Keywords: Life-Cycle Assessment, Organic Photovoltaics, Engineered Nanomaterials, Risk Assessment, Characterization Factor, Indoor Occupational

Titre : Analyse du cycle de vie de systèmes photovoltaïques organiques de 3ème génération : Élaboration d'un cadre pour étudier les avantages et les risques des technologies émergentes

Résumé : Les systèmes photovoltaïques organiques sont des technologies émergentes présentant de forts potentiels d'économie de ressources et de réduction des impacts sur l'environnement et la santé humaine par rapport aux dispositifs photovoltaïques conventionnels. La méthode de l'analyse du cycle de vie est appliquée afin d'évaluer la façon dont les différents procédés de fabrication, les caractéristiques des dispositifs, la phase d'utilisation et les scénarios de fin de vie des cellules photovoltaïques organiques influent sur ces avantages potentiels. Les impacts de cette technologie émergente sont comparés aux technologies conventionnelles à base de silicium pour établir un référentiel de performance des technologies photovoltaïques. En outre, les effets potentiels sur la santé humaine de l'utilisation de nanomatériaux dans les cellules photovoltaïques organiques ont été spécifiquement étudiés ; et la pertinence de l'analyse du cycle de vie pour évaluer cette catégorie d'impact a été examinée. Ainsi, un nouveau modèle d'évaluation de l'impact sur le cycle de vie est présenté afin de quantifier les dangers potentiels posés par les nanomatériaux. Enfin, ces impacts potentiels sont comparés aux avantages des cellules photovoltaïques organiques sur les cellules à base de silicium.

Mots clés : l'analyse du cycle de vie, photovoltaïques organiques, nanomatériaux fabriqués, l'evaluation des risques, l'exposition, énergie renouvelable Title : Life-cycle Assessment of 3rd-Generation Organic Photovoltaic Systems: Developing a Framework for Studying the Benefits and Risks of Emerging Technologies Abstract : Organic photovoltaics present an emerging technology with significant potential for increasing the resource efficiencies and reducing the environmental and human health hazards of photovoltaic devices. The discipline of life-cycle assessment is applied to assess how various prospective manufacturing routes, device characteristics, uses and disposal options of organic photovoltaics influences these potential advantages. The results of this assessment are further compared to silicon based photovoltaics as a benchmark for performance. A deeper look is given to the potential human health impacts of the use of engineered nanomaterials in organic photovoltaics and the appropriateness of life-cycle assessment to evaluate this impact criteria. A newly developed life-cycle impact assessment model is presented to demonstrate whether the use of and potential hazards posed by engineered nanomaterials outweighs any of the resource efficiencies and advantages organic photovoltaics possess over silicon photovoltaics. 22 ...... (3-2) (3-3) (3-4) (4-1) (5-1) (6-1) (6-2) (6-3) (6-4) (7-1) (7-2) (7-3) (7-4) (7-5) (7-6) (7-7) (7-8) (7-9) (7-10) (7-11) …………………………………………………………………………………………………………………………………………

Institut des Sciences Moléculaires

Executive Summary xvii

Executive Summary

Solar photovoltaics (PV) are poised to play a predominant role in energy production over the next century given imminent environmental and human health challenges such as climate change. While PV take freely available energy from the sun and convert it into electricity, there are still environmental and human health impacts that can occur along its life-cycle, albeit at lower levels than conventional fossil fuels. A new generation of PV technology is pushing the boundaries of these advantages by using extremely thin, lightweight, flexible materials. One such technology is the 3 rd -generation organic photovoltaic (OPV) device that uses organic, engineered nanomaterials (ENM) in its active layer.

The overall objective of this thesis was to demonstrate whether OPV have proven themselves to be a preferable energy supply option compared to silicon PV from an environmental and human health perspective. In turn, the thesis addresses whether life-cycle assessment (LCA) -an environmental management tool that can quantify resource efficiencies and environmental and human health impacts of products across their entire life-cycles, from material extraction to product disposal -is the most appropriate methodology for making such evaluations. Based on the outcomes of an extensive review of the existing OPV-LCA literature, further objectives were to address (i) a comprehensive range of OPV's environmental and human health impacts across (ii) all life-cycle stages, (iii) in comparison to silicon PV and (iv) the limitations to evaluating the human health impacts from ENM used in OPV devices. Two attributional LCA case-studies were completed from cradle-to-gate and cradle-to-grave. The results of the cradle-to-gate assessment demonstrate that per watt-peak of power production, OPV's resource efficiency was higher while their environmental and human health impacts were up to 93% lower than silicon PV's impacts, on average. While encouraging, OPV have lower efficiencies and shorter working lifetimes compared with silicon PV. Thus, changes in these outcomes of the LCA might occur after considering OPV's use and end-of-life stages. The results of the cradle-to-grave assessment demonstrated that, per kWh of energy produced over 25-years as a rooftop solar array, OPV's environmental and human health impacts were still 60% lower than silicon. These environmental and human health impact reductions could be pushed to 73%, on average, per 10 Wh of energy produced over 5-years as a portable solar charger.

While the results demonstrated a preference for OPV over silicon PV, there was no specific estimation regarding the toxicological impacts resulting from emissions of ENM across the OPV life-cycle. To address this, a human health risk assessment (HHRA) was performed. In general, LCA and HHRA are often used by Executive Summary xviii two different sets of decision makers and for different purposes. LCA is often used to address the resource efficiencies of a product, while HHRA is used to evaluate the toxicological risks of specific substances used in a product. A first-tier, qualitative HHRA identified that occupational scenarios were the life-cycle stages with the greatest risk potential due to interaction with large volumes of loose ENM particulates.

Additionally, inhalation posed the most significant exposure route for workers directly handling these materials. A focused, quantitative HHRA demonstrated that industrial-scale handling of nano-TiO2 where large amounts of power are dumped into open vessels presented airborne nano-TiO2 concentrations with statistically significant risks of inducing pulmonary inflammation to workers over a lifetime of work.

This Separate Use of LCA and HHRA is one possible approach for addressing the resource efficiencies and toxicological risks of emerging technologies such as OPV. Additionally, the results of both tools can be combined into a Complementary Use approach using multi-criteria decision analysis, for example. Another approach is Integration of these tools at the methodological level. The methods of the HHRA were integrated with life-cycle impact assessment, introducing a novel nano-specific characterization factor (CFNS) and presented as a case-study of nano-TiO2. After reassessing the OPV-LCA with the newly defined CFNS, there was a less than a 1% contribution of human health impacts resulting from the emissions of nano-TiO2 during occupational handling of the ENM. These findings suggest that ENM emissions across the life-cycle are marginal, however these results must be met with caution as they only include the impacts resulting from occupational nano-TiO2 emissions. Further emissions may occur during the use and end-of-life phases or from other ENM in the other layers of the OPV.

The results of the OPV-LCA case-studies and the further evaluation of ENM human health impacts indicate that OPV are a preferable energy production option compared to silicon PV from an environmental and human health standpoint. Outcomes were limited by uncertainties associated with OPV lifetime and efficiencies, which were marginalized when OPV were restricted to products with relatively short lifetimes (i.e. < 5 years) and moderate OPV efficiencies (i.e. > 5%). In terms of the assessment approach, LCA stands as the most comprehensive and appropriate methodology for evaluating the resource efficiencies and potential hazards of energy producing technologies such as OPV. Its broad environmental scope and focus on the entire life-cycle help to avoid burden shifting. While understanding the human health impacts to ENM is necessary, this is not fully achievable using existing life-cycle impact assessment methodologies.

An integrated HHRA and life-cycle impact assessment approach would dually ensure that the resource Executive Summary xix efficiencies of OPV do not come at the expense of shifting burdens to the occupational human health impacts. Energy is arguably the most important issue confronting society in the 21 st century. That is because energy seems to be at the heart of what is considered to be sustainability. Although sustainability means many things to many people, it is often reduced to three core pillars of sustainability development: economics, society and environment. 1 In this way, it is hard to imagine another factor over the previous two and a half centuries that has been so influential on society. The development and use of fossil fuels has spurred tremendous growth in industry and the overall global population as well as technological achievements on a scale never seen before the industrial revolution. Along the way, however, this has come at the expense of certain environmental and public health costs. The world is now, arguably, entering an era of peak-oil, 2 coincidentally while changes to the earth's atmosphere due to anthropogenic sources of greenhouse gases and pollution 3 are producing environmental conditions, such as global climate change, that will test the limit of society's capacity for adaptation and resiliency. 4 Thus, sustainability is not simply an energy issue but also an environmental and public health issue. The true cost of an energy source cannot be quantified solely as a function of its procurement, refinement and distribution transactions, for example, but it must also account for the "externalities" or indirect costs related to its environmental and human health damage. Taking this comprehensive vantage point is necessary to avoid burden shifting, for instance, as can be the case with biofuels and its issues of adverse land and freshwater use impacts [5][6][7] and increases in greenhouse gas emissions 8 or in regards to the use of solar photovoltaics (PV) and increases in the use and consumption of metals. 9 Therefore, research and development into new, sustainable sources of meeting the world's current and future energy demands will, consequently, continue to be an important topic throughout the 21 st century.

Keywords

Coupled with the urgent need to address greenhouse gas emissions and climate change, among a host of other environmental and public health challenges, renewable energy sources such as PV are poised to take on a more pronounced role in the world's energy procurement strategy.

Photovoltaic Technology: Background

The photovoltaic effect is the production of electric current under the application of light. Its discovery is attributed to the French physicist Edmond Becquerel who, in 1939, used silver bromide coated platinum electrodes to produce a current when illuminated in solution (Figure 1-1). 10,11 Mainstream and modern forms of PV technologies began to take shape in the mid-20 th century with the introduction of p-n junctions of silicon semiconductors as described by Russel Ohl. 13 The p-n junction exploits the difference in work function between these two materials, thus producing an electric field. [START_REF] Green | Photovoltaic principles[END_REF] Current silicon technologies, for example, artificially "dope" silicon with impurities such that there is a ptype silicon that has an excess of electrons and a n-type silicon that has a reduction of electrons (i.e. an excess of holes). Upon absorption of light, electrons are excited from their valence to conductions bands.

Ultimately, electrons will flow from the p-type material to the n-type material and then on to the rear (back) electrode. [START_REF] Green | Photovoltaic principles[END_REF] Simultaneously, the holes left behind by the electron will travel in the opposite direction to the top (front) electrode. [START_REF] Green | Photovoltaic principles[END_REF] From expensive, relatively low efficient solar panels, 14 silicon-based PV matured quickly achieving efficiencies of 20% by the mid-1980s. Lab-based record efficiencies have increased beyond 25% for most of the silicon-based cells. 15 Lab-based efficiency records have reached upwards of 45% conversion of light to electricity using triple-and quad-junction cells using combinations of gallium, indium and arsenic, for example. Silicon-based PV are still the most widely used PV technology globally, particularly in large scale ground and roof mounted systems, and capture over 90% of the global PV production annually. [START_REF] Krebs | Upscaling of polymer solar cell fabrication using full roll-to-roll processing[END_REF] The use of silicon PV also has tremendous resource efficiency and environmental benefits. Previous studies have shown, for example, that the greenhouse gas emissions per kWh of electricity produced using silicon PV are five-times lower than coal based production and over two-times lower than natural gas based production. 17 Moreover, since 2008 there has been a steady drop in market prices of silicon. 18 This has Chapter 1 Organic Photovoltaics as a Sustainable Technology 24 put PV technology at close price parity with conventional energy sources as such as coal. 19 Although this is great news for the renewable energy market in general, this trend may stabilize and even reverse in the near-term. 20 The competitive price of silicon PV may have the unintended consequences of reducing incentives to invest in and develop alternative PV technologies that may not yet be as cost competitive. 21 Organic Photovoltaics: The Third-Generation of Photovoltaics

The focus of this dissertation rests with the 3 rd -generation OPV technologies that utilize organic materials in their active layers instead of inorganics such as silicon. The birth of the first practical OPV technology began with a patent filed by C.W. Tang in 1979 for an organic bulk-heterojunction device. 23 That first device utilized an active layer of copper-phthalocyanine and a derivative of perylene, a 20 carbon, 5-ring, polycyclic hydrocarbon (C20H12). Since then, this technology has remained primarily as a lab-based technology, with small pilot-scale projects (www.infinitypv.com) coming online as recently as 2015 and large-scale deployment of OPV solar panels having been deployed on land for demonstrative and research purposes. 24 Conceptually, OPV cells are not much different from conventional PV technologies in that they are composed mainly of the same principal components (Figure 1-3) such as a front electrode, back electrode, active layer (i.e. where charge separation occurs). However, there is no one "standard" OPV geometry and device composition due to the fact that OPV are still very much a developing technology. While the large-scale application of silicon PV has been realized and its resource efficiencies and environmental impacts largely proven beneficial, 17,26 the overall objective of this thesis is to demonstrate Chapter 1 Organic Photovoltaics as a Sustainable Technology 28 whether OPV have proven themselves to be a preferable energy supply option compared to conventional silicon PV from an environmental and human health point of view.

Life-Cycle Assessment: A Primer

Central to the environmental evaluation of energy-related systems has been the use of life-cycle assessment (LCA). LCA is an environmental management tool outlined by ISO 14040:2006 and14044:2006. 27,28 A very brief introduction is provided here with a more detailed explanation of its use and methods in Chapter 2. It is viewed as a key tool to evaluate energy-related systems because of its ability to track all of the directly consumed and/or embedded energy in each life-cycle stage of the energyrelated system. This information can be used to determine how much time it will take for the system, during its use, to generate the energy that was consumed during the production of that same system. This is known as the energy payback time (EPBT). [29][30][31] Similar metrics such as the carbon payback time (CPBT) can also be calculated since LCA allows for the tracking of other non-energy materials flows such as greenhouse gases along the life-cycle. The EPBT and CPBT are core metrics to which all energy production systems are discriminated against and thus essential for defining the applicability of OPV in the energy sector.

LCA is composed of the (1) goal and scope definition, (2) life-cycle inventory analysis, (3) life-cycle impact assessment and (4) interpretation steps (Figure 1 -6). The goal and scope defines what is being studied in the LCA and includes defining the functional unit and the system boundaries. The functional unit describes a function being fulfilled such as a process (e.g. solvent regeneration), product (e.g. solar panel) or event (e.g. environmental remediation). The functional unit is directly related to the system boundaries, which further clarify which aspects of the functional unit are or are not included in the assessment. An important aspect of the system boundaries is identifying the life-cycle stages contained within the study (Figure 1 -6). For example, a LCA study could be classified as gate-to-gate if the assessment only includes one life-cycle stage (e.g. product manufacturing), cradleto-gate if the assessment includes raw material extraction through product manufacturing, or cradle-tograve if the assessment includes each life-cycle stage. In addition, identification and choosing the environmental and human health impact criteria for evaluation is to be completed as a part of the goal and scope definition.

The second step of a LCA, the life-cycle inventory, involves the identification, quantification and aggregation of all the material, energy and waste streams contained within the boundaries of the functional unit. Many times, the inventory data can be obtained from pre-existing life-cycle inventory databases such as Ecoinvent (The Ecoinvent Association, Zurich) which is the most widely used database for LCA studies. The life-cycle impact assessment involves the conversion of life-cycle inventory data into environmental and human health impacts, sometimes referred to as life-cycle impacts. These impacts may include, but are not limited to, acidification of land and water, climate change potential, resource depletion potential, ecotoxicity, eutrophication of waterways, human toxicity, ionizing radiation potential, land use changes, ozone layer depletion potential, particulate matter formation, and photochemical oxidant (smog) formation potential. The final step in an LCA, interpretation, is used to (a) understand and make the logical connections between the life-cycle impact assessment results, life-cycle inventory and the goal/scope definition, but also to (b) identify anomalies and errors that may present themselves in each of these LCA steps. Lastly, LCA does not quantify absolute environmental or human health impacts.

Instead, LCA uses generalized models that provide a potential, relative impact. This is in contrast to certain other impact analyses such as human health risk assessment (HHRA) 32 and environmental risk assessment (ERA). 33 In those approaches, individual substances contained within a product are identified and assessed for their probability of exposure in a population in distinct and specifically defined environmental conditions. Chapter 5 and Chapter 6 will introduce HHRA and ERA in greater detail.

Photovoltaic Life-Cycle Assessments: A Brief Background PV-related LCA studies date back to 1995 with a first such study published by Huber and Kolb. 34 That study demonstrated the first known life-cycle inventory for a silicon-based PV system as well as the first lifecycle impact assessment using 15 different environmental and human health criteria. By the time of the International Energy Agency's (IEA) 2011 report on Life Cycle Inventories and Life Cycle Assessments of Photovoltaic Systems, 35 there had been many dozens of PV-LCA studies published in the literature.

However, acknowledging the steep life-cycle inventory demands and technical nature of this technology, the IEA report aimed to provide further guidance on conducting PV-LCA to assure "consistency, balance, transparency and quality" as well as the "credibility and reliability of [LCA] results." While their report gathered the best-known life-cycle inventory data for crystalline silicon, cadmium telluride, and highconcentration PV, there was no consideration of OPV systems or other 3 rd -generation technologies in that report. This exclusion was understandable given the lack of maturity and consistency in the developments of OPV at that time. Consequently, the OPV-LCA literature itself is heterogeneous, representing material Chapter 1 Organic Photovoltaics as a Sustainable Technology 31 choices, processing routes and device structures as well as varying methods and environmental and human health impact results. The following sections outline the findings from a review of the OPV-LCA literature that was conducted at the start of this thesis and thus considers the published literature up to the end of 2013.

Review of Organic Photovoltaic Life-Cycle Assessments

The first OPV-LCA appeared in 2009 by Roes et al. 29 and between then and the end of 2013 there were a total of ten such studies (Table 1-1). [29][30][31][36][37][38][39][40][41][42] However, an additional eight peer-reviewed case studies, including the two from this thesis, have been published through October 2016.

Material Choices and Device Structures

Between 2009 and 2013, the types of OPV devices represented in the LCA literature have some overall consistency, owing to the fact that eight out of ten of these studies were either (a) from the same group of related authors 30,31,[36][37][38]41 or (b) were based on devices described in a prior LCA study that came before it. 39,40 Nearly all of the studies considered a flexible OPV device that had a plastic substrate, however the very first two published OPV-LCA considered OPV devices on rigid, glass substrates. 29,30 Glass substrates are typical for lab-based experiments and production, but since the time of the first OPV-LCA, it has been shown that the physics and performance of OPV are fully compatible with plastic, flexible substrates such as polyethylene terephthalate (PET). In conventional or normal device structures (Figure 1-4), light is incident through the top (front) facing electrode which acts as the anode, to which positive charge carriers pass into. Seven out of the ten studies considered an inverted device structure whereby light is intended to come through the bottom (rear) electrode which acts as an anode. In three of the studies indium tin oxide (ITO) was used as the front facing anode. 29,30,42 In two other studies, ITO was used as the cathode of an inverted device. 31,36 ITO is a commonly used transparent electrode given its high level of optical transparency and electrical conductivity. 44 However, most of these studies acknowledged the limitation ITO poses as a scalable technology due to its availability, cost and to a lesser extent its toxicity. [45][46][47] Thus, the other five studies investigated the use of various ITO substitutes for use as a cathode in an inverted structure such as other low-work function metals (e.g. aluminum), small molecule organics, nanoparticles and carbon nanotubes. [37][38][39][40][41] In the non-inverted devices that used ITO as the transparent front electrode, two of the studies used aluminum or aluminum with calcium as the back electrode, 29,30 while the third study used silver. 42 In the seven studies that assessed inverted structures, silver was uniformly employed as the anode. 31,[36][37][38][39][40][41] In addition, Espinosa et al. assessed the use of graphite as a non-silver substitute in order to begin to address the limitations silver poses in terms of abundance and cost if this technology were to be scaled to commercial and high volume scales.

Between electrodes and the active layer, optional hole-transport (electron-blocking) and an electron transport (hole-blocking) layers may be employed in order to increase efficiency and avoid premature recombination of the mobile charge carriers at the active layer. 48 In all ten of the OPV-LCA, poly (3,4ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) was used as the hole transport layer. In Anctil et al., both PEDOT:PSS and molybdenum oxide (MoO3) were individually tested. For the electron transport layer, three of the studies did not include this, one used a layer of lithium fluoride, five used nanoparticles of zinc oxide (nano-ZnO), 31,[38][39][40][41] and one other study individually assessed titanium dioxide (TiO2), bathocuproine and lastly bathophenanthroline. 42 At the heart of any PV device is the active layer, at which charge separation occurs. As described earlier, in a silicon-based device there are n-type and p-type silicon layers that donate and receive electrons, respectively, although in an OPV device these are all organic layers. In all ten of the reviewed OPV-LCA studies, the modified C60 fullerene, phenyl-C61-butyric acid methyl ester (PCBM), was used as the electron acceptor (Figure 1234567). Apart from PCBM, Anctil et al. assessed OPV's environmental impacts for devices using non-functionalized C60-fullerene as well as non-C60 PCBM alternatives such as PC70BM, bis-PCBM and the indene-bis-adduct. 42 PCBM is an engineered nanomaterial (ENM) and specifically a derivative of the Buckminsterfullerene or fullerene, named after the American engineer and architect Richard Buckminster Fuller. Its structure is a stable, geodesic cluster of carbon atoms that was first discovered by scientists at Rice University in 1985. 49 Clusters of 60 carbons with an approximate diameter of 0.7 nm is the most accessible forms of the fullerene and is commonly referred to as C60. 50 Market application and large scale use of C60-fullerenes began in the early 2000s, where these materials were used initially for enhancing sporting equipment.

However, their research, development and use for other sectors has grown beyond such applications. The principal properties specific to the functionality of C60-fullerenes are closely related to its small size and ability to reversibly accept upwards of six additional electrons, which is partly why they have been heavily researched for their electrochemical applications. 50,51 C60-fullerenes are strongly electronegative and provide high electron mobility and when in conjunction with a co-polymer provide reliable charge separation under illumintation. 43,[52][53][54] Ideally, the use of the so-called low bandgap polymers are most effective p-type materials for use with PCBM (Figure 1 -8). In all ten of the OPV-LCA studies, poly(3-hexylthiphene) (P3HT) was used as the co-polymer. However, Anctil et al. also assessed OPV environmental impacts using two non-P3HT co-polymers as well as a selection of small molecules such as zinc, copper and aluminum based phthalocyanines, 2,4,-bis[4-N,N-diisobutylamino_2,6-dihydroxyphenyl] squaraine, chloroboron subnaphthalocyanine and chloroboron subphthalocyanine. 42 For general protection and durability of the device, additional barrier and lamination layers are applied to the OPV panels. In all the OPV-LCA studies that involved flexible substrates, PET was used as the additional barrier and lamination layers. In some cases, silicon dioxide was also used for lamination. In the cases where a glass substrate was used, ethylvinylacetate and polyvinylfluoride served as the lamination and barrier layers. 29,30 All studies used some form of synthetic epoxy resin or acrylic adhesive to bind the barrier layers with the core OPV layers.

Among these ten OPV-LCA studies, there was significant differences in the way that the devices were constructed and how each layer was applied to the substrate of the PV device. Six of the studies, for Chapter 1 Organic Photovoltaics as a Sustainable Technology 36 example, utilized sputtering of ITO onto the PET using vacuum and/or intern atmospheric conditions. 29- 31,36,37,42 In the other four studies, these conditions were avoided altogether and substituted with other solution based deposition processes such as slot-die coating. [38][39][40][41] For other layers, various techniques were used such as screen printing, gravure printing and inkjet printing. Besides Garcia-Valverde et al., 30 all of the studies assumed non-batch processing using continuous roll-to-roll production.

The diversity in the materials, production routes and device structures used in these ten studies resulted in a range of OPV defined by differences in (i) the (photo)-active area of the panel, (ii) conversion efficiency and (iii) lifetime of the device. The active area of the OPV ranged from a low of 45% to a high of 90%.

These differences were sometimes actual measurements and in other times forecasted estimations of near-term feasible limits. 29,39,42 The efficiencies of the OPV ranged between 1% to 10%. Again, some of these values were based on actual measurements, while many of the studies engaged in sensitivity analyses to test various near-term changes in device efficiency. 30,[37][38][39]42 These projections are not unheard of, given that lab-scale OPV has reached upwards of 12% as of 2016. 15 Lastly, only seven of the OPV-LCA made any mention of device lifetime, with assumptions ranging from 2-15 year 30,[36][37][38][39][40][41] and in one case as high as 25 years. 29 Up to 2016, maximum reported OPV lifetimes have reached seven years. 56 Thus, the projected lifetimes of some of the reviewed OPV-LCA studies are overestimations and thus underestimate their reported environmental impacts.

Scope and Boundaries

The majority of the OPV-LCA studies were limited to a cradle-to-gate assessment from raw material extraction through the manufacture of the OPV panel (Table 12). [29][30][31][37][38][39][40][41][42] Accordingly, the functional units were defined for 1 m 2 of OPV panel. Anctil et al. defined their functional unit as a watt-peak (Wp) of power production. A Wp is defined as the nominal power of a solar cell, where the ability of a device to produce one Wp is tested under standardized conditions. Roes et al. stated that the scope of their study included the use-phase in the form of a rooftop mounted system using a functional unit defined as an average Wp of power produced over 25 years. As mentioned above, these results of their LCA study might be underestimating the environmental impacts given that the assumed lifetime of the OPV in that study was 25 years. Espinosa et al. was the only study that considered a use phase as well as an end-of-life phase. 36 Their use phase included the production of light using an OPVintegrated lamp and the end-of-life phase considered landfilling of the lamp. The functional unit for their study was the production of 0.31 Wp for light production over two years.

Environmental and Human Health Impact Assessment Criteria

All of the ten OPV-LCA studies published between 2009-2013 estimated the impact criteria of cumulative energy demand (CED), EPBT or a combination of both. This makes sense since OPV are an energy producing technology and one of the most obvious questions one can ask is how much energy is consumed in order to produce energy and how much time does it take for that technology to reproduce the energy it consumed (i.e. EPBT). These are useful calculations that also allow for comparisons between different types of energy producing systems (i.e. PV versus wind versus nuclear). In addition, six of the studies calculated the greenhouse gas emissions [29][30][31]36,37,41 and two of the studies assessed a more comprehensive set of environmental impacts beyond energy or greenhouse gas accounting as were presented in the previous "primer" on LCA. 29,41 Environmental and Human Health Impact Assessment Results

Because of the differences in the scope and boundaries of each study as well as differences in types of devices investigated and assumptions made on their performance, there was some variation in the results between each of the ten OPV-LCA studies. For example, these differences were reflected in the CED values estimated for each cradle-to-grave OPV study (Figure 1 -9). The results range from a low of 0.11 MJ per Wp to a high of 96 MJ per Wp. These results are specific for the baseline OPV assumptions defined in each study and do not reflect their various sensitivity analyses.

The CED value of 96 MJ per Wp reported by Espinosa et al. was much larger than all the other studies due to its greater scope and boundaries of the study, which included the use phase and the end-of-life phases. 36 More importantly, the use-phase included the production of many non-OPV parts that were associated with a functioning lamp such as a protective case, light emitting diode, cables and other circuitry. While Roes et al. also included the use-phase along with non-OPV parts such as the frame, inverter and cables, it is not apparent why exactly their CED is nearly 5-times lower than Espinosa et al.

Although, a small portion of this might be explained by the 23% greater active area assumed in Roes et al.

The results by Garcia-Valverde et al. report the second highest CED of 56 MJ per Wp. This is mainly a consequence of its use of fully lab-based, batch production methods of a rigid, glass OPV device. One of the main challenges cited by most of the studies with higher CED was reported as the energy demands for OPV (Espinosa et al. 2011) OPV (Espinosa et al. 2012) OPV (Espinosa et al. 2013) OPV (Anctil et al.) OPV (Roes et al.) OPV (Emmott et al.) OPV (Espinosa et al 2012) OPV (Espinosa et al. 2013) OPV (Emmott et al.) Chapter 1 Organic Photovoltaics as a Sustainable Technology 40 producing and sputtering ITO on the substrate under vacuum or inert atmospheric conditions. [29][30][31]36,37 In some cases ITO production and deposition account for upwards of 87% of the embodied energy of the entire OPV. 30,31 ITO itself is an energy intensive material to extract, produce and refine, however even if substituted with another material, it was found that the inert atmosphere needed for deposition could still account for nearly 17% of the energy demand. 37 In the cradle-to-grave study by Espinosa et al., the ITO substrate only constituted 35% of the overall CED, while the protective plastic layers, blocking diode, and light emitting diode represented 22%, 16% and 16% of the total demand. Those results again demonstrate the importance of and difference between cradle-to-gate and cradle-to-grave studies. When produced in normal atmosphere conditions, the reported CED values dropped significantly by over oneorder of magnitude. [38][39][40][41] Under these conditions, the main energy consuming aspects of the OPV shifted to the substrate, hole transport layer and electrodes.

It is curious to note that, since most of the studies were either from the same group of related authors 30,31,[36][37][38]41 or were based on devices described in a prior LCA study that came before it, 39,40 a lot of the life-cycle inventory data originate from a single source. If the data come from a primary, industrial source this could be reliable, however in many cases these data were being reported from lab-based processes, older techniques, expert judgement or stoichiometric calculations. One clear example of this influence on the results was brought to light in a LCA study by Anctil et al. 42 In a previous study, those authors completed an assessment of the CED for the processing and manufacturing of C60-fullerenes and PCBM. 57 They found their results were much higher than previously reported energy demands per kg of fullerene production and reasoned this to be on prior studies' use of older and/or incomplete data of the production route, particularly the purification steps. 57 When they integrated this information back into their OPV-LCA, they found that nearly 20% of the CED was due to the production of PCBM. 42 This is in contrast to the much lower percentages ranging between 0.21%-10% reported in the other OPV-LCA studies reviewed. Thus, whereas most of the other studies pointed to the non-active layer components as the main bottle-necks or targets for environmental improvement, Anctil et al. was the first study to identify the active layer as such.

Correspondingly, the EPBT reported in the OPV-LCA studies range from a low of 0.35 years to a high of 9.9 years. Because the EPBT is determined using the CED, the differences in the EPBT results can be attributed to the same factors driving the CED results. Once again, the highest EPBT of 9.9 years was reported by the cradle-to-grave study by Espinosa et al. that included the non-OPV components utilized during the use phase and impacts from the end-of-life landfilling process. 36 The glass-substrate OPV devices reported by respectively. 29,30 Those two studies also happened to report the highest active areas of 90%. Using Monte-Carlo uncertainty analysis, 58 Yue et al. demonstrated that the active area and efficiency of the OPV devices were the most significant sources of uncertainty in the calculation of the EPBT. Those authors also took into consideration the degradation rate of the OPV, as opposed to the other OPV-LCA studies, and found that it contributes upwards of 9% of the total uncertainty in the results. 39 When OPV manufacturing switched from inert to normal atmosphere conditions, the EPBT decreased by over one-order of magnitude similar to the CED values. 38- 41 The results from the two studies that reported a more comprehensive set of environmental impacts differed quite significantly. Some of the impacts reported by Espinosa et al. 41 In regards to conventional silicon PV, only half of the OPV-LCA studies made a comparison between OPV and conventional panels. 29,30,36,39,42 Garcia-Valverde et al. reported that the CED of a 5% efficient OPV panel was 56 MJ/Wp, nearly two-times and over three-times greater than the referenced values of 28.3-29.9 MJ/Wp and 15.9 MJ/Wp for m-Si and a-Si, respectively. 30 This resulted in a 4.0-year EPBT that was 1- For the glass-substrate, all impacts were lower for OPV, however the extent of which was not uniform for all impact categories. For instance, whereas ozone layer depletion for OPV was over 1order of magnitude smaller than m-Si, acidification for OPV was only 13% lower. The reductions over m-Si were more pronounced for the plastic-substrate OPV. For instance, the acidification potential was now 81% lower for OPV compared to silicon. However, the plastic-substrate scenario resulted in a photochemical oxidant formation potential that was 25% greater than m-Si due to the toluene emissions estimated for gravure printing. In comparison, the results presented by Espinosa et al. were unique in the fact that the LCA was cradle-to-grave, as opposed to the cradle-to-gate or cradle-to-use scopes that were applied in the other four studies that made comparisons to silicon-based PV. Espinosa et al. demonstrated that switching from an a-Si PV lamp to an OPV powered lamp can save 0.52 kg of greenhouse gas emissions per Wh of electricity produced and nearly 64 MJ of energy per lamp over a two-year period of use. 36 However, it should be noted that there were differences in the type of battery and other non-OPV components that contribute to this difference. The relative difference between OPV and the silicon PV reported by these five studies were of course influenced by the distinct types of OPV and methods of each study. However, different versions of pre-existing, background life-cycle inventory source data were used as a basis of the silicon-based analysis. 26,[59][60][61] Summary of the Review

The OPV systems represented in the ten OPV-LCA studies reviewed up to the end of 2013 represented a mix of different material choices and device structures for the OPV as well as a mix of scopes, boundaries, functional units, inventory data and impact assessment categories. Some of these studies were unique in that they happen to be produced at fairly high production volumes akin to pilot-scale setups, 31,[36][37][38] while their technical capacities, since 2013, have also been tested in large-scale outdoor demonstrations. 24,62 Even so, OPV still lack proper industrial scale technology or a market for use and consumption. Therefore, OPV may have the potential to reduce cradle-to-gate carbon emissions and energy consumption by over an order of magnitude compared to silicon (Figure 123456789), but such resource efficiencies may not necessarily remain after considering the use and end-of-life phases. 36 This is because OPV are also known to be less efficient 22,63 and have shorter lifetimes 45 compared to conventional PV. Consequently, environmental benefits seen during OPV manufacturing might be offset by the cumulative use and replacement of exhausted OPV panels over an entire lifetime of its service. In addition, important questions remain regarding how PV panels can and will be disposed of at their end-of-life. 64 This infrastructure is lacking for conventional PV, with landfilling being a default solution. 65 It is similarly uncertain how OPV panels might be disposed of once they are used at large scales or commercially. Thus, it is important to anticipate the influence a disposal route will have on the environmental impacts of these technologies.

In addition to considering the entire life-cycle, standard OPV-LCA should include impact categories beyond CED or greenhouse gas accounting as was limited to in a majority of the reviewed studies. While those two metrics are important for outlining baseline progress and advantages in this field, energy consumption and greenhouse gas emissions are only a limited selection of other environmental and human health indicators and impacts. A life-cycle approach should accommodate a broad range of environmental criteria in order to expose and reduce any potential environmental burden-shifting. 66 This recommendation is important because although CED may on occasion be a proxy for other potential impacts, 29 this may not always be the case. For example, what would be the correlation between CED and human toxicity? Even when there may be some correlation with CED and other potential impacts, that relationship is not necessarily linearly correlated. 45 Thus, comparing products based on one or two indicators may result in an unintended increase in other environmental impacts that were, as a consequence, not explicitly estimated. To date, only half of the currently published OPV-LCA completed such an assessment.

Furthermore, to demonstrate OPV advantages as an alternative, new source of sustainable energy, LCA studies should ideally compare them with conventional silicon-based PV, which currently dominate the market. [START_REF] Krebs | Upscaling of polymer solar cell fabrication using full roll-to-roll processing[END_REF] However, and as was discussed above, only five of the previously published OPV-LCA literature have done so, and of those, only one study has compared OPV and silicon PV across the entire cradle-tograve. 62 Thus, a review of OPV-LCA studies that are cradle-to-grave, multi-criterion based, and comparatively assessed with conventional silicon shows there were no such existing studies in the literature through at the start of the research put forth in this dissertation (i.e. the end of 2013) (Table 1-3).

○ ○ • ○

Overview of Problem Context for the Thesis

The future of energy production is an immediate problem facing the world's population, not only in terms of finding enough energy and at reasonable cost, but finding energy solutions that address pressing major environmental and human health challenges coinciding with our use of fossil fuels and impacting climate change effects. Conventional silicon-based PV technologies offer one means of providing a relatively low environmentally burdensome energy supply. At the beginning of this chapter, an overall objective of this thesis was put forth to answer if it is possible to demonstrate whether OPV have proven themselves to be a preferable energy supply option compared to conventional silicon-based PV from an environmental and human health point of view. While silicon-based PV have already proven themselves both in performance and environmental benefits, the results of preliminary OPV-LCA literature review only hint at OPV's potential greater resource efficiencies and reduced environmental hazards. However, a definitive view was not able to be taken based on those studies. The following chapters of this thesis explores this objective in greater detail based on the following set of hypotheses and research questions.

Hypotheses

1) The resource efficiencies and potential hazards of organic photovoltaics technologies can be adequately demonstrated, even from its current, early developmental stage.

2) The resource efficiencies are greater and potential hazards lower for prospective, near-term organic photovoltaic technology, compared with conventional silicon-based photovoltaics.

3) Life-cycle assessment is the most appropriate methodology for evaluating organic photovoltaics' resource efficiencies and potential hazards (i.e. human health exposure).

4) Human health risk assessment is the most appropriate tool for evaluating the human health impacts of potentially toxic substances used in organic photovoltaic technologies.

5) The approach and methodologies of life-cycle assessment and human health risk assessment are fully compatible for making evaluations of emerging technologies.

6) Life-cycle assessment and human health risk assessment can be used to inform the potential discussions and justifications for supporting the use and adoption of emerging technologies.

These hypotheses are intended to be supported or refuted based on the following set of core research questions posed in this thesis.

Questions Addressed in this Thesis:

1) What are the full range of cradle-to-grave environmental impacts of prospective organic photovoltaic technologies?

2) How do the environmental impacts of organic photovoltaic technologies compare with conventional silicon-based photovoltaics?

3) What are the current limitations of using life-cycle assessment to evaluate organic photovoltaic and other emerging technologies?

4) What tools are available for addressing the human health hazards and risks to engineered nanomaterials?

5) How can these human health impacts of engineered nanomaterials be integrated within life-cycle assessment methodologies?

Structure of the Thesis

The previous portion of this chapter referred to the literature leading up to the development of the objectives of this thesis. Specifically, the review was used to address to what degree did the state-of-theliterature support the notion that OPV are an environmentally preferable energy supply option compared to conventional silicon-based technologies. The literature largely demonstrated that the production of OPV on a watt-for-watt basis have shorter EPBT and lower greenhouse gas emissions compared to silicon.

There were also some indications that environmental and human health impacts may remain lower even after considering the use and disposal of OPV products. However, there is further support that needs to be demonstrated to support the claim that OPV are truly an environmentally preferable energy production option compared to conventional silicon.

In Chapter 2, a more detailed account of LCA is presented in order to better understand the environmental criteria and assessment methods that have been largely used to evaluate OPV. Chapter 3 introduces a cradle-to-gate LCA up to the point of the production of a prospective OPV cell. These results are further compared to the life-cycle results for conventional PV cells, discussing the implied advantages and drawbacks of each technology. Chapter 4 is a direct extension on the work of Chapter 3 and expands the LCA to explore the potential uses and end-of-life scenarios for OPV. Chapter 5 takes a further look at the possible issues of burden shifting, specifically examining LCA's limitations in estimating the human health impacts from the production, use and release of ENM across the OPV life-cycle. Chapter 6 then presents a more formal HHRA study to address these potential ENM impacts, and Chapter 7 reintegrates this information into the LCA methodology to reassess the overall environmental performance of the OPV.

Chapter 8 takes stock of the results provided by the updated LCA with human health impacts to address whether there is a case to be made regarding the environmental preference of OPV over silicon-based PV.

Lastly, ongoing work and future opportunities for developing OPV are presented. Following the main text, in order, are the Bibliography, Appendices and List of Publications. •Demonstrate whether organic photovoltaic technologies have proven themselves to be a preferable energy supply option compared to conventional, silicon-based photovoltaics from an environmental and human health perspective.

Chapter 1

•(Introduction and Background)

•Hypothesis #1

Chapter 2

•(LCA Background)

Chapter 3

•Hypothesis #2 •Research Question #1 •Research Question #2
Chapter 4

•Hypothesis #2 •Research Question #1 •Research Question #2
Chapter 5

•Hypothesis #3 •Research Question #3 •Hypothesis #4 •Research Question #4
Chapter 6

•Hypothesis #5

•Hypothesis #6

•Research Question #5

Chapter 7

•Hypothesis #5

•Hypothesis #6 •Research Question #5 •Research Question #6
Chapter 8

•Hypothesis #7

•(Conclusions and Perspectives) Overall Objective

•Demonstrate whether organic photovoltaic technologies have proven themselves to be a preferable energy supply option compared to conventional, silicon-based photovoltaics from an environmental and human health perspective.

Chapter 2 Life-Cycle Assessment and Its Application to Organic

Photovoltaics

In Chapter 1 the topic of LCA was briefly introduced as a primer and segue before discussing the OPVrelevant literature on the subject. It is worthwhile to discuss this tool and its methodology in greater detail, not only for providing the appropriate background for non-LCA experts, but also to describe the life-cycle inventory and life-cycle impact assessment methods that were applied in the case studies presented in Chapter 3 and Chapter 4. The latter point will also serve to orient the reader later in Chapter 5 and Chapter 7 during further review and analysis, respectively, of the impact assessment models created in this thesis.

Life-Cycle Assessment: A Brief History

Coinciding with the trends in industrial development and concerns over environmental protection and public health, the ideas of defining a product's resource efficiency date back to the early-mid 20 th century.

Early in the development of such assessments, energy was the main criteria of focus but soon after started to assess emissions, pollutants and waste generation. This was exemplified in one of the first types of these studies commissioned by the Coca-Cola Company in 1969, studying the effects of using different types of containers for their soda-products. 68 After a period of relative inactivity in this field of science, a formal report published in the mid-1980's by the Swiss Federal Laboratories for Materials Testing and

Research (EMPA) outlined the data requirements for producing a life-cycle inventory. 68 Yet, it was not until the beginning of the 1990s until LCA, in its current form, began to take shape. This period of time saw a number of workshops, frameworks, journal articles, codes of practice and standards being officially published and promulgated on an internationally recognized basis. 27,28,[69][70][71][72][73] Today, LCA has grown into a core scientific discipline finding its application in a myriad of other subjects which is reflected by the growth of published "life-cycle assessments" over the past two decades (Figure 2-1).

Figure 2-1

Growth in the number of peer reviewed scientific journal articles in the subject area of "lifecycle assessment." Results generated using SCOPUS (www.scopus.com).

As mentioned in Chapter 1, LCA is a broadly scoped environmental management tool outlined by ISO 14040:2006 and14044:2006. 27,28 These standards are founded on the concept of environmental management for measuring and communicating the relative environmental impacts of a product or process. The strengths of LCA are that it is a comprehensive tool, that models entire product systems, estimating the impacts of multiple environmental and sustainability metrics in response to the entire supply-chain of a product. Hence, it is often used by individuals and organizations promoting the concepts of resource efficiency, 74 eco-design or sustainable consumption and production. The results of an LCA can provide hot-spot analysis and identify burden-shifting, allowing for effective environmental management, guidance during early-stage technology development and environmental product declarations (ISO 14025). [START_REF] Hischier | Life cycle assessment of engineered nanomaterials: State of the art and strategies to overcome existing gaps[END_REF] More specifically, LCA is composed of the (1) goal and scope definition, (2) life-cycle inventory analysis, (3) life-cycle impact assessment and (4) interpretation steps (Figure 123456).

Goal and Scope Definition

The goal and scope defines what is being studied in the LCA and what research question is being addressed. This activity includes defining the functional unit and the system boundaries. The functional unit describes the function being fulfilled such as a process (e.g. solvent regeneration), product (e.g. solar panel) or event (e.g. environmental remediation). The functional unit is directly related to the system boundaries, which further clarify which aspects of the functional unit are or are not included in the assessment. An important aspect of the system boundaries is identifying the life-cycle stages contained within the study (Figure 1 geographic relevance, sources, uncertainty and specificity of the life-cycle inventory data. In addition, identification of environmental and human health impact criteria that will be assessed is to be completed as a part of the goal and scope definition.

Attributional and Consequential Life-Cycle Assessment

The inherent flexibility in ISO 14040:2006 and 14044:2006 allow for differences in the design of one's LCA study and the modeling approach taken in terms of the goal and scope definition and life-cycle inventory analysis. In general, two main modeling approaches are acknowledged within the LCA community. One approach is called the attributional-LCA approach and is described as an "accounting" or "descriptive" approach, in which the aim is to attribute the share of total, current global environmental impacts to a particular product or process. 76 The other approach is entitled consequential-LCA and is described as a "change-oriented" approach as is concerned with quantifying how changes in demand for a product influences total, global environmental impacts and therefore both direct and indirect environmental impacts of that product. These two approaches are illustrated in The decision to perform one modeling approach over the other will then determine the type of life-cycle inventory data and analysis that needs to be performed. This means that an attributional life-cycle inventory must be used for attributional-LCA and consequential life-cycle inventories for consequential-LCA. Specifically, an attributional life-cycle inventory is sourced from any typical, average supplier and deals with co-products using an allocation approach (see description in the following section). A consequential-LCA ideally uses data from the marginal supplier (i.e. one that is not constrained and by default responds to the change in demand for that product) and expands the system boundaries of its study to include the life-cycle stages and impacts from co-products (see description in the following section). The concepts of allocation and system expansion are further described in context of the life-cycle inventory in the following section.

Life-Cycle Inventory

Once the functional unit and system boundaries are defined, all material, energy and waste streams are identified, quantified and aggregated into a single inventory. More formally, the life-cycle inventory involves the quantification of all these inventory items per their use defined by each unit process within the boundaries of the functional unit that are initially defined within the goal and scope definition of the LCA. Figure 2-3 shows a generic flow diagram and system boundaries of a single unit-process that is typical in LCA for outlining and tracking the data required of a study. Typically, in LCA the inputs themselves are processes with their own set of inputs and outputs, and thus an LCA can be thought of as a series of unit-processes connected in series. The outputs are typically defined as products or elementary flows. Elementary flows represent resources taken from and pollution emitted to the environment, while products represent the technical flows out of the process. Products can be created in isolation or in conjunction with a co-product or by-product. In the latter cases, a formal decision must be made how the inventory is shared between these various product outflows. In attributional-LCA, it is common for these decisions to be made on a basis of "allocation." This means that distinct and separate portions of the total inventory items are ascribed to each co-product. For instance, if a process creates one kg of Product-A and one kg of Product-B, mass-based allocation (i.e. scaling by the fraction of total mass) would result in allocating 50% of the inventory to Product-A and 50% to Product-B. Similar forms of allocation can be made based on economic value or energy content of the products, for example.

In place of allocation, one can use "system expansion," which is a means of expanding the system boundaries to include the substitution of a primary product with the co-product. The result being a reduction of total life-cycle inventory data, because of avoiding primary production of the analogous coproduct. However, this approach is the default recommendation of the ISO 14040 and 14044 standards and also most typical of consequential-LCA modeling as opposed to attributional modeling. Besides the Chapter 2 Life-Cycle Assessment and Its Application to Organic Photovoltaics 53 management of co-products, other considerations must be made while building a life-cycle inventory such as the inclusion or exclusion of small material amounts, age-appropriateness of the data, spatialappropriateness of the data, and uncertainty in the data, for example. 78 The life-cycle inventory can be consolidated using pre-existing databases such as the previously mentioned Ecoinvent life-cycle database, extracted from scientific literature, professional reports or patents, empirically measured, modeled with detailed engineering and scientific tools, or estimated using expert judgment. As a rule of thumb, data extracted from pre-existing life-cycle databases, published literature or other sources that already pre-define a unit process and its inventory data is considered as background data. Conversely, primary data that is measured, modeled or estimated for the first time to create new life-cycle inventory process are considered as foreground data (Figure 234). Foreground data is often associated with the unit processes that are the principal focus of one's LCA study. GaBi is distinct in that it utilizes inventory data collected from industrial sources. Although such data provides tremendous value and validation to the LCA process, much of this data is "rolled up" into aggregated system processes (i.e. a collection of so-called smaller unit processes) to protect sensitive industrial data and provide insulation to any one company from targeted scrutiny. Ecoinvent on the other hand contains a greater number of unit processes and thus greater resolution at the impact assessment stage, although much of its data come from non-industrially supplied sources. However, many of the Ecoinvent inventories relevant to PV systems such as semiconductor-grade level silicon, production of PV cells, did include non-aggregated, industry-specific data. 79 Since a majority of environmental impacts are heavily influenced by background processes, it is beneficial to have non-aggregated data for understanding the sources of potential hazards related to PV systems. 35 Life-Cycle Impact Assessment

During the impact assessment step -the environmental and human health impact categories and methodologies are initially chosen in the goal and scope definition but then applied separately -inventory data are converted into potential impacts using their corresponding characterization (i.e. conversion) factors (CF) (equation (2-1)).

𝐼𝑚𝑝𝑎𝑐𝑡 𝐴𝑠𝑠𝑒𝑠𝑠𝑚𝑒𝑛𝑡 𝑆𝑐𝑜𝑟𝑒 = 𝐼x • 𝐶𝐹x,i • 𝑊i 𝑁i (2-1)
Ix: Inventory value for flow, x

CFi: Characterization factor for inventory flow, x, and impact category, i Wi: Weighting factor for impact category, i (optional)

Ni: Normalization value for impact category, i (optional)

The optional steps of normalization and weighting can be employed during the life-cycle impact assessment step. Weighting provides an approach expressing one's preference of concern for some impact categories over other. After weighting, normalization can be applied to harmonize impact category units so that each can be directly and relatively compared. External normalization involves dividing impact results by reference values, such as total global emissions. Internal normalization can be used when there are multiple alternatives fulfilling the same functional unit and division by the alternative with the greatest impact per impact category is used, for example.

There are a wide set of both environmental and human health impacts one can consider in an LCA. Often, these can be chosen from pre-defined impact assessment methodologies such as ReCiPe

• • ○ • • • • ○ ○ ○ • ○ • ○ CML, 2001 (Non-Baseline) • • ○ • • • • • • • • ○ • ○ Ecoinvent ○ ○ • ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ Eco-Indicator 99 • • ○ • • • • • • • • ○ • ○ Eco-Scarcity 2006 ○ ○ ○ • ○ ○ ○ ○ ○ ○ ○ ○ ○ • EDIP 2003 • • ○ ○ • • • ○ ○ ○ • ○ • ○ ILCD 2011 (Midpoint) • • ○ • • • • • • ○ • • • • IMPACT World+ • • ○ • • • • ○ • ○ • • • • LIME • ○ ○ • • • • ○ • ○ • • • • ReCiPe • • ○ • • • • • • ○ • • • • TRACI 2.1 • • ○ • • • • ○ ○ ○ • • • ○ USEtox ○ ○ ○ ○ • ○ • ○ ○ ○ ○ ○ ○
○ ‡Cumulative energy demand is not an impact category in the strict definition of the term, and instead it represents an approach for calculating an energy based inventory, and it is often reported by lifecycle assessment studies as a proxy-impact indicator. †Water use methods vary between inventory-based calculations, akin to cumulative energy demand, but can also represent actual impacts estimated across the cause and effect chain.

Chapter 2 Life-Cycle Assessment and Its Application to Organic Photovoltaics 57 These life-cycle impact assessment methods differ on the types of impact categories included, the spatial representativeness of the methods, and their modeling choices, among other things. Although Table 2-1 is not a comprehensive account of all possible impact categories, it is representative of 14 very common environmental and human health criteria. Some of these impacts, for instance, represent multiple impact categories such as ecotoxicity which can be sub-divided into freshwater or marine water ecotoxicity. Other impact categories which are nevertheless important, such as impacts from noise pollution, are not commonly included in these methods, lack formal reporting within life-cycle inventory databases and are, thus, not included in the Table 2-1. 83 The majority of the life-cycle impact assessment methodologies are specific for European conditions and impacts. These include CML, Eco-indicator99, EDIP, ILCD and ReCiPe.

TRACI was developed specifically for estimating the impacts within North America, while LIME was originally developed for the Japan but has since expanded its focus. Other impact methodologies such as Eco-Scarcity, Ecoinvent and USEtox focus on resource depletion, CED and toxicity, respectively. USEtox, in particular, is a consensus model focused on estimating freshwater ecotoxicity as well as non-cancerous and cancerous human health toxicity. 84 It was developed from authors of formerly and widely used environmental impact models such as CalTOX, 85 IMPACT 2002, 86 BETR, 87 EDIP, 88 WATSON, 89 USES-LCA 90 and EcoSense. 91 Inventory flows must be classified into specific impact categories. Inventory flows might contribute to more than one impact category (i.e. one compound might influence two separate types of environmental impacts). Thus, formal decisions must be made regarding how to partition the inventory flows, which will in turn affect how the final environmental and human health impacts. Impact assessment modeling can be divided into midpoint-or endpoint-modeling (Figure 2345). Midpoint impact assessment models consider the "relative" potency of an inventory flow somewhere in the middle of the cause and effect chain, while endpoint-models estimate levels of "damage" all the way through the cause and effect chain to what are known as "areas of protection." In other words, the midpoint can be thought of as an environmental mechanism of action while the endpoint is the ultimate effect. Midpoint impact assessment methods, thus, utilize fewer assumptions and have less uncertainty in their results, while endpoint-modelling has greater uncertainty but more comprehensiveness. Endpoint modeling also facilitates the decision-making processes because it reduces the LCA results into fewer, more manageable categories.

The midpoint indicators used in this thesis were derived mostly from the ReCiPe methodology but supplemented with USEtox for the human health and ecotoxicity impacts as well as the methods for CED outlined by Ecoinvent (Table 2-2). ‡Cumulative energy demand is not an impact category in the strict definition of the term, and instead it represents an approach for calculating an energy based inventory, and it is often reported by life-cycle assessment studies as a proxy-impact indicator. †Water use methods vary between inventory-based calculations, akin to cumulative energy demand, but can also represent actual impacts estimated across the cause and effect chain.

ReCiPe was chosen as the foundation due its focus on European impact modeling as well as it being a more recent update and successor of other European methods such as CML and Eco-Indicator99. While the 2011 ILCD Handbook 81 is a more recent recommendation for conducting European-based life-cycle impact assessments, previous OPV-LCA literature have not used these recommendations and instead have used methods such as CML and ReCiPe. Thus, for consistency it was decided to use ReCiPe as a basis for the impact assessment methodology. The decision to employ USEtox, which does happen to be an ILCD recommended method, was motivated by the problems addressed in Chapter 5-Chapter 7 regarding Chapter 2 Life-Cycle Assessment and Its Application to Organic Photovoltaics 60 compatibility of impact assessment methodologies with emerging technologies and to create consistency with the previous work in this area that had already considered USEtox in their methodological approaches. [93][94][95][96][97] A brief description of the midpoint impact indicators used in this thesis are included in the following subsections. Their descriptions are by no means exhaustive and serve only to provide sufficient information for understanding the fundamental implications of the results reported in this thesis. Additionally, it should be noted that ReCiPe adapts three different impact assessment "perspectives" that represent three different modeling choices and decision makers' perspectives: individualist (I), hierarchist (H) and egalitarian (E). 98 These perspectives only influence a portion of the total impact categories and differ mainly by their time-frames over which environmental and human health impacts to occur. Perspective I can be thought of as an optimistic model, Perspective E a precautionary model and Perspective H as the average model.

Climate Change Potential (Global Warming Potential)

The effects of climate change are in large part driven by the amounts of greenhouse gases contained in the Earth's atmosphere, such as carbon dioxide, methane, nitrous oxide and fluorinated gases. These gases, whose concentrations are growing due to human induced activities, have the capacity to trap and retain heat, raising near-surface temperatures of the planet. 99 Each gas has a different radiative forcing value and lifetime in the lower atmosphere (e.g. troposphere), meaning each has a distinct and relative "strength" for influencing climate change. 100 CO2 is considered the standard reference greenhouse gas to which all other substances are given CO2-equivalent values. These CO2-equivalent values represent the midpoint CF, while endpoint CF consider the relative temperature increase as well as the specific effect on the areas of protection due to that increase (e.g. extinction of species). 101

Ozone Layer Depletion

The ozone layer is defined as a portion of the stratosphere that contains an essential layer of ozone molecules (i.e. O3) that is effective at absorbing and thus blocking incoming radiation from the sun. Some of this radiation, such as ultra-violet B or UVB, is known to have both negative human health and environmental consequences such as contributing to skin cancer. By the 1970s it was identified that many man-made substances such as chlorofluorocarbons can react and deplete the ozone layer. 102 Similar to the effects of climate change with greenhouse gases, ozone depleting substances have different, relevant strengths and lifetimes in the atmosphere. One of these substances CFC11, a refrigerant, is used as the standard reference ozone depleting substance to which all others are defined by their CFC11-equivalent values. These values represent the midpoint CF, while endpoint CF are concerned with estimating the amount of increased radiation that results from the release of these substances and the corresponding influence on certain types of skin cancers as a result. 101

Photochemical Oxidation Formation (Smog) Potential

Like climate change potential, photochemical oxidation potential concerns itself with releases of pollutants in the lower troposphere that form photo-oxidants. Photo-oxidants form when nitrogen oxides (NOx) react with hydrocarbons in the presence of sunlight. 103 This reaction leads to the creation of "ground-level" ozone, as opposed to stratospheric ozone that comprises the protective ozone layer.

Ground level ozone can be dangerous to humans, causing various adverse pulmonary effects. 104 It can also have adverse impacts on terrestrial species, resulting in the loss of vegetation and plant life. 105 Midpoint CF are expressed as equivalents of non-methane volatile organic compounds (NMVOC-equivalents), expressing the marginal change in ground-level ozone per emission of a substance, x. 101 Endpoint CF are further defined by their level of exposure either by humans or terrestrial systems (e.g. plants) and their corresponding adverse effects per kg of ozone uptake. 101

Resource (Minerals) Depletion

This impact category represents multiple types of resources, often categorized by minerals and fossil fuels.

At the midpoint level, resource depletion represents the quantity of resources used in comparison to their known quantity of obtainable reserves existing in nature. These midpoints are reported in terms of kg of iron-equivalents (kg Fe-eq.) and kg of oil-equivalents (kg Oil-eq.) for mineral and fossil fuel depletion, respectively. 101 At the endpoint level, resource depletion includes the consideration of decreasing ore grades as a function of depletion and is reported in terms of monetary costs (e.g. USD) per mass of extracted resource. 101 Land Use, Land Occupation and Land Transformation Specific types of activities (e.g. mining) and structures (e.g. warehouses) require land either through which they directly use as a raw material or as a place to physically occupy. At the midpoint level, the impacts of land use are defined by the area (e.g. m 2 ) of physical land occupied or transformed as well as the timeframe (e.g. years) for these activities. 101 The endpoint impact may be defined by species-land Chapter 2 Life-Cycle Assessment and Its Application to Organic Photovoltaics 62 relationships whereby an increase in the area of available natural land, or a decrease in time of occupied/transformed land, implies an increase in the number of vegetative-species present in that same space. 101 Furthermore, the time of restoration is assumed to be linearly linked to the overall impact. Of course, this approach implies that the impacts of land use will be heavily dependent on the geographical location, considering that default species abundances and resiliency of the land to restore itself will be closely linked to the type of "natural" landscape present in a region.

Energy Use (Cumulative Energy Demand)

Although energy use is not a true impact category, 106 the concept of CED is important to energy related systems because it facilitates the calculation of how much time it will take for a device, during its use, to generate the energy that was consumed during the production of that same device (i.e. EPBT). The CED quantifies all the directly consumed energy across the entire life-cycle of a system as well as the indirect, embodied energy of materials consumed, created and discarded along the way. So far, there is no standardized approach for quantifying the CED in life-cycle impact assessment methodologies. The approach most commonly used in life-cycle impact assessment methods is the Ecoinvent approach that applies the upper heating value (e.g. MJ-energy per kg-material) for estimating the indirect energy consumed in a system. Other sources of non-combustible energy such as nuclear, hydro, wind and solar power are also included in the calculation and can be reported with or without the non-renewable sources of energy.

Water Depletion

The reporting of a water-based impact category has not been fully harmonized in impact assessment methodologies either. Some methods may report water use impacts similar to resource depletion impacts in terms of an overall summation of water use, potentially comparing it with known obtainable amounts for that resource. However, this is largely an inventory based statistic, similar to CED, and does not estimate any particular impact from water use specifically. Such values are typical of midpoint water use impact categories and are reported as m 3 of water used. 101

Acidification

There are a number of pollutants that have the capacity to increase the number of hydrogen (H + ) ions in the environment, including sulfur dioxide, nitrous oxides, hydrogen chloride and ammonia, for example. 107 The release or formation of H + can decrease the pH in both soil and water systems. Such changes can Chapter 2 Life-Cycle Assessment and Its Application to Organic Photovoltaics 63 cause imbalances in local ecosystems and ultimately result in the loss of species. 107 The fate and transport of these pollutants are defined by their behavior in the atmosphere as well as their behavior in the soil using steady-state and dynamic models, respectively. The midpoint CF are defined in equivalents of sulfur dioxide (SO2-equivalents) and represent the ratio of the fate for substance, x, with the fate of SO2. 101 Endpoint CF are defined by for a potentially disappeared fraction of plant species impacted by the change in pH of the media. 101

Eutrophication

Eutrophication is a phenomenon that occurs in fresh and marine water systems whereby an excess of nutrients, namely phosphorous and nitrogen, can cause unfavorable ecological conditions for living organisms. Specifically, excess phosphorous and nitrogen can promote the exponential growth of algae and other organisms. 108 In the long run, if growth is excessive, it can result in low oxygen conditions in the water, due to interference with photosynthesis-cycles of sub-surface plant life and the aerobic breakdown of the algae by bacteria. The lack of oxygen can ultimately cause stress and death in certain organisms within the hypoxic zones. 108 The midpoints for eutrophication are defined in terms of phosphorous-(Pequivalents) and nitrogen-equivalent (N-equivalents) mass emissions for freshwater and marine water environments, respectively. 101 Eutrophication endpoints are defined by a linear effect factor describing the number of disappeared species based on the change in phosphorous-or nitrogen-equivalent emissions in the environment. 101

Ionizing Radiation

The effects of radioactive isotopes may occur if humans encounter these substances either by directly working with them or after they are released into the environment. Although naturally occurring radioactive isotopes are the most abundant form of these substances, anthropogenic sources contribute to these levels through combustion of coal, production of nuclear fuel and use of military weapons, for example. 109 When these substances decay they release high amounts of ionizing radiation that can induce alterations in one's DNA and other organ systems. 110 The midpoint CF is defined by the internally absorbed dose, expressed using the mass of U235-equivalents and interpreted from the commonly applied unit Sievert (Sv), while the endpoint takes into consideration the incidence and severity of corresponding diseases (e.g. cancer) for this amount of internal dose. 101 Chapter 2 Life-Cycle Assessment and Its Application to Organic Photovoltaics 64

Particulate Matter Formation

In ReCiPe, particulate matter is defined as substances with diameters less than 10 μm (PM10). This type of particulate matter can form from emissions of sulfur dioxide, ammonia and nitrogen oxides, for example. 111 Particulate matter is specifically important for human health impacts, where they are associated with pulmonary diseases such as inflammation and/or cancer. 111 The fate of and exposure to particulate matter is calculated using steady-state models, while the resulting intake fraction is estimated directly from the number of persons exposed and their respective breathing rates. 101 The midpoint CF is then expressed in equivalents of the intake for PM10 (PM10-equivalents). 101 Endpoint CF are then defined based on the corresponding effect at that particular exposure dose, estimated from a corresponding doseresponse relationship. 101

Ecotoxicity

The life-cycle impact assessment methodology embodied by USEtox considers the potential ecotoxicological impacts that result from the exposure to organic chemicals and certain non-organic metals in freshwater. USEtox is a consensus model that came out of the Life-Cycle Initiative jointly partnered by the United National Environment Programme (UNEP) and the Society for Environmental Toxicology and Chemistry (SETAC). The goal of the initiative was to produce recommended ecotoxicity (and human health) CF for LCA. Multi-compartmental models are used to trace the fate and transport of a chemical from the source of its emission in one medium to the final concentration of another medium. Steadystate, equilibrium models are employed, largely dependent on partition (equilibrium) coefficients for characterizing these behaviors. 84 Exposure is estimated using steady-state bioaccumulation models using the same types of partition coefficients mentioned above. 84 This estimated level of exposure (e.g. bioavailable mass) is then combined with an estimation of the concentration at which 50% of the population has the observed impact, referred to as the effective concentration at 50% (EC50). 84 The EC50 value is calculated from in vivo toxicological studies for at least three different species/trophic levels. 84 The midpoint impact is defined here as a non-reference unit expressed as the comparative toxic unit for ecosystems (CTUe). 84 The endpoint may be combined with a damage factor that estimates the number of potentially disappeared fraction of species.

Human Toxicity

The life-cycle impact assessment methodology embodied by USEtox considers the potential human health impacts that result from the exposure to organic chemicals and certain non-organic metals. Multi-Chapter 2 Life-Cycle Assessment and Its Application to Organic Photovoltaics 65 compartmental models are used to trace the fate and transport of a chemical from the source of its emission in one medium to the final concentration of another medium. Steady-state, equilibrium models are employed, largely dependent on partition (equilibrium) coefficients for characterizing these behaviors (equation (2-2)). 84 𝐶𝐹i,j = 𝐹𝐹i • 𝑋𝐹i • 𝐸𝐹i,j = (𝑖𝐹 i ) • 𝐸𝐹i,j

(2-2)

FFi: fate factor (days) for substance i XFi: exposure factor (days -1 ) for substance i iFi: intake fraction for substance i

EFi: effect factor (cases/kg) for substance i and pathology j

The FF represents the various transformation and removal mechanisms from one compartment to another and is reported in days. Exposure is limited to inhalation and ingestion, without consideration of dermal exposure and is expressed as the XF in units of 1/days. It is assessed on a population-based level as opposed to an individual level, the latter being more conventional for HHRA methods. The results of the fate and exposure calculations provide a lifetime intake fraction (iF), which is defined as the ingested or inhaled amount per mass of emitted substance. This intake fraction is then combined with an estimation of the human-equivalent dose at which 50% of the population has the observed impact. This is referred to as the effective dose at 50% (ED50) and represented as the EF with units of cases of cancerous or non-cancerous disease per kg of emitted substance. 84 This dose is generally quantified using in vivo, animal dose-response toxicological data at which 50% of the test subjects gave a response. Both carcinogenic and non-carcinogenic doses may be defined depending. Midpoint impacts are expressed in non-equivalent unit defined as the comparative toxic unit for humans (CTUh). 84 Endpoints may be presented in terms of the disability-adjusted life years (DALY), where one DALY is defined as one year of lost life (YLL) due to mortality and/or due to disease and disabilities (YLD). 112

Interpretation

The interpretation stage is a process that is involved along each of the three other steps of the LCA (i.e.

the goal/scope definition, the life-cycle inventory analysis and the life-cycle impact assessment). LCA is commonly described as an iterative process, one in which the development of the objective, methods and result calculations are repeated and refined based on the previous set of outcomes. Interpretation should be used to (a) understand and make the logical connections between the life-cycle impact assessment Chapter 2 Life-Cycle Assessment and Its Application to Organic Photovoltaics 66 results, life-cycle inventory and the goal/scope definition, but also to (b) identify anomalies and errors that may present themselves in each of these LCA steps. Because LCA is involved with copious amounts of data, it is often best to use systematic approaches to the interpretation phase such as (i) contribution analysis (i.e. analyzing each impact category per life-cycle stage, unit-process and/or elemental flow), (ii) uncertainty analysis (e.g. defining the inventory data with distributions based on their inherent uncertainty and variability as opposed to deterministic values), and (iii) sensitivity analysis (e.g. modifying the life-cycle inventory to reflect specific technology decisions at the early development and design stages). 78 It should be noted that inventory items and their resulting impacts are aggregated over time and broad environmental landscapes/compartments. Consequently, LCA results do not communicate absolute values, as briefly mentioned in Chapter 1. Thus, the environmental impact results of an LCA derived for one product are best utilized when they are compared to the impacts of another product instead of being used to measure its definitive, absolute impact on human health or the environment, as is done in HHRA or ERA. Both HHRA and ERA will be presented in greater detail in Chapter 5 and Chapter 6.

Chapter 3 Cradle-to-Gate Life-Cycle Assessment of Organic

Photovoltaics

From a material and device structure perspective, there are a variety of OPV that have been studied using LCA (Chapter 1). While there are some general consistencies across the devices such as the use of a PCBM:P3HT active layer, there are some noticeable differences in such choices as the electrodes, for example. The results of those studies identified hot-spots that could be addressed for improving the environmental performance of OPV devices. Additionally, there were points raised about technical and functional aspects of material choices that should be addressed such as the assumptions regarding their lifetimes as well as their efficiencies. Furthermore, only three of the ten previous OPV-LCA studies looked beyond CED or greenhouse gas accounting. Of those that did, only one study compared those impact categories to conventional silicon PV. Illustrating the differences between OPV and silicon PV across a greater range of LCA impact categories, can help provide meaningful motivation for the development of this technology. Therefore, the objective of this chapter is to define a near-term, prospective OPV lifecycle inventory based on the recommendations and lessons from the review in Chapter 1 and complete a life-cycle impact assessment comparing OPV and silicon-based PV across a comprehensive set of LCA impact categories.

Organic Photovoltaic Device Structure and Material Choices in this Thesis

As was discussed in Chapter 1, all the previously reviewed LCA literature was completed on devices that exploit the functionality of ENM such as C60-fullerenes and their derivatives. C60-fullerenes have garnered the most interest in OPV research as an organic "n-type" (i.e. electron acceptor) semiconductor because of its strong electronegativity and high electron mobility. Specifically, the fullerene derivative PCBM is widely employed, often in conjunction with the common "p-type" (i.e. electron donor) polymer P3HT. 43,52- 54 During charge separation in the active layer, there is the creation of both a negative and positive charge carrier, the latter referred to as a "hole" or absence of an electron in an atom. Current is created when the negative charge is allowed to move through the PCBM and through to the (back) cathode while the holes simultaneously propagate through the P3HT and on to the transparent anode. 48 Substrates of early OPV devices were originally rigid structures such as glass, however it has become common and technologically feasible to print the components of an OPV cell directly onto a flexible substrate. 25,43 The substrate used in this work was polyethylene terephthalate (PET), which has proved to Chapter 3 Cradle-to-Gate Life-Cycle Assessment of Organic Photovoltaics 68 be an effective material particularly for large-volume roll-to-roll printing of OPV panels. 43 Immediately adjacent to the substrate is the transparent (front) anode layer (Figure 3-1), towards which the positive charge carriers (i.e. holes) travel. Commonly used transparent anodes are ITO, given its high level of optical transparency and electrical conductivity. 44 However, due to cost, scarcity and to a lesser extent toxicity, [45][46][47] fluorine doped tin oxide (FTO) was used. FTO was a suitable replacement due to favorable qualities such as high availability, lower market prices and relative effectiveness as a transparent conductive oxide compared to ITO. 44,113,114 FTO also has the potential to be printed 47 onto a flexible substrate. 115 While these assumptions were made in this work, this has yet to be effectively proven on a roll-to-roll basis using a plastic substrate.

As previously stated, the active layer of OPV cells consists of organic donor and acceptor materials. The bulk heterojunction is an interlacing morphology of donor:acceptor materials which maximizes the opportunity for charge separation. To avoid recombination of the mobile charge carriers at the active layer and increase efficiency, OPV cells are often given a hole-transport layer and an electron transport layer. PEDOT:PSS is a common hole transport layer used in OPV cells, but this material has been shown to have low stability in ambient conditions. An additional layer of nano-TiO2 was modeled in-between the active layer and the cathode to increase absorption of light. In many cases, the reflection of light off the (back) cathode redirects light towards the active layer for a second pass, and depending on the thickness of the overall cell, this can interfere with absorption. This is particularly true when the thickness of the active layer is on the tens-ofnanometers. 119,120 Optical spacers can avoid this problem and increase the spectrum of absorbed light (Figure 3-2). The optical spacer has the added benefit of acting as a hole-blocking layer and electron transport layer. 122 Even so, a lithium fluoride layer was modeled for increasing the efficiency and mobility of negative charge carriers to the (back) cathode. 123,124 Besides being an effective electron transport layer, lithium fluoride can also protect against pinhole damage during roll-to-roll processes, which leads to shorts and degradation of the device. 123 Lastly, a low work function (back) cathode is important for collecting the negative charge carriers. 125 A layer of aluminum was modeled as the cathode. Low work function metals can be relatively reactive, leading to potential oxidation and degradation of the device. 125 Thus, to inhibit these problems, the OPV was modeled with additional lamination layers (i.e. encapsulation and barrier layers).

Life-Cycle Assessment Methods

Goal and Scope Definition

The work in this chapter followed ISO 14040:2006 and 14044:2006 guidelines for conducting a LCA study and the recommendations of the IEA's 2011 guidelines and best practices for implementing an LCA for PV. 27,28,35 A cradle-to-gate LCA on the production of OPV cells in comparison to conventional technologies such as silicon is presented in this chapter. This work aims to define a baseline understanding of the lifecycle inventory and corresponding environmental and human health impacts for a prospective device with the most potential for real-world applications but also reduced environmental impacts. 45 The life-cycle inventory is built for the production of a default OPV cell produced as a bulk heterojunction cell of PCBM:P3HT as the active layer. The functional unit considered was one Wp. The OPV cell was assumed to require 200 cm² of surface area per Wp power produced at a module efficiency of 5%. 29 Efficiencies in the range of 2-3% were historically reported for lab-based PCBM-based cells without much improvement. 30,38,63 However, a large-scale deployment of OPV cells on land have already reported efficiencies near 2%. 24 Moreover, lab-scale efficiencies as high as 15% (Figure 1-2) have been reported in the literature for PCBM-based devices similar to what will be considered in this thesis. 126 Generally, as PV technologies mature, their lab-to-industrial scale efficiencies might decrease from 20%-50%. 127 Thus, the efficiency assumption in this work represents an optimistic yet realistic near-term objective.

The OPV cells were subsequently compared with conventional m-Si and a-Si PV. For each technology option, environmental flows were tracked across raw material extraction, material processing, manufacture, and transportation requirements for each solar cell design. Because the use phase was not considered in this LCA, the lifetime of the modules and any additional accessory components such as mounts, cables and invertors that PV panels may be required were not considered. 35 Additionally, capital equipment were also excluded from the assessment as such impacts are often negligible when considering the entire life-cycle and life-time of a product. 78 OPV are not currently manufactured on an industrial scale, therefore the foreground inventory data (Chapter 2)

developed for this LCA represented a mixture of lab and pilot scale processes, [29][30][31]37,39,42,44,57,128 patents, other professional materials, 129-131 stoichiometric calculations, and/or expert judgment. Background inventory data were taken from the Ecoinvent v.2.2 attributional inventory, 132 including an average European electricity mix and transportation processes. An attributional inventory was chosen to determine baseline environmental and human health impacts of OPV, as opposed to assessing the consequential impacts that arise from changes in the PV market and energy market due to OPV production and use, which are currently unknown. Therefore, where it was applicable, co-products were allocated.

Mass allocation, as opposed to economic allocation, was generally applied given that the industrial-scale, price information on specialty chemicals and substances was not available. All transportation requirements for incoming foreground chemicals and materials were taken into account using 100 km truck and 600 km rail transport, while outbound waste materials were estimated using ten km truck transport. 132 The LCA was conducted in OpenLCA v1.4 (GreenDelta, Berlin). The life-cycle impact categories (Table 2-2) were chosen as ReCiPe 80 v1.0.5 midpoint (H) impact categories with the substitution of USEtox 2.0 for the human health and freshwater ecotoxicity indicators as well as the addition of the CED. 92 A detailed account of the foreground inventory data collected and calculated in this chapter are explained in greater detail in Appendix: Chapter 3.

Life Cycle Inventory

Substrate

To produce one m² OPV panel, 0.074 kg of PET was used. 29 PET substrate production was modeled using the Ecoinvent process for "extrusion, plastic film" using the input "polyethylene terephthalate, granulate, amorphous, at plant" as the substrate source material.

Transparent (Front) Anode

The FTO anode was modeled as a solution produced from tin tetrachloride pentahydrate, ammonium fluoride, ethanol and water and heated to 60°C. 47,133 It was assumed that the only energy consuming step was for raising the temperature of water and ethanol from room temperature. The materials and energy to produce tin tetrachloride pentahydrate were based on stoichiometric calculations. 131 Wastes generated from these processes were not estimated. The amount of FTO solution applied was 0.042 liters per m² and was calculated as an average of the values reported for ITO in Garcia-Valverde et al. 30 and Roes et al. 29 FTO deposition in the default OPV cell was modeled via sputtering in a roll-to-roll process.

Hole Transporter

MoO3 was modeled as the hole transport layer. 116,117 Oxides of molybdenum do not occur in large amounts in nature, but rather as sulfides (e.g. molybdenite). Production of MoO3 was modeled by roasting molybdenite at high temperatures in the presence of oxygen. 134 The amount of MoO3 was estimated as 0.24g per m 2 of OPV. 30,31,128 Deposition of MoO3 was modeled via gravure printing in a roll-to-roll process as has been demonstrated for PEDOT:PSS. 43,135,136 Chapter 3 Cradle-to-Gate Life-Cycle Assessment of Organic Photovoltaics 73

Active Layer

The active layer consisted of a bulk heterojunction of PCBM and P3HT. PCBM can be produced in different ways including plasma and pyrolysis techniques. 57 Although plasma techniques can produce a more discrete distribution of C60-fullerenes, this technique also produces smaller quantities of C60-fullerenes, while pyrolysis can produce large quantities of various sized C60-fullerenes. 128 PCBM production was modeled via the pyrolysis technique using toluene as a feedstock. 57 P3HT is a region-regular polymer derived from 3-bromothiophene and bromohexane. 30 Data for bromothiophene production was taken from the inventory presented in Garcia-Valverde et al., 30 while thiophene production was calculated based on stoichiometric calculations. 130 The energy input for annealing the active layer was estimated from Espinosa et al. 38 as 5.07 MJ per m 2 and includes the energy for drying the electron transport and hole transport layers. Per m 2 of OPV, 0.21 g of PCBM and 0.235 g of P3HT were required. 30,31,128 Additionally, a 30 nm (0.13 g per m 2 of panel) optical spacer composed of nano-TiO2 particles was modeled on the backside of the active layer. 120,121,137,138 Deposition of P3HT:PCBM plus the optical spacer was modeled via gravure printing in a roll-to-roll process 135,136 using chlorobenzene as the solvent. 30 Cathode Per m 2 of OPV, 0.3 g of aluminum was modeled for the (back) cathode. 29,42 A layer of lithium fluoride was deposited along with aluminum to act as an electron transport layer. 29 Deposition of both layers was modeled via gravure printing. 11

Encapsulation and Lamination

Framing of the polymer solar cell is not considered in this inventory. Instead, several additional barrier layers were modeled. Per m 2 of OPV, 133 g PET, 0.44 g silica (SiO2), 0.99 g of an epoxy-silica ENMcomposite, and 9.25 g of an epoxy resin was used per the description in Roes et al. 29 Deposition of all layers was modeled as gravure printing. 11

Sensitivity Analysis

Alternative production and manufacturing options were assessed and compared with the default OPV production route as described above. The first alternative, labeled FTOinkjet, involved a change in the deposition method of the transparent electrode (i.e. FTO), using inkjet printing in place of sputtering. 44 Samad et al. 47 demonstrate the application of FTO solutions onto glass substrates using inkjet printing. As a prospective analysis, the approach presented by Samad et al. was assumed to be compatible with printing onto a flexible PET substrate and roll-to-roll compatible. 139 Electricity usage for inkjet printing was estimated as 21.3 kJ per m² substrate. 29 The second alternative, labeled PCBMdcb, was based on changes to the solvent used during PCBM manufacturing. Anctil et al. previously identified PCBM as a notable contributor to the total CED of OPV panel production. 57 PCBM requires large amounts of solvent, particularly toluene, during production. 57 Instead, another common industrial solvent, dichlorobenzene, was substituted for toluene as the solvent in PCBM production.

Additionally, a second OPV technology (OPV-PP) was considered as an all-polymer (i.e. polymer accepterpolymer donor) panel. 140 C60-fullerenes can represent nearly a quarter of an OPV's total material and production cost. They also require relatively large amounts of energy input for production, processing steps and purification to achieve electronic grade materials. Thus, some developments of alternative OPV devices such as those that utilize non-fullerene acceptors in the active layer are currently being researched and developed. The company Polyera reported a laboratory-based record of 6.4% with such an all polymer cell. Although, their materials are proprietary, these records have been verified by the National Renewable Energy Laboratory (NREL). 141 Others have demonstrated advancements upwards of 7% and 10% efficiency by using small molecule acceptors and non-bulk-heterojunction active layers. [142][143][144] However, average efficiencies for these devices are still low (i.e. circa 2%) and particularly lower than fullerene-based cells. 140 That being said, the effectiveness of this technology seems probable and the adaptation of existing PCBM:P3HT production routes to accommodate an all polymer active layer is extremely feasible. Thus, an additional consideration in this work was to evaluate the environmental impacts to produce an all polymer active layer. The alternative tested in this study exchanged PCBM with a n-type polymer. The inventory for the n-type polymer was based on the P3HT polymer as a proxy. For the sake of demonstration, an OPV-PP panel was modeled with the same 5% efficiency as the OPV-D panel outlined above and an identical geometry apart from the polymer substitution in place of PCBM.

Comparison to Conventional Silicon-Based Photovoltaics

The OPV devices described above were further compared to two conventional silicon-based PV: m-Si and a-Si. m-Si technology has the largest share of global PV production at 70% of the market. [START_REF] Krebs | Upscaling of polymer solar cell fabrication using full roll-to-roll processing[END_REF] a-Si is 2 ndgeneration thin film technology that once has nearly 10% market share in early 2000 but has since receded due to the influence of other 2 nd -generation technologies such as cadmium-telluride and copper-indiumgallium-selenium PV. [START_REF] Krebs | Upscaling of polymer solar cell fabrication using full roll-to-roll processing[END_REF] The inventories for the silicon modules were taken from Ecoinvent 2.2. 132 To appropriately compare the silicon-based PV to OPV, the silicon inventories excluded glass encapsulation.

Chapter 3 Cradle-to-Gate Life-Cycle Assessment of Organic Photovoltaics 75The m-Si cell was assumed to require 75 cm² of surface area per Wp power with an efficiency of 13.2%, and the a-Si cell was assumed to require 154 cm² of surface area per Wp power with an efficiency of 6.5%. 145

Energy Payback Time

The EPBT took into consideration how much energy was consumed across each life-cycle stage to produce the PV cell (i.e. CED) and how much would be generated (Eg) by the panel over its use (equation (3-1)). The values of the parameters used to quantify the EPBT are listed in Table 3-1.

Minimum Required Lifetime

The concept of a minimum required lifetime was used to communicate how long the OPV should be operational such that its environmental impacts are no greater than conventional PV (equation (3-4)):

𝑀𝑅𝐿 = To,n Ti,n • 𝐿i (3-4) MRL: Minimum required lifetime
To,n: Environmental and human health impact criteria (n) for OPV (o) Ti,n: Environmental and human health impact criteria (n) for the silicon PV (i)

Li: Lifetime of the ith technology

The minimum required lifetime was estimated using a-Si as the ith technology.

Results and Discussion

The greatest contribution to the overall environmental and human health impacts of the default OPV arose from the production and deposition (i.e. sputtering) of the FTO anode onto the substrate. FTO substrate production had an average contribution of 62% across all impact categories, with a high of 95%

for metal depletion and low of 57% for fossil fuel depletion (Figure 3 -4).

Figure 3-4

The contributions of life-cycle stages and production processes to the overall impacts of the default organic photovoltaic cell considered in this study.

In general, contributions from production of the FTO were on the order of a few percentage points, except for the metal depletion potential where upstream impacts from tin production accounted for roughly 90% of that impact. The high average contributions from FTO-substrate production was driven by the sputtering process, something also seen in previous OPV-LCA for ITO-based substrates. 45 Contributions from manufacturing PCBM, annealing of the active layer, and lamination of the solar cell also result in sizable average contributions of 11%, 11% and 6%, respectively. Contributions from PCBM production varied greatly and ranged from a high of 13% for ozone depletion to a low of 4% for water depletion. For PCBM production, the impacts were influenced heavily by both the production of C60-fullerene and subsequent modification and purification into PCBM. This was in part due to upstream impacts of producing toluene, ortho-dichlorobenzene and pyridine compounds. Previous estimations of CED per kg of PCBM ranged between 64-125 GJ amongst the studies reviewed (Chapter 1). Impacts from production of the nano-TiO2 optical spacer ranged from a high of 10% for marine eutrophication to a low of 0.01% for water depletion. Its overall contribution to all environmental and human health impacts were marginal and averaged 0.55%.

The annealing contribution of 11% was slightly higher compared to previously published literature which report estimations between roughly 0.2-10%. 30,37 However, in some studies the energy consumed during Chapter 3 Cradle-to-Gate Life-Cycle Assessment of Organic Photovoltaics 78 annealing of the active layer is not explicitly demonstrated, 128 or it is simply left out of the assessment altogether. 29 For lamination, the greatest influences were due to PET production and the epoxy resin.

Previously reported literature on the contribution of lamination to CED, for example, varies significantly from as little as 0.1% to as much as 37%. 29,30,38 In regards to the co-polymer P3HT, there was a smaller contribution of 2% on average across all impact categories. Previous studies demonstrated that P3HT can contribute between 0.1-0.8% of the CED, which is in line with the results of this thesis which show a 0.7% contribution to CED from P3HT production. Although this is a fairly moderate contribution to the overall environmental and human health impacts, the literature reports a trend moving away from P3HT for alternate low-bandgap polymers. 42 While these have the potential to increase OPV efficiency, it may come at the expense of increasing the environmental and human health impacts of the total device. 42 Sensitivity Analysis

For the FTOinkjet alternative, sputtering was removed from the production route and replaced with inkjet printing. Sputtering was previously identified as a highly energy intensive process and, when avoided, decreases the overall CED. 29,38,128 The results of this thesis confirmed this, but also demonstrate that the other life-cycle impact categories do not necessarily respond in-step with CED. For instance, whereas CED had an approximate 56% decrease, impact reductions ranged from 2% for metal depletion to 72% for ionizing radiation in other impacts (Figure 345).

Figure 3-5

Life-cycle impact results for the three alternative and one default organic photovoltaic cells considered in this life-cycle assessment. The impact results are internally normalized using division by the maximum impact value per impact category. Replacement of the PCBM with an additional polymer in the OPV-PP alternative resulted in an average decrease of 10% across all impact categories. Whereas PCBM has previously been identified as a low to moderate factor in the environmental and human health impacts of OPV production, 29,45,128,146 quite significant impact reductions from the polymer replacement were seen for ecotoxicity, where impacts were reduced by 20%. In general, polymers such as P3HT have shown much lower contributions to the environmental impacts of OPV production, however polymers are quite heterogeneous and their CED, for example, can differ by up to an order of magnitude. 42 In the PCBMdcb alternative, PCBM production was modeled with dichlorobenzene as the solvent instead of toluene. Impacts for this alternative resulted in an average increase of 2% in all categories except for fossil fuel depletion, which was only a fraction of a percent lower than the default case. Notably the human health impacts for dichlorobenzene were 10% higher than the default case using toluene. In contrast, ecotoxicity impacts were only 2% higher for the dichlorobenzene alternative. Nonetheless, production of C60-fullerenes requires large amounts of solvent for purification and separation, 45 thus solvent use could still be targeted as one way to further reduce OPV impacts. Dichlorobenzene is a widely used solvent in industry, and although the environmental impacts for this alternative were only slightly greater than the default case, concerns over potential human health impacts would be a limiting factor for its use.

Comparison to Conventional Silicon-Based Photovoltaics

The results show that all environmental and human health impacts were higher for both of the siliconbased PV compared with OPV (Figure 3 -6).

Figure 3-6

Comparison of life-cycle impacts for the organic photovoltaic cells and two conventional silicon cells. The impact results are internally normalized using division by the maximum impact value per impact category.

Particularly, m-Si cells had the greatest impacts for 14 of the 17 impact categories except for metal depletion, particulate matter formation, terrestrial acidification, and urban land occupation which were greatest for a-Si cells. The default OPV impacts were on average 93% lower when compared to the worst performing silicon cell. The impacts of the OPV cells that used inkjet printing for FTO deposition, OPV-PP and dichlorobenzene during PCBM production were on average 97%, 93% and 92% lower than the siliconbased cells, respectively.

Energy and Carbon Payback Times

The CED for the default OPV was 2.6 MJ/Wp. This decreased to 1.1 MJ/Wp, 2.6 MJ/Wp, and 2.3 MJ/Wp for the FTOinkjet, PCBMdcb and OPV-PP alternatives, respectively. Because OPV cells can be produced any number of ways (i.e. various materials and manufacturing routes), embodied energy for such systems has varied widely in the literature and ranges between 0.89-27 MJ/Wp (Chapter 1). [29][30][31]38,42,128 As is shown in Figure 34567, the CED for the default case is in line with previously published values for similar PCBM:P3HT 5% efficient solar cells. 3-2). For the OPV alternatives, EPBT decreased to 0.12 years (42 days) and 0.24 years (87 days), for the FTOinkjet and OPV-PP cells, respectively. These results are of importance given that the OPV cells only generate between 40% and 80% of the energy produced by m-Si and a-Si, respectively (Table 3-2). The results in this thesis show that OPV still recover their embodied energy one-order of magnitude quicker than their silicon-based counterparts. These results were in the range of previously reported literature, with reported EPBT between 52 days to 2 years. 29,30,38,128 It should be noted that EPBT calculations depend on the regions with which it operates and thus higher insolation values produce quicker payback-times. In terms of the CPBT, these were much shorter for OPV compared to conventional silicon. The default OPV CPBT was 32 days and decreased to 13 days for the FTOinkjet alternative and 29 days for the OPV-PP alternative. CPBT for m-Si and a-Si were 1.31 years and 1.16 years, respectively.

Minimum Required Lifetime

Compared to a-Si cells, the minimum required lifetimes of the default OPV cell ranged from 1.2-8.9 years and averaged 4.1 years (Table 3-3). For climate change potential and CED, the minimum lifetimes were 3.7 and 4.7 years, respectively. These results demonstrate that even for the worst performing environmental indicator, ionizing radiation, OPV cells only need to have a theoretical lifetime of 8.9 years such that its replacement over 25 years is not any worse than an analogous a-Si cell. This is both reasonable and encouraging given current maximum lifetimes of OPV have reached circa 7 years. 45 The OPV cells that used inkjet printing of the FTO layer, dichlorobenzene in PCBM production or an all polymer active layer resulted in minimum lifetime ranges of 0.6 years to 2.7 years, 1.3 years to 9.0 years, and 1.1 years to 8.5 years, respectively. In the case of the FTOinjet alternative, minimum required lifetimes ranged from 0.6 years for urban land occupation to a high of 2.7 years for ozone depletion. The average values for this alternative was only 1.8 years which is an even more promising result for the future of this technology.

Conclusion

The results from this LCA forecast a rather optimistic place for OPV as an energy producing technology, where all environmental and human health impact results indicate a much more favorable option for OPV on a watt-per-watt basis compared with conventional silicon cells. Although, the results of this LCA are applicable to the OPV described in this chapter, the materials used and modeling assumptions which were all chosen based on reasonable expectations that such a device could enter the market in the near-term.

As was demonstrated in this LCA, researchers in this field should be encouraged to consider a broader range of environmental and human health impacts for assessing the benefits of OPV over conventional technologies. As was presented, estimations of OPV minimum lifetimes to reach parity with a 25-year lifetime a-Si solar cell ranged from 1.2-8.9 years in the default case and as short as 0.6-2.7 in the FTOinkjet case. These represent moving targets for stakeholders, researchers and regulators in the field. More specifically and from an LCA perspective, the higher reported minimum required lifetimes reported for the default OPV demonstrate that OPV may be most advantageous in niche and short-term use scenarios compared to traditional long-term solar arrays. Although, the results for the FTOinkjet OPV point to potential justifications for using them in such traditional settings. However, these results were simplified theoretical calculations based on power production for just the PV cell (i.e. just the cradle-to-gate consideration up to the point of PV cell production were considered). The results implicitly imply that the PV would need no other components to be fully operational and would also have no impacts from the use-phase and/or end-of-life phases. Thus, this is an over simplified set of assumptions and no definitive determination can be given as to whether OPV are a preferred technology option over silicon-based PV from an environmental and human health perspective. Further research in this area should focus on how the use phase and end-of-life considerations will influence potential OPV deployment. For instance, recycling of precious metals has the potential to reduce the EPBT by 13% for OPV cells. 147 However, collection and the ultimate recovery rate of both the solar cells and materials with value will depend on where and how the OPV cells are employed. 148,149 The focus of Chapter 4 begins to address these questions by extending the system boundaries to include potential uses and end-of-life scenarios for OPV panels.

Chapter 4 Cradle-to-Grave Life-Cycle Assessment of Organic

Photovoltaics

In Chapter 1, a review of the OPV-LCA literature through the end of 2013 demonstrated that only two of the ten studies had considered the use phase, with one of those two studies also considering an end-oflife phase of the OPV. Of the two use phases considered, one was a rooftop glass-substrate system 29 while the other was a flexible-substrate OPV-powered street lamp. 36 The latter study also included the impacts from landfilling the OPV device, however as far as can be told, there does not seem to be any reporting of those end-of-life impacts in the results or mention of how this life-cycle stage influenced the overall LCA profile of the OPV. It should be noted that between the end of 2013 and the time this chapter was being prepared for submission, there were three other OPV-LCA studies published that contained use and/or end-of-life phases. In 2014, Espinosa et al. completed a cradle-to-use LCA for ground-installed solar arrays as well as three non-conventional onshore, offshore and balloon arrays. 24 In 2015, Espinosa et al.

completed a cradle-to-grave LCA for a ground-installed solar array with the additional consideration of either recycling or incinerating the entire solar-array. 62 Lastly, in 2014 Sondergaard et al. completed a LCA that considered the cradle-to-gate production of the OPV plus the additional consideration of recycling the silver content at its end-of-life (i.e. without the consideration of the use-phase). 147 The results of those studies are presented later in the results and discussion of and in context with the results of this chapter.

Methods

Goal and Scope

This LCA was conducted according to ISO 14040:2006 28 for incoming foreground chemicals and materials were taken into account using 100 km truck and 600 km rail transport, while outbound waste materials were estimated using ten km truck transport. 132 Capital equipment (e.g. buildings for solar panel production) were excluded from the OPV inventory as such environmental burdens are often negligible when considering the entire life-cycle and life-time of a product. 78 Where applicable, co-products resulting from the end-of-life treatment option of the solar panels (e.g. electricity from incineration) were handled as avoided products. Energy production from incineration was assumed to replace an average European medium-voltage electricity production mix (RER) defined by Ecoinvent v2.2. Only electrical energy, as opposed to thermal energy was considered.

Using an energy conversion efficiency half that of thermal energy, all potential thermal energy was converted to electrical energy. The LCA was conducted within openLCA v1.4.2 (GreenDelta, Berlin). The impact assessment was completed using ReCiPe 80 v1.0.5 midpoint (H) impact categories (Table 2-2) with the substitution of USEtox (www.usetox.org) for the human health and freshwater ecotoxicity indicators and the addition of CED. 92 Detailed inventories for each foreground data entry and stoichiometric estimation are supplied in Appendix: Chapter 4.

Life-Cycle Inventory: System 1 (Rooftop Solar Array)

Organic Photovoltaic Technology Description

The default technology (OPV-D) was based on a typical polymer-based bulk heterojunction that employed aPCBM:P3HT active layer as described in Chapter 3 for the FTOinkjet OPV. 52 The general geometry of the solar cell is depicted in Figure 1-3 and the main material and energy requirements are listed in The device efficiencies of OPV panels have been on a slight upward trend since the early 2000s, with labscale devices that are similar to what is considered in this current LCA reaching 15% in some cases. 150 Additionally, as PV technologies mature, their lab-to-industrial scale efficiencies might decrease from 20%-50%. 127 Given these considerations, a conservative 5% efficiency was assumed, requiring 200 cm 2 of panel per Wp of power produced. To fulfill the one kWh functional unit, 20 m 2 of OPV panel were required.

Historically, OPV lifetimes have been shorter than conventional silicon PV. More recently, maximum lifetimes for OPV reached seven years. 56 For any practical and mainstream use of OPV devices, typical lifetimes should start to approach these upper boundaries. Therefore, this LCA assumed a 5-year lifetime for the OPV. Given this estimation, the OPV panels needed to be replaced four times (five installations total) during the 25-year timeframe of S1, resulting in the total use of 100 m 2 of OPV panels.

Chapter 4 Cradle-to-Grave Life-Cycle Assessment of Organic Photovoltaics 88

Silicon-Based Photovoltaic Technology Description

For S1, the OPV panels were further compared to m-Si, which was chosen due to its 70% share of global annual production and high uptake in roof-mounted solar installations. [START_REF] Krebs | Upscaling of polymer solar cell fabrication using full roll-to-roll processing[END_REF] The inventory for m-Si panels was taken from the Ecoinvent process for "photovoltaic panel, multi-Si, at plant" and included the production and connection of individual panels plus the frame. 39 The m-Si panel was assumed to have an efficiency of 13.2%, requiring 75 cm² of surface area per Wp and a 25-year lifetime. 39 To fulfill the functional unit, 7.6 m 2 of silicon panels were used.

Balance of System

For each technology option, the rooftop installation included an estimation of a balance of system (BOS):

mounting-structure, an inverter and cables for electrical installation (Table 4-2). The inventory for the mounting-structure was based on Ecoinvent's "slanted-roof construction, mounted, on roof" process. 79 For the OPV-specific inventory, the mounting-structure was based on a modified version of this process (Appendix: Chapter 4), taking into account an aluminum-backing that the OPV panels would sit on. The aluminum backing was defined as a two mm thick panel with the exact dimensions of the OPV panel (20 m 2 ), requiring 3.9 kg of aluminum per functional unit. The backing panel was not completely solid (100% filled) but was modeled with repeating holes within it in since it was not foreseen that a filled structure would be necessary for support. For the m-Si inventory, the original Ecoinvent slanted-roof inventory was also used with the additional considerations of recycling the steel and aluminum components (Appendix: Chapter 4). In all cases, the mounting-structure was assumed to Chapter 4 Cradle-to-Grave Life-Cycle Assessment of Organic Photovoltaics 89 last 25 years and was recycled after use. Recycling estimations were made assuming 100% collection of the mounting components and entrance into the recycling waste stream, whereby aluminum and steel were used as scrap to produce secondary aluminum at conversion rates of 97% and 90%, respectively. As described above and at any single time during the use-phase, 20 m 2 of OPV panels were needed to fulfill the one kWh functional unit, requiring the installation of a 20 m 2 -equivalent OPV-specific mounting structure. Similarly, 7.6 m 2 -equivalent of the m-Si-specific mounting structure were installed for the m-Si panels.

The inventory for the inverter was based on a modified version the Ecoinvent "inverter, 2500W, at plant" process whereby recycling of the inverter was estimated using a mix of manual and mechanical dismantling processes and material transfer coefficients to recover aluminum, copper and steel (Appendix: Chapter 4). All three metals were assumed to be used as scrap in the secondary metals market with conversion rates of 97%, 76% and 90%, respectively, while the remaining waste was incinerated to generate electricity at 2.97 MJ/kg of waste. It was assumed that a single inverter was used throughout the duration of the functional unit.

Cabling requirements 151 included the recovery of copper used in secondary metal production at a 76% conversion rate. The remainder of the plastics was assumed incinerated, ultimately producing four MJ of electricity per kg of plastics waste. Twenty m 2 -equivalent of cabling and 7.6 m 2 -equivalent of cabling were required for both OPV panels and the m-Si panels, respectively.

End-of-Life Considerations

Assumptions were made that 100% of all PV devices were collected and directed to the appropriate waste stream, with 100% of the m-Si aluminum frame (19.99 kg) being dismantled and recycled along with the aluminum BOS mounting-structure components. No further energy or material inputs were considered for dismantling these components. Two end-of-life scenarios were considered: (a) incineration and (b) landfilling. Currently, the core infrastructure, economics, regulatory frameworks and the size of the waste stream indicate that most PV panels fall into these conventional disposal options as opposed to recycling. 64,65,152 Both disposal-process inventories were calculated using Ecoinvent's waste disposal modeling tools. 150 The tool was used to create a waste-inventory (Appendix: Chapter 4) based partially on the elemental composition of the waste as well as an assumed set of non-waste-specific technosphere and disposal related processes. The composition of the waste for each panel was based on the panel Life-Cycle Inventory: System 2 (Portable Solar Charger)

OPV Technology Description

The same OPV-D panels described above were considered in S2. To fulfill the ten Wh functional unit, 0.2 m 2 of OPV panels were needed (Table 4-3). The solar panels were used as portable chargers and were assumed to require a one mm thick PET-polyester case for protection, requiring 0.27 kg of PET and polyester each per charging unit.

Silicon-Based Photovoltaic Technology Description

The OPV portable chargers were compared to a-Si ones, which were chosen due to its thinness, flexibility and lighter weight, providing a conventional product mirroring some similarities to OPV. The inventory for a-Si panel was taken from the Ecoinvent process for "photovoltaic panel, a-Si, at plant" and includes the Chapter 4 Cradle-to-Grave Life-Cycle Assessment of Organic Photovoltaics 91 production and connection of a triple-junction silicon laminate that is roll-to-roll processed on a stainless steel substrate with the frame. 39 The a-Si panel was assumed to have an efficiency of 6.5%, requiring 159 cm² of surface area per Wp. 39 To fulfill the functional unit, 0.16 m 2 of a-Si panels were needed. The a-Si portable charger was assumed to require a one mm thick PET-polyester case for protection, requiring 0.22 kg of PET and polyester each per charging unit.

End-of-Life Considerations

The portable chargers were assumed to be collected at a rate of 100%, entering their appropriate waste streams without further consideration of dismantling. Incineration and landfilling were considered as the two end-of-life options. The lack of literature on the subject and low amounts of silicon in a-Si solar cells provides little support for recycling as a plausible disposal scenario, compared with m-Si. 152 A total of 0.044 kg of OPV-D panels were disposed, along with the 0.27 kg of PET and polyester in the casing. A total of 0.41 kg of a-Si panels were disposed of along with the 0.44 kg of casing. An average 4.95 MJ, 1.25 MJ, and 6.99 MJ of electricity production was estimated per kg of OPV incineration, a-Si incineration and plastic-casing incineration, respectively.

Energy Payback Time

To calculate the EPBT, the total CED, representing total energy consumption across the entire life-cycle of the PV device, was compared to the panel's Eg during that same time (equation (3-1)). EPBT was estimated using an average European insolation value of 1,300 kWh/m 2 /year (Appendix: Chapter 4) and the model parameters listed in Table 4-4. 153

Sensitivity Analysis

Although the default analysis rests on interpreting and estimating realistic efficiencies and lifetimes from their experimental maximums, 56 there is still considerable uncertainty in whether industrial production of such cells could achieve these estimates in the near-term. For instance, when transitioning from the lab to large-scale development, device efficiencies decline roughly by a factor of two, which could lead to situations where the default assumptions are potentially overly optimistic, particularly given the range of probable OPV devices in development and their corresponding range of lifetimes and efficiencies. The assumptions were tested assuming (a) lifetimes of one, three, five, seven and nine years with a constant efficiency of one percent and (b) assuming efficiencies of one, three, five, seven and nine percent with a constant lifetime of one year. The constants of one percent and one year were chosen in order to reflect more actual immediate-term scenarios which in effect act as a baseline set of characteristics from which industrial-level characteristics could be targeted.

1 Referring to the impacts that result from the production and disposal of the panel as opposed to the BOS components In both S1 and S2, the OPV panels have lower environmental and human health impacts compared to their silicon counterparts. In S1, OPV-D (with incineration) impacts ranged from 96% lower for water depletion to 40% lower for freshwater eutrophication, with the exception that metal depletion was approximately 21% greater for OPV-D mainly due to upstream demands of producing tin for the FTO electrode (Appendix: Chapter 4).

The OPV-D impact in S2 ranged from 92% lower for marine eutrophication to 56% lower for freshwater ecotoxicity compared with a-Si. In S2, the relative decrease between OPV and the silicon-based PV was more pronounced than in S1. The reason for the differences in OPV's relative impacts between S1 and S2

are in part explained by the different use phases. The use phase of S1 is 5-times longer than S2 (i.e. 25 years versus 5 years). Therefore, given the assumed 5-year OPV lifetime, there was a need to replace "exhausted" OPV panels many times over during the 25-year use-phase of S1. This contrasts with S2 which did not require replacements over the 5-year use-phase and thus the environmental and human health impacts were comparatively much lower for the portable charger compared with the rooftop setting. This is similar to the findings of Espinosa et al. where, on average, the majority of the cradle-to-grave impacts from their "solar-park" (duration 35 years) were due to the production and disposal of the panels themselves. 62 In that study, OPV-specific contributions were, for example, more than 90% for resource depletion, non-cancerous human toxicity and freshwater toxicity. However, in some cases the OPVspecific contributions were less than 50%, as was seen with particulate matter formation and ionizing radiation. Those authors ultimately attribute many of these high OPV-specific impacts to the production of silver electrode and its associated upstream processing and production. On the contrary, a 2014 study by Espinosa et al. reported that various on-land and offshore solar-array configurations contained support structures that were roughly 50% of the total CED. In the case of a balloon system, over 75% of the CED was due to components other than the OPV panel. Thus, use phase contributions to overall environmental and human health impacts over the OPV life-cycle should not be underestimated.

The results of this current chapter were mostly uniform regardless of the disposal route (i.e. incineration versus landfill). This was particularly true for S1 but not for S2, which had more noticeable impact reductions for incineration due to the added value of incinerating the plastic casing along with the OPV panel. It should be noted that climate change impacts for OPV-D were 3% (S1) and 16% (S2) greater when they are incinerated compared with when they are landfilled. This is a necessary consequence of combustion of the plastic components which were converted wholly into CO2 during the process.

Specifically looking at CED results, OPV-D saw reductions of 53% (S1, incineration) and 51% (S2, incineration) over their silicon-based counterparts. Previous studies have demonstrated that cradle-togate energy consumption savings for OPV panels can be nearly one-sixth and one-fourth the demand of m-Si and a-Si panels. 140 Roes et al. completed a cradle-to-gate LCA of a flexible OPV cell compared to a m-Si cell and estimated an average decrease of 73% in environmental impacts. 29 Similarly, the findings in Chapter 3 demonstrated average reductions in cradle-to-gate OPV impacts up to 97% and 92% over m-Si and a-Si cells, respectively. The results of the current cradle-to-grave LCA highlight that cradle-to-gate impacts were only part of the picture and that some amount of resource efficiency will be reduced when including the use and end-of-life phases for OPV.

Although the end-of-life options tested in this LCA were only marginally influential on the environmental and human health impacts, this analysis did not include potential effects from recycling of the PV panels.

Silicon-based PV may stand to gain the most from recycling since silicon-wafer and panel production were dominant influences on the overall impacts for both m-Si and a-Si (Appendix: Chapter 4). Müller et al., previously reported a nearly 57% reduction in energy consumption for m-Si modules made with recycled silicon wafers. 154 In the context of S1, the reduced cradle-to-gate energy demands reported by Müller et al. could lead to potential CED reductions of 49% for m-Si panels (Appendix: Chapter 4), resulting in a CED of 12,520 MJ. This estimate is just slightly higher than the CED estimate of 11,634 MJ for OPV-D, thus highlighting the potential importance of this end-of-life option. Although CED is not linearly related to the other impact categories, 146 a reasonable assumption would be that many of the other impact categories will also decrease, although at an unknown magnitude. This brings about some uncertainty surrounding the environmental benefits of OPV over conventional panels. Unlike m-Si, no relevant information on potential recycling options for a-Si is found in the literature, and due to its much lower silicon content the feasibility of recycling a-Si is not certain. 152 Recycling of OPV panels may also provide some environmental and human health benefits, albeit with lower yields compared to silicon PV. Sondergaard et al. demonstrated that use and subsequent recycling of silver for both front and back OPV electrodes may provide a decrease in cradle-to-grave CED for OPV panels by 12%. 147 They estimated a theoretical yield of 95% recycling rate of the original silver content used in the OPV panel. In almost all cases and as should be expected, recycling resulted in lower impacts across all impact categories they reported. This was not the case for climate change potential, however, which was higher than the non-recycling case. This increase in CO2-equivalent emissions was due to the Chapter 4 Cradle-to-Grave Life-Cycle Assessment of Organic Photovoltaics 96 energy and nitric acid requirements for isolating and recycling the silver back to an electronic grade state.

Furthermore, Espinosa et al. demonstrated the recyclability of both the PET laminate and silver electrodes in OPV panels used in their solar-parks, which led to a 57% average reduction in environmental and human health impacts compared with incineration of the panels. 62 However, these reductions were mainly linked to the recycling of their material choice using silver as an electrode, which in the end is arguably not a material that allows for the scalability OPV technologies.

Using the approach from Espinosa et al., 62 an estimated 1.7% reduction in the CED for S1 was calculated after inclusion of recycling the PET laminate. Thus, these results contrast with the findings of Espinosa et al. Ultimately, the potential motivation to recycle OPV technologies will depend on the geometry and material selection of the panel, particularly whether they include high valuable materials such as silver electrodes. 62,147 Energy and Carbon Payback Times

In S1, the EPBT (Table 4-5) for OPV-D panels were 436 days (incineration) and 449 days (landfill). These EPBT were approximately 53% and 51% shorter than the m-Si panels. In S2, the EPBT for OPV-D panels were noticeably much shorter at 220 days (incineration) and 265 days (landfill). These EPBT were approximately 65% (incineration) and 60% (landfill) shorter than the a-Si panels. As previously explained for other impacts in this chapter, it is likely that the EPBT for S1 were much higher due to the influence of having to replace the OPV-D panels every five years over 25 years. Additionally, a negative consequence of the energy production from incineration of the (mostly) plastic OPV-D panels could be seen by the shortened EPBT compared with landfilling. This effect was also slightly more influential in S2 due to the added amount of incinerated plastic from the casing materials, since those were assumed to follow the same end-of-life options as the panels. Although the results demonstrate that default OPV-D panels resulted in a 50%-65% reduction in EPBT compared to silicon panels, they were much higher than the payback-times previously seen for cradle-to-gate production of the panel alone which are on the order of days to weeks (Chapter 3). 29,38,128,146,149 This is in part due to the greater scope of this current chapter which evaluates the use and end-of-life phases, but also that previous studies calculated the EPBT and CPBT for regions with higher insolation values (e.g. Southern European conditions).

In S1, the CPBT for OPV-D panels were 192 days (incineration) and 185 days (landfill), approximately 47% and 50% shorter than the m-Si panels, respectively (Table 45). Similar to the EPBT, the CPBTs for S2 were also shorter at 118 days (incineration) and 98 days (landfill). These CPBT were approximately 59% and 64% shorter than the a-Si panels. It should be noted that the CPBT rose slightly for incineration of OPV panels due to direct releases of CO2 from combustion of the plastic components of the panel as discussed previously for the results of the climate change potential.

It should be noted that payback times calculated for S2 were purely theoretical measurements assuming chargers were consistently exposed to the sun under typical 24-hour patterns of light/dark 365 days per year. However, products like solar chargers are not intended to be an alternative energy supply but instead are a commodity. In reality, these chargers will be much less utilized by a consumer (i.e. compared with solar rooftop arrays) and in actuality EPBT will depend on the individual pattern of use by the consumer. To demonstrate this trade-off, a hypothetical example is presented whereby each PV was used to charge a typical 1.2 amps, 5-volt cell phone battery. It was assumed that the consumer used the portable charger five times (full-charging cycle) per week and that the charging cycle had a duration of two hours. Given these technical specifications and usage patterns, OPV-D consumer-adjusted EPBT of 5.3 years (incineration) and 6.3 years (landfill) would be expected, an increase of nearly 850% over the

Minimum Required Lifetime of Organic Photovoltaics

The minimum required lifetimes for S1 are listed in Table 456. (landfill), while specifically for incineration they were as low as 0.08 years (marine eutrophication) and as high as of 0.9 years (freshwater ecotoxicity). For landfilling, the minimum required lifetime was lowest for terrestrial acidification at 0.24 years, however it was highest at 1.18 years for marine eutrophication due to associated landfilling activities and processes leading to nitrate releases in the environment. The impacts of marine eutrophication were landfill-specific impacts that would have occurred irrespective of the type of material discarded.

These overall minimum require lifetimes are encouraging for OPV development given that experimental maximum lifetimes for this technology have surpassed all the results reported for S2 and have started to approach the upper bound minimum required lifetime values for S1. 56 However, it should be noted that these results are based on the assumed OPV efficiency of 5%. In the next section, this assumption is challenged against a range of different efficiencies as well as different lifetimes to better understand the influences these assumptions have on the overall environmental and human health impacts of OPV systems.

Chapter 4 Cradle-to-Grave Life-Cycle Assessment of Organic Photovoltaics 100

Influence of Lifetimes and Efficiencies on LCA Results

The influence from assumed OPV panel lifetimes ranging between one to nine years is shown in Figure 4-4.

(a) (b) Figure 4-4 Changes in life-cycle impacts for S1 (rooftop, incineration) according to forecasts in (a) lifetime of organic photovoltaic panels (with a 1% efficiency) and (b) efficiencies of organic photovoltaic panels (with a 1year lifetime). The impact results are internally normalized to the impact values of m-Si (i.e. m-Si's impacts are set at 100%). See Appendix: Chapter 4 for the sensitivity analysis for S1 with landfilling as the end-of-life option.

The time-sensitivity was calculated for an OPV device with a 1% efficiency. The increase in lifetime from Accordingly, the EPBT and CPBT never fell below that of m-Si. The EPBT ranged from a high of 16 years for panel lifetimes of one year to a low of 3.5 years for lifetimes of nine years, while the CPBT ranged from a high of 6.4 years to a low of 1.6 years, respectively. The results of increasing the efficiencies against a constant lifetime of one year were additionally tested. A similar trend was observed where increases in the efficiency from 1-3% was correlated with an initial sharp decrease in environmental and human health impacts (Figure 4-4). Beyond 3%, the impact reductions began to level off but not as sharply as in the case of the lifetime sensitivity. Thus, at efficiencies of 9% only metal depletion remained proportionally higher than m-Si. In particular, the CED and climate change potential were 18% and 16% lower than m-Si, with an EPBT and CPBT of 2.1 years and 305 days, respectively.

Chapter 4 Cradle-to-Grave Life-Cycle Assessment of Organic Photovoltaics 102

The trends in S2 were similar to S1 (Figure 45).

(a) (b) Figure 4-5 Changes in life-cycle impacts for System 2 (portable charger, incineration) according to forecasts in (a) lifetime of organic photovoltaic panels (with a 1% efficiency) and (b) efficiencies of organic photovoltaic panels (with a 1-year lifetime). The impact results are internally normalized to the impact values of a-Si (i.e. a-Si's impacts are set at 100%). See Appendix: Chapter 4 for the sensitivity analysis for System 2 with landfilling as the end-of-life option.

The increase in lifetime from one to three years also resulted in an initial, sharp decrease which then leveled off beyond 3 years. At the maximum lifetime tested of nine years, seven of the 17 impacts, including CED and climate change potential remained higher than a-Si. This resulted in EPBT and CPBT of 2.8 years and 1.5 years, respectively, which remained higher than a-Si. In contrast, the effects of increasing the efficiency resulted in a sharper and slightly more sustained decrease when moving from 1-9% efficiency (Figure 4-5). By 3% efficiency, over half of the OPV-D environmental and human health impacts were less than a-Si and by 5% all impacts were lower than a-Si. This means that by an efficiency of 5% the EPBT and CPBT were lower than that of a-Si. At an efficiency of 9% the EPBT and CPBT were 215 days and 101 days, respectively.

These results indicate that there might be more value in trying to achieve greater efficiencies than greater lifetimes. This is because with greater efficiencies, the PV panels become smaller in size per power output and thus require smaller amounts of materials for producing the framing structure (S1) or casing materials (S2). However, the same is not true for increasing the lifetime (i.e. the same amount of framing structure or casing materials are always needed). Moreover, due to the additional components in S1 that are not influenced by efficiency (e.g. inverter), there will be points at which further efforts to increase lifetimes and/or efficiencies level off without further improvement even with increasing effort to increase these characteristics. It should be noted that these results only test the assumption of the lifetime and efficiency of the currently envisioned OPV panels and do not attempt to account for material changes and/or production manufacturing differences. For example, reductions in the EPBT based on forecasted OPV efficiency gains may not always prove to have a direct relationship as moderate gains in efficiency may come at the expense of significant changes in embodied energy, materials consumed and pollutants emitted during production. 149,151 Impacts by Life-Cycle Stage For S1, there was a general trend that the BOS components such as the mounting structure and inverter where the major contributing factors to the environmental and human health impacts. For the scenario including incineration, the BOS contributed from 15% of the water depletion impacts up to 87% of the agricultural land occupation. In particular, the BOS accounted for 54% and 57% of the CED and climate change potential, respectively (Appendix: Chapter 4). Specifically, the mounting structure and inverter contributed 61% and 12% of the CED, while they contributed 63% and 13% of the climate change potential, respectively. Unlike an inverter that is always required for the use-phase of S1, a mounting structure is arguably questionable. Conceivably the OPV panels could themselves be directly placed on the roofing's surface. The greater decreases in ozone depletion potential, for example, were a consequence of eliminating the energy demand and aluminum oxide production (including aluminum mining) that would be consumed for the aluminum components. Furthermore, removing the mounting structure resulted in a 17% decrease in EPBT to 362 (incineration) and 376 days (landfill) over the default OPV-D scenario. Similarly, the CPBT decreased by 18% to 157 days (incineration) and 150 days (landfill). For S2, a similar trend was seen where the casing material heavily dominated the impacts for the OPV-D panels. Overall, the casing contributed from 21% of the metal depletion up to 91% of the photochemical oxidant formation potential in the incineration scenario. Alternatively, S2 could be envisioned without the need for external casing to protect the portable charger, for both the OPV-D and a-Si panels. Direct comparisons of the OPV (OPV-NC) and a-Si panels without casing (a-Si-NC) showed more pronounced decreases for the OPV-NC panels compared to a-Si-NC in contrast to the relative decreases found when both panels had casing. This is because a-Si impacts depend more on the contribution from the panel itself as opposed to the casing. A highly noticeable finding was the EPBT for OPV-NC panels that were on the order of 42 days (incineration) and 45 days (landfill) in total. Similarly, the CPBT for this technology decreased to 16 days and 14 days, respectively.

The high impact contribution from the BOS for OPV-D panels contrasts with the m-Si panels, whose environmental and human health impacts were most heavily influenced by production of the solar cell itself (Appendix: Chapter 4). This was somewhat expected knowing that the production of OPV-D panels is much less impactful on a watt-per-watt basis, compared with m-Si panels. 146 However, these results

were also dependent on the expected lifetime of the OPV-D panels. For instance, Epsinosa et al. found that the majority of the environmental and human health impacts from their OPV "solar-park" resulted from the production of the panels themselves, 62 whose lifetimes were estimated at one year compared to the default scenario of five years in this chapter. In contrast, S2 saw a decrease in the contribution arising from the production of the OPV-D panel itself. As discussed previously, S1 required that the OPV-D panels be replaced every five years, leading to a greater contribution of the panels themselves to the overall results. The shorter use-phase of S2 resulted in a situation where such replacements were not necessary and thus the impacts from the panels had diminished.

Conclusion

As was demonstrated in Chapter 3, the production of OPV panels (i.e. cradle-to-gate analysis) have shown greater resource efficiencies and lower environmental hazards compared to conventional silicon-based PV. The findings of the current chapter demonstrate that these potential advantages remain after including the use and end-of-life stages, albeit at a lower relative value. Obvious uncertainties remain regarding the assumed lifetimes and efficiencies that will be achieved at the industrial scale. Increasing both are necessary to reducing the environmental and human health impacts of this technology, however there is a non-linear relationship between lifetimes or efficiencies and impacts changes across the life- The current results suggest that OPV are a preferred energy producing technology compared with siliconbased PV from an environmental and human health perspective, particularly for uses where the product lifetime is relatively short (i.e. < 5 years) or where product systems are extremely simplified. This latter point was reflected in the LCA results for the solar rooftop array (S1) that had no mounting structure and the portable charger (S2) that had no case. This was also noticeable based on the quicker EPBT and CPBT for S2 compared with S1. Additionally, there may be important sources of uncertainty for the silicon panels as well. Although m-Si and a-Si are much more mature technologies, advances in silicon-cell technology at the lab-scale still continue. 15 However, their influence on the average market silicon-module efficiency

is not yet apparent. 22 Moreover, as more 1 st -generation silicon panels come to their end of serviceable life, the switch to full-scale silicon recycling could have a greater impact on the environmental profile of that technology compared with incremental adjustments to the efficiencies and lifetimes. transport layer, UV-blockers, coatings, barriers and/or optical spacers, for example. To achieve the thinness that is characteristic of OPV, these layers must contain very little material in extremely thin layers on the orders of tens to hundreds of nanometers. Consequently, the materials used often will be on the nanometer size range. In the OPV device considered throughout this thesis, PCBM nanoparticles were used in the active layer and TiO2 nanoparticles used as the optical spacer (Figure 3-1). While the previously reviewed OPV-LCA studies discussed in Chapter 1 have calculated the material and energy consumption during ENM production and OPV manufacturing, there was no estimation of any ENM emissions during their production, manufacture of the OPV panel, use nor end-of-life (Table 5-1). Engineered Nanomaterials: Resource Efficiencies and Hazards ENM are loosely defined as materials having a single dimension at or below 100 nanometers (nm) and generally in the range of 1-100 nm. 163 In the regulatory context, the European Commission defines ENM as intentionally produced materials in which at least 50% of the primary particles, aggregates and/or agglomerates have one or more external dimensions between 1-100 nm. 164 To put this in perspective, a human hair is roughly 100,000 nm in diameter, a single bacterium can be 2,500 nm in length, and DNA has a diameter of 2.5 nm. At the nanometer size range, there are new possible applications ranging from use in pharmaceuticals and agriculture to textile and energy sectors. 165 Use and production of ENM is also expected to assist in environmental protections, produce jobs and increase economic growth. 166,167 Apart from being manufactured at size ranges that are much smaller than their bulk counterparts, ENM can take on distinct shapes (e.g. fibers) and embodiments (e.g. coated materials). ENM are not only defined physically but by their distinct properties and behaviors. For instance, as materials decrease in size, their surface area to volume ratio increases to the point where surface-dominated properties are enhanced and potentially new physico-chemical properties may appear compared with micro-scale materials. 168 Thus, materials that are assumed to be inert or unreactive in their bulk-sizes, such as carbon, may take on greater abilities, such as C60-fullerenes acting as a semi-conductor. ENM are also foreseen to lead to more efficient chemical reactions and industrial processes, reducing energy consumption and avoiding waste production. 74,169 Thus, many 3 rd -generation PV have been exploiting ENM properties to achieve smaller, more efficient PV devices than were previously possible.

Coincidentally, the same physico-chemical characteristics leading to enhanced material properties, technological advantages and resource efficiencies may also introduce environmental and human health hazards [170][171][172][173] resulting from intentional and/or unintentional releases of ENM across the OPV lifecycle. 174,175 In the last two decades there has been tremendous growth in 'nanotoxicology' publications (Figure 5-1), driven by concerns that ENM may present new toxicological challenges compared to their Chapter 5 Options for Assessing the Toxicological Impacts from Engineered Nanomaterials Use in Organic Photovoltaics 109 bulk or non-particulate counterparts. [158][159][160][161]170,[176][177][178] At the same time, regulatory frameworks may require that companies collect and submit health and safety data to governmental bodies if their products fall under the definition of an ENM. [179][180][181] Figure In this example, it is assumed that the two pigments (i) are of the same chemical composition but different sizes, (ii) ENMversus bulk-manufacturing differ by the energy and materials (e.g. solvents) required to mechanically grind bulk material to ENM-sizes, (iii) due to its higher efficiency, less ENM-enabled material is required during the use-phase and thus less upstream raw material extraction is required and (iv) although less active ingredient is emitted during the use phase, it is distinct that it is being emitted in the ENM form as opposed to bulk emissions. The difference in energy consumption, material use, waste generation and emissions depicted by up/down arrows represent the relative life-cycle inventory of the ENM-enabled product (with up designating the ENM-enabled amounts are higher and vice versa but without any indication of the magnitude of change). Aggregation of all inventory values would then relate to their potential environmental impacts via its material specific characterization factor.

The gap in published literature is due to a lack of databases that contain ENM-specific life-cycle inventories for describing the material and energy inputs and waste outputs during production, but also due to the lack of life-cycle impact assessment methodologies that are able to characterize ENM-specific fate, exposure and human health impacts. [184][185][186][187][188] Chapter 5 Options for Assessing the Toxicological Impacts from Engineered Nanomaterials Use in Organic Photovoltaics 111

In regards to the emissions, there have been some attempts to model and calculate major environmental ENM flows leading from the production [189][190][191] but also the use [192][193][194] and disposal of these materials. 195 These approaches and models can be used to estimate average, global ENM emissions that might be used to create foreground life-cycle inventory emissions data of ENM-related products and processes. To be useful in LCA, each ENM emission must contain a corresponding characterization factor (CFNS) that converts its emission into a potential environmental or human health impact.

There are a handful of published papers that have made first approximations to model ENM fate, exposure and toxicity within LCA. [93][94][95][96] Those first approximations include one for human health 93 and three ii for ecotoxicity CF for nanomaterials. [94][95][96] In each case, the authors modified existing CF in USEtox to make them ENM-specific. Currently existing life-cycle impact assessment methods, such as USEtox, take advantage of a number of assumptions that conveniently describe the fate and transport of small organic molecules and metals quite well, but these methods are not appropriate for ENM or similarly described colloidal materials. 196 The major assumptions utilized in life-cycle impact assessment methods are that conditions are at steady-state and systems have reached thermodynamic equilibrium. 101,197 Consequently, these models assume that there are no changes in concentration of the pollutant over time and that the fate of these pollutants can be estimated using (equilibrium) partition coefficients (e.g. octanol-water partition coefficient, Kow). These coefficients are defined by the ratio of the resulting pollutant concentration in one medium versus another (e.g. octanol versus water), once the system involving both mediums has achieved equilibrium.

However, when released into a specific medium, ENM exist in their own phase and do not form uniform phases with the surrounding medium unlike organics. Therefore, and by definition, ENM are not thermodynamically stable, even if certain ENM may be kinetically stable for long periods of time. 196 In other words, an ENM concentration ratio between two mediums cannot be predicted from a previously measured partition coefficient if conditions were not exactly the same between the two scenarios. 196 This is because the behavior of ENM in the environment are heavily concentration dependent (i.e. the initial conditions of , the amount of ENM in, and the time at which the system is measured will produce different results). 95,196 Thus, in place of equilibrium partition coefficients, ENM should be described kinetically using dynamic fate and exposure models. For example, such models might employ the use of first-order rate ii Walser et al indirectly calculated the impacts from nanosilver by using experimentally derived bioavailable fraction values to estimate the amount of ionic silver released during use and disposal. The applied characterization factor was thus, technically not specific for nanosilver but for ionic silver.

constants to describe the dissolution of ENM into ions in the water column. 198 Furthermore, steady-state models are also not in agreement with the fact that ENM emissions rates are not typically constant events. This is particularly true for indoor, occupational scenarios. 199,200 Instead, ENM emissions are likely to be episodic, further influencing the concentration in the environment and the concentration-dependent transformations specific to that material. Lastly, the exposure to ENM, and specifically inhalation of ENM, will present concentration-dependent mechanisms for deposition, clearance and retention in the lung.

That is, inhalation of ENM is not only dependent on the inhalation rate of the individual who is exposed 201 but also the ENM characteristics, biological mechanisms and time at which exposure is measured. 202 It has been proposed to use a ENM-specific retention factor, R, for such purposes, calculated using human airway lung deposition and clearance models such as the Multiple-Path Particle Dosimetry (MPPD) model

. 97 The MPPD model is a tool typically used in HHRA for determining the internal lung dose from exposure to airborne concentrations of chemicals and particulates. However, the current MPPD model does not allow for estimating deposition or clearance while using exposure conditions that change over time.

Review of Previously Published Characterization Factors for Engineered Nanomaterials

In previously published CFNS, Eckelman et al. 94 estimated the fate and exposure of carbon nanotubes in the environment using empirically derived values for the partition coefficients already used in USEtox such as the octanol-water partition coefficient, Henry's law coefficient, and bioaccumulation factor in fish, for example. In addition, they added a parameter for aggregation and sedimentation that was reported as a deterministic fraction of the carbon nanotubes that are removed from the freshwater. Eckelman et al.

report a worst-case and realistic set of results, in which the worst-case fate calculation resulted in a residence time of carbon nanotubes in water of 143 days. Under more "realistic" conditions in which aggregation and sedimentation are considered, the residence time decreased to several days. Their estimation of the exposure calculated as a percentage of the total concentration of carbon nanotubes in water according to the USEtox procedure, resulted in 100% exposure in the worst-case scenario and 98%

in the realistic scenario. The resulting CFNS were 29,000 and 3,700 potentially affected fraction of species per kg of emitted carbon nanotubes in the worst-and realistic-case scenarios, respectively.

Miseljik and Olsen 96 presented a simplified USEtox fate calculation by artificially defining the residence time of nanoparticles of silver (nano-Ag) and TiO2 as one day. This was conceived on a quasi-kinetic basis representing the residence time given aggregation and sedimentation of these materials in freshwater systems. However, this kinetic parameter was ultimately used inside of a steady-state model, as was done Chapter 5 Options for Assessing the Toxicological Impacts from Engineered Nanomaterials Use in Organic Photovoltaics 113 in Eckelman et al. 96 These authors assumed an exposure factor of one in lieu of considering differences between external concentrations and uptake by the species. The resulting CFNS for nano-Ag and TiO2 were 8,573 and 26.2 potentially affected fraction of species per kg of emitted nanomaterial, respectively. Given the fate and exposure calculations were identical for both silver and TiO2, the differences in the CFNS resulted from their respective eco-toxicity dose-response relationships, whereby nano-Ag were found to be much more toxic than nano-TiO2.

Salieri et al. 95 defined their fate factor for nano-TiO2 using properly defined kinetic descriptors of fate and transport, unlike the two previous studies. Although, the fate factor was employed inside of a steady- of ENM by freshwater species. 95 After creating a weighted-average residence time for nano-TiO2, the resulting CFNS was reported as 0.28 potentially affected fraction of species per kg of emitted nano-TiO2.

This value is two-orders of magnitude smaller than what was reported by Miseljik and Olsen for nano-TiO2. While this difference can be partially explained by the difference in residence times calculated between the two studies, there were also differences in the reported eco-toxicity dose-response relationships. Salieri et al. reported an effective concentration at which 50% of species were effected between 0.013-0.018 kg/m 3 , while that of Mislejic and Olsen was reported as 0.019 kg/m 3 .

In regards to human health, Pini et al. 93 published a human health CFNS based on the framework of Walser et al. 97 The latter framework introduces the potential use of ENM-specific transformations such as aggregation, agglomeration and gravitational settling for calculating the fate of ENM in the indoor air setting. 97 In the end, the CFNS published by Pini et al. estimated the fate of nano-TiO2 as a function of advective air flow between the indoor and outdoor air, without the consideration of ENM-specific transformations that were outlined in Walser et al. 93 Pini et al. estimated exposure using a retention factor Chapter 5 Options for Assessing the Toxicological Impacts from Engineered Nanomaterials Use in Organic Photovoltaics 114 in order to account for the fact that particle deposition should not assumed to be 100%. Deposition of particles in the human airway may occur in the upper airway (i.e. nasal and mouth cavities), tracheobronchial region, air-exchange regions and/or the interstitial space, for example. The deposition and ultimate retention is a function of both bulk air movement through the airways but also a function of biological and chemical mechanisms that are responsible for sequestering, relocating and eliminating particles from the body. The retention factor calculated using the MPPD model in Pini et al. was specific for deposition, clearance and final retention under constant, steady-state exposure conditions. 97 However, the exposure to ENM, and specifically inhalation of ENM, will present concentration-dependent mechanisms for deposition, clearance and retention in the lung. That is, inhalation of ENM is not only dependent on the inhalation rate of the individual who is exposed 201 but also the ENM characteristics, biological mechanisms and time at which exposure is measured. 202 Since the current version of the MPPD model does not allow for estimating deposition or clearance while using variable exposure conditions, an alternate approach would need to be used to adapt steady-state models for handling these estimations of exposure.

To summarize, while it is possible -even if challenging -to build life-cycle inventories that estimate and quantify ENM emissions along the OPV life-cycle, 190,192,203,204 currently available life-cycle impact assessment software and methodologies do not contain CFNS that are necessary for quantifying the fate of, exposure to and impacts from those ENM. 187 Risk Assessment: Hazards and Risks Risk, in the context of environmental and public health disciplines, is defined as the probability that adverse ecological or human health impact results from exposure to an environmental stressor, which may be any physical, biological or chemical entity. To quantify substance-related risks, a HHRA or ERA must be conducted. In contrast to LCA, the aim of these other tools is to quantify absolute values of risk.

The National Research Council outlined the basic tenants to a generalized account of risk assessment in their 1983 publication on the subject and which was directly interpreted in this chapter to mean HHRA. 205 The main framework of a HHRA contains four steps: (1) hazard identification, (2) dose-response assessment, (3) exposure assessment, and (4) risk characterization (Figure 5-3). In brief, the hazard assessment involves an observation and determination of substance-specific adverse effects (i.e. hazards), while a dose-response analysis quantitatively characterizes the relationship between the dose or concentration of a substance and the level of adverse effect. The exposure assessment defines exposure scenarios that describe conditions in which a substance (on its own, in a mixture or embedded in an article) is manufactured, handled or used across its life-cycle. The magnitude of exposure is assessed for specified intake routes (e.g. inhalation), either through direct measurements or using models. In risk characterization, estimated exposure doses or concentrations are then compared to a recommended exposure limit (REL) to evaluate potential risk to calculate a risk characterization ratio (RCR) where values greater than or equal to one signify the existence of risk from exposure of to that substance (equation Lastly, an uncertainty analysis is conducted to analyze the level of ambiguity in each step of the HHRA process, pertaining to the quality and/or quantity of the input data and/or the applied models.

((5-1)).
Due to the existing regulatory framework governing high-volume, industrial-scale chemicals in commerce, HHRA and ERA are default tools used by scientists, regulators and commercial enterprises that need to ensure the safety of specific chemicals and substances used in their products. [206][207][208] In theory, ERA 33 and HHRA 32 could be used on a case-by-case basis to identify whether ENM also pose ecological or human health risks, similar to what is done for regular chemicals (Figure 5-4). 176 The former consisting of emissions, along with their fate and exposure, as well as elementary and techno-sphere flows, all of which are connected to various environmental and sustainability metrics used to measure levels of resource efficiency between products and processes. Human health risk assessment is depicted by its discreet scope that focuses on the toxicological risks. While both can evaluate issues of chemical toxicity, they do so with different methods, and in the case of life-cycle assessment, application of methods to determine ENM-toxicity are not currently employed in practice, they are for human health risk assessment.

Chapter 5 Options for Assessing the Toxicological Impacts from Engineered Nanomaterials Use in Organic Photovoltaics 117 However, HHRA is not immune to the same data and methodological gaps that have presented themselves for LCA, and thus HHRA may not necessarily serve as a panacea to the question of human health impacts for ENM-containing products such as OPV. For example, current HHRA efforts for ENM have not kept pace with their production and use in industry. To date, the U.S. Environmental Protection Agency (U.S. EPA) has released just one ENM-HHRA, 209 while the U.S. National Institute of Occupational Safety and Health (U.S. NIOSH) has released two ENM-specific occupational safety exposure levels. 161,210 Data gaps in toxicity and exposure assessments largely hinder the HHRA process from moving forward at sufficient speed. 174,211,212 While the U.S. 213 and E.U., 181 for example, can demand and gather data to assist in the HHRA procedure for ENM, an assessment of all registered ENM has not been completed. Although, some data and modeling efforts can be bypassed in HHRA, for example, by directly measuring the exposure doses or concentrations that one is subjected to. More commonly, however, qualitative HHRA [214][215][216][217] approaches have been put forth for prioritizing research efforts, environmental management and regulatory activities in the case of ENM. Nonetheless, many ENM-specific models, from those qualitative control banding approaches 218 to more sophisticated, dynamic approaches 219,220 have been or are currently being developed for ENM. Such tools are, thus, one option for addressing the human health impacts of ENM used in OPV.

Complementary and Integrated Approaches for Life-Cycle Assessment and Risk

Assessment

Developing nanotechnologies in a responsible and sustainable way should require that the net value to society is considered by considering their resource efficiencies as well as their potential human health and environmental risks. The general tendency is for different stakeholders to focus on these issues as distinct endpoints that must use distinct tools (e.g. LCA, HHRA, ERA), either out of familiarity or opinion, ultimately excepting the exclusion of a more comprehensive viewpoint. 74,221 Together, aspects from both LCA and HHRA may provide an adequate understanding of this information. 185,[222][223][224][225] However, their use as separate, distinct tools is only one potential option for doing so. In the remainder of this chapter, other options for achieving this objective are presented and discussed in greater detail. These options will be presented in a decision-depend context, exploring the application of ENM as an emerging technology and the corresponding evaluation objectives that may present themselves at different stages of product maturity. In total, five different options for using and combining LCA and HHRA for the environmental While having the ability to provide definitive estimates of ENM human health risk, a critical drawback of this option is the level of detail involved, which is not necessary in the case of early stage development of products containing ENM. Consequently, these tools are often utilized separately by different types of stakeholders who have distinctly different needs. Thus, the separate use of LCA or HHRA (i.e. without the use of the other) for emerging technologies will be limited to one set of results but not the other as well as limited by when along the development of the technology to use these tools.

Complementary Use of LCA and RA for Nanotechnologies However, Complementary Use, defined by using both tools separately and then combining their results, is an obvious approach when the evaluation of both the resource efficiencies and the human health effects of ENM needs to be considered. This option thus necessarily involves a detailed assessment of the exposure to and risk from ENM. Therefore, much like the Separate Use HHRA option discussed above, Complementary Use will be most appropriate during full-scale technology development (Figure 5-5) or when it is necessary to ensure human health safety. 214,226,227 Chapter 5 Options for Assessing the Toxicological Impacts from Engineered Nanomaterials Use in Organic Complementary Use also require that the different scopes or the results of the two tools be reconciled.

To clarify, LCA scales its results to a service or 'functional' unit (e.g. production of an average kWh of energy), while HHRA is defined by a relevant exposure scenario (e.g. the human health risk posed by nano-TiO2 emissions in an industrial plant). Thus, their results would require careful conversion (i.e. normalizing the results of each tool such that they can be compared on relative units, such as the conversion to damages) before useful comparisons of the results could be made. 222 Even then, the comparison essentially is likely to remain an apples-to-oranges scenario as opposed to apples-to-apples, unless the scopes of each tool were defined strategically and sufficiently to the point that they were in agreement In such a situation, one must choose whether they find apples or oranges more important, introducing explicit subjectivity into the decision making process. However, whether this is a true drawback of Complementary Use depends on the objectives of the decision maker. For instance, this could very well be a valid option for those looking to use LCA as an early stage eco-design tool while also looking to ensure occupational safety at an early stage of research, development and production.

Integration of LCA and RA for Nanotechnologies

As opposed to Complementary Use, Integration here refers to combining the methods of each tool instead of combining their results. 228 This approach utilizes methods generally attributed to HHRA to modify the approach for estimating the fate of, exposure to and/or effects from ENM. Because integration allows for the life-cycle, this approach helps to ensure that 'burden shifting' does not occur. 94 Integration can, therefore, be used as an alternative to existing uses of Separate or Complementary Use but it can be uniquely used for evaluation at early-stage product development, life-cycle eco-design or environmental product declarations, for example. 229

Figure 5-7 Key aspects of life-cycle and risk assessment integration at the methodological level. Integration is replaced on its level of spatial and temporal specificity as well as model and data complexity. For example, low temporal resolution (i.e. no changes in emissions over time) with a global spatial scope will result in a steady-state model that uses constant (i.e. nonvariable) and highly aggregated data, respectively, similar to what is used in current life-cycle assessment methods such as USEtox. This is the case labeled as "GI" or Global Integration, and represents one of the three levels of integration set forth in this thesis, however the degree of integration is dynamic for each axis, independent of one another. For instance, low temporal resolution with a local spatial scope (i.e. high spatial resolution) will similarly result in a steady-state model using constant, but, non-aggregated (i.e. site-specific) data. To illustrate these points further, ENM emissions modeling is shown in the figure as a practical representation. Global Integration, thus, results in the use of non-changing, aggregated emissions data that is averaged for a large continental region in a steady-state model. In terms of its relevancy to ENM, this approach has less predictive power than the other two options shown in the figure. For example, Site-specific Integration (SSI), uses the empirically determined changes in single-source emissions data measured over an unconditional timeframe in a fullydynamic model. Context-dependent Integration (CDI) models emissions using a generalized time-dependent assumption (e.g. positive, linear correlation) for classes of sources averaged at the country level in a partially-dynamic model.

Three different approaches which differ by their extent of methodological integration are presented in framework but supplemented with ENM-specific estimations of toxicity, for example, using the USEtox life-cycle impact assessment approach. [93][94][95][96]197 Global Integration, represents a feasible, first-tier tool that covers the scope of both resource efficiency and toxicological hazards. It is particularly well-suited for supporting eco-design or environmental product declarations which must be evaluated using current LCA standards. However, this approach relies on the predominant tools available, which ignore spatial and temporal acuity in both the life-cycle inventory and also the estimations of fate, exposure and hazard.

Models that do not possess sufficient spatial or temporal resolution cannot incorporate specific environmental details and exposure conditions necessary for appropriately estimating the fate of, and exposure to ENM, as discussed earlier in this chapter. 97 ENM are also influenced by both physical and molecular forces akin to colloidal materials, leading to an array of transformations (e.g. aggregation) that are important in determining their fate and exposure. 174,198,211,230 The context setting (e.g. indoor versus

outdoor emissions) and their local landscape parameters (e.g. ventilation rates) will also influence these behaviors in important ways.

Thus, emission, fate and exposure models that incorporate more spatial and temporal detail would be more appropriate for and increase the predictive power of ENM evaluation. Therefore, two further degrees of integration are considered: Context-dependent Integration (CDI) and Site-specific Integration (SSI) that represent models with greater levels of time and spatial resolution compared to Global Integration (Figure 567). Context-dependent Integration relies on models whose underlying parameters (e.g. emission magnitude, ENM size) are estimated per 'group' or 'class'. Groupings can be applied, but not limited, to emission sources (e.g. workplace activity), emission pattern (e.g. constant), emission timeframe, ENM-type (e.g. size), and conditions of the receiving compartment (e.g. ventilation rates). 231 Classes should be defined by statistically significant generalizations such that impact results differ from class to class but not within each class. 232 For example, statistically determined environmental conditions (e.g. meteorological data), operating parameters (e.g. stack height of emissions source) and receptor densities of the air pollution emissions were determined for waste treatment sites across Spain. 232 In this way, the spatial distribution around an emission point could be considered for estimating the concentration increment per receptor in geographically distinguishable environments. Indoor air ENM emissions and exposure scenarios could prove an extremely practical first case-study of the Contextdependent Integration approach, given that indoor environmental conditions are more constrained than ambient ones. Indoor, occupational scenarios may even present conditions whereby ENM fate and transport are more dependent on workplace conditions rather than ENM characteristics. 97,233 These Chapter 5 Options for Assessing the Toxicological Impacts from Engineered Nanomaterials Use in Organic Photovoltaics 123 conditions, together, could allow for the relatively simplified grouping of indoor, occupational model parameters, similar to what has been done for regular chemicals in residential indoor air, 234,235 industrial processes 232 and consumer products. [236][237][238] Site-specific Integration is the third-degree of the most ideal form of integration, whereby complete spatial and temporal resolution is known and incorporated into a fully-dynamic set of emission, fate and exposure models (Figure 567). This approach also assumes that all ENM emissions are known and quantifiable across the life-cycle of the ENM-containing product. Site-specific Integration would provide the most accurate results (e.g. absolute impact values), however it can only be applied on a case-by-case approach because of how inclusive the data and model requirements would be. The latter point most fitting for full-scale development and/or the need to ensure human health or ecological safety. Mature nanotechnology market products, such as certain paints and pigments, have well-established manufacturing routes, production volumes and intended uses that pose potential exposures across the life-cycle. In such real-world scenarios, absolute damage and evaluations of risk are necessary to ensure human health safety, local ecosystem integrity and ensure regulatory compliance. While Complementary Use would essentially allow an analogous evaluation of both the environmental resource efficiencies and risks of products containing ENM, as was discussed in the section above, this assessment option lacks harmony in the scopes of the two tools, leading to greater difficulty in interpreting the distinct LCA and RA results. Furthermore, it should be noted that as the number of ENM-emitting processes along the lifecycle become large (e.g. > 20), data collection may become untenable and the option of Site-specific Integration impractical. 232 As an alternative in such a situation, a special case of the Complementary Use approach might involve using Global Integration or Context-dependent Integration to identify the ENM emission hot-spots of greatest concern, which could then be followed by RA where it is warranted (e.g.

where ENM emissions contribute to a noticeably large fraction of the total human health impact across the life-cycle).

On the contrary, the level of data and model detail that is required of Site-specific Integration or Complementary Use would be unnecessary in cases of early product and process design of nanotechnologies since localized (i.e. site-specific) environmental conditions and operating parameters would not be known. To illustrate more clearly in context of this thesis, 3rd-generation PV devices are a mostly lab-scale technology that exploit the photoelectric properties of ENM for their functionality. In

Conclusion

LCA and HHRA offer decision makers the ability to evaluate the resource efficiencies and human health risks of their products and substances within their products, respectively, such as OPV. Specifically, LCA was used in Chapter 3 and Chapter 4 to compare the environmental and human health impacts of OPV with silicon-based PV. The results of those chapters demonstrate that OPV benefit from greater resource efficiency and have lower relative impacts overall. However, the results did not include the potential human health impacts from ENM.

The separate or complementary use of HHRA could potentially provide a solution to this assessment gap.

Making use of the HHRA makes most sense at the point of full-scale development or ensuring human health safety is required. Conversely, the detailed data needs required to complete a HHRA at early-stage product development would be highly uncertain and largely unwarranted. Instead, integrating HHRA approaches into LCA could provide an alternative option that is more appropriate for early-stage development and eco-design purposes, for example. Unfortunately, a lack of relevant data for most types of ENM remains a barrier for conducting both HHRA as well as LCA, and without such data neither approach will be feasible, let alone present opportunities for integration. For example, without known ENM emissions a life-cycle inventory cannot be calculated nor can an exposure assessment in HHRA be made. The same is true for ENM-specific data that is required for building fate and exposure models and determining dose-response relationship for both life-cycle impact assessment as well as HHRA.

Chapter 5 Options for Assessing the Toxicological Impacts from Engineered Nanomaterials Use in Organic Photovoltaics 125 While data is currently limited for ENM, materials such as certain metals, metal oxides and carbon based ENM have relatively more reported data in the literature compared with others and can serve as starting points for establishing HHRA or integrated, dynamic life-cycle impact assessment models similar to what has been done for steady-state models. [93][94][95][96] This is quite relevant for the case of OPV who use both carbon and metal-oxide ENM in their material layers and thus could be used to make further evaluations of the potential human health impacts resulting from the use of ENM in OPV. In terms of the three integrative approaches presented in this chapter, the previous steady-state adaptations made for these materials are what is referred to as Global Integration in this chapter. Given its use of existing steady-state models, this could serve as an immediate and near-term (i.e. circa 5 years) objective for most ENM evaluations.

However, as described previously, this approach also has less relevance for certain fate and exposure processes and, thus, less predictive power for estimating ENM human health impacts. Context-dependent Integration, on the other hand, can introduce a more dynamic process to the modeling approach and, hence, more relevant estimations of fate, exposure and impacts. This integration option will require greater coordination among the scientific community to identify the relevant data and appropriate emission, fate and exposure 'classes' that will be relevant to ENM development and uses.

In view of these potential options, Chapter 6 presents a more focused HHRA for the carbon and metal oxide ENM used in OPV. Chapter 7 then uses the approach and lessons learned from the HHRA to construct a Context-dependent Integration life-cycle impact assessment approach for estimating human health impacts of ENM. and human health impacts of OPV technologies that use ENM. In this current chapter, HHRA is used as a preliminary tool to evaluate the human health risks of those ENM, and then key methodological HHRA aspects are identified that can be integrated within life-cycle impact assessment methods.

Qualitative (Screening Level) Human Health Risk Assessment

Qualitative Exposure Assessment

As was discussed in Chapter 5, data and methodological gaps for HHRA present challenges to fully implementing this tool across the entire life-cycle of OPV devices. Thus, it was out of the scope of this thesis to focus quantitatively on all exposure scenarios (Figure 6-1) nor the risk therein posed by both PCBM and nano-TiO2. Proper emissions controls such as personal protective equipment may theoretically be in place, actual uptake of such equipment has been shown to be inconsistent and often low. 249 Downstream of ENM and OPV manufacturing, consumer hazards are hypothetically possible. Due to the fact that both PCBM and nano-TiO2 are embedded in a polymer matrix, it was assumed the use phase would not pose sufficient conditions to allow leaching of the ENM from the product. During the usephases explored in Chapter 4, exposure to naturally occurring weather including sun exposure and rainfall will occur. This is particularly true for the case where OPV are installed on rooftops for primary energy production, while portable chargers would likely have much less utilization, they would be subject to potentially greater amounts of physical stress from direct handling of the device. Rooftop systems would thus be hypothetically susceptible to leaching from acid rain or other extreme conditions, while portable chargers' physical integrity could be compromised (e.g. delaminated). There are no studies in the literature reporting any estimation of ENM leaching from OPV panels, although leaching of other Chapter 6 Human Health Risk Assessments: Quantitative Assessment of Titanium Dioxide and Qualitative Assessment of C60 Fullerene Nanoparticles 129 components from intentionally damaged (e.g. shredding) OPV devices has been shown. 250 At their endof-life, it is assumed that PCBM will be converted to CO2 during incineration. Based on previously reported results for cerium oxide nanoparticles that remained intact after incineration (i.e. in the bottom-ash), 195 there is a possible exposure scenario for nano-TiO2 during incineration for the OPV, particularly if the incineration residues are deposited in landfills. Similarly, depositing intact, whole OPV panels into a landfill was assumed to have possible leaching potential due to long term exposures of acidic, leachates typically found in such conditions. In these cases, the polymer matrix could breakdown, exposing the individual layers and leach certain materials over time. Given these considerations, it was found that occupational exposure scenarios involving production of either the ENM themselves or the OPV panel resulted in the greatest potential exposure (Figure 6-2).

Hazard Identification of C60-fullerenes and PCBM

The principal properties specific to the functionality of C60-fullerenes, and thus PCBM, are closely related to its ability to reversibly accept upwards of six additional electrons. 50,51 C60-fullerenes are also nearly insoluble, however functionalization can produce variants of this molecule such as PCBM that are, in effect, soluble. 251 Due to their very small size and classification as an ENM, there is an initial assumption that some of the toxicological trends seen with other spherical ENM may be relevant for C60-fullerenes. 252 On the contrary, and as a consequence of their electrophilic nature, C60-fullerenes have been shown to act as free-radical sponges and have anti-inflammatory effects. [253][254][255][256][257][258][259][260] This may be the reason why some inhalation in vivo toxicity studies have found minimal inflammation, 261 no inflammation 262,263 or even antiinflammatory 259 effects upon exposure to particles of C60-fullerenes. Several in vivo studies do, however, demonstrate the pro-inflammatory nature of C60-fullerenes as they have been associated with enhanced reactive oxygen species production, damage to cell membrane integrity and lipid peroxidation, for example. 264,265 Very few studies on dermal exposure have been carried out, demonstrating limited to no uptake by the skin and a lack of evidence that C60-fullerenes could result in skin irritation. 266,267 Upon oral administration, limited uptake by the digestive track has been demonstrated 268 with no obvious toxicological effects taking place either in the gut or systemically. 269,270 Hazard Identification of Titanium Dioxide Nanoparticles Titanium dioxide is an important inorganic metal oxide used in industry. Industrial-scale production of TiO2 began in the early 20 th century and has steadily increased since then. Industrial use of TiO2 is generally in the rutile or anatase forms (Figure 6-3), although the rutile form has found greater commercial use. Global TiO2 production reached 6.6 million metric-tons in 2012, 271 with ENM-forms increasingly dominating both the legacy 272-274 and emerging TiO2 markets. 275,276 Legacy markets were dominated by fine particle sizes of TiO2, mostly for use as pigments and coatings. This fine particulate form of TiO2 is generally considered thermodynamically stable and relatively inert. 161 For this reason, TiO2 was historically categorized as a nuisance dust in the occupational workplace. However, this is not necessarily true for nano-TiO2. Because of their large production volume and long historical use, there have been many more toxicological studies performed on TiO2 compared with PCBM, particularly those for inhalation exposure studies. [277][278][279][280][281][282] The range of nano-TiO2 induced in vivo toxicological responses in the respiratory system spans from inflammation, histopathology (e.g. endothelial or epithelial changes), cytotoxicity, oxidative stress, oncogenic effects (e.g. increase rates of carcinoma) and other genotoxic-related events. 160,161,272,[277][278][279][280][281][282][283][284][285][286] In certain cases, these effects were found to be more pronounced for nano-TiO2 compared to its bulk counterpart. 273 In their 2011 report, NIOSH specifically addressed concerns over nano-TiO2, acknowledging a potentially higher hazard potential in terms of inflammation and carcinogenicity of corresponding nanoparticles of this material as compared to bulk sizes. 161 For dermal exposure, there is limited to no evidence from in vivo animal studies showing nano-TiO2 is able to penetrate the (intact) skin. [287][288][289][290][291] Penetration often remains to the superficial layers of the epidermis 288 however dermal 292 and hypodermal penetration, such as in the hair follicles, 293,294 has been observed. Potential toxicological Chapter 6 Human Health Risk Assessments: Quantitative Assessment of Titanium Dioxide and Qualitative Assessment of C60 Fullerene Nanoparticles 131 effects from dermal exposure range from limited to no skin irritation 295 to oxidative stress 296 and lesions in certain organ systems. 292 Oral administration to animals has demonstrated their uptake particularly to the liver but also other organs such as the kidney, brain and testes. 297 Potential toxicological effects from oral exposure range from oxidative stress and neorinflammation 298 to necropathy, 297,299,300 for example.

Relevance of the Qualitative Exposure Assessment and Hazard Data Availability

The limitations to conducting a quantitative HHRA rest on the data availability and/or appropriate models available. For example, toxicological data with multiple dosing groups is required to complete a doseresponse analysis. Such data should also involve relevant exposure routes (e.g. inhalation as opposed to instillation) and preferably as long-term studies (e.g. chronic). Thus, the lack sufficient positively correlated toxicological data on C60-fullerenes or PCBM, particularly for in vivo long-term, multi-dose studies precludes its further analysis from the HHRA. This decision not to carry out a quantitative HHRA on C60-fullerenes should not be seen as a formal determination that these materials pose no human health risk across the life-cycle of OPV, since as the qualitative exposure assessment discussed, there is a high likelihood that emissions will occur along the OPV life-cycle. On the other hand, current human health relevant toxicological literature does not point to clear and consistent adverse responses nor is there sufficient data to carry out a quantitative assessment. There is, however, sufficient evidence in the literature points to both adverse non-cancerous and cancerous, chronic and sub-chronic human relevant toxicity of nano-TiO2. Sufficient data also exist to carry out an appropriate dose-response analysis of this material. The remainder of this chapter focuses on the quantitative HHRA of nano-TiO2 in occupational exposure scenarios in the OPV life-cycle.

Quantitative Risk Assessment

Methods

As described in Chapter 5, the four principle steps of HHRA involve (1) hazard identification, (2) doseresponse assessment, (3) exposure assessment, and (4) risk characterization (Figure 5-3). Hazard identification and the background on PCBM and nano-TiO2's toxicological concerns were addressed in the previous section of this chapter as a part of the qualitative HHRA. The methods sections below will describe steps ( 2), ( 3) and ( 4) in greater detail.

Chapter 6 Human Health Risk Assessments: Quantitative Assessment of Titanium Dioxide and Qualitative Assessment of C60 Fullerene Nanoparticles 132

Dose-Response Assessment

The benchmark dose (BMD) method was used to quantify the exposure concentration at which the adverse health effect occurs (i.e. dose-response relationship). This concentration was estimated from the corresponding, pre-defined benchmark response (BMR). The BMD is an alternative to the no-observedadverse-effect-level (NOAEL) and lowest-observed-adverse-effect-level (LOAEL). In this chapter, the terminology benchmark concentration (BMC) is used to clarify that the dose-response modeling used data correlating to exposure concentrations from whole body animal studies. Calculation of the BMC was completed using the Netherland's National Institute for Public Health and the Environment's (RIVM)

PROAST software (www.rivm.nl). PROAST was used to first calculate a BMCanimal (BMCa) based on the in vivo animal data. This value was converted to a distribution using PROAST's parametric bootstrap option (i.e. sampling with replacement) over 10,000 simulations. The BMCa was extrapolated to a human effect threshold referred to as the BMChuman (BMCh), by applying the appropriate extrapolation factors 301 using equation (6-1): Extrapolation factors represent differences between and among species according to anatomical differences (e.g. body size) and physiological functions (e.g. metabolism). Values are scaled with the extrapolation factors according the allocation scaling principle. The difference (i.e. increase) in breathing rates for persons exposed in the occupational setting are assumed to be elevated compared to persons at rest or not working and are captured within the EFintra. Log-normal distributions for these factors were defined using similar approaches presented by Slob et al. 302 A brief rationale follows. Biological mechanisms in a population (e.g. survival times after disease onset) are generally asymmetrical as opposed to being described by normal "bell-shape" curves. 303 In deterministic HHRA procedures, it is typical to use factors of ten to extrapolate to conservative yet acceptable human equivalent doses.

BMCh = BMCa EFinter • EFintra • UFi (6-
Although extrapolation factors of ten are supposed to be conservative protective values, it is understood that this does not always provide protection. However, for transparency, demonstration of the approach, and ease of interpretation the log-normal distributions were defined such that a value of ten was one-Chapter 6 Human Health Risk Assessments: Quantitative Assessment of Titanium Dioxide and Qualitative Assessment of C60 Fullerene Nanoparticles 133 order of magnitude greater than the mean and lies at the 99 th -percentile. In this regard, the selection of ten as a conservative extrapolation factor remains. 304 The geometric mean (GM) and geometric standard deviation (GSD) were 1 and 2.7, respectively (Figure 6-4).

(a) (b) Figure 6-4 Log-normal distributions of (a) the interspecies toxico-dynamic extrapolation factor with a geometric mean of 1 and geometric standard deviation of 3.27 and (b) the intraspecies extrapolation factor with a geometric mean of 1 and geometric standard deviation of 2.7. The x-axis is without units. 10,000 Monte-Carlo (MC) simulations were conducted using Crystal Ball (Oracle Corporation, USA) to complete the extrapolation of BMCa to BMCh distributions.

No relevant chronic in vivo multi-dose studies were found in the literature, although there was one such sub-chronic study 283 (i.e. 90-day exposure duration) whole body inhalation exposure to nano-TiO2.

Bermudez et al. exposed rats, mice and hamsters to uncoated, nano-TiO2 obtained from Evonik (formerly DeGussa) with a nominal particle diameter of 21 nm as supplied by the manufacturer. 283 Each species group was exposed to nano-TiO2 at concentrations of 0.0 mg/m 3 (control), 0.5 mg/m 3 , 2.0 mg/m 3 and 10 mg/m 3 for 6 hours/day, 5 days/week, for 13 weeks, while the control group received filtered air only (Table 6-1). Bronchoalveolar lavage (BAL) was completed on the lungs of the sacrificed animals at varying postexposure times to measure the counts of macrophages, neutrophils, eosinophils and lymphocytes. Only the effects at post exposure time zero (i.e. immediately following completion of exposure) were considered in the dose-response assessment. Bermudez et al. 283 reported that statistically significant changes in the BAL results were limited to the macrophages, neutrophil and lymphocyte cell types in the highest dose-groups. Furthermore, only data from the mice and rats were found to have statistically significant changes in percent cell counts over the control and thus the hamster data was not considered in the dose-response assessment.

Exposure Assessment

Measurements for occupational exposure to ENM are not abundant and the literature generally focuses on airborne concentrations as opposed to internal lung, internal oral, or dermal loading doses. 305,306 There are currently no models to estimate dermal or oral exposure from airborne concentrations. Thus, this HHRA focuses on the inhalation exposure to nano-TiO2 in the occupational setting. All exposure assessments were made with the near-field-far-field exposure assessment model in NanoSafer v. 1.1. 219,220,307 The three exposure scenarios (ES) considered in this chapter (Table 6-2) were based on prompts taken from the MARINA (www.marina-fp7.eu) and NANEX (www.nanex-project.eu) databases.

Table 6-2 Description of the exposure scenarios used for the human health risk assessment of inhalation exposure to nanoparticles of titanium dioxide in the occupational workplace. Hi: handling energy factor.

ES1

Transfer (dumping) of powder from a 10 kg bag into a mixing tank over 10-minute work cycles with 10-minute pauses inbetween over an 8-hour workday. Work is performed in a room with high ventilation rate. The process is assumed to be of high energy with the pouring height assumed to be 0.3 m -1 m corresponding to Hi = 0.8.

ES2

Transfer (dumping) of powder from a 560 kg containers into a larger holding vessel over 10-minute work cycles with 20minute pauses in-between over an 8-hour workday. Work is performed in a hall with low ventilation rate. The process is assumed to be of high energy with the pouring height assumed to be 0.3 m -1 m corresponding to Hi = 0.8.

ES3

Continuous filling (pouring) of bag bin with a total 250 kg of powder for 8 hours. Work is performed in a room with low ventilation rate. The process is assumed to be of high energy with the pouring height assumed to be 0.3 m -1 m corresponding to Hi = 0.8.

MARINA was an EU FP7 project aimed at developing risk management methods for ENM, including development of occupational release and ES. NANEX was also an EU FP7 project which aimed to catalogue potential exposure to EMN across the life-cycle including their manufacturing and industrial use. These scenarios correlated with workplace activities that take place during the production of nano-TiO2.

However, it was assumed that similar workplace scenarios would present themselves during OPV manufacturing activities. The near-field (NF) was defined as a box with a fixed volume of 2.3 m 3 that surrounds the ENM emission source. The far-field (FF) was defined as a box equal to the total volume of the work room (Vtot, m 3 ) minus the NF volume. Emissions (Ei, mg/min) were quantified (equation (6-2)) as a function of the respirable powder dustiness index (DIresp, mg/kg), determined using the rotating drum system. 308 and Hi is the handling energy factor (unit-less). Hi is based on a scale of zero to one, whereby zero is a noenergy event (e.g. no handling of the material) and one is a high energy event (e.g. crushing, dropping from greater than 2 m height). 309 In addition to those parameters used to derive source-term Ei, NanoSafer The equation shows that the standard deviation increased with larger DIresp values. This should be expected given that a larger DIresp generally results in a greater absolute standard deviation resulting from the higher release of particulates and thus uncertainty in fate and transport of this material. The DIresp for the TiO2 as considered in this chapter was 15 mg/kg, resulting in a standard deviation of 2.

Risk Characterization

Risk was calculated as a RCR (equation (6-4)).

RCR = EXPi

Results

Dose-Response Analysis

In general, a BMR (i.e. the relevant toxicological response of concern or safety) should approach a lower limit of reasonably measurable effects. For changes in total white blood cell counts relevant to inflammation, increases in 10% over the background response are often found to be significant. 310 However, due to uncertainty in measuring percent changes as opposed to total cell count, significant macrophage, neutrophil and lymphocyte percent changes were determined using a BMR of 20%. For both mice and rats, neutrophil and lymphocyte percent changes increased whereas macrophage changes decreased. Ultimately, the dose-response analysis was completed using the neutrophil rat data since (i) the decrease in macrophage percent change was directly correlated with the measured change in neutrophils and lymphocytes (Appendix: Chapter 6), (ii) neutrophil percent increases were much stronger and dominant compared to lymphocyte percent changes (Appendix: Chapter 6) and lastly (iii) rats showed inflammatory responses at lower concentrations than did mice (Figure 6-6). Figure 6-6 Fitted log-logistic models using PROAST software with reported confidence intervals to the mice and rat neutrophil percent changes upon inhalation of nano-TiO2. The dose-response results demonstrate differences in the slope of the lines per species, with a much more sensitive response for rats. The doesresponse curve shown in this example was fitted with a log-likelihood of -1374. The benchmark dose for rats is shown by the lower curve corresponding to data with large circles, while the benchmark dose for mice is shown for the upper curve using corresponding triangular data points. (see Appendix: Chapter 6 for full set of models fit to the dose-response data)

For rats, seven valid models were fitted to the neutrophil data (Table 6-4). The models were aggregated into an overall daily averaged inhalation concentration (i.e. BMCa) distribution that was normally distributed with a mean of 3.71 ± 0.56 mg/m 3 . After applying EFinter and EFintra over 10,000 Monte-Carlo simulations, the BMCh results followed a lognormal distribution with a geometric mean of 0.91 mg/m 3 and geometric standard deviation of 4.72 mg/m 3 (Figure 67). Approximately 59% and 40% of the distribution was explained by uncertainty in the values of EFinter and EFintra, respectively, while little variation was a consequence of the dose-response data used in the analysis (Figure 67).

(a) (b) Figure 6-7 Results of (a) the 10,000 Monte-Carlo simulations used to estimate the benchmark concentration for humans (BMCh) and (b) the contribution of each parameter used to estimate the benchmark concentrations for humans.

Exposure assessment

The daily NF and FF indoor air concentrations of nano-TiO2 were calculated for three different exposure scenarios that ultimately involved three different amounts of nano-TiO2 throughout the 8-hour workday.

The applied emission rates ranged from a low of 6.26 mg/min (ES3) to a high of 672 mg/min (ES2), while the value for ES1 was 12.0 mg/min. Figure 6-8 shows the time-integrated exposure concentrations in each of the exposure scenario. 6-5). The daily NF and FF indoor air concentrations were lognormally distributed in all exposure scenarios based on the geometric standard deviation of the DIresp of nano-TiO2 (Appendix: Chapter 6). Large variations in the NF and FF concentrations observed between the exposure scenarios were due to differences in the time-integrated substance emission, the dilution by room size, and the air exchange rate. As expected, the resulting exposure potentials varied directly with the scenario-specific integrated emission levels Chapter 6 Human Health Risk Assessments: Quantitative Assessment of Titanium Dioxide and Qualitative Assessment of C60 Fullerene Nanoparticles 141

given by the emission rate and duration of the process resulting in a higher exposure potential for ES2 than in the other scenarios. In the case of ES2, the high emissions and small workroom volume were coupled with a high handling energy factor (i.e. dumping powder into a vessel) and lower air exchange rate (4 h -1 ) as compared to the conditions in the other scenarios. Compared to the NF, the FF exposure concentrations were consistently lower but generally less than an order of magnitude lower. As was the case for the NF exposure scenarios, geometric means for FF exposure concentrations ranged from a low of 0.16 mg/m 3 for ES3 and a high of 11.8 mg/m 3 for ES2, while the value for ES1 was 0.259 mg/m 3 .

Risk Characterization and Uncertainty

Discussion

Risk Relevance to Engineered Nanomaterials Use in Production of Organic Photovoltaics

The results of the occupational HHRA presented in this chapter demonstrate a probability as high as 78%

for the lifetime risk of lung inflammation to workers handling nano-TiO2 during ENM production or OPV panel manufacturing. This risk was particularly relevant for ES2 where large-scale handling and use of high volume nano-TiO2 powders occurred over 8-hour workdays. The risk to lung inflammation was calculated as a 20% increase in certain white blood cells over background levels, however this response level only indicates the onset of inflammation as opposed to the degree and/or severity of the pathology. OPV are not produced at the industrial scale and thus the exposure scenarios presented in this thesis do not represent a specific OPV industrial profile. Instead, potential and realistic exposure scenarios that involve the production and handling of nano-TiO2 were presented and therefore provide a first-tier assessment for OPV. For example, if OPV devices utilizing PCBM and/or TiO2 come to full industrial scale and uptake to the marketplace, large volumes of these substances will be handled, loaded and dispensed either into other containers, holding tanks or vessels for mixing and/or application to the OPV panel. ES1 and ES3 involved exposure scenarios handling over an order of magnitude less nano-TiO2 powder compared with ES2. Thus, these represent production volumes that are much more moderate than ES2. Nevertheless, there remained a 10% lifetime probability of lung inflammation to workers handling these lower amounts of powders over an 8-hour workday.

Uncertainties within the Risk Assessment Procedure

The dose-response assessment was completed specifically from nano-TiO2 in vivo toxicological data, in accordance with the scientific literature that demonstrates the toxicological responses upon exposure to ENM and bulk substances can differ. Additionally, the emissions calculation was defined by a distribution that was defined by the standard deviation of nano-TiO2's dustiness index. While the dustiness index was material specific, its mass-based measurement does not provide detailed information about the sizedistributions in the dustiness tests. Overestimations for emissions and exposures from powder handling may be a result, whereas calculations for spray or fugitive-type releases might be closer to reality.

Airborne concentrations were further calculated using a two-zone near-and far-field model, which is particularly relevant for single source, non-fugitive emissions 311 that are very relevant for ENM. For instance, the persistence of uniformly small airborne ENM may quickly decline due to aggregation and agglomeration, 199,312,313 meaning that exposure to primary or small "nano" aggregates might be most relevant close to the emission source. [314][315][316] It should be noted that the fate and exposure model, NanoSafer, used in this work largely relied on advection and bulk air flow to estimate the concentration of nano-TiO2 in the workplace. The exposure also is based on external airborne concentrations as opposed to internal doses. Particle sizes are an important characteristic influencing the effectiveness of ventilation control, personal protective equipment and ultimately where ENM retention will occur in the lung. For example, ENM might lodge deeper and more uniformly in the lung (i.e. alveoli) as compared to larger-size materials. 314 Modifying factors (e.g. use of personal protective equipment) were not considered and should be considered for future assessments. For example, Fransman et al. 317 318 Additionally, a few occupational exposure studies have shown that particle size distributions in some cases may be similar for both ENM and conventional powders. 199,312 Such considerations could be included in models that utilize size-resolved concentration data (e.g. mass median aerodynamic diameters) in their models. 283,284,319 Given these relative uncertainties, the exposure assessments and corresponding risk calculations in this HHRA can be considered as worst-case scenarios.

The uncertainty arising from the use of a mass-based dose-metric was not addressed in this chapter.

Ideally, HHRA of ENM should use a dose-metric that is the best indication of toxicity as well as exposure. 246 For example, this might be indicated by mass concentration, particle number, or lung-deposited surface area. Although mass may not necessarily be the most suitable dose-metric to describe ENM doseresponse relationships, 320-323 it is the metric most easily and reliably determined for measuring and managing occupational exposures as management of particle numbers or surface area is not yet achievable. 161 The extrapolation of one dose-metric to another may be possible using specific uncertainty factors. For instance, the surface area can be estimated from a particle concentration size distribution of spherical ENM. However, there are considerable challenges in reliable measurements and, therefore, data conversion between these metrics for ENM with non-spherical morphologies 45 and their agglomerates and aggregates. 324,325 Conclusions These findings presented in this chapter suggest that the potential human health impacts from ENM used in OPV cannot, by default, be assumed to be negligible and may in fact be highly probable. This issue may not necessarily be relevant to the current technology and production level of OPV but should be considered for future scale-ups of these devices. It is important to note that the HHRA results are not scaled to the functional units defined by the LCA in Chapter 3 and Chapter 4. The difference in scopes means that the risk pertains to the handling a mass of the ENM that is independent of exact amounts of nano-TiO2 required to fulfill the functional unit (e.g. production of 1 Wp of OPV). Therefore, the risk cannot be directly interpreted in terms of its overall relevance to the OPV life-cycle, particularly any prospective analysis of future, large scale production of this technology. Of course, the exact exposure to these ENM used at current lab-and small-scale production per functional unit could, in some cases, be determined for the HHRA. While these results might be beneficial from a health and safety perspective for laboratory managers, for example, it may ultimately provide little relevance from an eco-design perspective where material choices and occupational workplace conditions at the industrial scale are being extrapolated A handful of studies have made first approximations to incorporate the ecotoxicity or human health impacts from ENM into their LCA studies, [93][94][95][96] but otherwise these impacts have been conspicuously

missing from the majority of published ENM-related LCA studies. 96,184 In those studies that addressed these issues, mainly ambient emissions of and ecotoxicity relevant CFNS have been published. Other studies show that pollutant emissions, including ENM, in occupational and indoor settings can be very important contributors to overall LCA results. 227,326,327,235,328 Occupational settings present unique scenarios where production 329 of pristine particles with small size distributions and thus exposure may occur. Neglecting such occupational, indoor ENM emissions in a LCA may result in burden shifting from the environment to workers. Currently, there is only one such CFNS relevant to indoor emissions of these ENM.

These previously reported CFNS have made use of existing life-cycle impact assessment methods such as USEtox. This approach takes advantage of a number of assumptions that conveniently describe the fate and transport of small organic molecules and metals quite well, but these methods are not appropriate Chapter 7 Life-Cycle Impact Assessment Nanomaterial Characterization Factors: Titanium Dioxide Case Study 147 for ENM. 196 The major assumptions utilized in human health life-cycle impact assessment methods are that conditions are at steady-state and systems have reached thermodynamic equilibrium. 101,197 However, ENM behave, in effect, like colloidal substances that, when released into a specific medium, exist in their own phase and will not form uniform phases with the surrounding medium unlike organics. Therefore and by definition, ENM are not thermodynamically stable, even if certain ENM may be kinetically stable for long periods of time. 196 Previous adaptations to current life-cycle impact assessment methodologies such as USEtox have been made to estimate the ecological [94][95][96] and human health impacts 93 of ENM. In all previous cases, steady-state conditions were kept in the model, and as was previously discussed in Chapter 5. Thus, the aim of this chapter is to present a dynamic life-cycle impact assessment fate and exposure model for estimating the human health impacts from indoor, occupational ENM air emissions.

The approach presented in this chapter integrates part of the HHRA methodology proposes in Chapter 6

with life-cycle impact assessment. The results of the integration are applied for nano-TiO2.

Methods

The calculation of a CFNS for use in LCA is further presented in this chapter, where CF is defined in an analogous fashion to equation (2-2). 234,330 However, unlike the that equation, the iF is not estimated using (equilibrium) partition coefficients inside of steady-state models. Instead, a dynamic fate and exposure model using kinetically defined fate and exposure parameters is used to define a newly introduced Retained-intake Fraction (RiF).

Emissions of and Exposure Scenarios for Nano-TiO2 in the Occupational Indoor Setting ENM emissions can be directly measured, estimated from published literature or calculated using models.

In many cases, means for measuring emissions or the primary data needed may not exist. Therefore, it can be useful and often necessary to model such emissions. A total of six exposure scenarios (ES1-ES6) (Table 7- These scenarios were more precisely six variations of a single representative workplace-activity that involved the handling of nano-TiO2. The description of this workplace activity was adapted from the NANEX (www.nanex-project.eu) database, an EU FP7 project which aimed to catalogue potential EMN exposure across the life-cycle including their manufacturing and industrial use. The emission scenarios in this case study represented occupational settings where pre-fabricated ENM were handled as opposed to scenarios that estimate exposure during the production of raw-ENM. The latter scenarios were not included due to the assumption that production of raw-ENM is more likely to occur under automated, enclosed settings where fugitive (i.e. accidents) exposures were assumed to be near zero.

ES1 represents a large (mass) emission event, involving the dumping of large amounts of nano-TiO2

powder into an open vessel over work-cycles lasting ten minutes and with 20-minute pauses in-between over an 8-hour workday. The magnitude of emission per minute was estimated as a function of the total amount (kg) of nano-TiO2 handled per minute, the dustiness index of the nanoparticles and the handling energy factor of the work-related activity as previously described in equation (6-2). 219

ES1 was characterized with a high handling energy factor and which took place in a modestly sized hall with moderately low ventilation (i.e. air exchange rate). ES2-ES6 ( for use with indoor, occupational ENM airborne emissions. The model assumes that there was only one emission source fully located inside a near-field zone. The NF is where exposure was assumed to take place exclusively. The remaining indoor air room volume was defined as the FF and is not to be confused with outdoor air compartments. The NF is defined as the volume of a hemisphere with a radius of 0.8 m.

This radius corresponds with being an arm's length away from the source of emission. 309 Both NF and FF zones were modeled as well-mixed compartments, thus the model can be thought of as two well-mixed one-box compartments linked by the airflow between them. 311 Existing LCA indoor air impact assessment methodologies utilize a one-box model under the assumption that there is only one emission source in a well-mixed room 331 (equation (7-1)). In the case of indoor air emissions from single point sources, it can be anticipated that large concentration gradients will exist between the point of emission and points further away from the source. 332 Uses of two-box models have thus shown that exposure near the source term can be 1.5-2 times greater than estimates for a one-box model. 332 To accommodate for imperfect mixing, life-cycle impact assessment methods conventionally use a mixing factor, m (equation (7-2)). 97,235,234,331

𝑉 • 𝑑𝐶i d𝑡 = 𝑆 -(𝐶 i-1 • 𝑄 • 𝑚) (7-2)
𝑚: mixing factor for an incompletely mixed one-compartment model However, the use of m can still result in underestimations of NF exposure upwards of 50% compared to the results of a two-zone model, depending on the assumptions of the inter-zonal air flow, β, between the NF and FF as well as the ratio of the NF and FF volumes. 311 𝛽 was defined as the volume of air that was exchanged between the NF and FF, equally entering and leaving through half of the curved surface area of a hemisphere (equation (7-3)). 311

𝛽 = 𝑠 • 𝜋 • 𝑟 2 (7-3)
𝑠: average air speed between the near-and far-fields (m/s) 𝑟: radius of the near-field (m 3 ) Thus, in this chapter a two-zone, dynamic model (equations (7-4) and (7-5)) was used to address the difference between NF and FF exposures. ENM settling velocities are relatively slow, 333 thus when indoor ENM emissions are sufficiently low and ventilation rates are high, steady-state ventilation models may adequately describe particle loss and estimate indoor ENM concentrations. 97 However, these conditions may not always be met, as has been demonstrated for regular chemicals. 330 For ENM, this would mean contributions from non-ventilation sources of particle loss such as homo-aggregation and gravitational settling. 97 The introduction of nonventilation sources of particle loss (k) in a two-zone, dynamic model is represented by mass balance equations (7-6) and (7-7). Gravitational settling is likely to be more important for ENM that are ≥ 100 nm while Brownian motion might be more important for ENM below 100 nm, although this distinction is not absolute and may differ based on the type of ENM under consideration. 97,201,336 The model was constructed in MatLab 9.0 (MathWorks, USA) across time-steps of one-minute and initially ran at iterative, increasing total durations starting with one-day, until pseudo-steady state concentrations, if any, were observed.

𝑉 • 𝑑𝐶NF d𝑡 = 𝑆 + (𝛽 • 𝐶FF-1) -(𝐶 NF-1 • 𝑄 • 𝑚) -(𝛽 • 𝐶NF-1) (7-4)

Exposure to Nano-TiO2 in Occupational Indoor Air

A physiologically-based pharmacokinetic (PBPK) model was adapted from Li et al. 337 to establish the deposition and retention of nano-TiO2 in the lung as a function of time. This PBPK model, which was originally built for estimating the fate and bio-distribution of inhaled nanoparticles of cerium oxide in rats, was adapted for human physiologically based pharmacokinetic modeling upon inhalation of nano-TiO2 using parameter values from published literature. [338][339][340][341][342][343] The reader is referred to Li et al. 337 for complete details on the model, but a brief explanation of the component parts and the adaptations made in this chapter follows. The model estimated bio-distribution in the lung after considering (i) mucociliary clearance, (ii) phagocytosis and (iii) entry of nano-TiO2 into the interstitium of lungs and subsequent systemic circulation via penetration of the alveolar cell walls. 337 In the lungs, the model estimated Retained-Intake Fraction of Nano-TiO2 Emissions to Occupational Indoor Air The results of the fate and exposure model were combined into an overall RiF, which represents the averaged internal wet lung mass of TiO2 (EXPint) per averaged lifetime emitted mass of TiO2 (Elife). This is different from traditional inhalation iF, which is the inhaled amount per emitted amount. 346 Two different time-horizons were used to represent acute and chronic exposure scenarios. These were defined using a (i) short-term, 1-year of work and (ii) lifetime, 45-years of work periods. The time-weighted average wet lung burden was calculated over an assumed life expectancy of 70 years, defined by periods of (i) nonwork between ages 1-20, (ii) work between ages 21-65 and (iii) non-work in their retirement between ages 66-70. Thus, the model assumed emissions that persisted throughout the complete set of working years of 20-65 (Elife) irrespective of whether the person was working or not the entire time. Therefore, the 1-year time-weighted lung burden assumed one year of exposure, while the lifetime time-weighted lung burden assumed 45 years of exposure. These wet lung burdens were then divided through by the 70-year life expectancy to obtain a total retained wet lung burden over lifetime. Finally, the 1-year and lifetime ratios of lifetime wet lung dose and lifetime of emissions were scaled by the number of workers (POP) inside of the exposure zones (equation (7-9)). The number of workers exposed was adapted from Walser et al. and estimated as a lognormal distribution with a geometric mean of 8.7 and geometric standard deviation of 2.8 (Appendix: Chapter 7). 97 Effect Factors for Nano-TiO2 in Occupational Indoor Air

The EF describes the human health impact of a substance and is dependent on its underlying doseresponse relationship (e.g. chemical, particle). 84 The EF was estimated based on the USEtox approach, 84 defined according to equation (7-10): 7-10) 𝐸𝐷50,h,int is defined as the human-equivalent (h) dose at which 50% of population experiences a carcinogenic or non-carcinogenic impact upon inhaled, internal (int) exposures 0.5: the fraction of the worker population that experiences the adverse human health impact Chapter 7 Life-Cycle Impact Assessment Nanomaterial Characterization Factors: Titanium Dioxide Case Study 155 Thus, when combined with the RiF, equation (7-10) assumes a linear relationship between the exposure and response, up to the point of 50% disease probability in the human population. 84 Unlike USEtox which is concerned with external exposure concentrations, the ED50 put forth in this chapter (equation (7-11)) is reported as an internal dose per g of wet-lung since the RiF represented lifetime-averaged internal exposure doses.

𝐸𝐹 = 0.5 𝐸𝐷50,h,int (
𝐸𝐷50,h,int = 𝐸𝐷50a,t,int 𝐴𝐹a • 𝐴𝐹t (7-11) 𝐸𝐷50a,t,int: animal (a) internal dose (int) of nano-TiO2 (mg/g-wet lung)

t: duration of the study (e.g. chronic)

𝐴𝐹a: interspecies extrapolation factor 𝐴𝐹t: study-time conversion factor

Interspecies extrapolation factors are conventionally used to account for anatomical and physiological differences (e.g. metabolic rates) between species, however no such factor was applied in this study as both the exposure and EF were reported as mass of TiO2 per g-wet lung, under the assumption that effects based on the internal doses and toxic mode of action were equivalent in animals and humans. 347 The 𝐸𝐷50a,t,int assumed, a 1:1 correlation of the disease and calculated as extra risk. Extra risk being the fraction of animals that respond to a dose, among animals who do not respond, effectively taking into account the background response rate. 310 Time conversion factors are used to account for differences in exposure

durations. An AFt of 2 was used to convert the sub-chronic non-carcinogenic dose-response data to chronic-equivalent values. 347 The dose-response data used to estimate the 𝐸𝐷50a,t,int was specific for drylung sections. Thus, the dry-lung 𝐸𝐷50a,t,int values were converted to a wet-lung value with the conversion factor of 0.11 based on previous results showing that nearly 89% of the lung-weight is lost after drying. 348 The non-carcinogenic 𝐸𝐷50a,t,int was estimated from experimental data found in the literature. Using the PROAST software (Chapter 6), the BMD approach 349 was employed to determine the 𝐸𝐷50a,t,inh,NC relationship between internal TiO2 mass loadings per gram of lung and the corresponding increase in lung inflammation. The BMD approach is an alternative to the NOAEL or LOAEL methods traditionally used to estimate the dose-response relationship (Appendix: Chapter 7). One of the advantages of the BMD approach is that it utilizes all the dose-response data in its estimation, whereas the N(L)OAEL method largely ignores the shape of the dose-response curve. 310,[349][350][351][352][353] The BMD is estimated and interpreted from a corresponding BMR, defined as the level of response (i.e. adverse health impact) considered significant.

The BMR was defined as the 50% increase in lung-inflammation, according to BAL results of rats and mice Chapter 7 Life-Cycle Impact Assessment Nanomaterial Characterization Factors: Titanium Dioxide Case Study 156 exposed to nano-TiO2 at concentrations of 0.0 mg/m 3 (control), 0.5 mg/m 3 , 2.0 mg/m 3 and 10 mg/m 3 for 6 hours/day, 5 days/week, for 13 weeks. 283 Lung inflammation was interpreted as the percent change in neutrophil count (i.e. neutrophil counts per 200 total cellular samples) and was measured as a function of internal TiO2 lung burden. 283 Typically a BMR of 10% would be analogous to a corresponding NOAEL value, and thus would require that the resulting BMD is extrapolated to the ED50. 354 Instead, the BMR was set to 50% extra risk and assumed to correspond directly to the ED50. While lung inflammation is not a disease in and of itself, it is used here as a general proxy-human health impact, representing a precursor of other non-carcinogenic health effects.

There were no carcinogenic animal studies involving exposure to multiple dosing levels, however in 2011 the U.S. NIOSH conducted a HHRA on nano-TiO2 by supplementing the lack of ENM-specific dose-response data with using previously published data from both fine-and nano-TiO2. 355,356 Using this same dataset, a carcinogenic ED50 was calculated as the dose where 50% of the exposed animals developed lung tumors.

Briefly, two chronic, whole-body, inhalation studies by Lee et al. and Muhle et al. studied the tumor rates of male and female Sprague-Dawley and Fischer-344 rats that were exposed to fine (rutile) particles of TiO2 with average mass-median-aerodynamic-diameter of 1.6 μm and 1.1 μm, respectively. 355,357 In Lee et al., there were four exposure levels of 0, 10, 50, and 250 mg/m 3 . In Muhle et al., there were two exposure levels of 0 and 5 mg/m 3 . Heinrich et al. completed a chronic, whole-body, inhalation study on Wistar female rats exposed to 10 mg/m 3 of nano-TiO2. 284 The dose-response relationships of ENM are known to be more strongly correlated with surface area as opposed to mass-based dose metrics for different diameter particles. Therefore, the doses were converted to surface area based doses using their reported conversion factors of 4.99 m 2 /g for the fine-particulate and 48 m 2 /g nano-TiO2. As was done for the noncarcinogenic ED50 estimation, the BMR was set to 50% extra risk in total lung tumor development.

Classes of Occupational Indoor Air Human Health Characterization Factors for Nano-TiO2

As discussed previously, both the physico-chemical properties and environmental parameters influence the fate of, exposure to and/or toxicity from ENM. Thus, LCA practitioners, not unlike risk assessors, will be forced to estimate these quantities on a case-by-case basis if the objective is to understand the absolute impacts of a particular exposure scenario. However, as LCA is generally used for comparative purposes, some level of aggregation and generalization of the model and its parameters is warranted. A

Context-dependent

Results

Emissions of Nano-TiO2 in the Occupational Indoor Setting

The emission rates for ES1-ES4 were all equal to 6.72E+02 mg/min. These also happened to be the scenarios with the highest emission rates, while emission rates for ES5 was 6.72E+00 mg/min and 6.72E-02 mg/min for ES6. Although the emission rates for ES1-ES4 were the same, their total daily emissions (Table 7- 3) were not equal due to changes in the frequency and duration of the emission events. Total daily emissions for ES1-ES3 differed by less than one-order of magnitude even though emission frequencies and durations were noticeably different. Particularly, ES2 was characterized by 60-minute emission cycles with 60-minute pauses in-between, resulting in a total daily nano-TiO2 emissions of 1.61E+05 mg. This was only 35% larger than ES1 even though emission events lasted ten minutes long with 20 minute breaks in-between. ES3 had a constant all day emission event that resulted in a total daily nano-TiO2 emission of 3.23E+05 mg, roughly 3-times the daily emission of ES1. ES4 was characterized by a single-pulse emission event that resulted in a total daily nano-TiO2 emission of 6.72E+02 mg. The total daily emissions for ES5 and ES6 were roughly 2-and 4-orders of magnitude smaller, respectively, than ES1.

This was in direct correlation with their 2-and 4-orders of magnitude decrease in emission rate.

Over the course of 1-year total emissions were greatest for ES3, corresponding with its high relative emission rate magnitude and continuous all-day emissions. The total yearly emissions of ES1 and ES2 were only slightly lower than ES3 even though their frequencies were much less often and shorter emission events. However, the single-pulse emission resulted in an over 2-orders of magnitude decrease in yearly emissions compared to ES1, ES2 and ES3 (Figure 7-1). An additional 0.0025% of the emissions had been transformed into larger aggregates and 0.0021% had settled to the surface due to gravitational settling. By the end of the first 10-minute emission cycle, the amount of total emissions remaining in the indoor air was only 55.84%, while 44% had been transferred

to the outdoor air. The proportion of the emissions that had been removed by homo-aggregation slightly increased to 0.0030% by the second and third minute and then fell back to 0.25% by the end of the emission event. The proportion of emissions removed by gravitational settling onto surfaces increased by over an order of magnitude to 0.03%. These trends continued through the workday whereby the exchange of indoor with outdoor air contributed to nearly 99% removal of the total daily emissions (Figure 7-4). The contributions from gravitational settling and homo-aggregation remained minimal and accounted for roughly 0.07% and 0.00005% of nano-TiO2 removal from the indoor air. By the end of the final minute of the last emission event of the workday, nearly 1.0% of the emissions remained in the indoor air compartment, as non-agglomerated nano-TiO2.

In ES2, the overall patterns and trends in removal of nano-TiO2 from the indoor air by the end of the workday were similar in ES2, ES3 and ES4. However, they differed by the rates at which the indoor air concentrations of nano-TiO2 decreased. For example, by the end of the 40 th minute, the amount of nano- Exposure to Nano-TiO2 in Occupational Indoor Air

The fractional percentage of inhaled nano-TiO2 deposited in the upper airway, tracheobronchial region and the pulmonary (i.e. alveolar) region was 9.8, 24.6 and 40.8, irrespective of the exposure concentration but specific for 21 nm diameter particles. These values were used to determine the relative deposition of the airborne nano-TiO2 concentrations that entered the lung and to which parts of the lung. The results of the clearance and ultimate retention of this deposited dose was then calculated by the PBPK model and displayed in Table 7-4. The results report the total wet lung burden and retained-intake fraction. The wet lung is considered as the pulmonary regions of the lung, the interstitial tissue of the lung, and corresponding blood and PCS found in those compartments (i.e. ignoring the upper airways and tracheabronchial regions of the lung). It was assumed that all exposures during the work cycles occurred within the near-field zone (i.e. all workers were in the near-field), while exposures in-between work-cycles were assumed to take place in the far-field. The final exposure values, therefore, represent the cumulative exposure between both the near-and far-field exposures throughout one 8-hour working day. The results of each exposure scenario are described below.

ES1 e-high, f-short

Chapter 7 Life-Cycle Impact Assessment Nanomaterial Characterization Factors: Titanium Dioxide Case Study 163

Over the course of 1-work day the wet lung burden increased 16 different times, corresponding with the 16 distinct work-cycles and emission events. Between work cycles, exposures increased very slightly. At the end of the 16-daily work cycles of the first workday, there was a maximum lung burden of 110 μg/gwet lung. The lung burden did not decrease enough between workdays or workweeks (i.e. over the weekends) to clear the lung of its total nano-TiO2 load. For example, the maximum exposure by the end of the first work week was 333 μg/g-wet lung, while the remaining lung burden at the beginning of the second work week was 238 μg/g-wet lung. This trend continued until the 5 th work week, after which maximum weekly accumulations slowed considerably, having already reached 453 μg/g-wet lung which was 95% of the maximum lung burden of 478 μg/g-wet lung observed at the end of the year (Figure 7-5). A two-week period of non-work activity (i.e. assumed standard holiday) between work years was assumed. The 1-year time-weighted lung burden over a lifetime was 5.72E+00 μg/g-wet lung. Additionally, the lifetime lung-burden was equal to 273 μg/g-wet lung. The corresponding 1-year and lifetime RiF values In contrast, the pulmonary region had cleared itself of all deposited nano-TiO2 by the beginning of each subsequent work week. Even so, the pulmonary region contributed up to 17% of the total maximum retention observed at the end of the first work-year. The PC located in the interstitial and pulmonary regions reached maximum retentions of 3202 μg and 4644 μg, respectively, very quickly within the first workday. This represented roughly 0.7% and 1.0% of the total lung burden.

The trachea-bronchial region is not defined as part of the wet lung and thus does not directly contribute to the overall lung burden under consideration. However, it is worth noting that retention in this region was similar to the retention pattern in the wet lung, whereby accumulation quickly increased and then leveled off by the 5 th work week. Its maximum retention was only 12% lower than the wet lung's value and thus represents an important consideration in the overall exposure since nanoparticles in this region can be transferred to other organ systems such as the gut. Comparatively, accumulation in the upper airway (i.e. head, nasal regions) was less significant (Figure 7-7). The upper airway cleared all nano-TiO2 by each subsequent workday. The maximum retention in this region by the end of the first work-year was 9.80E+03 μg, over 1-order of magnitude smaller than the retention of the wet lung. Over the course of 1-work day the wet lung burden increased 6 different times, corresponding with each of the 6 work-cycles and emission events. Between work cycles, exposures increased very slightly. At the end of the first workday, there was a maximum wet lung burden of 223 μg/g-wet lung. The lung burden did not decrease enough between workdays or workweeks (i.e. over the weekends) to clear the lung of its total nano-TiO2 load. For example, the maximum exposure by the end of the first work week was 656 μg/g-wet lung, while at the start of the second work week it was 475 μg/g-wet lung. This trend continued until the 6 th work week, after which maximum weekly accumulations slowed considerably, having already reached 917 μg/g-wet lung which is 94% of the maximum lung burden of 980 μg/g-wet lung at the end of the year (Appendix: Chapter 7). A two-week period of non-work activity (i.e. assumed standard holiday) between work years was assumed. The 1-year time-weighted lung burden over a lifetime was 1.16E+01 μg/g-wet lung. Additionally, the lifetime lung-burden was equal to 553 μg/g-wet lung. The corresponding 1-year and lifetime RiF values were 3.55E-11 and 1.69E-09, respectively (Figure 7-6).

ES2 e-high, f-long

Lung burden was mainly influenced by the retention of nanoparticles in the interstitial region, where it represented up to 81% of the retained wet lung mass by the end of the first work-year (Figure 7-7). In contrast, the pulmonary region cleared itself of all deposited nano-TiO2 by the beginning of each subsequent work week. Even so, the pulmonary region contributed up to 17% of the total maximum retention observed at the end of the first work-year. The PCS located in the interstitial and pulmonary regions reached maximum retentions of 3202 μg and 4644 μg, respectively, very quickly within the first workday. This represented roughly 0.3% and 0.5% of the total lung burden.

Retention in the trachea-bronchial region was similar to the retention pattern in the wet lung, whereby accumulation quickly increased and then leveled off by the 6 th work week. Its maximum retention was 10% lower than the wet lung's maximum retention value and thus represents an important consideration in the overall exposure as explained previously for ES1. Comparatively, accumulation in the upper airway was less significant. The upper airway cleared all nano-TiO2 by each subsequent workday. The maximum retention in this region by the end of the first work-year was 2.2E+04 μg, over 1-order of magnitude smaller than retention in the wet lung.

ES3 e-high, f-daily

Over the course of 1-work day the wet lung burden increased steadily without pause and correlated with the all-day, constant exposure emissions of this model. At the end of the first workday, the maximum lung Chapter 7 Life-Cycle Impact Assessment Nanomaterial Characterization Factors: Titanium Dioxide Case Study 167 burden was 486 μg/g-wet lung. The lung burden did not decrease enough between workdays or workweeks (i.e. over the weekends) to clear the lung of its total nano-TiO2 load. For example, the maximum exposure by the end of the first work week was 1.45E+03 μg/g-wet lung, while at the beginning of the second work week it was 1.02E+03 μg/g-wet lung. This trend continued until the 6 th work week, after which maximum weekly accumulations slowed considerably, having already reached 1.99E+03 μg/gwet lung which is 96% of the maximum lung burden of 2.08E+03 μg/g-wet lung at the end of the year (Appendix: Chapter 7). A two-week period of non-work activity (i.e. assumed standard holiday) between work years was assumed. The 1-year time-weighted lung burden over a lifetime was 2.50E+01 μg/g-wet lung. Additionally, the lifetime lung-burden was equal to 1.19E+03 μg/g-wet lung. The corresponding 1year and lifetime RiF values were 3.82E-11 and 1.82E-09, respectively (Figure 7-6).

Lung burden was mainly influenced by the retention of nanoparticles in the interstitial region, where it represented up to 90% of the retained wet lung mass by the end of the first work-year (Figure 7-7). In contrast, the pulmonary region cleared itself of all deposited nano-TiO2 by the beginning of each subsequent work week. Even so, the pulmonary region contributed up to 16% of the total maximum retention observed at the end of the first work-year. The PCS located in the interstitial and pulmonary regions reached maximum retentions of 3202 μg and 4644 μg, respectively, very quickly within the first workday. This represented roughly 0.20% and 0.23% of the total lung burden.

Retention in the trachea-bronchial region was similar to the retention pattern in the wet lung, whereby accumulation quickly increased and then leveled off by the 6 th work week. Its maximum retention was only 8% lower than the wet lung's maximum retention value and thus represents an important consideration in the overall exposure as explained previously for ES1. Comparatively, accumulation in the upper airway was less significant. The upper airway cleared all nano-TiO2 by each subsequent workday.

The maximum retention in this region by the end of the first work-year was 4.1E+04 μg, over 1-order of magnitude smaller than retention in the wet lung.

ES4 e-high, f-single pulse

Over the course of 1-work day there was an initial maximum daily lung burden seen within the first hour of exposure, which then slightly decreased by the end of the workday to 0.72 μg/g-wet lung. There were no decreases between workdays nor was there significant decreases between workweeks. This trend continued until the 18 th work week when there was a sudden spike in total wet lung retention. The peak A two-week period of non-work activity (i.e. assumed standard holiday) between work years was assumed. The 1-year time-weighted lung burden over a lifetime was 1.44E-01 μg/g-wet lung. Additionally, Chapter 7 Life-Cycle Impact Assessment Nanomaterial Characterization Factors: Titanium Dioxide Case Study 169 the lifetime lung-burden was equal to 7.04E+00 μg/g-wet lung. The corresponding 1-year and lifetime RiF values were 1.05E-10 and 5.16E-09, respectively (Figure 7-6).

Lung burden was mainly influenced by the retention of nanoparticles in the interstitial and pulmonary PCS, where they represented 29% and 42%, respectively, of the maximum retained dose seen at the end of the year (Figure 7-7). The interstitium had a maximum retention of 3.20E+03 μg by the end of the work year. This was 22% of the total wet lung retention and is in contrast to ES1-ES3 where the interstitium was the most significant contributor to the total wet lung burden. The pulmonary region cleared itself of all deposited nano-TiO2 by the beginning of each subsequent work week. This region only contributed up to 6% of the maximum wet lung retention observed at the end of the first work-year.

Retention in the trachea-bronchial region was similar to the retention pattern in the wet lung, however there was no secondary spike in this region's retention, which had leveled off by the 5 th work week. Its maximum retention was 74% lower than the wet lung's maximum retention value. Comparatively, accumulation in the upper airway was negligible and only represented 1% of the total wet lung retention by the end of the work-year.

ES5 e-medium, f-short

Over the course of 1-work day there were 16 distinct increases in wet lung retention that corresponded with the 16 emission events throughout the day. By the end of the first workday the maximum wet lung retention was 1.15 μg/g-wet lung. There was little decrease between workdays and between work-days.

This trend continued until the 11 th work week when there was a sudden spike in total wet lung retention.

The peak of this increase occurred at week 14, reaching 14 μg-wet lung, but then receding to approximately 12.6 μg/g-wet lung by week 17. Thereon, the maximum wet lung retention stayed consistent around 12.6 μg/g-wet lung until the end of the work-year (Appendix: Chapter 7). The 1-year time-weighted lung burden over a lifetime was 1.67E-01 μg/g-wet lung. Additionally, the lifetime lungburden was equal to 8.10E+00 μg/g-wet lung. The corresponding 1-year and lifetime RiF values were 7.64E-11 and 3.17E-09, respectively (Figure 7-6).

Lung burden was mainly influenced by the retention of nanoparticles in the pulmonary PCS, where they accounted for 38% of the maximum total retention by the end of the work-year (Figure 7-7). The interstitium accounted for 32% of the maximum total wet lung retention and was also correlated with the Retention in the trachea-bronchial region was similar to the retention pattern in the wet lung, however there was no secondary spike in this region's retention, which had leveled off by the 5 th work week. Its maximum retention was 65% lower than the wet lung's maximum retention value. Comparatively, accumulation in the upper airway was negligible and only represented < 1% of the total wet lung retention by the end of the work-year.

ES6 e-low, f-short

Over the course of 1-work day there were 16 distinct increases in wet lung retention that corresponded with the 16 emission events. By the end of the first workday the maximum wet lung retention was 1.15E-02 μg/g-wet lung. There was little decrease between workdays and between work-days. Total retention continued largely unabated, reaching a maximum total wet lung retention of 2030 μg/g-wet lung by the end of the first work-year (Figure 789). The 1-year time-weighted lung burden over a lifetime was 1.70E-02 μg/g-wet lung. Additionally, the lifetime lung-burden was equal to 3.99E+00 μg/g-wet lung. The corresponding 1-year and lifetime RiF values were 7.81E-10 and 1.83E-07, respectively (Figure 7-6).

Lung burden was mainly influenced by the retention of nanoparticles in the pulmonary PCS, where they accounted for 99.9% of the maximum total retention by the end of the work-year (Figure 7-7). Retention in the trachea-bronchial region was similar to the retention pattern in the wet lung, which increased largely unabated throughout the work-year. Its maximum retention was 97% lower than the wet lung's maximum retention value. Comparatively, accumulation in the upper airway was negligible and only represented < 0.01% of the total wet lung retention by the end of the work-year.

Chapter 7 Life-Cycle Impact Assessment Nanomaterial Characterization Factors: Titanium Dioxide Case Study 172

Influence of Worker Population on the Intake Fraction

The average worker population was estimated from survey-data reported by Walser et al. 97 Based on this dataset, the number of workers in the ENM manufacturing sector was log-normally distributed with a geometric mean of 8. As expected, the RiF increases linearly with the greater number of workers. Thus, the RiF for the highest worker population was 1.5-orders of magnitude greater than the RiF in the lowest worker population scenario. These results corresponded directly to the 1.5-order of magnitude difference in their respective worker populations.

Effect Factors for Nano-TiO2 in Occupational Indoor Air

The carcinogenic dose-response data is shown in Figure 7-11. ES4 (e-high, f-single pulse) ES5 (e-medium, f-short)

ES6 (e-low, f-short) The corresponding ED50,a (i.e. BMD) was 1.43 m 2 /g-dry lung, with the dose being explicitly expressed as the TiO2 surface area concentration per gram of dry lung. This was necessary given that both fine-and ultrafine-TiO2 data were used in the dose-response modeling. 358 After conversion to a mass-based dosemetric, assuming a value of 48 m 2 /g-TiO2, the ED50,a was 2.98E+04 μg/g-dry lung. The original doseresponse data was reported for dry-lung measurements, whereby the dry lung was 89.4% of its original wet lung weight. Therefore, the dose metric was converted to its corresponding wet lung ED50,a of 3.16E+03 μg/g-wet lung (Appendix: Chapter 7). After accounting the relevant extrapolation factors, the resulting ED50h value was 1.58E+03 μg-TiO2/g-wet lung and the final EF was 3.17E-04 cases/μg nano-TiO2 /g-wet lung.

The results of the non-carcinogenic dose-response analysis is shown in (Figure 7 -12). . The x-axis represents the internal lung dose reported as μg of nano-TiO2 per g-dry lung. The yaxis is reported as the fraction of the animals that result in inflammation (i.e. the 20% increase in neutrophil count over background rates). The log-likelihood of the fitted Hill model was -1386.37. The reported benchmark doses were 27352 μg/g-dry lung for mice and 7807 μg/g-dry lung for rats based on the excess risk of 50% over background inflammation rates.

BMD values were interpreted from the results of a sub-chronic whole-body inhalation study measuring the changes in BAL fluids upon exposure to nano-TiO2. Covariation, based on species type, showed that there were distinct dose-response slopes for both mice and rats (Figure 7-12), with rats having lower BMD values (i.e. more sensitive). Based on the approach put forth in USEtox, 84 the BMD for rats was used to estimate the EF because it was the most sensitive species. The original dose-response data was reported for dry lung measurements, whereby the dry lung was 89.4% of its original weight. Therefore, the dose metric was converted to a corresponding wet lung ED50a of 8.27E+02 μg/g-wet lung. The resulting ED50,h value was 4.14E+02 μg-TiO2/g-wet lung and the final EF was 1.21E-03 cases/μg (internal) TiO2 dose.

Classes of Occupational Indoor Air Human Health Characterization Factors for Nano-TiO2

For each exposure scenario, an acute, 1-year and chronic, lifetime human health CFNS were reported for nano-TiO2 occupational, airborne emissions (Table 7-5).

Chapter 7 Life-Cycle Impact Assessment Nanomaterial Characterization Factors: Titanium Dioxide Case Study

-- -- 4.19E+02 1.72E-02 3.41E-05 1.43E-02 5.85E-07 -- -- -- -- -- -- -- -- -- USEtox -- -- -- -- 1.19E-04 † -- -- -- -- -- -- -- -- -- --
*Reported as an ED4: 4% increase in inflammation rate †Reported for generic 'organics' and 'inorganics' and not specific for nanoparticles or titanium dioxide ‡Reported for the average number of workers §Reported for the lifetime exposure scenarios Chapter 7 Life-Cycle Impact Assessment Nanomaterial Characterization Factors: Titanium Dioxide Case Study 176

In this way, the CFNS can be used in a manner that is compatible with life-cycle impact assessment methodologies that contain different "perspectives" based on sources of uncertainty in the modeling choices. 101 In general, the 1-year CFNS were 1-2 orders of magnitude smaller than their corresponding lifetime values (Figure 7-13). ES4 (e-high, f-single pulse) ES5 (e-medium, f-short)

ES6 (e-low, f-short)

1.0E-03 1.0E-02 1.0E-01 1.0E+00 1.0E-05 1.0E-04 1.0E-03 1.0E-02
Non-Carcinogenic Characterization Factor (lifetime)

Non-Carcinogenic Characterization Factor (1year)

ES1 (e-high, f-short) ES2 (e-high, f-long) ES3 (e-high, f-all day)

ES4 (e-high, f-single pulse) ES5 (e-medium, f-short)

ES6 (e-low, f-short)

Chapter 7 Life-Cycle Impact Assessment Nanomaterial Characterization Factors: Titanium Dioxide Case Study 177 frequency of the emission. Emission magnitudes had the greatest influence on the CFNS, particularly at very low emission rates where the CFNS for the lowest emission rate tested was a greater than 1-order of magnitude larger than the medium or high emission rates. CFNS for emissions at the same rate but at different frequencies did not show as much sensitivity, where there was less than 1-order of magnitude difference between the single-pulse (i.e. one short emission per workday) and short-frequent emission scenarios. Thus, when using pre-defined CFNS "classes," these results suggest that assumptions about the emission rates will be more influential than the frequency aspect of the emission. This means that since current life-cycle inventories do not consider temporal dimensions of inventory flows, when deciding on which CFNS to use it is best to associate the emission with the magnitude of the emission (rate) in the inventory.

Discussion

Currently, the estimation of ENM emissions, fate, exposure and human health impacts within LCA have been largely un-addressed in the scientific literature. A main data gap concerns the known life-cycle emissions of ENM-related activities. Using the approaches from Hristozov et al. and Chapter 6 it was possible to make estimations of the emissions in workplaces that handle nano-TiO2 powders. However, this approach still requires somewhat specific data such as the amount of material handled during a workcycle and the length of time of a work-cycle. While this information might be too specific or not readily available for most life-cycle inventories, various "groups" or "classes" of these parameters can be estimated and used for sensitivity scoping as was done in this chapter. As was discussed in the results, this led to the findings that the emission magnitude had a stronger influence on the overall CFNS compared with the emission frequency pattern.

Regarding the fate and transport of ENM, recent adaptations to steady-state fate and exposure methodologies [93][94][95][96] provide a straightforward first approximation to understanding ENM behavior in the environment. However, such approaches may ultimately use models that are less relevant for ENM as they are for organics and certain metals. Results showed a large difference in the near-and far-field ENM concentrations, with the maximum daily near-field concentrations being twice as large as the far-field.

Such conditions were assumed for single, point-source emissions that did not have fugitive emissions.

When such conditions are not met, more uniform ENM concentrations might be expected and thus a simpler one-box model may be appropriate. 359 Airborne concentrations were modeled for two indoor air zones, a near-field zone immediately in the sphere of the emission source and a far-field that comprised Chapter 7 Life-Cycle Impact Assessment Nanomaterial Characterization Factors: Titanium Dioxide Case Study 178 the remaining indoor room volume. For example, a steady-state approximation is applied (Appendix:

Chapter 7) 311 to ES1, the resulting near-field airborne concentration would be 3.52 mg/m 3 . The result is approximately 95% smaller than the maximum near-field concentration predicted by the dynamic model.

Indoor occupational emissions are likely to be episodic, leading to non-constant airborne concentrations.

This was borne out by the results of the fate model that demonstrated only 2 of the 6 emission scenarios resulted in any pseudo-steady state conditions where airborne concentrations remained constant for at least some portion of the emission event. This only occurred in the two longest emission scenario of 60 minute and all-day emission. Such findings are particularly important since the fate and transport of ENM can be concentration dependent. As has been eluded to by Walser et al., the results of the fate and transport model showed that the overall air exchange rate between the indoor and outdoor air was most influential parameter on airborne nano-TiO2 concentrations in the workplace. 97 The air exchange rates were high at 8 hr -1 but typical for occupational working conditions. This resulted in only minor amounts of removal from homo-aggregation and gravitational settling mechanisms, which might be expected given that the settling velocity of even 1 μm sized particles is circa 12.5 cm/hr. 333 Still, the overall removal from gravitational settling was < 1% in most of the exposure scenarios. These removal mechanisms will likely become much more important when air exchange rates are kept low or particle number concentrations become very high. While it is assumed that conditions in the workplace are likely to be kept high for safety issues, some studies have reported much lower air exchange rates for ENM manufacturing conditions, 359 not to mention other non-industrial workplaces. 360 In the exposure model applied in this case study, there was a significant departure from currently applied life-cycle impact assessment methods that generally assume inhalation exposures are linearly related to an individual's respiration rate and the concentration of the chemical in air. Walser et al. 97 recently proposed, and Pini et al. 93 implemented, a retention factor based on the outputs from the MPPD model, however this value was actually interpreted from the assumed deposition calculation made by that model and did not consider clearance and ultimate retention of the nano-TiO2. This could be one reason that the lifetime RiF was over 2-orders of magnitude less than what was reported in Pini et al. The results in this chapter show that the retention of nano-TiO2 in the lung was similar to those from previously reported in vivo studies which were on the order of hundreds of days. 202,344,361 This has been shown to be particularly true for deposition in the air-exchange and interstitial regions of the lung, which the results were in agreement with. Of the three main pathways in the model that governed ENM retention in the lung, translocation of loaded-PCS from the pulmonary (i.e. alveolar) to the tracheobronchial region is the Chapter 7 Life-Cycle Impact Assessment Nanomaterial Characterization Factors: Titanium Dioxide Case Study 179 most sensitive parameter and subsequently the limiting factor determining clearance of ENM from the lung. This value was modified to reflect the slower clearance rate of particulates in the human lung compared with the rat lung. 344 In reality, it may be that the difference in retention times is influenced by the rate of phagocytosis by PC and also the penetration of ENM into the alveolar interstitium. However, at the cellular level, it is not clear that such dramatic differences would occur between rat and human rates of phagocytosis nor for the penetration into the interstitium, thus not being to fully explain the retention differences between the species. In the highest exposure scenario, PC were easily saturated and overburdened, leading to accumulation in many of the lung regions. The overall unlimited accumulation found in the air-exchange and interstitial regions for some of the exposure scenarios may be occurring due to mucociliary clearance becoming overburdened, which cannot remove particles at a fast enough rate to reach zero lung burden even at times of low-or no-exposure. However, these results may also be limited by the model's capability to adjust for the increase or decrease of mucociliary clearance activity depending on the mass loading of nanoparticles in the airway. The current version of the model uses a constant transfer factor to describe this mechanism. Additionally, the high concentration of nano-TiO2 found in the tracheobronchial region was expected given the primary particle sizes upon exposure were assumed to be 21 nm. Particles ≤ 100 nm will have a significant amount of deposition in the alveolar region, however particles ≤ 30 nm, particularly those below 10 nm, 344 will show greater deposition and retention in the tracheobronchial region. 202 Additionally, although the density of the particles do not affect deposition of ENM ≤ 100 nm, above this size, lower densities will result in greater deposition in the upper airways such as the tracheobronchial region. 202 The newly introduced RiF in this study shows an inverse relationship with the emission rate, which is counter-intuitive given that iF values in traditional LCIA are independent of the emission rate. This can be explained by the saturation of PC in the lungs and the relative contribution of PC-sequestered ENM to the overall wet lung burden. Neither of these scales linearly with emission rate, thus bearing the inverse relationship discovered in this study. Further examination on the details of the dynamics of the wet lung burden are shown below. As shown in the results for the exposure, after saturation, the relative contribution of PCS sequestered ENM to the overall wet lung burden decreases as emission rates increases. The amounts in the pulmonary region and interstitium of the lungs scales proportionally with the emission rate after the PCS are saturated. The lifetime wet lung burden increases 3.4 times while emission rate increases 100-fold from low to medium. This is because PCS still play a major role at medium emission rate. But at high emission, which is a 100-fold increase from medium emission, lifetime wet lung Chapter 7 Life-Cycle Impact Assessment Nanomaterial Characterization Factors: Titanium Dioxide Case Study 180 burden increases 31 times, closer to the magnitude of increase in emission rate. This is because the PCS are less important as emission rate goes even higher. This highlights the importance of the emission rate on the "iF" when considering the target organ's physiology.

It should be noted that the RiF and exposure results should be interpreted with caution. Although adaptations were made to the original rat-based model developed by Li et al. 337 to estimate biodistribution in humans, the results of this model have not been validated by experimental evidence. Li et al. validated their model with empirical data and found good agreement in the measured and estimated amounts of cerium oxide bio-distribution 337 . Also, and in general, reviews of previously used first-order clearance PBPK models tend to under-predict the retention of particulates in the lung when exposure concentrations are either very high or very low 344 . In addition, the model was built based on exposure concentrations that were 2-orders of magnitude lower than the highest exposure scenario presented in this study, which is in effect the worst-case scenario. The results of applying the PBPK-rat model to human exposure scenarios should be considered as potential impacts as opposed to absolute values. The resulting RiF calculated in this chapter did not include the effects and use of personal-protectionequipment in the work environment. At the time of this study, it was not evident which occupational scenarios would require PPE, at what rate PPE would be used and the effectiveness of the PPE to filtering ENM. However, if patterns of PPE usage are known, this could be included in the exposure model.

Additionally, the CFNS presented in this study were calculated for inhalation exposures in the occupational setting, since inhalation is the primary intake route in the workplace scenario. 97 However, the fate and transport model estimates the settling of particles out of the air that could be used for approximating dermal exposures. If there is potential inhalation exposure, there is likely to be potential dermal exposure in the same scenario either from direct settling of ENM onto the skin or settling of ENM onto indoor surfaces that later contact the skin of occupational persons. Dermal exposure and toxicity studies in the literature are much fewer than inhalation studies, potentially being the limiting factor to this approach.

As was discussed for fullerenes and titanium dioxide nano-powders in Chapter 6, penetration of such particles into the body through dermal uptake is not anticipated to be as efficient as through inhalation.

Furthermore, apart from direct impacts to the lungs and lung-related injuries, ENM that are deposited in the lung may translocate to other regions of the body after inhalation (Oberdorster 2005). [362][363][364] Although the exposure model presented in this approach allows for the estimation of ENM in 10 other organs of the body, these values were not yet used to address systemic-human health impacts upon inhalation to Chapter 7 Life-Cycle Impact Assessment Nanomaterial Characterization Factors: Titanium Dioxide Case Study 181 nano-TiO2. Future work in this area will address the compounded effects from multi-system organ toxicity.

The EFC reported in this study was almost 1-order of magnitude smaller than the EFNC. It should be noted that the non-carcinogenic endpoint was pulmonary inflammation, a precursor to the carcinogenic endpoint of pulmonary tumors. Thus, these results intuitively make sense. It would be expected that many more cases of a non-cancerous diseases occur compared with more advanced pathologies such as cancer.

This is in contrast to the only other study in the literature that has reported a human health EF for nano-TiO2. 93 Pini et al. report a EFC that is 4-orders of magnitude greater than their reported EFNC, thus implying that there are many more expected cases of cancer compared with inflammation. 93 However, a likely explanation regarding this discrepancy is the fact that Pini et al. used an inflammation study 283 as a proxy for the carcinogenic endpoint. 93 Therefore, a comparison of their EFC to the EFNC presented in this chapter is more appropriate. This comparison shows that this work's EFNC was still over 3-orders of magnitude greater than Pini et al.'s EFC value. Their EFC value was calculated for as an ED4 (i.e. a 4% increase over background response) and thus it seems appropriate that they report a lower value compared to ours which was calculated for a 50% increase over the background rate. Thus, while the overall difference in both the non-carcinogenic and carcinogenic CFNS reported in this thesis and what has been previously reported using steady-state models is partially explained by the difference in the fate, exposure and

(retained) intake-fractions, a significant difference arises from the selection of toxicological data used to calculate the effect factor (i.e. something that has nothing to do with steady-state versus dynamic models)

Life-Cycle Assessment of Organic Photovoltaics with ENM-Specific Characterization

Factors

Life-cycle inventories do not allow for time integrated emission-flows nor are they categorized by timescale (e.g. kg emissions per hour). If there is exact information regarding the rate of production for the reference-flow (i.e. product), then the reported emissions for that reference-flow could be defined as an emission rate for use in the fate and transport model. Often, this level of life-cycle inventory detail might not be attainable. In such as case, assumptions regarding the emission rate would have to be made. As explained above, the results of the CFNS calculations were most sensitive to the magnitude of the emission rate as opposed to emission frequency. Thus, static inventory flows should be interpreted as the magnitude of an emission rate for best correlation. Based on the previous set of results, it was assumed that the emissions reported in the OPV inventory (Appendix: Chapter 3 and Appendix: Chapter 4)

Chapter 7 Life-Cycle Impact Assessment Nanomaterial Characterization Factors: Titanium Dioxide Case Study 182 correspond with the highest emission rate. This is because the highest emission rate was characteristic of an industrial scale scenario, and since the purpose of this thesis is to forecast the potential environmental and human health risks for a prospective, large-scale OPV device, this assumption is warranted.

Furthermore, the highest emission rate was correlated with the highest level of risk based on the HHRA in Chapter 6, it was useful to explore whether the LCA results would echo this human health hazard.

Therefore, the highest emission rate was used as an initial proof of concept to estimate the human health hazards of nano-TiO2 use in OPV. Within this category of emission rates, there was a range of half an order of magnitude based on differences in emission frequencies. Since the original frequency was originally described for an actual occupational exposure scenario at the industrial scale, this value was chosen as a proof of concept due to its relevance in forecasting human health impacts at the industrial scale. Both the long-term (lifetime) and the short-term (1-year) CFNS were tested for their influence on the LCA results.

Thus, lifetime CFNS for ES1 was used as a worst-case scenario proof-of-concept for a first approximation of the human health impacts from nano-TiO2 handled and used at the industrial scale. Emissions across the life-cycle of the OPV panel were considered only during production of the nano-TiO2 power itself, and during production of the OPV panel. Emissions during nano-TiO2 production were estimated for the production route outlined by sulfate production route. Hischier et al. assumed there were no emissions to air during production, however this neglects the issues of handling, transferring, and packing the powder once it is created. 193 Thus, emissions were estimated using an average of lower-estimate and higher-estimate emission scenarios as described by the material flow analysis of various ENM reported by Gottschalk and Nowack. 191 This resulted in an estimated 0.25% of the total production rate being emitted to air, and, thus, per kg of nano-TiO2 produced and handled there were 0.32 mg of this material that ended up as occupational, indoor air emissions. Additionally, there were no known nano-TiO2 emissions during the production of the OPV panel, and the same modeling assumptions stated above applied for OPV production. It was further assumed that this emission was to indoor occupational air, as opposed to outdoor environmental emissions. The reported emission (e.g. 0.32 mg) was then combined with the both the non-carcinogenic and carcinogenic lifetime CFNS for ES1 to calculate the potential human health impacts. This implicit assumption is that the CFNS for ES1 represents an industrial-scale scenario and the inventory, including the emissions, built for the nano-TiO2 production and OPV manufacturing represent emissions of a similar magnitude over time at the industrial-scale. This is a significant caveat because there is no relatable time-scale on the inventory.

Preliminary Results

Preliminary cradle-to-gate results show there was no noticeable increase in the carcinogenic and noncarcinogenic human health impacts after including the human health impacts of nano-TiO2 either using the short-term (1-year) or the lifetime CFNS. The application of the long term (lifetime) CFNS resulted in greater impacts compared to the 1-year CFNS, as was expected given the latter is a one year time-weighted average exposure over an entire lifetime worth of emissions. After applying the lifetime CFNS, the human health impacts only slightly increased at around 1% increase overall (Table 7-6). These nano-TiO2 emissions to the indoor, occupational air resulted in a 0.28% and 0.41% contribution to the carcinogenic and non-carcinogenic impacts, respectively. Roughly 50% of these emissions came from handling nano-TiO2 during OPV-manufacturing and the other half during nano-TiO2 production. Thus, if a decision maker wanted to reduce the human health impact of OPV, there is not much support for targeting the nano-TiO2 emissions, for example (Figure 7 -14).

Chapter 7 Life-Cycle Impact Assessment Nanomaterial Characterization Factors: Titanium Dioxide Case Study 184 Such results must be met with caution as several assumptions were made in during the calculation. First, emissions from the production route of both the nano-TiO2 and the OPV panels were not known. These were estimated from generic release distributions previously reported in the literature for the production of ENM-related technologies and thus may not adequately describe the true emissions occurring in these industries. 191 It is likely that emissions to indoor air from powder handling and transfer are not adequately accounted for in these distributions, however this assumption is not clarified in those previous publications. 191 The nano-TiO2 CFNS were calculated using models that were emission-specific (i.e. the emission amount changed the resulting CFNS). The CFNS ultimately used to calculate the human health impacts from nano-TiO2 emissions was an industrial scale emission rate for a representative industrial scale emission scenario and number of workers exposed. While this CFNS might represent a valid industrial scale scenario for handling nano-TiO2, it is not specific to the actual emissions scenarios represented by the life-cycle inventory for OPV, and thus it was used to forecast impacts at the representative, prospective industrial scale of OPV production.

These preliminary results indicate that the human health impacts from occupational, indoor air emissions of ENM across the OPV life-cycle might be marginal at best. These results are only valid for nano-TiO2 and do not include the human health impacts of fullerenes. The magnitude of fullerene emissions during production, handling and use were not known, however Chapter 6 discussed the relevant fullerene have also been shown to have anti-inflammatory effects. Thus, its potential to significantly contribute to the human health impacts of OPV due to emissions in occupational indoor air is not anticipated.

Furthermore, these are just two material types used in OPV that have been explicitly called-out as ENM.

Since OPV are constructed on the order of hundreds of nanometers in thickness, this implies that the all the layers would necessarily have to be composed of materials in the nanometer size range. While this is not usually explicitly stated, this technically implies that the use of ENM comprises the entire panel (less the plastic-substrate). Consequently, the cumulative contribution of ENM emissions along the life-cycle of OPV could potentially be more influential than this preliminary assessment suggests. Lastly, while human health impacts from occupational indoor air emissions seemed to be the most relevant potential impact pathway, emissions during the use phase and end-of-life phase cannot be ruled out. Such emissions will also be important for the determination of potential environmental and ecological impacts.

Chapter 5 highlighted the gaps in life-cycle impact assessment methodologies in regards to the human health and ecotoxicity impacts resulting from ENM emissions along the OPV life-cycle. HHRA and ERA are two tools traditionally used for making assessments about the potential human health and environmental risks from regular chemicals. In principal, these tools can also be used to understand the impacts from ENM, although they too can suffer from data and methodological gaps similar to LCA. Chapter 5 expands on these issues and the potential approaches to use LCA and HHRA3 in separate, complementary or integrated uses for evaluating the environmental and human health benefits of emerging technologies These methodology options were the separate use of LCA, the separate use of HHRA, the combined use of LCA and HHRA (i.e. combining their results together), and three additional approaches to integration of HHRA and LCA at the methodological level. These latter three options were Global, Context-dependent and Site-specific integration. Global integration makes use of steady-state models that are less relevant for ENM, Context-dependent integration makes use of partially-dynamic models with aggregated environmental and ENM-specific parameters and Site-specific integration uses fully-dynamic models with perfect local and material-specific information. Chapter 6 presented the HHRA of the potential ENMemissions along the OPV life-cycle. Chapter 7 presented the methodology of Global Integration, culminating in the production of an occupational, indoor air, human health CFNS for nano-TiO2.

Overview of the Results of Each Methodological Option

Life-Cycle Assessment of Organic Photovoltaic Systems

The review of existing literature through the end of 2013 demonstrated a general finding that the resource efficiencies and environmental hazards of OPV were lower than that of conventional silicon PV. However, the majority of these studies were limited in their scope, focusing mainly on cradle-to-gate assessments up to the manufacture of the OPV panel as well as limiting their environmental criteria to CED, EPBT and greenhouse gas emissions. For example, CED for the reviewed literature showed values as low as 0.76 MJ/Wp of power production, compared with conventional m-Si which could be as high as 30 MJ/Wp.

Chapter 3 introduced a new, prospective OPV device based on the reviewed literature in terms of their reported material choices, structure and performance of the solar cells as well as other foreseen developments in this area of research. For instance, material choices eliminated the use of resource scarce or cost prohibitive materials such as indium and silver. The results of the CED, for example, ranged from 1.13-2.32 MJ/Wp and were largely in agreement with the prior studies. Looking beyond embodied energy, the environmental and human health impacts decreased across the board compared with conventional silicon-based PV. However, these differences were not uniform across all impact categories. For instance, whereas marine eutrophication impacts for OPV were between 94-97% lower than m-Si, decreases in ionizing radiation were between 77-94% lower. Further results, showed that at a 5% efficiency, OPV would need between 1-9 years of working lifetime such that they achieve environmental parity with siliconbased PV. However, these lifetimes are based on purely hypothetical estimations if there were no other impacts resulting from further use or end-of-life phases.

Chapter 4 expanded the scope of the LCA presented in Chapter 3 to include possible use and end-of-life phases for OPV systems. Two different use phases were considered: a rooftop, solar array and a portable solar charger. Two different end-of-life options were also considered: landfilling and incineration.

Whereas cradle-to-gate impacts were, on average, over 90% lower for OPV compared to silicon-based PV, cradle-to-grave impacts of using OPV panels on rooftops were only 60% lower. In one case, metal depletion potential, OPV impacts were in fact greater than m-Si PV. The increases in impacts were due to the need to continually replace the OPV panels every five years over the course of the 25-year rooftop lifetime. The difference between the cradle-to-gate and cradle-to-grave environmental impacts are thus not trivial and demonstrate how significant the use phase can be, even though technically there are no direct emissions during its use. This concept was reinforced by the results of using OPV as a portable charger, which were on average 74% lower than a-Si PV. In contrast to the rooftop scenario, metal depletion remained 87% lower than a-Si for the portable charger scenario.

Consideration of the end-of-life showed only minimal influence on the total cradle-to-grave impacts, however recycling of the panels was not considered. Currently recycling is not applied to solar panels, whereby the majority end up going to landfills. If recycling comes online as a major end-of-life route for silicon cells, there could be a significant reduction in their total impacts. OPV on the other hand do not stand a lot to gain from recycling as their total energy content is minimal given their thinness and low material mass. Thus, eco-design concepts for emerging technologies may stand to benefit by further developing and including prospective use and end-of-life phases to make targeted evaluations of how they can extract the most resource efficiencies and avoid the most environmental hazards.

Emissions of and Human Health Impacts from Engineered Nanomaterials using Risk Assessment

Those aforementioned environmental and human health impacts calculated using LCA did not include the impacts from the ENM used in the OPV technology. ENM-specific emissions had not been estimated either in the prior studies reviewed literature in 0. Main obstacles to including these potential impacts go beyond the reporting of emissions and are limited by current life-cycle impact assessment methodologies that were originally built for understanding the behavior of organic and some metals in the environment, but not ENM. To address this, a more focused HHRA was presented in which potential occupational exposure scenarios during production and use of nano-TiO2 was presented. The HHRA was conducted for the industrial-scale handling of nano-TiO2 in order to forecast potential human health risks in the OPV lifecycle if this were to reach the industrial level The results showed there was upwards of a 78% lifetime risk of lung inflammation to workers handling nano-TiO2 during ENM production or OPV panel manufacturing.

This estimation of risk was a worst-case scenario, for instance, under the assumption that no personal protective equipment was used and exposure to the individual was 100% of the concentration in the air.

The highest probability of risk was particularly relevant when handling and transfer of high volumes of dry nano-TiO2 powders occurred over 8-hour workdays. The results suggest that risk may exist along the OPV life-cycle if industrial-scale production of this technology is achieved. While these results might be beneficial from a health and safety perspective for laboratory managers, for example, it may ultimately provide little relevance from an eco-design perspective where material choices and occupational workplace conditions at the industrial scale, if reached, may not have the same exposure conditions that were presented in the HHRA. Instead, potential human health impacts calculated within LCA, as opposed to absolute values of risk, might prove more useful for early design and development of emerging technologies such as OPV.

Integrating Life-Cycle Assessment and Risk Assessment for the Evaluation of Organic Photovoltaics Instead, it would suit decision makers well if there were a tool harmonized at the methodological level to evaluated the human health impacts of occupational, indoor air emissions of ENM. There is one such study in the literature by Pini et al. who published an occupational, indoor air CFNS for nano-TiO2. Their model can be categorized as a Global integrative approach as defined in Chapter 5. In contrast, Chapter 7 introduced a dynamic, mechanistic fate and exposure model based on the approaches and experiences of applying HHRA to the case study of nano-TiO2. Since the approach to HHRA necessitates very specific exposure conditions, and would mean that strict integration of HHRA into LCA would have to be on a case-by-case basis (see Site-Specific Integration in Chapter 5). As an alternative, Context-Dependent integration was used to derive different CFNS "classes" based on certain parameters of the model. These classes were based on magnitude of the emission rates, emission frequency patterns (i.e. frequency as well as duration of the emission), workforce population (i.e. number of people exposed), and time-horizon (i.e. short-term versus long-term exposure). The lifetime nor the 1-year CFNS demonstrated any appreciable concern for nano-TiO2 human health impacts over the cradle-to-gate environmental impacts for producing the OPV panel. Unlike the results of the HHRA, these are results from a tool harmonized at the methodological level, therefore they can be directly interpreted and assessed in the context of all the other human health and life-cycle impact categories presented in the LCA. For example, it was possible to identify how significant the human health impact contributions were for nano-TiO2 compared to the rest of the human health impacts. Ultimately, nano-TiO2 emissions to air resulted in a marginal 0.41% and 0.28% contribution to the carcinogenic and non-carcinogenic impacts, respectively. Roughly 50% of these emissions came from OPV panel production and the other half from nano-TiO2 production. These results provide a clearer indication that efforts to reduce impacts from nano-TiO2 may not be well justified, in context of other relative human health impact contributors within the OPV life-cycle. All such analyses are either impossible or very difficult when using the Complementary Use approach of combing the individual results from LCA and HHRA.

Perspectives on the Environmental Preference of OPV Presented in this work was a comprehensive set of environmental and human health impacts for a prospective OPV product including its use and end-of-life phases. Compared with conventional silicon, OPV resource efficiencies are greater and their environmental and human health hazards lower, on average. These results are limited by how the PV devices are used and for how long. Uncertainties, mainly associated with the lifetime and efficiencies of OPV, prevent definitive answers in all cases as to whether

OPV prove themselves to be an environmentally preferable energy supply option. However, these uncertainties can be marginalized when OPV are limited to products with relatively short lifetimes (e.g.

products where the use is typically < 5 years), and the preference for OPV becomes evident for such technology choices and applications. Even so, and based on the results of this thesis, there is still some basis to support the notion that OPV should be employed for such uses as solar rooftop arrays where conventional silicon-based PV has dominated for decades. The human health hazards resulting from the use of ENM in OPV seems marginal at best per the results presented in Chapter 7. It must be noted that these results were specific only to nano-TiO2 emissions in the occupational indoor air compartment and Chapter 8 Conclusions and Perspectives 191 their resulting human health impacts. Emissions at other parts of the life-cycle may lead to additional impacts, particularly environmental and ecological toxicity due to emissions at the end-of-life.

Moreover, the resource efficiencies of OPV could be further exploited in other non-conventional and evolving applications and products. One example would be the integration of PV with automobiles and other modes of transportation. This is because the added benefit of producing additional solar power must not come at a price of adding too much additional weight to these application settings. Added weight would have the negative side effect of weighing down the transport device and requiring more energy to overcome inertia. Since OPV are much thinner and lighter than silicon cells or even other 2 nd -generation thin-films, the reductions in weight could see the biggest benefits for all-solar powered modes of transport (e.g. www.planetsolar.ch). The obvious assumption here is that OPV would have to have the same efficiency as conventional PV. This is currently not the reality and would require much larger surface areas of the transport system to be covered with the OPV, surface area that might not be feasibly attainable with current efficiencies.

Perspectives on Environmental and Human Health Modeling Options, Development and

Data Requirements

LCA stands as the most comprehensive and appropriate methodology for evaluating the resource efficiencies and potential hazards of energy producing technologies such as OPV. Its broad environmental scope and focus on the full extent of a product life-cycle help to ensure that the development or dissemination of a product does not simply just result in the shifting of burdens from one impact category to another. Although, understanding the human health and ecological hazards to ENM is necessary for the evaluation of this technology, which is not achievable using currently existing life-cycle impact assessment methodologies. While ecological risk is also important, the focus of this thesis was on human health modeling of ENM. Even so, the lessons learned in this thesis are applicable to environmental modeling as well. Ultimately, choosing the most appropriate methodological tool for human health evaluation depends on the context of the problem. HHRA is a valid tool for addressing human health risks to ENM and is most appropriate for industrial-scale production of emerging technologies or where human health safety must be ensured. Life-cycle impact assessment can also be adapted to include the human health impacts to ENM, albeit not with the same specificity and determination of risk like in HHRA. This type of approach that integrates the human health impacts of ENM into LCA is an effective tool for early product design and development, or so called eco-design purposes. This is evident by the fact that since Chapter 8 Conclusions and Perspectives

192

OPV are very much in the early stages of development, other approaches such as Complementary Use which involves HHRA, evaluations of absolute risk might be too ambitious since as the technology scales up, the exposure conditions that were specific to the risk assessment are likely to change. Thus, the Context-Dependent integrative approach provides a method of evaluating the relative human health impacts of ENM that would be more effective at OPV's current scale of development.

Consequential Life-Cycle Assessment of Organic Photovoltaics

A further distinction can be made within LCA between attributional and consequential modeling as was briefly presented in Chapter 2. The LCA work in this thesis employed attributional models, whereby the question being posed was "what is the share of current, total global environmental burden that is attributed to OPV technologies?" While this approach is relevant for hot-spot analysis and a baseline understanding of the environmental burdens associated with OPV, consequential-LCA could be used to better understand what the future and/or long-term effects would be for the large-scale production of and the energy introduced into the market by OPV. This approach would be appropriate for better understanding what, if any, influence energy produced by OPV would replace other technologies, for example.

Data Requirements

Furthermore, models and tools can be extremely useful but only if there is data to accompany it. The latter point represents an unfortunate problem when evaluating emerging technologies such as ENM and OPV. Lab-scale or recently introduced products and processes often mean that good representative data is hard to come by or that it simply doesn't exist. LCA and HHRA require quite comprehensive data and information. Fate and exposure models describing scenarios in which ENM emissions occur and how ENM behave in the environment will require somewhat specific contextual information. The occupational, indoor air environment presented in this thesis represents a much simpler context compared with outdoor fate and transport models that will use several nested and connected media compartments each controlled by their own relative model parameters and ENM-specific parameters. Similar to what was proposed in this thesis, different classes of archetypes or "classes" of environmental conditions should be focused on models with enough detail such that they are both manageable to build and operate yet meaningful in their results. Lastly, as was confronted in this thesis with C60-fullerenes, the existence of relevant toxicological information remains a major barrier to development of relevant LCA and HHRA models. Chronic, multi-dose, in vivo toxicological studies are time intensive and expensive to conduct. As a consequence, there is relatively little published data in vivo that can be used to calculate both cancerous and non-cancerous dose-response relationships that are relevant for humans. 97 The resulting set of relatively small amounts of data thus will carry with it greater amounts of uncertainty.

Data Requirements for Emissions of Engineered Nanomaterials from Organic Photovoltaics

In the past few years, models attempting to calculate the emissions from high volume production ENM have been published, however their uncertainty remains high and estimations are limited to certain industries, production routes or use phases, if the latter is included at all. [189][190][191]199,329,365,366 Espinosa et al. 250 assessed the leaching potential over the course of 1-year of both intact rolls of pristine and damaged OPV panels in situ while used in a standard solar array (1 kW grid) and in a simulated unintended disposal scenario where shredded and intact OPV panels were buried in soil columns. They found that up to 30% of the Ag (14.3 mg per m2 OPV) and 100% of the Zn (79.0 mg per m2 OPV) leached in the case of deliberately damaged panels. Interestingly, there were two sets of pristine panels tested, one in which stayed intact the entire testing period and another that started to de-laminate between days 90 and the end of the testing period (day 182). The intact panel leached no considerable amounts of Ag or Zn while the delaminated panel leached 30% of its total Ag and 54% of its total zinc content 250 . In their soil column experiments, no appreciable Zn leached however up to 15.7% (771.7 mg) and 0.5% (25.6 mg) of the total Ag content leached from the shredded and intact panels, respectively. 250 These results demonstrate that OPV can in fact leach substances from their individual layers over time, particularly when damaged or they have been physically compromised. However, there is no data showing whether ENM emissions are occurring as well. For reasons discussed in this thesis regarding the current Chapter 8 Conclusions and Perspectives 194 regulatory environment over ENM, for example, understanding the potential leaching of these materials will likely surface as notable concern. In addition, this information will be of interest in comparison to other 3 rd -generation PV such as perovskite solar cells. For example, there are some concerns over the use of heavy metals such as lead used in the perovskite cells, whose related eco-and human-toxicity impacts could potentially be a limiting factor for this technology.

Concluding Statements

The future is bright for OPV technologies. In terms of their resource efficiencies and potential hazards,

OPV have proven themselves to be a preferential option compared to conventional silicon-based PV and a sustainable energy supply option for certain applications. The limits of these conclusions rest on the uncertainties OPV possess in terms of their lifetimes and efficiencies. The results presented in this thesis recommend the use and application of OPV for products with short lifetimes as the greater resource efficiencies and lower environmental hazards compared with silicon-based PV were very compelling.

Further caveats to these findings involve the assumption that OPV material choices and device structures will continue along the trends pointed to in this thesis. Additionally, LCA, in many ways, was the most appropriate methodology for making these claims, particularly at its current, early-stage development as an emerging technology. HHRA, on the other hand, may be the most powerful methodology for evaluating the human health impacts from the ENM components used in the OPV, although this will be most appropriate at industrial-scales of production. In practice, the general tendency is for different stakeholders to hold on to their tool of choice (e.g. LCA, HHRA, ERA), either out of familiarity or opinion, ultimately excepting the exclusion of a more comprehensive viewpoint. 74,221 An integrated approach, however, that combines the methodologies of LCA and HHRA (as well as ERA) can offer the most flexibility and applicability for OPV given their current level of development while also addressing both the resource efficiencies and hazards of this technology.

I Appendices

Appendix: Chapter 3 Table A3-1 displays the impact assessment results for each of the OPV embodiments assessed. The default solar cell results in the highest impacts, except for human toxicity which was highest for the solar cell using dichlorobenzene during PCBM production instead of toluene. The foreground inventory data for the default organic photovoltaic cell and all components used across its life-cycle are shown in the following tables. Any background inventory data (i.e. a unit-process from Ecoinvent) are not included. The amounts of materials used per layer were estimated using well-documented descriptions used in the scientific literature. 29,30,38,128 Inventory Slanted roof mount construction inventory data is based on a modified Ecoinvent process "slanted-roof construction, mounted, on roof" for which an aluminum backing component was added and defined as: 0.001445 m 3 of aluminum (thickness 2 mm). Given a density of 2.7 g/cm 3 for aluminum, 3.9 kg of aluminum is required for the mounting structure. The previous Ecoinvent inventory entry for aluminum (including aluminum bar extrusion) used as u-profile mounting beams were removed and replaced with the above mention of aluminum backing. It is assumed that the mount would not have to be solid (100% filled) across the area of the backing in order to secure the OPV into place. Instead, holes measuring 12 cm in diameter are evenly spaced through the aluminum-backing leaving 6.7 cm between each hole and the sides. This amounts to 25 circles each measuring 113 cm 2 by area and 0.2 cm in thickness (22.6 cm 3 * 25 = 0.000555 m 3 ) of aluminum removed from the backing, and thus a total of 0.001445 m 3 of aluminum is used (3.9 kg). The mounting structure is assumed to last 25 years. The mounting structure for m-Si was based on the original Ecoinvent process "slanted-roof construction, mounted, on roof" but included the recycling of the main aluminum and steel components via secondary metal production pathways as inputs to and secondary metal products as avoided products from the system.

Inverter

The inventory for the inverter was based on the Ecoinvent process "inverter, 2500W, at plant" defined below. Disposal of the inverter was estimated for the major aluminium, copper and steel components. Elemental Composition of the Solar Panels for Use in Ecoinvent's Waste Disposal Tool Table 7 lists the elemental composition of each solar panel which was used to derive the disposal inventory using Ecoinvent's Waste Disposal Tool. 150 The composition was based on the panel composition at the time of manufacture (i.e. not considering losses or degradation of components over the lifetime of the PV device). See the Appendix A.1 (available with this article on the publisher's website) to view the final disposal inventory produced by the Ecoinvent Tool. Figure A4-8 Life-cycle impact changes for S2 OPV-D based on differences in the assumed (a) lifetimes (T) of the solar panels and while the efficiency was assumed to be 1% and (b) efficiencies (E) while the lifetime of the solar panels was assumed to be 1-year. The impact results are normalized to the impact values of a-Si (i.e. a-Si's impacts are set at 100%).

Mechanical and Manual Transfer Coefficients

Figure A4-9

EPBTs and CPBTs for S2 based on differences in the assumed lifetimes (T) and while the efficiency was assumed to be 1%.

Consumer-Adjusted EPBT Calculation for Portable Chargers

EPBTs for portable chargers will largely depend on the usage habits of the consumer (i.e. how frequently they are likely to use a portable charger compared to a fixed electrical outlet). A hypothetical situation is assumed involving a cell phone with charging specifications of 1.2 amp and 5 V. The power drawn from the charger was estimated as 6.00 W (Equation S1):

Power = (Ampere) • (Voltage) Eq. A4-1

Further assumptions were made that the consumer would use the portable charger 5 times a week (i.e. 5 full charging cycles) with each charging cycle lasting 2 hours. This amounts to 520 charging hours per year. The amount of energy generated -analogous to the amount of energy avoided from a conventional charger -was estimated as 11.2 MJ per year (Equation S2):

Energy Supplied by Charger = (Power) • (Charging time per year) † Eq. A4-2 †Actual charging times will depend on the technical aspects of the panel. It is assumed that both the OPV and silicon panels have the same wattage and voltage ratings, where Charging Time = (Battery A-h) / (Charging Source Ampere Rating) and Charging Source Ampere = W / V. Appendix: Chapter 4

XXVIII

The consumer-adjusted EPBTs are calculated using a ratio of the CED per functional unit to the Energy Supplied by Charger (Table 10). Similarly, the CPBT should be calculated according to consumer charging patterns. The same assumptions were considered above and the energy generated by the charging unit was assumed to replace carbon emissions generated from a general European medium-voltage electricity mix (RER) as described in the methods of the main text (Table 11). Recycling Potential of Silicon PV Panels Muller et al. previously reported potential reductions of 57% in the energy consumption for silicon wafer production when using recycled silicon wafers as the feedstock. 154 The CED for the production of 1m 2 of a m-Si wafer is 2,800 MJ. There are 7.6 m 2 of m-Si panels used in S1 (rooftop array) for a total of 21,280 MJ of energy consumed (assuming that the panel size is occupied 100% by wafers). Therefore, approximately 12,130 MJ of energy would be saved for the m-Si rooftop solar array if recycled silicon wafers were used. Fthenakis and Kim 151 estimate that it takes 0.34 MJ of energy to dismantle a kg of solar panels. A total of 88.08 kg of m-Si panels were used in S1, amounting in 29.9 MJ needed to dismantle those panels, reducing the potential energy savings to 12,101 MJ. This is a 49% reduction compared to the total CED of 24,621 that was calculated for the m-Si (incinerated) rooftop scenario. Extrapolation factors used in dose-response modeling Deterministic risk assessment procedures generally utilize single-point uncertainty factors such as interspecies extrapolation factors of 10. These values are meant to provide conservative and strongly protective regulatory exposure limits. To translate this stochastically, distributions for both an interspecies and intraspecies extrapolation factor were defined. Assumptions were made that these values would be log-normally distributed such that values of 10 were one-order of magnitude greater than the mean and occur at the 99 th -percentile. In this way, the protective yet conservative nature of these factors was conserved in the approach.

Results of the dose-response modeling of macrophage percent cell changes 283 using PROAST software Figure A6-1 Results for quantal analysis of percent change in macrophages for mice and rats as a potential covariate to the y-intercept (variable "a") and slope (variable "b"). Note that the response is an inverse of the actual change (i.e. macrophage counts actually decreased, and the inverse was necessary to calculate the doseresponse curves in PROAST. The benchmark response was set at 20% and analysis based on species covariation.

Neutrophil Data

Figure A6-2 Results for quantal analysis of percent change in neutrophil for mice and rats as a potential covariate to the y-intercep (variable "a") and slope (variable "b"). Note that the response is an inverse of the actual change (i.e. macrophage counts actually decreased, and the inverse was necessary to calculate the dose-response curves in PROAST. The benchmark response was set at 20% and analysis based on species covariation.

Appendix: Chapter 6 XXXIII

Summary Statistics of the Mice Data

Four valid mathematical models were fit to the mice macrophage dose-response data whose bootstrap summary statistics are listed in Table ##. The four distributions were aggregated into a single description of the BMCa that was normally distributed with a mean and standard deviation of 11.9 mg/m 3 and 1.1, respectively. For mice, there were 7 valid mathematical models that fit neutrophil dose-response data (Table ##). The summary statistics for the bootstrap results of each model are displayed in Table ##. The distributions for each model were aggregated into a single normal distribution defined by a mean and standard deviation of 12.44 mg/m 3 and 1.2, respectively.

Summary Results of the Rat Macrophage Data

Four valid mathematical models were (Table ##) fit to the rat macrophage data with a BMCa that was normally distributed with a mean and standard deviation of 3.47 mg/m 3 and 0.56.

Exposure Section

La technologie photovoltaïque organique étudiée dans cette thèse

Les ACV des études de cas présentées dans cette thèse concernent spécifiquement l'évaluation des dispositifs de PVO qui utilisent dans la couche active le [6,6] Une couche supplémentaire de nano-TiO2 a été ajoutée entre la couche active et la cathode, pour augmenter l'absorption de lumière. Dans de nombreux cas, la réflexion de la lumière à partir de la cathode (arrière) redirige la lumière vers la couche active pour une deuxième passe, et en fonction de l'épaisseur Résumé Détaillé LVI de la cellule, cela peut interférer avec l'absorption. Cela est particulièrement vrai lorsque l'épaisseur de la couche active est de l'ordre des dizaines de nanomètres 40,41 . Les espaceurs optiques peuvent éviter ce problème et augmenter le spectre de la lumière absorbée.

L'espaceur optique a l'avantage supplémentaire d'agir comme une couche bloquant les trous mais également comme une couche transportant les électrons 42 . Toutefois, une couche de fluorure de lithium a été modélisée pour accroître l'efficacité et la mobilité des porteurs de charge négative à la cathode (retour) 43,44 . En plus d'être une couche de transport d'électrons efficace, le fluorure de lithium peut aussi protéger contre les dommages au cours des processus « roll-to-roll », ce qui conduit à des dégradations de l'appareil. 43 Enfin, une cathode (arrière) est nécessaire pour la collecte des porteurs de charge négative. 45 Une couche d'aluminium a été intégrée en tant que cathode. Des métaux de faibles fonctions de travail peuvent être relativement réactifs, conduisant à une oxydation potentielle et à la dégradation de l'appareil 45 . Ainsi, pour résoudre ces problèmes, la cellule PVO a été conçu avec des couches supplémentaires de stratification (i.e. encapsulation et des couches de protection).

Analyse du cycle de vie du berceau à la porte de l'usine

Une ACV du berceau à la porte de l'usine sur la production de cellules PVO par rapport à des technologies classiques telles que le silicium est présenté. L'unité fonctionnelle considérée est 1 'Wp' (i.e. 'wattpeak'). La cellule PVO requiert 200 cm² par Wp produit pour un rendement hypothétique de 5% 20 . Des rendements de l'ordre de 2-3 % ont été rapportés pour les cellules à base de PCBM en laboratoire 21,46,47 (CED) 51 .

La plus grande contribution à l'impact global sur la santé humaine et l'environnement des cellules PVO standard provient de la production et du dépôt (i.e. la pulvérisation) de l'anode FTO sur le substrat. La production de substrats FTO a une contribution moyenne de 62 % pour toutes les catégories d'impact, avec un maximum de 95% pour la déplétion des métaux et de 57% pour l'épuisement des combustibles fossiles. La forte contribution de la production moyenne de FTO-substrat est due au procédé de pulvérisation cathodique, dont l'influence avait déjà été soulignée dans de précédentes études ACV sur les substrats à base d'ITO 31 . Les contributions de la fabrication de PCBM, le recuit de la couche active, et le laminage de la cellule solaire ont des contributions moyennes de 11%, 11% et 6%, respectivement. Les contributions de la production de PCBM varient considérablement (allant de 13% pour l'appauvrissement de l'ozone à un minimum de 4% pour la privation d'eau). Pour la production de PCBM, les impacts ont été fortement influencés à la fois par la production de C 60 -fullerène et sa purification en PCBM. Cela est dû en partie aux effets en amont de la production de toluène, de dichlorobenzène ortho et des composés de pyridine. Les estimations de la littérature pour la demande en énergie cumulée par kg de PCBM varient entre 64 et 125 GJ. Les impacts de la production de l'espaceur optique en nano-TiO2 varient de 10% pour l'eutrophisation marine à 0,01% pour la privation d'eau. Sa contribution globale à tous les impacts sur l'environnement et la santé humaine (en moyenne de 0,55%).

La contribution du recuit de 11% est plus élevée par rapport à la littérature qui estiment des contributions entre 0,2 et 10 % 21,52 . Cependant, dans certaines études, l'énergie consommée au cours du recuit de la couche active n'est pas explicitée 53 , ou elle est tout simplement omise de l'évaluation 20 . Pour la stratification, les influences majeures sont dues à la production de PET et de la résine époxy. Dans la littérature, la contribution de la stratification dans l'indicateur CED par exemple, varie considérablement : de 0,1 % à 37 % 20,21,46 . En ce qui concerne le copolymère P3HT, une contribution plus faible de 2 % en moyenne est observée pour toutes les catégories d'impact. Des études antérieures ont démontré que le P3HT peut contribuer entre 0,1 et 0,8% pour l'indicateur CED, ce qui est en accord avec les résultats de cette thèse. Bien que ce soit une contribution assez modérée par rapport à l'impact Résumé Détaillé LVIII global, la littérature fait état d'une tendance à remplacer le P3HT par de nouveaux polymères. 54 Bien que ceux -ci ont le potentiel d'accroître l'efficacité des cellules PVO, ils pourraient générer une augmentation des impacts 54 .

Une autre modélisation a été réalisée pour substituer la pulvérisation de l'électrode FTO par l'impression par jet d'encre de l'électrode. La pulvérisation a déjà été identifiée comme étant un processus énergivore et, lorsqu'il est évité, diminue l'ensemble de l'indicateur CED 20,46,53 . Les résultats de cette thèse ont confirmé cela, mais aussi démontré que les autres catégories d'impact ne répondent pas nécessairement à la même tendance que la CED. Par exemple, alors que la CED présente une diminution d'impact d'environ 56 %, les réductions d'impact variaient de 2 % pour l'épuisement des métaux à 72 % pour les rayonnements ionisants. En outre, une évaluation pour un dispositif remplaçant le PCBM avec un copolymère dans la couche active a été effectuée. Il en résulte une diminution moyenne de 10 % pour toutes les catégories d'impact. Considérant que le PCBM a déjà été identifié comme un facteur ayant une influence de faible à modérée dans les impacts de la production de la cellule PVO sur la santé humaine et l'environnement 20,31,53,55 , des réductions relativement importantes du au remplacement du polymère ont été observées pour l'écotoxicité, où les impacts ont été réduits de 20 %. En général, les polymères tels que le P3HT ont montré des contributions beaucoup plus faibles aux impacts environnementaux de la production de PVO, mais les polymères sont très hétérogènes et leur CED, par exemple, peut varier d'un ordre de grandeur 54 .

Par rapport à des cellules m-Si et a-Si, les impacts du PVO par défaut étaient en moyenne 93% plus panneaux PVO-D par défaut ont abouti à une réduction de 50% à 65% de TRE par rapport aux panneaux de silicium, les TRE sont plus élevés que ceux précédemment observés de l'ordre de quelques jours ou semaines (chapitre 3). 20,46,53,55,61 Les résultats de ce chapitre sont uniformes quel que soit le mode de gestion de fin de vie (c'est-à-dire l'incinération par rapport à la décharge). Cela est particulièrement vrai pour S1 mais pas pour S2, qui avait Ces résultats indiquent qu'il faudrait privilégier l'augmentation du rendement de la cellule PV plutôt que l'augmentation de sa durée de vie. En effet, avec une plus grande efficacité, les panneaux photovoltaïques sont plus petits pour une puissance de sortie similaire : de plus petites quantités de matériaux sont donc nécessaires pour la production de la structure de trame (S1), ou les matériaux d'enveloppe (S2).

L'augmentation de la durée de vie, quant à elle, n'influe pas sur la quantité de matériaux nécessaire.

Limitations de l'approche de l'évaluation actuelle du cycle de vie

Les résultats actuels des études de cas en ACV suggèrent que les PVO sont une technologie de production Les NM sont vaguement définis comme des matériaux ayant une seule dimension égale ou inférieure à 100 nanomètres (nm) et généralement dans la gamme de 1-100 nm 63 .A l'échelle nanométrique, il y a de nouvelles applications possibles allant de l'utilisation dans les produits pharmaceutiques jusqu'au secteur de l'énergie en passant par l'industrie textile et l'agriculture 64 . Il est également prévu que l'utilisation et la production de NM aident à la protection de l'environnement, à la création d'emplois et à l'augmentation de la croissance économique 65,66 . En plus d'être fabriqués dans des gammes de taille qui sont beaucoup plus petites que leurs homologues en vrac, les NM peuvent prendre différentes formes (par exemple des fibres) et différents modes de réalisation (matériaux enrobés, par exemple). Les NM ne sont pas seulement définis physiquement, mais aussi par leurs propriétés et comportements distincts. Par exemple, lorsque les matériaux diminuent en taille, le rapport entre la surface et le volume augmente jusqu'au point où de nouvelles propriétés physico-chimiques apparaissent par rapport aux matériaux à l'échelle micro 67 . Toutefois, ces mêmes caractéristiques physico-chimiques menant à l'amélioration des propriétés des matériaux, aux progrès technologiques et à un meilleur rendement des ressources peuvent également présenter des risques pour la santé humaine et l'environnement [68][69][70][71] résultant de rejets intentionnels et/ou non intentionnels de NM à travers le cycle de vie du PVO. 72,73 L'ACV, en théorie, peut être appliquée à des NM. La première utilisation formelle de l'ACV pour les technologies NM est venue au cours d'un atelier datant de 2006 et intitulé Nanotechnology and life cycle assessment 74 , où il a été reconnu que «[L'ACV] est tout à fait adaptée aux nanomatériaux et aux produits liés aux NM». Dans la pratique, cependant, peu d'études ACV complètes sur les NM ont été publiées, en particulier celles dont les limites du système sont du berceau à la tombe [START_REF] Hischier | Life cycle assessment of engineered nanomaterials: State of the art and strategies to overcome existing gaps[END_REF] . Cette lacune dans la littérature est due à un manque de bases de données qui contiennent des inventaires de cycle de vie spécifiques aux NM pour décrire les inputs de matière et d'énergie et la production de déchets lors de la production, mais aussi en raison de l'absence de méthodes d'évaluation des impacts capable de caractériser le devenir des NM, leur exposition et leurs impacts sur la santé humaine [START_REF] Hischier | Life cycle assessment of engineered nanomaterials: State of the art and strategies to overcome existing gaps[END_REF][76][77][78] .

Résumé Détaillé LXIV En ce qui concerne les émissions, il y a eu quelques tentatives de modéliser et de calculer les principaux flux de NM provenant de la production [79][80][81] mais aussi de l'utilisation [82][83][84] et de l'élimination de ces matériaux 85 . Ces approches et ces modèles peuvent être utilisés pour estimer les émissions moyennes mondiales de NM qui pourraient être utilisées pour créer des inventaires d'émissions directes des produits et des processus liés aux NM. Pour être utile dans l'ACV, chaque émission de NM doit contenir un facteur de caractérisation (FCNS) qui convertit son émission en un impact potentiel sur la santé humaine ou l'environnement.

Il y a une poignée d'articles publiés qui ont fait une première approximation pour modéliser le devenir des NM, leur exposition et leur toxicité dans les ACV [86][87][88][89] . Ces premières approximations comprennent 1 FC pour la santé humaine 89 et 3 FC 1 pour l'écotoxicité des nanomatériaux [86][87][88] . Dans chaque cas, les En alternative à l'ACV, l'Evaluation des risques environnementaux (ERE) 24 et l'EQRS 23 pourraient être utilisées sur une base au cas par cas pour déterminer si les NM posent également des risques écologiques ou de santé humaine, semblables à ce qui est fait pour des produits chimiques réguliers (Figure 5 -4) 99 .

Cependant, l'EQRS est sujet aux mêmes problèmes de manque de données et de lacunes

[

  CYVI Group, 351 Cours de la Libération 33405 Talence] ii Dedication This work is dedicated to my wife and daughter who are my foundation and to my parents who have continuously encouraged my educational and professional pursuits. x THE MAGNITUDE OF THE EMISSION, E, PER MINUTE AND THE FREQUENCY OF THE WORK-CYCLE ACTIVITY, F. HI = HANDLING ENERGY FACTOR, TWC IS WORK CYCLE TIME, PWC IS PAUSE BETWEEN WORK CYCLES, NWC IS NUMBER OF WORK CYCLES, AHANDLED IS AMOUNT OF MATERIAL TRANSFERRED PER TRANSFER EVENT WITHIN EACH WORK CYCLE, VTOT IS THE TOTAL VOLUME OF THE WORK ROOM, AND AER IS THE GENERAL AIR EXCHANGE RATIO IN THE WORK-ROOM ..................................................................................... TABLE 7-2 PARAMETERS AND THEIR VALUES USED IN THE FATE AND TRANSPORT MODEL ............................................................... TABLE 7-3 RESULTS FOR EMISSIONS AND FINAL NEAR-FIELD AND FAR-FIELD CONCENTRATIONS. ......................................................

FIGURE 1 -

 1 FIGURE 1-1 ILLUSTRATIONS OF (A) THE SETUP DESCRIBED BY BECQUEREL IN 1939 TO ACHIEVE THE FIRST KNOWN, INTENTIONALLY PRODUCED PHOTOVOLTAIC EFFECT AND (B) THE OLDEST CONTEMPORARY INTERPRETATION OF THE P-N SILICON SEMICONDUCTING CELL AS IT IS KNOWN TODAY. (SOURCE: GREET 2002) 12 .................................................................................................. FIGURE 1-2 LABORATORY-BASED SOLAR CELL EFFICIENCY RECORDS (SOURCE: U.S. NATIONAL RENEWABLE ENERGY LABORATORY) 22 ...... FIGURE 1-3 GEOMETRY AND LAYOUT OF A GENERIC PHOTOVOLTAIC CELL. .................................................................................... FIGURE 1-4 GEOMETRY AND LAYOUT OF A GENERIC ORGANIC PHOTOVOLTAIC CELL. ....................................................................... FIGURE 1-5 EXAMPLE OF PRINTED ORGANIC PHOTOVOLTAIC PANEL ............................................................................................ FIGURE 1-6THE (A) FOUR PRINCIPLE STEPS OF A LIFE-CYCLE ASSESSMENT BEGINNING WITH THE GOAL AND SCOPE DEFINITION WHICH IDENTIFIES THE PROBLEM AND THE BOUNDARIES OF THE STUDY. THE SECOND STEP IS THE INVENTORY ANALYSIS AND CORRESPONDS WITH ALL RELEVANT DATA COLLECTION REQUIREMENTS. NEXT THE IMPACT ASSESSMENT STAGE CONVERTS THE INVENTORY ENTRIES INTO POTENTIAL IMPACTS USING CHARACTERIZATION FACTORS. LASTLY, THE STEP OF INTERPRETATION IS CONDUCTED THROUGHOUT THE ENTIRE STUDY TO REFINE ANY NECESSARY STEPS AND DISCUSS THE SIGNIFICANCE OF THE INVENTORY AND IMPACT RESULTS IN CONTEXT OF THE ORIGINAL SCOPE OF THE STUDY. THE (B) GENERAL LIFE-CYCLE STAGES THAT CAN BE CONSIDERED IN A LIFE-CYCLE ASSESSMENT. (SOURCE: FIGURE (A) ADAPTED FROM ISO 14040 28 ) .................................................................................. FIGURE 1-7 TWO-DIMENSIONAL STRUCTURES OF (A) AN UNMODIFIED FULLERENE WITH 60 CARBON ATOMS AND (B) A FUNCTIONALIZED 60-CARBON RING FULLERENE (PCBM). (SOURCE: PUBCHEM OPEN CHEMISTRY ONLINE DATABASE) ....................................... FIGURE 1-8 SELECTION OF P-TYPE POLYMERS THAT COULD BE USED WITH PCBM AS A BASIS FOR THE ACTIVE LAYER OF AN ORGANIC PHOTOVOLTAIC SOLAR CELL. (SOURCE: YAN AND SAUNDERS 2014 55 ) ................................................................................ FIGURE 1-9 CUMULATIVE ENERGY DEMAND FOR ORGANIC PHOTOVOLTAIC CELLS AS REPORTED IN THE LITERATURE UP THROUGH DECEMBER 2013 PER WATT-PEAK OF POWER PRODUCTION ............................................................................................................. FIGURE 2-1 GROWTH IN THE NUMBER OF PEER REVIEWED SCIENTIFIC JOURNAL ARTICLES IN THE SUBJECT AREA OF "LIFE-CYCLE ASSESSMENT." RESULTS GENERATED USING SCOPUS (WWW.SCOPUS.COM).......................................................................FIGURE 2-2 THE (A) ATTRIBUTIONAL MODELING APPROACH IN LIFE-CYCLE ASSESSMENT DEPICTED AS A SHARE OF TOTAL, CURRENT GLOBAL ENVIRONMENTAL BURDENS OF A PRODUCT OR PROCESS AS OPPOSED TO THE (B) CONSEQUENTIAL MODELING APPROACH THAT IS CONCERNED WITH THE CHANGES IN TOTAL, GLOBAL ENVIRONMENTAL BURDENS DUE TO DECISIONS MADE REGARDING THE PRODUCT OR PROCESS. (SOURCE: WEIDEMA 2003 77 ) .................................................................................................................. FIGURE 2-3 GENERAL FLOW DIAGRAM CREATED DURING THE LIFE-CYCLE INVENTORY PHASE. THE FLOW DIAGRAM HELPS TO OUTLINE THE DATA REQUIREMENTS FOR EACH PROCESS AND ALONG EACH STEP OF A LIFE-CYCLE ASSESSMENT. (SOURCE: U.S. EPA 78 ) .............. FIGURE 2-4 ILLUSTRATION OF FOREGROUND AND BACKGROUND PROCESSES WITHIN A LIFE-CYCLE INVENTORY. FOREGROUND PROCESSES ARE DEFINED AS THE PRIMARY PROCESSES OF CONCERN AND/OR WHICH THE DEVELOPMENT OF NEW AND NOVEL DATA IS USED TO DEFINE THOSE PROCESSES. THUS, FOREGROUND PROCESSES CAN REPRESENT ANY STAGE OF THE LIFE-CYCLE ASSESSMENT SUCH AS DURING (A) RAW MATERIAL EXTRACTION, (B) PRODUCT MANUFACTURING, (C) USE AND/OR (D) END-OF-LIFE SCENARIOS. ............ FIGURE 2-5 A GENERAL CAUSE AND EFFECT CHAIN CONSIDERED IN LIFE-CYCLE IMPACT ASSESSMENT METHODOLOGIES, FOLLOWING THE CLASSIFICATION OF INVENTORY ITEMS INTO THEIR RESPECTIVE MIDPOINT AND/OR ENDPOINT LEVELS OF IMPACT AND CONVERTED TO IMPACT VALUES USING EACH CATEGORIES' SUBSTANCE-SPECIFIC CHARACTERIZATION FACTOR. (SOURCE: EUROPEAN COMMISSION JOINT RESEARCH CENTRE) ......................................................................................................................................... FIGURE 3-1 GENERAL DEPICTION OF THE BULK HETEROJUNCTION ORGANIC PHOTOVOLTAIC CELL CONSIDERED IN THIS CHAPTER, INCORPORATING (THE SKELETAL FORMULAS OF) (A) PHENYL-C61-BUTYRIC ACID METHYL ESTER (PCBM) WHICH ACTS AS THE ELECTRON ACCEPTOR AND (B) POLY(3-HEXYLTHIPHENE) (P3HT) WHICH ACTS AS THE ELECTRON DONOR IN THE ACTIVE LAYER. ...... FIGURE 3-2 GENERIC SCHEMATIC OF THE FIELD STRENGTH OF LIGHT AT THE ACTIVE LAYER FOR AN OPV DEVICE WITHOUT AN OPTICAL SPACER (LEFT) AND WITH AN OPTICAL SPACER (RIGHT). (SOURCE: KIM ET AL. 121 ) .................................................................. FIGURE 3-3 SYSTEM BOUNDARIES OF THE CRADLE-TO-GATE LIFE-CYCLE ASSESSMENT ILLUSTRATING THE MAIN COMPONENTS FOR THE PRODUCTION OF A PROSPECTIVE ORGANIC PHOTOVOLTAIC PANEL. ALL RELEVANT PROCESSES, MATERIALS, AND WASTE-STREAMS UP TO THE PRODUCTION OF THE SOLAR CELL ARE CONSIDERED, THUS EXCLUDING ANY CONSIDERATION OF THE USE-PHASE OR END-OF-LIFE CONSIDERATIONS. .............................................................................................................................................. FIGURE 3-4 THE CONTRIBUTIONS OF LIFE-CYCLE STAGES AND PRODUCTION PROCESSES TO THE OVERALL IMPACTS OF THE DEFAULT ORGANIC PHOTOVOLTAIC CELL CONSIDERED IN THIS STUDY. ...........................................................................................................

  ...................................................................................... FIGURE 7-2 RESULTS OF THE FATE AND TRANSPORT MODEL FOR EXPOSURE SCENARIO 1 SHOWING NEAR-FIELD (BLUE) AND FAR-FIELD (ORANGE) NANO-TIO2 AIRBORNE CONCENTRATIONS DURING 1 WORKING DAY OF 8 HOURS. ES1-ES6 ARE PRESENTED IN ORDER SEQUENTIAL ORDER (A)-(F). THE X-AXIS REPORTS TIME IN UNITS OF MINUTES AND THE Y-AXIS REPORTS NANO-TIO2 CONCENTRATION IN UNITS OF ΜG/M 3 ................................................................................................................................................ FIGURE 7-3 COMPARISON OF AVERAGE AND MAXIMUM DAILY AIRBORNE NANO-TIO2 CONCENTRATION. ......................................... FIGURE 7-4 PROPORTIONAL FATE AND TRANSPORT OF NANO-TIO2 PER "COMPARTMENT" DURING (A) THE FIRST 10-MINUTES OF THE FIRST EMISSION CYCLE OF ES1 AND (B) THE FINAL 10-MINUTE EMISSION CYCLE OF ES1 AT THE END OF THE WORK DAY ...................... FIGURE 7-5 RETENTION OF NANO-TIO2 IN THE LUNG ESTIMATED OVER 1 FULL WORK YEAR FOR ES1. THE X-AXIS REPRESENTS TIME IN MINUTES OVER 1-YEAR AND THE Y-AXIS REPRESENTS THE MASS (ΜG) OF NANO-TIO2 IN THE WET LUNG. THE GREEN TREND LINE REPRESENTS THE CHANGE IN MASS IN THE AIR-EXCHANGE (PULMONARY) REGIONS OF THE LUNG, THE BLUE TREND LINE REPRESENTS THE CHANGE IN MASS IN THE INTERSTITIAL REGIONS OF THE LUNG, THE PINK TREND LINE REPRESENTS THE CHANGE IN MASS IN THE TRACHEA-BRONCHIAL REGIONS OF THE LUNG, THE RED TREND LINE REPRESENTS THE TOTAL RETENTION IN THE WET LUNG INCLUDING THE AIR-EXCHANGE (PULMONARY) REGIONS, INTERSTITIAL REGIONS, TRACHEA-BRONCHIAL REGIONS AND THEIR MACROPHAGES. . FIGURE 7-6 TIME-WEIGHTED RETENTION IN THE WET LUNG OVER LIFETIME AS A FUNCTION OF THE (A) EMISSION RATE AND (B) YEARLY EMISSIONS.AS WELL AS THE RESULTING RETAINED INTAKE FRACTION AS A FUNCTION OF (C) THE EMISSION RATE AND (D) THE YEARLY EMISSIONS. ........................................................................................................................................................... FIGURE 7-7 RETENTION OF NANO-TIO2 IN THE (A) WET LUNG AND (B) TOTAL AIRWAY SYSTEM BASED ON AIRWAY REGIONS FOR ALL SIX EXPOSURE SCENARIOS. ............................................................................................................................................ FIGURE 7-8 RETENTION OF NANO-TIO2 IN THE LUNG ESTIMATED OVER 1 FULL WORK YEAR FOR ES4. THE X-AXIS REPRESENTS TIME IN MINUTES OVER 1-YEAR AND THE Y-AXIS REPRESENTS THE MASS (ΜG) OF NANO-TIO2 IN THE WET LUNG. THE GREEN TREND LINE REPRESENTS THE CHANGE IN MASS IN THE AIR-EXCHANGE (PULMONARY) REGIONS OF THE LUNG, THE BLUE TREND LINE REPRESENTS THE CHANGE IN MASS IN THE INTERSTITIAL REGIONS OF THE LUNG, THE PINK TREND LINE REPRESENTS THE CHANGE IN MASS IN THE TRACHEA-BRONCHIAL REGIONS OF THE LUNG, THE GREY TREND LINE REPRESENTS THE CHANGE IN MASS IN THE LUNG MACROPHAGES, THE DARK-BLUE LINE REPRESENTS THE CHANGE IN MASS IN THE PULMONARY MACROPHAGES, THE RED TREND LINE REPRESENTS THE TOTAL RETENTION IN THE WET LUNG INCLUDING THE AIR-EXCHANGE (PULMONARY) REGIONS, INTERSTITIAL REGIONS, TRACHEA-BRONCHIAL REGIONS AND THEIR MACROPHAGES. ......................................................................................................... FIGURE 7-9 RETENTION OF NANO-TIO2 IN THE LUNG ESTIMATED OVER 1 FULL WORK YEAR FOR ES4. THE X-AXIS REPRESENTS TIME IN MINUTES OVER 1-YEAR AND THE Y-AXIS REPRESENTS THE MASS (ΜG) OF NANO-TIO2 IN THE WET LUNG. THE PINK TREND LINE xv REPRESENTS THE CHANGE IN MASS IN THE TRACHEA-BRONCHIAL REGIONS OF THE LUNG, THE YELLOW TREND LINE REPRESENTS THE CHANGE IN MASS IN THE UPPER AIRWAY, THE DARK-BLUE LINE REPRESENTS THE CHANGE IN MASS IN THE PULMONARY MACROPHAGES, THE RED TREND LINE REPRESENTS THE TOTAL RETENTION IN THE WET LUNG INCLUDING THE AIR-EXCHANGE (PULMONARY) REGIONS, INTERSTITIAL REGIONS, TRACHEA-BRONCHIAL REGIONS AND THEIR MACROPHAGES. ........................... FIGURE 7-10 COMPARISON OF THE INTAKE FRACTION (SHOWN IN LOG-SCALE) AND NUMBER OF EXPOSED WORKERS .......................... FIGURE 7-11 BENCHMARK DOSE RESULTS FOR CANCEROUS IMPACTS TO BOTH MICE (BLACK CIRCLES) AND RATS (RED TRIANGLES). THE X-AXIS REPRESENTS THE INTERNAL LUNG DOSE REPORTED AS SURFACE AREA OF NANO-TIO2 PER G-DRY LUNG. THE Y-AXIS IS REPORTED AS THE FRACTION OF THE ANIMALS THAT RESULT IN CASES OF CANCER. THE LOG-LIKELIHOOD OF THE FITTED HILL MODEL WAS -208.82. THE REPORTED BENCHMARK DOSE WAS 1.43 M2/G-DRY LUNG BASED ON THE EXCESS RISK OF 50% OVER BACKGROUND CANCER RATES. ...................................................................................................................................................... FIGURE 7-12 BENCHMARK DOSE RESULTS FOR NON-CANCEROUS IMPACTS TO BOTH MICE (BLACK CIRCLES) AND RATS (RED TRIANGLES). THE X-AXIS REPRESENTS THE INTERNAL LUNG DOSE REPORTED AS ΜG OF NANO-TIO2 PER G-DRY LUNG.

Figure 1 - 1

 11 Figure 1-1 Illustrations of (a) the setup described by Becquerel in 1939 to achieve the first known, intentionally produced photovoltaic effect and (b) the oldest contemporary interpretation of the p-n silicon semiconducting cell as it is known today. (Source: Greet 2002) 12

  Nonetheless, the last four decades of PV development are marked by research and development of a wide range of viable technologies, spanning from conventional single-crystal silicon and multi-crystalline silicon (m-Si) PV to second generation technologies such as amorphous silicon (a-Si), cadmium-telluride and cadmiumindium-gallium-selenium cells. Since the mid-to late-1990s, increasing amounts of research and development have gone into the so-called third generation (3 rd -generation) PV technologies. This generation of PV are much more diverse from previous generations and include dye-sensitized, perovskite, quantum dot and organic photovoltaic (OPV) cells, among others (Figure 1-2).

Figure 1 - 2

 12 Figure 1-2 Laboratory-based solar cell efficiency records (Source: U.S. National Renewable Energy Laboratory) 22

Figure 1 - 3

 13 Figure 1-3 Geometry and layout of a generic photovoltaic cell.

Figure 1 - 4

 14 Figure 1-4 Geometry and layout of a generic organic photovoltaic cell.

Figure 1 - 5

 15 Figure 1-5 Example of printed organic photovoltaic panel

Figure 1 - 6

 16 Figure 1-6 The (a) four principle steps of a life-cycle assessment beginning with the goal and scope definition which identifies the problem and the boundaries of the study. The second step is the inventory analysis and corresponds with all relevant data collection requirements. Next the impact assessment stage converts the inventory entries into potential impacts using characterization factors. Lastly, the step of interpretation is conducted throughout the entire study to refine any necessary steps and discuss the significance of the inventory and impact results in context of the original scope of the study. The (b) general life-cycle stages that can be considered in a life-cycle assessment. (Source: Figure (a) adapted from ISO 14040 28 )

Figure 1 - 7

 17 Two-dimensional structures of (a) an unmodified fullerene with 60 carbon atoms and (b) a functionalized 60-carbon ring fullerene (PCBM). (Source: PubChem Open Chemistry Online Database)

Figure 1 - 8

 18 Figure 1-8 Selection of p-type polymers that could be used with PCBM as a basis for the active layer of an organic photovoltaic solar cell. (Source: Yan and Saunders 2014 55 )

Figure 1 - 9

 19 Figure 1-9 Cumulative energy demand for organic photovoltaic cells as reported in the literature up through December 2013 per watt-peak of power production

  were upwards of 3-orders of magnitude lower than Roes et al., for instance, in ozone depletion potential. These differences were most likely a consequence of the manufacturing differences taken by Espinosa et al. which used normal atmosphere conditions compared to Roes et al. who used an inert atmosphere for ITO sputtering onto the substrate.

2

  times longer than the EPBT values of 2.7-4.1 referenced for m-Si as well as 4-times longer than the values referenced for a-Si. Anctil et al. reported CED values ranging between 3-10 MJ/Wp for the 26 different OPV tested in their study. All such values were below the referenced CED of 16-24 MJ/Wp and 29 MJ/Wp for a-Si and m-Si, respectively. Yue et al. reported EPBT < 0.25 years for OPV which were one order of magnitude smaller than the nearly 2.4 years estimated for m-Si. Using two different functional units, Roes et al. compared a glass-substrate OPV and plastic-substrate OPV to both m-Si and a-Si. The EPBT for the glass-substrate OPV was 1.3 years compared with 2.33 years and 1.93 years for m-Si and a-Si PV, respectively. The EPBT for the plastic-substrate OPV was 0.2 years compared with 2.0 and 1.3 years for m-Si and a-Si, respectively. The reason why the EPBT for the silicon-based PV became shorter in the second comparison was due to the change in the functional unit which eliminated certain use-phase components (i.e. fewer physical materials used and thus a lower embodied energy of the entire system). Furthermore, Roes et al. compared OPV and silicon cells across 6 other impact categories: climate change potential, (abiotic) resource depletion, ozone layer depletion, photochemical oxidant formation, acidification and eutrophication.

Figure 2 Figure 2 - 2

 222 Figure 2-2The (a) attributional modeling approach in life-cycle assessment depicted as a share of total, current global environmental burdens of a product or process as opposed to the (b) consequential modeling approach that is concerned with the changes in total, global environmental burdens due to decisions made regarding the product or process. (Source:Weidema 2003 77 )

Figure 2 - 3

 23 Figure 2-3 General flow diagram created during the life-cycle inventory phase. The flow diagram helps to outline the data requirements for each process and along each step of a life-cycle assessment. (Source: U.S. EPA 78 )

Figure 2 - 4

 24 Figure 2-4 Illustration of foreground and background processes within a life-cycle inventory. Foreground processes are defined as the primary processes of concern and/or which the development of new and novel data is used to define those processes. Thus, foreground processes can represent any stage of the life-cycle assessment such as during (a) raw material extraction, (b) product manufacturing, (c) use and/or (d) end-of-life scenarios.

Figure 2 - 5

 25 Figure 2-5 A general cause and effect chain considered in life-cycle impact assessment methodologies, following the classification of inventory items into their respective midpoint and/or endpoint levels of impact and converted to impact values using each categories' substance-specific characterization factor. (Source: European Commission Joint Research Centre)

Figure 3 - 1

 31 Figure 3-1 General depiction of the bulk heterojunction organic photovoltaic cell considered in this chapter, incorporating (the skeletal formulas of) (a) phenyl-C61-butyric acid methyl ester (PCBM) which acts as the electron acceptor and (b) poly(3-hexylthiphene) (P3HT) which acts as the electron donor in the active layer.

Figure 3 - 2

 32 Figure 3-2 Generic schematic of the field strength of light at the active layer for an OPV device without an optical spacer (left) and with an optical spacer (right). (Source: Kim et al. 121 )

Figure 3 -

 3 3 depicts these lifecycle stages for the OPV cell.

Figure 3 - 3

 33 Figure 3-3 System boundaries of the cradle-to-gate life-cycle assessment illustrating the main components for the production of a prospective organic photovoltaic panel. All relevant processes, materials, and waste-streams up to the production of the solar cell are considered, thus excluding any consideration of the use-phase or end-of-life considerations.

  -to-Gate Life-Cycle Assessment of Organic Photovoltaics 81 Per m 2 , the EPBT for the default OPV cell was 0.27 years (97 days) compared with m-Si and a-Si's 3.6 and 2.8 years, respectively (Table

Figure 3 - 7

 37 Figure 3-7 Comparison of cumulative energy demand per watt-peak for organic photovoltaic cells reported in the literature as well as from this thesis.

Figure 4 Figure 4 - 1

 441 and 14044:200627 guidelines and the IEA's recommendations for implementing LCA for PV technology.35 All life-cycle stages and impacts from raw materials extraction, materials processing, PV manufacturing, use, end-of-life considerations were included (System boundaries for (a) System 1 (rooftop array) and (b) System 2 (portable charger). Incineration was just one of the end-of-life scenarios modeled in the life-cycle assessment and is shown for clarification of how the energy recovery was considered in the life-cycle inventory.OPV are not currently produced at industrial scale or used for commercial purposes. Therefore, two different systems (i.e. uses of the PV) were considered to better inform the potential role OPV could play for energy production and consumer product sectors. System 1 (S1) was defined by a functional unit of an average kWh of electricity generation over 25 years using a solar rooftop array (Figure4-2), while System 2 (S2) was defined by a functional unit of an average ten Wh of electricity generation over five years via a portable charging-device (Figure4-2).

Chapter 4

 4 Cradle-to-Grave Life-Cycle Assessment of Organic Photovoltaics 86 All foreground inventory data are explained in the following sections, while relevant background data were taken from the Ecoinvent v2.2 attributional life-cycle inventory. 132 All transportation requirements

Figure 4 - 2

 42 Figure 4-2 Examples of the two different systems (i.e. functional units) studied in this chapter

29 All

 29 layers of the panel were roll-to-roll printed under normal atmosphere conditions on a flexible substrate made from PET, using a transparent FTO electrode on the light-collecting face, an active layer composed of PCBM:P3HT, a hole transport layer made of MoO3, a back electrode made from aluminum with a thin layer of lithium fluoride, a nano-TiO2 optical spacer, encapsulation and lamination layers composed of PET, silica and various epoxy resins.

Chapter 4

 4 Cradle-to-Grave Life-Cycle Assessment of Organic Photovoltaics 90 composition at the time of manufacture (i.e. not considering losses or degradation of components over the lifetime of the PV device). For incineration, lower heating values for the major components were used to determine potential energy recovery from the system. Because the majority of the OPV panel is PET, its lower heating value of 22.9 was used, resulting in 4.95 MJ of electricity generated per kg of OPV panel incinerated. m-Si panels are composed almost entirely of materials that have insignificant lower heating values (i.e. silicon and glass) and thus it was assumed that incineration of m-Si panels generated no electricity. OPV panels weigh 0.22 kg per m 2 , resulting in the disposal of 22 kg of OPV panels. m-Si panels weigh 11.6 kg/m 2 , resulting in a total disposal of 88 kg of silicon panels.

Chapter 4 4 Figure 4 - 3

 4443 Cradle-to-Grave Life-Cycle Assessment of Organic Photovoltaics 93 Results and Discussion Results for the Default Organic Photovoltaic Technologies in Scenario 1 and Scenario 2 The relative impact differences between OPV-D and the silicon-based PV for S1 and S2 are shown in Figure Relative default impacts of (a) System 1 (rooftop array) comparing the default OPV-D scenario with m-Si panels and (b) System 2 (portable charger) comparing the default OPV-D with a-Si panels. Two separate disposal processes are additionally shown for each system. The impact results are internally normalized using division by the maximum impact value per impact category. -to-Grave Life-Cycle Assessment of Organic Photovoltaics 94

  Minimum required lifetimes ranged from a low of 0.01 years (freshwater ecotoxicity) to 11.4 years (metal depletion), with an average 1.4 years when considering incineration. These results indicate that in order for metal depletion OPV-D impacts to not exceed the impacts of m-Si, the OPV-D panels should have a lifetime of 9.8 years. Results of landfilling the panels were nearly identical, with an average minimum Chapter 4 Cradle-to-Grave Life-Cycle Assessment of Organic Photovoltaics 99 required lifetime of 1.6 years. The lifetime values for S2 averaged 0.46 years (incineration) and 0.55 years

  one to three years resulted in an initial, sharp decrease in environmental and human health impacts which then leveled off beyond three years. At the maximum lifetime tested of nine years, nine of 17 OPD-

Chapter 4 Figure 4 -Figure 4 - 6

 4446 Figure 4-6 displays the results for S1 when no mounting structure was used during the use phase. Impacts were reduced overall, with reductions over the default scenario ranging from 13% for metal depletion to 93% for water depletion.

  -to-Grave Life-Cycle Assessment of Organic Photovoltaics 105

Chapter 4

 4 Cradle-to-Grave Life-Cycle Assessment of Organic Photovoltaics 106 cycle. This is a consequence of the use-phase application setting which dictates what other contributing factors (i.e. auxiliary components other than the panel itself) will exist in the production, use and end-oflife phases.

Chapter 5

 5 Options for Assessing the Toxicological Impacts from Engineered Nanomaterials Use in Organic Photovoltaics 107 Chapter 5 Options for Assessing the Toxicological Impacts from Engineered Nanomaterials Use in Organic Photovoltaics Toxicological Hazards of Organic Photovoltaics An element missing from the LCA results presented in Chapter 3 and Chapter 4 was any estimation of ENM emissions and calculation of their human-and/or eco-toxicological impacts. There are only a few principle components of most PV, and for OPV these are generally the front electrode, back electrode, active layer, and the substrate. Additional components might include a hole transport layer, electron

Figure 5 - 2

 52 Figure 5-2 Consideration of life-cycle resource consumption and waste emissions using a generic nanotechnology example. The left side of the figure (a) provides a generalization about the considerations during life-cycle assessments of ENM-enabled products.Resource extraction and material processing would involve all relevant materials to the intended product but also those that are ENM-specific (i.e. extraction of the ENM precursor and processing it into the nanometer size range). Product manufacturing will outline the production of a single type of product (e.g. sporting equipment with carbon nanotubes) leading to its related use and potential end-of-life options (e.g. incineration). The right side of the figure (b) represents a hypothetical example of a ENM-enabled versus non-ENM (bulk) product applied in the function of wall-protection (i.e. a paint with a conventional pigment versus a paint using ENM-based pigments). In this example, it is assumed that the two pigments (i) are of the same chemical composition but different sizes, (ii) ENMversus bulk-manufacturing differ by the energy and materials (e.g. solvents) required to mechanically grind bulk material to ENM-sizes, (iii) due to its higher efficiency, less ENM-enabled material is required during the use-phase and thus less upstream raw material extraction is required and (iv) although less active ingredient is emitted during the use phase, it is distinct that it is being emitted in the ENM form as opposed to bulk emissions. The difference in energy consumption, material use, waste generation and emissions depicted by up/down arrows represent the relative life-cycle inventory of the ENM-enabled product (with up designating the ENM-enabled amounts are higher and vice versa but without any indication of the magnitude of change). Aggregation of all inventory values would then relate to their potential environmental impacts via its material specific characterization factor.

  state model like the two previous studies. Specifically,Salieri et al. considered the ENM-specific transformation processes of dissolution, homo-aggregation and hetero-aggregation, and sedimentation velocity inside of a freshwater and sediment compartmental model. The results of their fate calculations showed that the residence time for nano-TiO2 in freshwater was quite short, ranging from 0.1 days for 8 nm diameter particles to 0.001 days for 400 nm diameter particles. Such residence times are much shorter than the previously reported values ofEckelman et al. and Miseljik and Olsen, showing the potential importance of ENM-specific kinetic parameters in the calculation of fate and transport in life-cycle impact assessment models. Their reported fate and transport calculations were combined with a worst-case exposure factor of 1, citing no known knowledge about describing the bioaccumulation and bioavailability

Figure 5 - 3

 53 Figure 5-3 Illustration for carrying out a human health risk assessment to determine issues of chemical and substance-specific toxicity. Human health risk assessment involves identifying relevant health hazards, quantifying the critical dose of concern by evaluating the relationship between dose to the substance and toxicological response, estimating the measure of exposure of the receptor (i.e. the individual human) to the substance and calculating the risk involved by comparing the exposure value to the critical dose.

  Risk characterization ratio EXP: Exposure dose or concentration REL: Recommended exposure limit Chapter 5 Options for Assessing the Toxicological Impacts from Engineered Nanomaterials Use in Organic Photovoltaics 116

Figure 5 - 4

 54 Figure 5-4 Broad overview of the scope of life-cycle assessment (left) and of human health risk assessment (right).The former consisting of emissions, along with their fate and exposure, as well as elementary and techno-sphere flows, all of which are connected to various environmental and sustainability metrics used to measure levels of resource efficiency between products and processes. Human health risk assessment is depicted by its discreet scope that focuses on the toxicological risks. While both can evaluate issues of chemical toxicity, they do so with different methods, and in the case of life-cycle assessment, application of methods to determine ENM-toxicity are not currently employed in practice, they are for human health risk assessment.

Figure 5 - 5

 55 Figure5-5 One separate, one complementary and three integrated options for using of life-cycle assessment and risk assessment to evaluate products containing engineered nanomaterials. Choices are presented from the perspective of different particular stakeholders and their objectives. Decision makers, for example, may be concerned by both resource efficiency (e.g. changes in energy consumption for a ENM-enabled product) and toxicological impacts and risks of a nanotechnology or only a single dimension of these impacts.

Chapter 5

 5 Options for Assessing the Toxicological Impacts from Engineered Nanomaterials Use in Organic Photovoltaics 120 with one another. That is to say, the Complementary Use option will likely not be able to express how proportionate the resulting ENM-risk is in context of the toxicological hazards defined by LCA (Figure5-6).

Figure 5 - 6

 56 Figure 5-6 Illustration demonstrating that the results of a human health risk assessment (left side of figure) may not always be congruent with life-cycle assessment human health results, since they are calculated using different methods, most important of which is the scope by which these two tools are defined.

  (a) the interpretation of ENM toxicological hazards in the context of the intended product (i.e. functional unit) and (b) the direct comparison of ENM hazards to all other chemical-based hazards estimated across Chapter 5 Options for Assessing the Toxicological Impacts from Engineered Nanomaterials Use in Organic Photovoltaics 121

Figure 5 - 7 .

 57 Figure5-7. The first degree integration, referred to as Global Integration (GI), is based on the general LCA

Chapter 3 and

 3 Figure 5-5 a decision-maker at early-stage development (e.g. lab-or pilot-scale) concerned with addressing both the resource efficiency and the toxicological hazards would find Global Integration and/orContext-dependent Integration most suits their needs (i.e. effective hot-spot analysis to identify where in the life-cycle toxicological impacts and eventually risks may occur). This is because at the current scale of OPV development, there is too much uncertainty in production volumes or operating conditions, for example, and thus spending resources to determine the absolute damages (i.e. using Site-specific Integration) or risks (i.e. using Complementary Use) posed by ENM in such scenarios would have little to no basis.

Chapter 6

 6 Human Health Risk Assessments: Quantitative Assessment of Titanium Dioxide and Qualitative Assessment of C60 Fullerene Nanoparticles 126 Chapter 6 Human Health Risk Assessments: Quantitative Assessment of Titanium Dioxide and Qualitative Assessment of C 60 Fullerene Nanoparticles Chapter 3 and Chapter 4 explored the environmental and human health impacts of OPV technologies, their uses and their end-of-life considerations. The results of those assessments demonstrate that OPV have greater resource efficiencies and lower relative environmental and human health hazards compared with their silicon-based counterparts. Chapter 5 highlighted the absence of ENM-specific life-cycle inventory flows to the environment as well as the absence of their corresponding potential human health impacts in those LCA studies. A further analysis was provided in Chapter 5 regarding life-cycle impact assessment methodologies and HHRA, specifically what opportunities exist to use these tools in a complementary or integrative way in order to provide a comprehensive evaluation of the environmental

Figure 6 - 1 Chapter 6 Figure 6 - 2

 61662 Figure 6-1 Potential exposure to engineered nanomaterials across the life-cycle for a generic ENM-containing product. Exposure along the life-cycle of a ENM-enabled product can result from emissions of engineered nanomaterials at any stage, introducing potential for occupational, consumer and ecological exposures and corresponding toxicological impacts or risks.

Chapter 6 Figure 6 - 3

 663 Figure 6-3 Two-dimensional structure of titanium dioxide in its two most commonly found forms: rutile and anatase. (Source: U.S. National Institute of Occupational Safety and Health)

1 )

 1 BMCh: Human equivalent benchmark concentrationBMCa: Benchmark concentration based on animal toxicological dataEFinter: Interspecies extrapolation factor EFintra: Intraspecies extrapolation factor UFi: sources of uncertainty(i)

  Amount of nano-TiO2 handled during work-cycle ttransfer: Time length of work-cycle DIresp: Dustiness index of nano-TiO2 Hi: Handling energy factor of work-type Atransfer (kg) is the amount of powder transferred during a work cycle, of given time-length ttransfer (min),

v. 1 .

 1 1 includes the duration of the activities and work cycle (twc), the number of work cycles (nwc), the volume of work room (Vtot), and the general air-exchange rate (h -1 ) in the work-room to estimate the NF and FF concentrations. Daily(8-hour) exposure potentials were calculated in both the NF and FF.220 Uncertainty in the exposure estimations were correlated with the DIresp since the emission potential was found to be the most sensitive and strongest determinant input parameter in the model.307 This uncertainty (R 2 =65.8; p<0.001) is given by equation (6-3): log(σDI) = 0.871 • log(DI resp ) -0.625 (6-3) σDI: standard deviation of the DIresp. This equation was derived based on statistical analysis (Minitab® version 17.2.1) of the experimental standard deviation related to the dustiness indices of 59 powder samples available from the Danish National Research Centre for the Working Environment (Figure 6-5).

Figure 6 - 5

 65 Figure 6-5 Interpretations of the residual values of the log-transformed standard deviations for 59 particulate powder tests (Note: three tests included missing values). The upper and lower left graphs indicate that the assumption of normality for the error terms is valid, as there is no significant deviation from the central trend line and the highest frequency is for values equal to zero. The upper right graph plots the error terms against their fitted values, indicating whether that the mean value of zero holds true.

  from the exposure concentration distribution for scenario i A risky scenario was defined as RCR values ≥ 1.0 (i.e. exposure levels higher than the BMCh) and represents the lifetime risk of a worker exposed to the 8-hour time weighted indoor air nano-TiO2 concentrations over 45-years, 50 weeks/year, 5 days/week. The RCR distributions were calculated by sampling the BMCh distributions and the three NF and three FF exposure distributions over 10,000 Monte-Carlo simulations per exposure scenario.

Chapter 6

 6 Human Health Risk Assessments: Quantitative Assessment of Titanium Dioxide and Qualitative Assessment of C60 nano-TiO2 concentrations (mg/m 3 )

Figure 6 - 8

 68 Figure 6-8 Potential exposure time-series in the (a) near field and (b) far field. Note: This figure shows the results for a larger range of exposure scenarios than presented in this chapter. These additional scenarios were completed as a part of a larger publication out of direct context of this chapter. Additional information for the other scenarios can be found in Appendix: Chapter 6.

Figure 6 - 9

 69 Results of 10,000 Monte-Carlo risk characterization ratio simulations for exposure scenario 2 in the (a) near-field and (b) far-field. Note that right-end tails of the distribution are artificially truncated for presentation. Contributions to the uncertainty and variation are displayed in (c) for the near-field and in (d) for the far-field.

  the volume of the compartment (m 3 ) 𝐶i: the concentration at a given time-step (mg/m 3 ) 𝐶i-1: concentration at the previous time-step (mg/m 3 ) 𝑆: the emissions rate (mg/hr) 𝑄: ventilation rate (m 3 /hr)

  𝐶NF: near-field concentration at a given time-step (mg/m 3 ) 𝐶NF-1: near-field concentration at the previous time-step 𝐶FF-1: far field concentration at the previous time-step 𝛽: Inter-zonal air flow between the near-and far-fields 𝑉 • 𝑑𝐶FF d𝑡 = (𝛽 • 𝐶NF-1) -(𝐶 FF-1 • 𝑄 • 𝑚) -((𝛽 + 𝑄) • 𝐶FF-1) (7-5) 𝐶FF: near-field concentration at a given time-step (mg/m 3 )

Chapter 7

 7 Life-Cycle Impact Assessment Nanomaterial Characterization Factors: Titanium Dioxide Case Study 153 deposition in three airway regions: (i) the head or upper airways, (ii) the tracheobronchial region, and (iii) the pulmonary region (i.e. where air exchange occurs at the alveoli). The MPPD v3.01 dosimetry model was used to derive the regional (fractional) deposition of ENM in these regions (Appendix:Chapter 7). The inputs to the MPPD model, including the resulting fate model ENM concentrations, are shown in Appendix: Chapter 7. These values were used to determine the relative deposition of the airborne nano-TiO2 concentrations that entered the lung and to which parts of the lung. The results of the clearance and ultimate retention of this deposited dose was then calculated by the PBPK model.Phagocytizing cells (PCS) reside in both the pulmonary region of the lungs and the interstitium of the lungs.The ultimate retention of ENM in the lung was dependent on a number of processes including (i) translocation from the pulmonary region to the interstitium of lungs then to systemic circulation, (ii) sequestration of ENM by PCS, (iii) transfer of loaded-PCS to the tracheobronchial region via mucociliary clearance, and (iv) transfer from the tracheobronchial region to the pharynx via mucociliary clearance.Flow-and diffusion-limited processes, defined by their respective permeability and partition coefficients (Appendix: Chapter 7), governed the exchange of ENM with blood and tissues, while sequestration of ENM by PCS has organ-specific saturation levels and the sequestration rate decreased as the load in PCS approaches saturation. Finally, mucociliary clearance rate was defined by a constant value irrespective of ENM loading in the lung. It has been shown that pulmonary clearance of particulates found in lavages is 10-times faster in rats compared with humans and might be partially explained by the greater mucociliary clearance rate in rats compared with that in humans.344 Thus, the transport factor governing translocation of loaded-PCS to the tracheobronchial region was changed to 1.44E-6 (min -1 ), which is one-order of magnitude slower than in the original model ofLi et al. 337 Ideally, inhalation rates should fluctuate over time due to differences in metabolic activity.201 In the current model, an inhalation value of 34.0 L/min was assumed during the work-related activity and a value of 14L/min during non-work activities.345 Additionally, it was assumed that workers were in the FF during non-work activities. Blood flow was modeled for heavy exercise during the 8-hour workday and 5 workdays per week, while non-working hours and non-workdays reflected blood flow values for people at rest. A complete list of all PBPK parameters and their values of the model can be found in Appendix: Chapter 7. The PBPK model was implemented in Berkeley MadonnaTM version 8.3 (Berkeley, CA) and initially ran in iterative durations to determine when, if any, maximal retention in the lung occurred.

  Retained intake fraction (unitless) 𝐸𝑋𝑃int: Exposure as internal lung dose (μg/g-wet lung) 𝐸life: Lifetime emissions (kg) POP: Worker population (number of persons)

Figure 7 - 1 Figure 7 - 2

 7172 Figure 7-1 Comparison of yearly emissions and emission rate.

Figure 7 - 3 Figure 7 Figure 7 - 4

 73774 Figure 7-3 Comparison of average and maximum daily airborne nano-TiO2 concentration.

Figure 7 - 5

 75 Figure 7-5 Retention of nano-TiO2 in the lung estimated over 1 full work year for ES1. The x-axis represents time in minutes over 1-year and the y-axis represents the mass (μg) of nano-TiO2 in the wet lung. The green trend line represents the change in mass in the airexchange (pulmonary) regions of the lung, the blue trend line represents the change in mass in the interstitial regions of the lung, the

Figure 7 - 6 Figure 7 - 7

 7677 Figure 7-6 Time-weighted retention in the wet lung over lifetime as a function of the (a) emission rate and (b) yearly emissions.as well as the resulting retained intake fraction as a function of (c) the emission rate and (d) the yearly emissions.

Chapter 7

 7 Life-Cycle Impact Assessment Nanomaterial Characterization Factors: Titanium Dioxide Case Study 168 of this increase occurred at week 21, reaching 12.4 μg-wet lung, but then receded to approximately 11.1 μg/g-wet lung by week 25. Thereon, the maximum wet lung retention stayed consistent around 11.1 μg/gwet lung until the end of the year (Figure 7-8).

Figure 7 - 8

 78 Figure 7-8 Retention of nano-TiO2 in the lung estimated over 1 full work year for ES4. The x-axis represents time in minutes over 1-year and the y-axis represents the mass (μg) of nano-TiO2 in the wet lung. The green trend line represents the change in mass in the air-exchange (pulmonary) regions of the lung, the blue trend line represents the change in mass in the interstitial regions of the lung, the pink trend line represents the change in mass in the trachea-bronchial regions of the lung, the grey trend line represents the change in mass in the lung macrophages, the dark-blue line represents the change in mass in the pulmonary macrophages, the red trend line represents the total retention in the wet lung including the air-exchange (pulmonary) regions, interstitial regions, trachea-bronchial regions and their macrophages.

Chapter 7

 7 Life-Cycle Impact Assessment Nanomaterial Characterization Factors: Titanium Dioxide Case Study 170 rapid and steep spike in retention seen between weeks 11-17 similar to ES4. The interstitial PC contained 26% of the maximum total wet lung retention. The pulmonary and interstitial PC reached their maximum total retention by the end of the first work-week. The pulmonary region cleared itself of all deposited nano-TiO2 by the beginning of each subsequent week. This region only contributed up to 4% of the maximum wet lung retention observed at the end of the first year.

Figure 7 - 9

 79 Figure 7-9 Retention of nano-TiO2 in the lung estimated over 1 full work year for ES4. The x-axis represents time in minutes over 1-year and the y-axis represents the mass (μg) of nano-TiO2 in the wet lung. The pink trend line represents the change in mass in the trachea-bronchial regions of the lung, the yellow trend line represents the change in mass in the upper airway, the dark-blue line represents the change in mass in the pulmonary macrophages, the red trend line represents the total retention in the wet lung including the air-exchange (pulmonary) regions, interstitial regions, trachea-bronchial regions and their macrophages.

  7 and geometric standard deviation of 2.8 (Appendix: Chapter 7). The low-and highpopulation workforce estimates were defined by the 5 th -and 95 th -percent confidence intervals, with corresponding values of 1.6 and 47.3 persons. The influence of the worker population on the RiF is shown in Figure 7-10.

Figure 7 - 10

 710 Figure 7-10 Comparison of the intake fraction (shown in log-scale) and number of exposed workers

Figure 7 - 11

 711 Figure 7-11Benchmark dose results for cancerous impacts to both mice (black circles) and rats (red triangles). The x-axis represents the internal lung dose reported as surface area of nano-TiO2 per g-dry lung. The y-axis is reported as the fraction of the animals that result in cases of cancer. The log-likelihood of the fitted Hill model was -208.82. The reported benchmark dose was 1.43 m2/g-dry lung based on the excess risk of 50% over background cancer rates.

Figure 7 - 12

 712 Figure 7-12Benchmark dose results for non-cancerous impacts to both mice (black circles) and rats (red triangles). The x-axis represents the internal lung dose reported as μg of nano-TiO2 per g-dry lung. The yaxis is reported as the fraction of the animals that result in inflammation (i.e. the 20% increase in neutrophil count over background rates). The log-likelihood of the fitted Hill model was -1386.37. The reported benchmark doses were 27352 μg/g-dry lung for mice and 7807 μg/g-dry lung for rats based on the excess risk of 50% over background inflammation rates.

Figure 7 -

 7 Figure 7-13 Non-carcinogenic characterization factors (lifetime) as a function of the (a) total emissions per year, (b) emission rate in log-scale, (c) exposed population of workers (note: results only displayed for ES1.00 (e-high, f-short) scenario), and (d) acute, 1-year versus chronic, lifetime characterization factors.

Figure 7 - 14

 714 Figure 7-14 Contribution to human health impacts by life-cycle stage

Figure A4- 1 Figure A4- 2 Figure A4- 3 Figure A4- 4 Figure A4- 5

 12345 Figure A4-1 Contributions from each life-cycle stage for the OPV-D and m-Si panels when panels were (a) incinerated and (b) landfilled at their end-of-life for S1 (rooftop array).

Figure A4- 6

 6 Figure A4-6EPBTs and CPBTs for S1 based on differences in the assumed lifetimes (T) and while the efficiency was assumed to be 1%.

Figure A4- 7

 7 Figure A4-7EPBTs and CPBTs for S1 based on differences in the assumed efficiencies (E) and while the lifetime of the panel was assumed to be 1-year.

4 XXVIFigure A4- 10

 410 Figure A4-10EPBTs and CPBTs for S2 based on differences in the assumed efficiencies (E) and while the lifetime of the panel was assumed to be 1-year.

Figure A4- 11 Figure A4- 12

 1112 Figure A4-11 Comparison of OPV alternatives for S1 alternatives that involved removing the mounting structure. The impact results are all normalized by OPV-D per end-of-life option.

Figure A4- 13

 13 Figure A4-13 Comparison of S2 OPV alternatives based on the use of a copolymer instead of PCBM in the active layer of the solar cell.

  photovoltaïques organiques ne sont actuellement pas produites à l'échelle industrielle ou utilisées à des fins commerciales. Par conséquent, deux systèmes différents (deux utilisations du PV) ont été pris en compte afin de mieux éclairer le rôle que le PVO pourrait jouer pour les secteurs de la production d'énergie et des produits de consommation. Le système 1 (S1) est défini par une unité fonctionnelle d'une production d'un kWh moyen d'électricité durant 25 ans sur un toit (Figure 4 -2), tandis que le système 2 (S2) est défini par une unité fonctionnelle d'une production moyenne de 10 Wh d'électricité sur cinq ans via un dispositif de chargeur de portable. Pour S1, en plus de l'installation sur le toit des panneaux PVO et silicium, la structure de montage, un onduleur et des câbles pour l'installation électrique ont été également pris en compte. Pour S2, on a supposé que les chargeurs portatifs nécessitent un étui de protection en PET-polyester d'un mm d'épaisseur.

  des réductions d'impact plus marquées pour l'incinération en raison de la valeur ajoutée apporté par l'incinération du boîtier en plastique avec le panneau PVO. Il convient de noter que les impacts du changement climatique pour le VPO-D sont supérieurs de 3 % (S1) et de 16 % (S2) lorsqu'ils sont incinérés Résumé Détaillé LXI plutôt qu'enfouis. Ceci est une conséquence nécessaire de la combustion des composants en plastique qui ont été convertis entièrement en CO2 au cours du processus. Bien que les options de fin de vie testées dans cette ACV n'aient eu qu'une influence marginale sur les impacts sur l'environnement et la santé humaine, cette analyse n'a pas inclus les effets potentiels du recyclage des panneaux photovoltaïques. Le PV à base de silicium peut tirer le meilleur parti du recyclage puisque la production de plaquettes de silicium et de panneaux a une contribution majeure sur les impacts globaux pour le m-Si et l'a-Si. Müller et al., ont fait état d'une réduction de près de 57 % de la consommation d'énergie pour les modules m-Si fabriqués avec des plaques de silicium recyclé62 . Dans le cadre du S1, dans l'approche du berceau à la porte de l'usine, la diminution du besoin énergétique rapportée par Müller et al. pourrait conduire à des réductions potentielles de la CED de 49 % pour les panneaux m-Si, ce qui donne une CED de 12 520 MJ. Cette estimation est légèrement supérieure à l'estimation de la CED de 11 634 MJ pour le VPO-D, soulignant ainsi l'importance de cette option en fin de vie. Bien que la CED n'ait pas une relation linéaire avec les autres catégories d'impact,55 une hypothèse raisonnable serait que la plupart des autres catégories d'impact diminueront également, mais avec une amplitude différente. Cela entraîne une certaine incertitude entourant les avantages environnementaux du PVO sur les panneaux conventionnels. Contrairement aux m-Si, aucune information pertinente sur les options de recyclage possibles pour les a-Si ne se trouve dans la littérature, et en raison de sa teneur en silicium beaucoup plus faible, la faisabilité du recyclage des a-Si est incertaine.58 Ces résultats reposent sur les hypothèses que les PVO ont une durée de vie de 5 ans et une efficacité de conversion de 5%. Afin de tester l'influence de ces hypothèses sur les résultats globaux, différentes analyses de sensibilité ont été réalisées : dans un premier temps des panneaux ayant un rendement de 1 % et une durée de vie allant de 1 à 9 ans ont été testés puis dans un second temps, leur durée de vie a été fixée à 1 an et les rendements ont varié de 1 à 9 %. Pour S1, l'augmentation de la durée de vie d'un à trois ans a entraîné une forte baisse initiale des impacts sur la santé humaine et l'environnement, qui ensuite se stabilise après ces trois ans. Lorsqu'une durée de vie maximale de neuf ans est considérée, 9 des 17 impacts pour l'OPD-D, y compris le potentiel de changement climatique et la CED sont restés proportionnellement plus élevés que les panneaux m-Si. En conséquence, le TRE et le TRC ne sont jamais inférieurs aux m-Si. Le TRE varie d'un maximum de 16 ans pour une durée de vie d'1 an pour un minimum de 3,5 ans pour une durée de vie de 9 ans, alors que le TRC va d'un maximum de 6,4 ans à un minimum de 1,6 ans, respectivement. Une tendance similaire a été observée pour l'analyse de sensibilité sur le rendement du panneau : une augmentation de 1-3 % du rendement induit également une forte baisse Résumé Détaillé LXII initiale des impacts sur la santé humaine et l'environnement(Figure 4 -4). Au -delà de 3 %, les réductions d'impact ont commencé à se stabiliser, mais pas aussi fortement que dans le cas de la sensibilité sur la durée de vie. Ainsi, avec une efficacité de 9% seulement l'épuisement des métaux est resté proportionnellement plus élevé que m-Si. En particulier, le potentiel de DEC et le changement climatique étaient de 18% et 16% inférieure à m-Si, avec un TRE et TRC de 2,1 ans et 305 jours, respectivement.Pour S2, l'augmentation de la durée de vie d'un à trois ans a également entraîné une forte baisse initiale qui s'est ensuite stabilisée au -delà de 3 ans. Pour une durée de vie maximale de 9 ans, 7 des 17 impacts, y compris la CED et le potentiel de changement climatique sont restés plus élevés que pour l'a-Si. Cela a abouti à des TRE et TRC de 2,8 ans et 1,5 ans, respectivement, qui restent supérieurs à l'a-Si. En revanche, les effets de l'augmentation de l'efficacité a entraîné une diminution plus nette et légèrement plus soutenue lors de la variation de 1 à 9% d'efficacité (figure4 -5). Pour 3% d'efficacité, plus de la moitié des impacts sur la santé humaine et l'environnement des PVO-D étaient inférieurs à l'a-Si et à 5 % de rendement tous les impacts étaient inférieurs à ceux du a-Si. Cela signifie que pour un rendement de 5% le TRE et le TRC étaient inférieurs à ceux de l'a-Si. Avec un rendement de 9 %, le TRE et le TRC sont de 215 jours et 101 jours, respectivement.

  d'énergie préférable par rapport à des panneaux PV à base de silicium, d'un point de vue de la santé humaine et de l'environnement, en particulier pour les utilisations où la durée de vie du produit sont relativement courtes (<5 ans) ou lorsque les systèmes de produits sont extrêmement simplifiés. Un élément manquant dans les précédentes études était une estimation des émissions des NM et le calcul de leurs impacts sur la santé humaine et / ou éco-toxicologiques. Globalement une cellule PVO se compose d'une anode, d'une cathode, d'une couche active et d'un substrat. Des composants supplémentaires peuvent inclure une couche de transport de trous, une couche de transport d'électrons, Résumé Détaillé LXIII des bloqueurs d'UV, des revêtements, des barrières et / ou des espaceurs optiques, par exemple. Pour parvenir à la finesse caractéristique du PVO, ces couches doivent contenir peu de matériaux et ceux-ci doivent avoir une taille allant d'une dizaine à quelques centaines de nanomètres. Par conséquent, les matières utilisées seront souvent de taille nanométrique.

  auteurs modifiaient les FC existant dans USEtox pour les rendre propres au NM. Les méthodes d'évaluation de l'impact du cycle de vie actuelles, telles que USEtox, tirent parti d'un certain nombre d'hypothèses qui décrivent commodément le sort et le transport des petites molécules organiques et des métaux, mais ces méthodes ne sont pas appropriées pour les NM ou décrivent de manière similaire les matières colloïdales90 . Les principales hypothèses utilisées dans les méthodes d'évaluation de l'impact supposent que les conditions sont à l'état d'équilibre et que les systèmes ont atteint l'équilibre thermodynamique91,92 . Par conséquent, ces modèles supposent qu'il n'y a aucun changement dans la concentration du polluant dans le temps et que le sort de ces polluants peut être estimé à l'aide de coefficients (d'équilibre), de partition (par exemple, le coefficient de partition de l'octanol-eau, Kow). Ces coefficients sont définis par le rapport de la concentration de la pollution restante dans un milieu par rapport à un autre milieu (par exemple l'octanol par rapport à l'eau), une fois que le système impliquant les 2 milieux a atteint l'équilibre.Toutefois, lorsqu'ils sont libérés dans un milieu spécifique, les NM existent dans leur propre phase et ne forment pas de phases uniformes avec le milieu environnant contrairement aux matériaux organiques. Par conséquent, et par définition, les NM ne sont pas thermodynamiquement stables, même si certains NM peuvent être cinétiquement stables pendant de longues périodes de temps90 . En d'autres termes, un rapport de concentration de NM entre deux milieux ne peut pas être prédit à partir d'un coefficient de partage précédemment mesuré si les conditions n'étaient pas exactement les mêmes entre les deux scénarios90 . Cela s'explique par le fait que le comportement du NM dans l'environnement est fortement dépendant de la concentration (selon les conditions initiales, la quantité de NM, et le moment Résumé Détaillé LXV où le système est mesuré, les résultats seront différents)87,90 . Ainsi, à la place des coefficients de partage d'équilibre, les NM doivent être décrits en utilisant des modèles cinétiques dynamiques utilisant le devenir et l'exposition. Par exemple, ces modèles peuvent employer l'utilisation des constantes de vitesse du premier ordre pour décrire la dissolution du NM en ions dans la colonne d'eau93 . En outre, les modèles à l'état d'équilibre ne sont également pas en accord avec le fait que les taux d'émissions du NM ne sont généralement pas des événements réguliers. Cela est particulièrement vrai pour les scénarios en intérieur et d'occupation94,95 où les émissions de NM sont susceptibles d'être épisodiques. Enfin, l'exposition au NM, et plus particulièrement l'inhalation du NM, présentera des mécanismes dépendant de sa concentration elle-même influencée par les échanges avec les poumons (dégagement et rétention notamment). Autrement dit, l'inhalation du NM ne dépend pas seulement du degré d'inhalation de l'individu qui est exposé96 . Elle dépend également des caractéristiques du NM, des mécanismes et des temps biologiques au cours desquels l'exposition est mesurée97 . Il a été proposé d'utiliser un facteur de rétention spécifique au NM, appelé R, calculé en utilisant des modèles de dépôt et de dégagement des voies respiratoires des poumons tels que le modelé 'Multi-Pathway Particle Dosimetry' (MPPD)98 . Le modèle MPPD est un outil généralement utilisé dans l'Evaluation Quantitative des Risques Sanitaires (EQRS), équivalent du Human Health Risk Assessment (EQRS), pour déterminer la dose intra-pulmonaire venant de l'exposition à des concentrations dans l'air de produits chimiques et de particules. Cependant, le modèle MPPD actuel ne permet pas d'estimer le dépôt ou le dégagement tout en considérant des conditions d'exposition qui changent au fil du temps.

  photovoltaïques sont utilisés et de leurs durées de vie. Les incertitudes, principalement liées à la durée de vie et l'efficacité du PVO, empêchent de se prononcer définitivement afin de savoir si le PVO se révèle être une option d'approvisionnement en énergie écologiquement préférable. Cependant, ces incertitudes peuvent être marginalisées lorsque les technologies PVO sont limitées à des produits ayant des durées de vie relativement courtes (par exemple les produits où l'utilisation est typiquement <5 ans), pour lesquelles la préférence pour le PVO devient manifeste. Les résultats de cette thèse ont cependant montré que le PVO avait vocation à être utilisé pour des panneaux solaires sur les toits, là où le PV à base de silicium a dominé pendant des décennies.

TABLE 7 -

 7 4 RESULTS FOR THE INTERNAL WET LUNG BURDEN AND THE RETAINED-INTAKE FRACTION, REPORTED AS EITHER A LIFETIME OR 1-YEAR VALUE ..........................................................................................................................................................TABLE 7-5 EFFECT FACTORS (EF), INTAKE FRACTIONS (RIF) AND ENM-SPECIFIC CHARACTERIZATION FACTORS (CFNS) FOR SIX DIFFERENT EMISSION AND EXPOSURE SCENARIOS THAT INVOLVED DIFFERENCES IN EMISSION RATES (E) AND EMISSION INTERVAL FREQUENCIES (F) .......................................................................................................................................................................... TABLE 7-6 HUMAN HEALTH IMPACTS PER WATT-PEAK OF OPV CELL PRODUCTION WITHOUT A ENM-SPECIFIC CHARACTERIZATION FACTOR FOR NANO-TIO2 (LEFT COLUMNS) AND WITH A ENM-SPECIFIC CHARACTERIZATION FACTOR (RIGHT COLUMNS) ........................ FIGURE 1-1 ILLUSTRATIONS OF (A) THE SETUP DESCRIBED BY BECQUEREL IN 1939 TO ACHIEVE THE FIRST KNOWN, INTENTIONALLY PRODUCED PHOTOVOLTAIC EFFECT AND (B) THE OLDEST CONTEMPORARY INTERPRETATION OF THE P-N SILICON SEMICONDUCTING CELL AS IT IS KNOWN TODAY. (SOURCE: GREET 2002) 12 .................................................................................................. FIGURE 1-2 LABORATORY-BASED SOLAR CELL EFFICIENCY RECORDS (SOURCE: U.S. NATIONAL RENEWABLE ENERGY LABORATORY)

xi

List of Figures

  TWO SEPARATE DISPOSAL PROCESSES ARE ADDITIONALLY SHOWN FOR EACH SYSTEM. THE IMPACT RESULTS ARE INTERNALLY NORMALIZED USING DIVISION BY THE MAXIMUM IMPACT VALUE PER IMPACT CATEGORY. ................................................................................................... FIGURE 4-4 CHANGES IN LIFE-CYCLE IMPACTS FOR S1 (ROOFTOP, INCINERATION) ACCORDING TO FORECASTS IN (A) LIFETIME OF ORGANIC PHOTOVOLTAIC PANELS (WITH A 1% EFFICIENCY) AND (B) EFFICIENCIES OF ORGANIC PHOTOVOLTAIC PANELS (WITH A 1-YEAR LIFETIME). THE IMPACT RESULTS ARE INTERNALLY NORMALIZED TO THE IMPACT VALUES OF M-SI (I.E. M-SI'S IMPACTS ARE SET AT 100%). SEE APPENDIX: CHAPTER 4 FOR THE SENSITIVITY ANALYSIS FOR S1 WITH LANDFILLING AS THE END-OF-LIFE OPTION. ...... FIGURE 4-5 CHANGES IN LIFE-CYCLE IMPACTS FOR SYSTEM 2 (PORTABLE CHARGER, INCINERATION) ACCORDING TO FORECASTS IN (A) FIGURE 5-1 NUMBER OF PUBLICATIONS BETWEEN 1980-2013 THAT MATCH THE SEARCH CRITERIA OF "NANOTOXICOLOGY" (ADAPTED FIGURE 5-2 CONSIDERATION OF LIFE-CYCLE RESOURCE CONSUMPTION AND WASTE EMISSIONS USING A GENERIC NANOTECHNOLOGY EXAMPLE. THE LEFT SIDE OF THE FIGURE (A) PROVIDES A GENERALIZATION ABOUT THE CONSIDERATIONS DURING LIFE-CYCLE ASSESSMENTS OF ENM-ENABLED PRODUCTS. RESOURCE EXTRACTION AND MATERIAL PROCESSING WOULD INVOLVE ALL RELEVANT MATERIALS TO THE INTENDED PRODUCT BUT ALSO THOSE THAT ARE ENM-SPECIFIC (I.E. EXTRACTION OF THE ENM PRECURSOR AND PROCESSING IT INTO THE NANOMETER SIZE RANGE). PRODUCT MANUFACTURING WILL OUTLINE THE PRODUCTION OF A SINGLE TYPE OF PRODUCT (E.G. SPORTING EQUIPMENT WITH CARBON NANOTUBES) LEADING TO ITS RELATED USE AND POTENTIAL END-OF-LIFE OPTIONS (E.G. INCINERATION). THE RIGHT SIDE OF THE FIGURE (B) REPRESENTS A HYPOTHETICAL EXAMPLE OF A ENM-ENABLED VERSUS NON-ENM (BULK) PRODUCT APPLIED IN THE FUNCTION OF WALL-PROTECTION (I.E. A PAINT WITH A CONVENTIONAL PIGMENT VERSUS A PAINT USING ENM-BASED PIGMENTS). IN THIS EXAMPLE, IT IS ASSUMED THAT THE TWO PIGMENTS (I) ARE OF THE SAME CHEMICAL COMPOSITION BUT DIFFERENT SIZES, (II) ENM-VERSUS BULK-MANUFACTURING DIFFER BY THE ENERGY AND MATERIALS (E.G. SOLVENTS) REQUIRED TO MECHANICALLY GRIND BULK MATERIAL TO ENM-SIZES, (III) DUE TO ITS HIGHER EFFICIENCY, LESS ENM-ENABLED MATERIAL IS REQUIRED DURING THE USE-PHASE AND THUS LESS UPSTREAM RAW MATERIAL EXTRACTION IS REQUIRED AND (IV) ALTHOUGH LESS ACTIVE INGREDIENT IS EMITTED DURING THE USE PHASE, IT IS DISTINCT THAT IT IS BEING EMITTED IN THE ENM FORM AS OPPOSED TO BULK EMISSIONS. THE DIFFERENCE IN ENERGY CONSUMPTION, MATERIAL USE, FIGURE 5-3 ILLUSTRATION FOR CARRYING OUT A HUMAN HEALTH RISK ASSESSMENT TO DETERMINE ISSUES OF CHEMICAL AND SUBSTANCE-SPECIFIC TOXICITY. HUMAN HEALTH RISK ASSESSMENT INVOLVES IDENTIFYING RELEVANT HEALTH HAZARDS, QUANTIFYING THE CRITICAL DOSE OF CONCERN BY EVALUATING THE RELATIONSHIP BETWEEN DOSE TO THE SUBSTANCE AND TOXICOLOGICAL RESPONSE, ESTIMATING THE MEASURE OF EXPOSURE OF THE RECEPTOR (I.E. THE INDIVIDUAL HUMAN) TO THE SUBSTANCE AND CALCULATING THE RISK INVOLVED BY COMPARING THE EXPOSURE VALUE TO THE CRITICAL DOSE. ..................................................................... 115 FIGURE 5-4 BROAD OVERVIEW OF THE SCOPE OF LIFE-CYCLE ASSESSMENT (LEFT) AND OF HUMAN HEALTH RISK ASSESSMENT (RIGHT). THE FORMER CONSISTING OF EMISSIONS, ALONG WITH THEIR FATE AND EXPOSURE, AS WELL AS ELEMENTARY AND TECHNO-SPHERE FLOWS, ALL OF WHICH ARE CONNECTED TO VARIOUS ENVIRONMENTAL AND SUSTAINABILITY METRICS USED TO MEASURE LEVELS OF RESOURCE EFFICIENCY BETWEEN PRODUCTS AND PROCESSES. HUMAN HEALTH RISK ASSESSMENT IS DEPICTED BY ITS DISCREET SCOPE THAT FOCUSES ON THE TOXICOLOGICAL RISKS. WHILE BOTH CAN EVALUATE ISSUES OF CHEMICAL TOXICITY, THEY DO SO WITH DIFFERENT METHODS, AND IN THE CASE OF LIFE-CYCLE ASSESSMENT, APPLICATION OF METHODS TO DETERMINE ENM-TOXICITY ARE NOT CURRENTLY EMPLOYED IN PRACTICE, THEY ARE FOR HUMAN HEALTH RISK ASSESSMENT. ................................................. 116 FIGURE 5-5 ONE SEPARATE, ONE COMPLEMENTARY AND THREE INTEGRATED OPTIONS FOR USING OF LIFE-CYCLE ASSESSMENT AND RISK ASSESSMENT TO EVALUATE PRODUCTS CONTAINING ENGINEERED NANOMATERIALS. CHOICES ARE PRESENTED FROM THE PERSPECTIVE OF DIFFERENT PARTICULAR STAKEHOLDERS AND THEIR OBJECTIVES. DECISION MAKERS, FOR EXAMPLE, MAY BE CONCERNED BY BOTH RESOURCE EFFICIENCY (E.G. CHANGES IN ENERGY CONSUMPTION FOR A ENM-ENABLED PRODUCT) AND TOXICOLOGICAL IMPACTS AND RISKS OF A NANOTECHNOLOGY OR ONLY A SINGLE DIMENSION OF THESE IMPACTS. ........................................................ 119 FIGURE 5-6 ILLUSTRATION DEMONSTRATING THAT THE RESULTS OF A HUMAN HEALTH RISK ASSESSMENT (LEFT SIDE OF FIGURE) MAY NOT ALWAYS BE CONGRUENT WITH LIFE-CYCLE ASSESSMENT HUMAN HEALTH RESULTS, SINCE THEY ARE CALCULATED USING DIFFERENT METHODS, MOST IMPORTANT OF WHICH IS THE SCOPE BY WHICH THESE TWO TOOLS ARE DEFINED. ........................................ 120 FIGURE 5-7 KEY ASPECTS OF LIFE-CYCLE AND RISK ASSESSMENT INTEGRATION AT THE METHODOLOGICAL LEVEL. INTEGRATION IS REPLACED ON ITS LEVEL OF SPATIAL AND TEMPORAL SPECIFICITY AS WELL AS MODEL AND DATA COMPLEXITY. FOR EXAMPLE, LOW TEMPORAL POSITIVE, LINEAR CORRELATION) FOR CLASSES OF SOURCES AVERAGED AT THE COUNTRY LEVEL IN A PARTIALLY-DYNAMIC MODEL. .............................................................................................. 121 FIGURE 6-1 POTENTIAL EXPOSURE TO ENGINEERED NANOMATERIALS ACROSS THE LIFE-CYCLE FOR A GENERIC ENM-CONTAINING PRODUCT. EXPOSURE ALONG THE LIFE-CYCLE OF A ENM-ENABLED PRODUCT CAN RESULT FROM EMISSIONS OF ENGINEERED NANOMATERIALS AT ANY STAGE, INTRODUCING POTENTIAL FOR OCCUPATIONAL, CONSUMER AND ECOLOGICAL EXPOSURES AND CORRESPONDING TOXICOLOGICAL IMPACTS OR RISKS. ........................................................................................................................... 127 FIGURE 6-2 QUALITATIVE EXPOSURE ASSESSMENT OF PCBM AND NANO-TIO2 ACROSS THE LIFE-CYCLE OF THE OPV PANELS .............. 128 FIGURE 6-3 TWO-DIMENSIONAL STRUCTURE OF TITANIUM DIOXIDE IN ITS TWO MOST COMMONLY FOUND FORMS: RUTILE AND ANATASE. (SOURCE: U.S. NATIONAL INSTITUTE OF OCCUPATIONAL SAFETY AND HEALTH) ................................................................. 130 FIGURE 6-4 LOG-NORMAL DISTRIBUTIONS OF (A) THE INTERSPECIES TOXICO-DYNAMIC EXTRAPOLATION FACTOR WITH A GEOMETRIC MEAN OF 1 AND GEOMETRIC STANDARD DEVIATION OF 3.27 AND (B) THE INTRASPECIES EXTRAPOLATION FACTOR WITH A GEOMETRIC MEAN AND THE HIGHEST FREQUENCY IS FOR VALUES EQUAL TO ZERO. THE UPPER RIGHT GRAPH PLOTS THE ERROR TERMS AGAINST THEIR FITTED VALUES, INDICATING WHETHER THAT THE MEAN VALUE OF ZERO HOLDS TRUE. .................................................. FIGURE 6-6 FITTED LOG-LOGISTIC MODELS USING PROAST SOFTWARE WITH REPORTED CONFIDENCE INTERVALS TO THE MICE AND RAT NEUTROPHIL PERCENT CHANGES UPON INHALATION OF NANO-TIO2. THE DOSE-RESPONSE RESULTS DEMONSTRATE DIFFERENCES IN THE SLOPE OF THE LINES PER SPECIES, WITH A MUCH MORE SENSITIVE RESPONSE FOR RATS. THE DOES-RESPONSE CURVE SHOWN IN THIS EXAMPLE WAS FITTED WITH A LOG-LIKELIHOOD OF -1374. THE BENCHMARK DOSE FOR RATS IS SHOWN BY THE LOWER CURVE CORRESPONDING TO DATA WITH LARGE CIRCLES, WHILE THE BENCHMARK DOSE FOR MICE IS SHOWN FOR THE UPPER CURVE USING CORRESPONDING TRIANGULAR DATA POINTS. (SEE APPENDIX: CHAPTER 6 FOR FULL SET OF MODELS FIT TO THE DOSE-RESPONSE DATA) .................................................................................................................................................................. FIGURE 6-7 RESULTS OF (A) THE 10,000 MONTE-CARLO SIMULATIONS USED TO ESTIMATE THE BENCHMARK CONCENTRATION FOR HUMANS (BMCH) AND (B) THE CONTRIBUTION OF EACH PARAMETER USED TO ESTIMATE THE BENCHMARK CONCENTRATIONS FOR HUMANS. ............................................................................................................................................................. FIGURE 6-8 POTENTIAL EXPOSURE TIME-SERIES IN THE (A) NEAR FIELD AND (B) FAR FIELD. NOTE: THIS FIGURE SHOWS THE RESULTS FOR A LARGER RANGE OF EXPOSURE SCENARIOS THAN PRESENTED IN THIS CHAPTER. THESE ADDITIONAL SCENARIOS WERE COMPLETED AS A PART OF A LARGER PUBLICATION OUT OF DIRECT CONTEXT OF THIS CHAPTER. ADDITIONAL INFORMATION FOR THE OTHER SCENARIOS CAN BE FOUND IN APPENDIX: CHAPTER 6. ................................................................................................................... FIGURE 6-9 RESULTS OF 10,000 MONTE-CARLO RISK CHARACTERIZATION RATIO SIMULATIONS FOR EXPOSURE SCENARIO 2 IN THE (A) NEAR-FIELD AND (B) FAR-FIELD. NOTE THAT RIGHT-END TAILS OF THE DISTRIBUTION ARE ARTIFICIALLY TRUNCATED FOR PRESENTATION. CONTRIBUTIONS TO THE UNCERTAINTY AND VARIATION ARE DISPLAYED IN (C) FOR THE NEAR-FIELD AND IN (D) FOR THE FAR-FIELD. ...................................................................................................................................................... FIGURE 7-1 COMPARISON OF YEARLY EMISSIONS AND EMISSION RATE.

LIFETIME OF ORGANIC PHOTOVOLTAIC PANELS (WITH A 1% EFFICIENCY) AND (B) EFFICIENCIES OF ORGANIC PHOTOVOLTAIC PANELS (WITH A 1-YEAR LIFETIME). THE IMPACT RESULTS ARE INTERNALLY NORMALIZED TO THE IMPACT VALUES OF A-SI (I.E. A-SI'S IMPACTS ARE SET AT 100%). SEE APPENDIX: CHAPTER 4 FOR THE SENSITIVITY ANALYSIS FOR SYSTEM 2 WITH LANDFILLING AS THE END-OF-LIFE OPTION. ............................................................................................................................................................... FIGURE 4-6 COMPARISON OF ORGANIC PHOTOVOLTAIC ALTERNATIVES FOR (A) SYSTEM 1 THAT INVOLVED REMOVING THE MOUNTING STRUCTURE (NO MOUNT) AND (B) SYSTEM 2 BASED ON PORTABLE CHARGERS WITHOUT CASING (NC). THE IMPACT RESULTS IN SYSTEM 1 ARE ALL INTERNALLY NORMALIZED TO OPV-D AS THE MAXIMUM VALUE (I.E. 100%). THE IMPACT RESULTS IN SYSTEM 2 ARE INTERNALLY NORMALIZED BY TECHNOLOGY-TYPE (I.E. OPV-NC IS NORMALIZED BY OPV-D AND A-SI-NC IS NORMALIZED BY A-SI). SEE APPENDIX: CHAPTER 4 FOR THE RESULTS OF THE LANDFILLING OPTIONS. ................................................................ FROM H. F. KRUG 2014. 182 ). .................................................................................................................................. WASTE GENERATION AND EMISSIONS DEPICTED BY UP/DOWN ARROWS REPRESENT THE RELATIVE LIFE-CYCLE INVENTORY OF THE ENM-ENABLED PRODUCT (WITH UP DESIGNATING THE ENM-ENABLED AMOUNTS ARE HIGHER AND VICE VERSA BUT WITHOUT ANY INDICATION OF THE MAGNITUDE OF CHANGE). AGGREGATION OF ALL INVENTORY VALUES WOULD THEN RELATE TO THEIR POTENTIAL ENVIRONMENTAL IMPACTS VIA ITS MATERIAL SPECIFIC CHARACTERIZATION FACTOR. ............................................................ xiii RESOLUTION (I.E. NO CHANGES IN EMISSIONS OVER TIME) WITH A GLOBAL SPATIAL SCOPE WILL RESULT IN A STEADY-STATE MODEL THAT USES CONSTANT (I.E. NON-VARIABLE) AND HIGHLY AGGREGATED DATA, RESPECTIVELY, SIMILAR TO WHAT IS USED IN CURRENT LIFE-CYCLE ASSESSMENT METHODS SUCH AS USETOX. THIS IS THE CASE LABELED AS "GI" OR GLOBAL INTEGRATION, AND REPRESENTS ONE OF THE THREE LEVELS OF INTEGRATION SET FORTH IN THIS THESIS, HOWEVER THE DEGREE OF INTEGRATION IS DYNAMIC FOR EACH AXIS, INDEPENDENT OF ONE ANOTHER. FOR INSTANCE, LOW TEMPORAL RESOLUTION WITH A LOCAL SPATIAL SCOPE (I.E. HIGH SPATIAL RESOLUTION) WILL SIMILARLY RESULT IN A STEADY-STATE MODEL USING CONSTANT, BUT, NON-AGGREGATED (I.E. SITE-SPECIFIC) DATA. TO ILLUSTRATE THESE POINTS FURTHER, ENM EMISSIONS MODELING IS SHOWN IN THE FIGURE AS A PRACTICAL REPRESENTATION. GLOBAL INTEGRATION, THUS, RESULTS IN THE USE OF NON-CHANGING, AGGREGATED EMISSIONS DATA THAT IS AVERAGED FOR A LARGE CONTINENTAL REGION IN A STEADY-STATE MODEL. IN TERMS OF ITS RELEVANCY TO ENM, THIS APPROACH HAS LESS PREDICTIVE POWER THAN THE OTHER TWO OPTIONS SHOWN IN THE FIGURE. FOR EXAMPLE, SITE-SPECIFIC INTEGRATION (SSI), USES THE EMPIRICALLY DETERMINED CHANGES IN SINGLE-SOURCE EMISSIONS DATA MEASURED OVER AN UNCONDITIONAL TIMEFRAME IN A FULLY-DYNAMIC MODEL. CONTEXT-DEPENDENT INTEGRATION (CDI) MODELS EMISSIONS USING A GENERALIZED TIME-DEPENDENT ASSUMPTION (E.G. OF 1 AND GEOMETRIC STANDARD DEVIATION OF 2.7. THE X-AXIS IS WITHOUT UNITS. .......................................................... 133 FIGURE 6-5 INTERPRETATIONS OF THE RESIDUAL VALUES OF THE LOG-TRANSFORMED STANDARD DEVIATIONS FOR 59 PARTICULATE POWDER TESTS (NOTE: THREE TESTS INCLUDED MISSING VALUES). THE UPPER AND LOWER LEFT GRAPHS INDICATE THAT THE ASSUMPTION OF NORMALITY FOR THE ERROR TERMS IS VALID, AS THERE IS NO SIGNIFICANT DEVIATION FROM THE CENTRAL TREND xiv LINE

  THE Y-AXIS IS REPORTED AS THE FRACTION OF THE ANIMALS THAT RESULT IN INFLAMMATION (I.E. THE 20% INCREASE IN NEUTROPHIL COUNT OVER BACKGROUND RATES). THE LOG-LIKELIHOOD OF THE FITTED HILL MODEL WAS -1386.37. THE REPORTED BENCHMARK DOSES WERE 27352 ΜG/G-DRY LUNG FOR MICE AND 7807 ΜG/G-DRY LUNG FOR RATS BASED ON THE EXCESS RISK OF 50% OVER BACKGROUND INFLAMMATION RATES. ................................................................................................................................................................. FIGURE 7-13 NON-CARCINOGENIC CHARACTERIZATION FACTORS (LIFETIME) AS A FUNCTION OF THE (A) TOTAL EMISSIONS PER YEAR, (B) EMISSION RATE IN LOG-SCALE, (C) EXPOSED POPULATION OF WORKERS (NOTE: RESULTS ONLY DISPLAYED FOR ES1.00 (E-HIGH, F-SHORT) SCENARIO), AND (D) ACUTE, 1-YEAR VERSUS CHRONIC, LIFETIME CHARACTERIZATION FACTORS. .................................. FIGURE 7-14 CONTRIBUTION TO HUMAN HEALTH IMPACTS BY LIFE-CYCLE STAGE .........................................................................

	List of Equations
	(2-1)
	(2-2)
	(3-1)
	xvi

:

  Life-Cycle Assessment, Organic Photovoltaics, Engineered Nanomaterials, Risk Assessment,

	Characterization Factor, Indoor Occupational Exposure, Monte Carlo Analysis, Sustainable Production, Eco-Design, Renewable Energy, Life-Cycle Impact Assessment List of Abbreviations AER a-Si a-Si-NC BAL BMC BMCa BMCh BMD BMR BOS C60 CCE CCERER CDI CED CF CFNS CFNS,C CFNS,NC CPBT CTU DALY DI EFinter EFintra Intraspecies extrapolation factor XF Exposure Factor in life-cycle impact assessment Interspecies extrapolation factor Wp Watt-peak Dustiness index of particulate matter SSI Site-specific integration Disability adjusted life year RIVM Netherland's National Institute for Public Health and the Environment Comparative toxic unit RiF Retained-intake fraction for use in life-cycle impact assessment Carbon payback time REL Recommended exposure limit Engineered nanomaterial-specific, non-cancer characterization factor RA Risk assessment Engineered nanomaterial-specific, cancer characterization factor PV Photovoltaic Engineered nanomaterial-specific characterization factor POP Workplace population Characterization factor PET Polyethylene terephthalate Cumulative energy demand PEDOT:PSS Poly(3,4-ethylenedioxythiophene) polystyrene sulfonate Context-dependent integration PCS Phagocytizing cells Cumulative carbon equivalents for average European energy production mix PCBM [6,6]-phenyl C61 butyric acid methyl ester Cumulative carbon equivalents PBPK Physiologically-based pharmacokinetic Fullerenes P3HT Poly(3-hexyl)thiophene Balance of system OPV-NC Organic photovoltaics with no case Benchmark response OPV Organic photovoltaics Benchmark dose NOAEL No observed adverse effect level Human equivalent benchmark concentration NMVOC Non-methane volatile organic compounds Benchmark concentration based on animal toxicological data NIOSH U.S. National Institute of Occupational Safety and Health Benchmark concentration NF Near-field workroom volume Bronchi-alveolar lavage m-Si Multi-crystalline silicon Amorphous silicon with no case MRL Minimum required lifetime Amorphous silicon MC Monte-Carlo Analysis Air exchange rate LCA Life-cycle assessment LOAEL Low observed adverse effect level Chapter 1 Organic Photovoltaics as a Sustainable Technology
	EF	Effect factor in life-cycle impact assessment
	Eg	Energy generated over during the use of a photovoltaic panel
	ENM	Engineered nanomaterial or nanoparticle
	EPA	U.S. Environmental Protection Agency
	EPBT	Energy payback time
	ERA	Environmental risk assessment
	ES	Exposure scenario
	ETL	Electron transport layer
	EU	European Union
	FF	Fate factor for life-cycle impact assessment
	FP7	The Seventh Framework Programme (2007-2013) of the European Commission
	FTO	Fluorine-doped tin oxide
	GI	Global integration
	GM	Geometric mean
	GSD	Geometric standard deviation
	HHRA	Human health risk assessment
	HTL	Hole transport layer
	IEA	International energy agency
	iF	Intake fraction in life-cycle impact assessment
	ISO	International Organization for Standardization
	ITO	Indium tin oxide
		xx xxi

  43

Table 1 - 1

 11 Material choices of the life-cycle assessment studies on organic photovoltaic systems existing in the peer-reviewed literature up to December 2013.

	Case Study	Year	Substrate	Transparent Electrode	Hole Transport Layer	Active	Layer	Electron Transport Layer	Opaque Electrode	Lamination and Additional Components	Deposition
	Roes et al. 29	2009	Glass, PET	ITO	PEDOT:PSS	P3HT	PCBM	LiF	Aluminum	PVF, PET, EVA, SiO2 barrier layer	Sputtering, inkjet and gravure printing
	Garcia-Valverde et al. 30	2010	Glass	ITO	PEDOT:PSS	P3HT	PCBM	N/A	Aluminum, Calcium	EVA layer, SiO2 barrier layer, Aluminum frame	Sputtering in inert atmosphere
											Sputtering of ITO onto PET in a
	Espinosa et al. 31 2011	PET	Silver	PEDOT:PSS	P3HT	PCBM	nano-Zn	ITO	PET Barrier layer	vacuum roll-to-roll process, slot-die
											coating all other layers
	Espinosa et al. 36 2011	PET	Silver	PEDOT:PSS	P3HT	PCBM	N/A	ITO	AMCOR Barrier, MPF Adhesive	Sputtering of ITO onto PET in a vacuum roll-to-roll process, slot-die coating all other layers
											Sputtering of aluminum and
	Espinosa et al. 37 2012	Kapton (polyimide)	Silver	PEDOT:PSS	P3HT	PCBM	N/A	Aluminum, Chromium	AMCOR Barrier, MPF Adhesive	chromium on the Kapton in vacuum, Slot-die coating all other
											layers
									PEDOT:PSS,		Slot-die coating under ambient
	Espinosa et al. 38 2012	PET	Silver	PEDOT:PSS	P3HT	PCBM	nano-Zn	nano-Ag, Graphite,	PET Barrier layer	conditions for the front electrode, active layer, ETL and HTL. The silver
									Graphene		electrode was screen printed.
									PEDOT:PSS,		Slot-die coating under ambient
	Yue et al. 39	2012	PET	Silver	PEDOT:PSS	P3HT	PCBM	nano-Zn	nano-Ag, Graphite,	PET Barrier layer	conditions for the front electrode, active layer, ETL and HTL. The silver
									Graphene		electrode was screen printed.
											Various non-sputtering, normal
	Emmott et al. 40 2012	PET	Silver	PEDOT:PSS	P3HT	PCBM	nano-Zn	PEDOT:PSS, nano-Ag, CNT	PET Barrier layer	atmosphere deposition for the transparent electrode, all other
											layers were slot-die coated.
	Espinosa et al. 41 2013	PET	Silver or Graphite	PEDOT:PSS	P3HT	PCBM	nano-Zn	Silver, PEDOT, Graphite	PET Barrier layer	Various non-sputtering, normal atmosphere deposition for the transparent electrode, all other layers were slot-die coated.
	Anctil et al. 42 2013	PET	ITO	PEDOT:PSS, ZnO, MoO3	Various Polymers and Small Molecules	Various PC60BM, PC70BM	TiO2, Bathocuproine, Bathophenanthroline	Silver	PET Barrier layer	Sputtering in inert atmosphere
	EVA: ethylvinylacetate | PVF: polyvinylfluoride | MoO3: Molybdenum Oxide | Lithium Fluoride

Table 1 - 2

 12 Scope, boundaries and select number of assumptions that were made in the life-cycle assessment studies on organic photovoltaic systems existing in the peer-reviewed literature up to December 2013.

	Case Study	Year	Efficiency	Active Area	Lifetime	Functional Unit	Use Phase	End-of-Life
							Rooftop system including balance of system for the glass, no
							BOS for the plastic (assumed not producing energy that will
	Roes et al. 29	2009	5%	90%	25 years	25-year production of 1 Wp	be transmitted to grid in AC form, but used directly by	N/A
							personal device, however they do not account for any other
							use phase systems or change in the functional unit)
	Garcia-Valverde et al. 30 2010 5% and 10%	90%	15 years	1 m 2 of framed solar module	N/A	N/A
	Espinosa et al. 31	2011	2%-3%	67%	N/A	1 m 2 of framed solar module	N/A	N/A
	Espinosa et al. 36	2011	5%	67%	2 years	104 cm 2 lamp (0.1 Wp of OPV)	BOS: overlay frame, adhesives, spacer, battery, LED, circuits	Landfill
	Espinosa et al. 37	2012	1%-5%	68%	15	1 m 2 of framed solar module	N/A	N/A
	Espinosa et al. 38	2012	1%-5%	45%	15	1 m 2 of framed solar module	N/A	N/A
	Yue et al. 39	2012	3%-8%	45%-85%	15	1 m 2 of framed solar module	N/A	N/A
	Emmott et al. 40	2012	3%	67%	15	1 m 2 of framed solar module	N/A	N/A
	Espinosa et al. 41	2013	3%	46%	15	1 m 2 of framed solar module	N/A	N/A
	Anctil et al. 42	2013	3%-7%	85%	N/A	1 Wp	N/A	N/A

Wp: watt-peak | LED: light emitting diode | BOS: balance of system

Table 1 - 3

 13 Life-cycle assessment studies on organic photovoltaic systems existing in the peer-reviewed literature up to the end of 2013. (Adapted fromChatzisideris et al. 67 )

	Individual Case Studies Year	Cradle-to-	Full Range of Life-Cycle	Comparison to Conventional	Comprehensive
			Grave	Impact Criteria	Photovoltaics	Across All Indicators
	Roes et al. 29	2009	○	•	•	○
	Garcia-Valverde et al. 30 2010	○	○	•	○
	Espinosa et al. 31	2011	○	○	○	○
	Espinosa et al. 36	2011	•	○	•	○
	Espinosa et al. 37	2012	○	○	○	○
	Espinosa et al. 38	2012	○	•	○	○
	Yue et al. 39	2012	○	○	•	○
	Emmott et al. 40	2012	○	○	○	○
	Espinosa et al. 41	2013	○	•	○	○
	Anctil et al. 42	2013

Table 1 -

 1

4 Flow diagram of the structure, objectives, hypotheses and research questions put forth in this thesis Overall Objective

Table 2 -1

 2 Commonly applied life-cycle impact assessment methodologies and their commonly defined midpoint impact categories. (Adapted fromAcero et al. 82 )

	Methods

Acid- ification Climate change Cumulative Energy Demand ‡ Resource depletion Ecotoxicity Eutro- phication Human toxicity Ionising Radiation Land use Odour Ozone layer depletion Particulate matter Photo-chemical oxidation Water Use † CML, 2001 (Baseline)

Table 2 -2

 2 Life-cycle impact assessment categories considered in this study. ReCiPe 2008 midpoint (H) impact categories were used, with toxicity estimated using the midpoint USEtox 2.0 indicators and cumulative energy demand estimated based on Hischier et al.92

	Recipe Impact Category	Abbreviation	Unit
	Climate change potential	(CC)	kg (CO2-equivalents to air)
	Ozone depletion	(OD)	kg (CFC-115-equivalents to air)
	Terrestrial acidification	(TA)	kg (SO2-equivalents to air)
	Freshwater eutrophication	(FE)	kg (P-equivalents to freshwater)
	Marine eutrophication	(ME)	kg (N-equivalents to freshwater)
	Human toxicity	(HT)	CTUh
	Photochemical oxidant formation	(POF)	kg (NMVOC6-equivalents to air)
	Particulate matter formation	(PMF)	kg (PM10-equivalents to air)
	Freshwater ecotoxicity	(FET)	CTUe
	Ionizing radiation	(IR)	kg (U235 to air)
	Agricultural land occupation	(ALO)	m 2 • yr (agricultural land)
	Urban land occupation	(ULO)	m 2 • yr (urban land)
	Natural land transformation	(NLT)	m 2 (natural land)
	Water depletion †	(WD)	m 3 (water)
	Mineral resource depletion	(MRD)	kg (Fe)
	Fossil fuel depletion	(FD)	kg (oil)
	Cumulative energy demand ‡	(CED)	MJ
	CFC: chlorofluorocarbon | P: phosphorus | N: nitrogen | NMVOC: non-methane volatile organic carbon compound | U: uranium.

Table 3 - 1

 31 Estimated parameters used to calculate the energy-payback times for each solar cell considered. Insolation is based on an average European insolation of 1300 kWh per m 2 .To calculate the carbon payback time (CPBT), the cumulative, cradle-to-gate CO2-equivalent emissions (CCE) of each PV panel was compared to the CCE avoided from a European average electricity production mix (CCERER) for the same Wp produced by the PV panel per year of operation (equation(3-3)). Carbon payback time CCE: Cradle-to-gate CO2-eq. emissions generated for the PV device CCERER: CO2-eq. emissions avoided from an average European electricity mix per kWh of energy produced Eg: The amount of energy (kWh) produced by the PV device over one year

		Cell Power Conversion	System Performance	Insolation	Active Area of	Electrical Conversion
		Efficiency (%)	Ratio (%)	(MJ/m²/year)	Cell (%)	Efficiency (%)
	Default	5.00E-02	7.50E-01	4.68E+03	100	3.60E-01
	FTOinkjet	5.00E-02	7.50E-01	4.68E+03	100	3.60E-01
	PCBMdcb	5.00E-02	7.50E-01	4.68E+03	100	3.60E-01
	OPV-PP	5.00E-02	7.50E-01	4.68E+03	100	3.60E-01
	m-Si	1.32E-01	7.50E-01	4.68E+03	95	3.60E-01
	a-Si	6.50E-02	7.50E-01	4.68E+03	100	3.60E-01

CPBT:

CCERER was estimated as 4.9E-04 kg per Wp of the Ecoinvent process "electricity, medium voltage, production RER, at grid."

Table 3 -

 3

2 Power generation, embodied energy, energy payback time, embodied carbon, and carbon payback time for each solar cell considered in this chapter, assuming an average European insolation value of 1300 kWh per m 2 . Wp/

m² CED (MJ/m²) CED (MJ/Wp) CO2(kg)/Wp Energy Generated (MJ/m 2 /year) EPBT (yrs) CPBT (yrs)

	Default	50	130.0	2.6	0.115	4.88E+02	0.27	0.088
	FTOinkjet	50	57.1	1.1	0.049	4.88E+02	0.12	0.037
	PCBMdcb	50	130.0	2.6	0.116	4.88E+02	0.24	0.088
	OPV-PP	50	116.0	2.3	0.103	4.88E+02	0.27	0.078
	m-Si	210	4,580	21.8	1.088	1.29E+03	3.60	1.31
	a-Si	128	1,750	13.7	0.775	6.34E+02	2.81	1.16

Table 3 - 3

 33 Minimum lifetimes required of organic photovoltaic to achieve environmental and human health parity with amorphous silicon cells having 25-year lifetimes. Categories are displayed in descending order of results for the Default OPV.

	Impacts	Default (yrs)	FTOinkjet (yrs)	PCBMdcb (yrs)	OPV-PP (yrs)
	ionizing radiation	8.9	2.4	9.0	8.5
	water depletion	6.1	1.7	6.1	5.8
	ozone depletion	5.9	2.7	6.0	5.3
	freshwater eutrophication	5.1	1.6	5.1	4.9
	marine eutrophication	4.8	2.0	4.8	4.6
	cumulative energy demand	4.7	2.1	4.7	4.2
	fossil depletion	4.1	2.1	4.1	3.6
	average	4.0	1.8	4.1	0.2
	natural land transform	3.8	2.0	3.8	3.5
	climate change	3.7	1.6	3.7	3.3
	freshwater ecotoxicity	3.1	1.4	3.3	2.7
	smog	3.0	1.4	3.0	2.7
	agricultural land	2.9	1.2	2.9	2.7
	particulate matter	2.8	1.2	2.8	2.6
	metal depletion	2.7	2.6	2.7	2.7
	human toxicity	2.7	1.1	2.7	2.5
	terrestrial acidification	2.3	0.9	2.4	2.2
	urban land occupation	1.2	0.6	1.3	1.1

Table 4 -

 4

1. Source: novato.org Source: instapark.com

Table 4 - 1

 41 Generalized account of material components and energy requirements for producing one m 2 of an OPV-D panel based on the description in Chapter 3 for the FTOinkjet OPV. Indirect, upstream or auxiliary material and energy requirements as well as emissions are not reported here.

	Materials	Amounts	Notes	References
	Aluminum	0.3 g	Back electrode	52,140
	PET	74.0 g	Substrate	52
	FTO	1.80 g	Applied as an FTO solution	42,52,126
	PET	133 g	Lamination	52
	Lithium Fluoride	6.00E-5 g	Back electrode	52
	Molybdenum oxide	0.240 g	Hole transport layer	42,52,56,140
	Chlorobenzene	7.66 g	Solvent for active layer application	56
	P3HT	0.235 g	Active layer	42,52,56,140
	PCBM	0.205 g	Active layer	42,52,56,140
	Nano-TiO2	0.127 g	Optical Spacer	121
	Energy	Amounts	Notes	References
	Electricity	5.13 MJ	Annealing	145
	Electricity	2.56 MJ	For printing of panel components	29
	Electricity	0.0850 MJ	For lamination of the panel

Table 4 -

 4

	m 2	20.0

2 Inventory for an average kWh of installed OPV solar roofing array, mounted with support (S1)

Flow

Notes Unit Amount

OPV panel, at plant This inventory amount represents a total of 5 kWh of paneling in order to account for the subsequent replacement over 25 years of the use-phase while assuming an OPV lifetime of 5 years.

Inventory for the disposal of cabling per m 2 of PV capacity

	Disposal, OPV solar cell, incineration	Appendix: Chapter 4	kg	22.0
	OR
	Disposal, OPV solar cell, landfill	Appendix: Chapter 4	kg	22.0

Table 4 -

 4

	Flow	Notes	Unit	Amount
	OPV panel, at plant	This inventory amount represents a total of 5 kWh of	cm 2	2000.0
		paneling to account for the subsequent replacement
		over 25 years of the use-phase while assuming an OPV
		lifetime of 5 years.
		Used for the casing; based on Ecoinvent process for
	Plastic film, pet, at plant	extrusion of plastic film for packaging	g	273.7
	Polyester resin, unsaturated, at plant	Used for the casing	g	273.7
	Transport, lorry 16-32t, Euro3 -RER		tkm	0.010
	Transport, freight, rail -RER		tkm	0.063

3 Inventory for an average ten watt-hours of an organic photovoltaic portable solar charger (S2)

Inventory for the disposal of cabling per m 2 of PV capacity

	Disposal, OPV solar cell with casing, incineration Appendix: Chapter 4	kg	0.59
	OR
	Disposal, OPV solar cell with casing, landfill	Appendix: Chapter 4	kg	0.59

Table 4 - 4

 44 Estimated parameters used to calculate the energy and carbon payback times Cradle-to-Grave Life-Cycle Assessment of Organic Photovoltaics 92 estimated as 0.49 kg per kWh of the Ecoinvent process "electricity, medium voltage, production RER, at

		Power Conversion	System Performance	Insolation	Active Area	Electrical Conversion
		Efficiency (%)	Ratio (%)	(MJ/m²/year) *	of Panel (%)	Efficiency (%)
	OPV-D	5.00E-02	7.50E-01	4.68E+03	0.95E+00	3.60E-01
	m-Si	1.32E-01	7.50E-01	4.68E+03	100E+00	3.60E-01
	a-Si	6.50E-02	7.50E-01	4.68E+03	100E+00	3.60E-01

*Converted from the average European insolation of 1300 kWh

Carbon Payback Time

To calculate the CPBT, the CCE of each PV panel was compared to CCERER generated from a European average electricity production mix for the same Wh produced by the PV panel (equation

(3-3)

). CCERER was Chapter 4

Table 4 -

 4

	Scenario Alternative	Wp/m² Eg (MJ/m 2 /year) CED	EPBT	CCE (kg CO2-eq. /m²) CPBT
					(MJ/m²)	(Days)		(Days)
	S1	OPV-D (Incineration)	50	4.88E+02	1.22E+02 436	6.60E+00	192
	S1	OPV-D (Landfill)	50	4.88E+02	1.25E+02 449	6.34E+00	185
	S1	OPV-D (Incineration, No mount) 50	4.88E+02	9.67E+01 362	5.30E+00	157
	S1	OPV-D (Landfill, No mount)	50	4.88E+02	1.00E+02 376	5.05E+00	150
	S1	m-Si	210	1.29E+03	3.24E+03 919	1.68E+02	362
	S1	m-Si	210	1.29E+03	3.24E+03 918	1.68E+02	363
	S2	OPV-D (Incineration)	50	4.88E+02	2.93E+02 220	2.14E+01	118
	S2	OPV-D (Landfill)	50	4.88E+02	3.54E+02 265	1.76E+01	97
	S2	OPV-NC (Incineration)	50	4.88E+02	5.62E+01 209	2.86E+00	115
	S2	OPV-NC (Landfill)	50	4.88E+02	5.98E+01 255	2.61E+00	94
	S2	a-Si (Incineration)	128	6.34E+02	1.09E+03 627	6.68E+01	284
	S2	a-Si (Landfill)	128	6.34E+02	1.16E+03 666	6.39E+01	271
	S2	a-Si-NC (Incineration)	128	6.34E+02	8.55E+02 492	4.85E+01	206
	S2	a-Si-NC (Landfill)	128	6.34E+02	8.64E+02 498	4.90E+01	209

5 Power generation, cumulative energy demand and energy payback time for System 1 (rooftop array) and System 2 (portable charger)

  Chapter 4 Cradle-to-Grave Life-Cycle Assessment of Organic Photovoltaics 98 default OPV scenario (Appendix: Chapter 4). This was still 68% and 63% shorter than a-Si's 15.4-year (incineration) and 16.3-year (landfill) consumer-adjusted EPBT, respectively. Similarly, the OPV-D CPBT increased to 2.82 (incineration) and 2.3 (landfill) years compared to the default scenario (Appendix:

	Chapter 4). The CPBT of a-Si increased to 7.02 (incineration) and 6.68 (landfill).

Table 4 - 6

 46 Minimum required lifetimes (in years) of the default organic photovoltaic scenario for System 1 (rooftop array) and System 2 (portable charger) compared to their respective silicon-based photovoltaic counterparts

			Scenario 1			Scenario 2
		OPV-D	OPV-D	OPV-D	OPV-D	OPV-D	OPV-D	OPV-NC	OPV-NC
		(Incineration)	(Landfill)	(Incineration,	(Landfill, No	(Incineration)	(Landfill)	(Incineration)	(Landfill)
				No Mount)	Mount)
	Agricultural land occupation	0.3	0.3	0.3	0.4	0.26	0.29	0.24	0.26
	Climate change potential	1.6	1.5	1.5	1.3	0.41	0.36	0.37	0.33
	Fossil depletion	1.7	1.8	1.6	1.6	0.47	0.51	0.43	0.45
	Freshwater ecotoxicity	0.2	0.2	0.2	0.2	0.95	0.97	0.85	0.86
	Freshwater eutrophication	1.9	2.1	1.7	1.9	0.45	0.50	0.44	0.47
	Human toxicity	0.4	0.4	0.3	0.3	0.58	0.60	0.54	0.55
	Ionizing radiation	1.5	1.8	1.5	1.7	0.46	0.54	0.45	0.51
	Marine eutrophication	0.5	1.6	0.4	1.5	0.08	1.18	0.24	0.79
	Metal depletion	11.4	11.4	6.0	6.0	0.58	0.59	0.58	0.58
	Natural land transformation	1.2	1.2	1.0	1.1	0.43	0.44	0.41	0.42
	Ozone depletion	1.0	1.0	0.6	0.7	0.60	0.67	0.55	0.58
	Particulate matter formation	1.7	1.8	1.5	1.6	0.27	0.29	0.26	0.27
	Photochemical oxidant formation	0.9	0.9	0.8	0.8	0.16	0.34	0.31	0.31
	Terrestrial acidification	1.6	1.7	1.4	1.5	0.22	0.24	0.21	0.22
	Urban land occupation	1.3	1.5	0.9	1.0	0.15	0.27	0.15	0.16
	Water depletion	0.2	0.2	0.2	0.2	0.34	0.39	0.34	0.38
	Cumulative energy demand	1.5	1.6	1.4	1.5	0.39	0.44	0.41	0.43
	Average	1.5	1.6	1.1	1.2	0.46	0.55	0.44	0.48

Table 5 -

 5 Chapter 5 Options for Assessing the Toxicological Impacts from Engineered Nanomaterials Use in Organic Photovoltaics 108 Since the initial review up to the end of 2013, six additional OPV-LCA have been published, each without any estimation of ENM release along the life-cycle. Thus, toxicological hazards[158][159][160][161][162] resulting from ENM emissions have not been adequately assessed in the OPV-LCA literature nor in the prior case studies presented in Chapter 3 and Chapter 4. To avoid burden shifting and possible false assumptions about the human health impacts of OPV technologies, data and methodological adaptations in LCA are currently needed or other tools need to be used to address these issues.

	Individual Case Studies	Year	Energy and Material Flows During ENM	ENM Emissions ENM Human-or Eco-tox
			Production		Impacts
	Roes et al. 29	2009	•	○	○
	Garcia-Valverde et al. 30	2010	•	○	○
	Espinosa et al. 31	2011	•	○	○
	Espinosa et al. 36	2011	•	○	○
	Espinosa et al. 37	2012	•	○	○
	Espinosa et al. 38 s	2012	•	○	○
	Yue et al. 39	2012	•	○	○
	Emmott et al. 40	2012	•	○	○
	Espinosa et al. 41	2013	•	○	○
	Anctil et al. 42	2013	•	○	○
	Espinosa and Krebs 155	2014	•	○	○
	Espinosa et al. 24	2014	•	○	○
	Sondergaard et al. 147	2014	•	○	○
	Espinosa et al. 62	2015	•	○	○
	Sandwell et al. 156	2016	•	○	○
	Hengevoss et al. 157	2016	•	○	○

1 Consideration of nanomaterial-specific emissions and impacts across the previously published life-cycle assessments on organic photovoltaics

  Options for Assessing the Toxicological Impacts from Engineered Nanomaterials Use in Organic Photovoltaics 118 Separate Use of LCA and RA for Nanotechnologies LCA and HHRA are two separate tools that have distinct methodologies and report different results. WhileLCA allows the calculation of potential human health impacts from many competing sources of emissions along the life-cycle, decision makers might find it useful to employ this tool at all stages of technology development and eco-design purposes. However, for ENM-containing technologies, if evaluation of ENM is not possible, decision makers may choose to simply use HHRA instead, particularly once product development has increased to relatively large volumes, full-scale development or product adaption by society requires that human health risks are avoided. This is because full scale development will require registration with the appropriate regulatory agencies who may require HHRA knowledge as part of the registration or compliance with agencies tasked with ensuring occupational and consumer protections.

evaluation of ENM-related technologies are discussed: one separate, one complementary, and three integrative approaches.

Chapter 5

Table 6 - 1

 61 Summary of the study 283 and select dose-response data used to characterize the inflammatory response upon inhalation exposure to nano-TiO2

	Nanomaterial	Species	Exposure Type	Concentration (mg/m 3 ) Effects	BMR
	21 nm P25	Rat, Mice 13 weeks (6 h/day; 5 days/wk)	Control, 0.5, 2.0, 10	Pulmonary	20% increase in
	Degussa (Evonik)		whole-body inhalation		inflammation	neutrophil cell counts
	Control: Filtered air with corresponding concentration of 0.0 mg/m 3
	BMR: Benchmark response

Chapter 6

 6 Human Health Risk Assessments: Quantitative Assessment of Titanium Dioxide and Qualitative Assessment of C60 Fullerene Nanoparticles 135 The NanoSafer v.1.1 model parameters used for describing the emissions and estimation of exposure concentration are given inTable 6-3.

Table 6 - 3

 63 Parameters used in the NanoSafer v1.1 exposure assessment model where Hi = handling energy factor, twc is work cycle time, pwc is pause between work cycles, nwc is number of work cycles, Atransfer is amount of material transferred per transfer event within each work cycle, Vtot is the total volume of the work room, and AER is the general air exchange ratio in the work-room.

	No. Exposure scenario	Ei, [mg/min] Hi twc, [min] pwc, [min] nwc Atransfer, [kg] Vtot [m 3 ] AER [h -1 ]
	ES1 Manufacturer Dumping into Mixing Tank	1.20E+01	0.8	10	10	24	10	100	8
	ES2 Dumping Large Amount of Powder in Vessel	6.72E+02	0.8	10	20	16	560	75	4
	ES3 Bag Bin Filling	6.26E+00	0.8	480	0	1	250	70	4

  Human Health Risk Assessments: Quantitative Assessment of Titanium Dioxide and Qualitative Assessment of C60 Fullerene Nanoparticles 137 standard deviation was then applied to Ei for quantifying the exposure distribution of each ES. The final airborne exposure concentrations are reported as average 8-hour time weighted averages.

5 mg/kg. The Chapter 6

Table 6 - 4

 64 Daily averaged, inhalation benchmark concentrations (mg/m 3 ) for in vivo animal studies and corresponding models fit for a 20% increase in neutrophil count in mice.

	Model	Two-Stage Log-logistic Weibull Log-probabilistic Gamma Exponential Hill Aggregation of Models
	Median	12.76	12.54	13.04	12.39	12.77	11.44	11.61	12.32
	Mean	12.84	12.62	13.15	12.47	12.87	11.48	11.66	12.44
	Standard Deviation	1.15	1.05	1.24	1.06	1.11	0.64	0.99	1.20
	Minimum	9.25	9.58	9.60	9.37	9.66	9.57	8.82	8.82
	Maximum	18.20	19.54	21.01	18.39	18.85	14.66	17.78	21.01

Table 6 -5 Calculated

 6

near-field and far-field airborne concentrations of nano-TiO2 for the three separate exposure scenarios considered in the human health risk assessment No.

Exposure scenario CNF, [mg m -3 ] CFF, [mg m -3 ]

	ES1 Manufacturer Dumping into Mixing Tank	8.93E-01	2.59E-01
	ES2 Dumping Large Amount of Powder in Vessel	3.62E+01	1.18E+01
	ES3 Bag Bin Filling	8.25E-01	1.60E-01

C: exposure concentration. (Note: These are the geometric means 8-hour time weighted averages that were all defined as having a geometric standard deviation of 2.5)

Table 6 -

 6 6 displays the results of the risk characterization for the NF and FF exposure scenario, respectively, representing the lifetimes risk of a worker exposed to an 8-hour time weighted indoor air nano-TiO2 concentrations over 45-years, 50 weeks/year, 5 days/week.

Table 6 -

 6

	Exposure (Near Field)	ES1	ES2	ES3
	Risk characterization ratio (geometric mean)	9.91E-02	3.99E+00	9.05E-02
	Risk characterization ratio (geometric standard deviation)	6.07	5.96	6.04
	Probability risk characterization ratio ≥ 1	9.99%	78.06%	9.08%
	Exposure (Far Field)	ES1	ES2	ES3
	Risk characterization ratio (geometric mean)	2.83E-02	1.29E+00	1.77E-02
	Risk characterization ratio (geometric standard deviation)	6.21	6.10	6.27
	Probability risk characterization ratio ≥ 1	2.55%	55.68%	1.39%
	The RCR values for each exposure scenario were log-normally distributed (Appendix: Chapter 6). The risk
	characterization ratio distributions for ES1, ES2 and ES3 all contained some probability of risk to
	inflammation of the lung (i.e. RCR values ≥ 1). Scenario ES2 had a particularly high probability of risk
	compared to the other scenarios (Figure 6-9), with nearly 78% of the Monte-Carlo simulation results ≥ 1
	(i.e. 22% of the results resulted in no risk to the exposed workers). ES1 and ES3 resulted in 10% and 9% of
	their RCR ≥ 1 (Appendix: Chapter 6). For all of the exposure scenarios, roughly 75% of the variation in the
	RCR distributions was influenced by uncertainty in the dose-response analysis (i.e. EFinter and EFintra), while
	the remaining 25% was the result of uncertainty in the exposure estimations (Appendix: Chapter 6). In

6 Summary of the risk characterization (reported as risk characterization ratios) distributions for each near-and far-field exposure scenarios. Results represent 10,000 Monte-Carlo simulations.

accordance with the lower airborne nano-TiO2 concentrations found in the FF, the probability of individual FF RCR values ≥ 1 were much lower than compared with the NF. In total, approximately 56% of the results

Chapter 6

 6 Human Health Risk Assessments: Quantitative Assessment of Titanium Dioxide and Qualitative Assessment of C60 Fullerene Nanoparticles 145 from lab-or research-based scenarios and where uncertainty is thus high. Instead, potential human health impacts calculated within LCA, as opposed to absolute values of risk, might prove more useful for early design and development of emerging technologies. The next chapter presents the methods and results of calculating the potential human health impacts for ENM within the context of life-cycle impact assessment and the OPV-LCA case studies presented in earlier chapters.

	Chapter 7 Life-Cycle Impact Assessment Nanomaterial Characterization Factors: Titanium Dioxide Case
	Study
	Chapter 7 Life-Cycle Impact Assessment Nanomaterial Characterization
	Factors: Titanium Dioxide Case Study
	In the previous chapter, a HHRA was conducted to calculate the human health risk associated with
	handling and working with moderate to large amounts of nano-TiO2. The results demonstrate that there
	are high probabilities of lifetime risk to lung inflammation for workers handling these materials based on
	the 8-hour time weighted average exposures calculated with the NanoSafer fate and exposure model. The
	HHRA results will be particularly important and relevant for industries where workplace conditions
	particularly match the descriptions of the exposure scenarios tested in Chapter 6 (i.e. that represent large
	or industrial-scale production). It would be of benefit to be able to integrate these methods with life-cycle
	146

impact assessment methods. The advantages would allow the simultaneous analysis for other similarly related metal oxide ENM, with the potential to modify the approach for non-metal oxide ENM that are both found in OPV. Additionally, integration facilitates the comparison of ENM-specific human health impacts with non-ENM sources of human health impacts by directly converting all such impacts to the functional units used in Chapter 3 and Chapter 4. Such results could be used to identify if ENM is a driving influence in human health impacts over the OPV life-cycle and, thus, more appropriately prioritize ecodesign and product development efforts.

Table 7 - 1

 71 1) were modeled and identified from the previous HHRA in Chapter 6. Parameters used in the fate-transport model describing the exposure scenarios involved with "dumping large amounts of powder in a vessel" per the description in Chapter 6, Table6-2. The exposure scenarios differ based on the magnitude of the emission, e, per minute and the frequency of the workcycle activity, f. Hi = handling energy factor, twc is work cycle time, pwc is pause between work cycles, nwc is number of work cycles, Ahandled is amount of material transferred per transfer event within each work cycle, Vtot is the total volume of the work room, and AER is the general air exchange ratio in the work-room

	Chapter 7 Life-Cycle Impact Assessment Nanomaterial Characterization Factors: Titanium Dioxide Case
	Study

Table 7

 7

	-1) represent variations of ES1

the same emission rate but at frequencies of 60 min, 480 min and one min, respectively. Compared to ES1 whose frequency of ten min was defined as short, ES2, ES3 and ES4 represent long, all-day (i.e. noninterrupted) and single-pulse frequencies, respectively. Conversely, ES5 and ES 1.6 had the same, short

Table 7 - 2

 72 Volume of the far-field where ki is any source (i) of non-ventilation removal, VNF is the NF volume and VFF is the FF volume. The VNF equaled 1.07 m3 and was defined as the volume of a hemisphere with (near-field) radius of 0.8 m. The VFF was equal to the total volume of 100 m 3 less the VNF. All the values used in the fate and transport model are listed in Table 7-2. Parameters and their values used in the fate and transport model

	Chapter 7 Life-Cycle Impact Assessment Nanomaterial Characterization Factors: Titanium Dioxide Case
	Study

𝑉FF: 𝑠 : Cunningham slip correction factor 𝑛: viscosity of the medium 𝑥: dynamic shape factor (i.e. perfectly spherical materials have a value of 1) h: height of the emission source

  Life-Cycle Impact Assessment Nanomaterial Characterization Factors: Titanium Dioxide Case Study 157 to categorize CFNS was made based on the magnitude of the emission rates, emission frequencies, worker population size and time-frame of exposure.

Integration life-cycle impact assessment approach for estimating human health impacts of ENM, as presented in Chapter 5, is used to complete such aggregation. A first approximation Chapter 7

Table 7 - 3

 73 Results for emissions and final near-field and far-field concentrations.

	ES1 e-high, f-short	6.72E+02	1.08E+05	6.50E+04	3.45E+04	2.49E+04	1.48E+04
	ES2 e-high, f-long	6.72E+02	1.61E+05	8.15E+04	5.09E+04	4.07E+04	2.54E+04
	ES3 e-high, f-daily	6.72E+02	3.23E+05	8.15E+04	5.09E+04	8.08E+04	5.02E+04
	ES4 e-high, f-single pulse 6.72E+02	6.72E+02	3.63E+04	6.01E+03	1.70E+02	1.06E+02
	ES5 e-medium, f-short	6.72E+00	1.08E+03	6.77E+02	3.71E+02	2.71E+02	1.69E+02
	ES6 e-low, f-short	6.72E-02	1.08E+01	6.77E+00	3.71E+00	2.71E+00	1.69E+00

No. Exposure scenario Ei, [mg/min] Total Daily Emissions (mg) C ‡NF, [ug m -3 ] C ‡FF, [ug m -3 ] C † NF, [ug m -3 ] C † FF, [ug m -3 ] C: exposure concentration| † Average daily concentrations during the working hours | ‡ Maximum daily concentration

  ES4 since it was only a single pulse event without further addition of nano-TiO2 over time during the workday. Thus, the clearance mechanisms, mainly driven by the overall air exchange rate of the room, were more effective in this scenario. Although the magnitudes of ES5 and ES6's emission rates were lower than ES1, they had the same relative pattern and trends in removal of nano-TiO2 from the indoor air.

	Chapter 7 Life-Cycle Impact Assessment Nanomaterial Characterization Factors: Titanium Dioxide Case
	Study
	fastest rate for
	100%						100%
	80%							80%
	60%							60%
	40%							40%
	20%							20%
	0%	1	2	3	4 Work-Cycle Time 5 6 7	8	9 10	0%	1	2	3	4 Work-Cycle Time 5 6 7	8	9 10
	Air				Aggregate			Air				Aggregate
	Surface (Settling)	Outdoor (Ventilation)	Surface (Settling)	Outdoor (Ventilation)
							162

TiO2 emissions that remained in the indoor air were 25%, 19%, 19% and 0.49% for ES1, ES2, ES3 and ES4, respectively. The relative amount of nano-TiO2 emissions that remained in the indoor air decreased at the

Table 7 - 4

 74 Results for the internal wet lung burden and the retained-intake fraction, reported as either a lifetime or 1-year value

	Lifetime	1-Year

mg/min] Total Daily Emissions (mg) Lung Burden RiF Lung Burden RiF

	ES1 e-high, f-short	6.72E+02	1.08E+05	2.73E+02	1.25E-09	5.72E+00	2.62E-11
	ES2 e-high, f-long	6.72E+02	1.61E+05	5.53E+02	1.69E-09	1.16E+01	3.55E-11
	ES3 e-high, f-daily	6.72E+02	3.23E+05	1.19E+03	1.82E-09	2.50E+01	3.82E-11
	ES4 e-high, f-single pulse 6.72E+02	6.72E+02	7.04E+00	5.16E-09	1.44E-01	1.05E-10
	ES5 e-medium, f-short	6.72E+00	1.08E+03	8.10E+00	3.71E-09	1.67E-01	7.64E-11
	ES6 e-low, f-short	6.72E-02	1.08E+01	3.99E+00	1.83E-07	1.70E-02	7.81E-10

Table 7 - 5

 75 Effect factors (EF), intake fractions (RiF) and ENM-specific characterization factors (CFNS) for six different emission and exposure scenarios that involved differences in emission rates (e) and emission interval frequencies (f)

	Lifetime ‡	1-Year ‡	Low Population §	High Population §

  Chapter 7 Life-Cycle Impact Assessment Nanomaterial Characterization Factors: Titanium Dioxide Case Study 183

Table 7 - 6

 76 Human health impacts per watt-peak of OPV cell production without a ENM-specific characterization factor for nano-TiO2 (left columns) and with a ENM-specific characterization factor (right columns)

		No ENM-specific Characterization Factor	With ENM-specific Characterization Factor
				(Lifetime average)
	Percent Contribution by Life-Cycle stage	Human Health	Human Health	Human Health (non-	Human Health
		(non-carcinogenic)	(carcinogenic)	carcinogenic)	(carcinogenic)
	Annealing	32.08%	29.55%	31.95%	29.47%
	HTL	24.66%	2.46%	24.56%	2.45%
	PCBM	14.47%	18.02%	14.41%	17.97%
	Lamination	12.31%	14.56%	12.26%	14.51%
	FTO Substrate	9.61%	10.44%	9.57%	10.41%
	P3HT	0.80%	17.82%	0.80%	17.77%
	Printing	3.16%	3.14%	3.14%	3.13%
	ETL	1.59%	1.38%	1.58%	1.38%
	Aluminum Electrode	0.63%	1.96%	0.62%	1.95%
	Nano-TiO2 Spacer	0.05%	0.04%	0.25%	0.18%
	Other	0.64%	0.63%	0.86%	0.78%
	Total Impacts (CTU)	9.35E-09	3.55E-09	9.38E-09	3.56E-09
	Total contribution from nano-TiO2	N/A	N/A	0.41%	0.28%

  While some indication of inflammation, for instance, have been demonstrated, fullerenes

	Chapter 7 Life-Cycle Impact Assessment Nanomaterial Characterization Factors: Titanium Dioxide Case
	Study
	100% toxicity data.
	80%
	60%
	40%
	20%
	0%
	Human Health	Human Health C. Human Health	Human Health	Human Health C. Human Health
	N.C.	Total	N.C.	Total
	Annealing	HTL	PCBM	Lamination
	FTO Substrate	P3HT	Printing	ETL
	Aluminum Electrode	Nano-TiO2 Spacer	Other
		185

  This thesis was not able to address the question surrounding the ENM emissions during the use and end-of-life phases of the panels, however this remains an ongoing part of the work initiated by this thesis and will subsequently continue. A handful of previous studies have considered the leaching potential of OPV panels but none report the amounts of ENM in the leachates. For instance, Zimmerman et al.367 found 2.4 ± 0.77 μg Al, 1.3 ± 0.3 μg Ag and 4.4 ± 0.1 μg Zn per gram of PV leached with freshwater, 0.7 ± 0.1 μg Cu 2+ and 3.9 ± 0.7 μg Zn per gram of PV leached with acidic rainwater, and 0.9 ± 0.2 μg Al and 14.6 ± 1.8 μg Ag per gram of PV leached with seawater 367 . Brun et al. 368 made similar tests finding 63 ± 7 μg Zn 2+ per liter of mesotrophic water, 14 ± 2 μg Cu 2+ and 87 ± 7 μg Zn 2+ per liter of acidic water, and 78 ± 7 μg AgCl2 -and 17 ± 1 μg Zn 2+ per liter of seawater 368 .

Table A3 - 1

 A31 Absolute life-cycle impacts for each organic photovoltaic cell considered in this study. Values are reported with their respective reference unit.

	Impact category	Reference unit	Default	FTOinkjet	PCBMdcb
	Agricultural land occupation	m 2 a	8.89E-04	5.89E-04	6.80E-04
	Climate change	kg CO2-Eq	5.22E-02	3.45E-02	3.83E-02
	Fossil fuel depletion	kg Oil-Eq	2.09E-02	1.61E-02	1.68E-02
	Freshwater ecotoxicity	kg 1,4-DCB-Eq	1.16E-06	1.02E-06	1.06E-06
	Freshwater eutrophication	kg P-Eq	2.89E-05	1.39E-05	1.76E-05
	Human toxicity	kg 1,4-DCB-Eq	2.08E-03	1.74E-03	2.13E-03
	Ionizing radiation	kg U235-Eq	2.12E-02	7.72E-03	1.08E-02
	Marine ecotoxicity	kg 1,4-DCB-Eq	3.30E-05	2.84E-05	3.06E-05
	Marine eutrophication	kg N-Eq	1.05E-05	6.06E-06	7.19E-06
	Mineral resource depletion	kg Fe-Eq	3.52E-02	3.51E-02	3.52E-02
	Natural land transformation	m 2	9.64E-06	7.49E-06	8.04E-06
	Ozone depletion	kg CFC11-Eq	2.80E-09	2.00E-09	2.20E-09
	Particulate matter formation	kg PM10-Eq	6.84E-05	4.59E-05	5.14E-05
	Photochemical oxidant formation	kg NMVOC	1.27E-04	9.04E-05	9.87E-05
	Terrestrial acidification	kg SO2-Eq	1.94E-04	1.24E-04	1.41E-04
	Terrestrial ecotoxicity	kg 1,4-DCB-Eq	5.48E-06	4.99E-06	5.19E-06
	Urban land occupation	m 2 a	2.00E-04	1.40E-04	1.59E-04
	Water depletion	m 3	2.25E-01	8.89E-02	1.21E-01
	Cumulative Energy Demand	MJ-Eq	2.60E+00	8.37E-01	2.30E+00

Solar Insolation for European Union Member States

  Anctil et al. 57 The authors deviate from Anctil et al. by accounting for C60 regeneration. Often large amounts of C60 are not converted to PCBM and this would not be cost effective unless that amount was regenerated. TableA3-2 lists the average solar insolation for EU member state which were used to calculate an average European insolation value.

	Item Aluminum, primary, at plant, RER Annealing, process FTO substrate, sputtered, at plant Gravure printing Lamination flexible solar module, at plant Lithium fluoride, layer, application Molybdenum(VI) oxide, at plant Monochlorobenzene, at plant, RER poly(3-hexylthiophene-PCBM, purified 99%, at plant Nano-TiO2, sulphate precipitation, at plant Transport, freight, rail, RER Transport, lorry 16-32t, EURO3, RER Water, ultrapure, at plant, GLO poly(3-hexylthiophene-2,5-diyl), at plant Inventory Item Bromine, at plant Electricity, medium voltage, production RER, at grid Heat, heavy fuel oil, at industrial furnace 1MW, RER Hexane, at plant, RER Thiophene, at plant Transport, freight, rail, RER Transport, lorry 16-32t, EURO3, RER Water, ultrapure, at plant, GLO PCBM, purified 99%, at plant Inventory Item disposal, hazardous waste, 25% water, to waste incineration, CH Methanol, at regional storage, CH PCBM, Unpurified, at plant Solvent regeneration Transport, freight, rail, RER Transport, lorry 16-32t, EURO3, RER Methanol, at regional storage, CH PCBM, Unpurified, at plant Inventory Item C60 regeneration C60, C60-fullerenes , at plant disposal, hazardous waste, 25% water, to waste incineration, CH Electricity, medium voltage, production RER, at grid Methyl 4-Benzobutyrate p-tosylhydrazone, at plant Monochlorobenzene, at plant, RER Nitrogen, liquid, at plant, RER o-Dichlorobenzene, at plant RER pyridine-compounds, at regional storage, RER Sodium methoxide, at plant, GLO Solvent regeneration Steam, for chemical processes, at plant, RER Tap water, at user, RER Toluene, liquid, at plant, RER Transport, freight, rail, RER Transport, lorry 16-32t, EURO3, RER p-toluenesulfonyl chloride (TsCl), at RER plant plant production mix, at plant, pentahydrate, at Appendix: Chapter 3 Inventory Item Electricity, medium voltage, production RER, at grid Steam, for chemical processes, at plant, RER Sulfuryl chloride, at plant Toluene, liquid, at plant, RER Transport, freight, rail, RER Transport, lorry 16-32t, EURO3, RER p-toluenesulfonyl hydrazine, at plant Inventory Item Electricity, medium voltage, production RER, at grid Hydrazine, at plant p-toluenesulfonyl chloride (TsCl), at plant Steam, for chemical processes, at plant, RER Tetrahydrofuran, at plant, RER Transport, freight, rail, RER Transport, lorry 16-32t, EURO3, RER Solvent regeneration Chlorine, liquid, Appendix: Chapter 3 Electricity, medium voltage, production RER, at grid Steam, for chemical processes, at plant, RER Sulphur dioxide, liquid, at plant, RER Transport, freight, rail, RER Transport, lorry 16-32t, EURO3, RER Thiophene, at plant Inventory Item Aluminium oxide, at plant, RER Butanes from butenes, at plant, RER Chromium oxide, flakes, at plant, RER Heat, heavy fuel oil, at industrial furnace 1MW, RER Hydrogen sulfide, H2S, at plant, RER Secondary Sulphur, at refinery, RER Transport, freight, rail, RER Transport, lorry 16-32t, EURO3, RER Tin tetrachloride, at Tin tetrachloride Appendix: Chapter 3 Inventory Item Tin tetrachloride, at plant Water, ultrapure, at plant, GLO TiOSO4 production, at plant Inventory Item Chemical plant, organics -RER Electricity, medium voltage, production RER Heat, unspecific, in chemical plant -RER Ilmenite, 54% titanium dioxide, at plant -AU Sulphuric acid, liquid, at plant -RER Transport, freight, rail -RER Transport, lorry 16-32t, EURO3 -RER Water, cooling, unspecified natural origin Disposal, residue from TiO2 production SO4, 30% water, to residual material landfill -CH Hydrogen sulfide to description in Appendix: Chapter 3 Amount Units Other 0.001 kg 1 number of items 1 m 2 1 number of items 1 m 2 1 number of items 0.00024 kg 0.0076 kg 0.00024 kg 0.00021 kg 0.000127 kg 0.005 t*km Based on a distance traveled of 600km by train. 0.00018 t*km Based on a distance traveled of 100km by truck. 0.160 kg Reference Flow Amount: 1kg Reaction of thiophene, bromine and hexane. Amounts of Inventory Flow Source Ecoinvent This study This study This study This study This study This study Ecoinvent This study This study This study Ecoinvent Ecoinvent Ecoinvent inputs and energy adapted from García-Valverde et al. 30 Amount Units Other Inventory Flow Source 8.97 kg This study 131.07 MJ Ecoinvent 1.15 GJ Ecoinvent 2.58 kg Ecoinvent 7.81 kg This study 11.61 t*km Based on a distance traveled of 600km by train. Ecoinvent 1.94 t*km Based on a distance traveled of 100km by truck. Ecoinvent 11,136.15 kg Ecoinvent Reference Flow Amount: 1kg Functionalization of C60 into PCBM adapted from the description in Anctil et al. 57 Amount Units Other Inventory Flow Source 0.05 kg Ecoinvent 11.87 kg Ecoinvent 1.05 kg This study 237.5 kg This study 7.12 t*km Based on a distance traveled of 600km by train. Ecoinvent 1.19 t*km Based on a distance traveled of 100km by truck. Ecoinvent 8.9 kg Ecoinvent Reference Flow Amount: 1kg Functionalization of C60 into PCBM adapted from the Amount Units Other Inventory Flow Source 1.21 kg This study 2.12 kg This study 54.17 kg Estimated as: 90% of the methyl 4 benzoybutyrate p-tosylhydrazone; 100% of the pyridine-compounds; 10% of the oDCB; 100% of the sodium hydroxide as sodium chloride; 10% of the toluene; 10% of the monochlorobenzene Ecoinvent 159.23 MJ Ecoinvent 2.01 kg This study 0.087 kg Ecoinvent 18.90 kg Ecoinvent 219.63 kg Ecoinvent 39.50 kg Ecoinvent 0.30 kg Ecoinvent 347.00 kg This study 495.34 kg Ecoinvent 26,418.13 kg Ecoinvent 0.11 kg Ecoinvent 111.84 t*km Based on a distance traveled of 600km by train. Ecoinvent 18.64 t*km Based on a distance traveled of 100km by truck. Ecoinvent Reference Flow Amount: 1kg Production of p-toluenesulfonyl chloride adapted from Anctil Amount: 1kg 5H2O = SnCl4.5H2O. et al. 57 and with the precursors sulfuryl chloride, toluene Amount Units Other Inventory Flow Source 0.70 MJ Ecoinvent 1.20 kg Ecoinvent 0.74 kg This study 0.51 KG Ecoinvent 0.75 t*km Based on a distance traveled of 600km by train. Ecoinvent 0.13 t*km Based on a distance traveled of 100km by truck. Ecoinvent Reference Flow Amount: 1kg Production of p-toluenesulfonyl hydrazine adapted from Anctil et al. 57 Amount Units Other Inventory Flow Source 0.69 MJ Ecoinvent 0.62 kg This study 1.08 kg This study 1.20 kg Ecoinvent 0.39 kg Ecoinvent 1.25 t*km Based on a distance traveled of 600km by train. Ecoinvent 0.21 t*km Based on a distance traveled of 100km by truck. Ecoinvent Reference Flow Amount: 1kg Adapted from Geisler et al. 371 using steam, nitrogen and cooling water. Assumption that this represents the inventory Source 0.55 kg Ecoinvent 0.71 MJ Q Ecoinvent 1.21 Ecoinvent 0.50 kg Ecoinvent 0.63 t*km Based on a distance traveled of 600km by train. Ecoinvent 0.11 t*km Based on a distance traveled of 100km by truck. Ecoinvent Reference Flow Amount: 1kg Production of Thiophene (C4H4S) from Butane (C4H10) and sulfur (CS2). The inputs and outputs of production process were adapted from the Patent US 3939179 A [1]and from the Ullmann's Encyclopedia Of Industrial Chemistry. 130 Amount Units Other Inventory Flow Source 0.40 kg Ecoinvent 0.69 kg Ecoinvent 0.80 kg Ecoinvent 1.81 MJ Ecoinvent 1.22 kg Ecoinvent 1.52 kg Ecoinvent 1.58 t*km Based on a distance traveled of 600km by train. Ecoinvent 0.26 t*km Based on a distance traveled of 100km by truck. Ecoinvent Reference Flow Adapted from Ullman's encyclopedia of industrial chemistry Reference Flow Estimated using stoichiometric calculations following SnCl4 + Amount Units Other Inventory Flow Source 0.74 kg This study 0.26 kg Ecoinvent Reference Flow Amount: 1 kg Estimation of the production of 1 kg of nano-TiOSO4 based on the digestion of ilmenite (FeTiO3) with sulphuric acid as described in Hischier et al. 193 Amount Units Other Inventory Flow Source 4E-10 Items Ecoinvent 0.333 kWh Ecoinvent 2.17 MJ Ecoinvent 0.595 kg Ecoinvent 0.0248 kg Ecoinvent 0.387 tkm Ecoinvent 0.0645 tkm Ecoinvent 0.123 m3 Ecoinvent 0.162 kg Ecoinvent 1.58E-6 kg Ecoinvent Plastic film, pet, at plant Reference Flow Amount: 1kg for a process recovering 95% of solvent. plant Amount: 1kg following the reaction of tin and chlorine. 131 water The production of extruded plastic film adapted from the Inventory Item Amount Units Other Inventory Flow Inventory Item Amount Units Other Inventory Flow Sulfur dioxide to air 0.00025 kg Ecoinvent Ecoinvent process for extrusion of polyethylene terephthalate. Inventory Item Amount Units Other Source Source Inventory Flow Source Extrusion, plastic film, Polyethylene Transport, freight, rail, RER 0.6 t*km Based on a distance traveled of 600km by Transport, lorry 16-32t, EURO3, RER 0.1 t*km Based on a distance traveled of 100km by Inventory Item Amount Units Other Inventory Flow EURO3, RER truck. truck. plant Amount: 1kg dioxide and chlorine. Transport, lorry 16-32t, 0.1 t*km Based on a distance traveled of 100km by Ecoinvent Ecoinvent Sulfuryl chloride, at Reference Flow Adapted from Anctil et al. 57 using the precursors sulphur RER train. train. Transport, freight, rail, 0.6 t*km Based on a distance traveled of 600km by Ecoinvent Ecoinvent Tap water, at user, RER 80.00 kg Ecoinvent RER at plant, RER processes, at plant, RER Tin, at regional storage, 0.46 kg Ecoinvent granulate, amorphous, Steam, for chemical 1.50 kg Ecoinvent RER terephthalate, 1.03 kg plant, RER industrial furnace 1MW, Ecoinvent Nitrogen, liquid, at 0.01 kg Ecoinvent Heat, heavy fuel oil, at 0.13 MJ Ecoinvent RER 1.0 kg at grid RER Ecoinvent Electricity, medium voltage, production RER, 0.20 MJ Ecoinvent Chlorine, liquid, production mix, at plant, 0.54 kg Ecoinvent Average
	2,5-diyl), at plant
	IX X XI XII XIII

Table A3 - 2

 A32 Average solar insolation (kWh) for the individual European Union member states and an average153

	Appendix: Chapter 4
	Slanted Roof Mounting Structure Inventory Data
	Spain	1659
	Cyprus	1902
	Greece	1637
	Portugal	1632
	Italy	1494
	Albania	1556
	France	1259
	Montenegro	1468
	Serbia	1472
	Croatia	1334
	Bulgaria	1406
	XV

Table A4 - 1

 A41 Inventory data for production and disposal of OPV slanted roof mounting structure.

	Inventory per 1m 2 of slanted roof mounting and installation for OPV panels
	Flow	Notes	Unit	Amount
	aluminium, production mix, wrought alloy, at plant -RER		kg	3.90
	corrugated board, mixed fibre, single wall, at plant -RER		kg	0.133
	disposal, building, polyethylene/polypropylene products, to final	kg	0.00140
	disposal -CH
	disposal, building, polystyrene isolation, flame-retardant, to final	kg	0.00702
	disposal -CH
	disposal, OPV mounting frame		Item(s) 1
	disposal, packaging cardboard, 19.6% water, to municipal incineration -	kg	0.133
	CH
	polyethylene, HDPE, granulate, at plant -RER		kg	0.00140
	polystyrene, high impact, HIPS, at plant -RER		kg	0.00702
	section bar extrusion, aluminium -RER		kg	0
	sheet rolling, aluminium -RER		kg	1.35E-12
	sheet rolling, steel -RER		kg	1.49
	steel, low-alloyed, at plant -RER		kg	1.49
	transport, freight, rail -RER		t*km	1.50
	transport, lorry > 16t, fleet average -RER		t*km	0.225
	transport, van < 3.5t -RER		t*km	0.434
	Inventory for disposal of 1m 2 of OPV mounting structure
	Flow	Notes	Unit	Amount
	aluminium, secondary, from old scrap, at plant -RER	Secondary production of recycled	kg	3.90
		aluminum
	dismantling, industrial devices, manually, at plant -CH		kg	5.49
	steel, electric, un-and low-alloyed, at plant -RER	Secondary production of recycled steel	kg	1.49
	aluminium, primary, at plant -RER	Avoided product	kg	3.78
	steel, converter, unalloyed, at plant -RER	Avoided product	kg	1.34

Table A4 - 2

 A42 Inventory for 1m2 of slanted roof mounting and installation for m-Si panels including disposal (includes disposal of steel and aluminum components)

	Appendix: Chapter 4
	aluminium, secondary, from old scrap, at plant -RER	Secondary production of aluminum	kg	5.46
	corrugated board, mixed fibre, single wall, at plant -RER		kg	0.133
	dismantling, industrial devices, manually, at plant -CH		kg	5.80
	dismantling, industrial devices, mechanically, at plant -GLO		kg	5.80
	disposal, building, polyethylene/polypropylene products, to final
	disposal -CH		kg	0.00140
	disposal, building, polystyrene isolation, flame-retardant, to final
	disposal -CH		kg	0.00702
	disposal, packaging cardboard, 19.6% water, to municipal
	incineration -CH		kg	0.133
	polyethylene, HDPE, granulate, at plant -RER		kg	0.00140
	polystyrene, high impact, HIPS, at plant -RER		kg	7.02E-03
	section bar extrusion, aluminium -RER		kg	3.03
	sheet rolling, steel -RER		kg	1.50
	steel, electric, un-and low-alloyed, at plant -RER	Secondary production of steel	kg	1.50
	steel, low-alloyed, at plant -RER		kg	1.50
	transport, freight, rail -RER		t*km 1.50
	transport, lorry > 16t, fleet average -RER		t*km 0.225
	transport, van < 3.5t -RER		t*km 0.434
	steel, converter, unalloyed, at plant -RER	Avoided product	kg	1.35
	aluminium, primary, at plant -RER	Avoided product	kg	5.30
	Flow	Notes	Unit	Amount
	aluminium, production mix, wrought alloy, at plant -RER		kg	2.84
	XVI

Recycling Conversion Rates of Secondary Scrap Metal to Primary Metal

Table A4 - 3

 A43 Recycling Conversion Rates of Secondary Scrap Metal to Primary Metal

	Alumininum	97%
	Copper	76%
	Iron (Ferrous Materials)	90%

Table A4 - 4

 A44 Inventory for 1 inverter (18.5kg) as applied to both the OPVs and m-Si rooftop-installations

	resistor, metal film type, through-hole mounting, at plant -GLO		kg	0.005
	section bar extrusion, aluminium -RER		kg	1.40
	sheet rolling, steel -RER		kg	9.80
	steel, low-alloyed, at plant -RER		kg	9.80
	styrene-acrylonitrile copolymer, SAN, at plant -RER		kg	0.01
	transistor, wired, small size, through-hole mounting, at plant -GLO		kg	0.038
	transport, freight, rail -RER		t*km	7.11
	transport, lorry > 16t, fleet average -RER		t*km	2.30
	transport, transoceanic freight ship -OCE		t*km	36.3
	wire drawing, copper -RER		kg	5.51
	Inventory for disposal of 1 inverter per functional unit
	Flow	Notes		Unit Amount
	aluminium, secondary, from old scrap, at plant -RER	Secondary production of recycled aluminum kg	1.31
	copper, secondary, at refinery -RER	Secondary production of recycled copper	kg	5.50
	dismantling, inverter, manual, at plant			kg	4.25
	dismantling, inverter, mechanical, at plant			kg	14.3
	disposal, residues, mechanical treatment, industrial device, in
	MSWI -CH			kg	1.13
	steel, electric, un-and low-alloyed, at plant -RER	Secondary production of recycled steel		kg	10.6
	aluminium, primary, at plant -RER	Avoided product		kg	1.27
	copper, at regional storage -RER	Avoided product		kg	4.21
		Avoided product from incinerating inverter
	electricity, medium voltage, production RER, at grid -RER	plastic		MJ	3.35
	steel, converter, unalloyed, at plant -RER	Avoided product		kg	9.51
	Inventory for the production of 1 inverter per functional unit
	Flow	Notes	Unit	Amount
	aluminium, production mix, cast alloy, at plant -RER		kg	1.4
	capacitor, electrolyte type, > 2cm height, at plant -GLO		kg	0.256
	capacitor, film, through-hole mounting, at plant -GLO		kg	0.341
	capacitor, Tantalum-, through-hole mounting, at plant -GLO		kg	0.0230
	connector, clamp connection, at plant -GLO		kg	0.237
	copper, at regional storage -RER		kg	5.51
	corrugated board, mixed fibre, single wall, at plant -RER		kg	2.50
	diode, glass-, through-hole mounting, at plant -GLO		kg	0.0470
	disposal, inverter, to WEEE treatment		Item(s)	1
	disposal, packaging cardboard, 19.6% water, to municipal
	incineration -CH		kg	2.50
	disposal, polyethylene, 0.4% water, to municipal incineration -CH		kg	0.0600
	disposal, polystyrene, 0.2% water, to municipal incineration -CH		kg	0.310
	disposal, treatment of printed wiring boards -GLO		kg	1.703
	electricity, medium voltage, production UCTE, at grid -UCTE		kWh	21.2
	fleece, polyethylene, at plant -RER		kg	0.0600
	inductor, ring core choke type, at plant -GLO		kg	0.351
	integrated circuit, IC, logic type, at plant -GLO		kg	0.0280
	metal working factory -RER		Item(s)	8.97E-09
	polystyrene foam slab, at plant -RER		kg	0.300
	polyvinylchloride, at regional storage -RER		kg	0.0100
	printed wiring board, through-hole, at plant -GLO		m2	0.225
	XVII

Table A4 - 5

 A45 Mechanical and Manual Transfer Coefficients

	Appendix: Chapter 4
	copper, secondary, at refinery -RER			Secondary production of recycled copper	kg	0.083
	disposal, plastic, industry. electronics, 15.3% water, to
	municipal incineration -CH				kg	0.640
	copper, at regional storage -RER			Avoided product	kg	0.0830
				Avoided product from incineration of inverter
	electricity, medium voltage, production RER, at grid -RER	plastics	MJ	2.56
	Transfer Coefficients Per Material Per Dismantling Method
	Mechanical
	Metals, outside	50%	scrap, for metal production
	Metals, outside	50%	shredder
	Metals, inside	100%	shredder
	Plastics, inside	100%	shredder
	Printed Wiring Board	50%	treatment
	Printed Wiring Board	50%	shredder
	all else	100%	shredder
	Manual
	Metals, outside	100%	scrap, for metal production
	Metals, inside	100%	scrap, for metal production
	Plastics, inside	100%	incineration
	Printed Wiring Board	100%	treatment
	all else	100%	shredder
	Transfer Coefficients Per Mechanical Dismantling (Shredding)
	Alumininum	82.58%
	Copper	78.21%
	Iron (Ferrous materials)	95%
	Cables
	Table A4-6 Inventory for electric cabling as applied to both the OPVs and m-Si rooftop-installations
	Inventory during 1m 2 of PV installation
	Flow			Notes	Unit Amount
	acrylonitrile-butadiene-styrene copolymer, ABS, at plant -RER		kg	0.64
	copper, at regional storage -RER				kg	0.083
	disposal, cabling				m2	1.00
	wire drawing, copper -RER				kg	0.083
	Inventory for the disposal of cabling per m 2 of PV capacity
	Flow			Notes	Unit Amount
			XVIII

Table A4 -

 A4 7 PV panel elemental composition that was used to derive the disposal inventory per kg of PV panel considered in the LCA

	Sodium	1.35E-03	9.51E-04	5.59E-02
	Additional Impact Assessment Results from Chapter 4
	Component	OPV	a-Si	m-Si
	Oxygen (without O from H2O)	3.56E-01	2.39E-02	4.36E-01
	Hydrogen (without H from H2O)	6.16E-02	8.08E-02	2.29E-02
	Carbon	5.36E-01	4.87E-01	8.24E-02
	Sulfur	3.42E-04	3.34E-04	1.00E-05
	Nitrogen	6.04E-03	2.08E-03	1.62E-03
	Phosphor	0.00E+00	0.00E+00	0.00E+00
	Boron	0.00E+00	0.00E+00	0.00E+00
	Chlorine	1.67E-02	1.32E-03	1.70E-04
	Bromium	6.45E-05	5.57E-06	7.01E-07
	Fluorine	9.21E-04	1.57E-03	3.13E-03
	Iodine	0.00E+00	0.00E+00	0.00E+00
	Arsenic	1.75E-06	1.00E-06	1.90E-08
	Barium	4.61E-05	1.31E-04	1.87E-06
	Cadmium	3.04E-06	1.87E-05	2.61E-08
	Cobalt	2.90E-05	1.07E-06	3.16E-07
	Chromium	5.07E-06	7.83E-06	2.63E-04
	Copper	1.11E-05	2.63E-02	1.13E-02
	Mercury	8.30E-08	3.04E-08	8.50E-09
	Manganese	1.66E-05	1.68E-05	8.00E-07
	Molybdenum	7.18E-04	0.00E+00	0.00E+00
	Nickel	3.69E-06	6.25E-07	1.43E-05
	Lead	5.16E-06	4.19E-04	3.36E-05
	Antimony	1.49E-04	5.70E-06	4.30E-07
	Selenium	1.94E-06	1.11E-06	2.10E-08
	Tin	5.69E-03	6.11E-04	4.57E-04
	Vanadium	1.20E-03	1.20E-03	2.88E-06
	Zinc	6.42E-05	6.04E-04	3.96E-06
	Beryllium	4.61E-07	2.64E-07	5.00E-09
	Scandium	0.00E+00	0.00E+00	0.00E+00
	Strontium	8.16E-05	4.66E-05	8.86E-07
	Titanium	9.22E-04	5.27E-04	1.00E-05
	Thallium	3.69E-07	2.11E-07	4.00E-09
	Tungsten	0.00E+00	0.00E+00	0.00E+00
	Silicon	4.07E-03	2.20E-03	3.21E-01
	Iron	9.22E-05	3.66E-01	3.73E-05
	Calcium	2.77E-04	1.61E-03	6.30E-02
	Aluminium	4.68E-03	1.60E-03	2.00E-06
	Potassium	0.00E+00	0.00E+00	0.00E+00
	Magnesium	0.00E+00	4.08E-03	0.00E+00
		XIX

Table A8

 A8 Absolute life-cycle impacts for S1 (rooftop array)

		Appendix: Chapter 4
	1	(a)
		Table A9 Absolute life-cycle impacts for S2 (portable charger)
		Impact category		Reference unit	OPV-D (Incineration)	OPV-D (Landfill)	a-Si (Incineration)	a-Si (Landfill)	OPV-NC (Incineration)	OPV-NC (Landfill)	a-Si-NC (Incineration)	a-Si-NC (Landfill)
		Agricultural land occupation	m 2 • yr	7.53E-02	8.47E-02		2.25E-01	2.33E-01		8.31E-03	8.92E-03	1.72E-01	1.73E-01
	Impact category Climate change potential Fossil depletion Freshwater ecotoxicity Agricultural land occupation m 2 • yr Reference unit Freshwater eutrophication Climate change potential kg CO2-eq Human toxicity Fossil depletion kg Oil-eq Ionizing radiation Freshwater ecotoxicity kg 1,4-DCB-eq Marine ecotoxicity Freshwater eutrophication kg P-eq Marine eutrophication Human toxicity kg 1,4-DCB-eq Metal depletion Ionizing radiation kg U235-eq Natural land transformation Marine ecotoxicity kg 1,4-DCB-eq Ozone depletion Marine eutrophication kg N-eq Particulate matter formation Metal depletion kg Fe-eq Photochemical oxidant formation Natural land transformation m 2 Terrestrial acidification Ozone depletion kg CFC11-eq Terrestrial ecotoxicity Particulate matter formation kg PM10-eq Urban land occupation Photochemical oxidant formation kg NMVOC Water depletion Terrestrial acidification kg SO2-eq Cumulative energy demand	OPV-D (Incineration) kg CO2-eq kg Oil-eq kg 1,4-DCB-eq 2.44E+01 kg P-eq 6.94E+02 kg 1,4-DCB-eq 2.04E+02 kg U235-eq 2.35E-02 kg 1,4-DCB-eq 5.97E-01 kg N-eq 1.43E+02 kg Fe-eq 1.83E+02 m 2 2.21E+00 kg CFC11-eq 1.60E-01 kg PM10-eq 4.77E+02 kg NMVOC 1.17E-01 kg SO2-eq 6.11E-05 kg 1,4-DCB-eq 9.32E-01 m 2 • yr 1.82E+00 m 3 2.58E+00 MJ-eq	OPV-D (Landfill) 4.27E+00 1.19E+00 5.13E-05 2.47E+01 7.25E-04 6.68E+02 2.18E-01 2.08E+02 3.09E-01 2.37E-02 2.60E-03 6.12E-01 4.09E-04 1.43E+02 4.02E-01 1.96E+02 3.27E-04 2.22E+00 1.16E-07 2.95E-01 2.71E-03 4.78E+02 7.47E-03 1.18E-01 7.81E-03 6.19E-05 2.57E-04 9.52E-01 9.61E-03 1.84E+00 3.77E+00 2.64E+00 5.87E+01	m-Si (Incineration) 3.52E+00 1.34E+00 5.52E-05 7.23E+01 1.19E-03 1.31E+03 2.24E-01 3.81E+02 7.36E-01 1.77E-01 2.74E-03 8.78E-01 4.08E-03 4.67E+02 4.06E-01 3.64E+02 3.67E-04 8.73E+00 1.42E-07 7.13E-01 3.35E-03 3.77E+02 8.32E-03 2.57E-01 9.84E-03 1.15E-04 2.71E-04 1.59E+00 1.35E-02 4.98E+00 8.07E+00 4.71E+00 7.09E+01	m-Si (Landfill) 1.06E+01 3.06E+00 1.08E-04 7.22E+01 4.32E-03 1.31E+03 5.95E-01 3.80E+02 2.03E+00 1.77E-01 7.14E-03 8.77E-01 2.65E-03 4.67E+02 2.81E+00 3.64E+02 1.29E-03 8.73E+00 3.11E-07 7.07E-01 1.50E-02 3.77E+02 2.65E-02 2.57E-01 5.17E-02 1.15E-04 4.25E-04 1.58E+00 8.96E-02 4.94E+00 3.05E+01 4.68E+00 1.73E+02	1.02E+01 OPV-D 3.19E+00 (Incineration, 1.11E-04 No Mount) 1.47E+01 4.72E-03 5.68E+02 6.00E-01 1.67E+02 2.41E+00 2.03E-02 7.26E-03 5.33E-01 5.32E-03 1.06E+02 2.81E+00 1.60E+02 1.32E-03 1.72E+00 3.33E-07 1.34E-01 1.55E-02 4.04E+02 2.71E-02 8.30E-02 5.34E-02 2.38E-05 4.36E-04 7.92E-01 9.34E-02 1.44E+00 3.42E+01 2.16E+00 1.84E+02	5.73E-01 OPV-D (Landfill, 5.22E-01 1.97E-01 2.06E-01 No Mount) 1.32E-05 1.35E-05 3.60E-04 3.90E-04 1.50E+01 4.99E-02 5.09E-02 5.42E+02 1.76E-01 2.02E-01 1.72E+02 5.71E-04 5.86E-04 2.04E-02 1.12E-04 3.82E-04 5.48E-01 3.19E-01 3.20E-01 1.07E+02 9.23E-05 9.44E-05 1.73E+02 2.72E-08 2.87E-08 1.73E+00 7.04E-04 7.43E-04 2.69E-01 1.35E-03 1.40E-03 4.05E+02 2.01E-03 2.13E-03 8.41E-02 5.91E-05 6.03E-05 2.46E-05 2.54E-03 2.81E-03 8.12E-01 1.98E+00 2.24E+00 1.47E+00 2.22E+00 1.12E+01 1.20E+01	7.71E+00 2.28E+00 7.78E-05 4.03E-03 4.63E-01 1.93E+00 5.55E-03 2.42E-03 2.74E+00 1.11E-03 2.41E-07 1.34E-02 2.17E-02 4.71E-02 2.69E-04 8.41E-02 2.91E+01 1.36E+02	7.79E+00 2.30E+00 7.82E-05 4.09E-03 4.64E-01 1.99E+00 5.56E-03 2.40E-03 2.74E+00 1.11E-03 2.44E-07 1.35E-02 2.17E-02 4.73E-02 2.71E-04 8.50E-02 2.97E+01 1.37E+02
	Terrestrial ecotoxicity	kg 1,4-DCB-eq	1.16E-01	1.17E-01	3.69E+00		3.69E+00	9.57E-02		9.62E-02
	Urban land occupation	m 2 • yr	7.26E+00	7.40E+00	1.08E+01		1.09E+01	5.07E+00		5.21E+00
	Water depletion	m 3	1.04E+03	1.17E+03	2.62E+04		2.62E+04	1.78E+03		1.91E+03
	Cumulative energy demand (b)	MJ-eq	1.16E+04	1.20E+04	2.46E+04		2.46E+04	9.67E+03		1.00E+04
							XX
					XXI

Table A4 -

 A4 10 The consumer-adjusted EPBT, adjusted for consumer charging habits as described in the text.

		CED(MJ) / FU	Energy Supplied by Charger (MJ / year)	Consumer-adjusted EPBT (years)
	OPV-D (Incineration)	58.7	11.2	5.32
	OPV-D (Landfill)	70.9	11.2	6.30
	a-Si (Incineration)	173	11.2	15.4
	a-Si (Landfill)	184	11.2	16.3
	a-Si-NC (Incineration)	136	11.2	12.1
	a-Si-NC (Landfill)	137	11.2	12.2
	OPV-NC (Incineration)	11.2	11.2	1.00
	OPV-NC (Landfill)	11.9	11.2	1.03

Table A4 -

 A4 11 The consumer-adjusted CPBT for S2, adjusted for consumer charging habits as described in the text. Estimated from an avoidance of 0.487 kg CO2-eq. per kWh calculated in Ecoinvent 2.2 for general production of mediumvoltage electricity for an average European mix (RER)

		CO2-eq. / FU	Energy Supplied by Charger (kWh / year)	Avoided Carbon Emissions (kg CO2-eq. / year) ‖	Consumer-adjusted CPBT (years)
	OPV-D (Incineration)	4.27	3.11	1.51	2.82
	OPV-D (Landfill)	3.52	3.11	1.51	2.32
	a-Si (Incineration)	10.6	3.11	1.51	7.02
	a-Si (Landfill)	10.2	3.11	1.51	6.68
	a-Si-NC (Incineration) 7.71	3.11	1.51	5.08
	a-Si-NC (Landfill)	7.79	3.11	1.51	5.16
	OPV-NC (Incineration)	0.57	3.11	1.51	0.38
	OPV-NC (Landfill)	0.52	3.11	1.51	0.34
	‖

Table A6 -

 A6

	null	NA	2	-1501.87	--	NA	NA	NA
	full	NA	9	-1450.83	--	NA	NA	NA
	two-stage	b	5	-1457.2	no	4.22	NA	NA
	log-logist	b	5	-1454.87	yes	3.73	3.31	4.2
	Weibull	b	5	-1456.78	no	4.15	NA	NA
	log-prob	b	5	-1453.14	yes	3.51	3.14	3.92
	gamma	b	5	-1455.02	yes	3.87	3.46	4.33
	LVM: E3-	a	5	-1465.47	no	4.44	NA	NA
	LVM: H5-	b	6	-1452.73	yes	2.74	2.18	3.57
	BMR: 0.2 Extra Risk
	P-value (goodness-of-fit): 0.05
	Model	Covariation	npar Log-likelihood Accept	BMC	BMCL	BMCU
				XXXII

2 Fitted models to the dose-response macrophage data 283 with rats and mice as a possible covariate

Table A6 - 3

 A63 Fitted models to the dose-response neutrophil data 283 with rats and mice as a possible covariate

	Model	Covariation	npar Log-likelihood Accept	BMC	BMCL	BMCU
	null	NA	2	-1420.53	--	NA	NA	NA
	full	NA	9	-1370.09	--	NA	NA	NA
	two-stage	ab	6	-1373.14	yes	4.37	4	4.9
	log-logist	ab	6	-1370.87	yes	3.89	3	4.39
	Weibull	ab	6	-1373.02	yes	4.34	4	4.87
	log-prob	ab	6	-1368.35	yes	3.64	3	4.05
	gamma	ab	6	-1370.84	yes	4.02	4	4.51
	LVM: E4-	a	5	-1371.54	yes	3.48	NA	NA
	LVM: H5-	a	6	-1369.86	yes	3.21	3	3.39
	BMR: 0.2 Extra Risk
	P-value (goodness-of-fit): 0.05

Table A6 - 4

 A64 BMCa results (mg/m3) and models fit for a 20% increase in neutrophil count in mice dose-response data.

	Model	Two-Stage Log-logistic Weibull Log-probabilistic Gamma EXP4 Hill5 Aggregation of Models
	Median	4.26	3.81	4.25	3.56	3.89	3.00 3.17	3.76
	Mean	4.28	3.82	4.26	3.57	3.90	3.02 3.11	3.71
	Standard Deviation	0.29	0.27	0.29	0.24	0.25	0.21 0.49	0.56
	Minimum	3.26	2.99	3.38	2.84	3.03	2.42 2.11	2.11
	Maximum	5.48	5.17	5.54	4.69	5.08	4.24 4.58	5.54

Table A6 - 5

 A65 BMCa results (mg/m3) and models fit for a 20% change in macrophage count in mice dose-response data.

	Model	Log-Log Log-probabilistic	Gamma	Hill5	Aggregation of Models
	Median	4.26	3.81	4.25	3.56	3.89
	Mean	4.28	3.82	4.26	3.57	3.90
	Standard Deviation	0.29	0.27	0.29	0.24	0.25
	Minimum	3.26	2.99	3.38	2.84	3.03
	Maximum	5.48	5.17	5.54	4.69	5.08

Table A6 - 6

 A66 BMCa results (mg/m 3 ) and models fit for a 20% change in macrophage count in rat dose-response data.

	ES1			ES2
	ES3			ES4
	ES5			ES6
	ES7			ES8
	Model	Log-Log Log-probabilistic	Gamma	Hill5	Aggregation of Models
	Median	3.73	3.52	3.87	2.66	3.60
	Mean	3.74	3.53	3.89	2.73	3.47
	Standard Deviation	0.26	0.23	0.25	0.50	0.56
	Minimum	2.94	2.74	3.06	2.12	2.12
	Maximum	4.88	4.62	5.01	4.48	5.01
	ES9			ES10
				XXXV

Results: Monte Carlo simulations for the risk characterization ratios

  Distributions of the exposure concentrations (mg/m 3 ) per exposure scenario Results of the global sensitivity analysis that provide the percent contribution (x-axis) of each parameter that was used in estimating the risk characterization ratio. EF(inter): extrapolation factor used to extrapolate the BMCanimal to BMChuman. EF(intra): extrapolation factor used to extrapolate between different persons in the human population. Exposure: the resulting nano-TiO2 concentrations per exposure scenario.IntroductionL'énergie est une problématique fondamentale de la société au 21 ème siècle, et au centre des enjeux de durabilité. Au cours des deux derniers siècles, l'énergie a été le facteur le plus influent sur la modification de la société. Le développement et l'utilisation des combustibles fossiles ont stimulé une importante croissance de l'industrie et de la population mondiale ainsi que des réalisations technologiques à une échelle jamais vue avant la révolution industrielle. Cependant, cela s'est fait au détriment d'importants coûts environnementaux et de santé publique. Le monde est à présent entré dans une ère de pic pétrolier et les émissions de gaz à effet de serre d'origines anthropiques produisent des modifications environnementales majeures et problématiques telles que le changement climatique. Par conséquent, la recherche et le développement de nouvelles sources durables pour répondre aux demandes énergétiques actuelles et futures est un enjeu majeur du 21 eme siècle. Face à la nécessité urgente de diminuer les émissions de gaz à effet de serre, les sources d'énergie renouvelable comme le PV ont un rôle important dans la stratégie mondiale d'approvisionnement en énergie.D'abord coûteuses et de faible efficacité, les cellules PV à base de silicium 10 ont rapidement mûri pour atteindre des rendements de 20% au milieu des années 1980. A l'échelle du laboratoire, le rendement a été augmenté au -delà de 25% pour la plupart des cellules à base de silicium.11 Des records de rendement en laboratoire ont été atteint avec plus de 45 % de conversion de la lumière 90% de la production mondiale de PV par an. 12 L'utilisation du PV à base de silicium engendre des bénéfices environnementaux important et une meilleure gestion des ressources par rapport aux systèmes de production électriques classiques. Des études antérieures ont montré, par exemple, que les émissions de gaz à effet de serre par kWh d'électricité produit à l'aide de PV à base de silicium sont cinq fois plus faibles que la production à base de charbon et plus de deux fois plus faibles que la production à base de gaz naturel13 . En outre, les quatre dernières décennies de développement du PV sont marquées par la recherche et le développement d'une large gamme de technologies viables, allant du PV classique à base de silicium monocristallin et multi-cristallin (m-Si) aux technologies de deuxième génération, telles que le silicium amorphe (a-Si), le cadmium-tellurure et le cadmium-indium-gallium-sélénium. Depuis la fin des années 1990, les financements de R&D ont permis de développer les technologies dites de troisième génération. Cette génération de PV est beaucoup plus diversifiée par rapport aux générations précédentes et comprend notamment des cellules photovoltaïques sensibilisées par colorant, avec de la perovskite, des points quantiques ou bien des cellules photovoltaïques organiques (PVO). de trous, et des couches de revêtement barrière pour la protection. Cependant, il n'y a pas de géométrie PVO ni de composition "standard" les PVO étant encore une technologie en plein développement. Ceci étant dit, le PVO a de nombreuses caractéristiques qui en font un choix convaincant pour un développement ultérieur dans le domaine de l'énergie solaire. Ainsi, les cellules photovoltaïques sont extrêmement minces, flexibles, nécessitent de faibles quantités de matériaux et des processus de production à faible consommation énergétique[START_REF] Krebs | Upscaling of polymer solar cell fabrication using full roll-to-roll processing[END_REF] . L'objectif général de cette thèse est de vérifier si le PVO est préférable pour l'approvisionnement en énergie par rapport au PV classique à base de silicium Une très brève introduction est fournie ici, une explication plus détaillée de son utilisation et des méthodes se trouvant dans le chapitre 2. L'ACV est considérée comme un outil essentiel d'évaluation en raison de sa capacité à suivre l'ensemble de l'énergie utilisée à chaque étape du cycle de vie du système. Cette information peut être utilisée pour déterminer combien de temps il faudra au système, pendant son utilisation, pour générer l'énergie qui a été consommée pendant la production de ce même système. Ceci est connu comme le temps de retour énergétique (TRE)[20][21][22] . Des mesures similaires telles que le temps de retour carbone (TRC) peuvent également être calculées puisque l'ACV permet le suivi de flux de matières non énergétiques tels que l'émission de gaz à effet de serre tout au long du cycle de vie. Le TRE et le TRC sont des paramètres essentiels auxquels tous les systèmes de Par exemple, une étude peut être définie de porte à porte (gate-to-gate) si l'évaluation ne comprend qu'une étape du cycle de vie (par exemple, la fabrication d'un produit), du berceau à la porte de l'usine si l'évaluation inclut l'extraction de la matière première jusqu'à la fabrication du produit, et du berceau à la tombe si l'évaluation comprend chaque étape du cycle de vie. De plus, l'identification et le choix des critères d'évaluation de l'impact sur l'environnement et la santé humaine doivent être réalisés dans le cadre de la définition de l'objectif et du cadre de l'étude. De nombreuses fois, les données d'inventaire peuvent être obtenues à partir de bases de données d'inventaire de cycle de vie préexistantes telles que Ecoinvent qui est la base de données la plus utilisée pour les études d'ACV. L'évaluation de l'impact du cycle de vie implique la conversion des données d'inventaire du cycle de vie en impacts sur l'environnement et la santé humaine, parfois appelées impacts sur le cycle de vie. Ces effets peuvent comprendre l'acidification de la terre et de l'eau, le potentiel de changement climatique, le potentiel d'épuisement des ressources, l'écotoxicité, l'eutrophisation des cours d'eau, la toxicité humaine, le potentiel de rayonnement ionisant, les changements d'affectation des terres, le potentiel d'appauvrissement de la couche d'ozone, la formation de particules, et le potentiel de formation d'oxydant photochimique (smog). L'étape finale d'une ACV est l'interprétation utilisée pour (a) comprendre et établir les liens logiques entre les résultats de l'analyse d'impact du cycle de vie, l'inventaire du cycle de vie et la définition de l'objectif / du champ d'application, mais aussi (b) identifier les anomalies et erreurs qui peuvent se présenter dans chacune de ces étapes. Enfin, l'ACV ne quantifie pas les impacts absolus sur l'environnement ou la santé humaine. Au lieu

	9.91E-09 4.36E-03 9.91E-09 4.04E-03 Appendix: Chapter 6 Probability Probability Probability Probability ES11 ES13 ES15 ES17 Figure A6-3 4.36E-04 Probability 1.06E-06 Probability 5.79E-02 Probability 1.18E-07 Probability -100.0% -50.0% EF(inter) EF(intra) Exposure Other -100.0% -50.0% EF(inter) EF(intra) Exposure Other -100.0% -50.0% EF(inter) EF(intra) Exposure Other -100.0% -50.0% EF(inter) EF(intra) Exposure Other -100.0% -50.0% Appendix: Chapter 6 Other photovoltaïques classiques dans le sens où elles sont composées des mêmes composants principaux 3.43E-06 1.35E-03 4.67E-01 Probability 1.51E+00 3.68E-06 1.27E-03 Probability 3.43E-06 1.81E-01 6.28E+01 Probability 1.40E+00 4.95E-07 1.71E-04 Probability ES12 ES14 ES16 ES18 1.51E-01 1.27E-03 4.40E-01 Probability 3.68E-04 5.00E-09 1.73E-06 Probability 2.00E+01 7.85E-04 2.72E-01 Probability 4.07E-05 1.52E-09 5.26E-07 Probability ES1 ES2 ES3 ES4 ES5 ES6 ES7 ES8 43.5% 29.5% 26.3% 0.7% 0.0% 50.0% 100.0% 43.8% 30.5% 25.0% 0.6% -100.0% -50.0% 0.0% 50.0% 100.0% EF(inter) EF(intra) Exposure Other 43.5% 29.9% 26.0% 0.7% 0.0% 50.0% 100.0% 43.9% 29.5% 25.7% 0.8% -100.0% -50.0% 0.0% 50.0% 100.0% EF(inter) EF(intra) Exposure Other 43.0% 30.5% 25.5% 1.0% 0.0% 50.0% 100.0% 42.9% 31.3% 25.3% 0.5% -100.0% -50.0% 0.0% 50.0% 100.0% EF(inter) EF(intra) Exposure Other 43.1% 30.3% 26.1% 0.5% 0.0% 50.0% 100.0% 43.5% 30.2% 25.5% 0.8% -100.0% -50.0% 0.0% 50.0% 100.0% EF(inter) EF(intra) Exposure Other 0.0% 50.0% 100.0% -100.0% -50.0% 0.0% 50.0% 100.0% ES11 ES12 ES13 ES14 ES15 ES16 ES17 ES18 0.7% 0.6% Other comme deux électrodes et une couche active (i.e. où la séparation de charge se produit). Contrairement à d'autres technologies photovoltaïques, la couche active d'une cellule PVO est composée de couches de matériaux organiques donneurs d'électrons (semi-conducteur n) et accepteur d'électrons (semi-technologies photovoltaïques organiques potentielles ? 2) Comment les impacts environnementaux des technologies photovoltaïques organiques se comparent-ils aux photovoltaïques traditionnels à base de silicium ? 3) Quelles sont les limites actuelles de l'utilisation de l'évaluation du cycle de vie pour évaluer les Figure A6-5 44.4% 29.4% 25.4% 0.8% -100.0% -50.0% 0.0% 50.0% 100.0% EF(inter) EF(intra) Exposure Other 42.1% 31.4% 25.5% -1.0% 0.0% -100.0% -50.0% 0.0% 50.0% 100.0% EF(inter) EF(intra) Exposure BMD Rat Other 42.1% 31.2% 26.2% 0.5% -100.0% -50.0% 0.0% 50.0% 100.0% EF(inter) EF(intra) Exposure Other 43.3% 30.1% 25.8% 0.8% -100.0% -50.0% 0.0% 50.0% 100.0% EF(inter) EF(intra) Exposure Other 43.2% 30.3% 25.8% 0.6% -100.0% -50.0% 0.0% 50.0% 100.0% EF(inter) EF(intra) Exposure Other 42.8% 30.7% 25.4% -1.1% 0.0% -100.0% -50.0% 0.0% 50.0% 100.0% EF(inter) EF(intra) Exposure BMD Rat Other 43.0% 29.4% 27.0% -100.0% -50.0% 0.0% 50.0% 100.0% EF(inter) EF(intra) Exposure 43.1% 31.7% 24.6% -100.0% -50.0% 0.0% 50.0% 100.0% EF(inter) EF(intra) Exposure 0 32.7 21 M 200 1 200 0 0 0 15.5 21 M 200 1 200 0 0 0.5 381 21 M 200 0.995 199 0 0 0.5 335 21 M 200 1 200 0 0 0.5 349 21 M 200 0.995 199 0 0 0.5 361 21 M 200 0.99 198 0 0 0.5 365 21 M 200 1 200 0 0 2 1411 21 M 200 0.995 199 0.005 1 2 1458 21 M 200 1 200 0 0 2 1411 21 M 200 1 200 0 0 2 1400 21 M 200 0.995 199 0.005 1 2 1299 21 M 200 1 200 0 0 10 10860 21 M 200 0.8 160 0.195 39 10 9776 21 M 200 0.865 173 0.125 25 10 11080 21 M 200 0.915 183 0.07 14 10 10306 21 M 200 0.775 155 0.21 42 10 10540 21 M 200 0.875 175 0.125 25 0 42.4 21 R 200 1 200 0 0 0 35.3 21 R 200 0.995 199 0.005 1 0 35.5 21 R 200 1 200 0 0 0 38.6 21 R 200 0.99 198 0.01 2 0 31.4 21 R 200 0.99 198 0.005 1 0.5 423 21 R 200 0.995 199 0.005 1 0.5 454 21 R 200 0.985 197 0.01 2 0.5 483 21 R 200 0.99 198 0.005 1 0.5 382 21 R 200 0.99 198 0.005 1 0.5 380 21 R 200 1 200 0 0 2 1620 21 R 200 0.91 182 0.085 17 2 1769 21 R 200 0.97 194 0.03 6 2 1772 21 R 200 0.96 192 0.035 7 2 1605 21 R 200 0.87 174 0.13 26 2 1660 21 R 200 0.95 190 0.045 9 10 10211 21 R 200 0.235 47 0.73 146 10 12383 21 R 200 0.4 80 0.585 117 10 10110 21 R 200 0.365 73 0.625 125 10 11440 21 R 200 0.315 63 0.66 132 10 10793 21 R 200 0.35 70 0.64 128 Résumé Détaillé Bien que les technologies photovoltaïques captent le rayonnement solaire disponible et le transforment en une forme d'énergie plus utile (c.-à-d. l'électricité), des charges environnementales (des émissions de gaz à effet de serre par exemple) peuvent survenir pendant la production, l'utilisation ou l'élimination du panneau PV. Questions abordées dans cette thèse: 1) Quelle est la gamme complète des impacts environnementaux du berceau à la tombe des représente plus de démonstration 15 . Conceptuellement, les cellules PVO ne sont pas très différentes des technologies Résumé Détaillé couches de transport Objectif de cette thèse Résumé Détaillé Analyse du cycle de vie L'utilisation de l'ACV est l'élément central de l'évaluation environnementale des systèmes liés à l'énergie. L'ACV est un outil de gestion de l'environnement décrit par la norme ISO 14040:2006 et 14044:2006. 18,19 Résumé Détaillé frontières du système.
	Probability EF(inter) EF(intra) conducteur p), d'où le nom de cette technologie. De plus, pour atteindre leur extrême finesse, les 43.3% 29.4% 44.5% 30.3% EF(inter) EF(intra) dispositifs PVO utilisent généralement des nanomatériaux fabriqués (NMF) comme composants technologies photovoltaïques organiques et autres technologies émergentes ? 4) Quels sont les outils disponibles pour traiter les risques pour la santé humaine et les risques pour Probability 26.4% Exposure 24.6% Exposure principaux des couches actives et additionnelles. Les cellules PVO peuvent nécessiter des composants les nanomatériaux fabriqués ?
	0.8% 5) Comment ces impacts sur la santé humaine des nanomatériaux artificiels peuvent-ils être intégrés Other 0.6% Other
	4.95E-09 dans les méthodologies d'évaluation du cycle de vie ? 1.71E-06 5.00E-09 ES9	ES10	1.73E-06
	XXXVI XXXIX XL XLVIII LI LII LIII LIV

4

Dans ce contexte, la durabilité n'est pas simplement une question d'énergie, mais aussi une question d'environnement et de santé publique. Le coût réel d'une source d'énergie ne peut pas être quantifié uniquement en fonction de ses coûts d'extraction, de raffinage et de distribution, mais il doit également tenir compte des « externalités » ou des coûts indirects liés à ses dommages environnementaux. Ce point de vue global est nécessaire pour éviter un transfert d'impacts, comme cela peut être le cas avec les biocarburants (qui génèrent une utilisation des terres et une consommation d'eau douce accrues)

[5][6][7]

ou pour l'utilisation de l'énergie solaire avec des panneaux photovoltaïques (PV) qui génèrent une augmentation de l'utilisation de métaux.

9

en électricité en utilisant par exemple des cellules de gallium, d'indium et d'arsenic. Les cellules à base de silicium sont encore la technologie photovoltaïque la plus largement utilisée dans le monde, en particulier à grande échelle et pour les systèmes placés sur les toits. Elle L'objectif de cette thèse repose sur les technologies de 3 ème génération qui utilisent des matériaux organiques dans leurs couches actives au lieu de composés inorganiques tels que le silicium. La première technologie PVO a été développée par CW Tang en 1979 avec un dispositif organique d'hétérojonction 14 . Ce premier dispositif utilisait une couche active de phtalocyanine de cuivre et d'un dérivé de pérylène, un carbone polycyclique. Cette technologie est restée principalement une technologie de laboratoire, avec de petits projets pilotes (www.infinitypv.com) en ligne seulement depuis 2015 et le déploiement à grande échelle de panneaux solaires PVO déployés sur le terrain à des fins de recherche et de supplémentaires tels qu'un substrat sur lequel tous les autres composants sont déposés ainsi que des couches pour améliorer l'efficacité et la séparation des charges, telles que le transport d'électrons et les d'un point de vue environnemental et de santé humaine. Un objectif sous-jacent de la thèse est de vérifier que les méthodologies existantes, couramment utilisées pour évaluer la performance environnementale des systèmes d'approvisionnement en énergie, comme l'analyse du cycle de vie (ACV), sont appropriées. Cette thèse aborde également les questions concernant les limites actuelles de l'utilisation de l'ACV pour évaluer les impacts associés au PVO, tels que les impacts potentiels sur la santé humaine associés à l'utilisation de nanomatériaux, et de quelle façon et avec quels outils cette question peut-elle être traitée pour informer de manière constructive le développement de cette technologie. production d'énergie sont discriminés. L'ACV se compose de (1) l'objectif et la définition du champ d'application, (2) l'inventaire du cycle de vie, (3) l'évaluation d'impact du cycle de vie et (4) les étapes d'interprétation.

Le but et le champ d'étude définissent ce qui est étudié dans l'ACV et incluent la définition de l'unité fonctionnelle et des limites du système. L'unité fonctionnelle décrit une fonction remplie, telle qu'un processus (une régénération de solvant), un produit (un panneau solaire) ou un événement (une restauration environnementale). L'unité fonctionnelle est directement liée aux limites du système, ce qui clarifie davantage les aspects de l'unité fonctionnelle qui sont ou non inclus dans l'évaluation. Un aspect important des limites du système est l'identification des phases du cycle de vie contenues dans l'étude.

La deuxième étape d'une ACV, l'inventaire du cycle de vie, consiste à identifier, quantifier et regrouper tous les flux de matières, d'énergie et de déchets contenus dans l'unité fonctionnelle dans les limites des de cela, l'ACV utilise des modèles généralisés qui fournissent un impact potentiel, relatif. Ceci est en contraste avec certaines autres analyses d'impact telles que l'évaluation des risques pour la santé humaine (ERSA)

23

et de l'évaluation des risques environnementaux (ERA).

24

Dans ces approches, les substances individuelles contenues dans un produit sont identifiées et évaluées par leur probabilité d'exposition dans une population dans des conditions environnementales distinctes et spécifiquement définies.

  Toutefois, en raison d'un coût plus faible, d'une plus grande abondance et à un degré moindre de sa toxicité,[31][32][33] l'oxyde d'étain dopé au fluor (FTO) a été utilisé. Le FTO était un remplacement convenable en raison de qualités favorables telles que sa grande disponibilité, des prix du marché inférieurs et son efficacité relative comme un oxyde conducteur transparent par rapport au ITO30,34,35 . Le FTO a également le potentiel d'être imprimé33 sur un substrat flexible. de trous couramment utilisée dans les cellules PVO, mais il a été démontré que ce matériau a une faible stabilité dans des conditions ambiantes37,38 . Au lieu de cela, le matériau MoO 3 a été choisi pour la couche de transport de trous en raison de sa bonne stabilité, de son absence d'interférence avec le profil d'absorption de la lumière de la couche active (i.e. pas d'interférence avec le profil 350-700 nm d'absorption de la lumière de la couche active) et la compatibilité avec le dépôt et le traitement à base de solution39 .

	Résumé Détaillé
	Les substrats des premiers dispositifs PVO étaient des structures initialement rigides telles que le verre,
	mais il est devenu commun et technologiquement possible d'imprimer les composants d'une cellule de
	PVO directement sur un substrat flexible 16,26 . Le substrat utilisé dans ce travail était du polyéthylène
	téréphtalate (PET), qui est réputé pour être un matériau particulièrement efficace pour les gros
	volumes d'impression roll-to-roll de panneaux de PVO 26 . Immédiatement adjacente au substrat se situe
	la couche (avant) transparente d'anode, vers laquelle les porteurs de charge positifs (i.e. les trous)
	voyagent.
	Les anodes transparentes couramment utilisés sont constituées d'ITO (oxyde d'indium et d'étain), compte
	tenu de leur	niveau	élevé	de	transparence	optique	et de	leur conductivité
	électrique 30 .
							-phényl-C61-butanoate de méthyle (PCBM,
	un fullerène C 60 modifié) et le poly(3-hexylthiophène) (P3HT). Le fullerène C 60 a suscité le plus d'intérêt
	dans la recherche	PVO	comme		semi -conducteur	organique	de	"type	n"	(i.e.
	accepteur d'électrons) en raison de sa forte électronégativité et de sa mobilité électronique
	élevée. Plus précisément, le PCBM est largement utilisé, souvent en conjonction avec le polymère P3HT
	de "type p" (i.e. donneur d'électron) 25-28 . Pendant la séparation de charge dans la couche active, il y a
	création d'un porteur de charges à la fois positive et négative, ce dernier étant appelé "trou" ou absence
	d'un électron dans un atome. Le courant est créé lorsque la charge négative est autorisée
						LV

à se déplacer à travers le PCBM et à travers la cathode (arrière) tandis que les trous se propagent simultanément à travers le P3HT jusqu'à l'anode transparente

29 . 36

Comme indiqué précédemment, la couche active de cellules PVO est constituée de matériaux organiques donneurs et accepteurs. L'hétérojonction en vrac est une morphologie d'entrelacement des matériaux donneurs et accepteurs qui maximise l'opportunité de séparer les charges. Pour éviter la recombinaison des porteurs de charge mobiles dans la couche active et augmenter l'efficacité, les cellules PVO reçoivent souvent une couche de transport de trous et une couche de transport d'électrons. PEDOT:PSS est une couche de transport

  . Cependant, un déploiement à grande échelle de cellules PVO ont déjà montré des rendements de près de 2%15 . En outre, des rendements à l'échelle du laboratoire de l'ordre de 15% ont été rapportés dans la littérature pour les dispositifs à base de PCBM semblables à ceux qui sont considérés dans cette thèse48 . En règle générale, au fur et à mesure que les technologies photovoltaïques se développent, le passage d'une échelle laboratoire à une échelle traitement des matériaux, la fabrication et le transport pour chaque conception de cellules solaires. L'ACV a été conduite dans openLCA v1.4.2 (GreenDelta, Berlin). L'analyse d'impact a été réalisée en utilisant les catégories d'impact de ReCiPe (H) v1.0.550 , USEtox (www.usetox.org) pour les indicateurs de santé humaine et d'écotoxicité de l'eau douce et enfin la demande cumulée d'énergie

	Résumé Détaillé
	premières, le
	industrielle pourrait faire diminuer le rendement de 20% à 50% 49 . Ainsi, l'hypothèse de travail concernant
	le rendement représente un objectif à court terme optimiste mais réaliste.
	LVII

Les cellules PVO ont ensuite été comparées avec des cellules PV m-Si classique et des cellules PV a-Si. Pour chaque option technologique, les flux environnementaux ont été définis pour l'extraction des matières

Wp: watt-peak | Eg: energy generated | CED: cumulative energy demand | EPBT: energy payback time | CCE: cumulative CO2-equivalent emissions | CPBT: carbon payback time

This thesis took particular focus on the human health assessment modeling, however the use of ecological risk assessment, its data and methodological gaps, and its synergisms with life-cycle assessment also apply in the environmental (non-human health context)

Acknowledgements

A tremendous amount of gratitude is owed to the many people who were involved in and around my Ph.D. I, first, would like to thank my co-advisors Professors Guido W. Sonnemann and Dario M. Bassani

Chapter 8 Conclusions and Perspectives

The future of energy production and development of sustainable technology options are primed to be, arguably, the most important issues in the 21 st -century. In the face of mounting climate change and public health impacts, energy must be evaluated as a sustainability issue addressing not only economic impacts but also impacts on society and the environment. For instance, past experiences with biofuels have demonstrated that when objectives are limited to simply finding alternative or renewable energies, environmental burden shifting can occur. [5][6][7][8] The introduction of modern, conventional forms of PV systems in the mid-20 th century offered one option of producing electricity from limitless amounts of energy from the sun. Moreover, PV are basically an emission free and environmentally benign technology while it is being used, however there are environmental burdens (e.g. greenhouse gas emissions) that may occur during the production, use or disposal of the PV. Particularly for conventional silicon-based PV, the processing of silicon into semi-conducting wafers and entire panels is a fairly energy intensive process.

Consequently, the research and development into alternative PV technologies that are less energy intensive and environmentally preferable is an ongoing endeavor. In this regard, OPV have captured the imaginations of many researchers since it's a very thin, flexible technology requiring much fewer amounts of materials per Wp of power production. However, OPV are also known to have much lower conversion efficiencies and lower lifetimes compared with silicon-based PV. Therefore, the overall objective of this thesis was to demonstrate whether OPV have proven themselves to be an environmentally preferable energy supply option compared with conventional silicon PV.

The Methodological Options Presented in this Thesis

Implicit to the overall objective of the thesis was the question whether existing methodologies commonly used to evaluate the environmental performance of energy supply systems are fully compatible for carrying out such evaluations. In Chapter 1, a review of the literature up to the end of 2013 was made on the environmental performance of OPV. Central to the environmental evaluation of energy-related systems has been the use of LCA because of it facilitates the calculation of the EPBT (i.e. the time it takes to produce the same amount of energy that was consumed in creating the technology). However, LCA also facilitates the calculation of additional environmental and human health impacts of products.

Therefore, Chapter 3 and Chapter 4 employed the use of LCA to evaluate prospective OPV devices, their uses and their end-of-life options. 62 This would amount to nearly 325 MJ consumed for the recovery of the PET. The assumption can be made that this is directly consumed in a secondary PET production process and that this process consumes only half of the energy (1066 MJ) of primary production for the 13 kg of PET consumed in the functional unit, just for illustration, then a total 858 MJ (208 MJ savings) of energy would be consumed in order to recycle the PET.

XIV

Appendix: Chapter 6

XXX

Appendix: Chapter 6 XXXIV Currently, monitoring data on ENM-emission characteristics and/or source strengths are slowly emerging for some nano-TiO2 occupational ES, such as powder handling 199,242,323 and simulated sanding 372,373 . However, most current measurement devices are unable to distinguish ENMs from background natural or incidental nano aerosols 374 . Although there are powerful instruments available to measure actual workplace concentrations to ENMs 199,312,366 , such options are not always available due to technical and logistical challenges. When this is the case, risk assessors can use models. To demonstrate the feasilbilty of using models to estimate workplace exposure to ENMs, the exposure assessment was completed using an interim, un-released update to the NanoSafer 1.0 control banding exposure algorithm, titled NanoSafer v1.1β. Version 1.1β uses a two-box aerosol model to assess near-field (NF) and far-field (FF) maximum potential exposure without taking into account localized or personal exposure controls 375 (Table ##). For each exposure scenario, a total work-room volume equal to the combined volume of the NF (VNF) and FF (VFF) was defined, where the NF was always defined as 12.17 m 3 and the minimum value of Vtotal has been set to 38 m 3 . The emission source, Ei (mg/min) is described as

Regression Analysis: LOG(SD) versus LOG(DI)resp

The results of 59 particulate powder case tests used to determine the correlation between their dustiness index and standard deviations. Note that 3 cases contained missing values.

Results of the Emissions Distributions

Appendix

Global Sensitivity Analysis: Risk Characterization Results