Prof Emmanuel Cottancin

Prof Gilles

Prof Bernd Lerondel

Prof. Nowack

Prof. Regis Lengelle

Mme Isabelle Leclercq

Mme Pascal Denis

Mme Thé Rè Se Kazarian

Patricia Adnet

Kun Jia

Hongchang Han

Zhongmeng Wen

Weijie Liu

Feng Zhang

Feng Zhu

Yuan Li

Yanya Jin

Tzu-Heng Wu

Hyun Jeong

Josslyn Beltran Madrigal

Kiyan Vadoudi

Tatiana Perminova

Miravo Florian Bratec

Keywords:

Rakotozanany and many other friends in UTT. I could not complete my work without invaluable friendly assistance of these

Time is a river continuously running no matter day and night. When reviewing the past works and the life in France, I feel as if everything happened yesterday. The thesis could never be accomplished without the assistance, support and encourage of a number persons. First of all, I would like to thank the CSC -UT/INSA project cooperated between China Scholarship Council (CSC) and nine excellent grande é coles in France for giving me the opportunity to study in such a beautiful city far from China.

I would like to express my sincerest gratitude to my supervisors, Prof. Elena Rodica Ionescu and Dr. Bertrand Laratte. I greatly appreciate, and truly benefit from their profound knowledge, serious scientific attitudes, as well as the creativity and enthusiasms devoted to research. Thanks to Elena, especially, for her systematic, veteran and insightful guidance and advices on my research. She has been a considerate and supportive adviser to me throughout the 3.5 years of my PhD work, and she always gives me great freedom to pursue independent work. Thanks to Bertrand, in particular, for offering me a great chance to communicate with highly talented people in Research Centre on Environmental Studies & Sustainability (CREIDD) at UTT.

Thanks for his generous supports on my research and living. His dedicated guidance, suggestions and encouragements keep me on the correct path and allow me to explore new directions. I am extremely grateful for having been given great opportunities to attend overseas conferences that undoubtedly inspired my work and broadened my perspectives. The special thanks should be given to Dr. Xavier Gassmann for his kind and long-term assistance in my utilization of the public chemistry lab and to Mr. Anthony Buonanno for his help to collect the blood of pig in an abattoir (SICABA) in very early mornings. Thanks to Prof. Robert S. Marks from Ben-Gurion University of the Negev in Israel for his valuable and critical comments to the published article. He helps me make the complicated USEtox: http://www.usetox.org/. When a study included both ecotoxicity and human toxicity, it was considered as two applications. The records related to <nano> were collected and selected from the same sources as <conventional> but plus Scopus (https://www.scopus.com/). ........... 22 (Sun et al., 2014). PECs in Europe based on the predicted emission for the year (d) 2012 (Sun et al., 2014) and (e) 2014 (Sun et al., 2016)

Context, Motivation and Structure

Context and motivation

Modern nanotechnology has been developed rapidly since its emergence at the beginning of 1980s. As a very broad notion, it has been applied in various fields of science, such as life science [START_REF] Zhang | Nano-bioelectronics[END_REF], environmental science [START_REF] Tesh | Nano-composites for water remediation: A review[END_REF], optics [START_REF] Wen | Fluorescent/magnetic micro/nano-spheres based on quantum dots and/or magnetic nanoparticles: Preparation, properties, and their applications in cancer studies[END_REF]. Engineered nanoparticles (ENPs) are a type of man-made particles that are manufactured at the nano-scale. They usually exhibit special properties compared to their bulk counterparts, such as higher reactivity, increased specific surface area. It is these unique characteristics that make the ENPs become an indispensable part of our everyday life and make us benefit a lot. Over the last decade, the applications of ENPs have grown extensively. It was estimated that the production rate of ENPs will continue to rise in the coming years (Lopes et al., 2014).

The concerns on safety issues on nanotechnology have been raised since the beginning of 21 century [START_REF] Maynard | Nanotechnology: Assessing the risks[END_REF][START_REF] Wiesner | Assessing the risks of manufactured nanomaterials[END_REF]. The wide application of nanomaterials increases the potential exposure of ENPs to ecosystem and human (Gottschalk and Nowack, 2011;Keller and Lazareva, 2014). To investigate the impacts of ENPs, numerous bio-experiments were conducted on various organisms, and there is a great deal of evidences demonstrating the nanospecific toxicity of existing ENPs (Su et al., 2013;[START_REF] George | Differential effect of solar light in increasing the toxicity of silver and titanium dioxide nanoparticles to a fish cell line and zebrafish embryos[END_REF][START_REF] Jia | Influence of carbon-based nanomaterials on lux-bioreporter escherichia coli[END_REF][START_REF] Rosenfeldt | Heavy metal uptake and toxicity in the presence of titanium dioxide nanoparticles: A factorial approach using daphnia magna[END_REF]. Currently, there is a call for the legislation and regulations on nanomaterials (Bumbudsanpharoke and Ko, 2015). Both regulators and industrial sector have difficulties in understanding and managing potential risks resulted from nanomaterials [START_REF] Notter | Are nanosized or dissolved metals more toxic in the environment? A meta-analysis[END_REF]. The main reason can be explained by lack of knowledge about the impacts of nanomaterials on human and ecosystem.

The aim of toxicity testing should be collecting the appropriate results from test systems in order to forecast and prevent the risks to human and ecosystem (Bhattacharya et al., 2011). The development of toxicity testing tends to reduce the toxicity tests made on living animals (NRC, 2007; Berg et al., 2011). However, no guideline is available for extrapolating the in vitro results to human health effects (Pini et al., 2016). In addition, the toxicological data on ENPs remain poorly and unevenly distributed (Bondarenko et al., 2013). The lack of a database comprising all the toxicological data of various ENPs makes the life cycle impact assessment extremely hard (Pini et al., 2016;Pu et al., 2016).

Except for the inherent, more or less, toxicity of ENPs, the persistence time of ENPs in environment [START_REF] Adam | Characterization of engineered TiO 2 nanomaterials in a life cycle and risk assessments perspective[END_REF]Deng et al., 2016) and the routes of exposure to organisms (Caballero-Guzman and Nowack, 2016; Shakeel et al., 2016) can also affect the risks of ENPs.

Compared to their bulk counterparts, ENPs show many different fate behaviors (e.g. no degradation) in freshwater because of their nano-specific properties (such as low solubility, low vapor pressure) (Praetorius et al., 2012). The prior methods designed for modeling fate of conventional chemicals, thus, do not necessarily assess the behavior of ENPs in freshwater. In addition, the spatial differentiation of the fate of ENPs also requires to be addressed (ILCD, 2011; Quik et al., 2015;[START_REF] Meesters | Multimedia environmental fate and speciation of engineered nanoparticles: A probabilistic modeling approach[END_REF].

The general objective of this thesis is to advance the knowledge on the toxicity of ENPs to human and ecosystem. It is realized by the following steps:

(1) Conduct conventional in vitro experimentations to obtain the firsthand dose-response phenomena and toxicological data.

(2) Develop a fate model of ENPs in freshwater ecosystem and recommend regionalized input parameters to predict the persistence time of ENPs in different freshwaters.

General framework of this thesis

The structure of the thesis is shown in Figure 0. This thesis consists of five chapters. Chapter 1 presents background information on nanotechnology, engineered nanoparticles (ENPs), quantification of toxicity. It points out that the characterization factor (CF), as a toxicity indicator for human and ecosystem, is important in life cycle impact assessment (LCIA). The USEtox model is recommended for calculating the CFs of conventional chemicals by many organizations such as European Commission. However, some gaps remain before applying the model to ENPs. This chapter demonstrates the lack of methods on coordinating in vitro experimentations and human health effects. The second challenge is modeling the behaviors of ENPs in freshwater ecosystem with consideration of different hydrological properties all over the world. The necessity of collecting ecotoxicological data of various ENPs to aquatic species is also illustrated in this chapter.

Chapter 2 attempts to conduct conventional in vitro experimentations to obtain the firsthand dose-response phenomena and toxicological data. As an abundant type of white blood cell in mammals, the neutrophils are employed. In practice, the neutrophils isolated from pigs are exposed to three kinds of ENPs (i.e. copper, nickel and aluminum oxide nanoparticles) of different concentrations. During exposure, at certain time intervals, cells counting and chemiluminescence (CL) measurements were performed. The morphology of exposed neutrophils and ENPs was characterized by scanning electron microscope (SEM). The toxicological data of copper nanoparticles to porcine neutrophils are also reported.

Chapter 3 aims at developing a fate model to predict the persistence time of ENPs in different freshwaters. The fate model takes nano-specific behaviors of ENPs in freshwater into account and includes recommendations of regionalized hydrological parameters. Then, the functionality of the fate model is proved by comparing our computed results with the previously reported scenarios in North America, Switzerland, and Europe. The sensitivity analysis of the fate model is also necessary in order to distinguish which input parameters are essential to be focused on or discarded.

Chapter 4 aims to obtain the toxicological CFs consisting of fate factor (FF), exposure factor (XF) and effect factor (EF) for various ENPs by coordinating the toxicological data and environmental model. To do this, the fate mode proposed in Chapter 3 is applied to compute the FFs. According to the results of the sensitivity analysis in last chapter, one input parameters (i.e. the dissolution rate constants of ENPs) are paid more attention to. Human exposure factor (XF h ) and ecosystem exposure factor (XF e ) are estimated. Then, in order to calculate EFs, the toxicological data are extracted from the present in vitro experiments in Chapter 2 and a comprehensive literature survey. Finally, under the framework of USEtox model, the noncarcinogenic human toxicity CFs for copper nanoparticles and ecotoxicity CFs for 14 ENPs are presented.

Chapter 5 summarizes the advances and limitations of the thesis and presents more comprehensive thoughts on the toxicity assessment of nanomaterials.

Chapter 1 General Introduction

he objective of this chapter is to provide background information on engineered nanoparticles (ENPs) and highlight the requirements for the toxicity assessment of ENPs to human and freshwater ecosystem. It points out the importance of characterization factor (CF), as a toxicity indicator, in life cycle impact assessment (LCIA). The USEtox model, a scientific consensus environmental model, is recommended for calculating the ecotoxicological and human toxicological CFs of conventional chemicals by many organizations such as European Commission. However, it is not fully available for ENPs. This chapter demonstrates the lack of methods on coordinating in vitro experimentations and human health effects. The second requirement is to model the behaviors of ENPs in freshwater ecosystem with consideration of different hydrological properties all over the world. The necessity of collecting ecotoxicological data of various ENPs to aquatic species is also illustrated in this chapter.

Brief introduction to nanotechnology

From food packages (Bumbudsanpharoke and Ko, 2015) to biomedicine (Yang et al., 2013a), cosmetics [START_REF] Kabri | Physico-chemical characterization of nanoemulsions in cosmetic matrix enriched on omega-3[END_REF][START_REF] Nohynek | Nano-sized cosmetic formulations or solid nanoparticles in sunscreens: A risk to human health?[END_REF] [START_REF] Vlasov | Silicon cmos-integrated nano-photonics for computer and data communications beyond 100g[END_REF]; [START_REF] Kempa | Semiconductor nanowires: A platform for exploring limits and concepts for nano-enabled solar cells[END_REF], etc., nanotechnology plays an important role in our daily life. The International Organization for Standardization (ISO) defines nanotechnology as: -The study and application of scientific knowledge to manipulate and control matter predominantly in the nanoscale (length range approximately from 1 nm to 100 nm) to make use of size-and structure-dependent properties and phenomena distinct from those associated with individual atoms or molecules, or extrapolation from larger sizes of the same material‖ (ISO, 2015b).

Modern nanotechnology began in 1981 with the development of the scanning tunneling microscope (STM) and the subsequent atomic force microscope (AFM) that provided visualization of individual atoms and bonds [START_REF] Binnig | Atomic force microscope[END_REF][START_REF] Binnig | Scanning tunneling microscopy-from birth to adolescence[END_REF], just 7 years after the term -nanotechnology‖ had been coined by Professor Norio Taniguchi [START_REF] Taniguchi | On the basic concept of nanotechnology[END_REF]. The ideas and concepts of nanotechnology were created in fact much earlier.

They are usually considered to be proposed by physicist Richard Feynman in a talk entitled -There's Plenty of Room at the Bottom‖ at an American Physical Society meeting in December 1959 [START_REF] Feynman | There's plenty of room at the bottom[END_REF].

Although modern nanotechnology has been developed in recent decades, nanomaterials were manufactured and utilized for centuries or even thousands of years in different regions of the world. A famous example is the Lycurgus glass cup, which was probably created in Rome during the 4th century AD [START_REF] Freestone | The lycurgus cup-a roman nanotechnology[END_REF][START_REF] Vajtai | Springer handbook of nanomaterials[END_REF]. The cup is dichroic because of the embedded nanoparticles of gold and silver. It resembles jade with light green color, but it becomes ruby red when light shines through the glass [START_REF] Freestone | The lycurgus cup-a roman nanotechnology[END_REF]. A much earlier example is the use of carbon nanoparticles by human as far back as 40,000 years ago to depict and decorate. These carbon nanoparticles were created by burning the organic matters such as wood and grasses, then mixed with fat for painting [START_REF] Vajtai | Springer handbook of nanomaterials[END_REF].

Engineered nanomaterials (ENMs) and ENPs

In 2011, the European Commission (EC) recommended a definition of nanomaterial. Generally, nanomaterials (NMs) can be considered as having at least one dimension in the nanoscale (length range approximately from 1 nm to 100 nm) (EC, 2011). In addition, the materials with one or more external dimensions below 1 nm should also be understood as NMs, such as fullerences, graphene flakes, and single wall carbon nanotubes [START_REF] Ec | Definition of a nanomaterial[END_REF].

The rapid advancement in the field of nanotechnology is attributed to the excellent properties of nanomaterials (NMs) [START_REF] Gao | Modulating antibacterial immunity via bacterial membrane-coated nanoparticles[END_REF][START_REF] Ghafari | Critical review on eco-efficient ultra high performance concrete enhanced with nano-materials[END_REF]. It is usually considered that the size of NMs is intrinsic to their functions. For example, the silver in nanoscale has longer-term and broader spectrum antimicrobial activities compared with bulk silver [START_REF] Zhang | Nano-bioelectronics[END_REF].

The shape of NMs can also have significant influence on their performance. Yang et al. compared the electrochemical sensing properties of four kinds of carbon materials: carbon nanoonions (0-Dimension), multiwalled carbon nanotubes (1-Dimension), graphite nanoflakes (2-Dimension), and glassy carbon (bulk). The results showed that carbon nano-onions displayed remarkable electrochemical activities for the detection of biologically important molecules (Yang et al., 2016).

Basically, NMs are classified into three kinds by shapes: nanoparticle, nanofibre, and nanoplate.

Nanoparticle is defined by the ISO as: "Object with all external dimensions in the nanoscale where the lengths of the longest and the shortest axes of the nano-object do not differ significantly. If the dimensions differ significantly (typically by more than 3 times), terms such as nanofibre or nanoplate may be preferred to the term nanoparticle‖ (ISO, 2015a). Engineered nanomaterial (ENM) / nanoparticle (ENP) is a kind of nanomaterial / nanoparticle -designed for specific purpose or function‖ (ISO, 2015a).

It is noteworthy that the ENM is not completely equal to manufactured nanomaterial, because there are some non-engineered nanomaterials existing such as nanoparticles from motor vehicle emissions [START_REF] Morawska | Ambient nano and ultrafine particles from motor vehicle emissions: Characteristics, ambient processing and implications on human exposure[END_REF]. Indeed, it is currently unclear if the non-engineered, unintentional nanoparticles should fall under the same regulatory guidelines as other particulates or be included in nano-specific regulatory scrutiny [START_REF] Brame | EHS testing of products containing nanomaterials: What is nano release[END_REF].

Nanoproducts

The product contains manufactured NMs are usually labeled as nanoproduct [START_REF] Boldrin | Environmental exposure assessment framework for nanoparticles in solid waste[END_REF]Caballero-Guzman and Nowack, 2016). In 2013, a database of nanoproducts, Nanodatabase, was established under the collaboration between the Technical University of Denmark, the Danish Consumer Council and the Danish Ecological Council. By 4 October 2016, the number of nanoproducts in Nanodatabase is 2343. All the nanoproducts listed in the Nanodatabase are buyable online or in physical stores in Denmark (Nanodatabase, 2016). Thus, the actual number of nanoproducts available in daily life may be more.

Nanodatabase classifies the nanoproducts into 7 major categories. In each major category, there are several subcategories. According to the CPI, the number of nanoproducts increased more than 30 times from 54 in 2005 up to 1814 in 2014 (Vance et al., 2015). It was estimated that the volume of nanoproducts will continue to rise in the coming years but with a potential limiting factor, because the increasing regulatory scrutiny may slow the development of nanoproducts (Sun et al., 2016).

Release of ENMs in Environment

The wide use of ENMs in commercial products results in the inevitable release of ENMs into environment (Gottschalk and Nowack, 2011;[START_REF] Quadros | Release of silver from nanotechnology-based consumer products for children[END_REF]Keller and Lazareva, 2014).

The release of ENMs can be defined as the liberation of ENMs from their confinements (e.g. a material matrix preventing ENMs from entering natural environment) and subsequent transfer to a particular technical or environmental compartments (Caballero-Guzman and Nowack, 2016).

The technical compartments are possible to be any man-made place structures, such as transport vehicles, houses, swimming pools. The environmental compartments include air, soil and sediment, freshwater and seawater. The route of ENMs releasing into environment can be unintentional or intentional (e.g. the use of nanoscale zero-valent iron in water treatment) [START_REF] Mueller | Application of nanoscale zero valent iron (nzvi) for groundwater remediation in europe[END_REF][START_REF] Fu | The use of zero-valent iron for groundwater remediation and wastewater treatment: A review[END_REF].

It is noteworthy that not only nanoproducts but also some conventional products can release ENMs. Mitrano et al. observed that all (nano)silver-treated textiles became a source of silver nanoparticles during laundering fabrics. The textiles treated with -conventional‖ silver have equal or greater propensity to form silver nanoparticles during washing conditions than those treated with -nano‖-silver [START_REF] Mitrano | Presence of nanoparticles in wash water from conventional silver and nano-silver textiles[END_REF]. This fact requires attention when people calculate the volume of released ENMs and make risk regulations.

ENMs can be released from products throughout their entire life cycle including the manufacture, transport, use and disposal [START_REF] Köhler | Studying the potential release of carbon nanotubes throughout the application life cycle[END_REF][START_REF] Dahlben | Environmental life cycle assessment of a carbon nanotube-enabled semiconductor device[END_REF]Nowack et al., 2013). Both intrinsic and extrinsic factors can affect the release potential of ENMs from nanoproducts. Intrinsic factors include the inherent properties of the ENMs and how they are integrated in products. Extrinsic factors refer to the surroundings where products are processed, used or disposed. In addition, the economic development, law and regulation, consumer preferences, are also considered to have influences on ENMs release (Caballero-Guzman and Nowack, 2016). According to the likelihood of exposure, generally, the agrifood, chemistry/materials, textiles and health sectors should receive more attention and the information and communication technology, security and energy sectors needs less attention [START_REF] Nowack | Analysis of the occupational, consumer and environmental exposure to engineered nanomaterials used in 10 technology sectors[END_REF].

Cases of ENMs release affected by intrinsic factors

The following cases show the different ENMs releases due to different type of products: Köhler et al. tracked two case studies in order to investigate the release of carbon nanotubes (CNT) from nanoproducts [START_REF] Köhler | Studying the potential release of carbon nanotubes throughout the application life cycle[END_REF]. One of the nanoproducts was a rechargeable battery, lithium-ion secondary battery, and another one was synthetic textiles. The CNTs in batteries were loosely embedded in a porous matrix sealed in a casing, while in textiles were dispersed without any protective sealing. A considerable part of CNTs released from nanoproducts were found in both cases. weathering cycles, the total released Si was 2.3% of the SiO 2 in pre-exposed panels. Moreover, it was found that the released Si was mainly present in dissolved form other than in (nano)particulate form.

Cases of ENMs release affected by extrinsic factors

The following two cases show the different releases of ENMs from same products because of the different exposure pathways: Yan et al. investigated the release of silver from nano-silver textiles in three simulated body perspiration fluids (Yan et al., 2012). The results showed that different exposure solutions generated different release amounts and forms of nanosilver. In acid media (pH 3), the released silver was in ionic and sub-micro particulate, while in alkaline media (pH 8.5), majority of the released silver was in ionic form. In salt perspiration (pH 8), the most form of silver was nanoparticulate.

Quadros et al. conducted experiments to assess the release of silver from nanotechnology-based consumer products for children (plush toy, breast milk storage bags, et al.) [START_REF] Quadros | Release of silver from nanotechnology-based consumer products for children[END_REF]. They measured the release of silver (both ionic and particulate) from nanoproducts onto dermal wipes and into various solutions such as tap water, sweat, urine. The silver transferred from products to wipes was between 0.3 and 23 μg•m -2 . For the liquid media, less than 1.5% of the silver mass in products was released into tap water, while up to 38% was released into sweat and urine.

ENMs in freshwater

The wastewater treatment plant (WWTP) and waste incineration plant are important -stations‖ for ENMs release, since majority of the unintentional release of ENMs will be into wastewater or solid waste (Gottschalk and Nowack, 2011). Though much of released ENMs (95% -99%) will retain in the sewage sludge after conventional wastewater treatment processes [START_REF] Barton | Monte carlo simulations of the transformation and removal of Ag, TiO 2 , and ZnO nanoparticles in wastewater treatment and land application of biosolids[END_REF], the rest ENMs will go into wastewater effluent. Moreover, not all the wastewater will be collected and treated due to the limitation of economic development and sometimes the extreme weather or accident (Sun et al., 2014). Based on the data mainly from the year 2012, the European Commission (EC) published the 8 th report on the -Urban waste water treatment directive‖ at European level in 2016 (EC, 2016). According to the report, there were still 2% households in Europe not being connected to a sewerage system and this value had been 5% in 2009-2010. In addition, 9% of the urban wastewater already connected has to meet the performance of a secondary treatment (EC, 2016). It is reasonable to speculate that the proportion of untreated wastewater in underdeveloped regions would be much higher.

According to the European Environment Agency (data from 2007-2009), Markus et al. conservatively estimated that 15% of the ENMs actually enter the surface water in Europe [START_REF] Markus | Modelling the transport of engineered metallic nanoparticles in the River Rhine[END_REF].

Though the release of ENMs is widely recognized, the detection and quantification of ENMs in freshwater are still difficult. In fact, the analysis and characterization of ENMs in complex media (pH, ionic strength, dissolved organic matter, etc.) under laboratory conditions have been well established [START_REF] Li | Effects of water chemistry on the dissolution of ZnO nanoparticles and their toxicity to escherichia coli[END_REF]Nowack et al., 2015). However, the characterization and in particular the quantification of ENMs in natural samples still have many limitations, such as the current low concentrations of ENMs, difficulties in distinguishing the natural materials (e.g. inorganic colloid particles, biogenic debris, algae and bacteria) (Lartiges et al., 2001) from their anthropogenic counterparts (Nowack et al., 2015). Thus, modeling of predicted environmental concentration (PEC) has become a preliminary and popular substitute ( [START_REF] Neal | Titanium in UK rural, agricultural and urban/industrial rivers: Geogenic and anthropogenic colloidal/sub-colloidal sources and the significance of within-river retention[END_REF]. Different concentrations of Ti (0.55 -6.48 μg/L with an average of 2.1 μg/L) were measured in rivers draining from various land types (rural, agricultural, urban and industrial). The comparisons between several rivers indicated an urban/industrial signal which came from either truly dissolved components or very fine colloid emission to the rivers. The ultrafiltration results revealed that 79% of the Ti was nanoparticulate for the rural areas, while 28% for the urban/industrial rivers.

Gondikas et al. successfully detected the TiO 2 nanoparticles in the Old Danube Lake in Vienna, Austria [START_REF] Gondikas | Release of tio2 nanoparticles from sunscreens into surface waters: A one-year survey at the old danube recreational lake[END_REF]. By scanning electron microscopy, it was found that the detected TiO 2 nanoparticles were similar in shape and size with particles from a commercial sunscreen popular in Vienna and with particles from cosmetic grade TiO 2 . Ti based particles increased during the summer season when the amount of sunscreen used was much larger than other seasons.

General Review of the Researches on Toxicity of ENPs

Due to the increasing application of ENPs and their potential exposure to environment, there are serious safety concerns towards ecosystem and human. A large number of researches have demonstrated the higher toxicity of ENPs because of their special properties compared to their bulk counterparts (Heinlaan et al., 2008;[START_REF] Jiang | Bacterial toxicity comparison between nano-and micro-scaled oxide particles[END_REF][START_REF] Xiong | Effects of nano-scale TiO 2 , ZnO and their bulk counterparts on zebrafish: Acute toxicity, oxidative stress and oxidative damage[END_REF][START_REF] Musante | Toxicity of silver and copper to cucurbita pepo: Differential effects of nano and bulk-size particles[END_REF]. For example, Karlsson et al. studied the ability of nano-and micrometer particles of copper oxide (CuO) to cause damage to the human cell line A549. CuO nanoparticles were much more toxic, and one key mechanism might be the specific damage of mitochondria by nanoparticles [START_REF] Karlsson | Size-dependent toxicity of metal oxide particles--a comparison between nano-and micrometer size[END_REF].

The researches on toxicity of nanoparticles can be traced back to as early as when modern nanotechnology began. In 1982, Kante et al. carried out preliminary studies concerning the acute toxicity of placebo polyalkylcyanoacrylate nanoparticles (diameters from ~200 to ~400 nm) used as drug carrier to hepatocytes of rats [START_REF] Kante | Toxicity of polyalkylcyanoacrylate nanoparticles i: Free nanoparticles[END_REF]. These first toxicological data demonstrated that the nanoparticles induced cellular damage only at relatively high concentrations (1% in the culture medium). In 1984, Kreuter et al. studied the toxicity of another kind of nanoparticles used as carriers for drugs, polycyanoacrylate nanoparticles, to hepatocytes of rats [START_REF] Kreuter | Toxicity and association of polycyanoacrylate nanoparticles with hepatocytes[END_REF]. They found hepatocytes did not take up nanoparticles in a significant levels and got similar conclusion as Kante et., 1982 that the toxicity of nanoparticles towards hepatocytes was rather low.

However, the toxicity researches did not go through substantial development before 1991 based on the few scientific papers recorded in Web of Science of Thomson Scientific (Core Collection) (WebofScience, 2016). The reason may be because the studied nanoparticles had not revealed noticeable toxicity in previous studies [START_REF] Kante | Toxicity of polyalkylcyanoacrylate nanoparticles i: Free nanoparticles[END_REF][START_REF] Kreuter | Toxicity and association of polycyanoacrylate nanoparticles with hepatocytes[END_REF]. Figure 1.2 showed the number of records in recent 30 years in Web of Science of Thomson Scientific (Core Collection). From 1991 to 2001, the number of papers concerning on the toxicity of nanoparticles (searched as topic -nanoparticles + toxicity‖) was with an average 8 per year. It is obvious that not only the number of records with topics on -nanoparticles‖ and -nanoparticles + toxicity‖ but also the number of total records increase exponentially. The reason may be that the development of computer and internet accelerate the spread and publication of knowledge. In order to avoid the influences of information technology, the proportions of the records in Figure 1.2 were further calculated and the results are shown in Figure 1.3. For instance, the proportion of -nanoparticles‖ to whole science was calculated via dividing the number of total records in each year by records with topic on -nanoparticles‖. Kahru and Dubourguier considered that there was a lag of almost 10 years till number of nanotoxicological papers started to increase exponentially [START_REF] Kahru | From ecotoxicology to nanoecotoxicology[END_REF].The proportions of researches on -nanoparticles‖ and -nanoparticles + toxicity‖ shown rapidly increasing tendency during past ~20 or ~10 year respectively. It is reasonable to assume that the researches on -nanoparticles‖ and -nanoparticles + toxicity‖ will be continual.

Quantification of Toxicity

Toxicity testing

The approaches to toxicity testing have not changed appreciably since the past 50-80 years. It is still focusing on high-dose studies that measure adverse outcomes (such as hepatotoxicity, cancer, and neurotoxicity) in homogeneous animal populations (Bhattacharya et al., 2011). In vitro study in experimental biology usually refers to the experiments conducted in artificial containers (such as glass tubes) with components of an organism which are isolated from their normal biological context [START_REF] Liu | Carbon nanotubes in biology and medicine: In vitro and in vivo detection, imaging and drug delivery[END_REF][START_REF] Johnston | A review of the in vivo and in vitro toxicity of silver and gold particulates: Particle attributes and biological mechanisms responsible for the observed toxicity[END_REF][START_REF] Khlebtsov | Biodistribution and toxicity of engineered gold nanoparticles: A review of in vitro and in vivo studies[END_REF]. A Vision and a Strategy‖, which considered that it would be a right approach to enable future toxicity testing without animal experimentation (NRC, 2007). There is also a need by the European Commission (EC) regulations, as well as others, to reduce the in vivo toxicity tests made on animals (Berg et al., 2011).

Life cycle assessment

When evaluating the potential impact of nanoproducts (especially the under-design ones) to human, the experimentations fall short of expectation being either insufficient or impractical. Therefore, many assessment methods for human toxicity have been developed to help relieve the pressure of animal experimentation and provide the end-user with a decision-making tool [START_REF] Alvarez | Research priorities to advance eco-responsible nanotechnology[END_REF][START_REF] Gao | Monodisperse hollow silica nanospheres for nano insulation materials: Synthesis, characterization, and life cycle assessment[END_REF][START_REF] Nel | Nanomaterial toxicity testing in the 21st century: Use of a predictive toxicological approach and high-throughput screening[END_REF].

Efforts have constantly been made by manufacturers to improve the environmental performances of products because of the more stringent regulations and market demand (Igos et al., 2014). The environmental implications of ENPs along their whole life cycle on ecosystem and human should be correctly quantified by scientific consensus approaches. Life cycle assessment (LCA), an international standardized methodology (ISO14040, 2006; ISO14044, 2006), is usually acknowledged as the most reliable and advanced approach to assess the impacts of a product or process on environment (ILCD, 2011; Gao et al., 2013; Laratte et al.,

2014).

As early as 1960s and 1970s, the study of environmental impacts of consumer products have come out, which could be considered as the early environmental life cycle assessment (LCA) [START_REF] Guinee | Life cycle assessment: Past, present, and future †[END_REF]. Full-fledged life cycle impact assessment emerged in the 1980s and 1990s, and LCA has developed fast over the last two decades [START_REF] Gavankar | Life cycle assessment at nanoscale: Review and recommendations[END_REF]. LCA is presented with its four main steps, as shown in Figure 1 (1) The Goal and scope definition includes: the objective of the study (goals), the functional unit, the system description and the boundaries.

(2) Life cycle inventory (LCI) is quantification of all inputs (raw resources or material consumption, energy consumption, etc.) and out puts (such as emissions to air, water and land, waste production) of a system.

(3) LCIA is life cycle impact assessment. It analyzes the environmental impacts for the defined inventory, which converts ' inventoried' flows into simpler indicators. LCIA is an appropriate tool for helping to determine to what extent a particular product, process, ingredient or emission may be associated with a particular impact category (e.g. global warming potential, ecotoxicity).

(4) The life cycle interpretation phase considers several elements, as defined in the ISO standard, a) identification of the environmental significant issues; b) evaluation of study completeness, sensitivity and consistency; and c) definition of conclusions, limitations, and recommendations for reducing the environmental impact of the product system.

Life cycle impact assessment

In life cycle impact assessment (LCIA) stage, the category indicator (CI) that reflects ecotoxicity and human toxicity of the inventory data is calculated as (ISO14044, 2006):

∑
where CI is the impact score for category of ecotoxicity or human toxicity. CF i is the specific characterization factor for substance i, that expresses the potential impact of each single elementary material flow (M i ) contributing to the total impacts [START_REF] Hauschild | Identifying best existing practice for characterization modeling in life cycle impact assessment[END_REF].

Characterization factor (CF) thus facilitates the comparisons between the different substances in terms of their ability of contribution to ecotoxicity or human toxicity.

The LCIA indicators can be at the midpoint level (such as acidification, climate change, ecotoxicity) or endpoint level (such as damage to human health, decrease in biodiversity) (ILCD, 2010b). Midpoints are specific points in the cause-effect chain of an impact category, somewhere between the emissions of pollutants and the endpoints. The numbers of studies reported the midpoint and endpoint CIs are approximately equal [START_REF] Hischier | Life cycle assessment of engineered nanomaterials: State of the art and strategies to overcome existing gaps[END_REF].

Selection of LCIA methodologies

Many characterization methods are available to calculate the characterization factors (CFs) for ecotoxicity and human toxicity impact category (ILCD, 2010a), such as IMPACT 2002+ [START_REF] Pennington | Multimedia fate and human intake modeling: Spatial versus nonspatial insights for chemical emissions in western Europe[END_REF], ReCiPe [START_REF] Goedkoop | A life cycle impact assessment method which comprises harmonised category indicators at the midpoint and the endpoint level[END_REF], EDIP2003 [START_REF] Potting | Background for spatial differentiation in life cycle impact assessment: The EDIP2003 methodology[END_REF], MEEuP [START_REF] Kemna | Meeup methodology report[END_REF], USEtox (Rosenbaum et al., 2008) February and 18 July 2016, the USEtox 2.0, 2.01 and 2.02 were successively released.

Characterization factor (CF)

As a scientific consensus environmental model, the USEtox model provides two kinds of characterization factors (CFs): ecotoxicological and human toxicological CF (USEtox TM , 2016a).

CF for freshwater ecotoxicity

The USEtox model describes a characterization factor of a substance for freshwater ecotoxicity (CF e ). It can be calculated as the following equation (Rosenbaum et al., 2008;Fantke et al., 2015):

(1.1)
where FF (fate factor, unit: day) is the residence time of a substance in a particular environment (such as freshwater). The XF e (ecosystem exposure factor, dimensionless) represents the bioavailability of a chemical and can be represented by the fraction of the chemical dissolved in freshwater (dimensionless). Additionally, the EF e (effect factor, PAF• m 3 • kg -1 ) reflects the change in the potentially affected fraction (PAF) of species because of the change in substance concentration in freshwater. Thus, the characterization factor CF e has a unit of PAF• m 3 • day• kg -1 .

USEtox defines it as comparative toxic units (CTU e ) for ecosystem.

CF for human toxicity

In USEtox, a characterization factor (CF) describes a potential impact on human health related to the emission of a given substance. It can be expressed by: 22 (1.2)

The human toxicity CF h has a unit of comparative toxic units (CTU h ), which represents cases/kg emission . The fate factor, FF (unit: day), links the total released quantity to the chemical masses (or concentrations) in certain compartment (e.g. freshwater). It is the same for ecotoxicity and human toxicity. The human exposure factor, XF h (unit: day -1 ), reflects the rate at which a pollutant is able to transfer from a receiving compartment into the human population through a series of exposure pathways. The EF is the human effect factor (unit: cases/kg intake ) and reflects the change in the life time disease probability, due to the change in life time intake of a chemical.

USEtox has a large database containing various properties data for substances (e.g. molar mass, solubility, partitioning coefficient between n-octanol and water), which facilitating its applications. In the latest version, USEtox 2.02, the database includes 27 inorganics (e.g. silver, copper, selenium) and 3077 organics (e.g. ethanol, atrazine, carbofuran) (USEtox TM , 2016b).

Applications of USEtox model to conventional technology

The application of USEtox model on conventional substances can be traced back to the year 2009. Koehler and Wildbolz compared the environmental footprints of 9 home care and personal-hygiene products. The ecotoxicity characterization factors for ingredients of the 9 products were calculated by USEtox model (Koehler and Wildbolz, 2009). Up to now, the USEtox model has been applied to a variety of substances to produce recommended or interim human toxicity and ecotoxicity CFs. This thesis collected and selected the applications of USEtox on conventional substances or processes based upon the publications on the official USEtox: http://www.usetox.org/. When a study included both ecotoxicity and human toxicity, it was considered as two applications. The records related to "nano‖ were collected and selected from the same sources as "conventional‖ but plus Scopus (https://www.scopus.com/).

Applications of USEtox model to nanotechnology

The applications of USEtox model on nanotechnology are rarely reported. Up to today, only 11 articles used USEtox model to calculate ecotoxicological or/and human toxicological CFs on nano (see Table 1.1). The annual distribution is shown in Figure 1.5. Although the application of USEtox in 2013 is 0, the overall trend seems to be upward. The more detailed introductions to these applications are described in Section 1.5.5. Toxicology studies of ENPs on aquatic species are unevenly distributed (Bondarenko et al., 2013). It was recommended that the ecotoxicological EF calculation should be based on data reflecting the entire ecosystem (Hauschild, 2007). In USEtox, the data from laboratory tests with algae (primary producers), crustaceans (primary consumers) and fish (secondary consumers), which represent the three trophic levels of the ecosystem, are preferred (Hauschild, 2007;Fantke et al., 2015). However, even though some researchers may include several species in toxicity tests, it is rare to find a study containing all the tree tropic levels needed in USEtox.

For example, Ivask et al reported the EC50 values of silver nanoparticles to different types of organisms (bacteria, yeast, algae, and crustaceans) and a kind of mammalian cells. However, fish was not included in their study. The lack of a database comprising all the toxicological data of various ENPs makes the calculation of EF extremely hard (Pini et al., 2016).

Fate model for ENPs: necessity and challenges

Experimental Methods

In this work, each experiment with three replicas was based on one pig individual. For each kind of ENPs, three experiments were performed respectively. The average values with standard deviations are reported hereafter unless specially explained.

Materials and instrumentations

Materials

Heparin sodium salt from porcine intestinal mucosa (H3393 (1) Ficoll-Paque separation. Subsequently, 10 mL blood was respectively mixed with 10 mL PBS buffer in four 50 mL tubes. Then 12.5 mL of ficoll solution was slowly added to the bottom of the tubes. After centrifuging 30 min at 1500 rpm, the solution was separated into 4 layers shown in Figure 2.3 [START_REF] Quinn | Neutrophil methods and protocols[END_REF]. Upper layer is plasma, and the layer under it is lymphocytes and monocytes. The third layer is ficoll together with neutrophils which are laying on the bottom phase of red blood cells (RBC). Some neutrophils are also inside the bottom phase of the RBC. The two upper layers were discarded with pipette and the remaining volume was about 15 mL. (3) Hypotonic lysis. This procedure aims to remove all residual RBCs and platelets. Firstly, the pellet was dispersed into 20 ml of 0.2% saline solution (4 o C) and mixed well with pasture pipette. After waiting for 1.5 min, 20 ml of 1.6% saline solution (4 o C) was added in and mixed well. The final concentration of NaCl was 0.9%. Then, the solution was centrifuged at 4 o C, 1500 rpm for 10 min. The supernatant was removed with pipettes. The above procedures were repeated one time respectively with 10 ml 0.2% and 1.6% saline solution in order to thoroughly remove the RBC. Finally, the resulting neutrophils were resuspended in KRP medium and maintained at 4 o C before use. The time point here was defined as time 0 h.

Cell counting and mortality rate calculation

The cell counting was conducted by using trypan blue dye as previous reported [START_REF] Babin | 2 , CeO 2 and ZnO nanoparticles and modulation of the degranulation process in human neutrophils[END_REF]Aalapati et al., 2014). This dye can enter the necrotic cells and stain them blue, while the Secondly, 20 µL the fresh neutrophils solution was taken and diluted 10 times with KRP. And then 20 µL diluted neutrophils was mixed with 20 µL trypan blue. After waiting for 5 minutes, 10 µL mixture was pipetted and carefully introduced into the space between the slide and hemocytometer. Finally, all the cells on the 25 squares of the hemocytometer should be counted under an optical microscope equipped with a camera.

The concentration of neutrophils solution (cells/mL) was calculated by the formula below (Hemocytometer, 2016):

(2.1)

Then a certain volume of neutrophils solution was respectively diluted to 6.25 and 5 million cells/mL (mln/mL) with KRP buffer for further experiments. Mortality rate of neutrophils was calculated as following:

(2.2)

Statistical analysis

LC50 and LC20 are respectively defined as the concentration at which 50% or 20% of the species are dead compared with the control. In this work, the LC50/LC20 values and associated 95% confidence limits were calculated on log-transformed mortality data using the probit model in the statistical package SPSS v20.0 (IBM, 2016).

Preparation of ENPs solution and exposure neutrophils to ENPs

The ENPs (i.e. CuNPs, NiNPs and Al 2 O 3 NPs) stock solutions were obtained by dispersing 10 mg ENPs into 1 mL ddH 2 O within an Eppendorf tube, respectively. These stock suspensions (10 mg/mL) were then diluted further with KRP buffer to prepare the desired concentrations (2.5-2000 μg/mL). Before each dilution step, the mother solution must be ultrasonic 5 minutes at room temperature.

The diluted ENPs solutions were respectively mixed with a certain volume of 6.25 mln/mL diluted neutrophils in Eppendorf tubes as a volume proportion of 1:4 to make the concentration of neutrophils to be 5 mln/mL and the concentration of ENPs to be 0.5-400 µg/mL. It was assumed that both neutrophils and ENPs were well dispersed to homogeneity under the experimental conditions.

All the samples were kept incubating at 38 o C (normal temperature of pigs) (Brown et al., 2007) by using a water bath before the following tests. During exposure, at certain time intervals, cell counting and chemiluminescence measurements were performed and samples for morphology characterization were prepared. There is no reported longevity of porcine neutrophils. However, since the accepted longevity of human neutrophils is approximately 8-10 h, and many similarities between pigs and humans, we assumed that the lifespan of porcine neutrophils is ~10 h. Thus, the longest exposure duration in this study is ~11 h. The neutrophils exposed to selected concentrations of ENPs during certain time intervals were all fixed in 2.5% glutaraldehyde aqueous solutions overnight. Then, the neutrophil suspension was centrifuged (4000 rpm, 5 min), followed by a stepwise dehydration using a series of graded ethanol aqueous solutions of 30 %, 50 %, 70 %, and 100 % respectively. The dehydration with each ethanol solution took for 10 min at room temperature followed by a centrifugation (4000 rpm, 5 min). Hereafter, the neutrophils were well dispersed in absolute ethanol, and deposited 2.5-5 µL solution onto the surface of prepared glass substrates. Prior to scanning electron microscopy (SEM) characterization, the samples were dried freely in hood and covered with a thin layer (2-3 nm) of gold-palladium to suppress the charging effects. solution, 200 µg/mL ENPs in KRP solution was ultrasonicated for 5 min then followed the same procedures as for neutrophils to prepare the SEM samples.

Morphology characterization of neutrophils and ENPs

Chemiluminescence measurements

The standard white 96-well microtiter plates were used in the chemiluminescence measurements.

Generally, 40 µL KRP buffer, 120 µL neutrophils solution (with or without ENPs), 20 µL luminol and 20 µL zymosan solution were added into each well of the microtiter plate in sequence. The samples without ENPs were used as controls. The chemiluminescence was monitored in a luminometer at 38 o C for a period of 120 minutes. The CL values are presented in relative light units (RLU). In addition, it is noteworthy that no signal was detected in the samples without neutrophils. To better understand the influences of concentration of CuNPs and the time on mortality rates kinetics of neutrophils, the average mortality rate velocity (AMRV) of neutrophils shown in Figure 2.2d was calculated as the following method.

Results and Discussions

Mortality of neutrophils exposed to ENPs

The y-axis values in Figure 2.2d were calculated by the following equation:

(2.

3)

The x-axis values T in Figure 2.2d were obtained by:

(2.4)

where AMRV i and MR i respectively represent the average mortality rate velocity and the mortality rate of neutrophils exposed to CuNPs at time point T i . T i is a certain time point chosen for present mortality rate test, while T i+1 means a time point just after T i .

In this work, the mortality rates of neutrophils were tested at six time points (0.875, 2.925, but not precise information, because the time points were designed arbitrarily, and the values reported were in average.

Toxicological data of ENPs

Table 2.

Chemiluminescence of neutrophils exposed to ENPs

Examples of CL spectra of neutrophils exposed to ENPs were shown in Figure B2 NiNPs was just ~115% more to control. Another obvious difference between them is that CuNPs stimulated most the neutrophils exposed for 2.5-4.6 h, while NiNPs delayed it to 10.70 h.

All the above differences could be explained by the much more toxic property of CuNPs than NiNPs. In addition, it was found that the relative CL intensities increased over time for neutrophils exposed to NiNPs, Al 2 O 3 NPs and low concentration CuNPs (< 5 μg/mL), which indicates that the neutrophils may need time to be fully activated. The more toxic the invader is, the shorter time they take.

SEM characterization of CuNPs and neutrophils

The surface morphology of CuNPs is shown in It should be noted that after neutrophils were exposed to 200 µg/mL CuNPs for 8 h, the CL signal decreased to 0, despite the fact that there were still about 10 % living neutrophils (Figure

Rearrangement of copper nano-sheets initiated by neutrophils

2.2c

). There are two probable reasons. On one hand, neutrophils may ‗exhaust' themselves during their defense with the copper particles, thus reducing their light signal output drastically, even if observed to be alive; on the other hand, the layers of copper nano-sheets may scatter and quench some of the light produced by the CL reaction.

Moreover, it was found when the CuNPs concentration was lower than 5 µg/mL, there was no coverage of copper nano-sheets on neutrophils, while the coverage started to be observed from 10 µg/mL CuNPs onwards (Figure B4 in Appendix B).

Conclusions

This work reports the first study on the toxicity testing of ENPs to neutrophils. In comparison to the in vivo methods, exposure to porcine neutrophils of ENPs in vitro is a rapid and less expensive method with no animal purposeful sacrifice as the blood is collected fresh during

Introduction

Knowledge of the behavior of ENPs in water is usually considered as part of interface and colloid science, which is an interdisciplinary science, including chemistry, physics, nanoscience, etc. [START_REF] Lyklema | Particulate colloids[END_REF]

Fate Model of ENPs in Freshwater Ecosystem

Fate factor calculation

It was deduced that the overall kinetics of nanomaterials in water-sediment transport should be close to the first order (Quik et al., 2011). Therefore, the first-order kinetics is applied to estimate all the processes considered in this study, which is considered acceptable (Meesters et al., 2014;Salieri et al., 2015).

Fate matrix

A 2 × 2 fate matrix ( ̅̅̅̅ ) has been established to quantify the ENPs transport between the freshwater (w) and sediment (sed) compartments [START_REF] Rosenbaum | A flexible matrix algebra framework for the multimedia multipathway modeling of emission to impacts[END_REF].

̅̅̅̅ ( ) (3.1)
The

̅̅̅̅ ̅ ( ) (3.2)

Rate constants

The total removal rate constant of ENPs in freshwater (k w,w , s -1 ) is expressed as a combination of the rate constants for three removal processes for freshwater compartment: dissolution (k diss , s -1 ), total sedimentation (k sed , s -1 ) and advection (k adv , s -1 ) as equation 2.3:

(3.3)
The total receiving rate constant of ENPs in sediment (k sed,sed , s -1 ) is expressed as a combination of the rate constant for horizontal bed load transfer (k sed,transfer , s -1 ), burial (k burial , s -1 ) and resuspension (k resusp , s -1 ) as equation 2.4:

(3.4)
The transport rate constants of ENPs from freshwater to sediment (k w,sed , s -1 ) and from sediment to freshwater (k w,sed , s -1 ) can be calculated based on the following two equations: is the gravitational acceleration on earth, and the is the dynamic viscosity of water. The is the radius of particles. The calculations of the size and density of the altered species (attached or aggregated ENPs) are as following:

(3.5) (3.6)

Dissolution rate constant

Sedimentation rate constant

The density of the attached or aggregated species ρ as (kg• m -3 ):

(3.11)

The radius of the attached or aggregated species r as (m):

√ (3.12)
where the volume V as (m 3 ) and mass m as (kg -1 ) of the attached or aggregated species can be calculated as following:

(3.13) (3.14)
where V ENP and V SPs are the volume of an individual ENP and suspended (nano)particle, respectively. m ENP and m SPs are the mass of un individual ENP and suspended (nano)particle, respectively. β is the number of ENPs that attached or aggregated with one suspended (nano)particle, which depends on the number of ENPs and suspended (nano)particles in the freshwater. It was reported that the concentration of suspended particulate matter (SPM) is at least 2 orders of magnitude higher than that of TiO 2 nanoparticles in all cases (10 10 SPM particles m -3 vs 10 8 TiO 2 NPs m -3 ) (Praetorius et al., 2012). Thus, in this study, β is assumed to be 1 due to the relatively lower concentration of ENPs in the freshwater.

The volume of an individual ENP can be calculated as:

(3.15)
The volume of an individual suspended (nano)particle can be calculated as:

(3.16)
where r SPs respectively represents radius of larger suspended particles for attached ENPs (r LSP )

or radius of suspended (nano)particles for aggregated ENPs (r SNP ).

The mass of an individual ENP can be expressed as:

(3.17)

The mass of an individual suspended nanoparticle can be expressed as: where is the attachment ( ) or aggregation ( ) efficiency with a value between 0 and 1, representing the probability that two particles successfully remain attached after the collisions.

(3.
is the concentration (m -3 ) of larger suspended particles (C 2 ) or suspended nanoparticles (C 3 ) in the freshwater. There are three kinds of mechanism that correspond to the collision frequency Here, the three equations below are applied to calculate each collision frequency:

(3.21) (3.22) (3.23)
where k B is the Boltzmann constant and T water (K) is the temperature of the water. r ENPS is the radius of ENPs. r SPs is the radius of suspended particles, which can either be larger suspended particles (r LSPs ) for attachment or suspended nanoparticles (r SNPs ) for aggregation.

(m• s -1 ) are the settling rates of ENPs and suspended particles, which can be calculated using equation 3.10. where advflow (m 3 •s -1 ) represents the water outflow from freshwater to sea water on a continental geographic scale. V water is the volume of freshwater (m 3 ).

Other rate constants

: Resuspension rate constant: k resusp (s -1 ) is expressed as:

(3.25)
where Resusp (m• s -1 ) is the resuspension rate from sediment to freshwater. Depth sed is the depth of freshwater sediment compartment.

: Burial rate constant: k burial (s -1 ) is expressed as:

(3.26)
where Burial (m• s -1 ) is the burial rate in freshwater sediment. Depth sed is the depth of freshwater sediment compartment.

: Horizontal bed load transfer rate constant: k sed,transfer (s -1 ) is expressed as:

Dissolution of ENPs in freshwater

When ENPs release into water, the dissolution may occur (Figure 3. Theoretically, the sedimentation rates of dissolved ENPs should have been calculated and included in the FF calculation. However, it was assumed that the sedimentation fraction of the dissolved ENPs could be neglected due to the following reasons. There is neither data nor approach concerning the respective proportions of each dissolved form of ENPs. In addition, according to the Stokes's law for gravitational settling of particles (Quik et al., 2011;Meesters et al., 2014), the smaller particles tend to precipitate more slowly. Furthermore, there is resuspension from the sediment. Thus, it is acceptable to assume that the sedimentation rates of dissolved ENPs are negligible. However, it should be noted that the FFs may be overestimated due to the above assumption, which is a limitation of this work.

Ranges and recommended values for input parameters

The behavior of colloids in freshwater can be described based on a combination of fate model In order to make the fate model more flexible, a group of general input parameters (Table 3 indicate that all the obtained attachment efficiencies ranged from 0.82 to 1, and almost all the obtained aggregation efficiencies ranged from 0.102 to 1 (except one which is 0.00675).

Therefore, the default attachment and aggregation efficiencies are assumed to be 0.9 and 0.5 respectively. In addition, to do the sensitivity analysis, the ranges of α2 and α3 respectively,

Evaluation method

Although the proposed fate model does not aim for calculating the PECs of ENPs, it is still capable to do that with emission information of ENPs. Since it was assumed that the freshwater is the only compartment directly receiving the ENPs emissions, the resuspended ENPs are all initially from the freshwater but not sediment. The time-dependent mass concentrations C

(kg• m -3 ) of ENPs can be expressed as the total mass M (kg) in freshwater of volume V (m 3 ) at time t (s) with constant emission E (kg• s -1 ) as equation 3.28 (Meesters et al., 2014).

(

Here, it should be noted that all the PECs of ENPs are highly dependent on the emission values.

Thus, in this study, the PECs of five ENPs (copper, silver, titanium dioxide, zinc oxide, and fullerenes nanoparticles) were calculated using our fate model within the same emission scenarios as previous studies (one in North America, two in Switzerland and two in Europe) ). These studies predicted the annual emissions of the related ENPs in certain regions based on probabilistic material flow analysis of nanoproducts such as some cosmetics, textiles, paints. The emission values as well as their source and quality were described in Table C1 (in Appendix C) and its footnote. Then, the total removal rates of ENPs were obtained by present fate model.

(
To better assess the outputs of the present model, the same input values as previous studies (such as radius of ENPs and larger suspended particles in Switzerland.) were also kept to the greatest extent. In the case of no data of relevance reported by the previous studies, the recommended data (North America: Region W10; Switzerland and Europe: Region W13) in Table 3.2 were used. Table 3.3 summarizes the dissolution data and some specific parameters related to North America, Switzerland and Europe. Finally, the outcomes were compared with the previously reported PECs. shown in the study of Keller and Lazareva (Keller and Lazareva, 2014). Thus, in this study, the WWTP effluent of SF Bay area were calculated by using the emission of ENPs for the SF Bay area divided by ENPs concentrations.

Evaluation results and discussions

The comparison of the PECs of ENPs in North America, Switzerland and Europe from this study and the previous studies are presented in Figure 3 Therefore, even though different concentration calculating methods of PECs were used, it did not have influences on the PECs comparison results.

Sensitivity Analysis of the Fate Model

The PECs calculated by our fate model have many uncertainties, which is similar to other fate predictions (Meesters et al., 2014). There are three main reasons. Firstly, it is difficult to know the exact emission rates of the ENPs in a given region (Mueller and Nowack, 2008;Gottschalk et al., 2009). Secondly, there is an absence of knowledge about how the physicochemical properties of ENPs affect the reactions (e.g. attachment, aggregation behavior) with the environment. Thirdly, the many uncertainties of the complex natural ecosystem may result in a large range of input parameter values, which obviously affect the outcomes. Fortunately, the first difficulty should not cause a problem for the calculation of FF in this thesis, because the outcome of the fate model is not the PECs. The calculation has no relationship with the emission rates of ENPs. However, the other two problems still exist and are difficult to solve directly and completely. In order to ameliorate this situation, a sensitivity analysis was performed. By identifying the relative importance of the variables in our model, the more sensitive parameters may then receive more attention in the future, while the parameters having low influence on the outcome would not be so urgent. Then, by changing the previously fixed parameter values in the studied group, a maximum DFF(δi) with an arbitrary unit (a.u.) can be obtained. The value of maximum DFF(δi) reflects the relative importance of the parameter δi for the given region.

Sensitivity analysis methods

It

Sensitivity analysis results and discussions

It is well known that the more accurate the input data is, the closer to the reality the predicted FF becomes. Therefore, when calculating the freshwater FFs of ENPs, the best choice is to use the local input data related to the real situation. However, if time, budget, or other resources are limited, which parameter should be paid more attention to? The relative importance of the input parameters in Figure 3.5 can give some indications (the numeric values are listed in Table C3 in Appendix C). In addition, the results from -Single Parameter SA‖ are described in Figure C1 with the values listed in Table C4 (in Appendix C). These two methods produced similar results.

The relative importance of the thirteen studied parameters presents big differences. For example, the radius of larger suspended particles (r LSP ) has the first or second highest relative importance, while the number concentration of suspended nanoparticles (C 3 ) and T water have no influence on the FFs. It indicates that the r LSP needs to be paid more attention. In contrast, even though use the recommended values of C 3 and T water by this study, rather than the values obtained by actual detections, the calculated FFs should not have big differences. In addition, the sensitivity analysis indicates that there are big differences of importance between the parameters even for those in the same group. Each group has not only significant parameters but also less important parameters. Thus, we recommend focusing on the more important parameters in each group rather than all the parameters in one group.

Introduction

Characterization factor (CF) is an important element in life cycle impact assessment. However, as described in Section 1.5, the existing environmental model is still not fully available for calculating the CFs of ENPs. The absence of CFs for ENPs hampers the toxicity assessment of nanoproducts in using life cycle assessment method.

Among various ENPs, 17 Although this analysis is quite rough, it can still give us a general view of the 17 selected ENPs.

According to Figure 4.1a, for this study, the most difficult scenario tends to occur in the ENPs which has the smallest absolute number of toxicity studies (i.e. the ZrO 2 NPs with 9 records and Cu(OH) 2 NPs with 6 records).

Calculation of Freshwater Fate Factors for 17 ENPs

In USEtox, fate factor is the residence time of a substance in the considered environmental compartment (i.e. freshwater and sediment in this work). The longer the residence time, the larger potential impacts on environment or human.

Dissolution rate constants

As described in Chapter 3, predicting the dissolution rates by modeling remains highly speculative (Quik et al., 2011). In proposed fate model, the recommended dissolution rate constant k diss (s -1 ) of ENPs was assumed to be 5• 10 -6 s -1 for all ENPs based on the range of 0-10 2015). Thus, in this section, the dissolution rate constants of ENPs are preliminarily estimated by averaging the data from literatures. In order to improve the reliability of the results, some criteria have been applied to select the reported dissolution rate values. The basic criterion is to choose the studies whose experimentation conditions are closed to the natural freshwater.

However, the detailed selection of the criteria is difficult, because a strict criterion would lead to no data to use but a loose criterion would cause unreliable results. In this work, the pH, the ionic strength, and the concentration of organic matters in the experimental media were all took into account.

The pH and ionic strength in natural freshwater

The pH and the ionic strength of the natural freshwater have large variations. Thus, a random collection of these data from numerous literatures were performed. The balance of worldwide regions was also taken into account in the literature searching. Ionic strength of freshwaters was rarely reported, so it was calculated by reworking the previously published cases. The ionic strength is a measure of the concentration of ions in the solution. It is defined as (De Vicente,

2004):

∑ where I is the molar ionic strength (mmol• L -1 ). The m i and z i are the molality (mmol• L -1 ) and charge of each ionic species, respectively. One half is due to the including of both cations and anions [START_REF] Lewis | The activity coefficient of strong electrolytes[END_REF][START_REF] De Vicente | The concept of ionic strength eighty years after its introduction in chemistry[END_REF].

The results are shown in table D2 (Appendix D). The lowest pH value occurs in Suwannee River in the USA with 4.7 (Gao et al., 2009), while the highest is 9.0 in freshwater pan sites in South Africa (Riato et al., 2014). The calculated ionic strength could be as low as 0.38 in a Lake in Argentina (Catan et al., 2016) and as high as 475.17 in Suwannee River in USA (Gao et al., 2009). The ionic strength in natural freshwater may be higher than the reported values because usually not all kinds of ions were measured.

The concentration of organic matters in natural freshwater

In freshwaters of Europe and North America, almost all the DOC (dissolved organic carbon)

Calculation of dissolution rate constants

For some ENPs, such as ZnONPs (Reed et al., 2012) and NiNPs (Griffitt et al., 2008), even though their dissolution was investigated, the dissolution rate constants were not directly reported. Instead, these studies usually presented the concentrations of released ions for predesigned durations. In this case, the equation 4.2 has been used for the calculation of dissolution rate constants of ENPs. It determines the apparent dissolution rate experimentally (Meesters et al., 2014).

( ) ( ) (4.2)
where C 0 is the initial concentration of ENPs and C t is the concentration of ENPs at time t (s -1 ).

C ions represents the concentration of released ions from ENPs at time t. This equation assumes that the ENPs in water only have particulate form and ionic form.

Exposure Factor for ENPs

Human exposure factor for CuNPs

Human exposure factor (XF h, day -1 ) reflects the rate at which a chemical is able to transfer from a compartment into the human population (Fantke et al., 2015). The intake fraction iF=XF×FF (dimensionless, kg intake /kg emitted ) represents the fraction of a chemical emission that is eventually taken in by human population through different exposure pathways [START_REF] Rosenbaum | Usetox human exposure and toxicity factors for comparative assessment of toxic emissions in life cycle analysis: Sensitivity to key chemical properties[END_REF]. For conventional chemicals, the USEtox modeled many pathways such as inhalation, drinking water, ingestion of meat. In the calculation of XF h , the substance-specific bioaccumulation factors (BAF, kg/kg) are required. Bioaccumulation refers to the accumulation of chemicals (units: kg) in an organism (units: kg).

In this work, the calculation of human characterization factor of CuNPs is based on the in vitro tests on porcine neutrophils, but not the in vivo experiments in whole animals. Thus, the XF h was modified a bit and redefined as: the rate that ENP is able to transfer from freshwater into the blood of human. Since the bioaccumulation data of CuNPs remain scare, in this work, we ultra-conservatively assumed that 100% released CuNPs in freshwater would be taken in by human. However, not all the ingested ENPs would reach the blood stream of human after their uptake. The XF h was provisionally estimated based on the previous reported data as below.

Eight healthy volunteers with normal intestinal permeability were recruited by Pele et al. and were orally administered 100 mg pharmaceutical/food TiO 2 NPs (88.9-200.6 nm). The presence of these particles in human blood was proved by analyzing the blood samples collected from 0.5 In reality, it was not likely to keep such high dose every 6 hours. Thus, the 0.09174% was considered as a daily result. Finally, the daily proportion of TiO 2 NPs that can enter human blood was assumed to be 0.09174%. Due to lack of data on CuNPs, we assumed that the CuNPs have the same XF h as TiO 2 NPs. Therefore, the XF h of CuNPs is 9.174• 10 -4 day -1 .

In the above estimation, on the one hand some assumptions may overestimate the XF h value, such as the ultra-conservative 100% ingestion of released CuNPs in freshwater. On the other hand, some assumptions may result in the underestimate of XF h value. For example, oral is not the only route of ENPs entering body of human. Ryu et al. demonstrated that the skin was a potentially important route, since it is the largest organ in human body [START_REF] Ryu | Zinc oxide nanoparticles: A 90-day repeateddose dermal toxicity study in rats[END_REF].

Despite of the assumptions due to absence of data, the method for calculation of XF h is promising. The value of XF h can be updated once the corresponding data of CuNPs are available.

Freshwater ecosystem exposure factor for ENPs

In USEtox, the freshwater ecosystem exposure factor (XF e , dimensionless) represents the bioavailability of a chemical and can be represented by the fraction of a substance dissolved in freshwater (Rosenbaum et al., 2008). The chemical-specific data required for calculating the XF e of conventional substances are already gathered in the USEtox database, such as the partition coefficient between water and suspended solids, the partitioning coefficient between dissolved organic carbon and water (Fantke et al., 2015; USEtox TM , 2016). However, because

ENPs present as the thermodynamically unstable suspensions in freshwater, the partition coefficients are invalid for ENPs (Praetorius et al., 2014). Therefore, it was assumed that 100%

ENPs were bioavailable to freshwater aquatic organisms. In this case, an ultra-conservative XF was set to be 1. In this study, in order to compare the impacts of CuNPs on human and pig, the porcine EFs were also calculated based on equation 4.3. The ED50 a of pig can be calculated by:

Effect Factor for ENPs

(4.5)
where BW a represents average body weight of pig (110 kg, data from the abattoir). The lifetime of pig (LT a ) is between 10 to 20 years [START_REF] Meurens | The pig: A model for human infectious diseases[END_REF]. Thus, here, an average LT a value of 15 years is used.

In order to obtain the ED50 a,t , the dose value of CuNPs per kilogram body weight of pig is needed. Nevertheless, the toxicological data reported in Chapter 2 is EC50 a,t in mg• L -1 of CuNPs.

Thus, the following equation is applied for calculating the ED50 a,t (mg• kg -1 • day -1 ) of CuNPs for a pig based on the neutrophils quantity of a pig:

(4.6)
where ENC represents the exposed neutrophils concentration was 5 million• mL -1 .

(7600 million• L -1 ) represents the number of neutrophils per liter blood of pigs [START_REF] Jonasson | Differences in lymphocyte subpopulations and cell counts before and after experimentally induced swine dysentery[END_REF].

(56-69 mL• kg -1 ) is the blood volume per kilogram body weight of pig [START_REF] Wolfensohn | Handbook of laboratory animal management and welfare[END_REF]. Here, an average BV value of 62.5 mL• kg -1 is used. The t (day) is exposure time. Finally, the EFs for pig and human over time were obtained and summarized in Table 4.3.

Table 4.3

The EF values (cases/kg intake ) for human and pig over time.

Time (h) Human

Toxicity data selection criteria

A comprehensive literature review has been performed to collect the ecotoxicological data of ENPs. To control the reliability, some criteria were applied in selection of ecotoxicological data from numerous literatures. (1) Only the ENPs toxicity tests on freshwater aquatic organisms were considered. (2) It was recommended that the EF calculation should be based on data reflecting the entire ecosystem (Hauschild, 2007b). In practice, the data from laboratory tests with algae (primary producers), crustaceans (primary consumers) and fish (secondary consumers), which represent the three trophic levels of the ecosystem, are preferred (Hauschild, 2007b;Fantke et al., 2015). (3) The toxicity data of particles with a size 1-100 nm were preferred. When the data were not available for certain ENPs, the size of a few hundred nanometers was also accepted due to the following reason. As mentioned in Section 1.1.2, The ENPs are generally considered to have size with at least one dimension of less than 100 nm.

However, from a viewpoint of toxicity, the definition is a somewhat arbitrary size cut off and it may be more prudent to liberalize the restriction of size under certain circumstances (Handy et al., 2008).

Calculation method

In USEtox, the effect factor (EF, PAF• m In USEtox model, the HC50 can be calculated based on the geometric mean of the toxicity values on either species level (GM-SL) or trophic level (GM-TL) as equations 4.8 and 4.9 (Hauschild, 2007b;a). The HC50 calculation of GM-TL was conducted by respectively computing the geometric mean of the toxicity values related to each trophic level (such as fish)

and then calculating the geometric mean of the three trophic levels.

∑ (4.8) ∑ (4.9)
where HC50 s and HC50 t respectively represent the HC50 (m 3 • kg -1 ) based on species level and trophic level. n s is the number of species, while n t is the number of tropic levels (in this study, n t ≤ 3). EC50 s is the geometric mean of EC50 (mg• L -1 ) for every individual species; EC50 t is the geometric mean of EC50 (mg/L) for the species belonging to one tropic level. Always pay attention to the units.

Ecotoxicological Effect Factors

Ecotoxicological data for Cu(OH) 2 NPs, NiNPs and ZrO 2 NPs have not been found. Thus, only data of the other 14 ENPs were collected and listed from Table D7 to D20 in Appendix D. In this work, the EFs based on GM-SL and GM-TL were both computed and listed in Table D21 (Appendix D). The approach relying on the trophic level has been claimed as better practice to calculate HC50 and further FF. It avoids the disadvantages caused by the unequal distribution of EC50 in three trophic levels (Hauschild, 2007a). Thus, the EF values on the basis of GM-TL (Figure 4.

Calculation of Characterization Factors (CFs) for 14 ENPs

Based on the XF as well as the calculated EF and FFs of ENPs, the recommended regionalized CFs were obtained.

Human toxicological CFs of CuNPs

Human and porcine non-carcinogenic CFs for different exposure periods in 17 subcontinental regions are listed in Table 4.4. This is the first time that characterization factors of animals are reported. Many assumations were made during their calculations. The most important assumation is that the human and pig have same exposure factor to CuNPs. It indicates that the 100% released CuNPs in freshwater would be taken in by pigs as an ultra-conservative assumption. The proportion of ingested CuNPs entering the blood stream was assumed as the same for human and pig, which was considered acceptable, due to the many similarities between pig and human (genetics, physiology, anatomy, etc.) [START_REF] Brea | The pig as a model for investigating the role of neutrophil serine proteases in human inflammatory lung diseases[END_REF].

Freshwater ecotoxicological CFs of 14 ENPs

Recommended ecotoxicological freshwater characterization factors of 14 ENPs in 17

subcontinental regions are listed in Table 4

Conclusions

Among various ENPs, seventeen kinds of ENPs have been chosen due to their relatively wide application. In terms of the sensitivity analysis results of the fate model, many attentions have

Chapter 5 Conclusions and Perspectives

dvances and limitations of this thesis are to be summarized in this chapter. In addition, this chapter presents more comprehensive thoughts on the toxicity assessment of nanomaterials.

A

1 Conclusions

The objective of this thesis was to improve understandings of toxicity of various engineered nanoparticles (ENPs) to human and ecosystem. The rapid development of nanotechnology raises the safety concerns of nanomaterials towards consumers, production staff as well as ecosystem.

Life cycle assessment (LCA) is usually acknowledged as a reliable and advanced approach to assess the impacts of a product or process on human and ecosystem. However, the absence of characterization factors (CFs) for nanomaterials seriously hampers our ability to understand potential impacts and risks of nanoproducts by using LCA. In order to fill the gaps of lacking CFs for nanomaterials in current environmental models, this thesis proposed non-carcinogenic human toxicological CFs for copper nanoparticles and ecotoxicological CFs for 14 ENPs under the framework of the USEtox model. In USEtox, CF is composed by fate factor (FF), exposure factor (XF) and effect factor (EF). All the three elements were investigated in this study.

2 Perspectives

The

R2.2 Donné es toxicologiques des ENPs

Le

̅̅̅ ( ) (R1)

Les FF w,w et FF sed,sed dé crivent respectivement le temps de sé jour (exprimé en jour) des ENPs dans l'eau douce et le sé diment. Les é lé ments hors diagonales repré sentent le temps de transport des ENPs de l'eau au sé diment (FF w,sed ) et de sé diment à l'eau (FF sed,w ). Le ̅̅̅ est é gal à l'inverse né gatif de la matrice du coefficient de vitesse ( ̅ ) : Mê me si les ré sultats entre les recherches actuelles et pré cé dentes montrent une amplitude de 1 à 3 dans des ordresdiffé rents les raisons sont bien expliqué es. Par consé quent, le modè le de devenir proposé est applicable.

̅̅̅ -̅ - -( - - ) - (

R3.5 Analyse de sensibilité du modè le de devenir

Il est à noter que certaines valeurs des paramè tres d'entré e varient selon les ressources de la litté rature en raison de l'utilisation de diverses mé thodes, instruments, objets de recherche, etc.

Ainsi, une analyse de sensibilité a é té ré alisé e sous MATLAB afin d'évaluer l'influence des variations des valeurs des paramètres sur les facteurs de devenir des ENPs. Un total de treize variables d'entrée ont été choisies pour effectuer l'analyse de sensibilité. On a supposé que ces paramètres étaient indépendants les uns des autres.

L'importance relative des treize paramè tres é tudié s pré sente de grandes diffé rences (Figure R6).

Le mê me paramè tre peut avoir une importance relative diffé rente dans diffé rentes ré gions. La De plus, l'analyse de sensibilité indique qu'il existe de grandes différences entre les paramètres, mê me pour ceux du mê me groupe. Chaque groupe a non seulement des paramè tres significatifs mais é galement des paramè tres moins importants. Ainsi, nous recommandons de privilé gier les paramè tres les plus importants dans chaque groupe plutôt que tous les paramè tres d'un groupe.

R4 Facteurs de caractérisation des nanoparticules de synthèse R4.1 Calcul des facteurs du devenir d'eau douce pour 17 ENPs

Dans USEtox, le facteur de devenir est le temps de sé jour d'une substance dans le compartiment environnemental considé ré (l'eau douce et les sé diments dans ce travail). Le temps de sé jour plus long a des grands impacts potentiels sur l'environnement ou l'homme.

Selon les ré sultats de l'analyse de sensibilité du modè le du devenir dans le chapitre 3, le taux de dissolution des ENP est le paramè tre d'entré e le plus urgent qui doit recevoir plus d'attention.

Pour obtenir la vitesse de dissolution spé cifique de ENPs, dans ce travail, le pH, la force ionique et la concentration des matiè res organiques dans les milieux expé rimentaux ont tous é té pris en compte. Le Tableau R2 ré sume les constantes de vitesse de dissolution de 17 ENPs en eau douce.

Tableau R2 Les constantes de vitesse de dissolution recommandé es de 17 ENPs en eau douce.

No. ENPs

R4.3 Effect factor for ENPs

Le calcul du facteur d'effet toxicologique humain (EF h ) des CuNPs a é té effectué dans le cadre du modè le USEtox. Les donné es toxicologiques obtenues dans les expé riences in vitro (dans chapitre 2) ont é té utilisé es. L'EF (cases/kg intake ) reflè te le changement de la probabilité de maladie en raison de la modification de l'apport de CuNPs dans la vie entiè re de l'homme. Dans ce travail, on suppose que lorsque le taux de mortalité diminue à la quantité de 50% d'un é chantillon de contrôle, la probabilité de la maladie est de 50%. Les EFs humains au fil du temps ont é té obtenus et ré sumé s dans le Tableau R3.

Tableau R3 Les valeurs EF (cases/kg intake ) pour l'homme au fil du temps. October, 2016 by -Year published‖ (Total), -Year published‖ + topic -nanoparticles‖ (NPs), -Year published‖ + topic -nanoparticles + toxicity‖ (NPs + Tox), respectively.

Temps (h)

Records in each year

Proportions in each year (%)

Year Total NPs NPs + Tox NPs/Total "NPs + Tox"/Total "NPs + Tox"/NPs Always pay attention to molar mass of metal ions or metal oxides and the conversion of the units. , NO 2 -1 , Na + , Mg 2+ , Ca 2+ , K + , etc. The detailed composition of mesocosm water was described in the study of [START_REF] Vencalek | In situ measurement of CuO and Cu(OH) 2 nanoparticle dissolution rates in quiescent freshwater mesocosms[END_REF].

1985 893545 ---- 0.000895 ---- ---- 1986 909202 ---- 0.001540 ---- ---- 1987 920234 ---- 0.001521 ---- ---- 1988 901627 ---- 0.001220 ---- ---- 1989 856823 ---- 0.002334 ---- ---- 1990 881129 ---- 0.002497 ---- ---- 1991

Table D3 Continued

  It has been a great privilege to work in the Laboratory of Nanotechnology and Instrumental Optics (LNIO) at UTT where I enjoyed abundant research resources. I would like to thank our collaborators of Prof. Pierre-Michel Adam and Mr Feng Tang in LNIO. They have given many invaluable suggestions and comments to my research as well as the published article. Thanks Dr. Anne-Laure Baudrion, Ré gis Deturche, Serguei Kochecheev and Jé ré mie Bé al for their constantly and timely training and technical assistance on the operation of various experimental instruments. I have appreciated the insightful discussions with Prof. Nadè ge Troussier in CREIDD on my Ph.D. work. Thanks to Dr. Kim Junbeum in CREIDD for recommending me to one of my supervisors: Bertand. Thanks to Prof. Xiaolu Gong and Dr. Zhidan Sun in LASMIS at UTT for their kind suggestions to my study and living in France and to my future careers.

Figure 0 2 Figure 1 . 1 11 Figure 1 . 2 16 Figure 1 . 3

 021111121613 Figure 0 General framework of the thesis. ................................................................................... 2 Figure 1.1 Nanoproduct distribution by category derived from Nanodatabase (Nanodatabase, 2016). Total amount of nanoproducts registered in Nanodatabase is 2343, while the sum of the numbers in this figure is 2473. It is because some products belong to more than one category. *The category <Cleaning> was separated from <Home and garden>. ....................................... 11 Figure 1.2 Number of the records from 1985 to 2015 in Web of Science of Thomson Scientific (Core Collection) (Web of Science, 2016). Search was made on 2 October, 2016 by <Year published>, <Year published> + topic <nanoparticles>, <Year published> + topic <nanoparticles + toxicity>, respectively. Numeric values are shown in Table A1 in Appendix A. ..................................................................................................................................................... 16 Figure 1.3 Proportions of Records. Calculation was based on the search results (shown in Fig. 1) made in Web of Science of Thomson Scientific (Core Collection) on 2 October, 2016 (Web of Science, 2016). Numeric values are shown in Table A1 in Appendix A. ................................... 17

Figure 1 . 4 19 Figure 1 . 5

 141915 Figure 1.4 Representation of the general methodological framework for LCA (ISO14040, 2006). ..................................................................................................................................................... 19 Figure 1.5 Number of records on applications of USEtox model on conventional products/processes and nanotechnology. All the searches were made on 2 October 2016. The total records in 2016 were simply predicted via dividing the current records in 2016 by a ratio 10/12. The records regarding <conventional> were collected and selected from Web of Science of Thomson Scientific (Core Collection) (Web of Science, 2016) and the official website of

Figure 1 . 6

 16 Figure 1.6 The applications of USEtox model in evaluation the toxicity of nanoproducts according to the year published. The blue arrow shows the relationship between the two studies. Hischier et al., 2015 used freshwater CF of TiO 2 nanoparticles reported by Salieri et al., 2015. 26

Figure 2 . 1

 21 Figure 2.1 Stratification of porcine blood during Ficoll-Paque separation. ............................... 40

Figure 2 . 2 44 Figure 2 . 3 % 48 Figure 2 . 4 49 Figure 2 . 5 50 Figure 2 . 6 51 Figure 2 . 7 52 Figure 3 . 1 61 Figure 3 . 2 66 Figure 3 . 3 68 Figure 3 . 4

 22442348244925502651275231613266336834 Figure 2.2 Mortality rates of porcine neutrophils (5 million/mL) exposed to (a) NiNPs, (b) Al 2 O 3 NPs and (c) CuNPs of different concentrations. The 0 μg/mL represents the unexposed neutrophils as controls. For each of the three ENPs, the vertical error bars represent the standard deviation based on the results obtained in three independent experiments. The horizontal error bars indicate the ranges of time needed for counting cell numbers and the time differences in the

  (a) PECs in San Francisco Bay in North America estimated from the study of (Keller and Lazareva, 2014). (b) PECs in Switzerland extracted from two studies: (Mueller and Nowack, 2008) and (Meesters et al., 2014) (bar with *). (c) PECs in Switzerland based on the predicted emission for the year 2012 by

  . WWTP = wastewater treatment plant. ......................................................................................................... 76

Figure. 3 . 5

 35 Figure. 3.5 The relative importance of ten parameters in four groups by method <Group Parameter SA>. Another three parameters (i.e. Depth sed , C 3 and T water ), with relative importance approximating to 0, are not shown in the histogram. SPM is the abbreviation of suspended particulate matter. A brief description of the sub-continental regions is listed in Figure 3.3. .... 80

Figure 4 . 1

 41 Figure 4.1 (a) Number and (b) Proportion, of the records for 17 ENPs from 1985 to 2015 in Web of Science of Thomson Scientific (Core Collection) (Web of Science, 2016). Search was made on 2 October, 2016 by topics <ENPs> and <ENPs + toxicity>, respectively. The <ENPs> represents the full name of each studied nanoparticles. The <ENPs ± toxicity> represents the topic <ENPs> with or without topic <toxicity>. ......................................................................... 88

Figure 4 . 2

 42 Figure 4.2 The recommended values, <Recommended ranges> and <Ranges> of the fate factors of 10 ENPs in one default region and 16 sub-continental regions. ............................................. 93

Figure 4 . 3

 43 Figure 4.3 The transport rates of CuNPs between freshwater and sediment for (a) W3 and (b) W5 regions. ................................................................................................................................. 94

Figure 4 . 4

 44 Figure 4.4 Ecotoxicological EFs (on trophic levels) for 14 ENPs. The green circle represents that the calculation of this ENP was based on three trophic levels. ............................................ 99

Figure 4 . 5 Figure 4 . 6 Figure 4 . 7

 454647 Figure 4.5 Human and porcine non-carcinogenic CFs of CuNPs for different exposure periods in Europe (W13 region). Error bars represent the 95% confidence intervals. .......................... 100 Figure 4.6 Classification of freshwater ecotoxicological CFs of 14 ENPs in 17 subcontinental regions into five levels. ............................................................................................................. 104 Figure 4.7 World map of the CuNPs characterization factors (CFs), with histograms indicating the variation between the numerical values (Units: 10 4 CTU e ) of seventeen worldwide regions (one default region and sixteen sub-continental regions). The source of the underlying map was from Mapchart website (Mapchart, 2016). ................................................................................ 105

2 O 3

 23 NPs to porcine neutrophils. The corresponding 95% confidence intervals are in brackets. ........................................................... 46

97 Table 4 . 4 Table 4 . 5

 974445 ) for human and pig over time. .............................. Human and porcine characterization factors of CuNPs in 17 subcontinental regions ................................................................................................................................................... 101 Recommended ecotoxicological freshwater characterization factors of 14 ENPs in 17 subcontinental regions ............................................................................................................... 103

( 3 )

 3 Coordinate the toxicological data (extracted from the present in vitro experiments and literatures) and the environmental model to evaluate the toxicity of ENPs to human and ecosystem.

Figure 0

 0 Figure 0 General framework of the thesis.

TChapter 1 1 . 1 1 . 2

 11112 Contents General Introduction ................................................................................................ Nanotechnology and Engineered Nanoparticles (ENPs) ............................................. 1.1.1 Brief introduction to nanotechnology ....................................................................... 1.1.2 Engineered nanomaterials (ENMs) and ENPs .......................................................... 1.1.3 Nanoproducts .......................................................................................................... Release of ENMs in Environment ............................................................................... 1.2.1 Cases of ENMs release affected by intrinsic factors .............................................. 1.2.2 Cases of ENMs release affected by extrinsic factors .............................................. 1.2.3 ENMs in freshwater ................................................................................................ 1.3 General Review of the Researches on Toxicity of ENPs ........................................... 1.4 Quantification of Toxicity ............................................................................................ 1.4.1 Toxicity testing ....................................................................................................... 1.4.2 Life cycle assessment ................................................................ 错误!未定义书签。 1.4.2.1 Life cycle impact assessment........................................................................... 1.4.2.2 Selection of LCIA methodologies .................................................................... 1.5 USEtox Model ............................................................................................................... 1.5.1 Brief historical context of USEtox model ............................................................... 1.5.2 Characterization factor (CF) ................................................................................... 1.5.2.1 CF for freshwater ecotoxicity ......................................................................... 1.5.2.2 CF for human toxicity ..................................................................................... 1.5.3 Applications of USEtox model to conventional technology................................... 1.5.4 Applications of USEtox model to nanotechnology ................................................ 1.5.5 Necessity to develop CFs for ENMs ...................................................................... 1.5.6 Challenges in calculation of nano-specific CFs by USEtox model ........................ 1.5.6.1 Toxicological data for different ENPs ............................................................ 1.5.6.2 Fate model for ENPs: necessity and challenges ............................................. 1.5.6.3 Consideration of regionalized hydrology properties ...................................... References ..............................................................................................................................

Figure 1 .

 1 1 shows the current distribution of nanoproducts by category. Health and fitness major category comprises 55% of total nanoproducts. More precisely, the personal care products (e.g. skin cream, dental gel, shampoo, et al.) and clothing subcategories account for up to 40% of total nanoproducts. Similar results were reported by Vance et al. in 2015. They summarized the number of nanoproducts (excluding archived products) in CHAPTER 1 GENEGERAL INTRODUCTION Consumer Products Inventory (CPI) over time (2007 -2014). Health and fitness category included 42% of the nanoproducts and the personal care products comprise the largest subcategory (39% of Health and fitness category) (Vance et al., 2015).

Figure 1 . 1

 11 Figure 1.1 Nanoproduct distribution by category derived from Nanodatabase (Nanodatabase, 2016). Total amount of nanoproducts registered in Nanodatabase is 2343, while the sum of the numbers in this figure is 2473. It is because some products belong to more than one category. *The category -Cleaning‖ was separated from -Home and garden‖. Over the last decade, the volume of nanoproducts has grown extensively. Since 2005, the Project on Emerging Nanotechnology has reported the nanoproduct inventories called Consumer Products Inventory (CPI) almost every year (except the years between 2010 and 2013) (Nanotechproject, 2016). The CPI records the registered and readily purchased nanoproducts.

CHAPTER 1

 1 GENEGERAL INTRODUCTION Lorenz et al. studied the silver release from 8 different (nano)silver-textiles during a washing and rinsing cycle (Lorenz et al., 2012). The socks originated from the UK with silver nanoparticles incorporated into cotton fibers had the largest release percentages: 19.6% after washing and another 3.9% after rinsing. Interestingly, Lombi et al. investigated the speciation of silver by washing the five (nano)silver-textiles which had previously been used by Lorenz et al (Lorenz et al., 2012; Lombi et al., 2014). Silver nanoparticles were detected in the washing water for four textiles. In addition, several other kinds of silver based nanoparticles were found for all the studied textiles, such as AgCl and Ag 2 S nanoparticles. Al-Kattan et al. exposed panels coated with paint containing SiO 2 nanoparticles to undergo weathering cycles in a climate chamber (Al-Kattan et al., 2015). One weather cycle had 6 hours including 3-hour of UV light, 0.5-hour of irrigation and 2.5-hour of drying. During 89

  Mueller and Nowack, 2008; Gottschalk et al., 2009; Hendren et al., 2013; Gottschalk et al., 2015; Sun et al., 2016). Up to date, only a limited number of cases reported the evidences on release of ENMs into freshwater by analytical methods. By means of analytical electron microscopy and inductive coupled plasma mass spectrometry (ICP-MS), Kaegi et al. observed that the synthetic TiO 2 nanoparticles were detached from new and aged facade paints under natural weather conditions and then transported by facade runoff into natural waters (Kaegi et al., 2008). It is a direct evidence of the ENPs release from urban products into freshwater. Neal et al. investigated the operationally defined dissolved titanium (Ti) (size < 0.45 μm) in river waters in the UK

Figure 1 . 2

 12 Figure 1.2 Number of the records from 1985 to 2015 in Web of Science of Thomson Scientific (Core Collection) (Web of Science, 2016). Search was made on 2 October, 2016 by -Year published‖, -Year published‖ + topic -nanoparticles‖, -Year published‖ + topic -nanoparticles + toxicity‖, respectively. Numeric values are shown in Table A1 in Appendix A.

Figure 1 . 3

 13 Figure 1.3 Proportions of Records. Calculation was based on the search results (shown in Fig. 1) made in Web of Science of Thomson Scientific (Core Collection) on 2 October, 2016 (WebofScience, 2016). Numeric values are shown in Table A1 in Appendix A.

Figure 1 .

 1 Figure 1.3 provides an interesting curve that described the proportion of -nanoparticles + toxicity‖ to -nanoparticles‖. At the beginning of 1990s, up to 8% records of -nanoparticles‖ paid attention to toxicity, and then the proportions declined rapidly from 1994 to 1998. The proportions remained relatively stable from 1998 to 2004 before the remarkable increases. The supports from policy makers speeded up the development of nanotoxicity research in recent years. For example, on 7 June 2005, the EC launched an Action Plan -Nanosciences and nanotechnologies: an action plan for Europe 2005-2009‖ (Kahru and Dubourguier, 2010). The deeper reason for the advancement of nanotoxicity may be the rapid industrial development of nanotechnology and the evidences of specific nanotoxicity.

  For example, Demokritou et al. investigated the toxicological characterization of nanoscale CeO 2 on A549 lung epithelial cells (Demokritou et al., 2013). In vitro is often contrasted to in vivo experiment which is generally performed within living animals (including human) and whole plants (Liu et al., 2009; Johnston et al., 2010; Khlebtsov and Dykman, 2011). For example, Yang et al. studied the toxicity of functionalized nano-graphene oxide to mice by oral injection for different durations (Yang et al., 2013b). Su et al. studied the comprehensive effect of single-walled carbon nanotubes (SWCNTs) and phenanthrene in fish by exposure the fishes to solutions containing phenanthrene and SWCNTs (Su et al., 2013). The in vivo experiments are neither simple nor cheap. In 2007, the US National Research Council (NRC) reported an executive summary entitled -Toxicity Testing in the 21st Century:

  .4: (1) goal and scope definition, (2) life cycle inventory (LCI), (3) life cycle impact assessment (LCIA) and (4) results interpretation and the iterative procedure (ISO14040, 2006; ISO14044, 2006).

Figure 1 . 4

 14 Figure 1.4 Representation of the general methodological framework for LCA (ISO14040, 2006).

1

 1 . A comprehensive comparison between these models was performed by the Joint Research Centre of European Commission. The compared models were evaluated under the criteria defined in International Reference Life Cycle Data System (ILCD) Handbook -Recommendations for LCIA in the European context (ILCD, 2011). Several features make the USEtox model stand out compared to others. The USEtox model includes all vital model elements in a scientifically sound way for most substances, which makes it have the best compliance with the science-based criteria (ILCD, 2010b; 2011). For example, the USEtox model accounts for the species exposure that is required by ILCD -LCIA-Framework and requirements document, while the MEEup model is not. In addition, the USEtox model addresses the freshwater part of the environment problem in a scientifically up-to-date way. The spatial differentiation in the calculation of CFs has also been considered in USEtox model. Therefore, the USEtox is preferred as the default method for the midpoint evaluation of freshwater ecotoxicity impacts and human toxicity impacts (ILCD, 2011). It has also been recommended by many international organizations such as the European Commission (EC), the Business Council for Sustainable Development (WBCSD) (ILCD, 2011; Fantke et al., 2015; Hischier et al., 2015). Brief historical context of USEtox model In April 2004, a Task Force on Toxic Impacts officially launched in Prague under the auspices of the United Nations Environment Program (UNEP) and the Society for Environmental Toxicology and Chemistry (SETAC) (Westh et al., 2014). More details of the model building processes and full description of considered factors in USEtox were respectively provided by Hauschild and Rosenbaum (Hauschild et al., 2008; Rosenbaum et al., 2008). Even though until May 2010, the USEtox was officially announced, the version 1.0 of USEtox had already been freely available via http://www.usetox.org/ on 18 November 2009. USEtox is continuously being improved in order to adapt to the current and future needs. On 31 August 2015, 19

  website of USEtox: http://www.usetox.org/ and the records of topic -USEtox‖ from Web of Science of Thomson Scientific (Core Collection) (Web of Science, 2016). Only the studies that used USEtox model for calculating ecotoxicity and human toxicity were included. The summarized results are shown in TableA2(in Appendix A). A more visualized and brief description of the results is displayed in Figure1.5. The general trend of the applications of USEtox appears to be increasing but, to some extent, fluctuated.

Figure 1 . 5

 15 Figure 1.5 Number of records on applications of USEtox model on conventional products/processes and nanotechnology. All the searches were made on 2 October 2016. The total records in 2016 were simply predicted via dividing the current records in 2016 by a ratio 10/12. The records regarding "conventional‖ were collected and selected from Web of Science of Thomson Scientific (Core Collection) (Web of Science, 2016) and the official website of

Figure 1 .

 1 Figure 1.6 visualizes the applications of USEtox model in nanotechnology which were discussed in Section 1.5.5. It is clear that the development tendency of the USEtox model in nanotechnology is to include the nano-specific behavior into the calculation of CF of ENPs.

Figure 1 . 6

 16 Figure 1.6 The applications of USEtox model in evaluation the toxicity of nanoproducts according to the year published. The blue arrow shows the relationship between the two studies.

Figure 2 . 1 ( 2 )

 212 Figure 2.1 Stratification of porcine blood during Ficoll-Paque separation. (2) Dextran sedimentation. The tubes were completed to 20 mL with PBS and mixed well with 20 mL 3% dextran solution. After setting 30 min in room temperature without lids, upper volume (clear red) was taken to another 50 mL tubes by plastic Pasteur pipettes. The tubes were centrifuged at 4 o C, 1500 rpm for 10 min. Then the upper liquid was removed with pipettes and left the red pellet within approximately 3 mL solution. The tubes with pellet were kept in ice to maintain low temperature.

  Figure B1 in Appendix B. Firstly, a glass slide was placed on top of the hemocytometer.

  Classical microscope glass slides (Carl Roth GmbH & Co.KG, Germany) were cut to 25 × 25 mm and used as substrates. Before dried within air stream, the glass substrates were respectively immersed in 1:9 (detergent Deco 90 : ddH 2 O) solution and ddH 2 O in an ultrasonic water bath for 10 min and 5 min at 50 °C. Further, the dried clean glass substrates were covered with a thin layer (2-3 nm) of gold-palladium by a high resolution sputter coater (SC7640, Polaron, UK) to suppress the charging effects.

For the surface morphology

  characterization of ENPs, different sample preparation procedures were applied. (1) For the pristine sample, ENPs powder without any treatment was spread onto the carbon conductive tape; (2) For the ENPs in water, 200 µg/mL ENPs solution with ddH 2 O was ultrasonicated for 5 min, followed by deposition of one drop of solution onto the goldpalladium covered glass substrate then dried for SEM imaging; (3) For the ENPs in KRP buffer CHAPTER 2 TOXICITY OF ENGINEERED NANOPARTICLES TO PORCINE NEUTROPHILS

Figure 2 .

 2 Figure 2.2a, b and c show that the exposure durations and concentrations of ENPs both have positive correlations on the mortality rates of neutrophils. All the studied ENPs have toxicity to neutrophils with different extent. The mortality rate of unexposed freshly isolated neutrophils was less than 10%, and it became approximately 20% -30% after 10 hours incubation in KRP medium at 38 o C. The data of unexposed neutrophils was treated as a control in the following calculation of LC50. Compared to the other two ENPs, the CuNPs show higher toxicity to neutrophils. The mortality rate of neutrophils exposed to CuNPs (200 μg/mL) for ~11 h reached up to over 90%, while it was ~50% for those exposed to NiNPs and Al 2 O 3 NPs of same concentrations.

Figure 2 . 2

 22 Figure 2.2 Mortality rates of porcine neutrophils (5 million/mL) exposed to (a) NiNPs, (b) Al 2 O 3 NPs and (c) CuNPs of different concentrations. The 0 μg/mL represents the unexposed neutrophils as controls. For each of the three ENPs, the vertical error bars represent the standard deviation based on the results obtained in three independent experiments. The horizontal error bars indicate the ranges of time needed for counting cell numbers and the time differences in the three experiments. (d) The average velocity of mortality rate of neutrophils exposed to CuNPs of different concentrations (0 -200 μg/mL).

5 .

 5 025, 7.175, 9.025 and 11.025 h, respectively), which divided the whole test period into five stages (I, II, III, IV, V in Figure 2.2d). Generally, the velocity of mortality rates of neutrophils displayed a declining trend. In stages I, II and III, they decreased relatively faster than in stages IV and V. The neutrophils exposed to 200 μg/mL CuNPs presented a bit different phenomenon. The AMRS of them are observed to keep stable at the onset (stage I and first half of stage II) then decreases (second half of stage II and stage III). The reason may attribute to their very high mortality rate (57.4%) at the beginning. It indicates that the Figure 2.2d can only offer general

  1 summarizes the results of acute toxicity tests results under different exposure durations. For all the tests conducted with NiNPs or Al 2 O 3 NPs, less than 50% mortality could be achieved in the test solutions of 400 mg/L. Thus, the LC50 values were noted as greater than 400 mg/L. The LC50 values of CuNPs continued decreasing from > 200 to 40.69 mg/L over time. The LC20 values of all the three ENPs decreased over time obviously. It indicates that the neutrophils are very sensitive to the ENPs. The LC50 and LC20 values of CuNPs were much smaller than of corresponding NiNPs and Al 2 O 3 NPs. At ~11h, the LC20 value of CuNPs (2.25 mg/L) was about 30 folds smaller than LC20 values of NiNPs (64.85 mg/L) and Al 2 O 3 NPs (69.90 mg/L). It reveals that CuNPs is much toxic than NiNPs and Al 2 O 3 NPs. The range of NiNPs LC20 values was a bit lower than that of Al 2 O 3 NPs, indicating a bit more toxic of NiNPs than Al 2 O 3 NPs.

  in Appendix B. The CL intensities of neutrophils exposed to different ENPs show very different developing trends with the increasing concentrations of ENPs. To simplify the comparison among the three ENPs, the areas under CL curves (AUC) were respectively calculated based on the original CL spectrums by a build-in algorithm from the software Origin (OriginLab, 2016). Then, in order to eliminate the effects of control in different experiments, a ratio of sample to control was further calculated for each sample as the equation 2.5: (2.5) CHAPTER 2 TOXICITY OF ENGINEERED NANOPARTICLES TO PORCINE NEUTROPHILS Hereafter, the term "relative CL intensity‖ of one sample is used to represent the proportion of its CL to control CL. Figure 2.3 shows the relative CL intensities of neutrophils exposed to three ENPs for different durations. When neutrophils were exposed to CuNPs and NiNPs with low concentrations (≤ 5 μg/mL), the relative CL intensities raised with increasing concentrations of ENPs. When neutrophils were exposed to CuNPs and NiNPs with relative high concentrations (> 5 μg/mL), the relative CL intensities declined with increasing concentrations of ENPs. It indicates that low concentrations of CuNPs or NiNPs could stimulate neutrophils while high concentration could inhibit the oxidative burst of neutrophils. One difference between samples with CuNPs and with NiNPs is that the degree of change. Neutrophils exposed to CuNPs showed almost 180% increase of CL densities to control while neutrophils exposed to

Figure 2 . 3 %

 23 Figure 2.3 % of control CL for neutrophils exposed to (a) CuNPs; (b) NiNPs; (c) Al 2 O 3 NPs for different durations. Error bars represent the standard deviation based on the results obtained in three independent experiments. Even though NiNPs and Al 2 O 3 NPs caused similar mortality rates to neutrophils, the CL results show that the mechanisms are different (Figure 2.3b and c). With increasing Al 2 O 3 NPs concentrations, more neutrophils were dead, while their CL results showed an upward trend. It reveals that the rest living cells exposed to high concentrations Al 2 O 3 NPs released more ROS than those exposed to lower Al 2 O 3 NPs concentrations.We assumed that the death of neutrophils exposed to CuNPs and NiNPs with higher concentration mainly resulted from the inhibition of physiological activity of neutrophils by ENPs. The reason is because the proportion of inhibited relative CL intensities to control was

Figure 2 . 4

 24 Figure 2.4 SEM images of ENPs: (a) and (a1) Pristine NiNPs powder with 50k and 200k magnification times, respectively; (b) and (b1) Pristine Al 2 O 3 NPs powder with 50k and 200k magnification times, respectively.

Figure 2 .

 2 Figure 2.5 shows the morphology of unexposed neutrophils as well as the neutrophils (5 mln/mL) exposed to NiNPs and Al 2 O 3 NPs (200 µg/mL). The unexposed neutrophils (both fresh isolated and incubated 10 hours) exhibited a roughly spherical shape with numerous surface folds as similar as the results in previous literatures (Brinkmann et al., 2004)(Brea et al., 2012).The size of porcine neutrophils is 3~4 μm, a bit smaller that their human counterparts (~6-8 μm)(Khismatullin, 2009;[START_REF] Brea | The pig as a model for investigating the role of neutrophil serine proteases in human inflammatory lung diseases[END_REF]. The morphology of neutrophils incubated 10 hours did not show obvious changes compared to the fresh ones.

Figure 2 . 5

 25 Figure 2.5 SEM images of (a) unexposed freshly isolated neutrophils; (b) unexposed neutrophils incubated for 10 hours; (c) and (c1) neutrophils exposed to NiNPs for 10 hours with 8k and 100k magnification times, respectively; (d) and (d1) neutrophils exposed to Al 2 O 3 NPs for 10 hours with 8k and 100k magnification times, respectively. All the incubations were in KRP medium at 38 o C.

Figure 2 .

 2 Figure 2.5c and d show that NiNPs and Al 2 O 3 NPs are attached on the membrane of exposed neutrophils, respectively. All the membranes of the exposed neutrophils had intact structures as the unexposed ones. Increasing of membrane protrusions was observed in neutrophils exposed to NiNPs. The ENPs attached on the surface of neutrophils had very similar morphologies as the pristine ENPs shown in Figure 2.4.

Figure 2 . 6 .

 26 It reveals that the pristine CuNPs and the CuNPs dispersed in ddH 2 O are granulated, irregular and around 50-100 nm in diameter (Figure 2.6a, a1 and b). After being dispersed in a salt-based KRP buffer solution, these same CuNPs were observed to form Cu nano-sheets (Figure 2.6c, c1 and c2) due to some unknown mechanism. Cu nano-sheets were arranged together forming relative spherical structure with diameter of 15-25 μm. The plan dimension of highly anisotropic nano-sheets can reach over 15 µm (Figure 2.6c1) while the thickness is estimated around 25 nm (Figure 2.6c2 inset).

Figure 2 . 6

 26 Figure 2.6 SEM images of ENPs: (a) and (a1) Pristine CuNPs powder with 50k and 200k magnification times, respectively; (b) CuNPs powder extracted from their dispersion in ddH 2 O; (c), (c1) and (c2) Cu nano-sheets extracted from dispersed CuNPs in KRP buffer solution with different magnification times (0.5k, 2.5k, and 50k times, respectively). The inset in b2 is a SEM image showing that the thickness of the nano-sheet is ~25 nm.

Figure 2 .

 2 Figure 2.7 shows the morphology of neutrophils before and after exposed to 200 µg/mL CuNPs for different durations via SEM imaging. Similar characterization on neutrophils exposed to 100 µg/mL CuNPs was also presented in Figure B3 (in Appendix B).

Figure 2 . 7

 27 Figure 2.7 SEM images of (a) unexposed freshly isolated neutrophils; 5 million/mL neutrophils exposed to 200 μg/mL CuNPs for (b) 0.4 h; (c) 1.1 h; (d) 8 h; (d1) and (d2) with different magnification times (10k and 100k, respectively) are SEM images zoomed in from sample (d).The morphology of unexposed neutrophils is similar as shown in Figure2.5a. The 0.4 h exposed neutrophils kept the spherical shape and intact membranes as the unexposed fresh neutrophils and the size of nano-sheets is around 3 to 5 µm. After exposure to CuNPs for 1.1 h, some of the neutrophils changed their morphology (Figure2.7c). These neutrophils became flatten,

  slaughtering. CuNPs are much more toxic than NiNPs and Al 2 O 3 NPs. The mortality rate of neutrophils exposed to CuNPs (200 μg/mL) for ~11 h reached up to over 90%, while it was ~50% for those exposed to NiNPs and Al 2 O 3 NPs of same concentrations. The 11-h median lethal concentration (LC50) for CuNPs to porcine neutrophils is 40.59 mg/L. The LC20 values of neutrophils exposed to CuNPs, NiNPs and Al 2 O 3 NPs for ~11 h are 2.25, 64.85 and 69.90 mg/L, respectively. The chemiluminescence (CL) kinetic results indicate that low toxicity could stimulate neutrophils while high toxicity could inhibit the oxidative burst of neutrophils. The death of neutrophils exposed to CuNPs and NiNPs with higher concentration mainly results from the inhibition of physiological activity of neutrophils by ENPs. However, the death of neutrophils exposed to Al 2 O 3 NPs may be due to the excessive stimulation of neutrophils. The scanning electron microscope (SEM) characterization exhibits the morphology of exposed neutrophils and the three studied ENPs. An estimated rearrangement of copper nano-sheets initiated by porcine neutrophils is presented.(8) Brea, D.; Meurens, F.; Dubois, A. V.; Gaillard, J.; Chevaleyre, C.; Jourdan, M. L.; Winter, N.; Arbeille, B.; Si-Tahar, M.; Gauthier, F.; Attucci, S. The pig as a model for investigating the role of neutrophil serine proteases in human inflammatory lung diseases. Biochemical Journal 2012, 447,

3. 4

 4 Sensitivity Analysis of the Fate Model ........................................................................ 3.4.1 Sensitivity analysis methods ................................................................................... 3.4.2 Sensitivity analysis results and discussions ............................................................ 3.5 Conclusions ................................................................................................................... References ..............................................................................................................................

A

  new fate model for ENPs in freshwater has been developed based on the previous multimedia fate models (Praetorius et al., 2012; Liu and Cohen, 2014; Meesters et al., 2014; Salieri et al., 2015) introducing the pseudo-sedimentation process and taking different transport processes of ENPs into account. The hydrological data of one default region and 16 sub-continental regions are recommended for the fate model.

3. 2 . 1

 21 Fate model concept The fate model proposed in this thesis mainly focuses on the nano-specific behaviors of ENPs in freshwater compartment on a continental geographic scale. Since the ENPs are usually released into freshwater through the WWTP (wastewater treatment plant) (Gottschalk and Nowack, 2011), it was assumed that there is no direct ENPs emissions into sediment. The following behaviors as shown in Figure 3.1 are considered as the removal processes for freshwater: (1) dissolution (denoted as ); (2) sedimentation (denoted as , and ); (3) transport from continental freshwater to other water compartments: advection (denoted as ). In Figure 3.1, two different mechanisms of sedimentation for ENPs are described: one is individual ENPs sedimentation of free ENPs ( ) and another is the transformation of ENPs with suspended (nano)particles followed by pseudo-sedimentation to the sediment ( and ). Here, pseudosedimentation means that the sedimentation does not reflect the real situation but is calculated CHAPTER 3 FATE MODEL OF ENGINEERED NANOPARTICLES IN FRESHWATER ECOSYSTEM theoretically. The transformation can be divided into two parts based on the sizes of suspended (nano)particles. The association of ENPs with larger suspended particles (> 450 nm) is referred to as -attachment‖, whereas the hetero-aggregation of ENPs with suspended nanoparticles (< 450 nm) is regarded as -aggregation‖ (see Figure 3.1) (Meesters et al., 2014). In reality, the sedimentation process of aggregated (or attached) ENPs is restricted by the slower process of transformation and its corresponding pseudo-sedimentation.

Figure 3 . 1

 31 Figure 3.1 The behavior and transport of ENPs in freshwater and sediment. Theoretically, the aggregation process can be either homo-or hetero-aggregation. Nevertheless, homo-aggregation is disregarded in this model, which is considered a justified simplification because of the more dominant hetero-aggregation between ENPs and suspended particles (such as inorganic colloid particles, biogenic debris, algae and bacteria)(Lartiges et al., 2001) in freshwater (Quik et al., 2014). Recently, a fate model of metal oxide nanoparticles interpreted hetero-aggregation as a removal process (Salieri et al., 2015). However, in this study, the attached and aggregated ENPs were considered as altered species of the same ENPs. The behaviors of ENPs related to sediment are also shown in Figure 3.1. The resuspension from sediment to freshwater (denoted as ) was considered as a transport process for freshwater and a removal process for sediment. The burial into the deep sediment (denoted as ) and horizontal bed load transfer at the surface of the sediment (denoted as ) were only been considered as removal processes for sediment. All the three behaviors were taken into account when calculating the residence time of ENPs in sentiment.

  Dissolution of ENPs is an important property that transforms the nanoparticulate form to the dissolved form (such as ionic form, smaller form and intermediates)(Quik et al., 2011; Conway et al., 2015). Usually, once an ENP has been dissolved, it is no longer considered as an ENP(Meesters et al., 2014). Therefore, dissolution of ENPs was often considered as a removal process for freshwater(Quik et al., 2011; Salieri et al., 2015). Although it was demonstrated that the application of first-order dissolution kinetics for the environmental risk assessment was acceptable(Meesters et al., 2014), modeling dissolution remains highly speculative(Quik et al., 2011). In practice, as a preliminary simplification, the dissolution rate constant k diss (s -1 ) of ENPs is assumed to be 0-10 -5 s -1 for different ENPs(Quik et al., 2011; Salieri et al., 2015). More detailed description of dissolution of ENPs in freshwater can be found in Section 3.2.3.

  , and : The total rate constant of the sedimentation (k sed ) process is calculated as the sum of that for individual ENPs (k 1 , ), aggregated ENPs (k agg-sed , ) and attached ENPs (k att- sed , ). (3.7) As mentioned in Section 3.2.1, the sedimentation rates of attached ENPs and aggregated ENPs respectively depend on the slower process between transformation and its corresponding pseudo-sedimentation process. ; (3.8) where k 2 represents the attachment rate constant; k 3 represents the aggregation rate constant; k 4 and k 5 are the pseudo-sedimentation rate constants for aggregated ENPs and attached ENPs respectively. ENPs attached or aggregated to suspended particles are considered as the altered species. (1). Free ENPs sedimentation rate constant k 1 (s -1 ), pseudo-sedimentation rate constants for aggregated ENPs k 4 (s -1 ) and attached ENPs k 5 (s -1 ) can be calculated as following (Praetorius et al., 2012; Salieri et al., 2015): (3.9) where -particle‖ can be free ENPs, ENPs attached to larger suspended particles (> 450 nm) and ENPs aggregated with suspended nanoparticles (< 450 nm), and (m) is the depth of the water compartment. The gravitational settling velocity of particles (m• s -1 ) is given by: (3.10) where and (kg• m -3 ) is the density of particles or water respectively. The (m• s -2 )

(

  Meesters et al., 2014): (i) f Brown (m 3 •s -1 ): random diffusive agglomeration caused by Brownian motion; (ii) f intercept (m 3 •s -1 ): interception caused by shear flows; and (iii) f grav (m 3 •s -1 ): the particles depositing on top of each other caused by different gravitational settling velocities. Thus, the collision frequency (m 3 •s -1 ) for both attachment and aggregation can be obtained by the following equation (Praetorius et al., 2012; Meesters et al., 2014): (3.20)

  Advection rate constant: k adv (s -1 ) is expressed as (Praetorius et al., 2012; Salieri et al., 2015): (3.24)

( 3

 3 .27) where V sed,transfer (kg• s -1 ) is the velocity of horizontal sediment transfer. The V water (m 3 ) and Depth water (m) are respectively the volume and depth of freshwater. The Depth sed (m) and ρ sed (kg• m -3 ) are respectively the depth and density of freshwater sediment compartment.

  2). Several forms (such as particulate, dissolved form or intermediates) may co-exist because of the dissolution (Kasemets et al., 2009; Conway et al., 2015). The dissolved ENPs in particulate forms (e.g. smaller ENPs, particulate intermediates) have similar behaviors as the undissolved ENPs such as attachments with larger suspended particles (LSP, > 450 nm) and aggregations with suspended nanoparticles (SNP, < 450 nm) followed by sedimentations. The ENPs in ionic form may be absorbed to the suspended materials (e.g. LSP, SNP, ENPs in particulate form) resulting in the complexation and subsequent sedimentation (Misra et al., 2012).

Figure 3 . 2

 32 Figure 3.2 The dissolution of ENPs and subsequent behaviors in freshwater ecosystem.

  and the related input parameters[START_REF] Liu | Multimedia environmental distribution of engineered nanomaterials[END_REF] Meesters et al., 2014). The USEtox model used the regionalization method proposed by a Ph.D. dissertation(Shaked, 2011). The continents were divided into 16 subcontinental regions and a DEFAULT region for unknown regions (see Figure3.3). For each subcontinental region, the USEtox reports various landscape data, such as the surface of the area land, depth of freshwater, volume of freshwater(Fantke et al., 2015). Even though it is not possible to use the original USEtox fate model to model the fate of ENPs, the hydrological data (e.g. the average temperature and depth of freshwater in Europe) is objective and still usable when considering the situation on a continental or global scale(Salieri et al., 2015).

Figure 3 . 3

 33 Figure 3.3 Regionalized world map visualized based on the USEtox model (Fantke et al., 2015) and a Ph.D. dissertation (Shaked, 2011). The further details about the countries within each region can be found in the study of Shaked.

3 (

 3 went from 0.001 to 1(Praetorius et al., 2012). *9. We chose 1230 kg• m -3 to be the density of the suspended particles. According to the previous study(Praetorius et al., 2012), when the density of larger suspended particles (diameters between 1.5 and 80 μm) is fixed at 1100 kg•m -3 , their number concentration ranges from 2.1• 10 10 to 6.3• 10 10 m -3 . Since 1230 kg• m -3 is not far from 1100 kg• m -3 , the average value of 2.1• 10 10 and 6.3• 10 10 m -3 is 4.2• 10 10 m -3 , which was assumed to be the default number concentration of suspended particles. In addition, to do the sensitivity analysis, two extreme values from the previous study(Praetorius et al., 2012), 9.2• 10 9 and 6.3• 10 10 m -3 were used. *10. The number concentration of suspended nanoparticles was reported to be 1• 10 11 m -Meesters et al., 2014). In this thesis, a range of half to two times' recommended value (5•10 10 -2• 10 11 m -3 ) was used for doing sensitivity analysis. *11. Default volume of freshwater in USEtox is 6.76• 10 12 m3 . In order to do the sensitivity analysis, we assumed that the range from half to two times' recommended volume of freshwater.For example, a range from 3.38• 10 12 to 1.352• 10 13 m 3 was used for DEFAULT region (6.76• 10 12 m 3 ). *12. The calculation of dissolution rate of ENPs is quite complex and has high uncertainties due to the limit of current theory and the complex ecosystem(Quik et al., 2011; Meesters et al., 2014). For instance, the dissolution rate constant of TiO 2 NPs was set to 0 in the study by Meesters et al.(Meesters et al., 2014). However, the TiO 2 NPs could be dissolved under certain conditions[START_REF] Schmidt | Dissolution kinetics of titanium dioxide nanoparticles: The observation of an unusual kinetic size effect[END_REF]. As demonstrated by Quik et al., the dissolution rate of ENPs has a range of 0 -10 -5 s -1(Quik et al., 2011). Thus, this range was also applied in this thesis. In addition, as a simple assumption, the average value of 5• 10 -6 s -1 was used as recommend default dissolution rate constant of unknown ENPs. *13. The depth of freshwater sediment compartment in USEtox is 3• 10 -2 m. In order to do the sensitivity analysis, we assumed that the range of depth of freshwater sediment was from half (1.5• 10 -2 m) to two (6• 10 -2 m) times' recommended depth of freshwater sediment compartment (3• 10 -2 m).

* 2 * 3 * 4 5 ( 3 ( 3 ( 10 (

 23453310 The recommended default r LSPs value of 1.05• 10 -5 m was used for North America and Europe; the r LSPs value of 2.5• 10 -6 was applied for Switzerland(Praetorius et al., 2012). The dissolution rate constant of TiO 2 NPs was set to 0 in the study by Meesters et al.However, the TiO 2 NPs could be dissolved under certain conditions[START_REF] Schmidt | Dissolution kinetics of titanium dioxide nanoparticles: The observation of an unusual kinetic size effect[END_REF]. Considering the complex ecosystem, in this study, the range of 0 -10 -5 s -1 was applied (Quik et al., 2011). Mueller and Nowack calculated the water volume of Switzerland as 3.7• 10 9 m 3 ; Sun et al. demonstrated that the volume of the surface water in Switzerland is 5.2• 10 9 m 3 . Quik et al., 2011) Depth of Switzerland freshwater, Depth water (m) Meesters et al., 2014) Depth of Europe freshwater, Depth water (m) Sun et al., 2016) Sun et al., 2014; Sun et al., 2016) CHAPTER 3 FATE MODEL OF ENGINEERED NANOPARTICLES IN FRESHWATER ECOSYSTEM *5 The ENPs concentrations were derived using the calculated emission of ENPs for the SF Bay area divided by the total WWTP effluent. The ENPs concentrations and emission values were

Figure 3 . 4

 34 Figure 3.4 Predicted environmental concentrations (PECs, μg•L -1 ) of five ENPs (CuNPs, AgNPs, TiO 2 NPs, ZnONPs and Fullerenes) based on present fate model and previous studies. (a) PECs in San Francisco Bay in North America estimated from the study of (Keller and Lazareva, 2014). (b) PECs in Switzerland extracted from two studies: (Mueller and Nowack, 2008) and (Meesters et al., 2014) (bar with *). (c) PECs in Switzerland based on the predicted emission for the year 2012 by (Sun et al., 2014). PECs in Europe based on the predicted emission for the year (d) 2012 (Sun et al., 2014) and (e) 2014 (Sun et al., 2016). WWTP = wastewater treatment plant.

  is noteworthy that some input parameter values vary in different literature resources due to the use of diverse methods, instruments, research objects, etc. Thus, a sensitivity analysis was performed by MATLAB (MathWorks, 2016) to assess the influence of variations of parameter values on the fate factors of ENPs. A total of thirteen input variables were chosen for performing sensitivity analysis. It was assumed that these parameters were independent of each other. The ranges of these input variables were obtained from the USEtox model(Fantke et al., 2015) and other literature (Lartiges et al., 2001; Praetorius et al., 2012; Meesters et al., 2014), and listed in Table 3.1. Other input parameters in our fate model are considered constants.A sensitivity analysis (SA) can be performed by varying each parameter within its range while holding the others constant(Walser et al., 2014), which was labeled as -Single Parameter SA‖ in this study. Alternatively, vary each of the model parameters at a time(Igos et al., 2014).Since it was difficult to vary thirteen parameters within their ranges at a time, the studied parameters were divided into four groups according to their natures. The sensitivity analysis was performed by varying the parameters within minimum and maximum values in one group while holding those in other groups as constants. It was labeled as -Group Parameter SA‖. Both methods were used and the detailed method for -Group Parameter SA‖ was described in the following paragraph.For a certain region, if define δi (i=1, 2, 3, … 13) as the studied parameters (r LSPs , ρ SNPs , etc.), the fate factor equation is regarded as: FF (δ1, δ2, δ3, … δ13). When the parameters in one group change their values, those in other groups stay as constants. The maximum (worst scenario) and minimum (best scenario) fate factors can be obtained by changing the values of the parameters in one group while holding those in other groups as constants. When the variables in the studied group are fixed to certain values, the fate factor equation FF (δi) (i=1 or 2 or 3…or 9), respectively, has a maximum (FF(δi)max) and minimum (FF(δi)min) value. In this study, the difference between FF(δi)max and FF(δi)min and is defined as DFF(δi):(3.29)CHAPTER 3 FATE MODEL OF ENGINEERED NANOPARTICLES IN FRESHWATER ECOSYSTEM

Figure 3 . 5

 35 Figure 3.5 The relative importance of ten parameters in four groups by method -Group Parameter SA‖. Another three parameters (i.e. Depth sed , C 3 and T water ), with relative importance approximating to 0, are not shown in the histogram. SPM is the abbreviation of suspended particulate matter. A brief description of the sub-continental regions is listed in Figure 3.3.The same parameter may have different relative importance in different regions. The dissolution rate (k diss ) of ENPs in twelve regions (e.g. W9 and W10), especially in W5 and W6 regions, has much higher relative importance than other parameters. However, it has lower relative importance in DEFAUTL, W3, W4 as well as W14 regions. The reason may be that the dissolution process becomes more influential when ENPs stay longer in the freshwater.

CHAPTER 3 FATEChapter 4

 34 MODEL OF ENGINEERED NANOPARTICLES IN FRESHWATER ECOSYSTEM3.5 ConclusionsA new fate model for ENPs in freshwater has been developed based on the previous multimedia fate models. The dissolution, sedimentation, and advection processes of ENPs are regarded as the removal processes for freshwater, while the resuspension, burial into the deep sediment, and horizontal bed load transfer at the surface of the sediment are considered as the removal processes for sediment. One originality of this model is the sedimentation rates of attached ENPs and aggregated ENPs respectively depending on the slower process between transformation and its corresponding pseudo-sedimentation process. In addition, the regionalized input data of the fate model are derived according to the landscape data in USEtox database and the other fate models, such as SimpleBox4Nano(Meesters et al., 2014). Except for the recommended values, the ranges of some important parameters for one default region and 16 sub-continental regions are also reported.The output of the proposed fate model, usually, is the residence time of ENPs in freshwater or sentiment. Nevertheless, it is also able to calculate the predicted environmental concentrations (PECs) of ENPs with the emission information of ENPs. The functionality of present fate model is tested by comparing the PECs obtained by this model and by the previous studies (i.e. PECs of ENPs in North America, Switzerland and Europe). The PECs of ENPs in freshwater and in WWTP effluent are both compared. Even though the results between present and previous investigations show 1~3 orders of magnitude's differences, the reasons are well explained. Therefore, the proposed fate model is shown to be applicable. Furthermore, for each studied region, the relative importance of thirteen input parameters in the fate model has been evaluated by two methods (i.e. Single Parameter SA and Group Parameter SA). Similar results from the two methods are obtained and indicate that it would be better to focus on the more important parameters in each group rather than all the parameters in one group. Dissolution process of ENPs should receive more attention, in particular for the freshwater with high depth. Characterization factors of Engineered Nanoparticles he aim of this chapter is to obtain the human toxicological and freshwater ecotoxicological characterization factors (CFs) of various ENPs. The first section illustrates the selection criteria and general information of the 17 selected ENPs. Then, the regionalized fate factors (FFs) for ENPs are calculated with considering their specific dissolution rate. Human exposure factor (XF h ) and ecosystem exposure factor (XF e ) are estimated. To obtain the human toxicological effect factor (EF h ) of CuNPs, the method in USEtox model is adapted to the obtained toxicological data from in vitro experiments in this study. In addition, an extensive collection of the ecotoxicological data of ENPs from literatures is performed to calculate the ecotoxicological effect factor (EF e ). Finally, the regionalized noncarcinogenic human CFs of CuNPs and freshwater ecotoxicological CFs of 14 ENPs are presented.

T

Figure 4 .

 4 Figure 4.1 presents the records of 17 ENPs in recent 30 years in Web of Science of Thomson Scientific (Core Collection). Numeric values are summarized in TableD1in Appendix D. The numbers of studies on different ENPs show big differences. In Figure4.1a, it is obvious that the AuNPs is the most studied ENPs with records of 74763, while the toxicity of AgNPs receives the most attentions (with records of 3404). Interestingly, it is found that the developments of toxicological studies for different ENPs are unequal. For example, even the absolute number of studies on toxicity of CeO 2 NPs is 338, ten times smaller than that of AgNPs (3404), its proportion is the largest (14.63%) and almost two times larger than that of AgNPs (7.64%).

Figure 4 . 1

 41 Figure 4.1 (a) Number and (b) Proportion, of the records for 17 ENPs from 1985 to 2015 in Web of Science of Thomson Scientific (Core Collection) (Web of Science, 2016). Search was made on 2 October, 2016 by topics -ENPs‖ and -ENPs + toxicity‖, respectively. The -ENPs‖ represents the full name of each studied nanoparticles. The -ENPs ± toxicity‖ represents the topic -ENPs‖ with or without topic -toxicity‖.

  concentrations for periods from 1990 to 2004 are between 0.8-20 mg• L -1[START_REF] Monteith | Dissolved organic carbon trends resulting from changes in atmospheric deposition chemistry[END_REF].The DOC concentrations ranging from 4-7 mg• L -1 were monitored in Malse River at the border of the Czech Republic and Austria for 1969-2000 (DOC = 1.4 + 0.67 × COD, COD ranges from ~4-8 mg• L -1 )[START_REF] Hejzlar | The apparent and potential effects of climate change on the inferred concentration of dissolved organic matter in a temperate stream (the Malše river, south bohemia)[END_REF]. In addition, between September 2011 and August 2012, the DOC concentration of the water from a 24.8 km 2 reservoir adjacent upland area in UK are 9.0-16.2 mg• L -1[START_REF] Gough | Dissolved organic carbon and trihalomethane precursor removal at a uk upland water treatment works[END_REF]. Therefore, it is assumed that the common DOC concentration in freshwater ranges from 0.8-20 mg• L -1 .In order to simulate the real freshwater or to investigate the interactions between the natural organic matters (NOMs) and ENPs, many experiments added bovine serum albumin (BSA) or humic acid (HA) in solutions. The concentrations can have a range of several orders of magnitude. In this work, only the studies with BSA or HA concentration less than 50 mg•L -1 were took into account due to the following reasons. Generally, the studies related to dissolution of ENPs do not report the DOC or TOC values but the concentrations of HA or BSA. Based on the elemental composition of HA samples(Wang et al., 2015), the TOC (total organic carbon) of HA samples are approximately 40% -50% of NOM. In addition, the molecular formula of BSA is C 8 H 21 NOSi 2 . The mount of TOC is about 47.22% of BSA (via dividing the molecular weight of carbon by that of BSA).The percentages of DOC to TOC of all the samples are about 100%(Wang et al., 2015). Thus, the DOC concentration of a solution with 50 mg• L -1 NOM is about 20-25 mg• L -1 , which is similar as the assumed highest concentration in freshwater (20 mg• L -1 ).

Figure 4 .

 4 Figure 4.2 visualizes the recommended values, -Recommended ranges‖ and -Ranges‖ of the FFs for 9 ENPs in 17 subcontinental regions. The FFs of 9 insoluble ENPs were similar between different ENPs. Thus, only FFs of TiO 2 NPs are shown as an example in Figure 4.2.

Figure 4 . 2

 42 Figure 4.2 The recommended values, "Recommended ranges‖ and -Ranges‖ of the fate factors of 9 ENPs in one default region and 16 sub-continental regions.

Figure 4 .

 4 3 shows the transport rates of CuNPs between freshwater and sediment for two regions (W3 and W5, respectively). The transport rates of CuNPs for other regions are shown in Figure D1 (in Appendix D). The transport rate of CuNPs from freshwater to sediment (k w,sed ) for W3 region is more than 15 times faster than that for W5 region (2.073• 10 -5 versus 1.352• 10 -6 s -1 ), while the inverse transport rate (k sed,w ) for W3 region is less than half of that for W5 region (3.317• 10 -9 versus 7.800• 10 -9 s -1 ). It's the apparent cause of longer residence time in freshwater of W5 region than W3 region.

Figure 4 . 3

 43 Figure 4.3 The transport rates of CuNPs between freshwater and sediment for (a) W3 and (b) W5 regions. Usually, fate model and the hydrological parameters are combined together to predict the behaviors of ENPs in freshwater. In this study, a fate model considering all the possible behaviors of ENPs in freshwater has been proposed. That is to say, no matter in which freshwater, the ENPs must follow all or parts of the behaviors described in this fate model. Then, the input hydrological data becomes the only difference between different regions. Therefore, the subcontinental differences in freshwater parameters were regarded as the primary contributing factors to the different FF values. In the example shown in Figure 4.3, the sedimentation process of CuNPs in W3 region is ~15 times faster than in W5 region because the

to 10 h

 10 post ingestion. A peak of absorption was reached at 6 h post ingestion (~11 μg Ti per liter blood)(Pele et al., 2015). The blood volume of a typical adult is approximately 5 liters[START_REF] Nadler | Prediction of blood volume in normal human adults[END_REF] and titanium is not the essential element for human. Thus, by reworking the results of Pele et al., the amount of TiO 2 NPs in the blood of volunteers was ~91.74 μg per person. It indicates that at least 0.092% of ingested TiO 2 NPs were absorbed into blood stream of human in 6 hours. Although the ingestion of 100 mg TiO 2 was too much as a single dose, it was considered within the daily limits from the dietary sources[START_REF] Lomer | Dietary sources of inorganic microparticles and their intake in healthy subjects and patients with crohn's disease[END_REF] Pele et al., 2015).

  4) were used to calculate the freshwater characterization factors. It is noteworthy that due to lack of data, only 6 ENPs have the toxicological data of all three trophic levels.

Figure 4 . 4 1 -Figure 4 . 4 .

 44144 Figure 4.4 Ecotoxicological EFs (on trophic levels) for 14 ENPs. The green circle represents that the calculation of this ENP was based on three trophic levels. According to the EU-Directive 93/67/EEC classification scheme (EC, 1996; Blaise et al., 2008), chemicals can be classified into five toxicity levels based on the measurement endpoint values

Figure 4 .

 4 5 shows the results in Europe (W13 region) as a visualized example. Both human and porcine characterization factors display upward trends over time. The CFs values for ~11 h are about 30 times of CFs values for ~3 h. It indicates that time is a significant parameter when calculating CFs. Here, we give the recommendations for using CFs values of ~9 h, because the human neutrophils are considered short-lived cells with a half-life in the circulation of approximately 8 hours in humans (Kolaczkowska and Kubes, 2013). Therefore, the recommended human and porcine CFs for W13 region are 17.50• 10 -4 and 47.24• 10 -4 CTU respectively. In addition, no matter during which exposure period, the probability of illness for pig is ~2.7 times higher than that for human when both of them intake the same amount CuNPs during their whole life time. The main reason is the different body weight and lifespan between human and pig.

Figure 4 . 5

 45 Figure 4.5 Human and porcine non-carcinogenic CFs of CuNPs for different exposure periods in Europe (W13 region). Error bars represent the 95% confidence intervals.

. 5 .

 5 Inspired by the classification of EFs, the ecotoxicological CFs of 14 ENPs for 17 subcontinental regions were also classified into five levels: (1) extremely toxic (CF > 10 5 CTU e ); (2) very toxic (10 5 > CF > 10 4 CTU e ); (3) toxic (10 4 > CF > 10 3 CTU e ); (4) harmful (10 3 > CF > 10 2 CTU e ); (5) not toxic (< 10 2 CTU e ).

Figure 4 .

 4 6 displays the classification results. The horizontal order of ENPs in Figure 4.6 follows the EFs order in Figure 4.4. Obviously, the fate of ENPs in freshwater has influences on the toxicity of ENPs. For example, when comparing the ecotoxicological data of ENPs, AgNPs are more toxic than CuNPs (Figure 4.4). However, when taking the fate of ENPs into account, AgNPs are less toxic than CuNPs in W5 and W6 regions. It indicates the necessity to consider the regionalized fate of ENPs in evaluating their toxicity.

Figure 4 . 6

 46 Figure 4.6 Classification of freshwater ecotoxicological CFs of 14 ENPs in 17 subcontinental regions into five levels.As an visualized example, Figure4.7 shows that the potential influences of CuNPs on freshwater aquatic organisms may vary from one region to another. This is mainly because of the large differences in FFs between each subcontinental region. The region most likely to be affected by CuNPs is Africa with a maximum CF of 10.92• 10 4 CTU e . In contrast, CuNPs may

Figure 4 . 7

 47 Figure 4.7 World map of the CuNPs characterization factors (CFs), with histograms indicating the variation between the numerical values (Units: 10 4 CTU e ) of seventeen worldwide regions (one default region and sixteen sub-continental regions). The source of the underlying map was from Mapchart website (Mapchart, 2016).Although this map lacks some precision, it can still show a general view of CuNPs CFs in the world. In the future, we propose to develop a more detailed map. The regions which are geographically close to one another have similar CFs of CuNPs. The differences of CFs between each region are expressed by different colors and the deeper the color on the map the larger is the CF of CuNPs for the related region. Moreover, the color of this map can also represent the length of persistence time of CuNPs in the freshwater.

  been paid to the specific dissolution rate constants of ENPs in the calculation of their fate factors. To obtain the regionalized fate factors for ENPs, a criterion including pH, ionic strength, and concentration of organic matters of experimental media are took into account in the calculation of dissolution rate constants of 17 ENPs. The human exposure factor of CuNPs has been estimated to be 9.174• 10 -4 day -1 by reworking the results from a previous study. The ecosystem exposure factor of ENPs is estimated to be 1 under an ultra-conservatively the assumption that the released ENPs are 100% bioavailable. Human toxicological effect factors of CuNPs are obtained on the basis of the in vitro experiments in Chapter 2. The method in USEtox model has been modified to adapt to the toxicological data from exposing porcine neutrophils to CuNPs. An extensive collection of the ecotoxicological data of ENPs from literatures is performed to calculate the ecotoxicological effect factors. Finally, the regionalized non-carcinogenic human CFs of CuNPs and freshwater ecotoxicological CFs of 14 ENPs have been proposed. It is noteworthy that the CFs proposed in this study should be classified as -indicative‖ in terms of USEtox model because the relatively high uncertainty in addressing fate, exposure and effects of metal or inorganic chemical. It is demonstrated that the fate of ENPs in freshwater has important influences on the toxicity of ENPs and should be considered in the toxicity assessment of ENPs. The proposed CF values fill the gaps between toxicity testing and environmental modeling. These regionalized CFs values can be used in the future LCA, when assessing the impacts of products containing ENPs to human and ecosystem.

  The proposed fate model of ENPs considered the nano-specific behaviors of ENPs in freshwater and sediment. The functionality of present fate model was tested via comparing the predicted environmental concentrations (PECs) obtained by this model and by the previous studies. Even though the results between present and previous investigations showed 1~3 orders of magnitude's differences, the reasons were well explained. Meanwhile, the relative importance of thirteen input parameters in the fate model (such as the density of ENPs, depth of freshwater) has also been evaluated. Dissolution rate constant (k diss ) of ENP was the most important input parameter for this fate model. Therefore, in the following calculation of fate factors (FFs) of 17 different ENPs, dissolution was paid more attention. The ENP-specific k diss was estimated by averaging the reported values in previous literatures. In addition, the spatial differentiation of the toxicity of ENPs in freshwater was studied by using regionalized hydrology parameters. The CF values of a certain ENP in different regions could differ by an order of magnitude, which indicated the significance of considering the spatial properties of ecosystem in evaluation of nanoproducts' impacts.Based on the limited data from previous literatures, the human XF was provisionally estimated to be 9.174• 10 -4 day -1 . It could be updated once the corresponding exposure data of CuNPs are available. The calculations of human toxicological EF and CF were realized with the help of an emerging method that used the toxicological data obtained in in vitro experimentations. Freshly isolated porcine neutrophils were respectively exposed to copper, nickel and aluminum oxide nanoparticles (CuNPs, NiNPs and Al 2 O 3 NPs). The morphologies, mortality rates, and chemiluminescence of neutrophils were observed or monitored. Even though LC50 and LC20 values of the three ENPs were all reported, the precise LC50 values of NiNPs and Al 2 O 3 NPs were not available under the experimental concentrations (0-400 μg/mL ENPs and 5 million/mL neutrophils). Thus, only the human EF of CuNPs was obtained. This work listed the human CFs based on neutrophils exposed to CuNPs for different durations. The CF values of ~9 h were recommended, because the human neutrophils are considered short-lived cells with a half-life in the circulation of approximately 8 hours. The human CF values ranged from 3.12• 10 -4 to 36.12• 10 -4 CTU h for different subcontinental regions. To calculate the ecotoxicological EFs and CFs of ENPs, ecotoxicological data were collected by a comprehensive literature survey under the given criteria. Due to the scarce of data, among 17 kinds of ENPs, only EFs of 14 ENPs were calculated. The ecosystem XF of ENPs was conservatively set to be 1 on the basis of the assumption that 100% ENPs were bioavailable to freshwater aquatic organisms. The recommended ecotoxicological CFs of different ENPs presented large differences. The Fe 3 O 4 NPs had the highest CF in Southern African region (6.23• 10 5 CTU e ), while the NiONPs had the smallest in Southern Australia region (1.53• 10 1 CTU e ). These regionalized CF values could be useful in future when evaluating the environmental impacts of nanoproducts. In addition, the coordination of in vitro toxicological data and environmental model, as well as the proposed fate model for ENPs in freshwater are effective and promising methods for CF calculation. However, the discussion of their limitations is necessary in order to understand better the CF values and to develop more precise results in the future. The main limitations within this work are presented below. (1) The focus of this work is nanoparticles, while the other forms of engineered nanomaterials such as nanotubes, nanosheets are not included. In addition, it assumes that the ENPs have uniform sizes and noncoating surfaces. (2) The use of porcine neutrophils to assess the toxicity of ENPs is more proper for high toxic ENPs. (3) The radius of larger suspended particles in freshwater was treated as one value (1.05• 10 -5 m) in this fate model due to lack of worldwide data. However, in real situation, the suspended particles have a size distribution, which should be considered in future studies. (4) The ENPs are ultra-conservatively estimated that they would be 100% ingested by animals or aquatic organisms. (5) Only freshwater and sediment are considered when calculating the CFs of ENPs. The ecotoxicological CFs can only represent the potential impacts on freshwater system.

à

  30% aprè s 10 heures d'incubation en milieu KRP à 38 o C. Les donné es des neutrophiles non exposé s ont é té traité es comme un contrôle dans le calcul suivant du LC50. Par rapport aux deux autres ENPs, les CuNP pré sentent une toxicité plus é levé e pour les neutrophiles. Le taux de mortalité des neutrophiles exposés aux CuNPs (200 μg/mL) pendant environ 11 h a atteint plus de 90%, alors qu'il é tait de ~50% pour ceux exposé s aux NiNP et Al 2 O 3 NPs de mê mes concentrations.

Figure R1

 R1 Figure R1 Taux de mortalité des neutrophiles porcins (5 millions/mL) exposé s à (a) NiNPs, (b) Al 2 O 3 NPs and (c) CuNPs de différentes concentrations. Le 0 μg/mL représente les neutrophiles non exposé s comme contrôles. Pour chacune des trois ENP, les barres d'erreur verticales repré sentent l'é cart type basé sur les ré sultats obtenus dans trois expé riences indé pendantes. Les

  faible concentration (< 5 μg/mL). Ceci indique que les neutrophiles peuvent avoir besoin de temps pour ê tre complè tement activé s.

Figure

  Figure R2 variation en % de la CL par rapport aux mesures effectué es sur le contrôle pour les neutrophiles exposé s à (a) CuNPs; (B) NiNP; (C) Al 2 O 3 NPs et pour diffé rentes duré es. Les barres d'erreur repré sentent l'é cart type sur la base des ré sultats obtenus dans trois expé riences indé pendantes. Mê me si les NiNPs et les Al 2 O 3 NPs ont provoqué des taux de mortalité similaires aux neutrophiles, les ré sultats des CL montrent que les mé canismes sont diffé rents (Figure R2b et c).

Figure R3

 R3 Figure R3 Images de MEB de (a) neutrophiles fraî chement isolé s non exposé s; (b) Les neutrophiles non exposé s incubé s pendant 10 heures; (c) et (c1) les neutrophiles exposé s aux

Figure R4

 R4 Figure R4 Comportement et transport des ENP dans l'eau douce et le sé diment.

R3. 3 R3. 4

 34 R2) La constante de vitesse d'é limination totale des ENPs dans l'eau douce se traduit par une combinaison des constantes de vitesse pour trois processus d'é limination pour le compartiment d'eau douce: dissolution (k diss ), sedimentation totale (k sed ) et advection (k adv ) comme l'équation R3: (R3)La constante de vitesse de ré ception totale des NP dans le sé diment (k sed,sed , s -1 ) se traduit par une combinaison des constantes de vitesse pour le transfert horizontal (k sed,transfer , s -1 ), le enterrement (k burial , s -1 ) et la resuspension (k resusp , s -1 ) comme l'équation R4:(R4)Les constantes de vitesse de transport des ENPs de l'eau douce au sé diment (k w,sed , s -1 ) et de sé diment à l'eau douce peuvent ê tre calculé s sur la base des deux é quations suivantes: Gammes et valeurs recommandé es pour les paramè tres d'entré e Le comportement des colloï des en eau douce peut ê tre dé crit basé sur une combinaison du modè le de devenir et les paramè tres d'entré e . Le modè le USEtox a utilisé la mé thode de ré gionalisation proposé e par une thè se de doctorat . Les continents ont é té divisé s en 16 ré gions souscontinentales et une ré gion DEFAULT pour les ré gions inconnue. Pour chaque ré gion souscontinentale, USEtox rapporte diverses donné es de paysage, telles que la surface de la zone et la profondeur de l'eau douce, le volume d'eau douce . Mê me s'il n'est pas possible d'utiliser le modè le original du devenir de USEtox pour des ENPs, les donné es hydrologiques sont objectives et utilisables, lorsqu'on considè re la situation à l'é chelle continentale ou mondiale. Afin de rendre le modè le de devenir plus flexible, un groupe de paramè tres gé né raux d'entré e a é té recommandé comme valeurs par dé faut basé es sur la base de donné es du modè le USEtox. Lorsque les donné es USEtox n'é taient pas disponibles, les valeurs des paramè tres issues de la litté rature et d'autres modè les de devenir ont é té pré fé ré es. De plus, en raison des diffé rences significatives de paramè tres de l'eau douce entre les ré gions souscontinentales (tels que l'Afrique, l'Amé rique, l'Asie et l'Europe) cinq paramè tres ré gionaux spé cifiques au modè le USEtox pour 16 ré gions souscontinentales diffé rentes ont é galement é té recommandé s. Évaluation du modè le de devenir Dans cette é tude, les concentrations environnementales pré vues (PECs) de cinq ENPs (le cuivre, l'argent, le dioxyde de titane, l'oxyde de zinc et les fullerè nes, nanoparticules) ont é té calculé s en utilisant notre modè le de devenir dans les mê mes scé narios d'é missions que les é tudes anté rieures (un en Amé rique du Nord, deux en Suisse et deux en Europe) . La comparaison des PECs des ENPs de cette é tude et des é tudes pré cé dentes est pré senté e dans la Figure R5 (les valeurs numé riques sont indiqué es dans le Tableau C2 de l'annexe C).

Figure R5

 R5 Figure R5 Concentrations environnementales pré dites de cinq ENPs (CuNPs, AgNPs, TiO 2 NPs, ZnONPs and Fullerenes) basé es sur le modè le actuel du devenir et des études pré cé dentes. (a) PECs dans la baie de San Francisco en Amé rique du Nord. (b) Les PEC en Suisse extraits de deux études (un est noté avec *). (c) PEC en Suisse sur la base des émissions pré dictives pour l'anné e 2012. PEC en Europe sur la base des é missions pré dictives de l'anné e (d) 2012 et (e) 2014 . WWTP = station de traitement des eaux usé es.

  constante de vitesse de dissolution des ENPs (k diss ) dans douze ré gions (e.g. W9 et W10), en particulier dans les ré gions W5 et W6, a beaucoup plus d'importance relative que les autres paramè tres. Cependant, il a une importance relative moindre dans les ré gions DEFAULT, W3, W4 ainsi que W14 ré gions. La raison peut être que le processus de dissolution devient plus influent lorsque les ENPs restent plus longtemps dans l'eau douce.

Figure R6

 R6 Figure R6 Importance relative de dix paramètres dans quatre groupes par la méthode -Group Parameter SA‖. Trois autres paramètres (i.e. Depth sed , C 3 and T water ), avec une importance

Figure

  Figure R7 visualise les valeurs recommandé es, "gammes recommandé es" et "Gammes" des FFs pour 9 ENPs dans 17 ré gions subcontinentales. Les FF de 9 ENP insolubles é taient similaires entre les diffé rentes ENP. Ainsi, les FF de TiO 2 NPs sont seulement repré senté s à titre d'exemple dans la Figure R7.

Figure R7 R4. 2

 R72 Figure R7Les valeurs recommandé es, "Gammes recommandé es" et "Gammes" des facteurs de destin de 9 ENPs dans une ré gion par dé faut et 16 ré gions souscontinentales.

R4. 3

 3 Facteur d'effet é cotoxicologique en eau douce pour 14 ENPsUne revue de la litté rature a é té ré alisé e pour recueillir les donné es é cotoxicologiques pour le calcul du facteur d'effet é cotoxicologique en eau douce (EF e ). On pré fè re les donné es provenant d'essais en laboratoire avec des algues (producteurs primaires), des crustacé s (consommateurs primaires) et des poissons (consommateurs secondaires), qui repré sentent les trois niveaux trophiques de l'é cosystè me. Le calcul des valeurs EF é cotoxicologiques é tait basé sur le niveau trophique, ce qui é vite les inconvé nients causé s par la ré partition iné gale de la EC50 dans trois niveaux trophiques. La FigureR8pré sente les EF é cotoxicologiques pour 14 ENPs.

Figure R8 EF é cotoxicologiques pour 14 ENPs. R4. 4

 4 Figure R8 EF é cotoxicologiques pour 14 ENPs.

Figure R9

 R9 Figure R9 CFs Humains de CuNNPs pour diffé rentes pé riodes d'exposition dans la ré gion W13. Les barres d'erreur repré sentent les intervalles de confiance de 95%.

Figure R10

 R10 Figure R10 Carte du monde des facteurs de caracté risation des CuNPs, avec des histogrammes indiquant la variation entre les valeurs numé riques (unité s: 10 4 CTU e ) de 17 ré gions mondiales (une ré gion par dé faut et seize ré gions souscontinentales). La source de la carte sous-jacente provenait du site Mapchart (http://mapchart.net/).

Figure R11 4 àR5. 2

 R1142 Figure R11 Classification des CFs é cotoxicologiques de 14 ENPs pour 17 ré gions souscontinentales en cinq niveaux.

  (Koehler and Wildbolz, 2009) Nine home-care and personal-hygiene products E[START_REF] Gandhi | New method for calculating comparative toxicity potential of cationic metals in freshwater: Application to copper, nickel, and zinc[END_REF] Three cationic metals in freshwater: copper, nickel and zinc E[START_REF] Laurent | Carbon footprint as environmental performance indicator for the manufacturing industry[END_REF] Six greenhouse gas: CO 2 , CH 4 , N 2 O, SF 6 , HFCs and PFCs NC (Lemming et al., 2010) Three remediation options of a trichloroethene-contaminated site E NC C (Van Caneghem et al., 2010) Substances emitted to air by the Flemish industry NC C (Askham, 2011) Two competing epoxy-/polyester-based powder coating products E NC C (Bamard et al., 2011) Different petroleum mixtures; different fractions of the same mixture E (Berthoud et al., 2011) Pesticide application on winter wheat E (Demou et al., 2011) Thirty-eight solvents, such as acetone, acrolein, chloroform, etc. NC C (Fantke et al., 2011) Diffuse emissions of 121 pesticides to urban air and agricultural soil NC C (Gandhi et al., 2011) Cu, Ni and Zn for 24 Canadian ecoregions E (Juraske and Sanjuan, 2011) Twenty-five pesticides used in production of oranges in Spain E (Mattila et al., 2011) Emission inventory from Finnish Environmental Administration in 2005 E H (Pizzol et al., 2011b) Twelve metals: Sb, As, Ba, Be, Cd, Cr, CrVI, Pb, Hg, Ni, Ag, Tl NC C (Pizzol et al., 2011a) Sixteen metals: Sb, As, Ba, Be, Cd, Cr, CrVI, Co, Cu, Pb, Hg, Ni, Ag, Tl, V, Zn E (Querini et al., 2011) Three energy pathways: gasoline, diesel fuel and hard coal electricity NC C (Saouter et al., 2011) Sixty-nine chemicals common in personal care and cleaning products E (Van Hoof et al., 2011) Two laundry products with three formats: powder, dilute-and concentrated-liquid E (Bartl et al., 2012) Eight dominant crops produced in a Peruvian coastal valley NC (Herrmann et al., 2012) Eight pathways of the production and use of rapeseed biodiesel E C (Ingwersen, 2012) Fresh pineapple from the farm of Costa Rica to retail shelf in the US E H

(Figure B2

 B2 Figure B2 Representations of CL spectra of neutrophils exposed to (a) CuNPs for 4.60 h; (b) NiNPs for 4.74 h; (c) Al 2 O 3 NPs for 4.52 h with different ENPs concentrations.

Figure B3

 B3 Figure B3 SEM images of neutrophils exposed to 20 µg/mln CuNPs for 10 h with different magnification times: (a) 2,000 times; (a1) 15,000 times; (a2) 5,000 times; (a2') 10,000 times.

Figure B4 * 1 (

 B41 Figure B4 SEM images of neutrophils exposed to 1 µg/mln CuNPs for (a) 0.4 h; (b) 8 h and (c) to 2 ug/mln CuNPs for 8 h. (a1), (b1) and (c1) are the SEM images respectively zoomed in from (a), (b) and (c).

* 2

 2 Ag PVP-stabilized silver NPs: poly(vinylpyrrolidone) coated silver NPs. *3 Citrate-stabilized AgNPs. Though dissolution data during 1 min to 7 hours were reported, only those of 4 h and 7 h are selected in this study. The reason is described as following: The persistence time of NPs in freshwater was supposed to be longer than several hours up to several years(Pu et al., 2016). In addition, the dissolution rate of NPs declines fast at beginning (particularly within the first 2 h), then it becomes relative stable[START_REF] He | Effects of aggregate structure on the dissolution kinetics of citrate-stabilized silver nanoparticles[END_REF]. Thus, the dissolution data collected in less than 2 hours have not been considered in this work. 1mM = 0.001 mol• L -1 165 APPENDIX D

2 . 2944 ( 5 ( 9 * 1 (

 22944591 Rodea-Palomares et al., 2011) 13 Anabaena CPB4337 Chronic; luminescence inhibition (24h) EC50=0.56 0Rodea-Palomares et al., 2011) 176 Anabaena CPB4337 Chronic; luminescence inhibition (24h) EC50=8.Three different quantification methods were used. Here, the -cell counting‖, a conventional method, is selected. *2 Poly (acrylic acid)-stabilised CeO 2 NPs. Garcí a et al.

Fish

* 2 .

 2 The authors reported two LC50 values for CuNPs. The exposure conditions of the fishes were different. One was in a continuously stirring chamber and another was in a static beaker. Here, considering the dynamic natural flow of the freshwater, the data obtained from the stirring chamber was selected. APPENDIX D 192

  de cette thèse est d'améliorer la compréhension de la toxicité de diverses nanoparticules de synthèse (ENPs) pour l'homme et l'écosystème. Les travaux réalisés s'appuient sur la combinaison de données toxicologiques et d'un modèle environnemental -le modèle USEtox. En tant qu'élément important de l'évaluation de l'impact du cycle de vie, le facteur de caractérisation (CF) a été utilisé, dans ce travail, comme indicateur de toxicité pour l'homme et l'écosystème. Pour avoir accès aux courbes dose-réponse et à différentes données toxicologiques, des expériences in vitro ont été réalisées en exposant des neutrophiles porcins fraîchement isolés à trois types de nanoparticules de synthèse. Les modifications morphologiques, les taux de mortalité et la chimioluminescence des neutrophiles ont été évaluées. De plus, pour estimer le temps de persistance des nanoparticules de synthèse dans l'écosystème eau douce, un modèle basé sur la science des colloïdes a été développé. Il prend en compte les comportements spécifiques des nanoparticules de synthèse et inclut des recommandations sur le choix des paramètres hydrologiques régionaux. Enfin, une enquête documentaire exhaustive a été réalisée pour recueillir les données écotoxicologiques de diverses nanoparticules de synthèse. Dans le cadre du modèle USEtox, le CF toxicologique non cancérogène pour cuivre NPs et les CF écotoxicologiques pour 14 ENPs sont recommandés. Ces valeurs des CF pourraient être utiles à l'avenir pour évaluer les impacts environnementaux des produits contenant des ENPs. Mots clés : nanoparticules -écotoxicologieneutrophiles -analyse du cycle de vie -tests de toxicité in vitro -chimiluminescence. this thesis is to improve understandings of toxicity of various engineered nanoparticles (ENPs) to human and ecosystem. It is realized via coordinating toxicological data and a scientific consensus environmental model --the USEtox model. As an important element in life cycle impact assessment, the characterization factor (CF) is employed as a toxicity indicator for human and ecosystem in this work. To obtain the firsthand dose-response phenomena and human toxicological data, in vitro experiments have been conducted by exposing freshly isolated porcine neutrophils to three kinds of ENPs (i.e. copper, nickel and aluminum oxide nanoparticles). The morphologies, mortality rates, and chemiluminescence, of neutrophils are observed or monitored. Additionally, to estimate the persistence time of ENPs in freshwater ecosystem, a fate model on the basis of colloid science is developed. It takes nano-specific behaviors of ENPs into account and includes recommendations of regionalized hydrological parameters. Finally, a comprehensive literature survey is accomplished to collect the ecotoxicological data of various ENPs. Under the framework of USEtox model, the non-carcinogenic human toxicological CFs for copper NPs and the ecotoxicological CFs for 14 ENPs are recommended. These CF values could be useful in the future when evaluating the environmental impacts of products containing ENPs.

Table 1 . 1

 11 Summary of studies regarding ecotoxicity (E) and human toxicity (NC: noncarcinogenic; C: carcinogenic; H: Not specify NC or C) assessment on nanotechnology. ...23

Table 2 . 1

 21 Toxicological data for CuNPs, NiNPs and Al

Table 3 . 1 .

 31 Recommended default values and ranges of input parameters for the fate model* ..69

Table 3 . 2

 32 Hydrology data for one default region and 16 different continental regions parameters of ENPs, freshwater and sediment collected from the USEtox model (Frantke et al., 2015). .... 72

Table 3 . 3

 33 Dissolution data and some specific parameters related to North America, Switzerland and Europe. ................................................................................................................................. 74

Table 4 . 1

 41 Basic information and abbreviations of the 17 selected nanoparticles (NPs).* .......... 87

Table 4 . 2

 42 Recommended dissolution rate constants of 17 ENPs in freshwater.......................... 91

Table 4 . 3

 43 Effect factor values (cases/kg intake

  to textiles (Yan et al., 2012; Lombi et al., 2014; Mitrano et al., 2014) paints (Al-Kattan et al., 2015) to electronics

Table 1 .1 Summary of

 1

	studies regarding ecotoxicity (E) and human toxicity (NC:
	noncarcinogenic; C: carcinogenic; H: Not specify NC or C) assessment on nanotechnology.

Num. Reference Target products or processes Impact category Ecotoxicity Human toxicity Non- cancer Cancer

	1	(Eckelman et al., 2012)	Three methods of synthesis carbon nanotoubes (CNTs); CNTs releases	E
	2	(Rodriguez-Garcia et al., 2014)	Single-and multi-walled CNTs	E	NC
	3	(Pizza et al., 2014)	Epoxy-based composites, filled with graphite nanoplatelets	E	H
	4	(Upadhyayula et al., 2014)	Carbon nanotube enhanced field emission display	E	NC	C
	5	(Miseljic and Olsen, 2014)	Products respectively containing Ag ENPs and TiO 2 ENPs	E
	6	(Hischier et al., 2015)	Three faç ade coating systems -SiO 2 and -Ag respectively containing nano -TiO 2 ,	E	H
	7	(Pourzahedi and Eckelman, 2015)	Nanosilver-enabled medical bandage		NC	C
	8	(Salieri et al., 2015)	TiO 2 nanoparticles	E
	9	(Deng et al., 2016)	Graphene oxide nanomaterial	E
	10	(Pini et al., 2016)	Nano-TiO2 in indoor and outdoor environments		NC	C
	11	(Pu et al., 2016)	Cu nanoparticles	E
	1.5

.5 Necessity to develop CFs for ENMs

	Pizza et al. conducted a conventional LCA on nanocomposites made of thermally conductive nanoproducts, no releases of nanoparticles were considered because of no available
	graphite nanoplatelets (Pizza et al., 2014). USEtox model was applied to calculate the toxicological CFs for SiO 2 and Ag nanoparticles. They stressed the urgency of the development
	environmental impacts. However, the authors did not consider the nanowastes and nanoparticles of appropriate CFs for ENMs.
	emissions due to lack of robust inventory. Upadhyayula et al. evaluated the environmental
	impacts of a field emission display containing CNTs by USEtox model (Upadhyayula et al., 1.5
	2014). They assumed that there was no release of CNTs during the whole life cycle of the
	product.
	Eckelman et al. used the USEtox model to quantify and compare the freshwater ecotoxicity of
	CNTs over their life cycle (Eckelman et al., 2012). The distinction between single-walled and
	multi-walled CNTs was not considered due to lack of data. Rodriguez-Garcia et al. reported the
	human non-cancer CF and the freshwater ecotoxicity CFs for single-and multi-walled CNTs
	(Rodriguez-Garcia et al., 2014). The above two studies both treated carbon nanotubes (CNTs) as
	conventional substances by assuming many partitioning coefficients (such as octanol-water,
	organic carbon-water) required by USEtox. However, the assumptions of these partition
	coefficients for CNTs were considered to be likely to result in significant errors in the toxicity
	assessment (Praetorius et al., 2014).
	Miseljic and Olsen reported the ecotoxicity impact of nano-Ag and nano-TiO 2 containing
	products. They did not consider the nano-specific behavior of ENPs in freshwater but simply set
	the residence time of ENPs in freshwater as 1 day (Miseljic and Olsen, 2014).
	Pourzahedi and Eckelman performed a life cycle assessment for a commercially available
	nanosilver-enabled medical bandage (Pourzahedi and Eckelman, 2015). USEtox model was
	used to assess the impacts on human health and ecosystem. Their studies focused on the 6.8%
	silver nanoparticles deposited in surface water. It was estimated that 50% silver ions were
	dissociated from nanoparticles deposited in surface waters. The amount of silver dissociated per
	bandage was between 0.002 and 0.04 mg. Then, the characterization factors for silver
	nanoparticles were calculated based on the silver ions in surface waters.
	To my knowledge, except us, only three publications reported the CFs for ENMs under the
	framework of USEtox. (1) Salieri et al. calculated the freshwater ecotoxicological CF for TiO 2
	nanoparticles with considering nano-specific behavior of ENPs in freshwater (Salieri et al.,
	2015). (2) Deng et al. also took into account the nano-specific behavior of ENMs and derived
	CFs for freshwater ecotoxicity of graphene oxide (Deng et al., 2016). (3) Pini et al. proposed
	both human carcinogenic and non-carcinogenic CFs for nano-TiO 2 for indoor and outdoor
	One major bottleneck of using USEtox on nanotechnology is that there is no available CFs for environments with aide of a multimedia fate model (Pini et al., 2016).
	ENMs in the USEtox database. Even though several studies have employed USEtox for Hischier et al. performed a LCA of faç ade coating systems containing TiO 2 /SiO 2 /Ag nanotechnology as listed in Table 1.1, most of them did not fully consider the nano-specific nanoparticles by using USEtox model (Hischier et al., 2015). For the product containing TiO 2 behavior of ENMs in environment. nanoparticles, the CF reported by Salieri et al. was used. However, for the other two

.6 Challenges in calculation of nano-specific CFs by USEtox model

	The calculation of CFs for ENPs by using USEtox model may meet many challenges such as
	insufficient toxicity data (Eckelman et al., 2012), unclear behavior of ENPs (Pourzahedi and
	Eckelman, 2015), unknown bioavailability (Klaine et al., 2008), unavailable partition
	coefficients of ENPs (Praetorius et al., 2014).
	1.5

.6.1 Toxicological data for different ENPs

	Effect factor (EF) is an important element for CF calculation. USEtox calculates the EF based
	on the toxicological data reported in the literatures. Although there are many toxicological
	studies on ENPs, most of them focused on the toxic mechanisms of ENPs and did not report the
	toxicological data (e.g. EC50) required by USEtox (Aalapati et al., 2014; Sabella et al., 2014;
	Shakeel et al., 2016). The EC50 data is needed for calculating the ecotoxicological EF. It is the
	effective concentration of a substance at which 50% of a population (e.g., fish) displays a
	phenomenon (e.g., immobility, reproduction).
	Human toxicological EF calculates on the basis of the in vivo experiments on animals, such as
	pig, rat, and mouse. These data are usually related to inhalation or oral (ingestion) exposure
	(USEtox TM , 2016a). However, besides sacrificing a relative large number of animals, the in vivo
	toxicity testing is also considered low throughput, expensive and time consuming (NRC, 2007;
	Bhattacharya et al., 2011). The development of toxicity testing tends to acquire knowledge of
	key issues based on in vitro methods rather than employing animal in vivo methods (NRC, 2007;
	Berg et al., 2011). Thus, not only for ENPs but also for conventional substances, in order to
	calculate the human toxicological CF, there is a call for implementation of emerging methods to
	use the toxicological data based on the in vitro experimentations.

Hischier et al., 2015 used freshwater CF of TiO 2 nanoparticles reported by Salieri et al., 2015. The difficulty in predicting the residence time of ENPs in ecosystem is one major challenge for

	CHAPTER 1 GENEGERAL INTRODUCTION
	The model describing nanomaterial fate and transport always need a validation (Nowack et al.,
	2015). However, as demonstrated in Section 1.2.3, the detection and quantification of ENMs in
	freshwater by analytical methods are still difficult. Therefore, the validation of the proposed
	nano-specific fate model becomes a challenge.
	using USEtox. Compared to the conventional chemical substances, ENPs show many different
	fate behaviors in freshwater due to their nano-specific properties (such as low solubility, low
	vapor pressure, high surface reactivity) (Salieri et al., 2015). The degradation process of
	conventional chemicals is irrelevant for ENPs, because the ENPs will undergo transformation
	processes (such as aggregation, dissolution and surface transformation) altering their original
	properties (Quik et al., 2011). In addition, most conventional chemicals in water form a true
	solution of a low-molecular-weight substance, while ENPs in water form thermodynamically
	unstable suspensions of particles with high surface energy (Praetorius et al., 2012). Therefore,
	the fate of ENPs in freshwater is hard to predict by using the prior experience for conventional
	chemicals. It is necessary to propose a nano-specific fate model.

1.5.6.3 Consideration of regionalized hydrology properties

	(111) Westh, T. B.; Hauschild, M. Z.; Birkved, M.; Jørgensen, M. S.; Rosenbaum, R. K.; Fantke, P. The
	usetox story: A survey of model developer visions and user requirements. The International Journal
	of Life Cycle Assessment 2014, 299-310.
	(112) Xiong, D.; Fang, T.; Yu, L.; Sima, X.; Zhu, W. Effects of nano-scale TiO 2 , ZnO and their bulk
	counterparts on zebrafish: Acute toxicity, oxidative stress and oxidative damage. Science of the
	Total Environment 2011, 409, (8), 1444-1452.
	(113) Yan, Y.; Yang, H.; Li, J.; Lu, X.; Wang, C. Release behavior of nano-silver textiles in simulated
	perspiration fluids. Textile Research Journal 2012, 82, (14), 1422-1429.
	The importance of including spatial differentiation in the calculation of CFs for ecotoxicity has (114) Yang, J.; Zhang, Y.; Kim, D. Y. Electrochemical sensing performance of nanodiamond-derived
	carbon nano-onions: Comparison with multiwalled carbon nanotubes, graphite nanoflakes, and been hardly addressed in LCA (ILCD, 2011). Within the USEtox model, the fate factor of a glassy carbon. Carbon 2016, 98, 74-82. conventional substance depends on two elements (USEtox TM , 2016a): (1) the physicochemical (115) Yang, K.; Feng, L.; Shi, X.; Liu, Z. Nano-graphene in biomedicine: Theranostic applications. properties of the substance; (2) the regionalized landscape properties of the studied Chemical Society Reviews 2013a, 42, (2), 530-547.
	compartments (such as the depth of the freshwater in central Asia, surface wind speed of (116) Yang, K.; Gong, H.; Shi, X.; Wan, J.; Zhang, Y.; Liu, Z. In vivo biodistribution and toxicology of
	functionalized nano-graphene oxide in mice after oral and intraperitoneal administration. northern Europe, human population in southern Africa). Although the original fate model in Biomaterials 2013b, 34, (11), 2787-2795.
	USEtox is not applicable for ENPs, the landscape data are objective. However, not all the data (117) Zhang, C.; Hu, Z.; Deng, B. Silver nanoparticles in aquatic environments: Physiochemical behavior
	in USEtox database are usable for ENPs and not all the needed data for ENPs exist in the and antimicrobial mechanisms. Water Research 2016, 88, 403-427.
	USEtox database. Thus, it requires a recommendation for the regionalized hydrological
	parameters as input data of nano-specific fate model.

Chapter 2 Toxicity of Engineered Nanoparticles to Porcine Neutrophils his

  chapter reports the first study on the toxicity testing of ENPs to neutrophils.

	Porcine neutrophils are exposed to three kinds of ENPs (i.e. copper, nickel and
	aluminum oxide nanoparticles) of different concentrations (0-400 μg/mL) for up to
	~11 hours. During exposure, at certain time intervals, cells counting and chemiluminescence
	(CL) measurements were performed. The morphology of exposed neutrophils and ENPs was
	characterized by scanning electron microscope (SEM). In comparison to the in vivo methods,
	exposure to porcine neutrophils of ENPs in vitro is a rapid and less expensive method with no
	animal purposeful sacrifice. The mortality results show that CuNPs are much more toxic than
	NiNPs and Al 2 O 3 NPs. The median lethal concentration (LC50) and LC20 for ENPs to porcine
	neutrophils are also reported.

T

Contents Chapter 2 Toxicity of Engineered Nanoparticles to Porcine Neutrophils ............................ 2.1 Introduction .................................................................................................................. 2.2 Experimental Methods .................................................................................................

  Isolation of neutrophils .............................................................. 错误!未定义书签。 2.2.3 Cell counting and mortality rate calculation ........................................................... 2.2.4 Statistical analysis ................................................................................................... 2.2.5 Preparation of ENPs solution and exposure neutrophils to ENPs .......................... 2.2.6 Morphology characterization of neutrophils and ENPs .......................................... 2.2.7 Chemiluminescence measurements ........................................................................

	2.2.1 Materials and instrumentations ...............................................................................
	2.2.1.1 Materials .........................................................................................................
	2.2.1.2 Instrumentations .............................................................................................
	2.2.2

2.3 Results and Discussions ................................................................................................ 2

  .3.1 Mortality of neutrophils exposed to ENPs ..............................................................

	2.3.2 Toxicological data of ENPs ....................................................................................
	2.3.3 Chemiluminescence of neutrophils exposed to ENPs ............................................
	2.3.4 SEM characterization of ENPs and neutrophils .....................................................
	2.3.4.1 SEM characterization of NiNPs, Al 2 O 3 NPs and neutrophils ..........................
	2.3.4.2 SEM characterization of CuNPs and neutrophils ...........................................
	2.3.4.3 Rearrangement of copper nano-sheets initiated by neutrophils .....................
	2

.4 Conclusions ................................................................................................................... References .............................................................................................................................. 2.1 Introduction

	The many similarities between pig and human (genetics, physiology, anatomy, etc.) make pig as then he died 13 days after being exposed, which confirmed that NiNPs can cause acute disease
	Given the rapid development of nanotechnology, exposure of ENPs to human is unavoidable an excellent animal biomedical model for human (Brea et al., 2012). For example, Meurens et al. in humans (Rendall et al., 1994; Phillips et al., 2010). However, toxicity studies on NiNPs are
	(Oberdorster et al., 2005; Schilling et al., 2010; Von Goetz et al., 2013). Many evidences have highlighted that there were numerous advantages of the pig model for vaccine development and rarely reported, which is a reason for choosing NiNPs in this work.
	shown that ENPs can potentially reach the bloodstream of human or other mammals via various the use of pigs could acquire new knowledge on both animal and human health (Meurens et al.,
	routes, such as oral (Pele et al., 2015), inhalation (Oberdorster et al., 2002; Gosens et al., 2016) 2012).
	and skin penetration (Holmes et al., 2016). During the defense with microbial intruders, the consumption of molecular oxygen by
	When ENPs enter the blood of mammals, neutrophils are considered as the first responder to be neutrophils, called as oxidative burst, increases (Freitas et al., 2009). Meanwhile, neutrophils
	produce large amount of toxic oxygen radicals, which is known as reactive oxygen species recruited to resist this invasion (Kolaczkowska and Kubes, 2013). As a type of polymorphonuclear leukocytes [PMNs] (also known as granulocyte), neutrophils are formed (ROS) including superoxide radical (• O 2 -), hydroxyl radical (• OH) hydrogen peroxide (H 2 O 2 )
	from stem cells in the bone marrow and are the most abundant white blood cells in mammals (Galli et al., 2011; Kolaczkowska and Kubes, 2013). The amount of generated ROS reflects the
	(Nathan, 2006; Quinn et al., 2007). They maintain an essential part of the innate immune system organism's readiness for defense and thus serves as a highly predictive value (Prilutsky et al.,
	by many specific functions such as phagocytosis, degranulation, and bacteria killing (Witko-2011a). Recently, there are some articles studied the interactions between ENPs and neutrophils.
	Sarsat et al., 2000; Borregaard, 2010). Indeed, the markedly decreases of the amount of After investigating TiO 2 , CeO 2 and ZnO nanoparticles with human neutrophils, it was
	neutrophils would lead a severe immunodeficiency (Kolaczkowska and Kubes, 2013). concluded that nanoparticles could differentially affect all steps involved during neutrophil
	Braakhuis et al. exposed the rats to 15 nm silver nanoparticles (AgNPs, 179 μg•m -3 ) 6 hours per degranulation (Babin et al., 2013). Similarly, by a series of experiments in vitro, it was found
	day for four consecutive days (Braakhuis et al., 2014). A pronounced observed effect was the that, studied polyacrylic acid (PAA)-coated and non-coated iron nanoparticles triggered
	175-fold increased influx of neutrophils in the lungs of rats compared to the controls. Noë l et al. neutrophils' oxidative burst in a NADPH oxidase dependent manner (Couto et al., 2014).
	also reported a significant increase of neutrophils numbers (4-8 folds compared with the Chemiluminescence (CL) is a phenomenon of light emission resulting from an exoergic
	controls) in lungs of rats after the rats were inhaled exposed to TiO 2 nanoparticles (TiO 2 NPs, 20 chemical reaction. It occurs when chemical compounds with unpaired electrons relax back to
	mg• m -3 ) for 6 hours (Noë l et al., 2013). By roughly comparing the above two studies, it is found their ground state (Dodeigne et al., 2000). Due to its wide detection range, rapid and sensitive
	that the AgNPs are more toxic than the TiO 2 NPs due to the following reason. Although the analysis, convenient and inexpensive features, CL detection has been widely applied in food
	exposure concentration of AgNPs was ~110 times lower than that of TiO 2 NPs, the increased analysis (Gamiz-Gracia et al., 2009), DNA hybridization (Zhang et al., 2008), environmental
	neutrophils number was approximately 20-40 folds higher (Noë l et al., 2013; Braakhuis et al., evaluation (Guan et al., 2014), etc.
	2014). Neutrophils are usually considered as an excellent target to evaluate potential impacts of As early as the year 1973, it was reported that human neutrophils emitted CL after phagocytosis
	chemicals because of their importance to immune system (Babin et al., 2013; Couto et al., 2014). of opsonized bacteria (Stjernholm et al., 1973). Up to now, CL detection has been widely
	There are some debates in terms of the longevity of a neutrophil. Before the year 2010, applied to detect the oxidative burst of neutrophils by quantifying the amount of ROS produced
	neutrophils were always considered short-lived cells and the generally accepted circulatory half-(Freitas et al., 2009; Prilutsky et al., 2011b). As a substrate amplifier, luminol is one of the
	life of neutrophils was about 1.5 and 8-10 hours in mice and human, respectively (Dancey et al., earliest and most common CL reagents used in CL reaction (Prilutsky et al., 2011a). It is also
	1976; Suratt et al., 2001). In 2010, Pillay et al. challenged the concept and proposed that the considered as the most sensitive luminophore to detect extracellular CL response in neutrophils
	average circulatory lifespan of neutrophils is 12.5 hours for mice and 5.4 days for human (Pillay with zymosan as the particulate stimulant (Kopprasch et al., 2003).
	et al., 2010). Soon afterwards, Tofts et al. reported the doubts concerning the unprecedented In this work, the toxicity of copper, nickel and aluminum oxide nanoparticles (CuNPs, NiNPs
	lifespan of human neutrophils. They analyzed the methodologies used by Pillay et al. and and Al 2 O 3 NPs) were assessed by exposing these ENPs to porcine neutrophils in vitro.
	demonstrated that the 5.4 days was highly unlikely (Tofts et al., 2011). Compared to other ENPs, CuNPs have relatively high toxicity, roughly equal to silver NPs, but
	Except for human (Dahlgren and Karlsson, 1999; Cowland and Borregaard, 2016), many other less concerns (Musante and White, 2012). As a kind of metal oxide nanoparticles, Al 2 O 3 NPs are
	mammals have also been employed as donors for neutrophils studies, such as pig (Chabot-Roy considered as low toxic materials to organisms particularly in low concentrations (Doshi et al.,
	et al., 2006; De Buhr et al., 2014), cattle (Balachandran et al., 2015), dog (Baneth et al., 2015), 2008; Stanley et al., 2010). A human death case was reported in 1994. A 38-year-old previously
	horse (Krumrych et al., 2013), mice (von Brühl et al., 2012), and rat (Ng and Uetrecht, 2013). healthy male inhaled NiNPs (majority being 50 nm in diameter) while using a metal arc process

2.2.1.2 Instrumentations

	), ficoll histopaque (10771), dextran
	(D4876), trypan blue (T8154), glutaraldehyde solution (G7651), copper nanoparticles (CuNPs,
	<100 nm, 634220), nickel nanoparticles (NiNPs, <100 nm, 577995), aluminum oxide
	nanoparticles (Al 2 O 3 NPs, <50 nm, gamma phase, 544833) were purchased from Sigma-Aldrich.
	Ethanol (02860) was provided by Fluka (Lyon, France). Luminol/Enhancer (Bio-Rad HRP, CA)
	was used to amplify chemiluminescence activity. Phosphate buffered saline (PBS buffer, pH 7.4)
	was freshly prepared in our lab using sodium chloride (S7653), sodium phosphate dibasic
	(94046) and sodium phosphate monobasic (71505) purchased from Sigma (France). Zymosan A
	from Saccharomyces cerevisiae (Z4250, Sigma), serving as a stimulating agent, was opsonized
	for 30 min at 38 °C in porcine serum. Then zymosan was washed twice and resuspended in
	previously prepared Krebs-Ringer phosphate (KRP) medium (pH 7.4).
	Prior to preparing stock analyte solution, an autoclave-steam sterilizer (2540 ML-Tuttnauer,
	Netherlands) was used to sterilize deionized water produced by Millipore water purification
	system (Molsheim, France). A conventional optical microscope and a hemocytometer (Hausser
	scientific, Horsham, USA) were used to count cells. Different dilutions of ENPs solutions were
	well dispersed by an ultrasonic water bath (Elmasonic S30H) and a vortex mixer (G560E,
	Scientific Industries Inc., USA). A centrifuge (Universal 320R, Andreas Hettich GmbH,
	Germany) was employed in neutrophils isolation and dehydration steps. The morphology of
	cells was characterized with a field emission scanning electron microscopy (Hitachi SU8030,
	Japan) with an accelerating voltage of 10 kV. Chemiluminescence measurements were
	performed with standard 96 wells Costar microtiter-plates (Corning, USA) in a luminometer
	(Luminoskan Ascent, Thermo Fisher Scientific, USA). A water bath (Grant Instruments Type
	VF, Cambridge Ltd, UK) was used to maintain the 38 °C during the exposure process.

2.2.2 Isolation of neutrophils

	For each experiment, porcine blood was collected from the external jugular vein of a clinically
	healthy domestic pig (≤ 6 month, 100-110 kg) in the morning between 4:30 to 5:10 in an
	abattoir near Troyes (Sicaba, Pont-Sainte-Marie, France). The use of pigs in this study was
	approved by the Service of Animal Health Protection and Environment in the Prefecture of
	Aube (approval no. 10-387-901).
	Two 50 mL sterilized tubes were prepared to collect the blood. Within each tube, 7 mg heparin
	sodium was pre-dissolved in 1 mL KRP medium to anticoagulate blood. The neutrophils were
	isolated from the freshly collected blood by gradient density centrifugation method as previous
	reported (Quinn et al., 2007; Bartneck et al., 2010; Couto et al., 2014). The isolation contains
	three main steps: (1) Ficoll-Paque separation; (2) Dextran sedimentation; (3) Hypotonic lysis.

Table 2 . 1

 21 Toxicological data for CuNPs, NiNPs and Al 2 O 3 NPs to porcine neutrophils. The corresponding 95% confidence intervals are in brackets.

		Time (h) LC50 (mg/L)	LC20 (mg/L)
	CuNPs	0.875	> 200	73.03 (58.03-90.47)
		2.925	152.28 (108.95-259.75) 8.46 (5.61-11.91)
		5.025	72.08 (50.40-113.62)	4.21 (2.65-6.04)
		7.175	51.20 (36.37-78.15)	2.65 (1.55-3.98)
		9.025	49.90 (34.91-77.90)	2.18 (1.20-3.39)
		11.025	40.69 (29.55-59.66)	2.25 (1.31-3.39)
	NiNPs	0.68	> 400	312.51 (129.44-2359.13)
		2.78	> 400	180.10 (103.19-407.86)
		4.93	> 400	159.32 (87.52-379.57)
		6.93	> 400	159.47 (105.31-267.28)
		8.93	> 400	86.12 (51.68-144.04)
		10.93	> 400	64.85 (41.10-96.30)
	Al 2 O 3 NPs 0.65	> 400	390.94 (221.92-1090.25)
		2.65	> 400	337.02 (203.70-945.89)
		4.68	> 400	187.23 (104.29-453.47)
		7.08	> 400	147.83 (99.07-234.60)
		9.12	> 400	122.28 (83.73-183.53)
		11.1	> 400	69.90 (40.38-116.64)

Fate Model of Engineered Nanoparticles in Freshwater Ecosystem ate

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(35) Oberdorster, G.; Maynard, A.; Donaldson, K.; Castranova, V.; Fitzpatrick, J.; Ausman, K.; Carter, J.; Karn, B.; Kreyling, W.; Lai, D.; Olin, S.; Monteiro-Riviere, N.; Warheit, D.; Yang, H. Principles for characterizing the potential human health effects from exposure to nanomaterials: Elements of a screening strategy. Particle and Fibre Toxicology 2005, 2, 8. (36) OriginLab. Origin and originpro -data analysis and graphing software. http://originlab.com/ (accessed on 4 November 2016) Chapter 3 models are approximations and forecasts of the behaviors of chemicals in environment. According to the previous multimedia fate models, a new fate model of ENPs in freshwater ecosystem has been developed in this chapter. The regionalized input parameters are recommended for the fate model. The proposed fate model is shown to be applicable by comparisons with previous investigations. Additionally, for each studied region, the relative importance of thirteen input parameters in the fate model has been evaluated. The sensitivity analysis results show that the dissolution rate of ENPs in freshwater should be given priority in calculating the fate factors of ENPs for worldwide regions. F Contents Chapter 3 Fate Model of Engineered Nanoparticles in Freshwater Ecosystem .................. 3.1 Introduction .................................................................................................................. 3.2 Fate Model of ENPs in Freshwater Ecosystem .......................................................... 3.2.1 Fate model concept ................................................................................................. 33.2.2.1 Fate matrix ...................................................................................................... 3.2.2.2 Rate constants ................................................................................................. 3.2.2.3 Dissolution rate constant ................................................................................ 3.2.3 Dissolution of ENPs in freshwater.......................................................................... 3.2.4 Ranges and recommended values for input parameters ..........................................

3.3 Evaluation of the Fate Model ...................................................................................... 3.3.1 Evaluation method .................................................................................................. 3.3.2 Evaluation results and discussions ..........................................................................

  . In recent years, several fate models considering ENPs as colloids have been established to assess the fate of ENPs in ecosystem (Praetorius et al., 2012; Liu and Cohen, 2014; Meesters et al., 2014; Salieri et al., 2015).Praetorius et al. presented an approach for environmental fate modeling of ENPs on the basis of well-established multimedia models for organic pollutants(Praetorius et al., 2012). The authors compared the behaviors in water relevant to nanoparticles and to low-molecular-weight organic chemicals in detail. As a case study of their fate model, the behavior of TiO 2 nanoparticles in the Rhine River is reported. The heteroaggregation between TiO 2 nanoparticles and suspended particulate matter (SPM) was considered to be important. ranges for the regionalized input parameters (such as depth and volume of freshwater in Europe, depth of sediment in Asia) for fate models of ENPs.Fate models are approximations and forecasts of the real processes(Hendren et al., 2013). Many input parameters would affect the calculated results, such as the density and concentration of suspended particles in the freshwater, the temperature of the freshwater, the size of ENPs.

	Moreover, some of the models took into account several compartments (such as a combination
	of air, water and soil) (Liu and Cohen, 2014; Meesters et al., 2014), which makes the model
	much more complex. Even for the same compartment (such as freshwater), different models
	usually contain various removal processes of ENPs. For example, the SB4N model
	(SimpleBox4nano) reported by Meesters et al (Meesters et al., 2014), did not consider
	transformation processes (e.g. hetero-aggregation of ENPs with suspended matter) as removal
	processes. Nevertheless, Salieri et al. took hetero-aggregation as a removal process in their
	model (Salieri et al., 2015).
	Salieri et al. presented an adjusted fate model for metal oxide nanoparticles in freshwater
	ecosystem (Salieri et al., 2015). An estimated average landscape data (depth of sediment,
	volume of water, height of water column, etc.) extrapolated from the USEtox model was used.
	The processes of advection to other waters, hetero-aggregation, dissolution and sedimentation
	were considered as the removal processes for freshwater. Based on the fate model and
	fundamental of the USEtox model, they also calculated the freshwater fate factor of TiO 2
	nanoparticles.

Liu and Cohen proposed a compartmental multimedia model for evaluating the dynamic multimedia mass distribution and concentrations of engineered nanomaterials (ENMs)

[START_REF] Liu | Multimedia environmental distribution of engineered nanomaterials[END_REF]

. This model regarded the environment as a collection of well-mixed compartments (e.g. air, water, soil) and investigated the intermedia mass transport between adjacent compartments. Meesters et al. developed SimpleBox4Nano (SB4N) as the first screening level model for environmental assessment of ENPs. SB4N included the transport and concentrations of ENPs in and across air, rain, surface waters, soil, and sediment. It also considered the nanospecific processes such as aggregation, attachment, and dissolution. The predicted environmental concentrations predicted by SB4N were demonstrated useful as background concentrations in environmental risk assessment.

Although the above models are considered to be useful for predicting average environmental concentrations of ENPs on a regional or national scale, most of them do not consider spatial heterogeneity

(Quik et al., 2015)

.

However, the freshwater parameters between the subcontinental regions (Europe, America, Asia, etc.) may be much different

[START_REF] Kounina | Spatial analysis of toxic emissions in LCA: A sub-continental nested usetox model with freshwater archetypes[END_REF]

. To my knowledge, there are no recommended values and

(Praetorius et al., 2012; Salieri et al., 2015)

. However, it is still unclear which parameters are essential to be focused on or discarded. Thus, to ensure the model's practicability and to avoid over parameterization, a sensitivity analysis for studying the influences of main input parameters on output results was required

(Quik et al., 2015)

.

  FF w,w and FF sed,sed respectively describe the residence time (day) of ENPs in freshwater and sediment. The off-diagonal elements represent the transport time (day) of ENPs from water to sediment (FF w,sed ) and from sediment to water (FF sed,w ). The ̅̅̅̅ equals the negative inverse of

the rate coefficient matrix ( ̅ ) (Rosenbaum et al., 2007; Rosenbaum et al., 2008; Salieri et al., 2015):

Table 3 . 1

 31 The recommended default values and ranges of input parameters for the fate model* The parameters with a star (*) are related to the sensitivity analysis. Generally, these parameters have a wide range of values and thus are considered to have significant impact on the final fate factor values. For the purpose of this thesis, the most representative values have been employed as default values. The ranges from minimum to maximum values of these parameters are used to observe to what extent they will affect the fate factors. 2. In this thesis, a radius of 25 nm was chosen as an average scenario. A range from 0.5 to 50 nm of ENPs radius was used for doing sensitivity analysis. not predict them accurately without adding corrective terms (such as morphology, ionic strength, surface structure.)[START_REF] Petosa | Aggregation and deposition of engineered nanomaterials in aquatic environments: Role of physicochemical interactions[END_REF] Meesters et al., 2014). To our knowledge, there is only one study that reported both attachment and aggregation efficiencies for just four kinds of nanomaterials in different waters[START_REF] Quik | Fate of nanoparticles in the aquatic environment[END_REF]. Their results

	Parameter	Symbol	Unit	Default value	Range	Ref.
	Density of nanoparticles*1	ρ ENP	kg• m -3	----	1600 -19300	----
	Radius of nanoparticles*2	r ENP	m	2.5• 10 -8	5• 10 -10 -5• 10 -8 ----
	Gravitational acceleration on earth		m• s -2	9.81	----	----
	Dynamic viscosity of water	µ water	Pa• s	8.9• 10 -4	----	(Meesters et al., 2014)
	Density of water	ρ water	kg• m -3	1000	----	(Fantke et al., 2015)
	Density of suspended (nano)particles*3	ρ SPs	kg• m -3	1230	1100 -2500	(Praetorius et al., 2012; Fantke et al., 2015)
	Depth of freshwater*4	Depth water m	3.0	0.5-2 Depth water	(Fantke et al., 2015)
	Boltzmann constant	k B	J• K -1	1.38• 10 -23	----	----
	Water temperature*5	T water	K	285	237.15 -303.15	(Klein Tank and Können, 2003; Fantke et al., 2015)
	Surface water shear rate	G	s -1	10	----	(Praetorius et al., 2012; Meesters et al., 2014)
	Radius of larger suspended particles*6	r LSP	m	1.05• 10 -5	5• 10 -7 -1• 10 -4	(Lartiges et al., 2001; Praetorius et al., 2012)
	Radius of suspended nanoparticles	r SNP	m	3• 10 -7	----	(Meesters et al., 2014)
	Attachment efficiency*7	α 2	--	0.9	0.001 -1	(Quik, 2013; Meesters et al., 2014)
	Aggregation efficiency*8	α 3	--	0.5	0.001 -1	(Quik, 2013; Meesters et al., 2014)
	Number concentration of larger suspended particles*9	C 2	m -3	4.2• 10 10	9.2• 10 9 -6.3• 10 10	(Praetorius et al., 2012)
	Number concentration of suspended nanoparticles*10	C 3	m -3	1• 10 11	5• 10 10 -2• 10 11 (Meesters et al., 2014)
	Flow of continental freshwater to continental seawater	advflow	m 3 •s -1	5.45• 10 4	----	(Fantke et al., 2015)
	Volume of freshwater*11	V water	m 3	6.76• 10 12	0.5-2 V water	(Fantke et al., 2015)
	Resuspension rate from sediment to freshwater	Resusp	m• s -1	2.66• 10 -10	----	(Fantke et al., 2015)
	Dissolution rate constant of ENPs*12	k diss	s -1	5• 10 -6	0 -10 -5	(Quik et al., 2011)
	Depth of freshwater sediment compartment*13	Depth sed	m	3• 10 -2	1.5• 10 -2 -6• 10 -2	(Fantke et al., 2015)
	Burial rate in freshwater sediment	Burial	m• s -1	8.60• 10 -11	----	(Fantke et al., 2015)
	Velocity of horizontal sediment transfer	V sed,transfer kg• s -1	3.0	----	(Praetorius et al., 2012)
	Bulk density of sediment	ρ sed	kg• m -3	1230	----	(Fantke et al., 2015)

.1 and DEFAULT in Table

3

.2) has been recommended as default values based on the database of USEtox model

(Fantke et al., 2015)

. Where USEtox data was not available, parameter values derived from literatures and other fate models were preferred

(Lartiges et al., 2001; Praetorius et al., 2012; Meesters et al., 2014; Salieri et al., 2015)

. Additionally, due to the significant differences of freshwater parameters between subcontinental regions (such as Africa, America, Asia and Europe), five regional-specific parameters from the USEtox model

(Rosenbaum et al., 2008; Fantke et al., 2015)

for 16 different subcontinental regions were also recommended and listed in Table

3

.2. * **3. Densities of suspended (nano)particles is varying from 1100 -2500 kg• m -3 in the river Rhine (Praetorius et al., 2012). It is assumed that the density of suspended (nano)particles equals the bulk density of sediment. Hence, the value is assumed to be 1230 kg• m -3 as recommended by USEtox (Fantke et al., 2015). *4. Default depth of freshwater in USEtox is 3.0 m. A range of half to two times' recommended freshwater depth related to each studied region was used for doing sensitivity analysis. For example, the recommended freshwater depth of W1 region is 12.5 m. Thus, the freshwater depth range of 6.25 to 25 m was applied for W1 to do sensitivity analysis. *5. USEtox model suggests a water temperature of 285 K. Considering the realistic worldwide situation (Klein Tank and Können, 2003), a range from 237.15 to 303.15 K was employed for sensitivity analysis. *6. Larger suspended particles are highly variable in size within different freshwater zones.

Even in the same freshwater, they have different size distributions. In the SB4N model, the size of larger suspended particles is set to 280 μm (radius)

(Meesters et al., 2014)

. However in river Rhine, it has been demonstrated that the sizes of suspended matter are mainly distributed from 10 to 100 μm (diameter, mode values are around 20 -50 μm) for three samples and 10 to 200 μm (mode values around 80 μm) for another sample

(Lartiges et al., 2001)

. Also in river Rhine, the particle size distribution is represented as lognormal with a mode of 5 μm (diameter) in another study

(Praetorius et al., 2012)

, which is much smaller than others. The authors also 0.5 μm to 100 μm were used to do sensitivity analysis. *7&*8. Usually, it is preferable to obtain the attachment efficiency and aggregation efficiency by experiments rather than by theoretical calculation, because the classical DLVO theory of colloid stability could

Table 3 . 2

 32 The hydrology data for one default region and 16 different continental regions' parameters of ENPs, freshwater and sediment collected from the USEtox model(Fantke et al.,

	2015).
		Depth water	advflow	V water	Resusp	Burial
	Region ID	(m)	(m 3 •s -1 )	(m 3 )	(m• s -1 )	(m• s -1 )
	DEFAULT	3	5.45• 10 4 6.76• 10 12 2.66• 10 -10 8.60• 10 -11
	W1	12.5	5.51• 10 4 3.51• 10 12 1.89• 10 -10 9.38• 10 -11
	W2	13	7.27• 10 4 1.51• 10 12 2.96• 10 -10 9.96• 10 -11
	W3	3	6.33• 10 4 1.95• 10 11 9.95• 10 -11 9.57• 10 -11
	W4	3	2.83• 10 3 5.46• 10 10 1.18• 10 -10 9.81• 10 -11
	W5	46	6.73• 10 4 1.02• 10 13 2.34• 10 -10 9.74• 10 -11
	W6	46	7.63• 10 4 2.09• 10 13 2.08• 10 -10 9.30• 10 -11
	W7	8	2.85• 10 4 4.98• 10 11 1.78• 10 -10 9.74• 10 -11
	W8	8	2.43• 10 5 7.18• 10 11 1.06• 10 -10 9.32• 10 -11
	W9	20	1.48• 10 5 4.31• 10 12 2.99• 10 -10 1.00• 10 -10
	W10	20	1.26• 10 5 9.98• 10 12 2.80• 10 -10 1.03• 10 -10
	W12	16.5	1.11• 10 5 1.49• 10 13 3.00• 10 -10 1.14• 10 -10
	W13	15.4	2.78• 10 4 2.07• 10 12 1.80• 10 -10 9.61• 10 -11
	W14	3	2.03• 10 4 1.73• 10 11 2.70• 10 -10 9.89• 10 -11
	IND	13	5.45• 10 4 2.55• 10 12 2.96• 10 -10 1.01• 10 -10
	CHI	13	7.62• 10 4 3.81• 10 12 3.00• 10 -10 1.02• 10 -10
	JAP	13	1.36• 10 4 3.39• 10 11 3.07• 10 -10 9.90• 10 -11

Evaluation of the Fate Model Ideally

  , testing the robustness and predictability of the fate model should be performed by comparing the real concentrations of ENPs in freshwater and the predicted environmental concentrations (PECs) calculated by the fate model. However, the real concentrations of ENPs in the ecosystem are rarely reported (Quik et al., 2015), because the detection and quantification of ENPs in complex natural media are still in their infancy (Gottschalk et al., 2013; Nowack et al., 2015). Thus, the functionality of fate model was usually tested by comparing the PECs obtained by the proposed model and by the previous reports. For instance, Meesters et al. reworked the case of TiO 2 nanoparticles in Switzerland to demonstrate their model's capability for environmental exposure estimations of ENPs (Meesters et al., 2014).

Table 3 . 3

 33 The dissolution data and some specific parameters related to North America,

	Switzerland and Europe.

*1 In order to better compare the outputs of the present model with previous results, the recommended default r ENPs of 2.5• 10 -8 m (from Table

3

.1) was used for North America and Europe, while the 1• 10 -8 m was for Switzerland as the literature

(Mueller and Nowack, 2008)

.

  .4 (the numeric values are listed in TableC2in Appendix C). As described in Section 3.3.1, in present study, all the calculations of PECs of ENPs were based on the same background as each scenario (such as ENPs emissions, water volumes). The concentration of CuNPs in freshwater was rarely reported. However, Keller and Lazareva estimated the concentrations of CuNPs as well as other ENPs (such as AgNPs, TiO 2 NPs and ZnONPs) in San Francisco (SF) Bay wastewater treatment plant (WWTP) effluent

(Keller and Lazareva, 2014

). In addition, Sun et al. reported the PECs of several ENPs (such as AgNPs, TiO 2 NPs, ZnONPs and Fullerenes) in Switzerland and Europe WWTP effluent

(Sun et al., 2014; Sun et al., 2016)

. Therefore, except for the PECs in freshwater, those in WWTP effluent were also used for comparisons in this study.

Table 4 . 1

 41 different ENPs have been chosen in this work because of their relatively wide application. The selection of the 17 ENPs was according to many reported inventories of the available nanoproducts in the market (Piccinno et al., 2012; Vance et al., 2015; Zhang et al., 2015; Nanodatabase, 2016; West et al., 2016). Table4.1 lists some basic information of the 17 selected ENPs. Basic information and abbreviations of the 17 selected nanoparticles (NPs).* 2. A density range from 2200 to 2600 kg• m -3 of SiO 2 NPs is reported in the website of Sigma-Aldrich. In this study, an average value of 2400 kg• m -3 was used.

	No. ENPs	Abbreviation CAS number	Density (kg• m -3 )	Molecular Weight (g• mol -1 )
	1	Gold NPs	AuNPs	7440-57-5	19300	196.97
	2	Silver NPs	AgNPs	7440-22-4	10490	107.87
	3	Fullerene	C 60	99685-96-8	1600	720.64
	4	Copper NPs	CuNPs	7440-50-8	8940	63.55
	5	Platinum NPs	PtNPs	7440-06-4	21450	195.08
	6	Titanium dioxide NPs TiO 2 NPs	13463-67-7	4260	79.87
	7	Nickel NPs	NiNPs	7440-02-0	8902	58.69
	8	Zinc oxide NPs	ZnONPs	1314-13-2	5610	81.39
	9	Magnetite NPs *1	Fe 3 O 4 NPs	1317-61-9	4950	231.53
	10 Aluminum oxide NPs	Al 2 O 3 NPs	1344-28-1	4000	101.96
	11 Copper oxide NPs	CuONPs	1317-38-0	6320	79.55
	12 Nickel oxide NPs	NiONPs	1313-99-1	6670	74.69
	13 Cerium oxide NPs	CeO 2 NPs	1306-38-3	7130	172.11
	14 Silicon dioxide NPs *2 SiO 2 NPs	7631-86-9	2400	60.8
	15 Ferric oxide NPs	Fe 2 O 3 NPs	1309-37-1	5242	159.69
	16 Zirconium oxide NPs	ZrO 2 NPs	1314-23-4	5680	123.22
	17 Copper hydroxide NPs Cu(OH) 2 NPs 20247-59-2	3368	97.56
	* The density and molecular weight of ENPs were collected from Sigma-Aldrich website
	(Sigma-Aldrich, 2016).

*1. A density range from 4800 to 5100 kg• m -3 of Fe 3 O 4 NPs is reported in the website of Sigma-Aldrich. In this study, an average value of 4950 kg• m -3 was used.

*

Table D3 in

 D3 Appendix D lists and reworks the studies (by equation 4.2) that did not directly report dissolution rate constants. Finally, the recommend dissolution rate constant of certain ENPs is decided by averaging the corresponding values directly or indirectly from literatures.

Table 4 .

 4

2 lists the dissolution rate constants of 17 ENPs. Among them there are 9 insoluble ENPs (Dorniani et al., 2012; Vogel et al., 2013; Mokhtary and Torabi, 2014) and 8 soluble ENPs in the freshwater. The detailed sources of the data of the 8 soluble ENPs are shown in

Table D4 in Appendix D.

Table 4 . 2

 42 The recommended dissolution rate constants of 17 ENPs in freshwater. focused on the toxicity to aquatic species. Thus, if not specified, the FF refers to FF w,w .The ranges of FFs represent that there are the possibilities to reach such values under extreme conditions. Due to the large ranges of input parameters, the FF ranges can be very large that, sometimes, they may not properly predict the real situation(Pu et al., 2016). In this work, two kinds of ranges were reported: -Recommended range‖ and -Range‖. Their calculations both used the 12 input variables (except k diss ) designed for sensitivity analysis in Chapter 3. For each ENP, -Recommended range‖ calculation fixed the dissolution rate constant of ENPs to k diss , while -Range‖ calculation used 0.5k diss to 2 k diss . Here, the k diss represents the recommend value for one given ENP listed in Table4.2. Table D5 (in Appendix D) lists the recommended values, -Recommended ranges‖ and -Ranges‖ of the FF w,w and the recommended values of FF sed,sed for 17 ENPs in 17 subcontinental regions.

	No. ENPs	k diss (s -1 )	No.	ENPs	k diss (s -1 )
	1	AgNPs	3.4197• 10 -6	10	Fe 3 O 4 NPs 0
	2	Al 2 O 3 NPs	0	11	NiNPs	2.1818• 10 -7
	3	AuNPs	0	12	NiONPs	2.1818• 10 -7
	4	C 60	0	13	PtNPs	0
	5	CeO 2 NPs	0	14	SiO 2 NPs	3.7899• 10 -6
	6	CuNPs	5.4953• 10 -7	15	TiO 2 NPs	0
	7	CuONPs	3.8681• 10 -7	16	ZnONPs	5.1163• 10 -7
	8	Cu(OH) 2 NPs 4.9376• 10 -6	17	ZrO 2 NPs	0
	9	Fe 2 O 3 NPs	0
	4.2.2 Fate factors of the 17 ENPs
	The densities (in Table 4.1) and dissolution rate constants (in Table 4.2) of 17 ENPs were used

to calculate their freshwater FF w,w and sediment FF sed,sed . Since the USEtox model have not included the impacts of chemicals in sediment

(Fantke et al., 2015; Salieri et al., 2015)

, this work also mainly

  Most of the recommended FFs of Cu(OH) 2 NPs, SiO 2 NPs and AgNPs are less than 3 days, while those of the insoluble ENPs are less than ~30 days. The other ENPs (i.e. CuNPs, ZnONPs, CuONPs, NiONPs and NiNPs) have the recommended FFs less than 20 days. In addition the recommended values of FFs of ENPs were not only affected by types of ENPs but also affected largely by regions. For all the 17 ENPs, the recommended FFs have lowest values in W3 region, while the highest values occur in W5 region.In order to understand better the influence of different regions on FFs of ENPs, the results of CuNPs were analyzed as an example. The respective data for the other ENPs are summarized in

	Table D6 in Appendix D. A low persistence of CuNPs in the freshwater (~1.1 days) is
	observable in Australia regions (W3 and W4), while high FF values (~12 days) occur in Africa
	(W5 and W6).

4.4.1 Human toxicological effect factor for CuNPs One

  The EF (cases/kg intake ) reflects the change of the illness probability due to the change of the CuNPs intake in whole life time of human. In this work, it is assumed that when the mortality rate decreases to the 50% amount of a control sample, the ED50 a,t (mg• kg -1 • day -1 ) is the dose for animal during exposure time t (day).

	a AF is the
	extrapolation factor for interspecies differences (pig to human is 1.1) (Fantke et al., 2015),
	because the ED50 h is derived from the toxicological data of pigs. BW h and LT h respectively
	represent average body weight (70 kg, data from USEtox) and average lifetime of human (70
	years, data from USEtox). N is the number of days per year (365 days• year -1 ). Since chronic
	toxicity values have priority in USEtox, thus an extrapolation factor AF t of 5 for acute to
	chronic exposure is applied (Fantke et al., 2015).
	illness probability is 50%. Under the assumption of linearity in dose-response (up to where the
	life time disease probability is 0.5), EF equals (Rosenbaum et al., 2008):
	(4.3)
	ED50 h (kg• lifetime -1 ) is the dose of substance for human during whole life time that causes an

objective of this work is to use toxicological data obtained in in vitro experiments (in Chapter 2) for calculating the human toxicological effect factor (EF h ) of CuNPs under the framework of the USEtox model. effect (e.g. inflammation) with a probability of 50%. ED50 h of a human is calculated based on the following equation (Rosenbaum et al., 2008):

(4.4)

EFs Porcine EFs

	4.4.
			CN
			BV
	2.925	0.0130	0.0350
	5.025	0.2245	0.6062
	7.175	0.3161	0.8534
	9.025	0.3244	0.8757
	11.025	0.3978	1.0739

2 Freshwater ecotoxicological effect factor for 14 ENPs

Table 4 . 4

 44 Human and porcine characterization factors of CuNPs in 17 subcontinental regions

Human CF (10 -4 CTU h ) Porcine CF (10 -4 CTU h )

		2.925 h	5.025 h	7.175 h	9.025 h	11.025 h	2.925 h	5.025 h	7.175 h	9.025 h	11.025 h
	DEF.	0.2447	4.2415	5.9713	6.1268	7.5136	0.6606	11.4509	16.1207	16.5407	20.2846
	W1	0.6209	10.7634	15.1529	15.5476	19.0668	1.6763	29.0583	40.9086	41.9744	51.4751
	W2	0.7633	13.2311	18.6269	19.1121	23.4381	2.0606	35.7203	50.2874	51.5975	63.2764
	W3	0.1246	2.1603	3.0413	3.1205	3.8269	0.3365	5.8322	8.2107	8.4246	10.3315
	W4	0.1373	2.3803	3.3510	3.4383	4.2166	0.3707	6.4262	9.0469	9.2826	11.3836
	W5	1.4426	25.0074	35.2057	36.1229	44.2992	3.8947	67.5131	95.0458	97.5219	119.5955
	W6	1.4168	24.5591	34.5747	35.4754	43.5051	3.8249	66.3029	93.3421	95.7738	117.4518
	W7	0.4098	7.1041	10.0012	10.2617	12.5844	1.1064	19.1790	27.0005	27.7039	33.9745
	W8	0.3040	5.2692	7.4181	7.6114	9.3341	0.8206	14.2255	20.0268	20.5486	25.1996
	W9	1.0091	17.4915	24.6248	25.2663	30.9853	2.7242	47.2223	66.4802	68.2122	83.6517
	W10	0.9837	17.0519	24.0059	24.6313	30.2065	2.6557	46.0355	64.8094	66.4978	81.5493
	W12	0.8625	14.9518	21.0493	21.5977	26.4862	2.3286	40.3657	56.8273	58.3078	71.5055
	W13	0.6988	12.1127	17.0524	17.4967	21.4570	1.8865	32.7010	46.0369	47.2362	57.9279
	W14	0.2210	3.8301	5.3921	5.5326	6.7849	0.5965	10.3403	14.5572	14.9364	18.3172
	IND	0.7693	13.3363	18.7751	19.2642	23.6246	2.0770	36.0045	50.6875	52.0080	63.7798
	CHI	0.7714	13.3712	18.8241	19.3145	23.6862	2.0825	36.0984	50.8198	52.1438	63.9463
	JAP	0.7830	13.5722	19.1071	19.6049	24.0424	2.1138	36.6412	51.5840	52.9279	64.9078

des nanoparticules de synthèse sur des neutrophiles porcins

  toxicity assessment of ENPs by coordinating in vitro data and environmental model can be studied further. This work conducts a series of in vitro experiments to obtain toxicological data for the following calculation of human CF, because of no previously reported data. Nevertheless, it is not always necessary for the same research group to perform in vitro experiments and to use the assessment model. To accomplish this objective, the guidelines for toxicity testing of ENPs should be made and the model required toxicological data should be reported by biologists.Only with comprehensive data, the results calculated by environmental model can be reliable.Theoretically, the same type ENPs in the background freshwater can have influences on the FFs of ENPs by accelerating the homo-aggregation process between ENPs. The proposed fate model disregards the homo-aggregation because the concentration of suspended particles is several orders of magnitude higher than that of ENPs in freshwater (e.g. 10 10 suspended particles m -3 versus 10 8 TiO 2 NPs m -3 ) (Gottschalk and Nowack, 2011; Praetorius et al., 2012). It is unlikely that the same type ENPs collide frequently with each other. Nevertheless, due to the potential increases of ENPs concentrations in ecosystem (Sun et al., 2016), the consideration of ENPs background concentrations would be recommended once ENPs have comparable amount with suspended particles. Dissolution plays an important role in the fate of ENPs. The present work assumes that it follows first-order kinetics and obtains the recommended values by averaging the previous reported dissolution values. Many factors may affect the dissolution of ENPs, such as the coating (Booth et al., 2015), size, pH, ionic strength, and natural organic matters (Liu and Hurt, 2010; Conway et al., 2015). The size distributions of ENPs are demonstrated to be different throughout their life cycle. Most of the nanomaterials are no longer in the nanoscale due to leur capacité à contribuer à l'écotoxicité ou à la toxicité pour l'homme. Parmi les nombreuses mé thodes disponibles pour le calcul du CF pour l'é cotoxicité et la toxicité pour l'homme , le modèle USEtox se dé marque par rapport aux autres. En tant que modè le 'objectif gé né ral de cette thè se est de faire avancer les connaissances sur la toxicité des ENPs pour les humains et les é cosystè mes. Il est ré alisé e par les é tapes suivantes: Coordonner les donné es toxicologiques (extraits des expé riences et des litté ratures in vitro) et le modè le environnemental pour é valuer la toxicité des ENPs pour les humains et les é cosystè mes. En raison du dé veloppement rapide de la nanotechnologie, l'exposition des ENPs à l'homme est iné vitable. De nombreuses preuves ont montré que les ENPs peuvent potentiellement atteindre la circulation sanguine de l'homme ou d'autres mammifè res par diverses voies, telles que la voie orale, l'inhalation et la pé né tration cutané e. Lorsque les ENPs entrent dans le sang des mammifè res, le premier constituant à ê tre mobilisé pour ré sister à cette invasion est les neutrophiles. Ils sont les globules blancs les plus abondants chez les mammifè res et maintiennent une partie essentielle du systè me immunitaire inné par de nombreuses fonctions spé cifiques telles que la phagocytose, la dé granulation et la destruction des bacté ries. Les neutrophiles sont gé né ralement considé ré s comme une excellente cible pour é valuerr les impacts potentiels des produits chimiques en raison de leur importance pour le systè me NiNPs et Al 2 O 3 NPs) ont é té é valué s en exposant ces ENP à des neutrophiles porcins in vitro. Pendant l'exposition, à certains intervalles de temps, le comptage des cellules et des mesures de chimioluminescence (CL) ont é té ré alisé es.La morphologie des neutrophiles exposé s et des ENPs a é té caracté risé e par microscope é lectronique à balayage (MEB). Pour chaque type de ENPs, trois expé riences ont é té ré alisé es respectivement sur un porc. Spé cifiquement,

	The new species may have different toxicological properties as their initial form (Rosenfeldt et al., 2015). Sometimes, the ubiquitous organic matter in freshwater may also have influence on the bioavailability of ENPs (Fan et al., 2016). Even though the combined toxicity has received concerns in toxicology community (Su et al., 2013; Torre et al., 2015), the current models have not taken it into account. Résumé Étendu en Français R1 Introduction L'utilisation é tendue de nanomaté riaux (ENMs) dans des produits commerciaux aboutit à la libé ration iné vitable des ENM dans l'environnement. La libé ration des ENMs peut ê tre dé finie comme la libé ration des ENMs de leurs confinements (par exemple, une matrice de maté riaux empê chant les ENMs de pé né trer dans l'environnement naturel) et transfert ulté rieur à un compartiment technique ou environnemental. Les compartiments techniques peuvent ê tre toutes les structures artificielles, comme les vé hicules de transport, les maisons, les piscines. Les compartiments environnementaux comprennent l'air, le sol et les sé diments, l'eau douce et l'eau (ACV), une mé thodologie normalisé e internationale, est gé né ralement reconnue comme l'approche la plus fiable et la plus avancé e pour é valuer les impacts d'un produit ou d'un procé dé sur l'environnement. L'é valuation d'impact sur le cycle de vie (AICV) est l'une des quatre é tapes environnemental largement utilisé par la communauté scientifique, le modè le USEtox fournit deux types de facteurs de caracté risation: CF é cotoxicologiques et CF toxicologie humaine . Ces deux facteurs incluent un facteur de devenir (FF), un facteur d'exposition (XF) et un facteur d'effet (EF). En plus, USEtox a une grande base de donné es contenant diverses donné es sur les proprié té s des substances (par exemple, masse molaire, solubilité , coefficient de partage entre n-octanol et eau), qui facilite ses applications. Dans la derniè re version, USEtox 2.02, la base de donné es comprend 27 inorganiques (argent, cuivre, sé lé nium, etc.) et 3077 organiques (ethanol, atrazine, carbofuran, etc.) . (1) Effectuer des expé rimentations in vitro classiques pour obtenir les phé nomè nes de ré ponse à la dose de premiè re main et les donné es toxicologiques. (3) Chaque expérience a utilisé le sang d'un même porc. Les valeurs moyennes avec écarts types
	de l'ACV, celle-ci permet d'é valuer les impacts potentielsd'un produit chimique. Il s'agit de (2) Élaborer un modè le de devenir des ENPs dans les é cosystè mes d'eau douce et recommander sont indiqué es ci-aprè s, sauf indication spé ciale.
	l'é tape qui permet de voir dans quelle mesure un produit particulier, processus, substance ou des paramè tres d'entré e ré gionalisé s pour pré dire le temps de persistance des ENPs dans
	diffé rentes eaux douces. R2.1

aggregation, attachment, etc. (Caballero-Guzman and

Nowack, 2016)

. Therefore, in the future, the dissolution rate constants and other important physicochemical properties of ENPs (e.g. diameters, densities) should be studied more based on the real nanoproducts to be assessed.

The world is divided into 17 subcontinental regions in USEtox and this work. The hydrological data in a given region is considered uniform for the whole region. But it is obvious that the sitespecific hydrological data can be very different between each other. Moreover, different organism species have different toxicological reactions. The USEtox model only concentrates on algae, crustacean and fish. Furthermore, different ecosystems are very likely to have different organisms. However, the differences between localized ecosystems are not considered in current models. Thus, the site-specific information is expected to be investigated.

Since nanomaterials have large specific surface area, they probably adsorb other pollutants such as metal ions, organic chemicals and other nanomaterials

[START_REF] Pu | Adsorption and desorption of thallium(i) on multiwalled carbon nanotubes[END_REF] Wang et al., 2015)

. de mer. Le rejet des ENMs dans l'environnement peut ê tre involontaire ou intentionnel (par exemple, l'utilisation de fer à nanomé trique pour le traitement de l'eau). Les ENM peuvent ê tre libé ré es des produits tout au long de leur cycle de vie, y compris la fabrication, le transport, l'utilisation et l'é limination. Des facteurs intrinsè ques et extrinsè ques peuvent affecter le potentiel de libé ration des ENMs des nanoproduits.

Les pré occupations sur les questions de sé curité lié es aux nanotechnologies existent depuis le dé but du 21è me siè cle. Pour é tudier les impacts des nanoparticules de synthè se (ENPs), nombreuses bio-expé riences ont é té mené es sur divers organismes, et il existe de nombreuses preuves dé montrant la toxicité nano-spé cifique des ENPs existantes. Actuellement, il y a une demande de légiférer sur l'utilisation des nanomatériaux. Les organismes de contrôle et le secteur industriel ont des difficulté s à comprendre et à gé rer les risques potentiels ré sultant des nanomaté riaux. La raison principale peut s'expliquer par le manque de connaissances de l'impact des nanomatériaux sur les humains et les écosystèmes.

Lorsqu'on é value l'impact potentiel des produits contenant des ENPs (particuliè rement, ceux qui sont sous-dessiné s) sur l'humain, les expé rimentations ne ré pondent pas aux attentes, soit insuffisantes, soit impraticables. Par consé quent, de nombreuses mé thodes d'é valuation pour la toxicité humaine ont é té dé veloppé es pour aider à soulager la pression de l'expé rimentation animale et fournir à l'utilisateur final un outil de prise de dé cision . L'é valuation du cycle de vie é mission, peut ê tre peut gé né rer des impacts sur une caté gorie d'impact particuliè re (par exemple, potentiel de ré chauffement plané taire, é cotoxicité ). Le facteur de caracté risation (CF), é lé ment important de l'AICV, facilite les comparaisons entre les diffé rentes substances en termes de L'EF toxicologique pour l'homme est calculé sur la base des expé riences in vivo sur des animaux, comme le porc, le rat et la souris. Ces donné es sont gé né ralement lié es à l'inhalation ou à l'exposition orale (ingestion). Cependant, en plus de sacrifier un nombre relativement important d'animaux, le test de toxicité in vivo est é galement considé ré comme peu robuste, coûteux et consommateur de temps . Donc, Pour calculer l'EF toxicologique sur l'homme, non seulement pour les ENPs, mais aussi pour des substances classiques il y a une demande émergente quant à la mise en oeuvre de nouvelles méthodes afin d'utiliser les données toxicologiques basé es sur les expé rimentations in vitro. À l'exception de la toxicité inhé rente aux ENPs, le temps de persistance des ENPS dans l'environnement et les voies d'exposition aux organismes peuvent é galement affecter les risques des ENPs. Par rapport à leurs homologues, les ENPs pré sentent de nombreux comportements diffé rents en eau douce (par exemple, pas de dé gradation), en raison de leurs proprié té s nanospé cifiques (tels qu'une faible solubilité , une faible pression de vapeur). Ainsi, les mé thodes anté rieures conç ues pour modé liser le devenir des produits chimiques classiques n'é valuent pas né cessairement le comportement des ENPs en eau douce. En outre, la diffé renciation spatiale du devenir des ENPs doit é galement ê tre abordé e . LR2 Toxicité immunitaire. Les nombreuses similitudes entre le porc et l'humain (gé né tique, physiologie, anatomie, etc.) font du porc un excellent modè le biomé dical animal pour l'humain. Il existe de nombreux avantages du modè le porcin pour le dé veloppement de vaccins et l'utilisation de porcs permett d'acquérir de nouvelles connaissances sur la santé animale et humaine. Pour lutter contre des intrus microbiens, les neutrophiles produisent une grande quantité de radicaux d'oxygè ne toxiques, connus sous le nom d'espè ces ré actives d'oxygè ne (ROS). La quantité de ROS gé né ré e reflè te la disponibilité de l'organisme pour la dé fense et sert ainsi d'une valeur hautement pré dictive. La dé tection de la chimioluminescence (CL) a é té largement appliqué e pour dé tecter le pouvoir oxydant des neutrophiles en quantifiant la quantité de ROS produite. Dans ce chapitre, la toxicité des nanoparticules de cuivre, de nickel et d'oxyde d'aluminium (CuNPs,

Mortalité des neutrophiles exposé s aux ENPs Ce

  chapitre pré sente la premiè re é tude sur les tests de toxicité des ENPs aux neutrophiles. Les neutrophiles porcins sont exposé s à trois ENPs (CuNPs, NiNPs et Al 2 O 3 NPs) de diffé rentes concentrations (0-400 μg/mL) pendant un maximum de ~11 heures.

	Les figures R2.1a, b et c montrent que les duré es d'exposition et les concentrations des ENPs
	ont des corré lations positives sur les taux de mortalité des neutrophiles. Le taux de mortalité des
	neutrophiles fraî chement isolé s non exposé s é tait infé rieur à 10% et il est devenu d'environ 20%

R2.2 Chimiluminescence des neutrophiles exposé s aux ENPs

  Tableau R1 ré sume les ré sultats des tests de toxicité aiguë sous diffé rentes duré es d'exposition. Pour tous les tests effectué s avec des NiNPs ou des Al 2 O 3 NPs, moins de 50% de mortalité pourrait ê tre atteint dans les solutions de 400 mg/L. Les valeurs de LC50 des CuNPs ont continué à diminuer de > 200 à 40.69 mg/L avec le temps. Les valeurs de LC20 de toutes les trois ENP ont é videmment diminué avec le temps. Il indique que les neutrophiles sont trè s sensibles aux ENPs Les valeurs de LC50 et LC20 des CuNP é taient beaucoup plus petites que celles des NiNP et des Al 2 O 3 NP correspondants. À environ 11 h, la valeur LC20 des CuNPs (2.25 mg/L) é tait environ 30 fois plus petite que les valeurs LC20 de NiNPs (64.85 mg/L) et Al 2 O 3 NP (69.90 mg/L). Ceci ré vè le que CuNPs est beaucoup plustoxique que NiNPs et Al 2 O 3 NPs et que NiNPs l'est un peu plus que Al 2 O 3 NPs. Tableau R1 Donné es toxicologiques pour les CuNP, les NiNP et les Al 2 O 3 NP en interaction avec les neutrophiles porcins. The corresponding 95% confidence intervals are in brackets.

		Time (h) LC50 (mg/L)	LC20 (mg/L)
	CuNPs	0.875	> 200	73.03 (58.03-90.47)
		2.925	152.28 (108.95-259.75) 8.46 (5.61-11.91)
		5.025	72.08 (50.40-113.62)	4.21 (2.65-6.04)
		7.175	51.20 (36.37-78.15)	2.65 (1.55-3.98)
		9.025	49.90 (34.91-77.90)	2.18 (1.20-3.39)
		11.025	40.69 (29.55-59.66)	2.25 (1.31-3.39)
	NiNPs	0.68	> 400	312.51 (129.44-2359.13)
		2.78	> 400	180.10 (103.19-407.86)
		4.93	> 400	159.32 (87.52-379.57)
		6.93	> 400	159.47 (105.31-267.28)
		8.93	> 400	86.12 (51.68-144.04)
		10.93	> 400	64.85 (41.10-96.30)
	Al 2 O 3 NPs 0.65	> 400	390.94 (221.92-1090.25)
		2.65	> 400	337.02 (203.70-945.89)
		4.68	> 400	187.23 (104.29-453.47)
		7.08	> 400	147.83 (99.07-234.60)
		9.12	> 400	122.28 (83.73-183.53)
		11.1	> 400	69.90 (40.38-116.64)
	La figure R2a montre les intensité s de CL relatives des neutrophiles exposé s à trois ENP
	pendant des duré es diffé rentes. Il indique que des concentrations faibles de CuNPs ou NiNPs
	pourraient stimuler les neutrophiles alorsqu'une concentration élevée pourrait inhiber leur
	action. Les neutrophiles exposé s aux CuNPs ont montré une augmentation de prè s de 180% des
	valeurs de CL par rapport aux valeurs mesuré es sur lecontrôle contre ~ 115% pour ceux exposé s
	aux NiNPs. Une autre diffé rence é vidente entre ces deux types de ENPs est que les CuNPs
	stimulent la plupart des neutrophiles exposé s pour 2.5-4.6 h, tandis que les NiNPs le retardaient

à 10.70 h. Toutes les diffé rences ci-dessus pourraient s'expliquer par les proprié té s beaucoup plus toxiques des CuNPs que des NiNPs. En outre, on a constaté que les intensité s relatives de CL augmentaient au cours du temps pour les neutrophiles exposé s à NiNPs, Al 2 O 3 NPs et

Caracté risation MEB des ENPs et des neutrophiles

  Ces mesures de CL ré vè lent aussi que le reste des cellules vivantes exposé es à des concentrations é levé es de Al 2 O 3 NPs libè re plus de ROS que ceux exposé s à des concentrations plus faibles de Al 2 O 3 NPs. Nous avons supposé que la mort des neutrophiles exposé s aux CuNPs et aux NiNPs à concentration plus é levé e est principalement due à l'inhibition de l'activité physiologique des neutrophiles par les ENPs. La raison est que la proportion d'intensité s de CL relatives inhibé es au contrôle é tait similaire à la proportion de cellules mortes au contrôle. Cependant, la mort des neutrophiles exposé s aux Al 2 O 3 NPs peut ê tre due à la stimulation excessive des neutrophiles, car la surproduction de ROS pourrait avoir des effets né fastes sur les neutrophiles eux-mê mes. La Figure R3 montre la morphologie des neutrophiles non exposé s ainsi que des neutrophiles (5 mln/mL) exposé s aux NiNPs, aux Al 2 O 3 NPs (200 μg/mL) et aux CuNPs (400 μg/mL). La morphologie des neutrophiles non exposé s et incubé s pendant 10 heures ne pré sente pas de modifications é videntes par rapport aux cellules « fraî ches » . Les Figures R3c et d montrent que les NiNP et les Al 2 O 3 NP sont fixé s respectivement sur la membrane des neutrophiles exposé s.

	R2.3

Ainsi, avec des concentrations croissantes d'Al 2 O 3 NPs, l'augmentation de neutrophiles morts est associé e avec des ré sultats de CL qui ont montré une tendance à la hausse.

Toutes les membranes des neutrophiles exposé s avaient des structures intactes comme les non exposé es. L'augmentation de protubé rances membranaires a é té observé e sur les neutrophiles exposés à NiNPs. Fait intéressant, après 8 h d'exposition aux CuNPs, lesneutrophiles ont été entouré s denano-feuilles de cuivre, sur une grande é chelle (Figure

R3e

). L'é paisseur d'une seule nano-feuille de cuivre couvrant les neutrophiles, se situe entre 20 et 50 nm avec une surface rugueuse.

  Gammes recommandé es" et "Gammes". Leurs calculs ont utilisé les 12 variables d'entré e (sauf k diss ) pour l'analyse de sensibilité au chapitre 3. Pour chaque ENP, Le calcul de la "Gamme recommandé e" a fixé la constante de vitesse de dissolution des ENPs à k diss , tandis que le calcul de "Gamme" a utilisé 0.5 k diss à 2 k diss . Ici, le k diss repré sente la valeur recommandé e pour la ENP indiqué e dans le Tableau R2. Tableau D5 (annexe D) dresse la liste des valeurs recommandé es, "gammes recommandé es" et "Gammes" des FFs pour 17 ENPs dans 17 ré gions subcontinentales.

			k diss (s -1 )	No.	ENPs	k diss (s -1 )
	1	AgNPs	3.4197• 10 -6	10	Fe 3 O 4 NPs 0
	2	Al 2 O 3 NPs	0	11	NiNPs	2.1818• 10 -7
	3	AuNPs	0	12	NiONPs	2.1818• 10 -7
	4	C 60	0	13	PtNPs	0
	5	CeO 2 NPs	0	14	SiO 2 NPs	3.7899• 10 -6
	6	CuNPs	5.4953• 10 -7	15	TiO 2 NPs	0
	7	CuONPs	3.8681 • 10 -7	16	ZnONPs	5.1163• 10 -7
	8	Cu(OH) 2 NPs 4.9376• 10 -6	17	ZrO 2 NPs	0
	9	Fe 2 O 3 NPs	0

Les densité s et les constantes de vitesse de dissolution (dans le Tableau R2) de 17 ENP ont é té utilisé es pour calculer le FF d'eau douce. Dans ce travail, deux types de gammes ont é té signalé s: "

Table A1

 A1 Numeric values of the records and proportions in Web of Science of Thomson Scientific (Core Collection) from 1985 to 2015. Results are based on the search made on 2

Table A2 Continued Num. Reference Target products or processes Impact category Ecotoxicity Human toxicity

 A2

				Non-cancer Cancer
	(Laurent et al., 2012)	Four thousands different products, technologies, and services	E	NC	C
	(Vá zquez-Rowe et al., 2012a) Forty grape production exploitations in Spain	E
	(Vá zquez-Rowe et al., 2012b) Viticulture, vinification, bottling and packaging in a winery in Spain (2007-2010)	E
	(Ferreira et al., 2013)	Entire demilitarization processes of portuguese ammunition	E	NC	C
	(Lam et al., 2013)	Waste electrical and electronic equipment from the U.S. market	E	NC	C
	(Morais et al., 2013)	Twenty micropollutants, such as Atenolol, bezafibrate, clindamycin etc.	E
	(Owsianiak et al., 2013)	Copper and nickel in 760 worldwide soils	E
	(Pasquet et al., 2013)	Seven molecules used as carriers for dyeing of polyester fabrics	E	H
	(Roï z and Paquot, 2013)	A bio-based chainsaw oil made on the farm in Belgium	E
	(Silva et al., 2013)	An medium density particleboard panel produced in Brazil	E	H
	(Skaar and Jørgensen, 2013)	Emissions of toxic substances from products (case study: a chair)		NC	C
	(van Zelm et al., 2013)	415 organic acids and 496 organic bases	E
	(Yang, 2013)	Corn ethanol (in 2001, 2005, 2010) and gasoline in the U.S.	E	NC	C
	(Zhou et al., 2013)	Brine disposal from 2 seawater desalination plants	E
	(Dong et al., 2014)	Nine cationic metals in freshwater: Ba, Be, Cd, Co, Cr, Cu, Ni, Pb, Zn	E

Table C3

 C3 Relative importance numeric values of 13 parameters on ENPs' fate factors in 17 sub-continental regions by -Group Parameter SA‖ (arbitrary unit).

		k diss	r LSP	ρ SNP	α 2	Depth water	r ENP	ρ ENP	C 2	α 3	V water	Depth sed	C T water
	DEFAULT 1.5146 2.2838 1.3591 4.6437E-03	0.9922	4.7981E-03 4.6437E-03 3.1222E-02 1.2617E-03 2.4754E-03 1.5543E-15	0
	W1	6.2280 2.2807 1.3603 1.4579E-02	0.3315	1.5064E-02 1.4579E-02	0	0	8.0301E-03 1.3323E-15	0
	W2	8.2142 2.2655 1.3517 1.8111E-02	0.2704	1.8713E-02 1.8111E-02	0	0	2.6173E-02 1.5543E-15	0
	W3	0.5388 2.1476 1.2783 2.1797E-03	1.1437	2.2522E-03 2.1797E-03 4.5512E-02 1.9740E-03 5.1575E-02 8.8818E-16	0
	W4	0.6282 2.2636 1.3460 2.4780E-03	1.1992	2.5604E-03 2.4780E-03 4.7284E-02 2.0442E-03 1.0676E-02 8.8818E-16	0
	W5	27.5505 2.2598 1.3629 5.7453E-02	0	5.9363E-02 5.7453E-02	0	0	4.2453E-03 1.3323E-15	0
	W6	26.3685 2.2632 1.3636 5.5508E-02	0	5.7354E-02 5.5508E-02	0	0	2.3429E-03 1.3323E-15	0
	W7	3.2209 2.2618 1.3478 8.2336E-03	0.5731	8.5074E-03 8.2336E-03	0	2.9275E-04 2.4206E-02 1.3323E-15	0
	W8	2.0839 2.1426 1.2775 5.4295E-03	0.6090	5.6100E-03 5.4295E-03	0	3.3029E-04 1.1247E-01 1.1102E-15	0
	W9	13.1592 2.2670 1.3554 2.7773E-02	0.1369	2.8697E-02 2.7773E-02	0	0	2.0239E-02 1.5543E-15	0
	W10	12.5735 2.2780 1.3612 2.7381E-02	0.1458	2.8292E-02 2.7381E-02	0	0	7.4455E-03 1.3323E-15	0
	W12	10.0291 2.2830 1.3626 2.2410E-02	0.2080	2.3156E-02 2.2410E-02	0	0	4.2422E-03 1.5543E-15	0
	W13	7.1454 2.2817 1.3607 1.6468E-02	0.2724	1.7016E-02 1.6468E-02	0	0	7.2189E-03 1.3323E-15	0
	W14	1.3014 2.2350 1.3324 4.0706E-03	1.0001	4.2059E-03 4.0706E-03 3.2261E-02 1.3121E-03 3.2904E-02 1.5543E-15	0
	IND	8.3192 2.2777 1.3588 1.8722E-02	0.2750	1.9344E-02 1.8722E-02	0	0	1.1746E-02 1.5543E-15	0
	CHI	8.3540 2.2783 1.3592 1.8812E-02	0.2746	1.9438E-02 1.8812E-02	0	0	1.0995E-02 1.5543E-15	0
	JAP	8.5569 2.2690 1.3536 1.8892E-02	0.5616	1.9520E-02 1.8892E-02	0	0	2.2396E-02 1.5543E-15	0

Table C4

 C4 Relative importance numeric values of 13 parameters on ENPs' fate factors in seventeen sub-continental regions by -Single Parameter SA‖ (arbitrary unit).

			k diss	r LSP	ρ SNP	α 2	Depth water	r ENP	ρ ENP	α 3	V water	Depth sed	C 2 C T water
	DEFAULT 1.5142 2.2784 1.3009 0.9913	0.7703	5.2822E-04 2.7336E-04 3.6359E-04 1.3875E-03 1.5543E-15 0	0
		W1	6.2265 2.2274 1.2750 0.3315	0.5897	3.8519E-04 1.9932E-04	0	6.2856E-03 1.1102E-15 0	0
		W2	8.2123 2.1896 1.1801 0.2704	0.4962	3.2604E-04 1.6871E-04	0	2.1383E-02 1.3323E-15 0	0
		W3	0.5387 2.1476 1.0261 1.1420	0.6609	4.4831E-04 2.3202E-04 3.0864E-04 2.3401E-02 6.6613E-16 0	0
		W4	0.6280 2.2636 1.0912 1.1975	0.7007	4.7599E-04 2.4634E-04 3.2769E-04 4.8707E-03 8.8818E-16 0	0
		W5	27.5445 2.0289 0.7112	0	0.2284	1.4399E-04 7.4505E-05 Proportion of "ENPs	0	3.9391E-03 8.8818E-16 0	0
		W6	26.3627 2.0424 0.7342	0	0.2382	1.5040E-04 7.7819E-05 -toxicity" (%)	0	2.1656E-03 8.8818E-16 0	0
	W7 AuNPs	3.2201 2.2390 1.3474 0.5730 2634 72129	0.7029 3.52	4.7742E-04 2.4706E-04 1.5229E-04 1.6446E-02 1.3323E-15 0 96.48	0
	W8 AgNPs	2.0833 2.1286 1.2741 0.6088 3404 41142	0.6994 7.64	4.7776E-04 2.4723E-04 1.5240E-04 7.1757E-02 1.1102E-15 0 92.36	0
	C 60	W9	13.1564 2.1448 1.0098 0.1369 673 29325	0.3783 2.24	2.4401E-04 1.2626E-04 97.76	0	1.7621E-02 1.1102E-15 0	0
	W10 CuNPs	12.5706 2.1644 1.0468 0.1458 804 15214	0.3998 5.02	2.5868E-04 1.3385E-04 94.98	0	6.4155E-03 1.3323E-15 0	0
	W12 PtNPs	10.0268 2.1929 1.1372 0.2080 156 14344	0.4476 1.08	3.0052E-04 1.5550E-04 98.92	0	3.5455E-03 1.3323E-15 0	0
	W13 TiO 2 NPs	7.1437 2.2190 1.2414 0.2724 1716 11030	0.5455 13.46 3.6100E-04 1.8680E-04 86.54	0	5.7258E-03 1.1102E-15 0	0
	W14 NiNPs	1.3010 2.2309 1.2563 0.9991 208 10478	0.7525 1.95	5.1607E-04 2.6707E-04 3.5524E-04 1.8041E-02 1.5543E-15 0 98.05	0
	IND ZnONPs	8.3172 2.2027 1.1919 0.2750 1070 9015	0.5033 10.61 3.3094E-04 1.7124E-04 89.39	0	9.5681E-03 1.5543E-15 0	0
	CHI Fe 3 O 4 NPs	8.3520 2.2031 1.1913 0.2746 7679 260 7419	0.5028 3.39	3.3054E-04 1.7104E-04 96.61	0	8.9544E-03 1.3323E-15 0	0
	JAP Al 2 O 3 NPs	8.5549 2.1902 1.1713 0.2650 6861 131 6730	0.4883 1.91	3.2058E-04 1.6588E-04 98.09	0	1.8392E-02 1.3323E-15 0	0
	CuONPs	4761	434	4327	9.12		90.88
	NiONPs	3489	112	3377	3.21		96.79
	CeO 2 NPs	2310	338	1972	14.63	85.37
	SiO 2 NPs	1119	76	1043	6.79		93.21
	Fe 2 O 3 NPs	822	52	770		6.33		93.67
	ZrO 2 NPs	623	9	614		1.44		98.56
	Cu(OH) 2 NPs	355	6	349		1.69		98.31

Table D3

 D3 Calculation of dissolution rate constants for several ENPs (ZnONPs, AgNPs, NiNPs, Fe 2 O 3 NPs and CuNPs) based on equation 3.2.

	ENPs	Reference	pH	Time (hours)

C ions (mg• L -1 ) C 0 (mg• L -1 ) Dissolution rate constant (s -1 ) Average Value of k diss (s -1 ) Standard deviation

	162	Table D3 Continued ENPs	Reference	pH	Time (hours)	6	8.40		5.1126E-06	APPENDIX D
						24	13.00		2.0429E-06
		ZnONPs	(Franklin et al., 2007)	7.50±0.15	31	14.50	100	1.7835E-06	2.2114E-06	1.5081E-06
						48	15.80		1.2672E-06
						72	15.90		8.5080E-07
						24	1		1.4496E-07
						140	2.3		5.7630E-08
						160	3		6.6069E-08
		ZnONPs (Alfa Aesar 44904)	(Reed et al., 2012)	7.3		360 550	4.4 5.7	100	4.3459E-08 3.7166E-08	5.6416E-08	3.5330E-08
						610	6.1		3.5958E-08
						700	6.4		3.2941E-08
						810	7.4		3.3137E-08
						24	1.3		1.8881E-07
						140	2.6		6.5272E-08
						160	2.9		6.3825E-08
		ZnONPs (Alfa Aesar 45409)	(Reed et al., 2012)	7.3		360 550	4.4 5.4	1.2287E-03	4.3459E-08 3.5141E-08	6.2317E-08	4.9390E-08
						610	5.9		3.4733E-08
						700	6.8		3.5094E-08
						810	7.2		3.2198E-08
						24	1		1.4496E-07
						140	2.8		7.0383E-08
						160	3.7		8.1853E-08
		ZnONPs (Alfa Aesar 45588)	(Reed et al., 2012)	7.3		360 550	5.4 6.1	1.2287E-03	5.3687E-08 3.9880E-08	6.2389E-08	3.5235E-08
						610	6.6		3.9035E-08
						700	7		3.6174E-08
						810	7.4		3.3137E-08

C ions (mg• L -1 ) C 0 (mg• L -1 ) Dissolution rate constant (s -1 ) Average Value of k diss (s -1 ) Standard deviation

				3	8.3333E-03 mM	6.14 mM	1.2575E-07
	ZnO	(Miao et al., 2010) *1	5.6	4 6	1.3333E-02 mM 2.6667E-02 mM	6.14 mM 6.14 mM	1.5097E-07 2.0151E-07	1.6563E-07	2.9326E-08
				8	3.2500E-02 mM	6.14 mM	1.8428E-07
	AgNPs		6	24	6.00E-03	2	3.4774E-08	----	----
		(Fabrega et al., 2009)
			7.5	24	1.88E-02	2	1.0931E-07	----	----
	NiNPs/NiONPs	(Griffitt et al., 2008)	8.2 ± 1	48	3.7% × 270	270	2.1818E-07	----	----
	*1 1mM = 0.001 mol• L -1

Table D4

 D4 The dissolution rate constants of 9 soluble ENPs in literatures.

	ENPs	Reference	Size (nm)	C 0 (mg• L -1 )	Media	pH	Temp. ( o C)	Reported k diss (s -1 )
		(Liu and Hurt, 2010)	4.8	0.05	DI water *1	5.68	20	1.0185E-05
		(Liu and Hurt, 2010)	4.8	0.2	DI water	5.68	20	6.1343E-06
		(Liu and Hurt, 2010)	4.8	2	DI water	5.68	20	2.6620E-07
		(Kittler et al., 2010) *2	50±20	50	DI water	----	25	4.7222E-06
		(Kittler et al., 2010) *2	50±20	100	DI water	----	25	2.3889E-06
		(He et al., 2013) *3	19.0±1.3	0.02 mM 50 mM NaCl	8	22	4.1000E-07
	AgNPs	(He et al., 2013) *3 (He et al., 2013) *3	19.0±1.3 19.0±1.3	0.02 mM 50 mM NaCl 0.02 mM 50 mM NaCl	8 8	22 22	8.3000E-07 1.0900E-06
		(He et al., 2013) *3	19.0±1.3	0.02 mM 50 mM NaCl	8	22	6.4000E-07
		(Peretyazhko et al., 2014)	6.2±1.6	8	DI water	6-8	Room Temp.	1.4352E-05
		(Peretyazhko et al., 2014)	9.2±3.0	8	DI water	6-8	Room Temp.	4.9769E-06
		(Peretyazhko et al., 2014)	12.9±3.5	8	DI water	6-8	Room Temp.	1.7361E-06
		(Fabrega et al., 2009)	65±30	2	MDM media; I = 55 mM	6	25	3.4774E-08
		(Fabrega et al., 2009)	65±30	2	MDM media; I = 55 mM	7.5	25	1.0931E-07
	*1 DI water: Ultrapure deionized water.

Table D4 Continued

 D4 The ionic strength of 100 mM PBS (Phosphate-buffered saline) is about 300 mM. Dissolution data of NiNPs and NiONPs are rarely reported. Griffitt et al. investigated the NiNPs coated with thin layers (thickness, ~2-5 nm) of nickel oxide. Thus, we assume that the reported values are both valid for NiNPs and NiONPs. Mesocosm water is a natural water from a freshwater wetland mesocosm in Durham, USA. It contains Cl -1 , SO 4 -2 , NO 3

	166	ENPs CuNPs Table D4 Continued Reference (Wang et al., 2015) *4 ENPs Reference NiNPs NiONPs (Griffitt et al., 2008) *5 ZnONPs (Franklin et al., 2007) (Miao et al., 2010) (Reed et al., 2012) (Reed et al., 2012) (Reed et al., 2012) CuONPs (Vencalek et al., 2016) (Vencalek et al., 2016) *6 (Vencalek et al., 2016) (Vencalek et al., 2016) *6 Cu(OH) 2 (Vencalek et al., 2016) (Vencalek et al., 2016) (Vencalek et al., 2016) (Vencalek et al., 2016) *4 APPENDIX Size (nm) C 0 (mg• L -1 ) Media pH Temp. ( o C) Reported k diss (s -1 ) 100 100 5 mg• L -1 Sigma HA, 100 mM PBS 7 25±0.5 7.9026E-07 10 mg• L -1 Sigma HA, 100 mM PBS 1.0029E-06 20 mg• L -1 Sigma HA, 100 mM PBS 1.0522E-06 50 mg• L -1 Sigma HA, 100 mM PBS 1.3202E-06 5 mg• L -1 Extracted HA, 100 mM PBS 1.1632E-07 10 mg• L -1 Extracted HA, 100 mM PBS 1.6436E-07 20 mg• L -1 Extracted HA, 100 mM PBS 3.1204E-07 50 mg• L -1 Extracted HA, 100 mM PBS 5.1233E-07 5 mg• L -1 SRHA, 100 mM PBS 4.5681E-07 10 mg• L -1 SRHA, 100 mM PBS 5.9977E-07 20 mg• L -1 SRHA, 100 mM PBS 6.8295E-07 50 mg• L -1 SRHA, 100 mM PBS 9.5375E-07 5 mg• L -1 BSA, 100 mM PBS 1.1632E-07 10 mg• L -1 BSA, 100 mM PBS 1.9374E-07 20 mg• L -1 BSA, 100 mM PBS 2.3855E-07 50 mg• L -1 BSA, 100 mM PBS 2.7998E-07 D Size (nm) C 0 (mg• L -1 ) Media pH Temp. ( o C) Reported k diss (s -1 ) 6.1 ± 1.4 270 Hardness: 142±2 mg/L as CaCO 3 ; I= 5.67 mM 8.2 ± 1 Room Temp. 2.1818E-7 30 100 10 mM Ca(NO 3 ) 2 ; I= 40 mM 7.50±0.15 24 2.2114E-06 20 6.14 mM DI water 5.6 20 1.6563E-07 70 100 DI water 7.3 20 5.6416E-08 40 100 DI water 7.3 20 6.2317E-08 70 100 DI water 7.3 20 6.2389E-08 40 1 DI water 5.8 20 4.7222E-07 40 1 Mesocosm water; I= 1.58 mM 7.7 20 1.8611E-07 40 1 DI water 5.8 10-16 4.1667E-07 40 1 Mesocosm water; I= 1.58 mM 7.7 10-16 4.7222E-07 Length 120 1 DI water 5.8 20 1.5556E-05 Length 120 1 Mesocosm water; I = 1.58 mM 7.7 20 1.6111E-06 Length 120 1 DI water 5.8 10-16 2.1667E-06 Length 120 1 Mesocosm water; I = 1.58 mM 7.7 10-16 4.1667E-07 *5 -1

*6

Table D4 Continued

 D4 The reported parameter in reference is log(r +,mass ) with a units of mol/g/s[START_REF] Diedrich | The dissolution rates of sio2 nanoparticles as a function of particle size[END_REF]. The r +,mass denotes the mass-normalized far-from-equilibrium dissolution rate. Here, the dissolution rate constant was calculated by the following equation: kdiss = r +,mass × M, where M is molecular weight of SiO 2 (60.8 g• mol -1 ).

	ENPs	Reference	Size (nm)	C 0 (mg• L -1 )	Media	pH	Temp. ( o C)	Reported k diss (s -1 )
	SiO 2 NPs	(Diedrich et al., 2012) *7	39	----	10mM NaCl	7.2	25	3.0472E-06
		(Diedrich et al., 2012)	101	----	I < 10 mM	7.2	25	1.9227E-06
		(Diedrich et al., 2012)	25	----	I < 10 mM	6.9	25	2.4205E-06
		(Diedrich et al., 2012)	39	----	I < 10 mM	6.9	25	1.9227E-06
		(Diedrich et al., 2012)	101	----	I < 10 mM	6.9	25	9.6362E-06
	*7

Table D5

 D5 Ranges and recommended values of fate factors for ENPs.

				CuNPs
	Region ID	FF w,w	Recommended range of FF w,w Max Min	Range of FF w,w MAX MIN	FF sed,sed
	DEFAULT	1.3630	3.3752E+00	3.8636E-03 6.7319E+00 3.8592E-03 2805.8313
	W1	2.2760	3.3680E+00	1.1829E-02 6.7036E+00 1.1788E-02 2030.3436
	W2	2.4772	3.3368E+00	1.6184E-02 6.5810E+00 1.6107E-02 1549.8820
	W3	0.8322	3.0888E+00	1.9260E-03 5.6812E+00 1.9249E-03 3129.1865
	W4	0.8981	3.3315E+00	2.0789E-03 6.5602E+00 2.0776E-03 3015.0497
	W5	3.0270	3.3776E+00	4.8590E-02 6.7416E+00 4.7902E-02 1309.2238
	W6	3.0133	3.3805E+00	4.6256E-02 6.7531E+00 4.5632E-02 1434.0575
	W7	1.8589	3.3274E+00	7.1085E-03 6.5444E+00 7.0936E-03 2277.5259
	W8	1.5651	3.0781E+00	5.3760E-03 5.6452E+00 5.3675E-03 2717.4272
	W9	2.7341	3.3503E+00	2.4948E-02 6.6338E+00 2.4766E-02 1349.0998
	W10	2.7116	3.3715E+00	2.3264E-02 6.7172E+00 2.3106E-02 1389.2948
	W12	2.5923	3.3765E+00	1.8770E-02 6.7371E+00 1.8667E-02 1351.6402
	W13	2.3920	3.3704E+00	1.3879E-02 6.7128E+00 1.3822E-02 1941.5595
	W14	1.2725	3.3703E+00	1.3623E-02 6.7127E+00 1.3569E-02 2510.5986
	IND	2.4848	3.3627E+00	1.6019E-02 6.6827E+00 1.5943E-02 1544.1417
	CHI	2.4873	3.3641E+00	1.6062E-02 6.6880E+00 1.5986E-02 1526.2203
	JAP	2.5015	3.3446E+00	1.6698E-02 6.6112E+00 1.6616E-02 1523.2120
				Al 2 O 3 NPs
	Region ID	FF w,w	Recommended range of FF w,w Max Min	Range of FF w,w MAX MIN	FF sed,sed
	DEFAULT	2.2824	1.2774E+03	3.8680E-03 1.2774E+03 3.8680E-03 4032.5662
	W1	6.9498	6.9753E+02	1.1871E-02 6.9753E+02 1.1871E-02 3678.0702
	W2	9.2421	2.3711E+02	1.6262E-02 2.3711E+02 1.6262E-02 3385.1761
	W3	1.1037	3.5436E+01	1.9271E-03 3.5436E+01 1.9271E-03 3569.5957
	W4	1.2227	2.1558E+02	2.0802E-03 2.1558E+02 2.0802E-03 3524.0819
	W5	28.6607	1.6695E+03	4.9298E-02 1.6695E+03 4.9298E-02 3523.3863
	W6	27.4769	2.8918E+03	4.6897E-02 2.8918E+03 4.6897E-02 3710.9980
	W7	4.1244	1.9848E+02	7.1235E-03 1.9848E+02 7.1235E-03 3516.5134
	W8	2.9119	3.4054E+01	5.3846E-03 3.4054E+01 5.3846E-03 3555.7454
	W9	14.2285	3.3235E+02	2.5134E-02 3.3235E+02 2.5134E-02 3362.0043
	W10	13.6393	8.8033E+02	2.3426E-02 8.8033E+02 2.3426E-02 3334.2649
	W12	11.0762	1.4356E+03	1.8875E-02 1.4356E+03 1.8875E-02 3030.0024
	W13	8.1584	8.1255E+02	1.3936E-02 8.1255E+02 1.3936E-02 3590.1592
	W14	2.0396	8.1168E+02	1.3679E-02 8.1168E+02 1.3679E-02 3456.2349
	IND	9.3483	5.2497E+02	1.6095E-02 5.2497E+02 1.6095E-02 3393.1796
	CHI	9.3836	5.5988E+02	1.6138E-02 5.5988E+02 1.6138E-02 3362.6739
	JAP	9.5889	2.8392E+02	1.6781E-02 2.8392E+02 1.6781E-02 3415.9415

Table D5

 D5 Continued.

				AuNPs
	Region ID	FF w,w	Recommended range of FF w,w Max Min	Range of FF w,w MAX MIN	FF sed,sed
	DEFAULT	2.2815	1.1803E+03	3.8680E-03 1.1803E+03 3.8680E-03 4032.5682
	W1	6.9470	6.8716E+02	1.1871E-02 6.8716E+02 1.1871E-02 3678.0797
	W2	9.2385	2.3624E+02	1.6262E-02 2.3624E+02 1.6262E-02 3385.2155
	W3	1.1033	3.5274E+01	1.9271E-03 3.5274E+01 1.9271E-03 3569.6183
	W4	1.2222	2.1015E+02	2.0802E-03 2.1015E+02 2.0802E-03 3524.0862
	W5	28.6492	1.6371E+03	4.9298E-02 1.6371E+03 4.9298E-02 3523.4029
	W6	27.4658	2.7911E+03	4.6897E-02 2.7911E+03 4.6897E-02 3711.0070
	W7	4.1227	1.9653E+02	7.1234E-03 1.9653E+02 7.1234E-03 3516.5323
	W8	2.9108	3.3977E+01	5.3846E-03 3.3977E+01 5.3846E-03 3555.8085
	W9	14.2229	3.3087E+02	2.5134E-02 3.3087E+02 2.5134E-02 3362.0473
	W10	13.6338	8.6934E+02	2.3425E-02 8.6934E+02 2.3425E-02 3334.2797
	W12	11.0717	1.4040E+03	1.8875E-02 1.4040E+03 1.8875E-02 3030.0088
	W13	8.1551	7.9938E+02	1.3936E-02 7.9938E+02 1.3936E-02 3590.1682
	W14	2.0388	7.9829E+02	1.3678E-02 7.9829E+02 1.3678E-02 3456.2562
	IND	9.3446	5.2069E+02	1.6095E-02 5.2069E+02 1.6095E-02 3393.1974
	CHI	9.3798	5.5502E+02	1.6138E-02 5.5502E+02 1.6138E-02 3362.6904
	JAP	9.5851	2.8272E+02	1.6781E-02 2.8272E+02 1.6781E-02 3415.9763
				C 60
	Region ID	FF w,w	Recommended range of FF w,w Max Min	Range of FF w,w MAX MIN	FF sed,sed
	DEFAULT	2.2826	1.3321E+03	3.8680E-03 1.3321E+03 3.8680E-03 4032.5659
	W1	6.9502	7.1010E+02	1.1871E-02 7.1010E+02 1.1871E-02 3678.0688
	W2	9.2427	2.3815E+02	1.6262E-02 2.3815E+02 1.6262E-02 3385.1699
	W3	1.1038	3.5517E+01	1.9271E-03 3.5517E+01 1.9271E-03 3569.5921
	W4	1.2228	2.1839E+02	2.0802E-03 2.1839E+02 2.0802E-03 3524.0812
	W5	28.6625	1.6860E+03	4.9298E-02 1.6860E+03 4.9298E-02 3523.3837
	W6	27.4787	2.9443E+03	4.6897E-02 2.9443E+03 4.6897E-02 3710.9966
	W7	4.1246	1.9980E+02	7.1235E-03 1.9980E+02 7.1235E-03 3516.5105
	W8	2.9120	3.4105E+01	5.3846E-03 3.4105E+01 5.3846E-03 3555.7355
	W9	14.2294	3.3309E+02	2.5134E-02 3.3309E+02 2.5134E-02 3361.9975
	W10	13.6401	8.8593E+02	2.3426E-02 8.8593E+02 2.3426E-02 3334.2626
	W12	11.0769	1.4597E+03	1.8875E-02 1.4597E+03 1.8875E-02 3030.0014
	W13	8.1589	8.2427E+02	1.3936E-02 8.2427E+02 1.3936E-02 3590.1578
	W14	2.0397	8.2359E+02	1.3679E-02 8.2359E+02 1.3679E-02 3456.2316
	IND	9.3489	5.3017E+02	1.6095E-02 5.3017E+02 1.6095E-02 3393.1768
	CHI	9.3841	5.6577E+02	1.6138E-02 5.6577E+02 1.6138E-02 3362.6713
	JAP	9.5895	2.8537E+02	1.6781E-02 2.8537E+02 1.6781E-02 3415.9361

Table D5

 D5 Continued.

				CeO 2 NPs
	Region ID	FF w,w	Recommended range of FF w,w Max Min	Range of FF w,w MAX MIN	FF sed,sed
	DEFAULT	2.2822	1.2442E+03	3.8680E-03 1.2442E+03 3.8680E-03 4032.5666
	W1	6.9492	6.9383E+02	1.1871E-02 6.9383E+02 1.1871E-02 3678.0722
	W2	9.2413	2.3683E+02	1.6262E-02 2.3683E+02 1.6262E-02 3385.1842
	W3	1.1037	3.5383E+01	1.9271E-03 3.5383E+01 1.9271E-03 3569.6003
	W4	1.2226	2.1379E+02	2.0802E-03 2.1379E+02 2.0802E-03 3524.0828
	W5	28.6583	1.6588E+03	4.9298E-02 1.6588E+03 4.9298E-02 3523.3897
	W6	27.4746	2.8583E+03	4.6897E-02 2.8583E+03 4.6897E-02 3710.9998
	W7	4.1240	1.9763E+02	7.1235E-03 1.9763E+02 7.1235E-03 3516.5173
	W8	2.9116	3.4021E+01	5.3846E-03 3.4021E+01 5.3846E-03 3555.7583
	W9	14.2273	3.3187E+02	2.5134E-02 3.3187E+02 2.5134E-02 3362.0131
	W10	13.6381	8.7673E+02	2.3426E-02 8.7673E+02 2.3426E-02 3334.2679
	W12	11.0752	1.4251E+03	1.8875E-02 1.4251E+03 1.8875E-02 3030.0037
	W13	8.1577	8.0822E+02	1.3936E-02 8.0822E+02 1.3936E-02 3590.1611
	W14	2.0394	8.0728E+02	1.3679E-02 8.0728E+02 1.3679E-02 3456.2392
	IND	9.3476	5.2357E+02	1.6095E-02 5.2357E+02 1.6095E-02 3393.1833
	CHI	9.3828	5.5829E+02	1.6138E-02 5.5829E+02 1.6138E-02 3362.6773
	JAP	9.5881	2.8353E+02	1.6781E-02 2.8353E+02 1.6781E-02 3415.9487
				CuNPs
	Region ID	FF w,w	Recommended range of FF w,w Max Min	Range of FF w,w MAX MIN	FF sed,sed
	DEFAULT	2.0590	2.0707E+01	3.8673E-03 4.0730E+01 3.8666E-03 3734.7739
	W1	5.2250	2.0440E+01	1.1864E-02 3.9709E+01 1.1857E-02 3070.2015
	W2	6.4229	1.9341E+01	1.6249E-02 3.5760E+01 1.6237E-02 2620.5099
	W3	1.0487	1.3200E+01	1.9269E-03 1.9224E+01 1.9267E-03 3480.4226
	W4	1.1555	1.9167E+01	2.0800E-03 3.5170E+01 2.0798E-03 3418.8420
	W5	12.1396	2.0797E+01	4.9183E-02 4.1078E+01 4.9068E-02 2096.4536
	W6	11.9220	2.0907E+01	4.6792E-02 4.1510E+01 4.6689E-02 2263.3495
	W7	3.4486	1.9030E+01	7.1210E-03 3.4712E+01 7.1186E-03 3147.1420
	W8	2.5579	1.3006E+01	5.3832E-03 1.8816E+01 5.3818E-03 3335.5896
	W9	8.4911	1.9804E+01	2.5104E-02 3.7377E+01 2.5074E-02 2357.4340
	W10	8.2777	2.0567E+01	2.3399E-02 4.0190E+01 2.3374E-02 2380.1379
	W12	7.2582	2.0754E+01	1.8858E-02 4.0911E+01 1.8841E-02 2274.8472
	W13	5.8800	2.0526E+01	1.3926E-02 4.0033E+01 1.3917E-02 2938.9392
	W14	1.8593	2.0525E+01	1.3670E-02 4.0031E+01 1.3661E-02 3234.2097
	IND	6.4740	2.0246E+01	1.6082E-02 3.8984E+01 1.6070E-02 2619.0157
	CHI	6.4909	2.0295E+01	1.6126E-02 3.9166E+01 1.6113E-02 2592.5439
	JAP	6.5885	1.9605E+01	1.6768E-02 3.6673E+01 1.6754E-02 2614.8525

Table D5

 D5 Continued.

				CuONPs
	Region ID	FF w,w	Recommended range of FF w,w Max Min	Range of FF w,w MAX MIN	FF sed,sed
	DEFAULT	2.1205	2.9212E+01	1.3867E-01 5.7070E+01 3.8670E-03 3816.6896
	W1	5.6396	2.8682E+01	1.1866E-02 5.5085E+01 1.1861E-02 3216.2467
	W2	7.0608	2.6564E+01	1.6253E-02 4.7768E+01 1.6244E-02 2793.4734
	W3	1.0644	1.6207E+01	1.9270E-03 2.2227E+01 1.9268E-03 3505.8824
	W4	1.1746	2.6237E+01	2.0800E-03 4.6721E+01 2.0799E-03 3448.7757
	W5	14.6384	2.9390E+01	4.9217E-02 5.7756E+01 4.9136E-02 2312.2350
	W6	14.3232	2.9610E+01	4.6823E-02 5.8612E+01 4.6750E-02 2486.7783
	W7	3.6246	2.5981E+01	7.1218E-03 4.5915E+01 7.1201E-03 3243.2499
	W8	2.6535	1.5917E+01	5.3836E-03 2.1684E+01 5.3826E-03 3394.9849
	W9	9.6426	2.7446E+01	2.5113E-02 5.0697E+01 2.5092E-02 2559.0006
	W10	9.3683	2.8933E+01	2.3407E-02 5.6015E+01 2.3389E-02 2574.1732
	W12	8.0834	2.9305E+01	1.8863E-02 5.7426E+01 1.8851E-02 2438.0193
	W13	6.4102	2.8851E+01	1.3929E-02 5.5711E+01 1.3923E-02 3090.4344
	W14	1.9093	2.8850E+01	1.3672E-02 5.5706E+01 1.3666E-02 3295.7463
	IND	7.1226	2.8303E+01	1.6086E-02 5.3700E+01 1.6077E-02 2793.6545
	CHI	7.1430	2.8398E+01	1.6129E-02 5.4045E+01 1.6121E-02 2766.1171
	JAP	7.2614	2.7064E+01	1.6772E-02 4.9410E+01 1.6762E-02 2794.4644
				Cu(OH) 2 NPs
	Region ID	FF w,w	Recommended range of FF w,w Max Min	Range of FF w,w MAX MIN	FF sed,sed
	DEFAULT	1.1564	2.3398E+00	3.8553E-03 4.6711E+00 3.8553E-03 2529.7791
	W1	1.7529	2.3362E+00	1.1752E-02 4.6569E+00 1.1752E-02 1845.7656
	W2	1.8698	2.3211E+00	1.6039E-02 4.5973E+00 1.6039E-02 1384.9676
	W3	0.7504	2.1987E+00	1.9239E-03 4.1406E+00 1.9239E-03 2996.1935
	W4	0.8036	2.3189E+00	2.0765E-03 4.5886E+00 2.0765E-03 2866.4680
	W5	2.1669	2.3408E+00	4.7308E-02 4.6750E+00 4.7308E-02 1234.8758
	W6	2.1598	2.3422E+00	4.5092E-02 4.6806E+00 4.5092E-02 1354.5666
	W7	1.4946	2.3167E+00	7.0804E-03 4.5801E+00 7.0804E-03 2078.1094
	W8	1.2987	2.1931E+00	5.3600E-03 4.1210E+00 5.3600E-03 2551.3636
	W9	2.0125	2.3277E+00	2.4606E-02 4.6230E+00 2.4606E-02 1222.6482
	W10	2.0003	2.3379E+00	2.2967E-02 4.6633E+00 2.2967E-02 1262.6070
	W12	1.9346	2.3403E+00	1.8576E-02 4.6729E+00 1.8576E-02 1221.4390
	W13	1.8209	2.3373E+00	1.3772E-02 4.6613E+00 1.3772E-02 1778.1360
	W14	1.0906	2.3373E+00	1.3521E-02 4.6613E+00 1.3521E-02 2286.0113
	IND	1.8741	2.3337E+00	1.5877E-02 4.6467E+00 1.5877E-02 1379.5029
	CHI	1.8756	2.3343E+00	1.5919E-02 4.6493E+00 1.5919E-02 1363.1931
	JAP	1.8836	2.3249E+00	1.6544E-02 4.6121E+00 1.6544E-02 1358.0798

Table D5

 D5 Continued.

				Fe 2 O 3 NPs
	Region ID	FF w,w	Recommended range of FF w,w Max Min	Range of FF w,w MAX MIN	FF sed,sed
	DEFAULT	2.2823	1.2615E+03	3.8551E-03 1.2615E+03 3.8551E-03 4032.5664
	W1	6.9495	6.9559E+02	1.1750E-02 6.9559E+02 1.1750E-02 3678.0710
	W2	9.2418	2.3698E+02	1.6036E-02 2.3698E+02 1.6036E-02 3385.1793
	W3	1.1037	3.5411E+01	1.9239E-03 3.5411E+01 1.9239E-03 3569.5975
	W4	1.2227	2.1473E+02	2.0765E-03 2.1473E+02 2.0765E-03 3524.0822
	W5	28.6597	1.6646E+03	4.7284E-02 1.6646E+03 4.7284E-02 3523.3877
	W6	27.4760	2.8764E+03	4.5070E-02 2.8764E+03 4.5070E-02 3710.9987
	W7	4.1242	1.9809E+02	7.0799E-03 1.9809E+02 7.0799E-03 3516.5150
	W8	2.9118	3.4039E+01	5.3596E-03 3.4039E+01 5.3596E-03 3555.7505
	W9	14.2280	3.3212E+02	2.4600E-02 3.3212E+02 2.4600E-02 3362.0078
	W10	13.6388	8.7865E+02	2.2961E-02 8.7865E+02 2.2961E-02 3334.2661
	W12	11.0758	1.4308E+03	1.8572E-02 1.4308E+03 1.8572E-02 3030.0029
	W13	8.1581	8.1055E+02	1.3770E-02 8.1055E+02 1.3770E-02 3590.1600
	W14	2.0395	8.0964E+02	1.3519E-02 8.0964E+02 1.3519E-02 3456.2366
	IND	9.3480	5.2433E+02	1.5874E-02 5.2433E+02 1.5874E-02 3393.1811
	CHI	9.3832	5.5914E+02	1.5916E-02 5.5914E+02 1.5916E-02 3362.6752
	JAP	9.5886	2.8374E+02	1.6541E-02 2.8374E+02 1.6541E-02 3415.9444
				Fe 3 O 4 NPs
	Region ID	FF w,w	Recommended range of FF w,w Max Min	Range of FF w,w MAX MIN	FF sed,sed
	DEFAULT	2.2824	1.2653E+03	3.8680E-03 1.2653E+03 3.8680E-03 4032.5664
	W1	6.9496	6.9592E+02	1.1871E-02 6.9592E+02 1.1871E-02 3678.0708
	W2	9.2419	2.3701E+02	1.6262E-02 2.3701E+02 1.6262E-02 3385.1785
	W3	1.1037	3.5417E+01	1.9271E-03 3.5417E+01 1.9271E-03 3569.5971
	W4	1.2227	2.1494E+02	2.0802E-03 2.1494E+02 2.0802E-03 3524.0822
	W5	28.6600	1.6657E+03	4.9298E-02 1.6657E+03 4.9298E-02 3523.3874
	W6	27.4762	2.8797E+03	4.6897E-02 2.8797E+03 4.6897E-02 3710.9985
	W7	4.1243	1.9818E+02	7.1235E-03 1.9818E+02 7.1235E-03 3516.5146
	W8	2.9118	3.4042E+01	5.3846E-03 3.4042E+01 5.3846E-03 3555.7493
	W9	14.2281	3.3217E+02	2.5134E-02 3.3217E+02 2.5134E-02 3362.0069
	W10	13.6389	8.7904E+02	2.3426E-02 8.7904E+02 2.3426E-02 3334.2658
	W12	11.0759	1.4318E+03	1.8875E-02 1.4318E+03 1.8875E-02 3030.0028
	W13	8.1582	8.1100E+02	1.3936E-02 8.1100E+02 1.3936E-02 3590.1598
	W14	2.0396	8.1010E+02	1.3679E-02 8.1010E+02 1.3679E-02 3456.2362
	IND	9.3481	5.2447E+02	1.6095E-02 5.2447E+02 1.6095E-02 3393.1807
	CHI	9.3833	5.5931E+02	1.6138E-02 5.5931E+02 1.6138E-02 3362.6749
	JAP	9.5887	2.8378E+02	1.6781E-02 2.8378E+02 1.6781E-02 3415.9437

Table D5

 D5 Continued.

				NiNPs
	Region ID	FF w,w	Recommended range of FF w,w Max Min	Range of FF w,w MAX MIN	FF sed,sed
	DEFAULT	2.1880	5.0856E+01	3.8674E-03 9.7677E+01 3.8674E-03 3906.9280
	W1	6.1441	4.9274E+01	1.1865E-02 9.2001E+01 1.1865E-02 3394.2783
	W2	7.8700	4.3336E+01	1.6252E-02 7.3260E+01 1.6252E-02 3013.1846
	W3	1.0811	2.1218E+01	1.9270E-03 2.6522E+01 1.9270E-03 3533.0997
	W4	1.1950	4.2473E+01	2.0800E-03 7.0826E+01 2.0800E-03 3480.8698
	W5	18.6059	5.1400E+01	4.9206E-02 9.9703E+01 4.9206E-02 2655.0802
	W6	18.0997	5.2077E+01	4.6814E-02 1.0228E+02 4.6814E-02 2838.4160
	W7	3.8264	4.1806E+01	7.1215E-03 6.8991E+01 7.1215E-03 3353.8002
	W8	2.7600	2.0723E+01	5.3835E-03 2.5752E+01 5.3835E-03 3461.4416
	W9	11.2182	4.5733E+01	2.5110E-02 8.0383E+01 2.5110E-02 2835.0724
	W10	10.8486	5.0017E+01	2.3405E-02 9.4626E+01 2.3405E-02 2837.7966
	W12	9.1620	5.1139E+01	1.8862E-02 9.8724E+01 1.8862E-02 2651.5425
	W13	7.0701	4.9774E+01	1.3928E-02 9.3761E+01 1.3928E-02 3279.2730
	W14	1.9639	4.9770E+01	1.3671E-02 9.3747E+01 1.3671E-02 3363.1320
	IND	7.9469	4.8163E+01	1.6085E-02 8.8203E+01 1.6085E-02 3015.8873
	CHI	7.9723	4.8440E+01	1.6128E-02 8.9137E+01 1.6128E-02 2987.1247
	JAP	8.1201	4.4683E+01	1.6771E-02 7.7194E+01 1.6771E-02 3023.9527
				NiONPs
	Region ID	FF w,w	Recommended range of FF w,w Max Min	Range of FF w,w MAX MIN	FF sed,sed
	DEFAULT	2.1881	5.0885E+01	3.8674E-03 9.7783E+01 3.8674E-03 3906.9205
	W1	6.1444	4.9282E+01	1.1865E-02 9.2030E+01 1.1865E-02 3394.2622
	W2	7.8704	4.3341E+01	1.6252E-02 7.3274E+01 1.6252E-02 3013.1615
	W3	1.0812	2.1228E+01	1.9270E-03 2.6538E+01 1.9270E-03 3533.0944
	W4	1.1951	4.2511E+01	2.0800E-03 7.0930E+01 2.0800E-03 3480.8667
	W5	18.6067	5.1406E+01	4.9206E-02 9.9723E+01 4.9206E-02 2655.0456
	W6	18.1004	5.2083E+01	4.6814E-02 1.0230E+02 4.6814E-02 2838.3811
	W7	3.8266	4.1827E+01	7.1215E-03 6.9047E+01 7.1215E-03 3353.7888
	W8	2.7602	2.0729E+01	5.3835E-03 2.5763E+01 5.3835E-03 3461.4280
	W9	11.2187	4.5738E+01	2.5110E-02 8.0398E+01 2.5110E-02 2835.0437
	W10	10.8491	5.0023E+01	2.3405E-02 9.4648E+01 2.3405E-02 2837.7717
	W12	9.1624	5.1146E+01	1.8862E-02 9.8751E+01 1.8862E-02 2651.5232
	W13	7.0705	4.9783E+01	1.3928E-02 9.3792E+01 1.3928E-02 3279.2560
	W14	1.9640	4.9779E+01	1.3671E-02 9.3780E+01 1.3671E-02 3363.1238
	IND	7.9473	4.8169E+01	1.6085E-02 8.8224E+01 1.6085E-02 3015.8663
	CHI	7.9727	4.8446E+01	1.6128E-02 8.9158E+01 1.6128E-02 2987.1039
	JAP	8.1205	4.4688E+01	1.6771E-02 7.7209E+01 1.6771E-02 3023.9293

Table D5

 D5 Continued.

				PtNPs
	Region ID	FF w,w	Recommended range of FF w,w Max Min	Range of FF w,w MAX MIN	FF sed,sed
	DEFAULT	2.2814	1.1716E+03	3.8551E-03 1.1716E+03 3.8551E-03 4032.5685
	W1	6.9466	6.8629E+02	1.1750E-02 6.8629E+02 1.1750E-02 3678.0810
	W2	9.2379	2.3616E+02	1.6036E-02 2.3616E+02 1.6036E-02 3385.2211
	W3	1.1032	3.5259E+01	1.9239E-03 3.5259E+01 1.9239E-03 3569.6215
	W4	1.2221	2.0964E+02	2.0765E-03 2.0964E+02 2.0765E-03 3524.0868
	W5	28.6475	1.6341E+03	4.7284E-02 1.6341E+03 4.7284E-02 3523.4052
	W6	27.4642	2.7821E+03	4.5070E-02 2.7821E+03 4.5070E-02 3711.0083
	W7	4.1225	1.9643E+02	7.0799E-03 1.9643E+02 7.0799E-03 3516.5349
	W8	2.9106	3.3973E+01	5.3596E-03 3.3973E+01 5.3596E-03 3555.8174
	W9	14.2221	3.3075E+02	2.4600E-02 3.3075E+02 2.4600E-02 3362.0533
	W10	13.6330	8.6844E+02	2.2961E-02 8.6844E+02 2.2961E-02 3334.2817
	W12	11.0710	1.4010E+03	1.8572E-02 1.4010E+03 1.8572E-02 3030.0097
	W13	8.1546	7.9803E+02	1.3770E-02 7.9803E+02 1.3770E-02 3590.1695
	W14	2.0387	7.9692E+02	1.3519E-02 7.9692E+02 1.3519E-02 3456.2591
	IND	9.3441	5.2029E+02	1.5874E-02 5.2029E+02 1.5874E-02 3393.1999
	CHI	9.3792	5.5457E+02	1.5916E-02 5.5457E+02 1.5916E-02 3362.6928
	JAP	9.5846	2.8260E+02	1.6541E-02 2.8260E+02 1.6541E-02 3415.9812
				SiO 2 NPs
	Region ID	FF w,w	Recommended range of FF w,w Max Min	Range of FF w,w MAX MIN	FF sed,sed
	DEFAULT	1.3062	3.0468E+00	3.8582E-03 6.0796E+00 3.8582E-03 2729.6639
	W1	2.1217	3.0407E+00	1.1779E-02 6.0555E+00 1.1779E-02 1975.7699
	W2	2.2955	3.0152E+00	1.6090E-02 5.9550E+00 1.6090E-02 1500.4272
	W3	0.8108	2.8119E+00	1.9247E-03 5.2109E+00 1.9247E-03 3094.0825
	W4	0.8732	3.0116E+00	2.0774E-03 5.9409E+00 2.0774E-03 2975.6112
	W5	2.7599	3.0484E+00	4.7756E-02 6.0859E+00 4.7756E-02 1286.0924
	W6	2.7485	3.0507E+00	4.5499E-02 6.0953E+00 4.5499E-02 1409.3480
	W7	1.7547	3.0078E+00	7.0904E-03 5.9263E+00 7.0904E-03 2220.3363
	W8	1.4906	2.8028E+00	5.3657E-03 5.1797E+00 5.3657E-03 2670.8588
	W9	2.5143	3.0262E+00	2.4727E-02 5.9979E+00 2.4727E-02 1310.5093
	W10	2.4952	3.0434E+00	2.3072E-02 6.0661E+00 2.3072E-02 1350.6910
	W12	2.3939	3.0475E+00	1.8645E-02 6.0823E+00 1.8645E-02 1312.2856
	W13	2.2221	3.0426E+00	1.3810E-02 6.0627E+00 1.3810E-02 1892.8428
	W14	1.2229	3.0426E+00	1.3557E-02 6.0627E+00 1.3557E-02 2449.0813
	IND	2.3020	3.0364E+00	1.5927E-02 6.0382E+00 1.5927E-02 1494.7443
	CHI	2.3041	3.0375E+00	1.5969E-02 6.0425E+00 1.5969E-02 1477.2980
	JAP	2.3163	3.0215E+00	1.6599E-02 5.9797E+00 1.6599E-02 1473.6089

Table D5

 D5 Continued.

				TiO 2 NPs
	Region ID	FF w,w	Recommended range of FF w,w Max Min	Range of FF w,w MAX MIN	FF sed,sed
	DEFAULT	2.2824	1.2738E+03	3.8680E-03 1.2738E+03 3.8680E-03 4032.5663
	W1	6.9497	6.8716E+02	1.1871E-02 6.8716E+02 1.1871E-02 3678.0704
	W2	9.2420	2.3624E+02	1.6262E-02 2.3624E+02 1.6262E-02 3385.1768
	W3	1.1037	3.5430E+01	1.9271E-03 3.5430E+01 1.9271E-03 3569.5960
	W4	1.2227	2.1539E+02	2.0802E-03 2.1539E+02 2.0802E-03 3524.0820
	W5	28.6605	1.6684E+03	4.9298E-02 1.6684E+03 4.9298E-02 3523.3866
	W6	27.4767	2.8882E+03	4.6897E-02 2.8882E+03 4.6897E-02 3710.9981
	W7	4.1243	1.9839E+02	7.1235E-03 1.9839E+02 7.1235E-03 3516.5138
	W8	2.9118	3.4050E+01	5.3846E-03 3.4050E+01 5.3846E-03 3555.7465
	W9	14.2284	3.3229E+02	2.5134E-02 3.3229E+02 2.5134E-02 3362.0050
	W10	13.6392	8.7995E+02	2.3426E-02 8.7995E+02 2.3426E-02 3334.2651
	W12	11.0761	1.4344E+03	1.8875E-02 1.4344E+03 1.8875E-02 3030.0025
	W13	8.1583	8.1206E+02	1.3936E-02 8.1206E+02 1.3936E-02 3590.1594
	W14	2.0396	8.1118E+02	1.3679E-02 8.1118E+02 1.3679E-02 3456.2353
	IND	9.3483	5.2483E+02	1.6095E-02 5.2483E+02 1.6095E-02 3393.1799
	CHI	9.3835	5.5971E+02	1.6138E-02 5.5971E+02 1.6138E-02 3362.6741
	JAP	9.5888	2.8388E+02	1.6781E-02 2.8388E+02 1.6781E-02 3415.9421
				ZnONPs
	Region ID	FF w,w	Recommended range of FF w,w Max Min	Range of FF w,w MAX MIN	FF sed,sed
	DEFAULT	2.0732	2.2222E+01	3.8673E-03 4.3672E+01 3.8667E-03 3753.4071
	W1	5.3163	2.1909E+01	1.1864E-02 4.2479E+01 1.1858E-02 3102.2331
	W2	6.5613	2.0650E+01	1.6250E-02 3.7989E+01 1.6238E-02 2657.9028
	W3	1.0524	1.3803E+01	1.9269E-03 1.9862E+01 1.9268E-03 3486.2756
	W4	1.1600	2.0464E+01	2.0800E-03 3.7364E+01 2.0798E-03 3425.7190
	W5	12.6427	2.2318E+01	4.9191E-02 4.4045E+01 4.9084E-02 2139.8140
	W6	12.4069	2.2445E+01	4.6800E-02 4.4541E+01 4.6703E-02 2308.3791
	W7	3.4883	2.0302E+01	7.1212E-03 3.6828E+01 7.1190E-03 3168.6602
	W8	2.5797	1.3589E+01	5.3833E-03 1.9423E+01 5.3820E-03 3349.0161
	W9	8.7344	2.1179E+01	2.5106E-02 3.9818E+01 2.5078E-02 2399.9125
	W10	8.5088	2.2054E+01	2.3401E-02 4.3027E+01 2.3377E-02 2421.1409
	W12	7.4353	2.2270E+01	1.8859E-02 4.3855E+01 1.8844E-02 2309.7681
	W13	5.9958	2.2007E+01	1.3927E-02 4.2850E+01 1.3919E-02 2971.9080
	W14	1.8709	2.2007E+01	1.3670E-02 4.2847E+01 1.3662E-02 3248.2356
	IND	6.6146	2.1686E+01	1.6083E-02 4.1648E+01 1.6072E-02 2656.7493
	CHI	6.6322	2.1742E+01	1.6127E-02 4.1855E+01 1.6115E-02 2630.0397
	JAP	6.7342	2.0951E+01	1.6769E-02 3.9020E+01 1.6756E-02 2653.6054

Table D5

 D5 Continued.

				ZrO 2 NPs
	Region ID	FF w,w	Recommended range of FF w,w Max Min	Range of FF w,w MAX MIN	FF sed,sed
	DEFAULT	2.2823	1.2573E+03	3.8551E-03 1.2573E+03 3.8551E-03 4032.5665
	W1	6.9495	6.9515E+02	1.1750E-02 6.9515E+02 1.1750E-02 3678.0713
	W2	9.2417	2.3694E+02	1.6036E-02 2.3694E+02 1.6036E-02 3385.1804
	W3	1.1037	3.5404E+01	1.9239E-03 3.5404E+01 1.9239E-03 3569.5982
	W4	1.2227	2.1451E+02	2.0765E-03 2.1451E+02 2.0765E-03 3524.0824
	W5	28.6594	1.6631E+03	4.7284E-02 1.6631E+03 4.7284E-02 3523.3882
	W6	27.4757	2.8716E+03	4.5070E-02 2.8716E+03 4.5070E-02 3710.9990
	W7	4.1242	1.9798E+02	7.0799E-03 1.9798E+02 7.0799E-03 3516.5155
	W8	2.9117	3.4034E+01	5.3596E-03 3.4034E+01 5.3596E-03 3555.7523
	W9	14.2279	3.3206E+02	2.4600E-02 3.3206E+02 2.4600E-02 3362.0090
	W10	13.6387	8.7818E+02	2.2961E-02 8.7818E+02 2.2961E-02 3334.2665
	W12	11.0757	1.4293E+03	1.8572E-02 1.4293E+03 1.8572E-02 3030.0031
	W13	8.1580	8.0990E+02	1.3770E-02 8.0990E+02 1.3770E-02 3590.1602
	W14	2.0395	8.0899E+02	1.3519E-02 8.0899E+02 1.3519E-02 3456.2372
	IND	9.3479	5.2414E+02	1.5874E-02 5.2414E+02 1.5874E-02 3393.1816
	CHI	9.3831	5.5893E+02	1.5916E-02 5.5893E+02 1.5916E-02 3362.6757
	JAP	9.5885	2.8369E+02	1.6541E-02 2.8369E+02 1.6541E-02 3415.9454

Table D6

 D6 Transport rates of ENPs between freshwater and sediment.

	Table D6 Continued.
		AgNPs (k, s -1 ) CeO 2 NPs (k, s -1 )			Al 2 O 3 NPs (k, s -1 ) CuONPs (k, s -1 )
	k w,w k w,w	k sed,sed k sed,sed	k w,sed k w,sed	k sed,w k sed,w	k w,w k w,w	k sed,sed k sed,sed	k w,sed k w,sed	k sed,w k sed,w
	DEF. 2.073E-05 1.173E-08 2.072E-05 8.867E-09 2.112E-05 1.173E-08 2.072E-05 8.867E-09
	W1 4.989E-06 9.427E-09 4.974E-06 6.300E-09 5.376E-06 9.427E-09 4.974E-06 6.300E-09
	W2 4.831E-06 1.319E-08 4.783E-06 9.867E-09 5.217E-06 1.319E-08 4.782E-06 9.867E-09
	W3 2.105E-05 6.508E-09 2.072E-05 3.317E-09 2.144E-05 6.508E-09 2.072E-05 3.317E-09
	W4 2.078E-05 7.208E-09 2.072E-05 3.933E-09 2.116E-05 7.208E-09 2.072E-05 3.933E-09
	W5 1.358E-06 1.105E-08 1.352E-06 7.800E-09 1.745E-06 1.105E-08 1.352E-06 7.800E-09
	W6 1.355E-06 1.003E-08 1.352E-06 6.933E-09 1.742E-06 1.003E-08 1.352E-06 6.933E-09
	W7 7.829E-06 9.181E-09 7.772E-06 5.933E-09 8.215E-06 9.181E-09 7.771E-06 5.933E-09
	W8 8.110E-06 6.641E-09 7.772E-06 3.533E-09 8.497E-06 6.641E-09 7.771E-06 3.533E-09
	W9 3.143E-06 1.330E-08 3.109E-06 9.967E-09 3.530E-06 1.330E-08 3.109E-06 9.967E-09
	W10 3.121E-06 1.277E-08 3.109E-06 9.333E-09 3.508E-06 1.277E-08 3.109E-06 9.333E-09
	W12 3.775E-06 1.380E-08 3.768E-06 1.000E-08 4.162E-06 1.380E-08 3.768E-06 1.000E-08
	W13 4.051E-06 9.204E-09 4.037E-06 6.000E-09 4.437E-06 9.204E-09 4.037E-06 6.000E-09
	W14 2.084E-05 1.230E-08 2.072E-05 9.000E-09 2.123E-05 1.230E-08 2.072E-05 9.000E-09
	IND 4.804E-06 1.323E-08 4.783E-06 9.867E-09 5.191E-06 1.323E-08 4.782E-06 9.867E-09
	CHI 4.803E-06 1.340E-08 4.783E-06 1.000E-08 5.189E-06 1.340E-08 4.782E-06 1.000E-08
	JAP 4.823E-06 1.354E-08 4.783E-06 1.023E-08 5.209E-06 1.354E-08 4.782E-06 1.023E-08
		Cu(OH) 2 NPs (k, s -1 )			Fe 2 O 3 NPs (k, s -1 )
	k w,w	k sed,sed	k w,sed	k sed,w	k w,w	k sed,sed	k w,sed	k sed,w
	DEF. 2.567E-05 1.173E-08 2.072E-05 8.867E-09 2.073E-05 1.173E-08 2.072E-05 8.867E-09
	W1 9.927E-06 9.427E-09 4.973E-06 6.300E-09 4.989E-06 9.427E-09 4.974E-06 6.300E-09
	W2 9.768E-06 1.319E-08 4.782E-06 9.867E-09 4.830E-06 1.319E-08 4.782E-06 9.867E-09
	W3 2.598E-05 6.508E-09 2.072E-05 3.317E-09 2.105E-05 6.508E-09 2.072E-05 3.317E-09
	W4 2.571E-05 7.208E-09 2.072E-05 3.933E-09 2.077E-05 7.208E-09 2.072E-05 3.933E-09
	W5 6.296E-06 1.105E-08 1.351E-06 7.800E-09 1.358E-06 1.105E-08 1.352E-06 7.800E-09
	W6 6.293E-06 1.003E-08 1.351E-06 6.933E-09 1.355E-06 1.003E-08 1.352E-06 6.933E-09
	W7 1.277E-05 9.181E-09 7.771E-06 5.933E-09 7.828E-06 9.181E-09 7.771E-06 5.933E-09
	W8 1.305E-05 6.641E-09 7.771E-06 3.533E-09 8.110E-06 6.641E-09 7.771E-06 3.533E-09
	W9 8.080E-06 1.330E-08 3.108E-06 9.967E-09 3.143E-06 1.330E-08 3.108E-06 9.967E-09
	W10 8.059E-06 1.277E-08 3.108E-06 9.333E-09 3.121E-06 1.277E-08 3.108E-06 9.333E-09
	W12 8.713E-06 1.380E-08 3.768E-06 1.000E-08 3.775E-06 1.380E-08 3.768E-06 1.000E-08
	W13 8.988E-06 9.204E-09 4.037E-06 6.000E-09 4.050E-06 9.204E-09 4.037E-06 6.000E-09
	W14 2.578E-05 1.230E-08 2.072E-05 9.000E-09 2.084E-05 1.230E-08 2.072E-05 9.000E-09
	IND 9.741E-06 1.323E-08 4.782E-06 9.867E-09 4.804E-06 1.323E-08 4.782E-06 9.867E-09
	CHI 9.740E-06 1.340E-08 4.782E-06 1.000E-08 4.802E-06 1.340E-08 4.782E-06 1.000E-08
	JAP 9.760E-06 1.354E-08 4.782E-06 1.023E-08 4.822E-06 1.354E-08 4.782E-06 1.023E-08

Table D6

 D6 Continued.

		Fe 3 O 4 NPs (k, s -1 )			NiNPs (k, s -1 )
	k w,w	k sed,sed	k w,sed	k sed,w	k w,w	k sed,sed	k w,sed	k sed,w
	DEF. 2.073E-05 1.173E-08 2.072E-05 8.867E-09 2.095E-05 1.173E-08 2.073E-05 8.867E-09
	W1 4.989E-06 9.427E-09 4.974E-06 6.300E-09 5.208E-06 9.427E-09 4.974E-06 6.300E-09
	W2 4.830E-06 1.319E-08 4.782E-06 9.867E-09 5.049E-06 1.319E-08 4.783E-06 9.867E-09
	W3 2.105E-05 6.508E-09 2.072E-05 3.317E-09 2.127E-05 6.508E-09 2.073E-05 3.317E-09
	W4 2.077E-05 7.208E-09 2.072E-05 3.933E-09 2.100E-05 7.208E-09 2.073E-05 3.933E-09
	W5 1.358E-06 1.105E-08 1.351E-06 7.800E-09 1.576E-06 1.105E-08 1.352E-06 7.800E-09
	W6 1.355E-06 1.003E-08 1.351E-06 6.933E-09 1.573E-06 1.003E-08 1.352E-06 6.933E-09
	W7 7.828E-06 9.181E-09 7.771E-06 5.933E-09 8.047E-06 9.181E-09 7.772E-06 5.933E-09
	W8 8.110E-06 6.641E-09 7.771E-06 3.533E-09 8.329E-06 6.641E-09 7.772E-06 3.533E-09
	W9 3.143E-06 1.330E-08 3.108E-06 9.967E-09 3.361E-06 1.330E-08 3.109E-06 9.967E-09
	W10 3.121E-06 1.277E-08 3.108E-06 9.333E-09 3.340E-06 1.277E-08 3.109E-06 9.333E-09
	W12 3.775E-06 1.380E-08 3.768E-06 1.000E-08 3.994E-06 1.380E-08 3.768E-06 1.000E-08
	W13 4.050E-06 9.204E-09 4.037E-06 6.000E-09 4.269E-06 9.204E-09 4.037E-06 6.000E-09
	W14 2.084E-05 1.230E-08 2.072E-05 9.000E-09 2.106E-05 1.230E-08 2.073E-05 9.000E-09
	IND 4.804E-06 1.323E-08 4.782E-06 9.867E-09 5.022E-06 1.323E-08 4.783E-06 9.867E-09
	CHI 4.802E-06 1.340E-08 4.782E-06 1.000E-08 5.021E-06 1.340E-08 4.783E-06 1.000E-08
	JAP 4.822E-06 1.354E-08 4.782E-06 1.023E-08 5.041E-06 1.354E-08 4.783E-06 1.023E-08
		NiONPs (k, s -1 )			PtNPs (k, s -1 )
	k w,w	k sed,sed	k w,sed	k sed,w	k w,w	k sed,sed	k w,sed	k sed,w
	DEF. 2.095E-05 1.173E-08 2.072E-05 8.867E-09 2.074E-05 1.173E-08 2.073E-05 8.867E-09
	W1 5.208E-06 9.427E-09 4.974E-06 6.300E-09 4.991E-06 9.427E-09 4.976E-06 6.300E-09
	W2 5.049E-06 1.319E-08 4.782E-06 9.867E-09 4.832E-06 1.319E-08 4.784E-06 9.867E-09
	W3 2.127E-05 6.508E-09 2.072E-05 3.317E-09 2.106E-05 6.508E-09 2.073E-05 3.317E-09
	W4 2.099E-05 7.208E-09 2.072E-05 3.933E-09 2.078E-05 7.208E-09 2.073E-05 3.933E-09
	W5 1.576E-06 1.105E-08 1.352E-06 7.800E-09 1.359E-06 1.105E-08 1.352E-06 7.800E-09
	W6 1.573E-06 1.003E-08 1.352E-06 6.933E-09 1.356E-06 1.003E-08 1.352E-06 6.933E-09
	W7 8.047E-06 9.181E-09 7.771E-06 5.933E-09 7.832E-06 9.181E-09 7.775E-06 5.933E-09
	W8 8.328E-06 6.641E-09 7.771E-06 3.533E-09 8.113E-06 6.641E-09 7.775E-06 3.533E-09
	W9 3.361E-06 1.330E-08 3.109E-06 9.967E-09 3.144E-06 1.330E-08 3.110E-06 9.967E-09
	W10 3.339E-06 1.277E-08 3.109E-06 9.333E-09 3.122E-06 1.277E-08 3.110E-06 9.333E-09
	W12 3.994E-06 1.380E-08 3.768E-06 1.000E-08 3.777E-06 1.380E-08 3.769E-06 1.000E-08
	W13 4.269E-06 9.204E-09 4.037E-06 6.000E-09 4.052E-06 9.204E-09 4.039E-06 6.000E-09
	W14 2.106E-05 1.230E-08 2.072E-05 9.000E-09 2.085E-05 1.230E-08 2.073E-05 9.000E-09
	IND 5.022E-06 1.323E-08 4.782E-06 9.867E-09 4.806E-06 1.323E-08 4.784E-06 9.867E-09
	CHI 5.021E-06 1.340E-08 4.782E-06 1.000E-08 4.804E-06 1.340E-08 4.784E-06 1.000E-08
	JAP 5.041E-06 1.354E-08 4.782E-06 1.023E-08 4.824E-06 1.354E-08 4.784E-06 1.023E-08

Table D6

 D6 Continued. AgNPs: Reported EC50 from literatures as well as the geometric means of EC50 for individual species (EC50 s ) and for the species in tropic level (EC50 t ).

	Trophic levels	k w,w	SiO 2 NPs (k, s -1 ) k sed,sed k w,sed Reference	k sed,w ENPs size (nm)	k w,w	TiO 2 NPs (k, s -1 ) k sed,sed k w,sed	k sed,w
	DEF. 2.452E-05 1.173E-08 2.072E-05 8.867E-09 2.073E-05 1.173E-08 2.072E-05 8.867E-09
	W1 8.779E-06 9.427E-09 4.973E-06 6.300E-09 4.989E-06 9.427E-09 4.973E-06 6.300E-09
	W2 8.620E-06 1.319E-08 4.782E-06 9.867E-09 4.830E-06 1.319E-08 4.782E-06 9.867E-09
	W3 2.484E-05 6.508E-09 2.072E-05 3.317E-09 2.105E-05 6.508E-09 2.072E-05 3.317E-09
	W4 2.456E-05 7.208E-09 2.072E-05 3.933E-09 2.077E-05 7.208E-09 2.072E-05 3.933E-09
	W5 5.148E-06 1.105E-08 1.351E-06 7.800E-09 1.358E-06 1.105E-08 1.351E-06 7.800E-09
	W6 5.145E-06 1.003E-08 1.351E-06 6.933E-09 1.355E-06 1.003E-08 1.351E-06 6.933E-09
	W7 1.162E-05 9.181E-09 7.771E-06 5.933E-09 7.828E-06 9.181E-09 7.771E-06 5.933E-09
	W8 1.190E-05 6.641E-09 7.771E-06 3.533E-09 8.109E-06 6.641E-09 7.771E-06 3.533E-09
	W9 6.932E-06 1.330E-08 3.108E-06 9.967E-09 3.143E-06 1.330E-08 3.108E-06 9.967E-09
	W10 6.911E-06 1.277E-08 3.108E-06 9.333E-09 3.121E-06 1.277E-08 3.108E-06 9.333E-09
	W12 7.565E-06 1.380E-08 3.768E-06 1.000E-08 3.775E-06 1.380E-08 3.768E-06 1.000E-08
	W13 7.840E-06 9.204E-09 4.037E-06 6.000E-09 4.050E-06 9.204E-09 4.037E-06 6.000E-09
	W14 2.463E-05 1.230E-08 2.072E-05 9.000E-09 2.084E-05 1.230E-08 2.072E-05 9.000E-09
	IND 8.593E-06 1.323E-08 4.782E-06 9.867E-09 4.804E-06 1.323E-08 4.782E-06 9.867E-09
	CHI 8.592E-06 1.340E-08 4.782E-06 1.000E-08 4.802E-06 1.340E-08 4.782E-06 1.000E-08
	JAP 8.612E-06 1.354E-08 4.782E-06 1.023E-08 4.822E-06 1.354E-08 4.782E-06 1.023E-08
			ZnONPs (k, s -1 )			ZrO 2 (k, s -1 )
		k w,w	k sed,sed	k w,sed	k sed,w	k w,w	k sed,sed	k w,sed	k sed,w
	DEF. 2.124E-05 1.173E-08 2.072E-05 8.867E-09 2.073E-05 1.173E-08 2.072E-05 8.867E-09
	W1 5.501E-06 9.427E-09 4.974E-06 6.300E-09 4.989E-06 9.427E-09 4.974E-06 6.300E-09
	W2 5.342E-06 1.319E-08 4.782E-06 9.867E-09 4.830E-06 1.319E-08 4.782E-06 9.867E-09
	W3 2.156E-05 6.508E-09 2.072E-05 3.317E-09 2.105E-05 6.508E-09 2.072E-05 3.317E-09
	W4 2.129E-05 7.208E-09 2.072E-05 3.933E-09 2.078E-05 7.208E-09 2.072E-05 3.933E-09
	W5 1.870E-06 1.105E-08 1.352E-06 7.800E-09 1.358E-06 1.105E-08 1.352E-06 7.800E-09
	W6 1.867E-06 1.003E-08 1.352E-06 6.933E-09 1.355E-06 1.003E-08 1.352E-06 6.933E-09
	W7 8.340E-06 9.181E-09 7.771E-06 5.933E-09 7.828E-06 9.181E-09 7.771E-06 5.933E-09
	W8 8.621E-06 6.641E-09 7.771E-06 3.533E-09 8.110E-06 6.641E-09 7.771E-06 3.533E-09
	W9 3.654E-06 1.330E-08 3.108E-06 9.967E-09 3.143E-06 1.330E-08 3.109E-06 9.967E-09
	W10 3.633E-06 1.277E-08 3.108E-06 9.333E-09 3.121E-06 1.277E-08 3.109E-06 9.333E-09
	W12 4.287E-06 1.380E-08 3.768E-06 1.000E-08 3.775E-06 1.380E-08 3.768E-06 1.000E-08
	W13 4.562E-06 9.204E-09 4.037E-06 6.000E-09 4.050E-06 9.204E-09 4.037E-06 6.000E-09
	W14 2.135E-05 1.230E-08 2.072E-05 9.000E-09 2.084E-05 1.230E-08 2.072E-05 9.000E-09
	IND 5.315E-06 1.323E-08 4.782E-06 9.867E-09 4.804E-06 1.323E-08 4.782E-06 9.867E-09
	CHI 5.314E-06 1.340E-08 4.782E-06 1.000E-08 4.802E-06 1.340E-08 4.782E-06 1.000E-08
	JAP 5.334E-06 1.354E-08 4.782E-06 1.023E-08 4.822E-06 1.354E-08 4.782E-06 1.023E-08

Figure D1. The transport rates of CuNPs between freshwater and sediment for the studied subcontinental regions. APPENDIX D 182

Table

D7

Tested species Test type and endpoints Reported EC50 (mg/L) Selected EC50 (mg/L) Chronic- Equivalent EC50 (mg/L) EC50 s EC50 t

	Algae	(Griffitt et al., 2008)*1	26.6±8.8	P. subcapitata	Chronic; growth inhibition (72h)	LC50=0.19	0.19	0.19	0.37445
		(McLaughlin and Bonzongo, 2012)	25.4	P. subcapitata	Chronic; growth inhibition (96h) EC50=0.0046	0.0046	0.0046	0.1125
		(Ksiazyk et al., 2015)	< 100	P. subcapitata	Chronic; growth inhibition (72h)	EC50=1.63	1.63	1.63
		(Kim et al., 2011a)	50	L. paucicostata Chronic; growth inhibition (7 days) EC50=13.8	13.8	13.8	13.8
	Crustacean	(Li et al., 2010)*2	36	D. magna	Acute; mortality (48h)	EC50=0.03	0.03	0.003	0.00206
		(Li et al., 2010)*2	52	D. magna	Acute; mortality (48h)	EC50=0.039	0.039	0.0039
		(Li et al., 2010)*2	66	D. magna	Acute; mortality (48h)	EC50=0.034	0.034	0.0034
		(Kennedy et al., 2010)	31	D. magna	Acute; mortality (48h)	LC50=0.0018	0.0018	0.00018
		(Kim et al., 2011b)	60	D. magna	Acute; immobility (48h)	EC50=0.001	0.001	0.0001
		(Hoheisel et al., 2012)*3	10	D. magna	Acute; mortality (48h)	LC50=0.00431	0.00431	0.000431
		(Hoheisel et al., 2012)*3	10	D. magna	Acute; mortality (48h)	LC50=0.0096	0.0096	0.00096
									0.00238
		(Hoheisel et al., 2012)*3	10	D. magna	Acute; mortality (48h)	LC50=0.00279	0.00279	0.000279
		(Hoheisel et al., 2012)*3	10	D. magna	Acute; mortality (48h)	LC50=0.00215	0.00215	0.000215
		(Hoheisel et al., 2012)*3	20	D. magna	Acute; mortality (48h)	LC50=0.008	0.008	0.0008
		(Hoheisel et al., 2012)*3	20	D. magna	Acute; mortality (48h)	LC50=0.01362	0.01362	0.001362
		(Hoheisel et al., 2012)*3	30	D. magna	Acute; mortality (48h)	LC50=0.01843	0.01843	0.001843
	183	(Hoheisel et al., 2012)*3 (Hoheisel et al., 2012)*3	30 50	D. magna D. magna	Acute; mortality (48h) Acute; mortality (48h)	LC50=0.01757 LC50=0.02448	0.01757 0.02448	0.001757 0.002448	APPENDIX D

Table D7 AgNPs Continued Trophic levels Reference ENPs size (nm) Tested species Test type and endpoints Reported EC50 (mg/L) Selected EC50 (mg/L) Chronic- Equivalent EC50 (mg/L) EC50 s EC50 t

 D7

	184	Table D7 AgNPs Continued Trophic levels Reference	ENPs size (nm)						APPENDIX D
		(Hoheisel et al., 2012)*3	50	D. magna	Acute; mortality (48h)	LC50=0.03038	0.03038	0.003038
		(Asghari et al., 2012)	5-25	D. magna	Acute; immobility (48h)	EC50=0.004	0.004	0.0004
		(Asghari et al., 2012)	16.6	D. magna	Acute; immobility (48h)	EC50=0.002	0.002	0.0002
		(Asghari et al., 2012)	20	D. magna	Acute; immobility (48h)	EC50=0.187	0.187	0.0187
		(Jo et al., 2012)*4	<100	D. magna	Acute; immobility (24h)	EC50=3.844	3.844	0.3844
		(Jo et al., 2012)*4	<100	D. magna	Acute; immobility (24h)	EC50=1.154	1.154	0.1154
		(Jo et al., 2012)*4	<100	D. magna	Acute; immobility (24h)	EC50=0.5315	0.5315	0.05315
		(Jo et al., 2012)*4	<100	D. magna	Acute; immobility (24h)	EC50=0.0124	0.0124	0.00124
		(Jo et al., 2012)*4	<100	D. magna	Acute; immobility (24h)	EC50=0.0118	0.0118	0.00118
		(Jo et al., 2012)*4	<100	D. magna	Acute; immobility (24h)	EC50=0.0143	0.0143	0.00143
		(Jo et al., 2012)*4	<100	D. magna	Acute; immobility (24h)	EC50=1.991	1.991	0.1991
		(Jo et al., 2012)*4	<100	D. magna	Acute; immobility (24h)	EC50=0.009	0.009	0.0009
		(Jo et al., 2012)*4	<100	D. magna	Acute; immobility (24h)	EC50=1.405	1.405	0.1405
		(Jo et al., 2012)*4	<100	D. magna	Acute; immobility (24h)	EC50=0.0042	0.0042	0.00042
		(Griffitt et al., 2008)*1	26.6±8.8	C. dubia	Acute; mortality (48h)	LC50=0.067	0.067	0.0067
		(Gao et al., 2009)	20-30	C. dubia	Acute; mortality (48h)	LC50=0.00046	0.00046	0.000046
		(Gao et al., 2009)	15-45	C. dubia	Acute; mortality (48h)	LC50=0.002	0.002	0.0002	0.00023
		(McLaughlin and Bonzongo, 2012)	25.4	C. dubia	Acute; mortality (48h)	LC50=0.0048	0.00048	0.000048
		(Griffitt et al., 2008)*1	26.6±8.8	Daphnia pulex	Acute; mortality (48h)	LC50=0.040	0.040	0.004	0.004

Tested species Test type and endpoints Reported EC50 (mg/L) Selected EC50 (mg/L) Chronic- Equivalent EC50 (mg/L) EC50 s EC50 t

		(Yoo-Iam et al., 2014)	<100	M. macrocopa	Acute; immobility (48h)	LC50=1.11	1.11	0.111	0.111
	Fish	(Griffitt et al., 2008)*1	26.6±8.8 Zebrafish (adults)	Acute; mortality (48h)	LC50=7.07	7.07	0.3535	0.0531
		(Griffitt et al., 2008)*1	26.6±8.8	Zebrafish (juveniles)	Acute; mortality (48h)	LC50=7.20	7.20	0.36
		(Bar-Ilan et al., 2009)	3	Zebrafish	Acute; mortality (120h)	LC50=10.07	10.07	0.5035
		(Bar-Ilan et al., 2009)	10	Zebrafish	Acute; mortality (120h)	LC50=13.55	13.55	0.6775
		(Bar-Ilan et al., 2009)	50	Zebrafish	Acute; mortality (120h)	LC50=13.69	13.69	0.6845
		(Bar-Ilan et al., 2009)	100	Zebrafish	Acute; mortality (120h)	LC50=14.81	14.81	0.7405
		(Cowart et al., 2011)	12	Zebrafish	Acute; mortality (48h)	LC50=15.8	15.8	0.79	0.2069
		(Cowart et al., 2011)	21	Zebrafish	Acute; mortality (48h)	LC50=50.1	50.1	2.505
		(Bilberg et al., 2012)	81	Zebrafish	Acute; mortality (48h)	LC50=0.084	0.084	0.0042
		(Boyle et al., 2015)*5	62.9	Zebrafish	Acute; mortality (96h)	LC50=0.27	0.27	0.0135
		(Lacave et al., 2016)*6	24	Zebrafish	Acute; mortality (120h)	LC50=0.529	0.529	0.02645
		(Lacave et al., 2016)*6	44	Zebrafish	Acute; mortality (120h)	LC50=3.94	3.94	0.197
		(Lacave et al., 2016)*6	96	Zebrafish	Acute; mortality (120h)	LC50=1.973	1.973	0.09865
		(Kennedy et al., 2010)	31	Fathead minnow	Acute; mortality (48h)	LC50=0.009	0.009	0.00045
	185	(Laban et al., 2010) (Laban et al., 2010) (Hoheisel et al., 2012)	35 ≤100 10	Fathead minnow Fathead minnow Fathead minnow	Acute; mortality (96h) Acute; mortality (96h) Acute; mortality (96h)	LC50=9.4 LC50=10.6 LC50=0.0894	9.4 10.6 0.0894	0.47 0.53 0.00447	0.0266	APPENDIX D

Table D7 AgNPs Continued Trophic levels Reference ENPs size (nm) Tested species Test type and endpoints Reported EC50 (mg/L) Selected EC50 (mg/L) Chronic- Equivalent EC50 (mg/L) EC50 s EC50 t

 D7 The tests were conducted twice on separate occasions and each time it included three replicates. The differences between the two tests were believed to be caused by the more sensitive organisms in the second test. Thus, the toxic values were both used in this study. Al 2 O 3 NPs: Reported EC50 from literatures as well as the geometric means of EC50 for individual species (EC50 s ) and for the species in tropic level (EC50 t ).

	Trophic levels	Reference	ENPs size (nm)
		(Chae et al., 2009)		49.6	Japanese medaka	Acute; mortality (96h)	LC50=0.035	0.035	0.00175
		(Wu et al., 2010)		20-37	Japanese medaka	Acute; mortality (48h)	LC50=1.03	1.03	0.0515	0.0050
		(Kim et al., 2011b)		60	Japanese medaka	Acute; mortality (96h)	LC50= 0.028	0.028	0.0014
		(Shahbazzadeh et al., 2009)	4.5	Rainbow trout	Acute; mortality (48h)	LC50=0.035	0.035	0.00175
		(Shahbazzadeh et al., 2009)	4.5	Rainbow trout	Acute; mortality (72h)	LC50=0.030	0.030	0.0015	0.0015
		(Shahbazzadeh et al., 2009)	4.5	Rainbow trout	Acute; mortality (96h)	LC50=0.023	0.023	0.00115
	(Govindasamy and Rahuman, 2012)	60-80	Tilapia	Acute; mortality (8 days)	LC50=12.6	12.6	0.63	0.63
		(Yoo-Iam et al., 2014)	<100	Silver barbs	Acute; mortality (48h)	LC50=1.76	1.76	0.088	0.088
	*1 Coated with thin layers (thickness, ~2-5 nm) of metal oxide.

*2 These values are estimated based on the figure in (Li et al., 2010). *3 *4 In the study of (Jo et al., 2012), the authors investigated 10 types' AgNPs suspensions, which were related to stirring and sonication. *5 The authors reported two LC50 values for AgNPs. The exposure conditions of the fishes were different. One was in a continuously stirring chamber and another was in a static beaker. Here, considering the dynamic natural flow of the freshwater, the data obtained from the stirring chamber was selected. *6 Maltose-coated AgNPs. The reported values are metal basis. APPENDIX D 186

Table

D8

Tested species Test type and endpoints Reported EC50 (mg/L) Selected EC50 (mg/L) Chronic- Equivalent EC50 (mg/L) EC50 s EC50 t

	Crustacean	(Zhu et al., 2008)	80	D. magna	Acute; immobilization (48h)	EC50=114.357	114.357	11.4357	9.4192
									13.6274
		(Zhu et al., 2008)	80	D. magna	Acute; mortality (48h)	LC50=162.392	162.392	16.2392
		(Li et al., 2011)	17	C. dubia	Acute; growth inhibition (48h)	EC50=45	45	4.5	4.5
		(Hu et al., 2012)	5	C. dubia	Acute; mortality (48h)	LC50>200	----	----
	187								APPENDIX D

Table D9

 D9 AuNPs: Reported EC50 from literatures as well as the geometric means of EC50 for individual species (EC50 s ) and for the species in tropic level (EC50 t ).

	Trophic levels	Reference	ENPs Size (nm)

Tested species Test type and endpoints Reported EC50 (mg/L) Selected EC50 (mg/L) Chronic- Equivalent EC50 (mg/L) EC50 s (mg/L) EC50 t (mg/L)

	Algae	(Hartmann et al., 2013)*1	25±4	P. subcapitata	Chronic; growth inhibition (72h)	EC50=38; EC50=83	38	38	32.619	24.605
		(Dedkova et al., 2014)	43.7	P.subcapitata	Chronic; growth inhibition (72h)	EC50=28	28	28
		(Dedkova et al., 2014)	43.7	D.subspicatus	Chronic; growth inhibition (72h)	EC50=14	14	14	14
	Crustacean	(Li et al., 2010)	15	D. magna	Acute; mortality (48h)	EC50=70	70	7	7	7
	Fish	(Bar-Ilan et al., 2009)*2	3	Zebrafish	Acute; mortality (120h)	LC50>49.24	----			1.3809
		(Bar-Ilan et al., 2009)*2	10	Zebrafish	Acute; mortality (120h)	LC50>49.24	----
		(Bar-Ilan et al., 2009)*2	50	Zebrafish	Acute; mortality (120h)	LC50>49.24	----
		(Bar-Ilan et al., 2009)*2	100	Zebrafish	Acute; mortality (120h)	LC50>49.24	----		1.3809
		(Lacave et al., 2016)*3	4.4	Zebrafish	Acute; mortality (120h)	LC50=24.655	24.655	1.2328
		(Lacave et al., 2016)*3	13.5	Zebrafish	Acute; mortality (120h)	LC50=24.61	24.61	1.2305
		(Lacave et al., 2016)*3	40.4	Zebrafish	Acute; mortality (120h)	LC50=34.717	34.717	1.7359

*1 The two reported values were obtained in two different ways: fluorescence of pigment extracts (EC50 = 38 mg/L) and cell counting in haemocytometer (EC50 = 83 mg/L). It should be noted that both of the two values were beyond the range of tested concentrations. Thus, they were calculated by extrapolation. The fluorometric method was found to be most suitable. In this theis, EC50 = 38 mg/L is selected. *2 The original reported values were in μM/L. Here they are presented in mg/L. *3 Citrate-coated AuNPs. The reported values are metal basis. APPENDIX D 188

Table D10 C

 D10 60 : Reported EC50 from literatures as well as the geometric means of EC50 for individual species (EC50 s ) and for the species in tropic level (EC50 t ).

	Trophic levels	Reference	ENPs size (nm)

Tested species Test type and endpoints Reported EC50 (mg/L) Selected EC50 (mg/L) Chronic- Equivalent EC50 (mg/L) EC50 s EC50 t

  Due to the scarce toxicological value of fullerene, the LC50 values of C60 with < 200nm size and unknown size were also employed in this thesis.

	Crustacean	(Lovern and Klaper, 2006)	10-20	D. magna	Acute; mortality (48h)	LC50=7.9	7.9	0.79	0.9190
		(Zhu et al., 2008) *1	<200	D. magna	Acute; immobilization (48h)	EC50=9.344	9.344	0.9344	0.9190
		(Zhu et al., 2008) *1	<200	D. magna	Acute; mortality (48h)	LC50=10.515	10.515	1.0515
		(Blaise et al., 2008) *1	----	T. platyurus	Acute; mortality (24h)	LC50>463	----	----
	189								APPENDIX D

*1

Table D11

 D11 CeO 2 NPs: Reported EC50 from literatures as well as the geometric means of EC50 for individual species (EC50 s ) and for the species in tropic level (EC50 t ).

	Trophic levels	Reference	ENPs size (nm)

Tested species Test type and endpoints Reported EC50 (mg/L) Selected EC50 (mg/L) Chronic- Equivalent EC50 (mg/L) EC50 s EC50 t

	Algae	(Hoecke et al., 2009)	14	P. subcapitata	Chronic; growth inhibition (72h)	EC50=10.2	10.2	10.2	3.6142
		(Hoecke et al., 2009)	20	P. subcapitata	Chronic; growth inhibition (72h)	EC50=11.7	11.7	11.7
		(Hoecke et al., 2009)	29	P. subcapitata	Chronic; growth inhibition (72h)	EC50=19.1	19.1	19.1
		(Rogers et al., 2010)	7-25	P. subcapitata	Chronic; growth inhibition (72h)	EC50=10.3	10.3	10.3
		(Rodea-Palomares et al., 2011)*1	12	P. subcapitata	Chronic; growth inhibition (72h)	EC50=29.6	29.6	29.6
		(Rodea-Palomares et al., 2011)*1	13	P. subcapitata	Chronic; growth inhibition (72h)	EC50=9.7	9.7	9.7
								4.3676
		(Rodea-Palomares et al., 2011)*1	22	P. subcapitata	Chronic; growth inhibition (72h)	EC50=4.4	4.4	4.4
		(Rodea-Palomares et al., 2011)*1	28	P. subcapitata	Chronic; growth inhibition (72h)	EC50=16.4	16.4	16.4
		(Rodea-Palomares et al., 2011)*1	176	P. subcapitata	Chronic; growth inhibition (72h)	EC50=56.7	56.7	56.7
		(Manier et al., 2013)	10	P. subcapitata	Chronic; growth inhibition (72h)	EC50=5.6	5.6	5.6
		(Booth et al., 2015)*2	4-10	P. subcapitata	Chronic; growth inhibition (72h)	EC50=0.024	0.024	0.024
		(Booth et al., 2015)*2	4-10	P. subcapitata	Chronic; biomass production (72h) EC50=0.013	0.013	0.013
		(Rodea-Palomares et al., 2011)	12	Anabaena CPB4337 Chronic; luminescence inhibition (24h) EC50=6.3	6.3	6.3

Table D12

 D12 CuNPs: Reported EC50 from literatures as well as the geometric means of EC50 for individual species (EC50 s ) and for the species in tropic level (EC50 t ).

	Trophic levels	Reference	ENPs size (nm)

Tested species Test type and endpoints Reported EC50 (mg/L) Selected EC50 (mg/L) Chronic- Equivalent EC50 (mg/L) EC50 s EC50 t

	Algae	(Griffitt et al., 2008)*1	26.7±7.1	P. subcapitata	Chronic; growth inhibition (96h)	LC50=0.54	0.54	0.54	0.54 0.54
	Crustacean	(Gao et al., 2009)	15-45	C. dubia	Acute; mortality (48h)	LC50=0.002	0.002	0.0002
									0.0029
		(Griffitt et al., 2008)*1	26.7±7.1	C. dubia	Acute; mortality (48h)	LC50=0.419	0.419	0.0419
									0.0047
		(Xiao et al., 2015)	50	D. magna	Acute; mortality (48h)	LC50=0.093	0.093	0.0093	0.0093
		(Griffitt et al., 2008)*1	26.7±7.1	Daphnia pulex	Acute; mortality (48h)	LC50=0.060	0.060	0.006	0.006
	Fish	(Griffitt et al., 2007)	50-100	Zebrafish	Acute; mortality (48h)	LC50=1.5	1.5	0.075	0.0683
		(Griffitt et al., 2008)*1	26.7±7.1	Zebrafish (adults)	Acute; mortality (48h)	LC50=0.94	0.94	0.047
		(Griffitt et al., 2008)*1	26.7±7.1 Zebrafish (juveniles)	Acute; mortality (48h)	LC50=0.71	0.71	0.0355
		(Kovriznych et al., 2013)	<100	Zebrafish	Acute; mortality (48h)	LC50=4.2	4.2	0.21
		(Kovriznych et al., 2013)	<100	Zebrafish	Acute; mortality (96h)	LC50=3.8	3.8	0.19	0.0683
		(Hua et al., 2014)	25	Zebrafish	Acute; mortality (120h)	LC50=0.58	0.58	0.029
		(Hua et al., 2014)	50	Zebrafish	Acute; mortality (120h)	LC50=1.65	1.65	0.0825
		(Hua et al., 2014)	100	Zebrafish	Acute; mortality (120h)	LC50=1.90	1.90	0.095
		(Boyle et al., 2015)*2	20.3	Zebrafish	Acute; mortality (96h)	LC50=0.57	0.57	0.0285

*1 Coated with thin layers (thickness, ~2-5 nm) of metal oxide.

Table D13

 D13 CuONPs: Reported EC50 from literatures as well as the geometric means of EC50 for individual species (EC50 s ) and for the species in tropic level (EC50 t ).

	Trophic levels	Reference	ENPs size (nm)

Tested species Test type and endpoints Reported EC50 (mg/L) Selected EC50 (mg/L) Chronic- Equivalent EC50 (mg/L) EC50 s EC50 t

	Algae	(Aruoja et al., 2009)*1	30	P. subcapitata Chronic; growth inhibition (72h)	EC50=0.71	0.89	0.89	0.89 3.4505
		(Wang et al., 2011)*2	20-50	M. aeruginosa Chronic; growth inhibition (72h)	EC50=0.47	0.47	0.47
									0.8170
		(Wang et al., 2011)*2	20-50	M. aeruginosa Chronic; growth inhibition (72h)	EC50=1.42	1.42	1.42
		(Manusadzianas et al., 2012)*3	<50	N. obtusa	Chronic; lethality (96h)	LC50=4.3	5.38	5.38
									4.3456
		(Manusadzianas et al., 2012)*3	<50	N. obtusa	Chronic; lethality (96h)	LC50=2.8	3.51	3.51
		(Melegari et al., 2013)	30-40	C.reinhardtii	Chronic; growth inhibition (72h) EC50=150.45	150.45	150.45	150.45
	Crustacean	(Heinlaan et al., 2008)	30	D. magna	Acute; immobility (48h)	LC50=3.2	3.2	0.32	0.6625
		(Sovová et al., 2009)	<50	D. magna	Acute; immobility (48h)	EC50=5.903	5.903	0.5903
		(Blinova et al., 2010)*4	30	D. magna	Acute; immobility (48h)	EC50=2.6	3.25	0.325
		(Blinova et al., 2010)*4	30	D. magna	Acute; immobility (48h)	EC50=92.7	116.04	11.604
		(Blinova et al., 2010) *4	30	D. magna	Acute; immobility (48h)	EC50=149	186.52	18.652
		(Blinova et al., 2010) *4	30	D. magna	Acute; immobility (48h)	EC50=160	200.29	20.029
		(Blinova et al., 2010) *4	30	D. magna	Acute; immobility (48h)	EC50=200	250.36	25.036	0.3242
		(Blinova et al., 2010) *4	30	D. magna	Acute; immobility (48h)	EC50=224	280.40	28.04
		(Blinova et al., 2010) *4	30	D. magna	Acute; immobility (48h)	EC50>200	----	----
		(Heinlaan et al., 2011)	30	D. magna	Acute; immobility (48h)	EC50=4.0	4.0	0.4
		(Jo et al., 2012)*5	<50	D. magna	Acute; immobility (24h)	EC50=2.558	2.558	0.2558
	193	(Jo et al., 2012)*5 (Jo et al., 2012)*5	<50 <50	D. magna D. magna	Acute; immobility (24h) Acute; immobility (24h)	EC50=2.793 EC50=2.090	2.793 2.090	0.2793 0.209	APPENDIX D

Table D13 CuONPs: Continued Trophic levels Reference ENPs size (nm) Tested species Test type and endpoints Reported EC50 (mg/L) Selected EC50 (mg/L) Chronic- Equivalent EC50 (mg/L) EC50 s EC50 t

 D13

	194						APPENDIX
							D
	(Jo et al., 2012)*5	<50	D. magna	Acute; immobility (24h)	EC50=0.0972	0.0972	0.00972
	(Jo et al., 2012)*5	<50	D. magna	Acute; immobility (24h)	EC50=0.115	0.115	0.0115
	(Jo et al., 2012)*5	<50	D. magna	Acute; immobility (24h)	EC50=0.0894	0.0894	0.00894
	(Jo et al., 2012)*5	<50	D. magna	Acute; immobility (24h)	EC50=3.208	3.208	0.3208
	(Jo et al., 2012)*5	<50	D. magna	Acute; immobility (24h)	EC50=0.3197	0.3197	0.03197
	(Jo et al., 2012)*5	<50	D. magna	Acute; immobility (24h)	EC50=1.884	1.884	0.1884
	(Jo et al., 2012)*5	<50	D. magna	Acute; immobility (24h)	EC50=0.0545	0.0545	0.00545
	(Zhao et al., 2012)	40	D. magna	Acute; immobility (48h)	EC50=0.46	0.46	0.046
	(Zhao et al., 2012)	40	D. magna	Acute; mortality (48h)	LC50=0.99	0.99	0.099
	(Heinlaan et al., 2008)	30	T. platyurus	Acute; mortality (24h)	LC50=2.1	2.1	0.21
	(Blinova et al., 2010) *4	70	T. platyurus	Acute; mortality (24h)	LC50=1.7	2.13	0.213
	(Blinova et al., 2010) *4	70	T. platyurus	Acute; mortality (24h)	LC50=152	190.27	19.027
	(Blinova et al., 2010) *4	70	T. platyurus	Acute; mortality (24h)	LC50=217	271.64	27.164
	(Blinova et al., 2010) *4	70	T. platyurus	Acute; mortality (24h)	LC50=92.7	116.04	11.604
							3.93773
	(Blinova et al., 2010) *4	70	T. platyurus	Acute; mortality (24h)	LC50=112	140.20	14.02
	(Blinova et al., 2010) *4	70	T. platyurus	Acute; mortality (24h)	LC50=129	161.48	16.148
	(Blinova et al., 2010) *4	70	T. platyurus	Acute; mortality (24h)	LC50=90.3	113.04	11.304
	(Manusadzianas et al., 2012)*3	30	T. platyurus	Acute; mortality (24h)	LC50=9.8	12.27	1.227
	(Manusadzianas et al., 2012)*3	30	T. platyurus	Acute; mortality (24h)	LC50=8.5	10.64	1.064

Table D13 CuONPs: Continued Trophic levels Reference ENPs size (nm) Tested species Test type and endpoints Reported EC50 (mg/L) Selected EC50 (mg/L) Chronic- Equivalent EC50 (mg/L) EC50 s EC50 t

 D13 The size of CuONPs is estimated from the TEM images[START_REF] Wang | Toxicity and internalization of CuO nanoparticles to prokaryotic algamicrocystis aeruginosaas affected by dissolved organic matter[END_REF]. The two reported values are with or without Suwannee River fulvic acid respectively. *3 The reported values were in mg Cu/L and were related to both sonicated and nonsonicated nanoparticles suspensions. *4 The reported values were in mg Cu/L. These results were based on one artificial freshwater and six natural freshwater. *5 In the study of[START_REF] Jo | Acute toxicity of Ag and CuO nanoparticle suspensions against daphnia magna: The importance of their dissolved fraction varying with preparation methods[END_REF], the authors investigated 10 types' CuONPs suspensions, which were related to stirring and sonication.Table D14Fe 2 O 3 NPs: Reported EC50 from literatures as well as the geometric means of EC50 for individual species (EC50 s ) and for the species in tropic level (EC50 t ).

		(Griffitt et al., 2008)*6	6.1±1.4 Daphnia pulex	Acute; mortality (48h)	LC50=3.89	3.89	0.389	0.0389
		(Griffitt et al., 2008)*6	6.1±1.4	C. dubia	Acute; mortality (48h)	LC50=0.674	0.674	0.0674	0.0674
	Fish	(Griffitt et al., 2008)*6	6.1±1.4	Zebrafish	Acute; mortality (48h)	LC50>10	----	----
									3.2
		(Ganesan et al., 2016)	51	Zebrafish	Acute; mortality (48h)	LC50=64	64	3.2	4.8990
		(Abdel-Khalek et al., 2015)	35-37	Nile Tilapia	Acute; mortality (96h)	LC50=150	150	7.5	7.5
	Trophic levels	Reference	ENPs size (nm)

*1 The reported value was in mg Cu/L. *2 *6 Coated with thin layers (thickness, ~2-5 nm) of metal oxide.

Tested species Test type and endpoints Reported EC50 (mg/L) Selected EC50 (mg/L) Chronic- Equivalent EC50 (mg/L) EC50 s EC50 t

	195	Crustacean Fish	(Hu et al., 2012) (Rockne et al., 2012) (Rockne et al., 2012)	20-40 30 30	C. dubia Zebrafish Zebrafish	Acute; mortality (48h) Acute; hatching inhibition (168h) Acute; mortality (168h)	LC50>200 EC50=36.06 LC50=53.35	----36.06 53.35	----1.803 2.6675	----2.1931	----2.1931	APPENDIX D

Table D15

 D15 Fe 3 O 4 NPs: Reported EC50 from literatures as well as the geometric means of EC50 for individual species (EC50 s ) and for the species in tropic level (EC50 t ).

	Trophic levels	Reference	ENPs size (

nm) Tested species Test type and endpoints Reported EC50 (mg/L) Selected EC50 (mg/L) Chronic- Equivalent EC50 (mg/L) EC50 s EC50 t

	Crustacean	(Garcí a et al., 2011)	6	D. magna	Acute; mortality (48h)	LC50=0.23	0.23	0.023	0.023 0.023

Table D16

 D16 NiONPs: Reported EC50 from literatures as well as the geometric means of EC50 for individual species (EC50 s ) and for the species in tropic level (EC50 t ).

	Trophic levels	Reference	ENPs size (nm)	Tested species	Test type and endpoints	Reported EC50 (mg/L)	Selected EC50 (mg/L)	Chronic-EC50 (mg/L) Equivalent	EC50 s EC50 t
	Algae	(Gong et al., 2011)	20	C. vulgaris	Chronic; growth inhibition (72h)	EC50=32.38	32.28	32.28	37.8282
		(Gong et al., 2011)	20	C. vulgaris	Chronic; growth inhibition (120h)	EC50=44.33	44.33	44.33	37.8282
	Fish	(Kovriznych et al., 2013)*1	<50	Zebrafish	Acute; mortality (48h)	LC50=760	760	38	32.9917
		(Kovriznych et al., 2013)*2	<50	Zebrafish	Acute; mortality (96h)	LC50=420	420	21
		(Kovriznych et al., 2014)*3	<50	Zebrafish	Chronic; mortality (30 days)	LC50=45	45	45	32.9917

*1. Size no report. However, article *1 and *2 are from the same author published within 1 year, and the NiONPs used were both from Sigma-Aldrich Chemie GmbH (Germany). In addition, there are not much toxic values of NiONPs available. Thus, we the size of nano NiO in the article *1 is assumed as also less than 50 nm. APPENDIX D 196

Table D17

 D17 PtNPs: Reported EC50 from literatures as well as the geometric means of EC50 for individual species (EC50 s ) and for the species in tropic level (EC50 t ).

	Trophic levels	Reference	ENPs size (nm)

Tested species Test type and endpoints Reported EC50 (mg/L) Selected EC50 (mg/L) Chronic- Equivalent EC50 (mg/L) EC50 s EC50 t

  Table D18SiO 2 NPs: Reported EC50 from literatures as well as the geometric means of EC50 for individual species (EC50 s ) and for the species in tropic level (EC50 t ).

	Algae	(Ksiazyk et al., 2015)		< 50		P. subcapitata	Chronic; growth inhibition (72h) EC50=16.9	16.9	16.9	37.073
											15.2917
		(Sorensen et al., 2016)	7.6±0.3	P. subcapitata	Chronic; growth inhibition (48h)	EC50=15	15	15
		(Sorensen et al., 2016)	7.6±0.3	C. reinhardtii	Chronic; growth inhibition (48h) EC50=201	201	201	201
	Fish	(Thangam and Veeramani, 2015)	3-10		Cirrhinus Mrigala	Acute; mortality (24h)	EC50=12	12	0.6	0.4243	0.4243
		(Thangam and Veeramani, 2015)	3-10		Cirrhinus Mrigala	Acute; mortality (96h)	EC50=6	6	0.3
	Trophic levels	Reference	ENPs size (nm)	Tested species	Test type and endpoints	Reported EC50 (mg/L)	Selected EC50 (mg/L)	Chronic-EC50 (mg/L) Equivalent	EC50 s EC50 t
	Crustacean	(Yang et al., 2014)	10-20		D. magna	Acute; immobilization (24h) EC50=148.871	148.871	14.8871	31.368 31.368
		(Yang et al., 2014)	10-20		D. magna	Acute; mortality (24h)	LC50=660.943	660.943	66.0943
	Fish	(Ye et al., 2013)	10-20		Zebrafish	Acute; mortality (84h)	LC50=240	240	12	10.3507
		(Lacave et al., 2016)*1	15			Zebrafish	Acute; mortality (120h)	LC50>100	----	----
											10.3507
		(Lacave et al., 2016)*1	30			Zebrafish	Acute; mortality (120h)	LC50>100	----	----
		(Lacave et al., 2016)*1	70			Zebrafish	Acute; mortality (120h)	LC50=83.329	178.562	8.9281

*1 L-arginine stabilized SiO 2 NPs. The reported values are Si basis. D 198

Table D19 TiO 2 NPs Continued Trophic levels Reference ENPs size (nm) Tested species Test type and endpoints Reported EC50 (mg/L) Selected EC50 (mg/L) Chronic- Equivalent EC50 (mg/L) EC50 s EC50 t

 D19

	Crustacean	(Zhu et al., 2008)	≤20	D. magna	Acute; immobilization (48h)	EC50=35.306	35.306	3.5306	1.6252
		(Zhu et al., 2008)	≤20	D. magna	Acute; mortality (48h)	LC50=143.387	143.387	14.3387
		(Sovová et al., 2009)	<25	D. magna	Acute; immobility (48h)	EC50>300	----	----
		(Wiench et al., 2009)	10-50	D. magna	Chronic; mortality (21 days)	EC50=66.1	66.1	66.1
		(Zhu et al., 2010)	21	D. magna	Chronic; reproduction (21 days)	LC50=2.62	2.62	2.62
		(Dabrunz et al., 2011)	6	D. magna	Acute; immobility (72h)	EC50=3.8	3.8	0.38
		(Dabrunz et al., 2011)	6	D. magna	Chronic; immobility (96h)	EC50=0.73	0.73	0.73
		(Garcí a et al., 2011)	7.5	D. magna	Acute; mortality (48h)	LC50=16	16	1.6	1.4837
		(Das et al., 2013)	10	D. magna	Acute; mortality (48h)	LC50=7.75	7.75	0.775
		(Clé ment et al., 2013)	15	D. magna	Acute; immobility (72h)	EC50=1.30	1.30	0.13
		(Clé ment et al., 2013)	25	D. magna	Acute; immobility (72h)	EC50=3.15	3.15	0.315
		(Clé ment et al., 2013)	32	D. magna	Acute; immobility (72h)	EC50=3.44	3.44	0.344
		(Kim et al., 2014b)	<40	D. magna	Chronic; mortality (7 days)	LC50=2.7	2.7	2.7
		(Kim et al., 2014b)	<40	D. magna	Chronic; mortality (14 days)	LC50=1.9	1.9	1.9
		(Seitz et al., 2015)*3	90	D. magna	Chronic; immobility (96h)	EC50=1.15	1.15	1.15
		(Kim et al., 2014a)*4	70	M. macrocopa	Acute; immobility (48h)	EC50=1.9	1.9	0.19
									0.2307
		(Kim et al., 2014a)*4	130	M. macrocopa	Acute; immobility (48h)	EC50=2.8	2.8	0.28
	199	(Griffitt et al., 2008)	20.5±6.7	Daphnia pulex	Acute; mortality (48h)	LC50>10	----	----	APPENDIX D

Table D19 TiO 2 NPs Continued Trophic levels Reference ENPs size (nm) Tested species Test type and endpoints Reported EC50 (mg/L) Selected EC50 (mg/L) Chronic- Equivalent EC50 (mg/L) EC50 s EC50 t

 D19 The authors only showed a range of EC50 values (0.9-1.4 mg/L) for TiO 2 NPs after different aging durations (0, 1, 3, 6 days). However, their values are very similar, so the average value of 1.15 mg/L was used here.

		(Griffitt et al., 2008)	20.5±6.7 Ceriodaphnia dubia	Acute; mortality (48h)	LC50>10	----	----
		(Tomilina et al., 2011)	8-50 Ceriodaphnia affinis	Acute; mortality (48h)	LC50=120.5	120.5	12.05
		(Tomilina et al., 2011)	8-50 Ceriodaphnia affinis	Chronic; mortality (7 days)	LC50=210.9	210.9	210.9	9.1354
		(Tomilina et al., 2011)	8-50 Ceriodaphnia affinis	Chronic; fertility (7 days)	EC50=0.3	0.3	0.3
	Fish	(Boyle et al., 2015)	21	Zebrafish	Acute; mortality (96h)	LC50=915.5	915.5	45.775	10.4306
		(Samaee et al., 2015)	21	Zebrafish	Acute; hatching inhibition (96h)	EC50=107.2	107.2	5.36
									11.5162
		(Xiong et al., 2011)	30	Zebrafish	Acute; lethality (96h)	LC50=124.5	124.5	6.225
		(Griffitt et al., 2008)	20.5±6.7	Zebrafish	Acute; mortality (48h)	LC50>10	----
		(Ma et al., 2012)	21	Japanese medaka	Acute; mortality (96h)	LC50=155	155	7.75	7.75
	*3.

*4: Hydrodynamic size. APPENDIX D 200

Table D20

 D20 ZnONPs: Reported EC50 from literatures as well as the geometric means of EC50 for individual species (EC50 s ) and for the species in tropic level (EC50 t ).

	Trophic levels	Reference	ENPs size (nm)

Tested species Test type and endpoints Reported EC50 (mg/L) Selected EC50 (mg/L) Chronic- Equivalent EC50 (mg/L) EC50 s EC50 t

	Algae	(Franklin et al., 2007)*1	30	P. subcapitata	Chronic; growth inhibition (72h) EC50=0.068	0.068	0.068
		(Franklin et al., 2007)*1	30	P. subcapitata	Chronic; growth inhibition (72h) EC50=0.049	0.049	0.049	0.0558
									0.1064
		(Aruoja et al., 2009)*2	50-70	P. subcapitata	Chronic; growth inhibition (72h) EC50=0.042	0.052	0.052
		(Tang et al., 2015)	40-50	Anabaena sp.	Chronic; growth inhibition (96h)	EC50=0.74	0.74	0.74	0.74
	Crustacean	(Zhu et al., 2008a)	20	D. magna	Acute; immobilization (48h)	EC50=0.622	0.622	0.0622	0.2198
		(Zhu et al., 2008a)	20	D. magna	Acute; mortality (48h)	LC50=1.511	1.511	0.1511
		(Heinlaan et al., 2008)	50-70	D. magna	Acute; immobility (48h)	LC50=3.2	3.2	0.32
		(Wiench et al., 2009)	<200	D. magna	Acute; immobility (48h)	EC50=7.5	7.5	0.75
		(Wiench et al., 2009)	<200	D. magna	Acute; immobility (48h)	EC50=1.1	1.1	0.11
		(Wiench et al., 2009)	<200	D. magna	Acute; immobility (48h)	EC50=13.4	13.4	1.34
		(Wiench et al., 2009)	<200	D. magna	Acute; immobility (48h)	EC50=1	1	0.1
		(Wiench et al., 2009)	<200	D. magna	Acute; immobility (48h)	EC50>100	----		0.2762
		(Sovová et al., 2009)	<100	D. magna	Acute; immobility (48h)	EC50=2.632	2.632	0.2632
		(Blinova et al., 2010)*3	30	D. magna	Acute; immobility (48h)	EC50=2.6	3.24	0.324
		(Blinova et al., 2010)*3	30	D. magna	Acute; immobility (48h)	EC50=3.3	4.11	0.411
		(Blinova et al., 2010) *3	30	D. magna	Acute; immobility (48h)	EC50=9.0	11.20	1.12
	201	(Blinova et al., 2010) *3 (Blinova et al., 2010) *3 (Blinova et al., 2010) *3	30 30 30	D. magna D. magna D. magna	Acute; immobility (48h) Acute; immobility (48h) Acute; immobility (48h)	EC50=1.7 EC50=3.5 EC50=2.8	2.12 4.36 3.49	0.212 0.436 0.349	APPENDIX D

Table D20 ZnONPs Continued Trophic levels Reference ENPs size (nm) Tested species Test type and endpoints Reported EC50 (mg/L) Selected EC50 (mg/L) Chronic- Equivalent EC50 (mg/L) EC50 s EC50 t

 D20

	202							APPENDIX
								D
	(Blinova et al., 2010) *3	30	D. magna	Acute; immobility (48h)	EC50=3.4	4.23	0.423
	(Poynton et al., 2010)	20	D. magna	Acute; mortality (24h)	LC50=22.0	22.0	2.2
	(Naddafi et al., 2011)	50-70	D. magna	Acute; mortality (24h)	LC50=2.6	2.6	0.26
	(Naddafi et al., 2011)	50-70	D. magna	Acute; mortality (48h)	LC50=2.1	2.1	0.21
	(Zhao et al., 2012)	40	D. magna	Acute; immobility (48h)	EC50=0.76	0.76	0.076
	(Zhao et al., 2012)	40	D. magna	Acute; mortality (48h)	LC50=1.48	1.48	0.148
	(Santo et al., 2014)	<50	D. magna	Acute; immobility (48h)	EC50=1.9	1.9	0.19
	(Santo et al., 2014)	<100	D. magna	Acute; immobility (48h)	EC50=3.1	3.1	0.31
	(Xiao et al., 2015)	43	D. magna	Acute; mortality (48h)	LC50=0.99	0.99	0.099
	(Blinova et al., 2010) *3	70	T. platyurus	Acute; mortality (24h)	LC50=0.14	0.17	0.017
	(Blinova et al., 2010) *3	70	T. platyurus	Acute; mortality (24h)	LC50=1.1	1.37	0.137
	(Blinova et al., 2010) *3	70	T. platyurus	Acute; mortality (24h)	LC50=6.0	7.47	0.747
	(Blinova et al., 2010) *3	70	T. platyurus	Acute; mortality (24h)	LC50=3.6	4.48	0.448	0.2078
	(Blinova et al., 2010) *3	70	T. platyurus	Acute; mortality (24h)	LC50=1.5	1.87	0.187
	(Blinova et al., 2010) *3	70	T. platyurus	Acute; mortality (24h)	LC50=5.3	6.60	0.66
	(Blinova et al., 2010) *3	70	T. platyurus	Acute; mortality (24h)	LC50=1.4	1.74	0.174
	(Tomilina et al., 2011)	15-350 Ceriodaphnia affinis	Acute; mortality (48h)	LC50=0.09	0.09	0.009
	(Tomilina et al., 2011)	15-350 Ceriodaphnia affinis	Chronic; mortality (7 days)	LC50=0.17	0.17	0.17	0.04355
	(Tomilina et al., 2011)	15-350 Ceriodaphnia affinis	Chronic; fertility (7 days)	EC50=0.054	0.054	0.054

Table D20 ZnONPs Continued Trophic levels Reference ENPs size (nm) Tested species Test type and endpoints Reported EC50 (mg/L) Selected EC50 (mg/L) Chronic- Equivalent EC50 (mg/L) EC50 s EC50 t

 D20 The reported values were mg Zn/L. These results were based on one artificial freshwater and six natural freshwater.Table D21 Ecotoxicological effect factors of 14 ENPs based on GM-SL and GM-TL

	Fish	(Zhu et al., 2008b)	≤ 20	Zebrafish	Acute; mortality (96h)	LC50=1.79	1.79	0.0895	0.2277
		(Zhu et al., 2008b)	≤ 20	Zebrafish (embryos)	Acute; hatching inhibition (84h)	EC50=2.07	2.07	0.1035
		(Zhu et al., 2009)	20	Zebrafish	Acute; hatching inhibition (84h)	EC50=23.06	23.06	1.153	0.2277
		(Yu et al., 2011)	80±49	Zebrafish	Acute; lethality (96h)	LC50=3.969	3.969	0.1985
		(Xiong et al., 2011)	30	Zebrafish	Acute; lethality (96h)	LC50=4.92	4.92	0.246
		(Lacave et al., 2016)*4	20-70	Zebrafish	Acute; mortality (120h)	LC50=4.289	5.345	0.26725

*1 The ZnONPs were respectively supplied in a powdered and an aqueous forms. *2 The reported value was 0.042 mg Zn/L. *3 *4 Ecodis-P-90 stabilized ZnONPs. The reported values are metal basis.

Fe 3 O 4 NPs AgNPs CuNPs ZnONPs C60NPs Fe 2 O 3 NPs CuONPs CeO 2 NPs PtNPs AuNPs TiO 2 NPs Al 2 O 3 NPs SiO 2 NPs NiONPs SL 21739

  .130 16030.832 22077.810 2954.751 544.060 227.992 275.915 270.870 45.154

		61.342	35.894	63.849	27.749	14.153
	TL	21739.130 14502.094 8999.303 2863.120 544.060 227.992 223.484 139.396 126.073 80.701	56.684	53.083	27.749	14.153

Acknowledgements

ACKNOWLEDGEMENTS

ii science much easier to understand. I also would like to thank Marta Manno Magris and Riccardo Muzzi from University of Udine in Italy as well as Yi-Jeng Huang, Ting-Yao Shen, Yu-Ching Yu from National Yang-Ming University in Taiwan. The short-term cooperation and our friendship make me benefit a lot.

000E-08 W13 4.052E-06 9.204E-09 4.038E-06 6.000E-09 4.050E-

Appendix C

Table C1 The annual emissions (E, kg• year -1 ) of five ENPs to the freshwater extracted from previous studies.

Emissions

Ref. --------*1 The emissions were calculated on the basis of the high and low production estimates from a detailed market study (2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016). The authors demonstrated that the estimated emissions are higher than previous studies, which may result in an overestimate of releases. The fluorometric method was found to be most suitable. In this theis, EC50 = 200 mg/L is selected.

Appendix D

Publications and Communications

Journals