Camilo Duran Quintero
email: camilo.duranquintero@univ-nantes.fr

Anne Ventura

Olivier Lepine

Jérémy Pruvost

Olivier Lépine

Eco-design of spirulina solar cultivation: Key aspects to reduce environmental impacts using Life Cycle Assessment

Keywords: Spirulina, LCA (Life Cycle Assessment), microalgae solar cultivation system, microalgae growth modelling Cultivation, flocculation, centrifugation

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I. Introduction

Arthrospira platensis (usually named "Spirulina") is a blue-green cyanobacteria that can be naturally found in different aqueous media such as fresh and seawater, brackish water, marshes, sable and soil [START_REF] Ciferri | Spirulina, the Edible Microorganism[END_REF]. The natural proliferation of this photosynthetic microorganism in warm lakes of Mexico and Tchad as well as its harvest and massive consumption as food by earlier populations has been reported since the 15 th century [START_REF] Léonard | The 1964-65 Belgian Trans-Saharan Expedition[END_REF]. Spirulina is nowadays widely commercially produced. At the beginning of this decade, an optimistic estimation of the global annual production neared 10,000 tons [START_REF] Belay | Biology and Industrial Production of Arthrospira (Spirulina)[END_REF]. Some studies have related the ingestion of this microorganism to nutritional and therapeutic benefits such as hepatoprotective [START_REF] Ferreira-Hermosillo | Hepatoprotective effects of Spirulina maxima in patients with non-alcoholic fatty liver disease: a case series[END_REF] and anti-inflammatory effects [START_REF] Deng | Hypolipidemic, Antioxidant, and Antiinflammatory Activities of Microalgae Spirulina[END_REF]. In fact, Spirulina has a high protein content (up to 70% dw, [START_REF] Campanella | Chemical composition and nutritional evaluation of some natural and commercial food products based on Spirulina[END_REF] and is also naturally rich in vitamins (B12, E), provitamins (-carotene), minerals (iron, magnesium) and antioxidants such as phycocyanine [START_REF] Sara | Extraction and purification of high-value metabolites from microalgae: essential lipids, astaxanthin and phycobiliproteins[END_REF].

Besides, the composition of Spirulina includes other substances such as lipids and polysaccharides that have an interest in other domains, ranging from green chemistry (i.e. biomaterials, bio fertilizers) to energy. Some studies have in fact observed the aptitude of the Spirulina biomass to be used as a feedstock for biogas [START_REF] Aramrueang | Effects of hydraulic retention time and organic loading rate on performance and stability of anaerobic digestion of Spirulina platensis[END_REF], biodiesel [START_REF] Sumprasit | Biodiesel and biogas recovery from Spirulina platensis[END_REF] and hydrogen production [START_REF] Ainas | Hydrogen production with the cyanobacterium Spirulina platensis[END_REF]. Besides, some authors have pointed out encouraging results related to the biosorption capacity of Spirulina to remediate water contaminated with synthetic dye at batch laboratory scale [START_REF] Deniz | Bioremediation of contaminated water with unnatural dye using blue-green alga spirulina platensis[END_REF]. Likewise, Spirulina has shown certain ability to treat highly polluted wastewater from anaerobic digestion [START_REF] Sankaran | Nutrients uptake from anaerobically digested distillery wastewater by Spirulina sp. under xenon lamp illumination[END_REF].

Much of the interest raised worldwide in the last few decades by microalgae biomass (here prokaryote cyanobacteria and eukaryote microalgae), is partially due to their high photosynthetic efficiency compared to conventional vegetal crops (between 10 to 20% versus e.g. less than 0.5% for switchgrass which is considered the fastest growing terrestrial plant, [START_REF] Li | Biofuels from Microalgae[END_REF]. Furthermore, to supply their metabolic needs for inorganic carbon, these microorganisms are able to uptake dissolved CO2 which is an additional feature to counteract climate change.

However, within a global context of environmental deterioration (i.e. greenhouse effect, ecosystems pollution, exhaustion of limited resources), and the quest for the development of a sustainable industry, the operations related to growing microalgae processes need to demonstrate high yields as well as viable environmental performances compared to those attained by the manufacture of conventional or equivalent products.

A few studies have used the Life Cycle Assessment methodology (LCA, ISO 14040, 14044, 2006) to evaluate the environmental impacts of processes related to Spirulina and to identify the processes "hotspots", from the biomass growth to the transformation into different alternative products: edible tablets [START_REF] Ye | Life cycle assessment of industrial scale production of spirulina tablets[END_REF], phycocyanin [START_REF] Papadaki | Environmental impact of phycocyanin recovery from Spirulina platensis cyanobacterium[END_REF], food and feed [START_REF] Smetana | Autotrophic and heterotrophic microalgae and cyanobacteria cultivation for food and feed: life cycle assessment[END_REF], and biogas [START_REF] Rodríguez | Environmental analysis of Spirulina cultivation and biogas production using experimental and simulation approach[END_REF]. However, these previous studies (summarized in Table 1) are limited to some fixed operating conditions and do not investigate the variability of the operational parameters. Indeed, when processes include several correlated parameters that have certain variability, evaluating only a few scenarios appears to underrepresent the system heterogeneity and consequently leads to incomplete results and analysis, hence restricting and probably pre-orienting the choices of the decisions makers to reduce the environmental impacts. In this line, the main goal of this study is to couple a model of Spirulina biomass growth to LCA methodology in order to identify key operational aspects allowing the improvement of the environmental balance. These models should include parameters representing specific operational conditions to help make appropriate decisions in a given culture context. This sort of approach has been already developed to link detailed operational models with LCA, and has been applied on a case study related to hemp crop production [START_REF] Andrianandraina | Sensitivity Analysis of Environmental Process Modeling in a Life Cycle Context: A Case Study of Hemp Crop Production[END_REF] and its manufacture as an insulating material for buildings [START_REF] Senga-Kiessé | Introducing Economic Actors and Their Possibilities for Action in LCA Using Sensitivity Analysis: Application to Hemp-Based Insulation Products for Building Applications[END_REF]. More recently, it has also been implemented to identify key parameters for the design of cement concrete [START_REF] Ventura | Design of concrete : Setting a new basis for improving both durability and environmental performance[END_REF].

1 kg of microalgae biomass (10-15% dw).

ReciPe method using SimaPro software and Ecoinvent database 3.1 Modelling of pilot industrial scale based on relevant literature data and experimental data (lab and pilot scale).

Heating and cooling for cultivation steps included.

Chlorella in ORP: 28.08 kg CO2 eq. per kg of biomass (20% dw) Spirulina in ORP: 22.5 kg CO2 eq. per kg of microalgae (20% dw) Reduction of heating in ORP in more favourable climate conditions could reduce GWP (c.a 20%). In this article, we aim to apply a similar approach to the cultivation of Spirulina by combining a model of biomass surface productivity in solar conditions (Pruvost and Cornet, 2012) and a thermal model that estimates temperature variations of the culture [START_REF] Goetz | A generic temperature model for solar photobioreactors[END_REF], with LCA and sensitivity analysis. The coupled model is used to identify operational key parameters for the reduction of the process environmental impacts and then to compare various culture scenarios. Two configurations under greenhouse are studied and compared: Open and Covered Raceway Ponds (ORP and CRP). While the former is a widely implemented and assessed technology for the microalgae culture [START_REF] Collet | Recommendations for Life Cycle Assessment of algal fuels[END_REF], the latest is an alternative raceway pond with a transparent cover that could allow to save heat and water and whose more recent utilization has not been yet environmentally evaluated (according to our knowledge). This article focuses on the Global Warming Potential (GWP) indicator, though additional results concerning other impact categories are presented in Supplementary Data. In addition to minimizing GWP, the microalgae cultivator (i.e. the decision-maker) has other interests: increasing its surface productivity thus optimising the production per unit of cultivated surface and/or increasing its total annual production and thus increasing its sells. Thus, the study explores these three aspects: surface productivity, annual production and GWP.

II. Materials and Methods

II.1 Description of the approach

The implemented methodology (based on [START_REF] Andrianandraina | Sensitivity Analysis of Environmental Process Modeling in a Life Cycle Context: A Case Study of Hemp Crop Production[END_REF] follows six main steps:

1. Developing a detailed model of the foreground system, which in contrast to the background system, is the part of the studied system under the direct influence of decision makers [START_REF] Frischknecht | Life cycle inventory analysis for decision-making: Scope-Dependent Inventory System Models and Context-Specific Joint Product Allocation[END_REF]; this step is detailed in section II.3; 2. Classifying the model parameters of the studied foreground system as controllable or not by the decision maker; the parameters classification is presented in Supplementary Data ( To conduct this approach, a computer simulation program was developed using Matlab®. The main modules of this program are resumed herein. The program simulates the microalgae production during a complete year in a given geographical location, using a parametric model of microalgae growth considering the variability of different culture parameters (e.g. length of the production period, thermal regulation of culture media, percentage of water separation after harvest). Subsequently, it calculates inventory flows (IFs) associated to the annual production of biomass (e.g. water, salts, electricity, thermal energy, cleaning products, infrastructures, etc.) according to other foreground operational parameters (e.g. fraction of water recycling, coefficient of performance of the heat pump used for heating). The IFs are then connected to the Ecoinvent 3.3 cut-off database [START_REF] Weidema | Overview and methodology. Data quality guideline for the ecoinvent database version 3[END_REF] to obtain inventories corresponding to the background system. Next, the environmental impacts of the system are calculated. Finally, a sensitivity analysis module is able to run simulations on the LCA model to identify action levers.

II.2 Life Cycle Assessment

II.2.1 Goal and Scope

Our foreground system is the cultivation process of Spirulina in two solar culture modes under greenhouse: Open and Covered Raceway Ponds (ORP and CRP). Our functional unit (FU) is the production of 1 kg of Spirulina biomass (dw: dry weight) contained in wet slurry (20% dw). For simplification purposes it will be henceforth noted as 1 kg of biomass (dw).

The LCA is performed on the 'Cradle to Gate' system illustrated in Figure 1. The foreground system is represented in the central frame of this figure. The solar culture modes are assessed on the basis of a surface culture of 388.5 m². All simulations of the microalgae production were performed by considering the annual meteorological conditions of the city of Nantes in France. A full and detailed description of foreground processes is provided in the Supplementary Data ( § I). Above and below the central frame are represented the background processes, which provide input flows (e.g. water, electricity, nutrients, etc.) and undertake output flows (e.g. wastewater treatment) of the foreground system. The inventory of the foreground system has been divided into 3 sub-inventories: Process, Cleaning and Infrastructure. Full details of inventory data and models are provided in the Supplementary Data ( §III, Table S 4 to Table S12).

Our impact assessment method follows the International Reference Life Cycle Data System (ILCD) recommendations (European Commission, Joint Research Centre, 2012). Indicators are listed in Table S 13 in Supplementary Data.

The features of the cultivation facilities and the related operation data used are based on actual solar culture modes installed and operated in the city of Asserac (France), by the Algosource group.

II.2.2 Limitations

It should be noted that some IFs are not included in these inventories due to a lack of sufficiently robust data. As a result, their impact on the environment is not accounted for in the study. These flows are listed here below:

-For processes: Seeding inoculum of Spirulina, stirring energy for the preparation of the culture medium (salts + water). -For infrastructures: sieve filter, filter press. Energy of the construction process.

II.3 Dynamic modelling of the foreground system

The inventory of the foreground system is based on the simulation of the annual production of microalgae biomass. For a given set of parameters (production period of the year, geographical location, pond configuration) the annual production is estimated by following the next three steps, further described in the next section: i. Calculation of the hourly biomass maximum surface productivity considering the dynamics of solar radiation ii. Calculation of the culture's temperature based on the dynamic heat exchanges under the greenhouse. iii.

Calculation of the "corrected" hourly surface productivity based on the effect of temperature on the microalgae growth

II.3.1 Maximum surface productivity model

The model to estimate the maximum surface productivity has been previously developed by Pruvost and Cornet, 2012. It results from coupling photosynthetic biological kinetic parameters of the microalgae with the incident solar radiation on the reactor surface at a given instant. It estimates the average maximum surface productivity of the biomass (<Sx>max) with an error of about 15% as follows:

< 𝑆 > = (1 -𝑓 )𝜌 φ` 2𝛼 1 + 𝛼 𝑥̅ 𝐾 2 𝑙𝑛 1 + 2𝑞 𝐾 + 𝐾(1 -𝑥̅ )𝑐𝑜𝑠𝜃 𝑙𝑛 1 + 𝑞 𝐾𝑐𝑜𝑠𝜃 (g/m²) Eq. 1
fd, unlit volume fraction of the reactor (-), is 0 in solar culture. ρM, maximum photosynthetic yield (conversion of light energy into physicochemical energy, (-)) φ` , average mass quantum yield of photosynthesis (Z-scheme) (𝑔/𝜇 ) 𝛼, linear scattering modulus (-), characterizes the fraction of reflected light at the culture interface, specific for a given microorganism K, half-saturation constant of photosynthesis specific to the microorganism (𝜇 /m².s) 𝑞 , mean incident light energy density (𝜇 /m².s), also called PFD (Photon Flux Density) 𝑥̅ d, mean diffuse fraction of 𝑞 (-) 𝑐𝑜𝑠𝜃 , specification of the solar radiation on the PBR, with  the incident angle defined from the outward normal of the PBR and the sun's course (-) Parameters related to the photosynthetic yield and kinetic growth (ρM, φ` , 𝛼 and K) can be considered time independent for a given type of microalgae. The parameters 𝑥̅ , (𝑞 ) and cosθ, are dependent on the dynamics of solar radiation. These are actually determined by the total incident solar radiation (Gt), horizontal diffuse solar radiation (GH) and normal diffuse solar radiation (GN) which can be provided by meteorological databases such as Meteonorm® (Meteonorm, Meteotest, Switzerland). Thus, it is possible to calculate, for a given geographical location, the hourly maximum surface productivity throughout any other period of the year. For instance, Figure S 3 in Supplementary Data shows the incident luminous energy density (𝑞 ) and the average maximum surface productivity (< 𝑆 > ) of Spirulina for an ORP culture under greenhouse during a year (from January to December) and located in the city of Nantes in France.

For a culture in a greenhouse, this model considers that the incident light energy density on the reactor surface (𝑞 ) is attenuated when light goes through the transparent surfaces composing the greenhouse. This attenuation is determined by a luminous transmittance coefficient (TL, 0 <TL <1) characteristic of the greenhouse material. If TL is equal to 0.5, only half of the entire incident light energy density is transmitted through the material and is therefore available inside the greenhouse.

Note that Eq.1 only estimates the maximum biomass productivity, and is then only related to available light, as discussed in [START_REF] Cornet | A Simple and reliable formula for assessment of maximum volumetric productivities in photobioreactors[END_REF] If culture conditions such as medium, pH or temperature are not in the optimal range of the culture species, biomass productivity will be then lower. The same will be obtained if biomass concentration is too low. Available light will be not fully absorbed in the culture broth leading to a decrease in productivity. For cyanobacteria like Spirulina, those optimal conditions (including light attenuation conditions as discussed in Pruvost et al., 2012) are rather easy to obtain in practice. Only the control of temperature could be challenging, especially when energy input for thermal regulation needs to be minimized (see [START_REF] Pruvost | Thermal modeling and optimization of microalgal biomass production in the harsh desert conditions of State of Qatar[END_REF] for a complete discussion). The effect of culture temperature on productivity must be considered, which is detailed below.

II.3.2 Model of thermal exchanges in the greenhouse

Temperature is known to affect deeply microalgae growth [START_REF] Pruvost | Large-scale production of algal biomass: photobioreactors[END_REF]. The biomass surface productivity obtained by Eq.1 must then be corrected accordingly. Thus, the biomass productivity model is combined with a thermal model that states differential equations resulting from unsteady state energy balances based on the thermal exchanges that take place between the various media composing the surroundings of the reactor (as presented by [START_REF] Goetz | A generic temperature model for solar photobioreactors[END_REF] For the ORP in this work, these media are: the roof of the greenhouse, the air inside the greenhouse and the microalgae culture aqueous media. The temperature of the culture medium is assumed to be homogenous and it is considered that the heat transfer by conduction between the culture medium and the floor is non-significant. For the CRP, two more media are considered: the reactor cover and the air between this cover and the microalgae culture aqueous media. For each considered medium, the terms included in the energy balance are the medium enthalpy variation, the solar radiation (absorbed, transmitted and reflected) and the heat exchange with the surroundings by radiation, convection and water evaporation/condensation. Thus, heat exchanges are characterised by several of the medium's properties (e.g. absorbance, transmittance, reflectance, heat capacity and emissivity).

The differential equations of the thermal model are solved using the Matlab® 'ode23' tool (Ordinary Differential Equation) as well as the hourly meteorological data provided by Meteonorm® for the studied geographical location. Solving these equations allows to calculate the temperature of the different media over time during one year.

II.3.3 Thermal regulation of the culture medium and related energy consumption

The thermal model provides the option of setting up a temperature range inside which the culture of microalgae will be maintained [START_REF] Pruvost | Thermal modeling and optimization of microalgal biomass production in the harsh desert conditions of State of Qatar[END_REF]. When the thermal regulation is activated, the model establishes that outside this interval, there is no change of the culture's temperature (𝑑𝑇 / 𝑑𝑡 = 0). Due to heat exchanges between the microalgae culture and its surroundings in the greenhouse, the thermal regulation affects other media's temperatures in the greenhouse.

In order to estimate the energy required to maintain the temperature of the culture medium within the chosen temperature range, a specific term of 'energy for thermal regulation' is then added to the unsteady state differential equation of the energy balance expressed for the culture medium. The addition of this energy term (either heating or cooling), implies that the temperature of the culture medium no longer changes over time (𝑑𝑇 / 𝑑𝑡 = 0) when its value is outside the defined temperature range. As discussed in [START_REF] Pruvost | Thermal modeling and optimization of microalgal biomass production in the harsh desert conditions of State of Qatar[END_REF] this assumes a thermal regulation unit of sufficient power to consider an instantaneous and ideal thermal response of the system.

II.3.4 Corrected surface productivity model: Effect of culture's temperature

As previously mentioned, since the production of microalgal biomass is not only determined by their kinetic parameters and the available solar radiation, but also by the temperature within the bioreactors, this model considers the effect of non-optimal temperature on the decrease of biomass surface productivity, as given by Eq.1 (i.e. maximal value). Thus, results from Eq.1 on biomass productivity were corrected by an empirical correlation (an Arrhenius type equation) establishing that below the optimal culture's temperature (35°C for Spirulina), the maximum biomass surface productivity undergoes a reduction as the culture's temperature decreases. Details on the effects of this correction are provided in Supplementary Data (Figure S 4).

II.4 Sensitivity Analysis: Morris Indices

The sensitivity analysis method used in our approach, is the Morris method [START_REF] Morris | Factorial Sampling Plans for Preliminary Computational Experiments[END_REF] providing three indices:  and . It consists in simulating several random modifications (one at a time) of the values of the model parameters according to their variation range. For each simulation an Elementary Effect (EE) is obtained by calculating the ratio between the variation of one output value obtained by the variation of one input parameter. For a set of a sufficient number of simulations for each parameter, is the mean value of EEs, is the mean of absolute values of elementary effects |EEs|, and is the standard deviation of EEs. For a given varied input parameter of the model, the interpretation of Morris indices can be done as follows:

the indice provides a ranking of parameters according to their influence on the environmental impacts indicator: higher  the higher the influence of the parameter if || ~  the effect of the studied parameter is an increasing effect if  ~ - the effect of the studied parameter is a decreasing effect if ||  μ *  the effect of the studied parameter is non-monotonic if  the effect is linear like if is not negligible compared to the effect is not linear or there are some interactions between the studied parameter and others

To generate the random sampling matrix in this study, the variation space of each parameter was discretized into 10 levels and the number of simulations computed for each parameter was set to 30.

In order to implement the Sensitivity Analysis, ranges of variation of all parameters of the model have been defined. They are presented in Supplementary Data (Table S 14) with their probability distributions and annotations on their meaning. The most influent parameter is found to be the one that determines whether there is a thermal regulation for the temperature of the culture or not. Activating the thermal regulation leads to an increase of the GWP indicator (⇧). This parameter also presents the highest degree of interaction or non-linearity (σ=4.1). Indeed, this parameter is a discrete coded parameter which means that its increment from 0 to 1 does not have any physical signification but allows the system to operate under different conditions (e.g. the supply of electrical energy to produce heat or cold through a heat pump in order to regulate the culture's temperature). This explains its high interaction degree. We can note that the high influence of this parameter on GWP is in accordance with Pruvost et al., 2019 who concluded to the large energy consumption that could be obtained when adding a thermal regulation unit, even in the case of open systems. Although such conclusion is highly dependent of the strain temperature range and outdoor culture conditions, thermal regulation energy was found in most cases higher than the energy absorbed from solar radiation.

III. Results and Discussion

⇧

⇩ Parameter whose increase leads to a decrease of the indicator impact per FU.

⇧ Parameter whose increase leads to an increase of the indicator impact per kg of FU.

The number of summer-centred production months (SCPM) per year is found as the second most influential parameter on the GWP indicator. It has a non-monotonic effect (⇩⇧) because μ * ||. In other words, increasing the value of this parameter could lead to either a decrease or increase of the GWP indicator. Hence an optimal value should be determined.

The third most influent parameter is found to be the "low temperature setpoint of the culture medium" (Tcmlow). It shows a monotonic increasing effect: an increase of this parameter rises the GWP indicator.

A similar increasing effect is observed for the parameters "greenhouse height" and "biomass concentration after harvesting and filtering" but their influence is found to be less important.

Other model parameters such as "depth of the culture media" and "greenhouse material" present non-monotonic behaviours with respect to the GWP indicator. However, their influence on GWP is found less significant, especially concerning the type of material used for the greenhouse. For a better interpretation of the meaning of this nonmonotonic effect of this discrete coded parameter, several simulations were run to examine the impact of using these three types of greenhouse material. Results presented in Supplementary Data (Figure S 9) for operation under thermal regulation, show that the different results tend to overlap and confirm the small influence of this choice on the GWP indicator.

For all other parameters (i.e. ratio of PBR surface to greenhouse surface, coefficient of performance of the heat pump, ratio of recycled water, biomass concentration in the PBR before harvesting), they have a decreasing effect, their increment leading to reduce the GWP indicator (⇩), though their influence is found to be minor. ⇧ ⇩ Parameter whose increase leads to a decrease of the indicator impact per FU. ⇧ Parameter whose increase leads to an increase of the indicator impact per FU.

III.1.2 Culture in Covered Raceway Pond

III.2 Generating scenarios for most favorable production conditions

The sensitivity analysis provides the three most influent parameters on GWP: (i) the choice to use or not thermal regulation, (ii) the choice of the low temperature setpoint Tcmlow under thermal regulation, and (iii) the number of SCPM per year.

Further simulations are conducted in order to evaluate with more detail their influence on the GWP indicator, surface productivity and annual production as well as to obtain possible optimal values for SCPM which is found to be non-monotonic.

For these simulations, the values of all other parameters were set to their most probable value according to their probability distribution presented in Table S 14 in Supplementary Data (except for the material of the greenhouse, which was defined by default as Polyethylene).

III.2.1 Effect of influent parameters on surface productivity

Figure 2 presents the simulated average surface productivity with and without thermal regulation for both types of solar culture modes. First of all, these results show a high variability of the average surface productivity (between 2.9 and 9.0 g/m²/d) depending on the extent of the annual production period, the thermal regulation conditions and the reactor configuration. Under thermal regulation (Figure 2a), for a given annual production period (SCPM), the average surface productivity increases with the increment of the low temperature setpoint of the culture (Tcmlow), as defined by the productivity model described (cf. §II.3.4). Furthermore, for a given Tcmlow, higher surface productivities are reached for a production period of 2 months (SCPM=2), i.e. June and July. Indeed, for the city of Nantes, these are the 2 months accumulating the highest number of hours of sunshine (21% or 970h out of 4558h per year) and where the close-to-zenith position of the sun provides more favourable sunlight for microalgae growth (i.e. photon flux density (𝑞 ) and incident radiation angle (cos)). In that configuration, ORP maximum productivities are 7.0 and 9.0 g dw/m²/d, and CRP maximum productivities are 7.0 and 7.9 g dw/m²/d, for Tcmlow=15°C and 35°C respectively. Lower productivities of CRP are due to the reactor cover: although transparent, it generates a condensed-water film that reduces the transmission of light to the culture media. Besides, when the production period is extended, the surface productivity decreases since the hours of sunshine per day decrease and the sun is more distant to the zenith position (lower values of 𝑞 and cos).

For instance, the months of January and December provide only 281 and 261 hours of sunshine respectively (together 12% of the 4558 annual sunshine hours) and during these hours the sun rises far from the zenith and provides a low incident radiation (low 𝑞 and cos). Thus, an operation of 12 months per year (JanuaryDecember) would make the average surface productivity values the lowest because these low sunshine months are included in the production period.

Without thermal regulation (Figure 2b), the productivities obtained in ORP and CRP are very close. The surface productivity decreases progressively from 7.3 down to 2.9 g dw/m²/d as the production period (SCPM) extends from 2 to 12 months per year. These values are equivalent to the ones obtained under thermal regulation with Tcmlow=15°C.

III.2.2 Effect of influent parameters on biomass production

The simulation results concerning total annual biomass production with and without thermal regulation are presented in Figure 3. With thermal regulation (Figure 3a), the maximum annual production for ORP and CRP (respectively 819 and 691 kg/year) are obtained with a temperature of Tcmlow=35°C and logically with the longest operating period (SCPM=12, JanuaryDecember). Besides, as expected, for a given number of SCPM, increasing the Tcmlow leads to an increase in annual production. For 12 SCPM, the annual production rises from 427 to 819 kg/year in ORP (+92%) and from 432 to 691 kg/year for CRP (+60%) when Tcmlow is incremented from 15 to 35°C.

the increment in annual production with the extension of the cultivation period is not linear, but tends towards a plateau. For instance, extending the production period from 2 months per year (SCPM=2, JuneJuly) to 4 months per year (SCPM=4, MayAugust) in the ORP system with Tcmlow=15°C, results in a production increment of 132 kg/year (from 166 to 298 kg/year), i.e. around 30% of the production achievable for 12 months of operation (427.1 kg/year) under the same Tcmlow. Moreover, if the period of operation is extended from 8 to 10 months or from 10 to 12 months per year, the annual production increases by approximately 15 kg or 11 kg respectively, which corresponds to nearly 3% at maximum production achievable for 12 months of operation at this temperature. These results are explained by low surface productivities obtained when the production period takes place away from the most favorable sunlight conditions. Quite similar values are found for the CRP configuration and Tcmlow=15°C. However, at higher temperatures, the CRP culture mode tends to provide lower annual productions than the ORP configuration which is caused by the reactor cover of the CRP that attenuates the light transmission to the microalgae culture. For an operation period of 12 SCPM and Tcmlow=35°C, the CRP system provides 18% less biomass per year than the ORP configuration (691.4 and 818.5 kg/year respectively). Without thermal regulation (Figure 3b), the annual production trend shows a strong increase from 2 to 8 SCPM, and then only a slight increment from 8 to 12 SCPM. For annual production periods between 6 and 12 SCPM, the annual production tends to be slightly higher for the culture in CRP compared to ORP. The small difference between these two culture modes can be explained by the capacity of the CRP system to maintain a higher culture temperature (annual mean culture temperatures are respectively 21.8 and 18.8°C) thanks to the reactor cover that avoids water evaporation and heat exchange by convection with the air inside the greenhouse. These trends are similar compared to those obtained under thermal regulation with Tcmlow=15°C.

III.2.3 Effect of influent parameters on Global Warming Potential

Simulation results of the GWP indicator per kg of biomass (dw) are presented in Figure 4. Under thermal regulation (Figure 4a), for a given production period, the GWP indicator increases, as expected from the sensitivity analysis, with the increase of the low temperature setpoint (Tcmlow). Indeed, the energy consumption is directly linked to this value. Besides, at a given Tcmlow (varied between 15 and 35°C), reducing the annual cultivation period (SCPM) shows a non-monotonic influence on GWP (in accordance with previous results, Table 2 andTable 3), with an optimal value of SCPM (around 6 months) for which GWP is minimum for both ORP and CRP culture modes. For instance, under thermal regulation in ORP with Tcmlow=15°C, shortening the annual production period (SCPM) from 12 months to 6 months, reduces the environmental impact by around 46% (from 29.3 to 15.8 kg CO2 eq. /kg biomass dw). However, further reduction of the annual operation period (SCPM) down to 2 months (JuneJuly) results in an increase in the GWP indicator up to kg CO2 eq./kg of biomass dw (+ 41%).

To analyse the effect of the annual operation period (SCPM) on GWP impact, Figure 5 presents the simulation results for the heating and cooling energy required per year for thermally regulated cultures at Tcmlow=15°C, as well as the annual biomass production. In Figure 5, for ORP with SCPM shorter than 6 months, heating energy remains close to zero, because ambient temperature is sufficiently high and solar radiation conditions provide sufficient heat to fulfill the thermal requirements to reach the setpoint temperature Tcmlow. Simultaneously, the annual production rises significantly (+133%) when the annual operation extends from 2 to 6 SCPM per year (from 165 up to 385 kg dw/year respectively). In contrast, when SCPM exceeds 6 months, the annual production reaches a plateau (+13%), but energy demand to provide sufficient heat drastically increases (up to 106 MWh/year for 12 SCPM in OPR). Besides, though not shown in this graph, other resources and emissions are linked to this growing demand of thermal energy (heat pump, refrigerant R134a, leaks of refrigerant (Tetrafluoroethane emissions)), and contribute to the GWP impact. Duran Quintero, C., Ventura, A., Lépine, O., Pruvost, J., 2021. Eco-design of spirulina solar cultivation: Key aspects to reduce environmental impacts using Life Cycle Assessment. Journal of Cleaner Production 299, 126741. https://doi.org/10.1016/j.jclepro.2021.126741

19

For the CRP culture mode, the same observations can be made about the influence of SCPM. However, thermal energy requirements are different from the ORP culture mode, although their annual productions are found comparable. When the annual operation (SCPM) lasts between 2 and 8 months, the CRP culture requires cooling energy to respect Tcmlow. Instead, for longer annual operations that include the coldest months (SCPM>10) the CRP system requires less heating energy than ORP (80 and 106 MWh/year respectively) due to its cover that allows to keep the reactor's temperature without further energy demand. This explains why in Figure 4a the GWP impact related to the ORP system, tends to appear lower than GWP impact of the CRP for short annual operations (low SCPM) whereas for long annual production periods (high SCPM) it appears to be higher, this trend being more pronounced with the increment of Tcmlow, since the demand of thermal energy is increased. Thus, on one hand, an increase of SCPM increases the biomass annual production. This will contribute to reduce GWP as it is calculated on the basis of the functional unit in this study: total GWP is divided by total produced biomass (dw). On the other hand, a longer SCPM period increases (i) the amounts of resources (intermediate flows) required for the culture operation (water, salts, electricity, paddle wheel, pumps, etc.) and, in the case of thermal regulation (ii) the energy requirements and related flows for maintaining a growing biomass at a given Tcmlow (thermal energy, heat pump, refrigerant, etc.). An equilibrium point, at which SCPM is around 6 months, is found when production almost reaches a plateau as shown in Figure 5: the annual production is then the highest in respect to the needed intermediate flows associated with the growth of this biomass.

Finally, without thermal regulation (Figure 4b), independently of the length of the annual operations (SCPM) the CRP system is found to have a higher GWP impact per kg of biomass than ORP (from +12% up to +33%) and that in spite of similar annual productions in both systems. This difference can be attributed to other features differentiating both cultivation systems, like their infrastructure itself, and whose GWP impact was not highlighted in the previous analysis, though it can significantly contribute to the total GWP as it will be shown in §III.3.2.

III.2.4 Definition of most favourable scenarios

In the model, an increase of surface productivity can be reached by two means: the regulation of the culture's temperature and the increase of daily solar radiation intensity and availability (number of hours). However, these two means do not have the same environmental loads: whereas thermal regulation implies the need of additional energy, for a given geographical location the solar resource is only conditioned by the annual cultivation period. Thus, logically, the more favourable the solar conditions, the less needed the thermal regulation. This consequently leads to lower GWP values. This result can be generalized for all microalgae cultures, only the value of GWP would change for other species, especially if they require a different culture temperature than Spirulina.

Duran Quintero, C., Ventura, A., Lépine, O., Pruvost, J., 2021. Eco-design of spirulina solar cultivation: Key aspects to reduce environmental impacts using Life Cycle Assessment. Journal of Cleaner Production 299, 126741. https://doi.org/10.1016/j.jclepro.2021.126741 20 Such analysis is then in accordance and complementary to others studies in the field (see [START_REF] Pruvost | Thermal modeling and optimization of microalgal biomass production in the harsh desert conditions of State of Qatar[END_REF] for a similar discussion on the challenge to optimally produce microalgae outdoor all year round).

The detailed analysis shows that optimal value of SCPM is close to 6 months in order to obtain lower GWP. It also shows that the increase in production is small above these values, because surface productivity decreases due to shorter daily sunlight time, and lower sun positioning. Results also show that the use of thermal regulation is very influent on GWP increase. However, it appears unnecessary to be used during the summer (low SCPM), as both ORP and CRP have the similar productions with or without thermal regulation (see Figure 3). Thermal regulation appears useful to extend SCPM above 6 months, in order to increase production, only if the low temperature setpoint is at least 20°C (see Figure 3).

Based on this analysis, several specific and plausible production scenarios, corresponding to lowest values of GWP according to Figure 2, are proposed for both ORP and CRP (Table 4). These scenarios consider the same cultivation context described previously (i.e. area of 388.5 m² under greenhouse located in the city of Nantes, France). In order to identify the scenario that deals the best with this balance between high annual productions and low GWP impact per kg of biomass, the next steps were followed. First, the scenario providing both the highest annual production and the highest GWP indicator (Scenario 2 and ORP culture mode) was fixed as a reference.

Variations of both production and GWP were calculated for the other scenarios with respect to scenario 2 (Table 5). The ratio of production loss to GWP decrease was then calculated to identify the scenario providing an adequate compromise between annual production and GWP impact per kg of biomass.

The most interesting ratio (lowest value=0.53) is obtained for scenario 4 (SCPM=8 months, no thermal regulation) with an ORP culture mode. Compared to scenario 2, production loss is -23%, and GWP decrease is -44%. The CRP culture mode of scenario 4 has a slightly higher ratio (0.55), with a production loss of -19%, but a GWP decrease of -34%. For the ORP system, the group of operations related to the biomass Culture (cultivation and harvest) is the main contributor to the GWP indicator (between 62 and 81%, Figure 6a). For scenarios operated under thermal regulation (S1, S2 and S3), the electricity related to the heating energy supplied by the heat pump, plays a major role in this impact (Figure 6b). Its contribution can reach up to 44% (S2). Associated to thermal regulation, the leaks of refrigerant R134a from the heat pump (Tetrafluoroethane emissions) also participate to this GWP impact (up to 14% of the total impact). Moreover, the contribution of electricity for culture mixing, biomass harvesting and filtering, vary from 8 up to 31%. Some of the salts added to provide the elementary nutriments participate importantly to this impact especially NaNO3 (between 14% and 34%), followed by NaHCO3 (8 and 25%) and Na2CO3 and 11%). The contribution of tap water and wastewater flows to the GWP impact appears to be minor (less than 3%). On the other hand, the GWP impact fraction related to the Infrastructures varies between approximately 18 and 36% (Figure 6a). Most of this impact comes from the infrastructure of the bioreactor itself (between 55 and 69%), followed by the greenhouse infrastructure (between 24 and 30%) and by the heat pump infrastructure (from 2 up to 20%) whose contribution takes place when the thermal regulation is activated. The impact fraction of other considered infrastructures such as the engines for the paddle wheel and the harvesting pump appears to be negligible. To observe how much the materials composing the ORP and the greenhouse infrastructures contribute to their total GWP impact, see Figure S 5 and Figure S 7 in Supplementary Data. Near 30% of the GWP impact related to the ORP infrastructure comes from the concrete composing the screed the heating underfloor followed equivalently (ca. 14%) by the PVC (structural), the EPDM (liner) and the waste management of the EPDM. Concerning the greenhouse infrastructure, approximately 75% of its GWP impact relates mainly to the structural steel, the remaining fraction (25%) being equivalently caused by Polyethylene and waste management of Polyethylene. Regarding the CRP configuration, the GWP impact fraction related to the Culture operations varies from 51 up to 35% (Figure 7a), which is lower to the one observed for the ORP system. Contributions of the inventory flows to the total GWP impact are similar to the ones observed previously for the ORP system. Two additional flows are in this case part of the inventory: the electricity related to the cooling energy and the synthetic CO2. For scenarios where temperature regulation is activated, the electricity flow related to cooling energy is at least half of the total electricity related to thermal energy. It can reach up to 20% of the total GWP impact associated to the culture operations. The GWP contribution of the group of flows related to the thermal regulation (heating, cooling, R134a, leaks of R134a) varies between 28 and 48%. On the contrary, the contribution of the synthetic CO2 on this impact is negligible (less than 1%). Furthermore, the impact fraction associated to the CRP Infrastructures varies from 31 up to 47% of the total GWP impact (Figure 7a), which is higher than one obtained for the ORP configuration. This is partially due to the infrastructure of the CRP bioreactor that has almost twice the GWP impact found for the ORP configuration (cf. Table S 16 in Supplementary Data). In fact, the bioreactor infrastructure is responsible for at least 75% up to 81% of the total GWP impact related to Infrastructures. This significant impact caused by the CRP infrastructure helps to explain why in the absence of thermal regulation (b), the GWP impact related to the cultivation in the CRP system appears to be higher than the one estimated for the ORP configuration, independently of the annual operation period (SCPM). Besides, the remaining GWP impact related to the CRP infrastructure is caused by the greenhouse infrastructure (near 18%) followed by the GWP impact associated to the heat pump for cooling needs (about 3 to 8%) used when temperature regulation is activated. The contributions of the materials composing the infrastructure of the CRP bioreactor are shown on Figure S 6 in Supplementary Data. The Polycarbonate (PC) used for the reactor cover and whose service life is considered to be shorter compared with other infrastructure materials, is the main contributor to this GWP impact (41%), followed by HDPE used for the reactor structure, isolation tubes and paddle wheel (17%), concrete for the screed of the heating underfloor (16%) and waste management of HDPE (15%).

Besides, the GWP impact related to the Cleaning steps appears to be negligible (lower than 2% of the total GWP impact) for both ORP and CRP.

IV. Conclusion

The purpose of this study was to identify key operational parameters of the Spirulina production in two solar bioreactors (ORP and CRP) under greenhouse in order to reduce their potential environmental footprint. To this aim, we have developed a simulation platform that integrates a validated model of microalgae surface productivity, coupled to a thermal model, with LCA methodology and sensitivity analysis. The implemented approach can be used as a decision tool to guide biomass production with low environmental impact. The main findings of this study, mainly focused on the GWP indicator, can be summarized as follows:

 Simulations of average surface productivity, annual production and GWP per unit of produced biomass showed a large variability depending mainly on three most influential parameters: the culture annual operation period (centred on summer months), the use of thermal regulation, and the low temperature setpoint of culture medium (Tcmlow) when thermal regulation is used.

 To optimize the GWP impact per unit of produced biomass (i.e. to minimize the use of resources and emissions per FU), the results showed that annual operations should favour periods where solar conditions provide sufficient heat and light. The use of the thermal regulation and the increment of Tcmlow may help to reach higher annual productions, especially during colder periods, but also imply higher consumption of energy and associated resources, hence leading to higher GWP per FU.

 The comparison between ORP and CRP showed that both systems lead to different GWP impacts though they provide close annual productions. Under thermal regulation, the CRP has higher GWP impacts in warm periods (up to 61%) because of its needs of cooling energy whereas in cold periods it has lower GWP impact (down to -23%) since the reactor cover helps to keep heat in the culture which reduces the heating demand. Furthermore, without thermal regulation, ORP presented lower GWP impact than CRP, which due to the reactor cover, presents almost twice the GWP impact related to the ORP infrastructure.

 To balance the interest of maintaining high annual production whereas reducing GWP, the evaluation of specific scenarios showed that for both ORP and CRP culture modes, a trade-off was found for 8 months of culture (March to October) without thermal regulation in Nantes location (France).

Although the presented results are specific to Spirulina and the city of Nantes, the developed model can adapt to other strains and geographical locations by changing parameters related to microalgae and climate.

Complementary work to this study will include the implementation of the developed approach to assess and conceive cultivation facilities located in other latitudes around the world as well as industrial symbiosis where alternative resources are used to replace those significantly affecting the GWP impact (thermal energy, salts). Further studies will also evaluate the effect of the biomass cultivation conditions on downstream processes. Likewise, in the perspective of an industrial scale operation, a consequential LCA could be performed to account for the impacts on the feedstock suppliers.

Figure 1 .

 1 Figure 1. Representation of the Spirulina production process in this study

  for a flat panel and by Pruvost et al., 2019 for a semi-buried open raceway).

  Figure 2. Simulation results for average surface productivity in ORP and CRP for different number of SCPM per year and low temperature setpoint of culture medium (15°C≤Tcmlow≤35°C) (a) with thermal regulation and (b) without thermal regulation. Simulations are run for 389 m² of surface culture under greenhouse located in the city of Nantes.

Figure 3 .

 3 Figure 3. Simulation results for biomass annual production in ORP and CRP for different number of SCPM per year and low temperature setpoint of culture medium (15°C≤Tcmlow≤35°C) (a) with thermal regulation and (b) without thermal regulation. Simulations are run for 389 m² of surface culture under greenhouse located in the city of Nantes.

  Figure 4. Simulation results for GWP indicator per kg of biomass (dw) in ORP and CRP for different number of SCPM per year and low temperature setpoint of culture medium (15°C≤Tcmlow≤35°C) (a) with thermal regulation and (b) without thermal regulation. Simulations are run for 389 m² of surface culture under greenhouse located in the city of Nantes.

Figure 5 .

 5 Figure 5. Simulated thermal energy required to maintain the culture at Tcmlow = 15°C in ORP (heating) and CRP (heating+cooling) under greenhouse (389 m² of culture) and associated annual Spirulina production for different number of SCPM per year.

Figure 6 and

 6 Figure 6 and Figure 7 show fraction of the inventory flows to the GWP indicator obtained for the ORP and CRP respectively and the different evaluated scenarios.

Figure 6 .

 6 Figure 6. Contributions fractions of the IFs to the GWP indicator for the five simulated scenarios in ORP (389 m²) under greenhouse in Nantes, France. (a) IFs related to Infrastructure, Cleaning and Culture; b) IFs related to the Spirulina culture.

Figure 7 .

 7 Figure 7. Contributions fractions of the IFs to the GWP indicator for the five simulated scenarios in CRP (389 m²) under greenhouse in Nantes, France. (a) IFs related to Infrastructure, Cleaning and Culture; b) IFs related to the Spirulina culture.

  Table S 14); 3. Identifying and assigning a probability distribution to all parameters; this step is detailed in the Supplementary Data (Table S 14); 4. Performing a Sensitivity Analysis, through the calculation of Morris indices, on the listed parameters of the model in order to qualify their influence on the different environmental indicators, this step is described in section II.4; 5. Identify action levers as controllable foreground parameters as key action possibilities for the decision maker in order to reduce environmental impacts as a result of sensitivity analysis detailed in section III.1; 6. Defining and comparing impact indicators of the most favourable scenarios for which controllable foreground parameters are set to suitable values; this is detailed in sections III.2 and III.3.

Table 2 .

 2 Morris indices for the ORP process regarding the GWP indicator.

	N°	model parameter (culture in ORP)	μ *	μ	σ	effect
	1	thermal regulation	25.2	25.2	4.1	⇧
	3	number of summer-centred production months (SCPM) per year	17.9	14.8	3.3	⇩⇧
	2	low temperature setpoint of the culture medium under thermal regulation (Tcmlow)	11.0	11.0	2.4	⇧
	6	ratio of PBR surface to greenhouse surface	1.8	-1.8	0.2	⇩
	7	greenhouse height	1.0	1.0	0.1	⇧
	4	depth of the culture medium in the PBRs	1.0	0.8	0.2	⇩⇧
	11	coefficient of performance of the heat pump	0.9	-0.9	0.3	⇩
	5	greenhouse material (PE, glass, PC)	0.6	0.0	0.2	⇩⇧
	10	ratio of recycled water	0.5	-0.5	1.1E-02	⇩
	8	biomass concentration in the PBR before harvesting	0.2	-0.2	1.5E-02	⇩
	9	biomass concentration after harvesting and filtering	1.2E-03	1.2E-03	1.0E-04

  Table 3 presents the results of the Morris indices obtained for the model parameters in the CRP configuration. The three most influent parameters regarding the GWP indicator are similar to those found for the ORP configuration. Sensitivity analysis results related to other environmental indicators are presented in Supplementary Data in Table S 19 and Table S 20.

Table 3 .

 3 Morris indices for the CRP regarding the GWP indicator.

	N°	model parameter (culture in CRP)	μ *	μ	σ	effect
	1	thermal regulation	19.1	19.1	2.4	⇧
	2	low temperature setpoint of the culture medium under thermal regulation (Tcmlow)	8.4	8.4	2.1	⇧
	3	number of summer-centred production months (SCPM) per year	8.1	-0.4	2.0	⇩⇧
	5	greenhouse material (PE, glass, PC)	1.6	0.5	0.3	⇩⇧
	4	depth of the culture medium in the PBRs	1.3	-0.2	0.3	⇩⇧
	6	ratio of PBR surface to greenhouse surface	0.8	-0.8	0.1	⇩
	11	coefficient of performance of the heat pump	0.7	-0.7	0.2	⇧
	7	greenhouse height	0.7	0.7	4.3E-02	⇩
	10	ratio of recycled water	0.5	-0.5	1.3E-02	⇩
	8	biomass concentration in the PBR before harvesting	0.2	-0.2	1.4E-02	⇩
	9	biomass concentration after harvesting and filtering	4.2E-04	4.2E-04	7.8E-05

Table 4 .

 4 Specific simulated scenarios for the production of Spirulina in ORP and CRP under greenhouse (389 m² of culture surface) located in the city of Nantes, France.For each scenario, the estimated average surface productivity, annual production and related annual inventories of the intermediate flows (IFs), are presented in the Supplementary Data(Table S 22 and Table S 24 for ORP and CRP respectively). Likewise, the simulated environmental indicators (ILCD Midpoint method), associated to these inventories are presented in Supplementary Data inTable S 23 and Table S 25 (for ORP and CRP respectively). Results presented here next are related to the GWP indicator.Concerning both ORP and CRP, the production scenario S2 would reach the highest impacts per kg of produced biomass on all indicators, except on the Land Use indicator (cf. TableS 23 and Table S25in Supplementary Data). The GWP impact is 28.2 and 26.4 CO2 eq./kg of biomass (dw) in ORP and CRP respectively. Scenario 2 is characterized by an important annual consumption of electrical energy (45.7 and 24.7 MWh/year for ORP and CRP respectively) exclusively related to temperature regulation (160.1 MWh/year for heat in ORP and 43 MWh/y and -43.5 MWh/y for heat and cold respectively in CRP) to ensure a minimum culture temperature (Tcmlow) of 30°C during 6 SCPM per year (AprilSeptember). In the same time, the operating conditions of Scenario 2 allow achieving the highest surface productivity for both ORP (7.4 g/m²/y and 527 kg Spirulina dw/y respectively) and CRP (6.3 g/m²/y and 450.9 kg/y respectively, cf. TableS 22 and Table S 24in Supplementary Data). These estimated productivity values are comparable to experimental data obtained on site (Algosource's production site in Asserac).For both ORP and CRP, the lowest impact values per kg of produced biomass (dw), except for the Land Use indicator (TableS 23 and Table S 25in Supplementary Data), are obtained for the scenario 5 with similar results for both types of culture modes. GWP indicators for scenario 5 were 15.5 and 18 kg CO2 eq./kg biomass dw respectively for ORP and CRP. This scenario operates for 8 SCPM and without thermal regulation. Although it provides lower annual productions than scenario 2 (378.8 and 401.4 kg/year for ORP and CRP), it also avoids all impacts related to the use of temperature regulation (i.e. thermal energy, use of heat pump and R134a and Tetrafluoroethane emissions due to leaks of R134a).

		S1	S2	S3	S4	S5
	SCPM (months)	8	6	6	8	6
	production period MarchOct	AprSept	AprSept	MarchOct	AprSept
	thermal regulation	Yes	Yes	Yes	No	No
	Tcmlow (°C)	20	30	20	-	-
	III.3 Evaluation of defined scenarios
	III.3.1 Scenarios comparison

Table 5 .

 5 Spirulina annual production and GWP per FU for 5 different cultivation scenarios in ORP and CRP under greenhouse (389 m² of culture surface) located in the city of Nantes, France.

		Culture mode	S1	S2	S3	S4	S5
	annual production	ORP	430.5 526.9 384.6 404	378.8
	(kg biomass dw)	CRP	424.6 450.9	387	427.6 401.4
	GWP	ORP	21.4	28.2	17	15.8	15.5
	(kg CO2 eq/kg biomass dw)	CRP	23.8	26.4	22.4	18.5

V. Acknowledgments

The authors wish to thank the Ademe (French Environment & Energy Management Agency) for the financial support to the Cimentalgue project. We also would like to express our gratitude to Algosource members Christophe Lombard, Jérôme Leuranguer and Arnaud Artu for providing us with the base models to estimate the Spirulina productivity and the culture temperature through energy balances under the greenhouse as well as the operational data related to the Spirulina culture. Likewise we also wish to thank Alpha Biotech members for transmitting us relevant information concerning microalgae culture infrastructures and Algosolis Research Platform for letting us visit their microalgae culture facilities and supplying us with complementary operational data. Finally, we would like to thank the French networks EcoSD and AgorACV for providing general knowledge that favours the development of such projects.


Virgil Pasquier
email: virgil.pasquier@univ-brest.fr

Jofre Sans

Pierre

Lebeau Oanez

Liorzou Celine

Rabineau Marina

Acid digestion on river influenced shelf sediment organic matter: Carbon and Nitrogen contents and isotopic ratios

Keywords: 13Corg, TOC, C/N ratio, organic matter, decarbonatation, pre-analysis treatment method

Rationale

Natural stable isotopes ratios (d13Corg and 15N vales) and associated elemental concentrations (i.e. total organic carbon and total nitrogen contents) preserved in marine sediments are frequently used for the determination of paleoenvironmental processes such as the origin of organic matter. Previous studies highlighted biases in the determination of such geochemical proxies due to pre-analysis acid treatment methods. This study is the first systematic comparison of the effect of acid treatment methods on bulk organic matter using a unique sedimentary system, under two contrasting climatic contexts (i.e. glacial vs interglacial).

Methods

We used the most common method for pre-treatment analysis, which consists of the acidification of bulk sediments followed by several de-ionised water rinses. We investigated the effect of acid type (hydrochloric acid and acetic acid), and also acid strength (from 0.2 to 10 mol L-1) on the δ13Corg, δ15N, TOC, TN and C/N values of three samples from the Gulf of Lion. Two of them (S302 and S102) were deposited during glacial maxima (i.e. high sedimentation rate, low porosity and high terrestrial inputs) whereas S157 characterizes interglacial conditions (high porosity, high foraminifera content, low sedimentation rates and low terrestrial inputs).

Results

For all three samples the δ13Corg values are between -21.7 and -24.4‰ with the TOC varying from 0.56 and 0.84 %wt/wt. The δ15N values are more stable with an average value of 3.0 ± 0.1‰ with a TN average of 0.08 ± 0.002 %wt/wt.

Introduction

Sedimentary organic matter (OM) in marine environments is typically composed of a mixture of in-situ marine and land exported particles. Autochthonous grains derive from land erosion, land plant detritus and outcropping older marine deposits which have already been degraded to various degrees. The accurate determination of past total organic carbon content (TOC), total nitrogen content (TN), carbon and nitrogen isotopic composition ( 13 C org and  15 N tot values) of OM naturally preserved in coastal marine sediments has been widely used for past environmental processes interpretation. C/N ratios are commonly used as a tracer for OM sources and alteration during weathering processes [START_REF] Tesi | Source, transport and fate of terrestrial organic carbon on the western Mediterranean Sea, Gulf of Lions, France[END_REF][START_REF] Kim | Origin and distribution of terrestrial organic matter in the NW Mediterranean (Gulf of Lions): Exploring the newly developed BIT index[END_REF][START_REF] Cathalot | Temporal variability of carbon recycling in coastal sediments influenced by rivers: assessing the impact of flood inputs in the Rhône River prodelta[END_REF][START_REF] Tesi | Early diagenesis of recently deposited organic matter: A 9-yr time-series study of a flood deposit[END_REF][START_REF] Aller | Sedimentary Diagenesis, Depositional Environments, and Benthic Fluxes[END_REF]. Combined with  13 C org values, the C/N ratio is also a powerful proxy to discriminate carbon sources and different photosynthetic pathways (e.g. C3-vs C4-photosynthesis ( 6)). Nitrogen isotope ratios and content allow determination of trophic levels but they can also be used as proxy for primary productivity and redox state of past environments [START_REF] Ader | Ocean redox structure across the Late Neoproterozoic Oxygenation Event: A nitrogen isotope perspective[END_REF][START_REF] Voss | Nitrogen isotope patterns in the oxygendeficient waters of the Eastern Tropical North Pacific Ocean[END_REF][START_REF] Möbius | Diagenetic control of nitrogen isotope ratios in Holocene sapropels and recent sediments from the Eastern Mediterranean Sea[END_REF][START_REF] Robinson | A review of nitrogen isotopic alteration in marine sediments[END_REF][START_REF] Ader | Interpretation of the nitrogen isotopic composition of Precambrian sedimentary rocks: Assumptions and perspectives[END_REF].

In most coastal sediments, the carbon fraction is commonly divided in two forms: organic carbon (OC) and inorganic carbon (IC) which is usually enriched in 13 C relative to OC because of the isotopic fractionation induced by photosynthesis [START_REF] Hayes | Factors controlling 13C contents of sedimentary organic compounds: principles and evidence[END_REF].

The accurate determination of OC content and its carbon isotopic composition involves the complete removal of the IC fraction from the bulk sediment. This is performed using acid treatment methods, which may also introduce artificial modifications of the natural OC mix preserved in old sediments.

There are several studies which have investigated the effects of acid types on specific terrestrial or aquatic products with contradictory induced effects [START_REF] Brodie | Evidence for bias in C and N concentrations and δ 13 C composition of terrestrial and aquatic organic materials due to pre-analysis acid preparation methods[END_REF][START_REF] Brodie | Evidence for bias in C/N, δ 13 C and δ 15 N values of bulk organic matter, and on environmental interpretation, from a lake sedimentary sequence by pre-analysis acid methods[END_REF][START_REF] Lorrain | Decarbonation and preservation method for the analysis of organic C and N contents and stable isotope ratios of low-carbonated suspended particulate material[END_REF][START_REF] Schubert | Effects of decarbonation treatments on δ 13 C values in marine sediments[END_REF][START_REF] Ryba | Effects of sample preparation on the measurement of organic carbon, hydrogen, nitrogen, sulfur, and oxygen concentrations in marine sediments[END_REF].

Several fundamentally different acid pre-treatment methods have been reported, employing a range of acid reagents and strengths, types of Elemental Analyser capsules (tin or aluminium) or chemistry environment (i.e. wet or fumigation chemistry). The results of these studies show that the acidification step can influence the elemental and isotopic composition of both C and N, and therefore their subsequent interpretations. Brodie and collaborators [START_REF] Brodie | Evidence for bias in C and N concentrations and δ 13 C composition of terrestrial and aquatic organic materials due to pre-analysis acid preparation methods[END_REF][START_REF] Brodie | Evidence for bias in C/N, δ 13 C and δ 15 N values of bulk organic matter, and on environmental interpretation, from a lake sedimentary sequence by pre-analysis acid methods[END_REF] have shown that nonlinear and unpredictable biases on OM characterization can be related to the acid pre-treatment. Loss or gain of C and N appear to vary according to acidification type, characteristic of the depositional environment and IC content [START_REF] Froelich | Analysis of organic carbon in marine sediments[END_REF]. Therefore, there is no consensus on the effect of such essential pre-analyses treatments, which is commonly considered as negligible or within the instrumental precision. For  13 C org values, shift down to -7‰ have been reported [START_REF] Brodie | Evidence for bias in C and N concentrations and δ 13 C composition of terrestrial and aquatic organic materials due to pre-analysis acid preparation methods[END_REF], ruling out the possibility of distinguishing between C3 and C4 photosynthetic pathways. Therefore, environmental interpretation of OM data in both modern and paleoenvironments can be highly questionable. This paper presents the first systematic comparison of the most common acid treatment, IC removal using both hydrochloric and acetic acid of a wide range of acid strength on quaternary sediments. We examine the effect of increased acid strength on the reliability of TOC, TN, C/N ratio,  13 C org and  15 N values for three samples from the Gulf of Lion (Western Mediterranean Sea).

Experimental

1.1.

Samples origin and paleo-climatic context

The studied sediment originate from the Gulf of Lion (GoL), which is characterised by a wide continental shelf (70 km), that was sub-aerially exposed during Quaternary glacial times [START_REF] Rabineau | Sedimentary sequences in the Gulf of Lion: a record of 100,000 years climatic cycles[END_REF][START_REF] Jouet | Shoreface migrations at the shelf edge and sea-level changes around the Last Glacial Maximum (Gulf of Lions, NW Mediterranean)[END_REF]. The spatial and temporal distribution of sediment in the studied area is mostly governed by sea level changes (Figure 1).

The present day spatial composition of OM in the GoL has already been studied in detail [START_REF] Tesi | Source, transport and fate of terrestrial organic carbon on the western Mediterranean Sea, Gulf of Lions, France[END_REF][START_REF] Kim | Origin and distribution of terrestrial organic matter in the NW Mediterranean (Gulf of Lions): Exploring the newly developed BIT index[END_REF][START_REF] Cathalot | Temporal variability of carbon recycling in coastal sediments influenced by rivers: assessing the impact of flood inputs in the Rhône River prodelta[END_REF] showing that origin and spatial distribution of OM are strongly related to sediments deposited at river mouths. respectively. These sediments were deposited in typical deltaic environment characterized by rapid sedimentation allowing higher preservation of labile sediment [START_REF] Pasquier | Pyrite sulfur isotopes reveal glacialinterglacial environmental changes[END_REF].

Pre-analyses protocol and acid treatment

Once the borehole has been subsampled, sediments were freeze-dried prior to any treatment. A well-known portion of dried samples (1.5 ± 0.1g) was ground in agate mortar, and transferred to pre-cleaned 15-mL centrifuge tubes. Prior to elemental and isotopic analyses, the carbonate (IC) fraction was removed by dissolution, using an excess of hydrochloric acid (HCl) or acetic acid (AA) for 24 hours at room temperature. In detail, the homogenized sediments were split into numerous aliquots of 30 +/-0.5 mg. Seventeen different leaching procedures were employed, using HCl or AA at different acid strength: 0.2, 0.5, 1, 1.5, 2, 2.5, 3, 4, 5, 6, 7, 8, 9, 10 mol L -1 , 10 mol L -1 (and 2h at 80°C), 0.87 and 1.74 mol L -1 AA, corresponding to 5% and 10% AA, respectively. During digestion, centrifuge tubes were placed in an ultrasonic bath for 3h at room temperature to increase the mechanical separation of clay and calcium carbonates. After digestion, the residues were washed three times with distilled water, centrifuged and dried at 50°C overnight. The samples were then weighed in tin capsules and kept in a desiccator.

C and N elemental and isotopic analysis

All analyses were performed using an elemental analyser (EA, Flash 2000 -Thermo Scientific, Bremen, Germany) coupled to a Delta V+ isotope ratio mass spectrometer (Thermo Scientific) at the Pôle de Spectométrie Océan (PSO, Brest, France). Samples were first loaded into an autosampler then dropped automatically into the oxidation furnace maintained at 1020°C. The flash combustion was performed using an 8 second injection time of dioxygen at a flux of 240mL/min. The oxidation reactor was filled with chromium(III) oxide and silver-plated cobalt oxide to ensure the complete oxidation of the flash combustion products. The resulting gas was then transferred, by continuous helium flow (100 mL/min), to a reducing column filled with activated copper, heated at 650°C. This reducing step allow both the removal of excess oxygen and the reduction of NO x to N 2 . Water vapour was removed from the gas stream by an anhydrous Mg(ClO 4 ) 2 trap, and the remaining gas phases were separated by gas chromatography and introduced into the mass spectrometer via a Conflo IV interface (Thermo Scientific).

Carbon and nitrogen isotopes ratios were obtained against reference standards SED-IVA (IVA reference number: 33802151) and in-house standards (Acetanilide: Routine replicate measurements had internal deviations of less than 4% and 5%, respectively, for TN and TOC.

Results

Organic Carbon: content and isotopic results

For all samples (i.e. including all the experiments), the TOC is between 0.56 and 0.84 %wt/wt and the isotope ratios between -21.7 and -24.4‰. These values are in the classic range of organic-poor sediments deposited in coastal marine environments [START_REF] Pasquier | Pyrite sulfur isotopes reveal glacialinterglacial environmental changes[END_REF] .

The Inter Glacial sample, S157, has an average  13 C org value of -22.04 ± 0.13‰ with a TOC of 0.79 ± 0.03% wt/wt. No trend nor deviation was observed with the use of different acid treatments.

The Glacial samples, S304 and S102, show average  13 C org values of -24.62 ± 0.31‰ and -24.31 ± 0.33‰, respectively, and TOC of 0.63 ± 0.07% and 0.63 ± 0.04%, respectively. An effect of acid strength on the TOC values is clearly visible between 0.2 and 1.5 mol L -1 HCl acid treatment, where we can observe a decreasing trend from values around 0.8% down to 0.6% (Figure 2A). This loss of 0.2% of the TOC is accompanied by an increase of 1.5‰ in the  13 C org values (from -25.6‰ to -24.1‰), explaining the slightly higher standard deviation obtained for these two samples (Figure 2B). Above 1.5 mol L -1 HCl acid concentration, neither OC content nor isotopic ratios present any trends. Interestingly, the TOC and isotopic ratios are not affected by the type of acid used for the decarbonation step (i.e. HCl or AA): the AA values (both TOC and isotopic ratios) are similar to those obtained from HCl treatment above 1.5 mol L -1 for these two samples.

The interglacial sediment sample, S157, thus shows higher TOC and higher  13 C org values than the two glacial samples, S305 and S102 (0.8 vs 0.6% on average, and -22.0‰ vs -24.5‰ on average).

Total nitrogen content and isotopic ratio

The mean TN value for the three samples with the different decarbonatation methods is 0.08 ± 0.002%wt/wt (Table 1). The TN appears stable and low whatever the climatic context. The  15 N tot values obtained during this study show homogeneous values with a global average of 3.0 ± 0.1‰, with no significant influence of the acid strength on the isotopic ratio (Figures 2D and2E). Therefore, the TN and the isotopic ratio do not appear to be sensitive to the type of acid used.

C/N ratios

The C/N ratios obtained on interglacial samples show an average value of 10.0 ± 0.3, which is typical of marine particulate OM (a broad term used for phytoplankton and zooplankton OM; [START_REF] Lamb | A review of coastal palaeoclimate and relative sea-level reconstructions using δ13C and C/N ratios in organic material[END_REF] and references therein).

The C/N ratios obtained on glacial samples (i.e. S304 and S102) show an average value of 8.0 ± 0.8. The high variability observed, up to 30% in the two glacial samples, is due a decrease of the C/N ratios with increasing acid strength (from 10.5 to 7.5 for S304 and from 9.5 to 7 for S102, Figure 2C). For acid strength over 1.5 mol L -1 HCl, the C/N ratios do not show significant variations.

Discussion

Decarbonatation is a necessary step to remove all carbonate (IC) phases from samples before measuring the content and isotopic composition of the OC pool in sediments. In agreement with the data obtained in this study, no clear effect of the acid strength is observed on TN and isotopic composition, (Figures 2D and2E). This is surprising in the sense that nitrogen is usually considered as affected by acid treatment during the removal of labile proteins. Our results suggest that the pool of leachable organic matter does not contain so much nitrogen, perhaps because sediments have already experienced a first diagenetic effect that mineralised most of the N-rich labile proteins. In the light of these results for the nitrogen content and isotopic ratios, no further consideration on nitrogen will be addressed in this part of the Discussion. Below we discuss first the Glacial-Interglacial discrepancies of the sediment, which should have led to different acid attack response, then the presence of a leachable pool of Organic Carbon.

Glacial vs Interglacial samples geochemical response

A clear distinction between glacial (i.e. S102 and S304) and interglacial (i.e. Lower sea level during glacial times induced a seaward migration of the coast that brought the borehole location closer to the source of terrestrial materials (i.e. river mouths). This proximal environment is characterized by greater inputs of fresh terrestrial OM and deposition of low porosity silty-clay material. A higher proportion of fresh terrestrial OM is buried into the sediment with limited degradation by oxic respiration [START_REF] Pasquier | Pyrite sulfur isotopes reveal glacialinterglacial environmental changes[END_REF][START_REF] Sierro | Phase relationship between sea level and abrupt climate change[END_REF].

S157) samples can be observed in

In contrast, interglacial times are associated with higher temperature, higher sea level and a disconnection of coastal river from our site location. This is supported by an increase of  13 C org values, indicative of higher marine inputs.

Coastal rivers disconnection and high sea level are typically accompanied by lower sedimentation rates together with an increase of the porosity due to high foraminifera abundance. We propose that these changes increase oxygen diffusion depth into the sediment, which favoured aerobic respiration of the OM. Consequently, a lower proportion of reactive OM remains within the sediment.

We thus propose that glacial samples S102 and S304 may have preserved a higher proportion of OM including an acid leachable pool. This preservation may be linked to both physical sediment properties and sedimentation rates, which influenced early diagenesis processes. We highlight here that different samples with distinct pools of OM react differently to the acid attack.

A weak HCl acid attack resistant pool of Carbon

For glacial samples S304 and S102, the negative covariation observed between TOC and  13 C org values strongly suggests a loss of carbon with a different isotopic ratio. The effect of the exposure to concentrated or diluted HCl on the TOC is clearly visible in Figures 2A and2b, where the TOC content shifted from 0.8 to 0.6%. Strong acids (above 1,5 mol L -1 HCl) allow the removal of at least 25% of the OM compared with our lowest acid treatment (0.2 mol L -1 ). It appears that the loss is greater as the acid strength increase.

One hypothesis would be that the carbonate phases were not completely dissolved or that another resistant carbonate phase was present as proposed by [START_REF] Brodie | Evidence for bias in C and N concentrations and δ 13 C composition of terrestrial and aquatic organic materials due to pre-analysis acid preparation methods[END_REF] or [START_REF] Kolasinski | Effects of acidification on carbon and nitrogen stable isotopes of benthic macrofauna from a tropical coral reef[END_REF]. Indeed, it has been shown that some magnesium or iron carbonates, such dolomite or siderite, can resist acid treatment at 25°C for hours [START_REF] Lebeau | Organic matter removal for the analysis of carbon and oxygen isotope compositions of siderite[END_REF]. Interestingly, this decrease in OM content is concomitant with an increase in  13 C org values, (Figures 2A and2B). This suggest that the 25% pool of OM removed by strong acid is enriched in 12 C with a  13 C value lower than -25‰ (Figure 2B). Such a pool with a low  13 C value is incompatible with the remaining carbonate phase [START_REF] Sierro | Phase relationship between sea level and abrupt climate change[END_REF][START_REF] Cortina | Impact of climate and sea level changes on the ventilation of intermediate water and benthic foraminifer assemblages in the Gulf of Lions, off South France, during MIS 6 and 7[END_REF].

Moreover, the loss of isotopically light organic carbon seems proportional to the strength of HCl between 0.2 to 1.5 mol L -1 . Above 1.5 mol L -1 , this leaching of isotopically light OC seems complete and both isotopic and content values are stable.

An isotopically light terrestrial organic matter leachable pool at

HCl strength above 1.5N

In the GoL, the glacial sediments are mostly composed of homogenous clastic material with OM spots mainly exported from fresh and labile land OM, such as wood and leaf fragments, with carbon isotopic ratios ranging from -32‰ to -21‰ [START_REF] Khan | Stable carbon isotope and C/N geochemistry of coastal wetland sediments as a sea-level indicator[END_REF].

Therefore, the loss of terrestrial labile fine organic materials during increased HCl strength treatment could bias the natural sedimentary mixing and explain the observed dataset.

In the GoL watershed and prodeltaic sedimentary accumulation, the TOC content and the  13 C org of bulk OM from river beds have revealed values around 1.5% and -27 ± 1‰, respectively (1,2). To test our assumption of preferential leaching of terrestrial fresh OM, we used a linear mixing extrapolation to confirm the removal of terrestrial OM (Figure 3). Effectively, in a  13 C org vs 1/TOC plot (Figure 3)

we can see that the obtained data are aligned between (1) a terrestrial OC from the catchment area and (2) a stable mix between marine primary production (and subsequent derived-products) and detrital OM.

First, this trend confirms the main terrestrial origin of our samples.

Furthermore, a close examination of this Figure allows us to observe the influence of acid strength on the proportion of terrestrial organic carbon preserved in our glacial samples. These results indicate that a small but isotopically important proportion of the terrestrial organic carbon is preferentially lost during decarbonatation. In landderived sediments the OM is composed of a vast pool of organic molecules that can be resistant to weak acid conditions. For example, in soils where the pH can decrease strongly, humic acid has been shown to be a significant component of the OC pool, which moreover has low  13 C org values [START_REF] Tesi | Source, transport and fate of terrestrial organic carbon on the western Mediterranean Sea, Gulf of Lions, France[END_REF][START_REF] Hood | Sources of dissolved organic matter (DOM) in a Rocky Mountain stream using chemical fractionation and stable isotopes[END_REF][START_REF] Giani | Humic acids contribution to sedimentary organic matter on a shallow continental shelf (northern Adriatic Sea)[END_REF]. In such a context, we suggest that there is preferential leaching of the organic acid compounds during the decarbonatation step. Nevertheless, a careful investigation of the effect of the acid strength on the organic acid fraction is necessary to confirm this hypothesis.

3.4.

Impact of acid treatment on C/N ratios and interpretation.

C/N ratios are indicators for OM source; when combined with  13 C org values in a cross plot, they are commonly used to distinguish terrestrial (C3 vs C4 land plants)

and aquatic (marine vs fresh water) sources [START_REF] Lamb | A review of coastal palaeoclimate and relative sea-level reconstructions using δ13C and C/N ratios in organic material[END_REF][START_REF] Meyers | Organic geochemical proxies of paleoceanographic, paleolimnologic, and paleoclimatic processes[END_REF]. Figure 4 shows a classical biplot used for paleo environment reconstruction. It clearly indicates procedural bias on the data that is equal in magnitude to the difference between terrestrial and marine environment. We note that the bias in the C/N ratio is concomitant with the isotopic offset reported in this study, highlighting the clear leaching of labile terrestrial OM preserved in coastal marine sediments.

Summary and conclusions

We evaluated the effect of acid strength on recent geological sediment samples from a coastal environment by comparing different acid treatment methods (i.e. HCl and AA) during the decarbonatation step prior to TOC, TN,  15 N and  13 C org measurements. We summarize the main findings in the following points:

1/ We identified that the TOC, TN,  15 N and  13 C org values do not show significant variability when using strong (>1.5 mol L -1 ) HCl or AA treatment.

2/ Weak HCl acid treatment (<1.5 mol L -1 ) in the Glacial samples shows systematically higher TOC together with lower  13 C org values. Strong HCl acid and acetic acid treatments remove a distinct pool of isotopically light OM, which correspond to a 25% loss of the TOC.

3/ This effect depends on the lithology and is only seen in samples containing high terrestrial source organic matter. It does not affect the nitrogen content nor the nitrogen isotopic ratio.

4/ Variations observed in the TOC,  13 C org values and C/N ratio after the different acid pre-treatment protocols are sufficient to affect any interpretations of organic carbon sources (Figure 4).

These results call for more precautions when using classic acid treatment for sediments. It is highly probable that this pool, which is leached by acetic acid and hydrochloric acid up to 1.5 mol L -1 , is naturally removed during late diagenesis in old sediments. This mean that for old rocks the main problem would be to remove the IC fraction entirely and thus strong acid treatment is recommended. However, for modern sediments the situation is different.

CH 3

 3 CONHC 6 H 5 reference number: 274462 provided by Thermo Fisher, CAP: leaf litter and LIPG: yeast from the Institut de Physique du Globe de Paris, Paris, France). The in-house standards were calibrated against certified materials, such as IAEA-N1, IAEA-N2, IAEA-CH6 and IAEA-600 provided by the International Atomic Energy Agency (IAEA, Vienna, Austria). The  13 C org and  15 N values are given as the per mil deviation from the PDB standard and atmospheric N 2 , respectively. Routine replicate measurements on standards had internal deviations of ± 0.15‰ and ± 0.30‰ (2), respectively, for nitrogen and carbon. The TN and TOC percentages were measured using the Thermal Conductivity Detector (TCD) of the Flash 2000.

Figure 2 .

 2 Indeed, the carbon isotopic composition and TOC of the interglacial sample do not seem to be affected by the pre-treatment acidification, whereas glacial samples show a clear change in response to the acid strength between 0.2 and 1.5 mol L -1 . This different geochemical response may reflect different properties of the sediment, including different sources and reactivities of the sedimentary OM towards acid treatment. Differences in the physical sediment properties between glacial and interglacial sediments have been highlighted in previous studies of PRGL_1-4 (22). In the GoL shelf, glacial interglacial variations are characterized by substantial glacio-eustatic movements inducing migration of the coastline, and associated rivers (i.e. Rhône and Pyrenean rivers).

Figure 1 :

 1 Figure 1: Map of the Gulf of Lion with the position of the PRGL1-4 core (42.690N; 3.838E). The bold grey line highlights the present shoreline position and the contours reflect modern water depths. The bold blue line corresponds to the shoreline position during the last-glacial period (low sea level).

Figure 2 :

 2 Figure 2: a. Percentage of total organic carbon (i.e.% TOC wt/wt); b. Organic carbon isotopic (i.e. 13 C org ); c. C/N ratio of preserved organic matter; d. Nitrogen isotopic values; e. Percentage of total nitrogen (i.e. %TN wt/wt). Symbol colours indicate various climatic context: red diamonds for interglacial times related to sample S.157 and light and dark blue symbols show glacial samples, S.304 and S.102, respectively. Data are presented as function of the acid type and strength used during the decarbonation step. Error bars obtained on isotopic measurements

Figure 3 :

 3 Figure 3: Linear mixing model showing a parental source between terrestrial derived riverbed / prodelta and the glacial samples of this study (blues symbols). Red diamonds correspond to interglacial samples S.157; light and dark blue symbols show data obtained on glacial samples S. 304 and S. 102, respectively. Error bars are smaller than the dots size. Pro-delta values from (1); River bed data from (2).

Figure 4 :

 4 Figure 4:  13 C org vs C/N ratio currently used to determine the OM provenance. Error bars are smaller than the dots size. Typical  13 C org and C/N ranges for organic inputs to coastal environments are compiled from (23) and references therein. Pro-delta values from (1) (light brown dot); River bed data from (2) (dark brown dot). Figure modified after (23).

  or interglacial), GoL sediments are significantly rich in carbonates (> 40% wt/wt), and mainly composed of silty-clay sediments, which are poor in organic compounds (0.6 % and 0.08 % wt/wt for Total Organic Carbon (TOC) and Total Nitrogen (TN), respectively, n= 382 analyses; 22, Pasquier et al, in preparation).

	According to the age model (22) and the paleoenvironment reconstructions, the
	present study is based on 3 samples originating from two contrasted climatic and
	sedimentological settings:
	-Sample S.157 is a typical interglacial sediment deposited under high sea
	level context, during Marine Isotope Stage 7.d (~224 ka). Sediments
	deposited under such context are associated with low sedimentation rate
	due to the landward migration of coastal rivers (i.e. Rhône and Pyrenean
	rivers) and increased foraminiferal abundance, resulting in higher
	porosity.
	-Samples S.102 and S.304 correspond to low sea-level during the
	Penultimate glacial maximum MIS 6.2 (~138 ka) and MIS 12.4 (~468 ka)
	sediments can be interpreted as reflecting the relative proportion of terrestrial versus
	marine OC (i.e. seaward dilution; Pasquier et al, in preparation).
	The samples come from the PRGL 1-4 borehole (42.690N; 3.838E), a 300m
	continuous record drilled on the upper slope (298 meters water depth) of the GoL, on

These pro-deltaic sediments are characterized by low  13 C org values (around -25‰) and high sedimentation rates (i.e. 20 cm.years - 1 , (21)), contrasting with open ocean sedimentation (i.e. very low sedimentation rate, below 0.3 m.ka -1 , (26)) characterized by higher  13 C org values around -21‰ at the shelf break (originating from primary producers and derived outcomes). Depending on the shelf distribution, organic carbon isotope ratios ( 13 C org values) preserved in the interfluve between the Bourcart and Herault canyons. Borehole PRGL 1-4 was drilled in the framework of the European Union project PROMESS1 (http://www.pangaea.de/Projects/PROMESS1/). Whatever the climatic context (i.e.

glacial

Table 1 :

 1 The presence of such a pool could represent a major problem for paleo environment interpretation. The weak acid treatment commonly used for modern sediments (23 and references within) may induce a large bias. Variations in 13 C org values measured on organic matter that experienced such acid treatment could represent different proportions of this weakacid leachable organic pool over the total OM reservoir. It is thus essential to test different acid treatments on the main lithologies before any systematic down core study. Nitrogen stable isotopes ( 15 N) Organic carbon stable isotope ( 13 C org ); concentration of total nitrogen (TN); concentration of total organic carbon (TOC), and atomic C/N ratio reported against different acid type and concentration used during the pre-analyses treatment of three different layers of PRGL_1-4 borehole.According to the age model published in[START_REF] Pasquier | Pyrite sulfur isotopes reveal glacialinterglacial environmental changes[END_REF], S.304 and S.102 are glacial samples whereas S.157 corresponds to interglacial conditions.

	Sample	Acid Strengh	15Ntot	13Corg	TN	TOC	C/N
			(‰)	(‰)	(%)	(%)
		0.2N HCl	3.5	-25.2	0.08	0.79	10.40
		0.5N HCl	3.7	-25.1	0.08	0.75	9.81
		1N HCl	3.2	-24.8	0.07	0.64	8.97
		1.5N HCl	3.4	-25.0	.08	0.67	8.85
		2N HCl	3.4	-24.8	0.07	0.62	8.39
	S.304	2.5N HCl	3.4	-24.3	0.07	0.58	7.84
	≂468 ka	3N HCl	3.4	-24.3	0.07	0.60	8.11
		4N HCl	3.1	-24.3	0.07	0.56	7.83
		5N HCl	3.5	-24.4	0.08	0.60	8.01
		6N HCl	3.2	-24.5	0.07	0.58	7.87
		7N HCl	3.3	-24.4	0.07	0.59	8.04
		8N HCl	3.1	-24.5	0.07	0.57	8.03
		9N HCl	3.2	-24.4	0.07	0.58	8.02
		10N HCl	3.3	-24.6	0.07	0.61	8.21
		10N HCl
		+80°C	3.2	-24.7	0.08	0.72	9.45
		0.2N HCl	3.2	-25.4	0.08	0.76	9.21
		0.5N HCl	3.	-24.8	0.09	0.69	7.96
		1N HCl	3.1	-24.3	0.08	0.63	7.70
		1.5N HCl	2.9	-24.2	0.08	0.63	7.64
		2N HCl	2.9	-24.4	0.08	0.62	7.55
		2.5N HCl	3.0	-24.3	0.08	0.61	7.58
	S.102	3N HCl	3.1	-24.2	0.08	0.62	7.37
	≂138 ka	4N HCl	3.0	-24.0	0.08	0.62	7.32
		5N HCl	3.1	-24.4	0.09	0.61	7.03
		6N HCl	3.0	-24.1	0.08	0.6	7.10
		7N HCl	3.1	-24.3	0.08	0.58	6.88
		8N HCl	3.0	-24.2	0.0	0.61	7.14
		9N HCl	3.1	-24.2	0.08	0.59	7.02
		10N HCl	3.2	-24.1	0.09	0.0	7.05
		10N HCl
		+80°C	2.7	-24.1	0.08	0.66	8.23
		5%AA	3.0	-24.3	0.08	0.65	7.82

Acknowledgements

This work was supported by the "Laboratoire d'Excellence" LabexMER (ANR-10-LABX-19) and co-funded by a grant from the French government under the program "Investissements d'Avenir", and by a grant from the Regional Council of Brittany. Additional founding came from Actions Marges Program (Mediterranean Sea). The drilling operation was conducted within the European Commission Project PROMESS (contract EVR1-CT-2002-40024). The European Promess Scientific committee and colleagues at Ifremer are also thanked for previous contributions of data acquisition, processing, interpretations and permitting to re-sample the borehole.