Thibaut Triquet
email: thibaut.triquet@univ-tlse3.fr

Claire Tendero
email: claire.tendero@toulouse-inp.fr

Laure Latapie
email: laure.latapie@univ-tlse3.fr

Romain Richard
email: romain.richard@iut-tlse3.fr

Caroline Andriantsiferana
email: caroline.andriantsiferana@iut-tlse3.fr

The Use of Composite TiO2/Activated Carbon Fibers as a Photocatalyst in a Sequential Adsorption/Photocatalysis Process for the Elimination of Ciprofloxacin

Keywords: Activated carbon fiber, photocatalysis, adsorption, ciprofloxacin, hybrid process, MOCVD, TiO2

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Even if water is in abundance on Earth, preserving its purity in the natural environment is crucial for the future. Over the last few decades, many toxic molecules named micropollutants or endocrine disruptors were detected in the rivers and underground waters, with serious consequences for the environment [START_REF] Thuy | Antibiotic contaminants in coastal wetlands from Vietnamese shrimp farming[END_REF][START_REF] Tong | Disposal practices for unused medications in New Zealand community pharmacies[END_REF][START_REF] Lage | Ciprofloxacin degradation by first-, second-, and third-generation manganese porphyrins[END_REF]. Their occurrence in natural water sources is constantly increasing on a scale ranging from ng.L -1 to µg.L -1 due to anthropogenic activities [START_REF] Krzeminski | Performance of secondary wastewater treatment methods for the removal of contaminants of emerging concern implicated in crop uptake and antibiotic resistance spread: A review[END_REF]. Therefore, global and European legal limits have been lowered to reduce the environmental impacts of these micropollutants [START_REF]Energy, Sustainable Development and the Sea[END_REF]. Conventional wastewater treatments (i.e., filtration, settling, biological processes, etc.) cannot totally eliminate these molecules, and additional treatments are therefore necessary. These techniques include advanced oxidation processes (AOP), separation processes (adsorption, stripping...), and biological processes (membrane bioreactor [START_REF] González-Hernández | Improved integrated dynamic model for the simulation of submerged membrane bioreactors for urban and hospital wastewater treatment[END_REF]). Among all of these techniques, two processes seem to be adequate for micropollutants removal: adsorption and AOP processes. Adsorption processes are some of the most efficient and economic processes [START_REF] Jiuhui | Research progress of novel adsorption processes in water purification: A review[END_REF], and removal rates close to 100% can be achieved [START_REF] Gupta | Environmental water: Advances in treatment, remediation and recycling[END_REF][START_REF] Khan | Occurrence, sources and conventional treatment techniques for various antibiotics present in hospital wastewaters: A critical review[END_REF]. The most common adsorbent is activated carbon which can be found in three different forms (powder, granular, or fiber). Activated carbon is the most widely used adsorbent in the field of wastewater treatment because of its very good cost/efficiency ratio and its excellent adsorption capacity directly linked to its large specific surface [START_REF] Cárdenas-López | Design of an adsorbent employing activated carbon fiber to remove lead[END_REF][START_REF] Pignon | Coupling ultrafiltration and adsorption onto activated carbon cloth: Application to the treatment of highly coloured wastewaters[END_REF]. Activated carbon fibers (ACF), due to their small size (between 10 and 40 µm of diameter), have a much better pore volume/surface area ratio than other adsorbents [START_REF] Chen | Activated carbon fiber and textiles[END_REF]. In addition, these fibers are mostly microporous materials. As the size of the micropollutants is generally small (0.1 -3 nm), the use of this type of adsorbent allows fast kinetics since the internal transfer is more easily achieved (adsorbate size < pore size) [START_REF] Hassan | Recent trends in activated carbon fibers production from various precursors and applications-a comparative review[END_REF]. ACFs have proven to be effective for several micropollutants such as methylene blue [START_REF] Wang | Activated carbon fiber derived from the seed hair fibers of Metaplexis japonica: Novel efficient adsorbent for methylene blue[END_REF], bisphenol [START_REF] Li | Adsorption of ciprofloxacin, bisphenol and 2chlorophenol on electrospun carbon nanofibers: In comparison with powder activated carbon[END_REF][START_REF] Srivastava | Effective elimination of endocrine disrupting bisphenol A and S from drinking water using phenolic resin-based activated carbon fiber: Adsorption, thermodynamic and kinetic studies[END_REF], diclofenac [START_REF] Zhao | Simultaneous scavenging of persistent pharmaceuticals with different charges by activated carbon fiber from aqueous environments[END_REF], and ciprofloxacin [START_REF] Wang | Enhanced adsorption of ionizable antibiotics on activated carbon fiber under electrochemical assistance in continuous-flow modes[END_REF]. The main drawback of the adsorption process is that the molecules are not degraded. Thus, they remain fixed on the adsorbent, and it is, therefore, necessary to regularly change or regenerate the adsorbent in situ (often a very difficult and sometimes almost impossible operation). Advanced oxidation processes (AOP) include many different processes: Fenton reactions, anodic oxidation, oxidation [START_REF] Whyte | Influence of environmental parameters on the photocatalytic oxidation efficiency of acrylonitrile and isoflurane; two operating room pollutants[END_REF], electro-Fenton, photocatalysis [START_REF] Paul | Photolytic and photocatalytic decomposition of aqueous ciprofloxacin: Transformation products and residual antibacterial activity[END_REF][START_REF] Rico-Santacruz | Alveolar TiO2-β-SiC photocatalytic composite foams with tunable properties for water treatment[END_REF], corona discharge [START_REF] Ajo | Hospital wastewater treatment with pilot-scale pulsed corona discharge for removal of pharmaceutical residues[END_REF], catalytic, or UV, or H2O2 coupled with ozonation [START_REF] Agustina | A review of synergistic effect of photocatalysis and ozonation on wastewater treatment[END_REF], sonochemical reactions [START_REF] Labrada | Optimization of ciprofloxacin degradation in wastewater by homogeneous sono-Fenton process at high frequency[END_REF] etc. AOPs have been extensively studied, and their excellent ability to remove and degrade micropollutants has been demonstrated by many authors [START_REF] Krishnan | Removal of emerging micropollutants originating from pharmaceuticals and personal care products (PPCPs) in water and wastewater by advanced oxidation processes: A review[END_REF]. For heterogeneous photocatalysis, the catalyst is a solid semiconductor activated by light radiation. The photocatalyst absorbs light to create free electrons (e -) and holes (h + ), allowing the production of extremely reactive hydroxyl radicals (OH • ) [START_REF] Matthews | Photo-oxidation of organic material in aqueous suspensions of titanium dioxide[END_REF]. Photocatalysis has proven its efficiency for the elimination of many different micropollutants such as, for example, ciprofloxacin [START_REF] Li | Transformation products formation of ciprofloxacin in UVA/LED and UVA/LED/TiO2 systems: Impact of natural organic matter characteristics[END_REF], tylosin [START_REF] Pereira | Integration of nanofiltration, UV photolysis, and advanced oxidation processes for the removal of hormones from surface water sources[END_REF], and triazole [START_REF] Lhomme | Photocatalytic degradation of a triazole pesticide, cyproconazole, in water[END_REF]. Titanium dioxide (TiO2) is the most widely used photocatalyst. However, usually available in powder form, TiO2 must be removed at the end of the process by an expensive separation step. Moreover, there is a suspected risk of chronic intestinal inflammation and carcinogenesis from TiO2 use due to the size of the particles (mainly nanoparticles) [START_REF] Bettini | Food-grade TiO2 impairs intestinal and systemic immune homeostasis, initiates preneoplastic lesions and promotes aberrant crypt development in the rat colon[END_REF]. Consequently, most authors propose the use of a TiO2 coating on multiple materials: glass [START_REF] Dulian | Photocatalytic methylene blue degradation on multilayer transparent TiO2 coatings[END_REF], metal [START_REF] Schiemann | Synthesis of high surface area TiO2 coatings on stainless steel by electrophoretic deposition[END_REF], adsorbent [START_REF] Andriantsiferana | Photocatalytic degradation of an azo-dye on TiO2/activated carbon composite material[END_REF][START_REF] Alalm | Enhancement of photocatalytic activity of TiO2 by immobilization on activated carbon for degradation of pharmaceuticals[END_REF], membrane [START_REF] Tran | Study of permeate flux behavior during photo-filtration using photocatalytic composite membranes[END_REF], etc. When the catalyst is deposited on an adsorbent, the objective is to combine adsorption and an oxidation technique to degrade the adsorbed molecules and thus regenerate the adsorbent [START_REF] Ribeiro | Activatedcarbon/TiO2 composites preparation: An original grafting by milling approach for solar water treatment applications[END_REF]. Therefore, the drawbacks of the two processes (separation step for photocatalysis and saturation of the adsorbent for adsorption) can be overcome. These hybrid processes can be implemented either sequentially or simultaneously. Supported TiO2 has already been studied and showed excellent results [START_REF] Triquet | TiO2 MOCVD coating for photocatalytic degradation of ciprofloxacin using 365 nm UV LEDs-kinetics and mechanisms[END_REF]. However, ACF/photocatalyst composite materials are little studied in the field of wastewater treatment [START_REF] Foo | Decontamination of textile wastewater via TiO2/activated carbon composite materials[END_REF]. In this category, different types of fibers can be found, such as commercial fabrics [START_REF] Chen | Activated carbon fiber and textiles[END_REF][START_REF] Tsai | Adsorption characteristics of acetone, chloroform and acetonitrile on sludge-derived adsorbent, commercial granular activated carbon and activated carbon fibers[END_REF][START_REF] Yue | Activated carbon fibers from meltblown isotropic pitch fiber webs for vapor phase adsorption of volatile organic compounds[END_REF], fibers made from natural resources such as coconut [START_REF] Lopes | Ag/TiO2 photocatalyst immobilized onto modified natural fibers for photodegradation of anthracene[END_REF], carbon nanotubes [START_REF] Martínez | Aqueous degradation of diclofenac by heterogeneous photocatalysis using nanostructured materials[END_REF][START_REF] Barakat | Adsorption and photocatalytic scavenging of 2-chlorophenol using carbon nitride-titania nanotubes based nanocomposite: Experimental data, kinetics and mechanism[END_REF], and graphene-like materials [START_REF] Awfa | Photodegradation of pharmaceuticals and personal care products in water treatment using carbonaceous-TiO2 composites: A critical review of recent literature[END_REF]. For commercial activated carbon fibers, studies are often focused on the materials (i.e., on the synthesis or deposition of the coating and its characterization). In general, these studies follow the evolution of the concentration in the liquid over time for simultaneous or sequential implementation [START_REF] Wang | Hydrothermal synthesis and photocatalytic activity of combination of flowerlike TiO2 and activated carbon fibers[END_REF]; most report the reuse of the material over several cycles [START_REF] Yuan | Photocatalytic degradation of methylene blue by a combination of TiO2 and activated carbon fibers[END_REF][START_REF] Bai | Firmly-supported porous fabric fiber photocatalysts: TiO2/porous carbon fiber cloth composites and their photocatalytic activity[END_REF]. In general, the conclusion is that the catalytic material is highly efficient since most authors report elimination rates higher than 95% [START_REF] Alam | Photocatalytic oxidation of glyphosate and reduction of Cr (VI) in water over ACF-supported CoNiWO4-gCN composite under batch and flow conditions[END_REF][START_REF] Jiang | Hydrothermal deposition of CoFe2O4 nanoparticles on activated carbon fibers promotes atrazine removal via physical adsorption and photo-Fenton degradation[END_REF]. However, the performance may decrease after several reuses. Few authors are interested in the transformation products (TP) formed and the monitoring of their concentrations over time, and most authors choose global measures such as total organic carbon (TOC), chemical oxygen demand (COD), or biological oxygen demand (BOD).

For this study, the antibiotic Ciprofloxacin has been selected as the target molecule. This fluoroquinolone is representative of the recalcitrant micropollutants found in municipal wastewater treatment plants. A sequential process has been selected combining adsorption and photocatalysis. To carry out this coupling process, a hybrid material was synthesized: a TiO2 coating deposited on commercial activated carbon fibers using the Metal Organic Chemical Vapor Deposition (MOCVD) technique. Activated carbon fibers were chosen to concentrate as much as possible the target molecules close to the TiO2 coating and to be able to degrade them afterward by photocatalysis. To evaluate the performances of this process, the liquid phase was completely analyzed by following the CIP concentration, the production and the elimination of transformation products, the concentration of fluoride, and TOC. The objective of this study was to answer the following questions: does this kind of treatment provide better performance than a single adsorption process? Does this process limit the amount of TPs formed? Does it regenerate the catalytic material in situ?

The novelty of this work is to (i) follow all the molecules that are produced and degraded during an adsorption/photocatalysis coupling and (ii) highlight and clearly demonstrate the potential photocatalytic activity of the composite material. Finally, the durability of TiO2 coating is also investigated to make sure that it remains on the ACF fibers and is not dispersed in the treated water.

Materials and Methods

Chemicals

The target molecule for this study was ciprofloxacin, available at high analytical grade (CIP, 98% purity). All of its physico-chemical properties are presented in Table 1. Titanium tetraisopropoxide (TTIP, 99.999%) was used as the source of titanium for the coating of the ACF. Formic acid, CH2O2 (formic acid, >99% purity), and acetonitrile (suitable for HPLC, gradient grade, ≥99.9%) were used for liquid analysis. Each chemical product was purchased from Sigma Aldrich.

Table 1 Chemical and physical properties of CIP.

Activated Carbon Fibers

The Activated Carbon Fibers (ACFs) product used for this study (ACF10) was supplied by KYNOL (Osaka, Japan). The different commercial characteristics of ACF10 are listed in Table 2.

Deposition of TiO2 Coating on ACF

Metal Organic Chemical Vapor Deposition (MOCVD) was used to synthetized the deposit. A horizontal tubular hot-wall reactor (heated at 500 °C ) was used to grow the TiO2 coating [START_REF] Sarantopoulos | Photocatalyseurs à base de TiO2 préparés par infiltration chimique en phase vapeur (CVI) sur supports microfibreux[END_REF] with titanium tetraisopropoxide (TTIP, 99.999%, Sigma-Aldrich) as the precursor. This titanium source was thermoregulated in a bubbler at 50 °C and carried to the deposition zone under 99.9992% of pure nitrogen flow, which was used as both the carrier gas (25 cm 3 .min -1 ) and the dilution gas (500 cm 3 .min -1 ). The deposition was performed on each side of the ACF. To minimize the gradient of the coating thickness along the substrate, the deposit on each side was made twice, with 180° rotation of the ACF between the two steps. Each material was weighed before and after treatment to estimate the mass of TiO2 coated on ACF.

Experimental Set-up

The experimental setup is shown in Figure 1. The batch process is carried out in a photo-reactor of 12 mL and a stirred storage tank of 100 mL. The circulation of the liquid is ensured by a peristaltic pump operating at a constant flow rate (200 mL.min -1 ). The liquid is pumped from the storage tank through the photo-reactor equipped with a glass Pyrex® window (130 × 10 × 12 mm). The substrate is placed at the bottom of this reactor, and a monochromatic LED panel (365 nm) irradiates its upper face. The temperature inside the system was maintained at 25 °C (±1 °C ) thanks to an aeration system.

Experimental Protocols

Firstly, adsorption kinetics were measured without any TiO2 coating on the surface, using the system shown in Figure 1. The size chosen for the ACF10 was equivalent to one-third of the total irradiated surface of the Pyrex®-glass window to have a compromise between a sufficient amount of adsorbent and a good-sized area of irradiated surface. The ACF10 piece, corresponding to 193 mg of ACF, was placed at the bottom of the photoreactor. The storage tank was filled with a 100 mL solution at 20 mg.L -1 of CIP, and then the peristaltic pump was started. Samples were taken from the storage tank over a period of 72 h. Each liquid sample was filtered through a 0.45 µm Nylon filter before being analyzed. All experiments were performed three times.

For the adsorption/photolysis experiment, ACF10 (without TiO2 coating) was used while a composite material was selected for the adsorption/photocatalysis process: ACF10 with deposited TiO2 (labeled ACF10-TiO2). Adsorption without UV was performed first for 24 h, followed by 6 h of uniform irradiation (Irradiance = 10 mW.cm -², λ = 365nm). Then, another 18 h of adsorption without UV followed. For adsorption/photolysis experiments alone, a further sequence of 6 h of irradiation and 18 h of adsorption were carried out. In practice, it was found that for adsorption/photolysis experiments, more irradiation and adsorption steps were necessary to eliminate the CIP. Liquid samples were taken in regular intervals from the storage tank, filtered through a 0.45 µm Nylon filter, and analyzed. Each experiment was carried out three times. At the end of the treatment, the composite material (ACF10-TiO2) was characterized.

The irradiance was measured using a UVA light meter -RS232PC serial interface (Ultra-Violet Radiometer) purchased from Lutron (USA).

Liquid Analysis

Measurements of CIP concentration were carried out using a Thermo Accela HPLC-PDA from Shimadzu. For all analyses, a Phenomenex C18 column (2.6 µm, 100 mm x 3 mm) and a diode array UV detector (UV spectrum possible from 190 to 800 nm) were used. Two analytical methods were used (Table 3): one when only adsorption was involved (only CIP present) and one when irradiation was involved (CIP and transformation products present).

Table 3 Operating conditions for the HPLC-UV analysis. Transformation products were identified by an HPLC-MS. A ThermoFisher UltiMate 3000 HPLC was used, coupled with an Orbitrap High-Resolution Mass Spectrometer (HRMS-Exactive) equipped with an Electrospray ionization source (ESI). The monochromatic wavelength was set to 280 nm for the UV detector, and a Gemini C18 column was used (3 µm, 100 mm x 2 mm). A standard mobile phase for HPLC was used (Table 3), and a gradient mode was selected. The flow rate was set to 0.2 mL.min -1 at 40 °C . Initially, the mobile phase was maintained at 10/90 (v/v) for 2 min, then, to reach 90/10 (v/v), a linear increase was performed across 7 min and maintained for 8 min. Finally, the composition of the mobile phase was decreased by a linear decrease across 3 min to reach the initial composition 10/90 (v/v) and then held for 6 min until the next injection. To identify the intermediates, both positive and negative ionization modes of the MS were used.

Method

The amount of aliphatic acid and fluorine were determined by Ionic Chromatography (IC) using a Thermo Scientific ICS 5000+ ion chromatography with a conductimetric detector. An AS19 (4 µm, 2 × 250 mm) anion column was used. The flow rate was maintained at 0.250 mL.min -1 at 25 °C . KOH solution was used as the mobile phase, and a gradient method was employed. A concentration of 5 mmol.L -1 of KOH is maintained for 10 min. To achieve 45 mmol.L -1 of KOH, a linear increase was then carried out across 15 min, and the concentration was maintained for 10 min. Then, a decrease from 45 to 5 mmol.L -1 was done by another linear decrease across 2 min.

A TOC-L (Total Organic Carbon Analyzer) from Shimadzu (Japan) was used for TOC measurements. The value of total carbon was firstly determined by a 20 µL injection, then the inorganic carbon was determined with a second injection of 50 µL with hydrochloric acid (0.1 mol.L -1 ).

ICP measurements were carried out using ICP-AES ( Ultima2, Horiba, Japan) to determine the quantity of titanium in the liquid phase at the end of the treatment. The power source was set at 1,100 W, with a continuous plasma gas flow rate of 12 L.min -1 , a cladding gas flow rate of 0.2 L.min - 1, and a pump speed of 15 tr.min -1 . A glass concentric nebulizer and a glass cyclone chamber were used for every analysis.

Solid Analysis

Scanning Electron Microscopy (SEM) observations were done with an LEO-435 VP-PGT scanning electron microscope to visualize the morphology of both raw and coated activated carbon fibers and an FEI Quanta450 for further analysis of the chemical composition. The crystalline structure was then investigated by X-Ray Diffraction (XRD) on a GI-XRD Bruker D8 instrument.

The tensile tests were carried out on the INSTRON 3367: the elongation of the fabric (mm) as a function of the tensile load (N) was monitored via the Bluehill acquisition software. The speed rate was set at 1 mm.min -1 , and each ACF tested had the same dimension (150 × 20 × 0.5 mm 3 ) with 100 mm placed between the jaws. The tensile tests were performed to characterize both ACF materials (tensile strength, elasticity, Young's modulus).

To determine the BET specific surface area, measurements were carried out with the Autosorb-I_Quantachrome BELMaster, a volumetric gas adsorption device. Before each measurement, the sample was degassed under vacuum at 200 °C for 2 h. For the determination of the BET specific surface area, the BET method was used from the adsorption and desorption isotherms of nitrogen (N2) at -196 °C . This method was proposed in 1938 by Brunauer, Emmett, and Teller and is based on the multilayer adsorption theory [START_REF] Brunauer | Adsorption of gases in multimolecular layers[END_REF].

The pore size distribution was obtained with the same measuring device as that used for the determination of the BET specific surface area, but via a different method: the NLDFT method (Non-Localized Density Functional Theory). The NLDFT method was coupled with the "slit-shaped" geometric pore model, which has been adapted for granular or fiber-type activated carbon materials [START_REF] Lastoskie | Pore size distribution analysis of microporous carbons: A density functional theory approach[END_REF][START_REF] Seaton | A new analysis method for the determination of the pore size distribution of porous carbons from nitrogen adsorption measurements[END_REF].

Results

Adsorption

Characterization of the ACF

The physical properties of ACF10 have been measured. The BET surface area was 926 m².g -1 , a rather low value compared to that of other ACF of up to 3000 m².g -1 [START_REF] Chen | Activated carbon fiber and textiles[END_REF]. The total volume was 0.38 cm 3 .g -1 , and the microporous volume was 0.35 cm 3 .g -1 : 92% of the pores correspond to micropores. Figure 2 shows the pore size distribution of the adsorbent with two significant peaks in the range of microporous sizes. These results confirmed the microporous nature of ACF10. The macroscopic structure and weaving pattern were also investigated (see Figure 3). ACF10 shows particularly strong longitudinal lines (probably the warp direction). Scanning electron microscopy (SEM) confirms this first observation and suggests that ACF10 has a tight weave. Finally, the cross-section shows a compact fiber bundle. Finally, ACF10 was tested under tensile stress to determine whether its resistance and elasticity were affected or not by the thermal treatment (500 °C during 2-3 h) used during the TiO2 deposition step. All results are presented in Table 4. The heat treatment results in a 25% reduction of Young's modulus E and a slight decrease of the ultimate tensile strength and the failure strain. After this treatment, the ACF10 fabric was thus considered to remain resistant and rigid. In this study, an initial concentration (20 mg.L -1 ), which is higher than the value reported in the outlet of domestic wastewater treatment plants (100 ng.L -1 ), has been chosen [START_REF] Krzeminski | Performance of secondary wastewater treatment methods for the removal of contaminants of emerging concern implicated in crop uptake and antibiotic resistance spread: A review[END_REF][START_REF] Botero-Coy | An investigation into the occurrence and removal of pharmaceuticals in Colombian wastewater[END_REF][START_REF] Al-Maadheed | Antibiotics in hospital effluent and domestic wastewater treatment plants in Doha, Qatar[END_REF][START_REF] Morasch | Occurrence and fate of micropollutants in the Vidy Bay of Lake Geneva, Switzerland. Part II: Micropollutant removal between wastewater and raw drinking water[END_REF]. This choice is justified by the strong adsorption capacities of ACF -with lower initial concentrations, all the molecules would have been adsorbed, and nothing would have remained in the liquid phaseafter the different treatment steps. Figure 4 shows the adsorption kinetics for CIP with ACF10 (experimental results and modeled kinetics). The blank curve corresponds to experiments without ACF10 -around 10% of CIP can be adsorbed in the experimental device corresponding to the part of CIP retained in the system (tubes, pump, etc.). The adsorption kinetics obtained with ACF10 is rather slow: total adsorption is reached after 480 h (20 days), and only 71% is adsorbed after 24 h. In addition, several models have been used to represent the adsorption kinetics:

-External diffusion model: in this case, the external diffusion of CIP to the surface of ACF10 is the limitation step. Then the kinetics can be described with the conventional external diffusion model [START_REF] Ruthven | Principles of adsorption and adsorption processes[END_REF][START_REF] Furusawa | Diffusivities from dynamic adsorption data[END_REF]:

- 𝑑𝐶 𝑡 𝑑𝑡 = 𝑘 𝑒𝑥𝑡 (𝐶 𝑡 -𝐶 𝑒 )
Where Ct is the concentration of CIP in the liquid phase, t the adsorption time, Ce the concentration of CIP at the equilibrium, and kext the kinetic constant. Thus, this equation can easily be linearized as follows:

ln [ 𝐶 0 -𝐶 𝑒 𝐶 𝑡 -𝐶 𝑒 ] = 𝑘 𝑒𝑥𝑡 . 𝑡
A simple plot of ln [C0-Ce)/(Ct-Ce)] versus reaction time will allow the determination of the kinetic constant kext.

-Intra-particle diffusion model: in this case, the limitation step is the diffusion of CIP inside the pore of ACF10. This kinetics can be described by the following model [START_REF] Jr | Kinetics of adsorption on carbon from solution[END_REF]:

𝑞 𝑡 = 𝑘 𝑖𝑛𝑡𝑟𝑎 𝑡 0.5 + 𝐴
Where qt the actual quantity adsorbed on ACF, kintra the kinetic constant, t the adsorption time, and A a constant.

-pseudo first-order model. This model is generally applicable for short adsorption kinetics (about 30 min). The partial order is 1 with respect to the free site concentration and 0 with respect to the solute in solution. This model neglects desorption by coupling the adsorption and desorption constants into one [START_REF] Lagergren | About the theory of so-called adsorption of soluble substances[END_REF]:

𝑑𝑞 𝑑𝑡 = 𝑘 1 (𝑞 𝑒 -𝑞 𝑡 )
Where qt the actual quantity adsorbed on ACF, qe the quantity adsorbed on the ACF at the equilibrium, k1 the pseudo first-order kinetic constant. This equation can be linearized:

ln(𝑞 𝑒 -𝑞 𝑡 ) = ln(𝑞 𝑒 ) -𝑘 1 𝑡
-pseudo second-order. A more robust model than the pseudo first-order. This model can be considered as a simplified case of a case where the adsorption kinetics is managed by the surface reaction rate [START_REF] Ho | Pseudo-second order model for sorption processes[END_REF][START_REF] Rudzinski | Kinetics of solute adsorption at solid/solution interfaces: A theoretical development of the empirical pseudo-first and pseudo-second order kinetic rate equations, based on applying the statistical rate theory of interfacial transport[END_REF]:

𝑑𝑞 𝑑𝑡 = 𝑘 2 (𝑞 𝑒 -𝑞 𝑡 ) 2
Where qt is the actual quantity adsorbed on the ACF, qe the quantity adsorbed on the ACF at the equilibrium, and k2 the pseudo second-order kinetic constant. This equation can be linearized as follows:

𝑡 𝑞 = 1 𝑘 2 𝑞 𝑒 2 +

𝑞 𝑒 𝑡

All four models were used to represent the adsorption kinetics of CIP on ACF10, and the different constants obtained are presented in Table 5.

Table 5

Coefficients of the kinetics of CIP adsorption on ACF10 for the different kinetic models.

Limitation External diffusion Intra-particle diffusion

Pseudo first order Pseudo second order kext (h -1 ) 0.0056 kintra (mg.g -1 .h -0.5 ) 0.1227 qe (mg.g -1 ) 3.77 qe (mg.g -1 ) 9.48 A (mg.g -1 )

6.602 k1 (h -1 ) 0.0062 k2 (g.mg -1 .h -1 ) 0.0071 R² 0.9955 R² 0.9949 R² 0.8392 R² 0.9988

The results show that the pseudo first-order model is not suitable for CIP adsorption on ACF10 as confirmed by the theoretical curve calculated by using the specific pseudo first-order parameters. However, even if excellent R² values were found with the other models, only the external diffusion limitation model presents a very good fit between the experimental and model curves (Figure 4). This limitation of external diffusion was due to the tight weave of ACF10.

Coupling Process

The Adsorption and Photolysis Coupling Process

Kinetics of CIP Elimination. As CIP can be degraded by photolysis, it is necessary to study the coupling of adsorption and photolysis (degradation under UV irradiation) before using the material with TiO2 coating to investigate the adsorption/photocatalysis coupling process [START_REF] Triquet | TiO2 MOCVD coating for photocatalytic degradation of ciprofloxacin using 365 nm UV LEDs-kinetics and mechanisms[END_REF]. For this study, the same volume of CIP solution (20 mg.L -1 ) has been used during the following steps of the process:

-First adsorption step: 24 h (UV off) -First irradiation step: 6 h (UV on) -Second adsorption step: 18 h (UV off) -Second irradiation step: 6 h (UV on) -Final adsorption step: 18 h (UV off)

Figure 5 shows the CIP elimination kinetics during the complete process. For comparison, the kinetics of the adsorption on ACF across 72 h has been added to the same figure. Only 71% of CIP was adsorbed by using ACF10 after 24 h of adsorption, and only 80% after 70 h. For the sequential process, after the first phase of UV-irradiation, 95% of the CIP had been removed. This irradiation allowed the elimination of CIP in solution by photolysis and adsorption (simultaneous processes). This behavior has already been observed: faster methylene blue removal when photolysis and adsorption were coupled in a simultaneous process has been reported [START_REF] Fu | Preparation of activated carbon fibers supported TiO2 photocatalyst and evaluation of its photocatalytic reactivity[END_REF]. After the second adsorption step, the CIP amount adsorbed is of the order of the magnitude of the error: this step did not remove any CIP molecules. During the first UV-irradiation, transformation products were formed; thus, competitive adsorption between these molecules and CIP could take place, which would lead to slower adsorption kinetics of CIP. During the second irradiation phase, very little amount of CIP degrades: all the transformation products present in the liquid compete with CIP for photo-degradation. Finally, even if CIP is eliminated with the adsorption/photolysis sequential process, the duration of the treatment is too long to be industrially interesting (70 h). Aromatic Transformation Products. Every aromatic transformation product (A, B, C…) obtained during the photolysis of CIP was identified and their evolution investigated (each letter corresponds to one aromatic transformation product or isomer product -Table 6) [START_REF] Triquet | TiO2 MOCVD coating for photocatalytic degradation of ciprofloxacin using 365 nm UV LEDs-kinetics and mechanisms[END_REF]. Many ATPs were detected. The evolution of their concentration is shown in Figure 6. During the first phase of UV irradiation, most of the ATPs were produced, and some of them had started to be degraded. However, 6 h of irradiation is not sufficient for their total elimination since photolysis is a slow and selective reaction [START_REF] Speight | Redox Transformations[END_REF]. The second adsorption step showed adsorption of ATPs, which could explain the slower adsorption kinetics of CIP in Figure 5. After two UV-irradiation steps and three adsorption steps, some ATPs remained in the liquid phase. Mineralization and Efficiency. For CIP, as shown by Triquet et al. [START_REF] Triquet | TiO2 MOCVD coating for photocatalytic degradation of ciprofloxacin using 365 nm UV LEDs-kinetics and mechanisms[END_REF], further refractory ultimate transformation products could be present such as aliphatic acids (HCOO -and C2O4 2-)and even the fluoride ion F -. These transformation products were searched for in the solution, but only fluorine F -was detected and quantified.

Considering only the CIP present in the solution at the end of the first adsorption step (5.82 mg.L - 1 ), the maximum amount of F -formed could be 0.36 mg.L -1 of F -. The fluorine concentration has been measured at the end of the total treatment (after 72 h) and the value obtained, 0.48 mg.L -1 of F -, was greater than the maximum expected. This result showed that photolysis reactions have taken place with the adsorbed CIP molecules on the ACF10. Therefore, a partial regeneration of the adsorbent could be possible with UV irradiation.

TOC measurements of the final solution showed 76% carbon removal for AFC10 (final TOC 3.15 mgC.L -1 ). The treatment increased the removal rate from 72% (first adsorption) to 76%. This result is in agreement with the results shown in Figure 6: some aromatic transformation products remained after 3 days of treatment.

With the adsorption/photolysis sequential process, the irradiation step allowed an increase of the CIP removal kinetics, but transformation products remained in the liquid at the end of the treatment. These results show that this type of coupling is of little interest.

The Adsorption and Photocatalysis Coupling Process

Characteristic of the Composite Material. TiO2 columnar coatings, as shown in Figure 7, were performed on activated superficial carbon fibers via the MOCVD technique. The deposition only occurred on superficial fibers of the ACF10 (on both sides of the fabric) while the internal fibers remained uncoated -the MOCVD setup not allowing growth by chemical vapor infiltration. A significant number of molecules could then be adsorbed on the uncoated fibers. The XRD patterns shown in Figure 8 confirm the anatase structure of the TiO2 coating on the ACF fibers. Concerning ACF, the wide peaks at 23 and 44° are consistent with graphitic carbon structure with a significant dispersion of interreticular distance, allowing us to consider the ACF fibers as being rather amorphous [START_REF] Descamps | Etats amorphe et vitreux des composés moléculaires et pharamceutiques-Propriétés générale[END_REF]. BET measurements were performed to evaluate the impact of the TiO2 coating on the physical and adsorption properties of the ACF. Table 7 presents the comparison of the physical characteristics of the two ACFs with and without TiO2 deposition. The presence of the catalyst has a low impact on the physical characteristics of ACFs in terms of BET surface and pore volume. 926 965 VT,porous (cm 3 .g -1 ) 0.38 0.39 Vµporous (cm 3 .g -1 ) 0.35 0.37 Vmesoporous (cm 3 .g -1 ) 0.032 0.025 Liquid Phase Monitoring. The kinetics of CIP degradation by coupling adsorption and photocatalysis using the composite material ACF10-TiO2 was compared to that achieved with the adsorption on ACF10 (without UV), and the adsorption/photolysis process presented previously (Figure 9). Different adsorption kinetics were observed between ACF10 and ACF10-TiO2 during the first adsorption step. Faster adsorption was obtained with the composite material, and the adsorbed quantity seems to be higher after 24 h of adsorption: 71% of the CIP was adsorbed by ACF10 without deposited TiO2, whereas 85% of the CIP was adsorbed by the composite material. Huang et al. (2020) reported a similar behavior with a composite material: CIP adsorbed faster and in greater quantity on straw fibers (adsorbent) having a TiO2 deposit on its surface [START_REF] Huang | Photocatalytic hydrogel layer supported on alkali modified straw fibers for ciprofloxacin removal from water[END_REF]. During this first phase of adsorption, the presence of the TiO2 coating could therefore influence the nature of the interactions as reported by several authors for CIP [START_REF] Huang | Photocatalytic hydrogel layer supported on alkali modified straw fibers for ciprofloxacin removal from water[END_REF][START_REF] Tan | Biochar-based nano-composites for the decontamination of wastewater: A review[END_REF]. The difference between the two adsorption kinetics could be due to the influence of TiO2 on the physical properties of the ACF. This deposit can play a role in changing the surface tension of the material and, thus, on its hydrophobicity. Hydrophobicity is known to be a major factor in adsorption kinetics, so a change in surface tension due to the presence of hydrophilic TiO2 could modify the adsorption kinetics [START_REF] Moreno-Castilla | Adsorption of organic molecules from aqueous solutions on carbon materials[END_REF]. Moreover, the deposition temperature (500 °C ) can also affect the material. The surface tension of uncoated fibers could be modified by the desorption/degradation of various molecules (residues from manufacturing steps, surrounding atmosphere, etc.) at this temperature. Modification of the properties of the fiber (elasticity and resistance) could also occur, which may reduce external limitations and so increase adsorption kinetics.

Moreover, by using ACF10-TiO2, 3 h of irradiation are sufficient to fully eliminate the CIP, whereas, as discussed previously, 6 h of irradiation was not sufficient in the absence of TiO2. This result shows that the kinetics of the photocatalytic reactions is much faster than that of photolysis, as already reported by Triquet et al. for CIP [START_REF] Triquet | TiO2 MOCVD coating for photocatalytic degradation of ciprofloxacin using 365 nm UV LEDs-kinetics and mechanisms[END_REF]. Furthermore, since the concentration in the liquid is lower, faster kinetics is expected as reported for UV adsorption/photocatalysis coupling for the removal of bisphenol A and 2-chlorophenol [START_REF] Nguyen | Combination of La-TiO2 and activated carbon fiber for degradation of toxic organic pollutants under the visible light[END_REF].

As with the adsorption/photolysis coupling, aromatic transformation products were detected using ACF10-TiO2 (Figure 10). In this process, most of them are completely degraded. The presence of the TiO2 coating allows, thanks to the action of the UV, the creation of radicals with a strong oxidizing power (OH • type). These oxidants can rapidly degrade organic molecules in a non-selective way. At the end of the treatment, only two ATPs (F and J) remained in the liquid. The second indicator of photocatalytic reaction performance is the presence of aliphatic and fluoride in the effluent. At the end of the treatment with the ACF10-TiO2, only formic acid has been quantified (0.70 mg.L -1 of HCOO -) while oxalic acid C2O4 2-has been just detected. In addition, a pH measurement was performed. An initial pH of 6.8 was recorded. This decreased to 6.3 after 48 h of treatment. This confirms slight acidification of the effluent and the presence of aliphatic acids. However, such a small difference in pH likely has very little influence on the adsorption and photocatalysis processes. Aliphatics are usually refractory to advanced oxidation processes and are usually detected at the end of this treatment [START_REF] Wang | Chlorination decreases acute toxicity of iodophenols through the formation of iodate and chlorinated aliphatic disinfection byproducts[END_REF]. The fluoride concentration was greater (0.33 mg.L -1 of F -) than expected based on the CIP concentration at the end of the adsorption phase ([CIP]= 2.89 mg.L -1 ; 0.17 mg.L -1 of F -expected). This high amount of fluoride in the solution showed that photochemical reactions took place at the surface of composite material, bringing about its partial regeneration.

Figure 11 shows the evolution of TOC measurements for the different couplings (48 h for adsorption/photocatalysis and 72 h for adsorption/photolysis). For ACF10, the TOC removal rate was 76% for the adsorption/photolysis coupling, whereas it was 89% for the adsorption/photocatalysis. With ACF10-TiO2, the removal of the CIP is more efficient and more rapid. On the other hand, considering only the adsorption step, the removal rate was 85% (CIP concentration = 2.88 mg.L -1 ). These values being of the same order of magnitude, it can be concluded that the coupling treatments did not significantly modify the TOC of the solution. The molecules are degraded, but mineralization remained incomplete, and the transformation products (TP) remained in the liquid phase. However, with ACF10-TiO2, most of the TP were aliphatic acids known to be less toxic, confirming the appeal of this process. Figure 12 shows that the deposit survived the 48 h-experiment even if some cracks can be observed. Furthermore, an ICP analysis of the liquid phase showed that very little TiO2 deposit came off the adsorbent. After the 48 h experiment, less than 0.02 mg of Ti were present in the water (0.5% of initial Ti), which was very low in comparison with the initial Ti mass in the deposit (2.2 mg of Ti in 3.7 mg of TiO2). Even if the cracking is not significant, a pre-treatment by the acid of the activated carbon fibers could have improved the adsorbent-photocatalyst interactions and, thus, allowed a better adherence of the deposit [START_REF] Lei | In situ growth of boron doped g-C3N4 on carbon fiber cloth as a recycled flexible film-photocatalyst[END_REF]. No evidence of adsorbed CIP appears in the EDS graphs of ACF10-TiO2 -either no CIP remains adsorbed on the TiO2, or the fluorine signal is so low that it disappears in the continuum background. In contrast, CIP aggregates were detected on regular ACF10 after the adsorption/photolysis process, as shown in Figure 13.

Conclusions

This study investigated a sequential process combining adsorption and photocatalysis for CIP removal. The ACF selected was microporous, with the same range of pore sizes (from 0.5 to 3 nm) but a smaller BET surface area (926 m²/g) than typical ACFs. SEM observations and tensile tests showed that the fibers were little affected by the heat treatment accompanying the TiO2 deposition step. Moreover, the integrity of the TiO2 coating was shown to be largely preserved following the adsorption/photocatalysis treatment. The adsorption study demonstrated slow kinetics with only 71% of CIP adsorbed after 24 h (total adsorption after 20 days). The kinetics was successfully fitted with an external diffusion limitation model, confirming the limiting of the external transfer due to the tight weave of the ACF. The sequential adsorption/photocatalysis process with ACF10-TiO2 presents better performances with a quasi-total elimination of CIP and the aromatic transformation products -only a few aliphatic acids remained in solution. In addition, the large quantity of fluorine found in the solution showed that photo-oxidation reactions happened at the surface of the material. Consequently, it can be concluded that a partial regeneration of the material occurred.

Figure 1

 1 Figure 1 Experimental setup flow-diagram (a); experimental setup (b); photo-reactor (c) [37].

  CIP alone (Adsorption) CIP and transformation products Mobile phase (A/B) A: Acidified water with 0.1% formic acid (pH = 3) B: Acetonitrile Flow rate (mL.min -1 min -90/10 7 -12 min -90/10 12 -19 min -10/90 19 -25 min -10/90

Figure 2

 2 Figure 2 Pore size distribution of ACF10.

Figure 3

 3 Figure 3 Optical (a) and Scanning Electron Microscopy images of ACF10 at different scales. (b): top view, (c): cross-section.

Figure 4

 4 Figure 4 Adsorption kinetics of CIP on ACF10 (C0 = 20 mg.L -1 ; V = 100 mL; mACF10 = 193 mg; T = 25 ±2 °C).

Figure 5

 5 Figure 5 Elimination of ciprofloxacin by coupling adsorption and photolysis (UV alone) for both ACFs (C0 = 20 mg.L -1 ; V = 100 mL; mKIP1200 = 123 mg; mACF10 = 193 mg; I = 10 mW.cm -²; T = 25 °C ).

Figure 6

 6 Figure 6 Evolution of the aromatic transformation products during the combined adsorption/photolysis process with ACF10.

Figure 7

 7 Figure 7 SEM pictures of ACF10 with TiO2 coating.

Figure 8

 8 Figure 8 XRD patterns of ACF and ACF-TiO2.

Figure 9

 9 Figure 9 Elimination of ciprofloxacin by coupling adsorption and photocatalysis with ACF-TiO2 (C0 = 20 mg.L -1 ; V = 100 mL; mACF10 = 193 mg; I = 10 mW.cm -²; mTiO2,coating = 3,7 mg; T = 25 °C ; Qv = 200 mL.min -1 ).

Figure 10

 10 Figure 10 Evolution of ATPs over time for treatment with the ACF10-TiO2 composite material.

Figure 11

 11 Figure 11 TOC evolution according to the different hybrid processes.

Figure 12

 12 Figure 12 SEM-EDS observation of the ACF10-TiO2 with a backscattered electron detector after a 48-h adsorption/photocatalysis experiment.

Figure 13

 13 Figure13SEM-EDS observation of the ACF10 with a backscattered electron detector after a 48-h adsorption/photolysis experiment.

Table 2

 2 Supplier characteristics of the selected activated carbon fibers.

	Molecule	Formula	Molar (g.mol -1 )	Weight	pKa [50]	Solubility at 25 °C [51]
						75 ±10 mg.L -1 in
						water
		C17H18FN3O3 331.35		3.64 (N1); 5.05 (N1'); 6.95 (OH); 8.95 (N4')	150 ±5 mg.L -1 in acetone
						25 g.L -1 in acidic
						water (pH = 3)
		Supplier			KYNOL (Japan)
		ACF			ACF 10
		Supplier references		ACC-5092-10
		Precursor			Novoïd Fiber
		Fiber diameter (µm)		10
		Fabric thickness (mm)		0.66
		BET specific surface (m².g -1 )		1200
		Adsorption I2 (mg.g -1 )		1300
		Pore volume (cm 3 .g -1 )		0.35
		Mean pore size (nm)		0.8
		Weight (g.m -²)			205

Table 4

 4 Young's modulus E, ultimate tensile strength σmax, and failure strain Al of ACF10 with and without (reference) heat treatment (500 °C).

		Reference	Heat treatment
	E(MPa)	640 ±40	480 ±40
	σmax (MPa)	8 ±1	7.5 ±0.7
	Al %	3.5 ±0.1	3.8 ±0.1
	3.1.2 Kinetics of CIP Adsorption on the ACF

Table 6

 6 Aromatic transformation products after CIP photocatalytic degradation.

	Molecule	Molecule structure [27]	Retention time (min)	Molecule	Molecule structure [27]	Retention time (min)
	A C17H19N3O4		3.0	F C16H16FN3O4		4.6
	B C15H17N3O3		3.1	G C14H11FN2O4		4.72
	C C15H16FN3O3		3.3	H C13H11FN2O3		4.98
	D C17H16FN3O5		4.06	I C17H17N3O5		3.9
	E C13H12N2O3		4.37	J C16H17N3O4		4.3

Table 7

 7 Comparison of ACF physical characteristics with and without TiO2 deposition.

	ACF	ACF10
	TiO2 coating	Without	With
	SBET (m².g -1 )

Author Contributions

Conceptualization, TT, CT and CA; methodology, TT, CT and CA; analysis, TT and LL; modelisation, TT; experimentation, TT; writing-original draft preparation, TT, CT, LL, RR and CA; writing-review and editing, TT, CT, LL, RR and CA; supervision, CT and CA; project administration, CT and CA. All authors have read and agreed to the published version of the manuscript.

Competing Interests

The authors have declared that no competing interests exist.


Thibault Simon
email: thibault.simon@inria.fr

Pierre Rust
email: pierre.rust@orange.com

Romain Rouvoy
email: romain.rouvoy@univ-lille.fr

Joël Penhoat
email: joel.penhoat@orange.com

Uncovering the Environmental Impact of Software Life Cycle

Keywords: software life cycle, software environmental impact, green software development

The environmental footprint of a software service development may be impacted by several factors, spanning human factors to infrastructure choices. To the best of our knowledge, the state of the art in the domain of environmental footprint fails i) to address the full spectrum of software services life cycle, from design to operation and maintenance and ii) to cover different categories of impacts. In this paper, we therefore introduce a methodology and the associated model to guide the software development ecosystem and their stakeholders in estimating the environmental footprint of their projects along multiple impact categories. This holistic approach delivers actionable insights to identify potential shifting between phases, such as development and usage, but also hotspots among resources consumed to produce and operate software services. We report on a sample case study that emphasizes the importance of development impact on a software life cycle and shows the relative importance of phases, as well as resources consumed.

I. INTRODUCTION AND MOTIVATION

The latest Intergovernmental Panel on Climate Change (IPCC) report [START_REF]Climate Change[END_REF] states that emissions of greenhouse gas from human activities are responsible for approximately 1.1°C of global warming since 1850-1900 and that-unless there is an immediate, rapid, and large-scale reduction-limiting it to +1.5°C will be out of reach. To achieve this objective, the ICT sector is challenged to adopt a more sustainable approach to reduce its environmental footprint. Freitag et al. [START_REF] Freitag | The real climate and transformative impact of ICT: A critique of estimates, trends, and regulations[END_REF] estimate, based on previous studies [START_REF] Anders | On Global Electricity Usage of Communication Technology: Trends to 2030[END_REF][START_REF] Belkhir | Assessing ICT global emissions footprint: Trends to 2040 & recommendations[END_REF][START_REF] Malmodin | The Energy and Carbon Footprint of the Global ICT and E&M Sectors 2010-2015[END_REF], that it contributes between 2.1% and 3.9% to worldwide Greenhouse Gas (GHG) emissions and continues to grow, while it should reduce by 42% by 2030, 72% by 2040, and 91% by 2050 and be netzero by 2050 to stay within 1.5°C warming. According to Masanet et al. [START_REF] Masanet | Recalibrating global data center energy-use estimates[END_REF], despite an increase of compute instance by 550% between 2010 and 2018, the consumption was stable from 194 to 205 TWh, thanks to the progress of servers and data centers efficiency. However, it is uncertain whether future optimizations will be able to keep up with the rise of computing resource demand.

The most commonly used category of impact is climate change, quantified through GHG, but according to best practices and standards [START_REF]Methodology for environmental life cycle assessments of information and communication technology goods, networks and services[END_REF]34,35] several should be combined to get a global environmental footprint. Material footprint refers to the total amount of raw materials extracted (minerals, metals. . . ), notably for ICT hardware manufacturing, and reflects the pressures placed on the environment. Overall, it roses from 43 billion metric tons in 1990 to 92 billion in 2017, an increase of 70% since 2000, and 113% since 1990 [START_REF]SDG Indicators[END_REF]. The ICT sector also consumes water, and Siddik et al. [START_REF] Abu | The environmental footprint of data centers in the United States[END_REF] estimate the total annual operational water footprint of US data centers used for cooling and energy generation at 513, 000, 000 m 3 in 2018. At the end of its life, ICT hardware becomes e-waste, which is estimated to be recycled at less than 40% in the European Union [START_REF]Waste electrical and electronic equipment (WEEE) by waste management operations -open scope, 6[END_REF]. Its disposal generates disparities around the world as well as irremediable pollution to soil [START_REF] Mihai | Chapter 1 -Waste Electrical and Electronic Equipment (WEEE): Flows, Quantities, and Management-A Global Scenario[END_REF].

Although the ICT environmental impact comes from hardware, especially its manufacturing and usage, one can argue that the responsibility lies within the software. Indeed, ICT equipment are built and operated solely to run software on, therefore levers for actions must also be sought at the software level.

When developing software, developers follow documentation and guidelines to optimize their maintainability, performance, and usability. However, they lack guidance and knowledge on how to lower their environmental impact [START_REF] Ournani | On Reducing the Energy Consumption of Software: From Hurdles to Requirements[END_REF], as well as sustainability models [START_REF] Deraman | Green Measurements for Software Product Based on Sustainability Dimensions[END_REF][START_REF] Sara | A green model for sustainable software engineering[END_REF][START_REF] Dick | Enhancing Software Engineering Processes towards Sustainable Software Product Design[END_REF][START_REF] Naumann | The GREENSOFT Model: A reference model for green and sustainable software and its engineering[END_REF][START_REF] Sanath | Green software development model: An approach towards sustainable software development[END_REF]. They are however prone to apply them, if available and easily integrable [START_REF] Verdecchia | The future of sustainable digital infrastructures: A landscape of solutions, adoption factors, impediments, open problems, and scenarios[END_REF].

To the best of our knowledge, current efforts are mainly focused on lowering software energy footprint by applying performance optimizations [START_REF] Lago | A systematic literature review of green software metrics[END_REF], such as energy-efficient virtual machines placement in data-centers [START_REF] Kumar | Energy-efficient VMplacement in cloud data center[END_REF], energy monitoring along releases [START_REF] Jagroep | Software Energy Profiling: Comparing Releases of a Software Product[END_REF], or energy code-smells [START_REF] Carette | Investigating the energy impact of Android smells[END_REF]. However, the energy consumed throughout the usage phase of a service or product might not always be the main driver of impact, thus adopting a more holistic approach is essential. For instance, Gupta et al. [START_REF] Gupta | Chasing Carbon: The Elusive Environmental Footprint of Computing[END_REF] showed that reducing a data center's energy consumption alone fails to reduce its carbon emissions, as hardware manufacturing plays a bigger role in its environmental impact. Therefore, to account for these effects in software, we need to model its whole life cycle, across all its phases and over multiple environmental criteria.

Our study aims at answering the following research question: How does a holistic perspective provide actionable insights to reduce software environmental footprint? . To do so, we propose a model to ease the estimation of software environmental impact through its life cycle across multiple categories, to study potential shifts and hotspots.

The remainder of this paper is organized as follows. We start by describing the key standards on life cycle analysis and software life cycle (cf. Section II). We then discuss the state of the art on leveraging software life cycle to lower its environmental impact (cf. Section III). We later introduce our contribution to model a software life cycle and estimate its environmental impact (cf. Section IV). To foster widespread adoption of our approach, we propose an implementation in Section V. We subsequently show how it can help stakeholders to identify impact shifting between phases, resources, and criteria (cf. Section VI). Finally, we conclude in Section VIII.

II. BACKGROUND

This section describes the key standards on life cycle analysis and software life cycle. In our work, we rely on these standards, simplifying and complementing them when necessary to provide a holistic view of the environmental impact.

A Life Cycle Assessment (LCA), as defined by the standard ISO 14040 [34], is the study of the environmental impacts contribution of a product or service across its entire life cycle. The analysis is performed for a given functional unit, allowing to compare two systems with the same one from an environmental perspective. By using a systematic overview and perspective, the LCA approach helps to identify the shifting of a potential environmental burden between life cycle stages or individual processes [START_REF] Finkbeiner | The new international standards for life cycle assessment: ISO 14040 and ISO 14044[END_REF]. A screening LCA is simpler to conduct and is established based on readily available data [START_REF] Wenzel | Application dependency of lca methodology: Key variables and their mode of influenc-ing the method[END_REF], and thus provides a high-level view of impacts to identify the main sources and those that require deeper examination.

Ekvall [START_REF] Ekvall | Cleaner production tools: LCA and beyond[END_REF] defines two types of LCA: attributional, which aims at describing the environmental properties of a life cycle and its subsystems, and consequential which aims at describing the effect of changes within the life cycle. Whereas a consequential LCA aims a describing the long-term effect of changes induced by the product or service and thus support decisions in the long term, an attributional one map the environmental impacts that a product can be made accountable for and is thus better suited to support decisions that aim at improving its life cycle processes [START_REF] Björn | Positive and negative feedback in consequential life-cycle assessment[END_REF].

Guinée et al. [START_REF]Handbook on Life Cycle Assessment[END_REF] define an environmental impact as the consequence of an environmental intervention in the environment system. It is represented as an aggregate of impact categories, a class representing an environmental issue of concern [34], and groups consequences on climate change, acidification, ecotoxicity. . . Each one is represented as a quantity, which unit is an impact category indicator. For example, the impact category Climate Change Potential contains a quantity whose unit is the tons of Carbon Dioxide Equivalent (tCO 2 e), defined as the mass of CO 2 that would yield an equivalent effect of global warming [START_REF]Carbon dioxide-equivalent (CO2 -eq) emissions and concentrations[END_REF].

To define a Software Development Life Cycle (SDLC), the international standard ISO/IEC/IEEE 12207 [START_REF]ISO/IEC/IEEE 12207: 2017, Systems and software engineering-Software life cycle processes[END_REF], entitled Systems and software engineering -Software life cycle processes, defines a set of processes organized as requirements, design, implementation, testing, deployment, maintenance and retirement [START_REF] Sanath | Green software development model: An approach towards sustainable software development[END_REF]. In projects, these life cycle processes can further be organized in different models, such as Waterfall, V-Model, Scrum. . . [START_REF] Nayan | Software Development Lifecycle Models[END_REF] III. STATE OF THE ART The trends in green computing studies published since 2012 clearly show a growing interest [START_REF] Chandani | A Bibliometric Analysis of Green Computing[END_REF], and several approaches [START_REF] Dick | Enhancing Software Engineering Processes towards Sustainable Software Product Design[END_REF][START_REF] Kumar | A GREEN IT STAR MODEL APPROACH FOR SOFTWARE DEVELOPMENT LIFE CYCLE[END_REF][START_REF] Lami | Software Sustainability from a Process-Centric Perspective[END_REF][START_REF] Sara | A green model for sustainable software engineering[END_REF][START_REF] Naumann | The GREENSOFT Model: A reference model for green and sustainable software and its engineering[END_REF][START_REF] Sanath | Green software development model: An approach towards sustainable software development[END_REF] attempt to leverage software life cycle models to reduce its environmental impact.

Some authors propose to use the life cycle of software to lower the impacts of the different processes. Kumar et al. [START_REF] Kumar | A GREEN IT STAR MODEL APPROACH FOR SOFTWARE DEVELOPMENT LIFE CYCLE[END_REF] propose the Green Star model to assess and rate each process of the SDLC. Lami et al. [START_REF] Lami | Software Sustainability from a Process-Centric Perspective[END_REF] look at the sustainability factors of software processes and propose the following reduction objectives: carbon footprint, energy, waste, and travel. They also add new processes to assess that these objectives are fulfilled.

Processes can also be used to reduce the resulting software's environmental impact. Shenoy et al. [START_REF] Sanath | Green software development model: An approach towards sustainable software development[END_REF] propose changes in the SDLC and provide guidelines for each process. Dick and Naumann [START_REF] Dick | Enhancing Software Engineering Processes towards Sustainable Software Product Design[END_REF] add processes dedicated to ensuring the resulting product sustainability, such as sustainability reviews and sustainability retrospectives. The GREENSOFT model and associated metrics [START_REF] Naumann | The GREENSOFT Model: A reference model for green and sustainable software and its engineering[END_REF][START_REF] Sara | A green model for sustainable software engineering[END_REF] cover both the process and resulting software sustainability within a conceptual framework that emphasizes considering software products over their life cycle.

The approaches for computing an environmental impact can be classified as top-down and bottom-up. A top-down approach adopts global statistics and input/output analysis to model the participating systems and compute a smaller-scale output. Chan et al. [START_REF] Chien | Methodologies for Assessing the Use-Phase Power Consumption and Greenhouse Gas Emissions of Telecommunications Network Services[END_REF] use this approach to model the Internet energy intensity, by dividing its total estimated energy consumption by the traffic on a given period. They obtain an intensity expressed in kWh-hours per gigabyte of data transferred. Unfortunately, top-down approaches generally suffer from an averaging bias and do not help in identifying action levers. In this case for example, one can only identify two levers to reduce the Internet's impact: either reducing the amount of data transferred or reducing the environmental footprint of the ICT sector as a whole.

A bottom-up approach is based on direct observations made on case studies [START_REF] Vlad | Assessing Internet energy intensity: A review of methods and results[END_REF], which are then generalized to be representative of a larger scale. Schien et al. [START_REF] Schien | The Energy Intensity of the Internet: Edge and Core Networks[END_REF] use this approach to compute the Internet energy intensity, by scaling the energy consumption of devices simulating an Internet Service Provider (ISP) network. This approach will reflect changes made to the system directly, without relying on structural changes, as a top-down approach.

Most studies on the environmental footprint of ICT combine both approaches to obtain more accurate estimations [START_REF] Freitag | The real climate and transformative impact of ICT: A critique of estimates, trends, and regulations[END_REF]. Malmodin and Lundén [START_REF] Malmodin | The Energy and Carbon Footprint of the Global ICT and E&M Sectors 2010-2015[END_REF] used a bottom-up approach for the impact of user devices, and a top-down approach for data centers and networks to model their projections of emissions beyond 2020.

The lack of openly available data, the complexity of ICT systems and their variability make it difficult to assess their impact. While Andrae et al. [START_REF] Anders Sg Andrae | New perspectives on internet electricity use in 2030[END_REF] estimate the worldwide data centers consumption to be around 299 TWh in 2020, China's State Grid Energy Research Institute released a report stating that Chinese data centers consumption alone was around 200 TWh the same year [START_REF]Green data centers in focus[END_REF]. Modeling various systems implies adopting hypotheses and values with possibly high margins of errors and, per Fonseca et al. [START_REF] Fonseca | A Manifesto for Energy-Aware Software[END_REF], one should look at trends that help to understand patterns rather than absolute values to take sourced decisions.

To the best of our knowledge, only two papers tried to assess the carbon footprint of software products' life cycle. The first attempt was conducted by Taina et al. [START_REF] Taina | How green is your software?[END_REF], where the authors proposed different approaches to assess the carbon footprint of phases on an artificial software project. By giving estimates for each phase, they emphasize that the way software is developed and delivered matters, as well as how usable it is. They mostly focus on energy consumption and paper printing, but conclude that traveling would dominate the result, if included. Kern et al. [START_REF] Kern | Impacts of software and its engineering on the carbon footprint of ICT[END_REF] focused on whether employee commuting should be included in software carbon footprint assessment, and conduct two case studies on micro-enterprises in Germany. They compute factors of impact for this context to give an impression of the magnitudes, and show that the distribution of environmental impacts between the build and the run phases can vary greatly depending on the number of copies sold. Both of these approaches do not cover the complete life cycle of the resources used, especially the impact of ICT equipment manufacturing, but only account for their energy consumption.

The ITU L.1410 [START_REF]Methodology for environmental life cycle assessments of information and communication technology goods, networks and services[END_REF] standard complements the ISO 14040 [34] and 14044 [35] and proposes a Methodology for environmental life cycle assessments of information and communication technology goods, networks and services, by defining a set of requirements that the LCA practitioners should strive for, as compliance may not be possible. It was refined in France by the General Principles for the Environmental Labelling of Consumer Products, Methodological Standard for the Environmental Assessment of Digital Service [START_REF]General Principles for the Environmental Labelling of Consumer Products, Methodological Standard for the Environmental Assessment of Digital Service[END_REF], which aims to provide a set of common rules to assess and inform consumers about the environmental impact of digital services. The Blue Angel environmental label for Resource and Energy-Efficient Software Products is designed in Germany to award products that use hardware resources in a particularly efficient manner and consume a low amount of energy during their use. All these standards take only into account the usage of software services, but not their development.

The Greenhouse Gas Protocol Product Life Cycle Accounting and Reporting Standard (Product Standard) [START_REF] Bhatia | Greenhouse gas protocol product life cycle accounting and reporting standard[END_REF] gives requirements and guidance for organizations to quantify and report an inventory of GHG emissions and removals associated with a specific product. The ICT Sector Guidance [START_REF]ICT Sector Guidance built on the GHG Protocol Product Life Cycle Accounting and Reporting Standard[END_REF] define the assessment of software life cycle GHG impact with predefined phases (material acquisition and preprocessing, production, distribution and storage, use, and end of life), and consider the software development. However, unlike the LCA approaches, it uses a single category of impact and only accounts for GHG emissions. Besides, upstream emissions such as building and hardware manufacturing, are not taken into account.

Outside the research community, several initiatives also propose emerging methodologies. The Green Software Foundation defines the Software Carbon Intensity (SCI) [START_REF]software carbon intensity[END_REF], a methodology to compute the rate of carbon emissions per functional unit for a software system. It takes into account both energy consumption and reserved hardware embodied emissions, but only covers GHG and the usage phase of a project, not its development. The Sustainable Digital Infrastructure Alliance (SDIA) proposes a methodology to compute the carbon emissions of server-side applications [START_REF]Methodology for calculating the environmental footprint of server-side applications[END_REF]. This approach encompasses both energy consumption and embodied carbon emissions, but only accounts for backend impacts and excludes those of the network and end-user devices.

Despite a growing interest to estimate the environmental impact of ICT, few approaches consider software over its life cycle along multiple categories. Standards and indicators emerge to standardize its computation and communication to consumers, but they focus mainly on its usage phase, often only through energy consumption, and its climate change impact through carbon emissions rather than considering multiple categories.

IV. APPROACH

We propose a methodology and the associated model to assist software project stakeholders in estimating the environmental footprint of their project. When designing this methodology, our goals are threefold. It needs to be: 1) holistic by providing a comprehensive perception of the impacts resulting from a software project, 2) actionable by delivering actionable insights to project stakeholders, 3) easy to use to enforce a widespread adoption.

To meet our first objective, we elected to follow the core principles of an attributional screening LCA, as introduced in Section II. The perimeter we take into account in our methodology includes all the resources consumed when performing the activities identified in the SDLC, but also other activities to consider a complete cradle-to-grave view of the impacts arising from the ICT project under study. This includes for example (and not exhaustively) the distances traveled to sell the product, the man-days required for its development, the production and deployment of ICT equipment and the impact of end-user devices. This offers a high-level view of the impacts and supports identifying key causes within the processes. Additionally, such a holistic perspective allows the identification of potential burden shifts between life cycle phases or resources.

For a software product, such a shift can occur when reducing the hardware resources required, and thus the running phase impact, by adopting a more efficient programming language. However, this programming language might incur longer development time, which will increase the impacts of the building phase. Impact shifts can also occur between categories, for instance when replacing servers with more energyefficient ones, thus decreasing the climate change impact, while increasing the land pollution due to the manufacturing of new hardware.

The adoption of a bottom-up approach allows us to meet our second objective: delivering actionable insights. Once stakeholders have identified major impact sources in their project, they can investigate the effect of decisions taken at a micro-scale on a larger scale. Our approach therefore equips stakeholders with a new Key Performance Indicator (KPI) to consider the environmental impacts of their decisions and assess if the benefit outweighs the cost next to others, such as security level, financial costs, redundancy, high availability. . . Meeting our third objective, namely "ease of use", is generally difficult when using a bottom-up approach with a large scope. We answer this challenge by proposing a simple and flexible way of modeling a project's resources and activities, which allows using the same methodology for projects using different development models. Our estimates rely on an impact dataset (named secondary data in LCA vocabulary), that we have consolidated from existing openly available sources. Finally, we elect to simplify inventory and data collection by using a static view of the project, which we introduce in the following section.

A. Software life cycle modeling

To adapt to each stakeholder's context, the software life cycle modeling has to be suitable for all types of SLDC models, while keeping a common environmental impact computation methodology.

To do so, it is represented as a static view rather than a time-based one, such as a timeline, to lower the granularity of inputs required while keeping their relevance. For instance, it is not required for stakeholders to detail on each day how many developers worked on the project, the number of test servers used, which would be tedious without automation, but rather the number of man-days and server-hours consumed on a given period. Instead of a day-to-day basis, time is flattened and the life cycle is modeled as a tree, as depicted in Figure 1. In this example, the project contains two key phases, build and run, which can contain sub-phases, such as selling, development, and hosting. Stakeholders can add, move, and remove phases and sub-phases to model their software development life cycle, no matter the model and processes or phases they chose. To support them do so, we propose predefined trees that model the most common life cycles, which they can fully customize to model more accurately their daily activities.

Each phase consumes resources to be completed, which can be of multiple kinds. The phase coding consumes man-days, but also server-hours for developers to test their products. As software projects use common resources, we propose a default dataset with their associated environmental impact, which the model then uses to compute the whole software life cycle impact. This way, we allow stakeholders to set their resources usage accordingly to their needs and policies, in the right life cycle phases, while keeping the same computation methodology between multiple typologies of projects.

B. Environmental impact computation

As the project life cycle is flattened as a tree representation, its whole environmental impact, represented as an aggregate of impact categories as defined in Section II, can be computed using a tree traversal:

Impact(project) = p∈P Impact(phase p )
Each phase of the project-or node of the tree-can consume resources and require sub-phases. For example, the phase build requires the sub-phase development, which consumes man-days resources. As such, the environmental impact of a phase is the sum of its consumed resources, including the ones consumed by its sub-phases:

Impact(phase) = r∈R Impact(resources r ) + p∈P Impact(phase p )
The impact of a given phase mostly depends on the resources it consumes. Each one is estimated from the declared quantity weighted to its associated impact factor, which gives an environmental impact per functional unit consumed, such as a vCPU-hour, a plane-kilometer. . . A resource impact is therefore computed as:

Impact(resource) = quantity × impact f actor
The quantity has to be the same unit as the impact factor to obtain an environmental impact. However as the model uses a flattened time representation, this quantity cannot directly express recurring events such as a product owner going to meet the customer every month, by plane, for 6 months. The quantity is therefore aggregated from multiple fields: amount, the number of functional units used, period, the length of the resource consumption, and frequency. For instance, for the impact factor plane, expressed by kilometer, the amount will be 100 plane-kilometers, the frequency every month, and the period a year, giving a quantity equal to 12, 000 planekilometers, which corresponds to the impact factor functional unit.

If an impact factor is given by a time-based functional unit, such as vCPU-per-hour, the quantity field has to contain a time unit. A period of usage is therefore mandatory and the frequency field cannot be used in isolation anymore, to avoid canceling out the time unit. The field duration is added to represent a recurring scenario: to model a vCPU used two hours per day for a year, the duration will be a year, the frequency every day, and the duration two hours. The quantity value is thus computed as follows:

quantity = amount × duration × period f requency V. ENVIRONMENTAL MODEL
To foster widespread adoption of our approach to assess software environmental impact, we developed a web application shown in Figure 2 as well as an API implementing our approach, which will soon be available under Orange GitHub 1 . This application provides software project stakeholders with an easy way to model the life cycle of their project and get a first overview of the associated environmental impacts to quickly identify hotspots in their projects. They can model and save their project characteristics, through phases and resources consumed, and analyze the resulting impact from various standpoints. a) Tree view: Shown at the center of Figure 2, the tree view defines the project life cycle, by modeling its phases as nodes. Each node can be moved or deleted, and new ones can be created at all levels. Stakeholders can adopt a life cycle granularity that suits their study scope, which they can refine iteratively by updating the tree. Standard predefined life cycle trees are proposed to start modeling from the most common SDLC models.

b) Resources inputs: By switching to editing mode, the resources consumed by each phase can be updated. A form assists in conforming with the associated impact factor unit, while allowing entering time-based events despite the overall static view.

c) Impact factors: The impact factors are exposed by the API, and cannot be updated when modeling projects. Keeping the reference values in a single place, rather than distributing them, as spreadsheets for instance, avoid data tempering that would make comparisons between projects or solutions impossible. It also allows updating at once all the projects modeled using these impact factors, when refining their values. d) Results: To identify impact hotspots and shifts, the resulting environmental impact for the chosen impact category is displayed on the left side of the application, shown in Figure 2. The top figure is the Sankey diagram of the impact combined between the phases and resources, which is then refined solely by phases, as shown in Figure 3, and by resource, as shown in Figure 4. For the impact by resource, all impact categories are also reported simultaneously, as depicted in Figure 6.

e) Comparison: Each project can contain multiple models, which can be used to compare different approaches and scenarios. Shown on the left-hand side of Figure 2, these models can be compared between them, using a side-by-side view of the environmental impact described previously, as well as a direct comparison by resources shown in Figure 5.

f) Physical quantities: Each physical quantity, such as ones expressed in CO 2 e, is represented as a numerical value and a unit of measurement. Our implementation uses the Pint library [28], which allows conversions between units as well as ensures their consistency in computations.

VI. FINDINGS

This section demonstrates the benefits of our approach. We used the Gitlab project to estimate a sample application life cycle, considering the source code repository for modeling the building phase and the documented hardware requirements for the usage phase.

Using the CLOC tool [START_REF] Aldanial | cloc[END_REF], we extracted 6, 241, 291 Source Lines of Code (SLOC) from the project repository [START_REF] Gitlab | org / GitLab • GitLab[END_REF]. We used this value as an input for a Constructive Cost Model II (COCOMO II), the most commonly-used algorithmic method for software development cost estimation defined by Bohem et al. [START_REF] Boehm | Cost models for future software life cycle processes: COCOMO 2.0[END_REF]. Using a post mortem approach on an already developed application offers an estimation of the effort required to build a software, as well as its distribution among phases. To obtain a standard software effort distribution, we leave COCOMO cost drivers, such as team cohesion, programmer's capability, and multi-site developments with their default values. We obtained the effort distribution shown in Table I.

As discussing the COCOMO potential limits is out of this paper's scope, we consider the effort distribution as representative of a project this size, rather than the actual effort accomplished to design and develop the Gitlab application.

To model the usage phase, we adopted the Gitlab hosting reference architecture for 50, 000 users [START_REF] Gitlab | Reference architecture: up to 50,000 users[END_REF]. We chose the largest one, considering that the application is designed to Fig. 2. Screenshot of the web application implementing our approach be as scalable as its biggest instantiation, requiring more effort than for a smaller one, such as for 500 users. Hosting an instance requires hosting services not developed inhouse by Gitlab, such as Sidekiq or PostgreSQL, which is realistic for a real-world application. We only consider these services hosting impact and not their development, as done by Kern et al. [START_REF] Kern | Impacts of software and its engineering on the carbon footprint of ICT[END_REF]. As the requirements do not specify a storage size, we used the default repository size limit of 10 GB by project, and considered 2 projects per user with a 3× data redundancy; thus obtaining a total of 3, 000 TB of data to store. We do not include end-users in our case study, considering that their usage environmental impact-i.e., mainly through energy consumption-would be marginal in contrast to the manufacturing impact of their device, and that the property of the device does not rest on using Gitlab or not.

In this case study, we assume the development and hosting in France, and we use the corresponding impact factors.

A. Shifts between phases

As stated in Section III, the hypotheses taken when modeling and reference values used with possibly high margins of error imply uncertainty in an environmental impact assessment, to which there is no ground truth to compare. This implies caution when treating with absolute values, but stakeholders can identify impact hotspots and shifts using a high-level perspective, by manipulating orders of magnitude obtained through a common methodology and reference values.

We first focus on a specific impact category, Climate Change indicated in CO 2 e, to show how modeling a software life cycle environmental impact can provide actionable insights to lower its environmental impact. To respect the Paris Agreement climate objectives to limit global warming to +1.5°C above pre-industrial levels [52], the IPCC [START_REF]Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development[END_REF] gives a global carbon budget of 500 billion tons from 2020 to 2050-i.e., roughly under two tons per year by earth inhabitants.

1) Phases: When applying the methodology described in Section IV with a run phase duration of 15 years, we obtained the distribution of tCO 2 e emitted shown in Figure 3. The impact is largely dominated by the building phase, emitting approximately 13, 945 tCO 2 e, while the running phase is relatively low with roughly 279 tCO 2 e emitted.

This highlights the importance of using a life cycle approach, as considering only the software through its usage, which is commonly done, can hide the vast majority of impact which occur during its development: more than 95% in this case.

The build phase and its sub-phases only consume people resources. As such, their environmental impact distribution is the same one as the COCOMO software effort I, where the construction activity dominates the man-days required, resulting in more than 64% of the build phase CO 2 e emissions, especially for implementation, assessment, and design phases.

2) Amortization: Figure 3 highlights that in an ad-hoc development, the build phase can largely prevail over software's overall life cycle environmental impact. However, a project the size of Gitlab requires to be hosted for an unrealistically long duration to reach the tipping point where its run phase impact will be equal to the build phase. With a constant build phase emitting 13, 945 tCO 2 e and a year of run emitting 18.614 tCO 2 e, this tipping point is thus identified as roughly Fig. 3. CO 2 e emissions distribution between life cycle phases 749 years of usage. As Gitlab is hosted simultaneously across multiple instances, we consider a hosting-year as a single instance hosted for a year.

B. Shifts between resources consumed

A life cycle approach allows stakeholders to identify the most impactful phases and uncover impacts hidden by focusing on a single one, but does not provide actionable insight to reduce their respective impact. As defined in Section IV a phase does not have a direct impact, it inherently comes from the resources it consumes. By using a similar holistic approach for resources, stakeholders can identify hotspots and shifts in their environmental impact.

1) Hotspots: The Sankey diagram of Figure 4 depicts the flows of tCO 2 e emitted by the consumption of the different resources across the project life cycle. It models a project where impacts are at the tipping point between the build and run phases, previously defined and established at 749 hostingyears, to avoid having the people resource impact largely dominating the overall one.

For one day, the people resource emits roughly 1.21e-2 tCO 2 e and is constituted of transportation, a laptop and an external monitor, as well as an office. While the monitor and laptop impacts are relatively low, the office accounts for almost half of the impact, emitting 6, 178 tCO 2 e. Transportation emits the other half, 7, 326 tCO 2 e, based on a representative usage mix of car, motorbike, public transport and bike usage in France [START_REF]Résultats détaillés de l'enquête mobilité des personnes[END_REF][START_REF]Les cadres parcourent chaque année près de 3000 km de plus que les employés pour se rendre au travail | L'Observatoire des Territoires[END_REF] and their associated impact factors [2]. Among these, the car accounts for more than 95% of the overall commuting environmental impact and ultimately becomes one of the main impact hotspots among all resources consumed throughout the software life cycle. We thus assess Kern et al. [START_REF] Kern | Impacts of software and its engineering on the carbon footprint of ICT[END_REF] that commuting can be a key impact on the overall environmental impact of a software life cycle, not only on the building phase.

2) Manufacture and usage: Figure 4 shows that, despite the 758 vCPU and 1636 GB of memory required to host Gitlab instances, the 300 TB of storage largely prevails over the overall CO 2 e emissions. Among the 8, 853 tCO 2 e emitted, more than 95% comes from its manufacturing. By acknowledging only its usage impact-i.e., energy consumption-it would be considered a relatively low source of impact. However, when acknowledging the hardware's complete life cycle, its manufacturing is one of the main emissions flow, and storage becomes one of the main hotspots on the overall project life cycle. Identifying such a hotspot allows stakeholders to uncover hidden impacts, as well as prioritize actions, such as prolonging hardware life expectancy or decreasing the number of allocated resources.

3) Compare scenarios: To compare different approaches, Figure 2 depicts a scenario between a model at the tipping point between its build and run phases impact and a more hardware intensive one, where the overall number of personmonth required is reduced by 20%, leading to double the hardware resources required to host an instance.

Figure 5 points out the main drivers of emissions shifts between both approaches. The decrease of person-month required results in cars and offices emissions lowering by almost 2, 650 tCO 2 e. It is however not sufficient to compensate for the increase of hardware-related ones, resulting in an overall emissions increase of 2299 tCO 2 e. By modeling and comparing different scenarios, stakeholders can quickly get insights into the relative impact shifts between different implementation choices.

4) Multicriteria: We focused on the climate change impact category to highlight how the methodology can help stakeholders to identify impact shifts between phases and hotspots between resources, and highlight the importance of considering their life cycle impacts. However, as stated in Section II, an environmental impact is an aggregate of impact categories, and an impact can also shift between these categories.

To identify this shift, Figure 6 shows a holistic view of resources' environmental impact along all categories of impact proposed. Due to the scarcity of open-source data, discussed in Section VII, we were not able to find relevant multi-categories impact factors for transport and buildings. Thus, the figure focuses on the use phase, which uses hardware resources with environmental impact data sourced from [START_REF]Base Impacts -Documentation -Numérique(Négaoctet)[END_REF]. Following the category of impact looked at, the order of magnitude between the main sources can vary greatly. When focusing on climate change, a decision such as replacing processors with newer and more energy-efficient ones can reduce their contribution to this impact category. It will however increase their contribution to other categories, such as water depletion and resource depletion with a possibly worse environmental impact. Furthermore, a holistic view of multiple categories highlights the major role of hardware manufacturing on the software hosting environmental footprint.

Figure 6 displays each category separately and in its respective unit, which can complicate the decision-making process. To better understand their relative magnitude and importance, the ISO 14044 [35] defines optional steps of an LCA: normalization and weighting. Each indicator result is transformed by dividing it by a reference value, such as the one given by Sala et al. [START_REF] Sala | Global normalisation factors for the Environmental Footprint and Life Cycle Assessment[END_REF]. This allows for identifying and prioritizing the relevant categories, as well as obtaining a single score by conducting another optional step, grouping. However, as there is no broadly accepted method for weighting and grouping yet [START_REF] Roesch | Normalization and weighting: the open challenge in LCA[END_REF], we chose not to aggregate impact categories and report them separated to stakeholders.

VII. THREATS TO VALIDITY

Gitlab is used as a sampled case study to show the methodology and the actionable insights it offers on a realistic dataset of a complex application, but the impact cannot be considered as these of the project itself. Defining a scope, even with its limitation, is essential to a life cycle approach, as considering all upstream resources would be too large and thus impossible. As we limit our build scope to values obtained through the COCOMO effort estimation methodology, some key elements are missing for an organization, such as supporting employee infrastructure, accountancy, human resources, etc. Furthermore, the Gitlab company is all-remote, thus not using offices each day, which plays a key role associated with commuting in our impacts modeling. Their employees work across 65 countries, which we do not consider as we did not add workrelated travel. We used a "black-box" approach using the source code repository and documented hardware requirements as a best-effort solution, but encourage stakeholders to use the more precise project values available to them, and impact factors better suited to their context to obtain the best insights from the model.

Our scope does not include maintenance for the software, as well as for the hardware resources which include data centers through a Power Usage Effectiveness (PUE) only, not with a life cycle approach that includes water usage for example. Clusters monitoring infrastructure, overhead . . . are not taken into account, as well as the hardware used for development, such as test, qualification, and validation servers.

We chose not to consider Continuous Integration / Continuous Delivery (CI/CD) runners in our use case, as they would be predominant over the hosting impact. While relevant for the Gitlab product LCA, it is a peculiar use case, highly resource-intensive thus with a high impact. Using the default SaaS runner configuration with 1 vCPU and 3.75 GB of RAM, as well as the 10, 000 minutes per month or shared runner time allocated to premium users, we obtain just about 12.77 tCO 2 e per month for the runners, while the overall hosting impact is roughly 1.55 tCO 2 e.

The ecosystem lacks data on the environmental impact of resources it uses, as highlighted in [START_REF] Mytton | Assessing the suitability of the Greenhouse Gas Protocol for calculation of emissions from public cloud computing workloads[END_REF], all the more multicriteria impact, which we demonstrate the importance of. Our model outputs are strongly correlated to its reference values, and we modeled an environmental impact using impact factors from France where the electricity is low carbon. In this context, we observed a smaller resource usage than manufacturing footprint, but this cannot be generalized and we encourage stakeholders to use impact factors corresponding to their context.

As it uses an attributional approach, our model will be limited to delivering insights into its intrinsic characteristics, but not into the side effects that might occur. An ICT product life cycle analysis will only reveal the first-order effects, not its role as an enabling technology [START_REF] Lorenz | CO2 Reduction with ICT: Prospects and Barriers[END_REF]. For instance, reducing the commuting impact by encouraging employees to work from home will reduce the project's overall impact from the model's perspective, but teleworking may also increase the overall distance traveled as stated by Caldarola and Sorrell [START_REF] Caldarola | Do teleworkers travel less? Evidence from the English National Travel Survey[END_REF]. Offices-related impacts such as energy consumption for heating, cooling and lighting will be transferred to employees' homes. Hook et al. [START_REF] Hook | A systematic review of the energy and climate impacts of teleworking[END_REF] concluded through a systematic review that economy-wide energy savings due to teleworking are typically modest, and can even be negative or nonexistent.

Our solution is not intended to conduct a full-fledged LCA to obtain the environmental impact for a software product. It however helps software stakeholders to prepare the Life Cycle inventory (LCI) [35] step, by providing a flow model of the software life cycle.

VIII. CONCLUSION AND NEXT STEPS

In this paper, we introduce an approach to model a software life cycle environmental impact. We show how using a bottomup holistic approach can allow stakeholders to identify impact shifts between life cycle phases, resources consumed and categories of impact. As recommendations to lower software project impacts cannot be generalized due to their uniqueness, we propose a simple and flexible way to model project resources and activities to adapt to different development models. We publish an implementation to foster a widespread approach, and show on a sampled use case the actionable insights delivered to stakeholders by identifying impact hotspots in phases and as resources. We highlight the importance of considering software's environmental impact over its complete life cycle, and not only its usage through energy consumption. We emphasize on the role of commuting on the overall impact, as well as the importance of considering ICT hardware resources' complete life cycle, as their manufacturing impact can be larger than their usage one. We encourage stakeholders to consider multiple impact categories, to avoid shiftings by focusing on a single one.

Based on our work and due to the scarcity of reference data to model software environmental impact, we suggest that future work explore the ICT hardware impact along multiple impact categories, especially its propagation through abstraction layers.

Fig. 1 .

 1 Fig. 1. Software life cycle tree example

Fig. 4 .Fig. 5 .Fig. 6 .

 456 Fig. 4. CO 2 e emissions distribution between resources

ACKNOWLEDGMENTS

This work is partly supported by the ANR Distiller grant (ANR-21-CE25-0022).