Eléonore Loiseau
email: eleonore.loiseau@inrae.fr

Miriam Colin

Aude Alaphilippe

Gustave Coste

Philippe Roux

To what extent are short food supply chains (SFSCs) environmentally friendly? Application to French apple distribution using Life Cycle Assessment

Keywords: Environmental impacts, eco-design, tipping lines, GIS, decision making, fresh food products Highlights

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

Introduction

There has been growing interest in recent years in food sourcing. Many studies have focused on assessing the sustainability of global and local food supply [START_REF] Brunori | Sustainability of local and global food chains: Introduction to the special issue[END_REF], and a particular emphasis has been placed on short food supply chains (SFSC) [START_REF] Praly | Les circuits de proximité, cadre d'analyse de la relocalisation des circuits alimentaires[END_REF]. The popular concept of "food miles", which originated in the UK and provides a measure of how far food travels between the production stage and the final consumer [START_REF] Weber | Food-Miles and the Relative Climate Impacts[END_REF], often demonstrates that local sales can be a strategy to decrease the environmental impacts of supply chains. However, this concept has been defined as a poor indicator of environmental impacts of food production since a comprehensive analysis should include: (i) a life cycle perspective (including in particular the farm stage and the cold storage) and (ii) the consideration of multiple categories of environmental impacts (Edwards- [START_REF] Edwards-Jones | Testing the assertion that 'local food is best': the challenges of an evidence-based approach[END_REF]. In this context, Life Cycle Assessment (LCA), is a well-established and recognized methodology to be used for quantifying the environmental performance of food products [START_REF] Sala | In quest of reducing the environmental impacts of food production and consumption[END_REF]. Apples, which are the fruit with the second biggest production in the world after bananas and before oranges and grapes [START_REF] Demaria | Measuring the complexity of complying with phytosanitary standard: The case of French and Chilean fresh apples[END_REF], offer an interesting case study to discuss environmental performance of a fresh food supply chain where no transformation processes are required. Several LCA studies have been conducted to assess the environmental impacts of apples including entire apple supply chains, from the orchard stage to the last retailer or the final consumer. A particular emphasis has been placed on the origin of the products in order to compare the environmental impacts of domestic production and imported products (Goossens et al., 2018;[START_REF] Longo | Life Cycle Assessment of organic and conventional apple supply chains in the North of Italy[END_REF][START_REF] Sim | LCA Case Studies The Relative Importance of Transport in Determining an Appropriate Sustainability Strategy for Food Sourcing A Case Study of Fresh Produce Supply Chains[END_REF]. These studies show the large contributions of transportation to the environmental performance of entire apple supply chains when products are imported over long distances by trucks or ships (e.g. contribution in the order of 70% for climate change impacts). This has also been demonstrated in similar works on energy balances (Blanke and Burdick, 2005;[START_REF] Milà I Canals | Comparing domestic versus imported apples: a focus on energy use[END_REF]. However, aside from the discussion on the country of origin of the product, it is necessary to consider the environmental performance of different subnational supply chains, such as buying apples directly at the farm or outdoor markets rather than the supermarket [START_REF] Goossens | How origin, packaging and seasonality determine the environmental impact of apples, magnified by food waste and losses[END_REF]. [START_REF] Cerutti | Assessing environmental sustainability of different apple supply chains in northern Italy[END_REF] and [START_REF] Jones | An environmental assessment offood supply chains: a case study on dessert apples[END_REF] assessed different retailing scenarios (i.e. direct selling, or grocery store) and concluded on the importance of retailing strategies for the environmental sustainability of food systems. They showed that localization is a direct approach reducing or avoiding the negative environmental impacts of international transportation, distribution and car use. However, [START_REF] Van Hauwermeiren | Energy Lifecycle Inputs in Food Systems: A Comparison of Local versus Mainstream Cases[END_REF] highlighted that distribution modes can affect the performance of these supply chains rather than the distances travelled. [START_REF] Coley | Local food , food miles and carbon emissions : A comparison of farm shop and mass distribution approaches[END_REF] concluded that purchasing the most geographically local produce per se does not necessarily mean the lowest environmental impact. In addition, [START_REF] Mundler | The energy efficiency of local food systems : A comparison between different modes of distribution[END_REF] showed that there is an important potential for logistical optimization in local food chains. Finally, Milà i Canals et al. (2007) stressed that it is difficult to make general recommendations because of the variability in data and characteristics of these different food supply chains. For instance, the average distance travelled in French SFSCs is 70 km, with a standard deviation of 109 km [START_REF] Vaillant | Transport et logistique des circuits courts alimentaires de proximité : la diversité des trajectoires d'innovation[END_REF]. Consequently, the great diversity of SFSCs does not allow for generalization on their environmental impact or conclusions as to whether they are better or worse than other forms of distribution [START_REF] Ademe | Les circuits courts alimentaires de proximité, Les avis de l'ADEME[END_REF]. That is why, ultimately, for individual or collective decision making, the main issue is not to decide definitively if one kind of supply chain performs better than another one, but rather to establish the extent to which, and the conditions under which, one supply chain can be more environmentally efficient than another one. To explore this issue, this paper: i) defines the different archetypes of supply chain that can be used to provide apples to final consumers, ranging from international ones to local ones; ii) computes average data to model these archetypes from a life cycle perspective; iii) assess their environmental impacts through LCA to identify hotspots; and iv) discusses how one type of supply chain can be more efficient than another one, by varying the values of the main hotspot drivers. Apples are used as a case study to discuss the method and results for all fresh products such as fruits and vegetables where no processing is required. A particular focus will be placed on SFSCs. Although there is a growing societal demand for SFSCs as a driver of change in the food system and a policy tool for rural development, SFSCs are still rarely investigated through full LCA studies. This paper is organized as follows: Section 2 describes the overall methodology applied according to the four LCA stages described in ISO standards [START_REF] Isoa | ISO 14040 -Environmental Management -Life cycle assessment -Principles and framework[END_REF]ISOb, 2006) with a particular emphasis on the goal and scope definition, and the data collection stages; Section 3 presents, interprets and discusses the main results; and Section 4 outlines key research findings and follow-up research that could be conducted to improve sustainability assessment of food supply chains.

Material and method

Goal and scope definition

Aim of the study and functional unit definition

The aim of the study is to discuss the environmental sustainability of various supply chains that provide apples to final French consumers, from short and local supply chains to long and international ones under different conditions. As there is a wide diversity of final consumers with their specific purchasing patterns, the present study focuses on a typical French household defined as a family with at least one minor child living on the edge of a large city [START_REF] Buisson | Où vivent les familles en France ? INSEE Première[END_REF]. The chosen city is the metropolitan area of Montpellier located on the Mediterranean coast, with approximately 465,000 inhabitants (INSEE, 2016).

Montpellier is used as a proof of concept. Like all large French cities, it hosts all kinds of food supply chains that can be compared on the basis of representative data. There are several options for a family to buy apples (e.g., hypermarket, farm sale, open-air market, grocery store), which correspond to dedicated supply chains. In this paper, the environmental impacts of these different options are assessed through LCA. Archetypes of different supply chains are therefore defined from average data to identify the main hotspots within each one and to get first reference standards as a basis for comparison. The functional unit selected is the purchase of one kilogram of apples from a retail location. It is therefore a product-oriented study and attributional modelling is adopted according to ILCD guidelines (EC-JRC, 2010). In order to focus only on supply chain organization, from producer to consumer, it is assumed that apple variety and quality (Gala, conventional agricultural production) is identical in all the types of supply chain studied, meaning that the apple cultivation stage will be considered as the same in all the studied alternatives, no matter where the actual production takes place.

Definition of Short Food Supply Chains (SFSCs) and other supply chains

There is a large variety of types of SFSCs, and several attempts have been made to define what type of supply chain should be at the heart of the reflection on re-localization and re-connection of agriculture and food production [START_REF] Kneafsey | Short Food Supply Chains and Local Food Systems in the EU . A State of Play of their Socio-Economic Characteristics[END_REF]. [START_REF] Chiffoleau | Les circuits courts de commercialisation en agriculture : diversité et enjeux pour le développement durable[END_REF] referred to the French legislation to determine the main features of SFSCs, and identified important characteristics, i.e. number of intermediaries, geographical distances, and type of sale system (individual or collective). According to her and the food supply chain typologies proposed by [START_REF] Malak-Rawlikowska | Measuring the Economic, Environmental, and Social Sustainability of Short Food Supply Chains[END_REF], Figure 1 proposes a classification of food supply chains divided into SFSCs, medium supply chains and long supply chains.

Figure 1

Apple supply chain archetypes

Based on Figure 1 and the knowledge of experts on the French apple sector, five main archetypes of supply chain have been defined for apple provision to a French urban household in this paper. These are shown in Figure 2: i) international long supply chain (L1); ii) national long supply chain (L2); iii) medium supply chain (M); iv and v) two SFSCs (S1 and S2).

International long supply chain, L1, imports apples from Chile, one of the main suppliers during the European apple off-season [START_REF] Agreste | Les récoltes élevées de pommes dans l'UE pèsent sur les exportations françaises, voire sur le marché intérieur. Agreste Conjoncture -Fruits -Synthèses[END_REF], and apples are sold in hypermarkets after having passed through several intermediaries including wholesalers, and Retail Distribution Centers (RDC). In national long supply chains, L2, apples are also sold in hypermarkets, but they come only from French orchards. It is assumed that they are placed on the market by producer organizations (i.e. cold-store in Figure2) which currently concerns 60% of the volume distributed [START_REF] Agreste | Inventaire des vergers 2013[END_REF]. Medium supply chains rely on a regional supply of apples and regional intermediaries such as markets of national interest in charge of supplying food to large living areas through supermarkets and specialist retailers in grocery stores or outdoor markets.

Finally, there are no intermediaries in the two SFSCs. In one case, the sale does not take place on the farm but via a collective structure (community supported agriculture, farmers' shop) or an outdoor market (direct off-farm sale, S1), and in the other case, the sale is made directly on the farm (direct on-farm sale, S2).

Figure 2

In all these supply chains, the system boundaries include apple production, sorting and packaging, storage, transportation, sale and the final consumer trip to buy apples. The consumption stage (e.g. consumer food waste) and the product end-of-life are not included in the study (e.g. management of organic apple waste such as cores and peelings). Only the packaging end-of-life is considered as well as sorting and losses until the final purchase.

Final consumers can choose between different modes of transport to make their purchases. This is facilitated by the fact that the study focuses on a household living in an urban environment where shops are generally close to residential areas and where alternatives to private cars are possible, including nonmotorized vehicles or no vehicles (pedestrians), which are also assessed in this study (i.e. alternative scenarios Mb and S1b).

Seasonality of apple supply

In France, apple harvesting takes place from the end of August to the end of November, depending on the variety. Apples can withstand long periods of storage in cold rooms before being sold and consumed. The apple market in France is therefore dependent on the season and can be split into three main periods that have a direct effect on the average duration of cold storage: P1, from August to November (short storage duration -about 2 months); P2, from December to March (medium storage duration -about 5 months); and, P3, from March to July (long storage duration -about 3 months for Chilean apples and 9 months for French ones). In addition, the apple supply sources are seasonally dependent. Most Chilean apple imports take place in the off-season (P3), whereas M, S1 and S2 supply chains rarely take place during this same period.

Analytical design of scenario comparison

Different combinations of purchasing periods and supply chains are assessed and compared to a reference scenario to identify the main environmental hotspots concerning apple provision to a French urban household. The reference scenario is defined as the combination between the national long supply chain and the medium storage duration (noted L2-P2 in Figure 3).

Figure 3

The first point that will be tested is the comparison between different supply chains that are located entirely within France (i.e. comparison A in Figure 3), to analyze environmental performances of long and short supply chains. The second comparison will focus on the season effect to assess the storage effect on the overall performance of the reference scenario (i.e. comparison B in Figure 3). As the French electricity mix is particular (mainly composed of nuclear energy), the effect of the electricity mix will also be analyzed. The third comparison will assess the importation effect to analyze the trade-off between long distance transportation versus long storage periods (i.e. comparison C in Figure 3). Finally, the last comparison will assess the effect of consumer transportation mode to discuss the impacts of the final km (i.e. comparison D in Figure 3).

Life cycle inventory analysis

All supply chains studied are based on the same basket of unit processes, only the amounts used for each process vary from one scenario to another. Therefore, Life Cycle Inventory (LCI) analysis is divided into two steps. The first one is the LCI computation for each unit process included in the studied systems, from orchards to the purchase by the final consumer at different points of sale. The second step is to define the quantities used in each scenario for each of the unit processes to get the full LCIs of supply chains. These LCIs are based on average data to provide a preliminary modeling of archetype supply chains. All LCI modeling was done using the commercial LCA software SimaPro.

LCIs of unit processes included in apple supply chains

These unit processes cover all life cycle stages, from apple production to purchase by the final consumer, including transportation according to the types of vehicles that can be used, infrastructure for different types of building and equipment, materials (e.g. types of packaging), cooling and conservation systems, and electricity consumption of the different equipment used in the supply chains. Except for apple production, the LCI data used for these unit processes come from the ecoinvent database v3.2 (ecoinvent, 2015). When there were no processes directly usable in the ecoinvent database, processes were created by assembling existing ecoinvent LCI components as for the cold room. This assembly was done using data from the literature and the stakeholders' expertise (e.g. electricity consumption of a cold room to store one kg of apples for one day). All the detailed information regarding the LCI dataset of unit processes included in apple supply chains is provided in the appendices (see Appendix A). For the orchard step, an in-field dataset from an experimental orchard representative of French fruit grower practices was used with an average yield of 37.8 tons per year of commercialized fresh apples (national source Agreste 2015Agreste -2016)). The dataset covers a 9-year period [START_REF] Alaphilippe | Environmental impact of intensive versus semiextensive apple orchards : use of a speci fi c methodological framework for Life Cycle Assessments ( LCA ) in perennial crops[END_REF] in order to account for the climatic condition variability and thus to ensure both accuracy and representativeness.. The production system is conventional with applications of mineral N-fertilization (47 kg/ha/year) and pesticides (29 kg/ha/year active ingredients). LCA data for the orchard step was considered the same for all supply chain scenarios that occur in France or in Chili.

Full LCIs of apple supply chains

All LCI unit process quantities used in each of the scenarios are given in the Appendix B. These quantities are computed using supply chain archetypes described in section 2.1.3 (e.g. sea transport stage or not or purchase in hypermarket or outdoor market) and different types of data. All these data are summarized in Table 1, and the methods for estimating them are described below.

For this first modelling, some data are shared by all the archetypes of supply chains. This is the case for the packaging stage. Only secondary packaging has been taken into account as it is has been identified as an important hotspot compared to primary or tertiary packaging [START_REF] Goossens | How origin, packaging and seasonality determine the environmental impact of apples, magnified by food waste and losses[END_REF]. Moreover, secondary packaging is fairly similar among retailers, and it has been assumed that half of the apples are loaded in cardboard boxes (50%) or plastic crates (50%, IFCO boxes) according to Goossens et al. (2018). Cardboard boxes are equally disposed of in landfills, incinerated or recycled, while IFCO boxes are reused for 10 years and then recycled [START_REF] Ademe | Analyse du cycle de vie des caisses en bois , carton ondulé et plastique pour pommes[END_REF].

Another common data between the supply chains concerns apple sorting and losses at each stage.

Estimates were given after discussions with experts in the apple sector and the CTIFL (referent organization for applied research in the French fruit and vegetable sector). Apple sorting and losses during calibration are around 20%, those during storage (including water loss of the fruit, varying from 0.5 to 2% depending on storage time) and packaging are around 4%, and those in retail stores are around 5%. These are only orders of magnitude and deserve to be refined through field surveys. Yet these figures are close to those estimated in the recent study of [START_REF] Caldeira | Quantification of food waste per product group along the food supply chain in the European Union: a mass flow analysis[END_REF] on food losses and waste in Europe (i.e. for fruit, waste is around 16% in primary production, 9% in the processing stage, and 1% in the retail stage).

It is assumed that a part of the apple sorting and losses is not wasted and is used for processing into juice, puree and compote.

Table 1 Other data differ greatly between supply chains, such as transport distances or consumer practices. An innovative procedure has been developed to directly compute consistent data on transport distances between the different intermediaries for each supply chain noted ai, bi, ci and di in Figure 2. Generally, data on average transport distances come from survey data or general statistics, which are often based on small datasets or samples not representative of real practices. To overcome this limitation, the proposed procedure computes transport distances using Python programming software and the open source route planner called [START_REF] Openrouteservice | Open source route planner[END_REF]. First, all apple orchards with information on the orchard surface area were localized in each French municipality using French statistics on agriculture [START_REF] Agreste | Inventaire des vergers 2013[END_REF]. Producer organizations were also localized across France according to national data from the French Ministry of Agriculture [START_REF] Maaf | Liste des organisations de producteurs du secteur des fruits et légumes[END_REF]. The road distances between production sites (orchards) and producer organizations were estimated by computing the average road distances between the centroids of the municipalities producing apples and the nearest producer organization (see Figure 4).

Figure 4

The same method has been used to calculate the road distance from producer organizations to the RDC that corresponds to logistics warehouses in the case of hypermarket sales or to the market of national interest in other sales networks. The road distances between each producer organization and the RDC were computed and the average of these distances was weighted by the production surface areas of the producer organizations. The latter is the sum of the apple production surface areas of every municipality for which this producer organization is the nearest to their centroid. This approach has been used to determine road distances for M, S1 and S2 alternatives, although the apple supply pool has been reduced within 200 km for M, 50 km for S1 and 25 km for S2 (see Figure 4). With this method, average transport distances for all the different supply chains were computed (see Appendix C to get the Python code). In order to give more importance to the high apple growing regions, the distances can be weighted by apple production surface areas in the municipality. However, this gives little variation in the results (see italics in Table 1).

For transport distances and the logistics in Chile concerning imported apples, literature data were used [START_REF] Labouze | Etude de l'impact environnemental du transport des fruits et légumes frais importés et consommés en France métropolitaine[END_REF]. Distances between the consumer's home and the point of sale are derived based on the assumption that the household lives downtown and that shops are nearby. Finally, information on the types of vehicles used according to the different trips is provided (see Appendix D for the corresponding table of vehicles). It is assumed that long supply chains have optimized their logistics and use larger vehicles. To model consumer practices while purchasing apples, literature data were used. According to [START_REF] Rizet | Chaînes logistiques et consommation d'énergie : Cas des meubles et des fruits & légumes[END_REF], share of apples in the total basket and the average weight of apples purchased were estimated for different archetypes of supply chains.

Impacts assessment

The impact assessment method chosen is the hierarchist approach of ReCiPe v1.11 [START_REF] Goedkoop | ReCiPe 2008: A life cycle impact assessment method which comprises harmonised category indicators at the midpoint and the endpoint level[END_REF]. The impact categories are characterized at both the midpoint (18 categories of impacts: climate change, ozone depletion, terrestrial acidification, freshwater eutrophication, marine eutrophication, human toxicity, photochemical oxidant formation, particulate matter formation, terrestrial ecotoxicity, freshwater ecotoxicity, marine ecotoxicity, ionizing radiation, agricultural land occupation, urban land occupation, natural land transformation, water depletion, mineral depletion, and fossil depletion) and the endpoint level (3 categories of damages, i.e., human health, ecosystems and resources). Because ReCiPe combines midpoint and endpoint methods, the impact characterization at the endpoint level specifies the damage contribution of each midpoint impact category, which is very helpful for result interpretation. In addition, calculations are made with SimaPro v8.2.

Results and discussion

In order to present LCA results as clearly as possible, the preliminary contribution analyses are presented with midpoint indicators to maintain the multicriteria nature of LCA and to assess in detail the impacts of each stage of the apple life cycle. However, for scenario comparison, an endpoint indicator presentation is used to make interpretation easier, with only 3 impact categories corresponding to the 3 areas of protection commonly used in LCA (Human Health, Ecosystem, Resources).

Detailed contribution analysis for the reference scenario (L2-P2)

Figure 5 shows that life cycle stages that incur the main environmental impacts in the reference scenario (national long supply chain) are the production stage, refrigerated transportation, packaging, storage and the consumer trip. Their contribution depends on the given impact category.

Figure 5

Apple production has significant impacts (above 40%) on acidification, freshwater eutrophication, terrestrial ecotoxicity, agricultural land occupation and metal depletion, due to inputs such as fertilizers and pesticides. These inputs are correlated to orchards yields. Refrigerated transportation contributes substantially to climate change, fossil depletion, particulate matter formation, photochemical oxidant formation and impacts on natural, and urban lands due to fuel combustion and infrastructure. Packaging contributes significantly to marine eutrophication, ozone depletion, marine and freshwater ecotoxicity and agricultural land occupation generated by the primary products (i.e. cardboard box) and the processing stage. Cold storage contributes significantly to impacts induced by ionizing radiation due to electricity consumption but not so much to other impact categories. This is due to the particularity of the French nuclear electricity mix and therefore results are not representative of other countries. Finally, consumer trip for apple purchases is significant for almost all impact categories despite the low distance estimated between the consumer's house and the hypermarket in the supply chains studied (see Table 1). These results are in line with those of previous studies. For instance, the cultivation stage contributes little to the impacts on climate change (around 15% in Goossens et al. (2018)) or the use of fossil resources (less than 20% as in [START_REF] Longo | Life Cycle Assessment of organic and conventional apple supply chains in the North of Italy[END_REF]).

Comparison between different types of supply chains

The comparison between the reference scenario, defined as the national long supply chain, and alternative French options shows that there are few differences between the national, medium and direct off-farm sale supply chains (cf. Figure 6). Surprisingly, the main difference is with the direct on-farm sale supply chain when the final consumer goes directly to the farm to buy apples (S2-P2). This supply chain does not perform well because of the last transportation step, which is quite long (consumer-farm car transportation of about 23 km, or a 46 km round trip, for the specific case of Montpellier). The medium supply chain has a slight advantage over the other supply chains, followed by the reference scenario and the direct off-farm sale supply chain. However, these results are computed for a given set of data that do not reflect actual variability of practices and geographical particularities. It is therefore required to include this variability before providing any generic conclusions regarding food supply chains. This is what is proposed in Section 3.6 of the paper.

Figure 6

Figure 6 shows moreover that imported apples from overseas is the second most impactful supply chain (L1-P3). Import mainly occurs before the French apple harvest, when there are few domestic apples on the market. The impacts of import are much higher than the impacts of cold storage, regardless of storage time. In scenario L2-P1, apples are stored only for a period of two months, compared to five months in L2-P2, and nine months in L2-P3. Due to the particularity of the French electricity mix, based mainly on nuclear power, the impacts of electricity consumption during storage are relatively small compared to other life cycle stages, which makes it difficult to clearly distinguish the "storage time" effect (from 2 to 9 months). A comparison with another electricity mix is also proposed. The Polish electricity mix has been chosen in ecoinvent as it is mainly based on coal-fired power generation. It increases the total impacts for each variant of the reference scenario. This can change the damage to human health by up to 20% and to resources by up to 15%, but is not particularly damaging to ecosystems. Finally, in the studied scenarios, the distances between the consumer's home and the retail point are very short: between 1 and 1.6 km. Even with these limited distances, the impacts of consumer trips are significant (see Figure 5). For scenarios L2-P2, M-P2 and S1-P2, consumer's car trip has been substituted by a walking trip. This substitution meaningfully decreases the total impacts of the original scenario, and the medium supply chain that relies on regional sourcing stands out as the most environmentally efficient one (see Figure 6). Moreover, the scenario based on direct off-farm sales (S1-P2) is almost as efficient as the reference scenario showing the importance of consumer choice on the total impacts of apple purchases.

Tipping lines for decision making in highly variable contexts

The results on the effect of the mode of transportation should be interpreted with caution as they do not reflect the high variability of consumer practices concerning ways of purchasing apples, nor the territorial context (i.e. structuring of the supply chains, and location of producers and of all intermediaries). In particular, the final consumer-retailer distance is highly relevant as the share of the consumer trip in the total impacts of the studied supply chains is substantial (see Figure 5). To address this issue and help decision making in highly variable contexts, abacus have been built to see in which cases one scenario may be better than another. For that purpose, Figure 7 proposes to search for "tipping lines" for which direct on-farm supply chain S2-P2 performs better than the reference scenario L2-P2 regarding three key parameters: (i) the quantity of apples purchased, (ii) the distance between consumer and farm and (iii) the share of apples in purchases (%).

Figure 7

Rationally, it appears that direct on-farm sales can perform better than average national supply chains if distances between the consumer and the farm are reduced (less than 15 km for an apple share of 21%, or less than 5 km for an apple share of 76%), or if the quantities of apples purchased at the farm are increased (more than 12 kg at 22.7 km for an apple share of 21%), or even if the share of apples purchased in the total basket is decreased. The latter point also emphasizes the fact that the purchase of apples might not be the main or the only purpose of the consumer's trip, and the share of the trip could also be reduced, which at the same time increases the environmental performance of the short supply chain.

Data consistency and comparison with other studies

Data is a major issue in the environmental assessment of food supply chains, as there is a wide diversity of individual and collective practices and situations, depending on many factors (socio-economic contexts, geography, demography, consumer habits, or cultural identity). Table 2 illustrates the wide range of transportation data used in different studies to assess the environmental performance of long and short apple supply chains.

There is a large discrepancy in the numerical data used at each step of the supply chains, which shows the difficulty to define references and to provide generic recommendations for the wide diversity of food supply chain parameters. This is all the more problematic as these stages and the related data contribute significantly to the impacts of the entire supply chain. This finding supports the idea that this is not a question of comparing long and short supply chains based on a fixed reference, but rather of identifying what the hotspots are for these different supply chains, and at which time one can perform better than another according to the real conditions in which supply chains operate. This will be more instructive and useful for the eco-design of food supply chains as well as for labelling them. Important differences in data correspond to the distances travelled by trucks. Geographical and organizational contexts can differ substantially between studies and can partly explain the differences found in numerical data. However, for both studies located in the same country, i.e. Italy, there are large differences between the studied supply chains (up to more than 100 km). In comparison, there are smaller differences between distances travelled by ships, as shipping routes may be better documented and offer fewer opportunities for itineraries.

Table 2 Finally, data on the final kilometers are also important because they have a significant impact on the environmental performance of the whole supply chain. On this point too, data found in the literature are disparate (see Table 3).

In the proposed approach, average transport distances were computed using Python programming software and the open source route planner called Openrouteservice ( 2019) combined with ponderation of distances by the surface areas of apple orchards. This strategy seems more relevant (regarding in particular data heterogeneity) than combining national/global statistical data with local small sampling surveys on local practices. The big-data outlook should, in the near future, allow this type of approach to be further consolidated to provide more representative data.

Table 3

Studies show, however, (Table 3) that it is important to distinguish between rural and urban areas because the distances travelled in rural areas are generally greater.

Importance of the Functional Unit (FU)

The choice of the functional unit (FU) can largely determine the results obtained [START_REF] Huijbregts | Application of Uncertainty and Variability in LCA. Part I : A General Framework for the Analysis of Uncertainty and Variability in[END_REF]. This is the general case in agricultural LCAs where, depending on the FU selected, the results of the study may modify the LCA results and the order of the alternatives studied, according to their environmental performance [START_REF] Baumgartner | Environmental profiles of farm types in Switzerland based on LCA[END_REF][START_REF] Haas | LCA Methodology Life Cycle Assessment Framework in Agriculture on the Farm Level[END_REF]. The choice of the FU is essential when assessing food supply chains because, as for agriculture, these systems can be multifunctional. The growth of SFSCs can be explained for various reasons and meets a wide range of needs corresponding to various FUs, depending on whether the perspective is that of producers, consumers or citizens [START_REF] Fabbrizzi | The Short Food Supply Chain: A Concrete Example of Sustainability[END_REF]. The emergence of SFSCs can contribute to a better redistribution of the added value, the promotion of social and professional recognition of farmers, or the development of new skills from a producer's perspective [START_REF] Mundler | The contributions of short food supply chains to territorial development: A study of three Quebec territories[END_REF]. Consumers can choose to rely on SFSCs to access better quality products that are fresher, more authentic or more reliable in terms of traceability [START_REF] Giampietri | Exploring consumers' attitude towards purchasing in short food supply chains[END_REF]. In addition, SFSCs can be a driver of local development through job creation, or the creation of new farms and of the welfare of the community [START_REF] Mundler | The contributions of short food supply chains to territorial development: A study of three Quebec territories[END_REF]. Ultimately, SFSCs can provide a wide range of services and several FUs can be chosen. However, all LCA studies use the kg of apples provided to the retailer or the final consumer as the FU. Other FUs such as the euros that go back to farmers, the number of jobs created or the nutritional quality of the final products could be tested to assess the robustness of the conclusions that can be drawn [START_REF] Poore | Reducing food's environmental impacts through producers and consumers[END_REF]. This point is related to the modelling of the final consumer's trip, which has a major impact on the total performance of the supply chains studied. However, consumers may have several reasons for shifting to farmers and local producers. In addition to buying products, travelling to a different place allows people to walk around and can be combined with other activities such as buying other products, visiting natural or cultural sites, or sport practices. The farm may also be on the way home from work. It is moreover an opportunity to communicate and create ties between producers and consumers around the product itself and beyond (cultural aspects), which are not embedded within the FU "a kg of apples". If these other reasons were taken into account, it would significantly increase the environmental performance of the supply chains studied, and in particular that of direct on-farm sales, whose environmental impacts are based solely on the final consumer's trip. Moreover, this study deals with a household living in an urban area. The conclusions could be completely different in a rural context where supermarkets were further away from dwellings and where there is generally no alternative to the use of private cars. To pursue this inquiry, different territorial and organizational contexts should be studied.

Other modelling choices

The study aims at providing a general overview on the environmental performance of different types of food supply chains, from long ones to short ones. In accordance with this objective, "theoretical" scenarios representing archetypes of supply chain have been defined (see figure 2). It is not the intention here to specifically describe a real example of a short supply chain, for instance. Therefore, only secondary data has been used in the modeling of the systems, as defined by the ILCD handbook (EC-JRC, 2010). To go further, it would be necessary to study existing supply chains to obtain primary data and model the foreground system in coherence with these specific cases. It could be interesting to also include the apple consumption stage because it can have a significant impact, depending on the product's preservation method and the final waste rate [START_REF] Wikström | The influence of packaging attributes on recycling and food waste behaviour -An environmental comparison of two packaging alternatives[END_REF]. Loss and waste rates can moreover vary significantly, depending on the consumers and the type of supply chain studied. It has been shown that the SFSCs generate changes in practices among all actors in the chain, including consumers, who adopt more sustainable practices [START_REF] Chiffoleau | The participatory construction of new economic models in short food supply chains[END_REF]. This can affect the choice of products (purchases of downgraded products, limitation of losses when cooking, for example by using damaged products, etc.). These points should be further developed and included in future LCA studies of SFSCs by broadening the boundaries of the system through a cradle-to-grave perspective and conducting a sensitivity analysis on the consumption phase. More generally, changes in practice throughout the whole chain should be assessed, particularly on the producer side. In this study, the choice was made to take the same apple as a starting point, but to go further, different apple production methods should be taken into account. For instance, it is shown that SFSCs promote alternative production methods with more organic producers [START_REF] Aubert | Do short food supply chains go hand in hand with environment-friendly practices? An analysis of French farms[END_REF][START_REF] Mundler | The contributions of short food supply chains to territorial development: A study of three Quebec territories[END_REF]. These parameters should be considered to deepen the knowledge on the environmental performances of food supply chains. Yet, in the case of apples, this modelling choice affects the results only slightly because, as the analysis of contributions shows (see Figure 5), the production stage is not the most impactful stage compared to the other life cycle stages. It is also important to discuss the choice of the city used as a case study. If a city is distant to a greater or lesser degree from the main production areas, this can significantly affect the performance of the different supply chains. With proximity to production areas, the performance of long and short circuits can be significantly improved. On the other hand, if the first production areas are far from the city studied, the performance of the supply chains can be quite poor, especially for the SFSCs where distance is one of the main drivers of efficiency. However, in France the main apple production areas are spread fairly well over the territory, and there are orchards all over the country. Therefore, the choice of another city should not radically change the results of the study.

Conclusions and future prospects

The aim of the study was to discuss the environmental sustainability of different supply chains that provide food to final consumers, comparing archetypes of short and local supply chains to long and international ones using LCA methodology. This was achieved through a case study on apple supply chains in France. LCA can provide a valuable contribution to the development of sustainable strategies in food supply by identifying environmental hotspots through a life cycle and multicriteria perspective and drivers of changes. The results here show that the logistics phase contributes significantly to the impacts and that there is still room for improvement in supply chain performance. However, it is necessary to pay attention to the question raised in the LCA. Given the wide diversity of practices and cases in food supply chains and the advances of traditional ones (longer and more globalized), our intention is not to compare these supply chains to those of SFSCs, which are in full expansion. The question to be asked is rather how these SFSCs can improve their environmental performance and up to what point they could perform better than traditional supply chains. The results show that the answers to these questions may differ, depending on the context and organization of the SFSCs. On the one hand, national and medium supply chains are optimized both in terms of distances, transportation modes, and loading rates. On the other hand, emerging short supply chains have wide margins for progress in terms of logistics to improve their environmental performance. If these short supply chains implement strategies to optimize their logistics, and consumers optimize their food supply trips, performance would be greatly improved and could exceed that of conventional supply chains [START_REF] Vaillant | Transport et logistique des circuits courts alimentaires de proximité : la diversité des trajectoires d'innovation[END_REF]. Finally, consumers have significant impacts through the way they purchase foods, and active mobility should be promoted, especially in urban environments.

The lessons learnt from our case study can be generalized to all fresh products such as fruits and vegetables requiring little or no processing, and where the production phase is not the most impactful (e.g. fruits and vegetables grown in the field). However, there are still avenues of research to be investigated in further depth. First, more knowledge is needed to better understand how the supply chains are organized according to the types of food product categories. GIS-based tools, can be useful for compiling logistics data, for example, and developments could make it possible to better model the movements of final consumers according to their places of residence and work, and the location of all the stakeholders involved upstream of the supply chains. Surveys and field visits are however also necessary to deepen the knowledge of the supply chains, particularly on the practices of stakeholders both upstream (producers, transformers) and downstream (consumers) of the supply chain, where behavior can differ greatly and have a significant impact on the environmental performance of the whole supply chain. This is all the more true for product categories where the stages of production, processing or consumption have a heavy impact, such as products of animal origin and processed products [START_REF] Foster | Environmental impacts of food production and consumption: a re-port for the Department for Environment, Food and Rural Affairs[END_REF]. Second, a complete sensitivity and uncertainty analysis could be performed by stochastizing all input data. The method developed by [START_REF] Weidema | Data quality management for life cycle inventories -an example of using data quality indicators[END_REF] could be used to qualify the uncertainty on the input data. Note that stochasizing the calculations would not change the main conclusions of the study, particularly those presented in the abacuses in Figure 7. It would allow a fuzzy area to be drawn on these graphs where it would not be possible to differentiate the performance of a supply chain from another.

Furthermore, the study focused on the supply of one kg of apples to the final consumer. This functional unit may be restrictive. Food supply chains are multifunctional systems and provide a wide range of services that should be identified and quantified to consider them in LCA studies and move away from the strictly productivist rationale. Moreover, this choice implies modelling the system on a micro scale. Yet the supply of cities can also be studied at meso scales, through the use of territorial LCA approaches [START_REF] Loiseau | Territorial Life Cycle Assessment (LCA): What exactly is it about? A proposal towards using a common terminology and a research agenda[END_REF]. In this context, an attributional approach such as the one adopted in this study is not necessarily appropriate. The choice of one supply chain over another can generate indirect socio-economic effects that should be considered in the assessment according to a consequential approach (EC-JRC, 2010). Therefore, adopting modelling approaches focusing on large-scale systems and considering the socio-economic consequences of a decision would provide a more comprehensive view on the issue of urban food supply. Independently of food supply sustainability issues, a more generic conclusion on LCA use can also be drawn from this study. Stakeholders often expect LCA to give them a clear-cut answer to the question "is scenario A better than B?". Since LCA results are, in most of the cases, highly sensitive to specific local parameters, it would seem more relevant to reword the question as "under what conditions would A or B have the best environmental performance?". This would avoid decisions being made on the basis of scenarios that are not representative of the diversity of all real practices. This is exactly what has been illustrated in this paper with the apple case study and the provision of abacus based on tipping line computations. These results have direct managerial and policy implications. Firstly, in terms of ecodesign, LCA is traditionally used to identify the main hotspots of a system. The abacus provided goes further by quickly identifying conditions that can significantly improve the performance of a system. Secondly, there is currently much debate on environmental labelling of products and on how to communicate the LCA results [START_REF] Minkov | The product environmental footprint communication at the crossroad: integration into or co-existence with the European Ecolabel?[END_REF]. The outcome here shows that, in addition to the complexity of a multicriteria assessment, LCA can be used to produce very educational and simple results for the general public (e.g., how many products does a final consumer need to purchase to amortize his or her private car trip). This type of approach needs to be continued in order to improve the communication of LCA results (often considered too complex) and to determine what is important to share with the public (identifying the appropriate means of action) and in what form. et al., 2008). Shaded cells mean that the corresponding steps are not included in the systems studied.

List of tables and figures

Table 2 Review of the wide range of transportation data used in other apple studies, n/a = not available, ((l) = local distribution, (r) = regional distribution, (n) = national distribution, (i) = international) ((o) = organic production, (c) = conventional production), UK = United Kingdom, NZ = New Zealand, BE = Belgium, GWP = Global Warming Potential.

Table 3 Distances transportation between retailer and final consumer according to different types of retailer and geographic context (urban / rural). Figure 2 Description of different apple supply chains, from international long supply chain (L1), to direct on-farm sales (S2), through a wide range of combinations including national long supply chains (L2), medium supply chains with intermediaries (M) and direct off-farm sales (S1). Two variants are proposed for M and S1 with the possibility to rely on non-motorized vehicles or no vehicles (pedestrians) (scenarios Mb and S1b respectively). Distances between each stage of a supply chain are different for each scenario and are noted ai for the distance between the farm and the cold store, bi for the distance between the coldstore and the RDC, ci for the distance between the RDC and the retailer, and di for the distance between the point of retail and the consumer.

Figure 3 Summary of the main comparisons performed in the study to assess in an exhaustive way the environmental impacts of food supply chains, from international supply chains to direct selling (In parenthesis, storage duration in cold rooms).

Figure 4

Main apple production areas in France and calculation of average transport distances between intermediaries up to final consumers in the metropolitan area of Montpellier (see zoom). Distances from the different orchard locations are weighted by their respective surface areas.

Figure 5 Contribution analysis for the reference scenario (L2-P2) using ReCiPe midpoint (H) LCIA method.

Figure 6 Comparison of the environmental impacts of national apple supply chains (reference scenario L2-P2) with other types of supply chain, from international (L1-P3) to medium (M2-P2) and short (S1-P2 and S2-P2), including seasonal effect (L2-Px) and consumer transport mode effect (x2-x2 foodpath), using the ReCiPe endpoint (H) LCIA method for all types of supply chain.

Figure 7

Search for tipping lines where the direct on-farm sale performs better than the reference scenario due to optimal distance between consumer and point of sale, and quantities of apples purchased for two different apples' share in the final basket (21 and 76%). To what extent are Short Food Supply Chains (SFSCs) environmentally friendly? Application to French apple distribution using

Life Cycle Assessment

Figure 1

 1 Figure 1 Proposals for food supply chain classification according to Chiffoleau (2008) and Malak-Rawlikowska et al. (2019) (*supermarkets and hypermarkets are now offering some products directly from the producer).

FiguresFigure 1

 1 Figures

Figure 3

 3 Figure3Summary of the main comparisons performed in the study to exhaustively assess the environmental impacts of food supply chains, from international to direct selling (in parenthesis, storage duration in cold rooms).

Figure 4

 4 Figure 4 Main apple production areas in France and calculation of average transport distances between intermediaries up to final consumers in the metropolitan area of Montpellier (see zoom). Distances from the different orchard locations are weighted by their respective surface areas.

Figure 5 Figure 6 Figure 7

 567 Figure5Contribution analysis for the reference scenario (L2-P2) using the ReCiPe midpoint (H) LCIA method.0%20% 40% 60% 80% 100%

Eléonore

  Loiseau a,b* , Miriam Colin a,b , Aude Alaphilippe b,c , Gustave Coste c,d , Philippe Roux a,b a ITAP, Univ Montpellier, INRAE, Institut Agro, Montpellier, France b ELSA, Research Group for Environmental Lifecycle and Sustainability Assessment, Montpellier, France c UE0695, Domaine de Gotheron, INRAE, St-Marcel-les-Valence, France d LBE, Univ Montpellier, INRAE, Montpellier, France

Table 1

 1 Data used to model full LCIs of supply chains based on LCIs of unit processes (VP = private vehicle, types of vehicles C1, C2, C3, C4, C4R described in Appendix D) (*Labouze et al., 2007)(**Rizet

Table 3

 3 Distances of transportation between retailer and final consumer according to different types of retailer and geographic context (urban / rural).

	Sources		MTES (2008)	Rizet et al. (2008) Urban Rural	AGAM (2012) Urban (Marseille)
		Hypermarket 8.6 km	4.4 km	9.3 km	6.5 km
	Distances: retailer	Grocery	n/a	0.8 km	8.1 km	2.9 km
	consumer (km)
		Outdoor market	n/a	2.3 km	3.5 km	n/a

Acknowledgments

This research has been financially supported by the ANR (French national research agency) under the French research project n°ANR-13-ALID-004 Sustain'Apple. We thank in particular Pierre Varlet, Dominique Grassely, Anthony Langlais, Christian Hutin, and all the professionals in the apple sector for their expertise and precious advice, especially for the definition of realistic apple supply chain scenarios adapted to the French situation. We thank the steering committee and the coordinator of Sustain'Apple project, Jean-Marie Codron, as well as GIS Fruits, the instigator of this project.

Appendices

Appendices include: all the detailed information regarding the LCI dataset for all unit processes included in supply chain scenarios (A); the amounts of the unit processes used to model full LCIs of apple supply chains (B); the Python code used to compute average road distances between apple supply chain scenarios (C); and the corresponding table of vehicles used in the different supply chain scenarios described in Figure 2 (D).

Tables Table 1 Data et al., 2008). Shaded cells mean that the corresponding steps are not included in the systems studied. Table 2 Review of the wide range of transportation data used in other apple studies, n/a = not available, ((l) = local distribution, (r) = regional distribution, (n) = national distribution, (i) = international) ((o) = organic production, (c) = conventional production), UK = United Kingdom, NZ = New Zealand, BE = Belgium, GWP = Global Warming Potential. Shaded cells mean that the corresponding steps are not included in the systems studied. Figure 2 Description of different apple supply chains, from international long supply chain (L1), to direct on-farm sales (S2), through a wide range of combinations including national long supply chains (L2), medium supply chains with intermediaries (M) and direct off-farm sales (S1). Two variants are proposed for M and S1 with the possibility to rely on non-motorized vehicles or no vehicles (pedestrians) (scenarios Mb and S1b respectively). Distances between each stage of a supply chain are different for each scenario and are noted: ai for the distance between the farm and the cold store; bi for the distance between the coldstore and the RDC; ci for the distance between the RDC and the retailer; and di for the distance between the point of retail and the consumer.

Long supply chains Short supply chains

Mb S1b

Apple production (GALA)

Medium supply chains


Patrice Guyot
email: guyot@irit.fr

Julien Pinquier
email: pinquier@irit.fr

Régine André-Obrecht

Samova

Water flow detection from a wearable device with a new feature, the spectral cover

This paper presents a new system for water flow detection on real life recordings, and its application to medical context. The recognition system is based on an original feature for sound event detection in real life. This feature, called "spectral cover" shows an interesting behaviour to recognize water flow in a noisy environment. An experiment is realized with more than 7 hours of videos recorded by a wearable device. The system based on thresholds obtains good results for the water flow event recognition (Fmeasure of 66%). A comparison with classical approaches using MFCC or low levels descriptors and GMM classifiers is done to attest the good performance of our system. Adding the spectral cover to low levels descriptors also improve their performance and confirms the interest to this feature.

Introduction

Despite significant progress in audio information retrieval this last decades, identifying environmental sound events in everyday life is still a challenging task. Indeed, the amount of information holding in an acoustic scene can be very important. For example, sound of cooking utensils may lead to infer someone's cooking activity; vehicle passing sound may lead to infer a nearby road. That's why audio can be sometimes more accurate than video to recognize some events: as a gunshot in a crowd or the use of tap water. Thus, applications of Audio Event Detection (AED) are numerous, for instance humanoid robots audition [START_REF] Sasaki | Daily sound recognition using pitch-clustermaps for mobile robot audition[END_REF], sport (ball hit detection [START_REF] Zhang | Ball hit detection in table tennis games based on audio analysis[END_REF]), surveillance systems [START_REF] Clavel | Events detection for an audio-based surveillance system[END_REF], more recently life-logging [START_REF] Al Masum Shaikh | Automatic life-logging: A novel approach to sense real-world activities by environmental sound cues and common sense[END_REF] and activity detection for older adults [START_REF] Taati | Water flow detection in a handwashing task[END_REF].

Even if the feasibility of the AED in real life is increasing, most of audio segmentation and classification methods are used on clean corpus like extracts of radio recordings [START_REF] Galliano | The ester phase ii evaluation campaign for the rich transcription of french broadcast news[END_REF], films, or studio produced music tunes [START_REF] Downie | Music information retrieval[END_REF]. In this case, the use of signature [START_REF] Pinquier | Jingle detection and identification in audio documents[END_REF] can be an efficient solution to detect audio events. Nevertheless, a lot of acoustic features and retrieval methods which can be efficient in this particular context become obsolete in the everyday noisy life, due to the amount of learning data which would have been necessary to describe all acoustic cases [START_REF] Mesaros | Acoustic event detection in real-life recordings[END_REF].

A typical difficulty of identifying predetermined audio events in everyday life recordings comes from the possible overlapping of many different sound events. Indeed, this task can be seen as computational auditory scene analysis, where lots of different environmental sounds produce a mixture which must be analysed to obtain an auditory description of the environment for the listener [START_REF] Wang | Computational auditory scene analysis: Principles, algorithms, and applications[END_REF].

The unknown presence of speech and various noises make this task very hard, even sometimes by human. For example, noises from indoor could comes from doors, electric devices or air-conditioning. Those from outdoor could be cars, planes, birds or rain (see [START_REF] Schafer | The soundscape: Our sonic environment and the tuning of the world[END_REF] for a study in detail).

Another problem comes from the acoustic variability of sounds which could be labelled as the same audio event. Indeed, lot of objects which produce the same sound at a semantic level can produce very different acoustic sounds depending on their type or their material. For instance, if we focused on home environmental sounds, a door bell could differ a lot among different houses. The presence of different type of reverberation in houses may increase this difficulty.

Last but not least, the variability of the recording conditions like the choice of the microphone and it position in the space can deeply modify the acoustic mixture [START_REF] Smolders | On the importance of the microphone position for speech recognition in the car[END_REF]. Hence we can say that acoustic event detection in real life is a nondeterministic problem.

We present in this paper a robust audio water flow recognition system, from a wearable device recording, for activities indexing. This paper is organized as follows. Section 2 presents the AED systems and gives a review of previous works in water flow detection. Section 3 explains the IMMED project and the aim to build a water flow recognition system. We propose our water flow recognition system in section 4. Section 5 describes experiments and compari-son with a classical recognition system.

Previous works

The AED task purpose is to detect and recognize a closed set of pre-defined acoustic events in audio data. The most classical systems compute acoustic features to train some classifiers. The features can be extracted from the temporal domain (energy, zero-crossing rate, etc.) or from the spectral domain (spectral centroïd, spectral flux, spectral rolloff, etc). Some Low-Level Descriptors (LLD) have been standardized in the MPEG-7 norm. A large set of features have been described in [START_REF] Peeters | A large set of audio features for sound description (similarity and classification) in the cuidado project[END_REF].

If the Mel-Frequency Cepstral Coefficients (MFCC) are a very often used set of features in all types of audio recognition tasks and particularly speech [START_REF] Gauvain | The limsi broadcast news transcription system[END_REF], their efficiency for noisy sound recognition is discussed in several works in comparison to LLD [START_REF] Peltonen | Computational auditory scene recognition[END_REF][START_REF] Kim | How efficient is mpeg-7 for general sound recognition[END_REF]. Even if the potential space of extracted features is theoretically huge [START_REF] Pachet | Exploring billions of audio features[END_REF], most of the research works on acoustic events are not focused on specific features.

If different works have been done in environmental sound event detection [START_REF] Al Masum Shaikh | Automatic life-logging: A novel approach to sense real-world activities by environmental sound cues and common sense[END_REF][START_REF] Mesaros | Acoustic event detection in real-life recordings[END_REF], classification of environmental sound events for instance in a kitchen [START_REF] Kraft | Temporal ica for classification of acoustic events in a kitchen environment[END_REF], or context recognition [START_REF] Chu | Environmental sound recognition with time-frequency audio features[END_REF], only few studies deal specifically with water flow detection. These works are motivated by daily living activities recognition, underlying the hypothesis that several home human activities can be retrieved with water use analysis.

Some studies use sensors on the water pipes to provide an activity detection system. In [START_REF] Fogarty | Sensing from the basement: a feasibility study of unobtrusive and low-cost home activity recognition[END_REF], four microphones are installed in the basement, on hot water, cold water and drain pipes. This low cost system allows the recognition of eight daily living activities, with using the zero crossing rate (ZCR) and root mean square of the recording samples. In [START_REF] Ibarz | Design and evaluation of a sound based water flow measurement system[END_REF], Ibarz use a sensor attached to a water tap. The system detects if water is flowing in the pipe, but also quantifying the flow that allows to compute the amount of water used. The decision system is trained with Fast Fourrier Transform (FFT) coefficients and MFCC. The classification is perform by a k-Nearest Neighbor (kNN) system.

Two other studies use recordings from microphones to detect the Activities of Daily Living (ADL). Chen proposes in [START_REF] Chen | Bathroom activity monitoring based on sound[END_REF] to detect and recognize different bathroom activities with a single microphone close to the washing basin. The aim of this project is to monitor the bathroom ADL and increase understanding of personal hygiene behavioural problems of dementia patients. The system uses a classical approach: MFCC and Hidden Markov Models (HMM).

In [START_REF] Taati | Water flow detection in a handwashing task[END_REF], the purpose is water flow detection in a handwashing task with a fixed camera placed above the sink. Taati make use of audio and video features to detect the water flow. This system aims to be used within a system that provides reminding prompts for Alzheimer's sufferers. Taati compute a signal to noise ratio, the ZCR, the spectral centroïd, the spectral roll-off at 85%, the spectral flux and MFCC. Different classifiers have been tested with quite closed results.

Activities of daily living with a wearable device

We are working on the IMMED project [START_REF] Mégret | The immed project: wearable video monitoring of people with age dementia[END_REF], which proposes to help the doctors in their diagnosis with a video monitoring system. Indeed, with a longer lifetime expectancy, there is nowadays a real challenge in helping the elderly population to keep their autonomy as long as possible. The IMMED project follows the "aging in place" approach wich reduce the healthcare costs and allows the elders to be independent and socially connected. This approach is limited by the autonomy of the patients and their ability to relize the activities of daily living. The project uses a video acquisition wearable device that the voluntary patient wears at home while he executes ADL. With providing videos of the people in age of dementia in their activities of daily living, the goal of the project is to assist the doctors to measure autonomy decline as part of the diagnosis process. The videos are added to the traditional survey based on interview of the patient and the relatives which take part in the doctor diagnosis.

As the doctors will not watch hours of videos, an automatic system of activities indexing is developed to ameliorate the navigation inside the videos. One of the project's purpose is to create an automatic indexing system to segment the video in ADL. By this way, the doctors could directly watch specific activities to evaluate the patient autonomy. Thus, an original interface permits to access the videos.

The final decision system uses video and audio cues like water flow events in a hierarchical HMM to take a decision about activities of the patients [START_REF] Karaman | Activities of daily living indexing by hierarchical hmm for dementia diagnostics[END_REF]. Focusing on the audio part, we noticed that water flow recognition can help to index several human activities like cooking, or do the dishes. Moreover water sounds are more universal that sounds coming from other activities like make coffee which depend strongly of the houses and the object implies in.

In comparison to the other water flow detection studies (describes in section 2), the aim of the IMMED project is to observe the patient in his personal home with the less intrusive approach as possible. In this condition the use of sensors, placed on the basement of a house or on water taps, is inconceivable. The other studies which deal with water flow measurement use microphone in a fixed place. Consequently the machine learning methods can be efficient thanks to a certain homogeneity of the training data. By contrast, our wearable device implies an important variabil-ity of sounds due to different recording places and various activities executed by the device holder. First, each recording corresponds to a different home. Secondly, the device holder could be indoor, outdoor, and move from one room to another. Furthermore, the device holder can execute different activities, which imply so many different sounds. Hence these heterogeneous data makes very difficult the creation of a robust system. As the training data should be insufficient, we decide to focus on robust features.

Water flow detection

Classical features

A water flow sound usually presents a very noisy spectrum, which can be compared to a high colored noise. The main difficulty of our corpus is that water noises are overlapped with other noises and with speech. We can see on figure 1, an extract of 5 minutes composed by a long water flow event: a speech part overlaps the end of the water flow event and its amplitude is very high in comparison to the other parts. Lot of noises are present during all of the excerpt, mainly noises from the kitchen activities.

We have tested different low level descriptors on this excerpt: temporal features (energy, energy per band and ZCR) and spectral features (centroïd, spread, skewness, kurtosis, roll-off, flux, variation coefficient, and flatness).

According to the literature, variation coefficient is a good noise indicator [START_REF] Wilson | Signal detector employing mean energy and variance of energy content comparison for noise detection[END_REF], as well as spectral flatness [START_REF] Johnston | Transform coding of audio signals using perceptual noise criteria[END_REF].

All these features are computed on a 80 ms hamming window, and a smoothing is applied with a 4 s median filter. Figure 1 shows the most representative results: the zero crossing rate, the spectral centroïd and the spectral flatness: the curves of the ZCR and the spectral centroïd are impacted by the presence of the low frequencies of speech. The spectral flatness has a more interesting behaviour, but it is quite sensible to lots of other noises and silence.

Spectral cover

We introduce a new feature that we call spectral cover because it reacts to large spectral band sounds like high colored noises, even in presence of speech.

The spectral cover is given by:

SP = i (ampl(w i ) * w i ) 2 [ i ampl(w i )] γ , 1 ≤ γ ≤ 2 (1)
where w i represent the frequencies and ampl(w i ) their amplitudes, from the Fourrier transform .

Figure 1. Acoustic features comparison on a water flow event

If we compare the spectral cover with the well-known spectral centroïd given by:

µ = i ampl(w i ) * w i i ampl(w i ) (2)
An important difference is the introduction of a power in the numerator: it may boost the high frequencies, in com-parison of the spectral centroïd or the spectral spread. An other difference, the power γ, makes our feature sensitive to the absolute signal level, unlike spectral centroïd. The γ parameter allow to tune the sensibility of the feature to the signal level.

We can see on figure 1 that the spectral cover stays almost constant during the water flow event even in the presence of voice. It becomes lower outside the water flow sound event, that's why on this extract we can separate easily the water flow event with a simple threshold.

The water flow recognition system

Some experiments show the spectral cover is sensible to other audio events like the noise of a vacuum cleaner), so the system we propose operates in two steps. After computing the spectral cover on the signal, a first segmentation is performed to extract a family of sound events, the candidate sounds. We identify water flow events among this family in a second step. The system ends with a classical post-processing step. More precisely:

• We first compute a short-time Fourrier transform with a hamming window of 1024 samples (80 miliseconds) and an overlap of 50%. Then the spectral cover is computed on each frame. The minimum of the spectral cover values is extracted on a 2s window; this window is considered as a candidate sound if the value exceeds a threshold T 1 . With concatenating the juxtaposed values, we obtain a first selection, each segment is considered as a candidate sound or not.

• In the second step, we consider each candidate segment. If 85% of the frames included in the candidate segment, have a spectral cover value superior to a second threshold T 2 , the segment is rejected as water flow; if not, it is accepted. The rejected segment correspond to sounds event like vacuum cleaner sounds which implies high levels of spectral cover.

Figure 3. System diagram

• The post-processing step consists in removing all sound events smaller than 3 seconds.

Experiments

Corpus description

Our corpus is provided by the IMMED project: 20 videos of patients making activities of daily living in their own personal residence, recording with a camera (HD Go-Pro Fisheye) fixed on their shoulder. The videos are made in the presence of a psychologist who specifies a scenario of activities to realize. The records are from twenty minutes to two hours of duration. After extracting the audio part from the video, the audio sample rate is 12kHz . The listening reveals lots of environment noises, like opening and closing of doors, footsteps, kitchen utensils, and rubbing on the recording device. Some particular sounds like water flow or ring phone give obvious clues on the patient activity. Nevertheless, as the patient and the psychologist are in constant discussion, and due to the position of the microphone, speech is very present and can be loud in comparison to others sounds.

We annotated manually the videos in water flow events. We consider as water flow event each sound part containing at least one of the two sound events: the "fissing" sound created by the faucet, and the "splash" sound created by water impacts and bubbles. There is about 7 hours 30 minutes of videos with 85 water flow events for a total duration of 25 minutes.

Results

We fixed ours threshold values with one of the 20 videos. During its 39 minutes duration , 13 water flow events are present for a total duration of 3 minutes. The values are : T 1 = 30000, T 2 = 80000, and γ = 1.5

The results are evaluated by 4 classical metrics used in the french campaign ESTER2 [START_REF] Galliano | The ester phase ii evaluation campaign for the rich transcription of french broadcast news[END_REF]: Error-rate, Precision, Recall, and F-measure. The results of our system are presented in the table 1. Our system obtain 66% of F-measure. Among the missed events, some of them are not clearly audible or too short. The other missed events are only composed by little splash of water without a clearly audible fissing water flow sound. This kind of sound is never recognized by the system. To precise the nature of the false alarms, we analyse the system output on half of our corpus (4 hours of video): 22 false alarms are revealed and they are mainly due to the presence of speech and noise. Among them, we note speech overlaps (7 errors, 6 in the same file), manipulation noises like plastic bag or cookery (6 errors), television (3 errors), laughers (4 errors), manipulation of the camera (2 errors).

Comparison with MFFC & LLD in a GMM

We propose a comparative experiment between our system and a classical approach. This approach is based on a GMM classifier, and three different sets of features have been tested: MFCC, Low Level Descriptors (LLD), and LLD with our spectral cover (LLD+SC). MFCC and LLD are computed with the Yaafe audio extractor [START_REF] Mathieu | Yaafe, an easy to use and efficient audio feature extraction software[END_REF]. MFCC are composed of 24 coefficients from bands representing spectrum between 20Hz and 6kHz. To obtain performing LLD, a lot of feature combinations have been tested. Among them, the best results are obtained with energy, spectral flatness per band (per log-spaced band of 1/4 octave) and spectral shape statistics (including centroïd, skewness, and kurtosis). Several numbers of gaussians have been tested, and finally we have kept 4 Gaussians. As in our system, we have removed sound events smaller than three seconds. The standard leave-one-out testing protocol was used: one video is discarded from the database to train the GMM classifier with the 19 remaining videos. The excluded video is used as the test data.

Results are presented in the table 1. The results obtain with the GMM approach are not so good. We noticed that precision and recall are very unbalanced. That's could be the results of a specific learning data: the number of frames in the two classes non-water and water are quite unbalanced. Moreover, the water flow data are very heterogeneous, two much for a GMM of 4 components. As one could have predict, LLD gives better results than MFCC, and confirm some previous studies [START_REF] Peltonen | Computational auditory scene recognition[END_REF]. Adding spectral cover to LLD improves the performance; our spectral cover appears complementary to the chosen LLD. In comparison to these classical approaches, our system presents better performance. Moreover the computational cost of our system is insignificant compared with those of a GMM approach.

Conclusion

In this paper, we presented the problem of acoustic water flow detection in a specific and adverse environment: the audio signal is recorded by a wearable device inside personal home. Due to heterogeneous data and noisy environment, it was necessary to specify a new feature, called "Spectral Cover". A system based on robust thresholds was build to detect these water flow events. This system was assessed on IMMED corpus: more than 7 hours of real life records. The results (66% of F-measure and an error rate of 5%) are very satisfying regards to task difficulty. Moreover, our system was compared to classical stateof-the-art audio recognition systems (GMM with a leaveone-out protocol). In conclusion, adding the spectral cover to classical low level descriptors in a GMM allows to increase the performance, but this system isn't better than our initial proposition. This confirm the interest and pertinence of choosing this feature to detect water flow sounds.

Furthermore, we have noticed that the spectral cover is also suitable to detect other sound events. Indeed, among the candidate segments, we have detected cell phone rings and vacuum cleaner use and this allows us to imagine future applications.

Figure 2 .

 2 Figure 2. Threshold on the spectral cover

Table 1 . Comparative results

 1

		System		GMM
			MFCC LLD LLD+SC
	Error rate	0.04	0.09	0.08	0.07
	Precision	0.54	0.35	0.39	0.44
	Recall	0.83	0.87	0.91	0.88
	F-measure	0.66	0.45	0.50	0.53