Nicolas Loiseau
email: nicolas.loiseau@cnrs.fr

Sébastien Villéger
email: sebastier.villeger@cnrs.fr

Charles Le Bozec
email: charles.lebozec1@gmail.com

Marie Gimenez
email: mariegimenez34@yahoo.fr

Seiji Léo Kawahara
email: leo.kawahara@gmail.com

Thomas Claverie
email: tclaverie@gmail.com

Mesophotic reefs are not refugia for neither taxonomic nor functional diversity of reef fishes

Keywords: Coral reef, functional traits, beta diversity, video surveys, Western Indian Ocean Coral reef, functional traits, beta diversity, video surveys, Western Indian Ocean INTRODUCTION

Mesophotic reefs, defined as reefs between 30 m -150m, have been less investigated than their shallower counterparts. Yet, more protected from human activities and bleaching events, they could be refuge zones allowing the resilience of shallow reefs. However, it is currently unknown if their role as refugia for species diversity also extends to functional diversity and thus potentially ecosystem functioning. Using an extensive video field surveys from Mayotte Island in the Western Indian Ocean, we analyzed how taxonomic diversity and functional diversity of coral reef fishes changed along a depth gradient from 10 to 100 m. We found that shallow and mesophotic reefs host different species, but that these dissimilar assemblages share similar richness combinations of traits values. However, when accounting for biomass of species, it appears that functional structure of assemblages differs along the depth gradient, with, for instance, herbivorous fishes being very scarce in deep reefs while large-bodied carnivorous are more abundant. Therefore, surveys of mesophotic fishes should assess not only species, but also trait composition, abundance and biomass. Overall, our results support the claim that extending marine protected areas to mesophotic reefs is required to maximize the protection of both species and functional diversities to eventually maintain coral reef ecosystem functioning and associated services across the broader seascape.

Coral reefs cover close to 300,000 km 2 , which is just under 0.1% of the oceans' surface area [START_REF] Spalding | World atlas of coral reefs[END_REF]. Despite this restricted cover, coral reefs are one of the world's most diverse and productive ecosystems, supporting prodigious biodiversity and providing important ecosystem services to many millions of people. In fact, over 4,000 species of teleost fishes are associated with coral reefs (Sale 2013). Fishes are central to reef ecosystems, providing essential ecological functions such as biomass production, regulation of foodwebs, nutrient cycling and sand formation among others [START_REF] Villéger | Functional ecology of fish: current approaches and future challenges[END_REF][START_REF] Brandl | Coral reef ecosystem functioning: eight core processes and the role of biodiversity[END_REF]. Fishes also provide food security to hundreds of millions of people [START_REF] Sala | Protecting the global ocean for biodiversity, food and climate[END_REF]. However, coral reef fishes are severely affected by overexploitation, habitat loss and climate change, especially through marine heatwaves [START_REF] Eddy | Global decline in capacity of coral reefs to provide ecosystem services[END_REF]. In response to this "coral reef crisis," studies around the world have examined the social, institutional, and environmental conditions that influence fish biodiversity and ecosystem functions [START_REF] Cinner | Bright spots among the world's coral reefs[END_REF][START_REF] Cinner | Meeting fisheries, ecosystem function, and biodiversity goals in a humandominated world[END_REF]. However, most of these studies focused on shallow (< 30m) reef communities [START_REF] Rocha | Mesophotic coral ecosystems are threatened and ecologically distinct from shallow water reefs[END_REF]). As a result, the distribution, abundance and function of fish assemblages that occupy the deeper range of reefs are less known compared to shallow reefs. In other words, variation of the large diversity of biological traits (e.g. size, diet, mobility, behavior; [START_REF] Villéger | Functional ecology of fish: current approaches and future challenges[END_REF], and their participation in core processes [START_REF] Brandl | Coral reef ecosystem functioning: eight core processes and the role of biodiversity[END_REF]) along depth gradients remains largely unknown (see however [START_REF] Cure | Depth gradients in abundance and functional roles suggest limited depth refuges for herbivorous fishes[END_REF][START_REF] Carrington | How functionally diverse are fish in the deep? A comparison of fish communities in deep and shallow-water systems[END_REF]). Yet, unraveling the distribution of fishes and of their traits across depth is critical for effective ecosystem-based fisheries management [START_REF] Lindfield | Mesophotic depths as refuge areas for fishery-targeted species on coral reefs[END_REF].

Mesophotic coral ecosystems occur in the deeper parts of the ocean's photic zone, with depth ranging from 30 to 150 m [START_REF] Eyal | Mesophotic coral ecosystems of the Great Barrier Reef are understudied and underexplored[END_REF], experience reduced temperatures and light, and may be protected from localized anthropogenic stressors such as pollution and fishing. This suggests that cooler conditions in deeper coral reefs could buffer the impacts of the strongest stressors affecting fishes in shallow water. The deep reef refugia hypothesis emerged from genetic connectivity research (Lesser et al. 2009;[START_REF] Bivand | Assessing the 'deep reef refugia'hypothesis: focus on Caribbean reefs[END_REF][START_REF] Assis | Deep reefs are climatic refugia for genetic diversity of marine forests[END_REF] and was reinforced by the fact that many coral reef species occur across a wide depth range [START_REF] Rocha | Mesophotic coral ecosystems are threatened and ecologically distinct from shallow water reefs[END_REF][START_REF] Medeiros | Deep reefs are not refugium for shallow-water fish communities in the southwestern Atlantic[END_REF]. However, the increasing frequency and severity of marine heatwaves [START_REF] Oliver | Longer and more frequent marine heatwaves over the past century[END_REF][START_REF] Venegas | The rarity of depth refugia from coral bleaching heat stress in the western and central Pacific Islands[END_REF], and accumulating reports of impacts reaching mesophotic depths [START_REF] Jackson | Historical overfishing and the recent collapse of coastal ecosystems[END_REF], cast doubt on the ability of depth refuges to provide protection to fish communities over the long-term [START_REF] Rocha | Mesophotic coral ecosystems are threatened and ecologically distinct from shallow water reefs[END_REF]. A refugium is defined as an "habitat where some components of biodiversity retreat to, persist in and can potentially expand from under changing environmental conditions" (sensu, [START_REF] Keppel | Refugia: identifying and understanding safe havens for biodiversity under climate change[END_REF]. The role of refugia to protect species from extinction has been assessed worldwide for different taxa [START_REF] Pellissier | Quaternary coral reef refugia preserved fish diversity[END_REF][START_REF] Brown | Persistent Quaternary climate refugia are hospices for biodiversity in the Anthropocene[END_REF]. However, biodiversity is multifaceted and includes other components in addition to the number of species. A largely unexplored dimension of the role of refugia is their effects on functional diversity, despite the importance of functional diversity for community and ecosystem functioning. In other words, the ability of refugia to fully encapsulate diversity has been poorly investigated, especially as it relates to their capacity to reduce the loss of ecological function [START_REF] Ordonez | Geographic patterns in functional diversity deficits are linked to glacial-interglacial climate stability and accessibility[END_REF]. This is particularly true for mesophotic reefs. Indeed, despite the hypothesis suggesting that mesophotic reefs could be refugia for reef biodiversity [START_REF] Jankowski | Depth gradients in diversity, distribution and habitat specialisation in coral reef fishes: implications for the depth-refuge hypothesis[END_REF][START_REF] Lindfield | Mesophotic depths as refuge areas for fishery-targeted species on coral reefs[END_REF][START_REF] Coleman | Shifting reef fish assemblages along a depth gradient in Pohnpei, Micronesia[END_REF][START_REF] Rocha | Mesophotic coral ecosystems are threatened and ecologically distinct from shallow water reefs[END_REF][START_REF] Stefanoudis | Depth-dependent structuring of reef fish assemblages from the shallows to the rariphotic zone[END_REF], there is still a critical lack of evaluation of their role as refugia for species and trait diversity of fishes [START_REF] Carrington | How functionally diverse are fish in the deep? A comparison of fish communities in deep and shallow-water systems[END_REF][START_REF] Cure | Depth gradients in abundance and functional roles suggest limited depth refuges for herbivorous fishes[END_REF].

Thus, considering the multifaceted definition of biodiversity, the mesophotic reef refugia hypothesis could follow several expectations, depending on similarity in species and trait diversity between shallow and mesophotic reefs (Figure 1): (1) Enriched full refugia: mesophotic reefs host the same species and trait composition as shallow reefs, plus host unique species and traits not observed in shallow reefs; (2) Full refugia: mesophotic reefs host the same species and traits composition than shallow reef while maintaining the same biomass distribution among species;

(3) Functional refugia: mesophotic reefs host different species than shallow reefs but these distinct species have the same trait values; (4) Not refugia: mesophotic and shallow reefs host distinct species and traits composition.

Here, we tested the potential refugia role of mesophotic ecosystems for multiple facets of fish diversity using a video-based survey in coral reefs from Mayotte (an island in the Western Indian Ocean), along a depth gradient from 7.8 to 99.7 m. More specifically, we analyzed how taxonomic and functional diversity and composition of coral reef fishes changed along the depth gradient. Figure 1: A multifaceted approach to test whether deep reefs are refugia for taxonomic (circle) and functional (square) facets of biodiversity. The size of a circle or square represents the taxonomic or functional richness of an assemblage while overlapping represents similarity in species or functional composition between assemblages from mesophotic and shallow reefs. The associated table shows the expected value of four diversity metrics for each scenario. ∆Richness is the difference in richness between mesophotic and shallow and dissimilarity is the proportion of richness not shared by shallow and mesophotic reefs. A: No refugia, mesophotic and shallow reefs host highly distinct species and trait combinations. B & C: Partial refugia, shallow and mesophotic reefs share a subset of species and traits but also a proportion of unique species and traits, in B, shallow reefs host more species and trait combinations than mesophotic reef. D: Functional refugia, mesophotic reef host dissimilar species communities from shallow reefs, but similar traits composition. E: Full refugia, mesophotic reefs host the same composition of species and hence of traits as shallow reefs. F: Enriched full refugia, mesophotic reefs host all the species and trait combinations present in shallow reefs, plus host unique species which have unique traits (absent in shallow reefs).

MATERIALS AND METHODS

Underwater video surveys of coral reef fishes

Mayotte is a volcanic island (360 km 2 ) in the northern Mozambique Channel and is surrounded by one of the world's largest lagoons (area of ~1500 km 2 surface). The coral reefs of Mayotte hosts more than 700 fish species [START_REF] Wickel | Composition et structure des peuplements ichtyologiques marins de l'île de Mayotte (sud-ouest de l'océan Indien)[END_REF]), but recent and rapid human development (annual growth rate higher than 4%, [START_REF] Marie | More than half of all adults living in Mayotte were born elsewhere[END_REF]) is driving fast-paced change across the coral reef communities.

The choice of study sites was made using two complementary tools: observation of the habitat using rosette cameras and a 3D reconstruction of the site using data collected by the boat's sounder. The rosette camera system consisted of four cameras oriented in four different directions. This system was manually lowered to the bottom over the point of interest using a rope and the videos were viewed afterwards. For the 3D reconstruction, latitude, longitude and depth coordinates were recorded every second using a GPS plotter sonar (Lowrance, HDS10). These traces were then used to reconstruct the 3D profile of the substrate using the raster [START_REF] Hijmans | Package 'raster[END_REF], gstat [START_REF] Pebesma | Spatio-temporal interpolation using gstat[END_REF] and sp [START_REF] Bivand | Assessing the 'deep reef refugia'hypothesis: focus on Caribbean reefs[END_REF]) R packages (Figure 2). Using to this information, we selected six sites along the west coral reef barrier of the island with as much similarity as possible in coral cover and complexity within each four different depths, ranging from 7.8 to 99.7 m deep (Figure 2). Fish communities were surveyed using a non-baited remote underwater stereovideo system, which is a cost-effective technique for surveying reef fish across a range of depths and habitats and beyond the limits of regular scientific diving [START_REF] Mallet | Underwater video techniques for observing coastal marine biodiversity: a review of sixty years of publications (1952-2012)[END_REF]. We chose non-baited video systems to avoid over-representation of piscivore species. The stereovideo system consisted of two digital cameras (GoPro Hero 4+ and Hero 3+) inside 150m-rated housings mounted on an aluminum bar, 80 cm apart and each oriented 8° inward using 3D printed mounts to maximize pictures overlap 5 m away [START_REF] Letessier | Low-cost small action cameras in stereo generates accurate underwater measurements of fish[END_REF]. Four PVC legs (length 60 cm), each weighted with 1 kg, were attached under the bar to make a quadripod system. The recording was made using the cameras' medium view setting (84° of horizontal field of view in water) and at full HD definition (1080p) with a frame rate of 30 fps. Calibration of the system was performed using a 2x2x1 m black cubic frame with white dots of known coordinates situated 4 m from the system underwater. Distortion of the cameras was calibrated with an 80x50 cm chessboard situated 1 m from the cameras underwater [START_REF] Neuswanger | Measuring fish and their physical habitats: versatile 2D and 3D video techniques with user-friendly software[END_REF]. Measurement accuracy was verified by filming a graduated ruler underwater with each system.

Each of the 6 sites was surveyed several times (between 9 and 64 times, 20.2 ± 21.6, mean ± sd) from 26-Feb to 23-Mar 2019. All surveys were performed between 9:00 and 17:00. Weather ranged from warm and calm sunny days to rainy and windy days with as much as 1.5 m waves in that period. Current did not exceed 0.8 knots. To minimize fishes moving between drops, and potential spatial pseudo-replication, the four depths within a site were simultaneously surveyed, and video systems within a depth level were dropped at least 100m apart from each other [START_REF] Langlois | A field and video annotation guide for baited remote underwater stereo-video surveys of demersal fish assemblages[END_REF]. Videos recording lasted from 55 minutes to 171 minutes (98.9 ± 36.3).

Video analyses

We analyzed data from 121 drops (i.e., assemblages with fewer than five species were not included). For each video, the first two minutes were not analyzed to limit disturbance due to the video system deployment. As the total duration of video recorded varied among sites and depth level, and as analyzing strereovideos is a demanding task we analyzed five, one-minute nonoverlapping sequences randomly selected within each recording. Hence, we keep a constant sampling effort across all replicates for estimating abundance of fish species, even if this sampling effort did not allow to reach the asymptote of the species accumulation curve (Supplementary materials, Figure S1). For each of these sequences, all fishes up to 5 m from the camera were counted and identified to the lowest possible taxonomic level. Abundance of each species was estimated as the maximum number of individuals of each species observed in a single frame during each one-minute sequence (MaxN; [START_REF] Ellis | Evaluation of video camera technique for indexing abundances of juvenile pink snapper Pristipomoides filamentosus, and other Hawaiian insular shelf fishes[END_REF]. A total of 12,891 individuals (50%) belonging to 182 species were identified. 2206 individuals were identified at the genus level (44 genera) and 10,382 (40%) at the family level (26 families). We also did not consider cryptobenthic species (Apogonidae [7 individuals], Gobiidae [33 individuals] and Chondrichthyans [30 individuals]). Because a high proportion of individuals were not identified at the species levels, we also tested the sensitivity of our analyses to the taxonomic resolution by rerunning our analyses at the genus level. Size calibration calculation and 3D fish measurements were all performed using the opensource software VidSync (www.vidsync.org).

Biomass estimation

Measuring manually each individual for the 121 videos was impractical given their number. Thus, we measured all individuals for 45 videos (for a total of 7h18min) at one of the six sites. Then, for each individual in the present study, we assigned the measured average size at the lowest taxonomic level (species, genus, family) possible according to available data. This process may reduce the dissimilarity in species size between shallow and mesophotic reefs. However, comparison of the distribution in size between shallow reef (from 0 to 30m) and mesophotic reef (from 30 to 99m) for the most measured common species (37) show that body size did not significantly vary across the depth gradient (Supplementary materials, Figure S2). Some species or genera were not observed at the selected site for fish measurement (59%). Thus, we associated the average size for the given taxonomic level from the Reef Life Survey database (highly correlated with the average size from our dataset, cor = 0.78, p-value < 0.001, https://reeflifesurvey.com/, [START_REF] Edgar | Reef Life Survey: Establishing the ecological basis for conservation of shallow marine life[END_REF].

Functional traits

Five traits linked to the key functions performed by fishes were selected: Diet, Position in the water column, Gregariousness, Activity, Mobility, Size. These traits linked to food acquisition, locomotion and behaviour are a proxy of the ability of each species to impact ecosystem processes. Values were taken from the dataset used by [START_REF] Mouillot | Functional over-redundancy and high functional vulnerability in global fish faunas on tropical reefs[END_REF]. Diet characterized each species' main food source and was coded using seven categories: herbivore and detritivore, macro-algae herbivore, sessile invertebrate feeder, mobile invertebrate feeder, planktonivore, piscivore and omnivore. Position in the water column was coded using four ordered categories: benthic (sedentary on the bottom), demersal (swimming near the bottom), pelagic site-attached (swimming off the bottom within a reef), and pelagic mobile (swimming off the bottom between reefs). Mobility was coded using three ordered categories: sedentary, mobile within a reef, and mobile between reefs. Active period was coded as either diurnal (active during the day), nocturnal (active during the night) or both. Gregariousness was coded using five ordered categories from solitary to large schooling. Body size was coded using six ordered categories: 0-7 cm, 7.1-15 cm, 15.1-30 cm, 30.1-50 cm, 50.1-80 cm, and >80 cm. For individuals identified at the genus or family level, we used the average modality of each trait for the given genus or family. Functional distance between all pairs of species was computed using Gower's metric [START_REF] Gower | Metric and Euclidean properties of dissimilarity coefficients[END_REF].

Taxonomic and functional diversity

To evaluate the influence of depth on reef fish diversity of each assemblage we applied a framework based on a generalization of Hill numbers to measure taxonomic and functional diversity in all assemblages (Chao et al. 2019). Hill numbers provide a parametric family of diversity indices, differing by a parameter "q" that determines their sensitivity to species relative abundances, all expressed in the same unit as "equivalent number of species" (sensu species with even dominance and even functional distance). Taxonomic diversity was described using two indices: species richness (i.e., q = 0 indicates that all species have equal weight) and taxonomic entropy (i.e., q=1, as exponential of the Shannon index):

TD = 𝑒 # ∑ % & '()'() % & + &,-
Eq (1) where i is the number of species in an assemblage and pi is the relative dominance (abundance or biomass) of the ith species.

To estimate functional diversity, we also used the framework from Chao et al. ( 2019) setting the parameter τ to the mean functional distance over all pairs of species: FD = (∑ 𝑝 0 (∑ 11 -𝑓(𝑑 06 (𝜏))9𝑝 6

: 6;< ) =#< : 0;< ) </(<#=) Eq (2)
Functional richness (q = 0) was computed with species presence/absence (i.e., all 𝑝 0 equals to 1/S): FD = ∑ (∑ ?1 -𝑓 @𝑑 06 (𝜏)AB : 6;< ) : 0;< #< Eq (3) Functional entropy (q = 1) was computed on species biomass:

FD = 𝑒 # ∑ % & '() (∑ 1<#C(D &E (F))9 + E,- + &,- % E )
Eq (4) This metric is a generalization of the Shannon entropy with functional distance having equal importance as species dominance.

Taxonomic and functional dissimilarities

The dissimilarity in species and functional entities among assemblages was also computed using the framework of Chao et al. (2019) with, as for alpha-diversity, q=0 (i.e., dissimilarity in composition of species or functional entities) and q=1 (i.e., dissimilarity in dominance of species or functional entities). The dissimilarity indices are bounded between 0 and 1, with 0 indicating identical assemblages (in terms of species or trait values) and values close to 1 indicating a strong dissimilarity in species or functional entities. We applied a depth decay analysis (similar to the distance decay in biogeography, Nekola and White 1999) by comparing the dissimilarity between 0-20m assemblages and each of all other deeper assemblages.

Changes in functional richness and structure through depth were illustrated in a functional space built using Principal Coordinates Analysis (PCoA) computed on functional distances between species. Indices and functional space were computed and drawn using the mFD package [START_REF] Magneville | mFD: an R package to compute and illustrate the multiple facets of functional diversity[END_REF].

Habitat predictors

To include the environment of each assemblage as covariate, we measured variables related to abiotic and biotic habitat recorded by the video systems. It is however important to note that hard substrate was mainly targeted and any results on variation among habitat should be interpreted with caution. For the abiotic variables, we recorded the substrate type (soft, mix, hard), topography, habitat complexity (from 0 to 3), temperature and depth. For temperature and depth measurements, we used a custom-made data logger built by the company Syera (https://syera.fr/wp/glog/) and using the Bar30 temperature-depth sensor from the company blue robotics (https://bluerobotics.com/). Moreover, categorical abundance of dominant coral biota, sponge, non-scleractinian coral and scleractinian coral were recorded. We performed a principal component analysis of a mixture of numerical and categorical variables (Factor analysis of mixed data, FAMD, using FactoMineR). Depth was not included in this analysis. The first four orthogonal PCA axes explained 62% of the total variance in habitat predictors among assemblages and were retained as explanatory variables in the analyses to test environmental influence on fish assemblages (Supplementary materials, Figure S3).

Determinants of fish diversity

We used generalized linear mixed models (GLMMs) to investigate the effect of depth while controlling for environmental differences in the four diversity indices and biomass hypothesized to influence composition, entropy and traits of reef fish communities. Variables included the first four axes of the environmental FAMD and depth while accounting for the site and date of the sampling as random effects. For each model, we computed marginal R 2 providing the variance explained only by fixed effects and conditional R 2 providing the variance explained by the entire model, i.e., both fixed effects and random effects. Finally, we performed visreg analyses [START_REF] Breheny | Visualization of regression models using visreg[END_REF] to isolate the exclusive effect of depth on diversity after accounting for environmental confounding factors.

Determinants of fish dissimilarity

To test if depth was the best predictor of taxonomic and functional dissimilarities, we applied generalized dissimilarity modelling (GDM, [START_REF] Ferrier | Using generalized dissimilarity modelling to analyse and predict patterns of beta diversity in regional biodiversity assessment[END_REF]. GDM examines the dissimilarities among pairs of assemblages as a non-linear multivariate function of the depth and environment of those assemblages. GDM thus overcomes two potential problems: non-linearity in dissimilarity between assemblages and environmental dissimilarities, and uneven rates of dissimilarities along environmental gradients [START_REF] Ferrier | Using generalized dissimilarity modelling to analyse and predict patterns of beta diversity in regional biodiversity assessment[END_REF][START_REF] Manion | gdm: Functions for generalized dissimilarity modeling[END_REF]). We selected the three I-spline basis function options and accounted for spatial autocorrelation by including the geographic distance between pairs of assemblages as a predictor variable. To assess the impact of each environmental variable, multiple GDMs were run by removing one variable at a time. Percentage contribution of each variable was determined by comparing deviance of the full model to deviance of models without the variable of interest. Variables included the first four axes of the environmental PCA and depth.

RESULTS

Diversity of fishes along depth gradient

Total fish biomass and taxonomic richness (q = 0) significantly decreased with depth (Figure 3 & 4, p-value < 0.001, Table S1). On average, 29.3 (± 10.7 sd) species were observed between 0-20 m, and 8.4 (± 3.6 sd) beyond 80 m. Taxonomic entropy (q = 0) also decreased with depth, but less steeply than taxonomic richness, indicating that decreasing species richness was associated with an increase in species dominance (p-value = 0.002).

Functional richness values varied from 1.7 to 6.2 among assemblages (4.3 ± 1.0, Figure 4). Functional entropy varied from 1.1 to 4.8 among assemblages (2.8 ± 0.7, Figure 4). Unlike taxonomic diversity, functional richness and functional entropy were not significantly influenced by depth (p-value = 0.134, 0.498, respectively, Table S1). Figure 3 Partial regression plot showing changes in biomass of fish along the depth gradient while accounting for environmental variables. Statistical analyses were carried out using generalized linear mixed models and related statistics are reported in the Results. Figure 4: Partial regression plot showing changes in taxonomic richness (q=0, A) and entropy (q=1, B) and functional richness (q=0, τ = mean functional distance, C) and entropy (q=1, τ = mean functional distance, D) of fish communities along the depth gradient, while accounting for environmental variables. Statistical analyses were carried out using generalized linear mixed models and related statistics are reported in the Results.

Dissimilarity of fish assemblages along depth gradient

The dissimilarity in taxonomic composition and structure between assemblages was high overall, ranging from 0.4 to 1 (0.86± 0.09) and 0.1 to 1 (0.77 ± 0.2), respectively. The highest dissimilarity values were observed between shallow (0-20 m) and deep assemblages (>60 m) (i.e., high value in the difference in depth axis, Figure 5). Limited dissimilarity in functional composition was observed between assemblages (0.13 ± 0.13). The dissimilarity in functional structure was higher than functional composition (0.46 ± 0.25), highlighting an important change in the distribution of biomass between functional entities. Both taxonomic and functional dissimilarities increased with an increase in depth difference (all p-values < 0.001 and R 2 ranged between 0.30 and 0.47; Figure 5).

The diversity patterns described above were very similar to those computed only with the genus presence and dominance (Supplementary materials, Figure S4 andS5). Figure 5: Depth decay of fish diversity, mean dissimilarity (whiskers representing sd) in taxonomic and functional composition and structure between 0-20m assemblages (coded as being at 0m) and each all other deeper assemblages. The first row shows dissimilarity in species composition (q= 0) and structure (q = 1). The second row dissimilarity in functional composition (q= 0, τ = mean functional distance) and structure (q= 0, τ = mean functional distance). Yellow lines are linear regressions (with associated statistics) between the difference in depth and beta diversity indices. Shaded areas represent the 95% confidence interval of the regression. Finally, the projection of all species along the first two axes of the PCoA confirmed that mesophotic and shallow reefs mostly overlap in functional space but distribution of biomass within this shared functional volume markedly changed (Figure 6). Figure 6: Changes in functional composition and structure along the depth gradient. The traits space represented is made of the first two axes (Principal Components of a PCoA, relative importance: 32 and 30%, respectively). Species are represented by circles whose size is proportional to the biomass of the species. The gray area represents the portion of the trait space filled by all species present across all depths and colored areas represented the functional richness, i.e., trait space filled by species present in each depth range. Vertical and horizontal lines illustrate the biomass-weighted average position of species from each depth range.

Environmental drivers of taxonomic and functional diversity

Despite differences in habitat among sites, depth was the most important variable in explaining biomass, number of species and taxonomic entropy, followed by PC1 of the habitat data (Table S1). This axis is driven by the shift from scleractinian corals to non-scleractinian corals with depth (Supplementary materials, Figure S3). For taxonomic richness and taxonomic entropy, the models explain 66% (conditional R 2 = 0.66 and marginal R 2 = 0.45) and 22% (conditional R 2 = 0.22 and marginal R 2 = 0.19) of the variance, respectively. Visreg confirmed that depth, after controlling for environmental conditions, significantly explained the biomass, species richness and entropy pattern between assemblages (Figure 3 and4). Conversely, environmental conditions and depth poorly explain variation of functional richness and entropy (25 and 12%, respectively; conditional R 2 = 0.25, 0.12 and marginal R 2 = 0.21 and 0.09, respectively). The GDM explained 41 and 44% of the variation in species composition and structure, respectively. Depth was the most important explanatory variable for changes in species composition (Supplementary materials, Figure S6), contributing to 16% and 21% of the explained deviance, respectively, with a lower effect of environmental PC1 (11% of the deviance explained for both models). Variation in functional composition was poorly explained by differences in environmental variables and depth (9% of deviance explained). The GDM explained 41% of the variation in functional structure, and depth was the greatest driver, contributing to 23% of the deviance explained.

DISCUSSION

Here, thanks to a video survey, we documented how taxonomic and trait diversity of reef fishes changed along a depth gradient from 7.8 to 99.7m around Mayotte Island. Deeper ecosystems exhibit reduced species richness as already reported [START_REF] Piacenza | Patterns and variation in benthic biodiversity in a large marine ecosystem[END_REF]. However, our results demonstrate that shallow reefs and mesophotic reefs contain similar levels of functional richness with a partial overlap in trait space despite the difference in species composition. While such convergence has been observed at biogeographic scale [START_REF] Loiseau | Multi-component β-diversity approach reveals conservation dilemma between species and functions of coral reef fishes[END_REF][START_REF] Olivier | Functional-biogeography of the reef fishes of the islands of the Gulf of California: integrating functional divergence into marine conservation[END_REF]McLean et al. 2021), ours is a novel example showing trait convergence in marine fish communities driven by taxonomically different species.

Thus, while the taxonomic composition of the fish fauna varied over depth, species were often replaced by another with similar functional roles such that only minor differences in functional entities occurred across depth ranges. For instance, small planktivorous Chromis spp. were progressively replaced by Pseudanthias spp. another small planktivorous species, with a ratio of two Pseudanthias spp. per 100 Chromis spp. between 0 and 20 m versus a ratio of 49 per 100 beyond 60 m. This trait similarity across the depth gradient could limit the competition between species that have different distributions along the depth gradient and thus promote coexistence of fishes on the seascape [START_REF] Goetze | Increased connectivity and depth improve the effectiveness of marine reserves[END_REF]Bosch et al. 2021).

Thus, functional space remains relatively constant over the entire water column. This finding is similar to Carrington et al.'s (2021) findings that temperate shallow-water and deep-sea ecosystems have similar functional composition. The limited shift in trait composition could be explained by the relative homogeneity in the temperature of the water column from our sampling area, ranging only from 24.6 to 31°C. Even if biologically significant, this 6°C degree difference may not be strong enough to limite the establishment of adapted shallow reef functional entities at greater depths. For instance, in the Bay of La Paz, across the same range of depth temperature decreased by 11°C [START_REF] Hollarsmith | Habitats and fish communities at mesophotic depths in the Mexican Pacific[END_REF]. However, conversely to our study [START_REF] Medeiros | Deep reefs are not refugium for shallow-water fish communities in the southwestern Atlantic[END_REF] found a divergence in both species and functional composition with depth along the Northeastern Brazilian subprovince characterized by a temperature range similar to ours (7°C).

However, despite the functional richness of shallow and mesophotic reefs remaining relatively similar, traits dependent on light energy, like herbivory, do not occur beyond 60m depth (Supplementary materials, Figure S7). Such a decrease is expected to be due to harsher environmental conditions in deep habitat that filter out some species present on shallow reefs [START_REF] Price | Ocean-scale patterns of 'biodiversity'of Atlantic asteroids determined from taxonomic distinctness and other measures[END_REF][START_REF] Costello | Marine biodiversity, biogeography, deep-sea gradients, and conservation[END_REF]. Moreover, the overlap in trait space (i.e., functional similarity) is accompanied by changes in the structure of the traits with a significant shift in biomass between functional entities. Indeed, the increase in the dissimilarity of functional structure with depth highlights the fact that dominant and rare functional entities are different between shallow reefs and deep reefs (Figure 5). For instance, 75% of Chaetodontidae, corallivores, were observed between 0 and 34 m and only 5% were observed beyond 48 m. Conversely, 50% of Carangidae, which are highly mobile and piscivorous, were recorded beyond 48 m. Overall, variation in trait structure was characterized by a greater relative biomass and abundance of piscivores on deep reefs, while herbivorous fishes were relatively more abundant on shallow reefs (Supplementary materials, Figure S7). In fact, piscivorous species represent 8% of the total biomass in shallow reefs (0-20m), but represent 48% of the biomass beyond 60 m. The biomass of piscivorous and mobile fishes within reefs, such as grouper, is even higher beyond 60 m (538g ± 859, mean ± sd) than between 0 and 20 m (191g ± 222).

Coral reef ecosystems have been heavily impacted by temperature rise over the last few decades, with documented increases in intense heatwaves leading to massive bleaching events [START_REF] Genevier | Marine heatwaves reveal coral reef zones susceptible to bleaching in the Red Sea[END_REF]). Synergistically, fisheries strongly impact coral reef fish communities [START_REF] Macneil | Recovery potential of the world's coral reef fishes[END_REF]. Deep-sea ecosystems have been described as stable, with the incidence and intensity of heatwaves, occurrence of storms and fishing activities lower in mesophotic depths than in shallow water [START_REF] Kahng | Mesophotic coral ecosystems. Marine animal forests: the ecology of benthic biodiversity hotspots[END_REF]. This "stability" suggests that mesophotic reefs could provide protection to fish communities [START_REF] Bivand | Assessing the 'deep reef refugia'hypothesis: focus on Caribbean reefs[END_REF]). In the same vein, we found that the upper mesophotic (30-60 m) zone holds the greatest potential to aid in reef recovery following disturbance due to the functional overlap with the shallow reef, suggesting that the upper mesophotic could be a "functional refugia". However, the limited similarity in species composition between mesophotic assemblages and shallow reef assemblages suggests that only a relatively small number of deeper species may be able to live on shallow reefs. Moreover, the strong decrease in biomass with depth supports the hypothesis that mesophotic reefs are a limited functional refugia in the face of climate change (i.e. they host dissimilar species, but similar traits while exhibiting a decrease and a shift in biomass distribution across traits). Lastly, despite the minimal shift in functional trait composition, there is a marked shift from dominance of herbivorous fishes to piscivorous fishes with increasing depth. Herbivorous species can have positive effects on coral cover by controlling macroalgae populations, which can prevent coral mortality and promoting resilience of coral reefs in the face of anthropogenic disturbance and heatwaves [START_REF] Burkepile | Herbivore species richness and feeding complementarity affect community structure and function on a coral reef[END_REF]. Maintaining high herbivore biomass and diversity is an important goal for sustaining coral reef functioning [START_REF] Lefcheck | Tropical fish diversity enhances coral reef functioning across multiple scales. Science advances 5:eaav6420 Lesser MP[END_REF]) that cannot be supported by mesophotic reefs deeper than 60m. However, our results demonstrate the importance of mesophotic reefs to fishery-targeted species, since the biomass of some piscivorous fishes that are targets of fisheries increase with depth [START_REF] Lindfield | Mesophotic depths as refuge areas for fishery-targeted species on coral reefs[END_REF][START_REF] Weijerman | Shining a light on the composition and distribution patterns of mesophotic and subphotic fish communities in Hawai 'i[END_REF]Bosch et al. 2021). Even low fishing pressure on shallow reefs of Mayotte could also create a decreasing gradient of biomass of targeted piscivorous [START_REF] Doherty | Reconstructing domestic marine fisheries in Mayotte from 1950-2010[END_REF].

While we documented the convergence of trait diversity over depth, we are unable to explain the potential consequences for the ecosystem functioning, since studies linking fish traits to ecosystem functioning are still lacking [START_REF] Villéger | Functional ecology of fish: current approaches and future challenges[END_REF]Bellwood et al. 2018;Bosch et al. 2021b). However, since biomass distribution across traits determines the influence of fish on ecosystems functioning, we can easily assume that shallow and mesophotic reef fish communities do not have the same influence of reef ecosystems. As with all traits-based studies, trait choices ultimately influence research conclusions. Our results are limited to the set of traits used and to the depth range scales examined. Future studies should therefore attempt to identify the influence of depth on physiologic traits such as reproduction and direct functions such as nutrient cycling [START_REF] Allgeier | Fishing down nutrients on coral reefs[END_REF]) and productivity [START_REF] Morais | Principles for estimating fish productivity on coral reefs[END_REF] to better understand the functional complementarity of mesophotic and shallow reefs. Understanding how these functions vary along depth gradients is critically important to predict how mesophotic reefs may respond to future environmental change and for their management and conservation. Finally, fishes associated with mesophotic reefs may perform vertical movements over seasonal and diel time frames [START_REF] Bacheler | Environmental conditions, diel period, and fish size influence the horizontal and vertical movements of red snapper[END_REF][START_REF] Brazo | Seasonal influence on the bathymetric distribution of an endangered fish within a marine protected area[END_REF]. This behavior could increase the similarity between shallow and mesophotic reefs. Preserving marine ecosystems is a crucial priority, as they support the livelihood and well-being of millions of citizens (human food provisioning, water purification, disease regulation, cultural values, etc.) [START_REF] Cabral | A global network of marine protected areas for food[END_REF]. Despite their importance, and although the percentage of the coral reef surface devoted to protected areas has markedly increased globally [START_REF] Maxwell | Area-based conservation in the twenty-first century[END_REF], deep reefs are rarely the focus of conservation strategies. Fishing has historically impacted shallow coral reefs more than the mesophotic part, but as technology continues to improve, the exploitation of the deeper areas has been increasing [START_REF] Audzijonyte | Deep impact of fisheries[END_REF]. Thus, in the context of the ongoing rise in the severity of threats, mesophotic reefs are an important habitat for conserving coral reef fish diversity. The unique species composition hosted by mesophotic reefs supports the recommendation that protected areas should be representative of a broad range of habitats across depths. Expanding marine protected areas to adjacent mesophotic reefs is required to protect a mosaic of habitat, maximising the number of species and functional entities protected, critical to prevent future local population extirpations and maintain coral reef ecosystem functioning and associated services at all depths. Sustainable management of deep reef ecosystems will also have strong impacts on the management of fishery resources by providing shelters for targeted species. Acknowledgement This research was funded by the CUFR of Mayotte, by the second Research Consortium Iles eparse 2017-2020 (project MAPOR), by the French Foundation for Research on Biodiversity and its partners (FRB -www.fondationbiodiversite.fr) and by ANR (The French National Research Agency) under the "investissement d'avenir" program with the reference ANR-16-IDEX-0006. The authors thank Priscilla Dupont and Julien Wikel for helping with fish ID on videos and Yann Mercky and Elliott Sucre for their help on field operation.

  Figure2: Location of surveyed sites around Mayotte Island in the SW Indian Ocean. On the left, the 3D reconstruction of each site and on the right each red dot represents a drop of a remote underwater camera.Video analysesWe analyzed data from 121 drops (i.e., assemblages with fewer than five species were not included). For each video, the first two minutes were not analyzed to limit disturbance due to the video system deployment. As the total duration of video recorded varied among sites and depth level, and as analyzing strereovideos is a demanding task we analyzed five, one-minute nonoverlapping sequences randomly selected within each recording. Hence, we keep a constant sampling effort across all replicates for estimating abundance of fish species, even if this sampling effort did not allow to reach the asymptote of the species accumulation curve (Supplementary materials, FigureS1). For each of these sequences, all fishes up to 5 m from the camera were counted and identified to the lowest possible taxonomic level. Abundance of each species was estimated as the maximum number of individuals of each species observed in a single frame during each one-minute sequence (MaxN;[START_REF] Ellis | Evaluation of video camera technique for indexing abundances of juvenile pink snapper Pristipomoides filamentosus, and other Hawaiian insular shelf fishes[END_REF]. A total of 12,891 individuals (50%) belonging to 182 species were identified. 2206 individuals were identified at the genus level (44 genera) and 10,382 (40%) at the family level (26 families). We also did not consider cryptobenthic species (Apogonidae [7 individuals], Gobiidae [33 individuals] and Chondrichthyans [30 individuals]). Because a high proportion of individuals were not identified at the species levels, we also tested the sensitivity of our analyses to the taxonomic resolution by rerunning our analyses at the genus level. Size calibration calculation and 3D fish measurements were all performed using the opensource software VidSync (www.vidsync.org).Biomass estimationMeasuring manually each individual for the 121 videos was impractical given their number. Thus, we measured all individuals for 45 videos (for a total of 7h18min) at one of the six sites. Then, for each individual in the present study, we assigned the measured average size at the lowest taxonomic level (species, genus, family) possible according to available data. This process may reduce the dissimilarity in species size between shallow and mesophotic reefs. However, comparison of the distribution in size between shallow reef (from 0 to 30m) and mesophotic reef (from 30 to 99m) for the most measured common species (37) show that body size did not

All code and data will be provided on https://github.com/LoiseauN/Bubot


Rachna Bhoonah
email: rachna.bhoonah@minesparis.psl.eu.

Alice Maury-Micolier

Olivier Jolliet

Peter Fantke

Fine particulate matter exposure and health impacts from indoor activities

Keywords: indoor air quality, life cycle assessment, air pollution, PM2.5, cook stove

Exposure to fine particulate matter (PM2.5) is an important contributor to global human disease burden, particularly indoors where people spend the majority of their time and exposure is highest. We propose a framework linking indoor PM2.5 emissions from human activities to exposure and health impacts, expressed in Disability-Adjusted Life Years (DALY). Derived dynamic indoor PM2.5 concentrations-capturing temporal variations through different window opening scenarios and air renewal rates-are used to estimate uncertainty for a parametric model (up to a factor 114). Intake fractions (fraction of emitted substance taken in, µgintake/µgemitted), effect factors (µDALY/µgintake), related impact characterisation factors (health impact per unit mass emitted, µDALY/µgemitted) and impact scores (health impact per hour activity, µDALY/hactivity) are provided for 19 one-hour indoor activities, and can be flexibly scaled to real activity durations. Indoor concentrations exceeded recommended World Health Organization (WHO) limits for all activities at low ventilation rates. Per person, 98 to 119 µDALY/hactivity (52 to 63 minuteslost/hactivity) were associated with traditional fuel cook stoves, with high air renewal rates (3 and 14 h -1 ). The burning of candles, at low air renewal rates of 0.2 to 0.6 h -1 , results in 7 to 11 µDALY/hactivity (4 to 11 minuteslost/hactivity). Derived impact scores and characterisation factors serve as a starting point for integrating indoor PM2.5 emissions and exposure into life cycle impact and public health assessments.

Introduction

Ambient and household fine particulate matter (PM2.5) pollution is one of the major global health risk factors, representing 120 million and 92 million Disability-Adjusted Life Years (DALYs) each in 2019 (4.7% and 3.6% of total DALYs) [START_REF] Murray | Global burden of 87 risk factors in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019[END_REF]. Serious health outcomes are associated with PM2.5 exposure, including chronic obstructive diseases (COPD), ischemic heart disease (IHD), stroke and lung cancer (LC) for adults, and acute lower respiratory diseases (ALRI) for children who are still in the developmental stage (< 5 years) [START_REF] Burnett | An Integrated Risk Function for Estimating the Global Burden of Disease Attributable to Ambient Fine Particulate Matter Exposure[END_REF][START_REF] Fantke | Health effects of fine particulate matter in life cycle impact assessment: findings from the Basel Guidance Workshop[END_REF]. Indoor environments, where people spend a high fraction of their time, are particularly important to study: 83% to 90% of exposure occurs indoors [START_REF] Fantke | Characterizing Aggregated Exposure to Primary Particulate Matter: Recommended Intake Fractions for Indoor and Outdoor Sources[END_REF]. Since buildings have relatively small, enclosed volumes, indoor air concentrations can be particularly high as compared to ambient levels. For instance, PM2.5 air concentrations were higher than the World Health Organization's (WHO) annual guideline values in schools and kindergartens by a factor 4 to 15 [START_REF] Oqai | Une campagne nationale pour évaluer la qualité des environnements intérieurs des écoles françaises[END_REF][START_REF] Mandin | The Indoor Air Quality Observatory (OQAI): a unique project to understand air pollution in our living spaces[END_REF][START_REF] Mainka | Preschool children health impacts from indoor exposure to PM2.5 and metals[END_REF].

According to these guidelines, annual exposures should remain below 5 µg/m 3 , and 24-hour exposures can exceed 15 µg/m 3 for no more than 3 to 4 days per year [START_REF] Who | WHO global air quality guidelines: particulate matter (PM2.5 and PM10), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide[END_REF]. Inhabitants of rural areas, especially in developing countries, are particularly at risk due to wide usage of solid fuel combustion indoors for cooking or heating [START_REF] Smith | National burden of disease in India from indoor air pollution[END_REF][START_REF] Smith | Effect of reduction in household air pollution on childhood pneumonia in Guatemala (RESPIRE): a randomised controlled trial[END_REF][START_REF] Rohra | Mass distribution and health risk assessment of size segregated particulate in varied indoor microenvironments of Agra, India -A case study[END_REF].

Indoor PM2.5 concentrations depend on outdoor pollution levels, penetrating through unfiltered ventilation, indoor primary emissions from activities, and chemical reactions between substances such as the oxidation of volatile organic compounds (VOCs) with ozone, nitrate and hydroxyl radicals which can form secondary PM [START_REF] Srivastava | Formation of secondary organic aerosols from anthropogenic precursors in laboratory studies[END_REF]. Various studies have measured primary PM2.5 emission rates, indoor concentrations and particle size distributions [START_REF] Pagels | Chemical composition and mass emission factors of candle smoke particles[END_REF][START_REF] Aquilina | Impact of daily household activities on indoor PM2.5 and Black Carbon concentrations in Malta[END_REF][START_REF] Long | Characterization of Indoor Particle Sources Using Continuous Mass and Size Monitors[END_REF][START_REF] He | Contribution from indoor sources to particle number and mass concentrations in residential houses[END_REF][START_REF] Tissari | Fine particle and gaseous emissions from normal and smouldering wood combustion in a conventional masonry heater[END_REF][START_REF] Shen | Fugitive Emissions of CO and PM2.5 from Indoor Biomass Burning in Chimney Stoves Based on a Newly Developed Carbon Balance Approach[END_REF][START_REF] Demanega | Performance assessment of low-cost environmental monitors and single sensors under variable indoor air quality and thermal conditions[END_REF][START_REF] Du | Substantial leakage into indoor air from on-site solid fuel combustion in chimney stoves[END_REF][START_REF] Li | User behavior, influence factors, and impacts on real-world pollutant emissions from the household heating stoves in rural China[END_REF] for different activities. While fuel stoves are recognised as strong indoor PM2.5 sources and linked to premature mortality in developing countries, cooking (especially frying and grilling) and candle burning were also identified as important sources. Occupant contributions to indoor PM2.5 from the shedding of skin and cloth fibres or the resuspension of particles during activities [START_REF] Ferro | Source Strengths for Indoor Human Activities that Resuspend Particulate Matter[END_REF][START_REF] Corsi | Particle Resuspension During the Use of Vacuum Cleaners on Residential Carpet[END_REF][START_REF] Bhangar | Chamber bioaerosol study: human emissions of size-resolved fluorescent biological aerosol particles[END_REF][START_REF] Licina | Emission rates and the personal cloud effect associated with particle release from the perihuman environment[END_REF][START_REF] Assaad | Modeling of indoor particulate matter deposition to occupant typical wrinkled shirt surface[END_REF] have also been measured, and are highly dependent on dust coverage and, ultimately, occupant behaviour (e.g. frequency of cleaning or presence in dusty environments).

Indoor concentrations are affected by air changes per hour (ACH), the presence of filters for mechanical ventilation, and the size and orientation of windows for natural ventilation.

Human health impacts of products from outdoor PM2.5 emissions can be calculated using life cycle assessment (LCA), a tool used to evaluate the environmental performance of products and technologies over their life cycles, considering different impact categories. The UNEP/SETAC Life Cycle Initiative identified the PM2.5 impact category as one of the categories requiring refinements [START_REF] Fantke | Health effects of fine particulate matter in life cycle impact assessment: findings from the Basel Guidance Workshop[END_REF]. These include the need for representative indoor archetypes treating indoor sources of PM2.5 and consequent occupant exposures.

Archetypes have been defined by Fantke et al. [START_REF] Fantke | Characterizing Aggregated Exposure to Primary Particulate Matter: Recommended Intake Fractions for Indoor and Outdoor Sources[END_REF] according to parameters identified by Hodas et al. [START_REF] Hodas | Indoor inhalation intake fractions of fine particulate matter: review of influencing factors[END_REF], including air renewal rates and occupancy. Parametric models coupled with these indoor archetypes can provide average PM2.5 concentrations and intake fractions (mass taken in per unit mass emitted) [START_REF] Fantke | Characterizing Aggregated Exposure to Primary Particulate Matter: Recommended Intake Fractions for Indoor and Outdoor Sources[END_REF], but usually do not capture temporal variations in emission or ventilation rates. Dynamic simulations, using airflow simulation tools such as CONTAM (used by INCA-Indoor [START_REF] Mendez | Assessment of the impact of oxidation processes on indoor air pollution using the new time-resolved INCA-Indoor model[END_REF]) or COMIS, can provide full concentration/exposure profiles that can be coupled with effect factors to calculate health impacts. There is still, however, a need for defining emission scenarios linked to indoor sources and their emission rates. In addition, factors linking emissions, exposure and health effects to particular indoor activities are missing in LCA literature. To address this gap, our study aims to propose a framework for characterising human exposure to PM2.5 and related health impacts associated with common indoor activities and their emissions. To achieve this aim, we will address the following three specific objectives:

1. Propose a framework for linking human indoor activities to primary PM2.5 emissions, exposure, effects and health damage based on a dynamic modelling approach, 2. Calculate dynamic indoor PM2.5 concentrations and derive a parameterised exposure and effect model for integration in life cycle impact assessment, and 3. Provide a set of impact characterisation factors for different reference indoor activities under different natural ventilation scenarios.

Resuspension and secondary PM2.5 are not within the scope of this study, since they depend on dust coverage and the presence of precursors such as VOCs, which are not currently treated by the dynamic model.

Methods

Overall followed source-to-damage approach

The pollutant pathway from emission to impact is modelled using the framework recommended in global consensus-building efforts for PM2.5 exposure and effects [START_REF] Fantke | Health effects of fine particulate matter in life cycle impact assessment: findings from the Basel Guidance Workshop[END_REF][START_REF] Humbert | Intake Fraction for Particulate Matter: Recommendations for Life Cycle Impact Assessment[END_REF],

adapted to indoor contexts (Figure 1). The functional unit is defined as one hour of activity.

Figure 1: Framework for the calculation of activity impact scores from PM2.5 emission rates.

Indoor PM2.5 concentrations 𝐶 in (µg/m 3 ) are simulated with INCA-Indoor, considering 1) indoor emission rates of different activities 𝑚̇e mitted in,avg (µgemitted/hactivity) obtained from studies, 2) the penetration from outdoors with natural ventilation 𝑚̇p enetration,avg (µg/h) (5) and

3) air renewal rates (h -1 ). These are compared to a parametric model's results, using one hour as reference activity duration under multiple given air change rates 𝐴𝐶𝐻 (h -1 ). Using a fate factor 𝐹𝐹 (h) or the dynamic fate model INCA-indoor, we determine the evolution of the indoor PM2.5 concentrations and the resulting time integrated mass inside the room air. These masses are then multiplied by the exposure rates (h -1 ) to yield indoor PM2.5 intake fractions 𝑖𝐹 (µgintake/µgemitted) [START_REF] Burnett | An Integrated Risk Function for Estimating the Global Burden of Disease Attributable to Ambient Fine Particulate Matter Exposure[END_REF]. These 𝑖𝐹s are multiplied by effect factors 𝐸𝐹 (µDALY/µgintake) to obtain the characterisation factors 𝐶𝐹 (µDALY/µgemitted), i.e. the impact per unit of PM2.5 emitted.

Impact scores 𝐼𝑆 (µDALY/hactivity) for one person are then calculated as the product of the cumulative indoor emission 𝑚 emitted in (µgemitted) and 𝐶𝐹s. This can therefore be expressed as:

𝐼𝑆 activity = 𝐸𝐹 × 𝑖𝐹 × 𝑚 emitted in = 𝐶𝐹 × 𝑚 emitted in (1)
With

𝑖𝐹 = ∫ 𝐵𝑅 × 𝑃𝑂𝑃 × 𝐶 in,inc ∞ 𝑡=0 𝑑𝑡 𝑚 emitted in (2)
Where 𝑃𝑂𝑃 (cap) is the number of occupants, 𝐵𝑅 is the breathing rate of an occupant (m 3 /cap/h). 𝐶 in,inc (𝑡) is the increase in indoor PM2.5 concentration due to the activity-related emissions (µg/m 3 ) integrated up to infinity (in practice up to the time required to entirely evacuate the particles emitted by the activity). It is given by the difference between indoor concentration with activity (𝐶 in , in µg/m 3 ) and without (𝐶 base , in µg/m 3 ).

The effect factor depends on the average effective indoor concentration 𝐶 ̅ in and the annual average ambient concentration of the region 𝐶 ̅ out,𝑟 (Cohen et

𝑚̇p enetration,avg (µg/h) is the average penetration rate of PM2.5 from outdoors, defined by:

𝑚̇p enetration,avg = 𝐶 ̅ out × 𝑉 room × 𝐴𝐶𝐻 avg (5)
𝐶 ̅ out (µg/m 3 ) is the average outdoor PM2.5 concentration.

Individual Lifetime Risk

The total individual lifetime risk 𝐼𝐿𝑅 ̅̅̅̅̅ (DALY/person) represents the number of life years lost from exposure to PM2.5 over a lifetime for each one-hour activity. It is calculated using equation [START_REF] Mandin | The Indoor Air Quality Observatory (OQAI): a unique project to understand air pollution in our living spaces[END_REF].

𝐼𝐿𝑅 ̅̅̅̅̅ = 𝑁 activity × 𝐼𝑆 𝑎𝑐𝑡𝑖𝑣𝑖𝑡𝑦 (6)
Where 𝑁 𝑎𝑐𝑡𝑖𝑣𝑖𝑡𝑦 is the number of one-hour activities occurring during a lifetime.

Emission data

Primary PM2.5 emission rates are collected from various studies for 19 activities and are presented in Table 1. a. Fugitive emissions (leakage) from cooking with the burning of coal in an iron stove.

b. Fugitive emissions from cooking with the burning of wood in a brick stove.

c. Fugitive emissions from cooking with the burning of maize straw in a brick stove.

d. Emission rate (mg/min) calculated from the emission factor (g/kg), as described in SI.

Cook stove emission rates are highest (up to 120 mg/min) and correspond to common practices in certain rural Chinese homes [START_REF] Du | Substantial leakage into indoor air from on-site solid fuel combustion in chimney stoves[END_REF]. Model parameters, including reference outdoor PM2.5 concentration and air renewal rates are given in the supporting information (SI) section S.1.

Dynamic indoor PM2.5 concentration calculation

Dynamic concentrations are simulated using the INCA-Indoor multizone model [START_REF] Mendez | Assessment of the impact of oxidation processes on indoor air pollution using the new time-resolved INCA-Indoor model[END_REF].

The following inputs are necessary for the simulation: 1) building characteristics, including room dimensions, mechanical ventilation rates if any (not considered in this study), window sizes and layout (modelled with the Pleiades software [START_REF] Izuba Énergies | Logiciel Pleiades[END_REF]), 2) dynamic outdoor PM2.5 concentrations, 3) meteorological data (temperature, wind speed and direction), 3) indoor PM2.5 emission rates, 4) time and duration of emissions and 5) particle size distribution. Room volumes of 30 m 3 and 67 m 3 are considered, corresponding to 12 m² and 27 m² flooring area, respectively which are within the range of average bedrooms and kitchens [START_REF] Biesebeek | Room size and ventilation. General Fact Sheet: General default parameters for estimating consumer exposure: Updated version 2014[END_REF][START_REF] Pereira | Room-scale analysis of spatial and human factors affecting indoor environmental quality in Porto residential flats[END_REF][START_REF] Kumar | CO2 exposure, ventilation, thermal comfort and health risks in low-income home kitchens of twelve global cities[END_REF]. Air flows are simulated with CONTAM based on the opening of windows, infiltration rates under 4 Pa and meteorological data, considering a constant indoor temperature of 20 °C. Any interconnecting door between rooms is assumed to be closed, while infiltrations and airflows

from differences between open versus closed windows are explicitly considered.

Concentrations are calculated with a time step of 10 minutes as a function of airflow rates, emission rates, outdoor PM2.5 concentrations and deposition rates.

The INCA-Indoor model calculates air PM concentrations based on their size distribution, separated into 27 categories of 0.004 µm to 10 µm. Since particle size distributions are only available for some specific activities, we select a more general indoor distribution, irrespective of the emission source [START_REF] Abt | Relative Contribution of Outdoor and Indoor Particle Sources to Indoor Concentrations[END_REF]. The distributions are provided in SI section S.3.

Because the dynamic model allows to capture variations in air renewal rates, scenarios have been defined to evaluate the effect of window opening on concentrations and health impacts for each activity, hence estimating uncertainties of the parametric model. Air change exchange rates (ACH) between indoor and outdoor air are defined according to Fantke et al. [START_REF] Fantke | Characterizing Aggregated Exposure to Primary Particulate Matter: Recommended Intake Fractions for Indoor and Outdoor Sources[END_REF].The average ACH for OECD countries is 0.64 ACH [START_REF] Rosenbaum | Indoor Air Pollutant Exposure for Life Cycle Assessment: Regional Health Impact Factors for Households[END_REF], while low-end values are around 0.2 ACH for airtight buildings [START_REF] Persily | Modeled infiltration rate distributions for U.S. housing[END_REF]. High air change rates are around 3 ACH, and in non-OECD countries, they can reach 14 ACH.

Exposure model data

The intake fractions for one occupant are calculated using equation (2) for dynamic concentrations using a breathing rate 𝐵𝑅 of 16 m 3 /d [START_REF] Hodas | Indoor inhalation intake fractions of fine particulate matter: review of influencing factors[END_REF] and an exposure duration of 24 hours.

Effect (IER) model data

Global population data are obtained from world population prospects [44] and agespecific global mortality rates, 𝑀 (deaths/year), for the five diseases outcomes are obtained from the GBD Collaborative Network for 2019 [START_REF]Global Burden of Disease Study 2019 (GBD 2019) Results[END_REF]. An annual average ambient PM2.5 level of 16 µg/m 3 , corresponding to an average European city [START_REF] Who | Modelled estimates of air pollution from particulate matter[END_REF], is considered and average exposure concentrations 𝐶 ̅ in (µg/m 3 ) are calculated over 24 hours. The calculated effect factor 𝐸𝐹 only corresponds to exposure to one activity and ambient PM2.5 concentrations, without considering the occurrence of several activities at the same time. PM2.5 concentration increments are higher for higher emission rates: the use of a coal heating stove can lead to a peak of 4500 µg/m 3 , while toasting or cooking on an electric stove lead to a peak of 150 µg/m 3 . The area under the curve gives the concentration to which occupants are exposed over a given duration (µg.s/m 3 ), which is important to consider in health impact assessment. It is ultimately linked to the decay rate, mainly determined by the air change rate: higher ACH lead to higher decay rates. The increment in the larger room is on average 2.5 times lower than that in the smaller room, and the ratio of their volumes is 2.2. The difference can be explained by a higher deposition rate in the larger room due to larger available surface area. Higher ventilation rates lead to a decrease in concentrations if windows are always open or closed. We note from the other scenarios presented in Figure 3 (b), that least concentrations are linked to opening windows during the activity, since ventilation rates during emission are much higher, while opening before the activity does not affect concentrations: they are equal to the closed window scenario. Opening after the activity allows for partial evacuation of substances and hence a slight decrease in concentrations (see SI Figure S2).

Results and discussion

Parametric model indoor PM2.5 concentrations

The average PM2.5 concentration over 24 hours are calculated for each activity with the parametric model described by equation ( 4). Though the parametric model with a mean ACH throughout the day can provide a good estimation of 24-h average indoor air concentrations (e.g. for fixed mechanical ventilation), under certain conditions, the dynamic model is more precise. In most cases, the ACH changes throughout the day according to opening/closing of windows and meteorological conditions. The latter determines natural air flow rates through

Contribution of outdoor PM2.5 level on indoor concentrations

Dynamic and parametric model concentrations were calculated for an average European city with an average background PM2.5 level of 16 µg/m 3 , while, for different cities in the world, outdoor PM2.5 concentrations can range from 4 µg/m 3 to 200 µg/m 3 [START_REF] Who | Modelled estimates of air pollution from particulate matter[END_REF]. Figure 5 illustrates the effect of different outdoor PM2.5 levels on indoor concentrations for different ventilation scenarios and three activities representing low, medium and high emission rates: candle burning (low), frying (low) and cook stove (high). We note from Figure 5 that, for activities with high emissions such as incense burning, outdoor concentrations have low relative effect on indoor concentrations (only up to 6% increase), while they can lead to an 8-fold increase for low-emission activities such as candle burning (low). At very high outdoor concentrations and for low emission rates such as candle burning, average concentrations are lower for closed window scenarios since the highest contribution to indoor PM2.5 is outdoor air.

Intake fractions, effect factors and characterisation factors for different indoor activities

Intake quantities and impacts for each activity are calculated per occupant and per hour of activity. The uncertainty factors for the intake mass (µg) calculated by the parametric versus the dynamic model are 1.0, 1.05, 1.04 and 1.37, respectively, for the standard scenarios with 0.2, 0.6, 3 and 14 ACH.

Intake fractions (µgintake/µgemitted) and intake rates (µgintake/hactivity) calculated using the dynamic concentrations for all activities and ventilation scenarios are shown in Figure 6 (a).

Orange and red lines represent the annual and daily exposure recommendations from the WHO air quality guidelines respectively [START_REF] Who | WHO global air quality guidelines: particulate matter (PM2.5 and PM10), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide[END_REF]. Figure 6 (b) shows effect factors (µDALY/µgintake) for all activities and four standard ventilation scenarios, and Figure 6 (c) shows characterisation factors (µDALY/µgemitted) on the primary y-axis and impact scores (µDALY/hactivity and minuteslost/hactivity) on the secondary y-axes with diagonal iso-impact lines for all activities and scenarios. Figure 6: a) Intake fractions (µgintake/µgemitted) for all activities and ventilation scenarios on the primary y-axis and total intake (µgintake/ hactivity) on the secondary y-axis, with iso-intake diagonal lines in grey and annual and daily recommendations represented by yellow and red lines, (b) effect factors (µDALY/µgintake) for all activities and four standard ventilation scenarios and (c) characterisation factors (µDALY/µgemitted) on the primary y-axis and health damages (µDALY/hactivity and minuteslost/d) on the two secondary y-axes (left and right) with iso-impact diagonal lines in grey.

Intake fractions (µgintake/µgemitted) calculated by equation ( 2) are different for each scenario but independent of the emission rate: they depend on breathing rate, occupancy, particle deposition rate and ACH [START_REF] Fantke | Characterizing Aggregated Exposure to Primary Particulate Matter: Recommended Intake Fractions for Indoor and Outdoor Sources[END_REF]. Given a ventilation scenario, the total intake (µgintake/hactivity), represented by diagonal grey iso-intake lines, is higher for activities with higher emission rates. Markers in the orange and red zone indicate activity and window-opening combinations that lead to concentrations, and consequently intake quantities, beyond WHO guidelines. These include low-emission activities such as lighting a candle in a closed airtight building at 0.2 ACH, which is a possible scenario. Unless having a very high ventilation rate (windows open only during activity, with a 24-hour average of 14 ACH) during the use of a very high-emission cook stove, all scenarios lead to intake well above guidelines. with cross ventilation). We also note that indoor fuel burning for cooking (high, using maize straw) can lead to very high health impacts of 484 µDALY/hactivity (about 4 hourslost) in closed buildings at 0.2 ACH. However, these ACH are unlikely for this activity, occurring in rural homes in buildings with potentially high infiltration rates. Furthermore, occupants might ventilate during the use of the cook stove, which can otherwise can cause discomfort due to increased temperatures and high, perceptible, PM2.5 levels. Air renewal rates are more likely to be around 3 to 14 ACH, resulting in 98 to 119 µDALY/hactivity (52 to 63 minuteslost/hactivity). On the other hand, candle burning can potentially occur in airtight buildings with closed windows, at 0.2 or 0.6 ACH, leading to 7 to 20 µDALY/hactivity (4 to 11 minuteslost/hactivity).

For a given activity-window scenario combination in the larger room (67 m 3 ), concentrations are lower (see Figure 3), impact scores are also lower. Results for the larger room are given in SI.

Individual lifetime risk

The individual lifetime risk (𝐼𝐿𝑅) (DALY/person), representing the number of life years lost from the five disease outcomes, are calculated for each activity for an average scenario of 0.6 ACH air change rate from equation ( 6) and illustrated in Figure 7. It is considered that an individual is exposed PM2.5 resulting from daily one-hour activities over a lifetime of 86 years [START_REF] Who | WHO methods and data sources for global burden of disease estimates 2000-2019[END_REF]. The ILR for 30-minute and 2-hour activity durations are also given. given on the right y-axis

Activities with very high emissions can lead to a loss of up to 10 years of life, distributed as follows for the use of cook stove (high): 42% of the risk is related to IHD, 30% to stroke, 3% to LRI, 9% to LC and 15% to COPD.

From Figure 7, we note variations in the distance between the ILR for the default activity duration (one hour) and that of each two additional durations (30 minutes and 2 hours) for different activities. These are explained by two non-linearities, one associated with the effect factor and one associated with the intake. First, the exposure-response model underlying the effect factors is supra-linear and dependent on indoor PM2.5 concentrations (see equation 3), which are a function of both indoor activity emissions and outdoor PM2.5 levels. Second, intake considers the concentration increment associated with a specific activity, and is hence nonlinearly dependent on indoor PM2.5 concentrations (especially at very low emission rates, where outdoor PM2.5 have a higher relative influence on indoor PM2.5 concentrations).

Discussion

We In the particular case of the heating stove, opening windows during the activity is counterproductive, leading to higher heating needs and thus higher environmental impacts.

With a life cycle perspective, it is thus important to consider heating as a source of impacts (calculated using an energy simulation and LCA software) and identify best trade-offs that allow to reduce damages from PM2.5 and heating altogether.

The emission factor obtained in literature is dependent on the mass of coal burnt [START_REF] Li | User behavior, influence factors, and impacts on real-world pollutant emissions from the household heating stoves in rural China[END_REF],

which was calculated according to heating needs with Pleiades [START_REF] Izuba Énergies | Logiciel Pleiades[END_REF]. The emission rate could thus be adapted in the model if specific information on the mass burnt or heating need is known.

Furthermore, activities linked with indoor burning of fuels are more likely to occur in rural regions, in homes having high ventilation/infiltration rates. Different ventilation rates can be used as sensitivity analysis to reflect associations between indoor pollution levels and occupant behaviour (e.g. opening of windows when concentrations are uncomfortably high), especially if actual ventilation rates are unknown. In an LCA context, results are given for a large number of scenarios that combine different emission rates and building characteristics (i.e. ventilation rates and occupancy). Practitioners can thus apply those results that are relevant for their specific study context.

Health impacts are calculated for activities with a reference duration of one hour, but some activities can be shorter (e.g. smoking for a few minutes) or last longer (e.g. heating for a few hours). Scaling results to actual activity durations can provide approximations, since the calculated non-linear effect factors depend on average indoor air PM2.5 concentrations, which vary with different activity durations. A sensitivity analysis is given in SI section S.6. for the duration of 30 minutes and 2 hours for all activities at 0.6 ACH. Similarly, the factors provided do not consider multiple occurrences of different activities at the same time, which could increase concentrations and lead to variations in effect factors and impact scores. Finally, the emission of other contaminants from the activities (e.g. volatile organic compounds, VOCs, from candle burning) were not considered, which could be responsible for additional health impacts.

Future scopes include the study of the influence of PM toxicity if compositions of emitted particles for specific activities are known, and the calculation of characterisation factors and impact scores for more activities. Studies focusing on fugitive emissions are few and fairly recent. With growing awareness around health impacts of indoor solid fuel burning for heating or cooking, more data could be available and allow to derive impact scores for different types of stoves (e.g. certified heating stoves). The effect of a kitchen hood, which lead to 60 to 100% reduction in PM2.5 concentrations [START_REF] Eom | Particle dispersion and removal associated with kitchen range hood and whole house ventilation system[END_REF], on health impacts of cooking could be included.

Furthermore, effect factors were calculated based on ambient PM2.5 concentrations, though occupants are mainly exposed to indoor concentrations, which are often higher than outdoors: effect factors are hence possibly overestimated. Representative building archetypes with their respective activity scenarios can be modelled to calculate annual exposure concentrations considering the fractions of time spent indoors and outdoors.

Conclusion

In this paper, we have provided a set of intake fractions, effect factors, characterisation factors and impact scores for 19 one-hour activities and 10 different ventilation scenarios.

Indoor concentrations depend on indoor settings (e.g. window layout and opening scenarios), outdoor PM2.5 level and activity duration. These parameters influence the indoor exposure PM2.5 level used to calculate effect factors. We note that, at very low ACH, all activities induced exposure concentrations beyond WHO recommendations. High or very high ventilation during all activities allowed to reduce concentrations well below these recommendations. For instance, 98 to 119 µDALY/hactivity (52 to 63 minuteslost/hactivity) were associated with traditional fuel cook stoves, with high air renewal rates (3 and 14 ACH), while 7 to 11 µDALY/hactivity (4 to 11 minuteslost/hactivity) were associated with candle burning in closed buildings at 0.2 to 0.6 ACH. Characterisation factors for the one-hour activities (or any other activities with corresponding emission rates) provided in this study can be integrated to LCIA methods and the framework proposed can help to devise optimal ventilation strategies in building design.

The derived impact scores (𝐶𝐹 × 𝐸𝑅 𝑎𝑐𝑡𝑖𝑣𝑖𝑡𝑦 ) for an activity unit of one hour can be scaled by activity duration to obtain an approximation of actual activity impacts. This constitutes a valuable starting point for the integration of indoor activities and their PM2.5-related emissions, exposures and health effects into LCA and environmental footprint studies.

Four

  standard scenarios are defined: windows always closed with infiltration rates of 0.2 ACH and 0.6 ACH, and windows always open with high and very high ventilation rates of 3 ACH and 14 ACH respectively. The air change rates indicated are 24-hour averages, but air flows vary during the day for natural ventilation due to changes in wind speed and direction, temperature, and pressure. In the remaining six scenarios, windows are either open before, during or after activity, with high and very high average air renewal rates of 3 ACH and 14 ACH. Since windows are open for a limited duration in the last six scenarios (a minimum of one hour allowed by the model), the ACH can be much higher when open in order to reach the target 24-hour average, but do not exceed a reference comfort speed of 1 m/s [43]. Dynamic ACH are illustrated in SI Figure S2. Very high ventilation rates typically correspond to hot or tropical regions, where cross-ventilation is common. The 10 ventilation scenarios are summarised in SI section S.1.

3. 1 .

 1 Dynamic indoor PM2.5 concentrations Dynamic PM2.5 concentrations from four indoor activities in a reference room of 30 m 3 with closed windows at 0.6 ACH are presented in Figure 2 over 24 hours, a duration that allows to evacuate most activity-related PM2.5 from indoor air. Indoor and outdoor concentrations are dynamic. Indoor concentrations increase after emission, and decrease due to evacuation with air renewal. Outdoor concentrations vary independently of the indoor activity, due to change in outdoor emissions (e.g. fuel burning) or wind speeds.

Figure 2 :

 2 Figure 2: Indoor PM2.5 concentrations from outdoors (navy) and increment from one hour activities (orange) over 24 hours: (a) toasting or cooking on an electric stove, (b) grilling (low), (c) smoking or lighting a candle with essential oil diffusion and (d) coal heating stove.

24-h averageFigure 3 :

 3 Figure 3: Bars represent PM2.5 concentrations for an average scenario with 0.6 ACH without indoor emission (base) and with emissions from activities (increment) for a room with small volume and high occupancy (HO) and a room with high volume and low occupancy (LO). Markers represent concentrations for (a) three other ACH in the small room: 0.2 ACH (always closed), 3 ACH and 14 ACH (always open) and (b) two ACH: 3 ACH and 14 ACH for different opening scenarios (open before, open during and open after)

Figure 4 .

 4 Figure 4.

Figure 4 :

 4 Figure 4: INCA-Indoor v/s parametric model 24-h average indoor PM2.5 concentrations from different activities for (a) closed windows at 0.2 and 0.6 ACH and (b) air renewal of 3 and 14 ACH for windows always open, and uncertainties linked to window opening scenarios

Figure 5 :

 5 Figure 5: Indoor PM2.5 concentrations for three different activities and seven outdoor concentrations (4 -200 µg/m 3 ) for a) four standard ACH: (a) 0.2 ACH and 0.6 ACH with closed windows and 3 ACH and 14 ACH with windows always open, and (b) 3 ACH or

Figure 6 (

 6 Figure 6 (b) shows that effect factors decrease with increasing emission rate for each

Figure 7 :

 7 Figure 7: Total individual lifetime risks (DALY/person) given by black markers for one-hour activities at 0.6 ACH and grey markers for 30-minute and two-hour durations on the left yaxis, with contributions of each disease (ischaemic heart disease IHD, stroke, lower respiratory infections LRI, lung cancer LC and chronic obstructive pulmonary disease COPD)

  𝑆𝐹 𝑖,𝑟 (DALY/death) is the severity factor specific to the region and disease and 𝐵𝑅 𝑦𝑟 (m 3 /year) is the breathing rate. The effect factor depends on exposure concentrations and can hence be different for activities with different emission rates.The 𝐸𝐹 depends on the average effective indoor concentration 𝐶 ̅ in . This can be either calculated by INCA-Indoor, or as a comparison calculated using Fantke et al.[START_REF] Fantke | Characterizing Aggregated Exposure to Primary Particulate Matter: Recommended Intake Fractions for Indoor and Outdoor Sources[END_REF], adapted to consider intake and deposition for outdoor PM2.5:

	al. 2017, Fantke et al. × 𝑆𝐹 𝑖,𝑟 (3) Where 𝑅𝑅 𝑖 (-) is the relative risk of developing disease 𝑖 fom exposure to 𝐶 ̅ 𝐸𝐹(𝐶 ̅ in ) = 𝑑𝑀 PM 2.5 𝑑𝐼 in × 𝑆𝐹 𝑖,𝑟 = (𝑅𝑅 𝑖 (𝐶 ̅ in + ∆𝐶 in ) -𝑅𝑅 𝑖 (𝐶 ̅ in )) × 𝑀 𝑖,𝑟 𝑅𝑅 𝑖 (𝐶 ̅ out,𝑟 ) × 𝑃𝑂𝑃 𝑟 ∆𝐶 ̅ in × 𝐵𝑅 𝑦𝑟 in (see Fantke et al. 2019), ∆𝐶 ̅ 2019). in (µg/m 3 ) is the increment on the exposure-response curve, 𝑀 𝑖,𝑟 (deaths/year) the annual mortality in region 𝑟 due to disease 𝑖, 𝑃𝑂𝑃 𝐶 ̅ (𝑚̇e mitted in,avg + 𝑚̇p enetration,avg ) in = 𝑉 room × (𝐴𝐶𝐻 avg + 𝐷𝑅 avg + 𝐵𝑅 avg × 𝑃𝑂𝑃 avg 𝑉 room )

𝑟 (cap) is the population of the region,

Table 1 :

 1 Primary PM2.5 emission rates for 19 activities

		PM2.5
	Activity	emission rate	Reference
		(mg/min)
	Candle burning (low)	0.04	Pagels et al. (2009)
	Toasting	0.11	He et al. (2004)
	Cooking with electric stove (low)	0.11	He et al. (2004)
	Candle burning (medium)	0.15	Pagels et al. (2009)
	Incense -aromatic (low)	0.16	Lee and Wang (2004)
	Gas stove	0.24	He et al. (2004)
	Printer (high)	0.28	He et al. (2010)
	Frying (low)	0.43	Aquilina and Camilleri (2022)
	Grilling (low)	0.62	Aquilina and Camilleri (2022)
	Candle with eucalyptus oil diffusion (high)	0.91	He et al. (2004)
	Smoking (passive)	0.99	He et al. (2004)
	Cook stove (low) a	1.2	Du et al. (2021)
	Cooking (high, with burning)	1.33	Aquilina and Camilleri (2022)
	Frying (high)	2.68	He et al. (2004)
	Grilling (high)	2.78	He et al. (2004)
	Coal heating stove d	3.50	Li et al. (2022)
	Incense -traditional (high)	6.21	Lee and Wang (2004)
	Cook stove (medium) b	7.9	Shen et al. (2020)
	Cook stove (high) c	120	Du et al. (2021)

  have seen that the parametric model can be used with little or no variation in ACH during the day, but cannot integrate dynamic ventilation or occupancy scenarios. As for the dynamic model, window-opening scenarios can be defined with a minimum of one-hour time

steps, while in reality, occupants can open windows for only a few minutes, especially during cold weather. Ventilation durations are thus potentially overestimated, leading to underestimations in concentrations and impacts, in particular for the open during and open after scenarios.

Acknowledgements

The authors would like to thank the financial support by the Chair ParisTech VINCI Eco-design of buildings and infrastructure. The project acknowledges the UNEP GLAM project human health task force, and the financial support from the USEtox International Centre.

Supplementary description

In Supplementary Material 1, background model data, ventilation scenario descriptions, heat stove emission calculations, the particle size number distribution, indoor PM2.5 concentrations as a function of ventilation scenario and results for the larger room are provided.

In Supplementary Material 2, effect factors, characterisation factors and impact scores for all activities, scenarios and occupancy rates are given.


Chantal Le Mouël

Olivier Mora

Marie De Lattre-Gasquet

Agrimonde-Terra foresight study on'Land use and food security

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

The study seeks to answer these questions, highlighting levers that could modify ongoing land use patterns for improved food and nutrition security. The approach provides decision makers, stakeholders, non-governmental organizations and researchers with a tool for dialogue.

Method

Researchers firstly analyzed the long-term dynamics of the land use and food security system, with a focus on the five dimensions of land use (agronomic potential, access to land, degree of intensity of land use, distribution of land between different uses and services provided by land). This analysis allowed us to select key drivers of land use change. By identifying a range of variables influencing each driver and its dynamics, hypotheses on how each driver might evolve in 2050 were then produced. Secondly, five contrasted scenarios were built by combining one or several hypotheses per driver. Each scenario describes a situation of land use and food security in 2050 and has been translated into a narrative. Thirdly, the impacts of the scenarios in terms of land use, agricultural production and trade in the 14 world regions and globally have been assessed through quantitative simulations using the biomass balance model GlobAgri-AgT. The five scenarios and their outcomes were then appreciated both quantitatively and qualitatively.

The five scenarios of land use and food security by 2050

The first three scenarios are based on current competing trends identified in most world regions.

 'Metropolization' links the development of megacities with a nutrition transition led by global agri-food companies selling ultra-processed foods or increase of animal products consumption, in a global context of development through market forces and rapid climate change. Small farmers are marginalized.

 'Regionalization' relates the increase of medium-size cities and their networking with rural areas to the emergence of regional food systems based on family farming and traditional food diets, and a set of regional agreements, notably trade agreements.

 'Households' links strong individual mobility between rural and urban areas and a development of nonfarm employment to the emergence of hybrid diets based on both traditional and modern value chains, in a globalized world where family farms and cooperatives are major actors in land use.

The last two scenarios involve potential breaks that could change the entire land use and food security system.

 'Healthy' assumes that due to the increasing cost of malnutrition, a radical move towards healthy diets occurs driven by global cooperation and public policies in a context of climate change stabilization, involving a re-configuration of agricultural systems backed by new alliances between stakeholders.

 'Communities' assumes that in a context of recurrent crises, development based on small towns and rural communities occurs, focusing on managing common property in agriculture in order to ensure food security.

For each scenario variants have been applied reflecting, for example, a diet transition based on animal products or ultra-processed products in the 'Metropolization' scenario.

Lessons on land use and food security from the scenarios

 Ensuring world food availability in 2050 will involve expanding the world's agricultural land area to the detriment of forests. Indeed only the 'Healthy' scenario is likely to be able to ensure sustainably world food security in 2050. 'Metropolization' and 'Communities' are not able to achieve this objective without deforestation and the two other scenarios, 'Regionalization' and 'Households', lead to ambiguous results.

 In terms of nutrition 'Healthy' contributes most to reducing overnutrition and related diseases, but also to diminishing undernutrition. 'Metropolization' contributes most to the expansion of overweight, obesity and related diseases. 'Communities' would create a reduction in food availability at the world and regional levels. 'Regionalization' works in favour of world and regional food and nutrition security, but leads to ambiguous results for world food availability. 'Households' contributes to both a decrease in undernutrition and some ambiguous effects on overnutrition.

 The situation in India, West African and East, Central & South Africa will probably be difficult in 2050 both in terms of food security and land pressure because of demography, low agricultural performances and current diets that are insufficient in average. Increasing their imports would reduce the required growth in agricultural land area, avoiding some deforestation. Increasing agricultural performances in these regions would also be a land-saving lever for reduced deforestation.

 Whatever the scenario, international trade will play a key role in ensuring world food availability in 2050 and some regions, especially North Africa and Near & Middle-East, are likely to be highly dependent on food imports.

 Increasing food and nutritional diversity for healthier diets in 2050 while limiting the growth of agricultural land and deforestation will require greater diversification in cropping and livestock systems.

Conclusions

 There is no given pathway to food and nutrition security. Because the challenge is complex, with many overlapping and interlinked issues that cut across sectors, territories and actors, changing the course of ongoing trends requires systemic transformation, public policies and consistent actions from a wide range of actors. Each country and region will have to find its own pathway in coherence with common responsibilities for facing global challenges.

 Changes in both supply and demand are necessary for transition towards diversified and healthy diets and the reduction of waste and losses.

 Future cropping and livestock systems are a cornerstone issue for food security and further research is needed on their economic, environmental and social performance.

 The importance of trade and the roles of new actors require a rethink on how it is organised.  All of the above imply securing access to land for all types of farming structures and that we care about rural development.

  Agrimonde foresight study published in 2011 on 'World food security in 2050', the CIRAD and INRA have turned their attention to a new foresight exercise on 'Land use and food security in 2050'. The subject is particularly important because of uncertainties about the planet's capacity to feed a growing population (set to reach 9.7 billion in 2050) in a context of climate change and ongoing debates regarding land use change trajectories. What are the main drivers of land use changes and how do they interact and influence food and nutrition security? How will the agricultural land area change over the next 40 years, globally and regionally? What tensions will there be between food and nutrition security and climate change mitigation in 2050?