# Environmental justice in France: Analysis of the association between exposure to environmental pollution and social deprivation

Khadidiatou Diallo, Pauline Pouchin, Lucie Adélaïde, Johanna Lepeule, Ludivine Launay, Guy Launoy, Joséphine Bryere

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## To cite this version:

Khadidiatou Diallo, Pauline Pouchin, Lucie Adélaïde, Johanna Lepeule, Ludivine Launay, et al. "Environmental justice in France: Analysis of the association between exposure to environmental pollution and social deprivation." Environmental Research, 2025, 285, 10.1016/j.envres.2025.122434 hal-05198590

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# Environmental justice in France: Analysis of the association between exposure to environmental pollution and social deprivation

Khadidiatou Diallo<sup>a, *,</sup>, Pauline Pouchin<sup>a</sup>, Lucie Adélaïde<sup>b, f</sup>, Johanna Lepeule<sup>b</sup>, Ludivine Launay<sup>a, d, e</sup>, Guy Launoy<sup>a, c, d</sup>, Joséphine Bryere<sup>a, c</sup>

## ABSTRACT

**Background:** Some studies on environmental justice have demonstrated inequalities in pollution exposure based on socioeconomic gradients. However, French studies are limited to the local level, and showed varied associations between environmental pollution and social deprivation.

**Methods:** This study analysed the association between exposure to environmental pollution and social deprivation on a fine scale in France. Indicators for proximity to polluted sites and soils, and the number of days of overexposure to fine particles (PM₁₀, PM₂.₅) and nitrogen dioxide (NO₂), according to World Health Organisation (WHO) guidelines (2021), were calculated for each geographical unit (IRIS) in 2021. Social deprivation levels were measured using the French version of the European Deprivation Index (EDI) in 2017. Relationships between environmental pollution and deprivation were assessed using a conditional spatial autoregressive Poisson model.

**Findings:** IRIS in EDI quintiles 4 and 5 (the most deprived) were 2.005 (95 % CI: 1.892–2.125) and 1.991 (95 % CI: 1.873–2.117) times more likely to have a higher number of polluted sites and soils compared with IRIS in quintile 1 (the least deprived). Air pollution showed linear associations with risks of overexposure to PM₁₀, PM₂.₅, and NO₂, with relative risks of 1.087 (1.082–1.093), 1.027 (1.025–1.029), and 1.013 (1.011–1.016), respectively, in the most deprived quintile.

**Interpretation:** This study highlights the compounding effects of environmental and social inequities in France, underscoring the need for targeted interventions to reduce disparities in pollution exposure and associated health risks.

## 1. Introduction

Environmental justice is a concept that emerged in the USA in the 1980s (Blanchon et al., 2009). Its aim was to denounce the environmental injustices suffered by populations from ethnic minorities, namely African-Americans, Hispanics and Native Americans. These injustices concerned in particular the location of polluting activities (polluting factories and toxic waste dumps). This concept rapidly became a major field of research. Numerous studies have confirmed the existence of discrimination in exposure to environmental pollution, with overexposure of both poor and ethnic minority populations. This is the case, for example, of the national study carried out by the US Environmental Protection Agency (EPA) in 2001 on environmental indicators for risk assessment (Bouwes et al., 2001). Using the Risk-Screening Environmental model based on Toxics Release Inventory (TRI) data, this study showed the overexposure of ethnic minorities (Blacks, Asians and

Hispanics) and the unemployed to airborne toxic substances emitted by industries at the census block level. Another national study of urban areas using the same data (Zwickl et al., 2014) looked at how racial and ethnic disparities in exposure varied with median neighborhood income, on the one hand, and assessed the existence of regional variation in the distribution of these environmental inequalities, on the other. The results showed a significant effect of race, ethnic status and income on exposure to pollution, with an accentuation of the effect of minority status according to low income. These results persisted even after regional variations had been taken into account. In addition, a cross-sectional study conducted as part of the MESA (Multi-Ethnic Study of Atherosclerosis) cohort in the USA revealed a reduction in exposure to PM2.5 and NO2 pollution as neighborhood socio-economic status increased (Hajat et al., 2013). Beyond these borders, numerous other studies have also been carried out, confirming that these issues are far from unique to the USA. For example, a study in New Zealand found that

<footnote><sup>a</sup> U1086 Inserm Anticipe, Avenue Général Harris, Cedex, 14076, Caen, France</footnote>

<footnote><sup>b</sup> Université Grenoble Alpes, Inserm, CNRS, IAB, Site Santé, Allée des Alpes, 38700, La Tronche, France</footnote>

<footnote><sup>c</sup> University of Caen, Cedex, 14076, Caen, France</footnote>

<footnote><sup>d</sup> Plateforme MapInMed, US PLATON, Avenue Général Harris, Cedex, 14076, Caen, France</footnote>

<footnote><sup>e</sup> Centre François Baclesse, Avenue Général Harris, Cedex, 14076, Caen, France</footnote>

<footnote><sup>f</sup> Santé Publique France, 12 Rue Du Val D'Osne, 94415 Saint-Maurice Cedex, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail addresses: diallohadidiatou1997@gmail.com, khadidiatou.diallo@etu.unicaen.fr (K. Diallo).</footnote>
average particulate concentrations were up to five times lower in neighborhoods in high-income urban units than in low-income neighborhoods (Pearce and Kingham, 2008). Similarly, a study carried out on the island of Montreal concerning exposure to noise from transport and industry showed a positive correlation between noise exposure and socio-economic indicators, with higher levels of exposure in the most deprived areas (Dale et al., 2015). In a study carried out in three major Canadian metropolises (Montreal, Vancouver and Toronto) on NO₂ pollution exposure and socio-economic indicators of social and material deprivation, overexposure was observed among the most vulnerable populations, such as renters, those speaking neither English nor French, or those living alone (Toronto), with less than a high school education (Montreal), or unmarried (Vancouver) (Pinault et al., 2016).

In Europe, and especially in France, the notion of environmental justice emerged in the 2000s, with an approach focused on environmental inequalities. It highlights situations where socio-economic and environmental inequalities accumulate within territories. Unlike the American approach, the focus is no longer on belonging to different ethnic groups, but on environmental vulnerability according to social group (Deguen et al., 2022; Deldrève, 2020; Emelianoff, 2008; Lejeune and Deldrève, 2022; Lejeune, 2015). This new orientation is the result of specific geo-historical and political contexts that shape the way this field of research is appropriated. It underlines the central role of public action, seen as a lever for correcting and redistributing the mechanisms that induce these disparities (Emelianoff, 2006; Laigle and Tual, 2007). Environmental justice, or environmental inequality, thus reveals the existence of disparities that do not necessarily reflect injustice or discrimination, but rather an inequality of situation between social groups. It highlights differences in exposure to environmental nuisances or amenities, be they risks, the impacts of human practices or the social effects of environmental public policies (Blanchon et al., 2009; Deguen et al., 2022; Deldrève and Candau, 2014; Lejeune, 2015). This new focus on environmental justice issues requires us to provide public action with reliable and robust knowledge of existing environmental inequalities at fine scales, enabling the implementation of corrective measures. This study will approach environmental justice by analyzing socio-economic inequalities. It will be based on social categories defined using an ecological index of social disadvantage, such as that proposed by (Pornet et al., 2012).

From the point of view of territorial characteristics and dynamics in France, these social disparities are unevenly distributed. The literature reveals a very high concentration of social disparities in the most urbanised areas. In their study on the use of an ecological index on a national scale (Merville et al., 2022), showed higher levels of deprivation in Type 1, 2 and 5 zones of the typology of living territories indicator, according to the European index of social deprivation. These territories correspond to the highly urbanised and rather favorable areas, but with social difficulties and jobs often far away (Type 1) in the Paris region; the rather favorable areas with rapid access to facilities, but with socio-economic difficulties (Type 2) in regional metropolises (Lyon, Marseille, Lille, Toulouse ...) and fairly densely populated areas in unfavorable situations (Type 5) with former industrial and mining territories in the North and East (Reynard, 2016). These results thus underline the importance of analyzing social inequalities from a territorialized perspective, taking into account the local specificities of urban and post-industrial dynamics.

European studies also highlight the disproportionate exposure of socially disadvantaged groups to physical environments that are potentially harmful to health, although this situation varies from region to region (Fairburn et al., 2019; Hajat et al., 2015; Harper et al., 2009; Richardson et al., 2013; Samoli et al., 2019). In England, a study assessing geographical associations between environmental exposures, taking into account all levels of the environment-exposure chain (proximity to sources, emissions, environmental concentrations), showed higher pollution in the most deprived areas. However, these associations remain generally weak (R² < 0.10) and vary according to

the pollutants and socio-economic indicators used. In London, some groups with high socio-economic status have NOₓ (nitrogen oxides) concentrations similar to those of groups with low socio-economic status when a measure of socio-economic status is used over a small area (Goodman et al., 2011). In Spain, a study on pregnant women found no association between individual socio-economic status and NO₂ levels (Fernández-Somoano and Tardon, 2014).

The first study on this issue in French was carried out by Laurian (2008), which highlighted significant disparities in the location of polluting industries within cities, as a function of the proportion of immigrants, even after controlling for income, population size, manufacturing employment and unemployment, and after correcting for spatial autocorrelation. Subsequently, a limited number of studies have addressed this issue of environmental inequality, extending the analysis to other pollutants and other measures of deprivation. As in the European context, there are contrasting results depending on the geographical scale of analysis, particularly with regard to exposure to air pollution. For example, a study carried out in four major French cities revealed contrasting associations: in Paris, the population most exposed to air pollution was the one with the lowest social deprivation level, while the opposite was observed in Marseille and Lille. In the Lyon conurbation, the most exposed neighborhoods were those with intermediate social deprivation status (Padilla et al., 2014). In 2016, a study carried out in Grenoble and Lyon on small-scale risk assessment of pollution, health and social deprivation showed a trend towards a higher incidence of health effects (mortality, low birth weight, lung cancer) in areas with a moderate to high index of social deprivation (Morelli et al., 2016). In the same vein, a recent study analyzing exposure to heat, air pollution and lack of vegetation across mainland France highlighted an overexposure of the most deprived IRIS, particularly in urban areas, to "hotspots", i.e. areas combining levels of exposure to air pollutants above WHO guideline values, high temperatures and a low vegetation index (Adélaïde et al., 2024).

However, a number of criticisms can be made to explain, in part, the contrasting results observed in the European and French literature. Firstly, the majority of these studies have focused on specific localities, such as cities or regions (Havard et al., 2009; Lanier et al., 2019; Morelli et al., 2016; Padilla et al., 2014; Viel et al., 2011). Few of them have adopted a global analysis of the territory at fine (Adélaïde et al., 2024; Caudeville and Rican, 2016) or, on the contrary, very coarse scales (Laurian, 2008; Laurian and Funderburg, 2014; Salesse, 2023). Secondly, most previous research measured exposure to environmental pollution on the basis of pollutant concentration or emission levels in territories or populations. Although these measures are validated and recognized for their effectiveness in estimating actual or potential exposures, and are easily understood in scientific jargon, it is crucial, in the current state of research into inequalities in exposure to health risk factors, to incorporate international regulatory standards (Brunekreef and Holgate, 2002). These standards offer a global and recognized framework for assessing exposures, and the integration of these guidelines could enable a better assessment of public health issues, while facilitating the understanding of observed health inequalities and the proposal of appropriate solutions. Thirdly, as environmental data are spatial in nature, observed exposure values may be spatially auto-correlated. This spatial dependence can lead to errors in risk estimation when using conventional statistical models, which can bias the interpretation of relationships between variables. It is therefore essential to incorporate suitable techniques, such as spatial models, to account for this auto-correlation and obtain accurate, robust estimates. However, few studies have taken this dimension into account when analyzing the link between pollution exposure and social deprivation (Havard et al., 2009; Laurian, 2008; Laurian and Funderburg, 2014; Padilla et al., 2014; Samoli et al., 2019; Viel et al., 2011).

Thus, our study aimed to fill these gaps by examining the association between environmental pollution (polluted sites, soils and air pollution) and social deprivation in France at the smallest geographical scale
available, taking into account WHO air pollution standards, territorial specificities and spatial autocorrelation.

## 2. Materials and methods

### 2.1. Data and indicators

#### i. Geographical Scale

The geographic scale of analysis for this study corresponds to the “îlots regroupés pour l’information statistique” (IRIS) proposed by the French National Institute for Statistics and Economic Studies (INSEE). These are the smallest territorial census units available in France, based on an average population of around 2000. Communes with more than 10,000 inhabitants, and most of those with between 5000 and 10,000 inhabitants, are subdivided into several IRIS (Supplementary Fig. 1). Where necessary, a commune corresponds to an IRIS (Supplementary Fig. 1). This study focuses solely on residential IRIS. Non-residential IRIS, as well as those located in Corsica and the French overseas departments (DOM), were excluded from the analysis due to the absence of data on the Deprivation Index (EDI) for non-residential IRIS and DOM, and on air pollution for Corsica. In all, the study covers 48,006 IRIS in mainland France.

The IRIS base maps used in this study were derived from the IGN data and corresponded to the 2021 reference data (IGN, INSEE, n.d.).

#### ii. Exposure to environmental pollution

Exposure to environmental stresses has significant effects on people's health and well-being. In its report "Healthy environment, healthy lives: how the environment influences health and well-being in Europe" (EEA, 2019), the European Environment Agency (EEA) estimates that around 13% of deaths in the European Union are attributable to environmental factors, representing around 630,000 preventable deaths in 2012 (WHO, 2016). Beyond its harmful effects, it is also a source of health disparities. Indeed, the association between exposure to environmental risk factors and social disadvantage can increase health vulnerabilities. Numerous studies have shown that the environment-related burden of disease disproportionately affects vulnerable populations: children, the elderly and disadvantaged groups, thereby exacerbating social inequalities in health (EEA, 2018; WHO Europe, 2019). Socio-territorial constructs and environmental exposures combined with socio-economic disadvantage contribute to the persistence, or even increase, of social inequalities in health across territories (Deguen and Kihal-Talantikite, 2022). This observation brings us back to the issue of the vulnerability differential induced by an exposure differential. The most disadvantaged populations are often the most exposed to environmental pollution, but also the least able to withstand it, due to deteriorated living conditions, limited access to healthcare and a state of health often weakened by co-morbidities. Our analysis of environmental justice was therefore based on an inventory of environmental inequalities at the IRIS level in France. The data used corresponded to the environmental data available at this scale.

#### a. Contaminated sites and soils

The data on contaminated sites and soils comes from the Georisque<fnref n="1" /> platform, a tool developed by the French government to improve the management of natural, technological and industrial risks in France. It provides access to detailed, regularly updated geospatial data on the

major risks present in France. It brings together a wide range of information on risks such as soil pollution, flooding, landslides, polluting industries, etc.

Three databases have been selected.

* BASOL: Polluted or potentially polluted sites and soils requiring preventative or remedial action by public authorities.

* SIS (Secteurs d'Information pour les Sols): Data on land where soil pollution justifies action, such as soil surveys or pollution management measures, in case of land-use changes.

* ICPE: Industrial and agricultural facilities classified for environmental protection (ICPE), including hazardous facilities like oil refineries and combustion industries<fnref n="2" /> under the European IED directive (used for this study).

To assess exposure to polluted sites and soils, spatialized scores were constructed using a geographic information system (GIS). Buffer zones of 100 m around BASOL and SIS sites (geolocalized by polygons) and 1000 m around ICPE facilities (geolocalized by points) were defined. These distances were chosen in order to define closer impact zones around polluted sites and soils. These buffer zones were then added to the Insee IRIS map (reference 2021). An IRIS is considered exposed if it overlaps the defined impact zone. Based on this "exposed" variable, an indicator of the number of polluted sites and soils in the vicinity was calculated by summing the number of times an IRIS was intersected by an impact zone.

#### b. Air pollution

Air pollution concentration data (PM₁₀, PM₂.₅ and NO₂) were obtained from a recent study (Adélaïde et al., 2024), which used ensemble models integrating air quality monitoring data, satellite-based aerosol optical depth (AOD), meteorological factors and spatial predictors (Hough et al., 2021; Real et al., 2022). In this study, daily average concentrations for the period 2000–2018 were calculated with a spatial resolution of 1 km for PM₁₀ and PM₂.₅, 4 km for NO₂ between 2000 and 2017, and 2 km for the year 2018 over the whole of mainland France. For each IRIS, daily exposure to PM₂.₅, PM₁₀, NO₂ was estimated by calculating the area-weighted average of gridded concentration data. The average annual and summer differences between the daily average concentrations of PM₁₀, PM₂.₅ and NO₂ and the WHO's guidelines (WHO, 2021) values for air quality in 2021 (15 µg/m³, 5 µg/m³, and 10 µg/m³, respectively) were calculated to assess the exceedance days for each IRIS.

For our study, the latter indicator was used for the year 2017 to calculate an indicator of the number of days of overexposure. This was done by summing the binary variable 'exceedance days' by pollutant, for each IRIS.

#### iii. Social deprivation

Social deprivation level for each area was estimated using the European Deprivation Index (EDI). Its way of construction is published previously (Pornet et al., 2012). It is dedicated to measure in a robust and comparable way the relative deprivation in the whole European territory using data of the European Union Statistics on Income and Living Conditions (EU-SILC) survey and national census data. For France the 2017 version of its national version (F-EDI) is calculated using weighted rates of various socioeconomic indicators at IRIS level (smallest geographical unit for French census data): percentage of non-owners, overcrowded housing, unskilled workers, low education, foreign nationality, unemployment, single-parents household, no access to a car, household with two or more persons, unmarried. A deprivation

<footnote n="1">¹ Portal set up by the French Ministry of Ecology in partnership with the Geology and Mining Research Bureau (BRGM) to facilitate the use and sharing of data and cartographic information on natural and technological hazards within territories: Accueil - Particulier | Géorisques.</footnote>

<footnote n="2">² Classement par Activités « IED » | AIDA - Ineris.</footnote>
score was calculated for each IRIS. This score was standardized (centered and reduced), so that it had a mean of zero and a variance equal to 1. It was then transformed into a categorical variable, divided into five classes (quintiles), ranging from the least disadvantaged IRIS (quintile 1) to the most disadvantaged (quintile 5). In this study, this indicator was used as a categorical variable and as a continuous score.

#### iv. Territory Characteristics Data

The level of urbanization (highly urbanised or moderately urbanised), the territory's dominant vocation (industrial, agricultural, etc.) or other geographical characteristics can influence the pollution levels observed, independently of the degree of social disadvantage. These characteristics can also help explain levels of socio-economic precariouslyness, as shown by (Merville et al., 2022; Zwickl et al., 2014). In many studies focused on the analysis of environmental inequalities (Adélaïde et al., 2024; Laurian, 2008; Salesse, 2023; Viel et al., 2011; Zwickl et al., 2014) it is recommended to perform stratified analyses according to the type of territory (urban or rural), in order to assess more accurately the relationship between social disadvantage and pollution exposure.

However, our statistical model did not allow us to perform a stratified analysis of the link between exposure to environmental pollution and levels of territorial disadvantage. To overcome this methodological limitation, we took specific territorial characteristics into account in our analysis of the link between exposure to environmental pollution and social disadvantage, using the typology of living territories indicator proposed by (Reynard, 2016). This indicator classifies metropolitan France's 2677 living territories into 8 areas (Supplementary Table 2). These living territories correspond to geographical areas of less than 50, 000 inhabitants, in which populations have access to the most common service facilities.

For the purposes of our study, these areas were aggregated into three categories based on the level of urbanization and harmonized with IRIS (IGN, INSEE, n.d.) for analysis.

* Class 1: Highly urbanised areas, where major urban centres correspond to Types 1, 2, and 3 areas in the INSEE classification.

* Class 2: intermediately urbanised areas, mainly peri-urban, corresponding to types 4, 5 and 8.

* Class 3: Rural or isolated, less urbanised areas corresponding to Types 6 and 7.

The typology of the living area indicator extends beyond the classic distinction between urban and rural areas. In addition to economic and demographic aspects, it provides detailed information on living environments and housing quality.

For reasons of geographical scale, all these indicators have been harmonized with the 2021 IRIS referential, using the Communal Transition Table,<fnref n="3" /> or by simply joining the databases.

### 2.2. Statistical analysis

In the first part of this study, data on exposure to air pollution, contaminated sites and soils were described according to EDI quintiles and living territory categories. Spearman correlation analysis was used to examine the relationships between the EDI continuous score and indicators of exposure to environmental pollution. The Kruskal-Wallis comparison test was used to evaluate differences in the distribution of air pollution across EDI classes and territorial typology. A Bayesian approach was used rather than the classical Poisson regression because it allows the integration of extra-Poisson variability if it exists in the

data. Neighboring areas may not be independent and have similar environmental indicators and this phenomenon called spatial autocorrelation has to be integrated in a Bayesian approach. Spatial autocorrelation, testing for spatial dependency between geographical observations, was assessed using the Moran Index (Chen et al., 2019; Havard et al., 2009). A conditional autoregressive Poisson (CAR) model that takes into account the spatial correlation of the data, by adding a spatial component, $Ui$ (Wang et al., 2023), to the risk assessment model was used for each pollutant.

$$ \text{Log (Yi)} = \beta0 + \beta\text{EDI} + \text{Ui} \quad \text{(Model 1)} $$

Where $Yi$ represents the number of days of overexposure to PM10, PM2.5 and NO2 for the air pollution models, and the number of nearby polluted sites and soils for the contaminated sites and soils model. Spatial correlation was taken into account using $Ui$, incorporating connectivity criteria (e.g. common boundaries).

$$ (\cup_i | \cup_j = u_j, j \neq i) \sim N \left( \frac{\sum_{j \neq i} w_{ij} u_j}{\sum_{j \neq i} w_{ij}}, \frac{\sigma_u^2}{\sum_{j \neq i} w_{ij}} \right) $$

where $w_{ij}$ corresponds to the weight describing the geographical proximity of IRIS i and j, and $\sigma_u^2$ controlling the conditional variability of relative risks, and $u_j$ corresponds to the average of the values taken by j. The geographical proximity criterion used in the model is adjacency. IRIS i and j are adjacent if they share a common border, so $w_{ij}=1$ if they are adjacent and $w_{ij}=0$ otherwise.

To consider the effects of specific characteristics on each area, the models were adjusted for the typology of living areas.

$$ \text{Log (Yi)} = \beta0 + \beta\text{EDI} + \beta\text{Typology of living areas} + \text{Ui} \quad \text{(Model 2)} $$

In order to evaluate the stability of the results depending on the way of defining overexposure, two sensitivity analyses were carried out. The first focused on IRIS with at least one overexposure value greater than or equal to the 80th percentile of the indicator of the number of days by which WHO guide values are exceeded. The second focused exclusively on IRIS with multi-exposure, with overexposure values greater than or equal to the 80th percentile of this indicator for all pollutants.

All analyses in this section were performed using QGIS and RStudio with integrated nested Laplace approximation (INLA) for the Bayesian model.

### 3. Results

#### 3.1. Distribution of air pollution exposure and environmental degradation according to the levels of social deprivation and territorial typology

The number of polluted sites and soils in the vicinity varied between 0 and 101 per IRIS. Of the 48,006 IRISs included in this study, 67 % (32,197) had no polluted sites and soils nearby (score 0). According to their distribution by level of deprivation, quintile 1 (last deprived) represented 77 % of IRIS with a score of zero, compared with only 50 % for quintile 5 (most deprived) (Table 1).

<caption>**Table 1**</caption>

Distribution of the number and proportion of IRIS with a number of polluted sites and soils equal to 0 per EDI quintile.

<table><thead><tr><th>EDI</th><th>IRIS</th><th>Number of IRIS with a null score</th><th>Proportion of IRIS with a null score (%)</th></tr></thead><tbody><tr><td>Quintile 1 (last deprived)</td><td>9651</td><td>7408</td><td>77</td></tr><tr><td>Quintile 2</td><td>9645</td><td>6927</td><td>72</td></tr><tr><td>Quintile 3</td><td>9587</td><td>6677</td><td>70</td></tr><tr><td>Quintile 4</td><td>9550</td><td>6376</td><td>67</td></tr><tr><td>Quintile 5 (most deprived)</td><td>9573</td><td>4809</td><td>50</td></tr></tbody></table>

<footnote n="3">³ Listing all communes that have existed since January 1, 2003, and their Correspondence in the latest available geography. Tables de passage des communes | Insee.</footnote>
Table 2 shows the mean distribution of the number of nearby polluted sites and soils as a function of deprivation level and housing area typology, together with the results of the Kruskal-Wallis test. A significant difference was observed in the distribution of the number of polluted sites and soils in the vicinity as a function of deprivation level and living area typology (p < 2.2e-16). On average, the number of polluted sites and soils nearby was 1.45 for the most deprived IRIS (quintile 5) compared with 0.5 for the least deprived IRIS (quintile 1) (Table 2). According to the typology of living areas, highly urbanised zones (1.23) and intermediately urbanised zones (1.12) had the highest average exposure compared to less urbanised rural areas (0.38).

Air pollution showed similar trends (Table 3), with a significant difference (Kruskal-Wallis test, p < 2.2e-16) in the distribution of the number of days of overexposure to PM₁₀, PM₂.₅, and NO₂ according to the different EDI quintiles and living area typology. The most deprived quintile had the highest median number of days of overexposure to PM₁₀, PM₂.₅, and NO₂, at 193, 325, and 275 days, respectively (Table 3). However, depending on the level of deprivation, there is no gradient in the values of these medians. For example, for NO₂, quintiles 1 (last deprived) and 5 (most deprived) had the highest median number of days of over-exposure (Table 3). The typology of living areas showed that the most urbanised IRISs were the most overexposed. The highly urbanised IRIS had the highest number of days of overexposure to all pollutants, with medians of 216 for PM₁₀, 332 for PM₂.₅, and 305 for NO₂. In contrast, the least exposed IRIS corresponded to the least urbanised IRIS (Rural), with medians of 97 for PM₁₀, 261 for PM₂.₅, and 188 for NO₂.

## 3.2. Association between environmental exposure and social deprivation

Table 4 presents the results of the CAR model. There was a significant and positive association between the risk of exposure to air pollution and pollution at sites and soils and the index of social deprivation (EDI), even after adjustment for the typology of the living area. This risk increased by 1.024 (1.020–1.029), 1.006 (1.006–1.007), 1.002 (1.002–1.002), and 1.001 (1.001–1.001) for a one-unit increase in the level of deprivation for polluted sites and soils, PM₁₀, PM₂.₅, and NO₂, respectively.

Depending on the site and soil pollution indicator considered (Table 4), the risk distribution differed according to the level of deprivation. The relationship between proximity to polluted sites and soils was nonlinear, increasing from the most favoured areas (quintile 1) to the most deprived (quintile 5). Quintiles 4 and 5 were the most exposed, with 2.008 (1.895–2.125) and 1.994 (1.876–2.119) times more likely to have a greater number of polluted sites and nearby soils, respectively, than IRIS in quintile 1.

For air pollution (Table 4), monotonic and linear associations were observed, with a significant increase in risk from the least to the most deprived areas for PM₁₀ and PM₂.₅. IRIS in the most deprived quintile (quintile 5) had 1.088 (1.083–1.093) and 1.027 (1.025–1.029) times higher risks of having a significant number of days of overexposure to

<caption>**Table 2**</caption>

Description of the number of nearby polluted sites and soils by EDI quintiles and typology of living areas.

<table><thead><tr><th></th><th>Mean(sd)</th><th>p-value<sup>a</sup></th></tr></thead><tbody><tr><td><strong>EDI</strong></td><td></td><td>&lt;2.2e-16</td></tr><tr><td>Quintile 1 (least deprived)</td><td>0.50(1.31)</td><td></td></tr><tr><td>Quintile 2</td><td>0.76(1.98)</td><td></td></tr><tr><td>Quintile 3</td><td>0.81(1.95)</td><td></td></tr><tr><td>Quintile 4</td><td>0.90(1.97)</td><td></td></tr><tr><td>Quintile 5 (most deprived)</td><td>1.45(2.55)</td><td></td></tr><tr><td><strong>Typology of living areas</strong> &lt; 2.2e-16</td><td></td><td></td></tr><tr><td>Highly urbanised</td><td>1.23(2.38)</td><td></td></tr><tr><td>Intermediately urbanised</td><td>1.12(2.29)</td><td></td></tr><tr><td>Rural</td><td>0.38(1.15)</td><td></td></tr></tbody></table>

PM₁₀ and PM₂.₅, respectively, compared with IRIS in the least deprived quintile (quintile 1). For NO₂ pollution, a significant excess risk was observed only for quintiles 4 and 5, with 1.005 (1.003–1.007) and 1.013 (1.010–1.016), respectively, compared with quintile 1.

The results of the sensitivity analyses also showed similar trends (Supplementary Tables 3 and 4), even though the multi-exposure analysis was mostly insignificant due to the smaller number of IRIS considered as overexposed.

## 4. Discussion

The purpose of this study was to analyze the association between exposure to air pollution, polluted sites and soils, and social deprivation at a fine scale in France, using an adapted and methodology that considers possible spatial autocorrelation. The results revealed differential exposure to air pollution and polluted sites and soils according to the levels of social deprivation at the IRIS levels, with a social gradient of overexposure to the detriment of the most deprived populations.

The assessment of disparities in exposure to site and soil pollution revealed a more marked overexposure in the most deprived IRIS. This trend is generally consistent with previous studies (Bouwes et al., 2001; Briggs et al., 2008; Cambra et al., 2013; Caudeville and Rican, 2016; Harper et al., 2009; Laurian, 2008; Laurian and Funderburg, 2014; Viel et al., 2011; Zwickl et al., 2014). Although these studies have used different measures of deprivation, they have for the most part concluded that there is a higher risk of exposure to noxious installations and contaminated soils nearby in areas inhabited by deprived populations. However, these studies are often limited to local or coarse scales, with the exception of the study conducted by (Zwickl et al., 2014), which limits the assessment of variability in exposure within territories. What's more, there are sometimes based on the assumption of independence in observed exposure values from one territory to another, which can potentially lead to biases in the strength and direction of the association (Havard et al., 2009). With regard to exposure to air pollution, our results also showed an overexposure of the most deprived populations in France. These results are consistent with those observed in other countries, notably the USA, Canada, New Zealand and Sweden (Brochu et al., 2011; Buzzelli, 2020; Chaix et al., 2006; Deng et al., 2024; Hajat et al., 2013; Jerrett et al., 2001; Pearce et al., 2006; Pearce and Kingham, 2008; Yanosky et al., 2008). In France, previous studies had controversial results, the majority conducted in limited areas without accounting for spatial autocorrelation. Different trends have been observed depending on the level of urbanization (Adélaïde et al., 2024; Salesse, 2023). For example, Salesse (2023) has shown that disparities in exposure to air pollution are more marked in urban areas, where the most deprived populations are more overexposed. This observation was confirmed in a recent study by Adélaïde et al. (2024), which found a strong association between social deprivation and the risk of living in urban hotspots. In rural areas, these studies showed an opposite trend, with an overexposure of the most affluent populations. Local scale studies provided conflicting results (Havard et al., 2009; Padilla et al., 2014). In Marseille and Lille, a linear and positive association was found with overexposure of the most deprived populations (Padilla et al., 2014). By contrast, in Strasbourg (Havard et al., 2009), Lyon and Paris (Padilla et al., 2014), the middle-income and affluent populations were the most affected by pollution. These differences in results in France can be explained by the varied, local scales of analysis, which may reveal greater social disparities in exposure dynamics that do not necessarily appear on broader scales of analysis (Hajat et al., 2013). In addition, this variability may also be due to the scale and zoning effect known as the Modifiable Area Unit Problem (MAUP) (Deng et al., 2024), which can lead to changes in the spatial patterns of certain phenomena, thus impacting on the accuracy and reliability of results (Buzzelli, 2020).

Our study has several strengths and limitations. Firstly, it is the first study in France to integrate both spatial autocorrelation and the analysis of pollution exposure indicators measured according to WHO health

<footnote><sup>a</sup> Kruskal Wallis test.</footnote>
<table><caption>**Table 3**
Distribution of the number of days of exceedance of WHO guidelines values for air pollution by EDI quintiles and by typology of living.</caption><thead><tr><th rowspan="2"></th><th colspan="2">PM<sub>10</sub></th><th colspan="2">PM<sub>2.5</sub></th><th colspan="2">NO<sub>2</sub></th></tr><tr><th>Median(Q1-Q2)</th><th>p-value<sup>a</sup></th><th>Median(Q1-Q2)</th><th>p-value<sup>a</sup></th><th>Median (Q1-Q3)</th><th>p-value<sup>a</sup></th></tr></thead><tbody><tr><td><strong>EDI</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Quintile 1 (least deprived)</td><td>116(86-362)</td><td rowspan="5">&lt;2.2e-16</td><td>273(251-365)</td><td rowspan="5">&lt;2.2e-16</td><td>213(173-262)</td><td rowspan="5">&lt;2.2e-16</td></tr><tr><td>Quintile 2</td><td>111(81-364)</td><td>270(247-365)</td><td>197(160-249)</td></tr><tr><td>Quintile 3</td><td>114(81-364)</td><td>273(247-365)</td><td>194(156-250)</td></tr><tr><td>Quintile 4</td><td>123(87-365)</td><td>279(247-365)</td><td>203(161-264)</td></tr><tr><td>Quintile 5 (most deprived)</td><td>193(139-365)</td><td>325(284-365)</td><td>275(212-331)</td></tr><tr><td><strong>Typology of living areas</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Highly urbanised</td><td>216(105-269)</td><td rowspan="3">&lt;2.2e-16</td><td>332(287-355)</td><td rowspan="3">&lt;2.2e-16</td><td>305(235-352)</td><td rowspan="3">&lt;2.2e-16</td></tr><tr><td>Intermediately urbanised</td><td>127(94-165)</td><td>279(257-316)</td><td>207(160-259)</td></tr><tr><td>Rural</td><td>97(69-127)</td><td>261(239-288)</td><td>188(160-225)</td></tr></tbody></table>

guidelines, all carried out at a fine scale. This approach not only provides a better understanding of disparities in exposure to environmental pollution in a public health context, but also takes into account the geographical and social characteristics of territories.

Rigorous methods were used to identify areas exposed to polluted sites and soils, going beyond simple descriptive analysis, such as counting polluted soils or industrial facilities in each IRIS. Potential exposure was assessed using buffering methods based on proximity distances. However, this method has its limitations. It is based on the assumption of homogeneous exposure for all IRIS located in the same buffer zone, which is not necessarily the case. For example, an IRIS located 5 m from a pollution source may have a very different exposure level to one located 100 m away. In addition, other risk factors, such as the type of plant, pollutants emitted, emission control devices, as well as atmospheric and topographical conditions, can lead to significant variations in exposure levels (Fernández-Somoano and Tardon, 2014; Goodman et al., 2011). These factors are not always taken into account in the buffering approach. Another important limitation lies in the difficulty of accurately assessing the actual exposure of populations living within these IRIS. Analysis of pollution at an aggregate level (e.g., IRIS) may not reflect the actual exposure of residents. Indeed, pollution may affect only certain populations and spread to neighboring IRISs depending on the location of pollution sources, particularly in larger IRISs, often located in rural areas. In addition, individual behaviors (time spent outdoors, on transport, at school or at work) greatly influence exposure levels, making the assessment of individual exposure levels particularly complex. People living in the same IRIS may be exposed to very different levels of pollution, making it difficult to analyze their actual impact on health. However, by using the fine scale of IRIS, this study manages to capture local variations in pollution more accurately, enabling a more realistic estimate of population exposure (Maantay, 2002; Viel et al., 2011).

In addition, this study is limited to an analysis of inequalities in exposure to environmental pollution across territories as a function of an ecological indicator of levels of socio-economic deprivation. It does not establish cause-and-effect relationships, nor does it explain the mechanisms that lead to these disproportionate distributions. Indeed, the use of an ecological index makes it possible to measure the contextual deprivation of territories based on multiple individual components of residents' material and social deprivation. However, disproportionate exposure to pollutants may concern only certain components of deprivation rather than others, and may also vary according to the specific features of the city (Hajat et al., 2013). In the study of environmental justice linked to harmful industrial installations in France (Laurian, 2008), showed a significant association between the proportion of foreign-born people and the installation of these harmful industries, independently of income, population size and level of urbanization. In addition to socio-economic characteristics, other factors may explain the mechanisms of the environmental inequalities observed. For example, in their studies on the unequal distribution of hazardous industrial sites

(Viel et al., 2011; Laurian, 2008), explain that in some regions, concerns are more oriented towards the search for jobs and income-generating industries, while in others, well-being and environmental protection are prioritized. Moreover, the characteristics of cities' historical development and real estate market dynamics can influence these unequal distributions, in addition to socio-economic levels. Nevertheless, this work has enabled us to identify the disparities that may exist in exposure to environmental pollution, and their magnitude from one socio-economic level to another. In addition, the use of the French version of the Index of Social Disadvantage (EDI) adds reliability to the results, as it is a robust indicator of contextual social disadvantage due to its stability, its comprehensiveness over the whole territory of metropolitan France, and its potential comparability on an international scale (Merville et al., 2022).

Considering the spatial autocorrelation of the exposure data, our results are robust and precise. Numerous studies (Havard et al., 2009; Padilla et al., 2014; Viel et al., 2011; Yanosky et al., 2008) comparing simple statistical models with models considering spatial autocorrelation have shown variations in risk. Simple models tend to overestimate the association by assuming independence between evaluated data. This is not the case for the distribution of environmental phenomena influenced by territorial dynamics. Thus, the use of a conditional additive model considers the non-independence of data by capturing the dependence between IRIS (Wang et al., 2023).

Finally, several studies have also shown that inequalities in exposure to pollution are strongly influenced by territorial characteristics (Laurian, 2008; Padilla et al., 2014; Pearce and Kingham, 2008; Salesse, 2023; Viel et al., 2011). The inclusion of territorial characteristics in this study, through INSEE's territorial typology indicator, which categorizes territories according to qualitative aspects of life beyond territorial density and rural/urban character, enabled us to identify the proper risk linked with social deprivation. This indicator was divided into three classes according to level of urbanization. Although subjective, this classification facilitated the interpretation of the indicator in our results and significantly reduced the correlation observed between the living territory typology indicator and the EDI, due to some of its indicators being linked to social deprivation.

In conclusion, our study highlights the existence of environmental injustice in pollution exposure at the smallest scale available in France and using methods integrating the geographical structure of the data. The most deprived areas have the highest exposure to pollution, compared with the most advantaged. It also highlights the challenges for future studies on environmental justice issues in France, showing the need to apply them to fine and more global scales (i.e. France as a whole) and to use appropriate regression models in estimating associations.

### CRediT authorship contribution statement

**Khadidiatou Diallo:** Writing – review & editing, Writing – original draft, Validation, Software, Methodology, Formal analysis,

<footnote><sup>a</sup> Kruskal Wallis test.</footnote>
<table><caption>Table 4
Association between exposure to polluted sites and soils, PM10, PM2.5 and NO2 pollution and social deprivation adjusted for living area typology (Model 2).</caption><thead><tr><th rowspan="3"></th><th colspan="4">Polluted sites and soils</th><th colspan="4">PM<sub>10</sub></th><th colspan="4">PM<sub>2.5</sub></th><th colspan="4">NO<sub>2</sub></th></tr><tr><th>RR</th><th>IC 95 %</th><th>RR</th><th>IC 95 %</th><th>RR</th><th>IC 95 %</th><th>RR</th><th>IC 95 %</th><th>RR</th><th>IC 95 %</th><th>RR</th><th>IC 95 %</th><th>RR</th><th>IC 95 %</th><th>RR</th><th>IC 95 %</th></tr><tr><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th></tr></thead><tbody><tr><td>EDI</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Quintile 1 (least deprived)</td><td>1024</td><td>(1020 1028)</td><td>ref</td><td></td><td>1006</td><td>(1006 1007)</td><td>ref</td><td></td><td>1002</td><td>(1002 1002)</td><td>ref</td><td></td><td>1001</td><td>(1001 1001)</td><td>ref</td><td></td></tr><tr><td>Quintile 2</td><td></td><td></td><td>1377</td><td>(1302 1456)</td><td></td><td></td><td>1016</td><td>(1012 1020)</td><td></td><td></td><td>1004</td><td>(1002 1006)</td><td></td><td></td><td>1</td><td>(0.998 1002)</td></tr><tr><td>Quintile 3</td><td></td><td></td><td>1595</td><td>(1,5051 690)</td><td></td><td></td><td>1034</td><td>(1028 1038)</td><td></td><td></td><td>1009</td><td>(1007 1011)</td><td></td><td></td><td>1001</td><td>(0.998 1003)</td></tr><tr><td>Quintile 4</td><td></td><td></td><td>2008</td><td>(1895 2125)</td><td></td><td></td><td>1053</td><td>(1049 1059)</td><td></td><td></td><td>1015</td><td>(1013 1017)</td><td></td><td></td><td>1005</td><td>(1003 1007)</td></tr><tr><td>Quintile 5 (most deprived)</td><td></td><td></td><td>1994</td><td>(1876 2119)</td><td></td><td></td><td>1088</td><td>(1083 1093)</td><td></td><td></td><td>1027</td><td>(1025 1029)</td><td></td><td></td><td>1013</td><td>(1010 1016)</td></tr><tr><td>Typology of living areas</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Highly urbanised</td><td>1340</td><td>(1218 1477)</td><td>1454</td><td>(1322 1600)</td><td>1091</td><td>(1083 1100)</td><td>1093</td><td>(1084 1102)</td><td>1029</td><td>(1026 1034)</td><td>1030</td><td>(1026 1034)</td><td>1061</td><td>(1055 1065)</td><td>1.06</td><td>(1055 1064)</td></tr><tr><td>Intermediately urbanised</td><td>1301</td><td>(1210 1398)</td><td>1461</td><td>(1359 1570)</td><td>1031</td><td>(1026 1038)</td><td>1033</td><td>(1027 1039)</td><td>1011</td><td>(1009 1013)</td><td>1011</td><td>(1009 1014)</td><td>1019</td><td>(1017 1022)</td><td>1019</td><td>(1016 1022)</td></tr><tr><td>Rural</td><td></td><td>ref</td><td></td><td>ref</td><td></td><td></td><td>ref</td><td></td><td>ref</td><td></td><td>ref</td><td></td><td>ref</td><td></td><td></td><td></td></tr></tbody></table>

RR: relative risk; IC95 %: 95 % confidence interval.

Conceptualization. **Pauline Pouchin**: Validation, Software, Methodology. **Lucie Adélaïde**: Data curation. **Johanna Lepeule**: Data curation. **Ludivine Launay**: Data curation. **Guy Launoy**: Project administration, Methodology, Conceptualization. **Joséphine Bryere**: Project administration, Methodology, Formal analysis, Conceptualization.

## Funding

Ligue contre le cancer, Grant/award Number: Equipe Labellisée LIGUE 2022.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.envres.2025.122434.

## Data availability

Data will be made available on request.

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