# Perinatal air pollution exposure and autism spectrum disorder severity: The intertwined roles of green space, grey space and healthcare accessibility – A cohort study

Valentin Simoncic, Romain Wenger, Maëva Monnier, Cécile Michelon, Marianne Peries, Ian Hough, Itai Klook, Johanna Lepeule, Marion Mortamais, Amaria Baghdadli

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Valentin Simoncic, Romain Wenger, Maëva Monnier, Cécile Michelon, Marianne Peries, et al. Perinatal air pollution exposure and autism spectrum disorder severity: The intertwined roles of green space, grey space and healthcare accessibility – A cohort study. Environmental Research, 2025, 285, pp.122577. 10.1016/j.envres.2025.122577. hal-05277244

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# Perinatal air pollution exposure and autism spectrum disorder severity: The intertwined roles of green space, grey space and healthcare accessibility – A cohort study

Valentin Simoncic<sup>a,b,*</sup> <sup>ID</sup>, Romain Wenger<sup>c</sup> <sup>ID</sup>, Maëva Monnier<sup>a,d,e</sup> <sup>ID</sup>, Cécile Michelon<sup>a</sup>, Marianne Peries<sup>a</sup>, Ian Hough<sup>f,g</sup> <sup>ID</sup>, Itai Klook<sup>h</sup>, Johanna Lepeule<sup>f</sup> <sup>ID</sup>, Marion Mortamais<sup>i</sup> <sup>ID</sup>, Amaria Baghdadli<sup>a,b,j</sup> <sup>ID</sup>

## ARTICLE INFO

### Keywords:

- Air pollution

- Autism spectrum disorder

- Healthcare services accessibility

- Green space

- Grey space

- Urbanization level

## ABSTRACT

**Introduction:** Autism spectrum disorder (ASD) is influenced by environmental, genetic, and socio-economic factors. While air pollution exposure during development has been linked to ASD outcomes, the roles of green spaces, grey spaces, and healthcare accessibility in this relationship remain understudied. This research examines how these factors during the first 1000 days (from conception to the first two years of life), moderate the association between air pollution and ASD severity in children from the ELENA cohort ("Etude Longitudinale chez l'Enfant avec Autisme").

**Methods:** Data from 237 children with ASD were analyzed. Perinatal exposure to particulate matter (PM₁₀ and PM₂.₅) was estimated using a validated model, with green spaces quantified using the normalized difference vegetation index (NDVI) and grey spaces through impervious surface coverage. Healthcare accessibility was measured as the distance to autism resource centers (CRA) from residential addresses. ASD severity was assessed using the total score of the Social Responsiveness Scale. Linear regression models within structural equation modeling estimated the effects of air pollution, green spaces, grey spaces, and healthcare accessibility on ASD severity.

**Results:** Higher PM exposures were paradoxically associated with lower ASD severity. However, grey spaces significantly moderated this association, with greater impervious surface coverage attenuating the negative association. Healthcare accessibility was crucial: when considering center proximity, pollution was no longer associated with ASD severity.

**Conclusions:** These findings highlight the critical role of healthcare accessibility and environmental factors in shaping the relationship between air pollution and ASD severity. Integrating geographic and environmental contexts is crucial when evaluating the environmental determinants of ASD outcomes.

<footnote><sup>a</sup> Autism Resources Centre of Languedoc-Roussillon & Centre of Excellence for Autism and Neurodevelopmental Disorders (CeAND), Montpellier University Hospital, Montpellier, France</footnote>

<footnote><sup>b</sup> University of Paris-Saclay, Inserm, CESP, Villejuif, France</footnote>

<footnote><sup>c</sup> Image City Environment Laboratory (LIVE), UMR 7362, CNRS, University of Strasbourg, Strasbourg, France</footnote>

<footnote><sup>d</sup> University of Paris-Saclay, Doctoral School of Public Health (EDSP), Kremlin-Bicêtre, Paris, France</footnote>

<footnote><sup>e</sup> University of Versailles-Saint-Quentin en Yvelines, Versailles, Paris, France</footnote>

<footnote><sup>f</sup> University Grenoble Alpes INSERM CNRS Institute for Advanced Biosciences Team of Environmental Epidemiology Applied to Development and Respiratory Health, La Tronche, France</footnote>

<footnote><sup>g</sup> Department of Geography and Environmental Development, Ben-Gurion University of the Negev, Be'er Sheva, Israel</footnote>

<footnote><sup>h</sup> Department of Environmental Medicine and Climate Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA</footnote>

<footnote><sup>i</sup> Institute for Neurosciences of Montpellier (INM), University of Montpellier, Inserm, Montpellier, France</footnote>

<footnote><sup>j</sup> Faculty of Medicine, University of Montpellier, Montpellier, France</footnote>

<footnote>* Corresponding author. Centre de Ressources Autisme Languedoc-Roussillon, CHU de Montpellier - SMPEA Peyre Plantade, 291 avenue du Doyen Giraud, 34000, Montpellier, France.</footnote>

<footnote>E-mail address: valentin.simoncic@chu-montpellier.fr (V. Simoncic).</footnote>
# 1. Introduction

Autism spectrum disorder (ASD - referenced 6A02 in the 11th revision of the International Classification of Diseases - ICD 11) is a clinically heterogeneous neurodevelopmental disorder characterized by an association of socio-communication impairments and repetitive and restrictive behavior and interests, which manifest across a continuum of severity between individuals (Lord et al., 2018). ASD occurs in approximately 10 in 1000 children or adolescents worldwide with a median male-to-female ratio of 4.2 (Zeidan et al., 2022). The prevalence in Europe is February 6, 1000 ranging from February 4, 1000 (Italy) to March 31, 1000 (Iceland) and 7/1000 in France from 2012 to 2021 (Chiarotti and Venerosi, 2020). Individuals with ASD and caregivers face enduring and multifaceted challenges, encompassing social, behavioral and healthcare-related adversities throughout their lifespan. Although the etiology of ASD is still not fully understood (Kim et al., 2019). Current research suggests that genetic, epigenetic and environmental factors all make significant contributions (Atladóttir et al., 2010; Dietert et al., 2011; Durkin et al., 2008; Krakowiak et al., 2012; Landrigan et al., 2012; Lyall et al., 2017; Pagalan et al., 2022; Zerbo et al., 2015). Given the early onset of ASD, environmental risk factors are expected to exert their effects early in development, particularly during critical developmental windows spanning the prenatal and early postnatal periods, from preconception to the first years of life (Lyall et al., 2014). Among suspected environmental exposures, a growing number of studies have demonstrated that air pollution, especially particulate matter, may constitute a risk factor for ASD (Chun et al., 2020; Dutheil et al., 2021; Jo et al., 2019; Kaufman et al., 2019; McGuinn et al., 2020; Pagalan et al., 2019; Raz et al., 2018; Ritz et al., 2018). To date, only a handful of studies have specifically examined the relationship between air pollution exposure and ASD severity and their findings are mixed. An US study reported only limited effects (Kerin et al., 2018), whereas a Portuguese study found that prenatal exposure to PM<sub>2.5</sub> and PM<sub>10</sub> was associated with greater ASD severity (Santos et al., 2023). Nonetheless, these studies didn't take environmental variables into account as potential confounders or moderators.

When studying the effect of air pollution on ASD severity, it is crucial to consider other environmental variables (e.g., green spaces and grey spaces) as potential confounders or moderators because they may influence this association. Green spaces can refer to terrestrial areas dominated by natural or semi-natural vegetation: natural green spaces which are minimally modified by anthropogenic activities and urban green spaces characterized by natural surfaces, planted vegetation or managed ecosystems integrated into urban landscapes. Green spaces contribute positively to human health through various mechanisms including promoting physical activity, enhancing social interactions but also mitigating air pollution exposure by absorbing or dispersing ambient air pollutants (Maas et al., 2009; Mitchell and Popham, 2008; Wolch et al., 2014). Green space exposure supports maternal health and improves pregnancy outcomes (Coombes et al., 2010; Hystad et al., 2014; Lee and Maheswaran, 2011; Markevych et al., 2017; van den Berg et al., 2015; van den Bosch and Ode Sang, 2017), and it is further associated with enhanced attention, and healthier behavioral development in children (Amoly et al., 2014; Beyer et al., 2014; Flouri et al., 2015; Gascon et al., 2015; Li and Sullivan, 2016; Markevych et al., 2014). Long-term cognitive benefits for children have also been observed with increased exposure to green spaces (Dadvand et al., 2015; de Keijzer et al., 2016). Environmental research has focused on green spaces, and, to a lesser extent, blue-spaces, while largely overlooking the grey spaces that characterise the built environment. Urbanisation and densification both reduce contact with vegetation and expand hard, impermeable surfaces such as

concrete and asphalt, which in turn heighten health risks by intensifying urban heat-islands effects (Potter et al., 2023).

A recent case-control study using participants from the French ELENA (cases) and ELFE (controls) cohorts found a paradoxical, albeit

non-significant, association where higher prenatal air pollution exposure was associated with a reduced risk of ASD diagnosis (Mortamais et al., 2025). Comparable counterintuitive patterns have been reported in other research. An US study also found that increased PM<sub>10</sub> exposure in the third trimester was associated with improved adaptive functioning (8.24 %, 95 % CI [1.31 %, 15.2 %], *p* < .05), suggesting potentially unexpected effects of pollutant exposure on adaptive functioning (Kerin et al., 2018). Similarly, another US study found that green space and tree canopy coverage were linked with an increased risk of severe anxiety among youth with ASD (OR = 1.03, 95 % CI [1.01,1.05], *p* < .05). These findings challenge the widely held assumption that green space exposure uniformly benefits mental health and underscores the complex and multifaceted nature of environmental influences on neurodevelopmental disorders. The paradoxical effects noted in these studies may not directly result from green space or air pollution exposure but could instead reflect broader urbanization dynamics. Urbanization inherently reduces green space availability while increasing grey spaces and air pollution concentrations. Moreover, urbanization may confound underlying exposures with healthcare access factors such as the availability of diagnostic services, quality of care and follow-up support (Antezana et al., 2017; Chen et al., 2019).

In this context, this study investigates the association between prenatal and early-life exposure to PM<sub>10</sub> and PM<sub>2.5</sub> and the severity of ASD within the ELENA cohort (a French cohort of children diagnosed with ASD). To provide a comprehensive understanding of environmental influences, we examine the moderating role of green and grey spaces, while also accounting for healthcare accessibility as a potential confounding factor. As an explanation for the previously reported paradoxical results—pollutants appearing protective and green space looking harmful for ASD severity—we hypothesize that access to healthcare may act as a confounding variable. Grey, densely built urban areas tend to concentrate diagnostic and treatment services, whereas greener, less urbanized settings often offer fewer such resources, potentially biasing the observed associations. In these less built-up areas, ASD diagnoses may disproportionately capture more severe cases while milder cases remain undiagnosed.

Understanding the associations between ASD severity, environmental factors such as air pollutant exposure and healthcare accessibility is crucial for exploring how disparities in ASD severity may originate as early as the prenatal period. This research contributes to the expanding body of evidence available on the perinatal impacts of air pollution, with a specific focus on vulnerable populations including pregnant women and young children.

# 2. Methods

## 2.1. Participants

The study population was drawn from the ELENA cohort: a French prospective and multicentric cohort of children with a confirmed ASD diagnosis, established in 2012 to study developmental trajectories of ASD. Between 2012 and 2019, 876 children aged 2–16 years were recruited at the time of diagnosis across 15 autism research centers in six regions: Occitanie, Auvergne-Rhône-Alpes, Ile-de-France, Provence-Alpes-Côte d'Azur, Nouvelle Aquitaine and Grand-Est. Diagnoses were confirmed by a trained multidisciplinary team using Diagnostic and Statistical Manual of Mental Disorders, fifth Edition (DSM-5) criteria, the Autism Diagnostic Observation Schedule™ (ADOS-2) (Lord et al., 2012), the Autism Diagnostic Interview-Revised (ADI-R) (Lord et al., 1994), Vineland Adaptive Behavior Scales (VABS-II) (Sparrow et al., 2012) and an age-appropriate psychometric Intelligence Quotient (IQ) test (Wechsler intelligence scales or Kaufman assessment battery for children, based on the Howlin approach). Detailed procedures are described elsewhere (Baghdadli et al., 2019).

Individuals with missing residential address data, those whose mothers' pregnancies occurred outside mainland France, duplicates (e.
g., children in shared custody) and individuals with major missing clinical data at baseline (e.g., the Social Responsiveness Scale, Second Edition total score) were excluded, resulting in a final study population of 237 children (see FlowChart in Fig. 1).

The geographical distribution of the home addresses of the selected participants, as well as the autism resource centers and external units, can be seen in Fig. 2. Each participant may have up to seven recorded addresses (Address 1 to Address 7), corresponding to potential residential relocations during the study period.

The study adhered to the latest version of the Declaration of Helsinki and ethical approval as obtained through the Marseille Mediterranean Ethics Committee (ID RCB: 2014-A01423-44), France. The trial registration number is NCT02625116. Informed consent was obtained from all participants included in the study who were informed about the research objectives, procedures, potential risks and benefits. Their anonymity and confidentiality of their personal information were preserved in strict compliance with all subjects' right to privacy.

## 2.2. Outcomes

The severity of ASD traits was measured at the time of diagnosis (VO) using the Social Responsiveness Scale, Second Edition (SRS-2) (Bruni, 2014; Constantino, 2013). This 65-item questionnaire assesses social impairment across two main sub-scales: restricted interests and repetitive behavior (RIRB) and social communication and interaction (SCI). For the purpose of this study, only the total SRS-2 score was used as a global measure of ASD severity, with higher scores indicating more severe deficits. We used the chronological age-standardized SRS-2

<figure><img src="image_4.png" /><figcaption>Fig. 1. Flowchart of study sample selection.</figcaption></figure>

T-scores as our primary outcome variable throughout all models.

## 2.3. Covariates

Potential confounding variables were identified based on existing literature and selected using a backward approach. They included infant sex, father and mother's age at the time of birth, parental socio-economic status (SES) and age of the child at diagnosis. To create a nuanced measure reflecting the household socio-economic status, a composite 'Parental SES score' was constructed. This score was derived from several key variables obtained via parental questionnaire: i) The profession and socio-professional category of both parents. ii) Their employment status during the study period. iii) The child's primary custody arrangement in cases of parental separation. The final score was determined by the status of the parent with primary custody, or by the parent with the higher SES in two-parent households. This composite score was then categorized into three levels: high (e.g., business owners, executives), middle (e.g., supervisors, skilled craftsmen), and low (e.g., farm workers, laborers, service employees, unemployed). All these variables were collected at the time of diagnosis (VO).

## 2.4. Environmental characterization

### 2.4.1. Study period

The study period extended from conception to the time of diagnosis (VO), encompassing the critical window of the first 1000 days of life. Conceptions in the cohort occurred between 2000 and 2016, with over 75 % of participants conceived after 2009. The distribution of conception years is detailed in Supplementary Material (see Fig. S1). This timeframe was chosen to capture prenatal and early-life environmental exposures which are pivotal for neurodevelopment. The focus on this period also aligns with the timing of ASD diagnosis within the cohort, ensuring that exposures and outcomes are appropriately linked. To assess these exposures, residential history was collected between February 2019 and May 2021, through a standardized questionnaire, allowing us to reconstruct the participants' residential trajectories during this critical developmental period.

### 2.4.2. Green spaces

Green spaces were quantified using the normalized difference vegetation index (NDVI). To estimate the NDVI values, we utilized satellite imagery data for the period spanning from 1995 to 2020, corresponding to the earliest conception date in the cohort to the most recent diagnosis date. For each year, data were sourced from the most relevant satellite mission corresponding to the time period: the Landsat 5 (TM), Landsat 7 (ETM+) and Sentinel-2. These sensors also give a deep time series of data over each year as Landsat-5 and 7 have a temporal resolution of 16 days and Sentinel-2 of 5 days for each point of the globe. To ensure better comparability across the entire study period, all NDVI analyses were conducted at a consistent spatial resolution of 30 m. To ensure high-quality NDVI estimates, Sentinel-2 and Landsat imagery were filtered based on cloud cover thresholds. More specifically, we applied a filtering criterion that excluded images exceeding a predefined cloud cover percentage (30 %). With this threshold, we aimed to maximize the number of images in the time series to obtain a robust annual NDVI estimation. For each image, NDVI was computed using the normalized difference formula (Near-Infrared (NIR) - Red)/(NIR + Red); for Sentinel-2 images, this corresponded to band (B8 - B4)/(B8 + B4) and equivalent bands for Landsat. This formula takes advantage of the physiological and structural properties of vegetation: chlorophyll in plant leaves absorbs red light as part of the photosynthesis process while the cellular structure of leaves strongly reflects NIR light. Non-vegetated surfaces, such as bare soil or water, reflect less NIR and more red light, resulting in lower NDVI values. This mechanism allows NDVI to effectively distinguish areas with dense vegetation from those with sparse or absent vegetation (Tucker, 1979). Extensive pre-processing steps were
<figure><img src="image_5.png" /><figcaption>Fig. 2. Location of residential addresses of participants in France and the location of Autism resource centers and external units.</figcaption></figure>

applied, including scaling reflectance values, applying quality assurance (QA) masks to both surface reflectance and top-of-atmosphere reflectance and interpolating missing data to enhance data quality and consistency. NDVI values ranged from -1 to 1 where negative values indicate water (as water has small reflectance in red and infrared bands), values near zero correspond to barren or sparsely vegetated areas and positive values represent green vegetation (an example with a simulated address is presented in Fig. 3.) Annual NDVI layers were computed by averaging the NDVI values across the entire year for each satellite dataset.

The exposure window for each individual spans from conception to the date of diagnosis. Residential history data captures transition between addresses, with dates adjusted to indicate each residence period accurately. For each residential stay, an average NDVI is characterized for different buffer zones around the residence address (100 m, 250 m, 500 m and 1000 m). Weighted average NDVI exposure is calculated by multiplying NDVI values by the duration of residence within each buffer and summing these weighted values across all residential periods and years. The weighted mean NDVI values are then obtained by dividing the weighted sum by the total days of exposure but accounting for

<figure><img src="image_6.png" /><figcaption>Fig. 3. Ndvi and buffer sizes for a simulated address point.</figcaption></figure>
changes in residence and variations in annual NDVI values.

### 2.4.3. Grey spaces

We characterized grey spaces with impervious surface coverage using high-resolution layer imperviousness (HRLI) products from Copernicus. HRLI provides data on the extent and density of artificial surface sealing, with imperviousness values ranging from 0 % to 100 % at a 20 m resolution. Our analysis used HRLI from 2006, 2009, 2012, 2015 and 2018, selecting the most relevant years based on each participant's residential history between conception to the date of diagnosis.

### 2.4.4. Pregnancy ambient air pollution exposure

In this study, we assessed exposure to two key air pollutants: particulate matter with aerodynamic diameters of 10 µm or less (PM₁₀) and 2.5 µm or less (PM₂.₅). The exposure estimates were derived using a validated high-resolution spatio-temporal model, which provided daily PM concentrations across mainland France with a spatial resolution of 1 km² for the period 2000 to 2019 (Hough et al., 2021). The modelling approach involves dividing mainland France into a grid of 1 km² cells, each containing daily concentration estimates for PM₁₀ and PM₂.₅. We utilized these modelled data for each subject to construct daily exposure matrices spanning from the date of conception to the end of the second year of life. The PM values for each day were assigned based on the nearest grid cell to the subject's residential address, with adjustments made to take into account any residential relocations during the study period.

The prenatal exposure to PM₁₀ and PM₂.₅ was identified for each trimester of pregnancy (T1, T2 and T3) and subsequent life-year intervals, up to the second year. This approach allowed for a detailed, individualized characterization of PM exposure, expressed in micrograms per cubic meter (µg/m³), facilitating an in-depth analysis of the potential associations between air pollution and clinical outcomes within the cohort.

### 2.4.5. Healthcare services availability and proximity

Within the context of ASD care, healthcare services include a variety of stakeholders, with autism resource centers (CRAs) and their affiliated branches playing a critical role in diagnosing ASD, supporting families, training professionals and promoting autism awareness. Given their central role, CRAs can serve as a reasonable proxy for evaluating healthcare access and availability. Proximity to CRAs and their branches may influence both diagnosis rates and assessments of clinical severity, therefore potentially explaining the observed trends.

To assess healthcare services availability and proximity, we used three key indicators.

* "Diagnostic center distance": Euclidean distance between the participant's residence at the time of diagnosis and the specific diagnostic center that conducted the assessment, typically an autism resource center (CRA) or one of its affiliated branches.

* "Hub distance": distance between the most frequently occupied residence prior to diagnosis (which may differ from the residence at the time of diagnosis) and the nearest CRA or equivalent center (regardless of whether it was the diagnostic center). CRAs are specialized centers in France that play a pivotal role in diagnosing ASD, supporting families, training professionals, and raising awareness on autism-related issues. This measurement aimed to capture the broader influence of CRAs on professional training, awareness-raising and community support in the region.

* "Hub distance at recruitment": distance between the residence at the time of cohort recruitment and the nearest CRA or affiliated center, regardless of whether it was the diagnostic center, to assess potential influences of CRA proximity on healthcare accessibility at the time of recruitment.

### 2.5. Statistical analyses

Statistical analyses were performed to explore the influence of PM₁₀ or PM₂.₅ exposures (analyzed in separate models) on the clinical severity of ASD in children. The potential effect and moderating roles of green space (NDVI) and grey space (HRLI) on these associations were tested. Finally, healthcare accessibility was evaluated as a potential confounding variable in the models.

* Firstly, descriptive statistics for the overall population are presented in Table 1.

* Secondly, to evaluate the effects of air pollution (PM₁₀ or PM₂.₅) and green or grey spaces on ASD severity (SRS-2 total score) without adjustment variables as well as the moderating role of green or grey spaces on the relationship between air pollution and ASD severity, multiple linear regressions within a structural equation modelling (SEM) framework were conducted using the Lavaan package in R (Rosseel, 2012). The first model examined the effects of both PM₁₀ and green space (NDVI) on SRS-2 total score while also accounting for the potential moderating effect of green space without adjustment variables. The second model similarly assessed the effect without adjustment variables of PM₁₀ but this time included the effect and moderating role of grey space rather than green space. Lastly, these two models were repeated by replacing PM₁₀ with PM₂.₅. As a key preliminary step, we also formally tested the

<table><caption>**Table 1**
Participant characteristics and environmental exposure metrics.</caption><thead><tr><th></th><th>N = 237</th></tr></thead><tbody><tr><td>SRS-2 total score</td><td></td></tr><tr><td>Mean ± SD</td><td>95.31 (18.47)</td></tr><tr><td>Sex</td><td></td></tr><tr><td>Male</td><td>185 (78.1 %)</td></tr><tr><td>Female</td><td>52 (21.9 %)</td></tr><tr><td>IQ</td><td></td></tr><tr><td>Mean ± SD</td><td>79.2 (26.8)</td></tr><tr><td>Child's age at diagnosis</td><td></td></tr><tr><td>Mean ± SD</td><td>5.76 (3.57)</td></tr><tr><td>Father age at birth</td><td></td></tr><tr><td>Mean ± SD</td><td>34.6 (6.63)</td></tr><tr><td>Missing</td><td>1 (0.4 %)</td></tr><tr><td>Mother age at birth</td><td></td></tr><tr><td>Mean ± SD</td><td>31.5 (5.28)</td></tr><tr><td>Missing</td><td>1 (0.4 %)</td></tr><tr><td>Parental socioeconomic status</td><td></td></tr><tr><td>High</td><td>79 (33.3 %)</td></tr><tr><td>Mid</td><td>63 (26.6 %)</td></tr><tr><td>Low</td><td>80 (33.8 %)</td></tr><tr><td>Missing</td><td>15 (6.3 %)</td></tr><tr><td>PM<sub>10</sub> (µg/m<sup>3</sup>)</td><td></td></tr><tr><td>Mean ± SD</td><td>21.1 (5.19)</td></tr><tr><td>PM<sub>2.5</sub> (µg/m<sup>3</sup>)</td><td></td></tr><tr><td>Mean ± SD</td><td>14.2 (3.50)</td></tr><tr><td>Hub distance (km)</td><td></td></tr><tr><td>Mean ± SD</td><td>22.5 (21.9)</td></tr><tr><td>Hub distance at recruitment (km)</td><td></td></tr><tr><td>Mean ± SD</td><td>22.6 (21.6)</td></tr><tr><td>Missing</td><td>1 (0.4 %)</td></tr><tr><td>Diagnostic centre distance (km)</td><td></td></tr><tr><td>Mean ± SD</td><td>23.7 (23.5)</td></tr><tr><td>Missing</td><td>1 (0.4 %)</td></tr><tr><td>NDVI 100m</td><td></td></tr><tr><td>Mean ± SD</td><td>0.34 (0.09)</td></tr><tr><td>NDVI 250m</td><td></td></tr><tr><td>Mean ± SD</td><td>0.36 (0.09)</td></tr><tr><td>NDVI 500m</td><td></td></tr><tr><td>Mean ± SD</td><td>0.38 (0.09)</td></tr><tr><td>NDVI 1000m</td><td></td></tr><tr><td>Mean ± SD</td><td>0.39 (0.09)</td></tr><tr><td>Average impervious surface 1000m (HRLI %)</td><td></td></tr><tr><td>Mean ± SD</td><td>54.5 (26.4)</td></tr></tbody></table>
association between grey space and healthcare accessibility to inform our interpretation of the models.

* Thirdly, multiple linear regression models were developed within SEM to explore adjusted associations between PM₁₀ or PM₂.₅ and ASD severity, incorporating covariates as well as the multiplicative interaction terms and effects of green space or grey space (separately) on ASD severity, both identified in Step 2. In moderation analyses, it is considered best practice to systematically include both the main effects of the interacting variables and their interaction term. This approach ensures proper model specification and allows the estimated coefficients to accurately reflect the moderating effect, rather than being confounded by the main effects of the individual predictors (Hayes and Little, 2022).

* Fourthly, associations between the three indicators of healthcare accessibility ("Diagnostic center distance", "Hub distance" and "Hub distance at recruitment") and ASD severity were first examined separately to assess their effects without any adjustment.

* Fifthly, to systematically test if healthcare accessibility acts as a primary confounder, the significant healthcare accessibility indicators were included in the main adjusted models from Step 3. This allowed us to evaluate the influence of healthcare access on the previously observed effects of grey/green space and their moderation.

* Finally, the primary goal of this step was to assess the robustness of the moderation effects. If the inclusion of healthcare accessibility substantially diminished or nullified the interaction term for grey/green space, it would suggest that access to care, rather than the spatial variable itself, was the main driver of the observed association.

To ensure the robustness of our findings, we carried out sensitivity analyses to account for potential biases related to exposure estimation methodologies and missing data handling. More specifically, we assessed the possible shift in exposure levels due to the inclusion of the volatile fraction adjustment in 2007 for PM₁₀ and in 2008–2009 for PM₂.₅. Sensitivity analyses were conducted by excluding individuals whose exposure periods overlapped with these methodological changes. Given that the exposure periods considered for these pollutants corresponded to the 1000-day window, from conception to the child's second year, we excluded participants whose exposure periods fell within the methodological changes for each pollutant. For PM₁₀, we performed analyses on two groups separately for participants conceived before January 01, 2005 and after October 01, 2008. Similarly, for PM₂.₅, we grouped separately individuals conceived before January 01, 2006 and after October 01, 2010. A descriptive analysis of air pollution estimates and the number of affected individuals was performed, followed by sensitivity analyses if substantial discrepancies were identified. Additionally, air pollution exposure was estimated whenever at least one modelled value was available to maximize the sample size. However, to evaluate the potential influence of missing exposure data, we conducted complementary sensitivity analyses using a complete-case approach where only individuals with full exposure data were included.

SEM allowed the inclusion of missing data through full information maximum likelihood (FIML) estimation, enhancing robustness. Models were estimated using maximum likelihood robust (MLR) methods. Model adequacy was assessed using common fit indices (Hooper et al., 2008), including the Chi-square test, root mean square error of approximation (RMSEA, threshold ≤0.05) and comparative fit index (CFI, threshold ≥0.95). RStudio version 2024.04.2, with R V4.4.1 software were used for statistical analyses and p-values <0.05 were considered for statistical significance. All statistical tests were two-sided.

# 3. Results

## 3.1. Population description

Table 1 presents the characteristics of the study population, including socio-demographic, environmental and geographic variables. The sample was predominantly male, with 78.1 % males and 21.9 % females, consistent with the known higher prevalence of ASD in males. Mean age at diagnosis was 5.76 years (SD = 3.57), with a median of 5 years, reflecting variability in the timing of diagnoses among participants. Mean paternal and maternal ages at birth were 34.60 years (SD = 6.63) and 31.50 years (SD = 5.28) respectively. For the outcome SRS-2 total score, distribution showed a median score of 96, with a mean of 95.31 (SD = 18.47) ranging from 36 to 144. The Shapiro-Wilk test indicated approximate normality (p = .151), further supported by a unimodal histogram and a QQ-plot showing no extreme outliers. The detailed statistical table for SRS-2 total score and graphical representations are available in the Supplementary Material (see Table S1 and Figs. S2 and S3). SES was evenly distributed among participants, with 33.3 % classified as low SES, 28.6 % as middle SES and 33.8 % as high SES. A small proportion of SES data was missing (6.3 %).

To evaluate the comparability between included participants and those excluded due to missing environmental or residential data, we conducted a statistical comparison of key demographic and clinical characteristics. The age at diagnosis was similar between the two groups (mean M = 5.76 years, SD = 3.57 for included vs. mean M = 6.01 years, SD = 3.38 for excluded) as were parental ages at birth (fathers: 34.6 years, SD = 6.63 vs. 34.3 years, SD = 7.06; mothers: 31.5 years, SD = 5.28 vs. 30.9 years, SD = 5.57). Mean total SRS-2 score was slightly higher in the included group (95.3, SD = 18.5) versus the excluded group (91.6, SD = 19.4) although this difference was not statistically significant. The sex distribution was also comparable, with a slightly higher proportion of males in the excluded group (84.8 %) than in the included group (78.1 %). SES showed a slightly higher mean in the excluded group (mean = 2.17, SD = 0.87), although this comparison was limited by the high proportion of missing SES data in this group (55.7 %). These results indicate that the demographic and clinical profiles of included and excluded participants are similar overall. A detailed table of these comparisons is available in the Supplementary Materials (see Tables S2 and S3).

Exposure to air pollution showed a mean PM₁₀ concentration of 21.1 µg/m³ (SD = 5.19) and a mean PM₂.₅ concentration of 14.2 µg/m³ (SD = 3.50). These levels varied considerably, with maximum values reaching 54.2 µg/m³ for PM₁₀ and 43.3 µg/m³ for PM₂.₅, thereby highlighting spatial heterogeneity in exposure across the study region.

Geographic indicators of proximity to CRA revealed significant variability. The mean "Diagnostic center distance", measuring the Euclidean distance between participants' residences at the time of diagnosis and the CRA conducting the assessment, was 23,700 m (SD = 23,500). The "Hub distance", indicating the distance from the most frequently occupied residence before diagnosis to the nearest CRA or equivalent center, revealed a mean distance of 22,500 m (SD = 21,900). Finally, the "Hub distance at recruitment", representing the distance between the residence at cohort recruitment and the nearest CRA, showed similar results, with a mean of 22,600 m (SD = 21,600). These findings evoke potential geographic disparities in terms of access to diagnostic and support services.

Vegetation levels, measured using NDVI, were moderate. At a 100 m buffer, mean NDVI was 0.34 (SD = 0.10), increasing slightly to 0.40 (SD = 0.0932) at a 1000 m buffer. This gradient suggests greater vegetation coverage in more peripheral areas. The average impervious surface percentage within a 1000 m buffer was 54.5 %, ranging from 1 % to 100 % and reflective of the substantial variability in urbanization around participants' residences.
## 3.2. Environmental exposures analyses

At first, separate multiple linear regression models were conducted for PM₁₀ and PM₂.₅ exposures to evaluate potential relationships and interactions between environmental variables (air pollutant, grey space or green space) and SRS-2 total score. In each model, the effect of PM₁₀ or PM₂.₅ on total SRS-2 total score was examined, with either grey space or green space included as a potential moderator. Green space (NDVI) was assessed at different buffer distances (100 m, 250 m, 500 m, and 1000 m). Only results for green space in a 1000 m buffer are presented as similar results were found for other buffer distances and these results can be viewed in the Supplementary Materials (see Excel Table S1, S2 and S3).

The results revealed significant negative associations between PM₁₀ and ASD severity as measured by SRS-2 total score (β = -0.53, *p* = .016) and between PM₂.₅ and SRS-2 total score (β = -0.73, *p* = .033, see Table 2). These findings illustrate a paradoxical relationship where higher air pollution levels were associated with lower SRS-2 total score, indicating less severe ASD symptoms. However, no significant associations were observed between green or grey space and SRS-2 total score.

In the interaction model, the results still revealed significant negative associations between PM₁₀ and SRS-2 total score with grey space as moderator: (β = -0.67, *p* = .004) and between PM₂.₅ and SRS-2 total score (β = -0.90, *p* = .003); similar results were found for green space as moderator (see Table 2). The interaction between PM₁₀ and grey space was significant (β = 0.02, *p* = .025), illustrating that greater impervious surface coverage attenuated the negative relationship between PM₁₀ exposure and SRS-2 total score. Similarly, the interaction between PM₂.₅ and grey space was also significant (β = 0.01, *p* = .036) and indicative of a comparable effect. Conversely, no significant interactions were noted between green space and either PM₁₀ or PM₂.₅ exposures, suggesting that vegetation coverage did not moderate the association between air pollution exposure and ASD severity in this study (see Table 2). Therefore, further analyses were focused on grey space given that green space showed neither effects nor a moderating role in this context.

When adjusting the effect of PM₁₀ or PM₂.₅ on SRS-2 total score for

<table><caption>**Table 2**
Effects of Air Pollution and Green/Grey Spaces on ASD Severity, accounting for the potential Moderating Effects of Green/Grey Spaces.</caption><thead><tr><th>Outcome</th><th>Predictor</th><th>B<sup>a</sup></th><th>SE</th><th>p-value</th><th>95 % CI (b)</th></tr></thead><tbody><tr><td rowspan="10">SRS-2 total score</td><td>PM<sub>10</sub></td><td>-0.57</td><td>0.23</td><td><strong>.011</strong></td><td>[-1.02, -0.13]</td></tr><tr><td>Green space</td><td>5.94</td><td>12.95</td><td>0.647</td><td>[-19.45, 31.33]</td></tr><tr><td>Interaction: PM<sub>10</sub>'green space</td><td>-2.25</td><td>1.74</td><td>0.197</td><td>[-5.67, 1.17]</td></tr><tr><td>PM<sub>10</sub></td><td>-0.67</td><td>0.23</td><td><strong>.004</strong></td><td>[-1.12, -0.21]</td></tr><tr><td>Grey space</td><td>0.03</td><td>0.04</td><td>0.555</td><td>[-0.06, 0.11]</td></tr><tr><td>Interaction: PM<sub>10</sub>'grey space</td><td>0.02</td><td>0.01</td><td><strong>.025</strong></td><td>[0.00, 0.03]</td></tr><tr><td>PM<sub>2.5</sub></td><td>-0.83</td><td>0.30</td><td><strong>.006</strong></td><td>[-1.42, -0.24]</td></tr><tr><td>Green space</td><td>7.36</td><td>12.62</td><td>0.560</td><td>[-17.38, 32.10]</td></tr><tr><td>Interaction: PM<sub>2.5</sub>'green space</td><td>-4.03</td><td>2.43</td><td>0.096</td><td>[-8.79, 0.72]</td></tr><tr><td>PM<sub>2.5</sub></td><td>-0.90</td><td>0.30</td><td><strong>.003</strong></td><td>[-1.48, -0.31]</td></tr><tr><td>Grey space</td><td>0.01</td><td>0.04</td><td>0.739</td><td>[-0.07, 0.09]</td></tr><tr><td>Interaction: PM<sub>2.5</sub>'grey space</td><td>0.01</td><td>0.01</td><td><strong>.036</strong></td><td>[0.00, 0.03]</td></tr></tbody></table>

sex, father and mother's age at birth, parental socio-economic status, child's age at diagnosis and including the moderator effect of grey space, we observed consistent trends although the magnitude of association slightly decreased. The β coefficient for PM₁₀ was -0.56 (SE = 0.26, *p* = .029) while it was -0.73 (SE = 0.30, *p* = .015) for PM₂.₅, demonstrating that the significance was attenuated yet the negative trend persisted (see Table 3).

These findings led us to question what factors might influence the moderating role of grey space (HRLI) in the relationship between PM₁₀ or PM₂.₅ exposures and ASD severity. We have hypothesized that healthcare access could act as a confounding variable, a theory supported by our preliminary analysis which confirmed a significant negative correlation between grey space and distance to healthcare hubs (Std. β = -0.44, *p* < .001), This indicates that more urbanized areas are strongly associated with better access to care. Therefore, we reasoned that accounting for this confounding variable might eliminate the previously observed paradoxical protective effects of PM₁₀ (or PM₂.₅) exposure on ASD severity as well as the moderating effect of grey space.

## 3.3. Hypothesis of healthcare access

At first, we analyzed the effects of three healthcare access indicators (“Hub distance”, “Hub distance at recruitment” and “Diagnostic center

<table><caption>**Table 3**
Association Between Air pollution and SRS-2 Total Score Across Adjusted Models Accounting for the moderating effect of Grey Space.</caption><thead><tr><th rowspan="2">Outcome</th><th rowspan="2">Pollutant</th><th rowspan="2">Predictor</th><th colspan="4">Adjusted model</th></tr><tr><th>β*</th><th>SE</th><th>p-value</th><th>95 % CI (b*)</th></tr></thead><tbody><tr><td rowspan="10">SRS-2 total score</td><td rowspan="10">PM<sub>10</sub></td><td>PM<sub>10</sub></td><td>-0.56</td><td>0.26</td><td><strong>.029</strong></td><td>[-1.07, -0.06]</td></tr><tr><td>Grey space</td><td>-0.01</td><td>0.05</td><td>0.854</td><td>[-0.10, 0.08]</td></tr><tr><td>Interaction: PM<sub>10</sub>'grey space</td><td>0.01</td><td>0.01</td><td><strong>.043</strong></td><td>[0.00, 0.03]</td></tr><tr><td>Sex</td><td>7.04</td><td>2.95</td><td><strong>.017</strong></td><td>[1.26, 12.82]</td></tr><tr><td>Father age at birth</td><td>0.47</td><td>0.23</td><td><strong>.041</strong></td><td>[0.02, 0.93]</td></tr><tr><td>Mother age at birth</td><td>-0.41</td><td>0.30</td><td>0.169</td><td>[-1.00, 0.18]</td></tr><tr><td>Parental socioeconomic status</td><td>0.82</td><td>1.47</td><td>0.576</td><td>[-2.05, 3.70]</td></tr><tr><td>Child's age at diagnosis</td><td>-0.15</td><td>0.33</td><td>0.646</td><td>[-0.80, 0.49]</td></tr><tr><td>PM<sub>2.5</sub></td><td>-0.73</td><td>0.30</td><td><strong>.015</strong></td><td>[-1.32, -0.14]</td></tr><tr><td rowspan="10">PM<sub>2.5</sub></td><td>Grey space</td><td>-0.02</td><td>0.04</td><td>0.655</td><td>[-0.11, 0.07]</td></tr><tr><td>Interaction: PM<sub>2.5</sub>'grey space</td><td>0.01</td><td>0.01</td><td>0.051</td><td>[-0.00, 0.03]</td></tr><tr><td>Sex</td><td>6.85</td><td>2.97</td><td><strong>.021</strong></td><td>[1.02, 12.68]</td></tr><tr><td>Father age at birth</td><td>0.49</td><td>0.23</td><td><strong>.035</strong></td><td>[0.03, 0.94]</td></tr><tr><td>Mother age at birth</td><td>-0.43</td><td>0.30</td><td>0.150</td><td>[-1.01, 0.15]</td></tr><tr><td>Parental socioeconomic status</td><td>0.98</td><td>1.47</td><td>0.503</td><td>[-1.89, 3.85]</td></tr><tr><td>Child's age at diagnosis</td><td>-0.25</td><td>0.32</td><td>0.443</td><td>[-0.88, 0.39]</td></tr></tbody></table>

<footnote><sup>a</sup> Air Pollution: PM₁₀ or PM₂.₅ (β estimates correspond to a 1 µg/m³ increase in exposure), Green space: NDVI (β estimates correspond to a 1-unit increase), Grey Spaces: HRLI (β estimates correspond to a 1-unit increase).</footnote>

<footnote>Air pollution: PM₁₀ or PM₂.₅ (β estimates correspond to a 1 µg/m³ increase in exposure), Grey space: HRLI (β estimates correspond to a 1-unit increase); Model was adjusted for the sex (boy as reference), father's and mother's age at birth, parental socioeconomic status (high as reference), child's age at diagnosis and accounting for the moderating effect of grey space. The confounding variables were selected using a backward approach.</footnote>
distance”) on ASD severity (see Table 4). The results indicated that greater distances from CRAs and affiliated units were associated with higher severity of ASD symptoms with SRS-2. More specifically, “Hub distance” revealed a significant positive association with SRS-2 total score ($\beta$ = 0.16, $p$ = .002) and “Hub distance at recruitment” was similarly associated with worse outcomes for SRS-2 total score ($\beta$ = 0.12, $p$ = .026).

To examine whether grey space (HRLI) remains a moderator of the effect of air pollution exposure (PM₁₀/PM₂.₅) on ASD severity (SRS-2 total score) while accounting for healthcare accessibility, we included the indicators of distance to healthcare facilities (“Diagnostic center distance,” “Hub distance” and “Hub distance at recruitment”) one at a time in separate interaction models.

The interaction term between PM₁₀ and grey space (HRLI) was no longer significant after including “Hub distance” ($p$ = .051, see Table 5). However, “Hub distance” remained significantly associated with SRS-2 total score ($\beta$ = 0.14, $p$ = .016) even when taking into account the interaction between PM₁₀ exposure and grey space. Likewise, “Hub distance” remained significantly associated with SRS-2 total score ($\beta$ = 0.06, $p$ = .018), even when the interaction between PM₂.₅ and grey space was considered in the model although the interaction itself was not significant ($p$ = .062, see Table 5). Similar but weaker or non-significant results were observed for the other proximity indicators (“Diagnostic center distance” and “Hub distance at recruitment”), suggesting a consistent but less pronounced trend.

Overall, the inclusion of “Hub distance” revealed that the previously observed interactions between air pollution (PM₁₀ or PM₂.₅) and grey space (HRLI) might be better explained by disparities in healthcare accessibility. Given this finding, the effect of grey space on ASD severity as well as its moderating role, were removed from the models, to focus exclusively on the influence of healthcare accessibility. This allowed for a clearer examination of the effects of air pollution (PM₁₀ or PM₂.₅) alongside the three healthcare accessibility indicators (“Hub distance”, “Hub distance at recruitment” and “Diagnostic center distance”) on ASD severity. These effects were tested using separate models: one model for each combination of the three healthcare indicators and the two air pollution exposures (PM₂.₅ and PM₁₀), with each model being run both with and without adjustment for covariates.

When analyzing ASD severity scores with PM₁₀ or PM₂.₅ exposures, no significant associations between these pollutant exposures and ASD severity were observed after accounting for “Hub distance” (see Table 6) or “Hub distance at diagnosis”. This was evidenced in simple models without any adjustment. For example, in the regression analysis of SRS-2 total score with PM₁₀ or PM₂.₅ and “Hub distance”, the effect of PM₁₀ ($\beta$ = -0.35, $p$ = .106) and PM₂.₅ ($\beta$ = -0.47, $p$ = .131) lost statistical significance once distance was taken into account. Similar results were found with the adjusted models (adjusted for sex, father and mother’s age at birth, parental socio-economic status, child’s age at diagnosis, see Table 6). Comparable findings were observed for PM₂.₅ in adjusted models using “Hub distance” and for both pollutants when “Hub distance at recruitment” was included (see Table 6). In contrast, “Diagnostic center distance” evoked a less pronounced trend, with weaker or even non-significant results.

<table><caption>**Table 4**
Associations between Distance to CRA Facilities and ASD Outcomes in univariate Linear Regression Models.</caption><thead><tr><th>Outcome</th><th>Predictor<sup>a</sup> (in km)</th><th>β</th><th>SE</th><th>p-value</th><th>95% CI (β)</th></tr></thead><tbody><tr><td rowspan="3">SRS-2 total score</td><td>Hub distance</td><td>0.16</td><td>0.05</td><td><strong>.002</strong></td><td>[0.06, 0.26]</td></tr><tr><td>Hub distance at recruitment</td><td>0.12</td><td>0.05</td><td><strong>.026</strong></td><td>[0.01, 0.22]</td></tr><tr><td>Diagnostic center distance</td><td>0.10</td><td>0.05</td><td><strong>.033</strong></td><td>[0.01, 0.19]</td></tr></tbody></table>

<table><caption>**Table 5**
Association Between Air pollution (PM₁₀ or PM₂.₅) and SRS-2 total score Across interaction models, adjusted with healthcare accessibility indicator (Hub distance).</caption><thead><tr><th>Outcome</th><th>Predictor</th><th>β</th><th>SE</th><th>p-value</th><th>95% CI (b)</th></tr></thead><tbody><tr><td rowspan="8">SRS-2 total score</td><td>PM₁₀</td><td>-0.53</td><td>0.22</td><td><strong>.018</strong></td><td>[-0.97, -0.09]</td></tr><tr><td>Grey space</td><td>0.07</td><td>0.05</td><td>0.155</td><td>[-0.03, 0.16]</td></tr><tr><td>Interaction: PM₁₀*grey space</td><td>0.01</td><td>0.01</td><td>0.051</td><td>[-0.00, 0.03]</td></tr><tr><td>Hub distance</td><td>0.14</td><td>0.06</td><td><strong>.016</strong></td><td>[0.03, 0.26]</td></tr><tr><td>PM₂.₅</td><td>-0.71</td><td>0.29</td><td><strong>.015</strong></td><td>[-1.29, -0.14]</td></tr><tr><td>Grey space</td><td>0.06</td><td>0.05</td><td>0.206</td><td>[-0.03, 0.15]</td></tr><tr><td>Interaction: PM₂.₅*grey space</td><td>0.02</td><td>0.01</td><td>0.062</td><td>[-0.00, 0.03]</td></tr><tr><td>Hub distance</td><td>0.14</td><td>0.06</td><td><strong>.018</strong></td><td>[0.02, 0.26]</td></tr></tbody></table>

Air Pollution: PM₁₀ or PM₂.₅ ($\beta$ estimates correspond to a 1 µg/m³ increase in exposure), Green space: NDVI ($\beta$ estimates correspond to a 1-unit increase), Grey Spaces: HRLI ($\beta$ estimates correspond to a 1-unit increase); Interactions were modelled using SEM with the Lavaan package in R, accounting for HubDistance in km ($\beta$ estimates per 1 km increase).

Our results highlight critical findings, after adjusting for proximity to CRA facilities, specifically “Hub distance” and “Hub distance at recruitment”, no significant associations between PM₁₀ or PM₂.₅ exposures and ASD severity were observed. Overall, this pattern suggests that the noted associations between pollution and ASD severity may be influenced by healthcare accessibility, as measured by proximity to CRA facilities and potentially mask true environmental effects.

To assess the potential influence of methodological changes in air pollution exposure estimation, we conducted sensitivity analyses stratifying participants based on the period when exposure data were modelled. Given that our study focuses on pollutant exposure during the first 1000 days of life, we excluded individuals whose exposure periods overlapped with the introduction of the volatile fraction adjustment. Specifically, for PM₁₀, analyses were conducted separately for participants conceived before 2005 and after 2008. For PM₂.₅, we applied a similar approach, with two groups, one for individuals conceived before 2006 and one for individuals conceived after 2010. Accordingly, for PM₁₀, 149 individuals were included after 2008 and 16 were exposed before 2005. Similarly, for PM₂.₅, 162 individuals had exposure data available after 2010 and 24 were exposed before 2006. Mean air pollution concentrations remained relatively stable across these subgroups. In the full study population, mean PM₁₀ and PM₂.₅ concentrations were 21.1 µg/m³ (SD = 5.19) and 14.2 µg/m³ (SD = 3.50) respectively. In the sub-group of individuals conceived after 2008, mean PM₁₀ exposure was 20.8 µg/m³ (SD = 5.57) while in the pre-2005 sub-group mean PM₁₀ exposure was 21.6 µg/m³ (SD = 2.27) while in the post-2010 sub-group, mean PM₂.₅ exposures was 13.7 µg/m³ (SD = 3.04) while in the pre-2006 sub-group mean PM₂.₅ exposures was 16.9 µg/m³ (SD = 1.66). Details are available in the Supplementary Materials (see Excel Table S4). Separate analyses were performed for individuals conceived before 2005 and after 2008 to PM₁₀ and before 2006 and after 2010 to PM₂.₅, and in both cases, no significant differences were observed in the links between air pollution exposure and ASD severity compared to the main results. However, for PM₂.₅ after 2010, the interaction effect with grey space was no longer significant, while associations with healthcare accessibility remained unchanged. This may be due to the decreasing sample size in each subgroup. The full details of these analyses are available in the Supplementary Materials (see Excel Tables S5-S8). In addition, we conducted a complete-case analysis including only participants with full air pollution data (n = 178) to

<footnote><sup>a</sup> Predictor was assessed per 1 km increase.</footnote>
<table><caption>**Table 6**
Association Between Air pollution and SRS-2 total score Across non adjusted and adjusted model, accounting for healthcare accessibility indicator.</caption><thead><tr><th rowspan="2">Outcome</th><th rowspan="2">Pollutant</th><th rowspan="2">Predictor</th><th colspan="4">non adjusted model</th><th colspan="4">adjusted model</th></tr><tr><th>β</th><th>SE</th><th>p-value</th><th>95 % CI (b)</th><th>β*</th><th>SE*</th><th>p-value*</th><th>95 % CI (b*)</th></tr></thead><tbody><tr><td rowspan="12">SRS-2 total score</td><td rowspan="6">PM<sub>10</sub></td><td>PM<sub>10</sub></td><td>-0.35</td><td>0.22</td><td>0.106</td><td>[-0.78, 0.08]</td><td>-0.30</td><td>0.22</td><td>0.176</td><td>[-0.73, 0.13]</td></tr><tr><td>Sex</td><td></td><td></td><td></td><td></td><td>6.40</td><td>2.77</td><td><strong>.021</strong></td><td>[0.98, 11.82]</td></tr><tr><td>Father age at birth</td><td></td><td></td><td></td><td></td><td>0.45</td><td>0.23</td><td>0.051</td><td>[-0.00, 0.91]</td></tr><tr><td>Mother age at birth</td><td></td><td></td><td></td><td></td><td>-0.39</td><td>0.31</td><td>0.199</td><td>[-0.99, 0.21]</td></tr><tr><td>Parental socioeconomic status</td><td></td><td></td><td></td><td></td><td>0.89</td><td>1.47</td><td>0.542</td><td>[-1.98, 3.76]</td></tr><tr><td>Child's age at diagnosis</td><td></td><td></td><td></td><td></td><td>-0.17</td><td>0.33</td><td>0.613</td><td>[-0.82, 0.49]</td></tr><tr><td rowspan="6">PM<sub>2.5</sub></td><td>Hub Distance</td><td>0.13</td><td>0.05</td><td><strong>.015</strong></td><td>[0.02, 0.23]</td><td>0.13</td><td>0.05</td><td><strong>.011</strong></td><td>[0.03, 0.22]</td></tr><tr><td>PM<sub>2.5</sub></td><td>-0.47</td><td>0.31</td><td>0.131</td><td>[-1.08, 0.14]</td><td>-0.30</td><td>0.29</td><td>0.178</td><td>[-0.95, 0.18]</td></tr><tr><td>Sex</td><td></td><td></td><td></td><td></td><td>6.40</td><td>2.78</td><td><strong>.025</strong></td><td>[0.80, 11.69]</td></tr><tr><td>Father age at birth</td><td></td><td></td><td></td><td></td><td>0.45</td><td>0.23</td><td><strong>.050</strong></td><td>[0.00, 0.91]</td></tr><tr><td>Mother age at birth</td><td></td><td></td><td></td><td></td><td>-0.39</td><td>0.30</td><td>0.189</td><td>[-1.00, 0.20]</td></tr><tr><td>Parental socioeconomic status</td><td></td><td></td><td></td><td></td><td>0.89</td><td>1.47</td><td>0.508</td><td>[-1.90, 3.85]</td></tr><tr><td rowspan="6">PM<sub>2.5</sub></td><td>Child's age at diagnosis</td><td></td><td></td><td></td><td></td><td>-0.17</td><td>0.33</td><td>0.512</td><td>[-0.87, 0.43]</td></tr><tr><td>Hub Distance</td><td>0.13</td><td>0.05</td><td><strong>.012</strong></td><td>[0.03, 0.24]</td><td>0.13</td><td>0.05</td><td><strong>.009</strong></td><td>[0.03, 0.22]</td></tr></tbody></table>

Air pollution: PM₁₀ and PM₂.₅ (β estimates correspond to a 1 µg/m³ increase in exposure), healthcare accessibility indicator: Hub Distance, Model was adjusted for the sex (boy as reference), father's and mother's age at birth, parental socioeconomic status (high as reference), child's age at diagnosis and accounting for HubDistance in km (β estimates per 1 km increase.) The confounding variables were selected using a backward approach.

evaluate the impact of missing exposure data. In this sub-set, mean PM₁₀ and PM₂.₅ concentrations were 20.8 µg/m³ (SD = 4.52) and 14.0 µg/m³ (SD = 2.84) respectively, remaining consistent with the full population. The findings from these complete-case analyses also tallied with the primary results, with no significant differences noted. Overall, these sensitivity analyses suggest that the methodological choices regarding exposure estimation and missing data handling did not meaningfully impact the observed associations. The full details of these analyses are available in the Supplementary Materials (see Excel Table S9).

## 4. Discussion and conclusions

This study investigated the associations between prenatal exposure to particulate matter (PM₁₀ and PM₂.₅), green and grey space environments and ASD severity in children within the French ELENA cohort. Our findings exemplify the intricate interplay between environmental exposures, underscoring the nuanced influences on neurodevelopmental outcomes.

Our initial analyses revealed paradoxical protective links between PM exposure and ASD severity, measured using the SRS-2 scale. More specifically, higher PM₁₀ and PM₂.₅ levels were associated with lower ASD severity. While these findings align with some previous observations of paradoxical effects (Astell-Burt et al., 2022; Kerin et al., 2018; Larson et al., 2018), our research highlighted that these associations dissipated when accounting for urbanization levels and proximity to autism resource centers (CRA), thereby highlighting the confounding influence of healthcare access.

Adjustments for indicators of healthcare accessibility, particularly “Hub distance” (proximity to the nearest CRA prior to diagnosis) and “Hub distance at recruitment” (proximity to CRA at the time of cohort enrollment), consistently attenuated or nullified the links between air pollution and ASD severity. This indicates that regional disparities in access to specialized autism care may play a critical role in shaping these associations. Most notably, the effect of “Hub distance at diagnosis” (distance to the diagnostic center) was weaker and less consistent, reinforcing the idea that general accessibility to autism care infrastructure, rather than the specific location of diagnosis, is more influential in confounding these relationships.

These findings could suggest that for children living further away from specialized autism care centers, the decision to seek a consultation in these facilities may be primarily driven by the severity of clinical symptoms. In such cases, individuals residing in less urbanized areas, characterized by lower levels of grey space and greater distances to CRAs, are more likely to receive an ASD diagnosis only when symptoms are more pronounced. This self-selection process, which had not been

accounted for in previous studies, could explain the paradoxical associations observed between environmental exposures and ASD severity. Moreover, the specific diagnostic location appeared to be a less significant factor in our models, probably because families often initially sought care at the nearest CRA before being referred to a second CRA for formal diagnosis and inclusion in the ELENA cohort. This referral process suggests that proximity to the nearest CRA plays a more critical role in structuring access to autism care than the exact diagnostic location itself, which may explain why “Hub distance” (distance to the nearest CRA) showed stronger links with ASD severity than “Diagnostic center distance” (distance to the CRA where diagnosis occurred). By contrast, in urban settings where proximity to CRAs facilitates earlier and potentially more frequent diagnostic assessments, children with milder ASD traits may be identified, leading to an overall distribution of severity that differs from that of children diagnosed in more remote areas. Taken together, these results emphasize the necessity of integrating healthcare accessibility indicators into studies examining environmental risk factors for ASD. The potential misclassification of ASD severity due to regional differences in access to specialized care spotlights the importance of future research which incorporates both the structural (infrastructure, availability of trained professionals) and functional (ease of access, referral patterns) aspects of healthcare accessibility.

Our study's strengths lie in its comprehensive exposure assessment, leveraging high-resolution PM₁₀ and PM₂.₅ data, which allows for precise estimation of air pollution exposure levels. In addition, we provided nuanced environmental characterization by capturing both green and grey space exposures and incorporating detailed residential historicity. By accounting for changes in residence during critical developmental periods, we minimized exposure misclassification and offered a more accurate reflection of participants' true environmental contexts throughout prenatal and early childhood.

The use of the ELENA cohort represents another major strength of this study. As a French prospective and multicenter study comprising 473 children with confirmed ASD diagnoses from 15 autism research centers in various regions of France, this cohort provides robust and standardized data. Its extensive characterization of environmental exposures, urbanization metrics and ASD outcomes enables in-depth exploration of these complex relationships within a diverse sample.

However, based on the overall SRS-2 scores, our sample appears to represent a more severe subset of the ASD population. This is a common characteristic of specialized, research-oriented cohorts, where a potential selection bias may occur as families of children with more pronounced symptoms are often more likely to participate in long-term studies. Consequently, our findings regarding the influence of
environmental factors on ASD severity may not be fully generalizable. Then future research should explore multi-pollutant models since the use of single-pollutant models limits our ability to evaluate a potential cocktail effect. Moreover, upcoming research could provide deeper insights into cumulative environmental impacts on ASD severity and incorporate longitudinal designs to clarify causal pathways. Temporal specificity is another critical area for improvement. Current exposure assessments often average air pollution levels across entire trimesters of pregnancies, potentially overlooking critical windows of vulnerability. Exploring shorter, biologically relevant exposure windows would allow for more precise identification of the developmental stages most susceptible to environmental risks. Additionally, the assumption that maternal residence at delivery reflects exposure throughout pregnancy introduces potential biases; Bell and Belanger (2012) reported that between 9% and 32% of women move during pregnancy, with a median of 20%. This mobility may lead to misclassification of exposure estimates, introducing potential biases. In addition, incorporating data on daily mobility and adjusting exposure assessments accordingly could yield a more accurate understanding of environmental impacts during pregnancy. Furthermore, the interactions involving grey space (HRLI) remained close to statistical significance with the inclusion of healthcare accessibility indicators in the models. This near-significance may be attributed to the increased complexity of the model and the limited sample size. Subsequent large-scale studies are recommended to replicate these findings to better understand the interplay between grey spaces, healthcare access and ASD severity. Finally, incorporating vegetation indices such as the modified soil-adjusted vegetation index (MSAVI), which reduces background noise from bare soil and offers a refined measure of green space exposure, could further elucidate the interconnection between green spaces and neurodevelopmental outcomes, especially in urban areas. Applying such indices in subsequent analyses may better capture variations in vegetation density and their health implications.

In conclusion, this study highlights the complex relationships between air pollution, urban environments and ASD severity. Although initial results suggested protective effects of PM exposure, adjusted analyses emphasize the confounding role of urbanization and healthcare accessibility. These findings highlight the critical need for tailored public health strategies to address disparities in ASD diagnosis and care. By integrating more precise environmental assessments and longitudinal designs, future research can better elucidate the pathways linking early-life environmental exposures with neurodevelopmental outcomes and orient interventions towards promoting equity in ASD care and prevention.

### CRediT authorship contribution statement

**Valentin Simoncic:** Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Resources, Methodology, Funding acquisition, Formal analysis, Data curation, Conceptualization.
**Romain Wenger:** Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Methodology.
**Maëva Monnier:** Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Methodology, Formal analysis.
**Cécile Michelon:** Visualization, Validation, Resources, Methodology, Investigation.
**Marianne Peries:** Visualization, Validation, Resources, Methodology, Investigation.
**Ian Hough:** Visualization, Validation, Resources.
**Itai Klook:** Visualization, Validation, Resources.
**Johanna Lepeule:** Visualization, Validation, Resources.
**Marion Mortamais:** Visualization, Validation, Resources, Methodology, Investigation.
**Amaria Baghdadli:** Writing – review & editing, Visualization, Validation, Supervision, Project administration, Investigation, Funding acquisition.

### Declaration of generative AI and AI-assisted technologies in the writing process

During the preparation of this work the authors used DeepL and GPT-4o in order to optimize the scripts in R language and to correct English errors, as all the authors are French. After using this tool, the authors reviewed and edited the content as needed and took full responsibility for the content of the publication.

### Funding

This work received support from the French government, managed by the Agence Nationale de la Recherche (ANR) as part of the investment program France 2030 under the reference "ANR-21-EXES-0005", from the Occitanie Region, and from the ExposUM Institute of the University of Montpellier. Also, this study is part of the ETAP-ASD project which is supported by the French National Research Program for Environmental and Occupational Health of Anses (2020/01/031).

The ELENA cohort was funded by the French Health Ministry (DGOS) PHRCN 2013, grant number: 13-0232, and the Caisse Nationale de Solidarité pour l'Autonomie (CNSA), grant number: 030,319. The CHU of Montpellier (AOI) provided additional support.

### 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.

### Acknowledgments

The authors warmly thank all participants and their families, clinicians and research's teams.

### Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.envres.2025.122577.

### Data availability

Data will be made available on request.

HRLI were downloaded through the Copernicus web interface (https://land.copernicus.eu/pan-european/high-resolution-layers/imperviousness)

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