# Traffic-related air pollution and childhood acute leukemia in France: GEOCAP nationwide case-control study

Charlotte Salmon, André Some, Bénédicte Jacquemin, Danielle Vienneau, Kees de Hoogh, Laure Faure, Jacqueline Clavel, Stéphanie Goujon

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Charlotte Salmon, André Some, Bénédicte Jacquemin, Danielle Vienneau, Kees de Hoogh, et al. Traffic-related air pollution and childhood acute leukemia in France: GEOCAP nationwide case-control study. Environmental Research, 2025, 288 (2), pp.123303. 10.1016/j.envres.2025.123303. hal-05367051

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# Traffic-related air pollution and childhood acute leukemia in France: GEOCAP nationwide case-control study

Charlotte Salmon<sup>a, *</sup> <sup>□</sup>, André Some<sup>a</sup>, Bénédicte Jacquemin<sup>b, □</sup>, Danielle Vienneau<sup>c, d □</sup>, Kees de Hoogh<sup>c, d □</sup>, Laure Faure<sup>a, e □</sup>, Jacqueline Clavel<sup>a, e</sup>, Stéphanie Goujon<sup>a, e □</sup>

## ARTICLE INFO

Keywords:

Childhood leukemia

Air pollution

Traffic

NO2

Particulate matter

Black carbon

Case-control study

## ABSTRACT

Outdoor air pollution and particulate matter (PM) have been classified as carcinogenic to humans, and benzene exposure is known to increase the risk of leukemia in adults. Yet the evidence regarding childhood leukemia remains inconclusive. In this study, we sought to investigate whether traffic-related air pollution increases the risk of acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML).

We used data from GEOCAP, an ongoing population-based case-control study conducted in France. It includes 4611 ALL and 830 AML cases, diagnosed between 2002 and 2013 and identified through the French national registry of childhood cancer, and 60,189 contemporaneous controls representative of the French pediatric population. Annual average exposures to nitrogen dioxide (NO2), fine PM (PM2.5) and Black Carbon (BC) at the geocoded address of residence at diagnosis for cases and inclusion for controls were estimated using land-use regression models. We also evaluated major roads length within 150 m of the geocoded addresses. Polyto-mous logistic regression was used to derive odds ratios (ORs) and 95 % confidence intervals (CI).

Increased risks of AML were found with NO2 (OR per 10 µg/m3 = 1.09, 95 %CI = 1.03–1.15), PM2.5 (OR per 5 µg/m3 = 1.09, 95 %CI = 1.01–1.18) and BC exposures (OR per 0.5–5/m = 1.09, 95 %CI = 1.03–1.16). The length of major roads within 150 m of the residence was also associated with AML risk (OR per 300 m = 1.13, 95 %CI = 1.03–1.25). These increases in risk were more pronounced in the most urbanized areas. For ALL, inverse asso-ciations were suggested.

This study provides further evidence supporting a role of traffic-related air pollution in AML risk in children.

## 1. Introduction

Acute leukemia (AL) is the most common cancer among children under 15 years of age, accounting for about 25 % of all childhood cancer cases in France and other high-income countries (Johnston et al., 2021; RNCE, 2024). Depending on the type of progenitor stem cell affected, it can be classified into two main types: acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML), representing about 83 % and 15 % of the AL cases, respectively. In France, the annual age-standardized incidence rate of acute leukemia is estimated at 39.2

cases per one million children. Approximately 480 children are thus diagnosed with this cancer each year, almost half of the cases occurring before the age of 5 (RNCE, 2024).

Despite extensive research, the etiology of childhood leukemia re-mains poorly understood. Established risk factors include high dose ionizing radiation and some genetic factors such as Down and Li-Fraumeni syndrome, but these are likely to explain only a small pro-portion of observed cases (Erdmann et al., 2019). Growing evidence also suggests that environmental factors other than ionizing radiation may be involved in the occurrence of these cancers (Schüz and Erdmann, 2016).

<footnote><sup>a</sup> Université Paris Cité and Université Sorbonne Paris Nord, Inserm, INRAe, Center for Research in Epidemiology and StatisticS (CRESS), Paris, France</footnote>

<footnote><sup>b</sup> Univ Rennes, Inserm, EHESP, Institut de Recherche en Santé, Environnement Et Travail (Irset, UMR S 1085), Rennes, France</footnote>

<footnote><sup>c</sup> Swiss Tropical and Public Health Institute, Allschwil, Switzerland</footnote>

<footnote><sup>d</sup> University of Basel, Basel, Switzerland</footnote>

<footnote><sup>e</sup> French National Registry of Childhood Cancer, National Registry of Childhood Haematological Malignancies, AP-HP, GHU Paris-Sud, Paris, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail addresses: charlotte.salmon@inserm.fr (C. Salmon), andre.some@inserm.fr (A. Some), benedicte.jacquemin@inserm.fr (B. Jacquemin), danielle.vienneau@swisstph.ch (D. Vienneau), c.dehoogh@unibas.ch (K. de Hoogh), laure.faure@inserm.fr (L. Faure), jacqueline.clavel@inserm.fr (J. Clavel), stephanie.goujon@inserm.fr (S. Goujon).</footnote>
Suspected factors comprise outdoor air pollution, which has been clas-
sified as carcinogenic to humans by the International Agency for
Research on Cancer (IARC) (IARC, 2016), in particular for its association
with lung cancer in adults. Benzene, a volatile organic compound (VOC)
which can be found in outdoor air, is also recognized as leukemogen for
adults (IARC, 2018).

Traffic, a major contributor to outdoor air pollution, has been
extensively studied over the past decades as a risk factor for cancer in
adults and children. Yet the evidence remains inconclusive for child-
hood leukemias. Variation in sample size, exposure assessment, periods
of exposure and metrics under consideration may partly explain the
inconsistencies in the literature (Filippini et al., 2019). Motor vehicles
emit a complex mixture of pollutants, including nitrogen dioxide (NO2),
fine particle matter with an aerodynamic diameter ≤2.5 µm (PM2.5)
and VOCs. Simple metrics of traffic density near the child’s residence (e.
g. distance to roads) and benzene exposure have been associated with
childhood leukemia (AML particularly) in several studies (Houot et al.,
2015; Amigou et al., 2011; Crosignani et al., 2004; Heck et al., 2014;
Janitz et al., 2016; Pearson et al., 2000; Savitz and Feingold, 1989;
Spycher et al., 2015; Tamayo-Uria et al., 2018; Vinceti et al., 2012), as
summarized in the meta-analyses by Filippini et al. (2019). No firm
conclusion could be drawn for NO2 and PM2.5, with a limited number of
studies included for PM2.5. More recent studies reported an association
between NO2 exposure and the risk of AML (Kreis et al., 2022) or ALL
(Gaspard et al., 2025), whereas findings for PM2.5 were mixed, with one
study showing associations with ALL (Williams et al., 2024) and others
reporting no association (Gaspard et al., 2025; Hvidtfeldt et al., 2020).
Black carbon (BC), a traffic-related component of PM2.5, has to our
knowledge been investigated in relation to childhood leukemia only in
the latter study, which found no association.

In a previous study conducted within our research group and included in the recent meta-analysis, we highlighted an association between the length of major roads within 150 m of children's residences and the risk of AML over the period 2002–2007, in France (Houot et al., 2015). Background NO2 exposure, estimated on a 4 km² grid, was neither associated with ALL nor AML. The objective of the present study was to further investigate whether air pollution increases the risk of childhood leukemia, focusing our research on three markers of traffic: NO2, PM2.5 and Black Carbon, estimated at a fine spatial scale.

## 2. Methods

### 2.1. Study population

GEOCAP-Diag study is a population-based case-control study con-
ducted since 2002 in France to investigate the role of environmental
exposures at the time of diagnosis in the risk of childhood cancer. The
present study was carried out over the period 2002–2013.

Cases come from the French National Registry of Childhood Cancer (RNCE, https://rnce.inserm.fr/en/), which provides an exhaustive coverage of the pediatric population in France. Within the RNCE, case identification is conducted actively within all French pediatric hema-tology and oncology departments and is supplemented by information from additional sources, including treatment protocol lists, the National Health Insurance long-term disease files and the National Registry of Medical Causes of Death. For the present study, cases included all chil-dren diagnosed with primary ALL (International Classification of Childhood Cancer third edition [ICCC-3] subgroups 1. a) or AML (ICCC-3 subgroups 1. b) between January 1st, 2002 and December 31st, 2013, residents of mainland France and aged less than 15 years old at the end of the year of diagnosis. A total of 4611 ALL and 830 AML cases were included. Children's residential addresses at the time of diagnosis were systematically collected by the RNCE.

Approximately 5000 population controls were randomly selected each year from the income and council tax databases for French households by the French National Institute of Statistics and Economic

Studies (InseeInsee). Overall, 60,189 controls aged less than 15 years at the end of the year of inclusion and residents of mainland France were included in the analysis for the period 2002–2013. Of note, only the year of birth and the year of inclusion were available for the controls, so that age at the end of the year was considered for both controls and cases. We checked a *posteriori* that each annual sample of controls was representative of the French pediatric population, in terms of age and socio-demographic characteristics of the municipality of residence. Residential addresses of the controls at the time of inclusion were obtained from Insee.

2.2. Exposure assessment

Residential addresses of the children at index date, that is the time of
diagnosis for cases and inclusion for controls, were geocoded blinded to
the case-control status. Geocoding was carried out using the MapInfo
geographic information system and the BD Adresse 2.2 database of the
French National Institute of Geographic and Forest Information (IGN).
Information on the accuracy of geocoding was provided for each address
(from most to least accurate): 1) geocoding at the entrance of the resi-
dential plot, 2) by shifting the parcel centroid towards the road section,
3) by interpolation between two neighboring numbers, 4) in the middle
of the street, 5) in the center of an urban residential area, 6) in the center
of a rural hamlet and 7) at the town hall of the municipality.

To estimate annual average exposure to NO2, PM2.5 and BC at the geocoded residential address of the children, we used a European hybrid land-use regression (LUR) model developed for each pollutant at a resolution of 100 × 100 m. Details on the modeling and validation of models can be found elsewhere. In brief, LUR models were developed based on the European Environment Agency (EEA) AirBase V8 routine monitoring data for PM2.5 and NO2, for the year 2010, which was the first year with a sufficient coverage of PM2.5 monitoring sites in Western Europe. For BC, monitoring data for the year 2010 were obtained within the European Study of Cohorts for Air Pollution Effects (ESCAPE) project, using the reflectance of the PM2.5 filters (EEA, 2015). In addition to monitoring data, LUR models incorporated satellite observations, chemical-transport model estimates, land cover and road data. Models were validated using 5-fold cross-validation, yielding explained spatial variation (R²) of 66 % for PM2.5, 58 % for NO2, and 51 % for BC. The corresponding root mean square errors (RMSE) were 3.0 µg/m³ for PM2.5, 9.5 µg/m³ for NO2, and 0.6 10⁻⁵/m for BC (de Hoogh et al., 2018).

NO2, PM2.5 and BC concentrations were evaluated for each year of the study period using a ratio extrapolation method with 2010 as the reference year. Average pollutant concentrations estimated from the Danish Eulerian Hemispheric Model (DEHM) (Brandt et al., 2012) at the Nomenclature of Territorial Units for Statistics – level 1 (NUTS-1) spatial scale, were used to compute temporal ratios (Supplementary Fig. 1). These yearly ratios were calculated for each pollutant and each NUTS-1, by dividing the DEHM-modeled concentrations for a given year (2002–2013) by the corresponding 2010 concentration. The 2010 LUR-derived pollutant concentrations at each child’s residential address were then multiplied by the relevant NUTS-specific yearly ratio. Annual average concentrations of NO2, PM2.5, and BC were assigned to each child based on their residential address at the time of diagnosis for cases and the time of inclusion for controls, thus reflecting the average exposure during the corresponding year.

As a complementary indicators of traffic-related air pollution, we
calculated the cumulative length of major roads within 150 m of the
residence. The length of major roads around the geocoded residential
addresses was calculated using the French road network map from
HERE®, a company specialized in location data and mapping technol-
ogies. The mapping of the road network was available for the year of
diagnosis for the cases and for the year of inclusion for the controls. The
roads are classified into five categories according to their functional
class: 1) high volume and maximum speed traffic roads with few
changes (i.e. high-speed freeways), 2) roads with high volume and high speed traffic connecting roads from class 1, used to channel traffic, 3) roads connected to class 2 roads, with high volume and a lower level of speed, 4) moderate speed roads connecting neighborhoods and 5) roads with low volume and traffic movement, parking lanes or private roads. We considered as "Major roads" those classified in the first three classes. The length of major roads was calculated within a 150 m buffer around the geocoded address, as beyond this distance traffic-related pollution levels are significantly reduced (Pasquier and André, 2017; Hitchins et al., 2000; Karner et al., 2010).

## 2.3. Statistical analysis

Polytomous logistic regressions were applied to estimate odds ratios (ORs) and 95 % confidence intervals (CIs) for the associations between pollutants exposure, major roads length and risk of childhood ALL and AML. Unconditional logistic regression models were used for additional analyses involving the study of leukemia types separately.

Exposures were modeled independently (one model per exposure). All models were adjusted for age at the end of the calendar year of diagnosis or inclusion, in one-year categories. Exposures were included as linear terms and ORs were expressed per 300 m for road length in the 150 m buffer and 10 µg/m³, 5 µg/m³ and 0.5 10⁻⁵/m for NO2, PM2.5 and BC, respectively, based upon increments widely used in the literature. These increments also roughly corresponded to the interquartile range (IQR) of our study for most pollutants and to the median of the length of major roads. Restricted cubic splines with three knots were applied to evaluate the exposure-response function, and deviation from linearity was assessed using likelihood ratio tests comparing linear models with spline models.

Multicollinearity between the air pollutant variables was investigated using variance inflation factors (VIFs).

Air pollution levels, emission sources, and chemical compositions of pollutants may vary between urban and rural areas (Minguillón et al., 2012; Cazier et al., 2016). The analyses were thus stratified according to the population size of urban units, an urban unit referring to a municipality or a group of municipalities of 2000 inhabitants or more, with a continuous building area (INSEE). Using the Insee classification, we considered four main categories: <10,000, 10,000–99,999, 100,000–1, 999,999, >2,000,000 inhabitants (Paris urban unit). Stratified analyses by major roads length were also conducted, using the median value of road length in the control group to defined the categories.

Other sensitivity analyses were conducted to evaluate the robustness of the results. NO2, PM2.5 and BC estimates were categorized according to the quintiles of the distribution among controls. The cumulative length of major roads within a 150 m buffer was divided into 6 categories: 0 m (no major roads within 150 m of the residence), and 5 categories based on the quintiles of the distribution among the controls with major roads length >0 m. We also adjusted the analyses for NUTS-1 regions to account for possible heterogeneity in exposure assessment methods across the regions as well as regional differences in environmental contexts and air pollution sources. To reduce potential residual confounding, models were additionally adjusted for factors previously associated with acute leukemia risk in GEOCAP studies. For ALL, we adjusted for the French Deprivation Index (FDep) (Marquant et al., 2016) and the density of viticulture in the municipality of residence retrieved from the 2010 national agricultural census (Mancini et al., 2023; Coste et al., 2020). The FDep was calculated for the year of diagnosis or inclusion at the IRIS level, the smallest unit (approximately 2000 inhabitants) for which census data are available in France, using principal component analysis of four census variables (median income, unemployment rate, and proportions of blue-collar workers and of high school graduates) (Rey et al., 2009). The resulting score of the first component was split into categories based on the quintiles of the distribution of the variable in our controls' population, ranging from the least deprived category (Q1) to the most deprived (Q5). For AML, we

adjusted the models for FDep. Because children with Down syndrome have a significantly increased risk of developing acute leukemia, cases diagnosed with this syndrome were excluded from the analyses (N = 44 ALL and 43 AML). Lastly, we restricted the analyses to children with the best geocoding quality, i.e. address geocoded at the entrance of the residential plot (N = 40,281), to evaluate the robustness of our results with respect to potential exposure misclassification due to geocoding uncertainties.

All analyses were carried out using SAS 9.4 (SAS Institute Inc., Cary, North Carolina, USA) and R 4.3.1 (R Foundation for Statistical Computing, Vienna, Austria).

## 3. Results

The study population consisted of 830 AML cases, 4611 ALL cases and 60,189 controls. Their characteristics are displayed in Table 1. Overall, cases were younger than controls: 44.9 % of ALL cases and 40.1 % of AML cases were aged 1–4 years at the end of the calendar year, compared to 26.6 % of controls. The proportion of children living in the Paris urban unit was lower among ALL cases than among controls (16.2 % vs 18.2 %). Conversely, this proportion tended to be greater among AML cases (20.2 %). Overall, ALL cases were also less likely to live in the most deprived areas than controls. The distribution of the residential addresses by NUTS-1 region and geocoding accuracy was similar between cases and controls. Less than 10 % of the cases and the controls lived in each of the North, East or South-West regions, while 20 % lived in Ile-de-France or the surrounding area, the most densely populated regions in France. About 60 % of addresses for both cases and controls were geocoded at the entrance of the residential plot. No major differences were found between cases and controls with respect to the density of viticulture in the municipality of residence.

The distribution of air pollution concentrations among the controls, representative of the French pediatric population, is shown in Supplementary Table 1. In mainland France, the median exposure was estimated at 23.8 µg/m³, 18.6 µg/m³, and 1.6 10⁻⁵/m for NO2, PM2.5 and BC, respectively. As expected, pollutant concentrations increased with the size of the urban unit, ranging from a median of 16.0 µg/m³ in urban units with less than 10,000 inhabitants to 41.8 µg/m³ in the Paris urban unit for NO2, from 16.3 to 22.2 µg/m³ for PM2.5 and from 1.3 to 2.4 10⁻⁵/m for BC. About 29 % of the control population (N = 17,734) had major roads within 150 m of their residence. For the latter, the median length of roads in the buffer was 296.4 m. The median length showed little variation across the different strata of urban units, although some fluctuation could be observed for the highest values. The 95th percentile goes from 514.8 m in urban units with less than 10,000 inhabitants to 890.9 m in the Paris urban unit. In addition, the proportion of children living within 150 m of major roads increases gradually from 24 % to 42 %.

The estimates of NO2, PM2.5 and BC were highly correlated with each other (Spearman correlation coefficient [r] = 0.78 for NO2-PM2.5 and 0.92 for NO2-BC) (Supplementary Fig. 2). These correlations remained high regardless of the size of the urban units. For the length of major roads within 150 m of the residence, moderate correlations were found with NO2 and BC (r = 0.37). The correlation was weaker for PM2.5 (r = 0.18). Correlations between road length and the pollutants (in particular NO2 and BC) appeared to be slightly lower in urban units with less than 10,000 inhabitants, compared to other areas.

High multicollinearity was detected between the pollutants, particularly between NO2 and BC with VIFs >5 (Supplementary Table 2). This collinearity increased in the most urbanized areas.

Table 2 presents the results for the association between indicators of air pollution and acute leukemia risk. Overall, the risk of AML increased with residential exposure to NO2 (OR = 1.09, 95 %CI = 1.03–1.15 for a 10 µg/m³ increase), PM2.5 (OR = 1.09, 95 %CI = 1.01–1.18 for a 5 µg/m³ increase) and BC (OR = 1.09, 95 %CI = 1.03–1.16 for a 0.5 10⁻⁵/m increase). Likewise, the length of major roads within 150 m of residence
<table><caption>**Table 1**

Characteristics of the study population, GEOCAP, 2002–2013.</caption><thead><tr><th rowspan="2"></th><th colspan="2">Controls<br/>N = 60,189</th><th colspan="2">ALL<br/>N = 4611</th><th colspan="2">AML<br/>N = 830</th></tr><tr><th>N</th><th>(%)</th><th>N</th><th>(%)</th><th>N</th><th>(%)</th></tr></thead><tbody><tr><td><strong>Age<sup>a</sup></strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>&lt;1 year</td><td>3727</td><td>(6.2)</td><td>68</td><td>(1.5)</td><td>65</td><td>(7.8)</td></tr><tr><td>1–4 years</td><td>16,009</td><td>(26.6)</td><td>2070</td><td>(44.9)</td><td>333</td><td>(40.1)</td></tr><tr><td>5–9 years</td><td>20,190</td><td>(33.5)</td><td>921</td><td>(20.0)</td><td>239</td><td>(28.8)</td></tr><tr><td>10–14 years</td><td>20,263</td><td>(33.7)</td><td>1552</td><td>(33.7)</td><td>193</td><td>(23.3)</td></tr><tr><td><strong>Geocoding accuracy</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Entrance of the plot</td><td>37,020</td><td>(61.5)</td><td>2762</td><td>(59.9)</td><td>499</td><td>(60.1)</td></tr><tr><td>Projection towards the road</td><td>5047</td><td>(8.4)</td><td>354</td><td>(7.7)</td><td>55</td><td>(6.6)</td></tr><tr><td>Interpolation between neighboring numbers</td><td>7140</td><td>(11.9)</td><td>602</td><td>(13.1)</td><td>115</td><td>(13.9)</td></tr><tr><td>Middle of the street</td><td>2861</td><td>(4.8)</td><td>311</td><td>(6.7)</td><td>53</td><td>(6.4)</td></tr><tr><td>Center of urban residential area</td><td>597</td><td>(1.0)</td><td>34</td><td>(0.7)</td><td>8</td><td>(1.0)</td></tr><tr><td>Center of rural hamlet</td><td>6640</td><td>(11.0)</td><td>419</td><td>(9.1)</td><td>78</td><td>(9.4)</td></tr><tr><td>Townhall of the municipality</td><td>884</td><td>(1.5)</td><td>129</td><td>(2.8)</td><td>22</td><td>(2.7)</td></tr><tr><td><strong>Territorial units (NUTS-1), France</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>1- Ile de France</td><td>12,392</td><td>(20.6)</td><td>880</td><td>(19.1)</td><td>190</td><td>(22.9)</td></tr><tr><td>2- Surrounding Ile de France</td><td>10,323</td><td>(17.2)</td><td>824</td><td>(17.9)</td><td>122</td><td>(14.7)</td></tr><tr><td>3- North</td><td>4243</td><td>(7.1)</td><td>323</td><td>(7.0)</td><td>77</td><td>(9.3)</td></tr><tr><td>4- East</td><td>5005</td><td>(8.3)</td><td>406</td><td>(8.8)</td><td>72</td><td>(8.7)</td></tr><tr><td>5- West</td><td>8159</td><td>(13.6)</td><td>609</td><td>(13.2)</td><td>104</td><td>(12.5)</td></tr><tr><td>6- South-West</td><td>5766</td><td>(9.6)</td><td>474</td><td>(10.3)</td><td>77</td><td>(9.3)</td></tr><tr><td>7- Center East</td><td>7337</td><td>(12.2)</td><td>548</td><td>(11.9)</td><td>93</td><td>(11.2)</td></tr><tr><td>8- Mediterranean region</td><td>6964</td><td>(11.6)</td><td>547</td><td>(11.9)</td><td>95</td><td>(11.5)</td></tr><tr><td><strong>Size of urban unit (UU)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>&lt;10,000 inhabitants</td><td>17,549</td><td>(29.2)</td><td>1420</td><td>(30.8)</td><td>226</td><td>(27.2)</td></tr><tr><td>10,000–99,999 inhabitants</td><td>14,519</td><td>(24.1)</td><td>1102</td><td>(23.9)</td><td>193</td><td>(23.3)</td></tr><tr><td>100,000–1,999,999 inhabitants</td><td>17,188</td><td>(28.6)</td><td>1340</td><td>(29.1)</td><td>243</td><td>(29.3)</td></tr><tr><td>≥2,000,000 inhabitants (Paris UU)</td><td>10,933</td><td>(18.2)</td><td>749</td><td>(16.2)</td><td>168</td><td>(20.2)</td></tr><tr><td><strong>French deprivation index (FDep)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Q1 (least deprived)</td><td>12,029</td><td>(20.0)</td><td>951</td><td>(20.6)</td><td>174</td><td>(21.0)</td></tr><tr><td>Q2</td><td>12,029</td><td>(20.0)</td><td>964</td><td>(20.9)</td><td>158</td><td>(19.0)</td></tr><tr><td>Q3</td><td>12,027</td><td>(20.0)</td><td>928</td><td>(20.1)</td><td>169</td><td>(20.4)</td></tr><tr><td>Q4</td><td>12,029</td><td>(20.0)</td><td>913</td><td>(19.8)</td><td>162</td><td>(19.5)</td></tr><tr><td>Q5 (most deprived)</td><td>12,029</td><td>(20.0)</td><td>853</td><td>(18.5)</td><td>167</td><td>(20.1)</td></tr><tr><td><strong>Density of viticulture<sup>b</sup></strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>N Median (Q1-Q3)</td><td>17,314</td><td>0.4 (0.0–4.1)</td><td>1386</td><td>0.3 (0.0–4.3)</td><td>220</td><td>0.4 (0.0–4.0)</td></tr></tbody></table>

ALL, Acute lymphoblastic leukemia; AML, Acute myeloid leukemia; NUTS1, Nomenclature of Territorial Units for Statistics – level 1.

<table><caption>**Table 2**

Associations between indicators of air pollution and childhood acute leukemia, GEOCAP, 2002–2013.</caption><thead><tr><th rowspan="2"></th><th colspan="2">Controls<br/>N = 60,189</th><th colspan="3">ALL<br/>N = 4611</th><th colspan="3">AML<br/>N = 830</th></tr><tr><th>N</th><th>OR<sup>a</sup></th><th>(95 % CI)</th><th>N</th><th>OR<sup>a</sup></th><th>(95 % CI)</th><th>N</th><th>OR<sup>a</sup></th><th>(95 % CI)</th></tr></thead><tbody><tr><td>NO2, per 10 µg/m<sup>3</sup></td><td>60,189</td><td>0.96</td><td>(0.93–0.98)</td><td>830</td><td>1.09</td><td>(1.03–1.15)</td><td>60,189</td><td>0.96</td><td>(0.93–0.98)</td></tr><tr><td>PM2.5, per 5 µg/m<sup>3</sup></td><td>60,189</td><td>0.95</td><td>(0.92–0.99)</td><td>830</td><td>1.09</td><td>(1.01–1.18)</td><td>60,189</td><td>0.95</td><td>(0.92–0.98)</td></tr><tr><td>Black Carbon, per 0.5 10<sup>-5</sup>/m</td><td>60,189</td><td>0.95</td><td>(0.93–0.98)</td><td>830</td><td>1.09</td><td>(1.03–1.16)</td><td>60,189</td><td>0.98</td><td>(0.94–1.03)</td></tr><tr><td>Major roads length within 150 m of geocoded addresses, per 300 m</td><td>60,189</td><td>0.98</td><td>(0.94–1.03)</td><td>830</td><td>1.13</td><td>(1.03–1.25)</td><td>60,189</td><td>0.98</td><td>(0.94–1.03)</td></tr></tbody></table>

ALL, Acute lymphoblastic leukemia; AML, Acute myeloid leukemia.

a Age-adjusted odds ratios (ORs) and 95 % confidence intervals estimated by polytomous logistic regressions. Each exposure (NO2, PM2.5, Black Carbon, Major roads length) was modeled separately.

was associated with AML risk (OR = 1.13, 95 %CI = 1.03–1.25 for a 300 m increase). Exposure-response curves for PM2.5, BC and major roads derived from restricted cubic spline models were broadly consistent with a linear trend, but with large model uncertainty for high exposure values (Supplementary Fig. 3). For NO2, log odds tended to increase more markedly from 20 µg/m³, but no departure from linearity was detected (p-value = 0.26). For ALL, decreased ORs were observed with exposure to NO2, PM2.5 and BC (Table 2). Major roads length within 150 m of residence was not associated with ALL, and no deviation from linearity were detected (Supplementary Fig. 4). In the categorical analysis, elevated ORs were found for AML in the highest exposure categories, particularly for BC and the length of major roads (Supplementary Table 3).

Results from the stratified analyses by size of urban unit suggested no major differences according to urbanization level (Table 3). For ALL, odds ratios were generally close to 1 and fairly stable across strata, although confidence intervals were wider than those observed in the main analyses. For AML, no clear differences were observed between strata for PM2.5 exposure. Odds ratios for NO2, BC, and major roads length appeared slightly elevated in most strata except the least urbanized areas (urban units with fewer than 10,000 inhabitants), but confidence intervals overlapped and no formal interaction was detected. In the analyses stratified by major roads length, ORs for NO2 and BC exposures and AML risk tended to be higher for children with highest lengths of major roads within 150 m of their residence, although no significant interaction was detected (Supplementary Table 4).

<footnote>a Age at the end of the year of diagnosis for cases and inclusion for controls (on December 31).</footnote>

<footnote>b Ratio of the viticulture area in the municipality to the total area of the municipality, among children with viticulture in the municipality.</footnote>
<table><caption>**Table 3**
Associations between indicators of air pollution and childhood acute leukemia according to the size of urban unit, GEOCAP, 2002–2013.</caption><thead><tr><th rowspan="2"></th><th colspan="2">Controls</th><th colspan="3">ALL</th><th colspan="3">AML</th></tr><tr><th colspan="2">N = 60,189</th><th colspan="3">N = 4611</th><th colspan="3">N = 830</th></tr><tr><th></th><th>N</th><th></th><th>N</th><th>OR<sup>a</sup></th><th>(95 % CI)</th><th>p-int<sup>b</sup></th><th>N</th><th>OR<sup>a</sup></th><th>(95 % CI)</th><th>p-int<sup>b</sup></th></tr></thead><tbody><tr><td><strong>NO2, per 10 µg/m<sup>3</sup></strong></td><td></td><td></td><td></td><td></td><td></td><td>0.592</td><td></td><td></td><td></td><td>0.266</td></tr><tr><td>Urban units &lt;10,000 inhabitants</td><td>21,299</td><td>1712</td><td>1.02</td><td>(0.93-1.11)</td><td></td><td></td><td>274</td><td>0.95</td><td>(0.77-1.17)</td><td></td></tr><tr><td>Urban units 10,000-99,999 inhabitants</td><td>10,769</td><td>810</td><td>1.00</td><td>(0.90-1.11)</td><td></td><td></td><td>145</td><td>1.17</td><td>(0.93-1.47)</td><td></td></tr><tr><td>Urban units 100,000-1,999,999 inhabitants</td><td>17,188</td><td>1340</td><td>0.96</td><td>(0.90-1.03)</td><td></td><td></td><td>243</td><td>1.11</td><td>(0.97-1.28)</td><td></td></tr><tr><td>Urban unit ≥2,000,000 inhabitants (Paris)</td><td>10,933</td><td>749</td><td>0.96</td><td>(0.89-1.03)</td><td></td><td></td><td>168</td><td>1.22</td><td>(1.06-1.40)</td><td></td></tr><tr><td><strong>PM2.5, per 5 µg/m<sup>3</sup></strong></td><td></td><td></td><td></td><td></td><td></td><td>0.681</td><td></td><td></td><td></td><td>0.880</td></tr><tr><td>Urban units &lt;10,000 inhabitants</td><td>21,299</td><td>1712</td><td>0.96</td><td>(0.89-1.03)</td><td></td><td></td><td>274</td><td>1.05</td><td>(0.89-1.25)</td><td></td></tr><tr><td>Urban units 10,000-99,999 inhabitants</td><td>10,769</td><td>810</td><td>1.03</td><td>(0.93-1.13)</td><td></td><td></td><td>145</td><td>1.17</td><td>(0.93-1.46)</td><td></td></tr><tr><td>Urban units 100,000-1,999,999 inhabitants</td><td>17,188</td><td>1340</td><td>0.99</td><td>(0.92-1.06)</td><td></td><td></td><td>243</td><td>1.09</td><td>(0.93-1.28)</td><td></td></tr><tr><td>Urban unit ≥2,000,000 inhabitants (Paris)</td><td>10,933</td><td>749</td><td>0.96</td><td>(0.87-1.05)</td><td></td><td></td><td>168</td><td>1.04</td><td>(0.86-1.26)</td><td></td></tr><tr><td><strong>Black Carbon, per 0.5 10<sup>-5</sup>/m</strong></td><td></td><td></td><td></td><td></td><td></td><td>0.863</td><td></td><td></td><td></td><td>0.563</td></tr><tr><td>Urban units &lt;10,000 inhabitants</td><td>21,299</td><td>1712</td><td>1.03</td><td>(0.93-1.14)</td><td></td><td></td><td>274</td><td>1.03</td><td>(0.81-1.30)</td><td></td></tr><tr><td>Urban units 10,000-99,999 inhabitants</td><td>10,769</td><td>810</td><td>0.98</td><td>(0.88-1.10)</td><td></td><td></td><td>145</td><td>1.19</td><td>(0.94-1.51)</td><td></td></tr><tr><td>Urban units 100,000-1,999,999 inhabitants</td><td>17,188</td><td>1340</td><td>0.95</td><td>(0.89-1.01)</td><td></td><td></td><td>243</td><td>1.12</td><td>(0.98-1.28)</td><td></td></tr><tr><td>Urban unit ≥2,000,000 inhabitants (Paris)</td><td>10,933</td><td>749</td><td>0.96</td><td>(0.90-1.02)</td><td></td><td></td><td>168</td><td>1.12</td><td>(0.98-1.29)</td><td></td></tr><tr><td><strong>Major roads length within 150 m of geocoded addresses, per 300 m</strong></td><td></td><td></td><td></td><td></td><td></td><td>0.468</td><td></td><td></td><td></td><td>0.298</td></tr><tr><td>Urban units &lt;10,000 inhabitants</td><td>21,299</td><td>1712</td><td>0.96</td><td>(0.85-1.07)</td><td></td><td></td><td>274</td><td>0.94</td><td>(0.71-1.23)</td><td></td></tr><tr><td>Urban units 10,000-99,999 inhabitants</td><td>10,769</td><td>810</td><td>1.09</td><td>(0.96-1.23)</td><td></td><td></td><td>145</td><td>1.00</td><td>(0.74-1.33)</td><td></td></tr><tr><td>Urban units 100,000-1,999,999 inhabitants</td><td>17,188</td><td>1340</td><td>0.98</td><td>(0.89-1.07)</td><td></td><td></td><td>243</td><td>1.14</td><td>(0.96-1.36)</td><td></td></tr><tr><td>Urban unit ≥2,000,000 inhabitants (Paris)</td><td>10,933</td><td>749</td><td>1.01</td><td>(0.92-1.10)</td><td></td><td></td><td>168</td><td>1.22</td><td>(1.05-1.42)</td><td></td></tr></tbody></table>

ALL, Acute lymphoblastic leukemia; AML, Acute myeloid leukemia.

<sup>a</sup> Age-adjusted odds ratios (OR) and 95 % confidence intervals estimated by polytomous logistic regressions. Each exposure (NO2, PM2.5, Black Carbon, Major roads length) was modeled separately.

<sup>b</sup> P-value for interaction between air pollution indicators and urban unit size (Wald test, estimated separately for ALL and AML by unconditional logistic regressions).

Additional adjustments for NUTS-1 region, deprivation index and potential confounders (for ALL) did not substantially change the effects estimates (Supplementary Table 5). ORs remained also stable when we excluded cases diagnosed with a Down syndrome or when we restricted the analyses to addresses geocoded with the best quality (Supplementary Table 6).

## 4. Discussion

In this nationwide population-based case-control study, we assessed the associations between air pollution residential exposure and childhood acute leukemia risk. Overall, the risk of AML increased with NO2, PM2.5 and BC concentrations and with the length of major roads within 150 m of the residence. These associations did not appear in the least urbanized urban unit. For ALL, inverse associations were observed with residential exposure to the pollutants.

Our findings are consistent with those obtained in our previous study on air pollution at diagnosis and childhood acute leukemia (Houot et al., 2015). We reported an increased risk of AML on the period 2002–2007 with the length of major road within 150 m of the residence, while no association was observed for ALL (OR = 1.2, 95 %CI = 1.0–1.4 for AML; OR = 1.0, 95 %CI = 0.9–1.1 for ALL for a 300 m increase in road length within the 150 m buffer). NO2 concentration, however, was not associated with ALL or AML, and the results were stable across strata of urban unit size. The discrepancy in the findings for NO2 may be explained by the different methodology used to characterize exposure. In the study of Houot et al. a cokriging model was applied to estimate NO2 concentrations for the year 2000, on a much larger spatial scale (2 × 2km) than that of the present study. The low spatial resolution induced a smoothing of high NO2 concentration values, thereby reducing the exposure variability (IQR = 7.5 µg/m³ for Houot et al. vs 16.0 µg/m³ for this study) and leading to an estimation of background rather than traffic-related air pollution. These less precise exposure estimates could have led to non-differential errors and limited the power to detect a true association between exposure to NO2 at the address of residence and leukemia risk.

More recently, we investigated the effect of residential exposures to air pollution at the time of birth using the same methodology as that

used in the present study for exposure assessment (Danjou et al., 2025). In this paper, the analysis was based on a distinct case-control study conducted over the period 2010–2015, including 717 AL cases aged 0–6 years born and diagnosed during that period and 11,908 controls randomly selected from birth certificates. An increased risk of ALL was observed in relation to residential PM2.5 and BC exposures at birth. Interestingly, the results of stratified analyses by urban unit size, suggested an increase in ALL risk with BC exposure in the least urbanized areas. These results suggest an effect of prenatal exposure to PM2.5, possibly from sources other than traffic, on childhood ALL. Considering that we did not observe any increased risk of ALL with indicators of air pollution in the present study, if air pollution actually played a role in ALL development, the time of birth may be a more critical exposure window than the period close to diagnosis. Besides, in line with the present study, Danjou et al. reported elevated ORs for AML for the highest concentration of NO2 and PM2.5, but the number of cases was rather limited.

In the most recent meta-analysis on this topic, Filippini et al. reported little evidence of an association between traffic-related pollution indicators and both types of childhood acute leukemia (Filippini et al., 2019). The interpretation of the findings was however complicated, given the diversity of exposure indicators used (i.e. roads length, roads density, pollutant concentrations ...), different timings of exposure, and characteristics of the populations under study. In particular, heterogeneous results were found in studies investigating road density, defined as road length in the vicinity of the residence. Of six studies included in the meta-analysis, with a highest vs lowest exposure approach, four suggested no association with childhood leukemia (Janitz et al., 2016; Reynolds et al., 2004; Badaloni et al., 2013; Magnani et al., 2016), one found an increased risk of ALL (Amigou et al., 2011) and one an increased risk of AML (Houot et al., 2015). Overall, a non significant linear increase in risk of childhood leukemia was observed. This result was however highly unstable due to the small number of studies included (N = 3 for ALL and AML combined) (Houot et al., 2015; Reynolds et al., 2004; Magnani et al., 2016). For NO2, eight studies were included in the meta-analysis (Houot et al., 2015; Janitz et al., 2016; Badaloni et al., 2013; Feychting et al., 1998; Raaschou-Nielsen et al., 2001; Weng et al., 2008; Ghosh et al., 2013; Lavigne et al., 2017), with
four studies focusing on leukemia types (Houot et al., 2015; Janitz et al., 2016; Ghosh et al., 2013; Lavigne et al., 2017). In the highest vs lowest exposure approach, no association were found for AML or ALL risk, with significant heterogeneity between the study results for the latter ($I^2$ = 56 %). The quantitative analysis based on three studies suggested no association for AML and an excess risk of ALL limited to the highest exposure values (Houot et al., 2015; Heck et al., 2014; Janitz et al., 2016), an increase that we did not observe here or in our previous study (Houot et al., 2015). It should be noted that these studies suffer from the small number of cases included and a rather low exposure variability. More recently, a large Canadian population-based cohort study also reported an increased risk of ALL associated with NO2 exposure during the prenatal period (HR for 4.8 µg/m³ increment = 1.15, 95 %CI = 0.98–1.37), but no associations with AML (Gaspard et al., 2025). Instead, our findings are in accordance with those of a Swiss cohort study, which has shown an elevated risk of AML with NO2 exposure at diagnosis (Hazard Ratio [HR] for 10 µg/m³ increment = 1.31, 95 %CI = 1.00–1.71), but no association with ALL (HR = 1.00, 95 %CI = 0.88–1.13) (Kreis et al., 2022).

As with NO2, only a limited number of studies included in the meta-analysis of Filippini et al. have investigated the association between PM2.5 exposure and childhood leukemia (Hvidtfeldt et al., 2020; Badaloni et al., 2013; Lavigne et al., 2017; Heck et al., 2013), and they found no association for either type of leukemia. More recently, a case-control study reported an increased risk of ALL with PM2.5 exposure estimated during the child's year of birth (OR = 1.15, 95 %CI = 1.07–1.23) (Williams et al., 2024). In our study, we did not observe such an association with ALL, but the comparison of their findings to ours is not straightforward given differences in exposure assessment methods (exposure modeled at the census tract level) and timing of exposure (birth year). In the study by Gaspard et al. elevated HRs were suggested for PM2.5 exposure and AML (HR = 1.15, 95 %CI = 0.77–1.70), although based on a limited number of cases (N = 95) (Gaspard et al., 2025). Interestingly, this study also identified maternal comorbidities as potential effect modifiers that strengthened the observed associations.

The interpretation of the findings for PM2.5 is tricky as PM2.5 represent a complex mixture of different components, the type and quantity of which may vary depending on the local environment and the sources of exposure. In France, most PM2.5 pollution in urban areas comes from domestic heating, vehicle exhaust (with BC as the main component), and construction dust, while the main sources of PM2.5 in rural areas are domestic heating and agricultural activities (Ministère de la transition écologique, service des données et études statistiques (SDES), 2021). PM2.5 as such, therefore might not be a specific enough indicator of traffic-related air pollution. This is supported by the weak correlation reported in our study between the length of major roads and PM2.5 (r = 0.07–0.10).

To our knowledge, only two studies have investigated the effects of PM2.5 components separately in relation to childhood leukemia (Hvidtfeldt et al., 2020; Lavigne et al., 2020). In the Danish case-control study, which focused on the effect of BC, no association were reported for both types of leukemia, but the estimates were quite imprecise for AML (OR = 1.10, 95 %CI = 0.92–1.32 for ALL and 0.68, 95 %CI = 0.39–1.17 for AML; per 1 µg/m³ of BC), with only 193 AML cases (Hvidtfeldt et al., 2020). In the cohort study conducted in Canada, Lavigne et al. found no association between ultra-fines particles (UFPs) exposure during pregnancy or childhood and ALL risk (Lavigne et al., 2020). For AML, elevated odds ratios were observed for exposure to UFPs during the first trimester of pregnancy among children aged ≤6 years, but the confidence intervals were wide (HR = 1.69, 95 %CI = 0.98–2.89; 57 AML cases).

In our study, associations for AML tended to be more pronounced in the most urbanized areas, which could suggest an effect of pollutants particularly linked to road traffic. The results we observed in analyses stratified by road length also pointed in this direction. In the past, the few studies that performed stratified analyses did not show associations

that differed according to the degree of urbanization, but they were limited by the small number of cases included (Houot et al., 2015; Amigou et al., 2011; Spycher et al., 2015).

For ALL, we observed inverse associations with exposure to air pollutants, which tended to be attenuated in analyses stratified by size of urban units. These associations appear biologically implausible and contradict the well-established hypothesis that air pollution increases cancer risk. In a previous study, we have highlighted a reduction of approximately 20 % in the incidence of ALL in the Paris urban unit, an area that is highly populated, geographically concentrated and characterized by high levels of pollution (Marquant et al., 2016). Although this finding remains unexplained, it could account for the reduced odds ratios observed in our study. Overall, our findings do not provide evidence supporting a role of traffic-related air pollution in childhood ALL, but they may reflect a potential area-specific confounding effect.

Mechanistic evidence explaining the differences observed between AML and ALL remains limited. While both leukemias may share environmental risk factors, AML could be more susceptible to traffic-related pollutants, potentially due to differences in the progenitor cells involved and in leukemogenic mechanisms. PM2.5 components, including BC, can penetrate the air-blood barrier through the respiratory tract, enter the circulation and reach out the bone marrow, where it create a permissive environment for cancer by promoting inflammation, oxidative stress, and cellular damage (Samimi et al., 2018; Niranjan and Thakur, 2017; Bhattarai et al., 2024). It has been shown by myeloid progenitors are particularly vulnerable to the inflammatory responses induced by PM2.5 (Jin et al., 2016). In addition, PM2.5 may promote AML development by inducing epigenetic modifications that impede cellular differentiation, particularly through DNA methylation (Gruzieva et al., 2017; Møller et al., 2014). Regarding NO2, although it is not classified as a carcinogen itself, it is commonly used as a marker of traffic-related mixtures that include other pollutants with carcinogenic potential, such as benzene, which has been classified as carcinogenic to humans by the IARC based on sufficient evidence of its role in AML in occupationally exposed adults (IARC, 2018; Loomis et al., 2017). In our study, air pollution exposure was estimated around the time of diagnosis. This exposure window may be less relevant for ALL, particularly precursor B-cell acute lymphoblastic leukemia (pre-B ALL), which is thought to follow a “two-hit” model, with an initiating event occurring prenatally and a second event postnatally (Greaves, 2018). Further research is still needed to clarify the differential effects of air pollution on AML and ALL, particularly regarding critical windows of susceptibility.

Our study has several limitations. Due to the high collinearity between the pollutant estimates, we were unable to distinguish their individual effects or to assess their combined effects. NO2, PM2.5 and BC exposures as such may not perfectly represent traffic-related air pollution, as emissions from road traffic are composed of a complex mixture of pollutants. We acknowledge that the association between air pollution and AML risk observed in our population might be attributable to another traffic-related pollutant. Benzene, as mentioned earlier, could be a good candidate, but no national exposure maps were available for this pollutant during the study period. However, the concentration of benzene in the air has drastically decreased in the early 2000s following the implementation of European regulations limiting the benzene value contained in gasoline (EU, 1998; Bentayeb et al., 2014). We therefore consider unlikely that benzene could fully explain our associations. Similarly, we were not able to investigate the potential role of 1,3-butadiene, polycyclic aromatic hydrocarbons, or particulate organic matter. Given their suspected leukemogenic potential in adult populations (IARCa; IARCb; IARCc), future studies should assess whether these pollutants contribute to childhood leukemia risk, possibly in combination with other traffic-related pollutants.

Another limitation relates to the lack of data at individual level in our study. We were unable to take into account some factors suspected of being associated with acute leukemia, such as maternal occupational
exposure to pesticides and domestic uses of pesticides during pregnancy, paternal smoking status prior to conception or factors related to early immune stimulation, including breastfeeding (Schüz and Erdmann, 2016). However, to have an impact on our results, these factors would have to be strongly associated with our indicators of air pollution without being in the exposure-outcome causal pathway, which seems unlikely. In one of our previous studies, an interview-based case-control study carried out in 2003–2004, we showed that the association between traffic-related air pollution and leukemia risk remains unchanged after adjustment for individual potential confounders (Amigou et al., 2011). In addition, in the present study, adjustment for the neighboring socioeconomic environment, which may serve as a crude proxy for lifestyle or occupational exposures, did not change the results. Nevertheless, residual confounding by imperfectly measured or unknown factors cannot be entirely ruled out.

Similarly, information on sex was not available for controls. Although sex is known to be associated with childhood leukemia incidence rate, it is unlikely to confound the observed associations, as we do not expect air pollution exposure to differ significantly between boys and girls. Adjustment for sex in some previous studies did not actually change the conclusions (Lavigne et al., 2017; Heck et al., 2013; Peckham-Gregory et al., 2019). However, we cannot exclude the possibility of effect modification by sex. Detailed information on individual-level socioeconomic status or ethnicity, which could influence both exposure patterns and susceptibility (Palmer et al., 2024), was also lacking. Including such data in future studies could help clarify potential disparities in risk.

Exposures to air pollutants were estimated based on a land-use regression model designed for the year 2010, and were rescaled for the other years of the study period by NUTS-1 region using a back-and-forward extrapolation. Although spatial variations of pollutants concentrations are expected to be stable over time (de Hoogh et al., 2018), this approach could have introduced some imprecision in the exposure assessment, as it may have over- or under-estimated actual exposures for certain years of the study period and did not account for potential changes in pollutant composition over time. We also cannot exclude exposure misclassification due to geocoding uncertainties. However, we expect that these misclassifications due to geocoding or exposure assessment methods would be rather non-differential between cases and controls and, if so, that this would have biased the association towards the null value. It should be noted that sensitivity analyses restricted to geocoded addresses at the entrance of the plot, i.e. the most precisely geocoded addresses, confirmed the observed associations.

In addition to modeled NO2, PM2.5, and BC, we included the cumulative length of major roads within 150 m of the residence as a complementary indicator. We considered only roads associated with a high traffic volume and moderate to high speed (first three HERE® categories), which are more likely to be associated with elevated levels of air pollution. We acknowledge that this indicator is only a crude proxy for traffic-related air pollution, as actual exposure depends on several important factors (e.g., vehicle type, meteorological conditions, ...). Nevertheless, major road length provided additional insight that aided in interpreting our findings. It is a simple, robust, and highly spatially resolved metric derived from accurate geospatial data and independent of modeling assumptions and temporal averaging. Stratified analyses by major road length, in particular, helped us to disentangle local traffic-related exposures from other sources, especially for PM2.5.

A limitation of our study is that residential address was used as a proxy for personal exposure. This approach does not capture individual time-activity patterns, such as time spent at school, daycare, or other locations, which may lead to exposure misclassification and potentially attenuate observed associations.

Finally, exposures were estimated for the year of diagnosis for cases and the corresponding year for controls. Consequently, our analyses focused on postnatal exposure over a single year around the time of diagnosis and do not capture cumulative or long-term exposure.

Residential history was not available in this study, preventing the assessment of cumulative or long-term exposures, as well as exposures during the prenatal window, which seems critical for pediatric leukemogenesis, as previously discussed. Considering simultaneously prenatal and postnatal exposures in future research would be highly valuable to better identify critical windows of susceptibility to traffic-related air pollution in childhood leukemia.

Strengths of this study include its large sample size. With nearly 5500 acute leukemia cases and 60,000 controls, this is, to our knowledge, the largest case-control study conducted on this topic to date. This allowed us to efficiently perform analyses by acute leukemia types and, in particular, to have sufficient statistical power to detect associations for AML. It is worth noting that, despite the large size of our study population, the number of cases remained unfortunately insufficient to conduct meaningful analyses for specific molecular or cytogenetic subtypes. This remains an important avenue for future research, as such analyses could help determine whether specific AML subgroups are particularly susceptible to air pollution exposures.

The population-based and registry-based design is another major strength of our study, minimizing selection bias and ensuring representativeness. We also conducted several sensitivity analyses, which confirmed the robustness of our results. Finally, we used a European validated model to estimate exposure to air pollution and because our indicators are objectively determined, our study did not suffer from recall bias.

In conclusion, the results of this nationwide case-control study provide new evidence supporting a role for traffic-related air pollution in the risk of childhood AML, while inverse associations were found for ALL. To further investigate the effect of exposure to vehicle exhaust, future large-scale studies considering multi-pollutant mixtures, including benzene, are warranted.

### CRediT authorship contribution statement

**Charlotte Salmon:** Writing – review & editing, Writing – original draft, Visualization, Methodology, Formal analysis, Conceptualization.

**André Some:** Writing – review & editing, Software, Formal analysis.

**Bénédicte Jacquemin:** Writing – review & editing, Conceptualization.

**Danielle Vienneau:** Writing – review & editing, Resources, Methodology, Data curation.

**Kees de Hoogh:** Writing – review & editing, Resources, Methodology, Data curation.

**Laure Faure:** Writing – review & editing, Resources, Methodology, Data curation.

**Jacqueline Clavel:** Writing – review & editing, Project administration, Funding acquisition, Conceptualization.

**Stéphanie Goujon:** Writing – review & editing, Supervision, Project administration, Funding acquisition, Conceptualization.

### 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 are particularly grateful to the French services of pediatric hemato-oncology and to the SFCE for their help in data collection and to the RNCE research assistants who collected the data for the cases.

This research was funded by The French National Research Program for Environmental and Occupational Health of Anses with financial support from ITMO Cancer of Aviesan within the framework of the 2022–2030 Cancer Control Strategy, on funds administered by Inserm (ANSES-22-EST-186). The study was also supported by the Institut national du cancer (PEDIAC INCA-15670), the Fondation de France (WB-2022-42982) and the Agence Nationale de la Recherche (ANR_SEST_00146-05, ANR-10-COHO-0009).
# Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.envres.2025.123303.

## Data availability

Data will be made available on request.

## References

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## Glossary

**AL:** Acute Leukemia

**ALL:** Acute Lymphoblastic Leukemia

**AML:** Acute Myeloid Leukemia

**BC:** Black Carbon

**CI:** Confidence Interval

**DEHM:** Danish Eulerian Hemispheric Model

**DNA:** Deoxyribonucleic Acid

**EEA:** European Environment Agency

**ESCAPE:** European Study Of Cohorts For Air Pollution Effects

**GEOCAP:** Geolocalisation Des Cancers Pédiatriques

**HR:** Hazard Ratio

**IARC:** International Agency for Research on Cancer

**IGN:** National Institute Of Geographic And Forest Information

**INSEE:** National Institute Of Statistics And Economic Studies

**IQR:** Interquartile Range

**LUR:** Land-Use Regression

**NO2:** Nitrogen Dioxide;

**NUTS-1:** Nomenclature Of Territorial Units For Statistics – Level 1

**OR:** Odds Ratio

**PM:** Particulate Matter

**RNCE:** French National Registry Of Childhood Cancer

**RMSE:** Root mean square errors

**VIF:** Variance Inflation Factor

**VOC:** Volatile Organic Compound