# Long-term exposure to particulate air pollution and components in relation to breast cancer risk: A nested case-control study in the E3N-Generations cohort

Tasnim Fareh, Benoît Mercoeur, Thomas Coudon, Lény Grassot, Blandine Le Provost, Karen Leffondre, Elodie Faure, Camille Giampiccolo, Amina Amadou, Gianluca Severi, et al.

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Tasnim Fareh, Benoît Mercoeur, Thomas Coudon, Lény Grassot, Blandine Le Provost, et al. Long-term exposure to particulate air pollution and components in relation to breast cancer risk: A nested case-control study in the E3N-Generations cohort. Environment International, 2026, 207, pp.109987. 10.1016/j.envint.2025.109987. hal-05425623

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Full length article

# Long-term exposure to particulate air pollution and components in relation to breast cancer risk: A nested case-control study in the E3N-Generations cohort

Tasnim Fareh <sup>a,b</sup>, Benoît Mercoeur <sup>a,c</sup>, Thomas Coudon <sup>a,c</sup>, Lény Grassot <sup>a,c</sup>, Blandine Le Provost <sup>d</sup>, Karen Leffondre <sup>d</sup>, Elodie Faure <sup>e</sup>, Camille Giampiccolo <sup>a,c</sup>, Amina Amadou <sup>a,c</sup>, Gianluca Severi <sup>c,f</sup>, Francesca Romana Mancini <sup>e</sup>, Florian Couvidat <sup>g</sup>, Béatrice Fervers <sup>a,c</sup>, Delphine Praud <sup>a,c,*</sup>

ARTICLE INFO

Keywords:

Breast cancer

Air pollution

Particulate matter

Mixture exposure

Environmental health

ABSTRACT

**Background:** Previous studies on the association between airborne particulate matter (PM), particularly PM<sub>2.5</sub> and PM<sub>10</sub>, and breast cancer have shown inconsistent results, potentially due to variations in particle composition. To address this, we investigated the relationship between breast cancer and exposure to individual PM<sub>2.5</sub> and PM<sub>10</sub> components, as well as their combined effects, in the French E3N-Generation cohort.

**Methods:** We conducted a nested case-control study within the cohort (1990–2011), including 5,222 incident breast cancer cases matched to 5,222 controls. Annual mean concentrations (µg/m³) of pollutants at residential addresses were estimated using the CHIMERE chemistry-transport model from 1990 to the index date. Exposure assessment included nine PM components: ammonium, sulfate, black carbon, polychlorobiphenyl-153 (PCB153), nitrate, benzo[a]pyrene, cadmium, dioxins, and Saharan dust. We evaluated single-pollutant effects using logistic regressions, and mixture effects using Quantile G-computation (QGC) and Bayesian Kernel Machine Regression (BKMR).

**Results:** Significant positive associations with breast cancer (Odds Ratios and confidence intervals for one SD increase (controls distribution) were found for ammonium (OR = 1.19; 95%CI: 1.05–1.35), sulfate (OR = 1.17; 95%CI: 1.02–1.35), PCB153 (OR = 1.17; 95%CI: 1.08–1.26), nitrate (OR = 1.16; 95%CI: 1.01–1.33), black carbon (OR = 1.12; 95%CI: 1.05–1.20), cadmium (OR = 1.05; 95%CI: 1.00–1.11). QGC showed a positive association with breast cancer for a one-quartile increase in joint exposure (OR = 1.22; 95% CI: 1.00–1.50) with cadmium and nitrate as major contributors. BKMR confirmed a significant positive association between the exposure to the mixture and breast cancer.

**Conclusion:** The consistency between single-pollutant and mixture analyses supports a role for multiple PM components acting jointly on breast cancer risk. These results suggest that the chemical composition of PM, rather than individual pollutants alone, is a key determinant of breast cancer risk, highlighting the importance of considering pollutant composition in air pollution research.

<footnote><sup>a</sup> Department of Prevention Cancer Environment, Centre Léon Bérard, Lyon, France</footnote>

<footnote><sup>b</sup> Lyon-Est Faculty of Medicine, Lyon Claude-Bernard University, Lyon, France</footnote>

<footnote><sup>c</sup> Inserm U1296, Radiation: Defense, Health Environment, Lyon, France</footnote>

<footnote><sup>d</sup> Univ. Bordeaux, INSERM, BPH, U1219, F-33000 Bordeaux, France</footnote>

<footnote><sup>e</sup> Université Paris-Saclay, Institut national de la santé et de la recherche médicale (Inserm, National Institute of Health & Medical Research), Centre de recherche en Épidémiologie et Santé des Populations (CESP), Exposome and Heredity Team, Gustave Roussy, Villejuif, France</footnote>

<footnote><sup>f</sup> Departement of Statistics, Computer Science and Applications (DISIA), University of Florence, Italy</footnote>

<footnote><sup>g</sup> National Institute for industrial Environment and Risks (INERIS), Verneuil-en-Halatte, France</footnote>

<footnote>* Corresponding author at: Département Prévention Cancer Environnement, Centre Léon Bérard, Inserm U1296 Radiations: Défense, Santé, Environnement, 28 rue Laënnec, Cedex 08, 69373 Lyon, France.</footnote>

<footnote>E-mail address: delphine.praud@lyon.unicancer.fr (D. Praud).</footnote>
# 1. Introduction

Particulate matter (PM) has been classified as a Group 1 carcinogen by the International Agency for Research on Cancer (IARC) in 2013, based on evidence linking PM of aerodynamic diameter less than 10 µm (PM₁₀) and less than 2.5 µm (PM₂.₅) to lung cancer risk (Loomis et al., 2013; Hamra et al., 2014). According to the Global burden of Disease evaluation, ambient PM₂.₅ was estimated to be responsible for 307,680 lung cancer deaths worldwide in 2019 (Yang et al., 2022).

Current evidence linking PM₁₀ and PM₂.₅ to breast cancer, remains limited and inconclusive (White et al., 2024; Wang et al., 2025). The health effects of PM are strongly related to particle size, with the smallest particles able to penetrate the blood-alveolar barrier and subsequently reach various organs and tissues (Nemmar et al., 2002; Geiser et al., 2005; Peters et al., 2006; Farina et al., 2013). Studies have shown that inhaled toxicants can reach breast tissue (Nemmar et al., 2002; Geiser et al., 2005) supporting the plausibility that PM can do the same and potentially alter histological characteristics and affect biological pathways involved in breast cancer development. For instance, Niehoff et al. in 2020 found that increased PM₂.₅ levels were associated with a reduced involution of terminal duct lobular units (TDLUs), the histologic site where most cancers arise and an intermediate marker of breast cancer risk (Niehoff et al., 2020). Ish et al. in 2023, found associations between PM₂.₅ exposure and breast tissue modifications which varied according to the PM chemical composition (Ish et al., 2023). However, current studies on PM₁₀ and PM₂.₅ exposure and breast cancer risk have reported contrasting findings (Tippila et al., 2024; Wang et al., 2025; Wu et al., 2025).

Given these considerations, we hypothesize that heterogeneity across studies may partly reflect spatial and temporal variation in the composition of PM due to varying emission sources (Bell and Health Review Committee, 2012); Snider et al., 2016). To date, only one study has investigated the combined effects of five PM components namely black carbon, organic matter, ammonium, sulfate, and nitrate, on breast cancer risk in China (Song et al., 2024) using quantile g-computation and random forest algorithm. This study reported a significant positive association between increased exposure to the PM mixture and breast cancer risk, with black carbon and sulfate identified as major contributors (Song et al., 2024).

To our knowledge, no study has investigated the joint effects of specific PM components on breast cancer risk in the European population. In this nested case-control study within the French E3N-Generation cohort, we assessed a broad range of specific PM constituents beyond the major PM components. These included traffic- and industry- related pollutants (cadmium, black carbon, sulfate, nitrate), persistent organic pollutants (dioxins, benzo[a]pyrene (BaP), polychlorinated biphenyl congener 153 (PCB153), agricultural (ammonium); and natural (mineral dust, predominantly from the Sahara) sources. We evaluated their associations with breast cancer risk both individually and within the mixture accounting for high inter-correlation and investigating potential synergistic effects.

# 2. Method

## 2.1. Study population

### 2.1.1. The cohort study population

This study is based on a case-control study nested within the French national E3N-Generation cohort (Amadou et al., 2020). The E3N-Generation cohort is a large prospective study established in 1990 to investigate major risk factors for cancer and other chronic diseases in women (Clavel-Chapelon and E3N Study Group, 2015). Between June 1990 and November 1991, 98,995 women aged 40 to 65 years, residing in Metropolitan France and insured through the French National Education System Insurance (Mutuelle Générale de l'Éducation Nationale, MGEN), were recruited. Participants were monitored using self-

administered biennial questionnaires that collected information on reproductive history, hormonal treatments, lifestyle factors, past medical history (e.g., benign breast disease, gynecological screenings), and family history of cancer. Residential addresses were obtained through the mailing of baseline questionnaire and ten subsequent follow-up questionnaires.

Breast cancer cases were identified between cohort enrollment (1990) and 2011, primarily through self-reported data in the questionnaires, supplemented by information from the national service on causes of death registry. Self-reported cases were validated through medical records, with pathological confirmation achieved in 93% of cases. Tumor characteristics, including histological type, stage, grade and hormone receptor status (Estrogen Receptor positive or negative (ER+/ER-) and Progesterone Receptor positive or negative (PR+/PR-)), were extracted from the pathological reports. Given the cohort's low false-positive rate (<5%), cases lacking pathological confirmation were also included. Additionally, for one-quarter of the cohort, blood samples were collected between 1995 and 1998, stored in liquid nitrogen as aliquots of buffy coat, plasma, serum, and erythrocytes. Informed consent was obtained from all participants, and the study received approval from the French National Commission for Data Protection and Privacy (CNIL).

### 2.1.2. The nested case-control study

This nested case-control study included women who provided their home addresses at baseline, resided in metropolitan France during the follow-up period (1990–2011), and had no history of cancer at baseline. The process of cases and controls selection has been previously detailed (Amadou et al., 2020; Deygas et al., 2021). Briefly, 6,298 women were identified as cases of invasive breast cancer. Exclusions were made for individuals with phyllodes tumors due to their distinct biological and clinical characteristics (N = 19) (Zhang and Kleer, 2016), cases missing data on matching variables (N = 3), those with multiple missing addresses, and individuals residing outside metropolitan France during the follow-up period (N = 1,054) (Deygas et al., 2021).

For each case, one cancer-free control was randomly selected through an incidence density sampling, ensuring cases were matched with controls who were at risk of breast cancer at the index date (date of case diagnosis) (Amadou et al., 2020). In this design, calendar time is inherently controlled for because controls are selected from participants who were alive and at risk at the exact time each case was diagnosed. As a result, each case and its matched control share the same follow-up time window and experience the same underlying calendar period. This method is considered efficient for obtaining unbiased results for nested case-control studies (Richardson, 2004). Matching was performed based on the availability of biological samples (blood or saliva) to address additional study objectives, such as assessing the impact of exposures on DNA methylation and their interaction with genetic polymorphisms (Amadou et al., 2020). Cases with blood samples collected before diagnosis were matched to controls with available blood samples, based on department of residence (French administrative divisions of territories corresponding to “NUTS-3” in the classification of territorial divisions of the European Union (European Commission, n.d.)), age (±1 year), date (±3 months), and menopausal status all assessed at the time of blood collection. For cases without blood samples, matching was performed at inclusion using the same criteria but assessed at date of cohort entry, with an additional match on saliva sample availability. The final study population consisted of 5,222 women diagnosed with invasive breast cancer and 5,222 matched controls. The study selection flowchart is presented in Supplementary Fig. S1.

## 2.2. Exposure

### 2.2.1. Air pollutants considered in the present study

For this study, we considered exposure to PM₂.₅, PM₁₀, and 9 PM components. We selected PM components classified as Group 1 carcinogens by the International Agency for Research on Cancer (IARC), either
for a link with breast cancer or other cancer types in the general or in occupational populations. These included four components: BaP (ng/m³), cadmium (Cd) (pg/m³), dioxins and furans (PCCD.F) (fg/m³), which we will refer as dioxins for simplification, and PCB153 (pg/m³) (Straif et al., 2005; Nordberg, 2006; Becher et al., 1998; Lauby-Secretan et al., 2013). These carcinogenic compounds are primarily of anthropogenic origin. Their sources include, but are not limited to, urban heating, particularly the combustion of coal and other fossil fuels for BaP (Bukowska et al., 2022), waste incineration for dioxins (Quaß et al., 2004), and PCB (Othman et al., 2022), as well as various industrial processes for cadmium (Pan et al., 2010; Genchi et al., 2020). They are highly localized, mainly closed to their sources of emission (Fig. S2).

We further included elemental carbon (µg/m³), often referred to as black carbon, which represents 3% to 10% of PM₂.₅ mass in continental Europe (Shaap et al., 2024). Black carbon is a product of incomplete combustion of hydrocarbons, primarily emitted from traffic, biomass combustion, and industrial activities (Long et al., 2013), and is a major constituent of soot. While black carbon itself has not been individually classified by the IARC, occupational exposure to soot — such as among chimney sweeps — has been classified as carcinogenic to humans (Group 1) (Baan et al., 2009).

Moreover, we examined major non-organic components of particulate matter, including sulfate (SO₄) (µg/m³), nitrate (NO₃) (µg/m³), ammonium (NH₄) (µg/m³), and Saharan dust (µg/m³). These components are secondary compounds formed by chemical reactions in the atmosphere. They are broadly distributed spatially, with Saharan dust being more prevalent in the south (Fig. S2). Sulfate is due to the condensation of sulfuric acid (H₂SO₄) formed by the atmospheric oxidation of sulfur dioxide (SO₂) mainly coming from industrial sources (Badr and Probert, 1994; Calvert et al., 1985). Due to acid-base reactions, sulfate can adsorb the ammonia present in the atmosphere, 98% of which originates from agricultural emissions in France and 94% in Europe (Ramanantenasa et al., 2018; Wyer et al., 2022) to form part of ammonium. Ammonia can also react with nitric acid (HNO₃) formed by the oxidation of NOₓ, coming mostly from road traffic (Grice et al., 2009), around half of the emissions (Hasan Shahariar et al., 2022), in order to form ammonium nitrate (Viatte et al., 2023). In Europe, sulfate and nitrate account for approximately 20% of PM mass each, while ammonium accounts for about 10% (Philip et al., 2014; Harrison, 2020; Bressi et al., 2021; Font et al., 2024). Saharan dust contributions to PM₂.₅ vary by region and event intensity (Remoundaki et al., 2013; Wang et al., 2020; Gini et al., 2022). Although these components are not currently classified as carcinogenic, they represent a substantial fraction of PM mass. Excluding them from the analysis could lead to an underestimation of health effects, as they may interact with other potentially carcinogenic pollutants in complex ways through physicochemical reactions or synergistic effects within the mixture potentially enhancing the effects of harmful constituents in the mixture. Component concentrations were available for total particulate matter but not separately for PM₂.₅ and PM₁₀ fractions.

### 2.2.2. Air pollution exposure assessment

The methodology used to estimate participants' atmospheric exposure based on pollutant concentrations at their consecutive residential addresses has been previously applied and described in detail elsewhere (Deygas et al., 2021; Amadou et al., 2024; Giampiccolo et al., 2024).

Mean annual atmospheric concentrations of these particulate components were estimated at participants geocoded residential addresses from 1990 to their index date using the CHIMERE chemistry-transport model. Simulations were performed at a spatial resolution of 0.125° × 0.0625° (approximately 8 × 8 km) (Couvidat et al., 2018). CHIMERE is a deterministic model that simulates large-scale pollutant concentrations. Necessary inputs of the model include meteorological data, primary pollutant emissions, and chemical boundary conditions (Menut et al., 2013; Guerreiro et al., 2016; Couvidat et al., 2018). The model has been validated through comparisons with pollutant measurements (Menut

et al., 2021) and is widely used in Europe, as well as in Africa, the North Atlantic, Central America, and the United States (Guerreiro et al., 2016). The PM composition simulated over Europe by the model was evaluated by Couvidat et al. by comparison to measurements of PM chemical speciation (Couvidat et al., 2018).

Participants' residential addresses were geocoded using ArcGIS software (ArcGIS Locator version 10.0, Environmental Systems Research Institute, ESRI, Redlands, CA, USA) and the BD Adresse® street network database from the National Geographic Institute (IGN) (Faure et al., 2017). Weighted annual pollutant concentration estimates were assigned to the geocoded residential addresses for each year during the follow-up period (1990–2011). For each participant, mean pollutant concentration levels were calculated from cohort entry to the index date. In this study, exposure assessment began at cohort entry (1990) and ended at the index date. The average of the annual mean pollutant concentrations prior to diagnosis served as the exposure estimate for this study.

## 2.3. Statistical analysis

### 2.3.1. Descriptive analysis

Socio-demographic characteristics, and other covariates of the women were described using mean and standard deviation (SD) for continuous variables, and frequency with percentage for categorical variables, separately for cases and controls.

The average annual exposure to each pollutant prior to the index date was described using the mean (SD), median (IQR), minimum, and maximum values in both cases and controls. Exposure levels were expressed in the following units: µg/m³ for PM₂.₅, PM₁₀, ammonium, black carbon, nitrate, sulfate, and Saharan dust; ng/m³ for benzo[a] pyrene (BaP) and cadmium; pg/m³ for PCB153; and fg/m³ for dioxins. The evolution of the mean annual concentrations of each pollutant (µg/m³) at residential addresses in the overall population was plotted from 1990 to 2011.

The Spearman's rank correlation coefficient (ρ) was used to assess the correlations and potential multicollinearity between pollutants (Bellavia, 2025).

### 2.3.2. Handling of missing data

Under the assumption of missing data at random, multiple imputation was performed for the following variables: level of education (0.7% of missing data), urban/rural status of the birthplace (11.1% of missing data), body mass index (1.9% of missing data), alcohol drinking (28.9% of missing data), breastfeeding (1.4% of missing data), oral contraceptive use (0.9% of missing data), age at first full-term pregnancy and parity (1.1% of missing data), total physical activity (0.1% of missing data), smoking status (0.3% of missing data), age at menarche (2.0% of missing data) and previous family history of breast cancer (1.6% of missing data). We carried out 10 imputations, each with 10 iterations with a multivariate imputation via a chained equations approach (MICE) (van Buuren and Groothuis-Oudshoorn, 2011). Predictors were all variables considered in the different adjustment models, matching variables, and exposure variables. Models specified for multiple imputation were logistic regression models for binary variables, polynomial logistic regression models for categorical variables with more than two categories, and predictive matching models for quantitative variables.

Simple imputation was used for menopausal status at inclusion. If age at menopause was missing, women were classified as premenopausal at inclusion if they were younger than 51 years (the median age at menopause in the E3N-Generations cohort) otherwise, they were classified as postmenopausal. Although less than 5% of data were missing for menopausal hormone replacement therapy (MHT), no imputation was performed due to its strong dependency on age, time and clinical symptoms, and the “missing data” category was left.

For the multipollutant models (QGC and BKMR), multiple imputation was not available within the R packages. Therefore, simple
imputation techniques were performed for variables with less than 5% missing data. Continuous variables were imputed with the median, and categorical variables were imputed with the modal class, separately for cases and controls, except for menopausal status at inclusion and oral contraceptive use of MHT as mentioned above.

### 2.3.3. Individual-pollutant exposure regression

Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using conditional logistic regression models, with each individual pollutant exposure treated as continuous variable representing an increase of one standard deviation of exposure in controls. The models were conditioned on matching variables. Based on a literature review (Binachon et al., 2014; Rojas and Stuckey, 2016; Sun et al., 2017; Dong and Qin, 2020) and a Directed Acyclic Graph (DAG), two sets of covariates were identified for model adjustment (see Fig. S3). The first set included education level (secondary, 1–2 years of university, and ≥ 3 years of university, used as a proxy for socio-economic status) and the urban/rural status at inclusion. The second set comprised variables such as prior oral contraceptive use (ever, never), menopausal hormone replacement therapy use (ever, never), body mass index (BMI; <25, 25–30, ≥30 kg/m²), smoking status (never, current, former), breastfeeding history (ever, never), total physical activity (categorized by metabolic equivalent task-hours per week: <25.3, 25.3–35.5, 35.6–51.8, ≥51.8 MET-h/week), parity (0, 1–2 children with age at first pregnancy < 30, 1–2 children with age at first pregnancy ≥ 30, ≥3 children), alcohol consumption (never, ≤6.7 g/day, >6.7 g/day), and urban/rural status of both birthplace and inclusion location.

For each model, the linearity of the logit for quantitative variables was assessed using fractional polynomials.

The two models, which are equivalent based on the DAG, produced equivalent results for associations between particulate matter components and breast cancer risk in previous analyses of this cohort (Duboeuf et al., 2024) as well as in the present individual-pollutant analyses. Therefore, in line with the principle of parsimony, we selected the first model only for the multipollutant analysis, QGC and BKMR.

All statistical analyses were conducted using R software version 4.4.2.

### 2.3.4. QGC

QGC represents an approach assessing the effects of a mixture, by combining the inferential of Weighted Quantile Sum (WQS) regression with the flexibility of g-computation, a method for causal effect estimation (Keil et al., 2020; Schmidt, 2020). Unlike traditional WQS, QGC enables unbiased inference on mixture effects with appropriate confidence intervals, even when the assumption of directional homogeneity of the association is not met (Schmidt, 2020). Thus, in QGC, exposures may have both positive (harmful) and negative (protective) associations with the outcome of interest.

The equation of the logistic regression model was:

$$ \operatorname{logit}(P(Y_i = 1)) = \beta_0 + \sum_{j=1}^{p} \beta_j z_{ij}^q + \gamma^T x_i + \varepsilon_i $$

With $Y_i$ the observed outcome for subject i, $\beta_0$ the intercept, $\beta_j$ is the effect size of exposure j for subject i. q is for quantile so $z_{ij}^q$ is the quantized versions of $z_{ij}$, the level of exposure j for subject i, and $z_{ij}^q = \{0,1,2,3\} (X_j^q = 0, when the observed value of exposure z_{ij}^q is less than the first quartile of this variable), $x_i$ is the vector of covariates $\gamma$ the vector of corresponding coefficients, and $\varepsilon_i$ the error term of the regression. This model as specified here assumes no interaction between the pollutants, and linear effect of the quartiles of exposure

The Odds Ratio (OR) for the overall effect was defined by:

$$ OR = \exp(\psi) = \exp(\psi^+ + \psi^-) $$

With $\psi$ the overall effect that is the sum of all $\beta_j$ coefficients and can be

written as the sum of the positive partial effect $\psi^+$ and negative partial effect $\psi^-$ which are respectively the sum of the positive and negative $\beta_j$ coefficients. The OR and its 95% Confidence interval (CI) is interpreted as the increase in breast cancer risk for a quartile increase in all pollutant exposures simultaneously.

Positive and negative weights assigned to each pollutant, both sum to 1 and respectively, indicate the contribution of harmful, and protective exposure.

$$ \text{Positive Weights are defined by the equation: } w_k^+ = \frac{|\beta_k|}{\sum_{j \in P} |\beta_j|} $$

$$ \text{And Negative Weights: } w_k^- = \frac{|\beta_k|}{\sum_{j \in N} |\beta_j|} $$

With P and N the set of Positive and Negative coefficients $\beta_j$ respectively.

We further conducted exploratory QGC analysis for different subgroups including menopausal status at inclusion and at index date, hormonal receptor status, histological type, grade and stade of the tumor. When stratifying by menopausal status, we retained only matched pairs in which both the case and the control had the same menopausal status at the inclusion and at the index date, so having the same status during the whole follow-up. Pairs with discordant menopausal status (one transitioning during the follow-up and one staying in the premenopausal status) were therefore excluded from the stratified analyses, in accordance with the matched design.

For the QGC model, conditional logistic regression was not available within the respective R packages. Consequently, the models were adjusted for matching variables in addition to confounding variables, following the approach recommended by Pearce et al. in 2016 (Pearce, 2016).

For QGC analysis we used the R package gcomp version 2.16.4 and the function qgcomp.

### 2.3.5. BKMR

BKMR was also used as a complementary approach to QGC. BKMR uses a kernel function to flexibly model the non-linear exposure-response relationships between multiple exposures and health outcomes. This approach allows to evaluate interactions among the exposures within the mixture as well as non-linear effects in the exposure-outcome relationship without a priori specification of these non-linear relationships (Bobb et al., 2015, 2018; Gibson et al., 2019; Domingo-Relloso et al., 2019; Mínguez-Alarcón et al., 2019; Zhang et al., 2019).

We standardized the components concentrations of the PM component for the BKMR analysis. The model was performed with 5000 iterations using a Markov Chain Monte Carlo (MCMC) algorithm. The equation of the BKMR model was as follows:

$$ \Phi^{-1}[P(Y_i = 1)] = h^T[z_i] + x'_i\beta + \varepsilon_i $$

$\Phi^{-1}$ is the inverse of the probit link function, $Y_i$ is the binary health outcome for subject i. $h$ is the exposure-response function, corresponding to a kernel function that accounts for non-linearity and/or interaction between the mixture components. A kernel function is a mathematical tool that maps input data into a higher-dimensional space, enabling linear separation of data, which corresponds to a non-linear solution when projected back to the original space. The vector $z_i = (z_{i1}, \dots, z_{i9})^T$ is for the nine standardized exposure variables, ammonium, black carbon, cadmium, BaP, dioxins, nitrate and PCB153, Saharan dust and sulfate. $x'_i$ is the vector of covariates and $\beta$ the vector of corresponding coefficients. The residuals $\varepsilon_i N(0, \sigma^2)$ are assumed to be independent and normally distributed with common variance. We can use the predicted probabilities from the probit BKMR model to evaluate OR and their corresponding 95% CI.

We employed this model to generate four key results: (1) the overall mixture effect, represented by varying ORs when all pollutants are fixed at predefined percentiles compared to when all pollutants are fixed at
their median values; (2) Single-exposure OR estimating the differences in the outcome when a given pollutant is set at its 75th percentile compared with its 25th percentile, with all other pollutants set at pre-defined quantiles (25th, 50th or 75th percentiles). (3) the univariate exposure-response curves representing the shape of the relationship between a given pollutant and the outcome, with all other pollutants fixed at their median value. (4) Bivariate exposure-response functions identifying possible interactions between pollutants by estimating of the relationship between exposure to a given pollutant and the outcome while one other pollutant is fixed at predefined percentiles (10th, 50th, and 90th) and all the others are fixed at their median values. If the shape of the relationship between a pollutant and the outcome changes as a function of different percentiles of another pollutant, it suggests an interaction.

We use for this analysis the kmbayes function of the R package BKMR version 0.2.2.

# 3. Results

## 3.1. Study participants characteristics

Characteristics of the study participants by case-control status are presented in Supplementary Table S1. Cases were included on average at age 49.6 years (± 6.3) and were diagnosed on average at age 60.6 years (± 8.1). The mean age at inclusion was the same for cases and controls. Cases were more likely to have a higher level of alcohol intake, to be less physically active, to have a higher education level, to be nulliparous, to be more than 30-years-old at the first-term pregnancy, to use menopausal hormone therapy, and to have a family history of cancer or a previous history of personal benign cancer.

## 3.2. Exposure description

### 3.2.1. Exposure correlations

Results of the spearman correlations between the average annual mean concentrations of exposure to pollutants are presented in Fig. 1.

Overall, PM₁₀ and PM₂.₅ were highly correlated to black carbon and PCB153, BaP, dioxins, and sulfate (with a spearman correlation coefficient ρ > 0.80). Nitrate howed the weakest correlation with PM₁₀ and PM₂.₅ (ρ ~ 0.40). Saharan dust was the only component negatively correlated to PM₁₀, PM₂.₅, and all other PM components. These correlations remained relatively stable over time (Supplementary Fig. S2).

When examining the correlations between components, we observed two groups of highly correlated pollutants, excluding Saharan dust. The first group included black carbon, PCB153, cadmium, dioxins, and BaP with strong correlations: black carbon-PCB153 (ρ = 0.80), cadmium-dioxins (ρ = 0.90), and PCB153-dioxins (ρ = 0.89), BaP-PCB153 (ρ = 0.82), BaP-dioxins (ρ = 0.77), BaP-cadmium (ρ = 0.83). The second group included the secondary inorganic aerosols nitrate, ammonium, sulfate with the following correlations: ammonium-nitrate (ρ = 0.82), ammonium-sulfate (ρ = 0.80). These findings are consistent with known common sources of these pollutants (black carbon, PCB153, dioxins and BaP are mainly produced by combustion in urban and industrial areas) as well as with the well-established atmospheric interactions among ammonium, nitrate and sulfate.

### 3.2.2. Exposure evolution

The temporal and spatial variations in concentrations of PM₂.₅, PM₁₀, and their components are presented in figure 2 and in the supplementary materials (Fig. S4) respectively. In 1990, the highest concentrations were observed in the northern and northeastern regions, the Rhône Valley (southeastern France, from Lyon to Marseille), and the Paris metropolitan area. By 2010, although pollutant concentrations had significantly decreased across France, these regions and urban areas

<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Spearman's correlation matrix between the average of the annual mean concentrations of the pollutants estimated at subject addresses, the case-control study nested in the E3N cohort, France, 1990–2011. BaP = benzo[a]pyrene, PCB153 = polychlorinated biphenyl congener 153.</figcaption></figure>
remained among those with the highest pollutant concentrations. Consistent with these findings, PM concentrations at the residential addresses of the women in our study gradually declined over time before stabilizing in the mid-2000s (Fig. 2).

BaP, cadmium, black carbon, PCB153, dioxins, and sulfate showed temporal trends similar to those observed for PM₁₀ and PM₂.₅, with a marked decrease in concentrations from 1990 to 2011. In contrast, nitrate and Saharan dust followed different patterns. Nitrate concentrations were highest in northern France and the Rhône Valley; but remained stable between 1990 and 2011. In contrast to all other components, Saharan dust showed no temporal decline and exhibited a south-north concentration gradient (Fig. S2).

### 3.3. Individual-pollutant exposure regression

Fractional polynomial regression analysis indicated that the associations between breast cancer and PM₂.₅, PM₁₀, as well as their particulate components, were linear, except for BaP, which showed evidence of a non-linear relationship.

Compared to controls, cases exhibited slightly higher mean annual pollutant concentrations for black carbon (2.68 µg/m³ vs. 2.61 µg/m³), PCB153 (9.90 pg/m³ vs. 9.72 pg/m³), and sulfate (4.79 µg/m³ vs. 4.77 µg/m³). After adjusting for education level and urban/rural status at inclusion (Model 1), significant associations for one standard deviation (SD) increase of the exposure in controls were found for ammonium (OR = 1.19, 95% CI: 1.05–1.35), sulfate (OR = 1.17, 95% CI: 1.02–1.35), PCB153 (OR = 1.17, 95% CI: 1.08–1.26), nitrate (OR = 1.16, 95% CI: 1.01–1.33), black carbon (OR = 1.12, 95% CI: 1.05–1.20), cadmium (OR

= 1.05, 95% CI: 1.00–1.11). No significant associations were observed for dioxins (OR = 1.02, 95% CI: 0.97–1.07) and Saharan dust (OR = 0.93, 95% CI: 0.79–1.09). The observed associations remained similar after further adjusting for hormonal and lifestyle factors, including oral contraceptive use, menopausal hormone replacement therapy use, body mass index, smoking status, breastfeeding history, total physical activity, parity with age at first pregnancy, alcohol consumption, and urban/rural status of both birthplace and inclusion location (Model 2) (Table 1). Given that the linearity assumption for BaP was not met, the OR is not presented in Table 1. However, a fractional polynomial regression plot is provided in the supplementary materials (Fig. S5).

### 3.4. QGC regression

Table 2 shows the results of the QGC regression model assessing the joint effect of exposure to the nine pollutants and breast cancer risk, adjusted for potential confounders. A positive association was observed for a one quartile increase of each PM component conjointly and breast cancer risk in the overall study population (OR = 1.22 95% CI: 1.00–1.50, p = 0.049) (Table 2).

In the overall population, cadmium had the highest positive contribution to the OR (35%), followed by nitrate (19%), dioxins (14%), and Saharan dust (14%) (Supplementary Table S3). Black carbon and sulfate contributed negatively to the joint effect with a 83% and 17% weight respectively in the negative partial effect. However, the negative partial effect ($\psi' = -0.01$) is twenty times smaller than the positive partial effect ($\psi^+ = 0.21$), making the contribution of black carbon and sulfate negligible compared to the other components' contribution in the total

<figure><img src="image_3.png" /><figcaption>**Fig. 2.** Annual evolution of hourly particulate ammonium (NH₄), BaP (benzo[a]pyrene), black carbon, cadmium, dioxins, nitrate (NO₃), PCB153, Saharan dust, sulfate (SO₄), PM₂.₅ and PM₁₀ atmospheric concentrations in µg/m³ (median, 1st and 3rd quartile) at the residential addresses of the study subjects by year, from 1990 to 2011.</figcaption></figure>
<table><caption>**Table 1**
Estimated single pollutant exposure (average of the mean annual concentrations estimates) in cases and controls at their residential addresses and odds ratios (ORs) for the association with invasive breast cancer. The case-control study nested in the E3N-Generations<sup>a,b,c</sup> cohort, France, 1990–2011.</caption><thead><tr><th rowspan="2">Components</th><th colspan="3">Case</th><th colspan="3">Control</th><th rowspan="2">Adjusted OR<sup>a</sup><br>(95%IC)<br>Model 1<sup>b</sup></th><th rowspan="2">Adjusted OR<sup>a</sup><br>(95%IC)<br>Model 2<sup>c</sup></th></tr><tr><th>Mean (SD)</th><th>Median (IQR)</th><th>Min-Max</th><th>Mean (SD)</th><th>Median (IQR)</th><th>Min-Max</th></tr></thead><tbody><tr><td><strong>Ammonium</strong> (µg/m<sup>3</sup>)</td><td>2.27 (0.64)</td><td>2.30 (1.74-2.65)</td><td>0.86-4.67</td><td>2.26 (0.64)</td><td>2.29 (1.74-2.63)</td><td>0.83-4.93</td><td>1.19 (1.05-1.35)</td><td>1.19 (1.04-1.35)</td></tr><tr><td><strong>Black carbon</strong> (µg/m<sup>3</sup>)</td><td>2.68 (1.68)</td><td>2.11 (1.57-3.13)</td><td>0.46-9.11</td><td>2.61 (1.58)</td><td>2.10 (1.55-3.08)</td><td>0.46-8.88</td><td>1.12 (1.05-1.20)</td><td>1.12 (1.04-1.19)</td></tr><tr><td><strong>Cadmium</strong> (ng/m<sup>3</sup>)</td><td>0.15 (0.11)</td><td>0.12 (0.09-0.19)</td><td>0.03-2.07</td><td>0.15 (0.10)</td><td>0.12 (0.08-0.19)</td><td>0.03-2.16</td><td>1.05 (1.00-1.11)</td><td>1.05 (1.00-1.10)</td></tr><tr><td><strong>Dioxins</strong> (fg/m<sup>3</sup>)</td><td>10.00 (8.74)</td><td>7.94 (5.05-12.73)</td><td>1.34-227.69</td><td>9.90 (8.17)</td><td>7.86 (5.02-12.45)</td><td>1.18-218.17</td><td>1.02 (0.97-1.07)</td><td>1.02 (0.97-1.08)</td></tr><tr><td><strong>Nitrate</strong> (µg/m<sup>3</sup>)</td><td>2.75 (0.94)</td><td>2.91 (1.98-3.35)</td><td>0.69-4.97</td><td>2.74 (0.94)</td><td>2.90 (1.97-3.33)</td><td>0.62-4.90</td><td>1.16 (1.01-1.33)</td><td>1.16 (1.01-1.33)</td></tr><tr><td><strong>PCB153</strong> (pg/m<sup>3</sup>)</td><td>9.90 (4.20)</td><td>9.11 (6.87-12.07)</td><td>2.14-33.27</td><td>9.72 (3.96)</td><td>9.02 (6.84-11.93)</td><td>1.87-28.64</td><td>1.17 (1.08-1.26)</td><td>1.16 (1.07-1.26)</td></tr><tr><td><strong>Saharan dust</strong> (µg/m<sup>3</sup>)</td><td>0.61 (0.37)</td><td>0.43 (0.32-0.85)</td><td>0.19-1.64</td><td>0.61 (0.37)</td><td>0.44 (0.32-0.86)</td><td>0.19-1.59</td><td>0.93 (0.79-1.09)</td><td>0.94 (0.80-1.10)</td></tr><tr><td><strong>Sulfate</strong> (µg/m<sup>3</sup>)</td><td>4.79 (1.34)</td><td>4.63 (3.82-5.57)</td><td>1.69-10.53</td><td>4.77 (1.33)</td><td>4.61 (3.81-5.55)</td><td>1.72-11.34</td><td>1.17 (1.02-1.35)</td><td>1.16 (1.00-1.34)</td></tr><tr><td><strong>PM<sub>2.5</sub></strong><br>(µg/m<sup>3</sup>)</td><td>19.07 (6.06)</td><td>17.82 (14.7-22.03)</td><td>6.71-41.87</td><td>18.81 (5.78)</td><td>17.73 (14.71-21.80)</td><td>6.85-41.29</td><td>1.15 (1.07-1.24)</td><td>1.15 (1.06-1.24)</td></tr><tr><td><strong>PM<sub>10</sub></strong><br>(µg/m<sup>3</sup>)</td><td>21.64 (6.31)</td><td>20.36 (17.21-24.70)</td><td>7.78-45.08</td><td>21.37 (6.02)</td><td>20.27 (17.21-24.412</td><td>7.94-44.50</td><td>1.15 (1.07-1.24)</td><td>1.14 (1.06-1.23)</td></tr></tbody></table>

<sup>a</sup> The OR were estimated by logistic conditional regression on matching variables, with a standard deviation (SD) increment in controls, and 95% confidence intervals (CIs).

<sup>b</sup> Adjusted for level of education and the urban/rural status at inclusion.

<sup>c</sup> Adjusted for prior oral contraceptive use, menopausal hormone replacement therapy use, body mass index, smoking status, breastfeeding history, total physical activity, parity with age at first pregnancy, alcohol consumption, and urban/rural status of both birthplace and inclusion location.

<table><caption>**Table 2**
Quantile G computation results: Odds Ratio (OR) and its 95% Confidence Interval (CI) for a quartile joint increase of all pollutant in the mixture and breast cancer risk. The analysis comprises nine components including cadmium, nitrate, dioxins, PCB153, BaP, black carbon, sulfate, ammonium, and Saharan dust. The analysis was conducted in the overall population, by menopausal status, and by Estrogen Receptor/Progesterone Receptor status (ER/PR status). The case-control study nested within the E3N cohort, France, 1990–2011.</caption><thead><tr><th></th><th>Matched cases/controls (n)</th><th>OR (95%CI)<sup>a</sup></th><th>p-value<sup>b</sup></th></tr></thead><tbody><tr><td>Overall</td><td>5222/5222</td><td>1.22 (1.00-1.50)</td><td>0.049</td></tr><tr><td>Premenopausal at inclusion and at index date</td><td>591/591</td><td>0.93 (0.47-1.86)</td><td>0.84</td></tr><tr><td>Premenopausal at inclusion and postmenopausal at index date</td><td>1958/1958</td><td>1.04 (0.75-1.46)</td><td>0.80</td></tr><tr><td>Postmenopausal at inclusion and index date</td><td>1991/1991</td><td>1.15 (0.82-1.62)</td><td>0.41</td></tr><tr><td>ER+PR+</td><td>2459/2459</td><td>1.40 (1.04-1.88)</td><td>0.03</td></tr><tr><td>ER+PR-</td><td>825/825</td><td>1.10 (0.64-1.89)</td><td>0.72</td></tr><tr><td>ER-PR+</td><td>140/140</td><td>0.54 (0.11-2.67)</td><td>0.45</td></tr><tr><td>ER-PR-</td><td>612/612</td><td>0.94 (0.47-1.88)</td><td>0.88</td></tr></tbody></table>

<sup>a</sup> The model is adjusted for level of education and the urban/rural status at inclusion and matching variables including age, date, department of residence, and menopausal status at baseline, blood collection at baseline or existence of a biological sample.

<sup>b</sup> Wald-test p-value.

effect.

Subgroup analysis stratified by hormonal status receptors showed significant positive results in ER + PR+ (OR = 1.40 95% CI: 1.04–1.88). By menopausal status, there is a positive albeit not significant trend for postmenopausal women (OR = 1.15 95% CI: 0.82–1.62) Supplementary Table S2.

### 3.5. BKMR

Fig. 3 displays the overall effect of exposure to the mixture of the nine components on breast cancer, relative to a reference scenario in which all exposures are fixed at their median value (50th percentile). A positive trend in OR was observed as the components’ exposure increased, with higher OR values at higher exposure percentiles compared to the median. Notably, the OR for the 90th versus the 50th percentile is significantly positive, while the OR is significantly negative when comparing the lower percentiles (10th, 15th, 20th, 25th, 30th, 35th, 45th) and the 50th percentile (Fig. 3).

Fig. 4 shows the effect of each individual component on breast cancer, based on changes in exposure from the 25th to the 75th percentile, while holding all other components at the 25th, 50th, and 75th percentiles. Notably, no individual pollutant — when comparing the 75th to the 25th percentile—showed a significant association with breast cancer risk in this analysis.

Fig. 5 shows the univariate exposure-response relationship between

<figure><img src="image_4.png" /><figcaption>**Fig. 3.** Joint effect (estimates and 95%CI) of the nine PM components on breast cancer risk, using Bayesian kernel machine regression (BKMR). The figure plots the estimated change in breast cancer risks for each 5-percentile increase in exposure from the 10th to the 90th percentile compared to the 50th percentile. Adjusted for education level and rural/urban status at inclusion and matching variables including age, date, department of residence, and menopausal status, at blood collection or at baseline and existence of a biological sample.</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>Fig. 4. Single pollutant effect in the 9 components mixture (estimates and 95%CI) and breast cancer risk, using Bayesian kernel machine regression (BKMR) model. The figure compares breast cancer risk when a single pollutant is at the 75th versus 25th percentile, while all other exposures are fixed at the 25th 50th or 75th percentile. Models were adjusted for education level, rural/urban status at inclusion and the matching factors including age, date, department of residence, and menopausal status, at blood collection or at baseline and existence of a biological sample.</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Fig. 5. Univariate exposure-response function h(z) and 95% confidence bands between each standardized pollutant exposure (z) and breast cancer risk, with all other components fixed at their median, using Bayesian kernel machine regression (BKMR) model. Models were adjusted for education level, rural/urban status at inclusion, and the matching factors including age, date, department of residence, and menopausal status, blood collection or baseline, and existence of a biological sample.</figcaption></figure>
each individual component and breast cancer risk, when all other pollutants were held at their median level. Overall, although not statistically significant, all components showed positive exposure-response relationships with breast cancer risk when all other pollutants were held at their median levels. The relationships followed a linear trend, except for BaP and dioxins, which exhibited inverse U-shaped and sigmoid-like curves, respectively.

Finally, we investigated the bivariate exposure-response functions to assess potential interactions between PM components and breast cancer risk. This was done by assessing the exposure-response function of one pollutant while fixing another pollutant at three different levels (its 10th, 50th and 90th percentiles), with the remaining pollutants fixed at their 50th percentile. As shown in supplementary figure S6, overall, there was no qualitative interaction between the pollutants in the mixture. However, increasing black carbon and PCB153 levels seemed to increase the baseline risk of breast cancer associated with exposure to other pollutants.

## 4. Discussion

This is the first European study to assess the effects of nine PM components both individually and as part of a pollutant mixture. Our findings consistently showed a positive association between increased exposure and breast cancer risk. More specifically, in single-pollutant logistic regression models, significant associations were observed for ammonium, sulfate, PCB153, nitrate, black carbon, and cadmium. To capture mixture effects, we applied two complementary mixture models. QGC assumes that each pollutant contributes additively to the mixture, allowing the estimation of relative weights that can be interpreted as the proportional contribution of each component which gives interesting directions for public health approaches. In contrast, BKMR provides a more flexible framework by modelling complex and potentially nonlinear exposure-response relationships, and is therefore more informative for etiologic relationships although it is less straightforward for attributing effects to individual pollutants.

Regarding analyses stratified by menopausal status, we observed no substantial differences across strata, although all subgroup ORs appeared lower than the overall odds ratio.

QGC identified cadmium and nitrate as the most influential contributors; however, as weights are not accompanied by confidence intervals, it remains difficult to distinguish true effects from statistical noise. In BKMR analyses, single-component effects assessed while fixing all other components at the 25th, 50th or 75th percentile did not reach statistical significance, whereas the effect became significant when all components increased simultaneously. This pattern suggests that the combined exposure to multiple PM components may exert synergistic or cumulative effects that are not fully captured by single-pollutant models. Together, these complementary findings highlight that while single-pollutant analyses help identify specific chemical drivers, mixture approaches provide a more realistic assessment of joint exposure effects in complex ambient environments. Despite the methodological differences between the two approaches, both converged in showing that increasing joint exposure to multiple PM components was associated with a higher risk of breast cancer, reinforcing the robustness of our findings. These findings align with a study using a similar approach in a cohort of 85,504 women in China, which reported a stronger association between PM components and breast cancer risk (QGC OR = 1.44; 95% CI: 1.39–1.49 for a one-quartile increase in joint exposure) (Song et al., 2024).

The discrepancies observed between our single- and multi-component analyses suggest that significant associations identified in single-pollutant models may partly reflect the influence of correlated co-exposures, rather than the isolated effect of a single compound. For example, cadmium showed a relatively small effect in single-pollutant models but contributed strongly in the mixture analysis. This illustrates a key strength of mixture modeling: capturing the joint impact of correlated exposures that may not be evident in single-pollutant analyses.

For most components, the BKMR exposure-response curves appear monotonic and without threshold effect based on visual inspection, except for BaP and dioxins, which show potential indications of non-linearity. Moreover, the results from the bivariate dose-response analyses suggest that increasing exposure to black carbon and PCB153 may potentiate the effects of the other components examined in this study. This hypothesis warrants further investigation into future research.

Among the pollutants analyzed, cadmium exposure, which was significantly associated with breast cancer in single-pollutant logistic regression, emerged in the QGC analyses as the strongest contributor to breast cancer risk. This heavy metal, primarily emitted from anthropogenic sources such as tobacco smoking, metal refining, fossil fuel combustion, and waste incineration, is an airborne toxicant of concern (Pan et al., 2010; Genchi et al., 2020), with inhalation representing a key route of exposure (Pan et al., 2010). Due to its estrogenic properties, cadmium has been implicated in breast cancer progression and metastasis (Ponce et al., 2015; Wei and Shaikh, 2017; Liang et al., 2021). However, previous research has yielded conflicting findings regarding the association between atmospheric cadmium exposure and breast cancer risk. A dose-response meta-analysis of 10 cohort studies provided limited evidence supporting this link (Filippini et al., 2020), while the SISTER study, which included 50,884 participants, reported a hazard ratio (HR) of 1.1 (95% CI: 1.0–1.3) for postmenopausal breast cancer at the highest quintile of exposure compared to the lowest quintile (White et al., 2019b). In the context of multi-pollutant analyses, our findings are consistent with prior research on the E3N-Generation cohort, which identified cadmium—alongside other endocrine disruptors such as BaP, PCB153, and dioxins—as the primary contributor to breast cancer risk in multi-pollutant models (Giampiccolo et al., 2024). Interestingly, cadmium contributed the most to the total effect according to the QGC analysis, despite being part of the metals fraction of PM which constitutes a very small portion of the PM composition in outdoor pollution (Sarti et al., 2015; McNeill et al., 2020). This finding underscores the importance of not disregarding the potential impact of chronic low-level exposure to certain pollutants. The weights observed for cadmium should be interpreted with caution as weights are not provided with confidence intervals and warrants confirmation in other studies. Another key component was black carbon. Studies on black carbon and breast cancer risk show mixed results, likely due to differences in exposure assessment (White et al., 2019a; Hvidtfeldt et al., 2023; Tippila et al., 2024). In our study, black carbon was significantly associated with breast cancer risk; however, its contribution to the weighted index in the overall population was minimal, as shown by the QGC analysis. One possible explanation is that the observed association may be partially confounded by other correlated pollutants, such as PCB153, cadmium, dioxins, and BaP, whose effects are accounted for in the multi-pollutant analysis.

Nitrate ($NO_3$) and sulfate ($SO_4$), despite their prominent role in PM composition, remain relatively understudied in relation to cancer risk in general. Song et al in 2024, identified sulfate as a key component of air pollution mixtures linked to an increased risk of breast cancer in China (Song et al., 2024). In our analysis, conducted in metropolitan France, sulfate was also significantly associated with breast cancer risk in single-pollutant models. However, its relative contribution appeared limited in multi-pollutant models, where nitrate emerged as a more prominent factor. Interestingly, Ish et al. in 2023, and White et al. in 2024 found that in the United States, women clusters exposed to PM with a higher composition of nitrate were more at risk of developing breast cancer (Ish et al., 2023; White et al., 2024). Further mechanistic studies are needed to support these findings and address existing knowledge gaps.

In the present study, Saharan dust was the only compound negatively correlated with all other pollutants, likely due to its geographical distribution that is denser in the south of France whereas other components are more prevalent in the north. While individual regression analyses showed a negative non-significant association with breast cancer risk,
the QGC analysis identified it as a positive contributor. This apparent contradiction is consistent with the reversal paradox (Simpson's paradox) (Tu et al., 2008), which arises when confounding is not adequately accounted for. By adjusting for confounders or considering all exposures within a mixture approach, as applied here, this paradox can be mitigated. Nevertheless, to date, no physiological mechanism or epidemiological evidence supports this finding, and it should therefore be interpreted with caution. A possible explanation for the observed positive association is that Saharan dust, composed mostly of silicates, iron oxides and quartz (Sokolik and Toon, 1999), contributes to the formation of particulate matter (PM) and influences air quality (Wang et al., 2020; Gini et al., 2022; Loskot et al., 2024), potentially acting as a vector rather than a direct carcinogen.

The present study has notable strengths. By integrating multiple analytical approaches, it disentangles effects of nine correlated PM components selected based on biological hypotheses and major contributions to PM composition. Future research incorporating additional contaminants, such as specific metals or other chemical compounds released by anthropic activities, could provide further insights into their role in breast cancer risk. The large sample size enhances statistical power, allowing for the detection of small pollution-related risks. The E3N-generations cohort includes extensive data on confounders, enabling thorough adjustments. Exposure assessment was conducted blindly to case-control status, reducing bias. Moreover, residential histories provided long-term exposure estimates, covering up to 22 years for some women.

This study also presents some limitations. The study population, though geographically diverse across metropolitan France, is not fully representative of the general population, as most participants are highly educated (Clavel-Chapelon and E3N Study Group, 2015). However, due to the occupational homogeneity of the cohort—comprising primarily teachers and related professions—this population exhibits uniformly low exposure to occupational contaminants, thereby minimizing potential confounding. Exposure estimates rely on residential addresses with an 8 × 8 km² spatial resolution. Although more refined models could potentially enhance accuracy, Land Use Regression (LUR) models with a 50 × 50 m² resolution require fitting with air measurement data on the pollutants of interest that is scarce in the French territory during the 1991–2010 period. Additionally, the exposure estimation relies solely on residential addresses and does not account for daily mobility or commuting patterns. However, prior research on this nested case-control study investigating the association between breast cancer and PM (Duboeuf et al., 2024) found equal results when both residential and workplace addresses were considered, vs. when only residential addresses were taken into account. It remains possible that the observed effects of the PM mixture are confounded by other correlated airborne exposures or dietary co-exposures. However, acquiring quality data to comprehensively map a wider range of environmental exposures from multiple sources at large scales remains a significant challenge to this day. We were unable to distinguish PM₂.₅ and PM₁₀ components because size-resolved speciation data were not available in our exposure model. This limits direct comparison with studies that evaluated fine and coarse particle fractions separately. Nevertheless, because many sources contribute to both fractions, our integrated assessment still provides meaningful information about chemical composition patterns most relevant to breast cancer risk.

We acknowledge that the temporal distribution of breast cancer diagnoses, with most cases occurring within the first five years of follow-up, may introduce an imbalance that limits causal inference. Extending the follow-up period to include younger women, and applying models that account for exposure trajectories, temporal dynamics, and latency periods, could provide a more refined understanding of the timing and nature of exposures most relevant to disease onset. Nevertheless, owing to the matched design of our study, cases and controls were compared over the same follow-up period, from 1990 to the index date, within each case-control pair. Although some cases had a shorter follow-up

duration, previous analyses conducted on the same population (Duboeuf et al., 2024; Praud et al., 2025), in which the follow-up period was varied and restricted to participants followed for more than two years, showed no substantial changes in the observed associations. Moreover, analysis of the entire cohort or of a case-cohort sample could have been pertinent, but a nested case-control design was deemed more efficient in terms of both human and financial resources required for spatial data processing. Given that the nested case-control study provided sufficient statistical power, and considering the considerable human effort involved in manual geocoding to minimize misclassification bias due to positional errors (Faure et al., 2017), we aimed to balance the number of subjects with the precision of exposure assessment. Consequently, we selected a matched case-control design nested within the cohort. Study designs that enable the estimation of time-varying exposure-related risks, such as case-cohort analyses, could be considered in future research.

Our study builds on previous research within the same cohort showing that PM exposure alone was associated with breast cancer risk (Duboeuf et al., 2024), that women with combined high PM₂.₅ and NO₂ exposure were at greater risk (Giampiccolo et al., 2024), and that gaseous-phase xenoestrogen mixtures may also contribute (Amadou et al., 2024). This study provides deeper insight into the contribution of the different factions within the PM and highlights the need to consider composition and mixture effects when assessing PM-related health risks.

While our estimate appears higher than in many previous studies, it is consistent with the range of effects reported in similar French cohorts (Duboeuf et al., 2024; Lemarchand et al., 2021) and may reflect differences in exposure assessment, population characteristics, or particulate matter composition.

Future research integrating a multi-pollutant approach should explore long-term exposure timeframes in the cohort from birth to diagnosis to determine whether the findings remain consistent despite the overall decline in air pollution and explore potential windows of susceptibility in breast cancer development throughout the lifetime. Additionally, analyzing different profiles and trajectories of exposure over time could provide deeper insights into how particulate matter and its components contribute to breast cancer risk, as well as identify geographical areas with higher risks. Nonetheless, this study offers further evidence and valuable insights into the role of atmospheric PM in breast cancer risk. Importantly, this study reaffirms the need for public health stakeholders to move beyond siloed approaches to pollutant exposures and adopt a systems-thinking perspective, as multiple exposures can interact to increase health risks and contribute to health inequalities.

## 5. Conclusion

This study is the first in Europe to disentangle the role of PM and its constituents in breast cancer development using both individual and multi-pollutant models. These complementary findings indicate that the joint chemical composition of PM, rather than the effect of any single component, may be most relevant for investigating breast cancer etiology. Our findings show that increased joint exposure to PM constituents as black carbon, BaP, PCB, dioxins, cadmium, secondary inorganic pollutants, produced mainly by anthropogenic activities is significantly associated with a higher risk of breast cancer. These results add to the growing body of evidence linking air pollution to breast cancer and should encourage public health authorities to recognize breast cancer as a potential consequence of air pollution exposure and to integrate it into risk management strategies. Further research is needed to better characterize the role of PM components in breast cancer risk, particularly by identifying the most at-risk exposure profiles and by bridging the gap between certain air pollutant exposure and the underlying biological mechanisms of breast cancer development.
# 6. Authors' contributions

All authors contributed to the study conception and design. All authors read and approved the final manuscript. DP was responsible for the conception and design and supervising the work. TF and BM analysed the data and interpreted the results. TF drafted the manuscript. DP, AA, CG, BLP, KL and EF helped with the data collection, data analysis and results interpretation. TC, LG, EF, and FC participated to the data collection, exposure assessment and analyses (geocoding and spatial analyses). DP, BM, TC, LG, BLP, KL, EF, CG, AA, GS, FRM, FC and BF critically reviewed and approved the manuscript.

## CRediT authorship contribution statement

**Tasnim Fareh:** Writing – original draft, Validation, Software, Methodology, Formal analysis. **Benoît Mercoeur:** Writing – review & editing, Software, Methodology, Formal analysis. **Thomas Coudon:** Writing – review & editing, Methodology, Data curation. **Lény Grassot:** Writing – review & editing, Software, Methodology, Data curation. **Blandine Le Provost:** Writing – review & editing. **Karen Leffondre:** Writing – review & editing. **Elodie Faure:** Writing – review & editing, Methodology, Data curation. **Camille Giampiccolo:** Writing – review & editing, Methodology. **Amina Amadou:** Writing – review & editing, Methodology. **Gianluca Severi:** Writing – review & editing. **Francesca Romana Mancini:** Writing – review & editing. **Florian Couvidat:** Writing – review & editing, Data curation. **Béatrice Fervers:** Writing – review & editing. **Delphine Praud:** Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Project administration, Methodology, Funding acquisition, Conceptualization.

# 6. Funding

This work was supported by the French National Research Program for Environmental and Occupational Health of Anses (ANSES-22-EST-195), the French League against cancer, the Lyon Metropolis and the French League against Cancer of the Haute-Savoie Region. The E3N-Generation cohort receives financial support from the Ligue Contre le Cancer, the Mutuelle Générale de l'Éducation Nationale (MGEN), the Institut Gustave Roussy, the Institut National de la Santé et de la Recherche (INSERM). The E3N-Generation cohort is also supported by the French National Research Agency (ANR) under the Investment for the future Program (PIA; ANR-10-COHO-0006) and by the French Ministry of Higher Education, Research and Innovation (subsidiary for public service charges No. 2102 918823). The funding bodies had no role in the conception, design, planning, or writing of the study.

# 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.

# Acknowledgements

The authors would like to thank all participants for providing data and physicians for providing pathology reports. They also thank Charlotte Carretero, Maxime Guillou and Matthieu Dubuis for geocoding addresses and Céline Kernaleguen, Maxime Valdenaire, Laureen Dartois, Roselyn Gomes-Rima, and Amandine Gelot for data management and assistance for cohort data collection.

# Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.envint.2025.109987.

# Data availability

The datasets analysed during the current study may be available from the corresponding author under on reasonable request.

# References

Amadou, A., Coudon, T., Praud, D., Salizzoni, P., Leffondre, K., Lévêque, E., Boutron-Ruault, M.-C., Danjou, A.M.N., Morelli, X., Le Cornet, C., Perrier, L., Couvidat, F., Bessagnet, B., Caudeville, J., Faure, E., Mancini, F.R., Gulliver, J., Severi, G., Fervers, B., 2020. Chronic Low-Dose Exposure to Xenoestrogen Ambient Air Pollutants and Breast Cancer Risk: XENAIR Protocol for a Case-Control Study Nested within the French E3N Cohort. JMIR Res Protoc 9, e15167. https://doi.org/10.2196/15167

Amadou, A., Giampiccolo, C., Bibi Ngaleu, F., Praud, D., Coudon, T., Grassot, L., Faure, E., Couvidat, F., Frenoy, P., Severi, G., Romana Mancini, F., Roy, P., Fervers, B., 2024. Multiple xenoestrogen air pollutants and breast cancer risk: Statistical approaches to investigate combined exposures effect. Environ. Pollut. 351, 124043. https://doi.org/10.1016/j.envpol.2024.124043

Baan, R., Grosse, Y., Straif, K., Secretan, B., El Ghissassi, F., Bouvard, V., Benbrahim-Tallaa, L., Guha, N., Freeman, C., Galichet, L., Cogliano, V., WHO International Agency for Research on Cancer Monograph Working Group, 2009. A review of human carcinogens-Part F: chemical agents and related occupations. Lancet Oncol. 10, 1143-1144.

Badr, O., Probert, S.D., 1994. Atmospheric sulphur: Trends, sources, sinks and environmental impacts. Appl. Energy 47, 1-67. https://doi.org/10.1016/0306-2619(94)00030-2

Becher, H., Steindorf, K., Flesch-Janys, D., 1998. Quantitative cancer risk assessment for dioxins using an occupational cohort. Environ. Health Perspect. 106, 663-670. https://doi.org/10.1289/ehp.98106663

Bell, M.L., HEI Health Review Committee, 2012. Assessment of the health impacts of particulate matter characteristics. Res Rep Health Eff Inst 5-38.

Bellavia, A., 2025. Statistical Methods for Environmental Mixtures: A Primer in Environmental Epidemiology, Society, Environment and Statistics. Springer Nature Switzerland, Cham. DOI: 10.1007/978-3-031-78987-8.

Binachon, B., Dossus, L., Danjou, A.M.N., Clavel-Chapelon, F., Fervers, B., 2014. Life in urban areas and breast cancer risk in the French E3N cohort. Eur. J. Epidemiol. 29, 743-751. https://doi.org/10.1007/s10654-014-9942-z

Bobb, J.F., Claus Henn, B., Valeri, L., Coull, B.A., 2018. Statistical software for analyzing the health effects of multiple concurrent exposures via Bayesian kernel machine regression. Environ. Health 17, 67. https://doi.org/10.1186/s12940-018-0413-y

Bobb, J.F., Valeri, L., Claus Henn, B., Christiani, D.C., Wright, R.O., Mazumdar, M., Godleski, J.J., Coull, B.A., 2015. Bayesian kernel machine regression for estimating the health effects of multi-pollutant mixtures. Biostatistics 16, 493-508. https://doi.org/10.1093/biostatistics/kku058

Bressi, M., Cavalli, F., Putaud, J.P., Fröhlich, R., Petit, J.-E., Aas, W., Äijälä, M., Alstuey, A., Allan, J.D., Aurela, M., Berico, M., Bougiatioti, A., Bukowiecki, N., Canonaco, F., Crenn, V., Dusanter, S., Ehn, M., Elsasser, M., Flentje, H., Graf, P., Green, D.C., Heikkinen, L., Hermann, H., Holzinger, R., Hueglin, C., Keernik, H., Kiendler-Scharr, A., Kubelová, L., Lunder, C., Maasikmets, M., Makes, O., Malaguti, A., Mihalopoulos, N., Nicolas, J.B., O'Dowd, C., Ovadnevaite, J., Petralia, E., Poulain, L., Priestman, M., Riffault, V., Ripoll, A., Schlag, P., Schwarz, J., Sciare, J., Slowik, J., Sosedova, Y., Stavroulas, I., Teinemaa, E., Via, M., Vodicka, P., Williams, P.I., Wiedensohler, A., Young, D.E., Zhang, S., Favez, O., Minguillón, M.C., Prevot, A.S.H., 2021. A European aerosol phenomenology -7: High-time resolution chemical characteristics of submicron particulate matter across Europe. Atmos. Environ.: X 10, 100108. https://doi.org/10.1016/j.aeaoa.2021.100108

Bukowska, B., Mokra, K., Michalowicz, J., 2022. Benzo[a]pyrene-Environmental Occurrence, Human Exposure, and Mechanisms of Toxicity. Int. J. Mol. Sci. 23, 6348. https://doi.org/10.3390/ijms23116348

van Buuren, S., Groothuis-Oudshoorn, K., 2011. mice: Multivariate Imputation by Chained Equations in R. J. Stat. Soft. 45. https://doi.org/10.18637/jss.v045.i03

Calvert, J.G., Lazrus, A., Kok, G.L., Heikes, B.G., Walega, J.G., Lind, J., Cantrell, C.A., 1985. Chemical mechanisms of acid generation in the troposphere.

Clavel-Chapelon, F., E3N Study Group, 2015. Cohort Profile: The French E3N Cohort Study. Int J Epidemiol 44, 801-809. https://doi.org/10.1093/ije/duy184

Couvidat, F., Bessagnet, B., Garcia-Vivanco, M., Real, E., Menut, L., Colette, A., 2018. Development of an inorganic and organic aerosol model (CHIMERE 2017 β v1.0): seasonal and spatial evaluation over Europe. Geosci. Model Dev. 11, 165-194. https://doi.org/10.5194/gmd-11-165-2018

Deygas, F., Amadou, A., Coudon, T., Grassot, L., Couvidat, F., Bessagnet, B., Faure, E., Salizzoni, P., Gulliver, J., Caudeville, J., Severi, G., Mancini, F.R., Leffondré, K., Fervers, B., Praud, D., 2021. Long-term atmospheric exposure to PCB153 and breast cancer risk in a case-control study nested in the French E3N cohort from 1990 to 2011. Environ. Res. 195, 110743. https://doi.org/10.1016/j.envres.2021.110743

Domingo-Relloso, A., Grau-Perez, M., Briongos-Figuero, L., Gomez-Ariza, J.L., Garcia-Barrera, T., Duenas-Laita, A., Bobb, J.F., Chaves, F.J., Kioumourtzoglou, M.-A., Navas-Acien, A., Redon-Mas, J., Martin-Escudero, J.C., Tellez-Plaza, M., 2019. The association of urine metals and metal mixtures with cardiovascular incidence in an adult population from Spain: the Hortega Follow-up Study. Int. J. Epidemiol. 48, 1839-1849. https://doi.org/10.1093/ije/dy061

Dong, J.-Y., Qin, L.-Q., 2020. Education level and breast cancer incidence: a meta-analysis of cohort studies. Menopause 27, 113-118. https://doi.org/10.1097/GME.0000000000001425
Duboeuf, M., Amadou, A., Coudon, L., Grassot, L., Ramel-Delobel, M., Faure, E., Salizzoni, P., Gulliver, J., Severi, G., Mancini, F.R., Fervers, B., Praud, D., 2024. Long-term exposure to air pollution at residential and workplace addresses and breast cancer risk: a case-control study nested in the French E3N-générations cohort from 1990 to 2011. Eur. J. Cancer 210, 114293. https://doi.org/10.1016/j.ejca.2024.114293.

European Commission, n.d. Eurostat population database. Available at: http://ec.europa.eu/eurostat.

Farina, F., Sancini, G., Battaglia, C., Tinaglia, V., Mantecca, P., Camatini, M., Palestini, P., 2013. Milano summer particulate matter (PM10) triggers lung inflammation and extra pulmonary adverse events in mice. PLoS One 8, e56636. https://doi.org/10.1371/journal.pone.0056636.

Faure, E., Danjou, A.M.N., Clavel-Chapelon, F., Boutron-Ruault, M.-C., Dossus, L., Fervers, B., 2017. Accuracy of two geocoding methods for geographic information system-based exposure assessment in epidemiological studies. Environ. Health 16, 15. https://doi.org/10.1186/s12940-017-0217-5.

Filippini, T., Torres, D., Lopes, C., Carvalho, C., Moreira, P., Naska, A., Kasdagli, M.-I., Malavolti, M., Orsini, N., Vinceti, M., 2020. Cadmium exposure and risk of breast cancer: a dose-response meta-analysis of cohort studies. Environ. Int. 142, 105879. https://doi.org/10.1016/j.envint.2020.105879.

Font, A., de Brito, J.F., Riffault, V., Conil, S., Jaffrezo, J.-L., Bourin, A., 2024. Long-term measurements of aerosol closure at rural background sites in France: sources, seasonality and mass closure of PM2.5. Atmos. Environ. 334, 120724. https://doi.org/10.1016/j.atmosenv.2024.120724.

Geiser, M., Rothen-Rutishauser, B., Kapp, N., Schürch, S., Kreyling, W., Schulz, H., Semmler, M., Hof, V.I., Heyder, J., Gehr, P., 2005. Ultrafine Particles Cross Cellular Membranes by Nonphagogenic Mechanisms in Lungs and in Cultured Cells. Environ. Health 113, 1555-1560. https://doi.org/10.1289/ehp.8006.

Genchi, G., Sinicropi, M.S., Lauria, G., Carocci, A., Catalano, A., 2020. The Effects of Cadmium Toxicity. Int. J. Environ. Res. Public Health 17, 3782. https://doi.org/10.3390/ijerph17113782.

Giampiccolo, C., Amadou, A., Coudon, T., Praud, D., Grassot, L., Faure, E., Couvidat, F., Severi, G., Romana Mancini, F., Fervers, B., Roy, P., 2024. Multi-pollutant exposure profiles associated with breast cancer risk: a Bayesian profile regression analysis in the French E3N cohort. Environ. Int. 190, 108943. https://doi.org/10.1016/j.envint.2024.108943.

Gibson, E.A., Nunez, Y., Abuawad, A., Zota, A.R., Renzetti, S., Devick, K.L., Gennings, C., Goldsmith, J., Coull, B.A., Kioumourtzoglou, M.-A., 2019. An overview of methods to address distinct research questions on environmental mixtures: an application to persistent organic pollutants and leukocyte telomere length. Environmental Health: A Global Access Science Source 18, 76. https://doi.org/10.1186/s12940-019-0515-1.

Gini, M., Manousakas, M., Karydas, A.G., Eleftheriadis, K., 2022. Mass size distributions, composition and dose estimates of particulate matter in Saharan dust outbreaks. Environ. Pollut. 298, 118768. https://doi.org/10.1016/j.envpol.2021.118768.

Grice, S., Stedman, J., Kent, A., Hobson, M., Norris, J., Abbott, J., Cooke, S., 2009. Recent trends and projections of primary NO2 emissions in Europe. Atmos. Environ. 43, 2154-2167. https://doi.org/10.1016/j.atmosenv.2009.01.019.

Guerreiro, C.B.B., Horálek, J., de Leeuw, F., Couvidat, F., 2016. Benzo(a)pyrene in Europe: Ambient air concentrations, population exposure and health effects. Environ. Pollut. 214, 657-667. https://doi.org/10.1016/j.envpol.2016.04.081.

Hamra, G.B., Guha, N., Cohen, A., Laden, F., Raaschou-Nielsen, O., Samet, M., Vineis, P., Forastiere, F., Saldiva, P., Yorifuji, T., Loomis, D., 2014. Outdoor Particulate Matter Exposure and Lung Cancer: A Systematic Review and Meta-Analysis. Environ. Health Perspect. https://doi.org/10.1289/ehp.1408092.

Harrison, R.M., 2020. Airborne particulate matter. Philos. Trans. R. Soc. A. Math. Phys. Eng. Sci. 378, 20190319. https://doi.org/10.1098/rsta.2019.0319.

Hasan Shahariar, G.M., Sajjad, M., Suara, K.A., Jahirul, M.I., Chu-Van, T., Ristovski, Z., Brown, R.J., Bodisco, T.A., 2022. On-road CO2 and NOx emissions of a diesel vehicle in urban traffic. Transp. Res. Part D: Transp. Environ. 107, 103326. https://doi.org/10.1016/j.trd.2022.103326.

Hvidtfeldt, U.A., Chen, J., Rodopoulou, S., Strak, M., de Hoogh, K., Andersen, Z.J., Bellander, T., Brandt, J., Fecht, D., Forastiere, F., Gulliver, J., Hertel, O., Hoffmann, B.H., Katsouyanni, K., Ketzel, M., Brynedal, B., Leander, K., Ljungman, P.L.S., Magnusson, P.K.E., Nagel, G., Pershagen, G., Rizzuto, D., Boutron-Ruault, M.-C., Samoli, E., So, R., Stafoggia, M., Tjønneland, A., Vermeulen, R., Verschuren, W.M., Weinmayr, G., Wolf, K., Zhang, J., Zitt, E., Brunekreef, B., Hoek, G., Raaschou-Nielsen, O., 2023. Breast Cancer Incidence in Relation to Long-Term Low-Level Exposure to Air Pollution in the ELAPSE Pooled Cohort. Cancer Epidemiol. Biomarkers Prev. 32, 105-113. https://doi.org/10.1158/1055-9965.EPI-22-0720.

Ish, J.L., Abubakar, M., Fan, S., Jones, R.R., Niehoff, N.M., Henry, J.E., Gierach, G.L., White, A.J., 2023. Outdoor air pollution and histologic composition of normal breast tissue. Environ. Int. 176, 107984. https://doi.org/10.1016/j.envint.2023.107984.

Keil, A.P., Buckley, J.P., O'Brien, K.M., Ferguson, K.K., Zhao, S., White, A.J., 2020. A Quantile-based g-Computation Approach to addressing the Effects of Exposure Mixtures. Environ. Health. https://doi.org/10.1289/EHP5838.

Lauby-Secretan, B., Loomis, D., Grosse, Y., Ghissassi, F.E., Bouvard, V., Benbrahim-Tallaa, L., Guha, N., Baan, R., Mattock, H., Straif, K., 2013. Carcinogenicity of polychlorinated biphenyls and polybrominated biphenyls. Lancet Oncol. 14, 287-288. https://doi.org/10.1016/S1470-2045(13)70104-9.

Lemarchand, C., Gabet, S., Cénée, S., Tvardik, N., Slama, R., Guénel, P., 2021. Breast cancer risk in relation to ambient concentrations of nitrogen dioxide and particulate matter: results of a population-based case-control study corrected for potential selection bias (the CECILE study). Environ. Int. 155, 106604. https://doi.org/10.1016/j.envint.2021.106604.

Liang, Y., Zeng, T., Tian, J., Yan, J., Lan, Z., Chen, J., Xin, X., Lei, B., Cai, Z., 2021. Long-term environmental cadmium exposure induced serum metabolic changes related to renal and liver dysfunctions in a female cohort from Southwest China. Sci. Total Environ. 798, 149379. https://doi.org/10.1016/j.scitotenv.2021.149379.

Long, C.M., Nascarella, M.A., Valberg, P.A., 2013. Carbon black vs. black carbon and other airborne materials containing elemental carbon: physical and chemical distinctions. Environ. Pollut. 181, 271-286. https://doi.org/10.1016/j.envpol.2013.06.009.

Loomis, D., Grosse, Y., Lauby-Secretan, B., El Ghissassi, F., Bouvard, V., Benbrahim-Tallaa, L., Guha, N., Baan, R., Mattock, H., Straif, K., International Agency for Research on Cancer Monograph Working Group IARC, 2013. The carcinogenicity of outdoor air pollution. Lancet Oncol. 14, 1262-1263.

Loskot, J., Jezbera, D., Nalezinková, M., Smejkalová, A.H., Fernandes, D., Komárek, J., 2024. Impact of Saharan dust on particulate matter characteristics in an urban and a natural locality in Central Europe. Sci. Rep. 14, 32002. https://doi.org/10.1038/s41598-024-83603-0.

McNeill, J., Snider, G., Weagle, C.L., Walsh, B., Bissonnette, P., Stone, E., Abboud, I., Akoshile, C., Anh, N.X., Balasubramanian, R., Brook, J.R., Coburn, C., Cohen, A., Dong, J., Gagnon, G., Garland, R.M., He, K., Holben, B.N., Kahn, R., Kim, J.S., Lagrosas, N., Lestari, P., Liu, Y., Jeba, F., Joy, K.S., Martins, J.V., Misra, A., Norford, L.K., Quel, E.J., Salam, A., Schichtel, B., Tripathi, S.N., Wang, C., Zhang, Q., Brauer, M., Gibson, M.D., Rudich, Y., Martin, R.V., 2020. Large global variations in measured airborne metal concentrations driven by anthropogenic sources. Sci. Rep. 10, 21817. https://doi.org/10.1038/s41598-020-78789-y.

Menut, L., Bessagnet, B., Briant, R., Cholakian, A., Couvidat, F., Mailler, S., Pennel, R., Siour, G., Tuccella, P., Turquety, S., Valari, M., 2021. The CHIMERE v2020r1 online chemistry-transport model. Geosci. Model Dev. 14, 6781-6811. https://doi.org/10.5194/gmd-14-6781-2021.

Menut, L., Bessagnet, B., Khvorostyanov, D., Beekmann, M., Blond, N., Colette, A., Coll, I., Curci, G., Foret, G., Hodzic, A., Mailler, S., Meleux, F., Monge, J.-L., Pison, I., Siour, G., Turquety, S., Valari, M., Vautard, R., Vivanco, M.G., 2013. CHIMERE 2013: a model for regional atmospheric composition modelling. Geosci. Model Dev. 6, 981-1028. https://doi.org/10.5194/gmd-6-981-2013.

Mínguez-Alarcón, L., Messerlian, C., Bellavia, D., Gaskins, A.J., Chiu, Y.-H., Ford, J.B., Azevedo, A.R., Petrozza, J.C., Calafat, A.M., Hauser, R., Williams, P.L., 2019. Urinary concentrations of bisphenol a, parabens and phthalate metabolite mixtures in relation to reproductive success among women undergoing in vitro fertilization. Environ. Int. 126, 355-362. https://doi.org/10.1016/j.envint.2019.02.025.

Nemmar, A., Hoet, P.H.M., Vanquickenborne, B., Dinsdale, D., Thomeer, M., Hoylaerts, M.F., Vanbilloen, H., Mortelmans, L., Nemery, B., 2002. Passage of Inhaled Particles into the Blood Circulation in Humans. Circulation 105, 411-414. https://doi.org/10.1161/hc0402.104118.

Niehoff, N.M., Keil, A.P., Jones, R.R., Fan, S., Gierach, G.L., White, A.J., 2020. Outdoor air pollution and terminal duct lobular involution of the normal breast. Breast Cancer Res. 22, 100. https://doi.org/10.1186/s13058-020-01339-x.

Nordberg, G.F., 2006. Lung cancer and exposure to environmental cadmium. Lancet Oncol. 7, 99-101. https://doi.org/10.1016/S1470-2045(06)70548-4.

Othman, N., Ismail, Z., Selamat, M.I., Sheikh Abdul Kadir, S.H., Shibraumalisi, N.A., 2022. A Review of Polychlorinated Biphenyls (PCBs) Pollution in the Air: Where and How Much Are We Exposed to? International Journal of Environmental Research and Public Health 19, 13923. Doi: 10.3390/ijerph192113923.

Pan, J., Plant, J.A., Voulvoulis, N., Oates, C.J., Ihlenfeld, C., 2010. Cadmium levels in Europe: implications for human health. Environ. Geochem. Health 32, 1-12. https://doi.org/10.1007/s10653-009-9273-2.

Pearce, N., 2016. Analysis of matched case-control studies. BMJ 352, i969. https://doi.org/10.1136/bmj.i969.

Peters, A., Veronesi, B., Calderón-Garcidueñas, L., Gehr, P., Chen, L.C., Geiser, M., Reed, W., Rothen-Rutishauser, B., Schürch, S., Schulz, H., 2006. Translocation and potential neurological effects of fine and ultrafine particles a critical update. Part. Fibre Toxicol. 3, 13. https://doi.org/10.1186/1743-8977-3-13.

Philip, S., Martin, R.V., van Donkelaar, A., Lo, J.-W.-H., Wang, Y., Chen, D., Zhang, L., Kasibhatla, P.S., Wang, S., Zhang, Q., Lu, Z., Streets, D.G., Bittman, S., Macdonald, D. J., 2014. Global Chemical Composition of Ambient Fine Particulate Matter for Exposure Assessment. Environ. Sci. Technol. 48, 13060-13068. https://doi.org/10.1021/es02965b.

Ponce, E., Louie, M.C., Sevigny, M.B., 2015. Acute and chronic cadmium exposure promotes E-cadherin degradation in MCF7 breast cancer cells. Mol. Carcinog. 54, 1014-1025. https://doi.org/10.1002/mc.22170.

Praud, D., Amadou, A., Coudon, T., Duboeuf, F., Mercoeur, B., Faure, E., Grassot, L., Danjou, A.M., Salizzoni, P., Couvidat, F., Dossus, L., Severi, G., Mancini, F.R., Fervers, B., 2025. Association between chronic long-term exposure to airborne dioxins and breast cancer. Int. J. Hyg. Environ. Health 263, 114489. https://doi.org/10.1016/j.ijheh.2024.114489.

Quaß, U., Fermann, M., Bröker, G., 2004. The European Dioxin Air Emission Inventory Project—Final results. Chemosphere, Dioxins in the Air 54, 1319-1327. https://doi.org/10.1016/S0045-6535(03)00251-0.

Ramanantenasoa, M.M.J., Gilliot, J.-M., Mignolet, C., Bedos, C., Mathias, E., Eglin, T., Makowski, D., Géremont, S., 2018. A new framework to estimate spatio-temporal ammonia emissions due to nitrogen fertilization in France. Sci. Total Environ. 645, 205-219. https://doi.org/10.1016/j.scitotenv.2018.06.202.

Rundmaki, D., Papayannis, A., Kassomenos, P., Mantas, E., Kokkalis, P., Tsezos, P., 2013. Influence of Saharan Dust Transport events on PM2.5 Concentrations and Composition over Athens. Water Air Soil Pollut. 224, 1-14. https://doi.org/10.1007/s11270-012-1373-4.

Richardson, D.B., 2004. An incidence density sampling program for nested case-control analyses. Occup. Environ. Med. 61, e59.
Rojas, K., Stuckey, A., 2016. Breast Cancer Epidemiology and Risk Factors. Clin. Obstet. Gynecol. 59, 651–672. https://doi.org/10.1097/GRF.0000000000000239.

Sarti, E., Pasti, L., Rossi, M., Ascanelli, M., Pagnoni, A., Trombini, M., Remelli, M., 2015. The composition of PM1 and PM2.5 samples, metals and their water soluble fractions in the Bologna area (Italy). Atmos. Pollut. Res. 6, 708–718. https://doi.org/10.5094/APR.2015.079.

Schmidt, S., 2020. Quantile g-Computation: A New Method for Analyzing Mixtures of Environmental Exposures. Environ. Health Perspect. 128, 104004. https://doi.org/10.1289/EHP7342.

Shaap, M., et al., 2024. Modelling fine aerosol and black carbon over Europe to address health and climate effects. Air Pollution Modeling and Its Application XVII 321–330. https://doi.org/10.1007/978-0-387-68854-1_35.

Snider, G., Weagle, C.L., Murdymootoo, K.K., Ring, A., Ritchie, Y., Stone, E., Walsh, A., Akoshile, C., Anh, N.X., Balasubramanian, R., Brook, J., Qonitan, F.D., Dong, J., Griffith, D., He, K., Holben, B.N., Kahn, R., Lagrosas, N., Lestari, P., Ma, Z., Misra, A., Norford, L.K., Quel, E.J., Salam, A., Schichtel, B., Segev, L., Tripathi, S., Wang, C., Yu, C., Zhang, Q., Zhang, Y., Brauer, M., Cohen, A., Gibson, M.D., Liu, Y., Martins, J. V., Rudich, Y., Martin, R.V., 2016. Variation in global chemical composition of PM<sub>2.5</sub>: emerging results from SPARTAN. Atmos. Chem. Phys. 16, 9629–9653. https://doi.org/10.5194/acp-16-9629-2016.

Sokolik, I.N., Toon, O.B., 1999. Incorporation of mineralogical composition into models of the radiative properties of mineral aerosol from UV to IR wavelengths. J. Geophys. Res. Atmos. 104, 9423–9444. https://doi.org/10.1029/1998JD200048.

Song, Y., Yang, L., Kang, N., Wang, N., Zhang, X., Liu, S., Li, H., Xue, T., Ji, J., 2024. Associations of incident female breast cancer with long-term exposure to PM2.5 and its constituents: Findings from a prospective cohort study in Beijing, China. J. Hazard. Mater. 473, 134614. https://doi.org/10.1016/j.jhazmat.2024.134614.

Straif, K., Baan, R., Grosse, Y., Secretan, B., Ghissassi, F.E., Cogliano, V., 2005. Carcinogenicity of polycyclic aromatic hydrocarbons. Lancet Oncol. 6, 931–932. https://doi.org/10.1016/S1470-2045(05)70458-7.

Sun, Y.-S., Zhao, Z., Yang, Z.-N., Xu, F., Lu, H.-J., Zhu, Z.-Y., Shi, W., Jiang, J., Yao, P.-P., Zhu, H.-P., 2017. Risk Factors and Preventions of Breast Cancer. Int. J. Biol. Sci. 13, 1387–1397. https://doi.org/10.7150/ijbs.21635.

Tippila, J., Wah, N.L.S., Akbar, K.A., Bhummaphan, N., Wongsasuluk, P., Kallawicha, K., 2024. Ambient Air Pollution Exposure and Breast Cancer Risk Worldwide: a Systematic Review of Longitudinal Studies. Int. J. Environ. Res. Public Health 21, 1713. https://doi.org/10.3390/ijerph21121713.

Tu, Y.-K., Gunnell, D., Gilthorpe, M.S., 2008. Simpson's Paradox, Lord's Paradox, and suppression Effects are the same phenomenon – the reversal paradox. Emerg. Themes Epidemiol. 5, 2. https://doi.org/10.1186/1742-7622-5-2.

Viatte, C., Guendouz, N., Dufaux, C., Hensen, A., Swart, D., Van Damme, M., Clarisse, L., Coheur, P., Clerbaux, C., 2023. Measurement report: Ammonia in Paris derived from

ground-based open-path and satellite observations. Atmos. Chem. Phys. 23, 15253–15267. https://doi.org/10.5194/acp-23-15253-2023.

Wang, Q., Gu, J., Wang, X., 2020. The impact of Sahara dust on air quality and public health in European countries. Atmos. Environ. 241, 117771. https://doi.org/10.1016/j.atmosenv.2020.117771.

Wang, R., Wang, P., Zhou, Y., Wang, Y., Xu, C., Wang, Z., Wang, W., 2025. Association between long-term ambient air pollution exposure and the incidence of breast cancer: a meta-analysis based on updated evidence. Ecotoxicol. Environ. Saf. 289, 117472. https://doi.org/10.1016/j.ecoenv.2024.117472.

Wei, Z., Shaikh, Z.A., 2017. Cadmium stimulates metastasis-associated phenotype in triple-negative breast cancer cells through integrin and beta-catenin signaling. Toxicol. Appl. Pharmacol. 328, 70–80. https://doi.org/10.1016/j.taap.2017.05.017.

White, A.J., Fisher, J.A., Sweeney, M.R., Freedman, N.D., Kaufman, J.D., Silverman, D.T., Jones, R.R., 2024. Ambient fine particulate matter and breast cancer incidence in a large prospective US cohort. J. Natl Cancer Inst. 116, 53–60. https://doi.org/10.1093/jnci/djad170.

White, A.J., Keller, J.P., Zhao, S., Carroll, R., Kaufman, J.D., Sandler, D.P., 2019a. Air Pollution, Clustering of Particulate Matter Components, and Breast Cancer in the Sister Study: a U.S.-Wide Cohort. Environ. Health Perspect. 127, 107002. https://doi.org/10.1289/EHP5131.

White, A.J., O'Brien, K.M., Niehoff, N.M., Carroll, R., Sandler, D.P., 2019b. Metallic Air Pollutants and Breast Cancer Risk in a Nationwide Cohort Study. Epidemiology 30, 20–28. https://doi.org/10.1097/EDE.0000000000000917.

Wu, A.H., Wu, J., Tseng, C., Stram, D.O., Shariff-Marco, S., Larson, T., Goldberg, D., Fruin, S., Jiao, A., Inamdar, P.P., Ihenacho, U., Le Marchand, L., Wilkens, L., Haiman, C., Ritz, B., Cheng, I., 2025. Air Pollution and Breast Cancer Incidence in the Multiethnic Cohort Study. J. Clin. Oncol. 43, 273–284. https://doi.org/10.1200/JCO.24.00418.

Wyer, K.E., Kelleghan, D.B., Blanes-Vidal, V., Schaubberger, G., Curran, T.P., 2022. Ammonia emissions from agriculture and their contribution to fine particulate matter: a review of implications for human health. J. Environ. Manage. 323, 116285. https://doi.org/10.1016/j.jenvman.2022.116285.

Yang, X., Zhang, T., Zhang, X., Chu, C., Sang, S., 2022. Global burden of lung cancer attributable to ambient fine particulate matter pollution in 204 countries and territories, 1990–2019. Environ. Res. 204, 112023. https://doi.org/10.1016/j.envres.2021.112023.

Zhang, Y., Dong, T., Hu, W., Wang, Xu., Xu, B., Lin, Z., Hofer, T., Stefanoff, P., Chen, Y., Wang, X., Xia, Y., 2019. Association between exposure to a mixture of phenols, pesticides, and phthalates and obesity: Comparison of three statistical models. Environ. Int. 123, 325–336. https://doi.org/10.1016/j.envint.2018.11.076.

Zhang, Y., Kleer, C.G., 2016. Phyllodes Tumor of the Breast: Histopathologic Features, Differential Diagnosis, and Molecular/Genetic Updates. Arch. Pathol. Lab. Med. 140, 665–671. https://doi.org/10.5858/arpa.2016-0042-RA.