# 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

Margaux Duboeuf, Amina Amadou, Thomas Coudon, Lény Grassot, Marie Ramel-Delobel, Elodie Faure, Pietro Salizzoni, John Gulliver, Gianluca Severi, Francesca Romana Mancini, et al.

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Margaux Duboeuf, Amina Amadou, Thomas Coudon, Lény Grassot, Marie Ramel-Delobel, et al. 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. European Journal of Cancer, 2024, 210, pp.114293. 10.1016/j.ejca.2024.114293. hal-04993833

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Original research

# 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

<figure><img src="image_2.png" /></figure>

Margaux Duboeuf<sup>a,b</sup>, Amina Amadou<sup>a,b</sup>, Thomas Coudon<sup>a,b</sup>, Lény Grassot<sup>a,b</sup>,

Marie Ramel-Delobel<sup>a,b,d</sup>, Elodie Faure<sup>c</sup>, Pietro Salizzoni<sup>d,e</sup>, John Gulliver<sup>f</sup>, Gianluca Severi<sup>c,g</sup>,
Francesca Romana Mancini<sup>c</sup>, Béatrice Fervers<sup>a,b</sup>, Delphine Praud<sup>a,b,*</sup>

ARTICLE INFO

Keywords:

Air pollution

Breast cancer

Case-control study

Land use regression

Nitrogen dioxide

Particulate matters

Workplace history

Residential history

A B S T R A C T

**Background:** An increasing evidence links air pollution to breast cancer (BC) risk. Yet, pollutant exposure estimates at the workplace location in pollution exposure assessment have not been considered.

**Objectives:** This study investigates the association between particulate matters (PM<sub>2.5</sub>, PM<sub>10</sub>) and nitrogen dioxide (NO<sub>2</sub>) atmospheric concentrations (1990–2011), at the women’s residential and workplace locations, and BC risk.

**Methods:** This case-control study of 2419 BC cases and 2984 controls, was nested in the French prospective E3N cohort. The annual mean PM<sub>2.5</sub>, PM<sub>10</sub> and NO<sub>2</sub> concentrations were estimated using a Land Use Regression model (50 m x 50 m resolution) and assigned to the women’s geocoded residential and workplace locations, from cohort recruitment to their index date (date of case diagnosis). Odds ratios (OR) and 95 % confidence intervals (CI) were estimated using multivariate logistic regression models.

**Results:** An increased BC risk was observed for a 10 µg/m³ increase of the 1990–2011 average PM<sub>2.5</sub> concentration estimates (OR=1.28; CI 1.00, 1.63). An increased risk was suggested for a 10 µg/m³ increase for PM<sub>10</sub> (OR=1.09; CI 0.92, 1.30) and NO<sub>2</sub> (OR=1.05; CI 0.97, 1.13). No effect modification by menopausal status, nor difference by hormone receptor status were observed.

**Discussion:** This study is the first to estimate BC risk and long-term air pollutant exposure from both, residential and workplace location histories. Results suggest that residential PM<sub>2.5</sub>, PM<sub>10</sub> and NO<sub>2</sub> concentrations are strongly correlated with workplace ones, indicating that residential data may serve as proxy for overall exposure. Future studies should consider exposure during commuting.

# 1. Introduction

Breast cancer is the most common female cancer worldwide, with nearly 2.3 million cases in 2020, and the leading cause of cancer deaths among women (approximately 685,000 deaths in 2020) [1]. Advanced

age, genetic predisposition, hormonal and reproductive factors, and lifestyle are the main risk factors identified to date [2]. However, these remain insufficient to explain all cases of breast cancer. There is an increasing body of evidence and compelling data suggesting an association between some environmental exposures, including outdoor air

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

<footnote><sup>b</sup> Inserm, U1296 Unit, "Radiation: Defense, Health and Environment", Lyon, France</footnote>

<footnote><sup>c</sup> Paris-Saclay University, UVSQ, Inserm U1018 unit, Gustave Roussy, Centre de Recherche en Epidémiologie et Santé des Populations (CESP), Villejuif, France</footnote>

<footnote><sup>d</sup> Laboratoire de Mécanique des Fluides et d'Acoustique, University of Lyon, CNRS UMR 5509, Ecole Centrale de Lyon, INSA Lyon, Université Claude Bernard Lyon 1, Ecully, France</footnote>

<footnote><sup>e</sup> Department of Environmental, Land, and Infrastructure Engineering, Politecnico di Torino, Turin, Italy</footnote>

<footnote><sup>f</sup> Centre for Environmental Health and Sustainability, School of Geography, Geology and the Environment, University of Leicester, United Kingdom</footnote>

<footnote><sup>g</sup> Departement of Statistics, Computer Science and Applications "G. Parenti", University of Florence, Italy</footnote>

<footnote>* Correspondence to: Département Prévention Cancer Environnement, Centre Léon Bérard, Inserm U1296 Radiations: Défense, Santé, Environnement, 28 rue Laënnec, 69373 CEDEX 08 Lyon, France.</footnote>

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

<footnote>https://doi.org/10.1016/j.ejca.2024.114293</footnote>
pollution and breast cancer risk [3,4]. Outdoor air pollution is a major public health concern, classified carcinogenic to humans in 2013, by the International Agency for Research on Cancer (IARC) [5]. Air pollution is an ubiquitous complex mixture of solid and liquid particles and gases. Particulate matter (PM) and nitrogen dioxide (NO₂) are the main components of air pollution in particular in urban areas. PM originating from the residential sector, road traffic, industry and agriculture are classified according to their size, mainly particles with aerodynamic diameters of 10 µm or smaller (PM₁₀), and particles of 2-5 µm in diameter or smaller (PM₂.₅). The smaller the particles, the deeper they are able to penetrate into the respiratory system. NO₂ are principally emitted from road traffic and fuel combustion (e.g. heating, power generation). Inhalation is the main exposure route to these pollutants.

A causal link between air pollution and lung cancer and biological mechanisms involved, has been established [6]. However, the link with other cancer sites, including breast cancer, remains more controversial. Several studies supported a statistically significant increased breast cancer risk associated with higher NO₂ atmospheric concentration [7], as well as higher PM₂.₅ or PM₁₀ concentrations [8], while others reported no evidence of an association. The latest meta-analysis on the effect of NO₂, highlighted a small but significant increase in breast cancer risk with increasing NO₂ concentration (13 studies; RR=1.015; 95 % Confidence Interval (CI) 1.003, 1.028, for a 10 µg/m³ increase in NO₂) [7]. Conversely, the most recent meta-analysis on the effect of PM did not show a statistically-significant association neither for PM₂.₅ (8 studies; RR=1.006; CI 0.941, 1.076, for a 10 µg/m³ increase in PM₂.₅), nor PM₁₀ (7 studies; RR=1.047; CI 0.984, 1.113, for a 10 µg/m³ increase in PM₁₀) [8].

Reliable assessment of PM and NO₂ atmospheric concentrations, emitted from various sources unevenly distributed over the territory with large spatio-temporal concentration variability, is critical to epidemiological studies. The challenge to accurately estimate air pollution exposure is amplified in studies investigating cancer risk, as the latency requires the reconstruction of exposures over long periods [9]. Most published studies to date, in particular those investigating breast cancer risk, relied on exposure estimates at a single point in time (e.g. at inclusion) [10–15] and/or did not consider the subjects’ residential history [10–13,16–18]. The uncertainties arising from these estimates may be source of measurement errors, potentially resulting in biased estimates of the exposure effect [19,20]. Moreover, it has been suggested that outdoor air pollution exposure is insufficiently captured when limiting assessment to the residential location. Considering air pollution levels at the workplace location, may better account for variations in daily activity patterns and further enhance the reliability of exposure estimates [21]. However, to our knowledge, workplace location has not been considered in previous studies investigating the link between air pollution and risk of breast cancer.

To overcome these limitations, the present study examines the association of breast cancer risk with long-term PM₂.₅, PM₁₀ and NO₂ concentrations, estimated at a fine spatial scale at the residential and workplace locations combined. Since breast cancer is a heterogeneous disease, we also investigate this relationship by menopausal status and hormonal subtypes.

# 2. Material and Methods

## 2.1. E3N study

E3N-Générations (Etude Epidémiologique auprès de femmes de l'Education Nationale) is a prospective, national cohort study investigating factors associated with cancer and other chronic diseases in women. To participate in the E3N study, women had to be between 40 and 65 years old, live in metropolitan France, and be insured by a national health insurance plan covering mostly teachers (Mutuelle Générale de l'Education Nationale, MGEN) at the time of inclusion in the cohort between June 1990 and November 1991. They were included

after returning a self-questionnaire and signing an informed consent, in accordance with the French National Commission for Data Protection and Privacy (CNIL). At inclusion, 98,995 women completed the questionnaire, providing information on sociodemographic characteristics, anthropometric measurements, reproductive history, lifestyle, medical history and familial history of cancer [22]. In addition, the participants' birthplace was obtained from the first questionnaire and assigned an urban/rural status based on data from the closest national census. Follow-up questionnaires were sent every 2 to 3 years, with a total of 13 follow-up questionnaires to date. Alcohol-intake was collected from the dietary questionnaire sent in 1993. Breast cancer cases were identified mainly by self-reporting through follow-up questionnaires but for a few from insurance data or the National Service on Causes of Deaths. Overall, nearly 90 % of cases were histologically confirmed.

## 2.2. Study design

The present study used data from the XENAIR case-control study nested in the E3N-Générations cohort [23]. Histologically confirmed incident cases of invasive breast cancer diagnosed between 1990 (inclusion in the E3N-Générations cohort) and 2011 (the 10th questionnaire) were included in the nested case-control study. Breast cancer cases without pathology report confirmation were included because of the very low proportion of self-reported false positives (<5 %). For cases with multiple tumours detected at the same time, the most advanced tumour was considered. Cases must not have a prior diagnosis of any other cancers to be included.

For each breast cancer case, one control was randomly selected by incidence density sampling. At the index date of the case, the controls needed to be in follow-up and without any history of cancer diagnosis. Two groups of cases based on the availability of biological samples (blood or saliva) were used, to ensure the best choice of controls selection and to allow ancillary objectives based on biological samples. For the first group (with a blood sample), controls were matched to cases on the department of residence, age (± 1 year), date (± 3 months), and menopausal status at blood collection. Controls for the second group (without a blood sample) were matched on the same criteria, but at baseline, and additionally matched on availability of a saliva sample.

Women who had missing data on matching variables, as well as their pairs, were excluded (N = 6).

## 2.3. Collection of history of residential and workplace locations

In addition to the information collected in the inclusion and follow-up questionnaires, the addresses of residential and workplace locations (start and end dates for each residence and job) were collected through a specific questionnaire send out to the participants of the XENAIR case-control study in March 2017. Reminders were sent in June and November 2017. The present study focuses on residential and workplace addresses between 1990 and 2011, corresponding to the follow-up period covered by the XENAIR study [23].

## 2.4. Estimation of long-term PM₂.₅, PM₁₀ and NO₂ concentrations

In this study, we used modelled pollutant concentration estimates at the residence and workplace locations as a proxy for individual exposure to ambient PM₂.₅, PM₁₀ and NO₂ concentrations. PM₂.₅, PM₁₀ and NO₂ concentration levels were estimated at the residential and workplace addresses using a LUR model with 50 m x 50 m spatial resolution and coverage from 1990 to 2011. The LUR model has been described previously [24]. Briefly, LUR is a statistical method using geographical data such as topography (land use, road network, buildings height, etc.), and emission sources (road traffic volume, industrial density, etc.) to predict the concentrations at a given site. The parameters used in each LUR model differ according to the pollutant. In the present study, the LUR models for PM₂.₅, PM₁₀ and NO₂, included estimates from a chemical
transport model, length of roads and major roads, density of agriculture and forestry or urban land, residential and commercial density. This model has been validated for the years 2010 and 2011 by comparing its estimates with experimental data collected at monitoring sites, through a hold-out validation, and a back-extrapolation to reconstruct exposure until 1990.

Subjects' residential and workplace addresses were geocoded using a Geographic Information System software ArcMap 10.0 (ArcGIS Locator version 10.0, Environmental System Research Institute - ESRI, Redlands, CA, USA) and the reference street network database (BD Adresse from the French National Geographic Institute (X and Y coordinates) [23]. Addresses were geocoded automatically and 28 % of residential addresses and 65 % of occupational addresses were manually relocated by a trained GIS-technician to improve their accuracy – the high rate for occupational addresses is linked to the fact that people regularly provide the name of the workplace (such as school, university predominantly) rather than the street address. Overall a total of 75 % of residential and 70 % of occupational addresses were geocoded to the exact address, 14 % of residential and 15 % of occupational addresses were located to the street segment and 11 % of residential and 16 % of occupational addresses were geocoded with an accuracy level lower than street segment, i.e., town hall, postal code or city. Annual means of the PM<sub>2.5</sub>, PM<sub>10</sub> and NO<sub>2</sub> concentration estimates were further calculated for each woman, for each calendar year (from 1990 to the index date (date of diagnosis for cases and sampling date for controls)), separately for residential and workplace addresses. The average of the annual mean concentrations (from 1990 to index date) was then calculated for each pollutant, separately for residential and workplace locations. We assumed that E3N women working in the national education system, spent on average nine hours daily at the workplace (including two hours of lunchbreak onsite), five days a week, for at least 36 weeks (i.e. school time) per year, resulting in 18.5 % of the time spent at the workplace. Given that working hours varies according to positions, we rounded up this proportion to 20 %. We further weighted the average pollutant concentrations by the proportion of time spent at home (80 %) and at the workplace (20 %). From the women's retirement, only their residential addresses were taken into account. The combined residential-workplace concentrations for each pollutant refers hereafter to the average of the annual means of the pollutant concentration estimates, weighted according to the time spent at the residence and workplace locations.

## 2.5. Handling of missing data

Under the assumption of missing data at random, multiple imputation was performed for the following variables: education level (3.7 % of missing data), body mass index (BMI) (1.6 % of missing data), alcohol intake (20.7 % of missing data), smoking status (0.5 % of missing data), age at menarche (1.9 % of missing data), parity and age at first full-term pregnancy (AFP) (1.4 % of missing data), and family history of breast cancer (0.8 % of missing data) [25]. We carried out 10 imputations, each with 10 iterations with a multivariate imputation via a chained equations approach (MICE) [25]. Predictors were all variables considered in the different adjustment models. 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. The analyses were then performed in parallel for the 10 imputed datasets, and then pooled into imputed data sets and then pooled using Rubin's laws.

Simple imputation was used for menopausal status at inclusion. If age at menopause was missing and a woman's age at inclusion was less than 51 years (median age at menopause of women in the E3N-Générations cohort), women were considered premenopausal at inclusion, otherwise, women were considered postmenopausal at inclusion [26].

All women with missing data on oral contraceptive use at index date,

had reported 'never use' at baseline. We assumed for these women continued 'never use' during follow-up, since age at cohort inclusion was 40 to 65 years. No imputation was performed for menopausal hormone therapy (MHT) as this variable is highly dependent on age and menopausal status, but a missing data category was created.

## 2.6. Statistical methods

Baseline socio-demographic characteristics and main risk factors for breast cancer have been described by case-control status. The combined residential-workplace pollutant concentration (main exposure) was estimated on a continuous scale. The linearity of the logit of the effect of the quantitative variables on breast cancer risk was investigated using fractional polynomials of degree 2.

Associations of PM<sub>2.5</sub>, PM<sub>10</sub> and NO<sub>2</sub> with breast cancer risk were modelled using unconditional logistic regression models to estimate odds ratios (OR) for a 10-µg/m<sup>3</sup> increase in combined residential-workplace pollutant concentrations and their 95 % confidence intervals (CI). Unconditional logistic regression models were performed in order to prevent the exclusion of incomplete case-control pairs since not all cases and controls within the same pair from the XENAIR study answered to the specific questionnaire on residential and workplace.

Adjustment variables were selected from a literature review and a directed acyclic graph (DAG), and two models emerged from the DAG (Supplementary material, Fig. S1). The first model was adjusted for matching variables (department of residence, age, date, and menopausal status at baseline or at blood collection), education level, urban/rural status of birthplace and follow-up duration (Model 1). The second model was adjusted for matching variables, urban/rural status of birthplace, follow-up duration, and instead of education level was adjusted for physical activity, BMI, alcohol-intake, smoking- status, oral contraceptive use, MHT, mammography, parity, AFP, and breastfeeding (Model 2). We also considered an additional model adding other adjustment variables widely found in the literature: age at menarche, menopausal status at index date, previous history of personal benign breast disease, previous family history of breast cancer (Model 3). All adjustment variables were taken at baseline with the exception of oral contraceptive and MHT variables, collected from the last questionnaire before the index date, and alcohol intake from the dietary questionnaire (Q3, in 1993).

Additional subgroup analyses were conducted according to breast cancer hormone receptor status (ostrogen receptors (ER), progesterone receptors (PR)). These analyses were carried out using an unconditional logistic regression model comparing cases of each hormonal subtype with all controls, adjusted for the variables of the first model (Model 1).

Effect modification analyses by menopausal status during follow-up (considering menopausal status at inclusion and menopausal status at index date), physical activity, BMI, smoking status and urban/rural status of birthplace, were performed. Menopausal status during follow-up was considered in three categories: premenopausal (women who reported premenopausal on the last returned questionnaire), post-menopausal (women who reported postmenopausal on the last returned questionnaire) and women premenopausal at inclusion who transitioned to postmenopausal status before the index date. Test for interaction for each potential effect modifier was computed by the likelihood ratio test.

## 2.7. Additional and sensitivity analyses

Subjects that could not be included in the analyses because of incomplete workplace history have been described by case-control status.

Additional analyses were performed to investigate the association between air pollution and breast cancer risk depending on the grade, stage and histological subtype of the tumor.

A first sensitivity analysis was carried out considering that women spent 30% of the daily time at the workplace (vs. 20% in our main
analysis) [21]. In a second sensitivity analysis, subjects with short follow-up (<2years) were excluded. In addition, to facilitate comparisons with previous studies, we focused analyses on concentration data evaluated at the residential address only. Finally, we performed an analysis considering only active women at baseline.

All statistical tests were two-sided and p values< 0.05 were considered statistically-significant. All analyses were performed using R Studio software version 4.2.0.

## 3. Results

### 3.1. Characteristics of the study population

The selection flowchart of the participants in this case-control study is described in Fig. S2. Briefly, within the E3N cohort, 11,421 women were included in the nested XENAIR case-control study, and received the questionnaire collecting history of residence and workplace locations. Overall, 7824 women returned this questionnaire and were eligible for the present study. Of the 7806 women who completed at least one information in the questionnaire, 5403 had a complete residential and workplace history.

Characteristics of the 2419 breast cancer cases and the 2984 controls are presented in Table 1. The distribution by age was similar in the two groups. Cases were diagnosed, on average at 59.5 (±7.4) years. Cases and controls had a mean follow-up of 12.0 (±5.6) years. Education-level, urban/rural status at birthplace, lifestyle factors, age at menarche, menopausal status and use of oral contraceptives were similar between cases and controls. However, compared to controls, cases had less frequently a child (86.6% vs. 88.8%) and breastfed (54.8% vs. 56.7%). Conversely, cases were more likely to be MHT users (57.0% vs. 55.2%), to have a mammography before inclusion (76.6% vs. 72.0%), and to have a family history of breast cancer in first-degree relatives (17.5% vs. 11.6%) or history of personal benign breast disease (30.1% vs. 23.9%).

The distribution of the combined residential-workplace pollutant concentrations was similar in cases and controls (26.2 (±5.9) µg/m³ among cases vs. 26.2 (±5.8) µg/m³ among controls for PM₂.₅; 35.4 (±8.0) µg/m³ among cases vs. 35.3 (±7.7) µg/m³ among controls for PM₁₀; 30.7 (±15.1) µg/m³ among cases vs 30.1 (±14.6) µg/m³ among controls for NO₂) (Table 1, Figure 1). All women were exposed above the thresholds recommended by World Health Organization (WHO) in 2005 and 2021 [27].

Characteristics of the 1089 subjects (457 cases and 632 controls) who had a complete residential history but an incomplete workplace history are described in Table S1. These subjects were on average older compared to women with a complete residential and workplace history at inclusion (51.3 (±6.5) vs. 47.4 (±5.1) years for cases and 51.8 (±6.4) vs. 47.4 (±5.1) years for controls) and at diagnosis (62.9 (±8.3) vs. 59.4 (±7.4) years respectively) and were more likely to have a secondary education level (16.0% vs. 8.6% respectively). They were comparable in terms of lifestyle factors, except that they were more physically active. Age at menarche was also similar between the two groups. Subjects with an incomplete workplace history had used less frequently an oral contraceptive and were less likely to be nulliparous. However, they were more likely to have used MHT and to have breastfed. The proportion of women who had had menopause was higher at inclusion and at the index date. They were less likely to have a personal or family history of cancer or other breast diseases. Women with incomplete workplace history had similar average annual mean pollutant concentration estimates at the residential address, except for NO₂ whose concentrations were slightly lower compared to women with complete workplace history (28.6 (±14.9) µg/m³ vs 30.7 (±15.1) µg/m³ for cases and 28.1 (±13.9) µg/m³ vs 30.1 (±14.6) µg/m³ for controls, respectively).

<table><caption>**Table 1**

Characteristics of the 2419 cases and 2984 controls, France, 1990–2011.</caption><thead><tr><th rowspan="2">Characteristics</th><th>Cases (n = 2419)</th><th>Controls (n = 2984)</th></tr><tr><th>n (%) or mean ± sd</th><th>n (%) or mean ± sd</th></tr></thead><tbody><tr><td>Age at baseline (years)</td><td>47.4 ± 5.1</td><td>47.4 ± 5.1</td></tr><tr><td>Age at diagnosis (years)</td><td>59.5 ± 7.4</td><td>12.0 ± 5.5</td></tr><tr><td>Follow-up duration (years)</td><td>12.0 ± 5.7</td><td>26.2 ± 5.8</td></tr><tr><td>LUR PM<sub>2.5</sub> concentration<sup>a</sup> (µg/m<sup>3</sup>)</td><td>26.2 ± 5.9</td><td>35.3 ± 7.7</td></tr><tr><td>LUR PM<sub>10</sub> concentration<sup>a</sup> (µg/m<sup>3</sup>)</td><td>35.4 ± 8.0</td><td>30.1 ± 14.6</td></tr><tr><td>LUR NO<sub>2</sub> concentration<sup>a</sup> (µg/m<sup>3</sup>)</td><td>30.7 ± 15.1</td><td></td></tr><tr><td>Level of education</td><td></td><td></td></tr><tr><td>Secondary</td><td>208 (8.6)</td><td>246 (8.2)</td></tr><tr><td>1- to 2-year university degree</td><td>1138 (47.0)</td><td>1530 (51.3)</td></tr><tr><td>≥ 3 year university degree</td><td>978 (40.4)</td><td>1105 (37.0)</td></tr><tr><td>Missing</td><td>95 (3.9)</td><td>103 (3.5)</td></tr><tr><td>Status of the birthplace</td><td></td><td></td></tr><tr><td>Rural</td><td>730 (30.2)</td><td>921 (30.9)</td></tr><tr><td>Urban</td><td>1689 (69.8)</td><td>2063 (69.1)</td></tr><tr><td>Body mass index (kg/m<sup>2</sup>)</td><td>22.3 ± 2.8</td><td>22.2 ± 2.9</td></tr><tr><td>Missing</td><td>37 (1.5)</td><td>50 (1.7)</td></tr><tr><td>Physical activity (METs-h/week)</td><td>38.2 ± 24.4</td><td>39.7 ± 25.4</td></tr><tr><td>Alcohol drinking<sup>b</sup> (g/day)</td><td>12.6 (14.5)</td><td>11.5 (13.7)</td></tr><tr><td>Missing</td><td>521 (21.5)</td><td>595 (19.9)</td></tr><tr><td>Smoking status</td><td></td><td></td></tr><tr><td>Never or former smoker</td><td>1257 (52.0)</td><td>1582 (53.0)</td></tr><tr><td>Current smoker</td><td>1149 (47.4)</td><td>1388 (46.5)</td></tr><tr><td>Missing</td><td>13 (0.5)</td><td>14 (0.5)</td></tr><tr><td>Age at menarche (years)</td><td>12.7 ± 1.4</td><td>12.8 ± 1.4</td></tr><tr><td>Missing</td><td>46 (1.9)</td><td>59 (2.0)</td></tr><tr><td>Previous use of oral contraceptives<sup>c</sup></td><td></td><td></td></tr><tr><td>Yes</td><td>1600 (66.1)</td><td>2000 (67.0)</td></tr><tr><td>No</td><td>819 (33.9)</td><td>984 (33.0)</td></tr><tr><td>Parity and age at first full-term pregnancy (years)</td><td></td><td></td></tr><tr><td>Nulliparous</td><td>324 (13.4)</td><td>334 (11.2)</td></tr><tr><td>1 or 2 children and AFP &lt; 30</td><td>1239 (51.2)</td><td>1551 (52.0)</td></tr><tr><td>1 or 2 children and AFP ≥ 30</td><td>290 (12.0)</td><td>267 (8.9)</td></tr><tr><td>≥ 3</td><td>527 (21.8)</td><td>794 (26.6)</td></tr><tr><td>Missing</td><td>39 (1.6)</td><td>38 (1.3)</td></tr><tr><td>Breastfeeding</td><td>2.6 ± 4.0</td><td>2.8 ± 4.3</td></tr><tr><td>Menopausal status at inclusion</td><td></td><td></td></tr><tr><td>Premenopausal</td><td>1771 (73.2)</td><td>2206 (73.9)</td></tr><tr><td>Postmenopausal</td><td>646 (26.7)</td><td>769 (25.8)</td></tr><tr><td>Missing</td><td>2 (0.1)</td><td>9 (0.3)</td></tr><tr><td>Menopausal status at index date</td><td></td><td></td></tr><tr><td>Premenopausal</td><td>453 (18.7)</td><td>495 (16.6)</td></tr><tr><td>Postmenopausal</td><td>1966 (81.3)</td><td>2489 (83.4)</td></tr><tr><td>Use of menopausal replacement therapy<sup>d</sup></td><td></td><td></td></tr><tr><td>Yes</td><td>1378 (57.0)</td><td>1647 (55.2)</td></tr><tr><td>No</td><td>1020 (42.2)</td><td>1304 (43.7)</td></tr><tr><td>Missing</td><td>21 (0.9)</td><td>33 (1.1)</td></tr><tr><td>Mammography during the previous follow-up period</td><td></td><td></td></tr><tr><td>Yes</td><td>1853 (76.6)</td><td>2149 (72.0)</td></tr><tr><td>No</td><td>566 (23.4)</td><td>835 (28.0)</td></tr><tr><td>Previous family history of breast cancer</td><td></td><td></td></tr><tr><td>First-degree relative<sup>e</sup></td><td>424 (17.5)</td><td>347 (11.6)</td></tr><tr><td>Second degree relatives</td><td>362 (15.0)</td><td>379 (12.7)</td></tr><tr><td>No familial history</td><td>1616 (66.8)</td><td>2230 (74.7)</td></tr><tr><td>Missing</td><td>17 (0.7)</td><td>28 (0.9)</td></tr><tr><td>Previous history of personal benign breast disease</td><td></td><td></td></tr><tr><td>Yes</td><td>728 (30.1)</td><td>713 (23.9)</td></tr><tr><td>No</td><td>1691 (69.9)</td><td>2271 (76.1)</td></tr></tbody></table>

SD, standard deviation; MET, metabolic Equivalent of Task; AFP, age at first full-term pregnancy; PM₂.₅, particulate matter with a diameter < 2.5 µg/m³; PM₁₀, particulate matter with a diameter < 10 µg/m³; NO₂, nitrogen dioxide

a PM₂.₅, PM₁₀ and NO₂ concentrations correspond to the average of the annual means (1990-index date) at the subject's residential and workplace location combined

b Alcohol consumption collected through the food questionnaire (Q3)

c Use of oral contraceptive collected between Q1 and index date

d Use of menopausal replacement therapy collected between Q2 and index date

e Breast cancer among parents, siblings or children
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Distribution of the average annual mean exposure to PM<sub>2.5</sub>, PM<sub>10</sub> and NO<sub>2</sub>, at residential and workplace combined during follow-up by case/control status, France, 1990–2011. The solid curve represents the distribution of the mean annual concentration levels for PM<sub>2.5</sub>, PM<sub>10</sub> and NO<sub>2</sub>, if it followed a normal distribution. The vertical dashed line corresponds to the 2021 WHO reference guidelines for PM<sub>2.5</sub>, PM<sub>10</sub> and NO<sub>2</sub>.</figcaption></figure>

## 3.2. Evolution of PM<sub>2.5</sub>, PM<sub>10</sub> and NO<sub>2</sub> concentrations

In 1990, the median PM<sub>2.5</sub>, PM<sub>10</sub> and NO<sub>2</sub> concentrations, were respectively 29.2 µg/m<sup>3</sup> (interquartile range, IQR=9.4 µg/m<sup>3</sup>), 38.0 µg/m<sup>3</sup> (IQR=10.6 µg/m<sup>3</sup>) and 26.6 µg/m<sup>3</sup> (IQR=19.4 µg/m<sup>3</sup>) for residential addresses and respectively 29.7 µg/m<sup>3</sup> (IQR=9.5 µg/m<sup>3</sup>), 38.0 µg/m<sup>3</sup> (IQR=11.0 µg/m<sup>3</sup>) and 28.1 µg/m<sup>3</sup> (IQR=20.2 µg/m<sup>3</sup>) for workplace addresses (Figure 2). PM<sub>2.5</sub> and PM<sub>10</sub> concentrations decreased

largely until 2000, gradually between 2000 and 2011, except in 2003 when particulate concentrations peaked, mainly due to specific weather conditions (a very long European heatwave) resulting in poor dispersion of the pollutants. The decrease in NO<sub>2</sub> concentrations was progressive over the whole follow-up period. In 2011, the median PM<sub>2.5</sub>, PM<sub>10</sub> and NO<sub>2</sub> concentrations were respectively 16.2 µg/m<sup>3</sup> (IQR=4.3 µg/m<sup>3</sup>), 22.2 µg/m<sup>3</sup> (IQR=5.9 µg/m<sup>3</sup>) and 19.3 µg/m<sup>3</sup> (IQR=12.4 µg/m<sup>3</sup>) for residential locations and 16.4 µg/m<sup>3</sup> (IQR=4.4 µg/m<sup>3</sup>), 22.4 µg/m<sup>3</sup>

<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Evolution of the annual mean hourly atmospheric concentrations (median, 1st and 3rd quartiles) for PM<sub>2.5</sub>, PM<sub>10</sub> and NO<sub>2</sub>, at the residential addresses and workplace addresses of the study subjects, France, 1990–2011. Horizontal dashed lines correspond to 2005 and 2021 WHO reference guidelines for PM<sub>2.5</sub>, PM<sub>10</sub> and NO<sub>2</sub>.</figcaption></figure>
(IQR=6.1 µg/m³) and 20.4 µg/m³ (IQR=13.2 µg/m³) for workplace locations. In 2011, these concentrations remained well above the current WHO thresholds, especially for PM₂.₅. Correlation between the average of the annual mean exposure to PM₂.₅, PM₁₀ and NO₂ at residential and workplace combined were high, 0.91 for PM₂.₅ and PM₁₀, 0.82 for PM₂.₅ and NO₂ and 0.83 for PM₁₀ and NO₂ (Supplementary material, Table S2).

### 3.3. Association between PM2.5, PM10 and NO2 concentrations and breast cancer risk

There was a positive association between combined residential-workplace concentration estimates for PM₂.₅ and risk of breast cancer, with ORs of 1.28 (CI 1.00, 1.63) and 1.28 (CI 1.00, 1.64) per each 10-µg/m³ increment, for first and second model, respectively (Table 2). For the third adjustment model, the association with PM₂.₅ was not statistically-significant but suggested a trend towards increased risk of breast cancer with OR of 1.24 (CI 0.97, 1.58). For PM₁₀ and NO₂ concentrations, a statistically non-significant positive association with breast cancer risk was observed in the three models (as an example: OR=1.09; CI 0.92, 1.30 for PM₁₀ and OR=1.05; CI 0.97, 1.13 for NO₂ in Model 1) (Table 2). Given that risk estimates in the main analyses were similar for the three adjustment models, all additional and sensitivity analyses were performed using the first model, containing fewer variables and thus less uncertainties arising from multiple imputations.

When considering pollutant concentration estimates at the residential address only, ORs were similar to those obtained by weighted residential-workplace pollutant concentrations (OR=1.29; CI 1.02, 1.62 for PM₂.₅; OR=1.10; CI 0.93, 1.31 for PM₁₀ and OR=1.05; CI 0.98, 1.13 for NO₂) (Table 3).

By hormone receptor status, an increase of breast cancer risk, albeit

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

Odds ratios (ORs), 95% confidence intervals (95% CIs) and associated *p* values, of breast cancer among 2419 cases and 2984 controls, for the association with the average of the annual mean exposure to PM₂.₅, PM₁₀ and NO₂, at residential and workplace addresses combined, in all women, France, 1990–2011.

<table><thead><tr><th></th><th>OR (95% CI)</th><th>P value</th></tr></thead><tbody><tr><td colspan="3"><strong>PM₂.₅ (for an increase of 10 µg/m³)</strong></td></tr><tr><td>Model 1<sup>f</sup></td><td>1.28 (1.00, 1.63)</td><td>0.047</td></tr><tr><td>Model 2<sup>g</sup></td><td>1.28 (1.00, 1.64)</td><td>0.046</td></tr><tr><td>Model 3<sup>h</sup></td><td>1.24 (0.97, 1.58)</td><td>0.086</td></tr><tr><td colspan="3"><strong>PM₁₀ (for an increase of 10 µg/m³)</strong></td></tr><tr><td>Model 1<sup>f</sup></td><td>1.09 (0.92, 1.30)</td><td>0.318</td></tr><tr><td>Model 2<sup>g</sup></td><td>1.10 (0.92, 1.31)</td><td>0.291</td></tr><tr><td>Model 3<sup>h</sup></td><td>1.07 (0.90, 1.28)</td><td>0.455</td></tr><tr><td colspan="3"><strong>NO₂ (for an increase of 10 µg/m³)</strong></td></tr><tr><td>Model 1<sup>f</sup></td><td>1.05 (0.97, 1.13)</td><td>0.222</td></tr><tr><td>Model 2<sup>g</sup></td><td>1.05 (0.97, 1.13)</td><td>0.210</td></tr><tr><td>Model 3<sup>h</sup></td><td>1.05 (0.97, 1.13)</td><td>0.242</td></tr></tbody></table>

OR, Odds ratio; 95% CI, 95% confidence intervals; PM₂.₅, particulate matters with a diameter < 2.5 µg/m³; PM₁₀, particulate matters with a diameter < 10 µg/m³; NO₂, nitrogen dioxide

f Adjusted for matching variables (age at inclusion or at blood collection, date of return of inclusion questionnaire or at blood collection, menopausal status at inclusion or at blood collection, department of residence at inclusion or at blood collection, availability of a salivary sample), level of education, urban/rural status of birthplace and follow-up duration.

g Adjusted for matching variables (age at inclusion or at blood collection, date of return of inclusion questionnaire or at blood collection, menopausal status at inclusion or at blood collection, department of residence at inclusion or at blood collection, availability of a salivary sample), and physical activity, body mass index, alcohol intake, smoking status, oral contraceptive use, menopausal hormone therapy use, mammography, parity, age at first full-term pregnancy, breastfeeding and follow-up duration.

h Adjusted for model 1 variables, and age at menarche, previous family of breast cancer, personal history of benign breast cancer, menopausal status at index date and follow-up duration.

<caption>**Table 3**</caption>

Odds ratios (ORs) and 95% confidence intervals (95% CIs) for the association between breast cancer and average of the annual means concentrations at residential location, France, 1990–2011.

<table><thead><tr><th></th><th>Cases/controls</th><th>OR<sup>i</sup> (95% CI)</th></tr></thead><tbody><tr><td>PM₂.₅ (for an increment of 10 µg/m³)</td><td>2419/2984</td><td>1.29 (1.02, 1.62)</td></tr><tr><td>PM₁₀ (for an increment of 10 µg/m³)</td><td>2419/2984</td><td>1.10 (0.93, 1.31)</td></tr><tr><td>NO₂ (for an increment of 10 µg/m³)</td><td>2419/2984</td><td>1.05 (0.98, 1.13)</td></tr></tbody></table>

OR, Odds ratio; 95% CI, 95% confidence intervals; PM₂.₅, particulate matters with a diameter < 2.5 µg/m³; PM₁₀, particulate matters with a diameter < 10 µg/m³; NO₂, nitrogen dioxide

i Adjusted for matching variables (age at inclusion or at blood collection, date of return of inclusion questionnaire or at blood collection, department of residence at inclusion or at blood collection, availability of a salivary sample), level of education, urban/rural status of birthplace, and follow-up duration (Model 1)

not statistically significant, was observed for ER-positive (ER+) breast cancer (OR=1.31; CI 0.99, 1.73 for PM₂.₅ and OR=1.05; CI 0.96, 1.14 for NO₂) and for ER-positive and PR-positive (ER+PR+) breast cancer (OR=1.32; CI 0.97, 1.79 for PM₂.₅ and OR=1.04; CI 0.95, 1.14 for NO₂) (Table 4). Results were similar for ER-negative (ER-) breast cancer and for ER-negative PR-negative (ER-PR-) breast cancer, although with wider 95% CI probably due to the limited number of cases included in this analysis (Table 4).

There were no statistically significant interactions with menopausal status for any of the pollutants (all *p* values for interaction >0.05). Nevertheless, considering the relevance of menopausal status in the

<caption>**Table 4**</caption>

Odds ratios (ORs) and 95% confidence intervals (95% CIs) for the association between breast cancer and average of the annual mean exposure to PM₂.₅ PM₁₀ and NO₂, at residential and workplace combined, by hormone receptor status, France, 1990–2011.

<table><thead><tr><th></th><th>Cases/controls</th><th>OR<sup>i</sup> (95% CI)</th></tr></thead><tbody><tr><td colspan="3"><strong>PM₂.₅ (for an increment of 10 µg/m³)</strong></td></tr><tr><td>ER-</td><td>347/2984</td><td>1.29 (0.77, 2.16)</td></tr><tr><td>ER+</td><td>1662/2984</td><td>1.31 (0.99, 1.73)</td></tr><tr><td>PR-</td><td>681/2984</td><td>1.28 (0.87, 1.89)</td></tr><tr><td>PR+</td><td>1328/2984</td><td>1.30 (0.96, 1.76)</td></tr><tr><td>ER-/PR-</td><td>282/2984</td><td>1.36 (0.76, 2.41)</td></tr><tr><td>ER-/PR+</td><td>65/2984</td><td>0.97 (0.33, 2.83)</td></tr><tr><td>ER+/PR-</td><td>399/2984</td><td>1.26 (0.78, 2.03)</td></tr><tr><td>ER+/PR+</td><td>1254/2984</td><td>1.32 (0.97, 1.79)</td></tr><tr><td>Missing</td><td>403/2984</td><td>1.12 (0.72, 1.74)</td></tr><tr><td colspan="3"><strong>PM₁₀ (for an increment of 10 µg/m³)</strong></td></tr><tr><td>ER-</td><td>347/2984</td><td>1.06 (0.73, 1.53)</td></tr><tr><td>ER+</td><td>1662/2984</td><td>1.06 (0.87, 1.30)</td></tr><tr><td>PR-</td><td>681/2984</td><td>1.00 (0.75, 1.32)</td></tr><tr><td>PR+</td><td>1328/2984</td><td>1.09 (0.88, 1.35)</td></tr><tr><td>ER-/PR-</td><td>282/2984</td><td>1.06 (0.70, 1.61)</td></tr><tr><td>ER-/PR+</td><td>65/2984</td><td>0.97 (0.44, 2.11)</td></tr><tr><td>ER+/PR-</td><td>399/2984</td><td>0.97 (0.69, 1.38)</td></tr><tr><td>ER+/PR+</td><td>1254/2984</td><td>1.09 (0.88, 1.37)</td></tr><tr><td>Missing</td><td>403/2984</td><td>1.14 (0.84, 1.56)</td></tr><tr><td colspan="3"><strong>NO₂ (for an increment of 10 µg/m³)</strong></td></tr><tr><td>ER-</td><td>347/2984</td><td>1.06 (0.91, 1.24)</td></tr><tr><td>ER+</td><td>1662/2984</td><td>1.05 (0.96, 1.14)</td></tr><tr><td>PR-</td><td>681/2984</td><td>1.08 (0.96 -1.21)</td></tr><tr><td>PR+</td><td>1328/2984</td><td>1.03 (0.94, 1.13)</td></tr><tr><td>ER-/PR-</td><td>282/2984</td><td>1.08 (0.91, 1.29)</td></tr><tr><td>ER-/PR+</td><td>65/2984</td><td>0.96 (0.67, 1.36)</td></tr><tr><td>ER+/PR-</td><td>399/2984</td><td>1.07 (0.92, 1.24)</td></tr><tr><td>ER+/PR+</td><td>1254/2984</td><td>1.04 (0.95, 1.14)</td></tr><tr><td>Missing</td><td>410/2984</td><td>1.04 (0.90, 1.20)</td></tr></tbody></table>

OR, Odds ratio; 95% CI, 95% confidence intervals; PM₂.₅, particulate matters with a diameter < 2.5 µg/m³; PM₁₀, particulate matters with a diameter < 10 µg/m³; NO₂, nitrogen dioxide

j Adjusted for matching variables (age at inclusion or at blood collection, date of return of inclusion questionnaire or at blood collection, department of residence at inclusion or at blood collection, availability of a salivary sample), level of education, urban/rural status of birthplace and follow-up duration (Model 1)
aetiology of breast cancer; we performed stratified analyses on menopausal status during follow-up. The results suggested a similar increase risk associated with PM<sub>2.5</sub> and NO<sub>2</sub> in premenopausal women, in women who transitioned to postmenopausal status before the index date and in postmenopausal women (Table 5).

There was no evidence of effect modification by education level, total physical activity, BMI, smoking status and breastfeeding (p for interaction >0.05) (Table S3).

### 3.4. Additional and sensitivity analyses

The association between air pollution and breast cancer risk was not substantially different depending on the grade, stage and histological subtype (Supplementary material, Table S4). Similar results were also obtained when increasing the time spent at the workplace location (30%, vs. 20% in the main analyses) (OR=1.27; CI 0.99, 1.62 for PM<sub>2.5</sub>; OR=1.09; CI 0.91, 1.29 for PM<sub>10</sub> and OR=1.05; CI 0.97, 1.13 for NO<sub>2</sub>) (Supplementary material, Table S5). Moreover, after excluding women followed for less than two years (n = 248), the ORs associated with a 10-µg/m<sup>3</sup> increase of combined residential-workplace concentration were consistent with ORs for the overall population (OR=1.30; CI 1.01, 1.67 for PM<sub>2.5</sub>; OR=1.08; CI 0.90, 1.30 for PM<sub>10</sub> and OR=1.05; CI 0.97, 1.13 for NO<sub>2</sub>) (Supplementary material, Table S6).

When excluding women who were already retired at baseline (606 cases and 722 controls), the OR was slightly higher than for the overall study population for PM<sub>2.5</sub>, (OR=1.61; CI 0.96, 2.71) and similar for PM<sub>10</sub> and NO<sub>2</sub> (OR=1.02; CI 0.71, 1.45 for PM<sub>10</sub> and OR=0.99; CI 0.85, 1.16 for NO<sub>2</sub>) (Supplementary material Table S7).

### 4. Discussion

To the best of our knowledge, this case-control study is the first study

<caption>**Table 5**</caption>

Odds ratios (ORs) and 95% confidence intervals (95% CIs) for the association between breast cancer and average of the annual mean exposure to PM<sub>2.5</sub>, PM<sub>10</sub> and NO<sub>2</sub>, at residential and workplace combined, by menopausal status during follow-up, France, 1990–2011.

<table><thead><tr><th></th><th>Cases/ controls</th><th>OR<sup>k</sup> (95% CI)</th><th>p-value<sup>l</sup></th></tr></thead><tbody><tr><td>PM<sub>2.5</sub> (for an increment of 10 µg/m<sup>3</sup>)</td><td></td><td></td><td>0.99</td></tr><tr><td>Premenopausal</td><td>453/495</td><td>1.26 (0.94, 1.70)</td><td></td></tr><tr><td>Premenopausal at inclusion &amp; postmenopausal before index date</td><td>1312/1700</td><td>1.28 (0.98, 1.67)</td><td></td></tr><tr><td>Postmenopausal</td><td>633/756</td><td>1.27 (0.95, 1.70)</td><td></td></tr><tr><td>PM<sub>10</sub> (for an increment of 10 µg/m<sup>3</sup>)</td><td></td><td></td><td>0.90</td></tr><tr><td>Premenopausal</td><td>453/495</td><td>1.09 (0.87, 1.36)</td><td></td></tr><tr><td>Premenopausal at inclusion &amp; postmenopausal before index date</td><td>1312/1700</td><td>1.07 (0.89, 1.30)</td><td></td></tr><tr><td>Postmenopausal</td><td>633/756</td><td>1.13 (0.91, 1.39)</td><td></td></tr><tr><td>NO<sub>2</sub> (for an increment of 10 µg/m<sup>3</sup>)</td><td></td><td></td><td>0.86</td></tr><tr><td>Premenopausal</td><td>453/495</td><td>1.07 (0.96, 1.19)</td><td></td></tr><tr><td>Premenopausal at inclusion &amp; postmenopausal before index date</td><td>1312/1700</td><td>1.05 (0.97, 1.14)</td><td></td></tr><tr><td>Postmenopausal</td><td>633/756</td><td>1.04 (0.94, 1.15)</td><td></td></tr></tbody></table>

OR, Odds ratio; 95% CI, 95% confidence intervals; PM<sub>2.5</sub>, particulate matters with a diameter < 2.5 µg/m<sup>3</sup>; PM<sub>10</sub>, particulate matters with a diameter < 10 µg/m<sup>3</sup>; NO<sub>2</sub>, nitrogen dioxide

<sup>k</sup> Adjusted for matching variables (age at inclusion or at blood collection, date of return of inclusion questionnaire or at blood collection, department of residence at inclusion or at blood collection, availability of a salivary sample), level of education, urban/rural status of birthplace and follow-up duration (Model 1)

<sup>l</sup> P-values derived from a likelihood ratio test

to investigate combined residential-workplace air pollution exposure over 22 years, and its association with breast cancer risk. Our results suggest a positive association between cumulative long-term exposure to air pollution including PM<sub>2.5</sub>, PM<sub>10</sub> and NO<sub>2</sub> exposure. Results were consistent across the three adjustment models and robust to sensitivity analyses. Findings were comparable when using only residence concentration estimates suggesting that residential data could serve as a proxy for overall exposure in the population of the present study.

These findings are in line with several previous studies that have found an increase in breast cancer risk among subjects exposed to higher levels of PM<sub>2.5</sub> and similar to our study [8], two studies investigated PM<sub>2.5</sub> exposure level estimates over a period of over 20 years [12,28]. A recent meta-analysis summarized these results and reported a pooled RR of 1.006 (CI 0.941–1.076) [8]. However, none of these studies considered workplace concentrations in addition to residential concentrations. Of note, the mean of PM<sub>2.5</sub> exposure levels over all studies included in this meta-analysis of 10.9 µg/m<sup>3</sup>, was quite lower than in the present study.

Our results on PM<sub>10</sub> exposure are in line with the last published meta-analysis showing a border line positive association with breast cancer risk [8]. Among the seven studies published to date, only one reported a statistically significant increased breast cancer risk (OR=1.19; CI 1.09, 1.31), for a 10 µg/m<sup>3</sup> increment of PM<sub>10</sub> [8].

Current evidence for an association with breast cancer risk appears to be stronger for NO<sub>2</sub> [7]. Our findings of a positive association, was consistent with the results of a previous study on NO<sub>2</sub> exposure in a larger sample of the E3N cohort population, associated with a significant increase of breast cancer risk (OR=1.09; CI: 1.01, 1.31) for an interquartile range increase in NO<sub>2</sub> levels (estimated with a LUR model = 17.8 µg/m<sup>3</sup>) [24], as well as the results of two recent meta-analyses reporting an association for NO<sub>2</sub> with breast cancer risk (RR=1.015; CI 1.003, 1.028 and RR= 1.023; CI 1.005, 1.041) [7,8].

Our study did not suggest a differential effect according to menopausal status, unlike several previous studies [12,14,17,29–31]. However, the question of whether air pollution affects breast cancer risk differently according to menopausal status is still open and needs to be investigated further.

The results do not support that the relationship between pollutant concentrations and breast cancer risk varies by hormone receptor status. However we observed a trend towards an increased risk of ER+ and ER+PR+ breast cancers consistent with several other studies [4,28,32].

The differences might be due to the higher exposure levels observed in our study population compared to those in previous studies [7,8]. However, difference in exposure assessment methodology may also explain the heterogeneity of results between studies in terms of spatial resolution (from <100 m to 10 kilometres), geographical coverage (regional to national coverage) and temporal resolution [7,8]. Previous studies have investigated whether combined residential-workplace concentration of NO<sub>2</sub> better reflects overall personal concentration, suggesting that workplace locations are strong determinants of NO<sub>2</sub> concentration [33,34]. Nethery et al. in their review on air pollution exposure technologies in 2008 concluded that combining workplace location pollutant concentrations with residential location concentrations improves estimates of personal exposure by taking into account variation in time-activity patterns that result in exposure differences for individuals living in the same community, in fact correlation between NO estimates with land use regression and measurements changed from r = 0.49 to r = 0.55 [33]. Two studies assessed the impact of NO<sub>2</sub> and PM concentrations at the school or workplace on health effects (such as birth outcomes, overweight and obesity), over shorter periods, with divergent findings [35,36]. The study by de Bont et al. investigating the associations between ambient air pollution exposure at both home and school location, and obesity in children, reported higher risks for childhood overweight and obesity, especially for a higher exposure at school location [35]. Conversely, in the study by Dibben et al., studying maternal ambient air pollution exposure related to birth outcomes, the
inclusion of workplace location exposure showed negligible differences
in the effect size compared to considering residential exposure only
[36].

No previous studies considered combined residential-workplace
concentration over long periods, and none of the studies published to
date on the effects of air pollution on breast cancer risk considered
concentrations at the workplace addresses. In this study, the small dif-
ferences observed in breast cancer risk estimates, whether or not the
workplace concentration was considered and the strong correlation
between concentration at the residential and workplace locations
(r = 0.95 for PM<sub>2.5</sub>; r = 0.93 for PM<sub>10</sub>; r = 0.90 for NO<sub>2</sub>) may suggest
that concentrations at the residential address may be a good proxy of the
overall exposure [33]. Yet, we did not consider daily variations in
pollutant concentrations. Daytime concentrations at the workplace,
especially for NO<sub>2</sub> could be higher than concentrations averaged over
24 h. Moreover, the lack of difference in risk estimates could also be
explained by the high proportion of retired women (i.e. 38% were
already retired at baseline).

Although the biological mechanisms by which air pollution may
cause breast cancer remains unclear, several key biological mechanisms
have been proposed. Air pollution has been reported to increase breast
density [37], which is an important risk factor for breast cancer [38]. In
addition, several pollutants, including NO₂, are suspected of having
endocrine-disrupting properties [39]. Hormone exposure is one of the
key drivers in the development of breast cancer [40]. Also, air pollution,
including PM is reported to induce inflammation and oxidation stress,
associated with both breast cancer development and progression [41].
Air pollutants can also cause epigenetic modifications, such as DNA
methylation, involved in tumorigenesis [42]. Furthermore, PM₂.₅, PM₁₀
and NO₂ are traffic-related air pollutants (TRAP), representing a com-
plex mixture of several pollutants such as polycyclic aromatic hydro-
carbons (PAHs) suspected of being carcinogenic due to their potential
cytotoxic, mutagenic, epigenetic, inflammatory and
endocrine-disrupting effects [39,43]. Finally, exposure to air pollution
can alter immune function, resulting in suppression of immune re-
sponses and modulation of immune cell activity [44].

This study presents several strengths. First, it is based on a relatively
large sample size (5403 subjects), from a large prospective cohort with a
high participation rate during follow-up questionnaires, ranging from
77% to 92% [22]. The participation rate in the present study for the
specific questionnaire, gathering information of residential and work-
place history, is relatively high (65%) given the advanced average age of
the participants at the time of data collection (74 years) and that some
have since died. Thanks to the large amount of data collected within the
E3N-Générations cohort, we could adjust for most of the potential
confounding factors known to date. Moreover, the long-term follow-up
of up to 22 years allowed for consider the effect of chronic exposure to
PM<sub>2.5</sub>, PM<sub>10</sub> and NO<sub>2</sub>, in contrast to most other studies that measure
exposure over shorter periods [7,8]. A major strength of our study was
the availability of the residential and workplace history to obtain the
finest possible air pollutant concentration estimates. Furthermore, most
women from the E3N-Générations cohort were teachers or worked in
affiliated occupations, thus it can be assumed that exposures related to
the occupational tasks were homogeneously low among study
participants.

This present study has also several limitations. Despite the two de-
cades of follow-up, concentration information for the three pollutants
before 1990 was lacking. This is due to scarcity of historical air pollutant
concentration data precluding assessment of air pollution concentration
before 1990, leading to left truncation lifetime exposure data on
potentially relevant critical windows exposure for breast cancer (in
utero, early years of life, puberty, etc.) [39,45,46]. Nonetheless, we
adjusted for urban/rural status at the birthplace as a surrogate of air
pollution concentration before inclusion into the cohort. Moreover, we
took into account menopausal status in our analysis. We looked at this
association specifically in women who had undergone menopause

during follow-up (pre-menopausal at inclusion and post-menopausal
before index date) which is a significant period in a woman's life,
marked by hormonal changes that may influence her susceptibility to
various health conditions, including breast cancer due to structural and
functional changes in breast tissue [45]. Failure to consider exposure
during windows of susceptibility have led to non-differential bias in the
OR estimates, resulting in inaccurate estimation of the true impact of air
pollution on breast cancer risk [46]. Finally, given that the majority of
our population lived and worked in urban areas (where most of the
emission sources of the pollutants taken into account in this study are
located), the exposure contrast was perhaps insufficient to identify a
differential risk between residential and combined residential and
workplace exposure.

5. Conclusion

In conclusion, this study found a positive association between the
average of the annual means of PM2.5 atmospheric concentrations at
residential and workplace locations combined and the risk of overall
breast cancer. An increased risk of breast cancer was also suggested for
PM10 and NO2. Yet, these results need to be confirmed by future studies
in other population.

Future epidemiological studies should also explore the impact of
exposure while commuting and of diurnal variations in pollutants'
concentrations on the risk of breast cancer.

Funding

This work was financially supported by The French National
Research Program for Environmental and Occupational Health of Anses
(2019/1/242), Fondation de France (n°00099899) and the canceropole
of Lyon Auvergne Rhône-Alpes (Grant OncoStarter). The part of the
project allowing to collect residential and workplace addresses was
financially supported by the French National Institute for Cancer (INCa)
(SHSESP17-070). The research was carried out using data from the
French National Institute of Health and Medical Research (Inserm) E3N-
generation cohort, which was established and maintained with the
support of the Mutuelle Générale de l'Education Nationale (MGEN), the
Gustave Roussy Institute and the Ligue Contre le Cancer. E3N-
Generations 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 (#2103 586016). Marie Ramel-Delobel was
supported with a PhD fellowship from the French League against cancer.
Amina Amadou was supported by the Regional Committee of the French
League against Cancer of the Savoie Region. The funding body had no
role in the conception, design, planning, or writing of the study.

CRediT authorship contribution statement

Pietro Salizzoni: Writing – review & editing. John Gulliver:
Writing – review & editing. Gianluca Severi: Writing – review & edit-
ing. Francesca Romana Mancini: Writing – review & editing. Thomas
Coudon: Writing – review & editing, Funding acquisition, Data cura-
tion. Lény Grassot: Writing – review & editing, Software, Data curation.
Marie Ramel-Delobel: Writing – review & editing, Software, Method-
ology, Data curation. Elodie Faure: Writing – review & editing, Data
curation. Delphine Praud: Writing – review & editing, Validation, Su-
pervision, Project administration, Methodology, Funding acquisition,
Conceptualization. Margaux Duboeuf: Writing – original draft, Soft-
ware, Methodology, Formal analysis. Amina Amadou: Writing – review
& editing. Béatrice Fervers: Writing – review & editing, Funding
acquisition.
# 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

We gratefully acknowledge the E3N-generation cohort participants for providing data and the practitioners for providing pathology reports. We also thank Charlotte Carretero, Matthieu Dubuis and Kathleen Godet for addresses geocoding, Elodie Belladame for the datamanagement of residential and workplace addresses, Céline Kernaleguen, Maxime Valdenaire, Laureen Dartois, Roselyn Gomes-Rima, and Amandine Gelot for the data management and the assistance for the cohort data collection.

# Data sharing

Access to the data can be requested from the corresponding author upon reasonable request and after acceptance by the E3N-generation cohort coordinator and its scientific board.

# Appendix A. Supporting information

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.ejca.2024.114293.

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