# Air pollution and bone health outcomes: Periods of susceptibility from pregnancy to childhood

Lieke E J M Scheepers, Anne-Claire Binter, Susana Santos, Sami Petricola, Fernando Rivadeneira, Vincent W V Jaddoe, Mònica Guxens, Fay H Johnston

► To cite this version:

Lieke E J M Scheepers, Anne-Claire Binter, Susana Santos, Sami Petricola, Fernando Rivadeneira, et al. Air pollution and bone health outcomes: Periods of susceptibility from pregnancy to childhood. Environment International, 2025, 203, pp.109739. 10.1016/j.envint.2025.109739. hal-05249786

HAL Id: hal-05249786

https://hal.science/hal-05249786v1

Submitted on 15 Nov 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
Full length article

# Air pollution and bone health outcomes: Periods of susceptibility from pregnancy to childhood

Check for
updates

Lieke E.J.M. Scheepers<sup>a,∗</sup>, Anne-Claire Binter<sup>b,c,d,e,f</sup>, Susana Santos<sup>g,h,i,j</sup>, Sami Petricola<sup>b,c,e,f</sup>, Fernando Rivadeneira<sup>h,j,i</sup>, Vincent W.V. Jaddoe<sup>g,h,j</sup>, Mònica Guxens<sup>b,c,e,f,k,l</sup>, Fay H. Johnston<sup>a,∗</sup>

## ARTICLE INFO

Keywords:

Air pollution

Particulate matter (PM)

Bone mineral density

Time series

DLM

Children

Pregnancy

## ABSTRACT

**Background:** Early life exposure to environmental factors can impact skeletal development. We aimed to identify periods of susceptibility to air pollution in early life in relation to bone health outcomes at age six.

**Methods:** Data were from the Generation R study, a population-based pregnancy cohort study, The Netherlands. We estimated daily concentrations of nitrogen dioxide (NO2) and particulate matter (PM10, PM2.5 and PM2.5 absorbance) at the home addresses during pregnancy and childhood, using land-use regression models. Bone mineral density and area-adjusted bone mineral content were measured by dual-energy x-ray absorptiometry at age six. We performed distributed lag modelling (DLM) adjusted for several socioeconomic characteristics to assess the associations between bone health and air pollution, using 28-day averaged exposure levels, and identify windows of susceptibility.

**Results:** Among 5966 children, we identified windows of susceptibility from ~ 1 to ~ 4 years of age for PM2.5 and PM2.5 absorbance with bone mineral density (e.g., -10.3; 95% CI -15.8 to -4.7 per 5 µg/m<fnref n="3" /> increase in PM2.5) and for all air pollutants with bone mineral content (e.g., -14.6; 95% CI -20.7 to -8.4 per 5 µg/m<fnref n="3" /> increase in PM2.5). Also, we identified an association between NO2 and PM2.5 absorbance during pregnancy and higher bone mineral content (e.g., 4.0; 95% CI 1.4 to 6.6 per 10⁻<fnref n="5" /> m⁻<fnref n="1" /> increase in PM2.5 absorbance). In the sex-stratified analyses, associations across all exposures and outcome measures were in the same direction for both sexes, and similar to the main analyses, but statistically significance was observed only in boys.

**Conclusions:** Our findings suggest that exposure to air pollutants during childhood may already lead to poorer bone health outcomes.

<footnote><sup>a</sup> University of Tasmania, Menzies Institute for Medical Research, Hobart, Australia</footnote>

<footnote><sup>b</sup> ISGlobal, Barcelona, Spain</footnote>

<footnote><sup>c</sup> Universitat Pompeu Fabra (UPF), Barcelona, Spain</footnote>

<footnote><sup>d</sup> Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, Rennes, France</footnote>

<footnote><sup>e</sup> Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Spain</footnote>

<footnote><sup>f</sup> Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Centre, Rotterdam, the Netherlands</footnote>

<footnote><sup>g</sup> Department of Pediatrics, Erasmus MC - Sophia Children's Hospital, University Medical Centre Rotterdam, Rotterdam, the Netherlands</footnote>

<footnote><sup>h</sup> The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands</footnote>

<footnote><sup>i</sup> EPIUnit ITR, Instituto de Saúde Pública da Universidade do Porto, Universidade do Porto, Rua das Taipas, n° 135, 4050-600 Porto, Portugal</footnote>

<footnote><sup>j</sup> Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands</footnote>

<footnote><sup>k</sup> ICREA, Barcelona Spain</footnote>

<footnote>* Corresponding author at: University of Tasmania, Menzies Institute for Medical Research, 17 Liverpool Street, Hobart, TAS 7005 Australia.</footnote>

<footnote>E-mail addresses: lieke.scheepers@utas.edu.au (L.E.J.M. Scheepers), anne-claire.binter@inserm.fr (A.-C. Binter), susana.m.santos@ispup.up.pt (S. Santos), sami.petricola@isglobal.org (S. Petricola), f.rivadeneira@erasmusmc.nl (F. Rivadeneira), v.jaddoe@erasmusmc.nl (V.W.V. Jaddoe), monica.guxens@isglobal.org (M. Guxens), fay.johnston@utas.edu.au (F.H. Johnston).</footnote>

<footnote n="1">¹ Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, Rennes, France.</footnote>

<footnote n="2">² EPIUnit ITR, Instituto de Saúde Pública da Universidade do Porto, Universidade do Porto, Rua das Taipas, n° 135, 4050-600 Porto, Portugal.</footnote>

<footnote n="3">³ Barcelona Institute for Global Health ISGLOBAL, Doctor Aiguader, 88, 08003, Barcelona, Spain</footnote>

<footnote n="4">⁴ Erasmus University Medical Centre Rotterdam, Dr. Molewaterplein 40, 3015, Rotterdam, The Netherlands, +31 10 7044015.</footnote>

<footnote n="5">⁵ Erasmus University Medical Centre Rotterdam, Dr. Molewaterplein 40, 3015, Rotterdam, The Netherlands, +31 (0)10 7036742.</footnote>

<footnote n="6">⁶ Barcelona Institute for Global Health ISGLOBAL, Doctor Aiguader 88, 08003, Barcelona, Spain, +34 932 147 330.</footnote>

<footnote n="7">⁷ University of Tasmania, Menzies Institute for Medical Research, 17 Liverpool Street, Hobart, TAS 7005 Australia, +61(0)408266652.</footnote>
# 1. Introduction

Osteoporosis affects 1 in 5 people worldwide and is associated with fragility fractures, which can lead to chronic pain, disability, loss of independence, and even premature death (Clynes et al., 2020). Although osteoporosis typically manifest in older adults, its origins can be traced back much earlier in life (NIH Consensus Development Panel on Osteoporosis Prevention D, 2001). Early bone accrual during childhood and peak bone mass achieved in early adulthood are crucial determinants of bone health in later life, including the risk of osteoporosis (NIH Consensus Development Panel on Osteoporosis Prevention D, 2001; Hernandez et al., 2003; Weaver et al., 2016). Therefore, studying bone health in early life is essential for identifying modifiable early-life factors that may influence long-term bone outcomes. Exposure to environmental factors during early life may provoke responses that have a lasting impact on skeletal development and it is hypothesized that exposure to air pollution in early life could influence bone homeostasis (Prada et al., 2017; Nguyen, 2017). Several mechanisms have been put forward. Air pollution may (i) promote low-grade systemic inflammation, which affects bone accrual through specific effect of cytokines on osteoblast and osteoclast differentiation and functioning; (ii) cause oxidative damage in the airway and bone cells; (iii) act as endocrine disruptors when binding to the receptors in bone cells, changing their function; and (iv) directly and indirectly cause vitamin D deficiency (Prada et al., 2017).

Whilst plausible mechanisms exist to explain the influence of air pollution on bone metabolism, most evidence is derived from murine studies (Prada et al., 2020). The limited number of human studies are conflicting and most have significant methodological limitations related to exposure assessment, lack of longitudinal data, or inadequate adjustment for confounders (Pang et al., 2021; Shi et al., 2022). Further, most studies focused on adults, primarily the elderly, which overlooks the significance of early bone accrual during childhood and adolescence (Hernandez et al., 2003; Weaver et al., 2016).

Research in children is scarce and with conflicting findings. One study reported no difference in bone mineral density (BMD) between 20 children from Mexico City versus 15 controls of 6 years of age living in a low-exposure rural area (Calderón-Garcidueñas et al., 2013). In contrast, a cross-sectional study including ~2,000 participants from a larger birth cohort found that annual exposure to NO2 and PM10 was associated with elevated bone turnover markers at 10 years of age (Liu et al., 2015). Nonetheless, these are not reliable markers of bone health in children and are influenced by factors such as nutrition, growth, and puberty, and whether it indicates increased bone loss or poor bone quality is still unclear (Liu et al., 2015; Zhao et al., 2022). Furthermore, studies to date have investigated average air pollution exposures over a particular period (e.g., pregnancy or childhood), which might not capture critical developmental phases and neglected cumulative exposure and variability over time, leading to potential misclassification and bias (Loomis and Kromhout, 2004).

Therefore, in this exploratory analysis, we aimed to identify potential periods of susceptibility to air pollution in early life in relation to bone health measures in children with a mean age of ~6 years. We hypothesize that both prenatal exposure to air pollution and exposure during infancy are associated with poorer bone health outcomes in children, with the strongest effects observed during early childhood.

# 2. Methods

## 2.1. Study population and design

The current study is embedded in the Generation R study, a prospective population-based cohort study from early fetal life onwards in Rotterdam, The Netherlands (Jaddoe et al., 2006; Kooijman et al., 2016). The design and details of this cohort study have been described elsewhere (Jaddoe et al., 2006; Kooijman et al., 2016). In brief, recent

mothers or pregnant women with an expected delivery date between April 2002 and January 2006 and who were living in the study area of Rotterdam (defined by postal codes), were invited to participate in the study. In total, 9,901 pregnant women or mothers (response of 61 %) were enrolled in the study. Of these, 91 % (n = 8,879) were enrolled in pregnancy. We restricted the current analysis to singleton live-born children excluding extremely preterm births (i.e., born before 28 weeks of gestation), with available information on air pollution during pregnancy and childhood and bone outcomes at 6 years of age. This resulted in a final study population of 5,966 children, as shown in Fig. S1. The study protocol was approved by the Medical Ethical Committee of Erasmus University Medical Center, Rotterdam. We obtained written informed consent from all participant's parents.

## 2.2. Exposure to air pollution

We estimated air pollution exposure levels of nitrogen dioxide (NO2), particulate matter (PM) with aerodynamic diameter <2.5 µm (PM2.5), <10 µm (PM10), and absorbance of PM2.5 fraction (PM2.5 absorbance). PM2.5 absorbance reflects the light-absorbing fraction of PM2.5, which is indicative of carbonaceous particles, often derived from combustion sources. These exposures were assessed for each participant's address from conception until the date of the bone outcomes assessment. These models have been previously described (Methods S1) (Guxens et al., 2022). Briefly, three 2-week measurements of NO2 were performed in 2009–2010 at 80 sites spread across The Netherlands and Belgium, and measurements of PM10 and PM2.5 in 40 of those sites (Cyrys et al., 2012; Eeftens et al., 2012). PM2.5 absorbance was measured in the PM2.5 filters (Eeftens et al., 2012). A variety of geographic information systems variables were used in land use regression models to predict air pollution levels at each address (Eeftens et al., 2012; Beelen et al., 2013). To capture the temporal trend and increase the temporal resolution, daily data from seven routine background monitoring sites were used to extrapolate the air pollution levels to the exact period of residency at each address, resulting in daily air pollution levels from conception until the date of the bone outcomes assessment (Brunekreef, 2012). We averaged the daily levels of each pollutant into periods of four-weeks lags (28 days). The variability in monthly exposure is presented in Fig. S2.

## 2.3. Bone outcomes assessment

We measured total-body BMD, bone mineral content (BMC), and bone area (BA) using a DXA scan (iDXA, Geneal Electrics – Lunar, 2008, Madison, WI, USA) at the mean age of 6.2 ± 0.5 years. Well-trained research assistants obtained the DXA scans by using the same device and software (enCORE version 13.6) following standard manufacturer protocols (Heppe et al., 2014). As recommended by the International Society for Clinical Densitometry (ISCD) on DXA evaluation in children, we used areal total-body-less-head BMD as region of interest for all analyses (Bianchi et al., 2010). Measured BMD was expressed as BMC (in g) per projected BA (in cm²). BMC was calculated from BMD using the projected bone area. Area-adjusted BMC (aBMC) was derived as a measure of volumetric BMD by using linear regression to adjust BMC for bone area and adding the residuals to the mean BMC (Macdonald-Wallis et al., 2010).

## 2.4. Covariates and confounder selection

We collected the following data by questionnaires at enrolment: maternal and paternal age (years), maternal and paternal educational levels (primary or lower, secondary, higher), monthly household income (<1,200€, 1,200 – 2,000€, >2,000€), marital status (married, living together, no partner), and ethnicity of the child using grandparental origin, categorized according to the three largest transcontinental ancestral groups: Western (Dutch, Turkish, other European, American,
and Oceanic), African (Moroccan, other African, Antillean, Surinamese-Creole, and Cape Verdean), and Asian (Indonesian, other Asian, and Surinamese-Hindu) descent. We assessed maternal smoking during pregnancy (never, until pregnancy known, during pregnancy). We obtained information about the child's date of birth, sex, and gestational age at birth from medical records. At the age of 6 years, we also measured height to the nearest 0.1 cm using a Harpenden stadiometer (Holtain Limited, Dyfed, UK) and derived total body fat and lean mass from the DXA scan.

Potential confounders were identified *a priori* based on graphical criteria for confounding, using a directed acyclic graph (DAG) presented in **Methods S2**. In the basic models, we adjusted for child's sex, and ethnicity, and age, height at the DXA scan visit. To account for the influence of body composition on bone measures, we also included the sum of lean and fat mass. Additionally, to account for potential temporal trend, we adjusted for the year (2001–2005) and month (January–December) of conception. Fully adjusted models further included indicator variables for socioeconomic position (maternal and paternal age, maternal and paternal education, household income, marital status) and maternal smoking during pregnancy.

## 2.5. Statistical analyses

Distributed lag model (DLM) was used to estimate the association between each air pollutant and each measure of bone health, while simultaneously capturing the change of this exposure-response relationship through time (Gasparrini et al., 2010). DLM is based on two functions that define the exposure-response and the lag-response relationship (Methods S3) (Gasparrini et al., 2010).

As DLM requires the same observation period for all children and no missing exposure data, we considered exposure starting at conception and included only children born after 32 weeks of gestation (n = 5,910) in the main analysis. The pregnancy period spanned from conception until 36 weeks of gestation, corresponding to 9 four-week lags. For children born between week 33 and 36, we averaged their exposure during these weeks, to represent the 9th four-week lag. Exposure data for children born after week 36 were excluded. The childhood exposure period started at birth and was truncated at 5.5 years of age, the age of the youngest child undergoing a DXA scan. We applied this truncation to ensure the inclusion of all children regardless of the DXA scan age and corresponds to 72 four-week lags.

First, we assessed whether there were periods of susceptibility to each air pollutant between conception and 5.5 years in relation to each measure of bone health (BMD and aBMC) separately. To explore potential sex-specific associations, all analyses were also stratified by child's sex. A period of susceptibility was first identified by a statistically significant association between air pollution exposure and the outcome in a specific lag (*p*-value < 0.05). Then, we corrected the *p*-values for multiple testing considering four exposures and two measures of bone health. To confirm independence among the outcomes and the exposures, we extracted eigenvalues from individual-level matrix of phenotype data using the *meff* function from the *poolr* package in R to confirm the effective number of tests, which was estimated at 2 (2 effective exposures * 1 effective outcomes). The new *p*-value was 0.05/2 = 0.025. We estimated the association of each period of susceptibility by combining the estimates of the consecutive statistically significant lags that survived correction for multiple testing. For the confidence intervals, we considered the covariance matrix along consecutive statistically significant lags that survived correction for multiple testing.

To reduce bias related to missing data, we used expectation-maximization (EM) algorithm for obtaining maximum likelihood estimates when data are missing on confounders. Based on observed values, families of the included participants were more likely to have higher education, having higher household income, be Western, and be married, as compared to families of participants that were not included (Table S1). We thus corrected for potential attrition bias using inverse

probability weighting (Methods S4). (Weisskopf et al., 2015; Weuve et al., 2012) In brief, we imputed missing values for predictors for participation for all eligible participants (n = 9,655; Table S2), and we used all the available information to predict the probability of participating (n = 5,966). Predictors for participation included a wide set of baseline parental and child characteristics. These included sociodemographic factors (e.g., parental age, education, income, ethnicity, marital status), maternal behaviors during pregnancy (smoking, alcohol use), child characteristics at birth (e.g., gestational age, birth weight), and measurements at the DXA visit (e.g., age, body size and composition, and outdoor activity). We used the inverse of those probabilities as weights, so that results would be representative for the initial population of the cohort (Methods S4).

### 2.5.1. Sensitivity analyses

We conducted several sensitivity analyses for investigating the influence of our methodological choices: i) we repeated the selection of the lag-response relationship and reduced the number of knots from one to none knots (i.e., a linear relationship) in all models; ii) in the main analyses we decided not to exclude moderate to late preterm birth (i.e., children born between weeks 32 to 37), because prematurity may be a mediator in the pathway between air pollution and bone health and their exclusion could lead to an incorrect specification of the estimates due to collider bias. However as a sensitivity analyses, we investigated the influence of moderate to late premature birth by only including children born after week 36 and added a 10th four-week lag during pregnancy corresponding to the pregnancy period between week 36 and 40 (n = 5,658); and iii) to investigate a possible selection bias we included very preterm and moderate to late preterm births and truncated the pregnancy period to week 28 (n = 5,966); Sensitivity analyses were also corrected for multiple testing using the same threshold as the main analysis (i.e., *p*-value < 0.025). All analyses were carried out with R version 4.0.3 (R Core Team, 2020), the DLM analyses were performed using the R package *dlnm*.

## 3. Results

### 3.1. Study population characteristics

Child and parental characteristics for the total study population are shown in Table 1 and characteristics stratified by median average exposure to PM2.5 during pregnancy are presented in Table S3. At enrolment, the mothers were 30.9 (SD 5.2) years of age, 2509 (46 %) had achieved more than secondary education, and 665 (12 %) had no partner. The levels of each air pollutant during pregnancy and years in childhood are shown in Table 2. For all pollutants, mean air pollution exposure decreased between conception and 5.5 years of age. For example, NO2 concentrations were on average 39.1 (SD 6.6) µg/m³ during pregnancy and 32.1 (SD 6.1) µg/m³ between ages 5 and 6 years (Table 2). At age 6, BMD was 554 mg/cm² (SD 51) on average, and aBMC was 530 g (SD 40), with a correlation of 0.73 between BMD and aBMC (Table S5).

### 3.2. Periods of susceptibility to air pollution and measures of bone health

In the fully adjusted analyses, presented in Table 3 and Fig. 1, we observed that higher levels of PM2.5 between 1.1 years and 4.1 years of age were associated with lower BMD (−10.3; 95%CI −15.8 to −4.7; per 5 µg/m³ increase in PM2.5). This was the equivalent of a 2.5 – 3 % reduction in BMD at the age of six for each increase in PM2.5 of 5 µg/m³. Similarly, higher levels of PM2.5 absorbance between 1.3 year and 3.8 years of age were associated with lower BMD (−7.8; 95%CI −13.1 to −2.5; per 10⁻⁵ m⁻¹ increase in PM2.5 absorbance). We observed similar period of susceptibility for PM2.5 and PM2.5 absorbance with aBMC, with higher levels associated with lower aBMC. In addition, we found that higher levels of NO2 between 1.3 years and 3.9 years of age were
<table><caption>**Table 1**

Characteristics of the study population (Generation R, The Netherlands, 2002–2006 (n = 5,966).</caption><thead><tr><th>Variable</th><th>Distribution</th></tr></thead><tbody><tr><td><strong>Parental characteristics</strong></td><td></td></tr><tr><td>Maternal age at enrolment (years)</td><td>30.9 ± 5.2</td></tr><tr><td>Paternal age at enrolment (years)</td><td>33.2 ± 5.6</td></tr><tr><td>Maternal education</td><td></td></tr><tr><td>Primary or lower</td><td>22.5 % (1222)</td></tr><tr><td>Secondary</td><td>31.3 % (1699)</td></tr><tr><td>Higher</td><td>46.2 % (2509)</td></tr><tr><td>Paternal education</td><td></td></tr><tr><td>Primary or lower</td><td>21.5 % (771)</td></tr><tr><td>Secondary</td><td>27.1 % (969)</td></tr><tr><td>Higher</td><td>51.4 % (1842)</td></tr><tr><td>Monthly household income</td><td></td></tr><tr><td>&lt;1,200€</td><td>17.1 % (767)</td></tr><tr><td>1,200 – 2,000€</td><td>18.2 % (816)</td></tr><tr><td>&gt;2,000€</td><td>64.8 % (2911)</td></tr><tr><td>Family status</td><td></td></tr><tr><td>Married</td><td>51.0 % (2772)</td></tr><tr><td>Living together</td><td>36.8 % (2002)</td></tr><tr><td>No partner</td><td>12.2 % (665)</td></tr><tr><td>Maternal smoking during pregnancy</td><td></td></tr><tr><td>Never</td><td>74.3 % (3849)</td></tr><tr><td>Until pregnancy known</td><td>9.0 % (467)</td></tr><tr><td>During pregnancy</td><td>16.6 % (861)</td></tr><tr><td><strong>Child characteristics at DXA scan visit</strong></td><td></td></tr><tr><td>Sex</td><td></td></tr><tr><td>Boy</td><td>49.8 % (2972)</td></tr><tr><td>Girl</td><td>50.2 % (2994)</td></tr><tr><td>Ethnic background</td><td></td></tr><tr><td>Western</td><td>78.9 % (4593)</td></tr><tr><td>African</td><td>6.7 % (391)</td></tr><tr><td>Asian</td><td>14.4 % (836)</td></tr><tr><td>Age (years)</td><td>6.2 ± 0.5</td></tr><tr><td>Height (cm)</td><td>119.6 ± 6.0</td></tr><tr><td>Weight (kg)</td><td>23.3 ± 4.3</td></tr><tr><td>Body mass index (kg/m<sup>2</sup>)</td><td>16.2 ± 1.9</td></tr><tr><td>Bone-free mass (kg)</td><td>22.3 ± 4.1</td></tr><tr><td>Bone mineral density (BMD, mg/cm<sup>2</sup>)</td><td>554.2 ± 50.8</td></tr><tr><td>Bone mineral content (g)</td><td>529.6 ± 105.9</td></tr><tr><td>Area-adjusted bone mineral content (aBMC, g)</td><td>529.6 ± 39.8</td></tr><tr><td>Bone area (cm<sup>2</sup>)</td><td>949.0 ± 117.8</td></tr></tbody></table>

Values are mean ± standard deviations for continuous variables and percentage and (numbers) for categorical variables. Distribution is displayed of non-imputed values.

associated with lower aBMC only (−3.8; 95% CI −6.1 to −1.5; per 10 µg/m³ increase in NO2). Higher levels of PM10 between 1.6 year and 3.9 years of age were associated with lower aBMC (−6.5; 95% CI −10.8 to −2.2; per 5 µg/m³ increase in PM10).

We found that higher levels of NO2 from conception to 28 weeks gestation were associated with slightly higher aBMC. Similarly, higher levels of PM2.5 absorbance from conception to 1 month of age and between 4.8 years and 5.5 years of age were also associated with higher aBMC.

Results of the basic models are presented in Table S6.

We observed a sex-specific effect between air pollutants and bone health outcomes. In boys, the results were consistent with the main analyses, with stronger effect estimates (Fig. 2A and Table S10). In

contrast, for girls, all effects were in similar direction, but after correction for multiple testing none remained significant (Fig. 2B).

### 3.3. Sensitivity analyses

When we used a linear association between each air pollutant and each bone health parameter (Table S7 and Fig. S3), we found that during childhood higher levels of PM10 and PM2.5 exposure were associated with lower BMD and aBMC. Further, adding a 10th four-week lag during the prenatal period or excluding moderate to late preterm births yielded similar association estimates as our main results (Table S8 and Table S9, respectively).

### 4. Discussion

In this large prospective cohort study, we observed an association between exposure to PM2.5 and PM10 with lower BMD and to all air pollutants with lower aBMC, with a potential period of susceptibility from approximately 1 year to 4 years of age. We found stronger adverse association between air pollutants and bone health parameters in boys than in girls. Contrary to our a priori expectations, we found associations

<table><caption>**Table 3**

Associations between exposure to air pollutants at pregnancy and childhood with measures of bone health at ~ 6 years of age (n = 5,910).</caption><thead><tr><th rowspan="2"></th><th colspan="2">Bone mineral density (BMD)</th><th colspan="2">Area-adjusted bone mineral content (aBMC)</th></tr><tr><th>Air pollutants</th><th>Lags</th><th>Estimate</th><th>Lags</th><th>Estimate</th></tr></thead><tbody><tr><td>NO2 (Δ10 µg/m³)</td><td>--</td><td>--</td><td>--</td><td>Conception – 28 weeks of gestation</td><td>1.2 (95% CI 0.3 to 2.2)</td></tr><tr><td>PM10 (Δ 10 µg/m³)</td><td>--</td><td>--</td><td>--</td><td>1.3 year – 3.9 years</td><td>−3.8 (95% CI −6.1 to −1.5)</td></tr><tr><td>PM2.5 (Δ 5 µg/m³)</td><td>1.1 year – 4.1 years</td><td>−10.3 (95% CI −15.8 to −4.7)</td><td>9 months – 4.2 years</td><td>1.6 year – 3.9 years</td><td>−6.5 (95% CI −10.8 to −2.2)</td></tr><tr><td>PM2.5 absorbance (Δ 10<sup>−5</sup> m<sup>−1</sup>)</td><td>--</td><td>--</td><td>Conception – 1 months</td><td>4.0 (95% CI 1.4 to 6.6)</td><td></td></tr><tr><td></td><td>1.3 year – 3.8 years</td><td>−7.8 (95% CI −13.1 to −2.5)</td><td>1.0 year – 3.9 years</td><td>−13.4 (95% CI −19.4 to −7.4)</td><td></td></tr><tr><td></td><td>--</td><td>--</td><td>4.8 years – 5.5 years</td><td>5.4 (95% CI 1.8 to 9.0)</td><td></td></tr></tbody></table>

Estimates and 95% CI from distributed lag model, adjusted for maternal and paternal age, maternal and paternal educational levels, marital status, monthly household income, maternal smoking during pregnancy, child's sex and ethnicity, child's age, height, and bone free mass at the DXA scan visit, and year and month of conception.

Associations after correction for multiple testing (P-value <0.025).

Abbreviations: CI, confidence interval; NO2, nitrogen dioxide; PM2.5, particulate matter with aerodynamic diameter <2.5 µm; PM10, particulate matter with aerodynamic diameter <10 µm; PM2.5 absorbance, absorbance of PM2.5 filters.

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

Characteristics of the levels of air pollutants (Generation R, The Netherlands, 2002–2006 (n = 5,966).</caption><thead><tr><th></th><th>Pregnancy</th><th>Birth – 1 year</th><th>1 – 2 years</th><th>2 – 3 years</th><th>3 – 4 years</th><th>4 – 5 years</th><th>5 – 5.5 years</th></tr></thead><tbody><tr><td>NO2 (in µg/m³)</td><td>39.1 ± 6.6</td><td>38.0 ± 6.1</td><td>35.4 ± 5.9</td><td>33.4 ± 5.7</td><td>33.0 ± 6.0</td><td>32.3 ± 6.1</td><td>32.1 ± 6.1</td></tr><tr><td>PM10 (in µg/m³)</td><td>32.6 ± 4.3</td><td>31.0 ± 3.8</td><td>28.7 ± 2.3</td><td>27.6 ± 2.4</td><td>26.8 ± 2.4</td><td>26.1 ± 2.2</td><td>26.5 ± 2.5</td></tr><tr><td>PM2.5 (in µg/m³)</td><td>20.2 ± 2.4</td><td>19.2 ± 2.1</td><td>17.9 ± 0.9</td><td>17.3 ± 1.1</td><td>16.8 ± 1.1</td><td>16.4 ± 0.9</td><td>16.7 ± 1.1</td></tr><tr><td>PM2.5 absorbance (in 10<sup>−5</sup> m<sup>−1</sup>)</td><td>1.5 ± 0.3</td><td>1.47 ± 0.26</td><td>1.35 ± 0.24</td><td>1.27 ± 0.22</td><td>1.26 ± 0.22</td><td>1.23 ± 0.22</td><td>1.22 ± 0.22</td></tr></tbody></table>

Values are mean ± standard deviation.

NO2, nitrogen dioxide; PM2.5, particulate matter with aerodynamic diameter <2.5 µm; PM10, particulate matter with aerodynamic diameter <10 µm; PM2.5 absorbance, absorbance of PM2.5 filters.
<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Adjusted associations between exposure to air pollutants from conception to age 5.5 years and measures of bone health at ~6 years of age (n = 5,910). Adjusted for maternal and paternal age, maternal and paternal educational levels, marital status, monthly household income, maternal smoking during pregnancy, child's sex and ethnicity, child's age, height, and bone free mass at the DXA scan visit, and year and month of conception. Blackline represents the beta estimate of the association between the exposure at each specific lag and the outcome. Vertical grey line represents 95 % confidence interval (CI) and indicate no divergence from the null; blue and orange lines indicate 95 % CIs showing significant associations below and above the null value respectively. Darker blue and orange colors indicate associations after correction for multiple testing (p-value <0.025). Abbreviations: NO2, nitrogen dioxide; PM2.5, particulate matter with aerodynamic diameter <2.5 µm; PM10, particulate matter with aerodynamic diameter <10 µm; PM2.5 absorbance, absorbance of PM2.5 filters. Adjusted for maternal and paternal educational levels, monthly household income, maternal and paternal age, maternal smoking during pregnancy, marital status, child's ethnicity, child's age, height, bone free mass at the DXA scan visit, and year and month of conception. Black line represents the beta estimate of the association between the exposure at each specific lag and the outcome. Vertical grey and blue lines represent 95 % CI and indicates no divergence from the null or significant divergence from negative association, respectively. Abbreviations: NO2, nitrogen dioxide; PM2.5, particulate matter with aerodynamic diameter <2.5 µm; PM10, particulate matter with aerodynamic diameter <10 µm; PM2.5 absorbance, absorbance of PM2.5 filters. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

in the opposite direction for exposure to NO2 and PM2.5 absorbance during pregnancy with aBMC.

This is the first study to investigate the association between air pollution, estimated from conception to age 5.5 years on a monthly basis, with bone health parameters in children. Childhood appears to be a period of higher susceptibility, particularly between 1 to 4 years of age, with all pollutants associated with lower aBMC. Associations with BMD were similar but of smaller magnitude, reaching statistical significance only for PM2.5 and PM2.5 absorbance. Although BMD and aBMC are correlated (r = 0.73), aBMC is considered less influenced by body size and is suggested to provide a more accurate reflection of true volumetric bone mass in young children (Bianchi et al., 2010; Macdonald-Wallis et al., 2010).

To date, only one small study, conducted by Calderon-Garciduenas

et al., explored the relationship between air pollution exposure and BMD in children at age 6 years (Calderón-Garciduenas et al., 2013). This study found no significant differences in BMD z-scores between children from a highly polluted area (Mexico City, n = 20) and children from a low-exposure rural area (n = 15). However, the Mexico City group showed higher IL-6 and IL-10 levels, and lower IFN-gamma levels (Calderón-Garciduenas et al., 2013). Indeed inflammatory pathways are one of the proposed mechanisms how air pollution might influence bone homeostasis (Prada et al., 2020). For instance, in a pooled analyses from two Swedish birth cohorts, air pollution exposure during the first 2 years was associated with osteoprotegerin at age 1 and 2, a key molecular regulation system for bone remodeling through bone resorption (He et al., 2022). In German birth cohorts GINAplus and LISAplus, annual NO2 and PM10 exposure was linked with bone turnover markers
<figure><img src="image_3.png" /><figcaption>(A) Boys</figcaption></figure>

**Fig. 2.** Adjusted associations between cumulative exposure to air pollutants from conception to age 5.5 years and measures of bone health at ~6 years of age, for (A) boys only (n = 2,942) and for (B) girls only (n = 2,968). Vertical grey line represents 95 % confidence interval (CI) and indicate no divergence from the null; blue and orange lines indicate 95 % CIs showing significant associations below and above the null value respectively. Darker blue and orange colors indicate associations after correction for multiple testing (*p*-value <0.025). Abbreviations: NO2, nitrogen dioxide; PM2.5, particulate matter with aerodynamic diameter <2.5 µm; PM10, particulate matter with aerodynamic diameter <10 µm; PM2.5 absorbance, absorbance of PM2.5 filters. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

osteocalcin and c-terminal telopeptide of type 1 collagen (CTx) at age 10, reflecting enhanced bone turnover (Liu et al., 2015) and suggesting that ambient air pollution exposure may negatively impact bone development (Schini et al., 2022). However, these results should be carefully interpreted since a quantitative assessment between bone markers and BMD is still lacking for children.

In the current study, we observed a sex-specific effect difference. Between ~1 to ~4 years of age exposure to air pollutants had a negative effect on bone health, but for girls, the effects were of a smaller magnitude, and did not reach statistical significance. While requiring replication, several potential biological explanations can be formulated as to why boys might be more vulnerable to the negative effects of air pollution. During infancy and early childhood, boys generally have a greater inflammatory response than girls (Klein and Flanagan, 2016; Uekert et al., 2006). It may be postulated that girls might therefore be better protected against air pollution induced low-grade systemic inflammation. Epidemiological studies have also shown that the expression of inflammatory proteins following air pollution exposure differed by sex (Cabello et al., 2015; Hoffmann et al., 2009). However, most evidence is based on adult studies, and only few small studies investigated the sex-specific association in children, yielding inconsistent results (He et al., 2022; Gruzieva et al., 2017; Deng et al., 2022).

Further analyses are warranted to disentangle sex-specific effect of air pollutant on bone health outcomes.

Of note, we showed that exposure to NO2 and PM2.5 absorbance during pregnancy was associated with higher values of subsequent bone health parameters. This unexpected protective effect might be partially attributed to the greater uncertainty in the DLM model at the beginning and end of the assessment period. Given the novelty of our study, comparing the results to existing literature is challenging, as no studies have specifically investigated the effect of prenatal exposure to air pollutant and bone health outcomes later in childhood. The closest comparison are studies examining maternal smoking and its impact on offspring bone health. Two previous studies within the same (Generation R) and the large British ASLPAC pregnancy cohorts demonstrated an association between maternal smoking during pregnancy and higher offspring bone health parameters at 6 and 10 years of age, respectively (Heppe et al., 2015). However, unlike in our study, these association became non-significant after adjustment for body weight. Furthermore, a study among 391 children found that air pollution exposures during early pregnancy was negatively associated with certain immune markers (e.g., T lymphocytes subsets and cytokines) at 1-year and 2-years of age, while exposure during late pregnancy or postnatal exposure showed positive associations (Deng et al., 2022). This suggests that the effect of
<figure><img src="image_4.png" /><figcaption>Fig. 2. (continued).</figcaption></figure>

air pollution on bone health may differs depending on the timing of exposure (i.e., age). Future research is required to confirm our findings, and our results highlight the importance of studying different time windows of susceptibility instead of average exposure over the life course.

In the current study, a 5 µg/m³ increase in PM2.5 (range 14.7 to 22.6) was associated with a ~2.5 to 3 % decrease in BMD and aBMC. Numerous prospective studies have showed that bone health track strongly from childhood through adolescence (Weaver et al., 2016). It has been estimated that a 10 % increase in peak bone mass would delay the onset of osteoporosis by 13 years and reduce risk of osteoporotic fractures by 50 % (Hernandez et al., 2003). Nevertheless, further studies are needed to replicate our findings and to investigate whether the association between exposure to air pollutants during childhood persist with poorer bone health outcomes during adolescence and adulthood. Notably, the association between air pollution and bone health may vary by age and sex; and may be influenced by hormonal changes post-puberty and a greater inflammatory response in females later in life (Klein and Flanagan, 2016). Also, the chemical composition and physicochemical characteristics of particles may play a critical role in modulating the pathophysiological responses. Further research should also focus on different sources and composition, as this information can support interventions to reduce exposure and health impacts.

Strengths of this study include (i) its large sample size, of almost 6,000 children from a population-based pregnancy cohort; (ii) assessment of air pollution from conception to age 5.5 years in a very fine time scale of 28-days using a standardized and validated method; (iii) the

gold-standard DXA scan was used to assess bone health; (iv) adjustment of various socioeconomic and lifestyle variables that are likely to influence the association; and v) we used an advanced statistical method DLM to estimate the association between air pollution exposure from conception until outcome assessment, in a flexible, data-driven manner. This approach allowed us to identify potential periods of susceptibility without relying on predefined time windows (e.g., trimesters of pregnancy or years of age), which could introduce bias (Wilson et al., 2017).

Our study also has some limitations. First, our study was conducted in a single population using an exploratory, data-driven approach. As such, the identified susceptibility periods should be interpreted with caution, and replication in independent cohorts is essential. While DLM offers a more flexible, less assumption-driven alternative to traditional models and, likely yield less biased estimates, external validation remains necessary to confirm these findings and avoid overinterpreting them as a generalizable framework. Second, our sampling campaigns to estimate the exposure were conducted when participants were between 3.5 and 5.5 years of age. We relied on historical pollution data from routine monitoring stations to back-extrapolate the levels to the periods for each child. Although these data have been shown to remain spatially stable over extended periods of time (up to 8 or 18 years), (Eeftens et al., 2012; Gulliver et al., 2013) we cannot rule out misclassification errors. Furthermore, only residential addresses were used for estimating the exposure. However, prior studies demonstrated that home addresses adequately represent air pollution exposure in young children, given the proximity of their daycare and primary schools to residential address (McConnell et al., 2010). Third, the DLM utilized in our analysis requires
tuning parameters (i.e., shape of the relationship) that has demonstrated to be sensitive to the parameters used in the analyses. We have intended to overcome this methodological limitation by (i) determining the position and the number of knots on a parsimonious principle, to not overfit the model, and (ii) we repeated the analyses with a linear lag-response relationship to minimize the BIC.

Then, because DLM requires complete exposure data, the main analyses included prenatal exposure up to gestational week 32 – 36 and postnatal exposure up to 5.5 years of age. To assess whether this affected our findings, we performed several sensitivity analyses, including models with a shorter and longer pregnancy exposure period. These analyses yielded similar results, suggesting that our findings are robust.

Fourth, participants in our analysis differed in socioeconomic background and lifestyle characteristics from those without follow-up data. Although we employed inverse probability weighting to mitigate this limitation, the potential for selection bias remains, as important predictors of loss-to-follow-up risk may have been overlooked. Fifth, despite adjusting for potential confounders, the observational nature of our study leaves room for residual confounders.

## 5. Conclusions

We demonstrated that exposure to various air pollutants were associated with poorer bone health outcomes in 6-year-old children. Childhood appears to be a period of increased susceptibility, particularly between the ages of 1 and 4 years. Ours is the first large study addressing this question and the findings should be confirmed by further studies, including investigation of the age- and sex specific associations. The potential for improved air quality to also reduce the prevalence or severity of osteoporosis in later life adds to the weight of evidence that strongly supports policy interventions to reduce population level exposure to air pollution throughout the world (WHO, 2021).

### CRediT authorship contribution statement

**Lieke E.J.M. Scheepers:** Writing – review & editing, Writing – original draft, Methodology, Investigation, Formal analysis, Conceptualization.
**Anne-Claire Binter:** Writing – review & editing, Supervision, Methodology, Formal analysis, Conceptualization.
**Susana Santos:** Writing – review & editing.
**Sami Petricola:** Writing – review & editing, Data curation.
**Fernando Rivadeneira:** Writing – review & editing, Funding acquisition.
**Vincent W.V. Jaddoe:** Writing – review & editing, Funding acquisition.
**Mònica Guxens:** Writing – review & editing, Supervision, Methodology, Conceptualization.
**Fay H. Johnston:** Writing – review & editing, Supervision, Conceptualization.

### Ethics approval and consent to participate

Parents provided written informed consent for themselves and their children. The Medical Ethics Committee of the Erasmus Medical Center in Rotterdam, the Netherlands, granted ethical approval for 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.

### Acknowledgments

We gratefully acknowledge the contribution of the participating children, their mothers, general practitioners, hospitals, midwives, and pharmacies in Rotterdam.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.envint.2025.109739.

### Data availability

The datasets generated and analyzed during this study are not publicly available due to legal and ethical regulations, but can be made available upon request to Prof. Jaddoe v.jaddoe@erasmusmc.nl

### References

Clynes, M.A., Harvey, N.C., Curtis, E.M., Fuggle, N.R., Dennison, E.M., Cooper, C., 2020. The epidemiology of osteoporosis. Br. Med. Bull. 133 (1), 105–117.

NIH Consensus Development Panel on Osteoporosis Prevention D, Therapy. Osteoporosis Prevention, Diagnosis, and Therapy. JAMA 2001; 285(6): 785-95.

Hernandez, C.J., Beaupré, G.S., Carter, D.R., 2003. A theoretical analysis of the relative influences of peak BMD, age-related bone loss and menopause on the development of osteoporosis. Osteoporos. Int. 14 (10), 843–847.

Weaver, C.M., Gordon, C.M., Janz, K.F., et al., 2016. The National Osteoporosis Foundation's position statement on peak bone mass development and lifestyle factors: A systematic review and implementation recommendations. Osteoporos. Int. 27 (4), 1281–1386.

Prada, D., Jr, Z., Elena, Z.A., et al., 2017. Association of air particulate pollution with bone loss over time and bone fracture risk: analysis of data from two independent studies. The Lancet Planet. Heath 1 (8), e337.

Nguyen, T.V., 2017. Air pollution: A largely neglected risk factor for osteoporosis. The Lancet Planetary Health 1 (8), e311.

Prada, D., López, G., Solleiro-Villavicencio, H., Garcia-Cuellar, C., Baccarelli, A.A., 2020. Molecular and cellular mechanisms linking air pollution and bone damage. Environ. Res. 185, 109465.

Pang, K.-L., Ekeuku, S.O., Chin, K.-Y., 2021. Particulate air pollution and osteoporosis: A systematic review. Risk Management and Healthcare Policy 14, 2715–2732.

Shi, W., Huang, C., Chen, S., et al., 2022. Long-term exposure to air pollution increases hip fracture incidence rate and related mortality: analysis of National Hip Fracture Database. Osteoporos. Int.

Calderón-Garciduénas, L., Mora-Tiscareño, A., Francolira, M., et al., 2013. Exposure to urban air pollution and bone health in clinically healthy six-year-old children. Arch. Ind. Hyg. Toxicol. 64 (1), 23–34.

Liu, C., Fuertes, E., Flexeder, C., et al., 2015. Associations between ambient air pollution and bone turnover markers in 10-year old children: Results from the GINIplus and LISAplus studies. Int. J. Hyg. Environ. Health 218 (1), 58–65.

Zhao, T., Markevych, I., Standl, M., et al., 2022. Ambient ozone exposure and bone turnover markers in children: Results from the GINIplus and LISA birth cohorts. Environ. Res. 214 (Pt 1), 113784.

Loomis, D., Kromhout, H., 2004. Exposure variability: Concepts and applications in occupational epidemiology. Am. J. Ind. Med. 45 (1), 113–122.

Jaddoe, V.W.V., Mackenbach, J.P., Moll, H.A., et al., 2006. The Generation R Study: Design and cohort profile. Eur. J. Epidemiol. 21 (6), 475–484.

Kooijman, M.N., Kruithof, C.J., Van Duijn, C.M., et al., 2016. The Generation R Study: Design and cohort update 2017. Eur. J. Epidemiol. 31 (12), 1243–1264.

Guxens, M., Lubczynska, M.J., Perez-Crespo, L., et al., 2022. Associations of air pollution on the brain in children: A brain imaging study. Res. Rep. Health Eff. Inst. 2022 (209), 1–61.

Cyrbs, J., Eeftens, M., Heinrich, J., et al., 2012. Variation of NO2 and NOx concentrations between and within 36 European study areas: results from the ESCAPE study. Atmos. Environ. 62, 374–390.

Eeftens, M., Beelen, R., De Hoogh, K., et al., 2012. Development of land use regression models for PM<sub>2.5</sub>, PM<sub>2.5</sub> absorbance, PM<sub>10</sub> and PM<sub>coarse</sub> in 20 European study areas; results of the ESCAPE project. Environ. Sci. Technol. 46 (20), 11195–11205.

Eeftens, M., Tsai, M.-Y., Ampe, C., et al., 2012. Spatial variation of PM<sub>2.5</sub>, PM<sub>10</sub>, PM<sub>2.5</sub> absorbance and PM<sub>coarse</sub> concentrations between and within 20 European study areas and the relationship with NO<sub>2</sub> – results of the ESCAPE project. Atmos. Environ. 62, 303–317.

Beelen, R., Hoek, G., Vienneau, D., et al., 2013. Development of NO<sub>2</sub> and NOx land use regression models for estimating air pollution exposure in 36 study areas in Europe – the ESCAPE project. Atmos. Environ. 72, 10–23.

Brunkreef B. Procedure for extrapolation back in time. 2012. http://www. escapeproject.eu/manuals/Procedure_for_extrapolation_back_in_time.pdf [Accessed 07/11/2023].

Heppe, D.H., Medina-Gomez, C., De Jongste, J.C., et al., 2014. Fetal and childhood growth patterns associated with bone mass in school-age children: The generation R study. J. Bone Miner. Res. 29 (12), 2584–2593.

Bianchi, M.L., Baim, S., Bishop, N.J., et al., 2010. Official positions of the international society for clinical densitometry (ISCD) on DXA evaluation in children and adolescents. Pediatr. Nephrol. 25 (1), 37–47.

Macdonald-Wallis, C., Tobias, J.H., Smith, G.D., Lawlor, D.A., 2010. Relation of maternal prepregnancy body mass index with offspring bone mass in childhood: Is there evidence for an intrauterine effect? Am. J. Clin. Nutr. 92 (4), 872–880.

Gasparrini, A., Armstrong, B., Kenward, M.G., 2010. Distributed lag non-linear models. Stat. Med. 29 (21), 2224–2234.
Weisskopf, M.G., Sparrow, D., Hu, H., Power, M.C., 2015. Biased exposure-health effect estimates from selection in cohort studies: Are environmental studies at particular risk? *Environ. Health Perspect.* 123 (11), 1113–1122.

Weuve, J., Tchetgen, Tchetgen, E.J., Glymour, M.M., et al., 2012. Accounting for bias due to selective attrition: the example of smoking and cognitive decline. *Epidemiology* 23 (1), 119–128.

He, S., Klevebro, S., Baldanzi, G., et al., 2022. Ambient air pollution and inflammation-related proteins during early childhood. *Environ. Res.* 215, 114364.

Schini, M., Vilaca, T., Gossiel, F., Salam, S., Eastell, R., 2022. Bone turnover markers: Basic biology to clinical applications. *Endocr. Rev.* 44 (3), 417–473.

Klein, S.L., Flanagan, K.L., 2016. Sex differences in immune responses. *Nat. Rev. Immunol.* 16 (10), 626–638.

Uekert, S.J., Akan, G., Evans, M.D., et al., 2006. Sex-related differences in immune development and the expression of atopy in early childhood. *J. Allergy Clin. Immunol.* 118 (6), 1375–1381.

Cabello, N., Mishra, V., Sinha, U., et al., 2015. Sex differences in the expression of lung inflammatory mediators in response to ozone. *American Journal of Physiology-Lung Cellular and Molecular Physiology* 309 (10), L1150.

Hoffmann, B., Moebus, S., Dragano, N., et al., 2009. Chronic residential exposure to particulate matter air pollution and systemic inflammatory markers. *Environ. Health Perspect.* 117 (8), 1302–1308.

Gruzieva, O., Merid, S.K., Gref, A., et al., 2017. Exposure to traffic-related air pollution and serum inflammatory cytokines in children. *Environ. Health Perspect.* 125 (6), 067007.

Deng, Y.-L., Liao, J.-Q., Zhou, B., et al., 2022. Early life exposure to air pollution and cell-mediated immune responses in preschoolers. *Chemosphere* 286, 131963.

Heppe, D.H.M., Medina-Gomez, C., Hofman, A., Rivadeneira, F., Jaddoe, V.W.V., 2015. Does fetal smoke exposure affect childhood bone mass? *The Generation R Study. Osteoporosis International* 26 (4), 1319–1329.

Wilson, A., Chiu, Y.-H.-M., Hsu, H.-H.-L., Wright, R.O., Wright, R.J., Coull, B.A., 2017. Potential for bias when estimating critical windows for air pollution in children's health. *Am. J. Epidemiol.* 186 (11), 1281–1289.

Gulliver, J., de Hoogh, K., Hansell, A., Vienneau, D., 2013. Development and back-extrapolation of NO2 land use regression models for historic exposure assessment in great Britain. *Environ. Sci. Technol.* 47 (14), 7804–7811.

McConnell, R., Islam, T., Shankardass, K., et al., 2010. Childhood incident asthma and traffic-related air pollution at home and school. *Environ. Health Perspect.* 118 (7), 1021–1026.

**WHO Guidelines Approved by the Guidelines Review Committee.** WHO global air quality guidelines: Particulate matter (PM(25) and PM(10)), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide. Geneva: © World Health Organization 2021.; 2021.