Skip to main content
Download PDF
Download ePub

Identifying critical windows of air pollution exposure during preconception and gestational period on birthweight: a prospective cohort study

Environmental Health volume 22, Article number: 71 (2023) Cite this article

Abstract

Background

Few studies have assessed air pollution exposure association with birthweight during both preconception and gestational periods.

Methods

Leveraging a preconception cohort consisting of 14220 pregnant women and newborn children in Shanghai, China during 2016–2018, we aim to assess associations of NO2 and PM2.5 exposure, derived from high-resolution spatial-temporal models, during preconception and gestational periods with outcomes including term birthweight, birthweight Z-score, small-for-gestational age (SGA) and large-for-gestational age (LGA). Linear and logistic regressions were used to estimate 3-month preconception and trimester-averaged air pollution exposure associations; and distributed lag models (DLM) were used to identify critical exposure windows at the weekly resolution from preconception to delivery. Two-pollutant models and children’s sex-specific associations were explored.

Results

After controlling for covariates, one standard deviation (SD) (11.5 μg/m3, equivalent to 6.1 ppb) increase in NO2 exposure during the second and the third trimester was associated with 13% (95% confidence interval: 2 – 26%) and 14% (95% CI: 1 – 29%) increase in SGA, respectively; and one SD (9.6 μg/m3) increase in PM2.5 exposure during the third trimester was associated with 15% (95% CI: 1 – 31%) increase in SGA. No association have been found for outcomes of birthweight, birthweight Z-score and LGA. DLM found that gestational weeks 22–32 were a critical window, when NO2 exposure had strongest associations with SGA. The associations of air pollution exposure tended to be stronger in female newborns than in male newborns. However, no significant associations of air pollution exposure during preconception period on birthweight outcomes were found.

Conclusion

Consistent with previous studies, we found that air pollution exposure during mid-to-late pregnancy was associated with adverse birthweight outcomes.

Peer Review reports

Introduction

Ambient air pollution, especially its components of particulate matter with equal to or less than 2.5 μm aerodynamic diameter (PM2.5) and nitrogen dioxide (NO2), is one of most important environmental health risk factors [1, 2]. Even though annual averages of ambient air pollution levels have generally declined in Shanghai, a mega city in China, over the past 5 years [3, 4], PM2.5 and NO2 are still well above World Health Organization (WHO) ambient air quality guideline of PM2.5 as 5 μg/m3 and NO2 as 10 μg/m3 [5]. Over 1 million premature deaths due to lung cancer, cardiovascular diseases and lower-respiratory infection and chronic lung disease can be attributed to excessive levels of air pollution in China [1, 2]. In addition to asserting excess mortality, exposure to high levels of air pollution during pregnancy has also been associated with adverse birth outcomes, including preterm birth, low birthweight (LBW), small for gestational age (SGA), large for gestational age (LGA) and birth defects [6,7,8,9,10,11,12,13]. Infants born higher- or lower-than normal birthweight have been shown to be associated with abnormal growth trajectory and linked to increased risk of childhood obesity and other cardiometabolic disorders later in life [14,15,16].

While existing evidence suggests a link between PM2.5 and NO2 exposure during gestational period and birthweight [7, 17,18,19], the critical exposure time windows in the pregnancy period were inconsistent [12, 20]. On the other hand, the evidence is extremely limited concerning pre-conception period air pollution exposure and birth outcomes. Only a few studies have assessed the associations of air pollution during preconception period on birthweight [19, 21,22,23,24]. These studies assessed the associations of exposures to several air pollutants during the 3-month preconception period with the risk of adverse birthweight outcomes such as SGA. However, no consistent associations have been found for preconception exposure to PM2.5 and NO2 air pollution [21,22,23,24]. Preconception is a critical developmental window for gametogenesis; and air pollution exposure from both mothers and fathers during preconception or early stages of pregnancy could be as important as during pregnancy to health of children in the future [25]. Previous studies have found that air pollution exposure during these critical gametogenesis period could lead to adverse effects on development of sperm and ova cells [26, 27], disrupting first cell lineage segregation at the blastocyst stage [28], diminishing ovarian reserve [29], and finally leading to long-term adverse outcomes of fetal and neonatal development [30, 31]. Considering the potential importance of preconception exposure in fetal body growth, we leveraged the rich data resource of the Shanghai Pre-conception Cohort (SPCC) to investigate the effect estimates of air pollution during both preconception and gestational period on birthweight outcomes. We aim to identify critical time windows of exposure associated with birth outcomes during both the preconception period and the gestational period. In addition, we also aim to assess whether these associations differed by children’s sex, in order to understand sex-specific relationships between prenatal air pollution exposure and birthweight outcomes.

Method

Study design and population

This Growth and Air Pollution in Preconception (GAAP) study was based on pregnancy-planning women and men enrolled in the Shanghai Preconception Cohort (SPCC). The SPCC was established with its primary aim to investigate the associations of parental periconceptional nutritional factors with congenital heart disease, child growth and development and pediatric diseases. More details of SPCC cohort were reported elsewhere [32]. Briefly, SPCC enrolled N = 34,759 women at preconception or antenatal care clinics from 28 maternity institutions in 10 districts of Shanghai, between March 2016 to December 2018. Participants of the SPCC cohort who delivered between March 2016 to December 2018 and registered residential address during peri-conception were included in the GAAP study. The exclusion criteria of the GAAP study included (1) participants missing residential address or (2) moving out of Shanghai city. The GAAP study protocols were approved by the Ethics Committee of the Children’s Hospital of Fudan University (IRB number: 201,649), Duke University (IRB number: 00000560) and University of Southern California (IRB number: HS-19-00306), respectively.

Participants follow up and covariates

Participating women completed the baseline questionnaire of key demographics following their pregnancy consultation visits to a clinic (i.e., the preconception visits), after they signed a consent to participate. Information collected included maternal age, ethnicity, education levels and employment. When participants visited the first antenatal care around 14-weeks of gestational age, another questionnaire was administered to collect lifestyle covariates, including maternal smoking, exposure to secondhand smoking, nutrition supplementation information during the periconception period such as folic acid supplement. We defined maternal smoking and secondhand smoke as whether women participants self-reported active smoking and exposed to cigarette smoke from family members or colleagues in working space between 3-month preconception and first trimester, respectively. Routine antenatal care electronic medical record data were obtained from the Maternal Clinic Antenatal Medical Record System (MCAMRS), managed by the Shanghai Center for Women and Children’s Health, including gestational weeks measured by ultrasound or last menstrual period, anthropometry measurements of women participants such as height, gestational weight, last menstrual period, and childbearing history collected at first antenatal care visit. Maternal body mass index (BMI) at early pregnancy was calculated using maternal weight and height and categorized as normal (BMI < 24 kg/m2) or overweight (BMI > = 24 kg/m2 and BMI < 28 kg/m2) or obesity (BMI > = 28 kg/m2) according to the definition of the Chinese population recommend by Working Group on Obesity in China [33]. Participants were followed until delivery. Newborn information was also extracted from MCAMRS, including delivery date, delivery mode, birthweight, child’s sex, birth defects and pregnancy complications if any. Based on the conception date and delivery date, we calculated the gestational age in week at delivery for each newborn. We only included children born full-term [gestational age > = 37 weeks, N = 14,220 (95.0%)] in the analysis. The first trimester was defined as the period during gestational week 0–13 (day 0 – day 91), the second trimester was gestational week 14–26 (day 92 – day 182); and the third trimester was from gestational week 27 (day 183) to delivery. Additionally, the season of delivery (spring/summer/fall/winter), weekly average temperature (°C) and relative humidity in Shanghai were derived from the data retrieved from National Centers for Environmental Information of United States (https://www.ncdc.noaa.gov/).

Preconception air pollution exposure

Air pollution exposures were assessed based on a spatial-temporal model of PM2.5 and NO2 at three-day temporal resolution predicted at each participant’s residential address extracted from MCAMRS, between October 2012 and December 2019 [34], which covers entire preconception and pregnancy period of cohort of this study. This spatial-temporal model was specifically developed for this study based on smoothed temporal trends and the land-use regression approach in a universal kriging structure [35]. This model has shown good performance at both short-term (three-day average) and long-term (7-year average) scales, with leave-one-out cross-validation (LOOCV) rooted-mean square error (RMSE) 2.01 μg/m3 (R2 = 0.72) for PM2.5 and 3.07 μg/m3 (R2 = 0.87) for NO2 in the long-term averages and 5.82 μg/m3 (R2 = 0.94) for PM2.5 and 7.50 μg/m3 (R2 = 0.83) for NO2 in three-day averages [34]. We used this model to predict three-day average concentrations of PM2.5 and NO2 at each participant’s residential address. Then, based on the conception date and delivery date, we calculated weekly air pollution exposure during pregnancy as gestational air pollution exposure for each 7-day period from conception date, and estimated trimester-specific exposures to PM2.5 and NO2. Additionally, we estimated weekly exposures from conception date up to 15 weeks before conception date, and we used the mean of the air pollution exposure levels from conception to 12 weeks before conception date as the average preconception exposure.

Birthweight outcomes

Birth weights in gram of newborns were extracted from the MCAMRS, which recorded birthweight of every newborn in Shanghai and matched with study participants’ information. Birthweight was measured to nearest gram and recorded into the system within 72 h after birth by a nurse in obstetrics. We estimated sex- and gestational age-specific birthweight Z-scores and percentiles of each newborn using recorded birthweight, gestational age at delivery and reference percentile chart for Chinese representative children [36]. Based on the 10th and 90th percentile cut-offs, we categorized the sex and gestational age-specific birth weight into small for gestational age (SGA) as less than 10th percentile, appropriate for gestational age (AGA) as between 10th and 90th percentile, and large for gestational age (LGA) as above 90th percentile. The outcome variables in this study included birth weight in gram, birthweight Z scores, and gestational age-specific birth weight categories (SGA and LGA).

Statistical analysis

Firstly, we assessed the 12-week preconception and trimester-averaged gestational period air pollution associations with birth weight outcomes. We applied multivariate regressions with linear and non-linear exposure-response functions to assess effect estimates of air pollution during these periods on birthweight and birthweight Z-score. We applied logistic regressions to assess the associations with SGA and LGA. We included pre-specified covariates to adjust in the models based on previous studies and potential confounding effects of covariates [21,22,23, 37]. For multivariate regression models of birthweight, we included covariates including maternal age, ethnicity, education, occupation, maternal BMI at first trimester, maternal gravidity, maternal smoking status, exposure to secondhand smoking, season of delivery, gestational age, children’s sex, temperature, and relative humidity. For outcomes of birthweight Z-score, SGA and LGA, since gestational age and children’s sex were incorporated for the calculations, we included all the covariates other than gestational age and child’s sex.

Secondly, we attempted to identify the critical time window for birth weight outcomes during the preconception and pregnancy period and non-linear exposure-response associations of maternal air pollution on birthweight outcomes. In doing so, we applied distributed lag non-linear models (DLNM) to evaluate weekly-specific time-varying effect estimates of maternal exposure to air pollution [38]. We built DLNM models separately for each pollutant of PM2.5, and NO2. The framework of DLNM was based on a “cross-basis” function to flexibly model both exposure-response associations and lag structure of the associations between exposures at different times. DLNM included weekly air pollution data simultaneously and estimated associations between birthweight outcomes and air pollutant exposure for a given week after controlling for exposure at all other weeks, based on an assumption that association varies smoothly as a function of week. We included the weekly air pollution 15 weeks before conception as the pre-conception period exposure, and weekly air pollution exposure for 37 weeks after conception as the gestational period exposure. Only children born full-term ( > = 37 weeks) were included in the analysis, and for children born with more than 37 weeks, only air pollution exposure for first 37 weeks of gestation was included in the analysis. To develop DLNM model, we firstly investigated the non-linearity of exposure-response effect estimates of air pollution on the outcomes, and we found linear exposure-response provided best fit for minimizing Akaike Information Criteria (AIC). Supplemental Information (SI) Figure S1 shows the difference exposure-response function curves and associated AIC, comparing linear, quadratic polynomial and cubic polynomial functions. Therefore, we used simplified distributed-lag linear model (DLM) in the analysis. Then, we modeled the exposure lag-response function of weekly air pollution in DLM. We used the natural splines [39, 40] to model this smooth lag function based on minimizing AIC with degree of freedom (df) range from 3 to 7, with knots placed at equal space at all lags. Based on the AIC values, the natural cubic spline of the lag function with 4 df provided best fit of PM2.5 on birthweight and birthweight Z-score, SGA and LGA and best fit for NO2 on SGA and LGA; df of 5 provided best fit for NO2 on birthweight Z-score and birthweight. Like in the trimester-averaged air pollution models, same covariates were included in the DLM analysis.

Following these analyses, we conducted stratified analysis by children’s sex, and assessed whether associations of air pollution on birthweight differed by children’s sex. Lastly, we conducted sensitivity analysis to confirm the robustness of the findings. In the first sensitivity analysis, we assessed the weekly air pollution associations with the birthweight outcomes separately. In the second sensitivity analysis, we conducted two-pollution air pollution model, included both NO2 and PM2.5 trimester-averaged air pollution exposure in the model. Lastly, we conduct sensitivity analysis on birthweight without adjusting gestational age at birth, since gestational age could be an intermediate factor between air pollution exposure and birthweight outcomes. R (version 4.1.3) with mgcv and dlnm packages was used in the statistical analyses.

Results

Population characteristics

Between March 2016 to December 2018, 18767 pregnant women, from the total of 34759 SPCC cohort participants (26714 women participants), delivered a baby and were potentially eligible for the GAAP study. Among them, 18,767 participants had complete information on newborns’ birthweight and sex as well as gestational age at birth. We excluded 9 children with birthweight less than 200 gram (an implausible value) and removed 3786 additional mother-child pairs due to missing baseline address information, air pollution exposure assessment and outcome health information, and 752 children who were born with gestational age less than 37 weeks (preterm birth). We excluded preterm birth infants mainly due to that could be associated with other maternal factors such as complications, maternal age and delivery mode in China [41], potentially inducing bias in the analysis. Consequently, 14220 mother-child pairs had complete information and were included in our main analysis in this study. Figure 1 illustrates the flow diagram of the cohort of this study. In Table 1, we show the participants’ characteristics at enrollment during preconception or early pregnancy period. This shows that the mean (SD) of the maternal age is 33.4 (3.8) years at conception date. Near half of the participants did not report their ethnicity (N = 7206, 50.7%), while the rest of the women participants were of Han ethnicity (N = 6885, 48.4%). Additionally, participants mostly reported having a college degree (N = 9703, 68.2%), and working as a manufactural worker, office clerk or self-employed (N = 8257, 58.1%), followed by manager, entrepreneur, or technician (N = 4834, 34.0%). A very small proportion of the women reported smoking during preconception and early pregnancy (N = 155, 1.1%), whereas more women (N = 2318, 16.3%) reported that they were exposed to second-hand smoke during this period. Most women had no previous baby delivery (N = 10962, 77.2%), while 3,199 (22.4%) reported they had one delivery before this pregnancy. The mean (SD) maternal body mass index (BMI) in the first trimester was 20.7 (4.4) kg/m2. The vast majority (72.6%) of the women had BMI in the healthy range (18.5 ‒ 24 kg/m2), while 11.2% in the overweight range of 24–28 kg/m2 and 2.7% in the obese category of ≥ 28 kg/m2.

Fig. 1
figure 1

Flow diagram of the Growth and Air Pollution in Preconception (GAAP) Cohort participant screening, showing the final sample size of 14,220 used in the main data analysis

Full size image

Birthweight outcomes

In Table 1, we also summarize birthweight data for full-term birth children. The mean (SD) of the birthweight was 3358.7 (402.0) gram, and the Z-score of the birthweight was 0.2 (1.0) based on the Chinese infant birthweight chart [36]. Among all the full-term birth children included in our analysis, 820 (5.8%) and 1819 (12.8%) had birthweight classified as small for gestational age (SGA) and large for gestational age (LGA). The mean (SD) of the gestational age of these births was 39.2 (1.1) weeks, and slightly more than half of the births were delivered as vaginal birth (N = 8650, 60.8%). Deliveries occurred mostly in the fall season (N = 4189, 29.5%), followed by in summer (N = 3514, 24.7%), spring (N = 3253, 22.9%) and winter (N = 3264, 23.0%). Slightly more than half (N = 8070, 56.8%) of the children delivered were boys.

Table 1 Summary of Study Participants’ Characteristics and Birthweight Outcome and Meteorology Variables (N = 14220)
Full size table

Air pollution exposure assessment

In Table 2, we show the levels of NO2 and PM2.5 predicted for the 3-month preconception period, first trimester, second trimester, and third trimester averages, respectively. The correlations among pollutant exposures during the four periods are listed in Figure S2, showing that at the same period, NO2 and PM2.5 had a relatively high correlation (spearman correlation ρ > 0.75), while crossing different periods, the correlations were relatively low for NO2 (spearman correlation − 0.5 < ρ < 0.5) and the correlations were moderate and negative for PM2.5. Between preconception and second trimester and between the first and third trimester PM2.5 exposures had correlation coefficient of -0.72 and − 0.76, respectively. The density plots of air pollution exposure levels during these periods of time are shown in Figure S3. Approximately 66% of the NO2 exposure levels during pre-conception and gestational periods were above annual National Ambient Air Quality Standard in China of 40 μg/m3, and 85% of the PM2.5 exposure levels were above annual National Ambient Air Quality Standard in China of 35 μg/m3 [42].

Table 2 Summary of Ambient Air Pollution Exposure Levels During 3-month Preconception period, first trimester, second trimester, third trimester of pregnancy and the entire pregnancy period, unit: μg/m3
Full size table

Effect estimates of preconception and gestational air pollution exposure on birth weight outcomes

Table 3 shows the results of multivariate regression analysis of single pollutant NO2 and PM2.5 effect estimates on birthweight, birthweight Z-score, and logistic regression of small for gestational age (SGA) and large for gestational age (LGA). Trimester-specific exposures showed strongest and most consistent associations with small-for-gestational (SGA), whereas their associations with birthweight and birthweight Z-score, and large-for-gestational age were non-significant. Specifically, we found that a standard deviation (11.5 μg/m3, equivalent to 6.1 ppb) increase in NO2 exposure was associated with the increased odds ratio (OR) of small for gestational age (SGA) during the second (OR: 1.13, 95% confidence interval (CI): 1.02 – 1.26) and third trimester (OR: 1.14, 95% CI: 1.01 – 1.29) after controlling for covariates. We also found a significant positive association between PM2.5 and SGA during the third trimester (OR: 1.15, 95%: 1.01 – 1.31) per 1 standard deviation (9.6 μg/m3) increase.

Table 3 Differences in birthweight (g), Z-score and odds ratios (ORs) of SGA and LGA per 1 standard deviation (SD) increase in preconception and trimester-averaged NO2 and PM2.5 exposure (9.6 μg/m3 for PM2.5 and 11.5 for NO2), respectively. For birthweight outcome, models are controlled for maternal age, ethnicity, education, occupation, maternal BMI at first trimester, maternal gravidity, maternal smoking status, exposure to secondhand smoking, season of delivery, gestational age, children’s sex, temperature, and relative humidity; for Z-score, SGA and LGA outcomes, models controlled for same covariates except for children’s sex and gestational age
Full size table

Figure 2 illustrates the associations of 1 interquartile range (IQR, 17.4 μg/m3, equivalent to 9.3 ppb) increase in NO2 exposure on birthweight, birthweight Z-score, SGA and LGA, showing the lag-response relationships for weekly NO2 exposures during the preconception and gestational periods. Figure 3 illustrates the associations of the 1 IQR (16 μg/m3) increase in weekly PM2.5 exposures on birthweight, birthweight Z-score, SGA and LGA. Similar to the results of trimester-averaged air pollution exposure models, we found significant positive associations between NO2 and SGA at later pregnancy. We found that increased weekly NO2 exposure from 1st quartile (33.1 μg/m3) to 3rd quartile (50.5 μg/m3) during the 22nd gestational week to the 32nd gestational week was associated with increased odds of SGA (for example, OR = 1.02, 95% CI: 1.00–1.04 in gestational week 28) (SI Table S1 for numerical results for all week). However, we did not see significant associations between weekly PM2.5 exposures with birthweight Z-score, birthweight, SGA, or LGA during the preconception and gestational periods (Fig. 3 and SI Table S2).

Fig. 2
figure 2

Differences in birthweight Z-score, birthweight, SGA and LGA associated with 1 IQR increase (17.4 μg/m3, equivalent to 9.26 ppb) of NO2 during preconception and gestational periods. Distributed lag models were adjusted for the maternal age, ethnicity, education, occupation, maternal BMI at first trimester, maternal gravidity, maternal smoking status, exposure to secondhand smoking, season of delivery, gestational age (only for birthweight), children’s sex (only for birthweight), temperature, and relative humidity. The x-axis indicated the time in week of gestation (i.e. conception date is 0 week). The dashed line separated preconception period, first trimester, second trimester and third trimester from left to right, respectively

Full size image
Fig. 3
figure 3

Differences in birthweight Z-score, birthweight, SGA and LGA associated with 1 IQR (16 μg/m3) increase of PM2.5 concentrations during preconception and gestational periods. Distributed lag models were adjusted for the maternal age, ethnicity, education, occupation, maternal BMI at first trimester, maternal gravidity, maternal smoking status, exposure to secondhand smoking, season of delivery, gestational age (only for birthweight), children’s sex (only for birthweight), temperature, and relative humidity. The x-axis indicated the time in week of gestation (i.e. conception date is 0 week). The dashed line separated preconception period, first trimester, second trimester and third trimester from left to right, respectively

Full size image
Fig. 4
figure 4

Sex-stratified analysis of NO2 exposure during 3-month preconception and gestational period on Odds Ratio (OR) of SGA. A: DLM of NO2 on SGA using DLM among female children (girl); B: DLM of NO2 on SGA using DLM among male children (boy); C: preconception and trimester-averaged model by newborns’ sex. Same covariates were adjusted as full models

Full size image

In the sex stratified analysis, we found that girls were more vulnerable to air pollution exposure than boys during later pregnancy, as NO2 exposure at later pregnancy was significantly associated with increased odds of SGA in girls but not in boys shown in the DLM (Fig. 4A and B). In the trimester averaged model, we also only found that NO2 during the second trimester shown significant associations with SGA among girls (Fig. 4C). However, we did not find significant associations between NO2 on other birthweight outcomes stratified by children’s sex (SI Table S3 and Figure S4). For PM2.5, we did not find consistent children’s sex-specific associations between preconception and gestational PM2.5 exposure and birthweight outcomes (SI Table S4 and Figure S5). Our sensitivity analyses confirmed the robustness of the findings from the main analysis as shown above. In the analysis using weekly exposures (as opposed to preconception and trimester-specific exposures), increased NO2 and PM2.5 exposures during gestational week 12 and gestational week 27 exposure were each significantly associated with increased odds of SGA (SI Figure S6). This is consistent with our main analysis finding that NO2 exposure in the 2nd and 3rd trimester and PM2.5 exposure in the 3rd trimester was positively associated with increased odds of SGA. Lastly, two-pollutant models did not show significant associations between a pollutant with birthweight outcomes in trimester-averaged models (SI Table S5), only NO2 exposure during second trimester demonstrated a marginally significant association with SGA (OR = 1.13 per 10 μg/m3 increase, p = 0.06). Lastly, the model assessing the associations between preconception and trimester-averaged air pollution exposure and birthweight, not adjusting for gestational age did not significantly alter the effect estimates and no significantly associations between NO2 or PM2.5 air pollution exposure on birthweight outcomes were identified (SI Table S6).

Discussion

In this study, we found that exposure to NO2 and PM2.5 during gestational period was associated with increased risk of term SGA, and we identified that later pregnancy period including second and third trimester and gestational week 22–32 weeks is one of the critical periods of air pollutant health effects. No significant associations were found between preconception air pollution and birthweight outcomes. Additionally, we found newborn sex-specific associations of NO2 exposure on SGA outcome and identified that girls were more susceptible to prenatal air pollution exposure, especially during later pregnancy period.

Our findings on increased risks of adverse birth outcomes associated with gestational air pollution exposure were consistent with previous epidemiologic observations showing that PM2.5 and NO2 exposures were associated with increased of risk of SGA [12, 18, 43,44,45]. A systematic review conducted in 2012 [12] summarized from 10 studies that per 20 ppb (equivalent to 37.70 μg/m3) increase in NO2 exposure was associated with a 28.1 g (95% CI: 11.5 – 44.8 g) decrease in birthweight. Studies published subsequently provided similar supportive results, even though the critical windows in some studies were not same as we found. Le et al. (2012) have shown that among 164905 pregnant women in Detroit, Michigan, exposure to high levels of NO2 (> 18.7 ppb, equivalent to 35.1 μg/m3) during the first month of the pregnancy was associated with 10% (95% CI: 1 – 19%) increases of odds of SGA [43]. In a cohort of 628 women in Los Angeles, California, Niu et al., (2022) found that 1 IQR increase in PM2.5 (4 μg/m3) during 4–22 gestational weeks was associated with 9.5 g (95% CI: 8.6–10.4 g) decrease in birthweight, and 1 IQR increase in NO2 (11 ppb, equivalent to 5.8 μg/m3) from 9 to 14 gestational weeks was associated with 40.4 g (95% CI: 33.3–47.4 g) decrease in birthweight, respectively, and the associations were larger for pregnant women experiencing higher perceived stress. Rachel B Smith et al. (2017) found among half a million pregnant women, per 1 IQR (8.6 μg/m3, equivalent to 4.6 ppb) increase in NO2 exposure during pregnancy was associated with 3% (95% CI: 1 – 6%) increased in odds of SGA after adjusting for night noise, and the associations were strongest in the second trimester [44].

Similar results were also found in studies in China, showing that later pregnancy was a critical time of air pollution exposure in terms of birthweight outcomes. In a natural experimental design with a month-long air quality improvement during the 2008 Beijing summer Olympics and Paralympics, Rich et al. (2015) found that air pollution exposure in the eighth month of pregnancy was more strongly and significantly associated with decreased birthweight than exposure in other months of pregnancy. Specifically, they reported that 1 IQR increase in PM2.5 (19.8 μg/m3) and 1 IQR increase in NO2 (13.6 ppb) were associated with 18 g (95% CI: 3 – 32 g) and 34 g (95% CI: -3 – 72 g) decrease of birthweight. In the present GAAP study analysis, we found similar associations between NO2 and PM2.5 exposures and SGA outcomes, but non-significant associations with birthweight. Specifically, we found that exposure to NO2 during second and third trimester and exposure to PM2.5 during the third trimester were associated with SGA, respectively. Additionally, we identified that gestational weeks 22 to 32 were critical period. We mainly focused on SGA instead of low birthweight (LBW) in this study, this is due to (1) SGA is determined together by birthweight, children’s sex and gestational age at delivery with a reference chart specific for Chinese population [36], (2) LBW is relatively low prevalence and could be influenced by other factors pregnancy complications and preterm birth [46, 47]. The biological mechanisms of gestational air pollution exposure on SGA were not fully elucidated, but possible mechanisms included that exposure to air pollution could affect human placenta physiology [48], alter placenta DNA methylation [49] and suppress antioxidant defense systems [50, 51]. In mid-to-late pregnancy periods, previous studies indicated that air pollution exposure could particularly impact placental mitochondrial DNA content and increase oxidative stress and inflammation [51, 52], leading to the dysregulation of fetal weight gain.

In the two-pollutant model, however, we did not find significant associations between one air pollutant with birthweight outcomes (SI Figure S5). This is probably due to (1) relatively high correlation of NO2 and PM2.5 (SI Figure S2) and including correlated two variables in the model could lead to unstable estimates, (2) co-exposure confounding between NO2 and PM2.5. Since NO2 has stronger effects than PM2.5 in two-pollutant model, it seems to indicate that NO2 rather than PM2.5 may be the driver of the associations with SGA. In our study, we examined the potential children’s sex-specific associations and found higher estimated risks of SGA among newborn girls than boys (Fig. 4). Our findings on sex-difference are an important contribution to the literature, as only one previous study reported similar findings that newborn girls had a higher risk of SGA after gestational air pollution exposure [18]. Previous reviews and studies suggested that female newborns were more at an increased risk of lower birthweight and lower birthweight z-scores with air pollution exposure [53,54,55], which are in line with findings of our study on the outcome of SGA. The biological mechanisms behind sex-specific susceptibility remains to be uncovered thoroughly. However, compared to male fetuses, female fetuses have shown to have lower antioxidant responses than male fetuses such as microRNA and protein markers [56], and potential mechanisms underlie the effects of air pollution on SGA included reduction in transplacental oxygen and nutrient transport and the suppression of antioxidant defense system [53, 57]. Therefore, the lower antioxidative capacity of female fetus could partly explain higher susceptibility of female fetus to air pollution exposure.

We did not find significant associations between preconception air pollution exposure and birthweight outcomes. This non-significant associations of preconception air pollution on birthweight outcomes were also reported by most of the previous literatures [22,23,24]. However, the previous studies indicated the possible mechanisms linking preconception air pollution and children’s birthweight included alternations of epigenetics and DNA methylations in gametes, maternal oxidative stress, disruption of endocrine in gametogenesis, and changing placental mitochondrial DNA content [28, 58, 59]. Current systematic review and meta-analysis did not find a coherent finding of preconception air pollution and children’s birthweight outcome [60]; therefore, more epidemiological studies are warranted to assess causal effects of children’s health effects from preconception air pollution exposure.

Our study has a few strengths. First, this study leveraged a well characterized preconception birth cohort, with information collected during both preconception and gestational period. We collected maternal information and risk factors of birth outcome during both preconception period and gestational period, such as smoking exposure, maternal BMI. Second, we used a high-resolution spatial-temporal air pollution model to assess weekly exposures to NO2 and PM2.5 during both preconception and pregnancy periods at residential addresses. In addition, we conducted analysis of both trimester-averaged air pollution exposure as well as weekly averages of exposure using DLM, to identify critical time window in both the preconception and the gestational period of air pollution exposure. This study has some limitations that merit improvements in the future. Pregnant women could be exposed to air pollutants from indoor sources such as cooking, workplace, hospital, and other places. Our air pollution exposure model at individual address level does not include those exposure from other sources and will induce bias if pregnant women moved out of their residential address. Second, we do not have paternal information in this study. Since paternal exposure to environmental pollution and smoking could lead to lower birthweight [61, 62], assessing paternal exposure together with maternal exposure could further clarify whether air pollution exposure during preconception period can affect birth outcomes. Some medical conditions such as gestational diabetes, gestational hypertension are also factors affecting birthweight outcomes [63, 64], but not included in this study as covariates. In addition, our cohort has some missing covariates in ethnicity, occupation, and education levels and missingness was treated as a separate category in the analysis. These factors could contribute to the bias when estimating associations between maternal preconception air pollution and birthweight outcomes, potentially leading to the non-significant findings reported in this study. Lastly, this study was only based on population consisting of mostly Han Chinese population in urban or peri-urban areas in Shanghai of relatively high air pollution levels. The findings may not be extrapolated to populations with lower exposure levels.

Conclusion

We found increased risks for term SGA associated with maternal NO2 exposure in the second, third trimester and with maternal PM2.5 exposure in the third trimester. The magnitudes of NO2 and PM2.5 effect estimates were similar to those reported in the existing literature. We identified gestational weeks 22–32, within the entire 52 weeks of peri-conception period consist of 15 weeks of preconception and 37 weeks of gestational period, as a critical time window of exposure affecting birth outcomes in 14220 pregnant women in Shanghai, China. The associations between gestational NO2 air pollution exposure and SGA were stronger among newborn girls than boys. No significant associations were found between preconception air pollution exposure and birthweight or birthweight Z-scores, and no associations with LGA, birthweight and birthweight Z-score were found with air pollution exposure during pregnancy. Our novel finding on the sex difference warrants further epidemiologic and mechanistic investigation.

Data Availability

The data described in this article can be accessed upon request after institutional review board (IRB) approvals.

References

  1. Landrigan PJ, Fuller R, Acosta NJR, Adeyi O, Arnold R, Basu N (Nil), editors. The Lancet Commission on pollution and health. The Lancet. 2018;391:462–512.

  2. Murray CJL, Aravkin AY, Zheng P, Abbafati C, Abbas KM, Abbasi-Kangevari M, et al. Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the global burden of Disease Study 2019. The Lancet. 2020;396:1223–49.

    Article  Google Scholar 

  3. Fan H, Zhao C, Yang Y. A comprehensive analysis of the spatio-temporal variation of urban air pollution in China during 2014–2018. Atmos Environ. 2020;220:117066.

    Article  CAS  Google Scholar 

  4. Zeng Y, Cao Y, Qiao X, Seyler BC, Tang Y. Air pollution reduction in China: recent success but great challenge for the future. Sci Total Environ. 2019;663:329–37.

    Article  CAS  Google Scholar 

  5. World Health Organization. WHO global air quality guidelines. Particulate matter (PM2.5 and PM10), ozone, nitrogen dioxide, sulfer dioxide and carbon monoxide. 2021. Report No.: ISBN 978-92-4-003422-8.

  6. Bekkar B, Pacheco S, Basu R, DeNicola N. Association of Air Pollution and heat exposure with Preterm Birth, Low Birth Weight, and Stillbirth in the US: a systematic review. JAMA Netw Open. 2020;3:e208243–3.

    Article  Google Scholar 

  7. Dadvand P, Parker J, Bell ML, Bonzini M, Brauer M, Darrow LA, et al. Maternal exposure to Particulate Air Pollution and Term Birth Weight: a multi-country evaluation of Effect and Heterogeneity. Env Health Perspect. 2013;121:267–373.

    Article  Google Scholar 

  8. Hu C-Y, Huang K, Fang Y, Yang X-J, Ding K, Jiang W, et al. Maternal air pollution exposure and congenital heart defects in offspring: a systematic review and meta-analysis. Chemosphere. 2020;253:126668.

    Article  CAS  Google Scholar 

  9. Pedersen M, Giorgis-Allemand L, Bernard C, Aguilera I, Andersen A-MN, Ballester F, et al. Ambient air pollution and low birthweight: a european cohort study (ESCAPE). Lancet Respir Med. 2013;1:695–704.

    Article  CAS  Google Scholar 

  10. Ravindra K, Chanana N, Mor S. Exposure to air pollutants and risk of congenital anomalies: a systematic review and metaanalysis. Sci Total Environ. 2021;765:142772.

    Article  CAS  Google Scholar 

  11. Shah PS, Balkhair T. Knowledge synthesis group on determinants of Preterm/LBW births. Air pollution and birth outcomes: a systematic review. Environ Int. 2011;37:498–516.

    Article  CAS  Google Scholar 

  12. Stieb DM, Chen L, Eshoul M, Judek S. Ambient air pollution, birth weight and preterm birth: a systematic review and meta-analysis. Environ Res. 2012;117:100–11.

    Article  CAS  Google Scholar 

  13. Tsoli S, Ploubidis GB, Kalantzi O-I. Particulate air pollution and birth weight: a systematic literature review. Atmospheric Pollut Res. 2019;10:1084–122.

    Article  CAS  Google Scholar 

  14. Hong YH, Chung S. Small for gestational age and obesity related comorbidities. Ann Pediatr Endocrinol Metab. 2018;23:4–8.

    Article  Google Scholar 

  15. Martin A, Connelly A, Bland RM, Reilly JJ. Health impact of catch-up growth in low-birth weight infants: systematic review, evidence appraisal, and meta-analysis. Matern Child Nutr. 2017;13.

  16. Viswanathan S, McNelis K, Makker K, Calhoun D, Woo JG, Balagopal B. Childhood obesity and adverse cardiometabolic risk in large for gestational age infants and potential early preventive strategies: a narrative review. Pediatr Res. 2022;92:653–61.

    Article  Google Scholar 

  17. Gheissari R, Liao J, Garcia E, Pavlovic N, Gilliland FD, Xiang AH, et al. Health Outcomes in Children Associated with prenatal and early-life exposures to Air Pollution: a narrative review. Toxics. 2022;10:458.

    Article  CAS  Google Scholar 

  18. Lu C, Deng M, Norbäck D, Liu Z, Murithi RG, Deng Q. Effect of outdoor air pollution and indoor environmental factors on small for gestational age. Build Environ. 2021;206:108399.

    Article  Google Scholar 

  19. Niu Z, Habre R, Chavez TA, Yang T, Grubbs BH, Eckel SP, et al. Association between Ambient Air Pollution and Birth Weight by maternal individual- and Neighborhood-Level Stressors. JAMA Netw Open. 2022;5:e2238174.

    Article  Google Scholar 

  20. Jacobs M, Zhang G, Chen S, Mullins B, Bell M, Jin L, et al. The association between ambient air pollution and selected adverse pregnancy outcomes in China: a systematic review. Sci Total Environ. 2017;579:1179–92.

    Article  CAS  Google Scholar 

  21. Chen J, Li P-H, Fan H, Li C, Zhang Y, Ju D et al. Weekly-specific ambient fine particular matter exposures before and during pregnancy were associated with risks of small for gestational age and large for gestational age: results from Project ELEFANT. Int J Epidemiol [Internet]. 2021 [cited 2021 Nov 1]; https://doi.org/10.1093/ije/dyab166

  22. Ha S, Zhu Y, Liu D, SHerman S, Mendola P. Ambient temperature and air quality in relation to small for gestational age and term low birthweight. Environ Res. 2017;155:394–400.

    Article  CAS  Google Scholar 

  23. Nobles CJ, Grantz KL, Liu D, Williams A, Ouidir M, Seeni I, et al. Ambient air pollution and fetal growth restriction: physician diagnosis of fetal growth restriction versus population-based small-for-gestational age. Sci Total Environ. 2019;650:2641–7.

    Article  CAS  Google Scholar 

  24. Williams AD, Kanner J, Grantz KL, Ouidir M, Sheehy S, SHerman S, et al. Air pollution exposure and risk of adverse obstetric and neonatal outcomes among women with type 1 diabetes. Environ Res. 2021;197:111152.

    Article  CAS  Google Scholar 

  25. Gluckman PD, Hanson MA, Cooper C, Thornburg KL. Effect of in Utero and early-life conditions on Adult Health and Disease. N Engl J Med. 2008;359:61–73.

    Article  CAS  Google Scholar 

  26. Udagawa O, Furuyama A, Imai K, Fujitani Y, Hirano S. Effects of diesel exhaust-derived secondary organic aerosol (SOA) on oocytes: potential risks to meiotic maturation. Reprod Toxicol. 2018;75:56–64.

    Article  CAS  Google Scholar 

  27. Vecoli C, Montano L, Andreassi MG. Environmental pollutants: genetic damage and epigenetic changes in male germ cells. Environ Sci Pollut Res. 2016;23:23339–48.

    Article  CAS  Google Scholar 

  28. Perin PM, Maluf M, Czeresnia CE, Nicolosi Foltran Januário DA, Nascimento Saldiva PH. Effects of exposure to high levels of particulate air pollution during the follicular phase of the conception cycle on pregnancy outcome in couples undergoing in vitro fertilization and embryo transfer. Fertil Steril. 2010;93:301–3.

    Article  Google Scholar 

  29. Ogliari KS, Lichtenfels AJ, de FC MRR, Ferreira AT, Dolhnikoff M, Saldiva PHN. Intrauterine exposure to diesel exhaust diminishes adult ovarian reserve. Fertil Steril. 2013;99:1681–1688e2.

    Article  CAS  Google Scholar 

  30. Klepac P, Locatelli I, Korosec S, Kunzli N, Kukec A. Ambient air pollution and pregnancy outcomes: a comprehensive review and identification of environmental public health challenges. Env Res. 2018;167:144–59.

    Article  CAS  Google Scholar 

  31. Proietti E, Röösli M, Frey U, Latzin P. Air pollution during pregnancy and neonatal outcome: a review. J Aerosol Med Pulm Drug Deliv. 2013;26:9–23.

    Article  CAS  Google Scholar 

  32. Wang D, Zhang Y, Jiang Y, Ye Y, Ji M, Dou Y, et al. Shanghai Preconception Cohort (SPCC) for the association of periconceptional parental key nutritional factors with health outcomes of children with congenital heart disease: a cohort profile. BMJ Open. 2019;9:e031076.

    Article  Google Scholar 

  33. Xi B, Liang Y, He T, Reilly KH, Hu Y, Wang Q, et al. Secular trends in the prevalence of general and abdominal obesity among Chinese adults, 1993–2009. Obes Rev. 2012;13:287–96.

    Article  CAS  Google Scholar 

  34. Xu J, Yang Z, Han B, Yang W, Duan Y, Fu Q, et al. A unified empirical modeling approach for particulate matter and NO2 in a coastal city in China. Chemosphere. 2022;299:134384.

    Article  CAS  Google Scholar 

  35. Keller JP, Olives C, Kim S-Y, Sheppard L, Sampson PD, Szpiro AA, et al. A unified spatiotemporal modeling approach for predicting concentrations of multiple air pollutants in the multi-ethnic study of atherosclerosis and air pollution. Environ Health Perspect. 2015;123:301–9.

    Article  Google Scholar 

  36. Dai L, Deng C, Li Y, Zhu J, Mu Y, Deng Y, et al. Birth weight reference percentiles for Chinese. PLoS ONE. 2014;9:e104779.

    Article  Google Scholar 

  37. Yuan L, Zhang Y, Wang W, Chen R, Liu Y, Liu C, et al. Critical windows for maternal fine particulate matter exposure and adverse birth outcomes: the Shanghai birth cohort study. Chemosphere. 2020;240:124904.

    Article  CAS  Google Scholar 

  38. Gasparrini A, Guo Y, Hashizume M, Lavigne E, Zanobetti A, Schwartz J, et al. Mortality risk attributable to high and low ambient temperature: a multicountry observational study. The Lancet. 2015;386:369–75.

    Article  Google Scholar 

  39. Rahman MM, Shu Y-H, Chow T, Lurmann FW, Yu X, Martinez MP et al. Prenatal exposure to Air Pollution and Autism Spectrum Disorder: sensitive windows of exposure and sex differences. Environ Health Perspect. 130:017008.

  40. Wu H, Jiang B, Zhu P, Geng X, Liu Z, Cui L, et al. Associations between maternal weekly air pollutant exposures and low birth weight: a distributed lag non-linear model. Environ Res Lett. 2018;13:024023.

    Article  Google Scholar 

  41. Deng K, Liang J, Mu Y, Liu Z, Wang Y, Li M, et al. Preterm births in China between 2012 and 2018: an observational study of more than 9 million women. Lancet Glob Health. 2021;9:e1226–41.

    Article  CAS  Google Scholar 

  42. Ministry of Ecology and Environment. Ambient Air Quality Standards of China [Internet]. 2012. Report No.: GB 3095 – 2012. Available from: https://english.mee.gov.cn/Resources/standards/Air_Environment/quality_standard1/201605/W020160511506615956495.pdf

  43. Le HQ, BatTerman SA, Wirth JJ, Wahl RL, Hoggatt KJ, Sadeghnejad A, et al. Air pollutant exposure and preterm and term small-for-gestational-age births in Detroit, Michigan: long-term trends and associations. Environ Int. 2012;44:7–17.

    Article  CAS  Google Scholar 

  44. Rachel B, Smith D, Fecht J, Gulliver SD, Beevers D, Dajnak M, Blangiardo et al. Impact of London’s road traffic air and noise pollution on birth weight: retrospective population based cohort study. BMJ. 2017;359.

  45. Rich DQ, Liu K, Zhang J, Thurston SW, Stevens TP, Pan Y, et al. Differences in Birth Weight Associated with the 2008 Beijing Olympics Air Pollution reduction: results from a natural experiment. Environ Health Perspect. 2015;123:880–7.

    Article  CAS  Google Scholar 

  46. Tang W, Mu Y, Li X, Wang Y, Liu Z, Li Q, et al. Low birthweight in China: evidence from 441 health facilities between 2012 and 2014. J Matern Fetal Neonatal Med. 2017;30:1997–2002.

    Article  Google Scholar 

  47. Liu Y, Xu J, Chen D, Sun P, Ma X. The association between air pollution and preterm birth and low birth weight in Guangdong, China. BMC Public Health. 2019;19:3.

    Article  Google Scholar 

  48. Saenen ND, Martens DS, Neven KY, Alfano R, Bové H, Janssen BG, et al. Air pollution-induced placental alterations: an interplay of oxidative stress, epigenetics, and the aging phenotype? Clin Epigenetics. 2019;11:124.

    Article  CAS  Google Scholar 

  49. Cai J, Zhao Y, Liu P, Xia B, Zhu Q, Wang X, et al. Exposure to particulate air pollution during early pregnancy is associated with placental DNA methylation. Sci Total Environ. 2017;607–608:1103–8.

    Article  Google Scholar 

  50. Guxens M, Aguilera I, Ballester F, Estarlich M, Fern Ã-SA, Lertxundi A, et al. Prenatal exposure to Residential Air Pollution and Infant Mental Development: modulation by Antioxidants and Detoxification factors. Environ Health Perspect. 2012;120:144–9.

    Article  CAS  Google Scholar 

  51. Latzin P, Frey U, Armann J, Kieninger E, Fuchs O, Röösli M, et al. Exposure to Moderate Air Pollution during late pregnancy and cord blood cytokine secretion in healthy neonates. PLoS ONE. 2011;6:e23130.

    Article  CAS  Google Scholar 

  52. Janssen BG, Munters E, Pieters N, Smeets K, Cox B, Cuypers A, et al. Placental mitochondrial DNA content and Particulate Air Pollution during in Utero Life. Environ Health Perspect. 2012;120:1346–52.

    Article  CAS  Google Scholar 

  53. Ghosh R, Rankin J, Pless-Mulloli T, Glinianaia S. Does the effect of air pollution on pregnancy outcomes differ by gender? A systematic review. Environ Res. 2007;105:400–8.

    Article  CAS  Google Scholar 

  54. Lakshmanan A, Chiu Y-HM, Coull BA, Just AC, Maxwell SL, Schwartz J, et al. Associations between prenatal traffic-related air pollution exposure and birth weight: modification by sex and maternal pre-pregnancy body mass index. Environ Res. 2015;137:268–77.

    Article  CAS  Google Scholar 

  55. Wang Q, Miao H, Warren JL, Ren M, Benmarhnia T, Knibbs LD, et al. Association of maternal ozone exposure with term low birth weight and susceptible window identification. Environ Int. 2021;146:106208.

    Article  CAS  Google Scholar 

  56. Al-Gubory KH. Multiple exposures to environmental pollutants and oxidative stress: is there a sex specific risk of developmental complications for fetuses? Birth defects res part C embryo. Today Rev. 2016;108:351–64.

    CAS  Google Scholar 

  57. Kannan S, Misra DP, Dvonch JT, Krishnakumar A. Exposures to airborne particulate matter and adverse perinatal outcomes: a biologically plausible mechanistic framework for exploring potential effect modification by nutrition. Environ Health Perspect. 2006;114:1636–42.

    Article  CAS  Google Scholar 

  58. Li Z, Tang Y, Song X, Lazar L, Li Z, Zhao J. Impact of ambient PM2.5 on adverse birth outcome and potential molecular mechanism. Ecotoxicol Environ Saf. 2019;169:248–54.

    Article  CAS  Google Scholar 

  59. Yauk C, Polyzos A, Rowan-Carroll A, Somers CM, Godschalk RW, Van Schooten FJ, et al. Germ-line mutations, DNA damage, and global hypermethylation in mice exposed to particulate air pollution in an urban/industrial location. Proc Natl Acad Sci. 2008;105:605–10.

    Article  CAS  Google Scholar 

  60. Blanc N, Liao J, Gilliland F, Zhang J (Jim), Berhane K, Huang G A systematic review of evidence for maternal preconception exposure to outdoor air pollution on Children’s health, et al. editors. Environ Pollut. 2023;318:120850.

  61. Leonardi-Bee J, Smyth A, Britton J, Coleman T. Environmental tobacco smoke and fetal health: systematic review and meta-analysis. Arch Dis Child - Fetal Neonatal Ed. 2008;93:F351–61.

    Article  CAS  Google Scholar 

  62. Robledo CA, Yeung E, Mendola P, Sundaram R, Maisog J, Sweeney AM, et al. Preconception maternal and paternal exposure to Persistent Organic Pollutants and Birth size: the LIFE Study. Environ Health Perspect. 2015;123:88–94.

    Article  Google Scholar 

  63. Alberico S, Montico M, Barresi V, Monasta L, Businelli C, Soini V, et al. The role of gestational diabetes, pre-pregnancy body mass index and gestational weight gain on the risk of newborn macrosomia: results from a prospective multicentre study. BMC Pregnancy Childbirth. 2014;14:23.

    Article  Google Scholar 

  64. McCowan L, Horgan RP. Risk factors for small for gestational age infants. Best Pract Res Clin Obstet Gynaecol. 2009;23:779–93.

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Institute of Environmental Health Science (NIEHS) with grant number R01ES029945 and P30ES007048. The finding source had no role in the design, analysis, interpretation or writing of the manuscript.

Author information

Author notes

  1. Jiawen Liao and Yi Zhang contributed equally to the manuscript.

Authors and Affiliations

  1. Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States of America

    Jiawen Liao, Chenyu Qiu, Wu Chen, Frank Gilliland & Zhanghua Chen

  2. Department of Clinical Epidemiology & Clinical Trial Unit, Children’s Hospital of Fudan University, National Children’s Medical Center & Shanghai Key Laboratory of Birth Defects, Shanghai, China

    Yi Zhang, Weili Yan & Guoying Huang

  3. Duke Global Health Institute, Durham, NC, United States of America

    Zhenchun Yang & Junfeng Jim Zhang

  4. Division of Environmental Science and Policy, Nicholas School of the Environment, Duke University, Durham, NC, United States of America

    Junfeng Jim Zhang

  5. Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, United States of America

    Kiros Berhane

  6. State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, China

    Zhipeng Bai, Bin Han & Jia Xu

  7. Department of Genetics, Neuroscience, and Pediatrics, Yale University School of Medicine, New Haven, CT, United States of America

    Yong-hui Jiang

Authors
  1. Jiawen Liao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yi Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhenchun Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chenyu Qiu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wu Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Junfeng Jim Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Kiros Berhane
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Zhipeng Bai
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Bin Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Jia Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Yong-hui Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Frank Gilliland
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Weili Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Guoying Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Zhanghua Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Jiawen Liao: Formal analysis, Investigation, Methodology, Writing – original draft of all manuscript, Visualization. Yi Zhang: Data curation, Software, Investigation, Writing – original draft of all manuscript. Zhenchun Yang: Investigation, Methodology. Wu Chen: Investigation, Writing – review & editing of all manuscript.: Junfeng(Jim) Zhang: Funding acquisition, Project administration, Supervision and Editing of all manuscript. Kiros Berhane: Methodology. Zhipeng Bai: Methodology, Resources. Bin Hin: Methodology, Resources, Writing – review & editing. Jia Xu: Methodology, Resources. Yong-hui Jiang: Resources. Frank Gilliland: Funding acquisition, Project administration. Weili Yan: Project administration, Supervision. Guoying Huang: Project administration, Supervision. Zhanghua Chen: Investigation, Methodology, Supervision, Validation and Writing – review & editing of all manuscript.

Corresponding authors

Correspondence to Guoying Huang or Zhanghua Chen.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liao, J., Zhang, Y., Yang, Z. et al. Identifying critical windows of air pollution exposure during preconception and gestational period on birthweight: a prospective cohort study. Environ Health 22, 71 (2023). https://doi.org/10.1186/s12940-023-01022-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12940-023-01022-6

Keywords

Environmental Health

ISSN: 1476-069X