Associations between birth outcomes and maternal PM2.5 exposure in Shanghai: A comparison of three exposure assessment approaches

Effect of PM2.5 on macrosomia in China: A nationwide prospective cohort study

BACKGROUND

Macrosomia is associated with both neonatal complications and adult diseases (obesity, diabetes mellitus, etc.). Previous studies have reported maternal exposure to PM2.5 might influence metabolism and fetal development and cause adverse pregnancy outcomes. Studies conducted in areas with low PM2.5 concentration have found relationship between gestational PM2.5 exposure and birth weight. However, the impact of air pollution on macrosomia has not been studied, especially in highly polluted areas.

OBJECTIVE

To evaluate the association between fine particulate matter (PM2.5) exposure during pregnancy and the risk of macrosomia.

METHODS

Data from preconception health examination and prenatal and postnatal records were collected from 1 January 2010 to 31 December 2012 in the National Free Preconception Health Examination Project. Monthly mean of PM2.5 concentration during pregnancy was estimated from satellite data using an ensemble machine learning model. A newborn with birth weight above 4000 g was defined as macrosomia. Logistic regression models were used to examine the association between maternal exposure to PM2.5 and the risk of macrosomia, after adjusting for maternal age, pre-pregnancy body mass index, parity, neonatal sex, duration of gestation, seasonality, educational level, smoking and drinking habits, past history of diabetes mellitus and hypertension, and family history of diabetes mellitus. Restricted cubic spline models were used to evaluate the dose-response relationship between the risk of macrosomia and PM2.5 concentration.

RESULTS

Of 177 841 singleton nonlow birth weight newborns included, 14 598 (8.2%) had macrosomia. The mean PM2.5 concentrations were 70.7, 71.5, and 80.9 μg/m³ in the first, second, and third trimesters. In full-adjusted logistic regression models, significant associations were found between increased risk of macrosomia and every 10 μg/m³ increase of PM2.5 concentration over the first (odds ratio [OR]: 1.045; 95% CI, 1.037-1.052), second (OR: 1.035; 95% CI, 1.028-1.043), and third (OR: 1.033; 95% CI, 1.026-1.039) trimesters. There was a nonlinear does-response association between PM2.5 concentration and the risk of macrosomia.

CONCLUSIONS

Maternal exposure to PM2.5 during pregnancy was associated with an increased risk of macrosomia in China.

Estimating daily PM2.5 concentrations in New York City at the neighborhood-scale: Implications for integrating non-regulatory measurements

Previous PM_2.5 related epidemiological studies mainly relied on data from sparse regulatory monitors to assess exposure. The introduction of non-regulatory PM_2.5 monitors presents both opportunities and challenges to researchers and air quality managers. In this study, we evaluated the advantages and limitations of integrating non-regulatory PM_2.5 measurements into a satellite-based daily PM_2.5 model at 100 m resolution in New York City in 2015. Two separate machine learning models were developed, one using only PM_2.5 data from the US Environmental Protection Agency (EPA), and the other with measurements from both EPA and the New York City Community Air Survey (NYCCAS). The EPA-only model obtained a cross-validation (CV) R² of 0.85 while the EPA + NYCCAS model obtained a CV R² of 0.73. With the help of the NYCCAS measurements, the EPA + NYCCAS model predicted distinctly different PM_2.5 spatial patterns and more pollution hotspots compared with the EPA model, and its predictions were >15% higher than the EPA model along major roads and in densely populated areas. Our results indicated that satellite AOD and non-regulatory PM_2.5 measurements can be fused together to capture neighborhood-scale PM_2.5 levels and previous studies may have underestimated the disease burden due to PM_2.5 in densely populated areas.

Maternal ambient air pollution exposure with spatial-temporal variations and preterm birth risk assessment during 2013-2017 in Zhejiang Province, China

Preterm birth (PTB) can give rise to significant neonatal morbidity and mortality, as well as children's long-term health defects. Many studies have illustrated the associations between ambient air pollution exposure during gestational periods and PTB risks, but most of them only focused on one single air pollutant, such as PM_2.5. In this population-based environmental-epidemiology study, we recruited 6275 pregnant mothers in Zhejiang Province, China, and evaluated their gestational exposures to various air pollutants during 2013-2017. Time-to-event logistic regressions were performed to estimate risk associations after adjusting all confounders, and Quasi-AQI model and PCA-GLM analysis were applied to resolve the collinearity issues in multi-pollutant regression models. It was found that gestational exposure to ambient air pollutants was significantly associated with the occurrence of PTB, and SO₂ was the largest contributor with a proportion of 29.4%. Three new variables, prime factor (a combination of PM_2.5, PM₁₀, SO₂, and NO₂), carbon factor (CO), and ozone factor (O₃), were generated by PCA integration, contributing 63.4%, 17.1%, and 19.5% to PTB risks, respectively. The first and third trimester was the most crucial exposure window, suggesting the pregnant mothers better to avoid severe air pollution exposures during these sensitive periods.

Predicting gestational personal exposure to PM2.5 from satellite-driven ambient concentrations in Shanghai

BACKGROUND

It has been widely reported that gestational exposure to fine particulate matters (PM_2.5) is associated with a series of adverse birth outcomes. However, the discrepancy between ambient PM_2.5 concentrations and personal PM_2.5 exposure would significantly affect the estimation of exposure-response relationship.

OBJECTIVE

Our study aimed to predict gestational personal exposure to PM_2.5 from the satellite-driven ambient concentrations and analyze the influence of other potential determinants.

METHOD

We collected 762 72-h personal exposure samples from a panel of 329 pregnant women in Shanghai, China as well as their time-activity patterns from Feb 2017 to Jun 2018. We established an ambient PM_2.5 model based on MAIAC AOD at 1 km resolution, then used its output as a major predictor to develop a personal exposure model.

RESULTS

Our ambient PM_2.5 model yielded a cross-validation R² of 0.96. Personal PM_2.5 exposure levels were almost identical to the corresponding ambient concentrations. After adjusting for time-activity patterns and meteorological factors, our personal exposure has a CV R² of 0.76.

CONCLUSION

We established a prediction model for gestational personal exposure to PM_2.5 from satellite-based ambient concentrations and provided a methodological reference for further epidemiological studies.

Associations between long-term exposure to ambient air pollution and risk of type 2 diabetes mellitus: A systematic review and meta-analysis

Previous meta-analyses on associations between air pollution (AP) and type 2 diabetes mellitus (T2DM) were mainly focused on studies conducted in high-income countries. Evidence should be updated by including more recent studies, especially those conducted in low- and middle-income countries. We therefore conducted a systematic review and meta-analysis of epidemiological studies to conclude an updated pooled effect estimates between long-term AP exposure and the prevalence and incidence of T2DM. We searched PubMed, Embase, and Web of Science to identify studies regarding associations of AP with T2DM prevalence and incidence prior to January 2019. A random-effects model was employed to analyze the overall effects. A total of 30 articles were finally included in this meta-analysis. The pooled results showed that higher levels of AP exposure were significantly associated with higher prevalence of T2DM (per 10 μg/m3 increase in concentrations of particles with aerodynamic diameter < 2.5 μm (PM2.5): odds ratio (OR) = 1.09, 95% confidence interval (95%CI): 1.05, 1.13; particles with aerodynamic diameter < 10 μm (PM10): OR = 1.12, 95%CI: 1.06, 1.19; nitrogen dioxide (NO2): OR = 1.05, 95%CI:1.03, 1.08). Besides, higher level of PM2.5 exposure was associated with higher T2DM incidence (per 10 μg/m3 increase in concentration of PM2.5: hazard ratio (HR) = 1.10, 95%CI:1.04, 1.16), while the associations between PM10, NO2 and T2DM incidence were not statistically significant. The associations between AP exposure and T2DM prevalence showed no significant difference between high-income countries and low- and middle-incomes countries. However, different associations were identified between PM2.5 exposure and T2DM prevalence in different geographic areas. No significant differences were found in associations of AP and T2DM prevalence/incidence between females and males, except for the effect of NO2 on T2DM incidence. Overall, AP exposure was positively associated with T2DM. There still remains a need for evidence from low- and middle-income countries on the relationships between AP and T2DM.

Estimating the acute effects of ambient ozone pollution on the premature rupture of membranes in Xinxiang, China

While increasing evidence suggests that ozone (O₃) exposure is associated with adverse birth outcomes, only one study has focused on its impact on the premature rupture of membranes (PROM). Therefore, we thus examined the effect of O₃ on PROM in Xinxiang, China, using an over-dispersed Poisson generalized additive model. Several confounding factors, including meteorological factors, temporal trends, the day of the week, and public holidays, were considered in the model. We identified a total of 3255 instances of PROM from January 1, 2015 to December 31, 2017, and there was a significant association between the daily maximum 8-h mean concentrations (O_3-8h) and PROM. Each 10 μg/m³ increase in the 3-day average concentration (lag02) of O_3-8h corresponded to an increment in PROM of 5.42% (95% CI: 1.45-9.39%). Although the results of the stratified analyses were insignificant, a few trending results were observed: stronger associations between O₃ and PROM would occur in women with advanced age (≥35) or during the warm season than those in younger women (<35) or during the cool season. Our study indicates that O₃ exposure is an important risk factor of PROM and should be considered in its prevention and control in the study area.

Association of ambient air pollutants and birth weight in Ningbo, 2015-2017

Previous studies have suggested a change of birth weight linked with elevated ambient air pollutant concentrations during the pregnancy. However, investigations of the influence of higher pollutant levels on birth weight change are limited. The goal of this study is to evaluate whether the air pollution of Ningbo is associated with birth weight, and which trimester could be a window period for maternal exposure to air pollution. A total of 170,008 live births were selected in the Ningbo city of Zhejiang, China, from 2015 to 2017. We estimated the association between the decreased birth weight and the increased air pollutant concentrations in the three trimesters and full gestation. The effects of interaction among pollutants were identified using a co-pollutant adjustment model. An interquartile range increases in PM_2.5 (10.55 μg/m³), SO₂(4.6 μg/m³), CO (125.59 μg/m³), and O₃ (14.54 μg/m³) concentrations during the entire gestation were associated with 3.65 g (95% confidence interval: -6.02 g, -1.29 g), 5.02 g (-6.89 g, -3.14 g), 2.64 g (-4.65 g, -0.63 g) and 2.9 g (-4.8 g, 1 g) decreases, respectively, in birth weight. With each interquartile range increment in NO₂ concentration was associated with an 8.05 g (6.24 g, 9.85 g) increase in birth weight. In the first trimester, only the PM_2.5 exposure seemed to be associated with the greatest decline in birth weight. After adjustment for co-pollutant, both PM_2.5 and SO₂ were still associated with birth weight, except for CO for O₃ adjustment, O₃ for SO₂ adjustment, and O₃ for NO₂ adjustment. Maternal exposure to air pollution may be associated with a decrease of birth weight, but the contribution of various pollutants is necessary to verify by future research.

Ambient PM2.5 and birth outcomes: Estimating the association and attributable risk using a birth cohort study in nine Chinese cities

BACKGROUND

Previous studies have reported that maternal exposure to particles with aerodynamic diameter <2.5 μm (PM_2.5) is associated with birth outcomes. However, a multicity birth cohort study has not been conducted in China, and the attributable fraction of adverse birth outcomes due to PM_2.5 exposure remains unknown.

METHODS

We examined associations in a birth cohort of 1,455,026 mother-and-live-birth pairs who were followed up from the first hospital visit for pregnancy until the birth of the baby during 2014-2017 in nine cites of the Pearl River Delta (PRD) region, China. The PM_2.5 exposures were estimated based on the air pollution concentrations of the nearby monitors. Cox proportional hazards regressions were employed to examine the associations.

RESULTS

We found 1% (HR = 1.01; 95% CI: 1.00, 1.02), 6% (HR = 1.06; 95% CI: 1.05, 1.07), and 7% (HR = 1.07; 95% CI: 1.06, 1.08) increases in risk of PTB and 20% (HR = 1.20; 95% CI: 1.18, 1.22), 18% (HR = 1.18; 95% CI: 1.15, 1.20), and 20% (HR = 1.20; 95% CI: 1.17, 1.23) increases in risk of LBW, with each 10 μg/m³ increase in PM_2.5 from trimester 1 to trimester 3, respectively. For PTB, highest HRs were observed during trimester 3, as for LBW, stronger effect were observed during trimester 1 and trimester 3. We further estimated that 7.84% (95% CI: 6.21%, 9.50%) of PTB and 14.85% (95% CI: 13.00%, 16.61%) of the LBW cases could be attributable to PM_2.5 exposure during the third trimester.

CONCLUSION

The results indicate that maternal PM_2.5 exposure is a risk factor for both LBW and PTB, and responsible for considerable burdens of PTB and LBW in the Pearl River Delta region.

Prenatal exposure to ambient fine particulate matter induces dysregulations of lipid metabolism in adipose tissue in male offspring

Prenatal exposure to ambient fine particles (diameter < 0.25 μm, PM_2.5) has been found to be associated with abnormal growth and development in offspring. However, the effects of PM_2.5 on the lipid metabolism of adipose tissue in offspring are unclear. In the present study, we established a mouse model of prenatal exposure to PM_2.5 by intratracheal instillation to pregnant C57BL/6 female mice with PM_2.5 suspension or normal saline. We found that prenatal exposure to PM_2.5 of a mouse model reduced body weight in adult male offspring after 6 weeks old. Histological analysis showed that the adipocyte size was significantly reduced in epididymal adipose tissue (eWAT) in male offspring, but not in brown adipose tissue. The expression levels of genes related to fatty acid synthesis (ACC1, ACSL1) and oxidation (PPARα) in eWAT were also significantly decreased. In addition, downregulation of pro-inflammatory cytokines (TNFα, IL-1β, IL-6) was also observed. Lipidomics analysis of eWAT demonstrated that prenatal exposure of PM_2.5 reduced lysophosphatidylcholines (LPC), phosphatidylcholines (PC), phosphatidylethanolamines (PE), sphingomyelins (SM), and ceramides (Cer), indicating that metabolic pathways, including SM-Cer signaling and glycerophospholipids remodeling, were disrupted. In summary, prenatal exposure to PM_2.5 was associated with the dysregulations in lipid metabolism of eWAT and pro-inflammatory response in male offspring.

Space-time trends of PM2.5 constituents in the conterminous United States estimated by a machine learning approach, 2005-2015

Particulate matter with aerodynamic diameter less than 2.5 μm (PM_2.5) is a complex mixture of chemical constituents emitted from various emission sources or through secondary reactions/processes; however, PM_2.5 is regulated mostly based on its total mass concentration. Studies to identify the impacts on climate change, visibility degradation and public health of different PM_2.5 constituents are hindered by limited ground measurements of PM_2.5 constituents. In this study, national models were developed based on random forest algorithm, one of machine learning methods that is of high predictive capacity and able to provide interpretable results, to predict concentrations of PM_2.5 sulfate, nitrate, organic carbon (OC) and elemental carbon (EC) across the conterminous United States from 2005 to 2015 at the daily level. The random forest models achieved high out-of-bag (OOB) R² values at the daily level, and the mean OOB R² values were 0.86, 0.82, 0.71 and 0.75 for sulfate, nitrate, OC and EC, respectively, over 2005-2015. The long-term temporal trends of PM_2.5 sulfate, nitrate, OC and EC predictions agreed well with their corresponding ground measurements. The annual mean of predicted PM_2.5 sulfate and EC levels across the conterminous United States decreased substantially from 2005 to 2015; while concentrations of predicted PM_2.5 nitrate and OC decreased and fluctuated during the study period. The annual prediction maps captured the characterized spatial patterns of the PM_2.5 constituents. The distributions of annual mean concentrations of sulfate and nitrate were generally regional in the extent that sulfate decreased from east to west smoothly with enhancement in California and nitrate had higher concentration in Midwest, Metro New York area, and California. OC and EC had regional high concentrations in the Southeast and Northwest as well as localized high levels around urban centers. The spatial patterns of PM_2.5 constituents were consistent with the distributions of their emission sources and secondary processes and transportation. Hence, the national models developed in this study could provide supplementary evaluations of spatio-temporal distributions of PM_2.5 constituents with full time-space coverages in the conterminous United States, which could be beneficial to assess the impacts of PM_2.5 constituents on radiation budgets and visibility degradation, and support exposure assessment for regional to national health studies at county or city levels to understand the acute and chronic toxicity and health impacts of PM_2.5 constituents, and consequently provide scientific evidence for making targeted and effective regulations of PM_2.5 pollution.