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Exposure to fine particulate matter (PM2.5) during pregnancy has been associated with preterm birth (PTB). However, the existing evidence is inconsistent, and the roles of specific PM2.5 chemical constituents remain unclear. Based on the China Labor and Delivery Survey, we included birth data from 89 hospitals in 25 provinces in mainland China, and conducted a national multicenter cohort study to examine the associations of PM2.5 and its chemical constituents with PTB risk in China. We applied satellite-based models to predict prenatal PM2.5 mass and six main component exposure. Multilevel logistic regression analysis was used to examine the associations, controlling for sociodemographic characteristics, seasonality, and spatial variation. We observe an increased PTB risk with an increase in PM2.5 mass and the most significant association is found during the third trimester when the adjusted odds ratio (OR) per interquartile range increases in PM2.5 total mass is 1.12 (95% confidence Interval, CI: 1.05-1.20). Infants conceived by assisted reproductive technology (ART) show greater PTB risk associated with PM2.5 exposure (OR = 1.33, 95% CI: 1.05-1.69) than those conceived naturally (OR = 1.11, 95% CI: 1.03-1.19). We also find black carbon, sulfate, ammonium and nitrate, often linked to fossil combustion, have comparable or larger estimates of the effect (OR = 1.07-1.14) than PM2.5. Our findings provide evidence that components mainly from fossil fuel combustion may have a perceptible influence on increased PTB risk associated with PM2.5 exposure in China. Additionally, compared to natural conception, conception through ART may be more susceptible to PM2.5 exposure.

Exposure to fine particulate matter (PM

Acute coronary syndrome (ACS) is a major public health concern worldwide. Few studies have directly evaluated the associations between ambient temperature and ACS incidence. To explore the association between ambient temperature and ACS emergency hospitalizations in the area of subtropical monsoon climate, data on ACS emergency hospitalizations were collected from two highest-ranking hospitals in the central urban area of Yancheng, China, from January 1, 2013, to December 31, 2018. We applied the time-series method to investigate the potentially lagged and non-linear effects of ambient temperature on ACS using the generalized linear model combined with the distributed lag non-linear model after adjusting for time trend, day of the week, holiday, and relative humidity. We identified a total of 5303 cases of ACS emergency hospitalizations during the study period. The exposure-response curves between ambient temperature and ACS hospitalizations were inverse "J-shaped." The effects of extreme low temperature on ACS hospitalizations occurred on the present day and lasted for 3 days, followed by the harvesting effect. The effects of extreme high temperature occurred on the present day and lasted for 5 days. The cumulative relative risks of ACS were 2.14 [95% confident interval (CI): 1.32 to 3.47] for extremely low temperature and 1.66 (95% CI: 1.33 to 2.06) for extremely high temperature over the lag of 0-5 days, compared with the reference temperature (25.0 °C). Both low and high temperatures were significantly associated with higher risks of emergency hospital admissions for ACS in Yancheng, China.