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


Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, 30322, USA. Electronic address: [Email]


BACKGROUND : It has been widely reported that gestational exposure to fine particulate matters (PM2.5) is associated with a series of adverse birth outcomes. However, the discrepancy between ambient PM2.5 concentrations and personal PM2.5 exposure would significantly affect the estimation of exposure-response relationship.
OBJECTIVE : Our study aimed to predict gestational personal exposure to PM2.5 from the satellite-driven ambient concentrations and analyze the influence of other potential determinants.
METHODS : 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 PM2.5 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 PM2.5 model yielded a cross-validation R2 of 0.96. Personal PM2.5 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.
CONCLUSIONS : We established a prediction model for gestational personal exposure to PM2.5 from satellite-based ambient concentrations and provided a methodological reference for further epidemiological studies.


MAIAC AOD,Machine learning,PM(2.5),Personal exposure,Remote sensing,

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