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Chemosphere. 2019 May 31;233:452-461. doi: 10.1016/j.chemosphere.2019.05.251. [Epub ahead of print]

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

Author information

1
Key Lab of Health Technology Assessment, National Health Commission of the People's Republic of China, Fudan University, Shanghai, 200032, China; Key Laboratory of Public Health Safety, Ministry of Education, School of Public Health, Fudan University, Shanghai, 200032, China.
2
Key Lab of Health Technology Assessment, National Health Commission of the People's Republic of China, Fudan University, Shanghai, 200032, China; Key Laboratory of Public Health Safety, Ministry of Education, School of Public Health, Fudan University, Shanghai, 200032, China; State Environmental Protection Key Laboratory of Formation and Prevention of Urban Air Pollution Complex, Shanghai Academy of Environmental Sciences, Shanghai, 200233, China.
3
Songjiang Maternity & Child Health Hospital, Shanghai, 201600, China.
4
Songjiang Maternity & Child Health Institute, Shanghai, 201600, China.
5
Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine, Shanghai, 200126, China.
6
Key Laboratory of Public Health Safety, Ministry of Education, School of Public Health, Fudan University, Shanghai, 200032, China.
7
Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, 30322, USA. Electronic address: yang.liu@emory.edu.
8
Key Lab of Health Technology Assessment, National Health Commission of the People's Republic of China, Fudan University, Shanghai, 200032, China; Key Laboratory of Public Health Safety, Ministry of Education, School of Public Health, Fudan University, Shanghai, 200032, China. Electronic address: yhzhang@shmu.edu.cn.

Abstract

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.

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 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.

CONCLUSION:

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.

KEYWORDS:

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

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