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Predicting Fine Particulate Matter (PM2.5) in the Greater London Area: An Ensemble Approach using Machine Learning Methods
Open AccessArticle

Satellite-Derived PM2.5 Composition and Its Differential Effect on Children’s Lung Function

Keck School of Medicine, University of Southern California, Los Angeles, CA 90032, USA
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Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Remote Sens. 2020, 12(6), 1028; https://doi.org/10.3390/rs12061028
Received: 26 February 2020 / Revised: 19 March 2020 / Accepted: 19 March 2020 / Published: 23 March 2020
Studies of the association between air pollution and children’s health typically rely on fixed-site monitors to determine exposures, which have spatial and temporal limitations. Satellite observations of aerosols provide the coverage that fixed-site monitors lack, enabling more refined exposure assessments. Using aerosol optical depth (AOD) data from the Multiangle Imaging SpectroRadiometer (MISR) instrument, we predicted fine particulate matter, PM 2.5 , and PM 2.5 speciation concentrations and linked them to the residential locations of 1206 children enrolled in the Southern California Children’s Health Study. We fitted mixed-effects models to examine the relationship between the MISR-derived exposure estimates and lung function, measured as forced expiratory volume in 1 second (FEV 1 ) and forced vital capacity (FVC), adjusting for study community and biological factors. Gradient Boosting and Support Vector Machines showed excellent predictive performance for PM 2.5 (test R 2 = 0.68 ) and its chemical components (test R 2 = –0.71). In single-pollutant models, FEV 1 decreased by 131 mL (95% CI: 232 , 35 ) per 10.7-µg/m 3 increase in PM 2.5 , by 158 mL (95% CI: 273 , 43 ) per 1.2-µg/m 3 in sulfates (SO 4 2 ), and by 177 mL (95% CI: 306 , 56 ) per 1.6-µg/m 3 increase in dust; FVC decreased by 175 mL (95% CI: 310 , 29 ) per 1.2-µg/m 3 increase in SO 4 2 and by 212 mL (95% CI: 391 , 28 ) per 2.5-µg/m 3 increase in nitrates (NO 3 ). These results demonstrate that satellite observations can strengthen epidemiological studies investigating air pollution health effects by providing spatially and temporally resolved exposure estimates. View Full-Text
Keywords: Aerosol optical depth; particulate matter; particulate matter speciation; machine learning; exposure estimation; children’s health; children lung function Aerosol optical depth; particulate matter; particulate matter speciation; machine learning; exposure estimation; children’s health; children lung function
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Chau, K.; Franklin, M.; Gauderman, W.J. Satellite-Derived PM2.5 Composition and Its Differential Effect on Children’s Lung Function. Remote Sens. 2020, 12, 1028.

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