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

Abstract

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.

Temporal and Spatial Autocorrelation as Determinants of Regional AOD-PM2.5 Model Performance in the Middle East

“…On the other hand, model performance in the U.A.E. was similar to the improved results we found modeling PM 2.5 and PM 2.5 speciation over California [29], which incorporated meteorology. We observed that the non-linear relationship between PM 2.5 and satellite-retrieved AOD also affected the predictive performance of machine learning methods differently: gradient boosting, random forest, and support vector machines performed better than ridge and LASSO regression.…”

“…We used a selection of five different machine learning models [29,38] in a regression framework (LASSO regression, ridge regression, gradient boosting machines (GBM), random forests (RF), and support vector machines (SVM)) [39]. In previous studies, non-linear methods (GBM, RF, and SVM) demonstrated stronger predictive performance compared to linear learners (LASSO and ridge), due to the non-linear relationship between PM 2.5 and satellite-observed AODs [29,38,40]. We considered linear methods to assess whether the pattern of non-linearity held in this region with more extreme climate conditions.…”

“…Following previous modeling approaches [29], we incorporated meteorological data to improve PM 2.5 predictions. For this study, we relied on reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF), version 5 (ERA5).…”

“…ERA5 provides a wide range of meteorological data at hourly temporal and approximately 31 km horizontal resolution [30]. We extracted ERA5 meteorological variables including wind speed, wind direction, temperature, evaporation, surface pressure, cloud cover, precipitation, relative humidity, boundary layer height (BLH), and downward shortwave (UV) radiation, which we identified to be a key predictor variable in previous studies [29,31,32].…”

Toward an Early Warning System for Health Issues Related to Particulate Matter Exposure in Brazil: The Feasibility of Using Global PM2.5 Concentration Forecast Products

“…Satellite-derived PM 2.5 concentration and composition estimates were proven to be related to respiratory disorders (children's lung function) [28]. This demonstrates the possibility of overcoming the limits due to the discontinuity of the information provided by ground stations, both in space and time, using remotely sensed products.…”