A machine learning method to estimate PM2.5 concentrations across China with remote sensing, meteorological and land use information

Background: Machine learning algorithms have very high predictive ability. However, no study has used machine learning to estimate historical concentrations of PM2.5 (particulate matter with aerodynamic diameter <= 2.5 mu m) at daily time scale in China at a national level. Objectives: To estimate daily concentrations of PM2.5 across China during 2005-2016. Methods: Daily ground-level PM2.5 data were obtained from 1479 stations across China during 2014-2016. Data on aerosol optical depth (AOD), meteorological conditions and other predictors were downloaded. A random forests model (non-parametric machine learning algorithms) and two traditional regression models were developed to estimate ground-level PM2.5 concentrations. The best-fit model was then utilized to estimate the daily concentrations of PM2.5 across China with a resolution of 0.1 degrees (approximate to 10 km) during 2005-2016. Results: The daily random forests model showed much higher predictive accuracy than the other two traditional regression models, explaining the majority of spatial variability in daily PM2.5 [10-fold cross-validation (CV) R-2 = 83%, root mean squared prediction error (RMSE) = 28.1 mu g/m(3)]. At the monthly and annual time-scale, the explained variability of average PM2.5 increased up to 86% (RMSE = 10.7 mu g/m(3) and 6.9 mu g/m(3), respectively). Conclusions: Taking advantage of a novel application of modeling framework and the most recent ground-level PM2.5 observations, the machine learning method showed higher predictive ability than previous studies. Capsule: Random forests approach can be used to estimate historical exposure to PM2.5 in China with high accuracy. (C) 2018 Elsevier B.V. All rights reserved.