A machine learning method to estimate PM 2.5 concentrations across China with remote sensing, meteorological and land use information - Chen, Gongbo; Li, Shanshan; Knibbs, Luke D.; Hamm, Nicholas A.S.; Cao, Wei; Li, Tiantian; Guo, Jianping; Ren, Hongyan; Abramson, Michael J.; Guo, Yuming (Elsevier, 2018)
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 μm) 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 PM 2.5 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 PM 2.5 concentrations. The best-fit model was then utilized to estimate the daily concentrations of PM 2.5 across China with a resolution of 0.1° (≈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 PM 2.5 [10-fold cross-validation (CV) R2=83%, root mean squared prediction error (RMSE) = 28.1μg/m3]. At the monthly and annual time-scale, the explained variability of average PM 2.5 increased up to 86% (RMSE = 10.7μg/m3 and 6.9μg/m3, respectively). Conclusions: Taking advantage of a novel application of modeling framework and the most recent ground-level PM 2.5observations, the machine learning method showed higher predictive ability than previous studies.