High-resolution spatiotemporal mapping of PM_(2.5) concentrations at Mainland China using a combined BME-GWR technique

摘要

With rapid economic development, industrialization and urbanization, the ambient air PM2.5 has become a major pollutant linked to respiratory, heart and lung diseases. In China, PM2.5 pollution constitutes an extreme environmental and social problem of widespread public concern. In this work we estimate ground-level PM2.5 from satellite-derived aerosol optical depth (AOD), topography data, meteorological data, and pollutant emission using an integrative technique. In particular, Geographically Weighted Regression (GWR) analysis was combined with Bayesian Maximum Entropy (BME) theory to assess the spatiotemporal characteristics of PM2.5 exposure in a large region of China and generate informative PM2.5 space-time predictions (estimates). It was found that, due to its integrative character, the combined BME-GWR method offers certain improvements in the space-time prediction of PM2.5 concentrations over China compared to previous techniques. The combined BME-GWR technique generated realistic maps of space-time PM2.5 distribution, and its performance was superior to that of seven previous studies of satellite-derived PM2.5 concentrations in China in terms of prediction accuracy. The purely spatial GWR model can only be used at a fixed time, whereas the integrative BME-GWR approach accounts for cross space-time dependencies and can predict PM2.5 concentrations in the composite space-time domain. The 10-fold results of BME-GWR modeling (R-2 = 0.883, RMSE = 11.39 mu g/m(3)) demonstrated a high level of space-time PM2.5 prediction (estimation) accuracy over China, revealing a definite trend of severe PM2.5 levels from the northern coast toward inland China (Nov 2015 Feb 2016). Future work should focus on the addition of higher resolution AOD data, developing better satellite-based prediction models, and related air pollutants for space-time PM2.5 prediction purposes.