Satellite Based Mapping of Ground PM2.5 Concentration Using Generalized Additive Modeling - Bin Zou; Jingwen Chen; Liang Zhai; Xin Fang; Zhong Zheng (Remote Sensing, 2016)
Satellite-based PM2.5 concentration estimation is growing as a popular solution to map the PM2.5 spatial distribution due to the insufficiency of ground-based monitoring stations. However, those applications usually suffer from the simple hypothesis that the influencing factors are linearly correlated with PM2.5 concentrations, though non-linear mechanisms indeed exist in their interactions. Taking the Beijing-Tianjin-Hebei (BTH) region in China as a case, this study developed a generalized additive modeling (GAM) method for satellite-based PM2.5 concentration mapping. In this process, the linear and non-linear relationships between PM2.5 variation and associated contributing factors, such as the aerosol optical depth (AOD), industrial sources, land use type, road network, and meteorological variables, were comprehensively considered. The reliability of the GAM models was validated by comparison with typical linear land use regression (LUR) models. Results show that GAM modeling outperforms LUR modeling at both the annual and seasonal scale, with obvious higher model fitting-based adjusted R2 and lower RMSEs. This is confirmed by the cross-validation-based adjusted R2 with values of GAM-based spring, summer, autumn, winter, and annual models, which are 0.92, 0.78, 0.87, 0.85, and 0.90, respectively, while those of LUR models are 0.87, 0.71, 0.84, 0.84, and 0.85, respectively. Different to the LUR-based hypothesis of the “straight line” relations, the “smoothed curves” from GAM-based apportionment analysis reveals that factors contributing to PM2.5 variation are unstable with the alternate linear and non-linear relations. The GAM model-based PM2.5 concentration surfaces clearly demonstrate their superiority in disclosing the heterogeneous PM2.5 concentrations to the discrete observations. It can be concluded that satellite-based PM2.5 concentration mapping could be greatly improved by GAM modeling given its simultaneous considerations of the linear and non-linear influencing mechanisms of PM2.5.