Abstract
Over the past decades, regional haze episodes have frequently occurred in eastern China, especially in the Yangtze River Delta (YRD). Satellite derived Aerosol Optical Depth (AOD) has been used to retrieve the spatial coverage of PM2.5 concentrations. To improve the retrieval accuracy of the daily AOD-PM2.5 model, various auxiliary variables like meteorological or geographical factors have been adopted into the Geographically Weighted Regression (GWR) model. However, these variables are always arbitrarily selected without deep consideration of their potentially varying temporal or spatial contributions in the model performance. In this manuscript, we put forward an automatic procedure to select proper auxiliary variables from meteorological and geographical factors and obtain their optimal combinations to construct four seasonal GWR models. We employ two different schemes to comprehensively test the performance of our proposed GWR models: (1) comparison with other regular GWR models by varying the number of auxiliary variables; and (2) comparison with observed ground-level PM2.5 concentrations. The result shows that our GWR models of “AOD + 3” with three common meteorological variables generally perform better than all the other GWR models involved. Our models also show powerful prediction capabilities in PM2.5 concentrations with only slight overfitting. The determination coefficients R2 of our seasonal models are 0.8259 in spring, 0.7818 in summer, 0.8407 in autumn, and 0.7689 in winter. Also, the seasonal models in summer and autumn behave better than those in spring and winter. The comparison between seasonal and yearly models further validates the specific seasonal pattern of auxiliary variables of the GWR model in the YRD. We also stress the importance of key variables and propose a selection process in the AOD-PM2.5 model. Our work validates the significance of proper auxiliary variables in modelling the AOD-PM2.5 relationships and provides a good alternative in retrieving daily PM2.5 concentrations from remote sensing images in the YRD.
Highlights
Widespread air pollution has become a severe problem in China, with increasing population and pollution emissions
We argue that the inconsistent seasonal patterns of PM2.5 and Aerosol Optical Depth (AOD) are caused by different seasonal impacts of meteorological or geographical factors, rendering that the PM2.5 concentrations and AOD are scattered to different extents even in opposite directions
Compared with model fitting in four seasons, the averages of PM2.5 concentrations implemented in model evaluation are slightly higher
Summary
Widespread air pollution has become a severe problem in China, with increasing population and pollution emissions. China, has been suffering deterioration of air quality and even more frequent haze episodes, severely threatening both life and health of its people. Particulate matter with an aerodynamic diameter less than 2.5 μm (PM2.5 ) is one of most harmful components of pollution haze and it has severely toxic effects on climate, environment and human health [1,2]. 2017, 9, 346 validated the direct relation between high PM2.5 concentrations and rising human health problems like asthma, tumors, and lung cancer [3,4,5,6,7]. PM2.5 concentration monitoring is a significant and pressing issue for both assessing human health exposure and making effective air pollution control measures in the YRD region.
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