ABSTRACT
High concentration of fine particulate matter (PM2.5) has been shown to be a major contributor to haze weather, which has been associated with an increased prevalence in lung cancer. An accurate estimation and predication of PM2.5 historical levels, and its spatial-temporal variability can assist in strategically improving regional air quality and reducing its harmful effects on population health. This paper targets Beijing, Tianjin, and Hebei province (BTH), three northeast province of china (TNPC), Yangtze river delta (YRD) and pearl river delta (PRD) as the study areas. Data used in this study include PM2.5 measurements from April 2013 to December 2016, MODIS AOD raster imageries and five meteorological factors from 2000 to 2016. By combining back propagation artificial neural network (BPANN) and ε-support vector regression (ε-SVR), a novel hybrid model was constructed to impute the historical PM2.5 missing values in the long time series from 2000 to 2012, and to predict the concentration of PM2.5 from April 2014 to December 2017. The hybrid model produced results superior to BPANN and ε-SVR with a higher accuracy, lower error rate, and a stable performance. This model can be applied to the other four regions with consistent results. Results of spatial-temporal analysis indicated that the PM2.5 concentration has increased along with a pollution range expansion in BTH from 2000 to 2010. In addition, the PM2.5 concentration decreased slowly in PRD. The concentration and pollution range of PM2.5 in TNPC and YRD showed a stable trend. In 2012, the four research areas all showed decreased trend, and the pollution range narrowed. From 2013 to 2016, the PM2.5 concentration increased shortly then decreased; in particular, the high pollution areas saw a decrease in PM2.5 concentration, which correlated with control measures adopted by the state during the same time period. The hot spots of PM2.5 were mainly distributed in the inland cities.
