A Comparison Of Spatiotemporal Distribution Of PM_2.5between Coastal And Inland Areas Of China With Bavesian Maximum Entropy And Geographic Weighted Regression Methods

With the rapid increment of population and development of economic in China,the environment is getting worse and worse.PM2.5 in ambient air has become a major pathogenic pollutant causing respiratory,cardiopulmonary and immune disease.,PM2.5 pollution has drawn the attention of society in China.In recent years,Chinese government was gradually aware of the seriousness of air pollution and started to build the PM2.5 monitoring network.However,China still faced the two major difficulties in PM2.5 studies,i.e.,lacking accurate historical data and effective predictive models.Therefore,based on a variety of data such as aerosol,meteorology and terrain,this study used a new geostatistical model to estimate the spatial and temporal distribution of PM2.5 in coastal and inland areas of China from November 2015 to February 2016.The study area covered in the mainland China,including the entire coastal and all inland areas except Xinjiang,Tibet,Inner Mongolia,Qinghai and Heilongjiang.The research covered an area of about 4.52 million km2,accounting for 47.08%of the total area in China.The population of the research area was 1.272 billion,accounting for 93%of the total population.Due to such the dense population distributed in the study area,the present study may have a great contribution for guiding the environmental pollution control and assessing economic health benefits in our country.To improve the accuracy of the PM2.5 estimation model,we integrated data from different sources for constructing the model,including the ground monitoring data of PM2.5,NO2 and CO,the aerosol optical depth data of MODIS V5.2,digital elevation,population,meteorological data,land use and traffic data.Especially,this study used Geographically Weighted Regression(GWR)combined with Bayesian Maximum Entropy(BME)theory to analyze the space-time distribution of PM2.5 between coastal and inland areas of China and generated the space-time mapping of PM2.5 concentrations.GWR established the local spatial relationship between the PM2.5 concentrations and the above considered factors,can be depicted by GWR model,and GWR model was used to predict the PM2.5 concentration with the spatial resolution of 30X30 km2;then,the predicted PM2.5 values were used as soft data and PM2.5 monitoring sites were used as hard data,respectively,we used hard data and soft data to fit the spatiotemporal covariance function in BME and predicted the PM2.5 concentration at spatial resolution of 3×3 km2.Finally,10-fold Cross-validation method was used to test the performance of the BME-GWR model by comparing the predicted PM2.5 concentration with the ground monitoring data.The cross validation results showed that the BME-GWR method provided a further improvement on the spatiotemporal prediction of PM2.5 concentration compared to previous studies,due to the characteristics of the new method,the cross validation results of BME-GWR technology were superior to most of previous research results.The pure GWR model can’t capture the PM2.5 variation with time,but the integrated BME-GWR method can take into account the spatiotemporal correlation and could simultaneously predict the temporal and spatial variations of PM2.5 concentration to make up for the deficiencies of the GWR model.The fitting results of pure GWR model showed that R2 were 0.78,0.83,0.80,0.63(November to February),and the regression coefficients of AOD and pressure,two environmental variables had significant geographical differences in both space and time.Spatially,PM2.5 showed a tendency to aggregate in heavily polluted areas,high PM2.5 concentrations were mainly distributed in the northeast,southern of North China,eastern Central China and eastern Sichuan Basin,whereas the pollution level was relatively light in southeast of Yangtze River and northwestern China;timely,the winter pollution was relatively low in January and February,but PM2.5 pollution was most serious in November and December.The 10-fold cross-validation of the BME-GWR(R2 = 0.883,RMSE = 11.39μg/m3)showed that the model accuracy was high.By comparing coastal and inland areas,timely,the monthly average concentration of PM2.5 showed an increasing-decreasing trend.The monthly average PM2.5 in the study area mainly concentrated in 35～100 μg·m-3;spatially,the area of severe pollution showed a tendency of shifting from the coast to the inland and from northeast to southwest,and the area south area of the Yangtze River was always a less polluted area.These were mainly due to the increments in heating since entering the winter as well as unfavorable weather conditions and topography.Overall,the BME-GWR model showed good results in terms of forecasting dimension and accuracy,and could also be used to support and help the relevant environmental departments on decision-making of pollution control.In the future,work should focus on collecting and extracting higher-resolution AOD data and developing better prediction models based on satellite remote sensing data for spatio-temporal prediction of other air pollutants efficiently.