| With the rapid development of the domestic industrial economy,the problem of smog has become increasingly serious.The smog problem not only affects human health,but also restricts economic development.The main indicator for measuring the severity of haze is the concentration of PM2.5,which refers to particles that can enter the lungs with a diameter of 2.5 microns or less.The concentration and spatial distribution of PM2.5 in different regions are quite different;the PM2.5concentration values in the same region also have significant differences at different times.Therefore,the simple linear model is no longer applicable to PM2.5 data.Taking into account the spatial characteristics of PM2.5,this article first plans to adopt a spatial autoregressive model,and introduce the influence of time into the model as a non-parametric part,and establish a varying coefficient spatial autoregressive model.The spatial autocorrelation coefficients and the explanatory variable coefficients of the model can change over time,which enhances the interpretability and flexibility of the model.Therefore,it is more reasonable to use this model to study the spatial change mode of PM2.5.This paper studies the monthly average concentration data of PM2.5 in 31 key cities in China from 2015 to 2019.First,analyze the spatial distribution characteristics of PM2.5 concentration data and analyze the Moran scatter diagram.It is concluded that PM2.5 concentration data has periodicity and spatial correlation.Then,establish a varying coefficient spatial autoregressive model to deeply explore the relationship between PM2.5 and meteorological factors.The general research conclusions are as follows:（1）The spatial distribution of PM2.5 and the analysis of spatial correlation show that the PM2.5 of 31 key cities has a positive spatial correlation and show obvious spatial cluster pattern.The PM2.5 concentration of city is positively affected by the PM2.5 concentration of neighboring cities.The types of spatial agglomeration are mostly of the same kind,among which high-value agglomerations are mainly in areas centered on the Beijing-Tianjin-Hebei and Yangtze River Delta.The low-value agglomeration areas mainly include the Pearl River Delta urban agglomeration centered on Guangzhou,and most cities in the northwest and southwest regions.Some regions,such as Hohhot and Nanchang,present low-high-value clusters,while Shenyang and Xining are located in highlow-value clusters.（2）This paper establishes a varying coefficient spatial autoregressive model based on the monthly average concentration data of PM2.5 and meteorological data to study the relationship between PM2.5 and meteorological factors.The results show that the PM2.5 concentration is affected by an obvious positive spatial autocorrelation,and has a negative correlation with rainfall,relative humidity,meridional wind speed and zonal wind speed,and a positive correlation with ground pressure.Its relationship with temperature is related to the season,showing a negative correlation in summer and autumn,and a positive correlation in spring and winter.In addition,the effects of spatial effects and meteorological factors on PM2.5concentration are basically seasonal in time.In summary,PM2.5 in 31 major cities in China has a positive spatial correlation and is significantly affected by meteorological factors.Therefore,this paper proposes to use artificial rainfall and physical settlement to pre-control PM2.5 in high concentration areas.Then the pollution in different areas should be treated separately.In addition,it is recommended to take advantage of the spatial aggregation of PM2.5 to adopt a common treatment method for concentrated highconcentration areas to create a good atmospheric environment. |
PDF Full Text Request