Study On Spatial-temporal Characteristics And Influencing Factors Of PM_2.5 In Henan Province

With the increased of GDP year by year,the quality of air environment in my country was deteriorating day by day,among which haze pollution was the most serious,and haze pollution incidents had occurred in most areas of our country.As the main component of haze,PM2.5 had become one of the six major pollutants in air quality monitoring.Studying distribution characteristics of the temporal and spatial and influencing factors of PM2.5 had guiding role in the control of air pollution and had strong practical significance.This paper used the PM2.5 concentration data in Henan Province from 2015 to 2019 and the MODIS data in the same region during the same period.The dense dark vegetation was used to retrieve the missing part of PM2.5 data and build an improved aerosol optical depth and PM2.5 neural network model,to provide a reliable method for obtaining PM2.5 concentration data.On the temporal and spatial distribution of PM2.5 in Henan Province,the time series variation characteristics were analyzed according to the monthly and seasonal scales.Used the data on the proportion of PM2.5 concentration in Henan Province from 2015 to 2019 to analyze the spatial distribution of PM2.5 changes year by year.The spatial correlations were analyzed using spatial correlation and spatial hot-spot methods.Introducing the concept of center of gravity to explore the shift route and trend of PM2.5 concentration center in Henan Province within five years.Nine influencing factors were selected to analyze PM2.5 driving factors in Henan Province.The Geodetector was used to detect the explanatory power of single factor and double factor combined effect on PM2.5,and the following conclusions were obtained:(1)There was a significant positive correlation between PM2.5 and AOD,and which usually occurred at the same time in air pollution.The correlation was 0.7 in spring,0.84 in summer,0.68 in autumn,and 0.56 in winter.The accuracy of the improved neural network model had been significantly improved,and the RMSE value had been reduced to varying degrees in four seasons.R2 increased from 0.54 to 0.62 in spring,0.82 to 0.86 in summer,0.72 to 0.79 in autumn,and 0.52 to 0.63 in winter.The results showed that the improved neural network model had higher accuracy and were suitable for obtaining high-precision and long-term PM2.5 concentration data.(2)The analysis of time changes showed that the PM2.5 concentration in Henan Province had a decreasing trend as a whole from 2015 to 2019.The proportion of high pollution days in the five years decreased,the proportion of low pollution days increased,and high pollution gradually transformed into moderate pollution.Spatial change analysis showed that the PM2.5 concentration in Henan Province had obvious spatial aggregation characteristics.The spatial Moran index first decreased and then increased,showed a "U" shape.Local spatial hot spots were concentrated in the northern part of Henan Province(Hebi City,Xinxiang City,Anyang City and Jiaozuo City),Local spatial cold spots were concentrated in the western part of Henan Province(Sanmenxia City,Luoyang City and Nanyang City),The PM2.5 pollution situation in the northern part of Henan Province was more serious,and the spatial center of gravity shift showed an "N" type,with a trend of moving northward.(3)Among the nine influencing factors(Ozone concentration,PM10,NO2,Wind speed,Precipitation,Temperature,GDP,Population,Land use type),the single-factor detection showed that the Land use type(explaining strength was 0.511,the same below),Precipitation(0.312)and NO2(0.277)were the most obvious factors affecting PM2.5 concentration,and the other factors were ranked as PM10(0.255),Temperature(0.209),Wind speed(0.183),Ozone concentration(0.121),GDP(0.073)and Population(0.046).The interaction detection showed that the combined effect of two factors was more significant than the single factor.