| In recent years,urban air pollution problems have received more and more attention,and PM2.5 is one of the important atmospheric pollutants.This paper selects the PM2.5 concentration data released by 17 air quality monitoring points in the main urban area of Chongqing,and analyzes the spatial and temporal distribution characteristics of PM2.5 in 2016 and the correlation between PM2.5 and meteorological elements and other atmospheric pollutants.Combined with land use data,meteorological data and humanistic data of Chongqing,the LUR model of PM2.5 and land use types and geographic elements was constructed,and the PM2.5 concentration was simulated based on the LUR model.The study mainly draws the following conclusions:(1)From the time change,the change of PM2.5 in "24-hour and double-valley" in 24 hours,the lowest PM2.5 concentration at 4 and 16 o’clock,and the highest concentration at 10 and 23 o’clock.In the different months,the monthly variation of PM2.5 concentration in the main urban area generally showed a "U" shape.The highest PM2.5 concentration appeared in December and February.The lowest value appeared in July,and there was a big fluctuation in PM2.5 concentration in September;the PM2.5 season concentration was:winter>autumn>spring>summer.From the spatial distribution characteristics of PM2.5,it can be found that the PM2.5 concentration distribution is significantly different from north to south.The concentration of PM2.5 in the south of the main city is higher than that in the north The main performance is concentrated in the central and southwestern parts of the main urban area.The concentration of PM2.5 is higher in areas with crowded roads,such as Yuzhong District.The population with less population and high vegetation coverage has lower PM2.5 values,such as Banan.North Fujian,Beibei and other regions.(2)This study explored the impact of meteorological elements on PM2.5 concentration changes.The relationship between temperature,pressure,relative humidity,wind speed and rainfall and PM2.5 was analyzed.It was found that the concentration of M2.5 was negatively correlated with wind speed,rainfall and temperature,and positively correlated with relative humidity and air pressure.(3)According to the monitoring site,the buffers are extracted and 23 sets of land use characteristic variables are extracted,and then the correlation coefficient between PM2.5 and characteristic variables in different seasons is calculated.It is found that the variable with high correlation coefficient between spring and PM2.5 has 1km._Land use classification_forest area,site DEM,2km_industrial point density;summer with PM2.5 high correlation variable 1km_land use classification_forest area,5km_land use classification_building area,1km_road The length of the net;the variables with high correlation with PM2.5 in autumn are 3km_land use classification forest land area,3km_land use classification building area,2km_road network length,1km_industrial point density;winter PM2.5 correlation The high variables are 1km_land use classification_forest area,1km_land use classification_building area,site DEM,2km_road length.(4)Select the special independent variables with significant correlation,and establish a PM2.5 concentration model by multiple regression analysis to simulate the PM2.5 concentration distribution in different seasons of the main urban area.The spring,summer,autumn and winter prediction models R2 are 0.679,0.709,0.716,and 0.737,respectively.On the time scale,based on the linear relationship between meteorological data and PM2.5,the monthly mean value of PM2.5 for 17 years was simulated.The R2 of the simulation equation was 0.885,and the adjusted R2 was 0.79.The model was tested using Lijia and Nanping sites,and the model accuracy was high. |
