Inversion Of PM_2.5 With MODIS Data In Shenyang

In this paper,MODIS LIB lkm remote sensing data and meteorological elements data of 11 monitoring sites for the same pepriod in Shenyang in August 2014-July 2015,by 6S radiative transfer model inversion of the aerosol optical thickness of Shenyang city,analysis the correlation between the concentration of PM2.5 and revised AOD between the front and rear,on this basis,eatablished the inversion model of PM2.5,and use the meteorological factors analyzed the influence of PM2.5.The main conclusions of the study are as follows:(1)The seasonal changes of AOD in Shenyang,aerosol inversion results is generally higher in winter,autumn,spring and summer are minimum;spatial scales,in spring,eastern AOD values are generally higher than the western region,in autumn,the central of Liaoning AOD values is higher than the urban areas,summer and winter city limits of aerosol optical thickness values are generally higher than the surrounding area.(2)Analysis of obtained low correlation between AOD and PM2.5 mass concentration,after elevation and vertical revision revised,the correlation between them has been significantly improved,taking into account the differences between every seasons sources of AOD,to further analyze the correlation between them,the whole year is dicided into winter and summer months and six months,the correlation coefficient of PM2.5 mass concentration and AOD has been improved significantly.(3) Establish PM2.5 inversion model,the accuracy of the model tested,get RMSE error of winter months linear model is minimized,summer months exponential model RMSE error is minimized,description fit better,we have good explanatory power.By selecting the best fitting model to inversion of PM2.5,we found RMSE between PM2.5 mass concentration and the measured value inversion is the small,description inversion better,it can be used to reflect the PM2.5 of Shenyang.(4)In each season,effect of meteorological factors on PM2.5 is different.In spring,PM2.5 and temperature was a significant negative correlation,and pressure was a significant positive correlation; in summer, PM2.5 and temperature was a significant positive correlation,and relative humidity showed a significant negative correlation;in autumn,PM2.5 and temperature showed a significant positive correlation,and wind speed showed a significant positive correlation;in winner,PM2.5 and relative humidity showed a significant positive correlation,and wind speed was a significant negative correlation.