Evaluation Of The Aerosol Optical Depth Production And Retrieval Of The PM2.5 In China Using MODIS Images

Monitoring of the concentration of near-ground PM2.5 is one of the important contents of environmental monitoring,the conventional monitoring means is the continuous observation of the ground environment monitoring site,but it has the defects of the limited sites number and the uneven distribution of space.In addition,Chinese PM2.5 monitoring network established late,resulting in the lack of PM2.5 data before 2013,which limits the the study of PM2.5 in Chinese environmental and health effects.It can make up for the deficiency of conventional methods by using remote sensing method to retrieve PM2.5,which has important value and significance in the field of PM2.5 monitoring.AOD has a correlation with PM2.5,we can use the AOD data retrived by satellite remote sensing to estimate PM2.5 concentration.MODIS AOD products are widely used all over the world,and the new version has improved in accuracy and coverage compared with the previous version,but its applicability in China is still lacking.Based on the above background,this paper based on the aerosol products of MODIS satellite,verifide its applicability in our country,developed a hight-accuracy AOD-PM2.5 model,retrieved the history PM2.5 concentration of our country,analysed of temporal and spatial variation characteristics of PM2.5 in China,to provide scientific basis for studying the environmental and health effects of PM2.5 in China.Specific research work is as follows:(1)Using the Global Aerosol Robotic Network(AERONET)to validate the MODIS new version aerosol products.The results show that the new version of aerosol products is better than the previous version in China,which is better in the eastern region than in the western.Overall,it can be used as the basic data for the establish the AOD-PM2.5 modeling.(2)According to the geographical weighted regression(GWR)model is suitable for a wide range of regional modeling,based on the GWR to build a AOD-PM2.5 model in our country,and compared with multiple linear regression(MLR)model,validation of the GWR model inversion effect.The results show that the effect of the GWR model(R2=0.76)is much better than the MLR model(R2=0.54).(3)Based on the AOD-PM2.5 relationship is relatively stable in each of year,using AOD-PM2.5 model in 2015 to retrieve history PM2.5 concentration of our country,through the verification of different time scale model,obtained the monthly average PM2.5 concentration of China in 2006-2016.The results show that the PM2.5 distribution in China is closely related to terrain and human activities,and has obvious regional characteristics,there are two high-value regions.The annual average value concentration of PM2.5 in China shows a decreasing trend,further analysis shows that the PM2.5 pollution in winter is increasing and the other three seasons have improved.The concentration of PM2.5 varies widely from month to month,with a high of 12,1 and 2 months and relatively low in 5-9 months.Overall,based on remote sensing to inverse PM2.5 concentrations can be used as an effective supplement of ground monitoring methods,On the one hand,make up for the shortcomings of routine monitoring in time and space,on the other hand,inversion of historical PM2.5 data can be used to provide basic data to clarify PM2.5 pollution of the temporal and spatial variation characteristics in China.