Remote Sensing Estimation Model Of Atmospheric Fine Particulate Matter(PM2.5) Mass Concentration In The Main City Of Chongqing City

In recent years, along with the rapid development of society and economy, our country occur continued large-scale disasters, the smog weather frequency is more and more high. The main pollutants of smog weather disasters caused by is aerodynamic particle size of less than 2.5μm of atmospheric fine particulate matter, which can do direct harm to health through the respiratory into the body. Satellite remote sensing, one of the most important means of earth observation, is becoming more and more mature in terms of air pollutant monitoring. It can play an important use in PM2.5 concentration monitoring.In this paper, Chongqing city as the research area, utilizing MODIS L1 B data products, use the Dense Dark Vegetation method to retrieve aerosol optical thickness. According to the concentration data ground monitoring of atmospheric pollutant, Combining with ground measured the mass concentration data of atmospheric pollutants, based on temporal and spatial variation trend of fine particle mass concentration in the main urban area of Chongqing City and correlation between atmospheric aerosol optical thickness and ground measured the mass concentration of fine particle by inversion of MODIS, the model of satellite remote sensing estimation of fine particle mass concentration in winter in Chongqing City, which is a high incidence of smog, was fitted. And the estimation model was evaluated.The results show that the improved 6S model is suitable for the inversion of atmospheric aerosol optical thickness in the winter MODIS data of Chongqing City, which is not suitable for the meteorological conditions. In the time sequence, the Chongqing City Winter fine particulate matter concentration diurnal variation trend showed a bi-modal pattern. Among them, two high peak values respectively appeared every day between 12:00 to 13:00 and around 21:00 pm, two trough values occurred between 7:00 to 8:00 and about 16:00 pm, and Jinyun Mountain control point of fine particulate matter concentration daily variation tends to be stable. From the analysis of spatial trends, Chongqing City Winter fine particulate mass concentrations in east-west direction projection, north-south projection and southeast-northwest projection showed a downward trend from the center to both sides, but in southwest-northeast projection monotonically decreasing trend.By analyzing the correlation between the mass concentration of fine particles and other factors we can obtain:There is a positive correlation betweenMODIS inversion ofaerosol optical thicknessand mass concentration of fine particulate matter,the correlation coefficient is 0.481, and the confidence level through the 0.01 level(bilateral) significance test.The correlation coefficient between fine particle mass concentration and other pollutant factors is quite different.The correlation coefficient between fine particle mass concentration and PM10 reached 1, and the correlation coefficient of SO2 mass concentration reached 0.734, and the correlation coefficient of NO2 was 0.387,and both of them passed the significant test on the corresponding level.Chongqing City in winter air pollution has the typical characteristic of coal-burning.In the process of building a model we found that, in the polynomial regression model constructed by direct use of MODIS retrieval of atmospheric aerosol optical thickness and the fine particle mass concentration, the best model is a cubic function model.The certainty coefficient R2 is 0.591, and there is a large error in the estimation model of the aerosol optical thickness fitting directly the fine particle mass concentration.Multiple linear regression models with multiple pollutant factors have higher accuracy and stability.The evaluation analysis of the optimal fitting model shows that,the certainty coefficient of multiple linear regression models with multiple pollutant factors than the estimation model of the aerosol optical thickness fitting directly the fine particle mass concentration haveimproved, which determine coefficient R2 ofthe best goodness of fit of multiple linear regression model reaches 0.663,than directly through aerosol optical thickness fitting fine particulate matter concentration estimation model is improved. The estimation model based on the MODIS data of the fine particle mass concentration remote sensing can be in a certain extent reflects Chongqing city in winter near surface atmospheric fine particulate matter pollution.