Study On The Correlation Between MODIS AOD And Concentration Of Air PM10Pollutants In Wuhan

The atmospheric aerosol is an important part of the earth-atmosphere-ocean system, plays a very important role in the Earth’s radiation balance by scattering and absorption of solar radiation, the radiation balance of ayments of the earth-atmosphere system, which not only affects the climate change, but also closely related to human health.With the rapidly developed of economy, industrialization and urbanization process accelerated, the problem of air pollution becomes more and more serious. In most regions of China, a kind of inhalable particle-PM10has became the primary pollutant affecting the air quality. For the situation of air pollution in Wuhan city. put forward the utilization MODO4data (aerosol optical depth AOD) provided by NASA, and PM10daily average data at the same time, attempt to establish the regression analysis between the relational model, to achieve the purpose of using the remote sensing to monitor the ground air pollution. Then Use the online mode of HYSPLIT to analysis a typical pollution incident on November23,2011in Wuhan city for tracing the sources of pollutants. The research was supported by the following conclusions:(1) Constructed five types regression models contains linear, logarithmic, exponential, power, quadratic, between AOD based on MODIS LIB data and PM10, from January1,2008to December31.2010, according to analyzing the coefficient of determination R2. we got that the correlation of five regression models are all extremely signifacant and quadratic model is the best fitting model to reflect the annual relationship between AOD and PM10because the coefficient of determination of the quadratic model is0.242and higer than other’s.(2) Directly establish regression models between the annual daily average data of PM10and AOD, the correlation is extremely significant, but the coefficient of determination is only0.242, we taking into account that that Wuhan has four distinct seasons, and then try to explore seasonal models between them, establish regression models between the average data of PM10and AOD in spring and summer. we got the quadratic model are the best, coefficient of determination are0.505、0.570and extremely increased because of season divided, establish regression models between the average data of PM10and AOD in autumn and and winter. We got the quadratic model and the coefficient of determination are0.154、0.363. there are a slight increase in winter, but effect in autumn is not obvious.when step is30. annual and seasonal model accuracy are29%、69.8%、91.5%、78.2%、76.3%, and prediction accuracy are27.4%、69.2%、95.7%、85.2%、63.2%. Overall, the different seasons of the PM10and AOD relational model fitting and forecasting effect is still not ideal.(3) Introducing the meteorological factors (level of visibility, relative humidity) data to revise the AOD and PM10data. AOD data revised by elevation is the independent variable, PM10revised by humidity as the dependent variable, construct annual and seasonal models between the two factors, when step is30, annual and seasonal model accuracy are81.3%、88.7%、97.9%、83.6%、84.2%, and prediction accuracy are78.9%、76.9%、100%、96.3%、78.9%, both model accuracy and prediction accuracy have been enhanced significantly, the result shows that using AOD revised by humidity to indicate the air quality of Wuhan city is feasible and reliable.(4) Use the online mode of HYSPLIT to simulate the trajectory of the aerosol in a typical pollution incident on December23,2011in Wuhan city for tracing the sources of pollutants. The study found that the pollutants came from northwest of Wuhan. maybe come from Tongchuan Weinan or Yuncheng.