Authors: Yunyun Zhou*,
Topics: Remote Sensing, Environment
Keywords: air pollution, PM2.5, AOD, meteorological elemnets.
Session Type: Paper
Start / End Time: 5:00 PM / 6:40 PM
Room: Madison A, Marriott, Mezzanine Level
Presentation File: No File Uploaded
Fine particulate matter less than 2.5 microns in aerodynamic is called PM2.5, which has been considered to be main pollutant that threatens not only environment but also human health. Ground-observed sites can provide accurate PM2.5 concentration, but they have limitation on spatial discontinuity and uneven distribution. Satellite can exactly make up these limitations. Till now, satellite-based aerosol optical depth has been widely used to estimate large-scale and continuous ground-level PM2.5 concentration. And nowadays various AOD-PM2.5 regression models have been developed and have become an important way to obtain PM2.5 concentration. With the development of these models, former researchers found the accuracy of model can be improved by adding meteorological elements. However, most of AOD-PM2.5 models only incorporate surface meteorological elements, regardless of the influences of meteorological elements at other different heights and their vertical changes, which can also influence PM2.5 and AOD to some extents. Therefore, by using multivariate stepwise linear regression method, this study construct AOD-PM2.5 models over Huaihe River Basin on the basis of surface observation and reanalysis meteorology data, considering not only surface meteorological elements, but also meteorological elements at different heights and their vertical changes, and then to find out the contribution of these different factors. The results of this paper show that the incorporation of meteorological elements at different heights and their vertical changes can greatly improve the accuracy of AOD-PM2.5 model. And the influence of these meteorological elements to AOD-PM2.5 regression model has seasonal differences, which have the most obvious impact on spring.