Study On The Urban Aerosol Quantitative Retrieval Based On The Remote Sensing And Ground-based Air Quality Measurement Data

Aerosol is one of the most important factors which affect the balance of the outing and incoming solar energy of ground surface, and affect the climate system through their direct and indirect forcing mechanism on solar and long-wave radiation. They also have a crucial role in local, regional and global air pollution problems. There are five main studies were made based on the research of progress and problems of aerosol satellite remote sensing. The firstly, the theory of aerosol remote sensing was reviewed comprehensively, the secondly, applicability that AOT was used in field of air environment was affirmed based on the relation between AOT retrieved from CE318 and air pollution data ground-based, the thirdly, the height of pollution boundary layer was established based on the lidar data and meteorological sounding data in winter for Beijing, the fouth, the method of DTA was discussed and improved, in the end, a case study in Beijing, the procedure of modeling of the spatial distribution of aerosol and main air pollutant was studied with Landsat7/TM remote sensing data and isochronous ground measured air pollution data by using improved DTA and statistical regress methods.Based on calculations from statistical regress, a method combining sun-photometer observations and air pollution data is used to confirm aerosol loading can be used to indicate the degree of air pollution. The seasonal characteristics and variations of AOT and air pollution are analyzed using these data. In particular, the detailed seasonal changes and relation between AOT and air pollution were described. The results show there is discrepancy in mode and precision when AOT used to indicate air pollution for different pollutant in different season. In general, relation between PM10 and AOT is the simplest, and give good correlations, come to 0.7, and the good correlations was not affected by season. For NO₂, the correlation was affected obviously by season, the best correlations appeared in winter, and the better correlation appwared in spring and in autumn, there is a common correlation in summer. The relation between AOT and SO₂ is complex, with the exception of winter, there is no good correlation between them in any season.A simple relation is created linking data from mobile lidar AML-1 and meteorological sound data. The air pollution boundary layer(PBL) height data are obtained for Beijing in winter based on these data, which is important to link AOT and pollutant by statistical experiential model, for the assumption that aerosol was...