Investigation of relationship between aerosol optical depth and particulate matter for the application to satellite remote sensing

碩士 === 國立中央大學 === 遙測科技碩士學位學程 === 105 === Since the impact on global air quality and climate change are obvious and serious gradually, the observation of Particulate Matter (PM) has become essential issue all over the word. Due to the location of ground station and the large variance, the complete PM...

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Bibliographic Details
Main Authors: Chia-Hao Chung, 鍾嘉豪
Other Authors: 林唐煌
Format: Others
Language:zh-TW
Published: 2017
Online Access:http://ndltd.ncl.edu.tw/handle/n65k7v
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Summary:碩士 === 國立中央大學 === 遙測科技碩士學位學程 === 105 === Since the impact on global air quality and climate change are obvious and serious gradually, the observation of Particulate Matter (PM) has become essential issue all over the word. Due to the location of ground station and the large variance, the complete PM observation in spatiotemporal distribution has its limitation. Although satellite can provide widely observation, the short of information of aerosol vertical distribution is not satisfied to provide the surface PM concentration. Therefore, this study aims at the investigation of the relationship between aerosol optical depth (AOD) and PM from ground measurement with considering the key factors (vertical distribution, type and humility). Eventually, the results can be expected to benefit to a regional monitoring of PM concentration by means of satellite observation. To overcome the difficult of vertical distribution, this study assumes that the variation of total column AOD observed by satellite sensor is principally caused from the variation of PM near surface level. Then factors of aerosol type and water vapor effect are taken into account for the relationship between AOD and PM. Aerosol types of dust, anthropogenic pollutants and biomass burning are the main species of PM in this study. After the correction of water vapor effect, the correlation coefficients between △AOD and △PM based on the ground-based measurements are 0.71, 0.73 and 0.81 for dust, anthropogenic pollutants and biomass burning respectively. The results are further applied to MODIS AOD products in Taiwan. The root mean square error (RMSE) is 7.60 after compared with ground measurements, indicating highly feasibility of proposed approach for PM concentration monitor from satellite remote sensing.