Estimation of PM2.5 and PM10 mass concentrations in mining city cluster from gaofen-1 aerosol optical depth data and the WRF-chem model
编号:213 稿件编号:113 访问权限:仅限参会人 更新:2022-05-26 13:16:32 浏览:791次 口头报告

报告开始:2022年05月27日 12:05 (Asia/Shanghai)

报告时间:10min

所在会议:[S2] Environmental Science and Engineering » [S2-2] Theme 3 & 4

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摘要
ABSTRACT: Mining cities are an important part of China's urban agglomerations, and as mining cities continue to develop, ecological and environmental pollution has become a primary problem. the level of pollutant emissions of mining cities is much higher than that of non-mining cities, and the ability to repair the environment is lower than that of non-mining cities. City structure and air pollution are correlated, and studies of city cluster are different from previous air pollution analyses that have been conducted on regional basis. Particulate matter estimation for city clusters requires high spatial and temporal resolution observations to further understand the mechanisms of model, validate modelling results, and improve the modelling capabilities.
In this study, the aerosol optical depth (AOD) retrieval of major mining city clusters in China from 2013 to 2020 was carried out by using the Gaofen-1 satellite data, and a new hybrid model based on WRF-CHEM and GTWR was proposed for PM2.5 and PM10 mass concentration estimation. The temporal and spatial analysis of particulate matter characteristics according to the different transformation stages and urban structure of mining cities was carried out in mining city clusters in China in the past two decades. The estimated results for PM2.5 and PM10 were verified at ground stations with R2 of 0.81 and 0.78, and RMSE of 9.52 μg/m3 and 18.09 μg/m3, respectively. The results indicate that PM2.5 and PM10 have distinct spatial and temporal distribution patterns as mining cities in China are undergoing different types of transformation processes.
 
关键字
Mining City Cluster,Remote sensing (RS),Gaofen-1,PM2.5,PM10,AOD
报告人
Yuxin SUN
China University of Mining and Technology

稿件作者
玉新 孙 中国矿业大学环测学院
勇 薛 School of Environment and Spatial Informatics, China University of Mining and Technology;Artificial Intelligence Research Institute, China University of Mining and Technology;School of Electronics, Computing and Mathematics, College of Engineering and Technology
腾飞 崔 School of Environment and Spatial Informatics, China University of Mining and Technology
瑞 白 School of Environment and Spatial Informatics, China University of Mining and Technology
淑慧 吴 School of Environment and Spatial Informatics, China University of Mining and Technology
春林 靳 School of Environment and Spatial Informatics, China University of Mining and Technology
兴兴 蒋 School of Environment and Spatial Informatics, China University of Mining and Technology
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