北京市气溶胶光学厚度影响因子分析

刘福江; 唐家玉; 刘福寿; 陆姗姗; 李鹏

doi:10.3799/dqkx.2021.223

Volume 48 Issue 10

Oct. 2023

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Article Navigation > Earth Science > 2023 > 48(10): 3812-3819

Liu Fujiang, Tang Jiayu, Liu Fushou, Lu Shanshan, Li Peng, 2023. Analysis of Influencing Factors of Aerosol Optical Depth in Beijing. Earth Science, 48(10): 3812-3819. doi: 10.3799/dqkx.2021.223

Citation:

Liu Fujiang, Tang Jiayu, Liu Fushou, Lu Shanshan, Li Peng, 2023. Analysis of Influencing Factors of Aerosol Optical Depth in Beijing. Earth Science, 48(10): 3812-3819. doi: 10.3799/dqkx.2021.223

Liu Fujiang, Tang Jiayu, Liu Fushou, Lu Shanshan, Li Peng, 2023. Analysis of Influencing Factors of Aerosol Optical Depth in Beijing. Earth Science, 48(10): 3812-3819. doi: 10.3799/dqkx.2021.223

Citation:

Liu Fujiang, Tang Jiayu, Liu Fushou, Lu Shanshan, Li Peng, 2023. Analysis of Influencing Factors of Aerosol Optical Depth in Beijing. Earth Science, 48(10): 3812-3819. doi: 10.3799/dqkx.2021.223

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Analysis of Influencing Factors of Aerosol Optical Depth in Beijing

doi: 10.3799/dqkx.2021.223

1.
School of Geography and Information Engineering, China University of Geosciences, Wuhan 430078, China
2.
Shanghai Investigation, Design & Research Institute Co., Ltd., Shanghai 200000, China

Received Date: 2021-08-09
Available Online: 2023-10-31
Publish Date: 2023-10-25

Abstract

Abstract

Aerosol optical depth (AOD) is influenced by atmospheric pollution composition, road distribution, and surface cover type, etc. It is significant to study AOD for a regional scale considering multiple influencing factors. The study uses the high-resolution aerosol optical depth product data, air pollution data, road route data, surface classification data, and NPP night-light remote sensing data from February 2020 to February 2021 in Beijing, and proposes the extensible simplified aerosol retrieval algorithm (XSARA), using Pearson index, principal component analysis, multiple regression, and geographically weighted regression model to systematically analyze the aerosol optical thickness and its driving factors in Beijing from 2020 to 2021. The results show follows (1) Humidity is positively correlated with AOD, while wind direction is negatively correlated with AOD. The relationship equation between AOD and each combination factor is obtained by multiple linear regression, in which R² is 0.970 68 and the sum of squares of residuals is 0.029 10, indicating that the regression effect is good. (2) The AOD along the road is higher than the surrounding areas, and the AOD increases with the increasing density of the road. (3) The surface cover represented by man-made surface can enhance the AOD concentration, while the surface cover represented by vegetation can weaken the AOD concentration. (4) By using the night light data, it is found that the data has a high correlation with the spatial distribution pattern of AOD, and the R² obtained by geographic weighted regression model is greater than 0.9, which further indicates that the correlation between the two is high.
- impact factor,
- extensible simplified aerosol retrieval algorithm,
- aerosol optical depth,
- Beijing,
- atmospheric remote sensing,
- air population,
- meteorology

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References(19)

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