基于GF-5B卫星的随机森林模型反演京津冀地区PM2.5

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Abstract This study utilizes aerosol optical depth (AOD) data derived from the domestic GF-5B DPC payload, ERA- 5meteorological data, NDVI data, DEM data, nighttime light data, and ground-based PM_2.5 measurements. Based on a random forest model, an AOD-PM_2.5 inversion model is developed to estimate the PM_2.5 concentration in the Beijing-Tianjin-Hebei region for the year 2022. The results showed that the model's coefficient of determination (R²) for the entire year was 0.80, with a root mean square error (RMSE) of 11.43 μg/m³. For the seasonal models, the R² values were 0.84, 0.71, 0.88, and 0.87 for spring, summer, autumn, and winter, respectively, with corresponding RMSE values of 9.50, 7.37, 9.71, and 11.32μg/m³. Both the annual and seasonal models demonstrated good simulation accuracy. The PM_2.5 concentrations in the Beijing-Tianjin-Hebei region in 2022 exhibited significant seasonal variation, with the highest concentrations in winter and the lowest in summer. The estimated annual average PM_2.5 concentration was 30.86μg/m³. During the summer and autumn, AOD was the primary factor influencing PM_2.5 concentrations, while in spring and winter, the boundary layer height and relative humidity were the main influencing factors.

Key words： GF-5B RF model Beijing-Tianjin-Hebei AOD PM_2.5

Received: 01 April 2024

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X513

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	HANG Rui-jie
	ZHOU Chun-yan
	CHEN Hui
	ZHOU Wei
	XIE Hui-zhen
	CHEN Rui-zhi
	WANG Zhong-ting

Cite this article:

HANG Rui-jie,ZHOU Chun-yan,CHEN Hui等. Inversion of PM_2.5 in the Beijing-Tianjin-Hebei region using a random forest model based on GF-5B satellite[J]. CHINA ENVIRONMENTAL SCIENCECE, 2024, 44(11): 5961-5970.

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http://www.zghjkx.com.cn/EN/ OR http://www.zghjkx.com.cn/EN/Y2024/V44/I11/5961

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