基于时空优化模型的PM<sub>2.5</sub>遥感估测研究

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Abstract In order to obtain the continuous spatiotemporal distribution of PM_2.5 concentration and improve the estimation accuracy, this paper proposes a new PM_2.5 estimation model (SFRF) based on the optimization of spatiotemporal factors. The SFRF model integrates spatiotemporal factors into a random forest (RF) algorithm by integrating high-resolution (1km) satellite-retrieved aerosol optical depth (AOD) products, as well as meteorological data, nighttime light data, and vegetation. Using these data to build an SFRF model to accurately predict the PM_2.5 concentration in Shandong Province in 2019 and generate high spatial resolution (1km) PM_2.5 concentration in Shandong Province. The performance of the SFRF model was evaluated using the ten-fold cross-validation method and compared with the BPNN, SVM, XGBoost, RF and PCA-RF models. The results showed that the coefficient of determination and root mean square error (RMSE) values of the SFRF model verification are 0.85 and 8.10μg/m³, respectively, which are better than other models. The SFRF model can estimate PM_2.5 concentration in Shandong Province with high spatial resolution on daily, seasonal, and annual scales.

Key words： PM_2.5 spatiotemporal factor optimization model (SFRF) AOD Shandong province area

Received: 11 December 2023

PACS:	X513
	X87

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	Articles by authors
	ZHANG Na
	CHEN Wen-qian
	BAI Xue-song
	CAO Xiao-yi

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ZHANG Na,CHEN Wen-qian,BAI Xue-song等. PM_2.5 remote sensing estimation based on spatiotemporal factor optimization model[J]. CHINA ENVIRONMENTAL SCIENCECE, 2024, 44(7): 3625-3636.

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http://www.zghjkx.com.cn/EN/ OR http://www.zghjkx.com.cn/EN/Y2024/V44/I7/3625

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