基于PM<sub>2.5</sub>站点监测数据的京津冀AOD补值研究

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Abstract A spatiotemporal linear mixed effect model (STLME) and a spatiotemporal nested linear mixed effect model (STNLME) were presented using the PM_2.5 measurements of 318 ground monitoring stations in Beijing-Tianjin-Hebei (BTH) in 2020 to fill the missing data of AOD. The results indicated that the STLME and STNLME models in the days with AOD-PM_2.5 matchups showed similar performance with the cross-validation (CV) R² valued at 0.868 and 0.874, and the root mean square error (RMSE) valued at 0.112 and 0.109, respectively. However, the STNLME model with the CV R² valued at 0.63 outperforms STLME with the CV R² of 0.26 in the days without PM_2.5-AOD matchups. After models filling, the spatial valid value ratio of AOD data in the grid where the monitoring stations are located was increased from 44.35% to 99.35%, and the temporal valid value ratio was increased from 87.94% to 100%. Meanwhile, the annual mean AOD value of each station had increased significantly, and the missing AOD were filled under the condition of high PM_2.5 level, which could reduce the biases of exposure assessment in air pollution and health studies.

Key words： MAIAC AOD AOD filling of monitoring stations spatiotemporal linear mixed effects model spatiotemporal nested linear mixed effect model Beijing-Tianjin-Hebei

Received: 27 December 2021

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	SONG Chun-jie
	WEI Qiang
	FAN Li-hang
	WANG Wei
	HAN Fang
	LI Wei-miao
	LI Fu-xing
	CHENG He-xi

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SONG Chun-jie,WEI Qiang,FAN Li-hang等. Filling the missing data of AOD using the situ PM_2.5 monitoring measurements in the Beijing-Tianjin-Hebei region[J]. CHINA ENVIRONMENTAL SCIENCECE, 2022, 42(7): 3000-3012.

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http://www.zghjkx.com.cn/EN/ OR http://www.zghjkx.com.cn/EN/Y2022/V42/I7/3000

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