基于LME/BME的珠江三角洲PM<sub>2.5</sub>星地融合技术研究

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Abstract

By combining Linear Mixed Effect (LME) model and Bayesian Maximum Entropy (BME) method, ground-level PM_2.5 from October 2015 to March 2016 in Pearl River Delta region were estimated in this paper by AOD, NDVI and meteorological data. The results showed that the prediction accuracy of LME+BME method were greatly improved compared with that of the LME method. The cross-validation R² of LME+BME model was 0.751, and root mean squared prediction error (RMSE) was 6.886μg/m³, the mean prediction error (MPE) was 4.52μg/m³, while R²=0.703, RMSE=7.546μg/m³, and MAE=4.927μg/m³ for the LME method. The high PM_2.5 concentration was mainly located in Guangzhou, Foshan, Dongguan, and the low PM_2.5 concentration was mainly distributed in Zhaoqing, Huizhou, Jiangmen. In terms of seasonal variation, PM_2.5 pollution was more serious in mid-October in 2015, late November in 2015 and late March in 2016, while it was relatively low in early October in 2015, early December in 2015 and late January in 2016.

Key words： PM_2.5 MODIS AOD linear mixed-effect model Bayesian maximum entropy Pearl River Delta region

Received: 16 October 2018

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X513

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	Articles by authors
	ZHOU Shuang
	WANG Chun-lin
	SUN Rui
	TANG Jing
	HUANG Jun
	SHEN Zi-qi

Cite this article:

ZHOU Shuang,WANG Chun-lin,SUN Rui等. Fusion of satellite data and ground observed PM_2.5 in Pearl River Delta region with linear mixed effect and Bayesian maximum entropy method[J]. CHINA ENVIRONMENTAL SCIENCECE, 2019, 39(5): 1869-1878.

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http://www.zghjkx.com.cn/EN/ OR http://www.zghjkx.com.cn/EN/Y2019/V39/I5/1869

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