Fusion of satellite data and ground observed PM2.5 in Pearl River Delta region with linear mixed effect and Bayesian maximum entropy method
ZHOU Shuang1,2, WANG Chun-lin2,3, SUN Rui1, TANG Jing3, HUANG Jun3, SHEN Zi-qi3
1. State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China;
2. Guangzhou Institute of Tropical and Marine of Meteorology, Guangzhou 510640, China;
3. Guangzhou Climate and Agrometeorology Center, Guangzhou 511430, China
By combining Linear Mixed Effect (LME) model and Bayesian Maximum Entropy (BME) method, ground-level PM2.5 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 PM2.5 concentration was mainly located in Guangzhou, Foshan, Dongguan, and the low PM2.5 concentration was mainly distributed in Zhaoqing, Huizhou, Jiangmen. In terms of seasonal variation, PM2.5 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.
周爽, 王春林, 孙睿, 汤静, 黄俊, 沈子琦. 基于LME/BME的珠江三角洲PM2.5星地融合技术研究[J]. 中国环境科学, 2019, 39(5): 1869-1878.
ZHOU Shuang, WANG Chun-lin, SUN Rui, TANG Jing, HUANG Jun, SHEN Zi-qi. Fusion of satellite data and ground observed PM2.5 in Pearl River Delta region with linear mixed effect and Bayesian maximum entropy method. CHINA ENVIRONMENTAL SCIENCECE, 2019, 39(5): 1869-1878.
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