基于LUR/BME的海岸带地区PM<sub>2.5</sub>时空特性研究

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Abstract By combining Land Use Regression (LUR) and Bayesian Maximum Entropy (BME), this study constructed a LUR model based on the parameters of elevation, distance to sea, length of roads and Normalized Difference Vegetation Index (NDVI) to generate a global map of PM_2.5 distribution in a large costal area in 2015, china. The Bayesian Maximum Entropy was further introduced in the interpolation of LUR space-time residuals. Because of the introduction of BME, the cross-validation results showed that the R² increased from 0.36 to 0.85, and the root-mean-square error (RMSE) decreased from 23.53μg/m³ to 11.08μg/m³. The average concentration of PM_2.5 in the northern coastal areas was higher than that of the southern areas, and the highest concentration of PM_2.5 appeared in the inland area of Beijing, Tianjin, Hebei and Shandong provinces during winter times. The annual spatial distribution of PM_2.5 was further integrated with population density in Shandong province for risk exposure analysis. The outcome showed that the outdoor population exposure of PM_2.5 decreased from inland to sea, and the highest Per capita outdoor exposure value occurred in the central city, Jinan (85.5μg/m³), while the lowest value occurred in coastal areas of Yantai and Weihai.

Key words： land use regression bayesian maximum entropy PM_2.5 human exposure

Received: 18 June 2016

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X51

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	Articles by authors
	JIANG Qu-tu
	HE Jun-yu
	WANG Zhan-shan
	YE Guan-qiong
	CHEN Qian
	XIAO Lu

Cite this article:

JIANG Qu-tu,HE Jun-yu,WANG Zhan-shan等. Spatiotemporal analysis of PM_2.5 in large coastal domains by combining Land Use Regression and Bayesian Maximum Entropy[J]. CHINA ENVIRONMENTAL SCIENCECE, 2017, 37(2): 424-431.

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http://manu36.magtech.com.cn/Jweb_zghjkx/EN/ OR http://manu36.magtech.com.cn/Jweb_zghjkx/EN/Y2017/V37/I2/424

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