Spatiotemporal heterogeneity of PM2.5 and its relationship with urbanization in North China from 2000 to 2017

Xiangxue Zhang; Xinchen Gu; Changxiu Cheng; Dongyang Yang

doi:10.1016/j.scitotenv.2020.140925

Spatiotemporal heterogeneity of PM_2.5 and its relationship with urbanization in North China from 2000 to 2017

Sci Total Environ. 2020 Nov 20:744:140925. doi: 10.1016/j.scitotenv.2020.140925. Epub 2020 Jul 14.

Authors

Xiangxue Zhang¹, Xinchen Gu², Changxiu Cheng³, Dongyang Yang⁴

Affiliations

¹ Key Laboratory of Environmental Change and Natural Disaster, Beijing Normal University, Beijing 100875, China; State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China.
² College of Water & Architectural Engineering, Shihezi University, Shihezi 832003, China.
³ Key Laboratory of Environmental Change and Natural Disaster, Beijing Normal University, Beijing 100875, China; State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China; National Tibetan Plateau Data Center, Beijing 100101, China. Electronic address: chengcx@bnu.edu.cn.
⁴ Key Research Institute of Yellow River Civilization and Sustainable Development, Henan University, Kaifeng 475004, China. Electronic address: yangdy@lreis.ac.cn.

PMID: 32688000
DOI: 10.1016/j.scitotenv.2020.140925

Abstract

Fine particulate matter (PM_2.5) pollution is becoming an increasing global concern due to rapid urbanization and socioeconomic development, especially in North China. Although North China experiences poor air quality and high PM_2.5 concentrations, their spatial heterogeneity and relationship with the relative spatial risks of air pollution have not been explored. Therefore, in this study, the temporal variation trends (slope values) of the PM_2.5 concentrations in North China from 2000 to 2017 were first quantified using the unitary linear regression model, and the Bayesian space-time hierarchy model was introduced to characterize their spatiotemporal heterogeneity. The spatial lag model was then used to examine the determinant power of urbanization and other socioeconomic factors. Additionally, the correlation between the spatial relative risks (probability of a region becoming more/less polluted relative to the average PM_2.5 concentrations of the study area), and the temporal variation trends of the PM_2.5 concentrations were quantified using the bivariate local indicators of spatial association model. The results showed that the PM_2.5 concentrations increased during 2000-2017, and peaked in 2007 and 2013. Spatially, the cities at high risk of PM_2.5 pollution were mainly clustered in southeastern Hebei, northern Henan, and western Shandong where the slope values were low, as demonstrated by the value of Moran's I (-0.56). Moreover, urbanization and road density were both positively correlated with PM_2.5 pollution, while the proportion of tertiary industry was negatively correlated. Furthermore, a notable increasing trend was observed in some cities, such as Tianjin, Zaozhuang, Qingdao, and Xinyang. These findings can contribute to the development of effective policies from the perspective of rapid urbanization to relieve and reduce PM_2.5 pollution.

Keywords: Bayesian space-time hierarchy model; PM(2.5) concentrations; Spatiotemporal heterogeneity; Temporal variation trends; Urbanization.