The Effect of Socioeconomic Factors on Spatiotemporal Patterns of PM 2.5 Concentration in Beijing–Tianjin–Hebei Region and Surrounding Areas

Wenting Wang, Lijun Zhang, Jun Zhao, Mengge Qi and Fengrui Chen
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Wenting Wang: Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of Education/Collaborative Innovation Center of Yellow River Civilization, Henan University, Kaifeng 475004, China
Lijun Zhang: College of Environmental and Planning, Henan University, Kaifeng 475004, China
Jun Zhao: Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of Education/Collaborative Innovation Center of Yellow River Civilization, Henan University, Kaifeng 475004, China
Mengge Qi: Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of Education/Collaborative Innovation Center of Yellow River Civilization, Henan University, Kaifeng 475004, China
Fengrui Chen: Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of Education/Collaborative Innovation Center of Yellow River Civilization, Henan University, Kaifeng 475004, China

IJERPH, 2020, vol. 17, issue 9, 1-16

Abstract: The study investigated the spatiotemporal evolution of PM 2.5 concentration in the Beijing–Tianjin–Hebei region and surrounding areas during 2015–2017, and then analyzed its socioeconomic determinants. First, an estimation model considering spatiotemporal heterogeneous relationships was developed to accurately estimate the spatial distribution of PM 2.5 concentration. Additionally, socioeconomic determinants of PM 2.5 concentration were analyzed using a spatial panel Dubin model, which aimed to improve the robustness of the model estimation. The results demonstrated that: (1) The proposed model significantly increased the estimation accuracy of PM 2.5 concentration. The mean absolute error and root-mean-square error were 9.21 μg/m 3 and 13.10 μg/m 3 , respectively. (2) PM 2.5 concentration in the study area exhibited significant spatiotemporal changes. Although the PM 2.5 concentration has declined year by year, it still exceeded national environmental air quality standards. (3) The per capita GDP, urbanization rate and number of industrial enterprises above the designated size were the key factors affecting the spatiotemporal distribution of PM 2.5 concentration. This study provided scientific references for comprehensive PM 2.5 pollution control in the study area.

Keywords: PM 2.5; socioeconomic factors; spatiotemporal patterns; spatiotemporal heterogeneous; spatial panel Dubin model (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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