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Determinant Powers of Socioeconomic Factors and Their Interactive Impacts on Particulate Matter Pollution in North China

Author

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  • Xiangxue Zhang

    (Key Laboratory of Environmental Change and Natural Disaster, Ministry of Education, Beijing Normal University, Beijing 100875, China
    State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China)

  • Yue Lin

    (Department of Geography, The Ohio State University, Columbus, OH 43210, USA)

  • Changxiu Cheng

    (Key Laboratory of Environmental Change and Natural Disaster, Ministry of Education, 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)

  • Junming Li

    (School of Statistics, Shanxi University of Finance and Economics, Taiyuan 030006, China)

Abstract

Severe air pollution has significantly impacted climate and human health worldwide. In this study, global and local Moran’s I was used to examine the spatial autocorrelation of PM 2.5 pollution in North China from 2000–2017, using data obtained from Atmospheric Composition Analysis Group of Dalhousie University. The determinant powers and their interactive effects of socioeconomic factors on this pollutant are then quantified using a non-linear model, GeoDetector. Our experiments show that between 2000 and 2017, PM 2.5 pollution globally increased and exhibited a significant positive global and local autocorrelation. The greatest factor affecting PM 2.5 pollution was population density. Population density, road density, and urbanization showed a tendency to first increase and then decrease, while the number of industries and industrial output revealed a tendency to increase continuously. From a long-term perspective, the interactive effects of road density and industrial output, road density, and the number of industries were amongst the highest. These findings can be used to develop the effective policy to reduce PM 2.5 pollution, such as, due to the significant spatial autocorrelation between regions, the government should pay attention to the importance of regional joint management of PM 2.5 pollution.

Suggested Citation

  • Xiangxue Zhang & Yue Lin & Changxiu Cheng & Junming Li, 2021. "Determinant Powers of Socioeconomic Factors and Their Interactive Impacts on Particulate Matter Pollution in North China," IJERPH, MDPI, vol. 18(12), pages 1-15, June.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:12:p:6261-:d:572050
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    References listed on IDEAS

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    Cited by:

    1. Hansol Mun & Mengying Li & Juchul Jung, 2022. "Spatial-Temporal Characteristics and Influencing Factors of Particulate Matter: Geodetector Approach," Land, MDPI, vol. 11(12), pages 1-26, December.

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