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Spatiotemporal Dynamic Evolution of PM 2.5 Exposure from Land Use Changes: A Case Study of Gansu Province, China

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  • Fang Liu

    (School of Statistics and Data Science, Lanzhou University of Finance and Economics, Lanzhou 730020, China
    School of Science, Inner Mongolia University of Science and Technology, Baotou 014010, China)

  • Shanghui Jia

    (School of Statistics and Mathematics, Central University of Finance and Economics, Beijing 100081, China)

  • Lingfei Ma

    (School of Statistics and Mathematics, Central University of Finance and Economics, Beijing 100081, China)

  • Shijun Lu

    (School of Statistics and Data Science, Lanzhou University of Finance and Economics, Lanzhou 730020, China)

Abstract

Air pollution is a major trigger for chronic respiratory and circulatory diseases. As a key component of air pollution, fine particulate matter (PM 2.5 ) exposure is largely determined by land use type and population density. However, simultaneous consideration of their spatiotemporal distribution is lacking in existing studies on PM 2.5 exposure. In this paper, we first assess the dynamic evolution of land use patterns in Gansu Province, China, from 2000 to 2020, using a land use transfer matrix and dynamic degree. Population-weighted exposure (PWE) to PM 2.5 is then evaluated for each land use type at provincial, city, and county levels, with seasonal variations analyzed. Spatial autocorrelation analysis is finally performed to explore the spatiotemporal evolution of PM 2.5 exposure, whereas standard deviation ellipses and gravity center migration models highlight spatial distribution characteristics and shifting trends. Experimental results showed that 2010 was a turning point for annual PM 2.5 exposure at the provincial level in Gansu Province, with an initial increase followed by a decrease. Construction land had the highest annual PM 2.5 exposure, whereas forest had the lowest exposure (except in 2005). Exposure levels showed a seasonal pattern: higher in winter and spring and lower in summer and autumn. At city and county levels, southern Gansu indicated a continuous decline in annual PM 2.5 exposure across all land use types since 2000. Exposure levels exhibited a strong spatial positive correlation, with a fluctuating spatial convergence. This study comprehensively analyzes the multi-scale differences and spatiotemporal evolution patterns of PM 2.5 exposure across various land use types, contributing to provide scientific evidence and decision-making support for mitigating air pollution and enhancing coordinated air pollution control at multi-scale administrative levels.

Suggested Citation

  • Fang Liu & Shanghui Jia & Lingfei Ma & Shijun Lu, 2025. "Spatiotemporal Dynamic Evolution of PM 2.5 Exposure from Land Use Changes: A Case Study of Gansu Province, China," Land, MDPI, vol. 14(4), pages 1-29, April.
    • Handle: RePEc:gam:jlands:v:14:y:2025:i:4:p:795-:d:1629691
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    References listed on IDEAS

    as
    1. Basu, Tirthankar & Das, Arijit, 2024. "Urbanization induced changes in land use dynamics and its nexus to ecosystem service values: A spatiotemporal investigation to promote sustainable urban growth," Land Use Policy, Elsevier, vol. 144(C).
    2. Zhenjie Liu & Jun Li & Haonan Chen & Lizhe Wang & Jun Yang & Antonio Plaza, 2024. "Prediction of changes in war-induced population and CO2 emissions in Ukraine using social media," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-11, December.
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