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Using Multisource Data to Assess PM 2.5 Exposure and Spatial Analysis of Lung Cancer in Guangzhou, China

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  • Wenfeng Fan

    (School of Environment, Beijing Normal University, No. 19, Xinjiekouwai Street, Haidian District, Beijing 100875, China)

  • Linyu Xu

    (School of Environment, Beijing Normal University, No. 19, Xinjiekouwai Street, Haidian District, Beijing 100875, China)

  • Hanzhong Zheng

    (School of Environment, Beijing Normal University, No. 19, Xinjiekouwai Street, Haidian District, Beijing 100875, China)

Abstract

Elevated air pollution, along with rapid urbanization, have imposed higher health risks and a higher disease burden on urban residents. To accurately assess the increasing exposure risk and the spatial association between PM 2.5 and lung cancer incidence, this study integrated PM 2.5 data from the National Air Quality Monitoring Platform and location-based service (LBS) data to introduce an improved PM 2.5 exposure model for high-precision spatial assessment of Guangzhou, China. In this context, the spatial autocorrelation method was used to evaluate the spatial correlation between lung cancer incidence and PM 2.5 . The results showed that people in densely populated areas suffered from higher exposure risk, and the spatial distribution of population exposure risk was highly consistent with the dynamic distribution of the population. In addition, areas with PM 2.5 roughly overlapped with areas with high lung cancer incidence, and the lung cancer incidence in different locations was not randomly distributed, confirming that lung cancer incidence was significantly associated with PM 2.5 exposure. Therefore, dynamic population distribution has a great impact on the accurate assessment of environmental exposure and health burden, and it is necessary to use LBS data to improve the exposure assessment model. More mitigation controls are needed in highly populated and highly polluted areas.

Suggested Citation

  • Wenfeng Fan & Linyu Xu & Hanzhong Zheng, 2022. "Using Multisource Data to Assess PM 2.5 Exposure and Spatial Analysis of Lung Cancer in Guangzhou, China," IJERPH, MDPI, vol. 19(5), pages 1-17, February.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:5:p:2629-:d:758027
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    References listed on IDEAS

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    1. Maryum Javed & Muzaffar Bashir & Safeera Zaineb, 2021. "Analysis of daily and seasonal variation of fine particulate matter (PM2.5) for five cities of China," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(8), pages 12095-12123, August.
    2. Dubin, Robin A., 1998. "Spatial Autocorrelation: A Primer," Journal of Housing Economics, Elsevier, vol. 7(4), pages 304-327, December.
    3. Lisa C. Vinikoor-Imler & J. Allen Davis & Thomas J. Luben, 2011. "An Ecologic Analysis of County-Level PM 2.5 Concentrations and Lung Cancer Incidence and Mortality," IJERPH, MDPI, vol. 8(6), pages 1-7, June.
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