IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i5p2629-d758027.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/5/2629/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/19/5/2629/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Marco Helbich & Wolfgang Brunauer & Eric Vaz & Peter Nijkamp, 2014. "Spatial Heterogeneity in Hedonic House Price Models: The Case of Austria," Urban Studies, Urban Studies Journal Limited, vol. 51(2), pages 390-411, February.
    2. Schilling, Brian J. & Sullivan, Kevin P. & Duke, Joshua M., 2013. "Do Residual Development Options Increase Preserved Farmland Values?," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 38(3), pages 1-17.
    3. Marko Kryvobokov, 2011. "Defining apartment neighbourhoods with Thiessen polygons and fuzzy equality clustering," ERES eres2011_142, European Real Estate Society (ERES).
    4. Takafumi Kato, 2020. "Likelihood-based strategies for estimating unknown parameters and predicting missing data in the simultaneous autoregressive model," Journal of Geographical Systems, Springer, vol. 22(1), pages 143-176, January.
    5. Tianyue Ma & Jing Li & Shuang Bai & Fangzhe Chang & Zhai Jiang & Xingguang Yan & Jiahao Shao, 2022. "Optimization and Construction of Ecological Security Patterns Based on Natural and Cultivated Land Disturbance," Sustainability, MDPI, vol. 14(24), pages 1-19, December.
    6. Nyakundi Michieka & Archana Pradhan & Tesfa Gebremedhin, 2010. "A Spatial Analysis of Supplemental Nutrition Assistance Program and Economic Conditions in the Applachian Region," Working Papers Working Paper 2010-08, Regional Research Institute, West Virginia University.
    7. Eddie Chi Man Hui & Cong Liang & Ziyou Wang & Yuan Wang, 2016. "The roles of developer’s status and competitive intensity in presale pricing in a residential market: A study of the spatio-temporal model in Hangzhou, China," Urban Studies, Urban Studies Journal Limited, vol. 53(6), pages 1203-1224, May.
    8. Joshua C. Hall & Donald J. Lacombe & Timothy M. Shaughnessy, 2015. "Economic Freedom and Economic Growth Across U.S. States: A Spatial Panel Data Analysis," Working Papers 15-33, Department of Economics, West Virginia University.
    9. Hungria Gunnelin, Rosane, 2020. "Bidding strategies and winner’s curse in auctions of non-distressed residential real estate," Working Paper Series 20/13, Royal Institute of Technology, Department of Real Estate and Construction Management & Banking and Finance.
    10. Charlene M. Kalenkoski & Donald J. Lacombe, 2013. "Minimum wages and teen employment: A spatial panel approach," Papers in Regional Science, Wiley Blackwell, vol. 92(2), pages 407-417, June.
    11. Xiaolong Liu, 2013. "Spatial and Temporal Dependence in House Price Prediction," The Journal of Real Estate Finance and Economics, Springer, vol. 47(2), pages 341-369, August.
    12. Charlene Kalenkoski & Donald Lacombe, 2008. "Effects of Minimum Wages on Youth Employment: the Importance of Accounting for Spatial Correlation," Journal of Labor Research, Springer, vol. 29(4), pages 303-317, December.
    13. Helmuth Yesid Arias Gómez & Gabriela Antosová, 2018. "Spatial Patterns of Agriculture in Boyacá," Apuntes del Cenes, Universidad Pedagógica y Tecnológica de Colombia, vol. 37(66), pages 203-237, June.
    14. Mauro Ferrante & Giovanni Luca Lo Magno & Stefano De Cantis & Geoffrey J.D. Hewings, 2020. "Measuring spatial concentration: A transportation problem approach," Papers in Regional Science, Wiley Blackwell, vol. 99(3), pages 663-682, June.
    15. Helen R. Neill & David M. Hassenzahl & Djeto D. Assane, 2007. "Estimating the Effect of Air Quality: Spatial versus Traditional Hedonic Price Models," Southern Economic Journal, John Wiley & Sons, vol. 73(4), pages 1088-1111, April.
    16. Hunt, Len M. & Boxall, Peter & Englin, Jeffrey & Haider, Wolfgang, 2005. "Remote tourism and forest management: a spatial hedonic analysis," Ecological Economics, Elsevier, vol. 53(1), pages 101-113, April.
    17. Ana Lúcia Marto Sargento, 2007. "Empirical Examination of the Gravity Model in two Different Contexts: Estimation and Explanation," Review of Regional Research: Jahrbuch für Regionalwissenschaft, Springer;Gesellschaft für Regionalforschung (GfR), vol. 27(2), pages 103-127, August.
    18. Rahal, Charles, 2019. "A Grid Based Approach to Analysing Spatial Weighting Matrix Specification," SocArXiv nt2yq, Center for Open Science.
    19. Rahal, Charles, 2019. "A Grid Based Approach to Analysing Spatial Weighting Matrix Specification," SocArXiv nt2yq_v1, Center for Open Science.
    20. Joshua C. Hall & Donald J. Lacombe & Timothy M. Shaughnessy, 2019. "Economic Freedom And Income Levels Across U.S. States: A Spatial Panel Data Analysis," Contemporary Economic Policy, Western Economic Association International, vol. 37(1), pages 40-49, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:19:y:2022:i:5:p:2629-:d:758027. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.