IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i19p12671-d932943.html
   My bibliography  Save this article

Spatial Equity of PM 2.5 Pollution Exposures in High-Density Metropolitan Areas Based on Remote Sensing, LBS and GIS Data: A Case Study in Wuhan, China

Author

Listed:
  • Zhuoran Shan

    (School of Architecture and Urban Planning, Huazhong University of Science and Technology, Wuhan 430074, China
    Hubei Engineering and Technology Research Center of Urbanization, Wuhan 430074, China
    The Key Laboratory of Urban Simulation for Ministry of Natural Resources, Wuhan 430074, China)

  • Hongfei Li

    (Guangzhou Urban Planning Survey and Design Institute, Guangzhou 510060, China)

  • Haolan Pan

    (School of Architecture and Urban Planning, Huazhong University of Science and Technology, Wuhan 430074, China
    Hubei Engineering and Technology Research Center of Urbanization, Wuhan 430074, China
    The Key Laboratory of Urban Simulation for Ministry of Natural Resources, Wuhan 430074, China)

  • Man Yuan

    (School of Architecture and Urban Planning, Huazhong University of Science and Technology, Wuhan 430074, China
    Hubei Engineering and Technology Research Center of Urbanization, Wuhan 430074, China
    The Key Laboratory of Urban Simulation for Ministry of Natural Resources, Wuhan 430074, China)

  • Shen Xu

    (School of Architecture and Urban Planning, Huazhong University of Science and Technology, Wuhan 430074, China
    Hubei Engineering and Technology Research Center of Urbanization, Wuhan 430074, China
    The Key Laboratory of Urban Simulation for Ministry of Natural Resources, Wuhan 430074, China)

Abstract

In-depth studies have been conducted on the risk of exposure to air pollution in urban residents, but most of them are static studies based on the population of residential units. Ignoring the real environmental dynamics during daily activity and mobility of individual residents makes it difficult to accurately estimate the level of air pollution exposure among residents and determine populations at higher risk of exposure. This paper uses the example of the Wuhan metropolitan area, high-precision air pollution, and population spatio-temporal dynamic distribution data, and applies geographically weighted regression models, bivariate LISA analysis, and Gini coefficients. The risk of air pollution exposure in elderly, low-age, and working-age communities in Wuhan was measured and the health equity within vulnerable groups such as the elderly and children was studied. We found that ignoring the spatio-temporal behavioral activities of residents underestimated the actual exposure hazard of PM 2.5 to residents. The risk of air pollution exposure was higher for the elderly than for other age groups. Within the aging group, a few elderly people had a higher risk of pollution exposure. The high exposure risk communities of the elderly were mainly located in the central and sub-center areas of the city, with a continuous distribution characteristic. No significant difference was found in the exposure risk of children compared to the other populations, but a few children were particularly exposed to pollution. Children’s high-exposure communities were mainly located in suburban areas, with a discrete distribution. Compared with the traditional static PM 2.5 exposure assessment, the dynamic assessment method proposed in this paper considers the high mobility of the urban population and air pollution. Thus, it can accurately reveal the actual risk of air pollution and identify areas and populations at high risk of air pollution, which in turn provides a scientific basis for proposing planning policies to reduce urban PM 2.5 and improve urban spatial equity.

Suggested Citation

  • Zhuoran Shan & Hongfei Li & Haolan Pan & Man Yuan & Shen Xu, 2022. "Spatial Equity of PM 2.5 Pollution Exposures in High-Density Metropolitan Areas Based on Remote Sensing, LBS and GIS Data: A Case Study in Wuhan, China," IJERPH, MDPI, vol. 19(19), pages 1-22, October.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:19:p:12671-:d:932943
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/19/12671/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/19/19/12671/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Pengxiang Zhao & Mei-Po Kwan & Suhong Zhou, 2018. "The Uncertain Geographic Context Problem in the Analysis of the Relationships between Obesity and the Built Environment in Guangzhou," IJERPH, MDPI, vol. 15(2), pages 1-20, February.
    2. Chen, Juan & Chen, Shuo & Landry, Pierre F., 2013. "Migration, environmental hazards, and health outcomes in China," Social Science & Medicine, Elsevier, vol. 80(C), pages 85-95.
    3. Victor Brajer & Jane V. Hall, 2005. "Changes in the Distribution of Air Pollution Exposure in the Los Angeles Basin from 1990 to 1999," Contemporary Economic Policy, Western Economic Association International, vol. 23(1), pages 50-58, January.
    4. Yoo Min Park & Mei-Po Kwan, 2020. "Understanding Racial Disparities in Exposure to Traffic-Related Air Pollution: Considering the Spatiotemporal Dynamics of Population Distribution," IJERPH, MDPI, vol. 17(3), pages 1-14, February.
    5. Bin Chen & Yimeng Song & Tingting Jiang & Ziyue Chen & Bo Huang & Bing Xu, 2018. "Real-Time Estimation of Population Exposure to PM 2.5 Using Mobile- and Station-Based Big Data," IJERPH, MDPI, vol. 15(4), pages 1-14, March.
    6. Jing Ma & Bochu Liu & Gordon Mitchell & Guanpeng Dong, 2019. "A spatial analysis of air pollution and environmental inequality in Beijing, 2000–2010," Journal of Environmental Planning and Management, Taylor & Francis Journals, vol. 62(14), pages 2437-2458, December.
    7. Xinlin Ma & Xijing Li & Mei-Po Kwan & Yanwei Chai, 2020. "Who Could Not Avoid Exposure to High Levels of Residence-Based Pollution by Daily Mobility? Evidence of Air Pollution Exposure from the Perspective of the Neighborhood Effect Averaging Problem (NEAP)," IJERPH, MDPI, vol. 17(4), pages 1-19, February.
    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. Yiming Tan & Mei-Po Kwan & Zifeng Chen, 2020. "Examining Ethnic Exposure through the Perspective of the Neighborhood Effect Averaging Problem: A Case Study of Xining, China," IJERPH, MDPI, vol. 17(8), pages 1-17, April.
    2. Junghwan Kim & Mei-Po Kwan, 2018. "Beyond Commuting: Ignoring Individuals’ Activity-Travel Patterns May Lead to Inaccurate Assessments of Their Exposure to Traffic Congestion," IJERPH, MDPI, vol. 16(1), pages 1-20, December.
    3. Mark P.C. Cherrie & Niamh K. Shortt & Catharine Ward Thompson & Ian J. Deary & Jamie R. Pearce, 2019. "Association Between the Activity Space Exposure to Parks in Childhood and Adolescence and Cognitive Aging in Later Life," IJERPH, MDPI, vol. 16(4), pages 1-13, February.
    4. Nan Jia & Yinshuai Li & Ruishan Chen & Hongbo Yang, 2023. "A Review of Global PM 2.5 Exposure Research Trends from 1992 to 2022," Sustainability, MDPI, vol. 15(13), pages 1-15, July.
    5. Jue Wang & Mei-Po Kwan & Yanwei Chai, 2018. "An Innovative Context-Based Crystal-Growth Activity Space Method for Environmental Exposure Assessment: A Study Using GIS and GPS Trajectory Data Collected in Chicago," IJERPH, MDPI, vol. 15(4), pages 1-24, April.
    6. Xiang Kang & Mingxi Du & Siqin Wang & Haifeng Du, 2022. "Exploring the Effect of Health on Migrants’ Social Integration in China," IJERPH, MDPI, vol. 19(8), pages 1-20, April.
    7. Huang, Hongfu & Xing, Xinjie & He, Yong & Gu, Xiaoyu, 2020. "Combating greenwashers in emerging markets: A game-theoretical exploration of firms, customers and government regulations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 140(C).
    8. Lingling Su & Suhong Zhou, 2022. "Long-Term Residential Environment Exposure and Subjective Wellbeing in Later Life in Guangzhou, China: Moderated by Residential Mobility History," IJERPH, MDPI, vol. 19(20), pages 1-18, October.
    9. Janet Currie & John Voorheis & Reed Walker, 2023. "What Caused Racial Disparities in Particulate Exposure to Fall? New Evidence from the Clean Air Act and Satellite-Based Measures of Air Quality," American Economic Review, American Economic Association, vol. 113(1), pages 71-97, January.
    10. Wanting Liu & Ning Sun & Jingyu Guo & Zhenhua Zheng, 2022. "Campus Green Spaces, Academic Achievement and Mental Health of College Students," IJERPH, MDPI, vol. 19(14), pages 1-10, July.
    11. Tiina E. Laatikainen & Mohammad Haybatollahi & Marketta Kyttä, 2018. "Environmental, Individual and Personal Goal Influences on Older Adults’ Walking in the Helsinki Metropolitan Area," IJERPH, MDPI, vol. 16(1), pages 1-19, December.
    12. Zhen Yang & Weijun Gao & Jiawei Li, 2022. "Can Economic Growth and Environmental Protection Achieve a “Win–Win” Situation? Empirical Evidence from China," IJERPH, MDPI, vol. 19(16), pages 1-21, August.
    13. Bindong Sun & Xiajie Yao & Chun Yin, 2022. "An N-Shaped Association between Population Density and Abdominal Obesity," IJERPH, MDPI, vol. 19(15), pages 1-13, August.
    14. Franco, Sofia F. & Cutter, W. Bowman, 2022. "The determinants of non-residential real estate values with special reference to environmental local amenities," Ecological Economics, Elsevier, vol. 201(C).
    15. Daniel J. Hicks, 2020. "Census Demographics and Chlorpyrifos Use in California’s Central Valley, 2011–15: A Distributional Environmental Justice Analysis," IJERPH, MDPI, vol. 17(7), pages 1-20, April.
    16. Jue Wang & Mei-Po Kwan, 2018. "An Analytical Framework for Integrating the Spatiotemporal Dynamics of Environmental Context and Individual Mobility in Exposure Assessment: A Study on the Relationship between Food Environment Exposu," IJERPH, MDPI, vol. 15(9), pages 1-24, September.
    17. Siyu Ma & Lin Yang & Mei-Po Kwan & Zejun Zuo & Haoyue Qian & Minghao Li, 2021. "Do Individuals’ Activity Structures Influence Their PM 2 . 5 Exposure Levels? Evidence from Human Trajectory Data in Wuhan City," IJERPH, MDPI, vol. 18(9), pages 1-27, April.
    18. Jing Ma & Chunjiang Li & Mei-Po Kwan & Yanwei Chai, 2018. "A Multilevel Analysis of Perceived Noise Pollution, Geographic Contexts and Mental Health in Beijing," IJERPH, MDPI, vol. 15(7), pages 1-18, July.
    19. Chakraborti, Lopamudra & Shimshack, Jay P., 2022. "Environmental disparities in urban Mexico: Evidence from toxic water pollution," Resource and Energy Economics, Elsevier, vol. 67(C).
    20. Mingxiao Li & Song Gao & Feng Lu & Huan Tong & Hengcai Zhang, 2019. "Dynamic Estimation of Individual Exposure Levels to Air Pollution Using Trajectories Reconstructed from Mobile Phone Data," IJERPH, MDPI, vol. 16(22), pages 1-20, November.

    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:19:p:12671-:d:932943. 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.