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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)

Author

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  • Xinlin Ma

    (College of Urban and Environmental Science, Peking University, Beijing 100871, China)

  • Xijing Li

    (Department of Geography and Geographic Information Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA)

  • Mei-Po Kwan

    (Department of Geography and Resource Management, and Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Hong Kong, China
    Department of Human Geography and Spatial Planning, Utrecht University, 3584 CB Utrecht, The Netherlands)

  • Yanwei Chai

    (College of Urban and Environmental Science, Peking University, Beijing 100871, China)

Abstract

It has been widely acknowledged that air pollution has a considerable adverse impact on people’s health. Disadvantaged groups such as low-income people are often found to experience greater negative effects of environmental pollution. Thus, improving the accuracy of air pollution exposure assessment might be essential to policy-making. Recently, the neighborhood effect averaging problem (NEAP) has been identified as a specific form of possible bias when assessing individual exposure to air pollution and its health impacts. In this paper, we assessed the real-time air pollution exposure and residential-based exposure of 106 participants in a high-pollution community in Beijing, China. The study found that: (1) there are significant differences between the two assessments; (2) most participants experienced the NEAP and could lower their exposure by their daily mobility; (3) three vulnerable groups with low daily mobility and could not avoid the high pollution in their residential neighborhoods were identified as exceptions to this: low-income people who have low levels of daily mobility and limited travel outside their residential neighborhoods, blue-collar workers who spend long hours at low-end workplaces, and elderly people who face many household constraints. Public policies thus need to focus on the hidden environmental injustice revealed by the NEAP in order to improve the well-being of these environmentally vulnerable groups.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jijerp:v:17:y:2020:i:4:p:1223-:d:320471
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    References listed on IDEAS

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    1. Lin Zhang & Suhong Zhou & Mei-Po Kwan & Fei Chen & Rongping Lin, 2018. "Impacts of Individual Daily Greenspace Exposure on Health Based on Individual Activity Space and Structural Equation Modeling," IJERPH, MDPI, vol. 15(10), pages 1-18, October.
    2. Daniela Dias & Oxana Tchepel, 2018. "Spatial and Temporal Dynamics in Air Pollution Exposure Assessment," IJERPH, MDPI, vol. 15(3), pages 1-23, March.
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    5. Mei-Po Kwan, 2018. "The Neighborhood Effect Averaging Problem (NEAP): An Elusive Confounder of the Neighborhood Effect," IJERPH, MDPI, vol. 15(9), pages 1-4, August.
    6. 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.
    7. Mei-Po Kwan, 2018. "The Limits of the Neighborhood Effect: Contextual Uncertainties in Geographic, Environmental Health, and Social Science Research," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 108(6), pages 1482-1490, November.
    8. Spencer Banzhaf & Lala Ma & Christopher Timmins, 2019. "Environmental Justice: The Economics of Race, Place, and Pollution," Journal of Economic Perspectives, American Economic Association, vol. 33(1), pages 185-208, Winter.
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    10. David M. Drukker, 2003. "Testing for serial correlation in linear panel-data models," Stata Journal, StataCorp LP, vol. 3(2), pages 168-177, June.
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    Cited by:

    1. 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.
    2. 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.
    3. 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.

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