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Spatial Cluster Detection of Air Pollution Exposure Inequities across the United States

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Listed:
  • Bin Zou
  • Fen Peng
  • Neng Wan
  • Keita Mamady
  • Gaines J Wilson

Abstract

Air quality is known to be a key factor in affecting the wellbeing and quality of life of the general populous and there is a large body of knowledge indicating that certain underrepresented groups may be overexposed to air pollution. Therefore, a more precise understanding of air pollution exposure as a driving cause of health disparities between and among ethnic and racial groups is necessary. Utilizing 52,613 urban census tracts across the United States, this study investigates age, racial, educational attainment and income differences in exposure to benzene pollution in 1999 as a case. The study examines spatial clustering patterns of these inequities using logistic regression modeling and spatial autocorrelation methods such as the Global Moran's I index and the Anselin Local Moran's I index. Results show that the age groups of 0 to 14 and those over 60 years old, individuals with less than 12 years of education, racial minorities including Blacks, American Indians, Asians, some other races, and those with low income were exposed to higher levels of benzene pollution in some census tracts. Clustering analyses stratified by age, education, and race revealed a clear case of disparities in spatial distribution of exposure to benzene pollution across the entire United States. For example, people aged less than 4 years from the western south and the Pacific coastal areas exhibit statistically significant clusters. The findings confirmed that there are geographical-location based disproportionate pattern of exposures to benzene air pollution by various socio-demographic factors across the United States and this type of disproportionate exposure pattern can be effectively detected by a spatial autocorrelation based cluster analysis method. It is suggested that there is a clear and present need for programs and services that will reduce inequities and ultimately improve environmental conditions for all underrepresented groups in the United States.

Suggested Citation

  • Bin Zou & Fen Peng & Neng Wan & Keita Mamady & Gaines J Wilson, 2014. "Spatial Cluster Detection of Air Pollution Exposure Inequities across the United States," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-14, March.
    • Handle: RePEc:plo:pone00:0091917
    DOI: 10.1371/journal.pone.0091917
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    References listed on IDEAS

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    1. Margellos, H. & Silva, A. & Whitman, S., 2004. "Comparison of Health Status Indicators in Chicago: Are Black-White Disparities Worsening?," American Journal of Public Health, American Public Health Association, vol. 94(1), pages 116-121.
    2. Evan J. Ringquist, 2005. "Assessing evidence of environmental inequities: A meta-analysis," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 24(2), pages 223-247.
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    Cited by:

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    2. Carroll, Rachel & Lawson, Andrew B. & Jackson, Chandra L. & Zhao, Shanshan, 2017. "Assessment of spatial variation in breast cancer-specific mortality using Louisiana SEER data," Social Science & Medicine, Elsevier, vol. 193(C), pages 1-7.
    3. Bin Zou & Shan Xu & Troy Sternberg & Xin Fang, 2016. "Effect of Land Use and Cover Change on Air Quality in Urban Sprawl," Sustainability, MDPI, vol. 8(7), pages 1-14, July.
    4. Man Sing Wong & Fen Peng & Bin Zou & Wen Zhong Shi & Gaines J. Wilson, 2016. "Spatially Analyzing the Inequity of the Hong Kong Urban Heat Island by Socio-Demographic Characteristics," IJERPH, MDPI, vol. 13(3), pages 1-17, March.
    5. David W. S. Wong, 2021. "Issues in the Current Practices of Spatial Cluster Detection and Exploring Alternative Methods," IJERPH, MDPI, vol. 18(18), pages 1-20, September.
    6. Dana H. Z. Williamson, 2022. "Using the Community Engagement Framework to Understand and Assess EJ-Related Research Efforts," Sustainability, MDPI, vol. 14(5), pages 1-26, February.

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