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Analysis of Pollution Hazard Intensity: A Spatial Epidemiology Case Study of Soil Pb Contamination

Author

Listed:
  • Hoehun Ha

    (Department of Sociology, Anthropology and Geography, Auburn University at Montgomery, 7041 Senators Drive, Montgomery, AL 36117, USA)

  • Peter A. Rogerson

    (Department of Geography, University at Buffalo, Wilkeson Hall, Buffalo, NY 14261, USA)

  • James R. Olson

    (Departments of Pharmacology and Toxicology and Epidemiology and Environmental Health, Farber Hall, University at Buffalo, Buffalo, NY 14214, USA)

  • Daikwon Han

    (Department of Epidemiology and Biostatistics, School of Public Health, Texas A&M University, College Station, TX 77843, USA)

  • Ling Bian

    (Department of Geography, University at Buffalo, Wilkeson Hall, Buffalo, NY 14261, USA)

  • Wanyun Shao

    (Department of Sociology, Anthropology and Geography, Auburn University at Montgomery, 7041 Senators Drive, Montgomery, AL 36117, USA)

Abstract

Heavy industrialization has resulted in the contamination of soil by metals from anthropogenic sources in Anniston, Alabama. This situation calls for increased public awareness of the soil contamination issue and better knowledge of the main factors contributing to the potential sources contaminating residential soil. The purpose of this spatial epidemiology research is to describe the effects of physical factors on the concentration of lead (Pb) in soil in Anniston AL, and to determine the socioeconomic and demographic characteristics of those residing in areas with higher soil contamination. Spatial regression models are used to account for spatial dependencies using these explanatory variables. After accounting for covariates and multicollinearity, results of the analysis indicate that lead concentration in soils varies markedly in the vicinity of a specific foundry (Foundry A), and that proximity to railroads explained a significant amount of spatial variation in soil lead concentration. Moreover, elevated soil lead levels were identified as a concern in industrial sites, neighborhoods with a high density of old housing, a high percentage of African American population, and a low percent of occupied housing units. The use of spatial modelling allows for better identification of significant factors that are correlated with soil lead concentrations.

Suggested Citation

  • Hoehun Ha & Peter A. Rogerson & James R. Olson & Daikwon Han & Ling Bian & Wanyun Shao, 2016. "Analysis of Pollution Hazard Intensity: A Spatial Epidemiology Case Study of Soil Pb Contamination," IJERPH, MDPI, vol. 13(9), pages 1-15, September.
    • Handle: RePEc:gam:jijerp:v:13:y:2016:i:9:p:915-:d:78214
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    References listed on IDEAS

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    1. Kelejian, Harry H. & Prucha, Ingmar R., 2010. "Specification and estimation of spatial autoregressive models with autoregressive and heteroskedastic disturbances," Journal of Econometrics, Elsevier, vol. 157(1), pages 53-67, July.
    2. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-838, May.
    3. Kelejian, Harry H. & Prucha, Ingmar R., 2007. "HAC estimation in a spatial framework," Journal of Econometrics, Elsevier, vol. 140(1), pages 131-154, September.
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    Cited by:

    1. Deniz Yeter & Ellen C. Banks & Michael Aschner, 2020. "Disparity in Risk Factor Severity for Early Childhood Blood Lead among Predominantly African-American Black Children: The 1999 to 2010 US NHANES," IJERPH, MDPI, vol. 17(5), pages 1-26, February.

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