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

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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.

No MeSH data available.


Distribution of soil Pb concentration (ppm or mg/kg).
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ijerph-13-00915-f002: Distribution of soil Pb concentration (ppm or mg/kg).

Mentions: Pollution hazard intensity is modelled at the individual soil lead sample through linear regression on physical and socioeconomic profiles identified during the review of the literature. Ordinary least squares (OLS) stepwise regressions are estimated to find independent variables that are statistically significant in each model at the 0.05 significance level for entry and 0.10 for removal. Results are further checked for multicollinearity based on the variance inflation factor (VIF), where one of the variables concerned is dropped from further consideration. Because the distribution of lead concentrations is markedly skewed, with many small values and a small number of large values as shown in Figure 2, we use a logarithmic transformation of concentration as the dependent variable. Hence our initial equation is:(1)ln(lead)=b0+b1x1+…where lead is the observed concentration in mg/kg, and the x’s represent independent variables, while the b’s represent coefficients for each independent variable.


Analysis of Pollution Hazard Intensity: A Spatial Epidemiology Case Study of Soil Pb Contamination
Distribution of soil Pb concentration (ppm or mg/kg).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5036748&req=5

ijerph-13-00915-f002: Distribution of soil Pb concentration (ppm or mg/kg).
Mentions: Pollution hazard intensity is modelled at the individual soil lead sample through linear regression on physical and socioeconomic profiles identified during the review of the literature. Ordinary least squares (OLS) stepwise regressions are estimated to find independent variables that are statistically significant in each model at the 0.05 significance level for entry and 0.10 for removal. Results are further checked for multicollinearity based on the variance inflation factor (VIF), where one of the variables concerned is dropped from further consideration. Because the distribution of lead concentrations is markedly skewed, with many small values and a small number of large values as shown in Figure 2, we use a logarithmic transformation of concentration as the dependent variable. Hence our initial equation is:(1)ln(lead)=b0+b1x1+…where lead is the observed concentration in mg/kg, and the x’s represent independent variables, while the b’s represent coefficients for each independent variable.

View Article: PubMed Central - PubMed

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.

No MeSH data available.