Determining sources of neurotoxic metals in rural and urban soils is important for mitigating human exposure. Surface soil from four areas with significant clusters of mental retardation and developmental delay (MR/DD) in children, and one control site were analyzed for nine metals and characterized by soil type, climate, ecological region, land use and industrial facilities using readilyavailable GIS-based data. Kriging, principal component analysis (PCA) and cluster analysis (CA) were used to identify commonalities of metal distribution. Three MR/DD areas (one rural and two urban) had similar soil types and significantly higher soil metal concentrations. PCA and CA results suggested that Ba, Be and Mn were consistently from natural sources; Pb and Hg from anthropogenic sources; and As, Cr, Cu, and Ni from both sources. Arsenic had low commonality estimates, was highly associated with a third PCA factor, and had a complex distribution, complicating mitigation strategies to minimize concentrations and exposures.

Rural areas are often considered to have relatively uncontaminated soils; however few studies have measured metals in surface soil from low population areas. Many metals, i.e., arsenic (As), lead (Pb), and mercury (Hg), have well-documented negative neurological effects, and the developing fetus and young children are particularly at risk. Using a Medicaid database, two areas were identified: one with no increased prevalence of mental retardation and developmental delay (MR/DD) (Strip 1) and one with significantly higher prevalence of MR/DD (Strip 2) in children compared to the state-wide average. These areas were mapped and surface soil samples were collected from 0-5 cm depths from nodes of a uniform grid laid out across the sampling areas. Samples were analyzed for As, barium (Ba), beryllium (Be), chromium (Cr), copper (Cu), Pb, manganese (Mn), nickel (Ni), and Hg. Inverse distance weighting (IDW) was used to estimate concentrations throughout each strip area, and a principal component analysis (PCA) was used to identify common sources. All metal concentrations in Strip 2, the MR/DD cluster area, were significantly greater than those in Strip 1 and similar to those found in more urban and highly agricultural areas. Both Strips 1 and 2 had a high number of significant correlations between metals (33 for Strip 1 and 25 for Strip 2), suggesting possible similar natural or anthropogenic sources which was corroborated by PCA. While exposures were not assessed and direct causation between environmental soil metal concentrations and MR/DD cannot be concluded, the high metal concentrations in areas with an elevated prevalence of MR/DD warrants further consideration.

Urban and rural areas may have different levels of environmental contamination and different potential sources of exposure. Many metals, i.e., arsenic (As), lead (Pb), and mercury (Hg), have well-documented negative neurological effects, and the developing fetus and young children are particularly at risk. Using a database of mother and child pairs, three areas were identified: a rural area with no increased prevalence of mental retardation and developmental delay (MR/DD) (Area A), and a rural area (Area B) and an urban area (Area C) with significantly higher prevalence of MR/ DD in children as compared to the state-wide average. Areas were mapped and surface soil samples were collected from nodes of the uniform grid. Samples were analyzed for As, barium (Ba), beryllium (Be), chromium (Cr), copper (Cu), Pb, manganese (Mn), nickel (Ni), and Hg concentrations, and for soil toxicity and correlated to identify potential common sources. ArcGIS® was used to determine distances between sample locations and industrial facilities, which were correlated with both metal concentrations and soil toxicity. Results indicated that all metal concentrations (except Be and Hg) in Area C were significantly greater than those in Areas A and B (p ≤ 0.0001) and that Area C had fewer correlations between metals suggesting more varied sources of metals than in rural areas. Area C also had a large number of facilities whose distances were significantly correlated with metals, particularly Cr (maximum r = 0.33; p = 0.0002), and with soil toxicity (maximum r = 0.25; p = 0.007) over a large spatial scale. Arsenic was not associated with distance to any facility and may have different anthropogenic, or a natural source. In contrast to Area C, both rural areas had lower concentrations of metals, lower soil toxicity, and a small number of facilities with significant associations between distance and soil metals.

Lead (Pb) is a well-studied environmental contaminant that has many negative health effects, especially for children. Both racial/ethnic and income disparities have been documented with respect to exposure to Pb in soils. The objectives of this study were to assess whether soil Pb concentrations in rural and urban areas of South Carolina USA, previously identified as having clusters of intellectual disabilities (ID) in children, were positively associated with populations of minority and low-income individuals and children (≤6 years of age). Surface soils from two rural and two urban areas with identified clusters of ID were analyzed for Pb and concentrations were spatially interpolated using inverse distance weighted analysis. Population race/ethnicity and income-to-poverty ratio (ITPR) from United States Census 2000 block group data were aerially interpolated by block group within each area. Urban areas had significantly higher concentrations of Pb than rural areas. Significant positive associations between black, non-Hispanic Latino, individuals and children ≤6 years of age and mean estimated Pb concentrations were observed in both urban (r = 0.38, p = 0.0007) and rural (r = 0.53, p = 0.04) areas. Significant positive associations also were observed between individuals and children with an ITPR < 1.00 and Pb concentrations, though primarily in urban areas. Racial/ethnic minorities and low ITPR individuals, including children, may be at elevated risk for exposure to Pb in soils.

Bayesian kriging is a useful tool for estimating spatial distributions of metals; however, estimates are generally only verified statistically. In this study surface soil samples were collected on a uniform grid and analyzed for As, Cr, Pb, and Hg. The data were interpolated at individual locations by Bayesian kriging. Estimates were validated using a leave-one-out cross validation (LOOCV) statistical method which compared the measured and LOOCV predicted values. Validation also was carried out using additional field sampling of soil metal concentrations at points between original sampling locations, which were compared to kriging prediction distributions. LOOCV results suggest that Bayesian kriging was a good predictor of metal concentrations. When measured internode metal concentrations and estimated kriged values were compared, the measured values were located within the 5 th -95 th percentile prediction distributions in over half of the internode locations. Estimated and measured internode concentrations were most similar for As and Pb. Kriged estimates did not compare as well to measured values for concentrations below the analytical minimum detection limit, or for internode samples that were very close to the original sampling node. Despite inherent variability in metal concentrations in soils, the kriged estimates were validated statistically and by in situ measurement.