Heavy metals concentration in soils across the conterminous USA: Spatial prediction, model uncertainty, and influencing factors

Authors: Adhikari, Kabindra; MANCINI, MARCELO - University Of Arkansas; Libohova, Zamir; Blackstock, Joshua; Winzeler, Hans - Edwin; Smith, Douglas; Owens, Phillip; SILVA, SERGIO - Federal University Of Lavras; CURI, NILTON - Federal University Of Lavras

Submitted to: Science of the Total Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/12/2024
Publication Date: 2/13/2024
Citation: Adhikari, K., Mancini, M., Libohova, Z., Blackstock, J.M., Winzeler, H.E., Smith, D.R., Owens, P.R., Silva, S.H., Curi, N.C. 2024. Heavy metals concentration in soils across the conterminous USA: Spatial prediction, model uncertainty, and influencing factors. Science of the Total Environment. 919. Article 170972. https://doi.org/10.1016/j.scitotenv.2024.170972.
DOI: https://doi.org/10.1016/j.scitotenv.2024.170972

Interpretive Summary: Soils contaminated with heavy metals have concerning effects on human health and food production, and they pose serious health hazards. This study produced maps of Cd, Cu, Ni, Pb, and Zn across the conterminous USA using point measurements, environmental variables, and machine learning techniques. The maps showed high concentrations of heavy metals in the Lower Mississippi River Valley. High Pb concentrations were observed near urban areas. Similarly, Cu, Ni, and Zn concentrations were higher on the West Coast, and Cd concentrations were higher in the central USA. The maps also highlighted geogenic and anthropogenic sources of high heavy metal concentrations in the USA.

Technical Abstract: Assessment and proper management of sites contaminated with heavy metals require precise information on the spatial distribution of these metals. This study aimed to predict and map the distribution of Cd, Cu, Ni, Pb, and Zn across the conterminous USA using point observations, environmental variables, and Histogram-based Gradient Boosting modeling (HGB). Over 9180 surficial soil observations from the Soil Geochemistry Spatial Database (n=1150), the Geochemical and Mineralogical Survey of Soils (n=4857), and the Holmgren Dataset (n=3400), and 28 covariates representing climate, topography, soils, and environmental hot-spots were compiled. Model performance was evaluated on 20% of the data not used in calibration using the coefficient of determination, concordance correlation coefficient, and root mean square error (RMSE) indices. Uncertainty of predictions was calculated as the difference between the estimated 95 and 5% quantiles provided by HGB. The model explained up to 50% of the variance in the data with RMSE ranging between 0.16 (mg kg-1) for Cu and 23.4 (mg kg-1) for Zn, respectively. High Pb concentrations were observed near urban areas. Peak concentrations of all studied metals were found in the Lower Mississippi River Valley. Cu, Ni, and Zn concentrations were higher on the West Coast; Cd concentrations were higher in the central USA. Clay, pH, potential evapotranspiration, temperature, and precipitation were among the model's top five important covariates for spatial predictions of heavy metals. The combined use of point observations and environmental covariates coupled with machine learning provided a reliable prediction of heavy metals distribution in the soils of the conterminous USA. The updated maps could support environmental assessments, monitoring, and decision-making with this methodology applicable to other soil databases, worldwide.