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

Kabindra Adhikari; Marcelo Mancini; Zamir Libohova; Joshua Blackstock; Edwin Winzeler; Douglas R Smith; Phillip R Owens; Sérgio H G Silva; Nilton Curi

doi:10.1016/j.scitotenv.2024.170972

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

Sci Total Environ. 2024 Apr 1:919:170972. doi: 10.1016/j.scitotenv.2024.170972. Epub 2024 Feb 13.

Authors

Kabindra Adhikari¹, Marcelo Mancini², Zamir Libohova³, Joshua Blackstock³, Edwin Winzeler³, Douglas R Smith⁴, Phillip R Owens³, Sérgio H G Silva⁵, Nilton Curi⁵

Affiliations

¹ USDA-ARS, Grassland, Soil and Water Research Laboratory, Temple, TX 76502, USA. Electronic address: kabindra.adhikari@usda.gov.
² University of Arkansas, Department of Crop, Soil, and Environmental Sciences, Fayetteville, AR 72701, USA; Federal University of Lavras, Department of Soil Science, 37200-900 Lavras, Minas Gerais, Brazil.
³ USDA-ARS, Dale Bumpers Small Farms Research Center, Booneville, AR 72927, USA.
⁴ USDA-ARS, Grassland, Soil and Water Research Laboratory, Temple, TX 76502, USA.
⁵ Federal University of Lavras, Department of Soil Science, 37200-900 Lavras, Minas Gerais, Brazil.

PMID: 38360318
DOI: 10.1016/j.scitotenv.2024.170972

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 (HGB) modeling. Over 9180 surficial soil observations from the Soil Geochemistry Spatial Database (SGSD) (n = 1150), the Geochemical and Mineralogical Survey of Soils (GMSS) (n = 4857), and the Holmgren Dataset (HD) (n = 3400), and 28 covariates (100 m × 100 m grid) representing climate, topography, vegetation, soils, and anthropic activity were compiled. Model performance was evaluated on 20 % of the data not used in calibration using the coefficient of determination (R²), concordance correlation coefficient (ρ_c), 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. Likewise, ρ_c ranged between 0.55 (Cu) and 0.68 (Zn), respectively, and Zn had the highest R² (0.50) among all predictions. We observed high Pb concentrations 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.

Keywords: digital soil mapping; machine learning; metal pollution; prediction uncertainty; soil chemistry; soil contamination.

Published by Elsevier B.V.