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

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 (ρc), 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, ρc 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.

Spatial Distribution of Soil Phosphorus, Calcium, and pH after Long-term Broiler Litter Application.

Water movement over and through soil is largely driven by topography and soil management across landscapes. This research tested the hypothesis that the water movement determines the potential for P and Ca redistribution and pH variance across landscapes. This hypothesis was evaluated by using digital elevation model-derived terrain attributes in fields after 55 yr of broiler litter applications on pastures in Smith County, Mississippi. Results show that soils receiving broiler litter had mean Mehlich-3 P levels of 1221.8 mg kg at 0- to 15-cm depth and 618.6 mg kg at 15- to 30-cm depth, and Ca with mean values of 768.3 and 645.0 mg kg at 0- to 15-cm and 15- to 30-cm soil depths, respectively. Across fields, soils in areas of predicted convergent flow contained higher P, Ca, and lower pH values in the upper 0 to 15 cm, suggesting contributions via surface overland flow from areas with higher elevation and lower slope gradient. On the other hand, soils in areas with lesser slope and higher elevation also contained high levels of P, Ca, and pH for the subsurface soil depth, suggesting that vertical flow of water on this landscape is a mechanism for movement of P and Ca deeper in the profile. The incorporation of topographic characteristics across fields offers promising results that may be incorporated into improved P indices and management, making them more robust indicators of P mobilization to waterways.

Assessing soil organic carbon stock of Wisconsin, USA and its fate under future land use and climate change.

Carbon stored in soils contributes to a variety of soil functions, including biomass production, water storage and filtering, biodiversity maintenance, and many other ecosystem services. Understanding soil organic carbon (SOC) spatial distribution and projection of its future condition is essential for future CO2 emission estimates and management options for storing carbon. However, modeling SOC spatiotemporal dynamics is challenging due to the inherent spatial heterogeneity and data limitation. The present study developed a spatially explicit prediction model in which the spatial relationship between SOC observation and seventeen environmental variables was established using the Cubist regression tree algorithm. The model was used to compile a baseline SOC stock map for the top 30 cm soil depth in the State of Wisconsin (WI) at a 90 m × 90 m grid resolution. Temporal SOC trend was assessed by comparing baseline and future SOC stock maps based on the space-for-time substitution model. SOC prediction for future considers land use, precipitation and temperature for the year 2050 at medium (A1B) CO2 emissions scenario of the Intergovernmental Panel on Climate Change. Field soil observations were related to factors that are known to influence SOC distribution using the digital soil mapping framework. The model was validated on 25% test profiles (R2: 0.38; RMSE: 0.64; ME: -0.03) that were not used during model training that used the remaining 75% of the data (R2: 0.76; RMSE: 0.40; ME: -0.006). In addition, maps of the model error, and areal extent of Cubist prediction rules were reported. The model identified soil parent material and land use as key drivers of SOC distribution including temperature and precipitation. Among the terrain attributes, elevation, mass-balance index, mid-slope position, slope-length factor and wind effect were important. Results showed that Wisconsin soils had an average baseline SOC stock of 90 Mg ha-1 and the distribution was highly variable (CV: 64%). It was estimated that WI soils would have an additional 20 Mg ha-1 SOC by the year 2050 under changing land use and climate. Histosols and Spodosols were expected to lose 19 Mg ha-1 and 4 Mg ha-1, respectively, while Mollisols were expected to accumulate the largest SOC stock (62 Mg ha-1). All land-use types would be accumulating SOC by 2050 except for wetlands (-34 Mg C ha-1). This study found that Wisconsin soils will continue to sequester more carbon in the coming decades and most of the Driftless Area will be sequestering the greatest SOC (+63 Mg C ha-1). Most of the SOC would be lost from the Northern Lakes and Forests ecological zone (-12 Mg C ha-1). The study highlighted areas of potential C sequestration and areas under threat of C loss. The maps generated in this study would be highly useful in farm management and environmental policy decisions at different spatial levels in Wisconsin.

Soil legacy data rescue via GlobalSoilMap and other international and national initiatives.

Legacy soil data have been produced over 70 years in nearly all countries of the world. Unfortunately, data, information and knowledge are still currently fragmented and at risk of getting lost if they remain in a paper format. To process this legacy data into consistent, spatially explicit and continuous global soil information, data are being rescued and compiled into databases. Thousands of soil survey reports and maps have been scanned and made available online. The soil profile data reported by these data sources have been captured and compiled into databases. The total number of soil profiles rescued in the selected countries is about 800,000. Currently, data for 117, 000 profiles are compiled and harmonized according to GlobalSoilMap specifications in a world level database (WoSIS). The results presented at the country level are likely to be an underestimate. The majority of soil data is still not rescued and this effort should be pursued. The data have been used to produce soil property maps. We discuss the pro and cons of top-down and bottom-up approaches to produce such maps and we stress their complementarity. We give examples of success stories. The first global soil property maps using rescued data were produced by a top-down approach and were released at a limited resolution of 1km in 2014, followed by an update at a resolution of 250m in 2017. By the end of 2020, we aim to deliver the first worldwide product that fully meets the GlobalSoilMap specifications.