对被重金属污染的地点进行评估和适当管理需要有关这些金属的空间分布的准确信息。本研究旨在预测和绘制Cd的分布,Cu,Ni,Pb,使用点观测,环境变量,和基于直方图的梯度提升(HGB)建模。来自土壤地球化学空间数据库(SGSD)的9180多个表层土壤观测值(n=1150),土壤地球化学和矿物学调查(GMSS)(n=4857),和霍姆格伦数据集(HD)(n=3400),和28个协变量(100米×100米网格)代表气候,地形,植被,土壤,并编制了人类活动。使用决定系数(R2)对20%的校准中未使用的数据进行了模型性能评估,一致性相关系数(ρc),和均方根误差(RMSE)指数。预测的不确定性计算为HGB提供的估计的95%和5%分位数之间的差异。该模型解释了数据中多达50%的方差,其中Cu的RMSE介于0.16(mgkg-1)之间,Zn的RMSE介于23.4(mgkg-1)之间。分别。同样,ρc介于0.55(Cu)和0.68(Zn)之间,分别,在所有预测中,锌的R2最高(0.50)。我们在城市地区附近观察到高Pb浓度。在密西西比河下游河谷发现了所有研究金属的峰值浓度。Cu,Ni,西海岸的锌浓度较高;美国中部的镉浓度较高。粘土,pH值,潜在蒸散,温度,和降水是模型中重金属空间预测的五大重要协变量之一。点观测值和环境协变量的结合使用以及机器学习提供了对美国土壤中重金属分布的可靠预测。更新后的地图可以支持环境评估,监测,并利用适用于其他土壤数据库的这种方法进行决策,全世界。
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.