The quality and safety of soil are crucial for ensuring social and economic development and providing contaminant-free food. The availability and quality of soil data, particularly for multiple metals and metalloids, are often insufficient for comprehensive analysis. Soil formation and the distribution of metals are shaped by various factors such as geology, climate, topography, and human activities, making accurate modeling highly challenging. Additionally, agricultural intensification, urban expansion, road construction, and mining activities frequently result in soil pollution, posing serious risks to ecosystems and human health. This study aims to integrate diverse geospatial datasets with machine learning for high resolution soil contamination mapping (10 m spatial resolution) in a major agricultural region of Peruvian highlands. This study mapped 25 elements (Ca, Mg, Sr, Ba, Be, K, Na, As, Sb, Se, Tl, Cd, Zn, Al, Pb, Hg, Cr, Ni, Cu, Mo, Ag, Fe, Co, Mn, V) in the Peruvian Mantaro Valley using a training dataset of 109 topsoil samples combined with various geospatial datasets (remote sensing, climate, topography, soil data, and distance). The model provided satisfactory results in predicting the spatial distribution of the selected elements, with R2 values ranging from 0.6 to 0.9 for most elements. Edaphic, climate, and topographic covariates were the most significant predictors, particularly for croplands near rivers, whereas spectral variables were less important. The results reveal As, Pb, and Cd concentrations significantly above permissible limits, highlighting urgent health risks. These findings suggest that it is feasible to identify polluted soils and improve regulations based on widely available geospatial datasets with minimal training data. The study contributes to the development of models to assess the impact of pollutants on environmental and human health in the short-to-medium term, emphasizing the need for further research on the translocation of toxic metals into food crops and the implications for public health.

中文翻译：

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使用高级地理空间数据集成对秘鲁曼塔罗河谷的金属和准金属进行全面的空间映射


土壤的质量和安全对于确保社会和经济发展以及提供无污染食品至关重要。土壤数据的可用性和质量，特别是多种金属和准金属的数据，通常不足以进行全面分析。土壤形成和金属分布受地质、气候、地形和人类活动等各种因素的影响，这使得准确建模极具挑战性。此外，农业集约化、城市扩张、道路建设和采矿活动经常导致土壤污染，对生态系统和人类健康构成严重风险。本研究旨在将不同的地理空间数据集与机器学习相结合，在秘鲁高地的一个主要农业区进行高分辨率土壤污染测绘（10 m 空间分辨率）。本研究使用 109 个表层土壤样本的训练数据集结合各种地理空间数据集（遥感、气候、地形、土壤数据和距离）绘制了秘鲁曼塔罗山谷的 25 种元素（Ca、Mg、Sr、Ba、Be、K、Na、As、Sb、Se、Tl、Cd、Zn、Al、Pb、Hg、Cr、Ni、Cu、Mo、Ag、Fe、Co、Mn、V）。该模型在预测所选单元的空间分布方面提供了令人满意的结果，大多数单元的 R2 值范围为 0.6 到 0.9。Edaphic、气候和地形协变量是最重要的预测因子，特别是对于河流附近的农田，而光谱变量则不太重要。结果显示 As、Pb 和 Cd 浓度明显高于允许限值，凸显了紧迫的健康风险。这些发现表明，基于广泛可用的地理空间数据集和最少的训练数据来识别受污染的土壤并改进法规是可行的。 该研究有助于开发模型，以评估污染物在中短期内对环境和人类健康的影响，强调需要进一步研究有毒金属向粮食作物的转移及其对公共卫生的影响。