利用先进的地理空间数据集成,对秘鲁曼塔罗河谷的金属和类金属进行全面的空间测绘

IF 5.6 1区 农林科学 Q1 SOIL SCIENCE
Samuel Pizarro, Narcisa G. Pricope, Jesús Vera, Juancarlos Cruz, Sphyros Lastra, Richard Solórzano-Acosta, Patricia Verástegui Martínez
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引用次数: 0

摘要

土壤质量和安全对于确保社会和经济发展以及提供无公害食品至关重要。土壤数据的可得性和质量,特别是多种金属和类金属的数据,往往不足以进行全面分析。土壤的形成和金属的分布受到地质、气候、地形和人类活动等各种因素的影响,这使得精确的建模非常具有挑战性。此外,农业集约化、城市扩张、道路建设和采矿活动经常造成土壤污染,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制图(10米空间分辨率)。本研究利用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),绘制了秘鲁曼塔罗河谷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)。该模型对所选要素的空间分布预测效果较好,大部分要素的R2值在0.6 ~ 0.9之间。土壤、气候和地形协变量是最重要的预测因子,特别是对河流附近的农田,而光谱变量则不太重要。结果显示,砷、铅和镉浓度明显高于允许限度,突出了紧迫的健康风险。这些发现表明,基于广泛可用的地理空间数据集,以最少的训练数据识别污染土壤并改进法规是可行的。这项研究有助于建立模型,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众健康的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comprehensive spatial mapping of metals and metalloids in the Peruvian Mantaro Valley using advanced geospatial data Integration
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.
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来源期刊
Geoderma
Geoderma 农林科学-土壤科学
CiteScore
11.80
自引率
6.60%
发文量
597
审稿时长
58 days
期刊介绍: Geoderma - the global journal of soil science - welcomes authors, readers and soil research from all parts of the world, encourages worldwide soil studies, and embraces all aspects of soil science and its associated pedagogy. The journal particularly welcomes interdisciplinary work focusing on dynamic soil processes and functions across space and time.
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