Spatial distribution and source identification of metal contaminants in the surface soil of Matehuala, Mexico based on positive matrix factorization model and GIS techniques

IF 2.1 Q3 SOIL SCIENCE Frontiers in soil science Pub Date : 2022-12-06 DOI:10.3389/fsoil.2022.1041377
A. Saha, B. Gupta, S. Patidar, N. Martínez-Villegas
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引用次数: 2

Abstract

The rapid growth of urban development, industrialization, mining, farming, and biological activities has resulted in potentially toxic metal pollution of the soil all over the world. This has caused degradation of soil quality, lower crop production, and risk to human health. For this work, two study sites were selected to evaluate metal concentrations in the agricultural as well as the recreational soil around the Cerrito Blanco in Matehuala, San Luis Potosi, Mexico. The concentrations of eight metals, namely As, Ca, Mg, Na, K, Sr, Mn, and Fe were analysed in order to determine the level of contamination risk as well as their spatial distributions. However, this study is mainly focused on toxic metals, e.g. As, Sr, Mn, and Fe. The contamination indices techniques were used to evaluate the risk assessment of soil. Additionally, the positive matrix factorization (PMF) model as well as the geostatistical analysis was used to identify the contamination sources based on 64 surface soil samples. After implementing PMF to analyze the soils, it was possible to differentiate the variations in factors linked to the contaminants, farming impacts, and the reference soil geochemistry. The soil in the two studied locations included high concentrations of As, Ca, Mg, K, Sr, Mn, and Fe, including variations in their spatial compositions, which were caused by direct mining activities, the movement and deposition of smelting waste, and the extensive use of irrigated contaminated groundwater for irrigation. The four possible factors were identified for soil pollution including industrial, transportation, agricultural, and naturogenic based on the PMF and geostatistical analysis. The spatial distribution of metal concentrations in the soil was also presented using a geographical information system (GIS) interpolation technique. The identification of metal sources and contamination risk mapping presents a significant role in minimizing pollution sources, and it may be performed in regions with high levels of soil contamination risk.
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基于正矩阵分解模型和GIS技术的墨西哥Matehuala表层土壤金属污染物空间分布及来源识别
城市发展、工业化、采矿、农业和生物活动的快速增长导致了世界各地土壤的潜在有毒金属污染。这导致了土壤质量的退化、作物产量的下降以及对人类健康的风险。在这项工作中,选择了两个研究地点来评估墨西哥圣路易斯波托西Matehuala的Cerrito Blanco周围农业和休闲土壤中的金属浓度。分析了八种金属的浓度,即As、Ca、Mg、Na、K、Sr、Mn和Fe,以确定污染风险水平及其空间分布。然而,本研究主要集中在有毒金属上,如As、Sr、Mn和Fe。采用污染指数技术对土壤进行风险评价。此外,基于64个地表土壤样本,采用正矩阵因子分解(PMF)模型和地统计学分析来识别污染源。在实施PMF分析土壤后,可以区分与污染物、农业影响和参考土壤地球化学相关的因素的变化。两个研究地点的土壤含有高浓度的As、Ca、Mg、K、Sr、Mn和Fe,包括其空间组成的变化,这是由直接采矿活动、冶炼废物的移动和沉积以及灌溉污染地下水的广泛使用引起的。基于PMF和地质统计学分析,确定了土壤污染的四个可能因素,包括工业、交通、农业和自然成因。还利用地理信息系统(GIS)插值技术给出了土壤中金属浓度的空间分布。金属源的识别和污染风险绘图在最大限度地减少污染源方面发挥着重要作用,可以在土壤污染风险高的地区进行。
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