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Modelling trace metal background to evaluate anthropogenic contamination in arable soils of south-western France

机译:模拟痕量金属背景以评估法国西南部耕地土壤中的人为污染

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摘要

The trace metal (TM) content in arable soils has been monitored across a region of France characterised by a large proportion of calcareous soils. Within this particular geological context, the objectives were to first determine the natural levels of trace metals in the soils and secondly, to assess which sites were significantly contaminated. Because no universal contamination assessment method is currently available, four different methods were applied and compared in order to facilitate the best diagnosis of contamination. First, the TM geochemical background was determined by using basic descriptive statistics and linear regression models calculated with semi-conservative major elements as predictors. The natural concentrations of trace metals varied greatly due to the high soil heterogeneity encountered on the regional scale and were more-or-less accurately modelled according to the considered TM. Second, the basic descriptive statistics and the linear regression methods were then compared with the enrichment factor (EF) method and multivariate analysis (PCA), in order to evaluate whether the concentrations measured in soils were abnormally high or not. The advantages and disadvantages of each method were discussed and their results used to identify the most probable contamination cases, the influence of the soils characteristics, as well as the agricultural land cover. The basic descriptive method was good as a first and easy approach to describe the TM ambient concentrations, but may misinterpret the natural anomalies as contaminations. Based on geochemical associations, the linear regression method provided more realistic results even if the relationships between major and trace metals were not significant for the most mobile TM. The EF method was useful to identify high point source contaminations, but it was not suitable when considering a large dataset of low TM concentrations. Finally, the PCA method was a good preliminary tool for the description of the global TM concentrations in a studied area, but it could only give indication on the highest contaminated points. By comparing the results of the different methods in the studied region, we estimated that 24% of the arable soils were contaminated by at least one trace metal, mainly Cu in vineyards/orchards and Cd, Pb and/or Zn in grazing lands. In addition, the calcareous soils exhibited globally higher natural and anthropogenic TM concentrations than non-calcareous soils, probably because of the lower TM mobility at alkaline pH.
机译:在法国以钙质土壤比例较高为特征的地区,对可耕土壤中的痕量金属(TM)含量进行了监测。在这种特殊的地质背景下,目标是首先确定土壤中痕量金属的自然水平,其次是评估哪些地点受到了严重污染。由于目前尚无通用的污染评估方法,因此应用了四种不同的方法并进行了比较,以促进对污染的最佳诊断。首先,通过使用基本描述统计和以半保守主要元素作为预测因子计算的线性回归模型确定TM地球化学背景。痕量金属的天然浓度由于在区域范围内遇到的高度土壤异质性而变化很大,并且根据所考虑的TM或多或少都进行了精确建模。其次,将基本描述统计量和线性回归方法与富集因子(EF)方法和多元分析(PCA)进行比较,以评估土壤中测得的浓度是否异常高。讨论了每种方法的优缺点,并将其结果用于确定最可能的污染案例,土壤特性的影响以及农业用地的覆盖。基本的描述方法是描述TM环境浓度的第一种简便方法,很好,但是可能会将自然异常误解为污染物。基于地球化学联系,即使对于大多数移动性TM来说,即使主要金属和痕量金属之间的关系不显着,线性回归方法也提供了更为实际的结果。 EF方法对于识别高点污染源很有用,但是在考虑低TM浓度的大型数据集时并不适用。最后,PCA方法是描述研究区域中总TM浓度的很好的初步工具,但它只能指示最高污染点。通过比较研究区域不同方法的结果,我们估计24%的耕地土壤被至少一种微量金属污染,主要是葡萄园/果园中的Cu和牧场中Cd,Pb和/或Zn。另外,钙质土壤总体上显示出比非钙质土壤更高的天然和人为TM浓度,这可能是由于在碱性pH下较低的TM迁移率。

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