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Application of principal component analysis in the pollution assessment with heavy metals of vegetable food chain in the old mining areas

机译:主成分分析法在老矿区蔬菜食物链重金属污染评价中的应用

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Background The aim of the paper is to assess by the principal components analysis (PCA) the heavy metal contamination of soil and vegetables widely used as food for people who live in areas contaminated by heavy metals (HMs) due to long-lasting mining activities. This chemometric technique allowed us to select the best model for determining the risk of HMs on the food chain as well as on people's health. Results Many PCA models were computed with different variables: heavy metals contents and some agro-chemical parameters which characterize the soil samples from contaminated and uncontaminated areas, HMs contents of different types of vegetables grown and consumed in these areas, and the complex parameter target hazard quotients (THQ). Results were discussed in terms of principal component analysis. Conclusion There were two major benefits in processing the data PCA: firstly, it helped in optimizing the number and type of data that are best in rendering the HMs contamination of the soil and vegetables. Secondly, it was valuable for selecting the vegetable species which present the highest/minimum risk of a negative impact on the food chain and human health.
机译:背景技术本文的目的是通过主成分分析(PCA)评估土壤和蔬菜对重金属的污染,这些土壤和蔬菜广泛用于因长期采矿活动而生活在被重金属(HMs)污染的地区的人们的食物中。这种化学计量学技术使我们能够选择最佳模型来确定食物链以及人们健康中HM的风险。结果计算出许多PCA模型具有不同的变量:重金属含量和一些农业化学参数(表征受污染和未污染区域的土壤样品的特征),这些区域种植和食用的不同类型蔬菜的HMs含量以及复杂的目标危害指标商(THQ)。根据主成分分析讨论了结果。结论处理PCA数据有两个主要好处:首先,它有助于优化数据的数量和类型,从而最好地再现了土壤和蔬菜的HMs污染。其次,对于选择对食物链和人类健康产生负面影响的最大/最小风险的蔬菜种类,这是有价值的。

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