<br>废弃雄黄矿潜在有毒元素（PTE）的来源指定：多元统计分析和三种常见受体模型的结合,Chemosphere

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废弃雄黄矿潜在有毒元素（PTE）的来源指定：多元统计分析和三种常见受体模型的结合
Chemosphere ( IF 8.1 ) Pub Date : 2022-08-06 , DOI: 10.1016/j.chemosphere.2022.135923
Jingyun Wang ₁ , Jun Yang ₁ , Tongbin Chen ₁

Affiliation

识别污染源并量化其贡献对于提出土壤中潜在有毒元素（PTE）的管理和控制策略非常重要。在这项研究中，多元统计分析和受体模型相结合，以确定潜在的污染源并分配它们在废弃雄黄矿中的贡献。主成分分析（PCA）结果表明，三个因素是导致 PTE 的原因，聚类分析（CA）也支持这一点。相关性分析和空间分析也表明，来自同一污染源的重金属具有较高的相关系数和相似的空间分布。结合 3 个受体模型来分配污染源的贡献。采用绝对主成分分析-多元线性回归（APCA-MLR）检测 3 个污染源。相比之下，通过正矩阵分解（PMF）和 UNMIX 确定了 4 个来源。土壤母质对 Cr、Cu、Ni 和 Zn 的负载较重，平均贡献最大（30%–43%）。镉主要来自农业活动，贡献率高于 60%。砷积累主要与采矿和冶炼活动有关，贡献率高于 80%。PMF 和 UNMIX 模型显示，超过一半的 Pb 浓度受工业活动的影响。相对而言，APCA-MLR 在所有 PTE 中都是一个表现良好的模型，尽管它只检测到 3 个污染源。研究表明，应用多受体模型是一个好的选择，以获得更可靠和客观的来源预约结论。

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Source appointment of potentially toxic elements (PTEs) at an abandoned realgar mine: Combination of multivariate statistical analysis and three common receptor models

Identifying pollution sources and quantifying their contributions are of great importance for proposing management and control strategies of potentially toxic elements (PTEs) in soil. In this study, multivariate statistical analysis and receptor models were combined to identify potential pollution sources and apportion their contributions at an abandoned realgar mine. Principal component analysis (PCA) result shows that three factors are responsible for PTEs, which is also supported by cluster analysis (CA). Correlation analysis and spatial analysis also show that the heavy metals from the same pollution source are of higher correlation coefficients and similar spatial distribution. Three receptor models were combined to apportion contributions of pollution sources. Three pollution sources were detected by absolute principal component analysis-multiple linear regression (APCA-MLR). In contrast, four sources were identified by positive matrix factorization (PMF) and UNMIX. Soil parent material was heavily loaded on Cr, Cu, Ni and Zn, occupying the largest average contribution (30%–43%). Cadmium was mainly derived from agricultural activities with contribution higher than 60%. Arsenic accumulation was mainly associated with mining and smelting activity with contribution higher than 80%. PMF and UNMIX models showed that more than half of Pb concentrations were influenced by industrial activities. Comparatively speaking, APCA-MLR was a well-performing model for all PTEs even though it only detected three pollution sources. The study showed that it was a good choice to apply multiple receptor models in order to achieve more reliable and objective conclusions of source appointment.

更新日期：2022-08-06

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