污染
环境科学
多元统计
污染物
环境修复
鉴定(生物学)
聚类分析
污染
环境资源管理
统计
生态学
数学
生物
作者
Diego Baragaño,Gildas Ratié,Carlos Sierra,Vladislav Chrastný,Michael Komárek,J.R. Gallego
标识
DOI:10.1016/j.jhazmat.2021.127413
摘要
Industrial sites affected by anthropogenic contamination, both past and present-day, commonly have intricate pollutant patterns, and source discrimination can be thus highly challenging. To this goal, this paper presents a novel approach combining multivariate statistics and environmental forensic techniques. The efficiency of this methodology was exemplified in a severely polluted estuarine area (Avilés, Spain), where factor analysis and clustering were performed to identify sub-areas with distinct geochemical behaviour. Once six clusters were defined and a pollution index applied, forensic tools revealed that the As speciation, Pb isotopes, and PAHs molecular ratios were useful to categorise the cluster groups on the basis of distinct pollution sources: Zn-smelting, coaly particles and waste disposal. Overall, this methodology offers valuable insight into pollution sources identification, which can be extended to comparable scenarios of complexly polluted environmental compartments. The information gathered using this approach is also important for the planning of risk assessment procedures and potential remediation strategies.
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