Abstract

Heavy metal contamination was identified in groundwater monitoring wells surrounding a waste deposit facility at the Rönnskär Cu–Pb–Zn smelter in Skellefteå, northern Sweden. The main objective of this study is to identify the sources of contamination, utilizing element screening analyses and multivariate statistical techniques. A second objective is to determine the usefulness of these techniques in Environmental Forensics investigations of contaminated groundwater at a complex industrial site. Water samples were collected from four groundwater monitoring wells and six waste deposit cells surrounding the contaminated area. Seventy-two elements are statistically examined and the dataset is reduced to the variables representative of the contaminated source material from the smelting process. A three-component model is identified and explains 88% of the total variation in the dataset. Component 1 includes concentrations of Cd, Co, Ni, Rb, Re, and Zn. This component displays a high correlation with two of the deposit cells and their associated groundwater monitoring wells. Component 2 is comprised of Sb, Cu, and Mo. This component displays a correlation between all monitoring wells and deposits likely due to the high mobility of these elements as oxyanions. Component 3 is dominated by As and displays high correlation to three older deposit cells representing a completely different source than for Components 1 and 2. The application of screening analyses and multivariate statistics in this study has achieved a meaningful identification of sources of contamination in the investigated area. It was also shown to be useful as an initial survey aiming to optimize a full-scale monitoring program at the site.

摘要

在瑞典北部Skellefteå的RönnskärCu-Pb-Zn冶炼厂的废物沉积设施周围的地下水监测井中发现了重金属污染。这项研究的主要目的是利用元素筛选分析和多元统计技术来识别污染源。第二个目标是确定这些技术在复杂工业现场对污染的地下水进行环境取证调查中的有用性。从污染区域周围的四个地下水监测井和六个废物沉积池收集水样。统计检查了72个元素，并将数据集简化为代表冶炼过程中污染的原料的变量。确定了三部分模型，并解释了数据集中总变化的88％。组分1包括Cd，Co，Ni，Rb，Re和Zn的浓度。该组件与其中两个沉积单元及其相关的地下水监测井显示出高度相关性。组分2由Sb，Cu和Mo组成。该组分显示所有监测井和沉积物之间的相关性，这可能是由于这些元素作为氧阴离子的高迁移率所致。组分3以砷为主导，并且与三个较旧的沉积池（与组分1和2的来源完全不同）显示出高度相关性。筛选分析和多元统计数据在本研究中的应用已在研究中实现了对污染源的有意义的识别。区。