4.7 Article

The use of Pb, Sr, and Hg isotopes in Great Lakes precipitation as a tool for pollution source attribution

期刊

SCIENCE OF THE TOTAL ENVIRONMENT
卷 502, 期 -, 页码 362-374

出版社

ELSEVIER
DOI: 10.1016/j.scitotenv.2014.09.034

关键词

Heavy metals; Stable isotopes; Source attribution; Multivariate statistical receptor modeling; Wet deposition

资金

  1. University of Michigan Water Center of the Graham Sustainability Institute
  2. University of Michigan [EPD05005]
  3. Fred A. and Barbara M. Erb Family Foundation
  4. EPA through its Office of Research and Development [R-82971601]

向作者/读者索取更多资源

The anthropogenic emission and subsequent deposition of heavy metals including mercury (Hg) and lead (Pb) present human health and environmental concerns. Although it is known that local and regional sources of these metals contribute to deposition in the Great Lakes region, it is difficult to trace emissions from point sources to ithpacted sites. Recent studies suggest that metal isotope ratios may be useful for distinguishing between and tracing source emissions. We measured Pb, strontium (Sr), and Hg isotope ratios in daily precipitation samples that were collected at seven sites across the Great Lakes region between 2003 and 2007. Lead isotope ratios ((207)pb/(206)pb = 0.8062 to 0.8554) suggest that Pb deposition was influenced by coal combustion and processing of Mississippi Valley-Type Pb ore deposits. Regional differences in Sr isotope ratios (Sr-87/Sr-86 = 0.70859 to 0.71155) are likely related to coal fly ash and soil dust. Mercury isotope ratios (delta Hg-202 = -1.13 to 0.13%.) also varied among the sites, likely due to regional differences in coal isotopic composition, and fractionation occurring within industrial facilities and in the atmosphere. These data represent the first combined characterization of Pb, Sr, and Hg isotope ratios in precipitation collected across the Great Lakes region. We demonstrate the utility of multiple metal isotope ratios in parallel with traditional trace element multivariate statistical modeling to enable more complete pollution source attribution. (C) 2014 Elsevier B.V. All rights reserved.

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