USING METAL ISOTOPES AND STATISTICS TO UNRAVEL URBAN METAL POLLUTION CYCLING

Metal pollution continues to harm millions of children and adults throughout the globe, yet our understanding of pollutant sources, sinks, transport, and exposure in urban settings remains relatively rudimentary. Application of recently developed analytical geochemistry techniques and statistical tools can provide important insight into urban metal pollution in the environment, but these techniques and tools are drastically underutilized in the United States (U.S.) compared to other countries such as China. Here we show how a combination of Bayesian statistical techniques and Pb isotopes can aid in better quantification of pollution sources in the Midwest U.S. Additionally, we demonstrate that road sediment and lichens are two easily accessible environmental media that serve as excellent proxies of environmental pollution. Analysis of Pb isotopes in lichens and road sediment from two post-industrial Midwest cities reveals that industrial combustion is a pervasive source of Pb, but that leaded gasoline also persists as a source of Pb in the environment.

Our future work will expand on the aforementioned techniques to better understand the connection between metal pollution in soils and household dust in urban residential areas. Specifically, our forthcoming research will utilize citizen scientist-generated samples from individual homes, which we will analyze for metal isotopes such as Pb, Cu, and Zn, to better constrain pollution sources and variability in the urban environment. A combination of high spatial and temporal sampling resolution, multiple metal isotopes, electron microscopy, and statistical techniques such as the Bayesian MixSIAR package in R will all aid in better understanding metal pollutant cycling in the urban environment. Through this information, more efficient remediation and preventative measures can be produced to mitigate unnecessary exposure to toxic metals such as Pb, particularly for children in poorer, more socioeconomically disadvantaged areas.