Residential metal contamination and potential health risks of exposure in adobe brick houses in Potosí, Bolivia.

Abstract

Potosí, Bolivia, is the site of centuries of historic and present-day mining of the Cerro Rico, a mountain known for its rich polymetallic deposits, and was the site of large-scale Colonial era silver refining operations. In this study, the concentrations of several metal and metalloid elements were quantified in adobe brick, dirt floor, and surface dust samples from 49 houses in Potosí. Median concentrations of total mercury (Hg), lead (Pb), and arsenic (As) were significantly greater than concentrations measured in Sucre, Bolivia, a non-mining town, and exceeded US-based soil screening levels. Adobe brick samples were further analyzed for bioaccessible concentrations of trace elements using a simulated gastric fluid (GF) extraction. Median GF extractable concentrations of Hg, As, and Pb were 0.085, 13.9, and 32.2% of the total element concentration, respectively. Total and GF extractable concentrations of Hg, As, and Pb were used to estimate exposure and potential health risks to children following incidental ingestion of adobe brick particles. Risks were assessed using a range of potential ingestion rates (50-1000mg/day). Overall, the results of the risk assessment show that the majority of households sampled contained concentrations of bioaccessible Pb and As, but not Hg, that represent a potential health risk. Even at the lowest ingestion rate considered, the majority of households exceeded the risk threshold for Pb, indicating that the concentrations of this metal are of particular concern. To our knowledge, this is the first study to quantify key trace elements in building materials in adobe brick houses and the results indicate that these houses are a potential source of exposure to metals and metalloids in South American mining communities. Additional studies are needed to fully characterize personal exposure and to understand potential adverse health outcomes within the community.

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Citation

Published Version (Please cite this version)

10.1016/j.scitotenv.2016.03.152

Publication Info

McEwen, Abigail R, Heileen Hsu-Kim, Nicholas A Robins, Nicole A Hagan, Susan Halabi, Olivo Barras, Daniel deB Richter, John J Vandenberg, et al. (2016). Residential metal contamination and potential health risks of exposure in adobe brick houses in Potosí, Bolivia. The Science of the total environment, 562. pp. 237–246. 10.1016/j.scitotenv.2016.03.152 Retrieved from https://hdl.handle.net/10161/21242.

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Scholars@Duke

Hsu-Kim

Heileen Hsu-Kim

Professor in the Department of Civil and Environmental Engineering

Professor Heileen (Helen) Hsu-Kim is an environmental engineer who specializes in environmental aquatic chemistry and geochemistry. Her research tackles problems related to pollutant metals and the biogeochemical processes that alter their distribution in water, soil, and air. The applications of this work include environmental remediation technologies, the impacts of energy production on water resources, global environmental health, and the environmental implications and applications of nanotechnology.

Dr. Hsu-Kim's current research projects are focused on mercury biogeochemistry, the impacts of coal ash disposal on water quality, recovering valuable materials from geological wastes, and health impacts of trace metal/metalloid exposures. A central theme to her work is the utilization of chemical speciation for understanding and predicting the persistence, mobility and bioavailability of metals and minerals in the aquatic environment.

The methodologies her group employs for this research include laboratory techniques for quantifying trace element speciation, functional measures of reactivity and bioavailability of contaminant metals, and techniques to probe interactions at mineral, water and microbial interfaces.

Halabi

Susan Halabi

James B. Duke Distinguished Professor

Design and analysis of clinical trials, statistical analysis of biomarker and high dimensional data, development and validation of prognostic and predictive models.


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