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Your Environment. Your Health.

Progress Reports: Columbia University: Enhanced Remediation at U.S. Arsenic-Contaminated Sites

Superfund Research Program

Enhanced Remediation at U.S. Arsenic-Contaminated Sites

Project Leader: Benjamin C. Bostick
Co-Investigator: Steven N. Chillrud
Grant Number: P42ES010349
Funding Period: 2000-2021
View this project in the NIH Research Portfolio Online Reporting Tools (RePORT)

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Progress Reports

Year:   2019  2018  2017  2016  2015  2014  2013  2012  2011  2010  2009  2008  2007  2006  2005  2004  2003  2002  2001  2000 

Benjamin Bostick, Ph.D., and his team’s research continues to develop remediation strategies for arsenic-contaminated sites using magnetite-based approaches (Huang et al., submitted), and to upscale them to site-based remediation trials. In the last year, the team has conducted a field-based study of the Lot 84 site that examines both the origin of groundwater arsenic contamination in site groundwater, and the role of iron-nitrate additions in creating magnetite to remove it. This study examined changes in the microbial community that occurred in uncontaminated site sediments that contacted landfill leachate, finding that the microbial community changed rapidly as oxygen concentrations decreased, producing transient arsenic contamination. The researchers are using this information to develop a reactive transport model to predict arsenic off-site transport at the site for development of site-specific remediation plans. Bostick and his team are also improving nascent transport models that describe both arsenic sequestration and release mechanisms. Key to these improvements is a new method developed to trace groundwater flow in areas where hydrological data is sparse using water isotopes (Nghiem et al., 2019). The research team also using more informed set of geochemical reactions, including sulfur cycling, in reactive transport models. For mineralogical characterization, a basic need of environmental research, the team developed a series of high-throughput data streams to evaluate synchrotron-based spectroscopic characterization of sediments (Nghiem et al., submitted). These methods allow us to accurately identify iron minerals, but also to group sediments into categories based on their age and active redox processes, and to link those sediment properties to aqueous composition using machine learning.

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