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Final 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

Project-Specific Links

Final Progress Reports

Year:   2020  2016  2010  2005 

This project continues to develop remediation strategies for arsenic-contaminated sites using magnetite-based approaches (Huang 2020), 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 team is 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. The team is also improving nascent transport models that describe both arsenic sequestration and release mechanisms. Key to these improvements is a new method the team has developed to trace groundwater flow in areas where hydrological data is sparse using water isotopes (Nghiem 2019). The team is also using a more informed set of geochemical reactions, including sulfur cycling, in reactive transport models (Siade submitted). For mineralogical characterization, a basic need of the team’s environmental research, the team has developed a series of high-throughput data streams to evaluate synchrotron-based spectroscopic characterization of sediments (Nghiem 2020). These methods allow researchers 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. In the data supplement, the team is developing software that allows for inter-platform analysis and merging both biological and environmental datasets across different centers for distribution.

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