To ensure efficient and effective management of the Department of Defense’s (DoD's) remaining contaminated groundwater sites, conceptual site models (CSMs) must reflect all of the major factors that control contaminant fate, including those that are difficult to characterize and/or recently recognized. Abiotic natural attenuation (ANA) has both of these characteristics and is not adequately represented in current CSMs. Overcoming this limitation is one of the highest priority research objectives identified during a recent SERDP workshop on chlorinated solvents in groundwater.

The overall objective of this project is to address this need by developing a quantitative framework for characterizing site geochemistry and assessing site management scenarios with respect to their potential for natural abiotic attenuation, including changes in site conditions (esp. upon transitions from active to passive remedies). This project will address the need at three levels: specific data gaps (e.g., more and better kinetic data for ANA), fundamental process-level understanding (e.g., cause and effects of reactive mineral intermediate (RMI) phases, relative contributions of reductive vs oxidative degradation), and modeling tools that integrate ANA into CSMs for improved management of specific field sites.

Conceptual Model of the Relationship Between Biotic Natural Attenuation (BNA) and Abiotic Natural Attention (ANA) Processes, via Reactive Mineral Intermediate (RMI) Phases

Technical Approach

This project is structured into four specific objectives, each containing several experimental tasks to gather data, followed by a task that integrates the results into a model. Objective 1 will assemble available data relevant to ANA into a biogeochemical reactive transport model. Objective 2 will develop improved methods for site material characterization and develop a quantitative model for using these data to predict abiotic degradation of chlorinated ethenes (CEs). Objective 3 will measure a definitive data set of abiotic CE degradation rates by aquifer materials and RMI phases for both oxidative and reductive pathways and use it to calibrate and test the kinetic model developed in Objective 2. Objective 4 will measure CE degradation under conditions that simulate the dynamic characteristics of active to passive transitions, and extend the biogeochemical reactive transport model to apply to these transitions. Several specific aspects of the ANA issue—such as the role of microbiology in generating RMI phases and the role of these mineral phases in low permeability zones—will be addressed as cross-cutting issues that appear in several tasks. This project will focus on trichloroethene and its characteristic degradation products, but the tools developed will be applicable to other contaminants.


This project will provide the data and tools needed to quantitatively model CE degradation by ANA in order to provide site managers with a quantitative framework to assess the capacity for ANA at complex sites. This project will also address how ANA capacity changes as sites are transitioned from active-to-passive remedies and develop low-permeability zones. A rigorous, quantitative framework for predicting ANA will provide more reliable estimates of contaminant concentration reduction at sites and help site managers determine when “polishing” by ANA is sufficient to meet clean-up goals. The improved framework for evaluating ANA may allow earlier transitions from active to passive remedies. Ultimately, greater use of passive site management will result in substantial cost savings while remaining protective of human health and the environment. (Anticipated Project Completion - 2024)


Huang, J., A. Jones, T.D. Waite, Y. Chen, X. Huang, K.M. Rosso, A. Kappler, M. Mansor, P.G. Tratnyek, and H. Zhang. 2021. Fe(II) Redox Chemistry in the Environment. Chemical Reviews, 121(13):8161-8233. doi.org/10.1021/acs.chemrev.0c01286.

Gao, Y., S. Zhong, K. Zhang, and H. Zhang. 2023. Abiotic Reduction of Organic and Inorganic Compounds by Fe(II)-Associated Reductants: Comprehensive Data Sets and Machine Learning Modeling. Environmental Science and Technology, 57(46):17661-18390. doi.org/10.1021/acs.est.2c09724.