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==Hydrogeophysical methods for characterization and monitoring of surface water-groundwater interactions==
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==Estimating PCE/TCE Abiotic First-Order Reductive Dechlorination Rate Constants in Clayey Soils Under Anoxic Conditions==  
Hydrogeophysical methods can be used to cost-effectively locate and characterize regions of
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The U.S. Department of Defense (DoD) faces many challenges in restoring aquifers at contaminated sites, often due to back-diffusion of tetrachloroethene (PCE) and trichloroethene (TCE) from low-permeability clay zones. The uptake, storage, and subsequent long-term release of these dissolved contaminants from clays are key processes in understanding the longevity, intensity, and risks associated with many persistent chlorinated ethene groundwater plumes. Although naturally occurring abiotic and biotic dechlorination processes in clays may reduce stored contaminant mass and significantly aid natural attenuation, no standardized field method currently exists to verify or quantify these reactions. It is critical to remediation design efforts to demonstrate and validate a cost-effective in situ approach for assessing these dechlorination processes using first-order rate constants. An approach was developed and applied across eight DoD sites to support Remedial Project Managers (RPMs) and regulators in evaluating natural attenuation potential in clay-rich environments.
enhanced groundwater/surface-water exchange (GWSWE) and to guide effective follow up investigations based on more traditional invasive methods. The most established methods exploit the contrasts in temperature and/or specific conductance that commonly exist between groundwater and surface water.
 
 
<div style="float:right;margin:0 0 2em 2em;">__TOC__</div>
 
<div style="float:right;margin:0 0 2em 2em;">__TOC__</div>
  
 
'''Related Article(s):'''
 
'''Related Article(s):'''
*[[Geophysical Methods]]
 
*[[Geophysical Methods - Case Studies]]
 
  
'''Contributor(s):'''
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*[[Monitored Natural Attenuation (MNA)]]
*[[Dr. Lee Slater]]
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*[[Monitored Natural Attenuation (MNA) of Chlorinated Solvents]]
*Dr. Ramona Iery
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*[[Monitored Natural Attenuation - Transitioning from Active Remedies]]
*Dr. Dimitrios Ntarlagiannis
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*[[Matrix Diffusion]]
*Henry Moore
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*[[REMChlor - MD]]
  
'''Key Resource(s):'''
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'''Contributors:''' Dani Tran, Dr. Charles Schaefer, Dr. Charles Werth
*USGS Method Selection Tool: https://code.usgs.gov/water/espd/hgb/gw-sw-mst
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*USGS Water Resources: https://www.usgs.gov/mission-areas/water-resources/science/groundwatersurface-water-interaction
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'''Key Resource:'''
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*Schaefer, C.E, Tran, D., Nguyen, D., Latta, D.E., Werth, C.J., 2025. Evaluating Mineral and In Situ Indicators of Abiotic Dechlorination in Clayey Soils (3)
  
 
==Introduction==
 
==Introduction==
Discharges of contaminated groundwater to surface water bodies threaten ecosystems and degrade the quality of surface water resources. Subsurface heterogeneity associated with the geological setting and stratigraphy often results in such discharges occurring as localized zones (or seeps) of contaminated groundwater. Traditional methods for investigating GWSWE include [https://books.gw-project.org/groundwater-surface-water-exchange/chapter/seepage-meters/#:~:text=Seepage%20meters%20measure%20the%20flux,that%20it%20isolates%20water%20exchange. seepage meters]<ref>Rosenberry, D. O., Duque, C., and Lee, D. R., 2020. History and Evolution of Seepage Meters for Quantifying Flow between Groundwater and Surface Water: Part 1 – Freshwater Settings. Earth-Science Reviews, 204(103167). [https://doi.org/10.1016/j.earscirev.2020.103167 doi: 10.1016/j.earscirev.2020.103167].</ref><ref>Duque, C., Russoniello, C. J., and Rosenberry, D. O., 2020. History and Evolution of Seepage Meters for Quantifying Flow between Groundwater and Surface Water: Part 2 – Marine Settings and Submarine Groundwater Discharge. Earth-Science Reviews, 204 ( 103168). [https://doi.org/10.1016/j.earscirev.2020.103168 doi: 10.1016/j.earscirev.2020.103168].</ref>, which directly quantify the volume flux crossing the bed of a surface water body (i.e, a  lake, river or wetland) and point probes that locally measure key water quality parameters (e.g., temperature, pore water velocity, specific conductance, dissolved oxygen, pH). Seepage meters provide direct estimates of seepage fluxes between groundwater and surface- water but are time consuming and can be difficult to deploy in high energy surface water environments and along armored bed sediments. Manual seepage meters rely on quantifying volume changes in a bag of water that is hydraulically connected to the bed. Although automated seepage meters such as the [https://clu-in.org/programs/21m2/navytools/gsw/#ultraseep Ultraseep system] have been developed, they are generally not suitable for long term deployment (weeks to months). The US Navy has developed the [https://clu-in.org/programs/21m2/navytools/gsw/#trident Trident probe] for more rapid (relative to seepage meters) sampling, whereby the probe is inserted into the bed and point-in-time pore water quality and sediment parameters are directly recorded (note that the Trident probe does not measure a seepage flux). Such direct probe-based measurements are still relatively time consuming to acquire, particularly when reconnaissance information is required over large areas to determine the location of discrete seeps for further, more quantitative analysis.  
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Cost-effective methods are needed to verify the occurrence of natural dechlorination processes and quantify their dechlorination rates in clays under ambient in situ conditions in order to reliably predict their long-term influence on plume longevity and mass discharge. However, accurately determining these rates is challenging due to slow reaction kinetics, the transient nature of transformation products, and the interplay of biotic and abiotic mechanisms within the clay matrix or at clay-sand interfaces. Tools capable of quantifying these reactions and assessing their role in mitigating plume persistence would be a significant aid for long-term site management.
  
Over the last few decades, a broader toolbox of hydrogeophysical technologies has been developed to rapidly and non-invasively evaluate zones of GWSWE in a variety of surface water settings, spanning from freshwater bodies to saline coastal environments. Many of these technologies are currently being deployed under a Department of Defense Environmental Security Technology Certification Program ([https://serdp-estcp.mil/ ESTCP]) project ([https://serdp-estcp.mil/projects/details/e4a12396-4b56-4318-b9e5-143c3011b8ff ER21-5237]) to demonstrate the value of the toolbox to remedial program managers (RPMs) dealing with the challenge of characterizing surface water contamination via groundwater from facilities proximal to surface water bodies. This article summarizes these technologies and provides references to key resources, mostly provided by the [https://www.usgs.gov/mission-areas/water-resources Water Resources Mission Area] of the United States Geological Survey that describe the technologies in further detail.
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For reductive abiotic dechlorination under anoxic conditions, a 1% hydrochloric acid (HCl) extraction of a sample of native clay coupled with X-ray diffraction (XRD) data can be used as a screening level tool to estimate reductive dechlorination rate constants. These rate constants can be inserted into fate and transport models such as [[REMChlor - MD]]<ref>Falta, R., and Wang, W., 2017. A semi-analytical method for simulating matrix diffusion in numerical transport models. Journal of Contaminant Hydrology, 197, pp. 39-49. [https://doi.org/10.1016/j.jconhyd.2016.12.007 doi: 10.1016/j.jconhyd.2016.12.007]&nbsp; [[Media: FaltaWang2017.pdf | Open Access Manuscript]]</ref><ref>Kulkarni, P.R., Adamson, D.T., Popovic, J., Newell, C.J., 2022. Modeling a well-charactized perfluorooctane sulfate (PFOS) source and plume using the REMChlor-MD model to account for matrix diffusion. Journal of Contaminant Hydrology, 247, Article 103986. [https://doi.org/10.1016/j.jconhyd.2022.103986 doi: 10.1016/j.jconhyd.2022.103986]&nbsp; [[Media: KulkarniEtAl2022.pdf | Open Access Manuscript]]</ref> to quantify abiotic dechlorination impacts within clay aquitards on chlorinated solvent plumes. Thus, determination of the abiotic reductive dechlorination rate constant for a particular clayey soil can be readily utilized to provide a more accurate assessment of aquifer cleanup timeframes for groundwater plumes that are being sustained by contaminant back-diffusion.
  
==Hydrogeophysical Technologies for Understanding Groundwater-Surface Water Interactions==
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==Recommended Approach==
[[Wikipedia: Hydrogeophysics |Hydrogeophysical technologies]] exploit contrasts in the physical properties between groundwater and surface water to detect and monitor zones of pronounced GWSWE. The two most valuable properties to measure are temperature and electrical conductivity. Temperature has been used for decades as an indicator of groundwater-surface water exchange<ref>Constantz, J., 2008. Heat as a Tracer to Determine Streambed Water Exchanges. Water Resources Research, 44 (4).[https://doi.org/https://doi.org/10.1029/2008WR006996 doi: 10.1029/2008WR006996].[https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2008WR006996  Open Access Article]</ref> with early uses including pushing a thermistor into the bed of a surface water body to assess zones of surface water downwelling and groundwater upwelling. Today, a variety of novel technologies that measure temperature over a wide range of spatial and temporal scales are being used to investigate GWSWE. The evaluation of electrical conductivity measurements using point probes and geophysical imaging is also well-established. However, new technologies are now available to exploit electrical conductivity contrasts from GWSWE occurring over a range of spatial and temporal scales.
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[[File: TranFig1.png | thumb | 600 px | Figure 1: First-order rate constants for abiotic reductive dechlorination of TCE under anaerobic conditions (data from this study and prior research)]]
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[[File: TranFig2.png | thumb | 600 px | Figure 2: Flowchart diagram of field screening procedures]]
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The recommended approach builds upon the methodology and findings of a recent study<ref name="SchaeferEtAl2025">Schaefer, C.E., Tran, D., Nguyen, D., Latta, D.E., Werth, C.J., 2025. Evaluating Mineral and In Situ Indicators of Abiotic Dechlorination in Clayey Soils. Groundwater Monitoring and Remediation, 45(2), pp. 31-39. [https://doi.org/10.1111/gwmr.12709 doi: 10.1111/gwmr.12709]</ref>, emphasizing field-based and analytical techniques to quantify abiotic first-order reductive dechlorination rate constants for PCE and TCE in clayey soils under anoxic conditions. Key components of this evaluation are listed below:
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#<u>Zone Identification:</u> The focus of the investigation should be to delineate clayey zones adjacent to hydraulically conductive zones.
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#<u>Ferrous Mineral Quantification:</u> Assess ferrous mineral context in clay via 1% HCl extraction at ambient temperature over a 10-minute interval.
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#<u>Mineralogical Characterization:</u> Conduct XRD analysis with the specific intent of identifying the presence of pyrite and biotite.  
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#<u>Reduced Gas Analysis:</u> Measurement of reduced gases such as acetylene, ethene, and ethane concentrations in clay samples. Gas-tight sampling devices (e.g., En Core® soil samplers by En Novative Technologies, Inc.)  should be used to ensure sample integrity during collection and transport.
  
===Temperature-Based Technologies===
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Clay samples should be collected within a few centimeters of the high-permeability interface, with optional additional sampling further inward. For mineralogical analysis, a defined interval may be collected and subsequently subsampled. To preserve sample integrity, exposure to air should be minimized during collection, transport, and handling. Homogenization should occur within an anaerobic chamber, and if subsamples are required for external analysis, they must be shipped in gas-tight, anaerobic containers.
Several temperature-based GWSWE methodologies exploit the gradient in temperature between surface water and groundwater that exist during certain times of day or seasons of the year. The thermal insulation provided by the Earth’s land surface means that groundwater is warmer than surface water in winter months, but colder than surface water in summer months away from the equator. Therefore, in temperate climates, localized (or ‘preferential’) groundwater discharge into surface water bodies is often observed as cold temperature anomalies in the summer and warm temperature anomalies in the winter. However, there are times of the year such as fall and spring when contrasts in the temperature between groundwater and surface water will be minimal, or even undetectable. These seasonal-driven points in time correspond to the switch in the polarity of the temperature contrast between groundwater and surface water. Consequently, SW to GW gradient temperature-based methods are most effective when deployed at times of the year when the temperature contrasts between groundwater and surface water are greatest. Other time-series temperature monitoring methods depend more on natural daily signals measured at the bed interface and in bed sediments, and those signals may exist year round except where strongly muted by ice cover or surface water stratification. A variety of sensing technologies now exist within the GWSWE toolbox, from techniques that rapidly characterize temperature contrasts over large areas, down to powerful monitoring methods that can continuously quantify GWSWE fluxes at discrete locations identified as hotspots.
 
  
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Estimation of the abiotic reductive first-order rate constant for PCE and TCE is based on the “reactive” ferrous content in the clay. Reactive ferrous content (Fe(II)<sub>r</sub>) is estimated as shown in Equation 1:
  
[[File:AbioMCredFig4.png | thumb |600px|Figure 4. Chemical structure of commonly used hydroquinones in NACs/MCs kinetic experiments.]]
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::'''Equation 1:'''&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <big>''Fe(II)<sub><small>r</small></sub> = DA + XRD<sub><small>pyr</small></sub> - XRD<sub><small>biotite</small></sub>''</big>
The two most predominant forms of organic carbon in natural systems are natural organic matter (NOM) and black carbon (BC)<ref name="Schumacher2002">Schumacher, B.A., 2002. Methods for the Determination of Total Organic Carbon (TOC) in Soils and Sediments. U.S. EPA, Ecological Risk Assessment Support Center. [http://bcodata.whoi.edu/LaurentianGreatLakes_Chemistry/bs116.pdf Free download.]</ref>. Black carbon includes charcoal, soot, graphite, and coal. Until the early 2000s black carbon was considered to be a class of (bio)chemically inert geosorbents<ref name="Schmidt2000">Schmidt, M.W.I., and Noack, A.G., 2000. Black carbon in soils and sediments: Analysis, distribution, implications, and current challenges. Global Biogeochemical Cycles, 14(3), pp. 777–793.  [https://doi.org/10.1029/1999GB001208 DOI: 10.1029/1999GB001208]&nbsp;&nbsp; [https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/1999GB001208 Open access article.]</ref>. However, it has been shown that BC can contain abundant quinone functional groups and thus can store and exchange electrons<ref name="Klüpfel2014">Klüpfel, L., Keiluweit, M., Kleber, M., and Sander, M., 2014. Redox Properties of Plant Biomass-Derived Black Carbon (Biochar). Environmental Science and Technology, 48(10), pp. 5601–5611.  [https://doi.org/10.1021/es500906d DOI: 10.1021/es500906d]</ref> with chemical<ref name="Xin2019">Xin, D., Xian, M., and Chiu, P.C., 2019. New methods for assessing electron storage capacity and redox reversibility of biochar. Chemosphere, 215, 827–834.  [https://doi.org/10.1016/j.chemosphere.2018.10.080 DOI: 10.1016/j.chemosphere.2018.10.080]</ref> and biological<ref name="Saquing2016">Saquing, J.M., Yu, Y.-H., and Chiu, P.C., 2016. Wood-Derived Black Carbon (Biochar) as a Microbial Electron Donor and Acceptor. Environmental Science and Technology Letters, 3(2), pp. 62–66.  [https://doi.org/10.1021/acs.estlett.5b00354 DOI: 10.1021/acs.estlett.5b00354]</ref> agents in the surroundings. Specifically, BC such as biochar has been shown to reductively transform MCs including NTO, DNAN, and RDX<ref name="Xin2022"/>.
 
  
NOM encompasses all the organic compounds present in terrestrial and aquatic environments and can be classified into two groups, non-humic and humic substances. Humic substances (HS) contain a wide array of functional groups including carboxyl, enol, ether, ketone, ester, amide, (hydro)quinone, and phenol<ref name="Sparks2003">Sparks, D.L., 2003. Environmental Soil Chemistry, 2nd Edition. Elsevier Science and Technology Books.  [https://doi.org/10.1016/B978-0-12-656446-4.X5000-2 DOI: 10.1016/B978-0-12-656446-4.X5000-2]</ref>. Quinone and hydroquinone groups are believed to be the predominant redox moieties responsible for the capacity of HS and BC to store and reversibly accept and donate electrons (i.e., through reduction and oxidation of HS/BC, respectively)<ref name="Schwarzenbach1990"/><ref name="Dunnivant1992"/><ref name="Klüpfel2014"/><ref name="Scott1998">Scott, D.T., McKnight, D.M., Blunt-Harris, E.L., Kolesar, S.E., and Lovley, D.R., 1998. Quinone Moieties Act as Electron Acceptors in the Reduction of Humic Substances by Humics-Reducing Microorganisms. Environmental Science and Technology, 32(19), pp. 2984–2989.  [https://doi.org/10.1021/es980272q DOI: 10.1021/es980272q]</ref><ref name="Cory2005">Cory, R.M., and McKnight, D.M., 2005. Fluorescence Spectroscopy Reveals Ubiquitous Presence of Oxidized and Reduced Quinones in Dissolved Organic Matter. Environmental Science & Technology, 39(21), pp 8142–8149.  [https://doi.org/10.1021/es0506962 DOI: 10.1021/es0506962]</ref><ref name="Fimmen2007">Fimmen, R.L., Cory, R.M., Chin, Y.P., Trouts, T.D., and McKnight, D.M., 2007. Probing the oxidation–reduction properties of terrestrially and microbially derived dissolved organic matter. Geochimica et Cosmochimica Acta, 71(12), pp. 3003–3015.  [https://doi.org/10.1016/j.gca.2007.04.009 DOI: 10.1016/j.gca.2007.04.009]</ref><ref name="Struyk2001">Struyk, Z., and Sposito, G., 2001. Redox properties of standard humic acids. Geoderma, 102(3-4), pp. 329–346.  [https://doi.org/10.1016/S0016-7061(01)00040-4 DOI: 10.1016/S0016-7061(01)00040-4]</ref><ref name="Ratasuk2007">Ratasuk, N., and Nanny, M.A., 2007. Characterization and Quantification of Reversible Redox Sites in Humic Substances. Environmental Science and Technology, 41(22), pp. 7844–7850.  [https://doi.org/10.1021/es071389u DOI: 10.1021/es071389u]</ref><ref name="Aeschbacher2010">Aeschbacher, M., Sander, M., and Schwarzenbach, R.P., 2010. Novel Electrochemical Approach to Assess the Redox Properties of Humic Substances. Environmental Science and Technology, 44(1), pp. 87–93.  [https://doi.org/10.1021/es902627p DOI: 10.1021/es902627p]</ref><ref name="Aeschbacher2011">Aeschbacher, M., Vergari, D., Schwarzenbach, R.P., and Sander, M., 2011. Electrochemical Analysis of Proton and Electron Transfer Equilibria of the Reducible Moieties in Humic Acids. Environmental Science and Technology, 45(19), pp. 8385–8394.  [https://doi.org/10.1021/es201981g DOI: 10.1021/es201981g]</ref><ref name="Bauer2009">Bauer, I., and Kappler, A., 2009. Rates and Extent of Reduction of Fe(III) Compounds and O<sub>2</sub> by Humic Substances. Environmental Science and Technology, 43(13), pp. 4902–4908.  [https://doi.org/10.1021/es900179s DOI: 10.1021/es900179s]</ref><ref name="Maurer2010">Maurer, F., Christl, I. and Kretzschmar, R., 2010. Reduction and Reoxidation of Humic Acid: Influence on Spectroscopic Properties and Proton Binding. Environmental Science and Technology, 44(15), pp. 5787–5792.  [https://doi.org/10.1021/es100594t DOI: 10.1021/es100594t]</ref><ref name="Walpen2016">Walpen, N., Schroth, M.H., and Sander, M., 2016. Quantification of Phenolic Antioxidant Moieties in Dissolved Organic Matter by Flow-Injection Analysis with Electrochemical Detection. Environmental Science and Technology, 50(12), pp. 6423–6432.  [https://doi.org/10.1021/acs.est.6b01120 DOI: 10.1021/acs.est.6b01120]&nbsp;&nbsp; [https://pubs.acs.org/doi/pdf/10.1021/acs.est.6b01120 Open access article.]</ref><ref name="Aeschbacher2012">Aeschbacher, M., Graf, C., Schwarzenbach, R.P., and Sander, M., 2012.  Antioxidant Properties of Humic Substances. Environmental Science and Technology, 46(9), pp. 4916–4925.  [https://doi.org/10.1021/es300039h DOI: 10.1021/es300039h]</ref><ref name="Nurmi2002">Nurmi, J.T., and Tratnyek, P.G., 2002. Electrochemical Properties of Natural Organic Matter (NOM), Fractions of NOM, and Model Biogeochemical Electron Shuttles. Environmental Science and Technology, 36(4), pp. 617–624.  [https://doi.org/10.1021/es0110731 DOI: 10.1021/es0110731]</ref>.  
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where ''DA'' is the ferrous content from the dilute acid (1% HCl) extraction, ''XRD<sub><small>pyr</small></sub>'' is the pyrite content from XRD analysis, and ''XRD<sub><small>biotite</small></sub>'' is the biotite content from XRD analysis<ref name="SchaeferEtAl2025"/>.
  
Hydroquinones have been widely used as surrogates to understand the reductive transformation of NACs and MCs by NOM. Figure 4 shows the chemical structures of the singly deprotonated forms of four hydroquinone species previously used to study NAC/MC reduction. The second-order rate constants (''k<sub>R</sub>'') for the reduction of NACs/MCs by these hydroquinone species are listed in Table 1, along with the aqueous-phase one electron reduction potentials of the NACs/MCs (''E<sub>H</sub><sup>1’</sup>'') where available. ''E<sub>H</sub><sup>1’</sup>'' is an experimentally measurable thermodynamic property that reflects the propensity of a given NAC/MC to accept an electron in water (''E<sub>H</sub><sup>1</sup>''(R-NO<sub>2</sub>)):
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Abiotic dechlorination is unlikely to contribute to mitigating contaminant back-diffusion when reactive ferrous iron (Fe(II)<sub><small>r</small></sub>) concentrations are below 100 mg/kg (Figure 1). For Fe(II)<sub><small>r</small></sub> above 100 mg/kg, the first-order rate constant for PCE and TCE reductive dechlorination can be estimated using the correlation shown in Figure 1<ref name="SchaeferEtAl2018">Schaefer, C.E., Ho, P., Berns, E., Werth, C., 2018. Mechanisms for abiotic dechlorination of trichloroethene by ferrous minerals under oxic and anoxic conditions in natural sediments. Environmental Science and Technology, 52(23), pp.13747-13755. [https://doi.org/10.1021/acs.est.8b04108 doi: 10.1021/acs.est.8b04108]</ref><ref>Borden, R.C., Cha, K.Y., 2021. Evaluating the impact of back diffusion on groundwater cleanup time. Journal of Contaminant Hydrology, 243, Article 103889. [https://doi.org/10.1016/j.jconhyd.2021.103889 doi: 10.1016/j.jconhyd.2021]&nbsp; [[Media: BordenCha2021.pdf | Open Access Manuscript]]</ref>. The rate constant exhibits a strong positive correlation with the logarithm of reactive Fe(II) content (Pearson’s ''r'' = 0.82), with a slope of 4.7 × 10⁻⁸ L g⁻¹ d⁻¹ (log mg kg⁻¹)⁻¹.
  
:::::<big>'''Equation 1:'''&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;''R-NO<sub>2</sub> + e<sup>-</sup> ⇔ R-NO<sub>2</sub><sup>•-</sup>''</big>
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Figure 2 presents a decision flowchart designed to evaluate the significance and extent of abiotic reductive dechlorination. By applying Equation 1 to the dilute acid extractable Fe(II) plus measured mineral species data from clay samples, the reactive ferrous iron content (Fe(II)<sub><small>r</small></sub>) can be quantified, enabling a streamlined assessment of the extent to which abiotic processes are contributing to the mitigation of contaminant back-diffusion.
  
Knowing the identity of and reaction order in the reductant is required to derive the second-order rate constants listed in Table 1. This same reason limits the utility of reduction rate constants measured with complex carbonaceous reductants such as NOM<ref name="Dunnivant1992"/>, BC<ref name="Oh2013"/><ref name="Oh2009"/><ref name="Xu2015"/><ref name="Xin2021">Xin, D., 2021. Understanding the Electron Storage Capacity of Pyrogenic Black Carbon: Origin, Redox Reversibility, Spatial Distribution, and Environmental Applications. Doctoral Thesis, University of Delaware. [https://udspace.udel.edu/bitstream/handle/19716/30105/Xin_udel_0060D_14728.pdf?sequence=1 Free download.]</ref>, and HS<ref name="Luan2010"/><ref name="Murillo-Gelvez2021"/>, whose chemical structures and redox moieties responsible for the reduction, as well as their abundance, are not clearly defined or known. In other words, the observed rate constants in those studies are specific to the experimental conditions (e.g., pH and NOM source and concentration), and may not be easily comparable to other studies.
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==Study Design Considerations==
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===Diagnostic Resin Treatments===
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Several commercially available resins have been verified for use in the iTIE system. Investigators can select resins based on stressor classes of interest at each site. Each resin selectively removes a CoC class from site water prior to organism exposure.
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*[https://www.dupont.com/products/ambersorb560.html DuPont Ambersorb 560] for removal of 1,4-dioxane and other organic chemicals<ref>Woodard, S., Mohr, T., Nickelsen, M.G., 2014. Synthetic media: A promising new treatment technology for 1,4-dioxane. Remediation Journal, 24(4), pp. 27-40. [https://doi.org/10.1002/rem.21402 doi: 10.1002/rem.21402]</ref>
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*C18 for nonpolar organic chemicals
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*[https://www.bio-rad.com/en-us Bio-Rad] [https://www.bio-rad.com/en-us/product/chelex-100-resin?ID=6448ab3e-b96a-4162-9124-7b7d2330288e Chelex] for metals
 +
*Granular activated carbon for metals, general organic chemicals, sulfide<ref>Lemos, B.R.S., Teixeira, I.F., de Mesquita, J.P., Ribeiro, R.R., Donnici, C.L., Lago, R.M., 2012. Use of modified activated carbon for the oxidation of aqueous sulfide. Carbon, 50(3), pp. 1386-1393. [https://doi.org/10.1016/j.carbon.2011.11.011 doi: 10.1016/j.carbon.2011.11.011]</ref>
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*[https://www.waters.com/nextgen/us/en.html Waters] [https://www.waters.com/nextgen/us/en/search.html?category=Shop&isocode=en_US&keyword=oasis%20hlb&multiselect=true&page=1&rows=12&sort=best-sellers&xcid=ppc-ppc_23916&gad_source=1&gad_campaignid=14746094146&gbraid=0AAAAAD_uR00nhlNwrhhegNh06pBODTgiN&gclid=CjwKCAiAtLvMBhB_EiwA1u6_PsppE0raci2IhvGnAAe5ijciNcetLaGZo5qA3g3r4Z_La7YAPJtzShoC6LoQAvD_BwE Oasis HLB] for general organic chemicals<ref name="SteigmeyerEtAl2017"/>
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*[https://www.waters.com/nextgen/us/en.html Waters] [https://www.waters.com/nextgen/us/en/search.html?category=All&enableHL=true&isocode=en_US&keyword=Oasis%20WAX%20&multiselect=true&page=1&rows=12&sort=most-relevant Oasis WAX] for PFAS, organic chemicals of mixed polarity<ref>Iannone, A., Carriera, F., Di Fiore, C., Avino, P., 2024. Poly- and Perfluoroalkyl Substance (PFAS) Analysis in Environmental Matrices: An Overview of the Extraction and Chromatographic Detection Methods. Analytica, 5(2), pp. 187-202. [https://doi.org/10.3390/analytica5020012 doi: 10.3390/analytica5020012]&nbsp; [[Media: IannoneEtAl2024.pdf | Open Access Article]]</ref>
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*Zeolite for ammonia, other organic chemicals
  
{| class="wikitable mw-collapsible" style="float:left; margin-right:40px; text-align:center;"
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Resins must be adequately conditioned prior to use. Otherwise, they may inadequately adsorb toxicants or cause stress to organisms. New resins should be tested for efficacy and toxicity before being used in an iTIE system.  
|+ Table&nbsp;1.&nbsp;Aqueous&nbsp;phase one electron reduction potentials and logarithm of second-order rate constants for the reduction of NACs and MCs by the singly deprotonated form of the hydroquinones lawsone, juglone, AHQDS and AHQS, with the second-order rate constants for the deprotonated NAC/MC species (i.e., nitrophenolates and NTO<sup>–</sup>) in parentheses.
 
|-
 
! Compound 
 
! rowspan="2" |''E<sub>H</sub><sup>1'</sup>'' (V)
 
! colspan="4"| Hydroquinone [log ''k<sub>R</sub>''&nbsp;(M<sup>-1</sup>s<sup>-1</sup>)]
 
|-
 
! (NAC/MC)
 
! LAW<sup>-</sup>
 
! JUG<sup>-</sup>
 
! AHQDS<sup>-</sup>
 
! AHQS<sup>-</sup>
 
|-
 
| Nitrobenzene (NB) || -0.485<ref name="Schwarzenbach1990"/> || 0.380<ref name="Schwarzenbach1990"/> || -1.102<ref name="Schwarzenbach1990"/> || 2.050<ref name="Murillo-Gelvez2019"/> || 3.060<ref name="Murillo-Gelvez2019"/>
 
|-
 
| 2-nitrotoluene (2-NT) || -0.590<ref name="Schwarzenbach1990"/> || -1.432<ref name="Schwarzenbach1990"/> || -2.523<ref name="Schwarzenbach1990"/> || 0.775<ref name="Hartenbach2008"/> ||
 
|-
 
| 3-nitrotoluene (3-NT) || -0.475<ref name="Schwarzenbach1990"/> || 0.462<ref name="Schwarzenbach1990"/> || -0.921<ref name="Schwarzenbach1990"/> ||  ||
 
|-
 
| 4-nitrotoluene (4-NT) || -0.500<ref name="Schwarzenbach1990"/> || 0.041<ref name="Schwarzenbach1990"/> || -1.292<ref name="Schwarzenbach1990"/> || 1.822<ref name="Hartenbach2008"/> || 2.610<ref name="Murillo-Gelvez2019"/>
 
|-
 
| 2-chloronitrobenzene (2-ClNB) || -0.485<ref name="Schwarzenbach1990"/> || 0.342<ref name="Schwarzenbach1990"/> || -0.824<ref name="Schwarzenbach1990"/> ||2.412<ref name="Hartenbach2008"/> ||
 
|-
 
| 3-chloronitrobenzene (3-ClNB) || -0.405<ref name="Schwarzenbach1990"/> || 1.491<ref name="Schwarzenbach1990"/> || 0.114<ref name="Schwarzenbach1990"/> || ||
 
|-
 
| 4-chloronitrobenzene (4-ClNB) || -0.450<ref name="Schwarzenbach1990"/> || 1.041<ref name="Schwarzenbach1990"/> || -0.301<ref name="Schwarzenbach1990"/> || 2.988<ref name="Hartenbach2008"/> ||
 
|-
 
| 2-acetylnitrobenzene (2-AcNB) || -0.470<ref name="Schwarzenbach1990"/> || 0.519<ref name="Schwarzenbach1990"/> || -0.456<ref name="Schwarzenbach1990"/> || ||
 
|-
 
| 3-acetylnitrobenzene (3-AcNB) || -0.405<ref name="Schwarzenbach1990"/> || 1.663<ref name="Schwarzenbach1990"/> || 0.398<ref name="Schwarzenbach1990"/> || ||
 
|-
 
| 4-acetylnitrobenzene (4-AcNB) || -0.360<ref name="Schwarzenbach1990"/> || 2.519<ref name="Schwarzenbach1990"/> || 1.477<ref name="Schwarzenbach1990"/> || ||
 
|-
 
| 2-nitrophenol (2-NP) || || 0.568 (0.079)<ref name="Schwarzenbach1990"/> || || ||
 
|-
 
| 4-nitrophenol (4-NP) || || -0.699 (-1.301)<ref name="Schwarzenbach1990"/> || || ||
 
|-
 
| 4-methyl-2-nitrophenol (4-Me-2-NP) || || 0.748 (0.176)<ref name="Schwarzenbach1990"/> || || ||
 
|-
 
| 4-chloro-2-nitrophenol (4-Cl-2-NP) || || 1.602 (1.114)<ref name="Schwarzenbach1990"/> || || ||
 
|-
 
| 5-fluoro-2-nitrophenol (5-Cl-2-NP) || || 0.447 (-0.155)<ref name="Schwarzenbach1990"/> || || ||
 
|-
 
| 2,4,6-trinitrotoluene (TNT) || -0.280<ref name="Schwarzenbach2016"/> || || 2.869<ref name="Hofstetter1999"/> || 5.204<ref name="Hartenbach2008"/> ||
 
|-
 
| 2-amino-4,6-dinitrotoluene (2-A-4,6-DNT) || -0.400<ref name="Schwarzenbach2016"/> || || 0.987<ref name="Hofstetter1999"/> || ||
 
|-
 
| 4-amino-2,6-dinitrotoluene (4-A-2,6-DNT) || -0.440<ref name="Schwarzenbach2016"/>  || || 0.079<ref name="Hofstetter1999"/> || ||
 
|-
 
| 2,4-diamino-6-nitrotoluene (2,4-DA-6-NT) || -0.505<ref name="Schwarzenbach2016"/> || || -1.678<ref name="Hofstetter1999"/> || ||
 
|-
 
| 2,6-diamino-4-nitrotoluene (2,6-DA-4-NT) || -0.495<ref name="Schwarzenbach2016"/> || || -1.252<ref name="Hofstetter1999"/> || ||
 
|-
 
| 1,3-dinitrobenzene (1,3-DNB) || -0.345<ref name="Hofstetter1999"/> || || 1.785<ref name="Hofstetter1999"/> || ||
 
|-
 
| 1,4-dinitrobenzene (1,4-DNB) || -0.257<ref name="Hofstetter1999"/> || || 3.839<ref name="Hofstetter1999"/> || ||
 
|-
 
| 2-nitroaniline (2-NANE) || < -0.560<ref name="Hofstetter1999"/> || || -2.638<ref name="Hofstetter1999"/> || ||
 
|-
 
| 3-nitroaniline (3-NANE) || -0.500<ref name="Hofstetter1999"/> || || -1.367<ref name="Hofstetter1999"/> || ||
 
|-
 
| 1,2-dinitrobenzene (1,2-DNB) || -0.290<ref name="Hofstetter1999"/> || || || 5.407<ref name="Hartenbach2008"/> ||
 
|-
 
| 4-nitroanisole (4-NAN) || || -0.661<ref name="Murillo-Gelvez2019"/> || || 1.220<ref name="Murillo-Gelvez2019"/> ||
 
|-
 
| 2-amino-4-nitroanisole (2-A-4-NAN) || || -0.924<ref name="Murillo-Gelvez2019"/> || || 1.150<ref name="Murillo-Gelvez2019"/> || 2.190<ref name="Murillo-Gelvez2019"/>
 
|-
 
| 4-amino-2-nitroanisole (4-A-2-NAN) || || || ||1.610<ref name="Murillo-Gelvez2019"/> || 2.360<ref name="Murillo-Gelvez2019"/>
 
|-
 
| 2-chloro-4-nitroaniline (2-Cl-5-NANE) || || -0.863<ref name="Murillo-Gelvez2019"/> || || 1.250<ref name="Murillo-Gelvez2019"/> || 2.210<ref name="Murillo-Gelvez2019"/>
 
|-
 
| N-methyl-4-nitroaniline (MNA) || || -1.740<ref name="Murillo-Gelvez2019"/> || || -0.260<ref name="Murillo-Gelvez2019"/> || 0.692<ref name="Murillo-Gelvez2019"/>
 
|-
 
| 3-nitro-1,2,4-triazol-5-one (NTO) || || || || 5.701 (1.914)<ref name="Murillo-Gelvez2021"/> ||
 
|-
 
| Hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) || || || || -0.349<ref name="Kwon2008"/> ||
 
|}
 
  
[[File:AbioMCredFig5.png | thumb |500px|Figure 5. Relative reduction rate constants of the NACs/MCs listed in Table 1 for AHQDS<sup>–</sup>. Rate constants are compared with respect to RDX. Abbreviations of NACs/MCs as listed in Table 1.]]
+
===Test Organism Species and Life Stages===
Most of the current knowledge about MC degradation is derived from studies using NACs. The reduction kinetics of only four MCs, namely TNT, N-methyl-4-nitroaniline (MNA), NTO, and RDX, have been investigated with hydroquinones. Of these four MCs, only the reduction rates of MNA and TNT have been modeled<ref name="Hofstetter1999"/><ref name="Murillo-Gelvez2019"/><ref name="Riefler2000">Riefler, R.G., and Smets, B.F., 2000. Enzymatic Reduction of 2,4,6-Trinitrotoluene and Related Nitroarenes: Kinetics Linked to One-Electron Redox Potentials. Environmental Science and Technology, 34(18), pp. 3900–3906. [https://doi.org/10.1021/es991422f DOI: 10.1021/es991422f]</ref><ref name="Salter-Blanc2015">Salter-Blanc, A.J., Bylaska, E.J., Johnston, H.J., and Tratnyek, P.G., 2015. Predicting Reduction Rates of Energetic Nitroaromatic Compounds Using Calculated One-Electron Reduction Potentials. Environmental Science and Technology, 49(6), pp. 3778–3786.  [https://doi.org/10.1021/es505092s DOI: 10.1021/es505092s]&nbsp;&nbsp; [https://pubs.acs.org/doi/pdf/10.1021/es505092s Open access article.]</ref>.
+
Practitioners can also select different organism species and life stages for use in the iTIE system, depending on site characteristics and study goals. The iTIE system can accommodate various small test organisms, including embryo-stage fish and most macroinvertebrates. The following common toxicity tests can be adapted for application within iTIE systems<ref>U.S. Environmental Protection Agency, Office of Solid Waste and Emergency Response, 1994. Catalogue of Standard Toxicity Tests for Ecological Risk Assessment. ECO Update, 2(2), 4 pages. Publication No. 9345.0.05I [https://www.epa.gov/sites/default/files/2015-09/documents/v2no2.pdf Free Download]&nbsp; [[Media: usepa1994.pdf | Report.pdf]]</ref>.
 +
<ul><u>Freshwater acute toxicity:</u></ul>
 +
*[[Wikipedia: Daphnia magna | ''Daphnia magna'']] or [[Wikipedia: Daphnia pulex | ''Daphnia pulex'']] 24-, 48-, and 96-hour survival
 +
<ul><u>Freshwater chronic toxicity:</u></ul>
 +
*[[Wikipedia: Ceriodaphnia dubia | ''Ceriodaphnia dubia'']]  7-day survival and reproduction
 +
*''D. magna'' 7-day survival and reproduction
 +
*[[Wikipedia: Fathead minnow | ''Pimephales promelas'']] 7-day embryo-larval survival and teratogenicity
 +
*[[Wikipedia: Hyalella azteca | ''Hyalella Azteca'']] 10- or 30-day survival and reproduction
 +
<ul><u>Marine acute toxicity:</u></ul>
 +
*[[Wikipedia: Americamysis bahia | ''Americamysis bahia'']] 24- and 48-hour survival
 +
<ul><u>Marine chronic toxicity:</u></ul>
 +
*''Americamysis'' survival, growth and fecundity
 +
*[[Wikipedia: Topsmelt silverside | ''Atherinops affinis'']] embryo-larval survival and growth
  
Using the rate constants obtained with AHQDS<sup>–</sup>, a relative reactivity trend can be obtained (Figure 5). RDX is the slowest reacting MC in Table 1, hence it was selected to calculate the relative rates of reaction (i.e., log ''k<sub>NAC/MC</sub>'' – log ''k<sub>RDX</sub>''). If only the MCs in Figure 5 are considered, the reactivity spans 6 orders of magnitude following the trend: RDX ≈ MNA < NTO<sup>–</sup> < DNAN < TNT < NTO. The rate constant for DNAN reduction by AHQDS<sup>–</sup> is not yet published and hence not included in Table 1. Note that speciation of NACs/MCs can significantly affect their reduction rates. Upon deprotonation, the NAC/MC becomes negatively charged and less reactive as an oxidant (i.e., less prone to accept an electron). As a result, the second-order rate constant can decrease by 0.5-0.6 log unit in the case of nitrophenols and approximately 4 log units in the case of NTO (numbers in parentheses in Table 1)<ref name="Schwarzenbach1990"/><ref name="Murillo-Gelvez2021"/>.
+
Acute toxicity is quantifiable via organism survival rates immediately following the termination of an iTIE system field deployment. Chronic toxicity can be quantified by continuing to culture and observe test organisms in-lab. Common chronic endpoints include stunted growth, altered development such as teratogenicity in larval fish, decreased reproduction rates, and changes in gene expression.  
  
==Ferruginous Reductants==
+
Several gene expression endpoints have been detectable in bioassays following an iTIE system deployment and in-lab culturing period. Steigmeyer ''et al.''<ref name="SteigmeyerEtAl2017"/> were able to detect changes in the expression of two genes in ''D. magna'' after a 24-hour exposure to bisphenol A. In a separate study, Nichols<ref>Nichols, E., 2023. Methods for Identification and Prioritization of Stressors at Impaired Sites. Masters thesis, University of Michigan. University of Michigan Library Deep Blue Documents. [https://deepblue.lib.umich.edu/bitstream/handle/2027.42/176142/Nichols_Elizabeth_thesis.pdf?sequence=1 Free Download]&nbsp; [[Media: Nichols2023.pdf | Report.pdf]]</ref> found a significant decline in acetylcholinesterase activity in ''H. azteca'' after a 24-hour exposure to chlorpyrifos. These results indicate a potential to adapt other gene expression bioassays for use in conjunction with iTIE system field exposures to prove stressor-causality linkages.
{| class="wikitable mw-collapsible" style="float:right; margin-left:40px; text-align:center;"
 
|+ Table&nbsp;2.&nbsp;Logarithm&nbsp;of&nbsp;second-order rate constants for reduction of NACs and MCs by dissolved Fe(II) complexes with the stoichiometry of ligand and iron in square brackets
 
|-
 
! rowspan="2" | Compound
 
! rowspan="2" | E<sub>H</sub><sup>1'</sup>  (V)
 
! Cysteine<ref name="Naka2008"/></br>[FeL<sub>2</sub>]<sup>2-</sup>
 
! Thioglycolic acid<ref name="Naka2008"/></br>[FeL<sub>2</sub>]<sup>2-</sup>
 
! DFOB<ref name="Kim2009"/></br>[FeHL]<sup>0</sup>
 
! AcHA<ref name="Kim2009"/></br>[FeL<sub>3</sub>]<sup>-</sup>
 
! Tiron <sup>a</sup></br>[FeL<sub>2</sub>]<sup>6-</sup>
 
! Fe-Porphyrin <sup>b</sup>
 
|-
 
! colspan="6" | Fe(II)-Ligand [log ''k<sub>R</sub>'' (M<sup>-1</sup>s<sup>-1</sup>)]
 
|-
 
| Nitrobenzene || -0.485<ref name="Schwarzenbach1990"/> || -0.347 || 0.874 || 2.235 || -0.136 || 1.424<ref name="Gao2021">Gao, Y., Zhong, S., Torralba-Sanchez, T.L., Tratnyek, P.G., Weber, E.J., Chen, Y., and Zhang, H., 2021. Quantitative structure activity relationships (QSARs) and machine learning models for abiotic reduction of organic compounds by an aqueous Fe(II) complex. Water Research, 192, p. 116843.  [https://doi.org/10.1016/j.watres.2021.116843 DOI: 10.1016/j.watres.2021.116843]</ref></br>4.000<ref name="Salter-Blanc2015"/> || -0.018<ref name="Schwarzenbach1990"/></br>0.026<ref name="Salter-Blanc2015"/>
 
|-
 
| 2-nitrotoluene || -0.590<ref name="Schwarzenbach1990"/> || || || || || || -0.602<ref name="Schwarzenbach1990"/>
 
|-
 
| 3-nitrotoluene || -0.475<ref name="Schwarzenbach1990"/> || -0.434 || 0.767 || 2.106 || -0.229 || 1.999<ref name="Gao2021"/></br>3.800<ref name="Salter-Blanc2015"/> || 0.041<ref name="Schwarzenbach1990"/>
 
|-
 
| 4-nitrotoluene || -0.500<ref name="Schwarzenbach1990"/> || -0.652 || 0.528 || 2.013 || -0.402 || 1.446<ref name="Gao2021"/></br>3.500<ref name="Salter-Blanc2015"/> || -0.174<ref name="Schwarzenbach1990"/>
 
|-
 
| 2-chloronitrobenzene || -0.485<ref name="Schwarzenbach1990"/> || || || || || || 0.944<ref name="Schwarzenbach1990"/>
 
|-
 
| 3-chloronitrobenzene || -0.405<ref name="Schwarzenbach1990"/> || 0.360 || 1.810 || 2.888 || 0.691 || 2.882<ref name="Gao2021"/></br>4.900<ref name="Salter-Blanc2015"/> || 0.724<ref name="Schwarzenbach1990"/>
 
|-
 
| 4-chloronitrobenzene || -0.450<ref name="Schwarzenbach1990"/> || 0.230 || 1.415 || 2.512 || 0.375 || 3.937<ref name="Gao2021"/></br>4.581<ref name="Naka2006"/> || 0.431<ref name="Schwarzenbach1990"/></br>0.289<ref name="Salter-Blanc2015"/>
 
|-
 
| 2-acetylnitrobenzene || -0.470<ref name="Schwarzenbach1990"/> || || || || || || 1.377<ref name="Schwarzenbach1990"/>
 
|-
 
| 3-acetylnitrobenzene || -0.405<ref name="Schwarzenbach1990"/> || || || || || || 0.799<ref name="Schwarzenbach1990"/>
 
|-
 
| 4-acetylnitrobenzene || -0.360<ref name="Schwarzenbach1990"/> || 0.965 || 2.771 || || 1.872 || 5.028<ref name="Gao2021"/></br>6.300<ref name="Salter-Blanc2015"/> || 1.693<ref name="Schwarzenbach1990"/>
 
|-
 
| RDX || -0.550<ref name="Uchimiya2010">Uchimiya, M., Gorb, L., Isayev, O., Qasim, M.M., and Leszczynski, J., 2010.  One-electron standard reduction potentials of nitroaromatic and cyclic nitramine explosives. Environmental Pollution, 158(10), pp. 3048–3053.  [https://doi.org/10.1016/j.envpol.2010.06.033 DOI: 10.1016/j.envpol.2010.06.033]</ref> || || || || || 2.212<ref name="Gao2021"/></br>2.864<ref name="Kim2007"/> ||
 
|-
 
| HMX || -0.660<ref name="Uchimiya2010"/> || || || || || -2.762<ref name="Gao2021"/> ||
 
|-
 
| TNT || -0.280<ref name="Schwarzenbach2016"/> || || || || || 7.427<ref name="Gao2021"/> || 2.050<ref name="Salter-Blanc2015"/>
 
|-
 
| 1,3-dinitrobenzene || -0.345<ref name="Hofstetter1999"/> || || || || || || 1.220<ref name="Salter-Blanc2015"/>
 
|-
 
| 2,4-dinitrotoluene || -0.380<ref name="Schwarzenbach2016"/> || || || || || 5.319<ref name="Gao2021"/> || 1.156<ref name="Salter-Blanc2015"/>
 
|-
 
| Nitroguanidine (NQ) || -0.700<ref name="Uchimiya2010"/> || || || || || -0.185<ref name="Gao2021"/> ||
 
|-
 
| 2,4-dinitroanisole (DNAN) || -0.400<ref name="Uchimiya2010"/> || || || || || || 1.243<ref name="Salter-Blanc2015"/>
 
|-
 
| colspan="8" style="text-align:left; background-color:white;" | Notes:</br>''<sup>a</sup>'' 4,5-dihydroxybenzene-1,3-disulfonate (Tiron). ''<sup>b</sup>'' meso-tetra(N-methyl-pyridyl)iron porphin in cysteine.
 
|}
 
{| class="wikitable mw-collapsible" style="float:left; margin-right:40px; text-align:center;"
 
|+ Table&nbsp;3.&nbsp;Rate constants for the reduction of MCs by iron minerals
 
|-
 
! MC
 
! Iron Mineral
 
! Iron mineral loading</br>(g/L)
 
! Surface area</br>(m<sup>2</sup>/g)
 
! Fe(II)<sub>aq</sub> initial</br>(mM) ''<sup>b</sup>''
 
! Fe(II)<sub>aq</sub> after 24 h</br>(mM) ''<sup>c</sup>''
 
! Fe(II)<sub>aq</sub> sorbed</br>(mM) ''<sup>d</sup>''
 
! pH
 
! Buffer
 
! Buffer</br>(mM)
 
! MC initial</br>(&mu;M) ''<sup>e</sup>''
 
! log ''k<sub>obs</sub>''</br>(h<sup>-1</sup>) ''<sup>f</sup>''
 
! log ''k<sub>SA</sub>''</br>(Lh<sup>-1</sup>m<sup>-2</sup>) ''<sup>g</sup>''
 
|-
 
| TNT<ref name="Hofstetter1999"/> || Goethite || 0.64 || 17.5 || 1.5 || || || 7.0 || MOPS || 25 || 50 || 1.200 || 0.170
 
|-
 
| RDX<ref name="Gregory2004"/> || Magnetite || 1.00 || 44 || 0.1 || 0 || 0.10 || 7.0 || HEPES || 50 || 50 || -3.500 || -5.200
 
|-
 
| RDX<ref name="Gregory2004"/> || Magnetite || 1.00 || 44 || 0.2 || 0.02 || 0.18 || 7.0 || HEPES || 50 || 50 || -2.900 || -4.500
 
|-
 
| RDX<ref name="Gregory2004"/> || Magnetite || 1.00 || 44 || 0.5 || 0.23 || 0.27 || 7.0 || HEPES || 50 || 50 || -1.900 || -3.600
 
|-
 
| RDX<ref name="Gregory2004"/> || Magnetite || 1.00 || 44 || 1.5 || 0.94 || 0.56 || 7.0 || HEPES || 50 || 50 || -1.400 || -3.100
 
|-
 
| RDX<ref name="Gregory2004"/> || Magnetite || 1.00 || 44 || 3.0 || 1.74 || 1.26 || 7.0 || HEPES || 50 || 50 || -1.200 || -2.900
 
|-
 
| RDX<ref name="Gregory2004"/> || Magnetite || 1.00 || 44 || 5.0 || 3.38 || 1.62 || 7.0 || HEPES || 50 || 50 || -1.100 || -2.800
 
|-
 
| RDX<ref name="Gregory2004"/> || Magnetite || 1.00 || 44 || 10.0 || 7.77 || 2.23 || 7.0 || HEPES || 50 || 50 || -1.000 || -2.600
 
|-
 
| RDX<ref name="Gregory2004"/> || Magnetite || 1.00 || 44 || 1.6 || 1.42 || 0.16 || 6.0 || MES || 50 || 50 || -2.700 || -4.300
 
|-
 
| RDX<ref name="Gregory2004"/> || Magnetite || 1.00 || 44 || 1.6 || 1.34 || 0.24 || 6.5 || MOPS || 50 || 50 || -1.800 || -3.400
 
|-
 
| RDX<ref name="Gregory2004"/> || Magnetite || 1.00 || 44 || 1.6 || 1.21 || 0.37 || 7.0 || MOPS || 50 || 50 || -1.200 || -2.900
 
|-
 
| RDX<ref name="Gregory2004"/> || Magnetite || 1.00 || 44 || 1.6 || 1.01 || 0.57 || 7.0 || HEPES || 50 || 50 || -1.200 || -2.800
 
|-
 
| RDX<ref name="Gregory2004"/> || Magnetite || 1.00 || 44 || 1.6 || 0.76 || 0.82 || 7.5 || HEPES || 50 || 50 || -0.490 || -2.100
 
|-
 
| RDX<ref name="Gregory2004"/> || Magnetite || 1.00 || 44 || 1.6 || 0.56 || 1.01 || 8.0 || HEPES || 50 || 50 || -0.590 || -2.200
 
|-
 
| NG<ref name="Oh2004"/> || Magnetite || 4.00 || 0.56|| 4.0 || || || 7.4 || HEPES || 90 || 226 || ||
 
|-
 
| NG<ref name="Oh2008"/> || Pyrite || 20.00 || 0.53 || || || || 7.4 || HEPES || 100 || 307 || -2.213 || -3.238
 
|-
 
| TNT<ref name="Oh2008"/> || Pyrite || 20.00 || 0.53 ||  || || || 7.4 || HEPES || 100 || 242 || -2.812 || -3.837
 
|-
 
| RDX<ref name="Oh2008"/> || Pyrite || 20.00 || 0.53 || || ||  || 7.4 || HEPES || 100 || 201 || -3.058 || -4.083
 
|-
 
| RDX<ref name="Larese-Casanova2008"/> || Carbonate Green Rust || 5.00 || 36 || || || || 7.0 || || || 100 || ||
 
|-
 
| RDX<ref name="Larese-Casanova2008"/> || Sulfate Green Rust || 5.00 || 20 || || || || 7.0 || || || 100 || ||
 
|-
 
| DNAN<ref name="Khatiwada2018"/> || Sulfate Green Rust || 10.00 || || || || || 8.4 || || || 500 || ||
 
|-
 
| NTO<ref name="Khatiwada2018"/> || Sulfate Green Rust || 10.00 || || || || || 8.4 || || || 500 || ||
 
|-
 
| DNAN<ref name="Berens2019"/> || Magnetite || 2.00 || 17.8 || 1.0 || || || 7.0 || NaHCO<sub>3</sub> || 10 || 200 || -0.100 || -1.700
 
|-
 
| DNAN<ref name="Berens2019"/> || Mackinawite || 1.50 || || || || || 7.0 || NaHCO<sub>3</sub> || 10 || 200 || 0.061 ||
 
|-
 
| DNAN<ref name="Berens2019"/> || Goethite || 1.00 || 103.8 || 1.0 || || || 7.0 || NaHCO<sub>3</sub> || 10 || 200 || 0.410 || -1.600
 
|-
 
| RDX<ref name="Strehlau2018"/> || Magnetite || 0.62 ||  || 1.0 ||  ||  || 7.0 || NaHCO<sub>3</sub> || 10 || 17.5 || -1.100 ||
 
|-
 
| RDX<ref name="Strehlau2018"/> || Magnetite || 0.62 ||  ||  ||  ||  || 7.0 || MOPS || 50 || 17.5 || -0.270 ||
 
|-
 
| RDX<ref name="Strehlau2018"/> || Magnetite || 0.62 ||  || 1.0 ||  ||  || 7.0 || MOPS || 10 || 17.6 || -0.480 ||
 
|-
 
| NTO<ref name="Cardenas-Hernandez2020"/> || Hematite || 1.00 || 5.7 || 1.0 || 0.92 || 0.08 || 5.5 || MES || 50 || 30 || -0.550 || -1.308
 
|-
 
| NTO<ref name="Cardenas-Hernandez2020"/> || Hematite || 1.00 || 5.7 || 1.0 || 0.85 || 0.15 || 6.0 || MES || 50 || 30 || 0.619 || -0.140
 
|-
 
| NTO<ref name="Cardenas-Hernandez2020"/> || Hematite || 1.00 || 5.7 || 1.0 || 0.9 || 0.10 || 6.5 || MES || 50 || 30 || 1.348 || 0.590
 
|-
 
| NTO<ref name="Cardenas-Hernandez2020"/> || Hematite || 1.00 || 5.7 || 1.0 || 0.77 || 0.23 || 7.0 || MOPS || 50 || 30 || 2.167 || 1.408
 
|-
 
| NTO<ref name="Cardenas-Hernandez2020"/> || Hematite ''<sup>a</sup>'' || 1.00 || 5.7 ||  || 1.01 ||  || 5.5 || MES || 50 || 30 || -1.444 || -2.200
 
|-
 
| NTO<ref name="Cardenas-Hernandez2020"/> || Hematite ''<sup>a</sup>'' || 1.00 || 5.7 ||  || 0.97 ||  || 6.0 || MES || 50 || 30 || -0.658 || -1.413
 
|-
 
| NTO<ref name="Cardenas-Hernandez2020"/> || Hematite ''<sup>a</sup>'' || 1.00 || 5.7 ||  || 0.87 ||  || 6.5 || MES || 50 || 30 || 0.068 || -0.688
 
|-
 
| NTO<ref name="Cardenas-Hernandez2020"/> || Hematite ''<sup>a</sup>'' || 1.00 || 5.7 ||  || 0.79 ||  || 7.0 || MOPS || 50 || 30 || 1.210 || 0.456
 
|-
 
| RDX<ref name="Tong2021"/>  || Mackinawite || 0.45 ||  ||  ||  ||  || 6.5 || NaHCO<sub>3</sub> || 10 || 250 || -0.092 ||
 
|-
 
| RDX<ref name="Tong2021"/>  || Mackinawite || 0.45 ||  ||  ||  ||  || 7.0 || NaHCO<sub>3</sub> || 10 || 250 || 0.009 ||
 
|-
 
| RDX<ref name="Tong2021"/>  || Mackinawite || 0.45 ||  ||  ||  ||  || 7.5 || NaHCO<sub>3</sub> || 10 || 250 || 0.158 ||
 
|-
 
| RDX<ref name="Tong2021"/>  || Green Rust || 5 ||  ||  ||  ||  || 6.5 || NaHCO<sub>3</sub> || 10 || 250 || -1.301 ||
 
|-
 
| RDX<ref name="Tong2021"/>  || Green Rust || 5 ||  ||  ||  ||  || 7.0 || NaHCO<sub>3</sub> || 10 || 250 || -1.097 ||
 
|-
 
| RDX<ref name="Tong2021"/>  || Green Rust || 5 ||  ||  ||  ||  || 7.5 || NaHCO<sub>3</sub> || 10 || 250 || -0.745 ||
 
|-
 
| RDX<ref name="Tong2021"/>  || Goethite || 0.5 ||  || 1 || 1 ||  || 6.5 || NaHCO<sub>3</sub> || 10 || 250 || -0.921 ||
 
|-
 
| RDX<ref name="Tong2021"/>  || Goethite || 0.5 ||  || 1 || 1 ||  || 7.0 || NaHCO<sub>3</sub> || 10 || 250 || -0.347 ||
 
|-
 
| RDX<ref name="Tong2021"/>  || Goethite || 0.5 ||  || 1 || 1 ||  || 7.5 || NaHCO<sub>3</sub> || 10 || 250 || 0.009 ||
 
|-
 
| RDX<ref name="Tong2021"/>  || Hematite || 0.5 ||  || 1 || 1 ||  || 6.5 || NaHCO<sub>3</sub> || 10 || 250 || -0.824 ||
 
|-
 
| RDX<ref name="Tong2021"/>  || Hematite || 0.5 ||  || 1 || 1 ||  || 7.0 || NaHCO<sub>3</sub> || 10 || 250 || -0.456 ||
 
|-
 
| RDX<ref name="Tong2021"/>  || Hematite || 0.5 ||  || 1 || 1 ||  || 7.5 || NaHCO<sub>3</sub> || 10 || 250 || -0.237 ||
 
|-
 
| RDX<ref name="Tong2021"/>  || Magnetite || 2 ||  || 1 || 1 ||  || 6.5 || NaHCO<sub>3</sub> || 10 || 250 || -1.523 ||
 
|-
 
| RDX<ref name="Tong2021"/>  || Magnetite || 2 ||  || 1 || 1 ||  || 7.0 || NaHCO<sub>3</sub> || 10 || 250 || -0.824 ||
 
|-
 
| RDX<ref name="Tong2021"/>  || Magnetite || 2 || || 1 || 1 ||  || 7.5 || NaHCO<sub>3</sub> || 10 || 250 || -0.229 ||
 
|-
 
| DNAN<ref name="Menezes2021"/> || Mackinawite || 4.28 || 0.25 ||  ||  ||  || 6.5 || NaHCO<sub>3</sub> || 8.5 + 20% CO<sub>2</sub>(g) || 400 || 0.836 || 0.806
 
|-
 
| DNAN<ref name="Menezes2021"/> || Mackinawite || 4.28 || 0.25 ||  ||  ||  || 7.6 || NaHCO<sub>3</sub> || 95.2 + 20% CO<sub>2</sub>(g) || 400 || 0.762 || 0.732
 
|-
 
| DNAN<ref name="Menezes2021"/> || Commercial FeS || 5.00 || 0.214 ||  ||  ||  || 6.5 || NaHCO<sub>3</sub> || 8.5 + 20% CO<sub>2</sub>(g) || 400 || 0.477 || 0.447
 
|-
 
| DNAN<ref name="Menezes2021"/> || Commercial FeS || 5.00 || 0.214 ||  ||  ||  || 7.6 || NaHCO<sub>3</sub> || 95.2 + 20% CO<sub>2</sub>(g) || 400 || 0.745 || 0.716
 
|-
 
| NTO<ref name="Menezes2021"/> || Mackinawite || 4.28 || 0.25 ||  ||  ||  || 6.5 || NaHCO<sub>3</sub> || 8.5 + 20% CO<sub>2</sub>(g) || 1000 || 0.663 || 0.633
 
|-
 
| NTO<ref name="Menezes2021"/> || Mackinawite || 4.28 || 0.25 ||  ||  ||  || 7.6 || NaHCO<sub>3</sub> || 95.2 + 20% CO<sub>2</sub>(g) || 1000 || 0.521 || 0.491
 
|-
 
| NTO<ref name="Menezes2021"/> || Commercial FeS || 5.00 || 0.214 ||  ||  ||  || 6.5 || NaHCO<sub>3</sub> || 8.5 + 20% CO<sub>2</sub>(g) || 1000 || 0.492 || 0.462
 
|-
 
| NTO<ref name="Menezes2021"/> || Commercial FeS || 5.00 || 0.214 ||  ||  ||  || 7.6 || NaHCO<sub>3</sub> || 95.2 + 20% CO<sub>2</sub>(g) || 1000 || 0.427 || 0.398
 
|-
 
| colspan="13" style="text-align:left; background-color:white;" | Notes:</br>''<sup>a</sup>'' Dithionite-reduced hematite; experiments conducted in the presence of 1 mM sulfite. ''<sup>b</sup>'' Initial aqueous Fe(II); not added for Fe(II) bearing minerals. ''<sup>c</sup>'' Aqueous Fe(II) after 24h of equilibration. ''<sup>d</sup>'' Difference between b and c. ''<sup>e</sup>'' Initial nominal MC concentration. ''<sup>f</sup>'' Pseudo-first order rate constant. ''<sup>g</sup>'' Surface area normalized rate constant calculated as ''k<sub>Obs</sub>'' '''/''' (surface area concentration) or ''k<sub>Obs</sub>'' '''/''' (surface area × mineral loading).
 
|}
 
{| class="wikitable mw-collapsible" style="float:right; margin-left:40px; text-align:center;"
 
|+ Table&nbsp;4.&nbsp;Rate constants for the reduction of NACs by iron oxides in the presence of aqueous Fe(II)
 
|-
 
! NAC ''<sup>a</sup>''
 
! Iron Oxide
 
! Iron oxide loading</br>(g/L)
 
! Surface area</br>(m<sup>2</sup>/g)
 
! Fe(II)<sub>aq</sub> initial</br>(mM) ''<sup>b</sup>''
 
! Fe(II)<sub>aq</sub> after 24 h</br>(mM) ''<sup>c</sup>''
 
! Fe(II)<sub>aq</sub> sorbed</br>(mM) ''<sup>d</sup>''
 
! pH
 
! Buffer
 
! Buffer</br>(mM)
 
! NAC initial</br>(μM) ''<sup>e</sup>''
 
! log ''k<sub>obs</sub>''</br>(h<sup>-1</sup>) ''<sup>f</sup>''
 
! log ''k<sub>SA</sub>''</br>(Lh<sup>-1</sup>m<sup>-2</sup>) ''<sup>g</sup>''
 
|-
 
| NB<ref name="Klausen1995"/> || Magnetite || 0.200 || 56.00 || 1.5000 ||  ||  || 7.00 || Phosphate || 10 || 50 || 1.05E+00 || 7.75E-04
 
|-
 
| 4-ClNB<ref name="Klausen1995"/> || Magnetite || 0.200 || 56.00 || 1.5000 ||  ||  || 7.00 || Phosphate || 10 || 50 || 1.14E+00 || 8.69E-02
 
|-
 
| 4-ClNB<ref name="Hofstetter1999"/> || Goethite || 0.640 || 17.50 || 1.5000 ||  ||  || 7.00 || MOPS || 25 || 50 || -1.01E-01 || -1.15E+00
 
|-
 
| 4-ClNB<ref name="Elsner2004"/>  || Goethite || 1.500 || 16.20 || 1.2400 || 0.9600 || 0.2800 || 7.20 || MOPS || 1.2 || 0.5 - 3 || 1.68E+00 || 2.80E-01
 
|-
 
| 4-ClNB<ref name="Elsner2004"/>  || Hematite || 1.800 || 13.70 || 1.0400 || 1.0100 || 0.0300 || 7.20 || MOPS || 1.2 || 0.5 - 3 || -2.32E+00 || -3.72E+00
 
|-
 
| 4-ClNB<ref name="Elsner2004"/>  || Lepidocrocite || 1.400 || 17.60 || 1.1400 || 1.0000 || 0.1400 || 7.20 || MOPS || 1.2 || 0.5 - 3 || 1.51E+00 || 1.20E-01
 
|-
 
| 4-CNNB<ref name="Colón2006"/> || Ferrihydrite || 0.004 || 292.00 || 0.3750 || 0.3500 || 0.0300 || 7.97 || HEPES || 25 || 15 || -7.47E-01 || -8.61E-01
 
|-
 
| 4-CNNB<ref name="Colón2006"/> || Ferrihydrite || 0.004 || 292.00 || 0.3750 || 0.3700 || 0.0079 || 7.67 || HEPES || 25 || 15 || -1.51E+00 || -1.62E+00
 
|-
 
| 4-CNNB<ref name="Colón2006"/> || Ferrihydrite || 0.004 || 292.00 || 0.3750 || 0.3600 || 0.0200 || 7.50 || MOPS || 25 || 15 || -2.15E+00 || -2.26E+00
 
|-
 
| 4-CNNB<ref name="Colón2006"/> || Ferrihydrite || 0.004 || 292.00 || 0.3750 || 0.3600 || 0.0120 || 7.28 || MOPS || 25 || 15 || -3.08E+00 || -3.19E+00
 
|-
 
| 4-CNNB<ref name="Colón2006"/> || Ferrihydrite || 0.004 || 292.00 || 0.3750 || 0.3700 || 0.0004 || 7.00 || MOPS || 25 || 15 || -3.22E+00 || -3.34E+00
 
|-
 
| 4-CNNB<ref name="Colón2006"/> || Ferrihydrite || 0.004 || 292.00 || 0.3750 || 0.3700 || 0.0024 || 6.80 || MOPSO || 25 || 15 || -3.72E+00 || -3.83E+00
 
|-
 
| 4-CNNB<ref name="Colón2006"/> || Ferrihydrite || 0.004 || 292.00 || 0.3750 || 0.3700 || 0.0031 || 6.60 || MES || 25 || 15 || -3.83E+00 || -3.94E+00
 
|-
 
| 4-CNNB<ref name="Colón2006"/> || Ferrihydrite || 0.020 || 292.00 || 0.3750 || 0.3700 || 0.0031 || 6.60 || MES || 25 || 15 || -3.83E+00 || -4.60E+00
 
|-
 
| 4-CNNB<ref name="Colón2006"/> || Ferrihydrite || 0.110 || 292.00 || 0.3750 || 0.3700 || 0.0032 || 6.60 || MES || 25 || 15 || -1.57E+00 || -3.08E+00
 
|-
 
| 4-CNNB<ref name="Colón2006"/> || Ferrihydrite || 0.220 || 292.00 || 0.3750 || 0.3700 || 0.0040 || 6.60 || MES || 25 || 15 || -1.12E+00 || -2.93E+00
 
|-
 
| 4-CNNB<ref name="Colón2006"/> || Ferrihydrite || 0.551 || 292.00 || 0.3750 || 0.3700 || 0.0092 || 6.60 || MES || 25 || 15 || -6.18E-01 || -2.82E+00
 
|-
 
| 4-CNNB<ref name="Colón2006"/> || Ferrihydrite || 1.099 || 292.00 || 0.3750 || 0.3500 || 0.0240 || 6.60 || MES || 25 || 15 || -3.66E-01 || -2.87E+00
 
|-
 
| 4-CNNB<ref name="Colón2006"/> || Ferrihydrite || 1.651 || 292.00 || 0.3750 || 0.3400 || 0.0340 || 6.60 || MES || 25 || 15 || -8.35E-02 || -2.77E+00
 
|-
 
| 4-CNNB<ref name="Colón2006"/> || Ferrihydrite || 2.199 || 292.00 || 0.3750 || 0.3300 || 0.0430 || 6.60 || MES || 25 || 15 || -3.11E-02 || -2.84E+00
 
|-
 
| 4-CNNB<ref name="Colón2006"/> || Hematite || 0.038 || 34.00 || 0.3750 || 0.3320 || 0.0430 || 7.97 || HEPES || 25 || 15 || 1.63E+00 || 1.52E+00
 
|-
 
| 4-CNNB<ref name="Colón2006"/> || Hematite || 0.038 || 34.00 || 0.3750 || 0.3480 || 0.0270 || 7.67 || HEPES || 25 || 15 || 1.26E+00 || 1.15E+00
 
|-
 
| 4-CNNB<ref name="Colón2006"/> || Hematite || 0.038 || 34.00 || 0.3750 || 0.3470 || 0.0280 || 7.50 || MOPS || 25 || 15 || 7.23E-01 || 6.10E-01
 
|-
 
| 4-CNNB<ref name="Colón2006"/> || Hematite || 0.038 || 34.00 || 0.3750 || 0.3680 || 0.0066 || 7.28 || MOPS || 25 || 15 || 4.53E-02 || -6.86E-02
 
|-
 
| 4-CNNB<ref name="Colón2006"/> || Hematite || 0.038 || 34.00 || 0.3750 || 0.3710 || 0.0043 || 7.00 || MOPS || 25 || 15 || -3.12E-01 || -4.26E-01
 
|-
 
| 4-CNNB<ref name="Colón2006"/> || Hematite || 0.038 || 34.00 || 0.3750 || 0.3710 || 0.0042 || 6.80 || MOPSO || 25 || 15 || -7.75E-01 || -8.89E-01
 
|-
 
| 4-CNNB<ref name="Colón2006"/> || Hematite || 0.038 || 34.00 || 0.3750 || 0.3680 || 0.0069 || 6.60 || MES || 25 || 15 || -1.39E+00 || -1.50E+00
 
|-
 
| 4-CNNB<ref name="Colón2006"/> || Hematite || 0.038 || 34.00 || 0.3750 || 0.3750 || 0.0003 || 6.10 || MES || 25 || 15 || -2.77E+00 || -2.88E+00
 
|-
 
| 4-CNNB<ref name="Colón2006"/> || Hematite || 0.016 || 34.00 || 0.3750 || 0.3730 || 0.0024 || 6.60 || MES || 25 || 15 || -3.20E+00 || -2.95E+00
 
|-
 
| 4-CNNB<ref name="Colón2006"/> || Hematite || 0.024 || 34.00 || 0.3750 || 0.3690 || 0.0064 || 6.60 || MES || 25 || 15 || -2.74E+00 || -2.66E+00
 
|-
 
| 4-CNNB<ref name="Colón2006"/> || Hematite || 0.033 || 34.00 || 0.3750 || 0.3680 || 0.0069 || 6.60 || MES || 25 || 15 || -1.39E+00 || -1.43E+00
 
|-
 
| 4-CNNB<ref name="Colón2006"/> || Hematite || 0.177 || 34.00 || 0.3750 || 0.3640 || 0.0110 || 6.60 || MES || 25 || 15 || 3.58E-01 || -4.22E-01
 
|-
 
| 4-CNNB<ref name="Colón2006"/> || Hematite || 0.353 || 34.00 || 0.3750 || 0.3630 || 0.0120 || 6.60 || MES || 25 || 15 || 9.97E-01|| -8.27E-02
 
|-
 
| 4-CNNB<ref name="Colón2006"/> || Hematite || 0.885 || 34.00 || 0.3750 || 0.3480 || 0.0270 || 6.60 || MES || 25 || 15 || 1.34E+00 || -1.34E-01
 
|-
 
| 4-CNNB<ref name="Colón2006"/> || Hematite || 1.771 || 34.00 || 0.3750 || 0.3380 || 0.0370 || 6.60 || MES || 25 || 15 || 1.78E+00 || 3.59E-03
 
|-
 
| 4-CNNB<ref name="Colón2006"/> || Lepidocrocite || 0.027 || 49.00 || 0.3750 || 0.3460 || 0.0290 || 7.97 || HEPES || 25 || 15 || 1.31E+00 || 1.20E+00
 
|-
 
| 4-CNNB<ref name="Colón2006"/> || Lepidocrocite || 0.027 || 49.00 || 0.3750 || 0.3610 || 0.0140 || 7.67 || HEPES || 25 || 15 || 5.82E-01 || 4.68E-01
 
|-
 
| 4-CNNB<ref name="Colón2006"/> || Lepidocrocite || 0.027 || 49.00 || 0.3750 || 0.3480 || 0.0270 || 7.50 || MOPS || 25 || 15 || 4.92E-02 || -6.47E-02
 
|-
 
| 4-CNNB<ref name="Colón2006"/> || Lepidocrocite || 0.027 || 49.00 || 0.3750 || 0.3640 || 0.0110 || 7.28 || MOPS || 25 || 15 || 1.62E+00 || -4.90E-01
 
|-
 
| 4-CNNB<ref name="Colón2006"/> || Lepidocrocite || 0.027 || 49.00 || 0.3750 || 0.3640 || 0.0110 || 7.00 || MOPS || 25 || 15 || -1.25E+00 || -1.36E+00
 
|-
 
| 4-CNNB<ref name="Colón2006"/> || Lepidocrocite || 0.027 || 49.00 || 0.3750 || 0.3620 || 0.0130 || 6.80 || MOPSO || 25 || 15 || -1.74E+00 || -1.86E+00
 
|-
 
| 4-CNNB<ref name="Colón2006"/> || Lepidocrocite || 0.027 || 49.00 || 0.3750 || 0.3740 || 0.0015 || 6.60 || MES || 25 || 15 || -2.58E+00 || -2.69E+00
 
|-
 
| 4-CNNB<ref name="Colón2006"/> || Lepidocrocite || 0.027 || 49.00 || 0.3750 || 0.3700 || 0.0046 || 6.10 || MES || 25 || 15 || -3.80E+00 || -3.92E+00
 
|-
 
| 4-CNNB<ref name="Colón2006"/> || Lepidocrocite || 0.020 || 49.00 || 0.3750 || 0.3740 || 0.0014 || 6.60 || MES || 25 || 15 || -2.58E+00 || -2.57E+00
 
|-
 
| 4-CNNB<ref name="Colón2006"/> || Lepidocrocite || 11.980 || 49.00 || 0.3750 || 0.3620 || 0.0130 || 6.60 || MES || 25 || 15 || -5.78E-01 || -3.35E+00
 
|-
 
| 4-CNNB<ref name="Colón2006"/> || Lepidocrocite || 0.239 || 49.00 || 0.3750 || 0.3530 || 0.0220 || 6.60 || MES || 25 || 15 || -2.78E-02 || -1.10E+00
 
|-
 
| 4-CNNB<ref name="Colón2006"/> || Lepidocrocite || 0.600 || 49.00 || 0.3750 || 0.3190 || 0.0560 || 6.60 || MES || 25 || 15 || 3.75E-01 || -1.09E+00
 
|-
 
| 4-CNNB<ref name="Colón2006"/> || Lepidocrocite || 1.198 || 49.00 || 0.3750 || 0.2700 || 0.1050 || 6.60 || MES || 25 || 15 || 5.05E-01 || -1.26E+00
 
|-
 
| 4-CNNB<ref name="Colón2006"/> || Lepidocrocite || 1.798 || 49.00 || 0.3750 || 0.2230 || 0.1520 || 6.60 || MES || 25 || 15 || 5.56E-01 || -1.39E+00
 
|-
 
| 4-CNNB<ref name="Colón2006"/> || Lepidocrocite || 2.388 || 49.00 || 0.3750 || 0.1820 || 0.1930 || 6.60 || MES || 25 || 15 || 5.28E-01 || -1.54E+00
 
|-
 
| 4-CNNB<ref name="Colón2006"/> || Goethite || 0.025 || 51.00 || 0.3750 || 0.3440 || 0.0310 || 7.97 || HEPES || 25 || 15 || 9.21E-01 || 8.07E-01
 
|-
 
| 4-CNNB<ref name="Colón2006"/> || Goethite || 0.025 || 51.00 || 0.3750 || 0.3660 || 0.0094 || 7.67 || HEPES || 25 || 15 || 3.05E-01 || 1.91E-01
 
|-
 
| 4-CNNB<ref name="Colón2006"/> || Goethite || 0.025 || 51.00 || 0.3750 || 0.3570 || 0.0180 || 7.50 || MOPS || 25 || 15 || -9.96E-02 || -2.14E-01
 
|-
 
| 4-CNNB<ref name="Colón2006"/> || Goethite || 0.025 || 51.00 || 0.3750 || 0.3640 || 0.0110 || 7.28 || MOPS || 25 || 15 || -8.18E-01 || -9.32E-01
 
|-
 
| 4-CNNB<ref name="Colón2006"/> || Goethite || 0.025 || 51.00 || 0.3750 || 0.3670 || 0.0084 || 7.00 || MOPS || 25 || 15 || -1.61E+00 || -1.73E+00
 
|-
 
| 4-CNNB<ref name="Colón2006"/> || Goethite || 0.025 || 51.00 || 0.3750 || 0.3750 || 0.0004 || 6.80 || MOPSO || 25 || 15 || -1.82E+00 || -1.93E+00
 
|-
 
| 4-CNNB<ref name="Colón2006"/> || Goethite || 0.025 || 51.00 || 0.3750 || 0.3730 || 0.0018 || 6.60 || MES || 25 || 15 || -2.26E+00 || -2.37E+00
 
|-
 
| 4-CNNB<ref name="Colón2006"/> || Goethite || 0.025 || 51.00 || 0.3750 || 0.3670 || 0.0076 || 6.10 || MES || 25 || 15 || -3.56E+00 || -3.67E+00
 
|-
 
| 4-CNNB<ref name="Colón2006"/> || Goethite || 0.020 || 51.00 || 0.3750 || 0.3680 || 0.0069 || 6.60 || MES || 25 || 15 || -2.26E+00 || -2.27E+00
 
|-
 
| 4-CNNB<ref name="Colón2006"/> || Goethite || 0.110 || 51.00 || 0.3750 || 0.3660 || 0.0090 || 6.60 || MES || 25 || 15 || -3.19E-01 || -1.07E+00
 
|-
 
| 4-CNNB<ref name="Colón2006"/> || Goethite || 0.220 || 51.00 || 0.3750 || 0.3540 || 0.0210 || 6.60 || MES || 25 || 15 || 5.00E-01 || -5.50E-01
 
|-
 
| 4-CNNB<ref name="Colón2006"/> || Goethite || 0.551 || 51.00 || 0.3750 || 0.3220 || 0.0530 || 6.60 || MES || 25 || 15 || 1.03E+00 || -4.15E-01
 
|-
 
| 4-CNNB<ref name="Colón2006"/> || Goethite || 1.100 || 51.00 || 0.3750 || 0.2740 || 0.1010 || 6.60 || MES || 25 || 15 || 1.46E+00 || -2.88E-01
 
|-
 
| 4-CNNB<ref name="Colón2006"/> || Goethite || 1.651 || 51.00 || 0.3750 || 0.2330 || 0.1420 || 6.60 || MES || 25 || 15 || 1.66E+00 || -2.70E-01
 
|-
 
| 4-CNNB<ref name="Colón2006"/> || Goethite || 2.196 || 51.00 || 0.3750 || 0.1910 || 0.1840 || 6.60 || MES || 25 || 15 || 1.83E+00 || -2.19E-01
 
|-
 
| 4-CNNB<ref name="Colón2006"/> || Goethite || 0.142 || 51.00 || 0.3750 ||  ||  || 6.60 || MES || 25 || 15 || 1.99E-01 || -6.61E-01
 
|-
 
| 4-AcNB<ref name="Colón2006"/> || Goethite || 0.142 || 51.00 || 0.3750 ||  ||  || 6.60 || MES || 25 || 15 || -6.85E-02 || -9.28E-01
 
|-
 
| 4-ClNB<ref name="Colón2006"/> || Goethite || 0.142 || 51.00 || 0.3750 ||  ||  || 6.60 || MES || 25 || 15 || -5.47E-01 || -1.41E+00
 
|-
 
| 4-BrNB<ref name="Colón2006"/> || Goethite || 0.142 || 51.00 || 0.3750 ||  ||  || 6.60 || MES || 25 || 15 || -5.73E-01 || -1.43E+00
 
|-
 
| NB<ref name="Colón2006"/> || Goethite || 0.142 || 51.00 || 0.3750 ||  ||  || 6.60 || MES || 25 || 15 || -7.93E-01 || -1.65E+00
 
|-
 
| 4-MeNB<ref name="Colón2006"/> || Goethite || 0.142 || 51.00 || 0.3750 ||  ||  || 6.60 || MES || 25 || 15 || -9.79E-01 || -1.84E+00
 
|-
 
| 4-ClNB<ref name="Jones2016"/>  || Goethite || 0.040 || 186.75 || 1.0000 || 0.8050 || 0.1950 || 7.00 ||  ||  ||  || 1.05E+00 || -3.20E-01
 
|-
 
| 4-ClNB<ref name="Jones2016"/>  || Goethite || 7.516 || 16.10 || 1.0000 || 0.9260 || 0.0740 || 7.00 ||  ||  ||  || 1.14E+00 || 0.00E+00
 
|-
 
| 4-ClNB<ref name="Jones2016"/>  || Ferrihydrite || 0.111 || 252.60 || 1.0000 || 0.6650 || 0.3350 || 7.00 ||  ||  ||  || 1.05E+00 || -1.56E+00
 
|-
 
| 4-ClNB<ref name="Jones2016"/>  || Lepidocrocite || 2.384 || 60.40 || 1.0000 || 0.9250 || 0.0750 || 7.00 ||  ||  ||  || 1.14E+00 || -8.60E-01
 
|-
 
| 4-ClNB<ref name="Fan2016"/> || Goethite || 10.000 || 14.90 || 1.0000 ||  ||  || 7.20 || HEPES || 10 || 10 - 50 || 2.26E+00 || 8.00E-02
 
|-
 
| 4-ClNB<ref name="Fan2016"/> || Goethite || 3.000 || 14.90 || 1.0000 ||  ||  || 7.20 || HEPES || 10 || 10 - 50 || 2.38E+00 || 7.30E-01
 
|-
 
| 4-ClNB<ref name="Fan2016"/> || Lepidocrocite || 2.700 || 16.20 || 1.0000 ||  ||  || 7.20 || HEPES || 10 || 10 - 50 || 9.20E-01 || -7.20E-01
 
|-
 
| 4-ClNB<ref name="Fan2016"/> || Lepidocrocite || 10.000 || 16.20 || 1.0000 ||  ||  || 7.20 || HEPES || 10 || 10 - 50 || 1.03E+00 || -1.18E+00
 
|-
 
| 4-ClNB<ref name="Strehlau2016"/> || Goethite || 0.325 || 140.00 || 1.0000 ||  ||  || 7.00 || Bicarbonate || 10 || 100 || 1.14E+00 || -1.79E+00
 
|-
 
| 4-ClNB<ref name="Strehlau2016"/> || Goethite || 0.325 || 140.00 || 1.0000 ||  ||  || 6.50 || Bicarbonate || 10 || 100 || 1.11E+00 || -2.10E+00
 
|-
 
| NB<ref name="Stewart2018"/> || Goethite || 0.500 || 30.70 || 0.1000 || 0.1120 || 0.0090 || 6.00 || MES || 25 || 12 || -1.42E+00 || -2.61E+00
 
|-
 
| NB<ref name="Stewart2018"/> || Goethite || 0.500 || 30.70 || 0.5000 || 0.5150 || 0.0240 || 6.00 || MES || 25 || 15 || -7.45E-01 || -1.93E+00
 
|-
 
| NB<ref name="Stewart2018"/> || Goethite || 0.500 || 30.70 || 1.0000 || 1.0280 || 0.0140 || 6.00 || MES || 25 || 19 || -7.45E-01 || -1.93E+00
 
|-
 
| NB<ref name="Stewart2018"/> || Goethite || 1.000 || 30.70 || 0.1000 || 0.0960 || 0.0260 || 6.00 || MES || 25 || 13 || -1.12E+00 || -2.61E+00
 
|-
 
| NB<ref name="Stewart2018"/> || Goethite || 1.000 || 30.70 || 0.5000 || 0.4890 || 0.0230 || 6.00 || MES || 25 || 14 || -5.53E-01 || -2.04E+00
 
|-
 
| NB<ref name="Stewart2018"/> || Goethite || 1.000 || 30.70 || 1.0000 || 0.9870 || 0.0380 || 6.00 || MES || 25 || 19 || -2.52E-01 || -1.74E+00
 
|-
 
| NB<ref name="Stewart2018"/> || Goethite || 2.000 || 30.70 || 0.1000 || 0.0800 || 0.0490 || 6.00 || MES || 25 || 11 || -8.86E-01 || -2.67E+00
 
|-
 
| NB<ref name="Stewart2018"/> || Goethite || 2.000 || 30.70 || 0.6000 || 0.4890 || 0.0640 || 6.00 || MES || 25 || 14 || -1.08E-01 || -1.90E+00
 
|-
 
| NB<ref name="Stewart2018"/> || Goethite || 2.000 || 30.70 || 1.1000 || 0.9870 || 0.0670 || 6.00 || MES || 25 || 14 || 2.30E-01 || -1.56E+00
 
|-
 
| NB<ref name="Stewart2018"/> || Goethite || 4.000 || 30.70 || 0.1000 || 0.0600 || 0.0650 || 6.00 || MES || 25 || 11 || -8.89E-01 || -2.98E+00
 
|-
 
| NB<ref name="Stewart2018"/> || Goethite || 4.000 || 30.70 || 0.6000 || 0.3960 || 0.1550 || 6.00 || MES || 25 || 17 || 1.43E-01 || -1.95E+00
 
|-
 
| NB<ref name="Stewart2018"/> || Goethite || 4.000 || 30.70 || 1.0000 || 0.8360 || 0.1450 || 6.00 || MES || 25 || 16 || 4.80E-01 || -1.61E+00
 
|-
 
| NB<ref name="Stewart2018"/> || Goethite || 4.000 || 30.70 || 5.6000 || 5.2110 || 0.3790 || 6.00 || MES || 25 || 15 || 1.17E+00 || -9.19E-01
 
|-
 
| NB<ref name="Stewart2018"/> || Goethite || 1.000 || 30.70 || 0.1000 || 0.0870 || 0.0300 || 6.50 || MES || 25 || 5.5 || -1.74E-01 || -1.66E+00
 
|-
 
| NB<ref name="Stewart2018"/> || Goethite || 1.000 || 30.70 || 0.5000 || 0.4920 || 0.0300 || 6.50 || MES || 25 || 15 || 3.64E-01 || -1.12E+00
 
|-
 
| NB<ref name="Stewart2018"/> || Goethite || 1.000 || 30.70 || 1.0000 || 0.9390 || 0.0650 || 6.50 || MES || 25 || 18 || 6.70E-01 || -8.17E-01
 
|-
 
| NB<ref name="Stewart2018"/> || Goethite || 2.000 || 30.70 || 0.1000 || 0.0490 || 0.0730 || 6.50 || MES || 25 || 5.2 || 3.01E-01 || -1.49E+00
 
|-
 
| NB<ref name="Stewart2018"/> || Goethite || 2.000 || 30.70 || 0.5000 || 0.4640 || 0.0710 || 6.50 || MES || 25 || 14 || 8.85E-01 || -9.03E-01
 
|-
 
| NB<ref name="Stewart2018"/> || Goethite || 2.000 || 30.70 || 1.0000 || 0.9130 || 0.1280 || 6.50 || MES || 25 || 16 || 1.12E+00 || -6.64E-01
 
|-
 
| NB<ref name="Stewart2018"/> || Goethite || 1.000 || 30.70 || 0.1000 || 0.0630 || 0.0480 || 7.00 || MOPS || 25 || 5.3 || 6.12E-01 || -8.75E-01
 
|-
 
| NB<ref name="Stewart2018"/> || Goethite || 1.000 || 30.70 || 0.5000 || 0.4690 || 0.0520 || 7.00 || MOPS || 25 || 9 || 1.51E+00 || 2.07E-02
 
|-
 
| NB<ref name="Stewart2018"/> || Goethite || 1.000 || 30.70 || 1.0000 || 0.9360 || 0.1090 || 7.00 || MOPS || 25 || 18 || 1.33E+00 || -1.53E-01
 
|-
 
| NB<ref name="Stewart2018"/> || Goethite || 2.000 || 30.70 || 0.1000 || 0.0290 || 0.0880 || 7.00 || MOPS || 25 || 12 || 6.85E-01 || -1.10E+00
 
|-
 
| NB<ref name="Stewart2018"/> || Goethite || 2.000 || 30.70 || 0.5000 || 0.3950 || 0.1450 || 7.00 || MOPS || 25 || 15 || 1.59E+00 || -1.95E-01
 
|-
 
| colspan="13" style="text-align:left; background-color:white;" | Notes:</br>''<sup>a</sup>'' The NACs are Nitrobenzene (NB), 4-chloronitrobenzene(4-ClNB), 4-cyanonitrobenzene (4-CNNB), 4-acetylnitrobenzene (4-AcNB), 4-bromonitrobenzene (4-BrNB), 4-nitrotoluene (4-MeNB). ''<sup>b</sup>'' Initial aqueous Fe(II). ''<sup>c</sup>'' Aqueous Fe(II) after 24h of equilibration. ''<sup>d</sup>'' Difference between b and c. ''<sup>e</sup>'' Initial nominal NAC concentration. ''<sup>f</sup>'' Pseudo-first order rate constant. ''<sup>g</sup>'' Surface area normalized rate constant calculated as ''k<sub>Obs</sub>'' '''/''' (surface area × mineral loading).
 
|}
 
  
Iron(II)&nbsp;can&nbsp;be&nbsp;complexed by a myriad of organic ligands and may thereby become more reactive towards MCs and other pollutants. The reactivity of an Fe(II)-organic complex depends on the relative preference of the organic ligand for Fe(III) versus Fe(II)<ref name="Kim2009"/>. Since the majority of naturally occurring ligands complex Fe(III) more strongly than Fe(II), the reduction potential of the resulting Fe(III) complex is lower than that of aqueous Fe(III); therefore, complexation by organic ligands often renders Fe(II) a stronger reductant thermodynamically<ref name="Strathmann2011">Strathmann, T.J., 2011. Redox Reactivity of Organically Complexed Iron(II) Species with Aquatic Contaminants. Aquatic Redox Chemistry, American Chemical Society,1071(14), pp. 283-313.  [https://doi.org/10.1021/bk-2011-1071.ch014 DOI: 10.1021/bk-2011-1071.ch014]</ref>. The reactivity of dissolved Fe(II)-organic complexes towards NACs/MCs has been investigated. The intrinsic, second-order rate constants and one electron reduction potentials are listed in Table 2.
+
===Cost Effectiveness Study===
 +
Burton ''et al.''<ref name="BurtonEtAl2020"/> conducted a cost effectiveness study comparing the iTIE technology with the traditional US EPA Phase 1 TIE method. Comparisons were based on the estimated time required to complete various sub-tasks within each method. Sub-tasks included organism care, equipment preparation, mobilization and deployment, test maintenance, test termination, demobilization, and test termination analyses. It was ultimately estimated that the iTIE protocol requires 47% less time (67 fewer hours) to complete than the Phase 1 TIE method, with the largest time differences in equipment preparation, deployment, test maintenance, and demobilization. It is important to note that the iTIE method may require additional initial costs for equipment and training.
  
In addition to forming organic complexes, iron is ubiquitous in minerals. Iron-bearing minerals play an important role in controlling the environmental fate of contaminants through adsorption<ref name="Linker2015">Linker, B.R., Khatiwada, R., Perdrial, N., Abrell, L., Sierra-Alvarez, R., Field, J.A., and Chorover, J., 2015. Adsorption of novel insensitive munitions compounds at clay mineral and metal oxide surfaces. Environmental Chemistry, 12(1), pp. 74–84.  [https://doi.org/10.1071/EN14065 DOI: 10.1071/EN14065]</ref><ref name="Jenness2020">Jenness, G.R., Giles, S.A., and Shukla, M.K., 2020. Thermodynamic Adsorption States of TNT and DNAN on Corundum and Hematite. The Journal of Physical Chemistry C, 124(25), pp. 13837–13844.  [https://doi.org/10.1021/acs.jpcc.0c04512 DOI: 10.1021/acs.jpcc.0c04512]</ref> and reduction<ref name="Gorski2011">Gorski, C.A., and Scherer, M.M., 2011. Fe<sup>2+</sup> Sorption at the Fe Oxide-Water Interface: A Revised Conceptual Framework. Aquatic Redox Chemistry, American Chemical Society, 1071(15), pp. 315–343[https://doi.org/10.1021/bk-2011-1071.ch015 DOI: 10.1021/bk-2011-1071.ch015]</ref> processes. Studies have shown that aqueous Fe(II) itself cannot reduce NACs/MCs at circumneutral pH<ref name="Klausen1995"/><ref name="Gregory2004">Gregory, K.B., Larese-Casanova, P., Parkin, G.F., and Scherer, M.M., 2004. Abiotic Transformation of Hexahydro-1,3,5-trinitro-1,3,5-triazine by Fe<sup>II</sup> Bound to Magnetite. Environmental Science and Technology, 38(5), pp. 1408–1414.  [https://doi.org/10.1021/es034588w DOI: 10.1021/es034588w]</ref> but in the presence of an iron oxide (e.g., goethite, hematite, lepidocrocite, ferrihydrite, or magnetite), NACs<ref name="Colón2006"/><ref name="Klausen1995"/><ref name="Strehlau2016"/><ref name="Elsner2004"/><ref name="Hofstetter2006"/> and MCs such as TNT<ref name="Hofstetter1999"/>, RDX<ref name="Gregory2004"/>, DNAN<ref name="Berens2019">Berens, M.J., Ulrich, B.A., Strehlau, J.H., Hofstetter, T.B., and Arnold, W.A., 2019. Mineral identity, natural organic matter, and repeated contaminant exposures do not affect the carbon and nitrogen isotope fractionation of 2,4-dinitroanisole during abiotic reduction. Environmental Science: Processes and Impacts, 21(1), pp. 51-62. [https://doi.org/10.1039/C8EM00381E DOI: 10.1039/C8EM00381E]</ref>, and NG<ref name="Oh2004">Oh, S.-Y., Cha, D.K., Kim, B.J., and Chiu, P.C., 2004. Reduction of Nitroglycerin with Elemental Iron:  Pathway, Kinetics, and Mechanisms. Environmental Science and Technology, 38(13), pp. 3723–3730. [https://doi.org/10.1021/es0354667 DOI: 10.1021/es0354667]</ref> can be rapidly reduced. Unlike ferric oxides, Fe(II)-bearing minerals including clays<ref name="Hofstetter2006"/><ref name="Schultz2000"/><ref name="Luan2015a"/><ref name="Luan2015b"/><ref name="Hofstetter2003"/><ref name="Neumann2008"/><ref name="Hofstetter2008"/>, green rust<ref name="Larese-Casanova2008"/><ref name="Khatiwada2018">Khatiwada, R., Root, R.A., Abrell, L., Sierra-Alvarez, R., Field, J.A., and Chorover, J., 2018. Abiotic reduction of insensitive munition compounds by sulfate green rust. Environmental Chemistry, 15(5), pp. 259–266.  [https://doi.org/10.1071/EN17221 DOI: 10.1071/EN17221]</ref>, mackinawite<ref name="Elsner2004"/><ref name="Berens2019"/><ref name="Menezes2021">Menezes, O., Yu, Y., Root, R.A., Gavazza, S., Chorover, J., Sierra-Alvarez, R., and Field, J.A., 2021. Iron(II) monosulfide (FeS) minerals reductively transform the insensitive munitions compounds 2,4-dinitroanisole (DNAN) and 3-nitro-1,2,4-triazol-5-one (NTO). Chemosphere, 285, p. 131409.  [https://doi.org/10.1016/j.chemosphere.2021.131409 DOI: 10.1016/j.chemosphere.2021.131409]</ref> and pyrite<ref name="Elsner2004"/><ref name="Oh2008">Oh, S.-Y., Chiu, P.C., and Cha, D.K., 2008. Reductive transformation of 2,4,6-trinitrotoluene,  hexahydro-1,3,5-trinitro-1,3,5-triazine, and nitroglycerin by pyrite and magnetite. Journal of hazardous materials, 158(2-3), pp. 652–655.  [https://doi.org/10.1016/j.jhazmat.2008.01.078 DOI: 10.1016/j.jhazmat.2008.01.078]</ref> do not need aqueous Fe(II) to be reactive toward NACs/MCs. However, upon oxidation, sulfate green rust was converted into lepidocrocite<ref name="Khatiwada2018"/>, and mackinawite into goethite<ref name="Menezes2021"/>, suggesting that aqueous Fe(II) coupled to Fe(III) oxides might be at least partially responsible for continued degradation of NACs/MCs in the subsurface once the parent reductant (e.g., green rust or iron sulfide) oxidizes.
+
==Field Application==
 +
[[File: CraneFig6.png | thumb | left | 400px | Figure 6. iTIES deployment at the Rouge River, Detroit, MIIn the foreground is the iTIE Cooler Sub-System, which contains iTIE resin treatments and test organism groups, as well as the oxygenation coil and sample collection bottles. Next to the iTIE Cooler are the two pump cases. The Trident can be seen above the pump cases, installed in the river channel near shore.]]
 +
The&nbsp;iTIE&nbsp;system&nbsp;has&nbsp;been successfully deployed at a variety of marine and freshwater sites during the proof-of-concept phase of prototype development. One example is the 2024 iTIE system deployment completed near the mouth of the Rouge River in Detroit, MI (Figure 6). The Rouge River watershed has a long history of industrialization, with a legacy of chemical dumping, channelization, damming, and urban runoff<ref>Ridgway, J., Cave, K., DeMaria, A., O’Meara, J., Hartig, J. H., 2018. The Rouge River Area of Concern—A multi-year, multi-level successful approach to restoration of Impaired Beneficial Uses. Aquatic Ecosystem Health and Management, 21(4), pp. 398-408. [https://doi.org/10.1080/14634988.2018.1528816 doi: 10.1080/14634988.2018.1528816]</ref>. This has led to degraded environmental conditions, with previous detections of a wide range of chemicals including heavy metals and various organics.
  
The reaction conditions and rate constants for a list of studies on MC reduction by iron oxide-aqueous Fe(II)  redox couples and by other Fe(II)-containing minerals are shown in Table 3<ref name="Hofstetter1999"/><ref name="Larese-Casanova2008"/><ref name="Gregory2004"/><ref name="Berens2019"/><ref name="Oh2008"/><ref name="Strehlau2018">Strehlau, J.H., Berens, M.J., and Arnold, W.A., 2018. Mineralogy and buffer identity effects on RDX kinetics and intermediates during reaction with natural and synthetic magnetite. Chemosphere, 213, pp. 602–609.  [https://doi.org/10.1016/j.chemosphere.2018.09.139 DOI: 10.1016/j.chemosphere.2018.09.139]</ref><ref name="Cardenas-Hernandez2020">Cárdenas-Hernandez, P.A., Anderson, K.A., Murillo-Gelvez, J., di Toro, D.M., Allen, H.E., Carbonaro, R.F., and Chiu, P.C., 2020. Reduction of 3-Nitro-1,2,4-Triazol-5-One (NTO) by the Hematite–Aqueous Fe(II) Redox Couple. Environmental Science and Technology, 54(19), pp. 12191–12201. [https://doi.org/10.1021/acs.est.0c03872 DOI: 10.1021/acs.est.0c03872]</ref>. Unlike hydroquinones and Fe(II) complexes, where second-order rate constants can be readily calculated, the reduction rate constants of NACs/MCs in mineral suspensions are often specific to the experimental conditions used and are usually reported as BET surface area-normalized reduction rate constants (''k<sub>SA</sub>''). In the case of iron oxide-Fe(II) redox couples, reduction rate constants have been shown to increase with pH (specifically, with [OH<sup>– </sup>]<sup>2</sup>) and aqueous Fe(II) concentration, both of which correspond to a decrease in the system's reduction potential<ref name="Colón2006"/><ref name="Gorski2016"/><ref name="Cardenas-Hernandez2020"/>.
+
[[File: CraneFig7.png | thumb | 300px | Figure 7. Survival and healthy development of ''P. promelas'' embryos and larvae following a 48-hour iTIE exposure near the mouth of the Rouge River. Organisms were exposed to site porewater as embryos for 48 hours and cultured post-exposure for an additional 5 days.]]
 +
[[File: CraneFig8.png | thumb | 300px | Figure 8. Survival of ''C. dilutus'' larvae after an iTIE exposure near the mouth of the Rouge River. Organisms were exposed to site porewater for 48 hours and cultured post-exposure for an additional 5 days. Error bars show standard deviation.]]
 +
An&nbsp;iTIE&nbsp;system&nbsp;deployment&nbsp;was designed and completed to determine which chemical classes are most responsible for causing toxicity at the site. Resin treatments included glass wool (inert, non-fractionating substance), Chelex (metals sorption), Oasis HLB (general organics sorption), and Oasis WAX (organics sorption, with a high affinity for PFAS). The study utilized fathead minnow (''P. promelas'') embryos, due to their relative sensitivity to metals and PAHs, as well as second-instar midge ([[Wikipedia: Chironomus |''Chironomus dilutus'']]) larvae due to their relative sensitivity to PFAS.  
  
For minerals that contain structural iron(II) and can reduce pollutants in the absence of aqueous Fe(II), the observed rates of reduction increased with increasing structural Fe(II) content, as seen with iron-bearing clays<ref name="Luan2015a"/><ref name="Luan2015b"/> and green rust<ref name="Larese-Casanova2008"/>. This dependency on Fe(II) content allows for the derivation of second-order rate constants, as shown on Table 3 for the reduction of RDX by green rust<ref name="Larese-Casanova2008"/>, and the development of reduction potential (E<sub>H</sub>)-based models<ref name="Luan2015a"/><ref name="Gorski2012a">Gorski, C.A., Aeschbacher, M., Soltermann, D., Voegelin, A., Baeyens, B., Marques Fernandes, M., Hofstetter, T.B., and Sander, M., 2012. Redox Properties of Structural Fe in Clay Minerals. 1. Electrochemical Quantification of Electron-Donating and -Accepting Capacities of Smectites. Environmental Science and Technology, 46(17), pp. 9360–9368.  [https://doi.org/10.1021/es3020138 DOI: 10.1021/es3020138]</ref><ref name="Gorski2012b">Gorski, C.A., Klüpfel, L., Voegelin, A., Sander, M., and Hofstetter, T.B., 2012. Redox Properties of Structural Fe in Clay Minerals. 2. Electrochemical and Spectroscopic Characterization of Electron Transfer Irreversibility in Ferruginous Smectite, SWa-1. Environmental Science and Technology, 46(17), pp. 9369–9377. [https://doi.org/10.1021/es302014u DOI: 10.1021/es302014u]</ref><ref name="Gorski2013">Gorski, C.A., Klüpfel, L.E., Voegelin, A., Sander, M. and Hofstetter, T.B., 2013. Redox Properties of Structural Fe in Clay Minerals: 3. Relationships between Smectite Redox and Structural Properties. Environmental Science and Technology, 47(23), pp. 13477–13485.  [https://doi.org/10.1021/es403824x DOI: 10.1021/es403824x]</ref>, where E<sub>H</sub> represents the reduction potential of the iron-bearing clays. Iron-bearing expandable clay minerals represent a special case, which in addition to reduction can remove NACs/MCs through adsorption. This is particularly important for planar NACs/MCs that contain multiple electron-withdrawing nitro groups and can form strong electron donor-acceptor (EDA) complexes with the clay surface<ref name="Hofstetter2006"/><ref name="Hofstetter2003"/><ref name="Neumann2008"/>.
+
The test organisms were exposed to fractionated porewater ''in situ'' for 48 hours. Following exposure, organisms were cultured for an additional five days, and survival was recorded (Figures 7 and 8). Moderate declines in survival were seen in both species in the glass wool treatment, indicating toxicity at the site. For ''P. promelas'', the highest proportion of healthy development occurred in the Chelex treatment, supporting the hypothesis that metals are a dominant cause of toxicity. ''C. dilutus'' had the greatest survival in the Oasis WAX treatment, suggesting that an organic stressor class like PFAS is also present at harmful concentrations in the river.
  
Although the second-order rate constants derived for Fe(II)-bearing minerals may allow comparison among different studies, they may not reflect changes in reactivity due to variations in surface area, pH, and the presence of ions. Anions such as bicarbonate<ref name="Larese-Casanova2008"/><ref name="Strehlau2018"/><ref name="Chen2020">Chen, G., Hofstetter, T.B., and Gorski, C.A., 2020. Role of Carbonate in Thermodynamic Relationships Describing Pollutant Reduction Kinetics by Iron Oxide-Bound Fe<sup>2+</sup>. Environmental Science and Technology, 54(16), pp. 10109–10117.  [https://doi.org/10.1021/acs.est.0c02959 DOI: 10.1021/acs.est.0c02959]</ref> and phosphate<ref name="Larese-Casanova2008"/><ref name="Bocher2004">Bocher, F., Géhin, A., Ruby, C., Ghanbaja, J., Abdelmoula, M., and Génin, J.M.R., 2004. Coprecipitation of Fe(II–III) hydroxycarbonate green rust stabilised by phosphate adsorption. Solid State Sciences, 6(1), pp. 117–124.  [https://doi.org/10.1016/j.solidstatesciences.2003.10.004 DOI: 10.1016/j.solidstatesciences.2003.10.004]</ref> are known to decrease the reactivity of iron oxides-Fe(II) redox couples and green rust. Sulfite has also been shown to decrease the reactivity of hematite-Fe(II) towards the deprotonated form of NTO (Table 3)<ref name="Cardenas-Hernandez2020"/>. Exchanging cations in iron-bearing clays can change the reactivity of these minerals by up to 7-fold<ref name="Hofstetter2006"/>. Thus, more comprehensive models are needed to account for the complexities in the subsurface environment.
+
Water chemical analyses of fractionated and unfractionated water samples were completed to support biological results. Analyses were conducted for a range of stressor classes including metals, PAHs, PCBs, an organophosphate pesticide (chlorpyrifos), a PFAS compound (PFOS) and a pyrethroid insecticide (permethrin). Of these analytes, only heavy metals and PFOS were detected. Some chemical classes including PAHs and PCBs were not detected at the site.
 +
To reach similar conclusions using traditional Phase 1 TIE methods, one would need to complete the following tests: baseline toxicity, filtration, aeration, EDTA, C18 SPE, and methanol elution of C18 SPE. The iTIE method allows the same conclusions to be drawn with significantly less time and effort required.
  
The reduction of NACs has been widely studied in the presence of different iron minerals, pH, and Fe(II)<sub>(aq)</sub> concentrations (Table 4)<ref name="Colón2006"/><ref name="Klausen1995"/><ref name="Strehlau2016"/><ref name="Elsner2004"/><ref name="Hofstetter2006"/>. Only selected NACs are included in Table 4. For more information on other NACs and ferruginous reductants, please refer to the cited references.
+
==Summary==
<br clear="right" />
+
The ''in situ'' Toxicity Identification Evaluation technology and protocol is a powerful tool that investigators can use to strengthen causal linkages between chemical stressors and ecological toxicity. By fractionating sampled water and exposing test organisms ''in situ'', investigators can gather toxicity response data while minimizing sample manipulation and accurately representing environmental conditions.
 +
<br clear="right"/>
  
 
==References==
 
==References==
Line 559: Line 98:
  
 
==See Also==
 
==See Also==
*[https://www.serdp-estcp.org/Program-Areas/Environmental-Restoration/Contaminated-Groundwater/Persistent-Contamination/ER-2617 Measuring and Predicting the Natural and Enhanced Rate and Capacity of Abiotic Reduction of Munition Constituents]
 
 
*[https://www.epa.gov/fedfac/military-munitionsunexploded-ordnance Military Munitions/Unexploded Ordnance - EPA]
 

Latest revision as of 15:58, 14 April 2026

Estimating PCE/TCE Abiotic First-Order Reductive Dechlorination Rate Constants in Clayey Soils Under Anoxic Conditions

The U.S. Department of Defense (DoD) faces many challenges in restoring aquifers at contaminated sites, often due to back-diffusion of tetrachloroethene (PCE) and trichloroethene (TCE) from low-permeability clay zones. The uptake, storage, and subsequent long-term release of these dissolved contaminants from clays are key processes in understanding the longevity, intensity, and risks associated with many persistent chlorinated ethene groundwater plumes. Although naturally occurring abiotic and biotic dechlorination processes in clays may reduce stored contaminant mass and significantly aid natural attenuation, no standardized field method currently exists to verify or quantify these reactions. It is critical to remediation design efforts to demonstrate and validate a cost-effective in situ approach for assessing these dechlorination processes using first-order rate constants. An approach was developed and applied across eight DoD sites to support Remedial Project Managers (RPMs) and regulators in evaluating natural attenuation potential in clay-rich environments.

Related Article(s):

Contributors: Dani Tran, Dr. Charles Schaefer, Dr. Charles Werth

Key Resource:

  • Schaefer, C.E, Tran, D., Nguyen, D., Latta, D.E., Werth, C.J., 2025. Evaluating Mineral and In Situ Indicators of Abiotic Dechlorination in Clayey Soils (3)

Introduction

Cost-effective methods are needed to verify the occurrence of natural dechlorination processes and quantify their dechlorination rates in clays under ambient in situ conditions in order to reliably predict their long-term influence on plume longevity and mass discharge. However, accurately determining these rates is challenging due to slow reaction kinetics, the transient nature of transformation products, and the interplay of biotic and abiotic mechanisms within the clay matrix or at clay-sand interfaces. Tools capable of quantifying these reactions and assessing their role in mitigating plume persistence would be a significant aid for long-term site management.

For reductive abiotic dechlorination under anoxic conditions, a 1% hydrochloric acid (HCl) extraction of a sample of native clay coupled with X-ray diffraction (XRD) data can be used as a screening level tool to estimate reductive dechlorination rate constants. These rate constants can be inserted into fate and transport models such as REMChlor - MD[1][2] to quantify abiotic dechlorination impacts within clay aquitards on chlorinated solvent plumes. Thus, determination of the abiotic reductive dechlorination rate constant for a particular clayey soil can be readily utilized to provide a more accurate assessment of aquifer cleanup timeframes for groundwater plumes that are being sustained by contaminant back-diffusion.

Recommended Approach

File:TranFig1.png
Figure 1: First-order rate constants for abiotic reductive dechlorination of TCE under anaerobic conditions (data from this study and prior research)
File:TranFig2.png
Figure 2: Flowchart diagram of field screening procedures

The recommended approach builds upon the methodology and findings of a recent study[3], emphasizing field-based and analytical techniques to quantify abiotic first-order reductive dechlorination rate constants for PCE and TCE in clayey soils under anoxic conditions. Key components of this evaluation are listed below:

  1. Zone Identification: The focus of the investigation should be to delineate clayey zones adjacent to hydraulically conductive zones.
  2. Ferrous Mineral Quantification: Assess ferrous mineral context in clay via 1% HCl extraction at ambient temperature over a 10-minute interval.
  3. Mineralogical Characterization: Conduct XRD analysis with the specific intent of identifying the presence of pyrite and biotite.
  4. Reduced Gas Analysis: Measurement of reduced gases such as acetylene, ethene, and ethane concentrations in clay samples. Gas-tight sampling devices (e.g., En Core® soil samplers by En Novative Technologies, Inc.) should be used to ensure sample integrity during collection and transport.

Clay samples should be collected within a few centimeters of the high-permeability interface, with optional additional sampling further inward. For mineralogical analysis, a defined interval may be collected and subsequently subsampled. To preserve sample integrity, exposure to air should be minimized during collection, transport, and handling. Homogenization should occur within an anaerobic chamber, and if subsamples are required for external analysis, they must be shipped in gas-tight, anaerobic containers.

Estimation of the abiotic reductive first-order rate constant for PCE and TCE is based on the “reactive” ferrous content in the clay. Reactive ferrous content (Fe(II)r) is estimated as shown in Equation 1:

Equation 1:       Fe(II)r = DA + XRDpyr - XRDbiotite

where DA is the ferrous content from the dilute acid (1% HCl) extraction, XRDpyr is the pyrite content from XRD analysis, and XRDbiotite is the biotite content from XRD analysis[3].

Abiotic dechlorination is unlikely to contribute to mitigating contaminant back-diffusion when reactive ferrous iron (Fe(II)r) concentrations are below 100 mg/kg (Figure 1). For Fe(II)r above 100 mg/kg, the first-order rate constant for PCE and TCE reductive dechlorination can be estimated using the correlation shown in Figure 1[4][5]. The rate constant exhibits a strong positive correlation with the logarithm of reactive Fe(II) content (Pearson’s r = 0.82), with a slope of 4.7 × 10⁻⁸ L g⁻¹ d⁻¹ (log mg kg⁻¹)⁻¹.

Figure 2 presents a decision flowchart designed to evaluate the significance and extent of abiotic reductive dechlorination. By applying Equation 1 to the dilute acid extractable Fe(II) plus measured mineral species data from clay samples, the reactive ferrous iron content (Fe(II)r) can be quantified, enabling a streamlined assessment of the extent to which abiotic processes are contributing to the mitigation of contaminant back-diffusion.

Study Design Considerations

Diagnostic Resin Treatments

Several commercially available resins have been verified for use in the iTIE system. Investigators can select resins based on stressor classes of interest at each site. Each resin selectively removes a CoC class from site water prior to organism exposure.

  • DuPont Ambersorb 560 for removal of 1,4-dioxane and other organic chemicals[6]
  • C18 for nonpolar organic chemicals
  • Bio-Rad Chelex for metals
  • Granular activated carbon for metals, general organic chemicals, sulfide[7]
  • Waters Oasis HLB for general organic chemicals[8]
  • Waters Oasis WAX for PFAS, organic chemicals of mixed polarity[9]
  • Zeolite for ammonia, other organic chemicals

Resins must be adequately conditioned prior to use. Otherwise, they may inadequately adsorb toxicants or cause stress to organisms. New resins should be tested for efficacy and toxicity before being used in an iTIE system.

Test Organism Species and Life Stages

Practitioners can also select different organism species and life stages for use in the iTIE system, depending on site characteristics and study goals. The iTIE system can accommodate various small test organisms, including embryo-stage fish and most macroinvertebrates. The following common toxicity tests can be adapted for application within iTIE systems[10].

    Freshwater acute toxicity:
    Freshwater chronic toxicity:
    Marine acute toxicity:
    Marine chronic toxicity:
  • Americamysis survival, growth and fecundity
  • Atherinops affinis embryo-larval survival and growth

Acute toxicity is quantifiable via organism survival rates immediately following the termination of an iTIE system field deployment. Chronic toxicity can be quantified by continuing to culture and observe test organisms in-lab. Common chronic endpoints include stunted growth, altered development such as teratogenicity in larval fish, decreased reproduction rates, and changes in gene expression.

Several gene expression endpoints have been detectable in bioassays following an iTIE system deployment and in-lab culturing period. Steigmeyer et al.[8] were able to detect changes in the expression of two genes in D. magna after a 24-hour exposure to bisphenol A. In a separate study, Nichols[11] found a significant decline in acetylcholinesterase activity in H. azteca after a 24-hour exposure to chlorpyrifos. These results indicate a potential to adapt other gene expression bioassays for use in conjunction with iTIE system field exposures to prove stressor-causality linkages.

Cost Effectiveness Study

Burton et al.[12] conducted a cost effectiveness study comparing the iTIE technology with the traditional US EPA Phase 1 TIE method. Comparisons were based on the estimated time required to complete various sub-tasks within each method. Sub-tasks included organism care, equipment preparation, mobilization and deployment, test maintenance, test termination, demobilization, and test termination analyses. It was ultimately estimated that the iTIE protocol requires 47% less time (67 fewer hours) to complete than the Phase 1 TIE method, with the largest time differences in equipment preparation, deployment, test maintenance, and demobilization. It is important to note that the iTIE method may require additional initial costs for equipment and training.

Field Application

Figure 6. iTIES deployment at the Rouge River, Detroit, MI. In the foreground is the iTIE Cooler Sub-System, which contains iTIE resin treatments and test organism groups, as well as the oxygenation coil and sample collection bottles. Next to the iTIE Cooler are the two pump cases. The Trident can be seen above the pump cases, installed in the river channel near shore.

The iTIE system has been successfully deployed at a variety of marine and freshwater sites during the proof-of-concept phase of prototype development. One example is the 2024 iTIE system deployment completed near the mouth of the Rouge River in Detroit, MI (Figure 6). The Rouge River watershed has a long history of industrialization, with a legacy of chemical dumping, channelization, damming, and urban runoff[13]. This has led to degraded environmental conditions, with previous detections of a wide range of chemicals including heavy metals and various organics.

Figure 7. Survival and healthy development of P. promelas embryos and larvae following a 48-hour iTIE exposure near the mouth of the Rouge River. Organisms were exposed to site porewater as embryos for 48 hours and cultured post-exposure for an additional 5 days.
Figure 8. Survival of C. dilutus larvae after an iTIE exposure near the mouth of the Rouge River. Organisms were exposed to site porewater for 48 hours and cultured post-exposure for an additional 5 days. Error bars show standard deviation.

An iTIE system deployment was designed and completed to determine which chemical classes are most responsible for causing toxicity at the site. Resin treatments included glass wool (inert, non-fractionating substance), Chelex (metals sorption), Oasis HLB (general organics sorption), and Oasis WAX (organics sorption, with a high affinity for PFAS). The study utilized fathead minnow (P. promelas) embryos, due to their relative sensitivity to metals and PAHs, as well as second-instar midge (Chironomus dilutus) larvae due to their relative sensitivity to PFAS.

The test organisms were exposed to fractionated porewater in situ for 48 hours. Following exposure, organisms were cultured for an additional five days, and survival was recorded (Figures 7 and 8). Moderate declines in survival were seen in both species in the glass wool treatment, indicating toxicity at the site. For P. promelas, the highest proportion of healthy development occurred in the Chelex treatment, supporting the hypothesis that metals are a dominant cause of toxicity. C. dilutus had the greatest survival in the Oasis WAX treatment, suggesting that an organic stressor class like PFAS is also present at harmful concentrations in the river.

Water chemical analyses of fractionated and unfractionated water samples were completed to support biological results. Analyses were conducted for a range of stressor classes including metals, PAHs, PCBs, an organophosphate pesticide (chlorpyrifos), a PFAS compound (PFOS) and a pyrethroid insecticide (permethrin). Of these analytes, only heavy metals and PFOS were detected. Some chemical classes including PAHs and PCBs were not detected at the site. To reach similar conclusions using traditional Phase 1 TIE methods, one would need to complete the following tests: baseline toxicity, filtration, aeration, EDTA, C18 SPE, and methanol elution of C18 SPE. The iTIE method allows the same conclusions to be drawn with significantly less time and effort required.

Summary

The in situ Toxicity Identification Evaluation technology and protocol is a powerful tool that investigators can use to strengthen causal linkages between chemical stressors and ecological toxicity. By fractionating sampled water and exposing test organisms in situ, investigators can gather toxicity response data while minimizing sample manipulation and accurately representing environmental conditions.

References

  1. ^ Falta, R., and Wang, W., 2017. A semi-analytical method for simulating matrix diffusion in numerical transport models. Journal of Contaminant Hydrology, 197, pp. 39-49. doi: 10.1016/j.jconhyd.2016.12.007  Open Access Manuscript
  2. ^ Kulkarni, P.R., Adamson, D.T., Popovic, J., Newell, C.J., 2022. Modeling a well-charactized perfluorooctane sulfate (PFOS) source and plume using the REMChlor-MD model to account for matrix diffusion. Journal of Contaminant Hydrology, 247, Article 103986. doi: 10.1016/j.jconhyd.2022.103986  Open Access Manuscript
  3. ^ 3.0 3.1 Schaefer, C.E., Tran, D., Nguyen, D., Latta, D.E., Werth, C.J., 2025. Evaluating Mineral and In Situ Indicators of Abiotic Dechlorination in Clayey Soils. Groundwater Monitoring and Remediation, 45(2), pp. 31-39. doi: 10.1111/gwmr.12709
  4. ^ Schaefer, C.E., Ho, P., Berns, E., Werth, C., 2018. Mechanisms for abiotic dechlorination of trichloroethene by ferrous minerals under oxic and anoxic conditions in natural sediments. Environmental Science and Technology, 52(23), pp.13747-13755. doi: 10.1021/acs.est.8b04108
  5. ^ Borden, R.C., Cha, K.Y., 2021. Evaluating the impact of back diffusion on groundwater cleanup time. Journal of Contaminant Hydrology, 243, Article 103889. doi: 10.1016/j.jconhyd.2021  Open Access Manuscript
  6. ^ Woodard, S., Mohr, T., Nickelsen, M.G., 2014. Synthetic media: A promising new treatment technology for 1,4-dioxane. Remediation Journal, 24(4), pp. 27-40. doi: 10.1002/rem.21402
  7. ^ Lemos, B.R.S., Teixeira, I.F., de Mesquita, J.P., Ribeiro, R.R., Donnici, C.L., Lago, R.M., 2012. Use of modified activated carbon for the oxidation of aqueous sulfide. Carbon, 50(3), pp. 1386-1393. doi: 10.1016/j.carbon.2011.11.011
  8. ^ 8.0 8.1 Cite error: Invalid <ref> tag; no text was provided for refs named SteigmeyerEtAl2017
  9. ^ Iannone, A., Carriera, F., Di Fiore, C., Avino, P., 2024. Poly- and Perfluoroalkyl Substance (PFAS) Analysis in Environmental Matrices: An Overview of the Extraction and Chromatographic Detection Methods. Analytica, 5(2), pp. 187-202. doi: 10.3390/analytica5020012  Open Access Article
  10. ^ U.S. Environmental Protection Agency, Office of Solid Waste and Emergency Response, 1994. Catalogue of Standard Toxicity Tests for Ecological Risk Assessment. ECO Update, 2(2), 4 pages. Publication No. 9345.0.05I Free Download  Report.pdf
  11. ^ Nichols, E., 2023. Methods for Identification and Prioritization of Stressors at Impaired Sites. Masters thesis, University of Michigan. University of Michigan Library Deep Blue Documents. Free Download  Report.pdf
  12. ^ Cite error: Invalid <ref> tag; no text was provided for refs named BurtonEtAl2020
  13. ^ Ridgway, J., Cave, K., DeMaria, A., O’Meara, J., Hartig, J. H., 2018. The Rouge River Area of Concern—A multi-year, multi-level successful approach to restoration of Impaired Beneficial Uses. Aquatic Ecosystem Health and Management, 21(4), pp. 398-408. doi: 10.1080/14634988.2018.1528816

See Also