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==Hydrogeophysical methods for characterization and monitoring of surface water-groundwater interactions== 
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==Assessing Vapor Intrusion (VI) Impacts in Neighborhoods with Groundwater Contaminated by Chlorinated Volatile Organic Chemicals (CVOCs)==
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Hydrogeophysical methods can be used to cost-effectively locate and characterize regions of
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The VI Diagnosis Toolkit<ref name="JohnsonEtAl2020">Johnson, P.C., Guo, Y., Dahlen, P., 2020.  The VI Diagnosis Toolkit for Assessing Vapor Intrusion Pathways and Mitigating Impacts in Neighborhoods Overlying Dissolved Chlorinated Solvent Plumes.  ESTCP Project ER-201501, Final Report. [https://serdp-estcp.mil/projects/details/a0d8bafd-c158-4742-b9fe-5f03d002af71 Project Website]&nbsp;&nbsp; [[Media: ER-201501.pdf | Final Report.pdf]]</ref> is a set of tools that can be used individually or in combination to assess vapor intrusion (VI) impacts at one or more buildings overlying regional-scale dissolved chlorinated solvent-impacted groundwater plumes. The strategic use of these tools can lead to confident and efficient neighborhood-scale VI pathway assessments.
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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.
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<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):'''
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*[[Geophysical Methods]]  
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*[[Geophysical Methods - Case Studies]]
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*[[Vapor Intrusion (VI)]]
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*[[Vapor Intrusion – Sewers and Utility Tunnels as Preferential Pathways]]
  
 
'''Contributor(s):'''  
 
'''Contributor(s):'''  
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*[[Dr. Lee Slater]]
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*Dr. Ramona Iery
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*Paul C. Johnson, Ph.D.
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*Dr. Dimitrios Ntarlagiannis
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*Paul Dahlen, Ph.D.
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*Henry Moore
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*Yuanming Guo, Ph.D.
  
 
'''Key Resource(s):'''
 
'''Key Resource(s):'''
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*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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==Introduction==
 
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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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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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*The VI Diagnosis Toolkit for Assessing Vapor Intrusion Pathways and Impacts in Neighborhoods Overlying Dissolved Chlorinated Solvent Plumes, ESTCP Project ER-201501, Final Report<ref name="JohnsonEtAl2020"/>
  
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==Hydrogeophysical Technologies for Understanding Groundwater-Surface Water Interactions==
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*CPM Test Guidelines: Use of Controlled Pressure Method Testing for Vapor Intrusion Pathway Assessment, ESTCP Project ER-201501, Technical Report<ref name="JohnsonEtAl2021">Johnson, P.C., Guo, Y., Dahlen, P., 2021.  CPM Test Guidelines: Use of Controlled Pressure Method Testing for Vapor Intrusion Pathway Assessment.  ESTCP ER-201501, Technical Report. [https://serdp-estcp.mil/projects/details/a0d8bafd-c158-4742-b9fe-5f03d002af71 Project Website]&nbsp;&nbsp; [[Media: ER-201501_Technical_Report.pdf | Technical_Report.pdf]]</ref>     
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[[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
 
  
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[[File:AbioMCredFig4.png | thumb |600px|Figure 4. Chemical structure of commonly used hydroquinones in NACs/MCs kinetic experiments.]]
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*VI Diagnosis Toolkit User Guide, ESTCP Project ER-201501<ref name="JohnsonEtAl2022">Johnson, P.C., Guo, Y., and Dahlen, P., 2022. VI Diagnosis Toolkit User Guide, ESTCP ER-201501, User Guide.  [https://serdp-estcp.mil/projects/details/a0d8bafd-c158-4742-b9fe-5f03d002af71 Project Website]&nbsp;&nbsp; [[Media: ER-201501_User_Guide.pdf | User_Guide.pdf]]</ref>
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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"/>.
 
  
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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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==Background==
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[[File:ChangFig2.png | thumb | 400px| Figure 1. Example of instrumentation used for OPTICS monitoring.]]
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[[File:ChangFig1.png | thumb | 400px| Figure 2. Schematic diagram illustrating the OPTICS methodology. High resolution in-situ data are integrated with traditional discrete sample analytical data using partial least-square regression to derive high resolution chemical contaminant concentration data series.]]
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Nationwide, the liability due to contaminated sediments is estimated in the trillions of dollars. Stakeholders are assessing and developing remedial strategies for contaminated sediment sites in major harbors and waterways throughout the U.S. The mobility of contaminants in surface water is a primary transport and risk mechanism<ref>Thibodeaux, L.J., 1996. Environmental Chemodynamics: Movement of Chemicals in Air, Water, and Soil, 2nd Edition, Volume 110 of Environmental Science and Technology: A Wiley-Interscience Series of Texts and Monographs. John Wiley & Sons, Inc. 624 pages. ISBN: 0-471-61295-2</ref><ref>United States Environmental Protection Agency (USEPA), 2005. Contaminated Sediment Remediation Guidance for Hazardous Waste Sites. Office of Superfund Remediation and Technology Innovation Report, EPA-540-R-05-012. [[Media: 2005-USEPA-Contaminated_Sediment_Remediation_Guidance.pdf | Report.pdf]]</ref><ref>Lick, W., 2008. Sediment and Contaminant Transport in Surface Waters. CRC Press. 416 pages. [https://doi.org/10.1201/9781420059885 doi:  10.1201/9781420059885]</ref>; therefore, long-term monitoring of both particulate- and dissolved-phase contaminant concentration prior to, during, and following remedial action is necessary to document remedy effectiveness. Source control and total maximum daily load (TMDL) actions generally require costly manual monitoring of dissolved and particulate contaminant concentrations in surface water. The magnitude of cost for these actions is a strong motivation to implement efficient methods for long-term source control and remedial monitoring.  
  
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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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Traditional surface water monitoring requires mobilization of field teams to manually collect discrete water samples, send samples to laboratories, and await laboratory analysis so that a site evaluation can be conducted. These traditional methods are well known to have inherent cost and safety concerns and are of limited use (due to safety concerns and standby requirements for resources) in capturing the effects of episodic events (e.g., storms) that are important to consider in site risk assessment and remedy selection. Automated water samplers are commercially available but still require significant field support and costly laboratory analysis. Further, automated samplers may not be suitable for analytes with short hold-times and temperature requirements.
  
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:::::<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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Optically-based characterization of surface water contaminants is a cost-effective alternative to traditional discrete water sampling methods. Unlike discrete water sampling, which typically results in sparse data at low resolution, and therefore, is of limited use in determining mass loading, OPTICS (OPTically-based In-situ Characterization System) provides continuous data and allows for a complete understanding of water quality and contaminant transport in response to natural processes and human impacts<ref name="ChangEtAl2019"/><ref name="ChangEtAl2018"/><ref name="ChangEtAl2024"/><ref>Bergamaschi, B.A., Fleck, J.A., Downing, B.D., Boss, E., Pellerin, B., Ganju, N.K., Schoellhamer, D.H., Byington, A.A., Heim, W.A., Stephenson, M., Fujii, R., 2011. Methyl mercury dynamics in a tidal wetland quantified using in situ optical measurements. Limnology and Oceanography, 56(4), pp. 1355-1371. [https://doi.org/10.4319/lo.2011.56.4.1355 doi: 10.4319/lo.2011.56.4.1355]&nbsp;&nbsp; [[Media: BergamaschiEtAl2011.pdf | Open Access Article]]</ref><ref>Bergamaschi, B.A., Fleck, J.A., Downing, B.D., Boss, E., Pellerin, B.A., Ganju, N.K., Schoellhamer, D.H., Byington, A.A., Heim, W.A., Stephenson, M., Fujii, R., 2012. Mercury Dynamics in a San Francisco Estuary Tidal Wetland: Assessing Dynamics Using In Situ Measurements. Estuaries and Coasts, 35, pp. 1036-1048. [https://doi.org/10.1007/s12237-012-9501-3 doi: 10.1007/s12237-012-9501-3]&nbsp;&nbsp; [[Media: BergamaschiEtAl2012a.pdf | Open Access Article]]</ref><ref>Bergamaschi, B.A., Krabbenhoft, D.P., Aiken, G.R., Patino, E., Rumbold, D.G., Orem, W.H., 2012. Tidally driven export of dissolved organic carbon, total mercury, and methylmercury from a mangrove-dominated estuary. Environmental Science and Technology, 46(3), pp. 1371-1378. [https://doi.org/10.1021/es2029137 doi: 10.1021/es2029137]&nbsp;&nbsp; [[Media: BergamaschiEtAl2012b.pdf | Open Access Article]]</ref>. The OPTICS tool integrates commercial off-the-shelf ''in situ'' aquatic sensors (Figure 1), periodic discrete surface water sample collection, and a multi-parameter statistical prediction model<ref name="deJong1993">de Jong, S., 1993. SIMPLS: an alternative approach to partial least squares regression. Chemometrics and Intelligent Laboratory Systems, 18(3), pp. 251-263. [https://doi.org/10.1016/0169-7439(93)85002-X doi: 10.1016/0169-7439(93)85002-X]</ref><ref name="RosipalKramer2006">Rosipal, R. and Krämer, N., 2006. Overview and Recent Advances in Partial Least Squares, In: Subspace, Latent Structure, and Feature Selection: Statistical and Optimization Perspectives Workshop, Revised Selected Papers (Lecture Notes in Computer Science, Volume 3940), Springer-Verlag, Berlin, Germany. pp. 34-51. [https://doi.org/10.1007/11752790_2 doi: 10.1007/11752790_2]</ref> to provide high temporal and/or spatial resolution characterization of surface water chemicals of potential concern (COPCs) (Figure 2).
  
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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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==Technology Overview==
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The principle behind OPTICS is based on the relationship between optical properties of natural waters and the particles and dissolved material contained within them<ref>Boss, E. and Pegau, W.S., 2001. Relationship of light scattering at an angle in the backward direction to the backscattering coefficient. Applied Optics, 40(30), pp. 5503-5507. [https://doi.org/10.1364/AO.40.005503 doi: 10.1364/AO.40.005503]</ref><ref>Boss, E., Twardowski, M.S., Herring, S., 2001. Shape of the particulate beam spectrum and its inversion to obtain the shape of the particle size distribution. Applied Optics, 40(27), pp. 4884-4893. [https://doi.org/10.1364/AO.40.004885 doi:10/1364/AO.40.004885]</ref><ref>Babin, M., Morel, A., Fournier-Sicre, V., Fell, F., Stramski, D., 2003. Light scattering properties of marine particles in coastal and open ocean waters as related to the particle mass concentration. Limnology and Oceanography, 48(2), pp. 843-859. [https://doi.org/10.4319/lo.2003.48.2.0843 doi: 10.4319/lo.2003.48.2.0843]&nbsp;&nbsp; [[Media: BabinEtAl2003.pdf | Open Access Article]]</ref><ref>Coble, P., Hu, C., Gould, R., Chang, G., Wood, M., 2004. Colored dissolved organic matter in the coastal ocean: An optical tool for coastal zone environmental assessment and management. Oceanography, 17(2), pp. 50-59. [https://doi.org/10.5670/oceanog.2004.47 doi: 10.5670/oceanog.2004.47]&nbsp;&nbsp; [[Media: CobleEtAl2004.pdf | Open Access Article]]</ref><ref>Sullivan, J.M., Twardowski, M.S., Donaghay, P.L., Freeman, S.A., 2005. Use of optical scattering to discriminate particle types in coastal waters. Applied Optics, 44(9), pp. 1667–1680. [https://doi.org/10.1364/AO.44.001667 doi: 10.1364/AO.44.001667]</ref><ref>Twardowski, M.S., Boss, E., Macdonald, J.B., Pegau, W.S., Barnard, A.H., Zaneveld, J.R.V., 2001. A model for estimating bulk refractive index from the optical backscattering ratio and the implications for understanding particle composition in case I and case II waters. Journal of Geophysical Research: Oceans, 106(C7), pp. 14,129-14,142. [https://doi.org/10.1029/2000JC000404 doi: 10/1029/2000JC000404]&nbsp;&nbsp; [[Media: TwardowskiEtAl2001.pdf | Open Access Article]]</ref><ref>Chang, G.C., Barnard, A.H., McLean, S., Egli, P.J., Moore, C., Zaneveld, J.R.V., Dickey, T.D., Hanson, A., 2006. In situ optical variability and relationships in the Santa Barbara Channel: implications for remote sensing. Applied Optics, 45(15), pp. 3593–3604. [https://doi.org/10.1364/AO.45.003593 doi: 10.1364/AO.45.003593]</ref><ref>Slade, W.H. and Boss, E., 2015. Spectral attenuation and backscattering as indicators of average particle size. Applied Optics, 54(24), pp. 7264-7277. [https://doi.org/10.1364/AO.54.007264 doi: 10/1364/AO.54.007264]&nbsp;&nbsp; [[Media: SladeBoss2015.pdf | Open Access Article]]</ref>. Surface water COPCs such as heavy metals and polychlorinated biphenyls (PCBs) are hydrophobic in nature and tend to sorb to materials in the water column, which have unique optical signatures that can be measured at high-resolution using ''in situ'', commercially available aquatic sensors<ref>Agrawal, Y.C. and Pottsmith, H.C., 2000. Instruments for particle size and settling velocity observations in sediment transport. Marine Geology, 168(1-4), pp. 89-114. [https://doi.org/10.1016/S0025-3227(00)00044-X doi: 10.1016/S0025-3227(00)00044-X]</ref><ref>Boss, E., Pegau, W.S., Gardner, W.D., Zaneveld, J.R.V., Barnard, A.H., Twardowski, M.S., Chang, G.C., Dickey, T.D., 2001. Spectral particulate attenuation and particle size distribution in the bottom boundary layer of a continental shelf. Journal of Geophysical Research: Oceans, 106(C5), pp. 9509-9516. [https://doi.org/10.1029/2000JC900077  doi: 10.1029/2000JC900077]&nbsp;&nbsp; [[Media: BossEtAl2001.pdf | Open Access Article]]</ref><ref>Boss, E., Pegau, W.S., Lee, M., Twardowski, M., Shybanov, E., Korotaev, G. Baratange, F., 2004. Particulate backscattering ratio at LEO 15 and its use to study particle composition and distribution. Journal of Geophysical Research: Oceans, 109(C1), Article C01014. [https://doi.org/10.1029/2002JC001514 doi: 10.1029/2002JC001514]&nbsp;&nbsp; [[Media: BossEtAl2004.pdf | Open Access Article]]</ref><ref>Briggs, N.T., Slade, W.H., Boss, E., Perry, M.J., 2013. Method for estimating mean particle size from high-frequency fluctuations in beam attenuation or scattering measurement. Applied Optics, 52(27), pp. 6710-6725. [https://doi.org/10.1364/AO.52.006710 doi: 10.1364/AO.52.006710]&nbsp;&nbsp; [[Media: BriggsEtAl2013.pdf | Open Access Article]]</ref>. Therefore, high-resolution concentrations of COPCs can be accurately and robustly derived from ''in situ'' measurements using statistical methods.
  
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{| class="wikitable mw-collapsible" style="float:left; margin-right:40px; text-align:center;"
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The OPTICS method is analogous to the commonly used empirical derivation of total suspended solids concentration (TSS) from optical turbidity using linear regression<ref>Rasmussen, P.P., Gray, J.R., Glysson, G.D., Ziegler, A.C., 2009. Guidelines and procedures for computing time-series suspended-sediment concentrations and loads from in-stream turbidity-sensor and streamflow data. In: Techniques and Methods, Book 3: Applications of Hydraulics, Section C: Sediment and Erosion Techniques, Ch. 4. 52 pages. U.S. Geological Survey.&nbsp;&nbsp; [[Media: RasmussenEtAl2009.pdf | Open Access Article]]</ref>. However, rather than deriving one response variable (TSS) from one predictor variable (turbidity), OPTICS involves derivation of one response variable (e.g., PCB concentration) from a suite of predictor variables (e.g., turbidity, temperature, salinity, and fluorescence of chlorophyll-a) using multi-parameter statistical regression. OPTICS is based on statistical correlation – similar to the turbidity-to-TSS regression technique. The method does not rely on interpolation or extrapolation. 
−
|+ 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.]]
+
The OPTICS technique utilizes partial least-squares (PLS) regression to determine a combination of physical, optical, and water quality properties that best predicts chemical contaminant concentrations with high variance. PLS regression is a statistically based method combining multiple linear regression and principal component analysis (PCA), where multiple linear regression finds a combination of predictors that best fit a response and PCA finds combinations of predictors with large variance<ref name="deJong1993"/><ref name="RosipalKramer2006"/>. Therefore, PLS identifies combinations of multi-collinear predictors (''in situ'', high-resolution physical, optical, and water quality measurements) that have large covariance with the response values (discrete surface water chemical contaminant concentration data from samples that are collected periodically, coincident with ''in situ'' measurements). PLS combines information about the variances of both the predictors and the responses, while also considering the correlations among them. PLS therefore provides a model with reliable predictive power.
−
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>.  
 
  
−
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"/>.
+
OPTICS ''in situ'' measurement parameters include, but are not limited to current velocity, conductivity, temperature, depth, turbidity, dissolved oxygen, and fluorescence of chlorophyll-a and dissolved organic matter. Instrumentation for these measurements is commercially available, robust, deployable in a wide variety of configurations (e.g., moored, vessel-mounted, etc.), powered by batteries, and records data internally and/or transmits data in real-time. The physical, optical, and water quality instrumentation is compact and self-contained. The modularity and automated nature of the OPTICS measurement system enables robust, long-term, autonomous data collection for near-continuous monitoring.  
  
−
==Ferruginous Reductants==
+
[[File:ChangFig3.png | thumb | 400px| Figure 3. OPTICS to characterize COPC variability in the context of site processes at BCSA. (A) Tidal oscillations (Elev.<sub>MSL</sub>) and precipitation (Precip.). (B) – (D) OPTICS-derived particulate mercury (PHg) and methylmercury (PMeHg) and total PCBs (TPCBs). Open circles represent discrete water sample data.]] OPTICS measurements are provided at a significantly reduced cost relative to traditional monitoring techniques used within the environmental industry. Cost performance analysis shows that monitoring costs are reduced by more than 85% while significantly increasing the temporal and spatial resolution of sampling. The reduced cost of monitoring makes this technology suitable for a number of environmental applications including, but not limited to site baseline characterization, source control evaluation, dredge or stormflow plume characterization, and remedy performance monitoring. OPTICS has been successfully demonstrated for characterizing a wide variety of COPCs: mercury, methylmercury, copper, lead, PCBs, dichlorodiphenyltrichloroethane (DDT) and its related compounds (collectively, DDX), and 2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD or dioxin) in a number of different environmental systems ranging from inland lakes and rivers to the coastal ocean. To date, OPTICS has been limited to surface water applications. Additional applications (e.g., groundwater) would require further research and development.
−
{| 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.
+
==Applications==
 +
[[File:ChangFig4.png | thumb | 400px| Figure 4. OPTICS reveals baseflow daily cycling and confirms storm-induced particle-bound COPC resuspension and mobilization through bank interaction. (A) Flow rate (Q) and precipitation (Precip). (B) – (C) OPTICS-derived particulate mercury (PHg) and methylmercury (PMeHg). Open circles represent discrete water sample data.]]
 +
[[File:ChangFig5.png | thumb | 400px| Figure 5. Three-dimensional volume plot of high spatial resolution OPTICS-derived PCBs in exceedance of baseline showing that PCBs were discharged from the outfall (yellow arrow), remained in suspension, and dispersed elsewhere before settling.]]
 +
An OPTICS study was conducted at Berry’s Creek Study Area (BCSA), New Jersey in 2014 and 2015 to understand COPC sources and transport mechanisms for development of an effective remediation plan. OPTICS successfully extended periodic discrete surface water samples to continuous, high-resolution measurements of PCBs, mercury, and methylmercury to elucidate COPC sources and transport throughout the BCSA tidal estuary system. OPTICS provided data at resolution sufficient to investigate COC variability in the context of physical processes. The results (Figure 3) facilitated focused and effective site remediation and management decisions that could not be determined based on periodic discrete samples alone, despite over seven years of monitoring at different locations throughout the system over a range of different seasons, tidal phases, and environmental conditions. The BCSA OPTICS methodology and its results have undergone official peer review overseen by the U.S. Environmental Protection Agency (USEPA), and those results have been published in peer-reviewed literature<ref name="ChangEtAl2019"/>.  
  
−
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.
+
OPTICS was applied at the South River, Virginia in 2016 to quantify sources of legacy mercury in the system that are contributing to recontamination and continued elevated mercury concentrations in fish tissue. OPTICS provided information necessary to identify mechanisms for COPC redistribution and to quantify the relative contribution of each mechanism to total mass transport of mercury and methylmercury in the system. Continuous, high-resolution COPC data afforded by OPTICS helped resolve baseflow daily cycling that had never before been observed at the South River (Figure 4) and provided data at temporal resolution necessary to verify storm-induced particle-bound COC resuspension and mobilization through bank interaction. The results informed source control and remedy design and monitoring efforts. Methodology and results from the South River have been published in peer-reviewed literature<ref name="ChangEtAl2018"/>.  
  
−
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"/>.
+
The U.S. Department of Defense’s Environmental Security Technology Certification Program (ESTCP) supported an OPTICS demonstration study at the Pearl Harbor Sediment Site, Hawaii, to determine whether stormwater from Oscar 1 Pier outfall is a contributing source of PCBs to Decision Unit (DU) N-2 (ESTCP Project ER21-5021). High spatial resolution results afforded by ship-based, mobile OPTICS monitoring suggested that PCBs were discharged from the outfall, remained in suspension, and dispersed elsewhere before settling (Figure 5). More details regarding this study were presented by Chang et al. in 2024<ref name="ChangEtAl2024"/>.
  
−
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"/>.
+
==Summary==
 +
OPTICS provides:
 +
*High resolution surface water chemical contaminant characterization
 +
*Cost-effective monitoring and assessment
 +
*Versatile and modular monitoring with capability for real-time telemetry
 +
*Data necessary for development and validation of conceptual site models
 +
*A key line of evidence for designing and evaluating remedies.
  
−
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.
+
Because OPTICS monitoring involves deployment of autonomous sampling instrumentation, a substantially greater volume of data can be collected using this technique compared to traditional sampling, and at a far lower cost. A large volume of data supports evaluation of chemical contaminant concentrations over a range of spatial and temporal scales, and the system can be customized for a variety of environmental applications. OPTICS helps quantify contaminant mass flux and the relative contribution of local transport and source areas to net contaminant transport. OPTICS delivers a strong line of evidence for evaluating contaminant sources, fate, and transport, and for supporting the design of a remedy tailored to address site-specific, risk-driving conditions. The improved understanding of site processes aids in the development of mitigation measures that minimize site risks.  
−
 
 
−
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.
 
−
<br clear="right" />
 
  
 
==References==
 
==References==
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==See Also==
 
==See Also==
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*[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]
 
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*[https://www.epa.gov/fedfac/military-munitionsunexploded-ordnance Military Munitions/Unexploded Ordnance - EPA]
 

Latest revision as of 20:39, 15 July 2024

Assessing Vapor Intrusion (VI) Impacts in Neighborhoods with Groundwater Contaminated by Chlorinated Volatile Organic Chemicals (CVOCs)

The VI Diagnosis Toolkit[1] is a set of tools that can be used individually or in combination to assess vapor intrusion (VI) impacts at one or more buildings overlying regional-scale dissolved chlorinated solvent-impacted groundwater plumes. The strategic use of these tools can lead to confident and efficient neighborhood-scale VI pathway assessments.

Related Article(s):

Contributor(s):

  • Paul C. Johnson, Ph.D.
  • Paul Dahlen, Ph.D.
  • Yuanming Guo, Ph.D.

Key Resource(s):

  • The VI Diagnosis Toolkit for Assessing Vapor Intrusion Pathways and Impacts in Neighborhoods Overlying Dissolved Chlorinated Solvent Plumes, ESTCP Project ER-201501, Final Report[1]
  • CPM Test Guidelines: Use of Controlled Pressure Method Testing for Vapor Intrusion Pathway Assessment, ESTCP Project ER-201501, Technical Report[2]
  • VI Diagnosis Toolkit User Guide, ESTCP Project ER-201501[3]

Background

Figure 1. Example of instrumentation used for OPTICS monitoring.
Figure 2. Schematic diagram illustrating the OPTICS methodology. High resolution in-situ data are integrated with traditional discrete sample analytical data using partial least-square regression to derive high resolution chemical contaminant concentration data series.

Nationwide, the liability due to contaminated sediments is estimated in the trillions of dollars. Stakeholders are assessing and developing remedial strategies for contaminated sediment sites in major harbors and waterways throughout the U.S. The mobility of contaminants in surface water is a primary transport and risk mechanism[4][5][6]; therefore, long-term monitoring of both particulate- and dissolved-phase contaminant concentration prior to, during, and following remedial action is necessary to document remedy effectiveness. Source control and total maximum daily load (TMDL) actions generally require costly manual monitoring of dissolved and particulate contaminant concentrations in surface water. The magnitude of cost for these actions is a strong motivation to implement efficient methods for long-term source control and remedial monitoring.

Traditional surface water monitoring requires mobilization of field teams to manually collect discrete water samples, send samples to laboratories, and await laboratory analysis so that a site evaluation can be conducted. These traditional methods are well known to have inherent cost and safety concerns and are of limited use (due to safety concerns and standby requirements for resources) in capturing the effects of episodic events (e.g., storms) that are important to consider in site risk assessment and remedy selection. Automated water samplers are commercially available but still require significant field support and costly laboratory analysis. Further, automated samplers may not be suitable for analytes with short hold-times and temperature requirements.

Optically-based characterization of surface water contaminants is a cost-effective alternative to traditional discrete water sampling methods. Unlike discrete water sampling, which typically results in sparse data at low resolution, and therefore, is of limited use in determining mass loading, OPTICS (OPTically-based In-situ Characterization System) provides continuous data and allows for a complete understanding of water quality and contaminant transport in response to natural processes and human impacts[7][8][9][10][11][12]. The OPTICS tool integrates commercial off-the-shelf in situ aquatic sensors (Figure 1), periodic discrete surface water sample collection, and a multi-parameter statistical prediction model[13][14] to provide high temporal and/or spatial resolution characterization of surface water chemicals of potential concern (COPCs) (Figure 2).

Technology Overview

The principle behind OPTICS is based on the relationship between optical properties of natural waters and the particles and dissolved material contained within them[15][16][17][18][19][20][21][22]. Surface water COPCs such as heavy metals and polychlorinated biphenyls (PCBs) are hydrophobic in nature and tend to sorb to materials in the water column, which have unique optical signatures that can be measured at high-resolution using in situ, commercially available aquatic sensors[23][24][25][26]. Therefore, high-resolution concentrations of COPCs can be accurately and robustly derived from in situ measurements using statistical methods.

The OPTICS method is analogous to the commonly used empirical derivation of total suspended solids concentration (TSS) from optical turbidity using linear regression[27]. However, rather than deriving one response variable (TSS) from one predictor variable (turbidity), OPTICS involves derivation of one response variable (e.g., PCB concentration) from a suite of predictor variables (e.g., turbidity, temperature, salinity, and fluorescence of chlorophyll-a) using multi-parameter statistical regression. OPTICS is based on statistical correlation – similar to the turbidity-to-TSS regression technique. The method does not rely on interpolation or extrapolation.

The OPTICS technique utilizes partial least-squares (PLS) regression to determine a combination of physical, optical, and water quality properties that best predicts chemical contaminant concentrations with high variance. PLS regression is a statistically based method combining multiple linear regression and principal component analysis (PCA), where multiple linear regression finds a combination of predictors that best fit a response and PCA finds combinations of predictors with large variance[13][14]. Therefore, PLS identifies combinations of multi-collinear predictors (in situ, high-resolution physical, optical, and water quality measurements) that have large covariance with the response values (discrete surface water chemical contaminant concentration data from samples that are collected periodically, coincident with in situ measurements). PLS combines information about the variances of both the predictors and the responses, while also considering the correlations among them. PLS therefore provides a model with reliable predictive power.

OPTICS in situ measurement parameters include, but are not limited to current velocity, conductivity, temperature, depth, turbidity, dissolved oxygen, and fluorescence of chlorophyll-a and dissolved organic matter. Instrumentation for these measurements is commercially available, robust, deployable in a wide variety of configurations (e.g., moored, vessel-mounted, etc.), powered by batteries, and records data internally and/or transmits data in real-time. The physical, optical, and water quality instrumentation is compact and self-contained. The modularity and automated nature of the OPTICS measurement system enables robust, long-term, autonomous data collection for near-continuous monitoring.

Figure 3. OPTICS to characterize COPC variability in the context of site processes at BCSA. (A) Tidal oscillations (Elev.MSL) and precipitation (Precip.). (B) – (D) OPTICS-derived particulate mercury (PHg) and methylmercury (PMeHg) and total PCBs (TPCBs). Open circles represent discrete water sample data.

OPTICS measurements are provided at a significantly reduced cost relative to traditional monitoring techniques used within the environmental industry. Cost performance analysis shows that monitoring costs are reduced by more than 85% while significantly increasing the temporal and spatial resolution of sampling. The reduced cost of monitoring makes this technology suitable for a number of environmental applications including, but not limited to site baseline characterization, source control evaluation, dredge or stormflow plume characterization, and remedy performance monitoring. OPTICS has been successfully demonstrated for characterizing a wide variety of COPCs: mercury, methylmercury, copper, lead, PCBs, dichlorodiphenyltrichloroethane (DDT) and its related compounds (collectively, DDX), and 2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD or dioxin) in a number of different environmental systems ranging from inland lakes and rivers to the coastal ocean. To date, OPTICS has been limited to surface water applications. Additional applications (e.g., groundwater) would require further research and development.

Applications

Figure 4. OPTICS reveals baseflow daily cycling and confirms storm-induced particle-bound COPC resuspension and mobilization through bank interaction. (A) Flow rate (Q) and precipitation (Precip). (B) – (C) OPTICS-derived particulate mercury (PHg) and methylmercury (PMeHg). Open circles represent discrete water sample data.
Figure 5. Three-dimensional volume plot of high spatial resolution OPTICS-derived PCBs in exceedance of baseline showing that PCBs were discharged from the outfall (yellow arrow), remained in suspension, and dispersed elsewhere before settling.

An OPTICS study was conducted at Berry’s Creek Study Area (BCSA), New Jersey in 2014 and 2015 to understand COPC sources and transport mechanisms for development of an effective remediation plan. OPTICS successfully extended periodic discrete surface water samples to continuous, high-resolution measurements of PCBs, mercury, and methylmercury to elucidate COPC sources and transport throughout the BCSA tidal estuary system. OPTICS provided data at resolution sufficient to investigate COC variability in the context of physical processes. The results (Figure 3) facilitated focused and effective site remediation and management decisions that could not be determined based on periodic discrete samples alone, despite over seven years of monitoring at different locations throughout the system over a range of different seasons, tidal phases, and environmental conditions. The BCSA OPTICS methodology and its results have undergone official peer review overseen by the U.S. Environmental Protection Agency (USEPA), and those results have been published in peer-reviewed literature[7].

OPTICS was applied at the South River, Virginia in 2016 to quantify sources of legacy mercury in the system that are contributing to recontamination and continued elevated mercury concentrations in fish tissue. OPTICS provided information necessary to identify mechanisms for COPC redistribution and to quantify the relative contribution of each mechanism to total mass transport of mercury and methylmercury in the system. Continuous, high-resolution COPC data afforded by OPTICS helped resolve baseflow daily cycling that had never before been observed at the South River (Figure 4) and provided data at temporal resolution necessary to verify storm-induced particle-bound COC resuspension and mobilization through bank interaction. The results informed source control and remedy design and monitoring efforts. Methodology and results from the South River have been published in peer-reviewed literature[8].

The U.S. Department of Defense’s Environmental Security Technology Certification Program (ESTCP) supported an OPTICS demonstration study at the Pearl Harbor Sediment Site, Hawaii, to determine whether stormwater from Oscar 1 Pier outfall is a contributing source of PCBs to Decision Unit (DU) N-2 (ESTCP Project ER21-5021). High spatial resolution results afforded by ship-based, mobile OPTICS monitoring suggested that PCBs were discharged from the outfall, remained in suspension, and dispersed elsewhere before settling (Figure 5). More details regarding this study were presented by Chang et al. in 2024[9].

Summary

OPTICS provides:

  • High resolution surface water chemical contaminant characterization
  • Cost-effective monitoring and assessment
  • Versatile and modular monitoring with capability for real-time telemetry
  • Data necessary for development and validation of conceptual site models
  • A key line of evidence for designing and evaluating remedies.

Because OPTICS monitoring involves deployment of autonomous sampling instrumentation, a substantially greater volume of data can be collected using this technique compared to traditional sampling, and at a far lower cost. A large volume of data supports evaluation of chemical contaminant concentrations over a range of spatial and temporal scales, and the system can be customized for a variety of environmental applications. OPTICS helps quantify contaminant mass flux and the relative contribution of local transport and source areas to net contaminant transport. OPTICS delivers a strong line of evidence for evaluating contaminant sources, fate, and transport, and for supporting the design of a remedy tailored to address site-specific, risk-driving conditions. The improved understanding of site processes aids in the development of mitigation measures that minimize site risks.

References

  1. ^ 1.0 1.1 Johnson, P.C., Guo, Y., Dahlen, P., 2020. The VI Diagnosis Toolkit for Assessing Vapor Intrusion Pathways and Mitigating Impacts in Neighborhoods Overlying Dissolved Chlorinated Solvent Plumes. ESTCP Project ER-201501, Final Report. Project Website   Final Report.pdf
  2. ^ Johnson, P.C., Guo, Y., Dahlen, P., 2021. CPM Test Guidelines: Use of Controlled Pressure Method Testing for Vapor Intrusion Pathway Assessment. ESTCP ER-201501, Technical Report. Project Website   Technical_Report.pdf
  3. ^ Johnson, P.C., Guo, Y., and Dahlen, P., 2022. VI Diagnosis Toolkit User Guide, ESTCP ER-201501, User Guide. Project Website   User_Guide.pdf
  4. ^ Thibodeaux, L.J., 1996. Environmental Chemodynamics: Movement of Chemicals in Air, Water, and Soil, 2nd Edition, Volume 110 of Environmental Science and Technology: A Wiley-Interscience Series of Texts and Monographs. John Wiley & Sons, Inc. 624 pages. ISBN: 0-471-61295-2
  5. ^ United States Environmental Protection Agency (USEPA), 2005. Contaminated Sediment Remediation Guidance for Hazardous Waste Sites. Office of Superfund Remediation and Technology Innovation Report, EPA-540-R-05-012. Report.pdf
  6. ^ Lick, W., 2008. Sediment and Contaminant Transport in Surface Waters. CRC Press. 416 pages. doi: 10.1201/9781420059885
  7. ^ 7.0 7.1 Cite error: Invalid <ref> tag; no text was provided for refs named ChangEtAl2019
  8. ^ 8.0 8.1 Cite error: Invalid <ref> tag; no text was provided for refs named ChangEtAl2018
  9. ^ 9.0 9.1 Cite error: Invalid <ref> tag; no text was provided for refs named ChangEtAl2024
  10. ^ Bergamaschi, B.A., Fleck, J.A., Downing, B.D., Boss, E., Pellerin, B., Ganju, N.K., Schoellhamer, D.H., Byington, A.A., Heim, W.A., Stephenson, M., Fujii, R., 2011. Methyl mercury dynamics in a tidal wetland quantified using in situ optical measurements. Limnology and Oceanography, 56(4), pp. 1355-1371. doi: 10.4319/lo.2011.56.4.1355   Open Access Article
  11. ^ Bergamaschi, B.A., Fleck, J.A., Downing, B.D., Boss, E., Pellerin, B.A., Ganju, N.K., Schoellhamer, D.H., Byington, A.A., Heim, W.A., Stephenson, M., Fujii, R., 2012. Mercury Dynamics in a San Francisco Estuary Tidal Wetland: Assessing Dynamics Using In Situ Measurements. Estuaries and Coasts, 35, pp. 1036-1048. doi: 10.1007/s12237-012-9501-3   Open Access Article
  12. ^ Bergamaschi, B.A., Krabbenhoft, D.P., Aiken, G.R., Patino, E., Rumbold, D.G., Orem, W.H., 2012. Tidally driven export of dissolved organic carbon, total mercury, and methylmercury from a mangrove-dominated estuary. Environmental Science and Technology, 46(3), pp. 1371-1378. doi: 10.1021/es2029137   Open Access Article
  13. ^ 13.0 13.1 de Jong, S., 1993. SIMPLS: an alternative approach to partial least squares regression. Chemometrics and Intelligent Laboratory Systems, 18(3), pp. 251-263. doi: 10.1016/0169-7439(93)85002-X
  14. ^ 14.0 14.1 Rosipal, R. and Krämer, N., 2006. Overview and Recent Advances in Partial Least Squares, In: Subspace, Latent Structure, and Feature Selection: Statistical and Optimization Perspectives Workshop, Revised Selected Papers (Lecture Notes in Computer Science, Volume 3940), Springer-Verlag, Berlin, Germany. pp. 34-51. doi: 10.1007/11752790_2
  15. ^ Boss, E. and Pegau, W.S., 2001. Relationship of light scattering at an angle in the backward direction to the backscattering coefficient. Applied Optics, 40(30), pp. 5503-5507. doi: 10.1364/AO.40.005503
  16. ^ Boss, E., Twardowski, M.S., Herring, S., 2001. Shape of the particulate beam spectrum and its inversion to obtain the shape of the particle size distribution. Applied Optics, 40(27), pp. 4884-4893. doi:10/1364/AO.40.004885
  17. ^ Babin, M., Morel, A., Fournier-Sicre, V., Fell, F., Stramski, D., 2003. Light scattering properties of marine particles in coastal and open ocean waters as related to the particle mass concentration. Limnology and Oceanography, 48(2), pp. 843-859. doi: 10.4319/lo.2003.48.2.0843   Open Access Article
  18. ^ Coble, P., Hu, C., Gould, R., Chang, G., Wood, M., 2004. Colored dissolved organic matter in the coastal ocean: An optical tool for coastal zone environmental assessment and management. Oceanography, 17(2), pp. 50-59. doi: 10.5670/oceanog.2004.47   Open Access Article
  19. ^ Sullivan, J.M., Twardowski, M.S., Donaghay, P.L., Freeman, S.A., 2005. Use of optical scattering to discriminate particle types in coastal waters. Applied Optics, 44(9), pp. 1667–1680. doi: 10.1364/AO.44.001667
  20. ^ Twardowski, M.S., Boss, E., Macdonald, J.B., Pegau, W.S., Barnard, A.H., Zaneveld, J.R.V., 2001. A model for estimating bulk refractive index from the optical backscattering ratio and the implications for understanding particle composition in case I and case II waters. Journal of Geophysical Research: Oceans, 106(C7), pp. 14,129-14,142. doi: 10/1029/2000JC000404   Open Access Article
  21. ^ Chang, G.C., Barnard, A.H., McLean, S., Egli, P.J., Moore, C., Zaneveld, J.R.V., Dickey, T.D., Hanson, A., 2006. In situ optical variability and relationships in the Santa Barbara Channel: implications for remote sensing. Applied Optics, 45(15), pp. 3593–3604. doi: 10.1364/AO.45.003593
  22. ^ Slade, W.H. and Boss, E., 2015. Spectral attenuation and backscattering as indicators of average particle size. Applied Optics, 54(24), pp. 7264-7277. doi: 10/1364/AO.54.007264   Open Access Article
  23. ^ Agrawal, Y.C. and Pottsmith, H.C., 2000. Instruments for particle size and settling velocity observations in sediment transport. Marine Geology, 168(1-4), pp. 89-114. doi: 10.1016/S0025-3227(00)00044-X
  24. ^ Boss, E., Pegau, W.S., Gardner, W.D., Zaneveld, J.R.V., Barnard, A.H., Twardowski, M.S., Chang, G.C., Dickey, T.D., 2001. Spectral particulate attenuation and particle size distribution in the bottom boundary layer of a continental shelf. Journal of Geophysical Research: Oceans, 106(C5), pp. 9509-9516. doi: 10.1029/2000JC900077   Open Access Article
  25. ^ Boss, E., Pegau, W.S., Lee, M., Twardowski, M., Shybanov, E., Korotaev, G. Baratange, F., 2004. Particulate backscattering ratio at LEO 15 and its use to study particle composition and distribution. Journal of Geophysical Research: Oceans, 109(C1), Article C01014. doi: 10.1029/2002JC001514   Open Access Article
  26. ^ Briggs, N.T., Slade, W.H., Boss, E., Perry, M.J., 2013. Method for estimating mean particle size from high-frequency fluctuations in beam attenuation or scattering measurement. Applied Optics, 52(27), pp. 6710-6725. doi: 10.1364/AO.52.006710   Open Access Article
  27. ^ Rasmussen, P.P., Gray, J.R., Glysson, G.D., Ziegler, A.C., 2009. Guidelines and procedures for computing time-series suspended-sediment concentrations and loads from in-stream turbidity-sensor and streamflow data. In: Techniques and Methods, Book 3: Applications of Hydraulics, Section C: Sediment and Erosion Techniques, Ch. 4. 52 pages. U.S. Geological Survey.   Open Access Article

See Also

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