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| − | ==Hydrogeophysical methods for characterization and monitoring of surface water-groundwater interactions== | + | ==Sediment Porewater Dialysis Passive Samplers for Inorganics (Peepers)== |
| − | Hydrogeophysical methods can be used to cost-effectively locate and characterize regions of
| + | Sediment porewater dialysis passive samplers, also known as “peepers,” are sampling devices that allow the measurement of dissolved inorganic ions in the porewater of a saturated sediment. Peepers function by allowing freely-dissolved ions in sediment porewater to diffuse across a micro-porous membrane towards water contained in an isolated compartment that has been inserted into sediment. Once retrieved after a deployment period, the resulting sample obtained can provide concentrations of freely-dissolved inorganic constituents in sediment, which provides measurements that can be used for understanding contaminant fate and risk. Peepers can also be used in the same manner in surface water, although this article is focused on the use of peepers in sediment. |
| − | 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> |
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| | '''Related Article(s):''' | | '''Related Article(s):''' |
| − | *[[Geophysical Methods]]
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| − | *[[Geophysical Methods - Case Studies]]
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| − | '''Contributor(s):'''
| + | *[[Contaminated Sediments - Introduction]] |
| − | *[[Dr. Lee Slater]] | + | *[[Contaminated Sediment Risk Assessment]] |
| − | *Dr. Ramona Iery | + | *[[In Situ Treatment of Contaminated Sediments with Activated Carbon]] |
| − | *Dr. Dimitrios Ntarlagiannis | + | *[[Passive Sampling of Munitions Constituents]] |
| − | *Henry Moore | + | *[[Sediment Capping]] |
| | + | *[[Mercury in Sediments]] |
| | + | *[[Passive Sampling of Sediments]] |
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| − | '''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==
| + | '''Contributor(s):''' |
| − | 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.
| + | *Florent Risacher, M.Sc. |
| | + | *Jason Conder, Ph.D. |
| | | | |
| − | ==Hydrogeophysical Technologies for Understanding Groundwater-Surface Water Interactions==
| + | '''Key Resource(s):''' |
| − | [[Wikipedia: Hydrogeophysics |Hydrogeophysical technologies]] exploit contrasts in the physical properties between groundwater and surface water to detect and monitor zones of pronounced GWSWE. The two most valuable properties to measure are temperature and electrical conductivity. Temperature has been used for decades as an indicator of groundwater-surface water exchange<ref>Constantz, J., 2008. Heat as a Tracer to Determine Streambed Water Exchanges. Water Resources Research, 44 (4).[https://doi.org/https://doi.org/10.1029/2008WR006996 doi: 10.1029/2008WR006996].[https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2008WR006996 Open Access Article]</ref> with early uses including pushing a thermistor into the bed of a surface water body to assess zones of surface water downwelling and groundwater upwelling. Today, a variety of novel technologies that measure temperature over a wide range of spatial and temporal scales are being used to investigate GWSWE. The evaluation of electrical conductivity measurements using point probes and geophysical imaging is also well-established. However, new technologies are now available to exploit electrical conductivity contrasts from GWSWE occurring over a range of spatial and temporal scales.
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| − | ===Temperature-Based Technologies===
| + | *A review of peeper passive sampling approaches to measure the availability of inorganics in sediment porewater<ref>Risacher, F.F., Schneider, H., Drygiannaki, I., Conder, J., Pautler, B.G., and Jackson, A.W., 2023. A Review of Peeper Passive Sampling Approaches to Measure the Availability of Inorganics in Sediment Porewater. Environmental Pollution, 328, Article 121581. [https://doi.org/10.1016/j.envpol.2023.121581 doi: 10.1016/j.envpol.2023.121581] [[Media: RisacherEtAl2023a.pdf | Open Access Manuscript]]</ref> |
| − | Several temperature-based GWSWE methodologies exploit the gradient in temperature between surface water and groundwater that exist during certain times of day or seasons of the year. The thermal insulation provided by the Earth’s land surface means that groundwater is warmer than surface water in winter months, but colder than surface water in summer months away from the equator. Therefore, in temperate climates, localized (or ‘preferential’) groundwater discharge into surface water bodies is often observed as cold temperature anomalies in the summer and warm temperature anomalies in the winter. However, there are times of the year such as fall and spring when contrasts in the temperature between groundwater and surface water will be minimal, or even undetectable. These seasonal-driven points in time correspond to the switch in the polarity of the temperature contrast between groundwater and surface water. Consequently, SW to GW gradient temperature-based methods are most effective when deployed at times of the year when the temperature contrasts between groundwater and surface water are greatest. Other time-series temperature monitoring methods depend more on natural daily signals measured at the bed interface and in bed sediments, and those signals may exist year round except where strongly muted by ice cover or surface water stratification. A variety of sensing technologies now exist within the GWSWE toolbox, from techniques that rapidly characterize temperature contrasts over large areas, down to powerful monitoring methods that can continuously quantify GWSWE fluxes at discrete locations identified as hotspots.
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| − | ====Characterization Methods==== | + | *Best Practices User’s Guide: Standardizing Sediment Porewater Passive Samplers for Inorganic Constituents of Concern<ref name="RisacherEtAl2023">Risacher, F.F., Nichols, E., Schneider, H., Lawrence, M., Conder, J., Sweett, A., Pautler, B.G., Jackson, W.A., Rosen, G., 2023b. Best Practices User’s Guide: Standardizing Sediment Porewater Passive Samplers for Inorganic Constituents of Concern, ESTCP ER20-5261. [https://serdp-estcp.mil/projects/details/db871313-fbc0-4432-b536-40c64af3627f Project Website] [[Media: ER20-5261BPUG.pdf | Report.pdf]]</ref> |
| − | The primary use of the characterization methods is to rapidly determine precise locations of groundwater upwelling over large areas in order to pinpoint locations for subsequent ground-based observations. A common limitation of these methods is that they can only sense groundwater fluxes into surface water. Methods applied at the water surface and in the surface water column generally cannot detect localized regions of surface water transfer to groundwater, for which temperature measurements collected within the bed sediments are needed. This is a more challenging characterization task that may, in the right conditions, be addressed using electrical conductivity-based methods described later in this article.
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| − | =====Unmanned Aerial Vehicle Infrared (UAV-IR)=====
| + | *[https://serdp-estcp.mil/projects/details/db871313-fbc0-4432-b536-40c64af3627f/er20-5261-project-overview Standardizing Sediment Porewater Passive Samplers for Inorganic Constituents of Concern, ESTCP Project ER20-5261] |
| − | [[File:IeryFig1.png | thumb |600px|Figure 1. UAV IR orthomosaics with estimated scale of (a) a wetland in winter (modified from Briggs et al.<ref>Briggs, M. A., Jackson, K. E., Liu, F., Moore, E. M., Bisson, A., Helton, A. M., 2022. Exploring Local Riverbank Sediment Controls on the Occurrence of Preferential Groundwater Discharge Points. Water, 14(1). [https://doi.org/10.3390/w14010011 doi: 10.3390/w14010011] [https://www.mdpi.com/2073-4441/14/1/11 Open Access Article].</ref>) and (b) a mountain stream in summer (modified from Briggs et al.<ref>Briggs, M. A., Wang, C., Day-Lewis, F. D., Williams, K. H., Dong, W., Lane, J. W., 2019. Return Flows from Beaver Ponds Enhance Floodplain-to-River Metals Exchange in Alluvial Mountain Catchments. Science of the Total Environment, 685, pp. 357–369. [https://doi.org/10.1016/j.scitotenv.2019.05.371 doi: 10.1016/j.scitotenv.2019.05.371]. [https://pdf.sciencedirectassets.com/271800/1-s2.0-S0048969719X00273/1-s2.0-S0048969719324246/am.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEE0aCXVzLWVhc3QtMSJGMEQCIBY8ykhAP941wHO1NKj8EmXG3btdpgX6HaUV9zAo0PCMAiACRjzV0D2lbFFwnhUzEqBupGsgX6DkK62ZIEvb%2B0irbiq8BQj2%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F8BEAUaDDA1OTAwMzU0Njg2NSIMPmS2kZBwKKMGD%2F6GKpAFaY6lOuHO%2B1RkV%2FL6NkK74dL6YJculUqyZJn9s09njF1L%2Bb4LgjH%2FbawysWGvGeuH%2FQtSgwqFM90MQ4grDiDQPHUjSEDNVuN2II%2BqPK4oqkjqxwTmC2AObe%2FMY1c45L2nshYodZwtROh6Hl8Jp4B4HoDPE9wx1fEw7DGmB%2Bj70q5PG7%2FUUo3rLl6BCMT%2FWKFGfZSaOmaD5nweVaTRBUbgSVIcmCQKshE28TkHFpmwY58YNO0GjaKHXMsBNciZ2DvIPAHMyA1iymB7UFcoBRDicZJUDZvvnJNGj1bTX9tEQ49yil7IWD22hKPHL5nSogssocX5rRXiIglVT%2BAzHsMMyxfVxfFGBsmmSGAVG9FAeRPgx1T%2FIOqNo%2FOuyV9G%2BVSt5boUg4HBaZSvW5JNkL5bFpaMlrUTpMF%2F6Bbq3Q6EsiZMaFF0JOS3rvX5dkDlfu7OzJDBuRBszYoq%2B4%2FLQGJypfmarz8ZHEzi3Qw85nYbT68UGNa%2BZ9lZQG%2B47mF6Nj11%2F%2Fu%2FDTZD1p4r9nskTevwkRE%2BL7q3OSbqFj4YvN6qsMBLb%2FM7K2xSmaots0YGisZ09fVJBetJ1ILZpN5wCbS%2F77uFeQoxYXGIwz84wyqSueP7qcj3BQ%2FMkZRbmVpokj3vtESlfHvcZV2Ntu95JM9hetE9F5azaZ%2F%2Fm3WTE2mgW48FCbFI09p%2F7%2FSJyEWl54lNG7%2F2y0AayedFUs75otJauCpNJtr2pF4sbAGfgiagA2%2BzeDatKnI7MDhMD0R27wvaVwEup6vkLmTaJh4P8bGFd01Fwj96gZIKESW6HfwGXMBMj%2FoJn3CYpcfVelPmDr6jTeSJapUJoWE8gQVFjWuISuD4PdHYtbiSBL%2Fjn5jPvGMwvrqrrQY6sgEtK%2Fo3hSElpY%2Be20Xj4eNAJ%2BFmkb5nASAJvtygtnSdoc%2FBHMv4U3Je92nbunzwAwXaVCZ8FBK1%2F2cmq3sYLNOyPEJrCNqAo0Lgf137RvhaJb7erYXXfL7UCz1hePrG3I3bgKkBRN5PD%2FSlu%2BSSEimoEn4kCyxoaNYI9QvymaTlHZJM0ueXCYprlRfMneJXxnEVyC3qlMsTMtcL%2B45koHZeeTQJUMXWJB%2BYQxNDmNM3ZHH4&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20240119T205045Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAQ3PHCVTYV2JHRO6K%2F20240119%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=3befd4efcf96517aad4e02a2d76e82cd278f02be8a60a5136a4981889df64f00&hash=c0f70e64bfdb70375c685714475b258099c0d0b19a2a7a556e77182cc6cfac9c&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=S0048969719324246&tid=pdf-5d6462f0-c794-4158-b89d-2a1f5b96a226&sid=8b33666922432845420b6d75b151281148eegxrqa&type=client Open Access Manuscript]</ref>) that both capture multiscale groundwater discharge processes. Figure reproduced from Mangel et al.<ref>Mangel, A. R., Dawson, C. B., Rey, D. M., Briggs, M. A., 2022. Drone Applications in Hydrogeophysics: Recent Examples and a Vision for the Future. The Leading Edge, 41 (8), pp. 540–547. [https://doi.org/10.1190/tle41080540.1 doi: 10.1190/tle41080540].</ref>]]
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| − | [[Wikipedia: Unmanned aerial vehicle | Unmanned aerial vehicles (UAVs)]] equipped with thermal infrared (IR) cameras can provide a very powerful tool for rapidly determining zones of pronounced upwelling of groundwater to surface water. Large areas of can be covered with high spatial resolution. The information obtained can be used to rapidly define locations of focused groundwater upwelling and prioritize these for more intensive surface-based observations (Figure 1). As with all thermal methods, flights must be performed when adequate contrasts in temperature between surface water and groundwater are expected to exist. Not just time of year but, because of the effect of the diurnal temperature signal on surface water bodies, time of day might need to be considered in order to maximize the chance of success. Calibration of UAV-IR camera measurements against simultaneously acquired direct measurements of temperature is recommended to optimize the value of these datasets. UAV-IR methods will not work in all situations. One major limitation of the technology is that the temperature expression of groundwater upwelling must be manifested at the surface of the surface water body. Consequently, the technology will not detect relatively small discharges occurring beneath a relatively deep surface water layer, and thermal imaging over the water surface can be complicated by thermal IR reflection. The chances of success with UAV-IR will be strongest in regions of exposed banks or shallow water where there are no strong currents causing mixing (and thus dilution) of the upwelling groundwater temperature signals. UAV-IR methods will therefore likely be most successful close to shorelines of lakes/ponds, over shallow, low flow streams and rivers and in wetland environments. UAV-IR methods require a licensed pilot, and restrictions on the use of airspace may limit the application of this technology.
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| − | =====Handheld Thermal Infrared (TIR) Cameras===== | + | ==Introduction== |
| − | [[File:IeryFig2.png | thumb|left |600px|Figure 2. (a) A TIR camera set up to image groundwater discharges to surface water (b) TIR data inset on a visible light photograph. Cooler (blue) bank seepage groundwater is discharging into warmer (red) stream water (temperature scale in degrees). Both photographs courtesy of Martin Briggs USGS.]]
| + | Biologically available inorganic constituents associated with sediment toxicity can be quantified by measuring the freely-dissolved fraction of contaminants in the porewater<ref>Conder, J.M., Fuchsman, P.C., Grover, M.M., Magar, V.S., Henning, M.H., 2015. Critical review of mercury SQVs for the protection of benthic invertebrates. Environmental Toxicology and Chemistry, 34(1), pp. 6-21. [https://doi.org/10.1002/etc.2769 doi: 10.1002/etc.2769] [[Media: ConderEtAl2015.pdf | Open Access Article]]</ref><ref name="ClevelandEtAl2017">Cleveland, D., Brumbaugh, W.G., MacDonald, D.D., 2017. A comparison of four porewater sampling methods for metal mixtures and dissolved organic carbon and the implications for sediment toxicity evaluations. Environmental Toxicology and Chemistry, 36(11), pp. 2906-2915. [https://doi.org/10.1002/etc.3884 doi: 10.1002/etc.3884]</ref>. Classical sediment porewater analysis usually consists of collecting large volumes of bulk sediments which are then mechanically squeezed or centrifuged to produce a supernatant, or suction of porewater from intact sediment, followed by filtration and collection<ref name="GruzalskiEtAl2016">Gruzalski, J.G., Markwiese, J.T., Carriker, N.E., Rogers, W.J., Vitale, R.J., Thal, D.I., 2016. Pore Water Collection, Analysis and Evolution: The Need for Standardization. In: Reviews of Environmental Contamination and Toxicology, Vol. 237, pp. 37–51. Springer. [https://doi.org/10.1007/978-3-319-23573-8_2 doi: 10.1007/978-3-319-23573-8_2]</ref>. The extraction and measurement processes present challenges due to the heterogeneity of sediments, physical disturbance, high reactivity of some complexes, and interaction between the solid and dissolved phases, which can impact the measured concentration of dissolved inorganics<ref>Peijnenburg, W.J.G.M., Teasdale, P.R., Reible, D., Mondon, J., Bennett, W.W., Campbell, P.G.C., 2014. Passive Sampling Methods for Contaminated Sediments: State of the Science for Metals. Integrated Environmental Assessment and Management, 10(2), pp. 179–196. [https://doi.org/10.1002/ieam.1502 doi: 10.1002/ieam.1502] [[Media: PeijnenburgEtAl2014.pdf | Open Access Article]]</ref>. For example, sampling disturbance can affect redox conditions<ref name="TeasdaleEtAl1995">Teasdale, P.R., Batley, G.E., Apte, S.C., Webster, I.T., 1995. Pore water sampling with sediment peepers. Trends in Analytical Chemistry, 14(6), pp. 250–256. [https://doi.org/10.1016/0165-9936(95)91617-2 doi: 10.1016/0165-9936(95)91617-2]</ref><ref>Schroeder, H., Duester, L., Fabricius, A.L., Ecker, D., Breitung, V., Ternes, T.A., 2020. Sediment water (interface) mobility of metal(loid)s and nutrients under undisturbed conditions and during resuspension. Journal of Hazardous Materials, 394, Article 122543. [https://doi.org/10.1016/j.jhazmat.2020.122543 doi: 10.1016/j.jhazmat.2020.122543] [[Media: SchroederEtAl2020.pdf | Open Access Article]]</ref>, which can lead to under or over representation of inorganic chemical concentrations relative to the true dissolved phase concentration in the sediment porewater<ref>Wise, D.E., 2009. Sampling techniques for sediment pore water in evaluation of reactive capping efficacy. Master of Science Thesis. University of New Hampshire Scholars’ Repository. 178 pages. [https://scholars.unh.edu/thesis/502 Website] [[Media: Wise2009.pdf | Report.pdf]]</ref><ref name="GruzalskiEtAl2016"/>. |
| − | Hand-held thermal infrared (TIR) cameras are powerful tools for visual identification of localized seeps of upwelling groundwater. TIR cameras may be used to follow up on UAV-IR surveys to better characterize local seeps identified from the air using UAV-IR. Alternatively, a TIR camera is a valuable tool when performing initial walks of prospective study sites as they may quickly confirm the presence of suspected seeps. TIR cameras provide high resolution images that can define the structure of localized seeps and may provide valuable insights into the role of discrete features (e.g., fractures in rocks or pipes in soil) in determining seep morphology (Figure 2). Like UAV-IR, TIR provides primarily qualitative information (location, extent) of seeps and it only succeeds when there are adequate contrasts between groundwater and surface water that are expressed at the surface of the investigated water body or along bank sediments. The United States Geological Survey (USGS) has made extensive use of TIR cameras for studying groundwater/surface-water exchange.
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| − | =====Continuous Near-bed Temperature Sensing=====
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| − | When performing surveys from a boat a simple yet often powerful technology is continuous
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| − | near-bed temperature sensing, whereby a temperature probe is strategically suspended to float in the water column just above the bed or dragged along it. Compared to UAV-IR, this approach does not rely on upwelling groundwater being expressed as a temperature anomaly at the surface. The utility of the method can be enhanced when a specific conductance probe is co- located with the temperature probe so that anomalies in both temperature and specific conductance can be investigated.
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| − | ====Monitoring Methods====
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| − | Monitoring methods allow temperature signals to be recorded with high temporal resolution along the bed interface or within bank or bed sediments. These methods can capture temporal trends in GWSWE driven by variations in the hydraulic gradients around surface water bodies, as well as changes in [[Wikipedia: Hydraulic conductivity | hydraulic conductivity]] due to sedimentation, clogging, scour or microbial mass. If vertical profiles of bed temperature are collected, a range of analytical and numerical models can be applied to infer the vertical water flux rate and direction, similar to a seepage meter. These fluxes may vary as a function of season, rainfall events (enhanced during storm activity), tidal variability in coastal settings and due to engineered controls such as dam discharges. The methods can capture evidence of GWSWE that may not be detected during a single ‘characterization’ survey if the local hydraulic conditions at that point in time result in relatively weak hydraulic gradients.
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| − | =====Fiber-optic Distributed Temperature Sensing (FO-DTS)=====
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| − | [[File:IeryFig3.png | thumb|600px|Figure 3. (a) Basic concept of FO-DTS based on backscattering of light transmitted down a FO fiber (b) Example of riverbed temperature data acquired over time and space in relation to variation in river stage (black line) modified from Mwakanyamale et al.<ref>Mwakanyamale, K., Slater, L., Day-Lewis, F., Elwaseif, M., Johnson, C., 2012. Spatially Variable Stage-Driven Groundwater-Surface Water Interaction Inferred from Time-Frequency Analysis of Distributed Temperature Sensing Data. Geophysical Research Letters, 39(6). [https://doi.org/10.1029/2011GL050824 doi: 10.1029/2011GL050824]. [https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2011GL050824 Open Access Article]</ref> (c) spatial distribution of riverbed temperature and correlation coefficient (CC) between riverbed temperature and river stage for a 1.5 km stretch along the Hanford 300 Area adjacent to the Columbia River (modified from Slater et al.<ref name=”Slater2010”/>). Data are shown for winter and summer. Orange contours show uranium concentrations (μg/L) in groundwater measured in boreholes.]] | |
| − | Fiber-optic distributed temperature sensing (FO-DTS) is a powerful monitoring technology used in fire detection, industrial process monitoring, and petroleum reservoir monitoring. The method is also used to obtain spatially rich datasets for monitoring GWSWE<ref name=”Selker2006”>Selker, J. S., Thévenaz, L., Huwald, H., Mallet, A., Luxemburg, W., van de Giesen, N., Stejskal, M., Zeman, J., Westhoff, M., Parlange, M. B., 2006. Distributed Fiber-Optic Temperature Sensing for Hydrologic Systems. Water Resources Research, 42 (12). [https://doi.org/10.1029/2006WR005326 doi: 10.1029/2006WR005326]. [https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2006WR005326 Open Access Article]</ref><ref name=”Tyler2009”>Tyler, S. W., Selker, J. S., Hausner, M. B., Hatch, C. E., Torgersen, T., Thodal, C. E., Schladow, S. G., 2009. Environmental Temperature Sensing Using Raman Spectra DTS Fiber-Optic Methods. Water Resources Research, 45(4). [https://doi.org/https://doi.org/10.1029/2008WR007052 doi: 10.1029/2008WR007052]. [https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2008WR007052 Open Access Article]</ref>. The sensor consists of standard telecommunications fiber-optic fiber typically housed in armored cable and the physics is based on temperature-dependent backscatter mechanisms including Brillouin and Raman backscatter<ref name=”Selker2006”/>. Most commercially available systems are based on analysis of Raman scatter. As laser light is transmitted down the fiber-optic cable, light scatters continuously back toward the instrument from all along the fiber, with some of the scattered light at frequencies above and below the frequency of incident light, i.e., anti-Stokes and Stokes-Raman backscatter, respectively. The ratio of anti-Stokes to Stokes energy provides the basis for FO-DTS measurements. Measurements are localized to a section of cable according to a time-of-flight calculation (i.e., optical time-domain reflectometry). Assuming the speed of light within the fiber is constant, scatter collected over a specific time window corresponds to a specific spatial interval of the fiber. Although there are tradeoffs between spatial resolution, thermal precision, and sampling time, in practice it is possible to achieve sub meter-scale spatial and approximate 0.1°C thermal precision for measurement cycle times on the order of minutes and cables extending several kilometers<ref name=”Tyler2009”/>; thus, thousands of temperature measurements can be made simultaneously along a single cable. The method allows the visualization of a large amount of temperature data and rapid identification of major trends in GWSWE. Figure 3 illustrates the use of FO-DTS to detect and monitor zones of focused groundwater discharge along a 1.5 km reach of the Columbia River that is threatened by contaminated groundwater<ref name=”Slater2010”>Slater, L. D., Ntarlagiannis, D., Day-Lewis, F. D., Mwakanyamale, K., Versteeg, R. J., Ward, A., Strickland, C., Johnson, C. D., Lane Jr., J. W., 2010. Use of Electrical Imaging and Distributed Temperature Sensing Methods to Characterize Surface Water-Groundwater Exchange Regulating Uranium Transport at the Hanford 300 Area, Washington. Water Resources Research, 46(10). [https://doi.org/10.1029/2010WR009110 doi: 10.1029/2010WR009110]. [https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2010WR009110 Open Access Article]</ref>. As temperature is only sensed at the cable on the bed, FO-DTS can only detect groundwater inputs to surface water. It cannot detect losses of surface water to groundwater. The USGS public domain software tool [https://www.usgs.gov/software/dtsgui DTSGUI] allows a user to import, manage, visualize and analyze FO-DTS datasets.
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| − | =====Vertical temperature profilers (VTPs)=====
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| − | Analysis methods now allow for the accurate quantification of groundwater fluxes over time. Vertical temperature profilers (VTPs) are sensors applied for diurnal temperature data collection within saturated geologic matrices (Figure 4). Extensive experience with VTPs indicates that the methodology is equal to traditional seepage meters in terms of flux accuracy<ref>Hare, D. K., Briggs, M. A., Rosenberry, D. O., Boutt, D. F., Lane Jr., J. W., 2015. A Comparison of Thermal Infrared to Fiber-Optic Distributed Temperature Sensing for Evaluation of Groundwater Discharge to Surface Water. Journal of Hydrology, 530, pp. 153–166. [https://doi.org/10.1016/j.jhydrol.2015.09.059 doi: 10.1016/j.jhydrol.2015.09.059].</ref>. However, VTPs have the advantage of measuring continuous temporal variations in flux rates while such information is impractical to obtain with traditional seepage meters.
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| − | [[File:IeryFig4.png |thumb|600px|left|Figure 4. (a) Schematic of different VTP setups including (from left to right) thermistors in a piezometer, thermistors embedded in a solid rod and wrapped FO-DTS cable modified from Irvine et al.<ref name=”Irvine2017a”/>; (b) construction of VTPs showing thermistors embedded in rods and subsequent insulation; (c) example dataset plotted in 1DTempPro showing 5 days of streambed temperature at 6 streambed depths<ref>Koch, F. W., Voytek, E. B., Day-Lewis, F. D., Healy, R., Briggs, M. A., Lane Jr., J. W., Werkema, D., 2016. 1DTempPro V2: New Features for Inferring Groundwater/Surface-Water Exchange. Groundwater, 54(3), pp. 434–439. [https://doi.org/10.1111/gwat.12369 doi: 10.1111/gwat.12369].</ref>.]]
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| − | The low-cost design, ease of data collection, and straightforward interpretation of the data using open-source software make VTP sensors increasingly attractive for quantifying flux rates. These sensors typically consist of at least two temperature loggers installed within a steel or plastic pipe filled with foam insulation<ref name=”Irvine2017a”>Irvine, D. J., Briggs, M. A., Cartwright, I., Scruggs, C. R., Lautz, L. K., 2016. Improved Vertical Streambed Flux Estimation Using Multiple Diurnal Temperature Methods in Series. Groundwater, 55(1), pp. 73-80. [https://doi.org/10.1111/gwat.12436 doi: 10.1111/gwat.12436].</ref> although the use of loggers installed in well screens or FO-DTS cable wrapped around a piezometer casing (for high vertical resolution data) are also possible (Figure 4a). Loggers are inserted into the insulated housing at different depths, typically starting from one centimeter within the geologic matrix of interest<ref name=”Irvine2017b”> Irvine, D. J., Briggs, M. A., Lautz, L. K., Gordon, R. P., McKenzie, J. M., Cartwright, I., 2017. Using Diurnal Temperature Signals to Infer Vertical Groundwater-Surface Water Exchange. Groundwater, 55(1), pp. 10–26. [https://doi.org/10.1111/gwat.12459 doi: 10.1111/gwat.12459]. [https://ngwa.onlinelibrary.wiley.com/doi/am-pdf/10.1111/gwat.12459 Open Access Manuscript]</ref>. Temperature loggers usually remain within the first 0.2-meters of the geologic matrix based on the observed limits of diurnal signal influence<ref>Briggs, M. A., Lautz, L. K., Buckley, S. F., Lane Jr., J. W., 2014. Practical Limitations on the Use of Diurnal Temperature Signals to Quantify Groundwater Upwelling. Journal of Hydrology, 519(B), pp. 1739–1751. [https://doi.org/10.1016/j.jhydrol.2014.09.030 doi: 10.1016/j.jhydrol.2014.09.030].</ref>, though zones of strong surface water downwelling may necessitate deeper temperature data collection. Reliability of flux values generated from the temperature signal analysis is dependent in part on the temperature logger precision, VTP placement, sediment heterogeneity, flow direction, flow magnitude<ref name=”Irvine2017b”/>, and absence of macropore flow. Application of single dimension temperature-based fluid flux models assumes that all flow is vertical and therefore lateral flow within upwelling systems cannot be quantified using VTPs, emphasizing the importance of the VTP installation location over the active area of exchange<ref name=”Irvine2017b”/> at shallow depths. Thermal parameters of the geologic matrix where the VTP is installed can be measured using a thermal properties analyzer to record heat capacity and thermal conductivity for later analytical and numerical modeling.
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| − | Analytical and numerical solutions, used to solve or estimate the advection-conduction equation within the geologic matrix (bed sediments), continue to evolve to better quantify flux values over time. Analytical solutions to the heat transport equation are used to solve for flux values between sensor pairs from VTP datasets<ref name=”Gordon2012”>Gordon, R. P., Lautz, L. K., Briggs, M. A., McKenzie, J. M., 2012. Automated Calculation of Vertical Pore-Water Flux from Field Temperature Time Series Using the VFLUX Method and Computer Program. Journal of Hydrology, 420–421, pp. 142–158. [https://doi.org/10.1016/j.jhydrol.2011.11.053 doi: 10.1016/j.jhydrol.2011.11.053].</ref><ref name=”Irvine2015”>Irvine, D. J., Lautz, L. K., Briggs, M. A., Gordon, R. P., McKenzie, J. M., 2015. Experimental Evaluation of the Applicability of Phase, Amplitude, and Combined Methods to Determine Water Flux and Thermal Diffusivity from Temperature Time Series Using VFLUX 2. Journal of Hydrology, 531(3), pp. 728–737. [https://doi.org/10.1016/j.jhydrol.2015.10.054 doi: 10.1016/j.jhydrol.2015.10.054].</ref>. [https://data.usgs.gov/modelcatalog/model/a54608c5-ea6c-4d61-afc4-1ae851f46744 VFLUX] is an open-source MATLAB package that allows the user to solve for flux values from a VTP dataset using a variety of analytical solutions<ref name=”Gordon2012”/><ref name=”Irvine2015”/> based on the vertical propagation of diurnal temperature signals. Other emerging ‘spectral’ methods make use of a wide range of natural temperature signals to estimate vertical flux and bed sediment thermal diffusivity<ref>Sohn, R. A., Harris, R. N., 2021. Spectral Analysis of Vertical Temperature Profile Time-Series Data in Yellowstone Lake Sediments. Water Resources Research, 57(4), e2020WR028430. [https://doi.org/10.1029/2020WR028430 doi: 10.1029/2020WR028430]. [https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2020WR028430 Open Access Article]</ref>. VFLUX analytical solutions are limited by subsurface heterogeneity and diurnal temperature signal strength<ref name=”Irvine2017b”/>. [https://data.usgs.gov/modelcatalog/model/82fe0c15-97f5-4f6a-b389-b90f9bad615e 1DTempPro] (Figure 4c) provides a graphical user interface (GUI) for numerical solutions to heat transport<ref>Koch, F. W., Voytek, E. B., Day-Lewis, F. D., Healy, R., Briggs, M. A., Werkema, D., Lane Jr., J. W., 2015. 1DTempPro: A Program for Analysis of Vertical One-Dimensional (1D) Temperature Profiles v2.0. U.S. Geological Survey Software Release. [http://dx.doi.org/10.5066/F76T0JQS doi: 10.5066/F76T0JQS]. [https://data.usgs.gov/modelcatalog/model/82fe0c15-97f5-4f6a-b389-b90f9bad615e Free Download from USGS]</ref> and does not depend on diurnal signals. Numerical models can produce more accurate flux estimates in the case of complex boundary conditions and abrupt changes in flux rates, but require significant user calibration efforts for longer time series<ref name=”McAliley2022”> McAliley, W. A., Day-Lewis, F. D., Rey, D., Briggs, M. A., Shapiro, A. M., Werkema, D., 2022. Application of Recursive Estimation to Heat Tracing for Groundwater/Surface-Water Exchange. Water Resources Research, 58(6), e2021WR030443. [https://doi.org/10.1029/2021WR030443 doi: 10.1029/2021WR030443]. [https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2021WR030443 Open Access Article]</ref>. A hybrid approach between the analytical and numerical solutions, known as [https://www.sciencebase.gov/catalog/item/60a55c71d34ea221ce48b9e7 tempest1d]<ref name=”McAliley2022”/> improves flux modeling with enhanced computational efficiency, resolution of abrupt changes, evaluation of complex boundary conditions, and uncertainty estimations with each step. This new state-space modeling approach uses recursive estimation techniques to automatically estimate highly dynamic vertical flux patterns ranging from sub-daily to seasonal time scales<ref name=”McAliley2022”/>.
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| − | ===Electrical Conductivity (EC) Based Technologies===
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| − | The electrical conductivity (EC)-based technologies exploit contrasts in EC between surface water and groundwater<ref>Cox, M. H., Su, G. W., Constantz, J., 2007. Heat, Chloride, and Specific Conductance as Ground Water Tracers near Streams. Groundwater, 45(2), pp. 187–195. [https://doi.org/10.1111/j.1745-6584.2006.00276.x doi: 10.1111/j.1745-6584.2006.00276.x].</ref>. EC-based technologies are mostly applied as characterization tools, although the opportunity to monitor GWSWE dynamics with one of these technologies does exist. With the exception of specific conductance probes, the technologies measure the bulk EC of sediments, which will often (but not always) reveal evidence of GWSWE.
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| − | Electrical conduction (i.e., the transport of charges) in the Earth occurs via the ions dissolved in groundwater, with an additional contribution from ions in the electrical double layer (known as surface conduction)<ref name=”Binley2020”>Binley, A., Slater, L., 2020. Resistivity and Induced Polarization: Theory and Applications to the Near-Surface Earth. Cambridge University Press. [https://doi.org/10.1017/9781108685955 doi: 10.1017/9781108685955].</ref>. In relatively fresh surface water environments, groundwater is typically more electrically conductive than surface water due to the higher ion concentrations in groundwater. In these settings, groundwater inputs may be identified as zones of higher bulk EC beneath the bed. In coastal settings where surface water is saline, inputs of relatively fresh groundwater will give rise to zones of lower conductivity. Whereas the temperature-based methods rely on point measurements at the location of the sensor, the EC-based technologies
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| − | (with the exception of point specific conductance measurements) incorporate inverse modeling to estimate distributions of EC away from the sensors and beneath the bed. Consequently, these technologies may also image losses of surface water to groundwater<ref>Johnson, T. C., Slater, L. D., Ntarlagiannis, D., Day-Lewis, F. D., Elwaseif, M., 2012. Monitoring Groundwater-Surface Water Interaction Using Time-Series and Time- Frequency Analysis of Transient Three-Dimensional Electrical Resistivity Changes. Water Resources Research, 48(7). [https://doi.org/10.1029/2012WR011893 doi: 10.1029/2012WR011893]. [https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2012WR011893 Open Access Article]</ref>. Another advantage is that they may provide information on structural controls on zones of focused GWSWE expressed at the surface. However, interpretation of EC patterns from these technologies is inherently uncertain due to the fact that (with the exception of specific conductance probes) the bulk EC of the sediments is measured. Variations in lithology (e.g., porosity, grain size distribution, which determine the strength of surface conduction) can be misinterpreted as variations in the ionic composition of groundwater.
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| − | ====Characterization Methods====
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| − | =====Specific Conductance Probes=====
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| − | The simplest EC-based technology is a specific conductance probe, which measures the specific conductance of water between a small pair of metal plates at the end of the sensor probe. Many commercially available water quality sensors have a specific conductance sensor and a temperature sensor integrated into a single probe (they often also measure other water quality parameters, including pH and dissolved oxygen (DO) content). These are direct sensing measurements with a small footprint (the size of the sensor), so this is usually a time-consuming, inefficient method for detecting GWSWE dynamics. Furthermore, the sampling volume of the measurement is small (on the order of a cubic centimeter or less), so the degree to which the spot measurement is representative of larger-scale hydrological exchanges is often uncertain. However, specific conductance sensor remains popular, especially when integrated with a point temperature sensor, such as the [https://clu-in.org/programs/21m2/navytools/gsw/#trident Trident Probe].
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| − | =====Frequency Domain Electromagnetic (EM) Sensing Systems=====
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| − | [[File:IeryFig5.png |thumb|600px|Figure 5. (a) FDEM survey path within a stream/drainage channel network bisecting a wetland complex experiencing localized upwelling of contaminated groundwater (b) operation of an FDEM sensor (Dualem 421S, Dualem, CA) in this shallow stream environment (c) resulting imaging of EC structure in the upper 6 m of streambed sediments. Variations in EC may result from changes in sediment texture that determine the location of focused GWSWE. Dataset acquired under ESTCP project ER21-5237.]]
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| − | Electromagnetic (EM) sensors non-invasively sense the bulk EC of sediments (a function of both fluid composition and lithology as mentioned above) by measuring eddy currents induced in conductors using time varying electric and magnetic fields based on the physics of electromagnetic induction. Modern EM systems can simultaneously image across a range of depths. Frequency domain EM (FDEM) instruments generate a current that varies sinusoidally with time at a fixed frequency that is selected on the basis of desired exploration depth and resolution. State of the art FDEM sensors use a combination of different coil separations and/or frequencies to resolve conductivity structure over a range of depths. These instruments typically provide high-resolution (sub-meter) information on the EC structure in the upper 5 m (approximately, depending on EC) of the subsurface. Measurements are non-invasively and continuously made, meaning that large areas can be quickly surveyed on foot (e.g., along a shoreline) or from a boat in shallow water (1 m or less deep), for example when pulled along a river or stream channel. The method can also be deployed effectively in wetlands (Figure 5). FDEM data are often presented in terms of variations in the raw measurements because apparent EC values do not represent the true EC of the subsurface. However, with the increasing popularity of sensors with combinations of coil separations, the datasets can be inverted to obtain a model of the distribution of the true EC of the subsurface on land or below a water layer. Inversion of FDEM datasets is usually performed as a series of one-dimensional (1D) models, constrained to have a limited variance from each other, to generate a pseudo-2D model of the subsurface. Open-source software, such as [https://hkex.gitlab.io/emagpy/ EMagPy]<ref>McLachlan, P., Blanchy, G., Binley, A., 2021. EMagPy: Open-Source Standalone Software for Processing, Forward Modeling and Inversion of Electromagnetic Induction Data. Computers and Geosciences, 146, 104561. [https://doi.org/10.1016/j.cageo.2020.104561 doi: 10.1016/j.cageo.2020.104561].</ref>, is freely available to manage, visualize and interpret FDEM datasets.
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| − | =====Time Domain EM Sensing Systems=====
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| − | Time domain EM (TEM) systems transmit a current that is abruptly shut off (reduced to zero), resulting in a transient current flow that propagates (with decaying amplitude) into the earth. The time-decaying voltage recorded in a receiver coil contains information on the EC variation with depth below the instrument. TEM systems specifically designed for waterborne surveys provide investigation depths up to 70 m (again depending on electrical conductivity)<ref>Lane Jr., J. W., Briggs, M. A., Maurya, P. K., White, E. A., Pedersen, J. B., Auken, E., Terry, N., Minsley, B., Kress, W., LeBlanc, D. R., Adams, R., Johnson, C. D., 2020. Characterizing the Diverse Hydrogeology Underlying Rivers and Estuaries Using New Floating Transient Electromagnetic Methodology. Science of the Total Environment, 740, 140074. [https://doi.org/10.1016/j.scitotenv.2020.140074 doi: 10.1016/j.scitotenv.2020.140074]. [https://pdf.sciencedirectassets.com/271800/1-s2.0-S0048969720X00313/1-s2.0-S0048969720335944/am.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEFIaCXVzLWVhc3QtMSJIMEYCIQDZ%2B%2FCGoVTTeSPFPtk4OW69PC4KEHqVkJKlXr53AsvHdQIhAPZN6QAcBxRTVXEK7JzdlztbyC0YCiI8uy0GY9A0rXePKrwFCPr%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FwEQBRoMMDU5MDAzNTQ2ODY1IgwE9HI9XVU0l%2BzWSuoqkAXE3X7NIZ%2F%2FdOJUm0fUfbE9sV8pySpOwYC0486IvtPPTBowSKFbx3vAcqacG%2B6VAiPUlQJIbXyY10TtNJIVamtrdqKawz6kL9JuuoFesHagWsbHUu8xE0ZcEoSRoD%2Btocg7XxHtfdRC0cEM%2F6VDcKQg1h4j4Ak%2FrS2SJAvt0OmlvNNIXEp87MhMP7VU%2BTm788JJKs2VDuRNaz%2BoQ4W%2FpXpB5PxIB%2FvW55DtjmdUOTGB0d5Kwq1QTrX0z02SD3GaQFHvVlmwVtNbswzqgzLA%2BiHzqG9ZzmEqxJL8%2F%2F%2F9ZYahtdXZPWTTF0MqwAjskmy7aZoqn6H7bhO4tmQpgFLcVhkufPQkObVxTmCcSOUweT6yHq1K%2FysQrY9ba%2F6qCVFR2AhCvccsn0jTPVeMDhUkP0EAOZt3d4JvL9ZvViFh4WLjM69jB%2BBqXyhUEsOdPVC76RMMYWYtEhJq6bFKyAKX6VwvOnzoIcHxVuxa0ulPfshyymwNyeyXF30xrWDyUU10W5mThgljbwI1WWPubRFDCKiyuaEAJfMNZCM8I%2B8DaFm4qEpqgzOu28W0GnHova%2BLNza5yTpmNGZDRstWNTTeaE4VhgBuaLUc6TB0j7sH9yO5q5UOTqv4GN3X6w5GG758i7TgnNQPV5yjG%2Foyl46OgsVbq8ALyKvSFNYJeDS0Hv1s7pbwGHKi%2F7kZoOo30oLpN%2F8m1n5HYj%2Bxz7nkgzB5z7aelBYZERf3TypQaXlRiS%2FLgiqi6KzAsAKo07Mjn0lZNmTCrb7nsf3dPh2phYcVSRjSSZ4qTzF02Jc1kSHWGgNrt%2BaGRj2p%2FyNI%2Fb3WrvXffMSJ%2FpJfWJofKMlQP96TWBJt2mRZ6F6U5gWE4J5Dn%2BI8HTDduaytBjqwAf%2FpCFEnbS3RfOQ64c16pUa%2BCsCwOWuWoxV7sDyHaPuoDmfpmHbBMMQaUKp4iqCrDesa1Np01xsxOW6dUEHE9A2SmIS0eRtttMuf%2ByCQL8dXg3e5ptGM9VNkwpflS2rEpCCyDWN0rWMs7Dkzw232XzO9kR6ZNO5BJnQy1SOqoYn9kBTbY%2F6C0Nw3rkFIi%2FFHjxdyHk7pO0jf4p5graNK2kOB54cXa5PY5OcRRcv2irwk&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20240120T014358Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAQ3PHCVTY3WJS7ASS%2F20240120%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=5780b8a5381ef60b05df0e480b9c6d222c334b1d738bac8f9df7c3ae0b27fe59&hash=bcff28fddb45f4ac782f40fcc311db617d06d21300beb12018d4810d1baca112&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=S0048969720335944&tid=pdf-2543cf95-ab24-4e83-9d62-963bcb00db35&sid=27b3c4ac266c74410d0954f-878848e7f20agxrqa&type=client Open Access Manuscript]</ref>. Airborne TEM systems can also be deployed to look at large-scale surface water/groundwater dynamics, for example submarine discharge or saline intrusion along coastlines<ref>d’Ozouville, N., Auken, E., Sorensen, K., Violette, S., de Marsily, G., Deffontaines, B., Merlen, G., 2008. Extensive Perched Aquifer and Structural Implications Revealed by 3D Resistivity Mapping in a Galapagos Volcano. Earth and Planetary Science Letters, 269(3–4), pp. 518–522. [https://doi.org/10.1016/j.epsl.2008.03.011 doi: 10.1016/j.epsl.2008.03.011].</ref>. Inverse methods are employed to convert the raw measurements obtained along a transect into a distribution of conductivity.
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| − | =====Waterborne Electrical Imaging=====
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| − | [[File:IeryFig6.png |thumb|600px|left|Figure 6. Waterborne electrical imaging in a coastal setting with expected zones of upwelling groundwater (a) typical operation with floating electrode cable pulled behind boat (b) inverted 2D cross section of electrical resistivity along the survey path with possible zones of fresh groundwater discharges indicated from relatively high resistivity sediments. Dataset acquired under ESTCP project ER21-5237.]]
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| − | Electrical imaging techniques are based on galvanic (direct) contact between electrodes used to inject currents (and measure voltages) and the subsurface<ref name=”Binley2020”/>. Relative to EM methods, this can be a disadvantage when surveying on land. However, when making measurements from a water body, the electrodes used to acquire the data can be deployed as a floating array that is pulled behind a vessel. Waterborne electrical imaging relies on acquiring measurements of electrical potential differences between different pairs of electrodes on the array while current is passed between one pair of electrodes<ref>Day-Lewis, F. D., White, E. A., Johnson, C. D., Lane Jr, J. W., Belaval, M., 2006. Continuous Resistivity Profiling to Delineate Submarine Groundwater Discharge—Examples and Limitations. The Leading Edge, 25(6), pp. 724–728. [https://doi.org/10.1190/1.2210056 doi: 10.1190/1.2210056]</ref>. As the array is pulled behind the boat, thousands of measurements are made along a survey transect. Similar to the EM methods, inverse methods are used to process these datasets and generate a 2D image of the variation in the conductivity of the sediments below the bed. Open-source software such as [https://hkex.gitlab.io/resipy/ ResIPy] support 2D or 3D inversion of waterborne datasets. Figure 6 shows results of a waterborne electrical imaging survey conducted to locate regions where relative fresh (electrically resistive) groundwater is discharging into the near shore environment in a coastal setting. Beneath the saline (low resistivity) water layer, spatial variability in resistivity may partly be related to variations in the pore-filling fluid conductivity, with localized resistive zones possibly indicating upwelling fresh groundwater. However, the variation in resistivity in the sediments below the water layer may reflect variations in lithology. An extension of the electrical imaging method involves collecting induced polarization (IP) data<ref name=”Binley2020”/> in addition to electrical resistivity data. IP measurements capture the temporary charge storage characteristics of the subsurface, which are strongly controlled by lithology, with finer-grained (e.g. clay rich) sediments being more chargeable than coarser grained sediments. The method can be particularly useful for differentiating between conductivity variations resulting from variations in pore fluid specific conductance and those conductivity variations associated with lithology. For example, based on electrical imaging methods alone (or the EM method alone), it may not be possible to distinguish a zone of high specific conductance groundwater entering into freshwater from a region of relatively finer- grained sediments without additional supporting data (e.g. a core). IP measurements may be able to resolve this ambiguity as the region of finer-grained sediments will be more chargeable than the surrounding areas.
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| − | ====Monitoring Methods====
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| − | =====Land-based Electrical Monitoring=====
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| − | There is increasing interest in the use of electrical imaging methods as monitoring systems. Semi-permanent arrays of electrodes can be installed to monitor groundwater/surface water dynamics over periods of days to years. Low-power instrumentation has been developed to specifically address the needs for long-term monitoring, although such instrumentation is not yet commercially available. Consequently, electrical monitoring of groundwater/surface water interactions currently remains in the realm of the research-driven specialist.
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| − | ===Considerations for Using EM and Waterborne Electrical Imaging Methods===
| + | To address the complications with mechanical porewater sampling, passive sampling approaches for inorganics have been developed to provide a method that has a low impact on the surrounding geochemistry of sediments and sediment porewater, thus enabling more precise measurements of inorganics<ref name="ClevelandEtAl2017"/>. Sediment porewater dialysis passive samplers, also known as “peepers,” were developed more than 45 years ago<ref name="Hesslein1976">Hesslein, R.H., 1976. An in situ sampler for close interval pore water studies. Limnology and Oceanography, 21(6), pp. 912-914. [https://doi.org/10.4319/lo.1976.21.6.0912 doi: 10.4319/lo.1976.21.6.0912] [[Media: Hesslein1976.pdf | Open Access Article]]</ref> and refinements to the method such as the use of reverse tracers have been made, improving the acceptance of the technology as decision making tool. |
| − | The EM and waterborne electrical imaging methods both provide a way to determine variations in bulk electrical conductivity associated with groundwater/surface water interactions. However, each method has some advantages and some disadvantages. One consideration is maneuverability, particularly in shallow water environments. FDEM instruments are the most maneuverable, although they offer only limited investigation depths. Although bigger than the shallow-sensing frequency domain EM systems, TEM systems are still relatively maneuverable on water bodies. Whereas FDEM systems can be operated from a single small vessel, the TEM deployments require the use of pontoons as the transmitter and receiver coils need to be separated 9 m apart. This still equates to good maneuverability compared to waterborne electrical imaging where a floating electrode cable, typically 30-50 m long, is pulled behind a vessel.
| |
| | | | |
| − | In all three methods, variations in the water layer depth and the specific conductance of the water can significantly affect the data, especially in deeper water. Therefore, it is common to continuously record these parameters with an echo sounder and a specific conductance probe suspended in the water layer.
| + | ==Peeper Designs== |
| | + | [[File:RisacherFig1.png|thumb|300px|Figure 1. Conceptual illustration of peeper construction showing (top, left to right) the peeper cap (optional), peeper membrane and peeper chamber, and (bottom) an assembled peeper containing peeper water]] |
| | + | [[File:RisacherFig2.png | thumb |400px| Figure 2. Example of Hesslein<ref name="Hesslein1976"/> general peeper design (42 peeper chambers), from [https://www.usgs.gov/media/images/peeper-samplers USGS]]] |
| | + | [[File:RisacherFig3.png | thumb |400px| Figure 3. Peeper deployment structure to allow the measurement of metal availability in different sediment layers using five single-chamber peepers (Photo: Geosyntec Consultants)]] |
| | + | Peepers (Figure 1) are inert containers with a small volume (typically 1-100 mL) of purified water (“peeper water”) capped with a semi-permeable membrane. Peepers can be manufactured in a wide variety of formats (Figure 2, Figure 3) and deployed in in various ways. |
| | | | |
| − | ===Other Hydrogeophysical Technologies=== | + | Two designs are commonly used for peepers. Frequently, the designs are close adaptations of the original multi-chamber Hesslein design<ref name="Hesslein1976"/> (Figure 2), which consists of an acrylic sampler body with multiple sample chambers machined into it. Peeper water inside the chambers is separated from the outside environment by a semi-permeable membrane, which is held in place by a top plate fixed to the sampler body using bolts or screws. An alternative design consists of single-chamber peepers constructed using a single sample vial with a membrane secured over the mouth of the vial, as shown in Figure 3, and applied in Teasdale ''et al.''<ref name="TeasdaleEtAl1995"/>, Serbst ''et al.''<ref>Serbst, J.R., Burgess, R.M., Kuhn, A., Edwards, P.A., Cantwell, M.G., Pelletier, M.C., Berry, W.J., 2003. Precision of dialysis (peeper) sampling of cadmium in marine sediment interstitial water. Archives of Environmental Contamination and Toxicology, 45(3), pp. 297–305. [https://doi.org/10.1007/s00244-003-0114-5 doi: 10.1007/s00244-003-0114-5]</ref>, Thomas and Arthur<ref name="ThomasArthur2010">Thomas, B., Arthur, M.A., 2010. Correcting porewater concentration measurements from peepers: Application of a reverse tracer. Limnology and Oceanography: Methods, 8(8), pp. 403–413. [https://doi.org/10.4319/lom.2010.8.403 doi: 10.4319/lom.2010.8.403] [[Media: ThomasArthur2010.pdf | Open Access Article]]</ref>, Passeport ''et al.''<ref>Passeport, E., Landis, R., Lacrampe-Couloume, G., Lutz, E.J., Erin Mack, E., West, K., Morgan, S., Lollar, B.S., 2016. Sediment Monitored Natural Recovery Evidenced by Compound Specific Isotope Analysis and High-Resolution Pore Water Sampling. Environmental Science and Technology, 50(22), pp. 12197–12204. [https://doi.org/10.1021/acs.est.6b02961 doi: 10.1021/acs.est.6b02961]</ref>, and Risacher ''et al.''<ref name="RisacherEtAl2023"/>. The vial is filled with deionized water, and the membrane is held in place using the vial cap or an o-ring. Individual vials are either directly inserted into sediment or are incorporated into a support structure to allow multiple single-chamber peepers to be deployed at once over a given depth profile (Figure 3). |
| − | A number of other hydrogeophysical technologies exist, with proven applications to the characterization of settings where GWSWE occurs. Seismic [[Wikipedia:Reflection seismology | reflection]] and [[Wikipedia:Seismic refraction | refraction]] methods are used to image the depositional environments along coastlines. [[Wikipedia:Ground-penetrating radar | Ground penetrating radar]] has been effectively used to image depositional environments around freshwater lake shorelines, and across streams and rivers. Such information may help to identify depositional features that promote GWSWE but, unlike the temperature- and conductivity-based methods, do not sense changes in physical properties associated with the exchanging water itself.
| |
| | | | |
| − | One promising technique for detecting GWSWE is known as the
| + | ==Peepers Preparation, Deployment and Retrieval== |
| | + | [[File:RisacherFig4.png | thumb |300px| Figure 4: Conceptual illustration of peeper passive sampling in a sediment matrix, showing peeper immediately after deployment (top) and after equilibration between the porewater and peeper chamber water (bottom)]] |
| | + | Peepers are often prepared in laboratories but are also commercially available in a variety of designs from several suppliers. Peepers are prepared by first cleaning all materials to remove even trace levels of metals before assembly. The water contained inside the peeper is sometimes deoxygenated, and in some cases the peeper is maintained in a deoxygenated atmosphere until deployment<ref>Carignan, R., St‐Pierre, S., Gachter, R., 1994. Use of diffusion samplers in oligotrophic lake sediments: Effects of free oxygen in sampler material. Limnology and Oceanography, 39(2), pp. 468-474. [https://doi.org/10.4319/lo.1994.39.2.0468 doi: 10.4319/lo.1994.39.2.0468] [[Media: CarignanEtAl1994.pdf | Open Access Article]]</ref>. However, recent studies<ref name="RisacherEtAl2023"/> have shown that deoxygenation prior to deployment does not significantly impact sampling results due to oxygen rapidly diffusing out of the peeper during deployment. Once assembled, peepers are usually shipped in a protective bag inside a hard-case cooler for protection. |
| | | | |
| | + | Peepers are deployed by insertion into sediment for a period of a few days to a few weeks. Insertion into the sediment can be achieved by wading to the location when the water depth is shallow, by using push poles for deeper deployments<ref name="RisacherEtAl2023"/>, or by professional divers for the deepest sites. If divers are used, an appropriate boat or ship will be required to accommodate the diver and their equipment. Whichever method is used, peepers should be attached to an anchor or a small buoy to facilitate retrieval at the end of the deployment period. |
| | | | |
| | + | During deployment, passive sampling is achieved via diffusion of inorganics through the peeper’s semi-permeable membrane, as the enclosed volume of peeper water equilibrates with the surrounding sediment porewater (Figure 4). It is assumed that the peeper insertion does not greatly alter geochemical conditions that affect freely-dissolved inorganics. Additionally, it is assumed that the peeper water equilibrates with freely-dissolved inorganics in sediment in such a way that the concentration of inorganics in the peeper water would be equal to that of the concentration of inorganics in the sediment porewater. |
| | | | |
| | + | After retrieval, the peepers are brought to the surface and usually preserved until they can be processed. This can be achieved by storing the peepers inside a sealable, airtight bag with either inert gas or oxygen absorbing packets<ref name="RisacherEtAl2023"/>. The peeper water can then be processed by quickly pipetting it into an appropriate sample bottle which usually contains a preservative (e.g., nitric acid for metals). This step is generally conducted in the field. Samples are stored on ice to maintain a temperature of less than 4°C and shipped to an analytical laboratory. The samples are then analyzed for inorganics by standard methods (i.e., USEPA SW-846). The results obtained from the analytical laboratory are then used directly or assessed using the equations below if a reverse tracer is used because deployment time is insufficient for all analytes to reach equilibrium. |
| | | | |
| − | 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>)):
| + | ==Equilibrium Determination (Tracers)== |
| | + | The equilibration period of peepers can last several weeks and depends on deployment conditions, analyte of interest, and peeper design. In many cases, it is advantageous to use pre-equilibrium methods that can use measurements in peepers deployed for shorter periods to predict concentrations at equilibrium<ref name="USEPA2017">USEPA, 2017. Laboratory, Field, and Analytical Procedures for Using Passive Sampling in the Evaluation of Contaminated Sediments: User’s Manual. EPA/600/R-16/357. [[Media: EPA_600_R-16_357.pdf | Report.pdf]]</ref>. |
| | | | |
| − | :::::<big>'''Equation 1:''' ''R-NO<sub>2</sub> + e<sup>-</sup> ⇔ R-NO<sub>2</sub><sup>•-</sup>''</big>
| + | Although the equilibrium concentration of an analyte in sediment can be evaluated by examining analyte results for peepers deployed for several different amounts of time (i.e., a time series), this is impractical for typical field investigations because it would require several mobilizations to the site to retrieve samplers. Alternately, reverse tracers (referred to as a performance reference compound when used with organic compound passive sampling) can be used to evaluate the percentage of equilibrium reached by a passive sampler. |
| | | | |
| − | 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.
| + | Thomas and Arthur<ref name="ThomasArthur2010"/> studied the use of a reverse tracer to estimate percent equilibrium in lab experiments and a field application. They concluded that bromide can be used to estimate concentrations in porewater using measurements obtained before equilibrium is reached. Further studies were also conducted by Risacher ''et al.''<ref name="RisacherEtAl2023"/> showed that lithium can also be used as a tracer for brackish and saline environments. Both studies included a mathematical model for estimating concentrations of ions in external media (''C<small><sub>0</sub></small>'') based on measured concentrations in the peeper chamber (''C<small><sub>p,t</sub></small>''), the elimination rate of the target analyte (''K'') and the deployment time (''t''): |
| − | | + | </br> |
| − | {| class="wikitable mw-collapsible" style="float:left; margin-right:40px; text-align:center;" | + | {| |
| − | |+ Table 1. Aqueous 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.
| + | | || '''Equation 1:''' |
| | + | | [[File: Equation1r.png]] |
| | |- | | |- |
| − | ! Compound
| + | | Where: || || |
| − | ! rowspan="2" |''E<sub>H</sub><sup>1'</sup>'' (V)
| |
| − | ! colspan="4"| Hydroquinone [log ''k<sub>R</sub>'' (M<sup>-1</sup>s<sup>-1</sup>)]
| |
| | |- | | |- |
| − | ! (NAC/MC)
| + | | || ''C<small><sub>0</sub></small>''|| is the freely dissolved concentration of the analyte in the sediment (mg/L or μg/L), sometimes referred to as ''C<small><sub>free</sub></small> |
| − | ! 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"/> | + | | || ''C<small><sub>p,t</sub></small>'' || is the measured concentration of the analyte in the peeper at time of retrieval (mg/L or μg/L) |
| | |- | | |- |
| − | | 2-nitrotoluene (2-NT) || -0.590<ref name="Schwarzenbach1990"/> || -1.432<ref name="Schwarzenbach1990"/> || -2.523<ref name="Schwarzenbach1990"/> || 0.775<ref name="Hartenbach2008"/> || | + | | || ''K'' || is the elimination rate of the target analyte |
| − | |-
| |
| − | | 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"/>
| + | | || ''t'' || is the deployment time (days) |
| − | |-
| |
| − | | 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 elimination rate of the target analyte (''K'') is calculated using Equation 2: |
| − | 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] [https://pubs.acs.org/doi/pdf/10.1021/es505092s Open access article.]</ref>.
| + | </br> |
| − | | + | {| |
| − | 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"/>.
| + | | || '''Equation 2:''' |
| − | | + | | [[File: Equation2r.png]] |
| − | ==Ferruginous Reductants==
| |
| − | {| class="wikitable mw-collapsible" style="float:right; margin-left:40px; text-align:center;"
| |
| − | |+ Table 2. Logarithm of 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"/> | + | | Where: || || |
| | |- | | |- |
| − | | 2-nitrotoluene || -0.590<ref name="Schwarzenbach1990"/> || || || || || || -0.602<ref name="Schwarzenbach1990"/> | + | | || ''K''|| is the elimination rate of the target analyte |
| | |- | | |- |
| − | | 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"/> | + | | || ''K<small><sub>tracer</sub></small>'' || is the elimination rate of the tracer |
| | |- | | |- |
| − | | 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"/> | + | | || ''D'' || is the free water diffusivity of the analyte (cm<sup>2</sup>/s) |
| | |- | | |- |
| − | | 2-chloronitrobenzene || -0.485<ref name="Schwarzenbach1990"/> || || || || || || 0.944<ref name="Schwarzenbach1990"/> | + | | || ''D<small><sub>tracer</sub></small>'' || is the free water diffusivity of the tracer (cm<sup>2</sup>/s) |
| − | |-
| |
| − | | 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 3. Rate constants for the reduction of MCs by iron minerals
| + | The elimination rate of the tracer (''K<small><sub>tracer</sub></small>'') is calculated using Equation 3: |
| − | |-
| + | </br> |
| − | ! MC
| + | {| |
| − | ! Iron Mineral
| + | | || '''Equation 3:''' |
| − | ! Iron mineral loading</br>(g/L)
| + | | [[File: Equation3r2.png]] |
| − | ! 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>(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>''
| |
| − | |- | |
| − | | 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 || | + | | Where: || || |
| | |- | | |- |
| − | | 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 | + | | || ''K<small><sub>tracer</sub></small>'' || is the elimination rate of the tracer |
| | |- | | |- |
| − | | 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 | + | | || ''C<small><sub>tracer,i</sub></small>''|| is the measured initial concentration of the tracer in the peeper prior to deployment (mg/L or μg/L) |
| | |- | | |- |
| − | | 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 | + | | || ''C<small><sub>tracer,t</sub></small>'' || is the measured final concentration of the tracer in the peeper at time of retrieval (mg/L or μg/L) |
| | |- | | |- |
| − | | 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 | + | | || ''t'' || is the deployment time (days) |
| − | |-
| |
| − | | 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 4. 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) can be 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.
| + | Using this set of equations allows the calculation of the porewater concentration of the analyte prior to its equilibrium with the peeper water. A template for these calculations can be found in the appendix of Risacher ''et al.''<ref name="RisacherEtAl2023"/>. |
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| | + | ==Using Peeper Data at a Sediment Site== |
| | + | Peeper data can be used to enable site specific decision making in a variety of ways. Some of the most common uses for peepers and peeper data are discussed below. |
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| − | 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.
| + | '''Nature and Extent:''' Multiple peepers deployed in sediment can help delineate areas of increased metal availability. Peepers are especially helpful for sites that are comprised of coarse, relatively inert materials that may not be conducive to traditional bulk sediment sampling. Because much of the inorganics present in these types of sediments may be associated with the porewater phase rather than the solid phase, peepers can provide a more representative measurement of C<small><sub>0</sub></small>. Additionally, at sites where tidal pumping or groundwater flux may be influencing the nature and extent of inorganics, peepers can provide a distinct advantage to bulk sediment sampling or other point-in-time measurements, as peepers can provide an average measurement that integrates the variability in the hydrodynamic and chemical conditions over time. |
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| − | 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"/>.
| + | '''Sources and Fate:''' A considerable advantage to using peepers is that C<small><sub>0</sub></small> results are expressed as concentration in units of mass per volume (e.g., mg/L), providing a common unit of measurement to compare across multiple media. For example, synchronous measurements of C<small><sub>0</sub></small> using peepers deployed in both surface water and sediment can elucidate the potential flux of inorganics from sediment to surface water. Paired measurements of both C<small><sub>0</sub></small> and bulk metals in sediment can also allow site specific sediment-porewater partition coefficients to be calculated. These values can be useful in understanding and predicting contaminant fate, especially in situations where the potential dissolution of metals from sediment are critical to predict, such as when sediment is dredged. |
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| − | 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"/>.
| + | '''Direct Toxicity to Aquatic Life:''' Peepers are frequently used to understand the potential direct toxicity to aquatic life, such as benthic invertebrates and fish. A C<small><sub>0</sub></small> measurement obtained from a peeper deployed in sediment (''in situ'') or surface water (''ex situ''), can be compared to toxicological benchmarks for aquatic life to understand the potential toxicity to aquatic life and to set remediation goals<ref name="USEPA2017"/>. C<small><sub>0</sub></small> measurements can also be incorporated in more sophisticated approaches, such as the Biotic Ligand Model<ref>Santore, C.R., Toll, E.J., DeForest, K.D., Croteau, K., Baldwin, A., Bergquist, B., McPeek, K., Tobiason, K., and Judd, L.N., 2022. Refining our understanding of metal bioavailability in sediments using information from porewater: Application of a multi-metal BLM as an extension of the Equilibrium Partitioning Sediment Benchmarks. Integrated Environmental Assessment and Management, 18(5), pp. 1335–1347. [https://doi.org/10.1002/ieam.4572 doi: 10.1002/ieam.4572]</ref> to understand the potential for toxicity or the need to conduct toxicological testing or ecological evaluations. |
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| − | 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.
| + | '''Bioaccumulation of Inorganics by Aquatic Life:''' Peepers can also be used to understand site specific relationship between C<small><sub>0</sub></small> and concentrations of inorganics in aquatic life. For example, measuring C<small><sub>0</sub></small> in sediment from which organisms are collected and analyzed can enable the estimation of a site-specific uptake factor. This C<small><sub>0</sub></small>-to-organism uptake factor (or model) can then be applied for a variety of uses, including predicting the concentration of inorganics in other organisms, or estimating a sediment C<small><sub>0</sub></small> value that would be safe for consumption by wildlife or humans. Because several decades of research have found that the correlation between C<small><sub>0</sub></small> measurements and bioavailability is usually better than the correlation between measurements of chemicals in bulk sediment and bioavailability, C<small><sub>0</sub></small>-to-organism uptake factors are likely to be more accurate than uptake factors based on bulk sediment testing. |
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| − | 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.
| + | '''Evaluating Sediment Remediation Efficacy:''' Passive sampling has been used widely to evaluate the efficacy of remedial actions such as active amendments, thin layer placements, and capping to reduce the availability of contaminants at sediment sites. A particularly powerful approach is to compare baseline (pre-remedy) C<small><sub>0</sub></small> in sediment to C<small><sub>0</sub></small> in sediment after the sediment remedy has been applied. Peepers can be used in this context for inorganics, allowing the sediment remedy’s success to be evaluated and monitored in laboratory benchtop remedy evaluations, pilot scale remedy evaluations, and full-scale remediation monitoring. |
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| | ==References== | | ==References== |
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| | ==See Also== | | ==See Also== |
| − | *[https://www.serdp-estcp.org/Program-Areas/Environmental-Restoration/Contaminated-Groundwater/Persistent-Contamination/ER-2617 Measuring and Predicting the Natural and Enhanced Rate and Capacity of Abiotic Reduction of Munition Constituents] | + | *[https://vimeo.com/809180171/c276c1873a Peeper Deployment Video] |
| − | | + | *[https://vimeo.com/811073634/303edf2693 Peeper Retrieval Video] |
| − | *[https://www.epa.gov/fedfac/military-munitionsunexploded-ordnance Military Munitions/Unexploded Ordnance - EPA] | + | *[https://vimeo.com/811328715/aea3073540 Peeper Processing Video] |
| | + | *[https://sepub-prod-0001-124733793621-us-gov-west-1.s3.us-gov-west-1.amazonaws.com/s3fs-public/2024-09/ER20-5261%20Fact%20Sheet.pdf?VersionId=malAixSQQM3mWCRiaVaxY8wLdI0jE1PX Fact Sheet] |
