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| − | ==Hydrogeophysical methods for characterization and monitoring of surface water-groundwater interactions== | + | ==Munitions Constituents – Sample Extraction and Analytical Techniques== |
| − | Hydrogeophysical methods can be used to cost-effectively locate and characterize regions of
| + | Munitions Constituents, including [[Wikipedia: Insensitive munition | insensitive munitions]] IM), are a broad category of compounds and, in areas where manufactured or used, can be found in a variety of environmental matrices (waters, soil, and tissues). This presents an analytical challenge when a variety of these munitions are to be quantified. This article discusses sample extraction methods for each typical sample matrix (high level water, low level water, soil and tissue) as well as the accompanying [[Wikipedia: High-performance liquid chromatography | HPLC]]-UV analytical method for 27 compounds of interest (legacy munitions, insensitive munitions, and surrogates). |
| − | 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]] | + | *[[Munitions Constituents]] |
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| | '''Contributor(s):''' | | '''Contributor(s):''' |
| − | *[[Dr. Lee Slater]]
| + | |
| − | *Dr. Ramona Iery | + | *Dr. Austin Scircle |
| − | *Dr. Dimitrios Ntarlagiannis
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| − | *Henry Moore
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| | '''Key Resource(s):''' | | '''Key Resource(s):''' |
| − | *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==
| + | *[https://www.epa.gov/sites/default/files/2015-07/documents/epa-8330b.pdf USEPA Method 8330B]<ref name= "8330B">United States Environmental Protection Agency (USEPA), 2006. EPA Method 8330B (SW-846) Nitroaromatics, Nitramines, and Nitrate Esters by High Performance Liquid Chromatography (HPLC), Revision 2. [https://www.epa.gov/esam/epa-method-8330b-sw-846-nitroaromatics-nitramines-and-nitrate-esters-high-performance-liquid USEPA Website] [[Media: epa-8330b.pdf | Method 8330B.pdf]]</ref> |
| − | 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.
| + | *Methods for simultaneous quantification of legacy and insensitive munition (IM) constituents in aqueous, soil/sediment, and tissue matrices<ref name="CrouchEtAl2020">Crouch, R.A., Smith, J.C., Stromer, B.S., Hubley, C.T., Beal, S., Lotufo, G.R., Butler, A.D., Wynter, M.T., Russell, A.L., Coleman, J.G., Wayne, K.M., Clausen, J.L., Bednar, A.J., 2020. Methods for simultaneous determination of legacy and insensitive munition (IM) constituents in aqueous, soil/sediment, and tissue matrices. Talanta, 217, Article 121008. [https://doi.org/10.1016/j.talanta.2020.121008 doi: 10.1016/j.talanta.2020.121008] [[Media: CrouchEtAl2020.pdf | Open Access Manuscript.pdf]]</ref> |
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| − | ==Hydrogeophysical Technologies for Understanding Groundwater-Surface Water Interactions== | + | ==Introduction== |
| − | [[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.
| + | The primary intention of the analytical methods presented here is to support the monitoring of legacy and insensitive munitions contamination on test and training ranges, however legacy and insensitive munitions often accompany each other at demilitarization facilities, manufacturing facilities, and other environmental sites. Energetic materials typically appear on ranges as small, solid particulates and due to their varying functional groups and polarities, can partition in various environmental compartments<ref>Walsh, M.R., Temple, T., Bigl, M.F., Tshabalala, S.F., Mai, N. and Ladyman, M., 2017. Investigation of Energetic Particle Distribution from High‐Order Detonations of Munitions. Propellants, Explosives, Pyrotechnics, 42(8), pp. 932-941. [https://doi.org/10.1002/prep.201700089 doi: 10.1002/prep.201700089]</ref>. To ensure that contaminants are monitored and controlled at these sites and to sustainably manage them a variety of sample matrices (surface or groundwater, process waters, soil, and tissues) must be considered. (Process water refers to water used during industrial manufacturing or processing of legacy and insensitive munitions.) Furthermore, additional analytes must be added to existing methodologies as the usage of IM compounds changes and as new degradation compounds are identified. Of note, relatively new IM formulations containing NTO, DNAN, and NQ are seeing use in [[Wikipedia: IMX-101 | IMX-101]], IMX-104, Pax-21 and Pax-41 (Table 1)<ref>Mainiero, C. 2015. Picatinny Employees Recognized for Insensitive Munitions. U.S. Army, Picatinny Arsenal Public Affairs. [https://www.army.mil/article/148873/picatinny_employees_recognized_for_insensitive_munitions Open Access Press Release]</ref><ref>Frem, D., 2022. A Review on IMX-101 and IMX-104 Melt-Cast Explosives: Insensitive Formulations for the Next-Generation Munition Systems. Propellants, Explosives, Pyrotechnics, 48(1), e202100312. [https://doi.org/10.1002/prep.202100312 doi: 10.1002/prep.202100312]</ref>. |
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| − | ===Temperature-Based Technologies===
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| − | 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====
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| − | 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)=====
| + | Sampling procedures for legacy and insensitive munitions are identical and utilize multi-increment sampling procedures found in USEPA Method 8330B Appendix A<ref name= "8330B"/>. Sample hold times, subsampling and quality control requirements are also unchanged. The key differences lie in the extraction methods and instrumental methods. Briefly, legacy munitions analysis of low concentration waters uses a single cartridge reverse phase [[Wikipedia: Solid-phase extraction | SPE]] procedure, and [[Wikipedia: Acetonitrile | acetonitrile]] (ACN) is used for both extraction and [[Wikipedia: Elution | elution]] for aqueous and solid samples<ref name= "8330B"/><ref>United States Environmental Protection Agency (USEPA), 2007. EPA Method 3535A (SW-846) Solid-Phase Extraction (SPE), Revision 1. [https://www.epa.gov/esam/epa-method-3535a-sw-846-solid-phase-extraction-spe USEPA Website] [[Media: epa-3535a.pdf | Method 3535A.pdf]]</ref>. An [[Wikipedia: High-performance_liquid_chromatography#Isocratic_and_gradient_elution | isocratic]] separation via reversed-phase C-18 column with 50:50 methanol:water mobile phase or a C-8 column with 15:85 isopropanol:water mobile phase is used to separate legacy munitions<ref name= "8330B"/>. While these procedures are sufficient for analysis of legacy munitions, alternative solvents, additional SPE cartridges, and a gradient elution are all required for the combined analysis of legacy and insensitive munitions. |
| − | [[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=====
| + | Previously, analysis of legacy and insensitive munitions required multiple analytical techniques, however the methods presented here combine the two munitions categories resulting in an HPLC-UV method and accompanying extraction methods for a variety of common sample matrices. A secondary HPLC-UV method and a HPLC-MS method were also developed as confirmatory methods. The methods discussed in this article were validated extensively by single-blind round robin testing and subsequent statistical treatment as part of ESTCP [https://serdp-estcp.mil/projects/details/d05c1982-bbfa-42f8-811d-51b540d7ebda ER19-5078]. Wherever possible, the quality control criteria in the Department of Defense Quality Systems Manual for Environmental Laboratories were adhered to<ref>US Department of Defense and US Department of Energy, 2021. Consolidated Quality Systems Manual (QSM) for Environmental Laboratories, Version 5.4. 387 pages. [https://www.denix.osd.mil/edqw/denix-files/sites/43/2021/10/QSM-Version-5.4-FINAL.pdf Free Download] [[Media: QSM-Version-5.4.pdf | QSM Version 5.4.pdf]]</ref>. Analytes included in these methods are found in Table 1. |
| − | [[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.]]
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| − | 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=====
| + | The chromatograms produced by the primary and secondary HPLC-UV methods are shown in Figure 1 and Figure 2, respectively. Chromatograms for each detector wavelength used are shown (315, 254, and 210 nm). |
| − | 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==== | + | ==Extraction Methods== |
| − | 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.
| + | ===High Concentration Waters (> 1 ppm)=== |
| | + | Aqueous samples suspected to contain the compounds of interest at concentrations detectable without any extraction or pre-concentration are suitable for analysis by direct injection. The method deviates from USEPA Method 8330B by adding a pH adjustment and use of MeOH rather than ACN for dilution<ref name= "8330B"/>. The pH adjustment is needed to ensure method accuracy for ionic compounds (like NTO or PA) in basic samples. A solution of 1% HCl/MeOH is added to both acidify and dilute the samples to a final acid concentration of 0.5% (vol/vol) and a final solvent ratio of 1:1 MeOH/H2O. The direct injection samples are then ready for analysis. |
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| − | =====Fiber-optic Distributed Temperature Sensing (FO-DTS)===== | + | ===Low Concentration Waters (< 1 ppm)=== |
| − | [[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.]]
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| − | 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)=====
| + | Aqueous samples suspected to contain the compounds of interest at low concentrations require extraction and pre-concentration using solid phase extraction (SPE). The SPE setup described here uses a triple cartridge setup shown in '''Figure 3'''. Briefly, the extraction procedure loads analytes of interest onto the cartridges in this order: Strata<sup><small>TM</small></sup> X, Strata<sup><small>TM</small></sup> X-A, and Envi-Carb<sup><small>TM</small></sup>. Then the cartridge order is reversed, and analytes are eluted via a two-step elution, resulting in 2 extracts (which are combined prior to analysis). Five milliliters of MeOH is used for the first elution, while 5 mL of acidified MeOH (2% HCl) is used for the second elution. The particular SPE cartridges used are noncritical so long as cartridge chemistries are comparable to those above. |
| − | 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.
| + | ===Soils=== |
| | + | Soil collection, storage, drying and grinding procedures are identical to the USEPA Method 8330B procedures<ref name= "8330B"/>; however, the solvent extraction procedure differs in the number of sonication steps, sample mass and solvent used. A flow chart of the soil extraction procedure is shown in '''Figure 4'''. Soil masses of approximately 2 g and a sample to solvent ratio of 1:5 (g/mL) are used for soil extraction. The extraction is carried out in a sonication bath chilled below 20 ⁰C and is a two-part extraction, first extracting in MeOH (6 hours) followed by a second sonication in 1:1 MeOH:H<sub>2</sub>O solution (14 hours). The extracts are centrifuged, and the supernatant is filtered through a 0.45 μm PTFE disk filter. |
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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”/>.
| + | The solvent volume should generally be 10 mL but if different soil masses are required, solvent volume should be 5 mL/g. The extraction results in 2 separate extracts (MeOH and MeOH:H2O) that are combined prior to analysis. |
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| − | ===Electrical Conductivity (EC) Based Technologies=== | + | ===Tissues=== |
| − | 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
| + | Tissue matrices are extracted by 18-hour sonication using a ratio of 1 gram of wet tissue per 5 mL of MeOH. This extraction is performed in a sonication bath chilled below 20 ⁰C and the supernatant (MeOH) is filtered through a 0.45 μm PTFE disk filter. |
| − | (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==== | + | Due to the complexity of tissue matrices, an additional tissue cleanup step, adapted from prior research, can be used to reduce interferences<ref name="RussellEtAl2014">Russell, A.L., Seiter, J.M., Coleman, J.G., Winstead, B., Bednar, A.J., 2014. Analysis of munitions constituents in IMX formulations by HPLC and HPLC-MS. Talanta, 128, pp. 524–530. [https://doi.org/10.1016/j.talanta.2014.02.013 doi: 10.1016/j.talanta.2014.02.013]</ref><ref name="CrouchEtAl2020"/>. The cleanup procedure uses small scale chromatography columns prepared by loading 5 ¾” borosilicate pipettes with 0.2 g activated silica gel (100–200 mesh). The columns are wetted with 1 mL MeOH, which is allowed to fully elute and then discarded prior to loading with 1 mL of extract and collecting in a new amber vial. After the extract is loaded, a 1 mL aliquot of MeOH followed by a 1 mL aliquot of 2% HCL/MeOH is added. This results in a 3 mL silica treated tissue extract. This extract is vortexed and diluted to a final solvent ratio of 1:1 MeOH/H<sub>2</sub>O before analysis. |
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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=====
| + | Most federal, state, and local regulatory guidance for assessing and mitigating the [[Vapor Intrusion (VI) | vapor intrusion]] pathway reflects USEPA’s ''Technical Guide for Assessing and Mitigating the Vapor Intrusion Pathway from Subsurface Vapor Sources to Indoor Air''<ref name="USEPA2015">USEPA, 2015. OSWER Technical Guide for Assessing and Mitigating the Vapor Intrusion Pathway from Subsurface Vapor Sources to Indoor Air. U.S. Environmental Protection Agency, Office of Solid Waste and Emergency Response, OSWER Publication No. 9200.2-154, 267 pages. [https://www.epa.gov/vaporintrusion/technical-guide-assessing-and-mitigating-vapor-intrusion-pathway-subsurface-vapor USEPA Website] [//www.enviro.wiki/images/0/06/USEPA2015.pdf Report.pdf]</ref>. The paradigm outlined by that guidance includes: 1) a preliminary and mostly qualitative analysis that looks for site conditions that suggest vapor intrusion might occur (e.g., the presence of vapor-forming chemicals in close proximity to buildings); 2) a multi-step and more detailed quantitative screening analysis that involves site-specific data collection and their comparison to screening levels to identify buildings of potential VI concern; and 3) selection and design of mitigation systems or continued monitoring, as needed. With respect to (2), regulatory guidance typically recommends consideration of “multiple lines of evidence” in decision-making<ref name="USEPA2015" /><ref>NJDEP, 2021. Vapor Intrusion Technical Guidance, Version 5.0. New Jersey Department of Environmental Protection, Trenton, NJ. [https://dep.nj.gov/srp/guidance/vapor-intrusion/vig/ Website] [//www.enviro.wiki/images/e/ee/NJDEP2021.pdf Guidance Document.pdf]</ref>, with typical lines-of-evidence being groundwater, soil gas, sub-slab soil gas, and/or indoor air concentrations. Of those, soil gas measurements and/or measured short-term indoor air concentrations can be weighted heavily, and therefore decision making might not be completed without them. Effective evaluation of VI risk from sub-slab and/or soil gas measurements would require an unknown building-specific attenuation factor, but there is also uncertainty as to whether or not indoor air data is representative of maximum and/or long-term average indoor concentrations. Indoor air data can be confounded by indoor contaminant sources because the number of samples is typically small, indoor concentrations can vary with time, and because a number of household products can emit the chemicals being measured. When conducting VI pathway assessments in neighborhoods where it is impractical to assess all buildings, the EPA recommends following a “worst first” investigational approach. |
| − | 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.
| |
| | | | |
| − | =====Waterborne Electrical Imaging=====
| + | The limitations of this approach, as practiced, are the following: |
| − | [[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.]]
| |
| − | 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.
| |
| | | | |
| − | ====Monitoring Methods==== | + | *Decisions are rarely made without indoor air data and generally, seasonal sampling is required, delaying decision making. |
| | + | *The collection of a robust indoor air data set that adequately characterizes long term indoor air concentrations could take years given the typical frequency of data collection and the most common methods of sample collection (e.g., 24-hour samples). Therefore, indoor air sampling might continue indefinitely at some sites. |
| | + | *The “worst first” buildings might not be identified correctly by the logic outlined in USEPA’s 2015 guidance and the most impacted buildings might not even be located over a groundwater plume. Recent studies have shown [[Vapor Intrusion – Sewers and Utility Tunnels as Preferential Pathways |VI impacts in homes as a result of sewer and other subsurface piping connections]], which are not explicitly considered nor easily characterized through conventional VI pathway assessment<ref> Beckley, L, McHugh, T., 2020. A Conceptual Model for Vapor Intrusion from Groundwater Through Sewer Lines. Science of the Total Environment, 698, Article 134283. [https://doi.org/10.1016/j.scitotenv.2019.134283 doi: 10.1016/j.scitotenv.2019.134283] [//www.enviro.wiki/images/4/4e/BeckleyMcHugh2020.pdf Open Access Article]</ref><ref name="GuoEtAl2015">Guo, Y., Holton, C., Luo, H., Dahlen, P., Gorder, K., Dettenmaier, E., Johnson, P.C., 2015. Identification of Alternative Vapor Intrusion Pathways Using Controlled Pressure Testing, Soil Gas Monitoring, and Screening Model Calculations. Environmental Science and Technology, 49(22), pp. 13472–13482. [https://doi.org/10.1021/acs.est.5b03564 doi: 10.1021/acs.est.5b03564]</ref><ref name="McHughEtAl2017">McHugh, T., Beckley, L., Sullivan, T., Lutes, C., Truesdale, R., Uppencamp, R., Cosky, B., Zimmerman, J., Schumacher, B., 2017. Evidence of a Sewer Vapor Transport Pathway at the USEPA Vapor Intrusion Research Duplex. Science of the Total Environment, pp. 598, 772-779. [https://doi.org/10.1016/j.scitotenv.2017.04.135 doi: 10.1016/j.scitotenv.2017.04.135] [//www.enviro.wiki/images/6/63/McHughEtAl2017.pdf Open Access Manuscipt]</ref><ref name="McHughBeckley2018">McHugh, T., Beckley, L., 2018. Sewers and Utility Tunnels as Preferential Pathways for Volatile Organic Compound Migration into Buildings: Risk Factors and Investigation Protocol. ESTCP ER-201505, Final Report. [https://serdp-estcp.mil/projects/details/f12abf80-5273-4220-b09a-e239d0188421 Project Website] [//www.enviro.wiki/images/5/55/2018b-McHugh-ER-201505_Conceptual_Model.pdf Final Report.pdf]</ref><ref name="RiisEtAl2010">Riis, C., Hansen, M.H., Nielsen, H.H., Christensen, A.G., Terkelsen, M., 2010. Vapor Intrusion through Sewer Systems: Migration Pathways of Chlorinated Solvents from Groundwater to Indoor Air. Seventh International Conference on Remediation of Chlorinated and Recalcitrant Compounds, May, Monterey, CA. Battelle Memorial Institute. ISBN 978-0-9819730-2-9. [https://www.battelle.org/conferences/battelle-conference-proceedings Website] [//www.enviro.wiki/images/9/95/2010-Riis-Migratioin_pathways_of_Chlorinated_Solvents.pdf Report.pdf]</ref>. |
| | + | *The presumptive remedy for VI mitigation (sub-slab depressurization) may not be effective for all VI scenarios (e.g., those involving vapor migration to indoor spaces via sewer connections). |
| | + | |
| | + | The '''VI Diagnosis Toolkit''' components were developed considering these limitations as well as more recent knowledge gained through research, development, and validation projects funded by SERDP and ESTCP. |
| | | | |
| − | =====Land-based Electrical Monitoring===== | + | ==The VI Diagnosis Toolkit Components== |
| − | 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.
| + | [[File:DahlenFig1.png|thumb|450px|Figure 1. Vapor intrusion pathway conceptualization considering “alternate VI pathways”, including “pipe flow |
| | + | VI” and “sewer VI” pathways<ref name="JohnsonEtAl2020" />.]] |
| | + | The primary components of the VI Diagnosis Toolkit and their uses include: |
| | | | |
| − | ===Considerations for Using EM and Waterborne Electrical Imaging Methods===
| + | *'''External VI source strength screening''' to identify buildings most likely to be impacted by VI at levels warranting building-specific testing. |
| − | 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.
| + | *'''Indoor air source screening''' to locate and remove indoor air sources that might confound building specific VI pathway assessment. |
| | + | *'''Controlled pressurization method (CPM)''' testing to quickly (in a few days or less) measure the worst-case indoor air impact likely to be caused by VI under natural conditions in specific buildings. CPM tests can also be used to identify the presence of indoor air sources and diagnose active VI pathways. |
| | + | *'''Passive indoor sampling''' for determining long-term average indoor air concentrations under natural VI conditions and/or for verifying mitigation system effectiveness in buildings that warrant VI mitigation. |
| | + | *'''Comprehensive VI conceptual model development and refinement''' to ensure that appropriate monitoring, investigation, and mitigation strategies are being selected (Figure 1). |
| | | | |
| − | 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.
| + | Expanded discussions for each of these are given below. |
| | | | |
| − | ===Other Hydrogeophysical Technologies===
| + | '''External VI source strength screening''' identifies those buildings that warrant more intrusive building-specific assessments, using data collected exterior to the buildings. The use of groundwater and/or soil gas concentration data for building screening has been part of VI pathway assessments for some time and their use is discussed in many regulatory guidance documents. Typically, the measured concentrations are compared to relevant screening levels derived via modeling or empirical analyses from indoor air concentrations of concern. |
| − | A number of other hydrogeophysical technologies exist, with proven applications to the characterization of settings where GWSWE occurs. Seismic
| |
| | | | |
| | + | More recently it has been discovered that VI impacts can occur via sewer and other subsurface piping connections in areas where vapor migration through the soil would not be expected to be significant, and this could also occur in buildings that do not sit over contaminated groundwater<ref name="RiisEtAl2010" /><ref name="GuoEtAl2015" /><ref name="McHughEtAl2017" /><ref name="McHughBeckley2018" />. |
| | | | |
| − | 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>)):
| + | Therefore, in addition to groundwater and soil gas sampling, external data collection that includes and extends beyond the area of concern should include manhole vapor sampling (e.g., sanitary sewer, storm sewer, land-drain). Video surveys from sanitary sewers, storm sewers, and/or land-drains can also be used to identify areas of groundwater leakage into utility corridors and lateral connections to buildings that are conduits for vapor transport. During these investigations, it is important to recognize that utility corridors can transmit both impacted water and vapors beyond groundwater plume boundaries, so extending investigations into areas adjacent to groundwater plume boundaries is necessary. |
| | | | |
| − | :::::<big>'''Equation 1:''' ''R-NO<sub>2</sub> + e<sup>-</sup> ⇔ R-NO<sub>2</sub><sup>•-</sup>''</big>
| + | Using projected indoor air concentrations from modeling and empirical data analyses, and distance screening approaches, external source screening can identify areas and buildings that can be ruled out, or conversely, those that warrant building-specific testing. |
| | | | |
| − | 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.
| + | Demonstration of neighborhood-scale external VI source screening using groundwater, depth, sewer, land drain, and video data is documented in the ER-201501 final report<ref name="JohnsonEtAl2020" />. |
| | | | |
| − | {| class="wikitable mw-collapsible" style="float:left; margin-right:40px; text-align:center;"
| + | '''Indoor air source screening''' seeks to locate and remove indoor air sources<ref>Doucette, W.J., Hall, A.J., Gorder, K.A., 2010. Emissions of 1,2-Dichloroethane from Holiday Decorations as a Source of Indoor Air Contamination. Ground Water Monitoring and Remediation, 30(1), pp. 67-73. [https://doi.org/10.1111/j.1745-6592.2009.01267.x doi: 10.1111/j.1745-6592.2009.01267.x] </ref> that might confound building specific VI pathway assessment. Visual inspections and written surveys might or might not identify significant indoor air sources, so these should be complemented with use of portable analytical instruments<ref>McHugh, T., Kuder, T., Fiorenza, S., Gorder, K., Dettenmaier, E., Philp, P., 2011. Application of CSIA to Distinguish Between Vapor Intrusion and Indoor Sources of VOCs. Environmental Science and Technology, 45(14), pp. 5952-5958. [https://doi.org/10.1021/es200988d doi: 10.1021/es200988d]</ref><ref name="BeckleyEtAl2014">Beckley, L., Gorder, K., Dettenmaier, E., Rivera-Duarte, I., McHugh, T., 2014. On-Site Gas Chromatography/Mass Spectrometry (GC/MS) Analysis to Streamline Vapor Intrusion Investigations. Environmental Forensics, 15(3), pp. 234–243. [https://doi.org/10.1080/15275922.2014.930941 doi: 10.1080/15275922.2014.930941]</ref>. |
| − | |+ 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.
| |
| − | |-
| |
| − | ! Compound
| |
| − | ! 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)
| |
| − | ! LAW<sup>-</sup>
| |
| − | ! JUG<sup>-</sup>
| |
| − | ! AHQDS<sup>-</sup>
| |
| − | ! AHQS<sup>-</sup>
| |
| − | |-
| |
| − | | Nitrobenzene (NB) || -0.485<ref name="Schwarzenbach1990"/> || 0.380<ref name="Schwarzenbach1990"/> || -1.102<ref name="Schwarzenbach1990"/> || 2.050<ref name="Murillo-Gelvez2019"/> || 3.060<ref name="Murillo-Gelvez2019"/>
| |
| − | |-
| |
| − | | 2-nitrotoluene (2-NT) || -0.590<ref name="Schwarzenbach1990"/> || -1.432<ref name="Schwarzenbach1990"/> || -2.523<ref name="Schwarzenbach1990"/> || 0.775<ref name="Hartenbach2008"/> ||
| |
| − | |-
| |
| − | | 3-nitrotoluene (3-NT) || -0.475<ref name="Schwarzenbach1990"/> || 0.462<ref name="Schwarzenbach1990"/> || -0.921<ref name="Schwarzenbach1990"/> || ||
| |
| − | |-
| |
| − | | 4-nitrotoluene (4-NT) || -0.500<ref name="Schwarzenbach1990"/> || 0.041<ref name="Schwarzenbach1990"/> || -1.292<ref name="Schwarzenbach1990"/> || 1.822<ref name="Hartenbach2008"/> || 2.610<ref name="Murillo-Gelvez2019"/>
| |
| − | |-
| |
| − | | 2-chloronitrobenzene (2-ClNB) || -0.485<ref name="Schwarzenbach1990"/> || 0.342<ref name="Schwarzenbach1990"/> || -0.824<ref name="Schwarzenbach1990"/> ||2.412<ref name="Hartenbach2008"/> ||
| |
| − | |-
| |
| − | | 3-chloronitrobenzene (3-ClNB) || -0.405<ref name="Schwarzenbach1990"/> || 1.491<ref name="Schwarzenbach1990"/> || 0.114<ref name="Schwarzenbach1990"/> || ||
| |
| − | |-
| |
| − | | 4-chloronitrobenzene (4-ClNB) || -0.450<ref name="Schwarzenbach1990"/> || 1.041<ref name="Schwarzenbach1990"/> || -0.301<ref name="Schwarzenbach1990"/> || 2.988<ref name="Hartenbach2008"/> ||
| |
| − | |-
| |
| − | | 2-acetylnitrobenzene (2-AcNB) || -0.470<ref name="Schwarzenbach1990"/> || 0.519<ref name="Schwarzenbach1990"/> || -0.456<ref name="Schwarzenbach1990"/> || ||
| |
| − | |-
| |
| − | | 3-acetylnitrobenzene (3-AcNB) || -0.405<ref name="Schwarzenbach1990"/> || 1.663<ref name="Schwarzenbach1990"/> || 0.398<ref name="Schwarzenbach1990"/> || ||
| |
| − | |-
| |
| − | | 4-acetylnitrobenzene (4-AcNB) || -0.360<ref name="Schwarzenbach1990"/> || 2.519<ref name="Schwarzenbach1990"/> || 1.477<ref name="Schwarzenbach1990"/> || ||
| |
| − | |-
| |
| − | | 2-nitrophenol (2-NP) || || 0.568 (0.079)<ref name="Schwarzenbach1990"/> || || ||
| |
| − | |-
| |
| − | | 4-nitrophenol (4-NP) || || -0.699 (-1.301)<ref name="Schwarzenbach1990"/> || || ||
| |
| − | |-
| |
| − | | 4-methyl-2-nitrophenol (4-Me-2-NP) || || 0.748 (0.176)<ref name="Schwarzenbach1990"/> || || ||
| |
| − | |-
| |
| − | | 4-chloro-2-nitrophenol (4-Cl-2-NP) || || 1.602 (1.114)<ref name="Schwarzenbach1990"/> || || ||
| |
| − | |-
| |
| − | | 5-fluoro-2-nitrophenol (5-Cl-2-NP) || || 0.447 (-0.155)<ref name="Schwarzenbach1990"/> || || ||
| |
| − | |-
| |
| − | | 2,4,6-trinitrotoluene (TNT) || -0.280<ref name="Schwarzenbach2016"/> || || 2.869<ref name="Hofstetter1999"/> || 5.204<ref name="Hartenbach2008"/> ||
| |
| − | |-
| |
| − | | 2-amino-4,6-dinitrotoluene (2-A-4,6-DNT) || -0.400<ref name="Schwarzenbach2016"/> || || 0.987<ref name="Hofstetter1999"/> || ||
| |
| − | |-
| |
| − | | 4-amino-2,6-dinitrotoluene (4-A-2,6-DNT) || -0.440<ref name="Schwarzenbach2016"/> || || 0.079<ref name="Hofstetter1999"/> || ||
| |
| − | |-
| |
| − | | 2,4-diamino-6-nitrotoluene (2,4-DA-6-NT) || -0.505<ref name="Schwarzenbach2016"/> || || -1.678<ref name="Hofstetter1999"/> || ||
| |
| − | |-
| |
| − | | 2,6-diamino-4-nitrotoluene (2,6-DA-4-NT) || -0.495<ref name="Schwarzenbach2016"/> || || -1.252<ref name="Hofstetter1999"/> || ||
| |
| − | |-
| |
| − | | 1,3-dinitrobenzene (1,3-DNB) || -0.345<ref name="Hofstetter1999"/> || || 1.785<ref name="Hofstetter1999"/> || ||
| |
| − | |-
| |
| − | | 1,4-dinitrobenzene (1,4-DNB) || -0.257<ref name="Hofstetter1999"/> || || 3.839<ref name="Hofstetter1999"/> || ||
| |
| − | |-
| |
| − | | 2-nitroaniline (2-NANE) || < -0.560<ref name="Hofstetter1999"/> || || -2.638<ref name="Hofstetter1999"/> || ||
| |
| − | |-
| |
| − | | 3-nitroaniline (3-NANE) || -0.500<ref name="Hofstetter1999"/> || || -1.367<ref name="Hofstetter1999"/> || ||
| |
| − | |-
| |
| − | | 1,2-dinitrobenzene (1,2-DNB) || -0.290<ref name="Hofstetter1999"/> || || || 5.407<ref name="Hartenbach2008"/> ||
| |
| − | |-
| |
| − | | 4-nitroanisole (4-NAN) || || -0.661<ref name="Murillo-Gelvez2019"/> || || 1.220<ref name="Murillo-Gelvez2019"/> ||
| |
| − | |-
| |
| − | | 2-amino-4-nitroanisole (2-A-4-NAN) || || -0.924<ref name="Murillo-Gelvez2019"/> || || 1.150<ref name="Murillo-Gelvez2019"/> || 2.190<ref name="Murillo-Gelvez2019"/>
| |
| − | |-
| |
| − | | 4-amino-2-nitroanisole (4-A-2-NAN) || || || ||1.610<ref name="Murillo-Gelvez2019"/> || 2.360<ref name="Murillo-Gelvez2019"/>
| |
| − | |-
| |
| − | | 2-chloro-4-nitroaniline (2-Cl-5-NANE) || || -0.863<ref name="Murillo-Gelvez2019"/> || || 1.250<ref name="Murillo-Gelvez2019"/> || 2.210<ref name="Murillo-Gelvez2019"/>
| |
| − | |-
| |
| − | | N-methyl-4-nitroaniline (MNA) || || -1.740<ref name="Murillo-Gelvez2019"/> || || -0.260<ref name="Murillo-Gelvez2019"/> || 0.692<ref name="Murillo-Gelvez2019"/>
| |
| − | |-
| |
| − | | 3-nitro-1,2,4-triazol-5-one (NTO) || || || || 5.701 (1.914)<ref name="Murillo-Gelvez2021"/> ||
| |
| − | |-
| |
| − | | Hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) || || || || -0.349<ref name="Kwon2008"/> ||
| |
| − | |}
| |
| | | | |
| − | [[File:AbioMCredFig5.png | thumb |500px|Figure 5. Relative reduction rate constants of the NACs/MCs listed in Table 1 for AHQDS<sup>–</sup>. Rate constants are compared with respect to RDX. Abbreviations of NACs/MCs as listed in Table 1.]]
| + | The advantage of portable analytical tools is that they allow practitioners to expeditiously test indoor air concentrations under natural conditions in each room of the building. Concentrations in any room in excess of relevant screening levels trigger more sampling in that room to identify if an indoor source is present in that room. Removal of a suspected source and subsequent room testing can identify if that object or product was the source of the previously measured concentrations. |
| − | 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>.
| |
| | | | |
| − | 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"/>.
| + | '''Building-specific controlled pressurization method (CPM) testing''' directly measures the worst case indoor air impact, but it can also be used to determine contributing VI pathways and to identify indoor air sources<ref>McHugh, T.E., Beckley, L., Bailey, D., Gorder, K., Dettenmaier, E., Rivera-Duarte, I., Brock, S., MacGregor, I.C., 2012. Evaluation of Vapor Intrusion Using Controlled Building Pressure. Environmental Science and Technology, 46(9), pp. 4792–4799. [https://doi.org/10.1021/es204483g doi: 10.1021/es204483g]</ref><ref name="BeckleyEtAl2014" /><ref name="GuoEtAl2015" /><ref name="HoltonEtAl2015">Holton, C., Guo, Y., Luo, H., Dahlen, P., Gorder, K., Dettenmaier, E., Johnson, P.C., 2015. Long-Term Evaluation of the Controlled Pressure Method for Assessment of the Vapor Intrusion Pathway. Environmental Science and Technology, 49(4), pp. 2091–2098. [https://doi.org/10.1021/es5052342 doi: 10.1021/es5052342]</ref><ref name="JohnsonEtAl2020" /><ref name="GuoEtAl2020a">Guo, Y., Dahlen, P., Johnson, P.C., 2020a. Development and Validation of a Controlled Pressure Method Test Protocol for Vapor Intrusion Pathway Assessment. Environmental Science and Technology, 54(12), pp. 7117-7125. [https://dx.doi.org/10.1021/acs.est.0c00811 doi: 10.1021/acs.est.0c00811]</ref>. In CPM testing, blowers/fans installed in a doorway(s) or window(s) are set-up to exhaust indoor air to outdoor, which causes the building to be under pressurized relative to the atmosphere. This induces air movement from the subsurface into the test building via openings in the foundation and/or subsurface piping networks with or without direct connections to indoor air. This is similar to what happens intermittently under natural conditions when wind, indoor-outdoor temperature differences, and/or use of appliances that exhaust air from the structure (e.g. dryer exhaust) create an under-pressurized building condition. |
| | | | |
| − | ==Ferruginous Reductants==
| + | The blowers/fans can also be used to blow outdoor air into the building, thereby creating a building over-pressurization condition. A positive pressure difference CPM test suppresses VI pathways; therefore, chemicals detected in indoor air above outdoor air concentrations during this condition are attributed to indoor contaminant sources which facilitates the identification of any such indoor air sources. |
| − | {| 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"/>
| |
| − | |-
| |
| − | | 2-nitrotoluene || -0.590<ref name="Schwarzenbach1990"/> || || || || || || -0.602<ref name="Schwarzenbach1990"/>
| |
| − | |-
| |
| − | | 3-nitrotoluene || -0.475<ref name="Schwarzenbach1990"/> || -0.434 || 0.767 || 2.106 || -0.229 || 1.999<ref name="Gao2021"/></br>3.800<ref name="Salter-Blanc2015"/> || 0.041<ref name="Schwarzenbach1990"/>
| |
| − | |-
| |
| − | | 4-nitrotoluene || -0.500<ref name="Schwarzenbach1990"/> || -0.652 || 0.528 || 2.013 || -0.402 || 1.446<ref name="Gao2021"/></br>3.500<ref name="Salter-Blanc2015"/> || -0.174<ref name="Schwarzenbach1990"/>
| |
| − | |-
| |
| − | | 2-chloronitrobenzene || -0.485<ref name="Schwarzenbach1990"/> || || || || || || 0.944<ref name="Schwarzenbach1990"/>
| |
| − | |-
| |
| − | | 3-chloronitrobenzene || -0.405<ref name="Schwarzenbach1990"/> || 0.360 || 1.810 || 2.888 || 0.691 || 2.882<ref name="Gao2021"/></br>4.900<ref name="Salter-Blanc2015"/> || 0.724<ref name="Schwarzenbach1990"/>
| |
| − | |-
| |
| − | | 4-chloronitrobenzene || -0.450<ref name="Schwarzenbach1990"/> || 0.230 || 1.415 || 2.512 || 0.375 || 3.937<ref name="Gao2021"/></br>4.581<ref name="Naka2006"/> || 0.431<ref name="Schwarzenbach1990"/></br>0.289<ref name="Salter-Blanc2015"/>
| |
| − | |-
| |
| − | | 2-acetylnitrobenzene || -0.470<ref name="Schwarzenbach1990"/> || || || || || || 1.377<ref name="Schwarzenbach1990"/>
| |
| − | |-
| |
| − | | 3-acetylnitrobenzene || -0.405<ref name="Schwarzenbach1990"/> || || || || || || 0.799<ref name="Schwarzenbach1990"/>
| |
| − | |-
| |
| − | | 4-acetylnitrobenzene || -0.360<ref name="Schwarzenbach1990"/> || 0.965 || 2.771 || || 1.872 || 5.028<ref name="Gao2021"/></br>6.300<ref name="Salter-Blanc2015"/> || 1.693<ref name="Schwarzenbach1990"/>
| |
| − | |-
| |
| − | | RDX || -0.550<ref name="Uchimiya2010">Uchimiya, M., Gorb, L., Isayev, O., Qasim, M.M., and Leszczynski, J., 2010. One-electron standard reduction potentials of nitroaromatic and cyclic nitramine explosives. Environmental Pollution, 158(10), pp. 3048–3053. [https://doi.org/10.1016/j.envpol.2010.06.033 DOI: 10.1016/j.envpol.2010.06.033]</ref> || || || || || 2.212<ref name="Gao2021"/></br>2.864<ref name="Kim2007"/> ||
| |
| − | |-
| |
| − | | HMX || -0.660<ref name="Uchimiya2010"/> || || || || || -2.762<ref name="Gao2021"/> ||
| |
| − | |-
| |
| − | | TNT || -0.280<ref name="Schwarzenbach2016"/> || || || || || 7.427<ref name="Gao2021"/> || 2.050<ref name="Salter-Blanc2015"/>
| |
| − | |-
| |
| − | | 1,3-dinitrobenzene || -0.345<ref name="Hofstetter1999"/> || || || || || || 1.220<ref name="Salter-Blanc2015"/>
| |
| − | |-
| |
| − | | 2,4-dinitrotoluene || -0.380<ref name="Schwarzenbach2016"/> || || || || || 5.319<ref name="Gao2021"/> || 1.156<ref name="Salter-Blanc2015"/>
| |
| − | |-
| |
| − | | Nitroguanidine (NQ) || -0.700<ref name="Uchimiya2010"/> || || || || || -0.185<ref name="Gao2021"/> ||
| |
| − | |-
| |
| − | | 2,4-dinitroanisole (DNAN) || -0.400<ref name="Uchimiya2010"/> || || || || || || 1.243<ref name="Salter-Blanc2015"/>
| |
| − | |-
| |
| − | | colspan="8" style="text-align:left; background-color:white;" | Notes:</br>''<sup>a</sup>'' 4,5-dihydroxybenzene-1,3-disulfonate (Tiron). ''<sup>b</sup>'' meso-tetra(N-methyl-pyridyl)iron porphin in cysteine.
| |
| − | |}
| |
| − | {| class="wikitable mw-collapsible" style="float:left; margin-right:40px; text-align:center;"
| |
| − | |+ Table 3. Rate constants for the reduction of MCs by iron minerals
| |
| − | |-
| |
| − | ! MC
| |
| − | ! Iron Mineral
| |
| − | ! Iron mineral loading</br>(g/L)
| |
| − | ! Surface area</br>(m<sup>2</sup>/g)
| |
| − | ! Fe(II)<sub>aq</sub> initial</br>(mM) ''<sup>b</sup>''
| |
| − | ! Fe(II)<sub>aq</sub> after 24 h</br>(mM) ''<sup>c</sup>''
| |
| − | ! Fe(II)<sub>aq</sub> sorbed</br>(mM) ''<sup>d</sup>''
| |
| − | ! pH
| |
| − | ! Buffer
| |
| − | ! Buffer</br>(mM)
| |
| − | ! MC initial</br>(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 ||
| |
| − | |-
| |
| − | | NTO<ref name="Cardenas-Hernandez2020"/> || Hematite || 1.00 || 5.7 || 1.0 || 0.92 || 0.08 || 5.5 || MES || 50 || 30 || -0.550 || -1.308
| |
| − | |-
| |
| − | | NTO<ref name="Cardenas-Hernandez2020"/> || Hematite || 1.00 || 5.7 || 1.0 || 0.85 || 0.15 || 6.0 || MES || 50 || 30 || 0.619 || -0.140
| |
| − | |-
| |
| − | | NTO<ref name="Cardenas-Hernandez2020"/> || Hematite || 1.00 || 5.7 || 1.0 || 0.9 || 0.10 || 6.5 || MES || 50 || 30 || 1.348 || 0.590
| |
| − | |-
| |
| − | | NTO<ref name="Cardenas-Hernandez2020"/> || Hematite || 1.00 || 5.7 || 1.0 || 0.77 || 0.23 || 7.0 || MOPS || 50 || 30 || 2.167 || 1.408
| |
| − | |-
| |
| − | | NTO<ref name="Cardenas-Hernandez2020"/> || Hematite ''<sup>a</sup>'' || 1.00 || 5.7 || || 1.01 || || 5.5 || MES || 50 || 30 || -1.444 || -2.200
| |
| − | |-
| |
| − | | NTO<ref name="Cardenas-Hernandez2020"/> || Hematite ''<sup>a</sup>'' || 1.00 || 5.7 || || 0.97 || || 6.0 || MES || 50 || 30 || -0.658 || -1.413
| |
| − | |-
| |
| − | | NTO<ref name="Cardenas-Hernandez2020"/> || Hematite ''<sup>a</sup>'' || 1.00 || 5.7 || || 0.87 || || 6.5 || MES || 50 || 30 || 0.068 || -0.688
| |
| − | |-
| |
| − | | NTO<ref name="Cardenas-Hernandez2020"/> || Hematite ''<sup>a</sup>'' || 1.00 || 5.7 || || 0.79 || || 7.0 || MOPS || 50 || 30 || 1.210 || 0.456
| |
| − | |-
| |
| − | | RDX<ref name="Tong2021"/> || Mackinawite || 0.45 || || || || || 6.5 || NaHCO<sub>3</sub> || 10 || 250 || -0.092 ||
| |
| − | |-
| |
| − | | RDX<ref name="Tong2021"/> || Mackinawite || 0.45 || || || || || 7.0 || NaHCO<sub>3</sub> || 10 || 250 || 0.009 ||
| |
| − | |-
| |
| − | | RDX<ref name="Tong2021"/> || Mackinawite || 0.45 || || || || || 7.5 || NaHCO<sub>3</sub> || 10 || 250 || 0.158 ||
| |
| − | |-
| |
| − | | RDX<ref name="Tong2021"/> || Green Rust || 5 || || || || || 6.5 || NaHCO<sub>3</sub> || 10 || 250 || -1.301 ||
| |
| − | |-
| |
| − | | RDX<ref name="Tong2021"/> || Green Rust || 5 || || || || || 7.0 || NaHCO<sub>3</sub> || 10 || 250 || -1.097 ||
| |
| − | |-
| |
| − | | RDX<ref name="Tong2021"/> || Green Rust || 5 || || || || || 7.5 || NaHCO<sub>3</sub> || 10 || 250 || -0.745 ||
| |
| − | |-
| |
| − | | RDX<ref name="Tong2021"/> || Goethite || 0.5 || || 1 || 1 || || 6.5 || NaHCO<sub>3</sub> || 10 || 250 || -0.921 ||
| |
| − | |-
| |
| − | | RDX<ref name="Tong2021"/> || Goethite || 0.5 || || 1 || 1 || || 7.0 || NaHCO<sub>3</sub> || 10 || 250 || -0.347 ||
| |
| − | |-
| |
| − | | RDX<ref name="Tong2021"/> || Goethite || 0.5 || || 1 || 1 || || 7.5 || NaHCO<sub>3</sub> || 10 || 250 || 0.009 ||
| |
| − | |-
| |
| − | | RDX<ref name="Tong2021"/> || Hematite || 0.5 || || 1 || 1 || || 6.5 || NaHCO<sub>3</sub> || 10 || 250 || -0.824 ||
| |
| − | |-
| |
| − | | RDX<ref name="Tong2021"/> || Hematite || 0.5 || || 1 || 1 || || 7.0 || NaHCO<sub>3</sub> || 10 || 250 || -0.456 ||
| |
| − | |-
| |
| − | | RDX<ref name="Tong2021"/> || Hematite || 0.5 || || 1 || 1 || || 7.5 || NaHCO<sub>3</sub> || 10 || 250 || -0.237 ||
| |
| − | |-
| |
| − | | RDX<ref name="Tong2021"/> || Magnetite || 2 || || 1 || 1 || || 6.5 || NaHCO<sub>3</sub> || 10 || 250 || -1.523 ||
| |
| − | |-
| |
| − | | RDX<ref name="Tong2021"/> || Magnetite || 2 || || 1 || 1 || || 7.0 || NaHCO<sub>3</sub> || 10 || 250 || -0.824 ||
| |
| − | |-
| |
| − | | RDX<ref name="Tong2021"/> || Magnetite || 2 || || 1 || 1 || || 7.5 || NaHCO<sub>3</sub> || 10 || 250 || -0.229 ||
| |
| − | |-
| |
| − | | DNAN<ref name="Menezes2021"/> || Mackinawite || 4.28 || 0.25 || || || || 6.5 || NaHCO<sub>3</sub> || 8.5 + 20% CO<sub>2</sub>(g) || 400 || 0.836 || 0.806
| |
| − | |-
| |
| − | | DNAN<ref name="Menezes2021"/> || Mackinawite || 4.28 || 0.25 || || || || 7.6 || NaHCO<sub>3</sub> || 95.2 + 20% CO<sub>2</sub>(g) || 400 || 0.762 || 0.732
| |
| − | |-
| |
| − | | DNAN<ref name="Menezes2021"/> || Commercial FeS || 5.00 || 0.214 || || || || 6.5 || NaHCO<sub>3</sub> || 8.5 + 20% CO<sub>2</sub>(g) || 400 || 0.477 || 0.447
| |
| − | |-
| |
| − | | DNAN<ref name="Menezes2021"/> || Commercial FeS || 5.00 || 0.214 || || || || 7.6 || NaHCO<sub>3</sub> || 95.2 + 20% CO<sub>2</sub>(g) || 400 || 0.745 || 0.716
| |
| − | |-
| |
| − | | NTO<ref name="Menezes2021"/> || Mackinawite || 4.28 || 0.25 || || || || 6.5 || NaHCO<sub>3</sub> || 8.5 + 20% CO<sub>2</sub>(g) || 1000 || 0.663 || 0.633
| |
| − | |-
| |
| − | | NTO<ref name="Menezes2021"/> || Mackinawite || 4.28 || 0.25 || || || || 7.6 || NaHCO<sub>3</sub> || 95.2 + 20% CO<sub>2</sub>(g) || 1000 || 0.521 || 0.491
| |
| − | |-
| |
| − | | NTO<ref name="Menezes2021"/> || Commercial FeS || 5.00 || 0.214 || || || || 6.5 || NaHCO<sub>3</sub> || 8.5 + 20% CO<sub>2</sub>(g) || 1000 || 0.492 || 0.462
| |
| − | |-
| |
| − | | NTO<ref name="Menezes2021"/> || Commercial FeS || 5.00 || 0.214 || || || || 7.6 || NaHCO<sub>3</sub> || 95.2 + 20% CO<sub>2</sub>(g) || 1000 || 0.427 || 0.398
| |
| − | |-
| |
| − | | colspan="13" style="text-align:left; background-color:white;" | Notes:</br>''<sup>a</sup>'' Dithionite-reduced hematite; experiments conducted in the presence of 1 mM sulfite. ''<sup>b</sup>'' Initial aqueous Fe(II); not added for Fe(II) bearing minerals. ''<sup>c</sup>'' Aqueous Fe(II) after 24h of equilibration. ''<sup>d</sup>'' Difference between b and c. ''<sup>e</sup>'' Initial nominal MC concentration. ''<sup>f</sup>'' Pseudo-first order rate constant. ''<sup>g</sup>'' Surface area normalized rate constant calculated as ''k<sub>Obs</sub>'' '''/''' (surface area concentration) or ''k<sub>Obs</sub>'' '''/''' (surface area × mineral loading).
| |
| − | |}
| |
| − | {| class="wikitable mw-collapsible" style="float:right; margin-left:40px; text-align:center;"
| |
| − | |+ Table 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.
| + | Data collected during CPM testing, when combined with screening level VI modeling, can be used to identify which VI chemical migration pathways are significant contributors to indoor air impacts<ref name="GuoEtAl2015" />. CPM testing guidelines were developed and validated under ESTCP Project ER-201501<ref name="GuoEtAl2020a" /><ref name="JohnsonEtAl2021" />. |
| | | | |
| − | 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.
| + | '''Passive samplers''' can be used to measure long term average indoor air concentrations under natural conditions and during VI mitigation system operation. They will provide more confident assessment of long term average concentrations than an infrequent sequence of short term grab samples. Long term average concentrations can also be determined by long term active sampling (e.g., by slowly pulling air through a thermal desorption (TD) tube). However, passive sampling has the advantage that additional equipment and expertise is not required for sampler deployment and recovery. |
| | | | |
| − | 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"/>.
| + | Use of passive samplers in indoor air under time-varying concentration conditions was demonstrated and validated by comparing against intensive active sampling in ESTCP Project ER-201501<ref name="JohnsonEtAl2020" /><ref name="GuoEtAl2021">Guo, Y., O’Neill, H., Dahlen, P., and Johnson, P.C. 2021. Evaluation of Passive Diffusive-Adsorptive Samplers for Use in Assessing Time-Varying Indoor Air Impacts Resulting from Vapor Intrusion. Groundwater Monitoring and Remediation, 42(1), pp. 38-49. [https://doi.org/10.1111/gwmr.12481 doi: 10.1111/12481]</ref>. |
| | | | |
| − | For minerals that contain structural iron(II) and can reduce pollutants in the absence of aqueous Fe(II), the observed rates of reduction increased with increasing structural Fe(II) content, as seen with iron-bearing clays<ref name="Luan2015a"/><ref name="Luan2015b"/> and green rust<ref name="Larese-Casanova2008"/>. This dependency on Fe(II) content allows for the derivation of second-order rate constants, as shown on Table 3 for the reduction of RDX by green rust<ref name="Larese-Casanova2008"/>, and the development of reduction potential (E<sub>H</sub>)-based models<ref name="Luan2015a"/><ref name="Gorski2012a">Gorski, C.A., Aeschbacher, M., Soltermann, D., Voegelin, A., Baeyens, B., Marques Fernandes, M., Hofstetter, T.B., and Sander, M., 2012. Redox Properties of Structural Fe in Clay Minerals. 1. Electrochemical Quantification of Electron-Donating and -Accepting Capacities of Smectites. Environmental Science and Technology, 46(17), pp. 9360–9368. [https://doi.org/10.1021/es3020138 DOI: 10.1021/es3020138]</ref><ref name="Gorski2012b">Gorski, C.A., Klüpfel, L., Voegelin, A., Sander, M., and Hofstetter, T.B., 2012. Redox Properties of Structural Fe in Clay Minerals. 2. Electrochemical and Spectroscopic Characterization of Electron Transfer Irreversibility in Ferruginous Smectite, SWa-1. Environmental Science and Technology, 46(17), pp. 9369–9377. [https://doi.org/10.1021/es302014u DOI: 10.1021/es302014u]</ref><ref name="Gorski2013">Gorski, C.A., Klüpfel, L.E., Voegelin, A., Sander, M. and Hofstetter, T.B., 2013. Redox Properties of Structural Fe in Clay Minerals: 3. Relationships between Smectite Redox and Structural Properties. Environmental Science and Technology, 47(23), pp. 13477–13485. [https://doi.org/10.1021/es403824x DOI: 10.1021/es403824x]</ref>, where E<sub>H</sub> represents the reduction potential of the iron-bearing clays. Iron-bearing expandable clay minerals represent a special case, which in addition to reduction can remove NACs/MCs through adsorption. This is particularly important for planar NACs/MCs that contain multiple electron-withdrawing nitro groups and can form strong electron donor-acceptor (EDA) complexes with the clay surface<ref name="Hofstetter2006"/><ref name="Hofstetter2003"/><ref name="Neumann2008"/>.
| + | The purpose of maintaining an evergreen '''comprehensive VI conceptual model''' is to ensure that the most complete and up-to-date understanding of the site is informing decisions related to future sampling, data interpretation, and the need for and design of mitigation systems. The VI conceptual model can also serve as an effective communication tool in stakeholder discussions. |
| | | | |
| − | 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.
| + | Use of these tools for residential neighborhoods and in non-residential buildings overlying chlorinated solvent groundwater plumes is documented comprehensively in a series of peer reviewed articles<ref name="JohnsonEtAl2020" /><ref name="JohnsonEtAl2021" /><ref name="JohnsonEtAl2022" /><ref name="GuoEtAl2015" /><ref name="GuoEtAl2020a" /><ref name="GuoEtAl2020b">Guo, Y., Dahlen, P., Johnson, P.C. 2020b. Temporal variability of chlorinated volatile organic compound vapor concentrations in a residential sewer and land drain system overlying a dilute groundwater plume. Science of the Total Environment, 702, Article 134756. [https://doi.org/10.1016/j.scitotenv.2019.134756 doi: 10.1016/j.scitotenv.2019.134756] [//www.enviro.wiki/images/e/e5/GuoEtAl2020b.pdf Open Access Manuscript]</ref><ref name="GuoEtAl2021" /><ref name="HoltonEtAl2015" />. |
| | | | |
| − | The reduction of NACs has been widely studied in the presence of different iron minerals, pH, and Fe(II)<sub>(aq)</sub> concentrations (Table 4)<ref name="Colón2006"/><ref name="Klausen1995"/><ref name="Strehlau2016"/><ref name="Elsner2004"/><ref name="Hofstetter2006"/>. Only selected NACs are included in Table 4. For more information on other NACs and ferruginous reductants, please refer to the cited references.
| + | ==Summary== |
| − | <br clear="right" />
| + | In summary, the VI Diagnosis Toolkit provides a set of tools that can lead to quicker, more confident, and more cost effective neighborhood-scale VI pathway and impact assessments. Toolkit components and their use can complement conventional methods for assessing and mitigating the vapor intrusion pathway. |
| | | | |
| | ==References== | | ==References== |
| Line 619: |
Line 108: |
| | | | |
| | ==See Also== | | ==See Also== |
| − | *[https://www.serdp-estcp.org/Program-Areas/Environmental-Restoration/Contaminated-Groundwater/Persistent-Contamination/ER-2617 Measuring and Predicting the Natural and Enhanced Rate and Capacity of Abiotic Reduction of Munition Constituents]
| |
| | | | |
| − | *[https://www.epa.gov/fedfac/military-munitionsunexploded-ordnance Military Munitions/Unexploded Ordnance - EPA] | + | *[https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4000681 Evaluation of Radon and Building Pressure Differences as Environmental Indicators for Vapor Intrusion Assessment] |
| | + | *[https://pubs.acs.org/doi/10.1021/es4024767 Temporal Variability of Indoor Air Concentrations under Natural Conditions in a House Overlying a Dilute Chlorinated Solvent Groundwater Plume] |
| | + | *[https://serdp-estcp.mil/projects/details/e0d00662-c333-4560-8ae7-60f20b0e714b Integrated Field-Scale, Lab-Scale, and Modeling Studies for Improving Our Ability to Assess the Groundwater to Indoor Air Pathway at Chlorinated Solvent Impacted Sites] |
