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| − | ==Hydrogeophysical methods for characterization and monitoring of surface water-groundwater interactions== | + | ==Assessing Vapor Intrusion (VI) Impacts in Neighborhoods with Groundwater Contaminated by Chlorinated Volatile Organic Chemicals (CVOCs)== |
| − | Hydrogeophysical methods can be used to cost-effectively locate and characterize regions of
| + | The VI Diagnosis Toolkit<ref name="JohnsonEtAl2020">Johnson, P.C., Guo, Y., Dahlen, P., 2020. The VI Diagnosis Toolkit for Assessing Vapor Intrusion Pathways and Mitigating Impacts in Neighborhoods Overlying Dissolved Chlorinated Solvent Plumes. ESTCP Project ER-201501, Final Report. [https://serdp-estcp.mil/projects/details/a0d8bafd-c158-4742-b9fe-5f03d002af71 Project Website] [[Media: ER-201501.pdf | Final Report.pdf]]</ref> is a set of tools that can be used individually or in combination to assess vapor intrusion (VI) impacts at one or more buildings overlying regional-scale dissolved chlorinated solvent-impacted groundwater plumes. The strategic use of these tools can lead to confident and efficient neighborhood-scale VI pathway assessments. |
| − | 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]] | + | |
| − | *[[Geophysical Methods - Case Studies]] | + | *[[Vapor Intrusion (VI)]] |
| | + | *[[Vapor Intrusion – Sewers and Utility Tunnels as Preferential Pathways]] |
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| | '''Contributor(s):''' | | '''Contributor(s):''' |
| − | *[[Dr. Lee Slater]] | + | |
| − | *Dr. Ramona Iery | + | *Paul C. Johnson, Ph.D. |
| − | *Dr. Dimitrios Ntarlagiannis | + | *Paul Dahlen, Ph.D. |
| − | *Henry Moore
| + | *Yuanming Guo, Ph.D. |
| | | | |
| | '''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==
| + | *The VI Diagnosis Toolkit for Assessing Vapor Intrusion Pathways and Impacts in Neighborhoods Overlying Dissolved Chlorinated Solvent Plumes, ESTCP Project ER-201501, Final Report<ref name="JohnsonEtAl2020"/> |
| − | 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.
| + | *CPM Test Guidelines: Use of Controlled Pressure Method Testing for Vapor Intrusion Pathway Assessment, ESTCP Project ER-201501, Technical Report<ref name="JohnsonEtAl2021">Johnson, P.C., Guo, Y., Dahlen, P., 2021. CPM Test Guidelines: Use of Controlled Pressure Method Testing for Vapor Intrusion Pathway Assessment. ESTCP ER-201501, Technical Report. [https://serdp-estcp.mil/projects/details/a0d8bafd-c158-4742-b9fe-5f03d002af71 Project Website] [[Media: ER-201501_Technical_Report.pdf | Technical_Report.pdf]]</ref> |
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| − | ==Hydrogeophysical Technologies for Understanding Groundwater-Surface Water Interactions==
| + | *VI Diagnosis Toolkit User Guide, ESTCP Project ER-201501<ref name="JohnsonEtAl2022">Johnson, P.C., Guo, Y., and Dahlen, P., 2022. VI Diagnosis Toolkit User Guide, ESTCP ER-201501, User Guide. [https://serdp-estcp.mil/projects/details/a0d8bafd-c158-4742-b9fe-5f03d002af71 Project Website] [[Media: ER-201501_User_Guide.pdf | User_Guide.pdf]]</ref> |
| − | [[Wikipedia: Hydrogeophysics |Hydrogeophysical technologies]] exploit contrasts in the physical properties between groundwater and surface water to detect and monitor zones of pronounced GWSWE. The two most valuable properties to measure are temperature and electrical conductivity. Temperature has been used for decades as an indicator of groundwater-surface water exchange<ref>Constantz, J., 2008. Heat as a Tracer to Determine Streambed Water Exchanges. Water Resources Research, 44 (4).[https://doi.org/https://doi.org/10.1029/2008WR006996 doi: 10.1029/2008WR006996].[https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2008WR006996 Open Access Article]</ref> with early uses including pushing a thermistor into the bed of a surface water body to assess zones of surface water downwelling and groundwater upwelling. Today, a variety of novel technologies that measure temperature over a wide range of spatial and temporal scales are being used to investigate GWSWE. The evaluation of electrical conductivity measurements using point probes and geophysical imaging is also well-established. However, new technologies are now available to exploit electrical conductivity contrasts from GWSWE occurring over a range of spatial and temporal scales.
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| − | ===Temperature-Based Technologies=== | + | ==Background== |
| − | 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.
| + | [[File:ChangFig2.png | thumb | 400px| Figure 1. Example of instrumentation used for OPTICS monitoring.]] |
| | + | [[File:ChangFig1.png | thumb | 400px| Figure 2. Schematic diagram illustrating the OPTICS methodology. High resolution in-situ data are integrated with traditional discrete sample analytical data using partial least-square regression to derive high resolution chemical contaminant concentration data series.]] |
| | + | Nationwide, the liability due to contaminated sediments is estimated in the trillions of dollars. Stakeholders are assessing and developing remedial strategies for contaminated sediment sites in major harbors and waterways throughout the U.S. The mobility of contaminants in surface water is a primary transport and risk mechanism<ref>Thibodeaux, L.J., 1996. Environmental Chemodynamics: Movement of Chemicals in Air, Water, and Soil, 2nd Edition, Volume 110 of Environmental Science and Technology: A Wiley-Interscience Series of Texts and Monographs. John Wiley & Sons, Inc. 624 pages. ISBN: 0-471-61295-2</ref><ref>United States Environmental Protection Agency (USEPA), 2005. Contaminated Sediment Remediation Guidance for Hazardous Waste Sites. Office of Superfund Remediation and Technology Innovation Report, EPA-540-R-05-012. [[Media: 2005-USEPA-Contaminated_Sediment_Remediation_Guidance.pdf | Report.pdf]]</ref><ref>Lick, W., 2008. Sediment and Contaminant Transport in Surface Waters. CRC Press. 416 pages. [https://doi.org/10.1201/9781420059885 doi: 10.1201/9781420059885]</ref>; therefore, long-term monitoring of both particulate- and dissolved-phase contaminant concentration prior to, during, and following remedial action is necessary to document remedy effectiveness. Source control and total maximum daily load (TMDL) actions generally require costly manual monitoring of dissolved and particulate contaminant concentrations in surface water. The magnitude of cost for these actions is a strong motivation to implement efficient methods for long-term source control and remedial monitoring. |
| | | | |
| − | ====Characterization Methods====
| + | Traditional surface water monitoring requires mobilization of field teams to manually collect discrete water samples, send samples to laboratories, and await laboratory analysis so that a site evaluation can be conducted. These traditional methods are well known to have inherent cost and safety concerns and are of limited use (due to safety concerns and standby requirements for resources) in capturing the effects of episodic events (e.g., storms) that are important to consider in site risk assessment and remedy selection. Automated water samplers are commercially available but still require significant field support and costly laboratory analysis. Further, automated samplers may not be suitable for analytes with short hold-times and temperature requirements. |
| − | 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)''=====
| + | Optically-based characterization of surface water contaminants is a cost-effective alternative to traditional discrete water sampling methods. Unlike discrete water sampling, which typically results in sparse data at low resolution, and therefore, is of limited use in determining mass loading, OPTICS (OPTically-based In-situ Characterization System) provides continuous data and allows for a complete understanding of water quality and contaminant transport in response to natural processes and human impacts<ref name="ChangEtAl2019"/><ref name="ChangEtAl2018"/><ref name="ChangEtAl2024"/><ref>Bergamaschi, B.A., Fleck, J.A., Downing, B.D., Boss, E., Pellerin, B., Ganju, N.K., Schoellhamer, D.H., Byington, A.A., Heim, W.A., Stephenson, M., Fujii, R., 2011. Methyl mercury dynamics in a tidal wetland quantified using in situ optical measurements. Limnology and Oceanography, 56(4), pp. 1355-1371. [https://doi.org/10.4319/lo.2011.56.4.1355 doi: 10.4319/lo.2011.56.4.1355] [[Media: BergamaschiEtAl2011.pdf | Open Access Article]]</ref><ref>Bergamaschi, B.A., Fleck, J.A., Downing, B.D., Boss, E., Pellerin, B.A., Ganju, N.K., Schoellhamer, D.H., Byington, A.A., Heim, W.A., Stephenson, M., Fujii, R., 2012. Mercury Dynamics in a San Francisco Estuary Tidal Wetland: Assessing Dynamics Using In Situ Measurements. Estuaries and Coasts, 35, pp. 1036-1048. [https://doi.org/10.1007/s12237-012-9501-3 doi: 10.1007/s12237-012-9501-3] [[Media: BergamaschiEtAl2012a.pdf | Open Access Article]]</ref><ref>Bergamaschi, B.A., Krabbenhoft, D.P., Aiken, G.R., Patino, E., Rumbold, D.G., Orem, W.H., 2012. Tidally driven export of dissolved organic carbon, total mercury, and methylmercury from a mangrove-dominated estuary. Environmental Science and Technology, 46(3), pp. 1371-1378. [https://doi.org/10.1021/es2029137 doi: 10.1021/es2029137] [[Media: BergamaschiEtAl2012b.pdf | Open Access Article]]</ref>. The OPTICS tool integrates commercial off-the-shelf ''in situ'' aquatic sensors (Figure 1), periodic discrete surface water sample collection, and a multi-parameter statistical prediction model<ref name="deJong1993">de Jong, S., 1993. SIMPLS: an alternative approach to partial least squares regression. Chemometrics and Intelligent Laboratory Systems, 18(3), pp. 251-263. [https://doi.org/10.1016/0169-7439(93)85002-X doi: 10.1016/0169-7439(93)85002-X]</ref><ref name="RosipalKramer2006">Rosipal, R. and Krämer, N., 2006. Overview and Recent Advances in Partial Least Squares, In: Subspace, Latent Structure, and Feature Selection: Statistical and Optimization Perspectives Workshop, Revised Selected Papers (Lecture Notes in Computer Science, Volume 3940), Springer-Verlag, Berlin, Germany. pp. 34-51. [https://doi.org/10.1007/11752790_2 doi: 10.1007/11752790_2]</ref> to provide high temporal and/or spatial resolution characterization of surface water chemicals of potential concern (COPCs) (Figure 2). |
| − | [[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''===== | + | ==Technology Overview== |
| − | [[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.]] | + | The principle behind OPTICS is based on the relationship between optical properties of natural waters and the particles and dissolved material contained within them<ref>Boss, E. and Pegau, W.S., 2001. Relationship of light scattering at an angle in the backward direction to the backscattering coefficient. Applied Optics, 40(30), pp. 5503-5507. [https://doi.org/10.1364/AO.40.005503 doi: 10.1364/AO.40.005503]</ref><ref>Boss, E., Twardowski, M.S., Herring, S., 2001. Shape of the particulate beam spectrum and its inversion to obtain the shape of the particle size distribution. Applied Optics, 40(27), pp. 4884-4893. [https://doi.org/10.1364/AO.40.004885 doi:10/1364/AO.40.004885]</ref><ref>Babin, M., Morel, A., Fournier-Sicre, V., Fell, F., Stramski, D., 2003. Light scattering properties of marine particles in coastal and open ocean waters as related to the particle mass concentration. Limnology and Oceanography, 48(2), pp. 843-859. [https://doi.org/10.4319/lo.2003.48.2.0843 doi: 10.4319/lo.2003.48.2.0843] [[Media: BabinEtAl2003.pdf | Open Access Article]]</ref><ref>Coble, P., Hu, C., Gould, R., Chang, G., Wood, M., 2004. Colored dissolved organic matter in the coastal ocean: An optical tool for coastal zone environmental assessment and management. Oceanography, 17(2), pp. 50-59. [https://doi.org/10.5670/oceanog.2004.47 doi: 10.5670/oceanog.2004.47] [[Media: CobleEtAl2004.pdf | Open Access Article]]</ref><ref>Sullivan, J.M., Twardowski, M.S., Donaghay, P.L., Freeman, S.A., 2005. Use of optical scattering to discriminate particle types in coastal waters. Applied Optics, 44(9), pp. 1667–1680. [https://doi.org/10.1364/AO.44.001667 doi: 10.1364/AO.44.001667]</ref><ref>Twardowski, M.S., Boss, E., Macdonald, J.B., Pegau, W.S., Barnard, A.H., Zaneveld, J.R.V., 2001. A model for estimating bulk refractive index from the optical backscattering ratio and the implications for understanding particle composition in case I and case II waters. Journal of Geophysical Research: Oceans, 106(C7), pp. 14,129-14,142. [https://doi.org/10.1029/2000JC000404 doi: 10/1029/2000JC000404] [[Media: TwardowskiEtAl2001.pdf | Open Access Article]]</ref><ref>Chang, G.C., Barnard, A.H., McLean, S., Egli, P.J., Moore, C., Zaneveld, J.R.V., Dickey, T.D., Hanson, A., 2006. In situ optical variability and relationships in the Santa Barbara Channel: implications for remote sensing. Applied Optics, 45(15), pp. 3593–3604. [https://doi.org/10.1364/AO.45.003593 doi: 10.1364/AO.45.003593]</ref><ref>Slade, W.H. and Boss, E., 2015. Spectral attenuation and backscattering as indicators of average particle size. Applied Optics, 54(24), pp. 7264-7277. [https://doi.org/10.1364/AO.54.007264 doi: 10/1364/AO.54.007264] [[Media: SladeBoss2015.pdf | Open Access Article]]</ref>. Surface water COPCs such as heavy metals and polychlorinated biphenyls (PCBs) are hydrophobic in nature and tend to sorb to materials in the water column, which have unique optical signatures that can be measured at high-resolution using ''in situ'', commercially available aquatic sensors<ref>Agrawal, Y.C. and Pottsmith, H.C., 2000. Instruments for particle size and settling velocity observations in sediment transport. Marine Geology, 168(1-4), pp. 89-114. [https://doi.org/10.1016/S0025-3227(00)00044-X doi: 10.1016/S0025-3227(00)00044-X]</ref><ref>Boss, E., Pegau, W.S., Gardner, W.D., Zaneveld, J.R.V., Barnard, A.H., Twardowski, M.S., Chang, G.C., Dickey, T.D., 2001. Spectral particulate attenuation and particle size distribution in the bottom boundary layer of a continental shelf. Journal of Geophysical Research: Oceans, 106(C5), pp. 9509-9516. [https://doi.org/10.1029/2000JC900077 doi: 10.1029/2000JC900077] [[Media: BossEtAl2001.pdf | Open Access Article]]</ref><ref>Boss, E., Pegau, W.S., Lee, M., Twardowski, M., Shybanov, E., Korotaev, G. Baratange, F., 2004. Particulate backscattering ratio at LEO 15 and its use to study particle composition and distribution. Journal of Geophysical Research: Oceans, 109(C1), Article C01014. [https://doi.org/10.1029/2002JC001514 doi: 10.1029/2002JC001514] [[Media: BossEtAl2004.pdf | Open Access Article]]</ref><ref>Briggs, N.T., Slade, W.H., Boss, E., Perry, M.J., 2013. Method for estimating mean particle size from high-frequency fluctuations in beam attenuation or scattering measurement. Applied Optics, 52(27), pp. 6710-6725. [https://doi.org/10.1364/AO.52.006710 doi: 10.1364/AO.52.006710] [[Media: BriggsEtAl2013.pdf | Open Access Article]]</ref>. Therefore, high-resolution concentrations of COPCs can be accurately and robustly derived from ''in situ'' measurements using statistical methods. |
| − | 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 OPTICS method is analogous to the commonly used empirical derivation of total suspended solids concentration (TSS) from optical turbidity using linear regression<ref>Rasmussen, P.P., Gray, J.R., Glysson, G.D., Ziegler, A.C., 2009. Guidelines and procedures for computing time-series suspended-sediment concentrations and loads from in-stream turbidity-sensor and streamflow data. In: Techniques and Methods, Book 3: Applications of Hydraulics, Section C: Sediment and Erosion Techniques, Ch. 4. 52 pages. U.S. Geological Survey. [[Media: RasmussenEtAl2009.pdf | Open Access Article]]</ref>. However, rather than deriving one response variable (TSS) from one predictor variable (turbidity), OPTICS involves derivation of one response variable (e.g., PCB concentration) from a suite of predictor variables (e.g., turbidity, temperature, salinity, and fluorescence of chlorophyll-a) using multi-parameter statistical regression. OPTICS is based on statistical correlation – similar to the turbidity-to-TSS regression technique. The method does not rely on interpolation or extrapolation. |
| − | 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====
| + | The OPTICS technique utilizes partial least-squares (PLS) regression to determine a combination of physical, optical, and water quality properties that best predicts chemical contaminant concentrations with high variance. PLS regression is a statistically based method combining multiple linear regression and principal component analysis (PCA), where multiple linear regression finds a combination of predictors that best fit a response and PCA finds combinations of predictors with large variance<ref name="deJong1993"/><ref name="RosipalKramer2006"/>. Therefore, PLS identifies combinations of multi-collinear predictors (''in situ'', high-resolution physical, optical, and water quality measurements) that have large covariance with the response values (discrete surface water chemical contaminant concentration data from samples that are collected periodically, coincident with ''in situ'' measurements). PLS combines information about the variances of both the predictors and the responses, while also considering the correlations among them. PLS therefore provides a model with reliable predictive power. |
| − | Monitoring methods allow temperature signals to be recorded with high temporal resolution along the bed interface or within bank or bed sediments. These methods can capture temporal trends in GWSWE driven by variations in the hydraulic gradients around surface water bodies, as well as changes in [[Wikipedia: Hydraulic conductivity | hydraulic conductivity]] due to sedimentation, clogging, scour or microbial mass. If vertical profiles of bed temperature are collected, a range of analytical and numerical models can be applied to infer the vertical water flux rate and direction, similar to a seepage meter. These fluxes may vary as a function of season, rainfall events (enhanced during storm activity), tidal variability in coastal settings and due to engineered controls such as dam discharges. The methods can capture evidence of GWSWE that may not be detected during a single ‘characterization’ survey if the local hydraulic conditions at that point in time result in relatively weak hydraulic gradients.
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| − | =====''Fiber-optic Distributed Temperature Sensing (FO-DTS)''=====
| + | OPTICS ''in situ'' measurement parameters include, but are not limited to current velocity, conductivity, temperature, depth, turbidity, dissolved oxygen, and fluorescence of chlorophyll-a and dissolved organic matter. Instrumentation for these measurements is commercially available, robust, deployable in a wide variety of configurations (e.g., moored, vessel-mounted, etc.), powered by batteries, and records data internally and/or transmits data in real-time. The physical, optical, and water quality instrumentation is compact and self-contained. The modularity and automated nature of the OPTICS measurement system enables robust, long-term, autonomous data collection for near-continuous monitoring. |
| − | [[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)''=====
| + | [[File:ChangFig3.png | thumb | 400px| Figure 3. OPTICS to characterize COPC variability in the context of site processes at BCSA. (A) Tidal oscillations (Elev.<sub>MSL</sub>) and precipitation (Precip.). (B) – (D) OPTICS-derived particulate mercury (PHg) and methylmercury (PMeHg) and total PCBs (TPCBs). Open circles represent discrete water sample data.]] OPTICS measurements are provided at a significantly reduced cost relative to traditional monitoring techniques used within the environmental industry. Cost performance analysis shows that monitoring costs are reduced by more than 85% while significantly increasing the temporal and spatial resolution of sampling. The reduced cost of monitoring makes this technology suitable for a number of environmental applications including, but not limited to site baseline characterization, source control evaluation, dredge or stormflow plume characterization, and remedy performance monitoring. OPTICS has been successfully demonstrated for characterizing a wide variety of COPCs: mercury, methylmercury, copper, lead, PCBs, dichlorodiphenyltrichloroethane (DDT) and its related compounds (collectively, DDX), and 2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD or dioxin) in a number of different environmental systems ranging from inland lakes and rivers to the coastal ocean. To date, OPTICS has been limited to surface water applications. Additional applications (e.g., groundwater) would require further research and development. |
| − | 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.
| + | ==Applications== |
| | + | [[File:ChangFig4.png | thumb | 400px| Figure 4. OPTICS reveals baseflow daily cycling and confirms storm-induced particle-bound COPC resuspension and mobilization through bank interaction. (A) Flow rate (Q) and precipitation (Precip). (B) – (C) OPTICS-derived particulate mercury (PHg) and methylmercury (PMeHg). Open circles represent discrete water sample data.]] |
| | + | [[File:ChangFig5.png | thumb | 400px| Figure 5. Three-dimensional volume plot of high spatial resolution OPTICS-derived PCBs in exceedance of baseline showing that PCBs were discharged from the outfall (yellow arrow), remained in suspension, and dispersed elsewhere before settling.]] |
| | + | An OPTICS study was conducted at Berry’s Creek Study Area (BCSA), New Jersey in 2014 and 2015 to understand COPC sources and transport mechanisms for development of an effective remediation plan. OPTICS successfully extended periodic discrete surface water samples to continuous, high-resolution measurements of PCBs, mercury, and methylmercury to elucidate COPC sources and transport throughout the BCSA tidal estuary system. OPTICS provided data at resolution sufficient to investigate COC variability in the context of physical processes. The results (Figure 3) facilitated focused and effective site remediation and management decisions that could not be determined based on periodic discrete samples alone, despite over seven years of monitoring at different locations throughout the system over a range of different seasons, tidal phases, and environmental conditions. The BCSA OPTICS methodology and its results have undergone official peer review overseen by the U.S. Environmental Protection Agency (USEPA), and those results have been published in peer-reviewed literature<ref name="ChangEtAl2019"/>. |
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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”/>.
| + | OPTICS was applied at the South River, Virginia in 2016 to quantify sources of legacy mercury in the system that are contributing to recontamination and continued elevated mercury concentrations in fish tissue. OPTICS provided information necessary to identify mechanisms for COPC redistribution and to quantify the relative contribution of each mechanism to total mass transport of mercury and methylmercury in the system. Continuous, high-resolution COPC data afforded by OPTICS helped resolve baseflow daily cycling that had never before been observed at the South River (Figure 4) and provided data at temporal resolution necessary to verify storm-induced particle-bound COC resuspension and mobilization through bank interaction. The results informed source control and remedy design and monitoring efforts. Methodology and results from the South River have been published in peer-reviewed literature<ref name="ChangEtAl2018"/>. |
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| − | ===Electrical Conductivity (EC) Based Technologies===
| + | The U.S. Department of Defense’s Environmental Security Technology Certification Program (ESTCP) supported an OPTICS demonstration study at the Pearl Harbor Sediment Site, Hawaii, to determine whether stormwater from Oscar 1 Pier outfall is a contributing source of PCBs to Decision Unit (DU) N-2 (ESTCP Project ER21-5021). High spatial resolution results afforded by ship-based, mobile OPTICS monitoring suggested that PCBs were discharged from the outfall, remained in suspension, and dispersed elsewhere before settling (Figure 5). More details regarding this study were presented by Chang et al. in 2024<ref name="ChangEtAl2024"/>. |
| − | 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
| + | ==Summary== |
| − | (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.
| + | OPTICS provides: |
| − | | + | *High resolution surface water chemical contaminant characterization |
| − | ====Characterization Methods====
| + | *Cost-effective monitoring and assessment |
| − | | + | *Versatile and modular monitoring with capability for real-time telemetry |
| − | =====''Specific Conductance Probes''=====
| + | *Data necessary for development and validation of conceptual site models |
| − | 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].
| + | *A key line of evidence for designing and evaluating remedies. |
| − | | |
| − | =====''Frequency Domain Electromagnetic (EM) Sensing Systems''=====
| |
| − | [[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.]]
| |
| − | 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.
| |
| − | | |
| − | =====''Time Domain EM Sensing Systems''=====
| |
| − | 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''=====
| |
| − | [[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====
| |
| − | | |
| − | =====Land-based Electrical Monitoring=====
| |
| − | 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.
| |
| − | | |
| − | ===Considerations for Using EM and Waterborne Electrical Imaging Methods===
| |
| − | The EM and waterborne electrical imaging methods both provide a way to determine variations in bulk electrical conductivity associated with groundwater/surface water interactions. However, each method has some advantages and some disadvantages. One consideration is maneuverability, particularly in shallow water environments. FDEM instruments are the most maneuverable, although they offer only limited investigation depths. Although bigger than the shallow-sensing frequency domain EM systems, TEM systems are still relatively maneuverable on water bodies. Whereas FDEM systems can be operated from a single small vessel, the TEM deployments require the use of pontoons as the transmitter and receiver coils need to be separated 9 m apart. This still equates to good maneuverability compared to waterborne electrical imaging where a floating electrode cable, typically 30-50 m long, is pulled behind a vessel.
| |
| − | | |
| − | In all three methods, variations in the water layer depth and the specific conductance of the water can significantly affect the data, especially in deeper water. Therefore, it is common to continuously record these parameters with an echo sounder and a specific conductance probe suspended in the water layer.
| |
| − | | |
| − | ===Other Hydrogeophysical Technologies===
| |
| − | A number of other hydrogeophysical technologies exist, with proven applications to the characterization of settings where GWSWE occurs. Seismic [[Wikipedia:Reflection seismology | reflection]] and [[Wikipedia:Seismic refraction | refraction]] methods are used to image the depositional environments along coastlines. [[Wikipedia:Ground-penetrating radar | Ground penetrating radar]] has been effectively used to image depositional environments around freshwater lake shorelines, and across streams and rivers. Such information may help to identify depositional features that promote GWSWE but, unlike the temperature- and conductivity-based methods, do not sense changes in physical properties associated with the exchanging water itself. | |
| − | | |
| − | One promising technique for detecting GWSWE is known as the [https://www.epa.gov/environmental-geophysics/self-potential-sp self-potential (SP)] method. This simple to deploy geophysical technique is based on mapping voltage differences caused by natural sources of electric current in the Earth that are generated through a number of coupled flow processes, one being the coupling of pore fluid flow and transport of electric charge. Zones of enhanced seepage within a porous medium can result in a significant ‘streaming potential’ due to charge transport induced by fluid flow. This phenomenon has been effectively used to locate zones of leakage through dams and embankments<ref>Panthulu, T. V, Krishnaiah, C., Shirke, J. M., 2001. Detection of Seepage Paths in Earth Dams Using Self-Potential and Electrical Resistivity Methods. Engineering Geology, 59(3-4), pp. 281–295. [https://doi.org/10.1016/S0013-7952(00)00082-X doi: 10.1016/S0013-7952(00)00082-X].</ref>. Recently, floating SP measurements have been used to define gaining and losing portions of streams and to identify evidence of focused exchange<ref>Ikard, S. J., Teeple, A. P., Payne, J. D., Stanton, G. P., Banta, J. R., 2018. New Insights On Scale-Dependent Surface-Groundwater Exchange from a Floating Self-Potential Dipole. Journal of Environmental and Engineering Geophysics, 23(2), pp. 261–287. [https://doi.org/10.2113/JEEG23.2.261 doi: 10.2113/JEEG23.2.261].</ref>. Although the data acquisition is simple, consisting of a pair of non-polarizing electrodes and a voltmeter, the interpretation of SP measurements requires expert knowledge to filter out confounding contributions to the recorded signals.
| |
| − | | |
| − | ==Guidelines for Implementing Hydrogeophysical Methods into Groundwater/Surface Water Interaction Studies==
| |
| − | A number of factors will affect the success of individual hydrogeophysical methods at a specific
| |
| − | site of GWSWE. Depending on site conditions and the objective, some methods may be inappropriate to deploy. For example, temperature-based methods will most likely succeed at times of the year and times of day when contrasts between upwelling groundwater and surface water are greatest. In contrast, it is quite possible that some sites of groundwater/surface water exchange will have an insufficient contrast in the specific conductance of the groundwater versus the surface water to make techniques based on EC measurements effective. A groundwater-surface water method selection tool ([https://water.usgs.gov/water-resources/software/GW-SW-MST/ GW/SW-MST]<ref>Hammett, S., Day-Lewis, F. D., Trottier, B., Barlow, P. M., Briggs, M. A., Delin, G., Harvey, J. W., Johnson, C. D., Lane jr., J. W., Rosenberry, D. O., Werkema, D. D., 2022. GW/SW-MST: A Groundwater/Surface-Water Method Selection Tool. Groundwater, 60(6), pp. 784-791. [https://doi.org/10.1111/gwat.13194 doi: 10.1111/gwat.13194]. [https://ngwa.onlinelibrary.wiley.com/doi/am-pdf/10.1111/gwat.13194 Open Access Manuscript]</ref>) has recently been developed to assist practitioners in the informed selection of the methods that will be most effective for a particular site at a particular time. The tool guides the user through a series of questions that consider both the specific conditions at the site and the primary objectives of the investigation. The methods selection tool discusses the application of a number of additional technologies besides those included in this article. The selection tool is recommended as the starting point for any practitioner.
| |
| | | | |
| − | ==Summary==
| + | Because OPTICS monitoring involves deployment of autonomous sampling instrumentation, a substantially greater volume of data can be collected using this technique compared to traditional sampling, and at a far lower cost. A large volume of data supports evaluation of chemical contaminant concentrations over a range of spatial and temporal scales, and the system can be customized for a variety of environmental applications. OPTICS helps quantify contaminant mass flux and the relative contribution of local transport and source areas to net contaminant transport. OPTICS delivers a strong line of evidence for evaluating contaminant sources, fate, and transport, and for supporting the design of a remedy tailored to address site-specific, risk-driving conditions. The improved understanding of site processes aids in the development of mitigation measures that minimize site risks. |
| − | A number of temperature-based and electrical conductivity-based technologies exist for monitoring GWSWE over a range of spatial scales. Many of these technologies are most powerful when used as reconnaissance tools to rapidly identify probable locations of GWSWE to be verified with a limited campaign of direct sensing measurements (traditionally seepage meters). Vertical temperature profilers (VTPs) offer direct quantification of fluxes at sites identified by the reconnaissance tools, and some studies show that these methods are more reliable than traditional seepage meters. Given the number of sites across the globe where contaminated groundwater is impacting surface water resources, use of these technologies for both characterization and monitoring is expected to become more common. | |
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| | ==References== | | ==References== |
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| | ==See Also== | | ==See Also== |
| − | USGS Water Resources:
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| − | * https://www.usgs.gov/mission-areas/water-resources/science/geophysics-usgs-groundwatersurface-water-exchange-studies
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| − |
| |
| − | * https://www.usgs.gov/mission-areas/water-resources/science/thermal-imaging-cameras-studying-groundwatersurface-water
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| − |
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| − | * https://www.usgs.gov/mission-areas/water-resources/science/fiber-optic-distributed-temperature-sensing-technology
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| − |
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| − | * https://www.usgs.gov/mission-areas/water-resources/science/integration-suas-hydrogeophysical-studies
| |
Assessing Vapor Intrusion (VI) Impacts in Neighborhoods with Groundwater Contaminated by Chlorinated Volatile Organic Chemicals (CVOCs)
The VI Diagnosis Toolkit[1] is a set of tools that can be used individually or in combination to assess vapor intrusion (VI) impacts at one or more buildings overlying regional-scale dissolved chlorinated solvent-impacted groundwater plumes. The strategic use of these tools can lead to confident and efficient neighborhood-scale VI pathway assessments.
Related Article(s):
Contributor(s):
- Paul C. Johnson, Ph.D.
- Paul Dahlen, Ph.D.
- Yuanming Guo, Ph.D.
Key Resource(s):
- The VI Diagnosis Toolkit for Assessing Vapor Intrusion Pathways and Impacts in Neighborhoods Overlying Dissolved Chlorinated Solvent Plumes, ESTCP Project ER-201501, Final Report[1]
- CPM Test Guidelines: Use of Controlled Pressure Method Testing for Vapor Intrusion Pathway Assessment, ESTCP Project ER-201501, Technical Report[2]
- VI Diagnosis Toolkit User Guide, ESTCP Project ER-201501[3]
Background
Figure 1. Example of instrumentation used for OPTICS monitoring.
Figure 2. Schematic diagram illustrating the OPTICS methodology. High resolution in-situ data are integrated with traditional discrete sample analytical data using partial least-square regression to derive high resolution chemical contaminant concentration data series.
Nationwide, the liability due to contaminated sediments is estimated in the trillions of dollars. Stakeholders are assessing and developing remedial strategies for contaminated sediment sites in major harbors and waterways throughout the U.S. The mobility of contaminants in surface water is a primary transport and risk mechanism[4][5][6]; therefore, long-term monitoring of both particulate- and dissolved-phase contaminant concentration prior to, during, and following remedial action is necessary to document remedy effectiveness. Source control and total maximum daily load (TMDL) actions generally require costly manual monitoring of dissolved and particulate contaminant concentrations in surface water. The magnitude of cost for these actions is a strong motivation to implement efficient methods for long-term source control and remedial monitoring.
Traditional surface water monitoring requires mobilization of field teams to manually collect discrete water samples, send samples to laboratories, and await laboratory analysis so that a site evaluation can be conducted. These traditional methods are well known to have inherent cost and safety concerns and are of limited use (due to safety concerns and standby requirements for resources) in capturing the effects of episodic events (e.g., storms) that are important to consider in site risk assessment and remedy selection. Automated water samplers are commercially available but still require significant field support and costly laboratory analysis. Further, automated samplers may not be suitable for analytes with short hold-times and temperature requirements.
Optically-based characterization of surface water contaminants is a cost-effective alternative to traditional discrete water sampling methods. Unlike discrete water sampling, which typically results in sparse data at low resolution, and therefore, is of limited use in determining mass loading, OPTICS (OPTically-based In-situ Characterization System) provides continuous data and allows for a complete understanding of water quality and contaminant transport in response to natural processes and human impacts[7][8][9][10][11][12]. The OPTICS tool integrates commercial off-the-shelf in situ aquatic sensors (Figure 1), periodic discrete surface water sample collection, and a multi-parameter statistical prediction model[13][14] to provide high temporal and/or spatial resolution characterization of surface water chemicals of potential concern (COPCs) (Figure 2).
Technology Overview
The principle behind OPTICS is based on the relationship between optical properties of natural waters and the particles and dissolved material contained within them[15][16][17][18][19][20][21][22]. Surface water COPCs such as heavy metals and polychlorinated biphenyls (PCBs) are hydrophobic in nature and tend to sorb to materials in the water column, which have unique optical signatures that can be measured at high-resolution using in situ, commercially available aquatic sensors[23][24][25][26]. Therefore, high-resolution concentrations of COPCs can be accurately and robustly derived from in situ measurements using statistical methods.
The OPTICS method is analogous to the commonly used empirical derivation of total suspended solids concentration (TSS) from optical turbidity using linear regression[27]. However, rather than deriving one response variable (TSS) from one predictor variable (turbidity), OPTICS involves derivation of one response variable (e.g., PCB concentration) from a suite of predictor variables (e.g., turbidity, temperature, salinity, and fluorescence of chlorophyll-a) using multi-parameter statistical regression. OPTICS is based on statistical correlation – similar to the turbidity-to-TSS regression technique. The method does not rely on interpolation or extrapolation.
The OPTICS technique utilizes partial least-squares (PLS) regression to determine a combination of physical, optical, and water quality properties that best predicts chemical contaminant concentrations with high variance. PLS regression is a statistically based method combining multiple linear regression and principal component analysis (PCA), where multiple linear regression finds a combination of predictors that best fit a response and PCA finds combinations of predictors with large variance[13][14]. Therefore, PLS identifies combinations of multi-collinear predictors (in situ, high-resolution physical, optical, and water quality measurements) that have large covariance with the response values (discrete surface water chemical contaminant concentration data from samples that are collected periodically, coincident with in situ measurements). PLS combines information about the variances of both the predictors and the responses, while also considering the correlations among them. PLS therefore provides a model with reliable predictive power.
OPTICS in situ measurement parameters include, but are not limited to current velocity, conductivity, temperature, depth, turbidity, dissolved oxygen, and fluorescence of chlorophyll-a and dissolved organic matter. Instrumentation for these measurements is commercially available, robust, deployable in a wide variety of configurations (e.g., moored, vessel-mounted, etc.), powered by batteries, and records data internally and/or transmits data in real-time. The physical, optical, and water quality instrumentation is compact and self-contained. The modularity and automated nature of the OPTICS measurement system enables robust, long-term, autonomous data collection for near-continuous monitoring.
Figure 3. OPTICS to characterize COPC variability in the context of site processes at BCSA. (A) Tidal oscillations (Elev.
MSL) and precipitation (Precip.). (B) – (D) OPTICS-derived particulate mercury (PHg) and methylmercury (PMeHg) and total PCBs (TPCBs). Open circles represent discrete water sample data.
OPTICS measurements are provided at a significantly reduced cost relative to traditional monitoring techniques used within the environmental industry. Cost performance analysis shows that monitoring costs are reduced by more than 85% while significantly increasing the temporal and spatial resolution of sampling. The reduced cost of monitoring makes this technology suitable for a number of environmental applications including, but not limited to site baseline characterization, source control evaluation, dredge or stormflow plume characterization, and remedy performance monitoring. OPTICS has been successfully demonstrated for characterizing a wide variety of COPCs: mercury, methylmercury, copper, lead, PCBs, dichlorodiphenyltrichloroethane (DDT) and its related compounds (collectively, DDX), and 2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD or dioxin) in a number of different environmental systems ranging from inland lakes and rivers to the coastal ocean. To date, OPTICS has been limited to surface water applications. Additional applications (e.g., groundwater) would require further research and development.
Applications
Figure 4. OPTICS reveals baseflow daily cycling and confirms storm-induced particle-bound COPC resuspension and mobilization through bank interaction. (A) Flow rate (Q) and precipitation (Precip). (B) – (C) OPTICS-derived particulate mercury (PHg) and methylmercury (PMeHg). Open circles represent discrete water sample data.
Figure 5. Three-dimensional volume plot of high spatial resolution OPTICS-derived PCBs in exceedance of baseline showing that PCBs were discharged from the outfall (yellow arrow), remained in suspension, and dispersed elsewhere before settling.
An OPTICS study was conducted at Berry’s Creek Study Area (BCSA), New Jersey in 2014 and 2015 to understand COPC sources and transport mechanisms for development of an effective remediation plan. OPTICS successfully extended periodic discrete surface water samples to continuous, high-resolution measurements of PCBs, mercury, and methylmercury to elucidate COPC sources and transport throughout the BCSA tidal estuary system. OPTICS provided data at resolution sufficient to investigate COC variability in the context of physical processes. The results (Figure 3) facilitated focused and effective site remediation and management decisions that could not be determined based on periodic discrete samples alone, despite over seven years of monitoring at different locations throughout the system over a range of different seasons, tidal phases, and environmental conditions. The BCSA OPTICS methodology and its results have undergone official peer review overseen by the U.S. Environmental Protection Agency (USEPA), and those results have been published in peer-reviewed literature[7].
OPTICS was applied at the South River, Virginia in 2016 to quantify sources of legacy mercury in the system that are contributing to recontamination and continued elevated mercury concentrations in fish tissue. OPTICS provided information necessary to identify mechanisms for COPC redistribution and to quantify the relative contribution of each mechanism to total mass transport of mercury and methylmercury in the system. Continuous, high-resolution COPC data afforded by OPTICS helped resolve baseflow daily cycling that had never before been observed at the South River (Figure 4) and provided data at temporal resolution necessary to verify storm-induced particle-bound COC resuspension and mobilization through bank interaction. The results informed source control and remedy design and monitoring efforts. Methodology and results from the South River have been published in peer-reviewed literature[8].
The U.S. Department of Defense’s Environmental Security Technology Certification Program (ESTCP) supported an OPTICS demonstration study at the Pearl Harbor Sediment Site, Hawaii, to determine whether stormwater from Oscar 1 Pier outfall is a contributing source of PCBs to Decision Unit (DU) N-2 (ESTCP Project ER21-5021). High spatial resolution results afforded by ship-based, mobile OPTICS monitoring suggested that PCBs were discharged from the outfall, remained in suspension, and dispersed elsewhere before settling (Figure 5). More details regarding this study were presented by Chang et al. in 2024[9].
Summary
OPTICS provides:
- High resolution surface water chemical contaminant characterization
- Cost-effective monitoring and assessment
- Versatile and modular monitoring with capability for real-time telemetry
- Data necessary for development and validation of conceptual site models
- A key line of evidence for designing and evaluating remedies.
Because OPTICS monitoring involves deployment of autonomous sampling instrumentation, a substantially greater volume of data can be collected using this technique compared to traditional sampling, and at a far lower cost. A large volume of data supports evaluation of chemical contaminant concentrations over a range of spatial and temporal scales, and the system can be customized for a variety of environmental applications. OPTICS helps quantify contaminant mass flux and the relative contribution of local transport and source areas to net contaminant transport. OPTICS delivers a strong line of evidence for evaluating contaminant sources, fate, and transport, and for supporting the design of a remedy tailored to address site-specific, risk-driving conditions. The improved understanding of site processes aids in the development of mitigation measures that minimize site risks.
References
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See Also
