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| − | ==Downscaled High Resolution Datasets for Climate Change Projections== | + | ==Thermal In Situ Sustainable Remediation to Enhance Biotic and Abiotic Reactions and Accelerate Remediation Timeframes== |
| − | Global climate models (GCMs) have generated projections of temperature, precipitation and other important climate change parameters with spatial resolutions of 100 to 300 km. However, higher spatial resolution information is required to assess threats to individual installations or regions. A variety of “downscaling” approaches have been used to produce high spatial resolution output (datasets) from the global climate models at scales that are useful for evaluating potential threats to critical infrastructure at regional and local scales. These datasets enable development of information about projections produced from various climate models, about downscaling to achieve desired locational specificity, and about selecting the appropriate dataset(s) to use for performing specific assessments. This article describes how these datasets can be accessed and used to evaluate potential climate change impacts.
| + | Thermal In Situ Sustainable Remediation (TISR<sup>®</sup>; US Pat. Nos. 10,384,246 and 10,688,545) is a cost-effective and adaptable low temperature thermal remediation technology that accelerates contaminant treatment. It achieves this by enhancing biotic and abiotic contaminant degradation rates through a modest increase in subsurface temperature. TISR<sup>®</sup> is suitable for a wide range of hydrogeologic settings. It also offers reduced operational costs, a lower carbon footprint, and possibly shorter remedial timeframes than more conventional methods. |
| | <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):''' |
| − | * [[Climate Change Primer]]
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| − | '''Contributor(s):''' [[Dr. Rao Kotamarthi]]
| + | *[[Thermal Remediation]] |
| | + | *[[Thermal Remediation - Electrical Resistance Heating | Electrical Resistance Heating]] |
| | + | *[[Thermal Conduction Heating (TCH)]] |
| | + | *[[Thermal Remediation - Combined Remedies | Combined Remedies]] |
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| − | '''Key Resource(s):''' | + | '''Contributors:''' Jonah Munholland, Julia Vidonish, and Craig Divine |
| − | * Use of Climate Information for Decision-Making and Impacts Research: State of our Understanding<ref name="Kotamarthi2016">Kotamarthi, R., Mearns, L., Hayhoe, K., Castro, C.L., and Wuebble, D., 2016. Use of Climate Information for Decision-Making and Impacts Research: State of Our Understanding. Department of Defense, Strategic Environmental Research and Development Program (SERDP), 55pp. Free download from: [https://www.serdp-estcp.org/content/download/38568/364489/file/Use_of_Climate_Information_for_Decision-Making_Technical_Report.pdf SERDP-ESTCP]</ref>
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| − | * Applying Climate Change Information to Hydrologic and Coastal Design of Transportation Infrastructure, Design Practices<ref name="Kilgore2019">Kilgore, R., Thomas, W.O. Jr., Douglass, S., Webb, B., Hayhoe, K., Stoner, A., Jacobs, J.M., Thompson, D.B., Herrmann, G.R., Douglas, E., and Anderson, C., 2019. Applying Climate Change Information to Hydrologic and Coastal Design of Transportation Infrastructure, Design Practices. The National Cooperative Highway Research Program, Transportation Research Board, Project 15-61, 154 pages. Free download from: [http://onlinepubs.trb.org/Onlinepubs/nchrp/docs/NCHRP1561_DesignProcedures.pdf The Transportation Research Board]</ref> | + | '''Key Resources:''' |
| | + | *Thermal In Situ Sustainable Remediation to Enhance Biotic and Abiotic Reactions and Accelerate Remediation Timeframes, Final Report. ESTCP Project ER20-5028<ref>Divine, C., Munholland, J., Zardouzian, E., Justicia-Leon, S., Aspinall, J.V., Randhawa, D., Falta, R., Freedman, D., Becker, M., 2025. Thermal In Situ Sustainable Remediation to Enhance Biotic and Abiotic Reactions and Accelerate Remediation Timeframes, Final Report. Environmental Security Technology Certification Program (ESTCP), Project ER20-5028. [https://serdp-estcp.mil/projects/details/bab4bc87-b4bc-42df-ad0e-11ea0adf27dc/er20-5028-project-overview Project Website] [[Media: ER20-5028 FR DevineEtAl2025.pdf | Final Report.pdf]]</ref>. |
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| − | * Statistical Downscaling and Bias Correction for Climate Research<ref name="Maraun2018">Maraun, D., and Wildmann, M., 2018. Statistical Downscaling and Bias Correction for Climate Research. Cambridge University Press, Cambridge, UK. 347 pages. [https://doi.org/10.1017/9781107588783 DOI: 10.1017/9781107588783] ISBN: 978-1-107-06605-2</ref> | + | *Advances in Low-Temperature Thermal Remediation. Munholland ''et al.'', 2024<ref name="MunhollandEtAl2024">Munholland, J, Rosso, D., Randhawa, D., Devine, C., Pennington, A., 2024. Advances in Low-Temperature Thermal Remediation. In: García-Rincón, J., Gatsios, E., Lenhard, R.J., Atekwana, E.A., Naidu, R. (eds), Advances in the Characterisation and Remediation of Sites Contaminated with Petroleum Hydrocarbons, Chapter 18 pp. 623-653. Environmental Contamination Remediation and Management series, Springer Nature. [https://doi.org/10.1007/978-3-031-34447-3_18 doi: 10.1007/978-3-031-34447-3_18] [[Media: MunhollandEtAl2024.pdf | Open Access Chapter]]</ref>. |
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| − | * Downscaling Techniques for High-Resolution Climate Projections: From Global Change to Local Impacts<ref name="Kotamarthi2021">Kotamarthi, R., Hayhoe, K., Wuebbles, D., Mearns, L.O., Jacobs, J. and Jurado, J., 2021. Downscaling Techniques for High-Resolution Climate Projections: From Global Change to Local Impacts. Cambridge University Press, Cambridge, UK. 202 pages. [https://doi.org/10.1017/9781108601269 DOI: 10.1017/9781108601269] ISBN: 978-1-108-47375-0</ref> | + | *A Design Tool for Solar Thermal Remediation Using Borehole Heat Exchangers. Ornelles, Falta, and Divine, 2023<ref name="Ornelles">Ornelles, A.D., Falta, R.W., Divine, C.E., 2023. A Design Tool for Solar Thermal Remediation Using Borehole Heat Exchangers. Ground Water, 61(2), pp. 245-254. [https://doi.org/10.1111/gwat.13265 doi: 10.1111/gwat.13265] [https://ngwa.onlinelibrary.wiley.com/doi/epdf/10.1111/gwat.13265 Free download from NGWA]</ref>. |
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| − | ==Downscaling of Global Climate Models==
| + | *Low-Temperature Thermal Remediation: Gaining Traction as a Green Remedial Alternative. Horst, ''et al.'', 2018<ref name="HorstEtAl2018">Horst, J., Flanders, C., Klemmer, M., Randhawa, D.S., Rosso, D., 2018. Low-Temperature Thermal Remediation: Gaining Traction as a Green Remedial Alternative. Groundwater Monitoring and Remediation, 38(3), pp. 18-27. [https://doi.org/10.1111/gwmr.12295 doi: 10.1111/gwmr.12295]</ref>. |
| − | Some communities and businesses have begun to improve their resilience to climate change by building adaptation plans based on national scale climate datatsets ([https://unfccc.int/topics/adaptation-and-resilience/workstreams/national-adaptation-plans National Adaptation Plans]), regional datasets ([https://www.dec.ny.gov/docs/administration_pdf/crrafloodriskmgmtgdnc.pdf New York State Flood Risk Management Guidance]<ref name="NYDEC2020">New York State Department of Environmental Conservation, 2020. New York State Flood Risk Management Guidance for Implementation of the Community Risk and Resiliency Act. Free download from: [https://www.dec.ny.gov/docs/administration_pdf/crrafloodriskmgmtgdnc.pdf New York State] [[Media: NewYorkState2020.pdf | Report.pdf]]</ref>), and datasets generated at local spatial resolutions. Resilience to the changing climate has also been identified by the US Department of Defense (DoD) as a necessary part of the installation planning and basing process ([https://media.defense.gov/2019/Jan/29/2002084200/-1/-1/1/CLIMATE-CHANGE-REPORT-2019.PDF DoD Report on Effects of a Changing Climate]<ref name="DoD2019">US Department of Defense, 2019. Report on Effects of a Changing Climate to the Department of Defense. Free download from: [https://media.defense.gov/2019/Jan/29/2002084200/-1/-1/1/CLIMATE-CHANGE-REPORT-2019.PDF DoD] [[Media: DoD2019.pdf | Report.pdf]]</ref>). More than 79 installations were identified as facing potential threats from climate change. The threats faced due to changing climate include recurrent flooding, droughts, desertification, wildfires and thawing permafrost.
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| − | Assessing the threats climate change poses at regional and local scales requires data with higher spatial resolution than is currently available from global climate models. Global-scale climate models typically have spatial resolutions of 100 to 300 km, and output from these models needs to be spatially and/or temporally disaggregated in order to be useful in performing assessments at smaller scales. The process of producing higher spatial-temporal resolution climate model output from coarser global climate model outputs is referred to as “downscaling” and results in climate change projections (datasets) at scales that are useful for evaluating potential threats to regional and local communities and businesses. These datasets provide information on temperature, precipitation and a variety of other climate variables for current and future climate conditions under various greenhouse gas (GHG) emission scenarios. There are a variety of web-based tools available for accessing these datasets to evaluate potential climate change impacts at regional and local scales.
| + | ==Introduction== |
| | + | [[File: MunhollandFig1.png | thumb | 500 px | Figure 1: Components and layout of a TISR<sup>®</sup> system]] |
| | + | TISR<sup>®</sup> utilizes [[Wikipedia: Downhole heat exchanger | borehole heat exchangers (BHEs)]] to heat target treatment areas in the subsurface. This is achieved through a combination of solar collectors, which harness solar radiation as heat, and the utilization of other heat sources such as waste heat or hybrid heating with heat pumps. That heat is then transferred to the subsurface via a closed loop circulation system connected to the BHEs (Figure 1). By incorporating these additional heat sources, TISR<sup>®</sup> ensures a more robust and constant heat supply, enabling customization of the system to address specific site challenges and facilitate efficient heat transfer. |
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| − | ==Methods for Downscaling==
| + | The heating of the subsurface through TISR<sup>®</sup> accelerates various processes such as biodegradation, abiotic reactions, and contaminant desorption and dissolution. The technology is also suitable for diverse hydrogeologic environments, including sites with low permeability zones, due to the relatively uniform thermal conductivity of geologic materials, which is unaffected by grain size<ref name="MunhollandEtAl2024"/>. This versatility allows TISR<sup>®</sup> to integrate with conventional remediation technologies, enhancing their performance. |
| − | {| class="wikitable" style="float:right; margin-left:10px;text-align:center;"
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| − | |+Table 1. Two widely used methods for developing downscaled higher resolution climate model projections
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| − | |-
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| − | !Dynamical Downscaling
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| − | !Statistical Downscaling
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| − | |-
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| − | |Deterministic climate change simulations that output</br>many climate variables with sub-daily information ||Primarily limited to daily temperature and precipitation
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| − | |-
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| − | |Computationally expensive; hence, limited number of simulations – both</br>GHG emission scenarios and global climate models downscaled||Computationally efficient; hence, downscaled data typically</br>available for many different global climate models and GHG emission scenarios
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| − | |-
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| − | |May require additional bias correction||Method incorporates bias correction
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| − | |-
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| − | |Observational data at the downscaled location are not necessary</br>to obtain the downscaled output at the location||Best suited for locations with 30 years or more of observational data
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| − | |-
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| − | |Does not assume stationarity or in other words the model</br>simulates the future regardless of what has happened in the past||Stationarity assumption - assumes that the statistical relationship between global</br>climate model and observations will remain constant in the future
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| − | |}
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| − | There are two main approaches to downscaling. One method, commonly referred to as “statistical downscaling”, uses the empirical-statistical relationships between large-scale weather phenomena and historical local weather data. In this method, these statistical relationships are applied to output generated by global climate models. A second method uses physics-based numerical models (regional-scale climate models or RCMs) of weather and climate that operate over a limited region of the earth (e.g., North America) and at spatial resolutions that are typically 3 to 10 times finer than the global-scale climate models. This method is known as “dynamical downscaling”. These regional-scale climate models are similar to the global models with respect to their reliance on the principles of physics, but because they operate over only part of the earth, they require information about what is coming in from the rest of the earth as well as what is going out of the limited region of the model. This is generally obtained from a global model. The primary differences between statistical and dynamical downscaling methods are summarized in Table 1.
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| − | It is important to realize that there is no “best” downscaling method or dataset, and that the best method/dataset for a given problem depends on that problem’s specific needs. Several data products based on downscaling higher level spatial data are available ([https://cida.usgs.gov/gdp/ USGS], [http://maca.northwestknowledge.net/ MACA], [https://www.narccap.ucar.edu/ NARCCAP], [https://na-cordex.org/ CORDEX-NA]). The appropriate method and dataset to use depends on the intended application. The method selected should be able to credibly resolve spatial and temporal scales relevant for the application. For example, to develop a risk analysis of frequent flooding, the data product chosen should include precipitation at greater than a diurnal frequency and over multi-decadal timescales. This kind of product is most likely to be available using the dynamical downscaling method. SERDP reviewed the various advantages and disadvantages of using each type of downscaling method and downscaling dataset, and developed a recommended process that is publicly available<ref name="Kotamarthi2016"/>. In general, the following recommendations should be considered in order to pick the right downscaled dataset for a given analysis:
| + | One of the key advantages of TISR<sup>®</sup> is its ability to accelerate treatment processes while minimizing energy consumption. By harnessing renewable and sustainable energy sources such as solar radiation and effectively utilizing waste heat or hybrid heating with heat pumps, TISR<sup>®</sup> delivers an efficient, cost-effective remediation solution. Compatibility with multiple conventional remediation technologies makes it an effective option for addressing a variety of contamination challenges. |
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| − | * When a problem depends on using a large number of climate models and emission scenarios to perform preliminary assessments and to understand the uncertainty range of projections, then using a statistical downscaled dataset is recommended.
| + | ==Scientific Principles== |
| − | * When the assessment needs a more extensive parameter list or is analyzing a region with few long-term observational data, dynamically downscaled climate change projections are recommended.
| + | [[File: MunhollandFig2.png | thumb | 500 px | Figure 2: Effect of temperature on hydrolysis rates for VOCs]] |
| | + | During the TISR<sup>®</sup> heating process, thermal conduction and advection play a crucial role in boosting physical, biological, and chemical processes within the treatment zones, leading to enhanced attenuation, degradation, and removal of contaminants from the subsurface<ref>Dettmer, K., 2002. A Discussion of the Effects of Thermal Remediation Treatments on Microbial Degradation Processes. U.S. Environmental Protection Agency, Office of Solid Waste and Emergency Response, Technology Innovation Office, Washington, DC. [[Media: dettmer2002.pdf | free report]] [https://www.clu-in.org/download/techdrct/dettmer.pdf free download from clu-in.org]</ref><ref name="HorstEtAl2018"/>. TISR<sup>®</sup> systems are typically designed to enhance the biological activity and performance of other active technologies, such as [[Wikipedia: Air sparging | air sparge]]/[[Soil Vapor Extraction (SVE) | soil vapor extraction]] (AS/SVE) or [[Chemical Oxidation (In Situ - ISCO) | chemical oxidation]], by increasing the subsurface temperature by approximately 10°C to 20°C. The target groundwater temperature is often set to reach up to about 35°C, which is the optimal temperature for promoting the activity of mesophilic microbial communities responsible for biodegradation of certain contaminants. In cases where the objective is to maximize abiotic hydrolysis mechanisms, such as for some chlorinated alkanes, TISR<sup>®</sup> systems can be designed to reach a final groundwater temperature of up to 60°C. |
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| − | ==Uncertainty in Projections==
| + | The effectiveness of TISR<sup>®</sup> in enhancing treatment performance has been demonstrated through several mechanisms, which are discussed below: |
| − | {| class="wikitable" style="float:right; margin-left:10px;text-align:center;"
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| − | |+Table 2. Downscaling Model Characteristics and Output<ref name="Kotamarthi2016"/>
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| − | |-
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| − | !Model or</br>Dataset Name
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| − | !Model<br />Method
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| − | !Output<br />Variables
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| − | !Output<br />Format
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| − | !Spatial</br>Resolution
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| − | !Time</br>Resolution
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| − | |-
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| − | | colspan="6" style="text-align: left; background-color:white;" |'''Statistical Downscaled Datasets'''
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| − | |-
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| − | | [https://worldclim.org/data/index.html WorldClim]<ref name="Hijmans2005">Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G. and Jarvis, A., 2005. Very High Resolution Interpolated Climate Surfaces for Global Land Areas. International Journal of Climatology: A Journal of the Royal Meteorological Society, 25(15), pp 1965-1978. [https://doi.org/10.1002/joc.1276 DOI: 10.1002/joc.1276]</ref>
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| − | |Delta||T(min, max, avg), Pr||NetCDF||grid: 30 arc sec to 10 arc min||month
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| − | |-
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| − | |Coarse Sand||0.31 - 0.46||0.18 - 0.43||9×10<sup>-7</sup> - 6×10<sup>-3</sup>
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| − | |-
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| − | |Medium Sand||—||0.16 - 0.46||9×10<sup>-7</sup> - 5×10<sup>-4</sup>
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| − | |-
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| − | |Fine Sand||0.25 - 0.53||0.01 - 0.46||2×10<sup>-7</sup> - 2×10<sup>-4</sup>
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| − | |-
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| − | |Silt, Loess||0.35 - 0.50||0.01 - 0.39||1×10<sup>-9</sup> - 2×10<sup>-5</sup>
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| − | |-
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| − | |Clay||0.40 - 0.70||0.01 - 0.18||1×10<sup>-11</sup> - 4.7×10<sup>-9</sup>
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| − | |-
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| − | | colspan="4" style="text-align: left; background-color:white;" |'''Dynamical Downscaled Datasets'''
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| − | |-
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| − | |Karst and Reef Limestone||0.05 - 0.50||—||1×10<sup>-6</sup> - 2×10<sup>-2</sup>
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| − | |-
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| − | |Limestone, Dolomite||0.00 - 0.20||0.01 - 0.24||1×10<sup>-9</sup> - 6×10<sup>-6</sup>
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| − | |-
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| − | |Sandstone||0.05 - 0.30||0.10 - 0.30||3×10<sup>-10</sup> - 6×10<sup>-6</sup>
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| − | |-
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| − | |Siltstone||—||0.21 - 0.41||1×10<sup>-11</sup> - 1.4×10<sup>-8</sup>
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| − | |-
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| − | |Basalt||0.05 - 0.50||—||2×10<sup>-11</sup> - 2×10<sup>-2</sup>
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| − | |-
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| − | |Fractured Crystalline Rock||0.00 - 0.10||—||8×10<sup>-9</sup> - 3×10<sup>-4</sup>
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| − | |-
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| − | |Weathered Granite||0.34 - 0.57||—||3.3×10<sup>-6</sup> - 5.2×10<sup>-5</sup>
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| − | |-
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| − | |Unfractured Crystalline Rock||0.00 - 0.05||—||3×10<sup>-14</sup> - 2×10<sup>-10</sup>
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| − | |}
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| − | A primary cause of uncertainty in climate change projections, especially beyond 30 years into the future, is the uncertainty in the greenhouse gas (GHG) emission scenarios used to make climate model projections. The best method of accounting for this type of uncertainty is to apply a climate change model to multiple GHG emission scenarios (see also: [[Wikipedia: Representative Concentration Pathway]]).
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| − | The uncertainties in climate projections over shorter timescales, less than 30 years out, are dominated by something known as “internal variability” in the models. Different approaches are used to address the uncertainty from internal variability<ref name="Kotamarthi2021"/>. A third type of uncertainty in climate modeling, known as scientific uncertainty, comes from our inability to numerically solve every aspect of the complex earth system. We expect this scientific uncertainty to decrease as we understand more of the earth system and improve its representation in our numerical models. As discussed in [[Climate Change Primer]], numerical experiments based on global climate models are designed to address these uncertainties in various ways. Downscaling methods evaluate this uncertainty by using several independent regional climate models to generate future projections, with the expectation that each of these models will capture some aspects of the physics better than the others, and that by using several different models, we can estimate the range of this uncertainty. Thus, the commonly accepted methods for accounting for uncertainty in climate model projections are either using projections from one model for several emission scenarios, or applying multiple models to project a single scenario.
| + | *Primary dechlorinating bacteria, such as [[Wikipedia: Dehalococcoides | Dehalococcoides]] and [[Wikipedia: Dehalogenimonas | Dehalogenimonas]], are known to be [[Wikipedia: Mesophile | mesophilic]]. Therefore, increasing the temperature can significantly enhance microbial activity and biodegradation by multiple orders of magnitude<ref>Macbeth, T., Truex, M., Powell, T., Michalsen, M., 2012. Combining Low-Energy Electrical Resistance Heating with Biotic and Abiotic Reactions for Treatment of Chlorinated Solvent DNAPL Source Areas. [https://serdp-estcp.mil/ Environmental Security Technology Certification Program (ESTCP)] [https://serdp-estcp.mil/projects/details/877a3b41-6bed-4f4a-b43a-87fbcd9b7f0b/er-200719-project-overview Project ER-200719 webpage] [[Media: Macbeth2012_ER-200719-FR.pdf | free download]] [https://www.youtube.com/watch?v=-EbwKbs8scE Project Summary Presentation]</ref>. |
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| − | A comparison of the currently available methods and their characteristics is provided in Table 2 (adapted from Kotamarthi et al., 2016<ref name="Kotamarthi2016"/>). The table lists the various methodologies and models used for producing downscaled data, and the climate variables that these methods produce. These datasets are mostly available for download from the data servers and websites listed in the table and in a few cases by contacting the respective source organizations.
| + | *Reduced iron minerals, particularly iron sulfides, can participate in the abiotic degradation of chlorinated ethenes and other contaminants. The rates of these reactions generally depend on temperature, following the [[Wikipedia: Arrhenius equation | Arrhenius equation]]. |
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| − | The most popular and widely used format for atmospheric and climate science is known as [[Wikipedia:NetCDF | NetCDF]], which stands for Network Common Data Form. NetCDF is a self-describing data format that saves data in a binary format. The format is self-describing in that a metadata listing is part of every file that describes all the data attributes, such as dimensions, units and data size and in principal should not need additional information to extract the required data for analysis with the right software. However, specially built software for reading and extracting data from these binary files is necessary for making visualizations and further analysis. Software packages for reading and writing NetCDF datasets and for generating visualizations from these datasets are widely available and obtained free of cost ([https://www.unidata.ucar.edu/software/netcdf/docs/ NetCDF-tools]). Popular geospatial analysis tools such as ARC-GIS, statistical packages such as ‘R’ and programming languages such as Fortran, C++, and Python have built in libraries that can be used to directly read NetCDF files for visualization and analysis.
| + | *[[Wikipedia: Hydrolysis | Hydrolysis]] rates for certain compounds exhibit high temperature dependence. For example, a 10°C increase can lead to a 50% reduction in the half-life of [[Wikipedia: 1,1,1-Trichloroethane | 1,1,1-trichloroethane (1,1,1-TCA)]]<ref>Suthersan, S., Horst, J., Klemmer, M., Malone, D., 2012. Temperature-Activated Auto-Decomposition Reactions: An Under-Utilized In Situ Remediation Solution. Groundwater Monitoring and Remediation, 32(3), pp. 34-40. [https://doi.org/10.1111/j.1745-6592.2012.01407.x doi: 10.1111/j.1745-6592.2012.01407.x]</ref>. Figure 2 presents the calculated temperature-dependent hydrolysis rates for 13 common [[Wikipedia: Volatile organic compound | volatile organic compounds (VOCs)]] at a pH of 7. |
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| | + | *[[Wikipedia: Henry's law | Henry's Law]] constant, [[Wikipedia: Vapor pressure | vapor pressure]], solubility, and viscosity values are also temperature dependent. For instance, the Henry's Law constant values for many VOCs, including [[Wikipedia: Tetrachloroethylene | tetrachloroethene (PCE)]], have been observed to double between 8 and 24°C<ref>Chen, F., Freedman, D.L., Falta, R.W., Murdoch, L.C., 2012. Henry's law constants of chlorinated solvents at elevated temperatures. Chemosphere, 86(2), pp.156-165. [https://doi.org/10.1016/j.chemosphere.2011.10.004 doi: 10.1016/j.chemosphere.2011.10.004]</ref>. |
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| | + | *Rates of other processes that often limit the pace of remediation, such as dissolution, desorption, and [[Matrix Diffusion | back diffusion]], are positively correlated with temperature. This results in an enhanced bioavailability of contaminant mass that may otherwise be inaccessible. |
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| − | [[File: Gschwend1w2fig1.png | thumb | 300px | Figure 1. A representation of a clam living in a sediment bed that contains a chemical contaminant (depicted as red hexagons). The contaminant is partly dissolved in the sediment porewater between the solid grains, and partly associated with solid phases, like natural organic matter and "black carbons" such as soots from diesel engines and chars emitted during forest fires. All of these liquid and solid materials can exchange their contaminant loads with one another, with the distributions dependent on the chemical's relative affinity for each material. When an animal like the clam moves into this system, the chemical is also accumulated into the animal, until the animal is also equilibrated with the other solids and liquid(s) present.]]
| + | These various mechanisms collectively contribute to the improved performance of other active treatments when temperature is moderately increased by utilization of TISR®. |
| − | Environmental media such as sediments typically contain many different materials or phases, including liquid solutions (e.g. water, [[Light Non-Aqueous Phase Liquids (LNAPLs)| nonaqueous phase liquids]] like spilled oils) and diverse solids (e.g., quartz, aluminosilicate clays, and combustion-derived soots). Further, the chemical concentration in the porewater medium includes both molecules that are "truly dissolved" in the water and others that are associated with colloids in the porewater<ref name="Brownawell1986">Brownawell, B.J., and Farrington, J.W., 1986. Biogeochemistry of PCBs in interstitial waters of a coastal marine sediment. Geochimica et Cosmochimica Acta, 50(1), pp. 157-169. [https://doi.org/10.1016/0016-7037(86)90061-X DOI: 10.1016/0016-7037(86)90061-X] Free download available from: [https://semspub.epa.gov/work/01/268631.pdf US EPA].</ref><ref name="Chin1992">Chin, Y.P., and Gschwend, P.M., 1992. Partitioning of Polycyclic Aromatic Hydrocarbons to Marine Porewater Organic Colloids. Environmental Science and Technology, 26(8), pp. 1621-1626. [https://doi.org/10.1021/es00032a020 DOI: 10.1021/es00032a020]</ref><ref name="Achman1996">Achman, D.R., Brownawell, B.J., and Zhang, L., 1996. Exchange of Polychlorinated Biphenyls Between Sediment and Water in the Hudson River Estuary. Estuaries, 19(4), pp. 950-965. [https://doi.org/10.2307/1352310 DOI: 10.2307/1352310] Free download available from: [https://www.academia.edu/download/55010335/135231020171114-2212-b93vic.pdf Academia.edu]</ref>. As a result, contaminant chemicals distribute among these diverse media (Figure 1) according to their affinity for each and the amount of each phase in the system<ref name="Gustafsson1996">Gustafsson, Ö., Haghseta, F., Chan, C., MacFarlane, J., and Gschwend, P.M., 1996. Quantification of the Dilute Sedimentary Soot Phase: Implications for PAH Speciation and Bioavailability. Environmental Science and Technology, 31(1), pp. 203-209. [https://doi.org/10.1021/es960317s DOI: 10.1021/es960317s]</ref><ref name="Luthy1997">Luthy, R.G., Aiken, G.R., Brusseau, M.L., Cunningham, S.D., Gschwend, P.M., Pignatello, J.J., Reinhard, M., Traina, S.J., Weber, W.J., and Westall, J.C., 1997. Sequestration of Hydrophobic Organic Contaminants by Geosorbents. Environmental Science and Technology, 31(12), pp. 3341-3347. [https://doi.org/10.1021/es970512m DOI: 10.1021/es970512m]</ref><ref name="Lohmann2005">Lohmann, R., MacFarlane, J.K., and Gschwend, P.M., 2005. Importance of Black Carbon to Sorption of Native PAHs, PCBs, and PCDDs in Boston and New York Harbor Sediments. Environmental Science and Technology, 39(1), pp.141-148. [https://doi.org/10.1021/es049424+ DOI: 10.1021/es049424+]</ref><ref name="Cornelissen2005">Cornelissen, G., Gustafsson, Ö., Bucheli, T.D., Jonker, M.T., Koelmans, A.A., and van Noort, P.C., 2005. Extensive Sorption of Organic Compounds to Black Carbon, Coal, and Kerogen in Sediments and Soils: Mechanisms and Consequences for Distribution, Bioaccumulation, and Biodegradation. Environmental Science and Technology, 39(18), pp. 6881-6895. [https://doi.org/10.1021/es050191b DOI: 10.1021/es050191b]</ref><ref name="Koelmans2009">Koelmans, A.A., Kaag, K., Sneekes, A., and Peeters, E.T.H.M., 2009. Triple Domain in Situ Sorption Modeling of Organochlorine Pesticides, Polychlorobiphenyls, Polyaromatic Hydrocarbons, Polychlorinated Dibenzo-p-Dioxins, and Polychlorinated Dibenzofurans in Aquatic Sediments. Environmental Science and Technology, 43(23), pp. 8847-8853. [https://doi.org/10.1021/es9021188 DOI: 10.1021/es9021188]</ref>. As such, the chemical concentration in any one medium (e.g., truly dissolved in porewater) in a multi-material system like sediment is very hard to know from measures of the total sediment concentration, which unfortunately is the information typically found by analyzing for chemicals in sediment samples.
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| − | If an animal moves into this system, it will also accumulate the chemical in its tissues from the loads in all the other materials (Figure 1). This can lead to exposures of the chemical to other organisms, including humans, who may eat such animals. Predicting the quantity of contaminant in the animal requires knowledge of the relative affinities of the chemical for the animal versus the sediment materials. For example, if one knew the chemical's truly dissolved concentration in the porewater and could reasonably assume the chemical of interest in the animal has mostly accumulated in its lipids (as is often the case for very hydrophobic compounds), then one could estimate the chemical concentration in the animal (''C<sub><small>animal</small></sub>'', typically in units of μg/kg animal wet weight) using a lipid-water [[Wikipedia: Partition coefficient | partition coefficient]], ''K<sub><small>lipid-water</small></sub>'', typically in units of (μg/kg lipid)'''/'''(μg/L water), and the porewater concentration of the chemical (''C<sub><small>porewater</small></sub>'', in μg/L) with Equation 1.
| + | ==Implementation and Practical Considerations== |
| − | {|
| + | [[File: MunhollandFig3.png | thumb | 500 px | Figure 3: Transformation of 1,1-DCE and VC following installation of a TISR<sup>®</sup> system]] |
| − | |
| + | TISR<sup>®</sup> has proven successful in reducing contaminant concentrations across diverse hydrogeologic settings, including those with low and complex hydraulic conductivity conditions. Figure 3 shows a sharp initial decrease in [[Wikipedia: 1,1-Dichloroethylene | 1,1-dichloroethene (1,1-DCE)]] concentrations due to enhanced biodegradation, and then a temporary small increase followed by subsequent decreases in 1,1-DCE concentrations to below detection limits, possibly the result of temperature-enhanced desorption or dissolution of 1,1-DCE from the aquifer. There is also a temporary increase and then decrease in the concentrations of [[Wikipedia: Vinyl chloride | vinyl chloride (VC)]] (a biodegradation product) to very low levels. |
| − | |-
| |
| − | | || Equation 1.
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| − | | style="text-align:center;"| <big>'''''C<sub><small>animal</small></sub> '''=''' f<sub><small>lipid</small></sub> '''x''' K<sub><small>lipid-water</small></sub> '''x''' C<sub><small>porewater</small></sub>'''''</big>
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| − | |-
| |
| − | | where:
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| − | |-
| |
| − | | || ''f<sub><small>lipid</small></sub>'' || is the fraction lipids contribute to the total wet weight of the animal (kg lipid/kg animal wet weight), and
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| − | |-
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| − | | || ''C<sub><small>porewater</small></sub>'' || is the freely dissolved contaminant concentration in the porewater surrounding the animal.
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| − | |}
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| | | | |
| − | While there is a great deal of information on the values of ''K<sub><small>lipid-water</small></sub>'' for many chemicals<ref name="Schwarzenbach2017">Schwarzenbach, R.P., Gschwend, P.M., and Imboden, D.M., 2017. Environmental Organic Chemistry, 3rd edition. Ch. 16: Equilibrium Partitioning from Water and Air to Biota, pp. 469-521. John Wiley and Sons. ISBN: 978-1-118-76723-8</ref>, it is often very inaccurate to estimate truly dissolved porewater concentrations from total sediment concentrations using assumptions about the affinity of those chemicals for the solids in the system<ref name="Gustafsson1996"/>. Further, it is difficult to isolate porewater without colloids and/or measure the very low truly dissolved concentrations of hydrophobic contaminants of concern like [[Polycyclic Aromatic Hydrocarbons (PAHs) | polycyclic aromatic hydrocarbons (PAHs)]], [[Wikipedia: Polychlorinated biphenyl | polychlorinated biphenyls (PCBs)]], nonionic pesticides like [[Wikipedia: DDT | dichlorodiphenyltrichloroethane (DDT)]], and [[Wikipedia: Polychlorinated dibenzodioxins | polychlorinated dibenzo-p-dioxins (PCDDs)]]/[[Wikipedia: Polychlorinated dibenzofurans | dibenzofurans (PCDFs)]]<ref name="Hawthorne2005">Hawthorne, S.B., Grabanski, C.B., Miller, D.J., and Kreitinger, J.P., 2005. Solid-Phase Microextraction Measurement of Parent and Alkyl Polycyclic Aromatic Hydrocarbons in Milliliter Sediment Pore Water Samples and Determination of K<sub><small>DOC</small></sub> Values. Environmental Science and Technology, 39(8), pp. 2795-2803. [https://doi.org/10.1021/es0405171 DOI: 10.1021/es0405171]</ref>.
| + | The TISR<sup>®</sup> system construction utilizes off-the-shelf modular components, promoting cost-efficiency, reusability, and a reduced carbon footprint. Waste heat can supplement or replace the solar setup, and remote monitoring through dashboard apps reduces operation and maintenance costs. TISR® projects typically achieve an average capital cost payback within 1 to 3 years due to accelerated rates of remediation. |
| | | | |
| − | ==Passive Samplers==
| + | When implementing TISR<sup>®</sup>, [[Wikipedia: Solar irradiance | solar insolation]] and groundwater velocity must be considered for optimal equipment sizing and cycling. Depending on remedial goals and site history and conditions, the TISR<sup>®</sup> system and target temperatures may be designed to enhance specific remedial mechanisms (e.g. hydrolysis over bioremediation). Recently Ornelles ''et al.''<ref name="Ornelles"/> developed an easy to use Excel-based model that can be used to optimize TISR<sup>®</sup> design and predict subsurface temperature changes over time. Frequent monitoring of the temperature and microbial community composition relative to targets is useful for evaluating treatment effectiveness and optimizing system performance. The system and target temperatures can be customized to enhance specific remedial mechanisms based on site conditions and goals. |
| − | One approach to address this problem for contaminated sediments is to insert into the sediment billets of organic polymers like low density polyethylene (LDPE), polydimethylsiloxane (PDMS), or polyoxymethylene (POM) that can absorb such hydrophobic chemicals from their surroundings<ref name="Mayer2000">Mayer, P., Vaes, W.H., Wijnker, F., Legierse, K.C., Kraaij, R., Tolls, J., and Hermens, J.L., 2000. Sensing Dissolved Sediment Porewater Concentrations of Persistent and Bioaccumulative Pollutants Using Disposable Solid-Phase Microextraction Fibers. Environmental Science and Technology, 34(24), pp. 5177-5183. [https://doi.org/10.1021/es001179g DOI: 10.1021/es001179g]</ref><ref name="Booij2003">Booij, K., Hoedemaker, J.R., and Bakker, J.F., 2003. Dissolved PCBs, PAHs, and HCB in Pore Waters and Overlying Waters of Contaminated Harbor Sediments. Environmental Science and Technology, 37(18), pp. 4213-4220. [https://doi.org/10.1021/es034147c DOI: 10.1021/es034147c]</ref><ref name="Cornelissen2008">Cornelissen, G., Pettersen, A., Broman, D., Mayer, P., and Breedveld, G.D., 2008. Field testing of equilibrium passive samplers to determine freely dissolved native polycyclic aromatic hydrocarbon concentrations. Environmental Toxicology and Chemistry, 27(3), pp. 499-508. [https://doi.org/10.1897/07-253.1 DOI: 10.1897/07-253.1]</ref><ref name="Tomaszewski2008">Tomaszewski, J.E., and Luthy, R.G., 2008. Field Deployment of Polyethylene Devices to Measure PCB Concentrations in Pore Water of Contaminated Sediment. Environmental Science and Technology, 42(16), pp. 6086-6091. [https://doi.org/10.1021/es800582a DOI: 10.1021/es800582a]</ref><ref name="Fernandez2009">Fernandez, L.A., MacFarlane, J.K., Tcaciuc, A.P., and Gschwend, P.M., 2009. Measurement of Freely Dissolved PAH Concentrations in Sediment Beds Using Passive Sampling with Low-Density Polyethylene Strips. Environmental Science and Technology, 43(5), pp. 1430-1436. [https://doi.org/10.1021/es802288w DOI: 10.1021/es802288w]</ref><ref name="Arp2015">Arp, H.P.H., Hale, S.E., Elmquist Kruså, M., Cornelissen, G., Grabanski, C.B., Miller, D.J., and Hawthorne, S.B., 2015. Review of polyoxymethylene passive sampling methods for quantifying freely dissolved porewater concentrations of hydrophobic organic contaminants. Environmental Toxicology and Chemistry, 34(4), pp. 710-720. [https://doi.org/10.1002/etc.2864 DOI: 10.1002/etc.2864] [https://setac.onlinelibrary.wiley.com/doi/epdf/10.1002/etc.2864 Free access article.] [[Media: Arp2015.pdf | Report.pdf]]</ref><ref name="Apell2016"/>. In this approach, the polymer is inserted in the sediment bed where it absorbs some of the contaminant load via the contaminant's diffusion into the polymer from the surroundings. When the polymer achieves sorptive equilibration with the sediments, the chemical concentration in the polymer, ''C<sub><small>polymer</small></sub>'' (μg/kg polymer), can be used to find the corresponding concentration in the porewater, ''C<sub><small>porewater</small></sub>'' (μg/L), using a polymer-water partition coefficient, ''K<sub><small>polymer-water</small></sub>'' ((μg/kg polymer)'''/'''(μg/L water)), that has previously been found in laboratory testing<ref name="Lohmann2012">Lohmann, R., 2012. Critical Review of Low-Density Polyethylene’s Partitioning and Diffusion Coefficients for Trace Organic Contaminants and Implications for Its Use as a Passive Sampler. Environmental Science and Technology, 46(2), pp. 606-618. [https://doi.org/10.1021/es202702y DOI: 10.1021/es202702y]</ref><ref name="Ghosh2014">Ghosh, U., Kane Driscoll, S., Burgess, R.M., Jonker, M.T., Reible, D., Gobas, F., Choi, Y., Apitz, S.E., Maruya, K.A., Gala, W.R., Mortimer, M., and Beegan, C., 2014. Passive Sampling Methods for Contaminated Sediments: Practical Guidance for Selection, Calibration, and Implementation. Integrated Environmental Assessment and Management, 10(2), pp. 210-223. [https://doi.org/10.1002/ieam.1507 DOI: 10.1002/ieam.1507] [https://setac.onlinelibrary.wiley.com/doi/epdf/10.1002/ieam.1507 Free access article.] [[Media: Ghosh2014.pdf | Report.pdf]]</ref>, as shown in Equation 2.
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| − | {|
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| − | |
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| − | |-
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| − | | || Equation 2.
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| − | | style="width:600px; text-align:center;" | <big>'''''C<sub><small>porewater</small></sub> '''=''' C<sub><small>polymer</small></sub> '''/''' K<sub><small>polymer-water</small></sub>'''''</big>
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| − | |}
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| | | | |
| − | Such “passive uptake” by the polymer also reflects the availability of the chemicals for transport to adjacent systems (e.g., overlying surface waters) and for uptake into organisms (e.g., [[Wikipedia: Bioaccumulation | bioaccumulation]]). Thus, one can use the porewater concentrations to estimate the biotic accumulation of the chemicals, too. For example, for the concentration in the animal equilibrated with the sediment, ''C<sub><small>animal</small></sub>'' (μg/kg animal), would be found by combining Equations 1 and 2 to get Equation 3.
| + | ==Summary== |
| − | {|
| + | TISR<sup>®</sup> is a cost-effective and adaptable technology that can be customized to address specific site challenges. It can be used as a standalone solution or be integrated with existing remediation strategies to enhance their effectiveness. TISR<sup>®</sup> is particularly effective in challenging hydrogeological conditions and can be optimized to suit specific treatment mechanisms. By shortening the active remediation timeframe, TISR<sup>®</sup> reduces lifecycle costs and minimizes the carbon footprint associated with the remediation process. |
| − | |
| + | <br clear="right"/> |
| − | |-
| |
| − | | || Equation 3.
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| − | |style="width:700px; text-align:center;" |<big>'''''C<sub><small>animal</small></sub> '''=''' f<sub><small>lipid</small></sub> '''x''' K<sub><small>lipid-water</small></sub> '''x''' C<sub><small>polymer</small></sub> '''/''' K<sub><small>polymer-water</small></sub>'''''</big>
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| − | |}
| |
| − | [[File: Gschwend1w2fig2a.PNG | thumb | 300px | Figure 2a. Plot of the initial concentrations of a PRC (green lines) in a polyethylene (PE) sheet inserted in a sediment showing constant concentration across the PE and zero concentration outside the PE. At the same time, a target contaminant of interest (red lines) initially has a constant concentration in the sediment outside the PE and zero concentration inside the PE.]][[File: Gschwend1w2fig2b.PNG | thumb | 300px | Figure 2b. After the PE has been deployed for a time, the PRC is depleted from the PE (green lines), especially near the surfaces contacting the sediment, and its concentration is building up outside the PE and diffusing away into the sediment. Meanwhile, the target chemical leaves the sediment and begins to diffuse into the PE (red lines). The "jumps" in concentration at the PE-sediment boundary reflect the equilibrium paritioning coefficient,</br>''K<sub>PE-sed</sub> = C<sub>PE</sub> / C<sub>sediment</sub>''.]]
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| − | | |
| − | ==Performance Reference Compounds (PRCs)== | |
| − | Perhaps unsurprisingly, pollutants with low water solubility like PAHs, PCBs, etc. do not diffuse quickly through sediment beds. As a result, their accumulation in polymeric materials in sediments can take a long time to achieve equilibration<ref name="Fernandez2009b">Fernandez, L. A., Harvey, C.F., and Gschwend, P.M., 2009. Using Performance Reference Compounds in Polyethylene Passive Samplers to Deduce Sediment Porewater Concentrations for Numerous Target Chemicals. Environmental Science and Technology, 43(23), pp. 8888-8894. [https://doi.org/10.1021/es901877a DOI: 10.1021/es901877a]</ref><ref name="Lampert2015">Lampert, D.J., Thomas, C., and Reible, D.D., 2015. Internal and external transport significance for predicting contaminant uptake rates in passive samplers. Chemosphere, 119, pp. 910-916. [https://doi.org/10.1016/j.chemosphere.2014.08.063 DOI: 10.1016/j.chemosphere.2014.08.063] Free download available from: [https://www.academia.edu/download/44146586/chemosphere_2014.pdf Academia.edu]</ref><ref name="Apell2016b">Apell, J.N., Tcaciuc, A.P., and Gschwend, P.M., 2016. Understanding the rates of nonpolar organic chemical accumulation into passive samplers deployed in the environment: Guidance for passive sampler deployments. Integrated Environmental Assessment and Management, 12(3), pp. 486-492. [https://doi.org/10.1002/ieam.1697 DOI: 10.1002/ieam.1697]</ref>. This problem was recognized previously for passive samplers called [[Wikipedia: Semipermeable membrane devices | semipermeable membrane devices]] (SPMDs, e.g. polyethylene bags filled with triolein<ref name="Huckins2002">Huckins, J.N., Petty, J.D., Lebo, J.A., Almeida, F.V., Booij, K., Alvarez, D.A., Cranor, W.L., Clark, R.C., and Mogensen, B.B., 2002. Development of the Permeability/Performance Reference Compound Approach for In Situ Calibration of Semipermeable Membrane Devices. Environmental Science and Technology, 36(1), pp. 85-91. [https://doi.org/10.1021/es010991w DOI: 10.1021/es010991w]</ref>) that were deployed in surface waters. As a result, representative chemicals called performance reference compound (PRCs) were dosed inside the samplers before their deployment in the environment, and the PRCs' diffusive losses out of the SPMD could be used to quantify the fractional approach toward sampler-environmental surroundings equilibration<ref name="Booij2002">Booij, K., Smedes, F., and van Weerlee, E.M., 2002. Spiking of performance reference compounds in low density polyethylene and silicone passive water samplers. Chemosphere 46(8), pp.1157-1161. [https://doi.org/10.1016/S0045-6535(01)00200-4 DOI: 10.1016/S0045-6535(01)00200-4]</ref><ref name="Huckins2002"/>. A similar approach can be used for polymers inserted in sediment beds<ref name="Fernandez2009b"/><ref name="Apell2014"/>. Commonly, isotopically labeled forms of the compounds of interest such as deuterated or <sup>13</sup>C-labelled PAHs or PCBs are homogeneously impregnated into the polymers before their deployments. Upon insertion of the polymer into the sediment bed (or overlying waters or even air), the initially evenly distributed PRCs begin to diffuse out of the sampling polymer and into the surroundings (Figure 2).
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| − | | |
| − | Assuming the contaminants of interest undergo the same mass transfer restrictions limiting their rates of uptake into the polymer (e.g., diffusion through the sedimentary porous medium) that are also limiting transfers of the PRCs out of the polymer<ref name="Fernandez2009b"/><ref name="Apell2014"/>, then fractional losses of the PRCs during a particular deployment can be used to adjust the accumulated contaminant loads to what they would have been at equilibrium with their surroundings with Equation 4.
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| − | {|
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| − | |
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| − | |-
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| − | | || Equation 4.
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| − | | style="text-align:center;"| <big>'''''C(<sub>∞</sub>)<sub><small>polymer</small></sub> '''=''' C(<small>t</small>)<sub><small>polymer</small></sub> '''/''' f<sub><small>PRC lost</small></sub>'''''</big>
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| − | |-
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| − | | where:
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| − | |-
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| − | | || ''f<sub><small>PRC lost</small></sub>'' || is the fraction of the PRC lost to outward diffusion,
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| − | |-
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| − | | || ''C(<sub>∞</sub>)<sub><small>polymer</small></sub>'' || is the concentration of the contaminant in the polymer at equilibrium, and
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| − | |-
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| − | | || ''C(<small>t</small>)<sub><small>polymer</small></sub>'' || is the concentration of the contaminant in the polymer after deployment time, t.
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| − | |}
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| − | | |
| − | Since investigators are commonly interested in many chemicals at the same time, it is impractical to have a PRC for each contaminant of interest. Instead, a representative set of PRCs is used to characterize the rates of polymer-environment exchange as a function of the PRCs' properties (e.g., diffusivities, partition coefficients), the sediments characteristics (e.g., porosity), and the nature of the polymer used (e.g., film thickness, affinity for the chemicals)<ref name="Fernandez2009b"/><ref name="Lampert2015"/>. The resulting mass transfer model fit can then be used to estimate the fractional approaches to equilibrium for many other contaminants, whose diffusive and partitioning properties are also known. And these fractions can be used to adjust the target chemical concentrations that have accumulated from the sediment into the same polymeric sampler to find the equilibrated results<ref name="Apell2014"/>. Finally, these equilibrated concentrations can be used in Eq. 2 to estimate truly dissolved contaminant concentrations in the sediment's porewater.
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| − | ==Field Applications==
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| − | [[File: Gschwend1w2fig3.png | thumb |left| 450px | Figure 3. Passive sampler system made of polyethylene sheet loaded into an aluminum sheet metal frame, before (left), during (middle), and after (right) deployment in sediment.]]
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| − | Polymeric materials can be deployed in sediment in various ways<ref name="Burgess2017"/>. PDMS coatings can be incorporated into slotted silica rods called SPMEs (solid phase micro extraction devices), while thin sheets of polymers like LDPE or POM can be incorporated into sheet metal frames. In both cases, such hardware is used to insert the polymers into sediment beds (Figure 3).
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| − | | |
| − | Deployment of the assembled passive samplers can be accomplished via poles from a boat<ref name="Apell2014"/>, by divers<ref name="Apell2016"/>, or by attaching the samplers to a sampling platform lowered off a vessel<ref name="Fernandez2012">Fernandez, L.A., Lao, W., Maruya, K.A., White, C., Burgess, R.M., 2012. Passive Sampling to Measure Baseline Dissolved Persistent Organic Pollutant Concentrations in the Water Column of the Palos Verdes Shelf Superfund Site. Environmental Science and Technology, 46(21), pp. 11937-11947. [https://doi.org/10.1021/es302139y DOI: 10.1021/es302139y]</ref>. Typically, the method used depends on the water depth. Small buoys on short lines, sometimes with associated water-sampling polymeric materials in mesh bags (see right panel of Figure 3), are attached to the samplers to facilitate the sampler recoveries. After recovery, the samplers are wiped to remove any adhering sediment, biofilm, or precipitates and returned to the laboratory for PRC and target contaminant analyses. The resulting measurements of the accumulated target chemical concentrations can be adjusted using the observed PRC losses and publicly available software programs<ref name="Gschwend2014">Gschwend, P.M., Tcaciuc, A.P., and Apell, J.N., 2014. Guidance Document: Passive PE Sampling in Support of In Situ Remediation of Contaminated Sediments – Passive Sampler PRC Calculation Software User’s Guide, US Department of Defense, Environmental Security Technology Certification Program Project ER-200915. Available from: [https://www.serdp-estcp.org/Program-Areas/Environmental-Restoration/Contaminated-Sediments/Bioavailability/ER-200915 ESTCP].</ref><ref name="Thompson2015">Thompson, J.M., Hsieh, C.H. and Luthy, R.G., 2015. Modeling Uptake of Hydrophobic Organic Contaminants into Polyethylene Passive Samplers. Environmental Science and Technology, 49(4), pp. 2270-2277. [https://doi.org/10.1021/es504442s DOI: 10.1021/es504442s]</ref>.
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| − | Subsequently, since the passive sampling reveals the concentrations of contaminants in a sediment bed's porewater and the overlying bottom water<ref name="Booij2003"/>, the data can be used to estimate bed-to-water column diffusive fluxes of contaminants<ref name="Koelmans2010">Koelmans, A.A., Poot, A., De Lange, H.J., Velzeboer, I., Harmsen, J., and van Noort, P.C.M., 2010. Estimation of In Situ Sediment-to-Water Fluxes of Polycyclic Aromatic Hydrocarbons, Polychlorobiphenyls and Polybrominated Diphenylethers. Environmental Science and Technology, 44(8), pp. 3014-3020. [https://doi.org/10.1021/es903938z DOI: 10.1021/es903938z]</ref><ref name="Fernandez2012"/> and bioirrigation-affected fluxes<ref name="Apell2018">Apell, J.N., Shull, D.H., Hoyt, A.M., and Gschwend, P.M., 2018. Investigating the Effect of Bioirrigation on In Situ Porewater Concentrations and Fluxes of Polychlorinated Biphenyls Using Passive Samplers. Environmental Science and Technology, 52(8), pp. 4565-4573. [https://doi.org/10.1021/acs.est.7b05809 DOI: 10.1021/acs.est.7b05809]</ref>. The data are also useful for assessing the tendency of the contaminants to accumulate in benthic organisms<ref name="Vinturella2004">Vinturella, A.E., Burgess, R.M., Coull, B.A., Thompson, K.M., and Shine, J.P., 2004. Use of Passive Samplers to Mimic Uptake of Polycyclic Aromatic Hydrocarbons by Benthic Polychaetes. Environmental Science and Technology, 38(4), pp. 1154-1160. [https://doi.org/10.1021/es034706f DOI: 10.1021/es034706f]</ref><ref name="Yates2011">Yates, K., Pollard, P., Davies, I.M., Webster, L., and Moffat, C.F., 2011. Application of silicone rubber passive samplers to investigate the bioaccumulation of PAHs by Nereis virens from marine sediments. Environmental Pollution, 159(12), pp. 3351-3356. [https://doi.org/10.1016/j.envpol.2011.08.038 DOI: 10.1016/j.envpol.2011.08.038]</ref><ref name="Fernandez2015">Fernandez, L.A. and Gschwend, P.M., 2015. Predicting bioaccumulation of polycyclic aromatic hydrocarbons in soft-shelled clams (Mya arenaria) using field deployments of polyethylene passive samplers. Environmental Toxicology and Chemistry, 34(5), pp. 993-1000. [https://doi.org/10.1002/etc.2892 DOI: 10.1002/etc.2892]</ref>, and by extension into food webs that include such benthic species<ref name="vonStackelberg2017">von Stackelberg, K., Williams, M.A., Clough, J., and Johnson, M.S., 2017. Spatially explicit bioaccumulation modeling in aquatic environments: Results from 2 demonstration sites. Integrated Environmental Assessment and Management, 13(6), pp. 1023-1037. [https://doi.org/10.1002/ieam.1927 DOI: 10.1002/ieam.1927]</ref>. Furthermore, recent efforts have found that passive sampling observations can be used to infer ''in situ'' transformations of substances like nitro aromatic compounds<ref name="Belles2016">Belles, A., Alary, C., Criquet, J., and Billon, G., 2016. A new application of passive samplers as indicators of in-situ biodegradation processes. Chemosphere, 164, pp. 347-354. [https://doi.org/10.1016/j.chemosphere.2016.08.111 DOI: 10.1016/j.chemosphere.2016.08.111]</ref> and DDT<ref name="Tcaciuc2018">Tcaciuc, A.P., Borrelli, R., Zaninetta, L.M., and Gschwend, P.M., 2018. Passive sampling of DDT, DDE and DDD in sediments: accounting for degradation processes with reaction–diffusion modeling. Environmental Science: Processes and Impacts, 20(1), pp. 220-231. [https://doi.org/10.1039/C7EM00501F DOI: 10.1039/C7EM00501F] Open access article available from: [https://pubs.rsc.org/--/content/articlehtml/2018/em/c7em00501f Royal Society of Chemistry].</ref>.
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| | ==References== | | ==References== |
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| | ==See Also== | | ==See Also== |
| − | | + | [https://vimeo.com/1019489395 TISR<sup>®</sup> Explanation Video] |
| − | [https://www.serdp-estcp.org/Tools-and-Training/Tools/PRC-Correction-Calculator A PRC Correction Calculator for LDPE deployed in sediments] | |
