Difference between revisions of "User:Jhurley/sandbox"

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==1,2,3-Trichloropropane (TCP)==
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==Thermal In Situ Sustainable Remediation to Enhance Biotic and Abiotic Reactions and Accelerate Remediation Timeframes==  
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[[Wikipedia: 1,2,3-Trichloropropane | 1,2,3-Trichloropropane (TCP)]] is a chlorinated volatile organic compound (CVOC) that has been used in chemical production processes, in agriculture, and as a solvent, resulting in point and non-point source contamination of soil and groundwater. TCP is mobile and highly persistent in soil and groundwater. TCP is not currently regulated at the national level in the United States, but [[Wikipedia: Maximum contaminant level | maximum contaminant levels (MCLs)]] have been developed by some states.  Current treatment methods for TCP are limited and can be cost prohibitive. However, some treatment approaches—particularly [[Chemical Reduction (In Situ - ISCR) | ''in situ'' chemical reduction (ISCR)]] with [[Wikipedia: In_situ_chemical_reduction#Zero_valent_metals_%28ZVMs%29 | zero valent zinc (ZVZ)]] and [[Bioremediation – Anaerobic | ''in situ'' bioremediation (ISB)]]—have recently been shown to have potential as practical remedies for TCP contamination of groundwater.
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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>
  
 
'''Related Article(s):'''
 
'''Related Article(s):'''
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*[[Bioremediation – Anaerobic | Anaerobic Bioremediation]]
 
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*[[Chemical Reduction (In Situ - ISCR) | ''In Situ'' Chemical Reduction (ISCR)]]
 
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*[[Chemical Oxidation (In Situ - ISCO) | ''In Situ'' Chemical Oxidation (ISCO)]]
 
  
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'''Contributor(s):'''
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*[[Thermal Remediation]]
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*[[Dr. Alexandra Salter-Blanc | Alexandra J. Salter-Blanc]]
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*[[Thermal Remediation - Electrical Resistance Heating | Electrical Resistance Heating]]
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*[[Dr. Paul Tratnyek | Paul G. Tratnyek]]
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*[[Thermal Conduction Heating (TCH)]]
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*John Merrill
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*[[Thermal Remediation - Combined Remedies | Combined Remedies]]
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*Alyssa Saito
 
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*Lea Kane
 
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*Eric Suchomel
 
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*[[Dr. Rula Deeb | Rula Deeb]]
 
  
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'''Key Resource(s):'''
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'''Contributors:''' Jonah Munholland, Julia Vidonish, and Craig Divine
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* 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>
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'''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]&nbsp; [[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]&nbsp;&nbsp; ISBN: 978-1-107-06605-2</ref>
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*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]&nbsp; [[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]&nbsp;&nbsp; ISBN: 978-1-108-47375-0</ref>
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*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]&nbsp; [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>.
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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]&nbsp;&nbsp; [[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]&nbsp;&nbsp; [[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.
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==Introduction==
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[[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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[[File: Kotamarthi2w2Fig1.jpg | thumb |left| 450px | Figure 1.  Typical processes and spatial scales of Regional scale Climate Models. The models may calculate circulation in the atmosphere, cloud processes, precipitation, and land-atmospheric and ocean-atmospheric processes on a limited portion of the Earth, with boundary conditions provided by a Global Climate Model.<ref name="Kotamarthi2016"/>]]
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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.
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==Methods for Downscaling==
 
  
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{| class="wikitable" style="float:right; margin-left:10px;text-align:center;"
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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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|+Table 1.  Physical and chemical properties of TCP
 
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|-
 
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!Property
 
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!Value
 
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|-
 
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| Chemical Abstracts Service (CAS) Number || 96-18-4
 
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|-
 
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| Physical Description</br>(at room temperature) || Colorless to straw-colored liquid
 
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|-
 
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| Molecular weight</br>(g/mol) || 147.43
 
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|-
 
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| Water solubility at 25°C</br>(mg/L)|| 1,750 (slightly soluble)
 
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|-
 
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| Melting point</br>(°C)|| -14.7
 
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|-
 
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| Boiling point</br>(°C) || 156.8
 
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|-
 
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| Vapor pressure at 25°C</br>(mm Hg) || 3.10 to 3.69
 
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|-
 
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| Density at 20°C (g/cm<sup>3</sup>) || 1.3889
 
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|-
 
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| Octanol-water partition coefficient</br>(log''K<sub>ow</sub>'') || 1.98 to 2.27</br>(temperature dependent)
 
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|-
 
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| Organic carbon-water partition coefficient</br>(log''K<sub>oc</sub>'') || 1.70 to 1.99</br>(temperature dependent)
 
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|-
 
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| Henry’s Law constant at 25°C</br>(atm-m<sup>3</sup>/mol) || 3.17x10<sup>-4</sup> to 3.43x10<sup>-4</sup>
 
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|}
 
  
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{| class="wikitable" style="float:right; margin-left:10px;text-align:center;"
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==Scientific Principles==
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|+Table 2. Advantages and limitations of TCP treatment technologies
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[[File: MunhollandFig2.png | thumb | 500 px | Figure 2: Effect of temperature on hydrolysis rates for VOCs]]
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|-
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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]]&nbsp; [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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! Technology
 
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! Advantages
 
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! Limitations
 
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|-
 
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| ZVZ
 
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| style="text-align:left;" |
 
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* Can degrade TCP at relatively high and low concentrations
 
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* Faster reaction rates than ZVI
 
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* Material is commercially available
 
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| style="text-align:left;" |
 
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* Higher cost than ZVI
 
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* Difficult to distribute in subsurface ''in situ'' applications
 
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|-
 
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| Groundwater</br>Extraction and</br>Treatment
 
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| style="text-align:left;" |
 
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* Can cost-effectively capture and treat larger, more dilute</br>groundwater plumes than ''in situ'' technologies
 
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* Well understood and widely applied technology
 
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| style="text-align:left;" |
 
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* Requires construction, operation and maintenance of</br>aboveground treatment infrastructure
 
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* Typical technologies (e.g. GAC) may be expensive due</br>to treatment inefficiencies
 
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|-
 
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| ZVI
 
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| style="text-align:left;" |
 
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* Can degrade TCP at relatively high and low concentrations
 
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* Lower cost than ZVZ
 
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* Material is commercially available
 
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| style="text-align:left;" |
 
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* Lower reactivity than ZVZ, therefore may require higher</br>ZVI volumes or thicker PRBs
 
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* Difficult to distribute in subsurface ''in situ'' applications
 
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|-
 
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| ISCO
 
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| style="text-align:left;" |
 
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* Can degrade TCP at relatively high and low concentrations
 
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* Strategies to distribute amendments ''in situ'' are well established
 
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* Material is commercially available
 
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| style="text-align:left;" |
 
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* Most effective oxidants (e.g., base-activated or heat-activated</br>persulfate) are complex to implement
 
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* Secondary water quality impacts (e.g., high pH, sulfate, </br>hexavalent chromium) may limit ability to implement
 
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|-
 
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| ''In Situ''</br>Bioremediation
 
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| style="text-align:left;" |
 
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* Can degrade TCP at moderate to high concentrations
 
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* Strategies to distribute amendments ''in situ'' are well established
 
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* Materials are commercially available and inexpensive
 
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| style="text-align:left;" |
 
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* Slower reaction rates than ZVZ or ISCO
 
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|}
 
  
 +
The effectiveness of TISR<sup>®</sup> in enhancing treatment performance has been demonstrated through several mechanisms, which are discussed below:
  
 +
*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]&nbsp; [[Media: Macbeth2012_ER-200719-FR.pdf | free download]]&nbsp; [https://www.youtube.com/watch?v=-EbwKbs8scE Project Summary Presentation]</ref>.
  
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There&nbsp;are&nbsp;two&nbsp;main&nbsp;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.
+
*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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It&nbsp;is&nbsp;important&nbsp;to&nbsp;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:
+
*[[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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* 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.
+
*[[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>.
−
* 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.
 
  
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==Uncertainty in Projections==
+
*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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{| 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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|-
 
−
!Model or</br>Dataset Name
 
−
!Model<br />Method
 
−
!Output<br />Variables
 
−
!Output<br />Format
 
−
!Spatial</br>Resolution
 
−
!Time</br>Resolution
 
−
|-
 
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| colspan="6" style="text-align: left; background-color:white;" |'''Statistical Downscaled Datasets'''
 
−
|-
 
−
| [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,</br>avg), Pr||NetCDF||grid: 30 arc sec to</br>10 arc min||month
 
−
|-
 
−
| Bias Corrected / Spatial</br>Disaggregation (BCSD)<ref name="Wood2002">Wood, A.W., Maurer, E.P., Kumar, A. and Lettenmaier, D.P., 2002. Long‐range experimental hydrologic forecasting for the eastern United States. Journal of Geophysical Research: Atmospheres, 107(D20), 4429, pp. ACL6 1-15. [https://doi.org/10.1029/2001JD000659 DOI:10.1029/2001JD000659]&nbsp;&nbsp; Free access article available from: [https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2001JD000659 American Geophysical Union]&nbsp;&nbsp; [[Media: Wood2002.pdf | Report.pdf ]]</ref>
 
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|Empirical Quantile</br>Mapping||Runoff,</br>Streamflow||NetCDF||grid: 7.5 arc min||day
 
−
|-
 
−
| [https://cida.usgs.gov/thredds/catalog.html?dataset=dcp Asynchronous Regional Regression</br>Model (ARRM v.1)]<ref name="Stoner2013">Stoner, A.M., Hayhoe, K., Yang, X., and Wuebbles, D.J., 2013. An Asynchronous Regional Regression Model for Statistical Downscaling of Daily Climate Variables. International Journal of Climatology, 33(11), pp. 2473-2494.  [https://doi.org/10.1002/joc.3603 DOI:10.1002/joc.3603]</ref>
 
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|Parameterized</br>Quantile Mapping||T(min, max), Pr||NetCDF||stations plus</br>grid: 7.5 arc min||day
 
−
|-
 
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| [https://sdsm.org.uk/ Statistical Downscaling Model (SDSM)]<ref name="Wilby2013">Wilby, R.L., and Dawson, C.W., 2013. The Statistical DownScaling Model: insights from one decade of application. International Journal of Climatology, 33(7), pp. 1707-1719. [https://doi.org/10.1002/joc.3544 DOI: 10.1002/joc.3544]</ref>
 
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|Weather Generator||T(min, max), Pr||PC Code||stations||day
 
−
|-
 
−
| [https://climate.northwestknowledge.net/MACA/ Multivariate Adaptive</br>Constructed Analogs (MACA)]<ref name="Hidalgo2008">Hidalgo, H.G., Dettinger, M.D. and Cayan, D.R., 2008. Downscaling with Constructed Analogues: Daily Precipitation and Temperature Fields Over the United States. California Energy Commission PIER Final Project, Report CEC-500-2007-123. [[Media: Hidalgo2008.PDF | Report.pdf]]</ref>
 
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|Constructed Analogues||10 Variables||NetCDF||grid: 2.5 arc min||day
 
−
|-
 
−
| [http://loca.ucsd.edu/ Localized Constructed Analogs (LOCA)]<ref name="Pierce2013">Pierce, D.W., Cayan, D.R. and Thrasher, B.L., 2014. Statistical Downscaling Using Localized Constructed Analogs (LOCA). Journal of Hydrometeorology, 15(6), pp. 2558-2585. [https://doi.org/10.1175/JHM-D-14-0082.1 DOI: 10.1175/JHM-D-14-0082.1]&nbsp;&nbsp; Free access article available from: [https://journals.ametsoc.org/view/journals/hydr/15/6/jhm-d-14-0082_1.xml American Meteorological Society].&nbsp;&nbsp; [[Media: Pierce2014.pdf | Report.pdf]]</ref>
 
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|Constructed Analogues||T(min, max), Pr||NetCDF||grid: 3.75 arc min||day
 
−
|-
 
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| [https://www.nccs.nasa.gov/services/data-collections/land-based-products/nex-dcp30 NASA Earth Exchange Downscaled</br>Climate Projections (NEX-DCP30)]<ref name="Wood2002"/>
 
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|Bias Correction /</br>Spatial Disaggregation||T(min, max), Pr||NetCDF||grid: 30 arc sec||month
 
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|-
 
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| colspan="6" style="text-align: left; background-color:white;" |'''Dynamical Downscaled Datasets'''
 
−
|-
 
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| [http://www.narccap.ucar.edu/index.html North American Regional Climate</br>Change Assessment Program (NARCCAP)]<ref name="Mearns2009">Mearns, L.O., Gutowski, W., Jones, R., Leung, R., McGinnis, S., Nunes, A. and Qian, Y., 2009. A Regional Climate Change Assessment Program for North America. Eos, Transactions, American Geophysical Union, 90(36), p.311.  [https://doi.org/10.1029/2009EO360002 DOI: 10.1029/2009EO360002]&nbsp;&nbsp; Free access article from: [https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2009EO360002 American Geophysical Union]&nbsp;&nbsp; [[Media: Mearns2009.pdf  | Report.pdf]]</ref>
 
−
|Multiple Models||49 Variables||NetCDF||grid: 30 arc min||3 hours
 
−
|-
 
−
| [https://cordex.org/about/ Coordinated Regional Climate</br>Downscaling Experiment (CORDEX)]<ref name="Giorgi2009">Giorgi, F., Jones, C., and Asrar, G.R., 2009. Addressing climate information needs at the regional level: the CORDEX framework. World Meteorological Organization (WMO) Bulletin, 58(3), pp. 175-183. Free access article from: [https://public.wmo.int/en/bulletin/addressing-climate-information-needs-regional-level-cordex-framework World Meteorological Organization]&nbsp;&nbsp; [[Media: Giorgi2009.pdf | Report.pdf]]</ref>
 
−
|Multiple Models||66 Variables||NetCDF||grid: 30 arc min||3 hours
 
−
|-
 
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| [https://esrl.noaa.gov/gsd/wrfportal/ Strategic Environmental Research and</br>Development Program (SERDP)]<ref name="Wang2015">Wang, J., and Kotamarthi, V.R., 2015. High‐resolution dynamically downscaled projections of precipitation in the mid and late 21st century over North America. Earth's Future, 3(7), pp. 268-288.  [https://doi.org/10.1002/2015EF000304 DOI: 10.1002/2015EF000304]&nbsp;&nbsp; Free access article from: [https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2015EF000304 American Geophysical Union]&nbsp;&nbsp; [[Media: Wang2015.pdf | Report.pdf]]</ref>
 
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|Weather Research and</br>Forecasting (WRF v3.3)||80+ Variables||NetCDF||grid: 6.5 arc min||3 hours
 
−
|}
 
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A&nbsp;primary&nbsp;cause&nbsp;of&nbsp;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&nbsp;uncertainties&nbsp;in&nbsp;climate&nbsp;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.  
+
These various mechanisms collectively contribute to the improved performance of other active treatments when temperature is moderately increased by utilization of TISR®.
  
−
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.
+
==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.  
  
−
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.  
+
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.
−
<br clear="left" />
+
 
 +
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.  
 +
 
 +
==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"/>
  
 
==References==
 
==References==
 
<references />
 
<references />
 +
 
==See Also==
 
==See Also==
−
 
+
[https://vimeo.com/1019489395 TISR<sup>®</sup> Explanation Video]
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[https://serdp-estcp.org/Program-Areas/Resource-Conservation-and-Resiliency/Infrastructure-Resiliency/Vulnerability-and-Impact-Assessment/RC-2242/(language)/eng-US Climate Change Impacts to Department of Defense Installations, SERDP Project RC-2242]
 

Latest revision as of 19:44, 13 October 2026

Thermal In Situ Sustainable Remediation to Enhance Biotic and Abiotic Reactions and Accelerate Remediation Timeframes

Thermal In Situ Sustainable Remediation (TISR®; 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® 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.

Related Article(s):

Contributors: Jonah Munholland, Julia Vidonish, and Craig Divine

Key Resources:

  • Thermal In Situ Sustainable Remediation to Enhance Biotic and Abiotic Reactions and Accelerate Remediation Timeframes, Final Report. ESTCP Project ER20-5028[1].
  • Advances in Low-Temperature Thermal Remediation. Munholland et al., 2024[2].
  • A Design Tool for Solar Thermal Remediation Using Borehole Heat Exchangers. Ornelles, Falta, and Divine, 2023[3].
  • Low-Temperature Thermal Remediation: Gaining Traction as a Green Remedial Alternative. Horst, et al., 2018[4].

Introduction

File:MunhollandFig1.png
Figure 1: Components and layout of a TISR® system

TISR® utilizes 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® ensures a more robust and constant heat supply, enabling customization of the system to address specific site challenges and facilitate efficient heat transfer.

The heating of the subsurface through TISR® 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[2]. This versatility allows TISR® to integrate with conventional remediation technologies, enhancing their performance.

One of the key advantages of TISR® 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® 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.

Scientific Principles

File:MunhollandFig2.png
Figure 2: Effect of temperature on hydrolysis rates for VOCs

During the TISR® 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[5][4]. TISR® systems are typically designed to enhance the biological activity and performance of other active technologies, such as air sparge/ soil vapor extraction (AS/SVE) or 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® systems can be designed to reach a final groundwater temperature of up to 60°C.

The effectiveness of TISR® in enhancing treatment performance has been demonstrated through several mechanisms, which are discussed below:

  • Primary dechlorinating bacteria, such as Dehalococcoides and Dehalogenimonas, are known to be mesophilic. Therefore, increasing the temperature can significantly enhance microbial activity and biodegradation by multiple orders of magnitude[6].
  • 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 Arrhenius equation.
  • Rates of other processes that often limit the pace of remediation, such as dissolution, desorption, and back diffusion, are positively correlated with temperature. This results in an enhanced bioavailability of contaminant mass that may otherwise be inaccessible.

These various mechanisms collectively contribute to the improved performance of other active treatments when temperature is moderately increased by utilization of TISR®.

Implementation and Practical Considerations

File:MunhollandFig3.png
Figure 3: Transformation of 1,1-DCE and VC following installation of a TISR® system

TISR® 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 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 vinyl chloride (VC) (a biodegradation product) to very low levels.

The TISR® 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.

When implementing TISR®, 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® system and target temperatures may be designed to enhance specific remedial mechanisms (e.g. hydrolysis over bioremediation). Recently Ornelles et al.[3] developed an easy to use Excel-based model that can be used to optimize TISR® 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.

Summary

TISR® 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® is particularly effective in challenging hydrogeological conditions and can be optimized to suit specific treatment mechanisms. By shortening the active remediation timeframe, TISR® reduces lifecycle costs and minimizes the carbon footprint associated with the remediation process.

References

  1. ^ 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. Project Website  Final Report.pdf
  2. ^ 2.0 2.1 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. doi: 10.1007/978-3-031-34447-3_18  Open Access Chapter
  3. ^ 3.0 3.1 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. doi: 10.1111/gwat.13265  Free download from NGWA
  4. ^ 4.0 4.1 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. doi: 10.1111/gwmr.12295
  5. ^ 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. free report  free download from clu-in.org
  6. ^ 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. Environmental Security Technology Certification Program (ESTCP) Project ER-200719 webpage  free download  Project Summary Presentation
  7. ^ 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. doi: 10.1111/j.1745-6592.2012.01407.x
  8. ^ 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. doi: 10.1016/j.chemosphere.2011.10.004

See Also

TISR® Explanation Video