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==Downscaled High Resolution Datasets for Climate Change Projections==
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==Photoactivated Reductive Defluorination PFAS Destruction==  
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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.
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Photoactivated Reductive Defluorination (PRD) is a [[Perfluoroalkyl and Polyfluoroalkyl Substances (PFAS) | PFAS]] destruction technology predicated on [[Wikipedia: Ultraviolet | ultraviolet (UV)]] light-activated photochemical reactions. The destruction efficiency of this process is enhanced by the use of a [[Wikipedia: Surfactant | surfactant]] to confine PFAS molecules in self-assembled [[Wikipedia: Micelle | micelles]]. The photochemical reaction produces [[Wikipedia: Solvated electron | hydrated electrons]] from an electron donor that associates with the micelle. The hydrated electrons have sufficient energy to rapidly cleave fluorine-carbon and other molecular bonds of PFAS molecules due to the association of the electron donor with the micelle. Micelle-accelerated PRD is a highly efficient method to destroy PFAS in a wide variety of water matrices.
 
<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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* [[Climate Change Primer]]
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*[[Perfluoroalkyl and Polyfluoroalkyl Substances (PFAS)]]
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*[[PFAS Sources]]
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*[[PFAS Transport and Fate]]
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*[[PFAS Ex Situ Water Treatment]]
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*[[Supercritical Water Oxidation (SCWO)]]
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*[[PFAS Treatment by Electrical Discharge Plasma]]
  
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'''Contributor(s):''' [[Dr. Rao Kotamarthi]]
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'''Contributor(s):'''  
 +
*Dr. Suzanne Witt
 +
*Dr. Meng Wang
 +
*Dr. Denise Kay
  
 
'''Key Resource(s):'''
 
'''Key Resource(s):'''
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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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*Efficient Reductive Destruction of Perfluoroalkyl Substances under Self-Assembled Micelle Confinement<ref name="ChenEtAl2020">Chen, Z., Li, C., Gao, J., Dong, H., Chen, Y., Wu, B., Gu, C., 2020. Efficient Reductive Destruction of Perfluoroalkyl Substances under Self-Assembled Micelle Confinement. Environmental Science and Technology, 54(8), pp. 5178–5185. [https://doi.org/10.1021/acs.est.9b06599 doi: 10.1021/acs.est.9b06599]</ref>
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*Complete Defluorination of Perfluorinated Compounds by Hydrated Electrons Generated from 3-Indole-Acetic-Acid in Organomodified Montmorillonite<ref name="TianEtAl2016">Tian, H., Gao, J., Li, H., Boyd, S.A., Gu, C., 2016. Complete Defluorination of Perfluorinated Compounds by Hydrated Electrons Generated from 3-Indole-Acetic-Acid in Organomodified Montmorillonite. Scientific Reports, 6(1), Article 32949. [https://doi.org/10.1038/srep32949 doi: 10.1038/srep32949]&nbsp;&nbsp; [[Media: TianEtAl2016.pdf | Open Access Article]]</ref>
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*Application of Surfactant Modified Montmorillonite with Different Conformation for Photo-Treatment of Perfluorooctanoic Acid by Hydrated Electrons<ref name="ChenEtAl2019">Chen, Z., Tian, H., Li, H., Li, J. S., Hong, R., Sheng, F., Wang, C., Gu, C., 2019.  Application of Surfactant Modified Montmorillonite with Different Conformation for Photo-Treatment of Perfluorooctanoic Acid by Hydrated Electrons. Chemosphere, 235, pp. 1180–1188. [https://doi.org/10.1016/j.chemosphere.2019.07.032 doi: 10.1016/j.chemosphere.2019.07.032]</ref>
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*[https://serdp-estcp.mil/projects/details/c4e21fa2-c7e2-4699-83a9-3427dd484a1a ER21-7569: Photoactivated Reductive Defluorination PFAS Destruction]<ref name="WittEtAl2023">Kay, D., Witt, S., Wang, M., 2023. Photoactivated Reductive Defluorination PFAS Destruction: Final Report. ESTCP Project ER21-7569. [https://serdp-estcp.mil/projects/details/c4e21fa2-c7e2-4699-83a9-3427dd484a1a Project Website]&nbsp;&nbsp; [[Media: ER21-7569_Final_Report.pdf | Final Report.pdf]]</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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==Introduction==
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[[File:WittFig1.png | thumb |600px|Figure 1. Schematic of PRD mechanism<ref name="WittEtAl2023"/>]]
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The&nbsp;Photoactivated&nbsp;Reductive Defluorination (PRD) process is based on a patented chemical reaction that breaks fluorine-carbon bonds and disassembles PFAS molecules in a linear fashion beginning with the [[Wikipedia: Hydrophile | hydrophilic]] functional groups and proceeding through shorter molecules to complete mineralization. Figure 1 shows how PRD is facilitated by adding [[Wikipedia: Cetrimonium bromide | cetyltrimethylammonium bromide (CTAB)]] to form a surfactant micelle cage that traps PFAS. A non-toxic proprietary chemical is added to solution to associate with the micelle surface and produce hydrated electrons via stimulation with UV light. These highly reactive hydrated electrons have the energy required to cleave fluorine-carbon and other molecular bonds resulting in the final products of fluoride, water, and simple carbon molecules (e.g., formic acid and acetic acid). The methods, mechanisms, theory, and reactions described herein have been published in peer reviewed literature<ref name="ChenEtAl2020"/><ref name="TianEtAl2016"/><ref name="ChenEtAl2019"/><ref name="WittEtAl2023"/>.
  
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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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==Advantages and Disadvantages==
  
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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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===Advantages===
 +
In comparison to other reported PFAS destruction techniques, PRD offers several advantages:
 +
*Relative to UV/sodium sulfite and UV/sodium iodide systems, the fitted degradation rates in the micelle-accelerated PRD reaction system were ~18 and ~36 times higher, indicating the key role of the self-assembled micelle in creating a confined space for rapid PFAS destruction<ref name="ChenEtAl2020"/>. The negatively charged hydrated electron associated with the positively charged cetyltrimethylammonium ion (CTA<sup>+</sup>) forms the surfactant micelle to trap molecules with similar structures, selectively mineralizing compounds with both hydrophobic and hydrophilic groups (e.g., PFAS).
 +
*The PRD reaction does not require solid catalysts or electrodes, which can be expensive to acquire and difficult to regenerate or dispose.
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*The aqueous solution is not heated or pressurized, and the UV wavelength used does not cause direct water [[Wikipedia: Photodissociation | photolysis]], therefore the energy input to the system is more directly employed to destroy PFAS, resulting in greater energy efficiency.
 +
*Since the reaction is performed at ambient temperature and pressure, there are limited concerns regarding environmental health and safety or volatilization of PFAS compared to heated and pressurized systems.
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*Due to the reductive nature of the reaction, there is no formation of unwanted byproducts resulting from oxidative processes, such as [[Wikipedia: Perchlorate | perchlorate]]  generation during electrochemical oxidation<ref>Veciana, M., Bräunig, J., Farhat, A., Pype, M. L., Freguia, S., Carvalho, G., Keller, J., Ledezma, P., 2022. Electrochemical Oxidation Processes for PFAS Removal from Contaminated Water and Wastewater: Fundamentals, Gaps and Opportunities towards Practical Implementation. Journal of Hazardous Materials, 434, Article 128886. [https://doi.org/10.1016/j.jhazmat.2022.128886 doi: 10.1016/j.jhazmat.2022.128886]</ref><ref>Trojanowicz, M., Bojanowska-Czajka, A., Bartosiewicz, I., Kulisa, K., 2018. Advanced Oxidation/Reduction Processes Treatment for Aqueous Perfluorooctanoate (PFOA) and Perfluorooctanesulfonate (PFOS) – A Review of Recent Advances. Chemical Engineering Journal, 336, pp. 170–199. [https://doi.org/10.1016/j.cej.2017.10.153 doi: 10.1016/j.cej.2017.10.153]</ref><ref>Wanninayake, D.M., 2021. Comparison of Currently Available PFAS Remediation Technologies in Water: A Review. Journal of Environmental Management, 283, Article 111977. [https://doi.org/10.1016/j.jenvman.2021.111977 doi: 10.1016/j.jenvman.2021.111977]</ref>.
 +
*Aqueous fluoride ions are the primary end products of PRD, enabling real-time reaction monitoring with a fluoride [[Wikipedia: Ion-selective electrode | ion selective electrode (ISE)]], which is far less expensive and faster than relying on PFAS analytical data alone to monitor system performance.
  
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==Downscaling of Global Climate Models==
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===Disadvantages===
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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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*The CTAB additive is only partially consumed during the reaction, and although CTAB is not problematic when discharged to downstream treatment processes that incorporate aerobic digestors, CTAB can be toxic to surface waters and anaerobic digestors. Therefore, disposal options for treated solutions will need to be evaluated on a site-specific basis. Possible options include removal of CTAB from solution for reuse in subsequent PRD treatments, or implementation of an oxidation reaction to degrade CTAB.  
 +
*The PRD reaction rate decreases in water matrices with high levels of total dissolved solids (TDS). It is hypothesized that in high TDS solutions (e.g., ion exchange still bottoms with TDS of 200,000 ppm), the presence of ionic species inhibits the association of the electron donor with the micelle, thus decreasing the reaction rate.
 +
*The PRD reaction rate decreases in water matrices with very low UV transmissivity. Low UV transmissivity (i.e., < 1 %) prevents the penetration of UV light into the solution, such that the utilization efficiency of UV light decreases.  
  
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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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==State of the Art==
  
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==Methods for Downscaling==
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===Technical Performance===
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{| class="wikitable" style="float:right; margin-left:10px;text-align:center;"
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[[File:WittFig2.png | thumb |400px| Figure 2. Enspired Solutions<small><sup>TM</sup></small> commercial PRD PFAS destruction equipment, the PFASigator<small><sup>TM</sup></small>. Dimensions are 8 feet long by 4 feet wide by 9 feet tall.]]
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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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|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:
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{| class="wikitable mw-collapsible" style="float:left; margin-right:20px; text-align:center;"
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|+Table 1. Percent decreases from initial PFAS concentrations during benchtop testing of PRD treatment in different water matrices
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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. 
 
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* 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==
 
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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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|-
 
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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,</br>avg), Pr||NetCDF||grid: 30 arc sec to</br>10 arc min||month
 
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|-
 
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| 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
 
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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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|Sandstone||0.05 - 0.30||0.10 - 0.30||3×10<sup>-10</sup> - 6×10<sup>-6</sup>
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! Analytes
 +
!
 +
! GW
 +
! FF
 +
! AFFF<br>Rinsate
 +
! AFF<br>(diluted 10X)
 +
! IDW NF
 
|-
 
|-
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|Siltstone||—||0.21 - 0.41||1×10<sup>-11</sup> - 1.4×10<sup>-8</sup>
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| &Sigma; Total PFAS<small><sup>a</sup></small> (ND=0)
 +
| rowspan="9" style="background-color:white;" | <p style="writing-mode: vertical-rl">% Decrease<br>(Initial Concentration, &mu;g/L)</p>
 +
| 93%<br>(370) || 96%<br>(32,000) || 89%<br>(57,000) || 86 %<br>(770,000) || 84%<br>(82)
 
|-
 
|-
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|Basalt||0.05 - 0.50||—||2×10<sup>-11</sup> - 2×10<sup>-2</sup>
+
| &Sigma; Total PFAS (ND=MDL) || 93%<br>(400) || 86%<br>(32,000) || 90%<br>(59,000) || 71%<br>(770,000) || 88%<br>(110)
 +
|-  
 +
| &Sigma; Total PFAS (ND=RL) || 94%<br>(460) || 96%<br>(32,000) || 91%<br>(66,000) || 34%<br>(770,000) || 92%<br>(170)
 
|-
 
|-
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|Fractured Crystalline Rock||0.00 - 0.10||—||8×10<sup>-9</sup> - 3×10<sup>-4</sup>
+
| &Sigma; Highly Regulated PFAS<small><sup>b</sup></small> (ND=0) || >99%<br>(180) || >99%<br>(20,000) || 95%<br>(20,000) || 92%<br>(390,000) || 95%<br>(50)
 
|-
 
|-
−
|Weathered Granite||0.34 - 0.57||—||3.3×10<sup>-6</sup> - 5.2×10<sup>-5</sup>
+
| &Sigma; Highly Regulated PFAS (ND=MDL) || >99%<br>(180) || 98%<br>(20,000) || 95%<br>(20,000) || 88%<br>(390,000) || 95%<br> (52)
 
|-
 
|-
−
|Unfractured Crystalline Rock||0.00 - 0.05||—||3×10<sup>-14</sup> - 2×10<sup>-10</sup>
+
| &Sigma; Highly Regulated PFAS (ND=RL) || >99%<br>(190) || 93%<br>(20,000) || 95%<br>(20,000) || 79%<br>(390,000) || 95%<br>(55)
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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.
 
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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. 
 
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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.
 
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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.]]
 
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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]&nbsp;&nbsp; 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]&nbsp;&nbsp; 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.
 
−
 
 
−
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 &mu;g/kg animal wet weight) using a lipid-water [[Wikipedia: Partition coefficient | partition coefficient]], ''K<sub><small>lipid-water</small></sub>'', typically in units of (&mu;g/kg lipid)'''/'''(&mu;g/L water), and the porewater concentration of the chemical (''C<sub><small>porewater</small></sub>'', in &mu;g/L) with Equation 1.
 
−
{|
 
−
|
 
 
|-
 
|-
−
| || Equation 1.
+
| &Sigma; High Priority PFAS<small><sup>c</sup></small> (ND=0) || 91%<br>(180) || 98%<br>(20,000) || 85%<br>(20,000) || 82%<br>(400,000) || 94%<br>(53)
−
| 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>
 
 
|-
 
|-
−
| where:
+
| &Sigma; High Priority PFAS (ND=MDL) || 91%<br>(190) || 94%<br>(20,000) || 85%<br>(20,000) || 79%<br>(400,000) || 86%<br>(58)
 
|-
 
|-
−
| || ''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
+
| &Sigma; High Priority PFAS (ND=RL) || 92%<br>(200) || 87%<br>(20,000) || 86%<br>(21,000) || 70%<br>(400,000) || 87%<br>(65)
 
|-
 
|-
−
| || ''C<sub><small>porewater</small></sub>'' || is the freely dissolved contaminant concentration in the porewater surrounding the animal.
+
| Fluorine mass balance<small><sup>d</sup></small> || ||106% || 109% || 110% || 65% || 98%
−
|}
 
−
 
 
−
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>.
 
−
 
 
−
==Passive Samplers==
 
−
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]&nbsp;&nbsp;  [https://setac.onlinelibrary.wiley.com/doi/epdf/10.1002/etc.2864 Free access article.]&nbsp;&nbsp; [[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>'' (&mu;g/kg polymer), can be used to find the corresponding concentration in the porewater,  ''C<sub><small>porewater</small></sub>'' (&mu;g/L), using a polymer-water partition coefficient, ''K<sub><small>polymer-water</small></sub>'' ((&mu;g/kg polymer)'''/'''(&mu;g/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]&nbsp;&nbsp; [https://setac.onlinelibrary.wiley.com/doi/epdf/10.1002/ieam.1507 Free access article.]&nbsp;&nbsp; [[Media: Ghosh2014.pdf | Report.pdf]]</ref>, as shown in Equation 2.
 
−
{|
 
−
|
 
 
|-
 
|-
−
|&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;|| Equation&nbsp;2.
+
| Sorbed organic fluorine<small><sup>e</sup></small> || || 4% || 4% || 33% || N/A || 31%
−
| 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>
 
−
|}
 
−
 
 
−
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>'' (&mu;g/kg animal), would be found by combining Equations 1 and 2 to get Equation 3.
 
−
{|
 
−
|
 
 
|-
 
|-
−
|&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;|| Equation&nbsp;3.
+
| colspan="7" style="background-color:white; text-align:left" | <small>Notes:<br>GW = groundwater<br>GW FF = groundwater foam fractionate<br>AFFF rinsate = rinsate collected from fire system decontamination<br>AFFF (diluted 10x) = 3M Lightwater AFFF diluted 10x<br>IDW NF = investigation derived waste nanofiltrate<br>ND = non-detect<br>MDL = Method Detection Limit<br>RL = Reporting Limit<br><small><sup>a</sup></small>Total PFAS = 40 analytes + unidentified PFCA precursors<br><small><sup>b</sup></small>Highly regulated PFAS = PFNA, PFOA, PFOS, PFHxS, PFBS, HFPO-DA<br><small><sup>c</sup></small>High priority PFAS = PFNA, PFOA, PFHxA, PFBA, PFOS, PFHxS, PFBS, HFPO-DA<br><small><sup>d</sup></small>Ratio of the final to the initial organic fluorine plus inorganic fluoride concentrations<br><small><sup>e</sup></small>Percent of organic fluorine that sorbed to the reactor walls during treatment<br></small>
−
|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>
 
 
|}
 
|}
−
[[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>&nbsp;=&nbsp;C<sub>PE</sub>&nbsp;/&nbsp;C<sub>sediment</sub>''.]]
+
</br>
−
 
+
The&nbsp;PRD&nbsp;reaction&nbsp;has&nbsp;been validated at the bench scale for the destruction of PFAS in a variety of environmental samples from Department of Defense sites (Table 1). Enspired Solutions<small><sup>TM</sup></small> has designed and manufactured a fully automatic commercial-scale piece of equipment called PFASigator<small><sup>TM</sup></small>, specializing in PRD PFAS destruction (Figure 2). This equipment is modular and scalable, has a small footprint, and can be used alone or in series with existing water treatment trains. The PFASigator<small><sup>TM</sup></small> employs commercially available UV reactors and monitoring meters that have been used in the water industry for decades. The system has been tested on PRD efficiency operational parameters, and key metrics were proven to be consistent with benchtop studies.  
−
==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]&nbsp;&nbsp; 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).  
 
−
 
 
−
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.
 
−
{|
 
−
|
 
−
|-
 
−
| || Equation 4.
 
−
| style="text-align:center;"| <big>'''''C(<sub>&infin;</sub>)<sub><small>polymer</small></sub> '''=''' C(<small>t</small>)<sub><small>polymer</small></sub> '''/''' f<sub><small>PRC lost</small></sub>'''''</big>
 
−
|-
 
−
| where:
 
−
|-
 
−
| || ''f<sub><small>PRC lost</small></sub>'' || is the fraction of the PRC lost to outward diffusion,
 
−
|-
 
−
| || ''C(<sub>&infin;</sub>)<sub><small>polymer</small></sub>'' || is the concentration of the contaminant in the polymer at equilibrium, and
 
−
|-
 
−
| || ''C(<small>t</small>)<sub><small>polymer</small></sub>'' || is the concentration of the contaminant in the polymer after deployment time, t.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
 
−
|}
 
  
−
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.
+
Bench scale PRD tests were performed for the following samples collected from Department of Defense sites: groundwater (GW), groundwater foam fractionate (FF), firefighting truck rinsate ([[Wikipedia: Firefighting foam | AFFF]] Rinsate), 3M Lightwater AFFF, investigation derived waste nanofiltrate (IDW NF), [[Wikipedia: Ion exchange | ion exchange]] still bottom (IX SB), and Ansulite AFFF. The PRD treatment was more effective in low conductivity/TDS solutions. Generally, PRD reaction rates decrease for solutions with a TDS > 10,000 ppm, with an upper limit of 30,000 ppm. Ansulite AFFF and IX SB samples showed low destruction efficiencies during initial screening tests, which was primarily attributed to their high TDS concentrations. Benchtop testing data are shown in Table 1 for the remaining five sample matrices.
  
−
==Field Applications==
+
During treatment, PFOS and PFOA concentrations decreased 96% to >99% and 77% to 97%, respectively. For the PFAS with proposed drinking water Maximum Contaminant Levels (MCLs) recently established by the USEPA (PFNA, PFOA, PFOS, PFHxS, PFBS, and HFPO-DA), concentrations decreased >99% for GW, 93% for FF, 95% for AFFF Rinsate and IDW NF, and 79% for AFFF (diluted 10x) during the treatment time allotted. Meanwhile, the total PFAS concentrations, including all 40 known PFAS analytes and unidentified perfluorocarboxylic acid (PFCA) precursors, decreased from 34% to 96% following treatment. All of these concentration reduction values were calculated by using reporting limits (RL) as the concentrations for non-detects.  
−
[[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.]]
 
−
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).
 
  
−
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>.
+
Excellent fluorine/fluoride mass balance was achieved. There was nearly a 1:1 conversion of organic fluorine to free inorganic fluoride ion during treatment of GW, FF and AFFF Rinsate. The 3M Lightwater AFFF (diluted 10x) achieved only 65% fluorine mass balance, but this was likely due to high adsorption of PFAS to the reactor.
  
−
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]&nbsp;&nbsp; Open access article available from: [https://pubs.rsc.org/--/content/articlehtml/2018/em/c7em00501f Royal Society of Chemistry].</ref>.
+
===Application===
 +
Due to the first-order kinetics of PRD, destruction of PFAS is most energy efficient when paired with a pre-concentration technology, such as foam fractionation (FF), nanofiltration, reverse osmosis, or resin/carbon adsorption, that remove PFAS from water. Application of the PFASigator<small><sup>TM</sup></small> is therefore proposed as a part of a PFAS treatment train that includes a pre-concentration step.
  
−
<br clear="left" />
+
The first pilot study with the PFASigator<small><sup>TM</sup></small> was conducted in late 2023 at an industrial facility in Michigan with PFAS-impacted groundwater. The goal of the pilot study was to treat the groundwater to below the limits for regulatory discharge permits. For the pilot demonstration, the PFASigator<small><sup>TM</sup></small> was paired with an FF unit, which pre-concentrated the PFAS into a foamate that was pumped into the PFASigator<small><sup>TM</sup></small> for batch PFAS destruction. Residual PFAS remaining after the destruction batch was treated by looping back the PFASigator<small><sup>TM</sup></small> effluent to the FF system influent. During the one-month field pilot duration, site-specific discharge limits were met, and steady state operation between the FF unit and PFASigator<small><sup>TM</sup></small> was achieved such that the PFASigator<small><sup>TM</sup></small> destroyed the required concentrated PFAS mass and no off-site disposal of PFAS contaminated waste was required.
  
 
==References==
 
==References==
 
<references />
 
<references />
 +
 
==See Also==
 
==See Also==
−
 
−
[https://www.serdp-estcp.org/Tools-and-Training/Tools/PRC-Correction-Calculator A PRC Correction Calculator for LDPE deployed in sediments]
 

Revision as of 18:43, 8 May 2024

Photoactivated Reductive Defluorination PFAS Destruction

Photoactivated Reductive Defluorination (PRD) is a PFAS destruction technology predicated on ultraviolet (UV) light-activated photochemical reactions. The destruction efficiency of this process is enhanced by the use of a surfactant to confine PFAS molecules in self-assembled micelles. The photochemical reaction produces hydrated electrons from an electron donor that associates with the micelle. The hydrated electrons have sufficient energy to rapidly cleave fluorine-carbon and other molecular bonds of PFAS molecules due to the association of the electron donor with the micelle. Micelle-accelerated PRD is a highly efficient method to destroy PFAS in a wide variety of water matrices.

Related Article(s):

Contributor(s):

  • Dr. Suzanne Witt
  • Dr. Meng Wang
  • Dr. Denise Kay

Key Resource(s):

  • Efficient Reductive Destruction of Perfluoroalkyl Substances under Self-Assembled Micelle Confinement[1]
  • Complete Defluorination of Perfluorinated Compounds by Hydrated Electrons Generated from 3-Indole-Acetic-Acid in Organomodified Montmorillonite[2]
  • Application of Surfactant Modified Montmorillonite with Different Conformation for Photo-Treatment of Perfluorooctanoic Acid by Hydrated Electrons[3]
  • ER21-7569: Photoactivated Reductive Defluorination PFAS Destruction[4]

Introduction

Figure 1. Schematic of PRD mechanism[4]

The Photoactivated Reductive Defluorination (PRD) process is based on a patented chemical reaction that breaks fluorine-carbon bonds and disassembles PFAS molecules in a linear fashion beginning with the hydrophilic functional groups and proceeding through shorter molecules to complete mineralization. Figure 1 shows how PRD is facilitated by adding cetyltrimethylammonium bromide (CTAB) to form a surfactant micelle cage that traps PFAS. A non-toxic proprietary chemical is added to solution to associate with the micelle surface and produce hydrated electrons via stimulation with UV light. These highly reactive hydrated electrons have the energy required to cleave fluorine-carbon and other molecular bonds resulting in the final products of fluoride, water, and simple carbon molecules (e.g., formic acid and acetic acid). The methods, mechanisms, theory, and reactions described herein have been published in peer reviewed literature[1][2][3][4].

Advantages and Disadvantages

Advantages

In comparison to other reported PFAS destruction techniques, PRD offers several advantages:

  • Relative to UV/sodium sulfite and UV/sodium iodide systems, the fitted degradation rates in the micelle-accelerated PRD reaction system were ~18 and ~36 times higher, indicating the key role of the self-assembled micelle in creating a confined space for rapid PFAS destruction[1]. The negatively charged hydrated electron associated with the positively charged cetyltrimethylammonium ion (CTA+) forms the surfactant micelle to trap molecules with similar structures, selectively mineralizing compounds with both hydrophobic and hydrophilic groups (e.g., PFAS).
  • The PRD reaction does not require solid catalysts or electrodes, which can be expensive to acquire and difficult to regenerate or dispose.
  • The aqueous solution is not heated or pressurized, and the UV wavelength used does not cause direct water photolysis, therefore the energy input to the system is more directly employed to destroy PFAS, resulting in greater energy efficiency.
  • Since the reaction is performed at ambient temperature and pressure, there are limited concerns regarding environmental health and safety or volatilization of PFAS compared to heated and pressurized systems.
  • Due to the reductive nature of the reaction, there is no formation of unwanted byproducts resulting from oxidative processes, such as perchlorate generation during electrochemical oxidation[5][6][7].
  • Aqueous fluoride ions are the primary end products of PRD, enabling real-time reaction monitoring with a fluoride ion selective electrode (ISE), which is far less expensive and faster than relying on PFAS analytical data alone to monitor system performance.

Disadvantages

  • The CTAB additive is only partially consumed during the reaction, and although CTAB is not problematic when discharged to downstream treatment processes that incorporate aerobic digestors, CTAB can be toxic to surface waters and anaerobic digestors. Therefore, disposal options for treated solutions will need to be evaluated on a site-specific basis. Possible options include removal of CTAB from solution for reuse in subsequent PRD treatments, or implementation of an oxidation reaction to degrade CTAB.
  • The PRD reaction rate decreases in water matrices with high levels of total dissolved solids (TDS). It is hypothesized that in high TDS solutions (e.g., ion exchange still bottoms with TDS of 200,000 ppm), the presence of ionic species inhibits the association of the electron donor with the micelle, thus decreasing the reaction rate.
  • The PRD reaction rate decreases in water matrices with very low UV transmissivity. Low UV transmissivity (i.e., < 1 %) prevents the penetration of UV light into the solution, such that the utilization efficiency of UV light decreases.

State of the Art

Technical Performance

Figure 2. Enspired SolutionsTM commercial PRD PFAS destruction equipment, the PFASigatorTM. Dimensions are 8 feet long by 4 feet wide by 9 feet tall.
Table 1. Percent decreases from initial PFAS concentrations during benchtop testing of PRD treatment in different water matrices
Analytes GW FF AFFF
Rinsate 		AFF
(diluted 10X) 		IDW NF
Σ Total PFASa (ND=0)

% Decrease
(Initial Concentration, μg/L)

 93%
(370)		 96%
(32,000)		 89%
(57,000)		 86 %
(770,000)		 84%
(82)
Σ Total PFAS (ND=MDL) 93%
(400)		 86%
(32,000)		 90%
(59,000)		 71%
(770,000)		 88%
(110)
Σ Total PFAS (ND=RL) 94%
(460)		 96%
(32,000)		 91%
(66,000)		 34%
(770,000)		 92%
(170)
Σ Highly Regulated PFASb (ND=0) >99%
(180)		 >99%
(20,000)		 95%
(20,000)		 92%
(390,000)		 95%
(50)
Σ Highly Regulated PFAS (ND=MDL) >99%
(180)		 98%
(20,000)		 95%
(20,000)		 88%
(390,000)		 95%
(52)
Σ Highly Regulated PFAS (ND=RL) >99%
(190)		 93%
(20,000)		 95%
(20,000)		 79%
(390,000)		 95%
(55)
Σ High Priority PFASc (ND=0) 91%
(180)		 98%
(20,000)		 85%
(20,000)		 82%
(400,000)		 94%
(53)
Σ High Priority PFAS (ND=MDL) 91%
(190)		 94%
(20,000)		 85%
(20,000)		 79%
(400,000)		 86%
(58)
Σ High Priority PFAS (ND=RL) 92%
(200)		 87%
(20,000)		 86%
(21,000)		 70%
(400,000)		 87%
(65)
Fluorine mass balanced 106% 109% 110% 65% 98%
Sorbed organic fluorinee 4% 4% 33% N/A 31%
Notes:
GW = groundwater
GW FF = groundwater foam fractionate
AFFF rinsate = rinsate collected from fire system decontamination
AFFF (diluted 10x) = 3M Lightwater AFFF diluted 10x
IDW NF = investigation derived waste nanofiltrate
ND = non-detect
MDL = Method Detection Limit
RL = Reporting Limit
aTotal PFAS = 40 analytes + unidentified PFCA precursors
bHighly regulated PFAS = PFNA, PFOA, PFOS, PFHxS, PFBS, HFPO-DA
cHigh priority PFAS = PFNA, PFOA, PFHxA, PFBA, PFOS, PFHxS, PFBS, HFPO-DA
dRatio of the final to the initial organic fluorine plus inorganic fluoride concentrations
ePercent of organic fluorine that sorbed to the reactor walls during treatment


The PRD reaction has been validated at the bench scale for the destruction of PFAS in a variety of environmental samples from Department of Defense sites (Table 1). Enspired SolutionsTM has designed and manufactured a fully automatic commercial-scale piece of equipment called PFASigatorTM, specializing in PRD PFAS destruction (Figure 2). This equipment is modular and scalable, has a small footprint, and can be used alone or in series with existing water treatment trains. The PFASigatorTM employs commercially available UV reactors and monitoring meters that have been used in the water industry for decades. The system has been tested on PRD efficiency operational parameters, and key metrics were proven to be consistent with benchtop studies.

Bench scale PRD tests were performed for the following samples collected from Department of Defense sites: groundwater (GW), groundwater foam fractionate (FF), firefighting truck rinsate ( AFFF Rinsate), 3M Lightwater AFFF, investigation derived waste nanofiltrate (IDW NF), ion exchange still bottom (IX SB), and Ansulite AFFF. The PRD treatment was more effective in low conductivity/TDS solutions. Generally, PRD reaction rates decrease for solutions with a TDS > 10,000 ppm, with an upper limit of 30,000 ppm. Ansulite AFFF and IX SB samples showed low destruction efficiencies during initial screening tests, which was primarily attributed to their high TDS concentrations. Benchtop testing data are shown in Table 1 for the remaining five sample matrices.

During treatment, PFOS and PFOA concentrations decreased 96% to >99% and 77% to 97%, respectively. For the PFAS with proposed drinking water Maximum Contaminant Levels (MCLs) recently established by the USEPA (PFNA, PFOA, PFOS, PFHxS, PFBS, and HFPO-DA), concentrations decreased >99% for GW, 93% for FF, 95% for AFFF Rinsate and IDW NF, and 79% for AFFF (diluted 10x) during the treatment time allotted. Meanwhile, the total PFAS concentrations, including all 40 known PFAS analytes and unidentified perfluorocarboxylic acid (PFCA) precursors, decreased from 34% to 96% following treatment. All of these concentration reduction values were calculated by using reporting limits (RL) as the concentrations for non-detects.

Excellent fluorine/fluoride mass balance was achieved. There was nearly a 1:1 conversion of organic fluorine to free inorganic fluoride ion during treatment of GW, FF and AFFF Rinsate. The 3M Lightwater AFFF (diluted 10x) achieved only 65% fluorine mass balance, but this was likely due to high adsorption of PFAS to the reactor.

Application

Due to the first-order kinetics of PRD, destruction of PFAS is most energy efficient when paired with a pre-concentration technology, such as foam fractionation (FF), nanofiltration, reverse osmosis, or resin/carbon adsorption, that remove PFAS from water. Application of the PFASigatorTM is therefore proposed as a part of a PFAS treatment train that includes a pre-concentration step.

The first pilot study with the PFASigatorTM was conducted in late 2023 at an industrial facility in Michigan with PFAS-impacted groundwater. The goal of the pilot study was to treat the groundwater to below the limits for regulatory discharge permits. For the pilot demonstration, the PFASigatorTM was paired with an FF unit, which pre-concentrated the PFAS into a foamate that was pumped into the PFASigatorTM for batch PFAS destruction. Residual PFAS remaining after the destruction batch was treated by looping back the PFASigatorTM effluent to the FF system influent. During the one-month field pilot duration, site-specific discharge limits were met, and steady state operation between the FF unit and PFASigatorTM was achieved such that the PFASigatorTM destroyed the required concentrated PFAS mass and no off-site disposal of PFAS contaminated waste was required.

References

  1. ^ 1.0 1.1 1.2 Chen, Z., Li, C., Gao, J., Dong, H., Chen, Y., Wu, B., Gu, C., 2020. Efficient Reductive Destruction of Perfluoroalkyl Substances under Self-Assembled Micelle Confinement. Environmental Science and Technology, 54(8), pp. 5178–5185. doi: 10.1021/acs.est.9b06599
  2. ^ 2.0 2.1 Tian, H., Gao, J., Li, H., Boyd, S.A., Gu, C., 2016. Complete Defluorination of Perfluorinated Compounds by Hydrated Electrons Generated from 3-Indole-Acetic-Acid in Organomodified Montmorillonite. Scientific Reports, 6(1), Article 32949. doi: 10.1038/srep32949   Open Access Article
  3. ^ 3.0 3.1 Chen, Z., Tian, H., Li, H., Li, J. S., Hong, R., Sheng, F., Wang, C., Gu, C., 2019. Application of Surfactant Modified Montmorillonite with Different Conformation for Photo-Treatment of Perfluorooctanoic Acid by Hydrated Electrons. Chemosphere, 235, pp. 1180–1188. doi: 10.1016/j.chemosphere.2019.07.032
  4. ^ 4.0 4.1 4.2 Kay, D., Witt, S., Wang, M., 2023. Photoactivated Reductive Defluorination PFAS Destruction: Final Report. ESTCP Project ER21-7569. Project Website   Final Report.pdf
  5. ^ Veciana, M., Bräunig, J., Farhat, A., Pype, M. L., Freguia, S., Carvalho, G., Keller, J., Ledezma, P., 2022. Electrochemical Oxidation Processes for PFAS Removal from Contaminated Water and Wastewater: Fundamentals, Gaps and Opportunities towards Practical Implementation. Journal of Hazardous Materials, 434, Article 128886. doi: 10.1016/j.jhazmat.2022.128886
  6. ^ Trojanowicz, M., Bojanowska-Czajka, A., Bartosiewicz, I., Kulisa, K., 2018. Advanced Oxidation/Reduction Processes Treatment for Aqueous Perfluorooctanoate (PFOA) and Perfluorooctanesulfonate (PFOS) – A Review of Recent Advances. Chemical Engineering Journal, 336, pp. 170–199. doi: 10.1016/j.cej.2017.10.153
  7. ^ Wanninayake, D.M., 2021. Comparison of Currently Available PFAS Remediation Technologies in Water: A Review. Journal of Environmental Management, 283, Article 111977. doi: 10.1016/j.jenvman.2021.111977

See Also

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