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==Downscaled High Resolution Datasets for Climate Change Projections==
+
==Photoactivated Reductive Defluorination PFAS Destruction==  
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.
+
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):'''
* [[Climate Change Primer]]
+
*[[Perfluoroalkyl and Polyfluoroalkyl Substances (PFAS)]]
 +
*[[PFAS Sources]]
 +
*[[PFAS Transport and Fate]]
 +
*[[PFAS Ex Situ Water Treatment]]
 +
*[[Supercritical Water Oxidation (SCWO)]]
 +
*[[PFAS Treatment by Electrical Discharge Plasma]]
  
'''Contributor(s):''' [[Dr. Rao Kotamarthi]]
+
'''Contributor(s):'''  
 +
*Dr. Suzanne Witt
 +
*Dr. Meng Wang
 +
*Dr. Denise Kay
  
 
'''Key Resource(s):'''
 
'''Key Resource(s):'''
* 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>
+
*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>
 +
*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>
 +
*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>
 +
*[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>
  
* 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>
+
==Introduction==
 +
[[File:WittFig1.png | thumb |600px|Figure 1. Schematic of PRD mechanism<ref name="WittEtAl2023"/>]]
 +
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"/>.
  
* 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>
+
==Advantages and Disadvantages==
  
* 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>
+
===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.
 +
*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.
 +
*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.
  
==Downscaling of Global Climate Models==
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===Disadvantages===
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.  
+
*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.  
  
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.
+
==State of the Art==
  
[[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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===Technical Performance===
==Methods for Downscaling==
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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.]]
  
{| class="wikitable" style="float:right; margin-left:10px;text-align:center;"
+
{| class="wikitable mw-collapsible" style="float:left; margin-right:20px; text-align:center;"
|+Table 1. Physical and chemical properties of TCP
+
|+Table 1. Percent decreases from initial PFAS concentrations during benchtop testing of PRD treatment in different water matrices
 
|-
 
|-
!Property
+
! Analytes
!Value
+
!
 +
! GW
 +
! FF
 +
! AFFF<br>Rinsate
 +
! AFF<br>(diluted 10X)
 +
! IDW NF
 
|-
 
|-
| Chemical Abstracts Service (CAS) Number || 96-18-4
+
| &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)
 
|-
 
|-
| Physical Description</br>(at room temperature) || Colorless to straw-colored liquid
+
| &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)
 
|-
 
|-
| Molecular weight</br>(g/mol) || 147.43
+
| &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)
 
|-
 
|-
| Water solubility at 25°C</br>(mg/L)|| 1,750 (slightly soluble)
+
| &Sigma; Highly Regulated PFAS (ND=MDL) || >99%<br>(180) || 98%<br>(20,000) || 95%<br>(20,000) || 88%<br>(390,000) || 95%<br> (52)
 
|-
 
|-
| Melting point</br>(°C)|| -14.7
+
| &Sigma; Highly Regulated PFAS (ND=RL) || >99%<br>(190) || 93%<br>(20,000) || 95%<br>(20,000) || 79%<br>(390,000) || 95%<br>(55)
 
|-
 
|-
| Boiling point</br>(°C) || 156.8
+
| &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)
 
|-
 
|-
| Vapor pressure at 25°C</br>(mm Hg) || 3.10 to 3.69
+
| &Sigma; High Priority PFAS (ND=MDL) || 91%<br>(190) || 94%<br>(20,000) || 85%<br>(20,000) || 79%<br>(400,000) || 86%<br>(58)
 
|-
 
|-
| Density at 20°C (g/cm<sup>3</sup>) || 1.3889
+
| &Sigma; High Priority PFAS (ND=RL) || 92%<br>(200) || 87%<br>(20,000) || 86%<br>(21,000) || 70%<br>(400,000) || 87%<br>(65)
 
|-
 
|-
| Octanol-water partition coefficient</br>(log''K<sub>ow</sub>'') || 1.98 to 2.27</br>(temperature dependent)
+
| Fluorine mass balance<small><sup>d</sup></small> || ||106% || 109% || 110% || 65% || 98%
 
|-
 
|-
| Organic carbon-water partition coefficient</br>(log''K<sub>oc</sub>'') || 1.70 to 1.99</br>(temperature dependent)
+
| Sorbed organic fluorine<small><sup>e</sup></small> || || 4% || 4% || 33% || N/A || 31%
 
|-
 
|-
| 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>
+
| 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>
 
|}
 
|}
 +
</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.
  
{| class="wikitable" style="float:right; margin-left:10px;text-align:center;"
+
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.
|+Table 2. Advantages and limitations of TCP treatment technologies
 
|-
 
! Technology
 
! Advantages
 
! Limitations
 
|-
 
| ZVZ
 
| style="text-align:left;"
 
* Can degrade TCP at relatively high and low concentrations
 
* Faster reaction rates than ZVI
 
* Material is commercially available
 
| style="text-align:left;"
 
* Higher cost than ZVI
 
* Difficult to distribute in subsurface ''in situ'' applications
 
|-
 
| Groundwater</br>Extraction and</br>Treatment
 
| style="text-align:left;"
 
* Can cost-effectively capture and treat larger, more dilute groundwater plumes than ''in situ'' technologies
 
* Well understood and widely applied technology
 
| style="text-align:left;"
 
* Requires construction, operation and maintenance of aboveground treatment infrastructure
 
* Typical technologies (e.g. GAC) may be expensive due to treatment inefficiencies
 
|-
 
| ZVI
 
| style="text-align:left;"
 
* Can degrade TCP at relatively high and low concentrations
 
* Lower cost than ZVZ
 
* Material is commercially available
 
| style="text-align:left;"
 
* Lower reactivity than ZVZ, therefore may require higher ZVI volumes or thicker PRBs
 
* Difficult to distribute in subsurface ''in situ'' applications
 
|-
 
| ISCO
 
| style="text-align:left;"
 
* Can degrade TCP at relatively high and low concentrations
 
* Strategies to distribute amendments ''in situ'' are well established
 
* Material is commercially available
 
| style="text-align:left;"
 
* Most effective oxidants (e.g., base-activated or heat-activated persulfate) are complex to implement
 
* Secondary water quality impacts (e.g., high pH, sulfate, hexavalent chromium) may limit ability to implement
 
|-
 
| ''In Situ''</br>Bioremediation
 
| style="text-align:left;"
 
* Can degrade TCP at moderate to high concentrations
 
* Strategies to distribute amendments ''in situ'' are well established
 
* Materials are commercially available and inexpensive
 
| style="text-align:left;"
 
* Slower reaction rates than ZVZ or ISCO
 
|}
 
  
 +
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.
  
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.
+
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.
  
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:
+
===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.
  
* 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. 
+
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.
* 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.
 
 
 
==Uncertainty in Projections==
 
{| class="wikitable" style="float:right; margin-left:10px;text-align:center;"
 
|+Table 2.  Downscaling model characteristics and output<ref name="Kotamarthi2016"/>
 
|-
 
!Model or</br>Dataset Name
 
!Model<br />Method
 
!Output<br />Variables
 
!Output<br />Format
 
!Spatial</br>Resolution
 
!Time</br>Resolution
 
|-
 
| 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>
 
|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>
 
|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>
 
|Parameterized</br>Quantile Mapping||T(min, max), Pr||NetCDF||stations plus</br>grid: 7.5 arc min||day
 
|-
 
| [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>
 
|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>
 
|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>
 
|Constructed Analogues||T(min, max), Pr||NetCDF||grid: 3.75 arc min||day
 
|-
 
| [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"/>
 
|Bias Correction /</br>Spatial Disaggregation||T(min, max), Pr||NetCDF||grid: 30 arc sec||month
 
|-
 
| colspan="6" style="text-align: left; background-color:white;" |'''Dynamical Downscaled Datasets'''
 
|-
 
| [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
 
|-
 
| [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>
 
|Weather Research and</br>Forecasting (WRF v3.3)||80+ Variables||NetCDF||grid: 6.5 arc min||3 hours
 
|}
 
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]]).
 
 
 
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.
 
 
 
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. 
 
 
 
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.
 
<br clear="left" />
 
  
 
==References==
 
==References==
 
<references />
 
<references />
 +
 
==See Also==
 
==See Also==
 
[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]
 

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