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==Downscaled High Resolution Datasets for Climate Change Projections==
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==Munitions Constituents – Sample Extraction and Analytical Techniques==  
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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Munitions Constituents, including [[Wikipedia: Insensitive munition | insensitive munitions]] IM), are a broad category of compounds and, in areas where manufactured or used, can be found in a variety of environmental matrices (waters, soil, and tissues). This presents an analytical challenge when a variety of these munitions are to be quantified. This article discusses sample extraction methods for each typical sample matrix (high level water, low level water, soil and tissue) as well as the accompanying [[Wikipedia: High-performance liquid chromatography | HPLC]]-UV analytical method for 27 compounds of interest (legacy munitions, insensitive munitions, and surrogates).  
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<div style="float:right;margin:0 0 2em 2em;">__TOC__</div>
 
<div style="float:right;margin:0 0 2em 2em;">__TOC__</div>
  
 
'''Related Article(s):'''
 
'''Related Article(s):'''
* [[Climate Change Primer]]
 
  
'''Contributor(s):''' [[Dr. Rao Kotamarthi]]
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*[[Munitions Constituents]]
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 +
'''Contributor(s):'''  
 +
 
 +
*Dr. Austin Scircle
  
 
'''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>
 
  
* 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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*[https://www.epa.gov/sites/default/files/2015-07/documents/epa-8330b.pdf USEPA Method 8330B]<ref name= "8330B">United States Environmental Protection Agency (USEPA), 2006. EPA Method 8330B (SW-846) Nitroaromatics, Nitramines, and Nitrate Esters by High Performance Liquid Chromatography (HPLC), Revision 2. [https://www.epa.gov/esam/epa-method-8330b-sw-846-nitroaromatics-nitramines-and-nitrate-esters-high-performance-liquid USEPA Website]&nbsp; &nbsp;[[Media: epa-8330b.pdf | Method 8330B.pdf]]</ref>
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*Methods for simultaneous quantification of legacy and insensitive munition (IM) constituents in aqueous, soil/sediment, and tissue matrices<ref name="CrouchEtAl2020">Crouch, R.A., Smith, J.C., Stromer, B.S., Hubley, C.T., Beal, S., Lotufo, G.R., Butler, A.D., Wynter, M.T., Russell, A.L., Coleman, J.G., Wayne, K.M., Clausen, J.L., Bednar, A.J., 2020. Methods for simultaneous determination of legacy and insensitive munition (IM) constituents in aqueous, soil/sediment, and tissue matrices. Talanta, 217, Article 121008. [https://doi.org/10.1016/j.talanta.2020.121008 doi: 10.1016/j.talanta.2020.121008]&nbsp; &nbsp;[[Media: CrouchEtAl2020.pdf | Open Access Manuscript.pdf]]</ref>
  
* 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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==Introduction==
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[[File: ScircleFig1.png | thumb | 400px | Figure 1. Primary Method labeled chromatograms]]
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[[File: ScircleFig2.png | thumb | 400px | Figure 2. Secondary Method labeled chromatograms]]
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The primary intention of the analytical methods presented here is to support the monitoring of legacy and insensitive munitions contamination on test and training ranges, however legacy and insensitive munitions often accompany each other at demilitarization facilities, manufacturing facilities, and other environmental sites. Energetic materials typically appear on ranges as small, solid particulates and due to their varying functional groups and polarities, can partition in various environmental compartments<ref>Walsh, M.R., Temple, T., Bigl, M.F., Tshabalala, S.F., Mai, N. and Ladyman, M., 2017. Investigation of Energetic Particle Distribution from High‐Order Detonations of Munitions. Propellants, Explosives, Pyrotechnics, 42(8), pp. 932-941. [https://doi.org/10.1002/prep.201700089 doi: 10.1002/prep.201700089]</ref>. To ensure that contaminants are monitored and controlled at these sites and to sustainably manage them a variety of sample matrices (surface or groundwater, process waters, soil, and tissues) must be considered. (Process water refers to water used during industrial manufacturing or processing of legacy and insensitive munitions.) Furthermore, additional analytes must be added to existing methodologies as the usage of IM compounds changes and as new degradation compounds are identified.  Of note, relatively new IM formulations containing NTO, DNAN, and NQ are seeing use in [[Wikipedia: IMX-101 | IMX-101]], IMX-104, Pax-21 and Pax-41 (Table 1)<ref>Mainiero, C. 2015. Picatinny Employees Recognized for Insensitive Munitions. U.S. Army, Picatinny Arsenal Public Affairs.  [https://www.army.mil/article/148873/picatinny_employees_recognized_for_insensitive_munitions Open Access Press Release]</ref><ref>Frem, D., 2022. A Review on IMX-101 and IMX-104 Melt-Cast Explosives: Insensitive Formulations for the Next-Generation Munition Systems. Propellants, Explosives, Pyrotechnics, 48(1), e202100312. [https://doi.org/10.1002/prep.202100312 doi: 10.1002/prep.202100312]</ref>.
  
* 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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Sampling procedures for legacy and insensitive munitions are identical and utilize multi-increment sampling procedures found in USEPA Method 8330B Appendix A<ref name= "8330B"/>. Sample hold times, subsampling and quality control requirements are also unchanged. The key differences lie in the extraction methods and instrumental methods. Briefly, legacy munitions analysis of low concentration waters uses a single cartridge reverse phase [[Wikipedia: Solid-phase extraction | SPE]] procedure, and [[Wikipedia: Acetonitrile | acetonitrile]] (ACN) is used for both extraction and [[Wikipedia: Elution | elution]] for aqueous and solid samples<ref name= "8330B"/><ref>United States Environmental Protection Agency (USEPA), 2007. EPA Method 3535A (SW-846) Solid-Phase Extraction (SPE), Revision 1. [https://www.epa.gov/esam/epa-method-3535a-sw-846-solid-phase-extraction-spe USEPA Website]&nbsp; &nbsp;[[Media: epa-3535a.pdf | Method 3535A.pdf]]</ref>. An [[Wikipedia: High-performance_liquid_chromatography#Isocratic_and_gradient_elution | isocratic]] separation via reversed-phase C-18 column with 50:50 methanol:water mobile phase or a C-8 column with 15:85 isopropanol:water mobile phase is used to separate legacy munitions<ref name= "8330B"/>. While these procedures are sufficient for analysis of legacy munitions, alternative solvents, additional SPE cartridges, and a gradient elution are all required for the combined analysis of legacy and insensitive munitions.   
  
==Downscaling of Global Climate Models==
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Previously, analysis of legacy and insensitive munitions required multiple analytical techniques, however the methods presented here combine the two munitions categories resulting in an HPLC-UV method and accompanying extraction methods for a variety of common sample matrices. A secondary HPLC-UV method and a HPLC-MS method were also developed as confirmatory methods. The methods discussed in this article were validated extensively by single-blind round robin testing and subsequent statistical treatment as part of ESTCP [https://serdp-estcp.mil/projects/details/d05c1982-bbfa-42f8-811d-51b540d7ebda ER19-5078]. Wherever possible, the quality control criteria in the Department of Defense Quality Systems Manual for Environmental Laboratories were adhered to<ref>US Department of Defense and US Department of Energy, 2021. Consolidated Quality Systems Manual (QSM) for Environmental Laboratories, Version 5.4. 387 pages. [https://www.denix.osd.mil/edqw/denix-files/sites/43/2021/10/QSM-Version-5.4-FINAL.pdf Free Download]&nbsp; &nbsp;[[Media: QSM-Version-5.4.pdf | QSM Version 5.4.pdf]]</ref>. Analytes included in these methods are found in Table 1.
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.  
 
  
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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The chromatograms produced by the primary and secondary HPLC-UV methods are shown in Figure 1 and Figure 2, respectively. Chromatograms for each detector wavelength used are shown (315, 254, and 210 nm).
  
[[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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==Extraction Methods==
==Methods for Downscaling==
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===High Concentration Waters (> 1 ppm)===
{| class="wikitable" style="float:right; margin-left:10px;text-align:center;"
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Aqueous samples suspected to contain the compounds of interest at concentrations detectable without any extraction or pre-concentration are suitable for analysis by direct injection. The method deviates from USEPA Method 8330B by adding a pH adjustment and use of MeOH rather than ACN for dilution<ref name= "8330B"/>. The pH adjustment is needed to ensure method accuracy for ionic compounds (like NTO or PA) in basic samples. A solution of 1% HCl/MeOH is added to both acidify and dilute the samples to a final acid concentration of 0.5% (vol/vol) and a final solvent ratio of 1:1 MeOH/H<sub>2</sub>O. The direct injection samples are then ready for analysis.
|+Table 1.  Two widely used methods for developing downscaled higher resolution climate model projections
 
|-
 
!Dynamical Downscaling
 
!Statistical Downscaling
 
|-
 
|Deterministic climate change simulations that output</br>many climate variables with sub-daily information ||Primarily limited to daily temperature and precipitation
 
|-
 
|Computationally expensive; hence, limited number</br>of simulations – both GHG emission scenarios and</br>global climate models downscaled||Computationally efficient; hence, downscaled data</br>typically available for many different global</br>climate models and GHG emission scenarios
 
|-
 
|May require additional bias correction||Method incorporates bias correction
 
|-
 
|Observational data at the downscaled location</br>are not necessary to obtain the downscaled output</br>at the location||Best suited for locations with 30 years</br>or more of observational data
 
|-
 
|Does not assume stationarity or in other words</br>the model simulates the future regardless of</br>what has happened in the past||Stationarity assumption - assumes that the statistical</br>relationship between global climate model and</br>observations will remain constant in the future
 
|}
 
  
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.
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===Low Concentration Waters (< 1 ppm)===
 +
Aqueous samples suspected to contain the compounds of interest at low concentrations require extraction and pre-concentration using solid phase extraction (SPE). The SPE setup described here uses a triple cartridge setup shown in '''Figure 3'''. Briefly, the extraction procedure loads analytes of interest onto the cartridges in this order: Strata<sup><small>TM</small></sup> X, Strata<sup><small>TM</small></sup> X-A, and Envi-Carb<sup><small>TM</small></sup>. Then the cartridge order is reversed, and analytes are eluted via a two-step elution, resulting in 2 extracts (which are combined prior to analysis). Five milliliters of MeOH is used for the first elution, while 5 mL of acidified MeOH (2% HCl) is used for the second elution. The particular SPE cartridges used are noncritical so long as cartridge chemistries are comparable to those above.  
  
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:
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===Soils=== 
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Soil collection, storage, drying and grinding procedures are identical to the USEPA Method 8330B procedures<ref name= "8330B"/>; however, the solvent extraction procedure differs in the number of sonication steps, sample mass and solvent used. A flow chart of the soil extraction procedure is shown in '''Figure 4'''. Soil masses of approximately 2 g and a sample to solvent ratio of 1:5 (g/mL) are used for soil extraction. The extraction is carried out in a sonication bath chilled below 20 ⁰C and is a two-part extraction, first extracting in MeOH (6 hours) followed by a second sonication in 1:1 MeOH:H<sub>2</sub>O solution (14 hours). The extracts are centrifuged, and the supernatant is filtered through a 0.45 μm PTFE disk filter.
  
* 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 solvent volume should generally be 10 mL but if different soil masses are required, solvent volume should be 5 mL/g. The extraction results in 2 separate extracts (MeOH and MeOH:H<sub>2</sub>O) that are combined prior to analysis.
* 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==
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===Tissues===  
{| class="wikitable" style="float:right; margin-left:10px;text-align:center;"
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Tissue matrices are extracted by 18-hour sonication using a ratio of 1 gram of wet tissue per 5 mL of MeOH. This extraction is performed in a sonication bath chilled below 20 ⁰C and the supernatant (MeOH) is filtered through a 0.45 μm PTFE disk filter.  
|+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 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]]).  
 
  
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.  
+
Due to the complexity of tissue matrices, an additional tissue cleanup step, adapted from prior research, can be used to reduce interferences<ref name="RussellEtAl2014">Russell, A.L., Seiter, J.M., Coleman, J.G., Winstead, B., Bednar, A.J., 2014. Analysis of munitions constituents in IMX formulations by HPLC and HPLC-MS. Talanta, 128, pp. 524–530. [https://doi.org/10.1016/j.talanta.2014.02.013 doi: 10.1016/j.talanta.2014.02.013]</ref><ref name="CrouchEtAl2020"/>. The cleanup procedure uses small scale chromatography columns prepared by loading 5 ¾” borosilicate pipettes with 0.2 g activated silica gel (100–200 mesh). The columns are wetted with 1 mL MeOH, which is allowed to fully elute and then discarded prior to loading with 1 mL of extract and collecting in a new amber vial. After the extract is loaded, a 1 mL aliquot of MeOH followed by a 1 mL aliquot of 2% HCL/MeOH is added. This results in a 3 mL silica treated tissue extract. This extract is vortexed and diluted to a final solvent ratio of 1:1 MeOH/H<sub>2</sub>O before analysis.
  
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.   
+
==HPLC-UV and MS Methods==
 +
The Primary HPLC method uses a Phenomenex Synergi 4 µm Hydro-RP column (80Å, 250 x 4.6 mm), or comparable, and is based on both the HPLC method found in USEPA 8330B and previous work<ref name= "8330B"/><ref name="RussellEtAl2014"/><ref name="CrouchEtAl2020"/>. This separation relies on a reverse phase column and uses a gradient elution, shown in Table 2. Depending on the analyst’s needs and equipment availability, the method has been proven to work with either 0.1% TFA or 0.25% FA (vol/vol) mobile phase. Addition of a guard column like a Phenomenex SecurityGuard AQ C18 pre-column guard cartridge can be optionally used. These optional changes to the method have no impact on the method’s performance.
 +
The Secondary HPLC method uses a Restek Pinnacle II Biphenyl 5 µm (150 x 4.6 mm) or comparable column and is intended as a confirmatory method. Like the Primary method, this method can use an optional guard column and utilizes a gradient elution, shown in Table 3.
 +
 +
For instruments equipped with a mass spectrometer (MS), a secondary MS method is available and was developed alongside the Primary UV method. The method was designed for use with a single quadrupole MS equipped with an atmospheric pressure chemical ionization (APCI) source, such as an Agilent 6120B. A majority of the analytes shown in Table 1 are amenable to this MS method, however nitroglycerine (which is covered extensively in USEPA method 8332) and 2-,3-, and 4-nitrotoluene compounds aren’t compatible with the MS methodMS method parameters are shown in Table 4.
  
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.  
+
==Summary==
<br clear="left" />
+
The extraction methods and instrumental methods in this article build upon prior munitions analytical methods by adding new compounds, combining legacy and insensitive munitions analysis, and expanding usable sample matrices. These methods have been verified through extensive round robin testing and validation, and while the methods are somewhat challenging, they are crucial when simultaneous analysis of both insensitive and legacy munitions is needed.  
  
 
==References==
 
==References==
 
<references />
 
<references />
 +
 
==See Also==
 
==See Also==
 
+
*[https://serdp-estcp.mil/focusareas/9f7a342a-1b13-4ce5-bda0-d7693cf2b82d/uxo#subtopics  SERDP/ESTCP Focus Areas – UXO – Munitions Constituents]
[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]
+
*[https://denix.osd.mil/edqw/home/ Environmental Data Quality Workgroup]

Latest revision as of 00:31, 24 July 2024

Munitions Constituents – Sample Extraction and Analytical Techniques

Munitions Constituents, including insensitive munitions IM), are a broad category of compounds and, in areas where manufactured or used, can be found in a variety of environmental matrices (waters, soil, and tissues). This presents an analytical challenge when a variety of these munitions are to be quantified. This article discusses sample extraction methods for each typical sample matrix (high level water, low level water, soil and tissue) as well as the accompanying HPLC-UV analytical method for 27 compounds of interest (legacy munitions, insensitive munitions, and surrogates).

Related Article(s):

Contributor(s):

  • Dr. Austin Scircle

Key Resource(s):

  • Methods for simultaneous quantification of legacy and insensitive munition (IM) constituents in aqueous, soil/sediment, and tissue matrices[2]

Introduction

Figure 1. Primary Method labeled chromatograms
Figure 2. Secondary Method labeled chromatograms

The primary intention of the analytical methods presented here is to support the monitoring of legacy and insensitive munitions contamination on test and training ranges, however legacy and insensitive munitions often accompany each other at demilitarization facilities, manufacturing facilities, and other environmental sites. Energetic materials typically appear on ranges as small, solid particulates and due to their varying functional groups and polarities, can partition in various environmental compartments[3]. To ensure that contaminants are monitored and controlled at these sites and to sustainably manage them a variety of sample matrices (surface or groundwater, process waters, soil, and tissues) must be considered. (Process water refers to water used during industrial manufacturing or processing of legacy and insensitive munitions.) Furthermore, additional analytes must be added to existing methodologies as the usage of IM compounds changes and as new degradation compounds are identified. Of note, relatively new IM formulations containing NTO, DNAN, and NQ are seeing use in IMX-101, IMX-104, Pax-21 and Pax-41 (Table 1)[4][5].

Sampling procedures for legacy and insensitive munitions are identical and utilize multi-increment sampling procedures found in USEPA Method 8330B Appendix A[1]. Sample hold times, subsampling and quality control requirements are also unchanged. The key differences lie in the extraction methods and instrumental methods. Briefly, legacy munitions analysis of low concentration waters uses a single cartridge reverse phase SPE procedure, and acetonitrile (ACN) is used for both extraction and elution for aqueous and solid samples[1][6]. An isocratic separation via reversed-phase C-18 column with 50:50 methanol:water mobile phase or a C-8 column with 15:85 isopropanol:water mobile phase is used to separate legacy munitions[1]. While these procedures are sufficient for analysis of legacy munitions, alternative solvents, additional SPE cartridges, and a gradient elution are all required for the combined analysis of legacy and insensitive munitions.

Previously, analysis of legacy and insensitive munitions required multiple analytical techniques, however the methods presented here combine the two munitions categories resulting in an HPLC-UV method and accompanying extraction methods for a variety of common sample matrices. A secondary HPLC-UV method and a HPLC-MS method were also developed as confirmatory methods. The methods discussed in this article were validated extensively by single-blind round robin testing and subsequent statistical treatment as part of ESTCP ER19-5078. Wherever possible, the quality control criteria in the Department of Defense Quality Systems Manual for Environmental Laboratories were adhered to[7]. Analytes included in these methods are found in Table 1.

The chromatograms produced by the primary and secondary HPLC-UV methods are shown in Figure 1 and Figure 2, respectively. Chromatograms for each detector wavelength used are shown (315, 254, and 210 nm).

Extraction Methods

High Concentration Waters (> 1 ppm)

Aqueous samples suspected to contain the compounds of interest at concentrations detectable without any extraction or pre-concentration are suitable for analysis by direct injection. The method deviates from USEPA Method 8330B by adding a pH adjustment and use of MeOH rather than ACN for dilution[1]. The pH adjustment is needed to ensure method accuracy for ionic compounds (like NTO or PA) in basic samples. A solution of 1% HCl/MeOH is added to both acidify and dilute the samples to a final acid concentration of 0.5% (vol/vol) and a final solvent ratio of 1:1 MeOH/H2O. The direct injection samples are then ready for analysis.

Low Concentration Waters (< 1 ppm)

Aqueous samples suspected to contain the compounds of interest at low concentrations require extraction and pre-concentration using solid phase extraction (SPE). The SPE setup described here uses a triple cartridge setup shown in Figure 3. Briefly, the extraction procedure loads analytes of interest onto the cartridges in this order: StrataTM X, StrataTM X-A, and Envi-CarbTM. Then the cartridge order is reversed, and analytes are eluted via a two-step elution, resulting in 2 extracts (which are combined prior to analysis). Five milliliters of MeOH is used for the first elution, while 5 mL of acidified MeOH (2% HCl) is used for the second elution. The particular SPE cartridges used are noncritical so long as cartridge chemistries are comparable to those above.

Soils

Soil collection, storage, drying and grinding procedures are identical to the USEPA Method 8330B procedures[1]; however, the solvent extraction procedure differs in the number of sonication steps, sample mass and solvent used. A flow chart of the soil extraction procedure is shown in Figure 4. Soil masses of approximately 2 g and a sample to solvent ratio of 1:5 (g/mL) are used for soil extraction. The extraction is carried out in a sonication bath chilled below 20 ⁰C and is a two-part extraction, first extracting in MeOH (6 hours) followed by a second sonication in 1:1 MeOH:H2O solution (14 hours). The extracts are centrifuged, and the supernatant is filtered through a 0.45 μm PTFE disk filter.

The solvent volume should generally be 10 mL but if different soil masses are required, solvent volume should be 5 mL/g. The extraction results in 2 separate extracts (MeOH and MeOH:H2O) that are combined prior to analysis.

Tissues

Tissue matrices are extracted by 18-hour sonication using a ratio of 1 gram of wet tissue per 5 mL of MeOH. This extraction is performed in a sonication bath chilled below 20 ⁰C and the supernatant (MeOH) is filtered through a 0.45 μm PTFE disk filter.

Due to the complexity of tissue matrices, an additional tissue cleanup step, adapted from prior research, can be used to reduce interferences[8][2]. The cleanup procedure uses small scale chromatography columns prepared by loading 5 ¾” borosilicate pipettes with 0.2 g activated silica gel (100–200 mesh). The columns are wetted with 1 mL MeOH, which is allowed to fully elute and then discarded prior to loading with 1 mL of extract and collecting in a new amber vial. After the extract is loaded, a 1 mL aliquot of MeOH followed by a 1 mL aliquot of 2% HCL/MeOH is added. This results in a 3 mL silica treated tissue extract. This extract is vortexed and diluted to a final solvent ratio of 1:1 MeOH/H2O before analysis.

HPLC-UV and MS Methods

The Primary HPLC method uses a Phenomenex Synergi 4 µm Hydro-RP column (80Å, 250 x 4.6 mm), or comparable, and is based on both the HPLC method found in USEPA 8330B and previous work[1][8][2]. This separation relies on a reverse phase column and uses a gradient elution, shown in Table 2. Depending on the analyst’s needs and equipment availability, the method has been proven to work with either 0.1% TFA or 0.25% FA (vol/vol) mobile phase. Addition of a guard column like a Phenomenex SecurityGuard AQ C18 pre-column guard cartridge can be optionally used. These optional changes to the method have no impact on the method’s performance. The Secondary HPLC method uses a Restek Pinnacle II Biphenyl 5 µm (150 x 4.6 mm) or comparable column and is intended as a confirmatory method. Like the Primary method, this method can use an optional guard column and utilizes a gradient elution, shown in Table 3.

For instruments equipped with a mass spectrometer (MS), a secondary MS method is available and was developed alongside the Primary UV method. The method was designed for use with a single quadrupole MS equipped with an atmospheric pressure chemical ionization (APCI) source, such as an Agilent 6120B. A majority of the analytes shown in Table 1 are amenable to this MS method, however nitroglycerine (which is covered extensively in USEPA method 8332) and 2-,3-, and 4-nitrotoluene compounds aren’t compatible with the MS method. MS method parameters are shown in Table 4.

Summary

The extraction methods and instrumental methods in this article build upon prior munitions analytical methods by adding new compounds, combining legacy and insensitive munitions analysis, and expanding usable sample matrices. These methods have been verified through extensive round robin testing and validation, and while the methods are somewhat challenging, they are crucial when simultaneous analysis of both insensitive and legacy munitions is needed.

References

  1. ^ 1.0 1.1 1.2 1.3 1.4 1.5 1.6 United States Environmental Protection Agency (USEPA), 2006. EPA Method 8330B (SW-846) Nitroaromatics, Nitramines, and Nitrate Esters by High Performance Liquid Chromatography (HPLC), Revision 2. USEPA Website    Method 8330B.pdf
  2. ^ 2.0 2.1 2.2 Crouch, R.A., Smith, J.C., Stromer, B.S., Hubley, C.T., Beal, S., Lotufo, G.R., Butler, A.D., Wynter, M.T., Russell, A.L., Coleman, J.G., Wayne, K.M., Clausen, J.L., Bednar, A.J., 2020. Methods for simultaneous determination of legacy and insensitive munition (IM) constituents in aqueous, soil/sediment, and tissue matrices. Talanta, 217, Article 121008. doi: 10.1016/j.talanta.2020.121008    Open Access Manuscript.pdf
  3. ^ Walsh, M.R., Temple, T., Bigl, M.F., Tshabalala, S.F., Mai, N. and Ladyman, M., 2017. Investigation of Energetic Particle Distribution from High‐Order Detonations of Munitions. Propellants, Explosives, Pyrotechnics, 42(8), pp. 932-941. doi: 10.1002/prep.201700089
  4. ^ Mainiero, C. 2015. Picatinny Employees Recognized for Insensitive Munitions. U.S. Army, Picatinny Arsenal Public Affairs. Open Access Press Release
  5. ^ Frem, D., 2022. A Review on IMX-101 and IMX-104 Melt-Cast Explosives: Insensitive Formulations for the Next-Generation Munition Systems. Propellants, Explosives, Pyrotechnics, 48(1), e202100312. doi: 10.1002/prep.202100312
  6. ^ United States Environmental Protection Agency (USEPA), 2007. EPA Method 3535A (SW-846) Solid-Phase Extraction (SPE), Revision 1. USEPA Website    Method 3535A.pdf
  7. ^ US Department of Defense and US Department of Energy, 2021. Consolidated Quality Systems Manual (QSM) for Environmental Laboratories, Version 5.4. 387 pages. Free Download    QSM Version 5.4.pdf
  8. ^ 8.0 8.1 Russell, A.L., Seiter, J.M., Coleman, J.G., Winstead, B., Bednar, A.J., 2014. Analysis of munitions constituents in IMX formulations by HPLC and HPLC-MS. Talanta, 128, pp. 524–530. doi: 10.1016/j.talanta.2014.02.013

See Also