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==Passive Sampling of Sediments==
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==Assessing Vapor Intrusion (VI) Impacts in Neighborhoods with Groundwater Contaminated by Chlorinated Volatile Organic Chemicals (CVOCs)==  
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"Passive sampling" refers to a group of methods used to quantify the availability of organic contaminants to move between different media and/or to react in environmental systems such as indoor air, lake waters, or contaminated sediment beds. To do this, the passive sampling material is deployed in the environmental system and allowed to absorb chemicals of interest via diffusive transfers from the surroundings.  Upon recovery of the passive sampler, the accumulated contaminants are measured, and the concentrations in the sampler are interpreted to infer the chemical concentrations in specific surrounding media like porewater in a sediment bed.  Such data are then useful inputs for site assessments such as those seeking to quantify fluxes from contaminated sediment beds to overlying waters or to evaluate the risk of significant uptake into benthic infauna and the larger food web.
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The VI Diagnosis Toolkit<ref name="JohnsonEtAl2020">Johnson, P.C., Guo, Y., Dahlen, P., 2020.  The VI Diagnosis Toolkit for Assessing Vapor Intrusion Pathways and Mitigating Impacts in Neighborhoods Overlying Dissolved Chlorinated Solvent Plumes.  ESTCP Project ER-201501, Final Report. [https://serdp-estcp.mil/projects/details/a0d8bafd-c158-4742-b9fe-5f03d002af71 Project Website]&nbsp;&nbsp; [[Media: ER-201501.pdf | Final Report.pdf]]</ref> is a set of tools that can be used individually or in combination to assess vapor intrusion (VI) impacts at one or more buildings overlying regional-scale dissolved chlorinated solvent-impacted groundwater plumes. The strategic use of these tools can lead to confident and efficient neighborhood-scale VI pathway assessments.
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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):'''
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* [[Contaminated Sediments - Introduction]]
 
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* [[In Situ Treatment of Contaminated Sediments with Activated Carbon]]
 
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* [[Passive Sampling of Munitions Constituents]]
 
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'''Contributor(s):''' [[Dr. Philip M. Gschwend]]
 
  
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'''Key Resource(s):'''
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*[[Vapor Intrusion (VI)]]
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* Validating the Use of Performance Reference Compounds in Passive Samplers to Assess Porewater Concentrations in Sediment Beds<ref name ="Apell2014">Apell, J.N. and Gschwend, P.M., 2014. Validating the Use of Performance Reference Compounds in Passive Samplers to Assess Porewater Concentrations in Sediment Beds.  Environmental Science and Technology, 48(17), pp. 10301-10307.  [https://doi.org/10.1021/es502694g DOI: 10.1021/es502694g]</ref>
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*[[Vapor Intrusion – Sewers and Utility Tunnels as Preferential Pathways]]
  
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* ''In situ'' passive sampling of sediments in the Lower Duwamish Waterway Superfund site: Replicability, comparison with ''ex situ'' measurements, and use of data<ref name="Apell2016">Apell, J.N., and Gschwend, P.M., 2016. ''In situ'' passive sampling of sediments in the Lower Duwamish Waterway Superfund site: Replicability, comparison with ''ex situ'' measurements, and use of data. Environmental Pollution, 218, pp. 95-101.  [https://doi.org/10.1016/j.envpol.2016.08.023 DOI: 10.1016/j.envpol.2016.08.023]&nbsp;&nbsp; [[Media: ApellGschwend2016.pdf | Authors’ Manuscript]]</ref>
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'''Contributor(s):'''  
  
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* Laboratory, Field, and Analytical Procedures for Using Passive Sampling in the Evaluation of Contaminated Sediments: User’s Manual<ref name="Burgess2017">Burgess, R.M., Kane Driscoll, S.B., Burton, A., Gschwend, P.M., Ghosh, U., Reible, D., Ahn, S., and Thompson, T., 2017. Laboratory, Field, and Analytical Procedures for Using Passive Sampling in the Evaluation of Contaminated Sediments: User’s Manual, EPA/600/R-16/357. SERDP/ESTCP and U.S. EPA, Office of Research and Development, Washington, DC 20460.  [https://cfpub.epa.gov/si/si_public_record_report.cfm?Lab=NHEERL&dirEntryID=308731 Website]&nbsp;&nbsp; [[Media: EPA600R16357.pdf | Report.pdf]]</ref>
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*Paul C. Johnson, Ph.D.
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*Paul Dahlen, Ph.D.
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*Yuanming Guo, Ph.D.
  
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==Introduction==
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'''Key Resource(s):'''
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Environmental media such as sediments typically contain many different materials or phases, including liquid solutions (e.g. water, [[Light Non-Aqueous Phase Liquids (LNAPLs)| nonaqueous phase liquids]]like spilled oils) and diverse solids (e.g., quartz, aluminosilicate clays, and combustion-derived soots).  Further, the chemical concentration in the porewater medium includes both molecules that are "truly dissolved" in the water and others that are associated with colloids in the porewater<ref name="Brownawell1986">Brownawell, B.J., and Farrington, J.W., 1986. Biogeochemistry of PCBs in interstitial waters of a coastal marine sediment. Geochimica et Cosmochimica Acta, 50(1), pp. 157-169.  [https://doi.org/10.1016/0016-7037(86)90061-X DOI: 10.1016/0016-7037(86)90061-X]&nbsp;&nbsp; Free download available from: [https://semspub.epa.gov/work/01/268631.pdf US EPA].</ref><ref name="Chin1992">Chin, Y.P., and Gschwend, P.M., 1992. Partitioning of Polycyclic Aromatic Hydrocarbons to Marine Porewater Organic Colloids. Environmental Science and Technology, 26(8), pp. 1621-1626.  [https://doi.org/10.1021/es00032a020 DOI: 10.1021/es00032a020]</ref><ref name="Achman1996">Achman, D.R., Brownawell, B.J., and Zhang, L., 1996. Exchange of Polychlorinated Biphenyls Between Sediment and Water in the Hudson River Estuary. Estuaries, 19(4), pp. 950-965.  [https://doi.org/10.2307/1352310 DOI: 10.2307/1352310]&nbsp;&nbsp; Free download available from: [https://www.academia.edu/download/55010335/135231020171114-2212-b93vic.pdf Academia.edu]</ref>. As a result, contaminant chemicals distribute among these diverse media (Figure 1) according to their affinity for each and the amount of each phase in the system<ref name="Gustafsson1996">Gustafsson, Ö., Haghseta, F., Chan, C., MacFarlane, J., and Gschwend, P.M., 1996. Quantification of the Dilute Sedimentary Soot Phase: Implications for PAH Speciation and Bioavailability. Environmental Science and Technology, 31(1), pp. 203-209.  [https://doi.org/10.1021/es960317s  DOI: 10.1021/es960317s]</ref><ref name="Luthy1997">Luthy, R.G., Aiken, G.R., Brusseau, M.L., Cunningham, S.D., Gschwend, P.M., Pignatello, J.J., Reinhard, M., Traina, S.J., Weber, W.J., and Westall, J.C., 1997. Sequestration of Hydrophobic Organic Contaminants by Geosorbents. Environmental Science and Technology, 31(12), pp. 3341-3347.  [https://doi.org/10.1021/es970512m DOI: 10.1021/es970512m]</ref><ref name="Lohmann2005">Lohmann, R., MacFarlane, J.K., and Gschwend, P.M., 2005. Importance of Black Carbon to Sorption of Native PAHs, PCBs, and PCDDs in Boston and New York Harbor Sediments. Environmental Science and Technology, 39(1), pp.141-148.  [https://doi.org/10.1021/es049424+  DOI: 10.1021/es049424+]</ref><ref name="Cornelissen2005">Cornelissen, G., Gustafsson, Ö., Bucheli, T.D., Jonker, M.T., Koelmans, A.A., and van Noort, P.C., 2005. Extensive Sorption of Organic Compounds to Black Carbon, Coal, and Kerogen in Sediments and Soils: Mechanisms and Consequences for Distribution, Bioaccumulation, and Biodegradation. Environmental Science and Technology, 39(18), pp. 6881-6895.  [https://doi.org/10.1021/es050191b  DOI: 10.1021/es050191b]</ref><ref name="Koelmans2009">Koelmans, A.A., Kaag, K., Sneekes, A., and Peeters, E.T.H.M., 2009. Triple Domain in Situ Sorption Modeling of Organochlorine Pesticides, Polychlorobiphenyls, Polyaromatic Hydrocarbons, Polychlorinated Dibenzo-p-Dioxins, and Polychlorinated Dibenzofurans in Aquatic Sediments. Environmental Science and Technology, 43(23), pp. 8847-8853.  [https://doi.org/10.1021/es9021188 DOI: 10.1021/es9021188]</ref>. As such, the chemical concentration in any one medium (e.g., truly dissolved in porewater) in a multi-material system like sediment is very hard to know from measures of the total sediment concentration, which unfortunately is the information typically found by analyzing for chemicals in sediment samples.
 
  
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If an animal moves into this system, it will also accumulate the chemical in its tissues from the loads in all the other materials (Figure 1).  This is important if one is concerned with exposures of the chemical to other organisms, including humans, who may eat such shellfish.  Predicting the quantity of contaminant in the clam requires knowledge of the relative affinities of the chemical for the clam versus the sediment materials.  For example, if one knew the chemical's truly dissolved concentration in the porewater and could reasonably assume the chemical of interest in the clams has mostly accumulated in its lipids (as is often the case for very hydrophobic compounds), then one could estimate the chemical concentration in the clam (''C<sub><small>clam</small></sub>'', typically in units of &mu;g/kg clam wet weight) using a lipid-water [[Wikipedia: Partition coefficient | partition coefficient]], ''K<sub><small>lipid-water</small></sub>'', typically in units of (&mu;g/kg lipid)'''/'''(&mu;g/L water), and the porewater concentration of the chemical (''C<sub><small>porewater</small></sub>'', in &mu;g/L) with Equation 1.
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*The VI Diagnosis Toolkit for Assessing Vapor Intrusion Pathways and Impacts in Neighborhoods Overlying Dissolved Chlorinated Solvent Plumes, ESTCP Project ER-201501, Final Report<ref name="JohnsonEtAl2020"/>
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{|
 
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| || Equation 1.
 
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| style="text-align:center;"| <big>'''''C<sub><small>clam</small></sub> '''=''' f<sub><small>lipid</small></sub> '''x''' K<sub><small>lipid-water</small></sub> '''x''' C<sub><small>porewater</small></sub>'''''</big>
 
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|-
 
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| where:
 
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| || ''f<sub><small>lipid</small></sub>'' || is the fraction lipids contribute to the total wet weight of a clam (kg lipid/kg clam wet weight), and
 
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| || ''C<sub><small>porewater</small></sub>'' || is the freely dissolved contaminant concentration in the porewater surrounding the clam.
 
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While there is a great deal of information on the values of ''K<sub><small>lipid-water</small></sub>'' for many chemicals<ref name="Schwarzenbach2017">Schwarzenbach, R.P., Gschwend, P.M., and Imboden, D.M., 2017.  Environmental Organic Chemistry, 3rd edition. Ch. 16: Equilibrium Partitioning from Water and Air to Biota, pp. 469-521. John Wiley and Sons.  ISBN: 978-1-118-76723-8</ref>, it is often very inaccurate to estimate truly dissolved porewater concentrations from total sediment concentrations using assumptions about the affinity of those chemicals for the solids in the system<ref name="Gustafsson1996"/>. Further, it is difficult to isolate porewater without colloids and/or measure the very low truly dissolved concentrations of hydrophobic contaminants of concern like [[Polycyclic Aromatic Hydrocarbons (PAHs) | polycyclic aromatic hydrocarbons (PAHs)]], [[Wikipedia: Polychlorinated biphenyl | polychlorinated biphenyls (PCBs)]], nonionic pesticides like [[Wikipedia: DDT | dichlorodiphenyltrichloroethane (DDT)]], and [[Wikipedia: Polychlorinated dibenzodioxins | polychlorinated dibenzo-p-dioxins (PCDDs)]]/[[Wikipedia: Polychlorinated dibenzofurans | dibenzofurans (PCDFs)]]<ref name="Hawthorne2005">Hawthorne, S.B., Grabanski, C.B., Miller, D.J., and Kreitinger, J.P., 2005. Solid-Phase Microextraction Measurement of Parent and Alkyl Polycyclic Aromatic Hydrocarbons in Milliliter Sediment Pore Water Samples and Determination of K<sub><small>DOC</small></sub> Values. Environmental Science and Technology, 39(8), pp. 2795-2803.  [https://doi.org/10.1021/es0405171 DOI: 10.1021/es0405171]</ref>.
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*CPM Test Guidelines: Use of Controlled Pressure Method Testing for Vapor Intrusion Pathway Assessment, ESTCP Project ER-201501, Technical Report<ref name="JohnsonEtAl2021">Johnson, P.C., Guo, Y., Dahlen, P., 2021.  CPM Test Guidelines: Use of Controlled Pressure Method Testing for Vapor Intrusion Pathway Assessment. ESTCP ER-201501, Technical Report. [https://serdp-estcp.mil/projects/details/a0d8bafd-c158-4742-b9fe-5f03d002af71 Project Website]&nbsp;&nbsp; [[Media: ER-201501_Technical_Report.pdf | Technical_Report.pdf]]</ref>    
  
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==Passive Samplers==
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*VI Diagnosis Toolkit User Guide, ESTCP Project ER-201501<ref name="JohnsonEtAl2022">Johnson, P.C., Guo, Y., and Dahlen, P., 2022. VI Diagnosis Toolkit User Guide, ESTCP ER-201501, User Guide.  [https://serdp-estcp.mil/projects/details/a0d8bafd-c158-4742-b9fe-5f03d002af71 Project Website]&nbsp;&nbsp; [[Media: ER-201501_User_Guide.pdf | User_Guide.pdf]]</ref>
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One approach to address this problem for contaminated sediments is to insert organic polymers like low density polyethylene (LDPE), polydimethylsiloxane (PDMS), or polyoxymethylene (POM) that can absorb such chemicals in the sediment<ref name="Mayer2000">Mayer, P., Vaes, W.H., Wijnker, F., Legierse, K.C., Kraaij, R., Tolls, J., and Hermens, J.L., 2000. Sensing Dissolved Sediment Porewater Concentrations of Persistent and Bioaccumulative Pollutants Using Disposable Solid-Phase Microextraction Fibers. Environmental Science and Technology, 34(24), pp. 5177-5183.  [https://doi.org/10.1021/es001179g DOI: 10.1021/es001179g]</ref><ref name="Booij2003">Booij, K., Hoedemaker, J.R., and Bakker, J.F., 2003. Dissolved PCBs, PAHs, and HCB in Pore Waters and Overlying Waters of Contaminated Harbor Sediments. Environmental Science and Technology, 37(18), pp. 4213-4220.  [https://doi.org/10.1021/es034147c DOI: 10.1021/es034147c]</ref><ref name="Cornelissen2008">Cornelissen, G., Pettersen, A., Broman, D., Mayer, P., and Breedveld, G.D., 2008. Field testing of equilibrium passive samplers to determine freely dissolved native polycyclic aromatic hydrocarbon concentrations. Environmental Toxicology and Chemistry, 27(3), pp. 499-508. [https://doi.org/10.1897/07-253.1 DOI: 10.1897/07-253.1]</ref><ref name="Tomaszewski2008">Tomaszewski, J.E., and Luthy, R.G., 2008. Field Deployment of Polyethylene Devices to Measure PCB Concentrations in Pore Water of Contaminated Sediment. Environmental Science and Technology, 42(16), pp. 6086-6091.  [https://doi.org/10.1021/es800582a DOI: 10.1021/es800582a]</ref><ref name="Fernandez2009">Fernandez, L.A., MacFarlane, J.K., Tcaciuc, A.P., and Gschwend, P.M., 2009. Measurement of Freely Dissolved PAH Concentrations in Sediment Beds Using Passive Sampling with Low-Density Polyethylene Strips. Environmental Science and Technology, 43(5), pp. 1430-1436.  [https://doi.org/10.1021/es802288w DOI: 10.1021/es802288w]</ref><ref name="Arp2015">Arp, H.P.H., Hale, S.E., Elmquist Kruså, M., Cornelissen, G., Grabanski, C.B., Miller, D.J., and Hawthorne, S.B., 2015. Review of polyoxymethylene passive sampling methods for quantifying freely dissolved porewater concentrations of hydrophobic organic contaminants. Environmental Toxicology and Chemistry, 34(4), pp. 710-720.  [https://doi.org/10.1002/etc.2864 DOI: 10.1002/etc.2864]&nbsp;&nbsp;  [https://setac.onlinelibrary.wiley.com/doi/epdf/10.1002/etc.2864 Free access article.]&nbsp;&nbsp; [[Media: Arp2015.pdf | Report.pdf]]</ref><ref name="Apell2016"/>. In this approach, the polymer is inserted in the sediment bed where it absorbs some of the contaminant load via the contaminant's diffusion into the polymer from the surroundings. When the polymer achieves sorptive equilibration with the sediments, the chemical concentration in the polymer, ''C<sub><small>polymer</small></sub>'' (&mu;g/kg polymer), can be used to find the corresponding concentration in the porewater,  ''C<sub><small>porewater</small></sub>'' (&mu;g/L), using a polymer-water partition coefficient, ''K<sub><small>polymer-water</small></sub>'' ((&mu;g/kg polymer)'''/'''(&mu;g/L water)), that has previously been found in laboratory testing<ref name="Lohmann2012">Lohmann, R., 2012. Critical Review of Low-Density Polyethylene’s Partitioning and Diffusion Coefficients for Trace Organic Contaminants and Implications for Its Use as a Passive Sampler. Environmental Science and Technology, 46(2), pp. 606-618.  [https://doi.org/10.1021/es202702y DOI: 10.1021/es202702y]</ref><ref name="Ghosh2014">Ghosh, U., Kane Driscoll, S., Burgess, R.M., Jonker, M.T., Reible, D., Gobas, F., Choi, Y., Apitz, S.E., Maruya, K.A., Gala, W.R., Mortimer, M., and Beegan, C., 2014. Passive Sampling Methods for Contaminated Sediments: Practical Guidance for Selection, Calibration, and Implementation. Integrated Environmental Assessment and Management, 10(2), pp. 210-223.  [https://doi.org/10.1002/ieam.1507 DOI: 10.1002/ieam.1507]&nbsp;&nbsp; [https://setac.onlinelibrary.wiley.com/doi/epdf/10.1002/ieam.1507 Free access article.]&nbsp;&nbsp; [[Media: Ghosh2014.pdf | Report.pdf]]</ref>, as shown in Equation 2.
 
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{|
 
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|&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;|| Equation&nbsp;2.
 
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| style="width:600px; text-align:center;" | <big>'''''C<sub><small>porewater</small></sub> '''=''' C<sub><small>polymer</small></sub> '''/''' K<sub><small>polymer-water</small></sub>'''''</big>
 
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Such “passive uptake” by the polymer also reflects the availability of the chemicals for transport to adjacent systems (e.g., overlying surface waters) and for uptake into organisms (e.g., [[Wikipedia: Bioaccumulation | bioaccumulation]]).  Thus, one can use the porewater concentrations to estimate the biotic accumulation of the chemicals, too. For example, for the concentration in the clam equilibrated with the sediment, ''C<sub><small>clam</small></sub>'' (&mu;g/kg clam), would be found by combining Equations 1 and 2 to get Equation 3.
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==Background==
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{|
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[[File:ChangFig2.png | thumb | 400px| Figure 1. Example of instrumentation used for OPTICS monitoring.]]
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[[File:ChangFig1.png | thumb | 400px| Figure 2. Schematic diagram illustrating the OPTICS methodology. High resolution in-situ data are integrated with traditional discrete sample analytical data using partial least-square regression to derive high resolution chemical contaminant concentration data series.]]
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Nationwide, the liability due to contaminated sediments is estimated in the trillions of dollars. Stakeholders are assessing and developing remedial strategies for contaminated sediment sites in major harbors and waterways throughout the U.S. The mobility of contaminants in surface water is a primary transport and risk mechanism<ref>Thibodeaux, L.J., 1996. Environmental Chemodynamics: Movement of Chemicals in Air, Water, and Soil, 2nd Edition, Volume 110 of Environmental Science and Technology: A Wiley-Interscience Series of Texts and Monographs. John Wiley & Sons, Inc. 624 pages. ISBN: 0-471-61295-2</ref><ref>United States Environmental Protection Agency (USEPA), 2005. Contaminated Sediment Remediation Guidance for Hazardous Waste Sites. Office of Superfund Remediation and Technology Innovation Report, EPA-540-R-05-012. [[Media: 2005-USEPA-Contaminated_Sediment_Remediation_Guidance.pdf | Report.pdf]]</ref><ref>Lick, W., 2008. Sediment and Contaminant Transport in Surface Waters. CRC Press. 416 pages. [https://doi.org/10.1201/9781420059885 doi:  10.1201/9781420059885]</ref>; therefore, long-term monitoring of both particulate- and dissolved-phase contaminant concentration prior to, during, and following remedial action is necessary to document remedy effectiveness. Source control and total maximum daily load (TMDL) actions generally require costly manual monitoring of dissolved and particulate contaminant concentrations in surface water. The magnitude of cost for these actions is a strong motivation to implement efficient methods for long-term source control and remedial monitoring.
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|&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;|| Equation&nbsp;3.
 
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|style="width:700px; text-align:center;" |<big>'''''C<sub><small>clam</small></sub> '''=''' f<sub><small>lipid</small></sub> '''x''' K<sub><small>lipid-water</small></sub> '''x''' C<sub><small>polymer</small></sub> '''/''' K<sub><small>polymer-water</small></sub>'''''</big>
 
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==Performance Reference Compounds (PRCs)==
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Traditional surface water monitoring requires mobilization of field teams to manually collect discrete water samples, send samples to laboratories, and await laboratory analysis so that a site evaluation can be conducted. These traditional methods are well known to have inherent cost and safety concerns and are of limited use (due to safety concerns and standby requirements for resources) in capturing the effects of episodic events (e.g., storms) that are important to consider in site risk assessment and remedy selection. Automated water samplers are commercially available but still require significant field support and costly laboratory analysis. Further, automated samplers may not be suitable for analytes with short hold-times and temperature requirements.  
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Perhaps unsurprisingly, pollutants with low water solubility like PAHs, PCBs, etc. do not diffuse quickly through sediment beds.  As a result, their accumulation in polymeric materials in sediments can take a long time to achieve equilibration<ref name="Fernandez2009b">Fernandez, L. A., Harvey, C.F., and Gschwend, P.M., 2009. Using Performance Reference Compounds in Polyethylene Passive Samplers to Deduce Sediment Porewater Concentrations for Numerous Target Chemicals. Environmental Science and Technology, 43(23), pp. 8888-8894. [https://doi.org/10.1021/es901877a DOI: 10.1021/es901877a]</ref><ref name="Lampert2015">Lampert, D.J., Thomas, C., and Reible, D.D., 2015. Internal and external transport significance for predicting contaminant uptake rates in passive samplers. Chemosphere, 119, pp. 910-916.  [https://doi.org/10.1016/j.chemosphere.2014.08.063 DOI: 10.1016/j.chemosphere.2014.08.063]&nbsp;&nbsp; Free download available from: [https://www.academia.edu/download/44146586/chemosphere_2014.pdf Academia.edu]</ref><ref name="Apell2016b">Apell, J.N., Tcaciuc, A.P., and Gschwend, P.M., 2016. Understanding the rates of nonpolar organic chemical accumulation into passive samplers deployed in the environment: Guidance for passive sampler deployments. Integrated Environmental Assessment and Management, 12(3), pp. 486-492.  [https://doi.org/10.1002/ieam.1697 DOI: 10.1002/ieam.1697]</ref>. This problem was recognized previously for passive samplers called [[Wikipedia: Semipermeable membrane devices | semipermeable membrane devices]] (SPMDs, e.g. polyethylene bags filled with triolein<ref name="Huckins2002">Huckins, J.N., Petty, J.D., Lebo, J.A., Almeida, F.V., Booij, K., Alvarez, D.A., Cranor, W.L., Clark, R.C., and Mogensen, B.B., 2002. Development of the Permeability/Performance Reference Compound Approach for In Situ Calibration of Semipermeable Membrane Devices. Environmental Science and Technology, 36(1), pp. 85-91.  [https://doi.org/10.1021/es010991w DOI: 10.1021/es010991w]</ref>) that were deployed in surface waters. As a result, representative chemicals called performance reference compound (PRCs) were dosed inside the samplers before their deployment in the environment, and the PRCs' diffusive losses out of the SPMD could be used to quantify the fractional approach toward sampler-environmental surroundings equilibration<ref name="Booij2002">Booij, K., Smedes, F., and van Weerlee, E.M., 2002. Spiking of performance reference compounds in low density polyethylene and silicone passive water samplers. Chemosphere 46(8), pp.1157-1161.  [https://doi.org/10.1016/S0045-6535(01)00200-4 DOI: 10.1016/S0045-6535(01)00200-4]</ref><ref name="Huckins2002"/>. A similar approach can be used for polymers inserted in sediment beds<ref name="Fernandez2009b"/><ref name="Apell2014"/>. Commonly, isotopically labeled forms of the compounds of interest such as deuterated or <sup>13</sup>C-labelled PAHs or PCBs are homogeneously impregnated into the polymers before their deployments.  Upon insertion of the polymer into the sediment bed (or overlying waters or even air), the initially evenly distributed PRCs begin to diffuse out of the sampling polymer and into the sediment (Figure 2).  
 
  
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Assuming the contaminants of interest undergo the same mass transfer restrictions limiting their rates of uptake into the polymer (e.g., diffusion through the sedimentary porous medium) that are also limiting transfers of the PRCs out of the polymer<ref name="Fernandez2009b"/><ref name="Apell2014"/>, then fractional losses of the PRCs during a particular deployment can be used to adjust the accumulated contaminant loads to what they would have been at equilibrium with their surroundings with Equation 4.
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Optically-based characterization of surface water contaminants is a cost-effective alternative to traditional discrete water sampling methods. Unlike discrete water sampling, which typically results in sparse data at low resolution, and therefore, is of limited use in determining mass loading, OPTICS (OPTically-based In-situ Characterization System) provides continuous data and allows for a complete understanding of water quality and contaminant transport in response to natural processes and human impacts<ref name="ChangEtAl2019"/><ref name="ChangEtAl2018"/><ref name="ChangEtAl2024"/><ref>Bergamaschi, B.A., Fleck, J.A., Downing, B.D., Boss, E., Pellerin, B., Ganju, N.K., Schoellhamer, D.H., Byington, A.A., Heim, W.A., Stephenson, M., Fujii, R., 2011. Methyl mercury dynamics in a tidal wetland quantified using in situ optical measurements. Limnology and Oceanography, 56(4), pp. 1355-1371. [https://doi.org/10.4319/lo.2011.56.4.1355 doi: 10.4319/lo.2011.56.4.1355]&nbsp;&nbsp; [[Media: BergamaschiEtAl2011.pdf | Open Access Article]]</ref><ref>Bergamaschi, B.A., Fleck, J.A., Downing, B.D., Boss, E., Pellerin, B.A., Ganju, N.K., Schoellhamer, D.H., Byington, A.A., Heim, W.A., Stephenson, M., Fujii, R., 2012. Mercury Dynamics in a San Francisco Estuary Tidal Wetland: Assessing Dynamics Using In Situ Measurements. Estuaries and Coasts, 35, pp. 1036-1048. [https://doi.org/10.1007/s12237-012-9501-3 doi: 10.1007/s12237-012-9501-3]&nbsp;&nbsp; [[Media: BergamaschiEtAl2012a.pdf | Open Access Article]]</ref><ref>Bergamaschi, B.A., Krabbenhoft, D.P., Aiken, G.R., Patino, E., Rumbold, D.G., Orem, W.H., 2012. Tidally driven export of dissolved organic carbon, total mercury, and methylmercury from a mangrove-dominated estuary. Environmental Science and Technology, 46(3), pp. 1371-1378. [https://doi.org/10.1021/es2029137 doi: 10.1021/es2029137]&nbsp;&nbsp; [[Media: BergamaschiEtAl2012b.pdf | Open Access Article]]</ref>. The OPTICS tool integrates commercial off-the-shelf ''in situ'' aquatic sensors (Figure 1), periodic discrete surface water sample collection, and a multi-parameter statistical prediction model<ref name="deJong1993">de Jong, S., 1993. SIMPLS: an alternative approach to partial least squares regression. Chemometrics and Intelligent Laboratory Systems, 18(3), pp. 251-263. [https://doi.org/10.1016/0169-7439(93)85002-X doi: 10.1016/0169-7439(93)85002-X]</ref><ref name="RosipalKramer2006">Rosipal, R. and Krämer, N., 2006. Overview and Recent Advances in Partial Least Squares, In: Subspace, Latent Structure, and Feature Selection: Statistical and Optimization Perspectives Workshop, Revised Selected Papers (Lecture Notes in Computer Science, Volume 3940), Springer-Verlag, Berlin, Germany. pp. 34-51. [https://doi.org/10.1007/11752790_2 doi: 10.1007/11752790_2]</ref> to provide high temporal and/or spatial resolution characterization of surface water chemicals of potential concern (COPCs) (Figure 2).
  
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==Technology Overview==
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The principle behind OPTICS is based on the relationship between optical properties of natural waters and the particles and dissolved material contained within them<ref>Boss, E. and Pegau, W.S., 2001. Relationship of light scattering at an angle in the backward direction to the backscattering coefficient. Applied Optics, 40(30), pp. 5503-5507. [https://doi.org/10.1364/AO.40.005503 doi: 10.1364/AO.40.005503]</ref><ref>Boss, E., Twardowski, M.S., Herring, S., 2001. Shape of the particulate beam spectrum and its inversion to obtain the shape of the particle size distribution. Applied Optics, 40(27), pp. 4884-4893. [https://doi.org/10.1364/AO.40.004885 doi:10/1364/AO.40.004885]</ref><ref>Babin, M., Morel, A., Fournier-Sicre, V., Fell, F., Stramski, D., 2003. Light scattering properties of marine particles in coastal and open ocean waters as related to the particle mass concentration. Limnology and Oceanography, 48(2), pp. 843-859. [https://doi.org/10.4319/lo.2003.48.2.0843 doi: 10.4319/lo.2003.48.2.0843]&nbsp;&nbsp; [[Media: BabinEtAl2003.pdf | Open Access Article]]</ref><ref>Coble, P., Hu, C., Gould, R., Chang, G., Wood, M., 2004. Colored dissolved organic matter in the coastal ocean: An optical tool for coastal zone environmental assessment and management. Oceanography, 17(2), pp. 50-59. [https://doi.org/10.5670/oceanog.2004.47 doi: 10.5670/oceanog.2004.47]&nbsp;&nbsp; [[Media: CobleEtAl2004.pdf | Open Access Article]]</ref><ref>Sullivan, J.M., Twardowski, M.S., Donaghay, P.L., Freeman, S.A., 2005. Use of optical scattering to discriminate particle types in coastal waters. Applied Optics, 44(9), pp. 1667–1680. [https://doi.org/10.1364/AO.44.001667 doi: 10.1364/AO.44.001667]</ref><ref>Twardowski, M.S., Boss, E., Macdonald, J.B., Pegau, W.S., Barnard, A.H., Zaneveld, J.R.V., 2001. A model for estimating bulk refractive index from the optical backscattering ratio and the implications for understanding particle composition in case I and case II waters. Journal of Geophysical Research: Oceans, 106(C7), pp. 14,129-14,142. [https://doi.org/10.1029/2000JC000404 doi: 10/1029/2000JC000404]&nbsp;&nbsp; [[Media: TwardowskiEtAl2001.pdf | Open Access Article]]</ref><ref>Chang, G.C., Barnard, A.H., McLean, S., Egli, P.J., Moore, C., Zaneveld, J.R.V., Dickey, T.D., Hanson, A., 2006. In situ optical variability and relationships in the Santa Barbara Channel: implications for remote sensing. Applied Optics, 45(15), pp. 3593–3604. [https://doi.org/10.1364/AO.45.003593 doi: 10.1364/AO.45.003593]</ref><ref>Slade, W.H. and Boss, E., 2015. Spectral attenuation and backscattering as indicators of average particle size. Applied Optics, 54(24), pp. 7264-7277. [https://doi.org/10.1364/AO.54.007264 doi: 10/1364/AO.54.007264]&nbsp;&nbsp; [[Media: SladeBoss2015.pdf | Open Access Article]]</ref>. Surface water COPCs such as heavy metals and polychlorinated biphenyls (PCBs) are hydrophobic in nature and tend to sorb to materials in the water column, which have unique optical signatures that can be measured at high-resolution using ''in situ'', commercially available aquatic sensors<ref>Agrawal, Y.C. and Pottsmith, H.C., 2000. Instruments for particle size and settling velocity observations in sediment transport. Marine Geology, 168(1-4), pp. 89-114. [https://doi.org/10.1016/S0025-3227(00)00044-X doi: 10.1016/S0025-3227(00)00044-X]</ref><ref>Boss, E., Pegau, W.S., Gardner, W.D., Zaneveld, J.R.V., Barnard, A.H., Twardowski, M.S., Chang, G.C., Dickey, T.D., 2001. Spectral particulate attenuation and particle size distribution in the bottom boundary layer of a continental shelf. Journal of Geophysical Research: Oceans, 106(C5), pp. 9509-9516. [https://doi.org/10.1029/2000JC900077  doi: 10.1029/2000JC900077]&nbsp;&nbsp; [[Media: BossEtAl2001.pdf | Open Access Article]]</ref><ref>Boss, E., Pegau, W.S., Lee, M., Twardowski, M., Shybanov, E., Korotaev, G. Baratange, F., 2004. Particulate backscattering ratio at LEO 15 and its use to study particle composition and distribution. Journal of Geophysical Research: Oceans, 109(C1), Article C01014. [https://doi.org/10.1029/2002JC001514 doi: 10.1029/2002JC001514]&nbsp;&nbsp; [[Media: BossEtAl2004.pdf | Open Access Article]]</ref><ref>Briggs, N.T., Slade, W.H., Boss, E., Perry, M.J., 2013. Method for estimating mean particle size from high-frequency fluctuations in beam attenuation or scattering measurement. Applied Optics, 52(27), pp. 6710-6725. [https://doi.org/10.1364/AO.52.006710 doi: 10.1364/AO.52.006710]&nbsp;&nbsp; [[Media: BriggsEtAl2013.pdf | Open Access Article]]</ref>. Therefore, high-resolution concentrations of COPCs can be accurately and robustly derived from ''in situ'' measurements using statistical methods.
  
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The OPTICS method is analogous to the commonly used empirical derivation of total suspended solids concentration (TSS) from optical turbidity using linear regression<ref>Rasmussen, P.P., Gray, J.R., Glysson, G.D., Ziegler, A.C., 2009. Guidelines and procedures for computing time-series suspended-sediment concentrations and loads from in-stream turbidity-sensor and streamflow data. In: Techniques and Methods, Book 3: Applications of Hydraulics, Section C: Sediment and Erosion Techniques, Ch. 4. 52 pages. U.S. Geological Survey.&nbsp;&nbsp; [[Media: RasmussenEtAl2009.pdf | Open Access Article]]</ref>. However, rather than deriving one response variable (TSS) from one predictor variable (turbidity), OPTICS involves derivation of one response variable (e.g., PCB concentration) from a suite of predictor variables (e.g., turbidity, temperature, salinity, and fluorescence of chlorophyll-a) using multi-parameter statistical regression. OPTICS is based on statistical correlation – similar to the turbidity-to-TSS regression technique. The method does not rely on interpolation or extrapolation. 
  
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The OPTICS technique utilizes partial least-squares (PLS) regression to determine a combination of physical, optical, and water quality properties that best predicts chemical contaminant concentrations with high variance. PLS regression is a statistically based method combining multiple linear regression and principal component analysis (PCA), where multiple linear regression finds a combination of predictors that best fit a response and PCA finds combinations of predictors with large variance<ref name="deJong1993"/><ref name="RosipalKramer2006"/>. Therefore, PLS identifies combinations of multi-collinear predictors (''in situ'', high-resolution physical, optical, and water quality measurements) that have large covariance with the response values (discrete surface water chemical contaminant concentration data from samples that are collected periodically, coincident with ''in situ'' measurements). PLS combines information about the variances of both the predictors and the responses, while also considering the correlations among them. PLS therefore provides a model with reliable predictive power.
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[[File: Schwartz1w2Fig1.PNG | thumb | 500px | Figure 1.  Conceptual model of mercury speciation in the environment<ref>European Commission's Joint Research Centre, 2017. A new CRM to make mercury measurements in food more reliable. [https://ec.europa.eu/jrc/en/science-update/new-crm-make-mercury-measurements-food-more-reliable Website]</ref>]]
 
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[[Wikipedia: Mercury (element) | Mercury]] (Hg) is released into the environment typically in the inorganic form. Natural emissions of Hg(0) come mainly from volcanoes and the ocean. Anthropogenic emissions are mainly from artisanal and small-scale gold mining, coal combustion, and various industrial processes that use Hg ( see the [https://www.unep.org/explore-topics/chemicals-waste/what-we-do/mercury/global-mercury-assessment UN Global mercury assessment]). Industrial and natural emissions of gaseous elemental mercury, Hg(0), can travel long distances in the atmosphere before being oxidized and deposited on land and in water as inorganic Hg(II). The long range transport and atmospheric deposition of Hg results in widespread low-level Hg contamination of soils at concentrations of 0.01 to 0.3 mg/kg<ref name="Eckley2020"/>.  
 
  
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Hg-contaminated sites are most commonly contaminated with Hg(II) from industrial discharge and have soil concentrations in the range of 100s to 1000s of mg/kg<ref name="Eckley2020"/>.  Direct exposure to Hg(II) and Hg(0) can be a human health risk at heavily contaminated sites. However, the organic form of Hg, [[Wikipedia: Methylmercury | methylmercury]] (MeHg or CH<sub>3</sub>Hg<sup>+</sup>) is typically the greater concern. MeHg is a neurotoxin that is particularly harmful to developing fetuses and young children. Direct contamination of the environment with MeHg is not common, but has occurred, most notably in [https://www.minamatadiseasemuseum.net/10-things-to-know Minamata Bay, Japan] (see also [https://en.wikipedia.org/wiki/Minamata_disease Minamata disease]). More commonly, MeHg is formed in the environment from Hg(II) in oxygen-limited conditions in a processes mediated by anaerobic microorganisms. Because MeHg [[Wikipedia: Biomagnification | biomagnifies]] in the aquatic food web, MeHg concentrations in fish can be elevated in areas that have relatively low levels of Hg contamination. The MeHg production depends heavily on site geochemistry, and high total Hg sediment concentrations do not always correlate with MeHg production potential.
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OPTICS ''in situ'' measurement parameters include, but are not limited to current velocity, conductivity, temperature, depth, turbidity, dissolved oxygen, and fluorescence of chlorophyll-a and dissolved organic matter. Instrumentation for these measurements is commercially available, robust, deployable in a wide variety of configurations (e.g., moored, vessel-mounted, etc.), powered by batteries, and records data internally and/or transmits data in real-time. The physical, optical, and water quality instrumentation is compact and self-contained. The modularity and automated nature of the OPTICS measurement system enables robust, long-term, autonomous data collection for near-continuous monitoring.  
  
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==Biogeochemistry/Mobility of Hg in soils==
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[[File:ChangFig3.png | thumb | 400px| Figure 3. OPTICS to characterize COPC variability in the context of site processes at BCSA. (A) Tidal oscillations (Elev.<sub>MSL</sub>) and precipitation (Precip.). (B) – (D) OPTICS-derived particulate mercury (PHg) and methylmercury (PMeHg) and total PCBs (TPCBs). Open circles represent discrete water sample data.]] OPTICS measurements are provided at a significantly reduced cost relative to traditional monitoring techniques used within the environmental industry. Cost performance analysis shows that monitoring costs are reduced by more than 85% while significantly increasing the temporal and spatial resolution of sampling. The reduced cost of monitoring makes this technology suitable for a number of environmental applications including, but not limited to site baseline characterization, source control evaluation, dredge or stormflow plume characterization, and remedy performance monitoring. OPTICS has been successfully demonstrated for characterizing a wide variety of COPCs: mercury, methylmercury, copper, lead, PCBs, dichlorodiphenyltrichloroethane (DDT) and its related compounds (collectively, DDX), and 2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD or dioxin) in a number of different environmental systems ranging from inland lakes and rivers to the coastal ocean. To date, OPTICS has been limited to surface water applications. Additional applications (e.g., groundwater) would require further research and development.
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In the environment, Hg mobility is largely controlled by chelation with various ligands or adsorption to particles<ref name ="Hsu-Kim2018"/>. Hg(II) is most strongly attracted to the sulfur functional groups in dissolved organic matter (DOM) and to sulfur ligands. Over time, newly released Hg(II) “ages” and becomes less reactive to ligands and is less likely to be found in the dissolved phase. Legacy Hg(II) found in sediments and soils is more likely to be strongly adsorbed to the soil matrix and not very bioavailable compared to newly released Hg(II)<ref name ="Hsu-Kim2018"/>. MeHg has mobility tendencies similar to Hg, with DOM and sulfur ligands competing with each other to form complexes with MeHg<ref name="Loux2007">Loux, N.T., 2007. An assessment of thermodynamic reaction constants for simulating aqueous environmental monomethylmercury speciation. Chemical Speciation and Bioavailability, 19(4), pp.183-196.  [https://doi.org/10.3184/095422907X255947  DOI: 10.3184/095422907X255947]&nbsp;&nbsp; [https://www.tandfonline.com/doi/pdf/10.3184/095422907X255947?needAccess=true Free access article]&nbsp;&nbsp; [[Media: Loux2007.pdf | Report.pdf]]</ref>. However, unlike Hg-S complexes, MeHg-S does not have limited solubility.
 
  
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The bioavailability of Hg(II) is one of the factors controlling MeHg production in the environment. MeHg production occurs in anoxic environments and is affected by: (1) the bioavailability of Hg(II) complexes to Hg-[[Wikipedia: Methylation | methylating]] microorganisms, (2) the activity of Hg-methylating microorganisms, and (3) the rate of biotic and abiotic [[Wikipedia: Demethylation | demethylation]]. MeHg is produced by anaerobic microorganisms that contain the ''hgcAB'' gene<ref name="Parks2013">Parks, J.M., Johs, A., Podar, M., Bridou, R. Hurt, R.A., Smith, S.D., Tomanicek, S.J., Qian, Y., Brown, S.D., Brandt, C.C., Palumbo, A.V., Smith, J.C., Wall, J.D., Elias, D.A., Liang, L., 2013. The Genetic Basis for Bacterial Mercury Methylation. Science, 339(6125), pp. 1332-1335.  [https://science.sciencemag.org/content/339/6125/1332 DOI: 10.1126/science.1230667]</ref>. These microorganisms are a diverse group and include, sulfate-reducing bacteria, iron-reducing bacteria, and methanogenic bacteria. Site geochemistry has a significant effect on MeHg production. Methylating microorganisms are sensitive to oxygen, and MeHg production occurs in oxygen-depleted or anaerobic zones in the environment, such as anoxic aquatic sediments, saturated soils, and biofilms with anoxic microenvironments<ref name="Bravo2020">Bravo, A.G., Cosio, C., 2020. Biotic formation of methylmercury: A bio–physico–chemical conundrum. Limnology and Oceanography, 65(5), pp. 1010-1027. [https://doi.org/10.1002/lno.11366 DOI: 10.1002/lno.11366]&nbsp;&nbsp; [https://aslopubs.onlinelibrary.wiley.com/doi/epdf/10.1002/lno.11366 Free Access Article]&nbsp;&nbsp; [[Media: Bravo2020.pdf | Report.pdf]]</ref>. The activity of methylating microorganisms can be impacted by redox conditions, the concentrations of organic carbon, and different electron acceptors (e.g. sulfate vs iron)<ref name="Bravo2020"/>. Overall, MeHg concentrations and production are impacted by demethylation as well. Demethylation can occur both abiotically and biotically and occurs at a much faster rate than methylation. The main routes of abiotic demethylation are photochemical reactions and demethylation catalyzed by reduced sulfur surfaces<ref name="Du2019">Du, H. Ma, M., Igarashi, Y., Wang, D., 2019. Biotic and Abiotic Degradation of Methylmercury in Aquatic Ecosystems: A Review. Bulletin of Environmental Contamination and Toxicology, 102 pp. 605-611. [https://doi.org/10.1007/s00128-018-2530-2 DOI: 10.1007/s00128-018-2530-2]</ref><ref name="Jonsson2016">Jonsson, S., Mazrui, N.M., Mason, R.P., 2016. Dimethylmercury Formation Mediated by Inorganic and Organic Reduced Sulfur Surfaces. Scientific Reports, 6, Article 27958.  [https://doi.org/10.1038/srep27958 DOI: 10.1038/srep27958]&nbsp;&nbsp; [https://www.nature.com/articles/srep27958.pdf Free access article]&nbsp;&nbsp; [[Media: Jonsson2016.pdf | Report.pdf]]</ref>. Methylmercury can be degraded biotically by aerobic bacteria containing the mercury detoxification, ''mer'' [[Wikipedia: Operon | operon]] and through oxidative demethylation by anaerobic microorganisms<ref name="Du2019"/>.
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==Applications==
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[[File:ChangFig4.png | thumb | 400px| Figure 4. OPTICS reveals baseflow daily cycling and confirms storm-induced particle-bound COPC resuspension and mobilization through bank interaction. (A) Flow rate (Q) and precipitation (Precip). (B) – (C) OPTICS-derived particulate mercury (PHg) and methylmercury (PMeHg). Open circles represent discrete water sample data.]]
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[[File:ChangFig5.png | thumb | 400px| Figure 5. Three-dimensional volume plot of high spatial resolution OPTICS-derived PCBs in exceedance of baseline showing that PCBs were discharged from the outfall (yellow arrow), remained in suspension, and dispersed elsewhere before settling.]]
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An OPTICS study was conducted at Berry’s Creek Study Area (BCSA), New Jersey in 2014 and 2015 to understand COPC sources and transport mechanisms for development of an effective remediation plan. OPTICS successfully extended periodic discrete surface water samples to continuous, high-resolution measurements of PCBs, mercury, and methylmercury to elucidate COPC sources and transport throughout the BCSA tidal estuary system. OPTICS provided data at resolution sufficient to investigate COC variability in the context of physical processes. The results (Figure 3) facilitated focused and effective site remediation and management decisions that could not be determined based on periodic discrete samples alone, despite over seven years of monitoring at different locations throughout the system over a range of different seasons, tidal phases, and environmental conditions. The BCSA OPTICS methodology and its results have undergone official peer review overseen by the U.S. Environmental Protection Agency (USEPA), and those results have been published in peer-reviewed literature<ref name="ChangEtAl2019"/>.  
  
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==Bioaccumulation and Toxicology==
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OPTICS was applied at the South River, Virginia in 2016 to quantify sources of legacy mercury in the system that are contributing to recontamination and continued elevated mercury concentrations in fish tissue. OPTICS provided information necessary to identify mechanisms for COPC redistribution and to quantify the relative contribution of each mechanism to total mass transport of mercury and methylmercury in the system. Continuous, high-resolution COPC data afforded by OPTICS helped resolve baseflow daily cycling that had never before been observed at the South River (Figure 4) and provided data at temporal resolution necessary to verify storm-induced particle-bound COC resuspension and mobilization through bank interaction. The results informed source control and remedy design and monitoring efforts. Methodology and results from the South River have been published in peer-reviewed literature<ref name="ChangEtAl2018"/>.  
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Regulatory criteria are most often based on total Hg concentrations, however, MeHg is the form of Hg that can [[Wikipedia: Bioaccumulation | bioaccumulate]] in wildlife and is the greatest human and ecological health risk<ref name=”ATSDR1999”>Agency for Toxic Substances and Disease Registry (ATSDR), 1999. Toxicological Profile for Mercury.  [https://www.atsdr.cdc.gov/ToxProfiles/tp46.pdf Free download]&nbsp;&nbsp; [[Media: ATSDR1999.pdf | Report.pdf]]</ref>. MeHg represents over 95% of the Hg found in fish<ref name="Bloom1992">Bloom, N.S., 1992. On the Chemical Form of Mercury in Edible Fish and Marine Invertebrate Tissue. Canadian Journal of Fisheries and Aquatic Sciences 49(5), pp. 1010-117.  [https://doi.org/10.1139/f92-113 DOI: 10.1139/f92-113]</ref>. Hg and MeHg can be taken up directly from contaminated water into organisms, with the identity of the Hg-ligand complexes determining how readily the Hg is taken up into the organism<ref name="Kidd2012">Kidd, K., Clayden, M., Jardine, T., 2012. Bioaccumulation and Biomagnification of Mercury through Food Webs. Environmental Chemistry and Toxicology of Mercury, pp. 453-499. Liu, G., Yong, C. O’Driscoll, N., Eds. John Wiley and Sons, Inc. Hoboken, NJ.  [https://doi.org/10.1002/9781118146644.ch14 DOI: 10.1002/9781118146644.ch14]</ref>. Direct bioconcentration from water is the major uptake route at the base of the food web. Hg and MeHg can also enter the food web when benthic organisms ingest contaminated sediments<ref name="Mason2001">Mason, R.P., 2001. The Bioaccumulation of Mercury, Methylmercury and Other Toxic Elements into Pelagic and Benthic Organisms. Coastal and Estuarine Risk Assessment, pp. 127-149. Newman, M., Roberts, M., and Hale, R.C., Ed.s. CRC Press. ISBN: 978-1-4200-3245-1  Free download from: [https://www.researchgate.net/profile/Robert-Mason-13/publication/266354387_The_Bioaccumulation_of_Mercury_Methylmercury_and_Other_Toxic_Elements_into_Pelagic_and_Benthic_Organisms/links/55083eff0cf26ff55f80662d/The-Bioaccumulation-of-Mercury-Methylmercury-and-Other-Toxic-Elements-into-Pelagic-and-Benthic-Organisms.pdf ResearchGate]</ref>. Further up the food web organisms are exposed to Hg and MeHg both through exposure to contaminated water and through their diet. The higher up the trophic level, the more important dietary exposure becomes. Fish obtain more than 90% of Hg from their diet<ref name="Kidd2012"/>.  
 
  
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Humans are mainly exposed to Hg in the forms of MeHg and Hg(0). Hg(0) exposure comes from dental amalgams and industrial/contaminated site exposures. Hg(0) readily crosses the blood/brain barrier and mainly effects the nervous system and the kidneys<ref name="Clarkson2003">Clarkson, T.W., Magos, L., Myers, G.J., 2003. The Toxicology of Mercury — Current Exposures and Clinical Manifestations. New England Journal of Medicine, 349, pp. 1731-1737. [https://doi.org/10.1056/NEJMra022471 DOI: 10.1056/NEJMra022471]</ref>. MeHg exposure comes from the consumption of contaminated fish. In the human body, MeHg is readily absorbed through the gastrointestinal tract into the bloodstream and crosses the blood/brain barrier, affecting the central nervous system. MeHg can also pass through the placenta to the fetus and is particularly harmful to the developing nervous system of the fetus.  
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The U.S. Department of Defense’s Environmental Security Technology Certification Program (ESTCP) supported an OPTICS demonstration study at the Pearl Harbor Sediment Site, Hawaii, to determine whether stormwater from Oscar 1 Pier outfall is a contributing source of PCBs to Decision Unit (DU) N-2 (ESTCP Project ER21-5021). High spatial resolution results afforded by ship-based, mobile OPTICS monitoring suggested that PCBs were discharged from the outfall, remained in suspension, and dispersed elsewhere before settling (Figure 5). More details regarding this study were presented by Chang et al. in 2024<ref name="ChangEtAl2024"/>.
  
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MeHg and Hg toxicity in the body occurs through multiple pathways and may be linked to the affinity of Hg for sulfur groups. Hg and MeHg bind to S-containing groups, which can block normal bodily functions<ref name="Bjørklund2017">Bjørklund, G., Dadar, M., Mutter, J. and Aaseth, J., 2017. The toxicology of mercury: Current research and emerging trends. Environmental Research, 159, pp.545-554.  [https://doi.org/10.1016/j.envres.2017.08.051 DOI: 10.1016/j.envres.2017.08.051]</ref>.  
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==Summary==
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OPTICS provides:
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*High resolution surface water chemical contaminant characterization
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*Cost-effective monitoring and assessment
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*Versatile and modular monitoring with capability for real-time telemetry
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*Data necessary for development and validation of conceptual site models
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*A key line of evidence for designing and evaluating remedies.
  
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==Regulatory Framework for Mercury==
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Because OPTICS monitoring involves deployment of autonomous sampling instrumentation, a substantially greater volume of data can be collected using this technique compared to traditional sampling, and at a far lower cost. A large volume of data supports evaluation of chemical contaminant concentrations over a range of spatial and temporal scales, and the system can be customized for a variety of environmental applications. OPTICS helps quantify contaminant mass flux and the relative contribution of local transport and source areas to net contaminant transport. OPTICS delivers a strong line of evidence for evaluating contaminant sources, fate, and transport, and for supporting the design of a remedy tailored to address site-specific, risk-driving conditions. The improved understanding of site processes aids in the development of mitigation measures that minimize site risks.  
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In the United States, mercury is regulated by several different [[Wikipedia: Mercury regulation in the United States | environmental laws]] including: the Mercury Export Ban Act of 2008, the Mercury-Containing and Rechargeable Battery Management Act of 1996, the Clean Air Act, the Clean Water Act, the Emergency Planning and Community Right-to-Know Act, the Resource Conservation and Recovery Act, and the Safe Drinking Water Act<ref name=”USEPA2021”>US EPA, 2021. Environmental Laws that Apply to Mercury.  [https://www.epa.gov/mercury/environmental-laws-apply-mercury US EPA Website]</ref>.  
 
  
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In 2013, the United States signed the international [https://www.epa.gov/international-cooperation/minamata-convention-mercury Minamata Convention on Mercury]. The Minamata Convention on Mercury seeks to address and reduce human activities that are contributing to widespread mercury pollution. Worldwide, 128 countries have signed the Convention.
 
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==Remediation Technologies==
 
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As a chemical element, Hg cannot be destroyed, so the goal of Hg-remediation is immobilization and prevention of food web bioaccumulation. At very highly contaminated sites (>100s ppm), sediments are often removed and landfilled<ref name="Eckley2020"/>. ''In situ'' capping is also a common remediation approach. Both dredging and capping can be costly and ecologically destructive, and the development of less invasive, less costly remediation technologies for Hg and MeHg contaminated sediments is an active research field. Eckley et al.<ref name="Eckley2020"/>and Wang et al.<ref name="Wang2020">Wang, L., Hou, D., Cao, Y., Ok, Y.S., Tack, F., Rinklebe, J., O’Connor, D., 2020. Remediation of mercury contaminated soil, water, and air: A review of emerging materials and innovative technologies. Environmental International, 134, 105281.  [https://doi.org/10.1016/j.envint.2019.105281  DOI: 10.1016/j.envint.2019.105281]&nbsp;&nbsp; [https://www.sciencedirect.com/science/article/pii/S0160412019324754 Free access article]</ref> give thorough reviews of standard and emerging technologies.
 
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Recently application of ''in situ'' sorbents has garnered interest as a remediation solution for Hg<ref name="Eckley2020"/>. Many different materials, including biochar and various formulations of [[In Situ Treatment of Contaminated Sediments with Activated Carbon | activated carbon]], are successful in lowering porewater concentrations of Hg and MeHg in contaminated sediments<ref name="Gilmour2013">Gilmour, C.C., Riedel, G.S., Riedel, G., Kwon, S., Landis, R., Brown, S.S., Menzie, C.A., Ghosh, U., 2013. Activated Carbon Mitigates Mercury and Methylmercury Bioavailability in Contaminated Sediments. Environmental Science and Technology, 47(22), pp. 13001-13010.  [https://doi.org/10.1021/es4021074 DOI: 10.1021/es4021074]&nbsp;&nbsp; Free download from: [https://www.researchgate.net/profile/Steven-Brown-18/publication/258042399_Activated_Carbon_Mitigates_Mercury_and_Methylmercury_Bioavailability_in_Contaminated_Sediments/links/5702a10e08aea09bb1a30083/Activated-Carbon-Mitigates-Mercury-and-Methylmercury-Bioavailability-in-Contaminated-Sediments.pdf ResearchGate]</ref>. More research is needed to determine whether Hg and MeHg sorbed to these materials are available for uptake into organisms. Site biogeochemistry can also impact the efficacy of sorbent materials, with dissolved organic matter and sulfide concentrations impacting Hg and MeHg sorption. Overall, knowing site biogeochemical characteristics is important for predicting Hg mobility and MeHg production risks as well as for designing a remediation strategy that will be effective.
 
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Latest revision as of 20:39, 15 July 2024

Assessing Vapor Intrusion (VI) Impacts in Neighborhoods with Groundwater Contaminated by Chlorinated Volatile Organic Chemicals (CVOCs)

The VI Diagnosis Toolkit[1] is a set of tools that can be used individually or in combination to assess vapor intrusion (VI) impacts at one or more buildings overlying regional-scale dissolved chlorinated solvent-impacted groundwater plumes. The strategic use of these tools can lead to confident and efficient neighborhood-scale VI pathway assessments.

Related Article(s):

Contributor(s):

  • Paul C. Johnson, Ph.D.
  • Paul Dahlen, Ph.D.
  • Yuanming Guo, Ph.D.

Key Resource(s):

  • The VI Diagnosis Toolkit for Assessing Vapor Intrusion Pathways and Impacts in Neighborhoods Overlying Dissolved Chlorinated Solvent Plumes, ESTCP Project ER-201501, Final Report[1]
  • CPM Test Guidelines: Use of Controlled Pressure Method Testing for Vapor Intrusion Pathway Assessment, ESTCP Project ER-201501, Technical Report[2]
  • VI Diagnosis Toolkit User Guide, ESTCP Project ER-201501[3]

Background

Figure 1. Example of instrumentation used for OPTICS monitoring.
Figure 2. Schematic diagram illustrating the OPTICS methodology. High resolution in-situ data are integrated with traditional discrete sample analytical data using partial least-square regression to derive high resolution chemical contaminant concentration data series.

Nationwide, the liability due to contaminated sediments is estimated in the trillions of dollars. Stakeholders are assessing and developing remedial strategies for contaminated sediment sites in major harbors and waterways throughout the U.S. The mobility of contaminants in surface water is a primary transport and risk mechanism[4][5][6]; therefore, long-term monitoring of both particulate- and dissolved-phase contaminant concentration prior to, during, and following remedial action is necessary to document remedy effectiveness. Source control and total maximum daily load (TMDL) actions generally require costly manual monitoring of dissolved and particulate contaminant concentrations in surface water. The magnitude of cost for these actions is a strong motivation to implement efficient methods for long-term source control and remedial monitoring.

Traditional surface water monitoring requires mobilization of field teams to manually collect discrete water samples, send samples to laboratories, and await laboratory analysis so that a site evaluation can be conducted. These traditional methods are well known to have inherent cost and safety concerns and are of limited use (due to safety concerns and standby requirements for resources) in capturing the effects of episodic events (e.g., storms) that are important to consider in site risk assessment and remedy selection. Automated water samplers are commercially available but still require significant field support and costly laboratory analysis. Further, automated samplers may not be suitable for analytes with short hold-times and temperature requirements.

Optically-based characterization of surface water contaminants is a cost-effective alternative to traditional discrete water sampling methods. Unlike discrete water sampling, which typically results in sparse data at low resolution, and therefore, is of limited use in determining mass loading, OPTICS (OPTically-based In-situ Characterization System) provides continuous data and allows for a complete understanding of water quality and contaminant transport in response to natural processes and human impacts[7][8][9][10][11][12]. The OPTICS tool integrates commercial off-the-shelf in situ aquatic sensors (Figure 1), periodic discrete surface water sample collection, and a multi-parameter statistical prediction model[13][14] to provide high temporal and/or spatial resolution characterization of surface water chemicals of potential concern (COPCs) (Figure 2).

Technology Overview

The principle behind OPTICS is based on the relationship between optical properties of natural waters and the particles and dissolved material contained within them[15][16][17][18][19][20][21][22]. Surface water COPCs such as heavy metals and polychlorinated biphenyls (PCBs) are hydrophobic in nature and tend to sorb to materials in the water column, which have unique optical signatures that can be measured at high-resolution using in situ, commercially available aquatic sensors[23][24][25][26]. Therefore, high-resolution concentrations of COPCs can be accurately and robustly derived from in situ measurements using statistical methods.

The OPTICS method is analogous to the commonly used empirical derivation of total suspended solids concentration (TSS) from optical turbidity using linear regression[27]. However, rather than deriving one response variable (TSS) from one predictor variable (turbidity), OPTICS involves derivation of one response variable (e.g., PCB concentration) from a suite of predictor variables (e.g., turbidity, temperature, salinity, and fluorescence of chlorophyll-a) using multi-parameter statistical regression. OPTICS is based on statistical correlation – similar to the turbidity-to-TSS regression technique. The method does not rely on interpolation or extrapolation.

The OPTICS technique utilizes partial least-squares (PLS) regression to determine a combination of physical, optical, and water quality properties that best predicts chemical contaminant concentrations with high variance. PLS regression is a statistically based method combining multiple linear regression and principal component analysis (PCA), where multiple linear regression finds a combination of predictors that best fit a response and PCA finds combinations of predictors with large variance[13][14]. Therefore, PLS identifies combinations of multi-collinear predictors (in situ, high-resolution physical, optical, and water quality measurements) that have large covariance with the response values (discrete surface water chemical contaminant concentration data from samples that are collected periodically, coincident with in situ measurements). PLS combines information about the variances of both the predictors and the responses, while also considering the correlations among them. PLS therefore provides a model with reliable predictive power.

OPTICS in situ measurement parameters include, but are not limited to current velocity, conductivity, temperature, depth, turbidity, dissolved oxygen, and fluorescence of chlorophyll-a and dissolved organic matter. Instrumentation for these measurements is commercially available, robust, deployable in a wide variety of configurations (e.g., moored, vessel-mounted, etc.), powered by batteries, and records data internally and/or transmits data in real-time. The physical, optical, and water quality instrumentation is compact and self-contained. The modularity and automated nature of the OPTICS measurement system enables robust, long-term, autonomous data collection for near-continuous monitoring.

Figure 3. OPTICS to characterize COPC variability in the context of site processes at BCSA. (A) Tidal oscillations (Elev.MSL) and precipitation (Precip.). (B) – (D) OPTICS-derived particulate mercury (PHg) and methylmercury (PMeHg) and total PCBs (TPCBs). Open circles represent discrete water sample data.

OPTICS measurements are provided at a significantly reduced cost relative to traditional monitoring techniques used within the environmental industry. Cost performance analysis shows that monitoring costs are reduced by more than 85% while significantly increasing the temporal and spatial resolution of sampling. The reduced cost of monitoring makes this technology suitable for a number of environmental applications including, but not limited to site baseline characterization, source control evaluation, dredge or stormflow plume characterization, and remedy performance monitoring. OPTICS has been successfully demonstrated for characterizing a wide variety of COPCs: mercury, methylmercury, copper, lead, PCBs, dichlorodiphenyltrichloroethane (DDT) and its related compounds (collectively, DDX), and 2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD or dioxin) in a number of different environmental systems ranging from inland lakes and rivers to the coastal ocean. To date, OPTICS has been limited to surface water applications. Additional applications (e.g., groundwater) would require further research and development.

Applications

Figure 4. OPTICS reveals baseflow daily cycling and confirms storm-induced particle-bound COPC resuspension and mobilization through bank interaction. (A) Flow rate (Q) and precipitation (Precip). (B) – (C) OPTICS-derived particulate mercury (PHg) and methylmercury (PMeHg). Open circles represent discrete water sample data.
Figure 5. Three-dimensional volume plot of high spatial resolution OPTICS-derived PCBs in exceedance of baseline showing that PCBs were discharged from the outfall (yellow arrow), remained in suspension, and dispersed elsewhere before settling.

An OPTICS study was conducted at Berry’s Creek Study Area (BCSA), New Jersey in 2014 and 2015 to understand COPC sources and transport mechanisms for development of an effective remediation plan. OPTICS successfully extended periodic discrete surface water samples to continuous, high-resolution measurements of PCBs, mercury, and methylmercury to elucidate COPC sources and transport throughout the BCSA tidal estuary system. OPTICS provided data at resolution sufficient to investigate COC variability in the context of physical processes. The results (Figure 3) facilitated focused and effective site remediation and management decisions that could not be determined based on periodic discrete samples alone, despite over seven years of monitoring at different locations throughout the system over a range of different seasons, tidal phases, and environmental conditions. The BCSA OPTICS methodology and its results have undergone official peer review overseen by the U.S. Environmental Protection Agency (USEPA), and those results have been published in peer-reviewed literature[7].

OPTICS was applied at the South River, Virginia in 2016 to quantify sources of legacy mercury in the system that are contributing to recontamination and continued elevated mercury concentrations in fish tissue. OPTICS provided information necessary to identify mechanisms for COPC redistribution and to quantify the relative contribution of each mechanism to total mass transport of mercury and methylmercury in the system. Continuous, high-resolution COPC data afforded by OPTICS helped resolve baseflow daily cycling that had never before been observed at the South River (Figure 4) and provided data at temporal resolution necessary to verify storm-induced particle-bound COC resuspension and mobilization through bank interaction. The results informed source control and remedy design and monitoring efforts. Methodology and results from the South River have been published in peer-reviewed literature[8].

The U.S. Department of Defense’s Environmental Security Technology Certification Program (ESTCP) supported an OPTICS demonstration study at the Pearl Harbor Sediment Site, Hawaii, to determine whether stormwater from Oscar 1 Pier outfall is a contributing source of PCBs to Decision Unit (DU) N-2 (ESTCP Project ER21-5021). High spatial resolution results afforded by ship-based, mobile OPTICS monitoring suggested that PCBs were discharged from the outfall, remained in suspension, and dispersed elsewhere before settling (Figure 5). More details regarding this study were presented by Chang et al. in 2024[9].

Summary

OPTICS provides:

  • High resolution surface water chemical contaminant characterization
  • Cost-effective monitoring and assessment
  • Versatile and modular monitoring with capability for real-time telemetry
  • Data necessary for development and validation of conceptual site models
  • A key line of evidence for designing and evaluating remedies.

Because OPTICS monitoring involves deployment of autonomous sampling instrumentation, a substantially greater volume of data can be collected using this technique compared to traditional sampling, and at a far lower cost. A large volume of data supports evaluation of chemical contaminant concentrations over a range of spatial and temporal scales, and the system can be customized for a variety of environmental applications. OPTICS helps quantify contaminant mass flux and the relative contribution of local transport and source areas to net contaminant transport. OPTICS delivers a strong line of evidence for evaluating contaminant sources, fate, and transport, and for supporting the design of a remedy tailored to address site-specific, risk-driving conditions. The improved understanding of site processes aids in the development of mitigation measures that minimize site risks.

References

  1. ^ 1.0 1.1 Johnson, P.C., Guo, Y., Dahlen, P., 2020. The VI Diagnosis Toolkit for Assessing Vapor Intrusion Pathways and Mitigating Impacts in Neighborhoods Overlying Dissolved Chlorinated Solvent Plumes. ESTCP Project ER-201501, Final Report. Project Website   Final Report.pdf
  2. ^ Johnson, P.C., Guo, Y., Dahlen, P., 2021. CPM Test Guidelines: Use of Controlled Pressure Method Testing for Vapor Intrusion Pathway Assessment. ESTCP ER-201501, Technical Report. Project Website   Technical_Report.pdf
  3. ^ Johnson, P.C., Guo, Y., and Dahlen, P., 2022. VI Diagnosis Toolkit User Guide, ESTCP ER-201501, User Guide. Project Website   User_Guide.pdf
  4. ^ Thibodeaux, L.J., 1996. Environmental Chemodynamics: Movement of Chemicals in Air, Water, and Soil, 2nd Edition, Volume 110 of Environmental Science and Technology: A Wiley-Interscience Series of Texts and Monographs. John Wiley & Sons, Inc. 624 pages. ISBN: 0-471-61295-2
  5. ^ United States Environmental Protection Agency (USEPA), 2005. Contaminated Sediment Remediation Guidance for Hazardous Waste Sites. Office of Superfund Remediation and Technology Innovation Report, EPA-540-R-05-012. Report.pdf
  6. ^ Lick, W., 2008. Sediment and Contaminant Transport in Surface Waters. CRC Press. 416 pages. doi: 10.1201/9781420059885
  7. ^ 7.0 7.1 Cite error: Invalid <ref> tag; no text was provided for refs named ChangEtAl2019
  8. ^ 8.0 8.1 Cite error: Invalid <ref> tag; no text was provided for refs named ChangEtAl2018
  9. ^ 9.0 9.1 Cite error: Invalid <ref> tag; no text was provided for refs named ChangEtAl2024
  10. ^ Bergamaschi, B.A., Fleck, J.A., Downing, B.D., Boss, E., Pellerin, B., Ganju, N.K., Schoellhamer, D.H., Byington, A.A., Heim, W.A., Stephenson, M., Fujii, R., 2011. Methyl mercury dynamics in a tidal wetland quantified using in situ optical measurements. Limnology and Oceanography, 56(4), pp. 1355-1371. doi: 10.4319/lo.2011.56.4.1355   Open Access Article
  11. ^ Bergamaschi, B.A., Fleck, J.A., Downing, B.D., Boss, E., Pellerin, B.A., Ganju, N.K., Schoellhamer, D.H., Byington, A.A., Heim, W.A., Stephenson, M., Fujii, R., 2012. Mercury Dynamics in a San Francisco Estuary Tidal Wetland: Assessing Dynamics Using In Situ Measurements. Estuaries and Coasts, 35, pp. 1036-1048. doi: 10.1007/s12237-012-9501-3   Open Access Article
  12. ^ Bergamaschi, B.A., Krabbenhoft, D.P., Aiken, G.R., Patino, E., Rumbold, D.G., Orem, W.H., 2012. Tidally driven export of dissolved organic carbon, total mercury, and methylmercury from a mangrove-dominated estuary. Environmental Science and Technology, 46(3), pp. 1371-1378. doi: 10.1021/es2029137   Open Access Article
  13. ^ 13.0 13.1 de Jong, S., 1993. SIMPLS: an alternative approach to partial least squares regression. Chemometrics and Intelligent Laboratory Systems, 18(3), pp. 251-263. doi: 10.1016/0169-7439(93)85002-X
  14. ^ 14.0 14.1 Rosipal, R. and Krämer, N., 2006. Overview and Recent Advances in Partial Least Squares, In: Subspace, Latent Structure, and Feature Selection: Statistical and Optimization Perspectives Workshop, Revised Selected Papers (Lecture Notes in Computer Science, Volume 3940), Springer-Verlag, Berlin, Germany. pp. 34-51. doi: 10.1007/11752790_2
  15. ^ Boss, E. and Pegau, W.S., 2001. Relationship of light scattering at an angle in the backward direction to the backscattering coefficient. Applied Optics, 40(30), pp. 5503-5507. doi: 10.1364/AO.40.005503
  16. ^ Boss, E., Twardowski, M.S., Herring, S., 2001. Shape of the particulate beam spectrum and its inversion to obtain the shape of the particle size distribution. Applied Optics, 40(27), pp. 4884-4893. doi:10/1364/AO.40.004885
  17. ^ Babin, M., Morel, A., Fournier-Sicre, V., Fell, F., Stramski, D., 2003. Light scattering properties of marine particles in coastal and open ocean waters as related to the particle mass concentration. Limnology and Oceanography, 48(2), pp. 843-859. doi: 10.4319/lo.2003.48.2.0843   Open Access Article
  18. ^ Coble, P., Hu, C., Gould, R., Chang, G., Wood, M., 2004. Colored dissolved organic matter in the coastal ocean: An optical tool for coastal zone environmental assessment and management. Oceanography, 17(2), pp. 50-59. doi: 10.5670/oceanog.2004.47   Open Access Article
  19. ^ Sullivan, J.M., Twardowski, M.S., Donaghay, P.L., Freeman, S.A., 2005. Use of optical scattering to discriminate particle types in coastal waters. Applied Optics, 44(9), pp. 1667–1680. doi: 10.1364/AO.44.001667
  20. ^ Twardowski, M.S., Boss, E., Macdonald, J.B., Pegau, W.S., Barnard, A.H., Zaneveld, J.R.V., 2001. A model for estimating bulk refractive index from the optical backscattering ratio and the implications for understanding particle composition in case I and case II waters. Journal of Geophysical Research: Oceans, 106(C7), pp. 14,129-14,142. doi: 10/1029/2000JC000404   Open Access Article
  21. ^ Chang, G.C., Barnard, A.H., McLean, S., Egli, P.J., Moore, C., Zaneveld, J.R.V., Dickey, T.D., Hanson, A., 2006. In situ optical variability and relationships in the Santa Barbara Channel: implications for remote sensing. Applied Optics, 45(15), pp. 3593–3604. doi: 10.1364/AO.45.003593
  22. ^ Slade, W.H. and Boss, E., 2015. Spectral attenuation and backscattering as indicators of average particle size. Applied Optics, 54(24), pp. 7264-7277. doi: 10/1364/AO.54.007264   Open Access Article
  23. ^ Agrawal, Y.C. and Pottsmith, H.C., 2000. Instruments for particle size and settling velocity observations in sediment transport. Marine Geology, 168(1-4), pp. 89-114. doi: 10.1016/S0025-3227(00)00044-X
  24. ^ Boss, E., Pegau, W.S., Gardner, W.D., Zaneveld, J.R.V., Barnard, A.H., Twardowski, M.S., Chang, G.C., Dickey, T.D., 2001. Spectral particulate attenuation and particle size distribution in the bottom boundary layer of a continental shelf. Journal of Geophysical Research: Oceans, 106(C5), pp. 9509-9516. doi: 10.1029/2000JC900077   Open Access Article
  25. ^ Boss, E., Pegau, W.S., Lee, M., Twardowski, M., Shybanov, E., Korotaev, G. Baratange, F., 2004. Particulate backscattering ratio at LEO 15 and its use to study particle composition and distribution. Journal of Geophysical Research: Oceans, 109(C1), Article C01014. doi: 10.1029/2002JC001514   Open Access Article
  26. ^ Briggs, N.T., Slade, W.H., Boss, E., Perry, M.J., 2013. Method for estimating mean particle size from high-frequency fluctuations in beam attenuation or scattering measurement. Applied Optics, 52(27), pp. 6710-6725. doi: 10.1364/AO.52.006710   Open Access Article
  27. ^ Rasmussen, P.P., Gray, J.R., Glysson, G.D., Ziegler, A.C., 2009. Guidelines and procedures for computing time-series suspended-sediment concentrations and loads from in-stream turbidity-sensor and streamflow data. In: Techniques and Methods, Book 3: Applications of Hydraulics, Section C: Sediment and Erosion Techniques, Ch. 4. 52 pages. U.S. Geological Survey.   Open Access Article

See Also

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