This project sought to address the very common situation in which contaminated sediments in an aquatic ecosystem do not "respond" well to efforts to clean them up as measured by improved health of the organisms living in that environment. This, of course, translates to increased risks for humans and other predators (e.g., birds) that capture food from those sites.
Given the need for tools to guide site assessments and to make informed remedial designs for contaminated sediment sites, the team pursued the following coordinated modeling and passive sampling measurement objectives seeking:
- to develop a contaminated-sediment-site mass balance model that calculates the expected water column concentrations assuming a particular set of sources (e.g., diffusion from sediment in particular regions) and at least one quantifiable sink (e.g., flushing of the contaminants of concern from the aquatic site of interest). Hydrodynamic information was used to estimate bottom boundary layer thicknesses, and a polyethylene (PE) passive sampling-based method was used to characterize bed-to-water contaminant concentration gradients.
- to develop PE passive sampler-based methods suited to quantifying the freely dissolved concentrations of low-solubility contaminants like PCBs in the overlying water column, thereby allowing assessment of the accuracy of the water column concentration estimates using our mass balance model, the Environmental Fluid Dynamics Code (EFDC).
- to extend our understandings of the study site's set of PCB sources by using an Inverse Modeling approach that utilizes the known hydrodynamics (mixing, flushing) of the Lower Duwamish Waterway (LDW) and measures of the polychlorinated biphenyl (PCB) distributions in the water column to locate and quantify the sources needed to explain the distribution of PCBs in that estuary.
- to integrate the use of a Food Web Model (FWM) with the PCB concentration fields produced using passive sampling measures and synthesized using the EFDC to demonstrate the impacts of PCB bioaccumulation from surface water, pore water, and sediment solids.
- to exercise the mass balance and food web models together to assess the impacts of "what if" scenarios resulting from continuing low-level contaminant inputs after targeted sediment hot spots are remediated.
To improve the abilities to address such sites, the team explored a coordinated use of both measurement and modeling "tools" that could be applied to achieve better results. These tools included:
- using passive samplers to characterize the presence of contaminants like polycyclic aromatic hydrocarbons (PAHs), PCBs, and nonionic pesticides in all of the media including sediments. Such data allowed comparison of "passive sampler concentrations" in adjacent media such that directions of fluxes can be discerned. Further, translation to corresponding concentrations in other media like water allowed quantitative estimation of fluxes as well as a metric suited to calculating bioaccumulation.
- using mass balance modeling synthesis of the passive sampling results so as develop whole-ecosystem distributions of the chemicals evaluated by the passive samplers. Such whole-ecosystem expectations can then be compared with direct measures of the chemicals in well mixed fluid (e.g. surface waters) and any significant mismatch implies ignorance of the chemical's sources or sinks (as was found in this project for the surface water in the LDW).
- As found in this project, locating and quantifying important PCB sources in the LDW proved to be difficult for a variety of reasons (e.g., inability to access suspected source locations). This led the team to utilize inverse modeling so that the limited contaminant measures could be most effectively used to locate and quantify likely contaminant inputs.
- since environmental sampling is always limited in spatial and temporal coverage, coupling measures with modeling facilitated interpolation of contaminant presence at unsampled locations and times. This synthesis of the measurements has the huge advantage of greatly improving the team's knowledge of exposures to organisms in the environment of interest.
- the final "tool" used in this project was a food web model, FishRand. This modeling tool allowed the team to employ the measurement results, together with their synthesis via use of the mass balance modeling tool and the EFDC, to more fully drive the estimates of contaminant uptake by the various trophic levels present in the LDW.
Passive Sampling and Mass Balance Modeling Approaches - Utilizing passive sampling enabled the team to estimate the spatially varying diffusive fluxes from bed to water column. Integrating these fluxes over the entire LDW bottom yielded a total source strength for PCBs entering the water column from the bed. Using this spatially varying source strength for the 20 congeners the team specifically measured as inputs to the EFDC that had been tuned to match tidal amplitudes, the team could solve for the resulting distributions of each PCB in the LDW water column. It is important to note that passive sampling of the riverine influx of PCBs to the LDW was also performed to account for that PCB input source too. Combining these, the team could not explain the presence of most of the PCB load in the LDW water column.
Inverse Modeling - To perform this "inverse modeling", the team ran the EFDC with singular sources to generate a concentration profile that was unique to each putative source location. Then combining these concentration profiles using a least root mean error fitting approach such that the total PCB distributions in the water column could be be explained by only a few sources, we located a series of likely places in the estuary where PCBs were probably being introduced. The weightings of these inputs clearly indicated the relative importance for each of the PCB congeners that were fit.
Food Web Modeling - Operation of this model yielded biota concentrations that were consistent with past measures of PCBs in those organisms. Having established this linkage with the passive sampler measures and the EFDC modeling, the team showed that the food web model could be exercised for change PCB concentration conditions that might result from various remediation efforts. This effort clearly revealed the degree of biota response to particular changes in PCB exposures due to investments in clean up.
Combining all these results enable us to estimate the spatially varying diffusive fluxes from bed to water column. Integrating these fluxes over the entire LDW bottom yielded a total source strength for PCBs entering the water column from the bed.
Successful development of this integrated modeling-measurement system will allow DoD RPMs to: (i) identify their data needs, (ii) synthesize the data in terms of time- and space-varying exposures, (iii) estimate how those exposures translate into different food web responses for scenarios ranging from monitored natural recovery to targeted site clean-up, and (iv) evaluate potential improvements in FWMs seeking to predict organism concentrations given a more refined exposure field.
Apell, J. N. and P. M. Gschwend. 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, 218:95-101.
Apell, J.N. and P.M. Gschwend. 2017. The atmosphere as a source/sink of polychlorinated biphenyls to/from the Lower Duwamish Waterway Superfund site. Environmental Pollution, 227:263-270.
Apell, J.N., D.H. Shull, A.M. Hoyt, and P.M. Gschwend. 2018. Investigating the Effect of Bioirrigation on In Situ Porewater Concentrations and Fluxes of Polychlorinated Biphenyls Using Passive Samplers. Environmental Science Technology, 52(8):4565-4573.
Jonker, M.T.O., S.A. van der Heijden, D. Adelman, J.N. Apell, R.M. Burgess, Y. Choi, L.A. Fernandez, G.M. Flavetta, U. Ghosh, P.M. Gschwend, S.E. Hale, M. Jalalizadeh, M. Khairy, M.A. Lampi, W. Lao, R. Lohmann, M.J. Lydy, K.A. Maruya, S.A. Nutile, A.M.P. Oen, M.I. Rakowska, D. Reible, T.P. Rusina, R. Smedes, and Y. Wu. 2018. Advancing the Use of Passive Sampling in Risk Assessment and Management of Sediments Contaminated with Hydrophobic Organic Chemicals: Results of an International Ex Situ Passive Sampling Interlaboratory Comparison. Environmental Science Technology, 52(6):3574-3582.