Field Observation and Modeling of Dissolved Fraction Sediment−Water Exchange Coefficients for PCBs in the Hudson River

Abstract

Chemical fate and transport models that simulate sediment-water exchange of contaminants typically employ empirically determined sediment-water exchange coefficients for the dissolved fraction to describe the net effect of poorly understood mechanisms. This paper presents field-derived observations of the coefficient for 12 PCB congeners and two PCB mixtures in the Thompson Island Pool, Hudson River, and also presents an evaluation of a theoretical sediment-water exchange model. An extensive PCB data set was used to compute apparent coefficients for PCBs in the pool. Average exchange coefficients for the 12 congeners ranged from 2.6 to 18.8 cm/ day, and results showed a strong seasonal dependence. Peak coefficient values occurred in mid-May to early July, preceding peak water temperatures by 1 month and lagging the spring high-flow period. The coefficients increase with increasing partition coefficients, suggesting a dependence on congener properties. The large magnitude of the coefficients and the variation among the congeners is inconsistent with the pore-water molecular-diffusion transport process. A theory-based, mechanistic two-layer model reproduces the nonlinear relationship between the sediment-water exchange coefficients and partition coefficients. This model includes transfer through the mixed sediment layer by bioturbation and diffusion transfer through a water-side boundary layer governed by flow velocity. Results suggest that this algorithm can provide increased accuracyto future system-level fate and transport models for hydrophobic chemicals. The seasonal variation in the transfer coefficient appears to be a poorly understood interaction of physical and biological processes and merits further study.

Bioturbation-Driven Release of Organic Contaminants from Baltic Sea Sediments Mediated by the Invading Polychaete Marenzelleria neglecta

“…This trend was observed for all PCB congeners but was statistically significant for the low-chlorinated PCBs (one-way ANOVAs: CB-28, Table 2). Bioturbation is traditionally considered to enhance the resuspension of particle-associated contaminants (e.g., ref 46), but recent investigations suggest bioturbation-driven soluble contaminant release to be equally, or even more important (9,12). This was clearly the case in the present study.…”

Remobilization of Polychlorinated Biphenyl from Baltic Sea Sediment: Comparing the Roles of Bioturbation and Physical Resuspension

“…Laboratory experiments have shown a significant increase in the concentration of HOCs in the overlying water after simulated resuspension events [5][6][7], typically correlated with the increased concentration of suspended particles. The diffusive flux process, on the other hand, is driven by concentration differences between the sediment pore water and the overlying water, and it is facilitated by both physical and biological transport mechanisms [9]. The diffusive flux process, on the other hand, is driven by concentration differences between the sediment pore water and the overlying water, and it is facilitated by both physical and biological transport mechanisms [9].…”

An analytical model for chemical diffusion in layered contaminated sediment systems with bioreactive caps

“…Furthermore, the mass transfer coefficient reported by the field studies ranged from 2.8 × 10 −5 to 4.6 × 10 −4 cm/s. 60,61 The mass transfer coefficients obtained in the laboratory experiments range from 1.5 × 10 −6 to 5.1 × 10 −5 cm/s, which are much lower than those obtained by the field studies because of overestimation of pore water concentrations or artificially high fluxes. 62,63 In this study, 2.5 × 10 −5 , 5 × 10 −5 , and 10 −4 cm/s will be used in the following analysis.…”

“…At the top of the bioturbation layer, the maximum contaminant concentration at the surface of system for the case with k bl = 2.5 × 10 −5 cm/s can be 13 times larger than that of the case with k bl = 10 −4 cm/s. It should be noted that this study has certain limitations. For example, some important factors, such as solute dispersion, mineral dissolution ratio, medium permeability anisotropy, temperature effect, nonlinear adsorption, complex degradation processes, and advection and consolidation‐induced advection, were neglected in this study. To further improve the analytical solutions derived from this study, these factors need to be considered in the future study.…”