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Mind the exposure gaps—modeling chemical transport in sediment toxicity tests
Fischer, F.C.; Hiki, K.; Soetaert, K.; Endo, S. (2021). Mind the exposure gaps—modeling chemical transport in sediment toxicity tests. Environ. Sci. Technol. 55(17): 11885-11893. https://dx.doi.org/10.1021/acs.est.1c03201
In: Environmental Science and Technology. American Chemical Society: Easton. ISSN 0013-936X; e-ISSN 1520-5851, meer
Peer reviewed article  
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Author keywords
    numerical modeling; bioavailability and exposure; diffusion and partitioning; sediment toxicity; laboratory-field extrapolation; facilitated transport
Auteurs  Top 
  • Fischer, F.C.
  • Hiki, K.
  • Soetaert, K., meer
  • Endo, S.
Abstract
    Chemical exposure in flow-through sediment toxicity tests can vary in time, between pore and overlying water, and amid free and bound states, complicating the link between toxicity and observable concentrations such as free pore (Cfree,pore), free overlying (Cfree,over), or the corresponding dissolved concentrations (Cdiss, free + bound to dissolved organic carbon, DOC). We introduce a numerical model that describes the desorption from sediments to pore water, diffusion through pores and the sediment–water boundary, DOC-mediated transport, and mixing in and outflow from overlying water. The model explained both the experimentally measured gap between Cfree,over and Cfree,pore and the continuous decrease in overlying Cdiss. Spatially resolved modeling suggested a steep concentration gradient present in the upper millimeter of the sediment due to slow chemical diffusion in sediment pores and fast outflux from the overlying water. In contrast to continuous decrease in overlying Cdiss expected for any chemical, Cfree,over of highly hydrophobic chemicals was kept relatively constant following desorption from DOC, a mechanism comparable to passive dosing. Our mechanistic analyses emphasize that exposure will depend on the chemical’s hydrophobicity, the test organism habitat and uptake of bound chemicals, and the properties of sediment components, including DOC. The model can help to re-evaluate existing toxicity data, optimize experimental setups, and extrapolate laboratory toxicity data to field exposure.
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