Biogeochemical cycling of heavy metals in benthic sediments of Lake Coeur d'Alene

The rich mining history of the Western United States, since the late 1880's, has left many sites contaminated with toxic metals including Zn, Pb, and Cu. One example is Lake Coeur d'Alene (LCdA), located in northern Idaho, where lake sediments have been heavily impacted by decades of upstream mining activities. The bioavailability, fate and transport of these metals in the sediments are governed by complex biogeochemical processes. In particular, indigenous microbes are capable of catalyzing reactions that detoxify their environments, and thus are important to the biogeochemical cycling of these metals. To quantify biogeochemical reactions controlling metal fate and transport in lake benthic sediments, a diffusive reactive-transport model was developed. The model includes one-dimensional (1-D) diffusive transport coupled to a biotic reaction network with multiple terminal electron acceptors under redox disequilibrium conditions. The model is applied to evaluate the competing effects of heavy-metal mobilization through biotic reductive dissolution of Fe(III)-(hydr)oxides and immobilization as biogenic sulfide minerals. Results indicate that the relative rates of iron and sulfate reduction could play an important role in metal transport through the environment, and that the formation of (bi)sulfide complexes could significantly enhance metal solubility, as well as desorption from Fe(III)-(hydr)oxides. This effort provides a useful numerical tool to evaluate the important biogeochemical processes affecting cycling of metals in LCdA sediments and similar metal-impacted lakes. Microbial kinetics at growth-inhibiting concentrations of heavy metals have also been simulated to demonstrate the effect of zinc on Pseudomonas sp. as well as the effects of nickel and cobalt on a mixed bacterial culture and the subsequent delayed response in their metabolic potential. The delayed response in the microbial kinetic expressions due to the exposure to heavy metals is expressed by developing a metabolic potential functional to account for the metabolic state of the microorganisms. The simulations show that incorporation of a microbial lag functional can consistently represent the observations of microbial biodegradation under the exposure of toxic substances, such as heavy metals, and that the disregard of this metabolic lag may overpredict microbial activity.