ABSTRACT
Bioaccumulation of methylmercury in the aquatic food web is governed in part by the methylation of inorganic divalent mercury (Hg(II)) by anaerobic microorganisms. In sulfidic settings, a small fraction of total Hg(II) is typically bioavailable to methylating microorganisms. Quantification of this fraction is difficult due to uncertainties in the speciation of Hg(II) and the mechanisms of uptake by methylating microbes. However, recent studies have shown that the bioavailable fraction is likely to include a portion of Hg(II) associated with solid phases, that is, nanostructured mercuric sulfides. Moreover, addition of thiols to suspensions of methylating cultures coincides with increased uptake into cells and methylmercury production. Here, we present a thiol-based selective extraction assay to provide information on the bioavailable Hg fraction in sediments. In the procedure, sediment samples were exposed to a nitrogen-purged solution of glutathione (GSH) for 30 min and the amount of GSH-leachable mercury was quantified. In nine sediment samples from a marine location, the relative GSH-leachable mercury concentration was strongly correlated to the relative amount of methylmercury in the sediments (r2=0.91, p<0.0001) across an order of magnitude of methylmercury concentration values. The approach was further applied to anaerobic sediment slurry microcosm experiments in which sediments were cultured under the same microbial growth conditions but were amended with multiple forms of Hg with a known spectrum of bioavailability. GSH-leachable Hg concentrations increased with observed methylmercury concentrations in the microcosms, matching the trend of species bioavailability in our previous work. Results suggest that a thiol-based selective leaching approach is an improvement compared with other proposed methods to assess Hg bioavailability in sediment and that this approach could provide a basis for comparison of sites where Hg methylation is a concern.
ABSTRACT
Mercury emissions from coal combustion have become a global concern as growing energy demands have increased the consumption of coal. The effective implementation of treatment technologies requires knowledge of mercury speciation in the flue gas, namely concentrations of elemental, oxidized and particulate mercury at the exit of the boiler. A model that can accurately predict mercury species in flue gas would be very useful in that context. Here, a Bayesian regularized artificial neural network (BRANN) that uses five coal properties and combustion temperature was developed to predict mercury speciation in flue gases before treatment technology implementation. The results of the model show that up to 97 percent of the variation in mercury species concentration is captured through the use of BRANNs. The BRANN model was used to conduct a parametric sensitivity which revealed that the coal chlorine content and coal calorific value were the most sensitive parameters, followed by the combustion temperature. The coal sulfur content was the least important parameter. The results demonstrate the applicability of BRANNs for predicting mercury concentration and speciation in combustion flue gas and provide a more efficient and effective technique when compared to other advanced non-mechanistic modeling strategies.
