Terrestrial & Subsurface Ecosystems

Bioremediation of chlorinated ethenes via anaerobic reductive dechlorination relies upon the activity of specific microbial populations − most notably Dehalococcoides (DHC) strains. In the lab and field Dehalococcoides grow most robustly in mixed communities which usually contain both fermenters and methanogens. Recently, researchers have been developing quantitative molecular biomarkers to aid in field site diagnostics and it is hoped that these biomarkers could aid in the modeling of anaerobic reductive dechlorination. A comprehensive biokinetic model of a community containing Dehalococcoides mccartyi (formerly D. ethenogenes) was updated to describe continuously fed reactors with specific biomass levels based on
quantitative PCR (qPCR)-based population data (DNA and RNA). The model was calibrated and validated with subsets of chemical and molecular biological data from various continuous feed experiments (n = 24) with different loading rates of the electron acceptor (1.5 to 482 μeeq/L-h), types of electron acceptor (PCE, TCE, cis-DCE) and electron donor to electron acceptor ratios. The resulting model predicted the sum of dechlorination products vinyl chloride (VC) and ethene (ETH) well. However, VC alone was under-predicted and ETH was over predicted.
Consequently, competitive inhibition among chlorinated ethenes was examined and then added to the model. Additionally, as 16S rRNA gene copy numbers did not provide accurate model fits in all cases, we examined whether an improved fit could be obtained if mRNA levels for key functional enzymes could be used to infer respiration rates. The resulting empirically derived mRNA “adjustment factors” were added to the model for both DHC and the main methanogen in the culture (a Methanosaeta species) to provide a more nuanced prediction of activity. Results of this study suggest that at higher feeding rates competitive inhibition is important and mRNA provides a more accurate indicator of a population’s instantaneous activity than 16S rRNA gene copies alone as biomass estimates.

Surface functionalized magnetic nanoparticles (MNPs) are appealing candidates for analytical separation of heavy metal ions from waste water and separation of actinides from spent nuclear fuel. This work studies the separation dynamics and investigates the appropriate magnetic-field gradients. A dynamic study of colloidal MNPs was performed for steady-state flow. Measurements were conducted to record the separation time of particles as a function of magnetic field gradient. The drag and magnetic forces play a significant role on the separation time. A drop in saturation magnetization and variation of particle size occurs after surface functionalization of the MNPs; these are the primary factors that affect the separation time and velocity of the MNPs. The experimental results are correlated to a theoretical one-dimensional model.