Abstract: Groundwater biogeochemistry is adversely impacted when municipal solid waste leachate, rich in nutrients and anthropogenic compounds, percolates into the subsurface from leaking landfills. Detecting leachate contamination using statistical techniques is challenging because well strategies or analytical techniques may be insufficient for detecting low levels of groundwater contamination. We sampled profiles of the microbial community from monitoring wells surrounding a leaking landfill using terminal restriction fragment length polymorphism (T-RFLP) targeting the 16S rRNA gene. Results show in situ monitoring of bacteria, archaea, and the family Geobacteraceae improves characterization of groundwater quality. Bacterial T-RFLP profiles showed shifts correlated to known gradients of leachate and effectively detected changes along plume fringes that were not detected using hydrochemical data. Experimental sediment microcosms exposed to leachate-contaminated groundwater revealed a shift from a beta-Proteobacteria and Actinobacteria dominated community to one dominated by Firmicutes and delta-Proteobacteria. This shift is consistent with the transition from oxic conditions to an anoxic, iron-reducing environment as a result of landfill leachate-derived contaminants and associated redox conditions. We suggest microbial communities are more sensitive than hydrochemistry data for characterizing low levels of groundwater contamination and thus provide a novel source of information for optimizing detection and long-term monitoring strategies at landfill sites.
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Bongard's work focuses on understanding the general nature of cognition, regardless of whether it is found in humans, animals or robots. This unique approach focuses on the role that morphology and evolution plays in cognition. Addressing these questions has taken him into the fields of biology, psychology, engineering and computer science.
Danforth is an applied mathematician interested in modeling a variety of physical, biological, and social phenomenon. He has applied principles of chaos theory to improve weather forecasts as a member of the Mathematics and Climate Research Network, and developed a real-time remote sensor of global happiness using messages from Twitter: the Hedonometer. Danforth co-runs the Computational Story Lab with Peter Dodds, and helps run UVM's reading group on complexity.
Laurent studies the interaction of structure and dynamics. His research involves network theory, statistical physics and nonlinear dynamics along with their applications in epidemiology, ecology, biology, and sociology. Recent projects include comparing complex networks of different nature, the coevolution of human behavior and infectious diseases, understanding the role of forest shape in determining stability of tropical forests, as well as the impact of echo chambers in political discussions.
Hines' work broadly focuses on finding ways to make electric energy more reliable, more affordable, with less environmental impact. Particular topics of interest include understanding the mechanisms by which small problems in the power grid become large blackouts, identifying and mitigating the stresses caused by large amounts of electric vehicle charging, and quantifying the impact of high penetrations of wind/solar on electricity systems.
Bagrow's interests include: Complex Networks (community detection, social modeling and human dynamics, statistical phenomena, graph similarity and isomorphism), Statistical Physics (non-equilibrium methods, phase transitions, percolation, interacting particle systems, spin glasses), and Optimization(glassy techniques such as simulated/quantum annealing, (non-gradient) minimization of noisy objective functions).