Improving Site Characterization and Classifying Attenuation Processes Using Microbiological Profiles, Geochemistry, and Artificial Neural Networks from Landfill-Leachate Contaminated Groundwater
World Water and Environmental Resources Congress 2005, , , 2005
Abstract: Groundwater geochemistry and microbiology were sampled from a shallow aquifer contaminated by municipal landfill leachate in northeastern New York. Polymerase chain reaction (PCR) of the 16S rDNA molecule and sequencing was used to identify organisms associated with clean and leachate-contaminated monitoring wells. Groundwater samples were tested for general water quality parameters (pH, temperature, redox, turbidity, and specific conductance), metals, inorganic, and organic compounds. A principal component analysis revealed three groups of samples; highly contaminated, leachate-influenced, and uninfluenced groundwater monitoring locations. Microorganisms could also be classified into three groups, those typically found in extreme environments, those found in zones with changing redox conditions, and those typically found in agricultural or glacial soils corresponding to contaminated, leachate-influenced, and uninfluenced locations, respectively. The combined use of principal component scores and microorganism community for depicting the zone of leachate-influence gives managers a better estimate of the extent of contamination and attenuation processes. We will also show how an artificial neural network (ANN) can be trained to predict the geochemistry based on the microbiological profile for the purpose of improving site characterization using multiple types of data. Testing and validation was performed using data from subsequent sampling events.
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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).