Evaluation of Geostatistics for Combined Hydrochemistry and Microbial Community Fingerprinting at a Waste Disposal Site
World Water and Environmental Resources Congress 2004, , , 2004
Abstract: Traditional multivariate statistics and geostatistics were used to analyze groundwater hydrochemistry (alkalinity, Mn, Fe, Si, Al, Mg, NH4, Ca, K, Na, Cl, SO4, H2S, NO2, NO3, CH4, pH, DO, EC, and DOC) and microbial data (16S rDNA DGGE community profiles of Bacteria and Archaea) previously collected from a landfill leachate contaminated aquifer. Variograms and cross-variograms developed for principal components formed from hydrochemistry and rDNA correlation matrices showed spatial correlation between 30m–55m. Kriged principal components indicate the combination of hydrochemistry and Bacteria data may provide better estimates for unknown locations, particularly along the boundary of the plume. The principal components between these two types of data appear to complement each other, as hydrochemistry PC1 appears to separate sample locations vertically and Bacteria PC1 separates sample locations horizontally. These combined data with a multivariate-geostatistical approach may be useful for delineating leachate contaminated zones at waste disposal sites and provide managers with better estimates of risk at unknown locations.
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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).