A Multivariate Statistical Approach to Spatial Representation of Groundwater Contamination using Hydrochemistry and Microbial Community Profiles
Environmental Science and Technology, 39, 7551-7559, 2005
Abstract: Managers of landfill sites are faced with enormous challenges when attempting to detect and delineate leachate plumes with a limited number of monitoring wells, assess spatial and temporal trends for hundreds of contaminants, and design long-term monitoring (LTM) strategies. Subsurface microbial ecology is a unique source of data that has been historically underutilized in LTM groundwater designs. This paper provides a methodology for utilizing qualitative and quantitative information (specifically, multiple water quality measurements and genome-based data) from a landfill leachate contaminated aquifer in Banisveld, The Netherlands, to improve the estimation of parameters of concern. We used a principal component analysis (PCA) to reduce nonindependent hydrochemistry data, Bacteria and Archaea community profiles from 16S rDNA denaturing gradient gel electrophoresis (DGGE), into six statistically independent variables, representing the majority of the original dataset variances. The PCA scores grouped samples based on the degree or class of contamination and were similar over considerable horizontal distances. Incorporation of the principal component scores with traditional subsurface information using cokriging improved the understanding of the contaminated area by reducing error variances and increasing detection efficiency. Combining these multiple types of data (e.g., genome-based information, hydrochemistry, borings) may be extremely useful at landfill or other LTM sites for designing cost-effective strategies to detect and monitor contaminants.
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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).