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Microbial biodiversity in groundwater and soil presents a unique opportunity for improving characterization and monitoring at sites with multiple contaminants, yet few computational methods use or incorporate these data because of their high dimensionality and variability. We present a systematic, nonparametric decision-making methodology to help characterize a water quality gradient in leachate-contaminated groundwater using only microbiological data for input. The data-driven methodology is based on clustering a set of molecular genetic-based microbial community profiles. Microbes were sampled from groundwater monitoring wells located within and around an aquifer contaminated with landfill leachate. We modified a self-organizing map (SOM) to weight the input variables by their relative importance and provide statistical guidance for classifying sample similarities. The methodology includes the following steps: (1) preprocessing the microbial data into a smaller number of independent variables using principal component analysis, (2) clustering the resulting principal component (PC) scores using a modified SOM capable of weighting the input PC scores by the percent variance explained by each score, and (3) using a nonparametric statistic to guide selection of appropriate groupings for management purposes. In this landfill leachate application, the weighted SOM assembles the microbial community data from monitoring wells into groupings believed to represent a gradient of site contamination that could aid in characterization and long-term monitoring decisions. Groupings based solely on microbial classifications are consistent with classifications of water quality from hydrochemical information. These microbial community profile data and improved decision-making strategy compliment traditional chemical groundwater analyses for delineating spatial zones of groundwater contamination. © 2011 by the American Geophysical Union.

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© 2011. American Geophysical Union.



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