UVM Theses and Dissertations
Format:
Print
Author:
Mouser, Paula J.
Dept./Program:
Civil and Environmental Engineering
Year:
2006
Degree:
PhD
Abstract:
Many unlined historic municipal landfills have released liquids that have come in contact with waste products to the underlying soils and groundwater. This leachate-contaminated groundwater is rich in organic matter, nutrients, and metals, and distinctly changes the subsurface microbial communities. These microbial communities are intimately linked to changes in the hydrochemistry, including the leachate contaminants and contaminant concentration. Quantitative information about the microorganisms can improve our understanding of the movement and degradatation of the landfill contamination but is not well researched. The overall objective of this research was to evaluate the value of microbial community information for delineating and tracking leachate-contaminated groundwater for characterization and long-term monitoring purposes. We sampled the microbial communities in monitoring wells over time and space at a leachatecontaminated groundwater aquifer in northeastern New York using the 16S rDNA gene for Archae, Bacteria, and Geobacteraceae. Community profiles were generated for each group of organisms using terminal fragment restriction length polymorphism (T-RFLP), and the community shifts over time were quantified using the Jaccard Index of similarity.
Shifts in communities for three groundwater zones; clean, plume fringes, and contaminated, followed distinct temporal trends and were significantly correlated to sample-specific changes in groundwater hydrochemistry. Each group of microorganisms (Archaea, Bacteria, and Geobacteraceae) were correlated to different hydrochemical constituents. In general, Archaea were correlated to forms of nitrogen (Organic-N, NH₃) and electron acceptors (sulfate, Fe) while Bacteria and Geobacteraceae were correlated to organics (TOC, COD), essential nutrients (K, Mg, Ca), and electron acceptors (Mn, Fe, and sulfate). When contaminated and fringe monitoring locations were compared with clean monitoring locations they held 1) higher similarity indices through time, and 2) higher levels of biodiversity as measured by the number of distinct operational taxonomic units. These results indicate that a more stable and functionally diverse core community resides in impacted zones over clean groundwater throughout the year. In a principal component analysis, water chemistry information alone could not distinguish between clean and fringe monitoring locations at this site, but the inclusion of microbiological profiles provided information that clearly separated clean, fringe, and contaminated groundwater locations in the first two principal components. This separation could be explained by major electron accepting processes. Hydrochemical and microbial information were used to train a counterpropagation artificial neural network (ANN) to predict the primary redox processes at unknown locations across the site. Our results show that the microbiology, as monitored using molecular methods, can be used to improve the understanding of the state of the groundwater system and indicate the progress towards attenuation in time and space for a landfill leachate-contaminated site.
Shifts in communities for three groundwater zones; clean, plume fringes, and contaminated, followed distinct temporal trends and were significantly correlated to sample-specific changes in groundwater hydrochemistry. Each group of microorganisms (Archaea, Bacteria, and Geobacteraceae) were correlated to different hydrochemical constituents. In general, Archaea were correlated to forms of nitrogen (Organic-N, NH₃) and electron acceptors (sulfate, Fe) while Bacteria and Geobacteraceae were correlated to organics (TOC, COD), essential nutrients (K, Mg, Ca), and electron acceptors (Mn, Fe, and sulfate). When contaminated and fringe monitoring locations were compared with clean monitoring locations they held 1) higher similarity indices through time, and 2) higher levels of biodiversity as measured by the number of distinct operational taxonomic units. These results indicate that a more stable and functionally diverse core community resides in impacted zones over clean groundwater throughout the year. In a principal component analysis, water chemistry information alone could not distinguish between clean and fringe monitoring locations at this site, but the inclusion of microbiological profiles provided information that clearly separated clean, fringe, and contaminated groundwater locations in the first two principal components. This separation could be explained by major electron accepting processes. Hydrochemical and microbial information were used to train a counterpropagation artificial neural network (ANN) to predict the primary redox processes at unknown locations across the site. Our results show that the microbiology, as monitored using molecular methods, can be used to improve the understanding of the state of the groundwater system and indicate the progress towards attenuation in time and space for a landfill leachate-contaminated site.