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Title:Development and application of a microbial reliability model to analyze denitrifying biofilter stability
Author(s):Bartolerio, Nicholas
Advisor(s):Zilles, Julie L.; Rodriguez, Luis F.
Department / Program:Civil & Environmental Eng
Discipline:Environ Engr in Civil Engr
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Denitrifying Biofilter
Engineer Ecosystem
Abstract:The use of nitrogenous fertilizers to amend soil fertility has had a drastic effect on the global nitrogen cycle as excess nitrogen has contaminated surface waters, leading to marine hypoxic zones. One of the largest contributors to nitrogen loads in surface waterways is subsurface agricultural drainage. For this reason, efforts are underway to reduce drainage nitrogen losses to waterways. One proven method is the use of edge-of-field denitrifying biofilters. These biofilters redirect tile drainage flow through a woodchip bed, where microbial activity converts influent nitrate to nitrogen gases. As with many engineered ecosystems, performance stability of the biofilters is of concern. While much research has focused on stability concepts in ecology, there is no consensus on the nature of the relationships between microbial diversity, functional redundancy, and ecosystem stability. A better understanding of these relationships and other factors that affect performance stability of the denitrifying biofilters is necessary to further enhance their effectiveness. With the uncertainties that currently exist in the area of ecosystem stability in mind, it was my goal to develop methods using reliability theory as a new approach to analyze ecosystem stability, linking the stability of microbial populations to the stability of the system functional performance. To apply reliability theory to engineered ecosystems, microbial populations were represented as components in a system. We have developed a method of utilizing microbial fingerprinting data to quantify presence and longevity of microbial populations from which a reliability function for each population can be determined. We were able to target functional genes and identify populations directly responsible for the system functional performance using microbial fingerprinting techniques such as terminal restriction fragment length polymorphism (T-RFLP). This allowed us to get a better understanding of how to model the microbial populations as functional components in the system. In order to quickly and easily apply these methods to microbial fingerprinting data, we have developed the Ecosystem Reliability Analysis Tool (EcoReliAnT) in MATLAB®. This tool provides the functionality necessary for ecosystem reliability analysis through a user-friendly interface. The newly developed reliability analysis methods and EcoReliAnT software was first tested on a dataset acquired from the literature. This external dataset consisted of phenol degradation rate performance data from a sequencing batch reactor along with corresponding microbial functional gene information from restriction fragment length polymorphism analysis. This dataset provided a good trial dataset for the methods and software, as it showed clear changes in microbial community structure that corresponded to changes in system performance. Following the trial run on the external dataset, data from a field denitrifying biofilter in Decatur, Illinois was analyzed. This data consisted of nosZ T-RFLP microbial community information from a 135 day period in which there was continuous flow along with percent nitrate removal as a performance metric. Results from the reliability analyses of both the external dataset and the data from the field denitrifying biofilter demonstrated the capabilities of the reliability methods and EcoReliAnT software in analyzing engineered ecosystems. It was demonstrated that reliability functions could be determined for microbial populations based on their population dynamics and that the reliability of the system performance could be accurately modeled as a configuration of functional microbial components, with sum of squared error values between reliability functions of the system and model as low as 0.07. In both cases, incorporating microbial populations that were determined to be negatively correlated with system performance along with those that were positively correlated with system performance resulted in the best-fitting model. This suggests that ‘nuisance’ populations play an important role in the stability of engineered ecosystems. In addition to the development of reliability analysis methods, laboratory-scale denitrifying biofilters were designed and built to allow for a more controlled study of factors that affect system performance. These laboratory biofilters were fed synthetic tile drainage and allowed examination of the effects that changes in environmental and operational conditions have on the microbial community and system performance. Results from the startup of the laboratory-scale biofilters suggest that under constant environmental and operational conditions, denitrifying biofilters can exhibit very stable performance. This highlights the importance of understanding how changes in these conditions affect both the system performance and the microbial community structure in order to better understand the performance stability of the system. The successful startup of the laboratory-scale biofilters provides a platform for future experiments to enhance the understanding of how microbial population dynamics, environmental parameters, and operational conditions relate to biofilter performance and stability. The application of reliability theory methods to engineered ecosystems is a unique approach in the consideration of microbial diversity, functional redundancy, and engineered ecosystem stability. Developing a better understanding of the relationships between these concepts and other factors that affect the functional stability of denitrifying biofilters will allow for improved system design and operation, ultimately enhancing their efficacy as a treatment technology.
Issue Date:2012-02-06
Genre:Dissertation / Thesis
Rights Information:Copyright 2011 Nicholas Bartolerio
Date Available in IDEALS:2012-02-06
Date Deposited:2011-12

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