Reliability analysis of denitrifying biofilter stability

Subsurface agricultural drainage is a major contributor to nitrogen loads in surface waterways and has been associated with marine hypoxic zones. For this reason, efforts are underway to reduce 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 gas. As with many engineered ecosystems, performance stability is of concern. A novel approach to quantify engineered ecosystem stability is the use of reliability theory. To apply reliability theory to engineered ecosystems, microbial populations can be represented as components in a system. We are testing this approach by correlating nitrate removal with the composition of microbial communities in denitrifying biofilters over time using microbial fingerprinting techniques - automated ribosomal intergenic spacer analysis (ARISA) and terminal restriction fragment length polymorphism (T-RFLP). We have developed a method of utilizing microbial fingerprinting data to quantify presence and longevity of microbial populations and have applied it to data acquired from field biofilters in Central Illinois. This allows us to determine the best approach for modeling reliability for each system component, and thus the overall system. Current efforts are focused on collecting activity data for individual steps in the denitrification pathway from laboratory-scale denitrifying biofilters. This should enable the identification of components that limit overall system performance and identify avenues for improvement of the ecosystem performance. The use of microbial fingerprinting techniques to quantify presence and longevity is a unique approach in the consideration of microbial diversity, functional redundancy, and engineered ecosystem stability. This research will enhance the understanding of the effect of microbial diversity on biofilter stability, while demonstrating the use of reliability theory to analyze engineered ecosystems.