Doctoral Dissertations

Orcid ID


Date of Award


Degree Type


Degree Name

Doctor of Philosophy


Plant, Soil and Environmental Sciences

Major Professor

Jaehoon Lee

Committee Members

Michael E. Essington, Jie Zhuang, Sindhu Jagadamma, John S. Schwartz


Denitrifying bioreactor (DNBR) has become a popular edge-of-field practice applied to reduce nitrate over the Mississippi river and to prevent a downstream hypoxic zone occurring at the Gulf of Mexico. Despite widespread field and laboratory studies, fewer investigations have been directed toward a systematic means of evaluating the nitrate removal performance achieved by various filling materials, abiotic factors, and other critical parameters. Our ultimate goal is to improve the nitrate removal by choosing the optimum fill materials and operate under optimal conditions, meanwhile, modeling the DNBR by critical variables. This study begins by establishing a global database. Forty filling materials were systematically categorized by natural carbon (NC), non-natural carbon (NNC), inorganic materials (IC), and multi-material (MM). The results showed that MM is the best option under a comprehensive consideration. Subsequently, based on this established database, a data-driven approach was adopted to study how nitrate removal rate (NRR) and nitrate removal efficiency (NRE) respond to various abiotic factors, including media age, hydraulic retention time, influent nitrate concentration, pH, dissolved organic carbon, dissolved oxygen content, temperature, and effective porosity. The results showed that HRT is the most important abiotic factor for NRR. Then, biochar and silage leachate, two primary contributors of industrial and agricultural waste, were considered as amending materials to improve NRR and NRE in DNBR. The study showed that adding biochar and silage leachate together can significantly increase NRR. Following this, two nonlinear models were developed to describe the relationship between NRR, NRE, and hydraulic retention time (HRT). The results showed that the two models had a good performance (R2>0.9). Moreover, C/N ratio affect NRE was also studied in the above experiments. We found the relationship between NRE and C/N ratio can be well explained by a nonlinear model. Taken together, these investigations provide an improved understanding of how to choose suitable filling materials and prepare optimal environmental conditions to enhance nitrate removal performance in DNBR for stakeholders and practitioners.

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