Year Established: 2020 Start Date: 1970-01-01 End Date: 1970-01-01
Total Federal Funds: $10,000 Total Non-Federal Funds: $20,266
Principal Investigators: Tianyu Zhang
Abstract: Agricultural runoff contributes significantly to anthropogenic nitrate discharge to natural waters, and excessive nitrate in water leads to eutrophication â€“ the biggest water quality problem globally. Eutrophication often results in large-scale ecological damage and economic losses. This is particularly pertinent to Montana as an agriculture state, to the extent that nitrate abatement is a thematic direction in our EPSCoR Track I Research. Current technologies fall short of effective nitrate remediation, because they are prohibitively expensive, impractical (i.e., requiring injection of chemicals), or inefficient (permeable reactive barrier) for the largest source of anthropogenic nitrate discharge â€“ agricultural runoff. The aim of this project is to develop a new predictive model for Bioelectrochemical Denitrification (BED) through interdisciplinary research. In the PIâ€™s previous work, a promising nitrate remediation process â€“ bio-electrochemical denitrification â€“ has been developed in the lab. In BED, bacteria form biofilms on solid electrodes and directly use electrons via extra-cellular electron transfer (EET) for rapid reduction of nitrate to nitrogen gas. BED holds unique promise in agricultural runoff remediation, as it is chemical-free, solar-powered, and highly efficient, which overcomes the key limitation of existing technologies. Despite the considerable potential, preliminary results also suggest that currently-available predictive models cannot reliably predict the performance of BED. The PIsâ€™ preliminary collaboration showed that the BED performance may be influenced by stochastic processes that have not yet been characterized. For instance, lab results find that the proliferation of a key species, thiobacillus denitrificans and biofilm community diversity (as indicated by the Shannon index), are almost equally important in explaining the reactor-to-reactor functional variability. As such, advanced modeling is needed to predict the potential of BED and to elucidate the key factors determining the BED performance. The proposed model consists of a system of differential equations which takes into account engineering conditions, microbial community composition, and stochasticity in the process. This project is a unique interdisciplinary collaboration that synergistically combines mathematical modeling and experimental work. Data collected in experiments will be used to calibrate model parameter values such as kinetic parameters, and also provide critical input for the stochastic part of the model such as source and local community size and immigration rates of different species within the microbial community.