Denitrifying bioreactors (DNBs) are promising strategies for controlling nitrogen releases from nonpoint sources by creating reducing, carbon-rich environments to support the microbial conversion of nitrate to dinitrogen gas. However, dynamic hydrologic and temperature conditions may result in incomplete denitrification and accumulation of the greenhouse gas nitrous oxide, an obligate intermediate in the sequence of denitrification reactions. To better understand the performance of a passive DNB, we constructed a 1-D advection-dispersion-reaction model using finite difference methods to describe nitrate and nitrous oxide dynamics under a range of temperature and hydrological conditions. Ordinary differential equations describing reactive transport of nitrate and nitrous oxide are coupled via Michaelis-Menten biokinetic models for nitrate reduction/nitrous oxide production. Reaction velocities are adjusted for temperature using the Arrhenius equation. A two-year dataset of longitudinal profiles of dissolved nitrogen species (nitrate, nitrite, and nitrous oxide) and water level and temperature data was collected from a field DNB in central New York state to train and test the model. The field reactor experiences cold winter temperatures, with a mean air temperature of -3°C between December and February. Field data were used to parameterize the nitrate reduction model and batch experiments with field woodchip media were used to parameterize the nitrous oxide model. Model estimates will be compared with field data using a hold-out test to measure model accuracy. Nitrate removal efficiency estimates have a 22.5% RMSE compared to field data. Annual removal efficiency and nitrous oxide production trends will be forecast for climate-change scenarios corresponding to reactors facing less frequent, more intense precipitation and higher mean annual temperatures.

Parameterization and evaluation of a parsimonious model for nitrate removal and nitrous oxide production in a field denitrifying bioreactor experiencing hydroclimatic variability

ACS Spring Meeting 2021

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