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Improving accuracy of quantifying nitrate removal performance and enhancing understanding of processes in woodchip bioreactors using high-frequency data

Abstract

Woodchip bioreactors have gained popularity in many countries as a conservation practice for reducing nitrate load to freshwater. However, current methods for assessing their performance may be inadequate when nitrate removal rates (RR) are determined from low-frequency (e.g., weekly) concurrent sampling at the inlet and outlet. We hypothesised that high-frequency monitoring data at multiple locations can help improve the accuracy of quantifying nitrate removal performance, enhance the understanding of processes occurring within a bioreactor, and therefore improve the design practice for bioreactors. Accordingly, the objectives of this study were to compare RRs calculated using high- and low-frequency sampling and assess the spatiotemporal variability of the nitrate removal within a bioreactor to unravel the processes occurring within a bioreactor. For two drainage seasons, we monitored nitrate concentrations at 21 locations on an hourly or two-hourly basis within a pilot-scale woodchip bioreactor in Tatuanui, New Zealand. A novel method was developed to account for the variable lag time between entry and exit of a parcel of sampled drainage water. Our results showed that this method not only enabled lag time to be accounted for but also helped quantify volumetric inefficiencies (e.g., dead zone) within the bioreactor. The average RR calculated using this method was significantly higher than the average RR calculated using conventional low-frequency methods. The average RRs of each of the quarter sections within the bioreactor were found to be different. 1-D transport modelling confirmed the effect of nitrate loading on the removal process as nitrate reduction followed Michaelis-Menten (MM) kinetics. These results demonstrate that high-frequency temporal and spatial monitoring of nitrate concentrations in the field allows improved description of bioreactor performance and better understanding of processes occurring within woodchip bioreactors. Thus, insights gained from this study can be used to optimise the design of future field bioreactors.

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