Enhanced in situ biodenitrification (EIB) is a feasible technology to clean nitrate-polluted groundwater and reach
drinking water standards. Aimed at enabling a better monitoring and management of the technology at the field
scale, we developed a two-dimensional reactive transport model (RTM) of a cross section (26.5 × 4 m) of a fractured
aquifer composed of marls involving both biogeochemical processes and associated isotope fractionation.
The RTM was based on the upscaling of a previously developed batch-scale model and on a flow model that was
constructed and calibrated on in situ pumping and tracer tests. The RTM was validated using the experimental
data provided by Vidal-Gavilan et al. (2013). The model considers several processes including (i) exogenous and
endogenousmicrobial nitrate and sulfate respiration coupled to ethanol oxidation and linked tomicrobial growth
and decay, and (ii) geochemical interactions (dissolution/precipitation of calcite), and (iii) isotopic fractionation
of the reaction network (15N–NO3, 18O–NO3, 13C–DIC, 13C–ethanol, 13C–biomass, and 13C–calcite). Most of the
calibrated microbiological parameter values at field scale did not change more than one order of magnitude
from those obtained at batch scale, which indicates that parameters determined at the batch scale can be used as initial estimates to reproduce field observations provided that groundwater flow is well known. In contrast, the calcite precipitation rate constant increased significantly (fifty times)with respect to batch scale. The incorporation of isotope fractionation into the model allowed to confirm the overall consistency of the model and to test the practical usefulness of assessing the efficiency of EIB through the Rayleigh equation approach. The large underestimation of the Rayleigh equation of the extent of EIB (from 10 to 50%) was caused by the high value of hydrodynamic dispersion observed in this fractured aquifer together with the high reaction rates.
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Enhanced In situ Biodenitrification (EIB) is a capable technology for nitrate removal in subsurface water resources. Optimizing the performance of EIB implies devising an appropriate feeding strategy involving two design parameters: carbon injection frequency and C:N ratio of the organic substrate nitrate mixture. Here we model data on the spatial and temporal evolution of nitrate (up to 1.2 mM), organic carbon (ethanol), and biomass measured during a 342 day-long laboratory column experiment (published in Vidal-Gavilan et al., 2014). Effective porosity was 3% lower and dispersivity had a sevenfold increase at the end of the experiment as compared to those at the beginning. These changes in transport parameters were attributed to the development of a biofilm. A reactive transport model explored the EIB performance in response to daily and weekly feeding strategies. The latter resulted in significant temporal variation in nitrate and ethanol concentrations at the outlet of the column. On the contrary, a daily feeding strategy resulted in quite stable and low concentrations at the outlet and complete denitrification. At intermediate times (six months of experiment), it was possible to reduce the carbon load and consequently the C:N ratio (from 2.5 to 1), partly because biomass decay acted as endogenous carbon to respiration, keeping the denitrification rates, and partly due to the induced dispersivity caused by the well-developed biofilm, resulting in enhancement of mixing between the ethanol and nitrate and the corresponding improvement of denitrification rates. The inclusion of a dual-domain model improved the fit at the last days of the experiment as well as in the tracer test performed at day 342, demonstrating a potential transition to anomalous transport that may be caused by the development of biofilm. This modeling work is a step forward to devising optimal injection conditions and substrate rates to enhance EIB performance by minimizing the overall supply of electron donor, and thus the cost of the remediation strategy.

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Enhanced in-situ biodenitrification (EIB) is a potential technology for remediating nitrate-polluted groundwater. EIB aims to create optimal biodenitrification conditions through the addition of carbon sources, enabling the autochthonous microbial community to degrade nitrate via different redox pathways. Biogeochemical numerical models are useful tools for predicting and designing such biodenitrification applications. Compound-specific stable isotope analysis (CSIA) is another valuable method for determining the degree of nitrate transformation. Therefore, incorporating isotope fractionation in biogeochemical models combines the two tools and is a key step in the development of reactive transport models of EIB under field conditions. In this work, we developed such an integrated model using the Phreeqc code and calibrated the model with batch scale experimental data using either ethanol or glucose as external carbon sources. The model included the following: microbiological processes —exogenous and endogenous nitrate respiration coupled to microbial growth and decay; geochemical processes —precipitation or dissolution of calcite; and isotopic fractionation —δ15N-NO3−, δ18O-NO3−, and δ13C-DIC, incorporating the full δ13C isotope geochemistry involved in EIB. The modeled results fit well with the hydrochemical and isotopic experimental data. The model also incorporated nitrite accumulation observed during the glucose experiment. The biogeochemical model indicates that, depending on the added carbon source, calcite precipitates (using ethanol) or dissolves (using glucose). In both cases, changes in hydraulic conductivity can be induced for actual and long-term EIB applications. The incorporation of isotope fractionation in the model better enables to account for other natural attenuation processes, such as dilution and dispersion, in EIB applications at field scale. Both calibrated enrichment factors (+ 8‰ for ethanol and + 17‰ for glucose) suggest that an inverse fractionation effect occurred (in which the heavy isotope reacts faster than the light isotope) during their oxidation.@en