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We conducted simulations of oil transport from the footprint of the Macondo Well on the water surface throughout the Gulf of Mexico, including deposition on the shorelines. We used the U.S. National Oceanic Atmospheric Administration (NOAA) model General NOAA Operational Modeling Environment (GNOME) and the same parameter values and input adopted by NOAA following the Deepwater Horizon (DWH) blowout. We found that the disappearance rate of oil off the water surface was most likely around 20% per day based on satellite-based observations of the disappearance rate of oil detected on the sea surface after the DWH wellhead was capped. The simulations and oil mass estimates suggest that the mass of oil that reached the shorelines was between 10,000 and 30,000 tons, with an expected value of 22,000 tons. More than 90% of the oil deposition occurred on the Louisiana shorelines, and it occurred in two batches. Simulations revealed that capping the well after 2 weeks would have resulted in only 30% of the total oil depositing on the shorelines, while capping after 3 weeks would have resulted in 60% deposition. Additional delay in capping after 3 weeks would have averted little additional shoreline oiling over the ensuing 4 weeks.

A numerical reactive transport model was developed to simulate the bioremediation processes in a perchloroethene (PCE) contaminated single fracture system augmented with Dehalococcoides sp. (DHC). The model describes multispecies bioreactive transport processes that include bacterial growth and detachment dynamics, biodegradation of chlorinated species, competitive inhibition of various reactive species, and the loss of daughter products because of back‐partitioning effects. Two sets of experimental data, available in the study by Schaefer et al. (2010b), were used to calibrate and test the model. The model was able to simulate both datasets. The simulation results indicated that the yield coefficient and the DHC maximum utilization rate coefficient were the two important process parameters. A detailed sensitivity study was completed to quantify the sensitivity of the model to variations in these two parameter values. The results show that an increase in yield coefficient increases bacterial growth and thus expedites the dechlorination process, whereas an increase in maximum utilization rate coefficient greatly increased dechlorination rates. The proposed model provides a mathematical framework for simulating remediation systems that employ DHC bioaugmentation for restoring chlorinated‐solvent contaminated groundwater aquifers.

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We present the details of a numerical model, BIOB that is capable of simulating the biodegradation of oil entrapped in the sediment. The model uses Monod kinetics to simulate the growth of bacteria in the presence of nutrients and the subsequent consumption of hydrocarbons. The model was used to simulate experimental results of Exxon Valdez oil biodegradation in laboratory columns (Venosa et al., 2010). In that study, samples were collected from three different islands: Eleanor Island (EL107), Knight Island (KN114A), and Smith Island (SM006B), and placed in laboratory microcosms for a duration of 168 days to investigate oil bioremediation through natural attenuation and nutrient amendment. The kinetic parameters of the BIOB model were estimated by fitting to the experimental data using a parameter estimation tool based on Genetic Algorithms (GA). The parameter values of EL107 and KN114A were similar whereas those of SM006B were different from the two other sites; in particular biomass growth at SM006B was four times slower than at the other two islands. Grain size analysis from each site revealed that the specific surface area per unit mass of sediment was considerably lower at SM006B, which suggest that the surface area of sediments is a key control parameter for microbial growth in sediments. Comparison of the BIOB results with exponential decay curves fitted to the data indicated that BIOB provided better fit for KN114A and SM006B in nutrient amended treatments, and for EL107 and KN114A in natural attenuation. In particular, BIOB was able to capture the initial slow biodegradation due to the lag phase in microbial growth. Sensitivity analyses revealed that oil biodegradation at all three locations were sensitive to nutrient concentration whereas SM006B was sensitive to initial biomass concentration due to its slow growth rate. Analyses were also performed to compare the half-lives of individual compounds with that of the overall polycyclic aromatic hydrocarbons (PAHs).