James D. Bowen

Mechanistic and statistical models of total Vibrio abundance in the Neuse River Estuary.

Bacteria in the genus Vibrio are ubiquitous to estuarine waters worldwide and are often the dominant genus recovered from these environments. This genus contains several potentially pathogenic species, including Vibrio vulnificus, Vibrio cholerae, Vibrio parahaemolyticus, and Vibrio alginolyticus. These bacteria have short generation times, as low as 20-30 min, and can thus respond rapidly to changing environmental conditions. A five-parameter mechanistic model was generated based on environmental processes including hydrodynamics, growth, and death rates of Vibrio bacteria to predict total Vibrio abundance in the Neuse River Estuary of eastern North Carolina. Additionally an improved statistical model was developed using the easily monitored parameters of temperature and salinity. This updated model includes data that covers more than eight years of constant bacterial monitoring, and incorporates extreme weather events such as droughts, storms, and floods. These models can be used to identify days in which bacterial abundance might coincide with increased health risks.

Optimization of wastewater treatment plant operation for greenhouse gas mitigation

This study deals with the determination of optimal operation of a wastewater treatment system for minimizing greenhouse gas emissions, operating costs, and pollution loads in the effluent. To do this, an integrated performance index that includes three objectives was established to assess system performance. The ASMN_G model was used to perform system optimization aimed at determining a set of operational parameters that can satisfy three different objectives. The complex nonlinear optimization problem was simulated using the Nelder-Mead Simplex optimization algorithm. A sensitivity analysis was performed to identify influential operational parameters on system performance. The results obtained from the optimization simulations for six scenarios demonstrated that there are apparent trade-offs among the three conflicting objectives. The best optimized system simultaneously reduced greenhouse gas emissions by 31%, reduced operating cost by 11%, and improved effluent quality by 2% compared to the base case operation.

Using Eutrophication Modeling to Predict the Effectiveness of River Restoration Efforts

Massive summer fish kills in 1995 drew public attention to the deteriorating water quality in North Carolinas Neuse River Estuary. Nitrogen loading to the estuary has increased dramatically due to increases in population, agricultural activity, and livestock production. Widespread anoxia and fish kills have occurred occasionally in the Neuse River Estuary in the past, but occurred in both 1995 and 1996. In addition, problems associated with algal blooms and macrophytes are at all time highs. In response, the State has drafted regulations aimed at reducing nitrogen loading to the estuary by 30 percent. At the same time, the State has funded a research project to predict the water quality improvement that will result from reduced nutrient loading, and to plan for future management of the river basin. This three-phase project, referred to as MODMON (MODeling and MONitoring), will include collection of tightly coupled water quality, fisheries, hydrodynamic, and sediment data; application of a eutrophication model to predict the results of reduced nitrogen loading; and development of recommendations for longer-term development of a watershed-river-estuary water quality model focusing on management outcomes. Results from initial water quality monitoring showed the estuary to be intermittently anoxic even during good years such as 1997. The special characteristics of the predictive eutrophication modeling effort now underway are also discussed. These characteristics include a large database of water quality and ecosystem rate measurements, the existence of a multidisciplinary monitoring effort, parallel short-term and long-term model development efforts, and incorporation of an uncertainty analysis into the process-based eutrophication model.

Using Turbulence Model Results to Quantify Oxygen Reaeration in an Estuary Dissolved Oxygen Model

An alternate means of quantifying oxygen reaeration was investigated in a model of dissolved oxygen in an estuary. The three-dimensional hydrodynamic and water quality model EFDC was used to simulate dissolved oxygen (DO) conditions in North Carolinas Lower Cape Fear River Estuary. A review of DO monitoring data showed that the upper portion of the water column was frequently undersaturated with respect to dissolved oxygen even though hypoxia was not usually observed in the bottom waters. In the impaired area of the estuary, surface reaeration was expected to be a significant source of DO to the water column. Even though reaeration has been shown to be dependent on the local energy dissipation rate near the water surface, water quality models typically use macroscale measurements of wind and water velocity to establish the reaeration rate coefficient. In this study we investigated the use of results from the turbulence closure submodel of a hydrodynamic model to quantify the local energy dissipation rate and, in turn, the dissolved oxygen mass transfer coefficient. Significant differences were seen in the statistical distribution of reaeration rates. The existing formulation showed a log-normal distribution, with a relatively small number of high reaeration rates. The new formulation showed a higher abundance of relatively high reaeration rates, and these high rates were seen more often during the summer period when DO was lowest in the estuary. Although the differences in predicted DO between the two methods were not dramatic, minimum DO in the estuary was as much as 1.0 mg/L higher using the new formulation. Based upon these results, it does seem that further investigation of the method for quantifying reaeration rate in estuarine water quality models is justified.