| Summary: | Specialization: Environmental Engineering Degree: Doctor of Philosophy Abstract: This thesis documents the research undertaken to develop and assess modeling and monitoring tools to improve the water quality management in the Athabasca River, Alberta. The Upper Athabasca River (UAR) has experienced dissolved oxygen (DO) sags, which may affect the aquatic ecosystem. A water quality model for an 800 km reach of this river was customized, calibrated, and validated for DO and the factors that determine its concentration. The model showed that the sediment oxygen demand (SOD) represents about 50% of the DO sink in winter. The DO calibration was improved by implementing an annual SOD based on the biochemical oxygen demand (BOD) load. The model was used to estimate the assimilative capacity of the river based on a trigger DO concentration of 7 mg/L. The results revealed a maximum assimilative BOD load of 8.9 ton/d at average flow conditions, which is lower than the maximum permitted load. In addition, the model predicted a minimum assimilative flow at average BOD load of 52 m3/s. A three-level warning-system is proposed to manage the BOD load proactively at different river discharges. Other mitigation options were explored such as upgrading the wastewater treatment from the major BOD point source, and oxygen injection into the effluents. The model can be used as a management tool to forecast the DO in low flow years and evaluate mitigation measures. After improving the modeling tools for the UAR, monitoring tools for the Lower Athabasca River (LAR) were assessed. Naphthenic acids (NAs) have been identified as a main toxic component in the oil sands process affected water. However, it is desired to improve the current monitoring methods for NAs. Having a state-of-the-art monitoring system to quantify NAs in the LAR and its tributaries will allow calibrating robust models for this reach of the Athabasca River in the future. Passive samplers and the application of fluorescence spectroscopy using organic solvents were ...
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