Evaluating the Performance and Water Chemistry Dynamics of Passive Systems Treating Municipal Wastewater and Landfill Leachate
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This thesis consists of work conducted in two separate studies, evaluating the performance of passive systems for treating wastewater effluents. The first study involved the characterization of three wastewater stabilization ponds (WSPs) providing secondary and tertiary treatment for municipal wastewater at a facility in Amherstview, Ontario, Canada. Since 2003, the WSPs have experienced excessive algae growth and high pH levels during the summer months. A full range of parameters consisting of: pH, chlorophyll-a (chl-a), dissolved oxygen (DO), temperature, alkalinity, oxidation-reduction potential (ORP), conductivity, nutrient species, and organic matter measures; were monitored for the system and the chemical dynamics in the three WSPs were assessed through multivariate statistical analysis. Supplementary continuous monitoring of pH, chl-a, and DO was performed to identify time-series dependencies. The analyses showed strong correlations between chl-a and sunlight, temperature, organic matter, and nutrients, and strong time dependent correlations between chl-a and DO and between chl-a and pH. Additionally, algae samples were collected and analyzed using metagenomics methods to determine the distribution and speciation of algae growth in the WSPs. A strong shift from the dominance of a major class of green algae, chlorophyceae, in the first WSP, to the dominance of land plants, embryophyta – including aquatic macrophytes – in the third WSP, was observed and corresponded to field observations during the study period. The second study involved the evaluation of the performance and chemical dynamics of a hybrid-passive system treating leachate from a municipal solid waste (MSW) landfill in North Bay, Ontario, Canada. Over a three year period, monitoring of a full range of parameters consisting of: pH, DO, temperature, alkalinity, ORP, conductivity, sulfate, chloride, phenols, solids fractions, nutrient species, organic matter measures, and metals; was conducted bi-weekly and the dataset was analyzed with time series and multivariate statistical techniques. Regression analyses identified 8 parameters that were most frequently retained for modelling the five criteria parameters (alkalinity, ammonia, chemical oxygen demand, iron, and heavy metals), on a statistically significant level (p < 0.05): conductivity, DO, nitrite, organic nitrogen, ORP, pH, sulfate, and total volatile solids.