Potable water treatment in small communities is challenging due to a complexity of factors starting with generally poor raw water sources, a smaller tax and consumption base that limit capital and operating funds, and culminating in what is typically a less sophisticated and robust water treatment plant for production and delivery of safe, high quality potable water. The design and optimization of modular ozone-assisted biological filtration systems can address some of these challenges. In surface water treatment, the removal of organic matter (e.g., dissolved organic carbon - DOC), inorganic nutrients and other exposure-related contaminants (e.g., turbidity and dissolved solids) from the raw water source is essential. Thus, a combination of chemical and biological oxidation processes can produce an effective and efficient water treatment plant design that is also affordable and robust. To that end, the ozone-assisted biological filtration water treatment plants in two communities were evaluated to determine the efficacy of oxidation and contaminant removal processes. The results of testing for in-field system performance indicate that plant performance is particularly negatively impacted by high alkalinity, high organics loading, and turbidity. Both bicarbonate and carbonate alkalinity were observed to impede ozone contact and interaction with DOC, resulting in lower than anticipated DOC oxidation efficiency and bioavailability. The ozone dosage at both water treatment plants must be calculated on a more routine basis to better reflect both the raw water DOC concentration and presence of alkalinities to ensure maximized organics oxidation and minimization of trihalomethanes production.
Rural communities rely on surface water reservoirs for potable water. Effective removal of chemical contaminants and bacterial pathogens from these reservoirs requires an understanding of the bacterial community diversity that is present. In this study, we carried out a 16S rRNAbased profiling approach to describe the bacterial consortia in the raw surface water entering the water treatment plants of two rural communities. Our results show that source water is dominated by the Proteobacteria, Bacteroidetes, and Cyanobacteria with some evidence of seasonal effects altering the predominant groups at each location. A subsequent community analysis of sections through a biological carbon filter in the water treatment plant revealed a significant increase in the proportion of Proteobacteria, Acidobacteria, Planctomycetes, and Nitrospirae relative to raw water. Also, very few enteric coliforms were identified in either the source water or within the filter, although the abundance of Mycobacterium was high, and was found throughout the filter along with Aeromonas, Legionella, and Pseudomonas. This study provides valuable insight into bacterial community composition within drinking water treatment facilities, and the importance of implementing appropriate disinfection practices to ensure safe potable water for rural communities.
Quantification and scientific observations of the fate and transport of dissolved strontium in water systems, particularly cold climate water systems, are severely lacking. In this work, in an experiment conducted at a temperature of 6 °C, the observation of strontium precipitation along with calcium carbonate minerals from cold wastewater is investigated. ICP-MS is used for metal analyses where the distribution of the species and saturation state of minerals along with a surface complexation model was performed using the public-use USGS geochemical modeling software, PHREEQC (PH Redox Equilibrium (in C language)). Sample media were analyzed using XPS and Raman spectroscopy. The results suggest that the loss of strontium from natural waters is via the process of co-precipitation with calcite, a calcium carbonate polymorph. The observations and findings are intended to be useful to quantify the loss of 90Sr from the water, in the case of an unplanned release from a nuclear reactor-operated facility. The results indicate that the precipitation model is a robust and reliable approach to predicting and monitoring the behaviour and transport of strontium that may occur in natural environments as a result of an accidental nuclear release.
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