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The Effects of High Density Septic Systems on Surface Water Quality in Gwinnett County, GeorgiaAnderson, John R, II 15 December 2010 (has links)
Gwinnett County, Georgia experienced rapid growth in the 1970’s without the infrastructure so septic systems were installed for residential homes. The number of septic systems grew to over 85,000 with a density of 487 septic systems per square mile. This study mapped the distribution of septic systems to determine regions of potential pathogen surface water. This study addressed what potential health risks do high density septic systems have on surface water quality and how can the history of Gwinnett County assist in future development in the Metropolitan Atlanta area? It was found that the density of septic systems has reduced the surface water quality for streams in the Yellow and Alcovy River basins. An average rainfall cause septic flushing and an increase in the fecal coliform. Other trends observed in the surface water quality of increased BOD, water temperature, and various metals also indicated this flushing effect.
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Development of a TMDL Implementation Plan for Fecal Coliform Reduction in TennesseeScheuerman, Phillip R., Maier, Kurt J., Luffman, I. E., Craig, C. L., Chase, K. R. 01 January 2004 (has links)
No description available.
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Temporal Trends of Coliform Bacteria in Spencer Creek, HamiltonLymburner, Donna J. 04 1900 (has links)
<p> Coliform bacterial pollution was studied in the Spencer Creek in Hamilton, Ontario. Data, derived from the Ontario Ministry of the Environment files in Hamilton, were used in the determination of whether distinctive temporal patterns of coliform bacterial counts and loads exist in Spencer Creek at Cootes Drive in Dundas. An initial investigation of the general streamflow trends showed that the peak flow period is during the spring snowmelt (March) and a secondary peak flow occurs in late fall due to frontal storms passing through the area. This coincides with the minimums of coliform counts and loads which consistently occur during these times of high flow, particularly March and April. Peak coliform counts and loads occur 70% of the time in late summer with varying starting times in June, July, August and September. This is attributed to the low flow and high temperature of the water in the summer. The average coliform count is 334,154 coliform bacteria per 100 mL water, well above the water quality objectives set by the Ontario Water Resources Commission for Ontario Ministry of the Environment. Approximately 80% of the coliform counts, measured in Spencer Creek in this study, were well above this water quality objective. Therefore, in terms of coliform bacteria, Spencer Creek is polluted, particularly in the summer months. This study simply points out the necessity for further study and continued water quality testing in Spencer Creek.</p> / Thesis / Candidate in Philosophy
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FECAL BACTERIA INDICATOR TO DETERMINE POINT-SOURCE POLLUTION UPSTREAM OF THE CITY OF PITTSBURGH, WESTERN PENNSYLVANIA, USACelebrezze, Eric N. 27 March 2007 (has links)
No description available.
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Evaluating Coliform Monitoring Strategies for Contamination Scenarios in a Distribution System ModelBauer, Jeremy 28 May 2008 (has links)
Large public water utilities currently are required to take several 100 mL water samples every month in their distribution systems and to test these samples for the presence of total coliforms to comply with the Total Coliform Rule. How and where they take these samples is not specified. The Total Coliform Rule is currently undergoing review and revision. One possible revision is more specific rule language or guidance of where and when utilities take their monthly coliform water samples. This project considers various drinking water distribution system monitoring strategies for a large drinking water utility using simulations within an EPANET computer model of that utility's distribution system. A review of the literature and the use of best professional judgment help to inform the understanding of the primary causes of the occurrence of coliform bacteria in drinking water distribution systems. In this analysis, both contamination scenarios and coliform monitoring approaches are simulated in the EPANET computer model to better understand what types of contamination events might be detected by routine coliform monitoring and to what extent monitoring strategies affect observed results. In addition, statistical strengths of the various strategies and their respective results are considered in this project. Analysis of model output supports the assertion that water age may be a good factor to consider in designing a monitoring plan, especially if sampling cannot be conducted every day and that taking samples immediately after and in the vicinity of rapid changes in flow (e.g., during a firefighting incident) may help utility operators to better understand what associated risk, if any, exists to consumers of drinking water as a result of these flow conditions. / Master of Science
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Comparing Alternative Methods of Simulating Bacteria Concentrations with HSPF Under Low-Flow ConditionsHall, Kyle M. 27 September 2007 (has links)
During periods of reduced precipitation, flow in low-order, upland streams may be reduced and may stop completely. Under these "low flow" conditions, fecal bacteria directly deposited in the stream dominate in-stream bacteria loads. When developing a Total Maximum Daily Load (TMDL) to address a bacterial impairment in an upland, rural watershed, direct deposit (DD) fecal bacteria sources (livestock and wildlife defecating directly in the stream) often drive the source-load reductions required to meet water quality criteria. Due to limitations in the application of existing watershed-scale water quality models, under low-flow conditions the models can predict unrealistically high in-stream fecal bacteria concentrations. These unrealistically high simulated concentrations result in TMDL bacteria source reductions that are much more severe than what actually may be needed to meet applicable water quality criteria.
This study used the Hydrological Simulation Program-FORTRAN (HSPF) to compare three low-flow DD simulation approaches and combinations (treatments) on two Virginia watersheds where bacterial impairment TMDLs had been previously developed and where low-flow conditions had been encountered. The three methods; Flow Stagnation (FS), DD Stage Cut-off (SC), and Stream Reach Surface Area (SA), have all been used previously to develop TMDLs. A modified version of the Climate Generation (CLIGEN) program was used to stochastically generate climate inputs for multiple model simulations. Violations of Virginia's interim fecal coliform criteria and the maximum simulated in-stream fecal coliform concentration were used to compare each treatment using ANOVA and Kruskal Wallis rank sum procedures. Livestock DD bacteria sources were incrementally reduced (100%, 50%, 15%, 10%, 5%) to represent TMDL load reduction allocation scenarios (allocation levels).
Results from the first watershed indicate that the FS method simulated significantly lower instantaneous criterion violation rates at all allocation levels than the Control. The SC method reduced the livestock DD load compared to the Control, but produced significantly lower instantaneous criterion violation rates only at the 100% allocation level. The SA method did not produce significantly different instantaneous criterion violation rates compared to the Control. Geometric mean criterion violation rates were not significantly different from the Control at any allocation level. The distributions of maximum in-stream fecal coliform concentrations simulated by the combinations SC + FS and SC + SA + FS were both significantly different from the Control at the 100% allocation level.
The second watershed did not produce low-flow conditions sufficient to engage the FS or SC methods. However, the SA method produced significantly different instantaneous violation rates than the Control at all allocation levels, which suggests that the SA method continues to affect livestock DD loads when low-flow conditions are not simulated in the watershed. No significant differences were found in the geometric mean violation rate or distribution of maximum simulated in-stream fecal coliform concentrations compared to the Control at any allocation level.
This research suggests that a combination of the SC and FS methods may be the most appropriate treatment for addressing unrealistically high concentrations simulated during low-flow conditions. However, this combination must be used with caution as the FS method may increase the maximum simulated in-stream fecal coliform concentration if HSPF simulates zero volume within the reach. / Master of Science
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Estimating Bacterial Loadings to Surface Waters from Agricultural WatershedsPanhorst, Kimberly A. 29 April 2003 (has links)
Fecal bacteria and pathogens are a major source of surface water impairment. In Virginia alone, approximately 73% of impaired waters are impaired due to fecal coliforms (FC). Because bacteria are a significant cause of water body impairment and existing bacterial models are predominantly based upon laboratory-derived information, bacterial models are needed that describe bacterial die-off and transport processes under field conditions. Before these bacterial models can be developed, more field-derived information is needed regarding bacterial survival and transport. The objectives of this research were to evaluate bacterial survival under field conditions and to develop a comprehensive, spatially variable (distributed) bacterial model that requires little or no calibration. Three field studies were conducted to determine die-off or diminution (settling plus die-off) rates of FC and Escherichia coli (EC) over time in: 1) dairy manure storage ponds and turkey litter storage sheds, 2) pasture and cropland soils to which dairy manure was applied, and 3) beef and dairy fecal deposits. The dairy manure storage ponds were sampled just under the pond surface. The FC and EC diminution (settling plus die-off) rates for dairy manure storage ponds were 0.00478 day⁻¹ and 0.00781 day⁻¹, respectively. The five samples collected for turkey litter in storage were inadequate to draw any conclusions. Bacterial die-off rates in cropland and pastureland soils were found to be statistically different from each other at the α = 0.05 level. The FC and EC die-off rates in cropland soils were 0.01351 day⁻¹ and 0.01734 day⁻¹, respectively, while the FC and EC die-off rates in pastureland soils were 0.02246 day⁻¹ and 0.02796 day⁻¹, respectively. Die-off rates for bacteria from dairy heifer, dairy milker, and beef cow fecal deposits were not statistically different from each other. The resulting die-off rate constants for fecal deposits were 0.01365 day⁻¹ and 0.01985 day⁻¹ for FC and EC, respectively. The EC/FC ratio was also evaluated for the fecal deposits and land-applied manure to determine if a quantifiable relationship was discernable. In general the EC/FC ratio declined over time, but no quantifiable relationship was discerned.
The bacterial model simulates die-off, bacterial partitioning between soil and water, and bacterial transport to surface waters in free (in solution) and sediment-adsorbed forms. Bacterial die-off was modeled using Chick's Law, bacterial partitioning was modeled with a linear isotherm equation, and bacterial transport was modeled using continuity and flow equations. The bacterial model was incorporated into the ANSWERS-2000 model, a continuous, distributed, nonpoint source pollution model. The model was tested using data from two plot studies. Calibration was required to improve runoff and sediment predictions. Bacterial model predictions underpredicted bacterial concentrations in runoff with a maximum underprediction error of 92.9%, but predictions were within an order of magnitude in all cases. Further model evaluation, on a larger watershed with predominantly overland flow, over a longer time period, is recommended, but such data were not available at the time of this assessment. The overall conclusions of this research were 1) FC and EC die-off or diminution under the examined field conditions followed Chick's Law, 2) measured die-off rate constants in the field were much less than those cited in literature for laboratory experiments, and 3) for the conditions simulated for two plot studies, the bacterial model predicted bacterial concentrations in runoff within an order of magnitude. / Master of Science
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Avaliação da qualidade sanitária da água do Rio Uno e período de chuvaAline Briet de Almeida Silva 07 December 2007 (has links)
Este estudo teve por finalidade buscar informações sobre a qualidade da água do rio Una, por meio de análises de coliformes totais e coliformes termotolerantes, bem como de estudos sobre os impactos causados no entorno. Foram coletadas oito amostras ao longo do Rio Una. As taxas de coliformes totais foram de 1000 e ≥ 1600 NMP/mL, nos meses de dezembro de 2006, janeiro e março de 2007, enquanto nos meses de fevereiro e abril os resultados variaram de 17 a 300 por 100 mL. Os coliformes termotolerantes nos meses de dezembro de 2006, janeiro e março de 2007 em alguns pontos de coleta apresentaram-se fora do valor permitido para rio pertencente à classe 2. Desta forma, pode-se concluir que esses valores de coliformes evidenciam que o foco de contaminação é de despejos de esgotos "in natura" e a animais que ficam na margem do rio. A má ocupação do espaço vem causando sérios danos à conservação dos recursos hídricos. / The aim of this work is to determine the water quality of Rio Una, through analysis of total coliforms and thermotolerants coliform as well as studies on the environmental impacts. Eight samples were collected along the Rio Una. The rates of total coliforms were 1000 and ≥ 1600 MPN/100 mL, in the December 2006, January and March 2007, while in February and April results varied from 17 to 300/100 mL. Thermotolerants coliforms in December 2006, January and March 2007, in some points of collection had been out of the amount allowed for to the class 2 river. Thus, it was concluded that these values of coliforms show that the focus of contamination from sewage waste is "in natura" and to animals that are around the river. The bad occupation of the area has caused serious damage to the conservation of water resources.
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Some Impacts of Septic Effluent On Hydromorphic SoilsCampbell, James Alfred 06 1900 (has links)
<p> Measurements of soil atmosphere, soil physical parameters, coliform levels and ionic levels were made at 15 soil sites along Ancaster and Grindstone Creeks in winter, 1972-73 and July, 1973. Multivariate analysis of the data indicated large fluxes of septic effluent resulted in soil fabric alterations, anaerobosis, and elevated coliform, nitrate, chloride and sodium levels. According to ecological and health criteria, these hydromorphic soils have very serious limitations as processors and absorbers of septic effluent. </p> / Thesis / Master of Science (MSc)
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Watershed Based Analysis of Fecal Coliform within the Back Bay of Biloxi and its Surrounding StreamsRenick, Matthew Edward 04 August 2001 (has links)
In the development of the watershed, hydrodynamic, and water quality models for Back Bay of Biloxi in Mississippi, the Better Assessment Science Integrating Point and Nonpoint Sources (BASINS 2.0) - Nonpoint Source Model (NPSM) was selected as the watershed model. The hydrodynamic and water quality models DNYHYD5 and EUTRO5 were selected as the tidally influenced bay models. The watershed model simulated nonpoint source flow and pollutant loadings for all sub-watersheds, routed flow and water quality, and accounted for all major point source discharges in the Back Bay of Biloxi watershed. Time varying output from the watershed model was applied directly to the Back Bay of Biloxi model. The Bay models, in turn simulated hydrodynamics and water quality, including water depth, velocities, and fecal coliform concentrations. Both watershed and Bay models were calibrated and verified against observed data. The calibrated/verified model was used as a planning tool to assess the water quality in the Watershed and the Bay as well as for calculating Total Maximum Daily Load (TMDL) and Waste Load Allocation (WLA).
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