Thesis (M.S.)--Ohio University, November, 2002. / Title from PDF t.p. Includes bibliographical references (leaves 200-203).

Thesis (Ph. D.)--University of Washington, 1994. / Vita. Includes bibliographical references (leaves [289]-298).

Thesis (B.S.)--University of Missouri, School of Mines and Metallurgy, 1906. / The entire thesis text is included in file. Typescript. Illustrated by author. Title from title screen of thesis/dissertation PDF file (viewed December 22, 2008)

No description available.

Thesis (M.A.)--Central Connecticut State University, 2000. / Thesis advisor: Clayton A. Penniman. " ... in partial fulfillment of the requirements for the degree of Master of Arts in Biology." Includes bibliographical references (leaves 103-107).

No description available.

Thesis (M.S.)--West Virginia University, 2002. / Title from document title page. Document formatted into pages; contains xiv, 117 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 111-113).

Theses (M.S.)--Marshall University, 2007. / Title from document title page. Includes abstract. Document formatted into pages: contains iv, 72 pages. Bibliography: p. 57-60.

DeKalb and Fulton Counties, which share the metropolitan Atlanta area, have seen an increase in West Nile infected vector mosquitoes; the increase is associated with close proximity to combined sewer overflow facilities. Despite completion of the remediation system in 2008, the mosquito population testing positive for West Nile virus has increased each year from 2010 through 2012. Guided by the Geographical Information System framework and using spatial analysis and regression analyses, this study described and quantified the relationship between sewer system overflows and amplification of vector mosquitoes; an additional goal was to investigate their proximity to human cases of West Nile VIrus (WNV) infections. Comparing the prominence of all WNV vectors revealed how different mosquito species occupy the area. The Culex species was not detected in adult surveillance in 2012; however, the infection rate of mosquito pools increased by 15% and the human infection more than doubled. The influence of sewer system overflows became pronounced when this study analysis also identified that a proportion of West Nile-virus positive mosquito pools was significantly higher in approximately 58% of trap sites within 1 km of sewer overflow events and 30% over 1 km distance from sewer overflow events. Thus, the research contributes to shared information both in support of previous findings and considering novel sources that contribute to the proliferation of WNV. This research can help reduce the rate of WNV infection and decrease the resources needed to protect the public.

Following large rain events, excess flow in sanitary sewers from inflow and infiltration (I/I) cause sanitary sewer overflows (SSO), resulting in significant problems for Pinellas County and the Tampa Bay area. Stormwater enters the sanitary sewers as inflow from improper or illegal surface connections, and groundwater enters the system as infiltration through cracks in subsurface infrastructure. This pilot study was designed to develop methods to separate and quantify the components of I/I and to build a predictive model using flowmeter and rainfall data. To identify surface inflow, daily wastewater production and groundwater infiltration patterns were filtered from the flow data, leaving a residual signal of random variation and possible inflow. The groundwater infiltration (as base infiltration, BI) was calculated using the Stevens-Schutzbach method, and daily wastewater flow curves were generated from dry weather flow (DWF) data. Filtered DWF values were used to construct a range of expected residuals, encompassing 95% of the variability inherent in the system. Filtered wet weather flows were compared to this range, and values above the range were considered significant, indicating the presence of surface inflow. At all 3 flow meters in the pilot study site, no surface inflow was detected, and the I/I was attributed to groundwater infiltration (as BI). Flow data from 2 smaller sub-sewersheds within the greater sewershed allowed analysis of the spatial variability in BI and provided a method to focus in on the most problematic areas. In the sub-sewershed with the shallowest water table and most submerged sanitary sewer infrastructure, an average of 56% of the average daily flow consisted of groundwater, compared to 44% for the entire study site. Cross-correlation analysis suggests that rain impacts the water table for up to 9 days, with the highest impact 1 to 3 days after rain events, and the water table, in turn, impacts infiltration for up to 6 days. The highest correlation between rainfall and infiltration occurs 3 to 5 days after a rain event, which corroborates observations from Pinellas County that severe flows to the reclamation facility continue for 3 to 5 days after severe storms. These results were used to build a linear regression model to predict base infiltration (per mile of pipeline) during the wet season using the previous 7 days of daily rainfall depths. The model tended to under-predict infiltration response to large storm events with a R2 value of 0.52 and standard error of regression of 5.3. The results of the study show that inflow can be detected using simple time series analysis instead of traditional smoke and dye testing. In this study site, however, groundwater infiltration is the only significant source of I/I. Additionally, water table and sewer invert elevations serve as useful indicators of potential sites of groundwater infiltration. Infiltration can be modeled as a function of the previous 7 days of rainfall, however simple linear regression cannot fully capture the complexity of the system response.