Global ETD Search

1	SOURCE APPORTIONMENT OF PM2.5 SHIP EMISSIONS IN HALIFAX, NOVA SCOTIA, CANADA Toganassova, Dilyara 21 March 2013 (has links) This study investigated the source attribution of ship emissions to atmospheric particulate matter with a median aerodynamic diameter less than, or equal to 2.5 micron (PM2.5) in the port city of Halifax, Nova Scotia, Canada. The USEPA PMF model successfully determined the following sources with the average mass (percentage) contribution: Sea salt 0.147 µg m-3 (5.3%), Surface dust 0.23 µg m-3 (8.3%), LRT Secondary (ammonium sulfate) 0.085 µg m-3 (3.1%), LRT Secondary (nitrate and sulfate) 0.107 µg m-3 (3.9%), Ship emissions 0.182 µg m-3 (6.6%), and Vehicles and re-suspended gypsum 2.015 µg m-3 (72.8%). A good correlation was achieved between PM2.5 total mass predicted and observed with R2 = 0.83, bias = -0.23, and RMSE = 0.09 µg m-3. In addition, a 2.5 times (60%) reduction in sulfate was estimated, when compared to 2006-2008 Government data in Halifax.
2	A Re-Evaluation of the US EPA Radon Risk Categorization for Unicoi County, Tennessee. Parsons, William Grant 01 August 2003 (has links) (PDF) Effective risk communication is based on appropriate risk characterization. A reevaluation of the 1987 US EPA radon risk categorization of Unicoi County Tennessee was conducted using in-home radon concentrations, determined in a long-term monitoring study. Radon concentrations were measured in 69 homes using Electret Passive Environmental Radon Monitors (E-PERM’s), following standard methods. Radon concentrations determined in this study (avg. 4.03 ± 3.04) were significantly higher than those measured in the USEPA study (avg. 1.96 ± 1.08). Using this study’s data, the risk categorization was recalculated with the US EPA Radon Index Matrix Model. The model re-categorized Unicoi County from a moderate to a high risk zone classification. These results suggest that the health risks associated with in-home radon concentrations are inaccurately categorized and communicated to the citizens of Unicoi County, Tennessee. radon E-PERM USEPA matrix model Environmental Health and Protection Environmental Monitoring Environmental Sciences Physical Sciences and Mathematics
3	AN INTERNSHIP AS A GRADUATE ASSISTANT AT THE UNITED STATES ENVIRONMENTAL PROTECTION AGENCY Kramer, Elizabeth S. 09 December 2010 (has links) No description available. Technical Communication MTSC Internship report EPA USEPA Environmental Protection Agency water contamination techical communication technical writing
4	ASSESSING THE PERFORMANCE OF BROOKVILLE FLOOD CONTROL DAM Mingda Lu (5930987) 16 January 2019 (has links) <div>In this study, the performance of a flood control reservoir called Brookville Reservoir located in the East fork of the Whitewater River Basin, was analyzed using historic and futuristic data. For that purpose, USEPA HSPF software was used to develop the rainfall runoff modelling of the entire Whitewater River Basin up to Brookville, Indiana. Using uncontrolled flow data, the model was calibrated using 35 years of data and validated using 5 years by evaluating the goodness-offit with R2, RMSE, and NSE. Using historic data, the historic performances were accessed initially.</div><div>Using downscaled daily precipitation data obtained from. GCM for the considered region, flows were generated using the calibrated HSPF model. A reservoir operation model was built using the present operating policies. By appending the reservoir simulation model with HSPF model results, performance of the reservoir was assessed for the future conditions.</div> Brookville Dam USEPA BASINS GCM HEC-HMS Flood Control
5	Chemical and Geological Controls on the Composition of Waters and Sediments in Streams Located within the Western Allegheny Plateau: The Shade River Watershed Gbolo, Prosper 29 July 2008 (has links) No description available. Geology Western Allegheny Plateau (WAP) Shade River Watershed USEPA criteria dissolved oxygen total phosphate manganese iron alkalinity electric conductivity
6	Discovery of Nanostructured Material Properties for Advanced Sensing Platforms Wujcik, Evan K. 28 August 2013 (has links) No description available. Chemical Engineering Environmental Engineering Biomedical Engineering Analytical Chemistry detection sensor MWNT polycaprolactam polymer nanocomposite sodium sweat diagnosis TGA amperometry Fujiwara water contaminant colorimetric THM halogenated DBP material engineering fabrication design hydrophobic membrane USEPA SEM
7	Developing Artificial Neural Networks (ANN) Models for Predicting E. Coli at Lake Michigan Beaches Mitra Khanibaseri (9045878) 24 July 2020 (has links) <p>A neural network model was developed to predict the E. Coli levels and classes in six (6) select Lake Michigan beaches. Water quality observations at the time of sampling and discharge information from two close tributaries were used as input to predict the E. coli. This research was funded by the Indiana Department of Environmental Management (IDEM). A user-friendly Excel Sheet based tool was developed based on the best model for making future predictions of E. coli classes. This tool will facilitate beach managers to take real-time decisions.</p> <p>The nowcast model was developed based on historical tributary flows and water quality measurements (physical, chemical and biological). The model uses experimentally available information such as total dissolved solids, total suspended solids, pH, electrical conductivity, and water temperature to estimate whether the E. Coli counts would exceed the acceptable standard. For setting up this model, field data collection was carried out during 2019 beachgoer’s season.</p> <p>IDEM recommends posting an advisory at the beach indicating swimming and wading are not recommended when E. coli counts exceed advisory standards. Based on the advisory limit, a single water sample shall not exceed an E. Coli count of 235 colony forming units per 100 milliliters (cfu/100ml). Advisories are removed when bacterial levels fall within the acceptable standard. However, the E. coli results were available after a time lag leading to beach closures from previous day results. Nowcast models allow beach managers to make real-time beach advisory decisions instead of waiting a day or more for laboratory results to become available.</p> <p>Using the historical data, an extensive experiment was carried out, to obtain the suitable input variables and optimal neural network architecture. The best feed-forward neural network model was developed using Bayesian Regularization Neural Network (BRNN) training algorithm. Developed ANN model showed an average prediction accuracy of around 87% in predicting the E. coli classes. </p> Environmental Engineering Modelling ANN models E.coli cells Levenberg-Marquardt algorithm Bayesian Regularization Cross validation USEPA level real time magment Water Quality Monitoring regression model Hach Turbidity Meter

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