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SOURCE APPORTIONMENT OF PM2.5 SHIP EMISSIONS IN HALIFAX, NOVA SCOTIA, CANADAToganassova, 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.
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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.
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AN INTERNSHIP AS A GRADUATE ASSISTANT AT THE UNITED STATES ENVIRONMENTAL PROTECTION AGENCYKramer, Elizabeth S. 09 December 2010 (has links)
No description available.
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ASSESSING THE PERFORMANCE OF BROOKVILLE FLOOD CONTROL DAMMingda 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>
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Chemical and Geological Controls on the Composition of Waters and Sediments in Streams Located within the Western Allegheny Plateau: The Shade River WatershedGbolo, Prosper 29 July 2008 (has links)
No description available.
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Discovery of Nanostructured Material Properties for Advanced Sensing PlatformsWujcik, Evan K. 28 August 2013 (has links)
No description available.
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Developing Artificial Neural Networks (ANN) Models for Predicting E. Coli at Lake Michigan BeachesMitra 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>
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