Browsing by Subject "Hotspot"

Now showing 1 - 3 of 3
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    Data Driven Approach To Vehicle Emissions Reduction
    (2016-12) Kotz, Andrew
    Anthropogenic climate change and air pollution resulting from vehicle emissions are major issues facing society. Recent research and government regulations have focused on reducing these emissions through efficiency improvements, advanced aftertreatment technology and ever-tighter regulations. However, despite these advances vehicles still exhibit high emissions during actual use as compared to laboratory or certification testing. Using modern data collection techniques, I identified that electrification and hybridization of transit buses can reduce vehicle power consumption by up to 72% under “real-world” driving conditions, thus decreasing vehicle CO2 emissions. To illustrate the process behind vehicle data collection and offer a use-case for future vehicle connectivity, I discuss the development of a novel mass-based automatic passenger counter to provide a more accurate, lower-cost passenger counting method which can be retrofitted on existing transit buses. Initial results indicate this new passenger counting technology has an accuracy of 97%, however miscounts occur during times when the bus is kneeling, leaving an opportunity for future research. Using measurements from standard exhaust sensors I revealed that NOx emissions of 2013 model year (MY) transit buses were 3-9 times the federal test procedure (FTP) certification limit under “real-world” driving conditions while complying with all regulated standards. To help identify the systematic and physical causes for these high NOx emissions I developed a novel spatial emissions mapping technique called Lagrangian Hotspot Analysis, which used connected vehicle technology to identify spatial influences on vehicle emissions. Results indicate that a hotspot located in the vicinity of a bus stop and intersection had an emissions rate of roughly 3.3 times that of the route average, with these high emissions being attributed to the long idle periods and slow speeds. Instances of cold start and uphill accelerations were also found to increase NOx emissions. Finally, I compared a 2013MY and 2015MY conventional diesel bus to evaluate technology improvements between the generations with the key finding that NOx emissions from the 2015MY bus are reduced by 80% compared to the 2013MY. Further, the NOx emissions were lower than the FTP cycle limit under real-world driving. After the 2013MY bus was updated with the emissions aftertreatment technology of the 2015MY bus, I observed the same NOx reduction in the 2013MY bus. I attribute this reduction to increases in urea consumption through optimization of urea dosing strategy. Such findings imply that certification NOx levels are possible under real-world driving and that upgrading existing buses with modern aftertreatment systems can provide substantial NOx reductions.
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    Identifying erosional hotspots in streams along the North Shore of Lake Superior, Minnesota using high-resolution elevation and soils data
    (2013-09) Wick, Molly Jane
    Many streams on the North Shore of Lake Superior, Minnesota, USA, are impaired for turbidity driven by excess fine sediment loading. The goal of this project was to develop a GIS-based model using new, openly-available, high-resolution remote datasets to predict erosional hotspots at a reach scale, based on three study watersheds: Amity Creek, the Talmadge River, and the French River. The ability to identify erosional hotspots, or locations that are highly susceptible to erosion, using remote data would be helpful for watershed managers in implementing practices to reduce turbidity in these streams. Erosion in streams is a balance between driving forces, largely controlled by topography; and resisting forces, controlled by the materials that make up a channel's bed and banks. New high-resolution topography and soils datasets for the North Shore provide the opportunity to extract these driving and resisting forces from remote datasets and possibly predict erosion potential and identify erosional hotspots. We used 3-meter LiDAR-derived DEMs to calculate a stream power-based erosion index, to identify stream reaches with high radius of curvature, and to identify stream reaches proximal to high bluffs. We used the Soil Survey Geographic (SSURGO) Database to investigate changes in erodibility along the channel. Because bedrock exposure significantly limits erodibility, we investigated bedrock exposure using bedrock outcrop maps made available by the Minnesota Geological Survey (MGS, Hobbs, 2002; Hobbs, 2009), and by using a feature extraction tool to remotely map bedrock exposure using high-resolution air photos and LiDAR data. Predictions based on remote data were compared with two datasets. Bank Erosion Hazard Index surveys, which are surveys designed to evaluate erosion susceptibility of banks, were collected along the three streams. In addition, a 500-year flood event during our field season gave us the opportunity to collect erosion data after a major event and validate our erosion hotspot predictions. Regressions between predictors and field datasets indicate that the most significant variables are bedrock exposure, the stream power-based erosion index, and bluff proximity. A logistic model developed using the three successful predictors for Amity Creek watershed was largely unsuccessful. A threshold-based model including the three successful predictors (stream power-based erosion index, bluff proximity, and bedrock exposure) was 70% accurate for predicting erosion hotspots along Amity Creek. The limited predictive power of the models stemmed in part from differences in locations of erosion hotspots in a single large-scale flood event and long-term erosion hotspots. The inability to predict site-specific characteristics like large woody debris or vegetation patterns makes predicting erosion hotspots in a given event very difficult. A field dataset including long-term erosion data may improve the model significantly. This model also requires high resolution bedrock exposure data which may limit its application to other North Shore streams.
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    Identifying Erosional Hotspots in Streams along the North Shore of Lake Superior, Minnesota using High-Resolution Elevation and Soils Data
    (2013) Wick, Molly Jane
    This is a University of Minnesota Water Resources Science master’s thesis describing original research to determine fluvial erosion in three coastal streams (Amity, Talmadge and French) of Minnesota’s Lake Superior shoreline. All three streams have elevated levels of turbidity, with potential for damage to fisheries. The goal of this project was to develop a GIS-based model using new, openly-available, high-resolution LiDAR datasets to predict erosional hotspots at a reach scale. The abstract summarizing the study’s key findings is extracted and reproduced below. Abstract: “Many streams on the North Shore of Lake Superior, Minnesota, USA, are impaired for turbidity driven by excess fine sediment loading. The goal of this project was to develop a GIS-based model using new, openly-available, high-resolution remote datasets to predict erosional hotspots at a reach scale, based on three study watersheds: Amity Creek, the Talmadge River, and the French River. The ability to identify erosional hotspots, or locations that are highly susceptible to erosion, using remote data would be helpful for watershed managers in implementing practices to reduce turbidity in these streams. “Erosion in streams is a balance between driving forces, largely controlled by topography; and resisting forces, controlled by the materials that make up a channel’s bed and banks. New high-resolution topography and soils datasets for the North Shore provide the opportunity to extract these driving and resisting forces from remote datasets and possibly predict erosion potential and identify erosional hotspots. We used 3-meter LiDAR-derived DEMs to calculate a stream power-based erosion index, to identify stream reaches with high radius of curvature, and to identify stream reaches proximal to high bluffs. We used the Soil Survey Geographic (SSURGO) Database to investigate changes in erodibility along the channel. Because bedrock exposure significantly limits erodibility, we investigated bedrock exposure using bedrock outcrop maps made available by the Minnesota Geological Survey (MGS, Hobbs, 2002; Hobbs, 2009), and by using a feature extraction tool to remotely map bedrock exposure using high-resolution air photos and LiDAR data. “Predictions based on remote data were compared with two datasets. Bank Erosion Hazard Index surveys, which are surveys designed to evaluate erosion susceptibility of banks, were collected along the three streams. In addition, a 500-year flood event during our field season gave us the opportunity to collect erosion data after a major event and validate our erosion hotspot predictions. Regressions between predictors and field datasets indicate that the most significant variables are bedrock exposure, the stream power-based erosion index, and bluff proximity. A logistic model developed using the three successful predictors for Amity Creek watershed was largely unsuccessful. A threshold-based model including the three successful predictors (stream power-based erosion index, bluff proximity, and bedrock exposure) was 70% accurate for predicting erosion hotspots along Amity Creek. The limited predictive power of the models stemmed in part from differences in locations of erosion hotspots in a single large-scale flood event and long-term erosion hotspots. The inability to predict site-specific characteristics like large woody debris or vegetation patterns makes predicting erosion hotspots in a given event very difficult. A field dataset including long-term erosion data may improve the model significantly. This model also requires high resolution bedrock exposure data which may limit its application to other North Shore streams.”