No description available.

No description available.

No description available.

No description available.

A Westphalian (Coal Measures) sequence 900 m thick is preserved in the Durham coalfield of N.E. England, and includes the Westphalian A, B and C stages. This succession is condensed with respect to those of the central Pennine coalfields further south. The Durham Coal Measures were deposited on a coastal, deltaic plain, characterized by having little or no relief. This plain was crossed by bifurcating distributary channels, which were separated by shallow water lakes and bays. Trunk distributaries, probably of low-moderate sinuosity and perhaps 5 kms wide, fed sinuous major distributaries of up to 3 kms width. These in turn supplied minor distributaries, often sinuous in form, which were up to 100 m wide and fed shallow water deltas. Interdistributary lakes and bays were generally less than 10 m deep, up to 20+ kms wide, unstratified and often anoxic. These shallow basins were in filled by crevasse splay and minor delta sedimentation. Infilled lake and bay surfaces and abandoned channels were rapidly colonized by vegetation, and thick (perhaps up to 40 m) seams of peat were able to accumulate, often diachronously, over wide areas (100s of sq. kms).During Namurian and Lower Westphalian times, lower delta plain conditions prevailed, in which interdistributary bays were open to marine influence so that deltaic sands could be reworked to form quartz arenitic shoreline sandstone bodies. As a result of south ward deltaic progradation, this environment evolved into an upper delta plain which persisted through much of the Westphalian A and B, although during Upper Westphalian A times, the plain developed characteristics transitional to those of a fluvial plain. Occasional marine incursions caused only temporary drowning of the upper delta plain surface to 10-15 m depth. Sedimentation was controlled on the large scale (100s of sq. kms) by patterns of deltaic sediment distribution, on the medium scale (10s of sq. kms) by a combination of structurally- and compaction- induced subsidence, and on the small scale by local sedimentary processes. A comparison with other areas of Westphalian outcrop in Britain shows strong similarities to the depositional environment found in Durham.

No description available.

No description available.

<p> Eversource Energy, formerly Public Service of New Hampshire (PSNH), has worked closely with Plymouth State University (PSU) in the past, and present, to better predict weather-related power outage events and maximize the efficiency with which Eversource responds to them. This research paired weather data from thirteen stations throughout New Hampshire, Vermont, and Massachusetts with Eversource Trouble Report and Unsatisfactory Performance of Equipment Report (TRUPER) data in an effort to quantify weather situations that lead to power outages. The ultimate goal involved developing a predictive model that uses weather data to forecast the magnitude of power outages. The study focused on the Eversource Western/Central service territory and utilized data from 2006-2010. The first four years, 2006-2009, were analyzed using Classification and Regression Tree (CART) statistical analysis. The results of this CART analysis trained a predictive model, while the fifth year, 2010, served as the testing set for the predictive model. </p><p> To conduct the statistical analysis, a database was created pairing TRUPER reports with the closest available hourly weather observations. The database included nine weather variables matched with three variables from the TRUPER data: 1) customers, 2) customer minutes, and 3) outage duration. While the entire Eversource service territory saw 91,286 TRUPERs from 2006-2010, the Western/Central service territory, the focus of this study, accounted for 29,430. Before conducting the CART analysis, correlations between single weather variables and TRUPER data were calculated and, in general, proved xi weak. In addition to analyzing the complete four-year training data set, many portions/variations of the data set were analyzed. The analyses included a yearly analysis, time lag analysis, cold/warm-season analysis, and a single-station analysis. Although individual years and smaller data sets showed moderately higher correlations between weather and outage data, consistent relationships throughout the data set were fairly weak. CARTs were then created to examine the joint effect of the entire set of weather variables, such as interactions and nonlinear relationships, to improve the overall predictability of power outages. After creating the trees from the four-year training data set, their predictive ability was tested using the final year of data. </p><p> The CART predictive models showed that among Eversource TRUPER variables, the hardest to predict was customers per TRUPER. The best performing model predicted customers per TRUPER to an average error of 96 customers, or a percent mean average error (PMAER) of 131% of 2010 customers per TRUPER. This result could deal with the high variability seen in customer outages per TRUPER, across a single weather event, driven by widelyvarying population and customer density. The most accurately predicted TRUPER variable, outage duration, saw average PMAER values of 60% of the mean (e.g., if mean duration per TRUPER for the year was 100 minutes, the model would miss on average by 60 minutes). Overall, the model results show surface weather data has a weak correlation to the TRUPER variables analyzed. The model can predict situations when one would expect longer duration outages but is unable to accurately predict the magnitude of these variables. When xii adapting the predictive models to smaller portions of the data set, warm-season data showed the greatest predictability, considerably outperforming the other data sets (cold-season, 2006-2009, and single station). Cold-season showed the greatest volatility and, not surprisingly, proved the most difficult to predict.</p>

<p> The valleys of northern Utah, where most of Utah&rsquo;s population resides, experience episodic air pollution events well in excess of the National Ambient Air Quality Standards. Most of the events are due to an accumulation of particulate matter during persistent cold air pools in winter from both direct emissions and secondary chemical reactions in the atmosphere. High wintertime ozone concentrations are occasionally observed in the Uintah Basin, in addition to particulate matter. At other times of the year, blowing dust, wildland fires, fireworks, and summertime ozone formation contribute to local air pollution. The objective of this dissertation is to investigate one facet of the health effects of Utah&rsquo;s air pollution on its residents: the acute impacts of air pollution on gastrointestinal (GI) disease.</p><p> To study the health effects of these episodic pollution events, some measure of air pollution exposure must be matched to the health data. Time and place are used to link the health data for a person with the pollution data. This dissertation describes the method of kriging data from the sparse pollution monitoring network to estimate personal air pollution history based on the zip code of residence. This dissertation then describes the application of these exposure estimates to a health study on GI disease.</p><p> The purpose of the GI study is to retrospectively look at two groups of patients during 2000-2014: those with autoimmune disease of the GI tract (inflammatory bowel disease, IBD) and those with allergic disease of the GI tract (eosinophilic esophagitis, EoE) to determine whether disease exacerbations occur more commonly during and following periods of poor air quality compared to periods of good air quality. The primary analysis method is case crossover design. In addition to using the kriged air pollution estimates, the analysis was repeated using simpler empirical estimation methods to assess whether the odds ratios are sensitive to the air pollution estimation method.</p><p> The data suggests an association between particulate matter smaller than 2.5 microns and prednisone prescriptions, gastrointestinal infections in general, clostridium difficile infections specifically, and hospitalizations among people who have at least five entries of IBD diagnosis codes in their medical records. EoE exacerbations appear to be associated with high concentrations of particulate matter as well as ozone.</p>

<p> The Asian summer monsoon supplies around 3 billion people with much of their yearly supply of freshwater, necessary for human consumption as well as in agriculture and industry. In many regions, particularly along the Ganges River in India and in northern China, use of freshwater far exceeds natural recharge rates. Given the high population density of these regions, a substantial fraction of Asia's population is therefore critically sensitive to interannual changes in the supply of freshwater by the monsoon, as well as potential future change under 21st century warming. This dissertation focuses on understanding the atmospheric dynamics of the leading mode of July-August Asian Monsoon rainfall variability, which links two major subsystems: the South Asian and East Asian monsoons. (Abstract shortened by ProQuest.)</p>