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Large fires (400 ha +) occur about every seven to ten years in the vegetation types located at US Army Garrison Camp Williams (AGCW) practice range located near South Jordan, Utah. In 2010 and 2012, wildfires burned beyond the Camp’s boundaries into the wildland-urban interface. The political and public reaction to these fire escapes was intense. Researchers at Utah State University were asked to organize a system of fuel treatments that could be developed to prevent future escapes. The first step of evaluation was to spatially predict fuel model types derived from a random forests classification approach. Fuel types were mapped according to fire behavior fuel models with an overall validation of 72.3% at 0.5 m resolution. Next, using a combination of empirical and semi-empirical based methods, potential fire behavior was analyzed for the dominant vegetation types at AGCW on a climatological basis. Results suggest the need for removal of woody vegetation within 20 m of firebreaks and a minimum firebreak width of 8 m in grassland fuels. In Utah juniper (Juniperus osteosperma (Torr.) Little), results suggest canopy coverage of 25% or less while in Gambel oak (Quercus gambelii Nutt.) stands along the northern boundary of the installation, a fuelbreak width of 60 m for secondary breaks and 90 m for primary breaks is recommended.

The transportation sector has a significant impact on the environment both nationally and globally since it is a major vehicle fuel consumption and emissions contributor. These emissions are considered a major environmental threat. Consequently, decision makers desperately need tools that can estimate vehicle emissions accurately to quantify the impact of transportation operational projects on the environment. Microscopic fuel consumption and emission models should be capable of computing vehicle emissions reliably to assist decision makers in developing emission mitigation strategies. However, the majority of current state-of-the-art models suffer from two major shortcomings, namely; they either produce a bang-bang control system because they use a linear fuel consumption versus power model or they cannot be calibrated using publicly available data and thus require expensive laboratory or field data collection. Consequently, this dissertation attempts to fill this gap in state-of-the-art emission modeling through a framework based on the Virginia Tech Comprehensive Power-Based Fuel consumption Model (VT-CPFM), which overcomes the above mentioned drawbacks. Specifically, VT-CPFM does not result in a bang-bang control and can be calibrated using publicly available vehicle and road pavement parameters. The main emphasis of this dissertation is to develop a simple and reliable emission model that is able to compute instantaneous emission rates of carbon monoxide (CO), hydrocarbons (HC) and nitrogen oxides (NOx) for the light-duty vehicles (LDVs) and heavy-duty diesel trucks (HDDTs). The proposed extension is entitled Virginia Tech Comprehensive Power-Based Fuel consumption and Emission Model (VT-CPFEM). The study proposes two square root models where the first model structure is a cubic polynomial function that depends on fuel estimates derived solely from VT-CPFM fuel estimates, which enhances the simplicity of the model. The second modeling framework combines the cubic function of the VT-CPFM fuel estimates with a linear speed term. The additional speed term improves the accuracy of the model and can be used as a reference for the driving condition of the vehicle. Moreover, the model is tested and compared with existing models to demonstrate the robustness of the model. Furthermore, the performance of the model was further investigated by applying the model on driving cycles based on real-world driving conditions. The results demonstrate the efficacy of the model in replicating empirical observations reliably and simply with only two parameters. / Ph. D. / The transportation sector places a huge burden on our environment and is one of the major emitters of pollutants. The resulting emissions have a negative impact on human health and could be a concern for national security. Therefore, policymakers are keen to develop models that accurately estimate the emissions from on-road vehicles. Microscopic emission models are capable of estimating the instantaneous vehicle emissions from operational-level projects, and policymakers can use them to evaluate their emission reduction plans and the environmental impact of transportation projects. However, the majority of the current existing models indicate that to achieve the optimum fuel economy, the driver should accelerate at full throttle and full braking for deceleration to minimize the acceleration and deceleration times. This assumption is obviously incorrect since it requires aggressive driving which will result in increasing the fuel consumption rate. Also, the models cannot use publicly accessible and available data to estimate the emissions which require expensive laboratory or field data collection. Consequently, this dissertation attempts to fill this gap in emission modeling through a framework based on the Virginia Tech Comprehensive Power-Based Fuel consumption Model (VT-CPFM), which overcomes the above mentioned drawbacks. Specifically, VT-CPFM does not follow the mentioned assumption of aggressive driving to minimize the fuel consumption as previously explained and can use publicly available vehicle and road pavement variables to estimate the emissions. Also, it utilizes US Environmental Protection Agency (EPA) city and highway the fuel economy ratings to calibrate its parameters. The main emphasis of this dissertation is to develop a simple and reliable emission model that is able to compute instantaneous emission rates of carbon monoxide (CO), hydrocarbons (HC) and nitrogen oxides (NOx) for the light-duty vehicles (LDVs) and heavy-duty diesel trucks (HDDTs). The proposed extension is entitled Virginia Tech Comprehensive PowerBased Fuel consumption and Emission Model (VT-CPFEM). The study proposes two models where the first model structure that depends on fuel estimates derived solely from VT-CPFM fuel estimates, which enhances the simplicity of the model. The second modeling framework combines the VT-CPFM fuel estimates with the speed parameter. The additional speed term improves the accuracy of the model and can be used as a reference for the driving condition of the vehicle. The model framework is consistent in estimating the three emissions for LDVs and HDDTs. Moreover, the performance of the model was investigated in comparison with existing models to demonstrate the reliability of the model. Furthermore, the performance of the model was further evaluated by applying the model on driving cycles based on real-world driving conditions. The results demonstrate the capability of the model in generating accurate and reliable estimates based on the goodness of fit and error values for the three types of emissions.

Each year many forest fires have occurred and huge amount of forest areas in each country have been lost. Turkey like many world countries have forest fire problem. 27 % of Turkey&amp / #65533 / s lands are covered by forest and 48 % of these forest areas are productive, however 52 % of them must be protected. There occurred 21000 forest fires due to several reasons between 1993 and 2002. It is estimated that 23477 ha area has been destroyed annually due to wildfires. The fire management strategies can be built on the scenarios derived from the simulation processes. In this study a GIS &amp / #65533 / based fire simulating model is used to simulate a past fire occurred in Marmaris &amp / #65533 / &Ccedil / etibeli, Turkey, in August 2002. This model uses Rothermel&amp / #65533 / s surface fire model, Rothermel&amp / #65533 / s and Van Wagner&amp / #65533 / s crown fire model and Albini&amp / #65533 / s torching tree model. The input variables required by the model can be divided into four groups: fuel type, fuel moisture, topography and wind. The suitable fuel type classification of the vegetation of the study area has been performed according to the Northern Forest Fire Laboratory (NFFL) Fuel Model. The fuel moisture data were obtained from the experts working in the General Directorate of Forestry. The fire spread pattern was derived using two IKONOS images representing the pre- and post-fire situations by visual interpretation. Time of arrival, the rate of spread and the spread direction of the fire were obtained as the output and 70 % of the burned area was estimated correctly from the fire simulating model.