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No description available.

Neural network models were developed to model the short-term concentration distribution of aerosols released from point sources. Those models were based on data from a wide range field experiments (November 2002, March, May and August 2003). The study focused on relative dispersion from the puff centroid. The influence of puff/cloud meandering and large-scale gusts were not considered, the modelling was limited to studying the dispersion caused by small-scale turbulence. The data collected were based on short range/time dispersion, usually shorter than 150 s. The ANN (Artificial Neural Network) models considered explicitly a number of meteorological and turbulence parameters, as opposed to the Gaussian models that used a single fitting parameter, the dispersion coefficient. The developed ANN models were compared with predictions generated from COMBIC (Combined Obscuration Model for Battlefield Induced Contaminants), a sophisticated model based on Gaussian distributions, and a traditional Gaussian puff model using Slade’s dispersion coefficients. Neural network predictions have been found to have better agreement with concentration measurements than either of the other two Gaussian puff models. All models underestimate the maximum concentration, but ANN predictions are much closer to observations. Simulations of concentration distributions under different stability conditions were also checked using the developed ANN model, and it showed that, for a short time, Gaussian distributions are a good fit for puff dispersion in the downwind, crosswind and vertical directions. For Gaussian puff models, the key issue is to determine appropriate dispersion coefficients (standard deviations). ANN models for puff dispersion coefficients were trained and their average predictions were compared with the results of measurements. Very good agreement was observed, with a high correlation coefficient (>0.99). The ANN models for dispersion coefficients were used to analyze which input variables were more significant for puff expansions. Dispersion time, particle position relative to the centroid, turbulent kinetic energy and insolation showed the most significant influence on puff dispersion. The Gaussian puff model with dispersion coefficients from the ANN models was compared with COMBIC and a Gaussian puff model using Slade’s dispersion coefficients. Generally speaking, predictions generated by the Gaussian puff model with dispersion coefficients generated by ANN models showed better agreement with concentration measurements than the other two Gaussian puff models, by giving a much higher fraction within a factor of two, and lower normalised mean square errors. / Thesis (Ph.D, Chemical Engineering) -- Queen's University, 2007-10-17 12:13:42.923 / NSERC, DGNS

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No description available.

Vehicular emissions contribute significantly to poor air quality in urban areas. An integrated modelling approach is adopted to estimate microscale urban traffic emissions. The modelling framework consists of a traffic microsimulation model, a microscopic emissions model, and two dispersion models. This framework is applied to a traffic network in downtown Toronto to evaluate summer time morning peak traffic emissions during weekdays for carbon monoxide and nitrogen oxide. The model predicted results are validated against sensor observations with a reasonably good fit. Availability of local estimates of ambient concentration is useful for accurate comparisons of total predicted concentrations with observed concentrations. Both predicted and observed concentrations are significantly smaller than the National Ambient Air Quality Objectives established by Environment Canada. Sensitivity analysis is performed on a set of input parameters and horizontal wind speed is found to be the most influential factor in pollutant dispersion.

Vehicular emissions contribute significantly to poor air quality in urban areas. An integrated modelling approach is adopted to estimate microscale urban traffic emissions. The modelling framework consists of a traffic microsimulation model, a microscopic emissions model, and two dispersion models. This framework is applied to a traffic network in downtown Toronto to evaluate summer time morning peak traffic emissions during weekdays for carbon monoxide and nitrogen oxide. The model predicted results are validated against sensor observations with a reasonably good fit. Availability of local estimates of ambient concentration is useful for accurate comparisons of total predicted concentrations with observed concentrations. Both predicted and observed concentrations are significantly smaller than the National Ambient Air Quality Objectives established by Environment Canada. Sensitivity analysis is performed on a set of input parameters and horizontal wind speed is found to be the most influential factor in pollutant dispersion.

This work concerns the modelling of scenarios for a residential hydrogen refuelling system. Such a system is under construction within the Engineering Safe and Compact Hydrogen Energy Reserves (ESCHER) project. Non-reacting and reacting simulations are compared against experimental data before being applied to a residential garage scenario. The non-reacting simulations utilise natural ventilation, which utilises the natural buoyancy of hydrogen and vent locations to disperse flammable mixtures. This is favoured over mechanical ventilation, which could fail. The non-reacting work focuses on investigating the most suitable venting configuration for a release of hydrogen from a refuelling system located within a residential garage. Different vent configurations are examined initially before proceeding to take into account atmospheric conditions, wind, and the presence of a vehicle for the two best venting configurations. This is to determine the venting configuration that would diminish the accumulation of a flammable mixture, as well as dissipating the mixture quickest after the release has stopped. The modelling strategy utilised for this work is validated against two different sets of experimental data, prior to the investigation into residential garages. The predicted and experimental results show good agreement for the modelling procedure suggested. The reacting investigations are for both premixed and non-premixed combustion. The non-premixed combustion investigates the temperature distributions and as such the possible harm to people for such a scenario, compared against experimental data. The results show some over predictions of the temperatures. The premixed combustion investigates the potential overpressures that may occur if a homogeneous mixture was to form and ignite, within a residential garage. This work is preceded by a validation of the combustion model with the predicted results compared to data from The University of Sydney. The validation results show that the modelling strategy matches the peak overpressures accurately. The non-reacting studies show that having a lower vent opposite the release and an higher vent near the release produces the smallest flammable mixture as well as dissipating the mixture to the external surroundings quickest. The non-premixed reacting work shows good agreement with experimental results. The premixed reacting work shows that the garage would destruct with major consequences to people and surroundings. This work would be applicable to any potential usage of indoor refuelling for hydrogen vehicles, helping to determine a suitable configuration for mitigating hydrogen releases. It should be noted that all such work is geometrically dependent and as such the strategy proposed would be useful for investigating individual scenarios.

Nitrogen (N) fluxes show a substantial variability at the landscape scale. Emissions are transferred by atmospheric, hydrological and anthropogenic dispersion between different landscape elements or ecosystems, e.g. farms, fields, forests or moorland. These landscape N fluxes can cause impacts to the environment, such as loss of biodiversity. The aim of this study is to illustrate how landscape N fluxes can be quantified by integrating atmospheric and fluvial fluxes in a Scottish landscape of 6 km x 6 km that contains intensively managed poultry farming, extensively managed beef and sheep farming, semi-natural moorland and woodland. Atmospheric ammonia (NH3) emissions of two deep pit free range layer poultry houses were estimated by high time-resolution measurements of NH3 concentrations and meteorological variables downwind of layer poultry houses and the application of an inverse Gaussian plume model. Atmospheric NH3 concentrations and deposition fluxes across the study landscape were studied at a resolution of 25 m x 25 m. The approach combined a detailed landscape inventory of all farm activities providing high resolution NH3 emission estimates for atmospheric dispersion modelling and an intensive measurement programme of spatial NH3 concentrations for verifying modelled NH3 concentrations. The spatially diverse emission pattern resulted in a high spatial variability of modelled mean annual NH3 concentrations (0.3 to 77.9 μg NH3 m-3) and dry deposition fluxes (0.1 to >100 kg NH3-N ha-1 yr-1) within the landscape. Annual downstream fluxes and variation in spatial concentration of dissolved inorganic nitrogen (NH4 + and NO3 -) and dissolved organic nitrogen (DON) were studied in the two main catchments within the study landscape (agricultural grassland vs. semi-natural moorland catchment). The grassland catchment was associated with an annual downstream total dissolved nitrogen (TDN) flux of 14.4 kg N ha-1 yr-1, which was 66% higher than the flux of 8.7 kg ha-1 yr-1 from the moorland catchment. This difference was largely due to the NO3 - flux being one order of magnitude higher in the grassland catchment. The contribution of DON to the TDN flux varied between the catchments with 49% in the grassland and 81% in the moorland catchment. Fluvial and atmospheric N fluxes were combined to derive N budgets of the two catchments. Agricultural activities accounted for the majority of N input to the catchments, with atmospheric deposition also playing a significant role, especially in the moorland catchment. Both catchments showed large stream export fluxes compared to their net import which suggests that their capacity of N storage is limited. This thesis quantifies major N fluxes in a study landscape and shows their large spatial variability. Agricultural activities dominate landscape N dynamics. The work demonstrates the importance of considering landscape N variability when attempting to reduce the environmental impact of agricultural activities.

We describe the theory and implementation of a multipole method for calculating the modes of microstructured optical fibers (MOFs). We develop tools for exploiting results obtained through the multipole method, including a discrete Bloch transform. Using the multipole method, we study in detail the physical nature of solid core MOF modes, and establish a distinction between localized defect modes and extended modes. Defect modes, including the fundamental mode, can undergo a localization transition we identify with the mode�s cutoff. We study numerically and theoretically the cutoff of the fundamental and the second mode extensively, and establish a cutoff diagram enabling us to predict with accuracy MOF properties, even for exotic MOF geometries. We study MOF dispersion and loss properties and develop unconventional MOF designs with low losses and ultra-flattened near-zero dispersion on a wide wavelength range. Using the cutoff-diagram we explain properties of these MOF designs.