The level of air pollution in urban areas, which is largely affected by road traffic, is an issue of high political relevance. Congestion is most prevalent in urban areas and a common and increasingly present phenomenon worldwide. The first four chapters of this study have investigated how and to what extent models, which are used to predict emissions on road links in urban road networks, include the effects of congestion on emissions. In order to make this assessment, traffic engineering literature and empirical studies have been examined and used as a basis to review (current) emission models that exist or have been used around the world. Congestion causes changes in driving patterns of individual vehicles in a traffic stream, and these changes are subsequently reflected in changes in congestion indicators and changes in emission levels. This consideration and a literature review has led to a proposed 'congestion typology' of emission models, which reflects the different ways in which and the extent to which congestion has been incorporated in these models. The typology clarifies that six of in total ten families of emission models that were investigated in this thesis explicitly consider congestion in the modelling process (i.e. model variables are related to congestion), although this is done in different ways. For the remaining four families of emission models it was not possible to determine the extent to which congestion has been incorporated on the basis of literature review alone. Two families fell beyond the scope of this work since they cannot be used to predict emission on road links. For the other two families it became clear in the course of the thesis that the extent can be determined through analysis of driving pattern data (and other information with respect to e.g. data collection) that were used in the model development. A new methodology is presented in this thesis to perform this analysis and to assess the mean level of congestion in driving patterns (driving cycles). The analysis has been carried out for one important family of emission models, the so-called travel speed models ('average speed models'), which are used extensively in urban network modelling. For four current models (COPERT III, MOBILE 6, QGEPA 2002, EMFAC 2000), it is concluded that these models implicitly (i.e. congestion is inherently considered) take varying levels of congestion into account, but that this conclusion is subject to a number of limitations. It became clear in the course of this study that prediction of (the effects of) congestion in both traffic models and emission models is generally restricted to certain modelling dimensions. As a consequence, the effects of congestion are only partially predicted in current air emission modelling. Chapter 5 has attempted to address the question whether congestion is actually an important issue in urban network emission modelling or not. It also addressed the question if different types of emission models actually predict different results. On the basis of a number of selection criteria, two types of models were compared, i.e. one explicit model (TEE-KCF 2002) and two implicit models (COPERT III, QGEPA 2002). The research objectives have been addressed by applying these emission models to a case-study urban network in Australia (Brisbane) for which various model input attributes were collected from different sources (both modelled and field data). The findings are limited by the fact that they follow from one urban network with particular characteristics (fleet composition, signal settings, speed limits) and application of only a few particular emission models. The results therefore indicate that: 1. Changes in traffic activity (i.e. distribution of vehicle kilometres travelled on network links) over the day appear to have the largest effect on predicted traffic emissions. 2. Congestion is an important issue in the modelling of CO and HC emissions. This appears not to be the case for NOx emissions, where basic traffic composition is generally a more important factor. For the most congested parts in the urban network that have been investigated, congestion can more than double predicted emissions of CO and HC. 3. Different types of emission models can produce substantially different results when absolute (arithmetic) differences are considered, but can produce similar results when relative differences (ratio or percent difference) are considered.

Many models for predicting volatile organic compounds (VOC) emissions from latex paints have been developed. Earlier models were developed for solvent-borne paints, particularly since these paints evaporate rapidly and can be modeled with simple decay models. However, paint has changed in the past fifty years, and a transition has been made towards water-borne paints. These paints were introduced for indoor applications because they lacked the health hazards and odors of their solvent-borne counterparts. These paints also have organic modifiers, therefore it is very important to predict how these modifiers evaporate from the coated material. New mechanistic models that can predict slow emitting VOCs over long periods of time are not available. An improved ability to predict VOC emissions from latex paints could lead to improved understanding, better policy-making and promotion of environmental regulations that benefit both the consumer and producers of architectural coatings. This research improves on existing models used to estimate VOC emissions off-gassed from latex paints. The developed two layer model (2LM) has a layer for paint and substrate material, and accounts for mass transfer at the paint layer, and diffusion transport between paint and material layers. The model provides a semi-mechanistic way to predict paint drying and VOC emissions from coatings on a variety of substrates. The model only requires the estimation of one parameter (the paint layer diffusion coefficient), unlike other models available that require multiple parameter estimations. This model is robust in the sense that it could be used to predict VOC emissions from paint, as well as predicting the variation of the internal VOC distribution on both paint and material layers with time. The model was tested and validated with empirical data collected from previous controlled chamber experiments, and also with data collected from short evaporation experiments. Critical paint components like polymer and pigment composition and its relation to VOC fate and transport after paint application, both initially and over long periods of time, were explored. Modeling results indicated that the diffusion coefficient of 2,2,4-trimethyl-1,3-pentadediol monoisobutyrate (TMPD-MIB) in the paint layer does not depend on the thickness of the wet paint film, but it depends on the pigment volume concentration (PVC) of the paint. Additionally, a constant diffusion coefficient used in the 2LM was successful for modeling emissions of TMPD-MIB from low pigment volume concentration (LPVC) paints, but it failed to capture the physical mechanisms of the drying film for high pigment volume concentration (HPVC) paints. A major finding from this research was that a detailed gas phase analysis of mass transport for TMPD-MIB would have negligible effects on the predicted overall evaporation rate. Therefore, the entire wet and dry emissions processes are likely dominated by diffusion processes. / text

Studying nuclei beyond the proton dripline can provide valuable information on the structure of nuclei at the limits of stability, where the strong nuclear force starts to be overcome by Coulomb repulsion between protons. Simple experimental observables, such as excitation energies and lifetimes of excited states in these proton-unbound nuclei can provide information on the nuclear wave function. Experimental data, such as that presented in this work, can then be used to improve models of nuclear structure at the proton dripline. This thesis presents data from a recoil-decay tagged differential plunger experiment undertaken at the University of Jyvaskyla in 2014. A fusion-evaporation reaction was used to populate excited states in the deformed ground-state proton emitter 113Cs. The JUROGAM-RITU-GREAT experimental setup was used to correlate gamma rays emitted from these excited states with protons emitted from 113Cs and the differential plunger for unbound nuclear states (DPUNS) was placed at the target position to measure the excited state lifetimes. The lifetime of the (11/2+) state in the most intense rotational band of 113Cs was measured to be tau = 24(6) ps, while a limit of tau is less than or equal to 5 ps was found for the lifetime of the higher energy (15/2+) state. The lifetime of proton emission was measured to be tau = 24.2(2) microseconds. The experimental data were used to test the predictions of a non adiabatic quasi-particle model for proton-emitting nuclei, which was employed to deduce the deformation of the states in 113Cs. Wave functions from the non adiabatic quasi-particle model were used to independently calculate proton-emission rates, gamma-ray transition rates and excited state energies as functions of deformation. The deformation of 113Cs could then be extracted from the intersection of the different theoretical values and experimental observables. A deformation of beta2 = 0.22(6)was extracted from the (11/2+) excitation energy and lifetime. The deformation values taken from the proton-emission rate and the lifetime limit of the (15/2+) state were also consistent with this value. The consistency of the different deformations calculated shows the effectiveness of the non adiabatic quasi-particle method when used to calculate the properties of deformed ground-state proton-emitters.

Este estudo tem por objetivo propor um método para aprimorar a estimativa de emissões veiculares em áreas urbanas através da utilização de modelagem híbrida de tráfego associada a modelos de previsão de emissões. A modelagem híbrida agrega as vantagens individuais das abordagens agregada e desagregada de tráfego, uma vez que combina a micro-simulação de tráfego em áreas específicas com a simulação agregada em uma área de estudo mais abrangente. O método proposto neste trabalho foi consolidado a partir do desenvolvimento de um estudo de caso que consistiu na modelagem de uma rede viária com características distintas de infraestrutura e operação viárias. Os resultados do estudo de caso permitiram a identificação de trechos da rede viária nos quais as estimativas de emissões provenientes de modelos agregados foram significativamente diferentes das estimativas derivadas de modelos microscópicos, demonstrando a importância de uma abordagem híbrida. A utilização do método proposto pode embasar a elaboração e implementação de políticas de transportes que busquem reduzir a ocorrência de eventos responsáveis pela geração de elevados níveis de emissões. / This study aims to propose a method to improve the vehicle emissions estimation in urban area. The method associates hybrid traffic flow models with emission models. Hybrid traffic modeling combines the specific advantages of aggregate and disaggregated approaches, since they integrate traffic microssimulation in specific areas with agregated simulation in a wide area. The development of the proposed method was based on a case study consisting in the modeling a road network with different operations and infrastructure characteristics. Case study results indicated that emission estimates obtained from aggregated models were significantly different from emission estimates derived from microscopic models on some road segments, emphasizing the importance of a hybrid approach adopted in the method proposed in this work. The proposed method can be used to guide the development and implementation of transportation policies that aim to reduce the number of traffic events responsible for high levels of emissions.

Este estudo tem por objetivo propor um método para aprimorar a estimativa de emissões veiculares em áreas urbanas através da utilização de modelagem híbrida de tráfego associada a modelos de previsão de emissões. A modelagem híbrida agrega as vantagens individuais das abordagens agregada e desagregada de tráfego, uma vez que combina a micro-simulação de tráfego em áreas específicas com a simulação agregada em uma área de estudo mais abrangente. O método proposto neste trabalho foi consolidado a partir do desenvolvimento de um estudo de caso que consistiu na modelagem de uma rede viária com características distintas de infraestrutura e operação viárias. Os resultados do estudo de caso permitiram a identificação de trechos da rede viária nos quais as estimativas de emissões provenientes de modelos agregados foram significativamente diferentes das estimativas derivadas de modelos microscópicos, demonstrando a importância de uma abordagem híbrida. A utilização do método proposto pode embasar a elaboração e implementação de políticas de transportes que busquem reduzir a ocorrência de eventos responsáveis pela geração de elevados níveis de emissões. / This study aims to propose a method to improve the vehicle emissions estimation in urban area. The method associates hybrid traffic flow models with emission models. Hybrid traffic modeling combines the specific advantages of aggregate and disaggregated approaches, since they integrate traffic microssimulation in specific areas with agregated simulation in a wide area. The development of the proposed method was based on a case study consisting in the modeling a road network with different operations and infrastructure characteristics. Case study results indicated that emission estimates obtained from aggregated models were significantly different from emission estimates derived from microscopic models on some road segments, emphasizing the importance of a hybrid approach adopted in the method proposed in this work. The proposed method can be used to guide the development and implementation of transportation policies that aim to reduce the number of traffic events responsible for high levels of emissions.

Este estudo tem por objetivo propor um método para aprimorar a estimativa de emissões veiculares em áreas urbanas através da utilização de modelagem híbrida de tráfego associada a modelos de previsão de emissões. A modelagem híbrida agrega as vantagens individuais das abordagens agregada e desagregada de tráfego, uma vez que combina a micro-simulação de tráfego em áreas específicas com a simulação agregada em uma área de estudo mais abrangente. O método proposto neste trabalho foi consolidado a partir do desenvolvimento de um estudo de caso que consistiu na modelagem de uma rede viária com características distintas de infraestrutura e operação viárias. Os resultados do estudo de caso permitiram a identificação de trechos da rede viária nos quais as estimativas de emissões provenientes de modelos agregados foram significativamente diferentes das estimativas derivadas de modelos microscópicos, demonstrando a importância de uma abordagem híbrida. A utilização do método proposto pode embasar a elaboração e implementação de políticas de transportes que busquem reduzir a ocorrência de eventos responsáveis pela geração de elevados níveis de emissões. / This study aims to propose a method to improve the vehicle emissions estimation in urban area. The method associates hybrid traffic flow models with emission models. Hybrid traffic modeling combines the specific advantages of aggregate and disaggregated approaches, since they integrate traffic microssimulation in specific areas with agregated simulation in a wide area. The development of the proposed method was based on a case study consisting in the modeling a road network with different operations and infrastructure characteristics. Case study results indicated that emission estimates obtained from aggregated models were significantly different from emission estimates derived from microscopic models on some road segments, emphasizing the importance of a hybrid approach adopted in the method proposed in this work. The proposed method can be used to guide the development and implementation of transportation policies that aim to reduce the number of traffic events responsible for high levels of emissions.

In the last few years, scientific consensus is that emission of greenhouse gases (GHGs) into the atmosphere is contributing to changes in the earth's climate. While uncertainty remains over the pace and dimensions of the change, a consensus on the need for action has grown among the public and elected officials. In part, this shift has been accelerated by concern over energy security and rising fuel prices. The new political landscape has led many cities, states, and regions to institute policies aimed at reducing GHG emissions. These policies and emerging initiatives have significant implications for the transportation planning process. The transportation sector accounts for approximately 27% of GHG production in the U.S. (as of 2003) and while the U.S. accounts for only roughly 5% of the world's population, it is estimated that it produces over 20% of the world's GHG emissions. Note that this does not include "lifecycle" emissions that result from the processes undertaken to extract, manufacture, and transport fuel. Carbon dioxide represents approximately 96% of the transportation sector's radiative forcing effects. Unlike conventional air pollutants, carbon dioxide emissions are directly tied to the amount of fuel consumed and its carbon intensity. Therefore, emissions reductions can be achieved by increasing the use of low-carbon fuels, improving fuel economy, or reducing total vehicle miles of travel - often called the three legged stool. (A fourth leg is congestion reduction, at certain optimal speeds). These same factors are related to our use of imported oil, so actions taken to reduce GHG emissions may actually produce benefits in both policy areas. The climatic risks of additional emissions associated with capacity projects must be balanced against the mobility, safety, and economic needs of a community or region. Consequently, this dissertation attempts to quantify the impacts of high-emitting vehicles on the environment and to propose solutions to enhance the currently-used high-emitting vehicle detection procedures. In addition, fuel consumption and emission models for high-speed vehicles are developed in order to provide more reliable estimates of vehicle emissions and study the impact of vehicle speeds on vehicle emissions. The dissertation extends the state-of-the-art analysis of high emitting vehicles (HEVs) by quantifying the network-wide environmental impact of HEVs. The literature reports that 7% to 12% of HEVs account for somewhere between 41% to 63% of the total CO emissions, and 10% are responsible for 47% to 65% of HC emissions, and 10% are responsible for 32% of NOx emissions. These studies, however, are based on spot measurements and do not necessarily reflect network-wide impacts. Consequently, the research presented in this dissertation extends the state-of-knowledge by quantifying HEV contributions on a network level. The study uses microscopic vehicle emission models (CMEM and VT-Micro model) along with pre-defined drive cycles (under the assumption that the composite HEV and VT-LDV3 represent HEVs and NEVs, respectively) in addition to the simulation of two transportation networks (freeway and arterial) to quantify the contributions of HEVs. The study demonstrates that HEVs are responsible for 67% to 87% of HC emissions, 51% to 78% of CO emissions, and 32% to 62% of the NOX emissions for HEV percentages ranging from 5% to 20%. Additionally, the traffic simulation results demonstrate that 10% of the HEVs are responsible for 50% to 66% of the I-81 HC and 59% to 78% of the Columbia Pike HC emissions, 35% to 67% of the I-81 CO and 38% to 69% of the Columbia Pike CO emissions, and 35% to 44% of the I-81 NOX and 35% to 60% of the Columbia Pike NOX emissions depending on the percentage of the normal-emitting LDTs to the total NEVs. HEV emission contributions to total HC and CO emissions appear to be consistent with what is reported in the literature. However, the contribution of NOX emissions is greater than what is reported in the literature. The study demonstrates that the contribution of HEVs to the total vehicle emissions is dependent on the type of roadway facility (arterials vs. highways), the background normal vehicle composition, and the composition of HEVs. Consequently, these results are network and roadway specific. Finally, considering that emission control technologies in new vehicles are advancing, the contribution of HEVs will increase given that the background emission contribution will decrease. Given that HEVs are responsible for a large portion of on-road vehicle emissions, the dissertation proposes solutions to the HEV screening procedures. First, a new approach is proposed for estimating vehicle mass emissions from concentration remote sensing emission measurements using the carbon balance equation in conjunction with either the VT-Micro or PERE fuel consumption rates for the enhancement of current state-of-the-art HEV screening procedures using RSD technology. The study demonstrates that the proposed approach produces reliable mass emission estimates for different vehicle types including sedans, station wagons, full size vans, mini vans, pickup trucks, and SUVs. Second, a procedure is proposed for constructing on-road RS emission standards sensitive to vehicle speed and acceleration levels. The proposed procedure is broadly divided into three sub-processes. In the first process, HE cut points in grams per second are developed as a function of a vehicle's speed and acceleration levels using the VT-Micro and CMEM emission models. Subsequently, the HE cut points in grams per second are converted to concentration emissions cut points in parts per million using the carbon balance equation. Finally, the scale factors are computed using either ASM ETW- and model-year-based standards or engine-displacement-based standards. Given the RS emissions standards, the study demonstrated that the use of on-road RS cut points sensitive to speed and acceleration levels is required in order to enhance the effectiveness of RS. Finally, the dissertation conducted a study to develop fuel consumption and emissions models for high-speed vehicles to overcome the shortcomings of state-of-practice models. The research effort gathered field data and developed models for the estimation of fuel consumption, CO₂, CO, NO, NO2, NOx, HC, and PM emissions at high speeds. A total of nine vehicles including three semi-trucks, three pick-up trucks, and three passenger cars were tested on a nine-mile test track in Pecos, Texas. The fuel consumption and emission rates were measured using two portable emission measurement systems. Models were developed using these data producing minimum errors for fuel consumption, CO₂, NO2, HC, and PM emissions. Alternatively, the NO and NOx emission models produced the highest errors with a least degree of correlation. Given the models, the study demonstrated that the newly constructed models overcome the shortcomings of the state-of-practice models and can be utilized to evaluate the environmental impacts of high speed driving. / Ph. D.

Since the transportation sector is highly responsible for U.S. fuel consumption and emissions, assessing the environmental impacts of transportation activities is essential for air-quality improvement programs. Also, high emitting vehicles need to be considered in the modeling of mobile-source emissions, because they contribute to a large portion of the total emissions, although they comprise a small portion of the vehicle fleet. In the context of this research, the thesis quantifies the environmental impacts of roadway grades and proposes a procedure that can enhance the screening of high emitting vehicles. First, the study quantifies the environmental impacts of roadway grades. Although roadway grades are known to affect vehicle fuel consumption and emission rates, there do not appear to be any systematic evaluations of these impacts in the literature. Consequently, this study addresses this void by offering a systematic analysis of the impact of roadway grades on vehicle fuel consumption and emission rates using the INTEGRATION microscopic traffic simulation software. The energy and emission impacts are quantified for various cruising speeds, under stop and go conditions, and for various traffic signal control scenarios. The study demonstrates that the impact of roadway grade is significant with increases in fuel consumption and emission rates in excess of 9% for a 1% increase in roadway grade. Consequently, a reduction in roadway grades in the range of 1% can offer savings that are equivalent to various forms of advanced traffic management systems. Second, the study proposes a new procedure for estimating vehicle mass emissions from remote sensing device measurements that can be used to enhance HEV screening procedures. Remote Sensing Devices (RSDs) are used as supplementary tools for screening high emitting vehicles (HEVs) in the U.S. in order to achieve the National Ambient Air Quality Standards (NAAQS). However, tailpipe emissions in grams cannot be directly measured using RSDs because they use a concentration-based technique. Therefore, converting a concentration measurement to mass emissions is needed. The research combines the carbon balance equation with fuel consumption estimates to make the conversion. In estimating vehicle fuel consumption rates, the VT-Micro model and a Vehicle Specific Power (VSP)-based model (the PERE model) are considered and compared. The results of the comparison demonstrate that the VSP-based model under-estimates fuel consumption at 79% and produces significant errors (R2 = 45%), while the VT-Micro model produces a minimum systematic error of 1% and a high degree of correlation (R2 = 87%) in estimating a sample vehicle's (1993 Honda Accord with a 2.4L engine) fuel consumption. The sample vehicle was correctly identified 100%, 97%, and 89% as a normal vehicle in terms of HC, CO, NOX emissions, respectively, using its in-laboratory measured emissions. Its estimated emissions yielded 100%, 97%, and 88% of correct detection rates in terms of HC, CO, NOX emissions, respectively. The study clearly demonstrates that the proposed procedure works well in converting concentration measurements to mass emissions and can be applicable in the screening of HEVs and normal emitting vehicles for several vehicle types such as sedans, station wagons, full-size vans, mini vans, pickup trucks, and SUVs. / Master of Science