Dissertations / Theses on the topic 'Vehicle Emissions'

Abstract This thesis employs a statistical regression method to estimate models for testing the hypothesis of the thesis of vehicle emissions interdependencies. The thesis at the beginnings, reviews critically the formation of emissions in gasoline-fuelled engines, and also reviews existing and emerging models of automotive emissions. The thesis then, presents the relationships between the urban transport system and vehicle emissions. Particularly, it summarises different types of emissions and the contributory factors of the urban transport system to such emissions. Subsequently, the thesis presents the theory of vehicle emissions interdependencies and the empirical framework for testing the hypothesis of the thesis. The scope of testing the hypothesis of the thesis is only limited to gasoline-fuelled conventional vehicles in the urban transport environment. We use already available laboratory-based testing dataset of 542 passenger vehicles, to investigate the hypothesis of the thesis of vehicle emissions interdependencies. HC, CO, and NOX emissions were collected under six test drive-cycles, for each vehicle before and after vehicles were tuned. Prior to using any application, we transform the raw dataset into actionable information. We use three steps, namely conversion, cleaning, and screening, to process the data. We use classification and regression trees (CART) to narrow down the input number of variables in the models formulated for investigating the hypothesis of the thesis. We then, utilise initial results of the analysis to fix any remaining problems in the data. We employ three stage least squares (3SLS) regression to test the hypothesis of the thesis, and to estimate the maximum likelihood of vehicle variables and other emissions to influence HC, CO, and NOX emissions simultaneously. We estimate twelve models, each of which consists of a system of three simulations equations that accounts for the endogenous relations between HC, CO and NOX emissions when estimating vehicle emissions simultaneously under each test drive-cycle. The major contribution of the thesis is to investigate the inter-correlations between vehicle emissions within a well controlled data set, and to test the hypothesis of vehicle emissions interdependencies. We find that HC, CO, and NOX are endogenously or jointly dependent in a system of simultaneous-equations. The results of the analysis demonstrate that there is strong evidence against the null hypothesis (H0) in favour of the alternative hypothesis (H1) that HC, CO, and NOX are statistically significantly interdependent. We find, for the thesis sample, that NOX and CO are negatively related, whereas HC and CO emissions are positively related, and HC and NOX are positively related. The results of the thesis yield new insights. They bridge a very important gap in the current knowledge on vehicle emissions. They advance not only our current knowledge that HC, CO, and NOX should be predicted jointly since they are produced jointly, but also acknowledge the appropriateness of using 3SLS regression for estimating vehicle emissions simultaneously. The thesis measures the responses of emissions to changes with respect to changes in the other emissions. We investigate emission responses to a one percent increase in an emission with respect to the other emissions. We find the relationship between CO and NOX is of special interest. After vehicles were tuned, we find those vehicles that exhibit a one percent increase in NOX exhibit simultaneously a 0.35 percent average decrease in CO. Similarly, we find that vehicles which exhibit a one percent increase in CO exhibit simultaneously a 0.22 percent average decrease in NOX. We find that the responses of emission to changes with respect to other emissions vary with various test drive-cycles. Nonetheless, a band of upper and lower limits contains these variations. After vehicle tuning, a one percent increase in HC is associated with an increase in NOX between 0.5 percent and 0.8 percent, and an increase in CO between 0.5 percent and one percent Also, for post-tuning vehicles, a one percent increase in CO is associated with an increase in HC between 0.4 percent and 0.9 percent, and a decrease in NOX between 0.07 percent and 0.32 percent. Moreover, a one percent increase in NOX is associated with increase in HC between 0.8 percent and 1.3 percent, and a decrease in CO between 0.02 percent and 0.7 percent. These measures of the responses are very important derivatives of the hypothesis investigated in the thesis. They estimate the impacts of traffic management schemes and vehicle operations that target reducing one emission, on the other non-targeted emissions. However, we must be cautious in extending the results of the thesis to the modern vehicles fleet. The modern fleet differs significantly in technology from the dataset that we use in this thesis. The dataset consists of measurements of HC, CO, and NOX emissions for 542 gasoline-fuelled passenger vehicles, under six test drive-cycles, before and after the vehicles were tuned. Nevertheless, the dataset has a number of limitations such as limited model year range, limited representations of modal operations, and limitations of the measurements of emissions based only on averages of test drive-cycles, in addition to the exclusion of high-emitter emission measurements from the dataset. The dataset has a limited model year range, i.e., between 1980 and 1991. We highlight the age of the dataset, and acknowledge that the present vehicle fleet varies technologically from the vehicles in the dataset used in this thesis. Furthermore, the dataset has a limited number of makes - Holden, Ford, Toyota, Nissan, and Mitsubishi. There are also a limited number of modal operations. The model operations presented in the dataset are cold start, warming-up, and hot stabilised driving conditions. However, enrichment episodes are not adequately presented in the test-drive cycles of the dataset. Moreover, the dataset does not take into account driving behaviour influences, and all measurements are cycle-based averages. The emission measurements of laboratory-based testings are aggregated over a test drive cycle, and the test drive-cycle represents an average trip over an average speed. The exclusion of the measurements of high emitting vehicles from the dataset introduces further limitations. Remote sensing studies show that 20 percent of the on-road vehicle fleet is responsible for 80 percent of HC and CO emissions. The findings of the thesis assist in the identification of the best strategies to mitigate the most adverse effects of air-pollution, such as the most severe pollution that have the most undesirable pollution effects. Also, they provide decision-makers with valuable information on how changes in the operation of the transport system influence the urban air-quality. Moreover, the thesis provides information on how vehicle emissions affect the chemistry of the atmosphere and degrade the urban air-quality.

Individual carbon monoxide and hydrocarbon emissions were monitored from passing vehicles using the Fuel Efficiency Automobile Test at four survey sites (Bounds Green Road, Haringey (site A); Dixons Bank, Middlesborough (site B); Abbey Street, Southwark (site C); Uppingham Road, Leicester (site D)}. The remotely measured emissions data is described in terms of fleet emissions, model year emissions and model year contribution to fleet emissions. It was found that there were a large majority of low emitting vehicles contributing little to fleet emissions and a small minority of high emitting vehicles contributing significant proportions to fleet emissions. Model year analysis suggested a low association between vehicle age and mean emissions prior to 1983 but a much improved relationship after 1983. Analysis of model year contributions to fleet emissions shows new gross polluters to be the largest contributors and older vehicles playing only a minor role. The concentrations of carbon monoxide and nitrogen oxides in air were monitored, in conjunction with the FEAT measurement, at various distances from the road (roadside (on the kerb)), kerbside (3 metres from the road), 7.5 metres and 15 metres from the road). A decrease of carbon monoxide and nitric oxide concentrations with distance from the road was noted for all sites with the exception of site D where meteorological parameters exerted a greater influence upon air quality than did distance from the road. The expected increase of NO2 concentration with distance from the road, as NO is oxidised to NO2, did not occur. Moreover, NO2 concentrations decreased with distance from the road. However, the production of NO2 by oxidation of NO can be inferred in two ways. Firstly, a much more gradual decline in concentrations with distance from the road was noted for NO2 compared to CO and NO, possibly due to NO2 production counteracting the reduction in concentration caused by dispersion. Secondly, an analysis of the change of ratios between nitrogen dioxide and nitric oxide with distance from the road reveals a relative increase of NO2 with distance. The air quality data were compared with the remotely measured vehicle emissions data, wind speed and wind direction. A statistical examination of the data was undertaken on a halfhourly and five minute basis (no wind data was available on a five minute basis). The halfhourly analyses for both CO and NOx produced positive correlations between vehicle emissions data and air quality, and predominantly negative correlations between wind speed and air quality. Both positive and negative correlations were observed between wind direction and CO/NOx air quality . Regression analyses were undertaken where the results were statistically significant at a 0.1 level. This reduced the sample size for CO to data collected on eight individual sampling days and to only two days for NOx• All the analysed CO sampling days recorded r2 values of greater than 0.5, such that for each sampling day at least half the variation in CO air quality is explained by the variation in on-road vehicle emissions, wind speed and wind direction. The two analysed NOx sampling days recorded r values of approximately 0.8. The five minute analyses produced were less statistically significant giving only a low degree of correlation between CO and NOx air quality and on-road vehicle emissions. Regression analyses were undertaken for only two days for CO and only one day for NOx.

Thesis (Ph.D.)--Kent State University, 2007.
Title from PDF t.p. (viewed Mar. 19, 2009). Advisor: Jay Lee. Keywords: urban sprawl, vehicle emissions, air pollution, geographic information systems (GIS), home-work journey, simulation. Includes bibliographical references (p. 213-223).

Thesis (M.S.)--West Virginia University, 2001.
Title from document title page. Document formatted into pages; contains xviii, 100 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 98-100).

Introduction
Current instrumentation makes it possible to measure vehicle emissions with high temporal resolution. But the increased resolution of emissions signals does not equate with increased accuracy. A prerequisite for the derivation of accurate emission factors from instantaneous vehicle emissions data is a fine allocation of measured mass emissions to recorded engine or vehicle states. This poses a technical challenge, because vehicle emission test facilities are not designed to support instantaneous emissions modelling, and they introduce distorting effects that compromise the instantaneous accuracy of the measured signals.

Methodology
These distorting effects can be compensated through a combination of physical modelling and data post-processing. The main original contribution of this dissertation is a novel methodology for the compensation of instantaneous emission signals, which is fully described herein. Whereas previous methodologies relied on systems theory modelling, and on comprehensive testing to model the sub-systems of the measurement setup, the alternative approach uses CO2 as a tracer of the distortions brought about by the measurement setup, which is modelled as a 'lump' system.

Conclusions
The main benefits of this methodology are its low burden of experimental work and its flexibility. Furthermore, it has been fully implemented in the 'esto' software tool, which can perform the compensation of emission signals with minimal user intervention and speed up the creation of engine emission maps.

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 1999.
Includes bibliographical references (p. 345-361).
Motor vehicles are one of the largest sources of air pollutants worldwide. Despite their importance, motor vehicle emissions are inadequately understood and quantified. This is due in part to large variations in individual vehicle emissions with changing operating conditions, and to significant differences between vehicles. To better relate emissions with operating conditions, a new parameter termed "specific power" (SP) is presented. SP is the instantaneous tractive power per unit vehicle mass. This parameter has three main advantages: it can be calculated from roadside measurements, it captures most of the dependence of light-duty vehicle emissions on driving conditions, and it is directly specified in emissions certification cycles. The dependence of CO, HC, and NOx emissions on SP is better than on several other commonly used parameters, such as speed, acceleration, power, or fuel rate. Using SP as the basic metric allows meaningful comparisons to be made between data from different remote sensing sites, dynamometer driving cycles, and emission models. Modem U.S. vehicles are likely to operate under commanded enrichment when SP exceeds the maximum value on the Federal Test Procedure (-22 kW/Metric Ton). This may allow transient high emissions to be screened out during future remote sensing campaigns. Remote sensing can address the problem of inter-vehicle differences by quickly and cheaply measuring the emissions of large numbers of vehicles. Here, a tunable infrared laser differential absorption spectrometer (TILDAS) remote sensor was used to gather the first on-road measurements of N20 and N02, and the first high precision measurements of NO. NO was detected with a sensitivity of 5 ppm, which allowed even Ultra Low Emission Vehicles to be measured. On-road accuracy was demonstrated by comparing the TILDAS results with the on-board measurements of a heavy-duty diesel truck (HDDT). The remote sensor could operate with an optical path length of 88 meters, more than five times that of competing instruments. The NO and N20 emission distributions of passenger cars (PCs) and light-duty trucks (LDTs) were found to be highly skewed, while the NO emission distribution for HDDTs was not. N20 emissions from PCs and LDTs are estimated to contribute between 0.5% and 0.9% to U.S. greenhouse gas emissions.
by José Luis Jiménez-Palacios.
Ph.D.

Abstract Parmotrema austrosinense (Zahlbr.) Hale lichens were collected from the Pretoria central business district (CBD), as well as three sites to the east of Pretoria; the National Botanical Gardens, the CSIR campus and the suburb of Lynnwood, with the aim of utilising these lichens as biomonitors of air quality to determine the effects of the phasing out of leaded petrol and the simultaneous introduction of manganese anti-knock additives to fuel in South Africa. In addition to lichens, roadside dust and soil samples were collected from the CBD and CSIR campus, and all samples were analysed by flame atomic absorption spectrometry after acid digestion. There was no significant difference (95 % confidence) between the Mn content of lichens from all sampling sites (overall average of 97.1 ± 39.1 μg.g-1, n= 28), which was most likely due to an even suspension of Mn-containing particles arising from soil dust. Additional contributions to Mn loading as a result of vehicle emissions were currently not evident. For all non-CBD sites, higher Pb levels were found in lichens which were nearer to busy roads, suggesting an historical impact by vehicular emissions of Pb arising from leaded petrol usage. The Pb concentrations in lichens found in the CBD (average of 181.1 ± 98.0 μg.g-1, n=10) were significantly higher (95 % confidence limits) than those of lichens growing outside of the CBD area (average of 41.5 ± 36.4 μg.g-1, n=18), and the Pb levels were higher than those of Mn, which was contrary to that found in sites outside the CBD.

Thesis (M.S.)--West Virginia University, 2000.
Title from document title page. Document formatted into pages; contains x, 125 p. : ill. (some col.), map Includes abstract. Includes bibliographical references (p. 100-103).

Thesis (M.S.)--West Virginia University, 2001.
Title from document title page. Document formatted into pages; contains xv, 164 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 154-156).

The transportation sector is responsible for the majority of airborne particles and global energy consumption in urban areas. Its role in generating air pollution in urban areas is even more critical, as many visitors, commuters and citizens travel there daily for various reasons. Emissions released by transport fleets have an exhaust (tailpipe) and a non-exhaust (brake wears ) origin. Both exhaust and non-exhaust airborne particles can have destructive effects on the human cardiovascular and respiratory systems and even lead to premature deaths. This dissertation aims to estimate the amount of exhaust and brake emissions in a real case study by proposing an innovative methodology. For this purpose, different levels of study have been introduced, including the subsystem level, the system level, the environmental level and the suprasystem level. To address these levels, two approaches were proposed along with a data collection process. First, a comprehensive field survey was conducted in the area of Buonconsiglio Castle and data was collected on traffic and non-traffic during peak hours. Then, in the first approach, a state-of-the-art simulation-based method was presented to estimate the amount of exhaust emissions generated and the rate of fuel consumption in the case study using the VISSIM traffic microsimulation software and Enviver emission modeler at the suprasystem level. In order to calculate the results under different mobility conditions, a total of 18 scenarios were defined based on changes in vehicle speeds and the share of heavy vehicles (HV%) in the modal split. Subsequently, the scenarios were accurately modelled in the simulation software VISSIM and repeated 30 times with a simulation runtime of three hours. The results of the first approach confirmed the simultaneous effects of considering vehicle speed and HV % on fuel consumption and the amount of exhaust emissions generated. Furthermore, the sensitivity of exhaust emissions and fuel consumption to variations in vehicle speed was found to be much higher than HV %. In other words, the production of NOx and VOC emissions can be increased by up to 20 % by increasing the maximum speed of vehicles by 10 km/h. Conversely, increasing the HV percentage at the same speed does not seem to produce a significant change. Furthermore, increasing the speed from 30 km/h to 50 km/h increased CO emissions and fuel consumption by up to 33%. Similarly, a reduction in speed of 20 or 10 km/h with a 100% increase in HV resulted in a 40% and 27% decrease in exhaust emissions per seat, respectively. In the second approach, a novel methodology was proposed to estimate the number of brake particles in the case study. To achieve this goal, a downstream approach was proposed starting from the suprasystem level (microscopic traffic simulation models in VISSIM) and using a developed mathematical vehicle dynamics model at the system level to calculate the braking torques and angular velocities of the front and rear wheels, and proposes an artificial neural network (ANN) as a brake emission model, which has been appropriately trained and validated using emission data collected through more than 1000 experimental tribological tests on a reduced-scale dynamometer at the subsystem level (braking system). Consideration of this multi-level approach, from tribological to traffic-related aspects, is necessary for a realistic estimation of brake emissions. The proposed method was implemented on a targeted vehicle, a dominant SUV family car in the case study, considering real driving conditions. The relevant dynamic quantities of the targeted vehicle (braking torques and angular velocities of the wheels) were calculated based on the vehicle trajectory data such as speed and deceleration obtained from the traffic microsimulation models and converted into braking emissions via the artificial neural network. The total number of brake emissions emitted by the targeted vehicles was predicted for 10 iterations route by route and for the entire traffic network. The results showed that a large number of brake particles (in the order of billions of particles) are released by the targeted vehicles, which significantly affect the air quality in the case study. The results of this dissertation provide important information for policy makers to gain better insight into the rate of exhaust and brake emissions and fuel consumption in metropolitan areas and to understand their acute negative impacts on the health of citizens and commuters.

Engenharia Mecânica
Nos últimos anos, o número de vítimas de acidentes de tráfego por milhões de habitantes em Portugal tem sido mais elevado do que a média da União Europeia. Ao nível nacional torna-se premente uma melhor compreensão dos dados de acidentes e sobre o efeito do veículo na gravidade do mesmo. O objetivo principal desta investigação consistiu no desenvolvimento de modelos de previsão da gravidade do acidente, para o caso de um único veículo envolvido e para caso de uma colisão, envolvendo dois veículos. Além disso, esta investigação compreendeu o desenvolvimento de uma análise integrada para avaliar o desempenho do veículo em termos de segurança, eficiência energética e emissões de poluentes. Os dados de acidentes foram recolhidos junto da Guarda Nacional Republicana Portuguesa, na área metropolitana do Porto para o período de 2006-2010. Um total de 1,374 acidentes foram recolhidos, 500 acidentes envolvendo um único veículo e 874 colisões. Para a análise da segurança, foram utilizados modelos de regressão logística. Para os acidentes envolvendo um único veículo, o efeito das características do veículo no risco de feridos graves e/ou mortos (variável resposta definida como binária) foi explorado. Para as colisões envolvendo dois veículos foram criadas duas variáveis binárias adicionais: uma para prever a probabilidade de feridos graves e/ou mortos num dos veículos (designado como veículo V1) e outra para prever a probabilidade de feridos graves e/ou mortos no outro veículo envolvido (designado como veículo V2). Para ultrapassar o desafio e limitações relativas ao tamanho da amostra e desigualdade entre os casos analisados (apenas 5.1% de acidentes graves), foi desenvolvida uma metodologia com base numa estratégia de reamostragem e foram utilizadas 10 amostras geradas de forma aleatória e estratificada para a validação dos modelos. Durante a fase de modelação, foi analisado o efeito das características do veículo, como o peso, a cilindrada, a distância entre eixos e a idade do veículo. Para a análise do consumo de combustível e das emissões, foi aplicada a metodologia CORINAIR. Posteriormente, os dados das emissões foram modelados de forma a serem ajustados a regressões lineares. Finalmente, foi desenvolvido um indicador de análise integrada (denominado “SEG”) que proporciona um método de classificação para avaliar o desempenho do veículo ao nível da segurança rodoviária, consumos e emissões de poluentes.Face aos resultados obtidos, para os acidentes envolvendo um único veículo, o modelo de previsão do risco de gravidade identificou a idade e a cilindrada do veículo como estatisticamente significativas para a previsão de ocorrência de feridos graves e/ou mortos, ao nível de significância de 5%. A exatidão do modelo foi de 58.0% (desvio padrão (D.P.) 3.1). Para as colisões envolvendo dois veículos, ao prever a probabilidade de feridos graves e/ou mortos no veículo V1, a cilindrada do veículo oposto (veículo V2) aumentou o risco para os ocupantes do veículo V1, ao nível de significância de 10%. O modelo para prever o risco de gravidade no veículo V1 revelou um bom desempenho, com uma exatidão de 61.2% (D.P. 2.4). Ao prever a probabilidade de feridos graves e/ou mortos no veículo V2, a cilindrada do veículo V1 aumentou o risco para os ocupantes do veículo V2, ao nível de significância de 5%. O modelo para prever o risco de gravidade no veículo V2 também revelou um desempenho satisfatório, com uma exatidão de 40.5% (D.P. 2.1). Os resultados do indicador integrado SEG revelaram que os veículos mais recentes apresentam uma melhor classificação para os três domínios: segurança, consumo e emissões. Esta investigação demonstra que não existe conflito entre a componente da segurança, a eficiência energética e emissões relativamente ao desempenho dos veículos.
During the last years, the number of fatalities per million inhabitants in Portugal has always been higher than the average in the European Union. Therefore, at national level, there is a need for a more effective understanding of crash data and vehicles effects on crash severity. This research examined the effects of vehicle characteristics on severity risk, fuel use and emissions. The main goal of this research was to develop models for crash severity prediction in single vehicle-crashes and two-vehicle collisions. Furthermore, this research aimed at developing an integrated analysis to evaluate vehicle’s safety, fuel efficiency and emission performances. Crash data were collected from the Portuguese Police Republican National Guard records for the Porto metropolitan area, for the period 2006-2010. A total of 1,374 crashes were collected, 500 single-vehicle crashes and 874 two-vehicle collisions. For the safety analysis, logistic regressions were used. For single-vehicle crashes, the effect of vehicle characteristics to predict the probability of a serious injury and/or killed in vehicle occupants (designed as binary target) was explored. For two-vehicle collisions, additional binary targets were designed: one target to predict the probability of a serious injury and/or killed in vehicle V1) and another target to predict the probability of a serious injury and/or killed in vehicle V2). To overcome the challenge imposed by sample size and high imbalanced data (only 5.1% were severe crashes), research methodology was developed based on a resampling strategy and 10 stratified random samples were used for validation. During the modeling stage, the effect of vehicle characteristics, such as weight, engine size, wheelbase and age of vehicle were analyzed. For the vehicle’s fuel efficiency and emissions analysis, pollutants were estimated using CORINAIR methodology. Following, emissions data were fit into linear regression models. Finally, an integrated analysis indicator (entitled “SEG”) that provides rating classification for the evaluation of vehicle’s safety, fuel efficiency and emission performances, was developed. Regarding these results, for single-vehicle crashes, injury severity prediction model identified age of the vehicle and engine size as statistically significant, at 5% level. Model performance accuracy rate was 58.0% (S.D. 3.1). For two-vehicle collisions, when predicting injury severity in vehicle V1, the engine size of the opponent vehicle (vehicle V2) increased the risk for the occupants of the subject vehicle (vehicle V1), at 10% level. Injury severity prediction model for vehicle V1 revealed a good performance with a mean prediction accuracy rate of 61.2% (S.D. 2.4). When predicting injury severity for the other vehicle involved (vehicle V2), the engine size of the opponent vehicle (vehicle V1) increased the risk for the occupants of vehicle V2, at 5% level. Injury severity prediction model for vehicle V2 achieved a mean prediction accuracy rate of 40.5% (S.D. 2.1). The results of the integrated analysis indicator, SEG, revealed that recent vehicle achieved better rating simultaneously for all the three domains: safety, fuel efficiency and emissions performances. Newer vehicles showed a better overall safety rating, were more fuel efficient (less CO2 emissions) and reduced emissions (more environmental friendly). This research relevance showed that there is no trade-off between safety, fuel efficiency and emissions.

Pollutants emissions and fuel economy tests for passenger cars differ from region to region of the world, since different driving condition and vehicle fleet characterize different geographical areas. In particular, the European type approval procedure for passenger cars uses as reference cycle the New European Driving Cycle (NEDC), which is nowadays not representative of real driving conditions. Therefore, the European Commission has planned to introduce the Worldwide Harmonized Light Duty Test Procedure (WLTP) from September 2017. As a consequence, the CO2 emissions target should be adapted, since the current 2020 goals are based on NEDC assessment. The European Commission and the Joint Research Centre (JRC) are therefore developing a simulation tool called CO2MPAS (CO2 Module for Passenger and commercial vehicles Simulation) for the correlation of CO2 emissions from WLTP to NEDC, which will be used for the type approval of European passenger cars from 2017, avoiding expensive duplicate test campaigns for car manufactures. However, the implementation of CO2MPAS has so far involved solely conventional light duty vehicles. Within this context, a research project has been carried out in closed collaboration between Politecnico di Torino and JRC for the development of CO2MPAS for Hybrid Electric Vehicles (HEVs) and Plug-In Hybrid Electric Vehicles (PHEVs). The correlation model is based on a unique simplified physical approach, which should be able to detect the powertrain behavior along the NEDC cycle from the physical measurements along the new driving cycle, estimating with a good accuracy the CO2 emissions (within ± 3 g/km).

Thesis (M.S.)--West Virginia University, 2001.
Title from document title page. Document formatted into pages; contains xvi, 111 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 107-111).

Thesis (Ph. D.)--West Virginia University, 2005.
Title from document title page. Document formatted into pages; contains xv, 173 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 168-173).

Despite the increasingly stringent type approval limit values for vehicle emissions no quantitative difference has been seen in roadside concentrations of NOX concentrations (Carslaw et al., 2011b). This thesis aims to improve the ability of measuring and modelling the NOX emissions of passenger cars in urban environments by taking an in depth look at the emissions of vehicles observed in real driving environments over a number of years in Aberdeen, Cambridge, Leeds and Sheffield using a Remote Sensing Device. The remote sensing device is tested under controlled conditions to ascertain its measurement accuracy for both pollutants and vehicle specific power. A mathematical distribution function for describing the emissions of a fleet is presented and shown to be a good description of over 90% of the vehicles and a superposition of two of these distribution functions was able to describe the distribution of the rest of the fleet’s NO emissions with a high degree of accuracy. The distribution functions derived for one city were used to create a predictive model to determine how the average emission of a passenger car fleet vehicle performance would evolve over time showing that by 2025 a ≈ 30% reduction in NOX could be expected if the fleet was allowed to evolve naturally. In addition to these results a number of real world problems were assessed using the new framework developed in this thesis. The emissions of taxis compared to privately owned vehicles was assessed with taxis being shown to emit ≈ 50% more NOX than their equivalent vehicles in the fleet. The Volkswagen Group scandal, #dieselgate, is discussed and the data that the remote sensing device has been used to assess the real driving emissions of VWG passenger cars fitted with the EA189 engine. The observations show that whilst VWG vehicles are exceeding the limit values in real driving environments, they are observed to have equivalent or lower emissions factors than other marques.

The European Union has adopted a range of policies aiming at reducing greenhouse gas emissions from road transport, including setting binding targets for tailpipe CO2 emissions for new light-duty fleets. The legislative framework for implementing such targets allows taking into account the CO2 savings from innovative technologies that cannot be adequately quantified by the standard test cycle CO2 measurement. This paper presents a methodology to define the average productivity of vehicle-mounted photovoltaic roofs and to quantify the resulting CO2 benefits for conventional combustion engine-powered passenger cars in the European Union. The method relies on the analysis of a large dataset of vehicles activity data, i.e. urban driving patterns acquired with GPS systems, combined with an assessment of the shading effect from physical obstacles and indoor parking. The results show that on average the vehicle photovoltaic roof receives 58% of the available solar radiation in real-world conditions, making it possible to reduce CO2 emissions from passenger cars in a range from 1% to 3%, assuming a storage capacity of 20% of the 12 V battery dedicated to solar energy. This methodology can be applied to other vehicles types, such as light and heavy-duty, as well as to different powertrain configurations, such as hybrid and full electric.

Roadworks are a feature of the road network that can cause vehicles to deviate from their desired speed or trajectory. This may negatively impact traditional measures of network performance such as travel time, or result in changes to tailpipe emission rates. The impact of roadworks on tailpipe emission rates is of interest due to the harmful pollutants that are released during the combustion process. Pollutants such as nitrogen oxides (NOx) are toxic to humans, and carbon dioxide (CO2) is a greenhouse believed to influence human-induced global climate change. In order to investigate methods of reducing the environmental impact of roadworks and other obstacles in the road network, modelling tools may be used. However, it is essential that the tools are appropriate for modelling these features of the road network. In order to assess the suitability of existing traffic and emission modelling tools, an understanding of the variability in vehicle dynamics and emissions at urban obstacles is first required. In this thesis, a dataset that contains real-world tailpipe emissions and vehicle dynamics data, from vehicles in the vicinity of urban obstacles such as roadworks, is assembled. This is achieved using a portable emission measurement system (PEMS) and a high-resolution trajectory monitoring platform developed as part of this research. Through analysis of the acceleration behaviour and tailpipe emission rates at different urban obstacles and from different vehicles, an understanding of the variability is formed. The findings from the analysis of behaviours observed in the vicinity of urban obstacles are then used to adapt existing traffic and emissions modelling tools. The error between measured and modelled emissions is shown to reduce from over 30% to under 12% for CO2 emissions. Based on the findings of a roadworks case study, recommendations are made to policy makers and the modelling community.

Thesis (M. S.)--Civil and Environmental Engineering, Georgia Institute of Technology, 2009.
Committee Chair: Guensler, Randall; Committee Member: Rodgers, Michael; Committee Member: Ross, Catherine. Part of the SMARTech Electronic Thesis and Dissertation Collection.

Emissions from light-duty passenger vehicles represent a significant portion of total criteria pollutant emissions in the United States. Since the 1970s, emissions testing of these vehicles has been required in many major metropolitan areas, including Atlanta, GA, that were designated to be in non-attainment for one or more of the National Ambient Air Quality Standards. While emissions inspections have successfully reduced emissions by identifying and repairing high emitting vehicles, they have been increasingly inefficient as emissions control systems have become more durable and fewer vehicles are in need of repair. Currently, only about 9% of Atlanta area vehicles fail emissions inspection, but every vehicle is inspected annually. This research addresses explores ways to create a more efficient emissions testing program while continuing to use existing testing infrastructure. To achieve this objective, on road vehicle emissions data were collected as a part of the Continuous Atlanta Fleet Evaluation program sponsored the Georgia Department of Natural Resources. These remote sensing data were combined with in-program vehicle inspection data from the Atlanta Vehicle Inspection and Maintenance (I/M) program to establish the degree to which on road vehicle remote sensing could be used to enhance program efficiency. Based on this analysis, a multi-parameter model was developed to predict the probability of a particular vehicle failing an emissions inspection. The parameters found to influence the probability of failure include: vehicle characteristics, ownership history, vehicle usage, previous emission test results, and remote sensing emissions readings. This model was the foundation for a proposed emissions testing program that would create variable timing for vehicle retesting with high and low failure probability vehicles being more and less frequently, respectively, than the current annual cycle. Implementation of this program is estimated to reduce fleet emissions of 17% for carbon monoxide, 11% for hydrocarbons, and 5% for nitrogen oxides in Atlanta. These reductions would be achieved very cost-effectively at an estimated marginal cost of $149, $7,576 and $2,436 per-ton-per-year for carbon monoxide, hydrocarbons, and nitrogen oxides emissions reductions respectfully.

The main objectives of this research are; to develop on-road emission factor estimates for carbon monoxide (CO) and hydrocarbon (HC) emissions; to collect traffic and vehicle parameters that might be important in explaining variability in vehicle emissions; to develop an empirical traffic-based model that can predict vehicle emissions based upon observable traffic and vehicle parameters. Remote sensing technology were employed to collect exhaust emissions data. Traffic parameters were collected using an area-wide traffic detector, MOBILIZER. During the measurements, license plates were also recorded to obtain information on vehicle parameters. Data were collected at two sites, having different road grades and site geometries, over 10 days of field work at the Research Triangle area of North Carolina. A total of 11,830 triggered measurement attempts were recorded. After post-processing, 7,056 emissions were kept in the data base as valid measurements. After combining with the traffic and license vehicle parameters, a data base has been developed. Exploratory analysis has been conducted to find variables that are important to explain the variability of the emission estimates. Statistical methods were used to compare the mean of the emissions estimates for different sub-populations. For example, multi-comparison analysis has been conducted to compare the mean emissions estimates from vehicles having different model years. This analysis showed that the mean emissions from older vehicles were statistically different than the mean emissions estimates from the recent model year vehicles.One of the contributions of the research was developing an empirical traffic-based emission estimation model. For this purpose, data collected during the study were used to develop a novel model which combines the Hierarchical Tree-Based Regression method and Ordinary Least Squares regression. The key findings from this research include: (1) the measured mean CO emission estimate for Research Triangle park area of North Carolina is estimated as 340 grams/gallon, whereas the mean HC emissions estimate is found to be as 47 grams/gallon (2) inter-vehicle variability in vehicle emissions can be as high as two orders-of-magnitude; (3) intra-vehicle variability is lower compared to the inter-vehicle variability; (4) some vehicle variables such as vehicle model year and vehicle type are important factors in explaining the inter-vehicle variability in emissions estimates; (5) emission estimation model developed in this research can be applied to estimate the emissions from on-road vehicles.

A method for accurate emissions estimation that will contribute to promoting public health has been increasingly important. The purpose of this study is to develop a novel method that is designed to make accurate real-time emissions estimation from individual vehicles on freeways possible. The benefit of this method is that it can overcome the weakness of macroscopic emissions estimation methods, which underestimated emissions.

The most distinguishing feature of the Speed Profile Estimation (SPE) method is that it uses a speed profile (SP) that is generated by the sum of a basic SP (BSP), which is calculated by the basic travel information of an individual vehicle obtained from vehicle reidentification (REID), and a residual SP (RSP), which is estimated by categorized traffic information.

In order to estimate RSP this research employs Autoregressive (AR) model and Fourier series (FS). And to find the parameters of RSP, the total absolute difference between actual SP emissions and estimated SP emissions was optimized by genetic algorithm. For this, parameters are calculated for all possible combinations of three categorizations and clusters by K-mean clustering. Individual vehicle trajectories from two freeways, US101 and I-80, were provided by the Next Generation Simulation (NGSIM) dataset. US101 was examined for calibration, and I-80 for validation. And then, transferability tests were conducted for various section distances to verify model transferability. Finally, REID is simulated with low vehicle signatures match rates to test its applicability to real situations.

Unlike previous methods, the SPE is notable for its real-time, transferable, reliable, and cost efficient emissions estimation. The calibration and validation account only 4.0 % and 4.1 % MAPEs, respectively. Moreover, transferability tests showed that MAPEs are lower than 4.4 % in both longer and shorter section distances. Furthermore, REID simulation increases only 0.2 % MAPE even in low vehicle signatures match rates, which is lower than 5 % MAPE in emissions estimation.

Any signal-like formulation other than AR or FS can perform better emissions estimation when it replaces the RSP. Also, in this research the SPE method was calibrated only for LOS F, when it is arguably of greatest value, but further research should be coordinated to extend the models in other possible traffic conditions such as LOS A~E.

Master of Science
Department of Biological and Agricultural Engineering
Ronaldo G. Maghirang
Military installations in the United States may be large sources of fugitive dust emissions. Off-road vehicle training can contribute to air quality degradation resulting from increased wind erosion events as a result of soil disruption; however, limited information exists regarding the impacts of off-road vehicle maneuvering. This study was conducted to determine the effects of soil texture and intensity of training with off-road vehicles on fugitive dust emission potential due to wind erosion at military training installations. Multi-pass trafficking experiments, involving wheeled and tracked military vehicles (i.e., M1A1 Abrams tank, M925A1 water tanker and various HMMWV models), were conducted at three military training facilities with different climate and soil texture (i.e., Fort Riley, KS; Fort Benning, GA; and Yakima Training Center, WA). Dust emissions were measured on site using a Portable In-Situ Wind Erosion Laboratory (PI-SWERL) coupled with a DustTrak™ dust monitor. In addition, a top layer of soil was collected in trays and tested in a laboratory wind tunnel for dust emission potential. In wind tunnel testing, the amount of emitted dust was measured using glass-fiber filters through high-volume samplers. Also, the particle size distribution and concentration of the emitted dust were measured using a GRIMM aerosol spectrometer. Comparison of the PI-SWERL (with DustTrak™ dust monitor) and wind tunnel test (with GRIMM aerosol spectrometer) results showed significant difference and little correlation. Also, comparison of the filter and GRIMM aerosol spectrometer data showed significant difference but high correlation. The dust emission potential (as measured with the GRIMM spectrometer) was significantly influenced by soil texture, vehicle type and number of passes. For the light-wheeled vehicle, total dust emissions increased from 66 mg m-2 for undisturbed soil to 304 mg m-2 (357%) and 643 mg m-2 (868%) for 10 and 50 passes, respectively. For the tracked vehicle, an average increase in total dust emission of 569% was observed between undisturbed conditions and 1 pass, with no significant increase in emissions potential beyond 1 pass. For the heavy-wheeled vehicle, emissions increased from 75 mg m-2 for undisturbed soil to 1,652 mg m-2 (1,369%) and 4,023 mg m-2 (5,276%) for 10 and 20 passes, respectively. Soil texture also played an important role in dust emission potential. For all treatment effects, there was a 1,369% difference in emissions between silty clay loam soil and loamy sand soil.

Vehicle exhaust emissions predicted based on the outputs of traffic flow models are used directly to calculate traffic-related emissions, but also indirectly as input to 'air quality - human exposure' models. Both of which inform transport and environmental policies aimed at achieving sustainable mobility. To be effective, these must be based on robust modelling approaches that not only provide point-based emission predictions, but also inform these with an interval of confidence that properly accounts for the propagation of uncertainties and errors through the complex chain of models involved. This research develops a data-driven methodological framework to probabilistic average speed-based emission predictions using two widely deployed macroscopic traffic flow models. These are the Cell Transmission Model (CTM), a discretised first-order LWR-type model, and METANET, a discretised second-order Payne-type model. Studying both allows quantitative comparison in their application to predicting emissions. While this research discusses all potential sources of uncertainty in this modelling chain, it focusses on those arising from the traffic flow modelling output. The methodology starts with an ensemble-based optimisation approach to estimate both calibration and validation prediction errors in the traffic flow model, and then proposes a Monte Carlo sampling approach to propagate these to emission predictions. This allows predicting emissions alongside their upper and lower bounds for any time period and road network, at different levels of detail. To ensure transferability of findings, this methodology has been tested on three motorway road networks, one of which operates under Variable Speed Limits (VSL). This permits the quantitative assessment of VSL-modified traffic flow models. In the results of this research, emissions of Oxides of Nitrogen (NOx) and uncertainty associated with their prediction are specifically reported for each road network under study. Finally, this research argues that the methodological framework developed can (and should) be applied to any other (relatively) simple or complex integrated 'traffic flow - emission' modelling chain used as part of policy and decision making process.

This dissertation develops a framework for modeling vehicle emissions microscopically. In addition, the framework is utilized to develop the VT-Micro model using a number of data sources. Key input variables to the VT-Micro model include instantaneous vehicle speed and acceleration levels. Estimating accurate mobile source emissions is becoming more and more critical as a result of increasing environmental problems in large metropolitan urban areas. Current emission inventory models, such as MOBILE and EMPAC, are designed for developing large scale inventories, but are unable to estimate emissions from specific corridors and intersections. Alternatively, microscopic emission models are capable of assessing the impact of transportation scenarios and performing project-level analyses. The VT-Micro model was developed using data collected at the Oak Ridge National Laboratory (ORNL) that included fuel consumption and emission rate measurements (CO, HC, and NOx) for five light-duty vehicles (LDVs) and three light-duty trucks (LDTs) as a function of the vehicle's instantaneous speed and acceleration levels. The hybrid regression models predict hot stabilized vehicle fuel consumption and emission rates for LDVs and LDTs. The model is found to be highly accurate compared to the ORNL data with coefficients of determination ranging from 0.92 to 0.99. The study compares fuel consumption and emission results from MOBILE5a, VT-Micro, and CMEM models. The dissertation presents that the proposed VT-Micro model appears to be good enough in terms of absolute light-duty hot stabilized normal vehicle tailpipe emissions. Specifically, the emission estimates were found to be within the 95 percent confidence limits of field data and within the same level of magnitude as the MOBILE5a model estimates. Furthermore, the proposed VT-Micro model was found to reflect differences in drive cycles in a fashion that was consistent with field observations. Specifically, the model accurately captures the increase in emissions for aggressive acceleration drive cycles in comparison with other drive cycles. The dissertation also presents a framework for developing microscopic emission models. The framework develops emission models by aggregating data using vehicle and operational variables. Specifically, statistical techniques for aggregating vehicles into homogenous categories are utilized as part of the framework. In addition, the framework accounts for temporal lags between vehicle operational variables and vehicle emissions. Finally, the framework is utilized to develop the VT-Micro model version 2.0 utilizing second-by-second chassis dynamometer emission data for a total of 60 light duty vehicles and trucks. Also, the dissertation introduces a procedure for estimating second-by-second high emitter emissions. This research initially investigates high emitter emission cut-points to verify clear definitions of high emitter vehicles (HEVs) and derives multiplicative factors for newly developed EPA driving cycles. Same model structure with the VT-Micro model is utilized to estimate instantaneous emissions for a total of 36 light duty vehicles and trucks. Finally, the dissertation develops a microscopic framework for estimating instantaneous vehicle start emissions for LDVs and LDTs. The framework assumes a linear decay in instantaneous start emissions over a 200-second time horizon. The initial vehicle start emission rate is computed based on MOBILE6's soak time function assuming a 200-second decay time interval. The validity of the model was demonstrated using independent trips that involved cold start and hot start impacts with vehicle emissions estimated to within 10 percent of the field data. The ultimate expansion of this model is its implementation within a microscopic traffic simulation environment in order to evaluate the environmental impacts of alternative ITS and non-ITS strategies. Also, the model can be applied to estimate vehicle emissions using instantaneous GPS speed measurements. Currently, the VT-Micro model has been implemented in the INTEGRATION software for the environmental assessment of operational-level transportation projects.
Ph. D.

One of the major issues in road transport today is poor air quality and the associated negative health impacts. In particular, diesel vehicles are found to contribute substantially to atmospheric levels of particulate matter (PM) and ozone (O3). Pollutant mass emission rates from motor vehicles vary greatly according to their operating mode. Consequently, changes in road layout or traffic behaviour may have a significant impact on local air quality. Improved understanding of the underlying emission mechanisms can help at the traffic planning stage in order to assess environmental impacts and aid in the design of more efficient air quality management methods. However, existing planning tools do not adequately represent the variable and transient nature of pollutant emission rates from vehicles, especially with regard to emissions of PM. This thesis addresses the issue of how motor vehicles, and in particular, light-duty diesel vehicles contribute to local air pollution. It does so with reference to a series of experiments conducted with a 1999 model-year Ford Focus turbo-diesel test vehicle. The vehicle was equipped with an on-board monitoring system developed and validated for this research to address the shortcomings of existing devices. The resultant data are used to examine the second-by-second mass emission rates of pollutant species, including PM. The extension of an existing instantaneous model for gaseous pollutants to represent PM emissions is then examined, with an adjustment to the model structure found to improve emission estimates. The experimental techniques developed in this thesis are compared to reference data obtained using a chassis dynamometer emissions test facility. The models are compared to the on-board measurements for both laboratory and on-road tests. These comparisons show that on-board monitoring offers a more precise representation of the pollutant mass emission rates than the modelling techniques considered. Nevertheless, the model performance is sufficiently good to suggest that realistic estimates may be obtained through the simulation of vehicle trajectories and the associated pollutant emission rates. Importantly, the techniques developed in this research are able to replicate the high-emission episodes associated with transient vehicle operation. This offers the possibility to assess the impact of traffic management schemes on PM emission levels more accurately than has previously been possible.

Thesis (M.S.)--West Virginia University, 2001.
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Thesis (Ph. D.)--Civil and Environmental Engineering, Georgia Institute of Technology, 2006.
Michael O. Rodgers, Committee Chair ; Randall L. Guensler, Committee Member ; Michael D. Meyer, Committee Member ; Michael P. Hunter, Committee Member ; Jennifer H. Ogle, Committee Member.

The transportation sector is the dominant source of U.S. fuel consumption and emissions. Specifically, highway travel accounts for nearly 75 percent of total transportation energy use and slightly more than 33 percent of national emissions of EPA's six Criteria pollutants. Enactment of the Clean Air Act Amendment of 1990 (CAAA) and the Intermodal Surface Transportation Efficiency Act of 1991 (ISTEA) have changed the ways that most states and local governments deal with transportation problems. Transportation planning is geared to improve air quality as well as mobility. It is required that each transportation activity be analyzed in advance using the most recent mobile emission estimate model to ensure not to violate the Conformity Regulation.

Several types of energy and emission models have been developed to capture the impact of a number of factors on vehicle fuel consumption and emissions. Specifically, the current state-of-practice in emission modeling (i.e. Mobile5 and EMFAC7) uses the average speed as a single explanatory variable. However, up to date there has not been a systematic attempt to quantify the impact of various travel and driver-related factors on vehicle fuel consumption and emissions.

This thesis first systematically quantifies the impact of various travel-related and driver-related factors on vehicle fuel consumption and emissions. The analysis indicates that vehicle fuel consumption and emission rates increase considerably as the number of vehicle stops increases especially at high cruise speed. However, vehicle fuel consumption is more sensitive to the cruise speed level than to vehicle stops. The aggressiveness of a vehicle stop, which represents a vehicle's acceleration and deceleration level, does have an impact on vehicle fuel consumption and emissions. Specifically, the HC and CO emission rates are highly sensitive to the level of acceleration when compared to cruise speed in the range of 0 to 120 km/h. The impact of the deceleration level on all MOEs is relatively small. At high speeds the introduction of vehicle stops that involve extremely mild acceleration levels can actually reduce vehicle emission rates. Consequently, the thesis demonstrated that the use of average speed as a sole explanatory variable is inadequate for estimating vehicle fuel consumption and emissions, and the addition of speed variability as an explanatory variable results in better models.

Second, the thesis identifies a number of critical variables as potential explanatory variables for estimating vehicle fuel consumption and emission rates. These explanatory variables include the average speed, the speed variance, the number of vehicle stops, the acceleration noise associated with positive acceleration and negative acceleration noise, the kinetic energy, and the power exerted. Statistical models are developed using these critical variables. The statistical models predict the vehicle fuel consumption rate and emission rates of HC, CO, and NOx (per unit of distance) within an accuracy of 88%-96% when compared to instantaneous microscopic models (Ahn and Rakha, 1999), and predict emission rates of HC, CO, and NOx within 95 percentile confidence limits of chassis dynamometer tests conducted by EPA.

Comparing with the current state-of-practice, the proposed statistical models provide better estimates for vehicle fuel consumption and emissions because speed variances about the average speed along a trip are considered in these models. On the other hand, the statistical models only require several aggregate trip variables as input while generating reasonable estimates that are consistent with microscopic model estimates. Therefore, these models could be used with transportation planning models for conformity analysis.
Master of Science

Growing environmental and economic concerns have driven recent efforts to produce more fuel efficient and lower emissions vehicles. These goals are reflected by the Partnership for a New Generation of Vehicles (PNGV), a government, industry, and educational partnership in the United States. The major goal of this partnership is to have production vehicles by 2010 to address these concerns. Ideally, these vehicles will achieve three times the current fuel economy while drastically lowering emissions levels, without sacrificing the features, comfort, and performance of current conventional automobiles.

Hybrid Electric Vehicles (HEVs) are automobiles which have both electric drivetrains and fuel-consuming powerplants. HEVs provide some of the most promising designs with the capability of meeting the PNGV goals. However, the development of these vehicles within the next ten years will require accurate, flexible simulation tools. Such a simulation program is necessary in order to quickly narrow the technology focus of the PNGV to those HEV configurations and components which are best suited for these goals. Therefore, the simulation must be flexible enough to encompass the wide variety of components which could possibly be utilized. Finally, it must be able to assist vehicle designers in making specific decisions in building and testing prototype automobiles.

One of the most widely used computer simulation tools for HEVs is the ADvanced VehIcle SimulatOR (ADVISOR) developed by the National Renewable Energy Laboratory. This program is flexible enough to operate on most platforms in the popular MATLAB/SIMULINK programming environment. The structure of ADVISOR makes it ideal for interchanging a variety of components, vehicle configurations, and control strategies. Its modern graphical user interface allows for easy manipulation of various inputs and outputs. Also, the capability to quickly perform parametric and sensitivity studies for specific vehicles is a unique and invaluable feature of ADVISOR.

However, no simulation tool is complete without being validated against measured vehicle data so as to ensure the reliability of its predictions. ADVISOR has been tested using data from a number of student-built HEVs from the top engineering colleges and universities around the country. As ADVISOR evolves to meet the changing needs of the vehicle design teams, this testing continues to ensure that ADVISOR maintains its usefulness as a simulation tool. One current validation study was recently completed at Virginia Tech using the FutureCar Challenge entry.

This paper details the validation of ADVISOR using the Virginia Tech Lumina, a series HEV. The basic structure of the ADVISOR code is covered to ensure the validity of the vehicle modeling techniques used. The modeling process is discussed in detail for each of the major components of the hybrid system: transmission, electric motor and inverter, auxiliary power unit (fuel and emissions), batteries, and miscellaneous vehicle parameters. The integration of these components into the overall ADVISOR model is also described.

The results of the ADVISOR simulations are then explained and compared to measured vehicle data on energy consumption, fuel efficiency, emissions output, and control strategy function for a variety of driving cycles and test procedures. Uncertainties in the measured data are discussed. Finally, the discrepancies between predicted and actual behavior are analyzed. This validation process shows that ADVISOR has extensive value as a simulation tool for HEVs. The existing limitations of the program are also detailed, with recommendations for improvement.
Master of Science

COLYAR, JAMES DANIEL. An Empirical Study of the Relationships between Macroscopic Traffic Parameters and Vehicle Emissions. (Under the direction of Dr. Nagui Rouphail.)Understanding the relation between traffic parameters and vehicle emissions is an important step toward reducing the potential for global warming, smog, ozone depletion, and respiratory illness. Traffic engineers, through improved roadway design and traffic control, have the ability to reduce vehicle emissions. However, current vehicle emissions models do not allow traffic analysts to easily and accurately predict vehicle emissions based on commonly used macroscopic traffic parameters (i.e., control delay, corridor stops, average speed).The primary purpose of this thesis is to develop a corridor-level methodology for quantifying the individual effects of delay and stops on hydrocarbon (HC), nitric oxide (NO), and carbon monoxide (CO) vehicle emissions. A secondary, but equally important, purpose is to evaluate the impact of signal coordination on vehicle emissions through a before and after study. This is an important funding issue because signal coordination projects currently receive CMAQ funding with the expectation of a reduction in vehicle emissions.The study focused on three signalized arterials in Research Triangle Park and Cary, North Carolina. The data collection procedure differed from the majority of past emissions research in focusing on the collection of real-world, on-road data from instrumented vehicles. Sixteen different vehicles and ten drivers were tested, resulting in a total of approximately 825 corridor runs, 140 vehicle-hours, and 3,060 vehicle-miles of simultaneous vehicle emissions and engine diagnostic data. The latter were manipulated to produce macroscopic traffic parameters such as free flow speed, delays, and stops.An important result from this thesis is that vehicle emissions are generally highest while vehicles are accelerating and lowest while idling. In addition, control delay and corridor stops have a quantifiable effect on vehicle emissions, as an increase in control delay and corridor stops produces an increase in emissions. HC emissions show the strongest dependence on delay and stops, while NO and CO emissions show a weaker dependence.For the most part, the results of the before and after study showed no statistically significant changes in traffic parameters (speed, delay, and stops). As a result, no statistically significant changes occurred in the vehicle emissions. However, when arranging the data into groups of congested and uncongested runs, a significant direct relationship was found between HC emissions and traffic congestion. NO and CO emissions did not change significantly, even with significant changes in traffic congestion.Overall, this thesis presents a first-of-a-kind investigation into the trends between traffic parameters and real-world, on-road vehicle emissions.