The environmental impact of a transportation system is critical in the assessment of the transportation system performance. Eco-Cooperative Adaptive Cruise Control (Eco-CACC) systems attempt to minimize vehicle fuel consumption and emission levels by controlling vehicle speed and acceleration levels. The majority of previous research efforts developed and applied Eco-CACC systems on either freeway or signalized intersections independently on simple and small transportation networks without consideration of the interaction among these controls. This thesis extends the state-of-the-art in Eco-CACC evaluation by conducting a comprehensive evaluation on a complex network considering Eco-CACC control on both freeways and arterials individually and simultaneously. The goal of this study is to compare Eco-CACCs on arterial facilities (Eco-CACC-A), freeway facilities (Eco-CACC-F) and both facilities (Eco-CACC-F+A). The effects of Eco-CACC are evaluated considering various Measures of Effectiveness (MOEs), including: average vehicle delay, fuel consumption, and emission levels using simulated results from INTEGRATION, a microscopic traffic assignment and simulation software, considering different freeway speed limits, traffic demand levels and system market penetration rates. In total, 19 traffic scenarios for each of the four different cases (Eco-CACC-A, Eco-CACC-F and Eco-CACC-F+A plus a base no control case) were tested. In total 760 simulation runs were conducted (4 cases * 19 scenarios * 10 repetitions). T-tests and pairwise mean comparison (Tukey HSD) were conducted to identify any statistical differences between control cases and the base case from the simulation results. This thesis shows that arterial and freeway Eco-CACCs can work well together and their effects will be largely influenced by network characteristics. / Master of Science

A detailed multi-zone thermodynamic simulation has been developed for the direct-injection (DI) diesel engine combustion process. For the purpose of predicting heterogeneous type combustion systems, the model explores the formation of pre-ignition radicals, start of combustion, and eventual heat release. These mechanisms are described based on the current understanding and knowledge of the diesel engine combustion acquired through advanced laser-based diagnostics. Six zones are developed to take into account the surrounding bulk gas, liquid- and vapor-phase fuel, pre-ignition mixing, fuel-rich combustion products as well as the diffusion flame combustion products. A three-step phenomenological soot model and a nitric oxide emission model are applied based on where and when each of these reactions mainly occurs within the diesel fuel jet evolution process. The simulation is completed for a 4.5 liter, inline four-cylinder diesel engine for a range of operating conditions. Specifically, the engine possesses a compression ratio of 16.6, and has a bore and stroke of 106 and 127 mm. The results suggest that the simulation is able to accurately reproduce the fuel jet evolution and heat release process for conventional diesel engine combustion conditions. The soot and nitric oxide models are able to qualitatively predict the effects of various engine parameters on the engine-out emissions. In particular, the detailed thermodynamics and characteristics with respect to the combustion and emission formation processes are investigated for different engine speed/loads, injection pressures and timings, and EGR levels. The local thermodynamic properties and energy, mass distributions obtained from the simulation offer some fundamental insights into heterogeneous type combustion systems. The current work provides opportunities to better study and understand the diesel engine combustion and emission formation mechanisms for conventional diesel engine combustion modes. The flexible, low computational cost features of this simulation result in a convenient tool for conducting parametric studies, and benefits for engine control and diagnostics.

Mineral dust has manifold impacts on the Earth system. This includes land degradation at the dust sources, interaction with radiation in the atmosphere and effects on human health and economic activity. While it can be stated that most dust sources are found in arid and semi-arid environments, a general determination of characteristics that make a surface particularly susceptible to wind erosion cannot be given. One dust source type that has gained increasing attention in recent years is alluvial sediments. These sediments are formed and influenced by surface runoff and provide a large amount of fine grained material prone to wind erosion. Alluvial features are abundant in desert regions but are often small in size, for example dry river basins or alluvial fans. Due to their small size and despite their importance, these features are often underestimated or completely disregarded in dust-emission models. In this thesis, the spatio-temporal distribution of active alluvial dust sources is investigated and parameterized for a dust-emission model. For this, an approach to automatically detect alluvial features from two globally available satellite products is developed. These products comprise (1) surface reflectance at visible and near-infrared wavelengths derived from Sentinel-2 or MODIS and (2) HydroSHEDS flow accumulation data based on radar measurements. By combining these two datasets, an alluvial fines map (AFM) is created that shows the distribution of alluvial sediments. The AFM is implemented in a dust-emission model and multi-year model runs are performed for two study regions, one located around the Aïr Massif in the central Sahara, the other one covering western Namibia. Besides the distribution of fine alluvial sediments, another hydrologically influenced source type is analyzed in Namibia, i.e. the Etosha pan, a salt pan that is one of the most important dust sources in southern Africa. Dust activity from Etosha pan exhibits a strong seasonality due to regular flooding of the pan. These inundation events are implemented in the model by creating a monthly water mask from MODIS reflectance data. In the central Saharan study area, a comparison of the simulated dust flux with observed dust source activation frequency (DSAF) derived from the MSG SEVIRI Desert-Dust-RGB product shows that the model is able to reproduce the spatial and seasonal differences in the main activity of the identified sources. This seasonality cannot be reproduced by a control model run, in which the sediment supply by alluvial features is not included explicitly. For the Namibian study area, a model run is performed that includes the monthly water mask for Etosha pan and the AFM for the coastal ephemeral river basins. The simulated period covers 13 years from 2005 to 2017. With an empirical orthogonal function (EOF) analysis, constellations of pressure systems in the southern African region are determined that lead to an increased dust flux from the study area. Especially the Berg wind situation, a unique pressure pattern found in southern Africa with dry and hot continental winds, is identified as an atmospheric circulation pattern that leads to increased dust activity from the Namibian sources. The results highlight how important the consideration of alluvial features is for an accurate simulation of dust fluxes. Due to the global availability of the satellite data, the approach can be implemented in regional, continental or even global studies. Long-term emission fluxes can be used to identify the influence of meteorological patterns on dust emission and can help to estimate dust fluxes under current conditions but also in a changing climate.

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.

Continuous transportation demand growth in recent years has led to many traffic issues in urban areas. Among the most challenging ones are traffic congestion and the associated vehicular emissions. Efficient design of traffic signal control systems can be a promising approach to address these problems. This research develops a real-time signal control system, which optimizes signal timings at an under-saturated isolated intersection by minimizing total vehicular emissions. A combination of previously introduced analytical models based on traffic flow theory has been used. These models are able to estimate time spent per driving mode (i.e., time spent accelerating, decelerating, cruising, and idling) as a function of demand, vehicle arrival times, saturation flow, and signal control parameters. Information on vehicle activity is used along with the Vehicle Specific Power (VSP) model, which estimates emission rates per time spent in each operating mode to obtain total emissions per cycle. For the evaluation of the proposed method, data from two real-world intersections of Mesogion and Katechaki Avenues located in Athens, Greece and University and San Pablo Avenues, in Berkeley, CA has been used. The evaluation has been performed through both deterministic (i.e. under the assumption of perfect information for all inputs) and stochastic (i.e. without having perfect information for some inputs) arrival tests. The results of evaluation tests have shown that the proposed emission-based signal control system reduces emissions compared to traditional vehicle-based signal control system in most cases.

The work reported in this thesis explores innovative control structures and controller design for a heavy duty Caterpillar C6.6 diesel engine. The aim of the work is not only to demonstrate the optimisation of engine performance in terms of fuel consumption, NOx and soot emissions, but also to explore ways to reduce lengthy calibration time and its associated high costs. The test engine is equipped with high pressure exhaust gas recirculation (EGR) and a variable geometry turbocharger (VGT). Consequently, there are two principal inputs in the air-path: EGR valve position and VGT vane position. The fuel injection system is common rail, with injectors electrically actuated and includes a multi-pulse injection mode. With two-pulse injection mode, there are as many as five control variables in the fuel-path needing to be adjusted for different engine operating conditions.

<p>U okviru doktorske disertacije opisane su jedinstvene fiziĉko-hemijske karakteristike<br />grupe supstanci benzena, toluena, etilbenzena i ksilena koje pripadaju istraživanjima<br />najnovijeg trenda nauĉne javnosti. Posebno su opisane perzistencija i<br />pseudoperzistencija, transport, distribucija i negativan i toksiĉan uticaj na zdravlje<br />ĉoveka i životnu sredinu. Sprovedena su eksperimentalna istraživanja zavisnosti<br />koncentracionih nivoa BTEX jedinjenja u izduvnim gasovima i operativnih parametara<br />eksperimentalnog motora SUS. Na osnovu dobijenih rezultata eksperimentalnih<br />istraživanja definisan je emisioni model BTEX jedinjenja simulacijom kretanja<br />putniĉkog automobila Fiat Punto Classic u skladu sa NEDC ciklusom koji na<br />odgovarajući naĉin reprezentuje standarde uslove vožnje u evropskim gradovima.</p> / <p>In this thesis, the unique physicochemical characteristics of a group of substances<br />consisting of benzene, toluene, ethylbenzene and xylene have been described.<br />Special emphasis has been placed on their characteristics of persistence and pseudopersistence,<br />transport, distribution and the negative and toxic influence on human<br />health and the environment. The experimental research of the interdependence of the<br />concentration levels of BTEX compounds in the exhaust gases and the operational<br />parameters of the experimental IC engine has been conducted. Based on the<br />experimental research results, the emission model of the BTEX compounds has been<br />defined by a simulation of movement of a Fiat Punto Classic passenger car in<br />accordance with the NEDC cycle, which appropriately represents the standard driving<br />conditions in European cities. Research conducted within the thesis represents the<br />newest trend of investigation in the scientific world.</p>

Dust aerosol emission is a critical topic in agriculture, occurring either by aeolian process from bare or sparsely vegetated cropland or as fugitive emission during tilling, harvest and many other farming activities. Aerosols, which are in the case of agriculture either mineral dust, organic particles or a mixture, are known for impacting human health, cloud formation and ultimately, the earth’s climate and ecosystem. Coupled atmosphere and aerosol transport models are commonly used to study aerosol dispersion in the atmosphere, but so far, agricultural sources are under-represented. Hence, estimations of these emissions’ actual impact are still somewhat uncertain regarding their seasonality, spatial distribution and the fraction of the global aerosol load. To fill this gap, this study aims at identifying suitable approaches for modelling aeolian emissions from sparsely vegetated cropland and fugitive emissions from tilling. Fugitive emissions are challenging since they mainly depend on human activity that is not predictable, but observed events can be used as case studies. For this, a Lagrangian particle dispersion model was chosen, which can trace the trajectory of individual particles in the emitted dust plume. So the particle model “Itpas” was developed to tackle fugitive emissions and to be capable of simulating the complex turbulent mixing of dust particles inside the atmospheric boundary layer. This model was used to simulate a case study based on measured tilling emissions, showing the particle dispersion for a stable and unstable stratified boundary layer. It was shown that within a stably stratified boundary layer, the dust plume is restricted to the near-source region. In contrast, emissions in unstable boundary layers go into long-range transport. This illustrates the spatial range a single tillage operation can have an impact. Aeolian dust emissions are controlled by the wind. For cropland, the emission variability is caused mainly by the frequently changing vegetation cover. Emissions can only occur in the time between tillage and newly grown crops or during drought periods. A parametrisation based on high-resolution satellite observations of the vegetation cover was created to include this process into a model. With this, a new dust emission scheme for cropland emission was developed for the model system COSMO-MUSCAT. In a case study of a dust outbreak from cropland in Poland in 2019, the model’s ability was tested extensively on multiple spatial resolutions. Validation against satellite-measured AOD, ground-measured PM10 and the vertical profile of the PollyNET lidar in Warsaw showed an overall good agreement of the model simulation with the observations. In the framework of this thesis, one dedicated model approach was developed for both the fugitive emissions and the aeolian emissions and validated upon case studies. These approaches could help better understand agricultural dust emissions, their spatial distribution, seasonality and, ultimately, global impact.

La circulation routière est une source majeure de pollution atmosphérique dans les zones urbaines. Les décideurs insistent pour qu’on leur propose de nouvelles solutions, y compris de nouvelles stratégies de management qui pourraient directement faire baisser les émissions de polluants. Pour évaluer les performances de ces stratégies, le calcul des émissions de pollution devrait tenir compte de la dynamique spatiale et temporelle du trafic. L’utilisation de capteurs traditionnels sur route (par exemple, capteurs inductifs ou boucles de comptage) pour collecter des données en temps réel est nécessaire mais pas suffisante en raison de leur coût de mise en oeuvre très élevé. Le fait que de telles technologies, pour des raisons pratiques, ne fournissent que des informations locales est un inconvénient. Certaines méthodes devraient ensuite être appliquées pour étendre cette information locale à une grande échelle. Ces méthodes souffrent actuellement des limites suivantes : (i) la relation entre les données manquantes et la précision de l’estimation ne peut être facilement déterminée et (ii) les calculs à grande échelle sont énormément coûteux, principalement lorsque les phénomènes de congestion sont considérés. Compte tenu d’une simulation microscopique du trafic couplée à un modèle d’émission, une approche innovante de ce problème est mise en oeuvre. Elle consiste à appliquer des techniques de sélection statistique qui permettent d’identifier les emplacements les plus pertinents pour estimer les émissions des véhicules du réseau à différentes échelles spatiales et temporelles. Ce travail explore l’utilisation de méthodes statistiques intelligentes et naïves, comme outil pour sélectionner l’information la plus pertinente sur le trafic et les émissions sur un réseau afin de déterminer les valeurs totales à plusieurs échelles. Ce travail met également en évidence quelques précautions à prendre en compte quand on calcul les émissions à large échelle à partir des données trafic et d’un modèle d’émission. L’utilisation des facteurs d’émission COPERT IV à différentes échelles spatio-temporelles induit un biais en fonction des conditions de circulation par rapport à l’échelle d’origine (cycles de conduite). Ce biais observé sur nos simulations a été quantifié en fonction des indicateurs de trafic (vitesse moyenne). Il a également été démontré qu’il avait une double origine : la convexité des fonctions d’émission et la covariance des variables de trafic. / Road traffic is a major source of air pollution in urban areas. Policy makers are pushing for different solutions including new traffic management strategies that can directly lower pollutants emissions. To assess the performances of such strategies, the calculation of pollution emission should consider spatial and temporal dynamic of the traffic. The use of traditional on-road sensors (e.g. inductive sensors) for collecting real-time data is necessary but not sufficient because of their expensive cost of implementation. It is also a disadvantage that such technologies, for practical reasons, only provide local information. Some methods should then be applied to expand this local information to large spatial extent. These methods currently suffer from the following limitations: (i) the relationship between missing data and the estimation accuracy, both cannot be easily determined and (ii) the calculations on large area is computationally expensive in particular when time evolution is considered. Given a dynamic traffic simulation coupled with an emission model, a novel approach to this problem is taken by applying selection techniques that can identify the most relevant locations to estimate the network vehicle emissions in various spatial and temporal scales. This work explores the use of different statistical methods both naïve and smart, as tools for selecting the most relevant traffic and emission information on a network to determine the total values at any scale. This work also highlights some cautions when such traffic-emission coupled method is used to quantify emissions due the traffic. Using the COPERT IV emission functions at various spatial-temporal scales induces a bias depending on traffic conditions, in comparison to the original scale (driving cycles). This bias observed in our simulations, has been quantified in function of traffic indicators (mean speed). It also has been demonstrated to have a double origin: the emission functions’ convexity and the traffic variables covariance.

For Heavy-duty vehicles (HDVs), the distribution of vehicle miles traveled (VMT) by vehicle type is the most significant parameters for onroad mobile source emissions modeling used in the development of air quality management and regional transportation plans. There are two approaches for the development of the HDV VMT distribution; one approach uses HDV registration data and annual mileage accumulation rates, and another uses HDV VMT counts/observations collected with the FHWA truck classification. For the purpose of emissions modeling, the FHWA truck classes are converted to those used by the MOBILE6.2 emissions rate model by using either the EPA guidance or the National Research Council conversion factors. However, both these approaches have uncertainties in the development of onroad HDV VMT distributions that can lead to large unknowns in the modeled HDV emissions. This dissertation reports a new heavy-duty vehicle visual classification and activity estimation method that minimizes uncertainties in current HDV conversion methods and the vehicle registration based HDV VMT estimation guidance. The HDV visual classification scheme called the X-scheme, which classifies HDV/truck classes by vehicle physical characteristics (the number of axles, gross vehicle weight ratings, tractor-trailer configurations, etc.) converts FHWA truck classes into EPA HDV classes without losing the original resolution of HDV/truck activity and emission characteristics. The new HDV activity estimation method using publicly available HDV activity databases minimizes uncertainties in the vehicle registration based VMT estimation method suggested by EPA. The analysis of emissions impact with the new method indicates that emissions with the EPA HDV VMT estimation guidance are underestimated by 22.9% and 25.0% for oxides of nitrogen and fine particulate matter respectively within the 20-county Atlanta metropolitan area. Because the new heavy-duty vehicle visual classification and activity estimation method has the ability to provide accurate HDV activity and emissions estimates, this method has the potential to significantly influence policymaking processes in regional air quality management and transportation planning. In addition, the ability to estimate link-specific emissions benefits Federal and local agencies in the development of project (microscale), regional (mesoscale), and national (macroscale) level air quality management and transportation plans.