Maritime emissions have long time been a low priority issue for policy makers. However, maritime emissions will be included in the European National Emission Ceilings (NEC) and as maritime transport is increasing rapidly, its share in emissions is thought to become more significant.So maritime emissions are becoming more important for national and international policy makers, and pressure is rising to reduce emissions. As such, policy makers need a tool to estimate current emissions and to asses the impact of policy measures on emissions, especially with respect to harbor emissions. To this end, we have constructed an emission model to calculate and distribute maritime emissions geographically. Furthermore, we modeled future emissions starting from a traffic prognosis, taking into account fleet renewal, technological improvement, existing legislation, and increase (or decrease) of ship size. For Belgian maritime emissions, we found that total maritime emissions have been increasing slowly from 1990 to 2005, yet slower then traffic, e.g., NOx emissions increased 23% while traffic increased 36%. We furthermore found that more than half of all emissions are in-port emissions.With the model we calculated the effect of two policy measures: first, MARPOL annex VI concerning NOx emission standards, and second, European guideline 2005/33/EC concerning the sulphur content of maritime fuel. We found that the MARPOL annex had no significant impact on NOx emissions, while the European guideline will decrease emissions of SO2 in harbors to 36% in 2010 compared to 1990. Emissions of maritime transport are increasing rapidly. In a business-as-usual (BAU) scenario, emissions per traffic will decrease slowly, although measures to reduce emissions are available (shore-side electricity, exhaust aftertreatment, fuel quality,…) and can reduce emissions significantly.
In the last few years, scientific consensus is that emission of greenhouse gases (GHGs) into the atmosphere is contributing to changes in the earth's climate. While uncertainty remains over the pace and dimensions of the change, a consensus on the need for action has grown among the public and elected officials. In part, this shift has been accelerated by concern over energy security and rising fuel prices. The new political landscape has led many cities, states, and regions to institute policies aimed at reducing GHG emissions. These policies and emerging initiatives have significant implications for the transportation planning process. The transportation sector accounts for approximately 27% of GHG production in the U.S. (as of 2003) and while the U.S. accounts for only roughly 5% of the world's population, it is estimated that it produces over 20% of the world's GHG emissions. Note that this does not include "lifecycle" emissions that result from the processes undertaken to extract, manufacture, and transport fuel. Carbon dioxide represents approximately 96% of the transportation sector's radiative forcing effects. Unlike conventional air pollutants, carbon dioxide emissions are directly tied to the amount of fuel consumed and its carbon intensity. Therefore, emissions reductions can be achieved by increasing the use of low-carbon fuels, improving fuel economy, or reducing total vehicle miles of travel - often called the three legged stool. (A fourth leg is congestion reduction, at certain optimal speeds). These same factors are related to our use of imported oil, so actions taken to reduce GHG emissions may actually produce benefits in both policy areas. The climatic risks of additional emissions associated with capacity projects must be balanced against the mobility, safety, and economic needs of a community or region. Consequently, this dissertation attempts to quantify the impacts of high-emitting vehicles on the environment and to propose solutions to enhance the currently-used high-emitting vehicle detection procedures. In addition, fuel consumption and emission models for high-speed vehicles are developed in order to provide more reliable estimates of vehicle emissions and study the impact of vehicle speeds on vehicle emissions. The dissertation extends the state-of-the-art analysis of high emitting vehicles (HEVs) by quantifying the network-wide environmental impact of HEVs. The literature reports that 7% to 12% of HEVs account for somewhere between 41% to 63% of the total CO emissions, and 10% are responsible for 47% to 65% of HC emissions, and 10% are responsible for 32% of NOx emissions. These studies, however, are based on spot measurements and do not necessarily reflect network-wide impacts. Consequently, the research presented in this dissertation extends the state-of-knowledge by quantifying HEV contributions on a network level. The study uses microscopic vehicle emission models (CMEM and VT-Micro model) along with pre-defined drive cycles (under the assumption that the composite HEV and VT-LDV3 represent HEVs and NEVs, respectively) in addition to the simulation of two transportation networks (freeway and arterial) to quantify the contributions of HEVs. The study demonstrates that HEVs are responsible for 67% to 87% of HC emissions, 51% to 78% of CO emissions, and 32% to 62% of the NOX emissions for HEV percentages ranging from 5% to 20%. Additionally, the traffic simulation results demonstrate that 10% of the HEVs are responsible for 50% to 66% of the I-81 HC and 59% to 78% of the Columbia Pike HC emissions, 35% to 67% of the I-81 CO and 38% to 69% of the Columbia Pike CO emissions, and 35% to 44% of the I-81 NOX and 35% to 60% of the Columbia Pike NOX emissions depending on the percentage of the normal-emitting LDTs to the total NEVs. HEV emission contributions to total HC and CO emissions appear to be consistent with what is reported in the literature. However, the contribution of NOX emissions is greater than what is reported in the literature. The study demonstrates that the contribution of HEVs to the total vehicle emissions is dependent on the type of roadway facility (arterials vs. highways), the background normal vehicle composition, and the composition of HEVs. Consequently, these results are network and roadway specific. Finally, considering that emission control technologies in new vehicles are advancing, the contribution of HEVs will increase given that the background emission contribution will decrease. Given that HEVs are responsible for a large portion of on-road vehicle emissions, the dissertation proposes solutions to the HEV screening procedures. First, a new approach is proposed for estimating vehicle mass emissions from concentration remote sensing emission measurements using the carbon balance equation in conjunction with either the VT-Micro or PERE fuel consumption rates for the enhancement of current state-of-the-art HEV screening procedures using RSD technology. The study demonstrates that the proposed approach produces reliable mass emission estimates for different vehicle types including sedans, station wagons, full size vans, mini vans, pickup trucks, and SUVs. Second, a procedure is proposed for constructing on-road RS emission standards sensitive to vehicle speed and acceleration levels. The proposed procedure is broadly divided into three sub-processes. In the first process, HE cut points in grams per second are developed as a function of a vehicle's speed and acceleration levels using the VT-Micro and CMEM emission models. Subsequently, the HE cut points in grams per second are converted to concentration emissions cut points in parts per million using the carbon balance equation. Finally, the scale factors are computed using either ASM ETW- and model-year-based standards or engine-displacement-based standards. Given the RS emissions standards, the study demonstrated that the use of on-road RS cut points sensitive to speed and acceleration levels is required in order to enhance the effectiveness of RS. Finally, the dissertation conducted a study to develop fuel consumption and emissions models for high-speed vehicles to overcome the shortcomings of state-of-practice models. The research effort gathered field data and developed models for the estimation of fuel consumption, CO, CO, NO, NO2, NOx, HC, and PM emissions at high speeds. A total of nine vehicles including three semi-trucks, three pick-up trucks, and three passenger cars were tested on a nine-mile test track in Pecos, Texas. The fuel consumption and emission rates were measured using two portable emission measurement systems. Models were developed using these data producing minimum errors for fuel consumption, CO, NO2, HC, and PM emissions. Alternatively, the NO and NOx emission models produced the highest errors with a least degree of correlation. Given the models, the study demonstrated that the newly constructed models overcome the shortcomings of the state-of-practice models and can be utilized to evaluate the environmental impacts of high speed driving. ; Ph. D.
We apply deep kernel learning (DKL), which can be viewed as a combination of a Gaussian process (GP) and a deep neural network (DNN), to compression ignition engine emissions and compare its performance to a selection of other surrogate models on the same dataset. Surrogate models are a class of computationally cheaper alternatives to physics-based models. High-dimensional model representation (HDMR) is also briefly discussed and acts as a benchmark model for comparison. We apply the considered methods to a dataset which was obtained from a compression ignition engine and includes as outputs soot and NOx emissions as functions of 14 engine operating condition variables. We combine a quasi-random global search with a conventional grid-optimisation method in order to identify suitable values for several DKL hyperparameters, which include network architecture, kernel, and learning parameters. The performance of DKL, HDMR, plain GPs, and plain DNNs is compared in terms of the root mean squared error (RMSE) of the predictions as well as computational expense of training and evaluation. It is shown that DKL performs best in terms of RMSE in the predictions whilst maintaining the computational cost at a reasonable level, and DKL predictions are in good agreement with the experimental emissions data. ; This work was partly funded by the National Research Foundation (NRF), Prime Minister's Office, Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) programme, and by the European Union Horizon 2020 Research and Innovation Programme under grant agreement 646121. Changmin Yu was funded by a Shell PhD studentship. Markus Kraft gratefully acknowledges the support of the Alexander von Humboldt foundation.
Intro -- Abstract -- Nomenclature -- Contents -- List of Figures -- List of Tables -- Chapter 1: Introduction -- 1.1 Background and Context -- 1.2 Scope of the Brief -- References -- Chapter 2: Review of Energy-Economy-Environment Models -- 2.1 Overview of Energy Modeling Studies -- 2.2 Studies Examining Past Emissions and Emission Intensities -- 2.3 Optimal Energy Pathways -- 2.3.1 The TERI MARKAL Model -- 2.4 Energy Modeling Using Input-Output Methods -- 2.4.1 NCAER Model -- 2.4.2 IRADe Activity Analysis Model -- 2.5 Summary and Gaps -- 2.6 Developing an Approach to an Integrated Modeling Framework (IMF) -- References -- Chapter 3: Evaluation of Emission Indicators Using Decomposition Analysis -- 3.1 Introduction -- 3.2 Decomposition Analysis -- 3.2.1 Decomposition Analysis for India -- 3.2.2 Drivers of Energy and Emission Intensities -- 3.3 Scenarios for Target Year 2030 -- 3.3.1 Construction of Economic Baselines -- 3.3.2 Mitigation Targets for Developing Countries -- 3.4 Results and Discussion -- 3.5 Conclusion -- References -- Chapter 4: Optimal Pathways for Power Supply -- 4.1 Introduction -- 4.2 Data and Assumptions -- 4.2.1 Energy Requirements -- 4.2.2 Power Supply -- 4.2.3 Cost of Electricity -- 4.3 Power Sector Model: Methodology -- 4.4 Illustration of Power Sector Model Through Scenarios -- 4.5 Conclusion -- References -- Chapter 5: Analysis of Energy-Economy Linkages Using a Social Accounting Matrix -- 5.1 Introduction -- 5.2 Introduction to SAM -- 5.2.1 Multiplier Analysis Using SAM -- 5.3 Methodology Used for Constructing a Model Using the SAM -- 5.4 SAM 2003-2004 and Other Data -- 5.5 Illustration of the I-O Analysis Through Scenarios -- 5.6 Results and Discussion -- 5.7 Conclusions -- References -- Chapter 6: An Integrated Modeling Framework (IMF) for Energy-Economy-Environment Modeling.
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The work presented in this thesis is the result of the KTH CICERO project "Dynamic Engine Performance" in which the main objective was to develop simple models foremission formation. The demand for such models is increasing, mainly due to the tightening emission legislation for diesel engines which has lead to more complex engines and thereby more laborious development and calibration processes. Simple emission models can be a valuable tool during the development phase, e.g. when used with models for gas exchange - and after-treatment systems, and for precalibration of the engine control settings. Since engines in automotive application typically work under dynamic load, the main prerequisites were that the models should be comprehensive enough to handle the extreme conditions that can occur in engines during load transients but still simple enough to be used for calibration. Two main approaches have been used; one where the combustion and emission formation processes were modeled from the flame front and downstream using equilibrium chemistry. In the other approach, the entire mixing/entrainment process was modeled and emission formation was modeled with kinetic chemistry. Both approaches were found to meet the requirements but had different advantages; the first, simpler approach had shorter calculation time while the latter was more comprehensive and required less tuning. The latter also resulted in a model for heat release rate which can be useful as a stand-alone model and allows the emission models to be used for untested conditions. Another objective in this project was to identify techniques/instruments that can be used for emission measurements during transient operation since these are essential for understanding of emission formation in these conditions and as validation data for the emission models. ; QC 20110502
