CO₂ Emissions from Vehicles (Volume II)

Dear Colleagues,

The problem of greenhouse gas emissions is now the largest challenge facing humanity, one that must be solved as soon as possible. In particular, the emission of greenhouse gases from transport contributes a significant share of global human anthropogonic emissions. Therefore, it is crucial that the scientific community look for solutions that will allow us to reduce the emissions of these gases. One of the main gases emitted by fuel combustion in vehicles is CO₂.

This Special Issue aims to encourage scientists to look for solutions from a wider set of perspectives, both locally and globally. We welcome engine solutions, after-treatment systems, and concepts that have a chance of being implemented and thus contribute to environmental protection. The submission of articles on advanced, future-oriented topics will be important to this Special Issue, especially investigations of the large-scale electrification of vehicles and the impact of these solutions on the decarbonization of transport. We also encourage authors to submit papers related to the emissions, modelling different aspects of emissions using modern artificial intelligence and machine learning techniques. Submissions may either be in the form of original research articles or comprehensive reviews (e.g., legislative) on topics which are consistent with the aims and scope of this Special Issue.

Dr. Maksymilian Mądziel
Prof. Dr. Kazimierz Lejda
Dr. Artur Jaworski
Guest Editors

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• transport
• autonomous vehicles
• CO₂ emission
• emission modelling
• fuel consumption
• exhaust emission
• global warming
• greenhouse gases
• combustion engines
• electromobility
• hybrid and electric vehicles
• fuel cell vehicles

Title: Utilizing Artificial Neural Network Ensembles for Ship Design Optimization to Reduce Added Wave Resistance and CO2 Emissions
Authors: Tomasz Cepowski
Affiliation: Faculty of Navigation, Maritime University of Szczecin, 1-2 Wały Chrobrego St., 70-500 Szczecin, Poland
Abstract: Increased maritime cargo transportation has necessitated stricter management of emissions from ships. The primary source of this pollution is fuel combustion, which is influenced by factors such as a ship's added wave resistance. Accurate estimation of this resistance during ship design is crucial for minimizing exhaust emissions. The challenge is that, at the preliminary parametric design stage, only limited geometric data about the ship is available, and existing methods for estimating added wave resistance cannot be applied. This article presents the application of artificial neural network (ANN) ensembles for estimating added wave resistance based on dimensionless design parameters available at the preliminary design stage, such as the length-to-breadth ratio (L/B), breadth-to-draught ratio (B/T), length-to-draught ratio (L/T), block coefficient (CB), and the Froude number (Fn). Four different ANN ensembles are developed to predict this resistance using both complete sets of design characteristics (i.e., L/B, B/T, CB, and Fn) and incomplete sets, such as L/B, CB, and Fn; B/T, CB, and Fn; and L/T, CB, and Fn. This approach allows for the consideration of CO2 emissions at the parametric design stage when only limited ship dimensions are known. An example in this article demonstrates that minor modifications to typical container ship designs can significantly reduce added wave resistance, resulting in a daily reduction of up to 2.55 tons of CO2 emissions. This reduction is equivalent to the emissions produced by 778 cars per day, highlighting the environmental benefits of optimizing ship design.

Title: Correlation of the Smart City Concept with the Costs of Toxic Exhaust Gas Emissions based on the Analysis of a Selected Population of Motor Vehicles in Urban Traffic
Authors: Wojciech Lewicki; Milena Bera; Monika Spiewak- szyjka
Affiliation: West Pomeranian University of Technology in Szczecin
Abstract: The intensive development of road transport has resulted in a significant increase in air pollution. This phenomenon is particularly noticeable in urban areas. This creates the need for analyses and forecasts of the scale and extent of future emissions of harmful substances into the environment. The aim of the study was to estimate the costs of emission of toxic components of exhaust gases gener-ated by all users of conventionally propelled vehicles travelling on a section of urban road in the next 25 years. The traffic study was carried out on an urban traffic route playing a key role for road transport in the dimension of a given urban agglomeration. The traffic forecast for the analysed road section was based on the results of own measurements carried out in April 2023 and external data from the General Directorate for Roads and Motorways. The results of the observations concerned six categories of vehicles for the morning and afternoon rush hours. Based on the data obtained, the generic structure of the vehicle population on the analysed section and the average daily traffic were determined. Using the methodology contained in the Blue Book of Road Infrastructure, pa-rameters were calculated in the form of annual indicators of traffic growth on the analysed section, travel speed, and annual air pollution costs for selected vehicle categories. The results of the study confirmed that there was an increase in the cost of toxic emissions for each vehicle category over the projected 25-year period. The largest increases were seen for trucks with trailers and passenger cars. In total, for all vehicle categories, emission costs nearly doubled from 2024 to 2046, from €3,745,229 to €7,443,384. The analyses presented here provide an answer to the question of what pollution costs may be faced by cities in which road transport will continue to be based on conventional types of propulsion. In addition, the research presented can be used to develop urban mobility trans-formation plans for the coming years within the scope of the widely promoted Smart city concept and the idea of electromobility. By pointing out to local authorities the direct economic benefits of these changes.

Title: Emission Inventory for Road Transport Sector: A Case for Bangladesh
Authors: M. A. Bakkar; M. T. Farhan Fatin; F. M. Mohammedy; M. T. Islam
Affiliation: Bangladesh University of Engineering and Technology
Abstract: Sectoral greenhouse gas (GHG) emissions have become important indicators in country profiling, policy discussions, calculating national contributions in line with the Paris Agreement and various other intergovernmental and international dialogues. It also helps pinpoint which particular sector, and within a sector which particular category or sub-category, is the greatest emitter. Though Bangladesh is not among the top emitters of these gases, she will become one of the worst sufferers of the climate consequences. Her sectoral assessment on GHG emission inventory making has become important lately, for designing nationally determined contributions (NDC), for international negotiations on securing climate-related funds, and for decadal planning among others. This study utilizes the IPCC guideline and a tier-three activity-based model to estimate total emissions by considering vehicle quantity, engine capacity, fuel type, registration year, annual distance traveled, and emission factors. This paper estimates the total annual GHG emissions from the road transport sector to be 24.190 million tons. The greatest emission comes from carbon dioxide (99%) alone, while trace amounts of methane and nitrogen oxides (0.8%) are also present. Trucks are the largest emitters (29.6%), followed by buses (15%), plying on the roads. Trucks also emit the largest amount of CO2 and NOx. These estimates will be extremely useful for the transportation sector's carbon footprint and emission discussions.