Bus Emissions Research Articles

Well-to-Wheel Greenhouse Gas Emissions from Flying Cars: A Focus on Air Shuttles

Flying cars are becoming a key technology in future transportation, because of their ability to travel fast and to reduce traffic jams. Air shuttles are an important application scenario of flying cars. They can carry 19 passengers, playing a similar role as medium on-road buses. However, it is still unclear which has a better performance on carbon emissions, air shuttles or road buses. Based on the analysis of the flying car’s flying process, lifecycle greenhouse gas emissions from flying cars which are used as air shuttles are calculated and further compared with on-road bus emissions. For thorough consideration, improvements in battery specific energy are considered, and different propulsion systems for buses are also calculated. The results show that at the current battery technology level (200 Wh/kg specific energy), air shuttles have much more emissions than all kinds of road buses. But if the battery technology progresses to the level of 2030 (400 Wh/kg specific energy as predicted), air shuttles will have a lower emission than fuel cell buses within the range of 250 km, and will be competitive with battery electric bus that carries an equal number of passengers.

Study on the Emission Characteristics of Typical City Buses under Actual Road Conditions

Five typical in-use city buses in Tangshan with different emission standards, fuel power types, and emission control technology routes were selected as the research objects. PEMSs (Portable Emissions Measurement Systems) and OBD (On-Board Diagnostic) remote monitoring technologies were used to conduct research on actual road conditions and emission characteristics. The research aimed to analyze the driving characteristics of different power types of bus engines and after-treatment systems, as well as their impact on emissions. The results indicated that the actual road driving is mainly in the urban and suburban areas of the local region. Additionally, the VSP (Vehicle Specific Power) was found to be distributed in the low- and medium-speed range of mode Bin0~Bin24. The emission rates of each pollutant showed an increasing trend with the increase in VSP. THC and NOx emissions of CNG and LNG buses are significantly higher than those of gas–electric hybrid buses. However, the PM emissions are lower. Therefore, it is necessary to strengthen the NOx emission control of CNG and LNG buses. With the improvement in emission standards and the use of hybrid technology, CO2 and NOx emissions of China IV buses can be reduced by 13% and 53.5% compared with China III buses of the same tonnage. The CO2 and NOx emissions of China V buses using natural gas combustion and an electric mixture are 20% and 18.8% lower than those of China V buses of the same tonnage using only natural gas. In the actual operation of the diesel–electric hybrid bus equipped with the SCR (Selective Catalytic Reduction) system, the working time of the engine is about 35.5 ± 5%, and the working time of the SCR system only accounts for 65.5 ± 12%. The low working conditions of the SCR system and the low temperature of SCR system are the reasons for the high NOx concentration at the rear end of the SCR system when the hybrid electric bus is running at low speeds.

Reducing CO2 Emissions of Hybrid Heavy-Duty Trucks and Buses: Paving the Transition to Low-Carbon Transport

This study investigates the CO2 reduction potential of powertrain hybridisation on heavy-duty lorries and city buses. The analysis considers modern parallel and serial hybrid architectures, assessing their efficiency and limits in CO2 emission reduction through vehicle simulation in VECTO, which is the official tool of the European Commission for calculating heavy-duty vehicle fuel and energy consumption. The results reveal distinct trends for each vehicle type and architecture. In lorries, more significant improvements are observed in urban delivery profiles, reaching up to ~16%, indicating the benefits of hybridisation in transient conditions with energy recuperation opportunities. City buses, particularly those with serial architectures, exhibit significant emission reductions that reach 36%, making them suitable for urban environments. The optimisation of electric motor size and performance plays a crucial role in achieving emission reductions, while battery capacity must be carefully considered to avoid adverse effects. For lorries in urban delivery use, further improvements of 17.5% can be achieved by utilising a 160 kW engine motor and 30 kWh battery. Buses are already quite well optimised, with serial architecture presenting the highest benefits with a 120 kW electric motor and a battery of 11 kWh. Future research should focus on supercapacitors and gearboxes to improve efficiency at higher vehicle speeds and assess hybridisation potential in interurban coach travel. The heavy-duty vehicle sector can make significant strides towards low-carbon transport by maximising hybrid powertrain efficiency and emission reductions.

Real-Time LNG Buses Emissions Prediction Based on a Temporal Fusion Trans-Formers Model

Emissions from transportation are one of the key factors preventing the achievement of carbon peaking and carbon neutrality by 2050, with particular attention to emissions from buses. Specifically, few research has been conducted on the exhaust emissions characteristics of liquified natural gas (LNG) buses under different driving scenarios. This study proposed a framework for predicting exhaust emissions of LNG buses based on the portable emission measurement system and GPS collaborative perception data. Firstly, the emission distribution characteristics of CO2, CO, HC, and NOx from LNG buses in real-world driving were analyzed by visualization methods. Then, the real-time exhaust emissions of LNG buses were predicted based on the temporal fusion transformers model for both urban and suburban sections of Zhenjiang City, and the model validity was verified. The current and past 10 s driving states were used for predicting the emission rate of LNG buses. The results showed that the proposed model outperforms other advanced algorithms in real-time exhaust emissions prediction of LNG buses, with an average R2 value higher than 0.94 and an average MAPE reduction of 14.19%. The error assessment revealed that the emission values and average emission rates are higher when driving in the urban section compared to the suburban section. Among the influencing factors, traffic conditions have the most significant impacts on the exhaust emissions of LNG buses, followed by road conditions and driving states, with relative feature importance of 48.9, 34.8, and 16.3%, respectively. Additionally, the current and past 10 s driving states also significantly influenced real-time predictions. This study provides an essential theoretical reference for reducing exhaust emissions for city buses.

Calculation of Carbon Emissions and Study of the Emission Reduction Path of Conventional Public Transportation in Harbin City

As the northernmost megacity in China, the long winters, large population size, and unsustainable transport structure in Harbin determine that the city will produce relatively large carbon emissions. The transportation industry is one of the three greenhouse gas emission sources; therefore, the development of low-carbon transportation is imperative. This work compares commonly used carbon emission measurement methods and chooses a mileage method to classify the carbon emissions of conventional buses of different energy types used in Harbin in 2020. A multi-factor grey prediction model was constructed to predict the population size of Harbin and the number of conventional buses. After that, a scenario analysis method was used to analyze the fuel structure of buses in Harbin from three perspectives: a pessimistic scenario, a baseline scenario, and an optimistic scenario. The carbon emissions of conventional buses were calculated for Harbin from 2023 to 2030. Finally, by combining the prediction results and factors influencing carbon emission, a regular bus path to minimize carbon emissions is proposed. The outcome of this study shows that the carbon emission environment in Harbin will be improved by reducing vehicle energy consumption, optimizing energy structure, standardizing driving behavior, building intelligent transportation, giving priority to public transportation, and improving the road network structure.

Spatiotemporal analysis of CO2 emissions and emission reduction potential of Beijing buses using smart card data

Human activities, primarily carbon dioxide emissions, have undeniably caused global warming. The transportation sector contributes about a quarter of global CO2 emissions. While replacing traditional buses with electric ones has reduced emissions, it is crucial to consider the indirect emissions resulting from electricity consumption. This study proposes a framework for modeling bus emissions using smart card data, integrating spatiotemporal distribution characteristics and emission reduction potentials. Our analysis reveals that routes spanning 10–30 km contribute to 81% of total bus emissions, with an average emission rate of 56.2 gCO2/per-km for residents traveling by bus. Bus emissions also exhibit cyclical variations during holidays, weekdays, and weekends, indicating spatial clustering and trends. Although the area within Beijing's 4th Ring Road constitutes only 13% of the total area within the 6th Ring Road, it generates almost half of the CO2 emissions. With urban expansion, total bus emissions increase gradually, but emission intensity decreases. This study emphasizes the potential for reducing emissions through improved public transportation operations. It recommends fully electrifying the bus fleet and employing low grid emission factors, which could reduce emissions by up to 71% compared to diesel options. Electrification of buses and optimizing power generation on the grid are essential priorities for emission reduction.

Impact of powertrain electrification on the overall CO2 emissions of intercity public bus transport: Tenerife Island test case

The adoption of plug-in electric buses are seen as a promising alternative to reduce greenhouse gas (GHG) emissions in the public transport system. Although from an operational point of view this type of vehicles are zero-emissions, the upstream phase contribution can be considerably high in regions with high carbon grid intensities.This paper presents a simulation study to evaluate the energy consumption and the corresponding well-to-wheels (WTW) CO2 emissions of conventional diesel, parallel hybrid and battery electric buses. The test case is the island of Tenerife, an isolated power system highly dependent on fossil-fuels for power generation. Validated models of the aforementioned propulsion architectures were simulated over five representative cycles, covering city and intercity lines with noticeable differences in terms of driving behaviour, distance and gradient. Although battery electric buses present energy consumption reductions of 70% compared to conventional powertrains, the large carbon grid intensity of the island has a negative effect on their potential to reduce WTW CO2 emissions. The results evince that parallel hybrids provide the largest benefit to abate WTW CO2 emissions, reaching a reduction figure of 38% compared to its conventional diesel counterpart.

Research on Temporal and Spatial Distribution of Carbon Emissions from Urban Buses Based on Big Data Analysis

In recent years, global warming has become increasingly severe, and the ecological and environmental problems facing mankind have become increasingly serious. As the main areas of transportation activities, cities are also the main places of carbon emissions. As a necessary condition for human’s daily-life travel, it is particularly important to calculate the carbon emissions from urban transportation. Due to the different characteristics of economy and population in different regions of a city, the carbon emissions of urban buses show different characteristics in terms of temporal and spatial distribution. The developments of science and technology promote the application of big data analysis to specific practical life, enabling people to research and solve problems from a new perspective. This paper uses the GPS data of urban buses in Sanya City, China, to identify operation conditions from urban buses, and calculates the distance and time under different conditions. Based on the measured data of carbon emissions, this paper visualizes the distribution characteristics of carbon emissions by density analysis; explains the time distribution characteristics by the visual analysis of carbon emissions in different time periods, working days and rest days, and different energy types; and illustrates the spatial distribution characteristics by the spatial distributions of carbon emissions from Sanya’s buses on working days and rest days, as well as in different routes, providing reference for a low-carbon development of urban green transport.

Valuation of Urban Public Bus Electrification with Open Data and Physics-Informed Machine Learning

This research provides a novel framework to estimate the economic, environmental, and social values of electrifying public transit buses, for cities across the world, based on open-source data. Electric buses are a compelling candidate to replace diesel buses for their environmental and social benefits. However, the state-of-art models to evaluate the value of bus electrification require granular and bespoke data on bus operation that can be difficult to procure. This strict requirement on data and modeling can hinder potential collaborators on bus electrification, such as electric vehicle manufacturers or electricity providers, from selecting the candidate public transit agencies in the initial phase of the market investigation. Our valuation tool uses General Transit Feed Specification, a standard data format used by transit agencies worldwide, to provide high-level guidance on developing a prioritization strategy for electrifying a bus fleet. We develop physics-informed machine learning models to evaluate energy consumption, carbon emissions, health impacts, and the total cost of ownership for each transit route. We demonstrate our tool with a case study of the bus lines in the Greater Boston and Milan metropolitan areas. While the capital cost of electric buses and the price of diesel fuel may impact the economic benefit of electrification, we expect significant improvements in health and environmental performances. We estimate about 50% health cost reduction by electrifying only 4 routes out of 15 to 19 routes in each city. For Boston and Milan, some routes are estimated to produce less than 5% of the diesel bus emissions and 14%, respectively, for which investment in electrification can be prioritized.

Affective politics of air pollution: atmospheres and activism in the Tel Aviv Metropolitan Region

ABSTRACT In this article, we present a novel approach to studying how air pollution (AP) shapes environmental activism in urban-metropolitan contexts, drawing on geographies of affect. We focus on how people engage with AP affectively, through bodily, sensory, and emotional experiences, and highlight its powerful yet differential affective capacities to prompt activism. Through qualitative analysis of social media and interviews, we present two ‘scenes’ of AP activism across the Tel Aviv metropolitan region: residents attending to bus emissions in a marginalized inner-city neighborhood and to illegal waste-burnings in the mixed Jewish-Arab metropolitan edges. We offer a fine-grained reading of how affective atmospheres of pollution induce political agency both individually and collectively; and how, through spatially and temporally dynamic relations, they interact with broader political-ecological settings to direct activism in socially inclusionary/exclusionary paths. Our analysis highlights a distinct ‘affective politics’ of AP that differs from established approaches to environmental politics.

The impact on air quality of PM10 emissions from the bus fleet of Buenos Aires City

Abstract Automobile transport in megacities poses serious problems in the area of sustainability and environmental security. In the City of Buenos Aires (CABA) it represents 37% of GHG emissions and is an important source of pollutants dangerous to human health. As such, it is an energy consumption sector targeted for the implementation of methods that allow for sustainable urban transport. At the same time, very few previous studies about transport vehicle emissions have utilized the PM10 as an indicator of environmental contamination, considering that the negative impact of this environmental contaminant on human health is widely studied. An emission model linked to an atmospheric dispersion model, statistically validated, were used to study different scenarios of emissions generated by diesel buses. It was observed that in a scenario of zero bus emissions (E0), PM10 immission (air concentrations) concentration in CABA is reduced by half. When studying the energy transition from diesel buses to electric energy, while the rest of the vehicle fleet was maintained at the expense of fossil fuels, the local immission concentration of PM10 in CABA was of the same magnitude of that which was obtained when simulating the (E0) scenario of only private vehicle fleet. This study is relevant in the evaluation of public policy on vehicle emission mitigation that seeks to reduce health risks from poor air quality and to develop a more progressively sustainable city. Article Highlights Transition energy exclusively for buses could reduce PM10 immission values of CABA by 50%. Thermal energy needed to the energy transition does not significant impact the PM10 immission values on the local air quality. Electric buses could reduce the maximum PM10 immission value by half.

Applying a neural network-based approach for estimating instantaneous emissions of the heavy-duty buses: A case study

ABSTRACT The lack of a chassis and a transient engine dynamometer for heavy-duty vehicles in Vietnam is a significant impediment to develop the country-specific emission factors. This study presents a novel approach for modeling the emission rate of heavy-duty bus engines based on the artificial neural network (ANN) to overcome the above limitation while ensuring emission prediction’s accuracy and minimizing test costs. The ANN-based models with high reliability (R2 > 0.98 and MAPE <20%) were built based on the experimental data collected from the engine emission measurement on the steady-state engine dynamometer. The developed models were used to estimate the bus’s emission rate according to the real-world driving characteristic that was taken into the typical transient engine cycle. The estimated average emission factors in terms of distance-based ones for buses in Hanoi according the ANN-based models were close to those measured.

Key driver analysis of greenhouse gas emissions in Thailand's public bus transport with comparative study on metropolitan Bangkok hotspots

The 2007–2020 timeframe, an economic growth period coupled with a rising population for Thailand, witnessed an increase in fixed-route bus transportation options. It is expected that this sector will continue to grow and that additional routes will become accessible to the public in the near-term. It is generally accepted that the high Thai birth rate and the increased travel requirements of the less affluent will augment this growth. The objective of this study is to accurately estimate the 2007–2020 energy consumption and resultant greenhouse gas emissions (GHG) from public land transport. The focus will be on the regularly scheduled fixed-route bus schedules in Thailand that emit undesired pollutants, specifically CO2, N2O and CH4. Once estimates are reached, the past impact and the future ramifications of this mode of transport's contribution to the generation of toxic emissions into the environment will be analyzed. An evaluation of key carbon emission drivers will also be presented along with our identification of these pollutants. Furthermore, this study will focus comparatively on the areas of high development within Thailand, specifically, Bangkok localities referred to here as hotspots. These high density, economically vibrant areas contributed 15 % of Thailand's total fixed-route bus emissions in 2020. Being the capital of Thailand with the largest population center, Bangkok serves as a unique focal point where data extrapolations can be compared and contrasted with the nation as a whole. Although results from our research will show that fixed-route bus transport represents a small portion of total GHG emissions in Thailand, the data will show that significant improvement can be made to reduce this sector's carbon footprint. However, the most impactful contribution will be to the ‘quality of life’ benefit afforded to the large percentage of the Thai population that live within the concentrated areas serviced by fixed-route buses.

THE IMPACT OF DIESEL BUS EMISSIONS ON AIR POLLUTION IN ULAANBAATAR AND ATTEMPT TO REDUCE IT

The advantages of diesel engines have led it to become the only solution for heavy-duty vehicles (HDV), including city buses. Exhaust gas from diesel engines (EG) is a common environmental pollutant and carcinogenic to human health. The paper presents the results of measuring the emissions of city buses running on high sulfur fuel with and without diesel particulate filter (DPF). The study was conducted in real traffic conditions along the regular route of the city of Ulaanbaatar. The measurements were carried out using the HORIBA PEMS (Portable Emissions Measurement System) and the gravimetric method. The measured data was used to determine the actual emission levels from city buses. The actual particulate matter (PM) emissions from city buses were determined during the warm and cold seasons on a daily basis. It is found that a bus with average daily mileage of 242 km emits average of 166.155 g of PM into the atmosphere per day. This fluctuates depending on the season - 141.3 g in summer and 175.8 g in winter. The actual PM emissions of a city bus is 0.6866 g/km. The NOx concentration in the exhaust gases is 1410.94 ppm on average. As a result of 6 months of measurements, a total of 346.651 kg of soot was collected from 24 buses. Innovation: Actual on-road emissions from Ulaanbaatar buses and a cassette-type DPF system with “active” outside the bus regeneration, that can reduce conventional diesel engine PM emissions by up to 90% regardless of the sulfur content of the fuels.

Quantifying the Impact of Alternative Bus Stop Platforms on Vehicle Emissions and Individual Pollution Exposure at Bus Stops.

Due to stop-and-go events, bus stops are often treated as “hot spots” for air pollution. The design of bus stops should be optimized to reduce emissions and exposure for transit commuters. The objective of this study was to analyze the impact of bus stop platform types on vehicle emissions and individual pollution exposure. Second-by-second emissions data were first collected from one bus using a portable emission measurement system (PEMS). Microscopic traffic simulation was then used to estimate overall traffic emissions under six scenarios with different bus stop settings. Numerical simulation of pollutant dispersion was also conducted to calculate individual pollution exposure at bus stops. The results of PEMS tests showed no significant differences between bus emissions generated near two different types of stops. However, the effect of platform types on overall traffic emissions was revealed using traffic simulation. The results demonstrated that bus bays reduced the emissions of other heavy-duty vehicles. However, bus bays were not always effective during rush hours. The study also highlighted the importance of the location of bus stops, the number of bus lines, and the length of the platform, in addition to dynamic characteristics of traffic flows in the design of bus stop platforms. Bus stop platforms also affected individuals’ exposure due to the changes in the pollutant flow field. The passenger’s exposure at one bus stop was influenced by both the platform type and standing location. Results suggested that in a condition with a wind direction perpendicular to the bus stop shelter, the total exposure level to CO was lower at the bus bay stop if a passenger stood at the upstream of the station platform. However, the exposure was less at the downstream of the curbside bus stop.

Intermodal comparison of tailpipe emission rates between transit buses and private vehicles for on-road passenger transport

Modal shift from private vehicles (PVs) to transit buses has the potential to reduce energy use and emissions from on-road passenger transport. Comparisons between these modes may be sensitive to key factors, such as vehicle size, fuel and powertrains, passenger load, and travel routes. The objectives are to evaluate the sensitivity of emission rates to route alignment, and compare emission rates between PVs and buses accounting for variability in key factors. Real-world bus speed trajectories were measured on actual bus routes for four origin-destination pairs (ODPs). To evaluate the sensitivity of emission rates to route alignment, hypothetical alternative bus routes were posited based on shortest distance, shortest travel time, or observed PV routes for each ODP. Trajectories and emission rates for PVs were quantified based on prior measurements of two routes per ODP using portable emission measurement systems. Trip-based tailpipe CO2, CO, total hydrocarbons (THC), NOx, and particulate matter (PM) emission rates were estimated for each ODP for gasoline and gasoline-hybrid PVs based on a Vehicle Specific Power modal model and for compressed natural gas, diesel, and diesel-hybrid buses based on the Transit Bus Emissions Model. Break-even passenger load (BEPL) was quantified to assess the minimum bus passenger load needed to achieve lower per passenger-trip emissions compared to PVs. Bus emission rates per bus-trip on actual bus routes are generally higher than those on hypothetical routes. As a bounding analysis, compared to single-occupancy PVs, fully occupied buses are estimated to have 82%–94% lower CO2, 99% lower to 308% higher CO, 99% lower to 145% higher THC, 67% lower to 62% higher NOx, and 94%–99% lower PM emission rates per passenger-trip depending on vehicle size, fuel and powertrain, passenger load, and route. BEPL varies depending on vehicle size, fuel and powertrain, route, and pollutant. The relative importance of key factors affecting intermodal comparisons differs by pollutants. The intermodal comparison is also affected by interactions among key factors, such as passenger load and route alignment, which reinforces the need for joint consideration of key factors.

Global Warming Potential from Public Transportation Activities During COVID-19 Pandemic in Jakarta, Indonesia

During a pandemic, social distancing will affect the occupancy rate of public transportation in DKI Jakarta. The number of usually total passengers is partially occupied. Of course, this can change the carbon footprint generated for each person. For this reason, this research was conducted to determine the carbon footprint and greenhouse gas emissions released during the COVID-19 pandemic. A direct survey has been conducted to determine the occupancy rate of mass rapid transit (MRT) vehicles and Trans Jakarta buses. Online vehicles such as cars and motorbikes were based on government policy. The results show that the MRT occupancy rate was 63±32 passengers, and for Trans Jakarta, it was 21±9 passengers. The carbon footprint from transportation that produces the most negligible CO2 emissions was MRT. The comparison obtained between the MRT and Trans Jakarta Bus's emission values were 0.026 and 0.091 kg CO2 eq/passenger. As for the online taxi transportation mode with four people, it produced the highest CO2 emissions. Therefore, the government needs have planned MRT to improve the quality of public transportation and capacity, especially in the main corridors of DKI Jakarta.

Analyses of Well-to-Wheels Emissions of Battery-Powered Electric Buses from a Solar Generation System

Analyses of Well-to-Wheels Emissions of Battery-Powered Electric Buses from a Solar Generation System

Modeling research of city bus fuel consumption for different driving cycles

Simulation studies can be used to determine the fuel consumption and carbon dioxide emissions of city buses. The operating conditions of such vehicles are characterised by a very high variability of vehicle speed due to the large number of stops along the route of the bus. During vehicle testing, driving cycles are used to replicate the real-world conditions and to achieve repeatable test conditions. Such a driving cycle is a profile of speed represented as a function of time or as a function of distance. The speed profile over time can be an advantageous determinant, based on laboratory tests, for estimating fuel consumption and pollutant emissions of city buses. The research subject of this paper was the simulation of bus driving under simulated urban traffic conditions, carried out by means of the VECTO software. VECTO is a tool designed to perform the calculations of fuel consumption and carbon dioxide emissions of vehicles. It enables to model the powertrain of trucks and buses and to carry out simulations on various routes defined by driving cycles. The test object was a mega class bus, equipped with a 225 kW engine. The bus has three axles, including the rear drive axle. The scope of research included four cycles: urban, interurban, urbandelivery and interurban. Each of these was analysed in terms of speed and road gradient. The aim of this work was to perform a simulation study of the effect of the vehicle traffic conditions on the amount of CO2 emitted and fuel consumption. The obtained results were analysed.

Unraveling nonlinear and interaction effects of various determinants on bus gaseous emissions

Urban transit buses equipped with large-displacement engines operate on circular routes several times throughout the day, emitting large amounts of environmentally hazardous exhaust. Hence, understanding the intricate associations between bus emissions and multiple contributors is beneficial for creating sustainable transportation systems, while previous studies focusing on statistical methods fail to unravel them. This paper innovatively leverages the bagged decision tree approach to delineate such complex relationships based on the data collected from CNG-fueled and diesel-powered buses. Relative importance indicates that velocity appears to be the primary factor and is therefore selected as the research objective. Results suggest that the effects of different contributors on bus emissions present nonlinear patterns. More specifically, the influence of speed on CO, CO2, and NOx exhaust generally reveals an increasing-stabilizing tendency while that of HC represents a decreasing-stabilizing mode. Besides, the phenomenon of synergies between determinants is also prevalent, for instance, buses within high-speed and large-slope conditions tend to produce more emissions. These findings can provide nuanced guidance for policy-making and bus route planning issues in consideration of environmental protection and pollution mitigation.