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Road transport is a major source of air pollution especially in cities. Detailed calculations are needed to support road transport emission inventories due to the variance of technologies and operating conditions encountered on the roads. The annual distance driven by cars in relation to their characteristics is an important variable in such calcul...
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Context 1
... AAM only depends on vehicle age and it is the same for each year during the lifetime of the vehicle. AAM k values as a function of vehicle age are shown in Figure 2. This shows that the average mileage driven per year generally drops while the vehicles become older. ...
Context 2
... vehicle age is not taken into account, then the average mileage of our gasoline car sample is 10 636 km and 18 685 km for diesel cars. However, as shown in Figure 2, the true average will depend on the average age of the vehicles considered. This is not always taken into account in relevant studies. ...
Context 3
... of websites used to collect data (Table S1); Mean mileage (km) and standard deviation (km) of the vehicle sample per age bin (Table S2); Average cumulative mileage of gasoline cars as a function of end-of-life age ( Figure S1); Average cumulative mileage of diesel cars as a function of end-of-life age ( Figure S2). This information is available free of charge via Internet at http://www.atmospolres.com. ...
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Citations
... Estimates based on vehicle kilometers driven are justified in a sense that heavy driving shortens the lifespan of the vehicle and the battery. Furthermore, new cars are driven more than old cars (Caserini et al 2013), which puts emphasis of the manufacturing emissions on the early years of vehicle use. The survey includes several questions on the vehicle type. ...
While the greenhouse gas emissions of most sectors are declining in the EU, transport emissions are increasing. Passenger cars compose a large share of the transport sector emissions, and a lot of effort has been made to reduce them. Despite the significantly improved environmental performance of passenger cars, there is a prevailing belief that they are the most environmentally harmful mode of ground transport. In the study at hand, we illustrate how rebound effects of consumption may change this view. Passenger car is a relatively expensive transport mode. Expenditure on car-ownership reduces the remaining household budget and the related carbon footprint. Here, we compare the total consumer carbon footprints per capita between fossil-fuel car owners, green car owners, and car-free households in the Nordic countries, using survey data including 7 400 respondents. When income and household type are controlled with regression analysis, respondents without a car for climate reasons and ‘minimal drivers’, meaning the least driving 10% of fossil-fuel car owners, have the lowest carbon footprints. Other car-free households have 6% higher footprints, electric- and biofuel car owners 18%–24% higher footprints, and the increasingly driving fossil-fuel car owners 30%–189% higher carbon footprints than the first two groups. However, the working middle-income green car owners, minimal drivers, and car-free households have very similar sized carbon footprints. The results show some trade-off between car ownership and flying despite that the data was collected between 2021 and 2022, when COVID-19 was still partly affecting air travel.
... Still, large-scale studies of air pollutant emissions have yet to in the context of the changing operating age of vehicles. Less common studi the impact of vehicle age and technical conditions on air pollution emission search such as [11][12][13][14][15]. The age distribution of vehicles is an essential facto be considered when assessing the impact of air pollution emissions on the This type of research is described in [15] regarding the effects of vehicle agi lutant emissions in a sample of 600 vehicles. ...
... Still, large-scale studies of air pollutant emissions have yet to be conducted in the context of the changing operating age of vehicles. Less common studies examining the impact of vehicle age and technical conditions on air pollution emissions include research such as [11][12][13][14][15]. The age distribution of vehicles is an essential factor that should be considered when assessing the impact of air pollution emissions on the environment. ...
The increasing number of vehicles operating in Poland, especially passenger vehicles, justifies the need to conduct air pollution emission tests in the context of the impact of vehicles on the natural environment. Firstly, this article reviews the publications related to air pollutant emissions and passenger vehicles traveling on Polish roads. However, it presents a special method using advanced research equipment to determine air pollutant emissions. The above research methods are justified in implementing clean transport zones. Real Driving Emissions represent an essential procedure in the implementation of clean transport zones in Poland, verifying the actual emissions of air pollutants and modeling this phenomenon using the results of real air pollutant emissions. The results of this research state that establishing a link between a vehicle’s air pollutant emissions and its age can support making transport or delivery planning more sustainable and choosing less carbon-intensive means of transport to reduce the negative impact of transport on the environment. The scientific novelty of the proposed solutions is the verification of the actual emissions of Euro 6 vehicles and the modeling of air pollutant emissions as a function of speed and acceleration. The research results are included in this article and will become input data for further analysis in examining the impact of vehicle operating age on air pollution emissions. Consequently, the novelty of the present research also lies in its focus on the verification of the impact of operating age, particularly in the context of vehicles exceeding 15 years of age, on air pollutant emissions. By establishing a correlation between a vehicle’s air pollutant emissions and its operating age, it becomes possible to make transport or delivery planning more sustainable. Furthermore, the selection of less carbon-intensive means of transport can contribute to reducing the negative impact of transport on the environment.
... Vehicle age and number are significant factors when recommending policies to reduce emissions. Pastorello et al. (2017) stated that the PM emissions from vehicles with gasoline and diesel as fuels would decrease by approximately 20% after applying the vehicle scrapping policy with a certain VKT. They also found in their study that the average mileage traveled for a 10-year-old car was approximately 40% of the same car (gasoline or diesel) in its first year or decreased to 10% of the 20-year-old car (Pastorello et al., 2017). ...
... Pastorello et al. (2017) stated that the PM emissions from vehicles with gasoline and diesel as fuels would decrease by approximately 20% after applying the vehicle scrapping policy with a certain VKT. They also found in their study that the average mileage traveled for a 10-year-old car was approximately 40% of the same car (gasoline or diesel) in its first year or decreased to 10% of the 20-year-old car (Pastorello et al., 2017). Consequently, the PM emissions of a passenger vehicle decrease by more than 20% because the average kilometers traveled by an older car decreases. ...
... While the introduction of the new normative CO2 measurement procedure had a significant impact on the normative CO2 emissions, no impact on the CO2 emissions on the road are expected; however, the difference between normative and real CO2 emissions could be reduced significantly [6]. Estimating CO2 emissions involves employing calculation models that heavily rely on factors such as the vehicle fleet composition, fuel parameters, and average mileage of the vehicles [7][8][9][10]. ...
... Our focus was on utilizing a feature learning technique, which effectively learns representations in datasets with high dimensionality and significant uncertainties [15][16][17][18][19][20][21][22][23]. Additionally, our research aimed to develop a model for calculating average vehicle mileage for both inter-class and intra-class scenarios, thereby improving the accuracy of CO2 emission calculations and understanding the impact of vehicle class variations on the CO2 footprint of passenger vehicle fleets [9]. Ultimately, this study serves a greater purpose by facilitating a better understanding of the impact vehicle class variations have on the overall CO2 footprint of passenger vehicle fleets. ...
Accurately measuring vehicle mileage is pivotal in precise CO2 emission calculations and the development of reliable emission models. Nonetheless, mileage data gathered from surveys relying on self-estimation, garage reports, and other estimation-based sources often yield rough approximations that substantially deviate from the actual mileage. To tackle this issue, we present a comprehensive framework aimed at bolstering the accuracy of CO
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emission models. This paper harnesses two innovative techniques: the deep learning semi-supervised fuzzy C-means (SSFCM) and polynomial classifier models. By leveraging these sophisticated mathematical techniques, we achieve successful classification of passenger vehicles, enabling more precise evaluations of average mileage. Moreover, our framework supports segment-based analysis, which enables segment-specific assessment of average mileage. By implementing our proposed techniques, we aspire to enrich the precision of emission models, resulting in more dependable calculations and an enhanced understanding of the environmental implications associated with vehicles.
... Carbon monoxide, carbon dioxide, gaseous pollutants, and other primary pollutants are released into the environment when charcoal is burned uncontrollably and used as fuel for various purposes, such as food preparation (Abera et al. 2021) (Obanya et al. 2018). Today, vehicle transportation is also recognized as a significant contributor to air pollution in newly developing megacities (Nieuwenhuijsen 2016;Singh et al. 2021;Haslina et al. 2022); this contributes to both immediate health effects and future climate change (Caserini et al. 2013;Takuchev et al. 2014). ...
... The emission rate is also influenced by the average speed, the mass of the vehicle, and the technology used to reduce emissions. It is crucial to properly aggregate cars by classes and categories to obtain an accurate estimate of air pollutant emissions (Caserini et al. 2013). To improve the simulated data, the sensitivity test and data validation are used (Kerr et al. 2014). ...
In emerging countries’ expanding megacities, traffic is currently the main source of air pollution. Vehicles are the primary source of air pollution in Addis Ababa, the capital city of Ethiopia, because of the unimproved age of cars and bad road conditions. One of the city’s major hub squares, Megenagna (between the Bole and Yeka sub-cities of Addis Ababa), has six significant road crossings that clog up traffic. The purpose of this study was to evaluate and forecast air pollution levels in the Megenagna region using the dispersion model (AERMOD). A sample campaign was run for 2 months (January and February) at 43 sampling stations. Hand-held Air-test Model-CW-HAT2005 and Aeroqual series 5000 devices were used to measure gaseous pollutants (SO2 and NO2) and particulate matter (PM2.5 and PM10). There is a lot of spatial variation throughout the study site, as shown by the statistically significant difference between sampling locations (p < 0.05). The sensitivity variation of 1 m/s and the 45° wind direction with respect to the horizontal of the receptor of the self-monitored sample location were ideal for the prediction, calibration, and validation of pollutants in AERMOD. It was anticipated that gaseous and particulate pollution would vary from site to site, with SO2 exceeding the threshold. This study demonstrates the need for additional research into the spatiotemporal variance of emissions due to traffic in Addis Ababa.
... This refinement has been included in all TCOs in this paper and offers a further value to this work by recognising the impact of that mileage variation over time. Caserini et al. (2013) found that the average mileage driven in year one can be 2.5 times the average mileage driven in year 10 and some 10 times greater than the average mileage driven in year 20. Thus, this is an important aspect to consider, particularly for TCO analysis over varied time horizons. ...
Widespread adoption of electric vehicles (EVs) is a common and critical component of international strategies to mitigate environmental pollution, climate change and oil dependency. The ability of consumers to assess the total cost of ownership (TCO) of EVs relative to internal combustion engine vehicles (ICEVs) remains an important factor for EV uptake. The TCO of vehicles is not universal across different car segments and user profiles. We analyse and compare the TCO of ICEVs and EVs from 17 car segments across short- and long-term ownership periods, and further advance existing TCO approaches by integrating detailed activity-based driving profiles, taxation, grant structures and pricing. Results show that EV options in the most popular Irish car segments have existing battery EV options with a TCO averaging respectively 26% and 42% less than their equivalent petrol and diesel ICEV options over a 4-year ownership term when the current grant is included. This integrated method for granular TCO evaluation offers important insights for this market and affords scope to investigate how changes in travel patterns, car-segment pricing, taxation, grant policy, fuel costs, and carbon pricing and other transport policies can all affect TCO values over time across a broad range of market offerings.
... In Figure 1, the odometer reading accumulation of a car is described by taking a vehicle inspected annually in 2020 as an example. However, in previous studies [10,[12][13][14][15][16], annual VKT was found to be mainly affected by vehicle age, yet the influence of driving years is rarely discussed. In recent years, the number of vehicles in China has increased rapidly, and the growth rate has been higher than the scale of urban road construction. ...
... Mileage in the first year was about 70% of that in a stable period. This result was consistent with the above results based on the I/M data but is a deviation from previous results, which have generally shown that the mileage of vehicles gradually decreases with the increase in vehicle age [8,10,12,22]. As public service vehicles, taxis show more stable driving characteristics, and the odometer readings showed a complete linear correlation with the vehicle age, with the fitted slope around 80,000 km in 2019 (Figure 2b). ...
Vehicle mileage is one of the key parameters for accurately evaluating vehicle emissions and energy consumption. With the support of the national annual vehicle emission inspection networked platform in China, this study used big data methods to analyze the activity level characteristics of the light-duty passenger vehicle fleet with the highest ownership proportion. We found that the annual mileage of vehicles does not decay significantly with the increase in vehicle age, and the mileage of vehicles is relatively low in the first few years due to the run-in period, among other reasons. This study indicated that the average mileage of the private passenger car fleet is 10,300 km/yr and that of the taxi fleet was 80,000 km/yr in China in 2019, and the annual mileage dropped by 22% in 2020 due to the pandemic. Based on the vehicle mileage characteristics, the emission inventory of major pollutants from light-duty passenger vehicles in China for 2010–2020 was able to be updated, which will provide important data support for more accurate environmental and climate benefit assessments in the future.
... Urbanization is contributing to the increase in private vehicles in the cities (Gurjar et al. 2010;Prakash and Krishna 2013). It is the main source of air pollution in many cities around the world (Caserini et al. 2013). Most urban areas in emerging nations exhibit higher levels of urban air pollution than do industries (Schwela, 2012;Dionisio et al. 2010;Bai et al. 2018). ...
Vehicles are one of the main contributors to outdoor air pollution in urban areas of developing nations. Addis Ababa is experiencing the fastest rate of urbanization with increasing heavy traffic across the city. Megenagna is one of the city’s busiest transportation hubs, connecting traffic to most of Addis Ababa’s lower town via major highways and railways. The ever-increasing air pollution from heavy traffic in the area is an alarming environmental problem for the city. This research aimed to assess and evaluate traffic-related particulate and gaseous pollutants in Megenagna. There were 41 sampling points, 16 of which were near the root of the Megenagna bus station, and the rest 25 were taken on the six main road lines. The samples were collected for the 2-month variations of January and February during the rush hour. Sampling was done using the hand-held portable air test equipment (Model-CW-HAT2005) and Aeroqual Series 500 (2016). Geo-statistical analysis and descriptive and inferential statistical analysis were used. The mean values of PM2.5, PM10, SO2, and NO2 in the Megenagna area were 30.3 ± 2.2 µg/m3, 58.6 ± 3.1 µg/m3, 777.5 ± 151.2 µg/m3, and 58.6 ± 3.04 µg/m3, respectively. The difference between sampling locations was statistically significant (p < 0.05), suggesting that there is significant spatial variation between different parts of the study site. Individual comparisons, however, revealed that they are not significantly different from one another on some sites. The hotspot analysis also confirmed that there are hot and cold spots in the distribution of pollution over space and time.
... One of the reasons for traffic congestion is the rapid increase in the number of vehicles relative to the speed of road network expansion [2]. There is a high number of old vehicles, particularly vehicles aged more than 10 years [3]. These contribute significantly to the high level of traffic emissions in Bangkok. ...
Traffic information from the distance matrix application program interface (API), which is a part of the Google Maps API service, was used to develop a near real-time traffic emissions inventory in Bangkok. The information provided includes distance and traveling time, which can be used to develop an Underwood traffic model for traffic volume estimation. The speed-dependent emission factors, road distance and traffic volume, which were estimated based on the distance matrix API, and fleet composition, were used to estimate carbon monoxide (CO), hydrocarbon (HC), nitrogen oxide (NOx) and particulate matter (PM) emissions from eight types of vehicles, including passenger cars, motorcycles, pick-ups, taxis, vans, buses, tuk-tuks and trucks. On the weekend, in Bangkok, the traffic released 190 tons/day of CO, 34 tons/day of HC, 55 tons/day of NOx and 3 tons/day of PM. The traffic emissions on a weekday in Bangkok were 209 tons/day of CO, 39 tons/day of HC, 61 tons/day of NOx and 4 tons/day of PM. The spatial and temporal distribution of traffic emissions demonstrate that the area of highest traffic emissions was the center of Bangkok. Therefore, the Google Map API service can be used to develop near real-time traffic emission inventories.
... These urban areas emit a significant proportion of air pollutants globally and can be often be associated with poor air quality (Lawrence et al. 2007;Butler 2013). In the latest period of 10 Megacities, particularly in developing nations, has reported over 70-80% of air pollution, which is attributed to vehicular emissions caused by a large chunk of older vehicles exhibiting poor vehicle maintenance, inadequate road infrastructure and low fuel quality (Badami 2005;Singh et al. 2007;Wang et al. 2010;Pandey et al. 2016). Among the criteria pollutants, CO (Carbon Monoxide) is one of the most significant pollutants emitted by the transport segment, contributing to about 90% of total emissions with Hydrocarbons (HCs) following closely.This heterogeneous fleet of cars in Delhi is subject to periodic emission compliance testing in most of the developing countries, which allows them to remain in operation only upon passing the test. ...
... In case of the Italian fleet of passenger cars, annual mileage was found to drop significantly with age. Both diesel and gasoline cars drove half the annual distance when they reached an average age of approximately 8 years; hence, mileage must be considered along with the vehicle age while estimating emission from the transportation sector (Caserini et al. 2013).An investigation of 100 gasoline cars for their exhaust emission (CO and HC) under a basic I/M program in Lebanon was conducted. The vehicles reported higher failure rates indicating the need to develop country-specific emission standards. ...
The overall fleet of light-duty vehicles in most of the developing countries is characterized by various manufacturers of petrol-driven passenger cars. A thorough investigation of the exhaust emission from such cars is required to explore and address their contribution to the overall urban air quality. Notably, the fast-growing urban areas have a substantial mix of aged cars and also those having accumulated several kilometers of mileage during their present lifetime. It is, therefore, of paramount importance to understand how the vehicle age and mileage affect the tailpipe emission under different engine idling speed scenarios. The present study catered to 544 cars plying on the roads of Delhi, India and analyzed the exhaust emission monitored under two different engine speed, that is, idling and high idling conditions. The study reported significant correlation values pointing at the age and mileage as prominent vehicular aspects affecting tailpipe parameters (CO and HC at idling vs. age, R2 = 0.93 and 0.69; at fast idling vs. age, R2 = 0.58 and 0.51 respectively and vs. mileage, R2 = 0.80 and 0.61 at idling and, 0.58 and 0.51 at high idling respectively).
