International Vehicle Emission Research Articles

Emission characterisation of motorcycles and the potential of co-benefits from selected development scenarios in the urban ecosystem of Hanoi, Vietnam

Abstract Motorcycles are the largest proportion of transport in Hanoi. This study focuses on the emission characteristics and initially evaluates the potential co-benefits of low emission zones (LEZ) applied to motorcycles in Hanoi. The International Vehicle Emissions (IVE) model was designed by the U.S. EPA to calculate emissions from motor vehicles and was used to find the emission factor, the important index for emission characteristics of motorcycle activities, in Hanoi. This study surveyed motorcycle technologies and activities in Hanoi with questionnaires, collected data from GPS devices, and counted traffic flow through video cameras and collaborators teams from 2021 to 2023. EFs for the typical pollutants and GHGs of motorcycles in Hanoi were determined. During running, EFruning is 0.053g/km with PM, 4.8 g/km for CO, 0.006 g/km for SO2, 0.13 g/km NOx, CO2 is 72.55 g/km and CH4 is 0.24 g/km, respectively. When starting, EFstart-up is 0.12 g/time with PM, CO is 12.24 g/time, SO2 is 0.0007 g/time, NOx is 1.05 g/time, CO2 is 6.96 g/time, CH4 is 0.53 g/time and N2O is 0.0009 g/time, respectively. Using the EMISENS model, inventory emissions were calculated at tier 3 for this source via the Bottom-up approach. The EMISENS model calculates emissions resulting from traffic, developed by Bang Ho et al in 2010 uses input data related to traffic activities and three emission factors found from the IVE model to calculate the emissions. The total emissions from motorcycle operations in Hanoi are approximately 5.3 thousand tons of PM and 4.6 million tons of CO2e. The proposed LEZ solution for motorcycles brings climate co-benefits of reducing 1.62 million tons CO2e in scenario 1 - limiting all day and 563.2 thousand tons CO2e in scenario 2 – limit operations from 3:00 p.m. to 8:00 p.m. in 2025. Co-benefits on the environment corresponding to the two scenarios with PM are 1990 and 692 tons. Reducing motorcycle operations during 5 peak hours at LEZ can reduce total emissions from motorcycles in Hanoi by 12%. Meanwhile, the proposed 24-hour LEZ for motorcycles in the area has the potential to reduce GHGs by 35%. These results show that reducing emissions from motorcycles brings climate and environmental co-benefits.

Assessing the Additional Benefits of Thailand’s Approaches to Reduce Motor Vehicle Emissions

Air pollutants and greenhouse gases (GHGs) represent major challenges in our era, contributing to climate change and global health issues. These problems arise from a variety of well-known sources, including motor vehicles. Almost all nations, Thailand included, have formulated and implemented policies to curb greenhouse gas (GHG) emissions in line with the requirements and commitments of the Paris Agreement. The evaluation of specific air pollutants and GHG emissions originating from road vehicles utilises the Thailand database, referencing the year 2019. Data intersections from 2019 to 2022 are grounded in actual data collected from relevant departments in Thailand, while projections for 2023–2030 are forecasted based on the baseline year. The secondary database used in the International Vehicle Emission model is adjusted according to real-world driving data to accurately reflect country-specific emission factors. Dynamic emission factors for specific air pollutants and GHGs are evaluated and integrated with the average Vehicle Kilometres Travelled (VKT) for each vehicle category. The Business-As-Usual (BAU) scenario is then examined, based on existing policies aimed at reducing air pollutants and GHG emissions in Thailand’s transport sector. These policies include strategies for the adoption of electric vehicles and the promotion of public transport to reduce VKT. Under the BAU scenario, the overall number of road vehicles in Thailand, including passenger cars, motorcycles, pickups, vans, trucks, and buses, is expected to increase by approximately 6.58% by 2030, leading to a rise in specific air pollutants and GHG emissions compared to the 2019 baseline. However, by adhering to Thailand’s strategies and transitioning to new electric passenger cars and buses, greenhouse gas emissions and specific air pollutants from the road transport sector will be significantly reduced.

Managing Uncertainty in Urban Road Traffic Emissions Associated with Vehicle Fleet Composition: From the Perspective of Spatiotemporal Sampling Coverage

With pronounced differences in emission factors among vehicle types and marked spatiotemporal heterogeneity of vehicle fleet composition, extrapolating fleet composition from insufficient sample hour periods and road segments will introduce significant uncertainty in calculating regional daily road traffic emissions. We proposed a framework to manage uncertainty in urban road traffic emissions associated with vehicle fleet composition from the perspective of spatiotemporal sampling coverage. Initially, the respective relationships of the temporal and spatial sampling coverages of fleet composition with the resulting regional daily road traffic emission uncertainties were determined, using the core area of a typical small and medium-sized city in China with the widely-used International Vehicle Emissions (IVE) model as example. Subsequently, function models were developed to explore the determination of the spatiotemporal sampling coverage of fleet composition. These results of emission uncertainties and function models implied that gases with larger emission factor discrepancies between vehicle types, such as NOx, required greater spatiotemporal sampling coverage than gases with smaller discrepancies, such as CO2, under the same uncertainties target. Therefore, sampling efforts should be prioritized for gases with larger emission factor discrepancies. Additionally, increasing sampling coverage in one dimension (either spatial or temporal) can reduce the minimum required coverage in the other dimension. To further reduce uncertainty, enhancing both spatial and temporal sampling coverage of the fleet composition is more effective than enhancing one type of coverage alone. The framework and results proposed in this work can reduce the uncertainty of emissions calculations caused by insufficient sampling coverage and contribute to more accurate transport emission reduction policy formulation.

Estimation of on-road mobile emissions based on the vehicle technology in a high-traffic avenue in Panama City, Panama

This study addresses the emissions from mobile sources in a busy avenue. Latest mobile emission inventories estimated pollutants based on fuel sales activities in the country. While this is an approved methodology, it does not consider the characteristics of vehicles and their emissions control systems. Applying the International Vehicle Emissions (IVE) model involves low costs, the vehicle fleet’s technology features, and the study region’s environmental parameters. The IVE model allows a better understanding of how vehicle technology impacts air quality. This study aims to generate information about vehicle emissions in Panama using a model that considers factors like vehicle technology, driving patterns, and local conditions, all of which influence air pollution in the region. The focuses are on passenger vehicles, the most common in the country, particularly in one of the densest districts. Carbon monoxide (CO), sulfates (SOx), nitrogen oxide (NO, and particulate matter (PMx) emissions were estimated with the IVE model, the first one stood out, represented 98% (6479.82 g/km) of the air pollutants emitted into the atmosphere by these four gases. Finally, it is established that the combination of emission control technologies and vehicle features is what will determine its emission reduction efficiency. Only in diesel vehicles, an increase in PMx emissions was identified.

Assessment of sustainable school transport policies on vehicular emissions using the IVE model

The increased vehicle demand leads to a rise in congestion, exhaust emissions and travel duration. It makes countries adopt policies to reduce vehicular exhaust emissions to achieve sustainable development with improved air quality. The present study identifies factors that influence student's preferences, estimate vehicular emissions and examine policies to minimize use of passive school transport (PST). The responses of 945 students are statistically examined using the Multinominal Logit (MNL) model with factors that affect preferences of school transport. The MNL model revealed that private transports are preferred more by female students. Students covering up to 3 km distance preferred more to use active school transport (AST) than students with travel distance of above 5 km. The model has predicted that students with travel distance of 4 km or above are mostly opted for public travel modes. The representative driving cycle was developed for morning and afternoon school trips and used as an input parameter for the International Vehicle Emission (IVE) model to estimate exhaust emissions. School policies are proposed to reduce PST modes associated student kilometer travelled (SKT) and emissions. The study outcomes revealed that neighborhood school policy predicted significant reduction in yearly SKT for AST (6.07%) and PST (36.03%) modes. While increased walkability policy anticipates modest impact in minimizing the PST mode associated total exhaust emissions (3.18–40.34%). The private vehicle associated SKT and total exhaust emissions are reduced by nearly 27.58% and 34.66% under increased use of public transport policy respectively.

Towards sustainable transport: quantifying and mitigating pollutant emissions from heavy-duty diesel trucks in Northeast China.

Heavy-duty diesel trucks (HDDTs) have caused serious environmental pollution in China. Accurate estimation of their pollutant emission characteristics is essential to reduce emissions and associated environmental and public health impacts. To achieve sustainable development for transport emissions in Northeast China, we developed localized emission factors and a high-resolution emission inventory of HDDTs, based on on-board test, Guidebook and international vehicle emission (IVE) model. The results show that the total emissions of CO, NO, NO2, and PM from HDDTs in Northeast China in 2020 were 172.2 kt, 531.5 kt, 11.2 kt, and 921.4 t, respectively. In terms of spatial distribution, emissions decreased from the city center to the city fringe. Temporally, the NOx emission variation curves of different types of roads presented a "single-peak" emission characteristic, which was different from the peak of traffic flow. Three emission reduction scenarios are further developed in the paper. Scenario analysis shows that elimination of HDDTs that follow the old China III emission standard and installing tailpipe treatment devices are the most effective pollutant reduction measure. The reduction percentages for CO, NO, NO2, and PM ranged from 62.9 to 83.89%. The results of our study could inform policymakers to devise feasible strategies to reduce vehicle pollution in Northeast China.

Estimating the greenhouse gas emission factor of motorcycles under real-world driving conditions in Hanoi: a case study

The lack of country-specific greenhouse gas (GHG) emission factors vehicles in Vietnam makes the emissions inventory in the transport sector more difficult. This study developed the country-specific GHG emission factor in terms of CO2 equivalent for motorcycles (MCs) in Hanoi to overcome the above impediment. The real-world driving data, instantaneous speed versus time, were collected on the 30 road routes in Hanoi using a GPS device. A filtering process of nine-step was designed to repair misleading speed values and denoise signals before entering the International Vehicle Emissions (IVE) model. The typical GHG emission factor for the MC fleet in Hanoi was approximately 90.83 g/km. This study also found a remarkable difference in the MC’s specific power-related bins distribution between the real-world driving characteristics of the MC in Hanoi and the ones of the World Motorcycle Test Cycle. This contributes to reconfirming the necessity of developing the GHG emission factor based on the very real-world driving data of Vietnam

On-road vehicle emission inventory and its spatial and temporal distribution in the city of Guayaquil, Ecuador

Emissions from mobile sources have become a major concern for health, environmental sustainability and climate change and high-resolution inventories are needed to support the design and assessment of abatement measures in urban areas. This study addresses the development of a traffic emissions inventory for Guayaquil, the second largest city in Ecuador, using the International Vehicle Emissions Model (IVE). Emissions are allocated with a spatial resolution of 1 km × 1 km and a temporal resolution of 1 h using a top-down methodology. This application combines traffic statistics already available in the city with the data from a field campaign to characterize vehicle fleet composition and activity patterns. The estimated annual emissions for the city were 237.1 kt of CO, 46.4 kt of NOx, 28.5 kt of VOC, 7.7 kt of PM10, 0.70 kt of SO2 and 4549.7 kt of CO2. 92.3 % of CO and 85.4 % of VOC were emitted by light gasoline vehicles, including private passenger vehicles and taxis, which represents 68.6 % and 8.8 %, respectively of the total fleet and contributes 52 % and 22 % of the total vehicle kilometer traveled (VKT), respectively. 48.9 % of NOx and 82 % of PM10 were emitted by the bus fleet although buses only represent 7.5 % of the total fleet and contribute 10.6 % of total VKT in the city. 41.1 % and 36.5 % of CO2 were emitted by buses and private vehicles, respectively. Even though, the average age of the fleet is below 10 years, the fleet in Guayaquil presents outdated emission standards and high emission factors. We found the higher emission rates in dense populated areas are associated to secondary roads. There is not much variability of emissions between months, but the typical daily pattern of emissions shows a peak in the morning and another in the afternoon.

Assessment of different methods in analyzing motor vehicle emission factors.

To explore the emission characteristics of vehicle's pollutants is of great significance to prevent and control the diffusion of pollutants. Limited by geographic location and economic condition, the model- and guideline-based studies on vehicle's emission factor have become more concerned measures than the actual measurement. By analyzing the actual operating conditions of motor vehicles, this study obtains the emission factors of typical pollutants from different motor vehicles by adopting international vehicle emission (IVE) model and guideline method, respectively. Furthermore, the resulting emission factors by the above methods were compared and analyzed with on-road method. The results show that: (1) the emission factors of vehicle pollutants change regularly with velocity, emission standard, and accumulated mileage. Taking CO as an example, its emission factor shows a downward trend with the increase of velocity and emission standard and an upward trend with the increase of accumulated mileage; (2) compared with the actual measurement, the vehicle emission factor obtained by the guideline method has a large error, while the IVE model is close to the actual.

Real time roadway pollution in Indian cities: A comparative assessment with modelled emission

Roadway transport sector is one of the major contributors of atmospheric pollution. The study aims to understand the real time roadway pollution in megacities and medium sized Indian cities; therefore, the present study estimated on-road emission factors (EFs) and compared them with two other regulatory models for Delhi and Guwahati. Both the cities are different in traffic flow management strategies, traffic composition and vehicular density per 1000 population. Emission rate was found higher in Guwahati (17% to 38%) due to frequent stops-and-go patterns of driving profile and poor traffic flow management per vehicle as compared to Delhi, but overall roadway pollution load is higher in Delhi due to higher vehicular density. The real-time on-road EFs were compared with modelled EFs, estimated with two regulatory models International Vehicle Emission (IVE) model and COPERT-IV. The modelled EFs magnitude is lower than the real-time EFs. Pollutants HC, CO and CO2 were about 17% to 30% and 33% to 55% fewer from IVE and COPERT-IV respectively whereas NOx emissions are over predicted by both the models (COPERT-IV: -54% to -95% and IVE: -26% to -35%). Results demonstrate that efficient traffic flow management plays important role in reducing roadway pollution, which is major share of environmental pollution, but uncontrolled vehicular growth quickly offsets the benefit of emission control policies. Therefore, there is need for establishing equilibrium between emission control and vehicle growth rate in control policies for optimum benefits from policies for each city.

Effects of meteorological parameters and fuel composition on the air pollution production from motor vehicles.

The aim of this study was to investigate the effect of changes in meteorological parameters and fuel composition on the emission rate of air pollutants in the vehicle fleet of the Tehran Metropolis. The results of this study can be used in management decisions to reduce the emission of air pollutants. In this paper, based on the international vehicle emission model and using mathematical equations, the effects of changing meteorological parameters and fuel composition on the emission of pollutants were modeled. The emission rates of CO, VOCs, and NOx pollutants were the most sensitive to the changes in meteorological parameters, respectively. Among all parameters studied in this research, the changes in sulfur level had the greatest effect on the emission of pollutants from the vehicle fleet of Tehran Metropolis. If the fuel was replaced with Euro 5 standard instead of Euro 3, the emission rates of CO, VOCs, NOx, PM, and SOx pollutants from the vehicle fleet of Tehran Metropolis would be reduced by 9%, 6%, 5%, 14%, and 90%, respectively. Managing and reducing the sources of production and emission of air pollution is one of the best ways to reduce the air pollution. In general, since the emission of pollutants from the fleet of Tehran Metropolis in the cold seasons of the year is greater than during hot seasons and the problem of air pollution is exacerbated by air stability, using Euro 5 fuel in cold seasons is one of the efficient ways to reduce the air pollution.

An analysis of emission reduction strategy for light and heavy-duty vehicles pollutions in high spatial–temporal resolution and emission

Traffic as the main source of urban air pollution created severe problems for human health and sustainability. To propose a bottom-up method in emissions reduction, accurate emission inventories of vehicles in a medium-sized city are developed. Traffic emission factors were obtained using traffic flow data, traffic control cameras, and International Vehicle Emission (IVE) model to calculate CO, VOCs, NOx, SOx, and PM of passenger cars, taxis, and urban buses emission inventory. Annual overall emissions of CO, VOCs, NOx, SOx, and PM pollutants, respectively, are 346, 20.9, 25, 44.4, and 0.5 kt/year. VOC and CO emissions in the start-up phase are in the scale of the running phase, while NOx, SOx, and PM allocate much less than the running phase. The highest emission value of SOx and PM occurs in arterials while CO, VOC, and NOx in highways. Eight renovation scenarios have been designed to evaluate their environmental and economic efficiency. Two scenarios entitled "renovation of carbureted, Euro 1 and Euro 2 LDV" and "renovation of high mileage Euro 1 and Euro 2 standard urban buses" showed the highest decrease in pollution emission and pollution social costs. For these two important scenarios, implementation costs were calculated at 477 and 46M$, while social costs decrease are calculated to be 172.6 and 77.5M$, respectively. Renovation of vehicles could benefit both the government and society by reducing fuel consumption and pollutant emission. The emission mitigation scenarios considering mobile sources could be a guide for adopting policies in developing countries. Governmental and social cost-share and governmental and social repayment because of fuel-saving costs have also been calculated.

Emissions Control Scenarios for Transport in Greater Cairo.

Air pollution is a major cause of premature death in Greater Cairo, but studies on emission control are limited. We used local and international data to predict the impact of transport emission control measures on sector parameters including congestion. The International Vehicle Emission model accordingly estimated quantities of criteria, toxic and global warming emissions produced by on-road vehicles. Emissions were estimated for 2019 base case (2019-BC) and projected for 2030 under the ‘do nothing’ scenario (2030-DNS) and five scenarios: fuel subsidy removal (2030-FSR), road expansions (2030-RE), public transport improvements (2030-PTI), inspection and maintenance (I/M) programs (2030-I/MP), and fuel enhancements (2030-FE). The 2030-FSR would reduce emissions by 11.2% versus 2030-DNS. The 2030-RE resulted in an average increase of 37% in emissions compared with 2030-DNS since it induces more traffic. The 2030-PTI provides alternatives to car travel; hence, cars result in an average drop of 32.8% for all emission types compared with 2030-DNS. The 2030-I/MP exhibited reductions in PM10 and toxic pollutants, of 35–54.8% compared with 2030-DNS. The 2030-FE reduced SOx, benzene and N2O emissions by 91.8%, 81% and 39.1%, respectively, compared with 2030-DNS. The 2030-I/MP is most effective in reducing health damaging pollutants while 2030-PTI positively impacts commuters’ lifestyle.

Estimation of Pollutant Emissions in Real Driving Conditions Based on Data from OBD and Machine Learning.

This article proposes a methodology for the estimation of emissions in real driving conditions, based on board diagnostics data and machine learning, since it has been detected that there are no models for estimating pollutants without large measurement campaigns. For this purpose, driving data are obtained by means of a data logger and emissions through a portable emissions measurement system in a real driving emissions test. The data obtained are used to train artificial neural networks that estimate emissions, having previously estimated the relative importance of variables through random forest techniques. Then, by the application of the K-means algorithm, labels are obtained to implement a classification tree and thereby determine the selected gear by the driver. These models were loaded with a data set generated covering 1218.19 km of driving. The results generated were compared to the ones obtained by applying the international vehicle emissions model and with the results of the real driving emissions test, showing evidence of similar results. The main contribution of this article is that the generated model is stronger in different traffic conditions and presents good results at the speed interval with small differences at low average driving speeds because more than half of the vehicle’s trip occurs in urban areas, in completely random driving conditions. These results can be useful for the estimation of emission factors with potential application in vehicular homologation processes and the estimation of vehicular emission inventories.

The emission factor adjustments of the passenger cars in multi-story car parks under drive modes.

The drive mode is an important factor that affects the adjustment requirements for emission factors of the conventional simulation methods in multi-story car parks. In order to propose comprehensive emission factor adjustments for passenger cars based on the drive modes, the present study is aimed to investigate the effects of different drive modes on emission factors for multi-story car parks. Thus, to achieve this aim, the tailpipe emissions based on the on-board measurement and international vehicle emission (IVE) model are obtained. The results indicate that the drive modes significantly affect the emissions. Accordingly, the change in drive mode from minimum to maximum leads to an increase in the vehicle-specific power (VSP) by 106%. Furthermore, the results of emission factors show the discrepancy between on-board measurement and IVE model with the maximum and minimum adjustment factors by 3.28 and 1.28 for carbon monoxide (CO) and carbon dioxide (CO2), respectively.

Emission characterization and co-benefits of bus rapid transit: A case study in Hanoi, Vietnam

In this paper, the environmental efficiency of the bus rapid transit (BRT) in Hanoi was analyzed through estimating the co-benefits of air quality, climate and health. First, the real-world driving data, instantaneous velocity versus time, in the city was collected using the global positioning system (GPS) technology with the signal update of 1 Hz. Then, a data processing procedure consisting of nine steps was applied to remove errors and to smooth the GPS data. After that, the processed GPS data was used to develop the typical driving cycle for BRT in Hanoi based on the Markov chain theory. Emission factors of air pollutants including CO, CO2, VOC, NOx, SO2, and PM10 were determined based on the developed driving cycle with the use of the International Vehicle Emissions (IVE) model. The health effect was estimated using the AirQ+ model. It is found that, compared to the regular buses, deploying the BRT in Hanoi can reduce the emissions of air pollutants 17.3–22.6 % and the amount of CO2eq emission up to 22.4 %. It is also indicated that at least 4.8 % of prevalence of bronchitis symptoms in asthmatic children aged 5–14 years, and 2.4 % of total mortality due to all causes except accidents in aged 30+ years in Hanoi can be reduced through deploying the BRT. Although the average speed of the current BRT in Hanoi is only 1.3 times higher than the regular bus, it could bring to significant benefits in terms of environment and health.

ESTIMATION OF CARBON DIOXIDE EMISSIONS ON HETEROGENEOUS TRAFFIC BASED ON METROPOLITAN TRAFFIC EMISSIONS INVENTORY MODEL

Traffic on the arterial roads of Makassar City is categorized as heterogeneous traffic and can increase motor vehicle emissions. This study aims to estimate CO2 emissions from vehicles during morning and evening rush-hour traffic on nine arterial roads. This study used the MARNI model approach, which is a prediction model of vehicle emissions in heterogeneous traffic based on moving vehicles, and adapted it to the International Vehicle Emission Model (IVEM). The four factors that are considered to predict vehicle emissions are emission factors, driving cycle as a reference for vehicle speed, vehicle operational characteristics, and travel time. As a result, from 31 vehicle categories, the highest CO2 emissions are CV24: MPFI, 3 Way/EGR, Medium, and 0–6 years. The highest and lowest vehicle volumes and total CO2 emissions occurred on the RD3 and RD5 roads, respectively. This indicates a linear correlation between the vehicle volume and the levels of CO2 emissions. For future studies, the MARNI model can be used as a reference for predicting the emissions of motorcycles and heavy vehicles.

Unregulated Emissions from Natural Gas Taxi Based on IVE Model

Emissions from motor vehicles have gained the attention of government agencies. To alleviate air pollution and reduce the petroleum demand from vehicles in China, the policy of “oil to gas” was vigorously carried out. Qingdao began to promote the use of natural gas vehicles (NGVs) in 2003. By the end of 2016, there were 9460 natural gas (NG) taxis in Qingdao, which accounted for 80% of the total taxis. An understanding of policy implementation for emission reductions is required. Experiments to obtain the taxi driving conditions and local parameters were investigated and an international vehicle emissions (IVE) localization model was established. Combined with vehicle mass analysis system (VMAS) experiments, the IVE localization model was amended and included the taxi pollutant emission factors. The result indicates that annual total carbon monoxide (CO) emissions from actual taxis are 6411.87 t, carbureted hydrogen (HC) emissions are 124.85 t, nitrogen oxide (NOx) emissions are 1397.44 t and particulate matter (PM) emissions are 8.9 t. When the taxis are running on pure natural gas, the annual emissions of CO, HC, NOx and PM are 4942.3 t, 48.15 t, 1496.01 t and 5.13 t, respectively. Unregulated emissions of annual total formaldehydes, benzene, acetaldehyde, 1,3-butadience emissions from an actual taxi are 65.99 t, 4.68 t, 1.04 t and 8.83 t. When the taxi is running on pure natural gas, the above unregulated emissions are 12.11 t, 1.27 t, 1.5 t and 0.02 t, respectively.

An improved CAL3QHC model and the application in vehicle emission mitigation schemes for urban signalized intersections

As an important component of urban roads, signalized intersections have contributed significantly to motor vehicle emissions, which however has not been modelled in high accuracy. This paper uses the International Vehicle Emission (IVE) model to obtain the localized emission factors, so that a dispersion model, CAL3QHC was introduced and modified to incorporate the vehicle acceleration and deceleration states. Two emission reduction schemes for urban signalized intersections, the Green Light Optimal Speed Advisory (GLOSA) system and the no-obstacle driveway design, were proposed, by taking the intersection of Wen'er West Road and Liangmu Road in Hangzhou, China as an empirical example. The overall performance and roadside emission dispersion were simulated using VISSIM software package, with the improved CAL3QHC model. The GLOSA system and the no-obstacle driveway design were found to mitigate the roadside CO concentration by 21.2% and 22.1%, respectively. Findings of the study may assist to monitor roadside pollutant concentrations, formulate and evaluate emission mitigation strategies in urban intersections.

A high-resolution spatial and temporal on-road vehicle emission inventory in an Iranian metropolitan area, Isfahan, based on detailed hourly traffic data

The present study is primarily aimed to establish a detailed exhaustive emission inventory of urban motor vehicles in a medium-sized city, i.e. Isfahan. The International Vehicle Emission (IVE) model was utilized to estimate the motor vehicle Emission Factors (EFs) in different areas of Isfahan. IVE was adjusted using the results obtained by on-board real-world EF measurements. A high resolution 1 km × 1 km vehicle emission inventory for the year 2018 was then developed in a bottom-up approach. The method was employed based on the influencing factors such as the collected hourly-data on road network, traffic flow, vehicle driving cycles, and the vehicle population in Isfahan. Moreover, the variations of EFs with vehicle speed were investigated for different vehicle categories to assess the emission rates through Isfahan transportation fleet. The results showed that EFs of all pollutants emitted from vehicle exhausts usually decrease with vehicle speed. Also, gasoline vehicles have larger CO and VOCs EFs than the others, whereas vehicles fueled by diesel have the largest NOx, SOx, and PM10 EFs. The established emission inventory revealed that the annual amount of CO, VOCs, NOx, SOx, and PM10 emitted from Isfahan on-road mobile sources in 2018 are 267.6, 12.6, 20.3, 0.3, and 2.3 kt, respectively. The gasoline vehicles including cars and motorcycles were the main sources of CO, VOCs, and SOx, whereas the heavy-duty vehicles and buses equipped with diesel engines were the main sources of NOx and PM10. The hourly variations of EFs ensure an acceptable consistency with the hourly variation of the traffic flow at different areas. Moreover, the high-resolution spatial distribution of vehicle emission inventory represented that emissions were concentrated on the downtown, esp. on underdeveloped historical area of the city, where the most of the governmental and administrative departments are concentrated.