During the recent decades, rapid urbanization growth has led to even faster growth of motor vehicles and especially in large cities. Hence, evaluation of the actual level of traffic emissions has gained more interest. This paper, for the first time, presents a bottom-up approach for evaluation of vehicular emissions in Tehran- the capital of Iran- using the International Vehicle Emission (IVE) model. The IVE model uses local vehicle technology levels and its distributions, power based driving factors, vehicle soak distributions and meteorological parameters to tailor the model for specific evaluation of emissions. The results of this study demonstrate that carbon monoxide (CO) emission with 244.45 ton/hr during peak traffic hour is the most abundant criteria pollutant. About 25% of this quantity is emitted during start-up periods. Other pollutants such as NOX, VOCs, PM, VOCevap and SOX are ranked after CO accordingly. Also, carbon dioxide (CO2) emissions of 1744.22 ton/hr during the study period indicate that light vehicles are responsible for more than 82% of this amount. Based on IVE’s evaluation, about 25% of the total vehicle emissions in Tehran come from districts 2, 4 and 6 respectively. It has further been inferred that the development of public transportation systems and proper land-use and urban spatial planning for various centers in these districts are essential.

To determine the amount of air pollutants, produced by Iranian automakers, and compare it with old and retrofitted vehicles have become one of the important tools of urban management. The present research uses International Vehicle Emission (IVE) modeling software in order to verify SAIPA Co. fleet emissions, based on Euro 4 emission standard (SAIPA Co. recognized as a superior Iranian brand in vehicle industry). There has been attempts to determine pollutant emission from Saipa Co.-manufactured cars in the city of Tehran, in accordance with Tehran Driving Cycle along with modeling and lab results which have over 90% conformity with modeling and lab results of New European Driving Cycle. According to ISQI’ s 100, 000-km test results, the amount of CO2 emission modeling from X100 and Tiba2’ s has been about 160 gr/km, which has been within the range, whereas the modeled CO2 emission rate has been 232 gr/km in TDC, i. e., 1. 5 times more than laboratory test, due to different driving cycle usage. Significant differences between the values obtained in the emission lab and modeling at New European Driving Cycle, Tehran Driving Cycle, and Tehran Air Quality Control Company report, indicate that relying on hypothetical situation leads to inapplicable emissions value from light vehicles.

Air pollution remains one of the most pressing environmental challenges worldwide, with road transport identified as one of the primary contributors to harmful emissions. Within the European Union, road transport accounts for 40.3% of total NOx emissions and 16.5% of PM2.5 emissions, demonstrating its significant impact on air quality. Accurate modeling of vehicular emissions is a fundamental step toward effective air quality management, identifying pollution sources, and formulating targeted mitigation strategies. This study conducts a detailed comparative analysis of two widely used emission models, COPERT and IVE, focusing on their performance under varying regional contexts, levels of data availability, and specific emission-related tasks. Key aspects such as model accuracy, usability, input data sensitivity, and policy assessment applicability are critically evaluated. The rationale for selecting these two models lies in their extensive application in European and non-European regions, offering a unique perspective on their adaptability and limitations in diverse scenarios. By identifying the strengths and weaknesses of COPERT and IVE, this work provides practical guidance for their optimal use in emission inventories, policy evaluations, and reduction strategy designs. The findings aim to bridge the gap between theoretical modeling approaches and real-world applications, equipping environmental managers and policymakers with actionable insights to enhance air pollution control measures and protect public health.

Introduction: From an environmental and human health perspective, air quality management and pollution control from mobile and stationary sources are critically important. Estimating air pollution emissions from vehicles and other mobile sources is a key tool in this field. Given the diversity in vehicle performance under different conditions, this estimation presents significant challenges for researchers and industry professionals. Therefore, the use of accurate and reliable tools for conducting these estimations is essential. In the compilation of national air pollution emission inventories, the International Vehicle Emissions (IVE) modeling software is utilized as a primary tool. This comprehensive software can estimate pollutant emissions from vehicles and other mobile sources, aiding in the development of pollution inventories.Material and Methods: To investigate pollutant emission levels more accurately, this study involved a comparison between data from the IVE model and measured data from zero-kilometer domestic passenger car emissions. These measurements were conducted using the ISQI laboratory dynamometer test and adhering to Euro 4 emission standards and the New European Driving Cycle (NEDC).Results and Discussion: The comparison between dynamometer chassis emission test data from domestic vehicles and IVE model-generated data indicates significant differences in emission levels in some cases, highlighting that the IVE model is not closely aligned with reality and requires necessary modifications. Except for one instance where NOx emissions were equivalent with the Quick vehicle, other results showed NOx emissions ranging from 0.01 to 0.05, with the model displaying 0.03. Regarding CO, emissions ranged from 0.26 to 0.96, while the model displayed 0.48. Similarly, HC emissions ranged from 0.03 to 0.08, matching the model's result of 0.03. It is evident that the IVE model cannot accurately reflect real emission values in some instances and requires serious modifications to enhance accuracy in estimating these values.Conclusion: Managers and decision-makers in air quality and environmental fields should carefully consider research and experimental results. Based on emission analysis, it is clear that the IVE model is not closely aligned with reality and relies on hypothetical conditions for estimating pollutant values, making it unreliable. Necessary improvements are required in the IVE model to enhance assessment quality and performance in air quality and environmental management. Developing approaches and solutions based on accurate and transparent data are crucial and can significantly improve air quality management and pollution reduction. Therefore, the use of accurate and up-to-date data for assessing air pollution from mobile and stationary sources is critically important and requires modification and improvement of the models used for more accurate emission estimations.

The quick growth of vehicles is due to fast urbanization in mega cities during last decades. This phenomenon has serious impacts on air quality, as emission from mobile vehicles is the major source of air pollution. As a result, any attempt to reduce the emitted air pollutants is needed. This study aims at improving the fuel quality in transporting system with particular emphasis on taxis in Tehran in 2014. As a clean fuel, Euro IV is being used to reduce the emission of pollution, toxic substances, and greenhouse gases. A bottom-up approach to evaluate vehicular emission, using IVE (International Vehicle Emission) model in Tehran, has been presented, which employs the local vehicle technology and its distributions, vehicle soak distributions, power based driving factors, and meteorological parameters to evaluate the emission, itself. Results show that the most abundant air pollutant (CO) has been reduced by 87. 6% due to the clean fuel consumption (Euro IV). Also, the emission rates of the predominant toxic pollutant (Benzene) decreased by 98. 7%. As a clean fuel, Euro IV managed to increase the emitted amount of CO2 and NH3. It can be concluded that upgrading transportation system with updated fuel quality is an essential step to improve air quality in Tehran.

Urban air pollution caused by light-duty passenger vehicles poses critical environmental and public health challenges in megacities like Tehran. In this study, we dynamically estimated vehicular emissions by collecting second-by-second speed and acceleration data from 16 representative routes, including 2 residential, 8 urban, and 6 highway segments, across metropolitan Tehran. We integrated the Vehicle Specific Power (VSP) method with the International Vehicle Emissions (IVE) model to assess real-time emission patterns across four time intervals (08:00, 12:00, 16:00, and 23:00). Our measurements showed that average speeds ranged from 14.0 to 25.97 km/h in residential areas, 10.62 to 42.13 km/h in urban corridors, and 16.43 to 67.15 km/h on highways. We found that VSP values predominantly fell within bins 8–14, reflecting acceleration-intensive and stop-and-go traffic during peak hours. We estimated emissions per kilometer as follows: CO (0.47–0.57 g), NOₓ (0.11–0.23 g), CO₂ (240.7–411.5 g), VOC (0.13–0.19 g), and NMVOC (0.12–0.18 g). During peak hours, emissions increased by 40–50% compared to off-peak periods, correlating with VSP clustering around bins 8–10, while smoother traffic conditions (VSP ≥12) during off-peak hours reduced emissions. This study is among the first in the region to combine second-by-second VSP profiles with the IVE model to produce high-resolution, time-resolved urban emission estimates. Our findings highlight how dynamic traffic modeling can help policymakers design smart traffic signal systems, manage congestion, and improve air quality policies tailored to real-time conditions in megacities.

Air pollution is one of the most important problems in megacities. To provide comprehensive reduction plan for air pollution and pollutants emission prevention, first it must be estimated the amount and importance of major pollutants emission from various sources. The aims of this study were estimation the air pollutants emission from mobile sources and spatial modelling for each pollutant in Karaj. International vehicle emission model (IVE) was used to obtain air pollutants emission factors based on vehicle type. Finally, the annual pollutants emissions of vehicle fleet were calculated and spatial distribution of pollutants emissions based on traffic data and road networks were mapped in GIS environment. The results showed that the maximum annual emission of air pollutants including carbon monoxide (CO), nitrogen oxides (NOx), sulfur oxides (SOx), volatile organic compounds (VOCs), particulate matters (PM) and carbon dioxide (CO2) were estimated about 1688. 84, 185. 61, 6. 26, 31. 16, 120. 54 and 33737. 04 t y-1, respectively. The highways and the heavy vehicles were the major source for PM and NOx. In addition, the arterial roads and light vehicles were the main source for CO and VOCs. Despite the highest SOx emission factor was for heavy vehicles, the total SOx emission from light vehicles was higher than heavy vehicles because of their numbers. Also, the highest emissions amount of pollutants in municipal districts of Karaj were in 3, 4 and 6 and the lowest amount were in 1, 8 and 9.

Background & objectives: Air pollution is considered as an ultrastructural element in urban transportation systems as an important indicator of human health. Therefore, the purpose of this study was to investigate the PM10 pollution range of two main highways of Ardakan city and determine the contribution of these highways to the health risk of their residents due to exposure to these pollutants by modeling method. Methods: This descriptive-analytical, cross-sectional study was conducted on two highways of Meybod Ardakan and Ardakan Nain. IVE and AERMOD models were used for estimation of emission rate of PM10 in four seasons of the year, and also dispersion and exposure rates to PM10. The information required to run the models was collected by observational statistics, information from the police research center and the meteorological research center of Yazd province. By identifying six PM10 exposure groups in the pollution range of these two highways, the health risk assessment was performed using the proposed USEPA relationships. Results: In four seasons of the year, the mean and maximum concentration of PM10 of Meybod Ardakan highway were predicted more than Ardakan Nain highway. Although the minimum 24-hour and annual concentrations of PM10 dispersion in Ardakan Nain Highway were less than that of Meybod Ardakan, the pollution limit of this highway was predicted more. Among the six groups exposed to PM10 in all time intervals, the highest exposure belonged to Ardakan Naein road police and the least to residents of Shahid Paydar Park. Cancer and non-cancerous risk exposure to PM10 was estimated in acceptable range in all age groups and categories. Conclusion: With the modeling method used, the contribution of the two investigated highways in predicting the health risk of surrounding residents was within the acceptable range.

As the share of transport in the production and emission of air pollutants is higher than other sources of emissions in cities, this makes it important for urban managers to identify these sources in detail. This paper deals with the combined effect of emission sources due to transport in the case study of part 9 and part 21 of Tehran metropolitan area. The Mehrabad Airport is considered as a volume source of emission, the passenger terminal as an area source of emission, and the roads in this region as a linear source of emission. For this study, the emission factors for Mehrabad Airport were first calculated using ICAO proposed approaches. To model the emission of pollutants in the streets and west terminals, the emission factors were first obtained using the IVE model, which was developed for Iran with the obtained driving cycle. In addition, with the emission factors obtained for the airport, terminals and routes concerned, the emission distribution model is obtained by combining the emission sources modeling system (ADMS). In total, this paper uses two combined methods of IVE-ADMS for routes and terminals, as well as the ICAO-ADMS model for airports. The results showed that SOX emissions were predominant at Mehrabad airport. Also, the emission of various pollutants in the study area and its 24-hour impact on the surrounding areas with its geographical coordinates are shown.