Comparison of the Real Emissions of Domestic Passenger Cars with the IVE Model

Document Type : Original Article


Department of Environmental Technologies, Research Institute of Environmental Sciences, Shahid Beheshti University, Tehran, Iran


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.


Ali, M., Kamal, M.D., Tahir, A. and Atif, S., 2021. Fuel consumption monitoring through COPERT model—A case study for urban sustainability. Sustainability. 13(21),11614.DOI:10.3390/su132111614.
Alipourmohajer, S., Rashidi, Y. and Atabi, F., 2019. Verification of IVE model for SAIPA Co. Fleet Emission. Pollution. 5(2), 235-245.DOI: 10.22059/POLL.2018.256405.431.
Bernard, Y., Tietge, U., German, J. and Muncrief, R., 2018. Determination of Real-World Emissions from Passenger Vehicles Using Remote Sensing Data. The Real Urban Emissions Initiative: London, UK.
Bishop, G.A., Stedman, D.H., Burgard, D.A. and Atkinson, O., 2016. High-mileage light-duty fleet vehicle emissions: Their potentially overlookedimportance. Environmental Science and Technology. 50(10), 5405-5411.DOI:10.1021/acs.est.6b00717.
Cuba, C., Cuba, R., Arroyo, V., & Morales, J. (2021). Characterization of air pollution in pre-COVID 19 time using the IVE model applied to mobile sources in urban areas. In IOP Conference Series: Earth and Environmental Science, Vol. 943(1), p. 012003. IOP Publishing.
Dong, Y. and Xu, J., 2020. Estimation of vehicle carbon emissions in China accounting for vertical curve effects. Mathematical Problems in Engineering. 20(2). 1-20.DOI:10.1155/2020/1595974.
Ghaffarpasand, O., Talaie, M.R., Ahmadikia, H., Khozani, A.T. and Shalamzari, M.D., 2020. A high-resolution spatial and temporal on-road vehicle emission inventory in an Iranian metropolitan area, Isfahan, based on detailed hourly traffic data. Atmospheric Pollution Research. 11(9), 1598-1609.DOI: 10.1016/j.apr.2020.05.014.
Ghaffarpasand, O., Talaie, M.R., Ahmadikia, H., TalaieKhozani, A., Shalamzari, M.D. and Majidi, S., 2021. How does unsustainable urbanization affect driving behavior and vehicular emissions? Evidence from Iran. Sustainable Cities and Society. 72, 103065.DOI: 10.1016/j.scs.2021.103065.
Hirahara, Y., Rosnay, P.D. and Arduini, G., 2020. Evaluation of a microwave emissivity module for the snow-covered area with CMEM in the ECMWF integrated forecasting system. Remote Sensing. 12(18), 29-46.
DOI: 10.3390/rs12182911.
Jamshidi Kalajahi, M., Khazini, L., Rashidi, Y. and Zeinali Heris, S., 2020. Development of reduction scenarios based on urban emission estimation and dispersion of exhaust pollutants from light duty public transport: case of Tabriz, Iran. Emission Control Science and Technology. 6, 86-104. DOI: 10.1007/s40825-020-00177-1.
Kii, M., 2020. Reductions in CO2 emissions from passenger cars under demography and technology scenarios in Japan by 2050. Sustainability. 12(17), 6919.DOI: 10.3390/su12176919.
Le Hong, Z. and Zimmerman, N., 2021. Air quality and greenhouse gas implications of autonomous vehicles in Vancouver, Canada. Transportation Research Part D: Transport and Environment. 90, 102676. DOI: 10.1016/j.trd.2021.102676.
Leung, K.W., 2019. Development and assessment of high-resolution vehicle emission inventory in Hong Kong (Doctoral dissertation).
Li, X., Hu, Z., Cao, J. and Xu, X., 2022. The impact of environmental accountability on air pollution: A public attention perspective. Energy Policy. 161, 112-733.10. DOI: 1016/j.enpol.2022.112733.
MÄ…dziel, M., Campisi, T., Jaworski, A. and Tesoriere, G., 2021. The development of strategies to reduce exhaust emissions from passenger cars in Rzeszow city—Poland. a preliminary assessment of the results produced by the increase of e-fleet. Energies. 14(4), 1046.
Sanches, M.F., Oliveira, M.V.R., Ciceri, O.J., Ladeira, L.Z., Garcia, I.C., Da Fonseca, N.L. and Villas, L.A., 2021. July). EFIS-Ecological Fuel-consumption Intelligent System. In 2021 17th International Conference on Distributed Computing in Sensor Systems (DCOSS) (pp. 117-123). IEEE.DOI:10.1109DCOSS52099.2021.00027.
Shahbazi, H., Reyhanian, M., Hosseini, V. and Afshin, H., 2016. The relative contributions of mobile sources to air pollutant emissions in Tehran, Iran: an emission inventory approach. Emission control science and Technology. 2, 44-56.
Singh, H. and Kathuria, A., 2021. Analyzing driver behavior under naturalistic driving conditions: A review. Accident Analysis and Prevention. 150, 105908.DOI: 10.1016/j.aap.2021.105908.
Suarez-Bertoa, R., Valverde, V., Clairotte, M., Pavlovic, J., Giechaskiel, B., Franco, V. and Astorga, C., 2019. On-road emissions of passenger cars beyond the boundary conditions of the real-driving emissions test. Environmental research, 176, 108572.DOI: 10.1016/j.envres.2019.108572.
Wondifraw, B. A., Lemma, D. G. and Aschalwe, E.T., 2018. Estimation of exhaust emission from road transport using COPERT software.
Yu, Z., Li, W., Liu, Y., Zeng, X., Zhao, Y., Chen, K. and He, J., 2021. Quantification and management of urban traffic emissions based on individual vehicle data. Journal of Cleaner Production. 328, 129-386. DOI: 10.1016/j.jclepro.2021.129386.
Zhong, M., Saikawa, E., Avramov, A., Chen, C., Sun, B., Ye, W. and Panday, A.K., 2019. Nepal Ambient Monitoring and Source Testing Experiment (NAMaSTE): emissions of particulate matter and sulfur dioxide from vehicles and brick kilns and their impacts on air quality in the Kathmandu Valley, Nepal. Atmospheric Chemistry and Physics. 19(12), 8209-8228.DOI: 10.5194/acp-19-8209-2019.
Zhou, Z., Tan, Q., Liu, H., Deng, Y., Wu, K., Lu, C. and Zhou, X., 2019. Emission characteristics and high-resolution spatial and temporal distribution of pollutants from motor vehicles in Chengdu, China. Atmospheric Pollution Research. 10(3), 749-758.DOI: 10.1016/j.apr.2018.12.010.