Spatio-Temporal Modeling of Local-Scale Carbon Monoxide Dispersion from Traffic Using the GRAL Model in Urban Environments (Case Study: North Kargar Street, Tehran)

Document Type : Research Paper

Authors

1 Department of Remote sensing & GIS, Faculty of Geographical Sciences, University of Kharazmi, Tehran, Iran.

2 Assistant Professor GIS/RS Dept., Geographical Science Factulty, Kharazmi University

3 Department of Remote sensing & GIS, Faculty of Geography, University of Tehran, Tehran, Iran.

Abstract

Objective: This study uses a spatio-temporal approach to investigate the dispersion pattern of carbon monoxide emitted by vehicle traffic at near-ground altitude levels, on a local scale and within a part of Tehran, an environment whose complex and heterogeneous urban structure significantly influences the dispersion process of air pollutants.
Method: After identifying the parameters affecting the movement of carbon monoxide, these parameters were classified into 3 categories: climatic, spatial, and traffic-related criteria. The GRAL model was used to analyze the behavior of carbon monoxide. The movement dynamics and concentration of CO were modeled within a one-hour period after emission, at 5 altitude levels along a section of North Kargar Street in Tehran. To evaluate the model's performance, the Pearson correlation coefficient and the mean bias error statistical indices were evaluated.
Results: The highest correlation between predicted and measured values was obtained at the altitude level of 10.5 meters, and the lowest value at a height of 26 meters. It shows that building walls and the height of the sampling station play an effective role. At lower altitudes (2 and 4.5 meters), due to the presence of building obstacles and weak wind currents, higher CO concentrations and a positive model bias were observed. Nevertheless, the results obtained were statistically significant. Besides, the highest concentration of carbon monoxide was recorded at street level and near the entrances of alleys. If the wind direction is aligned with the street axis, the street acts as a duct for the evacuation of carbon monoxide; however, if the wind blows perpendicular to the traffic direction, the alley entrances become focus point for the accumulation and evacuation of pollutant.
Conclusions: Variables such as the geometry of urban thoroughfares, building heights, wind direction, and atmospheric stability give rise to various scenarios of CO dispersion in the urban environment. The evidence obtained indicates that the highest density of carbon monoxide, both in terms of persistence duration and accumulation volume, occurs at altitudes below 30 meters, coinciding with the typical human breathing height. These findings underscore the critical importance of local-scale air quality modeling that accounts for the complex morphological features and turbulent flow regimes characteristic of the urban environment. 

Keywords

Main Subjects

References

Alesheikh, A. A., Gharagozlu, A., & Sajadian, M. (2012). Study of air pollution resulting from the transportation traffic in Tehran metropolis by using LUR model combined with GIS and emission factors. Geographical Journal of Chashmandaz-e-Zagros, 4(11), 143-158. https://sid.ir/paper/175656/en [in Persian] 
Bakhshizadeh, F., Rezayan, H., & Akbary, M. (2015). 3D spatio-temporal modeling of NOx air pollution of vehicular traffic in Vali-e-Asr and Fatemi Streets intersection, Tehran city. Journal of Spatial Analysis Environmental Hazards, 2(1), 43-62. http://dx.doi.org/10.18869/acadpub.jsaeh.2.1.43 [in Persian]  
Baik, J. J., & Kim, J. J. (2002). On the escape of pollutants from urban street canyons. Atmospheric Environment, 36(3), 527-536.  
Berchet, A., Zink, K., Oettl, D., Brunner, J., Emmenegger, L., & Brunner, D. (2017). Evaluation of high-resolution GRAMM-GRAL (v15.12/v14.8) NOx simulations over the city of Zürich, Switzerland. Geoscientific Model Development, 10(9), 3441-3459. https://doi.org/10.5194/gmd-10-3441-2017.
Buccolieri, R., Sandberg, M., & Di Sabatino, S. (2010). City breathability and its link to pollutant concentration distribution within urban-like geometries. Atmospheric Environment, 44(15), 1894-1903. https://doi.org/10.1016/j.atmosenv.2010.02.022.
Blocken, B., Stathopoulos, T., & Carmeliet, J. (2007). CFD simulation of the atmospheric boundary layer: Wall function problems. Atmospheric Environment, 41(2), 238-252. https://doi.org/10.1016/j.atmosenv.2006.08.019.
Britter, R. E., & Hanna, S. R. (2003). Flow and dispersion in urban areas. Annual Review of Fluid Mechanics, 35, 469-496. https://doi.org/10.1146/annurev.fluid.35.101101.161147.
Department of Transportation & Traffic Organization of Tehran Municipality and Fuel, Combustion and Pollution Research Center. (2015). The hot exhaust pollution emission factors for petrol cars manufactured domestically based on pollutant standard Euro-2. Sharif University of Technology, Mechanical Engineering Department. [in Persian]
El-Harbawi, M. (2013). Air quality modelling, simulation, and computational methods: a review. Environmental Reviews, 21(3), 149-179. https://doi.org/10.1139/er-2012-0056.
Gertler, A. W., Koracin, D. R., Koracin, J., Lewis, J. M., Luria, M., Sagebiel, J. C., & Stockwell, W. R. (2004). Development and Validation of a Predictive Model to Assess the Impact of Coastal Operations on Urban Scale Air Quality. In: Borrego, C., Incecik, S. (eds) Air Pollution Modeling and Its Application XVI. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-8867-6_69.  
Gianquintieri, L., Oxoli, D., Caiani, E. G., & Brovelli, M. A. (2024). State-of-art in modelling particulate matter (PM) concentration: A scoping review of aims and methods. Environmental Development and Sustainability. https://doi.org/10.1007/s10668-024-04781-5.
GRAL, (2020), GRAL-Graz Lagrangian Model. Documentation and User Guides. Available online: https://gral.tugraz.at/index.php/files/category/3-documentation-and-user-guides (accessed 28 November 2020). 
Hang, J., Li, Y., Sandberg, M., Buccolieri, R., & Di Sabatino, S. (2012). The influence of building height variability on pollutant dispersion and pedestrian ventilation in idealized high-rise urban areas. Building and Environment, 56, 346-360. https://doi.org/10.1016/j.buildenv.2012.03.023.
Heidari Asl, S., Moradi, H., & Soleimani, M. (2021). Modeling of air particulate matter in the city of Isfahan with the use of IDW and Cokriging methods. Journal of Environmental Science and Technology, 23(6), 187-200. [in Persian] 
Karimi, M., Khosnavaz, S., Shamsipour, A., & Moghbel, M. (2020). Modeling the effect of street orientation on the air pollution dispersion (District Six of Tehran Municipality). Motaleate Shahri, 9(34), 77-90. [in Persian]
Katharopoulos, A., Galmarini, S., & Schmidli, J. (2022). Lagrangian particle dispersion models in the grey zone of turbulence: Adaptations to FLEXPART COSMO for simulations at 1 km grid resolution. Boundary-Layer Meteorology, 184, 241-267. https://doi.org/10.1007/s10546-022-00728-3.
Kahl, J. D., & Chapman, H. L. (2018). Atmospheric stability characterization using the Pasquill method: A critical evaluation. Atmospheric Environment, 187, 196-209. https://doi.org/10.1016/j.atmosenv.2018.05.058.
Kianmehr, A., & Bahrainy, H. (2016). The effect of wind direction and speed on ventilation and pollutant concentrations in street canyons. Advanced Environmental Sciences, 14(2), 97-108. https://envs.sbu.ac.ir/article_97719.html?lang=en [in Persian]
Lazić, L., Pejanović, G., & Živković, M. (2010). Wind forecasts for wind power generation using the Eta model. Renewable Energy, 35(6), 1236-1243. https://doi.org/10.1016/j.renene.2009.10.028.
Lee, C.-H., Lung, S.-C. C., & Chen, J.-P. (2023). Three-dimensional spatial inhomogeneity of traffic-generated urban PM2.5 in street canyons. Atmospheric Pollution Research, 14(5), 101748. https://doi.org/10.1016/j.apr.2023.101748.
Ling, H., Lung, S.-C. C., & Uhrner, U. (2020). Micro-scale particle simulation and traffic-related particle exposure assessment in an Asian residential community. Environmental Pollution, 266, 115046. https://doi.org/10.1016/j.envpol.2020.115046.
Liu, W., Ling, X., Xue, Y., Wu, S., Gao, J., Zhao, L., & He, B. (2024). Study on the concentration of top air pollutants in Xuzhou city in winter 2020 based on the WRF-Chem and ADMS-Urban models. Atmosphere, 15(1), 129. https://doi.org/10.3390/atmos15010129.
Moayyedi, M. K., & Talab, V. A. (2021). Modeling of turbulent atmospheric boundary layer and dispersion of solid pollutant particles in an urban area using large eddy simulation. Amirkabir Journal of Mechanical Engineering, 53(5), 2839-2856. https://doi.org/10.22060/mej.2020.17685.6656 [in Persian]
Oettl, D. (2015). Evaluation of the revised Lagrangian particle model GRAL against wind-tunnel and field observations in the presence of obstacles. Boundary-Layer Meteorology, 155(2), 271-287. https://doi.org/10.1007/s10546-014-9993-4.
Oettl, D., & Uhrner, U. (2011). Development and evaluation of GRAL-C dispersion model, a hybrid Eulerian-Lagrangian approach capturing NO-NO2-O3 chemistry. Atmospheric Environment, 45(4), 839-847. https://doi.org/10.1016/j.atmosenv.2010.11.028.
Pantusheva, M., Mitkov, R., Hristov, P. O., & Petrova-Antonova, D. (2022). Air pollution dispersion modelling in urban environment using CFD: A systematic review. Atmosphere, 13(10), 1640. https://doi.org/10.3390/atmos13101640.
Ruda Sarria, F., Guerrero Delgado, M., Monge Palma, R., Palomo Amores, T., Sánchez Ramos, J., & Álvarez Domínguez, S. (2025). Modelling pollutant dispersion in urban canyons to enhance air quality and urban planning. Applied Sciences, 15(4), 1752. https://doi.org/10.3390/app15041752.   
Shafiepor, M., & Kamalan, H. (2005). Air quality deterioration in Tehran due to motorcycles. Iranian Journal of Environmental Health Science & Engineering, 2(3), 145-152. http://hdl.handle.net/1807/9094.
Srivastava, A., & Rao, B. P. S. (2011). Urban air pollution modeling. In Air Quality-Models and Applications (p. 364). https://doi.org/10.5772/16776.
Soulhac, L., Salizzoni, P., Cierco, F. X., & Perkins, R. (2011). The model SIRANE for atmospheric urban pollutant dispersion; Part I, presentation of the model. Atmospheric Environment, 45(39), 7379-7395. https://doi.org/10.1016/j.atmosenv.2011.07.008
Tehran Air Quality Control Company. (1400). Annual report on Tehran air quality. [in Persian]
Vardoulakis, S., Fisher, B. E. A., Pericleous, K., & Gonzalez-Flesca, N. (2003). Modelling air quality in street canyons: a review. Atmospheric Environment, 37(2), 155-182. https://doi.org/10.1016/S1352-2310(02)00857-9.
Willmott, C. J., & Matsuura, K. (2005). Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Research, 30(1), 79-82. https://doi.org/10.3354/cr030079.
Zhang, S., Wu, Y., Huang, R., Wang, J., Yan, H., Zheng, Y., & Hao, J. (2016). High-resolution simulation of link-level vehicle emissions and concentrations for air pollutants in a traffic-populated eastern Asian city. Atmospheric Chemistry and Physics, 16(15), 9965-9981. https://doi.org/10.5194/acp-16-9965-2016.  
Zhang, Z., & Chen, Q. (2007). Comparison of the Eulerian and Lagrangian methods for predicting particle transport in enclosed spaces. Atmospheric Environment, 41(25), 5236-5248. https://doi.org/10.1016/j.atmosenv.2006.05.086.
Journal of Environmental Studies
Volume 52, Issue 1
June 2026
Pages 21-40

Files

Share

How to cite

Statistics