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Can private cars restriction policy help reduce air pollution in big cities? A case study of Tehran | ||
| AUT Journal of Civil Engineering | ||
| دوره 8، شماره 1، 2024، صفحه 33-42 اصل مقاله (654.33 K) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22060/ajce.2024.23216.5863 | ||
| نویسندگان | ||
| Fatemeh Rashidi1؛ Arash Rasaizadi* 2 | ||
| 1Transportation Planning Engineering, Sharif University of Technology, Tehran, Iran | ||
| 2Transportation Planning Engineering, Tarbiat Modares University, Tehran, Iran | ||
| چکیده | ||
| It is a common understanding that higher traffic volume leads to more pollutant air. Therefore, when the air quality deteriorates, policymakers restrict private vehicles. This study aims to challenge this assumption by using statistical models including multivariate regression and ordered logistic regression models. These models are calibrated by employing datasets on air pollution, traffic quality, and weather conditions, for Tehran the capital of Iran. The results show that the coefficient of the Traffic Quality Index (TQI), representing traffic volume, is not statistically significant. This finding suggests that traffic volume does not significantly impact air quality in Tehran. Among the variables, temperature has the most considerable effect on air pollution with a coefficient of -0.35 and has the highest significant coefficient with the p value of 0.083. The coefficients of all variables align with our previous knowledge. In fact, temperature and wind speed (coefficient is -0.14) show negative significant coefficients, implying that lower temperatures and slower wind speeds leads to higher levels of air pollution. Conversely, TQI and humidity exhibit positive significant coefficients which their values are 0.03 and 0.05 in order, showing that increased traffic volume and higher humidity levels are associated with more polluted air. In conclusion, restricting private cars may not be a practical solution for addressing the issue of air pollution in Tehran. | ||
| کلیدواژهها | ||
| Air Pollution؛ Private Cars Restriction؛ Traffic Quality Index؛ Ordered Logistic Regression؛ Multivariate Regression | ||
| مراجع | ||
|
[1] in: National Geographic, 2019 - National Geographic, 2019.
[2] in: World Health Organization, https://www.google.who.int.
[3] D. Blewitt, Traffic modelling guidelines, Traffic Manager and Network Performance Best Practice, Transp. London, London, UK, Tech. Rep. Version, 3 (2010).
[4] A. Hickman, D. Colwill, The estimation of air pollution concentrations from road traffic, 0266-7045, 1982.
[5] D.J. Briggs, S. Collins, P. Elliott, P. Fischer, S. Kingham, E. Lebret, K. Pryl, H. Van Reeuwijk, K. Smallbone, A. Van Der Veen, Mapping urban air pollution using GIS: a regression-based approach, International Journal of Geographical Information Science, 11(7) (1997) 699-718.
[6] M. Aldrin, I.H. Haff, Generalised additive modelling of air pollution, traffic volume and meteorology, Atmospheric Environment, 39(11) (2005) 2145-2155.
[7] B. Flicstein, D.M. Broday, The impact of a forced reduction in traffic volumes on urban air pollution, Atmospheric Environment, 42(3) (2008) 428-440.
[8] Q. Wang, H. Feng, H. Feng, Y. Yu, J. Li, E. Ning, The impacts of road traffic on urban air quality in Jinan based GWR and remote sensing, Scientific reports, 11(1) (2021) 15512.
[9] A. Hassani, A. Maleki, Projection of passenger cars’ air pollutants and greenhouse gas emissions and fuel consumption in Tehran under alternative policy scenarios, Case Studies on Transport Policy, 10(4) (2022) 2195-2207.
[10] R. Rossi, R. Ceccato, M. Gastaldi, Effect of road traffic on air pollution. Experimental evidence from COVID-19 lockdown, Sustainability, 12(21) (2020) 8984.
[11] Y. Xu, D. Weng, S. Wang, Q. Ge, X. Hu, Z. Wang, L. Zhang, Trends in Emissions from Road Traffic in Rapidly Urbanizing Areas, Sustainability, 16(17) (2024) 7400.
[12] G. Comert, S. Darko, N. Huynh, B. Elijah, Q. Eloise, Evaluating the impact of traffic volume on air quality in South Carolina, International Journal of Transportation Science and Technology, 9(1) (2020) 29-41.
[13] Z. Chen, X. Hao, X. Zhang, F. Chen, Have traffic restrictions improved air quality? A shock from COVID-19, Journal of cleaner production, 279 (2021) 123622.
[14] L.W. Davis, The effect of driving restrictions on air quality in Mexico City, Journal of Political Economy, 116(1) (2008) 38-81.
[15] Y. Chen, G.Z. Jin, N. Kumar, G. Shi, The promise of Beijing: Evaluating the impact of the 2008 Olympic Games on air quality, Journal of Environmental Economics and Management, 66(3) (2013) 424-443.
[16] R. Becker, V. Henderson, Effects of air quality regulations on polluting industries, Journal of political Economy, 108(2) (2000) 379-421.
[17] J.V. Henderson, Effects of air quality regulation, in, National Bureau of Economic Research Cambridge, Mass., USA, 1995.
[18] J.A. List, D.L. Millimet, P.G. Fredriksson, W.W. McHone, Effects of environmental regulations on manufacturing plant births: evidence from a propensity score matching estimator, Review of Economics and Statistics, 85(4) (2003) 944-952.
[19] G. Invernizzi, A. Ruprecht, R. Mazza, C. De Marco, G. Močnik, C. Sioutas, D. Westerdahl, Measurement of black carbon concentration as an indicator of air quality benefits of traffic restriction policies within the ecopass zone in Milan, Italy, Atmospheric Environment, 45(21) (2011) 3522-3527.
[20] D. Westerdahl, X. Wang, X. Pan, K.M. Zhang, Characterization of on-road vehicle emission factors and microenvironmental air quality in Beijing, China, Atmospheric Environment, 43(3) (2009) 697-705.
[21] V.B. Viard, S. Fu, The effect of Beijing's driving restrictions on pollution and economic activity, Journal of Public Economics, 125 (2015) 98-115.
[22] B. Bowerman, R. O’Connell, A. Koehler, Forecasting, time series, and regression, thomson learning, (2004).
[23] T.K. Tiemann, Introductory business statistics with interactive spreadsheets-1st Canadian Edition, (2010).
[24] Y. Yang, Development of the regional freight transportation demand prediction models based on the regression analysis methods, Neurocomputing, 158 (2015) 42-47.
[25] S. Xu, L. Zhang, J. Ma, Study on Linear Regression Prediction Model of County Highway Passenger Transport Volume, in: Advances in Computer Science, Intelligent System and Environment, Springer, 2011, pp. 151-156. | ||
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