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Real-time emissions of gaseous pollutants from vehicles under heterogeneous traffic conditions

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Języki publikacji
EN
Abstrakty
EN
Air quality problems in cities are often a cause for worry. The air quality index is increasing daily, leading to an increase in cancer and many respiratory problems. Road transport in an urban area is a significant cause of air pollution. The vehicles must meet Indian emission regulations for which the emissions are measured using legally mandated standard driving cycles that did not accurately reflect real-world driving emissions because of varying traffic conditions, meteorological conditions, driving behaviour, vehicle power, performance, etc. This study focuses on real-time emissions of gaseous pollutants hydrocarbon (HC), carbon dioxide (CO2), carbon monoxide (CO), and nitric oxide (NO) from vehicle exhaust pipes under heterogeneous traffic conditions. The emissions were measured using a Portable Emission Measurement System (PEMS). The PEMS used was an AVL MDS 450 analyser mounted on the vehicle, and onroad emissions were captured. The test sample consists of four passenger vehicles with varying engine sizes, manufacturers, and fuel. The test route comprises city and highway areas, and it was discovered that the emissions were reduced by 40 to 70% on highways compared to the city. In petrol BSIV and BSVI engines, the emission was reduced to 41.73% for CO, 46.90% for HC, and 64% for NO in the city area. Speed and emissions scatter graphs were plotted for the vehicles, and it was found that in the city area, the optimum speed for less emission is between 30-40 km/h, and on highways, the optimum speed is 80-90 km/h. The emissions were also sensitive to the rate and frequency of acceleration and decelerations. This type of study is very limited in India, and more such studies are required for the assessment of air quality in metropolitan areas and successful traffic management strategies, as well as for determining instantaneous projections of pollutant emissions.
Rocznik
Tom
Strony
55--75
Opis fizyczny
Bibliogr. 27 poz.
Twórcy
autor
  • M.Tech Scholar, Department of Civil Engineering, Maulana Azad National Institute of Technology, Bhopal, Madhya Pradesh, India
autor
  • Department of Civil Engineering, M.B.M. University, Jodhpur 342011 Rajasthan, India
  • Department of Civil Engineering, M.B.M. University, Jodhpur 342011 Rajasthan, India
  • Department of Civil Engineering, Maulana Azad National Institute of Technology, Bhopal, Madhya Pradesh, India
Bibliografia
  • 1. IQAir. 2021. “2021 World Air Quality Report”. Region & City PM2.5 Ranking. Available at: http://www.indiaenvironmentportal.org.in/files/file/2021%20world%20air%20quality%20report.pdf.
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  • 4. Manisalidis Ioannis, Elisavet Stavropoulou, Agathangelos Stavropoulos, Eugenia Bezirtzoglou. 2020. „Environmental And Health Impacts Of Air Pollution: A Review”. Frontiers In Public Health 8. DOI: 10.3389/fpubh.2020.00014.
  • 5. Jacyna M., J. Merkisz. 2014. “Proecological approach to modelling traffic organization in national transport system”. Archives of Transport 2(30): 43-56.
  • 6. Merkisz Jerzy, Marianna Jacyna, Agnieszka Merkisz-Guranowska, Jacek Pielecha. 2014. “The parameters of passenger cars engine in terms of real drive emission test”. Archives of Transport 32(4): 43-50. DOI: 10.5604/08669546.1146998.
  • 7. Liu Huan, Matthew Barth, George Scora, Nicole Davis, James Lents. 2010. „Using Portable Emission Measurement Systems For Transportation Emissions Studies”. Transportation Research Record: Journal Of The Transportation Research Board 2158(1): 54-60. DOI: 10.3141/2158-07.
  • 8. Franco Vicente, Marina Kousoulidou, Marilena Muntean, Leonidas Ntziachristos, Stefan Hausberger, Panagiota Dilara. 2013. „Road Vehicle Emission Factors Development: A Review”. Atmospheric Environment 70: 84-97. DOI: 10.1016/j.atmosenv.2013.01.006.
  • 9. Smit R., P. Kingston, D.H. Wainwright, R. Tooker. 2017. „A Tunnel Study To Validate Motor Vehicle Emission Prediction Software In Australia”. Atmospheric Environment 151: 188-199. DOI: 10.1016/j.atmosenv.2016.12.014.
  • 10. Shiva Nagendra S.M., Mukesh Khare. 2006. „Artificial Neural Network Approach For Modelling Nitrogen Dioxide Dispersion From Vehicular Exhaust Emissions”. Ecological Modelling 190(1-2): 99-115. DOI: 10.1016/j.ecolmodel.2005.01.062.
  • 11. Shiva Nagendra S.M., Mukesh Khare. 2010. „Artificial Neural Network Based Vehicular Pollution Prediction Model: A Practical Approach For Urban Air Quality Prediction”. International Journal Of Environment And Waste Management 5(3/4): 303. DOI: 10.1504/ijewm.2010.032010.
  • 12. Wyatt David W., Hu Li, James E. Tate. 2014. „The Impact Of Road Grade On Carbon Dioxide (CO2) Emission Of A Passenger Vehicle In Real-World Driving”. Transportation Research Part D: Transport And Environment 32: 160-170. DOI: 10.1016/j.trd.2014.07.015.
  • 13. Wang Haohao, Yunshan Ge, Lijun Hao, Xiaoliu Xu, Jianwei Tan, Jiachen Li, Legang Wu, Jia Yang, Dongxia Yang, Jian Peng, Jin Yang, Rong Yang. 2018. „The Real Driving Emission Characteristics Of Light-Duty Diesel Vehicle At Various Altitudes”. Atmospheric Environment 191: 126-131. DOI: 10.1016/j.atmosenv.2018.07.060.
  • 14. Jaikumar Rohit, S.M. Shiva Nagendra, R. Sivanandan. 2017. „Modeling Of Real Time Exhaust Emissions Of Passenger Cars Under Heterogeneous Traffic Conditions”. Atmospheric Pollution Research 8(1): 80-88. DOI: 10.1016/j.apr.2016.07.011.
  • 15. Jaikumar Rohit, S.M. Shiva Nagendra, R. Sivanandan. 2017. „Modal Analysis Of Real-Time, Real World Vehicular Exhaust Emissions Under Heterogeneous Traffic Conditions”. Transportation Research Part D: Transport And Environment 54: 397-409. DOI: 10.1016/j.trd.2017.06.015.
  • 16. Mahesh Srinath, Gitakrishnan Ramadurai, S.M. Shiva Nagendra. 2019. „Real-World Emissions Of Gaseous Pollutants From Motorcycles On Indian Urban Arterials”. Transportation Research Part D: Transport And Environment 76: 72-84. DOI: 10.1016/j.trd.2019.09.010.
  • 17. Nagendra Shiva S.M., Mukesh Khare. 2006. „Artificial Neural Network Approach For Modelling Nitrogen Dioxide Dispersion From Vehicular Exhaust Emissions”. Ecological Modelling 190(1-2): 99-115. DOI: 10.1016/j.ecolmodel.2005.01.062.
  • 18. Nagendra Shiva S.M., Mukesh Khare. 2009. „Univariate Stochastic Model For Predicting Carbon Monoxide On An Urban Roadway”. International Journal Of Environmental Engineering 1(3): 223. DOI: 10.1504/ijee.2009.027802.
  • 19. Lau Jason, W.T. Hung, C.S. Cheung. 2011. „On-Board Gaseous Emissions Of LPG Taxis And Estimation Of Taxi Fleet Emissions”. Science Of The Total Environment 409(24): 5292-5300. DOI: 10.1016/j.scitotenv.2011.08.054.
  • 20. Ho Sze-Hwee, Yiik-Diew Wong, Victor Wei-Chung Chang. 2014. „Developing Singapore Driving Cycle For Passenger Cars To Estimate Fuel Consumption And Vehicular Emissions”. Atmospheric Environment 97: 353-362. DOI: 10.1016/j.atmosenv.2014.08.042.
  • 21. Donateo Teresa, Mattia Giovinazzi. 2017. „Building A Cycle For Real Driving Emissions”. Energy Procedia 126: 891-898. DOI: 10.1016/j.egypro.2017.08.307.
  • 22. Stasinopoulos Peter, Nirajan Shiwakoti, Tobias Seidl, Alan Wong. 2018. „Comparison of Melbourne driving characteristics with the NEDC and WLTC”. In: Proceedings of the 40th Australasian Transport Research Forum (ATRF 2018): 1-11. Australasian Transport Research Forum (ATRF).
  • 23. Nowak Mateusz, Jacek Pielecha. 2017. „Comparison Of Exhaust Emission On The Basis Of Real Driving Emissions Measurements And Simulations”. MATEC Web Of Conferences 118: 00026. DOI: 10.1051/matecconf/201711800026.
  • 24. Nesamani K.S., K.P. Subramanian. 2006. „Impact Of Real-World Driving Characteristics On Vehicular Emissions”. JSME International Journal Series B 49 (1): 19-26. DOI: 10.1299/jsmeb.49.19.
  • 25. Kishan S., T. DeFries, et al. 1993. „A Study of Light-Duty Vehicle Driving Behavior: Application to Real-World Emission Inventories”. SAE Technical Paper Series. Paper 932659.
  • 26. Nakhawa Husein Adam, S.S. Thipse. 2015. „Characterization Of The Ultrafine And Nano Particle Emissions On Modified Indian Driving Cycle For Passenger Cars Operating On CNG Phase Wise Analysis”. Advanced Engineering Forum 14: 86-96. DOI: 10.4028/www.scientific.net/aef.14.86.
  • 27. AVL DiTest. 2022. „AVL Ditest MDS 450 – Modular Diagnostic System”. Available at: https://www.avlditest.com/en/emt-mds-450.html.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-36d2d971-cb00-4860-bf96-d76e5b6a8621
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