Analyzing The Role Of Public Transportation On Environmental Air Pollution In Select Cities

IJEP 41(5): 536-541 : Vol. 41 Issue. 5 (May 2021)

Neeraj Sharma1*, Rajat Agrawal2 and Anurag Silmana1

1. Graphic Era (Deemed to be University), Dehradun – 248 002, Uttarakhand, India
2. Indian Institute of Technology Roorkee, Roorkee – 247 667, Uttarakhand, India

Abstract

Amidst growing concern for rising air pollution levels in cities across the globe, this first of its kind research work attempts to study whether the usage of public transportation infrastructure and personal automobiles by citizens has any significant impact on the air pollution levels in a city, taking a sample of 59 urban settlements data points sample from 39 cities across the globe. Variance based structural equation modelling (SEM) procedure is used for estimating a series of relationships among the constructs of public transportation infrastructure, public transportation usage, personal automobiles ownership and environmental air pollution (represented by PM2.5, PM10 and greenhouse gas (GHG) levels) considered in the study and incorporating them into an integrated model. The results suggest that a significant relationship exists between the availability of public transportation infrastructure and its usage by its citizens on their personal automobiles ownership, which, in turn also impacts environmental air pollution levels in a city.

Keywords

Air pollution, public transportation, Structural equation modeling, PM2.5, PM10, Greenhouse gas

References

  1. ISO 37120. 2018. Sustainable development of communities – Indicators for city services and quality of life. International Organization for Standa-rdization, Geneva.
  2. ISO 37122. 2019. Sustainable cities and communities – Indicators for smart cities. International Organization for Standardization, Geneva.
  3. Ambarwati, L., et al. 2016. The influence of integrated space–transport development strategies on air pollution in urban areas. Transportation Res. Part D: Transport Env., 44:134-146.
  4. Urmetzer, P., D. E. Blake and N. Guppy. 1999. Individualized solutions to environmental problems: the case of automobile pollution. Canadian Public Policy. 25(3):345-359.
  5. Basagana, X., et al. 2018. Effect of public transport strikes on air pollution levels in Barcelona (Spain). Sci. Total Env., 610:1076-1082.
  6. Rojas-Rueda, D., et al. 2012. Replacing car trips by increasing bike and public transport in the greater Barcelona metropolitan area: a health impact assessment study. Env. int., 49:100-109.
  7. Monzon, A. and M.J. Guerrero. 2004. Valuation of social and health effects of transport-related air pollution in Madrid (Spain). Sci. Total Env., 334: 427-434.
  8. Guttikunda, S. K. and R.V. Kopakka. 2014. Source emissions and health impacts of urban air pollution in Hyderabad, India. Air Quality, Atmos. Health. 7(2):195-207.
  9. Meyer, J. R. and J.A. Go‘mez-Iba‘ñez. 1981. Autos transit and cities. Harvard University Press, Cambridge, USA.
  10. Reynolds, C. C. O. and M. Kandlikar. 2008. Climate impacts of air quality policy: switching to a natural gas-fueled public transportation system in New Delhi. Env. Sci. Tech., 42(16):5860-5865.
  11. Vafa-Arani, H., et al. 2014. A system dynamics modeling for urban air pollution: A case study of Tehran, Iran. Transportation Res. Part D : Transport Env., 31:21-36.
  12. Arman, A. A., A. E. Abbas and R. Hurriyati. 2015. Analysis of smart city technology initiatives for city manager to improve city services and quality of life based on ISO 37120. 2nd International Conference on Electronic governance and open society: Challenges in Eurasia. Proceedings, pp 193-198.
  13. Henseler, J¨org and Theo K. Dijkstra. 2015. Composite modeling. ADANCO 2.0. Kleve, Germany.
  14. Hu, L. T. and P.M. Bentler. 1998. Fit indices in covariance structure modeling sensitivity to under parameterized model misspecification. Psychological Methods. 3(4):424–453.
  15. Hu, L.T. and P.M. Bentler. 1999. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling. 6(1):1–55.
  16. Voorhees, C. M., et al. 2016. Discriminant validity testing in marketing : an analysis, causes for concern and proposed remedies. J. Academy Marketing Sci., 44(1):119–134.
  17. Cohen, J. 1988. Statistical power analysis for the behavioural sciences (2nd edn). Lawrence Erlbaum Associates Publishers, USA.