A Quantitative Approach to Prioritize Causes of Air Pollution in Delhi

IJEP 43(11): 1008-1013 : Vol. 43 Issue. 11 (November 2023)

Vibha Gaur1, Ramit Yadav1 and Ravneet Kaur2*

1. Acharya Narendra Dev College, Department of Computer Science, University of Delhi, New Delhi – 110 019, India
2. Acharya Narendra Dev College, Department of Electronics, University of Delhi, New Delhi – 110 019, India


Pollution is one of the most important issues that has attracted the interest of all countries and communities worldwide. India is the eighth most polluted nation in the world and Delhi is the second most polluted capital in 2022. The proposed work uses intuitionistic fuzzy sets (IFS) to find out what makes Delhi’s air dirty at different times of the year. As the association of causes with components of pollution involves the subjective opinion of environmental scientists, this work employs IFS. The objective of the study is to develop a method for prioritizing the causes to identify the primary sources of Delhi’s air pollution. This work ranks causes of pollution in terms of their contribution to AQI. Results of the study indicate that in winter, spring and autumn, vehicular emissions cause the most air pollution, followed by road dust, smoke, construction and industrial emissions. In contrast, during the summer and monsoon seasons, road dust has the greatest impact on air quality. Government agencies can use the results to address the serious causes of air pollution.


Air quality index, Air pollution, intuitionistic fuzzy sets, Particulate matter


  1. Manojkumar, N. and B. Srimuruganandam. 2021. Size-segregated particulate matter and health effects in air pollution in India: A review. Env. Chem. Lett., 19 (5): 3837-3858.
  2. Singh, B., et al. 2023. Substantial changes in gaseous pollutants and health effects during Covid-19 in Delhi, India. Peer. J., 11:e14489.
  3. Varshney, S., et al. 2021. Exploratory analysis of air quality in India. Indian J. Env. Prot., 41(12): 1410-1417.
  4. Somvanshi, S.S., et al. 2019. Delhi air pollution modelling using remote sensing technique. In Handbook of environmental materials management. Ed C. Hussain. Springer, Cham.
  5. Selokar, A., et al. 2020. PM2.5particulate matter and its effects in Delhi/NCR. Mater. Today Proc., 33: 4566-4572.
  6. Sinha, A. and S. Singh. 2021. Dynamic forecasting of air pollution in Delhi zone using machine learning algorithm. Quantum J. Eng. Sci. Tech., 2(3):40-53.
  7. Cusworth, D.H., et al. 2018. Quantifying the influence of agricultural fires in northwest India on urban air pollution in Delhi, India. Env. Res. Lett., 13: 044018.
  8. Saxena, P., et al. 2021. Impact of crop residue burning in Haryana on the air quality of Delhi, India. Heliyon. 7(5): e06973.
  9. Choudhary, M.C., H.D. Charan and B. Acharya. 2021. Potential of biochar derived from crop residues in soil remediation and controlling air pollution due to stubble burning. Indian J. Env. Prot., 41(2): 207-212.
  10. Somvanshi, A. 2021. Pandemic and air pollution in Delhi-NCR: Insights on World Environment Day. Center for Science and Environment, New Delhi.
  11. Gangwar, C., et al. 2021. Impact of Covid-19 lockdown on air quality index of the brass city of India. Indian J. Env. Prot., 41(11): 1263-1267.
  12. Singh, G., et al. 2021. Air pollution in Delhi– Impact of digital media on Denizen’s behaviour. Indian J. Env. Prot., 41(11): 1284-1289.
  13. Patel, K., et al. 2021. Sources and dynamics of submicron aerosol during the autumn onset of the air pollution season in Delhi, India. ACS Earth Space Chem., 5(1): 118-128.
  14. Gulia, S., et al. 2021. Policy interventions and their impact on air quality in Delhi city- an analysis of 17 years of data. Water Air Soil Poll., 232: 465.
  15. Atanassov, K.T. 2012. On intuitionistic fuzzy sets theory. Springer Berlin, Heidelberg.
  16. Ahmad, Y., et al. 2021. An intuitionistic fuzzy based approach to resolve detected ambiguities in the user requirements document. Ieee Access. 9:114547-114563.
  17. Li, D.F. 2005. Multi-attribute decision-making models and methods using intuitionistic fuzzy sets. J. Computer System Sci., 70(1):73-85.
  18. Bedi, P. and V. Gaur. 2007. Prioritizing quality specifications of multi-agent systems. World Congress on Engineering (WCE) 2007. Proceedings, pp 541-546.
  19. Ecer, F. 2022. An extended MAIRCA method using intuitionistic fuzzy sets for Coronavirus vaccine selection in the age of Covid-19. Neural Comput., Application. 34: 5603–5623.
  20. Thao, N.X. and S.Y. Chou. 2022. Novel similarity measures, entropy of intuitionistic fuzzy sets and their application in software quality evaluation. Soft Comput., 26: 2009–2020.
  21. Kejia, H., et al. 2020. Assessing technology portfolios of clean energy-driven desalination-irrigation systems with interval-valued intuitionistic fuzzy sets. Renew. Sustain. Energy Reviews. 132: 109950.
  22. Xue, Y. and Y. Deng. 2021. Decision making under measure-based granular uncertainty with intui-tionistic fuzzy sets. Appl. Intell., 51:6224–6233.
  23. Atanassov, K. 1999. Intuitionistic fuzzy sets: Theory and applications. Studies in fuzziness and soft computing (vol 35). Physica, Heidelberg. DOI: 10.1007/978-3-7908-1870-3_1.
  24. ARAI and TERI. 2018. Source apportionment of PM2.5 and PM10 of Delhi NCR for identification of major sources (report no. ARAI/16-17/DHI-SA-NCR/final report). The Automotive Research Association of India (ARAI) and The Energy and Resources Institute (TERI).