Air Quality Assessment Using Fuzzy Inference Systems

  • Seema A. NihalaniEmail author
  • Nandini Moondra
  • A. K. Khambete
  • R. A. Christian
  • N. D. Jariwala
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 949)


Air quality index (AQI) is a consistent pointer of the air quality for any locality. AQI may consider key air pollutants like sulphur dioxide, nitrogen dioxide, ground-level ozone, carbon monoxide and particulates. Several organisations worldwide calculate these indices, using different definitions. In India, Central Pollution Control Board, as well as State Pollution Control Boards, monitors the air quality level. A distinct sub-index is allocated to individual pollutant, and AQI is the maximum value of all these sub-indices. AQI is a numeral value that may be designated linguistically by stating that air quality is poor, fair and good [2]. When AQI upsurges, it is assumed that a substantial fraction of the residents is expected to face severe health effects. The present study is carried out to quantify air pollutant concentrations for four cities in Gujarat from 2011 to 2015. Conventional AQI is calculated by using a mathematical formula. Fuzzy logic system is used, and membership functions are fed as input to the Mamdani fuzzy inference system (FIS) to find out the fuzzy air quality index. Hence, the paper postulates a more consistent method for determining air quality index using the fuzzy logic system.


Air quality index Fuzzy logic Membership function Rule editor 


  1. 1.
    Anand, A., Ankita, B.: Revised air quality standards for particle pollution and updates to the Air Quality Index, vol. 25, pp. 24–29 (2011)Google Scholar
  2. 2.
    Anand, K., Ashish, G., Upendu, P.: A study of ambient air quality status in Jaipur city (Rajasthan, India), using Air Quality Index. Nat. Sci. 9(6), 38–43 (2011)Google Scholar
  3. 3.
    Balashanmugan, P., Ramanathan, A., Elango, E., Nehru Kumar, V.: assessment of ambient air quality Chidambaram a South Indian Town. J. Eng. Sci. Technol. 7(3) (2012)Google Scholar
  4. 4.
    Daniel, D., Alexandra, A., Emil, L.: Fuzzy inference systems for estimation of Air Quality Index. Rom. Int. J. 7(2), 63–70 (2011)Google Scholar
  5. 5.
    Gopal, U., Nilesh, D.: Monitoring of air pollution by using fuzzy-logic. Int. J. Comput. Sci. Eng. IJCSE 2(7), 2282–2286 (2010)Google Scholar
  6. 6.
    Kumaravel, R., Vallinayagam, V.: Fuzzy inference system for air quality in using Matlab, Chennai, India. J. Environ. Res. Dev. 7(1), 181–184 (2012)Google Scholar
  7. 7.
    Lokeshappa, B., Kamath, G.: Feasibility analysis of air quality indices using fuzzy logic. Int. J. Eng. Res. Technol. 5(8) (2016)Google Scholar
  8. 8.
    Mckone, T.E., Deshpande, A.W.: Can fuzzy logic bring complex environmental problems into focus? IEEE 15, 364–368 (2010)Google Scholar
  9. 9.
    Nagendra, S.M., Venugopal, K., Jones, S.L.: Assessment of air quality near traffic intersections in Bangalore city using air quality indices. J. Elsevier, 1361–9209 (2007)Google Scholar
  10. 10.
    Pankaj, D., Suresh, J., Nilesh, D.: Fuzzy rule based meta graph model of Air Quality Index to suggest outdoor activities. Int. J. Comput. Sci. Eng. Technol. (IJCSET) 2(1), 2229–3345 (2010)Google Scholar
  11. 11.
    Saddek, B., Chahra, B., Chaouch Wafa, B., Souad, B.: Air Quality Index and public health: modelling using fuzzy inference system. Am. J. Environ. Eng. Sci. 1(4), 85–89 (2014)Google Scholar
  12. 12.
    Sadhana, C., Pragya, D., Ravindra, S., Anand, D.: Assessment of ambient air Quality Status and Air Quality Index of Bhopal city (Madhya Pradesh), India. Int. J. Curr. Sci. 9, 96–101 (2013)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Seema A. Nihalani
    • 1
    • 2
    Email author
  • Nandini Moondra
    • 1
  • A. K. Khambete
    • 1
  • R. A. Christian
    • 1
  • N. D. Jariwala
    • 1
  1. 1.SVNITSuratIndia
  2. 2.Parul UniversityVadodaraIndia

Personalised recommendations