Fuzzy Inference of Air Quality – A case study of Vadodara City

Document Type : Original Research Paper


Civil Engg Dept, PIET, Parul University, Vadodara, Gujarat, India



Abstract: Air Quality Index (AQI) is derived from a series of observations of different air pollutants for reporting air quality. The severity of air pollution and its impacts on the general public are typically reported using the air quality index. Different methods have been developed by various regulatory agencies and scientists, to calculate the AQI using aggregation methods involving critical pollutants. This paper presents a comparison between conventional AQI and Fuzzy AQI. 20 sampling locations were chosen for Vadodara city in order to investigate the effects of urban air pollution, and ambient air quality was measured twice a week from October 2017 to February 2018. The Central Pollution Control Board (CPCB) method formulas were used to calculate the traditional Air Quality Index using the measured values of Coarse particulate matter (PM10), Sulphur dioxide  (SO2), and Oxides of nitrogen (NOX).  Additionally, the membership functions were provided as input to the Mamdani fuzzy inference system (FIS) for the fuzzy logic system, and the fuzzy air quality index (FAQI) was calculated. The computed conventional AQI values were compared with FAQI values. A close co-relation was observed between conventional AQI and fuzzy AQI values. The application of the fuzzy inference system demonstrates its capability to manage difficult issues including data ambiguity. The findings clearly show that the FIS is capable of resolving inherent discrepancies and interpreting complex conditions.


Main Subjects

Aggarwal, A., Choudhary, T., & Kumar, P. (2017, December). A fuzzy interface system for determining Air Quality Index. In 2017 International conference on infocom technologies and unmanned systems (trends and future directions)(ICTUS) (pp. 786-790). IEEE.
Balashanmugam, P., Ramanathan, A. R., & Kumar, V. N. (2012). Assessment of ambient air quality in Chidambaram a South Indian town. Journal of Engineering Science and technology, 7(3), 292-302.
Charan, P. D., & Sahel, H. (2014). Study of respirable dust in ambient air of Bikaner city and its impact on human health. Applied Journal of Hygiene, 3(1), 11-14.
Chaudhary, P., Singh, D., Singh, S. K., & Kumar, J. (2013). Assessment of ambient air quality in Northern India using Air Quality Index method. Bulletin of Environmental and scientific Research, 2(2-3), 12-17.
Chaurasia, S., Dwivedi, P., Singh, R., & Gupta, A. D. (2013). Assessment of ambient air quality status and air quality index of Bhopal city (Madhya Pradesh), India. Int. J. Curr. Sci, 9, 96-101.
Deshpande, A., Yadav, J., & Kharat, V. (2014). Zadeh-Deshpande Approach for Fuzzy Description of Air and Water Quality. BVICAM’s International Journal of Information Technology, 6(1).
Dunea, D., Pohoata, A. A., & Lungu, E. (2011). Fuzzy inference systems for estimation of air quality index. ROMAI J, 7(2), 63-70.
Ganesh, S. S., Reddy, N. B., & Arulmozhivarman, P. (2017, May). Forecasting air quality index based on Mamdani fuzzy inference system. In 2017 international conference on trends in electronics and informatics (ICEI) (pp. 338-341). IEEE.
Gorai, A. K. (2012). Application of Fuzzy Pattern Recognition Optimisation Model for Air Quality Assessment. International Journal of Environmental Protection, 2(5), 27-30.
Javid, A., Hamedian, A. A., Gharibi, H., & Sowlat, M. H. (2016). Towards the application of fuzzy logic for developing a novel indoor air quality, index (FIAQI). Iranian Journal of Public Health, 45(2), 203.
Juned, M. S., & Hemangi, D. (2014). Assessment of ambient air quality index of Surat city during early morning hours. Journal of Environmental Research and Development, 8(3), 384.
Katushabe, C., Kumaran, S., & Masabo, E. (2021). Fuzzy Based Prediction Model for Air Quality Monitoring for Kampala City in East Africa. Applied System Innovation, 4(3), 44.
Kumar, S. S., & Sharma, K. (2016). Ambient air quality status of Jaipur city, Rajasthan, India. Int Res J Environment Sci, 5, 43-48.
Kumaravel, R., & Vallinayagam, V. (2012). Fuzzy inference system for air quality in using Matlab, Chennai, India. J. Environ. Res. Dev, 7(1), 181-184.
Lokeshappa, B., & Kamath, G. (2016). Feasibility analysis of air quality indices using fuzzy logic. Int. J. Eng. Res. Technol, 5(8).
Mandal, T., Gorai, A. K., & Pathak, G. (2012). Development of fuzzy air quality index using soft computing approach. Environmental monitoring and assessment, 184, 6187-6196.
Mishra, D., & Goyal, P. (2015). Analysis of ambient air quality using fuzzy air quality index: a case study of Delhi, India. International Journal of Environment and Pollution, 58(3), 149-159.
Nagendra, S. S., Venugopal, K., & Jones, S. L. (2007). Assessment of air quality near traffic intersections in Bangalore city using air quality indices. Transportation Research Part D: Transport and Environment, 12(3), 167-176.
Nihalani, S. A., Moondra, N., Khambete, A. K., Christian, R. A., & Jariwala, N. D. (2020). Air quality assessment using fuzzy inference systems. In Advanced Engineering Optimization Through Intelligent Techniques: Select Proceedings of AEOTIT 2018 (pp. 313-322). Springer Singapore.
Nihalani, S., & Kadam, S. (2019, October). Ambient air quality assessment for Vadodara City using AQI and exceedence factor. In Proceedings of International Conference on Advancements in Computing & Management (ICACM).
Pankaj, D., Suresh, J., & Nilesh, D. (2010). 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.
Saddek, B., Chahra, B., Wafa, B. C., & Souad, B. (2014). Air quality index and public health: Modelling using fuzzy inference system. American Journal of Environmental Engineering and Science, 1(4), 85-89.
Saini, J., Dutta, M., & Marques, G. (2022). ADFIST: Adaptive dynamic fuzzy inference system tree driven by optimized knowledge base for indoor air quality assessment. Sensors, 22(3), 1008.
Sarella, G., & Khambete, A. K. (2015). Ambient air quality analysis using air quality index—A case study of Vapi. International Journal for Innovative Research in Science & Technology, 1(10).
Shafii, N. H., Mohd Ramle, N. A., Alias, R., Md Nasir, D. S., & Fauzi, N. F. (2021). Application of fuzzy inference system in the prediction of air quality index. Journal of Computing Research and Innovation (JCRINN), 6(3), 75-85.
Swarna, E., & Nirmala, M. (2017, August). Analysis of air quality indices using fuzzy inference system. In 2017 IEEE International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials (ICSTM) (pp. 203-207). IEEE.
Upadhyay, A., Kanchan, K., Goyal, P. G., Yerramilli, A., & Gorai, A. K. (2014). Development of a fuzzy pattern recognition model for air quality assessment of Howrah City. Aerosol and Air Quality Research, 14(6), 1639-1652.
Upadhyaya, G., & Dashore, N. (2010). Monitoring of air pollution by using fuzzy logic. International Journal on Computer Science and Engineering, 2(07), 2282-2286.
Uthayakumar, H., Thangavelu, P., & Ramanujam, S. (2021). Forecasting of outdoor air quality index using adaptive neuro fuzzy inference system. Journal of Air Pollution and Health, 6(3), 161-170.
Yadav, J. Y., Kharat, V., & Deshpande, A. (2011). Fuzzy description of air quality: a case study. In Rough Sets and Knowledge Technology: 6th International Conference, RSKT 2011, Banff, Canada, October 9-12, 2011. Proceedings 6 (pp. 420-427). Springer Berlin Heidelberg.
Yadav, S. K., Kumar, V., & Singh, M. M. (2012). Assessment of ambient air quality status in urban residential areas of Jhansi city and rural residential areas of adjoining villages of Jhansi city. International journal of advanced engineering technology, 3(1), 280-285.
Zadeh, L. A. (2010, August). A summary and update of “fuzzy logic”. In 2010 IEEE International Conference on Granular Computing (pp. 42-44). IEEE.