Abstract
In the current scenario, the determination of air quality is of utmost importance, as it is the most imperative factor for human health and the environment. Monitoring of air pollutants and their estimation is one of the essential prerequisites for predicting air pollution. The Air Quality Index (AQI) is a reliable indicator of air quality in any location. The chief air pollutants considered for assessment of AQI are particulate matter, SO2, NO2, ground-level ozone, and carbon monoxide. The Central Pollution Control Board and State Pollution Control Boards in India monitor the quality of the air. The conventional AQI determination implies a linear interpolation method to calculate the AQI. A discrete score is allocated to each pollutant based on its measured concentration, and the AQI is computed using the pollutant with the highest score. The present investigation is done to compute AQI in the territory of Gujarat from 2015 to 2017 by using a fuzzy inference system. In order to create the fuzzy air quality index in this work, the fuzzy logic system is used, and membership functions are fed into the Mamdani fuzzy inference system (FIS). The projected AQI values using a fuzzy system are compared with the conventionally calculated AQI values. It is observed that the Fuzzy logic-based system is a more consistent method and provides precise prediction. As a result, the article suggests a fuzzy logic-based method for choosing the Air Quality Index that becomes progressively dependable.
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References
Anand A, Ankita B (2011) Revised Air Quality Standards for Particle Pollution and Updates to the Air Quality Index 25:24–29
Anand K, Ashish G, Upendu P (2011) A study of ambient air quality status in Jaipur city (Rajasthan, India), using air quality index. Nat Sci 9(6):38–43
Balashanmugan P, Ramanathan A, Elango E, Nehru Kumar V (2012) Assessment of ambient air quality Chidambaram a South Indian Town. J Eng Sci Technol 7(3)
Daniel D, Alexandra A, Emil L (2011) Fuzzy inference systems for estimation of air quality index. Rom Int J 7(2):63–70
Gopal U, Nilesh D (2010) Monitoring of air pollution by using fuzzy-logic. Int J Comput Sci Eng IJCSE 2(7):2282–2286
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)
Mckone TE, Deshpande AW (2010) Can fuzzy logic bring complex environmental problems into focus? IEEE 15:364–368
Nagendra SM, Venugopal K, Jones SL (2007) Assessment of air quality near traffic intersections in Bangalore city using air quality indices. J Elsevier 1361–9209
Nihalani S, Kadam S (2019) Ambient air quality assessment for Vadodara City using AQI and exceedence factor. In: Proceedings of ınternational conference on advancements in computing and management (ICACM)
Nihalani SA et al (2020) Air quality assessment using fuzzy inference systems. In: Advanced engineering optimization through intelligent techniques. Springer, Singapore, pp 313–322
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, Chaouch Wafa B, Souad B (2014) Air quality index and public health: modelling using fuzzy inference system. Am J Environ Eng Sci 1(4):85–89
Sadhana C, Pragya D, Ravindra S, Anand 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
Zadeh LA (2010) A summary and update of “fuzzy logic”. In: 2010 IEEE ınternational conference on granular computing. IEEE
Acknowledgements
The study is part of the Industry Defined Research Project titled “AI, IoT and Digital Technologies for Future Sustainable Cities” funded by Royal Academy of Engineering under Newton Bhabha Fund in collaboration with L&T S&L, Nirveda Technologies and NASSCOM with Parul University.
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Nihalani, S.A. (2023). Fuzzy Air Quality Index for Air Quality Assessment in Gujarat. In: Al Khaddar, R., Singh, S.K., Kaushika, N.D., Tomar, R.K., Jain, S.K. (eds) Recent Developments in Energy and Environmental Engineering. TRACE 2022. Lecture Notes in Civil Engineering, vol 333. Springer, Singapore. https://doi.org/10.1007/978-981-99-1388-6_36
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DOI: https://doi.org/10.1007/978-981-99-1388-6_36
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