Abhishek Upadhyay1, Kanchan Kanchan1, Pramila Goyal Goyal2, Anjaneyulu Yerramilli3, Amit Kumar Gorai 1

  • 1 Environmental Science & Engineering Group, Birla Institute of Technology, Mesra, Ranchi -835215, India
  • 2 Centre for Atmospheric Sciences, IIT Delhi, Delhi-110016, India
  • 3 Trent Lott Geospatial and Visualisation Research Centre, Mississippi, Jackson State University, 1230 Raymond Road, Jackson, MS39204, USA

Received: April 11, 2013
Revised: January 6, 2014
Accepted: January 6, 2014
Download Citation: ||https://doi.org/10.4209/aaqr.2013.04.0118  

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Cite this article:
Upadhyay, A., Kanchan, K., Goyal, P.G., Yerramilli, A. and Gorai, A.K. (2014). Development of a Fuzzy Pattern Recognition Model for Air Quality Assessment of Howrah City. Aerosol Air Qual. Res. 14: 1639-1652. https://doi.org/10.4209/aaqr.2013.04.0118



The district of Howrah is one of the most highly industrialized districts in West Bengal, India. Howrah City continues to suffer from poor ambient air quality due to the dense siting of small scale industries without air pollution management, huge traffic congestion and high levels of human settlement. This paper presents the trends of air pollution concentration (O3, CO, SO2, NO2, and PM10) in Howrah City, and also demonstrates a new methodology for air quality assessment using an AHP coupled fuzzy pattern recognition model. The annual average of PM10 concentration has decreased from 2009 (185.57 ± 121.16 µg/m3) to 2011 (160.01 ± 117.32 µg/m3). A similar trend was observed for the CO concentration. The eight-hour average concentration of CO in 2011 (0.939 ± 0.632 mg/m3) was found to be lower than that in 2009 (1.59 ± 0.72 mg/m3), while the reverse trend was observed for SO2 and NO2. The annual average concentration of SO2 increased from 2009 (17.68 ± 20.92 µg/m3) to 2011 (43.048 ± 31.47 µg/m3). The annual average concentration of NO2 increased from 2009 (63.87 ± 39.73 µg/m3) to 2011(78 ± 61.51 µg/m3). There was no uniform trend observed in the annual the eight-hour average concentration of ozone. An approach was developed in this study to determine fuzzy air quality based on the observed air pollution concentration. This will help to identify the air pollution control measures that are required in a certain area. The proposed method is a multi-pollutant aggregation method with varying weighting, and has the capability to consider subjective factors like sensitivity and population density. The concentrations of the five air pollutant parameters (O3, CO, SO2, NO2, and PM10) were used to develop the model for air quality assessment.

Keywords: Air Quality assessment; Fuzzy pattern recognition; Optimisation

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