Abstract
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.
Highlights
Ambient air quality has been given continuous attention in the whole world for many years and air pollution affects human health, and restricts the city’s economic development
This paper presents the trends of air pollution concentration (O3, CO, SO2, NO2, and PM10) in Howrah City, and demonstrates a new methodology for air quality assessment using an analytical hierarchical process (AHP) coupled fuzzy pattern recognition model
The fuzzy air quality index values in fuzzy pattern recognition method reflected by the relative membership degree of the pollution concentration data
Summary
Ambient air quality has been given continuous attention in the whole world for many years and air pollution affects human health, and restricts the city’s economic development. Many cities continuously assess air quality using monitoring networks designed to measure and record air pollution concentrations at several points deemed to represent exposure of the population to these pollutants.There has been an increasing used by national and international agencies to inform environmental policies, and quantification of the impact of air pollution on public health has gradually become a critical component in policy discussions as governments weigh options for the control of pollution. Baldauf et al (2002) proposed risk assessment method for establishing air quality monitoring networks based on maximum concentration impacts or maximum total population. A more sophisticated tool has been developed to communicate the health risk of ambient concentrations by using an air pollution (or air quality, AQI) index (API). Various air quality indices have been developed to integrate air quality variables worldwide (ORAQI, 1970; USEPA, 1976; Ontario, 1991; GVAQI, 1997; Malaysia, 1997; UK, 1998; USEPA, 1998) but most of the methods have different characteristics with the type of aggregation and with the number and type of pollutants
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