Page 33 - 2023

To develop a new composite air quality index, the development of air quality indexing
systems from 1966 to 2021, fuzzy-based air quality indexing systems developed from
2010 to 2017, and existing air quality indexing systems in India have been studied. Based
on the chronological study of air quality indexing systems, it is concluded that the
aggregate index estimates more efficiently the pollutants exposure to the population than
US EPA-based AQI. And a fuzzy-based air quality indexing system is a more powerful tool
to represent uncertain environmental conditions. The literature review also concluded
that out of several fuzzy-based air quality indexing systems, the fuzzy synthetic
evaluation-based air quality indexing system is a more powerful tool for describing air
quality.

Ahmedabad city is selected as a study area to develop a fuzzy synthetic evaluation-based
air quality indexing system. The annual average concentration of air pollutants data from
2007 to 2018 has been collected, and bar charts have been prepared to analyze the air
pollution trend in Ahmedabad. The analysis of the collected data shows that the annual
average concentration of gaseous pollutants such as SO2, NO2, CO, O3, and HC is within
NAAQS, 2009. While the annual average concentration of respirable suspended
particulate matter (PM10) exceeds the standards at all the monitoring locations. From
2015 to 2018, particulate matter (PM10 and PM2.5) shows an increasing trend. So, based
on the trend analysis, it is concluded that particulate matter is responsible for air pollution
in Ahmedabad.
In Ahmedabad, six locations within the Ahmedabad Municipal Corporation (AMC)
boundary have been selected to monitor ambient air quality and to develop the fuzzy-
based composite air quality index (CAQI). Ten to fifteen days of monitoring have been
done at each location for the summer and winter seasons. So, 150 days, i.e., 3600 hours
of monitoring, have been done to collect the data. A sensor-based continuous ambient air
quality monitoring instrument known as Polludrone is used to monitor ambient air
quality. Six pollutants: PM10, PM2.5, NO2, SO2, CO, and O3 are selected, and aggregation
and synergetic effects of pollutants are considered. Experts' opinions are taken, and the
analytical hierarchical process (AHP) method is used to give the weights to the
pollutants. As per the weightage given to the pollutants, the impact of fine particulate
matter (PM2.5) is more on human health, followed by nitrogen dioxide (NO2), ozone
(O3), carbon

monoxide (CO), sulfur dioxide (SO2), and Respirable suspended particulate matter
(PM10).

Descriptive statistics have been calculated for the collected data, time series analysis has
been done, and a Pearson r correlation matrix has been prepared. The Pearson r

08