Data analysis

The air quality index (AQI) is a measure of the cumulative effect of individual pollutants concentration on the quality of air in different places, but the modification of the method which is termed as National Air Quality Index (NAQI) is based on the maximum operator approach to avoid the uncertainty (CPCB 2014; Dadhich et al. 2018; Joshi and Mahadev 2011; Mahato et al. 2020). Central pollution control board (CPCB), India, monitors the ambient air continuously and has been using the EPA-US method to calculate the air quality indices (AQI) (Chaurasia et al. 2013; Kumar and Dash 2018). Here we only briefly summarized it. Calculation of Sub-indices for each and individual pollutant and then the aggregation of breakpoint values (sub-indices), which depend upon the Indian National Ambient Air Quality Standards (NAAQS), are the two steps involved in AQI calculation. The maximum value of the sub-indices is taken as the AQI. The AQI standards are displayed in the number form that describes the quality rating (potential health effects) as shown in Table ​Table22.

Proposed breakpoints for AQI scale 0–500.

(Source: CPCB 2014)

As the study is carried out in KMC and HMC, the municipal ward boundary map is designed in a GIS environment. ArcGIS 10.3, distributed by the Environmental Systems Research Institute (ESRI), has been employed for spatial analysis to identify air pollution levels. Based on the ten monitoring sites, the interpolated maps of each pollutant and AQI have been generated from pre-lockdown tenure to during lockdown tenure. In this study, inverse distance weighting (IDW) interpolator by linear combination model has been used because it is easy to operate than others without any pre-program assumptions to opt for a semi-variogram model (Jumaah et al. 2019; Poshtmasari et al. 2012).

ANOVA (Two-way), cluster analysis (single linkage) based on the Euclidean distance and Ward’s methods, and Pearson moment correlation coefficient (r) were computed using the IBM SPSS version23. STATA 12 was used for the Box plots of AQI. The ANOVA helps to understand the variation in air quality index in each location as well as in pre-lockdown and during lockdown tenure, while correlation analysis denotes the degree of association among the different pollutants is done using JMP 15. Box plots are especially valid for comparing two or more distribution and also present a graphical display based on the order-statistic summaries of median and quartiles. Tables and charts have been used to report descriptive statistics by calculating the simple percentage, tabulation, and cross-tabulation. Using MS Excel line graphs for each pollutant is graphically represented.