Asian Journal of Pure and Applied Mathematics

Background: The Air Quality Index (AQI), developed by the Central Pollution Control Board (CPCB), is used to communicate overall air quality by combining multiple pollutants into a single value with defined health categories.

Aim: The present study conducted a comprehensive data-driven statistical assessment of ambient air quality parameters in Agartala Smart City, with comparative analysis of Guwahati and Delhi, representing different levels of urbanization, emission intensity, and meteorological variability.

Study Design: The study adopts a comparative and analytical research design integrating environmental chemistry with statistical methods to examine spatial and temporal variations in air pollution.

Place and Duration of Study: The study focuses on three Indian cities—Agartala, Guwahati, and Delhi. Secondary data were collected for a defined study period from official monitoring agencies.

Methodology: The research considers major air quality indicators such as PM₂.₅, PM₁₀, NO₂, and SO₂, which are key determinants of the Air Quality Index (AQI) and public health risk. Data were obtained from the Central Pollution Control Board and respective State Pollution Control Boards. A structured statistical framework was employed, including descriptive statistics, coefficient of variation, correlation analysis, and time-series-based linear trend modeling. These techniques were used to evaluate pollutant concentration patterns, variability, interrelationships, and trends across the selected cities. Comparative statistical diagnostics were applied to identify regional disparities in atmospheric pollution characteristics.

Results: Both North-Eastern (NE) cities show a sharp 2020 dip COVID-19 lockdown effect  followed by rapid recovery. Delhi's AQI remains persistently 2.5× higher than Agartala's. Agartala sits above Guwahati in most years but gap is closing sharply post-2020 in PM_2.5. PM₁₀ concentrations are driven primarily by road dust, construction, and industrial emissions. Guwahati's topographic trapping is especially visible — its PM₁₀ rose 31% from 2014 to 2023.

Discussion: The findings reveal significant inter-city variation in air quality. Delhi exhibits consistently higher by 11x according to WHO standards pollutant concentrations due to intense vehicular traffic, industrial emissions, and construction activities. Guwahati shows moderate pollution levels influenced by urban expansion and meteorological conditions. Agartala, although relatively less polluted, demonstrates increasing variability in particulate matter levels due to transportation, roadside dust, biomass burning, and seasonal effects. The coefficient of variation and correlation analysis indicate strong interdependence among pollutants, suggesting common anthropogenic sources and atmospheric transformation processes.

Conclusion: The study demonstrates that statistical analysis provides valuable insights into urban air pollution dynamics and regional variability. While Agartala currently maintains relatively better air quality, the observed increasing variability in pollutant levels indicates emerging environmental concerns. The findings highlight the need for proactive, region-specific air quality management strategies and reinforce the importance of data-driven approaches for sustainable urban environmental planning and policy formulation.