Delhi, one of the most polluted cities globally, serves as a critical case study for examining the
dynamics of particulate matter (PM2.5 and PM10), which significantly affects health, visibility,
and climate. Existing research predominantly focuses on short-term datasets or a limited
number of air quality monitoring stations, with little attention given to the long-term influence
of meteorology. The present work addresses these gaps by investigating the temporal
variability of particulate matter (PM2.5 and PM10) in Delhi from 2007 to 2022 across multiple
timescales daily, monthly, seasonal, annual, and inter-annual while exploring its relationship
with meteorological factors. Using robust non-parametric statistical techniques, including the
Mann-Kendall test, Theil-Sen slope estimator, and smooth-trends derived from Generalized
Additive Modelling (GAM), this study analyses long-term trends in PM2.5, PM10, and key
meteorological parameters. A unique aspect of this research is the examination of the COVID-
19 lockdown, which provided an unprecedented opportunity to observe the substantial
reduction in pollution levels due to the sudden cessation of anthropogenic activities. The
findings reveal how specific meteorological conditions such as low temperatures, calm winds,
and high relative humidity contributed to the formation of haze events and sharp increases in
PM2.5 concentrations during morning hours, underscoring the interaction between baseline
pollution and local meteorology.

Furthermore, the current topic decodes the monthly, seasonal, inter-annual variability, and
long-term trends in PM2.5 (2007–2022) and PM10 (2015–2022), offering a multi-year
comprehensive assessment of both meteorology-adjusted and unadjusted trends in PM2.5
using the AirGAM statistical model. The research highlights gradual air quality improvements
due to policy interventions and explores the non-linear relationships between particulate
matter and meteorological factors. Overall, presentation advances the understanding of
particulate pollution variability and its meteorological drivers, providing insights essential for
developing more effective air quality mitigation strategies.