Abstract:
About 8.1 million mortalities globally, including 2 million in South Asia, and 64,000 in
Pakistan are attributed to Particulate Matter (PM2.5) of size 2.5 microns or less (SOGA,
2019; UNICEF, 2024; World Bank, 2023). Despite being among the leading countries with
hazardous air quality, poor air quality monitoring infrastructure and repetition of
incompetent policies without proper enforcement, remain persistent challenges in Pakistan.
Therefore, spatiotemporal trend analysis of PM2.5 is crucial for building Pakistan's air
quality profile. This study aims to identify the good air quality conditions (referred to as
Blue-Sky conditions) in Punjab, Pakistan. A recently deployed network of Low-Cost
Sensors (LCS) is used for ground-based monitoring of PM2.5 in Punjab. These ground-
based sensors are known for their cost-effectiveness, portability, and applicability in
remote areas. Furthermore, satellite-based remote sensing data is used for long-term trend
analysis of air pollution. The 1 km spatial resolution product (MCD19A2) from the Multi-
Angle Implementation of Atmospheric Correction (MAIAC) algorithm derived from the
MODIS (Moderate Resolution Imaging Spectro-radiometer) satellite is exploited and
evaluated for the study period of 2000-2023. Consequently, the blue-sky conditions are
identified across Punjab, Pakistan based on AOD (Aerosol Optical Depth; 2000-2023) and
AQI (Air Quality Index; 2022-2023). While these retrieval algorithms have been
incorporated in most studies separately, the empirical relationship of AOD with PM2.5 has
yet to be investigated. Therefore, the relationship between AOD and PM2.5 is explored at
20 sites across Punjab at different temporal scales. AERONET and US Consulate monitors
are used for validation. Lastly, gaps are highlighted in the recent air pollution control policy
xvi
of Punjab along with their implications. This comprehensive and novel analysis of existing
blue-sky conditions across Punjab will serve as a baseline for academia, researchers, and
policymakers to address air pollution and its consequent health impacts.
