Impact assessment of land surface temperature on air pollution and smog formation in major cities of Pakistan using Google Earth engine

Abstract

Rapid urbanization, industrial expansion, and reliance on fossil fuels cause serious air pollution. Air pollution affects public health, especially in developing countries like Pakistan. Pakistan is among the most affected nations, particularly its major urban centers, which experience recurrent seasonal smog episodes. This study assesses the effectiveness of Sentinel-5P satellite products integrated with Google Earth Engine (GEE) for monitoring spatiotemporal variations of key air pollutants: nitrogen dioxide (NO₂), carbon monoxide (CO), and ozone (O₃) in Lahore, Faisalabad, Multan, and Gujranwala from 2018 to 2023. Monthly, seasonal, and annual composites of cloud-filtered Sentinel-5P TROPOMI data were analyzed using GEE, and statistical regression and correlation analyses were applied to examine relationships between pollutant concentrations and land surface temperature (LST). The results reveal pronounced seasonal variability in air pollutant levels, with elevated NO₂ and CO concentrations during winter due to intensified anthropogenic emissions and atmospheric stability, while O₃ concentrations peaked during spring and summer due to enhanced photochemical reactions driven by high temperatures and solar radiation. Quantitatively, wintertime NO₂ concentrations reached approximately 0.009 mol m⁻², whereas summer O₃ concentrations peaked at about 0.30 mol m⁻². Strong temperature–pollutant relationships were observed in highly urbanized cities such as Lahore and Faisalabad, reflecting the combined effects of dense infrastructure, industrial activity, and urban heat island intensity. Additionally, the COVID-19 lockdown period provided evidence of temporary reductions in pollutant concentrations and LST, highlighting the dominant role of anthropogenic activities in air quality degradation. Overall, the findings demonstrate that Sentinel-5P data, combined with automated cloud-based platforms such as GEE, provides a robust, cost-effective framework for large-scale air quality monitoring. This approach can support informed policymaking, targeted mitigation strategies, and sustainable urban planning to reduce smog intensity and protect public health in Pakistan.