Using a Generalized Additive Model (GAM) framework and satellite-derived pollutant measurements, this research studies the contribution of socio-climatic parameters and urban form to air pollution in 32 Iranian cities to explain linear and nonlinear associations. After retrieving high-resolution satellite-based air quality data from Tropospheric Monitoring Instrument (TROPOMI) aboard Sentinel-5, they were integrated with different indicators, e.g., landscape fragmentation, urban contiguity, humidity, and land surface temperature (LST). The results showed that compact urban forms are related to higher aerosols, CO, and NO 2 levels, particularly in cold season. Moreover, urban fragmentation affected the pollution pattern, with localized emissions, e.g., NO 2 and SO 2 , trapped in highly fragmented landscapes. Higher LST was related to higher NO 2 and CO concentrations. Seasonal changes indicated the necessity of adaptive pollution management because in hot seasons, the O 3 formation accelerates, while cold seasons cause increased pollutant retention. The present study underscores the necessity of seasonally attuned, data-driven urban planning. Heat mitigation policy and green space promotion may reduce pollution retention near urban landscapes, and specific zoning regulations may alleviate the effect of urban fragmentation. The study findings support the importance of sustainable air-quality management. • Analyzes the impacts of urban form and socio-climatic factors on air quality using GAMs. • Compact cities trap more aerosols, NO 2 , and CO in cold seasons. • Seasonal temperature and humidity strongly affect pollution levels. • Urban fragmentation creates localized pollution hotspots. • Seasonal, data-driven planning can improve air quality.
Abdollahi et al. (Wed,) studied this question.