Smog is a form of air pollution resulting from the chemical interaction of sunlight with emissions from vehicles, industrial facilities, and other sources, producing ground-level ozone and particulate matter that significantly degrades air quality and public health. As a GIS use case, smog analysis leverages geospatial data, remote sensing imagery, and spatial analysis techniques to monitor, model, and visualize pollution distribution across geographic areas. GIS professionals integrate multi-source datasets — including satellite-derived atmospheric readings, ground-based sensor networks, land use classifications, and meteorological data — within geospatial platforms to generate high-resolution smog concentration maps and predictive dispersion models. Spatial interpolation methods such as kriging and inverse distance weighting enable analysts to estimate pollution levels in unmonitored locations with measurable accuracy. These mapping workflows support critical decision-making for environmental agencies, urban planners, and public health officials by identifying pollution hotspots, assessing vulnerable population exposures, and evaluating the effectiveness of emission reduction policies. The integration of real-time geospatial data streams further enhances situational awareness, enabling dynamic monitoring dashboards that communicate air quality conditions to both technical stakeholders and the broader public.