Abstract This study investigates the spatial and temporal dynamics of air pollution in Guwahati, a rapidly urbanizing city in Northeast India, by integrating Geographic Information System (GIS)-based land-use analysis with advanced econometric techniques. Using daily air quality data from 1,461 observations across 2020–2023, the study examines major pollutants—PM2.5, PM10, NOₓ, SO₂, CO, NH₃, and O₃—through Robust Least Squares (RLS), Quantile Regression (QR), and Vector Autoregression (VAR) models. Spatial interpolation (IDW) and land-use overlays reveal high concentrations of PM2.5 and AQI values in traffic-heavy and construction-saturated zones such as GS Road and Beltola, coinciding with a 275% increase in built-up land over the past decade. Statistical findings confirm PM2.5 as the most consistent and dominant AQI predictor (RLS coefficient = 1.189; p < 0.01), followed by PM10, NH₃, and SO₂. Seasonal analysis shows winter peaks in PM2.5 (mean = 113.05 µg/m 3 ) due to temperature inversions and limited dispersion. QR results reveal pollutant impacts intensify at higher AQI quantiles, especially for PM2.5, SO₂, and CO, indicating disproportionate health risks during severe episodes. These findings have direct implications for targeted air quality management, urban planning, and health policy. By linking pollutant behavior with land-use patterns, temporal feedbacks, and exposure risks, the study contributes a multidimensional framework for understanding air pollution in mid-sized South Asian cities and underscores the urgency of localized, data-informed interventions.
Das et al. (2025) studied this question.