A COMPREHENSIVE SPATIAL-TEMPORAL ANALYSIS OF URBAN AIR QUALITY AND CLIMATE ADAPTATION DYNAMICS

Abstract

Urban agglomerations in developing nations are increasingly positioned at the intersection of severe ambient air pollution and large-scale climate change. This case study examines the spatiotemporal dynamics of global warming and air pollution in Lahore, Pakistan. Using longitudinal observational data from 2023 to 2025 and multivariate statistical modeling, including Pearson correlation and Ordinary Least Squares (OLS) multiple linear regression, the study evaluates the environmental drivers influencing Lahore’s Air Quality Index (AQI), fine particulate matter (PM2.5), coarse particulate matter (PM10), and key gaseous pollutants, including CO, NO₂, and SO₂. The results reveal severe winter peaks in AQI, exceeding 330, primarily associated with thermal inversions and regional crop burning (r = 0.811), while industrial activities and transportation remain persistent baseline contributors to air pollution. Econometric modeling produced an R² value of 0.98, indicating that the selected variables explain a substantial proportion of the variation in AQI. PM2.5 (β = 1.352), Industrial Index (β = 1.076), and Traffic Index (β = 0.939) exerted significant positive structural pressures on AQI, whereas temperature demonstrated a strong inverse relationship (β = −1.760). To address these interconnected environmental challenges, the study proposes an integrated urban adaptation framework incorporating spatial densification control, green infrastructure, thermal performance standards, and real-time cross-boundary environmental governance.