Artificial Intelligence Integrated Analysis of Weather and Emission Parameters for Characterizing Smog Dynamics and Mitigation Policy Design

In South Asia, smog has become a serious environmental problem that endangers public health, ecosystems, and the regional climate. To determine the primary causes of smog formation in Lahore in October and November, this study develops a dual analytical framework that combines cutting-edge machine learning with sector- and pollutant-specific emission analysis. To assess their relationship with AQI and create a high-accuracy predictive model, meteorological factors and emission data from key sectors were used to build Random Forest and XGBoost models. The study evaluates the joint effects of weather and emission loads on AQI variability by integrating atmospheric dynamics with comprehensive emission profiles. The XGBoost model forecasts important pollutants from the transportation, industrial, and agricultural sectors, including CO2, NOx, VOCs, and particulate matter, in the second analytical tier. The models consistently identified particulate matter, NOx, and transport-related pollutants as the major determinants of AQI, with high prediction performance (R² > 0.97). The transportation sector accounts for around 90% of Lahore's yearly emissions. These results offer policymakers a useful tool to anticipate air quality, identify important emission sources, and execute targeted initiatives to minimize smog and promote a healthier urban environment. They also clearly demonstrate the causes of atmospheric and sectoral pollution.