AI-Driven Analysis of Meteorological and Emission Characteristics Influencing Urban Smog: A Foundational Insight into Air Quality
Year: 2026
Type: article
Abstract: 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.
Source: Gases
DOI: 10.3390/gases6010010
Authors: Sadaf Zeeshan, Muhammad Ali Ijaz Malik
Institutions: University of Central Punjab, University of Technology Sydney
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,207
OpenAlex ID: W7127906148