Predicting Air Quality in Pakistan with a Focus on Smog Formation: A Machine Learning Approach

Smog is a pressing environmental and public health challenge in Pakistan, particularly in the Punjab province, where rapid urbanization, industrial emissions, and vehicular pollution contribute to a significant rise i n p articulate matter concentrations. The persistent increase in PM2.5 and PM10 levels has exacerbated air quality degradation, leading to frequent smog episodes that disrupt daily life, harm respiratory health, and strain the economy. Accurate forecasting of particulate matter is crucial for the healthcare sector of any country. To achieve, this study aims to develop a predictive model for air quality with a focus on smog formation, leveraging Artificial Neural Networks (ANN) to analyze and forecast PM2.5 and PM10 concentrations. Using historical air quality and meteorological data from key urban centers in Pakistan, the ANN model was trained and validated to capture complex patterns influencing smog events. Preliminary results demonstrate the model's high accuracy in predicting air quality indices and identifying critical thresholds for smog occurrence.