Smog Prediction in Punjab Using Machine Learning Approaches: A Literature Review

Smog in Punjab is a severe public health and environmental concern, driven by a combination of crop residue burning, urban emissions, and adverse meteorological conditions. Fine particulate matter and toxic gases accumulate during winter months due to low temperatures, high humidity, and temperature inversions, leading to repeated episodes of hazardous air quality. This literature review synthesizes recent studies on the environmental drivers of smog and its impacts on public health and the economy. The review highlights the role of post-harvest crop burning as the primary contributor to particulate emissions and examines how meteorological factors exacerbate pollution levels. Evidence from existing research demonstrates a clear association between smog events and increased respiratory and cardiovascular hospitalizations, particularly among vulnerable populations. Emerging studies indicate that machine learning models can offer predictive capabilities by integrating real-time environmental and anthropogenic data, enabling early warning systems and targeted interventions. The findings underscore the urgent need for region-specific forecasting systems that can mitigate health risks, guide policy decisions, and inform proactive public health strategies in Punjab.