AI-Driven Air Quality Index Forecasting: Advancing Cyber-Resilience in Environmental Monitoring and Public Health

Air pollution is a significant issue that impacts both individuals and the environment. It results from multiple variables such as industrial emissions, atmospheric pollution, the combustion of fossil fuels, and traffic and weather conditions. Accurate and timely air quality index (AQI) forecasts can assist industry and the public in minimizing exposure to dangerous pollution. Air quality forecasting has been extensively studied, but much of the study has been constrained by using short-term data, which hinders the consideration of seasonal and other variables. The article contains a detailed plan for projecting the Air Quality Index (AQI). The study presents a machine learning method that utilizes alternating gradient boosting, support vector, and random forest regression. This article discusses air quality forecasts, public awareness of declining air quality, and its health effects by compiling various techniques. The goal is to emphasize the best action to take in any situation.