Predictive Purity: Advancements in Air Pollution Forecasting through Machine Learning

Abstract

The world economy, human well-being, and the health of plants and animals have all suffered greatly as a result of rising air pollution. This survey investigates four different aspects of air pollution prediction using machine learning (ML). It examines the relationship between industrial processes and emissions, concentrating on factors and industries. Predictive models that can foretell pollution levels from industrial activity are created using machine learning techniques. ML models are used to forecast the amounts of pollution associated with vehicle traffic, as automobiles play a significant role in the degradation of urban air quality. The use of ML based approaches to predict pollution levels from natural phenomena like storms of dust, lava flows, and wildfires helps preventive measures and disaster preparedness. Lastly, ML algorithms are used to anticipate pollutant emissions from a range of combustion sources, including power plants, residential heating systems, and industrial boilers. In addition to discussing the consequences for pollution management strategies, the study assesses how well machine learning algorithms predict emissions. The objective is to further advance the creation of forecasting abilities that are essential for lowering the detrimental effects of air pollution on the environment and public health by providing insights into the quickly evolving field of air pollution forecasts through ML approaches.