Published Paper Details:

This research investigates the use of machine learning methods to examine the effect of atmospheric pollution on the global environment. By utilizing sophisticated algorithms, we aim to uncover patterns, trends, and relationships within extensive datasets pertaining to air quality and environmental health. Our strategy involves employing predictive models to evaluate the long-term effects of different pollutants on climate change, ecosystems, and human health. The findings highlight the capability of machine learning to offer deeper understanding of the intricate dynamics of atmospheric pollution and to support policy-making and mitigation efforts. The rapid progress of machine learning algorithms has improved the capacity to explore the chemical properties of various pollutants, analyze chemical reactions and their influencing factors, and simulate different scenarios. When integrated with data from multiple fields, machine-learning models become a potent resource for analyzing atmospheric chemical processes and assessing air quality management, warranting increased focus in the future. Six different machine-learning models are utilized to forecast air quality. Their results are assessed using standard metrics. The Random Forest model demonstrated the highest accuracy. This study emphasizes the capability of resource-constrained countries to predict air quality autonomously while they await larger datasets to enhance the accuracy of their predictions.