Integrating Machine Intelligence to Estimate PM2.5 Concentration for Sustainable Urban Air Quality Management

Abstract

Air quality degradation, particularly the proliferation of fine particulate matter (PM2.5), poses a critical threat to environmental sustainability and public health. This paper introduces a comprehensive machine learning (ML) framework designed to predict PM2.5 concentrations, addressing the complexities inherent in heterogeneous urban environments. Drawing from a review of existing literature encompassing diverse ML methodologies applied to PM2.5 prediction, this study proposes an innovative approach amalgamating various data sources, including meteorological, geographical, and anthropogenic factors. Leveraging ensemble learning techniques and novel algorithmic models, our framework aims to surpass limitations encountered in current predictive models, enabling accurate and localized PM2.5 predictions. The significance of this research lies in its potential to offer a robust tool for environmental policymakers and urban planners, facilitating informed decisions towards mitigating PM2.5 pollution and fostering sustainable environments. Through evaluation of multiple ML algorithms, this paper contributes a novel predictive model crucial for enhancing air quality management.