Use of Machine Learning and Regression Methods in processing PM2.5 Air Pollutants

Abstract

Air quality prediction is a critical aspect of environmental management and public health protection. Accurate forecasting of ground-level air pollutants is essential for implementing effective mitigation strategies. This study presents a new approach to enhance air quality prediction by integrating spatiotemporal observation system datasets with regression methods. The methods involve the collection of real-time data obtained from an observation system of ground-level monitoring stations equipped with low-cost air sensors. These stations are distributed across different areas to capture variations in air quality, specifically in particles that are 2.5 microns or less in diameter (PM2.5). To strengthen the predictive capabilities, simulations based on atmospheric data are employed. These models also take into account various influencing factors, such as emission and weather patterns. The observational data is then blended with the numerical simulations, ensuring a coherent and accurate representation of the spatiotemporal air quality patterns. The versatility of the methodology makes it suitable for various geographical locations and atmospheric conditions. As air quality remains a global concern, this research contributes significantly to advancing our ability to forecast ground-level pollutant concentrations accurately, ultimately facilitating more effective and targeted measures to mitigate air pollution and safeguard public health.