Paper Title
REAL-TIME AIR QUALITY PREDICTION USING SENSOR NETWORKS AND MACHINE LEARNING TECHNIQUES

Abstract
Air quality forecasting often falls short, hindering both public health responses and effective policy-making—a widespread problem that this dissertation aims to address. To this end, the research presents an advanced, real-time air quality prediction system. This system integrates data from a broad network of sensors, capturing high-resolution air quality information across time and space, alongside meteorological and environmental variables. Machine learning techniques are then applied to this integrated dataset. The study demonstrates that the proposed model provides a notably enhanced predictive capability. In most cases, it enhances not only the accuracy of air quality predictions but also furnishes actionable insights into pollution levels, thus enabling informed and timely responses to air quality emergencies. Generally speaking, reliable air quality data and improved predictive models are crucial for bolstering public health interventions and environmental policies, potentially benefiting population health. The findings underscore this relationship. Furthermore, this research highlights the potential of machine learning in environmental monitoring. Such methodologies could, in many instances, serve as a cornerstone for future studies focused on mitigating health risks from air pollution. By bridging technology and public health considerations, this work contributes to a more informed and proactive approach to environmental health challenges, paving the way for greater community awareness and engagement in air quality management. Keywords - Air Quality Prediction, Machine Learning, Sensor Networks, Real-Time Monitoring, IoT, Environmental Health