Leveraging Quantum Machine Learning for Accurate Indoor Air Quality Forecasting and Risk Mitigation

Air pollution is a global challenge that affects both outdoor and indoor environments. This issue is exacerbated in enclosed spaces where everyday activities like cooking or using cleaning products contribute to the accumulation of pollutants, leading to both acute and chronic health effects. Sick Building Syndrome (SBS) is one of the phenomena associated with poor indoor air quality. It is characterized by medical symptoms experienced by building occupants, which disappear once they leave the premises. In this context, the need for advanced predictive models to manage indoor air quality arises. Quantum Machine Learning (QML) offers an innovative solution for accurately predicting indoor air contaminants. By using historical and real-time data, QML can identify complex patterns and predict dangerous pollution levels, enabling proactive interventions. This model focuses on forecasting specific pollutants like CO2, TVOC, PM2.5, and PM10, and assesses its efficiency in determining risk levels. Integrating this model with IoT technologies allows for continuous and accurate monitoring, contributing to the creation of safer and healthier indoor environments.