Monitoring and Predicting Indoor Air Quality: An IoT and Machine Learning-Based Approach

Indoor Air Quality (IAQ) is crucial for human health, especially in homes where people spend most of their time. Common pollutants like VOCs (Volatile Organic Compounds) and particulate matter (PM), often produced by activities like cooking, can have a negative health impact, while proper ventilation is essential to reduce these risks. Growing interest in IAQ has led to the development of low-cost IoT-based monitoring systems using affordable sensors to track key parameters such as PM, CO, VOCs, temperature, and humidity. While these systems are effective for monitoring, limitations remain in accurately quantifying individual exposure. Furthermore, the ability to predict air quality is crucial to enable preventive actions. However, existing studies often lack comprehensive temporal analyses, focus on limited pollutants, or do not assess compliance with World Health Organization (WHO) guidelines, highlighting the need for more robust and integrated approaches. This study presents an IoT-based system for monitoring IAQ, collecting data on various pollutants in home kitchens. Data was gathered over seven days in three kitchens. The research includes time-based analysis, multivariate predictive models, and comparison with WHO standards. Strengths of this study include the simultaneous monitoring of multiple pollutants and the use of predictive models to support real-time decision-making, while limitations involve the relatively short monitoring period and potential variability between households. This comprehensive approach aims to enhance understanding of IAQ in kitchens, identify gaps in current knowledge, and facilitate proactive decision-making for the health and safety of occupants.