Advancing Indoor Air Pollution Research: A Novel Integration of AI, IoT, and Behavioural Science for Enhanced Indoor Air Quality Monitoring and Behaviour Change
This thesis addresses global concerns over air quality, focusing on Indoor Air Quality (IAQ). It presents an interdisciplinary exploration into the integration of Artificial Intelligence (AI), the Internet of Things (IoT), and Low-Cost Sensors (LCS) for air quality monitoring. The research evaluates LCS, augmented by AI and IoT, as an alternative to expensive monitoring systems, tackling challenges like sensor selection and calibration. Various AI techniques, including Random Forest and Neural Networks, are employed for calibration. The study also delves into the impact of human behaviour on IAQ, utilising the COM-B model to design digital interventions. Case studies with IoT-based devices in volunteer households showed significant IAQ improvement and increased awareness due to these interventions. The thesis further examines a digital visualisation platform's effectiveness in raising IAQ awareness, applying the Technology Acceptance Model (TAM) to understand technology adoption factors. The research underscores the potential of digital interventions in promoting better IAQ practices. It emphasises leveraging LCS-based IoT technologies, digital platforms, and citizen engagement to address IAQ challenges. The findings provide insights into AQ monitoring and improvement's technical, behavioural, and digital dimensions. This comprehensive exploration showcases how behavioural models, combined with IoT and AI, can drive behavioural change, offering valuable guidance for researchers, policymakers, and public health officials on IAQ.
Indoor air quality (IAQ), Low-cost sensors (LCS), Internet of Things (IoT), Calibration, Behavioural change, COM-B model, Artificial Intelligence (AI), Digital interventions, Web platform engagement, Air pollution awareness, Air quality monitoring
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