Publication

Advancing Indoor Air Pollution Research: A Novel Integration of AI, IoT, and Behavioural Science for Enhanced Indoor Air Quality Monitoring and Behaviour Change

Kureshi, Rameezraja S.
Publication Date
End of Embargo
Supervisor
Thakker, Dhaval
Mishra, Bhupesh K.
Keywords
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
Rights
Creative Commons License
The University of Bradford theses are licenced under a Creative Commons Licence.
Peer-Reviewed
Open Access status
Accepted for publication
Institution
University of Bradford
Department
School of Computer Science, AI and Electronics. Faculty of Engineering and Digital Technologies
Awarded
2023
Embargo end date
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Theses
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PhD Thesis
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Abstract
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.
URI
https://bradscholars.brad.ac.uk/handle/10454/20393
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Type
Thesis
Qualification name
PhD
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