Back to results

University of Bradford

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

Abstract

dc:description.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.

Degree

thesis:*
Grantor dc:publisher.institution
University of Bradford

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Kureshi, Rameezraja S.
Advisors dc:contributor.advisor
  • Thakker, Dhaval
  • Mishra, Bhupesh K.

Subjects

dc:subject × 11

Rights

dc:rights
Statement dc:rights
  • <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/"><img alt="Creative Commons License" style="border-width:0" src="http://i.creativecommons.org/l/by-nc-nd/3.0/88x31.png" /></a><br />The University of Bradford theses are licenced under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/">Creative Commons Licence</a>.
Language dc:language.iso
en

Identifiers

dc:identifier.*
Repository record dc:identifier.uri
https://bradscholars.brad.ac.uk/handle/10454/20393
OAI identifier oai:identifier
oai:bradscholars.brad.ac.uk:10454/20393

Chain of custody

source
Harvested from
University of Bradford
Base URL
bradscholars.brad.ac.uk/oai/request
Last updated
2026-07-24
Source record
OAI-PMH GetRecord
citation

Kureshi, Rameezraja S.. Advancing Indoor Air Pollution Research: A Novel Integration of AI, IoT, and Behavioural Science for Enhanced Indoor Air Quality Monitoring and Behaviour Change. University of Bradford, https://bradscholars.brad.ac.uk/handle/10454/20393