Air pollution is a global problem with substantial health impacts and costs millions of dollars of productivity loss each year. In modern cities with a high population density, people spend more than 80% of their time living or working in enclosed, indoor environments. Poor indoor air quality contributes to their overall pollutant exposures and potential for health impacts. It may also reduce the work performance and comfort of the occupants. Hence, there is a growing concern related to indoor air quality (IAQ) by the public. Measuring pollutants and factors of concern indoors is challenging; thus only limited actual data are available to establish what pollutant conditions are present in homes or other buildings. Smart sensor networks, which integrate multiple lower-cost sensor (LCS) n...[ Read more ]

Air pollution is a global problem with substantial health impacts and costs millions of dollars of productivity loss each year. In modern cities with a high population density, people spend more than 80% of their time living or working in enclosed, indoor environments. Poor indoor air quality contributes to their overall pollutant exposures and potential for health impacts. It may also reduce the work performance and comfort of the occupants. Hence, there is a growing concern related to indoor air quality (IAQ) by the public. Measuring pollutants and factors of concern indoors is challenging; thus only limited actual data are available to establish what pollutant conditions are present in homes or other buildings. Smart sensor networks, which integrate multiple lower-cost sensor (LCS) nodes through the Internet of Things (IoT) technology, is an emerging area with many benefits in the built environment.

The studies conducted during my research aimed to establish the campus-wide smart sensor network to provide real-time air quality monitoring in representative indoor environments, including lecture rooms, canteens, sports facilities, and common areas. To achieve the targets, the Smart Campus Air Network (SCAN) was assembled and deployed and tested. It consisted of sensor systems that tracked critical indoor air quality factors (CO₂, PM_2.5, PM₁₀, temperature, and humidity) and a data disclosure platform for real-time visualization of campus air quality. The relationships between indoor air pollutants and occupancy were examined integrating data from this project with other pilot smart campus projects. The result shows that the newly developed IAQ sensors were capable of good performance with high accuracy and data capture rates. In general, the indoor air quality on campus meets the IAQ Objectives(IAQO) under the EPD IAQ Certification Scheme. Spatial and temporal variations of indoor air pollution on campus were found in the study. While the CO₂ concentrations are highly correlated with the occupancy, it is worth noted that some locations had higher CO₂ concentrations during peak hours, indicating the ventilation may not be sufficient. PM concentration in food catering areas also has improvement space in controlling indoor emission sources. This study is a powerful demonstration of how real-time IAQ monitoring can provide responsive alerts and suggestions to facility management for evident-base intervention measures to empower sustainable development. The data collected in this study can further be used in the research for future green buildings operating informed behaviour change.