MULTISENSORY AND MACHINE LEARNING APPROACH TO IDENTIFY DRIVERS OF INDOOR AIR QUALITY

Air pollution can be attributed to an estimated 8 million global deaths per year. Tacking air pollution and its impact upon human health therefore remains a key global challenge. At present, there are many unresolved questions regarding indoor air quality and the factors that influence it. In particular, there is emerging evidence that occupant behaviours and activities have a strong influence upon indoor air quality. However, to date it has been challenging to gather robust evidence.
The advancement of miniaturised, discrete and low-cost sensors alongside advancements in machine learning and analytical methods offers new promise in uncovering these behaviours. Using a multi-sensor approach (e.g. multiple air pollutants, temperature, humidity, light, sound, energy) and machine learning techniques, it is hypothesized that many key activities may be successfully identified from long-term, non-intrusive monitoring. For example, simultaneous peaks in particulate matter, nitrogen dioxide and humidity might indicate a particular cooking activity, with noise levels and energy data further indicating the use of an extractor hood.
The overall aim of this PhD would be to unlock these signatures to better understand the influence of occupant activities and behaviours upon indoor air quality. This would inform strategies for healthy and low-energy building design as well as improved operational strategies and ventilation practices. More specifically the PhD would aim to:
Characterise the baseline signatures of key activities (cooking, cleaning, etc.).
Develop multisensory monitoring approach for data collection.
Identify and test appropriate machine learning techniques.
Validate this approach and deploy in wider field studies.