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Abstract (en): |
Indoor air quality is affected by many variables, such as temperature, humidity, particulate matter,
or gases like carbon dioxide. Considering that European citizens spend, on average, 90% of their
time indoors (cf. EPA 2021, WHO 2013, 9), the indoor air we breathe must be healthy and free of
pollutants.
Even though most people are aware of the harmful health effects caused by indoor air pollution,
many do not take the necessary steps to prevent them or to improve indoor air quality (cf.
Osagbemi, Adebayo and Aderibigbe 2009).
Based on this background, a prototype was developed to help occupants of a room to improve
indoor air quality. Therefore, a system was built to record crucial indoor air quality parameters.
Sensor measurements were recorded every 10 seconds over five months, from May 2022 until
September 2022. The focus was on the carbon dioxide concentration, a parameter easy to
measure that increases when humans exhale air (cf. Palmer, 2015) into the room and that
decreases when windows or doors are opened.
One crucial question was whether machine learning could reliably predict indoor air quality
parameters such as CO2 to notify room occupants at the right moment, for example, before a
parameter exceeds its limit or in further development to improve heating costs in winter. Therefore
a machine learning model was trained with the historical recordings of CO2 concentrations and
covariates such as the state of open doors and windows.
The other, more important question was whether the system’s suggestions could help room
occupants to improve indoor air quality significantly and efficiently in a user-friendly and practical
way. As a result, a notification- and recommendation service, a web application, and a voice
interface were developed.
Three methods were used to find answers: Historical forecasting to test the performance and
accuracy of the machine learning model, a user survey to receive feedback about practicality and
user-friendliness, and an experiment to evaluate whether the system can help improve indoor air
quality. Based on previously recorded sensor data, historical forecasting showed a prediction
accuracy of 94.58%. The user survey concluded that 96.9% of participants found the system’s
suggestions user-friendly and practical. The experiment showed that indoor air quality
significantly improved thanks to the system’s notifications and recommendations.
The results show an excellent potential of the system and an overall positive acceptance of users
towards IoT devices and sensor data helping to improve indoor air quality. |
