Poor indoor air quality is a significant burden to society that can cause health issues and decrease productivity. According to research, indoor air quality is intrinsically linked with human activity and mobility. Indeed, mobility is directly linked with transfer of small particles (e.g. PM2.5) and extent of activity affects production of CO2. Currently, however, estimation of indoor quality is difficult, requiring deployment of highly specialized sensing devices which need to be carefully placed and maintained. In this paper, we contribute by examining the suitability of infrastructure-based motion detectors for indoor air quality estimation. Such sensors are increasingly being deployed into smart environments, e.g., to control lighting and ventilation for energy management purposes. Being able to take advantage of these sensors would thus provide a cost-effective solution for indoor quality monitoring without need for deploying additional sensors. We perform a feasibility study considering measurements collected from a smart office environment having a dense deployment of motion detectors and correlating measurements obtained from motion detectors against air quality values. We consider two main pollutants,PM2.5 and CO2, and demonstrate that there indeed is a connection between extent of movement and PM2.5concentration. However, for CO2, no relationship can be established, mostly due to difficulties in separating between people passing by and those residing long-term in the environment.
|Nimi|| IEEE International Conference on Industrial Informatics (INDIN)|
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