Abstract

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.
Original languageEnglish
Title of host publication2019 IEEE 17th International Conference on Industrial Informatics (INDIN)
PublisherIEEE
Publication date2019
Publication statusAccepted/In press - 2019
MoE publication typeA4 Article in conference proceedings

Cite this

@inproceedings{d8a52c507f554ef3b4388bdb79844c31,
title = "Indoor Air Quality Monitoring Using Infrastructure-Based Motion Detectors",
abstract = "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.",
author = "{Hossein Motlagh}, Naser and Martha Zaidan and Eemil Lagerspetz and Samu Varjonen and Juhani Toivonen and Julien Mineraud and Andrew Rebeiro-Hargrave and Matti Siekkinen and Tareq Hussein and Petteri Nurmi and Sasu Tarkoma",
year = "2019",
language = "English",
booktitle = "2019 IEEE 17th International Conference on Industrial Informatics (INDIN)",
publisher = "IEEE",
address = "United States",

}

Indoor Air Quality Monitoring Using Infrastructure-Based Motion Detectors. / Hossein Motlagh, Naser; Zaidan, Martha; Lagerspetz, Eemil; Varjonen, Samu; Toivonen, Juhani; Mineraud, Julien; Rebeiro-Hargrave, Andrew; Siekkinen, Matti; Hussein, Tareq; Nurmi, Petteri; Tarkoma, Sasu.

2019 IEEE 17th International Conference on Industrial Informatics (INDIN). IEEE, 2019.

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

TY - GEN

T1 - Indoor Air Quality Monitoring Using Infrastructure-Based Motion Detectors

AU - Hossein Motlagh, Naser

AU - Zaidan, Martha

AU - Lagerspetz, Eemil

AU - Varjonen, Samu

AU - Toivonen, Juhani

AU - Mineraud, Julien

AU - Rebeiro-Hargrave, Andrew

AU - Siekkinen, Matti

AU - Hussein, Tareq

AU - Nurmi, Petteri

AU - Tarkoma, Sasu

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

M3 - Conference contribution

BT - 2019 IEEE 17th International Conference on Industrial Informatics (INDIN)

PB - IEEE

ER -