TY - GEN
T1 - A Framework for Monitoring and Identifying Indoor Air Pollutants Based on BIM with IoT Sensors
AU - Chung, Jihoon
AU - Tsamis, Alexandros
AU - Shelden, Dennis
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - Americans spend 86.9% of their life in buildings; however, about 1.64 million people died in 2019 due to diseases related to indoor air pollution. In the indoor air, thousands of chemical substances exist, and we have limited data to identify the source of the pollutants. Internet of Things (IoT) technology recently has addressed low resolution of spatio-temporal air quality data; but there remain limitations in connecting the sensor data to spatial information. This study integrates IoT sensors integrated with a Building Information Modeling (BIM) database, tracking indoor air quality in real-time, providing higher fidelity assessments of the pollutant sources using the locations and properties of building components. This paper proposes a framework for an indoor air quality monitoring system to achieve the following objectives: 1) Integrate IoT sensor data with BIM data sources into an integrated database, 2) Analyze the sensor data and estimate the probable area where the air pollutant sources can be located, 3) Suggest viable solutions for mitigating the air pollution. To demonstrate the framework, a system prototype has been developed, and two pilot tests and a case study have been implemented as proof-of-concept in a university laboratory. This result can be a basis to develop air quality monitoring infrastructure for understanding where the indoor air pollutants come from and how to deal with the problems in real-time.
AB - Americans spend 86.9% of their life in buildings; however, about 1.64 million people died in 2019 due to diseases related to indoor air pollution. In the indoor air, thousands of chemical substances exist, and we have limited data to identify the source of the pollutants. Internet of Things (IoT) technology recently has addressed low resolution of spatio-temporal air quality data; but there remain limitations in connecting the sensor data to spatial information. This study integrates IoT sensors integrated with a Building Information Modeling (BIM) database, tracking indoor air quality in real-time, providing higher fidelity assessments of the pollutant sources using the locations and properties of building components. This paper proposes a framework for an indoor air quality monitoring system to achieve the following objectives: 1) Integrate IoT sensor data with BIM data sources into an integrated database, 2) Analyze the sensor data and estimate the probable area where the air pollutant sources can be located, 3) Suggest viable solutions for mitigating the air pollution. To demonstrate the framework, a system prototype has been developed, and two pilot tests and a case study have been implemented as proof-of-concept in a university laboratory. This result can be a basis to develop air quality monitoring infrastructure for understanding where the indoor air pollutants come from and how to deal with the problems in real-time.
KW - Air Quality Monitoring
KW - Building Information Modeling
KW - Building Infrastructure
KW - Indoor Air Quality
KW - Internet of Things
UR - https://www.scopus.com/pages/publications/85169015962
U2 - 10.1007/978-3-031-37189-9_34
DO - 10.1007/978-3-031-37189-9_34
M3 - Conference contribution
AN - SCOPUS:85169015962
SN - 9783031371882
T3 - Communications in Computer and Information Science
SP - 518
EP - 531
BT - Computer-Aided Architectural Design. INTERCONNECTIONS
A2 - Turrin, Michela
A2 - Andriotis, Charalampos
A2 - Rafiee, Azarakhsh
PB - Springer Science and Business Media Deutschland GmbH
T2 - 20th International Conference on Computer-Aided Architectural Design Futures, CAAD Futures 2023
Y2 - 5 July 2023 through 7 July 2023
ER -