Abstract
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.
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Chung, J., Tsamis, A., Shelden, D. (2023). A Framework for Monitoring and Identifying Indoor Air Pollutants Based on BIM with IoT Sensors. In: Turrin, M., Andriotis, C., Rafiee, A. (eds) Computer-Aided Architectural Design. INTERCONNECTIONS: Co-computing Beyond Boundaries. CAAD Futures 2023. Communications in Computer and Information Science, vol 1819. Springer, Cham. https://doi.org/10.1007/978-3-031-37189-9_34
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