Quantifying spatiotemporal variability in occupant exposure to an indoor airborne contaminant with an uncertain source location

• Published in: Building simulation 2023, Vol.16 (6), p.889-913
• Main Authors: Castellini, John E., Faulkner, Cary A., Zuo, Wangda, Sohn, Michael D.
• Format: Article
• Language: English
• Subjects: Air quality; airborne contaminant; Atmospheric Protection/Air Quality Control/Air Pollution; Building Construction and Design; Computational fluid dynamics; Computing costs; Contaminants; ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; Engineering; Engineering Thermodynamics; Exposure; Heat and Mass Transfer; heterogeneity; Indoor air pollution; Indoor air quality; Mathematical models; Monitoring/Environmental Analysis; Parameter uncertainty; Research Article; Spatial distribution; spatiotemporal variability; uncertainty; variability; well-mixed
• ISSN: 1996-3599; 1996-8744
• DOI: 10.1007/s12273-022-0971-3

Well-mixed zone models are often employed to compute indoor air quality and occupant exposures. While effective, a potential downside to assuming instantaneous, perfect mixing is underpredicting exposures to high intermittent concentrations within a room. When such cases are of concern, more spatially resolved models, like computational-fluid dynamics methods, are used for some or all of the zones. But, these models have higher computational costs and require more input information. A preferred compromise would be to continue with a multi-zone modeling approach for all rooms, but with a better assessment of the spatial variability within a room. To do so, we present a quantitative method for estimating a room’s spatiotemporal variability, based on influential room parameters. Our proposed method disaggregates variability into the variability in a room’s average concentration, and the spatial variability within the room relative to that average. This enables a detailed assessment of how variability in particular room parameters impacts the uncertain occupant exposures. To demonstrate the utility of this method, we simulate contaminant dispersion for a variety of possible source locations. We compute breathing-zone exposure during the releasing (source is active) and decaying (source is removed) periods. Using CFD methods, we found after a 30 minutes release the average standard deviation in the spatial distribution of exposure was approximately 28% of the source average exposure, whereas variability in the different average exposures was lower, only 10% of the total average. We also find that although uncertainty in the source location leads to variability in the average magnitude of transient exposure, it does not have a particularly large influence on the spatial distribution during the decaying period, or on the average contaminant removal rate. By systematically characterizing a room’s average concentration, its variability, and the spatial variability within the room important insights can be gained as to how much uncertainty is introduced into occupant exposure predictions by assuming a uniform in-room contaminant concentration. We discuss how the results of these characterizations can improve our understanding of the uncertainty in occupant exposures relative to well-mixed models.