Modeling Indoor Air Concentrations Near Emission Sources in Imperfectly Mixed Rooms

Abstract

Assessments of exposure to indoor air pollutants usually employ spatially well-mixed models which assume homogeneous concentrations throughout a building or room. However, practical experience and experimental data indicate that concentrations are not uniform in rooms containing point sources of emissions; concentrations tend to be greater in close proximity to the source than they are further from it. This phenomenon could account for the observation that "personal air" monitors frequently yield higher concentrations than nearby microenvironmental monitors (i.e., the so-called "personal cloud" effect). In this project, we systematically studied the concentrations of a tracer gas at various distances from its emission source in a controlled-environment, room-size chamber under a variety of ventilation conditions. Measured concentrations in the proximity of the source deviated significantly above the predictions of a conventional well-mixed single-compartment mass balance model. The deviation was found to be a function of distance from the source and total room air flow rate. At typical air flow rates, the average concentration at IMPLICATIONS Assessments of human exposure to indoor air pollutants are frequently performed using indoor air quality models based on mass-balance equations that assume well-mixed conditions, with homogeneous concentrations in an indoor space such as a room or house. However, data exist that suggest that indoor air pollutant concentrations are not spatially uniform, and that concentrations in close proximity to emitting sources are higher than those further from the source, and higher than well-mixed model predictions. The research reported here is an attempt to develop and validate a modeling concept that more accurately simulates exposure concentrations for people in close proximity to indoor emission sources. This "source-proximate effect" model can help exposure assessors better understand and account for the so-called "personal cloud" effect, wherein measured personal exposure concentrations are frequently found to be higher than concurrently measured microenvironmental concentrations. arm's length (approximately 0.4 meters) from the source exceeds the theoretical well-mixed concentration by a ratio of about 2:1. However, this ratio is not constant; the monitored concentration appears to vary randomly from near the theoretical value to several times above it. Concentration data were fitted to a two-compartment model with the source located in a small virtual compartment within the room compartment. These two compartments were linked with a stochastic air transfer rate parameter. The resulting model provides a more realistic simulation of exposure concentrations than does the wellmixed model for assessing exposure to emissions from active sources. Parameter values are presented for using the enhanced model in a variety of typical situations.

Comparison of Geometrical Layouts for a Multi-Box Aerosol Model from a Single-Chamber Dispersion Study

“…However, this is not always the case. Furtaw et al [18] and Nicas et al [19] studied SF 6 gas concentrations in an imperfectly mixed chamber and the concept of a near field (NF) zone with higher concentrations than the far field (FF) zone was introduced. The box model was further developed by Cherrie and Schneider [20], and has been expanded to include several individual compartments in which airflow is restricted by physical barriers, such as walls [10,21,22].…”

A Near Field–Far Field model for assessing Oxygen Deficiency Hazard

“…Note that these two fields are a subdivision of the working environment, and the sum of their volumes is the overall volume of the working environment. In the literature there are several examples of application of this model to estimate the concentration of different chemicals: isoflurane (Sakhvidi et al, 2013), benzene (Nicas et al, 2006), solvent (Spencer and Plisko, 2007), methanol vapours (Gaffney et al, 2008), dusts (Jones et al, 2011), sulfur hexafluoride (Furtaw Jr. et al, 1996), laser-generated particulate matter (Lopez et al, 2015), cleaning products (Earnest and Corsi, 2013), toluene (Hofstetter et al, 2013), and unspecified substance (Feigley et al, 2002). One of these studies (Nicas et al, 2006) predicts concentrations using a non-constant emission rate, as done also in other papers: for example, Nicas and Armstrong (2003b) (a spreadsheet to compute a sine function emission rate), Nicas and Neuhaus (2008) (a formulation valid in the case of a variable emission rate), Nicas and Armstrong (2003a) (Excel spreadsheets and a Matlab code for studying the two-zone model with a constant emission rate and an exponentially decreasing contaminant emission rate), and Nicas (2016) (a revisited study of Nicas and Neuhaus (2008) with constant application of chemical mass and exponentially decreasing emission of the mass applied).…”

Dynamic sequential box modelling of inhalation exposure potential in multi-bed patient ward: Validation and baseline case studies

“…Regardless of these limitations, several indoor exposure studies, including the Wells-Riley infection risk model [14][15][16][17], are based on the one-box model. Sequential box models (SBM) provide useful alternatives that account for the limitations in one-box models [6,10,18]. Results of SBM for pollutant dispersals compare well with full-scale experiments [10] and CFD [19].…”

“…is the airflow rates into the zone (m 3 /h); is a coefficient (dimensionless) that account for the loss of contaminant on entering the zone due to the joint effects of air cleaning, air disinfection, and loss other than the zonal loss due to plate-out and/or decay (chemical, biological, or others). Accordingly, equals unity when there are no losses and nullity in the absence of the outdoor contaminants entry and/or room air recirculation; ( ) is the rate Although the one-box approach affords mathematical simplicity, treating a whole space as a single volume masks the spatial variability in exposure [7] and underestimates source proximate exposure intensity [6,[9][10][11][12][13]. Regardless of these limitations, several indoor exposure studies, including the Wells-Riley infection risk model [14][15][16][17], are based on the one-box model.…”