NDLI: A model to predict the breathing zone concentrations of particles emitted from surfaces.

Content Provider	World Health Organization (WHO)-Global Index Medicus
Author	Thornburg, Jonathan Kominsky, John Brown, G. Gordon Frechtel, Peter Barrett, William Shaul, Glenn
Description	Country affiliation: United States Author Affiliation: Thornburg J ( RTI International, Research Triangle Park, NC 27709, USA. jwt@rti.org)
Abstract	Activity based sampling (ABS) is typically performed to assess inhalation exposure to particulate contaminants known to have low, heterogeneous concentrations on a surface. Activity based sampling determines the contaminant concentration in a person's breathing zone as they perform a scripted activity, such as raking a specified area of soil, while wearing appropriate sample collection instrumentation. As an alternative approach, a probabilistic model based on aerosol physics and fluid dynamics was developed to predict the breathing zone concentration of a particulate contaminant emitted from a surface during activities of variable intensity. The model predicted the particle emission rate, tracked particle transport to the breathing zone, and calculated the breathing zone concentration for two scenarios. One scenario used an Eulerian model based on a Gaussian concentration distribution to quantify aerosol exposure in the trailing wake of a moving object. The second scenario modeled exposure in a quiescent environment. A Lagrangian model tracked the cumulative number of individual particles entering the breathing zone volume at a particular time. A Monte Carlo simulation calculated the breathing zone concentration probability distribution for each scenario. Both models predicted probability distributions of asbestos breathing zone concentrations that bracketed experimentally measured personal exposure concentrations. Modeled breathing zone concentrations were statistically correlated (p-value < 0.001) with independently collected ABS concentrations. The linear regression slope of 0.70 and intercept of 0.03 were influenced by the quantity of ABS data collected and model parameter input distributions at a site broader than those at other sites.
File Format	HTM / HTML
ISSN	14640325
Issue Number	4
Journal	Journal of Environmental Monitoring
Volume Number	12
e-ISSN	14640333
Language	English
Publisher	Royal Society of Chemistry
Publisher Date	2010-04-01
Publisher Place	Great Britain (UK)
Access Restriction	Subscribed
Subject Keyword	Discipline Environmental Health Asbestos Analysis Inhalation Exposure Models, Theoretical Particulate Matter Monte Carlo Method Journal Article Research Support, U.s. Gov't, Non-p.h.s.
Content Type	Text
Resource Type	Article
Subject	Public Health, Environmental and Occupational Health Management, Monitoring, Policy and Law

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