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A two-zone model to predict inhalation exposure to toxic chemicals in cleaning products

The use of cleaning products can lead to indoor concentrations of toxic air contaminants above regulatory levels. Studies show that the use of cleaning products is related to adverse respiratory health effects in adults ranging from irritation to asthma. Yet exposure to these chemicals is poorly understood. This thesis summarizes the current state of knowledge of inhalation exposure to toxic chemicals in consumer cleaning products. A new two-compartment model that treats personal air space as distinct from bulk room air is presented. The model accounts for air exchange between the two compartments and fresh air, dynamic source characteristics (i.e., the time-varying liquid concentrations and emission rates of pollutants within a mixture), the characteristics of chemical use (e.g., how frequently a cleaning chemical is applied to a new area), and reactive chemistry with ozone. The model’s applicability is restricted by limited data available for parameterization. Key components that are missing include composition data for consumer cleaning products and activity patterns. Extensive effort went into calculating the air exchange rate between the two zones.
Twelve computational fluid dynamic simulations and two model scenarios were completed. The predicted concentration in the inner-zone (Cin) was divided by the room concentration predicted by the traditional well-mixed model (Cwm). Concentration ratios (Cin/Cwm) ranged from 1.1 to 700. In terms of real cleaning events, results indicate that the beginning (where the only emission source is near the person) of events taking place in large indoor environments with high air exchange rates are the situations for which well-mixed models are most likely to fail in predicting actual exposures. / text

Identiferoai:union.ndltd.org:UTEXAS/oai:repositories.lib.utexas.edu:2152/ETD-UT-2009-05-137
Date03 September 2009
CreatorsEarnest, Clive Matthew, Jr.
Source SetsUniversity of Texas
LanguageEnglish
Detected LanguageEnglish
TypeThesis
Formatapplication/pdf

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