Abstract
Because people spend the majority of their time indoors, the variable efficiency with which ambient PM(2.5) penetrates and persists indoors is a source of error in epidemiologic studies that use PM(2.5) concentrations measured at central-site monitors as surrogates for ambient PM(2.5) exposure. To reduce this error, practical methods to model indoor concentrations of ambient PM(2.5) are needed. Toward this goal, we evaluated and refined an outdoor-to-indoor transport model using measured indoor and outdoor PM(2.5) species concentrations and air exchange rates from the Relationships of Indoor, Outdoor, and Personal Air Study. Herein, we present model evaluation results, discuss what data are most critical to prediction of residential exposures at the individual-subject and populations levels, and make recommendations for the application of the model in epidemiologic studies. This paper demonstrates that not accounting for certain human activities (air conditioning and heating use, opening windows) leads to bias in predicted residential PM(2.5) exposures at the individual-subject level, but not the population level. The analyses presented also provide quantitative evidence that shifts in the gas-particle partitioning of ambient organics with outdoor-to-indoor transport contribute significantly to variability in indoor ambient organic carbon concentrations and suggest that methods to account for these shifts will further improve the accuracy of outdoor-to-indoor transport models.