Modeling the residential infiltration of outdoor PM_2.5 in the multi-ethnic study of atherosclerosis and air pollution (MESA Air)

Background: Epidemiologic studies of fine particulate matter [aerodynamic diameter ≤ 2.5 μm (PM_2.5)] typically use outdoor concentrations as exposure surrogates. Failure to account for variation in residential infiltration efficiencies (F_inf) will affect epidemiologic study results. Objective: We aimed to develop models to predict F_inf for > 6,000 homes in the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air), a prospective cohort study of PM_2.5 exposure, subclinical cardiovascular disease, and clinical outcomes. Methods: We collected 526 two-week, paired indoor-outdoor PM_2.5 filter samples from a subset of study homes. PM_2.5 elemental composition was measured by X-ray fluorescence, and F_inf was estimated as the indoor/outdoor sulfur ratio. We regressed F_inf on meteorologic variables and questionnaire-based predictors in season-specific models. Models were evaluated using the R² and root mean square error (RMSE) from a 10-fold cross-validation. Results: The mean ± SD F_inf across all communities and seasons was 0.62 ± 0.21, and community-specific means ranged from 0.47 ± 0.15 in Winston-Salem, North Carolina, to 0.82 ± 0.14 in New York, New York. F_inf was generally greater during the warm (> 18°C) season. Central air conditioning (AC) use, frequency of AC use, and window opening frequency were the most important predictors during the warm season; outdoor temperature and forced-air heat were the best cold-season predictors. The models predicted 60% of the variance in 2-week F_inf, with an RMSE of 0.13. Conclusions: We developed intuitive models that can predict F_inf using easily obtained variables. Using these models, MESA Air will be the first large epidemiologic study to incorporate variation in residential F_inf into an exposure assessment.