Determinants of Indoor and Personal Exposure to PM(2.5) of Indoor and Outdoor Origin during the RIOPA Study

Abstract

Effects of physical/environmental factors on fine particle (PM(2.5)) exposure, outdoor-to-indoor transport and air exchange rate (AER) were examined. The fraction of ambient PM(2.5) found indoors (F(INF)) and the fraction to which people are exposed (alpha) modify personal exposure to ambient PM(2.5). Because F(INF), alpha, and AER are infrequently measured, some have used air conditioning (AC) as a modifier of ambient PM(2.5) exposure. We found no single variable that was a good predictor of AER. About 50% and 40% of the variation in F(INF) and alpha, respectively, was explained by AER and other activity variables. AER alone explained 36% and 24% of the variations in F(INF) and alpha, respectively. Each other predictor, including Central AC Operation, accounted for less than 4% of the variation. This highlights the importance of AER measurements to predict F(INF) and alpha. Evidence presented suggests that outdoor temperature and home ventilation features affect particle losses as well as AER, and the effects differ.Total personal exposures to PM(2.5) mass/species were reconstructed using personal activity and microenvironmental methods, and compared to direct personal measurement. Outdoor concentration was the dominant predictor of (partial R(2) = 30-70%) and the largest contributor to (20-90%) indoor and personal exposures for PM(2.5) mass and most species. Several activities had a dramatic impact on personal PM(2.5) mass/species exposures for the few study participants exposed to or engaged in them, including smoking and woodworking. Incorporating personal activities (in addition to outdoor PM(2.5)) improved the predictive power of the personal activity model for PM(2.5) mass/species; more detailed information about personal activities and indoor sources is needed for further improvement (especially for Ca, K, OC). Adequate accounting for particle penetration and persistence indoors and for exposure to non-ambient sources could potentially increase the power of epidemiological analyses linking health effects to particulate exposures.

Figure 1

The relationship between the fraction of ambient PM_2.5 found indoors (F_INF) and air exchange rate (a) for the 114 RIOPA homes with home-specific PM_2.5 F_INF values. The regression line and the equation on the plot are the results of least trimmed square regression.

Figure 2

The relationship between outdoor temperature and air exchange rate (a), fraction of ambient PM_2.5 found indoors, F_INF (b), and alpha (ambient exposure factor) (c), a LOESS smooth curve (solid line) and corresponding upper and lower 95% confidence levels (dash lines) are also shown on (a), (b), and (c); the relationship between the absolute difference of indoor and outdoor temperature and air exchange rate (d), infiltration factor (e) and alpha (f), the lines and the equations on (d), (e) and (f) are the results of the least trimmed square regression.

Figure 3

Estimated personal exposure concentrations using a microenvironmental model and direct personal exposure measurements for PM_2.5 mass (a) and Sulfur (b). The solid line is the least trimmed square robust regression line.