Use of real-time light scattering data to estimate the contribution of infiltrated and indoor-generated particles to indoor air.

The contribution of outdoor particulate matter (PM) to residential indoor concentrations is currently not well understood. Most importantly, separating indoor PM into indoor- and outdoor-generated components will greatly enhance our knowledge of the outdoor contribution to total indoor and personal PM exposures. This paper examines continuous light scattering data at 44 residences in Seattle, WA. A newly adapted recursive model was used to model outdoor-originated PM entering indoor environments. After censoring the indoor time-series to remove the influence of indoor sources, nonlinear regression was used to estimate particle penetration (P, 0.94 +/- 0.10), air exchange rate (a, 0.54 +/- 0.60 h(-1)), particle decay rate (k, 0.20 +/- 0.16 h(-1)), and particle infiltration (F(inf), 0.65 +/- 0.21) for each of the 44 residences. All of these parameters showed seasonal differences. The F(inf) estimates agree well with those estimated from the sulfur-tracer method (R2 = 0.78). The F(inf) estimates also showed robust and expected behavior when compared against known influencing factors. Among our study residences, outdoor-generated particles accounted for an average of 79 +/- 17% of the indoor PM concentration, with a range of 40-100% at individual residences. Although estimates of P, a, and k were dependent on the modeling technique and constraints, we showed that a recursive mass balance model combined with our censoring algorithms can be used to attribute indoor PM into its outdoor and indoor components and to estimate an average P, a, k, and F(inf), for each residence.