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Air pollution health studies of fine particulate matter (diameter ≤2.5 μm, PM2.5) often use outdoor concentrations as exposure surrogates. Failure to account for variability of indoor infiltration of ambient PM2.5 and time indoors can induce exposure errors. We developed and evaluated an exposure model for individuals (EMI), which predicts five tiers of individual-level exposure metrics for ambient PM2.5 using outdoor concentrations, questionnaires, weather, and time-location information. We linked a mechanistic air exchange rate (AER) model to a mass-balance PM2.5 infiltration model to predict residential AER (Tier 1), infiltration factors (Tier 2), indoor concentrations (Tier 3), personal exposure factors (Tier 4), and personal exposures (Tier 5) for ambient PM2.5. Using cross-validation, individual predictions were compared to 591 daily measurements from 31 homes (Tiers 1-3) and participants (Tiers 4-5) in central North Carolina. Median absolute differences were 39% (0.17 h(-1)) for Tier 1, 18% (0.10) for Tier 2, 20% (2.0 μg/m(3)) for Tier 3, 18% (0.10) for Tier 4, and 20% (1.8 μg/m(3)) for Tier 5. The capability of EMI could help reduce the uncertainty of ambient PM2.5 exposure metrics used in health studies.

The indoor environment is an important venue for exposure to fine particulate matter (PM2.5) of ambient (outdoor) origin. In this work, paired indoor and outdoor PM2.5 species concentrations from three geographically distinct cities (Houston, TX, Los Angeles County, CA, and Elizabeth, NJ) were analyzed using positive matrix factorization (PMF) and demonstrate that the composition and source contributions of ambient PM2.5 are substantially modified by outdoor-to-indoor transport. Our results suggest that predictions of "indoor PM2.5 of ambient origin" are improved when ambient PM2.5 is treated as a combination of four distinct particle types with differing infiltration behavior (primary combustion, secondary sulfate and organics, secondary nitrate, and mechanically generated PM) rather than as a "single internally mixed entity". Study-wide average infiltration factors (i.e., fraction of ambient PM2.5 found indoors) for Relationship of Indoor, Outdoor, and Personal Air (RIOPA) study homes were 0.51, 0.78, and 0.04 (consistent with P = 0.6, 0.9, and 0.09; k = 0.2, 0.1, and 0.6 h(-1)) for PM2.5 associated with primary combustion, secondary formation (excluding nitrate), and mechanical generation, respectively. Modification of the composition, properties, and source contributions of ambient PM2.5 in indoor environments has important implications for exposure mitigation strategies, development of health hypotheses, and evaluation of exposure error in epidemiological studies that use ambient central-site PM2.5 as a surrogate for PM2.5 exposure.

Epidemiological studies routinely use central-site particulate matter (PM) as a surrogate for exposure to PM of ambient (outdoor) origin. Below we quantify exposure errors that arise from variations in particle infiltration to aid evaluation of the use of this surrogate, rather than actual exposure, in PM epidemiology. Measurements from 114 homes in three cities from the Relationship of Indoor, Outdoor and Personal Air (RIOPA) study were used. Indoor PM2.5 of outdoor origin was calculated as follows: (1) assuming a constant infiltration factor, as would be the case if central-site PM were a "perfect surrogate" for exposure to outdoor particles; (2) including variations in measured air exchange rates across homes; (3) also incorporating home-to-home variations in particle composition, and (4) calculating sample-specific infiltration factors. The final estimates of PM2.5 of outdoor origin take into account variations in building construction, ventilation practices, and particle properties that result in home-to-home and day-to-day variations in particle infiltration. As assumptions became more realistic (from the first, most constrained model to the fourth, least constrained model), the mean concentration of PM2.5 of outdoor origin increased. Perhaps more importantly, the bandwidth of the distribution increased. These results quantify several ways in which the use of central site PM results in underestimates of the ambient PM2.5 exposure distribution bandwidth. The result is larger uncertainties in relative risk factors for PM2.5 than would occur if epidemiological studies used more accurate exposure measures. In certain situations this can lead to bias.