Toxicity data are unavailable for many thousands of chemicals in commerce and the environment. Therefore, risk assessors need to rapidly screen these chemicals for potential risk to public health. ...

A critical aspect of air pollution exposure models is the estimation of the air exchange rate (AER) of individual homes, where people spend most of their time. The AER, which is the airflow into and out of a building, is a primary mechanism for entry of outdoor air pollutants and removal of indoor source emissions. The mechanistic Lawrence Berkeley Laboratory (LBL) AER model was linked to a leakage area model to predict AER from questionnaires and meteorology. The LBL model was also extended to include natural ventilation (LBLX). Using literature-reported parameter values, AER predictions from LBL and LBLX models were compared to data from 642 daily AER measurements across 31 detached homes in central North Carolina, with corresponding questionnaires and meteorological observations. Data was collected on seven consecutive days during each of four consecutive seasons. For the individual model-predicted and measured AER, the median absolute difference was 43% (0.17 h⁻¹) and 40% (0.17 h⁻¹) for the LBL and LBLX models, respectively. Additionally, a literature-reported empirical scale factor (SF) AER model was evaluated, which showed a median absolute difference of 50% (0.25 h⁻¹). The capability of the LBL, LBLX, and SF models could help reduce the AER uncertainty in air pollution exposure models used to develop exposure metrics for health studies.

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) fo...