Air Pollution Exposure Model for Individuals (EMI) in Health Studies: Evaluation for Ambient PM2.5 in Central North Carolina.

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.

Modeling spatial and temporal variability of residential air exchange rates for the Near-Road Exposures and Effects of Urban Air Pollutants Study (NEXUS).

Air pollution health studies often use outdoor concentrations as exposure surrogates. Failure to account for variability of residential infiltration of outdoor pollutants can induce exposure errors and lead to bias and incorrect confidence intervals in health effect estimates. The residential air exchange rate (AER), which is the rate of exchange of indoor air with outdoor air, is an important determinant for house-to-house (spatial) and temporal variations of air pollution infiltration. Our goal was to evaluate and apply mechanistic models to predict AERs for 213 homes in the Near-Road Exposures and Effects of Urban Air Pollutants Study (NEXUS), a cohort study of traffic-related air pollution exposures and respiratory effects in asthmatic children living near major roads in Detroit, Michigan. We used a previously developed model (LBL), which predicts AER from meteorology and questionnaire data on building characteristics related to air leakage, and an extended version of this model (LBLX) that includes natural ventilation from open windows. As a critical and novel aspect of our AER modeling approach, we performed a cross validation, which included both parameter estimation (i.e., model calibration) and model evaluation, based on daily AER measurements from a subset of 24 study homes on five consecutive days during two seasons. The measured AER varied between 0.09 and 3.48 h(-1) with a median of 0.64 h(-1). For the individual model-predicted and measured AER, the median absolute difference was 29% (0.19 h­1) for both the LBL and LBLX models. The LBL and LBLX models predicted 59% and 61% of the variance in the AER, respectively. Daily AER predictions for all 213 homes during the three year study (2010-2012) showed considerable house-to-house variations from building leakage differences, and temporal variations from outdoor temperature and wind speed fluctuations. Using this novel approach, NEXUS will be one of the first epidemiology studies to apply calibrated and home-specific AER models, and to include the spatial and temporal variations of AER for over 200 individual homes across multiple years into an exposure assessment in support of improving risk estimates.

Source apportionment of particulate matter in the U.S. and associations with lung inflammatory markers.

Size-fractionated particulate matter (PM) samples were collected from six U.S. cities and chemically analyzed as part of the Multiple Air Pollutant Study. Particles were administered to cultured lung cells and the production of three different proinflammatory markers was measured to explore the association between the health effect markers and PM. Ultrafine, fine, and coarse PM samples were collected between December 2003 and May 2004 over a 4-wk period in each city. Filters were pooled for each city and the PM samples were extracted then analyzed for trace metals, ions, and elemental carbon. Particle extracts were applied to cultured human primary airway epithelial cells, and the secreted levels of interleukin-8 (IL-8), heme oxygenase-1, and cyclooxygenase-2 were measured 1 and 24 h following exposure. Fine PM sources were quantified by the chemical mass balance (CMB) model. The relationship between toxicological measures, PM sources, and individual species were evaluated using linear regression. Ultrafine and fine PM mass were associated with increases in IL-8 (r(2) = .80 for ultrafine and r(2) = .52 for fine). Sources of fine PM and their relative contributions varied across the sampling sites and a strong linear association was observed between IL-8 and secondary sulfate from coal combustion (r(2) = .79). Ultrafine vanadium, lead, copper, and sulfate were also associated with increases in IL-8. Increases in inflammatory markers were not observed for coarse PM mass and source markers. These findings suggest that certain PM size fractions and sources are associated with markers of lung injury or inflammation.

Distributions of PM2.5 source strengths for cooking from the Research Triangle Park particulate matter panel study.

Emission rates, decay rates, and cooking durations are reported from continuous PM2.5 (particulate matter less than 2.5 microm) concentrations measured using personal DataRam nephelometers (1-min time resolution) from the Research Triangle Park (RTP) PM panel study. The study (n = 37 participants) included monitoring for 7 consecutive days in each of four consecutive seasons (summer 2000 through spring 2001). Cooking episodes (n = 411) were selected using time-activity diaries and criteria for cooking event duration, peak concentration level, and decay curve quality. Averaged across all cooking events, mean source strengths were 36 mg/min (median = 12 mg/min), mean decay rates were 0.27 h(-1) (0.17 h(-1)), and mean cooking durations were 11 min (7 min). Cooking events were further separated into one of seven categories representing cooking method: burned food (oven cooking, toaster, or stovetop cooking), grilling, microwave, toaster oven, frying, oven cooking, and stovetop cooking. The highest mean source strengths were identified from burned food (mean = 470 mg/min), grilling (173 mg/min), and frying (60 mg/ min); differences between both burned food and grilling compared with all remaining cooking methods were statistically significant. Source strengths, decay rates, and cooking durations were also compared by season and typical meal times (8:00 a.m., 12:00 p.m., and 6:00 p.m.); differences were generally not statistically significant for these cases. Mean source strengths using electric appliances were typically a factor of 2 greater than those using gas appliances for identical cooking methods (frying, oven cooking, or stovetop cooking), although in all cases the difference was not statistically significant. Distributions of source strengths and decay rates for cooking events were also compared among study subjects to assess both within- and between-subject variability. Each subject's distribution of source strengths during the study tended to be either lower than the overall study average (and with lower variability) or higher than the overall study average (and with higher variability). No relationships could be found between source strength and either subject characteristics (age, gender, employment status) or home characteristic (daily air exchange rate). The large number of cooking events and the broad range of cooking activities included in this analysis makes the reported distributions of PM2.5 source strengths useful for probabilistic exposure modeling even though the study population was limited.