We measured fractional exhaled nitric oxide (FENO), spirometry, blood pressure, oxygen saturation of the blood (SaO2), and pulse rate in 16 older subjects with asthma or chronic obstructive pulmonary disease (COPD) in Seattle, Washington. Data were collected daily for 12 days. We simultaneously collected PM10 and PM2.5 (particulate matter ≤10 μm or ≤2.5 μm, respectively) filter samples at a central outdoor site, as well as outside and inside the subjects’ homes. Personal PM10 filter samples were also collected. All filters were analyzed for mass and light absorbance. We analyzed within-subject associations between health outcomes and air pollution metrics using a linear mixed-effects model with random intercept, controlling for age, ambient relative humidity, and ambient temperature. For the 7 subjects with asthma, a 10 μg/m3 increase in 24-hr average outdoor PM10 and PM2.5 was associated with a 5.9 [95% confidence interval (CI), 2.9–8.9] and 4.2 ppb (95% CI, 1.3–7.1) increase in FENO, respectively. A 1 μg/m3 increase in outdoor, indoor, and personal black carbon (BC) was associated with increases in FENO of 2.3 ppb (95% CI, 1.1–3.6), 4.0 ppb (95% CI, 2.0–5.9), and 1.2 ppb (95% CI, 0.2–2.2), respectively. No significant association was found between PM or BC measures and changes in spirometry, blood pressure, pulse rate, or SaO2 in these subjects. Results from this study indicate that FENO may be a more sensitive marker of PM exposure than traditional health outcomes and that particle-associated BC is useful for examining associations between primary combustion constituents of PM and health outcomes.

Most particulate matter (PM) health effects studies use outdoor (ambient) PM as a surrogate for personal exposure. However, people spend most of their time indoors exposed to a combination of indoor-generated particles and ambient particles that have infiltrated. Thus, it is important to investigate the differential health effects of indoor- and ambient-generated particles. We combined our recently adapted recursive model and a predictive model for estimating infiltration efficiency to separate personal exposure (E) to PM2.5 (PM with aerodynamic diameter ≤2.5 μm) into its indoor-generated (Eig) and ambient-generated (Eag) components for 19 children with asthma. We then compared Eig and Eag to changes in exhaled nitric oxide (eNO), a marker of airway inflammation. Based on the recursive model with a sample size of eight children, Eag was marginally associated with increases in eNO [5.6 ppb per 10-μg/m3 increase in PM2.5; 95% confidence interval (CI), −0.6 to 11.9; p = 0.08]. Eig was not associated with eNO (−0.19 ppb change per 10μg/m3). Our predictive model allowed us to estimate Eag and Eig for all 19 children. For those combined estimates, only Eag was significantly associated with an increase in eNO (Eag: 5.0 ppb per 10-μg/m3 increase in PM2.5; 95% CI, 0.3 to 9.7; p = 0.04; Eig: 3.3 ppb per 10-μg/m3 increase in PM2.5; 95% CI, −1.1 to 7.7; p = 0.15). Effects were seen only in children who were not using corticosteroid therapy. We conclude that the ambient-generated component of PM2.5 exposure is consistently associated with increases in eNO and the indoor-generated component is less strongly associated with eNO.

We evluated the association between mortality outcomes in elderly individuals and pculate matter (PM) of varying aerodynamic diameters (in micrometers) [PM10, PM25, and PMCF (PM1O minus PM25)], and selected particulate and; gaseous phase pollutants in Phoenix, Arizona, using 3 years of daily data (1995-1997). Although source apportionment and epidemiologic methods have been previously combined to investigate the efFects of air poliution on mortality, this is the first study to use detailed PM composition data in a time-series anabsis of mortality. Phoenix is in the arid Southwest and has apprxmately 1 million residents (9.7% of the residents are > 65 years of age). PM data were obtained from the'U.S. Environmental Protection Agency (EPA) National Exposure Research Laboratory Platform in central PhoeniL We obtained gaseous pollutant data, specifically carbon monoxide, nitrogen dioxide, ozone, and sulfur dioxide data, from the EPA Aerometric Infoaion Retrieval System Database. We used Poisson regression analysis to evaluate the associations between air pollution and nonaccidental mortality and cardiovascular mortality. Total mortality was signifintly associated with CO and NO2 (p < 0.05) and wealdy associated with S02, PM10, and PMCF (p < 0.10). Cardiovascula mortality was significandy associated with CO, NO2, SO2, PM2.5, PM1O, PMCF (p < 0.05), and elemental carbon. Factor analysis revealed that both combustion-related pollutants and secondary aerosols (sulfutes) were associated with cardiovascular mortality.

During the past three decades, receptor models have been used to identify and apportion ambient concentrations to sources. A number of groups are employing these methods to provide input into air quality management planning. A workshop has explored the use of resolved source contributions in health effects models. Multiple groups have analyzed particulate composition data sets from Washington, DC and Phoenix, AZ. Similar source profiles were extracted from these data sets by the investigators using different factor analysis methods. There was good agreement among the major resolved source types. Crustal (soil), sulfate, oil, and salt were the sources that were most unambiguously identified (generally highest correlation across the sites). Traffic and vegetative burning showed considerable variability among the results with variability in the ability of the methods to partition the motor vehicle contributions between gasoline and diesel vehicles. However, if the total motor vehicle contributions are estimated, good correspondence was obtained among the results. The source impacts were especially similar across various analyses for the larger mass contributors (e.g., in Washington, secondary sulfate SE ¼ 7% and 11% for traffic; in Phoenix, secondary sulfate SE ¼ 17% and 7% for traffic). Especially important for time-series health effects assessment, the source-specific impacts were found to be highly correlated across analysis methods/researchers for the major components (e.g., mean analysis to analysis correlation, r40.9 for traffic and secondary sulfates in Phoenix and for traffic and secondary nitrates in Washington. The sulfate mean r value is 40.75 in Washington.). Overall, although these intercomparisons suggest areas where further research is needed (e.g., better division of traffic emissions between diesel and gasoline vehicles), they provide support the contention that PM 2.5 mass source apportionment results are consistent across users and methods, and that today's source apportionment methods are robust enough for application to PM 2.5 health effects assessments.

We evaluated the association between mortality outcomes in elderly individuals and particulate matter (PM) of varying aerodynamic diameters (in micrometers) [PM(10), PM(2.5), and PM(CF )(PM(10) minus PM(2.5))], and selected particulate and gaseous phase pollutants in Phoenix, Arizona, using 3 years of daily data (1995-1997). Although source apportionment and epidemiologic methods have been previously combined to investigate the effects of air pollution on mortality, this is the first study to use detailed PM composition data in a time-series analysis of mortality. Phoenix is in the arid Southwest and has approximately 1 million residents (9. 7% of the residents are > 65 years of age). PM data were obtained from the U.S. Environmental Protection Agency (EPA) National Exposure Research Laboratory Platform in central Phoenix. We obtained gaseous pollutant data, specifically carbon monoxide, nitrogen dioxide, ozone, and sulfur dioxide data, from the EPA Aerometric Information Retrieval System Database. We used Poisson regression analysis to evaluate the associations between air pollution and nonaccidental mortality and cardiovascular mortality. Total mortality was significantly associated with CO and NO(2) (p < 0.05) and weakly associated with SO(2), PM(10), and PM(CF) (p < 0. 10). Cardiovascular mortality was significantly associated with CO, NO(2), SO(2), PM(2.5), PM(10), PM(CF) (p < 0.05), and elemental carbon. Factor analysis revealed that both combustion-related pollutants and secondary aerosols (sulfates) were associated with cardiovascular mortality.

Although the association between exposure to ambient fine particulate matter with aerodynamic diameter < 2.5 μm (PM2.5) and human mortality is well established, the most responsible particle types/sources are not yet certain. In May 2003, the U.S. Environmental Protection Agency’s Particulate Matter Centers Program sponsored the Workshop on the Source Apportionment of PM Health Effects. The goal was to evaluate the consistency of the various source apportionment methods in assessing source contributions to daily PM2.5 mass–mortality associations. Seven research institutions, using varying methods, participated in the estimation of source apportionments of PM2.5 mass samples collected in Washington, DC, and Phoenix, Arizona, USA. Apportionments were evaluated for their respective associations with mortality using Poisson regressions, allowing a comparative assessment of the extent to which variations in the apportionments contributed to variability in the source-specific mortality results. The various research groups generally identified the same major source types, each with similar elemental makeups. Intergroup correlation analyses indicated that soil-, sulfate-, residual oil-, and salt-associated mass were most unambiguously identified by various methods, whereas vegetative burning and traffic were less consistent. Aggregate source-specific mortality relative risk (RR) estimate confidence intervals overlapped each other, but the sulfate-related PM2.5 component was most consistently significant across analyses in these cities. Analyses indicated that source types were a significant predictor of RR, whereas apportionment group differences were not. Variations in the source apportionments added only some 15% to the mortality regression uncertainties. These results provide supportive evidence that existing PM2.5 source apportionment methods can be used to derive reliable insights into the source components that contribute to PM2.5 health effects.

As part of an EPA-sponsored workshop to investigate the use of source apportionment in health effects analyses, the associations between the participant's estimated source contributions of PM 2.5 for Phoenix, AZ for the period from 1995-1997 and cardiovascular and total nonaccidental mortality were analyzed using Poisson generalized linear models (GLM). The base model controlled for extreme temperatures, relative humidity, day of week, and time trends using natural spline smoothers. The same mortality model was applied to all of the apportionment results to provide a consistent comparison across source components and investigators/methods. Of the apportioned anthropogenic PM 2.5 source categories, secondary sulfate, traffic, and copper smelter-derived particles were most consistently associated with cardiovascular mortality. The sources with the largest cardiovascular mortality effect size were secondary sulfate (median estimate ¼ 16.0% per 5th-to-95th percentile increment at lag 0 day among eight investigators/methods) and traffic (median estimate ¼ 13.2% per 5th-to-95th percentile increment at lag 1 day among nine investigators/methods). For total mortality, the associations were weaker. Sea salt was also found to be associated with both total and cardiovascular mortality, but at 5 days lag. Fine particle soil and biomass burning factors were not associated with increased risks. Variations in the maximum effect lag varied by source category suggesting that past analyses considering only single lags of PM 2.5 may have underestimated health impact contributions at different lags. Further research is needed on the possibility that different PM 2.5 source components may have different effect lag structure. There was considerable consistency in the health effects results across source apportionments in their effect estimates and their lag structures. Variations in results across investigators/methods were small compared to the variations across source categories. These results indicate reproducibility of source apportionment results across investigative groups and support applicability of these methods to effects studies. However, future research will also need to investigate a number of other important issues including accuracy of results.

Epidemiologic studies report consistent adverse health effects of particulate matter (PM) air pollution in population studies (U.S. EPA 2001). However, describing the adverse health effects of fine PM exposure at the individual subject level remains a high priority. Studies designed to provide information concerning these adverse effects include panel studies with subjects followed for several days or longer, clinical exposure studies, and toxicologic studies, including controlled exposures of human subjects. Panel studies are usually designed to combine intensive personal, indoor and/or outdoor air monitoring in conjunction with measures of specific health outcomes. Recent panel studies have concentrated on subjects believed to be susceptible to air pollution, such as those with preexisting respiratory or cardiac disease. As a consequence, the health end points commonly measured are lung function, symptoms and medication use, arterial oxygen saturation, blood pressure, and heart rate variability. Recent results from panel studies indicate several adverse health effects associated with exposure to PM with aerodynamic diameters ≤ 2.5 µm (PM 2.5 ). In the United States, decreased heart rate variability was associated with indoor or outdoor PM 2.5 in 26 elderly subjects in Baltimore, Maryland (Liao et al. 1999), in healthy subjects in Boston, Massachusetts (Magari et al.