Therese F. Mar

PM source apportionment and health effects: 1. Intercomparison of source apportionment results

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=7% and 11% for traffic; in Phoenix, secondary sulfate SE=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, r>0.9 for traffic and secondary sulfates in Phoenix and for traffic and secondary nitrates in Washington. The sulfate mean r value is >0.75 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 PM2.5 mass source apportionment results are consistent across users and methods, and that todays source apportionment methods are robust enough for application to PM2.5 health effects assessments.

Associations between Health Effects and Particulate Matter and Black Carbon in Subjects with Respiratory Disease

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.

Pulmonary Effects of Indoor- and Outdoor-Generated Particles in Children with Asthma

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.

Workgroup Report: Workshop on Source Apportionment of Particulate Matter Health Effects—Intercomparison of Results and Implications

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.

PM source apportionment and health effects. 3. Investigation of inter-method variations in associations between estimated source contributions of PM2.5 and daily mortality in Phoenix, AZ

As part of an EPA-sponsored workshop to investigate the use of source apportionment in health effects analyses, the associations between the participants estimated source contributions of PM2.5 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 PM2.5 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=16.0% per 5th-to-95th percentile increment at lag 0 day among eight investigators/methods) and traffic (median estimate=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 PM2.5 may have underestimated health impact contributions at different lags. Further research is needed on the possibility that different PM2.5 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.

Exhaled nitric oxide in children with asthma and short-term PM2.5 exposure in Seattle

The objective of this study was to evaluate associations between short-term (hourly) exposures to particulate matter with aerodynamic diameters < 2.5 μm (PM2.5) and the fractional concentration of nitric oxide in exhaled breath (FeNO) in children with asthma participating in an intensive panel study in Seattle, Washington. The exposure data were collected with tapered element oscillation microbalance (TEOM) PM2.5 monitors operated by the local air agency at three sites in the Seattle area. FeNO is a marker of airway inflammation and is elevated in individuals with asthma. Previously, we reported that offline measurements of FeNO are associated with 24-hr average PM2.5 in a panel of 19 children with asthma in Seattle. In the present study using the same children, we used a polynomial distributed lag model to assess the association between hourly lags in PM2.5 exposure and FeNO levels. Our model controlled for age, ambient NO levels, temperature, relative humidity, and modification by use of inhaled corticosteroids. We found that FeNO was associated with hourly averages of PM2.5 up to 10–12 hr after exposure. The sum of the coefficients for the lag times associated with PM2.5 in the distributed lag model was 7.0 ppm FeNO. The single-lag-model FeNO effect was 6.9 [95% confidence interval (CI), 3.4 to 10.6 ppb] for a 1-hr lag, 6.3 (95% CI, 2.6 to 9.9 ppb ) for a 4-hr lag, and 0.5 (95% CI, −1.1 to 2.1 ppb) for an 8-hr lag. These data provide new information concerning the lag structure between PM2.5 exposure and a respiratory health outcome in children with asthma.

An Analysis of the Association Between Respiratory Symptoms in Subjects with Asthma and Daily Air Pollution in Spokane, Washington

The association between respiratory symptoms and ambient levels of particulate matter (PM) air pollution has been the focus of several panel studies. The majority of studies focused only on PM10, were conducted for relatively short periods, reported peak flow data, and involved children with asthma. The goal of our study was to evaluate the effect of particulate matter of various size fractions (PM10, PM2.5, PM1.0, and PM coarse fraction) on respiratory symptoms in both adults and children with asthma monitored over many months. Daily diary data on respiratory symptoms and medication use were collected. Air pollution data were collected by the local air agency and Washington State University. Data were collected in Spokane, WA, a semiarid city with diverse sources of particulate matter, including motor vehicles, woodstoves, agricultural burning, resuspended road dust, and dust storms. Sixteen adults and nine children living in Spokane participated in the study. The majority of adult subjects participated for over 1 yr and the children were studied for over 8 mo. In the children, we found a strong association between cough and PM10, PM2.5, PM coarse fraction, and PM1.0 (p <. 05). Sputum production and runny nose were associated with PM10 and coarse fraction. However, no association was found between the presence of any respiratory symptom any PM metric in the adult subjects. These positive associations between various metrics of PM and respiratory symptoms in children suggest that children are more sensitive than adults to the effects of increased levels of PM air pollution or that the central site monitor was more representative for children who spend more time outdoors than adults. These findings also suggest that both larger and smaller particles can aggravate asthma symptoms.

Changes in Lung Function and Airway Inflammation Among Asthmatic Children Residing in a Woodsmoke-Impacted Urban Area

Fine particulate matter (PM2.5) is associated with respiratory effects, and asthmatic children are especially sensitive. Preliminary evidence suggests that combustion-derived particles play an important role. Our objective was to evaluate effect estimates from different PM2.5 exposure metrics in relation to airway inflammation and lung function among children residing in woodsmoke-impacted areas of Seattle. Nineteen children (ages 6–13 yr) with asthma were monitored during the heating season. We measured 24-h outdoor and personal concentrations of PM2.5 and light-absorbing carbon (LAC). Levoglucosan (LG), a marker of woodsmoke, was also measured outdoors. We partitioned PM2.5 exposure into its ambient-generated (Eag) and nonambient (Ena) components. These exposure metrics were evaluated in relation to daily changes in exhaled nitric oxide (FENO), a marker of airway inflammation, and four lung function measures: midexpiratory flow (MEF), peak expiratory flow (PEF), forced expiratory volume in the first second (FEV1), and forced vital capacity (FVC). Eag, but not Ena, was correlated with combustion markers. Significant associations with respiratory health were seen only among participants not using inhaled corticosteroids. Increases in FENO were associated with personal PM2.5, personal LAC, and Eag but not with ambient PM2.5 or its combustion markers. In contrast, MEF and PEF decrements were associated with ambient PM2.5, its combustion markers, and Eag, but not with personal PM2.5 or personal LAC. FEV1 was associated only with ambient LG. Our results suggest that lung function may be especially sensitive to the combustion-generated component of ambient PM2.5, whereas airway inflammation may be more closely related to some other constituent of the ambient PM2.5 mixture.

Fine particulate air pollution and cardiorespiratory effects in the elderly.

Background: Past studies of air pollution effects among sensitive subgroups have produced inconsistent results. Our objective was to determine relationships between various measures of air pollution and cardiorespiratory effects in older subjects. Methods: We conducted a study that included repeated measurements of pulmonary function (arterial oxygen saturation) and cardiac function (heart rate and blood pressure) in a panel of 88 subjects (>57 years of age) in Seattle during the years 1999 to 2001. Subjects were healthy or had lung or heart disease. Each subject participated in sessions of 10 consecutive days of exposure monitoring and collection of health outcomes for up to 2 sessions. Associations between health outcomes and indoor, outdoor, and personal measures of particulate matter ≤2.5 micrometers (PM2.5) or particulate matter ≤10 micrometers (PM10) were evaluated using generalized estimating equations with an exchangeable working correlation matrix and robust standard errors. The model included terms for the within-subject, within-session effect; the within- subject, between-session effect; and an interaction term for medication usage. The model controlled for temperature, relative humidity, body mass index, and age. Results: Associations between air pollution and health measurements were found primarily in healthy subjects. Healthy subjects taking no medications had decreases in heart rate associated with indoor and outdoor PM2.5 and PM10. Healthy subjects on medication had small increases in systolic blood pressure associated with indoor PM2.5 and outdoor PM10. Heterogeneity analysis found differences among the health groups for associations with particulate air pollution in heart rate but not in blood pressure. Conclusion: Modest concentrations of air pollutants were associated with small changes in cardiac function.

Influence of exposure error and effect modification by socioeconomic status on the association of acute cardiovascular mortality with particulate matter in Phoenix.

Using ZIP code-level mortality data, the association of cardiovascular mortality with PM2.5 and PM10−2.5, measured at a central monitoring site, was determined for three populations at different distances from the monitoring site but with similar numbers of deaths and therefore similar statistical power. The % risk and statistical significance for the association of mortality with PM2.5 fell off with distance from the monitor, as would be expected if exposure error increased with distance. However, the % risk for PM10−2.5 increased in going from the population in Central Phoenix, where the monitoring site was located, to a population in a Middle Ring around Phoenix and fell off in an Outer Ring population. The % risks for the Outer Ring were low for each of the six lag days (0–5) and for the 6-day moving average. The lag structures for PM2.5 and PM10−2.5 also differed for the Central Phoenix and Middle Ring populations. These differences led us to examine the socioeconomic status (SES) of the populations. On the basis of education and income, the population in Central Phoenix had a lower SES than the Middle Ring. Thus, the differences between Central Phoenix and the Middle Ring may be due to effect modification by SES and differences in exposure error. However, the effect modification by SES may be different for thoracic coarse particulate matter (PM) than for fine PM. This study provides new information on the association of PM10−2.5 with cardiovascular mortality. In the Middle Ring, the % risk per 10 μg/m3 increase in PM10−2.5 concentration (lower and upper 95% confidence levels) for lag day 1 was 3.4 (1.0, 5.8) and for the 6-day distributed-lag was 3.8 (0.3, 7.5). The differences in lag structure for PM2.5 and PM10−2.5 provide evidence that the two particle size classes have health effects that are different and independent. This study also helps explain the high % risks for PM2.5 found for Central Phoenix, 6.6 (1.1, 12.5) for lag day 1, and 11.5 (2.8, 20.9) for the 6-day moving average. The smaller area may have a lower exposure error, and the lower SES population may be more susceptible to fine PM as compared to the larger areas and more heterogeneous populations used in many studies.