The relationship between daily cardiovascular mortality and daily ambient concentrations of particulate pollutants (sulfur, arsenic, selenium, and mercury) and daily source contributions from coal power plants and smelters (individually, combined, and with interaction) in Phoenix, AZ, 1995-1998: A multipollutant approach to acute, time-series air pollution epidemiology: I.

UNLABELLED: The objective of this paper is to estimate the increase in risk of daily cardiovascular mortality due to an increase in the daily ambient concentration of the individual particulate pollutants sulfur (S), arsenic (As), selenium (Se), and mercury (Hg) using single-pollutant models (SPMs) and to compare this risk to the combined increase in risk due to an increase in all four pollutants by including all four pollutants in the same model (multipollutant model, MPM) and to the risks from source contributions from power plants and smelters. Individual betas in a multipollutant model (MPM) were summed to give a combined beta. Interaction was investigated with a pollutant product term. SPMs (controlling for time trends, temperature, and relative humidity), for an interquartile range (IQR) increase in the pollutant concentration on lag day 0, gave these percent excess risks (±95% confidence levels): S, 6.9% (1.3-12%); As, 2.9% (0.4-5.5%); Se, 1.4% (-1.7 to 4.6); Hg, 9.6% (4.8-14.6%). The SPM beta for S (as sulfate) was higher than found in other studies. The SPM beta for Hg gave the largest t-statistic and beta per unit mass of any pollutant studied. An (IQR) increase in all four pollutants gave an excess risk of 15.4% (7.5-23.8%), slightly smaller than the combination of S and Hg, 16.7% (9.1-24.9%). The combined beta was 71% of the sum of the four individual SPM betas, indicating a reduction in confounding among pollutants in the combined model. As and Se were shown to be noncausal; their SPM betas could be explained as confounding by S. IMPLICATIONS: The combined effect of several pollutants can be estimated by including the appropriate pollutants in the same statistical model, summing their individual betas to give a combined beta, and using a variance-covariance matrix to obtain the standard error. This approach identifies and reduces confounding among the species in the multipollutant model and can be used to identify confounded species that have no independent relationship with mortality. The effect of several pollutants acting together may be higher than that of one pollutant. Further work is needed to understand the strong relationship of mortality with particulate mercury and sulfate.

Impact of covariate models on the assessment of the air pollution-mortality association in a single- and multipollutant context.

With the advent of multicity studies, uniform statistical approaches have been developed to examine air pollution-mortality associations across cities. To assess the sensitivity of the air pollution-mortality association to different model specifications in a single and multipollutant context, the authors applied various regression models developed in previous multicity time-series studies of air pollution and mortality to data from Philadelphia, Pennsylvania (May 1992-September 1995). Single-pollutant analyses used daily cardiovascular mortality, fine particulate matter (particles with an aerodynamic diameter ≤2.5 µm; PM(2.5)), speciated PM(2.5), and gaseous pollutant data, while multipollutant analyses used source factors identified through principal component analysis. In single-pollutant analyses, risk estimates were relatively consistent across models for most PM(2.5) components and gaseous pollutants. However, risk estimates were inconsistent for ozone in all-year and warm-season analyses. Principal component analysis yielded factors with species associated with traffic, crustal material, residual oil, and coal. Risk estimates for these factors exhibited less sensitivity to alternative regression models compared with single-pollutant models. Factors associated with traffic and crustal material showed consistently positive associations in the warm season, while the coal combustion factor showed consistently positive associations in the cold season. Overall, mortality risk estimates examined using a source-oriented approach yielded more stable and precise risk estimates, compared with single-pollutant analyses.

Associations between air pollution and peak expiratory flow among patients with persistent asthma.

Responses of patients with persistent asthma to ambient air pollution may be different from those of general populations. For example, asthma medications may modify the effects of ambient air pollutants on peak expiratory flow (PEF). Few studies examined the association between air pollution and PEF in patients with persistent asthma on well-defined medication regimens using asthma clinical trial data. Airway obstruction effects of ambient air pollutants, using 14,919 person-days of daily self-measured peak expiratory flow (PEF), were assessed from 154 patients with persistent asthma during the 16 wk of active treatment in the Salmeterol Off Corticosteroids Study trial. The three therapies were an inhaled corticosteroid, an inhaled long-acting beta-agonist, and placebo. The participants were nonsmokers aged 12 through 63 yr, recruited from 6 university-based ambulatory care centers from February 1997 to January 1999. Air pollution data were derived from the U.S. Environmental Protection Agency Aerometric Information Retrieval System. An increase of 10 ppb of ambient daily mean concentrations of NO2 was associated with a decrease in PEF of 1.53 L/min (95% confidence interval [CI] -2.93 to -0.14) in models adjusted for age, gender, race/ethnicity, asthma clinical center, season, week, daily average temperature, and daily average relative humidity. The strongest association between NO2 and PEF was observed among the patients treated with salmeterol. Negative associations were also found between PEF and SO2 and between PEF and PM(10), respectively. The results show that the two medication regimens protected against the effects of PM(10). However, salmeterol increased the sensitivity to NO2 and triamcinalone enhanced the sensitivity to SO2.

Measurement of fine particulate matter nonvolatile and semi-volatile organic material with the Sunset Laboratory Carbon Aerosol Monitor.

Semi-volatile organic material (SVOM) in fine particles is not reliably measured with conventional semicontinuous carbon monitors because SVOM is lost from the collection media during sample collection. We have modified a Sunset Laboratory Carbon Aerosol Monitor to allow for the determination of SVOM. In a conventional Sunset monitor, gas-phase organic compounds are removed in the sampled airstream by a diffusion denuder employing charcoal-impregnated cellulose filter (CIF) surfaces. Subsequently, particles are collected on a quartz filter and the instrument then determines both the organic carbon and elemental carbon fractions of the aerosol using a thermal/optical method. However, some of the SVOM is lost from the filter during collection, and therefore is not determined. Because the interfering gas-phase organic compounds are removed before aerosol collection, the SVOM can be determined by filtering the particles at the instrument inlet and then replacing the quartz filter in the monitor with a charcoal-impregnated glass fiber filter (CIG), which retains the SVOM lost from particles collected on the inlet filter. The resulting collected SVOM is then determined in the analysis step by measurement of the carbonaceous material thermally evolved from the CIG filter. This concept was tested during field studies in February 2003 in Lindon, UT, and in July 2003 in Rubidoux, CA. The results obtained were validated by comparison with Particle Concentrator-Brigham Young University Organic Sampling System (PC-BOSS) results. The sum of nonvolatile organic material determined with a conventional Sunset monitor and SVOM determined with the modified Sunset monitor agree with the PC-BOSS results. Linear regression analysis of total carbon concentrations determined by the PC-BOSS and the Sunset resulted in a zero-intercept slope of 0.99 +/- 0.02 (R2 = 0.92) and a precision of sigma = +/- 1.5 microg C/m3 (8%).

Use of the electrical aerosol detector as an indicator of the surface area of fine particles deposited in the lung.

Because of recent concerns about the health effects of ultrafine particles and the indication that particle toxicity is related to surface area, we have been examining techniques for measuring parameters related to the surface area of fine particles, especially in the 0.003- to 0.5-microm size range. In an earlier study, we suggested that the charge attached to particles, as measured by a prototype of the Electrical Aerosol Detector (EAD, TSI Inc., Model 3070), was related to the 1.16 power of the mobility diameter. An inspection of the pattern of particle deposition in the lung as a function of particle size suggested that the EAD measurement might be a useful indicator of the surface area of particles deposited in the lung. In this study, we calculate the particle surface area (micrometer squared) deposited in the lung per cubic centimeter of air inhaled as a function of particle size using atmospheric particle size distributions measured in Minneapolis, MN, and East St. Louis, IL. The correlations of powers of the mobility diameter, Dx, were highest for X = 1.1-1.6 for the deposited surface area and for X = 1.25 with the EAD signal. This overlap suggested a correspondence between the EAD signal and the deposited surface area. The correlation coefficients of the EAD signal and particle surface area deposited in the alveolar and tracheobronchial regions of the lung for three breathing patterns are in the range of Pearson's r = 0.91-0.95 (coefficient of determination, R2 = 0.82-0.90). These statistical relationships suggest that the EAD could serve as a useful indicator of particle surface area deposited in the lung in exposure and epidemiologic studies of the human health effects of atmospheric particles and as a measure of the potential surface area dose for the characterization of occupational environments.

The measurement of fine particulate semivolatile material in urban aerosols.

Ammonium nitrate and semivolatile organic material (SVOM) are significant components of fine particles in urban atmospheres. These components, however, are not properly determined with methods such as the fine particulate matter (PM2.5) Federal Reference Method (FRM) or other single filter samplers because of significant losses of semivolatile material (SVM) from particles collected on the filter during sampling. The R&P tapered element oscillating microbalance (TEOM) monitor also does not measure SVM, because this method heats the sample to remove particle bound water, which also results in evaporation of SVM. Recent advances in monitoring techniques have resulted in samplers for both integrated and continuous measurement of total PM2.5, including the particle concentrator-Brigham Young University organic sampling system (PC-BOSS), the real-time total ambient mass sampler (RAMS), and the R&P filter dynamics measurement system (FDMS) TEOM monitor. Results obtained using these samplers have been compared with those obtained with either a PM2.5 FRM sampler or a TEOM monitor in studies conducted during the past five years. These studies have shown the following: (1) the PC-BOSS, RAMS, and FDMS TEOM are all comparable. Each instrument measures both the nonvolatile material and the SVM. (2) The SVM is not retained on the heated filter of a regular TEOM monitor and is not measured by this sampling technique. (3) Much of the SVM is also lost during sampling from single filter samplers such as the PM2.5 FRM sampler. (4) The amount of SVM lost from single filter samplers can vary from less than one-third of that lost from heated TEOM filters during cold winter conditions to essentially all during warm summer conditions. (5) SVOM can only be reliably collected using an appropriate denuder sampler. (6) A PM2.5 speciation sampler can be easily modified to a denuder sampler with filters that can be analyzed for semivolatile organic carbon (OC), nonvolatile OC, and elemental carbon using existing OC/elemental carbon analytical techniques. The research upon which these statements are based for various urban studies are summarized in this paper.

Continuous determination of fine particulate matter mass in the Salt Lake City Environmental Monitoring project: a comparison of real-time and conventional TEOM monitor results.

Fine particulate matter (PM2.5) mass was determined on a continuous basis at the Salt Lake City Environmental Protection Agency Environmental Monitoring for Public Awareness and Community Tracking monitoring site in Salt Lake City, UT, using three different monitoring techniques. Hourly averaged PM2.5 mass data were collected during two sampling periods (summer 2000 and winter 2002) using a real-time total ambient mass sampler (RAMS), sample equilibration system (SES)-tapered element oscillating microbalance (TEOM), and conventional TEOM monitor. This paper compares the results obtained from the various monitoring systems, which differ in their treatment of semivolatile material (SVM; particle-bound water, semivolatile ammonium nitrate, and semivolatile organic compounds). PM2.5 mass results obtained by the RAMS were consistently higher than those obtained by the SES-TEOM and conventional TEOM monitors because of the RAMS ability to measure semivolatile ammonium nitrate and semivolatile organic material but not particle-bound water. The SES-TEOM monitoring system was able to account for an average of 28% of the SVM, whereas the conventional TEOM monitor loses essentially all of the SVM from the single filter during sampling. Occasional mass readings by the various TEOM monitors that are higher than RAMS results may reflect particle-bound water, which, under some conditions, is measured by the TEOM but not the RAMS.

Ambient particulate air pollution, heart rate variability, and blood markers of inflammation in a panel of elderly subjects.

Epidemiologic studies report associations between particulate air pollution and cardiopulmonary morbidity and mortality. Although the underlying pathophysiologic mechanisms remain unclear, it has been hypothesized that altered autonomic function and pulmonary/systemic inflammation may play a role. In this study we explored the effects of air pollution on autonomic function measured by changes in heart rate variability (HRV) and blood markers of inflammation in a panel of 88 elderly subjects from three communities along the Wasatch Front in Utah. Subjects participated in multiple sessions of 24-hr ambulatory electrocardiographic monitoring and blood tests. Regression analysis was used to evaluate associations between fine particulate matter [aerodynamic diameter less than or equal to 2.5 microm (PM2.5)] and HRV, C-reactive protein (CRP), blood cell counts, and whole blood viscosity. A 100- microg/m3 increase in PM2.5 was associated with approximately a 35 (SE = 8)-msec decline in standard deviation of all normal R-R intervals (SDNN, a measure of overall HRV); a 42 (SE = 11)-msec decline in square root of the mean of the squared differences between adjacent normal R-R intervals (r-MSSD, an estimate of short-term components of HRV); and a 0.81 (SE = 0.17)-mg/dL increase in CRP. The PM2.5-HRV associations were reasonably consistent and statistically robust, but the CRP association dropped to 0.19 (SE = 0.10) after excluding the most influential subject. PM2.5 was not significantly associated with white or red blood cell counts, platelets, or whole-blood viscosity. Most short-term variability in temporal deviations of HRV and CRP was not explained by PM2.5; however, the small statistically significant associations that were observed suggest that exposure to PM2.5 may be one of multiple factors that influence HRV and CRP.

Receptor modeling application framework for particle source apportionment.

Receptor models infer contributions from particulate matter (PM) source types using multivariate measurements of particle chemical and physical properties. Receptor models complement source models that estimate concentrations from emissions inventories and transport meteorology. Enrichment factor, chemical mass balance, multiple linear regression, eigenvector. edge detection, neural network, aerosol evolution, and aerosol equilibrium models have all been used to solve particulate air quality problems, and more than 500 citations of their theory and application document these uses. While elements, ions, and carbons were often used to apportion TSP, PM10, and PM2.5 among many source types, many of these components have been reduced in source emissions such that more complex measurements of carbon fractions, specific organic compounds, single particle characteristics, and isotopic abundances now need to be measured in source and receptor samples. Compliance monitoring networks are not usually designed to obtain data for the observables, locations, and time periods that allow receptor models to be applied. Measurements from existing networks can be used to form conceptual models that allow the needed monitoring network to be optimized. The framework for using receptor models to solve air quality problems consists of: (1) formulating a conceptual model; (2) identifying potential sources; (3) characterizing source emissions; (4) obtaining and analyzing ambient PM samples for major components and source markers; (5) confirming source types with multivariate receptor models; (6) quantifying source contributions with the chemical mass balance; (7) estimating profile changes and the limiting precursor gases for secondary aerosols; and (8) reconciling receptor modeling results with source models, emissions inventories, and receptor data analyses.

Designing monitoring networks to represent outdoor human exposure.

Measurements of outdoor human exposure to suspended particulate matter (PM) are always constrained by available resources. An effective network design requires tradeoffs between variables measured, the number of sampling locations, sample duration, and sampling frequency. Sampling sites are needed to represent neighborhood and urban spatial scales with minimal influences from nearby sources. Although most PM measurements for determining compliance with standards are taken over 24-h periods every third to sixth day, outdoor human exposure assessment requires measurements taken continuously throughout the day, preferably over durations of 1 h or less. More detailed particle size and chemistry data are also desirable, as smaller size fractions and specific chemicals may be better indicators of adverse health effects than total mass samples.

Chapter one: exposure measurements.

Determining human exposure to suspended particulate concentrations requires measurements that quantify different particle properties in microenvironments where people live, work, and play. Particle mass, size, and chemical composition are important exposure variables, and these are typically measured with time-integrated samples on filters that are later submitted to laboratory analyses. This requires substantial sample handling, quality assurance, and data reduction. Newer technologies are being developed that allow in-situ, time-resolved measurements for mass, carbon, sulfate, nitrate, particle size, and other variables. These are large measurement systems that are more suitable for fixed monitoring sites than for personal applications. Human exposure studies need to be designed to accomplish specific objectives rather than to serve too many purposes. Resources need to be divided among study design, field sampling, laboratory analysis, quality assurance, data management, and data analysis phases. Many exposure projects allocated too little to the non-measurement activities.

Regional variations in particulate matter composition and the ability of monitoring data to represent population exposures.

Epidemiologic studies have demonstrated that relative risks for mortality associated with ambient particulate matter (PM) concentrations vary with location in the U.S. with larger associations in both magnitude and strength observed in the East compared to the West. Two factors potentially contributing to the regional heterogeneity in PM-mortality associations observed are regional variations in PM composition and the ability of a single PM concentration estimate to represent the community-average exposure for an entire study area, which may lead to regional differences in exposure error. Variations in PM composition and the proportion of the population living in proximity to ambient monitors, an indicator of potential exposure error, are examined for the 20 most populated and 10 mid-size study areas included in the National Morbidity, Mortality and Air Pollution Study (NMMAPS). Clear differences in PM and in the proportion of the population living in proximity to ambient monitors are found for some of these cities. Differences in these exposure parameters may be interpreted more reasonably in terms of north-south differences compared to east-west differences, and may need to be considered when conducting future epidemiologic studies that aim to examine the factors that influence the regional variability in PM-mortality associations.

Interaction of ambient air pollution with asthma medication on exhaled nitric oxide among asthmatics.

The interaction between ambient air pollution and asthma medication remains unclear. The authors compared airway inflammation response to air pollution among asthmatics. Increases of 10 ppb of nitrogen dioxide (NO2) and of 10 microg/m3 of particulate matter < 10 micron in diameter (PM10) daily concentrations were associated with an increase in exhaled nitric oxide (eNO) of 0.13 ppb (95% confidence interval = 0.06, 0.19) and of 0.07 ppb (95% confidence interval = 0.02, 0.12), respectively, in models adjusted for important covariates. The results show that the medication could not counteract airway inflammation effects of air pollution. Specifically, the patients on triamcinolone decreased the sensitivity to PM10 but increased the sensitivity to NO2. The patients on salmeterol were more vulnerable to both NO2 and PM10. This study indicates that the current pollution levels may still enhance airway inflammation among patients with persistent asthma even when they are on asthma medications.

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 PM(2.5) and PM(10-2.5), 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 PM(2.5) fell off with distance from the monitor, as would be expected if exposure error increased with distance. However, the % risk for PM(10-2.5) 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 PM(2.5) and PM(10-2.5) 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 PM(10-2.5) with cardiovascular mortality. In the Middle Ring, the % risk per 10 microg/m3 increase in PM(10-2.5) 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 PM(2.5) and PM(10-2.5) 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 PM(2.5) 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.

Effects of exposure measurement error on particle matter epidemiology: a simulation using data from a panel study in Baltimore, MD.

Ascertaining the true risk associated with exposure to particulate matter (PM) is difficult, given the fact that pollutant components are frequently correlated with each other and with other gaseous pollutants; relationships between ambient concentrations and personal exposures are often not well understood; and PM, unlike its gaseous co-pollutants, does not represent a single chemical. In order to examine differences between observed versus true health risk estimate from epidemiologic studies, we conducted a simulation using data from a recent multi-pollutant exposure assessment study in Baltimore, MD. The objectives of the simulation were twofold: (a) to estimate the distribution of personal air pollutant exposures one might expect to observe within a population, given the corresponding ambient concentrations found in that location and; (b) using an assumed true health risk with exposure to one pollutant, to estimate the distribution of health risk estimates likely to be observed in an epidemiologic study using ambient pollutant concentrations as a surrogate of exposure as compared with actual personal pollutant exposures. Results from the simulations showed that PM2.5 was the only pollutant where a true association with its total personal exposures resulted in a significant observed association with its ambient concentrations. The simulated results also showed that true health risks associated with personal exposure to O3 and NO2 would result in no significant observed associations with any of their respective ambient concentrations. Conversely, a true association with PM2.5 would result in a significant, observed association with NO2 (beta=0.0115, 95% confidence interval (CI): 0.0056, 0.0185) and a true association with exposure to SO4(2-) would result in an observed significant association with O3 (beta=0.0035, 95% CI: 0.0021, 0.0051) given the covariance of the ambient pollutant concentrations. The results provide an indication that, in Baltimore during this study period, ambient gaseous concentrations may not have been adequate surrogates for corresponding personal gaseous exposures to allow the question to be investigated using central site monitors. Alternatively, the findings may suggest that in some locations, observed associations with the gaseous pollutants should be interpreted with caution, as they may be reflecting associations with PM or one of its chemical components.

Panel discussion review: session 1--exposure assessment and related errors in air pollution epidemiologic studies.

Examining the validity of exposure metrics used in air pollution epidemiologic models has been a key focus of recent exposure assessment studies. The objective of this work has been, largely, to determine what a given exposure metric represents and to quantify and reduce any potential errors resulting from using these metrics in lieu of true exposure measurements. The current manuscript summarizes the presentations of the co-authors from a recent EPA workshop, held in December 2006, dealing with the role and contributions of exposure assessment in addressing these issues. Results are presented from US and Canadian exposure and pollutant measurement studies as well as theoretical simulations to investigate what both particulate and gaseous pollutant concentrations represent and the potential errors resulting from their use in air pollution epidemiologic studies. Quantifying the association between ambient pollutant concentrations and corresponding personal exposures has led to the concept of defining attenuation factors, or alpha. Specifically, characterizing pollutant-specific estimates for alpha was shown to be useful in developing regression calibration methods involving PM epidemiologic risk estimates. For some gaseous pollutants such as NO2 and SO2, the associations between ambient concentrations and personal exposures were shown to be complex and still poorly understood. Results from recent panel studies suggest that ambient NO2 measurements may, in some locations, be serving as surrogates to traffic pollutants, including traffic-related PM2.5, hopanes, steranes, and oxidized nitrogen compounds (rather than NO2).

Estimation of ambient and non-ambient components of particulate matter exposure from a personal monitoring panel study.

To provide additional insight into factors affecting exposure to airborne particulate matter and the resultant health effects, we developed a method to estimate the ambient and nonambient components of total personal exposure. The ambient (or outdoor) component of total personal exposure to particulate matter (PM) (called ambient exposure) includes exposure to the ambient PM concentration while outdoors and exposure while indoors to ambient PM that has infiltrated indoors. The nonambient component of total personal exposure to PM (called nonambient exposure) refers to exposure to PM generated by indoor sources and an individual's personal activity. We used data collected from a personal monitoring study in Vancouver, Canada to demonstrate the methodology. In this study, ambient PM(2.5) exposure was 71% of the measured ambient PM(2.5) concentration and was responsible for 44% of the measured total personal PM(2.5) exposure. Regression analysis of the pooled data sets for ambient and total exposure against outdoor concentrations yielded similar slopes (0.76 for ambient and 0.77 for total) but a higher coefficient of determination for ambient exposure (R(2)=0.62) than for total exposure (R(2)=0.072). As expected, the nonambient exposure was not related to the ambient concentration (R(2)<10(-6)). For longitudinal analyses of the relationship between measured personal exposure and ambient concentrations for individual subjects, the correlation of total personal exposure with ambient concentration yielded values of Pearson's r from 0.83 to -0.68 with an average of 0.36. The relationship was statistically significant for only five of the 16 subjects. In contrast, the correlation of the estimated ambient exposure with ambient concentration yielded values of Pearson's r from 0.92 to 0.77 with an average of 0.88; 14 were significant. An example, taken from an epidemiologic analysis using the exposure data from this paper, demonstrates the usefulness of separating total exposure into its ambient and nonambient components.

Exposure to ambient and nonambient components of particulate matter: a comparison of health effects.

BACKGROUND: Numerous epidemiologic studies report associations between outdoor concentrations of particles and adverse health effects. Because personal exposure to particles is frequently dominated by exposure to nonambient particles (those originating from indoor sources), we present an approach to evaluate the relative impacts of ambient and nonambient exposures. METHODS: We developed separate estimates of exposures to ambient and nonambient particles of different size ranges (PM2.5, PM10-2.5 and PM10) based on time-activity data and the use of particle sulfate measurements as a tracer for indoor infiltration of ambient particles. To illustrate the application of these estimates, associations between cardiopulmonary health outcomes and the estimated exposures were compared with associations computed using measurements of personal exposures and outdoor concentrations for a repeated-measures panel study of 16 patients with chronic obstructive pulmonary disease conducted in the summer of 1998 in Vancouver. RESULTS: Total personal fine particle exposures were dominated by exposures to nonambient particles, which were not correlated with ambient fine particle exposures or ambient concentrations. Although total and nonambient particle exposures were not associated with any of the health outcomes, ambient exposures (and to a lesser extent ambient concentrations) were associated with decreased lung function, decreased systolic blood pressure, increased heart rate, and increased supraventricular ectopic heartbeats. Measures of heart rate variability showed less consistent relationships among the various exposure metrics. CONCLUSIONS: These results demonstrate the usefulness of separating total personal particle exposures into their ambient and nonambient components. The results support previous epidemiologic findings using ambient concentrations by demonstrating an association between health outcomes and ambient (outdoor origin) particle exposures but not with nonambient (indoor origin) particle exposures.

Dosimetric comparisons of particle deposition and retention in rats and humans.

Much of the information on the toxicity of particulate matter (PM) comes from studies in which laboratory rats were exposed to PM by inhalation or instillation. Optimal use of these toxicologic data requires extrapolation to the human scenario. Assuming that comparable doses should cause comparable effects across species and that species respond similarly to a given dose at a target site, extrapolations only require that dose be defined and then characterized. Dose may be defined in terms of a PM indicator (e.g., particle number or mass), a respiratory region, and the time over which the dose is integrated (i.e., deposited versus retained dose and incremental versus accumulated dose). Dose must also be normalized: for example, unit of dose per body mass, respiratory region surface area, or number of alveolar macrophages. The parameters chosen to define a normalized dose can drastically affect the rat exposure concentration required to provide a normalized dose equivalent to that occurring in a human. The publicly available multiple path particle dosimetry model developed by CIIT Centers for Health Research was used to predict particle deposition and retention in rats and humans. Estimates of particle concentration and exposure duration required for a rat to receive the same dose as received by a human were obtained with consideration of daily activity levels and ambient PM size distributions. These techniques were also used to compare dose and response between rats and humans in several published studies. Results indicate that the relationship between PM dose and response may differ between rats and humans. For acute PM exposures, rats may be less susceptible to inflammatory responses than humans. For chronic exposures to high levels of PM, however, an overload of alveolar clearance in rats may cause them to become more susceptible than humans to adverse pulmonary effects. The dosimetric calculations indicate that to achieve nominally similar acute doses per surface area in rats, relative to humans undergoing moderate to high exertion, PM exposure concentrations for rats would need to be somewhat higher than for humans. Since the clearance of PM is faster from the lung of rats than humans, much higher exposure concentrations are required for the rat to simulate retained burdens. In other cases, rats will require lower exposures than humans to have comparable doses, illustrating the complexity of such analyses. To make accurate estimates of dose, it is essential to have accurate and complete information regarding exposure conditions-that is, not only concentration and duration of exposure, but also the aerosol size distribution. Establishing a firm linkage between exposure and dose requires that consideration be given to particle characteristics, definitions of dose metrics, and biological normalizing factors.

Spatial variability of fine particle mass, components, and source contributions during the regional air pollution study in St. Louis.

Community time-series epidemiology typically uses either 24-hour integrated particulate matter (PM) concentrations averaged across several monitors in a city or data obtained at a central monitoring site to relate PM concentrations to human health effects. If the day-to-day variations in 24-hour integrated concentrations differ substantially across an urban area (i.e., daily measurements at monitors at different locations are not highly correlated), then there is a significant potential for exposure misclassification in community time-series epidemiology. If the annual average concentration differs across an urban area, then there is a potential for exposure misclassification in epidemiologic studies that use annual averages (or multi-year averages) as an index of exposure across different cities. The spatial variability in PM2.5 (particulate matter < or = 2.5 microm in aerodynamic diameter), its elemental components, and the contributions from each source category at 10 monitoring sites in St. Louis, Missouri were characterized using the ambient PM2.5 compositional data set of the Regional Air Pollution Study (RAPS) based on the Regional Air Monitoring System (RAMS) conducted between 1975 and 1977. Positive matrix factorization (PMF) was applied to each ambient PM2.5 compositional data set to estimate the contributions from the source categories. The spatial distributions of components and source contributions to PM2.5 at the 10 sites were characterized using Pearson correlation coefficients and coefficients of divergence. Sulfur and PM2.5 are highly correlated elements between all of the site pairs Although the secondary sulfate is the most highly correlated and shows the smallest spatial variability, there is a factor of 1.7 difference in secondary sulfate contributions between the highest and lowest site on average. Motor vehicles represent the next most highly correlated source component. However, there is a factor of 3.6 difference in motor vehicle contributions between the highest and lowest sites. The contributions from point source categories are much more variable. For example, the contributions from incinerators show a difference of a factor of 12.5 between the sites with the lowest and highest contributions. This study demonstrates that the spatial distributions of elemental components of PM2.5 and contributions from source categories can be highly heterogeneous within a given airshed and thus, there is the potential for exposure misclassification when a limited number of ambient PM monitors are used to represent population-average ambient exposures.