The amount of ambient PM found indoors is less than the ambient PM concentration. Since people spend much of their time indoors, the ambient concentration (AC) will be a noisy surrogate for personal exposure to ambient (PEA). In this paper, we will estimate the average daily personal exposure to both PM2.5 and PM10-2.5 and use this time series to estimate the relationship between daily PEA and daily cardiovascular mortality (CVM). We will compare these relationships with those found for AC and CVM. For a house with windows closed and few openings of doors, the air exchange rate, a, depends on the temperature difference between indoors and outdoors. From the literature, a=0.176+0.0162•ΔT(○C), based on daily measurements for a year. The ratio of the indoor concentration of an ambient air pollutant to the concentration of that ambient air pollutant outdoors, the infiltration factor, is given by Pa/a+k where P is the penetration efficiency and k is the deposition rate. From the literature we have k(PM2.5)=0.39, k(PM10-2.5)=1.01 and P=1 for both. ΔT=the absolute value of the difference between the average daily temperature and an estimated indoor temperature of 21.1○C (70○F). We can now estimate the daily, community-average, indoor concentrations of PM and use these values as surrogates for PEA. For a city like Phoenix, where >99% of the homes are air-conditioned, and using CVM of people ≥ 65, the indoor concentration should be a reasonable surrogate for PEA. Results will compare betas, t-statistics, and % Increases in Risk with ±95% CI for individual lag days out to lag day 10 and for a combined beta for all lag days with t>0.5 for both PMs. The relationship with AC is useful because air quality standards are based on ambient concentrations. However, PEA gives greater betas and greater statistical significance, supporting the importance of air pollution as a health hazard. For the same personal exposures, PM2.5 and PM10-2.5 are equally hazardous.

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.

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.

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.

Background and Aims: Due to current interest in multi-pollutant approaches to various aspects of air pollution, pollutant concentrations in Phoenix, AZ (1995-1998) have been analyzed for associations with cardiovascular mortality. Both the ability to determine accurately the independent effects of single pollutants and to estimate the joint effects of groups of pollutants are considered. Methods: A priori regression models were used based on previous analyses conducted in Phoenix. Subsequent sensitivity analyses give changes in beta from varying model parameters and thus an indication of the reliability of model results. Results: As, Se, Hg, and Sulfate are poorly correlated at the Phoenix measurement site. The joint effect (by including concentrations of all pollutants in a joint statistical model) gives a total beta very close to the sum of the individual pollutant betas. Therefore, betas for individual pollutants are meaningful and relative toxicities can be estimated. NO2, CO, and EC are highly correlated. The sum of the betas from a joint model is about equal to the largest single pollutant beta. Thus, it is difficult to obtain reliable betas for individual pollutants, only for their joint effect. Because of the high correlation between many pollutants, statistical models were used to produce poorly correlated factors which can often be associated with source categories (Source Category Models). Individual and joint results for factors from two such models are reported in interquartile range (IQR) units. Conclusions: It is difficult to estimate individual for highly correlated pollutants. Regression models using poorly correlated factors from Source Category Models (each factor including multiple pollutants) provide suggestions regarding the relative toxicity of source categories. Joint models using source categories yield an indication of the total effects of all identified sources acting jointly. (In making this presentation, Dr. Wilson is representing himself.)

Concentrations of size-resolved airborne particulate matter measured in Phoenix, Arizona in 1995-1996 indicate a strong linear relationship between the soil components of PM2.5 and the intermodal PM concentration and similarly between the nonsoil components of PM2.5 and the PM1 concentration. Moreover, the ratio of PM1 to intermodal PM varies considerably at all reported concentrations of PM2.5, and this variation appears to be systematically related to the proportion of PM2.5 comprised of soil components.

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 β-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 PM10, respectively. The results show that the two medication regimens protected against the effects of PM10. However, salmeterol increased the sensitivity to NO2 and triamcinalone enhanced the sensitivity to SO2.

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.

Abstract 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 σ = ± 1.5 µg C/m3 (8%).

Abstract 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-µm 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.

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 PM 2.5 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 O 3 and NO 2 would result in no significant observed associations with any of their respective ambient concentrations. Conversely, a true association with PM 2.5 would result in a significant, observed association with NO 2 ( β =0.0115, 95% confidence interval (CI): 0.0056, 0.0185) and a true association with exposure to SO 4 2− would result in an observed significant association with O 3 ( β =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.

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 μ g/m 3 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.

An intensive sampling campaign was performed in Fresno, CA during December 2003 measuring fine particulate matter including both the semi-volatile and nonvolatile fractions of the aerosol. Both the newly developed R&P FDMS Monitor and a PC-BOSS have been shown to measure total PM2.5 concentrations including semi-volatile nitrate and organic material. Good agreement was observed between the PC-BOSS and the R&P FDMS Monitor in this study with linear regression analysis resulting in a zero-intercept slope of 1.00 +/- 0.02 and an R-2 = 0.93. Several real-time measuring systems including the R&P Differential TEOM, the Met One BAMS, and a GRIMM Monitor were also employed and comparisons of total PM2.5 mass were made with the R&P FDMS Monitor. Agreement among these various monitors was generally good. However, differences were sometimes seen. Reasons for observed differences in the real-time mass measurement systems are explained by the composition and complexity of the measured aerosol, most importantly the composition of semi-volatile material. A newly automated ion chromatographic system developed by Dionex was also field tested and compared to both R&P 8400N Nitrate and integrated PC-BOSS inorganic species measurements. Sulfate and nitrate determined by the Dionex and PC-BOSS systems agreed. However, nitrate measured by the 8400N was low during fog events compared to the other two systems.

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 PM2.5 exposure was 71% of the measured ambient PM2.5 concentration and was responsible for 44% of the measured total personal PM2.5 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 (R2=0.62) than for total exposure (R2=0.072). As expected, the nonambient exposure was not related to the ambient concentration (R2<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.

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.

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.

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.