Faculty Bibliography

Using daily fine particulate matter (PM2.5) composition data from the 2000-2005 U.S. EPA Chemical Speciation Network (CSN) for over 200 sites, we applied multivariate methods to identify and quantify the major fine particulate matter (PM2.5) source components in the U.S. Novel aspects of this work were: (1) the application of factor analysis (FA) to multi-city daily data, drawing upon both spatial and temporal variations of chemical species; and, (2) the exclusion of secondary components (sulfates, nitrates and organic carbon) from the source identification FA to more clearly discern and apportion the PM2.5 mass to primary emission source categories. For the quantification of source-related mass, we considered two approaches based upon the FA results: 1) using single key tracers for sources identified by FA in a mass regression; and, 2) applying Absolute Principal Component Analysis (APCA). In each case, we followed a two-stage mass regression approach, in which secondary components were first apportioned among the identified sources, and then mass was apportioned to the sources and to other secondary mass not explained by the individual sources. The major U.S. PM2.5 source categories identified via FA (and their key elements) were: Metals Industry (Pb, Zn); Crustal/Soil Particles (Ca, Si); Motor Vehicle Traffic (EC, NO2); Steel Industry (Fe, Mn); Coal Combustion (As, Se); Oil Combustion (V, Ni); Salt Particles (Na, Cl) and Biomass Burning (K). Nationwide spatial plots of the source-related PM2.5 impacts were confirmatory of the factor interpretations: ubiquitous sources, such as Traffic and Soil, were found to be spread across the nation, more unique sources (such as Steel and Metals Processing) being highest in select industrialized cities, Biomass Burning was highest in the U.S. Northwest, while Residual Oil combustion was highest in cities in the Northeastern U.S. and in cities with major seaports. The sum of these source contributions and the secondary PM2.5 components agreed well with the U.S. PM2.5 observed during the study period (mean=14.3 ug/m3; R2= 0.91). Apportionment regression analyses using single-element tracers for each source category gave results consistent with the APCA estimates. Comparisons of nearby sites indicated that the PM2.5 mass and the secondary aerosols were most homogenous spatially, while traffic PM2.5 and its tracer, EC, were among the most spatially representative of the source-related components. Comparison of apportionment results to a previous analysis of the 1979-1982 IP Network revealed similar and correlated major U.S. source category factors, albeit at lower levels than in the earlier period, suggesting a consistency in the U.S. spatial patterns of these source-related exposures over time, as well. These results indicate that applying source apportionment methods to the nationwide CSN can be an informative avenue for identifying and quantifying source components for the subsequent estimation of source-specific health effects, potentially contributing to more efficient regulation of PM2.5.

Background: Past time-series studies of the health effects of fine particulate matter [aerodynamic diameter </= 2.5 microm (PM2.5)] have used chemically nonspecific PM2.5 mass. However, PM2.5 is known to vary in chemical composition with source, and health impacts may vary accordingly.Objective: We tested the association between source-specific daily PM2.5 mass and hospital admissions in a time-series investigation that considered both single-lag and distributed-lag models.Methods: Daily PM2.5 speciation measurements collected in midtown Manhattan were analyzed via positive matrix factorization source apportionment. Daily and distributed-lag generalized linear models of Medicare respiratory and cardiovascular hospital admissions during 2001-2002 considered PM2.5 mass and PM2.5 from five sources: transported sulfate, residual oil, traffic, steel metal works, and soil.Results: Source-related PM2.5 (specifically steel and traffic) was significantly associated with hospital admissions but not with total PM2.5 mass. Steel metal works-related PM2.5 was associated with respiratory admissions for multiple-lag days, especially during the cleanup efforts at the World Trade Center. Traffic-related PM2.5 was consistently associated with same-day cardiovascular admissions across disease-specific subcategories. PM2.5 constituents associated with each source (e.g., elemental carbon with traffic) were likewise associated with admissions in a consistent manner. Mean effects of distributed-lag models were significantly greater than were maximum single-day effect models for both steel- and traffic-related PM2.5.Conclusions: Past analyses that have considered only PM2.5 mass or only maximum single-day lag effects have likely underestimated PM2.5 health effects by not considering source-specific and distributed-lag effects. Differing lag structures and disease specificity observed for steel-related versus traffic-related PM2.5 raise the possibility of distinct mechanistic pathways of health effects for particles of differing chemical composition

Background: Previous studies have reported relationships between adverse respiratory health outcomes and residential proximity to traffic pollution, but have not previously shown this at a personal exposure level. Objective: To compare, among inner-city children with asthma, the associations of adverse asthma outcome incidences with increased personal exposure to fine particle (PM2.5) mass air pollution vs. with the diesel-related carbonaceous fraction of PM2.5. Methods: Daily 24-hr personal samples of PM2.5, including the elemental carbon (EC) fraction, were collected for forty fifth-grade children with asthma at four South Bronx schools (10 children per school) during approximately one month each. Spirometry and symptom scores were recorded several times daily during weekdays. Results: Significantly elevated same-day relative risks of cough (1.23 (95% CI -1.0, 1.545), wheeze 1.45 (95% CI 1.03, 10.4), shortness of breath 1.41 (95% CI 1.0, 1.99%) and total symptoms 1.30 (95% CI 4.0, 1.62) were found with an increase in personal EC, but not with personal PM2.5 mass. Increased risk of cough and total symptoms was found with increased one-day lag and two-day average school-site EC. No significant associations were found with school-site PM2.5 mass or sulfur. The EC effect estimate was robust to addition of gaseous pollutants. Conclusion: Adverse health associations were strongest with personal measures of EC exposure, suggesting that the diesel 'soot' fraction of PM2.5 is most responsible for pollution-related asthma exacerbations among children living proximal to roadways. Studies that rely on exposure to particulate mass may underestimate PM health impacts

Background: Recent time-series studies have indicated that both cardiovascular disease (CVD)mortality and hospitalizations are associated with particulate matter (PM). However, seasonal patterns of PM associations with these outcomes are not consistent, and PM components responsible for these associations have not been determined. We investigated this issue in New York City (NYC), where PM originates from regional and local combustion sources.Objective: In this study, we examined the role of particulate matter with aerodynamic diameter </= 2.5 microm (PM2.5) and its key chemical components on both CVD hospitalizations and on mortality in NYC.Methods: We analyzed daily deaths and emergency hospitalizations for CVDs among persons >/= 40 years of age for associations with PM2.5, its chemical components, nitrogen dioxide (NO2), carbon monoxide, and sulfur dioxide for the years 2000-2006 using a Poisson time-series model adjusting for temporal and seasonal trends, temperature effects, and day of the week. We estimated excess risks per interquartile-range increases at lags 0 through 3 days for warm (April through September) and cold (October through March) seasons.Results: The CVD mortality series exhibit strong seasonal trends, whereas the CVD hospitalization series show a strong day-of-week pattern. These outcome series were not correlated with each other but were individually associated with a number of PM2.5 chemical components from regional and local sources, each with different seasonal patterns and lags. Coal-combustion-related components (e.g., selenium) were associated with CVD mortality in summer and CVD hospitalizations in winter, whereas elemental carbon and NO2 showed associations with these outcomes in both seasons.Conclusion: Local combustion sources, including traffic and residual oil burning, may play a year-round role in the associations between air pollution and CVD outcomes, but transported aerosols may explain the seasonal variation in associations shown by PM2.5 mass

BACKGROUND: Recent toxicological and epidemiological studies have shown associations between particulate matter (PM) and adverse health effects, but which PM components are most influential is less well known. OBJECTIVES: In this study, we used time-series analyses to determine the associations between daily fine PM [PM </= 2.5 microm in aerodynamic diameter (PM2.5)] concentrations and daily mortality in two U.S. cities-Seattle, Washington, and Detroit, Michigan. METHODS: We obtained daily PM2.5 filters for the years of 2002-2004 and analyzed trace elements using X-ray fluorescence and black carbon using light reflectance as a surrogate measure of elemental carbon. We used Poisson regression and distributed lag models to estimate excess deaths for all causes and for cardiovascular and respiratory diseases adjusting for time-varying covariates. We computed the excess risks for interquartile range increases of each pollutant at lags of 0 through 3 days for both warm and cold seasons. RESULTS: The cardiovascular and respiratory mortality series exhibited different source and seasonal patterns in each city. The PM2.5 components and gaseous pollutants associated with mortality in Detroit were most associated with warm season secondary aerosols and traffic markers. In Seattle, the component species most closely associated with mortality included those for cold season traffic and other combustion sources, such as residual oil and wood burning. CONCLUSIONS: The effects of PM2.5 on daily mortality vary with source, season, and locale, consistent with the hypothesis that PM composition has an appreciable influence on the health effects attributable to PM

In this report we review the health effects of three short-lived greenhouse pollutants-black carbon, ozone, and sulphates. We undertook new meta-analyses of existing time-series studies and an analysis of a cohort of 352,000 people in 66 US cities during 18 years of follow-up. This cohort study provides estimates of mortality effects from long-term exposure to elemental carbon, an indicator of black carbon mass, and evidence that ozone exerts an independent risk of mortality. Associations among these pollutants make drawing conclusions about their individual health effects difficult at present, but sulphate seems to have the most robust effects in multiple-pollutant models. Generally, the toxicology of the pure compounds and their epidemiology diverge because atmospheric black carbon, ozone, and sulphate are associated and could interact with related toxic species. Although sulphate is a cooling agent, black carbon and ozone could together exert nearly half as much global warming as carbon dioxide. The complexity of these health and climate effects needs to be recognised in mitigation policies.

We conducted an extended follow-up and spatial analysis of the American Cancer Society (ACS) Cancer Prevention Study II (CPS-II) cohort in order to further examine associations between long-term exposure to particulate air pollution and mortality in large U.S. cities. The current study sought to clarify outstanding scientific issues that arose from our earlier HEI-sponsored Reanalysis of the original ACS study data (the Particle Epidemiology Reanalysis Project). Specifically, we examined (1) how ecologic covariates at the community and neighborhood levels might confound and modify the air pollution-mortality association; (2) how spatial autocorrelation and multiple levels of data (e.g., individual and neighborhood) can be taken into account within the random effects Cox model; (3) how using land-use regression to refine measurements of air pollution exposure to the within-city (or intra-urban) scale might affect the size and significance of health effects in the Los Angeles and New York City regions; and (4) what exposure time windows may be most critical to the air pollution-mortality association. The 18 years of follow-up (extended from 7 years in the original study [Pope et al. 1995]) included vital status data for the CPS-II cohort (approximately 1.2 million participants) with multiple cause-of-death codes through December 31, 2000 and more recent exposure data from air pollution monitoring sites for the metropolitan areas. In the Nationwide Analysis, the influence of ecologic covariate data (such as education attainment, housing characteristics, and level of income; data obtained from the 1980 U.S. Census; see Ecologic Covariates sidebar on page 14) on the air pollution-mortality association were examined at the Zip Code area (ZCA) scale, the metropolitan statistical area (MSA) scale, and by the difference between each ZCA value and the MSA value (DIFF). In contrast to previous analyses that did not directly include ecologic covariates at the ZCA scale, risk estimates increased when ecologic covariates were included at all scales. The ecologic covariates exerted their greatest effect on mortality from ischemic heart disease (IHD), which was also the health outcome most strongly related with exposure to PM2.5 (particles 2.5 microm or smaller in aerodynamic diameter), sulfate (SO4(2-)), and sulfur dioxide (SO2), and the only outcome significantly associated with exposure to nitrogen dioxide (NO2). When ecologic covariates were simultaneously included at both the MSA and DIFF levels, the hazard ratio (HR) for mortality from IHD associated with PM2.5 exposure (average concentration for 1999-2000) increased by 7.5% and that associated with SO4(2-) exposure (average concentration for 1990) increased by 12.8%. The two covariates found to exert the greatest confounding influence on the PM2.5-mortality association were the percentage of the population with a grade 12 education and the median household income. Also in the Nationwide Analysis, complex spatial patterns in the CPS-II data were explored with an extended random effects Cox model (see Glossary of Statistical Terms at end of report) that is capable of clustering up to two geographic levels of data. Using this model tended to increase the HR estimate for exposure to air pollution and also to inflate the uncertainty in the estimates. Including ecologic covariates decreased the variance of the results at both the MSA and ZCA scales; the largest decrease was in residual variation based on models in which the MSA and DIFF levels of data were included together, which suggests that partitioning the ecologic covariates into between-MSA and within-MSA values more completely captures the sources of variation in the relationship between air pollution, ecologic covariates, and mortality. Intra-Urban Analyses were conducted for the New York City and Los Angeles regions. The results of the Los Angeles spatial analysis, where we found high exposure contrasts within the Los Angeles region, showed that air pollution-mortality risks were nearly 3 times greater than those reported from earlier analyses. This suggests that chronic health effects associated with intra-urban gradients in exposure to PM2.5 may be even larger between ZCAs within an MSA than the associations between MSAs that have been previously reported. However, in the New York City spatial analysis, where we found very little exposure contrast between ZCAs within the New York region, mortality from all causes, cardiopulmonary disease (CPD), and lung cancer was not elevated. A positive association was seen for PM2.5 exposure and IHD, which provides evidence of a specific association with a cause of death that has high biologic plausibility. These results were robust when analyses controlled (1) the 44 individual-level covariates (from the ACS enrollment questionnaire in 1982; see 44 Individual-Level Covariates sidebar on page 22) and (2) spatial clustering using the random effects Cox model. Effects were mildly lower when unemployment at the ZCA scale was included. To examine whether there is a critical exposure time window that is primarily responsible for the increased mortality associated with ambient air pollution, we constructed individual time-dependent exposure profiles for particulate and gaseous air pollutants (PM2.5 and SO2) for a subset of the ACS CPS-II participants for whom residence histories were available. The relevance of the three exposure time windows we considered was gauged using the magnitude of the relative risk (HR) of mortality as well as the Akaike information criterion (AIC), which measures the goodness of fit of the model to the data. For PM2.5, no one exposure time window stood out as demonstrating the greatest HR; nor was there any clear pattern of a trend in HR going from recent to more distant windows or vice versa. Differences in AIC values among the three exposure time windows were also small. The HRs for mortality associated with exposure to SO2 were highest in the most recent time window (1 to 5 years), although none of these HRs were significantly elevated. Identifying critical exposure time windows remains a challenge that warrants further work with other relevant data sets. This study provides additional support toward developing cost-effective air quality management policies and strategies. The epidemiologic results reported here are consistent with those from other population-based studies, which collectively have strongly supported the hypothesis that long-term exposure to PM2.5 increases mortality in the general population. Future research using the extended Cox-Poisson random effects methods, advanced geostatistical modeling techniques, and newer exposure assessment techniques will provide additional insight.