Stefanie Ebelt Sarnat

Short-term associations between ambient air pollutants and pediatric asthma emergency department visits.

RATIONALE Certain outdoor air pollutants cause asthma exacerbations in children. To advance understanding of these relationships, further characterization of the dose-response and pollutant lag effects are needed, as are investigations of pollutant species beyond the commonly measured criteria pollutants. OBJECTIVES Investigate short-term associations between ambient air pollutant concentrations and emergency department visits for pediatric asthma. METHODS Daily counts of emergency department visits for asthma or wheeze among children aged 5 to 17 years were collected from 41 Metropolitan Atlanta hospitals during 1993-2004 (n = 91,386 visits). Ambient concentrations of gaseous pollutants and speciated particulate matter were available from stationary monitors during this time period. Rate ratios for the warm season (May to October) and cold season (November to April) were estimated using Poisson generalized linear models in the framework of a case-crossover analysis. MEASUREMENTS AND MAIN RESULTS Both ozone and primary pollutants from traffic sources were associated with emergency department visits for asthma or wheeze; evidence for independent effects of ozone and primary pollutants from traffic sources were observed in multipollutant models. These associations tended to be of the highest magnitude for concentrations on the day of the emergency department visit and were present at relatively low ambient concentrations. CONCLUSIONS Even at relatively low ambient concentrations, ozone and primary pollutants from traffic sources independently contributed to the burden of emergency department visits for pediatric asthma.

Fine Particle Sources and Cardiorespiratory Morbidity: An Application of Chemical Mass Balance and Factor Analytical Source-Apportionment Methods

Background Interest in the health effects of particulate matter (PM) has focused on identifying sources of PM, including biomass burning, power plants, and gasoline and diesel emissions that may be associated with adverse health risks. Few epidemiologic studies, however, have included source-apportionment estimates in their examinations of PM health effects. We analyzed a time-series of chemically speciated PM measurements in Atlanta, Georgia, and conducted an epidemiologic analysis using data from three distinct source-apportionment methods. Objective The key objective of this analysis was to compare epidemiologic findings generated using both factor analysis and mass balance source-apportionment methods. Methods We analyzed data collected between November 1998 and December 2002 using positive-matrix factorization (PMF), modified chemical mass balance (CMB-LGO), and a tracer approach. Emergency department (ED) visits for a combined cardiovascular (CVD) and respiratory disease (RD) group were assessed as end points. We estimated the risk ratio (RR) associated with same day PM concentrations using Poisson generalized linear models. Results There were significant, positive associations between same-day PM2.5 (PM with aero-dynamic diameter ≤ 2.5 μm) concentrations attributed to mobile sources (RR range, 1.018–1.025) and biomass combustion, primarily prescribed forest burning and residential wood combustion, (RR range, 1.024–1.033) source categories and CVD-related ED visits. Associations between the source categories and RD visits were not significant for all models except sulfate-rich secondary PM2.5 (RR range, 1.012–1.020). Generally, the epidemiologic results were robust to the selection of source-apportionment method, with strong agreement between the RR estimates from the PMF and CMB-LGO models, as well as with results from models using single-species tracers as surrogates of the source-apportioned PM2.5 values. Conclusions Despite differences among the source-apportionment methods, these findings suggest that modeled source-apportioned data can produce robust estimates of acute health risk. In Atlanta, there were consistent associations across methods between PM2.5 from mobile sources and biomass burning with both cardiovascular and respiratory ED visits, and between sulfate-rich secondary PM2.5 with respiratory visits.

Multipollutant modeling issues in a study of ambient air quality and emergency department visits in Atlanta

Multipollutant models are frequently used to differentiate roles of multiple pollutants in epidemiologic studies of ambient air pollution. In the presence of differing levels of measurement error across pollutants under consideration, however, they can be biased and as misleading as single-pollutant models. Their appropriate interpretation depends on the relationships among the pollutant measurements and the outcomes in question. In situations where two or more pollutant variables may be acting as surrogates for the etiologic agent(s), multipollutant models can help identify the best surrogate, but the risk estimates may be influenced by inclusion of a second variable that is not itself an independent risk factor for the outcome in question. In this paper, these issues will be illustrated in the context of an ongoing study of emergency visits in Atlanta. Emergency department visits from 41 of 42 hospitals serving the 20-county Atlanta metropolitan area for the period 1993–2004 (n=10,206,389 visits) were studied in relation to ambient pollutant levels, including speciated particle measurements from an intensive monitoring campaign at a downtown station starting in 1998. Relative to our earlier publications, reporting results through 2000, the period for which the speciated data are available is now tripled (6 years in length). Poisson generalized linear models were used to examine outcome counts in relation to 3-day moving average concentrations of pollutants of a priori interest (ozone, nitrogen dioxide, carbon monoxide, sulfur dioxide, oxygenated hydrocarbons, PM10, coarse PM, PM2.5, and the following components of PM2.5: elemental carbon, organic carbon, sulfate, and water-soluble transition metals). In the present analysis, we report results for two outcome groups: a respiratory outcomes group and a cardiovascular outcomes group. For cardiovascular visits, associations were observed with CO, NO2, and PM2.5 elemental carbon and organic carbon. In multipollutant models, CO was the strongest predictor. For respiratory visits, associations were observed with ozone, PM10, CO, and NO2 in single-pollutant models. In multipollutant models, PM10 and ozone persisted as predictors, with ozone the stronger predictor. Caveats and considerations in interpreting the multipollutant model results are discussed.

Factors Affecting the Association between Ambient Concentrations and Personal Exposures to Particles and Gases

Results from air pollution exposure assessment studies suggest that ambient fine particles [particulate matter with aerodynamic diameter ≤ 2.5 μg (PM2.5)], but not ambient gases, are strong proxies of corresponding personal exposures. For particles, the strength of the personal–ambient association can differ by particle component and level of home ventilation. For gases, however, such as ozone (O3), nitrogen dioxide (NO2), and sulfur dioxide (SO2), the impact of home ventilation on personal–ambient associations is untested. We measured 24-hr personal exposures and corresponding ambient concentrations to PM2.5, sulfate (SO42−), elemental carbon, O3, NO2, and SO2 for 10 nonsmoking older adults in Steubenville, Ohio. We found strong associations between ambient particle concentrations and corresponding personal exposures. In contrast, although significant, most associations between ambient gases and their corresponding exposures had low slopes and R2 values; the personal–ambient NO2 association in the fall season was moderate. For both particles and gases, personal–ambient associations were highest for individuals spending most of their time in high- compared with low-ventilated environments. Cross-pollutant models indicated that ambient particle concentrations were much better surrogates for exposure to particles than to gases. With the exception of ambient NO2 in the fall, which showed moderate associations with personal exposures, ambient gases were poor proxies for both gas and particle exposures. In combination, our results suggest that a) ventilation may be an important modifier of the magnitude of effect in time-series health studies, and b) results from time-series health studies based on 24-hr ambient concentrations are more readily interpretable for particles than for gases.

Ambient particulate air pollution and cardiac arrhythmia in a panel of older adults in Steubenville, Ohio

Objectives: Ambient particulate air pollution has been associated with increased risk of cardiovascular morbidity and mortality. Pathways by which particles may act involve autonomic nervous system dysfunction or inflammation, which can affect cardiac rate and rhythm. The importance of these pathways may vary by particle component or source. In an eastern US location with significant regional pollution, the authors examined the association of air pollution and odds of cardiac arrhythmia in older adults. Methods: Thirty two non-smoking older adults were evaluated on a weekly basis for 24 weeks during the summer and autumn of 2000 with a standardised 30 minute protocol that included continuous electrocardiogram measurements. A central ambient monitoring station provided daily concentrations of fine particles (PM2.5, sulfate, elemental carbon) and gases. Sulfate was used as a marker of regional pollution. The authors used logistic mixed effects regression to examine the odds of having any supraventricular ectopy (SVE) or ventricular ectopy (VE) in association with increases in air pollution for moving average pollutant concentrations up to 10 days before the health assessment. Results: Participant specific mean counts of arrhythmia over the protocol varied between 0.1–363 for SVE and 0–350 for VE. The authors observed odds ratios for having SVE over the length of the protocol of 1.42 (95% CI 0.99 to 2.04), 1.70 (95% CI 1.12 to 2.57), and 1.78 (95% CI 0.95 to 3.35) for 10.0 μg/m3, 4.2 μg/m3, and 14.9 ppb increases in five day moving average PM2.5, sulfate, and ozone concentrations respectively. The other pollutants, including elemental carbon, showed no effect on arrhythmia. Participants reporting cardiovascular conditions (for example, previous myocardial infarction or hypertension) were the most susceptible to pollution induced SVE. The authors found no association of pollution with VE. Conclusion: Increased levels of ambient sulfate and ozone may increase the risk of supraventricular arrhythmia in the elderly.

Short-term effects of air pollution on heart rate variability in senior adults in Steubenville, Ohio.

Objective: We examined the association between ambient air pollution levels and heart rate variability (HRV) in a panel study of 32 subjects. Methods: We used linear mixed models to analyze the effects of fine particles (PM2.5), sulfate (SO42−), elemental carbon (EC), and gases on log-transformed standard deviation of normal RR intervals (SDNN), mean square of differences between adjacent RR intervals (r-MSSD), and high- and low-frequency power (HF, LF). Results: An interquartile range (IQR) increase of 5.1 &mgr;g/m3 in SO42− on the previous day was associated with a decrease of −3.3% SDNN (95% confidence = −6.0% to −0.5%), −5.6% r-MSSD (−10.7% to −0.2%), and −10.3% HF (−19.5% to −0.1%). Associations with total PM2.5 were similar. HRV was not associated with EC, NO2, SO2, or O3. Conclusion: In addition to traffic-related particles, elevated levels of sulfate particles may also adversely affect autonomic function.

The Influences of Ambient Particle Composition and Size on Particle Infiltration in Los Angeles, CA, Residences

Abstract Particle infiltration is a key determinant of the indoor concentrations of ambient particles. Few studies have examined the influence of particle composition on infiltration, particularly in areas with high concentrations of volatile particles, such as ammonium nitrate (NH4NO3). A comprehensive indoor monitoring study was conducted in 17 Los Angeles–area homes. As part of this study, indoor/outdoor concentration ratios during overnight (nonindoor source) periods were used to estimate the fraction of ambient particles remaining airborne indoors, or the particle infiltration factor (FINF), for fine particles (PM2.5), its nonvolatile (i.e., black carbon [BC]) and volatile (i.e., nitrate [NO3 −]) components, and particle sizes ranging between 0.02 and 10 μm. FINF was highest for BC (median = 0.84) and lowest for NO3 − (median = 0.18). The low FINF for NO3 − was likely because of volatilization of NO3 − particles once indoors, in addition to depositional losses upon building entry. The FINF for PM2.5 (median = 0.48) fell between those for BC and NO3 −, reflecting the contributions of both particle components to PM2.5. FINF varied with particle size, air-exchange rate, and outdoor NO3 − concentrations. The FINF for particles between 0.7 and 2 μm in size was considerably lower during periods of high as compared with low outdoor NO3 − concentrations, suggesting that outdoor NO3 − particles were of this size. This study demonstrates that infiltration of PM2.5 varies by particle component and is lowest for volatile species, such as NH4NO3. Our results suggest that volatile particle components may influence the ability for outdoor PM concentrations to represent indoor and, thus, personal exposures to particles of ambient origin, because volatilization of these particles causes the composition of PM2.5 to differ indoors and outdoors. Consequently, particle composition likely influences observed epidemiologic relationships based on outdoor PM concentrations, especially in areas with high concentrations of NH4NO3 and other volatile particles.

Reactive Oxygen Species Generation Linked to Sources of Atmospheric Particulate Matter and Cardiorespiratory Effects.

Exposure to atmospheric fine particulate matter (PM2.5) is associated with cardiorespiratory morbidity and mortality, but the mechanisms are not well understood. We assess the hypothesis that PM2.5 induces oxidative stress in the body via catalytic generation of reactive oxygen species (ROS). A dithiothreitol (DTT) assay was used to measure the ROS-generation potential of water-soluble PM2.5. Source apportionment on ambient (Atlanta, GA) PM2.5 was performed using the chemical mass balance method with ensemble-averaged source impact profiles. Linear regression analysis was used to relate PM2.5 emission sources to ROS-generation potential and to estimate historical levels of DTT activity for use in an epidemiologic analysis for the period of 1998-2009. Light-duty gasoline vehicles (LDGV) exhibited the highest intrinsic DTT activity, followed by biomass burning (BURN) and heavy-duty diesel vehicles (HDDV) (0.11 ± 0.02, 0.069 ± 0.02, and 0.052 ± 0.01 nmol min(-1) μg(-1)source, respectively). BURN contributed the largest fraction to total DTT activity over the study period, followed by LDGV and HDDV (45, 20, and 14%, respectively). DTT activity was more strongly associated with emergency department visits for asthma/wheezing and congestive heart failure than PM2.5. This work provides further epidemiologic evidence of a biologically plausible mechanism, that of oxidative stress, for associations of adverse health outcomes with PM2.5 mass and supports continued assessment of the utility of the DTT activity assay as a measure of ROS-generating potential of particles.

Air pollution and acute respiratory response in a panel of asthmatic children along the U.S.-Mexico border.

Background: Concerns regarding the health impact of urban air pollution on asthmatic children are pronounced along the U.S.–Mexico border because of rapid population growth near busy border highways and roads. Objectives: We conducted the first binational study of the impacts of air pollution on asthmatic children in Ciudad Juarez, Mexico, and El Paso, Texas, USA, and compared different exposure metrics to assess acute respiratory response. Methods: We recruited 58 asthmatic children from two schools in Ciudad Juarez and two schools in El Paso. A marker of airway inflammation [exhaled nitric oxide (eNO)], respiratory symptom surveys, and pollutant measurements (indoor and outdoor 48-hr size-fractionated particulate matter, 48-hr black carbon, and 96-hr nitrogen dioxide) were collected at each school for 16 weeks. We examined associations between the pollutants and respiratory response using generalized linear mixed models. Results: We observed small but consistent associations between eNO and numerous pollutant metrics, with estimated increases in eNO ranging from 1% to 3% per interquartile range increase in pollutant concentrations. Effect estimates from models using school-based concentrations were generally stronger than corresponding estimates based on concentrations from ambient air monitors. Both traffic-related and non–traffic-related particles were typically more robust predictors of eNO than was nitrogen dioxide, for which associations were highly sensitive to model specification. Associations differed significantly across the four school-based cohorts, consistent with heterogeneity in pollutant concentrations and cohort characteristics. Models examining respiratory symptoms were consistent with the null. Conclusions: The results indicate adverse effects of air pollution on the subclinical respiratory health of asthmatic children in this region and provide preliminary support for the use of air pollution monitors close to schools to track exposure and potential health risk in this population.

Exposure prediction approaches used in air pollution epidemiology studies: key findings and future recommendations.

Many epidemiologic studies of the health effects of exposure to ambient air pollution use measurements from central-site monitors as their exposure estimate. However, measurements from central-site monitors may lack the spatial and temporal resolution required to capture exposure variability in a study population, thus resulting in exposure error and biased estimates. Articles in this dedicated issue examine various approaches to predict or assign exposures to ambient pollutants. These methods include combining existing central-site pollution measurements with local- and/or regional-scale air quality models to create new or “hybrid” models for pollutant exposure estimates and using exposure models to account for factors such as infiltration of pollutants indoors and human activity patterns. Key findings from these articles are summarized to provide lessons learned and recommendations for additional research on improving exposure estimation approaches for future epidemiological studies. In summary, when compared with use of central-site monitoring data, the enhanced spatial resolution of air quality or exposure models can have an impact on resultant health effect estimates, especially for pollutants derived from local sources such as traffic (e.g., EC, CO, and NOx). In addition, the optimal exposure estimation approach also depends upon the epidemiological study design. We recommend that future research develops pollutant-specific infiltration data (including for PM species) and improves existing data on human time-activity patterns and exposure to local source (e.g., traffic), in order to enhance human exposure modeling estimates. We also recommend comparing how various approaches to exposure estimation characterize relationships between multiple pollutants in time and space and investigating the impact of improved exposure estimates in chronic health studies.