Introduction
Various temporal metrics of daily pollution levels have been used to examine relationships between air pollutants and acute health outcomes. However, daily metrics of the same pollutant have rarely been systematically compared within a study. In this analysis, we describe the variability of effect estimates attributable to the use of different temporal metrics of daily pollution levels.
Methods
We obtained hourly measurements of ambient particulate matter (PM2.5), carbon monoxide (CO), nitrogen dioxide (NO2), and ozone (O3) from air monitoring networks in 20-county Atlanta for the time period 1993–2004. For each pollutant we created: 1) a daily 1-hour maximum; 2) a 24-hour average; 3) a commute average; 4) a day-time average; 5) a night-time average; and a daily 8-hour maximum (only for O3). Using Poisson generalized linear models, we examined associations between daily counts of respiratory emergency department visits and the previous day’s pollutant metrics.
Results
Variability was greatest across O3 metrics, with the 8-hour maximum, 1-hour maximum, and day-time metrics yielding strong positive associations and the night-time O3 metric yielding a negative association (likely reflecting confounding by air pollutants oxidized by O3). With the exception of the day-time metric, all of the CO and NO2 metrics were positively associated with respiratory emergency department visits.
Discussion
Differences in observed associations with respiratory emergency room visits among temporal metrics of the same pollutant were influenced by the diurnal patterns of the pollutant, spatial representativeness of the metrics, and correlation between each metric and copollutant concentrations. Overall, the use of metrics based on the US National Ambient Air Quality Standards (e.g., the use of a daily 8-hour maximum O3 as opposed to a 24-hour average metric) was supported by this analysis. Comparative analysis of temporal metrics also provided insight into underlying relationships between specific air pollutants and respiratory health.
Introduction
Relatively few studies have evaluated the impacts of heterogeneous spatiotemporal pollutant distributions on health risk estimates in time-series analyses that use data from a central monitor to assign exposures. We present a method for examining the impacts of exposure measurement error relating to spatiotemporal variability in ambient air pollutant concentrations on air pollution health risk estimates in a daily time-series analysis of emergency department visits in Atlanta, Georgia.
Methods
We used Poisson generalized linear models to estimate associations between current day pollutant concentrations and circulatory emergency department visits for the 1998–2004 period. Data from monitoring sites located in different geographical regions of the study area and at different distances from several urban geographic subpopulations served as alternative measures of exposure.
Results
We observed associations for spatially heterogeneous pollutants (CO and NO2) using data from several different urban monitoring sites. These associations were not observed when using data from the most rural site, located 38 miles from the city center. In contrast, associations for spatially homogeneous pollutants (O3 and PM2.5) were similar regardless of monitoring site location.
Conclusions
We found that monitoring site location and the distance of a monitoring site to a population of interest did not meaningfully impact estimated associations for any pollutant when using data from urban sites located within 20 miles from the population center under study. However, for CO and NO2, these factors were important when using data from rural sites, located greater than 30 miles from the population center, likely due to exposure measurement error. Overall, our findings lend support to the use of pollutant data from urban central sites to assess population exposures within geographically dispersed study populations in Atlanta and similar cities.
Background
Previous studies report associations between aeroallergen exposure and asthma exacerbations. Aeroallergen burdens and asthma prevalence are increasing worldwide and are projected to increase further with climate change, highlighting the importance of understanding population-level relationships between ambient pollen concentrations and asthma.
Objective
To examine short-term associations between ambient concentrations of various pollen taxa and emergency department (ED) visits for asthma and wheeze in the Atlanta metropolitan area between 1993 and 2004.
Methods
We assessed associations between the three-day moving average (lag 0-1-2) of Betulaceae (except Alnus), Cupressaceae, Quercus, Pinaceae (except Tsuga), Poaceae, and Ambrosia pollen concentrations and daily asthma and wheeze ED visit counts, controlling for covarying pollen taxa and ambient pollutant concentrations.
Results
We observed a 2–3% increase in asthma and wheeze ED visits per standard deviation increase in Quercus and Poaceae pollen and a 10–15% increased risk on days with the highest concentrations (comparing the top 5% of days to the lowest 50% of days). A standard deviation increase in Cupressaceae concentrations was associated with a 1% decrease in ED visits. The association for Quercus pollen was strongest for children age 5 to 17 years. Effects of Ambrosia pollen on asthma exacerbations were difficult to assess in this large-scale temporal analysis due to possible confounding by the steep increase in circulating rhinoviruses every September.
Conclusion
Poaceae and Quercus pollen contribute to asthma morbidity in Atlanta. Altered Quercus and Poaceae pollen production due to climate change could affect allergen-induced asthma morbidity in the southeastern United States.
Although short-term exposure to ambient ozone (O 3 ) can cause poor respiratory health outcomes, the shape of the concentration-response (C-R) between O 3 and respiratory morbidity has not been widely investigated. We estimated the effect of daily O 3 on emergency department (ED) visits for selected respiratory outcomes in 5 US cities under various model assumptions and assessed model fit. Population-weighted average 8-h maximum O 3 concentrations were estimated in each city. Individual-level data on ED visits were obtained from hospitals or hospital associations. Poisson log-linear models were used to estimate city-specific associations between the daily number of respiratory ED visits and 3-day moving average O 3 levels controlling for long-term trends and meteorology. Linear, linear-threshold, quadratic, cubic, categorical, and cubic spline O 3 C-R models were considered. Using linear C-R models, O 3 was significantly and positively associated with respiratory ED visits in each city with rate ratios of 1.02–1.07 per 25 ppb. Models suggested that O 3 -ED C-R shapes were linear until O 3 concentrations of roughly 60 ppb at which point risk continued to increase linearly in some cities for certain outcomes while risk flattened in others. Assessing C-R shape is necessary to identify the most appropriate form of the exposure for each given study setting.
Background: Given that fine particulate matter (≤ 2.5 μm; PM2.5) is a mixture of multiple components, it has been of high interest to identify its specific health-relevant physical and/or chemical features.
Objectives: We conducted a time-series study of PM2.5 and cardiorespiratory emergency department (ED) visits in the St. Louis, Missouri–Illinois metropolitan area, using 2 years of daily PM2.5 and PM2.5 component measurements (including ions, carbon, particle-phase organic compounds, and elements) made at the St. Louis-Midwest Supersite, a monitoring site of the U.S. Environmental Protection Agency Supersites ambient air monitoring research program.
Methods: Using Poisson generalized linear models, we assessed short-term associations between daily cardiorespiratory ED visit counts and daily levels of 24 selected pollutants. Associations were estimated for interquartile range changes in each pollutant. To allow comparison of relationships among multiple pollutants and outcomes with potentially different lag structures, we used 3-day unconstrained distributed lag models controlling for time trends and meteorology.
Results: Considering results of our primary models, as well as sensitivity analyses and models assessing co-pollutant confounding, we observed robust associations of cardiovascular disease visits with 17α(H),21β(H)-hopane and congestive heart failure visits with elemental carbon. We also observed a robust association of respiratory disease visits with ozone. For asthma/wheeze, associations were strongest with ozone and nitrogen dioxide; observed associations of asthma/wheeze with PM2.5 and its components were attenuated in two-pollutant models with these gases. Differential measurement error due to differential patterns of spatiotemporal variability may have influenced patterns of observed associations across pollutants.
Conclusions: Our findings add to the growing field examining the health effects of PM2.5 components. Combustion-related components of the pollutant mix showed particularly strong associations with cardiorespiratory ED visit outcomes.
Background
While several studies suggest that traffic-related air pollutants are detrimental for respiratory health, few studies have examined relationships between residential proximity to a major roadway and asthma control in children. Furthermore, a major limitation of existing research is reliance on self-reported outcomes. We therefore determined the spatial relationship between the distance from a major roadway and clinical, physiologic and inflammatory features of asthma in a highly characterized sample of asthmatic children 6–17 years of age across a wide range of severities. We hypothesized that a closer residential proximity to a major roadway would be associated with increased respiratory symptoms, altered pulmonary function and a greater magnitude of airway and systemic inflammation.
Methodology/Principal Findings
224 children 6–17 years with confirmed asthma completed questionnaires and underwent spirometry, plethysmography, exhaled nitric oxide determination, exhaled breath condensate collection and venipuncture. Residential distance from a major roadway was determined by mapping the geographic coordinates of the residential address in Geographic Information System software. The distance between the home address and the nearest major roadway was calculated according to the shortest distance between the two points (i.e., “as the crow flies”). Asthmatic children living in closer proximity to a major roadway had an increased frequency of wheezing associated with increased medication requirements and more hospitalizations even after controlling for potential confounders. These children also had increased airway resistance, increased airway inflammation reflected by a lower breath condensate pH, and higher plasma EGF concentrations.
Conclusions/Significance
These findings suggest that closer residential proximity to a major roadway is associated with poorer asthma control in school-age children. Assessment of residential proximity to major roadways may be useful in the clinical evaluation of asthma in children.
The adverse health effects of ambient ozone are well established. Given the high sensitivity of ambient ozone concentrations to meteorological conditions, the impacts of future climate change on ozone concentrations and its associated health effects are of concern. We describe a statistical modeling framework for projecting future ozone levels and its health impacts under a changing climate. This is motivated by the continual effort to evaluate projection uncertainties to inform public health risk assessment. The proposed approach was applied to the 20-county Atlanta metropolitan area using regional climate model (RCM) simulations from the North American Regional Climate Change Assessment Program. Future ozone levels and ozone-related excesses in asthma emergency department (ED) visits were examined for the period 2041-2070. The computationally efficient approach allowed us to consider 8 sets of climate model outputs based on different combinations of 4 RCMs and 4 general circulation models. Compared to the historical period of 1999-2004, we found consistent projections across climate models of an average 11.5% higher ozone levels (range: 4.8%, 16.2%), and an average 8.3% (range:-7%-24%) higher number of ozone exceedance days. Assuming no change in the at-risk population, this corresponds to excess ozone-related ED visits ranging from 267 to 466 visits per year. Health impact projection uncertainty was driven predominantly by uncertainty in the health effect association and climate model variability. Calibrating climate simulations with historical observations reduced differences in projections across climate models.
Background: Development of exposure metrics that identify contrasts in multipollutant air quality across space are needed to better understand multipollutant geographies and health effects from air pollution. Objective: Our aim is to improve understanding of: 1) long-term spatial distributions of multiple pollutants across urban environments; and 2) demographic characteristics of populations residing within areas that experience differing long-term air quality in order to assist in the development of future epidemiologic studies. Methods: Data available for this study included seven years of spatiotemporally resolved concentrations for ten ambient air pollutants across the Atlanta metropolitan area. To analyze, we first distinguish the long-term behavior of air pollution at each grid location (n=253) by calculating study period means for each pollutant (n=10). Then, we apply the self-organizing map (SOM) technique to derive patterns in the multipollutant combinations observed among grid cells, i.e., multipollutant spatial types (MSTs), project results onto an ‘organized map’, and classify each grid cell under its most similar MST. Finally, we geographically map grid cell classifications to delineate regions of similar multipollutant characteristics and characterize associated population demographics using geographic information systems. Results: We found six MSTs well describe the nature of multipollutant combinations experienced at locations in our study area. MST profiles highlighted a range of combinations, from locations experiencing generally clean air quality (all pollutants low) to locations experiencing conditions that were relatively dirty (high long-term concentrations of several pollutants). Mapping the spatial distribution of MSTs revealed strong within-class contiguity and highlighted that downtown areas were dominated by primary pollution and that suburban areas experienced relatively higher levels of secondary pollution. Demographics show that the largest proportion of the overall population of metro Atlanta resided in downtown locations experiencing relatively high levels of primary pollution. Moreover, higher proportions of nonwhites and children in poverty reside in these areas when compared to populations that resided in suburban areas exhibiting relatively lower pollution, moderate secondary pollution, or relatively high SO2. Conclusion: Placing multipollutant air quality within a geographic regionalization problem reveals the nature and spatial distribution of differential pollutant combinations across urban environments and provides helpful insights for identifying spatial exposure and demographic contrasts for future health studies.
Introduction: Advances in the development of high-resolution metabolomics (HRM) have provided new opportunities for their use in characterizing exposures to environmental air pollutants and air pollution-related disease etiologies. Exposure assessment studies have considered blood, breath, and saliva as biological matrices suitable for measuring responses to air pollution exposures. The current study examines comparability among these three matrices using HRM and explores their potential for measuring mobile-source air toxics. Methods: Four participants provided saliva, exhaled breath concentrate (EBC), and plasma before and after a 2 h road traffic exposure. Samples were analyzed on a Thermo Scientific QExactive MS system in positive electrospray ionization mode and resolution of 70 000 full-width at half-maximum with C18 chromatography. Data were processed using an apLCMS and xMSanalyzer on the R statistical platform. Results: The analysis yielded 7110, 6019, and 7747 reproducible features in plasma, EBC, and saliva, respectively. Correlations were moderate-to-strong (R = 0.41-0.80) across all pairwise comparisons of feature intensity within profiles, with the strongest between EBC and saliva. The associations of mean intensities between matrix pairs were positive and significant, controlling for subject and sampling time effects. Six out of 20 features shared in all three matrices putatively matched a list of known mobile-source air toxics. Conclusions: Plasma, saliva, and EBC have largely comparable metabolic profiles measurable through HRM. These matrices have the potential to be used in identification and measurement of exposures to mobile-source air toxics, though further, targeted study is needed.
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.