Final Report: A Multi-City Time-Series Study of Pollutant Mixtures and Acute Morbidity

EPA Grant Number: R834799C004
Subproject: this is subproject number 004 , established and managed by the Center Director under grant R834799
(EPA does not fund or establish subprojects; EPA awards and manages the overall grant for this center).

Center: The Southeastern Center for Air Pollution and Epidemiology: Multiscale Measurements and Modeling of Mixtures
Center Director: Tolbert, Paige
Title: A Multi-City Time-Series Study of Pollutant Mixtures and Acute Morbidity
Investigators: Sarnat, Stefanie Ebelt , Bilonick, Richard , Darrow, Lyndsey , Klein, Mitchel , Mulholland, James , Russell, Armistead G. , Talbott, Evelynn , Tolbert, Paige , Winquist, Andrea
Institution: Emory University , Georgia Institute of Technology
EPA Project Officer: Chung, Serena
Project Period: January 1, 2011 through December 31, 2016
RFA: Clean Air Research Centers (2009) RFA Text |  Recipients Lists
Research Category: Health Effects , Air

Objective:

Although associations between ambient air pollution and acute cardiorespiratory outcomes have been observed in numerous studies, questions remain about the degree to which these findings are generalizable between locations and whether the observed health effects are due to the individual pollutants measured or to pollutants acting in combination with other pollutants. In Project 4, we conducted a multi-city time-series study to clarify the impacts of air quality on acute cardiorespiratory morbidity in five U.S. cities (Atlanta, GA; Birmingham, AL; Dallas, TX; Pittsburgh, PA; St. Louis, IL-MO) using novel mixture characterization metrics. Analyses included consideration of factors related to air pollution mixtures, exposure measurement error, concentration-response functions, population susceptibility and vulnerability, and seasonality and climate to help explain heterogeneity in short-term associations between air quality measures and cardiorespiratory emergency department (ED) visits.

Summary/Accomplishments (Outputs/Outcomes):

Project 4 combined multi-year data on ambient mixture characterization methods and ED visits in five cities. Epidemiologic analyses focused on the following six areas:

1. Pollutant mixture effects. Over the course of the project, we applied various approaches to characterize different air pollution mixtures and their associated acute health effects in collaboration with the Air Quality and Biostatistics Cores. These approaches included: a) use of detailed PM2.5 components data as markers of source mixtures and/or as causal agents themselves (Sarnat, et al., 2015; Winquist, et al., 2015; Krall, et al., 2017; Ye, et al., in review); b) formal source apportionment approaches to estimate daily mass concentrations of PM2.5 by source type (Krall, et al., 2017); c) clustering approaches to determine pollutant groupings, including self-organizing maps (Pearce, et al. 2015) and classification and regression tree (Gass, et al., 2014, 2015) analyses; d) a priori pollutant groupings based on chemical structure (Ye, et al., 2017); e) joint effects estimation (Winquist, et al., 2014; Ye, et al., 2017); and f) PM2.5 oxidative potential from dithiothreitol (DTT) and ascorbic acid (AA) assay measurements as well as source-based models (Bates, et al., 2015; Fang, et al., 2016; Abrams, et al., in review). These different approaches presented varying advantages and disadvantages with regards to resources required (data, time, knowledge, expertise), ease of use, and interpretation of results. Taken together, however, some patterns emerged regarding associations of air pollution mixtures and cardiorespiratory ED visits. In analyses focused on respiratory-related ED visits, we observed associations with mixtures of O3, secondary organic aerosols, biomass burning, and traffic combustion-related pollution (e.g., Winquist, et al., 2014, 2015; Sarnat, et al., 2015; Pearce, et al., 2015; Krall, et al., 2017; Ye, et al., 2017); and for cardiovascular morbidity, we observed associations with primary traffic-related pollution mixtures (both tailpipe, combustion-related components as well as tire wear/brake pad related components) (e.g., Sarnat, et al., 2015; Winquist, et al., 2015; Ye, et al., 2017; Ye, et al., in review). In analyses estimating health effects of PM2.5 oxidative potential using back-cast estimates in Atlanta, DTT activity but not AA activity was associated with ED visits for asthma and congestive heart failure; these associations were stronger than those for PM2.5 (Bates, et al., 2015; Fang, et al., 2016), providing support for oxidative stress as a mechanism of PM2.5 effects. Given analyses assessing the source contributions to DTT activity, the findings point to the health-relevance of organic components and transition metals from biomass burning and mobile sources in the Atlanta area.

2. Exposure assignment approaches for large study areas. Population-based studies such as Project 4 relate day-to-day changes in ambient concentrations, often estimated using measurements made at ambient monitoring stations, to indicators of population health for a community, such as daily counts of deaths or ED visits. The exposure and health data in these studies are often misaligned in space, leading to concerns of bias in health effect estimates due to exposure measurement error. Such measurement error arises when the exposure measure does not capture the true average exposure of all at-risk individuals in the study area. In SCAPE, we documented and described the impacts of such measurement error through a series of early publications (Goldman, et al., 2011, 2012; Strickland, et al., 2011; Sarnat, et al., 2013). We then developed a data fusion approach that combines observational data from monitors and outputs from the Community Multiscale Air Quality (CMAQ) model (Friberg, et al., 2016; Friberg, et al., accepted) to estimate population exposure for Project 4 cities that aimed to better represent exposure for a range of pollutants across cities than use of monitoring data alone. Applied in epidemiologic analyses, we found that use of the data fusion metrics led to stronger health effect estimates compared to use of central monitoring site data across cities, particularly for spatiotemporally heterogeneous pollutants such as NO2 and SO2 (Sarnat, et al., ISEE 2015). The results provide a qualitative indication of reduced exposure measurement error for the data fusion exposure metric and indicate the importance of exposure assignment approach in large study areas.

3. Concentration-response (CR) function shape. Linearity in CR function shape for ambient pollutants and acute health outcomes often is assumed. In many cases, however, the assumption of linearity is made due to practical reasons, such as for ease of modeling linear relationships and ease of interpretation of parameter estimates. However, if the relationship is truly non-linear but modeled linearly, measures of association for particular pollution levels may be over or underestimated. Inaccurate risk estimates may have intervention and policy implications, and can impact comparisons of risk estimates across cities with different pollution levels. Here, we used Project 4 as a platform for specifically evaluating CR function shapes for O3 and respiratory ED visits in multiple cities under different model assumptions (linear, linear-threshold, quadratic, cubic, categorical, and cubic spline) (Barry, et al., ISEE 2016). O3 was positively associated with respiratory ED visits overall as well as with ED visits for asthma and upper respiratory infections in all models. Cubic and cubic spline functions best described the O3-respiratory disease relationship in all five cities; however, linear results were similar for O3 less than 60 ppb. Assessing CR function shape before analyzing and interpreting data can provide an idea as to which model assumptions may be most accurate for the specific study, city, and outcome.

4. Susceptible and vulnerable populations. Susceptibility to the health effects of ambient pollution may be influenced by both intrinsic factors, such as age and sex, and extrinsic factors, such as neighborhood socioeconomic status (SES). In Project 4, we conducted epidemiologic analyses to better understand the potential for these factors to confer susceptibility and vulnerability to ambient pollution, with a focus on respiratory health. Our findings suggest that age is a susceptibility factor for asthma exacerbations in response to air pollution, with school-age children having the highest susceptibility; strong observed associations among 5-18 year olds appeared to be partially driven by non-white and male patients, suggesting race/ethnicity and sex to be further factors conferring susceptibility (Alhanti, et al., 2016). In additional analyses focused on pediatric respiratory health, we found that neighborhood-level SES is a further factor contributing vulnerability to air pollution-related childhood morbidity (O’Lenick, et al., 2017a, 2017c). Children living in low SES environments appear to be especially vulnerable given positive health effect estimates and high underlying respiratory ED visit rates. We determined that inconsistent findings of effect modification by neighborhood SES among previous similar studies may be partially explained by choice of SES stratification criteria, and the use of multiplicative models combined with differing baseline risk across SES populations. Ongoing analyses are considering factors conferring susceptibility and vulnerability to the cardiovascular health effects of air pollution.

5. Ambient temperature as a main effect. Climate change is expected to cause higher ambient temperatures, especially in urban environments. Exposure to high ambient temperature may result in various adverse effects. In conjunction with funding from the National Institutes of Health, we conducted an investigation of the impacts of high temperatures and heat waves on ED visits for a broad range of ED visit outcomes in Atlanta (Winquist, et al., 2016; Heidari, et al., 2016; Chen, et al., 2017; O’Lenick, et al., 2017b). In main analyses, we observed associations between daily ambient maximum temperature and apparent temperature and ED visits for all internal causes, heat illness, fluid and electrolyte imbalances, renal diseases, cardiovascular diseases, asthma, diabetes, and intestinal infections (Winquist, et al., 2016). Age groups with the strongest observed associations were 65+ years for all internal causes and diabetes; 19-64 years for fluid and electrolyte imbalances and renal disease; and 5-18 years for asthma and intestinal infections. Based on these results, we concluded that optimal interventions and health-impact projections should account for varying heat-health impacts across ages. In further analyses, we examined the added effect of extreme heat over a sustained period beyond the continuous temperature-response relationships (Chen, et al., 2017). Our results suggest that prolonged heat exposure can confer added adverse health impacts beyond the risk due to higher daily temperature, particularly for renal diseases, cardiovascular diseases, and intestinal infection. We found some evidence that longer heat wave duration, later timing in the year, and higher heat wave intensity were associated with higher risks. We also found that associations of heat waves with ED visits were sensitive to heat wave definitions, which may be a result of different heat wave metrics representing different heat stress characteristics. Specifically, we concluded that minimum or nighttime temperatures also may be useful to consider in heat warning systems for some health outcomes.

6. Ambient temperature as a modifier of air pollution effects. We assessed modification of the acute respiratory effects of ambient air pollution by ambient temperature using splines to allow for nonlinearity in effect modification across multiple cities (Darrow, et al., ISEE 2016). Results suggest associations of ambient air pollution and acute respiratory outcomes vary by ambient temperatures, with higher estimated effects when mean ambient temperatures are mild compared to colder or warmer temperatures. These findings may reflect higher exposure to ambient pollution via increased time spent outdoors and/or higher air exchange rates (e.g., due to use of windows for ventilation, less A/C). Ongoing analyses are assessing estimated residential air exchange rates as a modifier of air pollution health associations (Sarnat, et al., 2013; Liang, et al., ISEE 2016).

Conclusions:

In Project 4, we observed associations of major ambient pollutants and specific air pollution mixtures and cardiorespiratory ED visits across several U.S. cities. Specifically, mixtures related to O3, secondary organic aerosols, biomass burning, and traffic combustion-related pollution appeared to have impacts on respiratory morbidity; and primary traffic-related pollution mixtures (both tailpipe, combustion-related components as well as tire wear/brake pad related components) were found to be important for cardiovascular morbidity. High ambient temperatures, expressed either as continuous warm-season temperatures or heat waves, were found to have strong impacts on acute morbidity, particularly dehydration and kidney-related outcomes, but also cardiorespiratory morbidity. Heat as an ambient exposure may be important to consider in the broad context of assessing health impacts of atmospheric mixtures. Finally, we found sociodemographic factors (age and SES) to be important modifiers of air pollution and temperature-related health associations. New work extending this research in additional cities with sufficiently resolved air quality data will contribute to better characterizing the generalizability of these findings.

References:

Project publications are included as references.


Journal Articles on this Report : 41 Displayed | Download in RIS Format

Other subproject views: All 101 publications 43 publications in selected types All 42 journal articles
Other center views: All 334 publications 136 publications in selected types All 132 journal articles
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Journal Article Alhanti BA, Chang HH, Winquist A, Mulholland JA, Darrow LA, Sarnat SE. Ambient air pollution and emergency department visits for asthma: a multi-city assessment of effect modification by age. Journal of Exposure Science & Environmental Epidemiology 2016;26(2):180-188. R834799 (2015)
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  • Journal Article Balachandran S, Pachon JE, Hu Y, Lee D, Mulholland JA, Russell AG. Ensemble-trained source apportionment of fine particulate matter and method uncertainty analysis. Atmospheric Environment 2012;61:387-394. R834799 (2012)
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  • Journal Article Bates JT, Weber RJ, Abrams J, Verma V, Fang T, Klein M, Strickland MJ, Sarnat SE, Chang HH, Mulholland JA, Tolbert PE, Russell AG. Reactive oxygen species generation linked to sources of atmospheric particulate matter and cardiorespiratory effects. Environmental Science & Technology 2015;49(22):13605-13612. R834799 (2015)
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  • Journal Article Baxter LK, Dionisio KL, Burke J, Sarnat SE, Sarnat JA, Hodas N, Rich DQ, Turpin BJ, Jones RR, Mannshardt E, Kumar N, Beevers SD, Ozkaynak H. Exposure prediction approaches used in air pollution epidemiology studies: key findings and future recommendations. Journal of Exposure Science & Environmental Epidemiology 2013;23(6):654-659. R834799 (2014)
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  • Journal Article Brown MS, Sarnat SE, DeMuth KA, Brown LAS, Whitlock DR, Brown SW, Tolbert PE, Fitzpatrick AM. Residential proximity to a major roadway is associated with features of asthma control in children. PLoS ONE 2012;7(5):e37044 ( pp.). R834799 (2012)
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  • Journal Article Chang HH, Hao H, Sarnat SE. A statistical modeling framework for projecting future ambient ozone and its health impact due to climate change. Atmospheric Environment 2014;89:290-297. R834799 (2014)
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  • Journal Article Chen T, Sarnat SE, Grundstein AJ, Winquist A, Chang HH. Time-series analysis of heat waves and emergency department visits in Atlanta, 1993 to 2012. Environmental Health Perspectives 2017;125(5):057009 (9 pp.). R834799 (2016)
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  • Journal Article Darrow LA, Hess J, Rogers CA, Tolbert PE, Klein M, Sarnat SE. Ambient pollen concentrations and emergency department visits for asthma and wheeze. Journal of Allergy and Clinical Immunology 2012;130(3):630-638. R834799 (2012)
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  • Journal Article Dionisio KL, Isakov V, Baxter LK, Sarnat JA, Sarnat SE, Burke J, Rosenbaum A, Graham SE, Cook R, Mulholland J, Ozkaynak H. Development and evaluation of alternative approaches for exposure assessment of multiple air pollutants in Atlanta, Georgia. Journal of Exposure Science & Environmental Epidemiology 2013;23(6):581-592. R834799 (2014)
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  • Journal Article Dionisio KL, Baxter LK, Chang HH. An empirical assessment of exposure measurement error and effect attenuation in bipollutant epidemiologic models. Environmental Health Perspectives 2014;122(11):1216-1224. R834799 (2014)
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  • Journal Article Fang T, Verma V, Bates JT, Abrams J, Klein M, Strickland MJ, Sarnat SE, Chang HH, Mulholland JA, Tolbert PE, Russell AG, Weber RJ. Oxidative potential of ambient water-soluble PM2.5 in the southeastern United States: contrasts in sources and health associations between ascorbic acid (AA) and dithiothreitol (DTT) assays. Atmospheric Chemistry and Physics 2016;16(6):3865-3879. R834799 (2015)
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  • Journal Article Flanders WD, Klein M, Darrow LA, Strickland MJ, Sarnat SE, Sarnat JA, Waller LA, Winquist A, Tolbert PE. A method for detection of residual confounding in time-series and other observational studies. Epidemiology 2011;22(1):59-67. R834799 (2013)
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  • Journal Article Flanders WD, Klein M, Darrow LA, Strickland MJ, Sarnat SE, Sarnat JA, Waller LA, Winquist A, Tolbert PE. A method to detect residual confounding in spatial and other observational studies. Epidemiology 2011;22(6):823-826. R834799 (2012)
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  • Journal Article Friberg MD, Zhai X, Holmes HA, Chang HH, Strickland MJ, Sarnat SE, Tolbert PE, Russell AG, Mulholland JA. Method for fusing observational data and chemical transport model simulations to estimate spatiotemporally resolved ambient air pollution. Environmental Science & Technology 2016;50(7):3695-3705. R834799 (2016)
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  • Journal Article Friberg MD, Kahn RA, Holmes HA, Chang HH, Sarnat SE, Tolbert PE, Russell AG, Mulholland JA. Daily ambient air pollution metrics for five cities: evaluation of data-fusion-based estimates and uncertainties. Atmospheric Environment 2017;158:36-50. R834799 (2016)
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  • Journal Article Gass K, Klein M, Chang HH, Flanders WD, Strickland MJ. Classification and regression trees for epidemiologic research: an air pollution example. Environmental Health 2014;13(1):17 (10 pp.). R834799 (2014)
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  • Journal Article Gass K, Klein M, Sarnat SE, Winquist A, Darrow LA, Flanders WD, Chang HH, Mulholland JA, Tolbert PE, Strickland MJ. Associations between ambient air pollutant mixtures and pediatric asthma emergency department visits in three cities: a classification and regression tree approach. Environmental Health 2015;14:58 (14 pp.). R834799 (2015)
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  • Journal Article Goldman GT, Mulholland JA, Russell AG, Strickland MJ, Klein M, Waller LA, Tolbert PE. Impact of exposure measurement error in air pollution epidemiology: effect of error type in time-series studies. Environmental Health 2011;10:61 (11 pp.). R834799 (2011)
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  • Journal Article Goldman GT, Mulholland JA, Russell AG, Gass K, Strickland MJ, Tolbert PE. Characterization of ambient air pollution measurement error in a time-series health study using a geostatistical simulation approach. Atmospheric Environment 2012;57:101-108. R834799 (2012)
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  • Journal Article Heidari L, Winquist A, Klein M, O’Lenick CR, Grundstein A, Sarnat SE. Susceptibility to heat-related fluid and electrolyte imbalance emergency department visits in Atlanta, Georgia, USA. International Journal of Environmental Research and Public Health 2016;13(10):982 (17 pp.). R834799 (2016)
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  • Journal Article Krall JR, Chang HH, Sarnat SE, Peng RD, Waller LA. Current methods and challenges for epidemiological studies of the associations between chemical constituents of particulate matter and health. Current Environmental Health Reports 2015;2(4):388-398. R834799 (2016)
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  • Journal Article Krall JR, Mulholland JA, Russell AG, Balachandran S, Winquist A, Tolbert PE, Waller LA, Sarnat SE. Associations between source-specific fine particulate matter and emergency department visits for respiratory disease in four U.S. cities. Environmental Health Perspectives 2017;125(1):97-103. R834799 (2016)
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  • Journal Article Maier ML, Balachandran S, Sarnat SE, Turner JR, Mulholland JA, Russell AG. Application of an ensemble-trained source apportionment approach at a site impacted by multiple point sources. Environmental Science & Technology 2013;47(8):3743-3751. R834799 (2013)
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  • Journal Article O'Lenick CR, Winquist A, Mulholland JA, Friberg MD, Chang HH, Kramer MR, Darrow LA, Sarnat SE. Assessment of neighbourhood-level socioeconomic status as a modifier of air pollution-asthma associations among children in Atlanta. Journal of Epidemiology and Community Health 2017;71(2):129-136. R834799 (2016)
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  • Journal Article O'Lenick CR, Winquist A, Chang HH, Kramer MR, Mulholland JA, Grundstein A, Sarnat SE. Evaluation of individual and area-level factors as modifiers of the association between warm-season temperature and pediatric asthma morbidity in Atlanta, GA. Environmental Research 2017;156:132-144. R834799 (2016)
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  • Journal Article O’Lenick CR, Chang HH, Kramer MR, Winquist A, Mulholland JA, Friberg MD, Sarnat SE. Ozone and childhood respiratory disease in three US cities: evaluation of effect measure modification by neighborhood socioeconomic status using a Bayesian hierarchical approach. Environmental Health 2017;16(1):36 (15 pp.). R834799 (2016)
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  • Journal Article Pachon JE, Balachandran S, Hu Y, Mulholland JA, Darrow LA, Sarnat JA, Tolbert PE, Russell AG. Development of outcome-based, multipollutant mobile source indicators. Journal of the Air & Waste Management Association 2012;62(4):431-442. R834799 (2012)
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  • Journal Article Pearce JL, Waller LA, Chang HH, Klein M, Mulholland JA, Sarnat JA, Sarnat SE, Strickland MJ, Tolbert PE. Using self-organizing maps to develop ambient air quality classifications: a time series example. Environmental Health 2014;13:56. R834799 (2014)
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  • Journal Article Pearce JL, Waller LA, Mulholland JA, Sarnat SE, Strickland MJ, Chang HH, Tolbert PE. Exploring associations between multipollutant day types and asthma morbidity: epidemiologic applications of self-organizing map ambient air quality classifications. Environmental Health 2015;14:55 (12 pp.). R834799 (2015)
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  • Journal Article Pearce JL, Waller LA, Sarnat SE, Chang HH, Klein M, Mulholland JA, Tolbert PE. Characterizing the spatial distribution of multiple pollutants and populations at risk in Atlanta, Georgia. Spatial and Spatio-temporal Epidemiology 2016;18:13-23. R834799 (2016)
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  • Journal Article Sarnat JA, Sarnat SE, Flanders WD, Chang HH, Mulholland J, Baxter L, Isakov V, Ozkaynak H. Spatiotemporally resolved air exchange rate as a modifier of acute air pollution-related morbidity in Atlanta. Journal of Exposure Science & Environmental Epidemiology 2013;23(6):606-615. R834799 (2013)
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  • Journal Article Sarnat SE, Sarnat JA, Mulholland J, Isakov V, Ozkaynak H, Chang HH, Klein M, Tolbert PE. Application of alternative spatiotemporal metrics of ambient air pollution exposure in a time-series epidemiological study in Atlanta. Journal of Exposure Science & Environmental Epidemiology 2013;23(6):593-605. R834799 (2014)
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  • Journal Article Sarnat SE, Winquist A, Schauer JJ, Turner JR, Sarnat JA. Fine particulate matter components and emergency department visits for cardiovascular and respiratory diseases in the St. Louis, Missouri-Illinois, metropolitan area. Environmental Health Perspectives 2015;123(5):437-444. R834799 (2015)
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  • Journal Article Sarnat SE, Chang HH, Weber RJ. Ambient PM2.5 and health: does PM2.5 oxidative potential play a role? American Journal of Respiratory and Critical Care Medicine 2016;194(5):530-531. R834799 (2016)
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  • Journal Article Strickland MJ, Darrow LA, Mulholland JA, Klein M, Flanders WD, Winquist A, Tolbert PE. Implications of different approaches for characterizing ambient air pollutant concentrations within the urban airshed for time-series studies and health benefits analyses. Environmental Health 2011;10:36 (9 pp.). R834799 (2011)
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  • Journal Article Winquist A, Klein M, Tolbert P, Sarnat SE. Power estimation using simulations for air pollution time-series studies. Environmental Health 2012;11:68 (12 pp.). R834799 (2012)
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  • Journal Article Winquist A, Klein M, Tolbert P, Flanders WD, Hess J, Sarnat SE. Comparison of emergency department and hospital admissions data for air pollution time-series studies. Environmental Health 2012;11:70 (14 pp.). R834799 (2012)
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  • Journal Article Winquist A, Kirrane E, Klein M, Strickland M, Darrow LA, Sarnat SE, Gass K, Mulholland J, Russell A, Tolbert P. Joint effects of ambient air pollutants on pediatric asthma emergency department visits in Atlanta, 1998-2004. Epidemiology 2014;25(5):666-673. R834799 (2014)
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  • Journal Article Winquist A, Schauer JJ, Turner JR, Klein M, Sarnat SE. Impact of ambient fine particulate matter carbon measurement methods on observed associations with acute cardiorespiratory morbidity. Journal of Exposure Science & Environmental Epidemiology 2015;25(2):215-221. R834799 (2014)
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  • Journal Article Winquist A, Grundstein A, Chang HH, Hess J, Sarnat SE. Warm season temperature and emergency department visits in Atlanta, Georgia. Environmental Research 2016;147:314-323. R834799 (2016)
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  • Journal Article Ye D, Klein M, Chang HH, Sarnat JA, Mulholland JA, Edgerton ES, Winquist A, Tolbert PE, Sarnat SE. Estimating acute cardiorespiratory effects of ambient volatile organic compounds. Epidemiology 2017;28(2):197-206. R834799 (2016)
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  • Supplemental Keywords:

    ambient air, health effects, sensitive populations, dose-response, cumulative effects, epidemiology, exposure, air quality modeling, PM2.5, organics, elemental carbon, metals, oxidants, sulfates, source characterization

     , Health, Scientific Discipline, Health Risk Assessment, Risk Assessments, Biochemistry, Environmental Monitoring, particulate matter, children's health, morbidity, ambient air monitoring, climate change, air pollution, susceptibility, ambient particle health effects, airshed modeling, human health risk

    Relevant Websites:

    www.scape.gatech.edu Exit  

    Progress and Final Reports:

    Original Abstract
  • 2011 Progress Report
  • 2012 Progress Report
  • 2013 Progress Report
  • 2014 Progress Report
  • 2015 Progress Report

  • Main Center Abstract and Reports:

    R834799    The Southeastern Center for Air Pollution and Epidemiology: Multiscale Measurements and Modeling of Mixtures

    Subprojects under this Center: (EPA does not fund or establish subprojects; EPA awards and manages the overall grant for this center).
    R834799C001 Development and Deployment of an Instrumentation Suite for Comprehensive Air Quality Characterization Including Aerosol ROS
    R834799C002 Examining In-Vehicle Pollution and Oxidative Stress in a Cohort of Daily Commuters
    R834799C003 Novel Estimates of Pollutant Mixtures and Pediatric Health in Two Birth Cohorts
    R834799C004 A Multi-City Time-Series Study of Pollutant Mixtures and Acute Morbidity