Impact of ambient fine particulate matter carbon measurement methods on observed associations with acute cardiorespiratory morbidity

Winquist, Andrea; Schauer, James J.; Turner, Jay R.; Klein, Mitch; Sarnat, Stefanie Ebelt

doi:10.1038/jes.2014.55

Cited by 22 publications

(19 citation statements)

References 40 publications

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“…To estimate associations between short-term exposure to source-specific PM 2.5 and respiratory disease ED visits, we applied overdispersed Poisson time-series regression models to data from each city controlling for potential confounders as in previous studies of PM 2.5 and cardiorespiratory ED visits (Winquist et al 2015). Specifically, we included indicator variables for holidays, day of the week, season, and the hospitals reporting data for each day.…”

Section: Methodsmentioning

confidence: 99%

Associations between Source-Specific Fine Particulate Matter and Emergency Department Visits for Respiratory Disease in Four U.S. Cities

Krall

Mulholland

Russell

et al. 2017

Environ Health Perspect

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128

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Background:Short-term exposure to ambient fine particulate matter (PM2.5) concentrations has been associated with increased mortality and morbidity. Determining which sources of PM2.5 are most toxic can help guide targeted reduction of PM2.5. However, conducting multicity epidemiologic studies of sources is difficult because source-specific PM2.5 is not directly measured, and source chemical compositions can vary between cities.Objectives:We determined how the chemical composition of primary ambient PM2.5 sources varies across cities. We estimated associations between source-specific PM2.5 and respiratory disease emergency department (ED) visits and examined between-city heterogeneity in estimated associations.Methods:We used source apportionment to estimate daily concentrations of primary source-specific PM2.5 for four U.S. cities. For sources with similar chemical compositions between cities, we applied Poisson time-series regression models to estimate associations between source-specific PM2.5 and respiratory disease ED visits.Results:We found that PM2.5 from biomass burning, diesel vehicle, gasoline vehicle, and dust sources was similar in chemical composition between cities, but PM2.5 from coal combustion and metal sources varied across cities. We found some evidence of positive associations of respiratory disease ED visits with biomass burning PM2.5; associations with diesel and gasoline PM2.5 were frequently imprecise or consistent with the null. We found little evidence of associations with dust PM2.5.Conclusions:We introduced an approach for comparing the chemical compositions of PM2.5 sources across cities and conducted one of the first multicity studies of source-specific PM2.5 and ED visits. Across four U.S. cities, among the primary PM2.5 sources assessed, biomass burning PM2.5 was most strongly associated with respiratory health.Citation:Krall JR, Mulholland JA, Russell AG, Balachandran S, Winquist A, Tolbert PE, Waller LA, Sarnat SE. 2017. Associations between source-specific fine particulate matter and emergency department visits for respiratory disease in four U.S. cities. Environ Health Perspect 125:97–103; http://dx.doi.org/10.1289/EHP271

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Section: Methodsmentioning

confidence: 99%

Associations between Source-Specific Fine Particulate Matter and Emergency Department Visits for Respiratory Disease in Four U.S. Cities

Krall

Mulholland

Russell

et al. 2017

Environ Health Perspect

Self Cite

128

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“…For example, ozone concentrations were found to be quite similar across our MSTs and thus it is likely that O 3 played a limited role in the formation of our spatial profiles. Nevertheless, we chose to include O 3 in this study to assess the approach and to identify which pollutants in our available data would be most appropriate for developing spatial profiles; O 3 is also an important health-relevant component of the air quality mixture in the Atlanta area (Strickland, Darrow et al 2010, Pearce, Waller et al 2015, Winquist, Schauer et al 2015). Another potential limitation of the work was the spatial resolution of the 12km data.…”

Section: Discussionmentioning

confidence: 99%

“…SOM performance is dependent on both α and N and thus mappings are sensitive to these parameters 30 . Therefore, in effort to provide guidance we note that α typically starts as small number and is specified to decrease monotonically (e.g., 0.05 to 0.01) as iterations increase.…”

Section: Methodsmentioning

confidence: 99%

“…Atlanta’s air quality issues are well known and several studies have documented associations with health outcomes including asthma, cardiorespiratory morbidity, and preterm births (Alhanti, Chang et al 2015, Chang, Warren et al 2015, Pearce, Waller et al 2015, Winquist, Schauer et al 2015). Moreover, a novel set of spatially and temporally resolved multipollutant data is available for the region (Sororian, Holmes et al 2014) that will allow us to more closely examine air pollution exposure across a unique and diverse population (Pooley 2015).…”

Section: Introductionmentioning

confidence: 99%

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Characterizing the spatial distribution of multiple pollutants and populations at risk in Atlanta, Georgia

Pearce

Waller

Sarnat

et al. 2016

Spatial and Spatio-temporal Epidemiology

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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.

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“…Many studies used standard covariate adjustment for total PM mass in models of PM constituents and adverse health outcomes (e.g., [36, 38, 41, 67, 68]), though this approach can lead to large standard errors for constituents highly correlated with total PM. Instead of adjusting for total PM 2.5 directly, several studies estimated associations between PM 2.5 constituents and health while controlling for the leftover PM 2.5 mass (total PM 2.5 mass−PM 2.5 constituent) [51, 53]. This approach minimizes multicollinearity between the constituent and total PM 2.5 , but the interpretation of health effects can be challenging since increases in the PM 2.5 constituent correspond to decreases in the leftover PM 2.5 fraction.…”

Section: Disentangling Health Effects Of Individual Constituentsmentioning

confidence: 99%

Current Methods and Challenges for Epidemiological Studies of the Associations Between Chemical Constituents of Particulate Matter and Health

Krall

Chang

Sarnat

et al. 2015

Curr Envir Health Rpt

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Epidemiological studies have been critical for estimating associations between exposure to ambient particulate matter (PM) air pollution and adverse health outcomes. Because total PM mass is a temporally and spatially varying mixture of constituents with different physical and chemical properties, recent epidemiological studies have focused on PM constituents. Most studies have estimated associations between PM constituents and health using the same statistical methods as in studies of PM mass. However, these approaches may not be sufficient to address challenges specific to studies of PM constituents, namely assigning exposure, disentangling health effects, and handling measurement error. We reviewed large, population-based epidemiological studies of PM constituents and health and describe the statistical methods typically applied to address these challenges. Development of statistical methods that simultaneously address multiple challenges, for example, both disentangling health effects and handling measurement error, could improve estimation of associations between PM constituents and adverse health outcomes.

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Impact of ambient fine particulate matter carbon measurement methods on observed associations with acute cardiorespiratory morbidity

Cited by 22 publications

References 40 publications

Associations between Source-Specific Fine Particulate Matter and Emergency Department Visits for Respiratory Disease in Four U.S. Cities

Associations between Source-Specific Fine Particulate Matter and Emergency Department Visits for Respiratory Disease in Four U.S. Cities

Characterizing the spatial distribution of multiple pollutants and populations at risk in Atlanta, Georgia

Current Methods and Challenges for Epidemiological Studies of the Associations Between Chemical Constituents of Particulate Matter and Health

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