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Exploring associations between multipollutant day types and asthma morbidity: epidemiologic applications of self-organizing map ambient air quality classifications.

Pearce JL, Waller LA, Mulholland JA, Sarnat SE, Strickland MJ, Chang HH, Tolbert PE - Environ Health (2015)

Bottom Line: Recent interest in the health effects of air pollution focuses on identifying combinations of multiple pollutants that may be associated with adverse health risks.Using a low pollution day type as the reference level, we found significant associations of increased asthma morbidity in three of nine categories suggesting adverse effects when combinations of primary (CO, NO2, NOX, EC, and OC) and/or secondary (O3, NH4, SO4) pollutants exhibited elevated concentrations (typically, occurring on dry days with low wind speed).We found that ED visits for pediatric asthma in Atlanta were more strongly associated with certain day types defined by multipollutant characteristics than days with low pollution levels; however, findings did not suggest that any specific combinations were more harmful than others.

View Article: PubMed Central - PubMed

Affiliation: Department of Public Health Sciences, College of Medicine, Medical University of South Carolina, 135 Cannon Street, Charleston, SC, 29422, United States. pearcejo@musc.edu.

ABSTRACT

Background: Recent interest in the health effects of air pollution focuses on identifying combinations of multiple pollutants that may be associated with adverse health risks.

Objective: Present a methodology allowing health investigators to explore associations between categories of ambient air quality days (i.e., multipollutant day types) and adverse health.

Methods: First, we applied a self-organizing map (SOM) to daily air quality data for 10 pollutants collected between January 1999 and December 2008 at a central monitoring location in Atlanta, Georgia to define a collection of multipollutant day types. Next, we conducted an epidemiologic analysis using our categories as a multipollutant metric of ambient air quality and daily counts of emergency department (ED) visits for asthma or wheeze among children aged 5 to 17 as the health endpoint. We estimated rate ratios (RR) for the association of multipollutant day types and pediatric asthma ED visits using a Poisson generalized linear model controlling for long-term, seasonal, and weekday trends and weather.

Results: Using a low pollution day type as the reference level, we found significant associations of increased asthma morbidity in three of nine categories suggesting adverse effects when combinations of primary (CO, NO2, NOX, EC, and OC) and/or secondary (O3, NH4, SO4) pollutants exhibited elevated concentrations (typically, occurring on dry days with low wind speed). On days with only NO3 elevated (which tended to be relatively cool) and on days when only SO2 was elevated (which likely reflected plume touchdowns from coal combustion point sources), estimated associations were modestly positive but confidence intervals included the .

Conclusions: We found that ED visits for pediatric asthma in Atlanta were more strongly associated with certain day types defined by multipollutant characteristics than days with low pollution levels; however, findings did not suggest that any specific combinations were more harmful than others. Relative to other health endpoints, asthma exacerbation may be driven more by total ambient pollutant exposure than by composition.

No MeSH data available.


Related in: MedlinePlus

Graphical and statistical evaluation measures used to aid in selection of number of day types. Panel a presents a principal components analysis (PCA) projection of our multipollutant data. The grey points represent the scores for daily observations along the first two principal components and the dark arrows indicate the corresponding loading vectors for each pollutant. Panel b displays the distribution of adjusted R2 values from simple regression models fit to each pollutant as a function of the number of day types. Each pollutant has a unique symbol and the trend line reflects the mean. Panel c displays the distribution of frequency assignments to each day type. Grey points reflect observed frequencies and trend line reflects the mean
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Fig1: Graphical and statistical evaluation measures used to aid in selection of number of day types. Panel a presents a principal components analysis (PCA) projection of our multipollutant data. The grey points represent the scores for daily observations along the first two principal components and the dark arrows indicate the corresponding loading vectors for each pollutant. Panel b displays the distribution of adjusted R2 values from simple regression models fit to each pollutant as a function of the number of day types. Each pollutant has a unique symbol and the trend line reflects the mean. Panel c displays the distribution of frequency assignments to each day type. Grey points reflect observed frequencies and trend line reflects the mean

Mentions: Plotting a PCA projection of our data (Fig. 1a) in combination with the component loading weights reveals a primary mode of variation (PC1) dominated by CO, NO2, NOX, EC, and OC, a subset of pollutants indicative of primary pollution, and a primary mode of variation (PC2) weighted towards SO4, NH4, and O3, marking it as a measure of secondary pollution. The relative lengths and direction of the SO2 and NO3 loading weights suggest behaviors independent from these two primary modes of variance (which capture approximately 65 % of the variance in our data). Although strong grouping is not evident in this display, PCA suggests that at least 4 separate modes of variance are needed to capture the primary features in this dataset.Fig. 1


Exploring associations between multipollutant day types and asthma morbidity: epidemiologic applications of self-organizing map ambient air quality classifications.

Pearce JL, Waller LA, Mulholland JA, Sarnat SE, Strickland MJ, Chang HH, Tolbert PE - Environ Health (2015)

Graphical and statistical evaluation measures used to aid in selection of number of day types. Panel a presents a principal components analysis (PCA) projection of our multipollutant data. The grey points represent the scores for daily observations along the first two principal components and the dark arrows indicate the corresponding loading vectors for each pollutant. Panel b displays the distribution of adjusted R2 values from simple regression models fit to each pollutant as a function of the number of day types. Each pollutant has a unique symbol and the trend line reflects the mean. Panel c displays the distribution of frequency assignments to each day type. Grey points reflect observed frequencies and trend line reflects the mean
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4477305&req=5

Fig1: Graphical and statistical evaluation measures used to aid in selection of number of day types. Panel a presents a principal components analysis (PCA) projection of our multipollutant data. The grey points represent the scores for daily observations along the first two principal components and the dark arrows indicate the corresponding loading vectors for each pollutant. Panel b displays the distribution of adjusted R2 values from simple regression models fit to each pollutant as a function of the number of day types. Each pollutant has a unique symbol and the trend line reflects the mean. Panel c displays the distribution of frequency assignments to each day type. Grey points reflect observed frequencies and trend line reflects the mean
Mentions: Plotting a PCA projection of our data (Fig. 1a) in combination with the component loading weights reveals a primary mode of variation (PC1) dominated by CO, NO2, NOX, EC, and OC, a subset of pollutants indicative of primary pollution, and a primary mode of variation (PC2) weighted towards SO4, NH4, and O3, marking it as a measure of secondary pollution. The relative lengths and direction of the SO2 and NO3 loading weights suggest behaviors independent from these two primary modes of variance (which capture approximately 65 % of the variance in our data). Although strong grouping is not evident in this display, PCA suggests that at least 4 separate modes of variance are needed to capture the primary features in this dataset.Fig. 1

Bottom Line: Recent interest in the health effects of air pollution focuses on identifying combinations of multiple pollutants that may be associated with adverse health risks.Using a low pollution day type as the reference level, we found significant associations of increased asthma morbidity in three of nine categories suggesting adverse effects when combinations of primary (CO, NO2, NOX, EC, and OC) and/or secondary (O3, NH4, SO4) pollutants exhibited elevated concentrations (typically, occurring on dry days with low wind speed).We found that ED visits for pediatric asthma in Atlanta were more strongly associated with certain day types defined by multipollutant characteristics than days with low pollution levels; however, findings did not suggest that any specific combinations were more harmful than others.

View Article: PubMed Central - PubMed

Affiliation: Department of Public Health Sciences, College of Medicine, Medical University of South Carolina, 135 Cannon Street, Charleston, SC, 29422, United States. pearcejo@musc.edu.

ABSTRACT

Background: Recent interest in the health effects of air pollution focuses on identifying combinations of multiple pollutants that may be associated with adverse health risks.

Objective: Present a methodology allowing health investigators to explore associations between categories of ambient air quality days (i.e., multipollutant day types) and adverse health.

Methods: First, we applied a self-organizing map (SOM) to daily air quality data for 10 pollutants collected between January 1999 and December 2008 at a central monitoring location in Atlanta, Georgia to define a collection of multipollutant day types. Next, we conducted an epidemiologic analysis using our categories as a multipollutant metric of ambient air quality and daily counts of emergency department (ED) visits for asthma or wheeze among children aged 5 to 17 as the health endpoint. We estimated rate ratios (RR) for the association of multipollutant day types and pediatric asthma ED visits using a Poisson generalized linear model controlling for long-term, seasonal, and weekday trends and weather.

Results: Using a low pollution day type as the reference level, we found significant associations of increased asthma morbidity in three of nine categories suggesting adverse effects when combinations of primary (CO, NO2, NOX, EC, and OC) and/or secondary (O3, NH4, SO4) pollutants exhibited elevated concentrations (typically, occurring on dry days with low wind speed). On days with only NO3 elevated (which tended to be relatively cool) and on days when only SO2 was elevated (which likely reflected plume touchdowns from coal combustion point sources), estimated associations were modestly positive but confidence intervals included the .

Conclusions: We found that ED visits for pediatric asthma in Atlanta were more strongly associated with certain day types defined by multipollutant characteristics than days with low pollution levels; however, findings did not suggest that any specific combinations were more harmful than others. Relative to other health endpoints, asthma exacerbation may be driven more by total ambient pollutant exposure than by composition.

No MeSH data available.


Related in: MedlinePlus