title: Ensemble-based source apportionment of fine particulate matter and emergency department visits for pediatric asthma.
abstract: Epidemiologic studies utilizing source apportionment (SA) of fine particulate matter have shown that particles from certain sources might be more detrimental to health than others; however, it is difficult to quantify the uncertainty associated with a given SA approach. In the present study, we examined associations between source contributions of fine particulate matter and emergency department visits for pediatric asthma in Atlanta, Georgia (2002-2010) using a novel ensemble-based SA technique. Six daily source contributions from 4 SA approaches were combined into an ensemble source contribution. To better account for exposure uncertainty, 10 source profiles were sampled from their posterior distributions, resulting in 10 time series with daily SA concentrations. For each of these time series, Poisson generalized linear models with varying lag structures were used to estimate the health associations for the 6 sources. The rate ratios for the source-specific health associations from the 10 imputed source contribution time series were combined, resulting in health associations with inflated confidence intervals to better account for exposure uncertainty. Adverse associations with pediatric asthma were observed for 8-day exposure to particles generated from diesel-fueled vehicles (rate ratio = 1.06, 95% confidence interval: 1.01, 1.10) and gasoline-fueled vehicles (rate ratio = 1.10, 95% confidence interval: 1.04, 1.17).
authors.forename: Katherine
authors.initials: K
authors.lastname: Gass
authors.forename: Sivaraman
authors.initials: S
authors.lastname: Balachandran
authors.forename: Howard H
authors.initials: HH
authors.lastname: Chang
authors.forename: Armistead G
authors.initials: AG
authors.lastname: Russell
authors.forename: Matthew J
authors.initials: MJ
authors.lastname: Strickland
mesh_terms: D000293:Adolescent; D000393:Air Pollutants / Q000009:adverse effects* / Q000032:analysis; D000397:Air Pollution / Q000009:adverse effects*; D001249:Asthma; D001499:Bayes Theorem; D002648:Child; D002675:Child, Preschool; D018450:Disease Progression; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data*; D005845:Georgia; D006801:Humans; D052638:Particulate Matter / Q000009:adverse effects* / Q000032:analysis; D001335:Vehicle Emissions / Q000032:analysis
chemical_list: D000393:Air Pollutants; D052638:Particulate Matter; D001335:Vehicle Emissions
keywords: PM2.5; air pollution; ensemble method; fine particulate matter; pediatric asthma; source apportionment; uncertainty