Source apportionment of primary and secondary PM2.5: Associations with pediatric respiratory disease emergency department visits in the U.S. State of Georgia. (December 2019)

• Record Type: Journal Article
• Authors: Huang, Mengjiao; Ivey, Cesunica; Hu, Yongtao; Holmes, Heather A.; Strickland, Matthew J.
• Is Part Of: Environment international. Volume 133(2019)Part A
• Publication Date: 2019-12
• Subjects: Hybrid chemical transport-receptor model -- PM2.5 -- Source apportionment -- Pediatric respiratory ED visits -- Epidemiologic model; Environmental protection -- Periodicals; Environmental health -- Periodicals; Environmental monitoring -- Periodicals; Environmental Monitoring -- Periodicals; Environnement -- Protection -- Périodiques; Hygiène du milieu -- Périodiques; Environnement -- Surveillance -- Périodiques; Environmental health; Environmental monitoring; Environmental protection; Periodicals; 333.705
• DOI: 10.1016/j.envint.2019.105167
• Languages: English
• ISSNs: 0160-4120

Abstract

Abstract: We developed a hybrid chemical transport model and receptor model (CTM-RM) to conduct source apportionment of both primary and secondary PM2.5 (particulate matter ≤2.5 μm in diameter) at 36 km resolution throughout the U.S. State of Georgia for the years 2005 and 2007. This novel source apportionment model enabled us to estimate and compare associations of short-term changes in 12 PM2.5 source concentrations (agriculture, biogenic, coal, dust, fuel oil, metals, natural gas, non-road mobile diesel, non-road mobile gasoline, on-road mobile diesel, on-road mobile gasoline, and all other sources) with emergency department (ED) visits for pediatric respiratory diseases. ED visits for asthma (N = 49, 651), pneumonia (N = 25, 558), and acute upper respiratory infections (acute URI, N = 235, 343) among patients aged ≤18 years were obtained from patient claims records. Using a case-crossover study, we estimated odds ratios per interquartile range (IQR) increase for 3-day moving average PM2.5 source concentrations using conditional logistic regression, matching on day-of-week, month, and year, and adjusting for average temperature, humidity, and holidays. We fit both single-source and multi-source models. We observed positive associations between several PM2.5 sources and ED visits for asthma, pneumonia, and acute URI. For example, for asthma, per IQR increase in the source contribution in the single-source model, odds ratios were 1.022 (95% CI: 1.013, 1.031) for dust; 1.050 (95% CI: 1.036, 1.063) for metals, and 1.091 (95% CI: 1.064, 1.119) for natural gas. These sources comprised 5.7%, 2.2%, and 6.3% of total PM2.5 mass, respectively. PM2.5 from metals and natural gas were positively associated with all three respiratory outcomes. In addition, non-road mobile diesel was positively associated with pneumonia and acute URI. Highlights: Source apportionment of primary and secondary PM2.5 species at 36 km resolution Epidemiologic models estimate acute associations with pediatric respiratory disease. Health associations observed with natural gas, metals, and non-road mobile diesel