Contribution of Tailpipe and Non-tailpipe Traffic Sources to Quasi-Ultrafine, Fine and Coarse Particulate Matter in Southern California
Creators: name: Habre, Rima
name: Girguis, Mariam
name: Urman, Robert
name: Fruin, Scott
name: Lurmann, Fred
name: Shafer, Martin
name: Gorski, Patrick
name: Franklin, Meredith
name: McConnell, Rob
name: Avol, Ed
name: Gilliland, Frank
Publisher: name: Taylor & Francis
Year: 2020
Subjects: subject: Biophysics
subject: Biochemistry
subject: Medicine
subject: Microbiology
subject: FOS: Biological sciences
subject: FOS: Biological sciences
subject: Physiology
subject: Biotechnology
subject: 59999 Environmental Sciences not elsewhere classified
subject: FOS: Earth and related environmental sciences
subject: FOS: Earth and related environmental sciences
subject: 39999 Chemical Sciences not elsewhere classified
subject: FOS: Chemical sciences
subject: FOS: Chemical sciences
subject: Ecology
subject: 19999 Mathematical Sciences not elsewhere classified
subject: FOS: Mathematics
subject: FOS: Mathematics
subject: Cancer
subject: Inorganic Chemistry

description: <b>Introduction</b>: Exposure to traffic-related air pollution (TRAP) in the near-roadway environment is associated with multiple adverse health effects. <b>Aim</b>: To characterize the relative contribution of tailpipe and non-tailpipe TRAP sources to particulate matter (PM) in the quasi-ultrafine (PM<sub>0.2</sub>), fine (PM<sub>2.5</sub>) and coarse (PM<sub>2.5-10</sub>) size fractions and identify their spatial determinants in southern California (CA). <b>Methods</b>: Month-long integrated PM<sub>0.2</sub>, PM<sub>2.5</sub> and PM<sub>2.5-10</sub> samples (n= 461, 265 and 298, respectively) were collected across cool and warm seasons in 8 southern CA communities (2008-9). Concentrations of PM mass, elements, carbons and major ions were obtained. Enrichment ratios (ER) in PM<sub>0.2</sub> and PM<sub>10</sub> relative to PM<sub>2.5</sub> were calculated for each element. The Positive Matrix Factorization model was used to resolve and estimate the relative contribution of TRAP sources to PM in three size fractions. Generalized additive models (GAMs) with bivariate loess smooths were used to understand the geographic variation of TRAP sources and identify their spatial determinants. <b>Results</b>: EC, OC, and B had the highest median ER in PM<sub>0.2</sub> relative to PM<sub>2.5</sub>. Six, seven and five sources (with characteristic species) were resolved in PM<sub>0.2</sub>, PM<sub>2.5</sub> and PM<sub>2.5-10</sub>, respectively. Combined tailpipe and non-tailpipe traffic sources contributed 66%, 32% and 18% of PM<sub>0.2</sub>, PM<sub>2.5</sub> and PM<sub>2.5-10</sub> mass, respectively. Tailpipe traffic emissions (EC, OC, B) were the largest contributor to PM<sub>0.2</sub> mass (58%). Distinct gasoline and diesel tailpipe traffic sources were resolved in PM<sub>2.5</sub>. Others included fuel oil, biomass burning, secondary inorganic aerosol, sea salt, and crustal/soil. CALINE4 dispersion model nitrogen oxides, trucks and intersections were most correlated with TRAP sources. The influence of smaller roadways and intersections became more apparent once Long Beach was excluded. <b>Conclusions</b>: Non-tailpipe emissions constituted ~8%, 11% and 18% of PM<sub>0.2</sub>, PM<sub>2.5</sub> and PM<sub>2.5-10</sub>, respectively, with important exposure and health implications. Future efforts should consider non-linear relationships amongst predictors when modeling exposures.