Contribution of tailpipe and non-tailpipe traffic sources to quasi-ultrafine, fine and coarse particulate matter in southern California

Posted on by Stephen Gee

Exposure to traffic-related air pollution (TRAP) in the near-roadway environment is associated with multiple adverse health effects. To characterize the relative contribution of tailpipe and non-tailpipe TRAP sources to particulate matter (PM) in the quasi-ultrafine (PM0.2), fine (PM2.5) and coarse (PM2.5–10) size fractions and identify their spatial determinants in southern California (CA). Month-long integrated PM0.2, PM2.5 and PM2.5–10 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 PM0.2 and PM10 relative to PM2.5 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. EC, OC, and B had the highest median ER in PM0.2 relative to PM2.5. Six, seven and five sources (with characteristic species) were resolved in PM0.2, PM2.5 and PM2.5–10, respectively. Combined tailpipe and non-tailpipe traffic sources contributed 66%, 32% and 18% of PM0.2, PM2.5 and PM2.5–10 mass, respectively. Tailpipe traffic emissions (EC, OC, B) were the largest contributor to PM0.2 mass (58%). Distinct gasoline and diesel tailpipe traffic sources were resolved in PM2.5. 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. Non-tailpipe emissions constituted ~8%, 11% and 18% of PM0.2, PM2.5 and PM2.5–10, respectively, with important exposure and health implications. Future efforts should consider non-linear relationships amongst predictors when modeling exposures.

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