Characterization of Annual Average Traffic-Related Air Pollution Concentrations in the Greater Seattle Area from a Year-Long Mobile Monitoring Campaign

• Published in: Environmental science & technology 2022-08, Vol.56 (16), p.11460-11472
• Main Authors: Blanco, Magali N., Gassett, Amanda, Gould, Timothy, Doubleday, Annie, Slager, David L., Austin, Elena, Seto, Edmund, Larson, Timothy V., Marshall, Julian D., Sheppard, Lianne
• Format: Article
• Language: English
• Subjects: Adult; Air Pollutants - analysis; Air pollution; Air Pollution - analysis; Area; Black carbon; Carbon Dioxide; Ecotoxicology and Public Health; Environmental Monitoring; Health risks; Humans; Land use; Mean square errors; Nitrogen dioxide; Nitrogen Dioxide - analysis; Outdoor air quality; Particulate matter; Particulate Matter - analysis; Pollutants; Pollution monitoring; Roadsides; Soot; Traffic; Ultrafines
• ISSN: 0013-936X; 1520-5851; 1520-5851
• DOI: 10.1021/acs.est.2c01077

Growing evidence links traffic-related air pollution (TRAP) to adverse health effects. We designed an innovative and extensive mobile monitoring campaign to characterize TRAP exposure levels for the Adult Changes in Thought (ACT) study, a Seattle-based cohort. The campaign measured particle number concentration (PNC) to capture ultrafine particles (UFP), black carbon (BC), nitrogen dioxide (NO2), fine particulate matter (PM2.5), and carbon dioxide (CO2) at 309 roadside sites within a large, 1200 land km2 (463 mi2) area representative of the cohort. We collected about 29 two-minute measurements at each site during all seasons, days of the week, and most times of the day over a 1-year period. Validation showed good agreement between our BC, NO2, and PM2.5 measurements and monitoring agency sites (R 2 = 0.68–0.73). Universal kriging–partial least squares models of annual average pollutant concentrations had cross-validated mean square error-based R 2 (and root mean square error) values of 0.77 (1177 pt/cm3) for PNC, 0.60 (102 ng/m3) for BC, 0.77 (1.3 ppb) for NO2, 0.70 (0.3 μg/m3) for PM2.5, and 0.51 (4.2 ppm) for CO2. Overall, we found that the design of this extensive campaign captured the spatial pollutant variations well and these were explained by sensible land use features, including those related to traffic.