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

Abstract

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 (NO 2 ), fine particulate matter (PM 2.5 ), and carbon dioxide (CO 2 ) at 309 roadside sites within a large, 1200 land km 2 (463 mi 2 ) 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, NO 2 , and PM 2.5 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/cm 3 ) for PNC, 0.60 (102 ng/m 3 ) for BC, 0.77 (1.3 ppb) for NO 2 , 0.70 (0.3 μg/m 3 ) for PM 2.5 , and 0.51 (4.2 ppm) for CO 2 . 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.

Multi-Season Mobile Monitoring Campaign of on-Road Air Pollution in Bengaluru, India: High-Resolution Mapping and Estimation of Quasi-Emission Factors

“…Hankey et al (2015), for example, conducted ~85 hours of bicycle-based mobile measurements of UFP, BC, and PM2.5 to build LUR models in urban Minneapolis. Blanco et al, (2022) in a stop-and-go mobile monitoring configuration, measured air pollution levels (UFP, BC, NO2, PM2.5, CO2) at 309 roadside sites over one year period in Greater Seattle Area.…”

Impact of Mobile Monitoring Network Design on Air Pollution Exposure Assessment Models

“…Concentrations were winsorized at the site level once (not after each subsample, see Section ) by setting values below each site’s 5th or above 95th quantile concentration to that threshold, respectively, to reduce the influence of a few extreme observations on annual averages. We previously showed that winsorizing prior to averaging slightly improved PNC and PM 2.5 models due to the reduction of influential points, while using approaches completely robust to influential points (i.e., medians), produced worse-performing models of pollutants with already limited spatial variability like CO 2 …”

“…New PLS components were calculated for each model (five models per sampling campaign for fivefold cross-validation and one for test set validation). We selected this subset of covariates from 348 original covariates because these had sufficient variability and a limited number of outliers in the training-validation set . The models were ln ( con c i ) = α + ∑ m = 1 M θ m Z mi + ε i where conc i is the pollutant concentration at the i th location, Z m are the first two PLS principal component scores ( M = 2), α and θ m are estimated model coefficients, and ε is the residual term with mean zero and a modeled geostatistical structure.…”

“…We previously showed that winsorizing prior to averaging slightly improved PNC and PM 2.5 models due to the reduction of influential points, while using approaches completely robust to influential points (i.e., medians), produced worse-performing models of pollutants with already limited spatial variability like CO 2 . 36 For comparability across pollutants, we excluded the ∼10% of visits that had incomplete measurements (i.e., had fewer than five pollutants); the resulting dataset had an average of 26 (range: 21−31) visits per site during 251 drive days. We split sites into training-validation (90%, n = 278 sites, 7327 stops) and test (10%, n = 31 sites, 810 stops) sets (see Figure S1 for the spatial distribution of these sites).…”

“…To address these gaps, we leverage an extensive, multipollutant mobile monitoring campaign in the greater Seattle area (1200 km 2 domain) intended to assess annual average air pollution exposure levels in an epidemiologic cohort. 36 The campaign consisted of 309 stationary sites, each with ∼29 temporally balanced visits per site, and measurements of PNC, BC, NO 2 , PM 2.5 , and CO 2 . We use Monte Carlo sampling to collect subsamples and investigate how the mobile monitoring design features, including the number of stops (sites × visits) and sampling temporality (seasons, days, hours), impact the resulting exposure prediction models.…”