Publicly available low-cost sensor measurements for PM2.5 exposure modeling: Guidance for monitor deployment and data selection

Gassett

Gould

et al. 2021

Preprint

Self Cite

Growing evidence links traffic-related air pollution (TRAP) to adverse health effects. Previous air pollution studies focused on a few commonly measured pollutants with poor spatial resolution. Well-designed mobile monitoring studies may address this limitation. We designed an extensive mobile monitoring campaign to characterize TRAP exposure levels for a Seattle-based cohort, the Adult Changes in Thought (ACT) study. The campaign measured particulate matter ≤ 2.5 µm in diameter (PM2.5), ultrafine particulates (UFP), black carbon (BC), nitrogen dioxide (NO2) and carbon dioxide (CO2) at 309 stop locations representative of the cohort. We collected about 29 two-minute measures at each location during all seasons, days of the week, and most times of day for a one-year period. Annual average concentrations of UFPs had similar degrees of spatial variability as BC and NO2 but higher degrees of variability as CO2 and PM2.5. UFPs had less temporal variability than other pollutants. Validation showed good agreement between our BC, NO2, and PM2.5 measurements and regulatory monitoring sites. The results from this campaign will be used to assess TRAP exposure in the ACT cohort.SynopsisWe assessed annual-average traffic-pollutant levels with high spatial resolution at locations representative of participant residences using a temporally balanced short-term mobile monitoring campaign.Abstract Figure

Section: Discussionmentioning

confidence: 90%

Characterization of annual average traffic-related air pollution levels (particle number, black carbon, nitrogen dioxide, PM_2.5, carbon dioxide) in the greater Seattle area from a year-long mobile monitoring campaign

Gassett

Gould

et al. 2021

Preprint

Self Cite

“…The average (SD) distance from an ACT cohort location to the nearest monitoring site was 611 (397) m rather than 5805 (2805) m to an AQS site, almost a 10-fold difference. Monitor proximity to prediction (i.e., cohort) locations, both in terms of geographic and covariate distance, is an important determinant of accurate exposure assessment. , For instance, closer monitor proximity to prediction sites can improve UK model predictions since these incorporate spatial correlation into LUR predictions. Additionally, we previously showed that more temporally balanced sampling across hours, days of the week, and seasons is expected to produce more accurate and largely unbiased annual average estimates as compared to more common campaigns with reduced temporal coverage …”

Section: Discussionmentioning

confidence: 99%

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

Gassett

Gould

et al. 2022

Environ. Sci. Technol.

Self Cite

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.

“…We do not address monitor placement in this analysis, although past work has indicated that if the goal is out-of-sample prediction, monitors should be placed near the desired prediction locations to achieve spatial coverage (spatial compatibility) and in locations with similar covariate makeups to capture the covariate variability (covariate compatibility). ,,− Furthermore, if using model predictions from mobile-monitoring campaigns in epidemiological applications, ensuring both spatial and covariate compatibility in their sampling design is critical for minimizing exposure measurement error . Spatial coverage may be especially important if the available modeling covariates do not capture the pollutant variability well and geostatistical approaches that take advantage of spatial correlation (e.g., kriging) are used.…”

Section: Discussionmentioning

confidence: 99%

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

Austin

et al. 2022

Environ. Sci. Technol.

Self Cite

Short-term mobile monitoring campaigns are increasingly used to assess long-term air pollution exposure in epidemiology. Little is known about how monitoring network design features, including the number of stops and sampling temporality, impacts exposure assessment models. We address this gap by leveraging an extensive mobile monitoring campaign conducted in the greater Seattle area over the course of a year during all days of the week and most hours. The campaign measured total particle number concentration (PNC; sheds light on ultrafine particulate (UFP) number concentration), black carbon (BC), nitrogen dioxide (NO2), fine particulate matter (PM2.5), and carbon dioxide (CO2). In Monte Carlo sampling of 7327 total stops (278 sites × 26 visits each), we restricted the number of sites and visits used to estimate annual averages. Predictions from the all-data campaign performed well, with cross-validated R 2s of 0.51–0.77. We found similar model performances (85% of the all-data campaign R 2) with ∼1000 to 3000 randomly selected stops for NO2, PNC, and BC, and ∼4000 to 5000 stops for PM2.5 and CO2. Campaigns with additional temporal restrictions (e.g., business hours, rush hours, weekdays, or fewer seasons) had reduced model performances and different spatial surfaces. Mobile monitoring campaigns wanting to assess long-term exposure should carefully consider their monitoring designs.