Use of Citizen Science-Derived Data for Spatial and Temporal Modeling of Particulate Matter near the US/Mexico Border

Atmosphere

卷:10

Luis Olmedo¹  Humberto Lugo¹  Ester Bejarano¹  Michael Jerrett²  Michael Yost³  GraemeN. Carvlin³  Jeff Shirai³  Timothy Larson³  Edmund Seto³  Amanda Northcross⁴  Alexa Wilkie⁵  PaulB. English⁵  Dan Meltzer⁵  Galatea King⁵  Michelle Wong⁵ 

[1] Comite Civico del Valle, Brawley, CA 92227, USA;

[2] Department of Environmental Health Sciences, University of California, Los Angeles, CA 90095, USA;

[3] Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA 98195, USA;

[4] Department of Environmental and Occupational Health, George Washington University, Washington, DC 98824, USA;

[5] Tracking California, Public Health Institute, Richmond, CA 94804, USA;

关键词: PM_2.5;    PM_coarse;    land-use regression;    community-based participatory research;    citizen science;    air sensors;    community air monitoring;   

DOI  :  10.3390/atmos10090495

来源: DOAJ

【 摘 要 】

This paper describes the use of citizen science-derived data for the creation of a land-use regression (LUR) model for particulate matter (PM_2.5 and PM_coarse) for a vulnerable community in Imperial County, California (CA), near the United States (US)/Mexico border. Data from the Imperial County Community Air Monitoring Network community monitors were calibrated and added to a LUR, along with meteorology and land use. PM_2.5 and PM_coarse were predicted across the county at the monthly timescale. Model types were compared by cross-validated (CV) R² and root-mean-square error (RMSE). The Bayesian additive regression trees model (BART) performed the best for both PM_2.5 (CV R² = 0.47, RMSE = 1.5 µg/m³) and PM_coarse (CV R² = 0.65, RMSE = 8.07 µg/m³). Model predictions were also compared to measurements from the regulatory monitors. RMSE for the monthly models was 3.6 µg/m³ for PM_2.5 and 17.7 µg/m³ for PM_coarse. Variable importance measures pointed to seasonality and length of roads as drivers of PM_2.5, and seasonality, type of farmland, and length of roads as drivers of PM_coarse. Predicted PM_2.5 was elevated near the US/Mexico border and predicted PM_coarse was elevated in the center of Imperial Valley. Both sizes of PM were high near the western edge of the Salton Sea. This analysis provides some of the initial evidence for the utility of citizen science-derived pollution measurements to develop spatial and temporal models which can make estimates of pollution levels throughout vulnerable communities.

【 授权许可】

Unknown