Extended Data Fig. 8: Station-based cross-validation performance for estimating all-source PM2.5 concentrations.
From: Long-range PM2.5 pollution and health impacts from the 2023 Canadian wildfires
a,b,c, Station-based twenty-fold cross-validation results for estimating all-source PM2.5 using GFED as a prior fire emission at daily (a), monthly (b), and yearly (c) scale in the year 2023, respectively. RMSE, root mean squared error. NMB, normalized mean bias. d,e,f, Station-based twenty-fold cross-validation results for estimating all-source PM2.5 using QFED as a prior fire emission at daily (d), monthly (e), and yearly (f) scale in the year 2023, respectively. g,h,i, Station-based twenty-fold cross-validation results for estimating all-source PM2.5 using GFAS as a prior fire emission at daily (g), monthly (h), and yearly (i) scale in the year 2023, respectively. Ground-based PM2.5 measurements collected globally, including data from North America (Environmental Canada: https://data-donnees.az.ec.gc.ca/data/air/monitor/national-air-pollution-surveillance-naps-program/Data-Donnees/, US Environmental Protection Agency: https://www.epa.gov/outdoor-air-quality-data/download-daily-data, Interagency Monitoring of Protected Visual Environments: https://views.cira.colostate.edu/fed/QueryWizard/Default.aspx, and AirFire program of the US Forest Service: https://info.airfire.org/airmonitor-package), Europe (European Air Quality Portal: https://eeadmz1-cws-wp-air02.azurewebsites.net/), China (China National Environmental Monitoring Center: http://www.cnemc.cn/), and other regions (OpenAQ: https://openaq.org/).
