• particulate matter;
  • air quality forecast;
  • modeling

[1] It is desirable for local air quality agencies to accurately forecast tropospheric PM2.5 concentrations to alert the sensitive population of the onset, severity, and duration of unhealthy air and to encourage the public and industry to reduce emissions-producing activities. Since elevated particulate matter concentrations are encountered throughout the year, the accurate forecast of the day-to-day variability in PM2.5 and constituent concentrations over annual cycles poses considerable challenges. In efforts to characterize forecast model performance during different seasons, PM2.5 forecast simulations with the Eta-Community Multiscale Air Quality system are compared with measurements from a variety of regional surface networks, with special emphasis on performance during the winter period. The analysis suggests that while the model can capture the average spatial trends and dynamic range in PM2.5 and constituent concentrations measured at individual sites, significant variability occurs on a day-to-day basis both in the measurements and the model predictions, which are generally not well correlated when paired both in space and time. Systematic overpredictions in regional PM2.5 forecasts during the cool season are noted through comparisons with measurements from different networks. The overpredictions are typically more pronounced at urban locations, with larger errors at the higher concentration range. Variability in aerosol sulfate concentrations were captured well, as well as the relative amounts of sulfur (IV) and sulfur (VI). The mix of carbon sources as represented by the ratio of organic to elemental carbon is captured well in the southeastern United States, but the total carbonaceous aerosol mass is underestimated. On average, during the wintertime the largest overpredictions among individual PM2.5 constituents were noted for the “other” category which predominantly represents primary-emitted trace elements in the current model configuration. The systematic errors in model predictions of both total PM2.5 and its constituents during the winter period are found to arise from a combination of uncertainties in the magnitude and spatial and temporal allocation of primary PM2.5 emissions, current uncertainties in the estimation of chemical production pathways for secondary constituents (e.g., NO3), and the representation of the impacts of boundary layer mixing on simulated concentrations, especially during nighttime conditions.