• fine particle;
  • air quality forecast;
  • 3-D Eta-CMAQ model

[1] The performance of the Eta-Community Multiscale Air Quality (CMAQ) modeling system in forecasting PM2.5 and chemical species is assessed over the eastern United States with the observations obtained by aircraft (NOAA P-3 and NASA DC-8) and four surface monitoring networks (AIRNOW, IMPROVE, CASTNet and STN) during the 2004 International Consortium for Atmospheric Research on Transport and Transformation (ICARTT) study. The results of the statistical analysis at the AIRNOW sites show that the model was able to reproduce the day-to-day and spatial variations of observed PM2.5 and captured a majority (73%) of PM2.5 observations within a factor of 2, with normalized mean bias of −21%. The consistent underestimations in regional PM2.5 forecast at other networks (IMPROVE and STN) were mainly due to the underestimation of total carbonaceous aerosols at both urban and rural sites. The significant underestimation of the “other” category, which predominantly is composed of primary emitted trace elements in the current model configuration, is also one of the reasons leading to the underestimation of PM2.5 at rural sites. The systematic overestimations of SO42− both at the surface sites and aloft, in part, suggest too much SO2 cloud oxidation due to the overestimation of SO2 and H2O2 in the model. The underestimation of NH4+ at the rural sites and aloft may be attributed to the exclusion of some sources of NH3 in the emission inventory. The systematic underestimations of NO3 may result from the general overestimations of SO42−. Note that there are compensating errors among the underestimation of PM2.5 species (such as total carbonaceous aerosols) and overestimation of PM2.5 species (such as SO42−), leading to generally better performance of PM2.5 mass. The systematic underestimation of biogenic isoprene (by ∼30%) and terpene (by a factor of 4) suggests that their biogenic emissions may have been biased low, whereas the consistent overestimations of toluene by the model under the different conditions suggest that its anthropogenic emissions might be too high. The contributions of various physical and chemical processes governing the distribution of PM2.5 during this period are investigated through detailed analysis of model process budgets using the integrated process rate (IPR) analysis along back trajectories at five selected locations in Pennsylvania and Georgia. The results show that the dominant processes for PM2.5 formation and removal vary from the site to site, indicating significant spatial variability.