Evaluation of a three-dimensional chemical transport model (PMCAMx) in the eastern United States for all four seasons

[1] A three-dimensional chemical transport model (PMCAMx) is used to simulate particulate matter (PM) mass and composition in the eastern United States during the four seasons of the year (July 2001, October 2001, January 2002, and April 2002). The model predictions are evaluated against daily average PM2.5 measurements taken throughout the eastern United States by the IMPROVE and STN monitoring networks and the EPA Supersites program. During the spring and summer the model reproduces the measured daily average PM2.5 concentrations with an error of less than 50%, two thirds of the time. The PM2.5 error is less than 30% for 43% of the measurements during these seasons. For the fall and winter the PM2.5 predictions are within 50% of the measurements for 51% of the data points and within 30% for 34% of the time. The performance of the model in reproducing sulfate, organic mass, elemental carbon, and total PM2.5 concentrations varies from average to good depending on the season. Uncertainties in ammonia emissions during the fall cause errors in the corresponding ammonium predictions, while the ammonia emissions inventory appears to be satisfactory during the other seasons. The ability of the model to reproduce the aerosol nitrate concentrations in the spring and summer is limited by difficulties in simulating the heterogeneous nighttime formation rate of nitric acid. During the summer and fall the model performance in reproducing the organic PM concentrations and diurnal patterns is good. The predicted organic PM during the summer is on average 60% primary and 40% secondary. The secondary contribution to organic PM drops to around 20% during the winter. The used average EC emission rate of approximately 0.55 ktons d(-1) (0.45 ktons d(-1) during the weekends) is consistent with the observed EC concentrations. The nighttime chemistry of NOx determines the PM nitrate concentrations during most days in the winter, spring, and fall and its mathematical description needs improvement. The good agreement between the predicted and observed temporal profiles for most species suggests a reasonable understanding and depiction in the model of the corresponding processes. Additional strengths and limitations of current modeling approaches for this modeling domain and for the different seasons are further discussed.