Abstract. The balance between turbulent transport and emissions is a key issue in understanding the formation of O3 and PM2.5. Discrepancies between observed and simulated concentrations for these species are often ascribed to insufficient turbulent mixing, particularly for atmospherically stable environments. This assumption may be inaccurate – turbulent mixing deficiencies may explain only part of these discrepancies, while the timing of primary PM2.5 emissions may play a much more significant role than previously believed. In a study of these issues, two regional air-quality models, CMAQ and AURAMS, were compared against observations for a domain in north-western North America. The air quality models made use of the same emissions inventory, emissions processing system, meteorological driving model, and model domain, map projection and horizontal grid, eliminating these factors as potential sources of discrepancies between model predictions. The initial statistical comparison between the models against monitoring network data showed that AURAMS' O3 simulations outperformed those of CMAQ, while CMAQ outperformed AURAMS for most PM2.5 statistical measures. A process analysis of the models revealed that the choice of an a priori cut-off lower limit in the magnitude of vertical diffusion coefficients in each model could explain much of the difference between the model results for both O3 and PM2.5. The use of a larger value for the lower limit in vertical diffusivity was found to create a similar O3 and PM2.5 performance in AURAMS as was noted in CMAQ (with AURAMS showing improved PM2.5, yet degraded O3, and a similar time series as CMAQ). The differences between model results were most noticeable at night, when the use of a larger cut-off in turbulent diffusion coefficients resulted in an erroneous secondary peak in predicted night-time O3. Further investigation showed that the magnitude, timing and spatial allocation of area-source emissions could result in improvements to PM2.5 performance with minimal O3 performance degradation. The use of a relatively high cut-off in diffusion may in part compensate for erroneously high night-time PM2.5 emissions, but at the expense of increasing model error in O3. While the strength of turbulence plays a key role in O3 and PM2.5 formation, more accurate primary PM2.5 temporal emissions data may be needed to explain observed concentrations, particularly in urban regions.
Turbulent transport, emissions, and the role of compensating errors in chemical transport models
