Turbulent transport, emissions and the role of compensating errors in chemical transport models

Abstract

Abstract. The balance between turbulent transport and emissions is a key issue in understanding the formation of O 3 and particulate matter with diameters less than 2.5 µm (PM 2.5 ). Discrepancies between observed and simulated concentrations for these species have, in the past, been ascribed to insufficient turbulent mixing, particularly for atmospherically stable environments. This assumption may be simplisticturbulent mixing deficiencies may explain only part of these discrepancies, and as turbulence parameterizations are improved, the timing of primary PM 2.5 emissions may play a much more significant role in the further reduction of model error. In a study of these issues, two regional air-quality models, the Community Multi-scale Air Quality model (CMAQ, version 4.6) and A Unified Regional Air-quality Modelling System (AURAMS, version 1.4.2), were compared to 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 and monitoring network data showed that AURAMS' O 3 simulations outperformed those of this version of CMAQ4.6, while CMAQ4.6 outperformed AU-RAMS for most PM 2.5 statistical measures. A process analysis of the models revealed that many of the differences between the models' results could be attributed to the strength of turbulent diffusion, via the choice of an a priori lower limit in the magnitude of vertical diffusion coefficients, with AURAMS using 0.1 m 2 s −1 and CMAQ4.6 using 1.0 m 2 s −1 .The use of the larger CMAQ4.6 value for the lower limit of vertical diffusivity within AURAMS resulted in a similar performance for the two models (with AURAMS also showing improved PM 2.5 , yet degraded O 3 , and a similar time series as CMAQ4.6). The differences between model results were most noticeable at night, when the higher minimum turbulent diffusivity resulted in an erroneous secondary peak in predicted night-time O 3 . A spatially invariant and relatively high lower limit in diffusivity could not reduce errors in both O 3 and PM 2.5 fields, implying that other factors aside from the strength of turbulence might be responsible for the PM 2.5 over-predictions. Further investigation showed that the magnitude, timing and spatial allocation of area source emissions could result in improvements to PM 2.5 performance with minimal O 3 performance degradation. AURAMS was then used to investigate a land-use-dependant lower limit in diffusivity of 1.0 m 2 s −1 in urban regions, linearly scaling to 0.01 m 2 s −1 in rural areas, as employed in CMAQ5.0.1. This strategy was found to significantly improve mean statistics for PM 2.5 throughout the day and mean O 3 statistics at night, while significantly degrading (halving) midday PM 2.5 correlation coefficients and slope of observed to mode...

Toxic volatile organic air pollutants across Canada: multi-year concentration trends, regional air quality modelling and source apportionment

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