Evaluation of Regional Air Quality Models over Sydney, Australia: Part 2, Comparison of PM2.5 and Ozone

Accurate air quality modelling is an essential tool, both for strategic assessment (regulation development for emission controls) and for short-term forecasting (enabling warnings to be issued to protect vulnerable members of society when the pollution levels are predicted to be high). Model intercomparison studies are a valuable support to this work, being useful for identifying any issues with air quality models, and benchmarking their performance against international standards, thereby increasing confidence in their predictions. This paper presents the results of a comparison study of six chemical transport models which have been used to simulate short-term hourly to 24 hourly concentrations of fine particulate matter less than and equal to 2.5 mu m in diameter (PM2.5) and ozone (O-3) for Sydney, Australia. Model performance was evaluated by comparison to air quality measurements made at 16 locations for O-3 and 5 locations for PM2.5, during three time periods that coincided with major atmospheric composition measurement campaigns in the region. These major campaigns included daytime measurements of PM2.5 composition, and so model performance for particulate sulfate (SO42-), nitrate (NO3-), ammonium (NH4+) and elemental carbon (EC) was evaluated at one site per modelling period. Domain-wide performance of the models for hourly O-3 was good, with models meeting benchmark criteria and reproducing the observed O-3 production regime (based on the O-3/NOx indicator) at 80% or more of the sites. Nevertheless, model performance was worse at high (and low) O-3 percentiles. Domain-wide model performance for 24 h average PM2.5 was more variable, with a general tendency for the models to under-predict PM2.5 concentrations during the summer and over-predict PM2.5 concentrations in the autumn. The modelling intercomparison exercise has led to improvements in the implementation of these models for Sydney and has increased confidence in their skill at reproducing observed atmospheric composition.