Investigating the influence of synoptic-scale meteorology on air quality using self-organizing maps and generalized additive modelling

The influence of synoptic-scale circulations on air quality is an area of increasing interest to air quality management in regards to future climate change. This study presents an analysis where the range of expected synoptic-scale circulation patterns over the region of Melbourne, Australia are determined and linked to regional air quality. A self-organizing map (SOM) has been applied to daily mean sea level pressure (MSLP) reanalysis to obtain twenty large-scale synoptic patterns in the Australian region. A time series of the occurrence of the synoptic archetypes was then employed within the framework of a generalized additive model (GAM) to identify links between synoptic-scale circulation and observed changes air pollutant concentrations. The GAM estimated shifts in pollutant concentration under the occurrence of each synoptic type after controlling for long-term trends, seasonality, weekly emissions, spatial variation, and temporal persistence. Results found the overall explanatory power of the synoptic archetypes in the models to be rather modest with 5.1% of the day-to-day variation in O3, 4.7% in PM10, and 7.1% in NO2 being explained. This indicates that synoptic-scale circulation features are not the primary driver of day-to-day pollutant concentrations. Nonetheless, further analysis of the partial residual plots identified that despite a modest response at the aggregate level, individual synoptic categories had differential effects on air pollutants. In particular, NO2 and O3 were 20% higher than average when synoptic conditions resulted in a northeasterly gradient wind over the Melbourne area. For PM10 maximum increases of up to 20% occurred when a strong anticyclonic system was centered directly over the Melbourne area. In sum, the unified approach of SOM and GAM provided a complementary suite of tools capable of identifying the entire range synoptic circulation patterns over a particular region and quantifying how they influence local air quality.