Meteorological modes of variability for fine particulate matter (PM2.5) air quality in the United States: implications for PM2.5 sensitivity to climate change

Projecting the effects of climate change on fine particulate matter (PM2.5) air quality requires an understanding of the relationships of PM2.5 with meteorological variables. We used a multiple linear regression model to correlate both observed and model simulated daily mean concentrations of total PM2.5 and its components with meteorological variables in the contiguous US for 2004-2008. We observe strong positive correlations of all PM2.5 components with temperature in most of the US, except for nitrate in the Southeast where the correlation is negative. Relative humidity (RH) is generally positively correlated with sulfate and nitrate but negatively correlated with organic carbon. We find that most of the correlations of PM2.5 with temperature and RH do not arise from direct dependence on these variables but largely from covariation with synoptic transport. Principal component analysis and regression show that 20-40% of the observed PM2.5 day-to-day variability can be explained by a single dominant meteorological mode: cold frontal passages in the eastern US and maritime inflows in the West, both associated with the movement of synoptic weather systems. We further show that interannual variability of PM2.5 in the US is strongly correlated with frequency of these dominant meteorological modes (e.g., cyclones and fronts) as diagnosed from spectral analysis. We then use projections from 15 climate models from the IPCC Fourth Assessment Report to diagnose future (2046-2065) changes in the frequency of these modes and the subsequent effects on PM2.5. We estimate a likely increase in annual mean PM2.5 in the eastern US by up to 0.3 µg m-3 due to a more stagnant atmosphere, with large inter-model variability. Our results point to the dominance of synoptic weather systems in controlling PM2.5 variability, and the potential of using the frequency of meteorological modes as the major climate variable for PM2.5 air quality. However, inter-model variability of the frequency trends of these modes undermines the certainty of future PM2.5 projections, at least on this time scale.