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Air pollution variability is strongly dependent on meteorology. However,quantifying the impacts of changes in regional climatology on pollutionextremes can be difficult due to the many non-linear and competingmeteorological influences on the production, transport, and removal ofpollutant species. Furthermore, observed pollutant levels at many sites showsensitivities at the extremes that differ from those of the overall mean,indicating relationships that would be poorly characterized by simple linearregressions. To address this challenge, we apply quantile regression toobserved daily ozone (O3) and fine particulate matter (PM2.5)levels and reanalysis meteorological fields in the USA over thepast decade to specifically identify the meteorological sensitivities ofhigher pollutant levels. From an initial set of over 1700 possiblemeteorological indicators (including 28 meteorological variables with 63different temporal options), we generate reduced sets of O3 andPM2.5 indicators for both summer and winter months, analyzing pollutantsensitivities to each for response quantiles ranging from 2 to 98 %. Primarycovariates connected to high-quantile O3 levels include temperature andrelative humidity in the summer, while winter O3 levels are mostcommonly associated with incoming radiation flux. Covariates associated withsummer PM2.5 include temperature, wind speed, and troposphericstability at many locations, while stability, humidity, and planetaryboundary layer height are the key covariates most frequently associated withwinter PM2.5. We find key differences in covariate sensitivities acrossregions and quantiles. For example, we find nationally averagedsensitivities of 95th percentile summer O3 to changes in maximumdaily temperature of approximately 0.9 ppb °C−1, whilethe sensitivity of 50th percentile summer O3 (the annual median)is only 0.6 ppb °C−1. This gap points to differingsensitivities within various percentiles of the pollutant distribution,highlighting the need for statistical tools capable of identifyingmeteorological impacts across the entire response spectrum

Investigating the observed sensitivities of air-quality extremes to meteorological drivers via quantile regression