Using synthetic tracers as a proxy for summertime PM2.5 air quality over the Northeastern United States in physical climate models

Over the Northeastern United States, the summer daily time series of SAt correlates strongly with that of PM2.5, with similar cumulative density functions under both present and future climate conditions. With a linear regression model derived from PM2.5 and SAt in the current-day simulation, we reconstruct both the current-day and future PM2.5 daily time series from the simulated SAt. This reconstruction captures the summer mean PM2.5, the incidence of days above the 24-h mean PM2.5 NAAQS, and PM2.5 responses to climate change.

This reconstruction also works over other polluted Northern Hemispheric regions and in spring. Our proof-of-concept study demonstrates that simple tracers can be developed to mimic PM2.5, including its response to climate change, as an easy-to-implement and low-cost addition to physical climate models that should help air quality managers to reap the benefits of climate models that have no chemistry.