Temperature-Dependent Composition of Summertime PM2.5 in Observations and Model Predictions across the Eastern U.S.

Throughout the U.S., summertime fine particulate matter (PM2.5) exhibits a strong temperature (T) dependence. Reducing the PM2.5 enhancement with T could reduce the public health burden of PM2.5 now and in the warmer future. Atmospheric models are a critical tool for probing the processes and components driving observed behaviors. In this work, we describe how observed and modeled aerosol abundance and composition vary with T in the present-day Eastern U.S., with specific attention to the two major PM2.5 components: sulfate (SO42–) and organic carbon (OC). Observations in the Eastern U.S. show an average measured summertime PM2.5-T sensitivity of 0.67 μg/m3/K, with CMAQv5.4 regional model predictions closely matching this value. Observed SO42– and OC also increase with T; however, the model has component-specific discrepancies with observations. Specifically, the model underestimates SO42– concentrations and their increase with T while overestimating OC concentrations and their increase with T. Here, we explore a series of model interventions aimed at correcting these deviations. We conclude that the PM2.5-T relationship is driven by inorganic and organic systems that are highly coupled, and it is possible to design model interventions to simultaneously address biases in PM2.5 component concentrations as well as their responses to T.