Abstract
Clouds are important physicochemical processors of atmospheric pollutants. Major contributors to secondary sulfate, clouds also provide media for the production and processing of secondary organic aerosol (SOA). Sulfate and organic compounds often dominate particulate mass, and the accurate representation of their important production and loss pathways in models is necessary to effectively address the adverse health, ecosystem, and climate effects associated with elevated particulate concentrations. In this study we investigate the impacts of an extended cloud-chemistry scheme on predictions of particulate sulfur and low molecular weight organic acids in an annual hemispheric application of the Community Multiscale Air Quality (CMAQ) modeling system, version 5.3. Building upon the previously developed Kinetic Mass Transfer (KMT) framework(1), the AQCHEM-KMT version 2 (KMT2) cloud-chemistry scheme supplements CMAQ's default (AQCHEM) seven-reaction cloud-chemistry parameterization with additional inorganic and organic aqueous-phase chemistry, including additional S(IV) reactions and replacement of the default in-cloud SOA parameterization with an explicit representation of the aqueous oxidation of small carbonyl compounds. Modeled impacts vary seasonally and spatially, and results indicate that, compared with the default seven-reaction cloud-chemistry scheme, the extended aqueous-phase chemistry mechanism contributes to predicted inorganic and organic aerosol fractions and can lead to increases in seasonally averaged PM(2.5) predictions up to ~1 μg m(-3), with greater episodic impacts. While model performance for particulate sulfur species is mixed and, in fact, slightly degraded over CONUS on average for these simulations, a comparison with seasonal oxalate observations indicates that the updated cloud chemistry code may lead to improved model performance for organic aerosol, particularly in areas and seasons where there is limited influence from primary organic acid and/or biomass emissions. The work here suggests there may be a potential benefit realized from re-evaluating and updating the simple cloud chemistry parameterizations that are common in chemical transport models. Future efforts should continue improving representation of the most important aqueous-phase chemical pathways in air quality models while minimizing computational cost.