pH Dependence of Brown Carbon Absorbance in Cloud Water

CHRISTOPHER HENNIGAN, Sara Lance, Bryanna Boegner, Grace Bounds, Lucia Garcia, Michael McKee, Madison McLaren, Vikram Pratap, Jasper Reno, Shawn Serafin, University of Maryland, Baltimore County

     Abstract Number: 603
     Working Group: Aerosols, Clouds and Climate

Abstract
Light-absorbing organic species present in aerosols, collectively called brown carbon (BrC), contribute important but highly uncertain effects on climate. Among absorbing species present in aerosols, BrC is unique because it is both emitted directly into the atmosphere (primary BrC) and formed from gas- and aqueous-phase reactions (secondary BrC). It is also unique because reactions initiated by oxidants and UV light can rapidly transform chromophores into non-absorbing (or more weakly absorbing) species in a process called bleaching. Clouds likely represent a significant medium for the production of secondary BrC and for a variety of bleaching reactions, though the relative importance of formation and loss processes in clouds is unknown at present. The acidity (or pH) of atmospheric particles and clouds affects the optical properties of BrC and bleaching rates, although the link between pH and BrC is yet another uncertainty in attempts to constrain its climate forcing effects. Given the wide variability of pH in the atmosphere (pH in particles and clouds ranges from -1 to 8), the optical properties of BrC and its bleaching behavior are expected to vary significantly in the atmosphere, as well. In this work, we characterize the pH dependence of BrC optical properties – including light absorption at 365 nm, mass absorption coefficient (MAC₃₆₅), and the absorption Ångström exponent (AAE) – in cloud water sampled from Whiteface Mountain, NY. The cloud water composition is used to inform thermodynamic predictions of aerosol pH upwind/downwind of Whiteface Mountain and the subsequent changes in BrC optical properties. Differences in BrC optical properties are linked with air mass history and cloud water composition to infer source influence.