AAAR 31st Annual Conference
October 8-12, 2012
Hyatt Regency Minneapolis
Minneapolis, Minnesota, USA
Abstract View
An Acid-Base Chemical Reaction Model for Nucleation Rates in the Polluted Boundary Layer
MODI CHEN, Mari Titcombe, Jingkun Jiang, Jun Zhao, Chongai Kuang, Ilja Siepmann, David Hanson, Peter McMurry, University of Minnesota
Abstract Number: 259 Working Group: Aerosol Nucleation: From Clusters to Nanoparticles
Abstract Nucleation is observed throughout the boundary layer, and evidence suggests it may affect global climate. Previous studies indicate that, in most cases, atmospheric nucleation rates vary in proportional to the nth (n is between 1 and 2) order of the sulfuric acid concentration, and are several orders of magnitude smaller than the sulfuric acid collision rate. The fundamental mechanism of atmospheric nucleation, though, remains unknown. In this work, a new conceptual model for nucleation in the polluted atmospheric boundary layer, involving a sequence of reactions between sulfuric acid and gaseous basic compounds (ammonia and amines), is proposed. This model is based on data from both a field study and a laboratory chamber study. These data indicate that the formation of clusters that contain two or three sulfuric acid molecules ("dimer" and "trimer") is the primary bottleneck for nucleation. The sulfuric acid vapor uptake rate of the tetramer cluster is found to be close to the collision rate. Our measurements show that basic gaseous compounds stabilize clusters, thereby enhancing nucleation rates. A closed form expression for nucleation rates in terms of the sulfuric acid and basic gas concentration is proposed. In this expression, nucleation rates vary approximately in proportion to the square of sulfuric acid concentration as has often been observed, and evaporation from dimer and trimer and coagulation losses are responsible for the smaller-than-one pre-factor. Nucleation rates obtained from previous field measurements (Mexico City 2006 and Atlanta 2009) are in reasonable agreement with values predicted by this model.