A new inorganic atmospheric aerosol phase equilibrium model (UHAERO)

Abstract

Abstract.A variety of thermodynamic models have been developed to predict inorganic gas-aerosol equilibrium. To achieve computational efficiency a number of the models rely on a priori specification of the phases present in certain relative humidity regimes. Presented here is a new computational model, named UHAERO, that is both efficient and rigorously computes phase behavior without any a priori specification. The computational implementation is based on minimization of the Gibbs free energy using a primal-dual method, coupled to a Newton iteration. The mathematical details of the solution are given elsewhere. The model computes deliquescence behavior without any a priori specification of the relative humidities of deliquescence. Also included in the model is a formulation based on classical theory of nucleation kinetics that predicts crystallization behavior. Detailed phase diagrams of the sulfate/nitrate/ammonium/water system are presented as a function of relative humidity at 298.15 K over the complete space of composition.

ANISORROPIA: the adjoint of the aerosol thermodynamic model ISORROPIA

“…A number of thermodynamic models have been developed to model aerosol thermodynamic equilibrium with varying degrees of comprehensiveness, accuracy, and efficiency (e.g., AIM (Clegg et al, 1998); GFEMIN (Ansari and Pandis, 1999a); EQSAM3 (Metzger et al, 2006;Metzger and Lelieveld, 2007); EQUISOLV II (Jacobson et al, 1996;Jacobson, 1999); ISORROPIA and ISORROPIA II (Nenes et al, 1998(Nenes et al, , 1999Fountoukis and Nenes, 2007); MARS-A (Saxena et al, 1986;Binkowski and Roselle, 2003); MESA (Zaveri et al, 2005); SCAPE2 (Kim et al, 1993a,b;Meng and Seinfeld, 1996); UHAERO (Amundson et al, 2006)). …”

The effect of meteorological and chemical factors on the agreement between observations and predictions of fine aerosol composition in southwestern Ontario during BAQS-Met

“…These equilibrium partitioning models, which are used to predict the gas/particle partitioning of inorganic semi-volatile species at atmospherically relevant conditions (RH, T ), differ in the type and variety of chemical species that they can treat, the type of input they can accept, the method they use to predict (solve for) the composition at thermodynamic equilibrium, and their computational efficiency (Fountoukis et al, 2009). For these reasons, the outputs of the models differ, and their ability to accurately predict the equilibrium partitioning is usually assessed by comparisons to other models and to measurements assuming that the thermodynamic equilibrium between gases and particles was established on the time scales of the measurements (Ansari and Pandis, 1999a;Zhang et al, 2000Zhang et al, , 2003Moya et al, 2001;Yu et al, 2005;Amundson et al, 2006;Fountoukis and Nenes, 2007). The differences and similarities between outputs of thermodynamic equilibrium models are discussed elsewhere (Ansari and Pandis, 1999a;Zhang et al, 2000;Amundson et al, 2006;Fountoukis and Nenes, 2007).…”

“…The more recently developed or revised thermodynamic partitioning models include SCAPE 2 Meng et al, 1995), AIM 2 (Clegg et al, 1998a, b;Wexler and Clegg, 2002), GFEMN (Ansari and Pandis, 1999a, b), EQUISOLV II (Jacobson, 1999), MESA (Zaveri et al, 2005), UHAERO (Amundson et al, 2006), and ISORROPIA II (Fountoukis and Nenes, 2007). These equilibrium partitioning models, which are used to predict the gas/particle partitioning of inorganic semi-volatile species at atmospherically relevant conditions (RH, T ), differ in the type and variety of chemical species that they can treat, the type of input they can accept, the method they use to predict (solve for) the composition at thermodynamic equilibrium, and their computational efficiency (Fountoukis et al, 2009).…”

Assessing the role of ammonia in sulfur transformation and deposition in China

“…In the past two decades, numerous inorganic thermodynamic equilibrium models have been developed to calculate aerosol composition (e.g., ADDEM (Topping et al, 2005a,b), AIM (Clegg and Pitzer, 1992;Clegg et al, 1998a,b), AIM2 (Wexler and Clegg, 2002), EQSAM (Metzger, 2000;Metzger et al, 2002a,b), EQSAM2 (Trebs et al, 2005;Metzger et al, 2006), EQSAM3 (Metzger and Lelieveld, 2007), EQUISOLV (Jacobson et al, 1996), EQUISOLVII (Jacobson, 1999), GFEMN (Ansari and Pandis, 1999), HETV (Makar et al, 2003), ISORROPIA (Nenes et al, 1998;Pilinis et al, 2000), ISORROPIA2 (Fountoukis and Nenes, 2007), MARS-A (Binkowski and Shankar, 1995), MESA (Zaveri et al, 2005a,b), SCAPE (Kim et al, 1993a,b;Kim and Seinfeld, 1995), SCAPE2 (Meng et al, 1995), UHAERO (Amundson et al, 2006)). Some of them were implemented in global models for simulating aerosol distributions (Metzger, 2000;Jacobson, 2001;Metzger et al, 2002b;Liao et al, 2003;Martin et al, 2004;Rodriguez and Dabdub, 2004;Feng and Penner, 2005;Liao and Seinfeld, 2005;Liao et al, 2006;Tsigaridis et al, 2006;Bauer et al, 2007a,b;Luo et al, 2007).…”