A Primal-Dual Active-Set Algorithm for Chemical Equilibrium Problems Related to Modeling of Atmospheric Inorganic Aerosols 1

Transcription

1 A Primal-Dual Active-Set Algorithm for Chemical Equilibrium Problems Related to Modeling of Atmospheric Inorganic Aerosols 1 N. R. Amundson 2, A. Caboussat 3, J.-W. He 4, J. H. Seinfeld 5, K.-Y. Yoo 6 1 This work has been partially supported by the United States Environmental Protection Agency through Cooperative Agreement CR to the University of Houston. The second author is supported by the Swiss National Science Foundation, Grant PBEL Cullen Professor of Chemical Engineering and Professor of Mathematics, Department of Mathematics, University of Houston, Houston, Texas. 3 Visiting Assistant Professor, Department of Mathematics, University of Houston, Houston, Texas. 4 Associate Professor, Department of Mathematics, University of Houston, Houston, Texas. 5 Louis E. Nohl Professor and Professor of Chemical Engineering, Department of Chemical Engineering, California Institute of Technology, Pasadena, California. 6 Assistant Professor, Department of Chemical Engineering, Seoul National University of Technology, Seoul, Korea

2 Abstract. A general equilibrium model for multiphase multicomponent inorganic atmospheric aerosols is proposed. The thermodynamic equilibrium is given by the minimum of the Gibbs free energy for a system involving an aqueous phase, a gas phase and solid salts. A primal-dual algorithm solving the Karush-Kuhn-Tucker conditions is detailed. An active set/newton method permits to compute the minimum of energy and track the presence or not of solid salts at the equilibrium. Numerical results show the efficiency of our algorithm for the prediction of multiphase multireaction chemical equilibria. Key Words. Inorganic aerosols, thermodynamic equilibrium, constrained minimization, primal-dual methods, active sets. 1 Introduction Over the last two decades, a series of thermodynamic modules, such as Equil (Ref. 1), Mars (Ref. 2), Sequilib (Ref. 3), Scape (Refs. 4-5), Scape2 (Refs. 6-7), Equisolv II (Refs. 8 10), and Isorropia (Refs ), has been developed in the atmospheric modeling community to predict the phase transition and multistage growth phenomena of inorganic aerosols. These modules calculate the composition of atmospheric aerosols by solving a set of nonlinear algebraic equations derived from chemical equilibrium relations. One of the most challenging parts is the prediction of the partitioning of the inorganic aerosol components between aqueous and solid phases. By relying on a priori and often incomplete knowledge of the presence of solid phases at a certain relative humidity and overall composition (information extracted from empirical data), these modules often fail to accurately predict the phase state and composition and the multistage growth phenomena of inorganic aerosols (Refs ). On the other hand, thermodynamic models that are based on the minimization of the Gibbs free energy, such as Gfemn (Ref. 14) and Aim (Refs ), implicitly predict phase transition and multistage aerosol growth without any a priori knowledge of the behavior of inorganic aerosols. However, such direct minimization of the Gibbs free energy is computationally intensive making its use in 3D air quality models infeasible (Refs ). In this paper, a primal-dual active set algorithm for the efficient and ac- 2

3 curate prediction of the phase transition and multistage growth phenomena of inorganic aerosols is presented. The mathematical framework for modeling solid-liquid equilibrium reactions is based on the canonical stoichiometry of inorganic aerosols (Ref. 21). The canonical form is elucidated from the analysis of the algebraic structure of aqueous electrolyte solution system and the Karush-Kuhn-Tucker (KKT) conditions for the constrained minimization of the Gibbs free energy. The concentrations of solid species in solid-liquid equilibrium are interpreted as the Lagrange multipliers of dual linear inequality constraints. This primal-dual relation is the key for the development of our primal-dual active set algorithm, whose principal features can be summarized as follows. The algorithm applies Newton s method to the reduced KKT system of equations that is projected on an active set of solid phases to find the next primal-dual approximation of the solution. The active set method permits us to add/delete salts to/from a working set of saturated salts until the equilibrium set of solid phases is obtained. The linear inequality constraints are enforced on the dual variables such that the solution remains dual feasible with respect to the solid constraints, until an inequality constraint becomes active at an iteration and the active set is modified by adding a saturated salt into it. The concentrations of the saturated salts in the active set are the Lagrange multipliers of the dual active constraints so that their nonnegativeness is enforced by deleting a saturated salt from the active set when its concentration becoming negative. A second order stability criterion is implemented by keeping the reduced Hessian of the Gibbs free energy positive definite so that the algorithm converges to a stable equilibrium (local minimum) rather than any other first order optimality point such as a maximum or a saddle point. To avoid the non-positiveness and poor-scaling of the concentrations in the computation, a logarithmic change of variables is performed so that the concentrations follow a path that is infeasible with respect to the mass balance constraints in the first few iterations, then converge quadratically to the minimum of the Gibbs free energy. The structure of this paper is the following: in Section 2, the steps of the mathematical modeling of inorganic aerosols are presented. In Section 3, the optimization problem is derived and modified until obtaining a suitable formulation from both the chemical and mathematical points of view. Then, in Section 4, an active set/newton method is detailed for solving this minimization problem. Numerical results are presented in Section 5 to illustrate 3

4 the efficiency of our approach. Section 6 consists in the conclusions. 2 Modeling of Inorganic Aerosols 2.1 Chemical Equilibrium Problem The multi-phase and multi-reaction chemical equilibrium for a closed inorganic aerosol system at constant temperature and pressure and a specified element-abundance feed vector ˆb is the minimization problem min G(n l, n g, n s ) = n T l µ l + n T g µ g + n T s µ s, (1a) s. t. n l > 0, n g > 0, n s 0, (1b)  l n l + Âgn g + Âsn s = ˆb, (1c) where n α R mα, µ α R mα and Âα R me mα are the concentration vector, the chemical potential vector and the element-based formula matrix for the species set α = l, g, s respectively. The subscripts l, g, s denote the liquid, gas and solid phases respectively. Here m e denotes the number of elements in the system and m α denotes the number of species in species set α. Let G α = n T αµ α denote the Gibbs free energy related to the phase α. The aqueous and gas phases are assumed to exist at the equilibrium. Remark 2.1. Let X 1,..., X m denote the m chemical species expected to be present in the system, whose molecular structures are described by the formula vectors â 1,..., â m, defined by m e X j = â ij E i, j = 1,..., m, (2) i=1 where the species are constituted as the linear combinations of the m e basic elements E 1,..., E me. Let E me, the last basic element in (2), be the electronic charge. Then, the first m e 1 rows of Âg,  l and Âs are non-negative, the last rows of Âg and  s are zeros, and the last equation in the element-balance constraints of (1) describes the electro-neutrality of the system. Let I be the index set of the species in the system and m := I. The index set I can be split into I g, I l and I s, with m g := I g, m l := I l, 4

5 m s := I s, according to the gas, aqueous and solid phases, respectively. The element-based formula matrices in (1) are defined by Âα = (â j ) j Iα for α = g, l, s. The chemical potential vectors µ α, α = g, l, s, in (1) are given by µ g = µ 0 g + RT log a g, (3) µ l = µ 0 l + RT log a l, (4) µ s = µ 0 s, (5) where R is the universal gas constant and T is the system temperature. In (3), µ 0 g is the standard chemical potential vector of the gas species at a pressure of 1 atm and the system temperature, and a g is the activity vector of the gas species. This activity vector is given by a g = f g (6) where f g is the fugacity vector of the gas species and is given by f g = (p 1 atm /n air )n g, where p 1 atm is the ratio P/(1 atm) with P denoting the system pressure and n air is the total number of moles of air in the system. In (4), µ 0 l is the standard chemical potential vector of the aqueous species at the system temperature and pressure, and a l is the activity vector of the aqueous phase that is predicted via an activity coefficient model. In (5), µ 0 s is the standard constant chemical potential vector of the solid species at the system temperature and pressure. Let us denote by R ++ the strictly positive real numbers. Note that the chemical potential vector µ l is defined as the gradient of the Gibbs free energy of the aqueous phase: G l : R m l ++ R +, so that, for all n l > 0, µ l = G l (n l ). The first-order homogeneity of G l is the basis for the relation and the Gibbs-Duhem equations: equivalently G l (n l ) = n T l µ l, 2 G l (n l )n l = 0, (7) ( µ l ) n l = 0 or ( log a l ) n l = 0. 5

6 The respective Hessian matrices are given by H l = 2 G l (n l ), H g = diag(1/n g ) and H s = 0. The above Gibbs-Duhem relations are used in the derivation of the optimality conditions for problem (1) in Section 3. 3 Description of the Problem and Optimality Conditions 3.1 KKT System and Canonical Stoichiometry Assuming that the aqueous and gas phases are present at equilibrium, the solution of the chemical equilibrium (1) is characterized by the Karush-Kuhn- Tucker (KKT) system of the first order necessary optimality conditions, see e.g. (Ref. 22): µ l + ÂT l λ = 0, (8a) µ g + ÂT g λ = 0, (8b) µ s + ÂT s λ 0, n s 0, n T s (µ s + ÂT s λ) = 0, (8c)  l n l + Âgn g + Âsn s = ˆb. (8d) The KKT system (8) is referred to as the non-stoichiometric form of the equilibrium conditions. These conditions can be expressed in an alternative and computationally more relevant form, the stoichiometric form, by extracting a set of component species from the aqueous species as described in the sequel. To ensure the feasibility of solid-liquid and gas-liquid equilibrium reactions in (1), the species sets I α, α = g, l, s, in the system are assumed to be consistent in the sense that range(âl, Âg, Âs) = range(âl). (9) The consistency (9) requires that all the gas and solid species in the system can be generated as a linear combination of the aqueous species. It is also assumed that m l > m e, i.e., the number of the aqueous species is larger than the number of the elements. Let m c ( m e ) be the rank of  l. The next 6

7 step is to select the m c chemical species which play the role of components of the system. Thus, the set I c ( I l ) is a set of m c aqueous species whose corresponding formula vectors â j are linearly independent. These species are called the components. Let Âc := (â i ) i Ic R me mc be the formula matrix for the components. If m c = m e, i.e.,  c is of full row rank, then  1 c exists; otherwise, the pseudoinverse of Âc is denoted by  1 c := (ÂT c Âc) 1  T c R mc me. The vector ˆb R me is assumed to belong to range(âl). Let I n be the set of the remaining m n = m l m c aqueous species with the formula matrix  n := (â i ) i In R me mn. These species are called the non-components. Let us define A α = (a α ij) :=  1 c  α, for α = c, n, g, s, the component-based formula matrix for species set α. Notice that A c = I mc. Remark 3.1. The matrices A α, α = c, n, g, s are also called the canonical stoichiometric matrices as their rows are formed of the stoichiometric coefficients associated to the canonical chemical equilibrium reactions: X j i I c a α ij X i, j I α, for α = c, n, g, s. (10) The corresponding canonical equilibrium-constant vector k α = (kj α) j I α is defined by RT log k α := A T αµ 0 c µ 0 α, for α = c, n, s, g (11) and expresses the relation between the chemical potentials. Note that log k c = 0. Let b =  1ˆb c be the component-based feed vector, and n c R mc + and n n R mn + be the concentration vector of the components and non-components, respectively. Then, the element balance equations in (1) can be replaced by the component balance equations 7

8 n c + A n n n + A g n g + A s n s = b. (12) The Gibbs free energy in the chemical equilibrium problem (1) can also be reformulated by replacing the chemical potentials in terms of activities and equilibrium constants via (3)-(5) and (11), and by taking into account (12): G(n l, n g, n s ) = b T µ 0 c + RT ( n T c log a c ) + n T n (log a n + log k n ) + n T g (log a g + log k g ) + n T s log k s. Let c f > 0 be a characteristic quantity of the feed vector b (for instance c f = e T b). Let us define the adimensional feed vector b, the adimensional concentration vectors ñ α, and the adimensional Gibbs free energy G by b = 1 c f b, ñ α = 1 c f n α, α = c, n, g, s, G = ñ T c log a c + ñ T n(log a n + log k n ) + ñ T g (log a g + log k g ) + ñ T s log k s. The problem (1) can be written in the adimensional canonical stoichiometric form (dropping the tilde in the notation): min G(n l, n g, n s ) = n T c log a c + n T n(log a n + log k n ) (13a) +n T g (log a g + log k g ) + n T s log k s, (13b) s. t. ( ) nc n l = > 0, n n n g > 0, n s 0, (13c) n c + A n n n + A g n g + A s n s = b. (13d) The KKT system of (13) can be written in the primal-dual canonical stoichiometric form: 8

9 log a c + λ = 0, (14) log a n + A T nλ = log k n, (15) log a g + A T g λ = log k g, (16) log k s + A T s λ 0, n s 0, (17) n T s (log k s + A T s λ) = 0, (18) n c + A n n n + A g n g + A s n s = b. (19) Remark 3.2. The above KKT system furnishes the mass action laws (14)- (18) in addition to the mass balance constraints (19). The mass action laws are in a logarithmic form. An immediate consequence of the logarithmic form is that the mass action laws in the primal-dual form (14)-(18) are linear with respect to the dual variable λ. 3.2 Chemical Equilibrium Problem at Fixed Relative Humidity In atmospheric aerosol thermodynamic calculations, the ambient relative humidity (RH) is usually treated as a known constant. Then the water deserves a special treatment in the system and is isolated from the other components. This is described in the following remark. Remark 3.3. The ratio p w /p 0 w between the water partial pressure p w and the saturation vapor pressure p 0 w is defined to be equal to the RH expressed in the 0 to 1 scale. From the definition of log a H2 O(g) in (6) and log k H2 O(g) in (11), it follows that log a H2 O(g) + log k H2 O(g) = log p w log p 0 w = log RH, with RH = RH Let the gas/particle partitioning of the total amount of H 2 O be n H2 O(total) = n H2 O(g) + n H2 O(pm), (20) where the subscript pm denotes the particulate matter, i.e. the water under particulate form. 9

10 In light of Remark 3.3, the total feed vector b is split into the no-water part b and the water part as follows: b = b + n H2 O(total)a H2 O, (21) where a H2 O is the formula vector for H 2 O. Let Īg = I g \ {H 2 O(g)} be the index set of gas species excluding the water vapor, and n g = (n g,j ) j Īg and Āg = (a g,j ) j Ī g be the corresponding concentration vector and formula matrix, respectively. Then, by combining (20) and (21) with the relations A g n g = Āg n g + n H2 O(g)a H2 O and n T g (log a g + log k g ) = n T g (log ā g + log k g ) + n H2 O(g) log RH, the (adimensional) component balance equations in (13) can be written as n c + A n n n + Āg n g + A s n s = b + n H2 O(pm)a H2 O, and the (adimensional) Gibbs free energy in (13) can be written as G(n l, n g, n s, n H2 O(pm)) = n T c log a c + n T n(log a n + log k n ) + n T g (log ā g + log k g ) + n T s log k s n H2 O(pm) log RH + n H2 O(total) log RH, where the last term n H2 O(total) log RH is a fixed quantity for a known relative humidity, so can be ignored. The chemical equilibrium problem gives min G(n l, n g, n s, n H2 O(pm)) = n T c log a c + n T n(log a n + log k n ) (22a) + n T g (log ā g + log k g ) + n T s log k s n H2 O(pm) log RH, (22b) ( ) nc s. t. n l = > 0, n g > 0, n s 0, n H2 O(pm) > 0 (22c) n n n c + A n n n + Āg n g n H2 O(pm)a H2 O + A s n s = b. (22d) The KKT system for the chemical equilibrium problem (22) can be written in the primal-dual canonical stoichiometric form: 10

11 log a c + λ = 0, (23) log a n + A T nλ = log k n, (24) log ā g + ĀT g λ = log k g, (25) log RH + a T H 2 Oλ = 0, (26) log k s + A T s λ 0, n s 0, (27) n T s (log k s + A T s λ) = 0, (28) n c + A n n n + Āg n g n H2 O(pm)a H2 O + A s n s = b. (29) Remark 3.4. In atmospheric aerosol models such as Sequilib, Scape2, Equisolv II, and Isorropia, the amount of the water partitioned in the particle phase is assumed to be equal to the aqueous water content n l,w, i.e. n H2 O(pm) = n l,w, by neglecting the part of n H2 O(pm) that is dissociated into electrolytes via H 2 O(aq) 2H + + OH. The aqueous water content n l,w is usually predicted using an empirical relationship (Zdanovskii, Robinson and Stokes equation, Zsr (Ref. 23)). The shortcomings of this empirical method are the additional needs of the saturated molarities of electrolytes according to the relative humidity and, as a result, the thermodynamic inconsistency with the specific activity coefficient model that is used for the prediction of the activity of the aqueous phase. From now on, let us drop the bar in the variables relative to the gas phase and replace n g by n g, etc. In the KKT system of the primal-dual canonical stoichiometric form (23)- (29), the primal variables n H2 O(pm) and n s occur only in the mass balance constraints (29). They can be viewed respectively as the multipliers of the saturation constraints (26) and (27) on the dual variable λ, thus will be eliminated by applying the so-called null-space method for the solution of (23)-(29). This observation is the key for the development of the primal-dual active set algorithm detailed in the next section. 11

12 4 Active Set/Newton Method 4.1 Phase Stability Criterion and Active Set A primal-dual solution of the KKT system (14)-(19) or (23)-(29), generally non-unique, is called a KKT point. But this solution may not be a local minimizer of the Gibbs free energy. One needs to perform a phase stability analysis to determine whether a postulated KKT point is thermodynamically stable with respect to any perturbation in n l, n g and n s. Let (n l, n g, n s, λ ) correspond to a KKT point of (14)-(19) or (23)-(29) and Ī s := {i I s : n s,i > 0}, (30) and m s := Ī s. It is important to note that Ī s is a priori unknown. In order to perform the phase stability analysis, the following second order sufficient condition is assumed. It states that, p T Hp > 0, for all nonzero vector p such that Āp = 0. (31) In (31), H = 2 n l,n g,n s G(n l, n g, n s ) is the Hessian matrix of the Gibbs free energy of the system, Ā = [A l, A g, Ās] and Ās := (a s i ). The following i Ī s assumptions are also made: (H1) The formula matrix Ās mc ms R is assumed to be of full column rank with m s m c. This assumption is consistent with the chemical relation called Gibbs phase rule, see for instance (Ref. 24), giving an a priori estimate for the number of phases existing at the equilibrium. The full rank assumption implies the feasibility for the dual solution λ with respect to the saturation constraints log k s + ĀT s λ = 0, with k s = (k s,i ) i Ī s, extracted from the complementary slackness conditions (18) or (28). (H2) The formula vectors of solids actually precipitated in the system (a s i ) i Ī s are assumed to be linearly independent (linear independent constraints qualification, see also (Ref. 25) for instance). (H3) The strict complementary condition holds, i.e. log k s + A T s λ 0, n s 0, n T s (log k s +A T s λ) = 0 (equivalent to (27) (28)), but also n s +(log k s + A T s λ) > 0 (i.e. n s and (log k s + A T s λ) are not simultaneously zero). 12

13 Recall that the inertia of a symmetric matrix is an ordered set of three integers (i +, i, i o ), where i + is the number of positive eigenvalues, i the number of negative eigenvalues, and i 0 the number of zero eigenvalues. For a generic matrix A R m n, let Z A R n (n m) denote a null space matrix of A (i.e. a matrix such that AZ A = 0). The relationship (31) is also equivalent to requiring the so-called KKT matrix ( ) H Ā K = T, Ā 0 to have a certain inertia. This is the subject of the next theorem. Theorem 4.1. Under assumptions (H1) and (H2) and if (31) is satisfied, the KKT matrix K is invertible. Proof. Based on an inertia result of Gould (Ref. 26), we have inertia(k) = inertia(z T Ā HZĀ ) + (m c, m c, 0), (32) where Z Ā is a null-space matrix for Ā. Then (31) implies The matrix K is thus invertible. inertia(k) = (m l + m g + m s, m c, 0). (33) The difficulty in solving (14)-(19) or (23)-(29) is mainly caused by the combinatorial aspect of the KKT system, or more precisely by the complementary slackness conditions (18) or (28). The problem is not only to determine the concentrations but also to guess the optimal active set of solids (in the dual sense) Ī s := { j I s : log k s,j + a T s,jλ = 0 }. (34) Under assumption (H3) that the strictly complementary slackness condition holds, Ī s is equal to the primal set defined in (30), i.e., Ī s := {i I s : n s,i > 0}, the set of solid salts actually precipitated at equilibrium and the complementary solid set of Ī s Ĩ s := I s \ Ī s = { j I s : log k s,j + a T s,jλ > 0 } (35) identifies the set of salts that are subsaturated with the aqueous solution. Under assumption (H3) again, the set Ĩ s is equal to Ĩ s = {i I s : n s,i = 0}, the set of possible solid salts that are not precipitated at equilibrium. 13

14 Based on a guess of the optimal active set of solid phases (34), the KKT system (14)-(19) or (23)-(29) can be transformed into a system of nonlinear equations, which is much more computationally tractable. With the notations Ā s := (a s j ) j Ī s, Ã s := (a s j ) j Ĩ s, n s := (n s,j ) j Ī s, and ñ s := (n s,j ) j Ĩ s, the exact solution of the chemical equilibrium problem (22) can be computed from the following KKT system of equations: log a c + λ = 0, (36) log a n + A T n λ = log k n, (37) log a g + A T g λ = log k g, (38) log RH + a T H 2 Oλ = 0, (39) log k s + ĀT s λ = 0, n s > 0, (40) log k s + ÃT s λ > 0, ñ s = 0, (41) n c + A n n n + A g n g n H2 O(pm)a H2 O + Ās n s = b, (42) where the complementary slackness conditions (27) and (28) are split into the equalities (40) and the strict inequalities (41), according to the optimal active and inactive sets of solid phases, Ī s and Ĩ s, respectively. In order to simplify the notations, let Ā λ = ( a H2 O, Ās) T, m λ = m s + 1, and b λ = (log RH, log k T s )T. The dual saturation constraints (39) and (40) are combined to form the dual linear equality constraint Ā λ λ = b λ. (43) The feasibility of (43) requires Āλ to be of full row rank, implying that Ā s must be of full column rank and that a H2 O / range(ās). Notice that the latter is always true if the solid salts do not contain hydrated water. The dual variable λ that satisfies (43) can be expressed in terms of a reduced variable η via 14

15 λ = λ + Z Āλ η, where Z Āλ is a null-space matrix of Ā λ and λ is a particular solution of (43). The primal variables n H2 O(pm) and n s are viewed as the multiplier of the combined dual equality constraint (43). Replacing λ by the reduced variable η and projecting the KKT system (36)-(42) onto the null-space Z Āλ to eliminate (n H2 O(pm), n s ), gives the following reduced KKT system of the primal-dual canonical stoichiometric equations: log a c + Z Āλ η = λ, (44) log a n + A T n ZĀ λ η = log k n A T n λ, (45) log a g + A T g Z Āλ η = log k g A T g λ, (46) Z T Ā λ n c + Z T Ā λ A n n n + Z T Ā λ A g n g = Z T Ā λ b. (47) Once the solution (n c, n n, n g, η) of the reduced system (44)-(47) is known, one can compute the primal variables n H2 O(pm) > 0 and n s > 0, from the mass-balance equations (42) via ( ) nh2 O(pm) = ( ) Ā T 1 ) λ ( b nc A n n n n A g n g (48) s where ( ) Ā T 1 λ is the left pseudoinverse of Ā T λ. From now on, let us denote by n s the union of n s and n H2 O(pm). Note that the set of all possible active sets grows exponentially with m s, the number of all possible solid salts considered. 4.2 Primal-Dual Active Set Method In order to solve (14)-(19) or (23)-(29), a primal-dual algorithm is presented, based on the active-set strategy that makes a sequence of sets converging to the optimal active set of solid phases. This sequence of the so-called active sets, denoted by Īs, is defined in the dual sense as it was done for the optimal active set Ī s in (34) by Ī s := { j I s : log k s,j + a T s,jλ = 0 }, (49) where the dual variable λ, together with the primal variable (n l, n g, n s ) consists of a sequence of iterates that converges to the primal-dual solution 15

16 (n l, n g, n s, λ ) of the KKT system (23)-(29). By the definition (49), Ī s is the set of the linear inequalities (27) becoming active at λ. Starting as an approximation of Ī s, a priori unknown, the set Īs is expected to converge quickly to the optimal active set Ī s as soon as λ is in a neighborhood of λ. The complementary solid set of Īs, denoted by Ĩs, is the set of the linear inequalities (27) being inactive at λ, i.e., Ĩ s := I s \ Īs = { j I s : log k s,j + a T s,jλ > 0 }. (50) In our active-set algorithm, the sequence of the dual variable λ is required to satisfy the active constraints in Īs as equalities, i.e., log k s,j + a T s,j λ = 0, j Īs, (51) and stay feasible with respect to the inequality constraints that are inactive, i.e., log k s,j + a T s,jλ 0, j Ĩs. (52) The principle of the algorithm is the following. The dual feasibility condition of inequalities (52) enforces the dual variables to remain feasible with respect to the inequality constraints, until the saturation is reached at an iteration and the inequality constraint is set to be active and is added into the active set. The dual feasibility condition of equalities (51) enforces every active constraint to remain active, until the corresponding primal variable n s,j becomes negative at a certain iteration and the corresponding equality constraint is set to be inactive and becomes an inequality. Thus the corresponding salt is removed from the active set. The problem is to construct a sequence of active sets which converges to Ī s. The active set strategy is the following. Along the process of applying the active-set strategy, the KKT system (23)-(29) is first projected onto the current active set Īs to form a reduced KKT system of the form similar to that of (44)-(47) with the particular solution λ of (43) being the current dual variable λ. Then one Newton iteration is applied to the reduced system to find the next primal-dual approximation (n l, n g, λ) of the solution, where the new estimate of λ is updated from the current one by stepping along a nullspace direction defined by Z Āλ. The displacement along this direction is restricted to a certain length so that λ stays feasible with respect to (52). 16

17 Finally, the next active set Īs is obtained by adding constraints that are encountered by the new λ and the KKT system (23)-(29) is projected onto the new active set. Once the sequence of (n l, n g, λ) has converged to a solution of the reduced KKT system, the concentrations n s of the saturated salts in the active set are computed via equation (48). Since n s is viewed as the Lagrange multipliers of the dual active constraints (51), its non-negativeness is enforced by removing a saturated salt from the active set Īs when its concentration becomes negative. The above process continues until the equilibrium set of solid phases Ī s is obtained. Let us ignore (for the moment) the fact that n s must remain non-negative, and simply apply Newton s method to the reduced KKT system projected on Īs to compute a displacement in (n l, n g, λ) denoted by (p nl, p ng, p λ ) with p λ = Z Āλ p η for a certain η as λ must satisfy (51). The reduced KKT system is the following symmetric indefinite system: where and H l 0 A T zl 0 H g A T zg A zl A zg 0 p l p g p η = b l b g b η H l = (log a l ), H g = (log a g ) = diag (1/n g ), A zl = Z T Ā λ A l, A zg = Z T Ā λ A g, Note that A zl is of full rank. Let b l = log k l log a l A T l λ, b g = log k g log a g A T g λ, b η = Z T Ā λ ( b Al n l A g n g ). Ĩ g := { j I g : Z T Ā λ a g j Note that Āzg is of full rank and we have then, (53) = 0 }, (54) Ī g := I g \ Ĩg, m g := Īg, (55) Ā g := (a g j ) j Īg, Ā zg := Z T Ā λ Ā g. (56) A zg P = (Āzg, 0), (57) 17

18 where P is a permutation matrix. The displacement in λ is obtained from p η as a displacement in the nullspace, defined by p λ = Z Āλ p η. A new estimate of the solution of the KKT system (23)-(29) is then obtained by The parameter α is a steplength computed by n + l = n l + α p l, (58a) n + g = n g + α p g, (58b) λ + = λ + α p λ. (58c) α = min(ᾱ, α max ), where α max is a fixed upper bound on the steplength and ᾱ is the maximum feasible steplength that can be taken along the direction p λ. The parameter α max is usually taken to be 1; it can also be adjusted to ensure that a merit function is sufficiently reduced so that the primal-dual method converges globally, see e.g. (Ref. 27). The maximum feasible steplength ᾱ is computed by using a ratio test { } log ks,j + a T s,jλ ᾱ = min a T s,j p : a T s,j p λ < 0, j / Īs, (59) λ so that the new estimate λ + stays feasible with respect to the inequality constraints (52), i.e. log k s + ÃT s λ + 0. A step α is called restricted if α < α max, i.e, a constraint is encountered by λ + in the line-search. Otherwise, the step is referred to as unrestricted. As λ + is updated along a null-space direction, it satisfies naturally (51). The updates of the active sets are now described, starting with the addition of constraints into the active set. The initial active-set Ī0 s is required to only contain constraints that are active at λ 0. By the feasibility arguments (43), the associated matrix Ā0 s is of full rank, so is Ā0 λ. Let Pa denote the index set of constraints that are encountered by λ + in the line-search at a Newton iteration, i.e., P a = { j / Īs : a T s,jp λ < 0, log k s,j + a T s,jλ + = 0 }. Note that P a may be the empty set. The new active set is then defined by 18

19 where Īa s Pa is required to satisfy Ī + s = Īs Īa s, P a implies Ī a s. (60) The implication of (60) is that if new constraints are encountered in the line-search, at least one of them must be added, and also that no all the constraints encountered need to be active. In practice, exactly one new constraint is added at one time. Since λ + satisfies all the constraints in Ī+ s, by the feasibility arguments (43), the associated matrix Ā+ s must have full rank, so does Ā+ λ, see also (Ref. 28). On the other hand, the choice of a rule for removing constraints from the active-set is now detailed. As described before, the solution (n l, n g, λ) is computed by Newton s method and the constraints encountered by λ in the line-search are added to the active set Īs, until the sequence of Newton iterates (n l, n g, λ) converges to a solution of the reduced KKT system (44)- (47) which satisfies the inequality constraints. The concentrations n s are then computed and the set P d = { j Īs : n s,j < 0 } is identified. If P d, the new active set is defined by where Īd s P d is required to satisfy Ī + s = Īs \ Īd s, P d implies Ī d s. (61) The implication of (61) is that if solid salts have negative concentrations, at least one of them must be removed, and also that no all the solid salts having negative concentrations need to be inactive. In practice, only one constraint is removed at one time. The KKT system is then projected on the new active set and a new loop of Newton iterations is restarted until convergence is achieved. If P d =, a feasible solution of (23)-(29) is reached, and the algorithm stops. This active set/newton algorithm is summarized in Table 1. 19

20 Table 1. Active set/newton method: summary of the algorithm. Step 0. Initial n 0 l, n0 g, λ 0 and Īs are given; Step 1. Compute the reduced Newton direction (p l, p g, p λ ) by solving (53); Step 2. Compute the steplength ᾱ with (59); Step 3. Update n + l, n+ g and λ + with (58); Step 4. Test if the Newton method converged; (a) If no, consider the steplength ᾱ: - if ᾱ < 1 (restricted step), update Ī+ s = Īs {i} and go to (1); - if ᾱ 1 (unrestricted step), go to (1). (b) If yes, compute the primal variables n + s and n + H 2 0 with (48): - If n + s, n+ H 2 0 0, Stop; - If j {1,..., n s } such that n + s,j < 0, update Ī+ s = Īs\{j} and go to (1) 20

21 Remark 4.1. In comparison with other thermodynamic models, this algorithm neither assumes n H2 O(pm) = n l,w, nor uses the Zsr relationship in its prediction of the amount of the water partitioned in the particulate phase at a fixed relative humidity. 4.3 Computation of Newton Direction Consider the linear KKT system (53) and define the associated KKT matrix H l 0 A T zl K = 0 H g A T zg. A zl A zg 0 First note that the Hessian matrix of the gas phase H g positive definite with inverse Hg 1 = diag(n g ). Lemma 4.1. The condition (33) is equivalent to where m zc = m c m λ. = diag(1/n g ) is inertia( K) = (m l + m g, m zc, 0), (62) Proof. The proof is a direct consequence of (32) applied to the matrix K. Let Āzg and A T zl have the following QR factorizations: ( Rg Ā zg = (Q g Qg ) 0 ) = Q g R g, A T zl = (Q l Q l ) ( Rl 0 ) = Q l R l, where (Q g Qg ) and (Q l Ql ) are orthogonal with Q g R mzc mg, Q g R mzc (mzc mg), Q l R m l m zc, and Q l R m l (m l m zc) mg mg, and R g R and R l R mzc mzc are nonsingular. Theorem 4.2. The condition (31), together with the assumptions (H1) (H2), is equivalent to ZA T zl H l Z Azl > 0, (63) Z A T zl H l ZAzl + Rg T H g Rg 1 > 0. (64) where Z Azl = Q l R T l Q g and H g = diag(1/n g j ) j Īg. Conditions (63) and (64) are sufficient conditions for the system (53) to be solvable. 21

22 Proof. The following proof is a constructive proof that also emphasize the solution method of (53) and the computation of the Newton direction. The range-space method is applied to eliminate p g from (53), giving ( Hl A T zl A zl S g ) ( pl p η ) = ( bl c η ), (65) where c η = b η A zg Hg 1 b g and S g = A zg Hg 1 A T zg is the Schur complement. Once (65) is solved, p g can be easily obtained from p η via Let p g = H 1 g (b g A T zg p η). (66) ( Hl A K = T zl A zl S g ). Lemma 4.2. Relationship (62) is equivalent to inertia( K) = (m l, m zc, 0). (67) Proof. The inertia relation, see for instance (Ref. 26), leads to inertia( K) = inertia(h g ) + inertia( K), and the conclusion holds since inertia(h g ) = (m g, 0, 0). Let us turn to the solution of (65). It follows from (57) that S g = A zg H 1 g A T zg = A zg P P T H 1 P P T A T zg = g 1 Āzg H g Ā T zg. Then, the inertia of S g is given by { ( mg, 0, m inertia(s g ) = zc m g ), if m zc m g, (m zc, 0, 0), otherwise. (68) The condition (68) implies that S g is positive definite only if the number of saturated salts becomes larger than the number of components subtracted by the number of active gas species ( m s m c m g ); otherwise, S g is positive semi-definite. Note that H l is singular, due to the Gibbs-Duhem relation (7). 22

23 For the solution of the system (65), the nullity of S g has to be eliminated first. Consider the QR factorization of A zg. Then it is easy to see that the Schur complement S g has the block structure S g = (Q g Qg ) ( Rg H 1 g R T g ) ( Q T g Q T g Lemma 4.3. By defining p η1 = Q T g p η and p η2 = Q T g p η, the linear system (65) is equivalent to solving ). ( Hl + S l A T Q zl g Q T g A zl 0 ) ( pl p η2 ) = ( cl Q T g c η ), (69) where Proof. Let Hence c l = b l + A T zlq g R T g S l = A T zl Q gr T g V = H g Rg 1 Q T g c η, (70) H g Rg 1 g A zl. (71) ( Iml 0 0 Q ). ( V T Hl A KV T = zl Q ) H l A T zl Q g A T Q zl g Q T A zl Q T = Q S g Q T g A zl R H 1 g g RT g 0. Q T g A zl 0 0 (72) The system (65) is multiplied by V T from the left on both sides and (72) is used to write the resulting system as a 3 3 block system. With symmetrically block rows and columns permutations, the resulting linear system is: R g H 1 g R T g Q T g A zl 0 A T zl Q g H l A T zl Q g 0 QT g A zl 0 p η1 p l p η2 = Q T g c η b l Q T g c η. (73) Since R H 1 g g Rg T, the (1, 1) block, is nonsingular, the range-space method is applied to eliminate p η1 from (73), giving (69) - (71). 23

24 Note that, with the definitions in Lemma 4.3, p η is obtained from ( ) pη1 p η = (Q g Qg ) = Q p g p η1 + Q g p η2. (74) η2 Once (69) is solved, the direction p η1 can easily be obtained from p l via p η1 = R T g H g R 1 g Q T g (c η A zl p l ). (75) Then let us define the matrix K of the linear system (69) by K = ( Hl + S l A T zl Q g Q T g A zl 0 ). (76) A stability analysis for the solvability of the system (69) is performed in the next lemma. Lemma 4.4. The condition (67) is equivalent to the condition Z T l (H l + S l )Z l > 0, (77) where Z l R m l (m l m zc+ m g) is a null-space matrix of Q T g A zl. Under condition (62) or (77), the linear system (69) is solvable. Proof. Relationship (32) leads to Since R H 1 g g Rg T inertia( K) = inertia( R H 1 g g Rg T ) + inertia( K). > 0, relationship (67) implies that inertia( K) = (m l, m zc m g, 0). (78) The Schur complement S l in (76) is positive semidefinite. Since Q T g A zl in (76) is of full rank m zc m g by (32), we have R (mzc mg) m l inertia( K) = inertia(zl T (H l + S l )Z l ) + (m zc m g, m zc m g, 0), Relationship (78) implies that inertia(zl T (H l + S l )Z l ) = (m l m zc + m g, 0, 0) which is equivalent to (77) and the desired conclusion follows 24

25 A particular matrix Z l is now considered. To construct Z l, let us consider the QR factorization of A T zl. Then, we have A zl = R T l QT l, A 1 zl = Q l R T where A 1 zl is the right pseudoinverse of A zl, i.e. A zl A 1 zl = I mzc. The matrix Z l is defined as the following null-space matrix of Q T g A zl : l, where Note that Z Azl = Q l, Z l = (Z Azl, Z Azl ), ZAzl = A 1 zl Q g = Q l R T l Q g. ( Zl T Z l = Q T l Q T g R 1 l Q T l From the condition (77), we have ) ( ( Q l Q l R T Iml m l Q g ) = zc 0 0 Q T g R 1 l R T l Q g ). 0 < Z T A zl (H l + S l )Z Azl = Z T A zl H l Z Azl + Z T A zl S l Z Azl, 0 < Z T A zl (H l + S l ) Z Azl = Z T A zl H l ZAzl + Z T A zl S l ZAzl. Taking into account of the definition of S l in (71), we have Z T A zl S l Z Azl = Z T A zl A T zlq g R T g Z T A zl S l ZAzl = Q T g A T zl = R T g A T zl Q gr T g H g R 1 g > 0. H g Rg 1 Q T g A zl Z Azl = 0, H g Rg 1 QT g A zla 1 zl Q g Thus, (77) is equivalent to require that the conditions (63) and (64) hold and the conclusion of the theorem holds. Remark 4.2. Note that the condition (63), i.e., ZA T zl H l Z Azl > 0, implies that ( ) Hl A inertia T zl = (m A zl 0 l, m zc, 0). That is, if m g = 0, the condition (63) alone implies that K has the desire inertia (33). For m g > 0, the additional condition (64) is required. 25

26 From the solvability condition (77) that is derived from the above analysis on the phase stability, the system (69) is then solved by the null-space method. To ensure that the primal-dual algorithm converges to a minimum or stable equilibrium rather than any other first order optimality point such as a maximum or a saddle point, the condition (31) is enforced by controlling H l at each iteration, i.e. by replacing H l by a modification H l if Zl T (H l+s l )Z l is not sufficiently positive definite so that Zl T ( H l + S l )Z l is sufficiently positive definite. Various methods for modifying H l may be found in (Ref. 29) for instance. Note that Z T l ( H l + S l )Z l = Z T l ( 0 0 H l Z l + 0 Rg T H g Rg 1 The system (69) or, more precisely, the system ). ( Hl + S l A T Q zl g Q T g A zl 0 ) ( pl p η2 ) = ( cl Q T g c η ) (79) has the solution given by: where and p l p l = Z l (Zl T ( H ( l + S l )Z l ) 1 Zl T c l ( H ) l + S l )p l + p l, (80) p η2 = (A Tzl Q ) 1 g (c l ( H ) l + S l )p l, (81) ( A T zl Q g ) 1 is the left pseudoinverse of A T zl Qg, given by ( A T zl Q g ) 1 = QT g (A 1 zl )T, is a particular solution of the second equation of (79), given by p l = A 1 zl c η. In summary, for the solution of (53), the combination of Schur complements and null-space methods permits to solve successively (79) to obtain p l and p η2. Then p η1 is obtained from (75), giving p η with (74) and p λ = Z Āλ p η. Finally p g is obtained with (66) to increment all the variables (n l, n g, λ) for next iteration. 26

27 5 Numerical Results The primal-dual active set algorithm has been implemented into UHAERO, a general thermodynamic model that can predict efficiently and accurately the phase transition and multistage growth phenomena of inorganic aerosols under a wide range of atmospheric conditions. Physical parameters as temperature, relative humidity and pressure are input parameters, as well as the feed vector b and stoichiometry matrix A = [A l, A g, A s ]. The Gibbs free energy and the activity coefficients are computed with the Extended UNI- QUAC model, see for instance (Ref. 30). Two numerical examples in the multi-stage growth of atmospheric aerosols are considered here to illustrate the efficiency of the algorithm. 5.1 Sulfate Aerosols A sulfate aerosol (NH 4 ) 2 SO 4 -H 2 SO 4 -H 2 O is assumed to be diluted in the air. Three solid phases (A:(NH 4 ) 2 SO 4, B:(NH 4 ) 3 H(SO 4 ) 2 and C:NH 4 HSO 4 ) may possibly appear at equilibrium. The chemical reactions which may appear between the chemical components of the system are given in Table 2. The vapor-liquid equilibrium reaction expresses the changes between water (l/aq = liquid/aqueous phase) and water vapor (g=gas phase), in which the relative humidity is a given constant; the speciation equilibria describe the interactions in the aqueous phase, while the solid-liquid equilibria are the reactions giving birth to a solid phase. In Figure 1, the reconstructed sulfate aerosol phase diagram is illustrated. For each weight ratio of the feed vector b, the method allows to predict the existence or non-existence of each solid phase and the weight amount of water in the aerosol particle due to the instantaneous vapor-liquid equilibrium. The level lines of the relative humidity show easily that, for high relative humidity, no solid salts appear at equilibrium. In Figure 2, the evolution of the aerosol particle with the feed vector b corresponding to three solids A, B and C in Figure 1, respectively, are illustrated in function of the relative humidity RH. For each feed ratios of the aerosol particle, the figure shows that the phase changes are very accurately tracked by discontinuities in the trajectory. Figure 3 shows the typical Newton iteration in UHAERO. 27

28 Table 2. Chemical equilibrium reactions in the sulfate aerosol. The first class denotes the vapor-liquid equilibrium; the second class describes the reactions in the aqueous phase, without phase changes; finally the third class is the reactions with phase changes which may lead to the creation of a solid. Vapor-Liquid Equilibrium: H 2 O(l) H 2 O(g) NH 3 (l) NH 3 (g) H 2 SO 4 (l) H 2 SO 4 (g) Speciation Equilibria: H 2 SO 4 (l) H + (aq) + HSO 4 (aq) HSO 4 (aq) H + (aq) + SO 2 4 (aq) NH 3 (l) + H 2 O(l) NH + 4 (aq) + OH (aq) H 2 O(l) H + (aq) + OH (aq) Solid-Liquid Equilibria: (NH 4 ) 2 SO 4 (s) 2NH + 4 (aq) + SO 2 4 (aq) (NH 4 ) 3 H(SO 4 ) 2 (s) 3NH + 4 (aq) + HSO 4 (aq) + SO 2 4 (aq) NH 4 HSO 4 (s) NH + 4 (aq) + HSO 4 (aq) 5.2 Urban and Remote Continental Aerosols The second example is two types of aerosols: urban and remote continental (Ref. 11), consisting of sulfate, nitrate and ammonium diluted in the air (with the specific ratio of H 2 O-H 2 SO 4 -HNO 3 -NH 3 ). Four solid phases (A, B, C and D) may possibly appear at equilibrium. They consist of the solids (NH 4 ) 2 SO 4, (NH 4 ) 3 H(SO 4 ) 2, NH 4 HSO 4 and NH 4 NO 3. Again, the chemical equilibrium reactions which are possible between the chemical components of the system are given in Table 3. In Figures 4 and 5, the mass ratios of inorganic components and water in typical urban and remote continental aerosols are given as a function of the 28

29 Figure 1. Modeling of a sulfate aerosol. Reconstruction of the phase diagram at 25 o C with tracking of the presence of each solid phases. For each region of space the existing phases at equilibrium are represented. A 1 (NH 4 ) 2 SO 4 H 2 SO 4 H 2 O B A (NH 4 ) 2 SO 4 B (NH 4 ) 3 H(SO 4 ) 2 C NH 4 HSO A + B + L wt% (NH 4 ) 2 SO A + L RH = B + L C B + C + L C + L L RH = wt% H SO 2 4 relative humidity. Figures 4 and 5 illustrate clearly that the phase transitions are accurately tracked without any a priori knowledge of the existing phases. In Figures 6 and 7, the evolution of solid contents is investigated. For high relative humidity, no salts appear at the equilibrium. When the humidity decreases, the mass of salts increases until reaching a constant value for low relative humidity. The distribution of solid salts at low relative humidity is totally dependant on the inorganic feed composition. 29

30 Figure 2. Modeling of a sulfate aerosol. Evolution of the particle mass in function of the relative humidity RH. The creation/disappearance of a solid phase appears through a discontinuity in the derivatives of the trajectories. n (f is the feed mole ratio: (NH4 ) 2 SO 4 and W 0 is the amount of inorganic feed). n (NH4 ) 2 SO 4 +n H2 SO (NH 4 ) 2 SO 4 H 2 SO 4 H 2 O system Particle Mass Change, W/W o, (W o = 20 µg/m 3 ) A = (NH ) SO B = (NH ) H(SO ) C = NH 4 HSO 4 L = Aqueous Phase B + L L L A + L (3) (2)(1) (1) f = 1.00 (2) f = 0.75 (3) f = 0.50 A + B + L L A + L C B + C + L B A RH 6 Conclusions The modeling of atmospheric inorganic aerosols has been proposed in the framework of the canonical stoichiometry. The thermodynamic equilibrium corresponds to the minimum of the Gibbs free energy for a system involving a gas phase, an aqueous phase and solid salts. A numerical method for the solution of this optimization problem has been investigated. It is based on an active set/newton method to take advantage of the constant chemical potentials for the solid phases. Numerical results have been presented to show the ability of our algorithm in the prediction of phase equilibria and its good numerical properties, especially in terms of convergence rate. 30

31 Figure 3. Modeling of a sulfate aerosol. Newton iteration at fixed RH = 0.85 in case of the inorganic feed B:(NH 4 ) 3 H(SO 4 ) Residue Newton Iteration Number References 1. Bassett, M. and Seinfeld, J. H., Atmospheric Equilibrium Model of Sulfate and Nitrate Aerosol, Atmospheric Environment, Vol. 17, No. 11, pp , Saxena, P., Hudischewskyj, A. B., Seigneur, C., and Seinfeld, J. H., A Comparative Study of Equilibrium Approaches to the Chemical Characterization of Secondary Aerosols, Atmospheric Environment, Vol. 20, pp , Pilinis, C. and Seinfeld, J. H., Continued Development of a General Equilibrium Model for Inorganic Multicomponent Atmospheric Aerosols, Atmospheric Environment, Vol. 21, pp ,

32 Table 3. Chemical equilibrium reactions in sulfate/nitrate system. Vapor-Liquid Equilibrium: H 2 O(l) H 2 O(g) NH 3 (l) NH 3 (g) H 2 SO 4 (l) H 2 SO 4 (g) HNO 3 (l) HNO 3 (g) Speciation Equilibria: HNO 3 (l) H + (aq) + NO 3 (aq) H 2 SO 4 (l) H + (aq) + HSO 4 (aq) HSO 4 (aq) H+ (aq) + SO 2 4 (aq) NH 3 (l) + H 2 O(l) NH + 4 (aq) + OH (aq) H 2 O(l) H + (aq) + OH (aq) Solid-Liquid Equilibria: (NH 4 ) 2 SO 4 (s) 2NH + 4 (aq) + SO2 4 (aq) (NH 4 ) 3 H(SO 4 ) 2 (s) 3NH + 4 (aq) + HSO 4 (aq) + SO 2 4 (aq) NH 4 HSO 4 (s) NH + 4 (aq) + HSO 4 (aq) NH 4 NO 3 (s) NH + 4 (aq) + NO 3 (aq) 4. Kim, Y. P., Seinfeld, J. H., and Saxena, P., Atmopsheric Gas- Aerosol Equilibrium I. Thermodynamic Model, Aerosol Science and Technology, Vol. 19, pp , Kim, Y. P., Seinfeld, J. H., and Saxena, P., Atmospheric Gas- Aerosol Equilibrium II: Analysis of Common Approximations and Activity Coefficient Calculation Methods, Aerosol Science and Technology, Vol. 19, pp , Kim, Y. P. and Seinfeld, J. H., Atmospheric Gas-Aerosol Equilibrium III: Thermodynamics of Crustal Elements Ca 2+, K +, and Mg 2+, 32

33 Figure 4. Deliquescence curve for sulfate/nitrate aerosol. Total SO 2 4 = 9.143µg/m 3, total NO 3 = 1.953µg/m 3 and total NH + 4 = 3.400µg/m 3. 7 Typical Urban Aerosol at 25 o C A = (NH 4 ) 2 SO 4 6 B = (NH 4 ) 3 H(SO 4 ) 2 Particle Mass Change, W/W D = NH 4 NO 3 L = Aqueous Phase L 2 A + L A + B + D A + B + L RH Aerosol Science and Technology, Vol. 22, pp , Meng, Z. Y., Seinfeld, J. H., Saxena, P., and Kim, Y. P., Atmospheric Gas-Aerosol Equilibrium IV: Thermodynamics of Carbonates, Aerosol Science and Technology, Vol. 23, pp , Jacobson, M. Z., Development and Application of a New Air Pollution Modeling System II: Aerosol Module Structure and Design, Atmospheric Environment, Vol. 31, No. 2, pp , Jacobson, M. Z., Development and Application of a New Air Pollution Modeling System III: Aerosol-Phase Simulations, Atmospheric Environment, Vol. 31, No. 4, pp ,

34 Figure 5. Deliquescence curve for sulfate/nitrate aerosol. Total SO 2 4 = µg/m 3, total NO 3 = 0.145µg/m 3 and total NH + 4 = 4.250µg/m 3. 7 Typical Remote Continental Aerosol at 25 o C 6 A = (NH 4 ) 2 SO 4 L = Aqueous Phase Particle Mass Change, W/W L 2 A + L RH 10. Jacobson, M. Z., Tabazadeh, A., and Turco, R. P., Simulating Equilibrium within Aerosols and Nonequilibrium between Gases and Aerosols, Journal of Geophysical Research, Vol. 101, No. D4, pp , Nenes, A., Pandis, S. N., and Pilinis, C., Isorropia: A New Thermodynamic Equilibrium Model for Multiphase Multicomponent Inorganic Aerosols, Aquatic Geochemistry, Vol. 4, pp , Nenes, A., Pandis, S. N., and Pilinis, C., Continued Development and Testing of a New Thermodynamic Aerosol Module for Urban and Regional Air Quality Models, Atmospheric Environment, Vol. 33, No. 10, pp ,