U.S. Environmental Protection Agency Office of Research and Development Towards building better linkages between aqueous phase chemistry and microphysics in CMAQ Kathleen Fahey, David Wong, Bill Hutzell, Shaocai Yu, and Jon Pleim Atmospheric Modeling and Analysis Division, National Exposure Research Laboratory, Office of Research and Development, U. S. Environmental Protection Agency Kathleen Fahey l l Introduction  Currently, CMAQ’s aqueous phase chemistry routine (AQCHEM-base) assumes Henry’s Law equilibrium and employs a forward Euler method to solve a small set of oxidation equations, considering the additional processes of Aitken scavenging and wet deposition in series and employing a bisection method to calculate H + concentrations. Limitations of this approach include o the mechanism is hardwired into the solver code so the module is difficult to expand with additional chemistry o it ignores the impacts of mass transfer limitations  Here, the Kinetic PreProcessor has been applied to generate a Rosenbrock solver for the CMAQ v5.0.1 aqueous phase chemistry mechanism.  The model simultaneously solves kinetic mass transfer between the phases, dissociation/association, chemical kinetics, Aitken scavenging, and wet deposition.  This allows for easier expansion of the chemical mechanism and a better link between aqueous phase chemistry and droplet microphysics. References: [1] Baek, J. et al., Developing Forward and Adjoint Aqueous Chemistry Module for CMAQ with Kinetic PreProcessor. 10th Annual CMAS Conference, Chapel Hill, NC. October 24-26, [2] CAPRAM 2.4 supplementary tables. [3] Damian, V. et al. (2002) The Kinetic PreProcessor KPP -- A Software Environment for Solving Chemical Kinetics. Computers and Chemical Engineering, v26(11), [4] Henderson et al. (2013) A database and tool for boundary conditions for regional air quality modeling: description and evaluation. Geosci. Model Dev. Discuss, 6, , [5] Hermann, H. et al. (2000) CAPRAM2.3: A Chemical Aqueous Phase Radical Mechanism for Tropospheric Chemistry. J.Atm Chem., 36: [6] Jacob, D. J. (1986) Chemistry of OH in remote clouds and its role in the production of formic acid and peroxymonosulfate. JGR, v91(d9), [7] Lim, H. et al. (2005) Isoprene Forms Secondary Organic Aerosol through Cloud Processing: Model Simulations. EST, 39,SI. [8] Sandu, A., Verwer, J.G., Blom, J.G., Spee, E.J., Carmichael, G.R., and F.A. Potra (1997) Benchmarking Stiff ODE Solvers for Atmospheric Chemistry Problems II: Rosenbrock Solvers. Atmospheric Environment, 31 (20), [9] Schwartz, S.E. (1986) Mass-transport considerations pertinent to aqueous-phase reactions of gases in liquid water clouds. In Chemistry of Multiphase Atmospheric Systens, NATO ASI Series, vol. G6, [10] Warneck, P. and J. Williams. (2012) The Atmospheric Chemist’s Companion: Numerical Data for Use in the Atmospheric Sciences. Springer. [11] Yu, S. et al. (2013) Aerosol indirect effect on the grid-scale clouds in the two-way coupled WRF-CMAQ: model description, development, evaluation and regional analysis”. Atmos. Chem. Phys. Discuss., 13, http://projects.tropos.de/capram/capram_24.html Model Description Gas* aa D g (m 2 /s) x 10 5b SO x HNO x CO x NH x H2O2H2O x O3O x HCOOH2.29 x MHP6.76 x PAA1.90 x HCL1.16 x GLY2.30 x 10 -2c 1.15 a MGLY2.30 x 10 -2c 1.15 d Table 2. Aqueous phase reactions. Rate coefficients are equal to those in AQCHEM- base. Often aqueous diffusion limitations can be neglected because the characteristic time for aqueous diffusion is much shorter than the timescales for other processes. An aqueous diffusion correction factor may be applied to reaction rates for those species most likely to be impacted by concentration gradients within the droplet (i.e., reaction rate >> mass transfer rate) (Jacob, 1986). *H 2 SO 4 is instantaneously transferred to aerosol SO 4 and N 2 O 5 to HNO 3 at the start of cloud processing. The gas phase hydroxyl radical concentration is held constant. a CAPRAM 2.4 tables. b Hermann et al. (2000). c Lim et al. (2005). d Assumed equal to D g for glyoxal HSO H 2 O 2 → SO H + SO 2 + O 3 → SO H + HSO O 3 → SO H + SO O 3 → SO 4 2- HSO MHP → SO H + HSO PAA → SO H + SO 2 (+ Mn(II)) → SO H + HSO 3 - (+ Mn(II)) → SO H + SO 3 2- (+ Mn(II)) → SO 4 2- SO 2 (+ Fe(III)) → SO H + HSO 3 - (+ Fe(III)) → SO H + SO 3 2- (+ Fe(III)) → SO 4 2- SO 2 + (Mn(II) + Fe(III)) → SO H + HSO 3 - (+ Mn(II) + Fe(III)) → SO H + SO 3 2- (+ Mn(II) + Fe(III)) → SO 4 2- GLY + OH* → 0.04 ORGC MGLY + OH* → 0.04 ORGC Table 1. Gas phase species that participate in phase transfer and associated gas phase diffusion and accommodation coefficients. Henry’s Law coefficients are equal to those in AQCHEM-base. Table 3. Dissociation reactions and equilibrium constants. Ionic species are considered explicitly and dissociation equilibrium reactions are described as a set of forward and backward reactions. Activity coefficients are rolled into the forward and backward reaction rates. ReactionK eq,298  H/R kbkb SO 2 ↔ HSO H x10 -2 a -1.87x x10 8 HSO 3 - ↔ SO H x10 -8 a -3.55x x10 10 HNO 3 ↔ NO H x10 1 a NA5.0x10 10 NH 4 OH ↔ NH OH x10 -5 a 7.10x x10 10 H 2 CO 3 ↔ HCO H x10 -7 a 9.95x x10 4 HCO 3 - ↔ CO H x a 1.79x x10 10 HCOOH ↔ HCOO - + H x10 -4 a 2.00x x10 10 HCl ↔ Cl - + H x10 6 b -6.90x x10 10 H 2 O ↔ OH - + H x a,c 6.95x x10 11 HSO 4 - ↔ SO H x10 -2 a -2.45x x10 11 a Warneck and Williams (2012). b Hermann et al. (2000). c This value of the equilibrium constant corresponds to K w = [H + ][OH − ]. k f = Keq i,T x k b Simulations Box Model Tests Box model comparisons for scenarios (varying liquid water content, cloud lifetime, precipitation rate, and initial concentrations of SO 2, H 2 O 2, O 3, NH 3, and HNO 3 ) were generated for stand-alone versions of the aqueous modules to estimate the impact of physical parameters and compare results for alternative solvers and tolerances (Figure 1). Figure 1. SO 4 predicted with the updated module versus AQCHEM-base for a range of scenarios. Such box model tests indicate that under certain conditions mass transfer and equilibrium assumptions may significantly impact the predicted concentrations of many species. CMAQ Simulations The model was implemented in CMAQ v5.0.1, and simulations were run over the continental U.S. at 12-kilometer resolution and 35 vertical layers for a two week period in January CMAQ was run in offline-mode, using the CB05TUCL gas phase chemical mechanism, and driven by WRFv.3.3 meteorological fields. Boundary conditions were derived from GEOS-Chem simulations (Henderson et al., 2013). While for most of the domain the impacts are below 0.1  g/m 3, there are some larger impacts on SO 4 concentrations in certain areas even for this short CMAQ simulation. Hourly differences for SO 4 range between -13 and 25  g/m 3. Depending on the assumed droplet diameter (10  m vs 30  m), weekly average SO 4 concentration differences range between -0.4 and 1.5  g/m 3. It is expected that larger wide-spread impacts would be seen for longer simulations and cloudier periods. The observed trends for NO 3, NH 4, and SO 2 are consistent with the above SO 4 results. Figure 2. Weekly average I+J mode SO 4 concentrations for AQCHEM-base and SO 4 difference between the base and updated model. The results are averaged for only the second week of the simulation in order to reduce the impacts of the clean air initial conditions. Droplet diameter =10  m Ongoing Work Aqueous chemistry box model tests indicate parameters like droplet size may have a noticeable effect on predicted concentrations (Figure 3). In addition to running longer term simulations to better assess seasonal and spatial impacts, we are currently implementing this module in WRF-CMAQ so we can better connect aqueous phase chemistry with WRF-modeled cloud microphysical parameters. This will build upon recent work by Yu et al. (2013) who estimates effective cloud droplet radius and activated fraction in the two-way coupled WRF-CMAQ model on the basis of the CMAQ-predicted aerosol size/composition distribution. We are now building the link to send that information back to the aqueous chemistry routine and examine the impacts on major species. As we continue to refine parameters and optimize for efficiency, this work will facilitate future mechanism expansion and better linkages with cloud microphysical parameters. Figure 3. Aqueous phase chemistry box model comparisons for SO 4, SO 2, total ammonium and total nitrate for different droplet sizes. These tests indicate that especially in the initial few minutes of cloud processing the droplet size can have observable impacts on major species concentrations for certain scenarios. When liquid water content surpasses a critical threshold of 0.01 g/m 3, cloud droplets form instantaneously on accumulation and coarse mode particles. The Aitken mode remains interstitial aerosol that gets scavenged by droplets over the cloud lifetime. For the duration of cloud processing we solve the following system of differential equations for gas (C g,i ) and aqueous phase (C aq,i ) species (i): with the individual terms representing (1) the mass transfer between the gas and aqueous phases, (2) chemical kinetics, (3) Aitken scavenging, (4) wet deposition, and (5) ionization. k mt is the mass transfer coefficient as described by Schwartz (1986) and is a function of the characteristic time for gas phase diffusion and interfacial transport across the surface of the drop. It is given by: where  = accommodation coefficient D g is gas phase diffusivity, is mean molecular speed a is the droplet radius (default = 5  m) Tables 1-3 provide details and list relevant parameters for the modeled processes. Aitken scavenging and wet deposition rate coefficients, as well as redistribution assumptions following cloud processing, remain consistent with AQCHEM-base. Following Baek et al. (2011), we use the Kinetic PreProcessor (KPP) (Damian et al., 2002) to automatically generate the Fortran90 code for the CMAQ version aqueous phase chemical mechanism. The Rosenbrock solver, RODAS3, was chosen for implementation due to its computational efficiency at lower accuracies for atmospheric aqueous systems (Sandu et al., 1997)