Abstract
Abstract. Accurately simulating secondary organic aerosols (SOA) in three-dimensional (3-D) air quality models is challenging due to the complexity of the physics and chemistry involved and the high computational demand required. A computationally-efficient yet accurate SOA module is necessary in 3-D applications for long-term simulations and real-time air quality forecasting. A coupled gas and aerosol box model (i.e., 0-D CMAQ-MADRID 2) is used to optimize relevant processes in order to develop such a SOA module. Solving the partitioning equations for condensable volatile organic compounds (VOCs) and calculating their activity coefficients in the multicomponent mixtures are identified to be the most computationally-expensive processes. The two processes can be speeded up by relaxing the error tolerance levels and reducing the maximum number of iterations of the numerical solver for the partitioning equations for organic species; conditionally activating organic-inorganic interactions; and parameterizing the calculation of activity coefficients for organic mixtures in the hydrophilic module. The optimal speed-up method can reduce the total CPU cost by up to a factor of 31.4 from benchmark under the rural conditions with 2 ppb isoprene and by factors of 10–71 under various test conditions with 2–10 ppb isoprene and >40% relative humidity while maintaining ±15% deviation. These speed-up methods are applicable to other SOA modules that are based on partitioning theories.
Highlights
Representing secondary organic aerosols (SOA) in three dimensional (3-D) atmospheric models is very important because they constitute a sizeable fraction of fine particulate mater (PM2.5), which has impacts on human health, visibility degradation, and climate change (Watson, 2002; Davidson et al, 2005; IPCC, 2007; Zhang et al, 2007)
SOA can be formed through the oxidation of the volatile organic compounds (VOCs) in the atmosphere and subsequent partitioning of their condensable products between gas- and particulate-phase
A combination of several speed-up parameters related to the numerical solver gives 47–55% CPU reductions with percentage deviations of −5.5 to 1.7%
Summary
Representing secondary organic aerosols (SOA) in three dimensional (3-D) atmospheric models is very important because they constitute a sizeable fraction of fine particulate. The two modules differ in two aspects They use different methods for partitioning coefficients for hydrophobic organic compounds (OC), despite the same partitioning equation. In MADRID 2, the partitioning coefficients of the hydrophobic OCs are calculated as a function of temperature and composition based on the Raoult’s law following the equation derived by Pankow (1994a, 1994b). Those of the hydrophilic OCs are calculated as a function of temperature, liquid water content, and composition based on the Henry’s law. The main objective of this study is to improve the computational efficiency of the SOA module for 3-D air quality long-term simulations and real-time forecasting
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