Coupling and evaluating gas/particle mass transfer treatments for aerosol simulation and forecast

Abstract

[1] Simulating gas/particle mass transfer in three-dimensional (3-D) air quality models (AQMs) represents one of the major challenges for both hindcasting and forecasting. The lack of an efficient yet accurate gas/particle mass transfer treatment for aerosol simulation and forecast in 3-D AQMs warrants its development, improvement, and evaluation. In this paper, several condensation/evaporation schemes (e.g., the Bott, Trajectory-Grid (T-G), Walcek, and analytical predictor of condensation (APC)) are first tested in a condensation-only case. The APC and Walcek schemes are shown to be more accurate than the Bott and T-G schemes. The Walcek and the APC schemes are then incorporated into the Model of Aerosol Dynamics, Reaction, Ionization, and Dissolution (MADRID) to solve the gas/particle mass transfer process explicitly. The test simulations with MADRID are initialized with measurements available from three sites with representative meteorological and emission characteristics. The results are evaluated using benchmark based on the kinetic approach with 500-section for all cases and available measurements from two sites. The box MADRID tests have shown that the bulk equilibrium approach fails to predict the distribution of semivolatile species (e.g., ammonium, chloride, and nitrate) because of the equilibrium and internal mixture assumptions. The hybrid approach exhibits the same problem for some cases as the bulk equilibrium approach since it assumes bulk equilibrium for fine particles. The kinetic approaches (including the APC and Walcek schemes for the condensation/evaporation equations) predict the most accurate solutions. Among all approaches tested, the bulk equilibrium approach is the most computationally efficient, and the kinetic/Walcek scheme provides an accurate solution but is the slowest due to its requirement for a small time step. Overall, the kinetic/APC and hybrid/APC schemes are attractive for 3-D applications in terms of both accuracy and computational efficiency.Citation: Hu, X.-M., Y. Zhang, M. Z. Jacobson, and C. K. Chan (2008), Coupling and evaluating gas/particle mass transfer treatments for aerosol simulation and forecast,

Size-resolved simulations of the aerosol inorganic composition with the new hybrid dissolution solver HyDiS-1.0: description, evaluation and first global modelling results

“…The remaining fraction of the size distribution is treated dynamically because the larger particles are not in equilibrium with the gas phase, as shown by theoretical studies (Wexler and Seinfeld, 1990;Meng and Seinfeld, 1996) and model investigations that demonstrate a much better agreement with observations (e.g. Hu et al, 2008).…”

The role of long-range transport and domestic emissions in determining atmospheric secondary inorganic particle concentrations across the UK

“…The EQSAM aerosol scheme was used here as it has demonstrated good performance in the TM5 atmospheric chemistry transport model (Karl et al, 2009;Huijnen et al, 2010). However, the bulk approach may lead to uncertainties in the simulated SIA, as shown in Hu et al (2008), as the particle sizes are not explicitly resolved in the model. The current aerosols scheme and size partitioning in the EMEP model has been validated and compared with observations across Europe as shown in Fagerli and Aas (2008) and in Simpson et al (2006).…”

Modeling intercontinental air pollution transport over the trans‐Pacific region in 2001 using the Community Multiscale Air Quality modeling system

“… Tan et al [2004] also estimated that TRACE‐P emission inventory for CO and particulate carbonaceous matter (BC + OC) are underestimated by 50 and 60–90%, respectively. The underpredictions of PM 10 over Beijing and SPM over Japan are also associated with the poor capability of CMAQ to simulate coarse PM [ Hu et al , 2008; Nolte et al , 2008], which is a very significant contributor to PM 10 /SPM over east Asia [ Kim et al , 2003; Cheng et al , 2005]. The concentrations of PM 2.5 over China account for more than 90% of PM 10 in CMAQ simulations (with 24% for SO 4 2− , 12% for NH 4 + , 14% for OC, ∼11% for NO 3 − , and 4% for BC), which is much higher than observed annual mean values of 65% and seasonal mean values of 55–73% in Beijing reported by Hao [2006].…”