Abstract. The development of chemical transport models with advanced physics
and chemical schemes could improve air-quality forecasts. In this study, the
China Meteorological Administration Unified Atmospheric Chemistry
Environment (CUACE) model, a comprehensive chemistry module incorporating
gaseous chemistry and a size-segregated multicomponent aerosol algorithm,
was coupled to the Weather Research and Forecasting (WRF) framework with chemistry (WRF-Chem)
using an interface procedure to build the WRF/CUACE v1.0 model. The latest
version of CUACE includes an updated aerosol dry deposition scheme and the
introduction of heterogeneous chemical reactions on aerosol surfaces. We
evaluated the WRF/CUACE v1.0 model by simulating PM2.5, O3,
NO2, and SO2 concentrations for January, April, July, and October
(representing winter, spring, summer and autumn, respectively) in 2013,
2015, and 2017 and comparing them with ground-based observations. Secondary
inorganic aerosol simulations for the North China Plain (NCP), Yangtze River
Delta (YRD), and Sichuan Basin (SCB) were also evaluated. The model captured well
the variations of PM2.5, O3, and NO2 concentrations
in all seasons in eastern China. However, it is difficult to accurately
reproduce the variations of air pollutants over SCB, due to
its deep basin terrain. The simulations of SO2 were generally
reasonable in the NCP and YRD with the bias at −15.5 % and 24.55 %,
respectively, while they were poor in the Pearl River Delta (PRD) and SCB. The sulfate and nitrate
simulations were substantially improved by introducing heterogeneous chemical
reactions into the CUACE model (e.g., change in bias from −95.0 % to
4.1 % for sulfate and from 124.1 % to 96.0 % for nitrate in the NCP).
Additionally, The WRF/CUACE v1.0 model was revealed with better performance
in simulating chemical species relative to the coupled Fifth-Generation Penn
State/NCAR Mesoscale Model (MM5) and CUACE model. The development of the
WRF/CUACE v1.0 model represents an important step towards improving
air-quality modeling and forecasts in China.