Performance Evaluation of PM2.5 Forecasting Using Multiscale WRF–CMAQ over East Asia

High PM2.5 concentrations can be detrimental to human and ecosystem health, as long-term exposure is associated with the aggravation of asthma and, in some cases, increased mortality. Chemical transport models (CTMs) have long been used to complement observation-based understanding of atmospheric conditions. These models are invaluable tools for scientists, as they provide spatially and temporally comprehensive representations of atmospheric states. The Community Multiscale Air Quality (CMAQ) model was developed by the U.S. Environmental Protection Agency (US EPA) and has been extensively used to investigate complex air quality issues from regional to hemispheric scales.

In this study, the nested-down approach of chemical transport modeling is employed to analyze the target region using finer grid resolutions. However, the nested-down approach may introduce unrealistic inflow and outflow of chemical species across the boundary regions. East Asia includes China, South Korea, and Japan, and China is one of the largest emitters globally. South Korea, which is the primary focus of this study, is located downwind of China; therefore, it is essential to represent boundary influences as realistically as possible.

In this work, East Asia was configured as a single domain with a horizontal resolution of 9 km, and an additional nested-down configuration with 27 km to 9 km horizontal resolution was constructed. Furthermore, two vertical configurations were applied: 22 vertical layers with a model top at 200 hPa and 29 vertical layers with a model top at 50 hPa. CMAQ simulations were conducted using an offline approach, in which meteorological and air quality models were run separately. The Weather Research and Forecasting (WRF, version 4.4.2) model and CMAQ (version 5.3.1) were used, with meteorological initial and boundary conditions provided by the Korea Integrated Model (KIM).

The representation of modeled meteorological variables—including near-surface temperature, humidity, wind speed, and wind direction—generally improved with increasing horizontal resolution across all cases examined in this study. Most variables showed the largest improvements when the grid spacing was reduced from 27 km to 9 km. PM2.5 statistical performance was best at the 9 km resolution, while little difference in model performance was observed with respect to vertical resolution.

Acknowledgment

"This research was supported by Particulate Matter Management Speciallized Graduate Program throu the Korea Environmental Industry & Technology Institute(KEITI) funded by the Ministry of Environment(MOE)"