One-year simulation of ozone and particulate matter in China using WRF/CMAQ modeling system
China has been experiencing severe air pollution in recent decades. Although an ambient air quality monitoring network for criteria pollutants has been constructed in over 100 cities since 2013 in China, the temporal and spatial characteristics of some important pollutants, such as particulate matter (PM) components, remain unknown, limiting further studies investigating potential air pollution control strategies to improve air quality and associating human health outcomes with air pollution exposure. In this study, a yearlong (2013) air quality simulation using the Weather Research and Forecasting (WRF) model and the Community Multi-scale Air Quality (CMAQ) model was conducted to provide detailed temporal and spatial information of ozone (O 3), total PM 2.5, and chemical components. Multi-resolution Emission Inventory for China (MEIC) was used for anthropogenic emissions and observation data obtained from the national air quality monitoring network were collected to validate model performance. The model successfully reproduces the O 3 and PM 2.5 concentrations at most cities for most months, with model performance statistics meeting the performance criteria. However, overprediction of O 3 generally occurs at low concentration range while underprediction of PM 2.5 happens at low concentration range in summer. Spatially, the model has better performance in southern China than in northern China, central China, and Sichuan Basin. Strong seasonal variations of PM 2.5 exist and wind speed and direction play important roles in high PM 2.5 events. Secondary components have more boarder distribution than primary components. Sulfate (SO 42−), nitrate (NO 3−), ammonium (NH 4+), and primary organic aerosol (POA) are the most important PM 2.5 components. All components have the highest concentrations in winter except secondary organic aerosol (SOA). This study proves the ability of the CMAQ model to reproduce severe air pollution in China, identifies the directions where improvements are needed, and provides information for human exposure to multiple pollutants for assessing health effects.