One-year simulation of ozone and particulate matter in Chinausing WRF/CMAQ modeling system

摘要

China has been experiencing severe air pollution in recent decades. Althoughan ambient air quality monitoring network for criteria pollutants has beenconstructed in over 100 cities since 2013 in China, the temporal and spatialcharacteristics of some important pollutants, such as particulate matter(PM) components, remain unknown, limiting further studies investigatingpotential air pollution control strategies to improve air quality andassociating human health outcomes with air pollution exposure. In thisstudy, a yearlong (2013) air quality simulation using the Weather Researchand Forecasting (WRF) model and the Community Multi-scale Air Quality(CMAQ) model was conducted to provide detailed temporal and spatial information ofozone (O), total PM, and chemical components. Multi-resolutionEmission Inventory for China (MEIC) was used for anthropogenic emissions andobservation data obtained from the national air quality monitoring networkwere collected to validate model performance. The model successfullyreproduces the O and PM concentrations at most cities for mostmonths, with model performance statistics meeting the performance criteria.However, overprediction of O generally occurs at low concentrationrange while underprediction of PM happens at low concentrationrange in summer. Spatially, the model has better performance in southernChina than in northern China, central China, and Sichuan Basin. Strong seasonalvariations of PM exist and wind speed and direction play importantroles in high PM events. Secondary components have more boarderdistribution than primary components. Sulfate (SO), nitrate(NO), ammonium (NH), and primary organic aerosol(POA) are the most important PM components. All components have thehighest concentrations in winter except secondary organic aerosol (SOA).This study proves the ability of the CMAQ model to reproduce severe airpollution in China, identifies the directions where improvements are needed,and provides information for human exposure to multiple pollutants forassessing health effects.