Abstract
Abstract. 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 (O3), total PM2.5, 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 O3 and PM2.5 concentrations at most cities for most months, with model performance statistics meeting the performance criteria. However, overprediction of O3 generally occurs at low concentration range while underprediction of PM2.5 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 PM2.5 exist and wind speed and direction play important roles in high PM2.5 events. Secondary components have more boarder distribution than primary components. Sulfate (SO42−), nitrate (NO3−), ammonium (NH4+), and primary organic aerosol (POA) are the most important PM2.5 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.
Highlights
Atmospheric pollutants have adverse effects on human health and ecosystems and are associated with climate change (Menon et al, 2008; Pöschl, 2005; Pui et al, 2014)
Changes were made to the original Community Multi-scale Air Quality (CMAQ) to improve the capability of the model in predicting secondary inorganic and organic aerosol, including (1) a modified SAPRC-11 gas phase photochemical mechanism to provide more detailed treatment of isoprene oxidation chemistry (Ying et al, 2015); (2) pathways of secondary organic aerosol (SOA) formation from surface controlled reactive uptake of dicarbonyls, isoprene epoxydiol (IEPOX), and methacrylic acid epoxide (MAE)
Overprediction of O3 occurs at low concentrations in winter while underprediction of PM2.5 happens at low concentration range in summer and in cities in the NW region
Summary
Atmospheric pollutants have adverse effects on human health and ecosystems and are associated with climate change (Menon et al, 2008; Pöschl, 2005; Pui et al, 2014). Developing countries usually experience severely high concentrations of air pollutants due to fast growth of population, industrialization, transportation, and urbanization without prompt emission controls. As one of such countries, China started to publish real-time concentration data of six criteria pollutants from the ambient air quality monitoring networks after multiple severe pollution events across the country Tao et al, 2014; D. As one of such countries, China started to publish real-time concentration data of six criteria pollutants from the ambient air quality monitoring networks after multiple severe pollution events across the country (Sun et al, 2014; M. Tao et al, 2014; D. Wang et al, 2014; Zheng et al, 2015).
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
