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

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 (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 2− 4 ), 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.

Multi-year downscaling application of two-way coupled WRF v3.4 and CMAQ v5.0.2 over east Asia for regional climate and air quality modeling: model evaluation and aerosol direct effects

“…The CMAQ performance of chemical predictions in this study was comparable to or even better than those of other air quality studies over east Asia (Wang et al, 2009(Wang et al, , 2012Liu et al, , 2016Zheng et al, 2015;Hu et al, 2016;Zhang et al, 2016a). This study predicted relatively well for Figure 7.…”

“…Compared with other regional modeling studies, WRF-CMAQ v5.0.2 used in this study outperformed MM5/CMAQ v4.6, which tended to underpredict the surface concentrations of major species with NMBs generally greater than −40 % and overpredict surface O 3 concentrations in most months with NMBs generally higher than 20 % over east Asia according to the evaluation results of Zhang et al (2016a). A relatively good performance of CMAQ v5.0.1 was also reported by Hu et al (2016). Global models such as GEOS-Chem and CESM tend to underpredict PM 2.5 concentrations (by about 50 % as reported by Jiang et al, 2013) and overpredict O 3 concentrations (by about 50 % as reported by in China/east Asia because of relatively coarse grid resolution and limitations in some model treatments (e.g., missing emissions of unspeciated primary PM 2.5 and discrepancies in surface layer height and vertical mixing).…”

“…Recently, the online-coupled Weather Research and Forecasting (WRF) model with Chemistry (WRF-Chem) was evaluated for decadal application over the continental US under RCP4.5 and RCP8.5 (Yahya et al, 2016(Yahya et al, , 2017a and applied to decadal projections of future climate and air quality under both scenarios (Yahya et al, 2017b). The Community Multi-scale Air Quality (CMAQ) model has historically been an offline model developed by the US Environmental Protection Agency (EPA) and widely used for air quality simulations over numerous countries and regions (Wang et al, 2009(Wang et al, , 2012Gao et al, 2013;Penrod et al, 2014;Sun et al, 2015;Zheng et al, 2015;Hu et al, 2016); it has recently been further developed to provide an online-coupled version with the Weather Research Forecast (WRF) model to simulate feedbacks between chemistry and meteorology (Wong et al, 2012). Several applications of the two-way coupled WRF-CMAQ model have been conducted to evaluate the performance of the coupled model system and to investigate aerosol direct effects Gan et al, 2015;Hogrefe et al, 2015;Xing et al, 2016) and indirect effects (Yu et al, 2014) on climate and air quality.…”

Severe air pollution events not avoided by reduced anthropogenic activities during COVID-19 outbreak

“…1). The model has been updated to better predict air pollutants in China and details can be found in Hu et al (2016). The meteorological inputs were generated using WRF v3.7.1 with initial and boundary conditions from the National Center for Environmental Prediction (NCEP) FNL Operational Model Global Tropospheric Analyses dataset (Zhang et al, 2012a;NCEP, 2000).…”

Effects of atmospheric transport and trade on air pollution mortality in China

“…The uncertainties in China's emission inventory were estimated to be −14-13, −13-37, −17-54, −25-136, and −40-121 % for SO 2 , NO x , PM 2.5 , black carbon (BC), and organic carbon (OC), respectively (Zhao et al, 2011). Although the quantitative uncertainties are not provided by the MEIC inventory, it has been widely used in chemical transport models and validated against surface and satellite observations (e.g., Chen et al, 2015;Geng et al, 2015;Zheng et al, 2015;Hu et al, 2016).…”