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One-Year Simulation of Ozone and Particulate Matter in China Using WRF/CMAQ Modeling System

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One-Year Simulation of Ozone and Particulate Matter in China Using WRF/CMAQ Modeling System Atmos. Chem. Phys., 16, 10333–10350, 2016 www.atmos-chem-phys.net/16/10333/2016/ doi:10.5194/acp-16-10333-2016 © Author(s) 2016. CC Attribution 3.0 License. One-year simulation of ozone and particulate matter in China using WRF/CMAQ modeling system Jianlin Hu1, Jianjun Chen2,1, Qi Ying3,1, and Hongliang Zhang4,1 1Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Engineering Technology Research Center of Environmental Cleaning Materials, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, 219 Ningliu Road, Nanjing 210044, China 2Air Quality Planning and Science Division, California Air Resources Board, 1001 I Street, Sacramento, CA 95814, USA 3Zachry Department of Civil Engineering, Texas A&M University, College Station, TX 77843, USA 4Department of Civil and Environmental Engineering, Louisiana State University, Baton Rouge, LA 70803, USA Correspondence to: Qi Ying ([email protected]) and Hongliang Zhang ([email protected]) Received: 17 February 2016 – Published in Atmos. Chem. Phys. Discuss.: 13 April 2016 Revised: 15 July 2016 – Accepted: 18 July 2016 – Published: 16 August 2016 Abstract. China has been experiencing severe air pollution PM2:5 events. Secondary components have more boarder dis- 2− in recent decades. Although an ambient air quality monitor- tribution than primary components. Sulfate (SO4 /, nitrate ing network for criteria pollutants has been constructed in − C (NO3 /, ammonium (NH4 /, and primary organic aerosol over 100 cities since 2013 in China, the temporal and spa- (POA) are the most important PM2:5 components. All com- tial characteristics of some important pollutants, such as par- ponents have the highest concentrations in winter except sec- ticulate matter (PM) components, remain unknown, limit- ondary organic aerosol (SOA). This study proves the abil- ing further studies investigating potential air pollution con- ity of the CMAQ model to reproduce severe air pollution trol strategies to improve air quality and associating hu- in China, identifies the directions where improvements are man health outcomes with air pollution exposure. In this needed, and provides information for human exposure to study, a yearlong (2013) air quality simulation using the multiple pollutants for assessing health effects. Weather Research and Forecasting (WRF) model and the Community Multi-scale Air Quality (CMAQ) model was conducted to provide detailed temporal and spatial informa- 1 Introduction tion of ozone (O3/, total PM2:5, and chemical components. Multi-resolution Emission Inventory for China (MEIC) was Atmospheric pollutants have adverse effects on human health used for anthropogenic emissions and observation data ob- and ecosystems and are associated with climate change tained from the national air quality monitoring network were (Menon et al., 2008; Pöschl, 2005; Pui et al., 2014). Devel- collected to validate model performance. The model success- oping countries usually experience severely high concentra- fully reproduces the O3 and PM2:5 concentrations at most tions of air pollutants due to fast growth of population, indus- cities for most months, with model performance statistics trialization, transportation, and urbanization without prompt meeting the performance criteria. However, overprediction emission controls. As one of such countries, China started to of O3 generally occurs at low concentration range while un- publish real-time concentration data of six criteria pollutants derprediction of PM2:5 happens at low concentration range from the ambient air quality monitoring networks after mul- in summer. Spatially, the model has better performance in tiple severe pollution events across the country (Sun et al., southern China than in northern China, central China, and 2014; M. Tao et al., 2014; D. Wang et al., 2014; Zheng et al., Sichuan Basin. Strong seasonal variations of PM2:5 exist 2015). and wind speed and direction play important roles in high Published by Copernicus Publications on behalf of the European Geosciences Union. 10334 J. Hu et al.: One-year simulation of ozone and particulate matter More than 1000 observation sites have been set up in more results were also reported by B. Zhang et al. (2015) with than 100 major cities in the country to routinely monitor the WRF/Chemistry (WRF/Chem) model. Using the Com- hourly concentrations of six criteria pollutants, i.e., O3, CO, prehensive Air Quality Model with extensions (CAMx) and NO2, SO2, PM2:5 (PM – particulate matter), and PM10, and the Particulate Source Apportionment Technology (PSAT), to inform the public on air quality status using the air qual- X. Li et al. (2015) determined the contributions of 7 emission ity index (AQI). Analysis of the observation provided a gen- categories and 11 source regions to regional air pollution in eral understanding of the spatial and temporal variation of China and suggested a strong need for regional joint emission the levels of air pollution (Hu et al., 2014a; Y. Wang et al., control efforts in Beijing. More recently, Hu et al. (2015a) 2014), the roles of meteorology in air pollution (H. Zhang used a tracer-based technique in a source-oriented CMAQ to et al., 2015), and the construction of AQI based on multiple determine source sector/region contributions to primary PM pollutants to better inform the public about the severity of air in different seasons in 2012–2013. It was found that residen- pollution (Hu et al., 2015b). However, the monitoring system tial and industrial emissions from local area and the neigh- only considers criteria pollutants and the key species such boring Hebei province contribute to high primary PM events as the volatile organic compounds (VOCs) and the chem- in Beijing. ical composition of PM that are needed to understand the All above modeling studies except Hu et al. (2015a) causes of air pollution and form cost-effective emissions con- were focused on the formation and source apportionment trols are not measured routinely. Monitoring networks fo- of airborne PM during the severe pollution episode of Jan- cusing on the chemical composition of gaseous and partic- uary 2013 in northern China. Although additional PM for- ulate air pollutants, such as the Photochemical Assessment mation pathways and/or emission adjustments were imple- Monitoring Stations (PAMS) and the Chemical Speciation mented and tuned to better predict this extreme episode, Network (CNS) in the United States, have not been estab- model predictions were only evaluated against a small num- lished in China. The lack of detailed chemical composition ber of measurements in and near Beijing for a relatively short information limits our capability to understand the forma- period of time. A few studies have been conducted to eval- tion mechanisms of O3 and PM, quantify the contributions uate the model performance in China for longer time pe- of different sources, and design effective control strategies. riods, such as a full year or several representative months In addition, the observation sites are mostly in highly devel- in different seasons (Gao et al., 2014; Liu et al., 2010; Liu oped urban areas but are very sparse in other suburban and et al., 2016; Wang et al., 2011; Zhang et al., 2016; Zhao rural regions which also have large population and experi- et al., 2013b). However, due to limited ambient observation ence high concentrations of certain pollutants, such as O3. data, model performance on temporal and spatial variations Insufficient spatial coverage in the monitoring system limits of air pollutants were mostly evaluated against available sur- the completeness of public air pollution risk assessment for face observation at a limited number of sites. In addition, the entire country. the surface observations were mostly based on the air pollu- Chemical transport models (CTMs) are often used to re- tion index numbers of the Ministry of Environmental Protec- produce past pollution events, test newly discovered atmo- tion (MEP), which could be used to calculate the concentra- spheric mechanisms, predict future air quality, and provide tions of the major pollutants of SO2, NO2, or PM10. Exten- high temporal and spatial resolution data for epidemiologi- sive model performance evaluation of O3 and PM is urgently cal studies. Several modeling studies have been reported to needed to build the confidence in the emission inventory, the analyze the severe air pollution events in January 2013. For predicted meteorological fields, as well as the capability of example, the Community Multiscale Air Quality (CMAQ) the model in predicting regional O3 and PM under a wide model was updated with heterogeneous chemistry to study range of topographical, meteorological, and emission condi- the formation of secondary inorganic aerosol in northern tions so that further modeling studies of pollutant formation China (Zheng et al., 2015). The CMAQ model was also ap- mechanisms, emission control strategies, and human expo- plied to identify the contributions of both source regions and sure and health risk assessment are based on a solid founda- sectors to PM2:5 in southern Hebei during the 2013 severe tion. haze episode with a brute force method (L. T. Wang et al., In this study, a yearlong (2013) air quality simulation us- 2014). It was found that industrial and domestic activities ing a WRF/CMAQ system was conducted to provide detailed were the most significant local sectors while northern Hebei temporal and spatial distribution of O3 and PM concentra- province, Beijing–Tianjin city cluster, and Henan province tions as well as PM2:5 chemical composition in China. The were the major regional contributors. Using the two-way publicly available observation data obtained from a total of coupled Weather Research and Forecasting (WRF)/CMAQ 422 air monitoring sites in 60 major cities in China were used system, L. T. Wang et al. (2014) simulated the impacts of to provide a thorough evaluation of the model performance aerosol–meteorology interactions of the PM pollution during in the entire year. The modeled spatial and temporal con- January 2013.
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