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Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
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Volume 17, issue 21 | Copyright
Atmos. Chem. Phys., 17, 13103-13118, 2017
https://doi.org/10.5194/acp-17-13103-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 07 Nov 2017

Research article | 07 Nov 2017

Ensemble prediction of air quality using the WRF/CMAQ model system for health effect studies in China

Jianlin Hu1, Xun Li1, Lin Huang1, Qi Ying2,1, Qiang Zhang3, Bin Zhao4, Shuxiao Wang4, and Hongliang Zhang5,1 Jianlin Hu et al.
  • 1Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Engineering Technology Research Center of Environmental Cleaning Materials, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, 219 Ningliu Road, Nanjing 210044, China
  • 2Zachry Department of Civil Engineering, Texas A&M University, College Station, TX 77843-3136, USA
  • 3Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System Science, Tsinghua University, Beijing, China
  • 4State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
  • 5Department of Civil and Environmental Engineering, Louisiana State University, Baton Rouge, LA 77803, USA

Abstract. Accurate exposure estimates are required for health effect analyses of severe air pollution in China. Chemical transport models (CTMs) are widely used to provide spatial distribution, chemical composition, particle size fractions, and source origins of air pollutants. The accuracy of air quality predictions in China is greatly affected by the uncertainties of emission inventories. The Community Multiscale Air Quality (CMAQ) model with meteorological inputs from the Weather Research and Forecasting (WRF) model were used in this study to simulate air pollutants in China in 2013. Four simulations were conducted with four different anthropogenic emission inventories, including the Multi-resolution Emission Inventory for China (MEIC), the Emission Inventory for China by School of Environment at Tsinghua University (SOE), the Emissions Database for Global Atmospheric Research (EDGAR), and the Regional Emission inventory in Asia version 2 (REAS2). Model performance of each simulation was evaluated against available observation data from 422 sites in 60 cities across China. Model predictions of O3 and PM2.5 generally meet the model performance criteria, but performance differences exist in different regions, for different pollutants, and among inventories. Ensemble predictions were calculated by linearly combining the results from different inventories to minimize the sum of the squared errors between the ensemble results and the observations in all cities. The ensemble concentrations show improved agreement with observations in most cities. The mean fractional bias (MFB) and mean fractional errors (MFEs) of the ensemble annual PM2.5 in the 60 cities are −0.11 and 0.24, respectively, which are better than the MFB (−0.25 to −0.16) and MFE (0.26–0.31) of individual simulations. The ensemble annual daily maximum 1h O3 (O3-1h) concentrations are also improved, with mean normalized bias (MNB) of 0.03 and mean normalized errors (MNE) of 0.14, compared to MNB of 0.06–0.19 and MNE of 0.16–0.22 of the individual predictions. The ensemble predictions agree better with observations with daily, monthly, and annual averaging times in all regions of China for both PM2.5 and O3-1h. The study demonstrates that ensemble predictions from combining predictions from individual emission inventories can improve the accuracy of predicted temporal and spatial distributions of air pollutants. This study is the first ensemble model study in China using multiple emission inventories, and the results are publicly available for future health effect studies.

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The model performance of CMAQ with WRF using four different emission inventories in China was validated and compared to obtain the best air pollutants prediction for health effect studies of severe air pollution. The differences in performance of chemical transport model were analyzed for different months and regions in the vast part of China and ensemble predictions were firstly obtained from different inventories for health analysis with minimized errors for pollutants including PM2.5 and O3.
The model performance of CMAQ with WRF using four different emission inventories in China was...
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