4.7 Article

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

Journal

ATMOSPHERIC CHEMISTRY AND PHYSICS
Volume 17, Issue 21, Pages 13103-13118

Publisher

COPERNICUS GESELLSCHAFT MBH
DOI: 10.5194/acp-17-13103-2017

Keywords

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Funding

  1. National Key R&D Program of China [2016YFC0203502]
  2. National Natural Science Foundation of China [41675125]
  3. Natural Science Foundation of Jiangsu Province [BK20150904]
  4. Jiangsu Distinguished Professor Project [2191071503201]
  5. Jiangsu Six Major Talent Peak Project [2015-JNHB-010]
  6. Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)
  7. Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control of Nanjing University of Information Science and Technology
  8. Jiangsu Province Innovation Platform for Superiority Subject of Environmental Science and Engineering [KHK1201]

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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 O-3 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 1 h O-3 (O-3-1h) 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 O-3-1h. 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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