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Estimates of Health Impacts and Radiative Forcing In Winter Haze In Eastern China Through Constraints of Surface Pm2.5 Predictions


The Gridpoint Statistical Interpolation (GSI) Three-Dimensional Variational (3DVAR) data assimilation system is extended to treat the MOSAIC aerosol model in WRF-Chem, and to be capable of assimilating surface PM2.5 concentrations. The coupled GSI-WRF-Chem system is applied to reproduce aerosol levels over China during an extremely polluted winter month, January 2013. After assimilating surface PM2.5 concentrations, the correlation coefficients between observations and model results averaged over the assimilated sites are improved from 0.67 to 0.94. At nonassimilated sites, improvements (higher correlation coefficients and lower mean bias errors (MBE) and root-mean-square errors (RMSE)) are also found in PM2.5, PM10, and AOD predictions. Using the constrained aerosol fields, we estimate that the PM2.5 concentrations in January 2013 might have caused 7550 premature deaths in Jing-Jin-Ji areas, which are 2% higher than the estimates using unconstrained aerosol fields. We also estimate that the daytime monthly mean anthropogenic aerosol radiative forcing (ARF) to be −29.9W/m2 at the surface, 27.0W/m2 inside the atmosphere, and −2.9W/m2 at the top of the atmosphere. Our estimates update the previously reported overestimations along Yangtze River region and underestimations in North China. This GSI-WRF-Chem system would also be potentially useful for air quality forecasting in China.

Article / Publication Data
Available Metadata
Accepted On
January 19, 2017
Fiscal Year
Peer Reviewed
Publication Name
Environmental Science & Technology
Published On
January 19, 2017
Publisher Name
American Chemical Society
Print Volume
Print Number
Page Range
Submitted On
July 26, 2016


Not available


Authors who have authored or contributed to this publication.

  • Meng Gao - lead None
  • Mariusz Pagowski - eighth Gsl
    Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder
    NOAA/Global Systems Laboratory