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Gao M, Saide PE, Xin J, Wang Y, Liu Z, Wang Y, Wang Z, Pagowski M, Guttikunda SK, Carmichael GR. Estimates of Health Impacts and Radiative Forcing in Winter Haze in Eastern China through Constraints of Surface PM 2.5 Predictions. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2017; 51:2178-2185. [PMID: 28102073 DOI: 10.1021/acs.est.6b03745] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
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.
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Affiliation(s)
- Meng Gao
- Department of Chemical and Biochemical Engineering, University of Iowa , Iowa City, Iowa 52242, United States
- Center for Global and Regional Environmental Research, University of Iowa , Iowa City, Iowa 52242, United States
| | - Pablo E Saide
- Atmospheric Chemistry Observations and Modeling (ACOM) Lab, National Center for Atmospheric Research (NCAR) , Boulder, Colorado 80305, United States
| | - Jinyuan Xin
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences , Beijing, China
| | - Yuesi Wang
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences , Beijing, China
| | - Zirui Liu
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences , Beijing, China
| | - Yuxuan Wang
- Department of Earth and Atmospheric Sciences, The University of Houston , Houston, Texas 77004, United States
- Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System Science, Tsinghua University , Beijing, China
| | - Zifa Wang
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences , Beijing, China
| | - Mariusz Pagowski
- NOAA Earth System Research Laboratory (ESRL), Boulder, Colorado 80305, United States
| | - Sarath K Guttikunda
- Division of Atmospheric Sciences, Desert Research Institute , Reno, Nevada 89119, United States
| | - Gregory R Carmichael
- Department of Chemical and Biochemical Engineering, University of Iowa , Iowa City, Iowa 52242, United States
- Center for Global and Regional Environmental Research, University of Iowa , Iowa City, Iowa 52242, United States
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Lee P, Liu Y. Preliminary evaluation of a regional atmospheric chemical data assimilation system for environmental surveillance. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2014; 11:12795-816. [PMID: 25514141 PMCID: PMC4276647 DOI: 10.3390/ijerph111212795] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2014] [Revised: 12/02/2014] [Accepted: 12/03/2014] [Indexed: 11/16/2022]
Abstract
We report the progress of an ongoing effort by the Air Resources Laboratory, NOAA to build a prototype regional Chemical Analysis System (ARLCAS). The ARLCAS focuses on providing long-term analysis of the three dimensional (3D) air-pollutant concentration fields over the continental U.S. It leverages expertise from the NASA Earth Science Division-sponsored Air Quality Applied Science Team (AQAST) for the state-of-science knowledge in atmospheric and data assimilation sciences. The ARLCAS complies with national operational center requirement protocols and aims to have the modeling system to be maintained by a national center. Meteorology and chemistry observations consist of land-, air- and space-based observed and quality-assured data. We develop modularized testing to investigate the efficacies of the various components of the ARLCAS. The sensitivity testing of data assimilation schemes showed that with the increment of additional observational data sets, the accuracy of the analysis chemical fields also increased incrementally in varying margins. The benefit is especially noted for additional data sets based on a different platform and/or a different retrieval algorithm. We also described a plan to apply the analysis chemical fields in environmental surveillance at the Centers for Disease Control and Prevention.
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Affiliation(s)
- Pius Lee
- Air Resources Laboratory, Office of Oceanic and Atmospheric Research, National Oceanic and Atmospheric Administration, College Park, MD 20740, USA.
| | - Yang Liu
- Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA.
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Ali A, Amin SE, Ramadan HH, Tolba MF. RETRACTED ARTICLE: On using stacked neural network for multi-sensor data merging to enhance aerosol data assimilation. Neural Comput Appl 2013. [DOI: 10.1007/s00521-012-1318-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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Tarasick DW, Jin JJ, Fioletov VE, Liu G, Thompson AM, Oltmans SJ, Liu J, Sioris CE, Liu X, Cooper OR, Dann T, Thouret V. High-resolution tropospheric ozone fields for INTEX and ARCTAS from IONS ozonesondes. ACTA ACUST UNITED AC 2010. [DOI: 10.1029/2009jd012918] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Wu L, Mallet V, Bocquet M, Sportisse B. A comparison study of data assimilation algorithms for ozone forecasts. ACTA ACUST UNITED AC 2008. [DOI: 10.1029/2008jd009991] [Citation(s) in RCA: 74] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Parrington M, Jones DBA, Bowman KW, Horowitz LW, Thompson AM, Tarasick DW, Witte JC. Estimating the summertime tropospheric ozone distribution over North America through assimilation of observations from the Tropospheric Emission Spectrometer. ACTA ACUST UNITED AC 2008. [DOI: 10.1029/2007jd009341] [Citation(s) in RCA: 77] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Constantinescu EM, Chai T, Sandu A, Carmichael GR. Autoregressive models of background errors for chemical data assimilation. ACTA ACUST UNITED AC 2007. [DOI: 10.1029/2006jd008103] [Citation(s) in RCA: 30] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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