Articles | Volume 19, issue 11
https://doi.org/10.5194/acp-19-7409-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-19-7409-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
05 Jun 2019
Research article |
| 05 Jun 2019
| 05 Jun 2019
Retrospective analysis of 2015–2017 wintertime PM2.5 in China: response to emission regulations and the role of meteorology
Institute of Urban Meteorology, China Meteorological Administration,
Beijing, 100089, China
National Center for Atmospheric Research, Boulder, CO 80301, USA
Junmei Ban
National Center for Atmospheric Research, Boulder, CO 80301, USA
Pusheng Zhao
Institute of Urban Meteorology, China Meteorological Administration,
Beijing, 100089, China
Min Chen
Institute of Urban Meteorology, China Meteorological Administration,
Beijing, 100089, China
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38 citations as recorded by crossref.
- Estimating the transboundary budget of sulfate aerosols in Northeast Asia with NASA MERRA aerosol reanalysis data D. Kwon et al. 10.1016/j.apr.2023.101981
- Evaluation of PM2.5 fluxes in the “2+26” cities: Transport pathways and intercity contributions M. Qi et al. 10.1016/j.apr.2021.03.011
- Influence of weather and air pollution on concentration change of PM2.5 using a generalized additive model and gradient boosting machine B. Cheng et al. 10.1016/j.atmosenv.2021.118437
- Three-dimensional variational assimilation of Lidar extinction profiles: Application to PM2.5 prediction in north China L. Gao et al. 10.1016/j.atmosenv.2021.118828
- Switching to electric vehicles can lead to significant reductions of PM2.5 and NO2 across China L. Wang et al. 10.1016/j.oneear.2021.06.008
- Continuous and comprehensive atmospheric observations in Beijing: a station to understand the complex urban atmospheric environment Y. Liu et al. 10.1080/20964471.2020.1798707
- Improved Modeling of Spatiotemporal Variations of Fine Particulate Matter Using a Three‐Dimensional Variational Data Fusion Method X. Zhang et al. 10.1029/2020JD033599
- Development and application of the WRFDA-Chem three-dimensional variational (3DVAR) system: aiming to improve air quality forecasting and diagnose model deficiencies W. Sun et al. 10.5194/acp-20-9311-2020
- Improving the sectional Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) aerosols of the Weather Research and Forecasting-Chemistry (WRF-Chem) model with the revised Gridpoint Statistical Interpolation system and multi-wavelength aerosol optical measurements: the dust aerosol observation campaign at Kashi, near the Taklimakan Desert, northwestern China W. Chang et al. 10.5194/acp-21-4403-2021
- Using machine learning to quantify drivers of aerosol pollution trend in China from 2015 to 2022 Y. Ji et al. 10.1016/j.apgeochem.2023.105614
- Impact of model resolution and its representativeness consistency with observations on operational prediction of PM2.5 with 3D-VAR data assimilation Y. Wei et al. 10.1016/j.apr.2024.102141
- Significant wintertime PM<sub>2.5</sub> mitigation in the Yangtze River Delta, China, from 2016 to 2019: observational constraints on anthropogenic emission controls L. Wang et al. 10.5194/acp-20-14787-2020
- Implementation of an ensemble Kalman filter in the Community Multiscale Air Quality model (CMAQ model v5.1) for data assimilation of ground-level PM<sub>2.5</sub> S. Park et al. 10.5194/gmd-15-2773-2022
- Spatiotemporally anthropogenic PM2.5- and O3-related health economic losses via weather normalization technique and hierarchical policies in Chinese cities J. Guo et al. 10.3389/fevo.2023.1192847
- The impacts of background error covariance on particulate matter assimilation and forecast: An ideal case study with a modal aerosol model over China J. Pang & X. Wang 10.1016/j.scitotenv.2021.147417
- Has the Three-Year Action Plan improved the air quality in the Fenwei Plain of China? Assessment based on a machine learning technique X. Dai et al. 10.1016/j.atmosenv.2022.119204
- Slower than expected reduction in annual PM2.5 in Xi'an revealed by machine learning-based meteorological normalization M. Wang et al. 10.1016/j.scitotenv.2022.156740
- The optimization of SO2 emissions by the 4DVAR and EnKF methods and its application in WRF-Chem Y. Hu et al. 10.1016/j.scitotenv.2023.163796
- Ammonium nitrate promotes sulfate formation through uptake kinetic regime Y. Liu et al. 10.5194/acp-21-13269-2021
- Length Scale Analyses of Background Error Covariances for EnKF and EnSRF Data Assimilation S. Park et al. 10.3390/atmos13020160
- Why did air quality experience little improvement during the COVID-19 lockdown in megacities, northeast China? D. Fu et al. 10.1016/j.envres.2023.115282
- Improving air quality forecasting with the assimilation of GOCI aerosol optical depth (AOD) retrievals during the KORUS-AQ period S. Ha et al. 10.5194/acp-20-6015-2020
- Quantification of aerosol and cloud effects on solar energy over China using WRF-Chem Y. Zhang et al. 10.1016/j.atmosres.2022.106245
- Characteristics of Winter Haze Pollution in the Fenwei Plain and the Possible Influence of EU During 1984–2017 Z. Zhao et al. 10.1029/2020EA001134
- PM2.5 Pollution in Six Major Chinese Urban Agglomerations: Spatiotemporal Variations, Health Impacts, and the Relationships with Meteorological Conditions Z. Li et al. 10.3390/atmos13101696
- The 2015 and 2016 wintertime air pollution in China: SO<sub>2</sub> emission changes derived from a WRF-Chem/EnKF coupled data assimilation system D. Chen et al. 10.5194/acp-19-8619-2019
- Implementation of aerosol data assimilation in WRFDA (v4.0.3) for WRF-Chem (v3.9.1) using the RACM/MADE-VBS scheme S. Ha 10.5194/gmd-15-1769-2022
- A 6-year-long (2013–2018) high-resolution air quality reanalysis dataset in China based on the assimilation of surface observations from CNEMC L. Kong et al. 10.5194/essd-13-529-2021
- Contributions of extremely unfavorable meteorology and coal-heating boiler control to air quality in December 2019 over Harbin, China D. Fu et al. 10.1016/j.apr.2021.101217
- Slower than Expected Reduction in Annual Pm2.5 in Northwest China Revealed by Machine Learning-Based Meteorological Normalization M. Wang et al. 10.2139/ssrn.4096148
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- Assessing the impact of clean air action on air quality trends in Beijing using a machine learning technique T. Vu et al. 10.5194/acp-19-11303-2019
- Turbulence-permitting air pollution simulation for the Stuttgart metropolitan area T. Schwitalla et al. 10.5194/acp-21-4575-2021
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- Synergistic interactions of fine particles and radiative effects in modulating urban heat islands during winter haze event in a cold megacity of Northeast China S. Yabo et al. 10.1007/s11356-023-26636-8
- Control of both PM2.5 and O3 in Beijing-Tianjin-Hebei and the surrounding areas S. Xiang et al. 10.1016/j.atmosenv.2020.117259
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Latest update: 29 Jun 2024
Short summary
To better characterize the anthropogenic emission-relevant aerosol species, the GSI-WRF/Chem data assimilation system was updated from GOCART to MOSAIC-4BIN scheme. Wintertime 2015–2017 (January) surface PM2.5 observations from more than 1600 sites were assimilated hourly. The observations and reanalysis data from the assimilation experiment were used to investigate year-to-year changes. Roles of emission and meteorology in driving the changes were also distinguished and quantitatively assessed.
To better characterize the anthropogenic emission-relevant aerosol species, the GSI-WRF/Chem...
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