Articles | Volume 7, issue 5
https://doi.org/10.5194/gmd-7-2243-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-7-2243-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Development and technical paper
| 
02 Oct 2014
Development and technical paper |
| 02 Oct 2014
Air quality forecast of PM10 in Beijing with Community Multi-scale Air Quality Modeling (CMAQ) system: emission and improvement
College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China
W. S. Xu
Beijing Municipal Environmental Protection Monitoring Center, Beijing 100048, China
A. J. Shi
Beijing Municipal Environmental Protection Monitoring Center, Beijing 100048, China
Y. T. Li
Beijing Municipal Environmental Protection Monitoring Center, Beijing 100048, China
X. J. Zhao
Environmental Meteorology Forecast Center of Beijing-Tianjin-Hebei, Beijing 100089, China
Z. F. Wang
State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
J. X. Li
Beijing Municipal Environmental Protection Monitoring Center, Beijing 100048, China
L. N. Wang
College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China
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30 citations as recorded by crossref.
- Artificial neural network application for forecasting the nitrogen oxides in the atmosphere at the microclimate conditions: example of Iğdır city in Turkey A. ALTIKAT https://doi.org/10.31015/jaefs.2020.1.5
- Effects of chemical mechanism and meteorological factors on the concentration of atmospheric pollutants in the megacity Beijing, China Y. Li et al. https://doi.org/10.1016/j.atmosenv.2024.120393
- Development of a vehicle emission inventory with high temporal–spatial resolution based on NRT traffic data and its impact on air pollution in Beijing – Part 1: Development and evaluation of vehicle emission inventory B. Jing et al. https://doi.org/10.5194/acp-16-3161-2016
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- Research of Air Pollutant Concentration Forecasting Based on Deep Learning Algorithms Y. Pan et al. https://doi.org/10.1088/1755-1315/300/3/032090
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- Numerical study of air pollution over a typical basin topography: Source appointment of fine particulate matter during one severe haze in the megacity Xi'an X. Yang et al. https://doi.org/10.1016/j.scitotenv.2019.135213
- Air Pollutant Concentration Forecasting Using Long Short-Term Memory Based on Wavelet Transform and Information Gain: A Case Study of Beijing B. Liu et al. https://doi.org/10.1155/2020/8834699
- New method for evaluating winter air quality: PM2.5 assessment using Community Multi-Scale Air Quality Modeling (CMAQ) in Xi'an X. Yang et al. https://doi.org/10.1016/j.atmosenv.2019.04.019
- Impact of anthropogenic aerosols on summer precipitation in the Beijing–Tianjin–Hebei urban agglomeration in China: Regional climate modeling using WRF-Chem J. Wang et al. https://doi.org/10.1007/s00376-015-5103-x
- Application of regional meteorology and air quality models based on the microprocessor without interlocked piped stages (MIPS) and LoongArch CPU platforms Z. Bai et al. https://doi.org/10.5194/gmd-17-4383-2024
- Model assessment of atmospheric pollution control schemes for critical emission regions S. Zhai et al. https://doi.org/10.1016/j.atmosenv.2015.08.093
- Weather Reduced the Annual Heavy Pollution Days after 2016 in Beijing Y. Sun et al. https://doi.org/10.2151/sola.2022-022
- High-resolution multi-scale air pollution system: Evaluation of modelling performance and emission control strategies D. Lopes et al. https://doi.org/10.1016/j.jes.2023.02.046
- Numerical study of the future PM2.5 concentration under climate change and best-health-effect (BHE) scenario D. Li et al. https://doi.org/10.1016/j.envpol.2024.124391
- Numerical study of the effects of initial conditions and emissions on PM2.5 concentration simulations with CAMx v6.1: a Xi'an case study H. Xiao et al. https://doi.org/10.5194/gmd-14-223-2021
- MP CBM-Z V1.0: design for a new Carbon Bond Mechanism Z (CBM-Z) gas-phase chemical mechanism architecture for next-generation processors H. Wang et al. https://doi.org/10.5194/gmd-12-749-2019
- The soil fugitive dust emission assessment using satellite data: A case study in Beijing-Tianjin-Hebei and its surrounding areas (BTHSA) Y. Li et al. https://doi.org/10.1016/j.apr.2025.102482
- Three-year, 5 km resolution China PM 2.5 simulation: Model performance evaluation Y. Wang et al. https://doi.org/10.1016/j.atmosres.2018.02.016
- Air pollutants concentrations forecasting using back propagation neural network based on wavelet decomposition with meteorological conditions Y. Bai et al. https://doi.org/10.1016/j.apr.2016.01.004
- PM2.5 Concentration Prediction Using GRA-GRU Network in Air Monitoring L. Qing https://doi.org/10.3390/su15031973
- Amplification Effects of Mountainous Terrain on PM2.5 Pollution in Winter in Anhui Province, China C. Shi et al. https://doi.org/10.1007/s00024-026-03977-2
- Comparative evaluation of the impact of GRAPES and MM5 meteorology on CMAQ prediction over Pearl River Delta, China T. Deng et al. https://doi.org/10.1016/j.partic.2017.10.005
- Development of a vehicle emission inventory with high temporal–spatial resolution based on NRT traffic data and its impact on air pollution in Beijing – Part 2: Impact of vehicle emission on urban air quality J. He et al. https://doi.org/10.5194/acp-16-3171-2016
- Accurate Prediction of Concentration Changes in Ozone as an Air Pollutant by Multiple Linear Regression and Artificial Neural Networks S. Bekesiene et al. https://doi.org/10.3390/math9040356
- Sensitivity Study of the Initial Meteorological Fields on the PM10 Concentration Predictions Using CMAQ Modeling Y. Jo et al. https://doi.org/10.5572/KOSAE.2017.33.6.554
- Improving PM2.5 simulations using LSTM: a study on spatiotemporal generalization X. Chen et al. https://doi.org/10.1016/j.apr.2025.102647
- Spatiotemporal networks for multi-city and multi-task air pollutant prediction ——Beijing, Shanghai and Shenzhen as examples D. Li et al. https://doi.org/10.1016/j.uclim.2025.102584
- Improvement of PM2.5 forecast over China by the joint adjustment of initial conditions and emissions with the NLS-4DVar method S. Zhang et al. https://doi.org/10.1016/j.atmosenv.2021.118896
- The amplification effect of El Niño on winter heavy pollution frequency in Beijing M. Lou et al. https://doi.org/10.1088/1748-9326/ade1ee
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