42 citations as recorded by crossref.

1. 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
2. Development of the CMA-ChemRA: China Regional Weakly Coupled Chemical-Weather Reanalysis System with product since 2007 T. Zhang et al. 10.1016/j.scitotenv.2024.177552
3. 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
4. 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
5. 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
6. 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
7. 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
8. Improved Modeling of Spatiotemporal Variations of Fine Particulate Matter Using a Three‐Dimensional Variational Data Fusion Method X. Zhang et al. 10.1029/2020JD033599
9. 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
10. 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
11. 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
12. 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
13. Significant wintertime PM2.5 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
14. Implementation of an ensemble Kalman filter in the Community Multiscale Air Quality model (CMAQ model v5.1) for data assimilation of ground-level PM2.5 S. Park et al. 10.5194/gmd-15-2773-2022
15. 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
16. 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
17. 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
18. 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
19. 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
20. Ammonium nitrate promotes sulfate formation through uptake kinetic regime Y. Liu et al. 10.5194/acp-21-13269-2021
21. Length Scale Analyses of Background Error Covariances for EnKF and EnSRF Data Assimilation S. Park et al. 10.3390/atmos13020160
22. 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
23. 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
24. Quantification of aerosol and cloud effects on solar energy over China using WRF-Chem Y. Zhang et al. 10.1016/j.atmosres.2022.106245
25. 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
26. 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
27. Different contributions of meteorological conditions and emission reductions to the ozone pollution during Shanghai’s COVID-19 lockdowns in winter and spring X. Dou et al. 10.1016/j.apr.2024.102252
28. The 2015 and 2016 wintertime air pollution in China: SO2 emission changes derived from a WRF-Chem/EnKF coupled data assimilation system D. Chen et al. 10.5194/acp-19-8619-2019
29. 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
30. 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
31. 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
32. 3DVAR meteorological data assimilation and aerosol impact on the simulation of heat wave 2022 over Haryana using WRF-Chem P. Deb et al. 10.1016/j.apr.2025.102440
33. 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
34. Anthropogenic factors of PM2.5 distributions in China’s major urban agglomerations: A spatial-temporal analysis X. Liu et al. 10.1016/j.jclepro.2020.121709
35. Pollutant emission reductions deliver decreased PM2.5-caused mortality across China during 2015–2017 B. Silver et al. 10.5194/acp-20-11683-2020
36. 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
37. Turbulence-permitting air pollution simulation for the Stuttgart metropolitan area T. Schwitalla et al. 10.5194/acp-21-4575-2021
38. Aerosol optical depth assimilation for a modal aerosol model: Implementation and application in AOD forecasts over East Asia J. Pang et al. 10.1016/j.scitotenv.2020.137430
39. 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
40. 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
41. Performance comparisons of the three data assimilation methods for improved predictability of PM2·5: Ensemble Kalman filter, ensemble square root filter, and three-dimensional variational methods U. Dash et al. 10.1016/j.envpol.2023.121099
42. Sensitivity studies of a four-dimensional local ensemble transform Kalman filter coupled with WRF-Chem version 3.9.1 for improving particulate matter simulation accuracy J. Lin et al. 10.5194/gmd-18-2231-2025