24 citations as recorded by crossref.

1. Development of an AI Model to Measure Traffic Air Pollution from Multisensor and Weather Data H. Ly et al. 10.3390/s19224941
2. Influences of planetary boundary layer mixing parameterization on summertime surface ozone concentration and dry deposition over North China Y. Zhao et al. 10.1016/j.atmosenv.2019.116950
3. Google Earth Engine-based mapping of land use and land cover for weather forecast models using Landsat 8 imagery M. Ganjirad & H. Bagheri 10.1016/j.ecoinf.2024.102498
4. High-Resolution Modeling of Air Quality in Abidjan (Côte d’Ivoire) Using a New Urban-Scale Inventory S. Gnamien et al. 10.3390/atmos15070758
5. Assessing the COVID‐19 Impact on Air Quality: A Machine Learning Approach Y. Rybarczyk & R. Zalakeviciute 10.1029/2020GL091202
6. Ozone air quality simulations with WRF-Chem (v3.5.1) over Europe: model evaluation and chemical mechanism comparison K. Mar et al. 10.5194/gmd-9-3699-2016
7. Uso do Modelo WRF-CHEM para a Simulação da Dispersão de Gases no Centro de Lançamento de Alcântara. P. Iriart & G. Fisch 10.1590/0102-7786312314b20150105
8. Evaluation of air quality forecasting system FORAIR-IT over Europe and Italy at high resolution for year 2017 M. Adani et al. 10.1016/j.apr.2022.101456
9. The influence of dry deposition on surface ozone simulations under different planetary boundary layer schemes over eastern China D. Li et al. 10.1016/j.atmosenv.2024.120514
10. Setting-up a Real-Time Air Quality Forecasting system for Serbia: a WRF-Chem feasibility study with different horizontal resolutions and emission inventories Z. Podrascanin 10.1007/s11356-019-05140-y
11. High-resolution multi-scale air pollution system: Evaluation of modelling performance and emission control strategies D. Lopes et al. 10.1016/j.jes.2023.02.046
12. Data imbalance causes underestimation of high ozone pollution in machine learning models: A weighted support vector regression solution L. Zhen et al. 10.1016/j.atmosenv.2024.120952
13. Evaluation of the offline-coupled GFSv15–FV3–CMAQv5.0.2 in support of the next-generation National Air Quality Forecast Capability over the contiguous United States X. Chen et al. 10.5194/gmd-14-3969-2021
14. Optimized neural network for daily-scale ozone prediction based on transfer learning W. Ma et al. 10.1016/j.scitotenv.2022.154279
15. Selection of the data time interval for the prediction of maximum ozone concentrations J. Kocijan et al. 10.1007/s00477-017-1468-y
16. Improving of local ozone forecasting by integrated models D. Gradišar et al. 10.1007/s11356-016-6989-2
17. Improving air quality assessment using physics-inspired deep graph learning L. Li et al. 10.1038/s41612-023-00475-3
18. Quantifying the sensitivity of aerosol optical properties to the parameterizations of physico-chemical processes during the 2010 Russian wildfires and heatwave L. Palacios-Peña et al. 10.5194/acp-20-9679-2020
19. Source attribution of carbon monoxide over Northern India during crop residue burning period over Punjab A. Sharma et al. 10.1016/j.envpol.2024.124707
20. The 1-way on-line coupled model system MECO(n) – Part 4: Chemical evaluation (based on MESSy v2.52) M. Mertens et al. 10.5194/gmd-9-3545-2016
21. Combined Effect of High-Resolution Land Cover and Grid Resolution on Surface NO2 Concentrations C. Silveira et al. 10.3390/cli10020019
22. PM2.5 and O3 concentration estimation based on interpretable machine learning S. Wang et al. 10.1016/j.apr.2023.101866
23. Evaluation of real-time PM2.5 forecasts with the WRF-CMAQ modeling system and weather-pattern-dependent bias-adjusted PM2.5 forecasts in Taiwan F. Cheng et al. 10.1016/j.atmosenv.2020.117909
24. Effects of the Tibetan Plateau and its second staircase terrain on rainstorms over North China: From the perspective of water vapour transport Y. Zhao et al. 10.1002/joc.6000