39 citations as recorded by crossref.

1. Global impact of COVID-19 restrictions on the surface concentrations of nitrogen dioxide and ozone C. Keller et al. 10.5194/acp-21-3555-2021
2. A machine learning emulator for Lagrangian particle dispersion model footprints: a case study using NAME E. Fillola et al. 10.5194/gmd-16-1997-2023
3. Assessing the feasibility of using a neural network to filter Orbiting Carbon Observatory 2 (OCO-2) retrievals at northern high latitudes J. Mendonca et al. 10.5194/amt-14-7511-2021
4. Physically regularized machine learning emulators of aerosol activation S. Silva et al. 10.5194/gmd-14-3067-2021
5. Modeling the atmospheric dispersion of SO2 from Mount Nyiragongo R. Opio et al. 10.1016/j.jafrearsci.2022.104771
6. Detecting Thyroid Disease Using Optimized Machine Learning Model Based on Differential Evolution P. Gupta et al. 10.1007/s44196-023-00388-2
7. Development of a recurrent spatiotemporal deep-learning method coupled with data fusion for correction of hourly ozone forecasts J. Li et al. 10.1016/j.envpol.2023.122291
8. Simulating the radiative forcing of oceanic dimethylsulfide (DMS) in Asia based on machine learning estimates J. Zhao et al. 10.5194/acp-22-9583-2022
9. Using an Explainable Machine Learning Approach to Characterize Earth System Model Errors: Application of SHAP Analysis to Modeling Lightning Flash Occurrence S. Silva et al. 10.1029/2021MS002881
10. Evaluation of WRF-chem simulations of NO2 and CO from biomass burning over East Africa and its surrounding regions R. Opio et al. 10.1007/s44195-022-00029-9
11. Responses of surface ozone under the tropical cyclone circulations: Case studies from Fujian Province, China K. Wang et al. 10.1016/j.apr.2024.102323
12. Using machine learning approach to reproduce the measured feature and understand the model-to-measurement discrepancy of atmospheric formaldehyde H. Yin et al. 10.1016/j.scitotenv.2022.158271
13. Improving Air Quality Zoning Through Deep Learning and Hyperlocal Measurements E. Fernández et al. 10.1109/ACCESS.2024.3374208
14. Interpretable machine learning for materials design J. Dean et al. 10.1557/s43578-023-01164-w
15. Assessing the climate and air quality effects of future aerosol mitigation in India using a global climate model combined with statistical downscaling T. Miinalainen et al. 10.5194/acp-23-3471-2023
16. Improving air quality assessment using physics-inspired deep graph learning L. Li et al. 10.1038/s41612-023-00475-3
17. Multidecadal ozone trends in China and implications for human health and crop yields: a hybrid approach combining a chemical transport model and machine learning J. Mao et al. 10.5194/acp-24-345-2024
18. Combining Machine Learning and Satellite Observations to Predict Spatial and Temporal Variation of near Surface OH in North American Cities Q. Zhu et al. 10.1021/acs.est.1c05636
19. Machine Learning Techniques for Vertical Lidar-Based Detection, Characterization, and Classification of Aerosols and Clouds: A Comprehensive Survey S. Lolli 10.3390/rs15174318
20. Machine Learning Emulation of Spatial Deposition from a Multi-Physics Ensemble of Weather and Atmospheric Transport Models N. Gunawardena et al. 10.3390/atmos12080953
21. Mechanism-learning coupling paradigms for parameter inversion and simulation in earth surface systems H. Shen & L. Zhang 10.1007/s11430-022-9999-9
22. Investigating the causes and reduction approaches of nocturnal ozone increase events over Tai'an in the North China Plain J. Li et al. 10.1016/j.atmosres.2024.107499
23. A machine learning methodology for the generation of a parameterization of the hydroxyl radical D. Anderson et al. 10.5194/gmd-15-6341-2022
24. Evaluation of Boosted Regression Tree for the Prediction of the Maximum 24-Hour Concentration of Particulate Matter W. Shaziayani et al. 10.18178/ijesd.2021.12.4.1329
25. An Online‐Learned Neural Network Chemical Solver for Stable Long‐Term Global Simulations of Atmospheric Chemistry M. Kelp et al. 10.1029/2021MS002926
26. Mapping Yearly Fine Resolution Global Surface Ozone through the Bayesian Maximum Entropy Data Fusion of Observations and Model Output for 1990–2017 M. DeLang et al. 10.1021/acs.est.0c07742
27. Correcting ozone biases in a global chemistry–climate model: implications for future ozone Z. Liu et al. 10.5194/acp-22-12543-2022
28. Exploring the Constraints on Simulated Aerosol Sources and Transport Across the North Atlantic With Island‐Based Sun Photometers S. Silva et al. 10.1029/2020EA001392
29. Critical Role of Secondary Organic Aerosol in Urban Atmospheric Visibility Improvement Identified by Machine Learning X. Peng et al. 10.1021/acs.estlett.3c00084
30. Delay recovery model for high-speed trains with compressed train dwell time and running time Y. Hou et al. 10.1007/s40534-020-00225-8
31. Surrogate Tree Ensemble Model Representing 2D Population Doses Over Complex Terrain in the Event of a Radiological Release Into the Air N. Hvala et al. 10.2139/ssrn.4181242
32. Surrogate tree ensemble model representing 2D population doses over complex terrain in the event of a radiological release into the air N. Hvala et al. 10.1016/j.pnucene.2023.104594
33. Global impact of the COVID-19 lockdown on surface concentration and health risk of atmospheric benzene C. Ling et al. 10.5194/acp-23-3311-2023
34. Description of the NASA GEOS Composition Forecast Modeling System GEOS‐CF v1.0 C. Keller et al. 10.1029/2020MS002413
35. Diesel passenger vehicle shares influenced COVID-19 changes in urban nitrogen dioxide pollution G. Kerr et al. 10.1088/1748-9326/ac7659
36. Technical note: Constraining the hydroxyl (OH) radical in the tropics with satellite observations of its drivers – first steps toward assessing the feasibility of a global observation strategy D. Anderson et al. 10.5194/acp-23-6319-2023
37. Sensitivity of Air Pollution Exposure and Disease Burden to Emission Changes in China Using Machine Learning Emulation L. Conibear et al. 10.1029/2021GH000570
38. 地球表层特征参量反演与模拟的机理<bold>-</bold>学习耦合范式 焕. 沈 & 良. 张 10.1360/SSTe-2022-0089
39. Short-term forecasting of ozone air pollution across Europe with transformers S. Hickman et al. 10.1017/eds.2023.37