63 citations as recorded by crossref.

1. 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
2. Global, high-resolution mapping of tropospheric ozone – explainable machine learning and impact of uncertainties C. Betancourt et al. 10.5194/gmd-15-4331-2022
3. Correcting ozone biases in a global chemistry–climate model: implications for future ozone Z. Liu et al. 10.5194/acp-22-12543-2022
4. Physics informed deep neural network embedded in a chemical transport model for the Amazon rainforest H. Sharma et al. 10.1038/s41612-023-00353-y
5. Prediction of Thermogravimetric Data in Bromine Captured from Brominated Flame Retardants (BFRs) in e-Waste Treatment Using Machine Learning Approaches L. Ali et al. 10.1021/acs.jcim.3c00183
6. Development and application of a multi-scale modeling framework for urban high-resolution NO2 pollution mapping Z. Lv et al. 10.5194/acp-22-15685-2022
7. Inverse modelling of Chinese NOx emissions using deep learning: integrating in situ observations with a satellite-based chemical reanalysis T. He et al. 10.5194/acp-22-14059-2022
8. Machine learning predictions of onset and oxidation potentials for methanol and ethanol electrooxidation: Comprehensive analysis and experimental validation T. von Zuben et al. 10.1016/j.electacta.2024.145285
9. An antibacterial lead identification of novel 1,3,4-oxadiazole derivatives based on molecular computer aided design approaches M. Manachou et al. 10.1016/j.sciaf.2024.e02078
10. An updated modeling framework to simulate Los Angeles air quality – Part 1: Model development, evaluation, and source apportionment E. Pennington et al. 10.5194/acp-24-2345-2024
11. Improving Air Quality Zoning Through Deep Learning and Hyperlocal Measurements E. Fernández et al. 10.1109/ACCESS.2024.3374208
12. A Machine Learning Approach for High-Resolution Modeling and Apportionment of Black Carbon Concentrations in an Impacted Community S. Hamilton & R. Harley 10.1021/acsestair.3c00082
13. Deep Learning for Air Quality Forecasts: a Review Q. Liao et al. 10.1007/s40726-020-00159-z
14. Conservation laws in a neural network architecture: enforcing the atom balance of a Julia-based photochemical model (v0.2.0) P. Sturm & A. Wexler 10.5194/gmd-15-3417-2022
15. Statistical Emulation of Winter Ambient Fine Particulate Matter Concentrations From Emission Changes in China L. Conibear et al. 10.1029/2021GH000391
16. Enhancing Fine Aerosol Simulations in the Remote Atmosphere with Machine Learning M. Lu & C. Gao 10.3390/atmos15111356
17. An Adaptive Auto‐Reduction Solver for Speeding Up Integration of Chemical Kinetics in Atmospheric Chemistry Models: Implementation and Evaluation in the Kinetic Pre‐Processor (KPP) Version 3.0.0 H. Lin et al. 10.1029/2022MS003293
18. Machine Learning Modeling of Environmentally Relevant Chemical Reactions for Organic Compounds K. Zhang & H. Zhang 10.1021/acsestwater.2c00193
19. Improving PM2.5 Forecasts in China Using an Initial Error Transport Model H. Wu et al. 10.1021/acs.est.0c01680
20. Interactions between atmospheric composition and climate change – progress in understanding and future opportunities from AerChemMIP, PDRMIP, and RFMIP S. Fiedler et al. 10.5194/gmd-17-2387-2024
21. A simplified non-linear chemistry transport model for analyzing NO2 column observations: STILT–NOx D. Wu et al. 10.5194/gmd-16-6161-2023
22. A machine learning examination of hydroxyl radical differences among model simulations for CCMI-1 J. Nicely et al. 10.5194/acp-20-1341-2020
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. Automated Experimentation Enables a Data-Driven Model for Palladium Removal with Aqueous Chelators S. Kochiashvili et al. 10.1021/acs.oprd.3c00442
25. 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
26. An adaptive method for speeding up the numerical integration of chemical mechanisms in atmospheric chemistry models: application to GEOS-Chem version 12.0.0 L. Shen et al. 10.5194/gmd-13-2475-2020
27. Machine learning calibration of low-cost NO<sub>2</sub> and PM<sub>10</sub> sensors: non-linear algorithms and their impact on site transferability P. Nowack et al. 10.5194/amt-14-5637-2021
28. Surrogate Models of Hydrogen Oxidation Kinetics based on Deep Neural Networks E. Akeweje et al. 10.1134/S004057952302001X
29. Insights from Machine Learning Techniques for Predicting the Efficiency of Fullerene Derivatives‐Based Ternary Organic Solar Cells at Ternary Blend Design M. Lee 10.1002/aenm.201900891
30. Future Co‐Occurrences of Hot Days and Ozone‐Polluted Days Over China Under Scenarios of Shared Socioeconomic Pathways Predicted Through a Machine‐Learning Approach C. Gong et al. 10.1029/2022EF002671
31. Суррогатные модели кинетики горения водорода на основе глубокого обучения И. Акевейе et al. 10.31857/S004035712302001X
32. Insight into Secondary Inorganic Aerosol (SIA) production enhanced by domestic ozone using a machine learning technique G. Lin et al. 10.1016/j.atmosenv.2023.120194
33. Unravelling the Relationship Between Microseisms and Spatial Distribution of Sea Wave Height by Statistical and Machine Learning Approaches A. Cannata et al. 10.3390/rs12050761
34. Mechanism-learning coupling paradigms for parameter inversion and simulation in earth surface systems H. Shen & L. Zhang 10.1007/s11430-022-9999-9
35. A mass- and energy-conserving framework for using machine learning to speed computations: a photochemistry example P. Sturm & A. Wexler 10.5194/gmd-13-4435-2020
36. Accelerating models for multiphase chemical kinetics through machine learning with polynomial chaos expansion and neural networks T. Berkemeier et al. 10.5194/gmd-16-2037-2023
37. Modeling real-world diesel car tailpipe emissions using regression-based approaches C. Chandrashekar et al. 10.1016/j.trd.2024.104092
38. A Neural Network-Assisted Euler Integrator for Stiff Kinetics in Atmospheric Chemistry Y. Huang & J. Seinfeld 10.1021/acs.est.1c07648
39. Improving air quality assessment using physics-inspired deep graph learning L. Li et al. 10.1038/s41612-023-00475-3
40. Neural Network Emulation of the Formation of Organic Aerosols Based on the Explicit GECKO‐A Chemistry Model J. Schreck et al. 10.1029/2021MS002974
41. Advecting Superspecies: Efficiently Modeling Transport of Organic Aerosol With a Mass‐Conserving Dimensionality Reduction Method P. Sturm et al. 10.1029/2022MS003235
42. Machine Learning-Enabled Predictions of Condensed Fukui Functions and Designing of Metal Pincer Complexes for Catalytic Hydrogenation of CO2 S. Parmar et al. 10.1021/acs.jpca.3c04494
43. A machine-learning-based global sea-surface iodide distribution T. Sherwen et al. 10.5194/essd-11-1239-2019
44. Emulation of an atmospheric gas-phase chemistry solver through deep learning: Case study of Chinese Mainland C. Liu et al. 10.1016/j.apr.2021.101079
45. Advances in Methane Emission Estimation in Livestock: A Review of Data Collection Methods, Model Development and the Role of AI Technologies J. Ghassemi Nejad et al. 10.3390/ani14030435
46. Remote Sensing of Tropospheric Ozone from Space: Progress and Challenges J. Xu et al. 10.34133/remotesensing.0178
47. Extending GRACE terrestrial water storage anomalies by combining the random forest regression and a spatially moving window structure W. Jing et al. 10.1016/j.jhydrol.2020.125239
48. Efficient machine learning based surrogate models for surface kinetics by approximating the rates of the rate-determining steps F. Döppel & M. Votsmeier 10.1016/j.ces.2022.117964
49. Developing a supervised learning-based simulation method as a decision support tool for rebalancing problems in bike-sharing systems A. Maleki et al. 10.1016/j.eswa.2023.120983
50. Dynamic Complex Network Analysis of PM2.5 Concentrations in the UK, Using Hierarchical Directed Graphs (V1.0.0) P. Broomandi et al. 10.3390/su13042201
51. Forecasting Air Quality in Taiwan by Using Machine Learning M. Lee et al. 10.1038/s41598-020-61151-7
52. Sensitivity of Air Pollution Exposure and Disease Burden to Emission Changes in China Using Machine Learning Emulation L. Conibear et al. 10.1029/2021GH000570
53. Can Terrestrial Water Storage Dynamics be Estimated From Climate Anomalies? W. Jing et al. 10.1029/2019EA000959
54. Deep Learning to Evaluate US NOx Emissions Using Surface Ozone Predictions T. He et al. 10.1029/2021JD035597
55. Reconstructing the Historical Terrestrial Water Storage Variations in the Huang–Huai–Hai River Basin With Consideration of Water Withdrawals C. Yang et al. 10.3389/fenvs.2022.840540
56. Predicting peak daily maximum 8 h ozone and linkages to emissions and meteorology in Southern California using machine learning methods (SoCAB-8HR V1.0) Z. Gao et al. 10.5194/gmd-15-9015-2022
57. A data-driven approach to generate past GRACE-like terrestrial water storage solution by calibrating the land surface model simulations W. Jing et al. 10.1016/j.advwatres.2020.103683
58. Toward Stable, General Machine‐Learned Models of the Atmospheric Chemical System M. Kelp et al. 10.1029/2020JD032759
59. An Online‐Learned Neural Network Chemical Solver for Stable Long‐Term Global Simulations of Atmospheric Chemistry M. Kelp et al. 10.1029/2021MS002926
60. Toward Emulating an Explicit Organic Chemistry Mechanism With Random Forest Models C. Mouchel‐Vallon & A. Hodzic 10.1029/2022JD038227
61. Deep learning-based gas-phase chemical kinetics kernel emulator: Application in a global air quality simulation case Z. Wang et al. 10.3389/fenvs.2022.955980
62. A numerical compass for experiment design in chemical kinetics and molecular property estimation M. Krüger et al. 10.1186/s13321-024-00825-0
63. A Deep Learning Parameterization for Ozone Dry Deposition Velocities S. Silva et al. 10.1029/2018GL081049