October 06, 2021 article Open access A machine learning model to estimate ground-level ozone concentrations in California using TROPOMI data and high-resolution meteorology Environment International DOI: 10.1016/j.envint.2021.106917 Full text (OA) OpenAlex Authors 0 Author list not loaded yet. Cited by 3 stored of 109 Search Sort Most cited Newest Oldest Patent citations Title Any typearticle review book-chapter conference-paper preprint dissertation book dataset other Any fieldAgricultural and Biological Sciences Arts and Humanities Biochemistry, Genetics and Molecular Biology Business, Management and Accounting Chemical Engineering Chemistry Computer Science Decision Sciences Dentistry Earth and Planetary Sciences Economics, Econometrics and Finance Energy Engineering Environmental Science Health Professions Immunology and Microbiology Materials Science Mathematics Medicine Neuroscience Nursing Pharmacology, Toxicology and Pharmaceutics Physics and Astronomy Psychology Social Sciences Veterinary Open access Characterizing Point‐Source Carbon Emissions by Combining TROPOMI CO and OCO CO 2 Data 2026 Journal of Geophysical Research Atmospheres article Environmental Science Atmospheric and Environmental Gas Dynamics Open access 0 citations Supervised Segmentation of NO2 Plumes from Individual Ships Using TROPOMI Satellite Data 2022 Remote Sensing article Environmental Science Maritime Transport Emissions and Efficiency Open access 19 citations Clustering-based spatial transfer learning for short-term ozone forecasting 2022 Journal of Hazardous Materials Advances article Environmental Science Air Quality Monitoring and Forecasting Open access 17 citations 3 results References 0