| Spatiotemporal distributions of surface ozone levels in China from 2005 to 2017: A machine learning approach R Liu, Z Ma, Y Liu, Y Shao, W Zhao, J Bi Environment international 142, 105823, 2020 | 151 | 2020 |
| Estimating daily ground-level PM2. 5 in China with random-forest-based spatiotemporal kriging Y Shao, Z Ma, J Wang, J Bi Science of The Total Environment 740, 139761, 2020 | 61 | 2020 |
| Associations between short-term ambient ozone exposure and cause-specific mortality in rural and urban areas of Jiangsu, China C Lin, Y Ma, R Liu, Y Shao, Z Ma, L Zhou, Y Jing, ML Bell, K Chen Environmental Research 211, 113098, 2022 | 12 | 2022 |
| Economic Growth Facilitates Household Fuel Use Transition to Reduce PM2.5-Related Deaths in China Y Shao, R Liu, J Yang, M Liu, W Fang, L Hu, J Bi, Z Ma Environmental Science & Technology 57 (34), 12663-12673, 2023 | 2 | 2023 |
| A MISR-Based Method for the Estimation of Particle Size Distribution: Comparison with AERONET over China Y Shao, R Liu, W Li, J Bi, Z Ma Journal of Remote Sensing 3, 0032, 2023 | 2 | 2023 |
| Estimation of daily NO2 with explainable machine learning model in China, 2007–2020 Y Shao, W Zhao, R Liu, J Yang, M Liu, W Fang, L Hu, M Adams, J Bi, Z Ma Atmospheric Environment 314, 120111, 2023 | 1 | 2023 |
| Data Imputation of Omi No2 by Combining Multi-Source Data Through a 2-Step Machine Learning Method Over China, 2007-2020 W Zhao, R Liu, Y Shao, W Li, J Bi, Z Ma Available at SSRN 4060131, 0 | | |
