| Fugitive Road Dust PM2.5 Emissions and Their Potential Health Impacts S Chen, X Zhang, J Lin, J Huang, D Zhao, T Yuan, K Huang, Y Luo, Z Jia, ... Environmental Science & Technology 53 (14), 8455-8465, 2019 | 122 | 2019 |
| New interpretable deep learning model to monitor real-time PM2. 5 concentrations from satellite data X Yan, Z Zang, N Luo, Y Jiang, Z Li Environment International 144, 106060, 2020 | 80 | 2020 |
| A Spatial-Temporal Interpretable Deep Learning Model for improving interpretability and predictive accuracy of satellite-based PM2. 5 X Yan, Z Zang, Y Jiang, W Shi, Y Guo, D Li, C Zhao, L Husi Environmental Pollution 273, 116459, 2021 | 76 | 2021 |
| Quantifying contributions of natural and anthropogenic dust emission from different climatic regions S Chen, N Jiang, J Huang, X Xu, H Zhang, Z Zang, K Huang, X Xu, Y Wei, ... Atmospheric Environment 191, 94-104, 2018 | 75 | 2018 |
| A deep learning approach to improve the retrieval of temperature and humidity profiles from a ground-based microwave radiometer X Yan, C Liang, Y Jiang, N Luo, Z Zang, Z Li IEEE transactions on geoscience and remote sensing 58 (12), 8427-8437, 2020 | 46 | 2020 |
| Dust modeling over East Asia during the summer of 2010 using the WRF-Chem model S Chen, T Yuan, X Zhang, G Zhang, T Feng, D Zhao, Z Zang, S Liao, X Ma, ... Journal of Quantitative Spectroscopy and Radiative Transfer 213, 1-12, 2018 | 39 | 2018 |
| Estimations of indirect and direct anthropogenic dust emission at the global scale S Chen, N Jiang, J Huang, Z Zang, X Guan, X Ma, Y Luo, J Li, X Zhang, ... Atmospheric environment 200, 50-60, 2019 | 35 | 2019 |
| Superior PM2.5 Estimation by Integrating Aerosol Fine Mode Data from the Himawari-8 Satellite in Deep and Classical Machine Learning Models Z Zang, D Li, Y Guo, W Shi, X Yan Remote Sensing 13 (14), 2779, 2021 | 29 | 2021 |
| Understanding global changes in fine-mode aerosols during 2008–2017 using statistical methods and deep learning approach X Yan, Z Zang, C Zhao, L Husi Environment International 149, 106392, 2021 | 22 | 2021 |
| Simplified and Fast Atmospheric Radiative Transfer model for satellite-based aerosol optical depth retrieval X Yan, N Luo, C Liang, Z Zang, W Zhao, W Shi Atmospheric Environment 224, 117362, 2020 | 21 | 2020 |
| Tree-based ensemble deep learning model for spatiotemporal surface ozone (O3) prediction and interpretation Z Zang, Y Guo, Y Jiang, C Zuo, D Li, W Shi, X Yan International Journal of Applied Earth Observation and Geoinformation 103 …, 2021 | 20 | 2021 |
| A global land aerosol fine-mode fraction dataset (2001–2020) retrieved from MODIS using hybrid physical and deep learning approaches X Yan, Z Zang, Z Li, N Luo, C Zuo, Y Jiang, D Li, Y Guo, W Zhao, W Shi, ... Earth System Science Data Discussions 2021, 1-27, 2021 | 18 | 2021 |
| Explainable and spatial dependence deep learning model for satellite-based O3 monitoring in China N Luo, Z Zang, C Yin, M Liu, Y Jiang, C Zuo, W Zhao, W Shi, X Yan Atmospheric Environment 290, 119370, 2022 | 15 | 2022 |
| New global aerosol fine-mode fraction data over land derived from MODIS satellite retrievals X Yan, Z Zang, C Liang, N Luo, R Ren, M Cribb, Z Li Environmental Pollution 276, 116707, 2021 | 10 | 2021 |
| An improved global land anthropogenic aerosol product based on satellite retrievals from 2008 to 2016 C Liang, Z Zang, Z Li, X Yan IEEE Geoscience and Remote Sensing Letters 18 (6), 944-948, 2020 | 10 | 2020 |
| Differences in sulfate aerosol radiative forcing between the daytime and nighttime over East Asia using the weather research and forecasting model coupled with chemistry (WRF … H Zhang, S Chen, N Jiang, X Wang, X Zhang, J Liu, Z Zang, D Wu, T Yuan, ... Atmosphere 9 (11), 441, 2018 | 5 | 2018 |
| Estimations of anthropogenic dust emissions at global scale from 2007 to 2010 S Chen, J Huang, N Jiang, Z Zang, X Guan, X Ma, Z Jia, X Zhang, ... Atmospheric Chemistry and Physics Discussions 2017, 1-46, 2017 | 4 | 2017 |
| Exploring Global Land Coarse-Mode Aerosol Changes from 2001–2021 Using a New Spatiotemporal Coaction Deep-Learning Model Z Zang, Y Zhang, C Zuo, J Chen, B He, N Luo, J Zou, W Zhao, W Shi, ... Environmental Science & Technology 57 (48), 19881-19890, 2023 | 2 | 2023 |
| Deep Learning with Pretrained Framework Unleashes the Power of Satellite-Based Global Fine-Mode Aerosol Retrieval X Yan, Z Zang, Z Li, HW Chen, J Chen, Y Jiang, Y Chen, B He, C Zuo, ... Environmental Science & Technology, 2024 | | 2024 |
| Wide and Deep Learning Model for Satellite-Based Real-Time Aerosol Retrievals in China N Luo, J Zou, Z Zang, T Chen, X Yan Atmosphere 15 (5), 564, 2024 | | 2024 |
