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代表性研究成果

  1. Wei, J.*, Li, Z., Lyapustin, A., Wang, J., Dubovik, O., Schwartz, J., Sun, L., Li, C., Liu, S., and Zhu, T. First close insight into global daily gapless 1 km PM2.5 pollution, variability, and health impact. Nature Communications, 2023, 14, 8349. https://doi.org/10.1038/s41467-023-43862-3 (Media Outlets: Nature Communities, UMD) (ESI Hot and Highly Cited Paper, ESSIC Best Paper Award)
  2. Wei, J.*, Wang, J., Li, Z., Kondragunta, S., Anenberg, S., Wang, Y., Zhang, H., Diner, D., Hand, J., Lyapustin, A., Kahn, R., Colarco, P., da Silva, A., and Ichoku, C. Long-term mortality burden trends attributed to black carbon and PM2.5 from wildfire emissions across the continental USA from 2000 to 2020: a deep learning modelling study. The Lancet Planetary Health, 2023, 7, e963–e975. https://doi.org/10.1016/S2542-5196(23)00235-8 (Media Outlets: CBS News, The Hill, Yahoo News, U.S. News, et al.) (ESI Highly Cited Paper)
  3. Wei, J., Li, Z., Lyapustin, A., Sun, L., Peng, Y., Xue, W., Su, T., and Cribb, M. Reconstructing 1-km-resolution high-quality PM2.5 data records from 2000 to 2018 in China: spatiotemporal variations and policy implications. Remote Sensing of Environment, 2021, 252, 112136. https://doi.org/10.1016/j.rse.2020.112136 (ESI Hot and Highly Cited Paper, ESSIC Best Paper Award, 中国百篇最具影响力国际学术论文)
  4. Wei, J., Huang, W., Li, Z., Xue, W., Peng, Y., Sun, L., and Cribb, M. Estimating 1-km-resolution PM2.5 concentrations across China using the space-time random forest approach. Remote Sensing of Environment, 2019, 231, 111221. https://doi.org/10.1016/j.rse.2019.111221 (ESI Hot and Highly Cited Paper)
  5. Wei, J.*, Li, Z., Li, K., Dickerson, R., Pinker, R., Wang, J., Liu, X., Sun, L., Xue, W., and Cribb, M. Full-coverage mapping and spatiotemporal variations of ground-level ozone (O3) pollution from 2013 to 2020 across China. Remote Sensing of Environment, 2022, 270, 112775. https://doi.org/10.1016/j.rse.2021.112775 (ESI Hot and Highly Cited Paper)
  6. Wei, J., Li, Z., Guo, J., Sun, L., Huang, W., Xue, W., Fan, T., and Cribb, M. Satellite-derived 1-km-resolution PM1 concentrations from 2014 to 2018 across China. Environmental Science & Technology, 2019, 53(22), 13265–13274. https://doi.org/10.1021/acs.est.9b03258 (ESI Hot and Highly Cited Paper)
  7. Wei, J.*, Li, Z., Chen, X., Li, C., Sun, Y., Wang, J., Lyapustin, A., Brasseur, G., Jiang, M., Sun, L., Wang, T., Jung, C., Qiu, B., Fang, C., Liu, X., Hao, J., Wang, Y., Zhan, M., Song, X., and Liu, Y. Separating daily 1 km PM2.5 inorganic chemical composition in China since 2000 via deep learning integrating ground, satellite, and model data. Environmental Science & Technology, 2023, 57(46), 18282–18295. https://doi.org/10.1021/acs.est.3c00272 (ESI Highly Cited Paper, ES&T Best Paper Award)
  8. Wei, J.*, Liu, S., Li, Z., Liu, C., Qin, K., Liu, X., Pinker, R., Dickerson, R., Lin, J., Boersma, K., Sun, L., Li, R., Xue, W., Cui, Y., Zhang, C., and Wang, J. Ground-level NO2 surveillance from space across China for high resolution using interpretable spatiotemporally weighted artificial intelligence. Environmental Science & Technology, 2022, 56(14), 9988–9998. https://doi.org/10.1021/acs.est.2c03834 (ESI Hot and Highly Cited Paper)
  9. Wei, J.*, Wang, Z., Li, Z., Li, Z., Pang, S., Xi, X., Cribb, M., and Sun, L. Global aerosol retrieval over land from Landsat imagery integrating Transformer and Google Earth Engine. Remote Sensing of Environment, 2024, 315, 114404. https://doi.org/10.1016/j.rse.2024.114404
  10. Wei, J., Huang, W., Li, Z., Sun, L., Zhu, X., Yuan, Q., Liu, L., and Cribb, M. Cloud detection for Landsat imagery by combining the random forest and super-pixels extracted via energy-driven sampling segmentation approaches. Remote Sensing of Environment, 2020, 248, 112005. https://doi.org/10.1016/j.rse.2020.112005