Peer-Reviewed Journal Articles

(*: Corresponding author; #: Co-first author. Please contact Fei if you cannot get access to any of these publications.)
  1. Yao, F.* and Palmer, P.I., 2022. Source sector mitigation of solar energy generation losses attributable to particulate matter pollution. Environmental Science & Technology, 56(12), pp.8619–8628. doi: 10.1021/acs.est.2c01175. Slides.
  2. Liu, J.*, Li, J. and Yao, F., 2021. Source-Receptor Relationship of Transboundary Particulate Matter Pollution between China, South Korea, and Japan: Approaches, Current Understanding, and Limitations. Critical Reviews in Environmental Science and Technology. In press. doi: 10.1080/10643389.2021.1964308. WeChat.
  3. Mogno, C.*, Palmer, P.I., Knote, C., Yao, F. and Wallington, T.J., 2021. Seasonal distribution and drivers of surface fine particulate matter and organic aerosol over the Indo-Gangetic Plain. Atmospheric Chemistry and Physics, 21(14), pp.10881–10909. doi: 10.5194/acp-21-10881-2021.
  4. Wu, J.*, Wang, Y., Liang, J. and Yao, F., 2021. Exploring common factors influencing PM2.5 and O3 concentrations in the Pearl River Delta: Tradeoffs and synergies. Environmental Pollution, 285, p.117138. doi: 10.1016/j.envpol.2021.117138.
  5. Yao, F.* and Palmer, P.I., 2021. A model framework to reduce bias in ground-level PM2.5 concentrations inferred from satellite-retrieved AOD. Atmospheric Environment, 248, p.118217. doi: 10.1016/j.atmosenv.2021.118217. Slides.
  6. Guo, H., Zhan, Q., Ho, H.C., Yao, F., Zhou, X., Wu, J. and Li, W.*, 2020. Coupling mobile phone data with machine learning: How misclassification errors in ambient PM2.5 exposure estimates are produced?. Science of The Total Environment, 745, p.141034. doi: 10.1016/j.scitotenv.2020.141034.
  7. Guo, H., Li, W.*, Yao, F., Wu, J., Zhou, X., Yue, Y. and Yeh, A.G., 2020. Who are more exposed to PM2.5 pollution: A mobile phone data approach. Environment international, 143, p.105821. doi: 10.1016/j.envint.2020.105821.
  8. Wu, J., Liang, J., Zhou, L., Yao, F. and Peng, J.*, 2019. Impacts of AOD Correction and Spatial Scale on the Correlation between High-Resolution AOD from Gaofen-1 Satellite and In Situ PM2.5 Measurements in Shenzhen City, China. Remote Sensing, 11(19), p.2223. doi: 10.3390/rs11192223.
  9. Yao, F., Wu, J.*, Li, W.* and Peng, J., 2019. A spatially structured adaptive two-stage model for retrieving ground-level PM2.5 concentrations from VIIRS AOD in China. ISPRS Journal of Photogrammetry and Remote Sensing, 151, pp.263-276. doi: 10.1016/j.isprsjprs.2019.03.011.
  10. Yao, F., Wu, J.*, Li, W. and Peng, J., 2019. Estimating daily PM2.5 concentrations in Beijing using 750-M VIIRS IP AOD retrievals and a nested spatiotemporal statistical model. Remote Sensing, 11(7), p.841. doi: 10.3390/rs11070841.
  11. Yao, F., Si, M., Li, W.* and Wu, J.*, 2018. A multidimensional comparison between MODIS and VIIRS AOD in estimating ground-level PM2.5 concentrations over a heavily polluted region in China. Science of the Total Environment, 618, pp.819-828. doi: 10.1016/j.scitotenv.2017.08.209. Slides.
  12. Wang, Z., Yao, F., Li, W. and Wu, J.*, 2017. Saturation correction for nighttime lights data based on the relative NDVI. Remote Sensing, 9(7), p.759. doi: 10.3390/rs9070759. Slides.
  13. Wu, J., Yao, F., Li, W.* and Si, M., 2016. VIIRS-based remote sensing estimation of ground-level PM2.5 concentrations in Beijing–Tianjin–Hebei: A spatiotemporal statistical model. Remote Sensing of Environment, 184, pp.316-328. doi: 10.1016/j.rse.2016.07.015. Slides.
  14. Yao, F., Ye, K. and Zhou, J.*, 2015. Automatic image classification and retrieval by analyzing plant leaf features. Journal of Zhejiang A&F University, 32(3), pp.426-433. doi: 10.11833/j.issn.2095-0756.2015.03.015.