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2022:

  1. Yang, X., Wang, Y., Zhao, C., Fan, H., Yang, Y., Chi, Y., Shen,L. & Yan, X. (2022). Health risk and disease burden attributable to long-term global fine-mode particles. Chemosphere, 132435.

 

2021:​

  1. Yan, X., Zang, Z. Liang, C., Luo, N., Ren, R., Cribb, M., Li, Z.* (2021). New global aerosol fine-mode fraction data over land derived from MODIS satellite retrievals. Environmental Pollution, 276, 116707.

  2. Yan, X., Zang, Z., Zhao, C.*, Husi, L. (2021). Understanding global changes in fine-mode aerosols during 2008–2017 using statistical methods and deep learning approach. Environment International, 149,106392.

  3. Yan, X.*,Zang, Z., Jiang, Y., Shi, W., Guo, Y., Li, D., Zhao, C., Husi, L. (2021). A Spatial-Temporal Interpretable Deep Learning Model for Improving Interpretability and Predictive Accuracy of Satellite-based PM2.5. Environmental Pollution,273, 116459.

  4. Zang, Z., Guo, Y., Jiang, Y., Zuo, C., Li, D., Shi, W., & Yan, X.*. (2021). Tree-based ensemble deep learning model for spatiotemporal surface ozone (O3) prediction and interpretation. International Journal of Applied Earth Observation and Geoinformation, 103, 102516.

  5. Zang, Z., Li, D., Guo, Y., Shi, W., & Yan, X.* (2021). Superior PM2. 5 Estimation by Integrating Aerosol Fine Mode Data from the Himawari-8 Satellite in Deep and Classical Machine Learning Models. Remote Sensing, 13(14), 2779.

  6. Yang, X., Zhao, C., Yang, Y., Yan, X., & Fan, H. (2021). Statistical aerosol properties associated with fire events from 2002 to 2019 and a case analysis in 2019 over Australia. Atmospheric Chemistry and Physics, 21(5), 3833-3853.

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2020:

  1. Yan, X.,Zang, Z.,Luo, N., Jiang, Y., & Li, Z.*(2020). New Interpretable Deep Learning Model to Monitor Real-Time PM2.5 Concentrations from Satellite Data. Environment International, 144,106060.

  2. Yan, X., Liang, C., Jiang, Y., Luo, N., Zang, Z., & Li, Z.* (2020). A Deep Learning Approach to Improve the Retrieval of Temperature and Humidity Profiles From a Ground-Based Microwave Radiometer. IEEE Transactions on Geoscience and Remote Sensing, 58, 8427-8437. doi: 10.1109/TGRS.2020.2987896.

  3. Liang, C., Zang, Z., Li, Z., & Yan, X.* (2020). An Improved Global Land Anthropogenic Aerosol Product Based on Satellite Retrievals From 2008 to 2016. IEEE Geoscience and Remote Sensing Letters. doi: 10.1109/LGRS.2020.2991730.

  4. Yan, X.*, Luo, N., Liang, C., Zang, Z., Zhao, W., & Shi, W. (2020). Simplified and Fast Atmospheric Radiative Transfer model for satellite-based aerosol optical depth retrieval. Atmospheric Environment, 224, 117362.

  5. Yang, X., Zhao, C., Luo, N., Zhao, W., Shi, W., & Yan, X.* (2020). Evaluation and Comparison of Himawari-8 L2 V1. 0, V2. 1 and MODIS C6. 1 aerosol products over Asia and the oceania regions. Atmospheric Environment, 220, 117068.

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2013-2019:

  1. Yan, X., Li, Z.*, Luo, N., Shi, W., Zhao, W., Yang, X., Liang, C., Zhang, F. & Cribb, M. (2019). An improved algorithm for retrieving the fine-mode fraction of aerosol optical thickness. Part 2: Application and validation in Asia. Remote Sensing of Environment, 222, 90-103.

  2. Nana Luo, Wenzhong Shi, Chen Liang, Zhengqiang Li, Haofei Wang, Wenji Zhao, Yingjie Zhang, Yuying Wang, Zhanqing Li, Xing Yan* (2019). Characteristics of atmospheric fungi in particle growth events along with new particle formation in the central North China Plain, Science of The Total Environment, 683, 389-398.

  3. Yang, X., Jiang, L., Zhao, W.*, Xiong, Q., Zhao, W., & Yan, X*. (2018). Comparison of Ground-Based PM2. 5 and PM10 Concentrations in China, India, and the US. International journal of environmental research and public health, 15(7), 1382.

  4. Yan, X., Li, Z.*, Luo, N., Shi, W., Zhao, W., Yang, X., & Jin, J. (2018). A minimum albedo aerosol retrieval method for the new-generation geostationary meteorological satellite Himawari-8. Atmospheric Research, 207, 14-27.

  5. Yan, X., Li, Z.*, Shi, W., Luo, N., Wu, T., & Zhao, W. (2017). An improved algorithm for retrieving the fine-mode fraction of aerosol optical thickness, part 1: algorithm development. Remote Sensing of Environment, 192, 87-97.

  6. Yan, X., Shi, W. *, Li, Z. *, Li, Z., Luo, N., & Zhao, W., et al. (2017). Satellite-based PM2.5 estimation using fine-mode aerosol optical thickness over China. Atmospheric Environment, 170,  290-302.

  7. Ou, Y., Chen, F., Zhao, W., Yan, X., & Zhang, Q. (2017). Landsat 8-based inversion methods for aerosol optical depths in the Beijing area. Atmospheric Pollution Research, 8(2), 267-274.

  8. Yan, X., Shi, W.*, Luo, N., & Zhao, W. (2016). A new method of satellite-based haze aerosol monitoring over the North China Plain and a comparison with MODIS Collection 6 aerosol products. Atmospheric Research, 171, 31-40.

  9. Luo, N., An, L., Nara, A., Yan, X., & Zhao, W. (2016). GIS-based multielement source analysis of dustfall in Beijing: A study of 40 major and trace elements. Chemosphere, 152, 123-131.

  10. Wang, H. F., Fang, N., Yan, X., Chen, F. T., Xiong, Q. L., & Zhao, W. J. (2016). Retrieving dustfall distribution in beijing city based on ground spectral data and remote sensing. Spectroscopy and Spectral Analysis, 36:2911-2918.

  11. Zheng, X., Guo, X., Zhao, W., Shu, T., Xin, Y., Yan, X., Xiong, Q., Chan, F. & Lv, M. (2016). Spatial variation and provenance of atmospheric trace elemental deposition in Beijing. Atmospheric Pollution Research, 7(2), 260-267.

  12. Yan, X., Shi, W.*, Zhao, W., & Luo, N. (2015). Mapping dustfall distribution in urban areas using remote sensing and ground spectral data. Science of the Total Environment, 506, 604-612.

  13. Zheng, X., Zhao, W., Yan, X., Shu, T., Xiong, Q., & Chen, F. (2015). Pollution characteristics and health risk assessment of airborne heavy metals collected from Beijing bus stations. International journal of environmental research and public health, 12(8), 9658-9671.

  14. Luo, N., Wong, M. S., Zhao, W., Yan, X., & Xiao, F. (2015). Improved aerosol retrieval algorithm using Landsat images and its application for PM10 monitoring over urban areas. Atmospheric Research, 153, 264-275.

  15. Chen, F. T., Zhao, W. J., & Yan, X. (2015). The study based on rectification of vegetation indices with dust impact. Spectroscopy and Spectral Analysis, 35(10), 2830-2835.

  16. Yan, X., Shi, W. Z.*, Zhao, W. J.*, & Luo, N. N. (2014). Impact of aerosols and atmospheric particles on plant leaf proteins. Atmospheric Environment, 88, 115-122.

  17. Yan, X., Shi, W.*, Zhao, W., & Luo, N. (2014). Estimation of atmospheric dust deposition on plant leaves based on spectral features. Spectroscopy Letters, 47(7), 536-542.

  18. Yan, X., Shi, W.*, Zhao, W., & Luo, N. (2014). Estimation of protein content in plant leaves using spectral reflectance: a case study in Euonymus japonica. Analytical Letters, 47(3), 517-530.

  19. Luo, N., Zhao, W., & Yan, X. (2014). Integrated aerosol optical thickness, gaseous pollutants and meteorological parameters to estimate ground PM2. 5 concentration. Fresenius Environmental Bulletin, 23(10 A), 2567-2577.

  20. Luo, N., Zhao, W., & Yan, X. (2013).Study on inversion of dust-fall on plant leaves based on high spectral data. Spectroscopy and Spectral Analysis, 33: 2715-2720.

Refereed Journal Publications: 

* Corresponding author

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