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Biography

Prof. Muhammad Bilal

Nanjing University of Information Science & Technology (NUIST), Nanjing, China


Email: muhammad.bilal@connect.polyu.hk


Qualifications

2014 Ph.D., Hong Kong Polytechnic University (PolyU), Department of Land Surveying and Geo-Informatics

2010 M.Sc., COMSATS University Islamabad, Department of Meteorology

2008 B.Sc., University of the Punjab, Department of Space Science


Publications (Selected)


  1. Shi, Y., Bilal, M., Ho, H.C., & Omar, A. (2020). Urbanization and regional air pollution across South Asian developing countries – A nationwide land use regression for ambient PM2.5 assessment in Pakistan. Environmental Pollution, 266
  2. Zhang, M., Su, B., Bilal, M., Atique, L., Usman, M., Qiu, Z., Ali, M.A., & Han, G. (2020). An Investigation of Vertically Distributed Aerosol Optical Properties over Pakistan Using CALIPSO Satellite Data. Remote Sensing, 12
  3. Nichol, J.E., Bilal, M., Ali, M.A., & Qiu, Z. (2020). Air Pollution Scenario over China during COVID-19. Remote Sensing, 12
  4. Su, B., Li, H., Zhang, M., Bilal, M., Wang, M., Atique, L., Zhang, Z., Zhang, C., Han, G., Qiu, Z., & Ali, M.A. (2020). Optical and Physical Characteristics of Aerosol Vertical Layers over Northeastern China. Atmosphere, 11
  5. Zhang et al. (2019). Optical and Physical Characteristics of the Lowest Aerosol Layers over the Yellow River Basin, Atmosphere, 10 (10), 638.
  6. Zhang et al. (2019). Evaluation of the Aqua-MODIS C6 and C6.1 Aerosol Optical Depth Products in the Yellow River Basin, China. Atmosphere 2019, 10, 426.
  7. Xie et al. (2019). Mapping daily PM2.5 at 500 m resolution over Beijing with improved hazy day performance. Science of The Total Environment, 659, 410-418.
  8. Chu and Bilal (2019). PM2.5 mapping using integrated geographically temporally weighted regression (GTWR) and random sample consensus (RANSAC) models. Environmental Science and Pollution Research, 26 (2), 1902-1910.
  9. Bilal et al. (2019). Characteristics of Fine Particulate Matter (PM2.5) over Urban, Suburban, and Rural Areas of Hong Kong, Atmosphere, 10(9), 496. DOI: https://doi.org/10.3390/atmos10090496.
  10. Bilal et al. (2019). A Simplified and Robust Surface Reflectance Estimation Method (SREM) for Use over Diverse Land Surfaces Using Multi-Sensor Data. Remote Sensing, 11, 1344, DOI: https://doi.org/10.3390/rs11111344.
  11. Bilal et a. (2019). Evaluation of Terra-MODIS C6 and C6.1 Aerosol Products against Beijing, XiangHe, and Xinglong AERONET Sites in China during 2004-2014. Remote Sensing, 11, 486, DOI: https://doi.org/10.3390/rs11050486.
  12. Bilal et at. (2018). Global Validation of MODIS C6 and C6.1 Merged Aerosol Products Over Diverse Vegetated Surfaces. Remote Sensing, DOI: 10.3390/rs10030475.
  13. Bilal et al. (2018). A New MODIS C6 Dark Target and Deep Blue Merged Aerosol Product at 3 km Spatial Resolution. Remote Sensing, DOI: 10.3390/rs10030463.
  14. Bilal et al. (2017). New customized methods for improvement of the MODIS C6 Dark Target and Deep Blue merged aerosol product. Remote Sensing of Environment, 197, 115-124. DOI: 10.1016/j.rse.2017.05.028.
  15. Bilal and Nichol (2017). Evaluation of the NDVI–based pixel selection criteria of the MODIS C6 Dark Target and Deep Blue combined aerosol product. IEEE JSTARS, DOI: 10.1109/JSTARS.2017.2693289.
  16. Bilal et al. (2017). Validation of MODIS and VIIRS derived aerosol optical depth over complex coastal waters. Atmospheric Research, 186, 43-50. doi: 10.1016/j.atmosres.2016.11.009.
  17. Bilal et al. (2017). A New Approach for Estimation of Fine Particulate Concentrations Using Satellite Aerosol Optical Depth and Binning of Meteorological Variables, Aerosol and Air Quality Research, 17, 356–367, doi: 10.4209/aaqr.2016.03.0097
  18. Bilal et al. (2016). Validation of Aqua–MODIS C051 and C006 Operational Aerosol Products Using AERONET Measurements Over Pakistan, IEEE JSTARS, 9(5), 2074-2080, doi: 10.1109/JSTARS.2015.2481460.
  19. Bilal and Nichol (2015). Evaluation of MODIS aerosol retrieval algorithms over the Beijing–Tianjin–Hebei region during low to very high pollution events, Journal of Geophysical Research-Atmosphere, 120, 7941–7957, doi: 10.1002/2015JD023082.
  20. Bilal et al. (2014). Validation and accuracy assessment of a Simplified Aerosol Retrieval Algorithm (SARA) over Beijing under low and high aerosol loadings and dust storms, Remote Sensing of Environment, 153, 50–60, doi: 10.1016/j.rse.2014.07.015.