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)
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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
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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
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Nichol, J.E., Bilal,
M., Ali, M.A., & Qiu, Z. (2020). Air Pollution Scenario over China during
COVID-19. Remote Sensing, 12
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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
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Zhang et al. (2019).
Optical and Physical Characteristics of the Lowest Aerosol Layers over the
Yellow River Basin, Atmosphere, 10 (10), 638.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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
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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.
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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.
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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.