Dr. Muhammad Bilal
Henan Polytechnic University, Jiaozuo, Henan, China
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Academic Career Length
16Years (2011-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications13 March 2023 in Science of The Total Environment
Journal Article
MJMost. Mastura Munia Farjana Jion
Most. Mastura Munia Farjana Jion
JJJannatun Nahar Jannat
Jannatun Nahar Jannat
YMYousuf Mia
Yousuf Mia
AAArfan Ali
Arfan Ali
SISaiful Islam
Saiful Islam
AIARM Towfiqul Islam
ARM Towfiqul Islam +6
1 September 2013 in Remote Sensing of Environment
Journal Article
Muhammad Bilal
Janet NicholMBMax P. Bleiweiss
Max P. Bleiweiss
DDDavid Dubois
David Dubois1 October 2021 in Remote Sensing of Environment
Journal Article
Muhammad BilalAMAlaa Mhawish
Alaa Mhawish
Janet NicholZQZhongfeng Qiu
Zhongfeng Qiu
Majid NazeerSKSong Ke
Song Ke
+10
1 November 2021 in Atmospheric Environment
Journal Article
WQWenmin Qin
Wenmin Qin
HFHejin Fang
Hejin Fang
LWLunche Wang
Lunche Wang
Jing WeiMZMing Zhang
Ming Zhang
XLXun Liang
Xun Liang
+2
29 February 2016 in IEEE Geoscience and Remote Sensing Letters
Journal Article
BZBin Zou
Bin Zou
QPQiang Pu
Qiang Pu
Muhammad Bilal
Qihao WengLZLiang Zhai
Liang Zhai
Janet NicholSatellite-retrieved aerosol optical depth (AOD) has been increasingly utilized for the mapping of fine particulate matter (PM 2.5 ) concentrations. An accurate estimation and mapping of PM 2.5 concentrations depends on the high-resolution AOD data and a robust mathematical model that takes into account the spatial nonstationary relationship between PM 2.5 and AOD. Take the core portion of the Beijing-Hebei-Tianjin (Jing-Jin-Ji) urban agglomeration as case study (the most seriously polluted region in China). Land use, population, meteorological variables, and simplified aerosol retrieval algorithm-retrieved AOD at 1-km resolution are employed as the predictors for the geographically weighted regression (GWR) and the ordinary least squares (OLS) model to map the spatial distribution of PM 2.5 concentrations. The GWR model shows significant spatial variations in PM 2.5 concentrations over the region than the traditional OLS model, which reveals relative homogeneous variations. Validation with ground-level PM 2.5 concentrations demonstrates that PM 2.5 concentrations predicted by the GWR model (R 2 = 0.75, RMSE = 10 μg/m 3 ) correlate better than those by the OLS model (R 2 = 0.53, RMSE = 16 μg/m 3 ). These results suggest that the GWR model offered a more reliable way for the prediction of spatial distribution of PM 2.5 concentrations over urban areas.
3 June 2020 in Environmental Science & Technology
Journal Article
AMAlaa Mhawish
Alaa MhawishTBTirthankar Banerjee
Tirthankar BanerjeeMSMeytar Sorek-Hamer
Meytar Sorek-Hamer
Muhammad BilalALAlexei I. Lyapustin
Alexei I. Lyapustin
DBDavid M. Broday
David M. Broday
+1
About Me
Dr. Bilal has served as a Distinguished Professor at Nanjing University of Information Science and Technology (NUIST), Nanjing, China, since October 2017. He is the first Pakistani to have completed his Ph.D. degree through the prestigious Hong Kong Ph.D. Fellowship. In October 2018, the Jiangsu Provincial Education Department (China) conferred him the special title of “Distinguished Professor” based on his outstanding research achievements. In October 2020, he was ranked in the Top 2% World's Scientists in Meteorology & Atmospheric Sciences based on a single year (2019) by Stanford University. He has published more than 100 research papers in top SCI(E) journals, including Remote Sensing of Environment, Journal of Geophysical Research-Atmosphere, Environment International, Environmental Pollution, IEEE Transactions on Geoscience and Remote Sensing, Atmospheric Environment, and Atmospheric Research, etc. He serves as a “peer-reviewer” for more than 63 scientific journals and has provided more than 280 reviews. He is a certified “top peer-reviewer” by Publons – part of Web of Science. He has devised five innovative methods (1) Simplified Aerosol Retrieval Algorithm (SARA), (2) Simplified Merge Scheme (SMS), (3) Simplified and Robust Surface Reflectance Estimation Method (SREM), (4) Integration of SREM and SARA (SEMARA), and (5) AEROsol classification using Satellite remote sensing Approach (AEROSA).