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Wenhao Xue
Researcher at Beijing Normal University
Publications - 27
Citations - 1573
Wenhao Xue is an academic researcher from Beijing Normal University. The author has contributed to research in topics: Environmental science & China. The author has an hindex of 8, co-authored 17 publications receiving 497 citations. Previous affiliations of Wenhao Xue include Qingdao University.
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Journal ArticleDOI
Estimating 1-km-resolution PM2.5 concentrations across China using the space-time random forest approach
Journal ArticleDOI
Reconstructing 1-km-resolution high-quality PM2.5 data records from 2000 to 2018 in China: spatiotemporal variations and policy implications
Jing Wei,Jing Wei,Zhanqing Li,Alexei Lyapustin,Lin Sun,Yiran Peng,Wenhao Xue,Tianning Su,Maureen Cribb +8 more
TL;DR: Li et al. as mentioned in this paper proposed a Space-Time Extra-Trees (STET) model to capture the spatiotemporal variations of PM2.5 at different spatial scales.
Journal ArticleDOI
Improved 1 km resolution PM 2.5 estimates across China using enhanced space–time extremely randomized trees
Jing Wei,Jing Wei,Zhanqing Li,Maureen Cribb,Wei Huang,Wenhao Xue,Lin Sun,Jianping Guo,Yiran Peng,Jing Li,Alexei Lyapustin,Lei Liu,Hao Wu,Yimeng Song +13 more
TL;DR: In this paper, the space-time extremely randomized trees (STET) model was enhanced by integrating updated spatiotemporal information and additional auxiliary data to improve the spatial resolution and overall accuracy of PM 2.5 estimates across mainland China.
Journal ArticleDOI
Satellite-Derived 1-km-Resolution PM1 Concentrations from 2014 to 2018 across China.
Jing Wei,Jing Wei,Zhanqing Li,Jianping Guo,Lin Sun,Wei Huang,Wenhao Xue,Tianyi Fan,Maureen Cribb +8 more
TL;DR: A space-time extremely randomized trees (STET) model is first developed to estimate PM1 concentrations at a 1-km spatial resolution from 2014 to 2018 across mainland China and shows superior performance in PM1 estimates relative to previous studies.
Journal ArticleDOI
The ChinaHighPM10 dataset: generation, validation, and spatiotemporal variations from 2015 to 2019 across China
TL;DR: The ChinaHighPM10 dataset is potentially useful for future small- and medium-scale air pollution studies by virtue of its higher spatial resolution and overall accuracy.