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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Improved 1 km resolution PM2.5 estimates across China using enhanced space–time extremely randomized trees
Jing Wei, Zhanqing Li, Maureen Cribb, et al.
Atmospheric chemistry and physics (2020) Vol. 20, Iss. 6, pp. 3273-3289
Open Access | Times Cited: 473

Showing 1-25 of 473 citing articles:

Reconstructing 1-km-resolution high-quality PM2.5 data records from 2000 to 2018 in China: spatiotemporal variations and policy implications
Jing Wei, Zhanqing Li, Alexei Lyapustin, et al.
Remote Sensing of Environment (2020) Vol. 252, pp. 112136-112136
Closed Access | Times Cited: 819
Full-coverage mapping and spatiotemporal variations of ground-level ozone (O3) pollution from 2013 to 2020 across China
Jing Wei, Zhanqing Li, Ke Li, et al.
Remote Sensing of Environment (2021) Vol. 270, pp. 112775-112775
Open Access | Times Cited: 375
Tracking Air Pollution in China: Near Real-Time PM2.5 Retrievals from Multisource Data Fusion
Guannan Geng, Qingyang Xiao, Shigan Liu, et al.
Environmental Science & Technology (2021) Vol. 55, Iss. 17, pp. 12106-12115
Closed Access | Times Cited: 353
The ChinaHighPM10 dataset: generation, validation, and spatiotemporal variations from 2015 to 2019 across China
Jing Wei, Zhanqing Li, Wenhao Xue, et al.
Environment International (2020) Vol. 146, pp. 106290-106290
Open Access | Times Cited: 289
Spatiotemporal distributions of surface ozone levels in China from 2005 to 2017: A machine learning approach
Riyang Liu, Zongwei Ma, Yang Liu, et al.
Environment International (2020) Vol. 142, pp. 105823-105823
Open Access | Times Cited: 211
Ground-Level NO2Surveillance from Space Across China for High Resolution Using Interpretable Spatiotemporally Weighted Artificial Intelligence
Jing Wei, Song Liu, Zhanqing Li, et al.
Environmental Science & Technology (2022) Vol. 56, Iss. 14, pp. 9988-9998
Open Access | Times Cited: 191
A Review of Ensemble Learning Algorithms Used in Remote Sensing Applications
Yuzhen Zhang, Jingjing Liu, Wenjuan Shen
Applied Sciences (2022) Vol. 12, Iss. 17, pp. 8654-8654
Open Access | Times Cited: 183
Separating emission and meteorological contributions to long-term PM<sub>2.5</sub> trends over eastern China during 2000–2018
Qingyang Xiao, Yixuan Zheng, Guannan Geng, et al.
Atmospheric chemistry and physics (2021) Vol. 21, Iss. 12, pp. 9475-9496
Open Access | Times Cited: 170
Himawari-8-derived diurnal variations in ground-level PM<sub>2.5</sub> pollution across China using the fast space-time Light Gradient Boosting Machine (LightGBM)
Jing Wei, Zhanqing Li, R. T. Pinker, et al.
Atmospheric chemistry and physics (2021) Vol. 21, Iss. 10, pp. 7863-7880
Open Access | Times Cited: 154
Ground-level gaseous pollutants (NO2, SO2, and CO) in China: daily seamless mapping and spatiotemporal variations
Jing Wei, Zhanqing Li, Jun Wang, et al.
Atmospheric chemistry and physics (2023) Vol. 23, Iss. 2, pp. 1511-1532
Open Access | Times Cited: 152
Separating Daily 1 km PM2.5 Inorganic Chemical Composition in China since 2000 via Deep Learning Integrating Ground, Satellite, and Model Data
Jing Wei, Zhanqing Li, Xi Chen, et al.
Environmental Science & Technology (2023) Vol. 57, Iss. 46, pp. 18282-18295
Closed Access | Times Cited: 94
Long-term mortality burden trends attributed to black carbon and PM2·5 from wildfire emissions across the continental USA from 2000 to 2020: a deep learning modelling study
Jing Wei, Jun Wang, Zhanqing Li, et al.
The Lancet Planetary Health (2023) Vol. 7, Iss. 12, pp. e963-e975
Open Access | Times Cited: 50
A review of machine learning for modeling air quality: Overlooked but important issues
Dié Tang, Yu Zhan, Fumo Yang
Atmospheric Research (2024) Vol. 300, pp. 107261-107261
Closed Access | Times Cited: 37
Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning
Siyuan Shen, Chi Li, Aaron van Donkelaar, et al.
ACS ES&T Air (2024) Vol. 1, Iss. 5, pp. 332-345
Closed Access | Times Cited: 17
Air quality predictions with a semi-supervised bidirectional LSTM neural network
Luo Zhang, Peng Liu, Lei Zhao, et al.
Atmospheric Pollution Research (2020) Vol. 12, Iss. 1, pp. 328-339
Closed Access | Times Cited: 134
Evaluation of gap-filling approaches in satellite-based daily PM2.5 prediction models
Qingyang Xiao, Guannan Geng, Jing Cheng, et al.
Atmospheric Environment (2020) Vol. 244, pp. 117921-117921
Closed Access | Times Cited: 131
Identifying the spatiotemporal dynamic of PM2.5 concentrations at multiple scales using geographically and temporally weighted regression model across China during 2015–2018
Bin Guo, Xiaoxia Wang, Lin Pei, et al.
The Science of The Total Environment (2020) Vol. 751, pp. 141765-141765
Closed Access | Times Cited: 115
An Evaluation of Eight Machine Learning Regression Algorithms for Forest Aboveground Biomass Estimation from Multiple Satellite Data Products
Yuzhen Zhang, Jun Ma, Shunlin Liang, et al.
Remote Sensing (2020) Vol. 12, Iss. 24, pp. 4015-4015
Open Access | Times Cited: 107
Abnormally Shallow Boundary Layer Associated With Severe Air Pollution During the COVID‐19 Lockdown in China
Tianning Su, Zhanqing Li, Youtong Zheng, et al.
Geophysical Research Letters (2020) Vol. 47, Iss. 20
Open Access | Times Cited: 102
Satellite-based ground PM2.5 estimation using a gradient boosting decision tree
Tianning Zhang, Weihuan He, Hui Zheng, et al.
Chemosphere (2020) Vol. 268, pp. 128801-128801
Closed Access | Times Cited: 101
A review of statistical methods used for developing large-scale and long-term PM2.5 models from satellite data
Zongwei Ma, Sagnik Dey, Sundar Christopher, et al.
Remote Sensing of Environment (2021) Vol. 269, pp. 112827-112827
Open Access | Times Cited: 100
New interpretable deep learning model to monitor real-time PM2.5 concentrations from satellite data
Xing Yan, Zhou Zang, Nana Luo, et al.
Environment International (2020) Vol. 144, pp. 106060-106060
Open Access | Times Cited: 97
Estimating PM2.5 concentrations via random forest method using satellite, auxiliary, and ground-level station dataset at multiple temporal scales across China in 2017
Bin Guo, Dingming Zhang, Lin Pei, et al.
The Science of The Total Environment (2021) Vol. 778, pp. 146288-146288
Closed Access | Times Cited: 93
A Spatial-Temporal Interpretable Deep Learning Model for improving interpretability and predictive accuracy of satellite-based PM2.5
Xing Yan, Zhou Zang, Yize Jiang, et al.
Environmental Pollution (2021) Vol. 273, pp. 116459-116459
Open Access | Times Cited: 80
The mechanisms and seasonal differences of the impact of aerosols on daytime surface urban heat island effect
Wenchao Han, Zhanqing Li, Fang Wu, et al.
Atmospheric chemistry and physics (2020) Vol. 20, Iss. 11, pp. 6479-6493
Open Access | Times Cited: 79
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