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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:

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

Showing 1-25 of 80 citing articles:

Machine learning and remote sensing integration for leveraging urban sustainability: A review and framework
Fei Li, Tan Yiğitcanlar, Madhav Prasad Nepal, et al.
Sustainable Cities and Society (2023) Vol. 96, pp. 104653-104653
Open Access | Times Cited: 95
A Comparison of Machine Learning-Based Approaches in Estimating Surface PM2.5 Concentrations Focusing on Artificial Neural Networks and High Pollution Events
Su-Huai Wei, Kyle Shores, Yangyang Xu
Atmosphere (2025) Vol. 16, Iss. 1, pp. 48-48
Open Access | Times Cited: 2
Interpretable deep learning model for building energy consumption prediction based on attention mechanism
Yuan Gao, Yingjun Ruan
Energy and Buildings (2021) Vol. 252, pp. 111379-111379
Closed Access | Times Cited: 82
Estimating daily full-coverage surface ozone concentration using satellite observations and a spatiotemporally embedded deep learning approach
Tongwen Li, Xiao Cheng
International Journal of Applied Earth Observation and Geoinformation (2021) Vol. 101, pp. 102356-102356
Open Access | Times Cited: 62
Machine learning and deep learning modeling and simulation for predicting PM2.5 concentrations
Jian Peng, Haisheng Han, Yong Yi, et al.
Chemosphere (2022) Vol. 308, pp. 136353-136353
Closed Access | Times Cited: 53
Explainable AI for earth observation: A review including societal and regulatory perspectives
Caroline Gevaert
International Journal of Applied Earth Observation and Geoinformation (2022) Vol. 112, pp. 102869-102869
Open Access | Times Cited: 49
Orbital collaborative learning in 6G space-air-ground integrated networks
Ming Zhao, Chen Chen, Lei Liu, et al.
Neurocomputing (2022) Vol. 497, pp. 94-109
Closed Access | Times Cited: 47
Deep-AIR: A Hybrid CNN-LSTM Framework for Fine-Grained Air Pollution Estimation and Forecast in Metropolitan Cities
Qi Zhang, Yang Han, Victor O. K. Li, et al.
IEEE Access (2022) Vol. 10, pp. 55818-55841
Open Access | Times Cited: 46
Deep neural networks for spatiotemporal PM2.5 forecasts based on atmospheric chemical transport model output and monitoring data
Pu-Yun Kow, Li‐Chiu Chang, Chuan‐Yao Lin, et al.
Environmental Pollution (2022) Vol. 306, pp. 119348-119348
Closed Access | Times Cited: 43
Explainable deep learning for insights in El Niño and river flows
Yumin Liu, Kate Duffy, Jennifer Dy, et al.
Nature Communications (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 22
Cooperative simultaneous inversion of satellite-based real-time PM2.5 and ozone levels using an improved deep learning model with attention mechanism
Xing Yan, Chen Zuo, Zhanqing Li, et al.
Environmental Pollution (2023) Vol. 327, pp. 121509-121509
Open Access | Times Cited: 22
SHAP-based insights for aerospace PHM: Temporal feature importance, dependencies, robustness, and interaction analysis
Yazan Alomari, Mátyás Andó
Results in Engineering (2024) Vol. 21, pp. 101834-101834
Open Access | Times Cited: 15
Data Augmentation Strategies for Improved PM2.5 Forecasting Using Transformer Architectures
Phoebe Pan, Anusha Srirenganathan Malarvizhi, Xianjun Hao
Atmosphere (2025) Vol. 16, Iss. 2, pp. 127-127
Open Access | Times Cited: 1
Machine learning-based estimation of ground-level NO2 concentrations over China
Yulei Chi, Meng Fan, Chuanfeng Zhao, et al.
The Science of The Total Environment (2021) Vol. 807, pp. 150721-150721
Open Access | Times Cited: 55
Spatiotemporal estimation of hourly 2-km ground-level ozone over China based on Himawari-8 using a self-adaptive geospatially local model
Yuan Wang, Qiangqiang Yuan, Liye Zhu, et al.
Geoscience Frontiers (2021) Vol. 13, Iss. 1, pp. 101286-101286
Open Access | Times Cited: 48
Superior PM2.5 Estimation by Integrating Aerosol Fine Mode Data from the Himawari-8 Satellite in Deep and Classical Machine Learning Models
Zhou Zang, Dan Li, Yushan Guo, et al.
Remote Sensing (2021) Vol. 13, Iss. 14, pp. 2779-2779
Open Access | Times Cited: 43
Full-coverage spatiotemporal mapping of ambient PM2.5 and PM10 over China from Sentinel-5P and assimilated datasets: Considering the precursors and chemical compositions
Yuan Wang, Qiangqiang Yuan, Tongwen Li, et al.
The Science of The Total Environment (2021) Vol. 793, pp. 148535-148535
Open Access | Times Cited: 42
Global spatiotemporal estimation of daily high-resolution surface carbon monoxide concentrations using Deep Forest
Yuan Wang, Qiangqiang Yuan, Tongwen Li, et al.
Journal of Cleaner Production (2022) Vol. 350, pp. 131500-131500
Closed Access | Times Cited: 28
Evaluation of four meteorological reanalysis datasets for satellite-based PM2.5 retrieval over China
Chen Zuo, Jiayi Chen, Yue Zhang, et al.
Atmospheric Environment (2023) Vol. 305, pp. 119795-119795
Closed Access | Times Cited: 19
Improving PM2.5 prediction in New Delhi using a hybrid extreme learning machine coupled with snake optimization algorithm
Adil Masood, Mohammed Majeed Hameed, Aman Srivastava, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 17
Spatiotemporal prediction of particulate matter concentration based on traffic and meteorological data
Jiayu Yang, Linchang Shi, Jaeyoung Lee, et al.
Transportation Research Part D Transport and Environment (2024) Vol. 127, pp. 104070-104070
Closed Access | Times Cited: 7
Modeling the impacts of 2D/3D urban structure on PM2.5 at high resolution by combining UAV multispectral/LiDAR measurements and multi-source remote sensing images
Rongfang Lyu, Jinming Zhang, Jili Pang, et al.
Journal of Cleaner Production (2024) Vol. 437, pp. 140613-140613
Closed Access | Times Cited: 6
Understanding global changes in fine-mode aerosols during 2008–2017 using statistical methods and deep learning approach
Xing Yan, Zhou Zang, Chuanfeng Zhao, et al.
Environment International (2021) Vol. 149, pp. 106392-106392
Open Access | Times Cited: 34
Combining Himawari-8 AOD and deep forest model to obtain city-level distribution of PM2.5 in China
Zhihao Song, Bin Chen, Jianping Huang
Environmental Pollution (2022) Vol. 297, pp. 118826-118826
Closed Access | Times Cited: 27
Estimation of high spatial resolution ground-level ozone concentrations based on Landsat 8 TIR bands with deep forest model
Muyu Li, Qianqian Yang, Qiangqiang Yuan, et al.
Chemosphere (2022) Vol. 301, pp. 134817-134817
Closed Access | Times Cited: 27
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