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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:

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

Showing 1-25 of 97 citing articles:

Microplastic/nanoplastic toxicity in plants: an imminent concern
Tapati Roy, Thuhin Kumar Dey, Mamun Jamal
Environmental Monitoring and Assessment (2022) Vol. 195, Iss. 1
Open Access | Times Cited: 129
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: 98
Estimating daily full-coverage near surface O3, CO, and NO2 concentrations at a high spatial resolution over China based on S5P-TROPOMI and GEOS-FP
Yuan Wang, Qiangqiang Yuan, Tongwen Li, et al.
ISPRS Journal of Photogrammetry and Remote Sensing (2021) Vol. 175, pp. 311-325
Open Access | Times Cited: 98
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
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
Health risk and disease burden attributable to long-term global fine-mode particles
Xingchuan Yang, Yuan Wang, Chuanfeng Zhao, et al.
Chemosphere (2021) Vol. 287, pp. 132435-132435
Open Access | Times Cited: 62
Short-Term Prediction of PM2.5 Using LSTM Deep Learning Methods
Endah Kristiani, Hao Lin, Jwu‐Rong Lin, et al.
Sustainability (2022) Vol. 14, Iss. 4, pp. 2068-2068
Open Access | Times Cited: 48
Flexible Bayesian Ensemble Machine Learning Framework for Predicting Local Ozone Concentrations
Xiang Ren, Zhongyuan Mi, Ting Cai, et al.
Environmental Science & Technology (2022) Vol. 56, Iss. 7, pp. 3871-3883
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
Reconstructing 6-hourly PM2.5 datasets from 1960 to 2020 in China
Junting Zhong, Xiaoye Zhang, Ke Gui, et al.
Earth system science data (2022) Vol. 14, Iss. 7, pp. 3197-3211
Open Access | Times Cited: 38
Application of machine learning in atmospheric pollution research: A state-of-art review
Zezhi Peng, Bin Zhang, Diwei Wang, et al.
The Science of The Total Environment (2023) Vol. 910, pp. 168588-168588
Closed Access | Times Cited: 31
Modeling air quality PM2.5 forecasting using deep sparse attention-based transformer networks
Zhen-Yu Zhang, Shiqing Zhang
International Journal of Environmental Science and Technology (2023) Vol. 20, Iss. 12, pp. 13535-13550
Open Access | Times Cited: 27
Taking Artificial Intelligence Into Space Through Objective Selection of Hyperspectral Earth Observation Applications: To bring the “brain” close to the “eyes” of satellite missions
Agata M. Wijata, M.F. Foulon, Yves Bobichon, et al.
IEEE Geoscience and Remote Sensing Magazine (2023) Vol. 11, Iss. 2, pp. 10-39
Closed Access | Times Cited: 26
Deep learning for medicinal plant species classification and recognition: a systematic review
Adibaru Kiflie Mulugeta, Durga Prasad Sharma, Abebe Haile Mesfin
Frontiers in Plant Science (2024) Vol. 14
Open Access | Times Cited: 14
Unlocking the black box: an in-depth review on interpretability, explainability, and reliability in deep learning
Emrullah Şahin, Naciye Nur Arslan, Durmuş Özdemir
Neural Computing and Applications (2024)
Closed Access | Times Cited: 10
Estimate hourly PM2.5 concentrations from Himawari-8 TOA reflectance directly using geo-intelligent long short-term memory network
Bin Wang, Qiangqiang Yuan, Qianqian Yang, et al.
Environmental Pollution (2020) Vol. 271, pp. 116327-116327
Closed Access | Times Cited: 62
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
Ground PM2.5 prediction using imputed MAIAC AOD with uncertainty quantification
Qiang Pu, Eun‐Hye Yoo
Environmental Pollution (2021) Vol. 274, pp. 116574-116574
Closed 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: 43
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
Estimation of Atmospheric PM10 Concentration in China Using an Interpretable Deep Learning Model and Top‐of‐the‐Atmosphere Reflectance Data From China’s New Generation Geostationary Meteorological Satellite, FY‐4A
Bin Chen, Zhihao Song, Jianping Huang, et al.
Journal of Geophysical Research Atmospheres (2022) Vol. 127, Iss. 9
Open Access | Times Cited: 29
Spatiotemporal neural network for estimating surface NO2 concentrations over north China and their human health impact
Chengxin Zhang, Cheng Liu, Bo Li, et al.
Environmental Pollution (2022) Vol. 307, pp. 119510-119510
Closed Access | Times Cited: 29
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
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: 20
BREATH-Net: a novel deep learning framework for NO2 prediction using bi-directional encoder with transformer
Abhishek Verma, Virender Ranga, Dinesh Kumar Vishwakarma
Environmental Monitoring and Assessment (2024) Vol. 196, Iss. 4
Closed Access | Times Cited: 6
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