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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.

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Showing 14 citing articles:

A comparative analysis of linear regression, neural networks and random forest regression for predicting air ozone employing soft sensor models
Zheng Zhou, Cheng Qiu, Yufan Zhang
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 28
Ground-level ozone estimation based on geo-intelligent machine learning by fusing in-situ observations, remote sensing data, and model simulation data
Jiajia Chen, Huanfeng Shen, Xinghua Li, et al.
International Journal of Applied Earth Observation and Geoinformation (2022) Vol. 112, pp. 102955-102955
Open Access | Times Cited: 20
A machine learning approach to downscale EMEP4UK: analysis of UK ozone variability and trends
Lily Gouldsbrough, Ryan Hossaini, Emma Eastoe, et al.
Atmospheric chemistry and physics (2024) Vol. 24, Iss. 5, pp. 3163-3196
Open Access | Times Cited: 2
Non-Destructive Prediction of Anthocyanin Content of Rosa chinensis Petals Using Digital Images and Machine Learning Algorithms
Xiuying Liu, Jun-Ru Yu, Heng-Nan Deng
Horticulturae (2024) Vol. 10, Iss. 5, pp. 503-503
Open Access | Times Cited: 2
Estimation of Near-Ground Ozone With High Spatio-Temporal Resolution in the Yangtze River Delta Region of China Based on a Temporally Ensemble Model
Zhen Li, Heng Dong, Zili Zhang, et al.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2023) Vol. 16, pp. 7051-7061
Open Access | Times Cited: 4
Joint estimation of PM2.5 and O3 concentrations using a hybrid model in Beijing-Tianjin-Hebei, China
Decai Gong, Ning Du, Wang Li, et al.
Atmospheric Pollution Research (2024) Vol. 15, Iss. 8, pp. 102174-102174
Closed Access | Times Cited: 1
Evaluating the effects of meteorology and emission changes on ozone in different regions over China based on machine learning
Boya Liu, Yuanyuan Li, Lili Wang, et al.
Atmospheric Pollution Research (2024), pp. 102354-102354
Closed Access | Times Cited: 1
Hourly Seamless Surface O3 Estimates by Integrating the Chemical Transport and Machine Learning Models in the Beijing-Tianjin-Hebei Region
Wenhao Xue, Jing Zhang, Xiaomin Hu, et al.
International Journal of Environmental Research and Public Health (2022) Vol. 19, Iss. 14, pp. 8511-8511
Open Access | Times Cited: 6
Estimation of Short-Term and Long-Term Ozone Exposure Levels in Beijing–Tianjin–Hebei Region Based on Geographically Weighted Regression Model
Zequn Qiao, Yusi Liu, Chen Cui, et al.
Atmosphere (2022) Vol. 13, Iss. 10, pp. 1706-1706
Open Access | Times Cited: 6
High-resolution full-coverage ozone (O3) estimates using a data-driven spatial random forest model in Beijing-Tianjin-Hebei region, China
Junyu Wang, Jian Qian, Jiayi Chen, et al.
Journal of Hazardous Materials (2024) Vol. 480, pp. 136047-136047
Closed Access
Comment on egusphere-2023-632

(2023)
Open Access
A machine learning approach to downscale EMEP4UK: analysis of UK ozone variability and trends
Lily Gouldsbrough, Ryan Hossaini, Emma Eastoe, et al.
(2023)
Open Access
Comment on egusphere-2023-632
Lily Gouldsbrough, Ryan Hossaini, Emma Eastoe, et al.
(2023)
Open Access
Response to reviewer comments
Lily Gouldsbrough
(2023)
Open Access
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