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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:

Using Explainable Machine Learning to Interpret the Effects of Policies on Air Pollution: COVID-19 Lockdown in London
Liang Ma, Daniel J. Graham, Marc Stettler
Environmental Science & Technology (2023) Vol. 57, Iss. 46, pp. 18271-18281
Open Access | Times Cited: 11

Showing 11 citing articles:

Exploring post-COVID-19 health effects and features with advanced machine learning techniques
Muhammad Nazrul Islam, Md Shofiqul Islam, Nahid Hasan Shourav, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 5
Challenges and opportunities offered by geostationary space observations for air quality research and emission monitoring
Tai‐Long He, Glenn-Michael Oomen, Wenfu Tang, et al.
Bulletin of the American Meteorological Society (2025)
Open Access
Heterogeneity analysis of CO2 emissions driving features on road transport: New perspective from interpretive machine learning
Xing Wang, Yikun Su, Zhizhe Zheng, et al.
Journal of Cleaner Production (2025), pp. 144726-144726
Closed Access
Advancements in machine learning for spatiotemporal urban on-road traffic-air quality study: a review
Zhanxia Du, Hanbing Li, Sha Chen, et al.
Atmospheric Environment (2025), pp. 121054-121054
Closed Access
Development of a multi-module data-driven integrated framework for identifying drivers of atmospheric particulate nitrate and reduction emissions: An application in an industrial city, China
Jiaqi Dong, Yulong Yan, Lin Peng, et al.
Environment International (2025), pp. 109394-109394
Open Access
Spatial-and-local-aware deep learning approach for Ground-Level NO2 estimation in England with multisource data from satellite-based observations and chemical transport models
Siying Wang, Shuangyin Zhang, Dawei Wang, et al.
International Journal of Applied Earth Observation and Geoinformation (2025) Vol. 139, pp. 104506-104506
Closed Access
Co-Occurring Extremes of PM2.5 and Ozone in Warm Seasons of the Yangtze River Delta of China: Insights from Explainable Machine Learning
Yan Lyu, Danni Wu, Fuliang Han, et al.
ACS ES&T Air (2025)
Closed Access
Black carbon in urban Jinan: variations, health risks, and driving factors analyzed with machine learning
Jiaqi Chen, Wenbin Yu, Xiaohan Cui, et al.
Journal of Environmental Sciences (2025)
Closed Access
Integration of interpretable machine learning and environmental magnetism elucidates reduction mechanism of bioavailable potentially toxic elements in lakes after monsoon
Ligang Deng, Yifan Fan, Mingjia Li, et al.
The Science of The Total Environment (2024) Vol. 954, pp. 176418-176418
Closed Access | Times Cited: 1
Insights on ozone pollution control in urban areas by decoupling meteorological factors based on machine learning
Yuqing Qiu, Xin Li, Wenxuan Chai, et al.
(2024)
Open Access
Comment on egusphere-2024-1576

(2024)
Open Access
Geographical Distribution of Atmospheric Nitrogen Deposition in China and Its Response to Emission Control Policy
Zhang Wen, Ziyin Yu, Ruotong Si, et al.
Chinese Geographical Science (2024) Vol. 34, Iss. 6, pp. 1017-1031
Closed Access
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