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:

Machine Learning Reveals the Parameters Affecting the Gaseous Sulfuric Acid Distribution in a Coastal City: Model Construction and Interpretation
Chen Yang, Hesong Dong, Yuping Chen, et al.
Environmental Science & Technology Letters (2023) Vol. 10, Iss. 11, pp. 1045-1051
Closed Access | Times Cited: 12

Showing 12 citing articles:

Exploring sludge yield patterns through interpretable machine learning models in China's municipal wastewater treatment plants
Y. Hu, Renke Wei, Ke Yu, et al.
Resources Conservation and Recycling (2024) Vol. 204, pp. 107467-107467
Closed Access | Times Cited: 10

Identifying Driving Factors of Atmospheric N2O5 with Machine Learning
Xin Chen, Wei Ma, Feixue Zheng, et al.
Environmental Science & Technology (2024) Vol. 58, Iss. 26, pp. 11568-11577
Closed Access | Times Cited: 5

Understanding the low-temperature drying process of sludge with machine learning in a sewage-source heat pump drying system
Yi Li, Guang‐Yu Yang, Wenlong Zhang, et al.
Journal of Environmental Management (2025) Vol. 375, pp. 124284-124284
Closed Access

Insights into ozone pollution control in urban areas by decoupling meteorological factors based on machine learning
Yuqing Qiu, Xin Li, Wenxuan Chai, et al.
Atmospheric chemistry and physics (2025) Vol. 25, Iss. 3, pp. 1749-1763
Open Access

Driving factors of TOC concentrations in four different types of estuaries (canal, urban, agricultural, and natural estuaries) identified by machine learning technique
Zhongyuan Yang, Sha Lou, Shizhe Chen, et al.
Marine Pollution Bulletin (2025) Vol. 214, pp. 117701-117701
Closed Access

New insights into the performance of biomass carbon-based supercapacitors based on interpretable machine learning approach
Pengfei Liu, Ge Yu, Huanhuan Li, et al.
Journal of Energy Storage (2025) Vol. 118, pp. 116300-116300
Closed Access

Key toxic components and sources affecting oxidative potential of atmospheric particulate matter using interpretable machine learning: Insights from fog episodes
Ruiyu Li, Caiqing Yan, Qingpeng Meng, et al.
Journal of Hazardous Materials (2023) Vol. 465, pp. 133175-133175
Closed Access | Times Cited: 14

New Insights on the Formation of Nucleation Mode Particles in a Coastal City Based on a Machine Learning Approach
Chen Yang, Hesong Dong, Yuping Chen, et al.
Environmental Science & Technology (2023) Vol. 58, Iss. 2, pp. 1187-1198
Closed Access | Times Cited: 8

Trends of peroxyacetyl nitrate and its impact on ozone over 2018–2022 in urban atmosphere
Ziyi Lin, Lingling Xu, Chen Yang, et al.
npj Climate and Atmospheric Science (2024) Vol. 7, Iss. 1
Open Access | Times Cited: 2

Unravelling nitrate transformation mechanisms in karst catchments through the coupling of high-frequency sensor data and machine learning
Xin Liu, Fu‐Jun Yue, Wei Wen Wong, et al.
Water Research (2024) Vol. 267, pp. 122507-122507
Closed Access | Times Cited: 1

Aqueous-phase chloramine production as a missing chain in atmospheric chlorine chemistry
Jingkun Jiang, Yijing Chen, Men Xia, et al.
Research Square (Research Square) (2024)
Open Access

Aqueous-phase chloramine production as a missing chain in atmospheric chlorine chemistry
Yijing Chen, Men Xia, Jinghui Zhang, et al.
Authorea (Authorea) (2024)
Open Access

Machine Learning-powered Pseudo-target Screening of Emerging Contaminants in Water: A Case Study on Tetracyclines
Ye Sun, WU Bao-li, Habit Dong, et al.
Water Research (2024), pp. 123039-123039
Closed Access

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