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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:

Machine-Learning Predictions of High Arsenic and High Manganese at Drinking Water Depths of the Glacial Aquifer System, Northern Continental United States
Melinda L. Erickson, Sarah Elliott, Craig J. Brown, et al.
Environmental Science & Technology (2021) Vol. 55, Iss. 9, pp. 5791-5805
Open Access | Times Cited: 77

Showing 1-25 of 77 citing articles:

Machine learning in natural and engineered water systems
Ruixing Huang, Chengxue Ma, Jun Ma, et al.
Water Research (2021) Vol. 205, pp. 117666-117666
Closed Access | Times Cited: 199
Application of machine learning in groundwater quality modeling - A comprehensive review
Ryan Haggerty, Jianxin Sun, Hongfeng Yu, et al.
Water Research (2023) Vol. 233, pp. 119745-119745
Open Access | Times Cited: 153
Artificial intelligence for geoscience: Progress, challenges and perspectives
Tianjie Zhao, Sheng Wang, Chaojun Ouyang, et al.
The Innovation (2024) Vol. 5, Iss. 5, pp. 100691-100691
Open Access | Times Cited: 57
Organic and Inorganic Carbon Sinks Reduce Long‐Term Deep Carbon Emissions in the Continental Collision Margin of the Southern Tibetan Plateau: Implications for Cenozoic Climate Cooling
Yingchun Wang, Sanyu Quan, Xin Tang, et al.
Journal of Geophysical Research Solid Earth (2024) Vol. 129, Iss. 4
Closed Access | Times Cited: 25
Using unsupervised machine learning models to drive groundwater chemistry and associated health risks in Indo-Bangla Sundarban region
Jannatun Nahar Jannat, Abu Reza Md. Towfiqul Islam, Md. Yousuf Mia, et al.
Chemosphere (2024) Vol. 351, pp. 141217-141217
Closed Access | Times Cited: 20
Predictions of groundwater PFAS occurrence at drinking water supply depths in the United States
Andrea K. Tokranov, Katherine M. Ransom, Laura M. Bexfield, et al.
Science (2024) Vol. 386, Iss. 6723, pp. 748-755
Closed Access | Times Cited: 17
Machine learning exploration of the direct and indirect roles of Fe impregnation on Cr(VI) removal by engineered biochar
Xinzhe Zhu, Zibo Xu, Siming You, et al.
Chemical Engineering Journal (2021) Vol. 428, pp. 131967-131967
Open Access | Times Cited: 77
Geogenic manganese and iron in groundwater of Southeast Asia and Bangladesh – Machine learning spatial prediction modeling and comparison with arsenic
Joel Podgorski, Dahyann Araya, Michael Berg
The Science of The Total Environment (2022) Vol. 833, pp. 155131-155131
Open Access | Times Cited: 56
Monitoring and prediction of high fluoride concentrations in groundwater in Pakistan
Yuya Ling, Joel Podgorski, Muhammad Sadiq, et al.
The Science of The Total Environment (2022) Vol. 839, pp. 156058-156058
Open Access | Times Cited: 49
Digital water: artificial intelligence and soft computing applications for drinking water quality assessment
Gyan Chhipi‐Shrestha, Haroon R. Mian, Saeed Mohammadiun, et al.
Clean Technologies and Environmental Policy (2023) Vol. 25, Iss. 5, pp. 1409-1438
Closed Access | Times Cited: 24
Prediction of arsenic and fluoride in groundwater of the North China Plain using enhanced stacking ensemble learning
Wengeng Cao, Zhuo Zhang, Yu Fu, et al.
Water Research (2024) Vol. 259, pp. 121848-121848
Closed Access | Times Cited: 16
High arsenic pollution of the eutrophic Lake Taihu and its relationship with iron, manganese, and dissolved organic matter: High-resolution synchronous analysis
Wenming Yan, Xiangyu He, Musong Chen, et al.
Journal of Hazardous Materials (2024) Vol. 467, pp. 133644-133644
Closed Access | Times Cited: 14
Estimating Lithium Concentrations in Groundwater Used as Drinking Water for the Conterminous United States
Melissa A. Lombard, Eric E. Brown, Daniel Saftner, et al.
Environmental Science & Technology (2024) Vol. 58, Iss. 2, pp. 1255-1264
Open Access | Times Cited: 12
Groundwater Salinity Across India: Predicting Occurrences and Controls by Field-Observations and Machine Learning Modeling
Soumyajit Sarkar, Kousik Das, Abhijit Mukherjee
Environmental Science & Technology (2024) Vol. 58, Iss. 8, pp. 3953-3965
Closed Access | Times Cited: 11
One-step method for the fabrication of pure and metal-decorated densified CNT films for effective electromagnetic interference shielding
Fan Yang, Shengcun Ma, Chia Miang Khor, et al.
Carbon (2023) Vol. 214, pp. 118370-118370
Open Access | Times Cited: 22
Hydrogeochemical and sediment parameters improve predication accuracy of arsenic-prone groundwater in random forest machine-learning models
Wenjing Guo, Zhipeng Gao, Huaming Guo, et al.
The Science of The Total Environment (2023) Vol. 897, pp. 165511-165511
Closed Access | Times Cited: 21
Human and natural impacts on the U.S. freshwater salinization and alkalinization: A machine learning approach
E Beibei, Shuang Zhang, Charles T. Driscoll, et al.
The Science of The Total Environment (2023) Vol. 889, pp. 164138-164138
Closed Access | Times Cited: 17
Application of machine learning in delineating groundwater contamination at present times and in climate change scenarios
Tridip Bhowmik, Soumyajit Sarkar, Somdipta Sen, et al.
Current Opinion in Environmental Science & Health (2024) Vol. 39, pp. 100554-100554
Closed Access | Times Cited: 7
A Statistical Approach for Identifying Private Wells Susceptible to Perfluoroalkyl Substances (PFAS) Contamination
Xindi C. Hu, Beverly Ge, Bridger J. Ruyle, et al.
Environmental Science & Technology Letters (2021) Vol. 8, Iss. 7, pp. 596-602
Open Access | Times Cited: 39
Controlling factors and health risks of groundwater chemistry in a typical alpine watershed based on machine learning methods
Huigui Shen, Wenbo Rao, Hongbing Tan, et al.
The Science of The Total Environment (2022) Vol. 854, pp. 158737-158737
Closed Access | Times Cited: 27
The Utility of Machine Learning Models for Predicting Chemical Contaminants in Drinking Water: Promise, Challenges, and Opportunities
Xindi C. Hu, Mona Dai, Jennifer Sun, et al.
Current Environmental Health Reports (2022) Vol. 10, Iss. 1, pp. 45-60
Open Access | Times Cited: 23
Breaking boundaries: Artificial intelligence for pesticide detection and eco-friendly degradation
Diyasha Banerjee, Satadal Adhikary, Suchandra Bhattacharya, et al.
Environmental Research (2023) Vol. 241, pp. 117601-117601
Closed Access | Times Cited: 15
Expanding the Spatial Reach and Human Impacts of Critical Zone Science
Kamini Singha, Pamela Sullivan, Sharon Billings, et al.
Earth s Future (2024) Vol. 12, Iss. 3
Open Access | Times Cited: 5
Interpreting optimised data-driven solution with explainable artificial intelligence (XAI) for water quality assessment for better decision-making in pollution management
Javed Mallick, Saeed Alqadhi, Hoang Thi Hang, et al.
Environmental Science and Pollution Research (2024) Vol. 31, Iss. 30, pp. 42948-42969
Closed Access | Times Cited: 5
Modelling of arsenic concentration in multiple water sources: A comparison of different machine learning methods
Bemah Ibrahim, Anthony Ewusi, Isaac Ahenkorah, et al.
Groundwater for Sustainable Development (2022) Vol. 17, pp. 100745-100745
Closed Access | Times Cited: 21
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