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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:

Deep learning model based on urban multi-source data for predicting heavy metals (Cu, Zn, Ni, Cr) in industrial sewer networks
Yiqi Jiang, Chaolin Li, H.K. Song, et al.
Journal of Hazardous Materials (2022) Vol. 432, pp. 128732-128732
Closed Access | Times Cited: 46

Showing 1-25 of 46 citing articles:

Deep learning for water quality
Wei Zhi, Alison P. Appling, Heather E. Golden, et al.
Nature Water (2024) Vol. 2, Iss. 3, pp. 228-241
Closed Access | Times Cited: 58
Performance of hydrogel immobilized bioreactors combined with different iron ore wastes for denitrification and removal of copper and lead: Optimization and possible mechanism
Liang Xu, Junfeng Su, Kai Li, et al.
Water Research (2022) Vol. 225, pp. 119196-119196
Closed Access | Times Cited: 57
Water Quality Prediction Based on Machine Learning and Comprehensive Weighting Methods
Xianhe Wang, Ying Li, Qian Qiao, et al.
Entropy (2023) Vol. 25, Iss. 8, pp. 1186-1186
Open Access | Times Cited: 34
Artificial intelligence and machine learning algorithms in the detection of heavy metals in water and wastewater: Methodological and ethical challenges
Brij Mohan Maurya, Nidhi Yadav, T. Amudha, et al.
Chemosphere (2024) Vol. 353, pp. 141474-141474
Closed Access | Times Cited: 12
Enhanced Insights into Effluent Prediction in Wastewater Treatment Plants: Comprehensive Deep Learning Model Explanation Based on SHAP
Ruojia Li, Kuanliang Feng, Tong An, et al.
ACS ES&T Water (2024) Vol. 4, Iss. 4, pp. 1904-1915
Closed Access | Times Cited: 9
Prediction models for bioavailability of Cu and Zn during composting: Insights into machine learning
Bing Bai, Lixia Wang, Fachun Guan, et al.
Journal of Hazardous Materials (2024) Vol. 471, pp. 134392-134392
Closed Access | Times Cited: 9
Surface water quality prediction in the lower Thoubal river watershed, India: A hyper-tuned machine learning approach and DNN-based sensitivity analysis
Md Hibjur Rahaman, Haroon Sajjad, Shabina Hussain, et al.
Journal of environmental chemical engineering (2024) Vol. 12, Iss. 3, pp. 112915-112915
Closed Access | Times Cited: 9
Machine learning approaches for monitoring environmental metal pollutants: Recent advances in source apportionment, detection, quantification, and risk assessment.
François Nkinahamira, Anqi Feng, Lijie Zhang, et al.
TrAC Trends in Analytical Chemistry (2024), pp. 117980-117980
Closed Access | Times Cited: 8
Bioaccessibility of arsenic, lead, and cadmium in contaminated mining/smelting soils: Assessment, modeling, and application for soil environment criteria derivation
Kunting Xie, Nangeng Xie, Zhiyang Liao, et al.
Journal of Hazardous Materials (2022) Vol. 443, pp. 130321-130321
Closed Access | Times Cited: 30
Novel production prediction model of gasoline production processes for energy saving and economic increasing based on AM-GRU integrating the UMAP algorithm
Jintao Liu, Liangchao Chen, Wei Xu, et al.
Energy (2022) Vol. 262, pp. 125536-125536
Closed Access | Times Cited: 28
A hybrid model combining mode decomposition and deep learning algorithms for detecting TP in urban sewer networks
Yituo Zhang, Chaolin Li, Yiqi Jiang, et al.
Applied Energy (2023) Vol. 333, pp. 120600-120600
Closed Access | Times Cited: 19
A comprehensive evaluation of influencing factors of neonicotinoid insecticides (NEOs) in farmland soils across China: First focus on film mulching
Jie Hou, Lixi Wang, Jinze Wang, et al.
Journal of Hazardous Materials (2024) Vol. 470, pp. 134284-134284
Closed Access | Times Cited: 6
The next generation of soil and water bodies heavy metals prediction and detection: New expert system based Edge Cloud Server and Federated Learning technology
Zaher Mundher Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬
Environmental Pollution (2022) Vol. 313, pp. 120081-120081
Closed Access | Times Cited: 22
Prediction of adsorption performance of ZIF-67 for malachite green based on artificial neural network using L-BFGS algorithm
Xiaoqing Wang, Shangkun Liu, Shaolei Chen, et al.
Journal of Hazardous Materials (2024) Vol. 473, pp. 134629-134629
Closed Access | Times Cited: 5
Utilizing machine learning to evaluate heavy metal pollution in the world's largest mangrove forest
Ram Proshad, Md. Abdur Rahim, Mahfuzur Rahman, et al.
The Science of The Total Environment (2024) Vol. 951, pp. 175746-175746
Closed Access | Times Cited: 5
Transcriptome and metabolome association analysis revealed key factors involved in melatonin-mediated copper-stress detoxication in tomato
Meng Geng, Yong Wang, Zhaopeng Ouyang, et al.
Journal of Cleaner Production (2025), pp. 144888-144888
Closed Access
Reserve and proliferation characteristics of antibiotic resistance genes and heavy metal resistance genes in the sewage pipe biofilm under the stress of Cu and Zn
Haodong Wei, Xin Wu, A. C. Chen, et al.
Journal of environmental chemical engineering (2025), pp. 116162-116162
Closed Access
Deep-Learning-Based Water Quality Monitoring and Early Warning Methods: A Case Study of Ammonia Nitrogen Prediction in Rivers
Xianhe Wang, Mu Qiao, Ying Li, et al.
Electronics (2023) Vol. 12, Iss. 22, pp. 4645-4645
Open Access | Times Cited: 10
Prediction of Dichloroethene Concentration in the Groundwater of a Contaminated Site Using XGBoost and LSTM
Feiyang Xia, Dengdeng Jiang, Lingya Kong, et al.
International Journal of Environmental Research and Public Health (2022) Vol. 19, Iss. 15, pp. 9374-9374
Open Access | Times Cited: 15
Experimental study and machine learning simulation of Pb (II) separation from aqueous solutions via a nanocomposite adsorbent
Hasan Abedpour, Jafarsadegh Moghaddas, Abobakr Sori, et al.
Journal of the Taiwan Institute of Chemical Engineers (2023) Vol. 147, pp. 104923-104923
Closed Access | Times Cited: 8
Predictive modeling of BOD throughout wastewater treatment: a generalizable machine learning approach for improved effluent quality
Offir Inbar, Moni Shahar, Dror Avisar
Environmental Science Water Research & Technology (2024) Vol. 10, Iss. 10, pp. 2577-2588
Closed Access | Times Cited: 2
Retrieval of chromium and mercury concentrations in agricultural soils: Using spectral information, environmental covariates, or a fusion of both?
Li Wang, Zhou Yong, Sun Xiao, et al.
Ecological Indicators (2024) Vol. 167, pp. 112594-112594
Open Access | Times Cited: 2
Maximizing Heavy Metal Removal and Precious Metal Recovery with Innovative Biowaste-Derived Biosorbents and Biochar
Behzad Murtaza, Rushan Arshad, Moon Kinza, et al.
Process Safety and Environmental Protection (2024)
Closed Access | Times Cited: 2
Hyperspectral Estimation of Soil Copper Concentration Based on Improved TabNet Model in the Eastern Junggar Coalfield
Yuan Wang, Abdugheni Abliz, Hongbing Ma, et al.
IEEE Transactions on Geoscience and Remote Sensing (2022) Vol. 60, pp. 1-20
Open Access | Times Cited: 12
Spatiotemporal analysis and prediction of water quality in Pearl River, China, using multivariate statistical techniques and data-driven model
Haonan Ding, Xiaojun Niu, Dongqing Zhang, et al.
Environmental Science and Pollution Research (2023) Vol. 30, Iss. 22, pp. 63036-63051
Closed Access | Times Cited: 6
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