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:
Improved O3 predictions in China by combining chemical transport model and multi-source data with machining learning techniques
Kaili Xiong, Xiaodong Xie, Lin Huang, et al.
Atmospheric Environment (2023) Vol. 318, pp. 120269-120269
Closed Access | Times Cited: 5
Kaili Xiong, Xiaodong Xie, Lin Huang, et al.
Atmospheric Environment (2023) Vol. 318, pp. 120269-120269
Closed Access | Times Cited: 5
Showing 5 citing articles:
Comprehensive 24-Hour Ground-Level Ozone Monitoring: Leveraging Machine Learning for Full-Coverage Estimation in East Asia
Yejin Kim, Seohui Park, Hyunyoung Choi, et al.
Journal of Hazardous Materials (2025) Vol. 488, pp. 137369-137369
Closed Access
Yejin Kim, Seohui Park, Hyunyoung Choi, et al.
Journal of Hazardous Materials (2025) Vol. 488, pp. 137369-137369
Closed Access
Systematic Review of Machine Learning and Deep Learning Techniques for Spatiotemporal Air Quality Prediction
Israel Edem Agbehadji, Ibidun Christiana Obagbuwa
Atmosphere (2024) Vol. 15, Iss. 11, pp. 1352-1352
Open Access | Times Cited: 3
Israel Edem Agbehadji, Ibidun Christiana Obagbuwa
Atmosphere (2024) Vol. 15, Iss. 11, pp. 1352-1352
Open Access | Times Cited: 3
Explainability of Machine Learning Using Shapley Additive exPlanations (SHAP): CatBoost, XGBoost and LightGBM for Total Dissolved Gas Prediction
Salim Heddam
Studies in big data (2024), pp. 1-25
Closed Access | Times Cited: 1
Salim Heddam
Studies in big data (2024), pp. 1-25
Closed Access | Times Cited: 1
A novel ensemble machine learning exposure model system for ground-level ozone at the national scale: A case of mainland China from 2013 to 2020
Jiawei Wang
Environmental Impact Assessment Review (2024) Vol. 109, pp. 107630-107630
Closed Access
Jiawei Wang
Environmental Impact Assessment Review (2024) Vol. 109, pp. 107630-107630
Closed Access
Spatiotemporal variations of PM2.5 and ozone in urban agglomerations of China and meteorological drivers for ozone using explainable machine learning
Yan Lyu, Haonan Xu, Haonan Wu, et al.
Environmental Pollution (2024), pp. 125380-125380
Closed Access
Yan Lyu, Haonan Xu, Haonan Wu, et al.
Environmental Pollution (2024), pp. 125380-125380
Closed Access
