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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:

A machine learning approach to investigate the build-up of surface ozone in Mexico-City
Mateen Ahmad, B. Rappenglück, Olabosipo Osibanjo, et al.
Journal of Cleaner Production (2022) Vol. 379, pp. 134638-134638
Closed Access | Times Cited: 11

Showing 11 citing articles:

Ozone Concentration Estimation and Meteorological Impact Quantification in the Beijing‐Tianjin‐Hebei Region Based on Machine Learning Models
Zheng Luo, Peilan Lu, Zhe Chen, et al.
Earth and Space Science (2024) Vol. 11, Iss. 2
Open Access | Times Cited: 5
Evaluation of Machine Learning Models for Ozone Concentration Forecasting in the Metropolitan Valley of Mexico
R. García, Magali Arellano-Vázquez
Applied Sciences (2024) Vol. 14, Iss. 4, pp. 1408-1408
Open Access | Times Cited: 4
Interpretable Machine Learning Approaches for Forecasting and Predicting Air Pollution: A Systematic Review
Anass Houdou, Imad El Badisy, Kenza Khomsi, et al.
Aerosol and Air Quality Research (2023) Vol. 24, Iss. 1, pp. 230151-230151
Open Access | Times Cited: 10
Temporal CNN-Based 72-hour Ozone Forecasting in South Korea: Explainability and Uncertainty Quantification
Ahmed Khan Salman, Yunsoo Choi, Deveshwar Singh, et al.
Atmospheric Environment (2024), pp. 120987-120987
Closed Access | Times Cited: 2
Unveiling air pollution patterns in Yemen: a spatial–temporal functional data analysis
Mohanned Abduljabbar Hael
Environmental Science and Pollution Research (2023) Vol. 30, Iss. 17, pp. 50067-50095
Open Access | Times Cited: 7
Estimating monthly surface ozone using multi-source satellite products in China based on Deep Forest model
Xueyao Chen, Zhige Wang, Yulin Shangguan, et al.
Atmospheric Environment (2023) Vol. 307, pp. 119819-119819
Closed Access | Times Cited: 7
Estimation of Near-Ground Ozone With High Spatio-Temporal Resolution in the Yangtze River Delta Region of China Based on a Temporally Ensemble Model
Zhen Li, Heng Dong, Zili Zhang, et al.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2023) Vol. 16, pp. 7051-7061
Open Access | Times Cited: 4
Machine Learning Integrated PMF Model Reveals Influencing Factors of Ozone Pollution in a Coal Chemical Industry City at the Jiangsu-Shandong-Henan-Anhui Boundary
Chaolong Wang, Xiaofei Qin, Yisheng Zhang, et al.
Atmospheric Environment (2024) Vol. 342, pp. 120916-120916
Closed Access | Times Cited: 1
Data imbalance causes underestimation of high ozone pollution in machine learning models: a weighted support vector regression solution
Ling Zhen, Baihua Chen, Lin Wang, et al.
Atmospheric Environment (2024), pp. 120952-120952
Closed Access | Times Cited: 1
Operational Ozone forecasting system in Mexico city: A machine learning framework integrating forecasted weather and historical Ozone data
Olmo Zavala‐Romero, Pedro Alberto Segura Chavez, Pablo Camacho-Gonzalez, et al.
Atmospheric Environment (2024), pp. 121017-121017
Closed Access | Times Cited: 1
Prediction of Ground-Level Ozone in Bengaluru Using Machine Learning Techniques
T Venuka, Kanchan Khare, Masabattula Teja Nikhil, et al.
2022 IEEE 7th International conference for Convergence in Technology (I2CT) (2024)
Closed Access
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