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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:

Revealing Drivers of Haze Pollution by Explainable Machine Learning
Linlu Hou, Qili Dai, Congbo Song, et al.
Environmental Science & Technology Letters (2022) Vol. 9, Iss. 2, pp. 112-119
Closed Access | Times Cited: 156

Showing 1-25 of 156 citing articles:

Machine Learning in Environmental Research: Common Pitfalls and Best Practices
Jun‐Jie Zhu, Meiqi Yang, Zhiyong Jason Ren
Environmental Science & Technology (2023) Vol. 57, Iss. 46, pp. 17671-17689
Closed Access | Times Cited: 215
Attribution of Air Quality Benefits to Clean Winter Heating Policies in China: Combining Machine Learning with Causal Inference
Congbo Song, Bowen Liu, Kai Cheng, et al.
Environmental Science & Technology (2023) Vol. 57, Iss. 46, pp. 17707-17717
Open Access | Times Cited: 51
Contributions of various driving factors to air pollution events: Interpretability analysis from Machine learning perspective
Tianshuai Li, Qingzhu Zhang, Yanbo Peng, et al.
Environment International (2023) Vol. 173, pp. 107861-107861
Open Access | Times Cited: 42
Explainable ensemble machine learning revealing the effect of meteorology and sources on ozone formation in megacity Hangzhou, China
Lei Zhang, Lili Wang, Dan Ji, et al.
The Science of The Total Environment (2024) Vol. 922, pp. 171295-171295
Closed Access | Times Cited: 15
Impacts of meteorology and precursor emission change on O3 variation in Tianjin, China from 2015 to 2021
Jing Ding, Qili Dai, Wenyan Fan, et al.
Journal of Environmental Sciences (2022) Vol. 126, pp. 506-516
Closed Access | Times Cited: 66
Enhanced ozone pollution in the summer of 2022 in China: The roles of meteorology and emission variations
Huang Zheng, Shaofei Kong, Yuan He, et al.
Atmospheric Environment (2023) Vol. 301, pp. 119701-119701
Closed Access | Times Cited: 37
Machine Learning Models for Inverse Design of the Electrochemical Oxidation Process for Water Purification
Ye Sun, Zhiyuan Zhao, Hailong Tong, et al.
Environmental Science & Technology (2023) Vol. 57, Iss. 46, pp. 17990-18000
Closed Access | Times Cited: 30
The application of machine learning to air pollution research: A bibliometric analysis
Yunzhe Li, Zhipeng Sha, Aohan Tang, et al.
Ecotoxicology and Environmental Safety (2023) Vol. 257, pp. 114911-114911
Open Access | Times Cited: 27
Trends of source apportioned PM2.5 in Tianjin over 2013–2019: Impacts of Clean Air Actions
Qili Dai, Jiajia Chen, Xuehan Wang, et al.
Environmental Pollution (2023) Vol. 325, pp. 121344-121344
Closed Access | Times Cited: 25
Understanding the relationship between 2D/3D variables and land surface temperature in plain and mountainous cities: Relative importance and interaction effects
Pinyang Luo, Bingjie Yu, Pengfei Li, et al.
Building and Environment (2023) Vol. 245, pp. 110959-110959
Closed Access | Times Cited: 23
Estimating particulate matter concentrations and meteorological contributions in China during 2000–2020
Shuai Wang, Peng Wang, Ruhan Zhang, et al.
Chemosphere (2023) Vol. 330, pp. 138742-138742
Closed Access | Times Cited: 21
PM2.5 and O3 concentration estimation based on interpretable machine learning
Siyuan Wang, Ying Ren, Bisheng Xia
Atmospheric Pollution Research (2023) Vol. 14, Iss. 9, pp. 101866-101866
Open Access | Times Cited: 21
Revealing the drivers of surface ozone pollution by explainable machine learning and satellite observations in Hangzhou Bay, China
Tianen Yao, Sihua Lu, Yaqi Wang, et al.
Journal of Cleaner Production (2024) Vol. 440, pp. 140938-140938
Closed Access | Times Cited: 9
Dissecting Drivers of Ozone Pollution during the 2022 Multicity Lockdowns in China Sheds Light on Future Control Direction
Yue Tan, Yingnan Zhang, Tao Wang, et al.
Environmental Science & Technology (2024) Vol. 58, Iss. 16, pp. 6988-6997
Closed Access | Times Cited: 9
Understanding Sources and Drivers of Size-Resolved Aerosol in the High Arctic Islands of Svalbard Using a Receptor Model Coupled with Machine Learning
Congbo Song, Silvia Becagli, David C. S. Beddows, et al.
Environmental Science & Technology (2022) Vol. 56, Iss. 16, pp. 11189-11198
Open Access | Times Cited: 34
Spatially heterogeneous relationships of PM2.5 concentrations with natural and land use factors in the Niger River Watershed, West Africa
Lixia Zhou, Tao Wu, Lijie Pu, et al.
Journal of Cleaner Production (2023) Vol. 394, pp. 136406-136406
Closed Access | Times Cited: 18
Quantifying the impacts of emissions and meteorology on the interannual variations of air pollutants in major Chinese cities from 2015 to 2021
Qili Dai, Tianjiao Dai, Linlu Hou, et al.
Science China Earth Sciences (2023) Vol. 66, Iss. 8, pp. 1725-1737
Closed Access | Times Cited: 18
Prediction of daily PM2.5 and ozone based on high-density weather stations in China: Nonlinear effects of meteorology, human and ecosystem health risks
Lili Wang, Boya Liu, Rong Li, et al.
Atmospheric Research (2023) Vol. 293, pp. 106889-106889
Closed Access | Times Cited: 17
Spatiotemporal variations in meteorological influences on ambient ozone in China: A machine learning approach
Tao Li, Yichen Lu, Xunfei Deng, et al.
Atmospheric Pollution Research (2023) Vol. 14, Iss. 4, pp. 101720-101720
Closed Access | Times Cited: 15
Large-scale deployment of intelligent transportation to help achieve low-carbon and clean sustainable transportation
Zhenyu Jia, Jiawei Yin, Zeping Cao, et al.
The Science of The Total Environment (2024) Vol. 949, pp. 174724-174724
Closed Access | Times Cited: 6
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
Global change progressively increases foliar nitrogen–phosphorus ratios in China's subtropical forests
Yuan Lai, Songbo Tang, Hans Lambers, et al.
Global Change Biology (2024) Vol. 30, Iss. 2
Closed Access | Times Cited: 5
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: 5
Application of machine learning to analyze ozone sensitivity to influencing factors: A case study in Nanjing, China
Chenwu Zhang, Xie Yu-min, Min Shao, et al.
The Science of The Total Environment (2024) Vol. 929, pp. 172544-172544
Closed Access | Times Cited: 5
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
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