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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:

Increased contribution to PM2.5 from traffic-influenced road dust in Shanghai over recent years and predictable future
Meng Wang, Yusen Duan, Zhuozhi Zhang, et al.
Environmental Pollution (2022) Vol. 313, pp. 120119-120119
Closed Access | Times Cited: 21

Showing 21 citing articles:

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
Simulation and prediction of PM2.5 concentrations and analysis of driving factors using interpretable tree-based models in Shanghai, China
Wei Qing, Yongqi Chen, Huijin Zhang, et al.
Environmental Research (2025), pp. 121003-121003
Closed Access | Times Cited: 1
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
Traffic volume and road network structure: Revealing transportation-related factors on PM2.5 concentrations
Chengcheng Yu, Yongqi Deng, Zhengtao Qin, et al.
Transportation Research Part D Transport and Environment (2023) Vol. 124, pp. 103935-103935
Closed Access | Times Cited: 25
Application of machine learning in atmospheric pollution research: A state-of-art review
Zezhi Peng, Bin Zhang, Diwei Wang, et al.
The Science of The Total Environment (2023) Vol. 910, pp. 168588-168588
Closed Access | Times Cited: 23
Assessment of human and meteorological influences on PM10 concentrations: Insights from machine learning algorithms
Puneet Verma, Ritu Verma, Marc Mallet, et al.
Atmospheric Pollution Research (2024) Vol. 15, Iss. 6, pp. 102123-102123
Open Access | Times Cited: 8
Advancements in machine learning for spatiotemporal urban on-road traffic-air quality study: a review
Zhanxia Du, Hanbing Li, Sha Chen, et al.
Atmospheric Environment (2025), pp. 121054-121054
Closed Access
Spatiotemporal correlations of PM2.5 and O3 variations: A street-scale perspective on synergistic regulation
Xinyuan Lin, Yangbin Dong, Z.K. Teng, et al.
The Science of The Total Environment (2025) Vol. 965, pp. 178578-178578
Closed Access
Elucidating ozone and PM2.5 pollution in the Fenwei Plain reveals the co-benefits of controlling precursor gas emissions in winter haze
Chunshui Lin, Ru‐Jin Huang, Haobin Zhong, et al.
Atmospheric chemistry and physics (2023) Vol. 23, Iss. 6, pp. 3595-3607
Open Access | Times Cited: 13
Does window/door opening behaviour during summer affect the bedroom environment and sleep quality in a high-density sub-tropical city
Yan Yan, Mengyuan Kang, Haodong Zhang, et al.
Building and Environment (2023) Vol. 247, pp. 111024-111024
Closed Access | Times Cited: 5
Evaluating Long-Term Reductions in Trace Metal Emissions from Shipping in Shanghai
Meng Wang, Yusen Duan, Juntao Huo, et al.
Journal of Hazardous Materials (2024) Vol. 480, pp. 136367-136367
Closed Access | Times Cited: 1
Reduction in vehicular emissions attributable to the Covid-19 lockdown in Shanghai: insights from 5 years of monitoring-based machine learning
Meng Wang, Yusen Duan, Zhuozhi Zhang, et al.
Atmospheric chemistry and physics (2023) Vol. 23, Iss. 18, pp. 10313-10324
Open Access | Times Cited: 4
Characteristic Analysis and Health Risk Assessment of PM2.5 and VOCs in Tianjin Based on High-Resolution Online Data
Yanqi Huangfu, Feng Wang, Qili Dai, et al.
Toxics (2024) Vol. 12, Iss. 9, pp. 622-622
Open Access | Times Cited: 1
Machine learning exploring the chemical compositions characteristics and sources of PM2.5 from reduced on-road activity
Dan Liao, Youwei Hong, Huabin Huang, et al.
Atmospheric Pollution Research (2024) Vol. 15, Iss. 11, pp. 102265-102265
Closed Access
Understanding the variability of ground-level ozone and fine particulate matter over the Tibetan Plateau with data-driven approach
Haobin Zhong, Ling Zhen, Lin Yang, et al.
Journal of Hazardous Materials (2024) Vol. 477, pp. 135341-135341
Closed Access
Measuring the fine particulate exposure levels of building occupants using localized sensors
Elaheh Samandi, Arezoo Shirazi, Sidney Newton
Building and Environment (2023) Vol. 242, pp. 110403-110403
Closed Access | Times Cited: 1
Meteorologically normalized spatial and temporal variations investigation using a machine learning-random forest model in criteria pollutants across Tehran, Iran
Mohammad Saleh Ali-Taleshi, Alireza Riyahi Bakhtiari, Philip K. Hopke
Urban Climate (2023) Vol. 53, pp. 101790-101790
Closed Access | Times Cited: 1
Comment on egusphere-2022-1440

(2023)
Open Access
Comment on egusphere-2022-1440
Chunshui Lin, Ru‐Jin Huang, Haobin Zhong, et al.
(2023)
Open Access
Comment on egusphere-2022-1440
Ru‐Jin Huang
(2023)
Open Access
Elucidating ozone and PM2.5 pollution in Fenwei Plain reveals the co-benefits of controlling precursor gas emissions in winter haze
Chunshui Lin, Ru‐Jin Huang, Haobin Zhong, et al.
(2022)
Open Access
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