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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:

Predicting PM10 and PM2.5 concentration in container ports: A deep learning approach
So-Young Park, Su‐Han Woo, Changwon Lim
Transportation Research Part D Transport and Environment (2023) Vol. 115, pp. 103601-103601
Closed Access | Times Cited: 19

Showing 19 citing articles:

A novel method for ship carbon emissions prediction under the influence of emergency events
Yinwei Feng, Xinjian Wang, Jianlin Luan, et al.
Transportation Research Part C Emerging Technologies (2024) Vol. 165, pp. 104749-104749
Open Access | Times Cited: 13
Modeling PM2.5 forecast using a self-weighted ensemble GRU network: Method optimization and evaluation
Hengjun Huang, Chonghui Qian
Ecological Indicators (2023) Vol. 156, pp. 111138-111138
Open Access | Times Cited: 19
Intrinsic and extrinsic techniques for quantification uncertainty of an interpretable GRU deep learning model used to predict atmospheric total suspended particulates (TSP) in Zabol, Iran during the dusty period of 120-days wind
Hamid Gholami, Aliakbar Mohammadifar, Reza Dahmardeh Behrooz, et al.
Environmental Pollution (2023) Vol. 342, pp. 123082-123082
Closed Access | Times Cited: 13
Predicting dust pollution from dry bulk ports in coastal cities: A hybrid approach based on data decomposition and deep learning
Wenyuan Wang, Bochi Liu, Qi Tian, et al.
Environmental Pollution (2024) Vol. 350, pp. 124053-124053
Closed Access | Times Cited: 5
Hourly PM2.5 concentration prediction for dry bulk port clusters considering spatiotemporal correlation: A novel deep learning blending ensemble model
Jinxing Shen, Q. Liu, Xuejun Feng
Journal of Environmental Management (2024) Vol. 370, pp. 122703-122703
Closed Access | Times Cited: 4
Combined Prediction of PM10 Concentration at Smart Construction Sites Based on Quadratic Mode Decomposition and Deep Learning
Xiaogang Li, Xin Li, Kaikai Kang, et al.
Sustainability (2025) Vol. 17, Iss. 2, pp. 616-616
Open Access
Explainable deep learning hybrid modeling framework for total suspended particles concentrations prediction
Sujan Ghimire, Ravinesh C. Deo, Ningbo Jiang, et al.
Atmospheric Environment (2025), pp. 121079-121079
Closed Access
Application of machine learning models for PM2.5 prediction in bengaluru using precursor air pollutants and meteorological data
Gourav Suthar, Saurabh Singh
Theoretical and Applied Climatology (2025) Vol. 156, Iss. 3
Closed Access
TEMDI: A Temporal Enhanced Multisource Data Integration model for accurate PM2.5 concentration forecasting
Ke Ren, Kangxu Chen, Chengyao Jin, et al.
Atmospheric Pollution Research (2024) Vol. 15, Iss. 11, pp. 102269-102269
Closed Access | Times Cited: 4
Applicability of denoising-based artificial intelligence to forecast the environmental externalities
Dongsheng Cai, Ghazala Aziz, Suleman Sarwar, et al.
Geoscience Frontiers (2023) Vol. 15, Iss. 3, pp. 101740-101740
Open Access | Times Cited: 10
A hybrid deep learning model–based LSTM and modified genetic algorithm for air quality applications
Oumaima Bouakline, Youssef El Merabet, Abdelhak Elidrissi, et al.
Environmental Monitoring and Assessment (2024) Vol. 196, Iss. 12
Closed Access | Times Cited: 1
Machine learning and deep learning approaches for PM2.5 prediction: a study on urban air quality in Jaipur, India
Saurabh Singh, Gourav Suthar
Earth Science Informatics (2024) Vol. 18, Iss. 1
Closed Access | Times Cited: 1
Optimizing air quality predictions: A discrete wavelet transform and long short‐term memory approach with wavelet‐type selection for hourly PM10 concentrations
Gökçe Nur Taşağıl Arslan, Serpil Kılıç Depren
Journal of Chemometrics (2024) Vol. 38, Iss. 4
Closed Access | Times Cited: 1
HUs Fleet Management in an Automated Container Port: Assessment by a Simulation Approach
Domenico Gattuso, Domenica Savia Pellicanò
Sustainability (2023) Vol. 15, Iss. 14, pp. 11360-11360
Open Access | Times Cited: 2
Evaluation of Deep Learning Models for Predicting the Concentration of Air Pollutants in Urban Environments
Edgar Tello-Leal, Ulises Manuel Ramirez-Alcocer, Bárbara A. Macías-Hernández, et al.
Sustainability (2024) Vol. 16, Iss. 16, pp. 7062-7062
Open Access
Charting sustainable vistas: Analysis of internal and external sustainability performance of Chinese ports
Yudan Kong, Xinyu Tian, Jinghui Sun, et al.
Transport Policy (2024)
Closed Access
Forecasting urban agglomeration air quality: A data-driven model with the Gaussian Decoupled Representation Extractor
W.X. Li, Yingfang Zhu
IEEE Access (2024) Vol. 12, pp. 183103-183116
Open Access
Comprehensive Analysis of PM2.5 Concentrations in the Seoul Metro Underground Stations: Relationships with Indoor Sources and Outdoor Air Quality
Hye-Ryun Oh, Doo‐Sun R. Park, Hua-Lun Ko, et al.
Asia-Pacific Journal of Atmospheric Sciences (2024) Vol. 61, Iss. 1
Closed Access
Predicting Dust Pollution from Dry Bulk Ports in Coastal Cities: A Hybrid Approach Based on Data Decomposition and Deep Learning
Wenyuan Wang, Bochi Liu, Qi Tian, et al.
(2023)
Closed Access | Times Cited: 1
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