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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:

Identifying sources of dust aerosol using a new framework based on remote sensing and modelling
Omid Rahmati, Farnoush Mohammadi, Seid Saeid Ghiasi, et al.
The Science of The Total Environment (2020) Vol. 737, pp. 139508-139508
Closed Access | Times Cited: 54

Showing 1-25 of 54 citing articles:

Atmospheric Dynamics and Numerical Simulations of Six Frontal Dust Storms in the Middle East Region
Nasim Hossein Hamzeh‎, Sara Karami, Dimitris G. Kaskaoutis, et al.
Atmosphere (2021) Vol. 12, Iss. 1, pp. 125-125
Open Access | Times Cited: 67
Predicting the number of dusty days around the desert wetlands in southeastern Iran using feature selection and machine learning techniques
Zohre Ebrahimi‐Khusfi, Ali Reza Nafarzadegan, Fatemeh Dargahian
Ecological Indicators (2021) Vol. 125, pp. 107499-107499
Open Access | Times Cited: 64
The Changes of Spatiotemporal Pattern of Rocky Desertification and Its Dominant Driving Factors in Typical Karst Mountainous Areas under the Background of Global Change
Bing Guo, Fei Yang, Junfu Fan, et al.
Remote Sensing (2022) Vol. 14, Iss. 10, pp. 2351-2351
Open Access | Times Cited: 47
Machine learning may accelerate the recognition and control of microplastic pollution: Future prospects
Fubo Yu, Xiangang Hu
Journal of Hazardous Materials (2022) Vol. 432, pp. 128730-128730
Closed Access | Times Cited: 44
Current applications and future impact of machine learning in emerging contaminants: A review
Lang Lei, Ruirui Pang, Zhibang Han, et al.
Critical Reviews in Environmental Science and Technology (2023) Vol. 53, Iss. 20, pp. 1817-1835
Closed Access | Times Cited: 25
A review on advancements in atmospheric microplastics research: The pivotal role of machine learning
Jiaer Yang, Zezhi Peng, Jian Sun, et al.
The Science of The Total Environment (2024) Vol. 945, pp. 173966-173966
Closed Access | Times Cited: 10
An integrated hybrid deep learning data driven approaches for spatiotemporal mapping of land susceptibility to salt/dust emissions
Bakhtiar Feizizadeh, Peyman Yariyan, Murat Yakar, et al.
Advances in Space Research (2025)
Closed Access | Times Cited: 1
Impact of Precipitation with Different Intensity on PM2.5 over Typical Regions of China
Xin Zhao, Yue Sun, Chuanfeng Zhao, et al.
Atmosphere (2020) Vol. 11, Iss. 9, pp. 906-906
Open Access | Times Cited: 59
Identifying sand and dust storm sources using spatial-temporal analysis of remote sensing data in Central Iran
Ramin Papi, A.A. Kakroodi, Masoud Soleimani, et al.
Ecological Informatics (2022) Vol. 70, pp. 101724-101724
Closed Access | Times Cited: 34
Dust source susceptibility mapping in Tigris and Euphrates basin using remotely sensed imagery
Ali Darvishi Boloorani, Najmeh Neysani Samany‬, Ramin Papi, et al.
CATENA (2021) Vol. 209, pp. 105795-105795
Closed Access | Times Cited: 41
Extracting Information on Rocky Desertification from Satellite Images: A Comparative Study
Junwei Pu, Xiaoqing Zhao, Pinliang Dong, et al.
Remote Sensing (2021) Vol. 13, Iss. 13, pp. 2497-2497
Open Access | Times Cited: 34
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: 15
Heavy metal pollution levels and health risk assessment of dust storms in Jazmurian region, Iran
Mojtaba Soleimani-Sardo, Mahboube Shirani, Vladimir Strezov
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 14
Visualized spatiotemporal data mining in investigation of Urmia Lake drought effects on increasing of PM10 in Tabriz using Space-Time Cube (2004-2019)
Hamed Ahmadi, Meysam Argany, Abolfazl Ghanbari, et al.
Sustainable Cities and Society (2021) Vol. 76, pp. 103399-103399
Closed Access | Times Cited: 29
Machine learning-based prediction of sand and dust storm sources in arid Central Asia
Wei Wang, Alim Samat, Jilili Abuduwaili, et al.
International Journal of Digital Earth (2023) Vol. 16, Iss. 1, pp. 1530-1550
Open Access | Times Cited: 11
Visual interpretation of satellite imagery for hotspot dust sources identification
Ali Darvishi Boloorani, Ramin Papi, Masoud Soleimani, et al.
Remote Sensing Applications Society and Environment (2022) Vol. 29, pp. 100888-100888
Open Access | Times Cited: 18
The contribution of erosive winds to dust pollution and analyzing influential factors in Yazd province, central Iran
Zohre Ebrahimi‐Khusfi, Mojtaba Soleimani-Sardo
International Journal of Environmental Science and Technology (2025)
Closed Access
The impact of natural factors and human activities on the dust-vulnerable regions in central Iran
Zohre Ebrahimi‐Khusfi, Abolfazl Ranjbar
Environmental Science and Pollution Research (2025)
Closed Access
Assessment of the impact of dust aerosols on crop and water loss in the Great Salt Desert in Iran
Mahdi Boroughani, Maziar Mohammadi, Fahimeh Mirchooli, et al.
Computers and Electronics in Agriculture (2021) Vol. 192, pp. 106605-106605
Closed Access | Times Cited: 22
Mapping of dust source susceptibility by remote sensing and machine learning techniques (case study: Iran-Iraq border)
Sima Pourhashemi, Mohammad Ali Zangane Asadi, Mahdi Boroughani, et al.
Environmental Science and Pollution Research (2022) Vol. 30, Iss. 10, pp. 27965-27979
Closed Access | Times Cited: 16
Mapping land degradation risk due to land susceptibility to dust emission and water erosion
Mahdi Boroughani, Fahimeh Mirchooli, Mojtaba Hadavifar, et al.
SOIL (2023) Vol. 9, Iss. 2, pp. 411-423
Open Access | Times Cited: 9
Dust detection and susceptibility mapping by aiding satellite imagery time series and integration of ensemble machine learning with evolutionary algorithms
Seyed Vahid Razavi-Termeh, Abolghasem Sadeghi‐Niaraki, Rizwan Ali Naqvi, et al.
Environmental Pollution (2023) Vol. 335, pp. 122241-122241
Closed Access | Times Cited: 9
Automatic identification of saltating tracks driven by strong wind in high-speed video using multiple statistical quantities of instant particle velocity
Hongji Zhou, Fanmin Mei, Chuan Lin, et al.
Aeolian Research (2024) Vol. 70-71, pp. 100940-100940
Closed Access | Times Cited: 3
Knowledge discovery of Middle East dust sources using Apriori spatial data mining algorithm
Ramin Papi, Sara Attarchi, Ali Darvishi Boloorani, et al.
Ecological Informatics (2022) Vol. 72, pp. 101867-101867
Closed Access | Times Cited: 14
Dust source susceptibility mapping based on remote sensing and machine learning techniques
Reza Jafari, Mohadeseh Amiri, Fatemeh Asgari, et al.
Ecological Informatics (2022) Vol. 72, pp. 101872-101872
Closed Access | Times Cited: 12
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