Bibliometric Analysis of Per- and Polyfluoroalkyl Substances (PFASs) from 2000 to 2023 Based on Web of Science Database DOI Open Access

Zixuan Yin,

Li Cui, Xingyang Li

et al.

Water, Journal Year: 2024, Volume and Issue: 17(1), P. 6 - 6

Published: Dec. 24, 2024

Perfluorinated and polyfluoroalkyl substances (PFASs) have been extensively used in many fields since the 1950s due to their distinctive chemical stability. PFASs are becoming emerging pollutants, they attracted special attention all over world because of environmental persistence, bioaccumulation, potential toxicity. Through bibliometric analysis, this study provides a visual analysis 6055 articles about Web Science database from 2000 2023. Research on can be divided into two stages, 2000–2014 2015–2023, number publications frequency citations increase rapidly latter stage. Studies highly interdisciplinary, mainly focusing cluster ecological environmental, involving science, engineering, toxicology. The authors come 106 countries, with United States China being most productive contributors. However, has relatively low per article. A total 2634 institutions participated studies, USA outstanding. An author cooperation shows that lead publication output research activity. Some Chinese rank among top contributors, but there is need for stronger international cooperation. Keyword clusters burst reveal key areas PFASs, including classification, behavior, health effects, removal methods. This comprehensive perspective offering valuable insights trends serving as critical reference future research, policy development, technological innovation.

Language: Английский

Global research landscape on coagulation-flocculation for wastewater treatment: A 2000–2023 bibliometric analysis DOI
Mohamed Hizam Mohamed Noor, Norzita Ngadi

Journal of Water Process Engineering, Journal Year: 2024, Volume and Issue: 64, P. 105696 - 105696

Published: July 1, 2024

Language: Английский

Citations

16
Artificial intelligence driven advances in wastewater treatment: Evaluating techniques for sustainability and efficacy in global facilities DOI Creative Commons

Dhanyashree Narayanan,

Manish Bhat,

Norottom Paul

et al.

Desalination and Water Treatment, Journal Year: 2024, Volume and Issue: 320, P. 100618 - 100618

Published: July 17, 2024

Language: Английский

Citations

7
A comprehensive overview of the applications of kernel functions and data-driven models in regression and classification tasks in the context of software sensors DOI Creative Commons
Joyce Chen Yen Ngu, Wan Sieng Yeo,

Teck Fu Thien

et al.

Applied Soft Computing, Journal Year: 2024, Volume and Issue: 164, P. 111975 - 111975

Published: July 10, 2024

Data-driven models can reduce the number of hardware sensors in a process plant by acting as low-cost substitutes for sensors. Since some data-driven have difficulty dealing with nonlinear data, kernel functions been integrated into due to their capability handle this behavior data. However, existing review studies on and regression classification are still limited. Moreover, functions, most research only focused radial basis function group, such gaussian hyperbolic tangent functions. Considering these gaps, study aims summarize up-to-date cumulative application categories, integration models. Different from other studies, discussed characteristics, advantages, disadvantages different Additionally, also summarizes critically reviews tasks, including advantages disadvantages. discovers state art that were used classification. Besides, found mostly task rather than task. In addition, is be applied various applications. Lastly, it recommended emphasize integrating adaptive industrial

Language: Английский

Citations

5
Enhancing Decision Making and Decarbonation in Environmental Management: A Review on the Role of Digital Technologies DOI Open Access
Abdel‐Mohsen O. Mohamed, Dina Mohamed, Adham Fayad

et al.

Sustainability, Journal Year: 2024, Volume and Issue: 16(16), P. 7156 - 7156

Published: Aug. 20, 2024

As global concerns about climate change intensify, the need for effective strategies to reduce carbon emissions, has never been more urgent. This review paper explores crucial role of digital technologies (i.e., data automation (DA) and decision support systems (DSSs)) in enhancing making achieving a ZERONET initiative (decarbonation efforts) within realms solid waste management (SWM), wastewater treatment (WWT), contaminated soil remediation (CSR). Specifically, provides (a) an overview footprint (CFP) relation environmental (EM) DA DSS decarbonization; (b) case studies areas SWM, WWT, CSR use (i) technology; ((ii) life cycle assessment (LCA)-based DSS; (iii) multi-criteria analysis (MCDA)-based (c) optimal contractual delivery method-based EM practices. concludes that adoption DSSs holds significant potential decarbonizing processes. By optimizing operations, resource efficiency, integrating renewable energy sources, smart can contribute reduction GHG emissions promotion sustainable demand eco-friendly solutions grows, will become increasingly pivotal decarbonization goals.

Language: Английский

Citations

5
Artificial intelligence in environmental monitoring: in-depth analysis DOI Creative Commons
Emran Alotaibi, Nadia Nassif

Discover Artificial Intelligence, Journal Year: 2024, Volume and Issue: 4(1)

Published: Nov. 18, 2024

Abstract This study provides a comprehensive bibliometric and in-depth analysis of artificial intelligence (AI) machine learning (ML) applications in environmental monitoring, based on 4762 publications from 1991 to 2024. The research highlights notable increase citations since 2010, with China, the United States, India emerging as leading contributors. Key areas include air water quality climate change modeling, biodiversity assessment, disaster management. integration AI technologies, such Internet Things (IoT) remote sensing, has significantly expanded real-time monitoring capabilities data-driven decision-making. In-depth reveals advancements AI/ML methodologies, including novel algorithms for soil mapping, land-cover classification, flood susceptibility sensing image analysis. Notable enhanced predictions, assessments, impact forecasting, automated wildlife using AI-driven recognition. Challenges “black-box” nature models, need high-quality data resource-constrained regions, complexity management are also addressed. ongoing efforts develop explainable (XAI) which aim improve model transparency trust critical applications. Future directions emphasize improving availability, fostering interdisciplinary collaborations across computer sciences, addressing ethical considerations These findings underscore transformative potential ML technologies sustainable management, offering valuable insights researchers policymakers global challenges.

Language: Английский

Citations

5
Application of Machine Learning in Plastic Waste Detection and Classification: A Systematic Review DOI Open Access
Edgar Ovidio Barrón Ramos, Arminda Lopes, Fábio Mendonça

et al.

Processes, Journal Year: 2024, Volume and Issue: 12(8), P. 1632 - 1632

Published: Aug. 3, 2024

The intersection of artificial intelligence and environmental sustainability has become a relevant exploration domain in the contemporary era rapid technological advancements complex global challenges. This work reviews application machine learning (ML) models to address pressing issue plastic waste (PW) management. By systematically examining state art with snowballing, this research aims determine efficiency effectiveness ML-based methods for PW detection classification. Considering increasing concerns information processing potential, article hypothesised that ML could contribute more sustainable management practices. For purpose, two scientific repositories were examined from 2000 2023, 188 articles identified. After systematic screening procedure, 28 selected. Additionally, included by snowballing. It was observed accuracy either or classification problems often exceeded 80% benchmark, further improving when model combination employed. As result, strong support reached applicable potential PW. also concluded based on convolutional neural networks most commonly used.

Language: Английский

Citations

4
Effective sonophotocatalytic degradation of tetracycline in water: Optimization, kinetic modeling, and degradation pathways DOI
Ansaf V. Karim, Grzegorz Boczkaj, Amritanshu Shriwastav

et al.

Chemical Engineering and Processing - Process Intensification, Journal Year: 2024, Volume and Issue: 205, P. 109979 - 109979

Published: Sept. 3, 2024

Language: Английский

Citations

4
Application of novel polymer materials for anti-fouling control of landfills: A comprehensive durability evaluation DOI

Ke Jiao,

Jia Li, Jingwei Zhang

et al.

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 376, P. 124354 - 124354

Published: Feb. 15, 2025

Language: Английский

Citations

0
Data governance and open sharing in the fields of life sciences and medicine: A bibliometric analysis DOI Creative Commons

Yinfeng Qiu,

Zhimin Hu

Digital Health, Journal Year: 2025, Volume and Issue: 11

Published: Jan. 1, 2025

Objective This study aims to conduct a bibliometric analysis of literature related data governance and open sharing in the fields life sciences medicine, so as clarify characteristics publications explore research hotspots trends. Methods A total 2529 valid documents published Web Science Core Collection database from 2000 2024 were included this study. VOSviewer was used for co-occurrence analysis, while CiteSpace employed clustering, burst detection, thematic evolution analysis. Results Between 2024, number studies on medicine has increased annually, indicating growing importance area. The USA led country with most output field. University Oxford institution highest publication volume, Amy L. McGuire active author, Journal Medical Internet Research American Informatics Association frequent outlets. cited reference ‘Comment: FAIR Guiding Principles Scientific Data Management Stewardship’. Conclusions Topics such principles, ethical issues, public attitudes toward sharing, quality, databases, big techniques are Potential frontiers include trust application artificial intelligence technology epidemiological health data, chronic diseases diabetes, construction models.

Language: Английский

Citations

0
Worldwide Research Progress and Trends in Application of Machine Learning to Wastewater Treatment: A Bibliometric Analysis DOI Open Access
Kun Zhou, Boran Wu, Xin Zhang

et al.

Water, Journal Year: 2025, Volume and Issue: 17(9), P. 1314 - 1314

Published: April 28, 2025

Efficient wastewater treatment with high-quality effluent and minimal operational costs carbon emissions is vital for safeguarding the ecological environment promoting human health. However, process extremely complicated due to characteristics of multiple mechanisms, high disturbance variability nonlinear behaviors; therefore, optimizing through intelligent control a long-standing challenge researchers operators. Machine learning models are regarded as effective tools better simulating controlling complex behaviors. With aid bibliometric analysis, this paper aimed summarize worldwide research progress trends in application machine among 1226 related publications. The findings indicate that China United States two leading countries, publications 342 209, respectively, while an outstanding global collaboration leader field. Research institutions authors mainly from developing accounts largest proportion these. analysis journal cited contributions report almost all top 10 journals belong Q1 quartile (9/10). Overall, future will likely focus on systematic, strong multi-objective treatment. A hybrid model could take advantage or more mechanistic models, which have been verified excellent tackling limited data. Thus, predicting pollutants rather than influent using attracting increasing attention because prediction contributes reducing loading shock sharp fluctuation quality. Also, development advanced data acquirement devices AI partially default should also be another research.

Language: Английский

Citations

0