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61,005 resultsShowing papers similar to Mapping the Rise in Machine Learning in Environmental Chemical Research: A Bibliometric Analysis
ClearThe application of machine learning to air pollution research: A bibliometric analysis
Researchers conducted a bibliometric analysis of 2,962 studies on machine learning applied to air pollution research from 1990 to 2021, finding that publications surged after 2017, with most research focused on pollutant characterization, short-term forecasting, detection improvement, and emission control. The analysis reveals that machine learning is becoming a powerful tool for understanding atmospheric chemistry and managing air quality, though global collaboration remains limited.
The supporting role of Artificial Intelligence and Machine/Deep Learning in monitoring the marine environment: a bibliometric analysis
This review examines the supporting role of artificial intelligence and machine learning in monitoring and managing plastic pollution, covering applications in remote sensing, image-based plastic detection, and predictive modeling of plastic fate. The authors identify deep learning for image classification and satellite-based detection as the most rapidly advancing AI applications in plastic pollution science.
Current applications and future impact of machine learning in emerging contaminants: A review
This review examines how machine learning is being applied to emerging contaminant research including microplastics, covering identification, environmental behavior prediction, bioeffect assessment, and removal optimization of these pollutants.
Application of Machine learning techniques in environmental governance: A review
This paper is not relevant to microplastics research — it reviews the application of machine learning methods in environmental governance broadly, covering air and water quality monitoring and land use management.
A Critical Review on Artificial Intelligence—Based Microplastics Imaging Technology: Recent Advances, Hot-Spots and Challenges
Researchers reviewed the use of artificial intelligence and machine learning techniques for detecting and identifying microplastics in environmental samples. The study found that AI-based imaging tools can significantly speed up analysis and improve accuracy compared to traditional manual methods. However, challenges remain around standardizing datasets and making these tools accessible for routine environmental monitoring.
Research Progress on the Application of Machine Learning in New Pollutants Studies
This review examines how machine learning is being applied to study new pollutants including microplastics, pharmaceuticals, and PFAS, covering detection, migration modeling, risk assessment, and remediation design. The authors identify that deep learning and ensemble methods have shown the strongest performance across these applications.
Artificial Intelligence-Driven Environmental Toxicology: Predictive Toxicity Modelling, Forensic Pollution Analysis, and AI-Enabled Public Health Surveillance
This research review shows how artificial intelligence and machine learning can help scientists better predict how environmental pollutants might harm human health, replacing slower traditional testing methods. AI can analyze huge amounts of environmental data to identify pollution sources, predict toxic effects, and track public health threats in real-time. This technology could help protect communities by catching environmental health risks earlier and providing better evidence for legal cases against polluters.
Machine learning: Next promising trend for microplastics study
This review explains how machine learning -- a type of artificial intelligence -- is being applied to microplastics research to speed up identification, predict pollution patterns, and analyze environmental risks. Traditional methods of identifying microplastics are slow and labor-intensive, but machine learning can process large datasets much faster and more accurately. Better detection tools are important because they help scientists understand the true scale of human microplastic exposure.
COVID-19 and the emerging research trends in environmental studies: a bibliometric evaluation
Researchers conducted a bibliometric analysis of 495 environmental science publications from the COVID-19 pandemic era, identifying key research trends including environmental quality assessment, increased chemical disinfectant exposure, worsening solid waste management, and strategies for post-pandemic urban planning. The study maps how the pandemic reshaped environmental research priorities and revealed both temporary environmental improvements and new pollution challenges.
Unveiling the research landscape of planetscope data in addressing earth-environmental issues: a bibliometric analysis
This bibliometric analysis examined scientific publications using PlanetScope satellite imagery from 2017 to 2023, analyzing 582 documents to map research trends and application areas. The study found growing use of high-resolution PlanetScope data for land use classification, agriculture, and environmental monitoring, with machine learning increasingly applied to enhance analysis.
机器学习在新污染物环境风险识别与防控上的研究进展与挑战
This Chinese review examines advances and challenges in applying machine learning to identify environmental risks from emerging contaminants, covering how AI-driven models can accelerate hazard screening, source tracing, and pollution control for novel pollutants including microplastics.
Artificial Intelligence and Machine Learning Approaches for Automatic Microplastics Identification and Characterization
This review examines how artificial intelligence and machine learning algorithms are being applied to identify, characterize, and model microplastic pollution in the environment. The authors found that these tools can analyze large sensor datasets to detect microplastics in water bodies, predict transport patterns, and model adsorption behavior under various environmental conditions. The study highlights the growing role of computational approaches in understanding and mitigating microplastic contamination.
[Overview of the Application of Machine Learning for Identification and Environmental Risk Assessment of Microplastics].
This review examines the application of machine learning (ML) methods for identifying microplastics and assessing their environmental risks, covering techniques for improving the accuracy and reliability of microplastic detection across different environmental media. Researchers highlight how ML can systematically analyse pollution characteristics and support ecological risk evaluation of microplastic contamination.
Trends, challenges, and research pathways in emerging contaminants: a comprehensive bibliometric analysis
This large-scale bibliometric analysis reviewed over 62,000 studies on emerging contaminants published between 2000 and 2024, finding that microplastics are among the fastest-growing areas of environmental health research. The analysis highlights that understanding how microplastics interact with other pollutants like pharmaceuticals and endocrine disruptors is a critical frontier for protecting human health.
Detecting Chemical Contaminants in Water Using AI
This review examines how artificial intelligence and machine learning tools are being applied to detect chemical contaminants in water, including microplastics, covering sensor technologies, data processing approaches, and the potential for real-time monitoring systems.
Bibliometric analysis of microplastics research: Advances and future directions (2020–2024)
This bibliometric study analyzed trends in microplastics research from 2020 to 2024, finding a rapid increase in publications with growing specialization in areas like ecotoxicology, detection methods, and pollution control. Key research hotspots include microplastic effects on human health, interactions with other pollutants, and removal technologies. The analysis reveals that while the field is maturing rapidly, significant gaps remain in understanding real-world health impacts and developing effective remediation strategies.
Impact of Machine/Deep Learning on Additive Manufacturing: Publication Trends, Bibliometric Analysis, and Literature Review (2013-2022).
This bibliometric review analyzes a decade of publications on the intersection of machine and deep learning with additive manufacturing (3D printing). The study is focused on manufacturing technology trends and is unrelated to microplastic research.
Advances in machine learning for the detection and characterization of microplastics in the environment
This review examines how machine learning and artificial intelligence are being used to speed up and improve the detection of microplastics in the environment. Techniques like neural networks and computer vision can now automatically identify plastic types and count particles much faster than traditional manual methods, though challenges remain in standardizing these approaches.
Enhancing global microplastic pollution analysis using machine learning: a longitudinal study of seasonal trends and anomaly detection
This study used machine learning — specifically the L-BFGS-B optimization algorithm — combined with traditional environmental data to analyze global microplastic pollution patterns, forecast concentrations in data-sparse regions, and identify seasonal trends and anomalies. The approach generated high-resolution global pollution heatmaps and identified clusters of similarly affected areas, offering a way to prioritize monitoring and cleanup resources worldwide. Applying AI to environmental data in this way could dramatically improve our ability to understand and respond to the global scale of plastic pollution.
Using artificial intelligence to rapidly identify microplastics pollution and predict microplastics environmental behaviors
This review summarizes how artificial intelligence and machine learning are being used to identify, track, and predict the environmental behavior of microplastics in soil and water. AI methods can analyze the chemical composition, shape, and distribution of microplastics faster and more accurately than traditional techniques. The technology could help scientists better understand where microplastics accumulate and what risks they pose to ecosystems and human health.
Mapping Global Research Trends in Groundwater Quality (2010–2025): a Bibliometric Perspective
Researchers conducted a bibliometric analysis of groundwater quality research published in Scopus between 2010 and 2025, identifying key publication trends, influential authors, and emerging themes including microplastic pollution, extreme event-driven contamination, and AI-based monitoring techniques, while mapping the evolution of interdisciplinary approaches integrating climate science, environmental engineering, and public health.
Comparative bibliometric trends of microplastics and perfluoroalkyl and polyfluoroalkyl substances: how these hot environmental remediation research topics developed over time
A bibliometric analysis compared publication trends for microplastics and PFAS research, identifying parallel trajectories driven by growing public concern, regulatory attention, and international research collaboration, with both fields experiencing rapid growth since the early 2010s.
Smart Water, Smart Models: Algorithmic Assessment of Water Quality under Evolving Chemical and Industrial Stressors
This review examines how machine learning approaches — including deep neural networks, hybrid physics-data models, and reinforcement learning — can be applied to detect and predict emerging chemical pollutants such as microplastics and recycling byproducts in water quality monitoring systems.
Detection of Microplastics Using Machine Learning
Researchers reviewed and demonstrated machine learning approaches for detecting and classifying microplastics in environmental samples, finding that automated image analysis and spectral classification methods can improve the speed and accuracy of microplastic monitoring compared to manual methods.