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Papers
61,005 resultsShowing papers similar to Advances and innovations in machine learning-based spectral detection methods for trace organic pollutants
ClearDetecting 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.
Artificial Intelligence (AI) Based Rapid Water Testing System
Researchers developed an AI-powered portable water testing system that combines multiple sensing techniques, including capacitance, resistance, UV, infrared, and Raman spectroscopy, to detect contaminants in real time. The system can identify a wide range of pollutants including microplastics, heavy metals, and organic compounds within seconds. The device aims to provide an accessible, rapid monitoring tool for water quality assessment in both industrial and domestic settings.
Integrating Machine Learning and IoT Technologies for Smart Water Quality Monitoring: Methods, Challenges, and Future Directions
Machine learning and IoT sensor technologies were integrated into a smart environmental monitoring system designed for real-time detection of pollutants including microplastics. The platform demonstrates how digital technologies can improve the spatial and temporal resolution of environmental contamination surveillance.
Application of Laser-Induced, Deep UV Raman Spectroscopy and Artificial Intelligence in Real-Time Environmental Monitoring—Solutions and First Results
Researchers tested a deep UV Raman spectrometer combined with artificial intelligence for real-time detection of nitrates, selected pharmaceuticals, and common microplastic polymers in water. The system demonstrated feasibility for continuous environmental monitoring of aquatic systems without extensive sample preparation.
Raman Spectroscopy Enhanced By Machine Learning For Effective Microplastic Detection In Aquatic Systems
Researchers explored combining Raman spectroscopy with machine learning techniques to improve microplastic detection and classification in aquatic systems. The study found that deep learning models, particularly convolutional neural networks, achieved high classification accuracy and significantly reduced reliance on labor-intensive manual spectral analysis for real-time environmental monitoring.
Advantages and Challenges of AI-Driven Water Quality Monitoring
This review outlined the opportunities and challenges of applying artificial intelligence to water quality monitoring, including real-time contaminant detection and predictive modeling. The authors highlight AI's potential to improve efficiency and reduce costs in monitoring systems, while noting data quality and model interpretability as key challenges.
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.
The Development of Sensors for Microplastic Detection Using Artificial Intelligence
This review examined AI-enhanced sensors developed for microplastic detection and characterization in aquatic environments, covering machine learning, deep learning, and spectroscopic sensor approaches. The authors found that AI substantially reduces the labor intensity of microplastic identification and improves detection of small particles, though training dataset standardization and real-world validation remain priority challenges.
Green Analytical Chemistry Strategies for Urban Wastewater Monitoring of Emerging Contaminants: a Machine Learning and Multi-technique Strategy
This study developed a green analytical chemistry strategy combining machine learning and multiple spectroscopic techniques to monitor emerging contaminants including microplastics, pharmaceuticals, and endocrine-disrupting chemicals in urban wastewater from Nigerian cities. The approach reduced toxic reagent use while improving detection accuracy for a wide range of contaminants.
Water Quality Monitoring And Ground Water Level Prediction Using Machine Learning
Researchers applied machine learning techniques to water quality monitoring and groundwater level prediction, demonstrating the potential of data-driven approaches for environmental sensing and resource management.
Possibilities of Real Time Monitoring of Micropollutants in Wastewater Using Laser-Induced Raman & Fluorescence Spectroscopy (LIRFS) and Artificial Intelligence (AI)
Researchers developed a real-time monitoring method combining deep-UV laser-induced Raman and fluorescence spectroscopy with AI-based analysis to detect micropollutants in wastewater treatment plants across different treatment stages.
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.
Monitoring Water Quality: Suggestions and Prospects
This review examined real-time water quality monitoring systems, evaluating sensors, data transmission technologies, and AI approaches for continuous assessment of physical, chemical, and biological parameters at scale. The authors proposed integrating IoT-connected sensor networks with machine learning to enable early warning of contamination events including microplastic and pathogen loads.
Monitoring Water Quality: Suggestions and Prospects
This review examined real-time water quality monitoring systems, evaluating sensors, data transmission technologies, and AI approaches for continuous assessment of physical, chemical, and biological parameters at scale. The authors proposed integrating IoT-connected sensor networks with machine learning to enable early warning of contamination events including microplastic and pathogen loads.
Recent Progresses in Machine Learning Assisted Raman Spectroscopy
This review covers how machine learning is being combined with Raman spectroscopy to improve the analysis of complex materials, including environmental samples. Traditional spectral analysis methods struggle with the volume and complexity of modern data, but AI techniques can extract meaningful patterns more efficiently. These advances are directly relevant to microplastic identification, where Raman spectroscopy is a primary detection tool.
Artificial intelligence (AI) based rapid water testing system
Researchers developed an AI-powered portable water testing system that integrates five analytical techniques for real-time water quality monitoring. The system can detect a range of contaminants including microplastics, heavy metals, and pathogens within seconds, offering a cost-effective alternative to traditional laboratory-based water testing for both industrial and domestic use.
Artificial Intelligence (AI) Based Rapid Water Testing System
Researchers developed an AI-powered portable water testing system that combines five analytical techniques to detect contaminants including heavy metals, pathogens, and microplastics in real time. The device uses an embedded machine learning model trained on diverse water samples to recognize contamination patterns. The study demonstrates a cost-effective approach to rapid water quality monitoring that could help identify microplastic pollution in both industrial and domestic water supplies.
Multi Analyte Concentration Analysis of Marine Samples Through Regression Based Machine Learning
Researchers used Raman spectroscopy combined with machine learning to identify concentrations of multiple chemical compounds in marine water samples. The study demonstrates that this approach offers a low-cost, portable method for monitoring ocean chemistry, which is relevant for understanding environmental health in marine ecosystems.
Recent advances in the application of machine learning methods to improve identification of the microplastics in environment
This review examined a decade of progress in applying machine learning algorithms to microplastic identification, finding that support vector machines and artificial neural networks significantly improve detection accuracy and efficiency when combined with spectroscopic techniques like FTIR and Raman.
Machine learning assisted Raman spectroscopy: A viable approach for the detection of microplastics
This review covers how machine learning combined with Raman spectroscopy can improve the detection and identification of microplastics in environmental samples. Traditional detection methods are slow and have limitations in resolution and particle size analysis, but AI algorithms can process spectral data more quickly and accurately. Better detection tools are essential for understanding the true scale of microplastic contamination in our water, food, and environment.
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.
Advances in the Development of Innovative Sensor Platforms for Field Analysis
This review examined advances in innovative sensor platforms for field environmental analysis, covering technologies for monitoring inorganic and organic air and water pollutants. The authors identified integration of sensing technologies with robotics and the Internet of Things as key future directions for enabling diffuse, real-time environmental monitoring campaigns.
Water Quality Management in the Age of AI: Applications, Challenges, and Prospects
This review examines how artificial intelligence is transforming water quality management through improved monitoring, prediction, and pollution tracking. Researchers found that combining AI with technologies like the Internet of Things and remote sensing has significantly enhanced real-time water quality analysis and early warning systems. However, major challenges remain around data quality, model transparency, and the ability to detect emerging pollutants like microplastics.
Prediction and Optimization of Process Parameters using Artificial Intelligence and Machine Learning Models
This review examined how artificial intelligence and machine learning models are being used to predict and optimize parameters for removing heavy metals and textile dyes from water. Researchers evaluated common AI approaches including artificial neural networks and genetic algorithms for improving water treatment efficiency. The study highlights the growing role of computational tools in designing more effective environmental remediation processes.