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Papers
61,005 resultsShowing papers similar to Chemical Detection Using Mobile Platforms and AI-Based Data Processing Technologies
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 and IoT driven technologies for environmental pollution monitoring and management
This review explores how artificial intelligence and Internet of Things sensors can be used to detect and monitor environmental pollutants, including microplastics, in air, water, and soil. Machine learning methods show promise for improving pollution tracking and prediction, but challenges remain around data sharing and model reliability. Advanced monitoring technology could play a key role in identifying and managing microplastic contamination in the environment.
How AI methods enhance the design and performance of nanophotonic environmental sensors: a systematical review
This review examines how artificial intelligence methods including machine learning and deep learning are being integrated with nanophotonic sensor platforms to enhance environmental monitoring capabilities, with applications including microplastic and contaminant detection in portable, real-time systems.
Recent developments on aerial lab-on-a-drone platforms for remote environmental monitoring: A review.
This review surveys recent advances in drone-mounted 'lab-on-a-drone' platforms designed for remote detection of environmental contaminants including microplastics, volatile organic compounds, heavy metals, and pesticides. Researchers examine sensing materials, miniaturised analytical systems, and deployment strategies that enable spatial and temporal mapping of pollutant distributions in water and air.
Advances in Portable Heavy Metal Ion Sensors
This review covers advances in portable sensors for detecting heavy metal ions in the environment, including electrochemical, optical, and smartphone-based devices. While focused on heavy metals rather than microplastics directly, the technology is relevant because microplastics often carry heavy metals that can leach into water and food. Better field-testing tools could help track how microplastics transport toxic metals into the environment and human food sources.
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.
How AI methods enhance the design and performance of nanophotonic environmental sensors: a systematical review
Researchers reviewed how combining artificial intelligence with nanophotonic sensors — devices that use light at the nanoscale — dramatically improves the detection of environmental pollutants including microplastics, heavy metals, and organic chemicals. The pairing enables faster, more accurate, and portable real-time environmental monitoring.
Recent progress and technological advancements for detection of micro/nano-plastics in the environment
This review surveys the latest analytical tools for detecting micro- and nanoplastics across environmental samples, covering imaging, spectroscopy, electrochemical sensors, and artificial intelligence. It highlights how the very small size and chemical complexity of nanoplastics makes detection especially challenging, and discusses how AI integration is improving accuracy and throughput. Advancing detection methods is foundational to understanding the true scale of microplastic contamination and its risks to ecosystems and human health.
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.
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.
Artificial Intelligence-Based Microfluidic Platform for Detecting Contaminants in Water: A Review
This review explores how microfluidic devices combined with artificial intelligence can detect water pollutants including microplastics and nanoplastics in real-time, outside the laboratory. Traditional water testing requires large lab equipment, but these portable chip-based systems can identify contaminants quickly and accurately using machine learning. This technology could improve monitoring of microplastic contamination in drinking water and other water sources.
Towards sustainable environmental chemistry: A comprehensive review
This review traces the evolution of environmental chemistry toward sustainability, covering recent advances in green catalysis, waste valorization, AI-assisted environmental monitoring, carbon capture, and bioremediation as tools for reducing chemical pollution including microplastics.
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.
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.
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.
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.
Artificial Intelligence (AI) to Trace the Pathways of MPs
This book chapter examines how artificial intelligence tools—including machine learning and remote sensing—can be used to trace microplastic transport pathways across environments, improving the accuracy and scale of MP distribution mapping beyond what conventional monitoring can achieve.
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.
Artificial intelligence in environmental health and public safety: A comprehensive review of USA strategies
This review explores how artificial intelligence is being used in the United States to improve environmental health monitoring and public safety, including pollution tracking and disease surveillance. While not specifically about microplastics, the AI tools discussed, such as real-time sensor networks and predictive models, could be applied to monitoring microplastic contamination in air and water. The review highlights how technology could help identify and reduce human exposure to environmental pollutants including microplastics.
Managing Marine Environmental Pollution using Artificial Intelligence
This review explores how artificial intelligence technologies are being developed to monitor and manage marine environmental pollution, including plastic contamination. The study suggests that AI-based approaches such as automated detection and predictive modeling offer promising opportunities for understanding ocean pollution and supporting sustainability goals.
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.
Artificial intelligence in microplastic detection and pollution control
This review examines how artificial intelligence is being combined with spectroscopy and imaging techniques to dramatically improve the speed and accuracy of microplastic detection in the environment. Better detection methods are essential for tracking the sources and spread of microplastic pollution that ultimately affects human health through contaminated food and water.
Artificial intelligence-empowered collection and characterization of microplastics: A review
This review examines how artificial intelligence tools like robots and machine learning are being used to collect, identify, and characterize microplastic pollution more efficiently. Better detection technology matters for human health because accurately measuring microplastic contamination in water and soil is the first step toward understanding and reducing our exposure.
Role of AI in Microplastic Pollution Detection and management studies
This review assessed how artificial intelligence approaches—including machine learning and deep learning—are being applied to detect, identify, and monitor microplastics in environmental and biological samples. The authors found AI substantially accelerates microplastic characterization workflows but that training data quality and standardization across studies remains a limiting factor.