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61,005 resultsShowing papers similar to Smart and Sustainable Technological Framework for Microplastic Pollution Mitigation
ClearIntegrating 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.
Microplastics Detection in Soil and Water: Leveraging IoT Technologies for Environmental Sustainability
Researchers explored the integration of IoT sensor technologies for detecting and monitoring microplastics in soil and water environments, proposing a connected sensing framework for real-time environmental surveillance. The system enables automated data collection and remote monitoring of microplastic contamination.
An Internet-of-Things (IoT) Sustainable Water Filtering and Monitoring System using Big Data Analysis and Clean Energy
Researchers developed MyRiiver, a solar-powered IoT-based water filtration and monitoring system designed to remove microplastics from freshwater ecosystems, integrating big data analytics for real-time environmental monitoring. The system addresses limitations of current microplastic filtration methods and demonstrates the potential of smart technologies for freshwater pollution management.
Real-time detection of microplastics in aquatic environments using emerging technologies
Researchers proposed a real-time microplastic detection system combining AI-enhanced optical sensors and IoT devices, capable of automatically classifying microplastics in ocean water without the time-consuming manual steps required by spectroscopy or microscopy.
Tracking microplastic pathways: Real-time IoT monitoring for water quality and public health
Researchers developed a low-cost, IoT-enabled system called TEMPT for real-time microplastic detection in water using turbidity sensors. The accompanying algorithm achieved 91.47 percent accuracy in identifying microplastic contamination, outperforming conventional methods. The study demonstrates how affordable sensor technology could enable large-scale monitoring of microplastic pollution in diverse water bodies.
A Novel Low-Cost Approach For Detection, Classification, and Quantification of Microplastic Pollution in Freshwater Ecosystems using IoT devices and Instance Segmentation
Researchers developed a novel low-cost IoT-based system combining instance segmentation algorithms for the automated detection, classification, and quantification of microplastic pollution in freshwater ecosystems, addressing the scalability limitations of conventional laboratory methods. The approach demonstrated feasibility for wide-scale environmental monitoring by enabling real-time microplastic analysis without expensive laboratory infrastructure.
Advancing environmental sustainability through emerging AI-based monitoring and mitigation strategies for microplastic pollution in aquatic ecosystems
This review explores how artificial intelligence technologies, including machine learning, computer vision, and remote sensing, can improve the detection, tracking, and removal of microplastic pollution in waterways. Researchers found that AI-based approaches offer significant advantages over traditional monitoring methods for identifying microplastic distribution patterns. The study highlights the potential of AI-driven robotic systems to support more efficient and scalable environmental cleanup efforts.
Smart and Sustainable Microplastic Removal: Hybrid Systems, Bio-Inspired Technologies, Real-Time Sensing, and Policy Integration
This review covers emerging technologies for removing microplastics from water, including hybrid systems that combine physical, chemical, and biological methods, as well as bio-inspired designs that mimic natural filtration. New sensor technologies and smart monitoring systems for real-time microplastic detection are also discussed. The authors emphasize that effective solutions will require both technological innovation and coordinated policy action across industries and governments.
Towards an IOT Based System for Detection and Monitoring of Microplastics in Aquatic Environments
This paper proposes using Internet of Things (IoT) sensors to build a real-time monitoring network for microplastics in aquatic environments. Automated, continuous monitoring systems could provide much better spatial and temporal coverage than current sampling-based approaches.
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.
Air and Water Microplastics Detection & Refinement System with Advanced Technology
Researchers proposed an integrated detection and remediation system for airborne and waterborne microplastics using sensor arrays and filtration technologies, designed to function in both urban and rural settings with minimal infrastructure requirements.
Microplastic menace: a path forward with innovative solutions to reduce pollution
This paper reviewed microplastic contamination as a complex and persistent pollutant class in aquatic ecosystems and surveyed innovative solutions being developed to reduce pollution, including advanced detection methods, filtration technologies, and biological degradation approaches. The review emphasized the need for integrated strategies spanning pollution prevention, monitoring, and remediation.
IoT-Integrated Image Recognition System for Microplastic Detection and Classification
Researchers developed an IoT-based system that combines a microscopic camera with a YOLOv8 deep learning model to detect and classify microplastics in real time, including types like LDPE, PE, PHA, and PS. The system achieves high accuracy across diverse environmental conditions and visualizes data through a cloud-based dashboard. This scalable approach offers a practical tool for monitoring microplastic pollution, with potential for future integration on marine vessels.
IoT-Driven Image Recognition for Microplastic Analysis in Water Systems using Convolutional Neural Networks
Researchers developed an IoT-based system using artificial intelligence to automatically detect and count microplastics in water samples through image recognition. The system uses cameras at distributed sensor points to continuously monitor waterways and can identify microplastics of different sizes, shapes, and colors. This technology could improve environmental monitoring of microplastic pollution in real time, helping communities and agencies respond faster to contamination threats in drinking water sources.
Survey on IoT Based Microplastic Detection
This research review summarizes new technology that uses internet-connected sensors to detect tiny plastic particles (microplastics) in water in real-time, rather than relying on slow lab tests. Microplastics are a growing health concern because they can get into our drinking water and food chain, potentially harming human health. Better detection methods could help protect our water supplies by catching pollution problems faster.
Harnessing Deep Learning for Real-Time Water Quality Assessment: A Sustainable Solution
Researchers developed a deep learning system that can predict water quality in real time based on measurements like pH, turbidity, and dissolved solids. While not directly about microplastics, this kind of AI-powered monitoring tool could eventually be adapted to detect microplastic contamination in water supplies more quickly and affordably than current lab-based methods.
Development of an Iot-Integrated AI System for Microplastic Detection in Water Samples
Researchers developed an IoT-integrated AI system using high-resolution microscopy, a Raspberry Pi platform, and machine learning to detect and classify microplastic particles in water samples in real time via MQTT, achieving detection accuracy exceeding 95% in simulated dataset validation.
Innovative Approaches for Microplastic Pollution Detection and Remediation in Aquatic Ecosystems
This study evaluated new technologies for detecting and cleaning up microplastic pollution in water environments, including advanced spectroscopy, sensor-based detection, bioremediation, and improved filtration systems. Researchers found that these innovative approaches significantly outperformed traditional methods in both identifying and removing microplastics. The work highlights the potential for emerging technologies to provide more effective solutions for tackling plastic pollution in rivers, lakes, and oceans.
Innovative prototype for the mitigation of water pollution from microplastics to safeguard the environment and health
Researchers developed an innovative prototype device for removing microplastics from water through a combination of filtration and electrocoagulation, demonstrating high MP removal efficiency from both synthetic and real water samples in controlled trials.
Advances in microplastic mitigation: current progress and future directions
This review synthesizes recent advances in biotechnology-based approaches to microplastic remediation, including microbial degradation, engineered enzyme systems, and AI-driven monitoring. Researchers found that while promising enzymes and engineered biofilm systems have been demonstrated in the lab, translating these solutions to diverse polymer types and real-world field applications remains a major challenge. The study proposes a unified roadmap for scaling sustainable biotechnology solutions to address the global microplastic crisis.
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.
Microplastics in ecosystems: ecotoxicological threats and strategies for mitigation and governance
This review provides a broad assessment of microplastic pollution across ecosystems, covering sources, detection methods, ecological impacts, and cleanup strategies. The study highlights recent advances including AI-enhanced detection tools and microbe-based degradation approaches, and proposes a roadmap for working toward microplastic-free environments through coordinated scientific and policy action.
An Artificial Intelligence based Optical Sensor for Microplastic Detection in Seawater
Researchers developed an AI-based optical sensor system combining an optical detection subsystem and an image acquisition subsystem to detect and identify microplastic particles in seawater, distinguishing them from naturally occurring marine particles. The device applies AI algorithms to analyze consecutive image frames and classify particles as microplastic or non-microplastic, with the full system housed in two portable cases.
Integrated Approaches to Water Quality Assessment and Treatment: A Comprehensive Review
This comprehensive review integrates physical, chemical, and biological water quality parameters, examines major pollution sources including emerging contaminants like microplastics, and surveys advances in real-time IoT-enabled monitoring and integrated treatment approaches.