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Papers
61,005 resultsShowing papers similar to An Internet-of-Things (IoT) Sustainable Water Filtering and Monitoring System using Big Data Analysis and Clean Energy
ClearSmart and Sustainable Technological Framework for Microplastic Pollution Mitigation
Researchers proposed a smart technological framework for microplastic pollution mitigation that integrates IoT-based monitoring, machine learning analytics, and eco-friendly remediation technologies. The system uses low-power sensors for continuous detection of microplastic contamination and sustainable filtration mechanisms with biodegradable adsorbent materials for cleanup. The framework emphasizes modular design and renewable energy integration to support long-term deployment across diverse aquatic environments.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
An IoT Based Low-Cost Optical System for Early Detection of Microplastics in Water Sources
Scientists have developed a low-cost system that can detect tiny plastic particles (microplastics) in drinking water using simple light sensors and internet technology. This matters because microplastics are found in tap water worldwide and may pose health risks when we drink them, but current detection methods are too expensive for regular monitoring. The new system could make it easier and cheaper to check water quality continuously, helping protect people from plastic pollution in their drinking water.
A Novel Application of Filtration for the Collection of Microplastics in Waterways
Researchers developed a novel filtration system for collecting microplastics from waterways, demonstrating its effectiveness as a scalable and practical tool for environmental monitoring and plastic pollution assessment.
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.
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.
Sustainable Microplastic Filter Development for River Conservation: A Case Study in Yogyakarta
Researchers developed a sustainable microplastic filter for protecting freshwater river environments, testing a pilot-scale filtration system in a real river setting. The filter reduced downstream microplastic concentrations and was designed for low-cost, low-maintenance deployment.
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.
Performance of polyvinyl alcohol graphene oxide membrane for microplastic removal in wastewater with an IoT based monitoring approach
Researchers developed a polyvinyl alcohol-graphene oxide membrane combined with IoT-based real-time monitoring to improve microplastic removal from wastewater treatment plants. Conventional treatment plants in the study achieved only about 84% microplastic removal, leaving significant amounts entering natural water bodies. The membrane filtration system coupled with continuous monitoring showed promise for improving microplastic capture rates in wastewater treatment.
An IoT Based Low-Cost Optical System for Early Detection of Microplastics in Water Sources
Researchers developed a low-cost device that can detect tiny plastic particles (microplastics) in drinking water using simple LED lights and sensors, which could make testing much cheaper and easier than current lab methods. This matters because microplastics are found in water supplies worldwide and may pose health risks, but expensive testing equipment has made it hard to monitor water quality regularly. The study shows this simpler technology could work, potentially helping communities better track plastic pollution in their water sources.
The Smart Drifter Cluster: Monitoring Sea Currents and Marine Litter Transport Using Consumer IoT Technologies
Researchers introduced a Smart Drifter Cluster concept — low-cost IoT-enabled floating sensors — to track ocean currents and monitor the transport of marine litter and plastic debris. This technology could provide real-time data on microplastic distribution across coastal and open ocean environments.
A Systematic Literature Study on IoT-Based Water Turbidity Monitoring: Innovation in Waste Management
This systematic literature review surveyed IoT-based water turbidity monitoring systems, assessing innovations in sensor technology, data transmission, and automated alerting that enable real-time tracking of water quality parameters for environmental and public health management.
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.
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.