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Papers
61,005 resultsShowing papers similar to Tracking microplastic pathways: Real-time IoT monitoring for water quality and public health
ClearDevelopment 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.
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.
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.
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.
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.
Economical and Novel Microplastic Detection Using a Arduino-Based Turbidity Sensor: A Comprehensive Investigation
Researchers developed a low-cost Arduino-based turbidity sensor system for microplastic detection as an accessible alternative to expensive FTIR and Raman spectroscopy methods. The sensor demonstrated the ability to detect microplastic-induced changes in water clarity, offering a practical monitoring tool for low-resource settings and smaller waterways that are typically undersampled.
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.
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.
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.
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.
Low-cost IoT based system for lake water quality monitoring
This study built a low-cost sensor system using Internet of Things technology to monitor basic water quality parameters like turbidity, pH, and dissolved oxygen in lakes. While not focused on microplastics specifically, affordable real-time water monitoring tools like this could eventually be adapted to track microplastic contamination. Better water quality monitoring is an important step toward understanding and reducing the pollutants, including microplastics, that end up in drinking water sources.
Smart 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.
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.
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.
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.
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.
Eco-Sensing System for Water Pollution and Microplastic Detection
This study evaluates new sensor-based and spectroscopic technologies for detecting microplastics in water in real time, comparing them with traditional lab-based methods. The portable systems showed improved accuracy and efficiency for field use, making it possible to monitor microplastic contamination as it happens. Better detection tools are essential for protecting drinking water sources and understanding the true scale of human microplastic exposure.
Automatic Detection of Microplastics in the Aqueous Environment
Researchers developed a deep-learning system for real-time detection and counting of microplastics in freshwater, achieving high accuracy for particles 1 mm and larger.
Cost-Effective and Wireless Portable Device for Rapid and Sensitive Quantification of Micro/Nanoplastics
Researchers developed a wireless portable device for rapid quantification of micro- and nanoplastics in water samples, offering a field-deployable alternative to laboratory-based analysis for environmental monitoring.
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.
Toward Nano- and Microplastic Sensors: Identification of Nano- and Microplastic Particles via Artificial Intelligence Combined with a Plasmonic Probe Functionalized with an Estrogen Receptor
Scientists created a sensor that combines artificial intelligence with a specialized light-based probe to detect and identify different types of nano- and microplastics in water. The AI-powered system could distinguish between various plastic types with high accuracy, offering a faster and more practical way to monitor plastic contamination in drinking water and environmental samples.
Real-Time Detection of Microplastics Using an AI Camera
Researchers developed a camera-based system using artificial intelligence to detect and measure microplastics in real time as they move through water. The system was tested with three different camera setups and could identify particles, measure their size, and track their speed. This technology could provide a faster and more practical alternative to the labor-intensive laboratory methods currently used to monitor microplastic pollution.
zero-plastic: AI-assisted Sensing for Microplastic Assessment
Researchers developed the 'zero-plastic' open-source imaging system combining flow microscopy with AI classification for low-cost, real-time microplastic monitoring in water, and integrated it with a digital twin infrastructure for distributed environmental sensing.