We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Papers
61,005 resultsShowing papers similar to Microplastics Detection in Soil and Water: Leveraging IoT Technologies for Environmental Sustainability
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.
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.
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.
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.
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.
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.
Enhancing Agricultural Sustainability Through Robotic-IoT Systems for Real-Time Monitoring Soil Contamination
Researchers developed an IoT-based robotic system integrating portable NIR spectroscopy sensors and machine learning, including a Random Forest algorithm, to monitor soil quality and detect microplastic contamination in real time, achieving 96% accuracy in microplastic detection and 91% accuracy in broader pollutant analysis.
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.
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.
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.
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-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.
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.
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.
Internet of Things and Health: A literature review based on Mixed Method
This literature review examines how Internet of Things technology is being applied in healthcare, covering areas like remote patient monitoring, diagnosis, and treatment. The review identified key trends and limitations in current implementations, noting the need for more interdisciplinary research. While not related to microplastics, the IoT sensing technologies discussed could potentially be adapted for real-time environmental monitoring of microplastic contamination in water and air.
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.
Smart Bin and IoT: A Sustainable Future for Waste Management System in Nigeria
Researchers proposed a smart waste bin system using Internet of Things technology to improve waste management in Nigerian cities. The system uses sensors and Wi-Fi connectivity to monitor bin fill levels remotely, enabling more efficient waste collection routes. The study highlights how affordable IoT-based solutions could help developing nations reduce plastic waste accumulation and environmental pollution.
Golden Seal Project: An IoT-Driven Framework for Marine Litter Monitoring and Public Engagement in Tourist Areas
Researchers developed the Golden Seal project, an IoT-driven framework that deploys sensor networks and gamified recycling systems in coastal tourist areas to monitor and reduce marine litter while increasing public engagement with plastic pollution issues.
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.
Air Quality Testing- a Design Thinking Approach
Not relevant to microplastics — this paper describes a design-thinking methodology for building IoT-based air quality monitoring systems, with no connection to plastic particle research.
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.
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.
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.