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61,005 resultsShowing papers similar to Monitoring Water Quality: Suggestions and Prospects
ClearMonitoring 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.
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.
Connected Sensors, Innovative Sensor Deployment, and Intelligent Data Analysis for Online Water Quality Monitoring
This review examines advances in sensor technology, autonomous deployment methods, and artificial intelligence for monitoring water quality in real time across rivers, lakes, and oceans. Researchers describe how networks of sensors on robotic boats, buoys, and drones can now measure physical, chemical, and biological water parameters more broadly than ever before. The study proposes that connecting water monitoring systems globally could help address challenges related to drinking water safety, aquaculture, and emerging contaminants like microplastics.
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.
Water Quality Management in the Age of AI: Applications, Challenges, and Prospects
This review examines how artificial intelligence is transforming water quality management through improved monitoring, prediction, and pollution tracking. Researchers found that combining AI with technologies like the Internet of Things and remote sensing has significantly enhanced real-time water quality analysis and early warning systems. However, major challenges remain around data quality, model transparency, and the ability to detect emerging pollutants like microplastics.
Cloud-Based Smart Water Quality Monitoring System using IoT Sensors and Machine Learning
Researchers developed a cloud-based smart water quality monitoring system using IoT sensors and machine learning to detect contamination parameters such as pH, nitrate, conductivity, and fecal coliform in real time. The system applies machine learning classification to correlated sensor data to enable early detection of health hazards from contaminated water sources.
Detecting 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.
Advantages and Challenges of AI-Driven Water Quality Monitoring
This review outlined the opportunities and challenges of applying artificial intelligence to water quality monitoring, including real-time contaminant detection and predictive modeling. The authors highlight AI's potential to improve efficiency and reduce costs in monitoring systems, while noting data quality and model interpretability as key challenges.
Water Quality Challenges and Technological Innovations for Sustainable Management
This review examines technological innovations for sustainable water quality management in the context of increasing contamination from chemical pollutants, pathogens, and emerging contaminants including microplastics. Researchers summarized advances in water quality assessment methods and treatment technologies that address these modern challenges. The study highlights how rapid urbanization and climate change are intensifying the need for more sophisticated water management approaches.
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.
Efficient Data-Driven Machine Learning Models for Water Quality Prediction
This study tested machine learning methods for predicting water quality based on physical, chemical, and biological measurements. While focused on water safety testing rather than microplastics specifically, the automated classification tools developed here could help water treatment facilities quickly identify contaminated water. Better monitoring technology is important because current methods for detecting microplastics in water are slow and expensive.
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.
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.
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.
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.
Next-Generation AI-IoT Integrated Systems for Dynamic Optimization of Water Disinfection and Removal of Emerging Contaminants
Researchers explored the integration of artificial intelligence and Internet of Things technologies into water management systems to improve disinfection and removal of emerging contaminants. The study found that AI-IoT integrated systems enable dynamic, real-time optimization of water treatment processes, offering more effective responses to complex water quality challenges.
Artificial Intelligence (AI) Based Rapid Water Testing System
Researchers developed an AI-powered portable water testing system that combines five analytical techniques to detect contaminants including heavy metals, pathogens, and microplastics in real time. The device uses an embedded machine learning model trained on diverse water samples to recognize contamination patterns. The study demonstrates a cost-effective approach to rapid water quality monitoring that could help identify microplastic pollution in both industrial and domestic water supplies.
Machine Learning to Access and Ensure Safe Drinking Water Supply: A Systematic Review
This systematic review examines machine learning applications for monitoring, predicting, and controlling drinking water quality, covering contaminants from disinfection byproducts to biofilms and antimicrobial resistance genes. While not specifically about microplastics, the ML approaches described are directly applicable to detecting and predicting microplastic contamination in engineered water systems.
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.
Advances in Catchment Science, Hydrochemistry, and Aquatic Ecology Enabled by High-Frequency Water Quality Measurements
This review covers advances in high-frequency water quality monitoring technology for streams and rivers, including automated sensors that measure pollutants in real time. While not focused on microplastics specifically, these monitoring tools are increasingly being adapted to track particulate pollutants including microplastics in waterways. Better real-time water monitoring could help communities identify and respond to microplastic contamination in their drinking water sources.
Artificial Intelligence Technologies in the Monitoring and Analysis of Water Resources Data (An Analytical Study)
This review examines the application of artificial intelligence technologies — including smart sensors, satellites, and unmanned aerial vehicles — to the monitoring and analysis of water resources data. Researchers found that AI-powered platforms significantly improve data collection efficiency and analytical capacity for managing water quality and quantity, including emerging contaminants such as microplastics.
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.
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.