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 Machine Learning to Access and Ensure Safe Drinking Water Supply: A Systematic Review
ClearMachine 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.
Microplastics contamination in water supply system and treatment processes
This systematic review found that microplastics are frequently detected in drinking and bottled water despite current treatment technologies, and that no existing method can completely remove them. Integrating advanced treatment approaches with life-cycle assessment and machine learning is needed to address this pervasive contamination of water supply systems.
Drinking water potability prediction using machine learning approaches: a case study of Indian rivers
Researchers applied machine learning techniques to predict drinking water quality in Indian rivers based on key parameters like pH, dissolved oxygen, and bacterial counts. Their models achieved high accuracy in classifying water as potable or non-potable. The study demonstrates how data-driven approaches could help developing countries monitor water safety more efficiently, especially in regions where traditional testing infrastructure is limited.
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.
Alleviating Health Risks for Water Safety: A Systematic Review on Artificial Intelligence-Assisted Modelling of Proximity-Dependent Emerging Pollutants in Aquatic Systems
This systematic review summarizes how artificial intelligence can help track emerging pollutants, including microplastics, in water systems. It highlights that AI-driven models can predict contamination patterns more efficiently than traditional methods, which could help protect drinking water safety and public health.
A Comprehensive Review of Machine Learning for Water Quality Prediction over the Past Five Years
This comprehensive review analyzes over 170 studies on using machine learning to predict water quality, covering both individual pollutant indicators and overall water quality indices. The authors highlight key challenges including data acquisition, model uncertainty, and the need to incorporate water flow dynamics into predictions. While broadly focused on water quality, these predictive tools are relevant to microplastics research because they could help forecast microplastic concentrations in water systems based on environmental conditions.
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.
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.
Enhancing water quality prediction: a machine learning approach across diverse water environments
Researchers compared seven machine learning models for predicting water quality parameters using six years of wastewater treatment plant data. The gradient boosting model performed best overall, accurately predicting parameters related to water contamination. While the study focuses on general water quality rather than microplastics specifically, these predictive tools could be applied to monitoring microplastic-relevant conditions in treatment systems.
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.
Microplastics in Drinking Water: A Review of Sources, Removal, Detection, Occurrence, and Potential Risks
This review examines how microplastics enter drinking water supply systems, evaluates methods for their detection and removal, and summarizes what is known about their occurrence in treated water. Researchers found that while conventional water treatment removes a significant portion of microplastics, no current method eliminates them completely. The study highlights the need for improved monitoring standards and further research into the long-term health effects of ingesting microplastics through drinking water.
Detecting and Quantifying Microplastics in Drinking Reservoirs
This study reviewed and evaluated methods for detecting and quantifying microplastics in drinking water reservoirs, highlighting the urgent need for standardized analytical approaches to accurately assess human exposure to microplastic contamination.
Microplastics in water: diagnosis and human health risk analysis
This systematic review summarizes existing research on microplastic contamination in drinking water and assesses the potential risks to human health. The findings confirm that microplastics are present in water intended for consumption, and while the exact health effects are still being studied, the evidence suggests we should take precautions to reduce our exposure.
Microplastics in Drinking Water:Current Knowledge, Quality Assuranceand Future Directions
This review synthesizes current knowledge on microplastics in drinking water, covering their occurrence in source waters, behavior during treatment processes, and potential health implications. Researchers found that while drinking water treatment plants remove a portion of microplastics, standardized quality assurance methods are still lacking. The study calls for improved monitoring protocols and treatment technologies to better address microplastic contamination in tap water.
Application of Artificial Intelligence in the Management of Coagulation Treatment Engineering System
Researchers reviewed the application of artificial intelligence and neural networks in water treatment coagulation systems. The study found that AI-based approaches can effectively predict water quality parameters and optimize chemical dosing, potentially improving the removal of contaminants including microplastics from drinking water treatment processes.
Science and Technology for Water Purification: Achievements and Strategies
This review covers the latest science and technology for purifying water, addressing the global challenges of water scarcity and pollution. It discusses emerging contaminants including microplastics and the treatment methods needed to remove them. The findings are relevant to human health because current water treatment systems may not fully remove microplastics and other new pollutants from drinking water.
Microplastics: review of removal methods for drinking water production
This review examined methods for removing microplastics from drinking water, responding to the growing detection of microplastic contaminants in rivers, lakes, reservoirs, and both tap and bottled water. The review surveys emerging treatment technologies capable of addressing microplastics as pollutants in drinking water production, synthesizing evidence on removal efficiency, limitations, and practical applicability for water utilities.
A Systematic Review of Microplastic Detection in Water
This systematic review summarizes current methods for detecting microplastics in water sources. The research highlights significant challenges in accurately measuring these tiny plastic particles, with different techniques yielding very different results. Better detection methods are essential for understanding how much microplastic is present in the water people drink and use daily.
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.
A solution for controling microplastics in drinking water
Researchers developed and tested a system for controlling microplastic contamination in drinking water, reporting on removal efficiency at levels relevant to public health. The approach offered effective microplastic reduction from drinking water sources including tap and bottled water.
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.
Drinking and Natural Mineral Water: Treatment and Quality–Safety Assurance
This review covers the sources, treatment methods, and safety standards for drinking water and natural mineral water across different regulatory frameworks. The authors discuss emerging contaminants including microplastics that are increasingly found in both tap and bottled water. The study highlights the need for updated regulations and monitoring to ensure drinking water safety as new pollutants are identified.
Emerging Drinking Water Borne Diseases: A Review on Types, Sources and Health Precaution
This review provides an overview of emerging waterborne diseases caused by physical, chemical, and biological contaminants in drinking water sources around the world. Researchers discuss how pollutants including microplastics, heavy metals, and microbial pathogens can enter water supplies through inadequate treatment and aging infrastructure. The study emphasizes the importance of improved water treatment and monitoring to protect public health from these diverse contamination sources.