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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 found that machine learning techniques can effectively monitor, predict, and control drinking water quality across engineered water systems. The applications span detection of physical, chemical, and microbiological contaminants, offering a scalable alternative to labor-intensive traditional methods for ensuring safe drinking water and identifying emerging pollutants like microplastics.
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.
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.
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.
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.
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.
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.
Current applications and future impact of machine learning in emerging contaminants: A review
This review examines how machine learning is being applied to emerging contaminant research including microplastics, covering identification, environmental behavior prediction, bioeffect assessment, and removal optimization of these pollutants.
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.
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.
Unveiling the Potential of Metagenomics for Eradicating Microplastics from Drinking Water
This review discussed metagenomics as a tool for identifying and characterizing microbial communities capable of degrading microplastics in drinking water systems. The paper addressed challenges in detecting microplastics in drinking water and proposed metagenomics-guided approaches for developing sustainable biological remediation strategies.
Identifying microplastic contamination in drinking water: analysis and evaluation using spectroscopic methods
Researchers developed analytical methods to identify and quantify microplastic contamination in drinking water, evaluating extraction efficiency and detection accuracy across different water types and plastic particle sizes. The study assessed health implications based on measured plastic loads in treated water.
[Overview of the Application of Machine Learning for Identification and Environmental Risk Assessment of Microplastics].
This review examines the application of machine learning (ML) methods for identifying microplastics and assessing their environmental risks, covering techniques for improving the accuracy and reliability of microplastic detection across different environmental media. Researchers highlight how ML can systematically analyse pollution characteristics and support ecological risk evaluation of microplastic contamination.
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.
Hygienic approaches to the safety levels identification of microplastics in water
Researchers developed a program of analytical and toxicological studies to establish safety levels for microplastics in water, addressing the international classification of microplastics as a new health hazard. The study combined literature analysis with sanitary-chemical and sanitary-microbiological experiments to propose indicators and criteria for assessing microplastic danger in water. The findings aim to support the development of regulatory standards for microplastic contamination in drinking water.
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.
A Review of the Current Literature on Sources and Mitigation Strategies of Microplastics in Drinking Water
Researchers reviewed the key sources of microplastic contamination in drinking water — including plastic waste, synthetic clothing, and microbeads in personal care products — and assessed strategies for reducing exposure through improved treatment technologies and stricter regulations on plastic production. The review emphasizes that effective policy, combined with public awareness about single-use plastics, is essential for protecting drinking water quality.
A critical review on recent research progress on microplastic pollutants in drinking water
This critical review synthesizes research on microplastic contamination in drinking water sources and treatment systems. The study highlights that microplastics have been found in rivers, lakes, and treatment facilities worldwide, and that bioaccumulation of these persistent particles through drinking water represents a potential concern that requires further investigation into health effects and improved removal technologies.
Microplastic: Unveiling the Stealthy Polluters in Our Water
This review covers microplastic contamination in water sources, documenting sources, environmental pathways, analytical detection methods, and potential human health risks from drinking water containing plastic particles, along with emerging mitigation strategies.
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 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.
Removal of microplastics via drinking water treatment: Current knowledge and future directions
This review examines what is currently known about microplastics in drinking water systems and how well existing water treatment processes remove them. Researchers found that while conventional treatment steps like coagulation and filtration do reduce microplastic levels, significant amounts can still persist through to tap water. The study calls for more research into optimizing treatment processes and developing monitoring strategies specifically targeting microplastic contamination in drinking water.
Machine Learning Advancements and Strategies in Microplastic and Nanoplastic Detection
This systematic review looks at how machine learning is improving our ability to detect tiny microplastics and nanoplastics in the environment. Better detection methods matter because accurately measuring plastic contamination is the first step toward understanding — and reducing — human exposure.
Applications of mathematical modelling for assessing microplastic transport and fate in water environments: a comparative review
This systematic review evaluates mathematical models used to predict how microplastics move through and accumulate in water systems. Better models help scientists understand where microplastics end up in the environment and, ultimately, how they might reach drinking water sources and affect human exposure.