Current Status of Emerging Contaminant Models and Their Applications Concerning the Aquatic Environment: A Review

Water, Journal Year: 2025, Volume and Issue: 17(1), P. 85 - 85

Published: Jan. 1, 2025

Increasing numbers of emerging contaminants (ECs) detected in water environments require a detailed understanding these chemicals’ fate, distribution, transport, and risk aquatic ecosystems. Modeling is useful approach for determining ECs’ characteristics their behaviors environments. This article proposes systematic taxonomy EC models addresses gaps the comprehensive analysis applications. The reviewed include conventional quality models, multimedia fugacity machine learning (ML) models. Conventional have higher prediction accuracy spatial resolution; nevertheless, they are limited functionality can only be used to predict contaminant concentrations Fugacity excellent at depicting how travel between different environmental media, but cannot directly analyze variations parts same media because model assumes that constant within compartment. Compared other ML applied more scenarios, such as identification assessments, rather than being confined concentrations. In recent years, with rapid development artificial intelligence, surpassed becoming one newest hotspots study ECs. primary challenge faced by outcomes difficult interpret understand, this influences practical value an some extent.

Language: Английский

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A Novel Single-Chamber Bio-Electro-Fenton for E2-3s Removal: Insights into the Effects of Wastewater-Derived Dom Composition from Molecular and Species Levels

Published: Jan. 1, 2025

Language: Английский

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Entropy Similarity-Driven Transformation Reaction Molecular Networking Reveals Transformation Pathways and Potential Risks of Emerging Contaminants in Wastewater: The Example of Sartans

Environmental Science & Technology, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 19, 2025

The transformation pathways and risks of emerging contaminants (ECs) in wastewater remain unclear due to the limited throughput nontarget screening. In this study, an improved method called entropy similarity-driven reaction molecular networking (ESTRMN) was developed identify products (TPs) wastewater. detail, similarity most effective algorithm for identifying parent-product spectrum pairs a threshold 0.5 it determined with guarantee high specificity. Additionally, TP structure database predicted according known structures reactions established assist identification. Sartan is one commonly used angiotensin II receptor blocker antihypertensive drugs. Take sartans as example, 69 TPs confidence levels above 3 were identified by ESTRMN, 43 which newly discovered. common included hydroxylation, hydrolysis, oxidation, resulting majority sartan exhibiting higher persistence, mobility, toxicity (PMT) than their parents. concentration 75% increased after treatment WWTP, overall risk has not been effectively mitigated. This study emphasizes role ESTRMN incorporating ECs into environmental monitoring protocols assessment frameworks management.

Language: Английский

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A novel single-chamber bio-electro-Fenton for E2-3S removal: Insights into the effects of wastewater-derived DOM composition from molecular and species levels

Journal of Hazardous Materials, Journal Year: 2025, Volume and Issue: unknown, P. 138147 - 138147

Published: April 1, 2025

Language: Английский

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Optimization of catalytic ozonation in a flotation cell through neural networks and genetic algorithms: A case study on caffeine degradation

Journal of Industrial and Engineering Chemistry, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 1, 2024

Language: Английский

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