Photodegradation of organic micropollutants in aquatic environment: Importance, factors and processes

Water Research, Год журнала: 2022, Номер 231, С. 118236 - 118236

Опубликована: Фев. 26, 2022

Язык: Английский

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Navigating the molecular landscape of environmental science and heavy metal removal: A simulation-based approach

Journal of Molecular Liquids, Год журнала: 2024, Номер 410, С. 125592 - 125592

Опубликована: Июль 20, 2024

Heavy metals pose a significant threat to ecosystems and human health because of their toxic properties ability bioaccumulate in living organisms. Traditional removal methods often fall short terms cost, energy efficiency, minimizing secondary pollutant generation, especially complex environmental settings. In contrast, molecular simulation offer promising solution by providing in-depth insights into atomic interactions between heavy potential adsorbents. This review highlights the for removing types pollutants science, specifically metals. These powerful tool predicting designing materials processes remediation. We focus on specific like lead, Cadmium, mercury, utilizing cutting-edge techniques such as Molecular Dynamics (MD), Monte Carlo (MC) simulations, Quantum Chemical Calculations (QCC), Artificial Intelligence (AI). By leveraging these methods, we aim develop highly efficient selective unravelling underlying mechanisms, pave way developing more technologies. comprehensive addresses critical gap scientific literature, valuable researchers protection health. modelling hold promise revolutionizing prediction metals, ultimately contributing sustainable solutions cleaner healthier future.

Язык: Английский

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Machine learning in the identification, prediction and exploration of environmental toxicology: Challenges and perspectives

Journal of Hazardous Materials, Год журнала: 2022, Номер 438, С. 129487 - 129487

Опубликована: Июнь 28, 2022

Язык: Английский

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The application of machine learning to air pollution research: A bibliometric analysis

Ecotoxicology and Environmental Safety, Год журнала: 2023, Номер 257, С. 114911 - 114911

Опубликована: Апрель 15, 2023

Machine learning (ML) is an advanced computer algorithm that simulates the human process to solve problems. With explosion of monitoring data and increasing demand for fast accurate prediction, ML models have been rapidly developed applied in air pollution research. In order explore status applications research, a bibliometric analysis was made based on 2962 articles published from 1990 2021. The number publications increased sharply after 2017, comprising approximately 75% total. Institutions China United States contributed half all with most research being conducted by individual groups rather than global collaborations. Cluster revealed four main topics application ML: chemical characterization pollutants, short-term forecasting, detection improvement optimizing emission control. rapid development algorithms has capability characteristics multiple analyze reactions their driving factors, simulate scenarios. Combined multi-field data, are powerful tool analyzing atmospheric processes evaluating management quality deserve greater attention future.

Язык: Английский

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Bioremediation of organohalide pollutants: progress, microbial ecology, and emerging computational tools

Current Opinion in Environmental Science & Health, Год журнала: 2023, Номер 32, С. 100452 - 100452

Опубликована: Янв. 29, 2023

Язык: Английский

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Investigation of the Binding Fraction of PFAS in Human Plasma and Underlying Mechanisms Based on Machine Learning and Molecular Dynamics Simulation

Environmental Science & Technology, Год журнала: 2022, Номер 57(46), С. 17762 - 17773

Опубликована: Окт. 25, 2022

More than 7000 per- and polyfluorinated alkyl substances (PFAS) have been documented in the U.S. Environmental Protection Agency's CompTox Chemicals database. These PFAS can be used a broad range of industrial consumer applications but may pose potential environmental issues health risks. However, little is known about emerging bioaccumulation to assess their chemical safety. This study focuses specifically on large high-quality data set fluorochemicals from related pharmaceutical chemicals databases, machine learning (ML) models were developed for classification prediction unbound fraction compounds plasma. A comprehensive evaluation ML shows that best blending model yields an accuracy 0.901 test set. The predictions suggest most (∼92%) high binding Introduction alkaline amino groups likely reduce affinities with plasma proteins. Molecular dynamics simulations indicate clear distinction between low fractions PFAS. computational workflows predict are also helpful molecular design prevent release high-bioaccumulation into environment.

Язык: Английский

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Computational Chemistry as Applied in Environmental Research: Opportunities and Challenges

ACS ES&T Engineering, Год журнала: 2023, Номер 4(1), С. 66 - 95

Опубликована: Окт. 12, 2023

The constant development of computer systems and infrastructure has allowed computational chemistry to become an important component environmental research. In the past decade, application quantum classical mechanical calculations model understand increased exponentially. this review, we highlight various applications techniques in areas research (e.g., wastewater/air treatment, sensing, biodegradation). We briefly describe each approach, starting with principle methods followed by molecular mechanics (MM), dynamics (MD), hybrid QM/MM methods. recent introduction artificial intelligence machine learning their potential disrupt field are also discussed. Challenges current future directions address them presented.

Язык: Английский

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A new ChatGPT-empowered, easy-to-use machine learning paradigm for environmental science

Eco-Environment & Health, Год журнала: 2024, Номер 3(2), С. 131 - 136

Опубликована: Фев. 3, 2024

The quantity and complexity of environmental data show exponential growth in recent years. High-quality big analysis is critical for performing a sophisticated characterization the complex network pollution. Machine learning (ML) has been employed as powerful tool decoupling complexities based on its remarkable fitting ability. Yet, due to knowledge gap across different subjects, ML concepts algorithms have not well-popularized among researchers sustainability. In this context, we introduce new research paradigm-"ChatGPT + Environment", providing an unprecedented chance reduce difficulty using models. For instance, each step involved applying models sustainability, including preparation, model selection construction, training evaluation, hyper-parameter optimization, can be easily performed with guidance from ChatGPT. We also discuss challenges limitations paradigm field Furthermore, highlight importance "secondary training" future application "ChatGPT Environment".

Язык: Английский

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Application of machine learning in the study of development, behavior, nerve, and genotoxicity of zebrafish

Environmental Pollution, Год журнала: 2024, Номер 358, С. 124473 - 124473

Опубликована: Июнь 28, 2024

Язык: Английский

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Origin of metabolites diversity and selectivity of P450 catalyzed benzo[a]pyrene metabolic activation

Journal of Hazardous Materials, Год журнала: 2022, Номер 435, С. 129008 - 129008

Опубликована: Апрель 26, 2022

Язык: Английский

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