Cited by Novel insights into halogenated carbazoles (HCZs) prediction in tap water: a comparative study of grey relational analysis-based neural networks

Using artificial intelligence to rapidly identify microplastics pollution and predict microplastics environmental behaviors DOI

Binbin Hu,

Yaodan Dai,

Haidong Zhou

et al.

Journal of Hazardous Materials, Journal Year: 2024, Volume and Issue: 474, P. 134865 - 134865

Published: June 12, 2024

Language: Английский

Citations

Revealing the Removal Behavior of Five Neglected Microplastics in Coagulation-Ultrafiltration Processes: Insights from Experiments and Predictive Modeling DOI

Guanyu Zhou, Guijing Chen, Peng Tang

et al.

Journal of Hazardous Materials, Journal Year: 2025, Volume and Issue: 491, P. 137857 - 137857

Published: March 6, 2025

Language: Английский

Citations

Pollution of the aquatic environment by plasticizers leached from microplastics originating from linear sewage systems DOI

Małgorzata Kida, Sabina Ziembowicz, Kamil Pochwat

et al.

Desalination and Water Treatment, Journal Year: 2025, Volume and Issue: unknown, P. 101122 - 101122

Published: March 1, 2025

Language: Английский

Citations

Modeling of microplastics degradation in aquatic environments using an experimental plan DOI

Małgorzata Kida, Michał Musiał, Kamil Pochwat

et al.

Journal of Hazardous Materials, Journal Year: 2024, Volume and Issue: 471, P. 134396 - 134396

Published: April 23, 2024

The aim of this article is to apply advanced predictive modeling techniques understand the degradation process microplastics in aquatic environments. Utilizing a Fractional Factorial Central Composite Experimental Plan, study seeks develop precise statistical models that enable forecasting quantity pollutants generated during under various environmental conditions. This tool was applied model changes DOC (dissolved organic carbon) and DEHP (bis(2-ethylhexyl) phthalate) values ecosystems. methods were developed using data derived from laboratory tests conducted GC-MS technique. obtained approximating functions, considering factors such as time, water temperature, particle size, significantly reduced analysis time. A two-stage verification functions conducted, accuracy function form, its adequacy, significance input variables, their correlation with DEHP. employed Plan allowed for simultaneous reduction number experiments prediction influence variables on output values. Precise support understanding microplastic process, facilitating development effective strategies managing pollution.

Language: Английский

Citations

Long-term Prediction Method for PM2.5 Concentration Using Edge Channel Graph Attention Network and Gating Closed-form Continuous-time Neural Networks DOI

Chen Zhang, Xiaofan Li,

Hongyang Sheng

et al.

Process Safety and Environmental Protection, Journal Year: 2024, Volume and Issue: 189, P. 356 - 373

Published: June 20, 2024

Language: Английский

Citations

Enabling data-driven process dynamic modeling for extractive leaching and chemical precipitation DOI

Wei Song, Fabian Diaz, Andrey Yasinskiy

et al.

Process Safety and Environmental Protection, Journal Year: 2024, Volume and Issue: 211, P. 179 - 183

Published: Oct. 9, 2024

Language: Английский

Citations

Derivation of marine water quality criteria for copper based on artificial neural network model DOI

Yang Li, Di Mu,

Hongqing Wu

et al.

Environmental Pollution, Journal Year: 2024, Volume and Issue: 363, P. 125172 - 125172

Published: Oct. 21, 2024

Language: Английский

Citations

Novel insights into halogenated carbazoles (HCZs) prediction in tap water: a comparative study of grey relational analysis-based neural networks DOI

Qianfeng He,

Wanting Xu, Guolong Chen

et al.

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: unknown, P. 144482 - 144482

Published: Dec. 1, 2024

Language: Английский

Citations