Microplastics in freshwater systems: Dynamic behaviour and transport processes

Resources Conservation and Recycling, Journal Year: 2024, Volume and Issue: 205, P. 107578 - 107578

Published: April 8, 2024

Freshwater ecosystems are viewed as a key medium for the transport of land-based plastics into oceans. Microplastic (MP) particles in freshwater environments demonstrate high persistence and an extensive range size shape distributions, which make their mobility, distribution, fate vary distinctly depending on prevailing environmental conditions. The inherent physical properties different plastic polymers constantly evolving at specific reaction rates due to complex weathering processes environment. This continuously alters underlying mechanisms governing MP dynamics further complicates ultimate natural aquatic systems. paper conducts comprehensive review dynamic behaviour MPs ecosystems, focusing investigating settling, aggregation, retention, suspension from source sink. We provide in-depth overview theoretical foundations ambient flows influential factors (i.e. size, density, shape, composition). Our findings highlight intricate interplays between behaviours local hydrodynamics water chemistry, lead continuous evolution physicochemical (e.g., surface charge) through interactions with suspended solids, organic matter, microorganisms under light wind exposure. poses significant challenges predicting fate. Gap analysis highlights discrepancy current models based controlled laboratory conditions environments, signifying need across wide (e.g. simulating flow patterns solution chemistries real bodies). Further research is needed expand field studies correlate environment abundance conduct mesoscale experiments that accurately reflect effects behaviours. Integrating detailed numerical modelling tools essential understanding among various overall impact facilitates robust reliable risk assessment control pollution management.

Language: Английский

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Investigating the impacts of climate change on hydroclimatic extremes in the Tar-Pamlico River basin, North Carolina

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 363, P. 121375 - 121375

Published: June 8, 2024

Language: Английский

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A water quality database for global lakes

Resources Conservation and Recycling, Journal Year: 2023, Volume and Issue: 202, P. 107401 - 107401

Published: Dec. 29, 2023

Language: Английский

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Predicting the hydraulic response of critical transport infrastructures during extreme flood events

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 133, P. 108573 - 108573

Published: May 11, 2024

Understanding the effects of extreme floods on critical infrastructures such as bridges is paramount for ensuring safety and resilient design in face climate change events. This study develops robust computational predictive modeling tools assessing impacts hydraulic response structural resilience bridges. A fluid dynamic (CFD) model utilizing RANS equations k-ω Shear Stress Transport (SST) simulating supercritical flows adopted to compute hydrodynamic pressures water levels bridge piers cylindrical rectangular shapes during a flood event. The CFD validated based case data obtained from Haj Omran Bridge, built Khorramabad River Iran. numerical simulations consider hydrological conditions exclude geotechnical parameters abutment damages. results are evaluated well-established guidelines. Machine learning techniques, including Extreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Regression (SVR), optimized with Grid Search Cross-Validation (GSCV), enhance accuracy pressure forecasting at piers. XGBoost exhibits superior performance (R2 = 0.908, RMSE 0.0279, E 3.41%) compared RF SVR models. All estimated by falls within ±6 percent error lines, highlighting model's robustness out-of-range prediction. Additionally, an Long Short-Term Memory (LSTM) effectively predict free surface flow profiles (i.e. depth) over 0.937 0.083), demonstrating its potential practical applications depth predictions infrastructures. proposed methodological framework outlined this can facilitate bridges, enabling assessment

Language: Английский

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Incorporation of water quality index models with machine learning-based techniques for real-time assessment of aquatic ecosystems

Environmental Pollution, Journal Year: 2024, Volume and Issue: 355, P. 124242 - 124242

Published: May 27, 2024

Language: Английский

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Assessment of Drinking Water Quality and Identifying Pollution Sources in a Chromite Mining Region

Journal of Hazardous Materials, Journal Year: 2024, Volume and Issue: 480, P. 136050 - 136050

Published: Oct. 4, 2024

Language: Английский

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Daily River flow Simulation Using Ensemble Disjoint Aggregating M5-Prime Model

Heliyon, Journal Year: 2024, Volume and Issue: 10(20), P. e37965 - e37965

Published: Sept. 30, 2024

Accurate prediction of daily river flow (Q t ) remains a challenging yet essential task in hydrological modeling, particularly crucial for flood mitigation and water resource management. This study introduces an advanced M5 Prime (M5P) predictive model designed to estimate Q as well one- two-day-ahead forecasts (i.e. t+1 t+2 ). The performance M5P ensembles incorporating Bootstrap Aggregation (BA), Disjoint Aggregating (DA), Additive Regression (AR), Vote (V), Iterative classifier optimizer (ICO), Random Subspace (RS), Rotation Forest (ROF) were comprehensively evaluated. proposed models applied case data Tuolumne County, US, using dataset comprising measured precipitation (P ), evaporation (E t), . A wide range input scenarios explored predicting , t+1, t+2. Results indicate that P significantly influence accuracy. Notably, relying solely on the most correlated variable (e.g., t-1) does not guarantee robust However, extending forecast horizon mitigates low-correlation variables Performance metrics DA-M5P achieves superior results, with Nash-Sutcliff Efficiency 0.916 root mean square error 23 m3/s, followed by ROF-M5P, BA-M5P, AR-M5P, RS-M5P, V-M5P, ICO-M5P, standalone model. ensemble modeling framework enhanced capability stand-alone algorithm 1.2 %-22.6 %, underscoring its efficacy potential advancing forecasting.

Language: Английский

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Groundwater for drinking and sustainable agriculture and public health hazards of nitrate: developmental and sustainability implications for an arid aquifer system

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104160 - 104160

Published: Jan. 1, 2025

Language: Английский

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Enhanced Prediction of Energy Dissipation Rate in Hydrofoil-Crested Stepped Spillways Using Novel Advanced Hybrid Machine Learning Models

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 103985 - 103985

Published: Jan. 1, 2025

Language: Английский

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Quantifying pluvial flood simulation in ungauged urban area; A case study of 2022 unprecedented pluvial flood in Karachi, Pakistan

Journal of Hydrology, Journal Year: 2025, Volume and Issue: unknown, P. 132905 - 132905

Published: Feb. 1, 2025

Language: Английский

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