Application of machine learning and multivariate approaches for assessing microplastic pollution and its associated risks in the urban outdoor environment of Bangladesh

Journal of Hazardous Materials, Journal Year: 2024, Volume and Issue: 472, P. 134359 - 134359

Published: April 18, 2024

Language: Английский

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Predicting microplastic quantities in Indonesian provincial rivers using machine learning models

The Science of The Total Environment, Journal Year: 2025, Volume and Issue: 961, P. 178411 - 178411

Published: Jan. 1, 2025

Language: Английский

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Global Occurrence and Environmental Fate of Microplastics in Stormwater Runoff: Unlock the In-depth Knowledge on Nature-Based Removal Strategies

Reviews of Environmental Contamination and Toxicology, Journal Year: 2025, Volume and Issue: 263(1)

Published: Jan. 28, 2025

Language: Английский

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Microplastic characteristics, transport, risks, and remediation in groundwater: a review

Environmental Chemistry Letters, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 8, 2025

Language: Английский

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Microplastics in aquaculture environments: Current occurrence, adverse effects, ecological risk, and nature-based mitigation solutions

Marine Pollution Bulletin, Journal Year: 2024, Volume and Issue: 209, P. 117168 - 117168

Published: Oct. 24, 2024

Language: Английский

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Water quality prediction of artificial intelligence model: a case of Huaihe River Basin, China

Environmental Science and Pollution Research, Journal Year: 2024, Volume and Issue: 31(10), P. 14610 - 14640

Published: Jan. 26, 2024

Language: Английский

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Detecting the interaction between microparticles and biomass in biological wastewater treatment process with Deep Learning method

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 951, P. 175813 - 175813

Published: Aug. 25, 2024

Investigating the interaction between influent particles and biomass is basic important for biological wastewater treatment. The micro-level methods allow this, such as microscope image analysis method with conventional ImageJ processing software. However, these are cost time-consuming, require a large amount of work on manual parameter tuning. To deal this problem, we proposed deep learning (DL) to automatically detect quantify microparticles free from entrapped in images. Firstly, introduced "TU Delft-Interaction Particles Biomass" dataset containing labeled Then, built DL models using seven state-of-the-art model architectures instance segmentation task, Mask R-CNN, Cascade Yolact YOLOv8. results show that R-CNN ResNet50 backbone achieves promising detection accuracy, mAP50

Language: Английский

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Landfill-mined soil-like fraction (LMSF) use in biopolymer composting: Material pre-treatment, bioaugmentation and agricultural prospects

Environmental Pollution, Journal Year: 2024, Volume and Issue: 355, P. 124255 - 124255

Published: May 28, 2024

Polylactic Acid (PLA) based compostable bioplastic films degrade under thermophilic composting conditions. The purpose of our study was to understand whether sample pre-treatment along with bioaugmentation the degradation matrix could reduce biodegradation time a simulated environment. Sepcifically, we also explored commercial composts be replaced by landfill-mined soil-like fraction (LMSF) for said application. effect on material analysed tests like tensile strength analysis, hydrophobicity morphological thermal profiling, etc. Subsequently, experiment performed in environment following ASTM D5338 standard, selected experimental setups. When novel approach and were applied combination, necessary 90% reduced 27% using compost 23% LMSF. Beyond improvement rate, water holding capacity increased significantly matrices. With pH, C: N ratio microbial diversity tested favourable through 16s metabarcoding studies, allow LMSF not only replace polymer but find immense application agricultural sector drought-affected areas (for better retention) after it has been used PLA degradation.

Language: Английский

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Common issues of data science on the eco-environmental risks of emerging contaminants

Environment International, Journal Year: 2025, Volume and Issue: 196, P. 109301 - 109301

Published: Jan. 27, 2025

Language: Английский

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Assessing heavy metal pollution levels and associated ecological risks in peatland areas in the Mekong Delta region

Environmental Research, Journal Year: 2025, Volume and Issue: unknown, P. 121319 - 121319

Published: March 1, 2025

Heavy metal (HM) pollution in soils and sediment is a significant concern, yet its levels ecological risks peatland areas remain unexplored. This study evaluates these aspects three regions of the Long An province Vietnam. Comparisons HM concentrations sediments from Tan Thanh, Thanh Hoa, Duc Hue provinces locations revealed highest values region. Specifically, Cu Ni were found at two to times higher than threshold effects level (TEL) range median (ERL) guidelines. The main sources area are predicted include production use fertilizers pesticides, surface processing, mechanical engineering electronics manufacturing, chemical plants. Further, positive correlations between factors such as pH, total organic carbon (TOC), clay-silt ratio identified through Spearman correlation analysis. results obtained analysis further corroborated by Bayesian network analysis, which was also applied this study. In addition, contamination factor (CF) index indicated that has "moderate degree" Hoa (CF = 1.3) "considerable 3.2), whereas, both 2.4). modified degree (mCd) ranked > Hue, with mCd indexes 1.3, 0.7, 0.4, respectively. potential risk (RI) "low risk" level, an average RI 35.6 across all sites. These findings address knowledge gaps peatlands but contribute development strategies for protection peatlands.

Language: Английский

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