With the omnipresence of plastic litter from oyster farming in marine coastal areas, the objective of this work was to better understand the weathering of plastics used in this field, focusing on oyster spat collectors. During their use, around fifteen years, collectors made of polypropylene (PP) undergo numerous degradations, alternatively submerged, emerged in seawater, and stored outdoor until the next cycle. They weaken, crack, break, end up fragmenting and disseminated in the environment as microplastics associated to persistent organic pollutants. In this work, a comparison of 55 months of in situ weathering with five months of artificial weathering in air or in artificial seawater in a homemade UV chamber was conducted to better understand the mechanisms involved. Chemical, thermal and surface characterizations of virgin and weathered samples were conducted using Fourier Transform Infrared Spectroscopy (FTIR), Differential Scanning Calorimetry (DSC) and Environmental Scanning Electron Microscopy (ESEM). After 55 months of in situ weathering, collectors were notably damaged with large fissures and loss of microplastics (MPs) associated with an increase of carbonyl index values and a decrease of melting temperatures and crystallinity rates. Considering only UV irradiation, five months of artificial weathering at 30 °C under continuous irradiation of 6.9 W/m under UV lamps (295-400 nm) reproduced approximately 4.4 months of natural sunlight. Artificial weathering confirmed that photooxidation by combined effects of UV rays and oxygen was the main weathering mechanism and was reduced in seawater. These results help to understand the mechanisms involved in the weathering of these collectors in the marine environment and provide valuable information for industrials and professionals. Our study suggests a better storage away from UV rays and a reduction of the duration of use compared to current practices.
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http://dx.doi.org/10.1016/j.scitotenv.2023.161638 | DOI Listing |
Sci Rep
January 2025
Agricultural Engineering Department, Faculty of Agriculture and Natural Resources, Aswan University, Aswan, Egypt.
The experiments were conducted at different levels of infrared power, airflow, and temperature. The relationships between the input process factors and response factors' physicochemical properties of dried garlic were optimized by a self-organizing map (SOM), and the model was developed using machine learning. Artificial Neural Network (ANN) with 99% predicting accuracy and Self-Organizing Maps (SOM) with 97% clustering accuracy were used to determine the quality characteristics of garlic.
View Article and Find Full Text PDFSci Rep
January 2025
Institute for Chemistry and Biology of the Marine Environment, Carl von Ossietzky University Oldenburg, Oldenburg, Germany.
Phytoplankton blooms exhibit varying patterns in timing and number of peaks within ecosystems. These differences in blooming patterns are partly explained by phytoplankton:nutrient interactions and external factors such as temperature, salinity and light availability. Understanding these interactions and drivers is essential for effective bloom management and modelling as driving factors potentially differ or are shared across ecosystems on regional scales.
View Article and Find Full Text PDFPLoS One
January 2025
Renewable Energy Science and Engineering Department, Faculty of Postgraduate Studies for Advanced Sciences (PSAS), Beni-Suef University, Beni-Suef, Egypt.
This study presents a comprehensive comparative analysis of Machine Learning (ML) and Deep Learning (DL) models for predicting Wind Turbine (WT) power output based on environmental variables such as temperature, humidity, wind speed, and wind direction. Along with Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN), the following ML models were looked at: Linear Regression (LR), Support Vector Regressor (SVR), Random Forest (RF), Extra Trees (ET), Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). Using a dataset of 40,000 observations, the models were assessed based on R-squared, Mean Absolute Error (MAE), and Root Mean Square Error (RMSE).
View Article and Find Full Text PDFFront Plant Sci
January 2025
Department of Computer Science and Engineering, Indian Institute of Information Technology Design and Manufacturing (III TDM), Kurnool, Andhrapradesh, India.
Climate change poses significant challenges to global food security by altering precipitation patterns and increasing the frequency of extreme weather events such as droughts, heatwaves, and floods. These phenomena directly affect agricultural productivity, leading to lower crop yields and economic losses for farmers. This study leverages Artificial Intelligence (AI) and Explainable Artificial Intelligence (XAI) techniques to predict crop yields and assess the impacts of climate change on agriculture, providing a novel approach to understanding complex interactions between climatic and agronomic factors.
View Article and Find Full Text PDFJ Hazard Mater
January 2025
Department of Environmental Science and Engineering, Sichuan University, Chengdu 610065, China. Electronic address:
Heavy metal natural background values play a crucial role in distinguishing anthropogenic sources from natural sources to assess human impacts in polluted areas, thereby accurately formulating environmental policies. However, due to limitations imposed by human activities, research methods, and regional constraints, the determination of heavy metal background values, particularly on site or profile scale, is often challenging, highlighting the urgent need for new methodologies. To establish a comprehensive dataset containing heavy metal concentrations and soil properties, the study systematically collected and screened 82 soil profiles from areas minimally affected by human activities, resulting in a total of 2185 data sets.
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