Nanobiotechnological advancements in agriculture and food industry: Applications, nanotoxicity, and future perspectives

The Science of The Total Environment, Год журнала: 2021, Номер 792, С. 148359 - 148359

Опубликована: Июнь 8, 2021

Язык: Английский

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Microalgae-bacteria consortium for wastewater treatment and biomass production

The Science of The Total Environment, Год журнала: 2022, Номер 838, С. 155871 - 155871

Опубликована: Май 11, 2022

Язык: Английский

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Nanotechnology – A new frontier of nano-farming in agricultural and food production and its development

The Science of The Total Environment, Год журнала: 2022, Номер 857, С. 159639 - 159639

Опубликована: Окт. 22, 2022

Язык: Английский

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Indices and models of surface water quality assessment: Review and perspectives

Environmental Pollution, Год журнала: 2022, Номер 308, С. 119611 - 119611

Опубликована: Июнь 15, 2022

Язык: Английский

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Eutrophication control of large shallow lakes in China

The Science of The Total Environment, Год журнала: 2023, Номер 881, С. 163494 - 163494

Опубликована: Апрель 15, 2023

Язык: Английский

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Nitrogen doped biochar derived from algae as persulfate activator for the degradation of tetracycline: Role of exogenous N doping and electron transfer pathway

Separation and Purification Technology, Год журнала: 2023, Номер 318, С. 123970 - 123970

Опубликована: Май 3, 2023

Язык: Английский

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Advancing reservoirs water quality parameters estimation using Sentinel-2 and Landsat-8 satellite data with machine learning approaches

Ecological Informatics, Год журнала: 2024, Номер 81, С. 102608 - 102608

Опубликована: Апрель 21, 2024

Reservoir eutrophication, caused by human activities and climate change, has emerged as a critical environmental concern that attracted both governmental public attention. However, accurate measurement of water quality parameters, such chlorophyll (CHL-a), clarity (Secchi depth; SD), total suspended solids (TSS), in inland waters is challenging due to the optical complexity individual bodies, which impedes optimization conventional bio-optical algorithms. The aim this study was demonstrate viability harmonizing Sentinel-2 Multi-Spectral Imager (MSI) Landsat-8 Operational Land (OLI) satellite imagery surface reflectance (SR) products facilitate monitoring reservoir CHL-a, SD, TSS using Google Earth Engine (GEE) platform machine learning Machine models were trained OLI MSI identify bands combinations predicting TSS. Among algorithms tested, random forest (RF) (S-2 MSI: R2 = 0.61, mean absolute error [MAE] 6.56%, root-mean-square [RMSE] 12.51 μg/L, L-8 OLI: 0.56, MAE 8.44%, RMSE 16.01 μg/L) yielded best results test set for CHL-a prediction from OLI, outperforming k-nearest neighbor (KNN), AdaBoost, artificial neural network (ANN) models. It also showed superior performance SD prediction. feature importance analysis revealed specific band ratios, (red/red edge1)*red edge2 red/blue significant predictors ratio green highly predictive respectively. fall predictions varying trophic levels reservoirs. indicated 2% reservoirs oligotrophic, while 46%, 43%, 9% mesotrophic, eutrophic, hypertrophic, Meanwhile, 51% 6%, 35%, 8% Overall, demonstrates effectiveness estimating parameters This approach potential yield valuable insights aiding assessment management at regional, national, global levels.

Язык: Английский

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Long-term (2003−2021) evolution trend of water quality in the Three Gorges Reservoir: An evaluation based on an enhanced water quality index

The Science of The Total Environment, Год журнала: 2024, Номер 915, С. 169819 - 169819

Опубликована: Янв. 6, 2024

Язык: Английский

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Predicting Chlorophyll-a Concentrations in the World’s Largest Lakes Using Kolmogorov-Arnold Networks

Environmental Science & Technology, Год журнала: 2025, Номер unknown

Опубликована: Янв. 16, 2025

Accurate prediction of chlorophyll-a (Chl-a) concentrations, a key indicator eutrophication, is essential for the sustainable management lake ecosystems. This study evaluated performance Kolmogorov-Arnold Networks (KANs) along with three neural network models (MLP-NN, LSTM, and GRU) traditional machine learning tools (RF, SVR, GPR) predicting time-series Chl-a concentrations in large lakes. Monthly remote-sensed data derived from Aqua-MODIS spanning September 2002 to April 2024 were used. The based on their forecasting capabilities March August 2024. KAN consistently outperformed others both test forecast (unseen data) phases demonstrated superior accuracy capturing trends, dynamic fluctuations, peak concentrations. Statistical evaluation using ranking metrics critical difference diagrams confirmed KAN's robust across diverse sites, further emphasizing its predictive power. Our findings suggest that KAN, which leverages KA representation theorem, offers improved handling nonlinearity long-term dependencies data, outperforming grounded universal approximation theorem algorithms.

Язык: Английский

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Approach based on TOPSIS and Monte Carlo simulation methods to evaluate lake eutrophication levels

Water Research, Год журнала: 2020, Номер 187, С. 116437 - 116437

Опубликована: Сен. 19, 2020

Язык: Английский

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