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Dye (textile) wastewater constitutes of highly complex toxic substances including salts and organic compounds that are to be separated and purified in secondary or tertiary treatment in order to provide reusable water. In this context, oxidation processes are gaining attention in textile effluent treatment due to their high efficiency and less harmful (CO2, H2O and Metal hydroxides or sulfates) by-products. AOPs (Advance Oxidation Processes) are promoted and stabilized by the use of nanoparticles-based catalysts (metal oxides or carbonaceous nanomaterials) with high surface area, stability and other beneficial relevant properties. The efficiency, reusability and characteristics of various metal, metal oxides and carbonaceous catalytic materials in hydroxyl radical and PDS (peroxydisulfate)/PMS (peroxymonosulfate) related oxidation processes of wastewater treatment are discussed in this review. The related activation mechanisms of the hydroxyl and sulfate radicals have been also elaborated, along with the effect of oxidation parameters, such as pH and pollutant concentration. The maximum removal efficiency of organic pollutant can reach values of 96%, while applying the adsorptive bubble separation method, can reach as high as 99% removal efficiency. This review describes the merits and demerits of unsupported and supported nanoparticles that are involved in AOPs. This study represents in selecting better suitability of oxidation catalyst with good regenerability in various parameter like pH, pollutant concentration and capacity.

Pt/SAPO-11 mesopores type materials has successfully synthesized using different templates, such as: diethylamine (DEA), dimethylamine (DMA), and n-propylamine (n-PA), under ultrasonication coupled with hydrothermal treatment or independently with hydrothermal treatment. The influences of structure directing agent (SDA) and synthesis method are investigated by different characterization techniques and the role of the material as catalyst in hydrotreating of sunflower oil has examined. The synthesized materials have been characterized by X-ray Diffraction (XRD), Scanning Electron Microscope (SEM), and Fourier Transform Infra Red (FT-IR) techniques. It is found that SAPO-11 material which has synthesized with n-PA as a template has the characteristics of high silicon incorporation. Hydrotreating of sunflower oil is carried out in a fixed bed reactor with Pt impregnated SAPO-11 catalyst and a detailed study on the isomerization is performed by varying the operating parameters like temperature and space velocity. The high selectivity of Pt/SAPO-11 catalyst is achieved by uniform pore size and acidity. Also the pore opening of the catalyst has a major effect in the selectivity of the catalyst. Further, it represents a higher ratio of isomers compared to other synthesized catalysts on hydro-treating of sunflower oil. Copyright © 2021 by Authors, Published by BCREC Group. This is an open access article under the CC BY-SA License (https://creativecommons.org/licenses/by-sa/4.0).

Ocean heat content (OHC) is a depth-integrated physical oceanographic variable used to precisely measure ocean warming. Because of the limitations associated with in-situ CTD data and Ocean Reanalysis system products, satellite-based approaches have gained importance in estimating the daily to decadal variability of OHC over the vast oceanic region. Efforts to minimize the biases in satellite-based OHC estimates are needed to realize the actual response of the ocean to the brunt of climate change. In the current study, an attempt has been made to better implement the satellite-based ocean thermal expansion method to estimate OHC at 17 depth extents ranging from the surface to 700m. To achieve this objective, an artificial neural network (ANN) model was developed to derive thermosteric sea level (TSL) from a given dataset of sea surface temperature, sea surface salinity, geographical coordinates, and climatological TSL. The model-derived TSL data were used to estimate OHC changes based on the thermal expansion efficiency of heat. Statistical analysis showed high correlation coefficients and low errors in satellite-derived TSL / OHC at 700 m water depth (N 388469, R 0.9926 / 0.9922, RMSE 1.16 m / 1.56 GJ m-2, MBE -0.1917 m / -0.2400 GJ m-2, MBPE -0.4560% / -0.0290%, MAE 0.763 m / 1.029 GJ m-2, and MAPE 2.34% / 0.13%) and nearly similar results at the remaining depth extents. These results suggest that the proposed ANN models are capable of accurately estimating OHC changes on real-time data and three-dimensional distribution patterns of depth-integrated OHC trends in the global ocean. In addition, the first-ever attempt to estimate the ocean thermal expansion component (i.e., TSL) from satellite data was successful and the model-derived TSL can be used to obtain high-end sea-level rise products in the global ocean. [ABSTRACT FROM AUTHOR]

Utilizing in situ measurement data to assess satellite-derived long-term ocean color products under different observational conditions is crucial for ensuring data quality and integrity. In this study, we conducted an extensive evaluation and analysis of Visible Infrared Imaging Radiometer Suite (VIIRS) remote sensing reflectance (Rrs) products using long-term OC-CCI in situ data from 2012 to 2021. Our research findings indicate that, well beyond its designed operational lifespan, the root mean square difference accuracy of VIIRS Rrs products across most spectral bands remains superior to 0.002 (sr−1). However, VIIRS Rrs products in shorter wavelength bands (e.g., at 412 nm) have exhibited significantly lower accuracy and a long-term bias in recent years. The annual precision of VIIRS Rrs products demonstrated a declining trend, particularly in coastal or eutrophic waters. This degradation in accuracy highlights the imperative for continuous monitoring of VIIRS performance and further advancements in the atmospheric correction algorithm, especially to address satellite records at high solar zenith angles (SZAs) and observation zenith angles (OZAs). Our analysis indicates that, in observation environments with high SZAs (greater than 70°), the accuracy of VIIRS Rrs products has declined by nearly 50% compared to typical solar zenith angle observation conditions. To address the challenge of declining accuracy under large observation geometries, we introduced the neural network atmospheric correction model (NN-V). Developed based on meticulously curated VIIRS products, the NN-V model exhibits outstanding performance in handling VIIRS data in conditions of extensive observation geometries. During the winter season in high-latitude marine regions, the NN-V model demonstrates a remarkable enhancement in ocean color product coverage, achieving an increase of nearly 20 times compared to traditional methods. [ABSTRACT FROM AUTHOR]