Biochar-based fixed filter columns for water treatment: A comprehensive review.

Biochar used in fixed filter columns (BFCs) has garnered significant attention for its capabilities in material immobilization and recovery, filtration mechanisms, and potential for scale-up, surpassing the limitations of batch experiments. This review examines the efficacy of biochar in BFCs, either as the primary filtering material or in combination with other media, across various wastewater treatment scenarios. BFCs show high treatment efficiency, with an average COD removal of 80 % ±15.

Revisiting triplet state dissolved organic matter (DOM): Advances in probes, photoreactivity, and environmental implications.

Triplet-state dissolved organic matter (DOM) plays a critical role in the photodegradation of organic pollutants in aquatic environments. This review offers a comprehensive overview of DOM, focusing on monitoring methods using various probes, formation mechanisms, and photoreactivity. Traditional probes, such as 2,4,6-trimethylphenol (TMP) and sorbic acid, are widely used, while novel probes promise improved accuracy and sensitivity.

Toxic metals in rice among Asian countries: A review of occurrence and potential human health risks.

Heavy metals such as cadmium (Cd), arsenic (As), and lead (Pb) pose significant health risks, particularly in Asia, where rice is a staple for nearly three billion people. Despite their known dangers and environmental prevalence, studies addressing their concentrations in rice across different regions and the associated health implications remain insufficient. This review systematically examines the occurrence and impact of these toxic elements, filling a critical gap in the literature.

Algae-constructed wetland integrated system for wastewater treatment: A review.

Integrating algae into constructed wetlands (CWs) enhances wastewater treatment, although the results vary. This review evaluates the role of algae in CWs and the performance of different algae-CW (A-CW) configurations based on literature and meta-analysis. Algae considerably improve N removal, although their impact on other parameters varies.

Meta-analysis review for pilot and large-scale constructed wetlands: Design parameters, treatment performance, and influencing factors.

Despite their longstanding use in environmental remediation, constructed wetlands (CWs) are still topical due to their sustainable and nature-based approach. While research and review publications have grown annually by 7.5 % and 37.

Exploring optical descriptors for rapid estimation of coastal sediment organic carbon and nearby land-use classifications via machine learning models.

This study utilizes ultraviolet and fluorescence spectroscopic indices of dissolved organic matter (DOM) from sediments, combined with machine learning (ML) models, to develop an optimized predictive model for estimating sediment total organic carbon (TOC) and identifying adjacent land-use types in coastal sediments from the Yellow and Bohai Seas. Our results indicate that ML models surpass traditional regression techniques in estimating TOC and classifying land-use types. Penalized Least Squares Regression (PLR) and Cubist models show exceptional TOC estimation capabilities, with PLR exhibiting the lowest training error and Cubist achieving a correlation coefficient 0.

Using CuMgFe layered double oxide to replace laccase as a catalyst for abiotic humification.

Humification offers a promising avenue for sequestering dissolved organic carbon while facilitating environmental cleanup. In this study, CuMgFe layered double oxides (LDO) were applied as a catalyst to replace conventional enzymes, such as laccase, thereby enhancing the in vitro polyphenol-Maillard humification reaction. CuMgFe LDO was synthesized through calcination of CuMgFe layered double hydroxides (LDH) at 500 °C for 5 h.

CuO/FeO/UiO-66 nanocomposite as an efficient fenton-like catalyst: Performance in organic pollutant degradation and influencing factors based machinelearning.

The persistent presence of organic pollutants like dyes in water environment necessitates innovative approaches for efficient degradation. In this research, we developed an advanced hybrid catalyst by combining metal oxides (CuO, FeO) with UiO-66, serving as a heterogeneous Fenton catalyst for for efficient RB19 breakdown in water with HO. The control factors to the catalytic behavior were also quantified by machine learning.

Recent advances in g-CN-based photocatalysis for water treatment: Magnetic and floating photocatalysts, and applications of machine-learning techniques.

Over the past decade, there has been a substantial increase in research investigating the potential of graphitic carbon nitride (g-CN) for various environmental remediations. Renowned for its photocatalytic activity under visible light, g-CN offers a promising solution for treating water pollutants. However, traditional g-CN-based photocatalysts have inherent drawbacks, creating a disparity between laboratory efficacy and real-world applications.

Isoryanodane diterpene derivatives from .

From the MeOH residue of branches and leaves, one new isoryanodane diterpene, barringisol (), and two new isoryanodane diterpene glucosides, barringisosides A and B ( and ), were obtained using various chromatographic isolations. The structural characterization was confirmed by spectroscopic methods including 1D, 2D NMR and HR-ESI-QTOF-MS. This is the first isolation of isoryanodane diterpene derivatives from species.

Roles, mechanism of action, and potential applications of sulfur-oxidizing bacteria for environmental bioremediation.

Sulfur (S) is a crucial component in the environment and living organisms. This work is the first attempt to provide an overview and critical discussion on the roles, mechanisms, and environmental applications of sulfur-oxidizing bacteria (SOB). The findings reveal that key enzymes of SOB embarked on oxidation of sulfide, sulfite, thiosulfate, and elemental S.

Phenolic Glycosides from the Leaves of Iodes cirrhosa Turcz. with Cytotoxic and Antimicrobial Effects.

In the present study, 14 phenolic glycosides, including one new neolignan glycoside, iodescirrhoside A (1), three new flavonol glycosides, iodescirrhosides B-D (2-4), and 10 known metabolites were obtained from the methanol extract of Iodes cirrhosa leaves. Structural elucidation was performed by interpretating the 1D- and 2D- NMR, HR-ESI-MS, and CD spectra in comparison with literature data. All compounds were noncytotoxic to LU-1, HepG2, MCF-7, SK-Mel-2, and LNCaP cancer cell lines.

Exploring potential machine learning application based on big data for prediction of wastewater quality from different full-scale wastewater treatment plants.

Water pollution generated from intensive anthropogenic activities has emerged as a critical issue concerning ecosystem balance and livelihoods worldwide. Although optimizing wastewater treatment efficiency is widely regarded as the foremost step to minimize pollutants released into the environment, this widespread application has encountered two major problems: firstly, the significant variation of influent wastewater constituents; secondly, complex treatment processes within wastewater treatment plants (WWTPs). Based on the data collected hourly using real-time sensors in three different full-scale WWTPs (24 h × 365 days × 3 WWTPs × 10 wastewater parameters), this work introduced the potential application of Machine Learning (ML) to predict wastewater quality.

Generation patterns and consumer behavior of single-use plastic towards plastic-free university campuses.

This study was conducted to estimate the generation of single-use plastics (SUPs) and elucidate consumer behavior towards a plastic-free university. The results show that the consumption rate of plastic bottles was the highest at 1.39 g per student per day (g.

Potential application of machine learning for exploring adsorption mechanisms of pharmaceuticals onto biochars.

The increasing accumulation of pharmaceuticals in aquatic ecosystems could impair freshwater quality and threaten human health. Despite the adsorption of pharmaceuticals on biochars is one of the most cost-effective and eco-friendly removal methods, the wide variation of experimental designs and research aims among previous studies pose significant challenge in selecting biochar for optimal removal. In this work, literature data of 1033 sets with 21 variables collected from 267 papers over ten years (2010-2020) covering 19 pharmaceuticals onto 88 biochars were assessed by different machine learning (ML) algorithms i.

Adsorption of norfloxacin from aqueous solution on biochar derived from spent coffee ground: Master variables and response surface method optimized adsorption process.

In this study, biochar derived from spent coffee grounds (SCGB) was used to adsorb norfloxacin (NOR) in water. The biochar properties were interpreted by analysis of the specific surface area, morphology, structure, thermal stability, and functional groups. The impacts of pH, NOR, and ion's present on SCGB performance were examined.

Developing a new approach for design support of subsurface constructed wetland using machine learning algorithms.

Knowing the effluent quality of treatment systems in advance to enable the design of treatment systems that comply with environmental standards is a realistic strategy. This study aims to develop machine learning - based predictive models for designing the subsurface constructed wetlands (SCW). Data from the SCW literature during the period of 2009-2020 included 618 sets and 10 features.

Application of Machine Learning for eutrophication analysis and algal bloom prediction in an urban river: A 10-year study of the Han River, South Korea.

The increasing release of nutrients to aquatic environments has led to great concern regarding eutrophication and the risk of unwanted algal blooms. Based on observational data of 20 water quality parameters measured on a monthly basis at 40 stations from 2011 to 2020, this study applied different Machine Learning (ML) algorithms to suggest the best option for algal bloom prediction in the Han River, a large river in South Korea. Eight different ML algorithms were categorized into several groups of statistical learning, regression family, and deep learning, and were then compared for their suitability to predict the chlorophyll-derived trophic index (TSI-Chla).

Metal salt-modified biochars derived from agro-waste for effective congo red dye removal.

Anionic Congo red dye (CR) is not effectively removed by conventional adsorbents. Three novel biochars derived from agro-waste (Acacia auriculiformis), modified with metal salts of FeCl, AlCl, and CaCl at 500 °C pyrolysis have been developed to enhance CR treatment. These biochars revealed significant differences in effluents compared to BC, which satisfied initial research expectations (P < 0.

Contamination, source attribution, and potential health risks of heavy metals in street dust of a metropolitan area in Southern Vietnam.

This study investigates distribution, pollution indices, and potential risk assessment for human health and ecology of eight heavy metals in twenty-five street dust samples collected from metropolitan area-Ho Chi Minh City, Vietnam. Results showed that Zn was of the highest concentration (466.4 ± 236.

Garcinoxanthones SV, new xanthone derivatives from the pericarps of Garcinia mangostana together with their cytotoxic and antioxidant activities.

Xanthones (9H-xanthene-9-ones) are considered to be very promising compounds due to a variety of interesting biological and pharmacological activities. In this study, column chromatography of the methanol extract of the Garcinia mangostana L. pericarps resulted in the isolation of four new xanthones (garcinoxanthones SV, 1-4) and five known analogs including garcinone E (5), 11-hydroxy-1-isomangostin (6) mangostenone E (7), 1,3,6,7-tetrahydroxyxanthone (8), and α-mangostin (9).

Vertical flow constructed wetlands using expanded clay and biochar for wastewater remediation: A comparative study and prediction of effluents using machine learning.

This study evaluated and compared the performance of two vertical flow constructed wetlands (VF) using expanded clay (VF) and biochar (VF), of which both are low-cost, eco-friendly, and exhibit potentially high adsorption as compared to conventional filter layers. Both VFs achieved relatively high removal for organic matters (i.e.