Recent advancements in modifications of metal-organic frameworks-based materials for enhanced water purification and contaminant detection.

Metal-organic frameworks (MOFs) have emerged as a key focus in water treatment and monitoring due to their unique structural features, including extensive surface area, customizable porosity, reversible adsorption, and high catalytic efficiency. While numerous reviews have discussed MOFs in environmental remediation, this review specifically addresses recent advancements in modifying MOFs to enhance their effectiveness in water purification and monitoring. It underscores their roles as adsorbents, photocatalysts, and in luminescent and electrochemical sensing. Advancements such as pore modification, defect engineering, and functionalization, combined synergistically with advanced materials, have led to the development of recyclable MOF-based nano-adsorbents, Z-scheme photocatalytic systems, nanocomposites, and hybrid materials. These innovations have broadened the spectrum of removable contaminants and improved material recyclability. Additionally, this review delves into the creation of multifunctional MOF materials, the development of robust MOF variants, and the simplification of synthesis methods, marking significant progress in MOF sensor technology. Furthermore, the review addresses current challenges in this field and proposes potential future research directions and practical applications. The growing research interest in MOFs underscores the need for an updated synthesis of knowledge in this area, focusing on both current challenges and future opportunities in water remediation.

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 0.79. In land-use classification, Support Vector Machines achieved 85.6 % accuracy in training and 92.2 % in testing. Maximum fluorescence intensity and ultraviolet absorbance at 254 nm were crucial factors influencing TOC variations in coastal sediments. This study underscores the efficacy of ML models utilizing DOM optical indices for near real-time estimation of marine sediment TOC and land-use classification.

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 37.6 %, respectively, from 2018 to 2022, a quantitative meta-analysis employing advanced statistics and machine learning to assess CWs has not yet been conducted. Further, traditional statistics of mean ± standard deviation could not convey the extent of confidence or uncertainty in results from CW studies. This study employed a 95 % bootstrap-based confidence interval and out-of-bag Random Forest-based driver analysis on data from 55 studies, totaling 163 cases of pilot and full-scale CWs. The study recommends, with 95 % confidence, median surface hydraulic loading rates (HLR) of 0.14 [0.11, 0.17] m/d for vertical flow-CWs (VF) and 0.13 [0.07, 0.22] m/d for horizontal flow-CWs (HF), and hydraulic retention time (HRT) of 125.14 [48.0, 189.6] h for VF, 72.00 [42.00, 86.28] h for HF, as practical for new CW design. Permutation importance results indicate influent COD impacted primarily on COD removal rate at 21.58 %, followed by HLR (16.03 %), HRT (12.12 %), and substrate height (H) (10.90 %). For TN treatment, influent TN and COD were the most significant contributors at 12.89 % and 10.01 %, respectively, while H (9.76 %), HRT (9.72 %), and HLR (5.87 %) had lower impacts. Surprisingly, while HRT and H had a limited effect on COD removal, they substantially influenced TN. This study sheds light on CWs' performance, design, and control factors, guiding their operation and optimization.

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. A suite of characterization methods confirmed the successful formation into mixed oxides (Cu2O, CuO, MgO, FeO, and Fe2O3) after thermal treatment. A rapid humification reaction was observed with CuMgFe LDO, occurring within a two-week span, likely due to a distinct synergy between copper and iron elements. Subsequent analyses identified that MgO in CuMgFe LDO also played a pivotal role in humification by stabilizing the pH of the reaction. In the absence of magnesium, LDO's humification activity was more pronounced in the early stages of the reaction, but it rapidly diminished as the reaction progressed. The efficiency of CuMgFe LDO was heightened at elevated temperatures (35 °C), while light conditions manifested a discernible effect, with a modest decrease in humification efficacy under indoor light exposure. CuMgFe LDO surpassed both laccase and MgFe LDH in performance, boasting a superior humification efficiency relative to its precursor, CuMgFe LDH. The catalysts' humification activity was modulated by their crystallinity and valence dynamics. Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) results suggested that introducing the amino acid, glycine, expedited the CuMgFe LDO-fueled humification, enhancing the formation of C-N and C-C bonds in the resultant products. The humic-like substances derived from the catalyst-enhanced reaction displayed an elevated presence of aromatic configurations and a richer array of oxygen functional groups in comparison to a typical commercial humic material.

Cu2O/Fe3O4/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 (Cu2O, Fe3O4) with UiO-66, serving as a heterogeneous Fenton catalyst for for efficient RB19 breakdown in water with H2O2. The control factors to the catalytic behavior were also quantified by machine learning. Experimental results show that the catalytic performance was much better than its individual components (P < 0.05 & non-zero 95% C.I). The improved catalytic efficiency was linked to the occurrence of active metal centers (Fe, Cu, and Zr), with Cu(I) from Cu2O playing a crucial role in promoting increased production of HO•. Also, UiO-66 served as a catalyst support, attracting pollutants to the reaction center, while magnetic Fe3O4 aids catalyst recovery. The optimal experimental parameters for best performance were pH at 7, catalyst loading of 1.6 g/L, H2O2 strength of 0.16 M, and reaction temperature of 25 °C. The catalyst can be magnetically separated and regenerated after five recycling times without significantly reducing catalytic activity. The reaction time and pH were ranked as the most influencing factors on catalytic efficiency via Random Forest and SHapley Additive exPlanations models. The findings show that developed catalyst is a suitable candidate to remove dyes in water by Fenton heterogeneous reaction.

Unveiling the drives behind tetracycline adsorption capacity with biochar through machine learning.

This study aimed to develop a robust predictive model for tetracycline (TC) adsorption onto biochar (BC) by employing machine learning techniques to investigate the underlying driving factors. Four machine learning algorithms, namely Random Forest (RF), Gradient Boosting Decision Tree (GBDT), eXtreme Gradient Boosting (XGBoost) and Artificial Neural Networks (ANN), were used to model the adsorption of TC on BC using the data from 295 adsorption experiments. The analysis revealed that the RF model had the highest predictive accuracy (R2 = 0.9625) compared to ANN (R2 = 0.9410), GBDT (R2 = 0.9152), and XGBoost (R2 = 0.9592) models. This study revealed that BC with a specific surface area (S (BET)) exceeding 380 cm3·g-1 and particle sizes ranging between 2.5 and 14.0 nm displayed the greatest efficiency in TC adsorption. The TC-to-BC ratio was identified as the most influential factor affecting adsorption efficiency, with a weight of 0.595. The concentration gradient between the adsorbate and adsorbent was demonstrated to be the principal driving force behind TC adsorption by BC. A predictive model was successfully developed to estimate the sorption performance of various types of BC for TC based on their properties, thereby facilitating the selection of appropriate BC for TC wastewater treatment.

Antimicrobial secondary metabolites from the aerial parts of Perilla frutescens.

Phytochemical research of Perilla frutescens aerial parts led to isolation of 12 secondary metabolites, including one new 3-benzoxepin glucoside, perillafrutoside A (1), one new megastigmane glycoside, perillafrutoside B (2), and 10 known compounds. Their chemical structures were identified based on 1D/2D NMR, HRESIMS, and ECD spectroscopic analyses. The structure of 2 was elucidated based on revision of the previously reported stereoisomer, (6R,9R)-blumenyl α-L-rhamnopyranosyl-(1→6)-ß-D-glucopyranoside. Evaluation of their antimicrobial effect revealed that compounds 1 and 5-11 inhibit Enterococcus faecalis growth, compounds 6, 7 and 9 suppress Staphylococcus aureus growth, whereas compounds 6 and 11 attenuate Candida albicans growth. This is the first report of the isolation of 3-5, 8-10 and 12 from the genus Perilla and the antimicrobial effect of compounds 3, 8 and 10.

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. Conversion of reduced S compounds was oxidatively catalyzed by various enzymes (e.g. sulfide: quinone oxidoreductase, flavocytochrome c-sulfide dehydrogenase, dissimilatory sulfite reductase, heterodisulfide reductase-like proteins). Environmental applications of SOB discussed include detoxifying hydrogen sulfide, soil bioremediation, and wastewater treatment. SOB producing S0 engaged in biological S soil amendments (e.g. saline-alkali soil remediation, the oxidation of sulfide-bearing minerals). Biotreatment of H2S using SOB occurred under both aerobic and anaerobic conditions. Sulfide, nitrate, and sulfamethoxazole were removed through SOB suspension cultures and S0-based carriers. Finally, this work presented future perspectives on SOB development, including S0 recovery, SOB enrichment, field measurement and identification of sulfur compounds, and the development of mathematical simulation.

Recent advances in nanomaterial developments for efficient removal of Hg(II) from water.

"Water" contamination by mercury Hg(II) has become the biggest concern due to its severe toxicities on public health. There are different conventional techniques like ion exchange, reverse osmosis, and filtration that have been used for the elimination of Hg(II) from the aqueous solutions. Although, these techniques have some drawbacks during the remediation of Hg(II) present in water. Adsorption could be a better option for the elimination of Hg(II) from the aqueous solutions. "Conventional adsorbents" like zeolite, clay, and activated carbons are inefficient for this purpose. Recently, nanomaterials have attracted attention for the elimination of Hg(II) from the aqueous solutions due to high porosity, better surface properties, and high efficiency. In this review, a thorough discussion has been carried out on the synthesis and characterization of nanomaterials along with mechanisms involved in the elimination of Hg(II) from aqueous solutions.

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. In this work, six different ML algorithms were examined and compared, varying from shallow to deep learning architectures including Seasonal Autoregressive Integrated Moving Average (SARIMAX), Random Forest (RF), Support Vector Machine (SVM), Gradient Tree Boosting (GTB), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Long Short-Term Memory (LSTM). These models were developed to detect total phosphorus in the outlet (Outlet-TP), which served as an output variable due to the rising concerns about the eutrophication problem. Irrespective of WWTPs, SARIMAX consistently demonstrated the best performance for regression estimation as evidenced by the lowest values of Mean Square Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and the highest coefficient of determination (R2). In terms of computation efficiency, SARIMAX exhibited acceptable time computation, acknowledging the successful application of this algorithm for Outlet-TP modeling. In contrast, the complex structure of LSTM made it time-consuming and unstable coupled with noise, while other shallower architectures, i.e., RF, SVM, GTB, and ANFIS were unable to address large datasets with nonlinear and nonstationary behavior. Consequently, this study provides a reliable and accurate approach to forecast wastewater effluent quality, which is pivotal in terms of the socio-economic aspects of wastewater management.

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. Five algorithms namely, Random forest, Classification and Regression trees, Support vector machines, K-nearest neighbors, and Cubist were compared to determine an optimal algorithm. All nine input features including the influent concentrations, C:N ratio, hydraulic loading rate, height, aeration, flow type, feeding, and filter type were confirmed as relevant features for the predictive algorithms. The comparative result revealed that Cubist is the best algorithm with the lowest RMSE (7.77 and 21.77 mg.L-1 for NH4-N and COD, respectively) corresponding to 84% of the variance in the effluents explained. The coefficient of determination of the Cubist algorithm obtained for NH4-N and COD prediction from the test data were 0.92 and 0.93, respectively. Five case studies of the application of SCW design were also exercised and verified by the prediction model. Finally, a fully developed Cubist algorithm-based design tool for SCW was proposed.

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.s-1.d-1), followed by plastic cups (0.20 g s-1.d-1), and plastic bags (0.14 g s-1.d-1). Approximately 94.41% of students were highly aware of the negative impacts of SUPs. More than four-fifths of the students (82.32%) assumed that they were responsible for the SUP pollution issue, whereas 59.52% considered SUP reduction (or lack thereof) by individuals, governments, and producers/businesses be important factors. Approximately 19.03% of the students supported implementing a high fine, one-tenth agreed for a total ban on SUPs, while nearly one-fifth believed reducing SUP consumption was unnecessary. Strategies for plastic-free universities was initiated by establishing the goal of "plastic-free university" and implementing integrated actions including a ban (plastic cups and bags) awareness-raising, and suitable alternatives.

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. The NOR adsorption mode of SCGB is best suited to the Langmuir model (R2 = 0.974) with maximum absorption capacity (69.8 mg g-1). By using a Response Surface Method (RSM), optimal adsorption was also found at pH of 6.26, NOR of 24.69 mg L-1, and SCGB of 1.32 g L-1. Compared with biochars derived from agriculture such as corn stalks, willow branches, potato stem, reed stalks, cauliflower roots, wheat straw, the NOR adsorption capacity of SCGB was 2-30 times higher, but less than 3-4 times for biochars made from Salix mongolica, luffa sponge and polydopamine microspheres. These findings reveal that spent coffee grounds biochar could effectively remove NOR from aqueous solutions. Approaching biochar derived from coffee grounds would be a promising eco-friendly solution because it utilizes solid waste, saves costs, and creates adsorbents to deal with emerging pollutants like antibiotics.

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.e., Linear regression model (LM), Feed-forward neural networks (NNET), Deep neutral networks (DNN), Cubist, K-nearest neighbor (KNN), and Random forest (RF), to predict equilibrium adsorption capacity (Qe) and explore adsorption mechanisms. LM showed the best performance on ranking importance of input variables. Except for initial concentration of pharmaceuticals, Qe was strongly governed by biochars' properties including specific surface area (BET), pore volume (PV), and pore structure (PS) rather than pharmaceuticals' properties and experimental conditions. The most accurate model for estimating Qe was achieved by Cubist, followed by KNN, RF, KNN, NNET and LM. The generalization ability was observed by the tuned Cubist with 26 rules for the prediction of the unseen data. This study not only provides an insightful evidence for data-based adsorption mechanisms of pharmaceuticals on biochars, but also offers a potential method to accurately predict the biochar adsorption performance without conducting any experiments, which will be of high interests in practice in terms of water/wastewater treatment using biochars.

Cembranoids from the Vietnamese soft coral Sarcophyton ehrenbergi.

Using various chromatographic separations, two new cembranoids, ehrenbergols F and G (1 and 2), along with three known analogs ehrenbergol D (3), (+)-isosarcophine (4) and sinulariol Z2 (5) were isolated from the soft coral Sarcophyton ehrenbergi. The structural elucidation was done by extensive analysis of the 1 D and 2 D NMR, HR-ESI-QTOF-MS as well as CD experiments. In addition, compounds 1 (IC50 of 38.38 ± 2.89 µM), 3 (IC50 of 37.14 ± 3.22 µM) and 4 (IC50 of 45.01 ± 2.49 µM) revealed moderate inhibitory activity on LPS-induced NO production in RAW264.7 cells, whereas 2 (IC50 of 73.32 ± 1.95 µM) and 5 (IC50 of 64.48 ± 4.93 µM) exhibited weak effect.

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). ML algorithms helped identify the most important water quality parameters contributing to algal bloom prediction. The ML results confirmed that eutrophication and algal proliferation were governed by the complex interplay between nutrients (nitrogen and phosphorus), organic contaminants, and environmental factors. Of the models tested, the adaptive neuro-fuzzy inference system (ANFIS) exhibited the best performance owing to its consistent and outperforming prediction both quantitatively (i.e., via regression) and qualitatively (i.e., via classification), which was evidenced by the lowest value of mean absolute error (MAE) of 0.09, and the highest F1-score, Recall and Precision of 0.97, 0.98 and 0.96, respectively. In a further step, a representative web application was constructed to assist common users to predict the trophic status of the Han River. This study demonstrated that ML techniques are not only promising for highly accurate water quality modeling of urban rivers, but also reduce time and labor intensity for experiments, which decreases the number of monitored water quality parameters, providing further insights into the driving factors of water quality deterioration. They ultimately help devise proactive strategies for sustainable water management.

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 FeCl3, AlCl3, and CaCl2 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 < 0.05). The salt concentration of 2 M realized optimal biochars with the highest CR removal of 96.8%, for AlCl3-biochar and FeCl3-biochar and 70.8% for CaCl2-biochar. The modified biochars were low in the specific surface area (137.25-380.78 m2 g-1) compared normal biochar (393.15 m2 g-1), had more heterogeneous particles and successfully integrated metal oxides on the surface. The CR removal increased with a decrease in pH and increase in biochar dosage, which established an optimal point at an initial loading of 25 mg g-1. Maximum adsorption capacity achieved 130.0, 44.86, and 30.80 mg g-1 for BFe, BCa, and BAl, respectively. As magnetic biochar, which is easily separated from the solution and achieves a high adsorption capacity, FeCl3-biochar is the preferred biochar for CR treatment application.

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 ± 236.5 mg/kg), followed by Mn (393.9 ± 93.2 mg/kg), Cu (153.7 ± 64.7 mg/kg), Cr (102.4 ± 50.5 mg/kg), Pb (49.6 ± 21.4 mg/kg), Ni (36.2 ± 15.4 mg/kg), Co (7.9 ± 1.9 mg/kg), and Cd (0.5 ± 0.5 mg/kg). The principal component analysis revealed that three sources of heavy metals measured in street dust include vehicular activities (32.38%), mixed source of vehicular and residential activities (26.72%), and mixture of industrial and natural sources (20.23%). The geo-accumulation index values showed levels of non-pollution to moderately pollution for Mn and Co; moderately pollution for Ni; moderately to strongly pollution for Cd, Cr, and Pb; and strongly pollution for Cu and Zn. The potential ecological risk values of all sampling sites were close to the high-risk category. Zn (28.9%), Cu (25.4%), and Mn (24.4%) dominantly contributed to the ecological risk. For non-carcinogenic risk, the hazard quotient values for both children and adults were within a safety level. For carcinogenic risk, the TCRChildren was about 3 times higher than TCRAdults, but still within a tolerable limit (1 × 10-6 to 1 × 10-4) of cancer risk. Cr was a major contribution to potential risks in humans. Such studies on heavy metal in street dust are crucial but are still limited in Vietnam/or metropolitan area in Southeast Asia. Therefore, this study can fill the information gap about heavy metal contaminated street dust in a metropolitan area of Vietnam.

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). The structures of the new compounds were elucidated by NMR, HRESIMS, and ECD spectra. Compound 8 (1,3,6,7-tetrahydroxyxanthone) was found from the G. mangostana pericarps for the first time. All the isolated compounds (1-8) were evaluated for their 2,2-diphenyl-1-picrylhydrazyl (DPPH) scavenging capacity and cytotoxicity in vitro against three human cancer cell lines including SK-LU-1, MCF7, and HT-29 cell lines. Compounds 3, 5, and 8 exhibited significant DPPH scavenging capacity with IC50 values of 68.55, 63.05, and 28.45 µM, respectively, in comparison with ascorbic acid (IC50 = 48.03 µM). Compounds 5 and 8 showed moderate cytotoxic effects against the three human cancer cell lines with IC50 value ranges of 19.86-27.38 µM.

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 (VF1) and biochar (VF2), 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. Biological oxygen demand during 5 days, BOD5) and nitrogen, accounting for 9.5 - 10.5 g.BOD5.m-2.d-1 and 3.5 - 3.6 g.NH4-N.m-2.d-1, respectively. The different filter materials did not exert any significant discrepancy to effluent quality in terms of suspended solids, organic matters and NO3-N (P > 0.05), but they did influence NH4-N effluent as evidenced by the removal rate of that by VF1 and VF2 being of 82.4 ± 5.7 and 84.6 ± 6.4%, respectively (P < 0.05). The results obtained from the designed systems were further subject to machine learning to clarify the effecting factors and predict the effluents. The optimal algorithms were random forest, generalized linear model, and support vector machine. The values of the coefficient of determination (R2) and the root mean square error (RMSE) of whole fitting data achieved 74.0% and 5.0 mg.L-1, 80.0% and 0.3 mg.L-1, 90.1% and 2.9 mg.L-1, and 48.5% and 0.5 mg.L-1 for BOD5_VF1, NH4-N_VF1, BOD5_VF2, and NH4-N_VF2, respectively.