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.

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.

Cytotoxic constituents from Isotrema tadungense.

In our search for cytotoxic constituents from Vietnamese plants, the methanolic extract of Isotrema tadungense was found to exhibit significant cytotoxic effect. Subsequent phytochemical investigation of ethyl acetate fractions of this plant led to isolation of 11 compounds including one new arylbenzofuran rhamnoside namely aristolochiaside (1), two aristololactams (2 and 3), three lignanamides (4-6) and five phenolic amides (7-11). Their structures were elucidated by 1 D and 2 D NMR and HR-QTOF-MS experiments. Among the isolated compounds, aristolochiaside (1), aristolactam AIIIa (2) and N-trans-sinapoyltyramine (10) exhibited strong and selective cytotoxicity on the HeLa human cancer cell line with IC50 values of 7.59 ± 1.03, 8.51 ± 1.73 and 9.77 ± 1.25 µM, respectively.[Formula: see text].

Cytotoxic steroid derivatives from the Vietnamese soft coral Sinularia brassica.

Using various chromatographic separations, six ergostane-type steroids, including one new compound sinubrassione (1), and two pregnene-type steroid glycosides, including one new compound sinubrassioside (7), were isolated from methanol extract of the Vietnamese soft coral Sinularia brassica. The structure elucidation was confirmed by spectroscopic methods including 1D, 2D NMR and HR-ESI-MS. The cytotoxic activities of all the isolated compounds against three human cancer cell lines were also evaluated using MTT-based colorimetric assays.

Potential of integrated vertical and horizontal flow constructed wetland with native plants for sewage treatment under different hydraulic loading rates.

In this study, a pilot-scale integrated constructed wetland with vertical flow (VF) and horizontal flow (HF) in series was designed and investigated to evaluate sewage wastewater treatment capacity. The VF unit was planted with Canna indica and was 1.2 m long, 1.2 m wide, and 1.2 m high; whereas the HF unit contained Colocasia esculenta and was 3.0 m long, 1.0 m wide, and 1.0 m high. The system was operated under different hydraulic loading rates (HLRs) of 0.1, 0.2, and 0.15 m/d. The effluent concentrations differed as HLR changed, and the means were total suspended solids (TSS): 87 mg/L; biological oxygen demand (BOD5): 31 mg/L; chemical oxygen demand (CODCr): 59 mg/L; ammonium nitrogen (NH4-N): 5.3 mg/L; nitrate nitrogen NO3-N: 8.4 mg/L; total nitrogen (TN): 7.1 mg/L; phosphate (PO4-P): 0.9 mg/L; and total coliforms (TCol): 1,485 most probable number (MPN)/100 mL. The average removal efficiencies for TSS, BOD5, TN, NH4-N, PO4-P, and TCol were 28.3, 74.9, 79, 76.2, 3.6, and 82%, respectively. There were significant differences in the effluent concentrations among the three HLRs (P < 0.05), except for PO4-P.

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.

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.

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.

Antifouling 26,27-cyclosterols from the Vietnamese marine sponge Xestospongia testudinaria.

Three new C29 sterols with a cyclopropane ring cyclized between C-26 and C-27 of the side chain, aragusterol I (1), 21-O-octadecanoyl-xestokerol A (4), and 7ß-hydroxypetrosterol (5b), were isolated from the Vietnamese marine sponge Xestospongia testudinaria, along with the known compounds, aragusterol B (2), xestokerol A (3), 7α-hydroxypetrosterol (5a), 7-oxopetrosterol (6), and petrosterol (7). The structures of the new compounds were established by analysis of spectroscopic data including 1D and 2D NMR, and high-resolution electrospray ionization mass spectrometry (HRESIMS). Their capacity to inhibit the adhesion of isolated bacteria from marine biofilms was evaluated against the bacterial strains Pseudoalteromonas sp. D41, Pseudoalteromonas sp. TC8, and Polaribacter sp. TC5. Aragusterol B (2) and 21-O-octadecanoyl-xestokerol A (4) exhibited the most potent antifouling activity with EC50 values close to these reported in the literature for tributyltin oxide, a marine anti-biofouling agent now considered to be a severe marine pollutant. Due to its comparable activity to tributyltin oxide and its absence of toxicity, the new 26,27-cyclosterol, 21-O-octadecanoyl-xestokerol A (4) constitutes a promising scaffold for further investigations.

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.

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.

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.

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.

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.

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.

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.

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.