Removal of trivalent chromium ions in model contaminated groundwater using hexagonal boron nitride as an adsorbent.

The feasibility of using hexagonal boron nitride (h-BN) to treat heavy metal Cr(III) from model contaminated groundwater was evaluated in this study by adsorption experiments and characterizations. To the best of our knowledge, this study is the first attempt to conduct the adsorption of Cr(III) by h-BN under various experimental conditions such as exposure time, ratio of adsorbates and adsorbents, solution pH, background ions with different ionic strength, and the presence of humic acids (HA) in model contaminated groundwater. The optimized h-BN showed excellent maximum adsorption capacity (i.e., 177 mg ∙ g-1) when the concentrations of Cr(III) and h-BN were 10 and 10 mg ∙ L-1, respectively. Subsequently, we confirmed there was a negligible change in the adsorption performance of Cr(III) by h-BN in the presence of co-ions (i.e., K and Mg) in concentrations in a range from 50 to 1000 mg ∙ L-1. Furthermore, the adsorption performance of Cr(III) gradually improved with HA concentrations from 2.5 to 25 mg ∙ L-1. Interestingly, the maximum adsorption performance of Cr(III) by both HA and h-BN increased until 500 mg ∙ g-1 in the presence of 25 mg ∙ L-1 HA. The adsorption mechanism was clarified by Fourier-transform infrared spectroscopy and X-ray photoelectron spectroscopy. Additionally, we successfully confirmed that h-BN could be reused until five cycles. On the basis of the adsorption performance results and characterizations, h-BN can be utilized as an efficient and practical adsorbent to treat Cr(III) in groundwater treatment.

Recent advances in the application of magnetic materials for the management of perfluoroalkyl substances in aqueous phases.

Perfluoroalkyl substances (PFASs) are a class of artificially synthesised organic compounds extensively used in both industrial and consumer products owing to their unique characteristics. However, their persistence in the environment and potential risk to health have raised serious global concerns. Therefore, developing effective techniques to identify, eliminate, and degrade these pollutants in water are crucial. Owing to their high surface area, magnetic responsiveness, redox sensitivity, and ease of separation, magnetic materials have been considered for the treatment of PFASs from water in recent years. This review provides a comprehensive overview of the recent use of magnetic materials for the detection, removal, and degradation of PFASs in aqueous solutions. First, the use of magnetic materials for sensitive and precise detection of PFASs is addressed. Second, the adsorption of PFASs using magnetic materials is discussed. Several magnetic materials, including iron oxides, ferrites, and magnetic carbon composites, have been explored as efficient adsorbents for PFASs removal from water. Surface modification, functionalization, and composite fabrication have been employed to improve the adsorption effectiveness and selectivity of magnetic materials for PFASs. The final section of this review focuses on the advanced oxidation for PFASs using magnetic materials. This review suggests that magnetic materials have demonstrated considerable potential for use in various environmental remediation applications, as well as in the treatment of PFASs-contaminated water.

Advances in Fe-modified lignocellulosic biochar: Impact of iron species and characteristics on wastewater treatment.

Lignocellulosic biomass is an attractive feedstock for biochar production owing to its high abundance and renewability. Various modified biochars have been extensively studied for wastewater treatment to improve the physical and chemical properties of lignocellulosic biochar (L-BC). Particularly, Fe-modified L-BCs have garnered attention owing to the abundance and eco-friendliness of Fe and the outstanding ability to remove various organic and inorganic contaminants via adsorption, oxidation, reduction, and catalytic reactions. Different iron species (e.g., Fe(0), Fe (hydr)oxide, Fe sulfide, and Fe-Metal) are formed during the preparation of Fe-L-BCs, which can completely differentiate the physical and chemical properties of BCs. This review discusses the advances in the synthesis of different Fe-L-BCs, specific changes in the physical and chemical properties of Fe-L-BCs upon Fe addition, and their impacts on wastewater treatment. The results of this review can demonstrate the unique advantages and drawbacks of Fe-L-BCs for the removal of different types of pollutants.

Machine Learning-Based Early Warning Level Prediction for Cyanobacterial Blooms Using Environmental Variable Selection and Data Resampling.

Many countries have attempted to mitigate and manage issues related to harmful algal blooms (HABs) by monitoring and predicting their occurrence. The infrequency and duration of HABs occurrence pose the challenge of data imbalance when constructing machine learning models for their prediction. Furthermore, the appropriate selection of input variables is a significant issue because of the complexities between the input and output variables. Therefore, the objective of this study was to improve the predictive performance of HABs using feature selection and data resampling. Data resampling was used to address the imbalance in the minority class data. Two machine learning models were constructed to predict algal alert levels using 10 years of meteorological, hydrodynamic, and water quality data. The improvement in model accuracy due to changes in resampling methods was more noticeable than the improvement in model accuracy due to changes in feature selection methods. Models constructed using combinations of original and synthetic data across all resampling methods demonstrated higher prediction performance for the caution level (L-1) and warning level (L-2) than models constructed using the original data. In particular, the optimal artificial neural network and random forest models constructed using combinations of original and synthetic data showed significantly improved prediction accuracy for L-1 and L-2, representing the transition from normal to bloom formation states in the training and testing steps. The test results of the optimal RF model using the original data indicated prediction accuracies of 98.8% for L0, 50.0% for L1, and 50.0% for L2. In contrast, the optimal random forest model using the Synthetic Minority Oversampling Technique-Edited Nearest Neighbor (ENN) sampling method achieved accuracies of 85.0% for L0, 85.7% for L1, and 100% for L2. Therefore, applying synthetic data can address the imbalance in the observed data and improve the detection performance of machine learning models. Reliable predictions using improved models can support the design of management practices to mitigate HABs in reservoirs and ultimately ensure safe and clean water resources.

A novel approach to measure carbonate alkalinity in aqueous solutions using a total organic carbon analyzer.

Carbonate alkalinity is crucial in regulating the pH and buffering capacity of natural water systems. Thus, its accurate measurement is essential to understand various water environments that affect water quality and ecosystem health. However, conventional potentiometric titration has some limitations. It results in inaccurate measurements of carbonate alkalinity when the alkalinity levels are low or when high dissolved organic matter or inorganic ion levels exist. Herein, we propose a novel approach to accurately measure carbonate alkalinity using a total organic carbon (TOC) analyzer. An extensive study comparing the accuracy and reliability of the conventional potentiometric titration method with those of the newly developed TOC method was conducted to develop and verify highly accurate measurements of carbonate alkalinity. The TOC method has several advantages over the conventional potentiometric titration methods, such as its ability to accurately measure carbonate alkalinity in the presence of high dissolved organic matter or inorganic ion levels and its ability to provide rapid and automated measurements with high reproducibility. Because, the limit of detection, limit of quantification, and the variation coefficient of the measurements was 0.016 mM (0.2 mgC/L), 0.050 mM (0.6 mgC/L), and 3.68 % respectively. Thus, the development of a novel TOC method has significant environmental implications as it provides a reliable and accurate means to measure carbonate alkalinity in solutions containing various organic matter types.

Mitigation of benzoic acid-driven autotoxicity in waste nutrient solution using O3 and O3/H2O2 treatments: Seed germination and root growth of Lactuca sativa L.

Benzoic acid (BA), a secondary metabolite released through root exudates, is considered to be the most common inhibitor that leads to plant autotoxicity, even at low concentrations in closed hydroponic systems. In this study, to mitigate BA-driven autotoxicity, the effects of O3 and O3/H2O2 oxidation treatment (O3 concentration: 1, 2, 4, 8 mg L-1, H2O2 concentration: 4, 8 mg L-1) on waste nutrient solution (WNS) were investigated in terms of BA degradation, the rate of germination inhibition (GI), and the rate of root growth inhibition (RI). In the case of O3 treatment, the BA degradation rate improved up to 14.1% as the O3 concentration increased, while alleviation of GI was insignificant (94.6-100%), confirming that a single O3 treatment was unsuitable for mitigating autotoxicity. On the other hand, O3/H2O2 treatment increased BA degradation by up to 24.8%, thereby significantly reducing GI (up to 7.69%) and RI (up to 0.88%). Both the highest BA mineralization rate and phytotoxicity mitigation was observed at BA125 (4-4) (BA mineralization: 16.7%, GI: 12.82%, RI: 11.69%) and BA125 (1-8) (BA mineralization: 17.7%, GI: 7.69%, RI: 0.88%) at each H2O2 concentration. In addition, the operating costs were evaluated by a chemical and electricity cost analysis at the different treatments. As a result, the operating costs of BA125 (4-4) and BA125 (1-8) were calculated to be 0.40 and 0.42 $ L-1 mg-1 of mineralized BA, respectively. After consideration of the mineralization rate, autotoxicity mitigation, and operating cost, BA125 (1-8) was suggested for the optimal treatment condition and our findings would contribute to the alleviation of BA-driven autotoxicity.

Evaluation of Efficiently Removing Secondary Effluent Organic Matters (EfOM) by Al-Based Coagulant for Wastewater Recycling: A Case Study with an Industrial-Scale Food-Processing Wastewater Treatment Plant.

The reuse of wastewater has been identified as an important initiative for the sustainable development of the environment; thus, the removal of secondary effluent organic matter (EfOM) to ensure the safety of reused wastewater is the key step and a subject of extensive research. In this study, Al2(SO4)3 and anionic polyacrylamide were selected as coagulant and flocculant, respectively, for the treatment of secondary effluent from a food-processing industry wastewater treatment plant to meet the standard regulatory specifications for water reuse. In this process, the removal efficiencies of chemical oxygen demand (COD), components with UV254, and specific ultraviolet absorbance (SUVA) were 44.61%, 25.13%, and 9.13%, respectively, with an associated reduction in chroma and turbidity. The fluorescence intensities (Fmax) of two humic-like components were reduced during coagulation, and microbial humic-like components of EfOM had a better removal efficiency because of a higher Log Km value of 4.12. Fourier transform infrared spectroscopy showed that Al2(SO4)3 could remove the protein fraction of the soluble microbial products (SMP) of EfOM by forming a loose SMP protein complex with enhanced hydrophobicity. Furthermore, flocculation reduced the aromaticity of secondary effluent. The cost of the proposed secondary effluent treatment was 0.034 CNY t-1 %COD-1. These results demonstrate that the process is efficient and economically viable for EfOM removal to realize food-processing wastewater reuse.

Enhanced selectivity and recovery of phosphate and nitrate ions onto coffee ground waste biochars via co-precipitation of Mg/Al layered double hydroxides: A potential slow-release fertilizer.

In this study, the feasibility of Mg/Al layered double hydroxides (LDH) functionalized coffee ground waste biochars (LDHMgAl@CWGB) as a potential adsorbent to selectively recover phosphate (PO43-) and nitrate (NO3-) ions in aqueous phases and their consecutive uses as a slow-release fertilizer for stimulating the plant growth were identified. The higher adsorption capacity of PO43- and NO3- ions by LDHMgAl@CWGB (PO43- = 6.98 mgP/g, NO3- = 2.82 mgN/g) compared with pristine coffee ground waste biochars (CWGB; PO43- = 0.19 mgP/g, NO3- = 0.32 mgN/g) was mainly due to the incorporation of Mg/Al mixed oxides and Cl contents. Chemisorption and intra-particle mainly controlled the adsorptive recovery of PO43- and NO3- ions by CWGB and LDHMgAl@CWGB in aqueous phases and their adsorption toward CWGB and LDHMgAl@CWGB proceeded endothermically and spontaneously. The changes in the major adsorption mechanisms of PO43- and NO3- ions from ligand exchange (CWGB) to electrostatic surface complexation and anion-exchange (LDHMgAl@CWGB) supported the conclusion that the alternation of the surface features through Mg/Al LDH functionalization might improve selectivity and adsorption capacity of PO43- and NO3- ions onto CWGB under the co-existence of Cl-, SO42-, and HCO3- ions. Since PO43-- and NO3--loaded LDHMgAl@CWGB exhibited much higher seed germination, root and shoot growth rates of garden cress seeds (Lepidium sativum L) than other liquid and solid matrices, including 5 mgP/L PO43- and 5 mgN/L NO3-, 10 mgP/L PO43- and 10 mgN/L NO3-, and LDHMgAl@CWGB, it can be postulated that PO43-- and NO3--loaded LDHMgAl@CWGB could be practically applicable to the agricultural field as a slow-release fertilizer to facilitate the seed germination, root and shoot growth of the plants.

Functionalization of pine sawdust biochars with Mg/Al layered double hydroxides to enhance adsorption capacity of synthetic azo dyes: Adsorption mechanisms and reusability.

This study determined that the adsorption of azo dyes, Methyl Orange (MO) and Sunset Yellow FCF (SYF), using the pristine pine sawdust biochar (PSB) and post-modified PSB with Mg/Al layered double hydroxides (PSB-LDHMgAl) was examined to offer valuable information into the differences in their adsorption mechanisms. Although a lower specific surface area of PSB-LDHMgAl (147.2 m2 g-1) than PSB (495.7 m2 g-1), LDHMgAl were successfully functionalized on the PSB surface through co-precipitation, which was highly related to the improvements of adsorption capacity of PSB-LDHMgAl toward MO and SYF. The MO and SYF adsorption kinetics by PSB and PSB-LDHMgAl were confirmed to the pseudo-second-order and considered chemisorption. The adsorption capacity of MO and SYF adsorbed onto PSB-LDHMgAl (MO = 21.8 mg g-1, SYF = 23.6 mg g-1) were significantly higher than that of PSB (MO = 2.2 mg g-1, SYF = 1.6 mg g-1). The adsorption isotherms of MO and SYF by PSB were well fitted by Freundlich isotherm, whereas the MO and SYF via PSB-LDHMgAl were by Langmuir isotherm. Even after 3 adsorption-desorption cycles using desorbents, the PSB-LDHMgAl remained excellent reusability (reuse efficiency: >81.2%). These findings suggest that post-modification with LDHMgAl might accelerate the adsorption performance (i.e., electrostatic interaction) of azo dyes to PSB in water.

An autopsy study of hollow fiber and multibore ultrafiltration membranes from a pilot-scale ultra high-recovery filtration system for surface water treatment.

The organic fouling characteristics of hollow fiber ultrafiltration (HFUF) and multibore ultrafiltration (MBUF) membranes from long-term ultrafiltration (UF) membrane systems were systemically investigated in this study. The objective was to obtain insights into the fouling behavior of dissolved organic matter (DOM) in a pilot-scale ultra-high-recovery membrane filtration system (p-UHMS) used for surface water treatment. The pilot system consisted of a series of two different UF membranes (1st stage: polyvinylidene fluoride (PVDF) HFUF and 2nd stage: polyethersulfone (PES) MBUF). It was designed to feed the HFUF concentrate to the MBUF membranes to achieve ≥99.5 % total water recovery for surface water treatment, as these advances might enhance the production efficiencies of drinking water. The experimental results confirmed that hydrophobic DOM controlled the formation of HFUF membrane organic fouling, whereas hydrophilic DOM, including polysaccharide-like and protein-like matter, promoted MBUF membrane fouling. These opposing trends were attributed to the hydrophilic characteristics of the MBUF membrane surfaces (contact angle: PVDF = 90-130° and PES ≤ 80°), which reduced the hydrophobic interactions between the UF membrane surfaces and foulants. The performance declines of the MBUF membrane due to fouling layer formation was considerably severer than those of the HFUF membrane, decreasing total permeate water in the p-UHMS. Moreover, the quantity of the desorbed MBUF membrane foulants via 0.1 N NaOH was roughly 7.2 times larger than that of the desorbed HFUF membrane foulants through 0.1 N NaOH, indicating that alkaline-based cleaning agent could much more efficiently recover the performance of the fouled MBUF membranes. Hence, adequate cleaning strategies using alkaline-based agent for the MBUF membrane appeared to be essential for preventing the performance deterioration of the p-UHMS.

Machine learning approaches to predict the photocatalytic performance of bismuth ferrite-based materials in the removal of malachite green.

This study focuses on the potential capability of numerous machine learning models, namely CatBoost, GradientBoosting, HistGradientBoosting, ExtraTrees, XGBoost, DecisionTree, Bagging, light gradient boosting machine (LGBM), GaussianProcess, artificial neural network (ANN), and light long short-term memory (LightLSTM). These models were investigated to predict the photocatalytic degradation of malachite green from wastewater using various NM-BiFeO3 composites. A comprehensive databank of 1200 data points was generated under various experimental conditions. The ten input variables selected were the catalyst type, reaction time, light intensity, initial concentration, catalyst loading, solution pH, humic acid concentration, anions, surface area, and pore volume of various photocatalysts. The MG dye degradation efficiency was selected as the output variable. An evaluation of the performance metrics suggested that the CatBoost model, with the highest test coefficient of determination (0.99) and lowest mean absolute error (0.64) and root-mean-square error (1.34), outperformed all other models. The CatBoost model showed that the photocatalytic reaction conditions were more important than the material properties. The modeling results suggested that the optimized process conditions were a light intensity of 105 W, catalyst loading of 1.5 g/L, initial MG dye concentration of 5 mg/L and solution pH of 7. Finally, the implications and drawbacks of the current study were stated in detail.

Pretreatments of lignocellulosic and algal biomasses for sustainable biohydrogen production: Recent progress, carbon neutrality, and circular economy.

Lignocellulosic and algal biomasses are known to be vital feedstocks to establish a green hydrogen supply chain toward achieving a carbon-neutral society. However, one of the most pressing issues to be addressed is the low digestibility of these biomasses in biorefinery processes, such as dark fermentation, to produce green hydrogen. To date, various pretreatment approaches, such as physical, chemical, and biological methods, have been examined to enhance feedstock digestibility. However, neither systematic reviews of pretreatment to promote biohydrogen production in dark fermentation nor economic feasibility analyses have been conducted. Thus, this study offers a comprehensive review of current biomass pretreatment methods to promote biohydrogen production in dark fermentation. In addition, this review has provided comparative analyses of the technological and economic feasibility of existing pretreatment techniques and discussed the prospects of the pretreatments from the standpoint of carbon neutrality and circular economy.

Inhibition of biofouling on reverse osmosis membrane surfaces by germicidal ultraviolet light side-emitting optical fibers.

Biofouling of membrane surfaces poses significant operational challenges and costs for desalination and wastewater reuse applications. Ultraviolet (UV) light can control biofilms while reducing chemical usage and disinfection by-products, but light deliveries to membrane surfaces in spiral wound geometries has been a daunting challenge. Thin and flexible nano-enabled side-emitting optical fibers (SEOFs) are novel light delivery devices that enable disinfection or photocatalytic oxidation by radiating UV light from light-emitting diodes (LEDs). We envision SEOFs as an active membrane spacer to mitigate biofilm formation on reverse osmosis (RO) membranes. A lab-scale RO membrane apparatus equipped with SEOFs allowed comparison of UV-A (photocatalysis-enabled) versus UV-C (direct photolysis disinfection). Compared against systems without any light exposure, systems with UV-C light formed thinner-but denser-biofilms, prevented permeate flux declines due to biofouling, and maintained the highest salt rejection. Results were corroborated by in-situ optical coherence tomography and ex-situ measurements of biofilm growth on the membranes. Transcriptomic analysis showed that UV-C SEOFs down-regulated quorum sensing and surface attachment genes. In contrast, UV-A SEOFs upregulated quorum sensing, surface attachment, and oxidative stress genes, resulting in higher extracellular polymeric substances (EPS) accumulation on membrane surfaces. Overall, SEOFs that deliver a low fluence of UV-C light onto membrane surfaces are a promising non-chemical approach for mitigating biofouling formation on RO membranes.

Synthesis and applications of bismuth-impregnated biochars originated from spent coffee grounds for efficient adsorption of radioactive iodine: A mechanism study.

The adsorption of radioactive iodine, which is capable of presenting high mobility in aquatic ecosystems and generating undesirable health effects in humans (e.g., thyroid gland dysfunction), was comprehensively examined using pristine spent coffee ground biochar (SCGB) and bismuth-impregnated spent coffee ground biochar (Bi@SCGB) to provide valuable insights into the variations in the adsorption capacity and mechanisms after pretreatment with Bi(NO3)3. The greater adsorption of radioactive iodine toward Bi@SCGB (adsorption capacity (Qe) = 253.71 µg/g) compared to that for SCGB (Qe = 23.32 µg/g) and its reduced adsorption capability at higher pH values provide evidence that the adsorption of radioactive iodine with SCGB and Bi@SCGB is strongly influenced by the presence of bismuth materials and the electrostatic repulsion between their negatively charged surfaces and negatively charged radioactive iodine (IO3-). The calculated R2 values for the adsorption kinetics and isotherms support that chemisorption plays a crucial role in the adsorption of radioactive iodine by SCGB and Bi@SCGB in aqueous phases. The adsorption of radioactive iodine onto SCGB was linearly correlated with the contact time (h1/2), and the diffusion of intra-particle predominantly determined the adsorption rate of radioactive iodine onto Bi@SCGB (Cstage II (129.20) > Cstage I (42.33)). Thermodynamic studies revealed that the adsorption of radioactive iodine toward SCGB (ΔG° = -8.47 to -7.83 kJ/mol; ΔH° = -13.93 kJ/mol) occurred exothermically and that for Bi@SCGB (ΔG° = -15.90 to -13.89 kJ/mol; ΔH° = 5.88 kJ/mol) proceeded endothermically and spontaneously. The X-ray photoelectron spectroscopy (XPS) analysis of SCGB and Bi@SCGB before and after the adsorption of radioactive iodine suggest the conclusion that the change in the primary adsorption mechanism from electrostatic attraction to surface precipitation upon the impregnation of bismuth materials on the surfaces of spent coffee ground biochars is beneficial for the adsorption of radioactive iodine in aqueous phases.

Unveiling the positive effect of mineral induced natural organic matter (NOM) on catalyst properties and catalytic dechlorination performance: An experiment and DFT study.

Herein, we report the significant effects of natural organic matter contained in natural zeolite (Z-NOM) on the physicochemical characteristics of a Ni/Fe@natural zeolite (NF@NZ) catalyst and its decontamination performance toward the dechlorination of trichloroethylene (TCE). Z-NOM predominantly consists of humic-like substances and has demonstrable utility in the synthesis of bimetallic catalysts. Compared to NF@NZ600C (devoid of Z-NOM), NF@NZ had increased dispersibility and mobility and showed significant enhancement in the catalytic dechlorination of TCE owing to the encapsulation of Ni0/Fe0 nanoparticles by Z-NOM. The results of corrosion experiments, spectroscopic analyses, and H2 production experiments confirmed that Ni0 acted as an efficient cocatalyst with Fe0 to enhance the dechlorination of TCE to ethane, and Z-NOM-capped Ni0 showed improved adsorption of TCE and atomic hydrogen on their reactive sites and oxidation resistance. The density functional theory (DFT) studies have substantiated the improved adsorption of TCE due to the presence of NOM (especially by COOH structure) and the enhanced charge density at the Ni site in the Ni/Fe bimetal alloy for the stronger adsorption of hydrogen atoms that ultimately enhanced the TCE reduction reaction. These findings illustrate the efficiency of NOM containing natural minerals toward the synthesis of bimetallic catalysts for practical applications.

NaOH-assisted H2O2 post-modification as a novel approach to enhance adsorption capacity of residual coffee waste biochars toward radioactive strontium: Experimental and theoretical studies.

In this study, NaOH-assisted H2O2 post-modification was proposed as a novel strategy to enhance the adsorption of radioactive strontium (Sr) onto residual coffee waste biochars (RCWBs). To validate its viability, the adsorption capacities and mechanisms of Sr(II) using pristine (RCWBP), H2O2 post-modified (RCWBHP), and NaOH-assisted H2O2 post-modified residual coffee waste biochars (RCWBNHP) were experimentally and theoretically investigated. The highest adsorption capacity of Sr(II) for RCWBNHP (10.91 mg/g) compared to RCWBHP (5.57 mg/g) and RCWBP (5.07 mg/g) was primarily attributed to higher negative surface zeta potential (RCWBNHP = -5.66 → -30.97 mV; RCWBHP = -0.31 → -11.29 mV; RCWBP = 1.90 → -10.40 mV) and decoration of Na on the surfaces of RCWBP via NaOH-assisted H2O2 post-modification. These findings agree entirely with the theoretical observations that the adsorption of Sr(II) onto RCWBP and RCWBHP was controlled by electrostatic interactions involving carbonyls whereas enriched carboxylic acids and decorated Na on the surfaces of RCWBNHP through the replacement of Mg and K by NaOH-assisted H2O2 modification stimulated electrostatic interactions and cation exchanges governing the adsorption of Sr(II). Hence, NaOH-assisted H2O2 post-modification seemed to be practically applicable for improving the adsorption capacity of Sr(II) using RCWB-based carbonaceous adsorbents in real water matrices.

Effects of two-step cleaning sequences on foulant extraction from multibore ultrafiltration membranes in a pilot-scale membrane filtration system for surface water treatment.

The cleaning efficiencies of fouled multibore ultrafiltration membrane (UFMB) operated from a pilot-scale UF process for surface water treatment were systemically investigated according to the sequences of two different cleaning solutions. The experimental results decisively confirmed that HPI DOM and HPO DOM/multivalent ions complexation significantly resulted in the fouling formations on UFMB due to their neutral charge characteristic. The basic cleaning agent effectively extracted the organic foulants attached on UFMB, indicating that the type of cleaning agent was a critical factor influencing on the cleaning efficiency of fouled UFMB. However, the cleaning sequence 1 (CS-1: 0.1 M NaOH >0.1 M HCl; the total DOC = 725.77 mgC∙m-2; the total TN = 146.35 mgN∙m-2, total inorganic contents = 132.62 mg m-2) much more effectively extracted the foulants on the UFMB surfaces than the cleaning sequence 2 (CS-2: 0.1 M HCl >0.1 M NaOH; the total DOC = 604.49 mgC∙m-2; the total of TN = 121.79 mgN∙m-2, total inorganic contents = 73.43 mg m-2). The morphological results also clearly showed that the cleaned UFMB surface using CS-1 were effectively recovered, as compared with those using CP-2. Overall, this study implied that the hydroxide ions from the basic cleaning agent promoted the infiltration of the acidic cleaning agent into the densely formed fouling layers on the UFMB surfaces and demonstrated that the cleaning sequences strategy could significantly govern the restoration of UFMB performance during the pilot-scale surface water treatment system operation.

Insights into the minimization of excess sludge production in micro-aerobic reactors coupled with a membrane bioreactor: Characteristics of extracellular polymeric substances.

The production of excess sludge by the activated sludge system of wastewater treatment plants is a problem. In this study, the EPS characteristics on production and degradation were investigated in the real-scale food processing wastewater treatment system (i.e., a micro-aerobic reactor coupled with a membrane bioreactor (MAR-MBR)) with a treatment capacity of 150 t d-1, which could cater for the low production of excess sludge (i.e., 9 t·a-1; 76% moisture content). The total organic carbon concentrations in the different EPS fractions were in the following order: soluble EPS (S-EPS) < loosely bound EPS (LB-EPS) < tightly bound EPS (TB-EPS). Although the components (e.g., protein and humic acid-like substances) of each EPS fraction changed significantly throughout the MAR-MBR process owing to the low production of excess sludge, the degrees of change in S-EPS, LB-EPS, and TB-EPS were significantly different from the corresponding change in their relative molecular weights. Furthermore, the microbial community composition was beneficial for the release and degradation of EPS, and the regulation of gene functions via the MAR-MBR enhanced this process.

Improving the performance of machine learning models for early warning of harmful algal blooms using an adaptive synthetic sampling method.

Many countries have attempted to monitor and predict harmful algal blooms to mitigate related problems and establish management practices. The current alert system-based sampling of cell density is used to intimate the bloom status and to inform rapid and adequate response from water-associated organizations. The objective of this study was to develop an early warning system for cyanobacterial blooms to allow for efficient decision making prior to the occurrence of algal blooms and to guide preemptive actions regarding management practices. In this study, two machine learning models: artificial neural network (ANN) and support vector machine (SVM), were constructed for the timely prediction of alert levels of algal bloom using eight years' worth of meteorological, hydrodynamic, and water quality data in a reservoir where harmful cyanobacterial blooms frequently occur during summer. However, the proportion imbalance on all alert level data as the output variable leads to biased training of the data-driven model and degradation of model prediction performance. Therefore, the synthetic data generated by an adaptive synthetic (ADASYN) sampling method were used to resolve the imbalance of minority class data in the original data and to improve the prediction performance of the models. The results showed that the overall prediction performance yielded by the caution level (L1) and warning level (L2) in the models constructed using a combination of original and synthetic data was higher than the models constructed using original data only. In particular, the optimal ANN and SVM constructed using a combination of original and synthetic data during both training (including validation) and test generated distinctively improved recall and precision values of L1, which is a very critical alert level as it indicates a transition status from normalcy to bloom formation. In addition, both optimal models constructed using synthetic-added data exhibited improvement in recall and precision by more than 33.7% while predicting L-1 and L-2 during the test. Therefore, the application of synthetic data can improve detection performance of machine learning models by solving the imbalance of observed data. Reliable prediction by the improved models can be used to aid the design of management practices to mitigate algal blooms within a reservoir.

Modified bentonite as a conditioning agent for stabilising heavy metals and retaining nutrients in sewage sludge for agricultural uses.

The management and disposal of excess sludge are emerging issues owing to the high costs associated with treatment. In this study, the viability of a modified bentonite was investigated as a conditioning agent for the stabilisation of heavy metals (i.e., Cu, Zn, Cr, Pb, and Cd) and the retention of nutrient species (i.e., total nitrogen (TN), total phosphorus (TP), available nitrogen (available N), and Olsen-phosphorus (Olsen-P)) in sewage sludge for agricultural use. Five grams of modified bentonite resulted in the highest stabilisation rate of heavy metals and strongly contributed to the stabilisation of heavy metals. However, increased amounts of modified bentonite might increase the TN, available N, and TP losses in the conditioned sewage sludge. Through the analytic hierarchy process modelling, optimal concentrations of nutrient species and heavy metals remaining in the conditioned sewage sludge were achieved when the ratio of bentonite to sewage sludge was 1:12.5 (4 g bentonite : 50 g sludge). Moreover, the optimal mixing ratio of the conditioned sewage sludge to the soil (1:2) was suggested for agricultural use. Based on these observations, modified bentonite allowed the sewage sludge to be used as a fertiliser in agriculture by stabilising heavy metals and retaining nutrient species.