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.

Changes in adsorption mechanisms of radioactive barium, cobalt, and strontium ions using spent coffee waste biochars via alkaline chemical activation: Enrichment effects of O-containing functional groups.

The single adsorption of radioactive barium (Ba(II)), cobalt (Co(II)), and strontium (Sr(II)) ions using pristine (SCWB-P) and chemically activated spent coffee waste biochars with NaOH (SCWB-A) were thoroughly explored in order to provide deeper insights into the changes in their adsorption mechanisms through alkaline chemical activation. The greater removal efficiencies of SCWB-A (76.6-97.3%) than SCWB-P (45.6-75.2%) and the consistency between the adsorptive removal patterns (Ba(II) > Sr(II) > Co(II)) and oxygen bond dissociation enthalpies (BaO (562 kJ/mol) > SrO (426 kJ/mol) > CoO (397 kJ/mol)) of radioactive species supported the assumption that the adsorption removal of radioactive species with spent coffee waste biochars highly depended on the abundances of O-containing functional groups. The calculated R2 values of the pseudo-first-order (SCWB-P = 0.998-0.999; SCWB-A = 0.850-0.921) and pseudo-second-order kinetic models (SCWB-P = 0.988-0.998; SCWB-A = 0.935-0.966) are evident that the physisorption mainly controlled the adsorption of radioactive species toward SCWB-P and the chemisorption played a crucial role in their adsorptive removal with SCWB-A. From the calculated intra-particle diffusion, isotherm, thermodynamic parameters, it can be concluded that the intra-particle diffusion and monolayer adsorption primarily governed the adsorption of radioactive species using SCWB-P and SCWB-A, and their adsorption processes occurred spontaneously and endothermically. The dominant adsorption mechanism of spent coffee waste biochars was changed from physisorption (ΔH° of SCWB-P = 21.6-29.8 kJ/mol) to chemisorption (ΔH° of SCWB-A = 42.4-81.3 kJ/mol) through alkaline chemical activation. The distinctive M-OH peak in the O1s XPS spectra of SCWB-A directly corresponding to the decrease in the abundances of O-containing functional groups confirms again that the enrichment of O-containing functional groups markedly facilitated the adsorption removal of radioactive species by chemisorption occurred at the inner and outer surfaces of spent coffee waste biochars.

Contributions of enhanced endogenous microbial metabolism via inoculation with a novel microbial consortium into an anoxic side-stream reactor to in-situ sludge reduction for landfill leachate treatment.

In-situ sludge reduction plays a significant role in reducing excess sludge production. This study investigated the role of beneficial microorganisms (BM) in the anoxic-oxic-settling-anoxic (A-OSA) process associated with the in-situ sludge reduction efficiency under synthetic landfill leachate treatment. The rates of excess sludge reduction with the inoculation of BM increased up to 53.6% (calculated as total suspended solids) and 38.3% (calculated as total volume), respectively. Side-stream reactors, as important components of the A-OSA process, were further studied to explore change of parameters related to in-situ sludge reduction. With the inoculation of BM, the release and conversion of extracellular polymeric substances and the dehydrogenase activity (increasing rate = 60.9%) were increased. Species richness and microbial diversity, as well as the microbial community composition (e.g., hydrolytic and fermentative bacteria), were improved via bioaugmentation. Moreover, potential gene functions of microorganisms were positively regulated and the abundance of gene expressions (e.g., nirK, norB) for in-situ sludge reduction could be improved.

A machine learning approach for early warning of cyanobacterial bloom outbreaks in a freshwater reservoir.

Understanding the dynamics of harmful algal blooms is important to protect the aquatic ecosystem in regulated rivers and secure human health. In this study, artificial neural network (ANN) and support vector machine (SVM) models were used to predict algae alert levels for the early warning of blooms in a freshwater reservoir. Intensive water-quality, hydrodynamic, and meteorological data were used to train and validate both ANN and SVM models. The Latin-hypercube one-factor-at-a-time (LH-OAT) method and a pattern search algorithm were applied to perform sensitivity analyses for the input variables and to optimize the parameters of the models, respectively. The results indicated that the two models well reproduced the algae alert level based on the time-lag input and output data. In particular, the ANN model showed a better performance than the SVM model, displaying a higher performance value in both training and validation steps. Furthermore, a sampling frequency of 6- and 7-day were determined as efficient early-warning intervals for the freshwater reservoir. Therefore, this study presents an effective early-warning prediction method for algae alert level, which can improve the eutrophication management schemes for freshwater reservoirs.

Real-time biomonitoring of oxygen uptake rate and biochemical oxygen demand using a novel optical biogas respirometric system.

In response to the ever-increasing need for monitoring-based process control of wastewater treatment plants, an online applicable respirometer shows great promise for real-time measurement of oxygen uptake rate (OUR) and biochemical oxygen demand (BOD) measurements as a surrogate of the biodegradability of wastewater. Here, we have developed a photosensor-assisted real-time respirometric system equipped with bubble counting sensors for accurate measurement of microbial oxygen consumption in a bottle. This system can measure OUR and BOD in a bottle equipped with a tube containing NaOH solution to absorb carbon dioxide and supplied with continuous atmospheric oxygen to the bottle, which reliably supplies non-limiting dissolved oxygen (DO) for aerobic biodegradation even at high organic loads. These technical improvements allow a sensitive and rapid analytical tool offering real-time profiles of oxygen uptake rate as well as BOD measurements with an extended measurable range (0-420 mg O2/L), enabling significant reduction or elimination of dilution steps. The respirometric system was used to elucidate the biodegradable kinetics of domestic and swine wastewaters as a function of the type and concentration of organic matters, depending on source characteristics including rapidly or slowly oxidizable organic substances by bacteria. Compared with conventional and manometric BOD methods, our method is reliable and accurate.

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.

Intrinsic pKa of Nanofiltration Membrane Surfaces to Assess Fouling and Cleaning Behaviors Induced by Foulant-Membrane Electrostatic Interactions.

The fouling and cleaning behaviors of m-phenylenediamine (MPD), coumarin-3-carboxylic acid (CCA), and d-(+)-glucose (DG) on polyamide nanofiltration (NF) membrane surfaces were investigated with a focus on the two intrinsic equilibrium constants (pKa,intr.) of carboxylic and amine functional groups determined using potentiometric titration. The charged foulants (MPD and CCA) strongly influenced the pKa,intr. of the membrane surface after the fouling layer formed via electrostatic interactions (Virgin = 3.4 and 9.2; MPD-fouled = 4.1 and 8.1; CCA-fouled = 1.5 and 12.4). Moreover, the pKa,intr. of electrostatically fouled membranes substantially recovered when using cleaning agents that released electrostatic interactions (cleaned MPD-fouled = 3.5 and 9.0; cleaned CCA-fouled = 3.3 and 9.6). In contrast, the neutral foulant (DG) did not affect the pKa,intr. (DG-fouled = 3.5 and 9.2); however, the ζ-potential of DG-fouled membrane was closer to zero than the virgin membrane (Virgin = -28.1 mV and DG-fouled = -7.2 mV at pH 7). The pKa,intr. value accurately represented the electrostatic interactions between organic foulants and membrane surfaces. Potentiometric titration is a facile method of determining the pKa,intr. that gives an in-depth understanding of the electrostatic interactions at the membrane surface associated with the membrane fouling and cleaning mechanism.

Enhanced mechanical deep dewatering of dewatered sludge by a thermal hydrolysis pre-treatment: Effects of temperature and retention time.

This study investigated effects of the thermal hydrolysis pre-treatment on mechanical deep dewaterability of dewatered sludge to extend understanding of dewatering characteristics of thermally hydrolyzed sludge. Floc sizes of dewatered sludge were gradually reduced during the thermal hydrolysis pre-treatment at 170 °C and 185 °C with increasing retention time whereas longer retention time (>60 min) increased floc sizes of thermally hydrolyzed sludges at 200 °C due to formation of undesired refractory organic materials (ROMs), which might hinder the disintegration of dewatered sludge flocs. Similar trends were found for thermal hydrolytic solubilization of dewatered sludge. This demonstrated that the efficiency of the thermal hydrolysis pre-treatment at a higher temperature (200 °C) with longer retention time (≥60 min) could be strongly influenced by the formation of ROMs associated with changes of solid fractions and some free amino acids (i.e., ß-aminobutyric acid, 4-hydroxyproline, and cysteine). Since the trade-off between the degradation of dewatered sludge and the formation of ROMs determined mechanical deep dewaterability of thermally hydrolyzed sludge, the lowest residual weight and moisture content were observed for thermally hydrolyzed sludges at 200 °C with retention time range of 60 min (residual weight = 0.165; moisture content = 55.38%) to 90 min (residual weight = 0.160; moisture content = 59.87%). These observations were intimately correlated to variations of extracellular polymeric substances during the thermal hydrolysis pre-treatment, but not in accordance with the change pattern of capillary suction time (CST) values. This is evident that the CST value was inadequate to estimate mechanical deep dewaterability of thermally hydrolyzed sludge.

An analysis of the effects of osmotic backwashing on the seawater reverse osmosis process.

Fouling control is an important consideration in the design and operation of membrane-based water treatment processes. It has been generally known that chemical cleaning is still the most common method to remove foultants and maintain the performance of reverse osmosis (RO) desalination. Regardless of the chemical membrane cleaning methods applied effectively, however, frequent chemical cleaning can shorten the membrane life. In addition, it also increases operating and maintenance costs due to the waste chemical disposal. As an alternative, osmotic backwashing can be applied to RO membranes by diluting the concentration polarization (CP) layer. In this study, the effects of osmotic backwashing were analysed under different total dissolved salts (TDSs) and backwashing conditions, and the parameters of the osmotic backwashing were evaluated. The results of the analysis based on the properties of the organic matters found in raw water showed that the cleaning efficiency in respect to the fouling by hydrophilic organic matters was the greatest. Osmotic backwashing was carried out by changing the TDS of the permeate. As a result, the backwashing volume decreased with time due to the CP of the permeate and the backwashing volume. The difference in the osmotic pressure between the raw water and the permeate (Delta pi) also decreased as time passed. It was confirmed that when the temperature of the effluent was high, both the cleaning efficiency and the backwashing volume, which inpours at the same time, increased. When the circulation flow of the effluent was high, both the cleaning efficiency and the backwashing volume increased.

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.

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.

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.

Advanced characterization of algogenic organic matter, bacterial organic matter, humic acids and fulvic acids.

Advanced characterization techniques of organic matter, including bulk organic characterization, size-exclusion chromatography, three-dimensional excitation-emission matrix, Fourier transform infrared spectroscopy, and fractionations using Amberlite XAD-8/4 resins, were used to investigate differences and similarities in the physicochemical properties of four different organic matter, namely algogenic organic matter (AOM), bacterial organic matter (BOM), Suwanee River humic acids (SRHA) and Suwanee River fulvic acids (SRFA). From the comparison of characteristics of the AOM, BOM, SRHA, and SRFA, it was identified that the specific UV absorbance, molar ratio of organic nitrogen to organic carbon, molecular weight, fluorescence characteristics, functional group compositions, and relative hydrophobicity/hydrophilicity of all the tested organic matter were considerably different from their sources. The SRHA and SRFA were mainly composed of hydrophobic fractions while the AOM and BOM included more hydrophilic fractions than the SRHA and SRFA due to the alcohol and amide functional groups. This indicated that the origin of organic matter in natural waters can be predicted by their physicochemical characteristics, and the source identification of organic matter provides a better understanding of the interactions between the origin of organic matter and water treatment processes (e.g., coagulation and membrane filtration).

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.

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.

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.

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.

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.

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.