Photocatalytic degradation of antibiotic sulfamethizole by visible light activated perovskite LaZnO3.

In this work, the perovskite LaZnO3 was synthesized via sol-gel method and applied for photocatalytic treatment of sulfamethizole (SMZ) antibiotics under visible light activation. SMZ was almost completely degraded (99.2% ± 0.3%) within 4 hr by photocatalyst LaZnO3 at the optimal dosage of 1.1 g/L, with a mineralization proportion of 58.7% ± 0.4%. The efficient performance of LaZnO3 can be attributed to its wide-range light absorption and the appropriate energy band edge levels, which facilitate the formation of active agents such as ·O2-, h+, and ·OH. The integration of RP-HPLC/Q-TOF-MS and DFT-based computational techniques revealed three degradation pathways of SMZ, which were initiated by the deamination reaction at the aniline ring, the breakdown of the sulfonamide moieties, and a process known as Smile-type rearrangement and SO2 intrusion. Corresponding toxicity of SMZ and the intermediates were analyzed by quantitative structure activity relationship (QSAR), indicating the effectiveness of LaZnO3-based photocatalysis in preventing secondary pollution of the intermediates to the ecosystem during the degradation process. The visible-light-activated photocatalyst LaZnO3 exhibited efficient performance in the occurrence of inorganic anions and maintained high durability across multiple recycling tests, making it a promising candidate for practical antibiotic treatment.

Organic carbon source-dependent properties of soluble microbial products in sequencing batch reactors and its effects on membrane fouling.

This study investigated the influence of three different organic carbon sources including sodium acetate (SOD), glucose (GLU), and starch (STAR), on soluble microbial products (SMP), which presumably have dissimilar uptake rates and metabolic pathways, in sequencing batch reactors (SBR) and their subsequent effects on membrane fouling of ultrafiltration (UF). SMP were mainly characterized by fluorescence excitation emission matrix coupled with parallel factor analysis (EEM-PARAFAC) and size exclusion chromatography (SEC). SMP produced in SOD-fed SBR showed higher abundances of protein-like fluorescent component and large sized aliphatic biopolymer (BP) than GLU- or STAR-fed counterpart did, while the STAR-based operation resulted in more SMP enriched with humic-like fluorescence. The differences in SMP exerted marked effects on UF membrane fouling as indicated by the highest fouling potential with reversibility shown for the SMP from the SOD-fed reactor. Regardless of the carbon source, BP fraction and protein-like component exhibited the greatest extent of reversible fouling, suggesting that size exclusion plays a critical role. However, notable differences in the reversible fouling propensity of relatively smaller size fractions among the three SBRs signified the possible involvement of chemical interactions as a secondary fouling mechanism and its dependency on different carbon sources. Our results provide a new insight into the roles of carbon sources in the characteristics of SMP in biological treatment systems and their effects on the post-treatment using membrane filtration, which is ultimately beneficial to the optimization of biological treatment design and membrane filtration operation.

Using fluorescence surrogates to track algogenic dissolved organic matter (AOM) during growth and coagulation/flocculation processes of green algae.

Tracking the variation of the algogenic organic matter (AOM) released during the proliferation of green algae and subsequent treatment processes is crucial for constructing and optimizing control strategies. In this study, the potential of the spectroscopic tool was fully explored as a surrogate of AOM upon the cultivation of green algae and subsequent coagulation/flocculation (C/F) treatment processes using ZrCl4 and Al2(SO4)3. Fluorescence excitation emission matrix coupled with parallel factor analysis (EEM-PARAFAC) identified the presence of three independent fluorescent components in AOM, including protein-like (C1), fulvic-like (C2) and humic-like components (C3). Size exclusion chromatography (SEC) revealed that C1 in AOM was composed of large-sized proteins and aromatic amino acids. The individual components exhibited their unique characteristics with respect to the dynamic changes. C1 showed the highest correlation with AOM concentrations (R2 = 0.843) upon the C/F processes. C1 could also be suggested as an optical predictor for the formation of trihalomethanes upon the C/F processes. This study sheds a light for the potential application of the protein-like component (C1) as a practical surrogate to track the evolution of AOM in water treatment or wastewater reclamation systems involving Chlorella vulgaris green algae.

Membrane-based nanoconfined heterogeneous catalysis for water purification: A critical review✰.

Progress in heterogeneous advanced oxidation processes (AOPs) is hampered by several issues including mass transfer limitation, limited diffusion of short-lived reactive oxygen species (ROS), aggregation of nanocatalysts, and loss of nanocatalysts to treated water. These issues have been addressed in recent studies by executing the heterogeneous AOPs in confinement, especially in the nanopores of catalytic membranes. Under nanoconfinement (preferably at the length of less than 25 nm), the oxidant-nanocatalyst interaction, ROS-micropollutant interaction and diffusion of ROS have been observed to significantly improve, which results in enhanced ROS yield and mass transfer, improved reaction kinetics and reduced matrix effect as compared to conventional heterogenous AOP configuration. Given the significance of nanoconfinement effect, this study presents a critical review of the current status of membrane-based nanoconfined heterogeneous catalysis system for the first time. A succinct overview of the nanoconfinement concept in the context of membrane-based nanofluidic platforms is provided to elucidate the theoretical and experimental findings related to reaction kinetics, reaction mechanisms and molecule transport in membrane-based nanoconfined AOPs vs. conventional AOPs. In addition, strategies to construct membrane-based nanoconfined catalytic systems are explained along with conflicting arguments/opinions, which provides critical information on the viability of these strategies and future research directions. To show the desirability and applicability of membrane-based nanoconfined catalysis systems, performance governing factors including operating conditions and water matrix effect are particularly focused. Finally, this review presents a systematic account of the opportunities and technological constraints in the development of membrane-based nanoconfined catalytic platform to realize effective micropollutant elimination in water treatment.

Improving algal bloom detection using spectroscopic analysis and machine learning: A case study in a large artificial reservoir, South Korea.

The prediction of algal blooms using traditional water quality indicators is expensive, labor-intensive, and time-consuming, making it challenging to meet the critical requirement of timely monitoring for prompt management. Using optical measures for forecasting algal blooms is a feasible and useful method to overcome these problems. This study explores the potential application of optical measures to enhance algal bloom prediction in terms of prediction accuracy and workload reduction, aided by machine learning (ML) models. Compared to absorption-derived parameters, commonly used fluorescence indices such as the fluorescence index (FI), humification index (HIX), biological index (BIX), and protein-like component improved the prediction accuracy. However, the prediction accuracy was decreased when all optical indices were considered for computation due to increased noise and uncertainty in the models. With the exception of chemical oxygen demand (COD), this study successfully replaced biochemical oxygen demand (BOD), dissolved organic carbon (DOC), and nutrients with selected fluorescence indices, demonstrating relatively analogous performance in either training or testing data, with consistent and good coefficient of determination (R2) values of approximately 0.85 and 0.74, respectively. Among all models considered, ensemble learning models consistently outperformed conventional regression models and artificial neural networks (ANNs). However, there was a trade-off between accuracy and computation efficiency among the ensemble learning models (i.e., Stacking and XGBoost) for algal bloom prediction. Our study offers a glimpse of the potential application of spectroscopic measures to improve accuracy and efficiency in algal bloom prediction, but further work should be carried out in other water bodies to further validate our proposed hypothesis.

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.

Characterization of dissolved organic matter for understanding the adsorption on nanomaterials in aquatic environment: A review.

Nanomaterials (NMs) have received tremendous attention as emerging adsorbents for environmental applications. The ever-increasing release into aquatic systems and the potential use in water treatment processes heighten the likelihood of the interactions of NMs with aquatic dissolved organic matter (DOM). Once DOM is adsorbed on NMs, it substantially modifies the surface properties, thus altering the fate and transport of NMs, as well as their toxic effects on (micro)organisms in natural and engineered systems. The environmental consequences of DOM-NMs interaction have been widely studied in the literature. In contrast, a comprehensive understanding of DOM-NM complexes, particularly regarding the controlling factors, is still lacking, and its significance has been largely overlooked. This gap in the knowledge mainly arises from the complex and heterogeneous structures of the DOM, which prompts the urgent need to further characterize the DOM properties to deepen the understanding associated with the adsorption processes on NMs. This review aims to provide in-depth insights into the complex DOM adsorption behavior onto NMs, whether they are metal- or carbon-based materials. First, we summarize the up-to-date analytical methods to characterize the DOM to unravel the underlying adsorption mechanisms. Second, the key DOM characteristics governing the adsorption processes are discussed. Next, the environmental factors, such as the nature of adsorbents and solution chemistry, affecting the DOM-NM interactions, are identified and discussed. Finally, future studies are recommended to fully understand the chemical traits of DOM upon its adsorption onto NMs.

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

The increasing release of nutrients to aquatic environments has led to great concern regarding eutrophication and the risk of unwanted algal blooms. Based on observational data of 20 water quality parameters measured on a monthly basis at 40 stations from 2011 to 2020, this study applied different Machine Learning (ML) algorithms to suggest the best option for algal bloom prediction in the Han River, a large river in South Korea. Eight different ML algorithms were categorized into several groups of statistical learning, regression family, and deep learning, and were then compared for their suitability to predict the chlorophyll-derived trophic index (TSI-Chla). ML algorithms helped identify the most important water quality parameters contributing to algal bloom prediction. The ML results confirmed that eutrophication and algal proliferation were governed by the complex interplay between nutrients (nitrogen and phosphorus), organic contaminants, and environmental factors. Of the models tested, the adaptive neuro-fuzzy inference system (ANFIS) exhibited the best performance owing to its consistent and outperforming prediction both quantitatively (i.e., via regression) and qualitatively (i.e., via classification), which was evidenced by the lowest value of mean absolute error (MAE) of 0.09, and the highest F1-score, Recall and Precision of 0.97, 0.98 and 0.96, respectively. In a further step, a representative web application was constructed to assist common users to predict the trophic status of the Han River. This study demonstrated that ML techniques are not only promising for highly accurate water quality modeling of urban rivers, but also reduce time and labor intensity for experiments, which decreases the number of monitored water quality parameters, providing further insights into the driving factors of water quality deterioration. They ultimately help devise proactive strategies for sustainable water management.

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

This study evaluated and compared the performance of two vertical flow constructed wetlands (VF) using expanded clay (VF1) and biochar (VF2), of which both are low-cost, eco-friendly, and exhibit potentially high adsorption as compared to conventional filter layers. Both VFs achieved relatively high removal for organic matters (i.e. Biological oxygen demand during 5 days, BOD5) and nitrogen, accounting for 9.5 - 10.5 g.BOD5.m-2.d-1 and 3.5 - 3.6 g.NH4-N.m-2.d-1, respectively. The different filter materials did not exert any significant discrepancy to effluent quality in terms of suspended solids, organic matters and NO3-N (P > 0.05), but they did influence NH4-N effluent as evidenced by the removal rate of that by VF1 and VF2 being of 82.4 ± 5.7 and 84.6 ± 6.4%, respectively (P < 0.05). The results obtained from the designed systems were further subject to machine learning to clarify the effecting factors and predict the effluents. The optimal algorithms were random forest, generalized linear model, and support vector machine. The values of the coefficient of determination (R2) and the root mean square error (RMSE) of whole fitting data achieved 74.0% and 5.0 mg.L-1, 80.0% and 0.3 mg.L-1, 90.1% and 2.9 mg.L-1, and 48.5% and 0.5 mg.L-1 for BOD5_VF1, NH4-N_VF1, BOD5_VF2, and NH4-N_VF2, respectively.

Fate and role of fluorescence moieties in extracellular polymeric substances during biological wastewater treatment: A review.

In biological wastewater treatment systems, extracellular polymeric substances (EPS) are continuously excreted as a response to environmental changes and substrate conditions. It could severely affect the treatment efficacy such as membrane fouling, dewaterability and the formation of carcinogenic disinfection by-products (DBPs). The heterogeneous dissolved organic matter (DOM) with varying size and chemical nature constitute a primary proportion of EPS. In the last few decades, fluorescence spectroscopy has received increasing attention for characterizing these organic substances due to the attractive features of this low-cost spectroscopic approach, including easy sample handling, rapid, non-destructive and highly sensitive nature. In this review, we summarize the application of fluorescence spectroscopy for characterizing EPS and provide the potential implications for online monitoring of water quality along with its limitations. We also link the dynamics of fluorescent dissolved organic matter (FDOM) in EPS with operational and environmental changes in wastewater treatment systems as well as their associations with metal binding, membrane fouling, adsorption, toxicity, and dewaterability. The multiple modes of exploration of fluorescence spectra, such as synchronous spectra with or without coupling with two-dimensional correlation spectroscopy (2D-COS), excitation-emission matrix (EEM) deconvoluted fluorescence regional integration (FRI), and parallel factor analysis (PARAFAC) are also discussed. The potential fluorescence indicators to depict the composition and bulk characteristics of EPS are also of interest. Further studies are highly recommended to expand the application of fluorescence spectroscopy paired with appropriate supplementary techniques to fully unravel the underlying mechanisms associated with EPS.

Exploring the relative changes in dissolved organic matter for assessing the water quality of full-scale drinking water treatment plants using a fluorescence ratio approach.

This study aims to extend and demonstrate the application of fluorescence spectroscopy for monitoring the water quality of three differently operated full-scale drinking water treatment plants located in the Shenzhen city (China). A ratio of fluorescent dissolved organic matter (FDOM), which describes relative changes in humic-like to protein-like fluorescence, was used to explain mechanisms behind the physicochemical processes. The fluorescence components obtained through individual and combined parallel factor analysis (PARAFAC) modeling revealed the presence of humic-like (C1) and protein-like (C2) structures in the DOM. The C1/C2 ratio provided a direct relationship between the seasonal variations and DOM composition. Wet season generated DOM enriched with humic-like fluorescence, while dry season caused a higher release of protein-like fluorescence. The fluorescence ratio presented unique patterns of DOM in treatment trains. The chemical pretreatment and disinfection unit processes showed a higher tendency to remove the humic-like fluorescence. However, the C1/C2 ratio increased during physical treatment processes such as coagulation-precipitation and sand filtration, indicating preferential removal of protein-like fluorescence. The DOM composition in influent directly (R2 = 0.77) influenced the relative intensities of fluorescence components in the treated water. Compared to the dry season, the wet season caused significant changes in DOM composition and produced treated water enriched with humic-like fluorescence. This fluorescence ratio offers an approach to explore the role of different treatment units and determine the factors affecting the composition of DOM in the surface water and drinking water treatment plants.

Seasonal occurrence of N-nitrosamines and their association with dissolved organic matter in full-scale drinking water systems: Determination by LC-MS and EEM-PARAFAC.

N-nitrosamines have been identified as emerging contaminants with tremendous carcinogenic potential for human beings. This study examined the seasonal changes in the occurrence of N-nitrosamines and N-nitrosodimethylamine formation potential (NDMA-FP) in drinking water resources and potable water from 10 drinking water treatment plants in a southern city of China. The changes in N-nitrosamines are well correlated with dissolved organic matter (DOM), particularly fluorophores, which were measured and compared between traditional fluorescence indices and excitation-emission matrix coupled with parallel factor analysis (EEM-PARAFAC). Four of N-nitrosamine species including N-nitrosodimethylamine (NDMA), N-Nitrosodibutylamine (NDBA), N-Nitrosopyrrolidine (NPYR), and N-Nitrosodiphenylamine (NDPhA) are found to be abundant compounds with an average of 29.5% (26.7%), 20.0% (25.2%), 18.9% (16.0%), and 9.0% (9.9%) in the source (and treated) water, respectively. The sum of N-nitrosamines concentration is recorded to be low in the wet season (July-September), whereas the dry season (October-December) provided opposite impacts. EEM-PARAFAC modeling indicated the predominance of humic-like component (C1) in the wet season while in the dry season the water was dominant in protein-like component (C2). All the N-nitrosamines excluding NDPhA and N-Nitrosomorpholine (NMOR) showed a strong association with protein-like component (C2). In contrast, humic-like C1, which was directly influenced by rainfall, was found to be a suitable proxy for NMOR and NDPhA. The results of this study are valuable to understand the correlation between different N-nitrosamines and DOM through adopting fluorescence signatures.

Applying a new pomelo peel derived biochar in microbial fell cell for enhancing sulfonamide antibiotics removal in swine wastewater.

A sequential anode-cathode double-chamber microbial fuel cell (MFC) is a promising system for simultaneously removing contaminants, recovering nutrients and producing energy from swine wastewater. To improve sulfonamide antibiotics (SMs)'s removal in the continuous operating of MFC, one new pomelo peel-derived biochar was applied in the anode chamber in this study. Results demonstrated that SMs can be absorbed onto the heterogeneous surfaces of biochar through pore-filling and π-π EDA interaction. Adding biochar to a certain concentration (500 mg/L) could enhance the efficiency in removing sulfamethoxazole, sulfadiazine and sulfamethazine to 82.44-88.15%, 53.40-77.53% and 61.12-80.68%, respectively. Moreover, electricity production, COD and nutrients removal were improved by increasing the concentration of biochar. Hence, it is proved that adding biochar in MFC could effectively improve the performance of MFC in treating swine wastewater containing SMs.

Visible light-activated degradation of natural organic matter (NOM) using zinc-bismuth oxides-graphitic carbon nitride (ZBO-CN) photocatalyst: Mechanistic insights from EEM-PARAFAC.

In this study, the complex degradation behavior of natural organic matter (NOM) was explored using photocatalytic oxidation systems with a novel catalyst based on a hybrid composite of zinc-bismuth oxides and g-C3N4 (ZBO-CN). The photooxidation system demonstrated the effective removal of NOM under low-intensity visible light irradiation, presenting removal rates of 53-74% and 65-88% on the basis of dissolved organic carbon (DOC) and the UV absorption coefficient (UV254), respectively, at 1.5 g/L of the catalyst. The NOM removal showed an increasing trend with a higher ZBO-CN dose. Comparative experiments with the hole and OH radical scavengers revealed that the direct oxidation occurring on the catalyst's surface might be the governing photocatalytic mechanism. Fluorescence excitation emission matrix-parallel factor analysis (EEM-PARAFAC) revealed the individual removal behavior of the different constituents in bulk NOM. Different tendencies towards preferential adsorption and subsequent oxidative removal were found among dissimilar fluorescent components within a bulk terrestrial NOM, following the order of terrestrial humic-like (C1) > humic-like (C2) > microbial humic-like (C3) components. The result suggests the dominant operation of π-π and/or hydrophobic interactions between the NOM and the catalyst. The discriminative removal behavior was more pronounced in visible light versus UV-activated systems, probably due to the incapability of visible light to excite è - h+ pairs of ZnO and the triplet state of NOM. The high photoactivity and structural stability of ZBO-CN under visible light implies its potential for an effective, low-cost and energy-saving treatment technology to selectively remove large sized humic-like substances from water.

Characteristics and influencing factors of organic fouling in forward osmosis operation for wastewater applications: A comprehensive review.

Wastewater reuse is considered one of the most promising practices for the achievement of sustainable water management on a global scale. In the context of the safe reuse of water, membrane filtration is a competitive technique due to its superior efficiency in several processes. However, membrane fouling by organics is an inevitable challenge that is encountered during the practical application of membrane processes. The resolution of the membrane fouling challenge requires an in-depth understanding of many complex interactions between organic foulants and the membrane. In the last few decades, the forward osmosis (FO) membrane process, which exploits osmosis as a driving force, has emerged as an effective technology for water production with low energy consumption, thus leveraging the water-energy nexus. However, their successful application is severely hampered by membrane fouling, which is caused by such complex fouling mechanisms as cake enhanced osmotic pressure (CEOP), reverse salt diffusion (RSD), internal, and external concentration polarization as well as by the traditional fouling processes encompassing colloids, microbial (biofouling), inorganic, and organic fouling. Of these fouling types, the fouling potential of organic matter in FO has not been given sufficient attention, in particular, when FO is applied to wastewater treatment. This paper aims to provide a comprehensive overview of FO membrane fouling for wastewater applications with a special focus on the identification of the major factors that lead to the unique properties of organic fouling in this filtration process. Based on the critical assessment of organic fouling formation and the governing mechanisms, proposals were advanced for future research aimed at the mitigation of FO membrane fouling to enhance process efficiency in wastewater applications.

Recent Progress in Carbon-Based Buffer Layers for Polymer Solar Cells.

Carbon-based materials are promising candidates as charge transport layers in various optoelectronic devices and have been applied to enhance the performance and stability of such devices. In this paper, we provide an overview of the most contemporary strategies that use carbon-based materials including graphene, graphene oxide, carbon nanotubes, carbon quantum dots, and graphitic carbon nitride as buffer layers in polymer solar cells (PSCs). The crucial parameters that regulate the performance of carbon-based buffer layers are highlighted and discussed in detail. Furthermore, the performances of recently developed carbon-based materials as hole and electron transport layers in PSCs compared with those of commercially available hole/electron transport layers are evaluated. Finally, we elaborate on the remaining challenges and future directions for the development of carbon-based buffer layers to achieve high-efficiency and high-stability PSCs.

Further insight into the roles of the chemical composition of dissolved organic matter (DOM) on ultrafiltration membranes as revealed by multiple advanced DOM characterization tools.

This study assessed the relative contributions of different constitutes in dissolved organic matter (DOM) with two different sources (i.e., urban river and effluent) to membrane fouling on three types of ultrafiltration (UF) membranes via excitation emission matrix - parallel factor analysis (EEM-PARAFAC), size exclusion chromatography (SEC), and Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS). Two polyethersulfone membranes with different pore sizes and one regenerated cellulose membrane were used as representative hydrophobic (HPO) and hydrophilic (HPI) UF membranes, respectively. Although size exclusion effect was found to be the most prevailing rejection mechanism, the behaviors of individual fluorescent components (one tryptophan-like, one microbial-humic-like, and terrestrial humic-like) and different size fractions upon the UF filtration revealed that chemical interactions (e.g., hydrophobic interactions and hydrogen bonding) between DOM and membrane might play important roles in UF membrane fouling, especially for small sized DOM molecules. Based on the molecular level composition determined by FT-ICR-MS, the CHOS formula group showed a greater removal tendency toward the HPO membrane, while the CHONS group was prone to be removed by the HPI membrane. The changes in the overall molecular composition of DOM upon UF filtration were highly dependent on the sources of DOM. The molecules of more acidic nature tended to remain in the permeate of effluent DOM, while the river DOM was shifted into more nitrogen-enriched composition after filtration. Regardless of the DOM sources, the HPO membrane with a smaller pore size led to the most pronounced changes in the molecular composition of DOM.

Effects of COD/N ratio on soluble microbial products in effluent from sequencing batch reactors and subsequent membrane fouling.

The relative ratios of chemical oxygen demand (COD) to nitrogen (N) in wastewater are known to have profound effects on the characteristics of soluble microbial products (SMP) from activated sludge. In this study, the changes in the SMP characteristics upon different COD/N ratios and the subsequent effects on ultrafiltration (UF) membrane fouling potentials were examined in sequencing batch reactors (SBR) using excitation emission matrix-parallel factor analysis (EEM-PARAFAC) and size exclusion chromatography (SEC). Three unique fluorescent components were identified from the SMP samples in the bioreactors operated at the COD/N ratios of 100/10 (N rich), 100/5 (N medium), and 100/2 (N deficient). The tryptophan-like component (C1) was the most depleted at the N medium condition. Fulvic-like (C2) and humic-like (C3) components were more abundant with N rich wastewater. Greater abundances of large size biopolymer (BP) and low molecular weight neutrals (LMWN) were found under the N deficient and N rich conditions, respectively. SMPs from various COD/N exhibited a greater degree on membrane fouling following the order of 100/2 > 100/10 > 100/5. C1 and C2 had close associations with reversible and irreversible fouling, respectively, while the reversible fouling potential of C3 depended on the COD/N ratios. No significant impact of COD/N ratio was observed on the relative contributions of SMP size fractions to either reversible or irreversible fouling potential. However, the COD/N ratios likely altered the BP foulants' composition with greater contribution of proteinaceous substances to reversible fouling under the N deficient condition than at other N richer conditions. The opposite trend was observed for irreversible fouling. Our results provided further insight into changes in different SMP constitutes and their membrane fouling in response to microbial activities under different COD/N ratios.

Unique characteristics of algal dissolved organic matter and their association with membrane fouling behavior: a review.

Over the last several decades, the frequent occurrence of algal bloom in drinking water supplies, driven by increasing anthropogenic input and climate change, has posed serious problems for membrane filtration processes, resulting in reduced membrane permeability and increased energy consumption. It is essential to comprehensively understand the characteristics of algal dissolved organic matter (DOM) and the subsequent effects on the filtration processes for better insight into membrane fouling mitigation. Many studies have revealed that algal DOM has displayed unique characteristics distinguished from other sources of DOM with respect to the chemical composition, the structures, and the molecular weight distributions. Algal DOM is considered to be a major obstacle in understanding membrane fouling due to its complicated interactions among dissimilar algal DOM constituents as well as between algal DOM and membrane material matrices. The present review article summarizes (1) recent characterizing methods for algal DOM, (2) environmental factors affecting the characteristics of algal DOM, (3) the discrepancies between algal DOM and other sources of aquatic DOM, particularly terrestrial sources, and (4) potential fouling effects of algal DOM on membrane filtration processes and their associations with algal DOM characteristics. A broad understanding of algal DOM-driven membrane fouling can lead to breakthroughs in efficient membrane filtration processes to treat algal bloom water sources.