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

Temporal drivers of tryptophan-like fluorescent dissolved organic matter along a river continuum

Tryptophan-like fluorescence (TLF) is used to indicate anthropogenic inputs of dissolved organic matter (DOM), typically from wastewater, in rivers. We hypothesised that other sources of DOM, such as groundwater and planktonic microbial biomass can also be important drivers of riverine TLF dynamics. We sampled 19 contrasting sites of the River Thames, UK, and its tributaries. Multivariate mixed linear models were developed for each site using 15 months of weekly water quality observations and with predictor variables selected according to the statistical significance of their linear relationship with TLF following a stepwise procedure. The variables considered for inclusion in the models were potassium (wastewater indicator), nitrate (groundwater indicator), chlorophyll-a (phytoplankton biomass), and Total bacterial Cells Counts (TCC) by flow cytometry. The wastewater indicator was included in the model of TLF at 89 % of sites. Groundwater was included in 53 % of models, particularly those with higher baseflow indices (0.50-0.86). At these sites, groundwater acted as a negative control on TLF, diluting other potential sources. Additionally, TCC was included positively in the models of six (32 %) sites. The models on the Thames itself using TCC were more rural sites with lower sewage inputs. Phytoplankton biomass (Chlorophyll-a) was only used in two (11 %) site models, despite the seasonal phytoplankton blooms. It is also notable that, the wastewater indicator did not always have the strongest evidence for inclusion in the models. For example, there was stronger evidence for the inclusion of groundwater and TCC than wastewater in 32 % and 5 % of catchments, respectively. Our study underscores the complex interplay of wastewater, groundwater, and planktonic microbes, driving riverine TLF dynamics, with their influence determined by site characteristics.

Changes of characteristics and disinfection by-products formation potential of intracellular organic matter with different molecular weight in metalimnetic oxygen minimum

Metalimnetic oxygen minimum (MOM) occurs in reservoirs or lakes due to stratification and algal blooms, which has low dissolved oxygen (DO) levels and leads to the deterioration of water quality. The transformation mechanism and the impact on the water quality of intracellular organic matter (IOM) derived from algae are poorly understood under MOM conditions. In this study, IOM extracted by Microcystis aeruginosa was divided into five components according to molecular weight (MW), and the changes of characteristics and correlated disinfection by-products formation potential (DBPFP) were analyzed and compared under MOM conditions. The removal efficiency of dissolved organic carbon (DOC) in the <5 kDa fraction (66.6%) was higher than that in the >100 kDa fraction (41.8%) after a 14-day incubation under MOM conditions. The same tendency also occurred in Fmax and DBPFP. The decrease in Fmax was mainly due to the decline in tryptophan-like and tyrosine-like for all IOM fractions. The diversity of microorganisms degrading the MW > 100 kDa fraction was lower than others. Besides low MW fractions, these findings indicated that more attention should be paid to high MW fractions which were resistant to biodegradation under MOM conditions during water treatment.

Analysing N-nitrosamine occurrence and sources in karst reservoirs, Southwest China

N-nitrosamines in reservoir water have drawn significant attention because of their carcinogenic properties. Karst reservoirs containing dissolved organic matter (DOM) are important drinking water sources and are susceptible to contamination because of the fast flow of various contaminants. However, it remains unclear whether N-nitrosamines and their precursor, DOM, spread in karst reservoirs. Therefore, this study quantitatively investigated the occurrence and sources of N-nitrosamines based on DOM properties in three typical karst reservoirs and their corresponding tap water. The results showed that N-nitrosamines were widely spread, with detection frequencies > 85%. Similar dominant compounds, including N-nitrosodimethylamine, N-nitrosomethylethylamine, N-nitrosopyrrolidine, and N-nitrosodibutylamine, were observed in reservoirs and tap water, with average concentrations of 4.7-8.9 and 2.8-6.7 ng/L, respectively. The average carcinogenic risks caused by these N-nitrosamines were higher than the risk level of 10-6. Three-dimensional fluorescence excitation-emission matrix modeling revealed that DOM was composed of humus-like component 1 (C1) and protein-like component 2 (C2). Fluorescence indicators showed that DOM in reservoir water was mainly affected by exogenous pollution and algal growth, whereas in tap water, DOM was mainly affected by microbial growth with strong autopoietic properties. In the reservoir water, N-nitrosodiethylamine and N-nitrosopiperidine were significantly correlated with C2 and biological indicators, indicating their endogenously generated sources. Based on the principal component analysis and multiple linear regression methods, five sources of N-nitrosamines were identified: agricultural pollution, microbial sources, humus sources, degradation processes, and other factors, accounting for 46.8%, 36.1%, 7.82%, 8.26%, and 0.96%, respectively. For tap water, two sources, biological reaction processes, and water distribution systems, were identified, accounting for 75.7% and 24.3%, respectively. Overall, this study presents quantitative information on N-nitrosamines' sources based on DOM properties in typical karst reservoirs and tap water, providing a basis for the safety of drinking water for consumers.

The apoptosis of Chlorella vulgaris and the release of intracellular organic matter under metalimnetic oxygen minimum conditions

Metalimnetic oxygen minimum (MOM) is a frequent occurrence in lakes and reservoirs, and its formation is related to the blooming and apoptosis of algae. In this study, the apoptosis mechanism of Chlorella vulgaris (C. vulgaris) and the release of intracellular organic matter (IOM) under different MOM conditions were analyzed by changing the dissolved oxygen (DO) (7.0 mg/L, 3.0 mg/L, and 0.3 mg/L) and water pressure (0.3 MPa and normal pressure). The integrity and auto-fluorescence of algae cells decreased rapidly in the first 8 days, and then stabilized gradually during the development of MOM. Compared with that of water pressures, DO had a significant effect on the activity of algal cells, and higher initial DO levels (3.0 mg/L and 7.0 mg/L) accelerated the lysis of algal cells. The integrity of algae cells decreased to 28.8 %, 31.8 % and 56.6 % at the initial DO of 7 mg/L, 3 mg/L and 0.3 mg/L under 0.3 MPa, respectively. Meanwhile, the concentration of dissolved organic carbon (DOC) and dissolved organic nitrogen (DON) continued to increase and reached their maximum at 8 or 12 days, respectively, due to the IOM release caused by algal cell rupture, and then gradually decreased due to microbial degradation. Consistent with the results of membrane integrity, the highest DOC and DON concentrations were found at higher initial DO conditions. By parallel factor analysis, the change in total organic matter fluorescence intensity was consistent with DOC, once again increasing in the first 8 days and then gradually decreasing. The increased humic-like component, which is related to higher aromaticity, led to the monotonic increase of HAAFPs and THMFPs. However, the released IOM of C. vulgaris had lower N-DBPFPs, with TCNMFP predominating primarily. In summary, these results shed new lights on exploring the apoptosis of algae and the release of IOM during the development of MOM.

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.

Elevated nano-α-Fe2O3 enhances arsenic metabolism and dissolved organic carbon release of Microcystis aeruginosa under a phytate environment

Little information is available on the effects of nano-α-Fe2O3 on arsenic (As) metabolism of algae and potential associated carbon (C) storage in As-contaminated water with dissolved organic phosphorus (DOP) as a phosphorus (P) source. In this study, Microcystis aeruginosa (M. aeruginosa) was used to investigate impacts of nano-α-Fe2O3 on cell growth and As metabolism of algae under a phytate (PA) environment as well as potential associated C storage. Results showed that nano-α-Fe2O3 had a subtle influence on algal cell growth in a PA environment. Herein, algal cell density (OD680) and chlorophyll a (Chla) were inhibited at elevated nano-α-Fe2O3 levels, which simultaneously limited the decrease of Yield. As suggested, the complexation of PA with nano-α-Fe2O3 could alleviate the negative influence on algal cell growth. Furthermore, the elevated nano-α-Fe2O3 increased As methylation in the PA environment due to higher monomethylarsenic (MMA) and dimethylarsenic (DMA) concentrations in the test media. Additionally, microcystins (MCs) in the media changed consistently with UV254, both of which were relatively lower at 10.0 mg·L-1 nano-α-Fe2O3. Enhanced As(V) methylation of algal cells was found to simultaneously reduce the release risk of As(III) and MC while increasing dissolved organic carbon (DOC) content in media, suggesting unfavorable C storage. Three-dimensional fluorescence analysis revealed that the main DOC constituent was the tryptophan-like component in aromatic proteins. Correlation analysis showed that decreases in pH and the zeta potential and an increase in Chla may lead to metabolic As improvements in M. aeruginosa. The obtained findings highlight the need for greater focus on the potential risks of DOP combined with nano-α-Fe2O3 on algal blooms as well as the biogeochemical cycling processes of As and C storage in As-contaminated water with DOP as the P source.

Production of Fluorescent Dissolved Organic Matter by Microalgae Strains from the Ob and Yenisei Gulfs (Siberia)

Dissolved organic matter (DOM) is an important component of aquatic environments; it plays a key role in the biogeochemical cycles of many chemical elements. Using excitation-emission matrix fluorescence spectroscopy, we examined the fluorescent fraction of DOM (FDOM) produced at the stationary phase of growth of five strains of microalgae sampled and isolated from the Ob and Yenisei gulfs. Based on the morphological and molecular descriptions, the strains were identified as diatoms (Asterionella formosa, Fragilaria cf. crotonensis, and Stephanodiscus hantzschii), green microalgae (Desmodesmus armatus), and yellow-green microalgae (Tribonema cf. minus). Three fluorescent components were validated in parallel factor analysis (PARAFAC): one of them was characterized by protein-like fluorescence (similar to peak T), two others, by humic-like fluorescence (peaks A and C). The portion of fluorescence intensity of humic compounds (peak A) to the total fluorescence intensity was the lowest (27 ± 5%) and showed little variation between species. Protein-like fluorescence was most intense (45 ± 16%), but along with humic-like fluorescence with emission maximum at 470 nm (28 ± 14%), varied considerably for different algae strains. The direct optical investigation of FDOM produced during the cultivation of the studied algae strains confirms the possibility of autochthonous production of humic-like FDOM in the Arctic shelf regions.

Change of algal organic matter under different dissolved oxygen and pressure conditions and its related disinfection by-products formation potential in metalimnetic oxygen minimum

Most of the reservoirs or lakes will form a metalimnetic oxygen minimum (MOM) with the characterization of a substantial fraction of dissolved oxygen (DO) depleted below the epilimnion. The effect of intracellular organic matter (IOM) of algal cells transformed under MOM conditions is completely different from that of the original IOM on water quality. In this study, the IOM changes of Microcystic aeruginosa under different MOM conditions and its related disinfection by-products formation potentials (DBPFPs) were investigated by changing the pressure and DO concentration of MOM. Total Fmax increased slightly and then decreased under different pressure conditions, finally decreasing by no more than 22.0%. Under aerobic condition, dissolved organic carbon (DOC) and total Fmax decreased significantly, and decreased by 60.4% and 38.8% within the first 2 days. The results of specific UV absorbance (SUVA) and UV₂₅₀/UV₃₆₅ indicated that aromatic compounds and average molecular weight of IOM were gradually increased under different MOM conditions. The total DBPFPs increased firstly and then decreased under different pressure conditions, and finally decreased by 26.2%-33.1%. The decrease of total DBPFPs was significantly higher under aerobic condition than that under anoxic condition, which finally decreased by 64.5%. Redundancy analysis showed that the fluorescence parameter (protein-like and humic-like fluorescence) could be expected as an index to predict the DBPFPs. Moreover, the results revealed that with the decrease of DO, the activity and diversity of natural microbial consortium decreased, which prevented the further degradation and utilization of organic matter by natural microbial consortium. Therefore, lower DO was a key player for the deterioration of water quality under MOM conditions.

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.

Molecular insights into formation of nitrogenous disinfection byproducts from algal organic matter in UV-LEDs/chlorine process based on FT-ICR analysis

Eutrophication is a globally concerned issue, which brings algal cells and algal organic matter (AOM) into drinking water treatment plants. AOM is an important branch of nitrogenous disinfection byproduct (N-DBP) precursors. The variation of AOM composition in UV-LEDs/chlorine process, and its relationship with N-DBP formation still remain much uncertainty. Herein, we used fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) to investigate AOM transformation in UV-LEDs/chlorine process, with UV₂₈₅ and UV₃₆₅ as light source, and screen for typical precursors of N-DBPs. We found that more nitrogen-containing compounds were generated after UV-LEDs/chlorine process, leading to the larger formation of N-DBPs in postchlorination. Compounds such as lignin, proteins, and amino sugars tends to be oxidized by reactive species in UV-LEDs/chlorine process. Further, compounds with higher O/C and higher weighted average double bond equivalence (DBE_w) are easier to form N-DBPs, including dichloroacetonitrile and trichloronitromethane. Also, influence factors including pH, UV fluence, post-chlorination time and bromide concentration on N-DBP formation were evaluated. The results show that N-DBP formation generally followed the order of UV₂₈₅/chlorine-postchlorination, UV₃₆₅/chlorine-postchlorination, and direct chlorination. Our study provides comprehensive information on N-DBP formation from AOM in UV-LEDs/chlorine-postchlorination from molecular levels.

Characteristics of extracellular organic matters and the formation potential of disinfection by-products during the growth phases of M. aeruginosa and Synedra sp

Extracellular organic matter (EOM) is an important precursor of disinfection by-products (DBPs). Nowadays, little is known about changes in molecular weight (MW) and hydrophilic (HPI)/hydrophobic (HPO) fractions of EOM during the entire algal growth phase. In this study, a combined approach of fractionation procedure and parallel factor (PARAFAC) analysis was applied to characterize the EOM during the entire growth phase of two algal species (M. aeruginosa and Synedra sp.), and investigated the relationships between fluorescent component and the DBP formation potential (FP) in MW and HPI/HPO fractions. Thereinto, three components (including one protein-like component (C1), one humic-like component (C2), and one fulvic acid-like component (C3)) were identified by the PARAFAC model. For two algae, the HPI and high MW (> 100 kDa) fractions were both the main components of algal EOM in the three growth phases in terms of the dissolved organic carbon. The high MW fraction had more C1 compared with other MW fractions, especially for M. aeruginosa. Besides, the formation risk of EOM-derived DBPs from M. aeruginosa was lower than that from Synedra sp. The result of this study showed the FP of DBPs varied with fluorescent components of algal EOM fractions and also indicated that the humic-like substances were tended to form trichloromethane and the tryptophan-like substances were associated with dichloroacetic acid by canonical correspondence analysis for both two algae.

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.

The characteristic of N-nitrosodimethylamine precursor release from algal organic matter and degradation performance of UV/H2O2/O3 technology

Seasonal cyanobacterial blooms in eutrophic water releases algal organic matter (AOM), which contains large amount of dissolved organic nitrogen (DON) and is difficult to be removed effectively by conventional treatment processes (e.g., coagulation and sand filtration) because of its high hydrophilicity. Moreover, N-nitrosodimethylamine (NDMA) can be generated by the reaction of AOM with disinfectants in the subsequent disinfection process. In this study, the formation of NDMA from different AOM components was explored and the control of algal-derived NDMA precursors by UV/H₂O₂/O₃ was evaluated. The results showed that the hydrophilic and polar components of AOM with the low molecular weight had higher NDMA yields. UV-based advanced oxidation process (AOPs) is effective in degrading NDMA precursors, while the removal rate can be affected greatly by UV doses. The removal rate of NDMA precursors by UV/H₂O₂/O₃ is higher than by UV/H₂O₂ or UV/O₃ which can reach 95% at the UV dose of 400 mJ/cm². An alkaline environment reduces the oxidation efficiency of UV/H₂O₂/O₃ technology, while an acidic environment is conducive to its function. Inorganic anions such as HCO₃^-, SO₄^2-, Cl^- and NO₃^- are potential to compete with target algal-derived NDMA precursors for the oxidants reaction and inhibit the degradation/removal of these precursors. The degradation of algal-derived NDMA precursors by UV/H₂O₂/O₃ is mainly accomplished by the oxidation of DON with secondary amide groups, and the main degradation mechanism by UV/H₂O₂/O₃ was through the initial decomposition of macromolecular organic compounds such as biopolymers and humic substances and the further degradation of resulting small molecular components.

A year-long cyclic pattern of dissolved organic matter in the tap water of a metropolitan city revealed by fluorescence spectroscopy

Delivering drinking water with stable quality in metropolitan cities is a big challenge. This study investigated the year-long dynamics of dissolved organic matter (DOM) in the tap water and source water of a metropolitan city in southern China using fluorescence spectroscopy. The DOM detected in the tap water, and source water of Shenzhen city was season and location-dependent. A year-long cyclic trend of DOM was found with predominate protein-like fluorescence in the dry season compared to the humic-like enriched DOM in the wet season. A general DOM pattern was estimated by measuring the shift in dominant fluorescence regions on the excitation-emission matrix (EEM). The difference in fluorescent DOM (FDOM) composition (in terms of the ratio of protein-like to humic-like fluorescence) was above 200% between wet and dry seasons. The taps associated with reservoirs receiving water from the eastern tributary of Dongjiang River showed significant changes in protein-like contents than the taps with source water originating from the western part of the river. This study highlights the importance of optimizing drinking water treatment plants' operational conditions after considering seasonal changes and source water characteristics.

Trade-off control of organic matter and disinfection by-products in the drinking water treatment chain: Role of pre-ozonation

Ozone is a strong oxidant commonly used in drinking water treatment, but its role in the transformation/formation of organic matters (OMs) and disinfection by-products (DBPs) in the drinking water treatment chain had not been systematically studied. In this work the occurrence and building up of OMs, DBPs of trihalomethanes (THMs) and nitrosamines (NAs) during water purification steps under different pre-ozonation dosages have been studied through lab-scale and pilot-scale studies. Results indicated that 0-0.4 mg/L of pre-ozonation dosage could reduce organic load of following-up process steps but insufficient to control DBPs. Seasonal performances of a pilot plant indicated that the accumulation of DBPs was much less in summer than in winter. Furthermore the formation potential of NAs was higher in winter than summer when 0.4 mg/L pre-ozonation was dosed while the maximum removal efficiency of organic matter was found at a pre-ozonation dosage of 0.8 mg/L in summer. Finally a seasonal trade-off control strategy for both OMs and DBPs was proposed with an elucidated role of pre-ozonation in the drinking water treatment chain. This study provided working principles on optimizing pre-ozonation dosage and a seasonal control strategy for trade-off control of both OMs and DBPs in drinking water treatment plants.

Recent advances in waste water treatment through transition metal sulfides-based advanced oxidation processes

With the ever-growing water pollution issues, advanced oxidation processes (AOPs) have received growing attention due to their high efficiency in the removal of refractory organic pollutants. Transition metal sulfides (TMSs), with excellent optical, electrical, and catalytical performance, are of great interest as heterogeneous catalysts. These TMSs-based heterogeneous catalysts have been demonstrated to becapable and adaptable in water purification through advanced oxidation processes. The aim of this review is to conduct an exhaustive analysis and summary of recent progress in the application of TMSs-based AOPs for water decontamination. Firstly, the commonly used tuning strategies for TMSs-based catalysts are concisely introduced, including artificial size and shape control, composition control, doping, and heterostructure manufacturing. Then, a comprehensive overview of the current state-of-the-art progress on TMSs-based AOPs (i.e., Fenton-like oxidation, photocatalytic oxidation, and electro chemical oxidation processes) for wastewater treatment is discussed in detail, with an emphasis on their catalytic performance and involved mechanism. In addition, influencing factors of water chemistry, namely, pH, temperature, dissolved oxygen, inorganic species, and natural organic matter on the catalytic performance of established AOPs are analyzed. Furthermore, the reusability and stability of TMSs-based catalysts in these AOPs are also outlined. Finally, current challenges and future perspectives related to TMSs-based catalysts and their applications for AOPs wastewater treatment are proposed. It is expected that this review would shed some light on the future development of TMSs-based AOPs towards water purification.

Characterization of algal organic matter as precursors for carbonaceous and nitrogenous disinfection byproducts formation: Comparison with natural organic matter

Algal organic matter (AOM) and natural organic matter (NOM) from a typical eutrophic lake were comprehensively investigated in terms of their physico-chemical property, components and disinfection byproduct formation potentials (DBPFPs). The relationships between specific chemical properties of AOM and NOM with their corresponding DBPFPs were further evaluated during chlorination. Results indicated that AOM had lower specific UV absorbance (SUVA) but richer organic nitrogen contents than NOM. Fluorescence excitation emission matrix spectroscopy further demonstrated that AOM were chiefly composed of aromatic protein-like and soluble microbial byproduct-like matters, while NOM were mainly contributed from humic acid-like and soluble microbial byproduct-like substances. Although the molecular weight (MW) distribution of AOM and NOM showed no significant difference, size-exclusion chromatography with organic carbon as well as organic nitrogen detection (LC-OCD-OND) revealed that AOM were concentrated with the fraction of building blocks and NOM had higher concentrations of biopolymers and humics (HS). Moreover, AOM displayed higher DBPFPs than NOM, especially for nitrogenous DBPFP (N-DBPFP). MW < 1 kDa fractions both in AOM and NOM contributed the largest proportion to the formation of carbonaceous disinfection byproducts (C-DBPs). In addition, Pearson correlation analysis showed that bulk parameter SUVA was significantly relevant to the formation potentials of trihalomethane both in AOM and NOM, but was ineffective for carbonaceous DBPFP (C-DBPFP) prediction. Dissolved organic nitrogen contents in biopolymer and HS characterized by LC-OCD-OND had strong correlations with N-DBPFPs from AOM and NOM, indicating that LC-OCD-OND quantitative analysis could improve the prediction accuracy of the DBP formation than bulk parameters during NOM and AOM chlorination.

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.

Cooking oil-surfactant emulsion in water for harvesting Chlorella vulgaris by sedimentation or flotation

In this study, a novel harvesting emulsion (HEM) consisting of cooking oil in an aqueous solution of cetyltrimethylammonium bromide (CTAB) was tested for the harvesting of a technologically important microalga, Chlorella vulgaris. The influence of HEM dose, biomass and bovine serum albumin (BSA) (model interferer compound) on harvesting efficiency (E) were studied. The HEM E was over 90% at pH 10 (0.33% (v/v) cooking oil, 6.7 mg/L of CTAB) and 12 (0.13% (v/v) cooking oil, 2.7 mg/L of CTAB). Harvesting efficiencies at pH 4 and 7 were < 73.5% due to the absence of precipitate formation. Bovine serum albumin (10 mg/L) increased the HEM dose necessary to achieve E ˃ 90% by 1.2 (pH 10), and 3 fold (pH 12). By manipulating the dose of HEM and pH, the method of harvesting (flocculation/sedimentation or flotation) was adjustable depending on the technological requirements.

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.