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One critical question for water security and sustainable development is how water quality responses to the changes in natural factors and human activities, especially in light of the expected exacerbation in water scarcity. Although machine learning models have shown noticeable advances in water quality attribution analysis, they have limited interpretability in explaining the feature importance with theoretical guarantees of consistency. To fill this gap, this study built a modelling framework that employed the inverse distance weighting method and the extreme gradient boosting model to simulate the water quality at grid scale, and adapted the Shapley additive explanation to interpret the contributions of the drivers to water quality over the Yangtze River basin. Different from previous studies, we calculated the contribution of features to water quality at each grid within river basin and aggregated the contribution from all the grids as the feature importance. Our analysis revealed dramatic changes in response magnitudes of water quality to drivers within river basin. Air temperature had high importance in the variability of key water quality indicators (i.e. ammonia-nitrogen, total phosphorus, and chemical oxygen demand), and dominated the changes of water quality in Yangtze River basin, especially in the upstream region. In the mid- and downstream regions, water quality was mainly affected by human activities. This study provided a modelling framework applicable to robustly identify the feature importance by explaining the contribution of features to water quality at each grid., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

The hydrological regimes and environmental changes in large riverine lakes are known for their complexity and high level of uncertainty. Scientifically uncovering the response mechanisms of water environments under complex hydrological conditions has become a challenging research objective, in the interdisciplinary of environmental science and hydrology. This study delved into the unstable response process between water level and quality of Poyang Lake, the largest freshwater lake as well as one of the most intense hydrological variability water bodies in China. We developed a non-steady state identification approach incorporates Seasonal and Trend decomposition using Loess (STL) and Wavelet Correlation (WTC) methods. The results showed that there were remarkable alterations in the hydrological regime and water quality at both seasonal and long-term scale of Poyang Lake over the past nine years. These alterations were accompanied by significant non-steady state characteristics, reflecting the changes in the response between water level and quality. The employment of the STL-WTC method revealed a significant nonlinear response between the long-term trends of water level and quality, in both the 4-month and 12-month frequency bands. In particular, our findings showed an intriguing shift towards in-phase behavior between water level and quality in the 12-month frequency band, rather than the anti-phase pattern observed previously. This correlation changed more significantly in seasons where the fluctuation pattern of water level varied sharply, such as summer and winter in Poyang Lake. Our study underscored the hydrological conditions and water quality of large lakes connected to rivers do not exhibit a long-term stable unidirectional response state, alterations in hydrological rhythms may induce a transition in the relationship from negative correlation towards nonlinear positive correlation between water level and water quality. Finally, this non-steady state fluctuation of water conditions can further exacerbate long-term and seasonal degradation of water quality., Competing Interests: Declaration of Competing Interest The authors declare no competing interests., (Copyright © 2023. Published by Elsevier Ltd.)

Planktonic fungi are important components of aquatic ecosystems, and analyses of their community composition and function have far-reaching significance for the ecological management and maintenance of reservoir environments. However, few studies have investigated the composition, distribution, and function of planktonic fungi in reservoir ecosystems and their relationship with water quality. Here, the composition of the planktonic fungal community in the surface water layer of the Danjiangkou Reservoir is investigated using Illumina MiSeq sequencing. According to the results, the reservoir community is primarily composed of 7 phyla, including Ascomycota, Rozellomycota, Basidiomycota, Chytridiomycota, and Zygomycota, comprising 294 genera, demonstrating the rich diversity of this community. Redundancy analysis (RDA) of the planktonic fungal community and environmental factors showed dissolved oxygen (DO), chemical oxygen demand (COD), total nitrogen (TN), chlorophyll a (Chl a), and permanganate (COD Mn ) to be important factors influencing the distribution of planktonic fungi. Spearman correlation analysis of the planktonic fungal community composition and diversity indices with physical and chemical water quality parameters showed that the impacts of TN, COD and DO were the most significant. The results of this study on the planktonic fungal community in the Danjiangkou Reservoir area using high-throughput sequencing revealed that the community is sensitive to water quality parameters. This result provides a reference for studying the composition and distribution of the planktonic fungal community in Danjiangkou Reservoir and its role in the biogeochemical cycle.

Nanofiltration (NF) membranes, which can consistently offer safe and reliable water quality, have become increasingly popular in drinking water treatment. In this study, the conventional (coagulation-sedimentation-sand filtration) and ozonation-biologically activated carbon filtration (O3-BAC) advanced treatment processes at a full-scale drinking water treatment plant (DWTP) were combined with a pilot-scale NF process for treatment of Taihu Lake water. The results showed that the "conventional + O3-BAC + NF" combined processes had superior effects on removing natural organic matter (NOM), Br−, and other common water quality parameters (e.g., turbidity, conductivity, TDS, and total hardness) with efficiencies of 88.8–99.8%, for which the NF process played a critical role. The conventional plus O3-BAC processes effectively removed formation potential of chlorinated disinfection by-products (Cl-DBPFPs, by 28.0–46.6%), but had poorer effect in reducing formation potential of brominated DBPs (Br-DBPFPs, by −2637.2–17.3%). NOM concentrations (characterized by dissolved organic carbon (DOC), ultraviolet absorbance at 254 nm (UV254), and/or fluorescent components) were the driving factors for most DBPFP species, while elevation of [Br−]/[DOC] ratio likely resulted in enhanced formation of brominated trihalomethanes (THMs) during chlorination of the BAC effluent. By adding the pilot-scale NF process, the "conventional + O3-BAC + NF" treatment train effectively controlled DBPFP, yielding the removal efficiencies of Cl-DBPFP and Br-DBPFP as 77.6–100% and 33.5–100%, respectively, with monochloroacetic acid, mono-bromo-acetic acid, and tribromomethane formation potentials (MCAA-FP, MBAA-FP, and TBM-FP) not detected in the final effluent. Low temperature in the winter season might be the primary reason for the rapid increase of transmembrane pressure when operating the NF membrane under flux of 25 L/(m2·h), which could be largely delayed by lowering the flux to 20 L/(m2·h). Characterization of the membrane cleaning solutions showed that macromolecular biopolymers (6000 Da–4000K Da) such as polysaccharides and proteins were the main contributors to membrane fouling. [ABSTRACT FROM AUTHOR]

Proper evaluation of water quality is pertinent to estuarine habitat restoration. Identifying the degrading factors of the water environment and predicting the trend of eutrophication are key to restore the habitat. Through trophic level index (TLI), water quality index (WQI), modified Nemerow pollution index (NPI), and the Random Forest (RF) model, water samples collected from various estuaries of Taihu Lake from 2017 to 2019 were evaluated. To predict the water quality development, four scenarios were set viz. S1: add or remove an ecological buffer, S2: increase or reduce the external nutrients, S3: open or close the dam/gate, and S4: increase or decrease the internal release. In Wuli Lake, the nutrient concentrations in the river regions were higher than in the lake regions, while a contrary trend was observed in Gonghu Bay. The estuarine water quality in the dry season (WQI = 40.91, NPI = 1.73) was merely worse than that in the wet season (WQI = 47.27, NPI = 1.67). On the other hand, the eutrophic status in the wet season (TLIWet = 57.93) was worse than that in the dry season (TLIDry = 57.23). The estuarine water quality of Taihu Lake has improved from 2017 to 2019 but still belongs to medium level. The principal component analysis (PCA) revealed that dam construction, land use types, unstable hydrodynamic conditions, and trumpet-shaped estuary were the main factors that aggravated the water quality degradation. The RF model has strong forecasting capabilities for estuarine water quality. When the estuaries are close to residential and industrial districts, controlling the surface runoff and improving sewage treatment efficiency are the most effective measures to improve the water quality. In the estuaries, the sediments are usually disturbed by the wind-waves. Conclusively, reducing sediment disturbance and internal contamination accumulation via biological and engineering measures is the key to estuarine restoration. [ABSTRACT FROM AUTHOR]

Estuarine zones are regarded as the ecotones connecting the rivers and lakes. Sediment dredging is a conventional treatment technology that is widely used to remove the internal loading in estuarine zones worldwide. However, what is the characteristic of nutrient release in the gate-controlled estuary and how long this practice is effective are still unclear. Hence, sediment and water samples were collected from dredged and undredged regions around the gate-controlled estuary of northern Taihu Lake for laboratory experiments, in which they were subjected to different temperatures, depths, and disturbance levels. The total nitrogen (TN) and total phosphorus (TP) concentrations of the dredged region were lower than those from the undredged region under stable hydrodynamic conditions. A high dynamic release rate (R) of nutrients in the dredged sediments (RTN = 164.75 mg/m2·d and RTP = 5.83 mg/m2·d) existed under dynamic release conditions (stirring speeds: 90 and 120 r/min). The effect of disturbance and temperature on release rate and nutrient form was completely different for the static and dynamic release cases. The nutrient loads from dynamic release were 4–17 times greater than those from static release. For unstable hydrodynamic conditions, the release rate from the bottom sediment exceeded that from the surface sediment in the undredged region. These results indicated that, under stable hydrodynamic conditions, dredging improves long-term water quality. However, dredging alone in unstable hydrodynamic conditions may not remove the potential risk of internal release in the long term. Specific ecological and engineering measures should be combined with dredging practice to restore estuary habitats and minimize the release of internal pollutants. [ABSTRACT FROM AUTHOR]

As the upper reach of the Yangtze River, the Jinsha River has experienced ecological degradation due to increased anthropogenic activities. The potential pollution sources affecting the Jinsha River watershed from 2016 to 2018 were investigated using an improved method in combination with correlation analysis and the absolute principal component score-multiple linear regression receptor modeling technique. Our results identified 5–7 potential pollution sources in the Jinsha main stream watershed and the Pudu, Niulan, and Yalong River watersheds of the Jinsha River. The water pollutant concentrations of the Jinsha main stream watershed were mainly influenced by environmental, agricultural, and human population factors. In the Pudu River watershed, the primary pollution sources changed to natural and sedimentary pollutant sources. It is necessary to control the sedimentary pollutants. The Niulan River watershed was also influenced by natural environment factors. Among those, mineral, sedimentary pollutant, and meteorological sources contributed the most to water quality. In the case of the Yalong River watershed, the influence of non-point source pollution caused by human activities and sedimentary pollutants was the main reason for the deterioration of the ecological environment. The multivariate statistical techniques presented good adaptability for the analysis of pollution sources in the Jinsha River watershed, and the results may be useful for the protection and management of the watershed eco-environment. [ABSTRACT FROM AUTHOR]

Deteriorating lake water quality has become a serious environmental issue around the globe. Heavy metals dissolved in the overlying water of lakes are notably more toxic than those found in lake sediment. Given this, we sought to better understand the characteristics of particular major ion and dissolved heavy metal in Dongting Lake—the second largest freshwater lake in China. Overlying water samples were collected from Dongting Lake to investigate the major ion geochemistry and to examine the relationship between the major ions and dissolved heavy metals. Chemical analysis of the overlying water showed that the average cation concentration was the highest for Ca2+, followed by Mg2+, Na+, and K+. Similarly, the highest anion concentration was SO42−, followed by Cl−, NO3−, and HCO3−. Total dissolved solids in the overlying water of Dongting Lake ranged from 66.19 to 159.20 mg/L, with an average value of 93.13 mg/L. The predominant hydrochemical type was Ca–SO4. The mean concentrations of dissolved heavy metal in both surface and deep waters decreased in the following order: Zn > Cr > Ni > Cu > Pb > Cd. Importantly, all of the selected heavy metals tested in the overlying water were lower than the corresponding toxicity reference values. Co-occurrence network analyses were performed and compared the correlations between all measured major ions and heavy metals. Results of the subsequent principal component analyses revealed that heavy metal levels in the aquatic environment primarily originated from natural processes and were enhanced by anthropogenic activities. [ABSTRACT FROM AUTHOR]

A high-resolution nutrient emission inventory can provide reliable and accurate identification of priority control areas, which is crucial for efficient decisions on water quality restoration. However, the inventories widely used in large-scale modeling are usually based on provincial inputs, which induce the challenges of lacking localized parameters and missing localized characteristic when provincial scale inputs are converted to finer scales with the down-scale methods. Based on elaborate investigations and statistical data at the county scale with multi-scale data conversion, the China Emission Inventory of Nutrients (CEIN) was developed with a spatial resolution of a 0.1° grid and sub-basin scales. The Yangtze River Basin was used as a case study to illustrate the potential applications of CEIN. The emissions of total nitrogen (TN) and total phosphorus (TP) of Yangtze River Basin is 0.43 Mt and 0.04 Mt for point sources, 11.09 Mt and 4.64 Mt for diffuse sources in 2017. The hotspot analysis for 2606 sub-basins indicated that cropland is the key source of nutrient emissions, accounting for 58.88% and 79.15% of TN and TP, respectively. Industrial sewage and freshwater aquaculture accounted for 27.39% (TN) and 21.98% (TP) of the point sources, which is substantial due to their direct discharge into surface waters. The current results also reveal that, in contrast to CEIN, the previously used common emission factors based on GDP per capita produced considerable overestimations of 2.37 and 2.65 times the actual TN and TP emissions, respectively. Additional advantages of the CEIN have been demonstrated in identifying priority control areas more accurately with reduced bias and quantifying the effects of policies at much smaller scales. For example, the CEIN helps to distinguish hotspots, which was neglected when identifying sources at the level-III sub-basin scale, and indicates that the management of fractional areas (TN: 16.97%; TP: 13.44%) provides the highest nutrient emissions control (TN: 44.34%; TP: 48.65%) for the entire basin. The evaluation of China's toilet revolution policy demonstrates that achieving equitable access to safe sanitation has resulted in a reduction of 7240 t of TN and 833 t of TP, which is extremely critical for rural water quality and health. [Display omitted] • High-resolution China Emission Inventory of Nutrients (CEIN) is developed with 0.1° grid. • Priority control areas were identified for 2606 sub-basins in the Yangtze River Basin. • Previous emission factors with GDP per capita produced considerable overestimations. • CEIN can quantify effects of policies at much smaller scales with reduced bias. [ABSTRACT FROM AUTHOR]

Suspended sediment concentration (SSC) is one of the most critical parameters in water quality and environmental evaluations. Remote sensing has the potential for monitoring the dynamics and spatial distribution of SSC efficiently. The primary objective of this study is to develop retrieval models that are reliable and sensitive to SSC levels in the Caofeidian area, a new seaport in northeast China, based on Landsat-5 Thematic Mapper (TM) images and a set ofin situdata sets, including spectral reflectance data and water quality data. The study finds that the band reflectance ratio and binary combination factor (i.e. the ratio of the reflectance to the particle size) are more effective than single band reflectance, and a non-linear model is more potent than a linear model for predicting SSC in the Caofeidian waters. A quadratic polynomial regression model of theRTM3/RTM2ratio is proposed as the optimal retrieval model after evaluating various models with respect to different sensitive factors. The accuracy of the model is acceptable with a relative error and a root mean square error of 25.35% and 7.22 mg l–1, respectively; the correlation coefficient between the observed and estimated SSCs is 0.986. This study also indicates that the band reflectance ratio and binary combination factor are effective in weakening and even partially eliminating the effects of the changes in the sediment type (i.e. particle size and refractive index). And the band reflectance ratio is more efficient. Using the proposed model and TM data, SSC levels for the entire region were estimated. Such results can serve as a baseline for future environmental monitoring efforts. [ABSTRACT FROM PUBLISHER]

A nutrient recirculation system (NRS) was used to assess the ability of four ornamental and three wetland plant species to remove nitrogen (N) and phosphorous (P) from stormwater runoff. The NRS was filled with a nutrient solution with total N and P concentrations of 11.3 and 3.1 mg.L-1, respectively, to simulate high levels of nutrient contaminations in stormwater. Nutrient removal abilities of herbaceous perennial ornamental plants, canna (Canna xgeneralis Bailey) 'Australia', iris (Iris pseudacorus L.) 'Golden Fleece', calla lily [Zantedeschia aethiopica (L.) Spreng], and dwarf papyrus (Cyperus haspan L.) were compared with those of wetland plants arrow arum [Peltandra virginica (L.) Schottl, pickerelweed (Pontederia cordata L.), and bulltongue arrowhead (Sagittaria lancifolia L.) in three experiments. 'Australia' canna had the greatest water consumption, total biomass production, and aboveground N and P content followed by pickerelweed. 'Golden Fleece' iris had higher tissue N concentrations than canna but much lower biomass production. Dwarf papyrus had similar total biomass as pickerelweed but less shoot biomass. N and P removed from the NRS units planted with canna (98.7% N and 91.8% P) were higher than those planted with iris and arrow arum (31.6% and 31.5% N, and 38.5% and 26.3% P, respectively). NRS units planted with dwarf papyrus had similar nutrient recovery rate as pickerelweed, but much less total N and P were removed as a result of less water consumption. The NRS units planted with calla lily had lower nutrient removal than canna and pickerelweed. Our results suggest that canna is a promising ornamental species for stormwater mitigation, and harvesting the aboveground biomass of canna can effectively remove N and P from the treatment system. However, more research needs to be done to evaluate factors that might affect plant performance in a floating biofiltration system. [ABSTRACT FROM AUTHOR]

Accurately obtaining the pollution sources and their contribution rates is the basis for refining watershed management. Although many source analysis methods have been proposed, a systematic framework for watershed management is still lacking, including the complete process of pollution source identification to control. We proposed a framework for identification and abatement of pollutants and applied in the Huangshui River Basin. A newer contaminant flux variation method based on a one-dimensional river water quality model was used to calculate the contribution of pollutants. The contributions of various factors to the over-standard parameters of water quality sections at different spatial and temporal scales were calculated. Based on the calculation results, corresponding pollution abatement projects were developed, and the effectiveness of the projects was evaluated through scenario simulation. Our results showed that the large scale livestock and poultry farms and sewage treatment plants were the largest sources of total nitrogen (TP) in Xiaoxia bridge section, with contribution rates of 46.02% and 36.74%, respectively. Additionally, the largest contribution sources of ammonia nitrogen (NH 3 –N) were sewage treatment plants (36.17%) and industrial sewage (26.33%). Three towns that contributed the most to TP were Lejiawan Town (14.4%), Ganhetan Town (7.3%) and Handong Hui Nationality town (6.6%), while NH 3 –N mainly from the Lejiawan Town (15.9%), Xinghai Road Sub-district (12.4%) and Mafang Sub-district (9.5%). Further analysis found that point sources in these towns were the main contributor to TP and NH 3 –N. Accordingly, we developed abatement projects for point sources. Scenario simulation indicated that the TP and NH 3 –N could be significantly improved by closing down and upgrading relevant sewage treatment plants and building facilities for large scale livestock and poultry farms. The framework adopted in this study can accurately identify pollution sources and evaluate the effectiveness of pollution abatement projects, which is conducive to the refined water environment management. • A framework of pollution source identification and abatement is proposed. • Contaminant flux variation method was put forward for quantifying contribution. • Water quality scenarios models provide pollution management strategies. • Point source contributes the biggest for ammonia nitrogen and total phosphorus. • Impact from point sources on water quality was greater during the dry season. [ABSTRACT FROM AUTHOR]

Scientific understanding of the scale effects of landscape structure on water quality is essential for national land planning and water environment management. However, there is no consensus on the optimal spatial landscape scale affecting water quality. To this end, we developed a novel mix scale division method, which is consist of the intersection of sub-basin, stream buffer zone and circular buffer zone. Based on the water quality data from 113 monitoring stations of the Yellow River Basin, we quantitatively identified the spatial scale effects of various landscape indexes on water quality, as well as the nonlinear response of the water quality along the gradient of landscape indicators in the Yellow River, by using the Redundancy analysis (RDA) and Generalized Additive Model (GAM). We show that landscape indexes related to buildup and agricultural lands had significant impact on water quality changes, and this impact was more influential during the rainy season compared with the dry season. We also identified that the optimal scale and landscape pattern corresponding to the main non-point source factors, i.e., COD Cr , total nitrogen (TN), and total phosphorus (TP). The variations of these three water quality indicators were predominated by buildup land (B-CA) at sub-basin (with a contribution percentage of 43.7%), agricultural land (A-CA) at the scale of stream buffer (bs) = 1000 m and circular buffer (bc) = 20 km (50.2%), and the largest patch index of agriculture (A-LPI) at the scale of bs = 1000m and bc = 25 km (41.7%), respectively. The B-CA>50000 ha at the sub-basin scale and A-LP = 50% at the mixed scale (bs = 1000m, bc = 25 km) would accelerate the deterioration of water quality. Results implied that water quality could be improved by implementing landscape regulation within an appropriate scale and considering the non-linear effects of landscape structure on water quality. • Developing a novel mixed buffer zone division method to analyze the influence range of landscape structure on water quality. • Locating the optimal scales of different landscape indices on water quality. • The nonlinear effect of the landscape on the water quality is found. • Water quality degradation in YRB is susceptible to human activities, particularly the agricultural landscape. [ABSTRACT FROM AUTHOR]

Ecosystem management in the face of uncertain disturbances has triggered increasing practices of resilience thinking. A multiscale probabilistic simulation-optimization framework is developed based on the nested nature of watersheds to inform decision robustness for Best Management Practices (BMPs). We presented a novel approach using hybrid Bayesian Networks (BNs) as interpretable and probabilistic emulators of process-based models. The hybrid BNs established at the scale of Hydrologic Response Units (HRUs) are embedded into simulation-optimization, whereby we analyze the cost-effectiveness-robustness of candidate BMP strategies at the subbasin scale. The optimal strategy is identified in compliance with water quality standards using watershed-scale integer programming. We apply the approaches in a typical intensively cultivated plateau watershed adjacent to Lake Dianchi, one of the three most eutrophic lakes in China. Our findings suggest that the hybrid BNs, incorporating both quantitative and qualitative information, are reliable emulators of the Soil and Water Assessment Tool (SWAT) in capturing critical pathways of diffuse phosphorus. Tradeoffs among cost, effectiveness, and robustness follow the law of diminishing marginal benefits. The optimum BMP strategies vary with policymakers' preference toward robustness levels. Our findings indicate that robustness should be accounted for as an additional decision attribute besides costs and pollution mitigation. The benefits of the modeling framework are to (i) reduce over 99% computation complexity and support efficient decision-making under multifaceted uncertainties; (ii) improve interpretability and reliability of machine learning emulators; and (iii) inform policymakers of robustness with the probability of water quality restoration success. [Display omitted] • A novel multiscale probabilistic simulation-optimization framework is presented. • The success probability of mitigation efforts is informed as decision robustness. • We use hybrid BNs as emulators of process-based models to improve interpretability. • The model effectively reduces computational budget whilst preserving model accuracy. [ABSTRACT FROM AUTHOR]

Abstract: This study was to explore the formation of iodinated disinfection by-products (I-DBPs), including iodoform (CHI3), iodoacetic acid (IAA) and triiodoacetic acid (TIAA), when iodide-containing artificial synthesized waters and raw waters are in contact with chlorine dioxide (ClO2). Among the investigated I-DBPs, CHI3 was the major species during ClO2 oxidation in artificial synthesized waters. Impact factors were evaluated, including the concentrations of ClO2, iodide (I–), dissolved organic carbon (DOC) and pH. Formation of CHI3, IAA and TIAA followed an increasing and then decreasing pattern with increased ClO2 or DOC concentration. I-DBPs yield was significantly affected by solution pH. High concentrations of I-DBPs were generated under circumneutral conditions with the maximum formation at pH 8. The increase of I– concentration can increase I-DBPs yields, but the increment was suppressed when I– concentration was higher than 50 μM. When 100 μg/L I–and ClO2 (7.5–44.4 μM) were spiked to the raw water samples from Yangshupu and Minhang drinking water treatment plant, certain amounts of CHI3 and IAA were found under pH 7 and the concentrations were strongly correlated with ClO2 dosage and water qualities, however, no TIAA was detected. Finally, we investigated I-DBPs formation of 18 model compounds, including 4 carboxylic acids, 5 phenols and 8 amino acids, treating with ClO2 when I– was present. Results showed that most of these model compounds could form a considerable amount of I-DBPs, especially for propanoic acid, butanoic acid, resorcinol, hydroquinone, alanine, glutamic acid, phenylalanine and serine. [Copyright &y& Elsevier]

Abstract: This paper investigates the characteristics of dissolved organic nitrogen (DON) in raw water from the Huangpu River and also in water undergoing treatment in the full-scale Yangshupu drinking water treatment plant (YDWTP) in Shanghai, China. The average DON concentration of the raw water was 0.34mg/L, which comprised a relatively small portion (~5%) of the mass of total dissolved nitrogen (TDN). The molecular weight (MW) distribution of dissolved organic matter (DOM) was divided into five groups: >30, 10–30, 3–10, 1–3 and <1kDa using a series of ultrafiltration membranes. Dissolved organic carbon (DOC), UV absorbance at wavelength of 254nm (UV254) and DON of each MW fraction were analyzed. DON showed a similar fraction distribution as DOC and UV254. The <1kDa fraction dominated the composition of DON, DOC and UV254 as well as the major N-nitrosodimethylamine formation potential (NDMAFP) in the raw water. However, this DON fraction cannot be effectively removed in the treatment line at the YDWTP including pre-ozonation, clarification and sand filtration processes. The results from linear regression analysis showed that DON is moderately correlated to DOC, UV254 and trihalomethane formation potential (FP), and strongly correlated to haloacetic acids FP and NDMAFP. Therefore, DON could serve as a surrogate parameter to evaluate the reactivity of DOM and disinfection by-products FP. [ABSTRACT FROM AUTHOR]

The effect of bromide and iodide on the transformation of humic acid (HA) and algal organic matter (AOM), and the formation of disinfection by-products (DBPs) during UV/chlorination were investigated. Experimental results indicated that the halides effectively inhibited mineralization, with multiple changes in organic molecule transformation due to differences in formation and speciation of reactive halogen species and free halogen. As a consequence, bromide and iodide also played important roles in DBP formation. The DBP yields in HA-containing water during UV/chlorination decreased in the order of iodide loaded > freshwater ≫ bromide loaded, whereas DBP formation in AOM-containing water decreased remarkably with halides added (freshwater > bromide loaded ≫ iodide loaded) at high UV fluence. Moreover, Pearson correlation analysis exhibited weaker correlation between DBPs and water parameters in AOM-containing water, while DBPs in HA-containing water exhibited better correlation with water parameters. For both simulated waters, the theoretical toxicity was calculated and peaked in bromide-containing water, whereas the calculated toxicity in iodide-containing water was comparable or slightly higher than that in freshwater. Therefore, UV/chlorine treatment may achieve good quality water with reduced DBP-associated toxicity in freshwater or iodide-containing water (iodide only), but careful consideration is needed when purifying source waters containing bromide (bromide only), especially for AOM/bromide-containing water. Image 1 • HA and AOM transformation was affected by halides during UV/chlorination. • DBP formation varied with the addition of Br− and I−. • DBP formed from HA decreased in the order of I− loaded > freshwater > Br− loaded. • Halides mitigated DBP formation from AOM (freshwater > Br− loaded ≫ I− loaded). • The highest DBP-associated cytotoxicity was observed in the presence of Br−. [ABSTRACT FROM AUTHOR]