Using simple and easy water quality parameters to predict trihalomethane occurrence in tap water

Chronic exposure to drinking water nitrate and trihalomethanes in the French CONSTANCES cohort

Trihalomethanes (THMs) and nitrate are widespread chemicals in drinking water. Chronic exposure has been associated with increased cancer risk despite inconclusive evidence, partly due to the challenges in long-term exposure assessment and potential exposure misclassification. We estimated concentrations of nitrate and THMs in drinking water using a public regulatory monitoring database (SISE-Eaux) for CONSTANCES, a French population-based prospective cohort. We obtained 26,322,366 measurements of drinking water parameters from 2000 to 2020. We excluded missing, implausible and duplicated measurements; we corrected or imputed missing geocodes of sampling locations; we calculated the annual median concentration of nitrate and THMs by surveillance area. To predict missing annual median concentrations, linear mixed models with random intercept using surveillance area as a clustering variable were developed for each region for nitrate and the four THM components (chloroform, chlorodibromomethane, bromodichloromethane and bromoform) separately. Concentrations in the nearest surveillance area from the household were merged per year among 75,462 participants with residential history geocoded for 2000-2020. Estimated concentrations resulting from this approach were compared with measured concentrations in 100 samples collected in Paris, Rennes and Saint-Brieuc in 2021. Median annual concentrations of total THMs and nitrate at study participants' homes for 2000-2020 were, respectively, 15.7 μg/l (IQR: 15.2) and 15.2 mg/l (IQR: 20.8). Among these, 35% were based on measurements for nitrate (16% for THMs), 44% (46%) were predicted using on linear mixed models, and 21% (38%) were based on distribution unit median values. Conditional R2 predictive models ranged from 0.71 to 0.91 (median: 0.85) for nitrate, and from 0.48 to 0.80 for THMs (median: 0.68). These concentrations will allow future association analyses with risk of breast and colorectal cancer. Our cleaning process introduced here could be adapted to other large drinking water monitoring data.

Effect of the residual levofloxacin on hydroponic vegetables with sewage treatment plant tailwater: Microbial community, discharge risk and control strategy

Tailwater-based hydroponic vegetable is a promising strategy for domestic wastewater recycling. However, the effect of residual antibiotics on the hydroponic vegetable system and the relation between hydroponic culture parameters and the residual water quality are still unclear. Here, the typical antibiotic Levofloxacin (LVFX) was employed, and the effect of LVFX (5 mg/L) on the residual water quality, plant growth and microbial community of water spinach hydroponic culture system were investigated under different hydraulic residence times (HRT). Obvious toxic effects on water spinach were observed, and the highest removal rate of LVFX (about 6 %) and TN (25.67±1.43 %) was observed when HRT was 7 days. Hydroponic culture increased the microbial abundance, diversity, and microbial community stability. To optimize the hydroponic culture, actual sewage plant tailwater spiked with 20 μg/L LVFX, along with three common planting substrates (sponge, ceramsite, and activated carbon) were used for the hydroponic culture of lettuce (seasonal reasons). The inhibition effect of LVFX on the removal of NO3--N and TN was observed even as the LVFX concentration decreased significantly (from 14.62 ± 0.44 μg/L to 0.65 ± 0.07 μg/L). The best growth situation of lettuce and removal rates of NH4+-N, NO3--N, TN, especially LVFX (up to 95.65 ± 0.54 %) were observed in the activated carbon treated group. The overall results indicate the negative effect of residual antibiotics on the hydroponic vegetable systems, and adding activated carbon as substrate is an effective strategy for supporting plant growth and controlling discharged risk.

Enhancing efficiency and quality control: The impact of Digital Twins in drinking water networks

This paper showcases the successful development and implementation of two Digital Twin prototypes within the Lab Digital Twins project, designed to enhance the efficiency and quality control of Aigües de Barcelona's drinking water network. The first prototype focuses on asset management, using (near) real-time data and statistical models, and achieving a 70% success rate in predicting pump station failures 137 days in advance. The second prototype addresses water quality monitoring, leveraging machine learning to accurately forecast trihalomethane levels at key points in the distribution system, and enabling proactive water quality management strategies, ensuring compliance with stringent safety standards and safeguarding public health. The paper details the methodology of both prototypes, highlighting their potential to revolutionize water network management. PRACTITIONER POINTS: Digital representation of assets and processes in the drinking water treatment network Early fault detection in assets, and predictions of trihalomethane formation in the drinking water distribution network Reduction on monitoring time and incident response for target assets by means of Digital Twins Improvement in visualization, prediction, and proactive measures for asset management and water quality control Contribution to the growing knowledge on Digital Twins and their potential to revolutionize water network operations.

Assessing the effectiveness of performic acid disinfection on effluents: focusing on bacterial abundance and diversity

Poorly-treated wastewater harbors harmful microorganisms, posing risks to both the environment and public health. To mitigate this, it is essential to implement robust disinfection techniques in wastewater treatment plants. The use of performic acid (PFA) oxidation has emerged as a promising alternative, due to its powerful disinfection properties and minimal environmental footprint. While PFA has been used to inactivate certain microbial indicators, its potential to tackle the entire microbial community in effluents, particularly resistant bacterial strains, remains largely unexplored. The present study evaluates the efficacy of PFA disinfection on the microbial communities of a WWTP effluent, through microbial resistance mechanisms due to their membrane structure. The effluent microbiome was quantified and identified. The results showed that the number of damaged cells increases with CT, reaching a maximum for CT = 240 mg/L•min and plateauing around 60 mg/L•min, highlighting the optimal conditions for PFA-disinfection against microbial viability. A low PFA level with a 10-min contact time significantly affected the microbial composition. It is worth noting the sensitivity of several bacterial genera such as Flavobacterium, Pedobacter, Massilia, Exiguobacterium, and Sphingorhabdus to PFA, while others, Acinetobacter, Leucobacter, Thiothrix, Paracoccus, and Cloacibacterium, showed resistance. The results detail the resistance and sensitivity of bacterial groups to PFA, correlated with their Gram-positive or Gram-negative membrane structure. These results underline PFA effectiveness in reducing microbial levels and remodeling bacterial composition, even with minimal concentrations and short contact times, demonstrating its suitability for widespread application in WWTPs.

Application of artificial intelligence in (waste)water disinfection: Emphasizing the regulation of disinfection by-products formation and residues prediction

Water/wastewater ((waste)water) disinfection, as a critical process during drinking water or wastewater treatment, can simultaneously inactivate pathogens and remove emerging organic contaminants. Due to fluctuations of (waste)water quantity and quality during the disinfection process, conventional disinfection models cannot handle intricate nonlinear situations and provide immediate responses. Artificial intelligence (AI) techniques, which can capture complex variations and accurately predict/adjust outputs on time, exhibit excellent performance for (waste)water disinfection. In this review, AI application data within the disinfection domain were searched and analyzed using CiteSpace. Then, the application of AI in the (waste)water disinfection process was comprehensively reviewed, and in addition to conventional disinfection processes, novel disinfection processes were also examined. Then, the application of AI in disinfection by-products (DBPs) formation control and disinfection residues prediction was discussed, and unregulated DBPs were also examined. Current studies have suggested that among AI techniques, fuzzy logic-based neuro systems exhibit superior control performance in (waste)water disinfection, while single AI technology is insufficient to support their applications in full-scale (waste)water treatment plants. Thus, attention should be paid to the development of hybrid AI technologies, which can give full play to the characteristics of different AI technologies and achieve a more refined effectiveness. This review provides comprehensive information for an in-depth understanding of AI application in (waste)water disinfection and reducing undesirable risks caused by disinfection processes.

Climate change and future water demand: Implications for chlorine and trihalomethanes management in water distribution systems

The global change in surface water quality calls for increased preparedness of drinking water utilities. The increasing frequency of extreme climatic events combined with global warming can impact source and treated water characteristics such as temperature and natural organic matter. On the other hand, water saving policies in response to water and energy crisis in some countries can aggravate the situation by increasing the water residence time in the drinking water distribution system (DWDS). This study investigates the individual and combined effect of increased dissolved organic carbon (DOC), increased temperature, and reduced water demand on fate and transport of chlorine and trihalomethanes (THMs) within a full-scale DWDS in Canada. Chlorine and THM prediction models were calibrated with laboratory experiments and implemented in EPANET-MATLAB toolkit for prediction in the DWDS under different combinations of DOC, temperature, and demand. The duration of low chlorine residuals (<0.2 mg/L) and high THM (>80 μg/L) periods within a day in each scenario was reported using a reliability index. Low-reliability zones prone to microbial regrowth or high THM exposure were then delineated geographically on the city DWDS. Results revealed that water demand reduction primarily affects chlorine availability, with less concern for THM formation. The reduction in nodal chlorine reliability was gradual with rising temperature and DOC of the treated water and reducing water demand. Nodal THM reliability remained unchanged until certain thresholds were reached, i.e., temperature >25 °C for waters with DOC <1.52 mg/L, and DOC >2.2 mg/L for waters with temperature = 17 °C. At these critical thresholds, an abrupt network-wide THM exceedance of 80 μg/L occurred. Under higher DOC and temperature levels in future, employing the proposed approach revealed that increasing the applied chlorine dosage (which is a conventional method used to ensure sufficient chlorine coverage) results in elevated exposure toTHMs and is not recommended. This approach aids water utilities in assessing the effectiveness of different intervention measures to solve water quality problems, identify site-specific thresholds leading to major decreases in system reliability, and integrate climate adaptation into water safety management.

Trihalomethane prediction model for water supply system based on machine learning and Log-linear regression

Laboratory determination of trihalomethanes (THMs) is a very time-consuming task. Therefore, establishing a THMs model using easily obtainable water quality parameters would be very helpful. This study explored the modeling methods of the random forest regression (RFR) model, support vector regression (SVR) model, and Log-linear regression model to predict the concentration of total-trihalomethanes (T-THMs), bromodichloromethane (BDCM), and dibromochloromethane (DBCM), using nine water quality parameters as input variables. The models were developed and tested using a dataset of 175 samples collected from a water treatment plant. The results showed that the RFR model, with the optimal parameter combination, outperformed the Log-linear regression model in predicting the concentration of T-THMs (N25 = 82-88%, rp = 0.70-0.80), while the SVR model performed slightly better than the RFR model in predicting the concentration of BDCM (N25 = 85-98%, rp = 0.70-0.97). The RFR model exhibited superior performance compared to the other two models in predicting the concentration of T-THMs and DBCM. The study concludes that the RFR model is superior overall to the SVR model and Log-linear regression models and could be used to monitor THMs concentration in water supply systems.

Performance evaluation of artificial intelligence with particle swarm optimization (PSO) to predict treatment water plant DBPs (haloacetic acids)

The prevention of water-borne diseases requires the disinfection of water consumed. Disinfection by-products, however, are an increasing concern, and they require advanced knowledge of water treatment plants before their release for human consumption. In this study, multivariate non-linear regression (MNR) and adaptive neuro-fuzzy inference system (ANFIS: Grid partition - GP and Sub-clustering - SC) integrated with particle swarm optimization (PSO) were proposed for the prediction of haloacetic acids (HAAs) in actual distribution systems. PSO-ANFIS-GP and PSO-ANFIS-SC were trained and verified for a total of 64 sets of data with eight parameters (pH, Temperature, UVA254, DOC, Br-; NH4+-N; NO2--N, residual free chlorine). With MNR, R2 is 0.5184 Collapse

Key Words

• Grid partition
• Haloacetic acids
• Multivariate non-linear regression
• Particle swarm optimization
• Sub-clustering

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MESH Headings

• Humans
• Artificial Intelligence
• Water Purification
• Neural Networks, Computer
• Fuzzy Logic
• Disinfection

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Grants Collapse

Affiliation(s)

Anthony I Okoji Department of Chemical Engineering, Covenant University, Ota, Ogun state, Nigeria.
Comfort N Okoji Department of Biology and Forensic, Admiralty University, Ibusa, Delta state, Nigeria
Olorunfemi S Awarun Department of Microbiology, Landmark University, Omu-Aran, Kwara state, Nigeria

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Identification and verification of PCDD/Fs indicators from four typical large-scale municipal solid waste incinerations with large sample size in China

Monitoring PCDD/Fs emissions from municipal solid waste incinerations (MSWIs) is of paramount importance, yet it can be time-consuming and labor-intensive. Predictive models offer an alternative approach for estimating their levels. However, robust models specific to PCDD/Fs were lacking. In this study, we collected 190 PCDD/Fs samples from 4 large-scale MSWIs in China, with the average PCDD/Fs levels and TEQ levels of 0.987 ng/m3 and 0.030 ng TEQ/m3, respectively. We developed and evaluated predictive models, including traditional statistical methods, e.g., linear regression (LR) as well as machine learning models such as back propagation-artificial neural networks (BP ANN) and random forest (RF). Correlation analysis identified 2,3,4,7,8-PeCDF, 1,2,3,6,7,8-HxCDF, 2,3,4,6,7,8-HxCDF were better indicator congeners for PCDD/Fs estimation (R2 > 0.9, p < 0.001). The predictive results favored the RF model, exhibiting a high R2 value and low root mean square error (RMSE) and mean absolute error (MAE). Additionally, the RF model showed excellent prediction ability during external validation, with low absolute relative error (ARE) of 10.9 %-12.6 % for the three indicator congeners in the normal PCDD/F TEQ levels group (<0.1 ng TEQ/m3) and slightly higher ARE values (13.8 %-17.9 %) for the high PCDD/F TEQ levels group (>0.1 ng TEQ/m3). In conclusion, our findings strongly support the RF model's effectiveness in predicting PCDD/Fs TEQ emission from MSWIs.

The mixed-order chlorine decay model with an analytical solution and corresponding trihalomethane generation model in drinking water

Ensuring effective drinking water disinfection, remaining a certain amount of residual chlorine, and controlling disinfection by-product formation were very important for guarantying water quality safety and protecting public health; thus, the chlorine decay model and corresponding disinfection by-product formation model were necessary. This paper proposed a mixed-order chlorine bulk decay model (two parameters) based on Taylor's formula and derived its analytical solution. The accuracy of the mixed-order model was evaluated by comparing it with the nth-order model. To optimize the model and reduce the number of parameters required to be calibrated, the relationship of parameters with temperature, initial chlorine concentration, TOC and inorganic substance (ammonia nitrogen and iodide ion) was explored. The result proved that one of the parameters could be regarded as temperature dependent only. Meanwhile, the temperature equation of the model parameters was established by the Arrhenius formula. Subsequently, this paper selected trihalomethane as the target and study the linear relationship between chlorine consumption and trihalomethane formation. The results indicated that the liner slope had little correlation with initial chlorine concentration and temperature. On this basis, the corresponding trihalomethane model was built and its performance was proven to be good. The modeling developed in this work could be applied to drinking water distribution systems for residual chlorine and trihalomethane prediction, and provided a reference for the decision involving water quality.

Full-scale multisampling and empirical modeling of DBPs in water and air of indoor pools

Disinfection by-products (DBPs) are formed in the water in swimming pools due to reactions between disinfectants (chlorine, bromine, ozone) and the organic matter introduced by bathers and supply water. High concentrations of DBPs are also reported in the air of indoor swimming pools. Based on a robust multisampling program, the levels and variations of DBPs in the air (trichloramine [TCAM] and trihalomethanes [THMs]) and water (THM) were assessed, as well as their precursors (total organic carbon, water temperature, pH, free, and total chlorine) and proxies (CO2 and relative humidity) in four indoor chlorinated swimming pools. High-frequency sampling was conducted during one high-attendance day for each pool. This study focused on parameters that are easy to measure in order to develop models for predicting levels of THMs and TCAM in the air. The results showed that the number of bathers had an important impact on the levels of THMs and TCAM, with a two-to-three-fold increase in air chloroform (up to 110 μg/m3) and a two-to-four-fold increase in TCAM (up to 0.52 mg/m3) shortly after pools opened. The results of this study for the first time showed that CO2 and relative humidity can serve as proxies for monitoring variations in airborne THMs and TCAM. Our results highlight the good predictive capacity of the developed models and their potential for use in day-to-day monitoring. This could help optimize and control DBPs formation in the air of indoor swimming pools and reduce contaminant exposure for both pool employees and users.

Using water quality parameters to prediction of the ion-based trihalomethane by an artificial neural network model

Trihalomethanes (THMs) are the first disinfectant by-products in the drinking water distribution network and are classified as potential carcinogens. The presence of THMs in chlorinated water depends on the pH, water temperature, contact time between water and chlorine, type and dose of disinfection, bromide ion concentration, and type and concentration of natural organic materials (NOMs). In the present study, the formation of THMs was evaluated by six simple and easy water quality parameters and modeled by an artificial neural network (ANN) approach through five water distribution networks (WDNs) and the Karoun River in Khuzestan province. The results of this study that was conducted from October 2014 to September 2015 showed that THM concentration ranged in five WDNs, including Shoushtar, Ahvaz (2), Ahvaz (3), Mahshahr, Khorramshahr, and total WDNs through N.D.-9.39 µg/L, 7.12-28.60, 38.16-67.00, 17.15-90.46, 15.14-29.99, and N.D.-156, respectively. The concentration of THMs exceeded Iran and EPA standards in many cases in Mahshahr and Khorramshahr WDNs. Evaluation of R², MSE, and RMSE showed the appropriate correlation between measured and modeled THMs, indicating a reasonable ANN potential for estimating THM formation in water sources.

Municipal wastewater contains antibiotic treatment using O2 transfer membrane based biofilm reactor: Interaction between regular pollutants metabolism and sulfamethoxazole degradation

The antibiotic sulfamethoxazole (SMX) is frequently detected in wastewater treatment plant effluents and has attracted significant attention owing to its significant potential environmental effects. We present a novel O₂ transfer membrane based biofilm reactor (O₂TM-BR) to treat municipal wastewater to eliminate containing SMX. Furthermore, conducting metagenomics analyses, the interactions in biodegradation process between SMX and regular pollutants (NH₄⁺-N and COD) were studied. Results suggest that O₂TM-BR yields evident advantages in SMX degradation. Increasing SMX concentrations did not affect the efficiency of the system, and the effluent concentration remained consistent at approximately 17.0 μg/L. The interaction experiment showed that heterotrophic bacteria tend to consume easily degradable COD for metabolism, resulting in a delay (>36 h) in complete SMX degradation, which is 3-times longer than without COD. It is worth noting that the taxonomic and functional structure and composition in nitrogen metabolism were significantly shifted upon the SMX. NH₄⁺-N removal remained unaffected by SMX in O₂TM-BR, and the expression of K10944 and K10535 has no significant difference under the stress of SMX (P > 0.02). However, the K00376 and K02567 required in the nitrate reductase is inhibited by SMX (P < 0.01), which hinders the reduction of NO₃^--N and hence the accumulation of TN. This study provides a new method for SMX treatment and reveals the interaction between SMX and conventional pollutants in O₂TM-BR as well as the microbial community function and assembly mechanism.

Combined application of EPANET and empirical model for possible formation of trihalomethanes in water distribution network of Chattogram city to identify potential carcinogenic health risk zone

The study identifies potential carcinogenic health risk-zone of Chattogram city for the occurrence of trihalomethanes (THMs) at its water distribution network. The EPANET-THMs simulation model along with an empirical model have been adopted in the study to predict THMs content of supply water of the distribution network of the city's Karnaphuli service area. The empirical model has estimated THMs level of supply water based on influential water quality parameters, and few of these have been used as pre-set values for subsequent EPANET simulation. The simulation (R2= 0.7) shows that THMs' concentrations throughout the network vary from 33 to 486 μg/L. Around 60% of total junctions showed THMs concentrations above 150 μg/L, while that is above 50 μg/L for most (99%) of the junctions. Residual Free chlorine, one of the precursors for the THMs formation in distribution line, has also been simulated by EPANET considering varying applied chlorine dose at the water purification unit and wall (Kw) and bulk (Kb) decay constants. The simulated free residual chlorine peaks are found to be closer to the actual values with chlorine dose of 2 mg/L, and decay constants, Kw = 1 d-1 and Kb = 1 d-1. A mean lifetime total risk of cancer due to the presence of THMs has been found to be very high. Spatial distribution of carcinogenic risk shows that the central zone of the service area is the most vulnerable zone, followed by the western and northern zone. The first ever zone wise risk identification could be used as baseline data for operational and regulatory purposes and may raise awareness among the city's inhabitants. Furthermore, the application of EPANET in combination with an empirical model could be an effective tool for predicting THMs' concentration in water distribution networks in developing countries like Bangladesh to minimize the expenses of measuring THMs.

Spatio-Temporal Analysis of Marine Water Quality Data Based on Cross-Recurrence Plot (CRP) and Cross-Recurrence Quantitative Analysis (CRQA)

In recent years, with the frequency of marine disasters, water quality has become an important environmental problem for researchers, and much effort has been put into the prediction of marine water quality. The temporal and spatial correlation of marine water quality parameters directly determines whether the marine time-series data prediction task can be completed efficiently. However, existing research has only focused on the correlation analysis of marine data in a certain area and has ignored the temporal and spatial characteristics of marine data in complex and changeable marine environments. Therefore, we constructed a spatio-temporal dynamic analysis model of marine water quality based on a cross-recurrence plot (CRP) and cross-recurrence quantitative analysis (CRQA). The time-series data of marine water quality were first mapped to high-dimensional space through phase space reconstruction, and then the dynamic relationship among various factors affecting water quality was visually displayed through CRP. Finally, their correlation was quantitatively explained by CRQA. The experimental results showed that our scheme demonstrated well the dynamic correlation of various factors affecting water quality in different locations, providing important data support for the spatio-temporal prediction of marine water quality.

Membrane separation of antibiotics predicted with the back propagation neural network

Antibiotics and antibiotic resistance genes (ARGs) have been frequently detected in the aquatic environment and are regarded as emerging pollutants. The prediction models for the removal effect of four target antibiotics by membrane separation technology were constructed based on back propagation neural network (BPNN) through training the input and output. The membrane separation tests of antibiotics showed that the removal effect of microfiltration on azithromycin and ciprofloxacin was better, basically above 80%. For sulfamethoxazole (SMZ) and tetracycline (TC), ultrafiltration and nanofiltration had better removal effects. There was a strong correlation between the concentrations of SMZ and TC in the permeate, and the R2 of the training and validation processes exceeded 0.9. The stronger the correlation between the input layer variables and the prediction target was, the better the prediction performances of the BPNN model than the nonlinear model and the unscented Kalman filter model were. These results showed that the established BPNN prediction model could better simulate the removal of target antibiotics by membrane separation technology. The model could be used to predict and explore the influence of external conditions on membrane separation technology and provide a certain basis for the application of the BPNN model in environmental protection.

The occurrence, formation and transformation of disinfection byproducts in the water distribution system: A review

Disinfection is an effective process to inactivate pathogens in drinking water treatment. However, disinfection byproducts (DBPs) will inevitably form and may cause severe health concerns. Previous research has mainly focused on DBPs formation during the disinfection in water treatment plants. But few studies paid attention to the formation and transformation of DBPs in the water distribution system (WDS). The complex environment in WDS will affect the reaction between residual chlorine and organic matter to form new DBPs. This paper provides an overall review of DBPs formation and transformation in the WDS. Firstly, the occurrence of DBPs in the WDS around the world was cataloged. Secondly, the primary factors affecting the formation of DBPs in WDS have also been summarized, including secondary chlorination, pipe materials, biofilm, deposits and coexisting anions. Secondary chlorination and biofilm increased the concentration of regular DBPs (e.g., trihalomethanes (THMs) and haloacetic acids (HAAs)) in the WDS, while Br^- and I^- increased the formation of brominated DBPs (Br-DBPs) and iodinated DBPs (I-DBPs), respectively. The mechanism of DBPs formation and transformation in the WDS was systematically described. Aromatic DBPs could be directly or indirectly converted to aliphatic DBPs, including ring opening, side chain breaking, chlorination, etc. Finally, the toxicity of drinking water in the WDS caused by DBPs transformation was examined. This review is conducive to improving the knowledge gap about DBPs formation and transformation in WDS to better solve water supply security problems in the future.

Appraisal of machine learning techniques for predicting emerging disinfection byproducts in small water distribution networks

Monitoring emerging disinfection byproducts (DBPs) is challenging for many small water distribution networks (SWDNs), and machine learning-based predictive modeling could be an alternative solution. In this study, eleven machine learning techniques, including three multivariate linear regression-based, three regression tree-based, three neural networks-based, and two advanced non-parametric regression techniques, are used to develop models for predicting three emerging DBPs (dichloroacetonitrile, chloropicrin, and trichloropropanone) in SWDNs. Predictors of the models include commonly-measured water quality parameters and two conventional DBP groups. Sampling data of 141 cases were collected from eleven SWDNs in Canada, in which 70 % were randomly selected for model training and the rest were used for validation. The modeling process was reiterated 1000 times for each model. The results show that models developed using advanced regression techniques, including support vector regression and Gaussian process regression, exhibited the best prediction performance. Support vector regression models showed the highest prediction accuracy (R² =0.94) and stability for predicting dichloroacetonitrile and trichloropropanone, and Gaussian process regression models are optimal for predicting chloropicrin (R² =0.92). The difference is likely due to the much lower concentrations of chloropicrin than dichloroacetonitrile and trichloropropanone. Advanced non-parametric regression techniques, characterized by a probabilistic nature, were identified as most suitable for developing the predictive models, followed by neural network-based (e.g., generalized regression neural network), regression tree-based (e.g., random forest), and multivariate linear regression-based techniques. This study identifies promising machine learning techniques among many commonly-used alternatives for monitoring emerging DBPs in SWDNs under data constraints.

Functional Beverages in the 21st Century

Underlying the dawn of humanity was primarily the search for food and access to drinking water. Over the course of civilization, there has been a significant increase in drinking water quality. By the average of the nutritional standards, the daily water demand is 2.5 L (also including liquid products such as tea, coffee, or soup). However, it is worth noticing that the need is strictly individual for each person and depends on two major factors, namely, epidemiological (sex, age state of health, lifestyle, and diet) and environmental (humidity and air temperature). Currently, our diet is more and more often enriched with isotonic drinks, functional drinks, or drinks bearing the hallmarks of health-promoting products. As a result, manufacturing companies compete to present more interesting beverages with complex compositions. This article will discuss both the composition of functional beverages and their impact on health.

Insight into the Behavior of Mortars Containing Glass Powder: An Artificial Neural Network Analysis Approach to Classify the Hydration Modes

In this paper, an artificial neural network (ANN) model is proposed to predict the hydration process of a new alternative binder. This model overcomes the lack of input parameters of physical models, providing a realistic explanation with few inputs and fast calculations. Indeed, four mortars are studied based on ordinary Portland cement (CEM I), cement with limited environmental impact (CEM III), and glass powder (GP) as the cement substitution. These mortars are named CEM I + GP and CEM III + GP. The properties of the mortars are characterized, and their life cycle assessment (LCA) is established. Indeed, a decrease in porosity is observed at 90 days by 4.6%, 2.5%, 12.4%, and 7.9% compared to those of 3 days for CEMI, CEMIII, CEMI + GP, and CEMIII + GP, respectively. In addition, the use of GP allows for reducing the mechanical strength in the short term. At 90 days, CEMI + GP and CEMIII + GP present a decrease of about 28% and 57% in compressive strength compared to CEMI and CEMIII, respectively. Nevertheless, strength does not cease increasing with the curing time, due to the continuous pozzolanic reactions between Ca(OH)₂ and silica contained in GP and slag present in CEMIII as demonstrated by the thermo-gravimetrical (TG) analysis. To summarize, CEMIII mortar provides similar performance compared to mortar with CEMI + GP in the long term. This can later be used in the construction sector and particularly in prefabricated structural elements. Moreover, the ANN model used to predict the heat of hydration provides a similar result compared to the experiment, with a resulting R² of 0.997, 0.968, 0.968, and 0.921 for CEMI, CEMIII, CEMI + GP, and CEMIII + GP, respectively, and allows for identifying the different hydration modes of the investigated mortars. The proposed ANN model will allow cement manufacturers to quickly identify the different hydration modes of new binders by using only the heat of hydration test as an input parameter.

Understanding the patterns and processes underlying water quality and pollution risk in West-Africa River using self-organizing maps and multivariate analyses

Rivers are dynamic systems in complex interactions with their surrounding environments. Reliable and fast interpretation of water quality is therefore needed for sustainable river management. Unfortunately, water quality and environmental status interactions have not yet been documented sufficiently in West-Africa. This study explored the spatial-latitudinal and seasonal features of water quality along the Sô River Basin (SRB, West Africa) using self-organizing map (SOM) and principal component analysis. Twenty-two water quality variables were measured in the surface layer at 12 different sampling sites during a twenty-four-month period from July 2016 to June 2018. The results revealed three water quality groups, following an upstream-downstream pollution gradient: (1) upstream and middle reach sites with high dissolved oxygen and Secchi disk depth values, which are more suitable for the aquatic biota; (2) downstream sites with high concentrations of ammonium, biochemical oxygen demand, and heavy metals especially in flood period, reflecting both high organic and heavy metal pollution; and (3) brackish downstream sites characterized by less heavy metal and organic pollutions. No significant variation was observed between seasons. However, the SRB relatively suffered from higher risks of heavy metal contamination and organic pollution in wet seasons. Although hydroclimatic processes affect the water quality, anthropogenic inputs of point and non-point sources were identified and discussed as a more prominent factor contributing to variation in the water quality condition. These results offer insights into the water quality dynamics in river-estuary system as well as potential pollution sources, crucial for defining sanitation, and management measures.

Multi-input multi-output temporal convolutional network for predicting the long-term water quality of ocean ranches

The prediction of water quality parameters is of great significance to the control of marine environments and provides a scientific decision-making basis for maintaining the stability of water environments and ensuring the normal survival and growth of marine aquatic products. However, the water quality in ocean ranches is affected by the complex, dynamic, and changeable environments of open water, which have complex nonlinear relationships, poor accuracy, high time complexity, and poor long-term predictability. Therefore, in this paper, a multi-input multi-output end-to-end prediction model based on a temporal convolutional network (MIMO-TCN) is proposed to predict water quality. A ConvNeXt module and TCN module were used as the model encoder and decoder, respectively. ConvNeXt was used to extract the features of the input data, and the TCN used the extracted feature data to achieve improved prediction accuracy. The model adds skip connections between its modules to solve the gradient disappearance problem as the number of network layers increases. To prove the effectiveness of the proposed method, a model robustness and prediction ability evaluation was conducted in this paper based on the dissolved oxygen in multiple ocean pasture validation samples. Compared with other learning models, the mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) of the MIMO-TCN prediction results were reduced by 60.77%, 30.88%, and 52.45% on average, respectively, and the R² improved by 6.07% on average over those of other models. The experimental results show that the proposed method has higher forecasting accuracy than competing approaches.

Molecular-level insights into the mitigation of magnesium-natural organic matter induced ultrafiltration membrane fouling by high-dose calcium based on DFT calculation

While magnesium cation (Mg²⁺) universally coexists with natural organic matter (NOM) in the water environment, influence of Mg²⁺ on NOM fouling in membrane filtration process is still unclear. This work was therefore performed to investigate effects of Mg²⁺ on NOM (sodium alginate (SA) as a model substance) fouling and role of Ca²⁺ in mitigating fouling from Mg²⁺ in the ultrafiltration (UF) water treatment process. Filtration tests showed two interesting fouling phenomena: (1) membrane fouling caused by combination of Mg²⁺ and SA maintained at a high value with the increased Mg²⁺ concentration; (2) the high fouling property of Mg²⁺ can be significantly improved by the prominent addition of calcium cation (Ca²⁺). It was found that changes of foulant morphology played essential roles through thermodynamic mechanisms represented by the Flory-Huggins lattice theory. Density functional theory (DFT) calculation showed that the combination of SA and Mg²⁺ tends to coordinate two terminal carboxyl groups in SA, beneficial to stretching alginate chains and forming a stable gel network at low doses. In addition, intramolecular coordination is difficult to occur between SA and Mg²⁺ due to the high hydration repulsion radius of Mg²⁺. Therefore, a dense and thick gel network remained even under high Mg²⁺concentration. Furthermore, due to the higher binding affinity of Ca²⁺ over Mg²⁺, high doses of Ca²⁺ trigger a transition of the stable SA-Mg²⁺ gel network to other configurations where flocculation and aggregation occur, thereby reducing the specific filtration resistance. The proposed thermodynamic mechanism satisfactorily explained the above interesting fouling behaviors, facilitating to development of new solutions to control membrane fouling.

Investigation of land use changes in rural areas using MCDM and CA-Markov chain and their effects on water quality and soil fertility in south of Iran

The purpose of the study is to predict drought changes in Dariun, Fars Province, and their impact on water and soil quality. To prepare drought, water, and soil quality zoning maps, Landsat satellite images and the kriging method were used. The fuzzy maps and weights for each parameter were then determined using fuzzy and analytic hierarchy process (AHP) methods. Additionally, cellular automata (CA)-Markov chains were used in order to predict the impact of drought changes on water and soil quality. Using the fuzzy-AHP method, water quality and soil fertility in 2020 were lower compared to previous years, mainly because of land use changes that increased pollution. Based on results of the Markov and CA-Markov chains, approximately 31% of the region will have very poor levels of soil fertility and water quality in 2050. Further, based on remote sensing indicators, it is determined that about 25% of the region will be at high risk of drought in 2050. Thus, if adequate management of the region is not done, the possibility of living in these areas may diminish in the coming years due to drought and deteriorated water and soil quality.

Comparison of the disinfection efficacy between ferrate(VI) and chlorine in secondary effluent

Disinfection is essential for the microbial safety of reclaimed water. Traditional chlorine disinfection leads to secondary problems such as disinfection by-products and chlorine-resistant bacteria. Ferrate (Fe(VI)) is a novel green disinfectant. However, research on the disinfection characteristics of Fe(VI) remains insufficient. This study compared the disinfection efficacy between Fe(VI) and chlorine in secondary effluent, including the inactivation efficiency of coliforms and heterotrophic bacteria and the control effect on typical chlorine-resistant bacteria. The results showed that Fe(VI) was more effective than chlorine in inactivating Escherichia coli and total coliforms at low doses, whereas chlorine was more effective than Fe(VI) in inactivating heterotrophic bacteria. A severe trailing phenomenon was observed in Fe(VI) disinfection. Based on bacterial community structure analysis, Fe(VI) was also found to be capable of controlling the relative abundance of some chlorine-resistant bacteria such as Sphingomonas, Bacillus, Mycobacterium and Legionella except for Pseudomonas. The results of this study could have implications in evaluating Fe(VI) disinfection ability and optimizing Fe(VI) dosing for disinfection.

Trichloroethylene remediation using zero-valent iron with kaolin clay, activated carbon and bacteria

Nanoscale particles of zero-valent iron were used to form a permeable reactive barrier whose performance in dechlorinating a solution of trichloroethylene was compared with that of a barrier formed from limestone. The iron was combined with kaolin by calcination. The test liquid contained sewage sludge, and also added NH₄Cl and KH₂PO₄. The average removal rates of trichloroethylene and phosphorus over 365 days both exceeded 94%. Chemical oxygen demand was reduced by 92% and ammonium nitrogen by 43.6%. All were significantly greater than the removals with the limestone barrier. The ceramsite barrier retained 85% of its effectiveness even after 365 days of use. Dechloromonas sp. was the main dechlorinating bacterium, but its removal ability is limited. The removal of trichloroethylene in such a barrier mainly depends on reduction by the zero-valent iron and biodegradation. The results show that the prepared ceramsite is stable and effective in removing trichloroethylene from water. It is a promising in-situ remediation material for groundwater.

Pilot Study of Pollution Characteristics and Ecological Risk of Disinfection Byproducts in Natural Waters in Hong Kong

Increased disinfection efforts in various parts of China, including Hong Kong, to prevent the spread of the novel coronavirus may lead to elevated concentrations of disinfectants in domestic sewage and surface runoff in Hong Kong, generating large quantities of toxic disinfection byproducts. Our study investigated the presence and distribution of four trihalomethanes (THMs), six haloacetic acids (HAAs), and eight nitrosamines (NAMs) in rivers and seawater in Hong Kong. The concentrations of THMs (mean concentration: 1.6 µg/L [seawater], 3.0 µg/L [river water]), HAAs (mean concentration: 1.4 µg/L [seawater], 1.9 µg/L [river water]), and NAMs (mean concentration: 4.4 ng/L [seawater], 5.6 ng/L [river water]) did not significantly differ between river water and seawater. The total disinfection byproduct content in river water in Hong Kong was similar to that in Wuhan and Beijing (People's Republic of China), and the total THM concentration in seawater was significantly higher than that before the COVID-19 pandemic. Among the regulated disinfection byproducts, none of the surface water samples exceeded the maximum index values for THM4 (80 μg/L), HAA5 (60 μg/L), and nitrosodimethylamine (100 ng/L) in drinking water. Among the disinfection byproducts detected, bromoform in rivers and seawater poses the highest risk to aquatic organisms, which warrants attention and mitigation efforts. Environ Toxicol Chem 2022;41:2613-2621. © 2022 SETAC.

Lignosulfonate valorization into a Cu-containing magnetically recyclable photocatalyst for treating wastewater pollutants in aqueous media

This study presents an eco-friendly and economical process for preparing a magnetic copper complex conjugated to modified calcium lignosulfonate (LS) through a diamine (Fe₃O₄@LS@naphthalene-1,5-diamine@copper complex; FLN-Cu) as a green and novel catalyst. The prepared catalyst was characterized by Fourier-transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), thermogravimetric analysis (TGA), vibrating sample magnetometer (VSM), Brunauer-Emmett-Teller (BET), energy-dispersive X-ray spectroscopy (EDS), elemental mapping, inductively coupled plasma-optical emission spectrometry (ICP-OES) and field emission scanning electron microscopy (FESEM) techniques. The photocatalytic performance of the synthesized FLN-Cu catalyst was investigated by the degradation of aqueous solutions of dyes such as Rhodamine B (RhB), methylene blue (MB), and Congo red (CR) under UV irradiation. The dye degradation was followed by UV-Vis (ultraviolet-visible) spectrophotometry by measuring the changes in absorbance. The effects of different factors such as pH, contact time, photocatalyst dosage, and initial concentration of dye on the adsorption percentage were also investigated. Moreover, the catalyst showed high stability and could be readily separated from the reaction media using a magnet and reused five times without a remarkable loss of catalytic ability.

Facile synthesis of Cu NPs@Fe3O4-lignosulfonate: Study of catalytic and antibacterial/antioxidant activities

Environmental pollution is one of the important concerns for human health. There are different types of pollutants and techniques to eliminate them from the environment. We hereby report an efficient method for the remediation of environmental contaminants through the catalytic reduction of the selected pollutants. A green method has been developed for the immobilization of copper nanoparticles on magnetic lignosulfonate (Cu NPs@Fe₃O₄-LS) using the aqueous extract of Filago arvensis L. as a non-toxic reducing and stabilizing agent. The characterization of the prepared Cu NPs@Fe₃O₄-LS was achieved by vibrating sample magnetometer (VSM), Fourier-transform infrared spectroscopy (FT-IR), transmission electron microscopy (TEM), high resolution TEM (HRTEM), X-ray diffraction (XRD), scanning TEM (STEM), thermogravimetry-differential thermal analysis (TG/DTA), fast Fourier transform (FFT), energy-dispersive X-ray spectroscopy (EDS), and X-ray photoelectron (XPS) analyses. The synthesized Cu NPs@Fe₃O₄-LS was applied as a magnetic and green catalyst in the reduction of Congo Red (CR), 4-nitrophenol (4-NP), and methylene blue (MB). The progress of the reduction reactions was monitored by UV-Vis spectroscopy. Finally, the biological properties of the Cu NPs@Fe₃O₄-LS were investigated. The prepared catalyst demonstrated excellent catalytic efficiency in the reduction of CR, 4-NP, and MB in the presence of sodium borohydride (NaBH₄) as the reducing agent. The appropriate magnetism of Cu NPs@Fe₃O₄-LS made its recovery very simple. The advantages of this process include a simple reaction set-up, high and catalytic antibacterial/antioxidant activities, short reaction time, environmentally friendliness, high stability, and easy separation of the catalyst. In addition, the prepared Cu NPs@Fe₃O₄-LS could be reused for four cycles with no significant decline in performance.

Precursor characteristics of mono-HAAs during chlorination and cytotoxicity of mono-HAAs on HEK-293T cells

Monohaloacetic acids (mono-HAAs), a class of disinfection by-products widely occurred in drinking water, receives significant attention due to their extremely high toxicity. Many studies on the biological toxicity of mono-HAAs have been reported, yet the toxic effects of mono-HAAs on human renal cells (kidney is one of the target organs for disinfection by-products) has not been involved. Studies on organic precursors for mono-HAAs formation were also very limited due to their lower levels as compared to di-HAAs and tri-HAAs. Based on this, the formation of mono-HAAs after chlorination of some typical source water samples and their relationship with water quality parameters were investigated. Meanwhile, the cytotoxicity of monochloroacetic acid (MCAA), monobromoacetic acid (MBAA), and monoiodoacetic acid (MIAA) were tested using human embryonic kidney cells (HEK-293 T cells). The results showed that the levels of mono-HAAs formed during chlorination of source water samples were between 0.44 and 0.87 μg/L. Formation of MBAA positively (p < 0.05) correlated with bromide ion and dissolved organic carbon, but negatively (p < 0.01) correlated with SUVA₂₅₄ (specific UV absorbance at 254 nm), while formation of MCAA was only positively (p < 0.05) related with SUVA₂₅₄. These results suggested that although MCAA and MBAA both belong to the mono-HAAs, the characteristics of their organic precursors differ significantly. MCAA precursors have high aromaticity and are more hydrophobic, yet MBAA precursors have low aromaticity and are more hydrophilic. The half-lethal concentrations (LC₅₀) of MCAA, MBAA, and MIAA on HEK293T cells were 1196-1211 μM, 16.07-18.96 μM, and 6.08-6.17 μM, respectively. An in-depth analysis showed that the cytotoxicity of mono-HAAs on HEK 293 T cells could not be explained by the parameters concerning cellular uptake (e.g., logP and pKa), but the S_N2 reaction of C-X bond with cellular molecules (e.g., glyceraldehyde-3-phosphate dehydrogenase, etc) may be the relevant cause for the cytotoxicity of mono-HAAs on HEK 293 T cells.

Sensor Data Fusion as an Alternative for Monitoring Chlorate in Electrochlorination Applications

As chlorate concentrations have been found to be harmful to human and animal health, governments are increasingly demanding strict control of the chlorate concentration in drinking water. Since there are no chlorate sensors available, the current solution is sampling and laboratory analysis. This is costly and time consuming. The aim of this work was to investigate Sensor Data Fusion (SDF) as an alternative approach, with a focus on chlorate formation in the electrochlorination process, and design an observer for the real-time estimation of chlorate. The pH, temperature and UV-a absorption were measured in real time. A reduced-order nonlinear model was derived, and it was found to be detectable. An Extended Kalman Filter (EKF), based on this model, was then used to estimate the chlorate formation. The EKF algorithm was verified experimentally and was found to be capable of accurately estimating chlorate concentrations in real time. Electrochlorination is an emerging and efficient method of disinfecting drinking water. Soft sensing of chlorate concentrations, as proposed in this paper, may help to better control and manage the process of electrochlorination.

A Review on Machine Learning, Artificial Intelligence, and Smart Technology in Water Treatment and Monitoring

Artificial-intelligence methods and machine-learning models have demonstrated their ability to optimize, model, and automate critical water- and wastewater-treatment applications, natural-systems monitoring and management, and water-based agriculture such as hydroponics and aquaponics. In addition to providing computer-assisted aid to complex issues surrounding water chemistry and physical/biological processes, artificial intelligence and machine-learning (AI/ML) applications are anticipated to further optimize water-based applications and decrease capital expenses. This review offers a cross-section of peer reviewed, critical water-based applications that have been coupled with AI or ML, including chlorination, adsorption, membrane filtration, water-quality-index monitoring, water-quality-parameter modeling, river-level monitoring, and aquaponics/hydroponics automation/monitoring. Although success in control, optimization, and modeling has been achieved with the AI methods, ML models, and smart technologies (including the Internet of Things (IoT), sensors, and systems based on these technologies) that are reviewed herein, key challenges and limitations were common and pervasive throughout. Poor data management, low explainability, poor model reproducibility and standardization, as well as a lack of academic transparency are all important hurdles to overcome in order to successfully implement these intelligent applications. Recommendations to aid explainability, data management, reproducibility, and model causality are offered in order to overcome these hurdles and continue the successful implementation of these powerful tools.

Analytical strategies to sense water stress level: An analysis of ground water fluctuations sensing SDGs under pandemic scenario

Groundwater fluctuation is directly linked with the consumption and wastage of water sources during the pandemic interval. That is why water resource planners directly target water resource and sanitation systems in line with the sustainable development goals (SDGs) concept. In this study, District Multan is designated as a study area with 85 distinct station points data sets from four zones taken to pursue this massive investigation. The data sets are studied analytically and graphically to explore the relationships among critical variables like population, average water consumption, groundwater elevation, water table depth, total consumption, wastage of water during the pandemic days, etc. For in-depth analysis, the statistical approaches are employed on these massive data sets to reveal the trend among each dataset point to generate predictive models. The results revealed that groundwater reservoirs and levels are continuously declining on an annual basis in the meantime, the water consumption and extraction are increasing simultaneously. The consumption during pandemic days has been increased so much at the same time the wastage and total consumption of water is rising a lot in contrast to previous daily consumption and water demand. The coefficient of determination (R-square) values vary from 0.41 to 0.93 in this investigation. It will help the utilization of developed models and water-providing organizations to forecast groundwater instabilities for the future. Moreover, the situation in the study area is very alarming in terms of water stress conditions. This study will help the decision-making agencies to produce a policy following the SDGs concept to control water consumption and higher extraction.

Modelling of transmembrane pressure using slot/pore blocking model, response surface and artificial intelligence approach

This work investigates the application of empirical, statistical and machine learning methods to appraise the prediction of transmembrane pressure (TMP) by oscillating slotted pore membrane for the treatment of two kinds of deformable oil drops. Here, we utilized the previous experimental runs with permeate flux, shear rate and filtration time as features, while TMP of crude oil and Tween-20 were two distinct targets. For 87 experimental runs, Response surface methodology (RSM) and Artificial Neural network (ANN) modelling were opted as statistical and machine learning tools, respectively, which were comprehensively compared with empirical slot-pore blocking model (SBM) considering accuracy and generalization. ANN with 10 neurons in the hidden layer could approximate the TMP of both oils better than RSM and SBM, which is reflected by computed performance metrics. Under the given conditions, almost similar analysis were predicted for TMP of both oils except changes in magnitude which were interpreted by (1) line plots, which showed that TMP of crude oil and Tween-20 were linearly related to flux rate and filtration time, and there was an inverse relationship between TMP and shear rate, (2) contour plots, which illustrated the strong interaction effect of flux rate and time on TMP, and (3)- sensitivity analysis, which revealed the influential sequence of variables on TMP as; flux rate > filtration time > shear rate, for both cases. The optimisation of the process showed that minimum TMP can be attained by maintaining higher shear rate and lower flux rate and time. Conclusively, the current findings indicate the utilization of ANN for the accurate assessment of TMP and can be helpful for the process designing and scale up.

A novel composite membrane for simultaneous separation and catalytic degradation of oil/water emulsion with high performance

It is of great significance to develop novel membranes with dual-function of simultaneously separating oil/water emulsion and degrading the contained water-miscible toxic organic components. To meet this requirement, a dual-functional Ni nanoparticles (NPs)@Ag/C-carbon nanotubes (CNTs) composite membrane was fabricated via electroless nickel plating strategy in this study. The as-prepared composite membrane possessed superhydrophilicity with water contact angle of 0° and splendid underwater oleophobic property with oil contact angle of 142°. When the membrane was applied for separation of surfactant stabilized oil-in-water emulsion, high permeate flux (about 97 L m^-2·h^-1 under gravity), oil rejection (about 98.8%) and antifouling property were achieved. Benefitting from the NiNPs@Ag/C-CNTs layer on membrane surface, the composite membrane exhibited high catalytic degradation activity for water-miscible toxic organic pollutant (4-nitrophenol) with addition of NaBH₄ in a flow-through mode. Meanwhile, the NiNPs@Ag/C-CNTs composite membrane possessed excellent durability, which was verified by the good structural integrity even under ultrasonic treatment. The cost-efficiency, high separation and degradation performance of the prepared membrane suggested its great potential for treatment of oily wastewater.

Effect of different fish feeds on water quality and growth of crucian carp (Carassius carassius) in the presence and absence of prometryn

Few data are available regarding comprehensive or quantitative assessment of fish feed considering both the environmental and feeding impacts. Aiming to fill the gap, an experimental study to investigate the effects of three fish feeds on concentrations of nutrients and crucian carp (Carassius carassius) growth was conducted in laboratory aquariums in the presence and absence of prometryn. Results showed that weight gain rates of crucian carp treated with Tong Wei (TW) feed were 106.3% and 2.0% higher than that of Zhong Shan (ZS) and Zhong Liang (ZL) feeds, a possible explanation was that the quality of protein in TW feed was highest as evidenced by the protein efficiency ratios. Meanwhile, TW feed posed relatively lighter effects on water qualities (between ZL and ZS). Prometryn significantly inhibited the growth of crucian carp and thus affected concentrations of nutrients in water indirectly. The relationships between weight gain rates of fish and concentrations of nutrients in water (R² = 0.929-0.990) were developed. In sum, this study suggested that it is realizable to obtain better fish growth performance with lesser degrading effects on water qualities by producing and selecting appropriate feed regardless of prometryn existence, and the developed equations could be used as a basis for future studies.