Water Environment Research最新文献

Excessive Fe²⁺ in coastal aquaculture source water will seriously affect the aquaculture development. This study used manganese sand to investigate the removal potential and mechanism of Fe²⁺ in coastal aquaculture source water by column experiments. The pseudo-first-order kinetic model could better describe Fe²⁺ removal process with R² in the range of 0.9451-0.9911. More than 99.7% of Fe²⁺ could be removed within 120 min while the removal rate (k) was positively affected by low initial concentration of Fe²⁺, high temperature, and low pH. Logistic growth (S-shaped growth) model could better fit the concentration variation of Fe²⁺ in the effluent of the column (R²>0.99). The Fe² breakthrough curve could be fitted by Bohart-Adams, Yoon-Nelson, and Thomas models (R²>0.95). Smooth slices with irregular shapes existed on the surface of manganese sand after the reaction while Fe content increased significantly on the surface of manganese sand after the column experiment. Moreover, FeO (OH) was mainly formed on the surface of manganese sand after the reaction. PRACTITIONER POINTS: Fe²⁺ in coastal aquaculture source water could be removed by manganese ores. The pseudo-first-order kinetic model better described the Fe²⁺ removal process. FeO (OH) was mainly formed on the surface of manganese sand after the reaction.

Submerged macrophytes can overgrow and negatively affect freshwater ecosystems. This study aimed to investigate the use of chlormequat (CQ) to regulate submerged Vallisneria natans growth as well as its impact on the microbial community of epiphytic biofilms. V. natans height under CQ dosages of 20, 100, and 200 mg/L decreased within 21 days by 12.57%, 30.07%, and 44.62%, respectively, while chlorophyll content increased by 1.94%, 20.39%, and 38.83%. At 100 mg/L, CQ reduced the diversity of bacteria in the biofilm attached to V. natans leaves but increased the diversity of the eukaryotic microbial community. CQ strongly inhibited Cyanobacteria; compared with the control group, the treatment group experienced a significant reduction from 36.54% to 2.61%. Treatment significantly inhibited Gastrotricha and Rotifera, two dominant phyla of eukaryotes in the leaf biofilm, reducing their relative abundances by 17.41% and 6.48%, respectively. CQ significantly changed the leaf biofilm microbial community correlation network. The treatment group exhibited lower modularity (2.012) compared with the control group (2.249); however, the central network of the treated group contained a higher number of microbial genera (13) than the control group (4), highlighting the significance of eukaryotic genera in the network. The results obtained from this study provide invaluable scientific context and technical understanding pertinent to the restoration of submerged macrophytes within aquatic ecosystems. PRACTITIONER POINTS: Chlormequat reduced the plant height but increased leaf chlorophyll content. Chlormequat reduced biofilm bacterial diversity but increased eukaryotic diversity. Chlormequat affected the bacterial-fungal association networks in biofilms.

Rising concerns over water scarcity, driven by industrialization and urbanization, necessitate the need for innovative solutions for wastewater treatment. This study focuses on developing an eco-friendly and cost-effective biochar-zeolite composite (BZC) adsorbent using waste materials-spent coffee ground biochar (CGB) and steel slag zeolite (SSZ). Initially, the biochar was prepared from spent coffee ground, and zeolite was prepared from steel slag; their co-pyrolysis resulted in novel adsorbent material. Later, the physicochemical characteristics of the BZC were examined, which showed irregular structure and well-defined pores. Dye removal studies were conducted, which indicate that BZC adsorption reach equilibrium in 2 h, exhibiting 95% removal efficiency compared to biochar (43.33%) and zeolite (74.58%). Moreover, the removal efficiencies of the novel BZC composite toward dyes methyl orange (MO) and crystal violet (CV) were found to be 97% and 99.53%, respectively. The kinetic studies performed with the dyes and phosphate with an adsorbent dosage of 0.5 g L^-1 suggest a pseudo-second-order model. Additionally, the reusability study of BZC proves to be effective through multiple adsorption and regeneration cycles. Initially, the phosphate removal remains high but eventually decreases from 92% to 70% in the third regeneration cycle, highlighting the robustness of the BZC. In conclusion, this study introduces a promising, cost-effective novel BZC adsorbent derived from waste materials as a sustainable solution for wastewater treatment. Emphasizing efficiency, reusability, and potential contributions to environmentally conscious water treatment, the findings highlight the composite's significance in addressing key challenges for the removal of toxic pollutants from the aqueous solutions. PRACTITIONER POINTS: A novel biochar-zeolite composite (BZC) material has been synthesized. Excellent removal of dyes by BZC (~95%) was achieved as compared to their counterparts The kinetic studies performed suggest a pseudo-second-order model. BZC proves to be highly effective for multiple adsorption studies. Excellent reusability showed potential as a robust adsorbent.

This study investigates the use of machine learning (ML) models for wastewater treatment plant (WWTP) sludge predictions and explainable artificial intelligence (XAI) techniques for understanding the impact of variables behind the prediction. Three ML models, random forest (RF), gradient boosting machine (GBM), and gradient boosting tree (GBT), were evaluated for their performance using statistical indicators. Input variable combinations were selected through different feature selection (FS) methods. XAI techniques were employed to enhance the interpretability and transparency of ML models. The results suggest that prediction accuracy depends on the choice of model and the number of variables. XAI techniques were found to be effective in interpreting the decisions made by each ML model. This study provides an example of using ML models in sludge production prediction and interpreting models applying XAI to understand the factors influencing it. Understandable interpretation of ML model prediction can facilitate targeted interventions for process optimization and improve the efficiency and sustainability of wastewater treatment processes. PRACTITIONER POINTS: Explainable artificial intelligence can play a crucial role in promoting trust between machine learning models and their real-world applications. Widely practiced machine learning models were used to predict sludge production of a United States wastewater treatment plant. Feature selection methods can reduce the required number of input variables without compromising model accuracy. Explainable artificial intelligence techniques can explain driving variables behind machine learning prediction.

Microalgal solutions to clean waste streams and produce biomass were evaluated in Nordic conditions during winter, spring, and autumn in Southeast Sweden. The study investigated nitrogen (N) removal, biomass quality, and safety by treating industrial leachate water with a polyculture of local microalgae and bacteria in open raceway ponds, supplied with industrial CO₂ effluent. Total N (TN) removal was higher in spring (1.5 g^-2d^-1), due to beneficial light conditions compared to winter and autumn (0.1 and 0.09 g^-2d^-1). Light, TN, and N species influenced the microalgal community (dominated by Chlorophyta), while the bacterial community remained stable throughout seasons with a large proportion of cyanobacteria. Winter conditions promoted biomass protein (19.6-26.7%) whereas lipids and carbohydrates were highest during spring (11.4-18.4 and 15.4-19.8%). Biomass toxin and metal content were below safety levels for fodder, but due to the potential presence of toxic strains, biofuels or fertilizer could be suitable applications for the algal biomass. PRACTITIONER POINTS: Microalgal removal of nitrogen from leachate water was evaluated in Nordic conditions during winter, spring, and autumn. Total nitrogen removal was highest in spring (1.5 g^-2d^-1), due to beneficial light conditions for autotrophic growth. Use of local polyculture made the cultivation more stable on a seasonal (light) and short-term (N-species changes) scale. Toxic elements in produced algal biomass were below legal thresholds for upcycling.

This study evaluated the influence of organic matter (OM) constituents on the potential for recovery of P from wastewaters when FeCl₃ treatment is employed for P removal. The presence of OM constituents did not influence P release from Fe-P sludges when alkaline and ascorbic acid treatments were employed. However, the overall recovery of P from wastewater was impacted by the presence of selected OM constituents through the reduction of P uptake during coagulation. The presence of protein and humic matter showed remarkably low P removal values (3.0 ± 0.4% and 23 ± 1% respectively) when compared to an inorganic control recipe (62 ± 2%). Elevated soluble Fe (SFe) residuals in the presence of proteins (87 ± 5%) and humics (51 ± 1%) indicated interactions between Fe(III) cations and negatively charged functional groups like hydroxyl, carboxyl, and phenolic groups available in these organics. Significant negative correlations between P removal and residual SFe were observed suggesting Fe solubilization by OM constituents was the mechanism responsible for reduced P removal. The findings of this study identify, for the first time, the impact of OM constituents on overall P recovery when Fe(III) salts are employed and provide insights into recoveries that can be expected when Fe is added to primary, secondary treated, and industrial wastewaters. PRACTITIONER POINTS: Low P removal values were observed for protein and humic dominated wastewater recipes. Iron(III) solubilization counted for P removal reduction by proteins and humic acids. There is no effect of OM on P release from Fe-P sludge at pH 10 and ascorbic acid treatments. OM and agent employed to release P from sludges affected overall recovery of P.

In this work, a Co-Ce@RM ozone catalyst was developed using red mud (RM), a by-product of alumina production, as a support material, and its preparation process, catalytic efficiency, and tetracycline (TCN) degradation mechanism were investigated. A comprehensive assessment was carried out using the 3E (environmental, economic, and energy) model. The optimal production conditions for Co-Ce@RM were as follows: The doping ratio of Co and Ce was 1:3, the calcination temperature was 400°C, and the calcination time was 5 h, achieving a maximum removal rate of 87.91% of TCN. The catalyst was characterized using different analytical techniques. Under the conditions of 0.4 L/min ozone aeration rate, with 9% catalyst loading and solution pH 9, the optimal removal rates and chemical oxygen demand by the Co-Ce catalytic ozonation at RM were 94.17% and 75.27%, respectively. Moreover, free radical quenching experiments showed that superoxide radicals (O₂ ^-) and singlet oxygen (1O₂) were the main active groups responsible for the degradation of TCN. When characterizing the water quality, it was assumed that TCN undergoes degradation pathways such as demethylation, dehydroxylation, double bond cleavage, and ring-opening reactions under the influence of various active substances. Finally, the 3E evaluation model was deployed to evaluate the Co-Ce@RM catalytic ozonation experiment of TCN wastewater. PRACTITIONER POINTS: The preparation of Co-Ce@RM provides new ideas for resource utilization of red mud. Catalytic ozonation by Co-Ce@RM can produce ₁O² active oxygen groups. The Co-Ce@RM catalyst can maintain a high catalytic activity after 20 cycles. The degradation pathway of the catalytic ozonation of tetracycline was fully analyzed. Catalytic ozone oxidation processes were evaluated by the "3E" (environmental, economic, and energy) model.

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.

Chlorophyll-a (Chl-a) concentrations, a key indicator of algal blooms, were estimated using the XGBoost machine learning model with 23 variables, including water quality and meteorological factors. The model performance was evaluated using three indices: root mean square error (RMSE), RMSE-observation standard deviation ratio (RSR), and Nash-Sutcliffe efficiency. Nine datasets were created by averaging 1 hour data to cover time frequencies ranging from 1 hour to 1 month. The dataset with relatively high observation frequencies (1-24 h) maintained stability, with an RSR ranging between 0.61 and 0.65. However, the model's performance declined significantly for datasets with weekly and monthly intervals. The Shapley value (SHAP) analysis, an explainable artificial intelligence method, was further applied to provide a quantitative understanding of how environmental factors in the watershed impact the model's performance and is also utilized to enhance the practical applicability of the model in the field. The number of input variables for model construction increased sequentially from 1 to 23, starting from the variable with the highest SHAP value to that with the lowest. The model's performance plateaued after considering five or more variables, demonstrating that stable performance could be achieved using only a small number of variables, including relatively easily measured data collected by real-time sensors, such as pH, dissolved oxygen, and turbidity. This result highlights the practicality of employing machine learning models and real-time sensor-based measurements for effective on-site water quality management. PRACTITIONER POINTS: XAI quantifies the effects of environmental factors on algal bloom prediction models The effects of input variable frequency and seasonality were analyzed using XAI XAI analysis on key variables ensures cost-effective model development.

The world's freshwater supply, predominantly sourced from rivers, faces significant contamination from various economic activities, confirming that the quality of river water is critical for public health, environmental sustainability, and effective pollution control. This research addresses the urgent need for accurate and reliable water quality monitoring by introducing a novel method for estimating the water quality index (WQI). The proposed approach combines cutting-edge optimization techniques with Deep Capsule Crystal Edge Graph neural networks, marking a significant advancement in the field. The innovation lies in the integration of a Hybrid Crested Porcupine Genghis Khan Shark Optimization Algorithm for precise feature selection, ensuring that the most relevant indicators of water quality (WQ) are utilized. Furthermore, the use of the Greylag Goose Optimization Algorithm to fine-tune the neural network's weight parameters enhances the model's predictive accuracy. This dual optimization framework significantly improves WQI prediction, achieving a remarkable mean squared error (MSE) of 6.7 and an accuracy of 99%. By providing a robust and highly accurate method for WQ assessment, this research offers a powerful tool for environmental authorities to proactively manage river WQ, prevent pollution, and evaluate the success of restoration efforts. PRACTITIONER POINTS: Novel method combines optimization and Deep Capsule Crystal Edge Graph for WQI estimation. Preprocessing includes data cleanup and feature selection using advanced algorithms. Deep Capsule Crystal Edge Graph neural network predicts WQI with high accuracy. Greylag Goose Optimization fine-tunes network parameters for precise forecasts. Proposed method achieves low MSE of 6.7 and high accuracy of 99%.