Water Environment Research最新文献

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.

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.

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.

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.

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%.

This study aims to investigate the response mechanisms of groundwater microbial-toxicological indicators, specifically total bacteria count (TBC) and total coliform count (TCC), to water quality indicators and environmental conditions. Using data from a water source in the western plateau of China, a predictive model focusing on TBC and TCC was developed. An orthogonal experimental design was employed to manipulate environmental conditions such as temperature, pH, and porosity, facilitating laboratory experiments. These experiments measured pH, chemical oxygen demand (COD), oxidation-reduction potential (ORP), TBC, and TCC at varying depths and environmental conditions. Principal component analysis elucidated the mechanisms by which water quality indicators and environmental conditions affect groundwater microbial-toxicological indicators. A prediction model for these indicators in plateau regions was established based on a backpropagation neural network (BP-NN), using TBC and TCC as target variables and the newly extracted principal components as influencing factors. The results demonstrate that environmental conditions and water quality indicators primarily influence the evolution of groundwater microbial-toxicological indicators by altering the ionic charge quantities, redox conditions, and temperature of the groundwater. The predictive model for groundwater microbial-toxicological indicators shows trends consistent with experimental outcomes, with an average relative error of less than 15%, meeting engineering requirements. PRACTITIONER POINTS: The values of total bacteria count (TBC) and total coliform count (TCC) under different conditions were obtained by column experiments. The influence mechanism of environmental conditions and groundwater indicators on TBC and TCC was elaborated by principal component analysis. TBC and TCC prediction models were established through the investigation of water sources in a plateau area and laboratory experiments.

In this study, the treatment of vegetable oil refinery plant condensate effluent (VORCE) having high total suspended solids (TSS) and chemical oxygen demand (COD) generated from acid oil unit was focused. The utilization of waste Brassica nigra meal (BNM) as protein flocculant in treating VORCE was explored. The B. nigra meal flocculant (BNMF) exhibited a crystalline nature, with the presence of amino and carboxyl functional groups, rendering it highly efficient (89.69% efficiency) in floc formation. Zeta potential and particle size (-5.6 mV and 240.68 nm, respectively) indicate BNMF's effectiveness in initiating floc formation. The interactive effects of pH, dosage, settling time on COD, and TSS removal were investigated using the Box-Behnken design. At an optimal pH of 6.9 and BNMF dosage of 0.77 g/L, a maximum removal of 85.38% COD and 72.56% TSS was obtained. The perikinetic theory for the coagulation-flocculation followed a second-order rate reaction with high K_c (0.0001 L/mg min), low settling time (37.04 min), and high collision efficiency (2.703 × 10¹⁷), indicating the model's significance in achieving maximum COD and TSS removal. These findings highlight the potential use of BNMF in the treatment of VORCE, leading to circular economy by valorizing waste from mustard oil extraction and zero discharge. PRACTITIONER POINTS: Valorization of waste Brassica nigra meal (BNM) as a potent protein flocculant Optimization for vegetable oil refinery condensate effluent (VORCE) treatment was done. Interactive effects of the process parameters were analyzed using Design expert. Perikinetic theory for VORCE treatment follows second-order reaction rate with high K_c.

Anaerobic treatment of industrial wastewater using upflow anaerobic reactors is an extended trend due to its high efficiency and biogas production potential, but its implementation in some sectors is limited due to the complexity and toxicity of the wastewaters. In this study, a two-stage expanded granular sludge bed (EGSB) reactors system has been investigated at both bench and pilot scale for the treatment of complex and toxic real wastewater from a petrochemical industry. The effect of different operational parameters including organic loading rate (OLR), hydraulic retention time (HRT) and influent characteristics over COD removal and biogas production and composition have been studied. Additionally, biomass specific methanogenic activity (SMA) and wastewater toxicity have been evaluated after long-term operation. Optimum total HRT of 24 h has been determined resulting in total COD and SO₄ ^2- removal of 56.30 ± 5.25% and 31.68 ± 14.71%, respectively, at pilot scale, and average biogas production of 93.47 ± 34.92 NL/day with 67.01 ± 10.23 %CH₄ content and 5210.11 ± 6802.27 ppmv of H₂S. SMA and toxicity tests have confirmed inhibitory and toxic effects of wastewater over anaerobic biomass with average maximum inhibition of 65.34% in the unacclimated anaerobic inoculum while chronic toxicity produced a decrease of an order of magnitude in SMA after 600 days of operation. This study demonstrates the feasibility of applying an anaerobic treatment to this wastewater using EGSB reactors between a 0.97-1.74 gCOD/L/day OLR range. Nonetheless, periodic reinoculation would be necessary for long-term operation due to chronic toxicity of the wastewater exerted on the anaerobic biomass. PRACTITIONER POINTS: A two-stage EGSB reactors system has been operated at bench and pilot scale to treat complex and toxic petrochemical wastewater. Optimal total HRT of 24 h resulted in average COD removal ranging from 40% to 60%. SMA and toxicity tests have been performed to study long-term acclimation, detecting an activity depletion of an order of magnitude.

Because of its low-lying location, urbanization, and inadequate infrastructure, Jakarta (Indonesia) has experienced an increase in annual flooding events, rising from an average of five significant floods per year in the 1990s to over 20 annually (2010-2020). With climate change exacerbating extreme weather events, Jakarta encounters escalating risks of flooding. Although the recurrent flooding is exacerbated by non-point source (NPS) of pollution such as urban runoff and agricultural discharge that contribute to 40% of total pollutants leading to flood-related issues in Jakarta, none has investigated this research gap. To reflect its novelty, this work explores the implications of climate change on the annual flooding in Jakarta by focusing on NPS and analyzes their impacts from social perspectives. This work also underscores the implications of flooding on livelihoods, health, and social cohesion in Jakarta. Focus group discussion with affected residents was used to shed light on the coping strategies employed in response to recurrent floods, ranging from community-based initiatives to reliance on informal networks. The empirical findings show that the implications of flooding extend beyond physical damages. Displacement of communities, loss of livelihoods, disruption of essential services, and increased health risks are among the social impacts experienced by local residents. Vulnerable populations, including low-income communities residing in informal settlements, bear their consequences. Economic losses from flooding amount to USD 500 million annually, impacting over 1 million residents. However, recent interventions have led to a 15% reduction in peak flood levels and a 20% reduction in flood duration in affected areas. Community resilience has also improved, with a 25% increase in flood insurance coverage and a 20% rise in community response initiatives. Overall, this study highlights that climate change exacerbates annual flooding in Jakarta, significantly impacting vulnerable communities through NPS pollution. Addressing the challenges requires integrated approaches combining effective pollution control, resilient infrastructure, and community engagement to mitigate social and long-term environmental impacts. PRACTITIONER POINTS: Climate-induced flooding disproportionately affects vulnerable communities in Jakarta. Non-point source pollution from urban runoff contributes to the severity of flooding in Jakarta. Waterborne diseases, disruption of livelihoods, and reduced access to clean water are major concerns identified in the study. The study highlights the importance of community-based adaptation strategies to mitigate the impact of flooding and pollution.