By integrating the successful case of the European Union emissions trading system, this study proposes a water emissions trading system, a novel method of reducing water pollution. Assuming that upstream governments allocate initial quotas to upstream businesses as the compensation standard, this approach defines the foundational principles of market trading mechanisms and establishes a robust watershed ecological compensation model to address challenges in water pollution prevention. To be specific, the government establishes a reasonable initial quota for upstream enterprises, which can be used to limit the emissions of upstream pollution. When enterprises exceed their allocated emissions quota, they face financial penalties. Conversely, these emissions rights can be transformed into profitable assets by participating in the trading market as a form of ecological compensation. Numerical simulations demonstrate that various pollutant emissions from upstream businesses will have various effects on the profits of other businesses. Businesses in the upstream region received reimbursement from the assigned emission rights through the market mechanism, demonstrating that ecological compensation for the watershed can be achieved through the market mechanism. This novel market trading system aims at controlling emissions management from the perspectives of individual enterprises and ultimately optimizing the aquatic environment.

In our contemporary world, demanding sustainable resource management, the study focuses on innovative fast flow channel designs. It investigates their efficacy in reducing flow kinetic energy, aiming to optimize water and energy management and diminish flood risks. Employing diverse methodologies, it analyzes and develops these designs, proving their substantial impact on stream energy management. These innovations not only enhance energy efficiency but also mitigate risks associated with excess kinetic energy, promoting safer stream management. This research significantly contributes to fluid dynamics and engineering, deepening the understanding of kinetic energy control in flows and offering potential solutions for water supply, environmental sustainability, and infrastructure safety challenges.

Accurate calculation of flow discharge for sluice gates is essential in irrigation, water supply, and structure safety. The measurement of discharge with the requirement of distinguishing flow regimes is not conducive to application. In this study, a novel approach that considers both free and submerged flow was proposed. The energy-momentum method was employed to derive the coefficient of discharge. Subsequently, the discharge coefficient was determined through the experiment which was performed on the physical model of a vertical sluice gate with a broad-crested weir. Feature engineering, incorporating dimensional analysis, feature construction, and correlation-based selection were performed. The best subset regression method was employed to develop regression equations of the discharge coefficient with the generated features. The derived formula was applied to compute the discharge coefficient in the vertical sluice gate and determine the flow discharge. The accuracy of adopted method was assessed by comparing it with recent studies on submerged flow, and the results demonstrate that the developed approach achieves a high level of accuracy in calculating flow discharge. The coefficient of determination for the calculated flow rate is 0.993, and the root mean square percentage error is 5.04%.

This study develops a novel double-loop contraction and C value sorting selection-based shrinkage frog-leaping algorithm (double-contractive cognitive random field [DC-CRF]) to mitigate the interference of complex salts and ions in seawater on the ultraviolet-visible (UV-Vis) absorbance spectra for chemical oxygen demand (COD) quantification. The key innovations of DC-CRF are introducing variable importance evaluation via C value to guide wavelength selection and accelerate convergence; a double-loop structure integrating random frog (RF) leaping and contraction attenuation to dynamically balance convergence speed and efficiency. Utilizing seawater samples from Jiaozhou Bay, DC-CRF-partial least squares regression (PLSR) reduced the input variables by 97.5% after 1,600 iterations relative to full-spectrum PLSR, RF-PLSR, and CRF-PLSR. It achieved a test R2 of 0.943 and root mean square error of 1.603, markedly improving prediction accuracy and efficiency. This work demonstrates the efficacy of DC-CRF-PLSR in enhancing UV-Vis spectroscopy for rapid COD analysis in intricate seawater matrices, providing an efficient solution for optimizing seawater spectra.

The study evaluated the impact of treated wastewater on plant growth through the use of hyperspectral and fluorescence-based techniques coupled with classical biomass analyses, and assessed the potential of reusing treated wastewater for irrigation without fertilizer application. Cherry tomato (Solanum lycopersicum) and cabbage (Brassica oleracea L.) were irrigated with tap water (Tap), secondary effluent (SE), and membrane effluent (ME). Maximum quantum yield of photosystem II (Fv/Fm) of tomato and cabbage was between 0.78 to 0.80 and 0.81 to 0.82, respectively, for all treatments. The performance index (PI) of Tap/SE/ME was 2.73, 2.85, and 2.48 for tomatoes and 4.25, 3.79, and 3.70 for cabbage, respectively. Both Fv/Fm and PI indicated that the treated wastewater did not have a significant adverse effect on the photosynthetic efficiency and plant vitality of the crops. Hyperspectral analysis showed higher chlorophyll and nitrogen content in leaves of recycled water-irrigated crops than tap water-irrigated crops. SE had 10.5% dry matter composition (tomato) and Tap had 10.7% (cabbage). Total leaf count of Tap/SE/ME was 86, 111, and 102 for tomato and 37, 40, and 42 for cabbage, respectively. In this study, the use of treated wastewater did not induce any photosynthetic-related or abiotic stress on the crops; instead, it promoted crop growth.

In this research, ascorbic acid (AA) was used to enhance Fe(II)/Fe(III)-activated permonosulfate (PMS) systems for the degradation of fluoranthene (FLT). AA enhanced the production of ROS in both PMS/Fe(II) and PMS/Fe(III) systems through chelation and reduction and thus improved the degradation performance of FLT. The optimal molar ratio in PMS/Fe(II)/AA/FLT and PMS/Fe(III)/AA/FLT processes were 2/2/4/1 and 5/10/5/1, respectively. In addition, the experimental results on the effect of FLT degradation under different groundwater matrixes indicated that PMS/Fe(III)/AA system was more adaptable to different water quality conditions than the PMS/Fe(II)/AA system. SO4·- was the major reactive oxygen species (ROS) responsible for FLT removal through the probe and scavenging tests in both systems. Furthermore, the degradation intermediates of FLT were analyzed using gas chromatograph-mass spectrometry (GC-MS), and the probable degradation pathways of FLT degradation were proposed. In addition, the removal of FLT was also tested in actual groundwater and the results showed that by increasing the dose and pre-adjusting the solution pH, 88.8 and 100% of the FLT was removed for PMS/Fe(II)/AA and PMS/Fe(III)/AA systems. The above experimental results demonstrated that PMS/Fe(II)/AA and PMS/Fe(III)/AA processes have a great perspective in practice for the rehabilitation of FLT-polluted groundwater.

Hyperparameter tuning is an important process to maximize the performance of any neural network model. This present study proposed the factorial design of experiment for screening and response surface methodology to optimize the hyperparameter of two artificial neural network algorithms. Feed-forward neural network (FFNN) and radial basis function neural network (RBFNN) are applied to predict the permeate flux of palm oil mill effluent. Permeate pump and transmembrane pressure of the submerge membrane bioreactor system are the input variables. Six hyperparameters of the FFNN model including four numerical factors (neuron numbers, learning rate, momentum, and epoch numbers) and two categorical factors (training and activation function) are used in hyperparameter optimization. RBFNN includes two numerical factors such as a number of neurons and spreads. The conventional method (one-variable-at-a-time) is compared in terms of optimization processing time and the accuracy of the model. The result indicates that the optimal hyperparameters obtained by the proposed approach produce good accuracy with a smaller generalization error. The simulation results show an improvement of more than 65% of training performance, with less repetition and processing time. This proposed methodology can be utilized for any type of neural network application to find the optimum levels of different parameters.

The algal-bacterial shortcut nitrogen removal (ABSNR) process can be used to treat high ammonia strength wastewaters without external aeration. However, prior algal-bacterial SNR studies have been conducted under fixed light/dark periods that were not representative of natural light conditions. In this study, laboratory-scale photo-sequencing batch reactors (PSBRs) were used to treat anaerobic digester sidestream under varying light intensities that mimicked summer and winter conditions in Tampa, FL, USA. A dynamic mathematical model was developed for the ABSNR process, which was calibrated and validated using data sets from the laboratory PSBRs. The model elucidated the dynamics of algal and bacterial biomass growth under natural illumination conditions as well as transformation processes for nitrogen species, oxygen, organic and inorganic carbon. A full-scale PSBR with a 1.2 m depth, a 6-day hydraulic retention time (HRT) and a 10-day solids retention time (SRT) was simulated for treatment of anaerobic digester sidestream. The full-scale PSBR could achieve >90% ammonia removal, significantly reducing the nitrogen load to the mainstream wastewater treatment plant (WWTP). The dynamic simulation showed that ABSNR process can help wastewater treatment facilities meet stringent nitrogen removal standards with low energy inputs.

The conventional building drainage system was constructed based on the theory of two-phase flow involving water and air. However, the drainage system contained a more intricate three-phase flow, encompassing water, air, and solids, which was relatively overlooked in research. This study addressed the impact of solids on pressure fluctuations, air flow rates, and hydraulic jump fullness within the drainage system, considering three factors: the mass factor, cross-section factor, and viscosity. The investigation was conducted within a single-stack system using both experimental methods and CFD simulations. The findings revealed a positive correlation between both positive and negative pressures and above three factors. The mass factor and the cross-section factor had a more significant impact on the negative pressure of the system. The maximum growth rates of negative pressure extremes under different mass and cross-section factors reached 7.72 and 16.52%, respectively. In contrast, the viscosity of fecal sludge had a slightly higher effect on the positive pressure fluctuation of the drainage system, with the maximum growth rate of positive pressure extremes at 3.41%.

In this study, further treatment of coking wastewater treated in anoxic-oxic-membrane bioreactor (A2O-MBR) was investigated to meet the standards of the ministry by means of nanofiltration (NF) (with two different membranes and different pressures), microfiltration -powder activated carbon (MF-PAC) hybrid system and NF-PAC (with two different membranes and five different PAC concentrations) hybrid system. In addition to the parameters determined by the ministry, other parameters such as ammonium, thiocyanate (SCN-), hydrogen cyanide (HCN), dissolved organic carbon (DOC), dissolved inorganic carbon (DIC), color were also examined to evaluate the flux performance and treatment efficiency of the hybrid processes. According to the results, chemical oxygen demand (COD) in the NF process, COD and total cyanide (T-CN) in the MF-PAC process could not meet the discharge standards. As for the NF-PAC hybrid system, XN45 membrane met the discharge standards in all parameters (COD = 96±1.88 mg/L, T-CN =<0,02 mg/L, phenol =<0.05 mg/L), with a recovery rate of 78% at 0.5 g/L PAC concentration.