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.

A water resource recovery facility sited in a region at a high elevation has experienced the effects of over-designing its blowers. In this case study, we used off-gas analysis and site-specific power tariffs to quantify actual process loading and air requirements, and we quantitatively evaluated various options for blower replacement or upgrade. Off-gas analysis mapped the oxygen uptake rate at the surface of the tank, suggesting that the tanks were not evenly loaded across their sections. The local cost of energy directly affects the return on the investment calculation and limits the available solutions. The payback of partial or complete blower replacement may not be justified even in the event of excessive aeration, and the sequencing of aeration system improvements including diffuser replacement, process controls, and blower modifications should be evaluated contemporaneously. PRACTITIONER POINTS: Off-gas analysis can be used to evaluate process loading imbalances by mapping the oxygen uptake rate. Alpha factors from off-gas testing are used in process models to evaluate air requirements and blower air demand. Comparative evaluation of blowers must be done considering the net present value of the status quo, upgrades, or replacement.

Biofilm modeling is inherently complex, often requiring multiple assumptions and simplifications. In biofilm modeling, default or literature-based values in biofilm systems are usually used to estimate biofilm parameters, including boundary layer, biofilm density, thickness, attachment, and detachment rates. This study aimed to characterize and model the biofilm of a specific rope-type fixed media system, removing carbon and total inorganic nitrogen, coupled with sensitivity analysis. Among the five model parameters, the sensitivity analysis of this study showed that boundary layer thickness is the most influential parameter for predicting effluent ammonia and nitrate concentrations, and biofilm density is most sensitive with respect to effluent chemical oxygen demand (COD). The least sensitive parameter is the detachment rate. Based on the calculated mean absolute error (MAE) and root mean squared error (RMSE), the calibrated BioCord fixed-film reactor (BFFR) model accurately predicted effluent ammonium and dissolved oxygen (DO) in the continuously aerated bench-scale reactor (R1) and failed to predict well in the intermittently aerated bench-scale reactor (R2). RMSE values calculated for NH₃-N and DO in R1 are 0.95 and 0.53 mg/L, respectively. In the BioCord pilot plant's case, ammonium-N predicted by the model fit the measured values well, while it overpredicted DO concentrations. PRACTITIONER POINTS: Fixed biofilm BioCord reactors were studied for primary effluent treatment. A methodology was developed to characterize biofilms. Boundary layer thickness is the most influential parameter for predicting effluent ammonia and nitrate concentrations. Biofilm density is the most sensitive parameter with respect to effluent COD. The calibrated BFFR model can predict effluent ammonium, nitrite, and nitrate-nitrogen.

This review presents a comprehensive analysis of current research on biological treatment processes for removing pharmaceutical compounds (PhCs) from wastewater. Unlike previous studies on this topic, our study specifically delves into the effectiveness and drawbacks of various treatment approaches such as traditional wastewater treatment facilities (WWTP), membrane bioreactors (MBRs), constructed wetlands (CW), and moving bed biofilm reactors (MBBR). Through the examination and synthesis of information gathered from more than 200 research studies, we have created a comprehensive database that delves into the effectiveness of eliminating 19 particular PhCs, including commonly studied compounds such as acetaminophen, ibuprofen, diclofenac, naproxen, ketoprofen, indomethacin, salicylic acid, codeine, and fenoprofen, amoxicillin, azithromycin, ciprofloxacin, ofloxacin, tetracycline, atenolol, propranolol, and metoprolol. This resource provides a depth and scope of information that was previously lacking in this area of study. Notably, among these pharmaceuticals, azithromycin demonstrated the highest removal rates across all examined treatment systems, with the exception of WWTPs, while carbamazepine consistently exhibited the lowest removal efficiencies across various systems. The analysis showcases the diverse results in removal efficiency impacted by factors such as system configuration, operation specifics, and environmental circumstances. The findings emphasize the critical need for continued innovation and research, specifically recommending the integration of advanced oxidation processes (AOPs) with existing biological treatment methods to improve the breakdown of recalcitrant compounds like carbamazepine. PRACTITIONER POINTS: Persistent pharmaceuticals harm aquatic ecosystems and human health. Biological systems show varying pharmaceutical removal efficiencies. Enhancing HRT and SRT improves removal but adds complexity and costs. Tailored treatment approaches needed based on contaminants and conditions.

Pyrolysis has been identified as a possible thermal treatment process for reducing perfluoroalkyl and polyfluoroalkyl substances (PFAS) from wastewater solids, though off-gas from the pyrolysis unit can still be a source of PFAS emissions. In this work, the fate of PFAS through a laboratory-scale pyrolysis unit coupled with a thermal oxidizer for treatment of off-gasses is documented. Between 91.5% and >99.9% reduction was observed through the entire system for specific compounds based on targeted analyses. Overall, the pyrolysis and thermal oxidizer system removed 99.4% of the PFAS moles introduced. Furthermore, shorter chain variants comprised the majority of reportable PFAS in the thermal oxidizer flue gas, indicating the longer chain compounds present in the dried biosolids fed to pyrolyzer decompose through the system. PRACTITIONER POINTS: Thermal oxidation is a promising treatment technology for exhaust systems associated with thermal biosolids treatments. Thermal oxidation demonstrated significant degradation capabilities, with gas phase emissions comprising only 0.200% of initial PFAS concentrations to the system. Short-chain PFAS made up a higher percent of thermal oxidizer emissions, ranging between 54.4% and 79.5% of PFAS in the exhaust on a molar basis. The possibility of recombinant PFAS formation and partial thermal decomposition of PFAS in thermal oxidation is a needed area of research.

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.

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.

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.