Search Results (2,811)

The anaerobic ammonium oxidation (anammox) process offers potential for saline wastewater treatment but is hindered by salt inhibition. This study investigates the salt tolerance mechanisms of granular (R1), biofilm-carrier (R2), and floccular (R3) sludge in up-flow anaerobic sludge blanket (UASB) reactors under 0–20 g/L NaCl. Granular sludge outperformed other biomass types, maintaining >90% ammonia nitrogen (${\mathrm{NH}}_4^{+}$-N) removal at 20 g/L NaCl due to structural stability and extracellular polymeric substances (EPS) adaptation (shift from hydrophobic proteins to polysaccharides). Microbial analysis revealed a transition from Planctomycetes/Proteobacteria to salt-tolerant Pseudomonadota, with Candidatus_Kuenenia replacing Candidatus_Brocadia as the dominant anaerobic ammonium oxidation bacteria (AnAOB) (reaching 14.5% abundance in R1). Genetic profiling demonstrated coordinated nitrogen metabolism: Hzs/Hdh inhibition (>85%) and NirBD/NrfAH activation (0.23%) elevated ${\mathrm{NH}}_4^{+}$-N, while NarGIV/NapA decline (1.10%→0.58%) increased nitrate nitrogen (${\mathrm{NO}}_3^{-}$-N). NxrB/NirSK maintained low nitrite nitrogen (${\mathrm{NO}}_2^{-}$-N), and GltBD upregulation (0.43%) enhanced osmoregulation. These findings underscore the superior resilience of granular sludge under high salinity, linked to microbial community shifts and metabolic adaptations. This study provides critical insights for optimizing anammox processes in saline environments, emphasizing the importance of biomass morphology and microbial ecology in mitigating salt inhibition. Full article

Phenol is a refractory organic pollutant that is difficult to degrade in wastewater treatment, and efficiently and stably degrading phenol presents a significant challenge. In this study, iron-doped humic acid-based nitrogen–carbon materials were prepared to activate peroxymonosulfate (PMS) for the degradation of phenol. The Fe-N-C/PMS system achieved a phenol degradation rate of 99.71%, which follows a first-order kinetic model, with the reaction rate constant of 0.1419 min⁻¹. The phenol degradation rate remained above 92% in inorganic anions (Cl⁻, SO₄²⁻, HCO₃⁻) and humic acid and the system maintained a 100% phenol removal rate over a wide pH range (3–9). The iron in the catalyst predominantly exists in the forms of Fe⁰ and Fe₃C, and Fe⁰, Fe²⁺/Fe³⁺ are the main active sites that promote PMS activation during the reaction. Additionally, Fe-N-C has a large specific surface area (1041.36 m²/g). Quenching experiments and electron spin resonance (ESR) spectroscopy detected the active free radicals in the Fe-N-C/PMS system: SO₄^•−, •OH, O₂^•−, and ¹O₂. The mechanism for phenol degradation was discussed, involving radical pathways (SO₄^•−, •OH, O₂^•−) and the non-radical pathway (¹O₂), in the Fe-N-C/PMS system activated by Fe⁰, Fe²⁺/Fe³⁺, sp² hybridized carbon, C-O/C-N, C=O, and graphitic nitrogen active sites. This study provides new insights into the synthesis of efficient carbon-based catalysts for phenol degradation and water remediation. Full article

Recent concerns regarding artificial intelligent (AI) technologies have spurred studies into improving wastewater treatment efficiency and identifying low-carbon processes. Treating one cubic meter of wastewater necessarily consumes a certain amount of chemicals and energy. Approximately 20% of the total chemical consumption is attributed to phosphorus and nitrogen removal, with the exact proportion varying based on treatment quality and facility size. To promote sustainability in wastewater treatment plants (WWTPs), there has been a shift from traditional control systems to AI-based strategies. Research in this area has demonstrated notable improvements in wastewater treatment efficiency. This review provides an extensive overview of the literature published over the past decades, aiming to advance the ongoing discourse on enhancing both the efficiency and sustainability of chemical dosing systems in WWTPs. It focuses on AI-based approaches utilizing algorithms such as neural networks and fuzzy logic. The review encompasses AI-based wastewater treatment processes: parameter analysis/forecasting, model development, and process optimization. Moreover, it summarizes six promising areas of AI-based chemical dosing, including acid–base regents, coagulants/flocculants, disinfectants/disinfection by-products (DBPs) management, external carbon sources, phosphorus removal regents, and adsorbents. Finally, the study concludes that significant challenges remain in deploying AI models beyond simulated environments to real-world applications. Full article

The sulfur content in crude oil varies between 1000 and 30,000 ppm (parts per million), meaning that its removal from fuels requires significant technical and economic effort. Growing concern about pollution, accompanied by stricter environmental regulations, have led to the development of strategies to mitigate the negative effects of sulfur-containing compounds in petroleum, which can cause malfunctions in manufacturing plants and refineries, such as causing catalyst poisoning in catalytic reforming equipment and sulfur dioxide emissions that have been generated through the use of fuels in vehicles, vessels, furnaces, etc. Sulfur is one of the main pollutants found in diesel and gasoline. The hydrodesulfurization method removes sulfur and nitrogen-containing compounds from diesel and gasoline, ensuring compliance with current environmental regulations established for the import and export of fuels. In addition, hydrodesulfurization contributes to reducing sulfur dioxide and nitrogen dioxide emissions into the environment and prevents corrosion, which increases safety for both manufacturing plants and end consumers. This situation is analyzed in this paper, considering Mexican legislation about fuels and their usage. Sulfur is an important pollutant contained in diesel and gasoline fuels; it exhibits lubricant properties, helping to reduce the maintenance intervals of the machines and increase engine life. Therefore, its removal from fuel blends is a topic of great scientific interest as researchers look for different lubricant alternatives, which are relevant to motor vehicle engines. Full article

The effective removal of oxygen (O), nitrogen (N), sulfur (S), and oxide inclusions from superalloy reverts is crucial for enhancing service life and achieving cost efficiency. However, refining DZ125 superalloy presents particular challenges, as conventional processes prove ineffective against hafnium (Hf) oxides. This study introduces an innovative purification method combining droplet electron-beam melting (EBM) with centrifugal directional solidification. Through this advanced EBM technique, we successfully produced ultrapure DZ125 superalloy with nitrogen content reduced below 5 ppm and total O + N + S content below 10 ppm. Most significantly, the process nearly eliminated Hf oxides from the reverts, meeting the stringent purity standards for DZ125 superalloy. We conducted a comprehensive analysis of inclusion morphology and composition in three distinct regions: the top slag layer, final solidification zone, and interior section of the ingot processed at varying EBM power levels. Our findings reveal that MC-type carbides at the slag–crucible interface were formed. There are HfO₂, TaC, and Al₂O₃ in the final solidification zone, with notable encapsulation of HfO₂ particulates within Al₂O₃ particles; and few HfO₂ and Al₂O₃ inclusions exist in the ingot interior. It is also found that increasing EBM power from 36 kW to 46 kW significantly improved impurity removal efficiency, as evidenced by substantial reductions in both inclusion quantity and size. This enhanced purification stems from two primary mechanisms: (1) flotation of inclusions during EBM melting, facilitated by Marangoni convection, droplet stirring effects, and centrifugal forces generated by ingot rotation; and (2) decomposition of stable oxides enabled by the high-energy density characteristic of EBM and high-vacuum processing environment. This combined approach demonstrates superior capability in overcoming the limitations of traditional refining methods, particularly for challenging Hf oxide removal, while establishing an effective pathway for superalloy revert recycling. Full article

The COVID-19 pandemic has caused a surge in the use of disposable surgical masks, primarily composed of polypropylene (>86% carbon), whose improper disposal contributes to persistent microplastic pollution. In alignment with circular economy principles, this study explores the valorization of surgical masks into carbonaceous adsorbent materials (ACMs) for dye removal from water. The masks were chemically treated with concentrated H₂SO₄ at 85 °C for 2 h and subsequently activated with air (400 °C), CO₂, or steam (800 °C, 1 h). The resulting ACMs were characterized by SEM, FT-IR, nitrogen adsorption at −196 °C, and pH of the aqueous carbon suspension (pH_Sus, 1.96–9.25). CO₂ and steam activation yielded the highest surface areas (525 and 632 m²·g⁻¹, respectively). FT-IR confirmed the introduction of sulfonic groups, enhancing dye interactions. Adsorption tests using methylene blue (MB), methyl orange (MO), and orange G (OG) in ultrapure and river water showed removal efficiencies up to 100% for MB with ACM-WV and ~94% with ACM. All dyes followed pseudo-second-order kinetics. These findings demonstrate that surgical mask waste can be effectively transformed into high-value adsorbents for water treatment applications. Full article

The environmental problems brought about by factory-based aquaculture have become increasingly prominent. Reducing nitrogen and phosphorus concentrations in tailwater has become the key to tailwater management. In order to assess the potential of microalgae in removing nitrogen and phosphorus ions from aquaculture wastewater, four microalgae species, i.e., Chlorella sp., Dicrateria zhanjiangensis, Nitzschia closterium minutissima, and Platymonas subcordiformis, were used in this study, and their growth and nitrogen and phosphorus removal rates in four nutrient concentrations of simulated aquaculture wastewater were systematically evaluated. After 15 days of cultivation, the cell counts of all four types of microalgae increased. Three species, i.e., Chlorella sp., N. closterium minutissima, and P. subcordiformis, grew best in high PO₄³⁻ and low NH₄⁺ medium, whereas D. zhanjiangensis possessed best growth in low PO₄³⁻ and high NH₄⁺ medium. The removal rate of PO₄³⁻, NH₄⁺, NO₃⁻, and NO₂⁻ by four microalgae species exceeded 82.64%, 89.06%, 59.27%, and 42.15%, respectively, even though the four microalgae had different performance in the removal of nitrogen and phosphorus. All microalgae in the low-phosphorus groups removed PO₄³⁻ at significantly lower rates than those in the high–phosphorus groups, while high NH₄⁺ removal rates were observed in all four microalgae groups. Moreover, in phosphorus-limited conditions, four microalgae exhibit lower removal rates of NO₃⁻ when nitrogen content was high. The chlorophyll a contents of microalgae in four culture media strictly corresponded to their final cell densities. P. subcordiformis exhibited the highest intracellular polysaccharide accumulation in high PO₄³⁻ and low NH₄⁺ type medium, whereas D. zhanjiangensis demonstrated the strongest protein synthesis capacity in high PO₄³⁻ and low NH₄⁺ medium. The activities of acid phosphatase in all microalgae were higher under phosphorus–deficient conditions than phosphorus-sufficient conditions. Our results might provide useful references for microalgae selection in the treatment of different aquaculture wastewater conditions. Full article

The aim of this study was to evaluate the treatment efficiency and applicability of using textile-based mats as roof biofilters on urban buildings for purifying preliminary treated wastewater (PTW) collected from a three-chamber septic tank. Therefore, a pilot plant with a 15° pitched wooden roof and two tracks for laying two mats made of different materials—polypropylene (PP), designated as Mat 1, and polyethylene terephthalate (PET), designated as Mat 2—was constructed at ground level under outdoor conditions. The plant was operated in parallel for a period of 455 days. Significant differences (p < 0.05) were observed in the results of the mass removal efficiencies between the two mats, with Mat 1 achieving mean removals of five-day biochemical oxygen demand (BOD₅), chemical oxygen demand (COD), ammonium-nitrogen (NH₄-N), and total nitrogen (TN) of 85%, 73%, 75%, and 38%, respectively, and Mat 2 achieving comparatively higher removals of 97%, 84%, 90%, and 57%, respectively. The mean concentrations of BOD₅ and COD at the outflow of both mats met the minimum water quality requirements for discharge and successfully met the minimum water quality class B for agricultural reuse. However, the comparatively low mean E. coli removal efficiencies of 2.0 and 2.4 log-units in Mat 1 and Mat 2, respectively, demonstrate the need for an effluent disinfection system. Highly efficient mass removal efficiencies were observed in the presence of dense vegetation on the mats, which may lead to a potential improvement in the urban climate through high daily evapotranspiration. Overall, this study demonstrates the potential for using lightweight, textile-based mats on rooftops to efficiently treat PTW from urban buildings, offering a promising decentralized wastewater management approach for climate-resilient cities. Full article

Nitrogen oxides (NO_x) emissions pose environmental and health risks. Selective catalytic reduction (SCR) is effective for NO_x removal, and using CO as a reductant can eliminate both NO_x and CO. This study explores CuCe catalysts on SiO₂, Al₂O₃, and TiO₂ for CO-SCR. Results show catalytic activity relates to the synergy between lattice oxygen and CuCe species. TiO₂ enhances this interaction, promoting Cu⁺ and lattice oxygen for NO adsorption and dissociation. The CuCe/TiO₂ catalyst achieves 100% NO conversion at 300 °C and 40.2% at 100 °C, indicating excellent low-temperature performance. These findings are valuable for developing efficient SCR catalysts. Full article

Microalgae offer a sustainable alternative for wastewater treatment by simultaneously removing pollutants and producing biomass of potential value. This study evaluated five species—Haematococcus pluvialis, Chlorella vulgaris, Chlamydomonas sp., Anabaena variabilis, and Scenedesmus sp.—in three undiluted food and beverage industry effluents from Mexico: nejayote (alkaline wastewater generated during corn nixtamalization for tortilla production), tequila vinasses (from tequila distillation), and cheese whey (from cheese making). Strains were adapted through UV mutagenesis and gradual acclimatization to grow without freshwater dilution. Bioremediation efficiency was assessed via reductions in chemical oxygen demand (COD), total nitrogen (TN), and total phosphates (TPO₄). C. vulgaris achieved complete TN and TPO₄ removal and 90.2% COD reduction in nejayote, while A. variabilis reached 81.7% COD and 79.3% TPO₄ removal in tequila vinasses. In cheese whey, C. vulgaris removed 55.5% COD, 53.0% TN, and 35.3% TPO₄. These results demonstrate the feasibility of microalgae-based systems for treating complex agro-industrial wastewaters, contributing to sustainable and circular wastewater management. Full article

Static chambers combined with isotopic (δ¹³C and δ¹⁸O) and flux (CO₂ and CH₄) measurements were applied, to explore the effects of mosses and long-term nitrogen (N) addition at two levels (22.5 and 45 kg N ha⁻¹ yr⁻¹) on δ¹³C and δ¹⁸O values of respired CO₂ across three autumn seasons under a temperate forest (northeastern China) and their relationships with CO₂ and CH₄ fluxes and with soil properties. Mosses generally depleted δ¹³C and enriched δ¹⁸O in respired CO₂, likely by altering soil microenvironments or/and substrate use. The effect of N addition on the δ¹³C and δ¹⁸O values of respired CO₂ varied with years, and its interaction with mosses had no effects on the isotopic values. The removal of mosses decreased CO₂ fluxes and the addition of N at a high dose increased CH₄ fluxes. The δ¹³C and δ¹⁸O values of respired CO₂ decreased at soil moisture levels below and above an optimum, and the moisture-dependent effect became more pronounced for the δ¹⁸O than for the δ¹³C. The results of structural equation modeling showed that 70% of the variability of δ¹³C values of respired CO_2. was accounted for by the N addition, mosses, soil moisture, and CH₄ and CO₂ fluxes, while only 22% of the variability of δ¹⁸O values of respired CO₂ was explained by these factors. The results highlight that moss–soil interaction drives the isotopic shifts, which is modulated by N availability. Soil moisture regulates the δ¹⁸O values of respired CO₂, but its drivers remain poorly understood. Future work should target processes influencing the δ¹⁸O shifts of respired CO₂ and deep soil property interactions. Full article

Controlling dissolved nitrogen is critical to meeting increasingly stringent steel quality targets, yet the variable kinetics of gas absorption and removal across production stages complicate real-time decision-making. Leveraging a total of 291 metal samples, the research applied ordinary least squares (OLS) regression, enhanced by cointegration diagnostics, to develop four stage-specific models covering pig iron after desulfurization, crude steel in the basic oxygen furnace (BOF) before tapping, steel at the beginning and end of secondary metallurgy processing. Predictor selection combined thermodynamic reasoning and correlation analysis to produce prediction equations that passed heteroscedasticity, normality, autocorrelation, collinearity, and graphical residual distribution tests. The k-fold cross-validation method was also used to evaluate models’ performance. The models achieved an adequate accuracy of 77.23–83.46% for their respective stages. These findings demonstrate that statistically robust and physically interpretable regressions can capture the complex interplay between kinetics and the various processes that govern nitrogen pick-up and removal. All data are from U. S. Steel Košice, Slovakia; thus, the models capture specific setup, raw materials, and production practices. After adaptation within the knowledge transfer, implementing these models in process control systems could enable proactive parameter optimization and reduce laboratory delays, ultimately minimizing excessive nitrogenation in finished steel. Full article

Drilling fluid plays a critical role in drilling engineering. With the deepening implementation of clean production concepts and increasingly stringent environmental regulations, the treatment standards for drilling wastewater at operational sites have been significantly elevated. In response to the characteristics of high oil content, high COD, high chromaticity, high ammonia nitrogen, and total phosphorus content in drilling, the use of fine bubbles to improve gas utilization efficiency and mass transfer effect, combined with ozone gas, is aimed at degrading difficult-to-degrade high-molecular-weight organic compounds, aiming to solve the problems of high viscosity and high oil content in drilling fluids returned from offshore platforms. Indoor simulation experiments have shown that by using ozone fine bubble technology to treat drilling fluids, the viscosity reduction rate can reach over 29%, and the oil removal rate can reach 40%. Ozone fine bubble technology has significant viscosity reduction and oil removal effects on high viscosity drilling fluids. Full article

Accurate monitoring of canopy nitrogen content in wolfberry (Lycium barbarum L.) is essential for optimizing fertilization management, improving crop yield, and promoting sustainable agriculture. However, the sparse, architecturally complex canopy of this perennial shrub—featuring coexisting branches, leaves, flowers, and fruits across maturity stages—poses significant challenges for canopy spectral-based nitrogen assessment. This study integrates methods across canopy spectral acquisition, transformation, feature spectral selection, and model construction, and specifically explores the potential of hyperspectral remote sensing, integrated with spectral mathematical transformations and machine learning algorithms, for predicting canopy nitrogen content in wolfberry. The overarching goal is to establish a feasible technical framework and predictive model for monitoring canopy nitrogen in wolfberry. In this study, canopy spectral measurements are systematically collected from densely overlapping leaf regions within the east, south, west, and north orientations of the wolfberry canopy. Spectral data undergo mathematical transformation using first-derivative (FD) and continuum-removal (CR) techniques. Optimal spectral variables are identified through correlation analysis combined with Recursive Feature Elimination (RFE). Subsequently, predictive models are constructed using five machine learning algorithms and three linear regression methods. Key results demonstrate that (1) FD and CR transformations enhance the correlation with nitrogen content (max correlation coefficient (r) = −0.577 and 0.522, respectively; p < 0.01), surpassing original spectra (OS, −0.411), while concurrently improving model predictive capability. Validation tests yield maximum R² values of 0.712 (FD) and 0.521 (CR) versus 0.407 for OS, confirming FD’s superior performance enhancement. (2) Nonlinear machine learning models, by capturing complex canopy-light interactions, outperform linear methods and exhibit superior predictive performance, achieving R² values ranging from 0.768 to 0.976 in the training set—significantly outperforming linear regression models (R² = 0.107–0.669). (3) The Random Forest (RF) model trained on FD-processed spectra achieves the highest accuracy, with R² values of 0.914 (training set) and 0.712 (validation set), along with an RPD of 1.772. This study demonstrates the efficacy of spectral transformations and nonlinear regression methods in enhancing nitrogen content estimation. It establishes the first effective field monitoring strategy and optimal predictive model for canopy nitrogen content in wolfberry. Full article

Two nitrogen-modified mesoporous MCM-41-type silicas were synthesized by the sol–gel route and post-grafting surface modification procedure, obtaining an aminopropyl-modified MCM-41 (denoted MCM-41-N) and an aminoethyl-aminopropyl-modified MCM-41 (denoted MCM-41-NN). Hexavalent chromium removal from acidified water by adsorption and reduction to Cr(III) on the solid mesophases was analyzed. The modified silicas were characterized by powder X-ray diffraction (XRD), Fourier transformed infrared spectra (FT-IR), nitrogen adsorption–desorption measurements at −196 °C, X-ray photoelectron spectroscopy (XPS), ²⁹Si solid state Nuclear Magnetic Resonance (²⁹Si-RMN), and thermogravimetric analysis (TGA). Both samples exhibited very high capacities for decreasing Cr(VI) concentrations in water, according to the Langmuir isotherm model: 129.9 mg·g⁻¹ for MCM-41-N and 133.3 mg·g⁻¹ for MCM-41-NN. The chromium speciation in the supernatant after 24 h indicates that MCM-41-N had a higher capacity to reduce Cr(VI) to the less toxic Cr(III) species than MCM-41-NN: 92.9% vs. 72.5% when the initial Cr(VI) concentration was 10 mg·g⁻¹. These differences were related to the different capacity of nitrogen atoms in MCM-41-N and MCM-41-NN to interact with the surrounding surface silanols which are required for the chemical reduction in the hexavalent species to take place, as evidenced by FT-IR and XPS analysis. Also, the Cr(III)/Cr(VI) atomic ratios on the solid’s surfaces were higher for MCM-41-N. These results highlight the characteristics that nitrogen atoms incorporated into silica matrices must possess in order to maximize the transformation of Cr(VI) into the trivalent species, thereby reducing the generation of toxic waste harmful to living organisms. Full article