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Treating hazardous landfill leachate poses significant environmental challenges due to its complex nature. In this study, we propose a novel approach for enhancing the anaerobic digestion of landfill leachate using silver nanoparticles (Ag NPs) conjugated with eco-friendly green silica nanoparticles (Si NPs). The synthesized Si NPs and Ag@Si NPs were characterized using various analytical techniques, including transmission electron microscopy, X-ray diffraction, and Fourier-transform infrared spectroscopy. The anaerobic digestion performance of Si NPs and Ag@Si NPs was tested by treating landfill leachate samples with 50 mg/L of each NP. The results demonstrated an enhancement in the biogas production rate compared to the control phase without the nanocomposite, as the biogas production increased by 14% and 37% using Si NPs and Ag@Si NPs. Ag@Si NPs effectively promoted the degradation of organic pollutants in the leachate, regarding chemical oxygen demand (COD) and volatile solids (VS) by 58% and 65%. Furthermore, microbial analysis revealed that Ag@Si NPs enhanced the activity of microbial species responsible for the methanogenic process. Overall, incorporating AgNPs conjugated with eco-friendly green Si NPs represents a sustainable and efficient approach for enhancing the anaerobic digestion of landfill leachate., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The demand for paper and paper-based packaging has seen a massive increase in past years, resulting in accelerated deforestation to meet the rising demand, negatively impacting the environment, and there is a need to look towards different non-woody raw materials. Kraft pulping (KP) is widely used in paper making, for which the chemical dose, temperature, time, and energy required must be optimized, for which many insignificant experimental trials are performed. An effort is made to solve this problem by developing the regression equations with the help of Excel using One Factor at a Time Analysis (OFAT), followed by carrying out design of experiments (DoE) using orthogonal approach and regression analysis in Minitab software. Life cycle Assessment (LCA) using the Open-LCA software estimates the effect of chemicals and energy required during pulping on human health, ecosystem quality, and resource depletion. Using regression analysis, the equations for predicting kappa number, yield (%), total energy consumed, and mechanical properties of the paper sheet showed a good fit with an R 2 value in the range of 0.90-0.99. Apart from that, the mechanical properties, namely tensile index (41.43 Nm/g), tear index (6.96 mN m 2 /g), bending stiffness (0.5 mN m), and burst index (3.92 kPa m 2 /g) of the unbeaten sheet, were determined experimentally at optimized conditions. Based on the Open-LCA result, the optimized pulping conditions had less impact on human health, ecosystem quality, and resource depletion. Industries can use the model to predict the values of kappa number, yield, mechanical properties, and energy consumption without performing optimization experiments that may impact the industry's economy to a greater extent., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Free nitrous acid (FNA) is a critical metric for stabilization of ANAMMOX but can not be directly and immediately measured by sensors or chemical measurement method, which hinders the effective management and operation for ANAMMOX. This study focuses on FNA prediction using hybrid model based on temporal convolutional network (TCN) combined with attention mechanism (AM) optimized by multiobjective tree-structured parzen estimator (MOTPE), called MOTPE-TCNA. A case study in an ANAMMOX reactor is carried out. Results show that nitrogen removal rate (NRR) is highly correlated with FNA concentration, indicating that it can forecast the operational status by predicting FNA. Then, MOTPE successfully optimizes the hyperparameters of TCN, helping TCN achieve a high prediction accuracy, and AM furtherly improves model accuracy. MOTPE-TCNA obtains the highest prediction accuracy, whose R 2 value gets 0.992, increasing 1.71-11.80% compared to other models. As a deep neural network model, MOTPE-TCNA has more advantages than traditional machine learning methods in FNA prediction, which is beneficial to maintain the stable operation and easy control for ANAMMOX process., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Releases of hydrogen sulphide (H 2 S) and sulphur ions (S 2- ) through sulphate reduction in black-odorous waterbody is a great environmental health concern. Aquatic planting for blackening and odour controls has received great attention in research and practice. Nitrate concentration in black-odorous waterbody can vary significantly but little is known about the responses of aquatic plants on H 2 S and S 2- releases under different nitrate levels. This controlled laboratory study explored the changes of H 2 S and S 2- releases in simulated black-odorous waterbody planted with Vallisneria natans and artificial plants (control). V. natans growth was stimulated by additional nitrate (6.6 mg/L NO 3 - -N), resulting in an increase of dissolved oxygen (DO) and pH in overlying water and an 11.0% decrease in removal efficiency of chemical oxygen demand (COD). At relatively low nitrate level (i.e., 2.0 mg/L NO 3 - -N in the absence of additional nitrate), V. natans after the 48th day inhibited H 2 S and S 2- releases by 81.5% and 66.8%, respectively, and their inhibition efficiencies were improved to 95.7% and 98.8% by the presence of additional nitrate. Additional nitrate reduced the relative abundance of sulphate-reducing bacteria (SRB) in the sediments while increased the relative abundance of sulphur-oxidizing bacteria (SOB) and nitrate-reducing sulphur-oxidizing bacteria (NR-SOB) in the leaf biofilms of V. natans and artificial plants. Genus compositions in leaf biofilms showed host specificity. Pearson correlation analysis showed that DO, pH, and nitrate concentration had a positive correlation with the relative abundance of SOB (Aeromonas) and NR-SOB (Hydrogenophaga), while were negatively correlated with the relative abundance of SRB (MSBL7). These results indicated that V. natans under additional nitrate altered microbial community to be unfavourable for H 2 S and S 2- releases. This study clarified the inhibition of H 2 S and S 2- releases by aquatic planting under additional nitrate and provided theoretical basis for improving black-odorous waterbody restoration technology., (Copyright © 2022 Elsevier Ltd. All rights reserved.)