Dynamic modeling of the activated sludge microbial growth and activity under exposure to heavy metals

Kinetic coefficients of cell growth and removal of organic substances for modeling Anaerobic-anoxic-aerobic method

Purpose

Anaerobic-anoxic-aerobic process is one of the biological removal processes of nutrients in wastewater treatment. Phosphorus removal by biological method is a new and developed technique that is done by changing the design of suspended growth systems.

Methods

This is a cross-sectional descriptive study, which is a pilot workshop based in the first module, and chemistry, physics and microbiological tests were carried out at the wastewater treatment plant laboratory in one of the cities (Pardis) of Tehran province in 2020-2021 during a period of 12 months. Was completed. In this research, a total of 500 samples were taken from raw wastewater, aeration pond, effluent, secondary sedimentation and return activated sludge.

Results

In this research, the internal decay coefficient and the growth efficiency coefficient are equal to d-1 0.1264 and 0.6579 gVSS/gCOD, respectively. And the maximum specific rate of consumption of food substance and the semi-saturation constant of food substance respectively were gCOD/gVSS.d 3.3467 gCOD/m 25.305. If the specific rate of consumption of food substance or efficiency factor in our research is 0.27 gCOD/gVSS.d and the semi-constant Ks saturation equal to 27.9 gCOD/m has been obtained, this actually shows that the organic matter (COD) in the waste water of Pardis city had a higher degradability (sbCOD).

Conclusion

According to the obtained results, the synthetic coefficients in the Lineweaver-Burk and Hanes models are suitable, but in the Hofstee model, the amount of K and Kd is less than the optimal amount for the proper exploitation of the pilot.

Performance evaluation, enzymatic activity change and metagenomic analysis of sequencing batch reactor under divalent zinc stress

Divalent zinc (Zn2+) are widely detected in domestic and industrial wastewater, and it is essential to evaluate the effect of Zn2+ on wastewater biological treatment process due to its bio-toxicity. In this study, the nitrogen removal rates and their corresponding enzymatic activities of sequencing batch reactor decreased with the increase of Zn2+ concentration. The Zn2+ accumulation in activated sludge caused significant antioxidant response, and the reactive oxygen species (ROS) production and antioxidant enzymatic activities were positively correlated with Zn2+ concentration. The presence of Zn2+ inhibited the metabolic pathways related to energy production and electron transport. The abundance decreases of nitrification and denitrification functional genes led to the deterioration of nitrogen removal performance under Zn2+ stress. The correlation analysis between functional gene modules and microbial genera revealed that Zoogloea had obvious Zn2+ resistance. This study can provide the insights into the influencing mechanism of Zn2+ on the biological nitrogen removal process.

Sublethal effects of binary mixtures of Cu2+ and Cd2+ on Daphnia magna: Standard Dynamic Energy Budget (DEB) model analysis

Dynamic Energy Budget theory (DEB) describes mass and energy fluxes that occur in living organisms. DEB models were successfully used to assess the influence of stress, including toxic substances, and changes in pH and temperature, on different organisms. In this study, the Standard DEB model was used to evaluate the toxicity of copper and cadmium ions and their binary mixtures on Daphnia magna. Both metal ions have a significant influence on daphnia growth and reproduction. Different physiological modes of action (pMoA) were applied to primary DEB model parameters. Model predictions for chosen modes of interaction of mixture components were evaluated. The goodness of model fit and the model prediction was assessed to indicate the most likely pMoA and interaction mode. Copper and cadmium influence more than one DEB model primary parameter. Different pMoAs can result in similar model fits, and therefore it is difficult to identify pMoA only by evaluation of the goodness of fit of the model to the growth and reproduction data. Some critical discussion and ideas for model development are therefore provided.

Tannic acid-based dual-network homogeneous hydrogel with antimicrobial and pro-healing properties for infected wound healing

The clinical treatment of infected skin injuries caused by exogenous bacteria faces great challenges. Conventional therapeutic approaches are difficult to achieve synergistic effects of infection control and induction of skin regeneration. In this study, a novel tannic acid-based physically cross-linked double network hydrogel (PDH gel) was prepared on demand by covalent cross-linking of tannic acid (TA) with polyvinyl alcohol (PVA) and chelating ligand of TA with Fe³⁺. The homogeneity of the hydrogel was achieved by the action of glycol dispersant. With the anti-inflammatory and antioxidant properties of Fe³⁺ and TA, this hydrogel exhibited excellent antibacterial properties by achieving 99.69% and 99.36% bacterial inhibition against E.coli and S. aureus, respectively. Moreover, the PDH gel exhibits good biocompatibility, stretchability (up to 200%) and skin-friendliness. After 14 days of PDH-1 gel implantation in a rat model infected by S. aureus, the wound healing rate was as high as 95.21%. PDH gel-1 showed more granulation tissue, more pronounced blood vessels, higher collagen fiber density and good collagen deposition, and its recovery effect was better than that of PSH gel and PDH gel-2 in vivo. Hence, this study provides a novel avenue for the design of future clinical infected wound healing dressings.

Perspectives of nanomaterials in microbial remediation of heavy metals and their environmental consequences: A review

Nanomaterials (NMs) have diverse applications in various sectors, such as decontaminating heavy metals from drinking water, wastewater, and soil. Their degradation efficiency can be enhanced through the application of microbes. As microbial strain releases enzymes, which leads to the degradation of HMs. Therefore, nanotechnology and microbial-assisted remediation-based methods help us develop a remediation process with practical utility, speed, and less environmental toxicity. This review focuses on the success achieved for the bioremediation of heavy metals by nanoparticles and microbial strains and in their integrated approach. Still, the use of NMs and heavy metals (HMs) can negatively affect the health of living organisms. This review describes various aspects of the bioremediation of heavy materials using microbial nanotechnology. Their safe and specific use supported by bio-based technology paves the way for their better remediation. We discuss the utility of nanomaterials for removing heavy metals from wastewater, toxicity studies and issues to the environment with their practical implications. Nanomaterial assisted heavy metal degradation coupled with microbial technology and disposal issues are described along with detection methods. Environmental impact of nanomaterials is also discussed based on the recent work conducted by the researchers. Therefore, this review opens new avenues for future research with an impact on the environment and toxicity issues. Also, applying new biotechnological tools will help us develop better heavy metal degradation routes.

Activated sludge process enabling highly efficient removal of heavy metal in wastewater

Activated sludge process was a low-cost alternative method compared to the conventional physicochemical process for the treatment of heavy metal-containing wastewater. In the present study, the removal efficiency of Pb²⁺, Cu²⁺, and Ni²⁺ from wastewater by a sequencing batch reactor (SBR) activated sludge system was investigated, and the mechanism was revealed by static adsorption experiment of activated sludge. The results showed that the activated sludge in the SBR system was effective in removing Pb²⁺ and Cu²⁺ from wastewater at 10 mg·L^-1 initial concentration, with a removal efficiency of 83.1 ~ 90.0% for Pb²⁺ and 74.3 ~ 80.6% for Cu²⁺, respectively. However, the removal efficiency for Ni²⁺ was only 0 ~ 6.2%. Static adsorption experiments showed that the adsorption capacity of activated sludge for three heavy metals was shown as Pb²⁺  > Cu²⁺  > Ni²⁺. When the initial concentration was 20 mg·L^-1, the equilibrium adsorption capacity of activated sludge for Pb²⁺, Cu²⁺, and Ni²⁺ was 18.35 mg·g^-1, 17.06 mg·g^-1, and 8.37 mg·g^-1, respectively. The main adsorption mechanisms for Pb²⁺ and Cu²⁺ were ligand exchange, electrostatic adsorption, and surface organic complexation processes, but Ni²⁺ removal mechanism mainly included electrostatic adsorption and surface organic complexation processes, showing that Ni²⁺ removal was inhibited in the presence of Pb²⁺ and Cu²⁺. The physicochemical properties and microbial diversity of activated sludge were greatly affected by the heavy metals in the SBR system, and genus Rhodobacter was found to be dominant bacteria enabling resistance to heavy metal ions.

Fungal Proteins from Sargassum spp. Using Solid-State Fermentation as a Green Bioprocess Strategy

The development of green technologies and bioprocesses such as solid-state fermentation (SSF) is important for the processing of macroalgae biomass and to reduce the negative effect of Sargassum spp. on marine ecosystems, as well as the production of compounds with high added value such as fungal proteins. In the present study, Sargassum spp. biomass was subjected to hydrothermal pretreatments at different operating temperatures (150, 170, and 190 °C) and pressures (3.75, 6.91, and 11.54 bar) for 50 min, obtaining a glucan-rich substrate (17.99, 23.86, and 25.38 g/100 g d.w., respectively). The results indicate that Sargassum pretreated at a pretreatment temperature of 170 °C was suitable for fungal growth. SSF was performed in packed-bed bioreactors, obtaining the highest protein content at 96 h (6.6%) and the lowest content at 72 h (4.6%). In contrast, it was observed that the production of fungal proteins is related to the concentration of sugars. Furthermore, fermentation results in a reduction in antinutritional elements, such as heavy metals (As, Cd, Pb, Hg, and Sn), and there is a decrease in ash content during fermentation kinetics. Finally, this work shows that Aspergillus oryzae can assimilate nutrients found in the pretreated Sargassum spp. to produce fungal proteins as a strategy for the food industry.

Deep learning model based on urban multi-source data for predicting heavy metals (Cu, Zn, Ni, Cr) in industrial sewer networks

The high concentrations of heavy metals in municipal industrial sewer networks will seriously impact the microorganisms of the activated sludge in the wastewater treatment plant (WWTP), thus deteriorating the effluent quality and destroying the stability of sewage treatment. Therefore, timely prediction and early warning of heavy metal concentrations in industrial sewer networks is crucial. However, due to the complex sources of heavy metals in industrial sewer networks, traditional physical modeling and linear methods cannot establish an accurate prediction model. Herein, we developed a Gated Recurrent Unit (GRU) neural network model based on a deep learning algorithm for predicting the concentrations of heavy metals in industrial sewer networks. To train the GRU model, we used low-cost and easy-to-obtain urban multi-source data, including socio-environmental indicator data, air environmental indicator data, water quantity indicator data, and easily measurable water quality indicator data. The model was applied to predict the concentrations of heavy metals (Cu, Zn, Ni, and Cr) in the sewer networks of an industrial area in southern China. The results are compared with the commonly used Artificial Neural Network (ANN) model. In this study, it was shown that the GRU had better prediction performance for Cu, Zn, Ni, and Cr concentrations, with the average R² significantly increased by 12.35%, 11.94%, 9.21%, and 8.13%, respectively, compared to ANN predictions. The sensitivity analysis based on Shapley (SHAP) values revealed that conductivity (σ), temperature (T), pH, and sewage flow (Flow) contributed significantly to the prediction results of the model. Furthermore, the three input variables including air pressure (AP), land area (A), and population (Pop.) were removed without affecting the prediction performance of the model, which maximized the modeling efficiency and reduced the operational cost. This study provides an economical and feasible technical method for early warning of abnormal heavy metal concentrations in urban industrial sewer networks.

Granular indigenous microalgal-bacterial consortium for wastewater treatment: Establishment strategy, functional microorganism, nutrient removal, and influencing factor

Granular indigenous microalgal-bacterial consortium (G-IMBC) system integrates the advantages of the MBC and granular activated sludge technologies, also with superior microalgal wastewater adaptation capacity. In this review, the concept of IMBC was firstly described, followed by its establishment and acclimation strategies. Characteristics and advantages of G-IMBC system compared to other IMBC systems (i.e., attached and floc IMBC systems) were then introduced. Moreover, the involved functional microorganisms and their interactions, as well as nutrient removal mechanisms were systematically and critically reviewed. Finally, the influencing factors including wastewater characteristics and operation factors were discussed. This study aims to provide a comprehensive up-to-date summary of the G-IMBC system for sustainable wastewater treatment.