Environmental Engineering Applications Research Articles

Assessment of the efficiency of functioning of the environmental management system of enterprises

Purpose. A multifactor model is developed to assess the efficiency level of an enterprise’s environmental management system, depending on the effectiveness of organizational environmental measures. Methodology. The authors’ method for determining a comprehensive criterion for monitoring the effectiveness of the environmental management system, which characterizes the efficiency of operation and determines the level of environmental safety of enterprises, is proposed and used. To solve the tasks, a complex research method was also used, which included the analysis and generalization of literary and patent sources, and analytical, experimental research using computer and mathematical modeling methods. Findings. According to the studies and calculations of the generalized environmental quality indicator – 0.64, 0.66 and 0.66, the largest negative impact on the environment is caused by the activities of enterprises in the Pecherskyi, Podilskyi, and Solomianskyi districts of Kyiv, respectively. The obtained data testify to the relationship between the effectiveness of the implemented environmental measures (saving and rational use of resources, application of environmental technologies, advanced training, and environmental competence of employees) and the level of efficiency of the environmental management system of enterprises. Originality. As a result of the studies conducted using actual data, a system of indicators of the generalized environmental quality indicator has been proposed for the first time, which allows determining the environmental efficiency and effectiveness of the implemented environmental measures to assess the effectiveness of the functioning of the implemented environmental management system of the enterprise. Practical value. Based on the research results, a technique is proposed to assess the effectiveness of the environmental management system of enterprises by determining a generalized environmental quality indicator in terms of reducing the negative impact of the enterprise’s activities on the environment. Such an assessment system will help the management of the enterprise to promptly introduce corrective actions to improve the efficiency of the environmental management system and increase the level of environmental safety.

Synthesis and characterization of novel electrospun nanofibers based on taro starch: influence of solvent and isolation agent on morphology and diameter

AbstractThe utilization of natural polymers in producing electrospun nanofibers has received significant attention due to their biocompatibility, sustainability and diverse range of applications. This research focuses on synthesizing electrospun nanofibers derived from taro starch isolated from tubers. The investigation utilized SEM to examine the structure and size of the electrospun nanofibers. Formic acid and dimethyl sulfoxide solvents were tested to determine the most efficient solvent system for synthesizing taro starch nanofibers. The results demonstrated that the taro starch nanofibers can be effectively synthesized when using formic acid as the primary solvent. The study also investigated the impact of altering the volumetric ratio of formic acid to water on nanofiber morphology and size, finding that a lower formic acid fraction produced smooth fibers while a higher fraction resulted in fused fibers. The electrospinnability was further evaluated by comparing the effects of different isolation agents—distilled water and sodium metabisulfite—during the isolation process. The isolation agent significantly affected the fiber diameter, with notable differences observed in the smoothness of taro starch nanofibers at starch solution concentrations of 13, 15 and 17 wt%. Overall, the results of the study showed that the formation of taro starch fibers was influenced by the type of solvent, the volume fraction of the solvent to water, and the starch isolation agent. Successful fabrication of nanofibers from taro starch and its optimization parameters can contribute to the development of environmentally friendly nanofiber materials and offer a variety of applications in biomedicine, food and environmental engineering, such as tissue engineering, wound dressing, drug delivery, functional food delivery and food packaging. © 2024 Society of Chemical Industry.

Advanced Kernel DM+V/W+ method for precise mapping of pollutant dispersion in indoor environments

This study introduces an advanced method for reconstructing pollutant concentration fields in indoor ventilated environments, aimed at enhancing air quality and ensuring occupant health. Our newly developed Kernel DM + V/W+ method significantly refines existing models by accurately capturing variations in pollutant dispersion in downwind areas while minimizing overestimations in upwind zones. This innovative approach overcomes the limitations of the traditional Kernel DM + V method, which neglects the impact of airflow, and the Kernel DM + V/W method, which inadequately differentiates between upwind and downwind dispersion. The effectiveness of this method is validated through a dataset derived from previously confirmed computational fluid dynamics (CFD) simulations across two standard indoor ventilation scenarios. A subset of this dataset was randomly selected for evaluation using the three methods, enabling a detailed performance analysis. The results demonstrate that the Kernel DM + V/W+ method markedly enhances accuracy in estimating concentrations at unsampled locations and significantly improves the identification of pollution sources, providing substantial benefits for environmental engineering applications.

Conceptual and Applied Aspects of Water Retention Tests on Tailings Using Columns

The water retention capacity of porous materials is crucial in various geotechnical and environmental engineering applications such as slope stability analysis, landfill management, and mining operations. Filtered tailings stacks are considered an alternative to traditional tailings dams. Nevertheless, the mechanical behaviour and stability of the material under different water content conditions are of concern because these stacks can reach considerable heights. The water behaviour in these structures is poorly understood, particularly the effects of the water content on the stability and potential for liquefaction of the stacks. This study aims to investigate the water retention and flow characteristics of compacted iron ore tailings in high columns to better understand their hydromechanical behaviour. The research used 5 m high columns filled with iron ore tailings from the Quadrilátero Ferrífero region in Minas Gerais, Brazil. The columns were prepared in layers, compacted, and instrumented with moisture content sensors and suction sensors to monitor the water movement during various stages of saturation, drainage, infiltration, and evaporation. The sensors provided consistent data and revealed that the tailings exhibited high drainage capacity. The moisture content and suction profiles were effectively established over time and revealed the dynamic water retention behaviour. The comparison of the data with the theoretical soil water retention curve (SWRC) demonstrated a good correlation which indicates that there was no hysteresis in the material response. The study concludes that the column setup effectively captures the water retention and flow characteristics of compacted tailings and provides valuable insights for the hydromechanical analysis of filtered tailings stacks. These findings can significantly help improve numerical models, calibrate material parameters, and contribute to the safer and more efficient management of tailings storage facilities.

Examining the influence of sintering temperatures on the efficiency of 3D-printed natural zeolite for methylene blue dye adsorption

Additive manufacturing technology, with its potential for improved material efficiency, cost reduction, and capability to fabricate complex structures, is increasingly being leveraged in environmental engineering applications. This study harnesses this technology for fabricating natural zeolite adsorbents using an extrusion-based 3D printer, examining the influence of different sintering temperatures (300, 600, 900, and 1200 °C) on the adsorbents' properties. The study finds that samples sintered at 300 °C disintegrated in water, while those at 1200 °C underwent melting, changing their shape. Focus was then shifted to samples sintered at 600 °C (3D-Ze-600) and 900 °C (3D-Ze-900), with the former displaying a remarkable methylene blue (MB) adsorption efficiency of 82.5%, significantly higher than the latter's 20.1%. This disparity in adsorption performance is closely linked to the difference in porosity; the 600 °C samples exhibited a notably higher apparent porosity, which was instrumental in their enhanced adsorption capability. In contrast, the 900 °C samples, despite their lower porosity, showed diminished adsorption efficiency. XRD analysis further revealed that the superior performance of the 600 °C samples could be attributed to their preserved crystalline structure, which was altered in the 900 °C samples. These findings highlight the critical role of sintering temperature in determining the structural and functional properties of 3D-printed zeolites, demonstrating their potential as efficient adsorbents in wastewater treatment and offering valuable insights for optimizing the fabrication process for environmental applications.

Advances in 3D bioprinting for environmental remediation and hazardous materials treatment.

The high-throughput method based on the micron-level structure that 3D bioprinting technology offers for various environmental microbiological engineering applications is made possible by its several printing paths and precision programming control. This versatility makes it an on-demand manufacturing technology. A novel 3D manufacturing technique called 3D bioprinting may be used to precisely uptake and disperse bacteria to create microbial active substances with a variety of intricate functionalities for environmental applications. The technological challenges that the current 3D bioprinting technology must face include the mechanical properties of materials, the creation of specific bioinks to adapt to different strains, and the exploration of 4D bioprinting for intelligent applications. Therefore, this analysis delves deeply into the core technological ideas of 3D bioprinting, bioink materials, and their environmental applications. It also offers recommendations about the challenges and opportunities associated with 3D bioprinting. Combined with the present advancements in microbe enhancement technology, 3D bioprinting will provide an enabling platform for multifunctional microorganisms and facilitate the management of in situ directional responses in the environmental domain. This review highlights the applications of 3D bioprinting in the environmental monitoring and bioremediation. 3D printing in solid waste management is also discussed in detail.

Distribution and response of electroactive microorganisms to freshwater river pollution

Electroactive microorganisms (EAMs) play a vital role in biogeochemical cycles by facilitating extracellular electron transfer. They demonstrate remarkable adaptability to river sediments that are characterized by pollution and poor water quality, significantly contributing to the sustainability of river ecosystems. However, the distribution and diversity of EAMs remain poorly understood. In this study, 16S rRNA gene high-throughput sequencing and real-time fluorescence quantitative PCR were used to assess EAMs in 160 samples collected from eight rivers within the Pearl River Delta of Southern China. The results indicated that specialized EAMs communities in polluted sediments exhibited variations in response to water quality and sediment depth. Compared to clean sediment, polluted sediments showed a 4.5% increase in the relative abundances of EAMs communities (59 genera), with 45- and 17-times higher abundances of Geobacter and cable bacteria. Additionally, the abundance of cable bacteria decreased with increasing sediment depth in polluted sediments, while the abundance of L. varians GY32 exhibited an opposite trend. Finally, the abundances of Geobacter, cable bacteria, and L. varians GY32 were positively correlated with the abundance of filamentous microorganisms (FMs) across all samples, with stronger interactions in polluted sediments. These findings suggest that EAMs demonstrate heightened sensitivity to polluted environments, particularly at the genus (species) level, and exhibit strong adaptability to conditions characterized by high levels of acid volatile sulfide, low dissolved oxygen, and elevated nitrate nitrogen. Therefore, environmental factors could be manipulated to optimize the growth and efficiency of EAMs for environmental engineering and natural restoration applications.

Geogrid-type geotextile made from Typha domingensis fibers with high tensile strength for erosion control

The present invention relates to a geotextile of the geogrid type manufactured with fibers from the Typha domingensis plant, known as cumbungi, recognized for its remarkable tensile strength. This geotextile is developed to mitigate erosive processes and is produced from natural fibers arranged in a mat configuration. This product fit in with the technical fields of civil engineering and textile materials. The central innovation of this geotextile lies in the provision of a natural alternative featuring biodegradable fibers and high tensile strength. Its applications in environmental engineering include erosion prevention, slope stabilization, and reinforcement of road structures, with the fundamental goal of preserving the soil against degradation and erosion. This high-strength geotextile is composed of Typha domingensis fibers skillfully interwoven and connected to form a grid structure, with each unit covering approximately 0.25 m2s. Furthermore, a double layer of waterproofing material is incorporated to provide greater cohesion, environmental durability, and tensile strength. In practical applications, this geotextile demonstrates a remarkable ability to withstand mass movements even before local vegetation develops. This makes it a technological alternative that synergistically combines technological and ecological processes, incorporating the principles of soil bioengineering while offering a sustainable alternative to synthetic geotextiles. The use of this geotextile in the field is preferably combined with living components such as seeds, plant stakes, wood, or rocks for slope or embankment stabilization, with a primary focus on effective erosion control and the promotion of environmental sustainability.

Numerical investigation of heat and mass transfer study on MHD rotatory hybrid nanofluid flow over a stretching sheet with gyrotactic microorganisms

This study signifies the advancing reader’s understanding of complex fluid dynamics, offering insights crucial for optimizing heat transfer processes, environmental engineering applications, and biomedical technologies. The focus of this work is to examine the heat and mass transference properties of electrically conductive hybrid nanofluid flow on a bi-directional stretching surface with gyrotactic microorganisms. The effects of thermophoresis, thermal radiation, Brownian motion, heat source, and chemical reaction are incorporated into the problem. The sheet is rotated about the z-axis with ω as its velocity. The modeled equations are changed to dimensionless form with the use of similarity variables and are then solved by employing the homotopy analysis method (HAM). It is concluded that the larger rotation factor boosts the velocity plot. Similarly, the thermal radiation and thermophoretic factor heighten the thermal gradient of the hybrid nanofluid. From the computed data, it is revealed that the microorganism plot shows a declining behavior against the Peclet and Lewis numbers. The findings of this work help in the optimization of cooling systems in electronics, power plants, and automotive engines by enhancing heat transfer efficiency. Moreover, in biomedical engineering, understanding microorganism behavior in fluid flow is critical for developing advanced drug delivery systems and biomedical diagnostics.

Inoculum source determines the stress resistance of electroactive functional taxa in biofilms: A metagenomic perspective

The inoculum has a crucial impact on bioreactor initialization and performance. However, there is currently a lack of guidance on selecting appropriate inocula for applications in environmental biotechnology. In this study, we applied microbial electrolysis cells (MECs) as models to investigate the differences in the functional potential of electroactive microorganisms (EAMs) within anodic biofilms developed from four different inocula (natural or artificial), using shotgun metagenomic techniques. We specifically focused on extracellular electron transfer (EET) function and stress resistance, which affect the performance and stability of MECs. Community profiling revealed that the family Geobacteraceae was the key EAM taxon in all biofilms, with Geobacter as the dominant genus. The c-type cytochrome gene imcH showed universal importance for Geobacteraceae EET and was utilized as a marker gene to evaluate the EET potential of EAMs. Additionally, stress response functional genes were used to assess the stress resistance potential of Geobacter species. Comparative analysis of imcH gene abundance revealed that EAMs with comparable overall EET potential could be enriched from artificial and natural inocula (P > 0.05). However, quantification of stress response gene copy numbers in the genomes demonstrated that EAMs originating from natural inocula possessed superior stress resistance potential (196 vs. 163). Overall, this study provides novel perspectives on the inoculum effect in bioreactors and offers theoretical guidance for selecting inoculum in environmental engineering applications.

Application of microbially induced calcium carbonate precipitation (MICP) process in concrete self-healing and environmental restoration to facilitate carbon neutrality: a critical review.

Soil contamination, land desertification and concrete cracking can have significant adverse impacts on sustainable human economic and societal development. Cost-effective and environmentally friendly approaches are recommended to resolve these issues. Microbially induced carbonate precipitation (MICP) is an innovative, attractive and cost-effective in situ biotechnology with high potential for remediation of polluted or desertified soils/lands and cracked concrete and has attracted widespread attention in recent years. Accordingly, the principles of MICP technology and its applications in the remediation of heavy metal-contaminated and desertified soils and self-healing of concrete were reviewed in this study. The production of carbonate mineral precipitates during the MICP process can effectively reduce the mobility of heavy metals in soils, improve the cohesion of dispersed sands and realize self-healing of cracks in concrete. Moreover, CO2 can be fixed during MICP, which can facilitate carbon neutrality and contribute to global warming mitigation. Overall, MICP technology exhibits great promise in environmental restoration and construction engineering applications, despite some challenges remaining in its large-scale implementation, such as the substantial impacts of fluctuating environmental factors on microbial activity and MICP efficacy. Several methods, such as the use of natural materials or wastes as nutrient and calcium sources and isolation of bacterial strains with strong resistance to harsh environmental conditions, are employed to improve the remediation performance of MICP. However, more studies on the efficiency enhancement, mechanism exploration and field-scale applications of MICP are needed.

A practical equation for predicting saturated hydraulic conductivity of fine-grained soils

The saturated hydraulic conductivity (ks) of soils plays a crucial role in various agricultural and environmental engineering applications, particularly in relation to pollution prevention and water migration. Firstly, the traditional Kozeny-Carman (KC) equation, which are commonly used to estimate ks, has limitations in predicting ks for different types of fine-grained soils based on the analyses of permeability test data in the literature. Secondly, to tackle this issue, a modified KC equation was proposed by introducing an expression for the shape parameter and establishing a relationship between the tortuosity equation and the plastic limit. Additionally, the validation of the proposed equation was conducted using ks values of 25 fine-grained soils obtained from other sources. Finally, a comparison was made with the four commonly used ks models to evaluate its performance. The results indicate that the proposed equation demonstrates a relatively high coefficient of determination and a low root mean square error. This suggests that the proposed equation has the potential to accurately assess ks for fine-grained soils. The predictions for ks using the modified equation showed good agreement with the experimental data mentioned in the literature.

Fuzzy Machine Learning Applications in Environmental Engineering: Does the Ability to Deal with Uncertainty Really Matter?

Statement of Problem: Environmental engineering confronts complex challenges characterized by significant uncertainties. Traditional modeling methods often fail to effectively address these uncertainties. As a promising direction, this study explores fuzzy machine learning (ML) as an underutilized alternative. Research Question: Although the potential of fuzzy logic is widely acknowledged, can its capabilities truly enhance environmental engineering applications? Purpose: This research aims to deepen the understanding of the role and significance of fuzzy logic in managing uncertainty within environmental engineering applications. The objective is to contribute to both theoretical insights and practical implementations in this domain. Method: This research performs a systematic review carried out in alignment with PRISMA guidelines, encompassing 27 earlier studies that compare fuzzy ML with other methods across a variety of applications within the field of environmental engineering. Results: The findings demonstrate how fuzzy-based models consistently outperform traditional methods in scenarios marked by uncertainty. The originality of this research lies in its systematic comparison and the identification of fuzzy logic’s transparent, interpretable nature as particularly suited for environmental engineering challenges. This approach provides a new perspective on integrating fuzzy logic into environmental engineering, emphasizing its capability to offer more adaptable and resilient solutions. Conclusions: The analysis reveals that fuzzy-based models significantly excel in managing uncertainty compared to other methods. However, the study advocates for a case-by-case evaluation rather than a blanket replacement of traditional methods with fuzzy models. This approach encourages optimal selection based on specific project needs. Practical Implications: Our findings offer actionable insights for researchers and engineers, highlighting the transparent and interpretable nature of fuzzy models, along with their superior ability to handle uncertainties. Such attributes position fuzzy logic as a promising alternative in environmental engineering applications. Moreover, policymakers can leverage the reliability of fuzzy logic in developing ML-aided sustainable policies, thereby enhancing decision-making processes in environmental management.

Exploration of uniform and sudden lifting on hybrid nanofluid flow induced by the ramped motion of magnetized porous plate suspended by dust particles

This study concentrates on heat transmission and momentum in the hydromagnetic flow of hybrid nano-dusty fluid over the thermal plate with radiation, Newtonian heating wall conditions, and heat sink/source. The outcome of this investigation finds application in wastewater management, groundwater treatment, soil remediation, magnetic separation, air pollution, biomedical diagnostics, continuous casting, and environmental engineering. MAPLE Software is used to solve flow-controlling equations numerically. The influence of physical parameters on the dust and fluid phases due to uniform lifting and sudden lifting of the thermal plate are compared by preparing graphs and tables. The obtained outcomes are validated. This study concludes that the fluid velocity and temperature are more significant in uniform lifting than the sudden lifting of the thermal plate. An increase in particle concentration, magnetic field, particle relaxation time, and porosity parameter results in growth in shearing stress with time. The fluid phase has a higher flow velocity and temperature than the dust phase. The temperature field is more in the fluid phase than the dust phase and is augmented with nanoparticle enhancement.

Numerical modeling of mixed convective nanofluid flow with fractal stochastic heat and mass transfer using finite differences

This study presents the first comprehensive numerical simulation of heat and mass transfer in fractal-like mixed convective nanofluid flows. The flow of non-Newtonian nanofluids over flat and oscillating sheets is modelled mathematically, and a finite difference scheme is used to solve this model. The two-stage scheme can tackle fractal and fractal stochastic mathematical models of partial differential equations. The consistency in the mean square is proved, and Fourier series stability analysis is adopted to find stability conditions for fractal stochastic partial differential equation. The scheme is applied to solve the unsteady Casson nanofluid flow over the flat and oscillatory sheet, which affects thermal radiation, heat source, and chemical reaction. The existence of the solution is also provided for the Navier-Stokes equation of the considered flow model using fractal time derivative. The graph illustrates that the proposed fractal technique achieves faster convergence than the Crank-Nicolson approach. Applications in energy systems, materials science, and environmental engineering are just a few of the domains that could benefit from a better understanding of mixed convective nanofluid flows with fractal features, and that is what this research study hopes to accomplish. Scientists and engineers may better develop efficient and environmentally friendly systems by simulating and analyzing these complicated processes with the suggested finite difference technique.

Demystifying Machine Learning: Applications in African Environmental Science and Engineering

This article delves into the transformative role of Machine Learning (ML) in Environmental Science and Engineering (ESE), illustrating its broad applications across diverse environmental issues and its potential to enhance decision-making and operational efficiency. It emphasizes the integration of ML techniques such as regression analysis, anomaly detection, and deep learning to address complex challenges in climate change, energy management, water resource management, and more. The document particularly focuses on the adaptation and challenges of ML in the African context, highlighting barriers such as infrastructure limitations and data scarcity, while proposing innovative solutions like cloud computing and lightweight models. Practical use cases in meteorology, energy optimization, and water management underscore the practical impacts of ML, showing significant advancements in forecasting, resource management, and system optimization. The article also discusses methodological considerations necessary for effective ML application in ESE, including model selection and rigorous benchmarking. Ultimately, it provides a comprehensive overview of current capabilities, challenges, and future directions for ML in environmental science, advocating for continued innovation and tailored solutions to meet the unique needs of different regions, particularly Africa.

Application of machine learning in the study of cobalt-based oxide catalysts for antibiotic degradation: An innovative reverse synthesis strategy

This study addresses antibiotic pollution in global water bodies by integrating machine learning and optimization algorithms to develop a novel reverse synthesis strategy for inorganic catalysts. We meticulously analyzed data from 96 studies, ensuring quality through preprocessing steps. Employing the AdaBoost model, we achieved 90.57% accuracy in classification and an R²value of 0.93 in regression, showcasing strong predictive power. A key innovation is the Sparrow Search Algorithm (SSA), which optimizes catalyst selection and experimental setup tailored to specific antibiotics. Empirical experiments validated SSA's efficacy, with degradation rates of 94% for Levofloxacin and 97% for Norfloxacin, aligning closely with predictions within a 2% margin of error. This research advances theoretical understanding and offers practical applications in material science and environmental engineering, significantly enhancing catalyst design efficiency and accuracy through the fusion of advanced machine learning techniques and optimization algorithms.

Fine-Grained Permeable Surface Mapping through Parallel U-Net.

Permeable surface mapping, which mainly is the identification of surface materials that will percolate, is essential for various environmental and civil engineering applications, such as urban planning, stormwater management, and groundwater modeling. Traditionally, this task involves labor-intensive manual classification, but deep learning offers an efficient alternative. Although several studies have tackled aerial image segmentation, the challenges in permeable surface mapping arid environments remain largely unexplored because of the difficulties in distinguishing pixel values of the input data and due to the unbalanced distribution of its classes. To address these issues, this research introduces a novel approach using a parallel U-Net model for the fine-grained semantic segmentation of permeable surfaces. The process involves binary classification to distinguish between entirely and partially permeable surfaces, followed by fine-grained classification into four distinct permeability levels. Results show that this novel method enhances accuracy, particularly when working with small, unbalanced datasets dominated by a single category. Furthermore, the proposed model is capable of generalizing across different geographical domains. Domain adaptation is explored to transfer knowledge from one location to another, addressing the challenges posed by varying environmental characteristics. Experiments demonstrate that the parallel U-Net model outperforms the baseline methods when applied across domains. To support this research and inspire future research, a novel permeable surface dataset is introduced, with pixel-wise fine-grained labeling for five distinct permeable surface classes. In summary, in this work, we offer a novel solution to permeable surface mapping, extend the boundaries of arid environment mapping, introduce a large-scale permeable surface dataset, and explore cross-area applications of the proposed model. The three contributions are enhancing the efficiency and accuracy of permeable surface mapping while progressing in this field.

Promoting denitrification via high electron conversion ratio in the iron valence Cycle: Prospects for Pseudomonas sp. JM-7 application in environmental engineering

The interdependence between iron and nitrogen in the environment is of utmost importance, and this study has explored the intricate relationship between denitrification and iron valence cycling using Pseudomonas sp. strain JM-7 (P. JM-7) as a model bacteria. Our research demonstrated that P. JM-7 had the ability to effectuate electron transfer from the periplasmic space to electron acceptors, such as nitrate, via Cytochrome c-proteins (c-Cyts). We found that iron valence state transformations with Fe(II)/Fe(III) promoted P. JM-7′s denitrification of nitrate coupled to yeast extract powder (YEP) oxidation. Denitrification rates depended on temperature (>21 °C) and pH (6–9). During logarithmic growth, 5 g/L YEP enabled 350 mg/L nitrate removal, increasing to 1650 mg/L in stable growth. Addition of 4 mM Fe(II)/Fe(III) increased denitrification to 500 mg/L during logarithmic growth. The dynamic test results displayed that compared to the SiO2 filler control group, iron-containing fillers could facilitate P. JM-7 to reduce 100 mg/L nitrate with 50 mg/L YEP electrons, corresponding to 6 times the maximum electron transfer efficiency in the static test. Overall, iron redox cycling significantly enhanced P. JM-7 denitrification, imparting insights into iron–nitrogen interactions. In summary, our research imparts valuable insights into the nexus between iron and nitrogen in the environment, highlighting the potential application of P. JM-7 in environmental engineering. Our findings indicate that P. JM-7 exhibits a significant denitrification capability, and further research is recommended to investigate its ability to remove phosphorus and promote sustainable environmental remediation practices.

Numerical exploration of the peristaltic flow of MHD Jeffrey nanofluid through a non-uniform porous channel with Arrhenius activation energy

This article describes the peristaltic flow of a Jeffrey nanofluid through a vertical divergent channel subject to the impacts of thermal radiation, heat generation, activation energy, and an angled magnetic field. Coupled governing non-linear differential equations are obtained by approximating the system with the lubrication approximation. Further, distributions of the velocity, temperature, concentration, heat, and mass transfer rates at the channel wall are all calculated numerically. In addition, the consequences of pertinent physical parameters on the flow quantities are analyzed via tabular and graphical illustrations. It is noted that the velocity distribution exhibits an increasing behavior for thermal and solute Grashof numbers. Augmentation in axial velocity is noticed by escalating the values of the Hall current parameter. For greater values of the activation energy parameter, the concentration and mass transfer rate were elevated. The present study has a wide range of applications in biomedical engineering and environmental engineering.