Environmental Processes Research Articles

Improvement of aerosol spray scavenging efficiency by pre-injecting electrically charged water mist

Efficient aerosol scavenging is crucial for gas purification to reduce environmental pollution in various chemical industries. In nuclear reactor decommissioning, especially for damaged reactors after severe accidents like Fukushima Daiichi, controlling radioactive aerosol generation and dispersion is essential. Radioactive aerosols sizing from 0.1 to 1 μm are expected during containment cleaning and fuel debris retrieval processes. To ensure safe decommissioning and mitigate environmental radioactivity, a containment water spray system is used for aerosol scavenging. However, conventional water spray has low efficiency for these submicron aerosols. Our recent experiments at UTARTS demonstrated that pre-injection of water mist enhances scavenging efficacy by increasing aerosol size and hydrophilicity through aerosol-mist agglomeration prior to spraying. On this basis, in this study, we introduced an innovative method using electrically charged water mist, which significantly facilitates aerosol-mist agglomeration through additional electrostatic coagulation, further improving scavenging efficacy (maximum 35 % in current configurations). The present experimental results attained under various mist conditions and charging configurations provided insights into optimizing the mist charging system. Our developed technology is expected to be useful in radioactive aerosol control during damaged reactor decommissioning and has potential applications in other environmental chemical engineering processes.

Parametric Analytical Modulation Transfer Function Model in Turbid Atmosphere with Application to Image Restoration

To address the issues of image blurring and color distortion in hazy conditions, an image restoration method based on a parametric analytical modulation transfer function model is proposed under turbid atmospheric conditions. A source database is established using a numerical radiative transfer method based on discrete ordinate. Through multivariate nonlinear fitting and linear interpolation, the quantitative relationships among critical spatial frequency, turbid atmospheric MTF, and key atmospheric optical parameters—such as optical thickness, single scattering albedo, and asymmetry factor—are examined. A fast and efficient parametric analytical MTF model for turbid atmospheres is developed and applied to restore images affected by fog. The results demonstrate that, within the applicable range of the model, the model’s maximum mean relative error and the root mean square error are 7.16% and 0.0454, respectively. The computational speed is nearly a thousand times faster than that of the numerical radiative transfer method, achieving high accuracy and ease of application. Images restored using this model exhibit enhanced clarity and quality, effectively compensating for the degradation in image quality caused by turbid atmospheres. This approach represents a novel solution to the challenges of image processing in complex atmospheric environments.

Sensory Processing Measure Tools for Adolescents Aged 12 to 21 Years

Background and Objectives: Impairment in the way of sensory information by the central nervous system can cause functional problems in all areas of an adolescent’s activities, including education, self-care, social participation, and leisure. Therefore, in the presence of problems in such activities, it is necessary to evaluate and screen for sensory processing. In the present study, the sensory processing assessment tools that cover the adolescent age group were investigated. Methods: In this review, PubMed, Science Direct, Google, OTseeker, and Google Scholar databases were searched, and articles published between 2000 and 2022 on sensory processing assessment tools in adolescents and their psychometrics were selected. Keywords used included sensory integration dysfunction (SID), sensory processing disorder (SPD), adolescence, adolescent, assessment, measurement, reliability, validity, psychometry, environment, scale, and instrument. Results: The assessment tools identified to evaluate sensory processing in the adolescent age group include the “adolescent/adult sensory profile” (AASP), “Glasgow sensory questionnaire” (GSQ), “sensory processing measure (SPM)”, “sensory response in autism spectrum” (SR-AS) questionnaire, “sensory processing scale” (SPS), and “SPM-second edition” (SPM-2). Conclusion: The environment was considered as a factor that needs to be evaluated only in the second version of the SPM among the selected tools. The results of this review also showed that the only tool available in Persian in this field is the AASP, which does not seem to be a suitable tool for use in adolescence because the population studied in the psychometric evaluation of the Persian version was elderly people with dementia. Therefore, it seems necessary to prepare the Persian version and psychometric evaluation of an appropriate assessment tool for evaluating the sensory processing of Iranian adolescents.

Exploring the potential of biosurfactants produced by fungi found in soil contaminated with petrochemical wastes

Biosurfactants are a diverse group of compounds derived from microorganisms, possessing various structures and applications. The current study was seeking to isolate and identify a new biosurfactant-producing fungus from soil contaminated with petrochemical waste. The bioprocess conditions were optimized to maximize biosurfactant production for Aspergillus carneus OQ152507 using a glucose peptone culture medium with a pH of 7.0 and a temperature of 35 °C. The carbon source was glucose (3%), and ammonium sulfate (0.25%) was utilized as the nitrogen source. For Aspergillus niger OQ195934, the optimized conditions involved a starch nitrate culture medium with a pH of 7.0 and a temperature of 30 °C. The carbon source used was sucrose (3.5%), and ammonium sulfate (0.25%) served as the nitrogen source. The phenol-H2SO4 and phosphate tests showed that the biosurfactants that were extracted did contain glycolipid and/or phospholipid molecules. They showed considerable antimicrobial activity against certain microbes. The obtained biosurfactants increased the solubility of tested polyaromatic hydrocarbons, including fluoranthene, pyrene, anthracene, and fluorine, and successfully removed the lubricating oil from contaminated soil and aqueous media surface tension reduction. Based on the obtained results, A. carneus and A. niger biosurfactants could be potential candidates for environmental oil remediation processes.

General Industrial Process Optimization Method to Leverage Machine Learning Applied to Injection Moulding

ABSTRACTThe development of machine learning (ML) technologies is driving changes across many sectors. In industrial settings, this is called the fourth industrial revolution and encompasses several technologies pushing the boundaries of industrial automation. In this study, a general industrial process optimization (GIPO) methodology is formulated in the context of Industry 4.0 and tested on an industrial injection moulding machine (IMM). GIPO aims to encourage the practical inclusion of industrial artificial intelligence at all levels of the manufacturing process, while enabling industrial equipment to adapt to a changing processing environment. Special attention is given to the generality of the methodology so that it can be extended to other applications. In the example case study presented here, GIPO combines K‐nearest neighbours classification and nearest neighbours optimization methods to optimize an injection moulding process effectively. Practical implementation conducted on the IMM demonstrates a novel methodology to leverage data mining and ML methods in a real‐world setting to improve production quality, production time and energy cost.

Enhancing spatial resolution of satellite soil moisture data through stacking ensemble learning techniques

Soil moisture (SM) is a critical variable influencing various environmental processes, but traditional microwave sensors often lack the spatial resolution needed for local-scale studies. This study develops a novel stacking ensemble learning framework to enhance the spatial resolution of satellite-derived SM data to 1 km in the Urmia basin, a region facing significant water scarcity. We integrated in-situ SM measurements (obtained using time-domain reflectometry [TDR]), Soil Moisture Active Passive (SMAP) and Advanced Microwave Scanning Radiometer 2 (AMSR2) SM products, Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature and vegetation indices, precipitation records, and topography data. Ten base machine-learning models were evaluated using the Complex Proportional Assessment (COPRAS) method, and the top-performing models were selected as base learners for the stacking ensemble. The ensemble model, incorporating Random Forest, Gradient Boosting, and XGBoost, significantly improved SM estimation accuracy and resolution compared to individual models. The XGBoost and Gradient Boosting meta-models achieved the highest accuracy, with an unbiased root mean square error (ubRMSE) of 1.23% m3/m3 and a coefficient of determination (R2) of 0.97 during testing, demonstrating the exceptional predictive capabilities of our approach. SHapley Additive exPlanations (SHAP) analysis revealed the influence of each base model on the ensemble’s predictions, highlighting the synergistic benefits of combining diverse models. This study establishes new benchmarks for soil moisture monitoring by showcasing the potential of ensemble learning to improve the spatial resolution and accuracy of satellite-derived SM data, providing crucial insights for environmental science and agricultural planning, particularly in water-stressed regions.

Geoenvironmental and geophysical methods applied to identify natural attenuation process on a complex hydrogeological environment contaminated by hydrocarbons

Environmental contamination found in urban areas generally has a chemical composition that favors the generation of a variety of physical, chemical and biological processes that differ from natural soil parameters. Thus, understanding natural attenuation processes in tropical environments is still a challenge, as is understanding how biogeochemical processes can influence and modify natural soil characteristics. The study aimed to apply methods that are not commonly used in investigations of contaminated areas, in order to assess the applicability of non-invasive methodology for detailed stratigraphic characterization and biogeochemical changes resulting from contamination by hydrocarbons in a complex hydrogeological environment site. To date, few studies have presented results obtained using non-invasive techniques to understand biochemical processes. The research was based on the interpretation of results obtained by high-resolution physical environment characterization techniques (such as gamma geophysical profiling and electrical conductivity surveys) and chemical analysis of groundwater. The data was interpreted to obtain a detailed characterization of the study area and evidence of the occurrence of natural attenuation of creosote contamination. The results indicated that the combination of geoenvironmental and geophysical methods is an interesting alternative for obtaining data and understanding the mineralogical and stratigraphic variation of the contaminated area. The physical parameters variations obtained by the methods suggested the presence of local geochemical alterations resulting from natural processes of attenuation of contamination by aliphatic and aromatic hydrocarbons in a tropical environment. The natural attenuation processes were confirmed through groundwater samples, biological analysis and metagenomic classification.

WATERFALL MODEL OVER PCD.UCT MODEL REVIEW

Abstract. The software development life cycle (SDLC) has seen the evolution of numerous models, each tailored to meet specific project needs. Among these, the Waterfall Model has been a longstanding, traditional approach, characterized by its linear and sequential phases. In contrast, the PcD.UcT Model, a relatively newer framework, advocates for a more iterative and user-centered approach. This paper presents a comprehensive review of the Waterfall Model, evaluating its strengths and limitations in modern software development. It contrasts these findings with the PcD.UcT Model, analyzing how its principles better align with contemporary project demands, particularly in terms of flexibility, user engagement, and iterative refinement. Through this comparative analysis, the paper aims to provide insights into the applicability and effectiveness of each model, offering guidance for software development teams in choosing the appropriate methodology for their projects. Since the waterfall development methodology, the key requirements in system development strategies have shifted from processes to users. Because the process as well as the customers are both equally important, employing either a predictive or an adaptive methodology is extremely difficult. The primary goal of this work is mainly combining and then evaluating all the relevant methodologies in order to create a HydroGIS that accurately automates the complexities of the hydrology process in a HydroGIS environment while meeting all user requirements.

A knowledge-guided process planning approach with reinforcement learning

With the widespread application of computer-aided technologies such as CAD and CAM in the manufacturing industry, a growing number of process documents and design documents generate multi-source process knowledge and expert experience. However, due to the diverse and complex representation of process knowledge, there is a need for more effective methods to mine a large amount of multi-source information and to exploit the explicit and implicit relationships between the knowledge contained in process knowledge. Effective knowledge reuse in process planning still needs to be improved. This paper proposes a reinforcement learning approach that combines knowledge graphs and process decision-making activities in process planning to exploit the learning potential of process knowledge graphs. Firstly, a reinforcement learning environment for process planning is introduced to model the process planning decision-making phase as a sequential recommendation of process knowledge. Then, this paper designs in detail the state representation method that combines process decision sequences and potential relationships between processes. This paper also creates the composite reward function that combines the process planning environment. In addition, a new algorithm is proposed for learning the proposed model more efficiently. Experimental results show that the network structure has more accurate recommendation results than other methods.

Decentralized Machine Learning Framework for the Internet of Things: Enhancing Security, Privacy, and Efficiency in Cloud-Integrated Environments

The Internet of things (IoT) presents unique challenges for the deployment of machine learning (ML) models, particularly due to constraints on computational resources, the necessity for decentralized processing, and concerns regarding security and privacy in interconnected environments such as the Internet of cloud. In this paper, a novel decentralized ML framework is proposed for IoT environments characterized by wireless communication, dynamic data streams, and integration with cloud services. The framework integrates incremental learning algorithms with a robust decentralized model exchange protocol, ensuring that data privacy is preserved, while enabling IoT devices to participate in collaborative learning from distributed data across cloud networks. By incorporating a gossip-based communication protocol, the framework ensures energy-efficient, scalable, and secure model exchange, fostering effective knowledge sharing among devices, while addressing the potential security threats inherent in cloud-based IoT ecosystems. The framework’s performance was evaluated through simulations, demonstrating its ability to handle the complexities of real-time data processing in resource-constrained IoT environments, while also mitigating security and privacy risks within the Internet of cloud.

A longitudinal study of health-impairment and motivational processes in the psychosocial work environment and the subsequent influence on teachers’ intention to remain at their workplace

ABSTRACT This study examines the concurrent influence of health-impairment and motivational processes proposed by the Job Demands-Resource model on teachers’ intention to remain at their workplace at 12 and 24 months among 308 certified teachers in Sweden. Teachers’ perceptions of job demands (quantitative demands, emotional demands, work pace) and job resources (possibilities for development, social support from supervisor, social support from colleagues, recognition) were measured at baseline whereas exhaustion, work engagement, and retention intention were measured at 12 months and 24 months. Data was analysed using structural equation modelling and subsidiary analyses explored the moderating effects of job resources on the health-impairment process at 12 and 24 months. The findings indicate that the health-impairment process is a stronger predictor of teachers’ retention intention at 12 and 24 months than the motivational process and that the primary focus of teacher retention policies should be optimising teachers job demands rather than enhancing teachers’ job resources.

Gypsum Crystals Formed by the Anhydrite–Gypsum Transformation at Low Temperatures: Implications for the Formation of the Geode of Pulpí

Determining the mechanisms of the formation of giant crystals is a challenging subject. Gypsum, calcium sulfate dihydrate (CaSO4·2H2O), is known to form crystals larger than one meter in several locations worldwide. These selenite crystals grow at different temperatures, either in sedimentary or hydrothermal systems. The famous selenite crystals of the geode of Pulpí (Almería, Spain) are known to have grown at a temperature T = 20 ± 5 °C and have been proposed to form in a subaqueous environment by a self-feeding mechanism triggered by anhydrite dissolution and the ripening of microcrystalline gypsum, enhanced by oscillations in temperature. This paper reports the monitored crystallization of gypsum crystals, from anhydrite powder dissolution, inside airtight evaporation-free reactors under oscillating low temperatures (15 °C < T < 25 °C). These crystals are clearly smaller than the ones in the Pulpí mine but exhibit similar habits (i.e., single blocky crystals and twins following the 100 twinning law). The growth rate of gypsum single crystals has been measured to be between 3.8 and 35.3 µm/day. Noteworthy, we document the occurrence of the 100 contact twinning law of gypsum, which is the most widespread twinning law in natural environments but never univocally reported in laboratory experiments. The selection of the 100 contact twinning law has been correlated to the low supersaturation values obtained in the experiment, where the concentration in these long-duration experiments can be safely assumed to be the equilibrium concentration, i.e., 0.3 (at 25 °C) ≤ SI ≤ 0.4 (at 15 °C). We discuss the relevance of our experiment for forming the gypsum crystals of Pulpí in the framework of the geological history of Pulpí mineralization. These laboratory model experiments contribute to a deeper understanding of mineral nucleation and growth processes in natural environments.

Tetrafunctionalized Azocalix[4]resorcinarene Dye: A Chromogenic Supramolecule Used for the Selective Liquid-Liquid Extraction and Spectrophotometric Determination of Cu(II)

Background: The detection and extraction of trace metal ions, particularly copper(II), are critical for environmental monitoring and industrial processes. Calixresorcinarene, with its unique cavity structure, offers excellent platforms for designing selective chemosensors and extractants. Functionalization of calixresorcinarene with azo groups can enhance their chromogenic properties, enabling both extraction and detection in a single step. Objective: This study aimed to evaluate its (Azocalix[4]resorcinaren) efficacy as a selective chemosensor for the liquid-liquid extraction and spectrophotometric determination of Cu(II) ions. Methods: Application in Extraction and Detection: The ability of the dye to selectively extract Cu(II) ions from aqueous solutions was investigated via liquid-liquid extraction experiments. The dye-Cu(II) complex formation was monitored by UV-Vis spectrophotometry, with systematic optimization of experimental conditions, including pH, solvent system, and extraction duration. Results: The synthesized azocalix[4]resorcinarene dye exhibited a pronounced selectivity towards Cu(II) ions, forming a stable, colored complex. The complexation induced a distinct bathochromic shift in the absorption spectrum, allowing for precise spectrophotometric detection. Optimal extraction was achieved at a specific pH and solvent combination, with the method demonstrating a low detection limit and high sensitivity. The dye showed minimal interference from other metal ions, confirming its selectivity for Cu(II). Conclusion: The tetrafunctionalized azocalix[4]resorcinarene dye is a highly effective chromogenic agent for the selective extraction and detection of Cu(II) ions. Its robust performance in both extraction efficiency and spectrophotometric detection underscores its potential utility in environmental analysis and industrial applications where trace metal detection is crucial.

Ten Years Later: Still Unchanged Susceptibility to Antibiotics of Listeria monocytogenes from Poultry Processing Environments.

Listeria monocytogenes is still recognized as being commonly susceptible to antibiotics; however, there have been reports of reduced susceptibility in recent years. The significance of this resistance is not clear, in part due to the disparity in the antimicrobial susceptibility testing methods used. EUCAST (European Committee on Antimicrobial Susceptibility Testing) has recently proposed a standardized method for antibiotic susceptibility testing of L. monocytogenes. The aim of this work was to evaluate the susceptibility to 11 antibiotics in clinical use of 50 pulsed-field gel electrophoresis types of L. monocytogenes representing 347 isolates from a poultry industry setting using the EUCAST method and to compare the results with those obtained 10 years before. All poultry strains were sensitive to all the antibiotics tested but one strain was resistant to benzylpenicillin according to the EUCAST criteria. The current findings supported the previous study and confirmed that in certain food-associated L. monocytogenes populations, antibiotic sensitivity has remained stable.

Anonymisation, Pseudonymisation and Secure Processing Environments Relating to the Secondary Use of Electronic Health Data in the European Health Data Space (EHDS)

Anonymisation, Pseudonymisation and Secure Processing Environments Relating to the Secondary Use of Electronic Health Data in the European Health Data Space (EHDS)

Carbon Nanoparticle Oxidation by NO2and O2: Chemical Kinetics andReaction Pathways.

Carbon nanoparticle interactions with gases are central to many environmental and technical processes, but the underlying reaction kinetics and mechanisms are not well understood. Here, we investigate the oxidation and gasification of carbon nanoparticles by NO2and O2under combustion exhaust conditions. We build on a comprehensive experimental data set and use a kinetic multilayer model (KM-GAP-CARBON) to trace the uptake and release of gas molecules alongside the temporal evolution of particle size and surface composition. The experimental results are captured by a model mechanism that involves different types of carbon atoms (edge/plane-like) and the formation of a reactive oxygen intermediate (activated CO complex) as the rate-limiting step. A transition between distinct chemical regimes driven by NO2at lower temperatures and O2at higher temperatures is reflected by an increase in the observable activation energy from ~60 kJ/mol to ~130 kJ/mol. We derive energy profiles for three alternative reaction pathways that involve uni- or bimolecular decomposition of reactive oxygen intermediates.

Carbon Nanoparticle Oxidation by NO2 and O2: Chemical Kinetics and Reaction Pathways

Carbon nanoparticle interactions with gases are central to many environmental and technical processes, but the underlying reaction kinetics and mechanisms are not well understood. Here, we investigate the oxidation and gasification of carbon nanoparticles by NO2 and O2 under combustion exhaust conditions. We build on a comprehensive experimental data set and use a kinetic multilayer model (KM‐GAP‐CARBON) to trace the uptake and release of gas molecules alongside the temporal evolution of particle size and surface composition. The experimental results are captured by a model mechanism that involves different types of carbon atoms (edge/plane‐like) and the formation of a reactive oxygen intermediate (activated CO complex) as the rate‐limiting step. A transition between distinct chemical regimes driven by NO2 at lower temperatures and O2 at higher temperatures is reflected by an increase in the observable activation energy from ~60 kJ/mol to ~130 kJ/mol. We derive energy profiles for three alternative reaction pathways that involve uni‐ or bimolecular decomposition of reactive oxygen intermediates.

Metagenomic insights into the mechanism of sophorolipid in facilitating co-anaerobic digestion of mushroom residues and cattle manure: Functional microorganisms and metabolic pathway analysis

The present study aimed to enhance the co-anaerobic digestion system of mushroom residues and cattle manure by incorporating biosurfactant sophorolipid. Results demonstrated that the addition of 75 mg/L sophorolipid increased cumulative methane production by 33.68%, acetate content by 9–10 times, and the abundance of Methanosarcina by 69.22%. The electroactive microorganisms (Bacteroides, Petrimonas, etc.) were enriched, while the up-regulation of functional genes associated with carbohydrate metabolism and methane metabolism was observed. The metagenomic analysis revealed the significant involvement of inter-microbial communication and extracellular electron transfer in anaerobic digestion. Petrimonas was identified as the predominant host involved in cellular processes and environmental information processing. The supplementation of sophorolipid significantly enhanced its abundance during the late anaerobic digestion period (by 12.30%–64.84%). The results emphasize the crucial function of sophorolipid as biosurfactant in enhancing the efficiency of anaerobic digestion.

Successional changes in aphyllophorales macromycetes at different stages of coniferous xylolysis

The state of modern protective plantations in coniferous-deciduous forests of the Non-Chernozem zone is characterized by great heterogeneity. Currently, both protective forest plantations and protective tree plantations are managed by an extensive method, often without taking into account biological relationships in the agrophytocenosis. Successional changes in Aphyllophorales macromycetes (AFMM) at different stages of xylolysis of large tree debris represent a complex biochemical process. The limiting conditions for the active growth of fungal mycelium inside wood include light, access to moisture and air, violations in water transport. As mycogenic xylolysis develops, there is an increase in the biodiversity of AFMM species, which reaches a maximum at stage III–IV. On a large data set of 332 model trees and 3,543 basidiomes of xylotrophic basidiomycetes, the presence of correlations between the settlement on the substrate of various ecological groups of AFMM involved in xylolysis was established. It has also been shown that the fruit bodies of fungi are actively formed under conditions of abiotic stress and their number increases in subsequent seasons. When using AFMM as indicators of the xylolysis stage of coniferous species, it is necessary to take into account that the development of mycelium depends on the structural features of the wood. The course of mycelium development in the trunk cross-section is associated with both the action of external environmental factors and interference processes between species. At the same time, the frequency of basidioma formation and their age are of great importance in field identification.

Strategies for effective high pressure germination or inactivation of Bacillus spores involving nisin.

Extremely resistant spore-forming bacteria are widely distributed in nature. They infiltrate the food chain and processing environments, posing risks of spoilage and food safety. Traditional heat-intensive inactivation methods often negatively affect the product quality. HP germination-inactivation offers a potential solution for better preserving sensitive ingredients while inactivating spores. However, the presence of ungerminated (superdormant) spores hampers the strategy's success and safety. Knowledge of strategies to overcome resistance to HP germination is vital to progress mild spore control technologies. Our study contributes to the evaluation and development of mild preservation processes by evaluating strategies to enhance the HP germination-inactivation efficacy. Mild preservation processes can fulfill the consumers' demand for safe and minimally processed food.