Hasil untuk "Environmental effects of industries and plants"

E. Rubin, Chao Chen, A. Rao

Cao Cuong Ngo, Thi Kim Thanh Nguyen, Thi Thanh Thuy Tran et al.

Vehicle-wash wastewater can contain petroleum-derived residues that accumulate in drainage sludge and pose persistent environmental risks. In this study, shotgun metagenomics of drainage sludge from a vehicle-wash ditch were combined with isolate level validation to assess indigenous hydrocarbon biodegradation potential. Taxonomic profiling revealed a community dominated by Proteobacteria (notably Gammaproteobacteria), while Bacillus was detected only at low relative abundance at the genus level. Functional annotation indicated strong genetic potential for alkane activation and downstream processing, together with multiple enzymes associated with aromatic?ring transformation. To link this community-level potential with experimentally verifiable activity, an indigenous isolate recovered from the sludge was identified as Bacillus amyloliquefaciens MD3.3 based on 16S rRNA gene sequencing (GenBank: PV550465). In microcosms prepared with wastewater from the same drainage source, GC-MS analysis demonstrated marked attenuation of mineral oil n-alkanes over 0-7-14 days. ?n?alkanes decreased from 185,346.63 ± 11,120.80 µg/L at Day 0 to 21,498.48 ± 3,224.77 µg/L at Day 7 and 260.82 ± 52.16 µg/L at Day 14, corresponding to 88.44 ± 1.05% and 99.86 ± 0.02% removal, respectively (n = 3). In contrast, abiotic sterilized controls showed only minor non-biological losses (4.23 ± 3.54% and 5.97 ± 0.75% removal at Days 7 and 14). Collectively, these results support the feasibility of site-relevant bioremediation for vehicle-wash wastewater.

X. Sui, Xuemei Wang, Yuhuan Li et al.

The petroleum industry’s development has been supported by the demand for petroleum and its by-products. During extraction and transportation, however, oil will leak into the soil, destroying the structure and quality of the soil and even harming the health of plants and humans. Scientists are researching and developing remediation techniques to repair and re-control the afflicted environment due to the health risks and social implications of petroleum hydrocarbon contamination. Remediation of soil contamination produced by petroleum hydrocarbons, on the other hand, is a difficult and time-consuming job. Microbial remediation is a focus for soil remediation because of its convenience of use, lack of secondary contamination, and low cost. This review lists the types and capacities of microorganisms that have been investigated to degrade petroleum hydrocarbons. However, investigations have revealed that a single microbial remediation faces difficulties, such as inconsistent remediation effects and substantial environmental consequences. It is necessary to understand the composition and source of pollutants, the metabolic genes and pathways of microbial degradation of petroleum pollutants, and the internal and external aspects that influence remediation in order to select the optimal remediation treatment strategy. This review compares the degradation abilities of microbial–physical, chemical, and other combination remediation methods, and highlights the degradation capabilities and processes of the greatest microbe-biochar, microbe–nutrition, and microbe–plant technologies. This helps in evaluating and forecasting the chemical behavior of contaminants with both short- and long-term consequences. Although there are integrated remediation strategies for the removal of petroleum hydrocarbons, practical remediation remains difficult. The sources and quantities of petroleum pollutants, as well as their impacts on soil, plants, and humans, are discussed in this article. Following that, the focus shifted to the microbiological technique of degrading petroleum pollutants and the mechanism of the combined microbial method. Finally, the limitations of existing integrated microbiological techniques are highlighted.

M. Munawar, A. Khoja, Salman Raza Naqvi et al.

Murad M. Khamayseh and Rana Kidak

Antibiotic removal poses a serious risk to the environment due to its intricate structure. Consequently, scientists are developing new and efficient techniques to remove antibiotic compounds from wastewater. The goal of this study is to employ green Pithophora macroalgae to remove the antibiotic amoxicillin (AMX) from a water-based solution. With a focus on understanding the process, this study assesses the application of reacting AMX biosorption on the biomass of Pithophora algae in aqueous solutions using thermodynamic modeling. The determined thermodynamic characteristics show that an endothermic process is used in the biosorption of the antibiotic AMX, considering that AMX has a positive electrical charge of ΔHº at 49.796 KJ.moL-1. As ΔGº has a positive charge (2.982 kJ.moL-1, 3.718 kJ.moL-1, and 4.793 kJ.moL-1) for AMX at (298 K, 303 K, and 308 K, respectively. This positive result indicates that the reaction is not feasible or spontaneous. The decrease in chaos at the liquid/solid interface caused by AMX biosorption on Pithophora macro algae is reflected in the negative charge of ΔSº, which was -176.735 kJ.moL-1. The effect of temperature on the biosorption of AMX was investigated for different initial AMX concentrations. At a lower temperature of 298 K, the AMX molecules were more likely to diffuse into the internal pores of the Pithophora algae. This suggests that the diffusion rate of the adsorbate (AMX) across the bulk and pore boundaries of the biosorbent particles may be increased at lower temperatures. The findings of this study indicate that the biomass of the macroalgae Pithophora is a valuable biosorbent for the biosorption of AMX antibiotics, and it may have potential applications in the treatment of wastewater.

Caspar Krampe, Anne-Jeth de Groot, William Hurst

Almost every food retailer offers them – temporary loyalty programs (TLPs). TLPs aim to enhance customer loyalty by offering enticing, product-based incentives. However, an emerging concern arises regarding the sustainability impact of these programs, and consumers have started questioning the sustainability of TLPs offered by retailers. This concern is primarily driven by the mounting consumer interest and awareness surrounding sustainability, requesting retailers to include sustainability dimensions in their TLPs and their sustainability strategy. Using conjoint analysis, this study investigates which of the three sustainability dimensions influences consumers' decision to participate in a TLP. As consumers often weigh sustainability attributes against other attributes, the competing TLP attributes of price, reward timing and brand were also included in this study. A total of 469 consumers were integrated in the data analysis. The results display that consumers have a higher intention to participate in a TLP when it focuses on the dimension of social sustainability, integrates a low-price level, delayed rewards, and provides unbranded rewards. However, as indicated by the results of a segmentation analysis, four heterogeneous consumer segments are identified, displaying an environmental sustainability-driven segment, a segment that favours no sustainability but delayed rewards, a consumer segment that favours social sustainability, and finally a segment that is price driven and in favour of delayed rewarded TLP products. Hence, the result indicates a need for more personalised TLPs that can greatly benefit from green data and technology communication approaches. The findings of this study are of relevance as they increase understanding of consumers’ preferences for sustainability in TLPs; while providing stakeholders fruitful (data-driven) direction to cope with sustainability demands expressed on the consumer side without putting their business at risk.

Ghasem Eghlima, Yasaman Mashhadi Tafreshi, Fateme Aghamir et al.

Abstract Identifying the optimal cultivation regions and evaluating the impact of environmental factors are crucial for selecting the best conditions for the commercial production of important medicinal and industrial plants. This study examined the effects of different cultivation areas-Rayen, Eghlid, Kalat, and Zanjan-on the agro-morphological and phytochemical traits of Glycyrrhiza glabra. The findings revealed that the location where the plants were grown significantly influenced their physical and chemical characteristics. The Kalat region produced the tallest plants, measuring 96.86 cm, along with the highest shoot dry weight at 205.17 g, root dry weight of 318.00 g, root yield of 1590.12 g/m², and glabridin content of 2.92 mg/g dry weight (DW). Conversely, samples from the Rayen region had the highest glycyrhizic acid content at 17.92 mg/g DW and liquritigenin content at 1.22 mg/g DW. The Eghlid region showcased the highest total phenol content and antioxidant activity. Additionally, the study found a negative and significant correlation between altitude and glabridin content, indicating that glabridin levels decrease with increasing altitude. Based on the needs of the food and pharmaceutical industries, the study recommends the Rayen region for the production of glycyrhizic acid, the Kalat region for glabridin, and the Eghlid region for phenolic compounds.

Endriani, Diah Listyarini, Yulfita Farni

Ultisol is generally characterized by a high clay content in the argillic horizon, easy compaction, slow permeability, and unstable aggregates, resulting in low total porosity. Organic soil amendments such as compost and biochar can be used to improve soil organic matter, aggregate stability, and other physical properties of Ultisol. A field experiment was conducted using a randomized block design to assess the potential effects of Leucaena compost (LC) and coconut shell biochar (CB) on the physical properties of Ultisol and maize yield. The treatments included combinations of compost (0, 5, and 10 t/ha), biochar (0, 5, and 10 t/ha), and inorganic fertilizer (50% and 100% of the recommended dose). The results showed that the application of Leucaena compost and coconut shell biochar improved the physical properties of the soil. Compost and/or biochar applications at 10 t/ha enhanced soil organic matter, total porosity, aggregate formation, aggregate stability, pore size distribution, and reduced soil bulk density, improving maize growth and yield.

Matthew C. Lange, Ran Li, John W. Apolzan et al.

Food loss and waste (FLW) is a global challenge. Interoperable FLW ontologies will foster more comprehensive data sharing and inform better solutions to reduce and recover excess food and to valorize wasted food and food byproducts. This review reveals that only eight ontologies currently address FLW with most emphasizing valorization. Notably, few are designed explicitly to support FLW reduction, and none facilitate food recovery, which is critical given that reduction and recovery are the preferred means of mitigating FLW. Furthermore, existing FLW ontologies show limited alignment with recognized gold-standard frameworks, for example the Open Biological and Biomedical Ontology (OBO) Foundry, and none support ongoing connectivity to external ontologies, restricting their utility across stakeholder domains. Looking ahead, there is a pressing need to create or expand ontologies that adhere to best practices from relevant foundries to ensure robust linkage and interoperability and undergird structured data ecosystems that support food systems stakeholders in FLW prevention and mitigation. Achieving this goal will require active collaboration among a diverse range of stakeholders, including builders of food systems cyberinfrastructure, scientists, innovators, regulators, public and private funders, community-based organizations, policymakers, and international NGOs as each rely on critical ontological elements to inform decision-making, measure impact, and drive improvement across the food supply chain. Finally, large language models offer promising capabilities for expediting ontology creation, broadening inclusivity in ontology creation, and enhancing the accuracy of resulting data infrastructures.

Yu-Wan Ma, Xin-Lan Lin, Qi-Han Ding et al.

Global warming-induced heat stress increasingly threatens the ornamental quality and productivity of roses. Previous studies have indicated that trehalose and the trehalose-6-phosphate synthase (TPS) gene family regulate plants' stress resistance, yet their roles in thermotolerance in rose remain uncharacterized. We previously identified RcTPS7b as a putative heat-responsive gene in Rosa chinensis 'Slater's Crimson China'. This study aimed to investigate whether exogenous trehalose enhances heat tolerance in rose, and elucidate the functional role of RcTPS7b in heat stress regulation. The results showed that exogenous application of 5 mmol/L trehalose significantly enhanced rose's heat tolerance by reducing the heat damage index, elevating superoxide dismutase (SOD) activity, and preserving the efficiency of Photosystem II (PSII). Overexpression of RcTPS7b in Arabidopsis thaliana enhanced the heat tolerance and antioxidant enzyme activities in transgenic plants, concomitant with upregulated expression of heat-resistant genes such as HEAT SHOCK FACTOR/PROTEIN (AtHSF/P). Furthermore, tobacco rattle virus-induced silencing of RcTPS7b in R. chinensis compromised thermotolerance and induced severe oxidative damage. Transient overexpression of RcTPS7b in rose petals reduced heat damage and maintained petal integrity through redox homeostasis under high temperature treatment. Collectively, this study demonstrates that exogenous trehalose potentiates RcTPS7b expression to enhance thermotolerance, revealing the trehalose metabolism pathway's pivotal role in heat stress regulation in R. chinensis, and will be helpful for the molecular breeding of heat tolerance in R. chinensis.

J. I. Ruiz-Martinez, A. Mendez-Vazquez, E. Rodriguez-Tello

In precision agriculture, one of the most important tasks when exploring crop production is identifying individual plant components. There are several attempts to accomplish this task by the use of traditional 2D imaging, 3D reconstructions, and Convolutional Neural Networks (CNN). However, they have several drawbacks when processing 3D data and identifying individual plant components. Therefore, in this work, we propose a novel Deep Learning architecture to detect components of individual plants on Light Detection and Ranging (LiDAR) 3D Point Cloud (PC) data sets. This architecture is based on the concept of Graph Neural Networks (GNN), and feature enhancing with Principal Component Analysis (PCA). For this, each point is taken as a vertex and by the use of a K-Nearest Neighbors (KNN) layer, the edges are established, thus representing the 3D PC data set. Subsequently, Edge-Conv layers are used to further increase the features of each point. Finally, Graph Attention Networks (GAT) are applied to classify visible phenotypic components of the plant, such as the leaf, stem, and soil. This study demonstrates that our graph-based deep learning approach enhances segmentation accuracy for identifying individual plant components, achieving percentages above 80% in the IoU average, thus outperforming other existing models based on point clouds.

Wentao Tang

While a characterization of plant-model mismatch is necessary for robust control, the mismatch usually can not be described accurately due to the lack of knowledge about the plant model or the complexity of nonlinear plants. Hence, this paper considers this problem in a data-driven way, where the mismatch is captured by parametric forms of integral quadratic constraints (IQCs) and the parameters contained in the IQC equalities are learned from sampled trajectories from the plant. To this end, a one-class support vector machine (OC-SVM) formulation is proposed, and its generalization performance is analyzed based on the statistical learning theory. The proposed approach is demonstrated by a single-input-single-output time delay mismatch and a nonlinear two-phase reactor with a linear nominal model, showing accurate recovery of frequency-domain uncertainties.

Lucas Dietrich, Benjamin Förster, Peter Langendörfer et al.

Plants come with sophisticated strategies to survive within a highly competing environment. In addition, they need to resist frequent attacks from a variety of herbivores acting alone, in small groups, or in swarms. Since the amount of energy a plant might invest in defense and reproduction is limited, a complex optimization problem emerges. In a shared habitat, plants fight herbivores by shape and camouflage, by the release of specific toxins, or by attracting predators of herbivores. Furthermore, plants alert their surrounding field by signaling substances in the event of an assault. Transported by air or through a network of roots, signaling substances reach neighbors to trigger their defense. The offsprings of a plant commonly grow within a certain distance to benefit from symbiotic protection. We introduce a grid-based visual simulation software for detailed configuration and subsequent processing of the behavior of the resulting system in time and space. In terms of solution to a computational optimization problem inspired by nature, settings with low energy need and long life able to cope with different patterns of attack can be figured out and analyzed. Applications include novel techniques for efficient construction and secure operation of sensor networks.

M. A. Pérez-Cutiño, J. Valverde, J. Capitán et al.

In the context of Concentrated Solar Power (CSP) plants, aerial images captured by drones present a unique set of challenges. Unlike urban or natural landscapes commonly found in existing datasets, solar fields contain highly reflective surfaces, and domain-specific elements that are uncommon in traditional computer vision benchmarks. As a result, machine learning models trained on generic datasets struggle to generalize to this setting without extensive retraining and large volumes of annotated data. However, collecting and labeling such data is costly and time-consuming, making it impractical for rapid deployment in industrial applications. To address this issue, we propose a novel approach: the creation of AerialCSP, a virtual dataset that simulates aerial imagery of CSP plants. By generating synthetic data that closely mimic real-world conditions, our objective is to facilitate pretraining of models before deployment, significantly reducing the need for extensive manual labeling. Our main contributions are threefold: (1) we introduce AerialCSP, a high-quality synthetic dataset for aerial inspection of CSP plants, providing annotated data for object detection and image segmentation; (2) we benchmark multiple models on AerialCSP, establishing a baseline for CSP-related vision tasks; and (3) we demonstrate that pretraining on AerialCSP significantly improves real-world fault detection, particularly for rare and small defects, reducing the need for extensive manual labeling. AerialCSP is made publicly available at https://mpcutino.github.io/aerialcsp/.

I. Bailo, F. Buonora, G. Ciarfaglia et al.

The energy transition is a key theme of the last decades to determine a future of eco-sustainability, and an area of such importance cannot disregard digitization, innovation and the new technological tools available. This is the context in which the Generative Artificial Intelligence models described in this paper are positioned, developed by Engineering Ingegneria Informatica SpA in order to automate the plant structures acquisition of SNAM energy infrastructure, a leading gas transportation company in Italy and Europe. The digitization of a gas plant consists in registering all its relevant information through the interpretation of the related documentation. The aim of this work is therefore to design an effective solution based on Artificial Intelligence techniques to automate the extraction of the information necessary for the digitization of a plant, in order to streamline the daily work of MGM users. The solution received the P&ID of the plant as input, each one in pdf format, and uses OCR, Vision LLM, Object Detection, Relational Reasoning and optimization algorithms to return an output consisting of two sets of information: a structured overview of the relevant design data and the hierarchical framework of the plant. To achieve convincing results, we extend a state-of-the-art model for Scene Graph Generation introducing a brand new Transformer architecture with the aim of deepening the analysis of the complex relations between the plant's components. The synergistic use of the listed AI-based technologies allowed to overcome many obstacles arising from the high variety of data, due to the lack of standardization. An accuracy of 91\% has been achieved in the extraction of textual information relating to design data. Regarding the plants topology, 93\% of components are correctly identified and the hierarchical structure is extracted with an accuracy around 80\%.

Ran Chen, Rohit S. Chandramouli, Federico Pozzoli et al.

The population of unresolved stellar-mass black hole binaries (sBBHs) is expected to produce a stochastic gravitational-wave background (SGWB) potentially detectable by the Laser Interferometer Space Antenna (LISA). In this work, we compute the imprint of astrophysical environmental effect--such as gas dynamical friction and accretion--on this background. Using the sBBHs population constraints obtained by the LIGO--Virgo--Kagra collaboration, we compute the expected SGWB and develop a phenomenological parametric model that can accurately capture the effect of dynamical friction and accretion. Using our model, we perform Bayesian inference on simulated signals to assess the detectability of these environmental effects. We find that even for large injected values of the Eddington ratio, the effect of accretion in the SGWB is undetectable by LISA. However, LISA will be able to constrain the effect of dynamical friction with an upper bound on the gas density of $ρ\lesssim 7.6 \times 10^{-10} \mathrm{g \, cm^{-3}}$, thus probing the sBBH environment forming in typical thin accretion disks around Active Galactic Nuclei (AGNs). For injected densities of $ρ\sim 10^{-10}-10^{-9} \mathrm{g} \, \mathrm{cm}^{-3}$, dynamical friction effects can be well measured and clearly distinguished from vacuum, with Bayes factors reaching up to $\sim$ 60, even when the Galactic foreground is included.

Karolina Stefanowska, M. Woźniak, R. Dobrucka et al.

Recently, the development of materials based on natural polymers have been observed. This is the result of increasing environmental degradation, as well as increased awareness and consumer expectations. Many industries, especially the packaging industry, face challenges resulting from legal regulations. Chitin is the most common biopolymer right after cellulose and is used to produce chitosan. Due to the properties of chitosan, such as non-toxicity, biocompatibility, as well as antimicrobial properties, chitosan-based materials are used in many industries. Many studies have been conducted to determine the suitability of chitosan materials as food packaging, and their advantages and limitations have been identified. Thanks to the possibility of modifying the chitosan matrix by using natural additives, it is possible to strengthen the antioxidant and antimicrobial activity of chitosan films, which means that, in the near future, chitosan-based materials will be a more environmentally friendly alternative to the plastic packaging used so far. The article presents literature data on the most commonly used natural additives, such as essential oils, plant extracts, or polysaccharides, and their effects on antimicrobial, antioxidant, mechanical, barrier, and optical properties. The application of chitosan as a natural biopolymer in food packaging extends the shelf-life of various food products while simultaneously reducing the use of synthetic plastics, which in turn will have a positive impact on the natural environment. However, further research on chitosan and its combinations with various materials is still needed to extent the application of chitosan in food packaging and bring its application to industrial levels.

L. A. Cuartas, A. P. Cunha, Jéssica Anastácia Alves et al.

Brazil has endured the worst droughts in recorded history over the last decade, resulting in severe socioeconomic and environmental impacts. The country is heavily reliant on water resources, with 77.7% of water consumed for agriculture (irrigation and livestock), 9.7% for the industry, and 11.4% for human supply. Hydropower plants generate about 64% of all electricity consumed. The aim of this study was to improve the current state of knowledge regarding hydrological drought patterns in Brazil, hydrometeorological factors, and their effects on the country’s hydroelectric power plants. The results show that since the drought occurred in 2014/2015 over the Southeast region of Brazil, several basins were sharply impacted and remain in a critical condition until now. Following that event, other regions have experienced droughts, with critical rainfall deficit and high temperatures, causing a pronounced impact on water availability in many of the studied basins. Most of the hydropower plants end the 2020–2021 rainy season by operating at a fraction of their total capacity, and thus the country’s hydropower generation was under critical regime.

Rui Zheng, Yan-ping Bao, Li-hua Zhao et al.

Alisa Kongthong, Pimprapa Chaijak

This study investigated the application of a microbial fuel cell (MFC) system integrated with freshwater microalgae Chlorella sp. TSU-FF67for wastewater treatment, electricity generation, and bio-oil production. The MFC with Chlorella sp. TSU-FF67achieved a significantly higher open-circuit voltage (OCV) of 413.67 ± 15.67 mV compared to the control (13.33 ± 6.38 mV), indicating enhanced bioelectrocatalytic activity. The system also demonstrated efficient organic matter removal from palm oil mill effluent (POME) with a maximum color removal of 95.12 ± 3.50%. Furthermore, Chlorella sp. TSU-FF67 recovered from the PMFC exhibited a remarkable docosahexaenoic acid (DHA) yield of 1,932.28 ± 88.69 µg/mL (1.93 ± 0.08 mg/mL), highlighting its potential as a feedstock for bio-oil production. This work presents a promising approach for sustainable wastewater treatment while simultaneously generating bioelectricity and bio-oil using microalgae-MFC integration.