Hasil untuk "Environmental effects of industries and plants"

Antoine Dechezleprêtre, Misato Sato

This article reviews the empirical literature on the impacts of environmental regulations on firms’ competitiveness as measured by trade, industry location, employment, productivity, and innovation. The evidence shows that environmental regulations can lead to statistically significant adverse effects on trade, employment, plant location, and productivity in the short run, in particular in a well-identified subset of pollution- and energy-intensive sectors, but that these impacts are small relative to general trends in production. At the same time, there is evidence that environmental regulations induce innovation in clean technologies, but the resulting benefits do not appear to be large enough to outweigh the costs of regulations for the regulated entities. As measures to address competitiveness impacts are increasingly incorporated into the design of environmental regulations, future research will be needed to assess the validity and effectiveness of such measures and to ensure they are compatible with the environmental objectives of the policies.

Dewi Junita, Budiastuti Kurniasih, Eka Tarwaca Susila Putra et al.

Increasing soil salinity led to a decline in rice yield, particularly in the coastal areas of West Aceh affected by the 2004 tsunami. This study aimed to examine the characteristics of soil salinity in the coastal rice fields of West Aceh during the rainy and dry growing seasons, and to evaluate its impact on rice yield. Soil sampling was conducted using purposive sampling during two growing seasons: the dry growing season (April-September 2024) and the rainy growing season (October 2024-March 2025). Clustering analysis showed that three clusters (sodic and saline-sodic) formed during the dry season, while two clusters (sodic and non-saline non-sodic) formed during the rainy season. The Suak Pante Breuh site changed cluster membership, being sodic in the dry season and non-saline non-sodic in the rainy season. Among all soil chemical properties, soil electrical conductivity (EC) and sodium adsorption ratio (SAR) showed the strongest negative correlations with rice yield, particularly during the dry growing season (r = -0.55; r = -0.50). This study found that rice yield was more affected by salinity than by sodicity. This was illustrated in the Suak Timah 5 site, which was classified as sodic during the rainy season but showed similar rice yields to non-saline and non-sodic sites. In contrast, during the dry season, this site was classified as saline–sodic and recorded lower rice yields than other locations that were classified as sodic.

Vivek Patil , Nagraj S. Patil and Shruthi R. G.

The strategic use of Soil and Water Conservation (SWC) techniques is essential for efficient watershed management and hydrological processes in semi-arid areas. This study elaborates on a scientific framework following a ridge-to-valley approach for model-based planning of land and drainage line treatments to develop a stage of the watershed known as watershed saturation, where the maximum generated runoff is conserved while maintaining environmental flow downstream. Using the Soil and Water Assessment Tool (SWAT), the study modelled the hydrologic processes in the Chikkodi sub-watershed, a semi-arid region. The assessment of existing SWC measures (bunding, trenching, Checkdam, etc.) formed a key baseline analysis; it revealed a significant 26.17% runoff reduction due to the combined effect of existing SWCs and provided insights for subsequent planning. Based on the model outputs, this study recommends land treatment, that is, bunding and trenching, in the identified critical areas, which effectively intercept the runoff at the source and maximize infiltration. Conservation of the remaining runoff through drainage line treatments (check dams) is proposed as the next crucial step in the ridge-to-valley strategy. This study highlights the necessity of a science-based framework for the sustainable management of semi-arid watersheds, emphasizing that with improved watershed saturation, there is increased local water availability, which supports environmental flow. By combining the assessment of existing treatments with hydrological modeling for proactive planning, the proposed methodology provides a flexible and transferable approach to SWC practice optimization for enhancing watershed water storage in similar semi-arid landscapes.

Fatih Merdan, Ozgur B. Akan

This paper develops an end-to-end odor communication model for stress signaling between plants using Green Leaf Volatiles (GLV). A damaged transmitter plant emits (Z)-3-hexenal, (Z)-3-hexenol, and (Z)-3-hexenyl acetate, which propagate through a time-varying diffusion-advection channel and undergo multiplicative loss at the receiver. The sink plant is modeled with a biochemical receiver network that converts the received GLVs into the defensive metabolite (Z)-3-hexenyl $β$-vicianoside, and an alarm decision is defined based on its concentration level. Numerical results show that (Z)-3-hexenol is the primary driver of the system and that plant perception generally operates in a non-linear region. These findings provide a framework for understanding the evolution of plant-plant communication and for developing next-generation precision farming technologies.

Angela Martina, Lorenzo Ferroni, E. Marrocchino

Rare earth elements (REEs) are increasingly present in the environment owing to their extensive use in modern industries, yet their interactions with plants remain poorly understood. This review explores the soil–plant continuum of REEs, focusing on their geochemical behavior in soil, the mechanisms of plant uptake, and fractionation processes. While REEs are not essential for plant metabolism, they interact with plant structures and interfere with the normal functioning of biological macromolecules. Accordingly, the influence of REEs on the fundamental physiological functions of plants is reviewed, including calcium-mediated signalling and plant morphogenesis. Special attention is paid to the interaction of REEs with photosynthetic machinery and, particularly, the thylakoid membrane. By examining both the beneficial effects at low concentrations and toxicity at higher levels, this review provides some mechanistic insights into the hormetic action of REEs. It is recommended that future research should address knowledge gaps related to the bioavailability of REEs to plants, as well as the short- and long-range transport mechanisms responsible for REE fractionation. A better understanding of REE–plant interactions will be critical in regard to assessing their ecological impact and the potential risks in terms of agricultural and natural ecosystems, to ensure that the benefits of using REEs are not at the expense of environmental integrity or human health.

Itzel Y. López-Pacheco, Arisbe Silva-Núñez, C. Salinas-Salazar et al.

Existence of anthropogenic contaminants (ACs) in different environmental matrices is a serious and unresolved concern. For instance, ACs from different sectors, such as industrial, agricultural, and pharmaceutical, are found in water bodies with considerable endocrine disruptors potency and can damage the biotic components of the environment. The continuous ACs exposure can cause cellular toxicity, apoptosis, genotoxicity, and alterations in sex ratios in human beings. Whereas, aquatic organisms show bioaccumulation, trophic chains, and biomagnification of ACs through different entry route. These problems have been found in many countries around the globe, making them a worldwide concern. ACs have been found in different environmental matrices, such as water reservoirs for human consumption, wastewater treatment plants (WWTPs), drinking water treatment plants (DWTPs), groundwaters, surface waters, rivers, and seas, which demonstrate their free movement within the environment in an uncontrolled manner. This work provides a detailed overview of ACs occurrence in water bodies along with their toxicological effect on living organisms. The literature data reported between 2017 and 2018 is compiled following inclusion-exclusion criteria, and the obtained information was mapped as per type and source of ACs. The most important ACs are pharmaceuticals (diclofenac, ibuprofen, naproxen, ofloxacin, acetaminophen, progesterone ranitidine, and testosterone), agricultural products or pesticides (atrazine, carbendazim, fipronil), narcotics and illegal drugs (amphetamines, cocaine, and benzoylecgonine), food industry derivatives (bisphenol A, and caffeine), and personal care products (triclosan, and other related surfactants). Considering this threatening issue, robust detection and removal strategies must be considered in the design of WWTPs and DWTPs.

Rodrigo A. Contreras, Karla Sepúlveda, Gustavo E. Zúñiga

The Antarctic vascular plant Colobanthus quitensis has developed unique mechanisms to survive in extreme environmental conditions, such as high UV-B radiation and nutrient scarcity. This study evaluated the effects of immersion frequency in a temporary immersion bioreactor (TIB) on biomass growth, phenolic compound accumulation, and flavonoid synthesis. Plants subjected to 24-hour immersion pulses exhibited the highest biomass yield (192.7%), phenylalanine ammonia-lyase (PAL) activity (52 μkats/kg protein), and total phenolic content (TPC; 40 mg GAE/g dry weight). Flavonoids such as neoschaftoside, saponarin, and schaftoside significantly accumulated under these conditions, demonstrating the metabolic shift induced by controlled nutrient deprivation and improved aeration. The findings highlight that intermittent stress optimises secondary metabolite production, providing a sustainable pathway for bioactive compound extraction. These phenolic metabolites have potential applications in agriculture as antifungal agents, in cosmetics for UV-B protection, and functional foods as antioxidants. This study underscores the relevance of TIB systems as scalable and efficient tools for enhancing plant-derived bioactive compounds, contributing to sustainability across various industries.

Sabreen Bashir, M. Siddiqui, A. Al-Khedhairy et al.

Cannabis sativa (Hemp) is renowned for its diverse applications across multiple industries. This versatile plant is utilized in food production, paper manufacturing, pharmaceutical development, cosmetic formulations, biofuel generation, and most notably, in the textile sector. The hemp fiber’s mechanical performance, low cost, and environmental sustainability make it a promising alternative to conventional fiber but the plant is highly susceptible to several agronomic and environmental factors, particularly herbicides. Our research investigates the impact of glyphosate and metribuzin herbicides on Cannabis sativa fiber quantity and quality. Cellulose and hemicellulose content, mechanical properties, and morphological features of fiber from treated plants were analyzed. Herbicide treatments significantly affected fiber composition and properties. Treatment with low-concentration glyphosate resulted in a reduction of both cellulose and hemicellulose content, whereas low-concentration metribuzin induced a slight increase in cellulose levels. Exposure to high concentrations of either herbicide led to a significant decrease in both cellulose and hemicellulose components. Mechanical tests and X-Ray Diffraction revealed that low-concentration glyphosate weakened fiber’s tensile strength, whereas low-concentration metribuzin enhanced it. However, high concentrations of both herbicides decreased tensile strength. Bast fiber content initially increased with low herbicide concentrations but declined at higher levels. Scanning electron microscopy revealed progressive structural damage to fiber with increasing herbicide concentrations. Glyphosate caused surface disruption, while metribuzin induced more severe degradation, including surface erosion and bubbling at high concentrations. These findings highlight the complex effects of glyphosate and metribuzin on Cannabis sativa fiber properties, emphasizing the need for careful consideration of herbicide use in hemp cultivation for textile production.

Elisabet Freyja Henriksson, Mugahid Elnour, Michael Martin

This study aimed to analyse the environmental performance of a commercial aquaponic system in Sweden. The system produces rainbow trout and 40+ varieties of leafy greens, edible flowers and herbs, about 50 % of which is lettuce, in total about 1200 kgs of plants was sold in 2022. Attributional life cycle assessment was employed to assess the environmental impacts of producing rainbow trout and leafy greens, with functional unit of 1 kg of leafy greens, employing system expansion for replaced conventionally produced trout. Results show a climate change impact of 3.94 kg CO2eq/kg plant. The results suggest that energy, infrastructure, and consumables were the main contributors to most environmental impact categories of the modelled system. Electricity contributed to 52 % of climate change impacts. Specifically, electricity used for artificial lighting represents 45 % of the electricity use. Reduced photoperiod was found to lead to reduced impact in all impact categories assessed. Another scenario explored the use of an insect-based feed using insects produced in Sweden as a possible pathway for increased circularity of the system. For the insect-based feed results depended on modelling choices (e.g. burden or no burden) for the food waste used for insect farming. Furthermore, the results were sensitive to the choice of electricity mix employed. This study advances the LCA literature in aquaponics by providing an actual case study with empirical evidence of aquaponics' sustainability and improvement pathways. The evidence shows results that existing and future aquaponics systems can use to optimise their performance and address potential trade-offs.

Ravi Sharma and Vinayak Patil

The present comprehensive study seeks to evaluate the institutional climate capacity of Community-based Organizations (CBOs) involved in coastal ecotourism conservation projects along the Maharashtra coastal region in India. The primary objective is to understand the community interactions, organizational structures, and adaptive capacities of CBOs in the face of climate change, utilizing an integrated approach through participative and stakeholder interaction. The research methodology employed through the integrated investigated assessment, which includes- focused group discussions (n=06) and a survey of key informants’ interviews and community participants (n=143), additionally were added to this set of data combined for a total of 204 respondents, to comprehensively evaluate the institutional climate capacity of the CSOs engaged in coastal ecotourism projects. The findings identify key dimensions influencing CBO-led conservation projects, emphasizing the importance of different actors’ interplay and processes reflected through the communities. Notable strengths include effective communication, inclusive planning, and budgetary processes contributing to climate action orientation, emphasizing strengths in communication, inclusive planning, and budgetary processes. Socially excluded groups actively participate, underscoring the significance of their involvement for project success. Integrating socio-cultural factors into climate change planning is highlighted, emphasizing the need for quantitative research in this area. These identified key dimensions influence the CSO’s institutional climate capacities.

Ziting Shen, Huimin Wu, Yanqing Liu et al.

Gesneriaceae is an important wildflower resource in China, with many new varieties of high ornamental value becoming increasingly popular in the market. Additionally, Gesneriaceae species have been used extensively in traditional ethnomedicine. Nevertheless, comprehensive studies on the chemical constituents and antioxidant activities of Gesneriaceae leaves remain limited. In this study, metabolomic analysis of leaf chemical components from ten Gesneriaceae taxa tentatively identified 48 metabolites across ten compound classes, including flavonoids, phenolic acids, and terpenoids. To further investigate the biological functions of these metabolites, the total flavonoid and phenolic contents were measured, and their in vitro antioxidant activities were evaluated. The results indicated significant variability in antioxidant activities across Gesneriaceae taxa, with phylloflavanine and isoacteoside as key components influencing this activity. Finally, through comprehensive evaluation, two new ornamental varieties—Primulina 'Purple Sundae' and Primulina 'Spring of Beilin'— were identified for their medicinal and edible development potential, providing a theoretical foundation for exploring multifunctional value in Gesneriaceae ornamental plants.

Naycari Forfora, Rhonald Ortega, Isabel Urdaneta et al.

This study assesses the environmental impact of producing consumer bath tissue (CBT) in the United States using Brazilian bleached eucalyptus kraft (BEK) and Canadian northern bleached softwood kraft (NBSK) market pulps, in comparison to bamboo-based CBT from China. Additionally, the analysis includes considerations of soil organic carbon (SOC) sequestration from plant growth, and the biogenic global warming potential (GWPbio) based on biomass rotation periods.Results indicate a cradle-to-grave carbon footprint (CF) of 1824 kg CO2eq/air-dry ton (ADt) for US CBT (70 % BEK/30 % NBSK) using Light Dry Creped (LDC) technology. Substituting BBK for BEK/NBSK increases CF to 2041 kg CO2eq/ADt, with Chinese manufactured CBT at 2400 kg CO2eq/ADt. Using Creped Trough Air Drying (CTAD), CF rises to 2531 and 2739 kg CO2eq/ADt for BEK-NBSK and BEK-BBK mixtures, respectively. Including SOC factors do not change the conclusions. While the GWPbio factors are highly dependent on the time horizon considered. These results emphasize production technologies' critical role in tissue sustainability and challenge bamboo's perceived environmental advantages.

Alen Joy, Chandni Thiruthikkatt

Heavy metals are an important category of elements that are commonly used in the industrial sector. Among these elements, cadmium is utilized in various types of industries. It may accumulate in the environmental systems such as water and soil as a result of industrial activities. Exposure to cadmium may evoke several ill effects on human, plant, and animal life. The well water sources in the industrial areas face a major threat from the exposure of cadmium used by the industries. The guidelines of the World Health Organization only permit a maximum amount of 3μg per liter of drinking water. In this study, 14 well water samples from the industrial area of Ollur in the Thrissur district of Kerala, India are collected and the concentration of cadmium is identified. The analysis was conducted by using an inductively coupled plasma optical emission spectroscope (ICP-OES), which is a highly sensitive instrument used in the identification of trace elements. Among the 14 samples analysed, several samples exceeded the maximum accepted concentration of cadmium according to the guidelines of WHO.

Dmitrii Usenko, David Helman, Chen Giladi

Accurate estimation of total leaf area (TLA) is crucial for evaluating plant growth, photosynthetic activity, and transpiration. However, it remains challenging for bushy plants like dwarf tomatoes due to their complex canopies. Traditional methods are often labor-intensive, damaging to plants, or limited in capturing canopy complexity. This study evaluated a non-destructive method combining sequential 3D reconstructions from RGB images and machine learning to estimate TLA for three dwarf tomato cultivars: Mohamed, Hahms Gelbe Topftomate, and Red Robin -- grown under controlled greenhouse conditions. Two experiments (spring-summer and autumn-winter) included 73 plants, yielding 418 TLA measurements via an "onion" approach. High-resolution videos were recorded, and 500 frames per plant were used for 3D reconstruction. Point clouds were processed using four algorithms (Alpha Shape, Marching Cubes, Poisson's, Ball Pivoting), and meshes were evaluated with seven regression models: Multivariable Linear Regression, Lasso Regression, Ridge Regression, Elastic Net Regression, Random Forest, Extreme Gradient Boosting, and Multilayer Perceptron. The Alpha Shape reconstruction ($α= 3$) with Extreme Gradient Boosting achieved the best performance ($R^2 = 0.80$, $MAE = 489 cm^2$). Cross-experiment validation showed robust results ($R^2 = 0.56$, $MAE = 579 cm^2$). Feature importance analysis identified height, width, and surface area as key predictors. This scalable, automated TLA estimation method is suited for urban farming and precision agriculture, offering applications in automated pruning, resource efficiency, and sustainable food production. The approach demonstrated robustness across variable environmental conditions and canopy structures.

Robin-Nico Kampa, Fabian Deuser, Konrad Habel et al.

Plant phenotyping involves analyzing observable characteristics of plants to better understand their growth, health, and development. In the context of deep learning, this analysis is often approached through single-view classification or regression models. However, these methods often fail to capture all information required for accurate estimation of target phenotypic traits, which can adversely affect plant health assessment and harvest readiness prediction. To address this, the Growth Modelling (GroMo) Grand Challenge at ACM Multimedia 2025 provides a multi-view dataset featuring multiple plants and two tasks: Plant Age Prediction and Leaf Count Estimation. Each plant is photographed from multiple heights and angles, leading to significant overlap and redundancy in the captured information. To learn view-invariant embeddings, we incorporate 24 views, referred to as the selection vector, in a random selection. Our ViewSparsifier approach won both tasks. For further improvement and as a direction for future research, we also experimented with randomized view selection across all five height levels (120 views total), referred to as selection matrices.

Yorick Estievenart, Sukanya Patra, Souhaib Ben Taieb

Efficient and reliable operation of Concentrated Solar Power (CSP) plants is essential for meeting the growing demand for sustainable energy. However, high-temperature solar receivers face severe operational risks, such as freezing, deformation, and corrosion, resulting in costly downtime and maintenance. To monitor CSP plants, cameras mounted on solar receivers record infrared images at irregular intervals ranging from one to five minutes throughout the day. Anomalous images can be detected by thresholding an anomaly score, where the threshold is chosen to optimize metrics such as the F1-score on a validation set. This work proposes a framework, using risk control, for generating more reliable decision thresholds with finite-sample coverage guarantees on any chosen risk function. Our framework also incorporates an abstention mechanism, allowing high-risk predictions to be deferred to domain experts. Second, we propose a density forecasting method to estimate the likelihood of an observed image given a sequence of previously observed images, using this likelihood as its anomaly score. Third, we analyze the deployment results of our framework across multiple training scenarios over several months for two CSP plants. This analysis provides valuable insights to our industry partner for optimizing maintenance operations. Finally, given the confidential nature of our dataset, we provide an extended simulated dataset, leveraging recent advancements in generative modeling to create diverse thermal images that simulate multiple CSP plants. Our code is publicly available.

Abdulrahman Bukhari, Bullo Mamo, Mst Shamima Hossain et al.

With rising demands for efficient disease and salinity management in agriculture, early detection of plant stressors is crucial, particularly for high-value crops like avocados. This paper presents a comprehensive evaluation of low-cost sensors deployed in the field for early stress and disease detection in avocado plants. Our monitoring system was deployed across 72 plants divided into four treatment categories within a greenhouse environment, with data collected over six months. While leaf temperature and conductivity measurements, widely used metrics for controlled settings, were found unreliable in field conditions due to environmental interference and positioning challenges, leaf spectral measurements produced statistically significant results when combined with our machine learning approach. For soil data analysis, we developed a two-level hierarchical classifier that leverages domain knowledge about treatment characteristics, achieving 75-86\% accuracy across different avocado genotypes and outperforming conventional machine learning approaches by over 20\%. In addition, performance evaluation on an embedded edge device demonstrated the viability of our approach for resource-constrained environments, with reasonable computational efficiency while maintaining high classification accuracy. Our work bridges the gap between theoretical potential and practical application of low-cost sensors in agriculture and offers insights for developing affordable, scalable monitoring systems.

Karim Khamaisi, Nicolas Keller, Stefan Krummenacher et al.

In the context of industrial factories and energy producers, unplanned outages are highly costly and difficult to service. However, existing acoustic-anomaly detection studies largely rely on generic industrial or synthetic datasets, with few focused on hydropower plants due to limited access. This paper presents a comparative analysis of acoustic-based anomaly detection methods, as a way to improve predictive maintenance in hydropower plants. We address key challenges in the acoustic preprocessing under highly noisy conditions before extracting time- and frequency-domain features. Then, we benchmark three machine learning models: LSTM AE, K-Means, and OC-SVM, which are tested on two real-world datasets from the Rodundwerk II pumped-storage plant in Austria, one with induced anomalies and one with real-world conditions. The One-Class SVM achieved the best trade-off of accuracy (ROC AUC 0.966-0.998) and minimal training time, while the LSTM autoencoder delivered strong detection (ROC AUC 0.889-0.997) at the expense of higher computational cost.

Xulong Yuan

In future space-borne \ac{GW} observations, matter arround the sources might influence the evolution and \ac{GW} signals from \ac{BBH} inspirals, which can be mistaken as deviations from \ac{GR}. Former research \cite{yuan2024} proposed a statistic $F$ that characterizes the dispersion of measured parameters to distinguish environmental effect(\ac{DF} from \ac{DM} spike) and theory of modified gravity effect(varying $G$). In this work we use the statistic to distinguish other couples of effects with \ac{GW} corrections at $-4$ PN order: \ac{DF} from \ac{DM} spike and the extra dimension theory, additionally try to determine the distinguishing threshold in more reasonable way to avoid arbitrariness, especially when the two effects to compare have more overlap in the $F$ distribution. Sources of different astronomical models are also considered, and two effects are still distinguishable but not much as in former work\cite{yuan2024}, so the threshold should be carefully selected. Following these procedures, we finally obtain the statistic thresholds of distinguishment between the three effects with \ac{GW} corrections at $-4$ PN order: \ac{DF} from \ac{DM} spike, the extra dimension theory, and varying $G$ theory. The method can be used to distinguish other effects among environmental effects and modified theories of gravity effects with the detections of \ac{GW} events.

Georges Dubourg, Zoran Pavlovic, B. Bajac et al.

The application of metal oxide nanomaterials (MOx NMs) in the agrifood industry offers innovative solutions that can facilitate a paradigm shift in a sector that is currently facing challenges in meeting the growing requirements for food production, while safeguarding the environment from the impacts of current agriculture practices. This review comprehensively illustrates recent advancements and applications of MOx for sustainable practices in the food and agricultural industries and environmental preservation. Relevant published data point out that MOx NMs can be tailored for specific properties, enabling advanced design concepts with improved features for various applications in the agrifood industry. Applications include nano-agrochemical formulation, control of food quality through nanosensors, and smart food packaging. Furthermore, recent research suggests MOx's vital role in addressing environmental challenges by removing toxic elements from contaminated soil and water. This mitigates the environmental effects of widespread agrichemical use and creates a more favorable environment for plant growth. The review also discusses potential barriers, particularly regarding MOx toxicity and risk evaluation. Fundamental concerns about possible adverse effects on human health and the environment must be addressed to establish an appropriate regulatory framework for nano metal oxide-based food and agricultural products.