Hasil untuk "Environmental effects of industries and plants"

P. Borowski

In the 21st century, it is becoming increasingly clear that human activities and the activities of enterprises affect the environment. Therefore, it is important to learn about the methods in which companies minimize the negative effects of their activities. The article presents the steps taken and innovative actions carried out by enterprises in the energy sector. The article analyzes innovative activities undertaken and implemented by enterprises from the energy sector. The relationships between innovative strategies, including, inter alia, digitization, and Industry 4.0 solutions, in the development of companies and the achieved results concerning sustainable development and environmental impact. Digitization has far exceeded traditional productivity improvement ranges of 3–5% per year, with a clear cost improvement potential of well above 25%. Enterprises on a large scale make attempts to increase energy efficiency by implementing the state-of-the-art innovative technical and technological solutions, which increase reliability and durability (material and mechanical engineering). Digitization of energy companies allows them to reduce operating costs and increases efficiency. With digital advances, the useful life of an energy plant can be increased up to 30%. Advanced technologies, blockchain, and the use of intelligent networks enables the activation of prosumers in the electricity market. Reducing energy consumption in industry and at the same time increasing energy efficiency for which the European Union is fighting in the clean air package for all Europeans have a positive impact on environmental protection, sustainable development, and the implementation of the decarbonization program.

Erni Saurmalinda Butar Butar, Jedtavong Thepvongsa

Soil erosion posed a significant environmental challenge in river basin environments, threatening agricultural productivity, compromising water quality, and eroding ecosystem integrity. The Teesta River Basin, an ecologically sensitive and economically important region, is increasingly affected by erosion driven by natural and anthropogenic factors. This study employed the Revised Universal Soil Loss Equation (RUSLE) alongside Google Earth Engine (GEE) and Geographic Information System (GIS) tools to evaluate the spatial distribution of soil erosion. The model incorporates rainfall erosivity (R), soil erodibility (K), slope length and steepness (LS), cover management (C), and support practices (P), processed with high-resolution remote sensing datasets. Results indicate strong spatial variability, with average soil loss estimated at 11.25 t/ha/yr. About 44% of the basin experiences very low erosion, 21% low, 6% moderate, 10% severe, and 7% very severe erosion. Agricultural land, the dominant cover type (391,796.9 ha), shows the highest average soil loss (112 t/ha/yr), largely due to continuous tillage, residue removal, and unsustainable practices. Nearly 59% of cropland faces high erosion risk compared to other land covers. Prioritization of erosion-prone areas reveals that 7% of the basin falls into high priority (very severe), 10% medium priority (severe), and over 70% low priority (low and very low). These findings offer crucial guidance for implementing targeted soil conservation measures and informing sustainable land use planning. The study highlights the effectiveness of integrating RUSLE with GEE for large-scale erosion assessment and watershed management.

Citra Dewi, Dyah Indriana Kusumastuti, Slamet Budi Yuwono et al.

Changes in land use and land cover (LULC) are among the main drivers of soil degradation, especially in urban areas under strong development pressure. The lack of land in urban areas often pushes development toward ecologically sensitive areas, such as hillslopes and riverbanks. These practices may alter soil biophysical characteristics and accelerate local-scale environmental degradation. Accordingly, predicting land-use and land-cover change is vital for assessing the potential risk of future soil degradation. Many spatial modeling methods have been developed to predict LULC change dynamics; however, their association with soil quality degradation has yet to be systematically illustrated in the scientific literature. Research on LULC change prediction and its implications for soil quality degradation is widely scattered across the scientific literature. This review conducted a literature search of the Scopus database and analyzed the research trends, methodological approaches, and the relationship between land cover change and soil quality degradation. The review results showed that LULC change is consistently linked to subsequent declines in soil characteristics, such as soil organic carbon, erosion, and soil structural stability. These results underscore the need for predictive models as valuable tools for anticipating soil degradation risks and guiding sustainable land use planning.

Payel Das, Dayana Das, Raghu Raman

This study provides a systematic literature review on the topic of sustainable fashion (SF) research through the lens of the United Nations Sustainable Development Goals (SDGs). The study employs the Theory–Context–Characteristics–Method to analyze 502 peer-reviewed journal articles published from 2015 to 2024 using BERTopic, an unsupervised machine learning approach that identifies latent thematic structures and research trends in the SF literature. The analysis centers on six main research themes Sustainable Fashion–Design Education, Consumption Dynamics, Consumption Pioneers, Circular Fashion–Industry Analysis, Eco-Textile Innovation, and Social Media Influence. Mapping these against the 17 SDGs showed strong associations with SDG 12 (Responsible Consumption and Production), SDG 9 (Industry, Innovation and Infrastructure), and SDG 13 (Climate Action), while SDGs 14 and 15 remain relatively unexplored. Regional disparities between the Global North and South are also illuminated as well as an over-reliance on conceptual and quantitative methods with a dearth of longitudinal, simulation-based, and biometric approaches. The results yield a systematic research agenda over TCCM dimensions. This review deepens and integrates existing sustainable fashion scholarship by systematically mapping dominant research themes and methodological patterns to the SDGs. By combining machine-learning-assisted topic modeling with expert validation and organizing insights through the TCCM framework, the study offers a comprehensive, SDG-anchored synthesis and a future-oriented research agenda for scholars, practitioners, and policymakers.

Anteneh D. Sewenet, Youssef Boulaksil, Paola Pisano

Circular economy (CE), circularity, and sustainability are conceptually distinct yet intrinsically interconnected frameworks that collectively offer transformative potential for addressing global environmental, economic, and social challenges. While each has received substantial scholarly attention, their integrative relationships and cumulative implications remain under-theorized and operationalized. In particular, the link between circularity (the extent to which materials and products circulate within closed loops) and broader sustainability outcomes lacks systematic examination. This study presents a systematic literature review of 151 peer-reviewed articles retrieved from the Scopus and Web of Science databases following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) protocol. Through a critical synthesis and thematic analysis, this review evaluates foundational conceptual constructs, emergent research themes, and enduring conceptual and methodological gaps within the CE–circularity–sustainability nexus. In response, this review proposes a conceptual framework that elucidates the dynamic interlinkages among these concepts, thereby providing a structured pathway to guide interdisciplinary research, policy development, and strategic practice towards integrated and systemic sustainable development.

Samuel Assefa, J. Gareth Polhill, Jianyu Chen et al.

The transition to a circular economy (CE) requires systemic perspectives to comprehend the complex behaviours of actors and support their decision-making processes. Agent-based modelling (ABM) has proven to be a promising approach to studying complex nonlinear phenomena in human and natural systems like CE. This critical review paper aims to understand the current state of ABM application in CE research and explore potential future study areas. Bibliometric and content analyses were applied with a stock of 45 articles employing ABM in CE, in addition to 13,797 CE articles that have not utilized ABM. The results called for expanding the application areas of CE research using ABM, focusing on policy and institutional environment, sustainable life cycle management, broadening scope of CE strategies, and social and environmental impacts. The content analysis revealed the need to employ more robust and up-to-date methodologies, such as theories underpinning agent behaviours, data sources and assumptions, validation and simulation procedure, replication of model runs, model representation and documentation, and stakeholders’ participation in the modelling process. This study introduces a participatory-integrated framework designed to equip CE researchers employing ABM with guidance for robust modelling and simulation practices. Such ABMs will offer valuable insights for developing effective policies, strategies, and business practices toward enhanced sustainability.

G. Mohan, S. Meenachi, K. Kiruthika and D. Kirthiga

This research explores the effectiveness of ZnO and Ag-doped ZnO photocatalysts in degrading organic pollutants, specifically focusing on phenol removal in wastewater treatment. The catalysts were synthesized using sol-gel and precipitation methods and characterized through XRD, SEM, and EDX analyses. The study assessed the degradation efficiency of phenol under various conditions, including different catalyst dosages, irradiation times, and initial phenol concentrations. UV-vis spectroscopy was used to measure degradation efficiency, revealing significant differences between the two catalysts. Ag-doped ZnO showed superior performance, achieving degradation efficiencies of over 90%, compared to ZnO’s 60-70%. Statistical analyses, including ANOVA and Response Surface Methodology (RSM), identified key factors influencing degradation efficiency. The enhanced performance of Ag-doped ZnO was attributed to its narrower band gap energy and improved irradiation responsiveness. These findings indicate that Ag-doped ZnO is a promising candidate for efficient and sustainable wastewater treatment, offering a robust solution for removing organic impurities and supporting environmental preservation. This research provides valuable insights into advanced photocatalytic processes and sets the stage for future wastewater treatment innovations.

Willis Muhammad Iqbal, Hashfi Hawali Abdul Matin and Prabang Setyono

The waste problem has become a big problem in Indonesia as the population continues to grow. The daily amount of waste generated in Yogyakarta City is 303.13 tons.day-1, with the composition of the largest waste source, namely household waste, around 63.75%. This data shows that there is a need for improvements related to management; 3R Temporary Waste Disposal Sites is an alternative for reducing waste before it is transported to the final processing place. This research aims to understand performance and waste transportation management and evaluate the level of waste management and sustainability of waste management at 3R Temporary Waste Disposal Sites Nitikan Yogyakarta. This research was conducted on 99 respondents using a purposive sampling method; the data analysis used was the evaluation of waste transportation, analysis of incoming, managed, and unmanaged waste data, categorization of questionnaire data, evaluation of waste management performance and analysis of the sustainability of waste management using RAPFISH software. The research results show that waste volume management at 3R Temporary Waste Disposal Sites Nitikan is 941.15 kg.day-1, and compost production is 190.65 kg.day-1. Transport management is carried out using the Stationary Container System (SCS) and is carried out 2 times. The evaluation of waste management performance is moderate, with a total relative value of 15.4, based on studies on the technical sector, institutional sector, financial sector, and the area of community participation. Based on the attribute index in each sector, it is concluded that the sustainability status of waste sorting and management at 3R Temporary Waste Disposal Sites Nitikan is 79.03, or very sustainable.

Milton Mbugano, Juma Rajabu Selemani, Baraka Kichonge et al.

Storing vaccines and perishable food in regions without access to the national grid presents significant challenges. Solar power generation technologies have emerged as a viable alternative solution to address these issues. This study conducted a life cycle assessment (LCA) and cost analysis (CA) of the locally developed solar-powered cooler to assess its economic viability and potential environmental impacts. The cooler was designed to preserve vaccines and perishable foods for use, especially in areas with no electricity connectivity, as a cheaper alternative to electricity-powered coolers. The results of LCA show that battery manufacturing was a slightly higher contributor to environmental impacts across various indicators, with terrestrial ecotoxicity identified as the highest impact among other environmental impacts. Cost analysis results further revealed that a solar-powered cooler project demonstrated a positive economic outlook, with the unit manufacturing cost estimated at USD 2682. This quantitative analysis of life cycle and cost will help decision-makers comprehend both the economic aspects and environmental impacts throughout the life cycle of locally manufactured solar-powered coolers. Such insights will be instrumental in enhancing the sustainability of these products.

Kai Rüdele, Barbara Linke, Matthias Wolf

The product carbon footprint (PCF) is a fundamental tool that offers a comprehensive analysis of emissions on the product level to re-think product designs and industrial processes to launch new climate benign products. Methodical issues associated with the determination of PCFs have been discussed for a long time. This article aims to assess the up to now unexplored inclusion of manufacturing tools as contributor to industrial process emissions. Based on literature and results of a previous survey, it can be concluded that the production and use of tools is barely considered in PCF studies and viewed as less significant than other inputs. However, supplementary case studies conducted in the automotive supply industry show that in individual cases greenhouse gas emissions related to tool use can have a considerable effect on the PCF, depending on the process and production volume. Based on these findings, we developed a guide to support the decision on whether to incorporate or exclude tool consumption in PCF studies.

Rianita Pertiwi, Junun Sartohadi, M. Anggri Setiawan et al.

Road construction with intensive slope cutting increases landslide susceptibility along the road section, especially in hilly areas such as Kaligesing, Indonesia. This study aimed to compile a landslide susceptibility map along the road section in Kaligesing and evaluate the level of susceptibility based on the main causal factors. GIS approach and quantitative statistical analysis Frequency Ratio (FR) were used in the susceptibility model. Eighty-two landslide points were randomly divided into training (70%) and testing (30%) datasets. Twelve causal factors were used in the analysis: slope direction, elevation, lithology, slope gradient, curvature, hemeroby degree, Topographic Wetness Index (TWI), distance from the river, distance from the road, rainfall, soil texture, and soil aggregate. Model validation used the Area Under Curve (AUC) value to evaluate model performance. The findings showed that the model is accurate, with an AUC value of 0.75 for the training set and 0.71 for the testing set. Furthermore, the level of landslide susceptibility is divided into four classes, namely very high (73 km), high (70.77 km), moderate (0.07 km), and very low (0.03 km). Thus, the findings can be used to support decision-making and planning for more adaptive road infrastructure development in landslide-prone areas.

Badariah Badariah, Sarah Fiebrina Heraningsih, Saldi Yulistian et al.

Contamination by heavy metals, especially lead (Pb) and cadmium (Cd), presents considerable environmental and public health hazards. This study examined the efficacy of a composite adsorbent composed of eggshell powder (ESP) and Palm Oil Fuel Ash (POFA) in eliminating Pb and Cd from polluted river water. BET analysis indicated that the ESP-POFA composite demonstrated enhanced surface area and pore volume post-activation, achieving a peak surface area of 38.79 m²/g at a 1:0.75 ESP:POFA ratio. The peak adsorption efficiency for Pb was 87.16%, achieved at a 1:0.25 ESP: POFA ratio after 180 minutes of agitation, whereas Cd adsorption peaked at 94.17% at a 1:0.75 ratio with the same agitation time. The adsorption capacity fluctuated according to the ESP: POFA ratio, with Pb attaining 0.00038 mg/g at the 1:0.25 ratio and Cd achieving 0.00099 mg/g at the 1:0.75 ratio. The findings demonstrate that the ESP-POFA composite is a sustainable and economical solution for water treatment and is exceptionally proficient in removing cadmium from river water. These findings endorse the composite's potential for extensive water treatment applications; nevertheless, additional modification is necessary to enhance Pb adsorption capability.

Ali Rhouma, Nikolaos Mellios, Floor Brouwer et al.

Optimizing agricultural inputs at the farm scale requires a holistic understanding of water, energy, food, and ecosystem (WEFE) interdependencies. This study develops a composite Water–Energy–Food–Ecosystem Nexus Indicator (WEFENI) and applies System Dynamics Modelling (SDM) to assess vineyard sustainability in northern Spain. This study is the first to introduce WEFENI at a fine spatial resolution, applying it at the grape variety and small-plot level to capture sustainability differences within a single farm. Five key indicators water footprint, carbon footprint, energy footprint, income, and productivity were selected based on their relevance to environmental and socio-economic performance. Primary data were collected through structured questionnaires and field measurements, complemented by secondary data from meteorological and governmental databases. The indicators were normalized, weighted using the CRiteria Importance Through Intercriteria Correlation (CRITIC) method, and aggregated into a composite WEFENI. The dynamic model was constructed to simulate monthly interactions within the WEFE nexus, enabling scenario-based analysis and capturing feedback-driven behaviour across resource systems.Results show substantial variation in WEFENI scores across agro-climatic zones and grape varieties. The Low Zone achieved the highest score (0.739) due to gravity-fed irrigation and low energy demand, while the High Zone scored lowest (0.556) because of energy-intensive pumping. At the variety level, 15 grape varieties demonstrated a balance between high sustainability and profitability demonstrating the added value of WEFENI in identifying optimal crop choices. Scenario analysis revealed that precision agriculture produced the greatest improvement in WEFENI (+0.102), followed by improved energy efficiency (+0.056), whereas reduced precipitation decreased the score (−0.056).The proposed framework enhances the replicability of farm-level sustainability assessments by explicitly defining indicator selection, system boundaries, and calculation procedures. The integration of WEFENI with SDM enables dynamic, scenario-based evaluation of trade-offs and synergies, providing a robust decision-support tool for sustainable resource management in agriculture.

Herve Goeau, Pierre Bonnet, Alexis Joly

Automated identification of plants has improved considerably thanks to the recent progress in deep learning and the availability of training data with more and more photos in the field. However, this profusion of data only concerns a few tens of thousands of species, mostly located in North America and Western Europe, much less in the richest regions in terms of biodiversity such as tropical countries. On the other hand, for several centuries, botanists have collected, catalogued and systematically stored plant specimens in herbaria, particularly in tropical regions, and the recent efforts by the biodiversity informatics community made it possible to put millions of digitized sheets online. The LifeCLEF 2020 Plant Identification challenge (or "PlantCLEF 2020") was designed to evaluate to what extent automated identification on the flora of data deficient regions can be improved by the use of herbarium collections. It is based on a dataset of about 1,000 species mainly focused on the South America's Guiana Shield, an area known to have one of the greatest diversity of plants in the world. The challenge was evaluated as a cross-domain classification task where the training set consist of several hundred thousand herbarium sheets and few thousand of photos to enable learning a mapping between the two domains. The test set was exclusively composed of photos in the field. This paper presents the resources and assessments of the conducted evaluation, summarizes the approaches and systems employed by the participating research groups, and provides an analysis of the main outcomes.

Hui Sun, Zheng Xie, Hao-Yuan He et al.

Unsupervised Domain Adaptation~(UDA) focuses on transferring knowledge from a labeled source domain to an unlabeled target domain, addressing the challenge of \emph{domain shift}. Significant domain shifts hinder effective knowledge transfer, leading to \emph{negative transfer} and deteriorating model performance. Therefore, mitigating negative transfer is essential. This study revisits negative transfer through the lens of causally disentangled learning, emphasizing cross-domain discriminative disagreement on non-causal environmental features as a critical factor. Our theoretical analysis reveals that overreliance on non-causal environmental features as the environment evolves can cause discriminative disagreements~(termed \emph{environmental disagreement}), thereby resulting in negative transfer. To address this, we propose Reducing Environmental Disagreement~(RED), which disentangles each sample into domain-invariant causal features and domain-specific non-causal environmental features via adversarially training domain-specific environmental feature extractors in the opposite domains. Subsequently, RED estimates and reduces environmental disagreement based on domain-specific non-causal environmental features. Experimental results confirm that RED effectively mitigates negative transfer and achieves state-of-the-art performance.

Zoe Pfister, Michael Vierhauser, Alzbeta Medvedova et al.

Forests are an essential part of our biosphere, regulating climate, acting as a sink for greenhouse gases, and providing numerous other ecosystem services. However, they are negatively impacted by climatic stressors such as drought or heat waves. In this paper, we introduce FORTE, an open-source system for environmental monitoring with the aim of understanding how forests react to such stressors. It consists of two key components: (1) a wireless sensor network (WSN) deployed in the forest for data collection, and (2) a Data Infrastructure for data processing, storage, and visualization. The WSN contains a Central Unit capable of transmitting data to the Data Infrastructure via LTE-M and several spatially independent Satellites that collect data over large areas and transmit them wirelessly to the Central Unit. Our prototype deployments show that our solution is cost-effective compared to commercial solutions, energy-efficient with sensor nodes lasting for several months on a single charge, and reliable in terms of data quality. FORTE's flexible architecture makes it suitable for a wide range of environmental monitoring applications beyond forest monitoring. The contributions of this paper are three-fold. First, we describe the high-level requirements necessary for developing an environmental monitoring system. Second, we present an architecture and prototype implementation of the requirements by introducing our FORTE platform and demonstrating its effectiveness through multiple field tests. Lastly, we provide source code, documentation, and hardware design artifacts as part of our open-source repository.

Sayak Mukherjee, Himanshu Sharma, Wenceslao Shaw Cortez et al.

This research investigates designing a supervisory feedback controller for a hybrid power plant that coordinates the wind, solar, and battery energy storage plants to meet the desired power demands. We have explored an online feedback control design that does not require detailed knowledge about the models, known as feedback optimization. The control inputs are updated using the gradient information of the cost and the outputs with respect to the input control commands. This enables us to adjust the active power references of wind, solar, and storage plants to meet the power generation requirements set by grid operators. The methodology also ensures robust control performance in the presence of uncertainties in the weather. In this paper, we focus on describing the supervisory feedback optimization formulation and control-oriented modeling for individual renewable and storage components of the hybrid power plant. The proposed supervisory control has been integrated with the hybrid plant co-simulation engine, Hercules, demonstrating its effectiveness in more realistic simulation scenarios.

Milapji Singh Gill, Javal Vyas, Artan Markaj et al.

Advances in Automation and Artificial Intelligence continue to enhance the autonomy of process plants in handling various operational scenarios. However, certain tasks, such as fault handling, remain challenging, as they rely heavily on human expertise. This highlights the need for systematic, knowledge-based methods. To address this gap, we propose a methodological framework that integrates Large Language Model (LLM) agents with a Digital Twin environment. The LLM agents continuously interpret system states and initiate control actions, including responses to unexpected faults, with the goal of returning the system to normal operation. In this context, the Digital Twin acts both as a structured repository of plant-specific engineering knowledge for agent prompting and as a simulation platform for the systematic validation and verification of the generated corrective control actions. The evaluation using a mixing module of a process plant demonstrates that the proposed framework is capable not only of autonomously controlling the mixing module, but also of generating effective corrective actions to mitigate a pipe clogging with only a few reprompts.

Gauthier Roussilhe, Thibault Pirson, David Bol et al.

Growing attention is given to the environmental impacts of the digital sector, exacerbated by the increase of digital products and services in our globalized societies. The materiality of the digital sector is often presented through the environmental impacts of mining activities to point out that digitization does not mean dematerialization. Despite its importance, such a narrative is often restricted to a few minerals (e.g., cobalt, lithium) that have become the symbols of extractive industries. In this paper, we further explore the materiality of the digital sector with an approach based on the diversity of elements and their purity requirements in the semiconductor industry. Semiconductors are responsible for manufacturing the key building blocks of the digital sector, i.e., microchips. Given that the need for ultra-high purity materials is very specific to the semiconductor industry, a few companies around the world have been studied, revealing new critical actors in complex supply chains. This highlights strong dependencies towards other industrial sectors with mass production and the need for a deeper investigation of interactions with the chemical industry, complementary to the mining industry.

Chunzhi Ou, W. Cheng, Zelu Wang et al.

The development of agriculture and industry has led to a gradual increase in the levels of cadmium (Cd) in the soil, which, due to its high mobility in soil, makes Cd deposition in plants a serious threat to the health of animals and humans. The important role of melatonin (MT) in regulating plant growth and adaptation to environmental stress has become a pertinent research topic, but the mechanisms of action of MT in Cd-stressed Platycladus orientalis seedlings are unclear. Here, we investigated the mitigation mechanism of exogenous MT application on P. orientalis seedlings under Cd stress. Cd stress significantly inhibited the growth of P. orientalis seedlings by disrupting photosynthetic pigments, mineral balance, osmotic balance, and oxidative balance. In contrast, the application of exogenous MT significantly increased the growth parameters of P. orientalis seedlings, reduced Cd accumulation and transfer in the seedlings, increased the content of iron, manganese, zinc, copper, chlorophyll, soluble protein, soluble sugar, and proline, reduced the content of glutathione, increased the activities of superoxide dismutase and peroxidase, and significantly enhanced the expression of antioxidant-related genes (POD, GST, and APX). It also effectively reduced the content of hydrogen peroxide and malondialdehyde to inhibit the production of reactive oxygen species, thus alleviating Cd-induced oxidative stress. In addition, MT significantly upregulated the expression of the ethanol dehydrogenase (ADH) gene, which is effective in removing the acetaldehyde produced by anaerobic respiration in seedlings under stress, thereby reducing the toxic effects on P. orientalis. The results showed that exogenous MT enhanced the tolerance of P. orientalis seedlings to Cd stress by regulating photosynthesis, mineral balance, osmotic balance, and the antioxidant system and that the optimal concentration of MT was 200 μmol·L-1.