Hasil untuk "Environmental effects of industries and plants"

Apichaya Sawasdee and Monthon Nethip

Biofuel pellet materials are a key renewable alternative to fossil fuels. Evaluating biomass quality is essential for both operational efficiency and environmental impact. This study aimed to produce and characterize biofuel pellets made from rice husks and straw. The pellets were analyzed using ASTM methods and compared against the ISO 17225-6 standard. The results indicated a low moisture content (2.53 ± 0.04 %) and a relatively high ash content (11.96 ± 0.05%). Thermally, the net calorific value was 3,951 ± 7.21 Cal/g. In terms of elemental composition, nitrogen (0.29 ± 0.02%), sulfur (0.15 ± 0.02%), and chlorine (0.34 ± 0.04%) contents were in line with the ISO 17225-6 standard. Additionally, the pellets made from both biomass met durability, length, and diameter specifications. The results indicate that mixing these rice-based biomass improves pellet quality and combustion performance. Lastly, this research supports SDG 7 (ensuring access to affordable, reliable, sustainable, and modern energy for all), SDG 12.2 (promoting the sustainable management and efficient utilization of natural resources), and SDG 12.5 (minimizing waste generation through prevention, reduction, recycling, and reuse).

Reny Mita Sari, Eka Tarwaca Susila Putra, Endang Sulistyaningsih et al.

This study evaluated whether reducing the dose of soil amendment and enriching it with vermicompost and biochar could maintain soil properties and support the growth of pineapple (Ananas comosus (L.) ‘GP3’) cultivated on degraded Ultisols. This experiment aimed to assess the effect of reducing the dose of soil amendment and decreasing the inorganic fertilizer application to 75% of the recommended dose on soil properties, nutrient uptake, and plant growth. The experiment used a split-plot design with varying doses of enriched compost combined with vermicompost and biochar, and two levels of inorganic fertilizer (75% and 100% of the recommended dose). Soil properties, nutrient uptake, and plant growth were measured and analyzed using Dunnett's test at ? = 0.05. The results showed that reducing the dose of enriched compost and decreasing the inorganic fertilizer by 25% generally resulted in soil properties, nutrient uptake, and plant growth comparable to those obtained using a higher dose of compost only and a full dose of inorganic fertilizer. Soil properties indicators generally remained stable across all treatments, with a small and transient decrease in soil nitrogen, which only occurred in the 25CVB1 treatment at later stages of plant growth. Although Ca and Mg uptake in the 25CVB3 treatment was lower at 4 months after planting, at 8 months after planting, the uptake levels were comparable across all treatments. These findings indicate that compost enriched with vermicompost and biochar allows for reduced doses of soil amendment and inorganic fertilizers without compromising soil quality or pineapple growth, thus providing a sustainable and cost-effective management strategy for pineapple cultivation on Ultisols.

Tiara Aprilia Putri Hernanda, Akhmad Fauzi, Baba Barus et al.

The expansion of oil palm in Indonesia increasingly occurs at the expense of traditional perennial crops such as coffee, reshaping land systems and livelihoods. This study analyzed coffee to oil palm conversion in Way Kanan Regency, Lampung Province, from 2018 to 2024 through GIS-based classification, satellite imagery, and field validation. Results revealed a sharp decline in forest cover of around 63% during those periods and the dominance of agricultural lands (197,000 ha), driven primarily by oil palm expansion. Results showed that in Kasui, coffee agroforestry followed a boom and bust trajectory with a 59% increase, but was later displaced by oil palm, which surged by 52%. Results indicated that in Rebang Tangkas, coffee maintained a modest presence with a 36% increase, while oil palm expanded aggressively by 329%, underscoring its dominant role in reshaping land use dynamics. Conversion patterns were amplified by topography and accessibility, with oil palm concentrated in lowlands and coffee surviving in uplands. Institutional frameworks and economic incentives reinforced oil palm dominance, while rising coffee prices have triggered localized reconversion. These findings highlight a dual transition: oil palm consolidation in accessible lowlands and the persistence of coffee agroforestry in upland niches. The study underscores the urgency of place-based governance to reconcile economic drivers with ecological sustainability and rural livelihood resilience.

Sadewa Purba Sejati, Rivi Neritarani

Studying groundwater conservation zones is vital for maintaining groundwater sustainability. A model for groundwater conservation zones should be based on groundwater pollution vulnerability to ensure that groundwater resource sustainability addresses both quantity and quality. Still, few studies have been conducted on groundwater conservation zone models based on groundwater pollution vulnerability zones. This study used the modified GOD method to explore a groundwater conservation zone model based on groundwater pollution vulnerability zones. The research variables included groundwater depth, aquifer type, natural materials of the aeration zone, and land use. The data was collected through a field survey and literature reviews, and then processed using scoring and overlay techniques through geographic information system software. The results indicated that the modified GOD generated a model with better accuracy. The modified GOD produces a model of groundwater conservation priority zones with four classifications: groundwater conservation priority zone I is in areas with high pollution vulnerability; zones II and III are in areas with medium and low pollution vulnerability, respectively; and zone IV is in areas with no pollution vulnerability.

Hang Chen, Wei Wu, Chen Li et al.

Yacouba Zoungranan, M’Bra Ignace Christian, Soro Sirata Ibrahima Francis, Ekou Lynda and Ekou Tchirioua

In developing countries, plastic packaging waste and the proliferation of cement plants have become real problems in terms of hygiene and public health. Common plastic packaging is produced by the petrochemical industry. It is (Ivory Coast) non-biodegradable and releases numerous toxic substances when heated or incinerated. In this study, building blocks were produced by incorporating waste plastic packaging (low-density polyethylene) as reinforcement in fired clay bricks. The incorporation into the raw clay matrix was carried out in proportions of 0%, 1%, 2%, 3%, 4%, and 5% of plastic, corresponding respectively to brick types Bcp0, Bcp1, Bcp2, Bcp3, Bcp4 and Bcp5. The Bcp4 bricks showed optimal physical properties (water absorption rate, apparent porosity, density, and compressive strength). The introduction of 4% plastic waste into the clay increased the compressive strength, decreased the water absorption rate, and significantly reduced the apparent porosity. The influence of firing temperature (Tf ), firing time (tf ), and amount of mixing water (mwater) was investigated on Bcp0 and Bcp4 bricks. The better plastic incorporation for the operating parameters Tf = 200°C, mwater = 20 g, and tf =.6 h. The study shows that it is possible to have eco-efficient brick production with low energy consumption.

Wawan Budianta, I Wayan Warmada, Ohta Hideki et al.

The extraction and processing of aggregates in quarrying operations leads to environmental degradation through various engineering methods. This study investigated the geo-engineering properties of tertiary volcanic rocks mined from various sites in Sleman and Bantul District, Yogyakarta Province. A total of twelve rock samples were obtained from the study area. This study conducted mineralogical and geotechnical engineering investigations, including microscopic analysis, unconfined compressive strength (UCS) tests, abrasiveness tests (CAI), and rock abrasivity index (RAI) calculations. Mineralogical observation using polarisation microscopy indicated that the rock samples consist of quartz, plagioclase, lithic fragments, and volcanic glass. The UCS test showed varying rock strengths due to resistant minerals in the samples. Similarly, the CAI values varied and were influenced by the quartz mineral content, which is representative of resistant minerals. The Rock Abrasivity Index (RAI) calculation classified the samples as less abrasive, and this characteristic is also affected by quartz content. A significant correlation was observed between the quartz mineral content and the UCS, CAI, and RAI values. This relationship suggests that the quartz mineral content substantially affects the UCS, CAI, and RAI values of the rock samples in the study area. The findings of this study can be used to enhance mining practices and minimize their ecological impact.

Ruijin Luo, Junhan Zhang, Petronella Margaretha Slegers et al.

Quantifying the environmental performance (EP) of citrus supply chains (SCs) via life cycle assessment is important for optimising fruit production for sufficient vitamin and micronutrient provision at lower environmental costs. As a part of national programme in China, green-labelled navel oranges use up to 50.0 ​% less chemical nitrogen fertilisers and become increasingly popular for their high quality. However, their EP remain unclear from the full SC perspective, and critical indicators, e.g. ecotoxicity and land occupation potential (LOP), have been mostly neglected in previous studies. Based on interviews with orange SC (OSC) stakeholders, this study analyses eleven ReCiPe2016 (H) midpoint indicators and normalises characterised results, followed by Monte Carlo simulation, to compare the EP of conventional, green-labelled, and organic-labelled OSCs from production to consumption. Green-labelled OSCs show lower impacts across most categories. Specifically, they reduce the LOP by 72.6 ​% compared with organic-labelled OSCs and decrease the ozone depletion potential by 65.5 ​% relative to conventional OSCs. Their total environmental index is 31.4 ​% and 24.5 ​% lower than conventional and organic-labelled OSCs, respectively. Packaging, transport, and production are significant contributing stages. Key contributing inputs include nitrogen fertiliser, corrugated boxes, long-distance transport, and land use. Beyond well-recognised fossil fuel potential, terrestrial ecotoxicity potential and LOP are newly identified critical indicators for OSC evaluation. Thus, green-labelled OSCs represent a more environment-friendly model for high-yield and high-quality fruit supply. This multi-stage and multi-indicator approach offers a transferable framework for comprehensive evaluation and optimisation of fruit SCs towards sustainable fruit provision and environment management.

H. T. Silva, L. C. S. Faria, T. A. Aversi-Ferreira et al.

The extensive use of glyphosate in agriculture has raised environmental concerns due to its adverse effects on plants, animals, microorganisms, and humans. This study investigates the interactions between ionized glyphosate and single-walled carbon nanotubes (CNT) using computational simulations through semi-empirical tight-binding methods (GFN2-xTB) implemented in the xTB software. The analysis focused on different glyphosate ionization states corresponding to various pH levels: G1 (pH < 2), G2 (pH ~ 2-3), G3 (pH ~ 4-6), G4 (pH ~ 7-10), and G5 (pH > 10.6). Results revealed that glyphosate in G1, G3, G4, and G5 forms exhibited stronger interactions with CNT, demonstrating higher adsorption energies and greater electronic coupling. The neutral state (G2) showed lower affinity, indicating that molecular protonation significantly influences adsorption. Topological analysis and molecular dynamics confirmed the presence of covalent, non-covalent, and partially covalent interactions, while the CNT+G5 system demonstrated moderate interactions suitable for material recycling. These findings suggest that carbon nanotubes, with their extraordinary properties such as nanocapillarity, porosity, and extensive surface area, show promise for environmental monitoring and remediation of glyphosate contamination.

Yen-hsuan Tseng

This paper presents a comprehensive analysis of power plant performance using the inverse Gaussian (IG) distribution framework. We combine theoretical foundations with practical applications, focusing on both combined cycle and nuclear power plant contexts. The study demonstrates the advantages of the IG distribution in modeling right-skewed industrial data, particularly in power generation. Using the UCI Combined Cycle Power Plant Dataset, we establishthe superiority of IG-based models over traditional approaches through rigorous statistical testing and model validation. The methodology developed here extends naturally to nuclear power plant applications, where similar statistical patterns emerge in operational data. Our findings suggest that IG-based models provide more accurate predictions and better capture the underlying physical processes in power generation systems.

Huei-Hsuan Tsai, Wolfgang Schmidt

Bert Scholtens

Hongwei Yu, Jun Chen, Nan Shen et al.

Yeneneh Tamirat Negash, Taufik Akhbar

The secondhand fashion market suffers from information asymmetry, creating consumer distrust and limited engagement in secondhand fashion (ESHF). The existing research on how sellers and product signals can mitigate this distrust is limited and produces conflicting results. This is particularly true in terms of understanding how signaling interacts with consumer-specific factors such as orientation and environmental awareness. Thus, this study contributes to understanding the factors driving consumer trust (CT) and ESHF through signaling theory and the nuanced role of consumer orientation and environmental awareness. Structural equation modeling, including multigroup analysis, is employed to test the proposed hypotheses with a sample of 203 Indonesian consumers from a secondhand market platform. The findings indicate that signals such as seller reputation, product history, and refurbishment details significantly enhance CT, with seller reputation being the most influential of the factors. The effectiveness of these signals varies by consumer orientation: functionality-oriented consumers respond more to remarketing information, whereas newness-conscious consumers are influenced more by refurbishment details. Additionally, consumer environmental awareness significantly strengthens the positive relationship between CT and ESHF, highlighting the importance of aligning environmental values with trust-building measures to enhance consumer ESHF. These insights enrich the theoretical understanding of signaling in secondhand markets and offer practical guidance for addressing the challenges associated with CT and ESHF.

Songliang Chen, Qinglin Mao, Youcan Feng et al.

Accurate hydrological predictions are often hindered by the lack of stream gauges in data-scarce regions, where traditional transfer learning (TL) models like Long Short-Term Memory (LSTM) networks often face limitations due to reduced accuracy and adaptability. To enhance runoff prediction in such regions, we developed DAformer, a novel TL approach that integrates domain adversarial neural networks with the Informer model. Trained on comprehensive runoff data from U.S. basins, DAformer was applied to three basins in Chile and the Chaersen basin in China, demonstrating an effective transfer from data-rich to data-scarce environments. Results show that DAformer significantly outperforms LSTM-based models, improving forecast accuracy by 16.1% for 1-day lead time and by 100.5% for 5-day lead time. These improvements indicate that the DAformer model not only enhances prediction accuracy but also holds substantial practical implications for flood risk management and water resource planning in regions with limited data availability. By clustering basins based on Shuttle Radar Topography Mission (SRTM) and other geographical data, we found that relying on multiple source basins further enhances the performance. DAformer, therefore, serves as a robust and scalable method for enhancing runoff prediction for regions with limited data.

D. F. Wardhani, D. Arisanty, A. Nugroho and U. B. L. Utami

The indigenous knowledge of the Dayak Paramasan in Indonesia holds the potential for environmental sustainability. This study aims to assess an environmental education framework grounded in the local wisdom of the Paramasan Dayak tribe. A survey was conducted among 300 individuals, including traditional leaders and members of indigenous communities residing in the Paramasan Subdistrict, Indonesia. Data collection occurred from May 2023 to July 2023 and was analyzed using Structural Equation Modelling (SEM). The findings indicate a significant association between indigenous values, local expertise, and community cohesion concerning environmental education. Local wisdom includes local skills, values, and community solidarity, which are crucial for environmental education. Local skills, like farming and hunting, have a significant impact on environmental protection. Passing down knowledge to younger generations needs improvement. Limited local resources create a gap between generations, but some believe traditional leaders can safeguard nature without formal education. Further exploration of implementing environmental education models within school settings will offer valuable insights for Indigenous communities and society, fostering environmentally conscious behaviors.

Yuki Okamoto, Ryotaro Nagase, Minami Okamoto et al.

Some datasets with the described content and order of occurrence of sounds have been released for conversion between environmental sound and text. However, there are very few texts that include information on the impressions humans feel, such as "sharp" and "gorgeous," when they hear environmental sounds. In this study, we constructed a dataset with impression captions for environmental sounds that describe the impressions humans have when hearing these sounds. We used ChatGPT to generate impression captions and selected the most appropriate captions for sound by humans. Our dataset consists of 3,600 impression captions for environmental sounds. To evaluate the appropriateness of impression captions for environmental sounds, we conducted subjective and objective evaluations. From our evaluation results, we indicate that appropriate impression captions for environmental sounds can be generated.

Chaoqun Li, Huanqian Yan, Lifeng Zhou et al.

Adversarial attacks in the physical world pose a significant threat to the security of vision-based systems, such as facial recognition and autonomous driving. Existing adversarial patch methods primarily focus on improving attack performance, but they often produce patches that are easily detectable by humans and struggle to achieve environmental consistency, i.e., blending patches into the environment. This paper introduces a novel approach for generating adversarial patches, which addresses both the visual naturalness and environmental consistency of the patches. We propose Prompt-Guided Environmentally Consistent Adversarial Patch (PG-ECAP), a method that aligns the patch with the environment to ensure seamless integration into the environment. The approach leverages diffusion models to generate patches that are both environmental consistency and effective in evading detection. To further enhance the naturalness and consistency, we introduce two alignment losses: Prompt Alignment Loss and Latent Space Alignment Loss, ensuring that the generated patch maintains its adversarial properties while fitting naturally within its environment. Extensive experiments in both digital and physical domains demonstrate that PG-ECAP outperforms existing methods in attack success rate and environmental consistency.

Jianchao Ci, Eldert J. van Henten, Xin Wang et al.

The 3D reconstruction of plants is challenging due to their complex shape causing many occlusions. Next-Best-View (NBV) methods address this by iteratively selecting new viewpoints to maximize information gain (IG). Deep-learning-based NBV (DL-NBV) methods demonstrate higher computational efficiency over classic voxel-based NBV approaches but current methods require extensive training using ground-truth plant models, making them impractical for real-world plants. These methods, moreover, rely on offline training with pre-collected data, limiting adaptability in changing agricultural environments. This paper proposes a self-supervised learning-based NBV method (SSL-NBV) that uses a deep neural network to predict the IG for candidate viewpoints. The method allows the robot to gather its own training data during task execution by comparing new 3D sensor data to the earlier gathered data and by employing weakly-supervised learning and experience replay for efficient online learning. Comprehensive evaluations were conducted in simulation and real-world environments using cross-validation. The results showed that SSL-NBV required fewer views for plant reconstruction than non-NBV methods and was over 800 times faster than a voxel-based method. SSL-NBV reduced training annotations by over 90% compared to a baseline DL-NBV. Furthermore, SSL-NBV could adapt to novel scenarios through online fine-tuning. Also using real plants, the results showed that the proposed method can learn to effectively plan new viewpoints for 3D plant reconstruction. Most importantly, SSL-NBV automated the entire network training and uses continuous online learning, allowing it to operate in changing agricultural environments.

Xiaming Chen, Ying Li