International Journal of Sustainable Energy and Environmental Research

Evaluating ecosystem impacts of biogas pathways using life cycle assessment and ecosystem service models

2025-08-31T13:36:11-05:00

The regional energy transition from fossil fuels to alternatives with lesser environmental impact has become significant in recent years. The UN and the Sustainable Development Goals (SDGs) emphasize the importance of cleaner, safer, and more modern energy production to support environmental and climate protection. Renewable energy-based agricultural feedstock is considered a better substitute; however, its increasing share among alternative technologies powered by biomass sources still requires comprehensive environmental impact assessments. This study employs Life Cycle Assessment (LCA) modeling to evaluate the environmental impacts of biogas pathways, focusing on maize silage production for biogas in Alberta, Canada. Using openLCA software and Eco-invent data, the analysis covers the entire supply chain from upstream to downstream, assessing emissions, land use, and climate change impacts. The methodology involved consulting literature reviews, utilizing databases such as Eco-Indicator 99, ReCiPe, and the Tool for the Reduction and Assessment of Chemical and Other Environmental Impacts (TRACI). Key findings indicate that nitrogen fertilizer use (above 120 kg/ha in the maize farm) significantly contributes to eutrophication. Additionally, drying maize silage with natural gas poses a high climate change potential. These insights suggest that while biogas from maize silage is not entirely environmentally benign, improvements are achievable through optimized practices.

Effect of pyrolysis temperature on the physicochemical properties of biochar derived from spent coffee grounds

2025-10-05T23:31:28-05:00

Biochar is a carbon-rich material widely recognized for its ability to enhance soil quality, adsorb heavy metals, and sequester carbon, thereby contributing to pollution mitigation and the reduction of greenhouse gas emissions. Spent coffee grounds (SCG), an abundant agricultural byproduct, represent a promising feedstock for biochar production. While coffee ground-derived biochar (CBC) supports waste reduction and aligns with sustainable development and circular economy principles, a significant volume of SCG remains underutilized globally, particularly in Vietnam, one of the world’s leading coffee producers. Therefore, this study aims to investigate the influence of pyrolysis temperature on the physicochemical properties of CBC to identify optimal conditions for improving its environmental applications. SCG collected from coffee stores in Vietnam was subjected to slow pyrolysis under anoxic conditions at five different temperatures: 300°C, 350°C, 400°C, 450°C, and 500°C, with a fixed residence time of 2 hours. The obtained biochar samples were analyzed for yield, moisture content, ash content, pH, and surface functional groups. The findings revealed that increasing the pyrolysis temperature led to a decrease in both biochar yield and moisture content, while ash content and pH values increased accordingly. Fourier transform infrared analysis revealed significant differences in surface functional groups between SCG and CBC across different pyrolysis temperatures, highlighting the critical role of temperature in shaping biochar properties for adsorption and environmental remediation. The study contributes to a deeper understanding of how pyrolysis conditions influence the quality of SCG-derived biochar and offers a basis for optimizing production parameters to tailor its characteristics for specific applications.

Research on super-resolution reconstruction of transmission lines based on SinGAN using a single image

2025-10-06T00:39:49-05:00

The purpose of this study is to address the problem of blurry transmission line images caused by foggy and cloudy weather, which severely hinders defect detection and intelligent recognition. To improve the quality and usability of inspection images, a SinGAN-based single-image super-resolution reconstruction method is proposed. The methodology exploits SinGAN’s advantage of requiring no paired datasets and adopts a self-supervised framework with an image pyramid structure. By learning texture features at multiple scales, the model progressively reconstructs high-resolution images and restores structural and textural details from a single blurry input. The findings demonstrate that the proposed method significantly enhances image clarity and improves structural fidelity. Quantitative evaluation is performed using PSNR and SSIM metrics for image quality and YOLOv7 mAP for detection performance. Results show clear improvements in detail restoration and detection accuracy compared with conventional super-resolution approaches, especially in scenarios with scarce samples. The practical implications of this study highlight that the SinGAN-based approach provides a scalable and efficient solution for real-world transmission line inspections. By eliminating the need for paired training data and enabling rapid deployment, this method enhances smart grid inspection capabilities under challenging conditions and offers strong potential for practical engineering applications.

Diurnal changes of mineral and organic compounds in the rhizosphere soil of plants growing in sites with different soil parameters

2025-10-25T06:30:17-05:00

There is a growing demand for efficient identification of different compounds in the rhizosphere soil. The main aims of this work were to conduct field experiments to identify possible differences in the composition of bulk soils in different sites, as well as in the rhizosphere soil, arising as a result of plant growth in sites with different soil characteristics and to study short-term (during daylight hours) variability in the composition of the rhizosphere soil. Soil samples were collected several times during the day at three sites characterized by different soil parameters. The soil taken from the roots of a widespread weed, nettle, and bulk soil (taken from the top layer of soil in the sites) were analyzed by Raman spectroscopy. The soil spectra showed bands of various organic and mineral compounds. The pH of bulk soils collected in the sites varied and depended on the type of soils and their composition, primarily on the amount of carbon and some minerals in the soils. This was observed even when the differences between the soil parameters in the sites were small. The content of different compounds in the rhizosphere soil was not constant but changed during the day. These changes were regular and varied across the three sites. It was assumed that the main factors influencing these variations were circadian fluctuations of root exudates and soil characteristics. Given the short-term variability of different compounds in the rhizosphere soil, sampling time should be chosen carefully to ensure correct interpretation of experimental data.

Greener pretreatment and the characterization of neem and babul tree barks for lignin extraction

2025-12-01T08:11:30-06:00

This research is motivated by the limited digital learning platforms capable of integrating various forms of representation to support STEAM-based in-depth learning, particularly in physics subjects that require high conceptual understanding. To address this need, this study aims to describe the feasibility of the Digital Multiple Representation Platform (DMRP) as an innovative learning medium designed to facilitate conceptual understanding and encourage interdisciplinary integration in the STEAM context. This research method uses a Research and Development (R&D) design based on the DDDE (Decide, Design, Develop, and Evaluation) model to systematically guide the platform creation and validation process. Data were collected using an expert validation questionnaire covering aspects of content feasibility, pedagogy, interface appearance, interactivity, and technical aspects. Validation was carried out by learning media experts and physics material experts to assess the quality and suitability of the platform for instructional purposes. In addition to quantitative data, the study also involved qualitative feedback from educators and students through open-ended interviews to obtain an in-depth overview of the platform's practicality and acceptability. Quantitative data were analyzed using descriptive techniques to calculate a feasibility score, while qualitative data were analyzed to identify suggestions for improvement and aspects that support the platform's effectiveness. The results of the study show that the DMRP achieved an average media validation score of 80% and a content validation score of 79%, both of which are categorized as very feasible to be implemented in learning. The conclusion of this study shows that integrating various representations in a digital environment can effectively improve conceptual understanding, creativity, and student engagement in STEAM-oriented physics education. Overall, the developed DMRP offers a pedagogically grounded model to support deep learning and interdisciplinary thinking, which contributes to the development of digital innovation in STEAM education.

Accurate prediction functions for turbine-scale wind energy resources from low-elevation measured data for Bangladesh

2025-12-01T08:18:19-06:00

This paper presents a highly accurate statistical modeling method for selecting prediction functions for turbine-scale wind energy resources in Bangladesh, which are not yet extensively covered in existing literature. The proposed resource modeling encompasses key turbine parameters, including turbine power, energy pattern factors, and wind energy outputs. The study, surveyed by the United Nations (UN), identifies prospective areas in mainland and coastal belt regions with the capacity to support commercial wind energy conversion systems for modeling purposes. As Bangladesh's current meteorological measurement facilities primarily collect low-elevation data (approximately 10 meters), the study employs prediction models and projection laws to estimate energy resources suitable for turbines of low-to-medium ratings (80 meters, less than 1 MW). The time-series probability distribution of available wind resources is analyzed in these potential regions, utilizing wind velocity prediction functions such as Weibull, Rayleigh, and Gumbel distributions. Their performances are compared against established statistical standards. Weibull factors are derived using graphical least squares (GLS) and modified maximum likelihood (MML) methods, and validated against parameter values reported in the literature. To enhance the analysis's coverage and accuracy, the Weibull function is expanded by incorporating the effect of output power ratio into its probability distribution. Wind power density (WPD) trends are confirmed through energy pattern factors, and a portable wind system model is employed to estimate the actual energy output at prospective locations, thereby increasing the comprehensiveness of the energy data modeling process.