Md.Nasir Uddin, Tanzeena Refat Tumpa, Mohammad Rashel Hawlader
et al.
The growing demand for sustainable textiles has prompted research into fiber blends that reduce environmental impact without compromising product performance. This study focuses on creating a sustainable tri-blend yarn consisted of recycled cotton, recycled polyester, and Tencel fibers. Recycled cotton replaces virgin cotton, recycled polyester enables the reuse of PET bottle and textile waste, and Tencel—derived from sustainably sourced wood pulp—enhances softness, comfort and tensile properties. MATLAB was used to perform Multiple Linear Regression (MLR) analysis to determine the optimal blend ratio that maximizes recycled fiber content while ensuring required yarn characteristics for fabric production. Based on Extreme Vertices Design, twenty-nine yarn samples with varying blend ratios were produced. Their quality parameters were evaluated, and the impacts of fiber composition on yarn properties were analyzed using Multiple Linear Regression (MLR) models. The MLR models generated predictive equations for each yarn parameter and performed blend optimization through two approaches: criteria-based optimization, which minimized yarn unevenness and imperfections while maximizing tensile properties, and target-based optimization, which aligned blends with the specific requirements of buyer or consumer. Validation of the optimized blends confirmed the model’s predictive reliability, with deviations between predicted and actual values remaining below 5%.
Open dumping of untreated waste is a very common practice in developing countries like Bangladesh. The biodegradation of this waste generates heat and gases that are often released without control, creating operational challenges in landfills. However, prior studies have not clearly quantified the resulting temperature increase. To address this gap, this study predicts the Land Surface Temperature (LST) of the Rajbandh landfill in Khulna using Generalized Additive Models (GAM), Multiple Linear Regression (MLR), and Artificial Neural Networks (ANN). The amount of waste received at the open dump was collected from the municipality’s logbook. LandGEM has been used for modeling methane emissions. NDBI, NDVI, specific humidity, wind speed and LST were obtained from Google Earth Engine. The GAM results show that all predictors of LST are statistically significant (p < 0.001). A strong cubic relationship was found between humidity and LST (R2 = 0.92), while NDVI and wind speed exhibited strong linear correlations (R2 = 0.73 and 0.71, respectively). Variable importance analysis highlighted that methane and humidity emerged as the most influential variables, contributing 25.40% and 24.35% to LST variation, whereas NDBI had a minor impact. The MLR also showed statistical significance (p < 0.001) with a moderate predictive power with an R2 of 0.60. These results confirm that combining remote sensing and waste management data enables accurate, low-cost LST prediction for proactive landfill monitoring in resource-constrained regions.
Emmanuel Yeboah Okyere, Jones Abrefa Danquah, Michael K. Miyittah
et al.
Microfibre pollution has emerged as a significant environmental challenge associated with the production, use, and disposal of textiles. This review synthesizes contemporary literature on circular economy innovations, policy responses, and implementation challenges to address microfibre emissions across the textile value chain. The study highlights key advancements in sustainable textile design, recycling and upcycling initiatives, and circular business models, all of which aim to reduce fiber shedding and extend product life. It also evaluates the role of national regulations, extended producer responsibility (EPR), eco-design strategies, and standards and certifications in governing microfibre release. Through comparative case studies and international policy analysis, the review highlights the uneven progress between high-income and low-income regions, underscoring the importance of public–private partnerships and community-driven interventions. Despite promising initiatives, the findings reveal persistent barriers related to technological limitations, weak enforcement, low public awareness, and infrastructural disparities. The paper concludes by identifying critical research gaps and calling for standardized shedding metrics, inclusive policy frameworks, consumer behavior change strategies, and scalable technological innovations. Addressing these gaps through integrated, equity-focused governance is essential for mitigating microfibre pollution and advancing a sustainable textile future.
Edilene da Silva Pereira, Ritieli Marostica, Jackeline Tatiane Gotardo
et al.
Anaerobic co-digestion (ACoD) of livestock waste with energy crops (EC) can increase methane yield and the profitability of biodigesters. The biochemical methane potential (BMP) of two commercial pumpkin species, “Paulista” (Cucurbita moschata) and “Moranga” (C. maxima), was evaluated in a laboratory setting. These pumpkins were either crushed or cooked and mixed with swine wastewater (SWW). The treatments included: AMonoD of SWW (T0) and ACoD of SWW + raw Paulista (T1), cooked Paulista (T2), raw Moranga (T3), and cooked Moranga (T4). The volume and composition of biogas were measured, and the modified Gompertz, Cone, modified First Order, and Logistic models were adjusted. T3 recorded the highest specific productions (1.39 m3 biogas and 0.91 m3 CH4 kg-1 VS), surpassing (p ≤ 0.05) the AMonoD and the other trials, without altering the methane content in biogas. It is concluded that crushed raw moranga is a low-cost EC that can significantly enhance the performance of ACoD with SWW, opening up exciting possibilities for the future of biogas technology. The integration of BMP with cone modelling provides reliable parameters for sizing more compact and economically attractive rural biodigesters.
Ilham Ku-nwa Hamid, Edna Korlekie Dapaah, Hamdala Hunsulu Hamid
Rapid urbanization in Ghana has intensified the challenges associated with municipal solid waste management, particularly in relation to open dumping and illegal disposal practices. This study investigates the drivers, patterns, and spatial distribution of informal waste disposal across six municipalities, across Accra and Kumasi, using a mixed-methods approach that integrates household surveys (n = 600), key informant interviews, and machine learning-based route optimization. Results reveal that over 60 % of households resort to informal dumping due to limited-service coverage, high collection costs, and socio-cultural perceptions of waste. The waste stream is predominantly organic, with compostable materials accounting for over 70 % of total waste generated. Engineering-oriented interventions such as community-scale composting, AI-enhanced collection routing, and participatory monitoring using geolocation tools are proposed to address these systemic inefficiencies. The findings underscore the need for decentralized, context-sensitive, and technology-supported waste governance models. Policy recommendations are offered to align local practices with circular economy principles and improve environmental health outcomes.
Saifullahi Shehu Imam, Sadiq Sani, Muhammad Mujahid
et al.
Palm oil mill effluent (POME) is a substantial by-product generated in the palm oil industry that has traditionally been regarded as waste due to its high pollutant content. However, recent advances emphasize the possibility of recovering valuable resources from POME, which would contribute to environmental sustainability and economic benefits. This made POME treatment the subject of numerous investigations. Various methods, mostly biological, have evolved over the past few years for the successful valorization of POME to produce useful resources including biofuels (biohydrogen, biogas, bioacetone, biobutanol, bioethanol, biohythane etc.), value-added chemicals (enzymes, volatile fatty acids, citric acid, carotenes, lipids etc.), nutrients (micronutrients, macronutrients), pesticides, solvents, and adsorbents. The current review’s objective is to summarize the possible approaches for conversion of POME waste to wealth with some outlooks. By transforming waste into valuable resources, the palm oil sector can improve its sustainability, minimize environmental footprints, and establish new revenue streams.
Shobhit Chaturvedi, Naimish Bhatt, Vatsal Shah
et al.
This paper presents a novel integrated Geographic Information System-Multi-Criteria Decision Making (GIS-MCDM) framework for evaluating landfill site suitability in Shimla, India, a rapidly urbanizing hill station. Combining Remote Sensing-Geographic Information Systems (RS-GIS) with the Analytical Hierarchy Process (AHP) and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) methods, the framework developed a Landfill Suitability Zoning map and ranked potential sites. The Land Suitability Index (LSI), derived using AHP, categorized the 124 sq. km study area into five suitability classes, with key factors influencing rankings: Landslide Proximity (weight: 0.162), Ground Slope (0.138), Land Use and Cover (0.122), Ground Elevation (0.114), and Road Proximity (0.095). From the Very High suitability zones, eight candidate sites were identified and ranked using VIKOR, with Kiargiri (score: 0.083) identified as the most suitable, followed by Baboloo (0.530), Karog (0.535), and Phayal Road (0.663). Sensitivity Analysis (SA) was conducted across five scenarios to account for possible variations in expert judgment, with the first three increasing beneficial weights (10 %, 15 %, and 20 %) and the last two decreasing beneficial weights (15 % and 20 %), proportionally adjusting non-beneficial weights. The SA confirmed the consistency and robustness of the rankings, with Kiargiri (0.083), Baboloo (0.530), Karog (0.535), and Phayal Road (0.663) maintaining top positions despite varying weight configurations. This approach offers a reliable, adaptable framework for landfill site selection in hilly urban areas, supporting waste management, sustainable development and environmental conservation.
Muazzam Mukhtar, Muhammad Rizwan, Atta Ullah
et al.
Optimizing municipal solid waste (MSW) management through the production of valuable products and energy conversion is crucial to mitigate environmental damage and promote economic sustainability. This study focuses on addressing the MSW supply chain problem by exploring the optimal location for the waste treatment. The supply chain network encompasses MSW transfer stations, treatment facilities, and markets with product demands. The methodological approach entails constructing a superstructure, gathering relevant data, and analyzing the results. Both deterministic MILP and two stage stochastic model are used in this study. A deterministic mixed-integer linear programming (MILP) model is employed to optimize the MSW supply chain problem, with the use of solver BARON. To account for uncertainties in supply–demand and transportation costs, a two-stage stochastic MILP model is developed. The deterministic equivalent approach is then employed to solve the stochastic model, resulting in an average solution across all scenarios. The decision variable pertaining to the selection of treatment technology locations is managed in the first stage. The second stage focuses on determining transportation and production-related decisions. Stochastic models can capture the inherent unpredictability of real-world systems by simulating a range of potential scenarios, helping to tackle uncertainty. To underscore the practical relevance of the mathematical programming formulation, a case study is presented and thoroughly analyzed.
Alternative Food Networks (AFNs) have gained growing attention for promoting sustainable food systems by addressing upstream food production, distribution, and waste reduction challenges. This study conducts a comprehensive bibliometric analysis of 830 peer-reviewed articles published between 1989 and 2024, retrieved from the Web of Science Core Collection. Utilizing VOSviewer and RStudio, the analysis maps the intellectual structure, thematic evolution, and global research dynamics of AFNs over the past three decades. The results reveal an average annual growth rate of 11.19%, with a sharp increase in publications after 2020, driven by heightened interest in resilient, local, and circular food systems. Co-word and cluster network analyses identify four major thematic domains: (1) local food and consumption patterns, (2) sustainability and environmental impacts, (3) governance, policy, and social justice, and (4) community participation and social innovation. Key theoretical frameworks underpinning the field include Actor-Network Theory, Convention Theory, and the Theory of Planned Behavior. This study distinguishes itself by framing AFNs as upstream system-level interventions with the potential to complement or substitute downstream technological approaches to waste management. It highlights AFNs’ unique contributions to reducing food loss and waste, strengthening local economies, and fostering inclusive development. The findings also reveal a research gap in the Global South, where AFNs could address food safety and local poverty. This review offers practical guidance for researchers and policymakers to advance AFNs’ research and design place-based, sustainable food governance strategies.
Cellulose nanofiber aerogels (CNFAs) exhibit desirable properties such as nontoxicity, high surface area-to-volume ratio, porosity, and biocompatibility. Their strong capacity to absorb moisture and blood exudates makes them suitable for wound dressings, with broad applications in biomedicine and antimicrobial textiles. Silver nanoparticles (AgNPs) can adhere to the surfaces of pathogenic bacteria, disrupting critical functions such as membrane permeability and respiration. In this study, cellulose nanofibers were prepared from waste carrot pulp and reacted with polyetheramines to form CNFAs. These aerogels were subsequently loaded with green synthesized AgNPs produced using pineapple leaf extract. Characterization revealed that 96.2 % of the AgNPs had diameters smaller than 100 nm. The resulting aerogels exhibited a water vapor transmission rate that exceeded 2000 g·m−2·d−1 and a water absorption capacity greater than 1000 % of their own weight. It was found that the water vapor transmission rate and water absorption capacity of the aerogels could be tuned by adjusting the hydrophilicity and molecular weight of the polyetheramines. Furthermore, polyetheramines containing difunctional primary amine groups formed highly cross-linked networks with CNFs, resulting in densely distributed pores. AgNP-loaded CNFAs also demonstrated excellent antibacterial activity against Escherichia coli. These findings highlight the potential of these materials for use in antimicrobial textiles and advanced wound care applications.
Wilfried Andlauer, Roméo Zuchuat, Ralf Schweiggert
et al.
Grape cane is a viticultural byproduct rich in bioactive stilbenoids and other phenolic compounds. Valorization of grape cane relies on effective extraction methods, traditionally using ethanol. This study aimed to compare a series of extraction techniques, including an extraction based on various ethanol–water mixtures as well as water-based methods combined with ultrasound-assisted extraction and microwave-assisted extraction. In this study, water-based extraction led to stilbenoid extraction yields determined by HPLC-DAD analyses that were significantly lower, even when applying ultrasound (43 mg/kg grape cane), than those obtained with 80 % ethanol maceration (4373 mg/kg). Water-based extraction utilizing microwave heating at optimal conditions (100 °C, 20 min) allowed yields of about 555 mg/kg. The spectrophotometrically measured total phenolic content showed a similar trend, with the highest yield achieved at 100 °C for 20 min under microwave-assisted extraction (13 g GAE /kg), remaining notably lower compared to using 80 % ethanol (20 g GAE /kg). These findings highlight the potential of increased temperature water-based extraction methods but also underscore the need for further optimization. Future research should aim to refine these techniques and explore more advanced methods, such as subcritical or high-pressure water extraction, to enhance the valorization of grape cane and support its use in producing bioactive extracts.
Sharmin Aktar Hasi, Samiul Ahsan Jyoti, Jagadish Chandra Joardar
et al.
Heavy metals (HMs) contamination is an emerging environmental and public health issues in south Asian country like Bangladesh. HMs from various anthropogenic sources accumulate in the soil, are taken up by plants and entering our food chain, which causes health disorder. However, this study synthesizes updated data on heavy metals contamination in agricultural soils, water, sediments, and food crops across the major industrial areas of Bangladesh from 2010 to 2024. We found that the agricultural soils surrounding of the Dhaka city is highly contaminated with Mn, Zn, Cu, Pb, Cr, Ni, As, Cd. Heavy metals content in water from Buriganaga and Sytalokka river of Dhaka city was greater in compared to countryside river. Similarly, the plants growing near Dhaka city were highly contaminated with HMs (Zn, Mn, Cu, Pb, Ni, Cr), which were Oryza sativa and Amaranthus dubius. Furthermore, in case of sediments, Sytalokka river sediment followed by Buriganga were contained higher content of heavy metals like Cd, Pb, Ni, Zn, Cr and Cu in compared to the other rivers across the Bangladesh. However, different remediation strategies like application of organic amendments, phytoremediation, bioremediation are currently using across the Bangladesh to limit the plant uptake and food chain contamination. This synthesis will provide baseline information and first nationwide comparison of heavy metals data among the different regions of Bangladesh that can be used by policymakers for risk prioritization over the country. Furthermore, this study underscores the urgent need for policy interventions to mitigate heavy metal pollution and ensure sustainable food safety.
Open burning of plastic waste may pose a significant threat to global health by degrading air quality, but quantitative research on this problem -- crucial for policy making -- has been stunted by lack of data. Many low- and middle-income countries, where open burning is most concerning, have little to no air quality monitoring. Here, we leverage remotely sensed data products combined with spatiotemporal causal analytic techniques to evaluate the impact of large-scale plastic waste policies on air quality. Throughout, we study Indonesia before and after 2018, when China halted its import of plastic waste, resulting in diversion of this massive waste stream to other countries. We tailor cutting-edge statistical methods to this setting, estimating effects of increased plastic waste imports on fine particulate matter (PM$_{2.5}$) near waste dump sites in Indonesia as a function of proximity to ports, an induced continuous exposure. We observe strong evidence that monthly PM$_{2.5}$increased after China's ban (2018-2019) relative to expected business-as-usual (2012-2017), with increases up to 1.68 $μ$g/m$^3$ (95\% CI = [0.72, 2.48]) at dump sites with medium-high port proximity. Effects were more modest at sites with very high port proximity, possibly reflecting smaller increases in dumping/burning where government oversight is greater.
Information systems generate a large volume of event log data during business operations, much of which consists of low-value and redundant information. When performance predictions are made directly from these logs, the accuracy of the predictions can be compromised. Researchers have explored methods to simplify and compress these data while preserving their valuable components. Most existing approaches focus on reducing the dimensionality of the data by eliminating redundant and irrelevant features. However, there has been limited investigation into the efficiency of execution both before and after event log simplification. In this paper, we present a prediction point selection algorithm designed to avoid the simplification of all points that function similarly. We select sequences or self-loop structures to form a simplifiable segment, and we optimize the deviation between the actual simplifiable value and the original data prediction value to prevent over-simplification. Experiments indicate that the simplified event log retains its predictive performance and, in some cases, enhances its predictive accuracy compared to the original event log.
Maria Korobeynikova, Alessia Battisti, Lukas Fischer
et al.
Current evaluation of German automatic text simplification (ATS) relies on general-purpose metrics such as SARI, BLEU, and BERTScore, which insufficiently capture simplification quality in terms of simplicity, meaning preservation, and fluency. While specialized metrics like LENS have been developed for English, corresponding efforts for German have lagged behind due to the absence of human-annotated corpora. To close this gap, we introduce DETECT, the first German-specific metric that holistically evaluates ATS quality across all three dimensions of simplicity, meaning preservation, and fluency, and is trained entirely on synthetic large language model (LLM) responses. Our approach adapts the LENS framework to German and extends it with (i) a pipeline for generating synthetic quality scores via LLMs, enabling dataset creation without human annotation, and (ii) an LLM-based refinement step for aligning grading criteria with simplification requirements. To the best of our knowledge, we also construct the largest German human evaluation dataset for text simplification to validate our metric directly. Experimental results show that DETECT achieves substantially higher correlations with human judgments than widely used ATS metrics, with particularly strong gains in meaning preservation and fluency. Beyond ATS, our findings highlight both the potential and the limitations of LLMs for automatic evaluation and provide transferable guidelines for general language accessibility tasks.
Abstraction or substitution and aggregation are the most widely used simulation model simplification operations. Abstraction involves replacing subsystems within a discrete-event simulation (DES) with one or more quantities - typically random variables - representing the lengths of stay in the subsystems(s) in question to create a `simplified' system comprising only of subsystems of interest to the analysis at hand. Aggregation involves replacing more than one subsystem of the original `parent' simulation with a single subsystem. However, the model simplification process itself can be expensive, in terms of the computational runtime and effort required to collect the data required to estimate the distributions of the length of stay variables, the distribution-fitting process, and testing and validation of the simplified model. Moreover, the savings in simulation runtime that the simplification process yields is \textit{a priori} unknown to the modeller. In this context, a method that predicts the runtime savings (RS) from DES model simplification operations before their execution - at the conceptualisation stage of the simplified model development process - may help judge whether its development is indeed worth undertaking. In this paper, we present a queueing-theoretic framework for the prediction of RS from model simplification operations. Our framework is applicable for DES models comprising $M/M/, M/G/ \text{ and } G/G/$ subsystems. The performance of the RS prediction framework is demonstrated using multiple computational experiments. Our proposed framework contributes to the literature around DES model complexity and more broadly to DES runtime prediction.
Background: The growth of the agricultural sector in Cipanas District, Cianjur Regency, West Java has increased the production of organic waste from vegetable packaging houses. This study aims to explore the method of converting vegetable waste into Liquid Organic Fertilizer (POC) for waste management and sustainable agriculture. Methods: This descriptive study evaluated the process of converting vegetable waste into POC in Cipanas District. The process includes soaking rice grits, mixing ingredients (rice soaking water, brown sugar solution, coconut water, vegetable waste), anaerobic fermentation for three weeks with periodic stirring, and harvesting. Findings: The study resulted in a description of the process of making POC from vegetable waste. POC has the potential to improve soil fertility holistically (physically, chemically, biologically) and has been proven effective in increasing the productivity of various types of vegetables. Conclusion: Utilization of vegetable waste as POC offers a sustainable solution for waste management and environmentally friendly agriculture. Although there are challenges in quality standardization and infrastructure, its economic potential and long-term benefits make this approach worthy of consideration in future agricultural strategies. Novelty: This study highlights the potential use of vegetable waste from packaging houses in Cipanas for POC production, integrating the concept of circular economy in agricultural waste management and the development of sustainable agricultural practices.