Hasil untuk "Standardization. Simplification. Waste"

Mohamed Abourida, Michael Short, Oleksiy V. Klymenko et al.

This review presents the first combined systematic and bibliometric review synthesising artificial intelligence (AI)-driven approaches to risk management in biogas production within wastewater treatment plants (WWTPs), with emphasis on decision-optimisation and operational safety. Seven academic databases: Scopus, Web of Science, IEEE Xplore, ScienceDirect, SpringerLink, Taylor & Francis, and Google Scholar were systematically searched from 2015 to March 2025, and screening followed PRISMA 2020 guidelines. Of 3,716 retrieved records, 109 studies met the inclusion criteria. Bibliometric mapping (VOSviewer) and qualitative synthesis identified five thematic clusters: (i) biogas process safety, (ii) IoT integration and renewable-energy, (iii) optimisation and supply-chain resilience, (iv) AI-driven decision-support frameworks, and (v) advanced machine-learning techniques. The analysis reveals a marked increase in publications since 2020, reflecting a shift from conceptual modelling toward applied digital risk solutions. Europe and China remain leading contributors, although collaboration networks are fragmented and methodological heterogeneity persists. Full-scale validation of AI models in operational WWTP-based biogas plants remains limited, with most studies relying on laboratory experiments, simulations, or pilot-scale data. Constraints include publication bias, database coverage, English-language restrictions, inconsistent performance metrics, and limited access to long-term Supervisory Control and Data Acquisition (SCADA) Systems datasets. The review demonstrates that AI-driven methods have significant potential to improve safety, operational efficiency, and regulatory assurance in biogas facilities. However, achieving practical and scalable implementation will require rigorous multi-site validation, standardised evaluation indicators, integration of explainable AI, and alignment with plant-level risk-governance frameworks.

Lars Klöser, Mika Beele, Bodo Kraft

While Large Language Models (LLMs) produce highly nuanced text simplifications, developers currently lack tools for a holistic, efficient, and reproducible diagnosis of their behavior. This paper introduces the Simplification Profiler, a diagnostic toolkit that generates a multidimensional, interpretable fingerprint of simplified texts. Multiple aggregated simplifications of a model result in a model's fingerprint. This novel evaluation paradigm is particularly vital for languages, where the data scarcity problem is magnified when creating flexible models for diverse target groups rather than a single, fixed simplification style. We propose that measuring a model's unique behavioral signature is more relevant in this context as an alternative to correlating metrics with human preferences. We operationalize this with a practical meta-evaluation of our fingerprints' descriptive power, which bypasses the need for large, human-rated datasets. This test measures if a simple linear classifier can reliably identify various model configurations by their created simplifications, confirming that our metrics are sensitive to a model's specific characteristics. The Profiler can distinguish high-level behavioral variations between prompting strategies and fine-grained changes from prompt engineering, including few-shot examples. Our complete feature set achieves classification F1-scores up to 71.9 %, improving upon simple baselines by over 48 percentage points. The Simplification Profiler thus offers developers a granular, actionable analysis to build more effective and truly adaptive text simplification systems.

Mohi ud Din, Muhammad Tanveer, Muhammad Faizan Khan

This study examines the influence of Green Transformation Leadership (GTL), Green Human Resource Practices (GHRP), and Green Culture (GC) on Environmental Sustainability (ES), with Employees’ Pro-Environmental Behavior (PEB) acting as a mediating variable. Data were collected from 358 employees across resource-intensive sectors including hospitals, transport, chemical industries, and tanneries in five major Pakistani cities. Structural Equation Modeling (SEM) was employed to evaluate the relationships among variables. Findings reveal that Green Culture exerts a strong direct and indirect impact on sustainability through PEB. GTL demonstrates the importance of leadership commitment, while GHRP enhances outcomes when integrated with cultural and leadership strategies. The R2 values indicate strong relationships between Green Transformation Leadership, Green HR Practices and Green Culture with Employees’ Pro-environmental Behavior and between these factors with Environmental Sustainability. These components together produce essential outcomes for promoting sustainable practices in organizations. The proposed model underscores the role of employee engagement in driving sustainability and offers practical recommendations for business leaders and policymakers. This study contributes to the sustainability literature by validating a holistic model and offering actionable insights for advancing green initiatives in emerging economies like Pakistan.

Karina A. Ramirez-Flores, Gilberto J. Colina-Andrade, Ruly Teran-Hilares et al.

The dairy industry is among of the fastest-growing agro-industries worldwide, driven by the rising demand for dairy products. However, it is also a major source of environmental pollution within the food sector due to the large volumes of whey it generates. This study presents a comparative analysis of alkaline-treated whey as a nutrient solution in hydroponic systems for the cultivation of lettuce (Lactuca sativa), cabbage (Brassica oleracea), and tomato (Solanum lycopersicum), by assessing various plant growth parameters. Alkaline precipitation at pH 11 resulted in substantial reductions in chemical oxygen demand (92.82 %), biological oxygen demand (75.58 %), phosphorus (22.12 %), hardness (75.62 %), and total dissolved solids (48.26 %). When diluted with freshwater at a 1:20 ratio, the treated whey exhibited performance comparable to the commercial hydroponic solutions. Notably, tomato exhibited showed excellent results, with 94 % similarity in cluster count, 73.33 % similarity in comparison to the control, and 97.13 % similarity in dry weight. Therefore, treated cheese whey presents a viable alternative to chemical nutrients in hydroponic systems.

Andry Alamsyah, Said Fikri Naufal Ramdhani

Plastic credit schemes are increasingly adopted to mitigate plastic pollution, yet existing systems remain centralized, opaque, and prone to double counting and fraud. This study proposes and validates a plastic credit system that leverages blockchain technology aimed at enhancing transparency, traceability, and accountability in plastic recovery efforts. A modular three-layer architecture was implemented, comprising a user interaction layer, a blockchain execution layer, and a utility layer for metadata and analytics integration. The system employs two smart contracts on the Polygon Proof-of-Stake (PoS) mainnet using Ethereum standards: ERC-20 for fungible tokenization of plastic credits and ERC-721 for non-fungible certificate issuance. Functional testing confirmed successful execution of token lifecycle operations. Stress testing across 5000 sequential transactions yielded stable performance, with average confirmation times of 5.29 s for fungible token operations and 5.59 s for non-fungible processes. A decentralized application (DApp) was developed to support role-based interaction, credit traceability, and certificate validation. User evaluation returned a high usability score (86.4%), while benchmarking against existing platforms demonstrated improved auditability, automation, and stakeholder control. These findings indicate that blockchain infrastructure can enable decentralized, tamper-resistant plastic credit systems. The proposed model provides a scalable foundation for Extended Producer Responsibility (EPR) compliance and plastic waste traceability, which could potentially support the credibility of Environmental, Social, and Governance (ESG) reporting and supporting circular economy transitions across diverse policy and economic contexts.

Susanna Raiskio, Aravin Periyasamy, Michael Hummel et al.

Fibre mechanical recycling is an efficient strategy to turn non-reusable post-consumer textiles into new textile products with a minimal environmental impact. It helps reduce the need for primary raw materials and prevents the incineration or landfill disposal of textile waste. To foster textile circularity, it is essential to use these recycled fibres as secondary raw materials for textiles. The focus of our study was, therefore, to create quality yarns for making knitted fabrics for long-lasting garments. In this study, mechanically recycled post-consumer cotton and linen were ring-spun into yarns. Recycled cotton (rCO) was blended with virgin cotton (CO) in ratios of 30/70, 50/50, and 70/30, and with virgin viscose (CV) at a 50/50 ratio. Recycled linen (rLI) was blended with virgin viscose in ratios of 30/70 and 50/50. The yarn appearance, breaking tenacity, and elongation were evaluated and compared to virgin viscose and cotton yarns. The knitting performance of rCO/CO 50/50 yarn and reference 100 CO yarn was assessed by knitting different knit structures and pattern designs using a flatbed knitting machine. Additionally, the abrasion resistance of the two yarns knitted into single jersey fabric was tested using the Martindale method, and the samples were inspected using scanning electron microscopy. Increasing recycled cotton and linen content in ring-spun yarns decreased yarn strength and increased the uneven appearance. In addition, the rCO/CO jersey fabric showed higher wear under abrasion than the reference 100 CO fabric. The knit structure and pattern design had a central influence on knittability and fabric appearance when using yarn containing recycled fibres. Therefore, these factors should be considered to promote the use of recycled fibres for creating long-lasting textile products.

Mobina Rostampour Gabanki, Hossein Yousefi, Ahmad Hajinezhad et al.

Biohydrogen production from organic waste offers a sustainable alternative to fossil fuel-derived hydrogen, contributing to the transition toward clean energy. This study explores advancements in microbial engineering, hybrid fermentation, and reactor optimization to enhance hydrogen yields and process efficiency. The objective is to develop innovative biohydrogen production methods that maximize substrate conversion efficiency while ensuring economic feasibility. Key innovations includenanoparticle-assisted hydrogenase activation, and hybrid fermentation integrating dark fermentation with microbial electrolysis cells (MECs) and photofermentation. Advanced continuous stirred tank reactors (CSTRs) and packed-bed bioreactors significantly improve hydrogen production. Results show hydrogen yields of 20–50 L H2/kg volatile solids (VS), with optimized systems increasing conversion efficiency by up to 35 %. Aspen HYSYS modeling identifies peak production at pH 5 and mesophilic temperatures (35–40 °C). A SimaPro-based life cycle assessment (LCA) reveals a global warming potential (GWP) reduction of −1.2E3 kg CO2 per ton of MSW and a resource impact benefit of −67 USD per ton, demonstrating economic feasibility. This study supports biohydrogen’s scalability as a waste-to-energy solution, aligning with circular bioeconomy principles. Future research should focus on microbial robustness, hybrid biorefineries, and AI-driven process control for enhanced sustainability. The findings position biohydrogen as a cost-effective and environmentally friendly clean energy source, accelerating global decarbonization efforts.

Salia S. Sheriff, Abdulfatah Abdu Yusuf, Oluwole O. Akiyode et al.

The rapid accumulation of plastic waste in the environment poses a significant global challenge, exacerbating ecosystem pollution and public health risks. Annually, approximately 8 million tons of plastic waste enter the oceans, contributing to ecosystem degradation and human exposure to toxic substances. Toxins such as phthalates, bisphenol A (BPA), dioxins, furans, and heavy metal residues released from plastic degradation cause severe health risks, including endocrine disruption, carcinogenesis, and respiratory diseases. This study reviews exposure pathways and bioaccumulation mechanisms of plastic-derived toxins, their health risks, mitigation strategies, and policy interventions. The findings reveal that BPA concentrations in rivers can exceed 12 µg/L, and dioxins in soil surpass 1000 ng Toxic Equivalency Quotient (TEQ)/kg in areas with open burning, exceeding WHO thresholds. In Poland, landfill leachate shows phthalate levels over 303 µg/L, while heavy metals in fish tissue reached over 2.26 ng/g wet weight in Sweden. Vulnerable populations in low- and middle-income countries (LMICs), particularly in Sub-Saharan Africa, face heightened risk exposure, with 39–45 % of urban waste being formally managed. Despite recycling efforts, only 9 % of plastic waste is recycled globally, while open burning and inadequate incineration release hazardous pollutants like dioxins and furans. Advanced solutions, such as chemical recycling, with recovery rates up to 97 % for polyethylene terephthalate (PET), and enzymatic degradation, achieving 90 % plastic breakdown in 10 h, show promise but face scalability challenges. Case studies from Germany, Japan, and Rwanda demonstrate effective strategies, including extended producer responsibility schemes and bans on single-use plastics, achieving recycling rates exceeding 41 % and reducing waste by 90 %. However, challenges persist, particularly in low- and middle-income countries with inadequate waste management infrastructure. This study concludes by recommending stricter regulations, investment in advanced recycling technologies, development of bioplastics, and international collaborations to mitigate health risks and environmental contamination from plastic waste.

Chandra Manivannan Arun, Logeshwaran Panneerselvan, Gunasekhar Nachimuthu et al.

Blended textile waste constitutes a substantial portion of the global textile waste stream, making recycling essential for minimizing the industry’s environmental impact. Although polyester/cotton recycling is well developed, many other blended textiles are routinely landfilled owing to a lack of effective recycling technologies. This review critically assesses existing strategies for recycling non-polyester/cotton blends, highlighting the key challenges and opportunities for innovation. In the sorting stage, integrating artificial intelligence (AI) and machine learning (ML) enhances efficiency and accuracy. Advanced methods, such as green chemistry, mechanical recycling, and enzymatic treatments, have proven effective for most blended textiles; however, fibers, such as polypropylene, still lack defined closed-loop recycling routes. Life cycle assessment (LCA) indicates that recycling textile waste can reduce environmental impacts by 60%, but the absence of comprehensive LCA studies on diverse recycling approaches limits reliability. Furthermore, while textile recycling is sustainable, concerns regarding the emission of hazardous additives and organic pollutants pose ecological and health risks. Therefore, advanced recycling technologies for non-polyester/cotton blends are crucial for achieving sustainability. Future research should focus on developing efficient recycling methods for complex blends, addressing the environmental impact of hazardous substances, and standardizing LCA methodologies to ensure economic and environmental viability.

Deborah Cristina Crominski da Silva Medeiros, Muhammad Usman, Pamela Chelme-Ayala et al.

Oil sludge (OS), a by-product of petroleum refinery (PR) waste, contains various toxic organic and inorganic compounds. Improper handling of OS poses serious environmental risks, highlighting the need for an effective and sustainable solution to mitigate these hazards and transform OS into a valuable product. This study converted OS into char-(OSC), including both pristine-OSC and ZnCl2-activated-OSC, for the removal of organic pollutants from refinery wastewater (WW) with a high COD concentration (89,233 mg/L). ZnCl2-activated-OSC produced at 400 °C (SB-Zn-400) showed superior adsorption capacity compared to pristine-OSC, due to enhanced oxygen-containing functional groups, crystallinity, thermostability, and superior degradation (OS). The adsorption process demonstrated rapid COD removal, reaching equilibrium within 2 h and achieving a 28 % reduction in COD. The adsorption capacity was found to be 420.5 mg-COD/g-OSC. SB-Zn-400 exhibited heterogeneous surface properties and supported multi-layer adsorption, with hydrogen-bonding and π–π interactions likely adsorption mechanisms. Moreover, the total concentration of organic compounds in PR-WW was >2700 mg/L, and SB-Zn-400 reduced this concentration to 34 mg/L, achieving >98 % removal. Although the adsorption treatment reduced the inorganic parameters of PR-WW, leaching of Mn, Ni, and Zn was observed, likely due to the nature of OS and the ZnCl2-activation process. Thermal regeneration of spent SB-Zn-400 allowed the reuse of OSC, with adsorption efficiency remaining higher than that of pristine-OSC, indicating that SB-Zn-400 has potential to be reused. These findings highlight the effectiveness of OSC in treating PR-WW, supporting a circular economy approach to enhance resource efficiency and minimize the environmental impact of OS from PR industries.

Alicia López-Ceballos, Alejandro Datas

Fully electrifying the building sector requires not only the widespread adoption of photovoltaic (PV) self-consumption and heat pumps, but also the integration of cost-effective energy storage solutions. Hybridizing lithium-ion (Li-ion) batteries with power to heat to power storage (PHPS) systems, thermal batteries capable of thermal-to-electric energy conversion, offers a promising and economically viable solution. PHPS systems dispatch combined heat and power by utilizing the low-temperature waste heat generated during the thermal to electric energy conversion process. This study investigates the technoeconomic impacts of waste heat use in PHPS systems integrated with Li-ion batteries and heat pumps to support the decarbonization of the building sector. Two distinct strategies are evaluated: direct use of waste heat to meet heating demands; and the use of waste heat to enhance the heat pump's coefficient of performance. Results show that supplying the waste heat at the demand setpoint temperature is the best solution to integrate PHPS cost-effectively, although enhancing the heat pump's COP with waste heat also yields notable economic gains. Additionally, leveraging waste heat significantly lowers the minimum thermal-to-electric conversion efficiency required for PHPS systems to achieve economic viability. Optimal PHPS designs enable large-scale energy storage and charging capacities, thereby enhancing PV self-consumption rates and reducing the levelized cost of energy. The analysis also reveals that hybridizing PHPS with Li-ion batteries may rise as the optimal solution for moderately priced PHPS systems, with the reduction in levelized cost being more pronounced in solar-dominated regions.

Mikhail Kaliteevski, Leonid Chechurin, Maxim Permyakov et al.

A method of waste incineration using pure oxygen or atmospheric air enriched with oxygen is proposed, demonstrating several advantages over conventional burning in atmospheric air. The higher flame temperature is predicted, even with low calorific value waste, ensuring the complete decomposition of harmful substances such as dioxins. This process also increases the efficiency of heat-to-electricity generation via steam turbine and facilitates the melting of ash and dust, leading to the production of gravel or rock fibre. Additionally, it enables the incineration of a wide range of waste, including sewage sludge. The higher partial pressure of water vapor in the combustion gases allows to develop a novel method of filtration: condensation filtration. The method promises less or next to zero fly ash by-production. The process produces concentrated carbon dioxide suitable for storage or use in industrial and agricultural applications. Moreover, air separation as part of this method generates large quantities of argon, which can be utilized in high-tech industries. This approach offers a comprehensive solution to waste management and resource recovery. The paper presents the principal scheme for the process, its initial modelling and general feasibility study demonstrating its technological and commercial potential.

Debojyoti Ghosh, Adrijit Goswami

Accurate waste classification is vital for achieving sustainable waste management and reducing the environmental footprint of urbanization. Misclassification of recyclable materials contributes to landfill accumulation, inefficient recycling, and increased greenhouse gas emissions. To address these issues, this study introduces HybridSOMSpikeNet, a hybrid deep learning framework that integrates convolutional feature extraction, differentiable self-organization, and spiking-inspired temporal processing to enable intelligent and energy-efficient waste classification. The proposed model employs a pre-trained ResNet-152 backbone to extract deep spatial representations, followed by a Differentiable Soft Self-Organizing Map (Soft-SOM) that enhances topological clustering and interpretability. A spiking neural head accumulates temporal activations over discrete time steps, improving robustness and generalization. Trained on a ten-class waste dataset, HybridSOMSpikeNet achieved a test accuracy of 97.39%, outperforming several state-of-the-art architectures while maintaining a lightweight computational profile suitable for real-world deployment. Beyond its technical innovations, the framework provides tangible environmental benefits. By enabling precise and automated waste segregation, it supports higher recycling efficiency, reduces contamination in recyclable streams, and minimizes the ecological and operational costs of waste processing. The approach aligns with global sustainability priorities, particularly the United Nations Sustainable Development Goals (SDG 11 and SDG 12), by contributing to cleaner cities, circular economy initiatives, and intelligent environmental management systems.

Kui Huang, Mengke Song, Shuo Ba et al.

Waste classification is crucial for improving processing efficiency and reducing environmental pollution. Supervised deep learning methods are commonly used for automated waste classification, but they rely heavily on large labeled datasets, which are costly and inefficient to obtain. Real-world waste data often exhibit category and style biases, such as variations in camera angles, lighting conditions, and types of waste, which can impact the model's performance and generalization ability. Therefore, constructing a bias-free dataset is essential. Manual labeling is not only costly but also inefficient. While self-supervised learning helps address data scarcity, it still depends on some labeled data and generally results in lower accuracy compared to supervised methods. Unsupervised methods show potential in certain cases but typically do not perform as well as supervised models, highlighting the need for an efficient and cost-effective unsupervised approach. This study presents a novel unsupervised method, Dual-Encoder Contrastive Learning with Multi-Clustering Voting (DECMCV). The approach involves using a pre-trained ConvNeXt model for image encoding, leveraging VisionTransformer to generate positive samples, and applying a multi-clustering voting mechanism to address data labeling and domain shift issues. Experimental results demonstrate that DECMCV achieves classification accuracies of 93.78% and 98.29% on the TrashNet and Huawei Cloud datasets, respectively, outperforming or matching supervised models. On a real-world dataset of 4,169 waste images, only 50 labeled samples were needed to accurately label thousands, improving classification accuracy by 29.85% compared to supervised models. This method effectively addresses style differences, enhances model generalization, and contributes to the advancement of automated waste classification.

Bennet van den Broek, Javad Pourmostafa Roshan Sharami

The improper disposal and mismanagement of medical waste pose severe environmental and public health risks, contributing to greenhouse gas emissions and the spread of infectious diseases. Efficient and accurate medical waste classification is crucial for mitigating these risks. We explore the integration of capsule networks with a pretrained DenseNet model to improve medical waste classification. To the best of our knowledge, capsule networks have not yet been applied to this task, making this study the first to assess their effectiveness. A diverse dataset of medical waste images collected from multiple public sources, is used to evaluate three model configurations: (1) a pretrained DenseNet model as a baseline, (2) a pretrained DenseNet with frozen layers combined with a capsule network, and (3) a pretrained DenseNet with unfrozen layers combined with a capsule network. Experimental results demonstrate that incorporating capsule networks improves classification performance, with F1 scores increasing from 0.89 (baseline) to 0.92 (hybrid model with unfrozen layers). This highlights the potential of capsule networks to address the spatial limitations of traditional convolutional models and improve classification robustness. While the capsule-enhanced model demonstrated improved classification performance, direct comparisons with prior studies were challenging due to differences in dataset size and diversity. Previous studies relied on smaller, domain-specific datasets, which inherently yielded higher accuracy. In contrast, our study employs a significantly larger and more diverse dataset, leading to better generalization but introducing additional classification challenges. This highlights the trade-off between dataset complexity and model performance.

Subhashish Dey, G.T.N. Veerendra, A.V. Phani Manoj et al.

Human and industrial activities pollute water resources with sulphur metal, endangering human health and ecosystems. Chemical precipitation and membrane filtration are expensive when treating large amounts of water, inefficient at low metal concentrations, and produce large amounts of toxic sludge and other products that must be disposed of waste water bio-sorption is eco-friendly and alternative. These methods are cheaper, more accessible, and reusable than conventional ones. This study investigates the bio-sorption of sulphur from contaminated water using neem leaf, custard apple leaf, mango tree leaf, orange peels, and banana peels biological waste materials. This work achieves 100 % removal efficiency. Biosorbents remove sulphates from contaminated water: custard leaves 100 % at 4gm, orange peels 40 % at 5gm, tea waste 50 % at 4gm, neem leaves 70 % at 5gm, and mango leaves 75 % at 5gm. The performances of biosorbents for removals of sulphates from water as follows: Custard leaves waste > Mango leaves > Neem leaves > Tea trash > Orange peels the sulphate reduction by biosorbents. The best biosorption occurred at basic pH 6.5, 3.7gm dosage, 90 min contact duration, 30 °C temperature, and 120 rpm agitation speed. The effects of contact time, agitation speed, adsorbent dosage, pH, and temperature are also examined. Before usage, biosorbents materials can be physically and chemically measured the changes. Regenerating and reusing bio-sorbent after sulphates removals makes the method cost-effective for removals of pollutants from water.

Srijana Rai, Aditi Gurung, Hari Bhakta Sharma et al.

Rapid urbanization and the growing tourism industry in the Himalayan region have raised significant concerns about the sustainable handling and management of solid waste in the area. This concern is particularly pronounced in the unique and vulnerable ecosystem of cities nestled in the eastern Indian Himalayas, where geography, ecology, and socio-economic factors converge to create distinctive challenges. This study undertakes a thorough investigation of solid waste management practices and challenges in two smart cities nestled in the eastern Himalayan region of India, specifically Gangtok and Namchi. The primary objective was to unveil the complex challenges confronting these cities by conducting an in-depth exploration of waste management practices. This comprehensive investigation was achieved through the collection of samples, conducting surveys within each ward of the cities, and performing physicochemical characterization analyses. The study also proposes a solution-oriented approach leveraging life cycle analysis. Despite the Himalayan backdrop, waste generation varies significantly, with Gangtok producing substantially more waste (50 tons per day (TPD)) compared to Namchi (4.6 TPD). Both cities excel in waste collection, but Gangtok leads in proactive treatment, with 22% (11 TPD) undergoing composting and recycling. However, shared challenges at riverside disposal sites, including overfilled dumpsites and leachate contamination, pose potential environmental hazards amid the Himalayan allure. Organic waste predominates at 40.9% and 40.01% in both cities, indicating composting potential. Plastic closely at 22.25% (Namchi) and 22.65% (Gangtok) was way above the global (12 %) and national (8.4 %) average respectively, emphasizing the urgency of plastic waste reduction strategy. The 16.95% cardboard in Gangtok highlights potential commerce-related waste. The study also highlights the importance of comprehensive policies for informal sector well-being. The lifecycle assessment of Namchi and Gangtok reveals significant methane emissions (99%) from waste anaerobic decomposition potentially due to poor source segregation. Implementing a futuristic waste strategy reduces emissions by 38% in Namchi and 40% in Gangtok, through biogas utilization and renewable energy adoption. The findings advocate for citizen engagement, improved collection, targeted campaigns, and circular economy principles. To ensure sustainability and resilience in waste management within mountain regions, tailored strategies such as material recycling facilities, community engagement initiatives, and landfill diversion mandates should integrate ecological, socioeconomic, and logistical considerations, fostering awareness and participation among communities.

Rafaela Orenga Panizza, Mazdak Nik-Bakht

The building sector is a major player in the world’s contribution to climate change, partly due to its dependence on large quantities of materials. The circular economy model of material flow has been gaining attention in the past decade as it seeks to promote the use of construction, renovation, and demolition (CRD) waste as inputs for new buildings or other applications, which would result in the diversion of materials from landfills. Developing a system capable of handling such waste requires a comprehensive knowledge of the composition of the building stock materials. This information, however, is rarely available. Thus, this research is proposing a conceptual model to aid city planners when considering the existing built environment as a resource for new construction. The methodology followed by this review includes a thorough analysis of 82 articles on quantity takeoff methods in the Urban Mining (UM) and CRD Waste Management (WM) fields. These articles were analyzed by considering a framework of four layers, i.e., (i) the approach, (ii) the analysis method, (iii) the granularity, and (iv) the performance analysis. The comprehensive analysis of the literature has highlighted the fact that the existing quantity takeoff methods need to consider more in-depth attributes and that the works performed by using machine learning methods are very important in the path toward the direction of improving these methods. With this conceptual model, waste management planners can select the appropriate methodology based on the available input data, and the type of output that they are looking.

Triastuti Sulistyaningsih, Dwi Atika Sari, Nuni Widiarti et al.

Magnetite, a widely synthesized compound utilized as an adsorbent in various processes, exhibits reduced adsorption efficiency due to aggregation in aqueous solutions. Addressing this limitation, modification of magnetite is necessary to mitigate agglomeration and enhance adsorption capacity. Herein, we propose the utilization of gaharu leaf extract for magnetite modification, leveraging its abundance as a byproduct of essential oil production and rich secondary metabolite content, which serves as a stabilizing agent in synthesis processes. Magnetite (M) and gaharu leaf extract-modified magnetite (MEDG) were synthesized and evaluated as adsorbents for methyl orange (MO) textile dyes. Characterization via FTIR, PSA, SEM-EDX, XRD, and VSM revealed distinctive features of MEDG, including vibrational peaks corresponding to organic functional groups derived from the secondary metabolites present in the gaharu leaf extract. These functional groups actively participate in the adsorption process and prevent magnetite aggregation. MEDG exhibited a particle size of 4408.7 nm, compared to 4625 nm for M, with both exhibiting face-centered cubic (fcc) crystal structures. Additionally, MEDG demonstrated a crystallinity percentage of 92.49 % and saturation magnetization of 34.43 emu/g, attributed to the incorporation of gaharu leaf extract. Adsorption studies demonstrated that MEDG and gaharu leaf simplicia (SDG) achieved maximal adsorption of MO at pH 3 for 60 min and pH 4 for 70 min, respectively, with an adsorption efficiency of 95–99 % for a 5 ppm MO solution. The adsorption kinetics followed Ho’s pseudo-second-order model, and the isotherm conformed to Freundlich’s model. Furthermore, M and MEDG exhibited high reusability, with up to 5 cycles of reuse, while SDG demonstrated 3 cycles. However, the adsorption efficiency decreased to 87 % for MEDG and 85.19 % for SDG upon the fifth repetition. In conclusion, modification of magnetite with gaharu leaf extract enhances its adsorption capacity, offering economic advantages over pure magnetite due to increased surface area and active sites.

Kaveh Andisheh

This article conducted a systematic literature review to find the most recent progress, barriers and opportunities in developing building material passports for structural steel reuse. To reuse steel a Circular Economy shall be implemented. A steel circular economy adoption requires the development of steel circularity passports, structural steel reusability assessment and reversible structural design. A programme rule was presented that can serves as a guideline for the development of a steel circularity passport in New Zealand. The programme rule outlined essential data and details, data resources and reliability, and steel identification process. Finally, a case study was used to demonstrate the significant benefits of applying the proposed programme rule in mitigating challenges presented by unidentified structural steel components. The 18-storey former council building has total 4,600 square meters located in Auckland downtown, New Zealand. It was found regulatory, technical, and economic barriers shall be addressed to enable steel reuse in New Zealand and presence of steel passports can facilitate steel reuse in practice by addressing the barriers in New Zealand.