Search Results (305)

Concrete production contributes approximately 4–8% of global cardon dioxide emissions, largely due to Portland cement. Incorporating municipal solid waste (MSW) into concrete offers a pathway to reduce cement demand while supporting circular economy objectives. This study evaluates the mechanical performance, environmental impacts, and optimization potential of concrete incorporating three MSW-derived materials: cardboard kraft fibers (KFs), recycled high-density polyethylene (HDPE), and textile fibers. A maximum 10% cement replacement strategy was adopted. Compressive strength was assessed at 7, 14, and 28 days, and a cradle-to-gate life cycle assessment (LCA) was conducted using OpenLCA to quantify global warming potential (GWP100) and other midpoint impacts. A surrogate-based optimization implemented using Non-dominated Sorting Genetic Algorithm II (NSGA-II) was applied to minimize cost and GWP while enforcing compressive strength as a feasibility constraint. The results show that fiber-based wastes significantly reduce embodied carbon, with KF achieving the largest GWP reduction (19%) and textile waste achieving moderate reductions (10%) relative to the control. HDPE-modified concrete exhibited near-control mechanical performance but increased GWP and fossil depletion due to polymer processing burdens. The optimization results revealed well-defined Pareto trade-offs for KF and textile concretes, identifying clear compromise solutions between cost and emissions, while HDPE was consistently dominated. Overall, textile waste emerged as the most balanced option, offering favorable environmental gains with minimal cost and acceptable mechanical performance. The integrated LCA optimization framework demonstrates a robust approach for evaluating MSW-derived concrete and supports evidence-based decision-making toward low-carbon, circular construction materials. Full article

Polyethylene terephthalate (PET) recycling technology has developed into a mature system, providing a key paradigm for the circular utilization of polyester waste. Its pathways are primarily divided into mechanical recycling and chemical recycling. Mechanical recycling converts waste PET into rPET through physical processes such as efficient sorting, deep cleaning, and melt extrusion. However, the resulting product often faces issues of decreased intrinsic viscosity and thermal oxidative degradation. Chemical recycling, particularly depolymerization techniques like saccharification, hydrolysis, and methanolysis, can reduce PET waste back to monomers. After purification, these monomers can be repolymerized into virgin-quality PET, achieving a closed-loop cycle. However, this approach faces challenges related to cost and process complexity. Against this backdrop, this paper further explores potential recycling methods for polyethylene terephthalate-1,4-cyclohexanedimethyleneterephthalate (PETG). This paper argues that the experience of PET recycling provides a crucial foundation for addressing PETG challenges but is not a direct solution. Future development directions include: developing intelligent sorting technologies, creating highly efficient selective catalysts to optimize depolymerization reactions, and other initiatives. These measures are essential for establishing an efficient recycling system for complex polyester waste. Full article

Alkali-activated materials (AAMs) or geopolymers have been considered for many years as a sustainable substitution for the traditional ordinary Portland cement (OPC) binder. However, their production needs energy consumption and creates carbon emissions. Since construction and demolition waste (CDW) can become precursors for manufacturing alkali-activated materials, their use as substitutes for traditional AAM (such as metakaolin, blast furnace slag, and fly ash) can solve both the problem of their disposal and the problem of sustainability. Furthermore, CDW can also be used as aggregate replacement, avoiding the exploitation of natural river sand and gravel. A new circular economy could be created based on CDW recycling, creating a new eco-friendly building practice. Unfortunately, this process is quite difficult owing to several variables that should be taken into consideration, such as the possibility of separating and sorting the CDW, the great variability of CDW composition, the cost of the mechanical and thermal treatment, the different parameters that compose an alkali-activated mix-design, and public opinion still being skeptical about the use of recycled materials in the construction sector. This review tries to describe all these aspects, summarizing the results of the most interesting studies performed on this subject. Today, thanks to a comprehensive protocol, the use of building information modeling (BIM) software and machine learning models, a large-scale reuse of CDW in the building industry appears more feasible. Full article

This article presents a narrative, traditional literature review summarizing current research on the integration of digital technologies in waste management. The study examines how intelligent technologies, including Geographic Information Systems, Big Data analytics, and artificial intelligence, can improve energy efficiency, support sustainable resource use, and enhance the development of low emission and circular waste management systems. The reviewed research shows that the combination of spatial analysis, large-scale data processing, and predictive computational methods enables advanced modeling of waste distribution, the optimization of collection routes, intelligent sorting, and the forecasting of waste generation. Geographic Information Systems support spatial planning, site selection for waste facilities, and environmental assessment. Big Data analytics allows the integration of information from Internet of Things sensors, global positioning systems, municipal databases, and environmental registries, which strengthens evidence-based decision making. Artificial intelligence contributes to automatic classification, predictive scheduling, robotic sorting, and the optimization of recycling and energy recovery processes. The study emphasizes that the integration of these technologies forms a foundation for intelligent waste management systems that reduce emissions, improve operational efficiency, and support sustainable urban development. Full article

This paper presents Greenopoli, an innovative framework for sustainability and waste management education that has engaged over 600 schools and 90,000 students since 2014. Greenopoli is founded on the idea that children and youth can grasp environmental issues as well as adults and act as agents of change within their families and communities. The Greenopoli approach combines scientific accuracy with playful, creative pedagogy to simplify complex topics and stimulate peer-to-peer learning. It includes storytelling, games, field visits, and “green raps” (original environmental songs co-created with students). The framework is adaptive, with content and activities tailored to education stages from kindergarten through university. Educators adopt the role of moderators or facilitators, encouraging students to discuss and discover concepts collaboratively. Greenopoli’s participatory method has been implemented across all age groups, yielding enthusiastic engagement and tangible outcomes in waste sorting and recycling behaviors. The program’s reach has extended beyond schools through collaborations with national recycling consortia, NGOs, municipalities, and media (TV programs, social media, TEDx talks). Numerous awards and recognitions (2017–2025) have highlighted its impact. A comparative analysis shows that Greenopoli’s use of peer-led learning, gamification, and creative communication aligns with global best practices while offering a unique blend of tools. Greenopoli is a novel best-practice model in environmental education, bridging theory and practice and contributing to the goals of Education for Sustainable Development and a circular economy. It demonstrates the effectiveness of engaging youth as change-makers through interactive and creative learning, and it can inspire similar initiatives globally. Full article

Innovations in manual waste sorting have stagnated for decades, despite the increasing global demand for efficient recycling solutions. The recAIcle system introduces an innovative AI-powered assistance system designed to modernise manual waste sorting processes. By integrating machine learning, continual learning, and projection-based augmentation, the system supports sorting workers by highlighting relevant waste objects on the conveyor belt in real time. The system learns from the decision-making patterns of experienced sorting workers, enabling it to adapt to operational realities and improve classification accuracy over time. Various hardware and software configurations were tested with and without active tracking and continual learning capabilities to ensure scalability and adaptability. The system was validated in initial trials, demonstrating its ability to detect and classify waste objects and providing augmented support for sorting workers with high precision under realistic recycling conditions. A survey complemented the trials and assessed industry interest in AI-based assistance systems. Survey results indicated that 82% of participating companies expressed interest in supporting their staff in manual sorting by using AI-based technologies. The recAIcle system represents a significant step toward digitising manual waste sorting, offering a scalable and sustainable solution for the recycling industry. Full article

With the increasing demand for sustainable building materials, it has become essential to identify sustainable alternatives to conventional sound absorbers, particularly in the context of waste reduction and the circular economy. The aim of this study was to compile and describe a structured dataset of sound absorption coefficients for laboratory-produced panels made from recycled textile materials. Five types of panels were developed using cotton, polyester, wool, linen, and a mixed composition of textiles. A biopolymer binder was applied to ensure structural stability of the materials. Following careful sorting, shredding, and homogenization of the textile waste, test specimens were prepared and examined under controlled laboratory conditions. The sound absorption coefficients were measured using an AFD 1000 impedance tube in accordance with the ISO 10534-2 standard, across a frequency range from 6.25 to 6393.75 Hz. For each material, three repeated measurements were performed, and mean values were calculated to ensure accuracy and reliability. The resulting dataset contains structured values of sound absorption coefficients, which can be applied in building acoustics modeling, comparative studies with conventional insulation materials, and the development of new sustainable products. In addition, the data can be used in educational contexts and machine learning applications to predict the acoustic properties of recycled textile composites. Full article

Brominated Flame Retardants (BFRs), widely used in Electrical and Electronic Equipment (EEE), pose severe health and environmental risks and complicate recycling at the end-of-life stage, calling for innovative, sustainable detection and sorting solutions. In this context, new strategies that are efficient, reliable, sustainable, and cost-effective are required. This study investigates Short-Wave Infrared (SWIR) spectroscopy for detecting brominated plastics and quantifying bromine (Br) and antimony (Sb) content in Cathode-Ray Tube (CRT) e-waste. X-Ray Fluorescence (XRF) provided reference measurements, while Support Vector Machine (SVM) models were trained on reflectance spectra acquired with a portable spectroradiometer. The SVM–Discriminant Analysis models achieved near-perfect classification, with 100% accuracy in distinguishing samples above and below the regulatory thresholds for Br (2000 mg/kg) and Sb (8354 mg/kg). SVM regression yielded excellent quantitative predictions, with R²_P = 0.996 and RMSEP = 2671 mg/kg for Br, and R²_P = 0.999 and RMSEP = 1056 mg/kg for Sb. These performances confirm the robustness of SWIR spectroscopy for rapid, non-destructive monitoring of hazardous plastics, even in highly heterogeneous waste streams. The integration of SWIR spectroscopy with machine learning supports selective recycling and safer resource recovery, directly contributing to United Nations Sustainable Development Goals on Decent Work and Economic Growth (SDG 8), Industry, Innovation and Infrastructure (SDG 9), and Responsible Consumption and Production (SDG 12). Full article

The construction industry is a major consumer of raw materials and a significant contributor to global waste. In Canada, the construction, renovation, and demolition (CRD) sector diverts only 16% of its waste from landfills, underscoring the urgent need for circular economy (CE) practices. This study develops a generalizable and reproducible framework for archetype identification to support CE strategies, with a focus on Montréal, Canada’s second-largest city. We define a new set of exterior shell archetypes for low-rise residential buildings and demonstrate their application in a neighborhood-scale case study. These archetypes enable systematic estimation of material and component stocks, as well as end-of-life recovery flows, across a representative sample of buildings in the Mercier–Hochelaga–Maisonneuve district. Results show that prioritizing reuse can nearly double material recovery compared to conventional sorting and recycling. More broadly, this framework advances engineering design for circular systems by integrating component-level data into reuse strategy assessment and providing a scalable approach for urban circularity. Full article

Plastic recycling has been a challenge worldwide due to various reasons, including limited profit margins, the demand for high-quality plastic reprocessing techniques to make products comparable to those from virgin materials, and challenges in sorting and processing. This problem became particularly urgent in the small towns in the U.S., where plastic waste was shipped overseas for treatment, but now it is not accepted in some countries. This study aims to understand the plastic value chain and find the necessary factors for a circular economy model of both environmental and economic settings. In this study, an educational plastics reprocessing workspace was developed with manufacturing processes such as shredding, filament extruding, 3D printing, and injection molding. A series of products was developed to increase the value of the recycled polymers. In addition, quality control of recycled polymers such as polylactic acid (PLA), acrylonitrile butadiene styrene (ABS), polyethylene terephthalate (PET), and polyethylene terephthalate glycol (PETG) was examined. By collaborating with a university manufacturing lab, this work illustrates how plastics can be collected, prepared, and reprocessed, serving as a platform for student learning and community outreach. This study contributes to the body of knowledge by presenting a case-based educational model for community-level plastic recycling and reprocessing in a college town context. Full article

The basis for the assessment of the quality of selective waste collection, as well as the direction of modernization of municipal waste sorting technology, should be morphological composition studies. According to research, the gold standard for the assessment of the quality of selective waste collection, as well as the direction of modernization of municipal waste sorting technology, is morphological composition studies. Selective collection of municipal waste is an indispensable element of the waste management system, particularly important in terms of the increasing recycling levels obtained. The recycling of problematic and polluting plastics is one of the main challenges of the European circular economy. By problematic plastics, we mean those that are not suitable for recycling, or their recycling is difficult and expensive for technological reasons, resulting in problems with their management on the market. In 2022, the level of plastic recycling in Europe reached 26.9%. This article presents the results of studies on the morphological composition of selectively collected plastics and metals carried out in the summer and autumn of 2023 for a large city in Poland in the Silesian agglomeration. As a result, the quality of selectively collected waste, the share of pollutants in it, depending on the type of development, and the real possibilities of its recovery were determined. As part of the results obtained, a simulation was also carried out on how the deposit system planned from the first of October 2025 in Poland will affect the morphological composition of plastics and metals currently collected in the yellow container/bag. Full article

The co-firing of coal and refuse-derived fuel (RDF) from municipal solid waste recycling is gaining support in countries in which energy production is based on solid fuels. It is the result of the rising priority given to renewable energy sources, the circular economy, and effective waste management through sorting, recycling, and thermal conversion. Despite the increasing efficiency of recycling and the ever-lower quantities of waste delivered to waste dumps, the problem of the residual fraction remains unsolved. The portion of mixed municipal waste that cannot be recycled exhibits a high energy value. For this reason, it should be neither stored nor burnt in household boiler rooms, as doing so would constitute an environmental hazard. However, the waste can be used as an additive to fine coal in power boilers, provided that they are equipped with flue gas monitoring and purification systems. Tests involving proportionally prepared compositions of fine coal and refuse-derived fuel burnt in a laboratory boiler revealed a major variability in the flue gas parameters (physicochemical), depending on the applied proportions of the individual components. For instance, when burning a composition of 50% fine coal and 50% refuse-derived fuel, a reduction in CO₂ emissions by about 12% was noted compared with that when burning fine coal exclusively. Furthermore, when burning refuse-derived fuel, an addition of 20% fine coal is enough to produce a 2.8% reduction in CO emission. Meanwhile, a composition of 80% fine coal and 20% refuse-derived fuel would reduce the emissions by 393 ppm. During the measurements, it was also noted that most of the measured parameters indicated a decrease in individual gas contents relative to the emissions obtained when burning fine coal or refuse-derived fuel exclusively. These relationships can be applied to prepare fuel compositions based on refuse-derived fuel and fine coal, depending on the power and flue gas purification capabilities of individual cogeneration systems. Full article

Effective recovery of materials from construction and demolition waste (CDW) remains a major problem and a real challenge in terms of implementing the circular economy. In many countries, this waste is landfilled due to the lack of modern technological lines for its recovery and recycling, including the sorting of materials suitable for reuse. Understanding the environmental impact of the CDW treatment process is important as it constitutes the final stage of building life cycle assessment and the basis for eco-design of construction processes. In addition, the recovered materials can be used as raw materials for construction, thereby closing the waste loop and aligning with the circular economy concept. The purpose of this study is to compare the environmental impact of three different CDW recovery technologies in order to identify the optimal option. The analysis was performed using the life cycle assessment (LCA) methodology, SimaPro 8.1 software, and the Ecoinvent v3.8 database. 1 Mg of processed CDW was adopted as the functional unit. It was found that the process of recovering materials from CDW allows for sorting over 13% of materials for recycling and approx. 40% of raw materials for reuse (stone aggregates). The conducted analyses showed that all three installations exert a negative impact on the environment. Solution No. 2 had the lowest total environmental impact (15.96 Pt) under the assumptions and datasets used in this study, presenting average electricity and fuel consumption and average weight of sorted materials for recycling. Installation No. 3, which sorts the largest volume of materials for recycling, also used the most electricity; therefore, it could not be considered as the solution with the minimal overall environmental impact. The research revealed that the treatment of CDW in a crusher, applied at all installations, is the process stage resulting in the greatest environmental pressure (16.92 Pt). The high level of sorted recyclable waste enabled a relatively low carbon footprint for processes No. 2 and No. 3, 18.7 and 17.6 kg CO₂ eq, respectively (more than four times lower than for installation No. 1). Future analyses should focus on optimizing the CDW recovery process by avoiding the use of impact crushers, as adding more waste sorting equipment does not significantly enhance environmental benefits. Full article

Driven by environmental concerns and aligned with the principles of the circular economy, urban plastic waste—including packaging materials, disposable items, non-functional objects, and industrial scrap—is increasingly being collected, recycled, and marketed as a potential substitute for virgin polymers. However, the use of recycled polymers introduces uncertainties that can significantly affect both the durability and the further recyclability of the resulting products. This paper demonstrates how spectroscopic analysis in the mid-infrared (MIR) and near-infrared (NIR) regions can be applied well beyond the basic identification of the main polymeric component, typically performed during the sorting stage of recycling processes. A detailed interpretation of spectral data, based on well-established correlations between spectroscopic response and material structure, enables the classification of recycled polymers according to specific physicochemical properties, such as chemical composition, molecular architecture, and morphology. In this context, infrared spectroscopy not only provides a reliable comparison with the corresponding virgin polymer references but also proves particularly effective in assessing the homogeneity of recycled materials and the reproducibility of their properties—factors not inherently guaranteed due to the variability of input sources. As a case study, we present a robust protocol for determining the polypropylene content in recycled polyethylene samples. Full article

Effective waste management is important for reducing environmental harm, improving recycling operations, and building urban sustainability. However, accurate waste classification remains a critical challenge, as many deep learning models struggle with diverse waste types. In this study, classification accuracy is enhanced using transfer learning, ensemble techniques, and custom architectures. Eleven pre-trained convolutional neural networks, including ResNet-50, EfficientNet variants, and DenseNet-201, were fine-tuned to extract meaningful patterns from waste images. To further improve model performance, ensemble strategies such as weighted averaging, soft voting, and stacking were implemented, resulting in a hybrid model combining ResNet-50, EfficientNetV2-M, and DenseNet-201, which outperformed individual models. In the proposed system, two specialized architectures were developed: EcoMobileNet, an optimized MobileNetV3 Large-based model incorporating Squeeze-and-Excitation blocks for efficient mobile deployment, and EcoDenseNet, a DenseNet-201 variant enhanced with Mish activation for improved feature extraction. The evaluation was conducted on a dataset comprising 4691 images across 10 waste categories, sourced from publicly available repositories. The implementation of EcoMobileNet achieved a test accuracy of 98.08%, while EcoDenseNet reached an accuracy of 97.86%. The hybrid model also attained 98.08% accuracy. Furthermore, the ensemble stacking approach yielded the highest test accuracy of 98.29%, demonstrating its effectiveness in classifying heterogeneous waste types. By leveraging deep learning, the proposed system contributes to the development of scalable, sustainable, and automated waste-sorting solutions, thereby optimizing recycling processes and minimizing environmental impact. Full article