Waste Management & Research最新文献

The management of end-of-life vehicles (ELVs) has become a significant environmental and economic challenge due to the substantial volumes of hazardous waste generated. This article analyses sustainable practices in ELV management across Europe, with a focus on contributions to the circular economy. The systematic literature review, conducted for articles published in the period 2016-2024, identifies five topics: (1) policy and regulatory frameworks evaluations and suggestions; (2) economic and environmental benefits through optimization modelling; (3) trends and performances analysis; (4) advanced treatment technologies and their impact and (5) economic and environmental impacts assessments. The findings highlight the importance of state-of-the-art recycling processes and coordinated stakeholder efforts in improving ELV management outcomes. In addition, the correlation between ELVs recycling and gross domestic product (GDP) was analysed. Data analysis for 27 European countries in the period 2016-2021 shows a moderate correlation. Specifically, countries with stronger economies tend to produce more ELVs, distinguishing two clusters when GDP is 35,000 € per capita. By adopting best practices and innovative approaches, European countries can enhance their ELV management systems, support a more circular economy and sustainable development. This work highlights the possible correlation between GDP per capita and ELV recycling rates across the European Union, the identification of economic clusters, and the critical role that advanced recycling technologies play in improving sustainability.

This article offers a reflective retrospective of the literature and practice on the informal waste and recycling sector. The authors have joined to share our experience and knowledge on the interface between the formal solid waste sector and informal recyclers and operators. Together, we discuss where this discourse has come from, where it is now, and where we, as practitioners, think it is going. We share our understanding of the waste and recycling sectors and how informality within them functions. The retrospective covers nearly 40 years of research, practice, advocacy, action, writing and intervention. The main storyline is how the public and private solid waste authorities and service providers relate to informal operators in both recycling ('the (private) value chains') and waste management ('the (public) service chain'). The recurring theme is how engaged scholarship and practice have interacted with, modified and improved the position of informal operators and workers and contributed to positive outcomes in both service and value chains. Throughout the period covered by this retrospective, opinions and framing on all sides have shifted substantially through the years, whereas the economic activities of informal recyclers and informal waste collection service providers have remained largely unchanged. Although we refer to both scientific and operational documents, we do not have the ambition to produce a scientific paper. Rather, we follow other authors of the special issue in referring to ourselves as involved witnesses who share a commitment to improving waste and recycling practices at the boundary of formal and informal systems.

The escalating volume of construction activities and resultant waste generation underscores the imperative for developing sophisticated segmentation models to facilitate efficient sorting and recycling processes. This study introduces WasteSAM, an enhanced iteration of the segment anything model (SAM), specifically tailored to address the intricate complexities inherent in construction waste imagery. Drawing upon a comprehensive dataset comprising over 15,000 masks representing five distinct categories of construction materials, WasteSAM exhibits notably superior segmentation capabilities. Quantitative analysis demonstrates significant performance improvements, with WasteSAM outperforming the original SAM model by an average of 23.9% in dice similarity coefficient and 30.0% in normalized surface distance metrics. The integration of stereo-image techniques in refining the training dataset has facilitated WasteSAM in more accurately discerning the three-dimensional structure of waste materials, thereby augmenting the precision of waste classification. Noteworthy is the model's adeptness in handling intricate textures and patterns across diverse imaging modalities, including varying lighting conditions and complex object interactions. While showing promising results, this study also highlights the need for high-quality, diverse datasets that reflect real-world construction site complexities, rather than merely larger datasets.

The complexity of risk assessment and the challenges in decision-making often impede the application of various models to renewable energy systems. This study introduces a comprehensive framework designed to streamline this process, facilitating informed decisions regarding the estimation of risks associated with solar photovoltaic (PV) technologies. Leveraging data and information available in the literature, the framework is particularly useful for manufacturers in selecting materials that balance low environmental risk with high efficiency. The framework emphasizes early-stage risk minimization by integrating changes during PV development, thereby promoting cleaner production systems. It's interconnected components encompass various approaches to risk assessment, control, and management, providing a structured methodology for risk reduction. Based on available information, the defined steps guide users through evaluating and mitigating risks. Applying risk minimization by metal substitution approach lowers the oral-ingestion and dermal-contact risk by a magnitude of four and six times, respectively. This framework will guide regulatory bodies throughout each step of the product life cycle, suggesting necessary changes and assessment strategies aligned with the perspectives of various stakeholders. By facilitating the identification and implementation of the most effective risk management strategies, the framework aims to advance the development of sustainable and safe PV technologies.

This study investigates, for the first time, the anaerobic digestion of food waste in Kuwait to optimize methane production through a combination of artificial neural network (ANN) modelling and continuous reactor experiments. The ANN model, utilizing eight hidden neurons and a 70-20-10 split for training, validation and testing sets, yielded mean squared error values of 0.0056, 0.0048 and 0.0059 and coefficient of determination (R²) values of 0.9942, 0.9986 and 0.9892, respectively. Methane percentages in biogas were predicted using six parameters: biomass type, pH, organic loading rate (OLR), hydraulic retention time (HRT), temperature and reactor volume. To validate the ANN results, continuous reactor experiments were conducted under an OLR of 3 kg VS m⁻³ d⁻¹ and HRT of 20 days at varying temperatures (35°C, 40°C, 45°C, 50°C and 55°C). The experiments demonstrated optimal methane production in the mesophilic range, with ANN predictions closely aligning with experimental data up to 45°C. However, deviations were observed at higher temperatures, particularly under thermophilic conditions beyond 50°C. This study provides novel insights into waste-to-energy initiatives in Kuwait and highlights the potential of integrating computational models with empirical data to enhance biogas production processes.

Piled smouldering has great potential for treatment and utilization of biomass wastes. However, its unsteady-state nature limits its industrial utilization, as well as treatment of smoke. This article addresses this issue by proposing the sequential operation of numerous smouldering chambers to realize steady- or quasi-steady-state piled smouldering. The superposition characteristics of sequential unsteady-state curves were analysed theoretically, and a code was developed to determine an appropriate number of piled chambers at an allowance oscillation percentage. Smouldering experiments were performed on a single mini chamber (length × width × height: 340 × 140 × 140 mm³) containing piled wood pellets mixed with wood powder. The superposition of sequential burning rate curves was demonstrated using the code based on the mass loss data of experiments. Analysis shows that the perfect-steady state is possible given the superposition value of the burning rate curve is a constant in this proposed system. Experiments show that the molar ratio of CO/CO₂ in smoke is almost a constant around 0.5 during densely piled smouldering, showing the great potential for self-sustained burning out the smoke. Based on the experimental results, the calculation results show that the relative oscillation range of burning rate (OSC) decreases from 75% to 3% while increasing the number of chambers from 2 to 7. This work provides a novel technology to enable quasi-steady-state smouldering for industrial utilization.

Discrimination of waste plastics according to brominated flame retardant (BFR) concentration is essential to ensure quality and safety in recycling. We present a sensor fusion approach to classify BFR-containing plastic waste by combining Raman and near-infrared (NIR) spectroscopies. We analysed 210 waste plastic samples sourced from waste electronics and electrical equipment stream and 25 laboratory-made plastics. The Raman spectra were acquired in the range 27-2481 cm^-1 using a time-gated Raman and the NIR spectra in the range 4000-5260 cm^-1 using a novel active hyperspectral sensor. Total elemental bromine concentrations were determined with X-ray fluorescence spectroscopy and used as reference for training extremely randomized trees classifiers for high- and low-bromine plastics with different thresholds of segmentation. The classifier models were built using Raman and NIR spectral data after reducing dimensions with principal component analysis, both separately and by fusing the data. We achieved over 80% balanced classification accuracies using all models, with significant improvements by data fusion.

Increasing efforts have been devoted to promoting sustainable demolition waste management (DWM) from a life cycle-thinking perspective. To this end, facilitating sustainability-oriented decision-making for DWM planning requires a sustainability assessment framework for assessing multifaceted criteria. This study develops a building information modelling (BIM)-based DWM sustainability assessment approach to facilitate the life cycle assessment (LCA) and decision-making by coupling the enriched Industry Foundation Classes model with hybrid multi-criteria decision-aiding (MCDA) methods using Dynamo visual scripting. To streamline the data-intensive LCA process, this study enriched the BIM properties and accommodated them into the LCA data template to enhance data interoperability, thus achieving seamless data transfer. Moreover, hybrid MCDA methods are integrated into the decision-making workflow for DWM scenario ranking. A pilot study is employed to verify the applicability of the decision-aiding framework. The results unveil that the sustainability score ascended with the recycling rate. The optimal DWM alternative with the highest recycling rate yields the highest sustainability score at 91.63. Conversely, a DWM alternative reflecting the 'status quo' in China's recycling industry has the lowest score at 8.37, significantly lower than the baseline scenario with a 50% recycling rate. It is worth noting that the 'growth curve' of the sustainability score continuously flattens as the target recycling rate escalates. The increment in recycling rate from the 'Australian standard' scenario to the optimal scenario is 18.4%, whereas the sustainability score merely increases by 2.3%, signalling that the former scenario arrived at an optimum point for maximising the cost-efficiency of DWM under the predefined framework and contexts.