Residual waste generation within municipal solid waste is characterized as a mix of various waste fractions that are either correctly or incorrectly discarded in residual bins. We denote this as residuals and assess its average and regional composition in Norway, based on the collection of 90 waste composition analysis. There is substantial variability between generation origins, and a clear pattern of lower food waste sorting in urban areas. Greenhouse gas emission of treating the various compositions with waste incineration were assessed, showing that CO2-equivalent emissions vary by up to 13% depending on waste origins, and estimate that approximately 54%, 55%, and 64% of rural, suburban, and urban residuals could potentially be recyclable, with a significant potential for reducing avoidable food waste. Successful implementation of the national “food waste avoidance” strategy could see the avoided generation and consequently production of 110kt of food for Norwegian households. Suggested improvements focus on convenient and clear sorting practices, better resolution on non-household residual compositions, and more multidisciplinary and regionalized research approaches.
Post disaster Construction and Demolition (C&D) waste quantification is critical in contexts where recurrent conflicts and natural disasters have led to significant infrastructure damage and large-scale debris accumulation. Debris quantification is a natural first step for efficient recovery planning, removal and management, and circular economy strategies. However, existing quantification methodologies lack national or regional standardization and fail to combine both rapid assessment as well as material characterization. Therefore, this study proposes a structured six-step debris quantification framework in post-disaster settings that integrates field data collection, material classification, density-based calculations, and demographic normalization to estimate per capita C&D waste generation. The framework was integrated in an automated decision-making tool using Visual Basic for Applications (VBA) macros in Microsoft Excel. The tool was applied to fully collapsed buildings, amid the 2023–2024 war in Lebanon. Based on an average household size of 4 to 5 persons and two apartments per floor in these regions, results indicated that the calculated C&D waste generation rate was approximately 55–69 tons per capita—an order of magnitude higher than typical demolition or municipal solid waste figures reported globally. Additionally, the study showed the tool’s effectiveness in producing rapid, replicable, and verifiable estimates, enabling faster operational decision-making compared to traditional, resource-intensive post-conflict assessments. Findings highlight the importance of standardized debris quantification protocols in enhancing disaster waste management readiness, with implications for contingency planning, resource allocation, and integration of remote sensing in damage assessment.
The bullwhip effect is a persistent challenge in multi-stage recycling supply chains, where fluctuations in waste availability, recovery rates, and downstream demand amplify variability across upstream recycling processors. These distortions increase operational costs, destabilize production planning, and reduce the efficiency of recycled-plastic markets. Although prior research has explored forecasting techniques for traditional supply chains, limited attention has been given to forecasting material flows derived from waste streams, where uncertainty is considerably higher. To address this gap, this study proposes a data-driven forecasting framework that combines machine learning models with ARIMAX and neural networks to predict demand for recycled plastic products. By incorporating external causal factors, macroeconomic indicators, and time-series patterns, the models improve forecast precision for recycled material flows. A comparative evaluation against conventional forecasting methods demonstrates that machine learning-based approaches significantly reduce demand distortion and enhance supply-chain coordination in recycling-focused systems. The findings reveal that proposed models outperform traditional ARIMA-type techniques, achieving up to 94% forecasting accuracy, thereby improving inventory planning, reducing variability, and strengthening the recycling system’s resilience. These results highlight the potential of advanced data-driven forecasting to support circular-economy objectives by enabling more stable, efficient, and predictable recycled-plastic supply chains.
In early December, the Court of Justice handed down a controversial ruling in Russmedia – indicating that online platforms can no longer confidently rely on EU intermediary liability law for protection against legal responsibility for user content in cases which involve data protection violations. Russmedia significantly weakens the intermediary liability protection – but the extent of the damage to the safe harbour remains unclear. The judgment can be read as having either a broad or narrow application, and the extent of the damage will depend upon how expansively the case is interpreted. And already, different actors are reading the judgment in very different ways.
Fatima Zahra Karmil, Abderrahman Abbassi, Omar Mounkachi
et al.
Reactive alkaline earth oxides (CaO and MgO) have recently received significant attention due to their low production costs, availability, and capacity for storing CO2. This study examined the high content of bivalent ions (Ca2+ and Mg2+) in RO reject brine for producing reactive quicklime and magnesia via selective precipitation. Various precipitating agents were evaluated at various molar ratios to discuss the high purity and process efficiency. The oxalic acid dosage was evaluated at a molar ratio (C2H2O4/Ca2+) of 2 which led to high recovery efficiency of calcium oxalate monohydrate (97.5 ± 0.5 %). The theoretical study using PhreeqCI3 via the Pitzer model was used to model the precipitation behavior at selected conditions. The alkaline caustic ash dosage was set at the molar ratio (NaOH/Mg2+) of 3, corresponding to the highest extent of precipitation of brucite (97 ± 0.5 %). Further, the calcination of the precipitated solids for 2 h at 900 °C and 500 °C produced the reactive quicklime and magnesia. The characterization of produced oxides at the optimized parameters was designated and discussed based on the composition, microstructure, and reactivity. The surface areas of the obtained oxides were 10.4 m2/g and 58.3 m2/g, respectively, according to the BET analysis. The energy consumption and production cost for generating high-purity oxides from desalination waste revealed that RO reject brine is an additional source for recovering alkaline earth oxides with high reactivities and allowed to minimize the environmental impacts resulting from desalination plants.
Environmental challenges associated with the disposal of organic farm waste and the growing demand for renewable energy underscore the importance of anaerobic digestion (AD), a process that converts organic matter into biogas and nutrient-rich digestate, offering a sustainable solution for waste management and energy production. This study evaluated a two-step AD process using cow manure and yeast extract through mono- and co-digestion trials at pilot-scale, serving as a preliminary step to assess feasibility and performance prior to scaling up for full-scale implementation on a dairy farm. Two 72 L continuously stirred tank reactors were operated, with the primary reactor maintained at mesophilic conditions and the secondary reactor acting as a thermophilic post-digester. This configuration was used to assess the influence of thermophilic post-digestion on biogas yield and digestate quality. During the mono-digestion of manure, mesophilic digestion yielded 138 L CH4/kg VS, while thermophilic post-digestion provided an additional methane recovery of 100 L CH4/kg VS. Co-digestion with yeast extract significantly enhanced methane yield, increasing it 1.77-fold to 421 L CH4/kg VS in the two-stage AD system. However, co-digestion resulted in elevated hydrogen sulphide (H2S) levels, posing potential challenges for biogas purification. Additionally, higher and more fluctuating volatile fatty acid concentrations were observed compared to manure mono-digestion. The quality and safety of the digestates remained comparable between mono- and co-digestion treatments, suggesting that co-digestion with yeast extract can offer advantages for manure-based AD systems, although an effective H2S mitigation strategy is recommended to optimise process sustainability.
Olufunke O. Oyebamiji, Akin S. Olaleru, Raifu B. Oyeleke
et al.
Utilizing agricultural waste presents a promising solution for sustainable energy production and efficient waste management. This study focuses on producing and characterizing biochar and briquettes derived from the pyrolysis of seven abundant agricultural residues: Corn Cob, Groundnut shell, Rice Bran, Sawdust, Corn Straw, Dry Leaf, and Sugar Cane peel. The process involves subjecting the raw materials to controlled pyrolysis conditions and compaction into briquettes. FTIR analysis of biochar and raw dried samples, physicochemical analysis, and percentage yield on biochar, and calorific value on the briquettes produced were conducted to characterize the agricultural wastes. The physicochemical parameters of their biochar revealed significant differences in their composition. The pH ranged from 8.76 (Dry leaves) to 14.09 (Corn cob), Cation Exchange Capacity ranged from 1.55 − 7.39 cmol (+)/Kg, moisture content ranged from 2.74 − 6.36 %, volatile matter ranged from 1.85 – 6.87 %, ash content ranged from 16.70 – 79.25 %, and fixed carbon ranged from 11.30 – 72.07 %. The percentage yield of biochar from raw materials ranged from 8.6 % (sugarcane) to 27 % (groundnut shell), while the calorific value of the briquettes produced ranged from 1,868.57 KJ/g (sugarcane) to 55,511.2 KJ/g (Rice bran). The FTIR analysis revealed distinct spectral peaks for all charred waste compared to their raw counterparts, indicating structural changes during pyrolysis. These findings show the potential of some agricultural waste-derived briquettes as an efficient, sustainable, and renewable alternative fuel source. The characterization tests demonstrate their viability as a practical energy source, offering agricultural waste management solutions.
This study presents a scoping review of peer-reviewed empirical and theoretical literature on the cultural determinants shaping sustainable solid waste management practices in rural Ghana. Drawing on interdisciplinary sources, the review synthesises evidence on how indigenous taboos, communal norms, and belief systems function as informal environmental governance mechanisms. Findings indicate that ritual prohibitions—such as bans on dumping in sacred groves or conducting waste activities on ancestral days—and gendered labour norms contribute to ecologically sustainable behaviours, often enforced through traditional authority structures. However, the efficacy of these systems is increasingly compromised by socio-cultural transformations, including urbanisation, religious pluralism, and declining customary leadership. The review also identifies significant gaps in the literature, notably spatial concentration in southern regions, limited gender-disaggregated analysis, and poor integration of indigenous ecological knowledge into formal waste policy frameworks. In addition, the evidence base is dominated by qualitative studies, which limits generalizability and underscores the need for mixed-methods and longitudinal research to capture the dynamic evolution of cultural practices. The study concludes that a hybrid governance model, incorporating indigenous principles within formal regulatory systems, offers a culturally contextualised pathway toward sustainable rural waste management. It recommends institutional recognition of traditional knowledge systems and participatory policy co-design as key to advancing integrated environmental governance.
Jason Kovacs, Richard Higgins, Nathalie Ionesco
et al.
A first-order thermodynamic model for the incineration of poly- and per-fluorinated alkyl substances (PFAS) in aqueous film-forming foam (AFFF) in a rotary kiln incinerator (i.e., a typical hazardous waste disposal) as well as decomposition at lower temperatures used in waste-to-energy incineration processes is used to discuss the global warming potential (GWP) impact of such incineration. Approximate orders of magnitude of tons of CO2 per ton of AFFF combusted are determined based on AFFF disposal data from a survey on the incineration of the US military’s AFFF supplies for complete and incomplete combustion of the AFFF wastewater. The model suggests that incomplete combustion of fluorosurfactants at low temperature will result in the release of high GWP waste products such as CF-alkanes. The risk of incomplete PFAS combustion may be amplified with diluted AFFF streams as the net reaction will be endothermic, potentially depressing the reactor temperature and promoting the formation of high-GWP byproducts. For more complete combustion cases evaluated, the estimated emission is on the order of 2 metric tons of CO2 per ton of AFFF combusted. In the worst-case scenario with CF-alkane release, the expected emission is between 439 and 537 metric tons of CO2 per ton of AFFF combusted. In this scenario, the incineration impact of 10,007 metric tons of AFFF combusted is equivalent to the emissions resulting from firing an average coal-fired power plant for approximately 3.6 years or the annual emissions of approximately 1 million automobiles.
This study presents the Hausdorff fractional NGBM (r,1), a novel prediction approach developed based on the original nonlinear grey Bernoulli model; NGBM(1,1). The approach integrates the Hausdorff fractional accumulation operator and provides greater degrees of freedom. The recurrence relation of the binomial in the discrete solution also provides simpler computation due to the elimination of the Gamma function calculation. The Jaya Algorithm is introduced to optimize the parameters of the new model to improve its adaptability. The proposed model and its findings are delineated with the help of two case studies utilizing e-waste data from United Kingdom and State of Connecticut. The proposed model is benchmarked with several existing forecasting models and the calculated Mean Absolute Percentage (MAPE) is compared. The findings demonstrate that the proposed model exhibits superior fitting and predictive accuracy in comparison to the existing models. It produced lower MAPE than its counterparts.
Riccardo Bianchini, Francesco Dagnino, Paola Giannini
et al.
We extend the semantics and type system of a lambda calculus equipped with common constructs to be "resource-aware". That is, the semantics keeps track of the usage of resources, and is stuck, besides in case of type errors, if either a needed resource is exhausted, or a provided resource would be wasted. In such way, the type system guarantees, besides standard soundness, that for well-typed programs there is a computation where no resource gets either exhausted or wasted. The extension is parametric on an arbitrary "grade algebra", modeling an assortment of possible usages, and does not require ad-hoc changes to the underlying language. To this end, the semantics needs to be formalized in big-step style; as a consequence, expressing and proving (resource-aware) soundness is challenging, and is achieved by applying recent techniques based on coinductive reasoning.
Effective waste sorting is critical for sustainable recycling, yet AI research in this domain continues to lag behind commercial systems due to limited datasets and reliance on legacy object detectors. In this work, we advance AI-driven waste detection by establishing strong baselines and introducing an ensemble-based semi-supervised learning framework. We first benchmark state-of-the-art Open-Vocabulary Object Detection (OVOD) models on the real-world ZeroWaste dataset, demonstrating that while class-only prompts perform poorly, LLM-optimized prompts significantly enhance zero-shot accuracy. Next, to address domain-specific limitations, we fine-tune modern transformer-based detectors, achieving a new baseline of 51.6 mAP. We then propose a soft pseudo-labeling strategy that fuses ensemble predictions using spatial and consensus-aware weighting, enabling robust semi-supervised training. Applied to the unlabeled ZeroWaste-s subset, our pseudo-annotations achieve performance gains that surpass fully supervised training, underscoring the effectiveness of scalable annotation pipelines. Our work contributes to the research community by establishing rigorous baselines, introducing a robust ensemble-based pseudo-labeling pipeline, generating high-quality annotations for the unlabeled ZeroWaste-s subset, and systematically evaluating OVOD models under real-world waste sorting conditions. Our code is available at: https://github.com/h-abid97/robust-waste-detection.
Sentence embeddings can be decoded to give approximations of the original texts used to create them. We explore this effect in the context of text simplification, demonstrating that reconstructed text embeddings preserve complexity levels. We experiment with a small feed forward neural network to effectively learn a transformation between sentence embeddings representing high-complexity and low-complexity texts. We provide comparison to a Seq2Seq and LLM-based approach, showing encouraging results in our much smaller learning setting. Finally, we demonstrate the applicability of our transformation to an unseen simplification dataset (MedEASI), as well as datasets from languages outside the training data (ES,DE). We conclude that learning transformations in sentence embedding space is a promising direction for future research and has potential to unlock the ability to develop small, but powerful models for text simplification and other natural language generation tasks.
The rise of convenience packaging has led to generation of enormous waste, making efficient waste sorting crucial for sustainable waste management. To address this, we developed DWaste, a computer vision-powered platform designed for real-time waste sorting on resource-constrained smartphones and edge devices, including offline functionality. We benchmarked various image classification models (EfficientNetV2S/M, ResNet50/101, MobileNet) and object detection (YOLOv8n, YOLOv11n) including our purposed YOLOv8n-CBAM model using our annotated dataset designed for recycling. We found a clear trade-off between accuracy and resource consumption: the best classifier, EfficientNetV2S, achieved high accuracy(~ 96%) but suffered from high latency (~ 0.22s) and elevated carbon emissions. In contrast, lightweight object detection models delivered strong performance (up to 80% mAP) with ultra-fast inference (~ 0.03s) and significantly smaller model sizes (< 7MB ), making them ideal for real-time, low-power use. Model quantization further maximized efficiency, substantially reducing model size and VRAM usage by up to 75%. Our work demonstrates the successful implementation of "Greener AI" models to support real-time, sustainable waste sorting on edge devices.
Research on text simplification has primarily focused on lexical and sentence-level changes. Long document-level simplification (DS) is still relatively unexplored. Large Language Models (LLMs), like ChatGPT, have excelled in many natural language processing tasks. However, their performance on DS tasks is unsatisfactory, as they often treat DS as merely document summarization. For the DS task, the generated long sequences not only must maintain consistency with the original document throughout, but complete moderate simplification operations encompassing discourses, sentences, and word-level simplifications. Human editors employ a hierarchical complexity simplification strategy to simplify documents. This study delves into simulating this strategy through the utilization of a multi-stage collaboration using LLMs. We propose a progressive simplification method (ProgDS) by hierarchically decomposing the task, including the discourse-level, topic-level, and lexical-level simplification. Experimental results demonstrate that ProgDS significantly outperforms existing smaller models or direct prompting with LLMs, advancing the state-of-the-art in the document simplification task.
Macdalyna Esther Ronie, Ahmad Hazim Abdul Aziz, Rovina Kobun
et al.
In the pursuit of sustainable and innovative food production, the utilisation of plant by-products have emerged as a promising frontier. Plant by-products have an inherent value that can be utilised to address both environmental concerns and the growing demand for food resources. This review provides a critical review of the environmental impact caused by the disposal of plant by-products and waste that are not being reutilised or repurposed to benefit other products. The multifaceted applications of agricultural residues, traditionally deemed as waste, in the creation of diverse and nutritionally enriched food products, were also presented in this review. The exploration encompasses a broad spectrum, from reducing waste through innovative utilisation to enhancing nutritional profiles and fostering culinary innovation. Through a detailed examination of specific instances, challenges, and prospects linked to this paradigm shift, we provide insights into the transformative potential of agriculture by-products in shaping a more sustainable and resilient food system.
Chinedu M. Agu, Kingsley A. Ani, Onuabuchi N. Ani
et al.
The processing of Petroleum-based oil has contributed to environmental challenges such as climate change. Recently, research attention has been shifted to oil extraction from different non-edible seeds as a suitable substitute. Accordingly, this study examines the physiochemical characterization, and the effect of extraction temperature on the yield of waste Citrus sinensis seed [orange seed (OS)] bio oil. Studies on the prevalent functional group, fatty acid methyl ester compositions and thermal oxidative stability were carried out using the Fourier transform infrared spectroscopy (FTIR), gas chromatography-mass spectroscopy (GCMS) and differential scanning calorimetry (DSC), respectively. The oil content of the OS bio oil was 46.26%w/w. The high iodine number (31.47 mg/100 g) and low pour point (4.50 °C) of the OS bio oil showed its high content of unsaturated fatty acid. The FTIR functional group of the OS bio oil showed predominantly alkane of CH stretching, aldehydes, esters, and carboxylic acid. The chemical composition of the OS bio oil as determined by GCMS (massHunter/library/NIST 14.1) showed the occurrence of octadecadienoic and oleic acids as poly-saturated and saturated fat found in plant glycosides and vegetable fats and oil. The DSC thermal stability analysis showed the possible existence of mixed triglycerides. Finally, the result from the physiochemical characterization compositions, and functional group the OS bio oil indicated its potential as a suitable substitute for petroleum-based oil.
Natalie Click, Randall Adcock, Theresa Chen
et al.
Toxic lead (Pb) in end-of-life silicon solar modules must be recovered to keep it out of the environment. Present literature on Pb recovery from solar waste is sparse and uses chemicals like nitric or hydrochloric acid. Previously, the authors reported Pb recovery from silicon modules by leaching and electrowinning in acetic acid. However, the Pb recovered from acetic solution contained a small amount of metallic Pb with the rest being lead oxides/acetates. These Pb compounds require further processing to obtain metallic Pb for reuse in solar panel solder, leading to additional cost and chemical waste. This paper reports recovery of metallic Pb using an electrochemical system with two half cells connected through a salt bridge. The salt bridge enables optimized recovery rates of Pb as high as 99.99 % for synthetic leachates. The first experiment to recover Pb from real silicon module waste shows 80 % recovery without optimization. The new method offers a low-cost, closed-loop, direct pathway to metallic Pb recovery from end-of-life silicon solar modules for reuse in new modules.
Md. Abul Hashem, Hridoy Paul, Md. Sabbir Rahman Akash
et al.
In a tannery at beamhouse, limed fleshing (LF) is generated during fleshing operation. It is the most generated solid waste of the entire tannery. In this study, atypical co-composting of tannery LF is presented to reduce the generated solid waste in the tannery. The collected LF was chopped and mixed with the chicken excreta (CE), sawdust (SD), and cow pats (CP). The mixed composting materials were placed in the soil with the top of the composting materials under 5 cm of soil. The physicochemical parameters of the compost met the requirements of the standard. The nutrient-nitrogen (N), phosphorous (P), potassium (K), and sulfur (S) content of compost were within the standard limits. The metal content-chromium (21.3 mg/kg), copper (11.7 mg/kg), zinc (125 mg/kg), and nickel (9.6 mg/kg) were below the standard limits. Lead and cadmium were below the detection levels. The photograph of Scanning Electron Microscope (SEM) analysis demonstrated the degradation of composting materials. The composting process suggests a pathway to reduce solid waste by producing a valuable product. The study recommends the LF transformation into nutrient-rich compost is a simple and progressive method without any additional pretreatment.
Studies in the field of machine learning utilization on air gasification of polyethylene terephthalate (PET) waste are of utmost importance and can contribute to solving the environmental challenges associated with PET waste while also promoting the development of advanced technologies in the field of waste management and renewable energy. The primary objective of this study is to focus on the gasification process of PET waste through the utilization of machine learning algorithms. The aim is also to assess how well these algorithms can predict and evaluate the gasification performance of PET waste. To achieve this, a model for air gasification of PET waste is created, and machine learning algorithms are developed and evaluated based on their performance. The results suggest that the H2/CO model has a high accuracy, as indicated by its R-sq value of 91.86 %. It is important to highlight that models developed for the lower heating values and cold gas efficiency show excellent accuracy, with R-sq values of 99.84 %. The high predicted R-sq values of models (higher than 90 % for the H2/CO model and higher than 99 % for models developed for the lower heating values and cold gas efficiency) indicate that these models excel in predicting future observations with great accuracy.