A one-dimensional analytical model was developed to simulate the migration of organic contaminants from an unregulated landfill through a damaged geomembrane into the underlying unsaturated soil. The model integrates both geomembrane defects (e.g., wrinkles, holes) and unsaturated soil properties, and an analytical solution for contaminant concentration (as a function of time and depth) was derived using boundary transformation, the shift theorem, and Laplace transform/inverse transform. Parametric analysis of five key factors showed: (1) Increases in geomembrane wrinkle size, hole number, and soil saturated water content significantly accelerated contaminant transport, deepened migration depth, and elevated risks to subsurface ecosystems and groundwater; (2) A higher soil unsaturated exponent markedly delayed migration and inhibited vertical penetration. Notably, unregulated landfills in high-saturation, low-unsaturated-exponent regions (e.g., coastal areas) face heightened contamination risks. This model provides a practical tool for predicting contaminant behavior and supporting environmental risk assessments, directly enhancing the management of containment systems in unregulated landfills.

The sustainable recycling of spent lithium-ion batteries (LiBs) requires the selective separation of cathode active materials (CAMs) from aluminum (Al) current collectors without degrading their functional properties. Electrical pulsed discharge (EPD) is a promising pretreatment method; however, delamination can be inconsistent owing to interfacial degradation and non-uniform current flow. We investigated a simple water-assisted approach in which a thin surface coating of water was applied before EPD. Experiments using commercial cathode sheets showed that water coating markedly enhanced CAM delamination, achieving over 90 % at 0.250 J/mg and reaching 94 % at 0.305 J/mg, whereas the uncoated samples failed to delaminate. Importantly, enhanced delamination was achieved without increasing the Al pulverization. High-speed imaging revealed that delamination initiated locally beneath the water-coated regions and propagated outward. Complementary simulations showed that thermal stress alone could not account for this behavior. The rapid vaporization of interfacial water generated transient opening pressures exceeding 100 MPa near the coated interface. These stresses (≈30 MPa) propagated across the CAM layer to the opposing side, contributing to delamination. These results identify water-assisted vapor expansion, rather than thermal stress alone, as the principal driver of coating-enhanced delamination. This facile, chemical-free method offers a robust pathway to improve direct LiB recycling efficiency and reduce environmental impact.

With the growing use of bioplastics due to their low environmental impact, interest in bioplastic-degrading microorganisms has also increased. To screen for such microorganisms, a clear zone test using plates containing bioplastic emulsions with detergents has been widely used as a key method for rapid and accurate identification. Although emulsifying detergents affect the hydrolytic activity of microbes, their effects have not been thoroughly evaluated across different detergent candidates. In this study, eight different detergents were evaluated for their effectiveness in screening poly(butylene adipate-co-terephthalate) (PBAT)-degrading strains. The anionic detergents N-Lauroylsarcosine sodium salt (Sarkosyl NL), sodium dodecyl sulfate (SDS), and non-ionic polysorbate 20 (Tween 20) enhanced clear zone formation. Among them, the non-ionic detergent Tween 20-based clear zone test system was the most effective for screening PBAT-degrading strains, showing rapid and distinct clear zone formation. This method was also applicable to polybutylene succinate (PBS), polycaprolactone (PCL), and polylactic acid (PLA). Using the Tween 20-based method, Bacillus sp. Y4 was isolated and exhibited a high PBAT degradation rate of 52 % over 14 days. Overall, this study demonstrated that the Tween 20-based clear zone assay offered a reliable and biologically stable screening platform.

The proper recycling of spent lithium-ion batteries (LIBs) is critical for both economic and environmental sustainability. This workproposes using the endogenous separator of spent LIBs as an energy material and the LiNiCoMnO pre-catalyst from the cathode material as a catalyst for catalytic reforming to produce H-rich syngas. The separator pyrolysis gas promoted the formation of LiCO and reduced transition metals in the pre-catalyst, resulting in a more robust NiCo alloy and MnO with good catalytic activity and thermostability in terms of H yield. The maximum H yield and selectivity were 40.19 mmol·g and 54.26 % at 800 °C, respectively. The H yield and selectivity during 10 cycle reaction ranged from 20-40 mmol·g and 40-50 %, respectively, with no carbon deposits on the catalyst surface. The work presents novel formulations and new insights into the endogenous recycling of spent LIBs.

With the increasing electronification of societies, the generation of Waste Electrical and Electronic Equipment (WEEE) continues to rise, while formal recycling rates are lagging behind at around 25 wt%. Apart from low collection rates, the presence of potentially hazardous brominated flame retardants (BFRs) in WEEE plastics further complicates recycling. As a result, restrictions have been implemented for flame retardants (FRs) classified as persistent organic pollutants (POPs). As regulatory thresholds tighten and more substances are added to restriction lists, effective analysis of these compounds in complex waste matrices becomes increasingly important. This study explores pyrolysis-GC-MS (Py-GC-MS) for the simultaneous identification and quantification of five flame retardants: HBCD, TBBPA, DBDPE, TPHP, and PolyFR. The main novelty of the work lies in the systematic development of the Py-GC-MS method, including the complementary capabilities of TGA and the optimal use of multi-shot analysis. In addition, the proposed analytical methodology has been applied to different plastic waste samples, collected from a mechanical recycling plant processing WEEE. Results confirm that most halogenated plastics can be isolated effectively via density-based sorting, with total bromine content reaching up to 2755 ± 430 mg kg. This study highlights the release of significant bromine levels (i.e., up to 300 kg) during extrusion, suggesting that brominated degradation products might escape from the extrusion lines in WEEE plastic reprocessing facilities. Among the investigated flame retardants, TBBPA was the most abundant (i.e., up to 1534 ± 244 mg kg). The study demonstrates that accurate quantification of multiple FRs simultaneously in complex waste samples by Py-GC-MS is feasible, though challenges remain due to potential interferences of reaction products.

Composting is a widely used method to process organic waste residues. It results in a valuable product for soil application and use in growing media. The aim of this study was to investigate the variation in characteristics of composts produced in the North Sea Region, and the factors determining this variation. A total of 107 composts were categorized into two composting practices (produced on a farm or on a commercial composting facility) and three feedstock groups (manure combined with other wastes; green waste; fruit, vegetable and garden waste (fvg)), and measured for 67 physical, chemical and biological characteristics. Variation in the results was large, e.g., up to a factor 20 and 11 for total microbial biomass and potassium content, respectively, underlining the importance of compost characterization to target the intended compost use. Organic matter (OM) content varied between 14 and 73% of dry matter and was larger for Belgian composts compared to composts from The Netherlands, Denmark, Germany and Scotland. The OM content was positively correlated with total microbial biomass, cation exchange capacity and content of nitrogen (N) and phosphorus (P) of composts. Farm composts, irrespective of the OM effect, exhibited higher total microbial biomass compared to commercial composts. Compost prepared from green waste had lower N and P contents compared to compost prepared from fvg or manure waste. The study documents characteristics in composts from diverse composting practices and feedstocks, providing a benchmark and enabling targeted improvements as a first step towards tailormade compost.

Composting is an important way of diverting municipal organics from landfills to reduce methane emissions. However, compost production is a source of greenhouse gas emissions (GHGs). To develop emissions mitigation strategies, methods to accurately measure GHGs from composting are needed. A systematic review of techniques for measuring GHGs from composting was carried out to evaluate different methodologies and their suitability for various applications. A literature search was performed using the Web of Science and Scopus databases to find information about different measurement methods used during composting from 2014 to 2024. Of the measurement methods identified, the static chamber method was the most widely applied due to its simplicity and cost-effectiveness, but it provides limited spatial representation and can disrupt emissions. Dynamic chambers and micrometeorological techniques give superior temporal resolution but are complex and costly. Emerging technologies, such as automated chambers and remote sensors on unmanned aerial vehicles and satellites, can potentially provide scalable, high-resolution data, but cost, high detection thresholds, and environmental interference present challenges. In this review, approaches for improving existing measurement techniques and the importance of developing standardized methodologies for measuring GHGs during composting have been spotlighted. To improve measurement accuracy and data quality, future research should focus on developing low-cost, automated chambers with large footprints and combining multiple technologies for data cross-validation. This should enable researchers and waste management practitioners to make guided decisions on methods that increase measurement accuracy, which will lead to the development of strategic policies to reduce emissions and fight against climate change.

The steel industry is responsible for 30 % of global industrial greenhouse gas emissions, which is mainly dependent on metallurgical coke utilization. Although biochar represents a promising carbon-neutral alternative, its suboptimal mechanical properties have hindered its direct substitution for conventional coke. This work introduces an innovative hydrothermal carbonization-repolymerization strategy to synthesize high-performance biocoke from sustainable feedstocks of oak residues and waste soybean oil. Our approach is centered on a novel and bio-based polymerization agent, synthesized by blending epoxidized soybean oil from oxidized waste oil and tannic acid extracted from oak residues in a 3:1 mass ratio. The epoxidized soybean oil-tannic acid effectively undergoes cross-linking reactions with oak biochar and oak bio-oil that both in-situ generated from the hydrothermal carbonization, resulting in a highly cross-linked biocoke. The ESO undergoes epoxy ring-opening in the cross-polymerization reaction, providing more reaction sites for cross-linked reactions. Moreover, the resulting biocoke shows a significant aromatization and graphitization structure. Therefore, the biocoke displays superior mechanical and thermochemical properties, including crushing strength (86.0 % of M), abrasion resistance (6.6 % of M), coke strength after reaction (64.2 %), and higher heating value (40.79 MJ/kg), meeting the performance of first-grade metallurgical coke. A cradle-to-gate LCA indicates that substituting 50 % of metallurgical coke by biocoke results in a reduction of CO emissions of 622.8 kg for per ton of iron production. This study not only provides a novel polymerization strategy for producing metallurgical-grade biocoke but also highlights its potential to significantly reduce the carbon footprint of the steel industry.

Municipal solid waste incineration (MSWI) fly ash, classified as hazardous waste, poses severe environmental risks. Conventional melting treatment requires high energy input and usually produces cooling products with limited resource utilization value. To overcome these limitations, this study proposes a synergistic co-melting technology that combines MSWI fly ash with electrolytic manganese residue, achieving compositional complementarity and a "waste-to-treat-waste" strategy. Furthermore, the sensible heat of the molten slag is recovered for the low-energy preparation of glass-ceramics. Focusing on this technical route, three critical issues were systematically investigated: 1) the harmlessness of fly ash after melting, verified through leaching tests of heavy metals under different cooling conditions; 2) the phase-change cooling and crystallization behavior of molten slag droplets, simulated using an enthalpy-based numerical model; and 3) the overall energy and cost performance of the proposed system. The results show that the leaching concentrations of all major heavy metals remained far below regulatory limits, confirming the environmental safety of the solidified products. The model successfully captured the cooling-crystallization process and identified an average cooling rate of 37.26 K·s as optimal for balancing energy recovery and material quality. In addition, the system-level assessment demonstrated energy savings of 62.89-74.53 % compared with conventional processes, highlighting the strong potential of this approach for sustainable and resource-efficient fly ash management.

Inland waterways can be an attractive under-utilized alternative to road transport. In the current situation, heavy trucks transport residual household waste from municipalities to incineration plants around the Netherlands. Logistics research groups have suggested using barge pushing ships for household waste transport to reduce emissions and costs. We analyze this suggestion for a case study involving the residual household waste of 55 municipalities across three provinces in the Netherlands. A Mixed Integer Linear Programming formulation is used to find the optimal combination of trucks and barge pushing ships in this waste network. The results demonstrate that adopting electric pusher ships can achieve significant reductions in costs (19%), emissions (41%), and waste carrying truck traffic (48%), compared to truck-only solutions. Diesel cargo ships are also shown to outperform truck-only approaches but are less effective than electric alternatives in most metrics. Sensitivity analysis shows that the solutions are fairly robust to parameter variations.

Effective recycling of spent LiNiCoMnO (NCM) battery is crucial to ensure sustainability of the lithium-ion battery industry. However, recycling is inherent with multiple operational steps and many effective factors. It is difficult to optimize the whole recycling process and identify the controlling steps, especially when the compositions and features of the raw materials are turbulent. This research demonstrates a machine learning (ML) strategy by mechanism analyzation to more accurately predict a spent NCM battery recycling process. Considering 28 input features under three categories (i.e., raw material properties, leaching reagent properties, operating conditions), Li, Ni, Co, and Mn leaching efficiency were analyzed with 4 typical ML models where extreme gradient boosting performed best. The leaching efficiency can be significantly improved when optimizing the leaching process by ML precisely forecasting. In addition to conventional operating conditions, the average key length of acid also significantly impacts metal leaching efficiency. Efficient leaching of Li can be achieved under malic acid (2.27 mol/L), S/L (48.25 g/L), stirring speed (528 rpm), temperature (55 ℃) and pH (2.15). This research could accurate predict NCM battery recovery process and pave the way for mechanism analyzation and industrial application under big data analyzation.

The bioaerosols contamination bind to PM leads to significant ecological and public health concerns, but studies on the bioaerosols contamination bind to PM during landfill leachate treatment are rare. This study comprehensively investigated the behavior of PM-bound bioaerosols and the potential pathogenicity that subsequently occurs during landfill leachate treatment. The results revealed that the mass concentration of PM was the lowest in the evaporation treatment workshop (ETW) and the highest in the biochemical treatment workshop (BTW). Burkholderia and Aureobasidium were the dominant strains in PM. The SO content in PM was significantly positively correlated with the Burkholderia and Aquabacterium abundances, whereas the Cl, NO and NO contents were negatively correlated with the Burkholderia and Ralstonia abundances. Burkholderia cepacia, Ralstonia pickettii and Stenotrophomonas maltophilia were the dominant bacterial pathogens. Hundreds of virulence factor genes (VFGs) and subtypes of antibiotic resistance genes (ARGs) were detected in PM during landfill leachate treatment, especially in the advanced membrane treatment of leachate and the evaporation treatment of concentrated leachate. VFGs and ARGs cooccur widely in PM-associated microbes, especially in Burkholderia and Pseudomonas. This study helps increase the understanding of the pollution behavior and potential disease risk of aerosol particles during landfill leachate treatment.

Neutral hydrothermal processing is a promising chemical recycling pathway for polyethylene terephthalate waste into its monomers. The process yields a solid and an aqueous product: the solid phase retains about 80 % of the carbon as terephthalic acid, while the aqueous phase contains the remaining 20 %, primarily as ethylene glycol (EG). If untreated, this dilute aqueous stream increases the process water footprint and limits recovery of valuable products. This study explores aqueous phase recirculation as a water management strategy at 250 °C, 280 °C, and 310 °C. Results show that EG and other soluble compounds accumulate during successive recirculation cycles, eventually stabilizing as makeup water dilutes the stream. Importantly, such accumulation does not affect reaction chemistry. Batch experiments extrapolated to continuous operation indicate a 67 % reduction in water consumption and an increase in EG concentration from 1.6 wt% to 4.8 wt%. Thus, aqueous recirculation reduces water use while enhancing EG recovery potential.

Mechanochemical (MC) treatment is a green and efficient approach that has shown great potential in reducing the environmental risks of potentially toxic metal(loid)s (PTMs) in municipal solid waste incineration fly ash (MSWIFA). However, accurately predicting the environmental risks of PTMs and optimizing the operating conditions remain challenge. In this study, six machine learning (ML) models were employed to predict the environmental risks of PTMs in MC-treated fly ash, quantified by the Overall Pollution Toxicity Index (OPTI). In particular, the eXtreme Gradient Boosting (XGB) model achieved the best performance, with R values of 0.986 for the training set and 0.921 for the test set, indicating excellent predictive accuracy and generalization. Feature importance analysis revealed the following ranking of influence on environmental risks: additives (48.2 %) > MC conditions (28.7 %) > PTMs properties (21.3 %) > fly ash composition (1.8 %). MC treatment time, initial concentration, Ca-based additive, P-based additive, Ca-P-based additive, Si-Al-based additive, leaching pH, and Cl were the eight important input features for predicting environmental risks of PTMs in MC-treated fly ash. Furthermore, a graphical user interface (GUI) was developed based on the trained models, enabling rapid environmental risks assessment and process optimization. This study presented a data-driven framework for predicting environmental risks in MC-treated fly ash and provided practical tools to support engineering-scale applications. The development of green and efficient additives represents the most effective approach for addressing the PTMs issues in MSWIFA.

Excessive use of antibiotics in animal husbandry leads to the accumulation of antibiotic residues and antibiotic resistance genes (ARGs) in manure, posing risks to the environment and public health. Although composting is widely used for manure treatment, its ability to reduce antibiotics and ARGs remains limited. Biochar, a porous material with unique physicochemical properties, shows great potential in improving composting performance. This review summarizes the current research on the removal of antibiotics and ARGs by different types of biochar during composting,and discusses the key influencing factors, removal efficiencies,and potential mechanisms. Biochar directly adsorbs antibiotics and ARGs, and indirectly inhibit their persistence by regulating microbial communities, changing the physicochemical conditions of composting, and reducing environmental selection pressures. Among various sources, plant-derived biochar (PDB) has been reported to perform better than animal-derived biochar in many cases. Furthermore, modification and compounding strategies can enhance its performance. However, the practical application of biochar still faces some challenges, including modification technology limitations, high production costs, and limited scalability. Future research should focus on low-cost and efficient modification strategies and explore the synergy between biochar and microbial processes. These efforts will contribute to the development of sustainable and effective biochar-based antimicrobial risk control technologies for organic waste management.

The growing accumulation of polyethylene terephthalate (PET) waste necessitates innovative recycling approaches that add value. In this study, PET plastic waste was upcycled into activated carbon materials via pyrolysis followed by KOH-based chemical activation, producing carbons with a high specific surface area of up to 2150 m/g and a total pore volume of 0.82 cm/g, with primary contributions from micropores. Characterization using SEM-EDS, XPS, and gas adsorption analyses confirmed the presence of hierarchical micro- and mesoporosity, as well as favorable surface functional groups. The resulting materials exhibited excellent electrochemical properties when evaluated as supercapacitor electrodes using both aqueous (potassium hydroxide) and organic (tetraethylammonium tetrafluoroborate) electrolytes. The specific capacitance, based on three-electrode testing, ranged from 100 to 800F/g, with the organic electrolyte demonstrating superior performance. The presence of prominent redox peaks in the organic electrolyte confirmed significant contributions from pseudocapacitance. Two types of supercapacitors were fabricated using these carbons: a glass-slide-based supercapacitor with both types of electrolytes and a coin-cell-based supercapacitor with organic electrolyte only. Their performance was assessed through galvanostatic charge-discharge (GCD) experiments. Both the energy and power densities were higher for the organic electrolyte compared to the aqueous electrolyte. Long-term cycling stability was outstanding, with approximately 99 % capacitance retention after 1000 cycles. These findings demonstrate that PET waste can serve as a low-cost, sustainable precursor for advanced energy storage applications. This work contributes both to mitigating plastic pollution and advancing the development of high-performance, waste-derived materials for next-generation supercapacitors.

This study investigated the effectiveness of municipal waste collection policies within England and Wales by examining how variations in local waste management strategies correlate with recycling rates. Using data from 297 council districts, we analysed the impact of different policy variables (frequency of residual waste and recycling collection, sorting requirements for recyclables, and the availability of food and yard waste collections) on recycling rates. We applied a logistic transformation to the dependent variable and fitted a linear regression model using the gathered predictors to evaluate policy effectiveness, while controlling for demographic factors. We validated the model with a series of beta regression models. The findings indicate that less frequent residual waste collection, the availability of weekly organic food waste and free organic yard waste significantly enhance recycling outcomes. Moreover, the research highlights the influence of socio-demographic factors. The results provide actionable insights for policymakers to optimise waste management practices and recycling rates within the framework of existing policies.

Postharvest losses in horticultural crops remain a major global challenge, primarily caused by microbial spoilage, physiological deterioration, and mechanical injury during handling, transport, and storage. Simultaneously, the agri-food industry generates vast quantities of postharvest residues, including peels, seeds, pomace, and trimmings that are frequently discarded despite their richness in bioactive phytochemicals, structural polysaccharides, and functional nutrients. The sustainable valorisation of these residues presents a dual opportunity: reducing waste while supplying renewable resources for postharvest preservation. This review provides a critical synthesis of recent advances in the conversion of postharvest waste into functional compounds and materials for disease control and quality maintenance in fruits and vegetables. It examines green extraction and bioprocessing techniques for recovering bioactives, their formulation into edible coatings, films, and biodegradable packaging, and the use of microbial fermentation for generating biocontrol agents and natural preservatives. Particular attention is given to the industrial scalability, techno-economic feasibility, and regulatory considerations of these approaches. Distinct from previous general reviews, this work integrates waste valorisation with postharvest quality management, highlighting circular-economy strategies capable of reducing chemical preservative dependence, enhancing product safety, and supporting sustainable food system transitions.

This study investigates the influence of pyrolysis temperature on the yield distribution, chemical composition, and fuel-relevant properties of polypropylene (PP) and low-density polyethylene (LDPE) waste streams in a pilot-scale, gas-fired auger reactor. The experiments were carried out at different pyrolysis temperatures between 525 and 650 °C in 25 °C increments to evaluate the trade-offs between gas production and oil quality. Gas phase composition was analyzed and HHV was calculated, while the liquid fractions were characterized in terms of density, HHV, and hydrocarbon class distribution. Results demonstrate that higher temperatures increase gas yields and improve energy self-sufficiency, but may lead to diminished oil quality regarding fuel applications due to elevated aromatic and benzene contents, particularly beyond 575 °C. The gasoline fractions were assessed against the EN 228 standard, revealing optimal compliance at intermediate temperatures.

Automated sorting is critical for improving the recycling rates of Construction and Demolition Waste (CDW), a significant global waste stream. While language-guided recognition-a technology known as Referring Image Segmentation (RIS)-offers a potential frontier for flexible and intelligent sorting, its practical deployment is hindered by a considerable lack of robustness. These systems often fail when operator instructions are ambiguous or do not correspond to objects in the visual scene-common occurrences in dynamic industrial environments-leading to sorting errors and reduced material purity. To address this challenge, this study introduces RefSegformer-CDW, a robust recognition framework explicitly designed to enhance the reliability of automated CDW sorting. The architecture integrates a cross-modal verification mechanism that enables the system to reject invalid commands, preventing costly sorting errors. The Ref-CODD dataset was developed to facilitate the development and validation of such robust systems, the first benchmark featuring real-world CDW imagery paired with adversarial negative samples that simulate challenging operational scenarios. Experimental results on the Ref-CODD dataset demonstrate the effectiveness of the proposed RefSegformer-CDW. It achieves a mean Intersection over Union (mIoU) of 94.03%, surpassing the state-of-the-art by 1.24 percentage points. The framework shows high stability under varying illumination, with the mIoU dropping by only 2.38 percentage points between dark and normal lighting conditions. Moreover, it maintains robust performance across diverse waste types, achieving an average overall Intersection over Union (oIoU) of 93.90% on ten different CDW categories.

Effective control of key parameters is critical for regulating pollutant emissions in municipal solid waste incineration (MSWI), but existing research on these parameters remains limited and lacks comprehensiveness. This study used over 600,000 industrial data records (June 9-15, 2024) from 140 sensors-covering the emission concentrations of four pollutants (HCl, SO, NO, CO)-employed the AntDE-DTFS algorithm for feature selection, and constructed eight machine learning models; among these, XGBoost achieved the best predictive performance, with R values of 0.92 (HCl), 0.87 (SO), 0.89 (NO), and 0.75 (CO). Results showed that temperature is the predominant factor for HCl and CO emissions: maintaining the furnace flue gas treatment outlet temperature below 140 °C effectively reduces HCl output, while optimizing the economizer inlet temperature control valve position feedback to exceed 11 % significantly mitigates CO emissions. In contrast, SO emissions are primarily governed by pressure, with steam drum pressure maintained below 6.8 MPa minimizing SO emissions, and NO emissions are affected by both temperature and pressure-with the temperature at the upper-left of the second flue gas duct as the key factor, and elevating this temperature above 780 °C contributing to lower NO concentrations. These findings provide valuable insights for emission control strategies, offering a scientific basis to optimize incineration processes and enhance environmental management.