Search Results (7,928)

At subsurface waste disposal sites, degradation of containment materials can cause leaks of chlorinated volatile organic compounds (Cl-VOCs) in the vadose zone. Material Disposal Area L (MDA L) is a heavily monitored waste site at Los Alamos National Laboratory in Northern New Mexico where a sharp increase in contaminant concentrations was measured in February 2019. Subsequently, soil vapor extraction (SVE) was performed as part of an ongoing interim measure. Here, we demonstrate a new method to introduce possible leakage within an existing numerical framework to bound possible leakage related to concentration increases seen in site monitoring data. A previously calibrated three-dimensional (3-D) model for SVE at MDA L is used to simulate the three conceptual stages from June 2017 to July 2024. The three conceptual stages based on the observed events are: leakage, passive diffusion, and soil vapor extraction. We use a 3-D multiphase flow simulator to introduce a simulated leak and attempt to approximately match monitoring data collected in February 2019, May 2024, and July 2024. After approximately matching the observed leak, outputs from the 3-D simulations were used to quantify the simulated mass of Cl-VOC leaked. Simulated results for a leak on the order of 40 kg of Cl-VOC showed general agreement with the monitoring data. Although the solution is non-unique, this paper presents a proof-of-concept addition to an existing case study, to show that a suspected subsurface container failure could create a signal consistent with the measured data and sets the stage for further analysis of future potential leak signals at the site. The work can also be adapted at other sites where changing subsurface conditions can require innovative modeling techniques to answer regulatory questions. Full article

The construction industry is characterised by high raw materials consumption and large waste generation. Upcycling waste construction materials offers an opportunity to reduce embodied carbon emissions while creating community assets. This paper examines how integrated design supports the effective reuse of waste materials from a modular building factory through the design of a community garden pavilion. Using Whole Lifecycle Assessment, the carbon impacts of three scenarios were evaluated. Case 1, the baseline scenario, represented the traditional temporary accommodation system using new materials with a hybrid steel–timber structure. Case 2 adopts new materials for the timber frame structure, combined with reused wooden pallets for the envelope. Case 3 represents an upcycling scenario where structural and envelope materials are reused from the modular building factory’s waste streams. Results show that the whole-life carbon emissions were 15,892.32 kgCO₂e for Case 1, 4293.25 kgCO₂e for Case 2, and 3044.99 kgCO₂e for Case 3, representing reductions of 73% and 81%, respectively, compared with the baseline. The findings demonstrate that integrated design and industrial material reuse can significantly reduce embodied carbon across a building’s life cycle. Recommendations for applying modular factory waste in community-led urban projects are provided. Full article

Break-bulk cargo operations are characterized by high variability and complex resource synchronization, yet they have received limited research attention compared to containerized logistics. This paper proposes an integrated lean-informed simulation framework for evaluating vessel service time (VST) in multipurpose terminals handling break-bulk cargo. The framework sequences three analytical stages: Value Stream Mapping paired with Ohno’s waste taxonomy to diagnose non-value-adding activities, a discrete-event simulation model built in Simio to quantify their impact on VST, and Sobol sensitivity analysis to decompose the remaining variability across operational factors. Demonstrated at DP World Lirquén, a multipurpose terminal in Chile, the lean diagnostic identified 101 min of waste per cycle across waiting, motion, and overproduction categories. Scenario evaluation showed that eliminating shift-transition delays and standardizing load composition reduced VST by 14.3% and 10.6%, respectively, without capital investment. The sensitivity decomposition revealed that warehouse machinery composition, particularly the interaction between equipment types, dominates VST variability, while truck fleet size operates as an independent factor. These findings demonstrate that coordination-related policy interventions outperform incremental resource additions. More specifically, machinery allocation must be optimized jointly rather than by equipment type in isolation. Full article

African soils face increasing levels of metal pollution due to industrialization, artisanal mining activities, improper waste management, and enhanced agricultural productivity. However, unlike many organic pollutants, heavy metals do not degrade naturally and therefore persist in environmental systems for prolonged periods. Heavy metals accumulate over many decades in the soil and bioaccumulate through the food chain causing severe health complications such as cancer, kidney problems, and neurological impairment. This paper reviews the current literature on the origin, prevalence, and behavior of the main pollutants Pb, Cd, Cr, As, Hg, and Cu. The major phytoremediation methods including phytoextraction, rhizofiltration, phytostabilization, and phytovolatilization are highlighted alongside in planta screening methods for hyperaccumulating plants including Berkheya coddii (Ni) and Haumaniastrum robertii (Co). The paper evaluates various enhancement techniques such as the use of chelators, Rhizobium inoculations, and genetic modifications. The significance of these approaches in tropical and subtropical climates is discussed. The paper suggests a holistic framework involving empirical kinetic modeling, geospatial machine learning (random forest, kriging), and molecular omics in prediction modeling. Major hurdles in such predictions include lack of field-based verification of the models, biotechnology safety of genetically modified (GM) organisms, and inadequate regulations. Future perspectives emphasize community-driven phytomining, biomass recycling, and resilient phytoremediation solutions. Full article

Biopolymers (BPs), obtained from urban and agricultural wastes, are known as active principles to manufacture ready-for-use finished products in several sectors of the agriculture and chemical industries. These findings prospect a biowaste-based refinery producing chemical specialities to replace products derived from fossil feedstock. The present paper reports new materials containing BPs. Composite granules containing Poly(Butylene Adipate-Co-Terephthalate (PBAT) as a matrix and BPs as fillers are manufactured by twin-screw extrusion. The granules are used to make single-layer PBAT-BP mulch films by single-screw extrusion and three-layer Starch-PBAT-BP films by blown co-extrusion. The films are tested for mechanical properties, and for structural stability and effects in the in vitro cress germination and the in-field horticulture. The results show that both the films’ effects on plant performance and the films’ structural degradation are regulated by the BP and polymeric matrix release kinetics in the operational germination medium or the field soil, and in turn, that the kinetics depend on the mulch film structural features. The horticulture trials prove that the three-layer mulch films have adequate mechanical strength (25 MPa maximum tensile strength and 520% elongation at break) and about 6 months lifespan to maintain and/or improve the soil protection and crop production (17 t/ha) over the plant seasonal cycle. These findings widen the range of renewable chemical specialities potentially producible by the envisioned biowaste-based refinery. Full article

The goaf backfilling with the coal gangue is an effective strategy for mitigating the mining-induced surface subsidence and reducing the solid waste accumulation. However, the conventional backfilling methods often suffer from limited transport efficiency, poor material distribution, and high operational cost. The present paper proposes a novel technique using an inclined spiral propeller to propel the gangue particles into the goaf, aiming to improve both the backfill rate and spatial uniformity. A three-dimensional parametric model of the inclined screw conveyor is developed, and the discrete element method (DEM) is employed to simulate the dynamic transport and placement of the gangue particles. An L9 ($3^3$) orthogonal experimental design is implemented to systematically evaluate the effects of the rotational speed (240, 300, 360 r/min), inclination angle ($30°$, $45°$, $60°$), and screw pitch (180, 240, 300 mm) on the two critical performance indicators, namely, filling mass and spreading coverage area. The range analysis and matrix analysis are performed to determine the primary influencing factors and to identify the optimal parameter combination for the multi-objective performance. The results show that the inclination angle is the dominant factor for the filling mass, with a $60°$ angle yielding the highest throughput (38.60 kg). In contrast, the rotational speed is the dominant factor for the spreading coverage area, where an increase from 240 to 360 r/min nearly triples the covered area. The optimal compromise for the comprehensive backfilling performance is the rotational speed 360 r/min, inclination angle $60°$, and screw pitch 300 mm, which simultaneously achieves the high transport capacity (36.65 kg) and the largest spreading area (2.87 ${\mathrm{m}}^2$). The present study provides a theoretical and methodological foundation for the engineering design of efficient, low-cost goaf backfilling systems. Full article

Accurate and automated extraction of solid waste sites from remote-sensing imagery constitutes a pivotal demand for contemporary environmental regulation and risk mitigation. However, in high-resolution remote-sensing imagery, solid waste sites are typically represented as a single semantic image object (SIO), which is composed of multiple physical image parcels (PIPs) exhibiting significant variations in scale, morphology, and spectral properties. This intrinsic heterogeneity substantially increases the complexity and uncertainty of multi-class site identification. To address this challenge, this paper proposes WasteMOE Net (WMN), which is developed based on the core concept of modeling the SIO–PIP relationship. WMN adopts a heterogeneous expert selection mechanism combined with a nested mixture-of-experts architecture. It thus enables adaptive perception of complex PIPs across diverse scenarios and their integrated discrimination at the SIO level. In addition, by incorporating the explicit nonlinear representation capability of the KAN network, WMN effectively improves multi-class recognition accuracy while maintaining computational efficiency. Furthermore, this study constructs a high-resolution solid waste site dataset in accordance with the SIO–PIP-aware annotation principle, encompassing five representative categories: tailings ponds (TP), construction spoil sites (CSS), landfill sites (LS), garbage dump sites (GDS), and excavation sites (ES). Experimental results show that WMN achieves mAP50 values of 74.2% (GDS), 63.5% (CSS), 80.9% (ES), 85.4% (TP), and 83.1% (LS) in detection tasks, and 75.4%, 64.1%, 83.0%, 86.7%, and 84.1% for the corresponding categories in segmentation tasks. It achieves competitive performance compared with state-of-the-art methods in both tasks. Further, in a real-world application over Loudi City, China, WMN completed the processing of a 490.67 km² area within 1.34 h. The recognition accuracies for GDS and ES reached 54.8% and 65.3%, respectively. Finally, the proposed method has been successfully integrated into a GIS-based solid waste pollution risk prevention system, which markedly boosts the overall efficiency of environmental monitoring and on-site inspections. Full article

Steel dust is a waste generated during steelmaking in an electric arc furnace (EAF), which contains a high proportion of iron-bearing compounds, leading to the inclusion of this waste as a resource in the circular economy for steelmaking. In addition to the limitation related to granulation (the waste must be processed to obtain larger particle sizes), a limiting factor is the increasingly high Zn content due to the low-quality ferrous charge. For the recycling of steelmaking dust, preliminary processing is necessary to reduce zinc. The paper presents, in addition to qualitative characterization of steel dust, laboratory experiments on the compositional changes associated with zinc redistribution applying the hydrometallurgical leaching process in an alkaline environment, using sodium hydroxide (NaOH). The changes in the chemical composition were identified and evaluated using X-ray fluorescence (XRF) and energy-dispersive X-ray spectroscopy (EDX). The experiments consisted of treating steel dust samples with 5 M NaOH at 25, 70, 80 and 90 °C for 60 min, using solid-to-liquid ratios of 10, 15, and 25 g/L. The results indicate a reduction in ZnO content ranging from 4.52% to 16.82%, as determined from Na₂O-free normalization data. Room-temperature samples show only marginal changes in ZnO content. The XRF and EDX analyses indicate a moderate and condition-dependent redistribution of zinc in the solid phase after alkaline treatment, as evaluated using Na₂O-free normalized data. These values are derived exclusively from solid-phase measurements (XRF/EDX) and do not include zinc in the leachate; therefore, true zinc extraction efficiency cannot be determined. The research results attest to the viability and efficiency (as a solid-phase compositional transformation process) using NaOH as a leaching agent for the studied steel dust, thus providing a potential pathway for improved waste recycling in the steel industry. Full article

The introduction of sustainable practices into waste management can have a favorable environmental impact, increase resource value, and yield economic gains. Hydrothermal technologies have strong potential for the production of up-cycled ingredients from biowaste (amino acids, sugars, phenols, pharmacologically active compounds, etc.), enabling high energy recovery (50–80%) from biowaste with net-negative carbon emissions. This review discusses the use of subcritical and supercritical water technologies for sustainable valorization of biowaste and conversion of biomass into high-value chemicals and biofuels. The potential for the extraction/generation of bioactive compounds from plant and animal waste is presented, emphasizing the efficiency, compound stability, and bioactivity of the fractions obtained. The possibilities of simultaneous extraction of added-value compounds and hydrolysis of feedstock biopolymers by these technologies are elaborated. The review further addresses the production of biofuels through hydrothermal carbonization for solid fuels, hydrothermal waste liquefaction for liquid fuels, and supercritical water gasification for gaseous fuels. The paper highlights the environmental and economic advantages of technologies based on sub- and supercritical water over conventional chemical and fermentative routes, emphasizing their contribution to a circular bioeconomy by converting biowaste into value-added products and sustainable energy sources. Full article

Numerous construction enterprises have insufficient efficiency in resource utilization for construction and demolition waste (CDW), restricting global circular economic development. How to improve resource utilization has become an urgent problem. While existing studies have extensively explored operational decisions in CDW resource supply chains, insufficient attention has been given to construction enterprises’ information misreporting and its interaction with on-site conversion efficiency. This paper aims to elucidate the mechanism of action of misreporting and systematically analyzes its effects on the pricing decisions of the CDW supply chain. Drawing on information misreporting theory, this study constructs a Stackelberg game model involving construction firms and recycled building materials manufacturers, and compares supply chain decision-making behaviors under two scenarios: information misreporting and honest disclosure. The main conclusions are as follows: (1) misreporting alters recycled building material pricing and profit distribution by affecting manufacturers’ supply capacity expectations; (2) higher on-site conversion efficiency enhances CDW treatment ability and affects stakeholders’ profits; and (3) misreporting is related to on-site conversion efficiency and onsite conversion costs—enterprises prefer misreporting for short-term gains under low on-site conversion efficiency or high costs, while higher on-site conversion efficiency makes truthful disclosure conducive to long-term stable returns. This paper reveals the CDW supply chain decision-making mechanism from enterprises’ perspective, providing a new theoretical basis and practical value for CDW utilization and supply chain optimization. Full article

The conversion of bio-waste into functional energy materials provides a robust platform for addressing both environmental and energy challenges. In this paper, discarded absorbent pads are transformed into carbon-rich frameworks, which is followed by the fabrication of composites through the incorporation of Cu₄SnS₄ (CSS) for dual electrochemical applications. Integrating CSS into the waste-derived carbon matrix induces strong synergistic effects, improving electrical conductivity, increasing active-site availability, and accelerating charge-transfer kinetics. Comprehensive physicochemical analyses confirmed the successful formation of a well-integrated heterostructure composite with favorable structural and surface characteristics. Electrochemical evaluations further demonstrated that CSS-modified carbon exhibits superior bifunctional performance. In a two-electrode configuration, the composite delivers an energy density of 12.08 Wh kg⁻¹ at a power density of 250 W kg⁻¹ along with excellent cycling stability in supercapacitor applications. As an electrocatalyst, it achieves a low overpotential of 268 mV at −10 mA cm⁻² and a small Tafel slope of 75 mV dec⁻¹, reflecting efficient reaction kinetics. The strong durability observed in both systems underscores the structural integrity and long-term operational stability of the material. Overall, this paper advances a sustainable waste-to-resource strategy for fabricating multifunctional carbon-based composites, offering a promising platform for integrated energy-storage and hydrogen-generation technologies. Full article

Stable control of the main combustion chamber temperature is critical for pollutant emission compliance, energy recovery, and equipment longevity in municipal solid waste incineration (MSWI). However, the response delays from manipulated variables such as primary air, secondary air, and feed rate to the furnace temperature span from seconds to tens of minutes, and a uniform-delay assumption is inadequate to characterize the true response lag. Moreover, without an action-smoothing constraint, optimizers tend to produce abrupt control commands that destabilize the temperature trajectory. Using real industrial distributed control system (DCS) data from a full-scale grate furnace, this paper develops a prediction–decision collaborative control framework. In the prediction module, mutual information (MI) is used to identify the optimal delay of each manipulated variable separately, and the time-aligned manipulated variables together with a low-order autoregressive component serve as input to XGBoost and yield a prediction RMSE of 6.85 °C with an $R^2$ of 0.9845. In the decision module, a normalized smoothing penalty is incorporated into the fitness function of particle swarm optimization (PSO) to constrain the step-to-step variation in manipulated variables. Offline predictor-in-the-loop simulation on the test set shows that, compared with a multi-loop PID controller, the proposed method reduces the standard deviation of the furnace temperature tracking error by about 35% (from 5.80 °C to 3.80 °C), and lowers the mean tracking error to 3.65 °C while improving actuator smoothness over both unconstrained PSO and a genetic algorithm. The framework provides a collaborative-control design for pre-deployment evaluation of data-driven controllers in MSWI operation. Full article

Precision feeding is an important foundation for improving production efficiency in aquaculture, reducing feed waste, mitigating water pollution, and promoting the intelligent development of aquaculture. Conventional feeding practices remain heavily dependent on operator experience and are typically executed at predetermined times or fixed ration levels. Such approaches frequently result in extensive feeding management, poor adaptability, low feed utilization efficiency, and delayed responses to environmental changes. Advances in machine vision, the Internet of Things, machine learning, deep learning, and automatic control have progressively shifted aquaculture feeding research beyond standalone automatic feeders toward integrated systems encompassing demand perception, intelligent decision-making, precise control, and equipment coordination. This paper reviews the state of the art in precision feeding technologies and equipment in aquaculture. At the technical level, it summarizes advances in feeding demand perception, intelligent feeding decision-making, and precise control and execution. At the equipment level, it reviews the main types, design features, and field application status of precision feeding equipment in intensive aquaculture, pond aquaculture, and offshore aquaculture scenarios. Despite the considerable progress achieved, the practical deployment of precision feeding still faces several limitations. Environmental disturbances, water turbidity, illumination variation, and sensor drift may compromise the reliability of feeding demand perception. Existing decision-making models frequently exhibit limited generalizability across species, growth stages, and aquaculture scenarios. Moreover, insufficient integration of sensing, decision-making, and execution restricts the development of fully closed-loop feeding systems. High initial investment, maintenance costs, and the shortage of skilled personnel further constrain the adoption of precision feeding equipment, particularly in resource-limited regions. On this basis, the main challenges including sensing accuracy, model practicability, closed-loop control, equipment reliability, and standardization, are examined. Future development trends are also discussed, covering multi-source information fusion, synergy between mechanistic models and data-driven methods, system-level closed-loop control, equipment modularization, and industrial application. This review is expected to provide a reference for subsequent research and engineering applications. Full article

To promote the sustainable development of agriculture and reduce resource waste, this paper investigates sustainable financing strategies for a green fresh agri-food supply chain. We employ a purely theoretical Stackelberg game model and numerical simulations based on hypothetical parameters to develop three financing models for a supply chain consisting of one capital-constrained farmer and one retailer, considering consumers’ dual sensitivity to product freshness and greenness. Analytical and numerical results reveal that: (1) with low financing rates, internal financing effectively alleviates under investment in preservation, leading to higher wholesale/retail prices. In a green-sensitive market, the resulting price premium compensates for cost increases, avoiding the “low quality–low price” trap under external financing. (2) The retailer’s total profit decreases as the internal financing rate rises; higher interest income cannot offset demand loss caused by reduced preservation effort. Thus, a low- or zero-interest strategy maximizes the retailer’s operational profit. (3) As consumer sensitivity to freshness and greenness increases, profit growth under internal financing displays convexity. However, under extremely high freshness sensitivity, external financing yields stronger marginal incentives, suggesting that retailers should adjust profit allocation in the high-end market. The findings provide theoretical guidance for financing mode selection and practical insights for promoting green agricultural sustainable development. Full article

Extended producer responsibility (EPR) is intended to link producer design decisions to end-of-life costs, but collective EPR schemes typically weaken this link by routing funding through producer responsibility organisations. We develop a multi-level framework of consumer-integrated environmental protection (CIEP) and argue that individual producer responsibility (IPR), where producers bear product-specific end-of-life liability, can function as a governance mechanism that reconnects design, consumer behaviour and waste governance. This paper is a qualitative multiple-case research study—not a systematic review—which draws on three funded research projects: (i) small and medium-sized enterprise (SME) tools for design-for-recyclability, (ii) an artificial intelligence (AI) application for household waste sorting, and (iii) closed-loop recycling of fishing gear in Vietnam. Within the first project (ToCoReRaM), a PRISMA-based systematic review of web-accessible circular economy tools finds that only 2 of 23 tools are SME-accessible through standard web searches. The AI-based waste-sorting application achieves approximately 75% classification accuracy under real-world conditions. The fishing gear study demonstrates technical and economic viability of closed-loop recycling, and a survey of more than 1500 Vietnamese fishers finds 95.8% willingness to return used gear given appropriate incentives. Together, the cases show that effective circular governance requires four complementary elements: IPR-based producer accountability, SME-accessible design tools, digital consumer guidance at the point of disposal, and context-sensitive governance capacity. These findings inform policy pathways for Sustainable Development Goal (SDG) 12 and SDG 14. Full article