Waste Management & Research最新文献

This research determines the potential impact of reducing food waste on future energy consumption and pollutant emissions. The study uses system dynamics modelling to simulate the complex link between population, food demand, food waste output and their interactions with energy consumption in the food system and carbon dioxide (CO₂) emissions. Scenarios are developed by considering two elements: a reduction in food waste and an increase in energy output. Based on a case study of Delhi, food demand and energy consumption are expected to rise by 6% and 35% every year, respectively, from 2023 to 2033. The model predicts that a 20% reduction in food waste, combined with a 20% increase in energy efficiency, could reduce CO₂ emissions by 23.17% by 2033. The combination scenario proved to be the most efficient in reducing carbon emissions and energy consumption. This significant reduction in emissions highlights the potential of integrated food waste and energy management strategies in mitigating environmental impact.

The war in Ukraine and its consequences are becoming a disaster not only on a national scale but also for many other countries. The overview and considerations on such consequences given in this article shall help in managing and restoring (Ukrainian) territories after war. A structured analysis of literature about the war and post-war impact on the environment paired with 'grey literature' and the collection of currently available information from regional and national government agencies and official organizations on the specific situation in Ukraine, with a particular focus on waste management issues, was conducted. The main groups of environmental components affected by military actions are analysed, systematized, and classified. It is shown that the negative impact of military actions has irreversible consequences for the environment not only in Ukraine but is also already gaining global proportions. In addition, much of the infrastructure in the waste management sector has been destroyed, meaning that large volumes of unsorted and untreated waste are now ending up in landfills, most of which are uncontrolled since other more effective and sustainable environmental and waste management options are currently missing. Therefore, it is necessary to develop remediation concepts and implement comprehensive measures to clean up the territories and restore environmental components and the waste management sector after war. This includes - among other issues - assessing or measuring the generation and composition of post-war demolition waste and municipal solid waste during and after the war as a basis for further planning and decision-making.

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 uses model materials to introduce a controlled, location- and manufacturer-independent internationally accepted method for assessing shredding machines based on large-scale tests. Furthermore, a better understanding of the comminution behaviour of shredders with fixed settings (gap width, shaft speed, cutting tool geometry) is in the focus of the present investigation and assessed, using the statistical analyses for particle size distribution in certain screen sections. Conclusions have been drawn on the comminution behaviour in general and the material-material interactions of different fractions in the grinding chamber of the shredder by showing significant differences in the pure fraction's comminution behaviour against the mixture's comminution behaviour. In addition, the effect of the wear condition of the cutting tools on the particle size distribution has been investigated using statistical tests. As a result, interactions of the materials in the shredder grinding chamber can be demonstrated. The null hypothesis of the statistical test can be rejected due to the p-values, which are around 0.01 and therefore less than the predetermined alpha, which is 0.05, meaning that the shredding behaviour of pure fractions and those in a mixture is different. Additional investigations of waste wood only have shown that the wear condition of the cutting tools does not exhibit any effect on the particle size distribution of waste wood, as seen in the p-value (0.29), which is higher than the used alpha value. This could mean that the cutting tools need to be changed less frequently, which could have a positive effect on plant operation.

Plastic pollution has been identified as one of the most pressing environmental issues of the 21st century, driven by excessive consumption and inadequate plastic waste management. This issue is particularly reflected in short lifespan of plastic products, particularly plastic packaging within the food and beverage (F&B) industry. Urgent and relevant policy actions are needed to promote plastic circularity and improve waste management practices. Developing countries such as Montenegro face significant challenges in managing plastic waste flows due to legal, institutional and infrastructural gaps. This research addresses these challenges by employing material flow analysis (MFA) and advanced techniques like artificial neural networks to estimate the quantities and types of plastic packaging used in the F&B sector in Montenegro. The findings reveal that over 21,300 tonnes of F&B plastic packaging were placed on the market in Montenegro in 2018. Approximately 11% of this amount ends up directly littered in the environment or dumpsites, whereas the remaining 89% is predominantly collected and deposited in controlled landfills. Detailed MFA models were developed separately for the eight most common polymer types used in the F&B sector, along with specific models for plastic bags and polyethene terephthalate bottles to explore closed-loop recycling systems. It was found that only a small fraction of all analysed types of plastic packaging placed on the market can be effectively treated and recycled. This research contributes to the understanding of plastic waste management in Montenegro and facilitates the formulation of effective strategies to mitigate plastic pollution in the F&B industry.

Landfill leachate characteristics vary depending on the type of waste facilities accept, such as municipal solid waste (MSW), construction and demolition debris (CDD) and MSW incineration (MSWI) ash. Optimizing disposal and treatment practices requires a thorough understanding of the behaviour of leachates from different classifications of refuse. This study provides a critical analysis of variation in leachate quality among over 80 sites based on landfill category: MSW, bulky debris, MSWI ash and MSW-MSWI ash co-disposal. Alkalinity was highest in leachates from facilities accepting MSW (average 2,810 mg L^-1), and the average pH from sites disposing of only ash (7.04) was lower than anticipated. As expected, all leachates were observed to have much greater concentrations of chemical oxygen demand compared to biochemical oxygen demand and require advanced secondary treatment to remove this recalcitrant organic matter. Unsurprisingly, leachates from facilities accepting only ash had elevated concentrations of salts (32,400 mg L^-1 TDS), and those from MSW disposing sites reported high ammonia-nitrogen (381 mg L^-1); co-disposal of MSW with ash resulted in elevated concentrations of both TDS and ammonia-nitrogen (19,400 mg L^-1 TDS, 543 mg L^-1 NH₃-N). Metal concentrations among all leachate types were similar, though arsenic was elevated in landfills accepting only CDD. Trace organic chemicals like benzene were much higher in leachates from sites disposing of unburned residuals compared to those only accepting ash. Variation among landfill types were attributed to leachate flow characteristics, pH, degradation, waste composition and other biogeochemical interactions. The results demonstrate co-disposal practices can potentially require more leachate treatment than separate disposal scenarios.

As the Internet of things (IoT) continues to transform modern technologies, innovative applications in waste management and air pollution monitoring are becoming critical for sustainable development. In this manuscript, a novel smart waste management (SWM) and air pollution forecasting (APF) system is proposed by leveraging IoT sensors and the fully Elman neural network (FENN) model, termed as SWM-APF-IoT-FENN. The system integrates real-time data from waste and air quality sensors including weight, trash level, odour and carbon monoxide (CO) that are collected from smart bins connected to a Google Cloud Server. Here, the MaxAbsScaler is employed for data normalization, ensuring consistent feature representation. Subsequently, the atmospheric contaminants surrounding the waste receptacles were observed using a FENN model. This model is utilized to predict the atmospheric concentration of CO and categorize the bin status as filled, half-filled and unfilled. Moreover, the weight parameter of the FENN model is tuned using the secretary bird optimization algorithm for better prediction results. The implementation of the proposed methodology is done in Python tool, and the performance metrics are analysed. Experimental results demonstrate significant improvements in performance, achieving 15.65%, 18.45% and 21.09% higher accuracy, 18.14%, 20.14% and 24.01% higher F-Measure, 23.64%, 24.29% and 29.34% higher False Acceptance Rate (FAR), 25.00%, 27.09% and 31.74% higher precision, 20.64%, 22.45% and 28.64% higher sensitivity, 26.04%, 28.65% and 32.74% higher specificity, 9.45%, 7.38% and 4.05% reduced computational time than the conventional approaches such as Elman neural network, recurrent artificial neural network and long short-term memory with gated recurrent unit, respectively. Thus, the proposed method offers a streamlined, efficient framework for real-time waste management and pollution forecasting, addressing critical environmental challenges.

Monitoring plastic consumption in emerging economies such as India is crucial because in these countries more than half of the consumption gets converted into waste within a year. In 2022, the Government of India enforced the Plastic Waste Management Rules to manage plastic waste. This article aims to find the potential impact of these rules on plastic waste generation from now until 2050. The simulation is carried out in two steps. In step one, a material flow analysis is conducted that maps plastic flow from production to its end-of-life. Through this it is estimated that at an optimistic level, off the 20 million metric tonnes (Mt) of plastic consumed, around 10 Mt is added to stock, 4.5 Mt is recycled, 2.5 Mt is reutilized and the balance 3 Mt is mismanaged. In step two, through system dynamics model, the impact of policy instruments is estimated on each stage of the flow of plastic assuming a phased implementation. Through this model, it is found that annually mismanaged plastic waste increases 11 to 28 Mt in 2050 in a business-as-usual scenario; however, implementing all the policies in a phased manner would stabilize waste generation from 2040 onwards.

High-quality recycling of photovoltaic (PV) modules starts with a delamination process. It aims to remove the encapsulation layer between glass and solar cells. Many studies have investigated the delamination of ethylene-vinyl acetate encapsulant, whereas the delamination of polyolefin elastomer (POE) encapsulation in solar modules remains a research gap. This study presents methods of solvent and thermal treatment for the separation of layers in a PV module encapsulated with POE polymer. Various organic compounds were tested for the solvent treatment. The results showed that most of the solvents did not separate the materials. However, with some of them, polymer swelling was achieved. Glycerol was the only solvent capable of separating glass from multi-material laminate. The separated glass does not include contaminants and is therefore suitable for the use as a secondary material. However, the solar cells remained encapsulated in the polymer, thus additional processing is needed to remove it. The time and solvent temperature for glycerol treatment were measured. The thermal treatment was conducted based on the results of thermogravimetric analyses, which determined the degradation of POE under heating conditions. Thermal treatment at 500°C for 1 hour in an air atmosphere was found to be the effective way to detach PV layers. Glass, solar cells and metal ribbons were separated without polymer contamination and are therefore suitable for further use.

In this systematic review, advancements in plastic recycling technologies, including mechanical, thermolysis, chemical and biological methods, are examined. Comparisons among recycling technologies have identified current research trends, including a focus on pretreatment technologies for waste materials and the development of new organic chemistry or biological techniques that enable recycling with minimal energy consumption. Existing environmental and economic studies are also compared. The findings highlight differences in the environmental characteristics of various recycling methods, including their ability to recover plastic resins, carbon footprint, electricity consumption and gas emissions. The comparisons also reveal the challenges associated with these methods: mechanical recycling often encounters economic barriers due to contamination and inefficiencies in sorting and cleaning processes; thermolysis is constrained by high energy demands and operational costs, whereas chemical and biological recycling faces limitations related to scalability and material costs. Additionally, current challenges, emerging research areas and future directions in plastic recycling are discussed. For example, the role of innovative techniques, such as artificial intelligence, in refining recycling processes is emphasized. The importance of incorporating circular economy principles in the integrated sustainable analysis of recycling processes is also highlighted. The innovative contribution of this review is to address both technological developments and their environmental and economic implications. The focus is placed on literature from the past 10 years to ensure coverage of the most recent advancements. Overall, the insights of this review article aim to guide researchers, policymakers and industry stakeholders in improving sustainable management practices for plastic waste.