Effects of biodegradable- and non-biodegradable-rich waste separation on ash deposition behaviour during coal and refuse-derived fuel co-combustion.

The potential impact of ash deposition during the combustion of separated biodegradable- and non-biodegradable-rich waste of refuse-derived fuel (RDF) was evaluated in this study. Theoretical prediction, drop tube furnace experimental combustion, and ash observation were performed to comprehensively investigate their ash deposit behaviour. The results show that high CaO and Cl in RDFs result in severe sintering and rust in the metal surface. The high ash deposit weight and aggregated sticky particles are observed during single-firing RDFs. Furthermore, adding 5 wt% of biodegradable-rich RDF or mixed RDF to coal has a less significant effect on ash deposition. However, several aggregate particles and metal degradation are observed during the combustion of mixed coal with the addition of 5 wt% non-biodegradable-rich RDF. The high Cl in non-biodegradable-rich RDF affects the ash deposition behaviour significantly. This research provides valuable insights into optimising coal-RDF co-combustion, especially with separating biodegradable- and non-biodegradable-rich RDFs.

Electricity out of electronic trash: Triboelectric nanogenerators from discarded smartphone displays for biomechanical energy harvesting.

In the context of escalating electronic waste (e-waste) generated by the rapid evolution of electronic devices, particularly smartphones/mobiles, the imperative for effective e-waste management to mitigate adverse environmental and health consequences has become increasingly apparent. Herein, novel mobile phone-based triboelectric nanogenerators (M-TENGs) are fabricated from discarded smartphone displays of eight different brands (B1-B8) for harvesting electrical energy. Analytical characterization techniques such as SEM and EDS are employed for morphological investigation. The tribopositivity and tribonegativity of the smartphone display layers are confirmed using the FTIR technique and test materials. The percentage tensile strength of the selected triboactive layers is measured to assess the mechanical durability. The electrical measurements are performed for all eight M-TENG devices, notably the device constructed from B8 smartphone display layers outperforms other brands by generating about three and five times higher voltage and current than the M-TENG device composed of B1 layers. Further, the optimized device is subjected to frequency, force, and stability tests, and also the impact of fluctuating humidity on the device performance is analyzed. Moreover, the M-TENG demonstrates its versatility by efficiently charging commercial electrolytic capacitors, powering LEDs, and effectively harvesting biomechanical energy. Thus, the present study represents a significant step towards mitigating the challenges posed by electronic smartphone waste disposal while simultaneously offering a viable pathway to harvest electricity and power a variety of applications.

DSYOLO-trash: An attention mechanism-integrated and object tracking algorithm for solid waste detection.

In a global context, the production of urban solid waste significantly varies with changes in living standards. This trend exhibits diversity across different countries and regions, reflecting shifts in lifestyles as well as varying needs and challenges in waste management strategies. However, current standards of waste recycling are too complex for the general public to follow. In this study, we propose a model called DSYOLO-Trash to identify solid waste by integrating the dual attention mechanisms convolutional block attention module (CBAM) and Contextual Transformer Networks(CotNet), which significantly enhance its ability to mine channel-related and spatial attention features while optimizing the learning process. We apply the deep simple online and realtime tracking (DeepSORT) object tracking algorithm to solid waste detection for the first time in the literature to enable the real-time identification and tracking of waste. We also develop a multi-label dataset of mixed solid waste, called MMTrash, to realistically simulate actual scenarios of waste classification. Our proposed DSYOLO-Trash delivered superior performance to classical detection algorithms on both the MMTrash and the TrashNet datasets. Our system combines the improved you only look once(YOLO) algorithm with DeepSORT technology by using industrial cameras and PLC-controlled robotic arms to intelligently sort waste. The work here constitutes an important contribution to intelligent waste management and the sustainable development of cities.

Smart-sight: Video-based waste characterization for RDF-3 production.

A material recovery facility (MRF) can transform municipal solid waste (MSW) into a valued commodity called refuse-derived fuel (RDF) as a promising solution to waste-to-energy conversion. The quality of the produced RDF significantly relies on the composition of in-feed waste and waste characterization method applied for auditing purposes, a process that is both time-consuming and fraught with potential hazards. This study focuses to enhance the workflow of the waste characterization process at an MRF. A solution named Smart Sight is proposed to detect and classify waste based on videos recorded after processing MSW through a mechanical sorting line consisting of bag breakers and trommel screens. A comprehensive dataset is created encompassing thirteen mixed waste classes from single and multi-family streams. The dataset is preprocessed with motion compensation techniques and frame differencing methods to extract and refine valuable frames. A one-stage YOLO detector model is then trained over the dataset. The experimental results show that the proposed method works efficiently at detecting and classifying waste objects in indoor MRF environments. Accuracy, precision, recall, and F1 score related to the proposed solution are found to be 0.70, 0.762, 0.69 and 0.72, respectively, with a mAP@0.5 of 0.716. The proposed approach is validated using data collected from local MRF by comparing the estimated waste composition values of the proposed solution with laboratory results obtained through current standardized industrial practices. Comparison reveals that waste characterization estimation obtained is consistent with the laboratory results, inferring that Smart-Sight is a viable tool for estimating waste composition.

Systematic surveillance tools to reduce rodent pests in disadvantaged urban areas can empower communities and improve public health.

Rodents are notorious pests, known for transmitting major public health diseases and causing agricultural and economic losses. The lack of site-specific and national standardised rodent surveillance in several disadvantaged communities has rendered interventions targeted towards rodent control as often ineffective. Here, by using the example from a pilot case-study in the Bahamas, we present a unique experience wherein, through multidisciplinary and community engagement, we simultaneously developed a standardised national surveillance protocol, and performed two parallel but integrated activities: (1) eight days of theoretical and practical training of selected participants; and (2) a three-month post-training pilot rodent surveillance in the urban community of Over-the-Hill, Nassau, The Bahamas. To account for social and environmental conditions influencing rodent proliferation in the Bahamas, we engaged selected influential community members through a semi-structured interview and gathered additional site-specific information using a modified Centers for Diseases Control and Prevention (CDC) exterior and interior rodent evaluation form, along with other validated instruments such as tracking plates and snap trapping, to test and establish a standardised site-specific rodent surveillance protocol tailored for the Bahamas. Our engagement with community members highlighted poor disposal of animal and human food, irregular garbage collection, unapproved refuse storage, lack of accessible dumpsters, poor bulk waste management, ownership problems and structural deficiencies as major factors fuelling rodent proliferation in the study areas. Accordingly, results from our pilot survey using active rodent signs (that is, the presence of rodent runs, burrows, faecal material or gnawed material) as a proxy of rodent infestation in a generalized linear model confirmed that the variables earlier identified during the community engagement program as significantly correlated with rodent activities (and capturing) across the study areas. The successful implementation of the novel site-specific protocol by trained participants, along with the correlation of their findings with those recorded during the community engagement program, underscores its suitability and applicability in disadvantaged urban settings. This experience should serve as a reference for promoting a standardised protocol for monitoring rodent activities in many disadvantaged urban settings of the Global South, while also fostering a holistic understanding of rodent proliferation. Through this pilot case-study, we advocate for the feasibility of developing sustainable rodent control interventions that are acceptable to both local communities and public authorities, particularly through the involvement of a multidisciplinary team of professionals and community members.

Investigation of biochemical, enzymatic, and metagenomic profiles of garbage enzymes and its application in dumping site leachate treatment.

The current article focuses on the preparation and characterization of garbage enzyme (GE) and explores its applications in treating leachate. GE is prepared from fruit and vegetable wastes and characterized via analysis of metabolites, carbohydrates, proteins, antioxidants, and enzymatic activities. This study extends our understanding of GE by reporting the presence of various metabolites. Moreover, a metagenomic analysis of GE is presented, shedding light on the microbial diversity. Firmicutes emerged as the dominant phylum, surpassing other phyla, including Proteobacteria and Actinobacteria. When exploring the potential for leachate treatment, the results indicate that vegetable GE shows 68% reduction in COD (chemical oxygen demand) and 39% reduction in ammoniacal nitrogen. Similarly, non-citrus GE also showed 64% reduction in COD and a 37% reduction in ammoniacal nitrogen, followed by citrus GE with a 33% reduction in COD and a 34% reduction in ammoniacal nitrogen compared to the control.

Giving people the words to say no leads them to feel freer to say yes.

We examine how to structure requests to help people feel they can say no (or yes) more voluntarily. Specifically, we examine the effect of having the requester provide the request-target with an explicit phrase they can use to decline requests. Part of the difficulty of saying no is finding the words to do so when put on the spot. Providing individuals with an explicit script they can use to decline a request may help override implicit scripts and norms of politeness that generally dictate compliance. This should make individuals feel more comfortable refusing requests and make agreement feel more voluntary. Hence, we hypothesized that telling people how to say no (by providing them with an explicit script) would make compliance decisions feel more voluntary above and beyond merely telling them they can say no. Across two experimental lab studies (N = 535), we find support for this prediction.

GCDN-Net: Garbage classifier deep neural network for recyclable urban waste management.

The escalating waste volume due to urbanization and population growth has underscored the need for advanced waste sorting and recycling methods to ensure sustainable waste management. Deep learning models, adept at image recognition tasks, offer potential solutions for waste sorting applications. These models, trained on extensive waste image datasets, possess the ability to discern unique features of diverse waste types. Automating waste sorting hinges on robust deep learning models capable of accurately categorizing a wide range of waste types. In this study, a multi-stage machine learning approach is proposed to classify different waste categories using the "Garbage In, Garbage Out" (GIGO) dataset of 25,000 images. The novel Garbage Classifier Deep Neural Network (GCDN-Net) is introduced as a comprehensive solution, adept in both single-label and multi-label classification tasks. Single-label classification distinguishes between garbage and non-garbage images, while multi-label classification identifies distinct garbage categories within single or multiple images. The performance of GCDN-Net is rigorously evaluated and compared against state-of-the-art waste classification methods. Results demonstrate GCDN-Net's excellence, achieving 95.77% accuracy, 95.78% precision, 95.77% recall, 95.77% F1-score, and 95.54% specificity when classifying waste images, outperforming existing models in single-label classification. In multi-label classification, GCDN-Net attains an overall Mean Average Precision (mAP) of 0.69 and an F1-score of 75.01%. The reliability of network performance is affirmed through saliency map-based visualization generated by Score-CAM (class activation mapping). In conclusion, deep learning-based models exhibit efficacy in categorizing diverse waste types, paving the way for automated waste sorting and recycling systems that can mitigate costs and processing times.

How does abstract and concrete garbage classification signage influence waste sorting behavior?

Despite the issuance of standardized garbage classification signage, the rate of garbage classification in China remains low. We conducted a pair of laboratory experiments to explore the cognitive processing differences between abstract (including recyclables, hazardous garbage, and food signs) and concrete (including paper, plastic, glass, metal, textiles, batteries, household chemicals, tubes, and food signs) classification signs. We tested a nudging strategy to enhance garbage classification behavior. In Experiment 1, we divided garbage classification signs into two conditions: an abstract condition (comprising abstract signs) and a concrete condition (comprising concrete signs). The Go/No Go task was used to simulate garbage classification behavior. Participants were instructed to press a key when the garbage stimulus matched the classification signs (Go condition) and to refrain from pressing the key when there was a mismatch (No Go condition). The results showed that responses under the concrete condition were expedited compared to those under the abstract condition. This suggests that concrete signage requires less cognitive exertion, thereby enhancing the efficiency of waste classification. In Experiment 2, we optimized the existing bin signage, which predominantly featured abstract signs (traditional condition), and transformed it into a bin signage that emphasized concrete classification signs. These concrete signs were strategically positioned on the upper part of the bins to draw attention (nudging condition). The results suggested that the nudging condition required fewer cognitive resources than the traditional condition, which in turn increased the efficiency of processing garbage classification. This study not only validates the effects of concreteness in garbage classification but also provides effective nudge strategies to complement existing garbage classification management policy tools in a realistic Chinese context.

Optimization of low-grade coal and refuse-derived fuel blends for improved co-combustion behavior in coal-fired power plants.

This study is aimed at utilizing three waste materials, i.e., solid refuse fuel (SRF), tire derived fuel (TDF), and sludge derived fuel (SDF), as eco-friendly alternatives to coal-only combustion in co-firing power plants. The contribution of waste materials is limited to ≤5% in the composition of the mixed fuel (coal + waste materials). Statistical experimental design and response surface methodology are employed to investigate the effect of mixed fuel composition (SRF, TDF, and SDF) on gross calorific value (GCV) and ash fusion temperature (AFT). A quadratic model is developed and statistically verified to apprehend mixed fuel constituents' individual and combined effects on GCV and AFT. Constrained optimization of fuel blend, i.e., GCV >1,250 kcal/kg and AFT >1,200 °C, using the polynomial models projected the fuel-blend containing 95% coal with 3.84% SRF, 0.35% TDF, and 0.81% SDF. The observed GCV of 5,307 kcal/kg and AFT of 1225 °C for the optimized blend were within 1% of the model predicted values, thereby establishing the robustness of the models. The findings from this study can foster sustainable economic development and zero CO2 emission objectives by optimizing the utilization of waste materials without compromising the GCV and AFT of the mixed fuels in coal-fired power plants.

Insights into the recent advances of agro-industrial waste valorization for sustainable biogas production.

Recent years have seen a transition to a sustainable circular economy model that uses agro-industrial waste biomass waste to produce energy while reducing trash and greenhouse gas emissions. Biogas production from lignocellulosic biomass (LCB) is an alternative option in the hunt for clean and renewable fuels. Different approaches are employed to transform the LCB to biogas, including pretreatment, anaerobic digestion (AD), and biogas upgradation to biomethane. To maintain process stability and improve AD performance, machine learning (ML) tools are being applied in real-time monitoring, predicting, and optimizing the biogas production process. An environmental life cycle assessment approach for biogas production systems is essential to calculate greenhouse gas emissions. The current review presents a detailed overview of the utilization of agro-waste for sustainable biogas production. Different methods of waste biomass processing and valorization are discussed that contribute towards developing an efficient agro-waste to biogas-based circular economy.

Cultural Considerations for Patient and Community Education in Global Health: A Qualitative Study in Lesotho.

Background: In global health, international nongovernmental organizations (NGOs) frequently hire, train, and partner with host-country clinicians who manage public outreach and patient care. Purpose and Research Design: We conducted a general interpretivist study of Basotho clinicians hired by NGOs and academic affiliates in Lesotho to identify cultural barriers and facilitators to community and patient education. Data Collection and Analysis: We conducted 13 interviews involving 16 participants (one physician, one nutritionist, 14 nurses). Using an inductive and iterative approach, we analyzed interview transcripts through the lens of social cognitive theory and identified 15 themes. Results: Major findings highlighted: 1) patient and community learners may view Basotho clinicians as authority figures; 2) family and community power dynamics affect healthcare access for vulnerable patient groups; and 3) village leaders may refuse community education when excluded from problem-solving and early planning. Conclusions: Although local clinicians and community members may identify with the same cultural group, clinicians can encounter cultural barriers to patient and community education.

Assessing the supply chain management of waste-to-energy on green circular economy in China: an empirical study.

One of the industries that makes a significant contribution to the overall amount of greenhouse gas emissions around the globe is agriculture. In this regard, the use of bioenergy in the agricultural and food processing industries might benefit from the implementation of circular economy techniques. Despite the fact that just roughly 9% of the global economy is circular, there have been worldwide efforts to improve that reality. The linear economy, commonly known as the "take-make-use-dispose" model, is in sharp contrast to the circular economy, also known as "grow-make-use-restore," which seeks to influence the flow of materials and energy in order to maximize the benefits to the environment and minimize any associated costs. Garbage-to-energy, also known as WTE, is the focus of both academics and businesses as a direct result of the increasingly diminishing number of energy supplies and the ever-increasing amount of garbage. This project intends to turn trash into profit, lessen the impact waste has on the environment, and generate energy from biowaste by conceptualizing a focus on the supply chain characteristics of waste-to-energy processing. The adoption of a waste-to-energy (WTE) supply chain as a district energy system should be a viable solution toward a circular industrial economy that can solve energy consumption, waste management, and greenhouse gas emission concerns all at once. In the framework of a "circular economy," this study investigates how the management of waste-to-energy supply chains impacts the performance of businesses. The present investigation makes use of life cycle assessments, technical innovation, waste-to-energy conversion, and capacities related to circular economies. The study makes use of data obtained from an online survey that was administered between March 2021 and November 2021 to employees of 285 representative samples drawn from 457 European enterprises and firms that have accepted the concepts of the circular economy. The data is examined using a technique known as partial least squares structural equation modeling (PLS-SEM for short). The findings indicate that waste-to-energy serves as a mediator between the life cycle assessment and the capabilities of the circular economy and that sustainable supply chain management, sustainable supply chain design, technological progress, and waste-to-energy all have positive effects on these metrics.

Household food waste and theory of planned behavior: A systematic review and meta-analysis.

The darker side of food behavior is that millions of tons of food have been shown the doors of garbage. Therefore, food waste behavior needs an eye to look upon. The purpose of this research is to inculcate the concept of systematic literature review along with meta-analysis in order to examine the Theory of Planned Behavior (TPB) with respect to food waste behavior. The methodology includes Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) that is conducted for the identification, screening, and inclusion of studies. In all, twenty-six independent studies with (N = 13373) met the inclusion criteria. For validating the related literature, random-effects meta-analysis has been applied for ascertaining the average correlation among the variables. More specifically, the present study also examines the sub-group analysis effect among TPB variables. The findings reveal that the strongest association was observed between Attitude and Intention followed by Subjective Norm (SN) and Intention (INT), Perceived Behavioral Control (PBC) and Intention, and Intention and Behavior. Furthermore, the subgroup analysis using multi-cultural groups explores the highest composite correlation in the case of other cultural groups that included countries like Canada. The outcomes of the present study seek to serve in the best interest of households, event management stakeholders, and food policy makers.

Evaluation of a data-driven intelligent waste classification system for scientific management of garbage recycling in a Chinese community.

Waste classification management is effective in addressing the increasing waste output and continuous deterioration of environmental conditions. The waste classification behaviour of resident is an important basis for managers to collect and allocate resources. Traditional analysis methods, such as questionnaire, have limitations considering the complexity of individual behaviour. An intelligent waste classification system (IWCS) was applied and studied in a community for 1 year. Time-based data analysis framework was constructed to describe the residents' waste sorting behaviour and evaluate the IWCS. The results showed that residents preferred to use face recognition than other modes of identification. The ratio of waste delivery frequency was 18.34% in the morning and 81.66% in the evening, respectively. The optimal time windows of disposing wastes were from 6:55 to 9:05 in the morning and from 18:05 to 20:55 in the evening which can avoid crowding. The percentage of accuracy of waste disposal increased gradually in a year. The amount of waste disposal was largest on every Sunday. The average accuracy was more than 94% based on monthly data, but the number of participating residents decreased gradually. Therefore, the study demonstrates that IWCS is a potential platform for increasing the accuracy and efficiency of waste disposal and can promote regulations implementation.

A waste classification method based on a capsule network.

Garbage recycling and automatic sorting are efficient ways to address the paradox of rising municipal waste. Although traditional image classification methods can solve the rubbish image classification problem, they ignore the spatial relationship between features, which can easily lead to misclassification of the same object. In this paper, we propose the ResMsCapsule network, which is a trash picture categorization model based on the capsule network. By combining the residual network and multi-scale module, the ResMsCapsule network can improve the performance of the basic capsule network greatly. Extensive experiments using the publicly available dataset TrashNet show that the ResMsCapsule method has a simpler network structure and higher garbage classification accuracy. The classification accuracy of the ResMsCapsule network is 91.41%, and the number of parameters is only 40% of that of ResNet18, which is better than other image classification algorithms.

Internet of Things enables smart solid waste bin management system for a sustainable environment.

Solid waste management (SWM) is a pressing concern and significant research topic that requires attention from citizens and government stakeholders. Most of the responsibility of waste management is on the municipal sector for its collection, reallocation, and reuse of other resources. The daily solid waste production is more than 54,850 tonnes in urban areas and is difficult to manage due to limited resources and different administrative and service issues. New technologies are playing their role in this area but how to integrate the technologies is still a question, especially for developing countries. This paper is divided into two main phases including a detailed investigation and a technological solution. In the first phase, the data is collected by using the qualitative method to investigate and identify the issues related to waste management. After a detailed investigation and results, the gap is identified by using statistical analysis and proposed a technological solution in the second phase. The technology-based solution is used to control and manage waste with a low-cost, fast, and manageable solution. The new sensor-based technologies, cellular networks, and social media are utilized to monitor the trash in the areas. The trash management department receives notification via cellular services to locate the dustbin when the dustbin reaches a maximum level so the department may send a waste collector vehicle to the relevant spot to collect waste. The smart and fast solution will connect all stakeholders in the community and reduce the cost and time and make the collection process faster. The experiment results indicated the issues and effectiveness of the proposed solution.

The key role of denitrification and dissimilatory nitrate reduction in nitrogen pollution along vertical landfill profiles from metagenomic perspective.

Landfill are persistent sources of nitrogen (N) pollution even in the decades after closure. However, the biological pathways of N-pollution, particularly N2O and NH4+, at different landfill depths have received little attention. In this study, metagenomic analysis was conducted on landfill refuse from vertical reservoir profiles in two closed landfills named XT and MT. NH4+ concentrations were found to be higher in deeper layers of MT, while greater potential for N2O emissions occurred in XT and the shallow layers of MT. Furthermore, the community structure and function of N-metabolizing microbes were more strongly defined by landfill depth than landfill type. Denitrification, involving abundant nirK and norB genes, was identified as the major pathway for N2O production in both XT and MT-shallow, while dissimilatory nitrate reduction with abundant nirBD genes was identified as the major pathway for NH4+ accumulation. Microbes of norB-type and nirBD-type were positively affected by NO3- in XT, whereas negatively affected by contents of organic material and moisture in MT-shallow. The mechanism by which nitrogen fixation, with abundant nifH genes, contributes to NH4+ accumulation in MT-deep should be further elucidated. These findings can provide a theoretical basis for governing scientific N-pollution control strategies throughout the entire landfill process.

Garbage enzyme-mediated treatment of landfill leachate: A sustainable approach.

The study evaluates the soluble chemical oxygen demand (sCOD) removal efficiency from landfill leachate by treating it with four different garbage enzymes at two temperatures (room temperature 27 ± 3 °C and higher temperature 42 ± 3 °C). The four different garbage enzymes were prepared by fermenting fruit peels such as pineapple, banana, orange, and lemon peels and treated with landfill leachate at different mixing ratios of 5%, 10%, 15% and 20%. The results show that garbage enzymes made from orange (10%) and lemon (15%) have maximum sCOD reduction of 68.24% and 67.89%, respectively, at room temperature. The maximum solubilization was found in the pineapple and lemon garbage enzyme at 5% concentration. The samples kept at room temperature showed better solubilization and sCOD removal compared to the samples at higher temperatures. The study demonstrates that the garbage enzyme could be used to increase the bioavailability of organics in leachate.