Improving Aerobic Digestion of Food Waste by Adding a Personalized Microbial Inoculum.

In the context of China's garbage classification policy, on-site aerobic food waste (FW) digestion is crucial for reducing transportation and disposal costs. The efficiency of this process is largely determined by the microbial community structure and its functions. Therefore, this study aimed to analyze the impact of a personalized microbial consortium (MCM) on the efficiency of aerobic FW digestion and to reveal the underlying mechanisms. An MCM, sourced from naturally degrading FW, was selected to enrich degrading bacteria with relatively high hydrolyzing ability. The functionality of the MCM was evaluated by tracing the successions of microbial communities, and comparing the differences in the forms of organic compounds, metabolic functions, and hydrolase activities. X-ray photoelectron spectroscopy demonstrated that the MCM metabolized faster, and produced more acidic metabolites. Metagenomic analysis indicated that FW inoculated with the personalized MCM increased abundance of Bacillaceae producing hydrolysis enzymes and promoted glycolysis metabolic pathways, enhancing energy generation for metabolism, compared to the commercial effective bacterial agent. This paper provides both theoretical and practical evidence for the improvement of biochemical processor of FW with the personalized MCM, which has promising application prospects and economic value.

The quantity and composition of household food waste: Implications for policy.

Studies on food waste in Southeast Asia are currently limited, with a notable absence of comparative analyses investigating the volume and composition of food waste in urban and rural areas through direct measurement. This study aimed to analyze the differences in the quantity, composition, and drivers of household food waste between urban and rural areas. Household food waste was assessed through waste compositional analysis for food and diaries for beverages. This cross-sectional study included 215 households in Bogor Regency, Indonesia. Comparisons between the two areas were performed using an independent t-test. The average of household food waste in Bogor Regency was 77 kg/cap/year (edible 37.7%, inedible 62.3%). Household food waste was higher in urban areas (79.4 kg/cap/year) than in rural areas (45.8 kg/cap/year) (p<0.001). Cereals, tubers and their derivatives (especially rice) and vegetables were the major contributors to edible food waste, whereas fruits were the main contributors to inedible food waste in both areas. Food waste drivers were spoilage/staleness/moldiness, changes in texture, short shelf life, cooking too much, and plate leftovers. Households in urban areas had a higher quantity of food waste and disposed of more edible food than those in rural areas. Meanwhile, the drivers of food waste generation were similar in both areas. Understanding the quantity, composition, and drivers of household food waste is pivotal for developing effective awareness campaigns and fostering behavioral changes to prevent household food waste.

Linking microbial population dynamics in anaerobic bioreactors to food waste type and decomposition stage.

A key question in anaerobic microbial ecology is how microbial communities develop over different stages of waste decomposition and whether these changes are specific to waste types. We destructively sampled over time 26 replicate bioreactors cultivated on fruit/vegetable waste (FVW) and meat waste (MW) based on pre-defined waste components and composition. To characterize community shifts, we examined 16S rRNA genes from both the leachate and solid fractions of the waste. Waste decomposition occurred faster in FVW than MW, as accumulation of ammonia in MW reactors led to inhibition of methanogenesis. We identified population succession during different stages of waste decomposition and linked specific populations to different waste types. Community analyses revealed underrepresentation of methanogens in the leachate fractions, emphasizing the importance of consistent and representative sampling when characterizing microbial communities in solid waste.

Revealing mechanisms of NH3 and N2O emissions reduction in the rapid bio-drying of food waste: Insights from organic nitrogen composition and microbial activity.

Inevitably, aerobic biological treatment processes generate emissions of ammonia (NH3) and greenhouse gas (GHGs) emissions, especially nitrous oxide (N2O). The rapid bio-drying process (RBD) for food waste (FW) alleviates issues arising from its substantial growth. However, its emissions of NH3 and N2O remain unknown, and the correlation with nitrogen components in the substrate remains unclear, significantly impeding its widespread adoption. Here, the nitrogen loss and its mechanisms in RBD were investigated, and the results are as follows: The total emission of NH3 and N2O were1.42 and 1.16 mg/kg FW (fresh weight), respectively, achieving a 98 % reduction compared to prior studies. Structural equation modeling demonstrates that acid ammonium nitrogen (AN) decomposition chiefly generates NH3 in compost (p < 0.001). Strong correlation (p < 0.001) exists between amino acid nitrogen (AAN) and AN. In-depth analysis of microbial succession during the process reveals that the enrichment of Brevibacterium, Corynebacterium, Dietzia, Fastidiosipila, Lactobacillus, Mycobacterium, Peptoniphilus, and Truepera, are conducive to reducing the accumulation of AN and AAN in the substrate, minimizing NH3 emissions (p < 0.05). While Pseudomonas, Denitrobacterium, Nitrospira, and Bacillus are identified as key species contributing to N2O emissions during the process. Correlation analysis between physicochemical conditions and microbial succession in the system indicates that the moisture content and NO3- levels during the composting process provide suitable conditions for the growth of bacteria that contribute to NH3 and N2O emissions reduction, these enrichment in RBD process minimizing NH3 and N2O emissions. This study can offer crucial theoretical and data support for the resource utilization process of perishable organic solid waste, mitigating NH3 and GHGs emissions.

Utilization strategy for algal bloom waste through co-digestion with kitchen waste: Comprehensive kinetic and metagenomic analysis.

Anaerobic co-digestion (AcoD) with kitchen waste (KW) is an alternative utilization strategy for algal bloom waste (AW). However, the kinetic characteristic and metabolic pathway during this process need to be explored further. This study conducted a comprehensive kinetic and metagenomic analysis for AcoD of AW and KW. A maximum co-digestion performance index (CPI) of 1.13 was achieved under the 12% AW addition. Co-digestion improved the total volatile fatty acids generation and the organic matter transformation efficiency. Kinetic analysis showed that the Superimposed model fit optimally (R2Adj = 0.9988-0.9995). The improvement of the kinetic process by co-digestion was mainly reflected in the increase of the methane production from slowly biodegradable components. Co-digestion enriched the cellulolytic bacterium Clostridium and the hydrogenotrophic methanogenic archaea Methanobacterium. Furthermore, for metagenome analysis, the abundance of key genes concerned in cellulose and lipid hydrolysis, pyruvate and methane metabolism were both increased in co-digestion process. This study provided a feasible process for the utilization of AW produced seasonally and a deeper understanding of the AcoD synergistic mechanism from kinetic and metagenomic perspectives.

Qualitative classification of waste garments for textile recycling based on machine vision and attention mechanisms.

The increasing volume of garment waste underscores the need for advanced sorting and recycling strategies. As a critical procedure in the secondary usage of waste clothes, qualitative classification of garments categorizes post-consumer clothes based on types and styles. However, this process currently relies on manual labor, which is inefficient, labor-intensive, and poses risks to workers. Despite efforts to implement automatic clothes classification systems, challenges persist due to visual complexities such as similar colors, deformations, and occlusions. In response to these challenges, this study introduces an enhanced intelligent machine vision system with attention mechanisms designed to automate the laborious and skill-demanding task of garment classification. Initially, a waste garment dataset comprising approximately 27,000 garments was curated using a self-developed automatic classification platform. Subsequently, the proposed attention method parameters were selected, and a series of benchmarks were conducted against state-of-the-art methods. Finally, the proposed system underwent a two-week online deployment to evaluate its running stability and sensitivity to similar colors, deformation, and occlusion in industrial production settings. The benchmarks indicate that the proposed method significantly improves classification accuracy across various models. The visualization interpretation of Grad-CAM reveals that the proposed method effectively handles complex environments by directing its focus toward garment-related pixels. Notably, the proposed system elevates classification accuracy from 68.28 % to human-level performance (>90 %) while ensuring greater running stability. This advancement holds promise for automating the classification process and potentially alleviating workers from labor-intensive and hazardous tasks associated with clothes recycling.

Distribution and survival of pathogens from different waste components and bioaerosol traceability analysis in household garbage room.

Household garbage rooms release abundant bioaerosols and are an important source of pathogens; however, information on the distribution and survival patterns of pathogens in different waste components is limited. In this study, a culture method and 16S rRNA high-throughput sequencing were used to determine bacterial communities, culturable pathogens, and human bacterial pathogens (HBPs). The results showed that abundant culturable bacteria were detected in all waste types, and a large number of S. aureus was detected on the surface of recyclable wastes, whereas S. aureus, total coliforms, Salmonella, Enterococcus, and hemolytic bacteria were detected in food waste and other waste. The activities of these detected pathogenic bacteria decreased after 24 h of storage but re-activated within one week. Factors affecting the emergence of pathogens varied with different waste components. Sequencing results showed that Pseudomonas, Acinetobacter, and Burkholderia were abundant in the waste samples, whereas Achromobacter, Exiguobacteriums, Bordetella, and Corynebacterium were the primary pathogens in the bioaerosol and wall attachment. The results of traceability analysis showed that bioaerosol microbes were mainly derived from raw kitchen waste (5.98%) and plastic and paper contaminated with food waste (19.93%) in garbage rooms. In addition, bioaerosols were the main source of microflora in the wall attachment, which possessed high HBP diversity and required more attention. These findings will help in understanding the microbial hazards in different waste components and provide guidance for the control and risk reduction of bioaerosols during waste management and recycling.

A systematic review on garbage enzymes and their applications in environmental processes.

Understanding the new insight on conversion of organic waste into value-added products can improve the environmental activities driven by microorganisms and return the nutrients to environment and earth. Here, we comprehensively review the available knowledge on application of garbage enzyme (GE) for different environmental activities including waste activated sludge, composting process, landfill leachate treatment, soil remediation and wastewater treatment with special focus on their efficiency. To identify peer-reviewed studies published in English-language journals, a comprehensive search was performed across multiple electronic databases including Scopus, Web of Science, Pubmed, and Embase. The search was conducted systematically using relevant keywords. The eligible studies were analyzed to extract data and information pertaining to components of GE, fermentation process operational parameters, type of hydrolytic enzymes and improved environmental performance. The findings derived from this current review demonstrated that GE produced from the fruit and vegetable peels, molasses or brown sugar (carbon source), and water within fermentation process contain different hydrolytic enzymes in order to facilitate the organic waste degradation. Therefore, GE can be considered as a promising and efficient pathway in order to improve the environmental activities depended on microorganism including, composting, wastewater and leachate treatment and bioremediation process.

Feasibility study of the use of concrete blocks produced from plastic marine garbage for use in civil construction.

The pursuit of development in an economically sustainable and ecologically sound manner is a goal of modern society. It has been investing in products that minimize their environmental impact, particularly concerning the use of plastic. This material is highly detrimental to nature due to its toxicity and long decomposition period. The present study aims to analyze the feasibility of producing blocks made of concrete with different amounts of waste plastic taken from coastal and estuarine areas. After laboratory analysis, it was found that blocks containing 5% plastic exhibit good compressive strength and are lighter. Additionally, there was an increase in the acidity of the sample, a decrease in the density of the block, and reductions in both flexural and compressive strength. The prototype, composed of 5% plastic by mass, proves to be efficient for constructing single-story houses. It meets the minimum requirements for normative resistance, effectively encapsulating the plastic within the block and thereby reducing its environmental impact.

Eco-efficiency as a prioritization tool in the reduction of food waste in restaurants.

Striving towards eco-efficiency means creating more value while generating a product or service with a reduced environmental impact. This quest makes it possible to link objectives associated with both the environmental and the economic pillars of sustainable development. Eco-efficiency could therefore be an interesting tool to evaluate the impacts of food waste (FW) and the potential of various food waste reduction strategies (FWRSs). However, the use of eco-efficiency to assess the interest in implementing FWRSs has never been explored in the foodservice sector. This work firstly aims to carry out an in-depth analysis of the costs of the FW generation of an independent restaurant. Secondly, based on these costs data and on some previously documented environmental impact data with a life cycle assessment according to ISO 14045:2012, this work also aims to model and evaluate the performance of FWRSs from a perspective of improving a restaurant eco-efficiency. The impact of each FWRS on the eco-efficiency of the restaurant under study was measured by modeling their economic and environmental net benefits over three implementation periods (one week, one month and six months) and under scenarios of strong and weak adherence. The study identified the most eco-efficient FWRSs to be implemented to reduce FW in the studied restaurant. In addition, key factors affecting eco-efficiency were raised, namely the period following the implementation of FWRSs, the FW reduction rate between FWRSs affecting the same type of FW, the specificity of the FWRSs and their ability to limit the waste of vegetables, meat, sea products and food requiring significant processing time by the cooks. Thus, these elements will guide foodservice managers in adopting FWRSs aimed at reducing FW generated in their restaurant and at improving its eco-efficiency. In addition, this work proposes a new methodology intended for the scientific community to identify FWRSs that have a strong impact on a restaurant eco-efficiency.

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.

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.

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.

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.

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.