Mini-Open Dumps in the Paraná River Floodplain: Local Problems with Global Effects.

The growing production of urban solid waste is a structural problem faced by most cities around the world. The proliferation of mini-open dumps (MOD; small spontaneous open-air waste dumps formed in urban and peri-urban areas) on the banks of the Paraná River is particularly evident. During the historical drought (June-December 2021), we carried out sampling campaigns identifying MODs of the Santa Fe River, a secondary channel of the Paraná River. MOD were geolocated, measured, described and classified by origin. The distance to the river and other sensitive places was considered (houses-schools-health facilities). Our results suggested a serious environmental issue associated with poor waste management. MOD were extremely abundant in the study area, being mostly composed of domestic litter. Plastics clearly dominated the MOD composition. Burning was frequently observed as a method to reduce the volume of MOD. We concluded that the proliferation of MOD is a multi-causal problem associated with a failure of public policies and a lack of environmental education.

Effective food waste management model for the sustainable agricultural food supply chain.

The extensive research examines the current state of agricultural food supply chains, with focus on waste management in Bandung Regency, Indonesia. The study reveals that a significant proportion of food within the agricultural supply chain goes to waste and discusses the various challenges and complexities involved in managing food waste. The research presents a conceptual model based on the ADKAR change management paradigm to promote waste utilization, increase awareness and change people's behaviors. The model emphasizes the importance of creating awareness, fostering desire, providing knowledge, implementing changes, and reinforcing and monitoring the transformation process. It also addresses the challenges, barriers, and drivers that influence waste utilization in the agricultural supply chain, highlighting the need for economic incentives and a shift in public awareness to drive meaningful change. Ultimately, this study serves as a comprehensive exploration of food waste management in Bandung Regency, shedding light on the complexities of the issue and offering a systematic approach to transition towards more sustainable waste utilization practices.

Integrating fuzzy-AHP and GIS for solid waste disposal site selection in Kenitra province, NW Morocco.

Selecting an optimal solid waste disposal site is one of the decisive waste management issues because unsuitable sites cause serious environmental and public health problems. In Kenitra province, northwest Morocco, sustainable disposal sites have become a major challenge due to rapid urbanization and population growth. In addition, the existing disposal sites are traditional and inappropriate. The objective of this study is to suggest potential suitable disposal sites using fuzzy logic and analytical hierarchy process (fuzzy-AHP) method integrated with geographic information system (GIS) techniques. For this purpose, thirteen factors affecting the selection process were involved. The results showed that 5% of the studied area is considered extremely suitable and scattered in the central-eastern parts, while 9% is considered almost unsuitable and distributed in the northern and southern parts. Thereafter, these results were validated using the area under the curve (AUC) of the receiver operating characteristics (ROC). The AUC found was 57.1%, which is a moderate prediction's accuracy because the existing sites used in the validation's process were randomly selected. These results can assist relevant authorities and stakeholders for setting new solid waste disposal sites in Kenitra province.

An evaluation model for city-scale construction and demolition waste management effectiveness: A case study in China.

The growing generation of construction and demolition waste (CDW) has emerged as a prominent challenge on global environmental agendas. However, the effectiveness of CDW management (CDWM) strategies varies among cities. Existing literature predominantly evaluates the effectiveness of CDWM at the project level, offering a localized perspective that fails to capture a city's comprehensive CDWM profile. This localized focus has certain limitations. To fill this gap in city-scale evaluations, this study introduces a novel model for assessing CDWM effectiveness at the municipal level. An empirical investigation was conducted across 11 cities within the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) to operationalize this model. The model defines five distinct levels of CDWM effectiveness. Findings indicate that Hong Kong consistently achieves the highest level (level I), while the majority of cities fall within levels III and IV. This pattern suggests that CDWM effectiveness in the GBA is moderately developed, with uneven progress in CDW management outcomes and supporting systems. Essentially, there is a lack of synchronous development of CDWM results and guarantee systems. The proposed evaluation model enriches existing CDWM research field and offers a framework that may inform future studies in other countries.

Agro-industrial waste management employing benefits of artificial intelligence.

By 2050, the world's population is predicted to reach over 9 billion, which requires 70% increased production in agriculture and food industries to meet demand. This presents a significant challenge for the agri-food sector in all aspects. Agro-industrial wastes are rich in bioactive substances and other medicinal properties. They can be used as a different source for manufacturing products like biogas, biofuels, mushrooms, and tempeh, the primary ingredients in various studies and businesses. Increased importance is placed on resource recovery, recycling, and reusing (RRR) any waste using advanced technology like IoT and artificial intelligence. AI algorithms offer alternate, creative methods for managing agro-industrial waste management (AIWM). There are contradictions and a need to understand how AI technologies work regarding their application to AIWM. This research studies the application of AI-based technology for the various areas of AIWM. The current work aims to discover AI-based models for forecasting the generation and recycling of AIWM waste. Research shows that agro-industrial waste generation has increased worldwide. Infrastructure needs to be upgraded and improved by adapting AI technology to maintain a balance between socioeconomic structures. The study focused on AI's social and economic impacts and the benefits, challenges, and future work in AIWM. The present research will increase recycling and reproduction with a balance of cost, efficiency, and human resources consumption in agro-industrial waste management.

Agricultural Waste Management by Production of Second-Generation Bioethanol from Sugarcane Bagasse Using Indigenous Yeast Strain.

In the wake of rapid industrialization and burgeoning transportation networks, the escalating demand for fossil fuels has accelerated the depletion of finite energy reservoirs, necessitating urgent exploration of sustainable alternatives. To address this, current research is focusing on renewable fuels like second-generation bioethanol from agricultural waste such as sugarcane bagasse. This approach not only circumvents the contentious issue of food-fuel conflicts associated with biofuels but also tackles agricultural waste management. In the present study indigenous yeast strain, Clavispora lusitaniae QG1 (MN592676), was isolated from rotten grapes to ferment xylose sugars present in the hemicellulose content of sugarcane bagasse. To liberate the xylose sugars, dilute acid pretreatment was performed. The highest reducing sugars yield was 1.2% obtained at a temperature of 121 °C for 15 min, a solid-to-liquid ratio of 1:25 (% w/v), and an acid concentration of 1% dilute acid H2SO4 that was significantly higher (P < 0.001) yield obtained under similar conditions at 100 °C for 1 h. The isolated strain was statistically optimized for fermentation process by Plackett-Burman design to achieve the highest ethanol yield. Liberated xylose sugars were completely utilized by Clavispora lusitaniae QG1 (MN592676) and gave 100% ethanol yield. This study optimizes both fermentation process and pretreatment of sugarcane bagasse to maximize bioethanol yield and demonstrates the ability of isolated strain to effectively utilize xylose as a carbon source. The desirable characteristics depicted by strain Clavispora lusitaniae shows its promising utilization in management of industrial waste like sugarcane bagasse by its conversion into renewable biofuels like bioethanol.

Plastic Pandemic after COVID-19: A Global Health Concern.

Abstract: COVID-19 pandemic has increased the amount of plastic burden to environment and complexities of plastic waste management. Change in behavioral pattern with advent of this pandemic led to increased practice of hygiene and increased use of different types of personal protective equipment. Unfortunately, rapid rise in production of the PPEs (like Hazmat suit, gloves, etc.) and single-use plastics used in RT-PCR and other testing are the biggest source for increased non-biodegradable plastic waste leading to amplified burden on plastic waste management. A number of measures like prioritizing the policies directed towards changes at behavioral, social and institutional level need to be started. Also, reduction in plastic waste along with proper plastic waste management policies should be implemented. To prevent the transition from one pandemic to other; improvement in government policies with public private partnership are the need of the hour.

Correlations and impact of anaerobic digestion operating parameters on the start-up duration: Database construction for robust start-up guidelines.

Anaerobic digestion (AD) has become a popular technique for organic waste management while offering economic and environmental advantages. As AD becomes increasingly prevalent worldwide, research efforts are primarily focused on optimizing its processes. During the operation of AD systems, the occurrence of unstable events is inevitable. So far, numerous conclusions have been drawn from full and lab-scale studies regarding the driving factors of start-up perturbations. However, the lack of standardized practices reported in start-up studies raises concerns about the comparability and reliability of obtained data. This study aims to develop a knowledge database and investigate the possibility of applying machine learning techniques on experimentation-extracted data to assist start-up planning and monitoring. Thus, a standardized database referencing 75 cases of start-up of one-stage wet continuously-stirred tank reactors (CSTR) processing agricultural, industrial, or municipal organic effluent in mono-digestion from 31 studies was constructed. 10 % of the total observations included in this database concern failed start-up experiments. Then, correlations between the parameters and their impacts on the start-up duration were studied using multivariate analysis and a model-based ranking methodology. Insights into trends of choices were highlighted through the correlation analysis of the database. As such, scenarios favoring short start-up duration were found to involve relatively low retention times (average initial and final hydraulic retention times, (HRTi) and (HRTf) of 26.25 and 20.6 days, respectively), high mean organic loading rates (average OLRmean of 5.24 g VS·d-1·L -1) and the processing of highly fermentable substrates (average feed volatile solids (VSfeed) of 81.35 g L-1). The model-based ranking of AD parameters demonstrated that the HRTf, the VSfeed, and the target temperature (Tf) have the strongest impact on the start-up duration, receiving the highest relative scores among the evaluated AD parameters. The database could serve as a reference for comparison purposes of future start-up studies allowing the identification of factors that should be closely controlled.

Evaluating factors promoting waste scavenging and its implications in a growing urban environ in Nigeria.

Despite the inherent risks associated with waste scavenging, it is surprising to note the age group involved in this activity. This study aimed to assess the motivating factors driving teenagers and youths to participate in waste scavenging, using a sample of 247 scavengers. The data underwent analysis using frequency counts, percentages, and factor analysis techniques. The findings revealed that all respondents were male and aged between 12 and 45 years. The primary sources of waste were dumpsites (61%), roadsides, and gutters (26%). Participants predominantly cited lack of formal education requirements and financial gain (93%) as key motivators for their involvement in waste scavenging. Additionally, 96% of scavengers were from northern Nigeria, with the remaining 4% from southern Nigeria. The factor analysis yielded significant results with a Kaiser-Meyer-Olkin (KMO) measure and Bartlett's test value of 67.770%, significant at p < 0.05. Four distinct factors emerged as significant motivators: (i) Lack of formal education (31.784%), (ii) Financial incentives (22.912%), (iii) Availability of wastes (14.297%), and (iv) Industrial demand for wastes (13.495%), in descending order of influence. Together, these factors accounted for 82.487% of the motivating factors behind youth engagement in waste scavenging in the study area. This outcome highlights potential challenges for the prospects of young individuals involved in scavenging unless substantial interventions are implemented. It is recommended that policies be developed to discourage scavenging activities while simultaneously promoting access to education and social welfare for this demographic. Such initiatives are crucial for redirecting young people away from scavenging and toward more sustainable livelihood options, ensuring a brighter future for them and contributing to overall societal development.

A review on the enhancement of circular economy aspects focusing on nanocellulose composites.

Researchers are now focusing on using the circular economy model to manufacture nanocellulose composites due to growing environmental issues related to waste management. The circular economy model offers a sustainable solution to the problem by optimizing resource efficiency and waste management by reducing waste, maintaining value over time, minimizing the use of primary resources, and creating closed loops for goods, components, and materials. With the use of the circular economy model, waste, such as industrial, agricultural, and textile waste, is used again to produce new products, which can solve waste management issues and improve resource efficiency. In order to encourage the use of circular economy ideas with a specific focus on nanocellulose composites, this review examines the concept of using circular economy, and explores ways to make nanocellulose composites from different types of waste, such as industrial, agricultural, and textile waste. Furthermore, this review investigates the application of nanocellulose composites across multiple industries. In addition, this review provides researchers useful insights of how circular economics can be applied to the development of nanocellulose composites, which have the goal of creating a flexible and environmentally friendly material that can address waste management issues and optimize resource efficiency.

Waste to resource recovery at a marina: Empirical evidence of upstream and downstream innovation for circularity.

In 2021, an Australian research centre partnered with a regional marina and shipyard where 90 businesses build, refit, and maintain boats in premium condition. Tenants and owners grapple with environmental waste management issues. Since there is a gap in applying action research but numerous calls to co-produce solutions and participate in translating ideas into practice, action research was used in this case study involving upstream and downstream innovation for circularity. Mixed methods data was collected through interviews, stakeholder workshops, and waste audits. A strategic action plan was created for closing the loop on waste. Interventions included tackling toxic, degradable products with natural alternatives, trading and remanufacturing materials to extend product life cycles, testing problematic materials, and pursuing product stewardship. This study is novel because it extends diffusion of innovation theory to real-world impact through a co-innovation process. Results underscore that ongoing achievements depend on properly sorting waste, accessing reprocessing facilities, and maintaining dedicated staff and partnerships, especially legislative support for making continuous improvement.

From waste management to circular economy: Leveraging thermophiles for sustainable growth and global resource optimization.

Waste of any origin is one of the most serious global and man-made concerns of our day. It causes climate change, environmental degradation, and human health problems. Proper waste management practices, including waste reduction, safe handling, and appropriate treatment, are essential to mitigate these consequences. It is thus essential to implement effective waste management strategies that reduce waste at the source, promote recycling and reuse, and safely dispose of waste. Transitioning to a circular economy with policies involving governments, industries, and individuals is essential for sustainable growth and waste management. The review focuses on diverse kinds of environmental waste sources around the world, such as residential, industrial, commercial, municipal services, electronic wastes, wastewater sewerage, and agricultural wastes, and their challenges in efficiently valorizing them into useful products. It highlights the need for rational waste management, circularity, and sustainable growth, and the potential of a circular economy to address these challenges. The article has explored the role of thermophilic microbes in the bioremediation of waste. Thermophiles known for their thermostability and thermostable enzymes, have emerged to have diverse applications in biotechnology and various industrial processes. Several approaches have been explored to unlock the potential of thermophiles in achieving the objective of establishing a zero-carbon sustainable bio-economy and minimizing waste generation. Various thermophiles have demonstrated substantial potential in addressing different waste challenges. The review findings affirm that thermophilic microbes have emerged as pivotal and indispensable candidates for harnessing and valorizing a range of environmental wastes into valuable products, thereby fostering the bio-circular economy.

Integral multi-valorization of agro-industrial wastes: A review.

Agriculture and industries related to the agriculture sector generate a large amount of waste each year. These wastes are usually burned or dumped, causing damage to the environment, the economy and society. Due to their composition, they have great potential for obtaining high value-added products in biorefineries. This fact, added to the growing demand for energy and chemicals from fossil resources, is driving the interest of the scientific community in them. Biorefinery processes are hardly profitable when applied individually, so a better alternative is to develop integrated multi-feedstock and multi-product biorefinery schemes using all biomass fractions in a zero-waste approach. However, for industrial scale application, extensive research, scale-up studies, and techno-economic and environmental feasibility analyses are needed. This review compiles information on integrated multi-biorefinery processes from agro-industrial wastes to shed light on the path towards sustainable development and circular bioeconomy.

Life cycle assessment and cost analysis of an innovative automatic system for sorting municipal solid waste: A case study at Milan Malpensa airport.

With the recent advancement in artificial intelligence, there are new opportunities to adopt smart technologies for the sorting of materials at the beginning of the recycling value chain. An automatic bin capable of sorting the waste among paper, plastic, glass & aluminium, and residual waste was installed in public areas of Milan Malpensa airport, a context where the separate collection is challenging. First, the airport waste composition was assessed, together with the efficiency of the manual sorting performed by passengers among the conventional bins: paper, plastic, glass & aluminium, and residual waste. Then, the environmental (via the life cycle assessment - LCA) and the economic performances of the current system were compared to those of a system in which the sorting is performed by the automatic bin. Three scenarios were evaluated: i) all waste from public areas, despite being separately collected, is sent to incineration with energy recovery, due to the inadequate separation quality (S0); ii) recyclable fractions are sent to recycling according to the actual level of impurities in the bags (S0R); iii) fractions are sorted by the automatic bin and sent to recycling (S1). According to the results, the current separate collection shows a 62 % classification accuracy. Focusing on LCA, S0 causes an additional burden of 12.4 mPt (milli points) per tonne of waste. By contrast, S0R shows a benefit (-26.4 mPt/t) and S1 allows for a further 33 % increase of benefits. Moreover, the cost analysis indicates potential savings of 24.3 €/t in S1, when compared to S0.

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.

Artificial Intelligence in enhancing sustainable practices for infectious municipal waste classification.

This research paper focuses on effective infectious municipal waste management in urban settings, highlighting a dearth of dedicated research in this domain. Unlike general or specific waste types, infectious waste poses distinct health and environmental risks. Leveraging advanced artificial intelligence techniques, we prioritize infectious waste categorization and optimization, integrating metaheuristics into optimization methods to create a robust dual-ensemble framework. Our model, the "Enhanced Artificial Intelligence for Infectious Municipal Waste Classification System," combines ensemble image segmentation methods and diverse convolutional neural network models. Innovative geometric image augmentation enhances model robustness, diversifies training data, and improves accuracy across waste types. A pivotal aspect is the integration of a reinforcement learning-differential evolution algorithm as a decision fusion strategy, optimizing classification by harmonizing outputs from ensemble methods and convolutional neural network models. Computational results, using a newly collected dataset, demonstrate our model's accuracy exceeding 96.54% while using the existing solid waste dataset we achieve the accuracy of 97.81%, outperforming advanced approaches by margins ranging from 2.02% to 8.80%. This research significantly advances sustainable waste management, showcasing artificial intelligence's transformative potential in addressing intricate waste challenges. It establishes a foundational framework prioritizing efficiency, effectiveness, and sustainability for future waste management solutions. Acknowledging the importance of diverse datasets, customization for urban contexts, and practical integration into existing infrastructures, our work contributes to the broader discourse on the role of artificial intelligence in evolving waste management practices.

Maximal fines and corruption: An experimental study on illegal waste disposal.

Corruption is known to be one of the real life situations which may jeopardize the effectiveness of fines in deterring crime. We present a model of 'crime with corruption' by which both the dilution of crime deterrence due to corruption, as well as the possibility of crime encouraging fines, are formally highlighted. More importantly, by running an experiment on a subject pool of students for the case of illegal waste disposal, we provide experimental evidence on the validity of our theoretical predictions. We find that increasing fine rate may become crime encouraging, or at least ineffective, beyond a context-specific fine threshold. From a policy perspective, we suggest that the optimal design of a crime-deterring sanctioning system must simultaneously account for both corruption practices and anti-corruption policies.

Assessing the quality and eco-beneficial microbes in the use of silkworm excrement compost.

Sericulture has become widespread globally, and the utilization of artificial diets produces a substantial quantity of silkworm excrement. Although silkworm excrement can be composted for environmentally friendly disposal, the potential utility of the resulting compost remains underexplored. The aim of this study was to assess the quality of this unique compost and screen for eco-beneficial microbes, providing a new perspective on microbial research in waste management, especially in sustainable agriculture. The low-concentration compost application exhibited a greater plant growth-promoting effect, which was attributed to an appropriate nutritional value (N, P, K, and dissolved organic matter) and the presence of plant growth-promoting bacteria (PGPB) within the compost. Encouraged by the "One Health" concept, the eco-benefits of potent PGPB, namely, Klebsiella pneumoniae and Bacillus licheniformis, in sericulture were further evaluated. For plants, K. pneumoniae and B. licheniformis increased plant weight by 152.44 % and 130.91 %, respectively. We also found that even a simple synthetic community composed of the two bacteria performed better than any single bacterium. For animals, K. pneumoniae significantly increased the silkworm (Qiufeng × Baiyu strain) cocoon shell weight by 111.94 %, which could increase sericulture profitability. We also elucidated the mechanism by which K. pneumoniae assisted silkworms in degrading tannic acid, a common plant-derived antifeedant, thereby increasing silkworm feed efficiency. Overall, these findings provide the first data revealing multiple beneficial interactions among silkworm excrement-derived microbes, plants, and animals, highlighting the importance of focusing on microbes in sustainable agriculture.

Closing the nutrient cycle in urban areas: The use of municipal solid waste in peri-urban and urban agriculture.

Cities face the challenges of supplying food and managing organic municipal solid waste (OMSW) sustainably amid increasing urbanization rates. Urban agriculture (UA) can help with this effort by producing local crops that are fertilized with nutrients recovered from compost generated from OMSW. This research aims to determine the potential of OMSW compost to supply the nitrogen-phosphorus-potassium (NPK) demand of UA and the environmental benefits of replacing mineral fertilizer from a life cycle perspective. The Metropolitan Area of Barcelona (AMB) serves as the case study given its commitment to reuse biowaste according to the Revised Waste Framework Directive and to promote UA as a signing member of the Milan Urban Food Policy Pact. Based on crop requirements and farmer surveys, we find that the annual NPK demands of the agricultural fields of the AMB that cover 5,500 ha and produce 70,000 tons of crops are approximately 769, 113, and 592 tons of NPK, respectively. Spatial material flow analysis and life cycle assessment were applied to found that the current waste management system can potentially substitute 8 % of the total NPK demanded by UA with compost, reduce the impacts by up to 39 % and yield savings in global warming of 130 %. The more ambitious future scenario of 2025 can potentially substitute 21 % of the total NPK demand and reduce environmental impacts up to 1,049 %, depending on the category considered. Avoiding processing of mixed OMSW, mineral fertilizer replacement and cogeneration of electricity from biogas are the major contributors to these environmental savings.