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Fly density can be a parameter for the success of waste management, both from containerization activities to final waste disposal, which must be integrated by sorting, collecting and transporting waste management. Waste that is not sorted can cause problems, one of which is disease caused by vectors. In this research, researchers used quantitative research as an approach. The research method used is an analytical survey while the type of research is cross sectional. The total population is 36 polling stations with a sample size of 20 polling stations using the Chi-Square test. Research shows that the independent variable value of sorting is significant (p-value = 0.014), with the level of fly density in the Madiun City Temporary Shelter (TPS). There is a relationship between waste sorting and the level of fly density in the Madiun City Temporary Shelter (TPS). [ABSTRACT FROM AUTHOR]

Waste management poses a pressing global challenge, necessitating innovative solutions for resource optimization and sustainability. Traditional practices often prove insufficient in addressing the escalating volume of waste and its environmental impact. However, the advent of Artificial Intelligence (AI) technologies offers promising avenues for tackling the complexities of waste management systems. This review provides a comprehensive examination of AI’s role in waste management, encompassing collection, sorting, recycling, and monitoring. It delineates the potential benefits and challenges associated with each application while emphasizing the imperative for improved data quality, privacy measures, cost-effectiveness, and ethical considerations. Furthermore, future prospects for AI integration with the Internet of Things (IoT), advancements in machine learning, and the importance of collaborative frameworks and policy initiatives were discussed. In conclusion, while AI holds significant promise for enhancing waste management practices, addressing challenges such as data quality, privacy concerns, and cost implications is paramount. Through concerted efforts and ongoing research endeavors, the transformative potential of AI can be fully harnessed to drive sustainable and efficient waste management practices.

Waste sorting at the source has been identified as a crucial solution for sustainable waste management in developing countries like Vietnam. Numerous previous studies have investigated the factors influencing waste sorting behavior among consumers. However, there has been limited research on consumers' capability in waste sorting at source, particularly the role of information. Without sufficient consumer capability in waste sorting, contamination continues to be a widespread problem, posing a challenge to the sustainability of such programs. A survey using the ASK (Attitude-Skill-Knowledge) model was conducted with 228 participants in Lao Cai city, Vietnam, to assess how information enhances consumer capability in waste sorting. This study indicated that: (1) Both information quality and information accessibility could affect overall consumer capability toward waste sorting at the source; (2) Information accessibility is important in determining all ASK component, while interestingly, information quality plays an important role in determining consumers' attitudes toward waste sorting at the source. The study suggested some solutions for enhancing consumer capability in waste sorting program, including engaging all stakeholders, leveraging technology, educational campaigns, and regular updates on waste sorting at the source information. [ABSTRACT FROM AUTHOR]

This paper highlights that metrics from the machine learning field (e.g., entropy and information gain) used to qualify a classifier model can be used to evaluate the effectiveness of separation systems. To evaluate the efficiency of separation systems and their operation units, entropy- and information gain-based metrics were developed. The receiver operating characteristic (ROC) curve is used to determine the optimal cut point in a separation system. The proposed metrics are verified by simulation experiments conducted on the stochastic model of a waste-sorting system. [ABSTRACT FROM AUTHOR]

This research assesses the determinants impacting the behavior in rural tourism destinations regarding tourists' waste sorting (WSB), emphasizing the significance of social norms and peer networks. Survey data was gathered from 955 participants in rural tourist destinations across Henan, Shandong, and Anhui provinces. The empirical findings illuminate several crucial aspects: (1) social norms and peer networks wield a positive, catalyzing influence on tourists' WSB in rural tourism settings. (2) Peer attitudes shape tourists' WSB, and environmental cognition plays a pivotal mediating role. The novelty of this study lies in its comprehensive exploration of the influence of social norms and peer networks on tourists' WSB in rural tourism destinations, highlighting the unique interplay of cognitive, behavioral, and attitudinal factors in this context. (3) Cognitive norms and behavioral norms have a more significant impact on female tourists. (4) For frequent travel groups, social norms affect waste‐sorting behavior less. [ABSTRACT FROM AUTHOR]

National and local governments implement municipal solid waste classification policies to reduce waste disposal and minimize environmental pollution. Beijing started implementing its classification of municipal solid waste policy in May 2020. This study evaluates the impact of Beijing's household waste classification policy on carbon emissions during collection, transportation, and treatment. The policy's introduction reduced the number of trash bins, influenced transportation modes, and altered waste treatment emissions. We found a notable reduction in average net carbon emissions post-policy implementation (2403 kg CO2eq per metric ton of waste) compared with pre-policy (3584 kg CO2eq per metric ton of waste). If Beijing reaches its 2025 waste target, these emissions will decrease further to 1760 kg CO2eq per metric ton of waste. Translating these findings into monetary terms confirms this policy's environmental and economic efficacy. [ABSTRACT FROM AUTHOR]

The increasing amount of household waste has caused harmful environmental pollution and land occupation problems. Implementing separate waste collection is considered an effective waste management method. This study used structural equation modelling (SEM) to investigate the garbage separation behaviour of 657 residents in Beijing. This study investigates the intention of residents to categorise behaviour from a psychological perspective, and previous studies on individual behaviour have overlooked the influence of their environment. Combining external factors of community convenience with the theory of planned behaviour (TPB) enriches research on environmental behaviour. The research results indicate that residents' attitudes, subjective norms, and perceived behavioral control have a positive and substantial impact on their willingness to participate in garbage classification behaviour. Community convenience as an exogenous variable plays an important role in directly and indirectly influencing personal garbage classification tendencies. The new model constructed has stronger explanatory power than the original model. The study also provides some practical suggestions, such as strengthening the dissemination of classification knowledge, improving residents' environmental awareness, increasing investment in classification facilities by the government and community, reducing the perceived difficulty of classification behaviour by residents, and creating a convenient atmosphere for garbage recycling and utilisation. [ABSTRACT FROM AUTHOR]

Sustainable development is increasingly recognized for its reliance on grassroots, bottom-up practices embedded in local communities. The economic literature has tested various influencing factors on household behaviors, such as culture or social cohesion, often considering proxies of so-called "intangible capitals" separately. This article aims to jointly consider the pivotal role of three of these potential capitals that could have a trigger effect on pro-environmental behaviors, specifically, social capital (sc), human capital (hc), and institutional quality (iq). In particular, our study, by adopting a PVAR approach, analyzes if and to what extent sc, hc, and iq influence the priority goal of increasing waste-sorting practices in the 20 Italian regions. Additionally, rooted in a robust theoretical framework, we present an in-depth analysis, with the trend of impulses from statistically significant variables—having observed the important roles played by hc, sc, and particularly iq, as well as the control variables GDP per capita and population density—in increasing the percentage of waste sorting. This impulse analysis suggests for policy interventions that there would be immediate effects (1–2 years) due to the improvement of our intangible capitals, but also a short duration. This suggests the need for lasting and structural interventions. [ABSTRACT FROM AUTHOR]

Although researchers have tried to explain why individuals are unwilling to sort waste, few studies focus on whether and how ego-depletion can inhibit an individual from separating waste. Here, we investigate the effectiveness of financial incentives in mitigating the impact of ego-depletion, a phenomenon triggered by demanding tasks that subsequently impair task performance. Experiment 1 revealed that when participants were involved in a task that expended substantial effort and energy in an initial task, they were more likely to discontinue a subsequent waste sorting task. Experiment 2, however, demonstrated a significant shift when financial incentives were introduced. Even in states of high depletion, the promise of financial rewards substantially increased participants' perseverance in waste sorting. These findings not only reveal the influence of ego-depletion as a barrier to effective waste sorting but also highlight the practical potential for policymakers and environmental practitioners to motivate individuals through appropriate incentives. By addressing psychological factors (i.e., ego-depletion) and integrating incentives, this research contributes to more sustainable waste management practices. [ABSTRACT FROM AUTHOR]

Waste sorting in urban villages is a less developed while important issue in waste management and it is of great importance to environmental sustainability. China is now facing challenges of combating waste sorting while little attention has been paid to these challenges in urban villages. This article aims to add to this knowledge by examining how waste sorting has been implemented in urban villages via a case study of Shenzhen, China. A co-production theory perspective is adopted to explore how actors are engaged in the process of waste sorting and what are the results. The findings suggest coproduction approach is neither a top-down nor bottom-up approach. It is a much more complex process that involves intensive interactions between the government and residents. It concludes that the co-production approach to waste sorting exemplified by urban villages' sustainable development has great significance for general environmental management and policy making in the long term. [ABSTRACT FROM AUTHOR]

The attention and sentiment of the public are crucial for better implementation of waste sorting behaviors and moving towards green living. In this study, web scraping technology was used to collect 367,856 Weibo posts related to waste sorting from Sina Weibo. Utilizing text co-occurrence networks, Latent Dirichlet Allocation (LDA) topic modeling, and a deep learning model combining the Affective Cognitive Model (OCC) with Long Short-Term Memory Model (LSTM) (referred to as OCC-LSTM), we comprehensively understand the text at both micro and macro levels, analyzing the attention and sentiment of the public towards waste sorting behaviors on the Sina Weibo platform. Several important findings emerged from the empirical results. First, highly engaging posts were predominantly published by users with a large following, and the number of posts fluctuated over time. This reflects the influence of social hot topics and the timeliness of information dissemination. Second, there was heterogeneity in the user groups and their locations, often influenced by cultural differences due to geographical location. Third, positive sentiment towards waste sorting behavior was higher than negative sentiment on the Weibo platform. Moreover, public attention varied under different emotional influences concerning the topic of waste sorting behavior. The innovation of this study lies in the development of a research framework combining co-occurrence networks and deep learning, expanding the analysis on both micro and macro levels. This framework broadens the research paradigms and dimensions of public perception in waste sorting. This study is significant for promoting waste sorting behaviors and implementing climate policies.

Waste management is a complex challenge for cities in developing countries, including Indonesia. This issue is particularly severe in heritage tourism areas, where unmanaged waste can affect the aesthetics and image of historic places. Therefore, this study aimed to investigate the sustainability aspects of waste management using heritage visitors’ destinations and waste management operator areas of the Old City of Semarang. A quantitative method was used to achieve this objective, and data were collected through questionnaires, interviews, field observations, document reviews and analyzed using descriptive methods. The results showed that visitors had positive behavior and awareness regarding waste management. Furthermore, sustainable waste management was implemented but not fully realized across all aspects. This study offered various measures to improve sustainable waste management in area, including waste sorting, collaboration between stakeholders, and policy advocacy on sustainable waste management.

[Objective] In the context of mandatory municipal solid waste (MSW) classification, traditional waste sorting practices face challenges in terms of participation rates and accuracy. In view of the increasing waste generation and resource recycling demand, the management and disposal level of MSW need to be further improved. This study aimed to thoroughly explore how digital technology paths enhance and transform waste sorting models, promoting the sustainable development of resource recycling and environmental protection. [Methods] This research conducted empirical case studies on the different application scenarios and practical cases of digital technology at various stages of domestic waste management, including generation, transfer, collection, and final disposal. It provided an in-depth analysis of different cases by integrating advanced algorithms such as deep learning and genetic algorithms. [Results] The application of digital technology at the front-stage can improve the participation rate and accuracy of MSW disposal and predict future waste production trends, with errors within 21.94%. The application in the middle-stage optimizes the inefficiencies in the traditional model of waste collection and transportation, planning the optimal routes using genetic algorithms to achieve unified objectives of economic cost, carbon emissions, and collection efficiency. The application at the end-stage helps enhance the overall efficiency of waste treatment and disposal, achieving a net benefit increase ranging from 8% to 39.7%. [Conclusion] This paper proposes a new model for waste sorting based on digital technology and centered on full-chain management. By leveraging information and automation technologies, it provides more efficient and precise solutions for waste sorting. Digital technology addresses the information gaps in traditional waste sorting methods, while the full-chain approach coordinates the front-end, mid-end, and back-end stages of waste sorting. This model overcomes the shortcomings of traditional waste sorting methods, enhances the recycling rate of typical urban waste, and achieves sustainable urban waste management.

[Display omitted] • The environmental and economic performance of Know-as-you-throw (KAYT) is unknown. • Four pilot KAYT schemes were implemented across Spain and Italy from 2019 to 2022. • KAYT reduced unsorted waste generation from 17 to 10% across pilots. • KAYT reduced the carbon footprint of waste management from 46 to 19% across pilots. • KAYT can notably reduce the carbon footprint of waste management at low cost. Considerable resources are spent globally on actions to improve the separate collection of municipal waste aiming to minimise the environmental and economic impacts of municipal waste management. One of such actions are know-as-you-throw (KAYT) schemes, which aim to change the behaviour of waste generators through tailored communication of information. KAYT schemes offer a relatively uncontroversial and simple tool, yet their environmental and economic performance remains unknown due to their limited implementation. To fill this gap, the LIFE-funded REthinkWASTE project applied a novel KAYT scheme in four pilot areas in Italy and Spain. The results of such pilots were evaluated in terms of carbon footprint and life-cycle costs of municipal waste management. The carbon footprint was notably reduced in all pilot areas, ranging from a 46% to 19% reduction, mainly due to notable reductions in unsorted waste (between 10 and 17% reduction) and subsequent lower treatment impacts. Life-cycle costs slightly increased overall, ranging from 4.6% to a −0.4% change. In addition to various sources of uncertainty, self-selection and recency biases are highlighted as major sources for potentially overestimating the benefits of KAYT in the context of large-scale and long-term KAYT implementation. The results however consistently show that the additional carbon footprint from KAYT actions can be offset with less than a 5% reduction in unsorted waste, well below the observed values. The results robustly reveal the potential of KAYT to notably reduce the carbon footprint of waste management systems with limited investment of economic resources. [ABSTRACT FROM AUTHOR]

The global plastic crisis is a significant concern. Addressing it requires a multi-faceted approach involving legislative, social, and technological measures to reduce post-consumer plastic waste and increase the recycling rate and material circularity. The EU Packaging Directive mandates a 50% recycling rate for plastics by the end of 2025 and 55% by 2030. Machine learning techniques are increasingly being explored to improve the efficiency of waste sorting, thereby contributing to achieving the aforementioned recycling rate goals. However, their effectiveness relies on the availability of high-quality and comprehensive datasets. One of the challenges in this field is the need for suitable datasets for training and testing machine learning models. Furthermore, access to detailed and real-world data, such as information from recycling plants, is often restricted due to paywalls, limiting the accessibility of critical data for research and development. This review sheds light on the situation of RGB (red, green, blue) image datasets and NIR (near-infrared) data availability to identify current dataset gaps and advocate for open access to relevant data sources. The target is to promote collaboration and innovation to combat the plastic crisis and promote sustainable practices collaboratively. [ABSTRACT FROM AUTHOR]

Waste management is a complex challenge for cities in developing countries, including Indonesia. This issue is particularly severe in heritage tourism areas, where unmanaged waste can affect the aesthetics and image of historic places. Therefore, this study aimed to investigate the sustainability aspects of waste management using heritage visitors' destinations and waste management operator areas of the Old City of Semarang. A quantitative method was used to achieve this objective, and data were collected through questionnaires, interviews, field observations, document reviews and analyzed using descriptive methods. The results showed that visitors had positive behavior and awareness regarding waste management. Furthermore, sustainable waste management was implemented but not fully realized across all aspects. This study offered various measures to improve sustainable waste management in area, including waste sorting, collaboration between stakeholders, and policy advocacy on sustainable waste management. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Community Service & Empowerment is the property of Journal of Community Service & Empowerment and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

[Display omitted] • This study shows how the delta robot was designed to physically pick-and-place MSW. • Combined with the YOLO-based model, the robot forms the Intelligent MSW Sorter. • IMSWS could automatically sort the multiple MSWs on the conveyor belt. • The mechanical efficiency of the robot dominated IMSWS performance. • Compared with the MSW detection, IMSWS must improve the mechanical efficiency first. Sorting Municipal Solid Waste (MSW) has helped promote the awareness of sustainable development of environment. A robot equipped with an intelligent deep learning (DL) detection algorithm have been proposed to improve the sorting task. But most of the related studies aimed to better the DL algorithms on MSW detection, and few studies integrated the DL algorithms with a robot to identify the dominated factors to Intelligent MSW Sorter (IMSWS). Therefore, this study is to develop IMSWS prototype to better sort MSW, based on the pick-and-place process, and preliminarily evaluate the dominated factors. First, the delta robot prototype was manufactured, and IMSWS was performed with a camera to acquire the RGB image and the height of a MSW in the conveyor belt. The DL algorithm, YOLOv3 or YOLOv4, detected the type and plane location of the MSWs in the conveyor belt. Next, the sequence program transferred the valid MSW data to the delta robot. After the calculation of the absorbed location of the target MSW was made, the arm of this delta robot moved to absorb and then transfer the MSW to the bin. Results showed that the IMSWS prototype could sort the multi-object MSWs in the MSW stream. Both YOLOv3 and YOLOv4 reached high detection accuracy on the MSW image dataset. However, the improvement should be made in the actually moving MSW stream even though the YOLOv4 performed the acceptable detection accuracy. The gripping stability of the arm mainly dominated the performance of IMSWS, and this should be improved first. [ABSTRACT FROM AUTHOR]

Copyright of Österreichische Wasser- und Abfallwirtschaft is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

An incredibly wide range of plastic products and enormous volumes of generated plastic wastes makes sorting technologies highly important. In order to enhance the efficiency and accuracy of sorting process, this article proposes an Internet of Video Things solution based on the deep learning algorithms for image recognition of plastic waste on a moving conveyor belt and embedded intelligence. In particular, the state-of-the-art object detection models, including Faster R-CNN, RetinaNet and YOLOv8 are used. Target categories of plastic are Polyethylene Terephthalate (PET) and Polypropylene (PP). Furthermore, we implemented quantization techniques for trained models on a commercial off-the-shelf embedded system for fast processing time. We achieved a high mean Average Precision (mAP) metric of 77.74% and accuracy of 95.67% on a test set and are fine-tuned and optimized for the deployment on an Nano embedded system providing 20 frames per second. This research contributes to the field of application of Internet of Things by demonstrating the efficacy of deep learning algorithms run on the embedded system in the industrial plastic waste sorting process. The findings highlight the practical applicability of these algorithms and offer insights into resource management and recycling practices.

Considering the multiplying effect their actions can have, teachers should be aware of the importance of promoting responsible consumption and waste separation in the classroom. To achieve this, drawing from previous experiences, a sequence of activities has been developed for students in the Degrees of Early Childhood Education and Primary Education. This sequence, consisting of six activities, aimed to improve the students´ knowledge and behaviour while equipping them with tools to implement similar sequences in the future. A positive impact of the sequence was observed, raising their awareness of the types of waste generated in the university, how it is managed, dispelling false myths and increasing their confidence to work on these issues in their future teaching. Based on the results, improvements have been established for the implementation of this sequence in subsequent courses. [ABSTRACT FROM AUTHOR]

Nowadays, the bilevel multi-objective optimization problem has been received extensive attention from scholars. As two mainstream algorithms to solve it, intelligent algorithm and interactive algorithm based on satisfaction have disadvantages such as slow operation speed and too much subjectivity in weight selection, respectively. This paper proposes a multi-attribute decision-making method based on fuzzy weight, which is a natural generalization of deterministic weight, and it can cope with the decision-making risks brought by uncertain environment, which has strong robustness. Meanwhile the new deviation function index proposed could reflect that the upper and lower decision-makers execute the final decision with the same enthusiasm, which is convenient to make exploratory decisions in unknown environment. Finally, compared with the traditional existing methods, the effectiveness of the proposed method and indicator is verified by the garbage classification game in the main urban district of Chongqing. [ABSTRACT FROM AUTHOR]

The article is devoted to the analysis of the problem of social attitude towards waste sorting in exceptional conditions. The author considers this problem within the framework of the general problematic of positions and practices of environmental consumption. The focus of the study is the peculiarities of attitudes towards environmental waste management practices in atypical conditions. The article examines Kharkiv as a front-line city in which military security threats come to the fore, create additional threats to food security, and at the same time disguise them as less important. The goal is to find out how mass consciousness in war conditions distributes empirical material for analysis - an online survey of 309 Kharkiv residents. The results of the study show that a positive attitude towards environmental waste sorting practices has been formed in the minds of Kharkiv residents. However, there is a gap between the declarative level of attitude and the implementation of practices. This gap can be explained both by the lack of a materially designed environment for the formation of sustainable waste sorting practices, and by the shortcomings of the information environment. At the same time, military living conditions in a front-line city do not affect the opinions of citizens about the importance of waste sorting. [ABSTRACT FROM AUTHOR]

ObjectiveWaste sorting has received considerable attention in recent decades. However, research on the mechanisms underlying the relationships among cultural worldview, environmental risk perception, and waste sorting is rather scarce. This study aims to explore the cultural worldviews, environmental risk perception, and waste sorting among urban Chinese and their mechanisms.MethodsThis was a cross-sectional study involving 744 urban Chinese residents (371 men and 373 women). A questionnaire was utilized to measure cultural worldviews, environmental risk perception, and waste sorting. Pearson correlation analysis and structural equation modeling were used to examine the relationship between cultural worldviews, perceptions of environmental risk, and waste sorting.ResultsWaste sorting had a relatively insignificant negative relationship with fatalism and individualism. The correlation between environmental risk perception and cultural worldviews was negative except for egalitarianism, and the correlation between hierarchy and environmental risk perception was higher than the others, while individualism was higher than fatalism. Heightened environmental risk perception mediates the relationship between egalitarianism and waste sorting. Reduced environmental risk perception mediates the relationship between hierarchy and waste sorting, and mediates the relationship between individualism and waste sorting.ConclusionThese new findings provide initial support for the mediating role of environmental risk perception in the relationship between cultural worldviews and waste sorting. Both theoretical and practical implications for understanding the psychological mechanisms of waste sorting are discussed.

The most significant source of waste in Indonesia is households. This community service aims to improve the environmental quality of Kesiman Village by educating residents to sort waste from their households. This activity begins with observations and interviews with residents. Preliminary survey results show that the waste problem has been around for a long time, and accumulation occurs in the Final Disposal Site (TPA). Based on interviews with villagers, they are willing to participate in the waste management assistance program. Furthermore, waste sorting facilities are provided for each resident's house by empowering village communities with expertise. Counseling or education is carried out at the home of a resident, as well as providing a real example of waste segregation. Sorting is done based on the categories of organic and non-organic waste. Two weeks after the counseling, the service team evaluated by direct surveying several residents' houses to see if the sorting had gone well and smoothly. The evaluation results show that residents understand and carry out waste segregation consistently. However, there are several obstacles; for example, some houses feel the trash can needs to be bigger, and sometimes they place organic and non-organic waste upside down. After the household waste sorting program runs smoothly, the community service team will plan and realize the Village waste management program so that the volume of waste is reduced and the environment is cleaner.

This paper highlights that metrics from the machine learning field (e.g., entropy and information gain) used to qualify a classifier model can be used to evaluate the effectiveness of separation systems. To evaluate the efficiency of separation systems and their operation units, entropy- and information gain-based metrics were developed. The receiver operating characteristic (ROC) curve is used to determine the optimal cut point in a separation system. The proposed metrics are verified by simulation experiments conducted on the stochastic model of a waste-sorting system.

Background and Objective: many countries have currently put waste management as a priority on the agenda and are seeking to enhance its effectiveness by encouraging public participation. Therefore, this study aimed to evaluate the willingness of rural residents to participate in waste management processes in Jajarm County, North Khorasan Province. Materials and Methods: The survey research method was used. The target population consisted of 4031 households from 37 villages in Jajarm County. The dependent variable of willingness to participate in waste management was measured with 5 variables, including willingness to pay for waste disposal, willingness to use special containers, eagerness to separate waste at source, eagerness to deliver separated waste, and tendency to cooperate with rural development. The impact of independent variables on the five dependent variables was evaluated by running five logit functions. Results: The results of the models showed that age, education, regular waste collection by village councils, cooperation with rural cooperatives and organizations, payment for waste collection, familiarity with waste management methods, health effects of waste, familiarity with the harmful effects of waste, communication with extension agents and rural health liaisons, participation in waste management training classes, and satisfaction with the village council have a positive and significant (p≤0.05) impact on the willingness to participate in waste management. Conclusion: Providing educational programs and introducing the consequences of nonseparation of waste on human health and the environment is effective in attracting the participation of people in waste management plans and should be prioritized in waste management programs. [ABSTRACT FROM AUTHOR]

Purpose: Occupational exposure to bioaerosols during waste handling remains a health concern for exposed workers. However, exposure-related health effects and underlying immunological mechanisms are still poorly described. Methods: The present study assessed the inflammatory potential of work-air samples (n = 56) in vitro and investigated biomarker expression in exposed workers (n = 69) compared to unexposed controls (n = 25). These quantitative results were compared to self-reported health conditions. Results: Personal air samples provoked an activation of TLR2 and TLR4 HEK reporter cells in one-third of all samples, indicating that the work environment contained ligands capable of inducing an immune response in vitro. Monocyte levels, as well as plasma biomarker levels, such as IL-1Ra, IL-18 and TNFα were significantly higher in exposed workers, compared to the control group when confounding factors such as BMI, sex, age and smoking habits were accounted for. Furthermore, a significant exposure-related increase in midweek IL-8 levels was measured among exposed workers. Tendencies of increased prevalence of health effects of the respiratory tract were identified in exposed workers. Conclusion: Inhalable dust provoked TLR activation in vitro, indicating that an exposure-related immune response may be expected in susceptible workers. However, despite significant differences in inflammatory plasma biomarker levels between exposed and unexposed workers, prevalence of self-reported health effects did not differ between the groups. This may be due to the healthy worker effect, or other factors such as adequate use of personal protective respiratory devices or adaptation to the work environment with reduced activation of the immune system. [ABSTRACT FROM AUTHOR]

Whether Waste Electrical and Electronic Equipment (WEEE) can be effectively sorted and recycled will affect the sustainable development of human society. However, Waste Small Electrical and Electronic Equipment (WSEEE) has often been neglected to meet the needs of the full range of WEEE governance. In this study, a YOLO-wseee classification model based on the YOLO (You Only Look Once)v5 framework, combined with a deepened-efficient layer aggregation networks (ELAN) structure (D-ELAN) and using efficient intersection over union (EIOU) loss, was designed to identify WSEEE. To make the model more suitable for detecting the real WSEEE state, a dataset called WASTE-SEEE was created, containing images of WSEEE in a variety of images are captured in real time. The experimental results show that the Precision and mAP@0.5 of the YOLO-wseee model are 98.24% and 99.32%, respectively, and the FOLPs of the model are only 23.9, and its model comprehensive index is significantly better than other classic models of YOLOv5. This method can help humans to detect WSEEE under real conditions more easily, thus helping to improve the problems facing the recycling and reuse of WSEEE, helping humans to improve efficiency, save resources and manage the environment. [ABSTRACT FROM AUTHOR]

Copyright of Experimental Technology & Management is the property of Experimental Technology & Management Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Waste sorting, as an embodiment of behavioral cognition, is regulated by two cognitive processes, namely, Categorization (C) and Category-Based Induction (CBI). This study employed the event-related potential (ERP) technique to assess whether there is a transformation between C and CBI in waste sorting cognition, in order to help individuals establish a correct waste sorting behavior. We reported a case of intervention in waste sorting education with a 58-year-old Chinese woman to discriminate whether such intervention facilitates the transition between C and CBI. The results showed that the waste sorting cognition follows a developmental model as C-CBI-C, where education may help the subject build a cognitive framework for waste sorting by altering inherent misperceptions and filling gaps in classification knowledge. The results also noticed that FN400 is identified as a characteristic waveform in the CBI process, by which it is indicated that the first 4 to 7 days of education is a critical period for establishing the cognitive framework. Through a comparison of the ERP waveforms at different stages of intervention, the results are insightful to help individuals improve their cognition of waste sorting. [ABSTRACT FROM AUTHOR]

Sustainable management of household solid waste (HSW) is an essential step in realizing the development of a circular economy (CE). However, for many developing countries, HSW sorting is evolving incrementally, and focusing on the intervention role of policy and the sorting behaviour of residents can help achieve sustainable waste management. This study aims to investigate the main determinants of residents' HSW sorting behaviour and how policy particularly contributes to it. Drawing upon the theory of planned behaviour (TPB) and incorporating experiences, a conceptual framework is tested on a sample of Chinese residents in Shanghai (n = 560, from November to December 2020) using structural equation modelling analysis. The empirical results show that experience and behavioural intention greatly influence HSW sorting behaviour. Moreover, the positive attitude of residents promotes the success of HSW sorting. Subjective norms and perceived behaviour control indirectly affect behavioural intention and behaviour via attitude. The findings of this study provide policymakers with critical factors for the successful implementation of HSW sorting. We also propose policy implications to maintain and improve sorting efficiency from the perspective of residents (education, communication and publicity), neighbourhood committees (degree and efficiency of supervision) and property management companies (good infrastructure, services and processes). [ABSTRACT FROM AUTHOR]

This study explored the relationship between egoistic, altruistic and biospheric values and pro-environmental attitudes, as well as their impact on the pro-environmental behaviours of Indian consumers. India is currently facing the burgeoning challenge of a rapidly increasing urban population, which is leading to waste segregation issues in households and the need for sustainable green products due to rising awareness among consumers. The goal of this research was to understand the effect of Indian consumers' values and pro-environmental attitudes on the following three specific behaviours: waste-sorting intention, green-consumption intention and social-activity intention. The authors found that values were positively related to pro-environmental attitudes, which in turn positively impacted the three pro-environmental behaviours. These findings suggest that, in order to strengthen individuals' perceptions of the individual benefits of a cleaner and better environment, marketers should promote responsible consumption in tandem with government policymakers. [ABSTRACT FROM AUTHOR]

The European Green Deal is the European Union’s latest expression of its ambition to become a world leader in addressing climate change (Kleinberga, 2020). Adaptation to climate change (CCh) is a key priority of the European Union (EU), exemplifi ed by the EU’s efforts to become “the first climate-neutral continent” (European Union, 2019) in the world by 2050. This article aims to analyse opportunities to increase consumer involvement in the context of waste sorting and climate change in Latvia, based on theoretical knowledge about consumer behaviour and empirical data analysis, to develop recommendations for marketing communication and consumer behaviour in the context of climate change content for waste management companies. There is a necessity to understand how to increase consumer involvement in waste-sorting due to the increasing amount of waste both in Latvia and the rest of the world. The following research methods have been used: the monographic method, secondary data analysis, the discourse analysis/coding method, the graphical method, and the qualitative method – a focus group interview. The most significant barriers that discourage consumers from sorting waste are the lack of infrastructure, the lack of information on how to sort waste properly and waste’s environmental impact, as well as the complex system that allows for differences in waste-sorting between municipalities and even neighbourhoods in the same city. Recommendations were developed as a result of the research for waste management companies in terms of communication, NGOs on education, and information for consumers, businesses, and waste management companies.

The paper has presented the control and development of an intelligent garbage sorting system with a robot arm. This system consists of a machine vision block, a 6DOF robot manipulator, and a control unit for sorting garbage based on analytical images. YOLOv4 software will be identified the object by the neural network. This method is used for detecting and image recognition of different sizes and types of waste, such as paper-based garbage, metal garbage, and plastic garbage. The results of offline testing on a database of more than 600 untrained images show that the trained model has an average accuracy of about 98.43% for classifying different types of garbage.

• A prototype platform is built for automated sorting of CDW. • Images of CDW are made openly accessible for model development and benchmarking. • Deterioration of model performance occurs in the presence of adherence and stacking. • Legacy Faster-RCNN models exhibit the most robustness to adherence and stacking. • YoloV7 models achieve the highest detection accuracy at lower inference times. Central to the development of a successful waste sorting robot lies an accurate and fast object detection system. This study assesses the performance of the most representative deep-learning models for the real-time localisation and classification of Construction and Demolition Waste (CDW). For the investigation, both single-stage (SSD, YOLO) and two-stage (Faster-RCNN) detector architectures coupled with various backbone feature extractors (ResNet, MobileNetV2, efficientDet) were considered. A total of 18 models of variable depth were trained and tested on the first openly accessible CDW dataset developed by the authors of this study. This dataset consists of images of 6600 samples of CDW belonging to three object categories: brick, concrete, and tile. For an in-depth examination of the performance of the developed models under working conditions, two testing datasets containing normally and heavily stacked and adhered samples of CDW were developed. A comprehensive comparison between the different models yields that the latest version of the YOLO series (YoloV7) attains the best accuracy (mAP 50 :95 ≈ 70%) at the highest inference speed (<30 ms), while also exhibiting enough precision to deal with severely stacked and adhered samples of CDW. Additionally, it was observed that despite the rising popularity of single-stage detectors, apart from YoloV7, Faster-RCNN models remain the most robust in terms of exhibiting the least mAP fluctuations over the testing datasets considered. [ABSTRACT FROM AUTHOR]

• Investigating intention-behavior gap with real Internet of Things (IoT)-based data. • Recycling intention found to have inadequate predictive power for actual behavior. • Forgetting/habituation exacerbates intention-behavior discrepancy. • IoT behavioral data offers promising direction for future environmental research. Improving residents' waste recycling behavior is crucial for enhancing resource efficiency and reducing carbon emissions. Previous questionnaire-based studies have reported that individuals exhibit a high willingness to recycle, yet often fail to convert this intention into action. Analyzing 180,417 Internet of Things (IoT) behavior data points, we discovered that the intention-behavior gap might be larger than anticipated. Our findings indicate that: 1) Intentions to recycle alone can predict self-reported recycling behavior (p < 0.01, t = 2.841), but not actual recycling behavior in the absence of other possible moderators (p > 0.1, t = 0.777); 2) Self-reported behavior predicts real behavior, but with limited explanatory power; and 3) The intention-behavior gap primarily results from forgetting or habituation (p < 0.01, t = 2.653), while social desirability plays an insignificant role (p > 0.1, t = 0.246). This study contributes to our understanding of the intention-behavior gap and provides direction for future pro-environmental behavior research. [ABSTRACT FROM AUTHOR]

In China, waste sorting has gradually entered "compulsory times". The beginning of the compulsory times of waste sorting is marked by the implementation of the policy to remove trash cans in residential building hallways. Since then, this policy has been controversial. Based on the theory of planned behavior and the public's perspective as well as using the Delphi method and entropy weight method, we investigated Longhua District in Shenzhen and designed an evaluation index system for the effect of the policy to remove trash cans from the following three dimensions: the policy cognitive level, the policy admissive degree, and the awareness of waste sorting. The data were supplemented by observations and interview methods as well as questionnaire surveys that were distributed in residential sub-districts in Longhua District. According to the quantitative research and variance analysis of the data, the policy promotes waste sorting. This paper provides a new idea on how to evaluate environmental policies such as the garbage-classification policy from the perspective of the public based on rigorous evaluation methods and processes. [ABSTRACT FROM AUTHOR]

The growing waste volume is an acute issue on the circular economy agenda and consumers play a key role by sorting recyclable materials from residual waste. In the literature, the experience of inconvenience is recurrently identified as a main barrier to sorting. Modeling inconvenience as the effort required for sorting, these studies suggest adjustments to communication and material arrangements, making recycling easier. This article offers a reappraisal of the experience of inconvenience and its conditioning. Using ethnographic data, the analysis explores the incongruities between participants’ sayings and doings – their articulated agreement with sorting and reluctance to do so. Waste biographies indicate that instead of overt efforts implied in the performance of sorting, embodied perceptions of normal waste practice govern which performances of sorting are experienced as inconvenient. This pre-reflective structuring of perception is anchored in and maintained by the relation of waste practices to co-occurring practices. Proposing a conceptual distinction, this article suggests that waste practices are performed as “secondary practices” enabling “primary practices,” that orchestrate participants’ dispositions and their immediate discernments of what are dispensable and, thus, inconvenient performances of sorting. This perspective elaborates on the understanding of inconvenience and the transition inertia of everyday practices toward sustainability.

The residential sector generates around 14% of the overall waste production in the Czech Republic. This essential share requires special attention to analyze with particular emphasis for citizen education, especially on young people. Thus, this research is dedicated to citizens' awareness about packaging waste to increase their knowledge. The experiment was conducted in the form of a survey, and students from a Czech university were asked to estimate the weight of the packaging waste presented to them in term to verify their knowledge about packaging waste. This experiment featured different groups of students over time. The result showed no difference in knowledge between these student groups over time. The long-term home study caused by COVID-19 did not appear to have affected students' knowledge of packaging waste. This study showed students' actual knowledge about packaging waste and highlighted the gap and importance of education in waste management. [ABSTRACT FROM AUTHOR]