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With the rapid increase in household waste, environmental degradation becomes more serious. It is imperative to promote household waste sorting in China. Therefore, this paper mainly studies the influencing factors of intention and behavior for household waste sorting. An extended theory of planned behavior (TPB) theoretical model is established by integrating TPB with the theory of environmental regulation. We investigate the moderating roles of knowledge, moral obligation and personal involvement on the relationship between perceived behavior control and low-carbon household waste sorting behavior. Based on 441 valid questionnaires data and structure equation model and multiple regression analysis and differencing analysing, we found that environmental regulation has the highest effect on behavioral intention for household waste sorting, followed by perceived behavioral control and attitude toward house waste sorting in the extended TPB model. In addition, intention and perceived behavior control for household waste sorting positively impact on behavior, followed by perceived behavior control. Knowledge, personal involvement, and moral responsibility have a positive moderating effect on the mechanism between perceived behavior control and household waste sorting behavior, respectively. It is important to enhance the residents' responsibility, personal involvement, and knowledge for household waste soring in China.

● MSWNet was proposed to classify municipal solid waste. ● Transfer learning could promote the performance of MSWNet. ● Cyclical learning rate was adopted to quickly tune hyperparameters. An intelligent and efficient methodology is needed owning to the continuous increase of global municipal solid waste (MSW). This is because the common methods of manual and semi-mechanical screenings not only consume large amount of manpower and material resources but also accelerate virus community transmission. As the categories of MSW are diverse considering their compositions, chemical reactions, and processing procedures, etc., resulting in low efficiencies in MSW sorting using the traditional methods. Deep machine learning can help MSW sorting becoming into a smarter and more efficient mode. This study for the first time applied MSWNet in MSW sorting, a ResNet-50 with transfer learning. The method of cyclical learning rate was taken to avoid blind finding, and tests were repeated until accidentally encountering a good value. Measures of visualization were also considered to make the MSWNet model more transparent and accountable. Results showed transfer learning enhanced the efficiency of training time (from 741 s to 598.5 s), and improved the accuracy of recognition performance (from 88.50% to 93.50%); MSWNet showed a better performance in MSW classsification in terms of sensitivity (93.50%), precision (93.40%), F1-score (93.40%), accuracy (93.50%) and AUC (92.00%). The findings of this study can be taken as a reference for building the model MSW classification by deep learning, quantifying a suitable learning rate, and changing the data from high dimensions to two dimensions.

Purpose The aim of this study is to develop and analyse a life cycle inventory of construction and demolition waste (C&DW) management systems based on primary data collected directly from Spanish enterprises involved in the life cycle of this type of waste material. Special emphasis is placed on assessing the environmental profile of inert waste sorting and treatment (IWST) facilities. Methods Taking the management of 1 t of C&DW as the functional unit, this study describes the boundaries of the C&DW management system and the scope of the research, which includes all stages from the temporary storage of waste in containers to its recovery or disposal on landfills. Primary data were collected directly from some Spanish enterprises involved in the life cycle of C&DW management: two firms that manufacture containers and bags, two companies responsible for the temporary storage of waste and transporting it, five firms devoted to sorting and treating the waste and two enterprises that operate inert landfills. Special attention was given to the IWST facilities, whose inventory data were related to four phases: pre-treatment and the primary, secondary and tertiary sectors. Finally, indicators were obtained for different impact categories. Results The environmental profiles of IWST facilities for mixed C&DW show that the greatest environmental impacts are produced in primary and tertiary sectors. From the life cycle analysis of C&DW management, it can be seen that transport, sorting and disposal make a net contribution to the environmental impact. Savings are due to the recycling of plastics, metals, aggregates and wood for all the impact assessment categories, except global warming in the case of wood and cardboard. Conclusions Impact of IWST can be reduced by selective collection at source, since it avoids the separation of light fractions at plants. Life cycle assessment of C&DW shows that transportation stage plays a decisive role and recycling is not always beneficial.

PurposeThis research investigates factors affecting the intention to sort waste. Specifically, this research aims to develop a waste-sorting intention model by extending the theory of planned behavior (TPB) model and to test the model to ensure the model's goodness-of-fit, validity and reliability.Design/methodology/approachThis research used a quantitative research methodology. Data were collected from 460 respondents using an online questionnaire. Some statistical analyses were performed to analyze the data: descriptive statistics, factor analysis, confirmatory factor analysis-structural equation modeling (CFA-SEM), SEM and Cronbach's alpha analysis.FindingsThe result shows that the intention to sort waste was directly affected by attitude, subjective norms, perceived behavioral control (PBC), moral obligation and facility support. Environmental concerns, waste-sorting knowledge and time availability indirectly influenced the intention to sort waste. The testing indicated that the proposed model was fit, valid and reliable.Practical implicationsThe model provides a more comprehensive understanding of waste-sorting intention. The central and local governments can use the results to encourage waste-sorting intention in the community.Originality/valueThis research is believed to be the first study to develop and test the waste-sorting intention model that extends the TPB model by incorporating moral obligation, facility support, policy and regulation support, environmental concerns, waste-sorting knowledge and time availability into the traditional TPB model.

Economic incentive is thought a good intervention type that can encourage residents to do food waste sorting by many cities’ government in China. However, there is a lack of long-term, large-scale study. So the business-led incentive scheme was studied by a case study in Nanjing, China, which focuses on food waste sorting. The results showed that the incentive can encourage at most an average 37% of residents to start and then continue to do food waste sorting regularly. Later, the incentive cannot encourage more even with many non-economic interventions. And most of these participating residents (31%) were encouraged at the first 12 months. The results also showed that house price had a negative relationship with the community sorting performance. The comparative study results showed that the community committee must be involved in the non-economic interventions to encourage more residents to take part; otherwise, the company will fail even after many attempts. So the government should apply the incentive policy by dialectical view in food waste sorting. And the incentive scheme should involve all the stakeholders to apply non-economic interventions to encourage more residents to do food waste sorting.

In recyclable waste management facilities, several contaminants, mainly bioaerosols and microorganisms, can be released and cause potential adverse health effects. Given that microbial volatile organic compounds (mVOCs) are metabolites developed by molds and since they can be considered as potential biomarkers of mold exposure, their concentrations in ambient air were monitored at a recyclable waste sorting plant (WSP) and a university campus (UC) serving as control environment for comparison. A recently developed analytical method was used for the detection of 21 selected mVOCs in real conditions. The impact of mVOC sampling duration, period of the day, and variability of their concentrations between different workstations at both sites was studied. At UC, only 3 mVOCs were detected in comparison with 13 mVOCs at the WSP. The sampling duration of 2 h was chosen to increase the sensitivity of the method. Statistical tests showed no significant difference in concentration for the detected mVOCs at WSP for different period of the day whereas an increase in concentrations for ethyl acetate was observed at UC. At WSP, the highest concentrations were measured at the pre-sorting room (ex. 4.04 ng/m 3 for pentan-2-one), followed by the sorting room (ex. 3.46 ng/m 3 for decanal), the optic sorting room (ex. 3.67 ng/m 3 for pentan-2-one), and the lowest concentrations were measured at the waste reception area (ex. 0.51 ng/m 3 for cyclopentanone). This study allows the optimization of the mVOC sampling method, the comparison of the mVOC profiles between contaminated and non-contaminated workplaces and the recommendation for mVOCs with better potential as biomarkers of mold exposure.

As the severity of global waste pollution continues to escalate, governments are increasingly prioritizing the promotion of waste sorting. This study engaged in the literature mapping of waste sorting and recycling behavior research currently available on the Web of Science using CiteSpace. First, studies on waste sorting behavior have grown rapidly since 2017. The top three continents publishing on this topic were Asia, Europe, and North America. Second, the top journals, Resources Conservation and Recycling and Environment and Behavior , were important to this field. Third, analyses of waste sorting behavior were mainly conducted by environmental psychologists. Ajzen had the highest co-citation count, as the theory of planned behavior has widely been used in this field. Fourth, the top three co-occurring keywords were “attitude,” “recycling behavior,” and “planned behavior.” There was also a recent focus on “food waste.” The research trend was found to be refined and accurately quantified.

Innovations in manual waste sorting have stagnated for decades, despite the increasing global demand for efficient recycling solutions. The recAIcle system introduces an innovative AI-powered assistance system designed to modernise manual waste sorting processes. By integrating machine learning, continual learning, and projection-based augmentation, the system supports sorting workers by highlighting relevant waste objects on the conveyor belt in real time. The system learns from the decision-making patterns of experienced sorting workers, enabling it to adapt to operational realities and improve classification accuracy over time. Various hardware and software configurations were tested with and without active tracking and continual learning capabilities to ensure scalability and adaptability. The system was validated in initial trials, demonstrating its ability to detect and classify waste objects and providing augmented support for sorting workers with high precision under realistic recycling conditions. A survey complemented the trials and assessed industry interest in AI-based assistance systems. Survey results indicated that 82% of participating companies expressed interest in supporting their staff in manual sorting by using AI-based technologies. The recAIcle system represents a significant step toward digitising manual waste sorting, offering a scalable and sustainable solution for the recycling industry.

A study was carried out in a waste sorting plant (WSP) located in France, treating dry recyclable household waste (DRHW) as well as dry recyclable commercial and industrial waste (DRCIW). Stationary and personal inhalable samples were collected in the WSP in order to investigate bioaerosols (sampling on a filter; 2 L/min and 10 L/min) and airborne dust (CIP; 10 L/min). The aim of the study was to assess the extent to which the measurement of concentration, species composition, and particle size distribution contributes to a better assessment of the biological risks associated with exposure. The results confirmed that waste and waste sorting activities are sources of airborne fungi. Indeed, ambient concentrations ranged from 7.3 × 103 to 8.5 × 105 colony-forming units (CFU)/m3 for culturable fungi and up to 4 mg/m3 for dust. Personal exposure to inhalable dust was found up to 3 mg/m3 for dust and ranged from 8.6 × 103 to 1.5 × 106 CFU/m3 for fungi. Airborne fungal communities were found to be dominated by the Penicillium genera in both bioaerosols and settled dust samples, followed by the Aspergillus, Cladosporium, Wallemia, Mucor, and Rhizopus genera. Fungi were carried by particles of aerodynamic diameters, mainly between around 2.0 and 10.0 µm. The findings dealing with size distribution and biodiversity of bioaerosols suggest that employees are exposed to complex bioaerosols during their work and help to make a finer diagnosis of the risks involved, which is often difficult in the absence of any occupational exposure limit (OEL) value for bioaerosols in general.

Safe working conditions must be guaranteed during waste sorting, which is crucial to maximizing recycling and reuse, in order to minimize workers’ exposure to chemical and biological hazards. This study determines the contribution of Aspergillus section Fumigati to the overall cytotoxicity of filtering respiratory protection devices (FRPD) and mechanic protection gloves (MPG) collected in 2019 from different workstations in one waste sorting industry in Portugal. The cytotoxicity of 133 Aspergillus section Fumigati isolates was determined as IC50 in human A549 epithelial lung cells and swine kidney cells, using the MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) assay. Aspergillus section Fumigati cytotoxicity results were compared with previous total cytotoxicity data from FRPD and MPG samples. A significant correlation was detected between the total cytotoxicity of samples and cytotoxicity of Aspergillus section Fumigati isolates in A549 cells (rS = −0.339, p = 0.030). The cytotoxicity of Aspergillus section Fumigati isolates explained 10.7% of the total cytotoxicity of the sample. On the basis of the comparison of cytotoxicity levels, it was possible to determine the contribution of Aspergillus section Fumigati isolates for the total cytotoxicity of protection devices used in the waste sorting industry. The results support in vitro toxicology as a relevant approach in risk assessments regarding cytotoxicity in passive sampling, and thus, useful in determining the contribution of relevant microbial contaminants to overall cytotoxicity. This approach can provide valuable answers in dose/response studies, and support innovations in risk characterization and their translation into occupational policies.