Waste management最新文献

Reducing urban fine particulate matter (PM_2.5) concentrations is essential for China to achieve the Sustainable Development Goals (SDGs). Identifying the key drivers of PM_2.5 will enable the development of targeted strategies to reduce PM_2.5 levels. This study introduces a machine-learning model that combines CatBoost and the Tree-Structured Parzen Estimator (TPE) to analyze PM_2.5 concentration across 297 cities between 2000 and 2021. SHapley Additive exPlanations (SHAP) were employed to identify the primary factors influencing urban PM_2.5 concentrations. The study revealed that the proposed model has high accuracy in predicting urban PM_2.5 concentrations, achieving a coefficient of determination (R²) score of 96.44%. Socioeconomic and industrial activity are key drivers of PM_2.5 concentrations. This study not only quantifies the primary factors exacerbating or alleviating pollution for each city or province during the 2000-2021 period but also evaluates the influence of operational factors such as technological and public financial expenditures. In 2000, the main contributors to pollution in four heavily polluted cities included substantial nitrogen oxide emissions, inadequate technology investments, and excessive population density and liquefied gas consumption. Due to the rapid reduction in nitrogen oxide emissions, pollution levels in these cities have improved substantially. In the future, the most effective strategies for pollution reduction in these cities will focus on controlling population density and slowing down mining development. The proposed framework serves as a robust evaluation tool and can propose tailored strategies to control PM_2.5 concentrations effectively in each city.

Decentralized thermal treatment is a common method for municipal solid waste (MSW) disposal in rural areas. However, evaluating the effect of incineration has always been challenging owing to the difficult and time-consuming measurements involved. Herein, this study presented a rapid image recognition method for assessing the effects of thermal treatment on MSW using a neural network algorithm and a BAEVA 1.0 software based on the relation between the ignition loss of the incinerated bottom ash and its color properties. Through Pearson correlation analysis, the results demonstrated a strong correlation (R² > 0.80) between the ignition loss and the R, G, and B color values. To enhance evaluation accuracy, we introduced the backpropagation artificial neural network (BPANN) algorithm, which exhibited an average evaluation error of only 3.21 in crossvalidation, 27.9 % lower than that of the linear regression model. Building upon the BPANN, we developed BAEVA 1.0 as a software tool for thermal treatment effect evaluation. This tool exhibited advantages in functionality, convenience, and accuracy compared to existing methods. Overall, this research provides an important rapid assessment approach for evaluating the effects of MSW incineration when measurement conditions are unavailable.

Sericulture waste poses significant challenges to industrial and environmental safety. Black soldier fly larvae (BSFL) offer a promising solution for organic waste management by converting it into insect protein. This study aimed to develop a microbial fermented method for utilizing sericulture waste to feed BSFL and explore the underlying mechanisms. Our results showed that all fermented sericulture waste groups had positive effects on body weight, survival rate, substrate consumption rate, and substrate conversion rate. Metagenomic analysis revealed a notable increase in the abundances of commensal genera, including Sedimentibacter, Clostridium, Enterococcus, Bacteroides, and Bacillus, in the gut of BSFL fed on sericulture waste fermented with the most effective combination of microbial strains (B. subtilis, B. licheniformis, and E. faecalis). In contrast, BSFL reared on unfermented sericulture waste exhibited higher abundances of potentially pathogenic and harmful genera, including Providencia, Klebsiella, Escherichia, Brucella, and Enterobacter. Clusters of orthologous genes (COG) analysis indicated that altered microbial communities in the fermented group mainly participated in metabolic pathways, defense mechanism, and signal transduction mechanism. Transcriptome analysis further revealed that the upregulated genes were functionally associated with key metabolic pathways and immune mechanisms in the fermented group. These findings underscore the pivotal role of selected microbial fermentation in utilizing sericulture waste as BSFL feed, providing a sustainable solution for organic waste management.

With the rapid increase in end-of-life smartphones, enhancing the automation and intelligence of their recycling processes has become an urgent challenge. At present, the disassembly of discarded smartphones predominantly relies on manual labor, which is not only inefficient but also associated with environmental pollution and high labor intensity. In the context of end-of-life smartphone recycling, complex situations such as stacking and occlusion are commonly encountered. Accurate pose information can provide critical data for precise robotic grasping, thereby improving the level of automation and efficiency in recycling and disassembly. This research proposes a pose estimation method tailored for stacked discarded smartphones, integrating an improved Mask R-CNN instance segmentation model with Iterative Closest Point (ICP) point cloud registration technology. The method begins by accurately segmenting stacked smartphones using both real and synthetic datasets. Subsequently, pose information is extracted through the proposed estimation approach, providing critical data to guide the robotic arm's grasping actions, thereby improving sorting efficiency and minimizing manual intervention. To enhance its practical applicability, a pose recognition interactive system is developed, enabling visualization and dynamic interaction with pose data. Experimental results demonstrate the effectiveness of the transfer learning algorithm, which leverages a large volume of synthetic data combined with a small batch of real-world data. This research offers valuable theoretical insights and technical solutions for advancing the automation and intelligent disassembly of end-of-life smartphones.

Battery technology has attained a key position as an energy storage technology in decarbonization of energy systems. Lithium-ion batteries have become the dominant technology currently used in consumer appliances, electric vehicles (EVs), and industrial applications. However, lithium-ion batteries are not alike and can have different cathode chemistries which makes their recycling more complex. In addition, as larger quantities of batteries are starting to enter their end-of-life (EOL) stage, efficient handling and management of batteries with different cathode chemistry types are required. By identifying the cathode chemistry type prior to mechanical treatment, mixing of different cathode chemistries could be decreased, resulting in an increase in overall recycling efficiency. This study investigated the applicability of a non-destructive battery diagnostic methods, namely incremental capacity analysis (ICA), for identifying EOL lithium-ion battery chemistry. The study conducted ICA both on known reference batteries and EOL batteries from the recycling industry. Next, EOL batteries were crushed and the resulting fine active material was analysed to validate the ICA result. In addition, released gaseous and airborne particles were measured during crushing. The ICA results showed reliable identification of lithium iron phosphate (LFP) from other chemistries. In addition, lithium cobalt oxide (LCO), lithium nickel cobalt aluminum oxide (NCA) and lithium nickel manganese cobalt oxide (NMC) could be identified with various degrees. The identification may suffer if the battery is heavily used, and its state of health is low.

Electroplating sludge (ES) is a hazardous waste, because it contains heavy metals. It poses severe environmental and health risk if not properly disposed. This study proposed a combined pyro-metallurgical process to separate and recover copper, nickel, chromium and iron from it. A chlorination roasting was firstly used to selectively recover copper and nickel, in which they were chlorinated and volatilized while chromium and iron retained in the residue in the forms of FeCr₂O₄ and Fe₂O₃. A certain FeS₂ promoted the conversion of the chlorinating agent of NaClO to Cl₂ (g), increasing the copper and nickel chlorination. Though Cr₂S₃ could be chlorinated and volatilized, a high O₂ partial pressure oxidized it to Cr₂O₃ and reduced it chlorination. Under the optimal condition, the chlorination of copper and nickel obtained 99.1 % and 92.6 % respectively, while that of chromium was only 5.7 %. In the followed silicothermic reduction, a silicon cutting waste (Si-CW) was employed as reductant to recover chromium and iron from the roasted residue, due to the reduction capacity of Si and SiC phases in it. The chromium and iron oxides were reduced and recycled in an Fe-Cr alloy ingot, and Si and SiC changed to a refractory SiO₂ and entered into the slag. CaO could be slagged with SiO₂ and converted to a slag-liquid phase, which accelerated the separation between alloy and slag. The chromium and iron yields could obtain 97.6 % and 98.9 %, respectively. This study supplied a new method to co-treat two wastes for recovering nickel, iron, copper and chromium.

Pyrolysis emerges as a strategy for handling waste textiles, wherein the conversion of high-carbon-content textile waste into carbonaceous materials facilitates the restoration of its economic value, concurrently mitigating the environmental impact posed by textile waste. The present study fabricated carbon felts for respiratory filter layers through single-step pyrolysis of acrylic filter felts. The advantage of employing conductive carbon felt as a respiratory filter layer is its capability to concurrently serve two functions: filtration and electrical heating for high-temperature disinfection. In order to achieve these two functions, both the respirator body and the embedded electrodes were designed to ensure the reliability of high-temperature disinfection. The breathability and water vapor permeability of the obtained carbon felt were examined to confirm its comfortability as a respiratory filter layer. The results of filtration efficiency and antimicrobial testing indicated that the carbon felt exhibited a filtration efficiency of over 90 % against inhalable particulate matter, while its antimicrobial properties effectively suppressed microbial growth. This method of reutilizing waste textiles maintained consistency in the usage of textiles before and after reuse, simplified the reusing process of waste acrylic fibers, and simultaneously reduced the manufacturing costs of respiratory filters. The designed respiratory filters have the potential for application in settings such as hospitals and virus research institutions.

Owing to the massive refractory lignocellulose and leachate-organic loads, the stabilization of municipal solid waste (MSW) landfill is often prolonged, resulting in environmental burdens. Herein, various assembled multifunctional microbial inoculums (MMIs) were introduced into the semi-aerobic bioreactor landfill (SABL) to investigate the bioaugmentation impacts. Compared to control (CK) and other MMIs treatments (G1-G3), LD + LT + DM inoculation (G4) significantly increased volatile solids degradation (9.72-45.03 %), while reducing chemical oxygen demand (COD) content (10.34-51.85 %) and ammonia nitrogen concentration (80.71-90.95 %) in the leachate. G4 also exhibited significantly higher degradation of cellulose and hemicellulose, achieving 0.99 and 1.94 times higher efficiency than CK, respectively. Microbial analysis revealed that LD + LT + DM reshaped microbial communities composition of SABL, with most of the introduced microorganisms (Enterobacter, Sphingobacterium, Streptomyces, etc.) successfully colonizing, and stimulating indigenous functional microbes associated with organic matter decomposition. Additionally, microbial interactions were strengthened in G4, accompanied by the higher abundance of 11 biomarkers and enzymes involved in lignocellulose degradation and ammonia nitrogen conversion. Overall, LD + LT + DM maximized MMI function by reconstructing synergistic core microbes. These findings highlight the superiority of LD + LT + DM in simultaneously regulating the microbial composition of lignocellulose-rich waste landfills, expediting MSW decomposition, improving leachate treatment, and mitigating odor emissions, offering valuable insights for efficient MSW management.

Although well-being is a fundamental human goal, few studies have clarified the causal relationship between well-being and waste separation, which strongly affects sustainable development. We propose that, assuming humans' innate affinity for nature (the biophilia theory), waste separation would be conducive to a sense of life meaning and well-being. To test this hypothesis, we systematically investigate how food waste separation and composting behaviors affect subjective well-being and meaning in life in a longitudinal field experiment. 226 valid residents were randomized into intervention (n = 113) or control (n = 113) groups. The participants in the intervention group were provided informational reminders (please separate food waste) for 9 weeks, and were asked to record their food waste separation and composting behaviors. We find that community residents who performed food waste separation and composting behaviors (intervention group) had a higher meaning in life and subjective well-being than those who did not (control group). Meanwhile, food waste separation and composting behaviors can promote subjective well-being through the sequential mediation effects of nature connectedness and meaning in life, validating the biophilia theory. These findings not only provide compelling evidence for how waste separation behaviors can promote well-being but also generate important implications for policy-makers and the understanding of pro-environmental behaviors.

This article presents a comparative analysis of the circularity and cost-efficiency of two distinct construction material recycling processes: ML-based automated sorting (MLAS) and conventional sorting technologies. Empirical data was collected from two Finnish companies, providing a robust foundation for this comparison. Our study examines the operational specifics, economic implications, and environmental impacts of each method, highlighting the advantages and drawbacks. By leveraging data-driven insights, we aim to illustrate how MLAS can enhance recycling efficiency and sustainability compared to traditional methods. In our cost modeling over a seven-year period, MLAS achieved a cumulative cost of €12.76 million, significantly lower than CS, which incurred €21.47 million, underscoring the long-term cost efficiency of MLAS. The findings underscore the potential for advanced AI technologies to revolutionize waste management practices, offering significant improvements in sorting accuracy, material recovery rates, and overall cost-effectiveness. This analysis provides valuable perspectives for stakeholders in the construction and waste management industries, emphasizing the importance of integrating innovative technologies to achieve higher circularity and sustainability goals.