Waste management最新文献

In order to achieve the ambitious goals of the European Union (EU) Green Deal, Member States must implement an efficient and modern recycling industry that can combine high environmental standards with high economic performance. According to Eurostat, the amount of waste recovered, both industrial and urban, increased by 33.9% from 2004 to 2020, and the share of recovery in total waste treatment rose, respectively, from 45.9% to 59.1%. Among the EU countries, Italy exhibits the highest waste recycling rate (83.2% in 2020). This paper empirically investigates the economic, institutional, and social aspects that may be correlated to the performance of firms involved in industrial and urban waste recycling. Better performing firms would definitely provide larger benefits to the recycling industry, since turning waste into resources is crucial for the transition to a cleaner, climate neutral and circular economy. Working on a panel dataset of 3715 Italian companies, it emerges that firms performance is positively influenced by several firm-specific factors, such as age of experience, size, degree of specialization and digitalization, and by territorial factors, such as separate collection rate, regional GDP, and regional political ideology.

Despite advances in anaerobic digestion (AD), full-scale implementation faces significant challenges, particularly during the start-up phase, where inoculum selection is crucial. This study examines the impact of inoculum choice on the operational and economic performance of thermophilic digesters during the start-up phase. Methanogenic reactors R3 and R4 were inoculated with digested sludge (DiS) and diluted sewage sludge (DSS), respectively, and fed with hydrolyzed source-sorted organic fraction of municipal solid waste (SS-OFMSW) and thickened sewage sludge, which were processed in R1 and R2, serving as acidogenic reactors. A two-stage AD configuration was employed to mitigate inhibitory effects associated with the undigested inoculum (DSS). This approach enabled the establishment of methanogenic activity in R4 when the AD system is initiated with DSS. However, R3 outperformed R4, achieving 49 % of the feedstock's theoretical methane potential compared to 15 % in R4. Methane production and volatile solids (VS) processing costs in R4 were 18 and 3 times higher than in R3, respectively. R3's superior performance was attributed to DiS's diverse bacterial community, with over 66 % of genera involved in hydrolysis, volatile fatty acid production, and syntrophic methane production. In contrast, DSS was dominated by Trichococcus and Lactococcus (75.4 %), primarily involved in butyrate oxidation and lactate production. This study provides valuable insights into effective inoculum selection for the start-up of full-scale digesters.

The utilization of natural waste gravel soil as base course material contributes to environmental protection and carbon emission reduction. The purpose of this research is to establish a new model for automated gradation design of the composite soil stabilizer-stabilized waste gravel soil (CSSWGS). A gradation range of CSSWGS has been proposed. The bearing capacity of the waste gravel soils was analyzed using the Particle Flow Code (PFC). The pavement structure performances of CSSWGS with different gradations were also evaluated using the asphalt pavement design method in China (APDM). A critical scientific challenge is to provide foundational predictive data for the gradation design. To address this, a deep learning neural network for small sample (DNNSS) was constructed to predict unconfined compressive strength (UCS) and frost resistance, offering analytical data for both of the aforementioned software. The Adaptive Moment Estimation (Adam) algorithm was employed to dynamically adjust the learning rate, thereby accelerating network; the Dropout function was used to alleviate overfitting; and the Rectified Linear Unit (ReLU) function was used as the activation function to solve the gradient vanishing problem. The results show that the DNNSS algorithm exhibits superior prediction performance compared to other deep learning algorithms. When employing the web version of APDM and the virtual California Bearing Ratio (CBR) test, the analysis results based on the predicted values from DNNSS and measured values were found to be consistent or closely aligned. Consequently, the new DNNSS-APDM-PFC model, leveraging the intelligent algorithm developed in this study, can be effectively utilized for designing the gradations of CSSWGS or analyzing the gradation performances of CSSWGS obtained from field applications.

At present, lead-containing wastes have increasingly become the raw materials together with primary lead concentrate for lead production to meet the ever-increasing lead demand market. PbSO₄ is the dominant component in the lead-containing wastes, nevertheless, its reaction behavior during lead smelting is not sufficiently investigated. This study investigated PbSO₄ decomposition behaviors and phase transformation mechanisms at oxidizing and reductive atmospheres and various gas flow rates. The investigations reveal that increasing the temperature and decreasing the oxygen partial pressure of the decomposition atmosphere can accelerate PbSO₄ decomposition degree. PbSO₄ decomposition intensity under different atmospheres follows the order of reducing atmosphere > inert atmosphere > oxidizing atmosphere. PbSO₄ decomposition path was identified: at a non-reductive atmosphere, the decomposition of PbSO₄ belongs to a multi-step decomposition process, PbSO₄ gradually decompose into xPbO·PbSO₄ (x = 1, 2, 4 in turn) and finally PbO. At a reductive atmosphere, the multi-step decomposition process was accelerated significantly, at the same time, the reduction decomposition path PbSO₄ → PbS was increasingly dominant with the extension of decomposition time. PbS and Pb were generated successively. Therefore, a suitable reducing atmosphere is suggested to co-smelt PbSO₄-bearing wastes in primary lead smelting furnace.

We explore the application of machine learning (ML) techniques to forecast door-to-door waste collection, addressing the challenges in municipal solid waste (MSW) management. ML models offer a promising solution to optimize waste collection operations, especially amid growing urban populations and evolving waste generation rates. Leveraging comprehensive data from a northeastern Italian municipality, including various waste types, our study investigates ML algorithms' efficacy in predicting household waste collection requirements. We examine two key tasks: predicting daily waste exposure likelihood and forecasting fulfilled pickups over monthly and weekly periods. Both tasks are developed at the user level, forecasting user behavior based on features that describe the user. We split the data based on its temporal distribution and evaluated the models by forecasting user behavior in a future period, using the data from earlier periods to train the models. This study addresses a novel and challenging scenario, as, to the best of our knowledge, no prior work has specifically focused on door-to-door waste management using machine learning techniques. Results highlight ML models' potential in enhancing waste collection efficiency, aiding route planning, resource allocation, and environmental sustainability in urban areas. Additionally, our findings underscore the importance of tailoring strategies to waste categories and pickup frequencies for optimal performance.

Rapid industrialization has led to the widespread accumulation of heavy metal-contaminated soils, posing significant environmental and health risks. Utilizing remediated soil for the production of sintered bricks offers a sustainable and effective solution. However, the migration and immobilization mechanisms of heavy metals during the sintering process, both within the tunnel kiln and the brick matrix, require detailed investigation. This study examined the distribution of heavy metals in the tunnel kiln and analyzed the differences in heavy metal content and leaching behavior between the inner and outer layers of the bricks. Bricks containing 15% remediated soil were prepared, and it was observed that the majority of heavy metals were effectively immobilized, with retention rates ranging from 90.6% to 99.9%. Selenium (Se), however, exhibited significant volatility, with a retention rate of only 64.3%. The outer layer of the brick body contained higher concentrations of As and Pb compared to the inner layer. Conversely, leaching tests showed lower leaching rates of Mn, Ni, Cu, and Zn in the inner layers, while As and Pb exhibited higher leaching rates in the inner layers. These findings underscore the varying migration and transformation behaviors of different heavy metals during sintering, both within the kiln and the brick layers. The study highlights the potential of doped sintered bricks as sustainable building materials, ensuring effective heavy metal stabilization with minimal environmental risk, and providing a promising pathway for the safe reuse of contaminated soils.

The ratio of nitrogen (N₂) to argon (Ar) in landfill gas was compared to the atmospheric gas ratio to quantify the balance between N₂ generating (anaerobic ammonium oxidation, denitrification) and N₂ consuming (nitrogen fixation) processes on three landfills undergoing in-situ stabilization. In the aerated landfills, as much as 22% of the extracted N₂ could be explained by net denitrification, with coexisting aerobic and anaerobic domains fostering nitrification-dependent denitrification. Nitrogen fixation was also occasionally observed. Removal of nitrogen via the gas phase exceeded nitrogen removed via the leachate by up to a factor of 33. Contrastingly, the anaerobic landfill under leachate recirculation showed a net reduction of N₂ in relation to Ar, indicating nitrogen fixation as the dominant mechanism, equivalent up to 28% of the nitrogen in the extracted landfill gas. The balance between denitrification and nitrogen fixation in the aerated sites varied seasonally, likely caused by increased evapotranspiration in the summer, allowing greater air intrusion through the cover soil, resulting in higher NO₃^- and NO₂^- availability for denitrification and anammox. No such variability was observed for the landfill under liquid recirculation. The nitrogen transforming microbial community comprised of species responsible for nitrification, ammonification, denitrification, and anammox, indicating all processes may coexist. The findings show aeration supports nitrogen removal through the gas phase, but also suggest that nitrogen fixation adds nitrogen to the waste body in anaerobic domains. This could delay reaching environmental compliance criteria for leachate nitrogen, both for in-situ treatment by aeration and by leachate recirculation.

Printed circuit boards represent an extraordinarily challenging fraction for the recycling of waste electric and electronic equipment. Due to the closely interlinked structure of the composing materials, the selective recycling of copper and closely associated precious metals from this composite material is compromised by losses during mechanical pre-processing. This problem could partially be overcome by a better understanding of the influence of particle size and shape on the recovery of finely comminuted and well-liberated metal particles during mechanical separation. Here, we propose a workflow to quantify the role of the size and shape of such particles in various separation processes. As a case study, we compare an analytical heavy liquid separation to a new type of eddy current separator. Using X-ray computed tomography, we were able to distinguish metallic and non-metallic phases and determine the size and 3D microstructure of individual particles. For both separation processes, we trained a particle-based separation model that predicts the probability of individual particles to end up in the processing products. In particular, elongated particles were found to display a negative correlation between particle size and sphericity of metallic particles. In line with this correlation, the predicted metal recoveries are positively correlated with particle size but negatively correlated with sphericity in both separation processes. The suggested workflow is easily transferred to other recycling material systems. It allows to quantify the role of 3D geometrical particle properties in separation processes and provide robust predictions for the recoverability of different raw materials in complex recycling streams.

The disposal of waste-printed circuit boards (WPCBs) poses significant environmental and health risks, as they are a major component of e-waste containing hazardous materials. However, WPCBs also contain valuable metallic elements, making them important resources for recycling. To address the dual challenge of hazardous waste management and resource recovery, sustainable approaches for metal extraction from WPCBs are imperative. The present study, thus aimed to explore the use of glycine as an environment-friendly alternative to conventional inorganic acid-leaching agents for copper extraction from WPCBs. The integration of glycine leaching with pre-treatment under supercritical conditions with methanol enhanced the copper liberation efficiency along with improved mass transfer processes. Under optimized conditions of 0.5 M glycine concentration, 5 % (v/v) H₂O₂ concentration, 1.5:100 g/mL solid-to-liquid ratio, and 40 °C temperature, a remarkably high copper extraction efficiency of 97.46 % was achieved within a 15 h leaching duration. Besides, the kinetic studies indicated a mixed-controlled reaction mechanism for the metal extraction process, with a calculated activation energy of 40.01 kJ/mol. Additionally, a thorough characterization of the recovered metal-leached salt provided insights into the compound's nature and leaching mechanism. This integrated approach developed thus offers a sustainable and environment-friendly method for reducing the hazardousness of WPCBs while simultaneously extracting valuable metals, contributing to the advancement of e-waste management practices and environmental sustainability.

Hydrothermal carbonization (HTC) treatment is a promising method to transforming waste biomass into valuable resources and promoting waste recycling, especially for high nitrogen feedstocks. While small-sized hydrochar particle (≥0.45 μm) released from its solid product (hydrochar) application demonstrated large knowledge gaps compared with its original hydrochar and "secondary char" from model biomass (like glucose, sucrose, and starch). Thus, hydrochar particles derived from typical high nitrogen biomass, kitchen garbage (KG), and blue-green algae mud (AM), were collected to investigate their basic properties, microstructures and corresponding formation mechanisms. The results were: 1) the micron-sized hydrochar particles with yields as 3.42-7.86 wt% presented special characteristics, i.e., poor porous structures, moderate pH value, negative surface charge and higher surface hydrophobicity (contact angles as 95.00-117.67°) relative to original hydrochar and secondary char; 2) micronuclei aromatic core and hydrophobic hydrothermal polymers (methoxyl groups/alkyl chain with ether and carboxy groups) were identified in these hydrochar microparticles (HMPs) by jointly using differential thermogravimetry (DTG) analysis, Gaussian fitting model and thermogravimetric analysis combined with Fourier transform infrared spectrometry and mass spectrometry (TG-FTIR-MS) analysis; 3) polycondensation/cyclization reactions and Maillard/Mannich reaction in the KGHMPs, as well as solid-solid conversion and Maillard/Mannich reaction, polymerization reaction in AMHMPs core and its shell were proposed as their dominated formation mechanisms. The conclusions of this study indicated strong binding of HMPs with NH₄⁺, metals, and hydrophobic contaminants, and further reinforcing these application effects as soil fertilizer and decontaminant in soil/water for the N conversion, which also significantly depend on HTC temperature and feedstock.