Waste management最新文献

This study systematically evaluates supercritical CO₂ (scCO₂) extraction for the decontamination of volatile compounds from post-consumer recycled high-density polyethylene (rHDPE), with a focus on co-solvent effects. Time-resolved extraction revealed that contaminant load strongly influenced removal efficiency, and flakes achieved 15% higher performance than pellets due to thinner morphology and reduced re-embedding. Co-solvent assistance further enhanced efficiency, with p-xylene, ethylbenzene, and cyclohexanone achieving up to 35% improvement compared to neat scCO₂. Hansen Solubility Parameter (HSP) analysis indicates that solvent compatibility with HDPE is a necessary but insufficient factor for the enhanced decontamination efficiency, and further molecular descriptor analysis provided additional mechanistic insight, highlighting aromaticity, polarizability, rigidity, and topological complexity as key determinants of enhanced efficiency. Aromatic co-solvents facilitated contaminant release through π-electron delocalization and planar rigidity, while cyclohexanone achieved broad solvation capacity via polar functionality and favorable topological descriptors. These results reveal structure-performance relationships that may inform co-solvent selection and contribute to the understanding of scCO₂-based decontamination strategies for rHDPE.

Addressing the dual challenges of food waste management and sustainable protein production requires circular economy solutions. This study demonstrates a sustainable approach to convert restaurant food waste (FW) into a protein-rich feed ingredient using black soldier fly (BSF) larvae. The nutritional profile of the FW-derived BSF meal was evaluated and compared to soybean meal (SBM). BSF meal had higher digestible (20.08 MJ/kg) and metabolizable (18.74 MJ/kg) energy than SBM (P < 0.01). The standardized ileal digestibility of amino acids in BSF meal (78.90%-85.12%) was comparable to or better than that of SBM. In the subsequent experiment, 72 pigs were assigned to either a control diet (Ctrl) or an experimental diet (BSF) with 20% SBM replaced by BSF meal. Throughout the trial, the BSF group showed a 5.80% higher average daily gain (P = 0.06) and a 4.29% higher average daily feed intake (P < 0.05). BSF meal increased shoulder backfat thickness and muscle decanoic acid content by 26.68% and 83.33%, respectively (P < 0.05). Furthermore, BSF meal improved lipid metabolism and antioxidant status, as characterized by reduced serum total cholesterol and malondialdehyde levels, and up-regulation of myosin heavy chain I expression (P < 0.05). BSF meal modulated the gut microbiota by enriching taxa linked to improved meat quality and health, including Clostridium_sensu_stricto_1 (1.97-fold) and Clostridiaceae (1.94-fold) (P < 0.05). In conclusion, BSF-based bioconversion effectively transformed FW into a sustainable, high-performance feed ingredient that enhanced pig productivity and meat quality, thereby contributing to a more circular and resilient food system.

Decarbonizing residential heating requires the widespread implementation of cleaner energy infrastructures. The absence of reliable technologies to phase out natural gas from this sector remains as a key barrier in arresting residential emissions. A methodology to quantify community resources with energetic value to meet residential heating demands is developed. The enabling technology is a seasonal energy storage model that relies on community scrap metals to produce hydrogen and heat on demand via metal-water reactions. A comprehensive geospatial quantification of suitable metal stocks at the city level is derived from municipal solid waste data. The model integrates open-source data and machine learning algorithms for waste generation forecasting. Results indicate that Hamilton, Ontario, can produce between 10 and 14.6 GWh of clean heat per year between 2021 and 2050. This represents an overall reduction of 2.9 ktonnes of space heating CO₂ emissions every year. An optimized machine learning model based on a gradient boosting algorithm captured 86% of the variability in the municipal waste data with a root mean square error of 11.8 kg/person. The methodology presents reproducible steps to replicate the analysis in different contexts to facilitate community energy planning.