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In the present study, a series of laboratory experiments were conducted to examine the impact of pyrolysis temperature on the outcome yields of waste coconut shells in a fixed bed reactor under varying conditions of pyrolysis temperature, from 400 to 800 °C. The temperature was increased at a stable heating rate of about 10 °C/min, while keeping the sweeping gas (Ar) flow rate constant at about 100 mL/min. The bio-oil was described by Fourier transform infrared spectroscopy (FTIR) investigations and demonstrated to be an exceptionally oxygenated complex mixture. The resulting bio-chars were characterized by elemental analysis and scanning electron microscopy (SEM). The output of bio-char was diminished pointedly, from 33.6% to 28.6%, when the pyrolysis temperature ranged from 400 to 600 °C, respectively. In addition, the bio-chars were carbonized with the expansion of the pyrolysis temperature. Moreover, the remaining bio-char carbons were improved under a stable structure. Experimental results showed that the highest bio-oil yield was acquired at 600 °C, at about 48.7%. The production of gas increased from 15.4 to 18.3 wt.% as the temperature increased from 400 to 800 °C. Additionally, it was observed that temperature played a vital role on the product yield, as well as having a vital effect on the characteristics of waste coconut shell slow-pyrolysis.

The selective detection of microplastics (MPs) in aquatic environments is hindered by particle size diversity and matrix-induced interferences. This study reports an excitation–emission matrix (EEM) fluorescence sensing platform using polyamide-derived carbon quantum dots (PACQDs; 0.5–2.6 nm) for the size- and concentration-resolved detection of polyethylene terephthalate MPs (PETMPs). PACQDs exhibited a pronounced fluorescence “turn-off” response upon PETMP interaction, governed by particle size (10–149 μm) and loading (4–8 g L−1). Small PETMPs (10 μm) followed linear Stern–Volmer behavior, achieving a detection limit of 1.67 mg L−1 in deionized water. Conversely, larger particles induced non-linear optical effects, including scattering-driven enhancement and inner-filter effects. Multivariate analysis using PCA and PARAFAC resolved three distinct components associated with surface-state quenching, scattering-mediated redistribution, and surface area-driven binding. Component-specific scores confirmed that PACQDs are most sensitive to small PETMPs, while larger particles primarily introduce optical interference. Selectivity tests showed distinct discrimination of PETMPs over polyamide and polypropylene. In tap water, significant matrix effects were corrected via matrix-matched calibration, achieving recoveries within 80–120%. This study establishes EEM-based multivariate fluorescence as a mechanism-informed strategy for PETMP sensing, highlighting the robust applicability of PACQDs for monitoring small PETMPs in real-world water matrices.

Petrochemical-based plastics are widely used due to their convenience and low cost, with polyethylene (PE) being the most produced globally. However, the lack of efficient and sustainable treatment methods for conventional plastic wastes has led to severe environmental pollution. A new fungus strain capable of degrading PE was isolated from soil samples collected at a waste disposal site in Henan province and identified as Aspergillus sydowii W144. After 30 days of incubation under solid-state culture conditions, the strain demonstrated significant oxidative depolymerization of low-density polyethylene (LDPE). FTIR results revealed a substantial increase in the carbonyl index of the LDPE film, while differential scanning calorimetry (DSC) analysis detected an enhanced crystallinity in the LDPE film. Notably, distinct pitting and erosion marks were observed on the surface of LDPE film using scanning electron microscopy (SEM). Quantitative analysis showed a weight loss rate of 6.39% and a reduction in Weight-Average Molecular Weight (Mw) by 50.93%. Among currently identified PE-degrading strains polyethylene, A. sydowii W144 exhibits particularly outstanding depolymerization efficiency, especially on untreated PE. Based on the whole-genome data of A. sydowii W144, a preliminary model of the putative polyethylene degradation pathway in A. sydowii W144 was constructed through homology-based sequence analysis and by referencing previously reported polyethylene degradation pathways. Laccase/multicopper oxidase plays a key role in the initial oxidation of PE. Heterologous expression of the candidate gene laccase4 in Pichia pastoris yielded an active enzyme (~56 kDa) with a laccase activity of 460 U/L, confirming its functionality. This study provides a novel microbial resource and potential enzymatic tools for PE biodegradation. The strain exhibits a promising application in complex ecosystems for PE pollution. IMPORTANCE: The polyethylene-degrading strain A. sydowii W144 isolated in this study exhibits highly efficient depolymerization capabilities, particularly under solid-state culture conditions. Genomic sequencing analysis enabled the construction of a potential polyethylene (PE) degradation pathway and facilitated the identification of key laccase and multicopper oxidase genes involved in this process. The isolation of this novel strain enriches the microbial resources available for PE waste treatment and offers new insights into the mechanisms of plastic biodegradation.

Because of its slow rate of disintegration, plastic debris has steadily risen over time and contributed to a host of environmental issues. Recycling the world’s increasing debris has taken on critical importance. Pyrolysis is one of the most practical techniques for recycling plastic because of its intrinsic qualities and environmental friendliness. For scale-up and reactor design, an understanding of the degradation process is essential. Using one model-free kinetic approach (Friedman) and two model-fitting kinetic methods (Arrhenius and Coats-Redfern), the thermal degradation of Polyethylene Terephthalate (PET) microplastics at heating rates of 10, 20, and 30 °C/min was examined in this work. Additionally, a powerful artificial neural network (ANN) model was created to forecast the heat deterioration of PET MPs. At various heating rates, the TG and DTG thermograms from the PET MPs degradation revealed the same patterns and trends. This showed that the heating rates do not impact the decomposition processes. The Friedman model showed activation energy values ranging from 3.31 to 8.79 kJ/mol. The average activation energy value was 1278.88 kJ/mol from the Arrhenius model, while, from the Coats-Redfern model, the average was 1.05 × 104 kJ/mol. The thermodynamics of the degradation process of the PET MPs by thermal treatment were all non-spontaneous and endergonic, and energy was absorbed for the degradation. It was discovered that an ANN, with a two-layer hidden architecture, was the most effective network for predicting the output variable (mass loss%) with a regression coefficient value of (0.951–1.0).

Thermochemical process of biomass is being considered as a latest technique for the restoration of energy source and biochemical products. In this study, the influence of the different heating rates on pyrolysis behaviors and kinetic of jute stick were investigated to justify the waste jute stick biomass as a potential source of bioenergy. Pyrolysis experiments were carried out at four several heating rates of 10, 20, 30 and 40 °C/min, by utilizing the thermogravimetric analyzer (TG-DTA) and a fixed-bed pyrolysis reactor. Two different kinetic methods, Kissinger–Akahira–Sunose (KAS) and Ozawa–Flynn–Wall (OFW) were used to determine the distinct kinetic parameters. The experimental results showed that, the heating rates influenced significantly on the position of TG curve and maximum Tm peaks and highest decomposition rate of the jute stick biomass. Both the highest point of TG and the lowest point of Derivative thermogravimetry (DTG) curves were shifted towards the maximum temperature. However, the heating rates also influenced the products of pyrolysis yield, including bio-char, bio-oil and the non-condensable gases. The average values of activation energy were found to be 139.21 and 135.99 kJ/mol based on FWO and KAS models, respectively.

The real-time monitoring of animal postures through computer vision techniques has become essential for modern precision livestock management. To overcome the limitations of current behavioral analysis systems in balancing computational efficiency and detection accuracy, this study develops an optimized deep learning framework named YOLOv8-BCD specifically designed for ovine posture recognition. The proposed architecture employs a multi-level lightweight design incorporating enhanced feature fusion mechanisms and spatial-channel attention modules, effectively improving detection performance in complex farm environments with occlusions and variable lighting. Our methodology introduces three technical innovations: (1) Adaptive multi-scale feature aggregation through bidirectional cross-layer connections. (2) Context-aware attention weighting for critical region emphasis. (3) Streamlined detection head optimization for resource-constrained devices. The experimental dataset comprises 1476 annotated images capturing three characteristic postures (standing, lying, and side lying) under practical farming conditions. Comparative evaluations demonstrate significant improvements over baseline models, achieving 91.7% recognition accuracy with 389 FPS processing speed while maintaining 19.2% parameter reduction and 32.1% lower computational load compared to standard YOLOv8. This efficient solution provides technical support for automated health monitoring in intensive livestock production systems, showing practical potential for large-scale agricultural applications requiring real-time behavioral analysis.

Cotton is planted on a large scale in China, especially in the Xinjiang Region. A large amount of agricultural waste from cotton plants is produced annually, and currently poses a disposal problem. In this study the product after liquefaction of cotton stalk powder was mixed with diphenylmethane diisocyanate to prepare polyurethane foams. The effects of the liquefaction conditions on the properties of the polyols and polyurethane foams produced using cotton stalk were investigated. The optimal processing conditions for the liquefied product, considering the quality of the polyurethane foams, were studied as a function of the residue fraction. Bio-polyols with promising material properties were produced using liquefaction conditions of 150 °C, reaction time of 90 min, catalyst content of 3 wt.%, and 20 w/w% cotton stalk loading. We investigated the optimal processing conditions for producing bio-foam materials with mechanical properties comparable to those of petroleum-based foam materials. This study demonstrated the potential of cotton stalk agricultural waste for use as a feedstock for producing polyols via liquefaction. It was shown that polyethylene glycol 400 (PEG400) and glycerin can be used as alternative solvents for liquefaction of lignocellulosic biomass, such as cotton stalk, to produce bio-polyol and polyurethane foams.

Ambient particulates of Shanghai with 5-stage particle sizes were firstly determined in spring, 2017. The particles’ mass concentrations were mainly observed in fine particle matter (PM 1.1 ) and coarse particles (diameter > 7.0 μm). Water-soluble ionic contents were also more distributed in PM 1.1 with the great contents of secondary particles (NO 3 − , SO 4 2− , and NH 4 + ). Higher ratios of NO 2 /SO 2 and NO 3 − /SO 4 2− indicated that the vehicle emissions might be made more greater contribution rather than coal combustions to the fine particles. Crustal enrichment factors (EFcs) of trace elements (V, Cr, Ni, Zn, As, Se, Rb, Cd, Pb, and Bi) in PM 1.1 in that called slight air pollution events were always higher than those in that called severe air pollution events and EFcs of Se were up to 2.5 × 10 4 , while EFcs of Pb, Bi, and Cd were over 100. Based on kinds of elemental ratios in PM 1.1 and PM 1.1–2.0 , atmospheric pollutants in Shanghai might be mainly from coal and oil combustions, diesel, and gasoline vehicles. Air masses backward trajectories also showed that the air masses from the northern part of China were one important air pollutant origins, but other ones might be the local sources, such as traffic and industries. Based on carcinogenic risk analysis of PM 2.0 , it was considerable that 12–60 children and 37–87 adults among millions of people living in Shanghai might be attacked by cancer during their lifetime. Moreover, the great carcinogenic risk was also observed according to the high concentrations of elemental Cr and As in PM 1.1 .

This study investigates the concentrations, health risks, and potential sources of heavy metal elements and polycyclic aromatic hydrocarbons (PAHs) in PM1.1 particles in Zhuji, a major copper-processing city in China. The ratios of heavy metals (summer: 0.906; winter: 0.619) and PAHs (>0.750 in both seasons) in PM1.1/PM2.0 suggest significant accumulation in ultrafine particles. In winter, heavy metal concentrations in PM1.1 reached up to 448 ng/m3, and PAH concentrations were 13.4 ng/m3—over ten times higher than in summer. Health risk assessments revealed that hazard index (HI) values exceeded 1.00 for five age groups (excluding infants) during winter, indicating chronic exposure risks. Incremental lifetime cancer risk (ILCR) values surpassed the upper acceptable limit (1.0 × 10⁻⁴) for four age groups, with Cr, As, Cd, and Pb as major contributors. PAH-related ILCRs were also elevated in winter, with benzo[a]pyrene (BaP) identified as the most potent carcinogen. Enrichment factor (EF) and principal component analysis (PCA) indicated that industrial activities and traffic emissions were the dominant anthropogenic sources of heavy metals. Diagnostic ratio analysis further showed that PAHs mainly originated from vehicle and coal combustion. These findings provide critical insights into pollution patterns in industrial cities and underscore the importance of targeted mitigation strategies.

The definition of environmental indexes is one of the most widely used methods and methodologies for the study of exposure to polluting agents, and it is a highly helpful instrument for describing the quality of the environment in a simple and straightforward manner. In this study, index models were presented and described that can be used in evaluating the contamination, pollution and health risks of environmental micro (MPs) and nanoplastics (NPs) to ecosystems and humans. Index models such as plastic contamination factors (pCf) and pollution load index (pPLI), plastic- bioconcentration or accumulation factors (pBCf or pBAf), plastic-biota-sediment accumulation factor (pBSAf), biota accumulation load index (BALI), polymer risks indices (pRi), polymer ecological risks index (pERI) while plastic estimated daily intake (pEDI) and plastic carcinogenic risks (pCR) were described for oral, dermal and inhalation pathways. All index modeled were further described based on polymer types of MPs/NPs. The final value is represented by a quantity that measures a weighted combination of sub-indices and defined by an appropriate mathematical function. The central concept is to present an indicator that can describe, in a clear and concise manner, the level of MPs/NPs in the environment, thereby indicating where it would be necessary to intervene and where it would not in order to improve overall environmental conditions.