Effective water quality management is essential in aquaponic systems for nutrient recovery from aquaculture sludge. To address limitations of conventional single-loop systems in handling high organic load and ammonium levels, a novel dual-loop aquaponic system (DLAS) was developed, incorporating a mainstream and a dedicated side-stream biofilter and hydroponics (BF + HP). This innovative design enables recovering nutrients from fish sludge while preventing the returned nutrient solutions from compromising mainstream water quality. A twostage pilot-scale experiment compared system performance with and without the BF + HP module. Results showed that in the absence of BF + HP, mainstream water quality deteriorated, with significant increases in soluble chemical oxygen demand (SCOD) and total ammonia nitrogen (TAN). However, when BF + HP was implemented, SCOD was reduced by 57.1 % and TAN by 92.1 %, demonstrating the system's capacity to maintain stable mainstream conditions. The side stream also achieved 74.6 % nitrogen recovery from fish sludge, highlighting its potential for sludge valorization. Microbial analysis revealed four distinct spatial clusters, dominated by Bacteroidota and Proteobacteria, including key genera such as Cloacibacterium, Flectobacillus, Flavobacterium, and Polynucleobacter, which support organic matter degradation and nitrogen cycling. These microbes play essential roles in the degradation of organic matter and nitrogen transformation, contributing to enhanced nitrogen recovery and system stability. DLAS offers an innovative, ecology-based solution for efficient nutrient recovery and sludge valorization, advancing the sustainability of closed-loop aquaponic systems.

Single-study regression models for compost total nitrogen (TNF) are calibrated under narrow experimental conditions and show limited transferability to other composting systems. This study develops an interpretable multiple linear regression (MLR) framework to estimate TNF in in-vessel food waste composting. The framework is based on 42 independent composting studies spanning a wide range of scales and feedstock compositions. Missing data were addressed through systematic screening and k-nearest-neighbour imputation. Model performance was evaluated using an independent 80/20 train-test split. The best-performing model (CBase) achieved a test R² of 0.26, RMSE of 0.57, and MAPE of 21.9%. The final equation retained initial TN, aeration rate, grinding treatment, and pile volume as predictors. Monte Carlo resampling across 200 iterations confirmed stable absolute prediction error, with median RMSE of 0.66 and median MAPE of 29%. R² varied substantially across splits, which is expected for small and heterogeneous cross-study datasets where error-based metrics are more reliable than variance-based measures. Benchmarking against published single-system TN equations revealed near-complete failure when applied to the multi-study dataset. Test R² values dropped as low as 4459 and MAPE exceeded 2000%, confirming the severe transferability limitations of narrowly calibrated composting models. The transparent regression equation supports early-stage planning, scenario comparison, and better nitrogen retention management across diverse composting systems. These outcomes support solid waste valorisation, resource efficiency, and circular economy principles in food waste management, contributing to Sustainable Development Goals 12 (Responsible Consumption and Production) and 13 (Climate Action) through reduced waste generation and lower nitrogen-related emissions.

Mono-crystalline silicon (mono-Si) PV modules account for around 70 % of global c-Si production, making robust environmental assessments essential. Life Cycle Assessment (LCA) is widely used for this purpose, yet inconsistent data reporting limits transparency and comparability. While prior reviews have explored environmental impacts and parameter trends, none have systematically evaluated reporting quality in mono-Si PV LCAs. To address this, the current review conducted a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-guided review of 32 mono-Si PV LCA studies published from 2019 to 2024. Each was assessed using the ISO 14040/14044 framework and IEA guidelines, with a focus on key elements such as functional unit, system boundary, data sources, software, characterization models, and data quality analysis. The review applied both a descriptive summary of common practices and a grading rubric to evaluate reporting transparency. Results reveal major inconsistencies, with fewer than half of the studies meeting international standards. This underscores the urgent need for harmonized reporting protocols in mono-Si PV LCA literature.

Petroleum-based fuels are finite, and the urgent need for alternative, non-petroleum fuels for internal combustion engines (ICEs) has become increasingly evident. In recent years, the automotive industry has faced increasing pressure to reduce the harmful emissions produced by direct-injection diesel engines. To address this challenge, various strategies have been explored, among which the 'dual-fuel concept' has emerged as a promising approach for controlling both nitrogen oxides (NOx) and soot emissions, even in existing diesel engines. One such strategy involves the incorporation of gaseous fuels with conventional diesel. These gaseous fuels, which typically possess high octane numbers and exhibit gasoline-like characteristics, are more resistant to auto-ignition. The primary goal of dual-fuel operations with diesel and gaseous fuels is to reduce emissions of particulate matter (PM) and NOx. The main objective of this study is to provide a comprehensive review on the effects of gaseous fuels, such as hydrogen gas, natural gas (NG), biogas, liquefied petroleum gas (LPG) and syngas in dual-fuel operation with diesel. The review focuses on comparing these effects to those of conventional diesel combustion in terms of engine emissions and performance. Understanding the synergy of the dual-fuel concept helps to revolutionize ICE technology, highlighting its potential economic benefits and contributions toward more sustainable and environmentally friendly transportation solutions.

There has been plenty of research on the influence of various socio-economic and demographic data on waste generation to develop effective and targeted waste reduction measures, including energy recovery. This study evaluates the relationship between the waste generation and Circular Material Use rate, Environmental Tax Revenue, and Global Innovation Index beyond the typical socio-economic factors (e.g., gross domestic product or population). Correlation analysis is conducted on the EU-27 datasets before the development of the predictive model. The correlation strength between the factors is discussed to identify the potential rebound effect from the central driver of economic growth and development. A positive correlation and partial rebound effect are identified in the data. The waste amount ending in disposal and energy recovery treatment increases with the Circular Material Use rate, suggesting that the expected gains from Circular Material Use rate are offset by other socio-economic factors such as increasing population or gross domestic product. However, a diminishing trend is observed in the rebound effect over the years. Multiple linear regression with validation is applied to identify the best fit model for predicting waste generation. Using population, gross domestic product, Circular Material Use rate, and Environmental Tax Revenues as independent variables, a model is generated with a mean absolute percentage error of 18.65% (7% lower than the benchmark) and R-2 (coefficient of determination) of 0.995.

Nutrient recovery from aquaculture sludge is vital for promoting hydroponic plant growth and achieving nearzero solid waste discharge in aquaponic systems. Modified biological aerated filters (MBAFs) are promising because of the dual capabilities of aquaculture sludge collection and aerobic mineralization. However, the bioconversion kinetics, which is indirectly related to the packed media, need to be improved. In this study, a novel polyhedral hollow sphere (PHS) medium was used in an MBAF (MBAF-PHS) to overcome the shortcomings of the current medium, facilitating fish sludge retention and enhancing subsequent bioconversion kinetics for nutrient recovery. An average rate of 36.9 g/d for dry weight of fish sludge was achieved during 29 d of filtration and an average reduction rate of 31.30 g/d during 26 d of bioconversion. The total mass of fish sludge was converted by 76.2% via the co-action of the solubilization of organic solids and degradation of dissolved organic matter. MBAF-PHS was competitive for macronutrient recovery compared with the MBAF-sponge previously used. The ratios of the final concentrations of the macronutrients (P, Mg, and S) to the concentrations in Hoagland solution (Cf/CH, %) were 278.1, 162.8, and 200.9%, respectively, whereas the ratios of N, K, and Ca were 65.9, 37.1, and 51.0%, respectively. High bioconversion kinetics of NO3--N and PO43--P were obtained within 7 d with an MNO3-N/MTN of 79.9% and MPO4-P/MDTP of 80.3%. The nutrient bioconversion of fish sludge was associated with the diversity of the microbial community in the MBAF-PHS, especially the population of nitrogen-removing microbial species that developed after 9 d of mineralization.

Biochar has gained attention for its role in soil improvement and environmental remediation. Predicting its physicochemical properties from various biomass sources is essential for optimising its applications. Existing linear regression models have investigated the effects of biomass type and pyrolysis conditions on biochar physicochemical properties and soil quality. The models are highly specific and lack predictive accuracy with limited biomass classifications. This study develops a multiple linear regression model, integrating Principal Component Analysis (PCA) to reduce dependent variables and enhance predictions of key biochar properties: pH, Cation Exchange Capacity (CEC), and Electrical Conductivity (EC). Three classification approaches-uncategorised (Combination UC), lignocellulosic analysis (Combination LA), and elemental analysis (Combination EA)-were compared. Classification significantly improved prediction accuracy, with Combination EA outperforming Combination LA. Applying PCA to Combination EA (EAPCA) further enhanced model efficiency, achieving high adjusted R2 values for pH, EC, and CEC in woody (0.817, 0.537, 0.875), herbaceous (0.795, 0.759, 0.732), and wet biomass (0.76, 0.787, 0.607) categories. The woody biomass case exhibited the strongest predictive performance for CEC. Key parameters identified through PCA included residence time, heating rate, nitrogen, hydrogen, and H/C ratio. The model's RMSE (15.4778) and R2 (0.875) indicate strong predictive capability, explaining 87.5 % of the variance in CEC. This study highlights the effectiveness of classification and PCA in improving biochar property predictions.

A novel stereoscopic mode of planting and breeding shed has been designed. The shed body comprises of a steel frame structure inserted with four transverse beams along the length direction of the shed through the middle part of the shed body. The transverse beams are covered with a partition. The top of the shed body is covered with 300 and 80 solar panels, while the lower and upper sides are covered with the first and second transparent materials. The partition is provided with 200 and 40 breeding cages, where the 200 and 40 manure drains connected to the breeding cages (each manure drain connected to one breeding cage) are vertically arranged under the partition. The two manure collection pipes are connected to the manure drains at a slope of 60 degrees. The transverse beams have 80 adjustable hammocks, which serve as space- saving hanging pots to grow plants. The lower side of the partition is provided with 80 spray heads through the pipes. The inner side of the shed body is provided with 20 heating devices. The novel planting and breeding shed can effectively utilize space, achieving the recycling of resources and waste, maximizing economic benefits and realizing the concept of low-carbon and green environmental protection.

Microplastics (MPs), as emerging indoor contaminants, have garnered attention due to their ubiquity and unresolved implications for human health. These tiny particles have permeated indoor air and water, leading to inevitable human exposure. Preliminary evidence suggests MP exposure could be linked to respiratory, gastrointestinal, and potentially other health issues, yet the full scope of their effects remains unclear. To map the overall landscape of this research field, a bibliometric analysis based on research articles retrieved from the Web of Science database was conducted. The study synthesizes the current state of knowledge and spotlights the innovative mitigation strategies proposed to curb indoor MP pollution. These strategies involve minimizing the MP emission from source, advancements in filtration technology, aimed at reducing the MP exposure. Furthermore, this research sheds light on cutting-edge methods for converting MP waste into value-added products. These innovative approaches not only promise to alleviate environmental burdens but also contribute to a more sustainable and circular economy by transforming waste into resources such as biofuels, construction materials, and batteries. Despite these strides, this study acknowledges the ongoing challenges, including the need for more efficient removal technologies and a deeper understanding of MPs' health impacts. Looking forward, the study underscores the necessity for further research to fill these knowledge gaps, particularly in the areas of long-term health outcomes and the development of standardized, reliable methodologies for MP detection and quantification in indoor settings. This comprehensive approach paves the way for future exploration and the development of robust solutions to the complex issue of microplastic pollution.

Sustainable transformation towards carbon neutrality can be achieved by the development of innovative low-carbon energy technologies.Despite the promulgation and subsequent implementation of various policies, many obstacles and challenges lie ahead in the long transition to the achievement of a sustainable transition towards carbon neutrality, including government behaviour, the market environment, and enterprise capacity.Seeking to overcome such substantial barriers to such development, this special issue focuses on urgent issues, with the theme of "Technological Innovations for Sustainable Transformation Towards Carbon Neutrality".The call welcomes researchers worldwide to contribute to extensive discussions on this theme and share their original thinking and high-quality research output.Its primary focus is to determine how stakeholders who have developed new technologies and designed green transformation paths can attain their carbon neutrality goals.

Bioproduction of 1,3-propanediol (1,3-PDO) is regarded as a low carbon footprint bioprocess with a 33% reduction of greenhouse gas (GHG) emissions compared to conventional chemical processes. In line with Sustainable Development Goal (SDG) 12, bioproduction of 1,3-PDO closes the loop between biodiesel waste glycerol and biobased 1,3-PDO to establish a circular bioeconomy. There are limited studies on the bioconversion of biodiesel-derived glycerol into 1,3-PDO via the immobilized cell biocatalysis route. In this study, the production of 1,3-PDO was enhanced by the wild-type Clostridium butyricum JKT 37 immobilized on the activated carbon of coconut shell (CSAC) as supporting material using the acidic-pretreated glycerol as a carbon source. Among various mesh sizes of CSAC tested, the 6-12 mesh immobilizer had enhanced cell density by about 94.43% compared to the suspended cell system. Following the acidic pretreatment in 37% (v/v) HCl, the pretreated glycerol had 85.60% glycerol with complete removal of soaps. The immobilized cell fermentation using pretreated glycerol produced 8.04 ± 0.34 g/L 1,3-PDO with 0.62 ± 0.02 mol/mol of yield, 15.81% and 27.78% higher than the control, respectively. Five repeated batches of immobilized cell fermentation had resulted in the average 1,3-PDO titer, yield, and productivity of 16.40 ± 0.58 g/L, 0.60 ± 0.03 mol/mol, and 0.68 ± 0.02 g/L.h, respectively. The metabolism pathway gradually shifted to a reductive branch when immobilized cells were reused in repeated batch fermentation, proven by the reduction in organic acid formation and the increased ratio of 1,3-PDO-to-total organic acids.

The outstanding performance of nanomaterials in chemical, magnetic, electrical, catalytic, and mechanical properties has paved the way for the huge market in material fabrication, energy storage, electronics, and many other industries. Traditional synthesis methods are relatively stunted in industrial applicability and scalability due to complex manufacturing processes and long preparation times, hampering the development and commercialisation of nanomaterials. On the other hand, the flame synthesis method emerges as an inexpensive, efficient, and easily scalable method for the commercial production of nanoparticles. The present review aims to highlight the research status and applications of the nanomaterials synthesised by the flame aerosol method. The advancement of flame aerosol synthesis technologies is reviewed, with emphasis on the state-of-the-art flame reactor configuration and design. Critical flame parameters that govern the formation of nanoparticles in the flame are reviewed to provide an understanding of the formation criteria and growth of nanomaterials in the flame environment. The properties and characteristics of carbon-based, platinum group metal, metal oxide, bimetallic nanoparticles, perovskite and high entropy oxide nanomaterials produced by flame synthesis are extensively reviewed. In addition, commercial manufacturing of flame-synthesised materials along with applications of the nanomaterials in the field of thermal or photocatalytic energy storage, fuel cells, and gas sensing are presented.

The diverse origins of biomass wastes provide a constraint on the practical application of pyrolysis, as it leads to uncertain chemical reactions, feedstock conversion, and energy consumption. Various model-fit methods are available to identify the decomposition kinetics, however, limited studies have compared the reliability and accuracy of those kinetic models. This work compared five different kinetic models, two based on model-fit methods (Coats–Redfern (CR), Kennedy-Clark (KC) and three based on the application forms of Criado Master Plots (CMP I, CMP II and CMP III). The mass loss data from the pyrolysis of horse manure (HM) through a thermogravimetric analyser was used as a biomass sample for evaluation. Results demonstrated that all five model-fit methods report different kinetics data. Although the model-fit methods (CR and KC) allow a direct determination of kinetic triplets, the accuracy of data is lower. The CMP-II and CMP-III methods (incorporating model-free data in the model-fit methods) showcase multi-step chemical kinetics and better describe the decomposition behavior of HM due to its multi-compositional properties. To obtain kinetic triplets with enhanced accuracy, it is recommended to incorporate model-free data into the model-fit method. Improved precision of the kinetics data is essential for developing experimental parameters and optimizing the process. Future research should include the selection criteria of model-fit methods shown in this study to improve the accuracy of kinetic data.

This study examined the premixed NH3/biogas combustion at near stoichiometric using an experimentally validated numerical method. Raising the NH3 wt.% in NH3/CH4 combustion at phi = 0.8 brought up the average reaction temperature (T-avg) due to heat retention. Intensified by CO2 addition, T-avg in NH3/biogas increased by a factor of 1.2 compared to NH3/CH4. At phi = 1.1, higher NH3 and CO2 wt.% reduced T-avg. The local Damk & ouml;hler number (Da) was reduced marginally in the absence of CO2 as phi increased from 0.8 to 1.1. Conversely, local Da grew considerably in the presence of CO2 and was particularly sensitive to variations in the excess air ratio. Increased NH3 wt.% promoted NO emission, primarily via N + OH -> NO + H and H + HCNO -> CH2 + NO pathways. NH3/biogas produced more NO than NH3/CH4 from phi = 0.9 to 1.1, but as phi approached 1.1, NO is generally lowered. N2O is produced mainly by NH + NO -> N2O + H. Fuel-lean operation generally results in a higher N2O than fuel-rich operation. The NH3/biogas combustion at phi = 0.8 is a potential clean fuel solution in lowering NO emissions, as compared to NH3/CH4 combustion.

Aligning with Sustainable Development Goal 12, National Biomass Action Plan 2023–2030 and the transition of carbon-emission-intensive development to low-carbon solutions, this research proposes a circular bioeconomy concept that utilizes byproduct of the biodiesel industry to create high-value 1,3-propanediol (1,3-PDO). There are limited studies on the bioconversion of biodiesel-derived glycerol into 1,3-PDO via the immobilized cell biocatalysis route. In this study, the production of 1,3-PDO was enhanced by the wild-type Clostridium butyricum JKT 37 immobilized on the coconut shell activated carbon (CSAC) as supporting material using the acidic-pretreated glycerol as a carbon source. Among various mesh sizes of CSAC tested, 6–12 mesh immobilization material had enhanced cell density by about 94.43 C_3H_8O_2 + 0.18NH_3→ 0.60C_3H_8O_2 + 0.06C_4H_8O_2 + 0.04C_2H_4O_2 + 0.18C_4H_7O_2N + 0.93H_2O + 0.16CO_2 . The metabolism pathway gradually shifted to a reductive branch when immobilized cells were reused in repeated batch fermentation, proven by the reduction in organic acid formation, increased ratio of 1,3-PDO-to-total organic acids, and experimental stoichiometry. An inclusive investigation on the variations of glycerol source and their impact on the carbonized immobilizer performance was conducted.

Waste management system plays a significant role in sustainable development and the Circular Economy (CE) transition. It is important but challenging to consider an integrated system in assessing and improving the sustainability of waste management. This article discusses the characteristics and contribution of an Integrated Waste Management System at the process and macro-level to reduce environmental impact. The potential pitfall of expected gain reduction in environmental footprint mitigation is also highlighted, emphasizing through burdening and unburdening footprint, rebound effect contributed by increasing waste generation and pollution haven hypothesis related to Environmental Kuznets curve.

This article reviews the current and potential trends in ventilation strategies within hospitals’ operating rooms (ORs), with a specific focus on vertical downward ventilation systems meeting cleanroom criteria. It also explores potential innovations, including thermal-guided mobile air supply units and the possibility of localised exhaust systems mounted on operating tables. By evaluating existing ventilation systems and emerging technologies, this review aims to provide valuable insights into optimising surgical environments to ensure improved patient safety and outcomes. Vertical downward ventilation systems, currently widely deployed in modern ORs, adhere to stringent cleanroom criteria, maintaining sterile conditions by efficiently controlling air flow and minimising airborne contaminants. These systems utilise High-Efficiency Particulate Air (HEPA) filters and controlled airflow patterns to create a clean and controlled environment conducive to surgical procedures. While vertical downward ventilation remains the prevailing practice, the potential integration of thermal-guided mobile air supply units represents an exciting innovation. These units offer dynamic airflow control based on real-time temperature gradients within the surgical zone, potentially enhancing contaminant control and settlement rate. Also, the possibility of localised exhaust systems mounted on operating tables presents another avenue for improving ventilation efficiency. By efficiently capturing and removing contaminants generated during surgical procedures, localised exhaust systems have the potential to further reduce the risk of surgical site infections and improve overall cleanliness in the vicinity of the surgical site. This review concludes that combining a mobile air supply unit with thermal control capabilities, in addition to integrating it with a vertical downward ventilation airflow system, appears to be the optimal choice for reducing low concentrations near the patient's vicinity. By synthesising current practices with potential innovations, this review could assist healthcare professionals and facility managers in the evolving landscape of ventilation strategies in ORs. The adoption of advanced ventilation technologies and practices contributes to advancing surgical care and enhancing patient safety in healthcare settings. Future studies could consider integrating the Internet of Things (IoT) for automatic indoor air quality optimisation through ventilation control.

Computational Fluid Dynamics (CFD) is a well-established tool to study fluid dynamics and particle movement, while Artificial Neural Network (ANN) models offer machine learning capabilities to accelerate indoor airflow predictions, but they still maintain a reasonable level of accuracy for prediction purposes. This study pioneers the integration of Deep Neural Network (DNN) models into indoor airflow dynamics, aiming to provide an accurate and accelerated prediction efficiency. The objective is to train two DNN models (classical and modified DNN models) to capture the complex relationships between ventilation rate, airflow patterns, and particle dispersion characteristics within buildings. Using a dataset generated from CFD simulations encompassing various air change rates, the trained modified DNN model significantly enhances prediction efficiency in term of the computational cost by 67 % reduction of CFD computational time (1 h to 20 min) while also resulting in very similar accuracy compared to the CFD outputs. The R2 values of classical and modified DNN models (plane 1) at air flow rate equals to 4 ach are 0.6867 and 0.9567 in term of the DPM distribution, respectively. The similar pattern is observed as the accuracy of modified DNN is higher than the classical DNN for other air flow rates in terms of the DPM and velocity distributions. Accordingly, the number of prediction errors is significantly decreased as the model alters from the classical DNN to modified DNN model. The significance of this research lies in its potential to enhance the efficiency of assessing particle dispersion, allowing for the more efficient design of targeted ventilation strategies and indoor air quality control measures tailored to diverse pollutant sources emitted from humans. Integrating DNN and CFD in assessing particle dispersion characteristics is promising for improving the understanding of indoor air dynamics and facilitating data-driven decision-making for ensuring healthier and safer indoor environments.