Search Results (273)

Photocatalysis is an emerging technology that harnesses light energy to facilitate chemical reactions. It has garnered considerable attention in the field of catalysis due to its promising applications in environmental remediation and sustainable energy generation. Recently, researchers have been exploring innovative techniques to improve the surface reactivity of ferroelectric materials for catalytic purposes, leveraging their distinct properties to enhance photocatalytic efficiency. With their switchable polarization and improved charge transport capabilities, ferroelectric materials show promise as effective photocatalysts for various reactions, including carbon dioxide (CO₂) reduction. Through a blend of experimental studies and theoretical modeling, researchers have shown that these materials can effectively convert CO₂ into valuable products, contributing to efforts to reduce greenhouse gas emissions and promote a cleaner environment. An artificial neural network (ANN) was employed to analyze parameter relationships and their impacts in this study, demonstrating its ability to manage training data errors and its applications in fields like speech and image recognition. This research also examined changes in charge separation, light absorption, and surface area related to variations in band gap and polarization, confirming prediction accuracy through linear regression analysis. Full article

It is critical to provide proper environmental conditions in poultry houses to maintain birds’ health, boost productivity, and improve the overall economic viability of the poultry industry. Among the myriad of environmental elements, indoor air quality has been a determining factor that directly affects poultry well-being. Elevated concentrations of harmful gases—in particular Carbon Dioxide ($C{O}_2$), Methane ($C{H}_4$), and Ammonia ($N{H}_3$)—decomposition products of poultry litter, feed wastage, and biological processes have draconian effects on bird health, feed efficiency, the growth rate, reproduction efficiency, and mortality rate. Despite their importance, traditional air quality monitoring systems are often operated manually, labor intensive, and cannot detect sudden environmental changes due to the lack of real-time sensing. To overcome these limitations, this paper presents an interdisciplinary approach combining cloud computing, Artificial Intelligence (AI), and Internet of Things (IoT) technologies to measure real-time poultry gas concentrations. Real-time sensor feeds are transmitted to a cloud-based platform, which stores, displays, and processes the data. Furthermore, a machine learning (ML) model was trained using historical sensory data to predict the next-day gas emission levels. A web-based platform has been developed to enable convenient user interaction and display the gas sensory readings on an interactive dashboard. Also, the developed system triggers automatic alerts when gas levels cross safe environmental thresholds. Experimental results of $C{O}_2$ concentrations showed a significant diurnal trend, peaking in the afternoon, followed by the evening, and reaching their lowest levels in the morning. In particular, $C{O}_2$ concentrations peaked at approximately 570 ppm during the afternoon, a value that was significantly elevated (p < 0.001) compared to those recorded in the evening (~560 ppm) and morning (~555 ppm). This finding indicates a distinct diurnal pattern in $C{O}_2$ accumulation, with peak concentrations occurring during the warmer afternoon hours. Full article

In the construction sector, cement and concrete are among the most widely utilized manufactured materials, yet their environmental impact remains a significant concern. The concrete industry is a major contributor to carbon dioxide emissions, accounting for over 8% of global greenhouse gas emissions annually. Several reports have estimated that between 1930 and 2013, a total of 4.5 gigatons of carbon was sequestered through the carbonation of cement-based materials. This process offset approximately 43% of the carbon dioxide (CO₂) emissions resulting from cement production during the same period, excluding emissions related to fossil fuel consumption in the manufacturing process. It is well established that producing one ton of cement results in approximately 0.60–0.98 tons of CO₂ emissions, coupled with substantial energy consumption. To mitigate these environmental effects, developing low-carbon or cement-free binders has become crucial. Alkali-activated binders (AABs), derived from industrial by-products or agricultural waste materials and activated with a low-molarity or one-part activator, are increasingly recommended as sustainable alternatives to reduce greenhouse gas emissions in the cement industry and minimize the consumption of natural resources. The production of alkali-activated concrete (AAC) involves several critical factors that significantly influence its mix design, fresh properties, and compressive strength (CS) performance. This study aims to provide a comprehensive review of the key factors affecting AAC’s mix design, workability, and CS characteristics. Firstly, the study discusses various methods employed for AAC mix design and the factors influencing these designs. Secondly, it examines the impact of binder type, source, chemical, mineralogical, and physical properties, as well as alkaline activator solutions, water content, and fillers on AAC’s workability, setting times, and strength development. Additionally, the study explores the correlation matrix and predictive performance models for fresh and strength properties. Lastly, the relationship between workability and CS is extensively analyzed. The review concludes by highlighting the existing challenges and prospects of AACs as sustainable construction materials to replace traditional cement and reduce carbon emissions. Full article

As an important part of global carbon neutrality strategies, carbon dioxide (CO₂) capture, utilization, and storage technologies have emerged as critical solutions for reducing carbon emissions. However, conventional CO₂ applications, including food preservation, industrial synthesis, and enhanced oil recovery, face inherent limitations such as suboptimal gas–liquid mass transfer efficiency and inadequate long-term stability. Recent advancements in CO₂ micro-nanobubbles (CO₂ MNBs) have demonstrated remarkable potential across multidisciplinary domains, owing to their distinctive physicochemical characteristics encompassing elevated internal pressure, augmented specific surface area, exceptional stability, etc. In this review, we try to comprehensively explore the unique physicochemical properties of CO₂ MNBs and their emerging applications, including industrial, agricultural, environmental, and energy fields. Furthermore, we provide a prospective analysis of how these minuscule bubbles can emerge as pivotal in future technological innovations. We also offer novel insights and directions for research and applications across related fields. Finally, we engage in predicting their future development trends as a promising technological pathway for advancing carbon neutrality objectives. Full article

Accurate forecasting of energy consumption in buildings is essential for achieving energy efficiency and reducing carbon emissions. However, many existing models rely on limited input variables and overlook the complex influence of indoor environmental quality (IEQ). In this study, we assess the performance of hybrid machine learning ensembles for predicting hourly energy demand in a smart office environment using high-frequency IEQ sensor data. Environmental variables including carbon dioxide concentration (CO₂), particulate matter (PM_2.5), total volatile organic compounds (TVOCs), noise levels, humidity, and temperature were recorded over a four-month period. We evaluated two ensemble configurations combining support vector regression (SVR) with either Random Forest or LightGBM as base learners and Ridge regression as a meta-learner, alongside single-model baselines such as SVR and artificial neural networks (ANN). The SVR combined with Random Forest and Ridge regression demonstrated the highest predictive performance, achieving a mean absolute error (MAE) of 1.20, a mean absolute percentage error (MAPE) of 8.92%, and a coefficient of determination (R²) of 0.82. Feature importance analysis using SHAP values, together with non-parametric statistical testing, identified TVOCs, humidity, and PM_2.5 as the most influential predictors of energy use. These findings highlight the value of integrating high-resolution IEQ data into predictive frameworks and demonstrate that such data can significantly improve forecasting accuracy. This effect is attributed to the direct link between these IEQ variables and the activation of energy-intensive systems; fluctuations in humidity drive HVAC energy use for dehumidification, while elevated pollutant levels (TVOCs, PM_2.5) trigger increased ventilation to maintain indoor air quality, thus raising the total energy load. Full article

The escalating threat of climate change has intensified the global urgency to accurately predict carbon dioxide (CO₂) emissions for sustainable development, particularly in developing economies experiencing rapid industrialization and globalization. Traditional Extreme Learning Machines (ELMs) offer rapid learning but often yield unstable performance due to random parameter initialization. This study introduces a novel hybrid model, Red-Billed Blue Magpie Optimizer-tuned ELM (RBMO-ELM) which harnesses the intelligent foraging behavior of red-billed blue magpies to optimize input-to-hidden layer weights and biases. The RBMO algorithm is first benchmarked on 15 functions from the CEC2015 test suite to validate its optimization effectiveness. Subsequently, RBMO-ELM is applied to predict Indonesia’s CO₂ emissions using a multidimensional dataset that combines economic, technological, environmental, and globalization-driven indicators. Empirical results show that the RBMO-ELM significantly surpasses several state-of-the-art hybrid models in accuracy (higher R²) and convergence efficiency (lower error). A permutation-based feature importance analysis identifies social globalization, GDP, and ecological footprint as the strongest predictors underscoring the socio-economic influences on emission patterns. These findings offer both theoretical and practical implications that inform data-driven Artificial Intelligence (AI) and Machine Learning (ML) applications in environmental policy and support sustainable governance models. Full article

This study combines convolutional neural network (CNN) recognition technology, Greenwich engineering software, and statistical yearbook methods to evaluate rural solar, wind, and biomass energy resources in pilot cities in China, respectively. The CNN method enables the rapid identification of the available roof area, and Greenwich software provides wind resource simulation with local terrain adaptability. The results show that the capacity of photovoltaic power generation reaches approximately 15.63 GW, the potential of wind power is 458.3 MW, and the equivalent of agricultural waste is 433,900 tons of standard coal. The city is rich in wind, solar, and biomass resources. By optimizing the hybrid power generation system through genetic algorithms, wind energy, solar energy, biomass energy, and coal power are combined to balance the annual electricity demand in rural areas. The energy trends under different demand growth rates were predicted through the LEAP model, revealing that in the clean coal scenario of carbon capture (WSBC-CCS), clean coal power and renewable energy will dominate by 2030. Carbon dioxide emissions will peak in 2024 and return to the 2020 level between 2028 and 2029. Under the scenario of pure renewable energy (H_WSB), SO₂/NO_x will be reduced by 23–25%, and carbon dioxide emissions will approach zero. This study evaluates the renewable energy potential, power system capacity optimization, and carbon emission characteristics of pilot cities at a macro scale. Future work should further analyze the impact mechanisms of data sensitivity on these assessment results. Full article

As climate change intensifies the frequency and magnitude of extreme weather events, such as storm surges, understanding how extreme weather events alter mangrove carbon dynamics is critical for predicting the resilience of blue carbon ecosystems under climate change. Mangrove forests are generally recognized for their resilience to natural disturbances, a characteristic largely attributed to the evolutionary development of species-specific functional traits. However, limited research has explored the impacts of storm surges on carbon flux dynamics in both natural and restored mangrove ecosystems. In this study, we analyzed short-term responses of storm surges on carbon dioxide flux and methane flux in natural and restored mangroves. The results revealed that following the storm surge, CO₂ uptake decreased by 51% in natural mangrove forests and increased by 20% in restored mangroves, while CH₄ emissions increased by 14% in natural mangroves and decreased by 22% in restored mangroves. GPP is mainly driven by PPFD and negatively affected by VPD and RH, while Reco and CH₄ flux respond to a combination of temperature, humidity, and hydrological factors. NEE is primarily controlled by GPP and Reco, with environmental variables acting indirectly. These findings highlight the complex, site-specific pathways through which extreme events regulate carbon fluxes, underscoring the importance of incorporating ecological feedbacks into coastal carbon assessments under climate change. Full article

This paper proposes an advanced forecasting model for predicting carbon dioxide (CO₂) emissions in Thailand’s electricity generation sector. The model integrates a multivariate gray model with the fminsearch optimization algorithm in MATLAB (R2025a) to address the critical challenge of accurate emission forecasting, a key driver of climate change. Historical data on CO₂ emissions, gross domestic product (GDP), peak electricity demand, and electricity user numbers are utilized to enhance predictive accuracy. Comparative analysis demonstrates that the optimized model significantly outperforms the conventional multivariate gray model, achieving mean absolute percentage error (MAPE) values of 7.74% for the training set and 1.75% for the testing set. The results highlight the effectiveness of the proposed approach as a robust tool for policymakers and stakeholders in Thailand’s energy sector, offering actionable insights to support informed decision-making in managing and reducing CO₂ emissions. Full article

It is a well-known fact that proper nutrition is essential for human beings to live healthy lives. For thousands of years, it has been considered that dates are one of the best nutrient providers. To have better-quality dates and to enhance the shelf life of dates, it is vital to preserve dates in optimal conditions that contribute to food security. Hence, it is crucial to know the shelf life of different types of dates. In current practice, shelf life assessment is typically based on manual visual inspection, which is subjective, error-prone, and requires considerable expertise, making it difficult to scale across large storage facilities. Traditional cold storage systems, whilst being capable of monitoring temperature and humidity, lack the intelligence to detect spoilage or predict shelf life in real-time. In this study, we present a novel IoT-based shelf life estimation system that integrates multichannel gas sensors and a lightweight machine learning model deployed on an edge device. Unlike prior approaches, our system captures the real-time emissions of spoilage-related gases (methane, nitrogen dioxide, and carbon monoxide) along with environmental data to classify the freshness of date fruits. The model achieved a classification accuracy of 91.9% and an AUC of 0.98 and was successfully deployed on an Arduino Nano 33 BLE Sense board. This solution offers a low-cost, scalable, and objective method for real-time shelf life prediction. This significantly improves reliability and reduces postharvest losses in the date supply chain. Full article

Marine transportation contributes approximately 2.5% of global greenhouse gas emissions. While previous studies have examined biodiesel effects on automotive engines, research on marine applications reveals critical gaps: (1) existing studies focus on single-parameter analysis without considering the complex interactions between biodiesel ratio, engine load, and operating conditions; (2) most research lacks comprehensive lifecycle assessment integration with real-time operational data; (3) previous optimization models demonstrate insufficient accuracy (R² < 0.80) for practical marine applications; and (4) no adaptive algorithms exist for dynamic biodiesel ratio adjustment based on operational conditions. These limitations prevent effective biodiesel implementation in maritime operations, necessitating an integrated multi-parameter optimization approach. This study addresses this research gap by proposing an integrated optimization model for fuel efficiency and emissions of marine diesel engines using biodiesel mixtures under diverse operating conditions. Based on extensive experimental data from two representative marine engines (YANMAR 6HAL2-DTN 200 kW and Niigatta Engineering 6L34HX 2471 kW), this research analyzes correlations between biodiesel blend ratios (pure diesel, 20%, 50%, and 100% biodiesel), engine load conditions (10–100%), and operating temperature with nitrogen oxides, carbon dioxide, and carbon monoxide emissions. Multivariate regression models were developed, allowing prediction of emission levels with high accuracy (R² = 0.89–0.94). The models incorporated multiple parameters, including engine characteristics, fuel properties, and ambient conditions, to provide a comprehensive analytical framework. Life cycle assessment (LCA) results show that the B50 biodiesel ratio achieves optimal environmental efficiency, reducing greenhouse gases by 15% compared to B0 while maintaining stable engine performance across operational profiles. An adaptive optimization algorithm for operating conditions is proposed, providing detailed reference charts for ship operators on ideal biodiesel ratios based on load conditions, ambient temperature, and operational priorities in different maritime zones. The findings demonstrate significant potential for emissions reduction in the maritime sector through strategic biodiesel implementation. Full article

The iron and steel industry is a major emitter of carbon. In the context of China’s dual-carbon goals, hydrogen-based reduction ironmaking technology has garnered unprecedented attention. It is considered a crucial approach to reducing carbon dioxide emissions in the steel sector and facilitating the realization of carbon neutrality. This work conducted isothermal thermogravimetric analysis on limonite ore in a N₂/H₂ atmosphere. The influences of reduction temperature, particle size, and hydrogen partial pressure on the hydrogen reduction reaction process of limonite were investigated. Based on the principles of isothermal thermal analysis kinetics and the unreacted core model for flat-plate particles, the mechanism function and kinetic parameters for the reduction of limonite particles were determined. The research results show that the hydrogen reduction process of limonite ore is influenced by multiple factors, including temperature, hydrogen partial pressure, and particle size. Increasing the reduction temperature and hydrogen partial pressure can significantly speed up the reduction reaction rate and enhance the degree of reduction. The kinetic parameters for the hydrogen reduction of limonite particles were obtained as follows: the reaction activation energy was 44.738 kJ·mol⁻¹, the pre-exponential factor was 31.438 m·s⁻¹, and the rate constant for the hydrogen reduction of limonite was $k=31.438\times e^{-{\displaystyle \frac{44.738\times 1000}{RT}}}\mathrm{m}\cdot {\mathrm{s}}^{-1}$. In addition, contour maps were plotted to predict the reaction time and reaction temperature required for a complete reduction of limonite particles of different sizes to iron (Fe) particles under varying hydrogen partial pressures. The research findings can serve as a scientific basis for optimizing hydrogen-based reduction ironmaking technology in the iron and steel industry and achieving carbon neutrality goals. Full article

With the growing severity of global climate change, forecasting and managing carbon dioxide (CO₂) emissions has become one of the critical tasks in addressing climate change. To improve the accuracy of CO₂ emission forecasting, an innovative framework based on variational mode decomposition (VMD), improved black-winged kite algorithm (IBKA), and BiLSTM networks is proposed. This framework aims to address the challenges associated with predicting non-stationary data and optimizing model hyperparameters. Initially, experiments were conducted on 29 benchmark functions using the IBKA algorithm, demonstrating its superior performance in highly nonlinear and complex environments. Subsequently, the BiLSTM model optimized by IBKA was employed to predict CO₂ emission trends across four major industries in China, confirming its enhanced prediction accuracy. Finally, a comparative analysis with other mainstream machine learning and deep learning models revealed that the BiLSTM model consistently achieved the best predictive performance across all industries. This research proposes an efficient and practical technical pathway for intelligent carbon emission prediction under the “dual-carbon” strategic goals, offering scientific support for policy formulation and the low-carbon transition. Full article

Carbon dioxide, the primary greenhouse gas responsible for global warming, represents today a critical environmental challenge for humans. Mitigating CO₂ emissions and other greenhouse gases is a pressing global concern. The primary goal of this study is to investigate the potential of particular ionic liquids (ILs) in capturing CO₂ for the sweetening of natural and other gases. The solubility of CO₂ was measured in three distinct ILs, which shared a common anion (triflate, TfO) but differed in their cations. The selected ionic liquids were {1-butyl-3-methylimidazolium triflate [BMIM][TfO], 1-butyl-1-methylpyrrolidinium triflate [BMP][TfO], and 1-butyl-4-methylpyridium triflate [MBPY][TfO]}. The solvents were screened based on results from a molecular computational study that predicted low CO₂ Henry’s Law constants. Solubility measurements were conducted at 303.15 K, 323.15 K, and 343.15 K and pressures up to 1.5 MPa using a gravimetric microbalance (IGA-003). The CO₂ experimental results were modeled using the Peng–Robinson Equation of state with three mixing rules: van der Waals one (vdWI), van der Waals two (vdWII), and the non-random two-liquid (NRTL) Wong–Sandler (WS) mixing rule. For the three ILs, the NRTL-WS mixing rule regressed the data with the lowest average deviation percentage of 1.24%. The three solvents had similar alkyl chains but slightly different polarities. [MBPY][TfO], with the largest size, exhibited the highest CO₂ solubility at all three temperatures. Calculation of its relative polarity descriptor (N) shows it was the least polar of the three ILs. Conversely, [BMP][TfO] showed the highest Henry’s Law constant (lowest solubility) across the studied temperature range. Comparing the results to published data, the study concludes that triflate-based ionic liquids with three fluorine atoms had lower capacity for CO₂ compared to bis(trifluoromethylsulfonyl) imide (Tf2N)-based ionic liquids with six fluorine atoms. Additionally, the study provided data on the enthalpy and entropy of absorption. A final comparison shows that the ILs had a lower CO₂ capacity than Selexol, a solvent widely used in commercial carbon capture operations. Compared to other ILs, the results confirm that the type of anion had a more significant impact on solubility than the cation. Full article

Despite global efforts to address climate change, carbon dioxide (CO₂) emissions are still on the rise. While carbon dioxide is essential for life on Earth, its increasing concentration due to human activities poses severe environmental and health risks. Therefore, accurately and efficiently predicting CO₂ emissions is essential. Hence, this research delves deeply into the prediction of CO₂ emissions by examining various deep learning models utilizing time series data to identify carbon dioxide levels in Oman. First, four important production materials of Oman (oil, gas, cement, and flaring), which have a great impact on CO₂ emissions, were selected. Then, the time series related to the release of CO₂ was collected from 1964 to 2022. After data collection, preprocessing was performed, in which outliers were removed and corrected, and data that had not been measured were completed using interpolation. Then, by dividing the data into two sections, education (1946–2004) and test (2022–2005) and creating scenarios, predictions were made. By creating four scenarios and modeling with two independent GRU and LSTM models and a hybrid LSTM-GRU model, annual carbon was predicted for Oman. The results were evaluated with three criteria: root mean square error (RMSE), mean absolute percentage error (MAPE), and correlation coefficient (r). The evaluations showed that the hybrid LSTM-GRU model with an error of 2.104 tons has the best performance compared to the rest of the models. By identifying key contributors to carbon footprints, these models can guide targeted interventions to reduce emissions. They can highlight the impact of industrial activities on per capita emissions, enabling policymakers to design more effective strategies. Therefore, in order to reduce pollution and increase the productivity of factories, using an advanced hybrid model, it is possible to identify the carbon footprint and make accurate predictions for different countries. Full article