Oil spills pose a significant environmental threat, necessitating the development of efficient and eco-friendly sorbent materials. In this study, a biodegradable cellulose aerogel was successfully synthesized from rambutan peel (RP) waste and evaluated for its diesel oil adsorption performance. The aerogel exhibited ultra-low density (0.027 ± 0.002 g/cm³), high porosity (97.88% ± 0.19%), and excellent hydrophobicity (152.7°). Adsorption experiments demonstrated a maximum diesel oil uptake capacity of 58.8235 g/g under optimal conditions: pH 7, oil concentration of 25 g/L, contact time of 25 s, salinity range of 0‰–35‰, and temperature range of 25–35 °C. Adsorption equilibrium data were best described by the Langmuir isotherm model (R²=0.9974), indicating monolayer adsorption with R_L values ranging from 0.0409 to 0.2301 at 25 °C. Kinetic studies revealed that the pseudo-second-order model provided the best fit (R²=0.9998), suggesting that adsorption occurred through both boundary layer diffusion and intraparticle diffusion mechanisms. Thermodynamic analysis confirmed the spontaneity and exothermic nature of the adsorption process. Furthermore, the adsorption mechanism was primarily governed by hydrophobic interactions, van der Waals forces, and hydrogen bonding. Overall, this study highlights the potential of cellulose aerogels derived from agricultural waste as sustainable and highly efficient sorbents for diesel oil removal, offering a promising solution for oil spill remediation in various aquatic environments.

As global climate change intensifies, city-level carbon accounting has become increasingly important, as cities are significant sources of carbon emissions. This paper provides an overview of key steps and considerations in city-level carbon accounting methods, with a particular focus on the bottom-up approach that involves defining accounting boundaries, selecting accounting perspectives, identifying emission sources, collecting data, determining calculation methods, and performing calculations and uncertainty analyses. Additionally, the paper introduces the top-down method that uses macro-level data to estimate carbon emissions at the city level and briefly discusses emerging methods for city-level carbon accounting. The strengths and limitations of these approaches are examined. The paper also provides an overview of different databases used for carbon accounting and evaluates their appropriateness for estimating carbon emissions at the city level. It also analyzes key research topics in the literature related to urban carbon accounting. Common challenges in city-level carbon accounting are discussed, along with recommendations for future research in this field.

The prediction of Aspergillus (A.)nidulans toxicities (log1/D₃₇) for a set of 55 halogenated aliphatic hydrocarbons (HAHs) was thoroughly investigated using density functional theory (DFT) computations. Different multiple linear regression (MLR)methods were employed to assess the reliability of the proposed quantitative structure-activity relationships (QSAR) model. The obtained E_LUMO, E_gap, molecular polarizability (α), and molar refractivity (MR) values offered informative indications in determining the toxicity of the HAHs. A promising three-descriptor linear model was constructed using 41 molecules as a training set; then, the model was validated on the remaining 14 molecules. Statistical comparisons between these models and others quoted from the literature were presented. Furthermore, the potential causes of the outlier molecules in the proposed QSAR models were explored. The most preferable interactions were obviously noticed within the 1-bromo-2-methylpropane∙∙∙α-glucan complex, followed by 2-bromo-2-methylpropane∙∙∙α-glucan and 2-chloro-2-methylpropane∙∙∙α-glucan complexes. Compared to other analogs, the higher number of bond paths and bond critical points within the 1-bromo-2-methylpropane∙∙∙α-glucan complex highlighted its high preferability.

In this study, Ca-alginate-Amidoxime (Ca-Alg-AO) beads synthesized from coral skeleton waste and functionalized with amidoxime groups were utilized for Sr(II) adsorption in aqueous solutions. The physicochemical characterization was conducted using FTIR, TGA-DTA, SEM-EDX, and XRF. Characterization confirmed successful functionalization, as evidenced by the presence of ACN functional groups at 2251 cm⁻¹, C=N stretching vibrations at 1651 cm⁻¹, and N–O vibrations at 939 cm⁻¹, as well as thermal stability up to 282.2–652.1 °C and enhanced structural density. XRF analysis revealed an increase in Ca content from 94.63% to 94.76%, while SEM showed improved surface structural density. In aqueous solutions, the beads achieved an adsorption capacity of 13.58 mg/g and an efficiency of 84.8%, following pseudo-second-order kinetics and the Langmuir isotherm model. Thermodynamic analysis indicated an exothermic and spontaneous adsorption process driven by entropy. In seawater, the adsorption capacity decreased to 4.02 mg/g due to ion competition and Sr(II) speciation changes. Adsorption mechanisms involved ion exchange and metal-ligand complexation at lower pH levels, while higher pH promoted electrostatic interactions and complexation, with Sr(OH)₂ precipitation at excessive pH. Reusability tests demonstrated that the beads retained 75.7% of their initial adsorption capacity after five cycles, highlighting excellent durability and potential for environmental applications.

Indonesia, as a nation with rich marine biodiversity, faces significant challenges in fostering ocean literacy due to the limited integration of ecological and technological content in formal education. In this study, we aimed to evaluate ocean literacy among university students through a project-based learning program incorporating deep learning and genomic data for fish species identification. The initiative responded to the urgent need for educational reform that supports sustainable ocean conservation practices. Using a quantitative approach, the program engaged 36 undergraduate biology students in various activities, including marine problem analysis, field-based fish species identification via the Fishiden platform, phylogenetic tree construction using genomic data, and public outreach through social media advocacy. Pretest and post-test evaluations measured changes in ocean literacy, while Likert-scale questionnaires assessed student engagement and perception. The results showed a significant increase in post-test scores, with a mean improvement from 58.11 to 71.88, and a moderate N-Gain of 0.30. Students who demonstrated positive responses to the program also achieved higher gains in ocean literacy, indicating a strong correlation between engagement and learning outcomes. The highest evaluation scores reflected students’ enhanced awareness, inquiry skills, and attitudes toward biodiversity conservation. Technological tools such as deep learning and genomic databases effectively support interactive and data-driven learning. These findings highlight the transformative potential of integrating technology into ocean literacy education. This research bridges technology, pedagogy, and marine conservation, emphasizing the integration of innovative educational strategies for sustainability. In the future, researchers should address study limitations and improve the accuracy of deep learning models.

This study used rice straw-based and palm fiber-based degradable plastics with glycerol and sorbitol. The strength of rice straw cellulose-based degradable plastics using 20% glycerol ranged from 2 to 5.75 MPa. Similarly, the strength of palm fiber cellulose-based degradable plastics using 40% sorbitol ranged from 5 to 11.13 MPa. In a chemical analysis, the peaks between 3444.87 cm^-1 and 3651.25 cm^-1 represented the O–H stretching of the alcohol group. This is shown by the C–O–H hydroxyl group at the wave numbers of 1627.92, 1724.36, and 1745.58 cm^-1. Moreover, these groups are hydrophilic, binding water, so they can be degraded by microbial activity in the soil. In the thermal analysis, degradable plastics from rice straw lost a lot of weight between 431.53 and 520.79 °C. Plastics derived from palm fibers as green products also showed extreme weight loss between 334.28 and 482.20 °C. Most of the material was decomposed at 600 °C. Both types of samples lost a lot of hydrogen groups and started to decompose and depolymerize. Rice straw plastic absorbed 10.73%–20.23% of water, while palm fiber plastic absorbed 15.34%–85.01%. The lowest water absorption rates were observed in rice straw and palm fiber degradable plastics. Rice straw and palm fiber cellulose plastics broke down in 45–48 days, in line with the American Standard Testing and Materials (ASTM) D-20.96 standard, which says that degradable plastic should take no more than 180 days to break down.

This research paper introduced a groundbreaking approach to greenhouse gas (GHG) monitoring by leveraging artificial intelligence (AI) technology to enhance both accuracy and efficiency. Traditional GHG monitoring methods are often hampered by high costs, labor-intensive processes, and significant delays in data analysis. This study harnessed advanced AI techniques, such as machine learning algorithms and neural networks, to facilitate real-time data collection and analysis from diverse sources including satellite imagery, Internet of Things (IoT) sensors, and atmospheric models. By implementing AI models like random forest, support vector machines, convolutional neural networks (CNNs), and long short-term memory (LSTM) networks, the research achieved substantial improvements such as reducing data reporting latency from 24 hours to just 1 hour, increasing spatial resolution from 30 meters to 10 meters, and enhancing detection accuracy from 80% to 95%. Additionally, the AI systems identified previously unknown emission sources and accurately forecasted future emission trends with a high correlation (R²=0.89). These advancements not only allow for more precise identification of emission hotspots and tracking of changes over time but also facilitate more effective regulatory responses and policy-making. The findings underscore the transformative potential of AI-driven monitoring systems in bolstering global sustainability efforts and combating climate change.

The deployment of real-time sensor calibration models for air pollution monitoring on resource-constrained Industrial Internet of Things (IIoT) edge devices presents significant challenges due to the computational complexity and memory requirements of deep learning models. This paper addressed these challenges by proposing a time-series-generative approach that integrated model quantization, generative artificial intelligence (AI), and temporal deep learning architectures to ensure efficient deployment. Specifically, we introduced a TimeGAN-augmented temporal fusion transformer (TFT) model optimized for edge devices. By leveraging model quantization, the approach reduces the memory footprint and computational demands of the model without compromising calibration accuracy. Furthermore, the integration of generative adversarial networks (GANs) enhances the robustness of the model by generating high-quality synthetic time-series data, compensating for sparse or noisy sensor readings. This ability to generate synthetic data mirrors the real sensor trends, ensuring reliable model performance even in data-limited environments. A comprehensive evaluation of the proposed model, comparing its performance against both float and quantized versions, demonstrates the effectiveness of the TimeGAN-augmented quantized TFT. This model achieves a significant 88% reduction in size (from 800.04 KB to 97.34 KB) while maintaining excellent predictive performance, evidenced by a mean squared error (MSE) of 0.3212 and a mean absolute error (MAE) of 0.4375. Additionally, the TimeGAN-augmented Float TFT model emerges as a strong contender for real-time applications, offering an optimal balance between inference speed and accuracy, with a rapid inference time of 23.4 ms, making it ideal for real-time pollution monitoring.