Search Results (364)

Parabens—specifically methylparaben (MeP), ethylparaben (EtP), propylparaben (PrP), and butylparaben (BuP)—are widely used substances in everyday life, particularly as preservatives in pharmaceutical and food products. However, these compounds are not effectively removed by conventional water and wastewater treatment processes, potentially causing disruptions to human homeostasis and the endocrine system. This study conducted a transport and dimensional analysis through simulation of the adsorption process for these parabens, using zinc chloride-activated carbon derived from spent coffee grounds (ACZnCl₂) as the adsorbent, implemented via Aspen Properties^® and Aspen Adsorption^®. Simulations were performed for two inlet concentrations (50 mg/L and 100 mg/L) and two adsorption column heights (3 m and 4 m), considering a volumetric flow rate representative of a medium-sized city with approximately 100,000 inhabitants. The results showed that both density and surface tension of the parabens varied linearly with increasing temperature, and viscosity exhibited a marked reduction above 30 °C. Among the tested conditions, the configuration with 50 mg∙L⁻¹ inlet concentration and a 4 m column height demonstrated the highest adsorption capacity and better performance under adsorption–desorption equilibrium. These findings indicate that the implementation of adsorption beds on an industrial scale in water and wastewater treatment systems is both environmentally and socially viable. Full article

Accurate above-ground biomass (AGB) quantification is confounded by signal saturation and data fusion challenges, particularly in structurally complex ecosystems like bamboo forests. To address these gaps, this study developed a two-stage framework to map the AGB of Dendrocalamus giganteus in a subtropical mountain environment. This study first employed Empirical Bayesian Kriging Regression Prediction (EBKRP) to spatialize sparse GEDI and ICESat-2 LiDAR metrics using Sentinel-2 and topographic covariates. Subsequently, a stacked ensemble model, integrating four machine learning algorithms, predicted AGB from the full suite of continuous variables. The stacking model achieved high predictive accuracy (R² = 0.84, RMSE = 11.07 Mg ha⁻¹) and substantially mitigated the common bias of underestimating high AGB, improving the predicted observed regression slope from a base model average of 0.63 to 0.81. Furthermore, SHAP analysis provided mechanistic insights, identifying the canopy photon rate as the dominant predictor and quantifying the ecological thresholds governing AGB distribution. The mean AGB density was 71.8 ± 21.9 Mg ha⁻¹, with its spatial pattern influenced by elevation and human settlements. This research provides a robust framework for synergizing multi-source remote sensing data to improve AGB estimation, offering a refined methodological pathway for large-scale carbon stock assessments. Full article

The salinization of agricultural soils is a serious threat to farming and ecological balance in arid and semi-arid regions. Accurate estimation of soil water-soluble ions (calcium, carbonate, magnesium, and sulfate) is necessary for correct monitoring of soil salinization and sustainable land management. Hyperspectral ground-based data are valuable in soil salinization monitoring, but the acquisition cost is high, and the coverage is small. Therefore, this study proposes a two-stage deep learning framework with multispectral remote-sensing images. First, the wavelet transform is used to enhance the Transformer and extract fine-grained spectral features to reconstruct the ground-based hyperspectral data. A comparison of ground-based hyperspectral data shows that the reconstructed spectra match the measured data in the 450–998 nm range, with R² up to 0.98 and MSE = 0.31. This high similarity compensates for the low spectral resolution and weak feature expression of multispectral remote-sensing data. Subsequently, this enhanced spectral information was integrated and fed into a novel multiscale self-attentive Transformer model (MSATransformer) to invert four water-soluble ions. Compared with BPANN, MLP, and the standard Transformer model, our model remains robust across different spectra, achieving an R² of up to 0.95 and reducing the average relative error by more than 30%. Among them, for the strongly responsive ions magnesium and sulfate, R² reaches 0.92 and 0.95 (with RMSE of 0.13 and 0.29 g/kg, respectively). For the weakly responsive ions calcium and carbonate, R² stays above 0.80 (RMSE is below 0.40 g/kg). The MSATransformer framework provides a low-cost and high-accuracy solution to monitor soil salinization at large scales and supports precision farmland management. Full article

Root pruning can promote the transplanting of young green plants, but the overall impact of pruning on root growth, morphology, and physiological functions remains unclear. This study integrated transcriptomics and physiological analyses to elucidate the effects of root pruning on blueberry growth. Appropriate pruning (CT4) significantly promoted plant growth, with above-ground biomass and leaf biomass significantly increasing compared to the control group within 42 days. Photosynthesis temporarily decreased at 7 days but recovered at 21 and 42 days. Transcriptomics analysis showed that the cellulose metabolism pathway was rapidly activated and influenced multiple key genes in the starch metabolism pathway. Importantly, transcription factors associated with vascular development were also significantly increased at 7, 21, and 42 days after root pruning, indicating their role in regulating vascular differentiation. Enhanced aboveground growth was positively correlated with the expression of photosynthesis-related genes, and the transport of photosynthetic products via vascular tissues provided a carbon source for root development. Thus, root development is closely related to leaf photosynthesis, and changes in gene expression associated with vascular tissue development directly influence root development, ultimately ensuring coordinated growth between aboveground and belowground parts. These findings provide a theoretical basis for optimizing root pruning strategies to enhance blueberry growth and yield. Full article

Light Detection and Ranging (LiDAR) provides three-dimensional information that can be used to extract tree parameter measurements such as height (H), canopy volume (CV), canopy diameter (CD), canopy area (CA), and tree stand density. LiDAR data does not directly give diameter at breast height (DBH), an important input into allometric equations to estimate biomass. The main objective of this study is to estimate tree DBH using existing allometric models. Specifically, it compares three global DBH pantropical models to calculate DBH and to estimate the aboveground biomass (AGB) of the Lake Broadwater Forest located in Southeast (SE) Queensland, Australia. LiDAR data collected in mid-2022 was used to test these models, with field validation data collected at the beginning of 2024. The three DBH estimation models—the Jucker model, Gonzalez-Benecke model 1, and Gonzalez-Benecke model 2—all used tree H, and the Jucker and Gonzalez-Benecke model 2 additionally used CD and CA, respectively. Model performance was assessed using five statistical metrics: root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), percentage bias (MBias), and the coefficient of determination (R²). The Jucker model was the best-performing model, followed by Gonzalez-Benecke model 2 and Gonzalez-Benecke model 1. The Jucker model had an RMSE of 8.7 cm, an MAE of −13.54 cm, an MAPE of 7%, an MBias of 13.73 cm, and an R² of 0.9005. The Chave AGB model was used to estimate the AGB at the tree, plot, and per hectare levels using the Jucker model-calculated DBH and the field-measured DBH. AGB was used to estimate total biomass, dry weight, carbon (C), and carbon dioxide (CO₂) sequestered per hectare. The Lake Broadwater Forest was estimated to have an AGB of 161.5 Mg/ha in 2022, a Total C of 65.6 Mg/ha, and a CO₂ sequestered of 240.7 Mg/ha in 2022. These findings highlight the substantial carbon storage potential of the Lake Broadwater Forest, reinforcing the opportunity for landholders to participate in the carbon credit systems, which offer financial benefits and enable contributions to carbon mitigation programs, thereby helping to meet national and global carbon reduction targets. Full article

Mangrove forests are vital blue carbon (BC) ecosystems that significantly contribute to climate change mitigation through carbon sequestration. Accurate, scalable, and cost-effective methods for estimating carbon stocks in these environments are essential for conservation planning. In this study, we assessed the potential of drones, also known as unmanned aerial vehicles (UAVs), for estimating above-ground biomass (AGB) and BC in Avicennia marina stands by integrating drone-based canopy measurements with field-measured tree heights. Using structure-from-motion (SfM) photogrammetry and a consumer-grade drone, we generated a canopy height model and extracted structural parameters from individual trees in the Melgonze mangrove patch, southern Iran. Field-measured tree heights served to validate drone-derived estimates and calibrate an allometric model tailored for A. marina. While drone-based heights differed significantly from field measurements (p < 0.001), the resulting AGB and BC estimates showed no significant difference (p > 0.05), demonstrating that crown area (CA) and model formulation effectively compensate for height inaccuracies. This study confirms that drones can provide reliable estimates of BC through non-invasive means—eliminating the need to harvest, cut, or physically disturb individual trees—supporting their application in mangrove monitoring and ecosystem service assessments, even under challenging field conditions. Full article

Typhoons are major climate disturbances that significantly impact coastal ecosystems, particularly mangrove forests. This study examines the effects of typhoons on mangrove communities at different stages of recovery, focusing on how environmental factors influence carbon storage and net ecosystem exchange (NEE). Three mangrove sites were selected based on their recovery age: young, moderately restored, and mature. The results revealed that typhoons had the most pronounced effect on young mangroves, resulting in significant reductions in both above-ground and soil carbon storage. In contrast, mid-aged and mature mangroves demonstrated greater resilience, with mature mangroves recovering most rapidly in terms of community structure and carbon storage. Key factors such as wind speed, heavy rainfall, and changes in photosynthetically active radiation (PAR) contributed to carbon storage losses, particularly in young mangrove forests. This study underscores the importance of recovery age in determining mangrove resilience to extreme weather events and offers insights for enhancing restoration and conservation strategies to mitigate the impacts of climate change on coastal carbon sequestration. Full article

Accurate quantification of above-ground biomass (AGB) and carbon sequestration is vital for monitoring terrestrial ecosystem dynamics, informing climate policy, and supporting carbon neutrality initiatives. However, conventional methods—ranging from manual field surveys to remote sensing techniques based solely on 2D vegetation indices—often fail to capture the intricate spectral and structural heterogeneity of forest canopies, particularly at fine spatial resolutions. To address these limitations, we introduce ForestIQNet, a novel end-to-end multimodal deep learning framework designed to estimate AGB and associated carbon stocks from UAV-acquired imagery with high spatial fidelity. ForestIQNet combines dual-stream encoders for processing multispectral UAV imagery and a voxelized Canopy Height Model (CHM), fused via a Cross-Attentional Feature Fusion (CAFF) module, enabling fine-grained interaction between spectral reflectance and 3D structure. A lightweight Transformer-based regression head then performs multitask prediction of AGB and CO₂e, capturing long-range spatial dependencies and enhancing generalization. Proposed method achieves an R² of 0.93 and RMSE of 6.1 kg for AGB prediction, compared to 0.78 R² and 11.7 kg RMSE for XGBoost and 0.73 R² and 13.2 kg RMSE for Random Forest. Despite its architectural complexity, ForestIQNet maintains a low inference cost (27 ms per patch) and generalizes well across species, terrain, and canopy structures. These results establish a new benchmark for UAV-enabled biomass estimation and provide scalable, interpretable tools for climate monitoring and forest management. Full article

To address the challenges of concrete construction in polar regions, this study investigates the feasibility of fabricating cement-based materials under severely low temperatures using electric-induced heating curing methods. Cement mortars incorporating fly ash (FA-CM), ground granulated blast furnace slag (GGBS-CM), and metakaolin (MK-CM) were cured at environmental temperatures of −20 °C, −40 °C, and −60 °C. The optimal carbon fiber (CF) contents were determined using the initial electric resistivity to ensure a consistent electric-induced heating curing process. The thermal profiles during curing were monitored, and mechanical strength development was systematically evaluated. Hydration characteristics were elucidated through thermogravimetric analysis (TG), X-ray diffraction (XRD), and Fourier-transform infrared spectroscopy (FTIR) to identify phase compositions and reaction products. Results demonstrate that electric-induced heating effectively mitigates the adverse effect caused by the ultra-low temperature constraints, with distinct differences in the strength performance and hydration kinetics among supplementary cementitious materials. MK-CM exhibited superior early strength development with strength increasing rates above 10% compared to the Ref. specimen, which was attributed to the accelerated pozzolanic reactions. Microstructural analyses further verified the macroscopic strength test results that showed that electric-induced heating curing can effectively promote the performance development even under severely cold environments with a higher hydration degree and refined micro-pore structure. This work proposes a viable strategy for polar construction applications. Full article

Underground metro stations are integral to urban transit infrastructure, and ensuring their safety during fire emergencies is crucial. This study proposes a novel evacuation strategy for underground metro stations wherein a segment of evacuees descends to the platform level via train, while the remaining individuals evacuate upward to the ground level through station exits. A novel safety assessment methodology is established to evaluate fire evacuation efficacy, incorporating the cumulative effects of smoke, elevated temperatures, carbon dioxide, and reduced oxygen levels. Employing an actual underground metro station in Guangzhou, China, as a case study, fire and evacuation models were developed to compare the traditional upward evacuation method with the proposed partial downward evacuation strategy. The analysis reveals that both evacuation strategies are effective under the assessed fire scenario. However, the partial downward evacuation is completed more swiftly—in 385.5 s compared to 494.8 s for upward evacuation—thereby mitigating smoke inhalation risks, as the smoke height remains above the critical threshold of 1.8 m for a longer duration than observed in the upward evacuation scenario. Simulations further indicate that neither high temperatures nor carbon monoxide concentrations reach hazardous levels in either evacuation mode, ensuring evacuee safety. The study concludes that, with appropriate training arrangements and under specific fire and evacuation conditions, the partial downward evacuation strategy is safer and more efficient than upward evacuation. Full article

The increasing pace of climate-driven changes in forest ecosystems calls for reliable remote sensing techniques for quantifying above-ground carbon storage. In this article, we compare the methodology and results of traditional field surveys, mobile laser scanning, optical drone imaging and photogrammetry, and both drone-based and light aircraft-based aerial laser scanning to determine forest stand parameters, which are suitable to estimate carbon stock. Measurements were conducted at four designated sampling points established during a large-scale project in deciduous and coniferous tree stands of the Dudles Forest, Hungary. The results of the surveys were first compared spatially and quantitatively, followed by a summary of the advantages and disadvantages of each method. The mobile laser scanner proved to be the most accurate, while optical surveying—enhanced with a new diameter measurement methodology based on detecting stem positions from the photogrammetric point cloud and measuring the diameter directly on the orthorectified images—also delivered promising results. Aerial laser scanning was the least accurate but provided coverage over large areas. Based on the results, we recommend adapting our carbon stock estimation methodology primarily to mobile laser scanning surveys combined with aerial laser scanned data. Full article

Carbon dioxide (CO₂) is a predominant greenhouse gas significantly contributing to atmospheric heat retention, primarily driven by anthropogenic activities intensifying the greenhouse effect. This study aims to evaluate the diversity of plant species, above-ground biomass (AGB), and carbon stock within a dry dipterocarp forest, which is a vital local natural resource. This study presents a comprehensive evaluation of plant species diversity, AGB, and carbon stock capacity within a dry dipterocarp forest at the Nature Study Center, Mahasarakham University, located in the Kham Riang Subdistrict of Kantharawichai District, Maha Sarakham Province, spanning an area of 20.80 hectares. Ten sample plots, each measuring 40 × 40 m, were established and distributed across the study area. The diameter at breast height (DBH) and the height of the trees were meticulously recorded for all trees within these plots. Advanced statistical techniques were employed to calculate the relative dominance (RD), relative frequency (RF), and Importance Value Index (IVI), alongside a comprehensive assessment of plant species diversity. The AGB was assessed using precise allometric equations, with a focus on analyzing carbon storage within woody biomass. The findings revealed the presence of 52 tree species across 26 families within the forest. The total AGB was measured at 144.510 tons, with carbon stock reaching 67.920 tCO₂. These results offer critical insights into enhancing land management strategies to optimize carbon stock, thereby playing a vital role in mitigating greenhouse gas emissions, a significant factor in climate change dynamics. Full article

Carbon (C), nitrogen (N), and phosphorus (P) act as pivotal regulators of biogeochemical cycles, steering organic matter decomposition and carbon sequestration in terrestrial ecosystems through the stoichiometric properties of photosynthetic organs. Deciphering their multi-scale spatiotemporal dynamics is central to unraveling plant nutrient strategies and their coupling mechanisms with global element cycling. In the current study, we modeled biogeochemical parameters (C/N/P contents, stoichiometry, and pools) in plant aboveground parts by using the growing mean temperature, total precipitation, total radiation, and maximum normalized difference vegetation index (NDVImax) across nine models (i.e., random forest model, generalized boosting regression model, multiple linear regression model, artificial neural network model, generalized linear regression model, conditional inference tree model, extreme gradient boosting model, support vector machine model, and recursive regression tree) in Xizang grasslands. The results showed that the random forest model had the highest predictive accuracy for nitrogen content, C:P, and N:P ratios under both grazing and fencing conditions (training R² ≥ 0.61, validation R² ≥ 0.95). Additionally, the random forest model had the highest predictive accuracy for C:N ratios under fencing conditions (training R² = 0.84, validation R² = 1.00), as well as for C pool and P content and pool under grazing conditions (training R² ≥ 0.62, validation R² ≥ 0.90). Therefore, the random forest algorithm based on climate data and/or the NDVImax demonstrated superior predictive performance in modeling these biogeochemical parameters. Full article

Above-ground biomass (AGB) is a core indicator for evaluating grassland ecosystem health and carbon storage. Traditional ground-based AGB measurements are labor-intensive and ill suited for large-scale monitoring. This study addresses this gap by developing a Multilayer Perceptron (MLP) model integrating Landsat 9 OLI/TIRS imagery acquired on 15 August 2024, with ground data from 78 sampling points (62 training, 16 testing). Incorporating fourteen multi-source features (seven vegetation indices, e.g., Modified Vegetation Index (MVI) and Green Chlorophyll Index (CIg); four meteorological variables; three soil properties), all data were standardized via z-score normalization before training. The MLP model, optimized via six-fold cross-validation, achieved an R² of 0.765 and RMSE of 38.066 g/m², outperforming XGBoost (R² = 0.723, RMSE = 41.354 g/m²) with a statistically significant 5.8% accuracy improvement (p < 0.05). Spatial analysis revealed a north-to-south AGB gradient, strongly correlated with precipitation gradients (250–350 mm/year) and soil organic carbon (R = 0.428). These findings provide a robust framework for climate-adaptive grassland management and carbon assessment in semi-arid regions. Full article

Cocoa (Theobroma cacao) is one of the world’s most traded commodities. Cocoa grown in agroforestry systems is considered a climate-smart agricultural practice, in part due to the role of shade trees as carbon reservoirs and carbon sinks. In Brazil, most production is concentrated in Bahia state, where traditional cocoa agroforests—locally known as cabrucas—are well known to harbor significant above- and below-ground carbon stocks, although their ability to act as carbon sinks is less well established. By analyzing previously published data on the dynamics of tree assemblages within a 1.7 ha area on a cabruca farm, we estimated an annual carbon increment of 3.46 Mg C ha⁻¹, a value comparable to other shade cocoa plantations elsewhere but more than three times the previous estimate for a cabruca. We discuss the importance of these findings and highlight the potential role of traditional cocoa shade plantations as climate-friendly crops, thus contributing to climate mitigation. It is also essential to highlight the importance of the carbon sequestration and storage services provided by tropical agroforestry systems. Full article