Search Results (500)

With the ongoing advancement of industrialization and rapid urbanization, the emission of volatile organic compounds (VOCs) has increased significantly. As key precursors of PM_2.5 and ozone formation, VOCs pose a growing threat to the health of ecosystems. Due to their complex and dynamic transformation processes across air, water, and soil media, the ecological risks associated with VOCs have attracted increasing attention from both the scientific community and policy-makers. This study systematically reviews the core literature on the ecological impacts of VOCs published between 2005 and 2024, based on data from the Web of Science and Google Scholar databases. Utilizing three bibliometric tools (CiteSpace, VOSviewer, and Bibliometrix), we conducted a comprehensive visual analysis, constructing knowledge maps from multiple perspectives, including research trends, international collaboration, keyword evolution, and author–institution co-occurrence networks. The results reveal a rapid growth in the ecological impact of VOCs (EIVOCs), with an average annual increase exceeding 11% since 2013. Key research themes include source apportionment of air pollutants, ecotoxicological effects, biological response mechanisms, and health risk assessment. China, the United States, and Germany have emerged as leading contributors in this field, with China showing a remarkable surge in research activity in recent years. Keyword co-occurrence and burst analyses highlight “air pollution”, “exposure”, “health”, and “source apportionment” as major research hotspots. However, challenges remain in areas such as ecosystem functional responses, the integration of multimedia pollution pathways, and interdisciplinary coordination mechanisms. There is an urgent need to enhance monitoring technology integration, develop robust ecological risk assessment frameworks, and improve predictive modeling capabilities under climate change scenarios. This study provides scientific insights and theoretical support for the development of future environmental protection policies and comprehensive VOCs management strategies. Full article

Vegetation and soil indices are able to indicate patterns of gradual plant growth. Therefore, productivity data may be used to predict performance in the development of pastures prior to grazing, since the morphology of the pasture follows repetitive cycles through the grazing of animals. Accordingly, in recent decades, much attention has been paid to the monitoring and development of vegetation by means of remote sensing using remote sensors. The current study seeks to determine the differences between three remote sensing products in the monitoring and development of white clover and perennial ryegrass ratios. Various grass and legume associations (perennial ryegrass, Lolium perenne, and white clover, Trifolium repens) were evaluated in different proportions to determine their yield and relationship through vegetation and soil indices. Four proportions (%) of perennial ryegrass and white clover were used, being 100:0; 90:10; 80:20 and 70:30. Likewise, to obtain spectral indices, a Spectral Evolution PSR-1100 spectroradiometer was used, and two UAVs with a MAPIR 3W RGNIR camera and a Parrot Sequoia multispectral camera, respectively, were employed. The data collection was performed before and after each cut or grazing period in each experimental unit, and post-processing and the generation of spectral indices were conducted. The results indicate that there were no significant differences between treatments for yield or for vegetation indices. However, there were significant differences in the index variables between sensors, with the spectroradiometer and Parrot obtaining similar values for the indices both pre- and post-grazing. The NDVI values were closely correlated with the yield of the forage proportions (R² = 0.8948), constituting an optimal index for the prediction of pasture yield. Full article

According to Victor Ernest Shelford’s ‘Law of Tolerance,’ organisms within ecosystems thrive optimally when environmental conditions are favorable. Applying this principle to ecosystems and agro-ecosystems facing water scarcity or environmental challenges can significantly enhance their productivity. In these ecosystems, phytocenosis adjusts its conditions by utilizing water with varying salinity levels. Moreover, establishing optimal drinking water conditions for human populations within an ecosystem can help mitigate future negative succession processes. The purpose of this study is to evaluate the quality of two distinct water sources in the Amudarya district of the Republic of Karakalpakstan, Uzbekistan: collector-drainage water and groundwater at depths of 10 to 25 m. This research is highly relevant in the context of climate change, as improper management of water salinity, particularly in collector-drainage water, may exacerbate soil salinization and degrade drinking water quality. The primary methodology of this study is as follows: The Food and Agriculture Organization of the United Nations (FAO) standard for collector-drainage water is applied, and the water quality index is assessed using the CCME WQI model. The Canadian Council of Ministers of the Environment (CCME) model is adapted to assess groundwater quality using Uzbekistan’s national drinking water quality standards. The results of two years of collected data, i.e., 2021 and 2023, show that the water quality index of collector-drainage water indicates that it has limited potential for use as secondary water for the irrigation of sensitive crops and has been classified as ‘Poor’. As a result, salinity increased by 8.33% by 2023. In contrast, groundwater quality was rated as ‘Fair’ in 2021, showing a slight deterioration by 2023. Moreover, a comparative analysis of CCME WQI values for collector-drainage and groundwater in the region, in conjunction with findings from Ethiopia, India, Iraq, and Turkey, indicates a consistent decline in water quality, primarily due to agriculture and various other anthropogenic pollution sources, underscoring the critical need for sustainable water resource management. This study highlights the need to use organic fertilizers in agriculture to protect drinking water quality, improve crop yields, and promote soil health, while reducing reliance on chemical inputs. Furthermore, adopting WQI models under changing climatic conditions can improve agricultural productivity, enhance groundwater quality, and provide better environmental monitoring systems. Full article

The rapid advancement of artificial intelligence is transforming agriculture by enabling data-driven plant disease monitoring and decision support. Soil-borne mosaic wheat virus (SBWMV) is a soil-transmitted virus disease that poses a serious threat to wheat production across multiple ecological zones. Due to the regional variability in environmental conditions and symptom expressions, accurately evaluating the severity of wheat soil-borne mosaic (WSBM) infections remains a persistent challenge. To address this, the problem is formulated as large-scale group decision-making process (LSGDM), where each planting plot is treated as an independent virtual decision maker, providing its own severity assessments. This modeling approach reflects the spatial heterogeneity of the disease and enables a structured mechanism to reconcile divergent evaluations. First, for each site, field observation of infection symptoms are recorded and represented using intuitionistic fuzzy numbers (IFNs) to capture uncertainty in detection. Second, a Bayesian graph convolutional networks model (Bayesian-GCN) is used to construct a spatial trust propagation mechanism, inferring missing trust values and preserving regional dependencies. Third, an enhanced spectral clustering method is employed to group plots with similar symptoms and assessment behaviors. Fourth, a feedback mechanism is introduced to iteratively adjust plot-level evaluations based on a set of defined agricultural decision indicators sets using a multi-granulation rough set (ADISs-MGRS). Once consensus is reached, final rankings of candidate plots are generated from indicators, providing an interpretable and evidence-based foundation for targeted prevention strategies. By using the WSBM dataset collected in 2017–2018 from Walla Walla Valley, Oregon/Washington State border, the United States of America, and performing data augmentation for validation, along with comparative experiments and sensitivity analysis, this study demonstrates that the AI-driven LSGDM model integrating enhanced spectral clustering and ADISs-MGRS feedback mechanisms outperforms traditional models in terms of consensus efficiency and decision robustness. This provides valuable support for multi-party decision making in complex agricultural contexts. Full article

Understanding soil infiltration at the watershed level is crucial to hydrological studies, as it significantly influences surface runoff, groundwater replenishment, and ecosystem sustainability. Research in this area—particularly employing the Soil and Water Assessment Tool (SWAT)—has seen sustained scholarly interest, with an upward trend in related publications. This study analyzed 141 peer-reviewed articles from the Web of Science (WOS) Core Collection. By applying bibliometric techniques through CiteSpace visualization software, it explored the key themes and emerging directions in the use of the SWAT model for soil infiltration studies across watersheds. Findings revealed that this field integrates multiple disciplines. Notably, the Journal of Hydrology and Hydrological Processes emerged as two of the most impactful publication venues. Researchers and institutions from the United States, China, and Ethiopia were the core contributors to this area. “Land use” and “climate change” are currently the hotspots of interest in this field. There are three development trends: (1) The scale of research is continuously expanding. (2) The research subjects are diversified, ranging from initially focusing on agricultural watersheds to surrounding areas such as hillsides, grasslands, and forests. (3) The research content becomes more systematic, emphasizing regional coordination and ecological sustainability. Overall, the research on soil infiltration at the watershed scale using the SWAT model presents a promising and thriving field. This study provides researchers with a framework that objectively presents the research hotspots and trends in this area, serving as a valuable resource for advancing academic inquiry in this domain. Full article

This study presents a discrete event simulation model to minimize operating costs in quarry mining processes by determining the optimal allocation of backhoes and dump trucks, which are the primary mining equipment. The modeling focuses on four principal vehicle types (24-ton dump truck, 2.0 m³ backhoe, 41-ton dump truck, 4.64 m³ backhoe) commonly deployed in quarry mining. The simulation replicates the sequential mining stages involving soil removal, rock ripping (weathered rock or weathered soil), and blasting operations. This methodology is applied to a case study of mining process planning under resource constraints, incorporating real-world quarry conditions in South Korea. Results demonstrate that optimizing the number of equipment units reduces construction costs and shortens the construction period by decreasing dump truck waiting times. When the number of backhoes is limited to 10 during operations, findings indicate an increase in costs and a gradual decline in net profit. Additionally, the interaction between the 24-ton and 41-ton dump trucks is shown to influence the optimal allocation strategy. The simulation-based optimization executes iterative experiments for each scenario, yielding statistically robust results within a 95% confidence interval, thereby supporting informed decision-making for managers. Full article

Artemisia absinthium L. contributes to ecological stabilization in arid regions through its deep root system for sand fixation and soil microenvironment modulation, thereby effectively mitigating desertification. Total terpenoids have been extracted from A. absinthium (AATP) and found to have antioxidant and anti-inflammatory activities. Terpenoids are a class of natural products derived from methyl hydroxypropanoic acid, for which their structural units consist of multiple isoprene (C5) units. They are one of the largest and most structurally diverse classes of natural compounds. However, there are still large gaps in knowledge regarding their exact biological activities and effects. Atherosclerosis (AS) is a prevalent cardiovascular disease marked by the chronic inflammation of the vascular system, and lipid metabolism plays a key role in its pathogenesis. This study determined the extraction and purification processes of AATP through single-factor experiments and response surface optimization methods. The purity of AATP was increased from 20.85% ± 0.94 before purification to 52.21% ± 0.75, which is 2.5 times higher than before purification. Studies have shown that the total terpenoids of A. absinthium significantly reduced four indices of serum lipids in atherosclerosis (AS) rats, thereby promoting lipid metabolism, inhibiting inflammatory processes, and hindering aortic wall thickening and hepatic fat accumulation. It is known from network pharmacology studies that AATP regulates the Janus kinase/signal transducer (JAK/STAT) signaling axis. Molecular docking studies have indicated that the active component of AATP effectively binds to Janus kinase (JAK2) and signal transducer (STAT3) target proteins. The results indicate that AATP can inhibit the release of pro-inflammatory mediators (such as reactive oxygen species (ROS)) in LPS-induced RAW264.7 macrophages. It also inhibits the M1 polarization of RAW264.7 macrophages. Protein immunoblotting analysis revealed that it significantly reduces the phosphorylation levels of Janus kinase (JAK2) and the signal transducer and activator of transcription 3 (STAT3). Research indicates that the active components in A. absinthium may exert anti-atherosclerotic effects by regulating lipid metabolism and inhibiting inflammatory responses. It holds potential value for development as a functional food or drug for the prevention and treatment of atherosclerosis. Full article

This research evaluated the recovery and reuse of dolomitic calcareous amendment saturated with nutrients adsorbed from hydroponic effluent as a soil improver and its impact on the agronomic performance of Phaseolus vulgaris. Initially, the dolomitic calcareous amendment (DCA) was saturated with nutrients from the hydroponic effluent through adsorption tests. The characterization of the DCA was conducted before and after nutrient saturation to verify its composition. Soil analysis was carried out prior to the trial, and a completely randomized experimental design was applied with four treatments and five replications, totaling 20 experimental units for each soil type (sandy and clayey): T1 (control), T2 (raw dolomitic calcareous amendment—DCA), T3 (saturated dolomitic calcareous amendment—DCAS), and T4 (granulated dolomitic calcareous amendment—DCAG). Agronomic performance parameters of Phaseolus vulgaris were assessed to determine nutrient availability to the plant: number of pods, pod length (cm), number of seeds per pod, and weight of 100 seeds (g). Data normality was verified using the Shapiro–Wilk test, and results were analyzed using ANOVA and mean comparisons through Tukey’s test (p < 0.05) using InfoStat software 2020I. Additionally, plant tissue was analyzed to determine nutrient absorption in the seeds, and both soil types were analyzed after harvest. Adsorption results indicated that the DCA retained phosphorus, manganese, calcium, and zinc. According to the characterization, DCA primarily consisted of calcium and magnesium carbonates; following the saturation process, an increase in carbonate groups was observed due to calcium adsorption from the hydroponic effluent. Results in both soil types showed no significant differences in pod number, pod length, or seeds per pod, except for the weight of 100 seeds in sandy soil, where T1, T2, and T3 differed significantly from T4. Based on references, the phosphorus content in the harvested seeds from T3 in sandy soil is classified as sufficient. The findings demonstrate the potential of recovering and reusing nutrients from hydroponic effluent using DCA and transforming it into a value-added agricultural input for soil improvement, presenting a promising alternative for more sustainable and efficient agriculture. Full article

Tropical montane cloud forests (TMCFs) are biodiversity hotspots that have been increasingly cleared to cultivate coffee under full sun exposure, replacing traditional shaded agroforestry systems. This study evaluated the impact of TMCF clearing on soil quality by analyzing 108 samples from undisturbed primary and secondary forests and deforested coffee plantations in the Dominican Republic and Haiti. Our findings indicate that forest clearing has a substantial adverse impact on soil nutrient status. Soils from undisturbed plots had total organic carbon (TOC) concentrations 4.83 units higher than those from cleared plots. Nitrogen levels were reduced by 28–61%, and available potassium declined by 23–51% in soils that had been cleared. Conversely, the available phosphorus levels exhibited a modest increase (ranging from 23% to 27%) following the clearing process, presumably attributable to diminished plant uptake and augmented mineralization in conditions characterized by diminished organic matter. However, given that phosphorus is not a limiting factor for coffee growth, this marginal gain does not compensate for the broader degradation of soil fertility. The study emphasizes that allowing TMCFs to be used for sun-grown coffee results in long-term nutrient depletion through erosion and leaching, which poses a threat to both the productivity of the soil and the ecological integrity of these valuable forest systems. Full article

The study examined seasonal variability in soil enzymatic activity and microbial abundance across five grassland vegetation units: Molinietum caeruleae, Alopecuretum pratensis, Arrhenatheretum elatioris, Lolio–Cynosuretum, and com. Poa pratensis–Festuca rubra. Soils under Molinietum caeruleae showed higher fungal abundance and greater plant diversity, while Lolio–Cynosuretum was notable for elevated Azotobacter spp. populations. Actinobacteria preferred soils with more organic matter, whereas Azotobacter spp. favored higher pH. A negative correlation was observed between the Shannon diversity index (H′) and heterotrophic bacterial abundance in Arrhenatheretum elatioris and with fungal abundance in com. Poa pratensis–Festuca rubra. Acid and alkaline phosphatase and catalase activities were also negatively correlated with H′. Redundancy analysis showed these enzymes were related to total nitrogen content, and enzyme activity decreased with rising soil pH. In autumn 2022, high fungal abundance coincided with a reduction in other microorganisms. Seasonal trends were evident: catalase and urease activities peaked in autumn 2023, while other enzymes were more active in spring 2022. The results emphasize the significance of seasonal shifts in shaping microbial and enzymatic soil processes, which are vital for nutrient cycling, carbon sequestration, and climate regulation. Further research is essential to guide sustainable grassland soil management. Full article

Groundwater storage refers to the water stored in the pore spaces of underground aquifers, which has been increasingly affected by both climate change and anthropogenic activities in recent decades. Therefore, monitoring their changes and the factors that affect it is of great importance. Although the influence of natural factors on groundwater is well-recognized, the impact of human activities, despite being a major contributor to its change, has been less explored due to the challenges in measuring such effects. To address this gap, our study employed an integrated approach using remote sensing and the Google Earth Engine (GEE) cloud-free platform to analyze the effects of various anthropogenic factors such as built-up areas, cropland, and surface water on groundwater storage in the Lake Urmia Basin (LUB), Iran. Key anthropogenic variables and groundwater data were pre-processed and analyzed in GEE for the period from 2000 to 2022. The processes linking these variables to groundwater storage were considered. Built-up area expansion often increases groundwater extraction and reduces recharge due to impervious surfaces. Cropland growth raises irrigation demand, especially in semi-arid areas like the LUB, leading to higher groundwater use. In contrast, surface water bodies can supplement water supply or enhance recharge. The results were then exported to XLSTAT software2019, and statistical analysis was conducted using the Mann–Kendall (MK) non-parametric trend test on the variables to investigate their potential relationships with groundwater storage. In this study, groundwater storage refers to variations in groundwater storage anomalies, estimated using outputs from the Global Land Data Assimilation System (GLDAS) model. Specifically, these anomalies are derived as the residual component of the terrestrial water budget, after accounting for soil moisture, snow water equivalent, and canopy water storage. The results revealed a strong negative correlation between built-up areas and groundwater storage, with a correlation coefficient of −1.00. Similarly, a notable negative correlation was found between the cropland area and groundwater storage (correlation coefficient: −0.85). Conversely, surface water availability showed a strong positive correlation with groundwater storage, with a correlation coefficient of 0.87, highlighting the direct impact of surface water reduction on groundwater storage. Furthermore, our findings demonstrated a reduction of 168.21 mm (millimeters) in groundwater storage from 2003 to 2022. GLDAS represents storage components, including groundwater storage, in units of water depth (mm) over each grid cell, employing a unit-area, mass balance approach. Although storage is conceptually a volumetric quantity, expressing it as depth allows for spatial comparison and enables conversion to volume by multiplying by the corresponding surface area. Full article

It is essential to emphasize the significant impacts of climate change, which are evident in the form of severe and prolonged droughts, hurricanes, snowstorms, and other climatic disturbances. These challenges are particularly pronounced in urban environments and among human populations. The situation is further aggravated by the increasing utilization of available open spaces for residential and industrial development, leading to heightened energy consumption, elevated pollution levels, and increased carbon emissions, all of which negatively affect public health. The primary objective of this review article is to provide a comprehensive evaluation of current research, with a particular focus on the innovative use of residual biomass from urban vegetation for biochar production in the United States. This research entails an exhaustive review of existing literature to assess the implementation of a carbon-negative wood biomass biochar system as a strategic approach to sustainable urban management. By transforming urban wood waste—including tree trimmings, construction debris, and storm-damaged timber—into biochar through pyrolysis, a thermochemical process that sequesters carbon while generating renewable energy, we can leverage this valuable resource. The resulting biochar offers a range of co-benefits: it enhances soil health, improves water retention, reduces stormwater runoff, and lowers greenhouse gas emissions when applied in urban green spaces, agriculture, and land restoration projects. This review highlights the advantages and potential of converting urban wood waste into biochar while exploring how municipalities can strengthen their green ecosystems. Furthermore, it aims to provide a thorough understanding of how the utilization of woody biomass biochar can contribute to mitigating urban carbon emissions across the United States. Full article

Soil moisture (SM) plays a fundamental role in the water cycle and is an important variable for all processes occurring at the lithosphere–atmosphere interface, which are strongly affected by climate change. Among the different fields of application, accurate SM measurements are becoming more relevant for all studies related to extreme event (e.g., floods, droughts, and landslides) mitigation and assessment. In this study, data acquired by the advanced microwave sounding unit (AMSU) onboard the European Meteorological Operational Satellite Program (MetOP) satellites were used for the first time to extract information on the variability of SM by implementing the original soil wetness index (SWI). Long-term monthly SWI time series collected for the Basilicata region (southern Italy) were analyzed for drought assessment during the period 2007–2021. The accuracy of the SWI product was tested through a comparison with SM products derived by the Advanced SCATterometer (ASCAT) over the 2013–2016 period, while the Standardized Precipitation-Evapotranspiration Index (SPEI) was used to assess the relevance of the long-term achievements in terms of drought analysis. The results indicate a satisfactory accuracy of the SWI, with the mean correlation coefficient values with ASCAT higher than 0.7 and a mean normalized root mean square error less than 0.155. A negative trend in SWI during the 15-year period was found using both the original and deseasonalized series (linear and Sen’s slope ~−0.00525), confirmed by SPEI (linear and Sen’s slope ~−0.00293), suggesting the occurrence of a marginal long-term dry phase in the region. Although further investigations are needed to better assess the intensity and main causes of the phenomena, this result indicates the contribution that satellite data/products can offer in supporting drought assessment. Full article

For every tractor test carried out on a concrete road under defined conditions, the Nebraska Tractor Test Laboratory (NTTL) provides values of the specific volumetric fuel efficiency (SVFE) in unit of kWh/L). Because soil tillage is a highly energy-intensive process and the energy consumption of tillage operations is a significant component of a farm budget, there is a growing amount of attention being given to the examination of the SVFE for tillage operations. Nonetheless, the study of the tillage process and a scientific approach to the tillage process are becoming more and more dependent on scientific modeling. Therefore, in this study based on real-tillage field operation, an artificial neural network (ANN) model was built to predict SVFE. This study aimed to confirm that the ANN model could incorporate 10 inputs for prediction: initial soil moisture content, draft force, initial soil bulk density, sand, silt, and clay proportions in the soil tractor power, plow width, tillage depth, and tillage speed. The Qnet v2000, as an ANN simulation software, was employed for the simulation of the SVFE. In this regard, 20,000 runs of Qnet v2000 were completed for the training and testing stages. The anticipated results displayed that the determination coefficient (R²) was larger than 0.96; using the training dataset, R² was 0.982 and using the testing dataset, R² was 0.9741, indicating that the recognition of a full ANN model makes it likely to reply to essential enquiries that were previously unanswerable regarding the impact of working and soil conditions on the SVFE of a tractor–tillage implement system. Additionally, sensitivity analyses were completed to specify which modeled parameters were more sensitive to the factors using the obtained ANN model. According to the sensitivity analysis, SVFE was more affected by changes in the tillage speed (21.07%), silt content in the soil (15.56%), draft force (11.01%), and clay content in the soil (10.86%). Predicting SVFE can lead to more appropriate decisions on tractor–chisel plow combination management. Therefore, it is highly advisable to use the newly created ANN model to appropriately manage SVFE to reduce tractor–tillage implement energy dissipation. Additionally, suitable management of some variables, for example, tillage depth, tillage speed, and soil moisture content, can help enhance fuel consumption in the tractor–tillage implementation system. Full article

To efficiently delineate mineral-induced geochemical anomalies within the Hongyahuo area, principal component analysis (PCA), S-A multifractal modeling, and singularity modeling were employed to examine multi-element datasets derived from 641 soil samples collected from natural gully systems. The isometric log-ratio (ilr) transformation was implemented in conjunction with histogram and quantile-quantile plot analysis to assess and compare the multivariate statistical properties of elemental data across three formats—original, logarithmic, and ilr-transformed. The findings demonstrate the following: (1) following ilr transformation, the issue of data closure was resolved, resulting in elemental distributions more closely approximating normality; (2) PCA revealed two distinguishable elemental associations: PC1 corresponds to the Cu-Fe-Mn-Ni-Pb-Zn group, indicative of a medium- to high-temperature hydrothermal mineralization assemblage associated with Cu-Pb-Zn polymetallic mineralization linked to magmatic intrusion and fracture systems, signifying overprinted copper polymetallic mineralization events; PC2 reflects the Au-As-Sb elemental combination, associated with low-temperature hydrothermal processes indicative of Au-Sb mineralization; (3) the decomposition of the S-A model indicated that low-background and high-anomaly zones for PC1 are primarily situated within andesitic units, where nearby intermediate to felsic intrusions and structural fracture zones have likely served as sources for Cu-polymetallic mineralization; (4) the spatial distribution of the singularity index suggested that anomalous regions characterized by a PC1 singularity index α < 2 were relatively confined, offering strategic implications for mineral exploration targeting; and (5) when integrated with regional metallogenic background, three prospecting targets were identified, leading to the subsequent discovery of two copper ore bodies through anomaly validation. Therefore, this integrative analytical framework is demonstrated to be a robust approach for delineating geochemical anomalies. Full article