Search Results (1,377)

To reveal the chemical composition and concentration distribution, spatial distribution characteristics, source composition, and health impacts of volatile organic compounds (VOCs) in the two multi-industry industrial parks in Tianjin, 57 VOC species were determined by using SUMMA canister sampling with preconcentration gas chromatography/mass spectrometry. Based on these measurements, the Positive Matrix Factorization (PMF) model was used for source analysis to achieve quantitative identification and contribution analysis of different source factors. The health risks of VOC concentrations were analyzed based on the assessment framework recommended by the United States Environmental Protection Agency (USEPA). The results showed that the average concentrations of TVOCs in A-1 (Industrial Park and Cluster A, 5 m), A-2 (Industrial Park and Cluster A, 10 m), B-1 (Industrial Park and Cluster B, 5 m), and B-2 (Industrial Park and Cluster B, 10 m) were 59.72 ppbv, 45.35 ppbv, 32.44 ppbv, and 21.65 ppbv, respectively. TVOC concentrations were higher at A-1 and B-1 than at A-2 and B-2, and were generally higher at site A than at site B. The VOC components were mainly alkanes (53.5%–71.4%), followed by aromatic hydrocarbons (15.6%–36.2%), alkenes (4.6%–9.9%), and alkynes (3.1%–7.6%). The proportion of aromatic hydrocarbons in A was higher (22.13% and 36.22%), while alkanes dominated absolutely in B (71.4% and 66.9%). n-Hexane was the key species driving the spatial differences (a typical source of VOCs in solvent usage). The concentration in A-1 was 3.1 times that of A-2, and B-1 was 3.3 times that of B-2. It was mainly controlled by local solvent unorganized emissions. The differences in species between the two points at site A for toluene, xylene, etc., were relatively gentle, while at site B, the concentration of the same aromatic hydrocarbons at B-1 was significantly higher than that at B-2, presenting a clearer spatial characteristic, indicating differences in the spatial distribution of organic solvent-related industrial activities in different sites. The source analysis showed that A-1 was dominated by solvent usage (38.64%) and natural gas/LPG sources (36.96%); A-2 by vehicle exhaust (40.20%) and natural gas/LPG sources (36.15%); B-1 by cleaning-agent usage (38.81%) and fuel combustion (30.33%); and B-2 by plastic and rubber production (36.90%) and fuel combustion (31.84%). The health risk assessment showed that benzene, n-hexane, and xylene dominated the non-carcinogenic risks, but the overall non-carcinogenic (HI < 1) and carcinogenic (CR < 1 × 10⁻⁶) risks were both below the threshold. This study reveals inter-site differences in VOC concentrations, compositions, and source contributions across two mixed industrial parks, providing a basis for site-specific VOC control priorities, source-targeted monitoring strategies, and risk management in mixed industrial areas. Full article

In crisis times, traditional models of water quality assessment and water resources management lose their effectiveness. In the current conditions of local climate change, accidental pollution, emergencies or military operations, there is an urgent need to transition from traditional descriptive hydrochemical monitoring toward intelligent analysis of spatial-temporal datasets. In this paper, the integrated approach combining spatial cluster analysis, GIS-based visualization, and machine learning is proposed for assessing the surface water quality under conditions of limited and incomplete hydrochemical monitoring data. A geospatial assessment of nutrient impacts on surface water quality was conducted using 192 hydrochemical observations collected during the 2024–2025 monitoring period at eight state monitoring stations located in the basins of the Teteriv, Uzh, Irsha, Ubort, Sluch, Hnylopiat, and Voznia rivers, Polissia, Ukraine. Permutation feature importance analysis based on the Random Forest model showed that nitrate concentration accounted for approximately 75% of the total relative importance, whereas phosphate concentration contributed approximately 14%, indicating that these variables were the most informative predictors among the investigated hydrochemical parameters. The latter parameters are associated with dissolved oxygen variability among the analyzed hydrochemical parameters. According to the results of this study, three interpretable groups of monitoring stations were formed: Cluster 1, representing moderate water quality with increased nutrient pressure, Cluster 2, representing comparatively favourable background conditions, and Cluster 3, representing a nitrate-dominated hydrochemical type. The Random Forest model demonstrated limited predictive performance (R² = 0.154), indicating that nutrient-related variables alone explain only a small proportion of dissolved oxygen variability. Hence, additional factors, including hydrological conditions, water temperature, organic matter decomposition, biological productivity, and catchment-specific characteristics, also play an important role in shaping oxygen dynamics. The spatial visualization of cluster membership showed that geographical location alone does not fully determine the surface water quality patterns in Ukrainian Polissia. Instead, the local catchment characteristics and land-use conditions appear to exert a stronger influence on the formation of nutrient-related water quality differences. The authors propose to employ the spatial cluster analysis and machine learning as a basic supporting tool for the transition from retrospective interpretation of hydrochemical monitoring data to predictive and adaptive water resources management. The integration of geospatial analysis and machine learning provides a practical decision-support framework for the early detection of anomalies, identification of potential pollution sources, and prioritization of river sub-basins for implementing nature-based solutions. Full article

Honey is a natural food product highly valued for its nutritional and biological properties. Its quality and safety are increasingly affected by environmental contamination and apicultural practices. Among the most relevant contaminants, pesticide residues, veterinary antibiotics, and heavy metals represent major concerns due to their potential impact on human health. This review provides a comprehensive overview of the occurrence, sources, and distribution of these contaminants in honey, with particular emphasis on their relationship with agricultural activities, environmental pollution, and beekeeping treatments. Advanced analytical techniques, including liquid chromatography–tandem mass spectrometry (LC-MS/MS), gas chromatography–mass spectrometry (GC-MS), and inductively coupled plasma–mass spectrometry (ICP-MS), are highlighted for their ability to enable sensitive multi-residue and trace-level detection. The application of chemometric tools for data analysis and sample classification is also addressed, supporting the identification of contamination patterns and origin-related differences. In addition, recent developments in rapid screening methods and environmentally sustainable analytical approaches are discussed. Overall, this review emphasizes the importance of continuous monitoring and the integration of advanced analytical strategies to ensure honey safety, support regulatory compliance, and protect consumers. Full article

The primary aim of this study is to identify how residents on the Hungarian and Slovak sides of the Lower Ipoly Valley perceive the most considerable material and immaterial local values and unique resources of their settlements. The research was designed to explore and compare the territorial capital of the two sides of the study area based on three major capital types: natural, social, and economic capitals. Data collection included 254 semi-structured interviews, conducted with 136 residents on the Slovak side (14 settlements) and 118 residents on the Hungarian side (12 settlements). Detailed interview summaries were analysed with qualitative content analysis using emergent coding. This resulted in an analytical framework comprising nine value dimensions. Across both sides of the border, we examined the same nine value dimensions: local workforce, local enterprises, civil organisations and cultural groups, local and community events, local gastronomy, natural values, built heritage, holders of local knowledge, and local products. Items mentioned that related to each value dimension were counted for each dimension and normalised to a 0–10 scale. The quantified values were aggregated at the settlement level (90 points being the maximum score). The average score of each dimension was also calculated for both sides of the study area. Our findings show that the Hungarian side achieved a total score of 40.4, while the Slovak side reached 36.6, both reflecting a similarly weak–moderate state of the local capitals with minimal differences. This indicates that the two sides of the study area share comparable developmental challenges but also considerable potential for improvement. The Hungarian side performed slightly better in nearly all dimensions except local gastronomy, where the Slovak side proved stronger. The Hungarian side’s relative advantages include a higher presence of local enterprises, a richer network of civil and cultural groups, and more diverse built heritage; in other dimensions, differences are marginal. One of the most pressing regional challenges is improving employment opportunities and stimulating entrepreneurial activity, which are essential for enhancing population retention. Overall, the results indicate that the settlements possess substantial—yet largely underutilised—value assets, whose conscious and consensus-based development could form a strong foundation for creative and innovative local development. Each settlement’s value matrix includes elements that define its uniqueness, enabling the identification of numerous potential development pathways, whether through nature-based educational and recreational programmes, the utilisation of culturally considerable built heritage, the revitalisation of living traditions, or the promotion of local traditional gastronomy on both sides of the study area. Full article

Ensuring long-term agricultural sustainability depends heavily on preserving soil health, a process fundamentally governed by humic substances (HSs) and their vital physicochemical and biological functions. However, because intensive farming rapidly degrades natural HSs reserves, external replenishment has become essential, driving the expansion of the humic preparations market. This article constitutes a comprehensive review of the entire technological chain of humic preparations: from the identification of raw materials, through advanced extraction techniques, up to agrochemical mechanisms in the soil–plant system. Both traditional fossil deposits (leonardite, brown coal, peat) and renewable waste sources fitting into the concept of the circular economy were discussed. Classical alkaline extraction was confronted with green methods such as ultrasound-assisted (UAE), microwave-assisted (MAE) or high voltage electrical discharge (HVED) extraction, which allow for shortening the operation time and reducing the consumption of reagents. Strategies of integrating HSs with mineral fertilizers (coating, liquid formulas, organo-mineral products) and their direct impact on improving nutrient use efficiency (NUE), mitigating plant abiotic stress, agricultural performance, and environmental impact were described in detail. Research perspectives were also presented, including, among others, economic aspects of scaling up humic technologies and an assessment of the development potential of innovative nanofertilizers functionalized with HSs. Full article

The application of natural bioactive compounds in cosmeceutical products, particularly as skin-lightening agents, has received increasing interest. Caulerpa racemosa, a green macroalga of the Chlorophyta division, contains beneficial nutrients that are applicable as food and cosmeceutical ingredients. This study investigated the in vitro effect of the ethanolic extract of C. racemosa (CR) on regulation of melanogenic-related signaling and gene expression in SK-MEL-5 human melanoma-derived cells. Identification of bioactive components revealed that catechin, rutin, and quercetin as flavonoid contents were found in CR extract, analyzed using HPLC. The expressions of microphthalmia-associated transcription factor (MITF), extracellular signal-regulated kinase (ERK) signaling molecules, and melanogenic-related molecules were analyzed via Western blotting and qPCR. The CR extract treatment applied to SK-MEL-5 cells decreased the MITF protein expression level, which correlated with increased pERK expression, and no cytotoxic effect was observed. The subsequent treatment reduced the expression of melanogenesis-related genes (TYR, TYRP1, MC1R, and DCT) that were downstream targets of MITF. This study provides preliminary evidence that CR extract may modulate melanogenesis-related signaling. However, the specific bioactive compounds responsible for the observed effects remain to be identified, as the extract contains a complex mixture of phytochemicals. Further fractionation studies are needed to pinpoint the active constituents. The variability of extract composition due to seasonal and geographical factors should be considered for future standardization. Full article

The BMP2-Smad signaling pathway serves as a central regulator of osteoblast differentiation and bone formation, rendering it a promising target for the discovery of osteogenic agents from natural sources. Nonetheless, direct BMP2-binding ligands derived from complex herbal extracts remain poorly characterized. In the present study, surface plasmon resonance (SPR)-based target fishing against BMP2, in conjunction with UPLC-Q-TOF-MS identification, was employed to screen for bioactive ligands from Periploca forrestii, a traditional Miao medicinal plant used for bone-related conditions. Six caffeoylquinic acid derivatives, namely neochlorogenic acid (NCA), 3-O-caffeoyl-4-O-sinapoylquinic acid, chlorogenic acid (CA), cryptochlorogenic acid (CCA), isochlorogenic acid B (IB), and isochlorogenic acid C (IC), were captured as direct BMP2-binding ligands. All six compounds promoted osteoblast differentiation and mineralization in MC3T3-E1 Subclone 14 cells, with IB displaying the strongest binding affinity and bioactivity. Mechanistically, IB failed to rescue the osteogenic suppression induced by the BMP type I receptor inhibitor LDN-193189, indicating its dependence on BMP signaling. In an LPS-induced inflammatory model, IB significantly reversed the downregulation of key proteins in the BMP2-Smad pathway (p-Smad1, Smad4, and Runx2) and osteogenic marker genes (Osterix, COL1A1, and OCN), demonstrating its capacity to restore osteogenic function under compromised conditions. Collectively, these findings establish that caffeoylquinic acid derivatives, particularly IB, function as BMP2-targeting osteogenic constituents of P. forrestii that activate BMP2-Smad signaling to promote osteoblast differentiation, thereby offering a pharmacological basis for the development of natural product-derived osteogenic agents. Full article

Accelerated urban expansion and socioeconomic growth have led to substantial changes in territorial spatial organization and growing pressures among spatial functions. Therefore, conducting fine-scale identification, functional evaluation, and coordination analysis of production-living-ecological space (PLES) carries practical value for improving spatial allocation and fostering harmonious regional coordination. By integrating remote sensing imagery and point-of-interest (POI) data, this study employed a hierarchical identification method combining the object-oriented random forest method with the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm to map the PLES spatiotemporal evolution in Fuzhou’s rapidly urbanizing territory over the 2012–2024 period. PLES functions were quantitatively assessed through a comprehensive evaluation index system, while the standard deviation ellipse, GeoDetector, and coupling coordination degree models were further employed to reveal their evolutionary patterns, driving mechanisms, and synergistic interactions. The results indicated that ecological-dominated spaces occupied the dominant share (nearly 63%). Meanwhile, living-dominated spaces expanded significantly, growing by 68.93 km² during the study period. The PLES comprehensive function increased by 15.50%, with the living function and production function rising by 57.19% and 61.84%, respectively. Distinct spatial differentiation existed in PLES functions across urban and rural territories, shaping mutually complementary functional systems. The coupling coordination level of PLES functions continuously improved, although notable regional disparities existed, with highly coordinated areas concentrated in midwestern urban areas. PLES functional evolution was jointly driven by natural and socioeconomic factors, which interacted through nonlinear or two-factor enhancement effects. This study provides a replicable methodological framework for fine-scale PLES research and offers scientific support for regional spatial governance and high-quality coordinated development. Full article

Endophytic fungi are promising sources of chemically diverse metabolites. Fusarium species exhibit diverse biological roles, ranging from beneficial applications to detrimental effects, including plant diseases and mycotoxin production. A few reports have investigated Fusarium equiseti bioactivity, but no reports have evaluated its anti-inflammatory potential. In the present study, the ethyl acetate (EtOAc) extract obtained from an endophytic F. equiseti, isolated for the first time from the bulb of Pancratium maritimum L., was evaluated for its in vitro antioxidant, cytotoxic, anti-acetyl- and butyrylcholinesterases, and anti-inflammatory activities. The extract was chemically profiled by LC-MS/MS, evaluated in complementary antioxidant and cytotoxicity screens, and assessed for inhibition of nitric oxide (NO) production in lipopolysaccharide-stimulated RAW264.7 macrophages. In addition, putatively annotated metabolites were further examined by molecular docking against inducible nitric oxide synthase (iNOS), as a promising biological activity providing a target directly aligned with the NO-based assay. The EtOAc extract showed total phenolics content of 29.31 mg GAE/g and a total alkaloid content of 8.43 mg AE/g, with a DPPH radical scavenging IC₅₀ value of 2.18 mg/mL. Cytotoxicity assessment against the RAW264.7 cell line revealed an IC₅₀ value of 49.95 μg/mL, while the Artemia salina lethality assay yielded an LC₅₀ value of 1.86 mg/mL. Notably, the extract demonstrated potent anti-inflammatory activity through nitric oxide inhibition, with an IC₅₀ value of 4.70 μg/mL compared with 18.46 μg/mL for quercetin. LC-MS/MS analysis offered the tentative identification of 31 metabolites in the extract. Docking against iNOS identified fusarioxazin as the highest-scoring ligand (−8.8 kcal/mol), although these computational predictions require experimental validation with purified compounds. The data identify suppression of macrophage NO production as the principal bioactivity of the crude F. equiseti TU-64 extract. Collectively, these findings highlight the potential of endophytic F. equiseti as a promising source of bioactive metabolites for the development of natural therapeutic agents. Full article

Natural resource management under climate change generates interdependent economic, social, and environmental impacts. However, the scientific evidence remains fragmented. This fragmentation limits the design of integrated policies capable of reducing vulnerability and preventing the degradation of natural capital. The objective of this study is to analyze recent scientific literature to assess how natural resource management in the context of climate change simultaneously influences economic stability, social resilience, and environmental sustainability. To this end, a systematic review of literature published in indexed journals on environmental economics, climate change, and natural resource management was conducted, selecting quantitative and mixed-methods studies that examine economic, social, or biophysical impacts associated with environmental degradation, extractive dependence, and adaptation and mitigation strategies. The review integrated research at macroeconomic, microeconomic, and ecological scales, organized using comparative matrices that allowed for the identification of common patterns in indicators of economic loss, emissions, natural capital depreciation, and effects on social welfare. Subsequently, a comparative analysis was conducted to detect relationships between management failures, social vulnerability, and long-term costs, as well as to identify conceptual, methodological, and geographical gaps in the literature. The results show that the degradation of natural resources under climate change produces simultaneous effects on macroeconomic stability, household income, and ecosystem resilience, increasing the costs of inaction when policies are designed sectorally. The evidence synthesized in this review indicates that dependence on extractive activities, limited productive diversification, and institutional weaknesses are frequently associated with greater economic and social vulnerability, particularly in communities dependent on natural resources. The reviewed studies also suggest that adaptation and mitigation strategies that incorporate participatory governance, social capital, and natural capital conservation may contribute to strengthening resilience. However, given the heterogeneity of methodologies, spatial scales, and indicators among the analyzed studies, these findings should be interpreted as evidence of consistent patterns rather than causal relationships. Therefore, integrated approaches that consider economic, social, and environmental dimensions represent a promising direction for sustainable natural resource management under climate change, although further empirical research is required to evaluate their effectiveness across different contexts. Full article

Wild plant biodiversity represents a largely untapped source of chemically diverse metabolites. However, many wild edible species remain poorly characterized despite their potential as sources of antioxidant and anti-inflammatory phytochemicals. Daucus carota L. subsp. carota, commonly known as wild carrot, has attracted interest due to its long history of medicinal use. In this study, seed extracts collected from populations growing in two Italian regions (Lazio and Molise) were investigated to evaluate their phytochemical composition, mineral content, antioxidant activity, and anti-inflammatory potential. The metabolite profiles were characterized by qualitative Liquid Chromatography-Mass Spectrometry analysis, allowing the identification of several phenolic compounds and other secondary metabolites. Antioxidant activity was assessed using in vitro assays, while anti-inflammatory activity was evaluated through the inhibition of cyclooxygenase and soluble epoxide hydrolase, two key enzymes involved in the arachidonic acid cascade. To gain further insight into the possible mechanisms underlying these activities, molecular docking analyses were performed on selected metabolites identified in the extracts, exploring their interactions with the target enzymes. The extracts displayed promising antioxidant and anti-inflammatory properties, while docking results supported the potential role of specific metabolites in enzyme modulation. These findings highlight the value of underexplored wild D. carota seeds as a source of natural compounds with potential applications in the development of nutraceutical and cosmeceutical products. Full article

Categorical raster maps derived from remote sensing classifications are widely used to describe land cover, landforms, and other environmental characteristics. However, these products are usually analysed at the pixel level, which limits the identification of larger spatial structures and coherent landscape units. The J-image Segmentation (JSEG) algorithm provides a promising framework for region delineation, but it was originally developed for natural color imagery and relies on fixed scale assumptions that are poorly suited to thematic geospatial data. To address these limitations, we developed GeoJSEG, a modified version of JSEG designed for remote sensing categorized spatial datasets. The method operates directly on classified raster layers, introduces user-defined scale parameters, and employs Jensen–Shannon Divergence during region merging, enabling segmentation that better reflects the spatial organization of geographic phenomena. GeoJSEG was evaluated using synthetic categorical maps, natural RGB images, orthophoto-derived data, land-cover maps, and geomorphon representations of terrain forms. For the synthetic class map, the segmentation quality measure $\overline{J}$ decreased from 0.088 to 0.012 (better quality), while for orthophoto data, it decreased from 0.059 to 0.025 (better quality). Improvements were also observed for land-cover data and most natural-image datasets. The results demonstrate that GeoJSEG extends JSEG toward scale-aware regionalization of thematic raster data and provides a practical tool for post-classification analysis of remote sensing products. Full article

In the context of product diversification and personalized requirement, deeply exploring the unstructured characteristics of online reviews and conducting differentiated analyses empowered by big data can enhance the precision of differentiated product design. However, although traditional research has recognized the asymmetry between satisfaction and dissatisfaction when analyzing online reviews, insufficient attention has been paid to the asymmetry of emotional causes, resulting in deviations in product design and limitations in satisfaction improvement. Accordingly, this study takes intelligent In-Vehicle Infotainment Systems (IVISs) of New Electric Vehicles (NEVs) as a case, employs fine grained structural deconstruction methods and natural language analysis to analyze factors influencing satisfaction, examines the types of cause symmetry, proposes a cause asymmetry analysis framework, and conducts differentiated product design strategy analysis for IVISs from the perspective of cause asymmetry. The findings indicate that both symmetry and asymmetry exist between the factors causing user satisfaction and dissatisfaction. Symmetry mainly involves single technical or functional indicators, and their causes exhibit linear symmetric characteristics. Asymmetry manifests in four forms, specifically embodied asymmetry, pluralistic asymmetry, interval asymmetry, and contradictory asymmetry. Based on different symmetry characteristics, this study integrates the theory of inventive problem solving (TRIZ) method to propose targeted product design strategies to better satisfy users’ differentiated requirements. The results of this study not only provide a digital intelligence path of requirement identification, differentiated design, and innovative iteration for optimizing intelligent IVISs of NEVs, but its universal methodology also offers a powerful tool for broader intelligent interaction product innovation. Full article

Natural fractures strongly influence fluid flow, hydraulic-fracturing performance, and production heterogeneity in tight oil reservoirs. Their identification from conventional logs remains challenging because image-log and core coverage is limited, fracture-related logging responses are non-unique, and discrete fracture interpretations are difficult to align with regularly sampled logging sequences. This study used conventional logging data and electrical image-log interpretations from 17 wells in the Xifeng Oilfield, Ordos Basin, together with core observations from selected intervals, to develop a fracture response mapping and triangular topology-optimized bidirectional long short-term memory model (FRM-BiLSTM-TTAO). After sliding-window construction and density-based undersampling, 1713 samples were retained and partitioned at the well level into 14 training wells and three independent test wells, yielding an approximate training-to-test sample ratio of 75:25. FRM extracts lithologic-background, local-abrupt-change, multiscale-fluctuation, and integrated fracture response features; BiLSTM captures bidirectional depth dependencies; and TTAO selects fracture response features and optimizes the network architecture and training parameters. On the test set, the model achieved a ROC-AUC of 0.9079, a recall of 0.8671, and an F1-score of 0.8464, outperforming CNN, MLP, ResNet1D, XGBoost, and the corresponding ablation models. The predicted high-probability intervals were generally consistent with image-log interpretations and core observations, indicating the feasibility of the proposed method for identifying fracture-prone intervals within the study area. Full article

Global food security remains under severe strain, and the conversion of cultivated land to non-grain uses poses a significant threat to the stability of food production capacity. Driven by mineral development and urbanization, the risk of non-grain conversion in resource-based counties has increased. This study selected Qian’an City as the study area to accurately identify the spatiotemporal evolution characteristics of non-grain production at the county scale. GF-1 satellite (China Centre for Resources Satellite Data and Application, Beijing, China) imagery from 2016, 2020, and 2024 was employed, integrated with a vegetation index, multi-scale segmentation, and a decision tree model. Non-grain conversion rate measurement, spatial trend surface analysis, and kernel density estimation were then applied for analysis. The results were as follows. (1) The high-precision identification system for the non-grain conversion of cultivated land accurately distinguished grain crops from non-grain land uses, achieving an overall accuracy of over 91%. (2) The process of non-grain conversion of cultivated land in Qian’an City showed significant stage characteristics. The non-grain conversion area first increased and then decreased, with the non-grain rate fluctuating from 49.38 to 53.48% before declining to 46.62%. (3) The driving mechanism exhibited a phased evolution: from natural economy-led, to policy-driven strong intervention, and finally to market location-led. In 2016, the terrain undulation (q = 0.63) and GDP (q = 0.551) were dominant. In 2020, the proportion of ecological protection red line area (q = 0.539) took the lead. By 2024, GDP (q = 0.66) and the distance from the main road (q = 0.544) were dominant. This reflected the coupling effect of market and location within the policy baseline and interpreted the dynamic evolution of cultivated land use management. The spatial and temporal evolution characteristics of non-grain conversion for cultivated land were quantitatively revealed. The findings provide a scientific basis and decision support for regional cultivated land protection, food security, and optimal allocation of land resources. Full article