Search Results (521)

Sustainable groundwater management represents a main goal for the future in the context of climate change and increasing anthropogenic pressure. In recent decades, intrinsic vulnerability assessment and risk mapping have been established as some of the most important tools for groundwater preservation, but they have also shown limitations due to their static nature and their failure to account for the inherent uncertainty of hydrogeological parameters. This study proposes an innovative hybrid framework that integrates traditional overlay-index methodology (SINTACS Release 5) with stochastic numerical modeling to assess groundwater contamination risk and evolve it into a dynamic time-dependent tool. This methodology was applied to a case study of the Lapisina Valley phreatic aquifer (Northeastern Italy), a strategic area for drinking water supply. Numerical simulations were implemented to reproduce groundwater flow using the MODFLOW-NWT code. To address parametric uncertainty, 237 stochastic realizations of the modeling domain were generated using the Latin Hypercube Sampling method, randomizing hydraulic conductivity values. Advective transport was simulated through forward particle tracking using the MODPATH code, starting from the identified and classified hazard sources within the study area. Assuming the absence of attenuation during transport allowed for a conservative worst-case scenario. The result was the definition of a probabilistic contaminant propagation factor, a time-dependent indicator that quantifies the probability of pollution arrival to a specific discrete portion of the domain. This probabilistic factor was combined with three indexes commonly utilized for risk assessment (the intrinsic vulnerability index, hazard index, and value of the resource) to generate four contamination risk maps representing different timestep scenarios (5, 10, 20, and 50 years) after the arrival of a hypothetical contaminant in the saturated zone. This approach transforms risk mapping from being a useful but static snapshot to a predictive dynamic framework. Full article

Riverbed dynamics and erosion processes remain an important research issue, particularly under increasing anthropogenic pressure on river systems. This study investigates long-term channel changes and bed-incision processes in selected Carpathian rivers—the Skawa, Raba, and Dunajec—with particular emphasis on bridge-affected reaches. The analysis combined hydrological and geomorphological data with one-dimensional MIKE 11 hydraulic modelling to assess local changes in flow parameters and indicators of erosion potential under Q1% flow conditions. In the analysed cross-sections, riverbed lowering ranged from 1.0 to more than 3.5 m over the observation period, confirming the occurrence of long-term channel degradation. The results indicate that this process was primarily related to historical gravel extraction and channel regulation, whereas bridges mainly modified local hydraulic conditions. In the vicinity of bridge structures, flow velocity increased to as much as 7.31 m/s, and local changes in water surface elevation reached 0.90 m, indicating increased susceptibility to local scour near piers and abutments. The modelling also showed marked local increases in bed shear stress. At the same time, the results do not support the conclusion that bridges are the primary cause of systemic erosion at the scale of entire river reaches. This research contributes to sustainable development because it provides the knowledge needed for better management of rivers and bridge infrastructure in a way that is environmentally, socially, and economically safe: it shows that long-term riverbed degradation results mainly from earlier anthropogenic transformations, such as aggregate extraction and river regulation, while bridges primarily alter local flow conditions and may increase the risk of erosion around piers and abutments. Full article

Anthropogenic polymers have become an increasingly important class of emerging contaminants in terrestrial ecosystems. While extensive research has focused on microplastics in aquatic environments, their interactions with soil systems and particularly with soil organic matter (SOM) remain insufficiently understood. Soil represents a major environmental sink for polymer residues originating from agricultural practices, urban activities, and atmospheric deposition. Accordingly, associations between polymers and SOM, including humic substances, may significantly influence the retention, mobility, and transformation of carbon in soil systems. This review synthesizes current knowledge on the influence of synthetic polymers on soil organic matter dynamics. A bibliometric and qualitative literature analysis based on publications indexed in Web of Science and Scopus from 1979 to 2025 was conducted to identify major research trends and knowledge gaps. The results indicate that polymer particles can alter soil structure, microbial activity, and sorption processes, thereby affecting the stability and cycling of soil organic carbon. Interactions between polymer surfaces and humic substances may modify aggregation processes and influence the persistence and mobility of both polymers and organic carbon compounds. Despite the rapid growth of research on microplastics, studies addressing polymer–SOM interactions remain limited and methodologically heterogeneous. Greater integration between polymer research, soil science, and land use studies is necessary to better understand the implications of polymer contamination for soil quality and carbon cycling. The findings highlight the need for standardized analytical approaches and interdisciplinary research frameworks to assess the long-term effects of polymers in soil ecosystems. Full article

The rapid growth of hiking tourism presents a critical challenge for balancing visitor safety with the sustainable management of ecologically fragile mountain environments. Traditional models developed in urban settings struggle to capture the highly non-linear, heterogeneous, and zero-inflated characteristics of wilderness trekking behavior. In order to quantify the nonlinear and threshold-based effects of environmental variables on hikers’ spatial decisions in unstructured wilderness and to identify distinct behavioral regimes for segmented management, this study introduces an explainable machine learning framework to reconstruct hikers’ spatial decision-making in a complex mountainous system in Inner Mongolia, China. Random Forest (RF), XGBoost, and LightGBM were compared in predicting trail density and the Euclidean distance to the nearest trail. Results show that transforming behavioral traces into continuous proximity surfaces dramatically improves model performance, with XGBoost achieving the highest predictive accuracy for Trail_Dist. By integrating the SHapley Additive exPlanations framework, this study moves beyond black-box prediction to reveal the nonlinear mechanisms driving hiker behavior. Key findings include: (1) Nighttime light range exhibits a U-shaped threshold effect as the primary anthropogenic attractor. (2) Elevation shows an exponential inhibitory trend above 1238 m. (3) Strong spatial coupling exists between elevation and slope, alongside a landscape compensation effect where high Normalized Difference Vegetation Index (NDVI) areas attract off-trail movements. This research provides a robust methodological pathway for predicting behavior in unstructured outdoor environments. It offers a scientific foundation for smart scenic area management, including optimized route planning, precise ecological protection zoning, and targeted emergency rescue preparedness. Full article

Microbial communities inhabiting natural and anthropogenically impacted environments are exposed to diverse abiotic stressors that can influence the distribution of functional traits. However, distinguishing the processes underlying phenotypic patterns remains challenging in microbial systems, where ecological and evolutionary dynamics often overlap. In this study, we experimentally assessed the distribution of biofilm formation and plastic degradation capacity in bacterial isolates across environments characterized by different stress regimes, to evaluate whether these traits are primarily associated with environmental context rather than phylogenetic relatedness, and may therefore reflect environment-dependent phenotypic modulation on a lineage-specific functional background. Taxonomic affiliation was assessed using 16S rRNA gene sequencing, while expressed biochemical profiles were characterized by Fourier-transform infrared (FTIR) spectroscopy. Multivariate ordination and Partial Least Squares analyses were used to explore relationships among taxonomy, biochemical profiles, functional phenotypes, and environment of isolation. Phylogenetic signal analysis confirmed that neither trait was strongly constrained by vertical inheritance, with Blomberg’s K ≈ 0 and Fritz & Purvis’ D = 0.51, consistent with environment-driven rather than phylogenetically conserved trait distributions. Both biofilm production and plastic degradation capacity showed significant environment-dependent differences in their relative frequencies (Fisher’s exact test, biofilm: p = 5.5 × 10⁻⁵; PCL degradation: p = 2.5 × 10⁻⁴) and were not directly associated with each other (Wilcoxon rank-sum test, p = 0.45; linear model, p = 0.68). Overall, these results indicate that microbial functional traits are unevenly distributed across environments and weakly constrained by taxonomy, consistent with the contribution of multiple, non-mutually exclusive processes that remain difficult to disentangle empirically. Full article

Crop planting structure adjustments in irrigated agricultural regions alter irrigation and drainage regimes, with potential consequences for regional surface water dynamics. However, the nature and scale dependence of these linkages remain insufficiently understood. This study investigates the spatiotemporal dynamics of crop planting structure and surface water bodies in the Ningxia Plain from 2004 to 2023, and systematically quantifies their scale-dependent coupling mechanisms. Annual crop maps were generated using a Random Forest classifier (Sentinel-2, 2019–2023) and a Transformer-based model applied to multi-source satellite imagery (2004–2018). Surface water bodies were derived from long-term remote sensing datasets covering the full study period. Results show that the agricultural system underwent a pronounced transition toward maize dominance. Maize area expanded by 50.8%, whereas wheat and rice declined by 74.3% and 44.6%, respectively. Crop diversity also decreased, with the Shannon Diversity Index declining from 1.41 to 1.06 in 2023, indicating progressive system simplification. Meanwhile, surface water bodies exhibited a sustained downward trend, decreasing at an average rate of −5.32 km² per year after 2013 and reaching a minimum in 2022. The Yellow River water surface area also contracted by 14.41% (p = 0.001), indicating a basin-scale reduction in surface water extent. Lake classification results reveal strong scale-dependent hydrological responses. Small lakes (≤18 ha), accounting for 73.2% of lake numbers, are primarily controlled by local irrigation–drainage processes. Medium lakes (18–80 ha) are influenced by both anthropogenic regulation and natural variability. Large lakes (>80 ha), although representing only 4.9% of lake numbers but 62.9% of total water area, are mainly sustained by climatic variability and ecological water supplementation. Principal component analysis explains 84.44% of total variance, highlighting agricultural structural change and irrigation–drainage dynamics as key system drivers. Correlation analysis further reveals strong climate sensitivity of large lakes and the Yellow River (ρ = 0.50, p = 0.031), while small lakes are predominantly influenced by agricultural drainage processes. Overall, crop planting structure affects regional water dynamics through scale-dependent processes, with maize expansion altering irrigation and diversion patterns and local irrigation–drainage processes controlling small water bodies. Full article

Rapid urbanization and agricultural expansion in arid regions have profoundly altered carbon cycles and landscape stability. Focusing on the Hexi Corridor, China, this study integrates multi-source geospatial data (1990–2020) to analyze the spatiotemporal evolution and driving factors of land-use carbon emissions (LUCE) and landscape ecological risks (LER). By integrating carbon accounting, LER assessment, bivariate spatial autocorrelation, and the Optimal Parameter Geographic Detector (OPGD), we quantify the intricate relationship between carbon dynamics and landscape integrity. Results indicate a transformative pattern of anthropogenic expansion and natural contraction, with a 2315.49 km² net loss of unused land. Net carbon emissions surged 4.6-fold, while forest and grassland sinks exhibited a significant “lock-in effect” due to fragile ecological foundations. Simultaneously, LER followed an “inverted U-shaped” trajectory; the refined 5 × 5 km grid scale revealed a significant drop in high-risk areas from 44.65% to 10.96% following ecological restoration. Spatial analysis reveals a significant “spatial mismatch” between LUCE and LER, with oases manifesting “high carbon–low risk” clustering. Driver detection confirms a driving asymmetry. LUCE is dominated by anthropogenic factors (nighttime light, q > 0.90), whereas LER is profoundly constrained by natural backgrounds. Future governance must shift toward a collaborative system centered on source-based emission control and precise regional management to synergize low-carbon transition with landscape security. Full article

The growing diversity of anthropogenic chemicals in the environment far exceeds the scope of routine analytical monitoring. Non-target screening (NTS) using high-resolution mass spectrometry (HRMS) has thus emerged to discover unknown organic contaminants. Liquid or gas chromatography (LC/GC) coupled with ion mobility–mass spectrometry (IM-MS) further enhances NTS by providing multidimensional, structurally informative data. Machine learning (ML) offers a powerful solution by efficiently processing high-dimensional data and uncovering patterns. Both supervised and unsupervised learning approaches show strong potential to streamline labor-intensive processes. This review provides an overview of key ML algorithms and representative workflows in LC/GC-(IM-) MS-related NTS, followed by a critical synthesis of recent advances in ML-enabled applications across the entire NTS procedure, from sample analysis to data acquisition, and ultimately risk assessment. Continued advances in ML are expected to transform NTS into a more efficient and robust tool for risk assessment. Full article

The pervasive environmental dispersal of glyphosate has established this herbicide as a dominant anthropogenic xenobiotic, necessitating advanced bioremediation strategies to restore soil integrity. This study assessed the bioremediation efficacy of biosurfactants produced by Serratia ureilytica BM01-BS in glyphosate-contaminated soils, establishing their adsorption dynamics and ecotoxicological safety. The strain S. ureilytica BM01-BS gave a biosurfactant yield of 3.7 g·L⁻¹ with promising surface properties, utilizing babassu (Attalea speciosa) waste as the sole nutrient source. Whole-Genome Sequencing and Biosynthetic Gene Cluster mining identified a Nonribosomal Peptide Synthetase cluster homologous to rhizomide-type lipopeptides responsible for biosurfactant production. Bioremediation assays in glyphosate-contaminated soils demonstrated a removal efficiency exceeding 95% in approximately 60 min, outperforming the synthetic surfactant SDS (20–30% efficiency). Kinetic and isothermal modeling suggest that the bioremediation process is governed by chemisorption, adhering to a pseudo-second-order model (R² = 0.998) with a maximum adsorption capacity of 845 µg·kg⁻¹. Fourier-Transform Infrared spectroscopy confirmed that the biosurfactant effectively removes glyphosate and restores the soil’s mineral integrity, as evidenced by the complete disappearance of glyphosate-associated phosphonic and carboxylic bands. Ecotoxicological assessments verified the environmental safety of the bioremediation process. These findings position the BM01-BS biosurfactant as a sustainable, biodiversity-based adjuvant for enhancing ecological resilience in glyphosate-impacted landscapes. Full article

A comparative analysis of the long-term dynamics of phytobenthos on the Black Sea coast from 1964 to 2020 has been conducted. The aim of the work was to assess changes in species composition, quantittive characteristics, and distribution of bottom vegetation under the influence of natural and anthropogenic factors. The research was carried out at three transects using standard hydrobotanical methods and analysis of climatic data. The results revealed significant structural reorganization of the communities: a decrease in the proportion of key brown algae (Ericaria crinita and Gongolaria barbata) by the middle of the observation period with partial recovery by 2020, an overall increase in biomass and species diversity, and increased role of epiphytes and green algae. An expansion of the depth range of the phytal zone and an increase in the presence of the deep-water species Phyllophora crispa were established. The main drivers of the transformation are increased anthropogenic pressure and climate change, which aligns with global trends. The obtained data are important for developing measures to preserve coastal ecosystems and can be used in monitoring the ecological state of the aquatic area. A promising direction for further research is the quantitative assessment of the role of the macrophytobenthos in this area in carbon sequestration. Full article

Understanding the accumulation, ecological risk, and source interactions of potentially toxic elements (PTEs) in reservoir sediments is essential for protecting drinking water safety, yet such processes remain insufficiently understood in karst tea-plantation watersheds influenced by mixed anthropogenic activities. In this study, sediment cores collected from four sites (CY-1 to CY-4) during 2022–2024 were analyzed, and an integrated framework combining the Potential Ecological Risk Index (RI), Spearman correlation analysis, Principal Component Analysis (PCA), and Positive Matrix Factorization (PMF) was applied to evaluate contamination characteristics and quantify source contributions. The results revealed significant spatial–vertical heterogeneity of PTEs, with Zn (up to 153 mg/kg) and Cr (up to 64.6 mg/kg) showing the greatest variability, and strong co-enrichment among Cu, Zn, Pb, and Ni (r > 0.85, p < 0.01). Although the overall ecological risk was low (RI = 83.15–106.69), As contributed the highest proportion of risk (28–35%). PCA indicated distinct grouping patterns among elements, while PMF resolved three major sources: domestic sewage and agricultural runoff, agricultural and coal-combustion inputs, and industrial–traffic emissions. Notably, physicochemical parameters (TP, TN, and COD) played important roles in regulating the mobility and partitioning of PTEs by influencing nutrient-associated adsorption processes, organic matter complexation, and redox-related transformations. These findings highlight the multi-source-driven accumulation mechanisms of PTEs in karst reservoirs and provide a scientific basis for targeted pollution control and watershed management in agriculturally impacted regions. Full article

The protection of landscape and biological diversity on small Mediterranean islands represents a significant challenge in the context of intensive anthropogenic pressure and land-use change. The aim of this study was to determine the composition of ornamental flora in private gardens on the island of Koločep (IPA, Natura 2000 site), the smallest inhabited island in the Croatian part of the Adriatic, with special emphasis on invasive (IAS) and potentially invasive (PIAS) plant species, and to analyse their relationship with landscape changes and property types. A total of 161 private gardens were analysed, representing all private gardens on the island. In total, 2095 plant records corresponded to 255 unique horticultural taxa from 82 families. Allochthonous species dominate in the gardens (73%). Private gardens represent the primary pathway for the introduction of IAS and PIAS taxa on the island. The taxa with the highest invasion intensity were Ailanthus altissima and Carpobrotus edulis, while among PIAS species, high invasive potential was observed for Mirabilis jalapa and Diospyros virginiana. The study highlights the need for systematic monitoring of ornamental flora and landscape transformation, and the promotion of horticultural practices focused on autochthonous species in gardens, in order to preserve island biological and landscape diversity. Full article

The genus Juniperus species is widely distributed in the Northern Hemisphere of the planet Earth. These species are notable for their ability to adapt to extreme environmental conditions, playing a crucial role in ecosystem structure and function. Currently, their expansion is being driven by anthropogenic activities and climate change, posing significant challenges for both control and conservation. The objective of this review was to synthesize the available evidence regarding the ecological importance and impacts of Juniperus on ecosystems, promoting a holistic perspective that contributes to the achievement of the United Nations 2030 Agenda for Sustainable Development. A systematic literature search was conducted using the Scopus database, and only the documents published between 2001 and 2025 were considered for the investigation. The results showed that these species possess a high ecological versatility, favoring their invasive success in disturbed ecosystems, particularly under the influence of climate change and land-use changes. Conversely, Juniperus species facilitate positive ecological outcomes by providing essential ecosystem services that benefit both the human population and the flora and fauna present in these ecosystems. Nevertheless, their expansion also causes negative effects, such as the suppression of herbaceous shrubs and understory cover, alteration of the hydrological function, and accelerated soil erosion, among others. Consequently, the genus Juniperus exhibits a dual ecological role, acting as a hero to many species within these ecosystems, yet a villain to others. In this sense, given its remarkable adaptive dynamism under scenarios of climate change and continuous anthropogenic alterations, it is imperative to promote comprehensive conservation and restoration strategies. These should include ecological monitoring, invasive species control, genetic management, and habitat restoration. Such efforts must be supported by long-term interdisciplinary research to understand and mitigate the ecological, genetic, and social impacts resulting from its expansion. Furthermore, these investigations and strategies must be flexible and locally contextualized to promote genuine ecosystem resilience in the face of the ongoing environmental transformations. Full article

The synergistic impacts of land use/land cover (LULC) transformations and weather pattern variabilities (WPV) represent a primary driver of hydro-geological instability, threatening agricultural productivity, soil conservation, and water quality. Disentangling the discrete contributions of these stressors to runoff and sediment yield (SY) remains a significant challenge, particularly in complex, confluence-proximal watersheds lacking major hydraulic regulations. This study investigates the Tirumakudalu Narasipura watershed in Karnataka, India, an agriculturally intensive system undergoing rapid peri-urbanization. Leveraging the process-based geospatial interface of the Water Erosion Prediction Project (GeoWEPP), we analyzed hydrological responses over a 24-year period (2000–2023) and projected future trajectories through 2030. To overcome the traditional constraints of GeoWEPP, which was developed for small-scale watersheds (<260 ha), we present a novel upscaling framework utilizing a multi-site multivariate temporal calibration of hydrological response variables to exploit its process-based precision in capturing distributed soil erosion and landscape heterogeneity. This approach is further reinforced by an ancillary data validation to minimize error propagation while model-upscaling. Our findings reveal projected increases in runoff and SY of 14.69% and 49.23%, respectively, between 2000 and 2030. Notably, the sub-decadal acceleration from 2023 to 2030 (17.32% for runoff and 18.51% for SY) underscores a shifting dominance where LULC-driven surface modifications now outweigh climatic variance in forcing hydrologic change. Furthermore, the study quantifies how anthropogenic interventions such as strategic crop selection, tillage intensity, and irrigation regimes act as critical determinants of topsoil preservation. These results provide a scalable, economically feasible framework for precision land stewardship and sustainable watershed management in rapidly developing tropical landscapes. Full article