Search Results (1,430)

This review examines the emerging role of manganese-based nanoparticles (Mn-NPs) in detecting heavy metal pollutants in environmental matrices. Heavy metals such as cadmium, lead, zinc, and copper pose serious environmental and health concerns due to their tendency to persist in ecosystems and accumulate in living organisms. As a result, there is a growing need for reliable methods to detect and remove these pollutants. Manganese nanoparticles offer unique advantages that scientists could consider as replacing other metal nanoparticles, which may be more expensive or more toxic. The physicochemical properties of Mn-NPs—including their multiple oxidation states, magnetic susceptibility, catalytic capabilities, and semiconductor conductivity—enable the development of multi-modal sensing platforms with exceptional sensitivity and selectivity. While Mn-NPs exhibit inherently low electrical conductivity, strategies such as transition metal doping and the formation of composites with conductive materials have successfully addressed this limitation. Compared to noble metal nanoparticles (Au, Ag, Pd) and other base metal nanoparticles (Bi, Fe₃O₄), Mn-NPs demonstrate competitive performance without the drawbacks of high cost, complex synthesis, poor distribution control, or significant aggregation. Preliminary studies retrieved from the Scopus database highlight promising applications of manganese-based nanomaterials in electrochemical sensing of heavy metals, with recent developments showing detection limits in the sub-ppb range. Future research directions should focus on addressing challenges related to scalability, cost-effectiveness, and integration with existing water treatment infrastructure to accelerate the transition from laboratory findings to practical environmental applications. Full article

Clarifying the spatiotemporal relationship between urban ecosystem services and changes in landscape patterns is essential, as it has significant implications for balancing ecological protection with socio-economic development. However, existing studies have largely focused on the one-sided impact of landscape patterns on either the supply or demand of ESs, with limited investigation into how changes in these patterns affect the growth rates of both supply and demand. The central urban area, characterized by complex urban functions, intricate land use structures, and diverse environmental challenges, further complicates this relationship; yet, the spatiotemporal differentiation patterns of ecosystem services’ supply–demand dynamics in such regions, along with the underlying influencing mechanisms, remain insufficiently explored. To address this gap, the present study uses Shenyang’s central urban area, China as a case study, integrating multiple data sources to quantify the spatiotemporal variations in landscape pattern indices and five ecosystem services: water retention, flood regulation, air purification, carbon sequestration, and habitat quality. The XGBoost model is employed to construct non-linear relationships between landscape pattern indices and the supply–demand ratios of these services. Using SHAP values and LOWESS analysis, this study evaluates both the magnitude and direction of each landscape pattern index’s influence on the ecological supply–demand ratio. The findings outlined above indicate that: there are distinct disparities in the spatiotemporal distribution of landscape pattern indices at the patch type level. Additionally, the changing trends in the supply, demand, and supply–demand ratios of ecosystem services show spatiotemporal differentiation. Overall, the ecosystem services in the study area are developing negatively. Further, the impact of landscape pattern characteristics on ecosystem services is non-linear. Each index has a unique effect, and there are notable threshold intervals. This study provides a novel analytical approach for understanding the intricate relationship between landscape patterns and ESs, offering a scientific foundation and practical guidance for urban ecological protection, restoration initiatives, and territorial spatial planning. Full article

Heavy metals represent long-lasting contaminants that pose significant risks to both human health and ecosystem integrity. Originating from both natural and anthropogenic activities, they bioaccumulate in organisms through the food web, leading to widespread and long-lasting contamination. Industrialization, agriculture, and urbanization have exacerbated soil and water contamination through activities such as mining, industrial production, and wastewater use. In response to this challenge, biochar produced from waste materials such as sewage sludge has emerged as a promising remediation strategy, offering a cost-effective and sustainable means to immobilize heavy metals and reduce their bioavailability in contaminated environments. Here we explore the potential of phosphate-enriched biochar, derived from sewage sludge, to adsorb and stabilize heavy metals in polluted soils. Sewage sludge was pyrolyzed at various temperatures to produce biochar. A soil incubation experiment was conducted by adding phosphate-amended biochar to contaminated soil and maintaining it for one month. Heavy metals were extracted using a CaCl₂ extraction method and analyzed using atomic absorption spectrophotometry. Results demonstrated that phosphate amendment significantly enhanced the biochar’s capacity to immobilize heavy metals. Amending soils with 2.5 wt% phosphate-enriched sewage sludge biochar led to reductions in bioavailable Cd (by 65–82%), Zn (40–75%), and Pb (52–88%) across varying pyrolysis temperatures. Specifically, phosphate-amended biochar reduced the mobility of Cd and Zn more effectively than unamended biochar, with a significant decrease in their concentrations in soil extracts. For Cu and Pb, the effectiveness varied with pyrolysis temperature and phosphate amendment, highlighting the importance of optimization for specific metal contaminants. Biochar generated from elevated pyrolysis temperatures (500 °C) showed an increase in ash content and pH, which improved their ability to retain heavy metals and limit their mobility. These findings suggest that phosphate-amended biochar reduces heavy metal bioavailability, minimizing their entry into the food chain. This supports a sustainable approach for managing hazardous waste and remediating contaminated soils, safeguarding ecosystem health, and mitigating public health risks. Full article

Periphytic algae, as representative aquatic epiphytic communities, play a vital role in the material cycling and energy flow in rivers. Through physiological processes such as photosynthetic carbon fixation and nutrient absorption, they perform essential ecological functions in water self-purification, maintenance of primary productivity, and microhabitat formation. This study investigates the interaction mechanisms between these highly flexible organisms and the hydrodynamic environment, thereby addressing the limitations of traditional hydraulic theories developed for rigid vegetation. By incorporating the coupled effects of biological flexibility and water flow, an innovative nonlinear resistance model with velocity-dependent response is developed. Building upon this model, a coupled governing equation that integrates water flow dynamics, periphytic algae morphology, and layered Reynolds stress is formulated. An analytical solution for the vertical velocity distribution is subsequently derived using analytical methods. Through Particle Image Velocimetry (PIV) measurements conducted under varying flow velocity conditions in an experimental tank, followed by comprehensive error analysis, the accuracy and applicability of the model were verified. The results demonstrate strong agreement between predicted and measured values, with the coefficient of determination R² greater than 0.94, thereby highlighting the model’s predictive capacity in capturing flow velocity distributions influenced by periphytic algae. The findings provide theoretical support for advancing the understanding of ecological hydrodynamics and establish mechanical and theoretical foundations for river water environment management and vegetation restoration. Future research will build upon the established nonlinear resistance model to investigate the dynamic coupling mechanisms between multi-species periphytic algae communities and turbulence-induced pulsations, aiming to enhance the predictive modelling of biotic–hydrodynamic feedback processes in aquatic ecosystems. Full article

Groundwater quality in arid and semi-arid regions of Morocco is under increasing pressure due to both anthropogenic influences and climatic variability. This study investigates the physicochemical and heavy metal characteristics of groundwater across four Moroccan regions (Tangier-Tetouan-Al Hoceima, Oriental, Souss-Massa, and Marrakech-Safi) known for being argan tree habitats. Thirteen groundwater samples were analyzed for twenty-five parameters, including major ions, nutrients, and trace metals. Elevated levels of ammonium, turbidity, electrical conductivity, and dissolved oxygen were observed in multiple samples, surpassing Moroccan water quality standards and indicating significant quality deterioration. Inductively Coupled Plasma-Atomic Emission Spectroscopy (ICP-AES) detected arsenic concentrations exceeding permissible limits in sample AW11 alongside widespread lead contamination in most samples except AW5 and AW9. Spatial patterns of contamination were characterized using Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), K-means clustering, and GIS-based Inverse Distance Weighted (IDW) interpolation. These multivariate approaches revealed marked spatial heterogeneity and highlighted the dual influence of geogenic processes and anthropogenic activities on groundwater quality. To assess consumption suitability, a Water Quality Index (WQI) and Human Health Risk Assessment were applied. As a result, 31% of samples were rated “Fair” and 69% as “Good”, but with notable non-carcinogenic risks, particularly to children, attributable to nitrate, lead, and arsenic. The findings underscore the urgent need for systematic groundwater monitoring and management strategies to safeguard water resources in Morocco’s vulnerable dryland ecosystems, particularly in regions where groundwater sustains vital socio-ecological species such as argan forests. Full article

An advanced anodic stripping voltammetry (ASV)-based Micro Electro Mechanical System (MEMS) sensor for cadmium (Cd) detection is presented in this study, which is cost-effective and efficient for in situ water monitoring, providing a crucial early warning mechanism, streamlining environmental monitoring, and facilitating timely intervention to safeguard public health and environmental safety. The rationale behind this work is to address the critical need for an in situ monitoring system for cadmium (Cd) in freshwater sources, particularly those adjacent to agricultural fields. Cd(II) is a highly toxic heavy metal that poses a significant threat to agricultural ecosystems and human health due to its rapid bioaccumulation in plants and subsequent entry into the food chain. The developed analytic device is composed of a commercial mercury salt-modified graphite screen-printed electrode (SPE) with a custom-designed innovative polydimethylsiloxane (PDMS) flow detection cell. The flow cell was prototyped using 3D printing and replica moulding, with its design and performance validated through COMSOL Multiphysics simulations to optimize inflow conditions and ensure maximum analyte dispersion on the working electrode surface. Chemical detection was performed using square wave voltammetry, demonstrating a linear response for Cd(II) concentrations of 0 to 20 µg/L. The system exhibited robust analytical performance, enabling 25–30 daily analyses with consistent sensitivity within the Limit of Detection (LoD) set by the law of 3 µg/L. Full article

Water, an indispensable resource for life and not a complete substitute, is indispensable for energy production, industry, agriculture, and ecosystem sustainability. In particular, the limited and unequal distribution of freshwater reserves makes water a strategic power element on a global scale, making competition inevitable. Increasing water demand and decreasing water resources increase regional and global security risks, causing water to go beyond being a vital natural resource and become a determining factor in diplomacy, conflict, and the balance of power. This study aimed to examine the relationship between freshwater resources and geopolitical risk between 1961 and 2021 using the ARDL model. All models had long-run relationships between water resources and geopolitical risk. In the long-run, a 1% decrease in water resources increased geopolitical risk by 0.37% in Chile, 0.30% in Colombia, 0.46% in the Netherlands, 0.42% in Thailand, 0.44% in Ukraine, and 0.29% in Venezuela. The adjustment rates for the prior period imbalances were estimated to be 0.75% in Switzerland, 0.68% in Chile, 0.28% in Colombia, 0.45% in the Netherlands, 0.86% in Thailand, 0.14% in Ukraine, and 0.59% in Venezuela. Full article

Lake Taihu, a large, shallow freshwater lake in China, has experienced severe eutrophication for decades under intense human activities occurring around cities. Through long-term water quality management since 1995, the eutrophication of Lake Taihu has been controlled. This review examines the eutrophication characteristics, source identification methods, and control measures in Lake Taihu. Phosphorus is a primary driver of eutrophication, correlating strongly with chlorophyll a. The lake exhibits significant temporal and spatial variability in nutrient dynamics, influenced by human activities and the climate. Historical data show fluctuating nutrient levels and persistent algal blooms despite government efforts. A critical assessment of various source apportionment methods, including statistical analysis, physical modeling, and empirical models, is presented to elucidate the relative contributions of different nutrient sources. These methods identify agricultural non-point and urban point sources as major external contributors, with sediment nutrient release as a significant internal source. Implemented controls, including wastewater treatment plants and non-point-source management, have had limited success. Increased sewage and sediment nutrients necessitate integrated watershed management. Future research should prioritize advanced source tracking, sediment dynamics, climate impacts, and integrated ecological models. Sustainable eutrophication management in Lake Taihu requires integrated science, policy, and public engagement to ensure ecosystem health. Full article

This study investigates the composition and transformation of soil organic matter (SOM) across seven sites in Maritime Antarctica, focusing on the impact of bird activity and vegetation cover on SOM dynamics. There is limited knowledge of the stability of Antarctic SOM and the effects of seabird colonies on it. This study aims to address the knowledge gap regarding drivers of soil organic matter transformations in polar ecosystems. Hot water-extractable carbon (HWC) and carbon extracted with phosphoric acid (PHP-C) were chosen as parameters for the labile carbon pool. A stable carbon pool was here characterized as one with alkali-soluble organic compounds opposing microbial decomposition. This carbon pool has long (decades) turnover rates, and therefore is regarded stable. The mentioned carbon pools were used to calculate humification indices. The HWC in studied soils ranged from 1.5 to 4.3% of total carbon, while the PHP-C varied largely and was not correlated with HWC. Soils influenced by current or historical bird colonies (particularly penguins and skuas) exhibited elevated labile carbon fractions, indicating active microbial processing. In contrast, sites without bird influence showed lower biological activity. The stable carbon peaked at 18.9% of total carbon, indicating distinct soil transformation stages. The humification degree (HD) and labile-to-stable carbon (L/S) ratio were used to assess SOM stability, revealing that former bird rookeries had the most stabilized SOM, while recently deglaciated sites were in early stages of organic matter accumulation. Vegetation cover, though secondary to bird impact, was positively correlated with SOM humification, supporting the role of vascular plant-derived organic input in carbon stabilization. The study showed a clear link between bird activity and SOM dynamics, supporting the concept of biological legacies in soil formation in Antarctica. It highlighted the role of vegetation in SOM stabilization, which is crucial for understanding how terrestrial ecosystems may evolve as ice retreats and plant colonization expands. Full article

Urban water, defined as water not used for agriculture or to support natural ecosystems, is increasingly impacted by anthropogenic pollution. Among the key concerns are emerging contaminants (ECs), a diverse group of largely unregulated chemical compounds that pose growing threats to both water and the life it supports. This review critically examines the challenges associated with the presence of ECs in urban water through two complementary approaches that together offer both scientific and policy-oriented insights. The first approach focuses on evaluating the difficulties in classifying, characterizing, detecting, monitoring, enforcing policies, and assessing the risks of ECs. The second approach focuses on assessing whether current efforts in research, public awareness, regulation, treatment, recycling, and international collaboration align with the United Nations Sustainable Development Goals (SDGs), particularly SDG 6 (clean water and sanitation), SDG 11 (sustainable cities and communities), and SDG 12 (responsible consumption and production). Current efforts to address the challenges posed by ECs and to achieve SDG targets remain insufficient, particularly in the areas of treatment and recycling. Globally, only 56% of household wastewater is treated safely, and industrial wastewater treatment in low-income countries remains severely lacking, with coverage under 30%. Globally, the effective management of ECs is hindered by outdated and inadequate treatment infrastructure, low recycling rates, and the technical complexity of handling multi-contaminant waste streams. In developing regions, these challenges are compounded by weak regulatory enforcement and limited public awareness. To effectively address ECs in urban water and fully meet the SDG targets, more integrated and globally coordinated efforts are necessary. Full article

A comprehensive exploration of the trade-offs/synergies and drivers of ecosystem services (ESs) is essential for formulating ecological plans. However, owing to the limited attention given to multiple scales, the relationship of ESs still needs to be further explored. Taking the Yangtze River Delta region of China as the study area, a multiscale data framework with a 1 km grid and 10 km grid and county was established, and six ESs were evaluated for 2000, 2010, and 2020. Then, the trade-offs and synergies between ESs were explored by Spearman’s correlation analysis and geographically weighted regression (GWR), and the ecosystem service bundles (ESBs) were identified by self-organizing maps (SOMs). Finally, the socioecological drivers of ESs were further analyzed via GeoDetector. The results showed that (1) the distribution of ESs exhibited spatial heterogeneity. (2) At the grid scale, there were very strong trade-off effects between crop production and the other ESs. The synergistic effects between ESs at the county level were further strengthened. (3) The ESBs identified at different temporal and spatial scales were different. (4) Land use had the strongest explanatory power for all the ESs. At the grid scale, climatic and biophysical factors had great impacts on ESs, whereas population density and night light remote sensing had significant impacts on crop production, carbon storage, and water yield at the county scale. Full article

Sandy beaches are vital geomorphic units with ecological, social, and economic significance, playing a key role in coastal protection and ecosystem regulation. However, they are increasingly threatened by climate change and human activities, highlighting the need for large-scale, high-precision monitoring to support sustainable management. Existing remote-sensing-based sandy beach extraction methods face challenges such as suboptimal feature selection and reliance on single data sources, limiting their generalization and accuracy. This study proposes a novel sandy beach extraction framework that integrates multi-source data, feature optimization, and collaborative modeling, with Fujian Province, China, as the study area. The framework combines Sentinel-1/2 imagery, nighttime light data, and terrain data to construct a comprehensive feature set containing 44 spectrum, index, polarization, texture, and terrain variables. The optimal feature subsets are selected using the Recursive Feature Elimination (RFE) algorithm. Six machine learning models—Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGBM), Gradient Boosting Machine (GBM), Adaptive Boosting (AdaBoost), and Categorical Boosting (CatBoost)—along with an ensemble learning model, are employed for comparative analysis and performance optimization. The results indicate the following. (1) All models achieved the best performance when integrating all five types of features, with the average overall F1-score and accuracy reaching 0.9714 and 0.9733, respectively. (2) The number of optimal features selected by RFE varied by model, ranging from 19 to 36. The ten most important features across models were Band 2 (B2), Elevation, Band 3 (B3), VVVH_SUM, Spatial Average (SAVG), VH, Enhanced Water Index (EWI), Slope, Variance (VAR), and Normalized Difference Vegetation Index (NDVI). (3) The ensemble learning model outperformed all others, achieving an average overall accuracy, precision, recall, and F1-score of 0.9750, 0.9733, 0.9725, and 0.9734, respectively, under the optimal feature subset. A total of 555 sandy beaches were extracted in Fujian Province, covering an area of 43.60 km² with a total perimeter of 1263.59 km. This framework demonstrates strong adaptability and robustness in complex coastal environments, providing a scalable solution for intelligent sandy beach monitoring and refined resource management. Full article

In arid regions with limited water resources, improving cropland water-use efficiency (WUEc) is crucial for maintaining crop production. This study aims to investigate how changes in meteorological and vegetation factors affect WUEc in drylands and to identify its primary drivers, which are essential for understanding how cropland ecosystems respond to complex environmental changes. Using remote sensing data, we analyzed the spatiotemporal patterns of WUEc in Xinjiang from 2002 to 2022 by applying STL decomposition, Sen’s slope combined with the Mann–Kendall test, and an XGBoost–SHAP model, quantifying its key controlling factors. The results indicate that from 2002 to 2022, WUEc in Xinjiang showed an overall declining trend. Prior to 2007, WUEc increased at 0.05 gC·m⁻¹·m⁻²·a⁻¹, after which it fluctuated downward at −0.01 gC·m⁻¹·m⁻²·a⁻¹. Intra-annual peaks consistently occurred in May and during September–October. Spatially, WUEc exhibited significant heterogeneity, increasing from south to north, with 53.26% of the region showing declines. Temperature (T) and leaf area index (LAI) emerged as the primary meteorological and vegetation drivers, respectively, influencing WUEc change in 45.7% and 17.6% of the area. Both variables were negatively correlated with WUEc, with negative correlations covering 60% of the region for T and 83% for LAI. These findings provide scientific guidance for optimizing crop structure and water-resource management strategies in arid regions. Full article

Agricultural activities and dry climatic conditions promote the evaporation and salinization of groundwater in arid areas. Long-term irrigation alters the groundwater circulation and environment in arid plains, as well as its hydraulic connection with surface water. A comprehensive assessment of groundwater irrigation suitability and its interaction with surface water is essential for water–ecology–agriculture security in arid areas. This study evaluates the irrigation water quality and groundwater–surface water interaction influenced by agricultural activities in a typical arid plain region using hydrochemical and stable isotopic data from 51 water samples. The results reveal that the area of cultivated land increases by 658.9 km² from 2000 to 2023, predominantly resulting from the conversion of bare land. Groundwater TDS (total dissolved solids) value exhibits significant spatial heterogeneity, ranging from 516 to 2684 mg/L. Cl⁻, SO₄²⁻, and Na⁺ are the dominant ions in groundwater, with a widespread distribution of brackish water. Groundwater δ¹⁸O values range from −9.4‰ to −5.4‰, with the mean value close to surface water. In total, 86% of the surface water samples are good and suitable for agricultural irrigation, while 60% of shallow groundwater samples are marginally suitable or unsuitable for irrigation at present. Groundwater hydrochemistry is largely controlled by intensive evaporation, water–rock interaction, and agricultural activities (e.g., cultivated land expansion, irrigation, groundwater exploitation, and fertilizers). Agricultural activities could cause shallow groundwater salinization, even confined water deterioration, with an intense and frequent exchange between groundwater and surface water. In order to sustainably manage groundwater and maintain ecosystem stability in arid plain regions, controlling cultivated land area and irrigation water amount, enhancing water utilization efficiency, limiting groundwater exploitation, and fully utilizing floodwater resources would be the viable ways. The findings will help to deepen the understanding of the groundwater quality evolution mechanism in arid irrigated regions and also provide a scientific basis for agricultural water management in the context of extreme climatic events and anthropogenic activities. Full article

Protecting threatened elasmobranch species despite limited data on their distribution and abundance is a critical challenge, particularly in the context of increasing human impacts on marine ecosystems. In the northeastern Atlantic, species such as the leafscale gulper shark, Portuguese dogfish, spurdog, and spotted ray are facing pressures from overfishing, bycatch, habitat degradation, and climate change. The OSPAR Commission has listed these species as threatened and/or declining and aims to protect them by reliably identifying suitable habitats and integrating these areas into Marine Protected Areas (MPAs). In this study, we present a spatial modelling framework using regression-based approaches to identify suitable habitats for these four species. Results show that suitable habitats of the spotted ray (25.8%) and spurdog (18.8%) are relatively well represented within existing MPAs, while those of the deep-water sharks are underrepresented (6.0% for leafscale gulper shark, and 6.8% for Portuguese dogfish). Our findings highlight the need for additional MPAs in deep-sea continental slope areas, particularly west and northwest of Scotland and Ireland. Such expansions would support OSPAR’s goal to protect 30% of its maritime area by 2030 and could benefit broader deep-sea biodiversity, including other vulnerable demersal species and benthic communities. Full article