Search Results (487)

Enhancing blue infrastructure is a critical pathway to strengthening urban water resilience and improving living environments. However, divergent perceptions and demands among multiple stakeholders may lead to misalignment between governance priorities and implementation pathways, thereby limiting governance effectiveness. Recognizing and addressing these differences has become essential for enhancing the performance of blue infrastructure governance and public satisfaction. Taking Shaanxi Province as a case study, this research systematically identifies core issues and disparities in public demands regarding water governance of blue infrastructure by analyzing governmental documents and public demands. The study aims to support a shift in governance strategy from a “provision-driven” to a “demand-driven” approach. A “topic identification–demand extraction–problem diagnosis” framework is adopted: first, the LDA model is used to analyze government platform texts and derive a macro-level thematic framework; subsequently, the BERTopic model is applied to mine public comments and identify micro-level demands; finally, the Jaccard similarity algorithm is employed to compare the two sets of topics, revealing the gap between policy provisions and public demands. The findings indicate the following: first, government agendas are highly concentrated on macro-level strategies (the topic “Integrated Water Ecosystem Management and Strategic Planning” accounts for 72.91% of weighting), whereas public appeals focus on specific, micro-level daily concerns such as infrastructure quality, drinking water safety, and drainage blockages; second, the Jaccard semantic correlation between the two is generally low (ranging from 6.05% to 14.62%), confirming a significant “topic-term overlap”; third, spatial analysis further reveals a geographical mismatch, particularly in core urban areas, which exhibit a “system-lag” type of misalignment characterized by high public demand but insufficient governmental attention. The research aims to clarify governance discrepancies, providing a basis for optimizing policy priorities and enabling targeted governance, while also offering insights for establishing a sustainable water resource management system. Full article

Fine-grained building function identification (BFI) is essential for sustainable urban development, land-use analysis, and data-driven spatial planning. Recent progress in fully supervised semantic segmentation has advanced multimodal BFI; however, most approaches still rely on static fusion and lack explicit multi-scale alignment. As a result, they struggle to adaptively integrate heterogeneous inputs and suppress cross-modal interference, which constrains representation learning. To overcome these limitations, we propose BuildFunc-MoE, an adaptive multimodal Mixture-of-Experts (MoE) network built on an effective end-to-end Swin-UNet backbone. The model treats high-resolution remote sensing imagery as the primary input and integrates auxiliary geospatial data such as nighttime light imagery, DEM, and point-of-interest information. An Adaptive Multimodal Fusion Gate (AMMFG) first refines auxiliary features into informative fused representations, which are then combined with the primary modality and passed through multi-scale Swin-MoE blocks that extend standard Swin Transformer blocks with MoE routing. This enables fine-grained, dynamic fusion and alignment between primary and auxiliary modalities across feature scales. BuildFunc-MoE further introduces a Shared Task-Expert Module (STEM), which extends the MoE framework to share experts between the main BFI task and auxiliary tasks (road extraction, green space segmentation, and water body detection), enabling parameter-level transfer. This design enables complementary feature learning, where structural and contextual information jointly enhance the discrimination of building functions, thereby improving identification accuracy while maintaining model compactness. Experiments on the proposed Wuhan-BF multimodal dataset show that, under identical supervision, BuildFunc-MoE outperforms the strongest multimodal baseline by over 2% on average across metrics. Both PyTorch and LuoJiaNET implementations validate its effectiveness, while the latter achieves higher accuracy and faster inference through optimized computation. Overall, BuildFunc-MoE offers a scalable solution for fine-grained BFI with strong potential for urban planning and sustainable governance. Full article

The rapid urbanization of coastal regions has intensified the demand for durable underground infrastructure like shield tunnels, where reinforced concrete (RC) structures are critical yet susceptible to long-term degradation in marine environments. This study develops an integrated machine vision-based framework for assessing the long-term durability of RC in marine shield tunnels by synergistically combining point cloud analysis and deep learning-based damage recognition. The methodology involves preprocessing tunnel point clouds to extract the centerline and cross-sections, enabling the quantification of geometric deformations, including segment misalignment and elliptical distortion. Concurrently, an advanced YOLOv8 model is employed to automatically identify and classify surface corrosion damages—specifically water leakage, cracks, and spalling—from images, achieving high detection accuracies (e.g., 95.6% for leakage). By fusing the geometric indicators with damage metrics, a quantitative risk scoring system is established to evaluate structural durability. Experimental results on a real-world tunnel segment demonstrate the framework’s effectiveness in correlating surface defects with underlying geometric irregularities. This integrated approach offers a data-driven solution for the continuous health monitoring and residual life prediction of RC tunnel linings in marine conditions, bridging the gap between visual inspection and structural performance assessment. Full article

Rapid urbanization has intensified ecological problems such as landscape fragmentation and biodiversity decline, underscoring the need to maintain regional ecological integrity. The construction of ecological security patterns and the optimization of ecological restoration areas are crucial for addressing these ecological issues. However, research on how to couple ecological security patterns with ecological risk assessment to scientifically identify priority areas for ecological restoration and guide spatially targeted restoration remains insufficient. To address this gap, we investigated Liaoning Province by integrating morphological spatial pattern analysis, landscape connectivity assessment, and ecosystem service hotspot analysis to identify ecological sources. We then applied the minimum cumulative resistance model and circuit theory to extract ecological corridors, constructing a comprehensive ecological security pattern. Integrating landscape ecological risk assessment with ecological security patterns established a conservation and restoration-oriented ecological security framework. The results show that the ecological security pattern comprises 40 ecological source patches and 89 potential ecological corridors. Ecological sources encompass a total of 17,628 km² (approximately 12% of the province), primarily comprising water bodies, grasslands, shrublands, and forests. The ecological corridors span a total of 3533.9 km, with an average length of 39.7 km. We also identified 139 ecological pinch points and 109 ecological barrier points. Integrating these findings with landscape ecological risk zoning delineates ecological restoration zones, revealing a spatial pattern characterized by east–west differentiation and north–south continuity. This ecological conservation and restoration network provides a clear spatial guide and a robust scientific foundation for territorial spatial planning, ecological conservation, and restoration efforts. Full article

Across the Baltic Sea region, areas situated in climate-sensitive water zones are increasingly exposed to environmental and socio-economic challenges. Gdańsk, Poland, is a prominent example where the rising threat of climate-related hazards, particularly connected with flooding, coincides with growing demand for resilient and adaptive housing solutions. Located in the Vistula Delta, the city’s vulnerability is heightened by its low-lying terrain, polder-based land systems, and extensive waterfronts. These geographic conditions underscore the urgent need for flexible, climate-responsive design strategies that support long-term adaptation while safeguarding the urban fabric and the well-being of local communities. This study provides evidence-based guidance for adaptive housing solutions tailored to Gdańsk’s waterfronts. It draws on successful architectural and urban interventions across the Baltic Sea region, selected for their environmental, social, and cultural relevance, to inform development approaches that strengthen resilience and social cohesion. To achieve this, an exploratory case study methodology was employed, supported by desk research and qualitative content analysis of strategic planning documents, academic literature, and project reports. A structured five-step framework, comprising project identification, document selection, qualitative assessment, data extraction, and analysis, was applied to examine three adaptive housing projects: Hammarby Sjöstad (Stockholm), Kalasataman Huvilat (Helsinki), and Urban Rigger (Copenhagen). Findings indicate measurable differences across nine sustainability indicators (1–5 scale): Hammarby Sjöstad excels in environmental integration (5/5 in carbon reduction and renewable energy), Kalasataman Huvilat demonstrates strong modular and human-scaled adaptability (3–5/5 across social and housing flexibility), and Urban Rigger leads in climate adaptability and material efficiency (4–5/5). Key adaptive measures include flexible spatial design, integrated environmental management, and community engagement. The study concludes with practical recommendations for local planning guidelines. The guidelines developed through the Gdańsk case study show strong potential for broader application in cities facing similar challenges. Although rooted in Gdańsk’s specific conditions, the model’s principles are transferable and adaptable, making the framework relevant to water sensitivity, flexible housing, and inclusive, resilient urban strategies. It offers transversal value to both urban scholars and practitioners in planning, policy, and community development. Full article

Using Shanghai as a case study, this paper develops a multi-source fusion and interpretable machine learning framework. Sentiment indices were extracted from Weibo check-ins with ERNIE 3.0, street-view elements were identified using Mask2Former, and urban indicators like the Normalized Difference Vegetation Index, floor area ratio, and road network density were integrated. The coupling between residents’ sentiments and streetscape features during heatwaves was analyzed with Extreme Gradient Boosting, SHapley Additive exPlanations, and GeoSHAPLEY. Results show that (1) the average sentiment index is 0.583, indicating a generally positive tendency, with sentiments clustered spatially, and negative patches in central areas, while positive sentiments are concentrated in waterfronts and green zones. (2) SHapley Additive exPlanations analysis identifies NDVI (0.024), visual entropy (0.022), FAR (0.021), road network density (0.020), and aquatic rate (0.020) as key factors. Partial dependence results show that NDVI enhances sentiment at low-to-medium ranges but declines at higher levels; aquatic rate improves sentiment at 0.08–0.10; openness above 0.32 improves sentiment; and both visual entropy and color complexity show a U-shaped relationship. (3) GeoSHAPLEY shows pronounced spatial heterogeneity: waterfronts and the southwestern corridor have positive effects from water–green resources; high FAR and paved surfaces in the urban area exert negative influences; and orderly interfaces in the vitality corridor generate positive impacts. Overall, moderate greenery, visible water, openness, medium-density road networks, and orderly visual patterns mitigate negative sentiments during heatwaves, while excessive density and hard surfaces intensify stress. Based on these findings, this study proposes strategies: reducing density and impervious surfaces in the urban area, enhancing greenery and quality in waterfront and peripheral areas, and optimizing urban–rural interfaces. These insights support heat-adaptive and sustainable street design and spatial governance. Full article

Accurately estimating urban floodwater depth is a critical step in enhancing urban resilience and strengthening disaster prevention and mitigation capabilities. Traditional methods relying on hydrological monitoring stations and numerical simulations suffer from limitations such as sparse spatial coverage, insufficient validation data, limited accuracy, and delayed fast performance. In contrast, social media data—characterized by its vast volume and fast availability, can effectively compensate for these shortcomings. When processed using artificial intelligence (AI) algorithms, such data can significantly improve credibility, disaster perception speed, and water depth estimation accuracy. To address these challenges, this paper proposes a robust and widely applicable method for rapid urban flood depth perception. The approach integrates AI technology and social media data to construct an AI framework capable of perceiving urban physical parameters through multimodal big data fusion without costly model training. By leveraging the near real-time and widespread nature of social media, an automated web crawler collects flood images and their textual descriptions (including reference objects), eliminating the need for additional hardware investments. The framework uses predefined prompts and pre-trained models to automatically perform relevance verification, duplicate filtering, object detection, and feature extraction, requiring no manual data annotation or model training. With only a minimal amount of water depth annotated data and compressed cross-modal feature vectors as training input, a lightweight Multilayer Perceptron (MLP) achieves high-precision depth estimation based on reference objects. This method avoids the need for large-scale model fine-tuning, allowing rapid training even on devices without GPUs. Experiments demonstrate that the proposed method reduces the Mean Square Error (MSE) by over 80%, processes each image in less than 0.5 s (more than 20 times faster than existing large-model approaches), and exhibits strong robustness to changes in perspective and image quality. The solution is fully compatible with existing infrastructure such as surveillance cameras, offering an efficient and reliable approach for fast flood monitoring in urban hydrology and water engineering applications. Full article

As a key indicator for measuring urban green visibility, the Green View Index (GVI) reflects actual visible greenery from a human perspective, playing a vital role in assessing urban greening levels and optimizing green space layouts. Existing studies predominantly rely on single-source remote sensing image analysis or traditional statistical regression methods such as Ordinary Least Squares and Geographically Weighted Regression. These approaches struggle to capture spatial variations in human-perceived greenery at the street level and fail to identify the non-stationary effects of different drivers within localized areas. This study focuses on the Luolong District in the central urban area of Luoyang City, China. Utilizing Baidu Street View imagery and semantic segmentation technology, an automated GVI extraction model was developed to reveal its spatial differentiation characteristics. Spearman correlation analysis and Multiscale Geographically Weighted Regression were employed to identify the dominant drivers of GVI across four dimensions: landscape pattern, vegetation cover, built environment, and accessibility. Field surveys were conducted to validate the findings. The Multiscale Geographically Weighted Regression method allows different variables to have distinct spatial scales of influence in parameter estimation. This approach overcomes the limitations of traditional models in revealing spatial non-stationarity, thereby more accurately characterizing the spatial response mechanism of the Global Vulnerability Index (GVI). Results indicate the following: (1) The study area’s average GVI is 15.24%, reflecting a low overall level with significant spatial variation, exhibiting a “polar core” distribution pattern. (2) Fractal dimension, normalized vegetation index (NDVI), enclosure index, road density, population density, and green space accessibility positively influence GVI, while connectivity index, Euclidean nearest neighbor distance, building density, residential density, and water body accessibility negatively affect it. Among these, NDVI and enclosure index are the most critical factors. (3) Spatial influence scales vary significantly across factors. Euclidean nearest neighbor distance, building density, population density, green space accessibility, and water body accessibility exert global effects on GVI, while fractal dimension, connectivity index, normalized vegetation index, enclosure index, road density, and residential density demonstrate regional dependence. Field survey results confirm that the analytical conclusions align closely with actual greening conditions and socioeconomic characteristics. This study provides data support and decision-making references for green space planning and human habitat optimization in Luoyang City while also offering methodological insights for evaluating urban street green view index and researching ecological spatial equity. Full article

This article examines the effects of climate change on the 32 million inhabitants of the Megalopolis of Central Mexico (MCM), which is threatened by chaotic urbanization, land-use changes, the deforestation of the Forest of Water by organized crime, unsustainable agriculture, and biodiversity loss. Expensive hydraulic management extracting water from deep aquifers, long pipes exploiting water from neighboring states, and sewage discharged outside the endorheic basin result in expensive pumping costs and air pollution. This mismanagement has increased water scarcity. The overexploitation of aquifers and the pollution by toxic industrial and domestic sewage mixed with rainfall has increased the ground subsidence, damaging urban infrastructure and flooding marginal neighborhoods with toxic sewage. A system approach, satellite data, and participative research methodology were used to explore potential water scarcity and weakened water security for 32 million inhabitants. An alternative nature-based approach involves recovering the Forest of Water (FW) with IWRM, including the management of Natural Protected Areas, the rainfall recharge of aquifers, and cleaning domestic sewage inside the valley where the MCM is found. This involves recovering groundwater, reducing the overexploitation of aquifers, and limiting floods. Citizen participation in treating domestic wastewater with eco-techniques, rainfall collection, and purification filters improves water availability, while the greening of urban areas limits the risk of climate disasters. The government is repairing the broken drinking water supply and drainage systems affected by multiple earthquakes. Adaptation to water scarcity and climate risks requires the recognition of unpaid female domestic activities and the role of indigenous people in protecting the Forest of Water with the involvement of three state authorities. A digital platform for water security, urban planning, citizen audits against water authority corruption, and aquifer recharge through nature-based solutions provided by the System of Natural Protected Areas, Biological and Hydrological Corridors [SAMBA] are improving livelihoods for the MCM’s inhabitants and marginal neighborhoods, with greater equity and safety. Full article

Floods, especially in urbanised areas, incur enormous economic and social losses. The structural flood management is often limited by urbanization and environmental issues. Following the catastrophic flood events of 1997 and 2010, a relatively large dry polder was constructed in Racibórz Dolny, Poland, with the highest flood retention capacity in Central Europe. During the 2024 flood in Czechia and Poland, the polder was filled to 80%, which significantly reduced the floodwave crest on the Odra River (by 1.65 m), halved the peak discharge, and delayed the floodwave passage by two days according to hydrological calculations. The operation of the polder enables multifunctional use of the river valley—ranging from agriculture and mineral extraction to environmental protection—without the need for permanent water impoundment. Aggregate extraction carried out within the basin contributed to shaping the reservoir, reducing the demand for transport and construction materials, while the overburden was reused for engineering and reclamation purposes. Mining activities between 2007 and 2023 increased the retention capacity of the polder by 13%, providing an example of rational environmental resource management combined with effective flood protection. The findings demonstrate that integrating retention functions with mineral resource management represents an efficient and sustainable approach to mitigating flood impacts in large European river valleys. Full article

Globally, intensive groundwater extraction has led to widespread land subsidence, posing severe threats to urban infrastructure, structural safety, and flood control capacity, and resulting in substantial economic losses and ecological degradation. Based on dynamic monitoring data and a poroelastic fluid–solid coupling model developed using COMSOL Multiphysics 6.2, this study systematically investigates the characteristics and evolution of land subsidence in Decheng District before and after the implementation of a groundwater extraction ban. Furthermore, recommendations and strategies for the sustainable management of regional groundwater resources are proposed. The results indicate that after the ban was enforced in 2020, the extraction volumes of deep and shallow groundwater in Decheng District decreased from 830,000 m³/a and 33,070,000 m³/a to 178,000 m³/a and 20,775,000 m³/a, respectively. The ban significantly influenced groundwater levels, with the recovery rate of deep groundwater increasing markedly from approximately 0.5 m/a before the ban to about 5 m/a afterward. Groundwater levels directly govern the rate of land subsidence; their decline increases the effective stress within the strata, leading to aquifer compaction and subsequent subsidence. Following the ban, the subsidence rate in Decheng District decreased significantly, with the annual subsidence volume reduced by more than 80% compared to the pre-ban period. Predictive analysis using the fluid–solid coupling model reveals that extraction from deep confined aquifers is the main driver of regional subsidence, with a time lag of approximately five years between groundwater level changes and subsidence response. After the implementation of the extraction ban, the subsidence rate slowed considerably. Over the long term, the subsiding strata tend to stabilize, although most of the subsidence that has already occurred is irreversible, making it difficult for the strata to return to their original state. In summary, the groundwater extraction ban has effectively facilitated groundwater recovery and mitigated land subsidence in Decheng District, though the response exhibits both temporal lag and spatial variability. Future work should focus on establishing an integrated monitoring and regulation system for land subsidence and groundwater dynamics to ensure the coordinated security of both water resources and the geological environment. These findings provide a scientific basis for informing land subsidence prevention and guiding the rational exploitation of groundwater resources in Decheng District. Full article

The Tibetan Plateau is a globally critical climate-sensitive and ecologically fragile region. Vegetation phenology serves as a key indicator of ecosystem responses to climate change and simultaneously influences regional carbon cycling, water regulation, and ecological security. However, systematic quantitative assessments of phenological responses under the combined effects of multiple climate factors remain limited. This study integrates multi-source remote sensing data (MODIS MCD12Q2) and ERA5-Land meteorological data from 2001 to 2023, leveraging the Google Earth Engine (GEE) cloud platform to extract key phenological metrics, including the start (SOS) and end (EOS) of the growing season, and growing season length (GSL). Sen’s slope estimation, Mann–Kendall trend tests, and partial correlation analyses were applied to quantify the independent effects and spatial heterogeneity of temperature, precipitation, solar radiation, and evapotranspiration (ET) on GSL. Results indicate that: (1) GSL on the Tibetan Plateau has significantly increased, averaging 0.24 days per year (Sen’s slope +0.183 days/yr, Z = 3.21, p < 0.001; linear regression +0.253 days/yr, decadal trend 2.53 days, p = 0.0007), primarily driven by earlier spring onset (SOS: Sen’s slope −0.183 days/yr, Z = −3.85, p < 0.001), while autumn dormancy (EOS) showed limited delay (Sen’s slope +0.051 days/yr, Z = 0.78, p = 0.435). (2) GSL changes exhibit pronounced spatial heterogeneity and ecosystem-specific responses: southeastern warm–wet regions display the strongest responses, with temperature as the dominant driver (mean partial correlation coefficient 0.62); in high–cold arid regions, warming substantially extends GSL (Z = 3.8, p < 0.001), whereas in warm–wet regions, growth may be constrained by water stress (Z = −2.3, p < 0.05). Grasslands (Z = 3.6, p < 0.001) and urban areas (Z = 3.2, p < 0.01) show the largest GSL extension, while evergreen forests and wetlands remain relatively stable, reflecting both the “climate sentinel” role of sensitive ecosystems and the carbon sequestration value of stable ecosystems. (3) Multi-factor interactions are complex and nonlinear; temperature, precipitation, radiation, and ET interact significantly, and extreme climate events may induce lagged effects, with clear thresholds and spatial dependence. (4) The use of GEE enables large-scale, multi-year, pixel-level GSL analysis, providing high-precision evidence for phenological quantification and critical parameters for carbon cycle modeling, ecosystem service assessment, and adaptive management. Overall, this study systematically reveals the lengthening and asymmetric patterns of GSL on the Tibetan Plateau, elucidates diverse land cover and climate responses, advances understanding of high-altitude ecosystem adaptability and climate resilience, and provides scientific guidance for regional ecological protection, sustainable management, and future phenology prediction. Full article

Water supply systems (WSS) face various threats such as climate change, declining freshwater availability, and over-extraction of groundwater. To improve the resilience and sustainability of WSS, both technological innovation and effective institutional and economic mechanisms are required. This study evaluates China’s recent water resource fee-to-tax reform as a quasi-natural experiment. It analyzes panel data from 222 prefecture-level cities between 2012 and 2023 and applies a multi-period difference-in-differences model to assess the impact of this reform on water use structure and efficiency. The two main research goals are to examine whether the reform has enhanced the structural resilience of WSS in terms of the shift from groundwater dependence to surface water, and whether it has improved water use efficiency to ensure sustainable water use. Our results show that the reform significantly reduced reliance on groundwater and increased the proportion of surface water use, thereby enhancing the structural resilience of urban water supply systems. Further analyses confirm that these effects are most pronounced in eastern and central regions, where water stress is higher. On the other hand, while the reform improved water use patterns, its positive impact on water use efficiency remains limited due to the current tax design. Overall, our research results demonstrate how fiscal instruments can be leveraged to improve sustainability of WSS. They provide policy insights for strengthening resilience of WSS against resource scarcity and environmental risks. Full article

The increasing demand for sustainable substrates in agriculture and urban greening calls for alternatives to peat, whose extraction poses significant environmental risks. This study assesses the potential of olive pomace biochar (OB), wood biochar (WB), and green compost (GC), alone or in combination, to partially replace peat in growing media and improve substrate properties and plant development. Ten different substrates were formulated by substituting 10–20% of a commercial peat-based substrate with these organic amendments, using the commercial substrate alone as a control. The effects of such replacements were evaluated in the following experiments: a germination test conducted in Petri dishes using four forage species (Medicago polymorpha, Lolium perenne, Festuca arundinacea, and Lolium rigidum); and two parallel pot experiments lasting 100 days each (one with M. polymorpha and L. perenne, and another with young Olea Europaea var. Arbequina saplings). This study evaluated the impact on plant development, as well as the physical properties and composition of the substrates during the incubation process. Germination and survival of forage species were comparable or improved in most treatments, except those including 20% OB, which consistently reduced germination—likely due to high electrical conductivity (>10dS/m). In the pot experiments, substrate pH and total carbon content increased significantly with biochar addition, particularly with 20% WB, which doubled total C relative to control. Both forage species (Medicago polymorpha and Lolium perenne) and the olive saplings (Olea Europaea) exhibited normal growth, with no significant differences in biomass, water content, or physiological stress indicators when compared to the control group. Nutrient uptake was found to be stable across treatments, although magnesium levels were below sufficiency thresholds without triggering visible deficiency symptoms. Overall, combining compost and biochar—particularly WB and GC—proved to be a viable strategy to reduce peat use while maintaining substrate quality and supporting robust plant growth. This approach proved effective across the different plant varieties tested, including Medicago polymorpha, Lolium perenne, and young olive plants, which together encompass a wide spectrum of agronomic and horticultural applications as well as contrasting growth and nutrient requirements. Adverse effects on early plant development can be avoided by carefully selecting and characterizing biochars, with specific attention to salinity and C/N ratio. This finding is crucial for the successful large-scale implementation of sustainable alternatives to peat. Full article

The contamination of water bodies with heavy metals from various anthropogenic sources has become a prominent global issue. New industrial establishments and rapid urbanization have led to heavy metal intrusion into various surface water bodies, deteriorating water quality and causing numerous health issues for people consuming it. Removal of heavy metals from water is a complicated and costly process; hence, researchers are adopting various techniques to remove them naturally. This paper assesses the performance of a biosorption technique to remove heavy metal iron from Gostani Velpuru Canal, India. The techniques involved using biomass of Tinospora cordifolia in the form of green stem (GSB), dry stem (DSB), and extracted powder (PB). The efficiency of iron removal was measured from water samples collected diurnally from the canal. The study focused on the variations of T. cordifolia biomass combinations in iron absorption using static and agitated methods. The results indicated that PB with agitation had the highest mean iron removal efficiency of 72.43%, followed by DSB (41.77%) and GSB (35.32%) in the collected GVC samples. These findings suggest that T. cordifolia, regardless of its form, can be used for diverse water resource applications. Full article