Search Results (9,261)

This study presents an integrated computational framework for quantifying industrial impacts on marine ecosystems through the combined assessment of multiple environmental quality indices. The Aquatic Water Quality Index (AWQI) and four diagnostic pollution indices, namely the Heavy Metal Pollution Index (HPI), Metal Index (MI), Degree of Contamination (C_d), and Pollution Index (PI), were applied across 23 offshore sites in Mesaieed Industrial City, Qatar, to establish a high-resolution baseline for evaluating the effects of industrial effluents and brine discharge. Multivariate statistical analyses, including Principal Component Analysis (PCA) and Cluster Analysis (CA), identified Cr, Pb, Mn, Ni, and Zn as the principal drivers of water quality variability, effectively distinguishing anthropogenic influences from natural background conditions. To enable rapid and automated marine environmental assessment, three machine learning models—Artificial Neural Networks (ANN), Random Forest (RF), and Decision Trees (DT)—were developed and evaluated for predicting the investigated indices. Model performance was assessed through rigorous training–testing validation and the Diebold–Mariano test. The results demonstrated that model selection significantly influences predictive accuracy. Among the evaluated algorithms, RF achieved the highest predictive performance for AWQI (R² = 0.88) and C_d (R² = 0.92), whereas ANN performed best for HPI (R² = 0.89), and DT yielded the most accurate predictions for MI (R² = 0.82). Despite the index-specific strengths of individual models, RF emerged as the most robust and generalizable approach, consistently providing superior performance across heterogeneous environmental datasets. The proposed framework advances marine water quality assessment from conventional descriptive monitoring toward a proactive, data-driven paradigm, offering a scalable and cost-effective decision support tool for environmental management, pollution mitigation, and evidence-based coastal governance in industrialized coastal regions. Full article

The integrated usage of metro and bus services plays a key role in long-distance trips in big cities. Revealing the nonlinear relationship between the intermodal transfer demand and the built environment is significant for building a sustainable public transport system. This paper proposes a stacking ensemble explainable machine learning framework, which uses meta-learner to learn the prediction results of diverse base learners to improve performance, to detect how the impact factors impact the intermodal demand, including metro-to-bus and bus-to-metro directions. In this framework, the ensemble model is the stacking model; the ridge regression model is the second model. The base learners contain tree-based models (e.g., Random Forest, XGBoost and CatBoost) and non-tree-based models (e.g., SVR and KNN). The framework is applied to the case study of Beijing, China, based on one weekday (13 May 2019) and one weekend day (18 May 2019) of smart card data covering the main urban districts within the Sixth Ring Road. The results indicate that the stacking ensemble learning model outperforms the base learning models. For the metro-to-bus direction, transfer time, bus station count, and degree centrality are the top three influential factors; for the bus-to-metro direction, transfer time, bus station count, and shopping POI count are the top three, with lower predictive performance due to greater variability in this direction. However, the interaction effect of transfer time and bus station count is negative. This study could provide new insights into public transport planning and management. Full article

This study investigated rubber additives and relevant transformation products (RARTPs) in surface waters of Kaifeng, a city linking the Yellow River and Huaihe River basins. Seven of fifteen target analytes were detected in >10% of samples. The hydrolysis product 4-hydroxydiphenylamine (4OH) showed the highest detection frequency (70%), followed by 1,2-Dihydro-2,2,4-trimethylquinoline (TMQ, 57%) and N-(1,3-Dimethylbutyl)-N′-phenyl-p-phenylenediamine (6PPD, 27%). TMQ had the highest average concentration (6.16 ± 4.17 ng·L⁻¹). Urban rivers (14.20 ± 4.72 ng·L⁻¹) were contamination hotspots, driven by management practices (e.g., dredging of urban lakes). Although detected at lower levels (0.09 ± 0.21 ng·L⁻¹), 6PPD-quinone (6PPD-Q) was associated with elevated risk (risk quotient, RQ ≥ 1) at 19% of sites. The chronic daily intake assessment showed that drinking water ingestion contributed 66.7% of total exposure in daily use, whereas dermal absorption dominated during swimming. Children, especially girls, were the most vulnerable subgroup. Although estimated chronic daily intakes (CDIs) from surface water accounted for only a negligible proportion of the daily urinary excretion of p-phenylenediamine antioxidants (PPDs) reported in a Chinese population, the ecological risk of 6PPD-Q warrants continued attention. These findings highlight the need for improved management of water bodies receiving urban runoff and aquaculture inputs, and further health risk assessment of RARTPs. Full article

Background: Biliary drainage is a key component of palliative management in patients with malignant biliary obstruction. In cases where endoscopic approaches are unsuccessful or cannot be performed, percutaneous transhepatic biliary drainage (PTBD) represents an established alternative for achieving biliary decompression. The C-reactive protein–albumin–lymphocyte (CALLY) index combines inflammatory, nutritional, and immune-related parameters into a single marker, while the modified Glasgow Prognostic Score (mGPS), based on C-reactive protein and albumin concentrations, reflects the systemic inflammatory status of the patient. This study aimed to evaluate the prognostic value of the preprocedural CALLY index and mGPS in predicting 30-day mortality among patients with advanced malignant biliary obstruction undergoing palliative PTBD. Methods: This single-center retrospective study was conducted in a total of 179 patients who underwent palliative PTBD for malignant biliary obstruction at Ankara Etlik City Hospital between December 2022 and June 2025. Results: The 30-day mortality rate was 25.1%. The cut-off value for CALLY was determined as 67 based on receiver operating characteristic (ROC) curve analysis, and mGPS was categorized as 0–1 versus 2. In univariable Cox regression analyses, pancreaticobiliary tumor type, mGPS = 2, and CALLY < 67 were associated with early mortality. In multivariable Cox analysis, CALLY ≥ 67 was independently associated with a reduced risk of 30-day mortality, whereas pancreaticobiliary tumor type was independently associated with an increased risk. In the CALLY–mGPS risk stratification, 30-day mortality rates were 8.0%, 13.5%, and 44.1% in the low-, intermediate-, and high-risk groups, respectively. Conclusions: In this retrospective cohort, preprocedural inflammation- and nutrition-based markers were found to be associated with early mortality in patients with malignant biliary obstruction undergoing PTBD. Accordingly, risk stratification using readily available parameters such as CALLY and mGPS appears feasible in the preprocedural setting. The CALLY–mGPS-based approach may provide a practical framework for clinical risk assessment; however, prospective multicenter validation, including tumor-specific subgroup analyses, is warranted. Full article

The housing sector in major cities is facing escalating challenges due to rapid population growth and land scarcity. Consequently, vertical growth has been adopted as a strategic solution to optimize land use while balancing economic, social, and environmental needs. This study examines the phenomenon of vertical growth of the Dammam Metropolitan Area (DMA) in Saudi Arabia, from an urban sustainability perspective, focusing on evaluating the current state of multi-story buildings, their determinants, and their impact on quality of life and infrastructure efficiency. This study utilizes a systematic review methodology and a conceptual approach to develop an integrated framework for sustainable vertical growth. Furthermore, an empirical validation was conducted by projecting this framework onto vertical housing projects in Dammam, focusing on challenges related to design, construction quality, shared service management, and the suitability of apartments for family needs. The results indicate that the shift toward vertical growth achieves land-use efficiency, limits random horizontal expansion, and provides economic opportunities. However, it faces social and cultural constraints, most notably the resistance of some families to changing traditional ownership patterns, limited privacy and green spaces, and challenges in building maintenance and operations. The study highlights the importance of integrating urban planning, governance, architectural design, and infrastructure to ensure the sustainability of vertical growth and provide suitable housing alternatives. The study recommends further field research to assess social acceptance, improve quality-of-life indicators, and develop policies encouraging sustainable vertical expansion in alignment with Saudi Vision 2030 and the 2030 Sustainable Development Goals (SDGs), ensuring cities are more resilient, efficient, sustainable, and liveable. Full article

Smart tourism destinations, embedded by the internet and information and communication technologies, have been improving tourists’ experiences and connectivity. However, Destination Management Organisations (DMOs) still lack knowledge of how digital technologies can enhance their role and bring greater competitive advantage to destinations. In this sense, this study aims to develop an integrated smart tourism destination management ecosystem model that clarifies the relationships between digital technologies, managerial functions, benefits and implementation barriers within the broader smart city context. The study adopts a mixed-review design, combining bibliometric analysis and a systematic literature review. Bibliometric mapping was conducted using VOSviewer to analyse co-occurrence networks, thematic clusters and research trends. At the same time, the systematic review, with a systems thinking approach, enabled an in-depth qualitative examination of technological applications, managerial roles and governance implications. Data was gathered from 29 Scopus-indexed articles. The analysis identifies key benefits, including enhanced visitor experiences, improved decision-making and increased destination competitiveness, alongside persistent barriers related to governance, digital literacy, interoperability and cybersecurity. Based on these findings, the study proposes a conceptual ecosystem model that illustrates how DMOs can orchestrate digital technologies to support smart, sustainable and adaptive destination management. This research contributes to the smart tourism and smart cities literature by integrating bibliometric insights with a systems thinking perspective to develop a holistic destination management ecosystem model. Unlike prior reviews that address technologies or outcomes in isolation, this study offers a structured and actionable framework that advances theoretical understanding of smart tourism destinations while providing practical guidance for DMOs engaged in digital transformation. Full article

In national park sections adjacent to large cities, protected wildlife habitats often intersect with roads, tourism, agriculture, forestry, and other community-use spaces. This overlap complicates the joint prioritization of multi-species conservation and potential human-wildlife conflict governance. Using species trace-point data from the Fourth National Giant Panda Survey, we developed 30 m MaxEnt distribution models for 12 mammal species in the Chengdu section of the Giant Panda National Park and integrated protected-species’ conservation priority with potential wild-boar-related conflict pressure. Test AUC values ranged from 0.702 to 0.897, and elevation was the dominant predictor for 11 species. The Top 15% weighted conservation priority area, based on protection status and rarity, covered 350.1 km². Potential wild boar conflict pressure was defined as wild boar suitability multiplied by human exposure, and the Top 15% risk area covered 348.3 km². Overlaying the two layers identified 61.6 km² of high-conservation-high-conflict areas. Functional-zone statistics showed that the core conservation zone concentrated higher multi-species conservation value, whereas the general control zone carried stronger potential wild boar conflict pressure. This framework provides a spatial basis for coordinating protected mammal monitoring, crop-damage warning, and community co-management. Full article

The water–energy–food (WEF) system acts as a critical nexus of social–ecological systems. However, rapid urbanization has intensified the regional imbalance in the supply and demand of ecosystem services (ESs). Clarifying the spatiotemporal matching of ecosystem services supply and demand (ESSD) within the WEF framework and revealing the driving mechanisms behind such imbalances are essential to formulating reasonable zoning schemes and targeted optimization strategies for the coordinated development of the regional WEF system. Taking Zhejiang Province as a case study, this research uses water yield (WY), carbon sequestration (CS), and grain production (GP) to characterize the WEF nexus system. It uses the InVEST model to assess WY and CS, applies spatial allocation methods to characterize GP, and integrates socioeconomic data to quantify the demand for the above three ESs. All indicators were standardized and integrated with equal weights to further clarify the comprehensive levels of ESSD. By integrating the Geodetector and K-Means clustering methods, the study analyzes the supply–demand matching of ecosystem services and its driving mechanisms in Zhejiang Province during this period, thereby exploring ecological management zoning and optimization strategies within the WEF system. The study findings indicate that: (1) From the supply perspective, Zhejiang Province’s WY services demonstrate a trend of elevated activity in the southwest and diminished presence in the northeast; high values for CS services are predominantly found in the vegetation-rich areas of the northwest, while high values for GP services are clustered in the northern Zhejiang Plain; from the demand perspective, high values for all three ESs in Zhejiang Province are primarily located in economically active, densely populated urban areas. (2) The correlation between ESSD within Zhejiang Province’s WEF system exhibits significant spatial heterogeneity and is driven by the combined effects of natural and socioeconomic factors, with the interaction between these two factors often producing a synergistic effect. Specifically, annual average precipitation and population density are the dominant factors influencing WY services, NDVI and human footprint are the dominant factors influencing CS services, and population density and GDP are the dominant factors influencing GP services. (3) From 2000 to 2020, the supply–demand ratio for comprehensive ESs in Zhejiang Province generally followed a pattern of being lower in the east and higher in the west. The supply–demand imbalance of ESs intensified in the core areas of eastern cities, whereas the western regions maintained a relatively sound supply–demand balance. (4) The study classifies the counties in Zhejiang Province into four ecological management zones—ecological stable zones, ecological conservation zones, ecological control zones, and ecological restoration zones—and explores differentiated approaches to optimizing these zones and implementing control strategies. Full article

Due to climate change and rapid urbanization, cities worldwide face the dual challenge of improving flood resilience and providing accessible green space within limited land resources. Sponge City parks offer a landscape-based approach for integrating stormwater management with park services. However, how park morphology structures this combined performance remains insufficiently understood. This study examines 26 Sponge City parks in Shanghai and evaluates how node-, line-, and patch-type morphologies are linked to stormwater storage and service provision. Using geospatial analysis, DEM-derived catchment delineation, land-cover interpretation, and statistical analysis, this study compares estimated stormwater storage, storage efficiency, local park availability, and land-cover composition across different park morphologies. The results show that estimated performance of stormwater management and park service provision vary across morphological types, but these differences do not follow a simple node–line–patch hierarchy. Rather, the observed patterns are jointly shaped by park morphology, catchment setting, land-cover allocation, and surrounding urban context. These findings suggest that Sponge City parks should not only be evaluated by total stormwater storage. Their contribution depends on morphology, scale, catchment setting, land-cover allocation, and urban context. The study provides a morphology–performance perspective to support more differentiated planning of multifunctional green infrastructure. Full article

Background and Objectives: Severe snowfall and icing are associated with weather-related trauma presentations, especially in cities unaccustomed to prolonged winter conditions. However, the clinical characteristics of these injuries and their implications for surgical management remain incompletely understood. This study aimed to describe injury patterns, treatment approaches, and factors associated with the need for surgery among patients presenting with extremity trauma during an intense snowfall and icing episode in Diyarbakır. Materials and Methods: This single-center retrospective observational study included patients presenting to the emergency department with extremity trauma during a severe snowfall and icing period. Demographic characteristics, injury features, imaging modality, ambient temperature, anatomical localization, and treatment approaches were analyzed. Patients were categorized according to nonoperative versus operative management. Factors associated with the need for surgery were evaluated using univariable and multivariable logistic regression analyses. Receiver operating characteristic analysis was used to assess the discriminative ability of age and ambient temperature for predicting the need for surgery. Results: A total of 943 patients were included. The largest age group was 18–44 years (38.6%), and 55.9% were male. Fractures were identified in 50.7% of cases, whereas 46.7% had no fracture and 2.7% had joint dislocation. Upper-extremity injuries predominated (65.2%), with distal segment involvement observed in 55.0% of cases. Most presentations occurred on days with mean ambient temperatures ≤ 0 °C (81.5%). Overall, 82.1% of patients were managed nonoperatively, while 17.9% required surgical treatment. In multivariable analysis, increasing age and the use of computed tomography were independently associated with the need for surgery, whereas ambient temperature was not. Conclusions: Fall-related extremity injuries during severe snowfall and icing were predominantly upper-extremity and distal injuries, and most were managed nonoperatively. The need for surgery was more strongly associated with patient age and injury complexity than with ambient temperature alone. These findings describe a distinct trauma profile during short-term winter events in mild-climate cities. Full article

Improving land green utilization efficiency (LGUE) is essential for achieving sustainable development in China. This study investigates the spatiotemporal evolution and localized driving mechanisms of land green utilization efficiency across 127 cities in six major Chinese urban agglomerations from 2011 to 2023. Previous research frequently overlooks the spatial non-stationarity and structural interactions within regional land governance. To address this theoretical gap, a comprehensive multiscale framework is employed. This framework integrates the Super-SBM model, Dagum Gini decomposition, Spatial Markov chains, and Multiscale Geographically Weighted Regression. The empirical results reveal an overall upward efficiency trajectory alongside persistent spatial inequalities. A pronounced scale-efficiency inversion is observed between developed eastern coastal and developing central-western inland regions. Furthermore, spatial interaction analysis identifies a significant backwash effect. This mechanism constrains the upward mobility of peripheral cities adjacent to high-efficiency core nodes. The multiscale regression demonstrates substantial spatial heterogeneity in the effects of key driving factors. Elements such as industrial structure and financial development exhibit highly localized associations dependent on regional institutional contexts. These findings bridge macroeconomic growth models with micro-environmental governance. The study provides critical empirical evidence for shifting from uniform administrative management to spatially targeted regional policy frameworks. Full article

Artificial intelligence has become constitutive of smart city governance, yet data and algorithmic challenges remain analytically separated in existing scholarship, obscuring their recursive coupling and consequences for the built environment. This review synthesises 82 peer-reviewed studies (2020–2025) drawn from a deduplicated corpus of 876 records, combining PRISMA-guided methodology with VOSviewer and CiteSpace bibliometric mapping. Annual output rose from 78 publications in 2020 to 224 in 2024, with ten leading countries contributing roughly 84% of the corpus. The keyword network organises into five thematic clusters spanning AI technical foundations, data governance, algorithmic governance, sustainability, and built-environment governance; emerging 2023–2025 couplings between digital twin and SDG 11, and between generative AI and SDG 11, mark a shifting research frontier, while the algorithmic governance → SDG 16 linkage constitutes the strongest single ribbon in the synthesis. The study advances a double-helix coupling mechanism specifying directional propagation, reverse modulation, and structural cross-linking between data and algorithmic strands, reframing building energy management, digital-twin operation, and smart infrastructure as governance arrangements whose sustainability legitimacy depends on the simultaneous integrity of both strands. Full article

Shopping mobility accounts for a significant share of total travel, while the growth of e-commerce is reshaping consumer purchasing behaviour and retail dynamics. Comprehending how territorial and sociodemographic factors shape the choice between physical and digital retail channels is therefore a key issue for transport planning and sustainable urban mobility. In this context, it is important to understand how accessibility to different classes of retailers is configured and how it can impact purchasing choices. Through a discrete choice analysis, this study examines the sociodemographic and territorial determinants of purchasing behaviour, focusing on the clothing market. Four purchase alternatives are considered: medium-sized and small urban retail stores, shopping malls, online purchasing, and no purchase. This multi-alternative framework enables the direct estimation of substitution patterns not only between physical and digital retail, but also between distinct forms of physical retail. Data were collected through a survey conducted in Southern Italy, providing empirical evidence from a territorial setting that is structurally underrepresented in the existing literature. A multinomial logit model and a two-level hierarchical logit model incorporating pedestrian accessibility—measured as walking time from residence to the nearest clothing store—alongside sociodemographic and territorial attributes were calibrated to analyse alternative choice behaviour. The calibrated models show interesting results, highlighting the role of pedestrian accessibility in the choice of clothing stores in city centres. Age, income, and territorial variables further differentiate channel preferences across population segments. The findings offer relevant implications for policymakers, governance managers, urban planners, and researchers concerned with retail location, sustainable accessibility, and consumer behaviour. These insights are highly valuable for developing planning that addresses the United Nations 2030 Agenda, particularly Sustainable Development Goal 11. Full article

Worldwide, flash floods are among the most unpredictable and hazardous hydrological phenomena, particularly in arid and semi-arid regions such as the Kingdom of Saudi Arabia, where sudden heavy rainfall follows prolonged periods of drought. This work presents an effective integrated model for flood hazard evaluation in the Hanefah Catchment, a socioeconomically vital area in the central part of Saudi Arabia that includes the capital city, Riyadh. Using high-resolution ALOS PALSAR 12.5 m Digital Elevation Model spatial data, we extracted and investigated indicative linear, areal, and relief morphometric keys of 64 sub-catchments. This paper employs a dual-method concept that integrates a multi-criteria ranking method and the El-Shamy approach in conjunction with morphotectonic analysis to model flood-susceptibility zones. Furthermore, this paper suggests a comparative assessment of low-cost morphometric models under data-scarce conditions, assessing the multi-criteria ranking method against El-Shamy’s approach, using the topographic position index (TPI) as an internal terrain scale benchmark. The ranking method successfully assigned 85.7% of the historically recorded flood locations to the high-hazard zone that covers ~24.22% of the Hanefah catchment. In contrast, the El-Shamy approach systematically underestimated flood susceptibility because regional tectonic activity increases bifurcation ratios, resulting in just ~42.9% of the historical floods being assigned to the high-hazard zone. The final results highlight the northern and northwestern parts of the catchment as high-hazard zones, characterized by high drainage density and steep relief. This study provides a refined, cost-effective model that aligns with the strategic objectives of Saudi Vision 2030 for sustainable water resources management and significant urban development. Full article

Urban freight distribution is essential for supplying commercial activities, but it also increases pressure on curb space, vehicular circulation, pedestrian movement, and public space management, especially in medium-sized cities where dedicated loading and unloading infrastructure is often limited. Although recent literature emphasizes the need for data-driven urban logistics planning, empirical evidence from intermediate Latin American cities remains scarce. This study develops and applies a spatial-operational framework to characterize urban freight distribution, identify patterns of conflict and informality, estimate loading and unloading bay requirements, and prioritize intervention areas in a medium-sized city. A quantitative, observational, exploratory–descriptive, and correlational design was applied, based on 642 georeferenced loading and unloading operations recorded through a digital field survey. The analysis integrated data cleaning, descriptive and inferential statistics, logistic models, an operational sustainability risk/pressure index, DBSCAN spatial clustering, logistics pressure and sustainable transport priority indices, and a capacity model based on average daily operations. The results revealed spatial concentration of logistics activity, a predominance of light trucks, frequent use of paid parking areas and roadways, and a high presence of operational conflicts. The study provides a replicable and planning-oriented framework for prioritizing curbside management interventions for sustainable urban freight distribution in medium-sized Latin American cities. Full article