Search Results (789)

Artificial intelligence (AI) is increasingly used in water research. However, many AI models, particularly complex machine learning models, often generate outcomes that are difficult for humans to interpret. Explainable artificial intelligence (XAI) has been developed to address these challenges by providing transparent and human-interpretable explanations of model behavior and predictions. We conducted a structured narrative review using predefined searches of Web of Science Core Collection and Scopus to synthesize empirical XAI applications across six water-research domains: hydrological processes, water quality and pollution, groundwater systems, urban water systems, climate–water interactions, and water and wastewater treatment. The review covers feature-importance methods, SHapley Additive exPlanations (SHAP), Local Interpretable Model-agnostic Explanations (LIME), partial dependence plots (PDPs), individual conditional expectation (ICE) plots, accumulated local effects (ALE) plots, counterfactual explanations, and deep-learning attribution methods. Building on previous reviews and perspectives focused on particular water domains or methodological priorities, we provide a cross-domain synthesis of XAI spanning natural and engineered water systems, with emphasis on method selection, model and data compatibility, explanation reliability, and operational implementation. These capabilities, however, must be interpreted with appropriate caution because XAI explanations remain conditional on the data, fitted model, and explanation method, and therefore should not be treated as evidence of causal mechanisms or environmental controls. Recognizing these limitations, we provide practical guidance for selecting and evaluating XAI methods and outline priorities for developing reliable, scalable, and operationally useful AI systems for water research and management. Full article

Immersive technologies support heritage interpretation and management, yet research remains divided between cultural and archaeological heritage and urban planning. This systematic review, reported according to PRISMA 2020, aimed to examine the design, application, and evaluation of augmented, virtual, and mixed reality systems in archaeological parks, historic districts, and urban-planning contexts. Peer-reviewed English-language journal articles and full conference papers published from 2010 to 15 July 2026 were eligible; museum-only, artifact-only, non-immersive, and methodologically insufficient reports were excluded. Scopus, Web of Science Core Collection, IEEE Xplore, ACM Digital Library, and SpringerLink were searched through 15 July 2026. A five-domain, study-type-adapted rubric was used for critical appraisal, and findings were synthesized narratively by spatial scale, delivery mode, technology pipeline, and evaluation. Of 10,790 records, 21 studies were included: 17 direct applications, three enabling methods, and one foundational survey. Twelve reported user or stakeholder evaluations, but only ten reported numerical sample sizes, precluding calculation of a pooled participant total. Evidence was heterogeneous, predominantly descriptive, and rarely longitudinal. The original screening, data extraction, and critical appraisal were performed by a single reviewer. Following peer review, all 21 included reports and the key coding and appraisal variables underwent retrospective independent verification and reconciliation. No supplementary hand-searching or citation searching was conducted. The urban dimension of ancient cities remains underexplored, while communication of uncertainty in city-scale reconstructions remains unresolved. The review proposes a cross-domain taxonomy, core evaluation outcomes, and an integrated research agenda. No external funding was received. The review was not registered, and no formal protocol was prepared. Full article

Buildings account for roughly 37% of energy-related CO₂ emissions, and space cooling already consumes nearly 10% of global electricity. Cooling demand is rising fastest in tropical cities, where air-conditioning could reach 45% of peak load, especially in India by 2050. This review critically examines thermal energy storage (TES) as a flexibility resource across three distinct scales: individual buildings, district heating and cooling networks, and city-level multi-energy systems. Using a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-based search of Scopus, Web of Science, and IEEE Xplore with primary and supplementary strings, 4447 records were identified, of which 174 were included. Each quantitative study was classified by validation level (simulation, laboratory, pilot, or operational) and by the centrality of thermal storage. Sensible, latent, and thermochemical storage technologies are compared using energy density (10–500 kWh/m³), efficiency (40–95%), cycle stability, and technology readiness. The review then evaluates the role of artificial intelligence (AI), machine learning, and Internet of Things platforms in forecasting, predictive control, and operational optimization of TES networks. Thirteen method families, grouped into AI and machine learning methods, optimization methods, control methods, and digital enabling technologies, are assessed against six explicitly defined criteria with evidence-coded scores. Among 47 quantitative studies, 37 (78.7%) are simulation-only, and only four (8.5%) report operational data. Direct TES-AI studies report simulated energy savings of 8–64% and peak load reductions of about 35%, whereas field-validated intelligent control reports 17% energy savings in a single real building experiment. The review also identifies inherent drawbacks of artificial intelligence-based operations, including limited interpretability, high data and computational demands, concept drift, and cyber vulnerabilities that increased peak electric load by 17.4% in a simulated attack. A structural imbalance in the literature is evident: most validated deployments remain at the building-scale, whereas urban-scale evidence is confined to district cooling, aquifer and pit storage, and multi-energy hub studies; no study reports the coordinated operation of distributed TES assets across multiple districts. A conceptual framework and a staged roadmap linking building, district, and urban scales are proposed. Priority research needs include urban-scale pilots in tropical climates, techno-economic assessment, interpretable and drift-robust AI, and interoperability standards that support United Nations’ Sustainable Development Goals 7, 11, and 13. Full article

Freshwater scarcity in arid and semi-arid regions, coupled with increased population density, leads to greater demand and pressure on aquatic ecosystems and limited groundwater resources. This study, conducted using the PRISMA 2020 methodology, aimed to evaluate the effectiveness and sustainability of hybrid technologies that combine advanced oxidation processes (AOPs) with biological filtration in the treatment of urban and industrial wastewater. A systematic review was conducted between 2015 and 2026 using six main databases (Scopus, Web of Science, ScienceDirect, SpringerLink, PubMed, and Google Scholar). A total of 1248 studies were identified, of which 78 met the eligibility criteria and provided sufficient quantitative data for comparative synthesis. The results showed that hybrid systems, such as ozone biofiltration, Fenton-Membrane Bioreactor (MBR), and photocatalytic biofilm, have higher removal efficiencies for COD (>95%), microorganisms (>90%), and pathogens (>99%), with minimal residual sludge. The environmental assessment also demonstrates the strong potential of these processes when integrated into arid regions such as southwestern Algeria, due to their contribution to conservation of biodiversity and sustainable reuse of treated wastewater. Through this study, our objective is to highlight the role of integrated approaches in circular water management, as well as the urgent need to standardize protocols to assess the magnitude of long-term environmental impacts. Full article

The integration of geo-spatial databases, advanced modeling, and artificial intelligence provides novel opportunities to investigate regional differentiation with direct implications for urban science and regional development. To avoid homogeneous competition and support regional tourism planning, it is important to examine how tourists perceive different tourism elements across large regional spaces. However, existing studies still lack a systematic workflow for extracting destination-specific perceptual characteristics and linking them to specific tourism locations. This study develops a UGC-based framework to identify tourism elements and perceptual characteristics, construct POI-level perceptual weights, and map the spatial patterns of tourism perception across Northeast China using 196,028 Ctrip reviews from 1491 POIs. The framework integrates jieba segmentation with BERT ranking for keyword extraction, ChatGPT-assisted filtering and manual review for keyword standardization, K-means clustering for perceptual dimension identification, and GIS-based kernel density analysis for spatial visualization. The main findings are as follows. First, tourism perception in Northeast China presented a diversified composite structure. Second, different perceptual dimensions and representative keywords showed differentiated spatial distributions. Third, the spatial structure of tourism perception in Northeast China was summarized as a composite spatial pattern centered on the Five-Dimensional Composite Perception Core Belt, supported by the Natural-Landscape Perception Belt and the Water Recreation Perception Area, and supplemented by multi-level urban nodes and inter-node perceptual linkages. This study provides methodological support and planning implications for understanding regional tourism differentiation and guiding evidence-based destination planning. The construction of geo-spatial databases plays a fundamental role in linking tourists’ perceptions to specific tourism elements and locations, enabling fine-grained and spatially explicit analysis that is difficult to achieve using conventional survey-based data. Full article

Urban green infrastructure (UGI) serves as a core pillar of sustainable urban development, delivering multiple benefits including urban heat island mitigation, stormwater regulation, and health promotion. However, allergenic pollen released by urban vegetation represents a typical ecosystem disservice, creating a prominent sustainability trade-off between greening benefits and residents’ allergy risk. For a long time, research in this field has evolved along two largely independent lines: vegetation allergenic hazard assessment and atmospheric pollen exposure analysis. Marked disconnections persist in indicator systems, spatial scales, and outcome translation, and an integrated spatial assessment framework remains lacking. Based on a systematic review of 127 publications from the Web of Science Core Collection (2000~2025) using framework synthesis methods, this study constructs a hazard–exposure–risk–planning (HERP) framework for the spatial assessment of allergenic pollen risk in UGI. This paper systematically synthesizes the core indicators, technical approaches and inherent limitations of each framework layer and identifies five key barriers: inconsistent indicator definitions, lack of cross-validation between surface vegetation and atmospheric pollen data, missing population vulnerability layer, insufficient multi-scale coupling, and weak translation of research into planning practice. The proposed framework provides a potential comparable and reproducible standardized pathway for cross-regional allergenic risk assessment and offers methodological support for healthy urban planning that balances ecosystem services and residents’ respiratory health. Full article

Open urban data portals are increasingly proposed as infrastructures for citizen-facing locative services, yet there are no explicit criteria for deciding which georeferenced entities qualify as urban digital locative elements, nor a principled basis for organising them. This hinders semantic interoperability, discovery, and reuse. This article pursues three objectives: to define eligibility criteria distinguishing such elements from other georeferenced data; to derive a faceted, multi-label typology organising them for discovery and reuse; and to assess operational feasibility in a real setting. Following a design-science approach, the artefact, five eligibility criteria and a nine-category typology, was derived by synthesising three evidence sources: a structured literature review, a comparative analysis of six CKAN-based portals in Portugal and Brazil, and an exploratory pilot in Fortaleza, with explicit mappings to FIWARE, schema.org/Place, OpenStreetMap, and CityGML/INSPIRE. The framework was then exercised on the portal corpus and in a working platform, complemented by a small in-situ user study (n = 17). The criteria filtered locative elements effectively, all categories were populated by real datasets, and category-based discovery was understandable in situ, though these findings are preliminary. Open government data thus emerges as an active layer for operationalising locative services, with broader validation left to future work. Full article

Accurate land cover classification is critical for geographic information science. However, the fusion of hyperspectral and LiDAR data remains constrained by insufficient spectral-geometric coupling and limited scale representation. To address these challenges, we propose H-FANet, a hierarchical fusion attention network with a three-branch backbone (spectral, spatial, and elevation). Multi-scale enhancement modules are embedded in the spatial and elevation branches to capture scale-invariant features through hierarchical aggregation with convolutional splitting. For cross-modal interaction, H-FANet adopts a two-level fusion strategy: shape-level shallow cross-attention for geometric alignment and spectral-level deep residual fusion for semantic integration. Experiments on three benchmark datasets showed that H-FANet achieved overall accuracies of 99.13 ± 0.06% on Trento, 97.43 ± 0.06% on Houston 2013, and 92.11 ± 0.05% on the Muufl Gulfport datasets. The network outperformed five comparison methods by approximately 1.7–2.2% in overall accuracy. Ablation studies confirm the contributions of the multi-scale enhancement and hierarchical fusion modules. This GeoAI-driven framework improved land cover identification accuracy and could be applied to fields such as environmental monitoring, urban land use analysis, and ecological protection. Full article

Brazil presents a distinctive convergence of continental-scale climatic diversity, extensive urbanization, large-scale biomass burning, rapid land-use change, persistent air-quality monitoring gaps, and deep social inequalities, producing highly heterogeneous and compound environmental health risks. In this context, treating air pollution and climate change as parallel environmental crises obscures their structural interconnections through shared emission sources, mutually reinforcing exposure pathways, and overlapping health and social consequences. In this narrative review, we critically synthesize scientific and institutional lines of evidence and argue that air pollution and climate risks can be more effectively addressed in Brazil through a single strategic agenda for science, public health, and governance. We first discuss why these challenges cannot be managed in isolation, emphasizing the effects of heat, drought, stagnation events, biomass burning, and extreme weather on pollutant formation, dispersion, and health burden. We then examine Brazil as a critical case where recent regulatory advances coexist with structural limitations in monitoring, data integration, and territorial coverage. Based on this diagnosis, we propose an integrated national agenda organized around five mutually reinforcing priorities: monitoring through hybrid networks; predictive science through climate-informed modeling and early warning; public health through the convergence of epidemiology, toxicology, and mechanistic research; equity-oriented research and action through the explicit incorporation of vulnerability, inequality, and climate justice; and policy appraisal through the assessment of disease burden, economic costs, mitigation co-benefits, and trade-offs. We further discuss the governance mechanisms needed to connect these priorities and translate evidence into coordinated action and adaptive public policies. We also argue that the Amazon should be approached not as an isolated ecological exception but as a central component of a broader Brazilian and Global South discussion on environmental health, land-use change, and climate justice. In this scenario, Brazil has the scientific capacity and regulatory momentum to become a reference in the integrated management of air pollution and climate risks, but this will depend on replacing fragmented approaches with a coordinated framework capable of linking exposure, mechanism, burden, inequality, and action. Full article

Stresses of climate change, urbanization, and segregation make the City of Norfolk in Virginia’s Tidewater region a fitting context for qualitative research into coastal resilience, urban nature, and residents’ perceptions of the environment. Dissertation research into sea level rise and urban development provided a rich environment for developing a practical multi-method system for community-engaged investigations of flooding effects, gentrification, and the loss of urban nature. Multi-method research illuminates social, racial, and economic dynamics in Norfolk residents through participant observation, archival research, semi-structured interviews, and walking interviews. The data generated by archival content analysis and semi-structured interviews can offer insights into citizens’ roles and experiences regarding climate change, urban development and redevelopment, and distressed nature in the city. Innovative use of participant observation and the walking interview can engage with questions of respondents’ personal beliefs about nature and the city, highlighting new possible applications of these methods. This research adds to the literature on the effects of climate change and displacement in Norfolk’s planning policies. It supersedes discussions of the mechanics of climate change to assess a range of residents’ perceptions on their own terms. The article provides a clear framework for engaging with residents around issues on climate, urbanization, and environmental stress. It therefore offers a methodological contribution to the field of qualitative research in coastal climate change science and the human experience of the dynamic, modern city. The article’s findings and methods may not be applicable in rural areas or in areas which are not facing acute climate change. Full article

Rift Valley fever (RVF) is a climate-sensitive mosquito-borne zoonosis long regarded as a rural, pastoral disease, yet accelerating tropical urbanization and intensifying climate variability may be reshaping its epidemiology at the urban–peri-urban interface. This narrative review examines how global climate change and urban-specific climatic conditions jointly shape RVF virus (RVFV) vector habitats, transmission, and burden in tropical and subtropical cities, synthesizing 51 of 412 English-language records identified by a structured, non-systematic search of PubMed, Scopus, Web of Science, and Google Scholar (2009–2026) and selected for relevance to urban and peri-urban RVF. This research draws on human, livestock, and vector evidence from Sub-Saharan Africa, the Arabian Peninsula, and Indian Ocean islands across epidemic and inter-epidemic periods. The synthesis indicates that impervious surfaces, poor drainage, and open water storage can recreate the water-retaining function of rural dambos, sustaining a year-round larval habitat, and that Culex quinquefasciatus dominance together with peri-urban cattle may form an amplification bridge to humans. Direct evidence remains scarce, anchored by a single peri-urban serosurvey and limited urban slaughterhouse entomology. Critical gaps include urban primary-vector ecology, infection-rate data, urban-heat effects, city-specific exposure studies, and coupled climate–urban burden models. We conclude that RVF is a plausible emerging urban threat warranting proactive inter-epidemic surveillance and integration of RVF into urban planning and One Health systems. Full article

Urban regeneration has become a primary strategy for addressing the spatial, social, environmental, and economic challenges associated with rapid urbanization. By using the PRISMA framework, this paper provides a systematic review of urban regeneration, focusing on its objectives, indicator systems, and estimation approaches. Journal articles published between 2008 and 2025 were retrieved from Web of Science, and 50 studies were retained after screening. The findings indicate that regeneration has increasingly highlighted long-term economic efficiency and environmental performance, along with growing attention to social equity, urban safety, and resilience. Life cycle costs are recognized as a core framework for capturing long-term financial and environmental performance. In terms of methodological evolution, a progression from conventional estimation techniques toward machine learning and hybrid approaches is observed. Several challenges remain, including the absence of standardized data frameworks and limited cross-regional transferability of existing models. To advance the field, future research could prioritize the establishment of common data standards, the adoption of explainable machine learning for improved model transparency, and the validation of models across diverse geographical and project settings. Full article

Climate change is intensifying heat-related risks in Mediterranean urban environments, increasing the need for pedestrian-scale approaches that combine spatial diagnostics with lived thermal experience. The pilot study develops a three-layer diagnostic framework for assessing outdoor thermal comfort in the central neighbourhoods of Urla, İzmir, Türkiye. The framework integrates UMEP-SOLWEIG microclimatic modelling, citizen science field measurements and structured thermal perception diaries. First, Physiological Equivalent Temperature (PET) and mean radiant temperature (Tmrt) were modelled for 24 November 2023 and used as spatial diagnostic layers to identify three pilot areas with distinct urban morphologies. Second, an independent citizen science thermal walk campaign was conducted on 5 November 2025, during which 12 volunteers recorded in situ air temperature and relative humidity at predefined measurement points. Third, participants completed thermal diaries based on ASHRAE Standard 55 to document thermal sensation, comfort, preference, acceptability and adaptive responses. The modelling and fieldwork components were not designed as same-day validation datasets but as complementary layers for interpreting spatial thermal patterns and perceived thermal conditions. The results show that modelled PET values were relatively homogeneous across the three areas, while field-measured air temperature and subjective thermal responses varied more clearly at the pedestrian scale. These differences suggest that surface conditions, shading, spatial openness and user perception may jointly shape outdoor thermal experience, although the findings should be interpreted as descriptive tendencies due to the single autumn fieldwork session and small sample size. The study contributes a cautious, repeatable pilot framework for linking spatial screening, citizen-generated microclimatic data and structured perception evidence in climate-responsive urban design and local climate governance. Full article

Park audits can inform neighbourhood-scale urban design strategies that support health. However, interpreting park audit data is time-consuming and requires specialised expertise. Recent advances in large language models suggest potential for assisting with structured data interpretation, but their application to park audits remains unexplored. This study examined whether a large language model (ChatGPT-5) can assist in interpreting park audit data from public parks in Dhaka City, Bangladesh. Using a modified park audit tool, 59 parks were audited, of which 26 met the predefined completeness threshold and were included in the analysis. ChatGPT-5 and human experts received identical park audit datasets and instructions for interpretation. Outputs were evaluated using content analysis and a pre-defined scoring framework to assess accuracy and comprehensiveness. Paired t-tests compared ChatGPT-5 outputs with the expert benchmark. ChatGPT-5 showed no statistically significant difference from the expert benchmark in overall accuracy in this sample (p = 0.13). The unadjusted analysis showed a higher recommendation-match count for ChatGPT-5 (p < 0.05), but this result should be interpreted as exploratory because multiple criteria were tested. No statistically significant differences were detected across accuracy domains or other comprehensiveness criteria. These findings suggest that ChatGPT-5 may assist with preliminary interpretation of structured park audit data. However, the results do not establish equivalence with expert assessment. Expert review remains necessary to assess feasibility, contextual relevance, and alignment with local planning standards. Full article

Low-emission zones (LEZs) require cities to define policy rules, predict effects before deployment, and verify outcomes afterwards, yet mobility, emissions, meteorology, exposure, data governance, and public communication are commonly handled in separate systems. This paper presents envair360, a Physical Intelligence architecture and a four-stage, evidence-gated LEZ methodology connecting project definition, baseline feasibility, digital-twin design, deployment, and verified impact closure. A design-science method is combined with an operational case study of Cartagena, Spain, because the research object is both a socio-technical artefact and a context-dependent municipal deployment. The technology chain is selected to bridge complementary scales and functions: SUMO for link- and vehicle-level traffic, WRF and CHIMERE for meteorology and regional chemistry, MUNICH and street-canyon parameterisation for computationally tractable street resolution, model-output calibration anchored to measurements, and FIWARE/NGSI-LD for governed context exchange. The manuscript distinguishes city observations, peer-reviewed component validation, demonstrated platform capabilities, and policy or engineering targets. A Murcia component study reports lower hourly than daily agreement after deep-learning calibration (NO₂: $r=0.79$ hourly and $0.94$ daily; O₃: $r=0.85$ hourly and $0.97$ daily), illustrating the importance of temporal aggregation and transfer limits. Digitisation of the prior Madrid ozone-density figure indicates modal shifts of approximately +32.0 and +27.8 source-axis units at two stations; the supplied source does not permit a numerical NO_x bias estimate. A separate six-city export audit covers 24,384 records and 4064 street segments and demonstrates a common model-output schema, not predictive validation. In Cartagena, project documentation reports elevated PM₁₀/PM_2.5, urban heat and solar-radiation stress, and a plausible role for dry-climate dust resuspension, supporting a superblock-oriented LEZ proposal with a long-term 30% vehicular CO₂ reduction target. The paper’s specific contribution is the governed orchestration, evidence taxonomy, quality gates, reproducible lineage, explicit policy-scenario representation, and portable city-onboarding protocol; it does not claim that the individual scientific models, the Cartagena deployment, or the cited project targets originated in this manuscript. Full article