Ferhat Karaca

Remote sensing enables building-level flood vulnerability assessment without field surveys, yet existing approaches require site-specific calibration or produce categorical outputs without physical interpretability. We present the Global Flood Vulnerability Model (GFVM), integrating six remotely sensed components (elevation, slope, topographic position index, distance to water, building height, and basement depth) through geographic context classification to quantify vulnerability from terrain and structural characteristics across coastal, fluvial, and pluvial settings. Building heights are extracted primarily from the Global Building Atlas, with gaps filled using a ConvNeXt neural network trained on high-resolution Light Detection and Ranging (LiDAR) ground truth from four cities (within-city MAE 1.35–1.91 m, cross-city MAE 2.05–3.47 m). Terrain metrics are derived from a combination of hierarchical digital elevation models (DEM) (USGS 3DEP 10 m, AHN LiDAR 0.5 m, UK Environment Agency DTM 1 m, Australia 5 m) and global datasets (NASADEM 30 m, Copernicus GLO-30). Hydrographic networks are sourced from OpenStreetMap and Natural Earth. Implementation through Google Earth Engine requires only coordinates as input, returning a five-level vulnerability index with multi-hazard decomposition (fluvial, coastal, pluvial) and SHapley Additive exPlanations (SHAP)-based attribution identifying dominant drivers. Validation across 183 independent locations in Germany, UK, and USA demonstrates robust performance: Area Under Curve 0.855 for separating flooded from non-flooded sites, weighted Cohen’s kappa 0.493 across regulatory zones, and Spearman ρ 0.746 against Federal Emergency Management Agency (FEMA) classifications. Sensitivity analysis across 625 parameter configurations confirms stability, and DEM resolution experiments show that global 30 m elevation data produces category reclassification in only 5.3–8.6% of locations compared to high-resolution sources. Application to the 2024 Kazakhstan floods identifies 118 high-vulnerability locations across 581 assessment points, with vulnerability patterns matching documented inundation. GFVM advances remote sensing applications for disaster risk assessment by demonstrating that multi-source geospatial data fusion enables building-level vulnerability screening without local calibration or field surveys.

The widespread adoption of circularity principles in the building sector fuels the need for robust and comprehensive evaluation systems, which could benefit from the approaches and indicators employed in widely accepted building sustainability assessment (BSA) methods. Simultaneously, the effective consideration of circular economy (CE) principles into BSA methods becomes increasingly urgent. An important step towards achieving these targets is the investigation of whether, and to which degree, the existing BSA methods encompass and express circularity principles; this study focuses on this relatively underexplored theme. Specifically, this study investigates the degree of association between five widely used BSA methods and the circularity strategies included in the 10R Framework. The methods examined are BREEAM, DGNB, LEED, Level(s) and SBTool (versions and criteria for new buildings). The examination was conducted at the lowest self-contained and score-attributing level of each method and was undertaken by five expert groups—each assigned one method. A quantitative scale from 0 to 5 was used to assess the strength of the association. The results are analysed in terms of (i) the criteria/thematic areas within each method receiving high/low scores, and (ii) the circularity strategies deduced to be strongly/weakly represented in and across the BSA methods. Common trends and milder differences across these axes are observed. Generally, the associations appear stronger in thematic areas relevant to, among others, resources and lifecycle performance, and weaker regarding parameters linked to user comfort. The R-strategies Reduce, Reuse, Recycle and Rethink emerge as more intensely represented in the examined methods. The study’s results indicate areas for further research and potential methodological enhancement.

Despite severe particulate matter (PM) pollution in Central Asia, limited air composition and health impact data are hindering sustainable air quality management and resilient urban planning. This study provides the first comprehensive assessment of PM_2.5 and PM_2.5–10 in the urban environment of Astana, Kazakhstan, a rapidly expanding city with intense winter heating demands. We characterized PM and atmospheric precipitation and assessed health risks using bioaccessible fractions of PM-bound potentially toxic elements (PTEs). Among 388 samples, PM_2.5 and PM_2.5–10 concentrations peaked at 534 and 1564 μg·m⁻³, respectively. Scanning electron microscopy (SEM) identified soot and coal fly ash, indicating fossil fuel combustion as a major source. Precipitation characterization also showed elevated SO₄²⁻ (17.8 μg⋅L⁻¹), V (108 μg⋅L⁻¹), Ni (84.0 μg⋅L⁻¹), and Mn (63.2 μg⋅L⁻¹). Bioaccessibility tests showed high solubility for Fe (16,229 mg·kg⁻¹) followed by V: key indicators of combustion emissions. Non-carcinogenic risk for Ni and V exceeded acceptable limits for adults and children (e.g., HQ: 6.07 for V for adults). Carcinogenic risk exceeded the threshold 10⁻⁶ for Cd (adults), Co, Cr, and Ni. These findings may help advance urban air quality management via integrating bioaccessibility-based health risk assessment and source apportionment, supporting evidence-driven policies for environmentally responsible development in rapidly urbanizing cold-climate regions.

Circular economy and sustainability have both seen rapid growth in academic literature, often leading to ambiguity and the overuse of these terms. This obscures their true objectives and makes it challenging to discern their distinct intentions. Manually analyzing the vast body of recent publications to understand how these concepts connect to environmentally beneficial practices is laborious and time-consuming. This study aims to compare and analyze existing literature on sustainable and circular construction using natural language processing (NLP) techniques to elucidate the similarities and overlaps between these concepts within the construction industry. To achieve this, we employed three NLP methods: (1) TextRank, a graph-based ranking algorithm that extracts key structural relationships between terms in a document; (2) term frequency–inverse document frequency, a statistical measure that identifies the most significant terms based on their frequency and uniqueness within the corpus; and (3) semantic annotation (Wikifier), a method that links text tokens to structured knowledge bases such as Wikipedia for better contextual understanding. These methods are used to analyze a dataset of 480 academic articles focusing on sustainability and circular economy in the construction sector. Our analysis revealed that circular construction is more specific and practical, emphasizing resource efficiency, waste management, and industry-specific processes, targeting the operational aspects of recycling and resource recovery. In contrast, sustainable construction encompasses a broader and more holistic scope, including urban planning, community development, and long-term environmental impacts. This study demonstrates how NLP methods can systematically disentangle closely related frameworks in construction literature, providing a replicable methodological framework for future data-driven investigations. By clarifying the distinctions and overlaps between the terms “circular construction” and “sustainable construction”, our research offers enhanced understanding for policymakers, industry practitioners, and academics aiming to integrate sustainable and circular principles effectively within the construction sector.