Exposure to elevated levels of ambient particulate matter (PM 2.5 ) leads to premature mortality and considerable economic losses.

This study explores how circular economy (CE) principles are incorporated in construction-related policy roadmaps worldwide, addressing the growing need for effective resource strategies in a highly material-intensive sector. National strategic CE documents from 57 countries were analyzed using natural language processing techniques, and then their policy statements were systematically mapped to the 10R hierarchy. A constrained cluster analysis identified five distinct country groupings: preventive, downstream revalorization, upstream revalorization, downstream life extension, and upstream life extension. The results show that revalorization-focused strategies, particularly recycling and recovery, dominate globally and represent the most accessible entry point for CE adoption in construction. Contrary to conventional assumptions, advanced circular strategies are not confined to economically developed countries in the policy documents but are shaped by institutional capacity and contextual factors. By revealing multiple policy pathways to circular construction, the study provides a cluster-based analytical framework that supports more context-sensitive and effective CE policy design.

Source apportionment of organic carbon (OC) and elemental carbon (EC) is essential for understanding combustion-related pollution, secondary aerosol formation, and their impacts on climate and human health. Conventional receptor models, such as Positive Matrix Factorization (PMF), remain widely used but are constrained by linear assumptions, sensitivity to input uncertainty, and limited ability to resolve overlapping and evolving sources. The integration of machine learning (ML) has emerged as a promising solution to these challenges, bridging the gap between linear statistical assumptions and the dynamic reality of ambient air quality monitoring. This review evaluates the role of ML in OC/EC source apportionment by organizing existing studies according to their functional roles within the workflow, including PMF-assisted hybrid approaches, data structuring methods, end-to-end frameworks, and physics-informed models. Particular attention is given to challenges related to data resolution, transferability, interpretability, and uncertainty quantification. The analysis shows that ML can help improve nonlinear source discrimination, high-resolution source prediction, and integration of heterogeneous datasets, yet most approaches remain dependent on receptor-model-derived labels and lack standardized validation protocols. Hybrid frameworks that combine receptor-model interpretability with ML predictive capability currently represent the most widely adopted approach. At the domain level, ML-assisted source apportionment can support improved characterization of emission sources and atmospheric processes, contributing to advances in aerosol chemistry and modeling. As a complementary tool that strengthens traditional receptor modeling, the integration of ML into source apportionment provides a more consistent evidence base for air quality policies and public health protection.

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.

Carbonaceous aerosols, comprising organic carbon (OC) and elemental carbon (EC), constitute up to 70% of atmospheric particulate matter (PM) and significantly impact human health, climate forcing, and air quality. Despite their significance, accurate characterization of OC and EC fractions remains challenging due to aerosols’ complex and variable composition and the constraints in conventional analytical techniques. This review aims to assess how artificial intelligence (AI) can advance OC-EC characterization by enhancing data interpretation, automation, and measurement reliability. Recent developments in photo-optical methods for OC-EC quantification, including real-time monitoring, destructive, and non-destructive techniques, are comprehensively analyzed. The review further examines emerging efforts to integrate AI into conventional analytical frameworks, critically evaluating the potential of semi-supervised learning, active learning, incremental learning, TinyML, explainable AI, and large language models (LLMs) for improving OC-EC analysis. Notably, the underexplored potential of these AI approaches in source apportionment and real-time monitoring is emphasized, alongside a proposed roadmap for implementing AI-driven strategies in carbonaceous aerosol analysis and air quality assessments. AI integration with existing OC-EC analytical techniques can minimize measurement uncertainties and enable real-time, cost-effective monitoring systems. This approach facilitates high-resolution source apportionment with broad applications in environmental monitoring research, ultimately improving urban air quality diagnostics and evidence-based pollution control strategies.

Accurate estimation of earthwork material volume is essential for achieving circular and resource-efficient urban development, enabling material reuse in construction planning. Traditional survey techniques cannot efficiently cover large areas, while grid-based approaches often overlook terrain curvature. This study presents a novel and scalable framework for earthwork volume estimation across five cities over eleven years. We introduce a curvature-aware methodology using sub-meter digital terrain models (DTMs) derived from Maxar stereo imagery. DTMs were validated against commercial datasets (RMSE 1.12-2.10 m) and literature benchmarks. Our approach combines adaptive grid refinement with spline-based volume integration, yielding 20.6% lower error than grid-based methods. Pre-earthwork terrain surfaces were reconstructed using Natural Neighbor Interpolation (RMSE 0.06 m in Astana). Temporal analysis reveals diverse urban development trajectories, including volume decline in Astana, stable growth in Almaty and Porto, and seasonal drops in Prague. These findings support sustainable construction strategies through precise city-scale earthwork volume estimation.

Background: Household air pollution is a major environmental factor that has been linked to adverse cognitive outcomes in older adults. While indoor ventilation has been shown to mitigate these effects, evidence remains limited, particularly regarding ventilation in cooking areas. Methods: Using data from a cross-sectional study of older adults without clinically diagnosed dementia in Kazakhstan, we examined the association between indoor ventilation in cooking and living areas and screen-detected dementia as well as potential interactions, using multivariable regression models. Participants scoring ≥6 on the Quick Dementia Rating System (QDRS) were classified as having screen-detected dementia. Results: Among 578 participants (median age 65, 60% women), 10.4% screened positive for dementia by QDRS. In adjusted models, having a ventilation system in the cooking area was significantly associated with lower odds of QDRS screening-positive dementia compared with not having ventilation (adjusted odds ratio [aOR] 0.41, 95% confidence interval [CI]: 0.21–0.81). Similarly, significantly lower odds of QDRS screening-positive dementia were observed among participants who ventilated the cooking (aOR 0.45, 95% CI: 0.23–0.86) and living (aOR 0.25, 95% CI: 0.08–0.65) areas 2–3 times daily compared to 0–1 ventilation. A significant interaction was found between daily cooking duration and ventilation presence in the cooking area (p=0.009). Conclusion: We found a potentially high burden of dementia in Kazakhstan and observed that better ventilation in cooking and living areas was associated with lower odds of QDRS screening-positive dementia. These findings highlight the potential of improving kitchen ventilation as a public health strategy in the context of dementia.

Post-Soviet urban renewal generates large material flows, yet building-level mapping of an entire city for urban mining remains absent from the literature. This study fills that gap using Astana as a case, quantifying material stocks in 193 Soviet-era residential buildings scheduled for demolition by 2030. A bottom-up Material Flow Analysis estimates material composition from geometric parameters drawn from technical passports, Soviet construction norms for material densities, and municipal demolition records. Buildings are classified into six typologies, reflecting Soviet industrialization phases between 1930 and 1990. Total material stocks reach 509 kilotonnes. Khrushchevki buildings account for 78% of total mass. Material composition profiles shift from wood-dominated low-rise structures (43.5-48% wood) to concrete-intensive mid-rises (52-59% concrete). Spatial analysis identifies concentrated material clusters near the Baikonur-Saryarka boundary, forming 1 to 3-kilometer catchments suitable for localized recovery infrastructure. The framework uses these clusters to identify where selective demolition yields the highest material returns, how demolition sequencing can reduce transport demand, and which districts justify targeted investment in recovery facilities. The standardized building typologies, municipal demolition schedules, and accessible geometric data are common across post-war prefabricated housing stocks globally, supporting broader applicability beyond the post-Soviet context.

Circularity is increasingly recognised as a critical paradigm for sustainability in the built environment, yet existing efforts to assess it-whether focused on material flow analysis, design-for-disassembly strategies, durability metrics, or carbon accounting-remain fragmented and operate at different scales. Despite numerous indicator sets, the literature lacks an integrated framework that combines both technical design factors and the enabling organisational conditions required to support circular outcomes at the building level. This paper introduces CircularB-DfC (CircularB COST Action - Design for Circularity), a decision-Support Tool with a structured matrix for prioritising building design factors to enhance circular material flows. The framework consolidates insights from a systematic literature review and a multi-stage expert engagement process, resulting in 35 technical indicators and 20 enabling factors. These are organised into four technical categories: Material Selection; Design for Disassembly; Embodied Energy and Carbon Footprint; Waste Minimisation, and one enabling category, Circular Construction Management, including Governance, Certification, Stakeholder Engagement, Digitalisation, and Socio-economic aspects. Indicators and enablers are aggregated into a Design Score and an Enabler Score to support early decision-making. The tool was applied to three illustrative scenarios: a reinforced-concrete industrial hall in the Western Balkans, a steel office building in Central Europe, and a timber residential project in East London. The steel scenario achieved the highest Design and Enabler Scores, the concrete scenario performed strongest in Waste Minimisation through prefabrication and site-based strategies, and the timber scenario scored lowest overall due to limited reuse and disassembly provisions in the original design. While CircularB-DfC offers a simple and transparent basis for integrating circularity in design, it is limited by the subjectivity of expert-based weighting and its static structure. Future research will focus on dynamic modelling, integration with digital tools, and broader validation to enhance applicability.

The circular economy (CE) offers transformative potential for sustainability in the built environment. However, its implementation remains hindered by numerous barriers, including technical challenges. Although analysis of barriers in multiple impact areas is widely available, a comprehensive understanding of technical challenges has not yet been achieved. Moreover, barriers are commonly approached focusing on the urban, building, or material level without an analysis across built environment levels, even though implementing the CE implies a multi-level approach.This study carried out a systematic literature review applying a multi-level problem-based approach. It identified, classified, and analysed the key technical obstacles affecting CE implementation across the urban, building, and material levels which emerged from the literature. The research adopted an analytical framework across the levels, examining which technical barriers appear, where they occur in the building layers, when they occur along the lifecycle stages, who is involved as a stakeholder, and how the technical barriers influence the application of circular strategies.The results revealed that research on CE in the built environment has focused mainly on visible market or application-related issues, while placing less emphasis on foundational barriers to knowledge, skills, processes, and infrastructure. Because of these underlying gaps, the ability to develop, select, or apply technical solutions is constrained at every level of the built environment. By revealing inter-level interrelations and highlighting overlooked layers and strategies, this study provides a comprehensive understanding of current research on technical barriers affecting CE implementation in the built environment to inform future studies and direct target measures.

Developing countries, undergoing rapid urbanization, have a keen interest in circular economy and its application in the construction sector in hopes of alleviating stresses on urban resources and waste management systems. This study presents a novel approach to identifying the demolition candidates from a city master plan, estimating natural building turnover from past demolitions, and quantifying the resultant materials with a potential to close the loop. Astana, the capital of Kazakhstan, was selected as a representative case due to its booming construction development along with scarce data on demolition activities. The methodology included (1) using ArcGIS and its fine-tuned Mask RCNN model to extract building footprints, (2) manual identification of demolition activities, (3) characterization of the building stock based on the city’s master plan to estimate the demolition waste composition, and using results obtained, (4) the prediction of annual demolition material volumes until 2035. The model for footprint extraction from past satellite imagery achieved 70.1

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 construction industry generates substantial waste, yet circular economy (CE) adoption remains sporadic despite EU regulatory pressure. Financial uncertainty and fragmented guidance keep firms hesitant. This study examines how construction stakeholders perceive CE’s financial costs and benefits across the building lifecycle. In total, 125 construction professionals participated, including 87 respondents who evaluated lifecycle-based costs and benefits and 38 Norwegian respondents who provided qualitative accounts of CE implementation cases. The combined analysis reveals that sectoral resistance stems primarily from financial concerns, underscoring the need for clearer cost–benefit expectations and regulatory incentives that make circularity economically viable. Three enablers emerge as critical: enhanced digital tools, policy innovation, and multi-stakeholder collaboration. The study contributes by empirically demonstrating how regulatory frameworks shape CE adoption through the integration of quantitative perceptions and qualitative case evidence. The implication is that targeted interventions addressing practitioners’ financial realities can accelerate the circular transition, but policies must move beyond aspiration to provide the economic certainty required for widespread adoption.

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 PM2.5 and PM2.5–10 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, PM2.5 and PM2.5–10 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.

In recent years, the number of studies on in vitro lung bioaccessibility of potentially toxic elements (PTEs) has increased; however, physiological parameters for these tests have yet to be optimized. This study aims to (1) evaluate the effect of adding cholesterol to synthetic lung fluid on PTEs bioaccessibility, and to (2) assess the effect of other selected test parameters on bioaccessibility. The bioaccessibility of Cd, Co, Cr, Cu, Mn, Ni, Pb, Sb, V, and Zn have been investigated using seven formulations of Gamble’s solution (GS, with/without cholesterol/DPPC) and one artificial lysosomal fluid (ALF) on two reference materials (SRM 2691, BGS 102). The bioaccessibility of certain PTEs increased in GS modified with 5

Effective air quality management in Central Asian cities is hindered by limited knowledge of PM2.5 sources. This study employed Positive Matrix Factorization (PMF) for source apportionment of PM2.5 in two urban cities in Kazakhstan (Almaty and Astana), representing the first comprehensive analysis for Almaty. An extensive year-long sampling campaign (August 2022-July 2023) collected PM2.5 samples for detailed chemical characterization, including organic/elemental carbon, elements, anions, and cations. PMF results revealed five distinct sources, contributing between 14 % and 32 % to ambient PM2.5 in both cities. Although pollution sources showed a relatively uniform distribution, several factors strongly correlated with coal and biomass combustion, which emerged as the predominant contributors to PM2.5, highlighting their key role in urban air quality in both cities. Regional and local source influences were identified using HYSPLIT backward trajectory and Conditional Probability Function (CPF) analysis. In Almaty, dominant trajectory clusters at different altitudes involved slow-moving air masses, indicating a significant impact from urban road dust resuspension. In Astana, backward trajectory analysis demonstrated that a substantial proportion of air masses traverse nearby industrial regions (Karagandy and Pavlodar), elevating PM2.5 levels. The findings highlighted the considerable contribution of coal combustion to PM2.5 pollution and emphasized the need for stringent emission controls and evidence-based strategies to improve air quality and safeguard public

Public awareness and understanding of air quality in Central Asia remain considerably low. The present study assesses the perception, attitude and environmental knowledge of local air quality among adult urban residents (n = 870) in a city with high air pollution among cities of Central Asia: Astana, Kazakhstan. Structural equation modelling (SEM) was employed to investigate the causal relationship between perceived air quality, environmental literacy and willingness to pay for environmental protection. Over half of the Kazakhstani population has higher education, yet environmental literacy remains low compared to countries with fewer university graduates. Participants' age, education and health status significantly affected (p < .001) their environmental knowledge and awareness. The SEM indicates knowledge as a major determinant in improving public awareness and perception of air pollution. The present study provides valuable insights for researchers and governmental institutions to promote a better understanding of air quality within a rapidly growing urban environment.