Search Results (2,840)

This study evaluates the short-term effects of nine commercially available fire retardants (FRs) on water quality under controlled laboratory conditions. FRs were diluted to a 1:10 FR:water stock solution to approximate readily water-extractable fractions, and ion solubility was quantified together with key physicochemical parameters, including pH, electrical conductivity (EC), and dissolved oxygen (DO). Results revealed substantial heterogeneity among formulations in the concentrations of solubilized ions. Notably, FR4 (NP+) exhibited exceptionally high concentrations of NH₄⁺, NO₃⁻, NO₂⁻, PO₄³⁻, and Br⁻, as well as elevated Ca²⁺ and Na⁺. Other formulations also released considerable loads of common nutrients, particularly NO₃⁻ and PO₄³⁻, although at lower magnitudes. Despite the pronounced ionic enrichment, pH and DO remained relatively stable across most treatments. However, DO decreased in several cases at higher FR concentrations, suggesting increased oxygen demand associated with dissolved constituents. EC displayed strong, concentration-dependent increases across treatments, reflecting the rapid dissolution of ionic components in water. Cluster analysis identified three distinct groups of FRs based on their water-quality responses. The PCA biplot further revealed two main gradients structuring the formulations: mineralization and compositional diversity (PC1), and physicochemical solution conditions (PC2), which together distinguish the chemical and physicochemical signatures of the tested products. Overall, the results highlight substantial formulation-specific differences in nutrient and metal release, with potential short-term implications for aquatic chemistry and ecosystem functioning. Although derived from controlled laboratory conditions, these findings provide a first-order assessment of FR impacts on water quality and underscore the importance of considering formulation composition in watershed risk assessments and post-fire water quality management strategies. Full article

Natural-gas-fired boilers remain widely used for building and industrial heat, making NOx control relevant even as energy systems decarbonize. This comprehensive review synthesizes the published literature on staged combustion, flue-gas recirculation (FGR), premixed combustion, oxy-fuel combustion, humidified combustion, catalytic combustion, flameless/MILD combustion, and integrated systems. The evidence indicates that staged burners and moderate external FGR are the most mature retrofit options, whereas lean premixed combustion is generally better suited to new or deeply retrofitted small and medium boilers. Humidification coupled with waste-heat recovery can reduce NOx while increasing total heat recovery, but water management, corrosion, fouling, and auxiliary demand must be considered. Oxy-fuel/FGR systems facilitate CO₂ capture but impose substantial oxygen-production, recycle, and CO₂-conditioning requirements. Hydrogen blending widens lean operability while increasing flashback sensitivity and altering thermal-NO and NNH chemistry. Technology selection should therefore balance NOx, CO, efficiency, stability, auxiliary resources, retrofit constraints, and long-term reliability. Full article

Metro stations, while essential for urban transportation, pose unique evacuation challenges due to confined layouts and high densities; existing models often struggle to accurately capture individual pedestrian behaviors and the dynamic spread of fires. This study introduces a human–fire–agent interaction model designed to enhance the understanding and simulation of critical interactions among pedestrians, fire dynamics, and the underground environment of a metro station. The model integrates social force modeling and fluid dynamics to accurately represent pedestrian behavior and fire spread, for a more complete analysis of evacuation scenarios. Using Lumu Station in Suzhou as a case study, this study develops a detailed simulation framework implemented in an integrated PyroSim-Python-AnyLogic platform to model the evacuation process. The framework is employed to evaluate the effectiveness of turnstile reversal strategies—an approach that involves temporarily reversing the direction of turnstiles to facilitate faster evacuation during emergencies. Beyond mitigation, this study extends to the preparedness phase by functioning as a high-fidelity digital twin. It enables immersive “Serious Game” training and provides a quantitative tool for railway managers, decision-makers, and engineers to optimize operating procedures and performance-based station designs. Full article

Forest fires are a major environmental concern in Northern Thailand, contributing to ecosystem degradation, biodiversity loss, and seasonal air pollution. This study identified the environmental factors influencing forest fire occurrence and developed a forest fire susceptibility map using an integrated Geographic Information System (GIS) and Logistic Regression (LR) framework in Chom Thong District, Chiang Mai Province, Thailand. Fire occurrence data were derived from Visible Infrared Imaging Radiometer Suite (VIIRS) active fire hotspots detected by the Suomi National Polar-orbiting Partnership satellite (Suomi-NPP satellite) during 2023–2025. A total of 1674 hotspots were identified (616 in 2023, 889 in 2024, and 169 in 2025). Ten environmental variables, including elevation, slope, aspect, Topographic Wetness Index (TWI), stream density, rainfall, Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI), Land Surface Temperature (LST), and land-use, were analyzed. The LR model was trained using 2293 training samples (70%) and validated using 983 samples (30%). The results revealed that slope, rainfall, stream density, and LST were significant predictors of forest fire occurrence, with deciduous and evergreen forests exhibiting the highest susceptibility among land-use classes. The resulting forest fire susceptibility map classified 235.12 km² (21.16%) and 204.16 km² (18.38%) of the district as very high and high susceptibility, respectively, primarily in mountainous forest areas. The model achieved an overall accuracy of 77.5% and an Area Under the Curve (AUC) value of 0.852, indicating good predictive performance. Furthermore, the proposed Geographic Information System-Logistic Regression (GIS-LR) framework provides an interpretable and transferable approach for forest fire susceptibility assessment and generates spatial information that can support forest fire prevention, resource allocation, and environmental management in Northern Thailand and other fire-prone regions. Full article

AI-based decision support is moving into fire practice and governance, where it is used to prioritise inspections, analyse building and community risk, examine station coverage, support evacuation planning, interpret warnings, and explore fire scenarios. These tools can extend analytical capacity, but they also create a decision role migration problem: an output developed for prediction, prioritisation, warning, simulation, or planning may later be treated as clearance, justification, or authority. Existing fire model evaluation guidance recognises that validation is use-specific; AI systems add a further challenge because outputs can migrate across dashboards, reusable software components, interfaces, and institutional procedures. This article develops a role-sensitive framework for bounded deployment of AI decision support in urban fire risk management. The framework classifies AI outputs by epistemic role, decision proximity, validation basis, temporal coupling, consequence asymmetry, and governance explicitness. Its central synthesis is that evidence sufficient to warn may be insufficient to clear. Probabilistic outputs can support screening, investigation, prioritisation, and scenario analysis; permissive or safety-proximate claims require stronger assurance, uncertainty communication, fallback rules, and explicit authority allocation. The contribution is a governance logic for keeping exploratory, advisory, policy-shaping, and safety-proximate AI roles separate in urban fire management and policy formulation. Full article

Accurate quantification of wildfire risk is essential for balancing wildfire mitigation and prescribed fire management in grassland ecosystems, yet existing fire danger indices do not explicitly distinguish seasonal fuel dynamics from daily weather variability. This study presents a hierarchical framework for quantifying wildland fire risk by explicitly separating seasonal wildfire potential from daily weather-driven fire activity. The framework introduces the Daily Burned Area Ratio (DBAR) as a quantitative measure of realized wildfire risk and decomposes it into the Seasonal Burned Area Ratio (SBAR) and the Daily Burn Activity Index (DBAI). Wildfire records from the U.S. Forest Service Fire Program Analysis Fire-Occurrence Database, Oklahoma Mesonet weather observations, and remotely sensed vegetation data collected between 1995 and 2020 were used to develop and evaluate the framework in the Flint Hills of Kansas and Oklahoma. SBAR was modeled using grass curing and air temperature to characterize the seasonal baseline of wildfire activity, whereas DBAI was modeled using dead fuel moisture content (DFMC) and wind speed to quantify day-to-day departures from that seasonal baseline. The SBAR model accurately reproduced the characteristic bimodal wildfire regime of the Great Plains, whereas the DBAI model identified DFMC as the dominant control on daily wildfire activity, with wind speed providing an important secondary influence. Compared with the Burning Index (BI) and the Grassland Fire Danger Index (GFDI), the hierarchical framework achieved superior performance in discriminating fire days from non-fire days. Global sensitivity analysis further demonstrated that the framework provides a more balanced representation of the influences of grass curing, relative humidity, air temperature, and wind speed than the conventional indices. By explicitly separating seasonal fuel dynamics from short-term weather variability, the proposed framework provides an ecologically interpretable, locally calibratable, and operationally practical approach to wildfire risk assessment. Because the seasonal and daily components can be calibrated independently, the framework is readily transferable to other grassland ecosystems and provides a flexible foundation for adaptive wildfire and prescribed fire management under changing climatic conditions. Full article

Coupled fire–atmosphere models (CFAMs) are increasingly proposed as an important tool for investigating a range of scientific and management questions, including the design of fuel management strategies. Uncertainty in CFAM outputs arises from environmental and fuel inputs, and a host of user-defined simulation choices such as fire approach angle and ignition timing. In this study, we used the Wildland–Urban Interface Fire Dynamics Simulator (WFDS) to quantify uncertainty in rate of spread and canopy consumption across pre- and post-restoration ponderosa pine stands. Two ensembles were conducted: (1) varying fire approach angles across 12 rotations and (2) varying ignition time in 80 simulations (five per plot, with 0 s delay, 250 s delay, and three random intervals in between). Metrics evaluated were rate of spread and percent canopy consumption. Variability was quantified using coefficients of variation (CVs). Approach-angle variation produced a mean CV of 5.46% for rate of spread, with treated stands exhibiting reduced variability relative to untreated stands. Canopy consumption showed a mean CV of 6.16%, with treatment having no effect. Ignition-time variation produced smaller CVs (rate of spread: 2.47%; canopy consumption: 3.60%) with no differences between management conditions. These results indicate that user-defined configuration choices contribute measurable but modest uncertainty, and that structural modifications from management can reduce sensitivity of fire spread. Incorporating these sources of uncertainty into formal frameworks will improve interpretation of CFAM outputs for operational and research applications. Full article

Tropical forests sustain exceptional biodiversity and regulate global carbon and water cycles, yet degradation and incomplete recovery increasingly compromise these functions. We integrated bibliometric mapping with structured systematic synthesis to characterize research at the tropical-forest degradation–restoration interface, identify its most influential contributors and publications, and evaluate evidence on drivers, interventions, monitoring, and knowledge gaps. This study addresses three guiding scientific questions: (i) How has the field developed over time and across geographic space? (ii) Which authors, institutions, journals and publications have been most influential? (iii) What does the evidence indicate about degradation drivers, restoration strategies, monitoring approaches and knowledge gaps? Scopus and Web of Science were searched for peer-reviewed articles and reviews published from 1980 to 2025. After deduplication and PRISMA-based screening, 1075 publications were analyzed bibliometrically, and the 400 most-cited studies were coded against twenty predefined questions. Scientific output increased by 10.68% annually, with 483 publications appearing during 2020–2025. The corpus comprised 318 journals and 4470 authors. Forest Ecology and Management was the leading source (141 publications; 13.1%), while the twenty most productive journals accounted for 44.3% of the corpus. Brancalion P.H.S. was the most productive author (23 publications), followed by Chazdon R.L. and Tabarelli M. (20 each). Citation influence was concentrated: the twenty most-cited documents received 25.7% of all citations, led by Ribeiro M.C. (3339 citations), whereas Hua F. achieved the highest publication-year-normalized citation score among this group. Agricultural expansion, pasture establishment, and logging were the most frequently reported degradation pressures, but interactions among logging, fire, drought, and fragmentation were rarely quantified. Restoration evidence supported a context-dependent continuum from natural regeneration to assisted and active interventions, although planting and enrichment were more visible than direct passive–active comparisons. Carbon, biomass, plant diversity, and forest structure dominated outcome assessment, whereas fauna, ecological interactions, governance, and socioeconomic dimensions received less attention. Monitoring relied mainly on satellite imagery and field inventories, with limited evaluation of tool performance. Long-term trajectories and evidence from Africa, Southeast Asia, seasonally dry forests, and montane systems remained scarce. Overall, the field is rapidly consolidating but remains geographically and thematically uneven. Future research should quantify interacting degradation processes, evaluate multidimensional and long-term recovery, connect remote sensing with ecologically meaningful field indicators, and integrate governance and social conditions into restoration planning. The synthesis supports preventing further degradation and treating restoration as a context-specific complement, rather than a substitute, for protecting remaining native forests. Full article

Disaster preparedness and the equitable distribution of emergency relief supplies remain pressing priorities for governments worldwide. Situated along the Pacific Ring of Fire, Taiwan is particularly vulnerable to natural disasters that frequently cause severe property damage and endanger public safety, making the efficient allocation of limited relief resources in the immediate aftermath a persistent challenge for emergency management. Although prior research has addressed risk assessment and resource allocation as separate problems, few studies have incorporated region-specific social vulnerability values directly into an optimization framework for relief distribution. This raises the question of how region-specific social vulnerability can be quantitatively embedded into a relief-supply allocation model to achieve a more equitable, risk-sensitive distribution than conventional population-based approaches. To address this gap, the present study develops an integrated decision-making model combining a fuzzy inference system (FIS) with fuzzy multiple objective linear programming (FMOLP). The FIS first derives social risk values from four vulnerability dimensions, namely exposure, disaster mitigation and preparedness, readiness, and recovery, for Taiwan’s 17 counties and municipalities, drawing on multidimensional indicators from the National Science and Technology Center for Disaster Reduction (NCDR) Disaster Mitigation Database. These values are subsequently incorporated as weighting coefficients within the FMOLP model, which jointly maximizes distributional utility and minimizes procurement cost under population-based supply constraints. The results identify Hsinchu, Taichung, Chiayi, Yunlin, and Hualien as the five highest-risk regions. The model further yields a compromise allocation plan spanning all 17 administrative units, with sensitivity analysis confirming its robustness across alternative performance metrics. This study offers a replicable, data-driven decision-support tool to assist disaster preparedness planners and government agencies in relief resource allocation. Full article

The construction industry is a critical pillar of Hong Kong’s economic development and urban growth, yet it continues to face significant safety challenges amid increasing construction activity and workforce demands. While efforts to accelerate housing development and infrastructure projects are essential for addressing societal needs, they must be balanced with effective measures to prevent accidents and manage workplace hazards. This paper reviews the common causes, patterns, and emerging trends of construction-related accidents in Hong Kong and examines the Tai Po Wang Fuk Court fire as a representative case study of systemic safety failure. Using documentary evidence on a fire that caused 168 deaths, 79 injuries, and spread across seven of eight towers, the study identifies five aligned failure layers. Drawing upon archival records, official reports, legislative documents, and public accounts, the study applies James Reason’s Swiss Cheese Model to demonstrate how deficiencies in material selection, worker behaviour, fire protection systems, contractor management, and regulatory oversight aligned to enable the incident. Building on these findings, the paper proposes a 5E framework—Engineering, Education, Enforcement, Engagement, and Evaluation—to provide a structured approach for strengthening construction safety management and fire risk governance. The framework offers practical guidance for policymakers, regulators, contractors, property managers, and other stakeholders seeking to enhance safety culture, improve regulatory compliance, and promote resilience in the construction sector. The findings contribute to the broader discourse on construction safety by demonstrating how systemic failures can be translated into targeted interventions for preventing similar incidents in the future. Full article

The increasing deployment of lithium-ion batteries in electromobility, industrial logistics, and stationary energy storage systems has introduced new operational safety challenges associated with thermal runaway, fire development, and the release of hazardous substances. Although significant attention has been devoted to battery design and fire suppression technologies, less emphasis has been placed on the development of integrated monitoring systems capable of identifying abnormal thermal behaviour during routine storage and handling operations. This paper presents an operational framework for the early detection of thermal anomalies in lithium-ion battery storage facilities based on the integration of thermal imaging technology, multi-level alarm logic, automated notification processes, and predefined response procedures. The proposed framework was developed using a risk-based approach and implemented within an industrial environment where lithium-ion batteries and battery modules are routinely stored and handled. The methodology included hazard identification, determination of critical monitoring zones, configuration of thermal detection devices, establishment of alarm thresholds, and integration with existing fire protection infrastructure. Particular attention was devoted to ensuring rapid identification, localization, verification, and escalation of abnormal thermal conditions before the occurrence of visible fire manifestations. The implemented monitoring framework comprised a total of 21 thermal imaging cameras, including four fixed radiometric thermal imaging cameras and seventeen local thermal monitoring cameras, covering five risk-prioritized monitoring zones within an industrial lithium-ion battery storage facility. During operational deployment, the system recorded 21 Yellow Alerts, 6 Red Alerts, and 4 false alarms, with an average response time of 4.2 min. Experimental verification further demonstrated that, although directly exposed battery modules were measured at approximately 60 °C, enclosure within the battery pack significantly attenuated the externally detectable thermal signature, with surface temperatures decreasing to approximately 23–31 °C after prolonged enclosure. The results demonstrate that the proposed framework enables continuous operational monitoring, supports timely identification of abnormal thermal behaviour, and provides a structured basis for rapid decision-making and emergency response in industrial lithium-ion battery storage facilities. The integration of thermal monitoring with structured alarm management and response procedures creates a comprehensive safety chain that contributes to reducing the probability of delayed incident recognition. The presented approach provides practical guidance for industrial operators seeking to improve lithium-ion battery safety and may serve as a foundation for the future development of operational safety requirements for battery storage facilities. The principal contribution of this study is the documented implementation and operational evaluation of an integrated thermal monitoring and response system under routine automotive production conditions. Full article

The increasing integration of ecosystem services (ES) into environmental and agricultural policies creates the need for operational approaches linking ecosystem service assessment with territorial planning and incentive design. This study develops a functional–territorial typology of ES provision for mainland Portugal based on the assessment of 26 ecosystem services derived from the Common International Classification of Ecosystem Services (CICES v5.2) across 278 municipalities. Territorial clusters were identified using provisioning and regulating ES, while cultural ES were subsequently incorporated into functional bundles and territorial–functional analyses. The analysis identified three functional ES bundles and six territorial socio-ecological regimes representing distinct patterns of ES provision. Functional bundle identification proved highly robust, with 92.3% of ES retaining the same bundle assignment across alternative correlation thresholds. A sensitivity analysis including cultural ES confirmed the stability of the main territorial regimes. Between 70% and 86% of municipalities remained within the same territorial group, while most reclassifications occurred near the boundaries between neighbouring regimes, indicating local refinements rather than changes in the overall territorial organization. Cross-analysis showed that territorial regimes differ systematically in ES configurations, baseline conditions, and patterns of synergies and trade-offs, indicating that additionality is inherently territorial and cannot be adequately interpreted using uniform baselines. Building on these findings, we propose a framework of territorially differentiated Ecosystem Service Provision Units (UPSE) incorporating variations in ES configurations and additionality potential across socio-ecological contexts. The framework provides a methodological basis for more context-sensitive ES incentives, restoration strategies, and environmental policy instruments. Full article

Conventional poly(vinyl alcohol) (PVA) hydrogels struggle to integrate the mechanical robustness, thermal management, and fire safety demanded by extreme environments. To overcome this, we fabricate a multifunctional hydrogel via a synergistic strategy combining freeze–thawing and citrate-driven Hofmeister salting-out. Kosmotropic citrate ions aggressively strip polymer hydration shells, driving intense intermolecular hydrogen bonding, elevated crystallinity, and severe network densification. Consequently, the optimized cit@PVA hydrogel exhibits a balanced mechanical performance, achieving a tensile strength of 1.31 MPa and an elongation at break of approximately 150%. The citrate-induced network densification not only reinforces the mechanical integrity of the hydrogel but also regulates its thermal transport characteristics. Benefiting from the dense polymer framework and intrinsic infrared-active chemical structures, the cit@PVA hydrogel demonstrates excellent thermal management capability, including effective high-temperature thermal insulation and high mid-infrared emissivity (~85%) for passive radiative cooling. Furthermore, the incorporated citrate shifts the degradation pathway toward catalytic charring, rapidly forming a dense carbonaceous shield to completely prevent burn-through during direct flame exposure. This scalable structural design overcomes traditional performance limitations, creating resilient soft materials for advanced flexible electronics and smart protective wearables. Full article

Forest landscape configurations in Mediterranean regions often promote fuel continuity, low structural heterogeneity, and inefficient spatial allocation of fuel treatments, increasing wildfire intensity and spread. This study improves wildfire risk management by applying spatially explicit Integer Linear Programming (ILP) models to optimize fuel-treatment allocation under operational constraints in two Mediterranean case studies: Vale do Sousa, Portugal, and Lesvos Island, Greece. In Portugal, wildfire risk was represented using a stand-level wildfire resistance indicator that accounts for the spatial influence of neighboring stands, whereas in Greece, simulation-derived fire spread probabilities from the FSim fire behavior model were used to prioritize high-risk treatment zones. Despite differences in modelling complexity and data availability, both optimization frameworks generated spatially targeted treatment strategies under contrasting wildfire risk representations. Optimized treatment allocation increased landscape-level wildfire resistance in Portugal from 0.9907 to 0.9959 and reduced expected wildfire risk in Greece from 0.1751 to 0.1701 across increasing treatment-area scenarios. Although these improvements were modest in magnitude, they were consistent across all scenarios. The novelty of this study lies in demonstrating the transferability of an ILP-based optimization framework across contrasting Mediterranean contexts, providing a practical decision-support tool for prioritizing fuel treatments and supporting adaptive, risk-informed forest management. Full article

Forest fires are a recurring disturbance in Mediterranean ecosystems, but they also impact air quality and public health, particularly given recent trends towards increasingly widespread and extreme fires. This study analyzed six large fires that occurred in Sardinia, Italy, between 2009 and 2021, in order to evaluate their impact on ground-level PM₁₀ concentrations and to investigate the influence of fire size, fuel type, and meteorological conditions. The analysis included data on fire perimeters and land cover, meteorological conditions, smoke plume trajectory simulations using HYSPLIT, satellite imagery, and PM₁₀ concentration measurements from the regional air quality monitoring network. The six case studies differed markedly in terms of burned area, vegetation composition, duration, and weather context. The results showed that the extent of the fire is likely not the most significant factor influencing the increase in PM₁₀ observed in the days following the fires. The most pronounced increases in PM₁₀ concentrations were recorded during the Isili and Montiferru fires, which differed in burned area but were similar in terms of fuel composition, dominated by forest and shrubland vegetation. These factors, together with favorable atmospheric conditions for plume transport and particulate matter deposition, likely contributed to the observed increases in PM₁₀, including exceedances of WHO and national daily limit values. By contrast, Bonorva, Ittiri, and Borore showed limited or no clear accumulation of PM₁₀, despite large burned areas in some cases. These findings suggest that the effects of wildfires on air quality in the Mediterranean region can be influenced by several features, such as meteorological conditions, biomass burned, area burned, severity and intensity of fires. Furthermore, the observed exceedance of WHO thresholds highlights the need to integrate public health considerations into wildfire risk management in the Mediterranean basin. Full article