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Abstract Background Postnatal care is crucial to prevent the child mortality. Despite the improvement in the PNC coverage for the neonates, it is still far away from the universal health coverage. Along with, some specific regions mostly are natural hazard prone areas of India show very under coverage of PNC for the neonates. Considering the substantial spatial variation of PNC coverage and natural hazard prevalence, present study aimed to examine spatial variation of PNC coverage and its association with natural hazard at the district level. Methods The cross-sectional exploratory study utilized National Family Health Survey, 2019-21, which included 1,76,843 children using multistage stratified sampling method to examine postnatal care within 42 days for neonates born within five years prior to the survey. Additionally, the study utilized Vulnerability Atlas of India,2019 maps to categorize regions into hazardous (flood, earthquake, and landslide) and non-hazardous areas. Spatial univariate and bivariate analyses, logistic and geographically weighted regressions were conducted using ArcGIS Pro, GeoDa, and Stata 16.0 software to identify associations between PNC coverage, hazard exposure, and spatial variation. Results The univariate spatial analysis showed some specific regions such as north, east, and north-east region of India had a high concentration of natural hazard and low access of PNC coverage. Bivariate analysis also showed that PNC coverage was low in flood (75.9%), earthquake (68.3%), and landslide (80.6%) effected areas. Compared to the national PNC coverage (81.1%), all these natural hazards effected areas showed low coverage. Further, logic regression showed that these hazard prone areas were less (OR:0.85 for flood, 0.77 for earthquake, and 0.77 for landslide) likely to get PNC coverage than their counterparts. LISA cluster maps significantly showed low PNC and high disaster concentration in these disaster-prone areas. Geographic weighted regression results also showed similar result. Conclusions The present study elucidates notable heterogeneity in the coverage of postnatal care (PNC) services, with lower concentrations observed in disaster-prone areas. In order to enhance the accessibility and quality of PNC services in these areas, targeted interventions such as the deployment of mobile health services and fortification of health systems are recommended.

No abstract available.

In the Himalaya, natural hazards are common and their places, as well as timings, are difficult to predict. Rough terrain, unpredictable weather conditions and poor road networks of the Himalaya make the mitigation measures even more challenging as well as difficult. On April 4, 2023, 7 people have died and 13 were injured due to a small loose snow avalanche at 14th Mile, on the way to Nathula Pass, near Indo-China Border in Sikkim state (India). State government had advised the people, not to visit the area due to the poor weather conditions. However, people have ignored this precious advice and lost their lives. Along with Indo-China border, the region is famous for its scenic beauty. In addition, the region is one of the most difficult border areas with the limited infrastructural development for armed forces. The present study explores the causes and consequences of this killer avalanche from an environmental determinism. The results suggest that, due to the western disturbances, the region received heavy snowfall. Higher average temperature and flowing stream beneath snow, make the snow unstable. Weight of humans put pressure on the weak snow which led to formation of avalanche. Our finding suggests that peoples’ negligence about government advices have led to casualties. The purpose of this case study is to highlight the human folly which invites such unfortunate incidences across the globe. Further, this study emphasis the need of public-government partnership to minimize the impact of such upcoming hazards.

AbstractKarakoram Himalayas are vulnerable to glacial lake outburst floods (GLOFs), which cause catastrophic floods in the surrounding areas. The increasing natural and anthropogenic activities, especially in the Indo-Gangetic Plains at the southern flank of the towering Himalayas, could be the cause of climate change affecting the frequency of the natural hazards in the Himalayas. In the present study, a detailed analysis of the Shisper Lake breach of 7 May 2022 is carried out using satellite remote sensing. A decreasing trend in the glacial mass balance is observed between 2017 and 2021; in this period, frequent GLOF episodes occurred. A pronounced decline in the snow cover and glacial debris (before the cascade) and an abnormal increase in land surface temperature (LST) retrieved from satellite data could be responsible for the enhanced snow/glacial melting prior to the breach. Alteration in glacial mobility (cm/12 days) is observed at the locations where the glacier melts due to an increase in LST with increased debris cover. In the paper, we have studied changes in water quality and land cover along the stream after the GLOF. An increment in normalized difference turbidity index (NDTI) is observed before the flood showing turbulence and sediment load. Increased glacial melt contribution into the stream results in increased flow rate and decreased water quality as observed through normalized difference water index (NDWI) and normalized difference chlorophyll index (NDCI), near the Karakoram Highway Bridge. Sentinel-1 backscatter coefficient indicates increased discharge from the melting of glaciers. A significant reduction in co-flood coherence is observed, attributed to the bridge collapse and severe bank erosion.

A hybrid machine learning method is proposed for wildfire susceptibility mapping. For modeling a geographical information system (GIS) database including 11 influencing factors and 262 fire locations from 2013 to 2018 is used for developing an integrated multivariate adaptive regression splines (MARS). The cat swarm optimization (CSO) algorithm tunes the parameters of the MARS in order to generate accurate susceptibility maps. From the Pearson correlation results, it is observed that land use, temperature, and slope angle have strong correlation with the fire severity. The results demonstrate that the prediction capability of the MARS-CSO model outperforms model tree, reduced error pruning tree and MARS. The resulting wildfire risk map using MARS-CSO reveals that 20% of the study areas is categorized in the very low wildfire risk class, whereas 40% is under the very high class of fire hazard.

Increasing interest in the natural environment and greater hiking activity have resulted in higher anthropogenic pressure in areas characterized by a geographic/physical setting that could present hazardous conditions. The development of these activities is influenced by the peculiar geomorphological and climatic conditions of the area. Visitors and hikers do not always have adequate cultural background and full awareness of natural dynamics, including the possibility of incurring hazardous conditions. For its cultural and landscape value and extraordinary trail network, the Portofino Promontory is frequented by more than a hundred thousand of hikers a year. However, due to the geomorphological characteristics of the area, the morphological features of the trail network (i.e., exposed paths, steep ups and downs, rocky sections with cables, etc.) and the peculiar meteo-climatic conditions, the number of accidents involving hikers has increased in the most recent years. This research uses a detailed LiDAR survey, a morphometric analysis, and a significant dataset of information on the frequentation of the hiking trail network and on the number of rescue operations carried out by the National Mountain Rescue and Speleological Service (CNSAS). These data have been related to the physical-geographical characteristics of the area. The results can be a useful tool for land management by the Park Authority. [ABSTRACT FROM AUTHOR]

In coastal regions, accurate storm surge prediction is crucial for effective disaster management and risk mitigation. This study presents a comparative analysis between BALSSA (Bidirectional Attention-based LSTM for Storm Surge Architecture) and the Japan Meteorological Agency (JMA) numerical storm surge model, focusing on the Saola-induced storm surge in Macao, September 2023. To train and assess the model, we leverage an extensive dataset comprising meteorological and tide level information from more than 80 typhoon occurrences in Macao spanning the period from 2017 to 2023. The results provide evidence of BALSSA's effectiveness in capturing the complex spatio-temporal dynamics of storm surges, with a lead time of up to 72 h, as reflected by its MAE of 0.019 and RMSE of 0.024. It demonstrates reliable accuracy in predicting storm surge magnitude, timing, and spatial extent, potentially contributing to more precise and timely warnings for coastal communities. Furthermore, the real-time data assimilation feature of BALSSA ensures up-to-date information, aligned with the latest observations, which is essential for effective emergency preparedness and response. The high-resolution grids enhance risk assessment, highlighting BALSSA's potential for early warnings, emergency planning, and coastal risk management. This study contributes valuable insights to the broader field of storm surge prediction, guiding decision-making processes and supporting the development of effective strategies to enhance coastal resilience. [ABSTRACT FROM AUTHOR]

This article reflects on the management of active fault surface rupture hazard after the Canterbury and Kaiköura earthquakes. To understand mitigation barriers, interviews were conducted with planners and natural hazard risk specialists in selected districts with active faults, but without land use provisions. The interviews revealed issues with the interpretation and implementation of the Ministry for the Environment's Active Fault Guidelines, Planning for Development of Land on or Close to Active Faults: a guideline to assist resource management planners in New Zealand. The purpose and intended use of the New Zealand Active Faults Database (NZAFD) is also explored. [ABSTRACT FROM AUTHOR]

This paper addresses the riverine flood events that have occurred in Greece over the last 136 years (i.e., during the 1886–2022 period), focusing, amongst others, on the case of urban floods. The flood record of various sites of the country has been collected and analyzed to determine their spatial and temporal distribution. Greece is a country where flood data and records are very scarce. Therefore, as there is not an integrated catalog of Greek floods spanning from the 19th century to recently, this is the first attempt to create an integrated catalog for Greece. The sources used include published papers, local and regional newspapers and public bodies (mainly the Ministry of Environment and Energy and the official websites of Greek municipalities). Additionally, the main factors responsible for their occurrence have been issued, regarding the country's climatic, geological and geomorphological setting, as well as human interventions. In addition, the atmospheric circulation driving factors of floods are assessed via an unsupervised neural network approach (i.e., Self-Organizing Maps). Based on the results of this research, an online GIS-based database has been created, depicting the areas that have been struck by riverine floods in Greece. By clicking a flood event in the online database, one can view several characteristics, depending on data availability, such as duration and height of the rainfall that caused them and number of fatalities. Long-term trends of mean and extremes seasonal precipitation also linked to the spatial distribution of floods. Our analysis shows that urban floods are a very large portion of the overall flood record, and they mainly occur in the two large urban centers, Athens and Thessaloniki, as well as near large rivers such as Pineios. Autumn months and mainly November are the periods with higher flood hazards, based on past records and cyclonic atmospheric circulation constitutes the principal driving factor. Our results indicate that a flood catalog at national level is of fundamental importance, as it can provide valuable statistical insights regarding seasonality, spatial distribution of floods, etc., while it can also be used by stakeholders and researchers for flood management and flood risk analysis and modelling. [ABSTRACT FROM AUTHOR]

Developing a susceptibility map is a crucial primary step for dealing with undesirable natural phenomena, gully erosion included. On the other hand, recent computational progress call for employing new methodologies to keep the solutions updated. In this work, the performance of a conventional artificial neural network (ANN) is improved by applying a metaheuristic algorithm (symbiotic organisms search—SOS) for generating the gully erosion susceptibility map of an area in Golestan Province, Northern Iran. A geo-database is created from the gully erosion inventory and twenty conditioning factors. After analyzing the interrelated relationships between the geo-database components, training and testing data sets are formed. The models are executed with proper configurations and according to the results, the SOS algorithm could enhance the training accuracy of the ANN from 92.8% to 98.4%, and testing accuracy from 89.8% to 91.4%. In addition, comparing the performance of the SOS with shuffled complex evolution (SCE-NN) and electromagnetic field optimization (EFO-NN) algorithms revealed the greater accuracy of the SOS. However, the SCE-NN and EFO-NN performed more accurately than conventional ANN. Therefore, it can be concluded that the use of metaheuristic techniques may improve the prediction ability of the ANN in gully erosion susceptibility mapping. Finally, a monolithic equation is extracted from the SOS–ANN model to be used as a predictive formula for similar purposes. [ABSTRACT FROM AUTHOR]

The glacial and torrential basin of Taconnaz (Mont-Blanc massif, France) dominates the Chamonix valley. It is one of the major paths for snow avalanches in the Alps, often triggered by serac falls from the Taconnaz glacier. On 24 November 2018, the basin's multi-risk nature was further accentuated by a new type of hazard with a rockfall triggered at c. 2700 m a.s.l. It travelled down over a distance of 1.85 km and stopped 165 m away from the construction site of a micro-hydroelectric power station. We studied the triggering conditions at the permafrost lower limit, the effects of the supra-glacial path on the flow patterns, and the fate of the scar and the deposit on torrential activity. By comparing a pre-event Structure from Motion model with a post-event LiDAR model, we estimated the volume of the scar to be 42,900 m3 (±5%). A numerical model was employed to simulate the rapid runout. It revealed the complexity of the flow, attributed to the sequestration of a part of the deposit in crevasses, the incorporation of a significant volume of ice resulting in a transition from a dry granular flow to a mud-like flow, and the presence of numerous deposit zones. Subsequent monitoring of the area after the event allowed for the documentation of the scar's evolution, including a landslide, as well as the progressive degradation and evacuation of the deposit by the torrent without producing debris flow. The study of the triggering factors indicated glacial retreat as the probable main cause, assisted by the melting of ice lenses left by the permafrost disappearance. Finally, we present replicable methods for managing risks at the site following the event. This event improves the understanding of cascading processes that increasingly impact Alpine areas in the context of climate change. [ABSTRACT FROM AUTHOR]

This empirical study explores the interplay between place attachment, risk perception, and coping behavior, within two small Norwegian communities exposed to flash flooding. Through a mixed-methods narrative approach, we found that most of our respondents exhibited positive attachment to their communities, aligning with previous studies in rural settings. Using a conceptual model of place attachment (Raymond et al. 2010), the study identified five dimensions of attachment among the respondents: place identity, place dependency, family bonding, friend bonding, and nature bonding. The most dominant narratives centered around practical ties to the area through family and place dependency, combined with symbolic attachments in the form of nature and identity. Place attachment through generational ties, closeness to nature, and access to historical knowledge influenced residents’ risk perception, here displayed as heightened awareness of flash flood-related risks. This risk awareness did however not translate directly into feeling at risk. Findings point to residents’ underestimating risk close to home, which could partially be explained by strong place attachment. Coping behavior was mainly motivated by previous hazard experiences. Place dependency and family bonding contribute to explaining the resident’s reluctancy to relocate as a coping strategy. We found that place attachment can act as both a mediator and a moderator between risk perception and coping behavior, and the relationship between place attachment, risk perception, and coping behavior is complex. While no clear causal relationship was established, understanding common elements in risk narratives can enhance community resilience and inform strategies to address community concerns. [ABSTRACT FROM AUTHOR]

This study provides a science map on social media application in disaster management through bibliometric analysis. Citation, co-citation, and co-words analysis were applied to identify influential social media publications in disaster management, evaluate the prospective evolution of social media and disaster management themes, and predict future research streams based on social media in disaster management. 862 journal publications were retrieved from the Web of Science, with 33,758 cited references. From the co-citation analysis, 3 clusters were classified: (1) Twitter in disaster management, (2) post-disaster recovery process, and (3) information and knowledge sharing, while the co-word analysis also produced 3 clusters (1) social media communication in disaster events, (2) social media and disaster preparedness, and (3) social media and disaster recovery. Furthermore, three research streams were discussed through inductive interpretation from the three analyses performed. This study provides theoretical and practical understanding to relevant stakeholders facing disaster through social media applications. [ABSTRACT FROM AUTHOR]

Abstract Voluminous pumice rafts produced by the 2021 phreatomagmatic eruption of Fukutoku-Oka-no-Ba, a submarine volcano located in the Izu-Bonin Islands, reached many Japanese ports and islands, damaging fisheries and hindering marine traffic and trade. This event emphasized the necessity for governments and stakeholders to develop plans in advance and prepare disaster mitigation measures before an eruption. To investigate when and to what extent pumice might intersect trade routes and arrive at islands after future eruptions, we conducted particle tracking simulations of eruptions at major volcanic islands and submarine volcanoes near Japan using the velocity field from the ocean reanalysis dataset. Seven major volcanoes that have produced pumice rafts in the past century were selected: the submarine volcano NNE of Iriomote Island, Izu-Tobu Volcanoes, Miyakejima, Bayonnaise Rocks, Nishinoshima, Kaitoku Seamount, and Fukutoku-Oka-no-Ba. We partly reproduced the distribution of pumice arrivals recorded after the 1986 Fukutoku-Oka-no-Ba eruption, demonstrating the potential effectiveness of the simulations. We report likely pumice raft arrivals and drifting durations for the investigated eruptive scenarios, which may aid future risk assessments for pumice arrivals.

Floods are one of the natural disasters that occur most frequently in the Brahmani River of Eastern India. Frequent floods in the area, results from anthropogenic activities, climate change, unpredictable weather, and heavy rainfall cause loss of life, property, and resources. Flood hazard susceptibility maps will help in identifying risk-prone locations, which aids in flood management and the decision-making process. The main objective of this study is to evaluate the performance of bivariate statistical and hybrid models such as Frequency ratio, Weight of Evidence (WoE), Evidential Belief Function (EBF), Index of Entropy, Analytical Hierarchy Process (AHP), AHP–TOPSIS and AHP VIKOR to prepare flood susceptibility maps of the Brahmani River basin, India. The flood conditioning factor includes Slope, Aspect, Elevation, Curvature, Geology, Geomorphology, Topographic Wetness Index, Topographic Ruggedness Index, Distance from Streams, Distance from Roads, Rainfall, Normalized Difference Vegetation Index (NDVI), Stream Power Index, Soil, Drainage Density, and Land use Land cover. The flood inventory was prepared using various atlas and news reports. Multicollinearity among all the causative parameters is measured using the Variance Inflation factor (VIF) and Tolerance (TOL). The collection of a spatial database and flood inventory from various sources is used to prepare flood susceptibility maps. The flood susceptibility map is categorised into very high, high, moderate, low, and very low classes using the natural break method. The models are validated using flood training (70%) and testing (30%) points. The accuracy of flood susceptibility maps is determined using Area Under Receiver Operating Characteristics (AUROC) curves and Seed Cell Area Index (SCAI) values. The success rate of Frequency ratio (AUC 0.91), EBF (AUC 0.90) and WoE (AUC 0.86) show the highest accuracy among all other models in AUC. WoE, IoE, and FR gave reliable results in the SCAI validation process. The susceptibility maps will help in identifying hazard-prone areas, which will help policy makers better assessment of natural hazards. [ABSTRACT FROM AUTHOR]

Climate change has a direct impact on flash floods, and indirectly on the environment, society, and economy, due to the rapid development and difficulty of predicting this hydrological phenomenon. The main objective of this study is to assess the potential flash flood hazard areas in the Likodra watershed (218.62 km2), one of the most vulnerable parts to flash floods in Serbia, using the flash flood potential index (FFPI) and analytic hierarchy process (AHP) method. Recurring events from 1995 to this day and the devastating impact on settlements of the analyzed area show that this territory is extremely vulnerable. The data used include hydrological statistics (maximum daily rainfall) and spatial data on watershed geographical characteristics (slope, soils, land use, vegetation, drainage density) obtained or derived from various sources (maps, satellite images, digital databases) which were integrated into the GIS environment. The results indicate a severe flash flood hazard level, with high flash flood susceptibility classes occupying 76.20%, 87.78%, and 91.73% of the area, depending on the considered criteria and weights assigned to them. [ABSTRACT FROM AUTHOR]

This article explores the factors that influence citizens' confidence in public and nonprofit sectors in the context of natural hazards. Utilizing a survey of residents in Austin, Texas, USA, we model the influence of residents' hazard risk awareness on the perceived confidence in formal institutions that are responsible for hazard preparedness and response. The empirical results of structural equation modeling show that perceived confidence in public and nonprofit sectors is differentially affected by residents' hazard risk awareness. Importantly, we also find the role of civic participation to positively mediate the effect of hazard risk awareness on the levels of confidence. Our findings contribute to the growing literature on coproduction of services for preparing urban residents for the impacts of climate change and emphasize the importance of community-driven development of community resilience. [ABSTRACT FROM AUTHOR]

Background: Postnatal care is crucial to prevent the child mortality. Despite the improvement in the PNC coverage for the neonates, it is still far away from the universal health coverage. Along with, some specific regions mostly are natural hazard prone areas of India show very under coverage of PNC for the neonates. Considering the substantial spatial variation of PNC coverage and natural hazard prevalence, present study aimed to examine spatial variation of PNC coverage and its association with natural hazard at the district level. Methods: The cross-sectional exploratory study utilized National Family Health Survey, 2019-21, which included 1,76,843 children using multistage stratified sampling method to examine postnatal care within 42 days for neonates born within five years prior to the survey. Additionally, the study utilized Vulnerability Atlas of India,2019 maps to categorize regions into hazardous (flood, earthquake, and landslide) and non-hazardous areas. Spatial univariate and bivariate analyses, logistic and geographically weighted regressions were conducted using ArcGIS Pro, GeoDa, and Stata 16.0 software to identify associations between PNC coverage, hazard exposure, and spatial variation. Results: The univariate spatial analysis showed some specific regions such as north, east, and north-east region of India had a high concentration of natural hazard and low access of PNC coverage. Bivariate analysis also showed that PNC coverage was low in flood (75.9%), earthquake (68.3%), and landslide (80.6%) effected areas. Compared to the national PNC coverage (81.1%), all these natural hazards effected areas showed low coverage. Further, logic regression showed that these hazard prone areas were less (OR:0.85 for flood, 0.77 for earthquake, and 0.77 for landslide) likely to get PNC coverage than their counterparts. LISA cluster maps significantly showed low PNC and high disaster concentration in these disaster-prone areas. Geographic weighted regression results also showed similar result. Conclusions: The present study elucidates notable heterogeneity in the coverage of postnatal care (PNC) services, with lower concentrations observed in disaster-prone areas. In order to enhance the accessibility and quality of PNC services in these areas, targeted interventions such as the deployment of mobile health services and fortification of health systems are recommended. Highlights: • PNC coverage was low in the area affected by flood (75.9%), earthquake (68.3%), and landslide (80.6%). • All-natural disaster-affected areas had low coverage of Post-Natal Care (PNC) compared to the national PNC coverage (81.1%). • Focusing at the geographical variation for PNC coverage, all the states and UTs performed well, with the exception of the central, eastern, and northeastern states. Results clearly demonstrated that certain regions—such as the north, east, and northeast regions—performed poorly in terms of PNC coverage because they were more vulnerable. [ABSTRACT FROM AUTHOR]

Environmental hazards invariably lead to significant human and financial repercussions, underscoring the crucial need to thoroughly investigate and recognize hazardous areas. Recognizing the importance of this matter, this study focuses on identifying regions susceptible to natural hazards within Roodehen's urban area. To achieve the objectives, a variety of data sources, including a digital model with a 30-meter height resolution, a geological map at a scale of 1:100,000, a topographic map at a scale of 1:50,000, and additional information layers were harnessed as primary research tools. The research method integrated multiple analytical tools, incorporating a digital model with a 30-meter height resolution, a geological map at a scale of 1:100,000, a topographic map at a scale of 1:50,000, and various information layers. ArcGIS software served as a pivotal component in data analysis, bolstered by the fuzzy logic model and the ANP model. The approach encompassed a multi-step process commencing with the utilization of the integrated fuzzy logic and ANP model, driven by diverse parameters, to pinpoint areas susceptible to flood hazards, amplitude movements, and earthquake vulnerability. Consequently, a comprehensive hazard map of Roodehen's urban area was formulated based on the obtained outcomes. The study underscores Roodehen's urban area as being at a heightened risk of natural hazards. Particularly, the western and northern regions exhibit elevated vulnerability due to their proximity to fault lines, earthquake centers, and steep slopes, making them susceptible to slope movements and earthquake hazards. Conversely, the central parts of the region, situated in close proximity to the river with low slopes and altitude, face a higher risk of flooding, and demonstrate significant earthquake vulnerability. [ABSTRACT FROM AUTHOR]

Accurate storm surge forecasting is vital for saving lives and avoiding economic and infrastructural damage. Failure to accurately predict storm surge can have catastrophic repercussions. Advances in machine learning models show the ability to improve accuracy of storm surge prediction by leveraging vast amounts of historical and realtime data such as weather and tide patterns. This paper proposes a bidirectional attention-based LSTM storm surge architecture (BALSSA) to improve prediction accuracy. Training and evaluation utilized extensive meteorological and tide level data from 77 typhoon incidents in Hong Kong and Macao between 2017 and 2022. The proposed methodology is able to model complex non-linearities between large amounts of data from different sources and identify complex relationships between variables that are typically not captured by traditional physical methods. BALSSA effectively resolves the problem of long-term dependencies in storm surge prediction by the incorporation of an attention mechanism. It enables selective emphasis on significant features and boosts the prediction accuracy. Evaluation has been conducted using real-world datasets from Macao to validate our storm surge prediction model. Results show that accuracy and robustness of predictions were significantly improved by the incorporation of attention mechanisms in our models. BALSSA captures temporal dynamics effectively, providing highly accurate storm surge forecasts (MAE: 0.0126, RMSE: 0.0003) up to 72 h in advance. These findings have practical significance for disaster risk reduction strategies, saving lives through timely evacuation and early warnings. Experiments comparing BALSSA variations with other machine learning algorithms consistently validate BALSSA's superior predictive performance. It offers an additional risk management tool for civil-protection agencies and governments, as well as an ideal solution for enhancing storm surge prediction accuracy, benefiting coastal communities. [ABSTRACT FROM AUTHOR]

Copyright of Runas. Journal of Education & Culture is the property of CICSHAL - Centro de Investigaciones en Ciencias Sociales y Humanidades desde America Latina and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The inhalation of natural nanoparticles (NPs) emitted from volcanic activity may be a risk to human health. However, the literature rarely reports the fate and response of NPs once in contact with lung fluids. In this work, we studied the particle size distribution of ashfall from Popocatépetl volcano, Mexico. The collected ashes (n = 5) were analyzed with scanning electron microscopy (SEM) to obtain the elemental composition and morphology, and to determine the size of the ash particles using ParticleMetric software (PMS). The PMS reported most of the ash to have submicrometric size (<1 μm) and an average equivalent circle of 2.72 μm. Moreover, to our knowledge, this study investigated for the first time the behavior of ash NPs at different times (0 to 24 h) while in contact with in vitro lung fluid, Gamble Solution (GS) and Artificial Lysosomal Fluid (ALF) using dynamic light scattering (DLS). We found a large variability in the hydrodynamic diameter, with values less than 1 nm and greater than 5 μm. Furthermore, aggregation and disaggregation processes were recognized in GS and ALF, respectively. The results of this study increase the knowledge of the interaction between NPs and lung fluids, particularly within the alveolar macrophage region. [ABSTRACT FROM AUTHOR]

Copyright of Revista de la Asociación Geológica Argentina is the property of Asociacion Geologica Argentina and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista de la Asociación Geológica Argentina is the property of Asociacion Geologica Argentina and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A soil depleted of its organic carbon content is typically destabilized, i.e., its capacity to maintain its microstructure intact under various stress conditions weakens, and consequently, landslides and mudflows can be triggered and propagated more easily. In a previous work, we showed with a rheological analysis that the removal of the sole water-soluble organic carbon "destabilized" the slurry very similarly to what occurs with the removal of the vast majority of soil organic carbon. In principle, the water-soluble organic carbon can be dissolved by rainfall, during which water can infiltrate the soil, eventually leaving it either by percolation or evaporation. These two processes are mimicked here with two different soil water wetting procedures. The stability of the treated (wetted) soils is studied with rheological and granulometric experiments. The former run on concentrated suspensions, while the latter run on very diluted ones. Despite this, the results agreed very well, indicating that the two wetting procedures induce the same destabilization of the soil which behaves as the one depleted by the whole water-soluble organic carbon. Our results concluded that a soil destabilized by a wetting procedure, i.e., by a rainfall event, will be more prone to trigger a landslide that will propagate more swiftly and will stop with more difficulties. [ABSTRACT FROM AUTHOR]

Some of the most devastating natural events on Earth, such as earthquakes and tropical cyclones, are prone to trigger other natural events, critical infrastructure failures, and socioeconomic disruptions. Man-made disasters may have similar effects, although to a lesser degree. We investigate the space of possible interactions between 19 types of loss-generating events, first by encoding possible one-to-one interactions into an adjacency matrix A, and second by calculating the interaction matrix M of emergent chains-of-events. We first present the impact of 24 topologies of A on M to illustrate the non-trivial patterns of cascading processes, in terms of the space of possibilities covered and of interaction amplification by feedback loops. We then encode A from 29 historical cases of cascading disasters and compute the matching matrix M. We observe, subject to data incompleteness, emergent cascading behaviors in the technological and socioeconomic systems, across all possible triggers (natural or man-made); disease is also a systematic emergent phenomenon. We find interactions being mostly amplified via two events: network failure and business interruption, the two events with the highest in-degree and betweenness centralities. This analysis demonstrates how cascading disasters grow in and cross over natural, technological, and socioeconomic systems.

The tourism sector, which is a significant driver of economic development, is particularly vulnerable to the risks of forest fires. Forest fires are a natural disaster that can have enormous consequences for tourism. This paper analyses the possibility of forest fires occurrence and their impact on tourist facilities in the Sokobanja municipality. Forest fires can occur naturally or as a result of anthropogenic impact. For the purposes of this analysis data about land cover, relief characteristics (slope and aspect) and distance from roads and settlements were used and processed in GIS environment (open source software “QGIS”). Based on the forest fire susceptibility index, areas with different degrees of endangerment were identified, which were then overlapped with the locations of tourist facilities. The applied methodology can be used except for the identification of endangered areas and the prediction of the degree of danger to the tourist offer and for the planning of appropriate prevention measures.

Abstract Background As the climate continues to warm, hurricanes will continue to increase in both severity and frequency. Hurricane damage is associated with cardiovascular events, but social capital may moderate this relationship. Social capital is a multidimensional concept with a rich theoretical tradition. Simply put, social capital refers to the social relationships and structures that provide individuals with material, financial, and emotional resources throughout their lives. Previous research has found an association between high levels of social capital and lower rates of cardiovascular (CVD) mortality. In post-disaster settings, social capital may protect against CVD mortality by improving access to life-saving resources. We examined the association between county-level hurricane damage and CVD mortality rates after Hurricane Matthew, and the moderating effect of several aspects of social capital and hurricane damage on this relationship. We hypothesized that (1) higher (vs. lower) levels of hurricane damage would be associated with increased CVD mortality rates and (2) in highly damaged counties, higher (vs. lower) levels of social capital would be associated with lower CVD mortality. Methods Analysis used yearly (2013-2018) county-level sociodemographic and epidemiological data (n = 183). Sociodemographic data were compiled from federal surveys before and after Hurricane Matthew to construct, per prior literature, a social capital index based on four dimensions of social capital (sub-indices): family unity, informal civil society, institutional confidence, and collective efficacy. Epidemiological data comprised monthly CVD mortality rates constructed from monthly county-level CVD death counts from the CDC WONDER database and the US Census population estimates. Changes in CVD mortality based on level of hurricane damage were assessed using regression adjustment. We used cluster robust Poisson population average models to determine the moderating effect of social capital on CVD mortality rates in both high and low-damage counties. Results We found that mean levels of CVD mortality increased (before and after adjustment for sociodemographic controls) in both low-damage counties (unadjusted. Mean = 2.50, 95% CI [2.41, 2.59], adjusted mean = 2.50, 95% CI [2.40, 2.72]) and high-damage counties (mean = 2.44, CI [2.29, 2.46], adj. Mean = 2.51, 95% CI [2.49, 2.84]). Among the different social capital dimensions, institutional confidence was associated with reduced initial CVD mortality in low-damage counties (unadj. IRR 1.00, 95% CI [0.90, 1.11], adj. IRR 0.91 CI [0.87, 0.94]), but its association with CVD mortality trends was null. The overall effects of social capital and its sub-indices were largely nonsignificant. Conclusion Hurricane damage is associated with increased CVD mortality for 18 months after Hurricane Matthew. The role of social capital remains unclear. Future research should focus on improving measurement of social capital and quality of hurricane damage and CVD mortality data.

Greece, like the rest of the Mediterranean countries, faces wildland fires every year. Besides their short-term socioeconomic impacts, ecological destruction, and loss of human lives, forest fires also increase the burnt areas’ risk of flash flood phenomena, as the vegetation, which acted in a protective way against runoff and soil erosion, is massively removed. Among the most severe wildland fire events in Greece were those of summer 2021, which were synchronous to the very severe heat waves that hit the broader area of the Balkan Peninsula. More than 3600 km2 of land was burnt and a significant amount of natural vegetation removed. Three of the burnt areas are examined in this work, namely, Attica, Northern Euboea, and the Peloponnese, in order to assess their risk of future flash flood events. The burnt areas were mapped, and their geological and geomorphological features studied. Flash flood hazard assessment was accomplished through a Boolean logic-based model applied through Geographic Information Systems (GIS) software, which allowed the prioritization of the requirement for protection by identifying which locations were most prone to flooding. The largest part of our study areas is characterized by geomorphological and geological conditions that facilitate flash flood events. According to our findings, in almost all study areas, the regions downstream of the burnt areas present high to very high flash flood hazard, due to their geomorphological and geological features (slope, drainage density, and hydrolithology). The only areas that were found to be less prone to flood events were Vilia and Varimpompi (Attica), due to their gentler slope inclinations and overall geomorphological characteristics. It is known that vegetation cover acts protectively against flash floods. However, in this case, large areas were severely burnt and vegetation is absent, resulting in the appearance of flash floods. Moreover, imminent flooding events are expected to be even more intense in the areas downstream of the burnt regions, possibly bearing even worse impacts on the local population, infrastructure, etc.

Forest fires are one of the most frequently occurring natural hazards, causing substantial economic loss and destruction of forest cover. As the Gangwon-do region in Korea has abundant forest resources and ecological diversity as Korea's largest forest area, spatial data on forest fire susceptibility of the region are urgently required. In this study, a forest fire susceptibility map (FFSM) of Gangwon-do was constructed using Google Earth Engine (GEE) and three machine learning algorithms: Classification and Regression Trees (CART), Random Forest (RF), and Boosted Regression Trees (BRT). The factors related to climate, topography, hydrology, and human activity were constructed. To verify the accuracy, the area under the receiver operating characteristic curve (AUC) was used. The AUC values were 0.846 (BRT), 0.835 (RF), 0.751 (CART). Factor importance analysis was performed to identify the important factors of the occurrence of forest fires in Gangwon-do. The results show that the most important factor in the Gangwon-do region is slope. A slope of approximately 17° (moderately steep) has a considerable impact on the occurrence of forest fires. Human activity and interference are the other important factors that affect forest fires. The established FFSM can support future efforts on forest resource protection and environmental management planning in Gangwon-do.

The frequency of forest fires in Gangwon-do has increased in recent years due to advanced climate change and dry weather. The Gangwon-do area, the largest forest area in South Korea, has rich forest resources and ecological diversity, therefore there is an urgent need for more effective monitoring and prevention of forest fires. This study proposed a method to establish a multi-hazard probability map (MHPM) for two related hazards (forest fires and droughts) based on a multi-layer hazards approach and machine learning algorithms for monitoring forest fire susceptibility areas. First, extreme drought years were selected using the standardized precipitation index (SPI). An inventory drought map was constructed using the enhanced vegetation index (EVI) based on satellite image data. Then, 70% of the inventory maps based on forest fires and droughts were used to construct hazard susceptibility maps and 30% of these were used to validate three machine learning models: Classification and Regression Trees (CART), Random Forest (RF) and boosted Regression Trees (BRT). Eleven conditioning factors related to climate, topography, hydrology, and human activities were considered for the analysis. The results of the three models were then validated using the area under the receiver operating characteristic (ROC) curve (AUC), and the best performing model was selected (BRT; forest fire: 85%, drought: 80%). Finally, the susceptibility maps of forest fires and droughts were combined to construct the MHPM for the Gangwon Province, South Korea. The results show that the MHPM of forest fires and droughts constructed in this study is valid and reliable. This multi-hazard map can provide key information for planners and decision-makers to develop forest fire prevention and management plans and to more effectively prevent and reduce the frequency of forest fires.

The birth of a new monogenetic volcano is difficult to forecast with precision, both in space and time. Nevertheless, seismic activity can alert of the imminence of such an eruption because it usually occurs as small-magnitude earthquake swarms that can last for a few weeks to months prior to an eruption. These swarms are usually related to magma that becomes stalled in the Earth's crust for variable periods of time before its eventual eruption at the surface. For several reasons, volcanic seismic swarms have rarely been recorded with seismometers before the birth of a new monogenetic volcano. Over the past 25 years, six distinct seismic swarms (in 1997, 1999, 2000, 2006, 2020, and 2021) were detected between Tancítaro and Paricutin volcanoes, in the southwestern part of México's Michoacán-Guanajuato volcanic field. They are believed to represent repeated attempts of magma to reach the surface hinting that in this region magma might become stalled for some time, so as to not reach the surface in a single ascent event from its source in the mantle. To better understand the magma's migration path through the crust, we re-located with greater precision some of these seismic swarms by using the same methodology and velocity model to the entire data set. Our results show that these swarms originated within a small area beneath the NE flank of Tancítaro at depths of between 15 and 8 km below sea level (bsl). Apparently, magma is trying to reach the surface within the same conduit network at these crustal depths, but stalls when reaching a depth of ~ 8 km bsl. It is crucial to study these swarms because they might be precursors to a new eruption in this part of the Michoacán-Guanajuato volcanic field. This monogenetic field has been very active, producing several dozen eruptions during the Holocene, the last two Jorullo (1759–1774) and Paricutin (1943–1952). Furthermore, the Tancítaro area displays one of the highest densities of Holocene volcanoes within the entire field, making it a probable candidate location for the birth of a future monogenetic volcano. For these reasons, a permanent seismic network should be installed as soon as possible. [ABSTRACT FROM AUTHOR]

There are numerous talus cones that have formed by long-term geological processes and sudden hydrological events in the Small Cold Valley (High Tatras National Park in Slovakia). Frequent hiking trails lead here; therefore, their safeness needs to be monitored due to recent rock avalanches and landslides. A complex methodology for monitoring changes in talus cones was developed to determine the extent, pace, nature, and origin of the morphological changes in the land in this complex high-mountain terrain. Non-contact UAS photogrammetry with SfM-MVS processing was applied as a quick, reliable, and environment-friendly data acquisition method. For proper georeferencing, a network of GCPs and stabilized surveying points were established by terrestrial geodetic surveying. Together with an evaluation of the methodology, the results comparing the actual state of a talus cone in 2018 and 2022 (after the significant hydrological event) are presented. Comparing and analyzing spatial models represented by point clouds, with an accuracy of centimeter level, was obtained. The detected morphological changes reached values in meters. A differential model expresses the distribution of the morphological changes. In conclusion, geodetic and geological knowledge is synthesized to evaluate the phenomena occurring in this territory. [ABSTRACT FROM AUTHOR]

Avalanches and other hazardous mass movements pose a danger to the population and critical infrastructure in alpine areas. Hence, understanding and continuously monitoring mass movements are crucial to mitigate their risk. We propose to use Distributed Acoustic Sensing (DAS) to measure strain rate along a fiber‐optic cable to characterize ground deformation induced by avalanches. We recorded 12 snow avalanches of various dimensions at the Vallée de la Sionne test site in Switzerland, utilizing existing fiber‐optic infrastructure and a DAS interrogation unit during the winter 2020/2021. By training a Bayesian Gaussian Mixture Model, we automatically characterize and classify avalanche‐induced ground deformations using physical properties extracted from the frequency‐wavenumber and frequency‐velocity domain of the DAS recordings. The resulting model can estimate the probability of avalanches in the DAS data and is able to differentiate between the avalanche‐generated seismic near‐field, the seismo‐acoustic far‐field, and the mass movement propagating on top of the fiber. By analyzing the mass‐movement propagation signals, we are able to identify group velocity packages within an avalanche that propagate faster than the phase velocity of the avalanche front, indicating complex internal structures. Importantly, we show that the seismo‐acoustic far‐field can be detected before the avalanche reaches the fiber‐optic array, highlighting DAS as a potential research and early warning tool for hazardous mass movements. Plain Language Summary: Avalanches and other hazardous mass movements pose a danger to the population and critical infrastructure in alpine areas. Therefore, it is important to be able to reliably measure and detect these hazardous events. We show a successful example to measure and characterize avalanches recorded with a Distributed Acoustic Sensing device that measures deformation along a fiber optic cable. We apply unsupervised machine learning to our avalanche recordings and are able to identify consistent properties between 12 avalanches. Ultimately, our results indicate that DAS might be a useful tool for detecting hazardous mass movements. Key Points: Distributed Acoustic Sensing measurements near the interface between avalanche and the subsurface reveal flow dynamicsStrain rate measurements of seismo‐acoustic waves are registered up to 30 s before avalanches reach the sensorsInternal group velocities larger than the propagation speed suggest the presence of complex internal structures [ABSTRACT FROM AUTHOR]

Tehran, the capital of Iran, is the largest and most populous city in Iran, which is of great importance due to its large population and abundant infrastructure. One of the most critical issues in this city is its need for resilience against all kinds of threats, including natural hazards, because its development was not based on territorial geography. In other words, in developing this 700 square kilometer area, attention has yet to be paid to its different zones. Different zones include the mountains, Shemiranat's alluvial cone area, the Tehran plain, etc. Main and minor faults, surface and underground water resources of the land, differences in formations between various parts of the land, the microclimate of the land in its multiple aspects, local and synoptic air currents, etc., have not been influential in urban development. The most crucial goal of this study is to identify and screen natural hazards in Tehran to improve this city's resilience by introducing a novel integrated MCDM method based on ANP and The Combined Compromise Solution method with Maximum Variance (MV-CoCoSo). Therefore, to increase the strength of Tehran against these disasters, the natural hazards of Tehran must first be identified and ranked. In this regard, practical criteria for evaluating Tehran's resilience were identified using library resources and the formation of expert groups. Then, using the ANP method, the comparative weightings of these effective criteria was investigated. Based on the results obtained, the disaster consequence criterion had the highest importance with a weight of 0.4361, followed by the disaster severity scale criterion with a weight of 0.2371, and the secondary threat possibility criterion (with a weight of 0.1232) was ranked third. Finally, using the MV-CoCoSo method, the natural hazards of Tehran city were classified based on the evaluated criteria. Tehran City's three significant disasters were earthquakes, floods, and landslides. In addition, two experiments were designed to assess the robustness of the research methodology. [ABSTRACT FROM AUTHOR]

Voluminous pumice rafts produced by the 2021 phreatomagmatic eruption of Fukutoku-Oka-no-Ba, a submarine volcano located in the Izu-Bonin Islands, reached many Japanese ports and islands, damaging fisheries and hindering marine traffic and trade. This event emphasized the necessity for governments and stakeholders to develop plans in advance and prepare disaster mitigation measures before an eruption. To investigate when and to what extent pumice might intersect trade routes and arrive at islands after future eruptions, we conducted particle tracking simulations of eruptions at major volcanic islands and submarine volcanoes near Japan using the velocity field from the ocean reanalysis dataset. Seven major volcanoes that have produced pumice rafts in the past century were selected: the submarine volcano NNE of Iriomote Island, Izu-Tobu Volcanoes, Miyakejima, Bayonnaise Rocks, Nishinoshima, Kaitoku Seamount, and Fukutoku-Oka-no-Ba. We partly reproduced the distribution of pumice arrivals recorded after the 1986 Fukutoku-Oka-no-Ba eruption, demonstrating the potential effectiveness of the simulations. We report likely pumice raft arrivals and drifting durations for the investigated eruptive scenarios, which may aid future risk assessments for pumice arrivals. [ABSTRACT FROM AUTHOR]

We studied a newly identified, multiple-kilometer-long rock slope failure in the Aiguilles Rouges massif (Chamonix valley, France). Owing to a high-resolution light detection and ranging (LiDAR) digital elevation model (DEM) and field work, we mapped morphostructures, including scarps, open fractures, and counterscarps. In some places, vertical offsets can reach tens of meters and crevasses can be meters wide. The evidence of gravitational activity (boulder displacements from analyses of archival satellite images) and the sharpness of the scarp outcrops together suggest very recent movements. These observations agree with ground displacement rates of a few millimeters per year estimated by interferometric synthetic aperture radar (InSAR) time series between 2014 and 2018. We sampled two vertical profiles along the top scarps to define the chronology of the slope failure using beryllium-10 (10Be) surface exposure dating. Glacially polished surfaces cut by these gravitational scarps were also sampled to determine glacial retreat timing as well as to constrain the pre-exposure 10Be inheritance. In total, 11 samples were studied. Our results highlight a significant time lag (approximately 15 ka) between the first evidence of nonglacial activity and the initiation of the slope failure that happened 1.3–2.5 ka ago, depending on the inheritance schemes. This suggests that the delayed opening of the crevasse is only one stage of a process that began when the valley was deepened and the glacial debuttressing is not the unique driving factor. This process of progressive failure of an excessively steep slope may continue, and the evolution of this slope failure may constitute a hazard for the upper part of the Chamonix valley. [ABSTRACT FROM AUTHOR]