In order to achieve the dual carbon goal and simplify the decarbonization process, a CO2 recovery process of the fire-flooding exhaust was designed. The process adopted the two-stage membrane separation method to recover CO2 and obtain high purity CO2 rich flow, and then through the mixed refrigerant liquefaction and rectification column, food grade liquid CO2 was obtained. HYSYS process simulation shows that for the fire-flooding exhaust with a flow rate of 1000 kg/h and a given composition, the process can achieve a CO2 recovery of 69.02%, total product sales S=2086.8 yuan/h, and specific power consumption of 2.818 kW∙h/kg. On this basis, a hybrid simulation platform of HYSYS & Matlab was established. Taking the specific power consumption of the product as the objective function, sequential quadratic programming (SQP) algorithm was adopted to optimize the process, and the optimized specific power consumption was reduced by 18.8%. Finally, by adjusting N2/CO2 components, the adaptability of the optimization condition was analyzed.

The 2022-2023 Chilean summer showed increased temperatures and similar burned area, compared to the 2016-2017 season, where more than 500,000 hectares were compromised, mainly in the rural areas. After a brief review, it is revealed that the effects of forest fires on soil and hydrological properties are barely debated in Chile. Here, we showed a climatological analysis where temperature records in the 2016-2017 season were unusual, as well as another unexpected increase in the summer of 2022-2023, resulting in high-severity fires known as ‘mega-fires’ or “storm-fires”. Mega-fires affect forest plantations and native forests mainly from 33º S (Maule Region) to 39º S (Los Ríos Region) and they are expected to become frequent due to climate change, moving from the north to the south. We present an overview of the influence of wildfires on soil components in the most affected areas (inland, Coastal, and Andes ranges), their hydrological impacts, and potential erosion risk due to high winter precipitation. We propose several management practices that could help to prevent or mitigate these events, including pre-and post-fire interventions, such as afforestation and seeding, selective logging, mulching, erosion barriers, soil preparation, and dam monitoring. We argue that any effective plan in fire-prone and affected areas should include a combination of actions taken at the hillslope scale at integral ecosystem management, whose effectiveness should be monitored and verified regionally at the watershed scale.

In this study, we focus on the effects of fuel bed representation and fire heat and smoke distribution in a coupled fire-atmosphere simulation platform for two landscape-scale fires: the 2018 Camp Fire and the 2021 Caldor Fire. The fuel bed representation in the coupled fire-atmosphere simulation platform WRF-Fire currently includes only surface fuels. Thus, we enhance the model by adding canopy fuel characteristics and heat release, for which a method to calculate the heat generated from canopy fuel consumption is developed and implemented in WRF-Fire. Furthermore, the current WRF-Fire heat and smoke distribution in the atmosphere is replaced with a heat-conserving Truncated Gaussian (TG) function and its effects are evaluated. The simulated fire perimeters of case studies are validated against semi-continuous, high-resolution fire perimeters derived from NEXRAD radar observations. Furthermore, simulated plumes of the two fire cases are compared to NEXRAD radar reflectivity observations followed by buoyancy analysis using simulated temperature and vertical velocity fields. The results show that while the improved fuel bed and the TG heat release scheme have small effects on the simulated fire perimeters of the wind-driven Camp Fire, they affect the propagation direction of the plume-driven Caldor Fire, leading to better-matching fire perimeters with the observations. However, the improved fuel bed representation together with the TG heat smoke release scheme leads to more realistic plume structure in comparison to the observations in both fires. The buoyancy analysis also depicts more realistic fire-induced temperature anomalies and atmospheric circulation when the fuel bed is improved.

The purpose of this paper is to develop an index system for measuring the compliance of high-rise residential buildings with fire requirements and also to develop an index system for measuring the fire response efficiency, which is linked to the index of compliance with fire requirements. The higher the compliance rate, the greater the chance of a successful response. The two systems de-pend on the automation of the firefighting system management processes using the techniques of the fourth industrial revolution and developed based on the consultation of subject matter experts in the field. The main elements of the indexes were identified, which were based mainly on several variables, including the fire administrative system, the firefighting system, the residents, the loca-tion of the high-rise residential buildings from the fire extinguishing station, and the height of the high-rise residential building. The two systems can contribute to improving emergency prepared-ness in high-rise residential buildings in the Emirate of Sharjah and are also considered as a measurement index for compliance with fire requirements in the Emirate of Sharjah.

Fires are the leading cause of death, serious injury and property damage. In the past, schools, temples and government offices had more frequent fires than they should. Statistics showed that the number of fires between 2017 to 2022 amounted 13,593 cases which mostly occurred in the school, temple and government offices (40.0% of all buildings). Moreover, it causes more damage among disabled especially the blinds who has a limited vision. Therefore, this cross-sectional purpose of this study was to assess fire risk including management model in school for the blind. The fire checklists, brainstorming and Analytic Hierarchy Process (AHP) were applied to estimate the fire risk in school for the blind building. The findings revealed an inherent fire hazard factors with a risk score equal to 3.2830 and evacuation factors with a risk score equal to 3.3178 were acceptable risk except the fire control factors with a risk score equal to 1.4320 was unacceptable risk may cause an impact on life, health, property and public communities. Eventually, efforts should be made to supervise those risk factors by designing suitable activities to reduce undesirable conditions in school for the blind.

Fires in houses are inevitable and they cause loss of life and major repair costs for the damage structure and contents. There is a clear need to prevent its ignition and spread to adjacent areas. This article presents the steps taken, namely: contacts, data collection, tests, analysis and conclusions regarding the accident resulting from the outbreak and spread of a fire, on December 25, 2020, in a traditional Portuguese house in Arganil, Portugal. The owners and the court requested to carry out an expert opinion by a person with specialized technical-scientific knowledge, which led to this work as an expert. the work consists of carrying out observations and data analysis. Then, the observed and analysed data were laboratory tested in order to consistently identify characteristics that lead to the ignition and propagation of the fire. This work identified construction problems, materials used and heat sources that are responsible for the accident. The conclusions may be used to avoid future incidents or accidents with houses of same construction type.

Expandable graphite (EG) was modified with a charring agent and organic-inorganic hybridised intumescent flame retardants (MEG) were synthesized. This study uses a cone calorimeter (CCT) and a DaqPRO 5300 radiation heat flow meter to evaluate the fire-resistant properties influenced by MEG on intumescent fire-retardant coatings. The impact of MEG on the thermal degradation of these coatings was investigated through the use of thermogravimetric analysis (TGA). The results obtained by CCT demonstrated that the incorporation of MEG markedly diminished the heat release rate and total heat release rate of the coating, in addition to enhancing the char residue compared to coatings with only expandable graphite (EG). Furthermore, TGA results demonstrate that adding MEG increases the weight of the char residue at elevated temperatures, suggesting improved thermal stability. Based on these findings, MEG exhibits a synergistic flame retardant effect when combined with intumescent fire retardant (IFR) systems. This synergy not only improves the flame-retardant properties of the coatings but also enhances their overall thermal stability, making MEG a promising additive for developing more efficient fire-retardant materials. Thus, MEG-modified coatings offer superior protection against fire hazards, highlighting their potential for practical applications in fire safety.

Using lightweight fire-rated board (LFRB) presents cost-effective opportunities for various passive fire protection measures. The aim of the project is to develop a LFRB with enhanced fire resistance, acoustic properties, and mechanical properties. These properties were determined using the Bunsen burner, furnace, energy dispersive X-Ray, impedance tube instrument, and Instron universal testing machine. To fabricate the LFRBs, vermiculite and perlite were blended with flame-retardant bind-ers, and four types of LFRBs were produced. A fire test was conducted to compare the fire-resistance performance of the LFRBs with a commercially available flame retardant board. The B2 prototype showed exceptional fire-resistant properties, with a temperature reduction of up to 73.0 °C as compared to the commercially available fire-rated magnesium board. Incorporating nano chicken eggshell into the specially formulated flame-retardant binder preserved the LFRBs’ structural integrity, enabling them to withstand fire for up to 120 minutes with an equilibrium temperature of 92.6 °C. This approach also provided an absorption coefficient of α = 2.0, a high flexural strength of 3.54 MPa, and effective flame retardancy properties with a low oxygen/carbon ratio of 2.60. These results make the LFRBs valuable for passive fire protection applications in the construction and building materials industry.

Samarinda City is one of the most attractive cities in Borneo Island (Indonesia) and also as a capital city of EastBorneo Province. The expansion of urban areas becomes essential due to rapid population and housing demand. Base on the statistical report, the annual population growth rate is 0.018% from the year 2016-2017 with a total population of 843446 inhabitants. Many natural disasters occur in some areas in this city, especially flooding. This natural disaster occurs almost every year, many people suffered and forced to evacuate. In 2018 there is 3 flood event with 28311 people was suffered and evacuated, and 5170 houses were flooded [1]. During the flood event, it was very possible to gain damages to their property and make traffic stuck. One common way to reducing the damages is using Early Warning Systems (EWS). Early warning is a major element for disaster risk reduction, including damages. To prevent and mitigate the impact of a disaster, many countries had taken action to build various methods of a public warning system. An effective early warning system focused on people-centered and comprises the following element, such as risk knowledge, technical monitoring and service, communication and dissemination of warnings, and community response capability [2]. Related to the existing condition which Samarinda is a Muslim-dominated city and obviously has a lot of a number of mosques. This is a good potency to develop an early warning system because every mosque has a loudspeaker for echoing Adzan (Muslim prayer-calling). With this existing condition, the loudspeaker can be utilized as a flood outdoor-voice warning announcer. The aim of this study is to briefly introduce the strategy of dissemination early warning by utilizing mosques. The hope of early warning dissemination is giving enough time to the people to evacuate their property to reduce damages and possibly to giving information to avoiding traffic stuck (in a certain location)due to flooding. The results of this study can be used as input for decision-makers to develop effective flood management strategies and policies, especially in the case of an early warning system where not well-developed in Samarinda.

Fire risk can be defined as the probability that a fire will spread. Reliable monitoring of fire risk is essential for effective landscape management. Compilation of fire risk records enable identification of seasonal and inter-annual patterns and provide a baseline to evaluate the trajectories in response to climate change. Typically, fire risk is estimated from meteorological data. In regions with sparse meteorological station coverage environmental proxies provide important additional data stream for estimating past and current fire risk. Here we use a 60-year record of daily flows from two rivers (Franklin and Davey) in the remote Tasmanian Wilderness World Heritage Area (TWWHA) to characterize seasonal patterns in fire risk in temperate Eucalyptus and rainforests. We show that river flows are strongly related to landscape soil moisture estimates derived from down-scaled re-analysis of meteorological data available since 1990. To identify river flow thresholds where forests are likely to burn, we relate river flows to known forest fires that have occurred in the previously defined ecohydrological domains that surround the Franklin and Davey catchments. Our analysis shows that the fire season in the TWWHA is centered on February (70% of all years below the median threshold), with shoulders on December-January and March. Since 1954 forest fire can occur in at least one month for all but four summers in the ecohydrological domain that includes the Franklin catchment, and since 1964 fire fires could occur in at least one month in every summer in the ecohydrological domain that includes the Davey catchment. Our analysis shows that mangers can use river flows as a simple index that provide a landscape-scale forest fire risk in the TWWHA.

Agricultural land fires have been linked to various and adverse impacts on ecosystems, food security and the agriculture sector. Understanding the patterns and drivers of agricultural land fires is essential for effective agricultural land fire management. The key objectives of this study were to (1) analyze the temporal and spatial patterns of agricultural land fires using satellite remote sensed data, (2) assess a range of environmental conditions that could drive the occurrence of agricultural land fires, (3) determine the best model for predicting agricultural land fires and (4) determine the relative contribution of each environmental condition variable on the best predictive model. We used both univariate and multivariate regressions for the fire prediction capability of four independent environmental conditions (fuel, weather, topographic and anthropogenic). Analysis of historical satellite data revealed that agricultural land fires were more frequent than forested land fires. Our analyses also revealed that fuel condition was the most important variable for predicting agricultural land fires followed by weather, topographic and anthropogenic conditions. This study provides a novel multivariate model for predicting agricultural land fires that harbors the potential to improve agricultural land fire management and reduce fire risk within the agricultural sector.

Throughout its history Western science has tended to treat complexity as a problem to be avoided or solved. A philosophy of simplification achieved significant improvements in human life and prevails as a model for achieving societal good. However, misplaced efforts to control complexity worsen our sustainability challenges. Effective responses to modern day dilemmas such as floods and climate change require ways of thinking that are more congruent with the complexity of the world. Further, governmental programs to reduce flood risk depend on the support of an informed and engaged public. It has proven difficult to communicate risk in ways that align scientific and technical expertise based on an objective worldview, with communal and individual under-standings of risk based on subjective experiences and perspectives. I introduce Paul Cilliers’ philosophy of critical complexity as the basis of a new conceptual framework that values and balances complexity and simplicity. This paper shows how objective strategies that rely on analytic and probabilistic thinking can be integrated with subjective approaches that draw on the experiential and possibilistic. An example depicts how the problematic framing of the “100-year flood” can be replaced by a model of the data that covers a range of possibilities within an experiential time frame. Cilliers’ work opens an ethical dimension in which we make more intentional choices and acknowledge our limitations. Complexability – understanding how to live with complexity rather than aiming to simplify it – is a vital component of sustainability communication and education.

Slimy sculpin (Uranidea cognata) inhabitat coldwater streams in southeastern Minnesota, USA, many of which were subjected to probable 2000-year flood events in August 2007. Floods scoured streambeds, created new stream channels, and greatly reduced benthic invertebrate communities that serve as the primary food resource for sculpin. Diets and Fulton condition of sculpin in Gilmore Creek (with moderate flooding) and Garvin Brook (with very severe flooding) had been examined just prior to flooding, and were re-examined two weeks after flooding to assess possible diet and condition changes. Diets, condition, and reproductive fitness of sculpin were examined seven months post-flood in these same two streams, plus nearby Trout run (also experienced very severe flooding). Sculpin condition declined significantly post-flood in Garvin Brook, but improved in Gilmore Creek. Prior to spring spawning, condition of Garvin Brook sculpin had improved, but Gilmore Creek fish condition worsened. Sculpin diets were more diverse before and after flooding in Gilmore Creek than in Garvin Brook, although diets of fish from both streams were dominated (>55%) by midge (Diptera: Chironomidae) larvae. Diets differed significantly before versus after flooding in the more severely flooded Garvin Brook, but not in Gilmore Creek. Prey number per sculpin stomach declined post-flood in Gilmore Creek, but not in Garvin Brook, although dry mass of prey/fish wet mass declined post-flood in both streams. Pre-spawn sculpin displayed no patterns in reproductive fitness (gonadosomatic index, hepatosomatic index, oocyte number) among the three streams that may have been related to flooding severity the previous summer. Although sculpin diets and condition were altered significantly immediately post-flood in the more severely flooded stream, food resource recovery apparently was rapid enough to prevent longer-term impacts on sculpin condition and reproductive fitness.

Although the vegetation of the Cerrado biome is prone to fire, plant species composition can be altered according to its severity and frequency. Furthermore, many Cerrado plant species are resistant and can adapt to frequent fires. Little is known about how plant species of Cerrado environments respond to fire and the extent to which fire is beneficial for their conservation. The present study analyzed the responses of plant species of areas of campo sujo to fire and changes in floristics and vegetation structure at different post-fire intervals. Areas with frequent fires had a greater diversity of species, while the area with 10 years without burning had lower diversity and greater DBH. The distribution of species differed among the four the studied areas. We conclude that fire was beneficial for species diversity in the studied areas of Cerrado since it was able to promote the insertion and removal of species during intervals between fires.

Fire accidents are common accidents amongst residential house occupants in Libya. Some of the factors that can mitigate fire accidents in Libyan residential houses include fire safety awareness and knowledge. This study investigated the level of awareness and knowledge of fire safety amongst residential house occupants in Libya. The possibility and consequences of fire hazards and risk existing in residential houses in Libya were also studied. Experimental data obtained for 1.5 m/s and 3.5 m/s as walking and running evacuation time, respectively amongst residential house occupants in Libya were also compared. The sample consist of 90 respondents with instruments of fire safety awareness and knowledge, fire hazard and consequences, and the involvement of residential house occupants in fire emergency. Statistical analysis was performed at p < 0.05 to determine the levels of residential awareness and knowledge regarding fire safety and the hazard and consequences. The findings illustrated that the items were highly reliable (α > 0.7) and normally distributed. Also, the results illustrated that the majority of residential house occupants in Libya have fair level of fire safety awareness and knowledge with overall percentage of 80% and 90%, respectively. On the other hand, the results further showed that majority of residential house occupants have good level of fire emergency involvement with 90%. This paper proposes a fire safety assessment method which may form the basis of future fire safety practice in Libya.

Forest fires are part of a set of natural disasters that have always affected regions of the world typically characterized by a tropical climate with long periods of drought. However, due to climate change in recent years, other regions of our planet have also been affected by this phenomenon, never seen before. One of them is certainly the Italian peninsula, and especially the regions of southern Italy. For this reason, the scientific community, as well as remote sensing one, is highly concerned in developing reliable techniques to provide useful support to the competent authorities. In particular, three specific tasks have been carried out in this work: (i) fire risk prevention, (ii) active fire detection, and (iii) post-fire area assessment. To accomplish these analyses, the capability of a set of spectral indices, derived from spaceborne remote sensing (RS) data, is assessed to monitor the forest fires. The spectral indices are obtained from Sentinel-2 multispectral images of the European Space Agency (ESA), which are free of charge and openly accessible. Moreover, the twin Sentinel-2 sensors allow to overcome some restrictions on time delivery and observation repeat time. The performance of the proposed analyses were assessed experimentally to monitor the forest fires occurred in two specific study areas during the summer of 2017: the volcano Vesuvius, near Naples, and the Lattari mountains, near Sorrento (both in Campania, Italy).

A neglected dimension of the fire regime concept is fire patchiness. Habitat mosaics that emerge from the grain of burned and unburned patches (pyrodiversity) are critical for the persistence of a diverse range of plant and animal species. This issue is of particular importance in frequently burned tropical Eucalyptus savannas, where coarse fire mosaics have been hypothesized to have caused the recent drastic population declines of small mammals. Satellites routinely used for fire mapping in these systems are unable to accurately map fine-grained fire mosaics, frustrating our ability to determine whether declines in biodiversity are associated with local pyrodiversity. To advance this problem, we have developed a novel method (we call ‘double-differenced dNBR’) that combines the infrequent (c. bi-monthly) detailed spatial resolution Landsat with daily coarse scale coverage of MODIS and VIIRS to map pyrodiversity in the savannas of Kakadu National Park. We used seasonal Landsat mosaics and differenced Normalized Burn Ratio (dNBR) to define burned areas, with a modification to dNBR that subtracts long-term average dNBR to increase contrast. Our results show this approach is effective in mapping fine-scale fire mosaics in the homogenous lowland savannas, although inappropriate for nearby heterogenous landscapes. Comparison of this methods to other fire metrics (e.g., area burned, seasonality) based on Landsat and MODIS imagery suggest this method is likely accurate and better at quantifying fine-scale patchiness of fire, albeit it demands detailed field validation.

Floods are among the most serious natural disasters worldwide; they cause enormous crop losses every year and threaten world food security. There are many studies focused on flood impact assessments for administrative districts but fewer on postdisaster impact assessments for specific crops. Therefore, this study used remote sensing data, including the normalized difference vegetation index (NDVI), elevation data, slope data, and rainfall data, combined with crop growing season data to construct a crop flood damage assessment index (CFAI). First, an analytic hierarchy process (AHP) was used to assign weights to the impact factors; then, the weighted composite score method was used to calculate the CFAI; and finally, the impact was classified as slight, moderate, or severe based on the magnitude of the CFAI . This method was used for the Missouri River floods of 2019 in the U.S. Due to the lack of measured data, the disaster vegetation damage index (DVDI) was used to validate the results. The combined approach achieved a high degree of consistency in spatial distribution. The CFAI can respond well to the degree of crop impact after flooding, providing new ideas and reference standards for agriculture-related departments.

IoTs and WSNs utilize their connectivity to enable solutions supporting a spectrum of industries in different and volatile environments. To effectively enhance the security and quality of service of networks, empirical research should take into account a variety of factors and be structured in a replicable manner. This will not only ensure scalability but also enable verification of conclusions, leading to more constructive outcomes. Cooja offers limited performance analysis capabilities of simulations, which are often extracted and calculated manually. In this paper, we introduce a framework and a toolkit that enable researchers to conduct structured and conclusive experiments considering different factors and metrics, experiment design to results analysis. Besides, this tool summarizes multiple network metrics and allows multiple scenarios. As proof of concept, this paper studies the flooding attacks on IoT and illustrates their impact on the network, leveraging the proposed framework and tools.

The naphtha cracking process results in the production of ethylene, propylene, mixed C4, and pygas, which are all olefins. Distillation columns and cracking furnaces are the primary units of operation. The price of raw materials accounts for 80% of production costs. There is also the pos-sibility of using LPG, a less expensive raw material, in place of some of the naphtha. A change in the raw material will affect the operability of the distillation columns and influence the yield on the cracking side. To determine the best naphtha substitution for LPG without creating a hydraulic problem (Jet Flooding) in distillation columns, analysis utilizing simulation tools must be done. To simulate the substitution of naphtha for other feedstocks, a reliability model is being built by comparing simulation results with data from the actual plant. LPG flow is a free variable that is being used to substitute naphtha. The effects of economically advantageous naphtha substitution for LPG can be assessed with the use of simulation tools without impairing plant operability. Optimum naphtha substitution rate is 21.14% from base case which resulting jet flooding occurred at Propylene Fractionator No 2. By implementing this substitution case, benefit that can be driven $22,772.02/ hour.

Samarinda’s flooding issue is threatening future city development. As the most populated city in Kalimantan, Samarinda (the municipality of East Borneo) plays a role-model in disaster management for a neighboring city. This paper introduces current flood disaster handling in this city. History of disaster management in Indonesia is started from the earlier of Indonesian independence. Year 2008, after hit by severe Tsunami in Aceh and its surrounding, Government of Republic of Indonesia form special agency to manage disaster specifically, namely National Board for Disaster Management (in Bahasa called: Badan Penanggulangan Bencana Nasional [BNPB]) and follows by the regional and local government to form similar agency in provincial and local scale (including Samarinda), called Regional Board for Disaster Management (in Bahasa: Badan Penanggulangan Bencana Daerah[BPBD]) which is formed in 2009 and 2011, respectively. The aim of this paper is to explain flood management in Samarinda where is flood hazard increase gradually and need to be a priority. Descriptive analysis is used in this study including secondary data and interviewed stakeholders. Finally, the finding of study obtains found five constraints related to Samarinda’s flood management including administrative and policy, social, economic, environment and technical and knowledge constraint. This study also promotes several schemes of non-structural approach to enlarge alternative perspective in flood management.

This study develops an intelligent fire suppression device that links microcapsule-based IoT to extinguish early fires, such as blind spots that cannot be seen by humans or places that are difficult to recognize fires. The microcapsule is a micro-unit collection that collects Novak 1230 gas generated in the semiconductor production process. The microcapsule is molded into a form with a fire suppression function, and when a fire occurs, the molded body explodes and absorbs ambient oxygen to suppress the fire. The complex sensor IoT executes smoke and heat detection generated when a fire is suppressed within 10 seconds, which derives the reliability of the detector by notifying the fire and detecting the ignition point through communication linkages such as Ieee 485 and WiFi or LoRa.

Wildland fires are responsible for large socio-economic impacts. Fires affect the environment, damage structures, threaten lives, cause health issues, and involve large suppression costs. These impacts can be mitigated via accurate fire spread forecast to inform the incident management team. We show that a fire forecast system based on a numerical weather prediction (NWP) model coupled with a wildland fire behavior model can provide this forecast. This is illustrated with the Chimney Tops II wildland fire responsible for large socio-economic impacts. The system is run at high horizontal resolution (111 m) over the region affected by the fire to provide a fine representation of the terrain and fuel heterogeneities and explicitly resolve atmospheric turbulence. Our findings suggest that one can use the high spatial resolution winds, fire spread and smoke forecast to minimize the adverse impacts of wildland fires.

Estimation of the potential consequences from events occurring downstream of a dam are part of the risk assessment needed during the installation phase of a new dam. In case of specific Natural or Man Made ongoing or prospected events, it may be also important to carry out fast computations that can provide information on the areas at risk either because the original design analyses are not available or because the parameters needed are different. This study is aimed to develop a procedure that strongly facilitates the preparation of the input deck and the derivation of the output quantities to allow a fast analysis of a dam break event using a shallow water model, NAMI DANCE, as the analysis tool.

The development effect of water flooding in ultra-low permeability reservoir was poor due to its poor physical property and high shale content, the experimental study of air flooding which help to complement producing energy was carried out. Based on the Accelerating Rate Calorimeter experimental results, the crude oil of N block in L oilfield can produce low-temperature oxidation reaction, which was the basic condition of air flooding. Three groups of experiment natural cylinder core were designed for oil displacement, water flooding and air flooding were used respectively, and the relationship of differential pressure, oil recovery, injection capacity with injection volume was investigated. It has been showed on the result that the recovery efficiency improved sharply than water flooding, the effect of depressurization and augmented injection was obvious, and the air displacement was validation.

Fire hazards present a multifaceted global challenge that endangers human lives and environmental safety. Despite advancements in fire risk management technologies and methods, fires continue to be a prime concern for human security. The Arabian Gulf region, influenced by its climatic and environmental conditions, is especially susceptible to these threats. The Emirate of Sharjah, part of the United Arab Emirates, has made significant progress in reinforcing fire safety measures within residential structures, achieving a decrease in the number of fire-related accidents compared to neighboring Emirates. However, the incidence rate remains relatively high. This study undertook a thorough review and assessment of the existing Fire Management System (FMS) for Sharjah's residential sector, leading to the recommendation of a more robust framework. Through a series of literature reviews, interviews with stakeholders, and surveys, using analytical techniques such as the Delphi method and Failure Mode Effect and Criticality Analysis, vital factors that influence the FMS's effectiveness were pinpointed. The study exposed that the current FMS was largely reactive. In response, a proactive framework incorporating nine essential components was designed to strengthen the Sharjah FMS. These components span across various domains such as fire assurance, assessment of fire risks, management of accidents, evaluation of performance, training programs, research and development initiatives, digital services, monitoring of regulatory compliance, firefighting strategies, automated responses to fires, recovery processes, as well as the use of resilient materials and architectural design considerations. Furthermore, the research introduced two specific fire indexes, the HRBFI and the EFRI. Anchored in the principles of Systems Thinking and Industry 4.0, this proposed framework is aimed at streamlining Sharjah's approach to fire management in dwellings, with the objective of diminishing the frequency of fire incidents yearly. The ultimate aspiration of this framework is to enhance the Emirate's standing in terms of safety and to guarantee a living environment that is safeguarded against the risk of fires..

Erwinia amylovora, the causative agent of fire blight, leads to important economic losses of apple and pear crops worldwide. This study aimed to investigate the potential of the resident bacterial flora of the apple blossom in combatting plant disease-causing organisms, with a focus on strains that show potential for controlling fire blight. We collected samples of apple blossoms from dif-ferent sites around Canton Zurich, some grown with and some without the use of conventional pesticides, and isolated 538 strains which we tested for activity against Pectobacterium carotovorum and E. amylovora. We also tested the isolates against oomycete and fungal pathogens, to evaluate them for additional potential as biocontrol agents. Nine isolates showed activity against P. ca-rotovorum and eight of these against E. amylovora. Furthermore, 117 showed antifungal and 161 anti-oomycete activity. Five strains showed activity against all classes of pathogen and were tested in a detached apple model for anti-E. amylovora activity. Of these five strains, two were able to antagonise E. amylovora; Bacillus velezensis #124 and Pantoea agglomerans #378. We also show that B. velezensis #124 exhibits strong activity against three different fungi and two oomycetes in vitro, suggesting that this strain might have broad potential for biocontrol. Our results showcase the potential of the natural apple blossom flora. We isolated two candidate biocontrol strains from apple blossoms, suggesting that they can persist at the most common entry point for the causative agent of fire blight. Furthermore, they are probably already part of the human diet, suggesting they are safe for consumption and thus are promising candidates for biocontrol applications.

Prescriptions for fuels management are universally applied across forest types in British Columbia, Canada, to reduce fire behaviour potential in the wildland-urban interface. Fuel thinning treatments are assumed to reduce the potential for sustained ignition and crown fire initiation by reducing surface fuel loading. We hypothesized that these prescriptions are not appropriate for the coastal wet forests in the Whistler region of the province. Our study measured the efficacy of fuel thinning treatments in four stands located in the Whistler community forest. We examined several in-stand microclimate variables during snow melt in the spring and at the height of fire danger in late summer, at thinned and unthinned locations paired using GIS- analysis. We found that thinning increased the wildfire risk based on the differences between unthinned and thinned areas in the same forest stand.

Shrubland and forestland covers are highly prone to fire. The Normalized Difference Vegetation Index (NDVI) has been widely used for biomass quantitative assessment. This study aims were the following: (1) to compute the NDVI annual curve for two types of land cover eucalypts and shrubland areas; (2) to collect field data in these two types of land cover to estimate aboveground biomass (AGB); and (3) to produce AGB maps for eucalypts and shrubland areas by modelling AGB with NDVI and validate them with other data sources. A study area in the central inland Portugal was considered. The Sentinel-2 MSI imagery for the year of 2022 and 2023 were used to compute the NDVI. The NDVI annual curve for 2022 showed a minimum observed between July and August, in accordance with the climatological data, and allowed differentiating eucalypts from shrubland areas. Spectral signatures also confirmed this differentiation. The fitted linear models for AGB prediction using the NDVI imagery showed good fitting performances (R2 of 0.76 and 0.69). The AGB maps provide a relevant decision support tool for forest management and for fire hazard and fire severity mitigation. Further research is needed using more robust data sets for models’ independent validation.

Two extreme wind-driven wildfire events impacted northern and southern California in late 2017 leading to 46 fatalities and thousands of structures lost. This study describes the meteorological and climatological factors that drove and enabled these wildfire events and quantifies the rarity of such conditions over the observational record. Both extreme wildfire events featured fire-weather metrics that were unprecedented in the observational record in addition to a sequence of climatic conditions that preconditioned fuels. The North Bay fires that affected portions of northern California in early October occurred coincident with strong downslope winds. The vast majority of the fires’ devastating effects and acres burned occurred overnight and within the first twelve hours of ignition. By contrast, the southern California fires of December were characterized by the longest Santa Ana wind event on record and included the largest wildfire in California’s history. Both fire events occurred following an exceptionally wet winter that was preceded by the drought of record in California. Fuels were further preconditioned as the warmest summer and autumn on record occurred in northern and southern California, respectively. Accelerated curing of fuels coupled with the delayed onset of autumn precipitation allowed for critically low dead fuel moisture leading up to the foehn wind events. Fire weather conditions were well forecasted several days prior to the fire. However, the rarity of fire-weather conditions that occurred in the wildland urban interface, along with other societal factors were key contributors to wildfire impacts to communities.

Flooding, exacerbated by climate change, poses a significant threat to certain areas, increasing in frequency and severity. In response, the construction of supplementary dams has emerged as a reliable solution for flood management. This study employs a Remote Sensing (RS) approach integrated with GIS to assess the feasibility of constructing a supplementary dam near Linville, Brisbane, Australia, with the aim of mit-igating floods and preventing overtopping failure at the Wivenhoe Dam. Using QGIS software and a 25-meter resolution DEM from the Queensland Spatial Catalogue ‘QSpatial’ website, four potential dam sites were analysed, considering cross-sections, watershed characteristics, and water volume calculations. Systematic selection criteria were applied to identify the most optimal option based on dam wall di-mensions, volume-to-area, and volume-to-cost ratios. The selected option was further assessed against predefined criteria, yielding one optimal choice. The study provides insights into the feasibility and ef-fectiveness of supplementary dam construction for flood mitigation in the region, with recommendations for future research and implementation.

(1) Background: Cameron County, which is located in the Rio Grande Valley, holds historical records for storm surges with noticeable property damage, fatalities, and injuries; (2) Methods: using storm surge hazard datasets from the National Oceanic and Atlantic Agency (NOAA), and American Community Survey (ACS) 2019 datasets and Geographic Information System (GIS), the study estimates at-risk population and their socio-demographic attributes; (4) Conclusions: Estimated water levels of a storm surge could be reached up to 5 feet in category 1 event, 9 feet in category 2, 17 feet in category 3, and above 20 feet in category 4 and 5. In the category 5 event, there is an estimated 37% (159,659) of the total county’s population (434,294) will be under flooded water. Suggestions are made to better prepare and successfully evaluate.

On 5-7 April 2018 a landfalling atmospheric river resulted in widespread heavy precipitation in the Sierra Nevada of California and Nevada. Observed snow levels during this event were among the highest snow levels recorded since observations began in 2002 and exceeded 2.75 km for 31 hours in the northern Sierra Nevada and 3.75 km for 12 hours in the southern Sierra Nevada. The anomalously high snow levels and over 80 mm of precipitation caused flooding, debris flows, and wet snow avalanches in the upper elevations of the Sierra Nevada. The origin of this atmospheric river was super typhoon Jelawat, whose moisture remnants were entrained and maintained by an extratropical cyclone in the northeast Pacific. This event was notable due to its April occurrence, as six other typhoon remnants that caused heavy precipitation with high snow levels (mean = 2.92 km) in the northern Sierra Nevada all occurred during October.

Polymer flooding is an established enhanced oil recovery (EOR) method, still many aspects of polymer flooding are not well understood. This study investigates the influence of mechanical degradation on flow properties of polymers in porous media. Mechanical degradation due to high shear forces may occur in the injection well and at the entrance to the porous media. The polymers that give high viscosity yields at a sustainable economic cost are typically large, MW > 10 MDa, and have wide molecular weight distributions. Both MW and the distributions are altered by mechanical degradation, leading to changes in the flow rheology of the polymer. The polymer solutions were subjected to different degrees of pre-shearing and pre-filtering before injected into Bentheimer outcrop sandstone cores. Rheology studies of injected and produced polymer solutions were performed and interpreted together with in-situ rheology data. The core floods showed a predominant shear thickening behavior at high flow velocities which is due to successive contraction/expansion flow in pores. When pre-sheared, shear thickening was reduced but with no significant reduction in in-situ viscosity at lower flow rates. This may be explained by reduction in the extensional viscosity. Furthermore, the results show that successive degradation occurred which suggests that the assumption of the highest point of shear which determines mechanical degradation in a porous media does not hold for all field relevant conditions.

This work analyzes a new indoor laboratory data set looking at early fire indicators in controlled and realistic experiments representing different incipient fire scenarios. The experiments were performed within the confines of an indoor laboratory setting using multiple distributed sensor nodes on different room positions. Each sensor node collected data of particulate matter (PM), volatile organic compounds (VOC), CO, CO2, H2, UV, air temperature and humidity in terms of a multivariate time series. These data hold immense value for researchers within the machine learning and data science communities who are keen on exploring innovative advanced statistical and machine learning techniques. They serve as a valuable resource for the development of early fire detection systems. The analysis of the collected data was carried out in dependence of the manhattan distance between the fire source and the sensor node. We found that especially larger particles (&gt; 0.5 μm) and VOCs show a significant dependency with respect to the intensity as a function of the manhattan distance to the source. Moreover, we observed differences in the propagation behavior of VOCs, PM, and CO, which are particularly relevant in incipient fire scenarios due to the presence of strand popagation effects.

In Summer 2017, huge wildfires in the British Columbia region (Canada) led to the injection of a remarkably high concentration of biomass burning aerosol in the atmosphere. These aerosol masses reached the city of Naples, Italy, since the end of August 2017, where they were characterized by means of a multiwavelength lidar and a sun-sky-lunar photometer. Here we report on the optical and microphysical properties of this aerosol in an intriguing condition, occurring on 4th September 2017, which is characterized by an interesting multi-layered vertical distribution of the aerosol. The Lidar profiles highlighted the presence of four aerosol layers, with two located in the lower troposphere and other two at stratospheric altitudes. A rather thorough characterization of the biomass burning aerosol was carried out. The aerosol depolarization ratio showed an increasing dependence on the altitude with averaged values of 2-4% for the tropospheric layers, which are indicative of almost spherical smoke particles, and larger values in the stratospheric layers, suggestive of aspheric particles. Lidar-derived size distributions were retrieved for the first three aerosol layers, highlighting a higher particle concentration in the fine mode fraction for the layers observed at higher altitude. A dominance of fine particles in the atmosphere (Fine Mode Fraction &gt; 0.8) with low absorption properties (absorption AOD &lt; 0.0025 and SSA &gt; 0.97) was also observed over the whole atmospheric column by sun-photometer data. The space-resolved results provided by the lidar data are consistent with the columnar features retrieved by the AERONET sun-photometer, thus evidencing the reliability and capability of lidar characterization of atmospheric aerosol in a very interesting condition of multiple aerosol layers originating from Canadian fires overpassing the observation station.

The enormous potential of renewable bioresources is expected to play a key role in the development of the EU’s sustainable circular economy. In this context, inexhaustible, biodegradable, non-toxic and carbon-neutral forest-origin resources are very attractive for the development of novel sustainable products. The main structural component of wood is cellulose, which, in turn, is the feedstock of nanocellulose, one of the most explored nanomaterials. Different applications of nanocellulose have been proposed, including packaging, functional coatings, insulating materials, nanocomposite fields among others. However, the intrinsic flammability of nanocellulose restricts its use in some industries where fire risk is a concern. This paper overviews the most recent studies of the fire resistance of nanocellulose-based materials, focusing on thin films, coatings and aerogels. Along with effectiveness, increased attention to sustainable approaches is considered in developing novel fire-resistant coatings. The great potential of bio-based fire-resistant materials, combined with conventional non-halogenated fire retardants FRs, has been established. The formulation methods, types of fire retardants and their action modes, and methods used for analysing fire retardancy are discussed in the frame of this overview.

The dependence of longleaf pine (Pinus palustris) ecosystems on fire is well-understood, and anthropogenic alteration of fire cycles within its natural range has contributed to its decline. This is increasingly exacerbated in areas of urban interfaces, wherein use of prescribed fire can be problematic. The purpose of this study—the University of West Florida Campus Ecosystem Study—was to examine effects of fire exclusion on longleaf pine in the unique urban interface of a university campus. This was conceived as an interconnected series of investigations on the main campus and three associated natural areas that comprise remnant longleaf stands following cessation of widespread longleaf pine harvesting ~120 years ago. This period of chronic fire exclusion has allowed for a distinct shift in stand structure and composition. The open, savanna-like structure of fire-maintained longleaf stands has transitioned into closed-canopy forests with the increased prevalence of southern evergreen oaks (especially live oak—Quercus virginiana) and Magnolia spp., resulting in complete absence of longleaf regeneration. Fire exclusion has also appeared to have decreased soil fertility. Significant variation in mean age of longleaf pine stems of the main campus and natural areas suggest that the natural areas were likely under separate ownership with contrasting land-use history prior to purchase by the State of Florida for campus construction in 1963.

This paper aims to develop a hierarchical risk assessment model using the newly-developed evidential reasoning (ER) rule, which constitutes a generic conjunctive probabilistic reasoning process. In this paper, we first provide a brief introduction to the basics of the ER rule and emphasize the strengths for representing and aggregating uncertain information from multiple experts and sources. Further, we discuss the key steps of developing the hierarchical risk assessment framework systematically, including (1) formulation of risk assessment hierarchy, (2) representation of both qualitative and quantitative information, (3) elicitation of attribute weights and information reliabilities, (4) aggregation of assessment information using the ER rule and (5) quantification and ranking of risks using utility-based transformation. The proposed hierarchical risk assessment framework can potentially be implemented to various complex and uncertain systems. A case study on the fire/explosion risk assessment of marine vessels demonstrates the applicability of the proposed risk assessment model.

Prescribed fire is increasingly utilized for conservation and restoration goals, yet there is limited empirical evidence supporting its effectiveness in reducing wildfire-induced damages to valued resources of significant worth—whether natural, cultural, or economic. This study evaluates the efficacy of prescribed fire in reducing wildfire severity to LANDFIRE-defined vegetation classes and valued resources impacted by the 2017 West Mims event. Wildfire severity, measured using the differenced normalized burn ratio (dNBR) index, was highly heterogeneous both within and between vegetation classes; however, profound differences between treated and untreated areas were evident. The beneficial effects of prescribed fire were most pronounced within ca. two-years post-treatment but remained evident beyond three-years relative to untreated areas. For example, actively managed areas that were treated with prescribed fire just one month prior to the West Mims event exhibited an 88% reduction in mean wildfire severity relative to unmanaged areas. When post-treatment duration reached 38 months, mean wildfire severity remained 17% lower than untreated areas. Findings from this study reveal significant reductions in both average and peak wildfire severities in areas treated with frequent fire, supporting burn rotations of one to two years to balance fuel reduction and ecological conservation.

The effects of climate change are already being felt around the world; however, data on its socio-economic impacts in regions such as the Amazon are still insufficient. Thus, this article aims to reveal the socio-environmental vulnerability of riverine communities to climate change through an analysis carried out in the middle Juruá region, Brazil, focusing on the loss of agricultural production that occurred after the extreme flooding in 2021. Data were collected through semi-structured questionnaires with the participation of 638 agro-extractivists from traditional riverine communities in the municipality of Carauari in the Brazilian Amazon, as well as the collection of documentary information and field visits to quantify and qualify the damage caused by the flood. The loss of agricultural production in the territory exceeded the value of US$ 1,300,000 with greater losses in the production of cassava flour, an essential food for the riverine communities. Production chains of non-timber products, such as the case of rubber tapping and the harvesting of oilseeds, were also directly impacted. The damages caused by climate change to the Amazonian riverine sociological systems are already being experienced by the traditional communities of the middle Juruá region. Governments need to assist these populations in building adaptive measures and strengthening local resilience.

The article presents the results of a morpho-physiological assessment of rice samples by the strength of growth, as well as an assessment of the degree of development of the conducting system of the flag leaves of rice plants. The resistance of varieties to flooding is very relevant in the fight against weeds due to the deep layer of water that weeds cannot overcome. Currently, there are no zoned varieties in Russia that would meet these requirements. Therefore, the problem of creating such rice varieties is urgent, as it will reduce production costs, reduce grain losses during harvesting, improve the quality of the products obtained, and also reduce the pesticide load on the ecosystem. As a result of research, a number of studied samples revealed the potential to quickly lengthen the first leaves, overcome a large layer of water and accumulate vegetative mass. As a result of the evaluation of the conducting system of the flag leaf of rice plants, it was found that in plants with the flood resistance gene Sub1A, which stops growth under water, the conductive beams were smaller in comparison with other fast-growing samples with the AG, Sk genes. Consequently, fast-growing samples have bigger sizes of the conducting system as a resistance mechanism.

Climate change and development have increased urban flooding, requiring modernization of stormwater infrastructure. Retrofitting standard passive systems with controllable valves/pumps is promising, but requires real-time control (RTC). One method of automating RTC is reinforcement learning (RL), a general technique for sequential optimization and control in uncertain environments. The notion is that an RL algorithm can use inputs of real-time flood data and rainfall forecasts to learn a policy for controlling the stormwater infrastructure to minimize measures of flooding. In real-world conditions, rainfall forecasts and other state information, are subject to noise and uncertainty. To account for these characteristics of the problem data, we implemented Deep Deterministic Policy Gradient (DDPG), an RL algorithm that is distinguished by its capability to handle noise in the input data. DDPG implementations were trained and tested against a passive flood control policy. Three primary cases were studied: (i) perfect data, (ii) imperfect rainfall forecasts, and (iii) imperfect water level and forecast data. Rainfall episodes (100) that caused flooding in the passive system were selected from 10 years of observations in Norfolk, Virginia, USA; 85 randomly selected episodes were used for training and the remaining 15 unseen episodes served as test cases. Compared to the passive system, all RL implementations reduced flooding volume by 70.5% on average, and performed within a range of 5%. This suggests that DDPG is robust to noisy input data, which is essential knowledge to advance the real-world applicability of RL for stormwater RTC.

Sea level rise and increased storm events, urge cities to develop governance capacity. However, a cohesive conceptual and empirical-based understanding of what governance capacity implies, how to measure it, and what cities can learn, is largely lacking. Understanding the influence of context is critical to address this issue. Accordingly, we aim to identify crosscutting contextual factors and their influence in impeding, enhancing or prioritising different elements of governance capacity to address urban flood risk. By assessing governance capacity through nine conditions and 27 indicators in two Dutch and two cities in the UK, three crosscutting contextual factors are identified: 1) flood probability and impact, 2) national imposed institutional setting, and 3) level of authority to secure long-term financial support. We found that contextual factors explain differences in urban capacity-priorities within and between both countries. The institutional setting in the UK and recent political devolution emphasized the role of citizen awareness, stakeholder engagement, entrepreneurial agents, and the overall necessity for local capacity-development. The Dutch focus on flood safety through centralised public coordination reduces flood probability but also inhibits incentives to reduce flood impacts and reduced public awareness. In conclusion, the three identified contextual factors enable a better understanding of capacity-building priorities.

Texas ranks first in number of natural hazard fatalities in the United States (U.S.). Based on data culled from the National Climatic Data Center databases from 1959 to 2016, the number of hydrometeorological fatalities in Texas have increased over the 58-year study period, but the per capita fatalities have significantly decreased. Spatial review found that flooding is the predominant hydrometeorological disaster in a majority of the Texas counties located in “Flash Flood Alley” and accounts for 43% of all hydrometeorological fatalities in the state. Flooding fatalities are highest on “Transportation Routes” followed by heat fatalities in “Permanent Residences”. Seasonal and monthly stratification identifies Spring and Summer as the deadliest seasons, with the month of May registering the highest number of total fatalities dominated by flooding and tornado fatalities. Demographic trends of hydrometeorological disaster fatalities indicated that approximately twice as many male fatalities occurred during the study period than female fatalities, but with decreasing gender disparity over time. Adults are the highest fatality risk group overall, children most at risk to die in flooding, and the elderly at greatest risk of heat-related death.

A general procedure is described, to generate material parameter sets to simulate fire propagation in horizontal cable tray installations. Cone Calorimeter test data is processed in an inverse modelling approach. Here, parameter sets are generated procedurally and serve as input for simulations conducted with the Fire Dynamics Simulator (FDS). The simulation responses are compared with the experimental data and ranked based on their fitness. The best fitness was found for a test condition of \SI{50}{\kilo\watt\per\meter^2}. Low flux conditions \SI{25}{\kilo\watt\per\meter^2} and less exhibited difficulties to be simulated accurately. As a validation step, the best parameter sets are then utilised to simulate fire propagation within a horizontal cable tray installation and are compared with experimental data. It is important to note, the inverse modelling process is focused on the Cone Calorimeter and not aware of the actual validation step. Despite this handicap, the general features in the fire development can be reproduced, however not exact. The fire in the tray simulation extinguishes earlier and the total energy release is slightly higher as compared to the experiment. The responses of the material parameter sets are briefly compared with a selection of state of the art procedures.

Abstract: Petrol is considered the most common fire accelerant. However, the identification and classification of petrol sources through the years has been proven to be a challenging field in the investigation of fire debris analysis. This research explored the possibility of identifying petrol sources by high field NMR methods accompanied by ML (Machine Learning). The automated identification and classification of petrol brands were achieved for first time based on the ML clas-sification model developed in this research. A hierarchical classification model was constructed using local classifiers to categorize neat or weathered petrol into its sources.

The soils represent a valuable renewable resource on human timescales and interact with distinctive grassland ecosystems characterized by their elevated biodiversity and essential provision of ecosystem services, such as water supply and carbon sequestration. However, knowledge concerning the effects of wildfires on soil properties and nutrient availability remains limited in the Andes. Andean grasslands currently stand as ecosystems most affected by wildfires in the Peruvian Andes. Our objective is to analyze the effect of fire activity on soil physicochemical properties and analyze its social context in Cusco, situated in the southern Andes of Peru. Five soil samples were collected during both the dry and rainy seasons to characterize changes in soil properties. Additionally, the vegetation restored after the wildfire was analyzed. Changes in soil properties subsequent to the fire indicate slight increases in pH, electrical conductivity, organic matter, nitrogen, phosphorus, and potassium during the onset of the rainy season; thereafter, a gradual reduction in these values was observed. This reduction can be attributed to the seasonal rainfall and runoff regime contributing to the leaching process. Our findings suggest that the complete regeneration of vegetation following a wildfire may require approximately four to five years. This assertion is supported by the perceptions of the affected population, as revealed through interviews conducted within two local farming communities. These results hold significance for decision-makers in formulating policies and regulations regarding grasslands and their seasonal restoration.

Polyurethane is used extensively in the furniture industry, primarily in beds and sofas. There is a significant volume of research pertaining to the flammability of polyurethane materials, but very little regarding smoke toxicity. Given toxic fire effluents are a primary cause of injury and fatalities, more work needs to be done to test the toxicity of polyurethane materials and the fire retardants they are coated/bound with. The aim of the review is to provide a comprehensive analysis of the decomposition of polyurethane and discuss the development and application of modern fire retardants and their effect on smoke toxicity. This work then provides discussion on the challenges faced when attempting to quantify the toxicity of the effluent produced during combustion. Future work should aim to reduce the toxicity and flammability of polyurethane simultaneously by; reducing the pyrolysis of the polymer, preventing the escape of volatile products, reducing radicals within the system and lowering the temperature of the system. Before this can be achieved, a better understanding of smoke toxicity and the ability to test materials is needed.

Tropical forest and savanna biomes are pivotal in the functioning of the Earth system. Tropical forests are one of the largest terrestrial biosphere carbon pools, whereas savannas exchange carbon between the biosphere and atmosphere via frequent and extensive landscape fires. Both are biodiverse and under increasing threat due to land clearing and anthropogenic climate change. Reliable mapping of tropical forest and savanna is essential to provide understanding of how anthropogenic impacts are affecting the extent of these biomes. Using Google Maps satellite imagery, we manually classified 24,239 random points as forest, savanna, or anthropogenic landscapes within the tropics. Because fire and climate are correlated, we developed separate geospatial models to rank the importance of climate, topography, and human influence on vegetation present. This modelling confirmed that those areas with more fires had lower probabilities of tropical forest, that forest was most likely in areas with high mean annual rainfall with little seasonal variation in precipitation, and that anthropogenic factors disrupt this environmental predictability. We found there were environments where tropical forest and savanna were equally probable are geographically restricted. These relationships suggest that future drier climates projected under anthropogenic climate change, combined with clearing and burning that have reduced tropical forest extent to a subset of its theoretical distribution, will lead to irreversible loss of tropical forests. Our modelling provides global mapping that can be used track further changes to distribution of rainforests.

Forest fire identification is important for forest resource protection. Effective monitoring of forest fires requires the deployment of multiple monitors with different viewpoints, while most traditional recognition models can only recognize images from a single source. By ignoring the information from images with different viewpoints, these models produce high rates of missed and false alarms. In this paper, we propose a graph neural network model based on the similarity of dynamic features of multi-view images to improve the accuracy of forest fire recognition. The input features of the nodes on the graph are converted into relational features of different gallery pairs by establishing pairs (nodes) representing different viewpoint images and gallery images. The new feature library relationship is used to update the image gallery with dynamic features in order to achieve the estimation of similarity between images and improve the image recognition rate of the model. In addition, to reduce the complexity of image pre-processing process and extract key features in images effectively, this paper also proposes a dynamic feature extraction method for fire regions based on image segment ability. By setting the threshold value of HSV color space, the fire region is segmented from the image, and the dynamic features of successive frames of the fire region are extracted. The experimental results show that, compared with the baseline method Resnet, this paper's method is more effective in identifying forest fires, and its recognition accuracy is improved by 2%. And the scheme of this paper can adapt to different forest fire scenes, with better generalization ability and anti-interference ability.

The rate of fires, and particularly residential-fires, is a serious concern in industrialized countries. However, there is considerable uncertainty regarding the reported numbers of residential-fire incidents as official figures are based on fires reported to fire response agencies only. This population-based study aims to quantify the total number of residential-fire incidents regardless of reporting status. The cohort comprised linked person-level data from Fire and Rescue New South Wales (FRNSW) and health system and death records. It includes all persons residing at a residential address in New South Wales, Australia, that experienced a fire between 1 January 2005 – 31 December 2014. Capture-Recapture method was used to estimate the underreporting number of residential fire-related incidents. Over the study period, 43,707 residential-fire incidents were reported to FRNSW and there were 2,795 residential fire-related health service utilization of which 2,380 were not reported. Using Capture-Recapture method, the total number of residential-fire incidents was estimated at 267,815 to 319,719 which is more than six times the official records. This study found that 15% of residential-fire incidents that were identified in health administrative dataset were reported. The residential-fire incidents that were not reported occurred mainly in socio-economically disadvantaged areas among the males and adults.

Water mist fire suppression technology has attracted an increasing interest from the field of fire protection services such as fire safety for buildings, ships, spacecraft, libraries and museums due to its non-toxic and high efficiency in the suppression of a wide variety of fires. To support the technological development of water mist fire suppression system and its application areas, this review introduces the concept of water mist system and discusses its suppression mechanisms in comparison with other fire protection systems. The recent application areas of water mist system are surveyed for class A fires involving combustible solid materials such as wood, paper and textiles; class B fires involving flammable liquids such as petrol, oils, lubricants, paints and waxes; class C fires involving flammable gasses such as natural gas and liquefied petroleum gas; fires involving electrical (class E) equipment such as computers and information technology facilities; and the class F fires involving flammable cooking oils and fats. Finally, the paper concludes the review by identifying the current research trends, and providing the future direction for water mist technology and applications.

Warming-induced drought stress and El Nino associated summer precipitation failure are responsible for increased forest fire intensities of tropical and temperate forests in Asia and Australia. However, both effects are unclear for boreal forests, the largest biome and carbon stock over land. Here we combined fire frequency, burned area and climate data in the Altai boreal forests, the southmost extension of Siberia boreal forest into China, and explored their link with ENSO (El Nino and South Oscillation). Surprisingly, both summer drought severity and fire occurrence have shown significant (P&lt;0.05) teleconnections with La Nina events of the previous year, and therefore provide an important reference for forest fire prediction and prevention in Altai. Despite a significant warming trend, the increased moisture over Altai has largely offset the effect of warming-induced drought stress, and lead to an insignificant fire frequency trend in the last decades, and largely reduced burned area since the 1980s. The reduced burned area could also benefit from the fire suppression efforts and greatly increased investment in fire prevention since 1987.

The peri-urban and urban forests in Greece occupy a total area of 105.353 ha. In these vulnerable ecosystems, fire constitutes a natural disaster presenting particular challenges and specific difficulties. These include the high number of visitors as well as forests characteristics - such as the presence of particularly flammable tree species and high accumulation of combustible biomass – that make the on-start of fires more likely. The main purpose of the current research is to identify the optimum combination of silvicultural treatments to efficiently reduce potential forest fire severity and to facilitate their successful suppression by firefighting crews. In order to simulate the basic fire environment of urban forests, two main experimental plots were established and several tree and topographical characteristics were measured. Additionally, the NEXUS wildfire system was used to simulate forest fire potential behavior before and after the adoption of the silvicultural treatments that altered critical characteristics of the forest fire environment. The results clearly show that specific silvicultural prescriptions altered the type of forest fire spreading potential, revealing the overall efficiency of preventing actions during forest management.

Experimental quantification of retention factors related to post-fire strength as well as post-fire ductility of intentionally selected stainless steel grades applied in construction is the objective of research presented here. These steel grades are characterized by a two-phase austenitic-ferritic microstructure of the duplex type. In this context two mutually corresponding chromium-nickel-molybdenum steel grades have been subjected to analysis, namely X2CrNiMoN22-5-3 steel belonging to the standard duplex group (DSS 22% Cr) and X2CrMnNiN21-5-1 steel belonging to the lean duplex group (LDSS). The similarities and differences in the mechanical properties exhibited by these steel grades after effective cooling, following more or less prolonged simulated fire action conforming to several development scenarios, have been identified and indicated. The resistance of given steel grade to permanent structural changes induced by the heating program proved to be the critical factor determining these properties and resulting in many cases in increased susceptibility to brittle fracture.

The article addresses the complex climatic situation faced by the state of Rio Grande do Sul, Brazil, which began on April 27, 2024, and extended for weeks. This unprecedented extreme weather event is yet another chapter in the global climate crisis. Initially, this rainfall seemed typical for the season. However, it quickly escalated into one of the worst climatic catastrophes in Brazil's history. In recent years, there has been a global increase in extreme weather events, including heavy precipitation, flooding, landslides, cyclones, and heatwaves, highlighting the ongoing climate crisis as one of the most serious threats we face today. In light of this, our study aims to elucidate the impact of climate change by examining one of Brazil's most severe climate disasters. We investigate the complex situation in Rio Grande do Sul, analyzing the ongoing climate crisis and projecting what can be expected in the near future.

The Hindu Kush Himalaya (HKH) is one of the most flood-prone regions in the world, yet heavy cloud cover and limited in-situ observations have hampered efforts to monitor the impact of heavy rainfall, flooding, and inundation during severe weather events. This paper introduces HydroSAR, a Sentinel-1 SAR-based hazard monitoring service that was co-developed with in-region partners to provide year-round, low-latency weather-hazards information across the HKH. The paper describes the end-user focused concept and overall design of the HydroSAR service. It introduces the main processing algorithms behind HydroSAR’s broad product portfolio, which includes qualitative visual layers as well as quantitative products measuring surface water extent and water depth. We summarize the cloud-based implementation of the developed service, which is providing capability to scale automatically with event size. A performance assessment of our quantitative algorithms is described, demonstrating capabilities to map flood extent and water depth at an accuracy of >90% and

The planned closure of potash mines, achieved through the injection of highly mineralized brines into the worked-out area, is a complex and not fully studied process. A critical concern arises when brines obstruct the aerodynamic connections between the flooded mine's airspace and the atmosphere, potentially leading to the formation of closed cavities where explosive gases can accumulate. To address this hazard, it is imperative to develop systems capable of extracting the gas-air mixture from the unflooded domed portions of the worked-out area. This is the focus of this study, in which we analyze gas-dynamic processes within the potassium mine worked-out area during planned flooding. Two distinct scenarios are examined: the first involves controlled flooding with saturated brines, while the second contemplates flooding resulting from a hypothetical breakthrough of supra-salt strata, leading to the ingress of groundwater into the worked-out area. A novel mathematical model is introduced to predict the evolution of gas-air mixture parameters in the unflooded dome segment of the worked-out area. Utilizing this model, we assess the effectiveness of proposed measures designed to eliminate explosive gases from the worked-out area. Specifically, a pipeline system is proposed for the removal of gases, followed by their dilution below the maximum permissible concentrations in the effluent jet. The findings from this study contribute valuable insights into ensuring the safe and efficient closure of potash mines, shedding light on potential risks and effective mitigation strategies for gas-related hazards during planned flooding.

Infill well location optimization poses significant challenges due to its complexity and time-consuming nature. Currently, determining the scope of infill wells relies heavily on field engineers' experience, often using single indices such as remaining oil saturation or abundance of remaining oil reserves to evaluate the potential of remaining oil. However, this approach lacks effectiveness in guiding the precise tapping of remaining oil in ultra-high water cut reservoirs. To address this, our study comprehensively considers factors influencing the recoverable potential of remaining oil in such reservoirs. We characterize differences in reservoir heterogeneity, the scale of recoverable remaining oil reserves, water flooding conditions, and oil-water flow capacity to construct a quantitative evaluation index system for the recoverable potential of remaining oil. Recognizing the varying degrees of influence of different indices on the recoverable potential of remaining oil, we determine the objective weight of each evaluation index by combining an accelerated genetic algorithm with the projection pursuit model. This approach enables the construction of a recoverable potential index for remaining oil and forms a quantitative evaluation method for the recoverable potential of remaining oil in ultra-high water cut reservoirs. Subsequently, we establish a mathematical model for infill well location optimization, integrating and optimizing infill well location coordinates, well length, well inclination angle, and azimuth angle. Using the main layer sand body of an oilfield in Bohai as a case study, we conducted evaluations of remaining oil potential and infill well location optimization. Results demonstrate that the assessment of remaining oil potential comprehensively characterizes the influence of reservoir physical properties and oil-water diversion capacity on remaining oil potential across different regional positions. This evaluation can effectively guide the determination of infill well location ranges based on the evaluation results. Furthermore, further optimization of infill well locations can effectively enhance reservoir development outcomes.

In today's context of rising sea levels and subsiding land topography, coastal flooding is a major concern for coastal areas, both nationally, regionally, and internationally. A detailed understanding of these two phenomena is a prerequisite for effective coastal flood management. The main objective of the present study is to contribute to the understanding of the impact of subsidence and sea-level rise on coastal flooding in Saint-Louis, and in particular the Langue de Barbarie, by producing a vulnerability map of submerged surfaces. To this end, the Synthetic Aperture Radar (SAR) interferometry technique is used to process a time series of Sentinel-1 images and estimate land subsidence in the Saint-Louis and Langue de Barbarie area. Interferometric SAR (InSAR) measurements are merged with RCP 2.6 sea-level rise scenarios to identify predicted flooded areas and provide a flood vulnerability map. At the Langue de Barbarie, topographic subsidence is estimated at between -6.4 and -0.4mm/year. In this study, we show that local land subsidence may increase vulnerability to flooding caused by sea-level rise based on 2100 projections. This may represent an increase in the flood zone in the study area. Based on the RCP 2.6 scenario and the subsidence rates obtained, a significant part of the St. Louis coastline and city would be subject to coastal and fluvial flooding for the years 2040 and 2100. For these dates, 29 to and 36 Km2 of the studied area would be occupied by water. Given this situation, research in Saint-Louis needs to focus on adaptation scenarios to coastal risks, to protect coastal communities and safeguard the city's historical heritage.

This research assesses the changes in the total water storage (TWS) during the twentieth century and their future projections in the Nile River Basin (NRB) via TWSA (TWS anomalies) records from GRACE (Gravity Recovery and Climate Experiment), GRACE-FO (Follow-On), data-driven-reanalysis TWSA and land surface model (LSM), in association with precipitation, temperature records, and standard drought indicators. The analytical approach incorporates the development of 100+ yearlong TWSA records using a probabilistic conditional distribution fitting approach by the GAMLSS (Generalized Additive Model for Location, Scale, and Shape) model. The drought and flooding severity, duration, magnitude, frequencies, and recurrence were assessed during the studied period. The results showed, 1- The NRB between 2002 to 2020 has transited to substantial wetter conditions. 2- The TWSA reanalysis records between 1901 to 2002 revealed that the NRB had experienced a positive increase in TWS during the wet and dry seasons. 3- The projected TWSA between 2021 to 2050 indicated slight positive changes in TWSA during the rainy seasons. The analysis of drought and flooding frequencies between 1901 to 2050 indicated the NRB has ~64 dry-years compared to ~86 wet-years. The 100+ yearlong TWSA records assured that the NRB transited to wetter conditions relative to few dry spells. These TWSA trajectories call for further water resources planning in the region especially during flood seasons. This research contributes to the ongoing efforts to improve the TWSA assessment and its associated dynamics for transboundary river basins. It also demonstrates how an extended TWSA record provides unique insights for water resources management in the NRB and similar regions.

Grasping the spatial and temporal patterns of forest fires, along with the key factors that drive them and the ability to forecast such events accurately, is essential for the successful management of forests. In China's southern region, forest fires significantly endanger the ecological system, public safety, and economic well-being. Through the application of Geographic Information Systems (GIS) and LightGBM (Light Gradient Boosting Machine) model, this study investigates the determinants of fire incidents and formulates a predictive model for forest fire occurrences, alongside a zoning strategy, within the Central-South area. The results indicate: (i) Spatially, fire points exhibit significant clustering and autocorrelation characteristics; (ii) The Central-South Forest Fire Prediction Model shows exceptional accuracy, reliability, and predictive power, with high performance metrics across training and validation sets, including over 85% accuracy, precision, recall, and F1 scores, along with AUC values above 89%, underscoring its efficacy in forecasting forest fires and distinguishing between fire events; (iii) Throughout the year, forest fire risks in the Central-South region of China vary by region and season, with risk spikes from March to May in Guangdong, Guangxi, Hunan, Hubei, and Hainan. From June to August, localized risks are observed in Heyuan and Huangshi, while from September to November, an increase in risk is noted in Guangdong (Meizhou, Heyuan, Shaoguan), Guangxi (Nanning, Hezhou, Yulin), and Hunan (Binzhou, Yongzhou, Hengyang) due to cooler, drier conditions and leaf litter accumulation. From December to February, the risk continues in specific areas across Guangdong, Guangxi, Hunan, and Hubei.

Fire regimes in mountain landscapes of southern Europe have been shifting from their baselines due to rural abandonment and fire exclusion policies. Understanding the effects of fire on biodiversity is paramount to implement adequate management. Herein, we evaluated the relative role of burn severity and heterogeneity on bird abundance in an abandoned mountain range located in the biogeographic transition between the Eurosiberian and Mediterranean region (the Natural Park ‘Baixa Limia–Serra do Xurés’). We surveyed the bird community in 206 census plots distributed across the Natural Park, both inside and outside areas affected by wildfires over the last 11 years (from 2010 to 2020). We used satellite images of Sentinel 2 and Landsat missions to quantify the burn severity and heterogeneity of each fire within each surveyed plot. We also accounted for the past land use (forestry or agropastoral use) by using a land cover information for year 2010 derived from satellite image classification. We recorded 1,735 contacts from 28 bird species. Our models, fitted by using GLMs with Poisson error distribution (pseudo-R²_-average of 0.22 ± 0.13), showed that up to 71% of the modelled species were linearly correlated with at least one attribute of the fire regime. The spatiotemporal variation in burnt area and severity were relevant factors for explaining the local abundance of our target species (39% of the species; Akaike weights > 0.75). We also found a quadratic effect of at least one fire regime attribute on bird abundance for 60% of the modeled species. The past land use, and its legacy after 10 years, was critical to understand the role of fire (Akaike weights > 0.75). Our findings confirm the importance of incorporating remotely sensed indicators of burn severity into the toolkit of decision makers to accurately anticipate the response of birds to fire management.

Accurate and prompt determination of fire types is essential for effective firefighting and reducing damages. However, traditional methods such as smoke detection, visual analysis, and wireless signals pose unavailability in identifying fire types. This paper introduces FireSonic, an acoustic sensing system that leverages commercial speakers and microphones to actively probe the fire using acoustic signals, effectively identifying indoor fire types. By incorporating beamforming technology, FireSonic first enhances signal clarity and reliability, thus mitigating signal attenuation and distortion. To establish a reliable correlation between fire types and sound propagation, FireSonic quantifies the Heat Release Rate (HRR) of flames by analyzing the relationship between fire heated areas and sound wave propagation delays. Furthermore, the system extracts spatiotemporal features related to fire from channel measurements. Experimental results demonstrate that FireSonic attains an average fire type classification accuracy of 95.5% and a detection latency of less than 400 ms, satisfying the requirements for real-time monitoring. This system significantly enhances the formulation of targeted firefighting strategies, boosting fire response effectiveness and public safety.

Repeated fires cause peatlands to degrade. Fire management has been carried out, but fires continue to occur, especially during the dry season. Through social capital that applies in the community based on the local wisdom of the Banjar people in environmental management, it is hoped that peatland fires can be overcome in this area. This research aims to analyze the social capital of the Banjar people in mitigating land fires based on local wisdom and the environment. The sample in this study was 250 people who live in an area prone to fires on peatlands in Banjarbaru, South Kalimantan, Indonesia. There are three variables in this study, namely Trust (T), Network (N) and Norm (N). There are 33 Likert scale questions (1-4, strongly agree-strongly disagree). We also conducted interviews with 20 members of the Fire Care Community (MPA) to obtain information about the social capital of the Banjar community in mitigating land fires. The analysis used is Structural Equation Modeling (SEM). The study results show norms have the greatest influence on disaster mitigation of peatland fires compared to trust and social networks. Norms in the community in the form of values from peatlands, rules, norms and sanctions regulate the community more to deal with the problem of peatland fires than trust and social networks

Fire safety is a critical concern in various industries necessitating the development of sustainable and effective fire-resistant materials. Sustainable fire-resistant polysaccharide-based composite aerogels are regarded as innovative solutions in fire safety applications, and as such, research in this field has increased consistently over the past few years. Despite the plethora of literature on this important subject, only a few studies have attempted to map the global research of sustainable fire-resistant polysaccharide-based composite aerogels to identify the geospatial collaborative network and trend of research. This study utilizes a scientometric review of global trends in sustainable fire-resistant polysaccharide-based composite aerogels research between 2003–2023 by VOSviewer and biblioshiny to analyze co-author, co-word, co-citation, clusters, and geospatial map. A total of 234 bibliographic records from the Scopus database were analyzed to generate the study's research power networks and geospatial map. The most significant contributions in sustainable fire-resistant polysaccharide-based composite aerogels comes from China, the United States, Australia, Canada, and India with records of 194, 20, 11, 9, and 8 respectively. The top five sources for articles in this area of research include ACS Applied Materials and Interfaces, Chemical Engineering Journal, Composite Engineering, ACS Sustainable Chemistry and Engineering, and Carbohydrate Polymers. The application of sustainable fire-resistant polysaccharide-based composite aerogels spans the engineering and construction fields. The versatility in the fabrication and customization allows for seamless integration into diverse applications. The article concludes by emphasizing the significance of sustainable fire-resistant polysaccharide-based composite aerogels as a promising advancement in fire safety technology, combining sustainability, fire resistance, versatility, and mechanical strength to address critical challenges in the field. This study provides readers with an extensive understanding of the salient research themes, trends, and patterns of sustainable fire-resistant polysaccharide-based composite aerogel research worldwide.

Fires pose a significant threat to human life and property. An autonomous fire detection and fighting robot has the potential to mitigate the challenges associated with firefighting. This paper presents the implementation of an autonomous fire detection and fighting robot equipped with sensors, algorithms, and actuators to detect, locate, and suppress fires autonomously. The robot's mechanical and electrical components were designed, prototyped, and tested in a controlled environment to evaluate its ability to navigate through obstacles, detect fires, and suppress them autonomously. The results show that the robot effectively detects and suppresses fires, making it a promising solution for firefighting applications. The study contributes to the development of autonomous robots for firefighting, enhancing the safety of firefighters and the effectiveness of firefighting operations.

Background: In long-term care facilities, there are many residents who do not have the ability to seek shelter by themselves in the case of an emergency. Thus, it is extremely important that the staff of nursing homes are equipped with correct disaster prevention concepts, emergency survival responses, and hazard mitigation measures. Purpose: Discuss the intervention effectiveness of different fire prevention and emergency response trainings at nursing homes and the relationship and predictivity of awareness to self-efficacy. Method: Recruit staff from two nursing homes through purposive sampling, using a two-team pre-and post-test design to collect results from 41 individuals in the experiment group and 40 individuals in the control group. The research tool is the “Nursing Home Fire Prevention and Emergency Response Awareness and Self-Efficacy Scale,” to compare the effectiveness of advanced and general fire safety trainings. Result: After receiving improved advanced fire safety training, the total score and the result of the experiment group on fire prevention and emergency response awareness and self-efficacy had both performed better than the control group who received general fire safety training (p < .001); fire prevention and emergency response awareness has significant and positive correlation with self-efficacy (r=.601, p < .001), and awareness is a significant predictor variable to self-efficacy (p < .001). Conclusion/Practical Application: This study finds that the key to improving learning effectiveness includes adding fire science concept chapter when creating fire safety training material in order to strengthen basic awareness; fire safety training should comprehensively introduce all related duty responsibilities of staff fire defense formation, in turn enabling mutual responsive support for the needs of the site; also, to become familiarized with the knowledge requires appropriate frequency of training and enhancing the staff’s awareness to fire prevention and emergency response, which is the most important key of learning effectiveness.

Fires are complex multi-physics problems that span wide spatial scale ranges. Capturing this complexity in computationally affordable numerical simulations for process studies and “outer-loop” techniques (e.g., optimization and uncertainty quantification) is a fundamental challenge in reacting flow research. Further complications arise for propagating fires where a priori knowledge of the fire spread rate and direction is typically not available. In such cases, static mesh refinement at all possible fire locations is a computationally inefficient approach to bridging the wide range of spatial scales relevant to fire behavior. In the present study, we address this challenge by incorporating adaptive mesh refinement (AMR) in fireFoam, an OpenFOAM solver for simulations of complex fire phenomena involving pyrolyzing solid surfaces. The AMR functionality in the extended solver, called fireDyMFoam, is load balanced, models gas, solid, and liquid phases, and allows us to dynamically track regions of interest, thus avoiding inefficient over-resolution of areas far from a propagating flame. We demonstrate the AMR capability and computational efficiency for fire spread on vertical panels, showing that the AMR solver reproduces results obtained using much larger statically refined meshes, but at a substantially reduced computational cost. We then leverage the computational efficiency of the AMR solver to demonstrate an optimization framework for fire suppression based on the open-source Dakota toolkit.

Fire-related deaths are issues for forensic pathologists particularly in ascertaining if death occurred before or during fire. The authors highlight a unique bronchiolar epithelial cytological clue (nuclear heat-induced elongation) determined by active inhalation of hot gases and fumes, not yet described in the literature at distal small airways level.

The designs of two large arenas of different epochs are discussed, which are the most famous antique amphitheater the Roman Colosseum (Italy) and the modern Gazprom Arena stadium (St. Petersburg, Russia). 3D-computer models of the arenas have been built and evacuation of people has been simulated in the Sigma FS software (Russia). The arena designs are analyzed in terms of organizing pedestrian evacuation, a comparative analysis is made, and the specific characteristics affecting the evacuation time are established.

Wildland-Urban Interfaces (W-UI) are at high risk of wildfires. Defensible spaces and home ignition zones are the two main aspects to protect lives and livelihoods of W-UI in the United States, Canada and Australia. The different part of the world has different rules and regulations for W-UI land management. We have discussed the defensible spaces in fire-prone areas, current ignition zone distances from structures, building materials, architectural design, the fire resistance trees, ground cover, landscaping and some other precautions to save lives and assets in the prominent fire-prone zones for three different countries (United States, Canada and Australia) of the world.

Forest fire is a major ecological disaster, which has economic, social and environmental impacts on humans and also causes the loss of biodiversity. Forest officials issue the warnings to the public on the basis of fire danger index classes. There is no fire danger index for the country India due to the sparsely distributed meteorological stations. Previous studies suggest that Static Fire Danger Index (SFDI) as well as Dynamic Fire Danger Index (DFDI) have been derived from the satellite datasets. In this study, we have made an attempt to integrate both the Static and Dynamic fire danger indices and also used the Near Real Time (NRT) data sets that can be available for download through NASA FTP server after one hour of the satellite overpass. In this study, DFDI has been calculated from the MODIS Terra NRT Land Surface Temperature (MOD11_L2) and MODIS TERRA NRT surface reflectance MOD09. Finally, The Forest Fire Danger Index (FFDI) has been developed from the static and dynamic fire danger indices by the additive model and the overall accuracy was around 81.27%. Thus, the FFDI has been useful to predict the fire danger accurately and can be useful anywhere, where the meteorological stations are un-available.

Chemical flooding methods are used to recover the remaining oil after water flooding, and surfactant-polymer (SP) flooding is an important chemical flooding technology. It is of great significance to design and screen high-performance oil displacement agents in order to overcome the increasingly challenging high temperature (&gt;70 ℃) and high salinity (>10,000 mg/L) reservoirs. Based on the characteristics of high temperature (70 ℃) and high salinity (151,000 mg/L) in the Qinghai Gasi reservoir, the performance of the SP flooding system was studied. This study considered interfacial tension, emulsification, viscosity, compatibility, and oil displacement efficiency. The selected formula demonstrated high resistance to both salinity (151,000 mg/L) and temperature (70 ℃), characterized by a low oil-water interfacial tension (10-2 mN/m) and high viscosity (18.13 mPa.s). After water flooding achieved an oil recovery rate of 64.64%, the subsequent replacement rate of crude oil increased by 26.95%, confirming that SP flooding can be used as a technology for enhancing oil recovery in high-temperature, high-salinity reservoirs represented by Qinghai Gasi reservoirs.

In the last decades, the impacts of urbanization on the hydrological cycle have led to an increase in the frequency and magnitude of urban flooding events, also amplified by the effects of climate change. Sustainable Drainage Systems (SuDS) provide a revolutionary change in this field, improving the sustainability and resilience of cities. This research explores the integration of different SuDS with the aim of significantly reducing both flow volume and celerity of floods in a residual urban catchment area of metropolitan city of Querétaro (Mèxico), where extreme rainfall frequently occurs. This catchment is a representative suburb of urban pressure and environmental degradation problems. Currently, managing storm water under climate uncertainty through a multi-disciplinary approach is a major concern in this urban area. A 1D-2D coupling model of shallow water equations, finite volume method, unstructured meshing method, and hybrid parallel computing application defined the optimal configuration of SuDS at catchment scale to reduce the flood vulnerability in Querétaro. Specifically, in this paper we explore the management issues of the proposed SuDS configuration that acts as a water resource system with multiple purposes. A generic simulation model called MODSIM was applied to simulate the designed urban drainage system under a balanced IPCC future climate scenario in terms of reliability, resilience and vulnerability against water scarcity.

Significant amount of oil remains in the reservoir after primary and secondary operations and to recover the remaining oil, enhanced oil recovery (EOR) can be applied as one of the feasible options remaining nowadays. In this study new nano polymeric materials have been prepared from purple yam and cassava starches. The yield of purple yam nanoparticles (PYNPs) was 85% and that of cassava nanoparticles (CSNPs) is 90.53%. Synthesized materials were characterized through particle size distribution (PSA), zeta potential distribution, Fourier transform infrared spectroscopy (FTIR), differential scanning calorimetry (DSC) and transmission electron microscopy (TEM). The performance of PYNPs in recovering oil was better than CSNPs as found from recovery experiments. Zeta potential distribution results confirmed the stability of PYNPs over CSNPs (-36.3 mV for PYNPs and -10.7 mV for CSNPs). The optimum concentration for these nanoparticles has been found from interfacial tension measurements and rheological properties and it was 0.60 wt.% for PYNPs and 0.80 wt.% for CSNPs. More incremental recovery (33.46%) has achieved for the polymer that contained PYNPs in comparison to the other nano-polymer (31.3%). This opens the inception for a new technology for polymer flooding that may replace the conventional method that depends on partially hydrolyzed polyacrylamide (HPAM).

Groundwater (GW) flooding mechanisms differ from river flooding both spatially and temporally, and preventative methods against groundwater flooding must take this into account. Although groundwater flooding caused by river water rise occurs seldom, it can occasionally become severe and last for a long time if the river is significantly flooded. In the southwest portion of the research domain, Friedrichshafen with a few urban communities, the level of the groundwater table was discovered to be roughly 1 m below the surface. It was discovered that the urban settlement area only has one-story buildings. In the study region, it is typical for the single-story building's foundation bottom level to extend up to a depth of about 1.5 meters. Therefore, flood mitigation methods are taken into account for the southwest portion of the study region. The installation of a pumping well, drainage, and a barrier in the affected area are three different flood control strategies that are taken into consideration for the study area. From a technical and cost-benefit perspective, installing a pumping well that withdraws water and lowers the groundwater table was found to be the most effective flood control measure locally in a small region (e.g., 1km x 1km). By contrast, removing groundwater by building drainage and barriers was also shown to be ineffective to lower the groundwater table over an extended region and was significantly more expensive than the installation of wells. Additionally, when river flooding is taken into account compared to the default scenario where no intake of water from the river is included along the western border of the study area, it is discovered that the spread of pollution is significantly greater.

The mangrove plant Acanthus ilicifolius and its relative, A. mollis, have been previously proved to possess diverse pharmacological effects. Therefore, evaluating the differentially expressed proteins of these species under tidal flooding stress is essential to fully exploit and benefit from their medicinal values. The roots of A. ilicifolius and A. mollis were exposed to 6 h of flooding stress per day for 10 days. The dry weight, hydrogen peroxide (H2O2) content, anatomical characteristics, carbon and energy levels, and two-dimensional electrophoresis coupled with MALDI-TOF/TOF MS technology were used to reveal the divergent flooding resistant strategies. A. ilicifolius performed better under tidal flooding stress, which was reflected in the integrity of the morphological structure, more efficient use of carbon and energy, and a higher percentage of up-regulated proteins associated with carbon and energy metabolism. A. mollis could not survive in flooding conditions for a long time, as revealed by incomplete cell structures of the roots, less efficient use of carbon and energy, and a higher percentage of down-regulated proteins associated with carbon and energy metabolism. Energy provision and flux balance played a role in the flooding tolerance of A. ilicifolius and A. mollis.

Green Stormwater Infrastructure (GSI), a sustainable engineering design approach for managing urban stormwater runoff, has long been recommended as an alternative to conventional conveyance-based stormwater management strategies to mitigate the adverse impact of sprawling urbanization. Hydrological and hydraulic simulations of small-scale GSI measures in densely urbanized micro watersheds require high-resolution spatial databases of urban land use, stormwater structures, and topography. This study presents a highly resolved Storm Water Management Model developed under considerable spatial data constraints. It evaluates the cumulative effect of the implementation of dispersed, retrofitted, small-scale GSI measures in a heavily urbanized micro watershed of Costa Rica. Our methodology includes a high-resolution digital elevation model based on Google Earth information, whose accuracy was sufficient to determine flow patterns and slopes, as well as to approximate the subsurface stormwater structures. The model produced satisfactory results in event-based calibration and validation, which ensured the reliability of the data collection procedure. Simulating the implementation of GSI shows that dispersed, retrofitted, small-scale measures could significantly reduce impermeable surface runoff (peak runoff reduction up to 40%) during frequent, less intense storm events and delay peak surface runoff 5-10 minutes. The presented approach can benefit stormwater practitioners and modelers conducting small scale hydrological simulation under spatial data constraint.

Water pollution associated with flooding is one of the major problems in cities in the global South. However, studies of water quality dynamics during flood events are not often reported in literature, probably due to difficult conditions for sampling during flood events. Water quality parameters in open water (canals, rivers, and lakes), floodwater on roads and water in sewers have been monitored during the extreme fluvial flood event on 7 October 2013 in Can Tho city, Vietnam. This is the pioneering study of urban flood water pollution in real time in Vietnam. The results showed that water quality is very dynamic during flooding, especially at the beginning of the event. In addition, it was observed that the pathogen and contaminant levels in the floodwater are almost as high as in sewers. The findings show that population exposed to flood water runs a health risk that is nearly equal to that of being in contact with sewer water. Therefore the people of Can Tho not only face physical risk due to flooding, but also exposed to health risk.

Amid increasing deforestation, surface fires reaching the forest understory are one of the primary threats to Amazonian ecosystems. Despite extensive research on post-fire mortality in woody species, literature on palm resilience to fire is scant. This study investigates post-fire mortality in four understory palms—Bactris maraja Mart., Chamaedorea pauciflora Mart., Geonoma deversa (Poit.) Kunth, Hyospathe elegans Mart., and juvenile individuals of Euterpe precatoria Mart. Objectives included: a) comparing post-fire responses; b) developing mortality models based on severity variables; and c) evaluating if diameter protects bud stems from heat flux. Conducted at the edge of an Ombrophylous Forest in Alto Juruá Acre, Brazil (7°45'S, 72°22'W), the experiment subjected 85 individuals to controlled burning in a 1 m² area near the stipe, with temperature sampling using K thermocouples. Results showed varying mortality rates among species, with larger stipe diameter correlating to reduced mortality. Canopy burning patterns significantly influenced mortality, especially for Euterpe precatoria. Species exhibited diverse regrowth capacities, with B. maraja showing the highest number and tallest basal resprouts. This study underscores post-fire plant mortality as a critical indicator of fire severity, essential for understanding its ecological impacts.

Mountain fire can become more complex than fires at lower elevation due to the complex interaction of fire, topography, and weather. The Gell River Fire in Tasmania, Australia occurred in rugged terrain where there are abundant fire sensitive vegetation communities, as well as the presence of infrastructure including high-voltage transmission lines. The fire began at the end of December 2018 and lasted a few months, with a final burnt area of approximately 350km² despite significant fire suppression effort. The fire was investigated by employing wind vector maps, numerical weather model vertical sounding charts (NWMVS) and Prototype 2, which is an integrated fire simulator and can detect lateral fire channeling (LFC). Our analysis of the fire found its spread was likely to be introduced into a valley by forced channeling (FC), which is modified synoptic wind, and showed rapid spread in the valley. The simulated fire also showed wider spread than the observed data in the valley, with the simulated fire impacting highly sensitive vegetation communities on the fringes of the valley. This alludes to some potential conclusions: (1) The loss of fire sensitive vegetation would have increased if fire suppression activity had not been conducted. (2) Spotting fires could be produced by LFC because these spotting fire would allow spreading fire in a shorter period. (3) Heterogeneity of vegetation, such as combination of buttongrass and forest, could help carry fire rapidly in the valley with LFC. Fire can propagate faster in buttongrass than in forests while the forests allow the spotting fire.

In the overall context of national eco-conservation civilization development and considering the hills in the Beijing-Tianjin-Hebei-Shanxi region, the local forest coverage rate is gradually on the rise, and so is the risk of forest fire. Every year, satellites can monitor and detect hundreds of fire spots on average. In recent years, serious fire took place for several times, leading to loss of life and personal injury. Then the fire-fighting work consumed huge amount of labor and material resources, exerting negative social impacts in many ways. Thus, the analysis of fire climate background in this region and the forecast based on fire weather indexes constitute an important part in the forest fire risk management. Located in the tropical monsoon climate zone, four provinces and municipalities of Beijing, Tianjin, Hebei and Shanxi are similar in terms of climate characteristics. This study tries to analyze the characteristics about fire spots under satellite monitoring, fire climate and fire weather index. The probability distribution analysis of weather index about fire spots in history and key meteorological observation stations as well as the weather index analysis in the early stage of typical fire indicate that the Fine Fuel Moisture Content (FFMC) and the Initial Spread Index (ISI) in the Fire Weather Index (FWI) system is applicable to the fire risk analysis in the Beijing-Tianjin-Hebei-Shanxi region, which can also effectively indicate the fuel moisture and spread conditions. As regards to the average level in Beijing-Tianjin-Hebei-Shanxi, when the FFMC is smaller than 93.5 and the ISI is also smaller than 5.0, there is a low risk of fire; when the FFMC is above 94 and the ISI is larger than 10.0, there is a high risk of fire; and the fire risk will be extremely high if the FFMC is over 96 and the ISI is above 13.0. In the practical fire-fighting management, the fire danger class can be released in reference to the ISI classes and threshold values of key meteorological observation stations under analysis provided by this study.

There is only a very short reaction time for people to find the best way out of a building in a fire outbreak. Software applications can be used to assist the rapid evacuation of victims; however, this is an arduous task, which requires an understanding of advanced technologies. Since well-known pathway algorithms (such as, Dijkstra, Bellman-Ford and A*) can lead to serious problems over performance, we decided to make use of deep reinforcement learning techniques. A wide range of strategies including a random initialization of replay buffer and transfer learning were assessed in three projects involving schools of different sizes. The results showed the proposal was viable and that in most cases the performance of transfer learning was superior. In addition, the study raised challenges that had to be faced in the future.

This study probes the probabilistic features of major fire hazards in enclosed spaces to establish their importance to the occurrence of fires onboard ro-ro passenger ships and in turn to raise effective operational countermeasures. Distinct from the previous studies, the present research employs Bayesian Network (BN) analysis to determine the probabilities of fire hazards more effectively. The findings of the research include five critical basic events (BE) identified namely, vehicle engine fire (fuel system fault), vehicle electric fire (electrical equipment defect or short circuit), used car electrical fire, reefer units electrical fire (electrical appliances defects or short circuit), and cargo spontaneous combustion. Additionally, the risk of fire for lithium - ion battery powered vehicles is also highlighted in the process of BN analysis, which prompts the authors to propose preventive measures for mitigating the possibility of fire occurrence on this type of electric vehicles. It is hoped that these measures can be essential justifications for establishing relevant rules regarding carrying LIB vehicles in enclosed spaces on international level.

This paper presents the results of quantitative research regarding the examination of the levels of efficiency of the fire protection system and the training of employees in Electrical power distribution of Serbia for fire protection, ie the analysis of the manner of implementation of fire protection measures, and determining whether these measures are adequately applied. Following the subject set in this way, the aim of the research was set, and that is the scientific analysis of the manner of application of fire protection measures. The results of this research showed that the employees of Electro distribution Belgrade perceive the preparedness of their company highly positively and certain socio-demographic characteristics also affect certain attitudes, especially those related to the experience with fire, the knowledge of employees to react in case of fire, and the choice of the appropriate response, assessment of the readiness of the company and the employees themselves. It was found that knowledge of how to react in case of fire is influenced by gender and level of education; the choice of one of the methods of action in case of fire is influenced by gender; the assessment of the readiness of the employees themselves is influenced by the marital status; the desire of employees to participate in training to increase their preparedness for fires is influenced by gender. The results of the research can be used to improve the preparedness of employees in various organizations to respond to disasters caused by fires.

Researchers have focused on collaborative governance as an effective measure to realise sustainable natural resource management through the participation of various stakeholders. However, the literature has indicated that issues such as power imbalances tend to undermine the effectiveness of collaborative governance. Powerful actors represented by the government tend to control collaborative processes and produce benefits for dominant groups, while less empowered local communities are often deprived of opportunities for livelihood improvement. Although numerous researchers have analysed the key factors that influence the processes and outcomes of collaborative governance, few have identified a concrete measure to reduce the risk of failure, particularly when managing power imbalances in developing countries. This study explored a methodology to address the power imbalances in collaborative governance based on a case study of a participatory peatland fire prevention project implemented in West Kalimantan Province, Indonesia. Semi-structured interviews and questionnaire surveys conducted with project participants suggested that measures such as establishing a joint team of government officers and villagers, providing a common facilitation training programme, training villagers as facilitators, promoting equal knowledge sharing, and allowing villagers to make their own decisions mitigated the power imbalances between the two groups.

An open source computational fluid dynamics (CFD) solver has been incorporated into the WindNinja modeling framework widely used by wildland fire managers as well as researchers and practitioners in other fields, such as wind energy, wind erosion, and search and rescue. Here we describe incorporation of the CFD solver and evaluate its performance compared to the conservation of mass (COM) solver in WindNinja and previously published large-eddy simulations (LES) for three field campaigns conducted over isolated terrain obstacles of varying terrain complexity: Askervein Hill, Bolund Hill, and Big Southern Butte. We also compare the effects of two important model settings in the CFD solver and provide guidance on model sensitivity to these settings. Additionally, we investigate the computational mesh and difficulties regarding terrain representation. Two important findings from this work are: (1) the choice of discretization scheme for advection has a significantly larger effect on the simulated winds than the choice of turbulence model and (2) CFD solver predictions are significantly better than the COM solver predictions at windward and lee side observation locations, but no difference was found in predicted speed-up at ridgetop locations between the two solvers.

Prescribed fire, intentionally ignited low-intensity fires, and managed wildfires, wildfires that are allowed to burn for land management benefit, could be used as a land management tool to create forests that are resilient to wildland fire. This could lead to fewer large catastrophic wildfires in the future. However, we must consider the public health impacts of the smoke that is emitted from wildland and prescribed fire. The objective of this synthesis is to examine the differences in ambient community-level exposures to particulate matter (PM_2.5) from smoke in the United States from two smoke exposure scenarios – wildfire fire and prescribed fire. A systematic search was conducted to identify scientific papers to be included in this review. Web of Science Core Collection and PubMed for scientific papers, and Google Scholar were used to identify any grey literature or reports to be included in this review. Sixteen studies that examined particulate matter exposure from smoke were identified for this synthesis – nine wildland fire studies and seven prescribed fire studies. PM_2.5 concentrations from wildfire smoke were found to be significantly lower than reported PM_2.5 concentrations from prescribed fire smoke. Wildfire studies focused on assessing air quality impacts to communities that were nearby fires and urban centers that were far from wildfires. However, the prescribed fire studies used air monitoring methods that focused on characterizing exposures and emissions directly from and next to the burns. This review highlights a need for a better understanding of wildfire smoke impact over the landscape. It is essential for properly assessing population exposure to smoke from different fire types.

This paper presents the results of hot smoke tests, conducted in a real road tunnel. The tunnel is located within the expressway S69 in southern Poland between cities Żywiec and Zwardoń. Its common name is Laliki tunnel. It is a bi-directional non-urban tunnel. The length of the tunnel is 678 m and it is inclined by 4%. It is equipped with the longitudinal ventilation system. Two hot smoke tests have been carried out according to Australian Standard AS 4391-1999. Hot smoke tests corresponded to a HRR (Heat Release Rate) equal to respectively 750 kW and 1500 kW. The fire source was located in the middle of the road lane imitating an initial phase of a car fire (respectively 150 m and 265 m from S portal). The temperature distribution was recorded during both tests using a set of fourteen thermocouples mounted at two stand poles located at the main axis of the tunnel on windward. The stand poles were placed at distances of 5 m and 10 m. The recorded data were applied to validate of a numerical model built and solved using Ansys Fluent. The calculated temperature distribution matched the measured values.

This research presents a comprehensive framework for efficiently generating forest fire datasets from Google Earth Engine data sources. The primary contribution of this work lies in providing a methodology to swiftly extract forest fire factors without the need for permissions or access to private datasets, rendering the dataset openly accessible and shared without barriers. Furthermore, given that the remote sensing data used is a global dataset, it can be applied in any region without restrictions. In this study, Peninsular Malaysia is chosen as a case study to demonstrate the framework's effectiveness. The generated dataset includes essential variables including the climate and environment, landcover, topography, and anthropogenic factors facilitating the analysis of fire occurrences. The methodology empowers data scientists, enabling them to leverage their analytical skills on the extracted dataset without requiring specialized remote sensing knowledge. Additionally, this study also showcases the adoption of large language models, specifically GPT-4 with the Noteable plugin, as a tool for conducting preliminary analyses on the generated dataset. Sample analyses reveal that several key features, including the KBDI, LST, PDSI, climate water deficit, and precipitation, significantly impact forest fire occurrences in Peninsular Malaysia. Despite the successful application of the GPT-4 with Noteable plugin, certain limitations and challenges are identified, highlighting the necessity for further validation of the tool's applicability and limitations. This study encourages future research to (1) adopt the proposed framework in other regions, (2) explore more detailed analyses encompassing all variables, and (3) leverage machine learning for advanced forecasting.

Fire-tolerant eucalypt forests of south eastern Australia are assumed to fully recover from even the most intense fires but surprisingly very few studies have quantitatively assessed that recovery. Accurate assessment of horizontal and vertical attributes of tree crowns after fire is essential to understand the fire’s legacy effects on tree growth and on forest structure. In this study, we quantitatively assessed individual tree crowns 8.5 years after a 2009 wildfire that burnt extensive areas of eucalypt forest in temperate Australia. We used airborne lidar data validated with field measurements to estimate multiple metrics that quantified the cover, density, and vertical distribution of individual-tree crowns in 51 plots of 0.05 ha in fire-tolerant eucalypt forest across four wildfire severity types (unburnt, low, moderate, high). Significant differences in the field-assessed mean height of fire scarring as a proportion of tree height, and in the proportions of trees with epicormic (stem) resprouts were consistent with the gradation in fire severity. Linear mixed-effects models indicated persistent effects of both moderate- and high-severity wildfire on tree crown architecture. Trees at high-severity sites had significantly less crown projection area and live crown width as a proportion of total crown width than those at unburnt and low-severity sites. Significant differences in lidar-based metrics (crown cover, evenness, leaf area density profiles) indicated that tree crowns at moderate- and high-severity sites were comparatively narrow and more evenly distributed down the tree stem. These conical-shaped crowns contrasted sharply with the rounded crowns of trees at unburnt and low-severity sites, and likely influenced both tree productivity and the accuracy of biomass allometric equations for nearly a decade after the fire. Our data provide a clear example of the utility of airborne lidar data for quantifying the impacts of disturbances at the scale of individual trees. Quantified effects of contrasting fire severities on the structure of resprouter tree crowns provide a strong basis for interpreting post-fire patterns in forest canopies and vegetation profiles in lidar and other remotely-sensed data at larger scales.

The microscopic pore structure of tight sandstone reservoir significantly influences the characteris-tics of CO2 flooding after water flooding. The research was conducted using various techniques such as casting thin sections, high pressure mercury injection, scanning electron microscopy, Nu-clear magnetic resonance (NMR) testing, and a self-designed high-temperatures and high-pressures microscopic visualization displacement system. Three type cores with different pore structures were selected for the flooding experiments and the microscopic visualization displace-ment experiments, including CO2 immiscible flooding, near-miscible flooding, and miscible flood-ing after the conventional water flooding. The characteristics of CO2 flooding and the residual oil distribution after water flooding were quantitatively analyzed and evaluated. The results show that: (1) During the water flooding process, the oil produced from type I and type III samples mainly comes from large and some medium pores. Oil utilization of all pores is significant for type II samples. The physical properties and pore types have a greater impact on water flooding. Type I and II samples are more suitable for near-miscible flooding after water flooding. Type III samples are more suitable for miscible flooding after water flooding. (2) In the CO2 flooding, oil recovery increases gradually with increasing pressure for all three types of samples. Type II core samples have the highest recovery. Before miscibility, the oil recovered from type I and type II samples was primarily from large pores, however, the oil recovery mainly comes from medium pores when reaching miscibility. As for the type III samples, the oil produced in the immiscible state mainly comes from large and medium pores, and the enhanced oil recovery mainly comes from medium and small pores after reaching the near-miscible phase. (3) It can be seen from the microscopic re-sidual oil distribution that oil recovery will increase as the petrophysical properties of the rock model improve. The oil recovery of near-miscible flooding after water flooding regarding the type II model is up to 68.11%. The oil recovery of miscible flooding after water flooding about type III model is the highest at 74.57%. With increasing pressure, the proportion of flake residual oil grad-ually decreases, while the proportion of droplet-like and film-like residual oil gradually increases. Type II samples have a relatively large percentage of reticulated residual oil in the near-miscible stage.

This study assesses coastal flooding extension mapping based on two innovative approaches. The first is based on the coupling of two robust numerical models (SWASH and LISFLOOD), in this case, discharges were extracted from the wave overtopping results from SWASH 1D and set as boundary conditions for LISFLOOD on the crest of an existing seawall where overtopping typically occurs. The second, hereby called the ‘Tilted Bathtub Approach’ (TBTA), is based on wave run-up levels and buffering the affected area of a prior flooding event, adjusting it for expected sea-states according to different return periods. The proposed approaches are applied to a case study on the Northern Portuguese coast, at Furadouro beach, in the municipality of Ovar, which has been facing multiple flooding episodes throughout recent years, including a dramatic storm in February 2014. This event was used as validation for the proposed methods. A 30 years long hourly local wave climate time series was used both to perform an extreme value analysis in order to obtain expected sea-states according to different return periods, and also for performing a sensitivity test for established empirical formulas to estimate wave run-up at this particular case. Results indicate both approaches are valuable: they yield coherent flood extension predictions that align well with the real inundated area from the 2014 storm. The convergence of these findings underscores the potential for these methods in future coastal flood risk assessment and planning.

In order to solve the problem of the poor oil displacement effect of high molecular weight alkali/surfactant/polymer (ASP) solution in low permeability reservoirs, Daqing Oilfield uses a partial quality tool to improve the oil displacement effect in low permeability reservoirs. Without changing the oil displacement capability of high molecular weight ASP solution in high permeability oil layers, the ASP solution is actively sheared in low permeability oil layers by using a partial quality tool to increase the injection capability of the solution and improve the overall oil recovery. In order to study the ability of the partial quality tool to improve the oil displacement effect, firstly, the matching degree of high molecular weight ASP solution to low permeability cores is studied, and the ability of quality control tools to change the molecular weight is studied. Then, experimental research on the pressure and oil displacement effect of high molecular weight ASP solution before and after the actions of the partial quality tool is carried out. The results show that ASP solutions with molecular weights of 1900 × 104 and 2500 × 104 have a poor oil displacement effect in low permeability reservoirs. After the action of the partial quality tool, the injection pressure is reduced by 5.22 MPa, and the oil recovery is increased by 7.79%. The injection pressure of the ASP solution after shearing by the partial quality tool is lower than that of the ASP solution with the same molecular weight and concentration without shearing, but the oil recovery is lower. On the whole, the use of the partial quality tool can obviously improve the oil displacement effect in low permeability reservoirs.

In this study, seven mortar mixes were prepared by replacing natural sand by crumb rubber ,CR, (10%, 25%, 50%, 60%, 75% and 100%). The properties of mortars, both in their fresh and hardened states, were studied. Key characterisitcs, including density, water absorption coefficient, porosity, thermal conductivity, heat capacity, shrinkage/swelling, elastic modulus, compressive strength, flexure strength, fracture energy and performance after exposure to heat, were evaluated. The results obtained showed that as the CR fraction increased, density, initial setting time, and workability decreased but mortars with CR fractions ranged between 25% and 50% are lightweight and can be used for repairing as they fulfill satndards. It was observed that strengths and stiffness decreased with higher CR content while shrinkage increased for CR percentage higher than 60%. However, up to 50% of CR content the mortars obey to the standards for repair. Interestingly, incorporating up to 50% waste tires proved to improve comfort and thermal resistance without affecting thermal inertia. A slight increase in the mass loss is observed when mortars are exposed to high temperatures. Mortars containing CR between 25% and 50% can be classified as lightweight and thermally insulating mortars, suitable for repairing concrete structures in accordance with industry standards.

The exposure of Mediterranean forests to large wildfires requires mechanisms to prevent and mitigate their negative effects on the territory and ecosystems. Fuel models synthesize the complexity and heterogeneity of forest fuels and allow understanding and modelling of fire behavior. However, it is sometimes challenging to define the fuel type in a structurally heterogeneous forest stand due to the mixture of characteristics from different types and limitations of qualitative field observation and passive and active airborne remote sensing. This can impact the performance of classification models that rely on in situ identification of fuel type as ground-truth, which can lead to a mistaken prediction of fuel types over larger areas in fire prediction models. In this study, a handheld mobile laser scanner (HMLS) system were used to assess its capability to define Prometheus fuel types in 43 forest plots in Aragón (NE Spain). The HMLS system captured the vertical and horizontal distribution of fuel at extremely high resolution to derive high-density three-dimensional point clouds (average: 63,148 points/m2) which were discretized into voxels of 0.05 m3. The total number of voxels in each 5 cm height stratum was calculated to quantify the fuel volume in each stratum, providing the vertical distribution of fuels (m3/m2) for each plot at centimetric scale. Additionally, fuel volume was computed for each Prometheus height stratum (0.60, 2, and 4 m) for each plot. The Prometheus fuel types were satisfactory identified for each plot, which was compared with the fuel type estimated in the field. This led to the modification of the ground-truth in 10 out of the 43 plots, with estimation errors between types 2–3, 5–6, and 6–7. These results demonstrate the ability of HMLS systems to capture fuel heterogeneity at centimetric scales for the definition of fuel types in Mediterranean forests, making them powerful tools for fuel mapping and fire modelling, and ultimately for improving wildfire prevention and forest management.

Despite being considered as natural components of many ecosystems, forest fires pose significant threats to the environment and human health. In order to ensure public safety and effective fire suppression planning, it is necessary to develop reliable prediction models to mitigate forest fire danger. These models should account for specific environmental conditions. The advent of big data in recent years has opened new avenues for improving forest fire predictions. Machine learning techniques, surpassing traditional forecasting methods, have shown significant promise in this area. By applying the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology to a real-world dataset, this paper explores the application of machine learning approaches to understand forest fire patterns and predict fire danger. We consider six distinct time stages and incorporate feature selection to refine our predictions. It is important to note that forest fire behavior models are not universally effective due to geographical variations in data. Nevertheless, advanced decision-making techniques are vital in forest fire management. Our research presents a systematic exploration of the topic, comparing various machine learning models, thereby providing a comprehensive baseline for future investigations in this crucial environmental arena.

Fire outbreaks continue to cause damage despite the improvements in fire-detection tools and algorithms. It is still challenging to implement a well performing and optimized approach, which is sufficiently accurate, and has tractable complexity and low false alarming rate. Small amount of fire and identification of fire from a long distance is also a challenge in previously proposed techniques. In this study, we propose a novel hybrid model based on Convolutional Neural Networks (CNN) to detect and analyze fire intensity. 21 convolutional layers, 24 Rectified Linear Unit (ReLU) layers, 6 pooling layers, 3 fully connected layers, 2 dropout layers, and a softmax layer are included in the proposed 57-layer CNN model. Our proposed model performs instance segmentation in order to distinguish between fire and non-fire events. To reduce the intricacy of the proposed model, we also propose a key-frame extraction algorithm. The proposed model uses Internet of Things (IoT) devices to alert the relevant person by calculating the severity of fire. Our proposed model is tested on a publicly available dataset having fire and normal videos. The achievement of 95.25 % classification accuracy, 0.09% False Positive Rate (FPR), 0.65 percent False Negative Rate (FNR), and a prediction time of 0.08 seconds validates the proposed system.