Start Of Fire Research Articles

Fire Risk Model for Fires in Ro-Ro Ship Ro-Ro Spaces

In recent years, fire accidents on Ro-Ro ships have led to numerous fatalities and significant economic losses. The response of the crew and the ship's protection systems are crucial in managing these incidents and mitigating their consequences. To assess fire safety improvements, this study has focused on developing and quantifying a risk model that captures the dynamics of a fire starting in a Ro-Ro space. Various risk modelling techniques were reviewed to construct the model, which was then quantified using historical data, simulations, and expert judgments. A Delphi-based, fully digital approach to expert elicitation was introduced, utilizing Microsoft Teams and Microsoft Excel-based questionnaires. This method ensured full anonymity for the experts, reducing the risk of group bias and eliminating the need for travel. To enhance understanding and verify the results, uncertainty and sensitivity analyses were performed. They revealed that the potential loss of life deviated, with 90% confidence, from the calculated mean value by less than 26%. Overall, the questionnaire-based method proved effective for expert elicitation and for quantifying nodes in the risk model, demonstrating its utility in the risk assessment process.

Comparing Large-Eddy Simulation and Gaussian Plume Model to Sensor Measurements of an Urban Smoke Plume

The fast prediction of the extent and impact of accidental air pollution releases is important to enable a quick and informed response, especially in cities. Despite this importance, only a small number of case studies are available studying the dispersion of air pollutants from fires in a short distance (O(1 km)) in urban areas. While monitoring pollution levels in Southampton, UK, using low-cost sensors, a fire broke out from an outbuilding containing roughly 3000 reels of highly flammable cine nitrate film and movie equipment, which resulted in high values of PM2.5 being measured by the sensors approximately 1500 m downstream of the fire site. This provided a unique opportunity to evaluate urban air pollution dispersion models using observed data for PM2.5 and the meteorological conditions. Two numerical approaches were used to simulate the plume from the transient fire: a high-fidelity computational fluid dynamics model with large-eddy simulation (LES) embedded in the open-source package OpenFOAM, and a lower-fidelity Gaussian plume model implemented in a commercial software package: the Atmospheric Dispersion Modeling System (ADMS). Both numerical models were able to quantitatively reproduce consistent spatial and temporal profiles of the PM2.5 concentration at approximately 1500 m downstream of the fire site. Considering the unavoidable large uncertainties, a comparison between the sensor measurements and the numerical predictions was carried out, leading to an approximate estimation of the emission rate, temperature, and the start and duration of the fire. The estimation of the fire start time was consistent with the local authority report. The LES data showed that the fire lasted for at least 80 min at an emission rate of 50 g/s of PM2.5. The emission was significantly greater than a ‘normal’ house fire reported in the literature, suggesting the crucial importance of the emission estimation and monitoring of PM2.5 concentration in such incidents. Finally, we discuss the advantages and limitations of the two numerical approaches, aiming to suggest the selection of fast-response numerical models at various compromised levels of accuracy, efficiency and cost.

A MACHINE LEARNING BASED SYSTEM TO CLASSIFY FOREST FIRE PREDICTION FOR FOREST SAFETY

Forecasting forest fires requires the application of different techniques and instruments to evaluate the likelihood and potential seriousness of a fire starting in a forested region. Elements like drought, extreme temperatures, and human behaviors like bonfires, cigarettes, and pyrotechnics can all play a role in sparking forest fires. Various methods such as statistical examination, AI algorithms, and satellite pictures are employed to gather and dissect data on climate conditions, moisture levels, topography, and other factors that may heighten the chance of a forest fire. Models for predicting forest fires can furnish advanced caution systems to inform officials and locals of potential fire hazards. Predicting forest fires in the future is expected to decrease the impact of fires. This study focuses on developing a system for predicting forest fires, which calculates the likelihood of a fire starting based on various meteorological factors such as location (latitude and longitude) and temperature. The Random Forest regression algorithm was utilized to create this predictive model. Key Words: Forest Fire, Machine Learning, Temparature, Random Forest, Regression

Urban Environment and People’s Perception on Risk Reduction in Fire Related Hazards in Srinagar City

This study aims to understand people’s risk perception to environmental hazards especially that of fires in context to an urban area. The fire is one of the major disasters in an urban environment but evidence of the effectiveness of education interventions against fire risks is limited. Fires start in three main ways i.e. accidents (misuse of appliances), deliberate ignition and equipment failure (electrical malfunction) and produce smoke and toxic gases which could be extremely fatal to those exposed to it hence the need for prevention and protection from spreading fires by delaying ignition period to allow people more time to escape and for the fire brigade to arrive at the incident. Urban disasters especially fires have tended to receive a baffling lack of response from aid agencies indicating major gaps in urban preparedness. Srinagar city is faced with inadequacy in responding to fire disasters of high magnitude and rescue teams have failed in many of the occasions to live up to expectations. The study area (Srinagar City, J & K) which is urban in nature and character has recorded highest number of fire incidents in last so many years, claiming dozens of lives and damaging property worth crores. The present study is an attempt to assess the people’s risk perception to fire hazards and their prevention, mitigation and preparedness measures in residential, commercial, educational and health related buildings. For this purpose both primary as well as secondary data has been used. The overall scenario reveals that the perception of selected respondents gives a very poor result, as more than 40 % of them either have no knowledge about the fire fighting equipment or lack their use during fire incidents. Factor identification has been done on prior knowledge upon which emphasis was on people’s perception, preparedness and mitigation measures adopted by different stakeholders.

MONITORING PEMAKAIAN ARUS DAN PENGENDALI INTENSITAS CAHAYA PADA LAMPU BERBASIS INTERNET OF THINGS MENGGUNAKAN ANDROID

The switch on and off of a light takes time when it has to be done in a lot of space. Not to mention in a multistoried building that had to turn the lights on and off on each floor of the building and in each room. Not only that, the omission in turning on and off lights would be very dangerous if left unchecked. Besides electrical waste, a fire starter will occur when lights will run continuously. To make it easier for users to engage in daily activities in turn off lights especially in many areas, it is suggested in this study that a prototype of the current monitoring system and light intensity control for lights based on the Internet of things which the design would be integrated with wi-fi and android. The prototype was built to provide the solution by turning off and turning on the lights without having to move from place to place. Designed by simple looks, the user can control it via the android. With the prototypes and created systems it is expected to make user activity easier and minimise the user negligence factor in turning on and off lights that can overload electricity to fire triggers.

Methodology for probabilistic tsunami-triggered oil spill fire hazard assessment based on Natech cascading disaster modeling

A novel modeling methodology is presented for cascading disasters triggered by tsunami hazards considering uncertainties. The proposed methodology focuses on tsunami-triggered oil spills and subsequent fires, a type of natural hazard-triggered technological (Natech) event. The methodology numerically simulates the time-varying behavior of tsunami-triggered oil spill fires for numerous stochastically generated scenarios and performs a probabilistic mapping of the maximum radiative heat flux as a quantitative measure of the fire hazard. To enable these assessments, probabilistic tsunami hazard assessments are extended to include the tsunami-induced movement of oil storage tanks, resulting oil spills, tsunami-driven oil fire spread, and thermal radiation from fires. The uncertainty of the earthquake fault slip distribution, oil filling level of storage tanks, and fire starting time and position is incorporated into the new assessments. To demonstrate the methodology, a realistic case study is conducted for a coastal petrochemical industrial park in Japan conditioned on possible offshore moment magnitude 9.1 earthquakes. Contrary to typical tsunami direct impact assessments, the results highlight the cascading effects of tsunamis and large variability in key output variables concerning oil spills and fires. This indicates that the methodology is useful for deepening stakeholders’ understanding of tsunami-triggered cascading disasters and improving risk reduction plans.

Kitchen fine particulate matter (PM2.5) concentrations from biomass fuel use in rural households of Northwest Ethiopia.

Combustion of solid biomass fuels using traditional stoves which is the daily routine for 3 billion people emits various air pollutants including fine particulate matter which is one of the widely recognized risk factors for various cardiorespiratory and other health problems. But, there is only limited evidences of kitchen PM2.5 concentrations in rural Ethiopia. This study is aimed to estimate the 24-h average kitchen area concentrations of PM2.5 and to identify associated factors in rural households of northwest Ethiopia. The average kitchen area PM2.5 concentrations were measured using a low-cost light-scattering Particle and Temperature Sensor Plus (PATS+) for a 24-h sampling period. Data from the PATS+ was downloaded in electronic form for further analysis. Other characteristics were collected using face-to-face interviews. Independent sample t-test and one-way analysis of variance were used to test differences in PM2.5 concentrations between and among various characteristics, respectively. Mixed fuels were the most common cooking biomass fuel. The 24-h average kitchen PM2.5 concentrations was estimated to be 405 μg/m3, ranging from 52 to 965 μg/m3. The average concentrations were 639 vs. 336 μg/m3 (p < 0.001) in the thatched and corrugated iron sheet roof kitchens, respectively. The average concentration was also higher among mixed fuel users at 493 vs. 347 μg/m3 (p = 0.042) compared with firewood users and 493 vs. 233 μg/m3 (p = 0.007) as compared with crop residue fuel users. Statistically significant differences were also observed across starter fuel types 613 vs. 343 μg/m3 (p = 0.016) for kerosene vs. dried leaves and Injera baking events 523 vs. 343 μg/m3 (p < 0.001) for baked vs. not baked events. The average kitchen PM2.5 concentrations in the study area exceeded the world health organization indoor air quality guideline value of 15 μg/m3 which can put pregnant women at greater risk and contribute to poor pregnancy outcomes. Thatched roof kitchen, mixed cooking fuel, kerosene fire starter, and Injera baking events were positively associated with high-level average kitchen PM2.5. concentration. Simple cost-effective interventions like the use of chimney-fitted improved stoves and sensitizing women about factors that aggravate kitchen PM2.5 concentrations could reduce kitchen PM 2.5 levels in the future.

Production and characterization of ecological fire starter from sawdust and vegetable oil

The objective of this article aims to reduce indoor air pollution through the use of ecological fire starter (EFS) made from sawdust and vegetable oils. In the Far North region of Cameroon, plastic waste is used to ignite and stoke solid fuels fires, exposing mainly women and children who are responsible for cooking to health risks from indoor air pollution. Thus, the survey conducted among the population of the region shows that 96% of urban households use plastics waste made with LDPE, HDPE, PET, PS, PP and EVA as fire starter for solid fuels. In the region, 5544 tons of plastic used by households could emit approximately 15,314 tons of CO2 eq per year. The region has a manufacturing capacity of 1000 tons of EFS while its need is 894 tons in 2022. The lower heating value of the EFS varies between 31.914 ± 0.810 and 25.127 ± 0.026 MJ/kg, and have many ecological health and economic advantages. A household needs about 10 g of EFS to ignite solid fuel per day, with an annual expenditure ranging from 5.5 and US$ 7 to purchase EFS. Therefore, it is important to promote EFS through developing countries and look for another way to recover plastic waste.

Preignition risk mitigation model for analysis of wildfires caused by electrical power conductors

Most wildfires started by electrical power are caused by the contact of trees and surface fuels with power lines. We propose a preignition risk mitigation model to reduce the risk of crown fires and prevent power outages. An optimization formulation model considering power flow under wildfire conditions is proposed. The heating and cooling processes are formulated to find the conductor temperature. We investigate the heat energy of conductors’ surface to fuels and trees. We determine the probability of wildfire risk using the non-stationary weather dynamics due to climate change and surface fuel variable. Moreover, we propose a fuel treatment strategy for managing vegetation and surface fuel variable that reduces the risk of wildfires around power transmission lines. Combining all these together, a mathematical model is proposed that minimizes the total expected costs associated with the generator cost and wildfire risks, consistent with conditions for the start and spread of a crown fire.

Local knowledge and the utilization of non-medicinal plants in home garden by the people of Donorejo Village in the Menoreh Karst Area, Purworejo, Central Java, Indonesia

Abstract. Sholekha AM, Yulia IT, Hanun Z, Perwitasari IG, Cahyaningsih AP, Sunarto, Sutarno, Sugiyarto, Buot Jr IE, Setyawan AD. 2023. Local knowledge and the utilization of non-medicinal plants in home garden by the people of Donorejo Village in the Menoreh Karst Area, Purworejo, Central Java, Indonesia. Biodiversitas 24: 645-657. Donorejo Village, in Purworejo District, Central Java, Indonesia, is one of the villages in the Menoreh Karst Mountains that uses non-medicinal plants. Donorejo villagers typically own vast plots of land that might be used for a wide range of productive purposes if planted with suitable vegetation. This study aims to investigate the knowledge of local people in the Menoreh Karst Mountain Area, Donorejo Village regarding the use of non-medicinal plants for daily life and document the use of plants by locals that can be useful to prevent delays in the transmission of local knowledge to future generations. Interviews and surveys conducted in the field using a purposive sampling strategy provided the bulk of the study's information. The age range of the 48 informants was from 25 to 91 years old, and the vast majority of them had only completed elementary school. A total of 119 species of non-medicinal plants were identified, representing 62 families and 12 different purposes, based on the results of the inventory of plants indicated by respondents and field observations. These plants were utilized as ornamental plants (48 species), food plants (44 species), cooking spices (16 species), animal feed (13 species), firewood (6 species), building materials (6 species), household items (2 species), and funeral ritual, prayer beeds, food wrapper, fence, fire starter (1 species). Based on these findings, it is clear that the residents of Donorejo Village continue to employ various non-medicinal plants in their gardens for various purposes.

Система персонального поиска людей при пожарах и чрезвычайных ситуациях

PURPOSE. The article deals with the issues of justifying possibility for using personal mobile device to search for people by Wi-Fi signal emission power. In case of fire individuals from technical staff at the facility under protection may not notice or hear the warning about fire and evacuation start. Having lost time to evacuate people may be cut off from main escape routes and become fire hazardous factors’ victims. METHODS. Using statistical model (ITU-R 1238) for calculations inside buildings and premises and data on routers location and placement order, an experiment has been conducted to locate a person when his mobile phone emits only residual signal power at this person’s specific location. FINDINGS. To simulate search for people and calculate matrix of signal power loss indoors and matrix of signal power levels at receiver input MatLab R2018 system has been used. Using two-way radio channel (based on Wi-Fi wireless network) is mandatory for system functioning to search for people in buildings, which allows not only identifying a person but also determining his exact location. It should be taken into account that dividing the room into cubes with dimensions 0.5×0.5×0.5 m makes it possible to determine person’s position in a particular place – lying or standing. RESEARCH APPLICATION FIELD. It is advisable to include study results in research and development work of EMERCOM of Russia research organizations and other ministries and departments involved in fire safety systems creation for critical protected facilities. CONCLUSIONS. Using the system for personal search during fires and emergencies will make it possible to locate people carrying mobile devices in real time and evacuate them to a safe zone when required.

The effect of using a fried rice machine on product effectiveness and productivity

In general, fried rice is a dish that is popular with the public, conventionally prepared using a set of conventional equipment with problems: (1) its operation requires a lot of physical effort, is tiring and sweaty, not intensive, (2) the equipment is relatively unhygienic, the quality of the results is less stable. The SEGORO fried rice-making machine is the solution to this problem. The SEGORO fried rice unit consists of: a set of heaters with gas energy, equipped with an electric fire starter, a 12 Volt DC motor that drives the stirrer and rotates the Wok, equipped with a relevant wok, equipped with a gas handle valve. Analysis results: (1) one processing batch for 15-20 portions; (2) processing time from 12 minutes to 7 minutes, productivity increased up to 2x; (3) conventionally workers work tiringly and produce a lot of sweat. ‘SEGORO’ fried rice machine, equipped with an electric stirrer motor and Wok turning motor does not require high physical effort, and is not tiring. In conclusion, the Segoro fried rice machine uses little electricity, low voltage, saves gas and physical energy, its operation is driven by a stirrer motor, and is equipped with a wok turning motor whose speed can be adjusted. The Segoro fried rice machine is made from stainless steel 304, it is more hygienic and meets food-grade standards, and the quality of the processed product is more stable.

Влияние высоты помещения на расчётное время обнаружения пожара

Introduction. Within the framework of this article, a quantitative and qualitative assessment of the influence of the height of the room on the estimated time of fire detection was carried out. Purpose. Assessment of the effect of the room height on the estimated fire detection time. Methods. Using the Fire Dynamics Simulator software system used to simulate the fire process, numerical experiments were carried out to study the fire detection time, which is considered in this article as an interval from the moment of fire start to reaching the values of optical density of smoke, at which point smoke fire detectors are triggered. Results and their discussion. As a result of numerical modeling of fire in rooms of different heights, data on the change in the optical density of smoke in the initial stage of fire are established. Based on the information received, the estimated time of fire detection by automatic fire alarm was determined according to the algorithms of triggering A and B at different values of permissible sensitivity for point smoke fire detectors, taking into account the height of the room. The results of numerical experiments can be used for expert assessment of the design time of fire detection as a "technical" component of the evacuation start time taken into account when determining the individual fire risk and conditions for safe evacuation of people. Conclusions. It was found that the height of the room has a slight impact on the estimated fire detection time. However, this effect is manifested in the case of the operation of point smoke detectors at the upper threshold of permissible sensitivity. To take into account the time of fire detection as part of the evacuation start time, further study of this issue is required with comparison of modeling results with the results of field experiments. Keywords: optical density of smoke; fire alarm; evacuation start time; fire detection time.

Fire behavior prediction with artificial intelligence in thinned black pine (Pinus nigra Arnold) stand

Modeling forest fire behavior is very important for the effective control of forest fires and the setting up of necessary precautions before fires start. However, studies of forest fire behavior are complex studies that depend on many variables and usually involve large data sets. For this reason, the predictive power and speed of classical forecasting models are lower than of artificial intelligence models in cases involving big data and many variables. Moreover, classical forecasting models must satisfy certain statistical assumptions, unlike artificial intelligence methods. Thus, in this study, predictions were made of surface fire behavior, especially the rate of fire spread and the fire intensity, at the location at which fires started using two artificial intelligence methods, an artificial neural network and a decision tree. The accuracy of the developed models was fitted and tested. Finally, the classical regression model for predicting surface fire behavior was compared with the two artificial intelligence methods. The accuracy measures of the artificial intelligence models were found to be better than those of the classical model.

Leveraging a Wildfire Risk Prediction Metric with Spatial Clustering

Fire authorities have started widely using operational fire simulations for effective wildfire management. The aggregation of the simulation outputs on a massive scale creates an opportunity to apply the evolving data-driven approach to closely estimate wildfire risks even without running computationally expensive simulations. In one of our previous works, we validated the application with a probability-based risk metric that gives a series of probability values for a fire starting at a start location under a given weather condition. The probability values indicate how likely it is that a fire will fall into different risk categories. The metric considered each fire start location as a unique entity. Such a provision in the metric could expose the metric to scalability issues when the metric is used for a larger geographic area and consequently make the metric hugely intensive to compute. In this work, in an investigative effort, we investigate whether the spatial clustering of fire start locations based on historical fire areas can address the issue without significantly compromising the accuracy of the metric. Our results show that spatially clustering all fire start locations in Tasmania into three risk clusters could leverage the probability-based risk metric by reducing the computational requirements of the metric by a theoretical factor in thousands with a mere compromise of approximately 5% in accuracy for two risk categories of high and low, thereby validating the possibility of the leverage of the metric with spatial clustering.

Лесные пожары в удаленных населенных пунктах сибири: причины и совершенствование технологии тушения

Проведен анализ лесных пожаров на территории Иркутской области, выявлены причины возгорания и масштабного распространения пожаров. Предложен новый подход к решению проблемы тушения лесных пожаров, который заключается в применении комбинированной мобильной техники, позволяющей выполнять сельскохозяйственные и хозяйственные функции, а также функции пожаротушения. Рассмотрен один из вариантов такой техники на примере устройства для метания грунта и пробивания водой. Предложена модель для оценки необходимых для тушения сил и средств, учитывающая новый подход. Forest fires, as shown by the results of numerous studies, are a complex uncontrollable process that leads to significant material damage, changes in the ecosystem and soil erosion. The reason for such a large number of forest fires and significant damage is that a long time passes from the moment of detection and warning to the fire localization start. In most cases, the protection of nearby forest areas is carried out by residents of adjacent settlements (creation of mineralized belts and plowing of adjacent areas). But the available technical means that are located in remote settlements do not meet the requirements for intensive fire extinguishing and suppression in the shortest possible time. The carried out studies were related to a greater extent to model the control of the extinguishing intensity, taking into account the technical equipment and a certain extinguishing technology. The introduction of scientific research in relation to fire hazards in forest plantations determines the relevance of the results obtained. The main goal of this work was to study the effect on the fire intensity. Taking into account our proposed extinguishing technology, using a device for throwing soil and simultaneously water spilling, the impact intensity of each type of control actions on fire is set by a vector that is included into the natural fire model. Based on the results of the study there was determined a control action for fire extinguishing in the form of a vector, which would allow to control the impact intensity on a fire by each of the types of controlled factors. Particular attention was paid to special mobile technical appliances, which should include a unit for throwing soil and cutting reclamation strips in the aisles of coniferous forest plantations, as well as the required amount of fire extinguishing liquid with a motor pump for spilling fires. Technical hitch and fire fighting equipment must be aggregated with mini-tractors of sufficient power, which can move freely in the aisles of coniferous forest stands. The results obtained should be used to extinguish forest fires in remote settlements, to develop methods, technologies and methods for extinguishing such fires, allowing to reduce the intensity of fire spread. The proposed mathematical model allows to select the most significant control parameters to achieve the maximum function of the impact intensity on the fire. The use of technology with special ground throwers for timely response and cutting of militia strips working with general-purpose tractors, the creation of artificial reservoirs, near remote settlements will allow to reduce the area of fires as well as the damage from forest fires in the territories of remote settlements of Siberia.

An analysis of fire dynamics in and around indigenous territories and protected areas in a Brazilian agricultural frontier

Sustainable land management is integral to conserving tropical ecosystems and reducing carbon emissions. Human-set fires are part of the land cover change process and are the most common driver of deforestation and carbon emissions in Amazonia. Analyzing the behavior of major fire events provides insight into the effectiveness of current protections. This study aims to quantify the role of Indigenous Territories and Protected Areas (ITPAs) in characterizing anthropogenic fire regimes over the 2020 fire season in Mato Grosso, Brazil. Because of the rapid rate of land cover change and lack of widespread ground validation data in the region, we used a combination of ESA’s Sentinel-5 Precursor Aerosol Index, NASA’s Visible Infrared Imaging Radiometer Suite, and high resolution Planet satellite imagery for the Monitoring of the Andean Amazon Project to analyze spatial and temporal patterns by fire type. ITPAs cover 25% of the study area’s land and contained approximately 20% of significant recorded fires in 2020. Recently deforested areas, forest, grassland, and cropland fires showed varying seasonality and lower frequencies inside ITPAs, but mean fire start dates for all fire types occurred in mid-September. Results suggest that the overall density of major fires is reduced in ITs. PAs only inhibit the density of crop or pastureland fires, but no major fires occurred past 10 km inside their borders. Burn severity of major fires had a weak relationship to distance from ITPAs for some fire types. This study highlights the advantages of near real-time data for individual fire events, provides further evidence of the effect of ITPAs on fire behavior, and demonstrates the importance of adequate protection strategies for mitigating fire activity.

Influence of Compartment Fire Behavior at Ignition and Combustion Development Stages on the Operation of Fire Detectors

This paper presents experimental research findings for those involved in the early phase of fire in office buildings. Class A model fires with a reaction area from 5 cm2 to 300 cm2 were chosen for investigation. To mock up a fire, the following combustible materials typical of offices were used: wood pieces, heat-insulated linoleum, paper and cardboard. The main characteristics of a model fire were recorded: temperature in the combustion zone, heat release, time of complete burnout and concentration of flue gas components. Typical trends and histograms of changes of these characteristics over time were presented; stages of ignition, flame combustion and smoldering were illustrated. The key characteristics of fire detector activation at different stages of model fire combustion were analyzed. Dead bands and operation conditions of a group of detectors (smoke, heat, optical, flame), their response time and errors were identified. It has been established that the most effective detectors are flame and smoke detectors. Specific operational aspects of detectors were established when recording the ignition of different types of model fires. The viability of combining at least two detectors to record fire behavior was established. Recommendations were made on using the obtained findings when optimizing the systems for detecting and recording the start of a compartment fire.

Synoptic weather patterns conducive to lightning-ignited wildfires in Catalonia

Abstract. Wildfires cause substantial losses to socio-economic and natural assets, especially in Mediterranean climate regions. Despite human activity being the main cause of wildfires in Mediterranean European countries, lightning-ignited wildfires should also be considered a major disruptive agent as they can trigger large fires. In addition, recent studies on the potential climate change effects on wildfires pointed out that lightning-ignited wildfires may gain relevance in Mediterranean areas in the years to come. The present study analyses the synoptical weather patterns favouring lightning-ignited wildfires in Catalonia (NE Iberian Peninsula). Being able to identify areas with an elevated lightning-ignition survival at daily timescales would be of great assistance to wildfire management agencies, i.e. locating ignitions and potential holdover fires, preparing for days with multiple ignitions or routing detection flight paths. It is worth noticing that one of the reasons that lightning-caused wildfires are difficult to manage is that they can survive for several days after the ignition, emerging days later once surface vegetation becomes dry enough to support sustained combustion. For this reason, in a first step, a reliable lightning–wildfire association is needed to properly identify the date and time of the fire starter for each wildfire. Afterwards, the circulation types on the days of ignition are analysed. The study relies on a dataset of 870 lightning-ignited wildfires, gathered by the Forest Protection Agency of the autonomous government of Catalonia between 2005 and 2020. Lightning data were provided by the Lightning Location System operated by the Meteorological Service of Catalonia. Results show that lightning-ignited wildfires in Catalonia are related to a typical synoptic weather pattern dominated by a short-wave trough at 500 hPa, with three distinct associations: an Iberian thermal low (51 % of the fires), a northern flow (24 %) and prefrontal convection (13 %).

Efficient Model for Gas Leakage Detection Using IoT

Abstract: In today's world, safety is critical, and good safety systems must be put in place. The principal objective of this study is to design a harmful gas sensing and warning system based on embedded systems. LPG is used for cooking and in industries. Since LPG is stored in a bottle, it is easily damaged. In any case, when the gas cylinder, controller, and tube are not in great state, breaks occur, resulting in a issue. Accidents can cause problems such as suffocation and can start of fire. Installing gas leakage detectors in vulnerable areas is the most important measure to avoid accidents caused by gas spill. The primary purpose of this study is to propose a framework for detecting and eliminating gas spills in vulnerable areas. The gas spill sensor is one such way of detecting and alerting people to gas spills in their initial stages. The gas leak detection system includes a GSM module that sends SMS messages when a gas leak is noticed. Keywords: Node MCU, MQ6 Sensor, Liquid crystal display, ESP8266 WIFI, Servo Motor, GSM module.