High-risk Zone Research Articles

Flood risk transfer analysis based on the “Source-Sink” theory and its impact on ecological environment: A case study of the Poyang Lake Basin, China

Driven by climate change, the frequent occurrence of regional destructive floods poses a grave threat to socio-economic systems and ecological environments. Previous flood risk studies have disregarded risk transfer within a region, resulting in inadequate flood risk assessment and ineffective disaster prevention and mitigation outcomes. Therefore, this study introduced the “Source-Sink” theory into flood disaster field to constructing flood risk transfer model. Flood risk assessment and transfer was conducted in the Poyang Lake Basin, China, where the impacts of the initial and transfer statuses on ecosystem service values were quantified. The results showed that the flood risk in the Poyang Lake Basin was relatively low, with high spatial distribution characteristics in the central-north areas but low in the surrounding areas. High-risk zones were mainly distributed southwest of the Poyang Lake. The lower-risk zones exhibited a contiguous distribution and were surrounded by higher-risk zones. Following the completion of the flood risk transfer, high-risk zones increased significantly; but there were a few zones where the risk was transferred to other zones, thereby lowering their risks. Flood risk transfer occurs primarily in low- and medium-risk zones, with high-risk zones being the most important growth targets. The change in risk transfer was most evident in the area surrounding Poyang Lake, while that in the Upper Gan River Basin was lower and less sensitive to the transfer effect. Accounting for flood disaster risk, the ecosystem service values of the Poyang Lake Basin decreased by 8.18 %, with the most significant impacts observed in the surrounding environment and southwest Poyang Lake. After the completion of the flood risk transfer, the ecosystem service value in the Poyang Lake Basin declined by 24.66 %. This study provides a reference point for flood risk management and sustainable regional development that account for risk transfer.

Multi-Disaster Hazard Analysis, The Case of Elazığ Province

In this study, a comprehensive assessment of disaster hazards in Elazığ province, where the Eastern Anatolian Fault Zone passes through, was conducted. Hazard maps for earthquakes, floods, landslides, rockfalls, avalanches, desertification, and erosion were integrated to create a multi-hazard map. Various methods, such as the Analytic Hierarchy Process (AHP) and machine learning models, including the Random Forest algorithm, were employed to assess the severity and probability of exposure for each hazard type. Independent variables, including VS30, liquefaction potential, Digital Elevation Model (DEM)-derived data, and climatic data, were selected based on relevant literature and the study area. For earthquake and erosion hazards, intuitive models were used due to the absence of a single dependent variable. The desertification map was obtained from the Ministry of Environment, Urban Planning, and Climate Change. The Random Forest model was used for other disaster hazard maps. All hazard maps were combined using a hierarchical approach with the Weighted Overlay tool. The study generated a spatial synthesis and database intended to offer proactive insights into disaster preparedness, optimizing resource allocation, and expediting recovery efforts post-disaster within the Elazığ Province. Its primary objective is to provide assistance to local authorities and emergency response teams. In the province, a significant portion of urban settlements and the majority of rural areas face high earthquake hazards. Floods pose a considerable risk, particularly in low-lying areas downstream of numerous dams scattered across the province, as well as at the confluence points of seasonal riverbeds. The hazard of landslides is high in the rugged areas along the EAF and in steep terrains eroded by rivers. Moreover, rock falls occur more frequently in mountainous areas along the Hazarbaba-Akdağ axis due to erosion and physical dissolution. Erosion and desertification represent significant slow-moving hazards, with erosion intensifying on steep slopes and barren lands, while desertification notably affects Baskil and its surrounding low-lying areas in the western part of the province. Considering multiple hazards, areas with concentrated settlements and economic activities such as Elazığ, Baskil, Kovancılar, Karakoçan, and Behrimaz plains are categorized as very high and high-risk zones.

An Assessment of Flood Risk in the Gambia: A Data-Driven Approach to Flood Risk Management and Prevention

Coastal African countries, including Gambia, are increasingly vulnerable to a range of natural disasters, notably flooding, which poses significant challenges to sustainable development and safety. This study set out to comprehensively assess the flood risk in Gambia using a data-driven approach, aiming to enhance flood risk management and prevention strategies. Utilizing geospatial data and Geographic Information System (GIS) techniques, the research involved mapping and categorizing flood risk zones across various regions. The analysis focused on the identification and categorization of flood risk zones, assessment of the vulnerability of key infrastructures, health facilities, road networks, and settlements to flooding. The findings reveal that a total of 2,721 square kilometers of land is at flood risk categorized as either high or very high. This constitutes 26.89 percent of the country’s of terrestrial area. A total of 25 critical health infrastructure were in areas of this high flood risk categorization – ranging from general hospitals to clinics. Up to 24.5% of the nation's road network was found to be in this flood risk zone. Of the country’s five administrative regions, the West Coast Region which is home to most of its urban centers was most at risk of flooding. Here, of the 353 settlements in this region, 172 of them fell under the high to very high-risk flood zone categorization, constituting 48.7% of the settlements at a significant level of flood vulnerability. This study enhances the understanding of flood hazards in Gambia, providing essential perspectives for decision makers, city planners, and emergency response teams. This research highlights the need for using data-driven methods in identifying and managing environmental risks. It also advocates for the adoption of long-term strategic planning, infrastructure enhancements, and community-based activities to reduce the effects of floods.

Landslide risk management using geospatial technique: comparative insights of China and Indonesia

Landslides are defined as the movement of soil and rocks that form slopes. Landslides can cause environmental damage, property losses, and deaths for people in disaster-prone areas. This study aims to review and compare landslide risk management patterns in China and Indonesia from research conducted in 2019-2023. The method used in this study is a Systematic Literature Review (SLR). While searching for literature using Scopus, Mendeley has a publication period of 2019-2023. The research findings show that disaster risk management also focuses on more than community knowledge in disaster emergency response. However, other elements need attention, namely road sections most vulnerable to landslides, slope conditions, river density, land use, GIS, resources, community participation, and training. In Fengjie County, China, landslide vulnerability is a significant problem, with about 70% of areas in the vulnerability zone very high. In Pengasih Sentolo district, Indonesia, nine villages are included in the very high-risk site, showing significant landslide vulnerability. The integration and application of GIS technology have greatly assisted in assessing landslide susceptibility and identifying high-risk zones. Conclusion: The case study in Fengjie County, China and the study in Pengasih Sentolo District, Kulon Progo, Indonesia, emphasize the importance of using geospatial techniques, particularly GIS, for landslide risk assessment.

Spatial evaluation of groundwater vulnerability using the DRASTIC-L model with the analytic hierarchy process (AHP) and GIS approaches in Edo State, Nigeria

The drinking of contaminated water is potentially hazardous to the well-being of humanity. Effective groundwater management depends on detecting groundwater contamination before it hits a critical point. The DRASTIC framework, modified globally to include land use (L), is utilized to evaluate groundwater vulnerability in various regions. In this study, an assessment of groundwater's susceptibility to contamination and a water quality index map to determine its fitness for Edo State have been done using the DRASTIC, DRASTIC-AHP, and DRASTIC-L-AHP models. The distinction of potential-risk water zones in Edo State, Nigeria, was made possible by the GIS that was used to create and combine several attribute maps. Results established that in the study area, very low (1,741.683 km2; 45%), low (1,741.683 km2; 10%), moderate (3,483.366 km2; 20%), and high (4,354.2075 km2; 25%) risk zones of groundwater contamination were identified on the overall DRASTIC-L-AHP map. The study found a significant pollution risk in one-fourth of the area, primarily in the Edo north and south regions of the study area. The significance of each factor for groundwater susceptibility and contamination risk was evaluated through a sensitivity analysis (SA). The SA indicated that the impact of the vadose zone is the most efficient variable in the DRASTIC-L-AHP model, with an effective weight value of 33.45% that is much larger than the theoretical value of 21.51%. The DRASTIC-L-AHP model, validated through hydrochemical analysis, is the least inaccurate and suitable for the current study area, serving as a useful pre-decisional tool for managing and preserving groundwater.

Evidence for partial melting and earthquake activity in the crust from a 2-D electrical resistivity model in the vicinity of the Chayu region, southeastern Jiali-Chayu fault, Tibetan Plateau

The southeastern segment of the Jiali-Chayu fault is a prominent strike-slip fault that separates the eastern Lhasa terrane. Its formation and activity are influenced by the subduction of the Indian plate towards the Lhasa terrane and clockwise rotation in the Himalayan Eastern syntaxis region. Notably, the Chayu 1950 Ms 8.6 earthquake, triggered by this fault, remains the largest recorded inland earthquake to date. However, due to a lack of high-precision geophysical evidence, the deep crustal structure of the hypocenter area remains challenging to reconstruct, limiting our understanding of earthquake activity in the region. In this paper, the Magnetotelluric data is consisted of 70 sites spaced 2 km apart. Through a joint inversion of the transverse electric and magnetic modes, we recovered the 2-D resistivity model that is approximately 140 km long and 50 km deep. The resistivity model provides compelling evidence that the presence of magmatic rocks and metamorphic basement is responsible for the high resistivity anomaly. Conversely, the low resistivity anomaly observed in the northern region can be attributed to crustal melting. These results suggest that the variation in earthquake activity between the Gongrigabu fault and Guyu fault can be attributed to the contrasting characteristics of the crustal material. The Gongrigabu fault represents a brittle plate with low water content and high crystallinity, resulting in a distinctive high resistivity anomaly. On the other hand, the Guyu fault corresponds to a molten plate, exhibiting a low resistivity anomaly. We believe that the Chayu earthquake is a typical intraplate earthquake in the Lhasa terrane and its seismogenic fault is a deeper hidden fault located below the Gongrigab fault, challenging the belief held by other scholars that the Gongrigabu fault is solely responsible. We think the Gongrigabu fault is a high-risk zone for severe earthquakes that demands continuous vigilance in the future.

Smart personal protective equipment for intelligent construction safety monitoring

Purpose The construction industry remains one of the most hazardous industries worldwide, with a higher number of fatalities and injuries each year. The safety and well-being of workers at a job site are paramount as they face both immediate and long-term risks such as falls and musculoskeletal disorders. To mitigate these dangers, sensor-based technologies have emerged as a crucial tool to promote the safety and well-being of workers on site. The implementation of real-time sensor data-driven monitoring tools can greatly benefit the construction industry by enabling the early identification and prevention of potential construction accidents. This study aims to explore the innovative method of prototype development regarding a safety monitoring system in the form of smart personal protective equipment (PPE) by taking advantage of the recent advances in wearable technology and cloud computing.Design/methodology/approach The proposed smart construction safety system has been meticulously crafted to seamlessly integrate with conventional safety gear, such as gloves and vests, to continuously monitor construction sites for potential hazards. This state-of-the-art system is primarily geared towards mitigating musculoskeletal disorders and preventing workers from inadvertently entering high-risk zones where falls or exposure to extreme temperatures could occur. The wearables were introduced through the proposed system in a non-intrusive manner where the safety vest and gloves were chosen as the base for the PPE as almost every construction worker would be required to wear them on site. Sensors were integrated into the PPE, and a smartphone application which is called SOTER was developed to view and interact with collected data. This study discusses the method and process of smart PPE system design and development process in software and hardware aspects.Findings This research study posits a smart system for PPE that utilises real-time sensor data collection to improve worksite safety and promote worker well-being. The study outlines the development process of a prototype that records crucial real-time data such as worker location, altitude, temperature and hand pressure while handling various construction objects. The collected data are automatically uploaded to a cloud service, allowing supervisors to monitor it through a user-friendly smartphone application. The worker tracking ability with the smart PPE can help to alleviate the identified issues by functioning as an active warning system to the construction safety management team. It is steadily evident that the proposed smart PPE system can be utilised by the respective industry practitioners to ensure the workers' safety and well-being at construction sites through monitoring of the workers with real-time sensor data.Originality/valueThe proposed smart PPE system assists in reducing the safety risks posed by hazardous environments as well as preventing a certain degree of musculoskeletal problems for workers. Ultimately, the current study unveils that the construction industry can utilise cloud computing services in conjunction with smart PPE to take advantage of the recent advances in novel technological avenues and bring construction safety management to a new level. The study significantly contributes to the prevailing knowledge of construction safety management in terms of applying sensor-based technologies in upskilling construction workers' safety in terms of real-time safety monitoring and safety knowledge sharing.

Analysis of Factors Influencing Spatial Distribution of Soil Erosion under Diverse Subwatershed Based on Geospatial Perspective: A Case Study at Citarum Watershed, West Java, Indonesia.

The application of remote sensing data has been significant in modeling soil erosion. However, previous studies have fallen short in elucidating and lacked an understanding of the multifactor influencing erosion. This study addresses these limitations by employing the InVEST and the Geodetector models. Specifically, it aims (1) to delineate both spatial and temporal variations in soil erosion within the Citarum watershed from 2010 to 2020, (2) to identify the key drivers of soil erosion and unravel the underlying mechanisms, and (3) to identify the high-risk zones for soil erosion. Both models consider a range of natural predictors, including topography (slope factor), climate (precipitation factor), and vegetation cover (vegetation factor). In addition, they incorporate social parameters such as income per capita and population density, which interact with the watershed's position in the downstream, middle, and upper streams. The results reveal that, over a decade, the average soil erosion increased by 15.50 × 106 tons, marking a 16.65% surge. The impact of factors varies significantly across different subwatershed areas. For example, fraction vegetation cover interactions influence upper- and middle-stream regions, while the downstream area is notably affected by precipitation interactions. The high-risk erosion areas in the watershed are primarily influenced by slope, precipitation, and fractional vegetation cover. In these areas, factors causing high erosion risks include slope, precipitation, and other environmental variables categorized into strata. The study highlights the varying influential factors in different watershed areas.

Tracing the COVID-19 spread pattern in India through a GIS-based spatio-temporal analysis of interconnected clusters

Spatiotemporal analysis is a critical tool for understanding COVID-19 spread. This study examines the pattern of spatial distribution of COVID-19 cases across India, based on data provided by the Indian Council of Medical Research (ICMR). The research investigates temporal patterns during the first, second, and third waves in India for an informed policy response in case of any present or future pandemics. Given the colossal size of the dataset encompassing the entire nation’s data during the pandemic, a time-bound convenience sampling approach was employed. This approach was carefully designed to ensure a representative sample from advancing timeframes to observe time-based patterns in data. Data were captured from March 2020 to December 2022, with a 5-day interval considered for downloading the data. We employ robust spatial analysis techniques, including the Moran’s I index for spatial correlation assessment and the Getis Ord Gi* statistic for cluster identification. It was observed that positive COVID-19 cases in India showed a positive auto-correlation from May 2020 till December 2022. Moran’s I index values ranged from 0.11 to 0.39. It signifies a strong trend over the last 3 years with r^2 of 0.74 on order 3 polynomial regression. It is expected that high-risk zones can have a higher number of cases in future COVID-19 waves. Monthly clusters of positive cases were mapped through ArcGIS software. Through cluster maps, high-risk zones were identified namely Kerala, Maharashtra, New Delhi, Tamil Nadu, and Gujarat. The observation is: high-risk zones mostly fall near coastal areas and hotter climatic zones, contrary to the cold Himalayan region with Montanne climate zone. Our aggregate analysis of 3 years of COVID-19 cases suggests significant patterns of interconnectedness between the Indian Railway network, climatic zones, and geographical location with COVID-19 spread. This study thereby underscores the vital role of spatiotemporal analysis in predicting and managing future COVID-19 waves as well as future pandemics for an informed policy response.

Integrated Assessment of Coastal Subsidence in Nansha District, Guangzhou City, China: Insights from SBAS-InSAR Monitoring and Risk Evaluation

Monitoring and assessing coastal subsidence is crucial to mitigating potential disaster risks associated with rising sea levels. Nansha District in Guangzhou City, representing global coastal soft-soil urban areas, faces significant challenges related to ground subsidence. However, the current understanding of the status, causative factors, and risk (includes subsidence susceptibility and vulnerability) assessment of ground subsidence in Nansha District is unclear. To address this gap, we utilized the SBAS-InSAR technique, analyzing 49 Sentinel-1A images from December 2015 to June 2019, for systematic ground subsidence monitoring. Subsequently, we assessed subsidence risk using a comprehensive index method and a risk matrix. Our findings indicate that subsidence velocity primarily ranged from −40 to −5 mm/a, with a spatial pattern of increasing subsidence from inland to coastal areas. The cumulative subsidence process unfolded in four distinct stages. The genesis of land subsidence was linked to an endogenous geological context dominated by soft-soil deposition, influenced by external factors such as surface loading and groundwater extraction. High-risk zones were concentrated in key engineering development areas, transportation pipeline trunk lines, and densely populated regions, demanding special attention. This study provides a foundational resource for disaster prevention and control strategies in Nansha District and similar coastal cities.

Traveling to the Danger Zone: Emotional Dynamics and Hermeneutics

Although emotions have garnered scholarly attention in tourism, an opportunity remains to examine the interplay between different emotions and their implications on tourism outcomes. This multi-method research was carried out in Venezuela and assesses the physiological arousal and cognitive appraisal of emotions. A guided walking tour through Venezuela’s high-risk zones resulted in varied degrees of emotional stimulation among participants. Their responses were associated with individual differences, such as travel history and personal/professional interests. The appraisal process showed that people who visit conflict-ridden destinations develop cognitively and emotionally. These findings underscore the significance of non-hedonic trips in emotional processes and illustrate how tourism can foster beneficial outcomes. Travel can especially shape tourists’ attitudes towards residents, conflict, and themselves. This research also broadens the literature on risk taking and danger zone tourism.

An integrated approach of machine learning and remote sensing for evaluating landslide hazards and risk hotspots, NW Himalaya

Landslides have become more frequent in the last decade in the NW Himalayan region, threatening people and damaging infrastructure. The study presented here aims to identify landslide hazards and risk hotspots in the NW Himalaya. The area is strategically important due the many holy pilgrimage sites and major hydropower projects. A Multilayer Perceptron (MLP) was used to generate the landslide susceptibility map, which was combined with the rainfall intensity map to create the hazard map. To determine the spatial landslide vulnerability, land use and land cover components were taken into account with their respective monetary values. The landslide risk map is the combination of landslide hazard and vulnerability maps, showing that ∼5% of the area falls in high and very high-risk zones, ∼6% in moderate, ∼47% in low, and ∼42% in very low landslide risk zones. High to very high landslide risk zones are mainly confined to Uttarkashi and its surroundings in the southwestern part of the study area, as well as the Tehri, Karanprayag, and Pithoragarh regions in the southern area. Societal risk was also analyzed and revealed that ∼53% of the human population resides in high to very high landslide risk-prone areas. The findings of this study will be beneficial for promoting sustainable development and safe urbanization in the Himalayan region, if used for planning.

Spatio-temporal dynamics and distributional trend analysis of African swine fever outbreaks (2020-2021) in North-East India.

African swine fever (ASF) is a highly contagious, notifiable, and fatal hemorrhagic viral disease affecting domestic and wild pigs. The disease was reported for the first time in India during 2020, resulted in serious outbreaks and economic loss in North-Eastern(NE) parts, since 47% of the Indian pig population is distributed in the NEregion. The present study focused on analyzing the spatial autocorrelation, spatio-temporal patterns, and directional trend of the disease in NE India during 2020-2021. The ASF outbreak data (2020-2021) were collected from the offices of the Department of Animal Husbandry and Veterinary Services in seven NE states of India to identify thepotential clusters, spatio-temporal aggregation, temporal distribution, disease spread, densitymaps, and risk zones. Between 2020 and 2021, a total of 321 ASF outbreaks were recorded, resulting in 59,377 deaths. The spatial pattern analysis of the outbreak data (2020-2021) revealed that ASF outbreaks were clustered in 2020 (z score = 2.20, p < .01) and 2021 (z score = 4.89, p < .01). Spatial autocorrelation and Moran's I value (0.05-0.06 in 2020 and 2021) revealed the spatial clustering and spatial relationship between the outbreaks. The hotspot analysis identified districts of Arunachal Pradesh, Assam and districts of Mizoram, Tripura as significant hotspots in 2020 and 2021, respectively. The spatial-scan statistics with a purely spatial and purely temporal analysis revealed six and one significant clusters, respectively. Retrospective unadjusted, temporal, and spatially adjusted space-time analysis detected five, five, and two statistically significant (p < .01) clusters, respectively. The directional trend analysis identified the direction of disease distribution as northeast-southwest (2020) and north-south (2021),indicatethe possibility of ASF introduction to India from China. The high-risk zones and spatio-temporal pattern of ASF outbreaks identified in the present study can be used as a guide for deploying proper prevention, optimizing resource allocation and diseasecontrol measures in NE Indian states.

Landslide risk assessment integrating susceptibility, hazard, and vulnerability analysis in Northern Pakistan

The purpose of this study is to assess the landslide risk for Hunza–Nagar Valley (Northern Pakistan). In this study, different conditioning factors, e.g., topographical, geomorphological, climatic, and geological factors were considered. Two machine learning approaches, i.e., logistic regression and artificial neural network were used to develop landslide susceptibility maps. The accuracy test was carried out using the receiving operative characteristic (ROC) curve. Which showed that the success and prediction rates of LR model is 82.60 and 81.60%, while 77.90 and 75.40%, for the ANN model. Due to the physiographic condition of the area, the rainfall density was considered as the primary triggering factor and landslide index map was generated. Moreover, using the Aster data the land cover (LC) map was developed. The settlements were extracted from the LC map and used as the elements at risk and hence, the vulnerability index was developed. Finally, the landslide risk map (LRM) for the Hunza–Nagar valley was developed. The LRM indicated that 37.25 (20.21 km2) and 47.64% (25.84 km2) of the total settlements lie in low and very high-risk zones. This landslide risk map can help decision-makers for potential land development and landslide countermeasures.

Reinforced Soil Slope, an innovative sustainable grade separation system utilized in civil infrastructure

Reinforced Soil slope is an innovative solution for sustainable grade separation systems that has gained popularity in recent years. This construction method involves stabilizing the soil with reinforcing elements like geogrids, geotextiles, or woven wire mesh, combining the advantages of these elements' tensile strength and the shear strength of natural soil. This fusion yields a cost-effective and eco-friendly alternative for grade separation, particularly beneficial in areas where it is not feasible or economical to construct conventional grade separation systems. These RSS systems are successful for earth-retaining structures, embankments, and road cuttings and can be seamlessly integrated into landscapes, making them an appealing choice for environmentally sensitive areas. Their sustainability is evident in utilizing on-site fill materials, minimizing the need for frequent maintenance and repairs, thereby reducing the construction's carbon footprint. Moreover, their affordability compared to traditional methods makes them accessible to communities with limited budgets. Leveraging local materials and labor not only cuts costs but also boosts the local economy and generates employment opportunities. Crucially, reinforced soil slopes exhibit enhanced durability, distributing loads evenly and bolstering resistance against natural calamities like earthquakes or landslides. Consequently, they offer safer alternatives for communities in high-risk zones. In this paper, we will see a real case study of a project in San Francisco, where RSS has been used to repair a slope damaged during a past seismic event.

Multimodal linguistic landscape ecological optimization strategy for Nanchang urban area based on big data analysis

Abstract How to better coordinate construction development and regional landscape ecology in the process of urbanization is a realistic problem to be solved. This paper firstly constructs a multimodal language landscape ecological analysis and prediction model based on the patch-corridor-substrate theory for the urban geographical characteristics of Nanchang city and analyzes the urban dynamic data and landscape element change transfer by Markov transfer matrix combined with the entropy weight method to realize the analytical functions of land use change, landscape ecological pattern, spatial measurements, and landscape ecological health risk evaluation. It has been found that the landscape ecological condition of Nanchang City has generally deteriorated from 2005 to 2023, and the average value of the landscape ecological index has decreased. With the improvement of ecological environment laws and regulations, the city will develop in a more scientific, orderly, and sustainable direction, the degree of damage to the landscape will gradually decrease, and the landscape patches become more regular. The degree of forest land separation decreased from 1.932 to 1.228. The area of unutilized land continued to decrease, the degree of disturbance increased to 0.994, and the degree of loss increased to 0.342. The area of the lower ecological risk zone continued to decrease to 1,364.96km2. The proportion of the medium ecological risk zone decreased from 33.42% to 5.26%, and the area decreased by 1,996.77km2. The area of the high ecological risk and high ecological risk zones decreased by 400.72% respectively. Higher ecological risk and high ecological risk areas were reduced by 400.72km² and 31.91km², respectively, according to which this paper puts forward three landscape ecological optimization strategies, such as constructing an ecological network system, strengthening monitoring and early warning, and rationally planning land space. This paper is of great significance in carrying out a regional landscape ecological evaluation, regulating the spatial pattern of Nanchang, optimizing the use of regional land resources, and guaranteeing the sustainable development of the region.

Vulnerability, Preparedness, and Previous Experience of Disasters Among the Population of a High Hazard Region-Rural Southern Sindh, Pakistan.

A cross-sectional, mixed-method study was conducted in Badin, a rural area in southern Sindh, which is considered a high-risk disaster zone, to assess the vulnerability, preparedness, and disaster experiences of the coastal population. A multistage sampling technique was employed to select the villages, study area, and 3 distinct populations (I, II, and III). Family heads of households were recruited for population I, village heads for population II, and community support group leaders from selected clusters for population III. The survey was conducted through face-to-face interviews. The results revealed that the population of rural southern Sindh, Pakistan, is highly vulnerable to disasters and exhibits lower levels of preparedness. The statistics about the vulnerable population may prove helpful in designing policies targeting specific groups to mitigate hazards in the future.

The Tsunami Generated by the Gorringe Bank Fault: Analyzing Wave Heights and Travel Time in the Atlantic Coast of Morocco

Two hundred sixty-eight years (about three centuries) since the 1755 Lisbon tsunami, which provoked large-scale damage in Portugal, Spain and Morocco. Scientists in several countries have mobilized to develop investigative methods to study this extraordinary tsunami, with the aim of reducing the impact of waves in future events and protecting and warning populations at risk. However, the most important question remains whether the entire damaged community has sufficient information about the impact to prepare for tsunami risks. The aim of this paper is to determine the maximum wave heights in selected cities located within high-risk tsunami zones in Morocco, as well as the tsunami arrival time for each site. We used the Non-linear Shallow Water model With Nested Grids (NSWING) code to model tsunami propagation. Six cities were determined as observation points as being the subject of tsunami studies in previous work, namely Tangier, Asilah, Rabat, Casablanca, El-Jadida and Safi. The maximum wave height calculated within the Atlantic coast of Morocco exceeds 5 m in some locations and the first waves reach the Moroccan coast in 60 minutes. The authorities might utilize these results to develop evacuation plans.

Identifying risk zones and landscape features that affect common leopard depredation.

Human-wildlife conflict (HWC) is a pressing issue worldwide but varies by species over time and place. One of the most prevalent forms of HWC in the mid-hills of Nepal is human-common-leopard conflict (HLC). Leopard attacks, especially in forested areas, can severely impact villagers and their livestock. Information on HLC in the Gorkha district was scarce, thus making it an ideal location to identify high-risk zones and landscape variables associated with such events. Registered cases were collected and reviewed from the Division Forest Office (DFO) during 2019-2021. Claims from DFO records were confirmed with herders and villagers via eight focus group discussions. To enhance modeling success, researchers identified a total of 163 leopard attack locations on livestock, ensuring a minimum distance of at least 100 meters between locations. Using maximum entropy (MaxEnt) and considering 13 environmental variables, we mapped common leopard attack risk zones. True Skill Statistics (TSS) and area under receiver-operator curve (AUC) were used to evaluate and validate the Output. Furthermore, 10 replications, 1,000 maximum iterations, and 1000 background points were employed during modeling. The average AUC value for the model, which was 0.726 ± 0.021, revealed good accuracy. The model performed well, as indicated by a TSS value of 0.61 ± 0.03. Of the total research area (27.92 km2), about 74% was designated as a low-risk area, 19% as a medium-risk area, and 7% as a high-risk area. Of the 13 environmental variables, distance to water (25.2%) was the most significant predictor of risk, followed by distance to road (16.2%) and elevation (10.7%). According to response curves, the risk of common leopard is highest in the areas between 1.5 to 2 km distances from the water sources, followed by the closest distance from a road and an elevation of 700 to 800 m. Results suggest that managers and local governments should employ intervention strategies immediately to safeguard rural livelihoods in high-risk areas. Improvements include better design of livestock corrals, insurance, and total compensation of livestock losses. Settlements near roads and water sources should improve the design and construction of pens and cages to prevent livestock loss. More studies on the characteristics of victims are suggested to enhance understanding of common leopard attacks, in addition to landscape variables. Such information can be helpful in formulating the best management practices.

Design of a WiFi sensor network to identify transmission zones of respiratory diseases through air quality indicators

infants and postoperative patients. While air extraction and droplet filtration systems are effective in containing these viruses, interconnected ventilation ducts can still facilitate virus transmission in areas with lower viral concentration. This study introduces a Wireless Sensor Network based on WiFi technology, designed to monitor crucial environmental variables such as CO2, TVOC, temperature, and humidity, collectively influencing indoor air quality. Moreover, this network connects to an online server, collaborating with subscribers to store historical data. The stored information aids in identifying patterns and high-risk zones associated with the transmission of respiratory diseases. The outcomes demonstrate a comprehensive, adaptable, and scalable WiFi-based Indoor Air Quality sensor network. Employing the publish-subscribe pattern, the MQTT message transport protocol, and open-access platforms like Streamlit, Supabase, and HiveMQ, along with affordable boards and sensors, this solution offers an innovative, scalable, and cost-effective approach to identifying areas at risk of airborne disease transmission. The system's design prioritizes data precision and reliability, making it a valuable addition to indoor air quality monitoring systems.