High-risk Areas Research Articles

Landslide susceptibility evaluation in the Beas River Basin of North-Western Himalaya: A geospatial analysis employing the Analytical Hierarchy Process (AHP) method

In the North-Western Himalayas, particularly within the Beas Basin of Himachal Pradesh, landslide incidents are frequent, primarily due to the unique interplay of adverse geological conditions, heavy rainfall, and human factors. These incidents result in substantial loss of life and property each year. To mitigate such issues, systematic landslide research is essential, encompassing aspects like inventory mapping and risk assessment. This study leverages the Analytical Hierarchy Process (AHP) for an in-depth Landslide Susceptibility Index (LSI) mapping in the Beas River basin, employing remote sensing data to analyze key factors contributing to the region's instability. The process involved a detailed selection and mapping of landslide conditioning variables, guided by validated landslide inventory data and high-resolution remote sensing images, ensuring an accurate representation of the basin's geographical variations. The creation of the landslide susceptibility map utilized the weighted overlay approach, categorizing the area into five levels of susceptibility: very low, low, moderate, high, and very high. This classification incorporated ten critical factors influencing landslide occurrence, including elevation, slope aspect, slope angle, distance from drainage, lithology, distance from lineament, geomorphology, rainfall, and land use/land cover (LULC). The LSI was calculated through the Weighted Linear Combination (WLC) technique, leveraging the weights and ratings derived via the AHP method. This analysis revealed that approximately 634.1 square kilometers, or 12.8% of the region, face very high landslide susceptibility, followed by 22.6% at high, 25.8% with moderate, 24.4% at low, and 14.5% at very low susceptibility. The LSI map's accuracy in predicting landslides was affirmed through Receiver Operating Characteristic (ROC) and Area Under Curve (AUC) evaluations, showcasing an 86.3% precision rate. This classification facilitates focused interventions in high-risk areas, guiding planners in landslide-conscious development and infrastructure planning. It directs engineers toward engineering solutions like slope stabilization and drainage improvements to mitigate landslide effects. Moreover, this approach supports the creation of evacuation and emergency response plans, bolstering community resilience to landslide threats in the river basin.

Artificial Intelligence (AI) Model Development Framework for the Protection of State Borders, with a Focus on Analyzing Behavioral Patterns

This study explores the development and implementation of an artificial intelligence (AI) model designed to predict illegal border crossing locations, thereby enhancing the effectiveness of national border security measures. By integrating and analyzing diverse data sources, -including satellite imagery, social media, and environmental factors, -this AI model aims to identify potential migration patterns and high-risk areas for illegal crossing. This research highlights the model's ability to provide real-time risk assessments, offering a novel approach to border security that surpasses traditional methods in terms of both efficiency and cost-effectiveness. The model's adaptability, continuous learning capabilities, and user-friendly interfaces ensure its relevance in addressing contemporary border-security challenges. This article contributes to the ongoing discourse on the application of AI in national security by proposing a solution that leverages technology for improved coordination among immigration services, government organizations, and international bodies.

Advancements in mapping landslide susceptibility in Bafoussam and its surroundings area using multi-criteria decision analysis, statistical methods, and machine learning models

Landslides pose a significant threat to lives and socio-economic stability globally. In this study, we conducted a comprehensive landslide susceptibility mapping (LSM) in the western region of Cameroon, focusing on Bafoussam and its surroundings. The integration of multi-criteria decision analysis models (AHP), statistical methods (Information Value IV, Shannon Entropy SE, Frequency Ratio FR), and machine learning algorithms (Naïve Bayes and Logistic Regression) provided a robust assessment of landslide risk. Our analysis, based on 54 recorded landslides, carefully selected Landslide Conditioning Factors (LCF), and influential parameters such as lithology, slope, altitude, and precipitation, resulted in susceptibility maps categorizing the area into five risk zones. The Spatial distribution shows the centre and northwestern regions as high-risk areas. Model sensitivity differences underscore the need for tailored LSM selection. Validation using the Area Under Curve/Receiver Operating Characteristics (AUC/ROC) method indicates the LR and NB methods have the highest accuracy (82.7% and 84.1%, respectively). Comparative analysis of landslide events in Gouaché, Sichuan, Souk Ahras, and Kekem reveals correlations between heavy rainfall and geological conditions. The study supplies valuable insights for decision-makers in landslide-prone areas, emphasizing the importance of integrating multiple methodologies for comprehensive risk assessment and management.

Abstract 2160: Utilizing geospatial analytics to identify neighborhood cancer hot spots for recruitment of underserved populations in clinical prevention research

Abstract Background: The term “precision public health” has increasingly been used in literature to describe public health practices that utilize new technologies and big data to precisely target the most vulnerable populations for disease control. Following the principles of precision public health, we used geospatial technologies and population-based health data to identify the locations and socioeconomic status of neighborhoods with high cancer incidences for recruitment of participants in prevention research. Methods: Using Florida cancer registry data, geospatial hot spot analyses were carried out to identify statistically significant clusters of census tracts with high incidences of prostate, gastric, and oral cancers within the catchment area of the Sylvester Comprehensive Cancer Center in South Florida. The locations, social determinants of health (US Census’ American Community Survey), and behavioral risk factor data (CDC’s Behavioral Risk Factor Surveillance System) of these hot spots were analyzed to identify the locations of underserved communities facing high risk for these three cancers. Results: Hot spots with high incidences for each of these three cancers overlap in many neighborhoods with high proportions of Hispanic and Non-Hispanic Black populations and high levels of socioeconomic disparities, such as low income, low education level, rental housing, no vehicles, and no access to health insurance. These neighborhoods also have high rates for behavioral risk factors in smoking, lack of physical activity, obesity, and high incidence of cervical cancer (high risk areas for HPV-associated oral cancers). Mobile screening units called the Game Changer Vehicles have been deployed to the locations to recruit participants who were tested on-site for prostate specific antigen (prostate cancer), H. pylori (gastric cancer), or solCD44 (a soluble tumor marker for squamous cell carcinoma) and total protein levels in oral rinse specimens, which can distinguish individuals with molecular features at risk for oral cancers due to tobacco, alcohol use, and human HPV infection. Conclusions: Geospatial analyses of population health data can be used to produce metrics that precisely target neighborhoods with high cancer risk and health disparities to recruit research participants. The same precision approach can also be applied for cancer screening purposes, such as offering cervical self-sample and fecal blood test kits in disadvantaged communities facing heightened risk for cervical and colorectal cancers. Citation Format: Ming S. Lee, Rebecca Kaiser, Ridhi Vyas, Amando Vera, Daniela Flores Quetant, Nancy Elliott, Valerie Bethel, Amanda Rivera, Shria Kumar, Brandon Mahal, Elizabeth Franzmann, Erin Kobetz. Utilizing geospatial analytics to identify neighborhood cancer hot spots for recruitment of underserved populations in clinical prevention research [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2160.

Molecular epidemiology of foot-and-mouth disease (review)

Molecular epidemiological studies are an important tool for regional and global surveillance of foot-and-mouth disease (FMD). These tests are based on constantly progressing technologies of nucleic acid sequencing and phylogenetic analysis. The use of these technologies made it possible to assess the genetic diversity of the FMD virus, to analyze the evolution of the virus in the enzootic regions, and track the pathways of FMD epizootic and panzootic spread. Molecular epidemiological studies have shown that within the long-known seven serotypes of the FMD virus, there are numerous topotypes (geographical types), genetic lineages and sublineages. Usually, the foot-and-mouth disease virus of a certain topotype and genetic lineage evolves within a certain area, periodically causing regional epizootics. However, over the past 30 years, two FMD panzootics have occurred, invloving several continents. The first panzootic occurred in the late 1990s – early 2000s and was caused by O/ME-SA/PanAsia FMDV, and the second, caused by O/ME-SA/Ind-2001 virus, began in 2013 and continues to the present. The emergence of FMD panzootics is probably a consequence of the economic globalization. FMD is not enzootic in Russia, but sporadic outbreaks of this disease are periodically reported. Molecular epidemiological studies have shown that these outbreaks are caused by the infection introduction from neighboring Asian countries, mainly from China. The FMD virus, which has come to the Russian Federation from other countries, is characterized by great genetic diversity and belongs to three serotypes, five topotypes and eight genetic lineages: O/Cathay, O/ME-SA/PanAsia, O/SEA/Mya-98, O/ME-SA/Ind-2001, O/ME-SA/unnamed, A/Asia/Iran-05, A/Asia/Sea-97, Asia1/V. The results of molecular epidemiological studies are taken into account when vaccine strains are to be selected for preventive vaccination of livestock in FMD high-risk areas. The review is based on the analysis of 68 literature sources.

Monitoring and Early Warning Method of Debris Flow Expansion Behavior Based on Improved Genetic Algorithm and Bayesian Network

Based on an improved genetic algorithm and debris flow disaster monitoring network, this study examines the monitoring and early warning method of debris flow expansion behavior, divides the risk of debris flow disaster, and provides a scientific basis for emergency rescue and post-disaster recovery. The function of the debris flow disaster monitoring network of the spreading behavior disaster chain is constructed. According to the causal reasoning of debris flow disaster monitoring information, the influence factors of debris flow, such as rainfall intensity and duration, are selected as the inputs of the Bayesian network, and the probability of a debris flow disaster is obtained. The probability is compared with the historical data threshold to complete the monitoring and early warning of debris flow spreading behavior. Innovatively, by introducing niche technology to improve traditional genetic algorithms by learning Bayesian networks, the optimization efficiency and convergence speed of genetic algorithms are improved, and the robustness of debris flow monitoring and warning is enhanced. The experimental results show that this method divides debris flow disasters into the following five categories based on their danger: low-risk area, medium-risk area, high-risk area, higher-risk area, and Very high-risk area. It accurately monitors the expansion of debris flows and completes early warning. The disaster management department can develop emergency rescue and post-disaster recovery strategies based on early warning results, thus providing a scientific basis for debris flow disasters. The improved genetic algorithm has a higher learning efficiency, a higher accuracy, a faster convergence speed, and higher advantages in learning time and accuracy of the Bayesian network structure.

GIS-Based Spatial Analysis Model for Assessing Impact and Cumulative Risk in Road Traffic Accidents via Analytic Hierarchy Process (AHP)—Case Study: Romania

On a global scale, traffic incidents are a leading cause of mortality and material damage. Romania exhibits the highest rate of road traffic fatalities both in the European Union and worldwide, requiring a comprehensive examination of its overall influence on a national level. The current study uses an extensive approach centering on a spatial analysis model based on the Analytic Hierarchy Process (AHP). Employing a series of spatial databases, this model delineates the geographical distribution and characteristics of road accidents to establish both their cumulative national impact and the identification of high-risk areas. The spatial database, containing traffic incident data, is constructed using geolocation techniques and integrated through network analysis to evaluate the impact in relation to distance. The AHP framework is applied in analyzing the impact across five key dimensions: accident severity, occurrence mode, prevailing weather conditions, traffic restrictions, and road markings. This multi-level AHP analysis not only identifies high-risk hotspots but also confirms the effectiveness of the proposed spatial model.

Elephants rest more when the poaching risk is high and do not recover the lost time within a diel cycle

Poaching for ivory has caused disturbances in the behaviour of elephants. The nature and magnitude of such disturbance are not yet fully understood. Here, we studied the daily activity cycles of 10 elephants tracked for a minimum of two years each in the Samburu ecosystem in Kenya. The elephants were tracked over a spatial or temporal gradient of poaching risk in their distribution area. Laikipia Samburu landscape is shared with humans and is a mosaic of land ownership and management practices, all of which lead to varied levels of illegal killing. Using movement data of elephants tracked on various dates between 2002 and 2016, we studied the daily activity cycle when they were in their different core areas. Using Generalized Additive Models, we found that elephants moved less around midday in high poaching areas. Despite the adaptive shift in activity times, elephants were moving for fewer hours per day in areas with higher risk, reducing total movement daily in risky areas or times. The elephants lost one hour daily from their usual movement time when they went into high-risk areas, which they did not compensate for despite increased activity at dawn and dusk. The level of illegal killing was the best explanatory variable for altering the activity cycle. We infer that such risk avoidance behaviour culminates in the potential reduction of foraging efforts in a risky area. A deficit in their activity time may have consequences for their social life, reproduction, or overall foraging success, aspects of elephant ecology that are not fully understood yet. We discuss the results in light of the increasing need for more fine-scale temporal analyses of the influence of risk on the diel activity of elephants for sites that achieve both movement data and verified records of causes of elephant deaths.

Ecological niche modeling analysis (Cx. pipiens), potential risk and projection of Dirofilaria spp. infection in Greece

Vector-borne diseases continue to increase worldwide. Dirofilariosis is one of the most common vector-borne zoonotic diseases, mainly caused by Dirofilaria spp. (D. immitis and D. repens) and spread by culicid mosquitoes of different species. Greece is one of the countries in southern Europe where it is traditionally endemic, and its distribution is not homogeneous. The aim of this study was to develop an environmental model for Greece that reflects the suitability of the ecological niche for Dirofilaria spp. infection risk and its projection until 2080. For this purpose, we used the potential distribution of suitable habitats for Culex pipiens calculated using an ecological niche model (ENM) and the potential number of generations of Dirofilaria spp. The ecological niche model of Cx. pipiens in Greece showed good predictive power (AUC=0.897) with the parasite at a resolution of 1 km2. The variables that contributed most to the model were mean annual temperature, rivers and human footprint. The highest risk of infection was found in coastal areas and in riverside areas of the main river basins, as well as in irrigated areas of the mainland and peninsular regions and in the whole territory of island areas, and the lowest risk was found in areas of higher altitude. A positive relationship was found between the risk of dirofilariosis and the location of infected dogs, with 86.65% located in very high and high risk areas. In 2080, the percentage of territory gained by Cx. pipiens will increase by 261.52%. This model provides a high predictive value, predicted presence, and risk of Dirofilaria spp. infection and can serve as a tool for the management and control of this disease.

Effect of ambient ozone pollution on disease burden globally: A systematic analysis for the global burden of disease study 2019

BackgroundExposure to ambient ozone pollution causes health loss and even death, and both are the main risk factors for the disease burden worldwide. We comprehensively evaluated the ozone pollution-related disease burden. MethodsFirst, numbers and age-standardized rates of deaths and disability-adjusted life years (DALYs) were assessed globally and by sub-types in 2019. Furthermore, the temporal trend of the disease burden was explored by the linear regression model from 1990 to 2019. The cluster analysis was used to evaluate the changing pattern of related disease burden across Global Burden of Disease Study (GBD) regions. Finally, the age-period-cohort (APC) model and the Bayesian age-period-cohort (BAPC) model were used to predict the future disease burden in the next 25 years. ResultExposure to ozone pollution contributed to 365,222 deaths and 6,210,145 DALYs globally in 2019, which accounted for 0.65 % of deaths globally and 0.24 % of DALYs globally. The disease burden was consistently increasing with age. Males were high-risk populations and low-middle socio-demographic index (SDI) regions were high-risk areas. The disease burden of ozone pollution varied considerably across the GBD regions and the countries. In 2019, the number of deaths and DALYs cases increased by 76.11 % and 56.37 %, respectively compared to those in 1990. The predicted results showed that the number of deaths cases and DALYs cases for both genders would still increase from 2020 to 2044. ConclusionIn conclusion, ambient ozone pollution has threatened public health globally. More proactive and effective strategic measures should be developed after considering global-specific circumstances.

Spatial clusters, temporal behavior, and risk factors analysis of rabies in livestock in Ecuador

Rabies, a globally distributed and highly lethal zoonotic neglected tropical disease, has a significant impact in South America. In Ecuador, animal rabies cases are primarily linked to livestock, and hematophagous bats play a crucial role in disease transmission. This study aims to identify temporal trends, spatial patterns, and risk factors for animal rabies in Ecuador between 2014 and 2019. Epidemiological survey reports from the official Animal Rabies Surveillance Program of the Phyto and Zoosanitary Regulation and Control Agency of Ecuador (AGROCALIDAD) were used. The Animal Rabies Surveillance Program from AGROCALIDAD consists of an official passive surveillance program that receives reports from farmers or individuals (both trained or untrained) who have observed animals with neurological clinical signs and lesions compatible with bat bites, or who have seen or captured bats on their farms or houses. Once this report is made, AGROCALIDAD personnel is sent for field inspection, having to confirm the suspicion of rabies based on farm conditions and compatibility of signs. AGROCALIDAD personnel collect samples from all suspicious animals, which are further processed and analyzed using the Direct Fluorescent Antibody (DFA) test for rabies confirmatory diagnosis. In this case, study data comprised 846 bovine farms (with intra-farm sample sizes ranging from 1 to 16 samples) located in different ecoregions of Ecuador; out of these, 397 (46.93%) farms tested positive for animal rabies, revealing six statistically significant spatial clusters. Among these clusters, three high-risk areas were identified in the southeast of Ecuador. Seasonality was confirmed by the Ljung-Box test for both the number of cases (p < 0.001) and the positivity rate (p < 0.001). The Pacific Coastal lowlands and Sierra regions showed a lower risk of positivity compared to Amazonia (OR = 0.529; 95% CI = 0.318 – 0.883; p = 0.015 and OR = 0.633; 95% CI = 0.410 – 0.977; p = 0.039, respectively). The breeding of non-bovine animal species demonstrated a lower risk of positivity to animal rabies when compared to bovine (OR = 0.145; 95% CI = 0.062 – 0.339; p < 0.001). Similarly, older animals exhibited a lower risk (OR = 0.974; 95% CI = 0.967 – 0.981; p < 0.001). Rainfall during the rainy season was also found to decrease the risk of positivity to animal rabies (OR = 0.996; 95% CI = 0.995 – 0.998; p < 0.001). This study underscores the significance of strengthening the national surveillance program for the prevention and control of animal rabies in Ecuador and other countries facing similar epidemiological, social, and geographical circumstances.

Correlations between ecosystem service value and landscape ecological risk and its spatial heterogeneity in Jilin Province, China.

Exploring the correlations between ecosystem service value (ESV) and landscape ecological risk and the driving factors of their spatial variations is crucial for maintaining regional ecological security and promoting sustainable human well-being. We carried out a grid resampling size of 5 km×5 km assessment units of Jilin Pro-vince based on the remote sensing monitoring data of land use in 2000, 2005, 2010, 2015, and 2020. We quantitatively evaluated the landscape ecological risk and ESV, and analyzed their spatial-temporal variations. Employing bivariate spatial autocorrelation analysis and the geographical detector models, we examined the correlation between the landscape ecological risk and ESV and explored the driving factors for their spatial variations. The results showed that ESV in Jilin Province decreased from 385.895 billion yuan to 378.211 billion yuan during 2000-2020. The eastern region was dominated by extremely low risk, medium risk, and low risk areas. In contrast, the western region was mainly composed of extremely high risk and high risk areas. There was a significant negative correlation and spatial negative correlation between landscape ecological risk and ESV in Jilin Province. Human activity and land use type were the important driving factors for spatial differentiation in both landscape ecological risk and ESV. Our findings suggested that scientific land use regulation and appropriate control of human activities are critically needed to optimize Jilin Province's ecological environment.

Identifying High-Risk ZIP Codes for Childhood Lead Exposure: A Statewide ZCTA-Level Priority List for North Carolina

Background Research has consistently shown that there is no safe blood lead level (BLL) for children. Despite progress in lead poisoning prevention, lead exposure remains a persistent threat to the health and neurological development of children. To identify high-risk ZIP codes for use by families and health care providers for the entire state of North Carolina, we developed a risk model using ZIP Code Tabula­tion Area (ZCTA)-level census data. Methods We obtained all available BLL testing data from the North Carolina Department of Health and Human Services for the years 2010–2015 via data use agreement. We fit a multivariable regression model with the ZCTA-level mean of log normalized BLLs as the de­pendent variable and ZCTA-level census data for known risk factors of childhood lead exposure as predictors. We used this model to create a priority risk categorization. Results We organized ZCTAs into 20 quantiles, or priority risk categories, that can be used in local and statewide screening programs. The first six (of 20) quantiles were identified as particularly high-risk areas for childhood lead exposure. Limitations Because BLL testing is not universal, the BLL testing data used in this study are likely biased toward those most at risk for lead exposure. Conclusion This study demonstrates the utility of ZCTA-level census data in identifying high-risk ZIP codes for childhood lead exposure, which can be used to ensure that the highest-risk children are tested in a timely manner. This approach can be replicated to address lead exposure nationally.

Detection of long-term slope displacement using time-series DInSAR and geological factor analysis for susceptibility assessment of landslides in northwestern Kyushu Island

Ongoing climate change has increased the impact of landslides and related slope disasters on infrastructure and human lives. Microwave satellite remote sensing, particularly interferometric analysis of synthetic aperture radar (InSAR) data, is a powerful tool for routine monitoring of the displacement of slopes on small wavelength scales, independently from solar illumination and cloud coverage. Although various sophisticated techniques for time-series InSAR have been developed, practical application in conjunction with geological analysis to reveal intrinsic and triggering factors of slope displacement is still limited. In this paper, we describe a practical case of time-series InSAR analysis with a special focus on its geological implications in northwestern Kyushu, a high-risk area for slope-related disasters in Japan. The extracted susceptible polygons from the InSAR results show accuracy comparable with that of other recent research on landslide mapping. Intrinsic factor analysis based on a geographic information system reveals that the displacement occurs on relatively gentle slopes instead of steep areas, corresponding to the transient zone of Paleogene-Neogene sedimentary rocks and basalt. Triggering factor analysis based on correlation coefficients reveals a significant link between some displacement areas with mean and/or maximum precipitation for each observation duration. Those findings confirm the importance of carefully accounting for the geological background for landslide susceptibility assessment and policy making, apart from topographic and meteorological conditions.

Research on the Deployment of Professional Rescue Ships for Maritime Traffic Safety under Limited Conditions

This paper focuses on optimizing the deployment plan for standby points of professional rescue vessels based on the data of maritime incidents in the Beihai area of China. The primary objective is to achieve multi-level and multiple coverage of the jurisdictional waters of the Beihai Rescue Bureau. Models including the coverage quality of the jurisdictional waters, the coverage quality in high-risk areas, the maximum coverage of jurisdictional areas, and the maximum coverage of high-risk areas are constructed and solved using 0–1 integer programming. The optimal plan for eight standby points and their corresponding deployment plans for rescue vessels are obtained. A comparison with the current site selection plan of the Beihai Rescue Bureau validates the superiority of the proposed deployment plan for rescue vessel standby points in this paper.

Adapting vector surveillance using Bayesian experimental design: An application to an ongoing tick monitoring program in the southeastern United States

Maps of the distribution of medically-important ticks throughout the US remain lacking in spatial and temporal resolution in many areas, leading to holes in our understanding of where and when people are at risk of tick encounters, an important baseline for informing public health response. In this work, we demonstrate the use of Bayesian Experimental Design (BED) in planning spatiotemporal surveillance of disease vectors. We frame survey planning as an optimization problem with the objective of identifying a calendar of sampling locations that maximizes the expected information regarding some goal. Here we consider the goals of understanding associations between environmental factors and tick presence and minimizing uncertainty in high risk areas. We illustrate our proposed BED workflow using an ongoing tick surveillance study in South Carolina parks. Following a model comparison study based on two years of initial data, several techniques for finding optimal surveys were compared to random sampling. Two optimization algorithms found surveys better than all replications of random sampling, while a space-filling heuristic performed favorably as well. Further, optimal surveys of just 20 visits were more effective than repeating the schedule of 111 visits used in 2021. We conclude that BED shows promise as a flexible and rigorous means of survey design for vector control, and could help alleviate pressure on local agencies by limiting the resources necessary for accurate information on arthropod distributions. We have made the code for our BED workflow publicly available on Zenodo to help promote the application of these methods to future surveillance efforts.

Unraveling trends in schistosomiasis: deep learning insights into national control programs in China.

To achieve the ambitious goal of eliminating schistosome infections, the Chinese government has implemented diverse control strategies. This study explored the progress of the 2 most recent national schistosomiasis control programs in an endemic area along the Yangtze River in China. We obtained village-level parasitological data from cross-sectional surveys combined with environmental data in Anhui Province, China from 1997 to 2015. A convolutional neural network (CNN) based on a hierarchical integro-difference equation (IDE) framework (i.e., CNN-IDE) was used to model spatio-temporal variations in schistosomiasis. Two traditional models were also constructed for comparison with 2 evaluation indicators: the mean-squared prediction error (MSPE) and continuous ranked probability score (CRPS). The CNN-IDE model was the optimal model, with the lowest overall average MSPE of 0.04 and the CRPS of 0.19. From 1997 to 2011, the prevalence exhibited a notable trend: it increased steadily until peaking at 1.6 per 1000 in 2005, then gradually declined, stabilizing at a lower rate of approximately 0.6 per 1000 in 2006, and approaching zero by 2011. During this period, noticeable geographic disparities in schistosomiasis prevalence were observed; high-risk areas were initially dispersed, followed by contraction. Predictions for the period 2012 to 2015 demonstrated a consistent and uniform decrease. The proposed CNN-IDE model captured the intricate and evolving dynamics of schistosomiasis prevalence, offering a promising alternative for future risk modeling of the disease. The comprehensive strategy is expected to help diminish schistosomiasis infection, emphasizing the necessity to continue implementing this strategy.

Geographical distribution of radon and associated health risks in drinking water samples collected from the Mulazai area of Peshawar, Pakistan

Geospatial methods, such as GIS and remote sensing, map radon levels, pinpoint high-risk areas and connect geological traits to radon presence. These findings direct health planning, focusing tests, mitigation, and policies where radon levels are high. Overall, geospatial analyses offer vital insights, shaping interventions and policies to reduce health risks from radon exposure. There is a formidable threat to human well-being posed by the naturally occurring carcinogenic radon (222Rn) gas due to high solubility in water. Under the current scenario, it is crucial to assess the extent of 222Rn pollution in our drinking water sources across various regions and thoroughly investigate the potential health hazards it poses. In this regard, the present study was conducted to investigate the concentration of 222Rn in groundwater samples collected from handpumps and wells and to estimate health risks associated with the consumption of 222Rn-contaminated water. For this purpose, groundwater samples (n = 30) were collected from handpumps, and wells located in the Mulazai area, District Peshawar. The RAD7 radon detector was used as per international standards to assess the concentration of 222Rn in the collected water samples. The results unveiled that the levels of 222Rn in the collected samples exceeded the acceptable thresholds set by the US Environmental Protection Agency (US-EPA) of 11.1 Bq L−1. Nevertheless, it was determined that the average annual dose was below the recommended limit of 0.1 mSv per year, as advised by both the European Union Council and the World Health Organization. In order to avoid the harmful effects of such excessive 222Rn concentrations on human health, proper ventilation and storage of water in storage reservoirs for a long time before use is recommended to lower the 222Rn concentration.

Monitoring and Analysis of the Collapse at Xinjing Open-Pit Mine, Inner Mongolia, China, Using Multi-Source Remote Sensing

The collapse of open-pit coal mine slopes is a kind of severe geological hazard that may cause resource waste, economic loss, and casualties. On 22 February 2023, a large-scale collapse occurred at the Xinjing Open-Pit Mine in Inner Mongolia, China, leading to the loss of 53 lives. Thus, monitoring of the slope stability is important for preventing similar potential damage. It is difficult to fully obtain the temporal and spatial information of the whole mining area using conventional ground monitoring technologies. Therefore, in this study, multi-source remote sensing methods, combined with local geological conditions, are employed to monitor the open-pit mine and analyze the causes of the accident. Firstly, based on GF-2 data, remote sensing interpretation methods are used to locate and analyze the collapse area. The results indicate that high-resolution remote sensing can delineate the collapse boundary, supporting the post-disaster rescue. Subsequently, multi-temporal Radarsat-2 and Sentinel-1A satellite data, covering the period from mining to collapse, are integrated with D-InSAR and DS-InSAR technologies to monitor the deformation of both the collapse areas and the potential risk to dump slopes. The D-InSAR result suggests that high-intensity open-pit mining may be the dominant factor affecting deformation. Furthermore, the boundary between the collapse trailing edge and the non-collapse area could be found in the DS-InSAR result. Moreover, various data sources, including DEM and geological data, are combined to analyze the causes and trends of the deformation. The results suggest that the dump slopes are stable. Meanwhile, the deformation trends of the collapse slope indicate that there may be faults or joint surfaces of the collapse trailing edge boundary. The slope angle exceeding the designed value during the mining is the main cause of the collapse. In addition, the thawing of soil moisture caused by the increase in temperature and the reduction in the mechanical properties of the rock and soil due to underground voids and coal fires also contributed to the accident. This study demonstrates that multi-source remote sensing technologies can quickly and accurately identify potential high-risk areas, which is of great significance for pre-disaster warning and post-disaster rescue.

Geographically induced and the spatially differentiated dimension of flood vulnerability in Greater Kumasi Metropolis, Ghana

ABSTRACT Over the past several years, the constant flooding events and their lethal consequences have reignited the debate on the need for vulnerability assessment of flood-prone communities in urban areas as a flood risk mitigation and adaptation measure. This article focuses on Ghana and uses remotely sensed high-resolution data, and community mapping to assess the flood vulnerability of 442 urban communities of the Greater Kumasi Metropolitan Area (GKMA). The Compound Topographic Index and the Shuttle Radar Topographic Mission were used systematically. The results showed that 43% of communities were found in low-risk areas, 30% were in medium-risk areas, and 27% were in high-risk areas. Flood intensity and frequencies were found to be dependent on location relative to slopes and undulating terrain. We argue that the identified flood hazard communities should guide policymakers in proffering specific and targeted interventions toward flood risk reduction and community resilience strategies