High-risk Areas Research Articles

Spatial analysis of teen births near the New Bedford Harbor Superfund site

Abstract Few studies have examined teen births at an individual-level to identify sociodemographic and environmental risk factors that may contribute to geographically high-risk areas. Communities around the New Bedford Harbor (NBH) Superfund site in southeast Massachusetts are racially and socioeconomically diverse and face potential environmental burdens. These psychosocial, chemical and physical stressors may be associated with behaviors that increase risk of teen birth. We examined associations between teen births (age < 20 years) from 1993–2009 and maternal residence at time of birth among mothers living in towns adjacent to the NBH Superfund site. We used generalized additive models with a smooth of residential location to predict teen birth risk among 29 472 births, adjusted for maternal race/ethnicity, birth country, and source of payment for prenatal care. Odds ratios (OR) and 95% confidence intervals (CIs) were calculated by location across the study area. Approximately 13% of births were to teen mothers. Residential location was significantly associated with teen birth. However, both the strength of associations and areas of elevated risk were attenuated after adjustment. The highest risk was observed north and northeast of the harbor (OR: 95% CI—3.19: 2.08, 4.88 and 2.93: 1.47, 5.82), which aligns with the prevailing wind direction from the harbor. Our study illustrates the importance of assessing the independent contributions of sociodemographic factors to geographic variation in teen birth risk. After accounting for maternal characteristics, results highlight areas within the study region where high-risk individuals may have also experienced environmental stressors.

Long-Term Effects of Crop Treatments and Fertilization on Soil Stability and Nutrient Dynamics in the Loess Plateau: Implications for Soil Health and Productivity

Soil degradation and erosion pose significant threats to agricultural sustainability in fragile ecosystems, such as the Loess Plateau in northern China. This study examines the long-term impacts of fertilization regimes and land-use systems on soil health, focusing on soil aggregate stability, fertility, and crop productivity. Six treatment combinations were evaluated in our study, including three continuous alfalfa fields (AL-CK, AL-P, and AL-NPM) and three continuous wheat fields (WH-NPM, WH-NP, and WH-P), each representing a combination of land use and three fertilization treatments: (1) no fertilization (CK), (2) inorganic fertilization (120 kg ha−1 N, 60 kg ha−1 P-NP), and (3) a combination of organic and inorganic fertilization (75 t ha−1 cow manure-NPM). Soil samples were collected from three depths (0–10 cm, 10–20 cm, and 20–30 cm) to assess physical and chemical properties. We evaluated the long-term effects of different fertilization treatments on soil stability, fertility, and crop yield to explore the interactions among soil’s physical and chemical properties under two land-use types and to assess the effectiveness of combined organic and inorganic fertilization strategies in improving soil health and mitigating erosion in vulnerable landscapes. The study revealed significant depth-specific variations with surface layers (0–10 cm) showing the greatest improvement under NPM treatments, particularly in continuous alfalfa fields, which exhibited higher soil fertility, improved soil structure, and crop yield. In contrast, continuous wheat fields with minimal fertilization demonstrated significantly lower soil quality and productivity. Using the combination of mineral fertilizers and organic amendments, such as cow manure, proved to be the most effective strategy for significantly enhancing nutrient availability and overall soil health. Partial Least Squares Modeling (PLS-M) and Mantel analysis highlighted the critical role of fertilization management in maintaining soil quality, boosting crop productivity, and mitigating erosion in high-risk areas. This study emphasizes the importance of integrated nutrient management for sustainable land use and soil conservation in erosion-prone regions.

Integrating Hydrological Models for Improved Flash Flood Risk Assessment and Mitigation Strategies in Northeastern Thailand

This study focuses on assessing flash flood risks in Northeastern Thailand, particularly within the Lam Saphung, Phrom, and Chern River Basins, which are highly susceptible to flash floods and debris flows. Using the HEC-RAS hydraulic model integrated with GIS tools, the research analyzes historical and scenario-based flood events to evaluate the impact of land use changes and hydrological dynamics. The model was calibrated and validated with statistical metrics such as R2 values ranging from 0.745 to 0.994 and NSE values between 0.653 and 0.893, indicating strong agreement with the observed data. This study also identified high-risk areas, with up to 5.49% and 5.50% increases in flood-prone areas in the Phrom and Chern River Basins, respectively, from 2006 to 2019. Key findings highlight the critical role of proactive risk management and targeted mitigation strategies in enhancing community resilience. The integration of advanced hydraulic modeling with detailed datasets enables precise flood hazard mapping, including flood depths exceeding 1.5 m in certain areas and high-risk zones covering up to 105.2 km2 during severe flood events. These results provide actionable insights for emergency response and land use planning. This research significantly contributes to hydrological risk assessments by advancing modeling techniques and delivering practical recommendations for sustainable flood management. The outcomes are particularly relevant for stakeholders, including urban planners, emergency management officials, and policymakers, who aim to strengthen resilience in vulnerable regions. By addressing the complexities of flash flood risk assessments with robust quantitative evidence, this study not only enhances the understanding of flood dynamics, but also lays the groundwork for developing adaptive strategies to mitigate the adverse impacts of flash floods, safeguarding both communities and infrastructure in the region.

DNA-Based Nanobiosensor for the Colorimetric Detection of Dengue Virus Serotype 2 Synthetic Target Oligonucleotide

Annually, the Philippines is burdened by a high number of infections and deaths due to Dengue. This disease is caused by the Dengue virus (DENV) and is transmitted from one human host to another by the female Aedes aegypti mosquito. Being a developing country, most of the high-risk areas in the Philippines are resource-limited and cannot afford equipment for detection and monitoring. Moreover, traditional clinical diagnoses of DENV infection are costly and time-consuming and require expertise. Hence, it is important to establish effective vector control and surveillance measures. In this study, we developed a DNA-based nanobiosensor for the colorimetric detection of Dengue virus serotype 2 (DENV-2) synthetic target DNA (stDNA S2) using gold nanoparticles (AuNPs). We successfully functionalized dextrin-capped gold nanoparticles with the designed DENV-2 oligonucleotide probes. The detection of the complementary stDNA S2, indicated by the pink-colored solution, was successfully performed within 15 min using 0.40 M NaCl solution. We were able to detect up to 36.14 ng/μL of stDNA S2 with some cross-reactivity observed with one non-complementary target. We believe that our study offers a basis for developing nanobiosensors for other DENV serotypes.

Risk effects of environmental factors on human brucellosis in Aksu Prefecture, Xinjiang, China, 2014–2023

The context of rapid global environmental change underscores the pressing necessity to investigate the environmental factors and high-risk areas that contribute to the occurrence of brucellosis. In this study, a maximum entropy (MaxEnt) model was employed to analyze the factors influencing brucellosis in the Aksu Prefecture from 2014 to 2023. A distributed lag nonlinear model (DLNM) was employed to investigate the lagged effect of meteorological factors on the occurrence of brucellosis. A total of 17 environmental factors were identified as affecting the distribution of brucellosis to varying degrees. The largest contributing was the normalized difference vegetation index (NDVI), followed by gross domestic product (GDP), and then meteorological factors such as average temperature, average relative humidity, and average wind speed. The receiver operating characteristic (ROC) curve demonstrated that the MaxEnt model exhibited a high degree of predictive efficacy, with an area under the curve (AUC) value of 0.921. The impact of high temperature (25℃ with a 2-month lag, RR = 3.130, 95% CI 1.642 ~ 5.965), low relative humidity (28% with a 2.5-month lag, RR = 1.795, 95% CI 1.298 ~ 2.483), and low wind speed (1.9 m/s with a 0-month lag, RR = 2.408, 95% CI 1.360 ~ 4.264) are the most significant meteorological factors associated with the incidence of brucellosis. The trends in the impact of extreme meteorological conditions on the spread of brucellosis were found to be generally consistent. Stratified analyses indicated that males were more affected by meteorological factors than females. The prevalence of brucellosis is influenced by a range of socio-economic and meteorological factors, and a multifaceted approach is necessary to prevent and control brucellosis.

Spatial analysis of traffic accidents using geographic information systems: case study of the wilaya of Sétif (Algeria)

The road network of the wilaya of Sétif is facing problems with congestion and intense traffic, which calls for special attention to traffic detection. The aim of this study is to highlight the capabilities of GIS to identify areas where road accidents are frequent, to analyze their causes, and to guide the efforts of the authorities of the wilaya of Sétif to resolve this situation. Thus, we propose solutions and measures to improve road planning and effective traffic management. The spatial analysis of the phenomenon, which is based on a quantitative and descriptive analysis method using GIS, reveals that the national roads are the most exposed to road accidents, with an incidence of 56.69%, or 257 injuries and 35 total deaths for the year of 2022. This type of road network is heavily utilized, and the current statistical analysis of 515 accidents, compared to the years 2021 and 2020, shows an improving trend, which is worrying. The results highlight the urgent need for targeted interventions and strategic planning to improve road safety in the region. By using GIS technology, authorities can not only identify high-risk areas, but also develop data-driven policies that focus on infrastructure improvements and effective traffic management strategies to reduce the increase in accidents.

Criticality disaster risk assessment for urban rail transport in Indonesia

The increasing frequency of extreme weather events due to climate change poses significant risks to urban rail systems, especially in disaster-prone regions like Indonesia. Urban railways, with limited rerouting options, are highly vulnerable to natural disasters such as floods, landslides, and earthquakes. These disruptions can cause cascading economic impacts, including infrastructure damage, productivity loss, and prolonged travel times. This study develops a methodological framework to assess disaster risk and climate vulnerability in urban rail systems, with Bandung, Indonesia, as the case study. The framework integrates Climate Vulnerability and Risk Assessment (CVRA) with the Movement and Place (M&P) framework, leveraging Geographic Information System (GIS) technology to identify and prioritize high-risk areas. CVRA evaluates hazards, exposure, and vulnerabilities, while M&P assesses the functional significance of transport nodes based on movement patterns and land use density. The combined analysis produces a risk matrix, enabling targeted mitigation strategies that integrate engineering solutions, urban planning, and policy interventions. Findings highlight critical vulnerabilities in Bandung’s proposed LRT system, particularly at stations susceptible to earthquakes and landslides. Recommended mitigation measures include resilient infrastructure designs, strategic planning for high-risk zones, and stakeholder engagement for prioritization. This framework offers practical guidance for policymakers to enhance urban rail resilience, reduce climate-related risks, and ensure sustainable urban mobility. It serves as a scalable model for other cities in Indonesia and globally, supporting adaptive and sustainable transport systems.

Longitudinal observational (single cohort) study on the causes of trypanocide failure in cases of African animal trypanosomosis in cattle near wildlife protected areas of Northern Tanzania.

African animal trypanosomosis (AAT) in cattle is primarily managed through trypanocide administration and insecticide application. Trypanocides can be used for both treatment and prophylaxis, but failure is often reported; this may occur due to resistance, substandard drugs, or inappropriate administration. This study in Tanzania aims to quantify reasons for trypanocide failure. An observational year-long longitudinal study was conducted in high-risk AAT areas in Serengeti District between June 2021-October 2022. Purposive sampling targeted herds with high utilization of the prophylactic trypanocide isometamidium chloride (ISM). When a farmer administered a trypanocide (ISM, diminazine aceturate, homidium), the project veterinarian assessed administration and treatment outcomes were determined based on PCR results from blood samples. A multivariable mixed model was utilized to evaluate risk factors for prophylaxis failure. Quality analysis was performed on trypanocide samples using High Performance Liquid Chromatography. A total of 630 cattle from 21 farms were monitored for a year-long period. A total of 295 trypanocide administrations were reported, predominantly being ISM (56%) used for prophylaxis (87%). One-third of trypanocide administrations were not given adequately, and many trypanocides were given to animals that tested negative for trypanosome infections by PCR. Failures occurred in 7% (95% CI 3.0-14%) of curative treatments, and 44% (95% CI 35-42%) of prophylactic administrations. The brand of ISM was significantly associated with odds of prophylaxis failure (p = 0.011). On quality analysis, two ISM samples had no detectable ISM isomers, but the remainder of ISM and DA samples (n = 46) fell within the range of acceptable levels. Drug counterfeiting, inadequate use of trypanocides, and resistance are all contributing to trypanocide failure, limiting effective AAT control and with implications for human disease risk. In order to curb trypanocide failure a multi-modal approach to managing the use of trypanocides is required to address all contributing factors.

Fibroblast activation protein peptide-targeted NIR-I/II fluorescence imaging for stable and functional detection of hepatocellular carcinoma.

Cancer-associated fibroblasts (CAFs) are the primary stromal component of the tumor microenvironment in hepatocellular carcinoma (HCC), affecting tumor progression and post-resection recurrence. Fibroblast activation protein (FAP) is a key biomarker of CAFs. However, there is limited evidence on using FAP as a target in near-infrared (NIR) fluorescence imaging for HCC. Thus, this study aims to develop a novel NIR fluorescent imaging strategy targeting FAP+ CAFs in HCC. The ICG-FAP-TATA probe was synthesized by conjugating a novel cyclization anti-FAP peptide with an indocyanine green derivative (ICG-NH2) as fluorophore, capable for NIR window I (NIR-I, 700-900nm) and II (NIR-II, 1000-1700nm) imaging. Its efficacy in lesion localization and other potential applications was evaluated. In vivo imaging of subcutaneous HCC models revealed that ICG-FAP-TATA specifically targeted FAP+ CAFs in the stroma and detected differences in CAFs loading within lesions. The fluorescence intensity/tumor-to-background ratio (TBR) positively correlated with FAP expression (R2 > 0.8, p < 0.05). Ex vivo incubation of tumor tissues with ICG-FAP-TATA provided stable fluorescence imaging of tumors in subcutaneous and orthotopic HCC models, including different cell line co-culture systems (LM3-luc, MHCC97H-luc, HepG2-luc + LX2), and various liver backgrounds (healthy/fibrotic) (n = 5 per group). TBR of the tumor mice models was higher for NIR-II than NIR-I imaging (3.89 ± 1.27 vs. 2.64 ± 0.64, p < 0.05). Moreover, NIR-I/II imaging of fresh tissues from seven patients with HCC undergoing surgery incubated with ICG-FAP-TATA visually provided the spatial distribution heterogeneity of CAFs. The targeted fluorescence was relatively enriched more in the blood flow direction and at the tumor edge, both of which were associated with tumor metastasis (all p < 0.05). This study presents a rapid and effective method for detecting HCC lesions, locating FAP+ CAFs, and visualizing high-risk areas for tumor metastasis at the macroscopic level. It offers a new promising approach with translational potential for imaging HCC.

Predicting dengue incidence in high-risk areas of China through the integration of Southeast Asian and local meteorological factors.

Dengue, a climate-sensitive mosquito-borne viral disease, is endemic in many tropical and subtropical areas, with Southeast Asia bearing the highest burden. In China, dengue epidemics are primarily influenced by imported cases from Southeast Asia. By integrating monthly maximum temperature and precipitation from Southeast Asia and local provinces in China, we aim to build models to predict dengue incidence in high-risk areas of China. From 2005-2023, a total of 117,839 dengue cases were reported, with Guangdong and Yunnan provinces accounting for 57.8 % and 26.2 % of cases, respectively. Large outbreaks occurred in 2014 (47,052 cases), 2019 (22,688 cases), and 2023 (19,936 cases), with peak incidence typically observed from August to October. The number of provinces reporting cases and outbreaks gradually linearly increased from 10 and 0 in 2005 to a peak of 30 and 11 in 2019, respectively, before declining in 2021 and 2022, then rebounding to 29 and 8 in 2023. Of the 13,927 imported cases, 91.3 % were from Southeast Asia, primarily from Myanmar (41.3 %) and Cambodia (27.3 %). Predictive models for dengue incidence in Guangdong Province showed high adjusted R2 values (0.993-0.999) and deviance explained values (0.975-0.989). The models from Cambodia, Thailand, and the Philippines outperformed the other six models. In Yunnan, adjusted R2 values and deviance explained values ranged from 0.81-0.84 and 0.90-0.92, respectively, with models from Laos, Myanmar and Cambodia achieving the best predictive performance. Incorporating meteorological data from Southeast Asia along with local data from China, we were able to develop accurate predictive models for dengue incidence in the high-risk areas of China.

Evaluation of snow-drifting influencing factors and susceptibility of transportation infrastructure lines

The construction of transportation infrastructure lines alters the natural terrain, leading to uneven redistribution of snow under the influence of snow-drifting, which can compromise road safety. With the ongoing expansion of transportation networks in cold regions, transportation engineers are increasingly focusing on snow-drifting phenomena and the associated disasters. This study, based on a GIS platform, conducts a case study of a transport infrastructure project in Xinjiang to analyze the geographical and environmental conditions along the route. Through on-site monitoring and investigation, factors related to the occurrence of snow-drifting disasters were implied. The WOE (Weight of Evidence) model was selected as the base evaluation model, and the BP-GA algorithm was applied to optimize the weights of evaluation indicators. This led to the establishment of a susceptibility evaluation index system for snow-drifting disasters, improving both the computational efficiency and the accuracy of the evaluation model. The results indicate that the evaluation accuracies of the WOE, WOE-BP, and WOE-BP-GA models were 72.32%, 75.32%, and 85.18%, respectively. The use of the GA-BP algorithm effectively captured the complex nonlinear relationships among various factors, producing evaluation results highly consistent with real-world conditions. This method may efficiently identify high-risk areas of snow-drifting along transportation infrastructure lines, providing valuable insights for disaster prevention using ArcGIS.

A GIS based spatiotemporal modelling approach for cycling risk mapping using crowd-sourced sensor data

ABSTRACT The major objective of this study is to apply integrated data-driven methods to estimate cyclist risk and discomfort in Berlin based on the OpenSenseMap dataset. The proposed approach makes use of crowd-sourced sensor data collected during cycling (speed, bike vibration, distance to other objects), spatial statistics and multi-criteria decision analysis to provide a continuous estimation of cycling discomfort and risk in the traffic network. We employed cycling traffic volume and route discomfort estimation techniques to determine the spatiotemporal patterns of cycling traffic volume and discomfort levels. Accordingly, a GIS-based multiple criteria analysis approach was applied to map areas with high cycling traffic volume based on the condition of the cycling lanes, environmental, road traffic, land use and sociodemographic characteristics. The results show that the central area of Berlin has a high cycling traffic volume as well as a high level of discomfort. In this context, we found a significant spatial correlation between the cycling traffic volume and discomfort with the land use characteristics such as commercial or residential areas, motor vehicle traffic volume and sociodemographic characteristics in Berlin. Furthermore, results revealed a correlation between intensive traffic volume and commercial zones, schools and university areas. As we identified high-risk cycling directions and their autocorrelation with relevant indicators, the obtained results from this study will support decision-makers and authorities in recognizing the high-risk cycling areas and optimizing the risk areas, which accordingly increase the cycling safety and wellbeing of citizens.

Ecological risk assessment and response prediction caused by LUCC in the central Yunnan Province

With the acceleration of urbanization, unreasonable land use poses a serious threat to ecological security. However, there is still some space for improvement in the existing assessment of ecological risks (ERs) caused by land use/land cover change (LUCC). Therefore, this study takes the central Yunnan Province (CYP) as an example, and uses the Patch-Generating Land Use Simulation (PLUS) model and the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model to simulate the land use/land cover (LULC) in different scenarios in the future, calculate the ecosystem services (ESs) from 2000 to 2020 and the next 20 years, as well as ERs of various types of ESs caused by LUCC. In addition, the Pearson correlation coefficient and geographically weighted regression (GWR) are used to analyze the trade-off and synergy relationships between various ecological risks, and the geographic detector method (GDM) is used to analyze their driving factors. The research results indicate that: (1) there have been significant changes in LULC in the CYP in the past 20 years. (2) There was significant spatial heterogeneity among various types of ESs in the CYP. (3) In the past 20 years, the distribution range of ERs has been relatively large, with significant spatial differences. (4) The ERs caused by LUCC in the next 20 years generally show a decreasing trend, and the ecological high-risk areas are mainly concentrated on construction land. (5) The ERs associated with ecosystem service types are mainly synergistic. ER is influenced by a combination of natural factors, socio-economic factors, and distance factors, with some factors having strong interactions. In short, the future ERs of CYP have decreased, and the ERs corresponding to various ecosystem services (ESs) interact with each other, causing a ripple effect. Finally, this study proposes policy recommendations to alleviate ERs in the CYP.

Spatiotemporal evolution of landscape ecological risk and its driving factors of the Beijing–Tianjin–Hebei major mineral belt, 1985–2022

The Beijing-Tianjin-Hebei major mineral belt represents a significant economic development area in China. Effective monitoring and assessment of the regional landscape ecological risk can provide a scientific basis for an ecological protection strategy for the environmental protection of the Beijing-Tianjin-Hebei major mineral belt. In this study, a landscape ecological risk index was constructed based on land use/land cover, and the spatial and temporal variations of landscape ecological risk were subsequently analyzed. Furthermore, the contribution of the main driving factors of landscape ecological risk was quantified in the Beijing-Tianjin-Hebei major mineral belt. The results demonstrate that: (1) The land use types within the study area underwent significant changes from 1985 to 2022. The predominant type of transfer was cropland, which was primarily converted to construction land, grassland, and woodland. (2) The landscape ecological risk in central-northern and western parts of the Beijing-Tianjin-Hebei major mineral belt is higher, while the landscape ecological risk in the southwest parts is lower. Using 2015 as the time point, the landscape ecological risk in the study area was found to change, with the average value of landscape ecological risk for all classes of landscape ecological risk within 2015–2022 being lower than that of 1985–2015, with the exception of the high-risk area. The mean annual landscape ecological risk is obviously higher during the 1985–2015 period in comparison to the 2015–2022 period, with the exception of regions exhibiting high risk. (3) There is a significant positive spatial correlation between landscape ecological risks in different periods. The pattern of landscape ecological risk exhibits both ‘high-high’ aggregation and ‘low-low’ aggregation. The ‘high-high’ aggregations are primarily located in the northern, central and western parts of the study area, while the ‘low-low’ aggregation zones are mainly located in the southeastern study region. (4) The spatial distribution of landscape ecological risk is predominantly shaped by population density and slope. In the context of interactive factor detection, the positive interaction between slope and average annual temperature, night-time illumination and slope, population density and annual precipitation were identified as exerting a more significant influence on the observed spatial differentiation of landscape ecological risk. It was found that the interaction of multiple drivers had a more pronounced impact on landscape ecological risk than any single factor. The findings of the research project provide a scientific rationale and reference for future land use, resource optimization, landscape ecological risk differential management and ecological restoration. Furthermore, the findings are of considerable importance in terms of maintaining ecological security patterns.

Evaluation of Water Inrush Risk in the Fault Zone of the Coal Seam Floor in Madaotou Coal Mine, Shanxi Province, China

As coal seams are mined at greater depths, the threat of high water pressure from the confined aquifer in the floor that mining operations face has become increasingly prominent. Taking the Madaotou mine field in the Datong Coalfield as the research object, in the context of mining under pressure, for the main coal seams in the mining area, first of all, an improved evaluation method for the vulnerability of floor water inrush is adopted for hazard prediction. Secondly, numerical simulation is used to conduct a simulation analysis on the fault zones in high-risk areas. By using the fuzzy C-means clustering method (FCCM) to improve the classification method for the normalized indicators in the original variable-weight vulnerability evaluation, the risk zoning for water inrush from the coal seam floor is determined. Then, through the numerical simulation method, a simulation analysis is carried out on high-risk areas to simulate the disturbance changes of different mining methods on the fault zones so as to put forward reasonable mining methods. The results show that the classification of the variable-weight intervals of water inrush from the coal seam floor is more suitable to be classified by using fuzzy clustering, thus improving the prediction accuracy. Based on the time effect of the delayed water inrush of faults, different mining methods determine the duration of the disturbance on the fault zones. Therefore, by reducing the disturbance time on the fault zones, the risk of karst water inrush from the floor of the fault zones can be reduced. Through prediction evaluation and simulation analysis, the evaluation of the risk of water inrush in coal mines has been greatly improved, which is of great significance for ensuring the safe and efficient mining of mines.

Spatial Modeling of Forest Fire Risks in A Watershed Using Remote Sensing

Objective: This study investigates remote sensing techniques for monitoring and environmental planning in areas with a higher risk of wildfires in the São Nicolau watershed, located in the municipality of São João Evangelista-MG, Brazil. Method: The research employed a quantitative approach, analyzing a 30-year historical series and incorporating nine significant variables influencing wildfire risk. The data were processed and analyzed using advanced geotechnologies, enabling the mapping of risk areas. Results and Discussion: The results indicate that the study area comprises 3.02 ha of low risk (0.13%), 3,835.66 ha of moderate risk (16.35%), 17,540.28 ha of high risk (74.79%), 2,073.87 ha of very high risk (8.84%), and 0.18 ha of extremely high risk (0.00%). These findings reveal the predominance of high-risk areas, highlighting the urgent need for mitigation measures. Wildfire risk maps represent the areas most prone to fire on a base map, integrating factors that influence fire ignition and spread. Based on this information, various measures can be implemented, such as increased surveillance in high-risk areas, restricted access, construction of preventive firebreaks, and reorganization of management practices (Ferraz &amp; Vettorazzi, 1998). Research Implications: The findings provide valuable input for strategic planning of preventive actions against wildfires, contributing to public policies for environmental conservation and sustainable management in critical areas.

Spatial Analysis of Maritime Disasters in the Philippines: Distribution Patterns and Identification of High-Risk Areas

Maritime accidents frequently occur in the Philippine archipelagic waters, often resulting in significant loss of life. These incidents highlight the urgent need for improvements in the country’s maritime safety systems. By utilising accident data from the Philippine Coast Guard and the GISIS IMO databases, spatial analytical approaches were employed to determine incident distribution patterns and resulted in an overall depiction of the likelihood component of risk across the country’s territorial waters. Kernel density and hotspot analysis revealed areas where incidents were concentrated and where statistically significant hotspots occurred. The Maxent tool was used to develop risk likelihood models for the incident locations using environmental rasters representing wind speed, significant wave height, depth, surface current, land distance and port distance. Model performance metrics including the AUC, TSS and Kappa were used to compare the two datasets and provide confidence on model robustness. Variable contribution figures showed that land distance is the most influential variable, with the majority of high-risk areas predominantly located near population centres. The resulting maps provide an intuitive and informative depiction of the characteristic patterns of maritime accidents in the country, identify areas of high risk requiring immediate attention and offer valuable insights to support strategies for improving and enhancing the country’s maritime safety.

Application of Statistical Methods for the Characterization of Radon Distribution in Indoor Environments: A Case Study in Lima, Peru

This study evaluates the effectiveness of advanced statistical and geospatial methods for analyzing radon concentration distributions in indoor environments, using the district of San Martín de Porres, Lima, Peru, as a case study. Radon levels were monitored using LR-115 nuclear track detectors over three distinct measurement periods between 2015 and 2016, with 86 households participating. Detectors were randomly placed in various rooms within each household. Normality tests (Shapiro–Wilk, Anderson–Darling, and Kolmogorov–Smirnov) were applied to assess the fit of radon concentrations to a log-normal distribution. Additionally, analysis of variance (ANOVA) was used to evaluate the influence of environmental and structural factors on radon variability. Non-normally distributed data were normalized using a Box–Cox transformation to improve statistical assumptions, enabling subsequent geostatistical analyses. Geospatial interpolation methods, specifically Inverse Distance Weighting (IDW) and Kriging, were employed to map radon concentrations. The results revealed significant temporal variability in radon concentrations, with geometric means of 146.4 Bq·m−3, 162.3 Bq·m−3, and 150.8 Bq·m−3, respectively, across the three periods. Up to 9.5% of the monitored households recorded radon levels exceeding the safety threshold of 200 Bq·m−3. Among the interpolation methods, Kriging provided a more accurate spatial representation of radon concentration variability compared to IDW, allowing for the precise identification of high-risk areas. This study provides a framework for using advanced statistical and geospatial techniques in environmental risk assessment.

Relatedness of White-Tailed Deer from Culling Efforts Within Chronic Wasting Disease Management Zones in Minnesota.

In white-tailed deer (Odocoileus virginianus), closely related females form social groups, avoiding other social groups. Consequently, females infected with chronic wasting disease (CWD) are more likely to infect social group members. Culling has been used to reduce CWD transmission in high-risk areas; however, its effectiveness in removing related individuals has not been assessed. We analyzed 11 microsatellites and a mitochondrial DNA fragment to assess: (1) the genetic structure in white-tailed deer in Minnesota, USA and (2) the effectiveness of localized culling to remove related deer. For (1), we genotyped deer culled in 2019 and 2021 in three CWD management zones, and deer collected in between zones. For (2), we only included culled deer, defining "culled groups" as deer obtained in the same township-range-section and year. We compared mean relatedness among deer from the same culled group (intra-group relatedness) and among deer from different culled groups (inter-group relatedness). We did not find evidence of genetic structure, suggesting that an outbreak in any of the management zones could naturally spread to the others. Culling removed deer that were on average more related than expected by chance (intra-group relatedness > inter-group relatedness), and most highly-related deer were culled in the same bait site.

Trends and analysis of risk factor differences in the global burden of chronic kidney disease due to type 2 diabetes from 1990 to 2021: A population-based study.

Chronic kidney disease (CKD) is a significant contributor to the global burden of disease. Among its causes, chronic kidney disease due to type 2 diabetes (CKD-T2D) is the primary subtype. This study aims to provide an updated assessment of the global disease burden of CKD-T2D from 1990 to 2021. It will analyse the trends in the global burden of CKD-T2D and the differences in risk factors, as well as project changes over the next 15 years. The data for this study were derived from the Global Burden of Disease, Injuries, and Risk Factors Study (GBD) 2021. Estimates of prevalence, incidence, deaths and disability-adjusted life years (DALYs) for CKD-T2D, along with their 95% uncertainty intervals (UIs), were extracted. The trends in CKD-T2D burden from 1990 to 2021 were analysed from overall and local perspectives. An age-period-cohort model was used to estimate the age, period and cohort effects on the prevalence and incidence of CKD-T2D between 1990 and 2021. A decomposition analysis was conducted to assess the contribution of population size, age structure and epidemiological changes to the burden of CKD-T2D. Population-attributable fractions were determined for each risk factor, and a difference analysis was conducted. Additionally, projections were made regarding changes in the burden of CKD-T2D over the next 15 years. In 2021, the global burden of CKD-T2D remained significant, with a total of 107 559 955 cases. The age-standardized prevalence rate (ASPR) was 1259.63 per 100 000 people. The age-standardized incidence rate (ASIR) was 23.07 per 100 000 people, and the age-standardized death rate (ASDR) was 5.72 per 100 000 people. The age-standardized disability-adjusted life years (DALYs) was 131.08 per 100 000. The global burden of CKD-T2D showed variation across different socio-demographic index (SDI) regions. In 2021, the overall burden of CKD-T2D continued to rise, with the age effect increasing with age. Both prevalence and incidence risks showed an upward trend over time. Decomposition analysis indicated that population growth and ageing were the primary contributors to the global burden of DALYs related to CKD-T2D. Metabolic risk factors such as high fasting plasma glucose and high body mass index (BMI) are the most significant attributable risk factors. It is projected that by 2036, the trends in ASPR, ASIR, ASDR and age-standardized DALYs will stabilize. However, ASIR and age-standardized DALYs are expected to continue rising, and the number of cases of prevalence, incidence, mortality and DALYs will persist in their upward trend. CKD-T2D imposes a significant global disease burden, with health disparities and unequal disease outcomes continuing to worsen across countries and regions due to differences in socio-economic development levels. This burden is primarily driven by population growth, ageing and metabolic risks such as obesity, hyperglycaemia and hypertension. Although the rate of increase in disease burden may slow over the next 15 years, the number of cases is expected to rise substantially. Therefore, enhancing prevention, early screening and effective treatment interventions, particularly in high-risk areas, is crucial for reducing the disease burden and narrowing health disparities.