Spatial Distribution Of Risk Research Articles

Health Risk Assessment of Residents' Exposure to Air Pollutants around the Sinpyeong-Jangrim Industrial Complex in Busan.

In this study, we aimed to evaluate the health effects of heavy metals and volatile organic compounds (VOCs) in fine particulate matter (PM2.5) in the air around the Sinpyeong-Jangrim Industrial Complex, Busan, and the necessity of reduction measures. We measured the concentrations of heavy metals and VOCs in PM2.5 in five areas with dense population around the Sinpyeong-Jangrim Industrial Complex. A health risk assessment was conducted, and the spatial risk distribution for the entire Saha-gu area was predicted using inverse distance weighting (IDW). Our results indicated that the carcinogens benzene, As, and Cr6+, and the non-carcinogen m,p-xylene, have the potential to adversely affect health. In particular, As was found to have the potential to express health effects at all measurement points. Additionally, based on the IDW results, the minimum values of benzene, arsenic, and m,p-xylene exceeded the threshold level, potentially affecting the health of residents across the entire Saha-gu area. In contrast, Cr6+ could potentially impact health only in some parts of Saha-gu as only its maximum value exceeded the threshold level. We demonstrated the importance of reducing air pollutant emissions from general industrial complexes. Our results will be useful in establishing more systematic mitigation measures to protect the health of residents near the Sinpyeong-Jangrim Industrial Complex and developing air pollutant management systems.

Assessing the impact of rising ambient temperatures on urban mortality rates using satellite mapping

Introduction. In the context of the change in global climate, the gain in the mortality rate due to an elevation of the temperature in the urban environment with the appearance of “heat waves” becomes an urgent problem. Materials and methods. To analyze the spatial distribution of risks and damages from premature mortality, there were selected cities located in several climatic zones of Russia including Omsk, Astrakhan, Rostov-on-Don, Lipetsk, Arkhangelsk. For satellite mapping of risks and damages from premature deaths caused by an elevation of the temperature in urban spaces, materials from summer surveys by Landsat series satellites were used. Results. The analysis of all satellite maps allows concluding the risks of an increase in urban temperature, potential deaths, and economic damage within the territories of the studied cities to be unevenly distributed. Advanced values of potential mortality are localized in places where high population density coincides with an enhanced risk of the temperature rise. The highest damage values were noted in Rostov-on-Don and Astrakhan, the lowest – in Arkhangelsk. Limitations. Mortality analysis was carried out without taking into account the age and sex structure of the population. Conclusion. The results of applying the methodology for satellite mapping of risks and economic losses from premature mortality due to rising urban temperatures showed accuracy acceptable for making management decisions to protect the health of the urban population. The results of satellite mapping of these events can be used in the development of preventive measures aimed at reducing premature mortality.

Identifying disproportionate impacts in Arkansas: New considerations for environmental justice mapping and implications for leaders

In 2021, Charles Lee defined disproportionate environmental and public health impacts and offered an approach for environmental justice (EJ) leaders to identify disproportionate impacts. However, Lee's model has not yet been compared to current mapping models, nor has it been applied to the highly disadvantaged landscape of Arkansas. In this study, we extend Lee's model and offer a complementary theory of disadvantage to identify, characterize, and describe disproportionate impacts in Arkansas. We ask: What communities are most disproportionately impacted? What is the spatial distribution of risk to environmental burdens and population vulnerabilities? How do these burdens and vulnerabilities cluster together? How might the theory of disadvantage assist in disproportionate impacts research? We deploy a descriptive, mixed-methods design using data from national mapping tools. Findings reveal regions of Arkansas that are disproportionately impacted. However, no region is devoid of high risk. Minority populations are the most impacted groups, and populations living in disproportionately impacted communities tend to suffer from specific burdens and vulnerabilities that erode well-being. The theory of disadvantage is found to be useful for disproportionate impacts research. These results offer EJ leaders a new lens to view and design mapping tools and they clarify intervention points.

Study on agricultural drought disaster risk assessment in Heilongjiang reclamation area based on SSAPSO optimization projection pursuit model.

Heilongjiang reclamation area serves as a crucial hub for commodity grain production and strategic reserves in China, playing a vital role in maintaining national food security. Investigating the assessment of agricultural drought risk in this region can yield valuable insights into spatial and temporal variations in drought risk. Such insights can aid in formulating effective strategies for disaster prevention and mitigation, thereby minimizing food losses caused by drought disasters. This study employs a comprehensive indicator system comprising 17 indicators categorized into hazard, exposure, vulnerability, and resistance capacity. The projection pursuit model is applied to evaluate regional drought risk, while the PSO algorithm, optimized by the SSA algorithm, addresses the limitations of low local search ability and search accuracy during the large-scale search process of the PSO optimization algorithm. This study examines and compares the optimization and convergence capabilities of three algorithms: real number encoding-based genetic algorithm (RAGA), particle swarm optimization algorithm (PSO), and sparrow algorithm-based improved particle swarm optimization algorithm (SSAPSO). The analysis demonstrates that SSAPSO exhibits superior optimization performance and convergence properties, establishing it as a highly effective algorithm for optimization tasks. The findings reveal the following trends: over time, agricultural drought risk in Heilongjiang reclamation area has generally declined, with fluctuations observed in hazard and vulnerability, an increase in exposure, and a continuous enhancement of resistance capacity. Spatially, the western region exhibits significantly higher agricultural drought risk compared to the eastern region, primarily due to elevated hazard and vulnerability, coupled with lower resistance capacity. As the agricultural economy grows and agricultural expertise accumulates, the risk of agricultural drought decreases. However, variations in economic growth among different regions lead to diverse spatial distributions of risk.

Temporal dynamics of urban gas pipeline risks

Urban gas pipelines pose significant risks to public safety and infrastructure integrity, necessitating thorough risk assessment methodologies to mitigate potential hazards. This study investigates the dynamics of population distribution, demographic characteristics, and building structures to assess the risk associated with gas pipelines. Using geospatial analysis techniques, we analyze population distribution patterns during both day and night periods. Additionally, we conduct an in-depth vulnerability assessment considering multiple criteria maps, highlighting areas of heightened vulnerability in proximity to gas pipelines and older buildings. This study incorporated the concept of individual risk and the intrinsic parameters of gas pipelines to develop a hazard map. Hazard analysis identifies areas with elevated risks, particularly around main pipeline intersections and high-pressure zones. Integrating hazard and vulnerability assessments, we generate risk maps for both day and night periods, providing valuable insights into spatial risk distribution dynamics. The findings underscore the importance of considering temporal variations in risk assessment and integrating demographic and structural factors into hazard analysis for informed decision-making in pipeline management and safety measures.

Risk Assessment and Analysis of Its Influencing Factors of Debris Flows in Typical Arid Mountain Environment: A Case Study of Central Tien Shan Mountains, China

The Tien Shan Mountain range connects Central Asia with northwestern China and is a crucial transport junction between East and West Asia. It is a common location for regional debris flows, which pose a significant risk to ecological security and the safety of people and property. Nevertheless, limited knowledge exists about the distribution of disaster risks and the impacted populations. This study uses advanced machine learning techniques to identify the key natural and social factors influencing these hazards and incorporates the Social Vulnerability Index (SoVI) to assess societal vulnerability. The outcomes demonstrate that (1) the debris flow hazard in the Tien Shan Mountain area is primarily governed by the geological structure, which dictates the material source and, in turn, dictates the onset of debris flows. (2) The vulnerability demonstrates a high spatial tendency in the north and a low one in the south, with evident spatial clustering characteristics. (3) A total of 19.13% of the study area is classified as high-hazard, with specific distribution zones including the northern foothills of the Tien Shan Mountains, the low-mountain zones of the southern foothills of the Tien Shan Mountains, and the Yili Valley zone. This holistic approach offers valuable insights into the spatial distribution of risks, aiding in prioritizing disaster preparedness and mitigation efforts. Also, our findings and conclusions are beneficial for local decision makers to allocate resources effectively and promote sustainable development practices in the region.

Köppen climates and Scheffer index as indicators of timber risk in Europe (1901–2020)

Timber heritage faces numerous risks, with the threat from insect and fungal attack particularly sensitive to climate. The Scheffer index estimates the deterioration of wood by combining average temperature and days of rain. We determined the Scheffer index from twenty sites in Europe across the 20th and early 21st century using meteorological observations, largely from the European Climate Assessment Dataset. Results showed that Scheffer indices across Europe loosely align with Köppen climate classes, which are also defined in terms of temperature and rainfall. However, within a given Köppen class, a wide range of Scheffer indices were found, thus representing very different threats to heritage. This suggests that specific heritage climate parameters, such as the Scheffer index, are needed. Trends in the Scheffer index provide a fairly robust guide to management strategy by identifying locations where the risk for timber is increasing. However, a simple index cannot necessarily describe the complex interactions between biological pests and climate; such complexity is better handled with well-constructed models. Nevertheless, the Scheffer index offers heritage managers an easily calculated estimate of the spatial distribution of risk to timber and the likely direction of future change.

Method of Wildfire Risk Assessment in Consideration of Land-Use Types: A Case Study in Central China

Research on wildfire risk can quantitatively assess the risk of wildfire damage to the population, economy, and natural ecology. However, existing research has primarily assessed the spatial risk of wildfires across an entire region, neglecting the impact of different land-use types on the assessment outcomes. The purpose of the study is to construct a framework for assessing wildfire risk in different land-use types, aiming to comprehensively assess the risk of wildfire disasters in a region. We conducted a case study in Central China, collecting and classifying historical wildfire samples according to land-use types. The Light Gradient Boosting Machine (LGBM) was employed to construct wildfire susceptibility models for both overall and individual land-use types. Additionally, a subjective and objective combined weighting method using the Analytic Hierarchy Process (AHP) and Entropy Weight Method (EWM) was utilized to build the wildfire vulnerability model. By integrating susceptibility and vulnerability information, we comprehensively assessed the combined risk of wildfire disasters across land-use types. The results demonstrate the following: (1) Assessing wildfire susceptibility based on different land-use types compensated for limitations in analyzing overall wildfire susceptibility, with a higher prediction performance and more detailed susceptibility information. (2) Significant variations in wildfire susceptibility distribution existed among different land-use types, with varying contributions of factors. (3) Using the AHP-EWM combined weighting method effectively addressed limitations of a single method in determining vulnerability. (4) Land-use types exerted a significant impact on wildfire risk assessment in Central China. Assessing wildfire risk for both overall and individual land-use types enhances understanding of spatial risk distribution and specific land use risk. The experimental results validate the feasibility and effectiveness of the proposed evaluation framework, providing guidance for wildfire prevention and control.

Large-scale risk assessment on snow avalanche hazard in alpine regions

Abstract. Snow avalanches are recurring natural hazards that affect the population and infrastructure in mountainous regions, such as in the recent avalanche winters of 2018 and 2019, when considerable damage was caused by avalanches throughout the Alps. Hazard decision makers need detailed information on the spatial distribution of avalanche hazards and risks to prioritize and apply appropriate adaptation strategies and mitigation measures and thus minimize impacts. Here, we present a novel risk assessment approach for assessing the spatial distribution of avalanche risk by combining large-scale hazard mapping with a state-of-the-art risk assessment tool, where risk is understood as the product of hazard, exposure and vulnerability. Hazard disposition is modeled using the large-scale hazard indication mapping method RAMMS::LSHIM (Rapid Mass Movement Simulation::Large-Scale Hazard Indication Mapping), and risks are assessed using the probabilistic Python-based risk assessment platform CLIMADA, developed at ETH Zürich. Avalanche hazard mapping for scenarios with a 30-, 100- and 300-year return period is based on a high-resolution terrain model, 3 d snow depth increase, automatically determined potential release areas and protection forest data. Avalanche hazard for 40 000 individual snow avalanches is expressed as avalanche intensity, measured as pressure. Exposure is represented by a detailed building layer indicating the spatial distribution of monetary assets. The vulnerability of buildings is defined by damage functions based on the software EconoMe, which is in operational use in Switzerland. The outputs of the hazard, exposure and vulnerability analyses are combined to quantify the risk in spatially explicit risk maps. The risk considers the probability and intensity of snow avalanche occurrence, as well as the concentration of vulnerable, exposed buildings. Uncertainty and sensitivity analyses were performed to capture inherent variability in the input parameters. This new risk assessment approach allows us to quantify avalanche risk over large areas and results in maps displaying the spatial distribution of risk at specific locations. Large-scale risk maps can assist decision makers in identifying areas where avalanche hazard mitigation and/or adaption is needed.

The assessment of personal exposure in restaurants considering heat sources and ventilation strategies

Outbreaks of airborne infections during meal consumption in diverse restaurant settings have been extensively reported. It is widely recognized that effective ventilation strategies are essential to minimize the infection risk in indoor environments, and these strategies should be tailored to the heat sources. The purpose of this study is to compare the spatial distribution of risk in restaurant rooms that use mixing or displacement ventilation, specifically focusing on the heat sources used for different food types, namely hotpot, normal Chinese food, and iced food. Computational Fluid Dynamics (CFD) was employed to assess exposure risk. Our results indicate that the use of low-temperature heat sources can elevate the risk of infection by increasing the local vertical temperature gradient. In comparison to no heat source, the risk increased by 190.9% and 99.6% for displacement and mixing ventilation strategies, respectively. Under mixing ventilation, both low-temperature and no heat sources showed lower infection risks when compared to displacement ventilation. However, displacement ventilation is found to be highly effective in reducing the risk of infection when using a high-temperature heat source, with only 12.3% of the infection risk observed in mixing ventilation. Furthermore, the use of displacement ventilation resulted in a significant reduction in the odors emitted by hotpot, which were instead absorbed by clothes in the mixing ventilation scenario. Our findings provide crucial insights into the development of appropriate ventilation strategies for reducing personal exposure to airborne infections in diverse restaurant settings. Specifically, we recommend using displacement ventilation in restaurants that utilize high-temperature heat sources, as it can substantially reduce the risk of infection.

Trending and analysis of payload vs. All low earth conjunction data messages below 1000 km, from 2016 through 2021

The Air Force Safety Center is developing a program to quantify the distribution of risks to the orbital environment across time and space by trending and analyzing orbital conjunction events. Previous efforts described automating the intake of Conjunction Data Messages (CDM) previously generated by the 18th and 19th Space Defense Squadrons (18 SDS, 19 SDS) and developing pre-processing scripts necessary to populate information germane to orbital collision risk assessment. The intent of the current effort is to account not only for the probability of collision (Pc), but also the consequence of the debris products from a collision to the low Earth orbital environment, should it occur. To accomplish this, the team analyzed all reported CDMs of conjunction events below 1000 km in altitude, between 1 Jan 2016 and 31 Dec 2021. 18 SDS reported Pcs were re-computed using the Foster-92 method. A circular 2-dimensional interaction area was applied instead of a square, to reduce conservatism and lend closer towards realism. Hard Body Radii estimates were pooled from externally sourced information, including The Aerospace Corporation and various open sources. When not available, radius estimates were imputed based on available data. Covariance information was sourced from CDMs. For consequence, the amount of damaging, lethal non-trackable and trackable debris products were estimated using The Aerospace Corporation's IMPACT 8.0 model. The model accounts for relative velocity, object masses, mass distribution, and structure. Object distribution and structure were inferred from the object type field in the CDMs. Relative velocity was also sourced from CDMs. The object masses were sourced from data furnished by The Aerospace Corporation and Union of Concerned Scientists satellite database. The expected number of damaging, lethal non-trackable, and trackable debris products were multiplied by the Pc to produce a quantitative risk metric for each conjunction event. Risks were adjusted to account for likely collision avoidance maneuvers based on satellite age, mass, and Pc. The results show a logarithmic increase in total risk over time, particularly after 2020. This change correlates to the launch of Starlink and OneWeb satellites into orbit, and analysis confirmed that the uptick in risk is attributable to the launch of those large constellations into orbit. The risk metric for Starlink vs. Starlink conjunctions was further adjusted to more accurately account for proprietary processes and high accuracy ephemerides which enables better information for performing collision avoidance maneuvers. This adjustment reduced the slope of the risk trends starting in 2020, but they are still positive and logarithmic. Spatial domain analysis revealed that the preponderance of debris production risk for reported conjunctions below 1,000 km resides between the altitudes of 450 km and 850 km. The spatial distribution of risk by altitude changed starting in 2020 due to the launch of large constellations, resulting in a higher and more evenly distributed risk of debris production between 350 and 1,000 km. Results also indicate that more trackable debris would have likely been generated by collisions between payloads and trackable objects, with perigees below 1,000 km than the 37 that have been confirmed and cataloged during that time period. However, analysis of the four confirmed payload collision events with cataloged debris produced a bootstrapped confidence interval of trackable debris generated per year suggesting the trackable debris production rate estimated here is within a plausible range. Planned future work looks to address known assumptions and limitations. This work is the first known effort to quantitatively analyze historical risks of conjunction events using full sets of CDM data and fuse them with the latest semi-analytical breakup algorithms. Analyses such as these are necessary to better inform ongoing and future Space Safety and Space Traffic Management policy development efforts within the US Government and internationally.

Spatial-Temporal Risk Field for Intelligent Connected Vehicle in Dynamic Traffic and Application in Trajectory Planning

The intelligent connected vehicle (ICV) is an organic combination of connectivity and intellectuality, making it possible to exchange information and drive autonomously. How to assess and describe the traffic risk for the ICV is a fundamental and crucial problem. Unlike the existing studies that only obtain instantaneous and quasi-static risk fields due to the separation between space and time, this study proposes a novel Spatial-Temporal Risk Field (STRF) that represents the dynamic driving risk from the perspective of time-space coupling for ICVs in dynamic traffic is proposed. In order to generate the field for the different traffic elements, the influential traffic elements are divided into concrete elements and abstract elements according to their features and influencing mechanisms. And then, A biased sweeping method (BSM) is developed for concrete elements, and a modeling method based on Gaussian distribution is developed for abstract elements. The results of the STRF for a traffic scenario are visualized and analyzed in two forms: complete form and slice form. The results show that the STRF can precisely describe the risk distribution of each traffic element in a specific and characteristic shape according to the spatial-temporal situation. Additionally, this study also provides an application example of the STRF in trajectory planning to demonstrate the applicability and availability of the STRF. The STRF-based trajectory planning method greatly benefits from the STRF and shows the potential ability of flexible and personalized trajectory planning. The STRF proposed in this paper can express the dynamics and continuity of the spatial-temporal risk and describe the instantaneous risk by extracting the spatial risk distribution at a certain moment. The STRF can be used in risk assessment, decision making, trajectory planning, driving behavior modeling, and automatic testing.

Multi-Scenario Simulation to Predict Ecological Risk Posed by Urban Sprawl with Spontaneous Growth: A Case Study of Quanzhou.

The rapid expansion of different types of urban land continues to erode natural and semi-natural ecological space and causes irreversible ecological damage to rapidly industrialized and urbanized areas. This work considers Quanzhou, a typical industrial and trade city in southeastern China as the research area and uses a Markov chain integrated into the patch-generating land use simulation (PLUS) model to simulate the urban expansion of Quanzhou from 2005 to 2018. The PLUS model uses the random forest algorithm to determine the contribution of driving factors and simulate the organic and spontaneous growth process based on the seed generation mechanism of multi-class random patches. Next, leveraging the importance of ecosystem services and ecological sensitivity as indicators of evaluation endpoints, we explore the temporal and spatial evolution of ecological risks from 2018 to 2031 under the scenarios of business as usual (BAU), industrial priority, and urban transformation scenarios. The evaluation endpoints cover water conservation service, soil conservation service, biodiversity maintenance service, soil erosion sensitivity, riverside sensitivity, and soil fertility. The ecological risk studied in this work involves the way in which different types of construction land expansion can possibly affect the ecosystem. The ecological risk index is divided into five levels. The results show that during the calibration simulation period from 2005 to 2018 the overall accuracy and Kappa coefficient reached 91.77% and 0.878, respectively. When the percent-of-seeds (PoS) parameter of random patch seeds equals 0.0001, the figure of merit of the simulated urban construction land improves by 3.9% compared with the logistic-based cellular automata model (Logistic-CA) considering organic growth. When PoS = 0.02, the figure of merit of the simulated industrial and mining land is 6.5% higher than that of the Logistic-CA model. The spatial reconstruction of multiple types of construction land under different urban development goals shows significant spatial differentiation on the district and county scale. In the industrial-priority scenario, the area of industrial and mining land is increased by 20% compared with the BAU scenario, but the high-level risk area is 42.5% larger than in the BAU scenario. Comparing the spatial distribution of risks under the BAU scenario, the urban transition scenario is mainly manifested as the expansion of medium-level risk areas around Quanzhou Bay and the southern region. In the future, the study area should appropriately reduce the agglomeration scale of urban development and increase the policy efforts to guide the development of industrial land to the southeast.

Behavioural syndromes going wild: individual risk-taking behaviours of free-ranging wild boar

Behavioural syndromes theory predicts animals will exhibit sets of correlated risk-taking behaviours, with individuals displaying a constant willingness to take risks across different situations. Because this phenomenon has mostly been investigated under artificial experimental conditions, we aimed to establish whether wild animals spontaneously exhibit correlated risk-taking behaviours. We merged a large data set of spatial positions from 43 wild boar, Sus scrofa , with the spatial distribution of risks, modelling their risk-induced resource selection. Elaborating these data by means of a resource selection analysis (step selection functions) and focusing only on periods when individuals were active represents an innovative approach to the study of spontaneous risk-taking behaviour exhibited across different situations. We sampled wild boar from two populations in southern Europe living in extremely different environmental conditions (mountainous versus Mediterranean area). We used model coefficients and parameters as quantitative measures of four different risk-taking behaviours (human avoidance, site fidelity, selection for covered habitat and mobility), for both the population and the individual level. A risk-taking syndrome was detected within both populations, with wild boar clustering in two groups sharing homogeneous sets of risk-taking behaviours. Contrary to the theoretical expectation which predicted individual use of consistent strategies, we observed a compensation among pairs of risk-taking behaviours, with individuals that could be considered risk avoiders on account of strong site fidelity and human avoidance being risk prone in terms of a low selection for covered habitats and a high mobility. Our results suggest that wild animals may adaptively exhibit strategies trading off different risk-taking behaviours. The similarities of risk-taking strategies in the two monitored populations suggested that the observed syndrome was not shaped by biotic or abiotic factors, but rather unintentionally caused by humans. • Telemetry data allow to study behavioural syndromes of free-ranging animals. • Resource selection modelling successfully described wild boar risk-taking behaviours. • Wild boar exhibited a syndrome which traded-off different risk-taking behaviours. • The spontaneous risk-taking syndrome differed from the theoretical expectation. • Humans were likely the main driver of the observed risk-taking syndrome.

Challenges to maintaining disaster relief supply chains in island communities: disaster preparedness and response in Honolulu, Hawai’i

Due to global warming and rising sea levels, Honolulu, Hawai’i—a city on a remote island in the Pacific Ocean—is becoming more vulnerable to climate disasters. This article utilizes a mixed-method approach to investigate the challenges of maintaining disaster relief supply chains in the face of the emerging risks to Honolulu. We conduct eighteen in-depth interviews with key emergency management stakeholders to understand current approaches to supply chain risk management. Based on the research, three main challenges influence disaster preparedness in Hawai’i. First, the physical location of the island and the spatial distribution of risk, assets, and capabilities supporting emergency relief present large, unusual constraints not found in other jurisdictions. Second, the challenges of supply chain management, planning, communications and coordination are exacerbated during disruptions and disaster events. Unlike other states, with neighboring, contiguous jurisdictions, there are severe limitations to mutual aid and assistance from outside sources. Third, our research has also identified cultural challenges associated with social, political, and economic development and change. Stronger institutional collaboration both within the state and beyond its boundaries are key to increased supply chain resilience and effective response and recovery from disasters. We conduct social network analysis focusing on measures of density, degree, betweenness, and centrality to better understand the status and gaps in institutional collaboration. The social network analysis reveals that the current levels of collaboration are relatively low with significant gaps between government agencies and the private sector, as well as limited vertical collaborations with respect to supply chain management between federal, state and local partners. There is a need for stronger leadership with deeper engagement across stakeholders. A pathway forward includes: (1) vulnerability analyses focused on private sector supply chain management and public sector transport hubs, networks, and facilities, (2) exercises and training on emergency supply chain management, and (3) longer-term actions to support sustainability, self-sufficiency, local production, and community resilience. Our research is relevant not just to Hawai’i and other island and isolated communities but also to communities threatened by supply chain disruptions.

Human-elephant conflict risk assessment under coupled climatic and anthropogenic changes in Thailand

As natural resources decrease, competition between humans and large endangered wildlife increases, hindering the sustainability of animal conservation and human development. Despite the multi-dimensional nature of such interactions, proactive assessments that consider both the biosphere and anthroposphere remain limited. In this study, we proposed a human elephant conflict risk assessment framework and analyzed the spatial distribution of risk at the baseline (2000–2019) and in the near future (2025–2044) for Thailand, so that it may address the multifaceted characteristics and impending effects of climate change. Future scenarios were based on the combination of RCP45/SSP2 or RCP85/SSP5 and spatial policy, with or without elephant buffer zones. The composite risk index, comprised of hazard, exposure, and vulnerability, was constructed using the geometric mean, and validation was performed with the area under the curve (AUC). Our results projected a shift with increasing future risk toward higher latitudes and altitudes. Increasing future risk (average +1.7% to +7.4%) in the four forest complexes (FCs) in northwestern regions was a result of higher hazard and vulnerability from more favorable habitat conditions and increasing drought probability, respectively. Reduction in future risk (average −3.1% to −57.9%) in other FCs in lower regions was mainly due to decreasing hazard because of decreasing habitat suitability. Our results also highlight geographically explicit strategies to support long-term planning of conservation resources. Areas with increasing future risk are currently facing low conflict; hence it is recommended that future strategies should enhance adaptive capacity and coexistence awareness. Conversely, areas with lowering future risk from a decrease in habitat quality are recommended to identify buffer strategies around protected areas to support existing large elephant populations.

The emergence of imperfect philopatry and fidelity in spatially and temporally heterogeneous environments

Repeatability in habitat use - often termed site fidelity - is widespread and ubiquitous. Often, used habitat areas may be inherited by offspring in a phenomenon called natal philopatry, leading to intergenerational repeatability in habitat preferences. Both fidelity and philopatry may represent strategies that could optimize fitness in spatially and temporally heterogeneous environments; however, the suite of ecological factors that encourage or discourage fidelity and philopatry remain poorly understood in most taxa. While turtles (Order Testudines) exhibit both fidelity and natal philopatry when selecting nesting areas, emerging evidence suggests that nest choice may be more plastic than previously thought. However, few studies to date have attempted to explore the behavioral choices that may produce deviations from nest site fidelity and philopatry. We used a spatially-explicit, individual-based model to explore how behavioral responses to a heterogeneous landscape can generate population-level patterns of fidelity and philopatry. We demonstrate that this model can dispersal patterns inferred from behavioral and genetic data from a Blanding's Turtle (Emydoidea blandingii) population in central Wisconsin. Reduced philopatry was associated with increasing error rates in habitat discrimination, and reduced site fidelity was associated with decreasing risk tolerance. Unpredictable spatial distributions of risk increased natal philopatry but decreased site fidelity overall, suggesting that strong natal imprinting may sometimes be associated with rapid shifts in habitat preferences in unpredictable environments. Associating risk exposure with increased adult or nest mortality led to increased nest site fidelity in both cases, but with relatively weak effects on natal philopatry. We also demonstrate the utility of this model for other landscapes and species by exploring a simplified seabird system. Our model holds promise for more detailed exploration of the ecological and evolutionary factors that may give rise to fidelity and philopatry.

Early-life environmental exposures and anaemia among children under age five in Sub-Saharan Africa: An insight from the Demographic & Health Surveys

BackgroundReports show that the majority (60%) of children under age five years in Sub-Saharan Africa are anaemic. Studies in the region have mainly focused on the effect of individual, maternal and household socioeconomic status on the prevalence of anaemia. Currently, there is limited understanding of the association between early-life environmental exposures and anaemia among children in Sub-Saharan Africa. ObjectiveThe study examines the association between early-life environmental exposures and anaemia among children under five in Sub-Saharan Africa. MethodsThe study used health and demographic data from the Demographic and Health Survey (DHS) program and environmental data from NASA's Geospatial Interactive Online Visualization ANd aNalysis Infrastructure (GIOVANNI) and Atmospheric Composition Analysis Group. Three exposure periods were defined for the study, namely: in-utero, post-utero and cumulative life exposures. Multilevel mixed-effect models were used to assess the associations between environmental exposures and anaemia in each exposure period. ResultsThe findings show that 63% of children in the study were anaemic. It also reveals that mean PM 2.5 exposure for in-utero (34.93 μgm−3), post-utero (35.23 μgm−3) and cumulative exposure (35.08 μgm−3) were seven times higher than the new air quality guideline WHO recommended. A 10 μgm−3 increase in in-utero, post-utero and cumulative PM 2.5 exposures was associated with 4% to 5% increase in the prevalence of anaemia among children. A 10ppbv increase in in-utero, post-utero and cumulative carbon monoxide exposures was associated with 1% increase in the prevalence of anaemia among children. The spatial risk distribution maps show that socioeconomic factors modify the spatial risk distribution pattern. ConclusionThe findings of the study suggest that early-life exposure to ambient air pollution is significantly associated with anaemia among children in Sub-Saharan Africa. Thus, policies aimed at addressing air quality should be incorporated into targeted interventions for anaemia among children in the region.

Karst Collapse Risk Zonation and Evaluation in Wuhan, China Based on Analytic Hierarchy Process, Logistic Regression, and InSAR Angular Distortion Approaches

The current study presents a detailed assessment of risk zones related to karst collapse in Wuhan by analytical hierarchy process (AHP) and logistic regression (LR) models. The results showed that the LR model was more accurate with an area under the receiver operating characteristic (ROC) curve of 0.911 compared to 0.812 derived from the AHP model. Both models performed well in identifying high-risk zones with only a 3% discrepancy in area. However, for the medium- and low-risk classes, although the spatial distribution of risk zoning results were similar between two approaches, the spatial extent of the risk areas varied between final models. The reliability of both methods were reduced significantly by excluding the InSAR-based ground subsidence map from the analysis, with the karst collapse presence falling into the high-risk zone being reduced by approximately 14%, and karst collapse absence falling into the karst area being increased by approximately 6.5% on the training samples. To evaluate the practicality of using only results from ground subsidence maps for the risk zonation, the results of AHP and LR are compared with a weighted angular distortion (WAD) method for karst risk zoning in Wuhan. We find that the areas with relatively large subsidence horizontal gradient values within the karst belts are generally spatially consistent with high-risk class areas identified by the AHP- and LR-based approaches. However, the WAD-based approach cannot be used alone as an ideal karst collapse risk assessment model as it does not include geological and natural factors into the risk zonation.

Riesgo de infestaciones por los descortezadores Dendroctonus mexicanusHopkinsy DendroctonusfrontalisZimmermann en bosques de Michoacán

Introduction: Severe Dendroctonus spp. infestations are reported in North and Central America. Dendroctonus mexicanus Hopkins and Dendroctonus frontalis Zimmermann are recognized as forest pests and are common in the state of Michoacán, Mexico.Objective: To model current and future (2015-2039) spatial distribution of risk D. mexicanus and D. frontalis infestation in forests of Michoacán, Mexico.Materials and methods: Multicriteria evaluation techniques, including the analytic hierarchy process and fuzzy membership functions, were combined with climate and biophysical variables to obtain forest infestation risk maps for D. mexicanus and D. frontalis under current and future climate scenarios.Results and discussion: Climate, fire, tree density and topography were identified as relevant criteria influencing bark beetle outbreaks. The maximum risk value estimated for D. mexicanus was 0.78 and 0.83 for the current and future scenarios, respectively; for D. frontalis these values correspond to 0.84 and 0.85, respectively. In terms of area, high risk of infestation by D. mexicanus increased from 3.9 % (current scenario) to 5.0 % (future scenario); for D. frontalis it decreased from 10.8 % to 9.6 %. The very high-risk value remained constant (0.35 %) for both species and scenarios.Conclusions: Forests of the Transversal Volcanic Belt (in the northeastern part of Michoacán) have the highest risk of bark beetle infestation in the two modeled scenarios.