A novel climate and health decision support platform: Approach, outputs, and policy considerations.

BACKGROUND: The adverse health impacts of climate change are increasingly apparent and the need for adaptation activities is pressing. Risks, drivers, and decision contexts vary significantly by location, and high-resolution, place-based information is needed to support decision analysis and risk reduction efforts at scale. METHODS: Using the Intergovernmental Panel on Climate Change (IPCC) risk framework, we developed a causal pathway linking heat with a composite outcome of heat-related morbidity and mortality. We used an existing systematic literature review to identify variables for inclusion and the authors' expert judgment to determine variable combinations in a hierarchical model. We parameterized the model for Washington state using observational (1991-2020 and June 2021 extreme heat event) and scenario-driven temperature projections (2036-2065), compared outputs against relevant existing indices, and analyzed sensitivity to model structure and variable parameterization. We used descriptive statistics, maps, visualizations and correlation analyses to present results. RESULTS: The Climate and Health Risk Tool (CHaRT) heat risk model contains 25 primary hazard, exposure, and vulnerability variables and multiple levels of variable combinations. The model estimates population-weighted and unweighted heat health risk for selected periods and displays estimates on an online visualization platform. Population-weighted risk is historically moderate and primarily limited by hazard, increasing significantly during extreme heat events. Unweighted risk is helpful in identifying lower population areas that have high vulnerability and hazard. Model vulnerability correlate well with existing vulnerability and environmental justice indices. DISCUSSION: The tool provides location-specific insights into risk drivers and prioritization of risk reduction interventions including population-specific behavioral interventions and built environment modifications. Insights from causal pathways linking climate-sensitive hazards and adverse health impacts can be used to generate hazard-specific models to support adaptation planning.

Tropical and subtropical Asia's valued tree species under threat.

Tree diversity in Asia's tropical and subtropical forests is central to nature-based solutions. Species vulnerability to multiple threats, which affect provision of ecosystem services, is poorly understood. We conducted a region-wide, spatially explicit assessment of the vulnerability of 63 socioeconomically important tree species to overexploitation, fire, overgrazing, habitat conversion, and climate change. Trees were selected for assessment from national priority lists, and selections were validated by an expert network representing 20 countries. We used Maxent suitability modeling to predict species distribution ranges, freely accessible spatial data sets to map threat exposures, and functional traits to estimate threat sensitivities. Species-specific vulnerability maps were created as the product of exposure maps and sensitivity estimates. Based on vulnerability to current threats and climate change, we identified priority areas for conservation and restoration. Overall, 74% of the most important areas for conservation of these trees fell outside protected areas, and all species were severely threatened across an average of 47% of their native ranges. The most imminent threats were overexploitation and habitat conversion; populations were severely threatened by these factors in an average of 24% and 16% of their ranges, respectively. Our model predicted limited overall climate change impacts, although some study species were likely to lose over 15% of their habitat by 2050 due to climate change. We pinpointed specific natural areas in Borneo rain forests as hotspots for in situ conservation of forest genetic resources, more than 82% of which fell outside designated protected areas. We also identified degraded areas in Western Ghats, Indochina dry forests, and Sumatran rain forests as hotspots for restoration, where planting or assisted natural regeneration will help conserve these species, and croplands in southern India and Thailand as potentially important agroforestry options. Our results highlight the need for regionally coordinated action for effective conservation and restoration.

Especies de Árboles Valoradas y Amenazadas de Asia Tropical y Subtropical Resumen La diversidad de árboles en los bosques tropicales y subtropicales de Asia es un eje central para las soluciones basadas en la naturaleza. La vulnerabilidad de las especies ante las múltiples amenazas, las cuales afectan el suministro de servicios ambientales, es un tema poco comprendido. Realizamos una evaluación regional espacialmente explícita de la vulnerabilidad de 63 especies de árboles de importancia socioeconómica ante la sobreexplotación, incendios, sobrepastoreo, conversión del hábitat y cambio climático. Los árboles se seleccionaron para su evaluación a partir de listas nacionales de prioridades, y las selecciones fueron validadas por una red de expertos de 20 países. Usamos el modelado de idoneidad Maxent para predecir el rango de distribución de las especies, conjuntos de datos espaciales de libre acceso para mapear la exposición a las amenazas y rasgos funcionales para estimar la susceptibilidad a las amenazas. Con base en la vulnerabilidad a las amenazas actuales y al cambio climático, identificamos las áreas prioritarias para su conservación y restauración. En general, el 74% de las áreas más importantes para la conservación de estos árboles quedó fuera de las áreas protegidas y todas las especies estaban seriamente amenazadas en promedio en el 47% de su distribución nativa. Las amenazas más inminentes fueron la sobreexplotación y la conversión del hábitat; las poblaciones estuvieron seriamente amenazadas por estos factores en promedio en el 24% y 16% de su distribución, respectivamente. Nuestro modelo predijo un impacto general limitado del cambio climático, aunque algunas especies estudiadas tuvieron la probabilidad de perder más del 15% de su hábitat para el 2050 debido a este factor. Identificamos áreas naturales específicas en las selvas de Borneo como puntos calientes para la conservación in situ de los recursos genéticos forestales, más del 82% de los cuales estaban fuera de las áreas protegidas designadas. También identificamos áreas degradadas en los Ghats Occidentales, los bosques secos de Indochina y las selvas de Sumatra como puntos calientes para la restauración, en donde la siembra o la regeneración natural asistida ayudarán a conservar estas especies. Además, identificamos campos de cultivo al sur de India y Tailandia como potenciales opciones importantes de agrosilvicultura. Nuestros resultados resaltan la necesidad de acciones regionales coordinadas para la conservación y restauración efectivas.

Mapping the effects of drought on child stunting.

As climate change continues, it is expected to have increasingly adverse impacts on child nutrition outcomes, and these impacts will be moderated by a variety of governmental, economic, infrastructural, and environmental factors. To date, attempts to map the vulnerability of food systems to climate change and drought have focused on mapping these factors but have not incorporated observations of historic climate shocks and nutrition outcomes. We significantly improve on these approaches by using over 580,000 observations of children from 53 countries to examine how precipitation extremes since 1990 have affected nutrition outcomes. We show that precipitation extremes and drought in particular are associated with worse child nutrition. We further show that the effects of drought on child undernutrition are mitigated or amplified by a variety of factors that affect both the adaptive capacity and sensitivity of local food systems with respect to shocks. Finally, we estimate a model drawing on historical observations of drought, geographic conditions, and nutrition outcomes to make a global map of where child stunting would be expected to increase under drought based on current conditions. As climate change makes drought more commonplace and more severe, these results will aid policymakers by highlighting which areas are most vulnerable as well as which factors contribute the most to creating resilient food systems.

Geospatial indicators of exposure, sensitivity, and adaptive capacity to assess neighbourhood variation in vulnerability to climate change-related health hazards.

BACKGROUND: Although the frequency and magnitude of climate change-related health hazards (CCRHHs) are likely to increase, the population vulnerabilities and corresponding health impacts are dependent on a community's exposures, pre-existing sensitivities, and adaptive capacities in response to a hazard's impact. To evaluate spatial variability in relative vulnerability, we: 1) identified climate change-related risk factors at the dissemination area level; 2) created actionable health vulnerability index scores to map community risks to extreme heat, flooding, wildfire smoke, and ground-level ozone; and 3) spatially evaluated vulnerability patterns and priority areas of action to address inequity. METHODS: A systematic literature review was conducted to identify the determinants of health hazards among populations impacted by CCRHHs. Identified determinants were then grouped into categories of exposure, sensitivity, and adaptive capacity and aligned with available data. Data were aggregated to 4188 Census dissemination areas within two health authorities in British Columbia, Canada. A two-step principal component analysis (PCA) was then used to select and weight variables for each relative vulnerability score. In addition to an overall vulnerability score, exposure, adaptive capacity, and sensitivity sub-scores were computed for each hazard. Scores were then categorised into quintiles and mapped. RESULTS: Two hundred eighty-one epidemiological papers met the study criteria and were used to identify 36 determinant indicators that were operationalized across all hazards. For each hazard, 3 to 5 principal components explaining 72 to 94% of the total variance were retained. Sensitivity was weighted much higher for extreme heat, wildfire smoke and ground-level ozone, and adaptive capacity was highly weighted for flooding vulnerability. There was overall varied contribution of adaptive capacity (16-49%) across all hazards. Distinct spatial patterns were observed - for example, although patterns varied by hazard, vulnerability was generally higher in more deprived and more outlying neighbourhoods of the study region. CONCLUSIONS: The creation of hazard and category-specific vulnerability indices (exposure, adaptive capacity and sensitivity sub-scores) supports evidence-based approaches to prioritize public health responses to climate-related hazards and to reduce inequity by assessing relative differences in vulnerability along with absolute impacts. Future studies can build upon this methodology to further understand the spatial variation in vulnerability and to identify and prioritise actionable areas for adaptation.

Assessing and modelling vulnerability to dengue in the Mekong Delta of Vietnam by geospatial and time-series approaches.

Dengue fever has continuously been a disease burden in Vietnam during the last 20 years, particularly in the Mekong Delta region (MDR), which is one of the most vulnerable to climate change. Variations in temperature and precipitation are likely to alter the incidence and distribution of vector-borne diseases such as dengue. This study focuses on assessing dengue risk via the vulnerability concept, which is composed of exposure and susceptibility using a combined approach of mapping and modelling for the MDR of Vietnam during the period between 2001 and 2016. Multisource remote sensing data from Global Satellite Mapping of Precipitation (GSMaP) and Moderate Resolution Imaging Spectrophotometer (MODIS) was used for presenting climate and environment variables in mapping and modelling vulnerability. Monthly and yearly maps of vulnerability to dengue in the MDR, produced for 15-year period, aided analysis of the temporal and spatial patterns of vulnerability to dengue in the study region and were used for constructing time-series modelling of vulnerability for the following year. The results showed that there is a clear seasonal variation in the vulnerability due to variability of the climate factor and its strong dispersion across the study region, with higher vulnerability in the scattered areas of urban and mixed horticulture land and lower vulnerability in areas covered by forest and bare soil lands. The Pearson's correlation was applied to evaluate the association between dengue rates and vulnerability values aggregated at the provincial level. Reasonable linear association, with correlation coefficients of 0.41-0.63, was found in two-thirds of the provinces. The predicted vulnerabilities to dengue during 2016 were comparable with the estimated values and trends for most provinces of the MDR. Our demonstrated approach with integrated geospatial data seems to be a promising tool in supporting the public health sector in assessing potential space and time of a subsequent increase in vulnerability to dengue, particularly in the context of climate change.

Vulnerability Indexing to Saltwater Intrusion from Models at Two Levels using Artificial Intelligence Multiple Model (AIMM).

Unplanned groundwater exploitation in coastal aquifers results in water decline and consequently triggers saltwater intrusion (SWI). This study formulates a novel modeling strategy based on GALDIT method using Artificial Intelligence (AI) models for mapping the vulnerability to SWI. This AI-based modeling strategy is a two-level learning process, where vulnerability to SWI at Level 1 can be predicted by such models as Artificial Neural Network (ANN), Sugeno Fuzzy Logic (SFL), and Neuro-Fuzzy (NF); and their outputs serve as the input to the model at Level 2, such as Support Vector Machine (SVM). This model is applied to Urmia aquifer, west coast of Lake Urmia, where both are currently declining. The construction of the above four models both at Levels 1 and 2 provide tools for mapping the SWI vulnerability of the study area. Model performances in the paper are studied using RMSE and R2 metrics, where the models at Level 1 are found to be fit-for-purpose and the SVM at Level 2 is improved particularly with respect to the reduced scale of scatters in the results. Evaluating the result and groundwater samples by Piper diagram confirms the correspondence of SWI status with vulnerability index.

What is in an index? Construction method, data metric, and weighting scheme determine the outcome of composite social vulnerability indices in New York City.

Mapping social vulnerability is a prominent way to identify regions in which the lack of capacity to cope with the impacts of weather extremes is nested in the social setting, aiding climate change adaptation for vulnerable residents, neighborhoods, or localities. Calculating social vulnerability usually involves the construction of a composite index, for which several construction methods have been suggested. However, thorough investigation of results across methods or applied weighting of vulnerability factors is largely missing. This study investigates the outcome of the variable addition-both with and without weighting of single vulnerability factors-and the variable reduction approach/model on social vulnerability indices calculated for New York City. Weighting is based on scientific assessment reports on climate change impacts in New York City. Additionally, the study calculates the outcome on social vulnerability when using either area-based (person/km2) or population-based (%) input data. The study reveals remarkable differences between indices particularly when using different methods but also when using different metrics as input data. The variable addition model has deductive advantages, whereas the variable reduction model is useful when the strength of factors of social vulnerability is unknown. The use of area-based data seems preferable to population-based data when differences are taken as a measure of credibility and quality. Results are important for all forms of vulnerability mapping using index construction techniques.

Assessing climate change and health vulnerability at the local level: Travis County, Texas.

We created a measure to help comprehend population vulnerability to potential flooding and excessive heat events using health, built environment and social factors. Through principal component analysis (PCA), we created non-weighted sum index scores of literature-reviewed social and built environment characteristics. We created baseline poor health measures using 1999-2005 age-adjusted cardiovascular and combined diabetes and hypertension mortality rates to correspond with social-built environment indices. We mapped US Census block groups by linked age-adjusted mortality and a PCA-created social-built environment index. The goal was to measure flooding and excessive heat event vulnerability as proxies for population vulnerability to climate change for Travis County, Texas. This assessment identified communities where baseline poor health, social marginalisation and built environmental impediments intersected. Such assessments may assist targeted interventions and improve emergency preparedness in identified vulnerable communities, while fostering resilience through the focus of climate change adaptation policies at the local level.

Active Case Finding among Adults using an Individual's Vulnerability Score for Pulmonary Tuberculosis in a Rural Village of Goa: A Cross-sectional Study.

Background: "Detect-Treat-Prevent-Build" to achieve tuberculosis (TB)-free India is envisaged in the National Tuberculosis Elimination Program (NTEP). To be able to achieve this, it is important to address the fact that the most vulnerable and hard-to-reach groups need to undertake screening. The present review aimed to examine the vulnerability in connection with TB disparities faced by distinct sub-populations generally viewed as vulnerable and follow these for testing. Materials and Methods: The community-based cross-sectional study was conducted in the field practice area of sub-center Carambolim in a rural area of Goa for 3 months. The households were visited, and data collected via personal interviews were recorded on the questionnaire study tool. Based on the data, the participants' vulnerability mapping was done per the parameters identified. Results: Among 223 households, 528 persons were screened for vulnerability. The 47 highly vulnerable participants were advised sputum CBNAAT, of which 9 (19%) tested positive for pulmonary TB, while of the 86 moderately vulnerable participants, 4 (5%) tested positive for pulmonary TB. Among the 34 with symptoms suggestive of TB, 3 (9%) tested positive for pulmonary TB. Conclusions: The study detected 16 new TB patients from the population and found a higher incidence of pulmonary TB among the vulnerable group with no symptoms of Pulmonary TB. A further state-wide survey is recommended to diagnose such cases.

Mapping Vulnerability to Potential Crisis Events in Surabaya City: A GIS-Based Approach.

Background: This study aims to develop a vulnerability map for Surabaya using GIS-based Multi-Criteria Decision Analysis (MCDA) to assess the city's vulnerability to COVID-19. Methods: Six key factors influencing vulnerability were identified and their relative importance determined through the Analytic Hierarchy Process (AHP) pairwise comparison matrix. GIS was utilized to classify Surabaya's vulnerability into five levels: very low, low, medium, high, and very high. Results: The resulting vulnerability map provides essential insights for decision-makers, healthcare professionals, and disaster management teams. It enables strategic resource allocation, targeted interventions, and formulation of comprehensive response strategies tailored to specific needs of vulnerable districts. Conclusions: Through these measures, Surabaya can enhance its resilience and preparedness, ensuring the well-being of its residents in the face of potential emergency outbreaks.

Seed quality as a proxy of climate-ready orphan legumes: the need for a multidisciplinary and multi-actor vision.

In developing countries, orphan legumes stand at the forefront in the struggle against climate change. Their high nutrient value is crucial in malnutrition and chronic diseases prevention. However, as the 'orphan' definition suggests, their seed systems are still underestimated and seed production is scanty. Seed priming is an effective, sustainable strategy to boost seed quality in orphan legumes for which up-to-date guidelines are required to guarantee reliable and reproducible results. How far are we along this path? What do we expect from seed priming? This brings to other relevant questions. What is the socio-economic relevance of orphan legumes in the Mediterranean Basin? How to potentiate a broader cultivation in specific regions? The case study of the BENEFIT-Med (Boosting technologies of orphan legumes towards resilient farming systems) project, developed by multidisciplinary research networks, envisions a roadmap for producing new knowledge and innovative technologies to improve seed productivity through priming, with the long-term objective of promoting sustainability and food security for/in the climate-sensitive regions. This review highlights the existing drawbacks that must be overcome before orphan legumes could reach the state of 'climate-ready crops'. Only by the integration of knowledge in seed biology, technology and agronomy, the barrier existing between research bench and local agricultural fields may be overcome, generating high-impact technical innovations for orphan legumes. We intend to provide a powerful message to encourage future research in line with the United Nations Agenda 2030 for Sustainable Development.

Exhaustive mutational analysis of severe acute respiratory syndrome coronavirus 2 ORF3a: An essential component in the pathogen's infectivity cycle.

Detailed knowledge of a protein's key residues may assist in understanding its function and designing inhibitors against it. Consequently, such knowledge of one of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)'s proteins is advantageous since the virus is the etiological agent behind one of the biggest health crises of recent times. To that end, we constructed an exhaustive library of bacteria differing from each other by the mutated version of the virus's ORF3a viroporin they harbor. Since the protein is harmful to bacterial growth due to its channel activity, genetic selection followed by deep sequencing could readily identify mutations that abolish the protein's function. Our results have yielded numerous mutations dispersed throughout the sequence that counteract ORF3a's ability to slow bacterial growth. Comparing these data with the conservation pattern of ORF3a within the coronavirinae provided interesting insights: Deleterious mutations obtained in our study corresponded to conserved residues in the protein. However, despite the comprehensive nature of our mutagenesis coverage (108 average mutations per site), we could not reveal all of the protein's conserved residues. Therefore, it is tempting to speculate that our study unearthed positions in the protein pertinent to channel activity, while other conserved residues may correspond to different functionalities of ORF3a. In conclusion, our study provides important information on a key component of SARS-CoV-2 and establishes a procedure to analyze other viroporins comprehensively.

A copula model integrating atmospheric moisture demand and supply for vegetation vulnerability mapping.

Drought caused by various meteorological factors negatively affects vegetation. Constructing a joint probability distribution between vegetation and drought information may be appropriate to understand the vulnerability of vegetation to drought. In this study, a copula-based trivariate joint probability model is proposed to investigate the effects of various aspects of meteorological drought on vegetation (vegetation drought). Because drought can be caused by insufficient precipitation or excessive evapotranspiration, the meteorological drought risk for vegetation was divided into two aspects (atmospheric moisture supply and moisture demand). The vulnerability of vegetation drought was mapped when two aspects of meteorological drought occurred separately or simultaneously at high spatial resolution using remote sensing data. The results revealed that the response of vegetation was significantly different depending on the climatic stressors. Although the sensitivity of vegetation to each drought condition varied from region to region, it was found that vegetation was more vulnerable to drought caused by atmospheric moisture demand in most regions of Far East Asia. It has also been shown that drought conditions, which overlapped with insufficient precipitation and excessive evapotranspiration, can drive vegetation to a far more lethal level. Meanwhile, through comparison with the existing VTCI, the proposed Normalized Vegetation Temperature Condition Index (nVTCI) was found to be able to more rationally monitor vegetation drought in the Far East Asian region.

A novel flood risk mapping approach with machine learning considering geomorphic and socio-economic vulnerability dimensions.

Quantifying flood hazards by employing hydraulic/hydrodynamic models for flood risk mapping is a widely implemented non-structural flood management strategy. However, the unavailability of multi-domain and multi-dimensional input data and expensive computational resources limit its application in resource-constrained regions. The fifth and sixth IPCC assessment reports recommend including vulnerability and exposure components along with hazards for capturing risk on human-environment systems from natural and anthropogenic sources. In this context, the present study showcases a novel flood risk mapping approach that considers a combination of geomorphic flood descriptor (GFD)-based flood susceptibility and often neglected socio-economic vulnerability components. Three popular Machine Learning (ML) models, namely Decision Tree (DT), Random Forest (RF), and Gradient-boosted Decision Trees (GBDT), are evaluated for their abilities to combine digital terrain model-derived GFDs for quantifying flood susceptibility in a flood-prone district, Jagatsinghpur, located in the lower Mahanadi River basin, India. The area under receiver operating characteristics curve (AUC) along with Cohen's kappa are used to identify the best ML model. It is observed that the RF model performs better compared to the other two models on both training and testing datasets, with AUC score of 0.88 on each. The socio-economic vulnerability assessment follows an indicator-based approach by employing the Charnes-Cooper-Rhodes (CCR) model of Data Envelopment Analysis (DEA), an efficient non-parametric ranking method. It combines the district's relevant socio-economic sensitivity and adaptive capacity indicators. The flood risk classes at the most refined administrative scale, i.e., village level, are determined with the Jenks natural breaks algorithm using flood susceptibility and socio-economic vulnerability scores estimated by the RF and CCR-DEA models, respectively. It was observed that >40 % of the villages spread over Jagatsinghpur face high and very high flood risk. The proposed novel framework is generic and can be used to derive a wide variety of flood susceptibility, vulnerability, and subsequently risk maps under a data-constrained scenario. Furthermore, since this approach is relatively data and computationally parsimonious, it can be easily implemented over large regions. The exhaustive flood maps will facilitate effective flood control and floodplain planning.

GIS-based COVID-19 vulnerability mapping in the West Bank, Palestine.

From the beginning of the COVID-19 pandemic, the world stands idly by in the face of the virus spreading. The prediction of highly vulnerable population and the implementation of proper actions are very important steps to break the infection chain of any virus. This will, in turn, reduce the economic and social impact of this virus outbreak. In this study, the COVID-19 vulnerability map for the West Bank, Palestine was developed. Analytic Hierarchy Process (AHP) was used to develop the COVID-19 vulnerability map. The Geographic Information system (GIS) in combination with multi-criteria decision analysis (MCDA) was adopted to estimate the COVID-19 vulnerability index (CVI) based on some selected potential criteria including population, population density, elderly population, accommodation and food service activities, school students, chronic diseases, hospital beds, health insurance, and pharmacy. The results of this study show that Nablus, Jerusalem, and Hebron governorates are under very high vulnerability. Tulkarm, Ramallah & Al-Bireh and Jenin governorates are high vulnerable to COVID-19. Additionally, 82 % of the West Bank population are under high to very high COVID-19 vulnerability classes. Moreover, 14% and 4 % are medium and low to very low vulnerable, respectively. The obtained results are of high value to help decision-makers to take proper actions as early as possible mainly in the highly COVID-19 vulnerable governorates to control the risk associated with the potential outbreak of the virus and accordingly to protect social life and to sustain economic conditions.

Oil spill trajectory modelling and environmental vulnerability mapping using GNOME model and GIS.

This study develops an oil spill environmental vulnerability model for predicting and mapping the oil slick trajectory pattern in Kota Tinggi, Malaysia. The impact of seasonal variations on the vulnerability of the coastal resources to oil spill was modelled by estimating the quantity of coastal resources affected across three climatic seasons (northeast monsoon, southwest monsoon and pre-monsoon). Twelve 100 m3 (10,000 splots) medium oil spill scenarios were simulated using General National Oceanic and Atmospheric Administration Operational Oil Modeling Environment (GNOME) model. The output was integrated with coastal resources, comprising biological, socio-economic and physical shoreline features. Results revealed that the speed of an oil slick (40.8 m per minute) is higher during the pre-monsoon period in a southwestern direction and lower during the northeast monsoon (36.9 m per minute). Evaporation, floating and spreading are the major weathering processes identified in this study, with approximately 70% of the slick reaching the shoreline or remaining in the water column during the first 24 h (h) of the spill. Oil spill impacts were most severe during the southwest monsoon, and physical shoreline resources are the most vulnerable to oil spill in the study area. The study concluded that variation in climatic seasons significantly influence the vulnerability of coastal resources to marine oil spill.

Vulnerability mapping and risk analysis of sand and dust storms in Ahvaz, IRAN.

In this work, a sand and dust storm vulnerability mapping (SDS-VM) approach is developed to model the vulnerability of urban blocks to SDS using GIS spatial analysis and a range of geographical data. The SDS-VM was carried out in Ahvaz, IRAN, representing one of the most dust-polluted cities in West Asia. Here, vulnerability is defined as a function of three components: exposure, sensitivity, and adaptive capacity of the people in the city blocks to sand and dust storms. These components were formulated into measurable indicators (i.e. GIS layers) including: PM2.5, wind speed, distance from dust emission sources, demographic statistics (age, gender, family size, education level), number of building floors, building age, land surface temperature (LST), land use, percentage of literate population, distance from health services, distance from city facilities (city center, shopping centers), distance from infrastructure (public transportation, main roads and highways), distance from parks and green spaces, and green area per capita. The components and the indicators were weighted using analytical hierarchy process (AHP). Different levels of risks for the components and the indicators were defined using ordered weighted averaging (OWA). Urban SDS vulnerability maps at different risk levels were generated through spatial multi-criteria data analysis procedure. Vulnerability maps, with different risk levels, were validated against field-collected data of 781 patients hospitalized for dust-related diseases (i.e. respiratory, cardiovascular, and skin). Results showed that (i) SDS vulnerability map, obtained from the developed methodology, gives an overall accuracy of 79%; (ii); regions 1 and 5 of Ahvaz are recognized with the highest and lowest vulnerabilities to SDS, respectively; and (iii) ORness equal to 0 (very low risk) is the optimum SDS-VM risk level for decision-making to mitigate the harmful impacts of SDS in the deposition areas of Ahvaz city.

Significance of validation for karst aquifers' vulnerability assessments: Antalya Travertine Plateau (Turkey) application.

Vulnerability maps were generated for Altinova Region within the Antalya Travertine Plateau based on DRASTIC, SINTACS, EPIK, COP and PI methods. Majority of the study area is covered by productive karstic aquifer, which is composed of travertine. Travertine includes typical karstic features such as dolines, springs and caves where groundwater of travertine aquifer is the sole source for irrigation. Areal extends of low, medium, high and very high vulnerability classes and their areal extends were determined for all methods and compared with each other. High and very high vulnerable areas covered >74% of the study area as investigated by all methods, except PI. Although PI is a specific method for karstic aquifers, this method could not generate a reasonable vulnerability map based on the assigned parameter definitions and scores. Only areal extents were not sufficient to decide about the proper vulnerability method for the study area. Therefore, NO3- concentration based validation method was performed for all generated vulnerability maps. Consequently, the areas which had NO3- concentrations higher than 30 mg/L were matched with high-very high vulnerable areas. According to this validation method, application of SINTACS with "karstic aquifer" weights could validate 95% of the area with NO3- concentrations higher than the selected threshold level of 30 mg/L for Altinova region. This study showed that simulation performance of vulnerability methods was highly related to the defined parameter definitions, score ranges and weights of each method. Similar parameters with variable score ranges could create considerably distinct vulnerability maps. Validation is the essential interpretation step for taking decision on the proper vulnerability method. Additionally, site-specific contaminant observations are critical for validation of vulnerability maps. Validated vulnerability maps could be used as a valuable water resources management tool.

Regional Aquifer Vulnerability and Pollution Sensitivity Analysis of Drastic Application to Dahomey Basin of Nigeria.

Shallow groundwater vulnerability mapping of the southwestern Nigeria sedimentary basin was assessed in this study with the aim of developing a regional-based vulnerability map for the area based on assessing the intrinsic ability of the aquifer overlying beds to filter and degrade migrating pollutant. The mapping includes using the established seven parameter-based DRASTIC vulnerability methodology. Furthermore, the developed vulnerability map was subjected to sensitivity analysis as a validation approach. This approach includes single-parameter sensitivity, map removal sensitivity, and DRASTIC parameter correlation analysis. Of the Dahomey Basin, 21% was classified as high-vulnerability and at risk of pollution, 61% as moderate vulnerability, and 18% as low vulnerability. Low vulnerability areas of the basin are characterised by thick vadose zones, low precipitation, compacted soils, high slopes, and high depth to groundwater. High-vulnerability areas which are prone to pollution are regions closer to the coast with flat slopes and frequent precipitation. Sensitivity of the vulnerability map show the greatest impact with the removal of topography, soil media, and depth to groundwater and least impact with the removal of the vadose zone. Due to the subjectivity of the DRASTIC method, the most important single parameter affecting the rating system of the Dahomey Basin DRASTIC map is the impact of the vadose zone, followed by the net recharge and hydraulic conductivity. The DRASTIC vulnerability map can be useful in planning and siting activities that generate pollutants (e.g., landfill, soak away, automobile workshops, and petrochemical industries) which pollute the environment, groundwater, and eventually impact the environmental health of the Dahomey Basin's inhabitants.

Examination of Human Health Impacts Due to Adverse Climate Events Through the Use of Vulnerability Mapping: A Scoping Review.

Government officials, health professionals, and other decision makers are tasked with characterizing vulnerability and understanding how populations experience risks associated with exposure to climate-related hazards. Spatial analyses of vulnerable locations have given rise to climate change vulnerability mapping. While not a new concept, the spatial analyses of specific health outcomes remain limited. This review explores different methodologies and data that are used to assess vulnerability and map population health impacts to climate hazards. The review retrieved scholarly articles and governmental reports concerning vulnerability mapping of human health to the impacts of climate change in the United States, published in the last decade. After review, 37 studies were selected for inclusion. Climate-related exposures were distributed across four main categories, including: high ambient temperatures; flood hazards; vector-borne diseases; and wildfires. A number of different methodologies and measures were used to assess health vulnerability to climate-related hazards, including heat vulnerability indices and regression analyses. Vulnerability maps should exemplify how variables measuring the sensitivity and adaptive capacity of different populations help to determine the potential for climate-related hazards to have an effect on human health. Recommendations address methodologies, data gaps, and communication to assist researchers and stakeholders in directing adaptations to their most efficient and effective use.