Vulnerability Index Map Research Articles

Modeling land-degradation vulnerability in coal-mined environs through geospatial and AHP techniques: potential strategies for eco-restoration.

Coal is the most important and abundant source of energy in India, where 66% of the thermal power is generated from the combustion of coal. Therefore, understandinghow coal-mining activities increase land-degradation risks and vulnerabilities in different land uses is essential to devise site-specific restoration plans for complementing the national targets of land degradation neutrality (LDN). This study was carried out in the Chirimiri coal-mining region of Chhattisgarh, India, employing geospatial methods. The Landsat Thematic Mapper (TM) and Operational Land Imager (OLI) data corresponding to 2000, 2010, and 2020 were digitally classified using maximum-likelihood algorithm in ERDAS IMAGINE and ArcGIS platforms. Spatio-temporal satellite data analysis revealed that an area encompassing ~ 112 km2 (48.9%) of forests and 6.63 km2 (37.6%) of waterbodies was lost due to the expanse of mining between 2000 and 2020. Consequently, the degraded land and built-up areas rapidly expanded during the same period accounting for a net increase of 101.1 km2 (47%) and 9.13 km2 (322%). The mining disturbances degenerated the soil health, drastically reducing soil pH, soil organic carbon (SOC), and available nitrogen (N) in the mined area exacerbating the region's vulnerability to degraded lands. Land Degradation Vulnerability Index (LDVI) map shows that mined area was highly vulnerable, followed by barren wasteland, agriculture, and forest area, while destructive mining and other associated activities in the past 20years made 52% of the landscape vulnerable. The study discusses the implications of effective eco-restoration for minimizing the land degradation processes and building resilient ecosystems for mitigating land degradation in coal-mined landscapes.

Assessing and mapping the vulnerability index of Bangladesh to natural and climate-induced disasters: A spatial analysis at the subdistrict level

This study assesses and maps the vulnerability index of Bangladesh at the subdistrict level to a range of natural and climate-induced disasters. Four vulnerability index maps are created using principal component analysis and categorized into five risk levels: (1) no/very low risk, (2) low risk, (3) moderate risk, (4) high risk and (5) very high risk for each sub-district. The results reveal that the south east region is highly vulnerable to cyclones, Haor region stands out as the most vulnerable area for flash floods, with numerous subdistricts facing very high to high risk levels; northern and north-eastern regions are prone to cold waves, while the western part of Bangladesh is highly vulnerable to heat waves. This comprehensive spatial analysis provides critical information for disaster risk reduction and adaptation strategies, assisting decision-makers in identifying the most vulnerable areas and prioritizing interventions. The findings of this study might be useful for policymakers as well as planners.

Spatio-temporal soil loss modelling using RUSLE and sediment delivery into a reservoir in a semi-arid region of northern Nigeria

Soil loss is aggravated by uncontrolled deforestation, indiscriminate land clearing for agricultural activities, overgrazing, and urban development that leads to severe soil erosion over the land surface. The main objective of this research is to apply the Revised Universal Soil Loss Equation (RUSLE), in conjunction with remote sensing and GIS, to determine the temporal variation of soil loss from the Gubi watershed in the years 2000 and 2017 and to estimate the sediment delivery into the Gubi reservoir in Northern Nigeria. Datasets of rainfall, soil type, topography, cover management, and support practice were utilized to determine the five RUSLE factors. The research identified five levels of erosion ranging from severe to very low-risk areas. The spatial distribution of erosion hotspots in the catchment area was highlighted. Results revealed increasing levels of erosion from 14,244.31 tons/year in 2000 to 16,792.33 tons/year in 2017. Consequently, the catchment's sediment yield was 6903.65 tons/year and 9172.71 tons/year corresponding to sediment delivery ratios of 48.46 % and 54.62 % in 2000 and 2017 respectively. The sediment inflow into Gubi reservoir was 747.38 tons/year and 950.76 tons/year corresponding to 10.83 % and 10.37 % of the total sediment yield in 2000 and 2017 respectively. Soil erosion vulnerability index maps were produced and the erosion is expected to increase with time as agricultural activities and deforestation continue to occur. The sediment deposits have the potential to reduce the designed life of the reservoir. Best management practices, such as tree planting, mulching, and contour ridging are recommended for soil conservation. There is a need to assess the reservoir sediment deposits so that measures can be taken to maintain the reservoir operations properly.

Interpretation of Microtremor Data Through Analysis Horizontal to Vertical Spectral Ratio (HVSR) for Microzonation of Seismic Earthquake Hazards in Baubau City

Research has been carried out on seismic hazard microzonation in the city of Bau – bau using microtremor data. The aim of this research is to determine the seismic vulnerability index based on horizontal to vertical spectral ratio (HVSR) analysis and create a dominant frequency microzonation map (F0), dominant period map (T0), amplification factor map (A0), and seismic vulnerability index map (Kg) from microtremor data which can describe the level of vulnerability of an area to seismic hazards. Microtremor measurements were carried out using a portable three-component seismograph (TDS. 303), the data was analyzed with gepsy software via the HVSR method. The research results show that the distribution of dominant frequency values ​​(F0), ranges from 0.4795 Hz to 16.6383 Hz, dominant period values ​​(T0), ranges from 0.0601 seconds to 2.0854 seconds, amplification factor values ​​(A0), ranges from 1.2285 to 4.9043, and the seismic vulnerability index (Kg) value ranges from 0.1043 to 11.0276. The values ​​(F0), (T0), (A0), and (Kg) are interpreted by referring to literature data and surface geological maps of the research area. The results show that the study area has a small portion of sediment layers that are vulnerable to seismic hazards.

Geospatial based groundwater resources susceptibility and contamination risk mapping for Yeşilköy aquifer, Cyprus

In rural areas where the goals of sustainable agriculture are intertwined with the availability and supply of groundwater resources, it becomes imperative to perceive and search the status of groundwater resource’s susceptibility to contamination. A well-known overlay & index method, the DRASTIC model that assesses the state of susceptibility to contamination using vulnerability index map has been applied to the Yeşilköy aquifer. The seven different hydro-geologic parameters; aquifer media, vadose zone, and soil media, surface topography, recharge capacity, water table depth, and the hydraulic conductivity maps were overlaid to find the vulnerability of the aquifer. Results depicted that the aquifer is categorized into two vulnerability zones; low and moderate. The available susceptibility of the aquifer due to ongoing anthropogenic activities like agriculture was also studied by implementing the Weighted Arithmetic Water Quality Index (WQI) method. The aquifer’s overall water quality was “poor water quality”. Hence, there was no significant relationship between WQI and the vulnerability of the aquifer. A modification of the DRASTIC to enhance the relationship with WQI is proposed by utilizing the Analytic Hierarchy Process (AHP), as one of the successful multi-criteria decision-making models. The modification of the DRASTIC model with AHP revealed that the performance of the DRASTIC model was improved and the correlation between WQI and the DRASTIC vulnerability index was significantly enhanced. The results also show that the effective contributing parameters for groundwater vulnerability in the Yeşilköy aquifer were the aquifer media and the vadose zone. On the other hand, the water table’s depth and topography were almost independent of the aquifer’s vulnerability.

Spatial mapping of the seismic vulnerability index in Kathmandu Valley: insight from dominant frequency and amplification factor

Abstract Kathmandu Valley situated in Nepal has experienced several earthquakes in the past. The expanding population of Kathmandu Valley is driving a significant acceleration in building construction reaching record levels and posing risks to both human lives and the economy, despite this surge in development. This study aimed to spatially map the dominant frequency, amplification factor, and vulnerability index map of Kathmandu Valley, Nepal using ArcGIS. This paper presents the nonlinear response analysis of 225 boreholes using the Gorkha earthquake as input motion and DEEPSOIL software for determining the amplification ratio and dominant frequency. The results revealed a range of values for dominant frequency from 0.08 to 7.65 Hz and soil amplification ratio from 0.13 to 8.14. Furthermore, the seismic vulnerability index was derived from the dominant frequency and soil amplification ratio ranging from 0.22 to 21.84. The resulting maps acquired through this study enable the decision-makers and stakeholders, to prioritize resources and implement targeted measures for enhancing seismic resilience in the region.

GIS-Based Analytical Hierarchy Process Modelling and Mapping of Erosion Vulnerability in the Coastal Areas of Rivers State, Nigeria

The problem of coastal erosion in rivers State Nigeria is a significant issue that has far-reaching consequences for the environment and local communities. Despite the efforts of previous research there remains a lack of comprehensive understanding of the factors contributing to erosion vulnerability and their relative importance, hindering effective decision-making and management practices aimed at mitigating the effects of coastal erosion in Rivers State. Therefore, this study aimed at a GIS-based analytical hierarchy process modeling and mapping of coastal erosion vulnerability in Rivers State, Nigeria. The objectives are to establish and classify the geophysical factors according to the levels of coastal erosion risk, calculate the reliability index of the classified geophysical factors, determine the coastal vulnerable areas across Rivers State using analytical hierarchical process and to produce a coastal vulnerability index map defining the extent of erosion vulnerability in Rivers State. The methodology comprises of the acquisition of primary and secondary data, image pre-processing, image classification, DEM processing, classification and standardization of factors, development of pairwise comparism, and weighted linear combination analysis. The study revealed three distinct coastal erosion vulnerability zones: high, moderate, and low vulnerability. The high vulnerability zone encompassed a total expanse of 545.29 square kilometers, constituting 6.38% of the study area. In contrast, the moderate and low vulnerability zones covered 1941.33 square kilometers and 6052.51 square kilometers, respectively, making up 22.73% and 70.89% of the total area. Bonny (139.28 sq km) was ranked as the most vulnerable due to its role as an oil and gas hub. Degema (111.28 sq km) ranked second and requires urgent erosion control. Okrika and Andoni (71.73 sq km and 62.20 sq km) were third and fourth respectively. It is recommended that an advocate for the systematic approach to coastal vulnerability zoning be introduced in the study. The categorization of areas into high, moderate, and low vulnerability zones provides a standardized framework for assessing coastal regions' susceptibility to erosion. This approach can be applied to other regions to facilitate consistent vulnerability assessments.

Design and Implementation of an Interactive Visualization Dashboard for Monitoring the Flood Vulnerability and Mapping

This study aims to build a web-based interactive visualization dashboard from granular flood vulnerability index estimation maps using data from satellite imagery. The approach used to build this visualization dashboard is a two-dimensional (2D) approach created with the qgis2web python plugin facilitated with a JavaScript leaflet. Raw data from satellite imagery consisting of indicators of the causes of flooding are extracted in comma-separated value (CSV) format. Furthermore, the data is integrated based on its spatial attributes and stored in Geographic JavaScript Object Notation (GeoJSON) format to produce a visualization of the flood vulnerability index map. In web views, dashboards are built by utilizing hypertext markup language (HTML), cascading style sheets (CSS), and JavaScript (JS). This interactive dashboard has several useful features in helping the process of monitoring the flood vulnerability of an area such as zoom, "show me where I am", measure distance, search, legend, and change year. Thus, the flood vulnerability estimation map dashboard is expected to assist the government in monitoring areas with extreme flood vulnerability and support the decision-making process related to mitigation of areas that have high flood vulnerability.

Vulnerability to urban flooding assessed based on spatial demographic, socio-economic and infrastructure inequalities

Urban flooding is a priority in natural risk management and mitigation because it is the most frequent natural disaster in densely urbanised environments. This research explores flood vulnerability in cities by developing an index that can be easily implemented across the world. Our methodology is based on the arrangement of a series variables into three different classes (demography, socioeconomics and infrastructure) and the determination of their spatial variability through a Principal Component Analysis (PCA). We tested the proposed approach in the city of Santander (Spain) where a vulnerability index map was generated based on the combination of the proposed classes. The analysis show that we can reduce complexity from an initially identified 159 relevant variables to 16 representative and impactful variables in terms of spatial variance. Classification of the variables into three different classes made it possible to quantify the main causes of vulnerability to flooding across space. We produce a flood risk map by integrating our findings with a flood hazard map for the same area. This flood risk map gives urban planners detailed information about the most affected areas and allows them to design measures that mitigate the severity and effects of floods optimising available resources.

Groundwater vulnerability assessment to pollution using GIS-based DRASTIC and GOD methods in Araihazar upazila of Narayanganj district, Bangladesh

A groundwater vulnerability map is a preliminary way to assess the risk of groundwater pollution from a variety of sources and across a range of geological and hydrological contexts. With rapid urbanization and industrialization, the possibility of groundwater contamination has greatly increased in Bangladesh. The focus of this study is on an industrialized peri-urban area in Bangladesh, where the regional aquifer has already been depressurized owing to pumping to fulfill the ascending municipal and industrial water demand of the Dhaka megacity and adjacent areas. Two vulnerability index maps were created utilizing a Geographic Information System (GIS) and the contamination risk indices DRASTIC (Depth to water table (D), Net recharge (R), Aquifer media (A), Soil media (S), Topography (T), Impact of vadose zone (I) and hydraulic Conductivity of the aquifer (C)) and GOD (Groundwater or hydraulic confinement in the aquifer under consideration (G), Overlying strata in terms of lithological characters that determine the pollutant attenuation capacity (O) and Depth to groundwater (D)). Vulnerabilities varied greatly over the study area. The DRASTIC analysis identified three vulnerability classes: one-fifth of the area exhibited high vulnerability, more than half of the study area showed moderate vulnerability and rest of the area is low in vulnerability. In contrast, GOD analysis was somewhat conservative, showed two classes where vulnerability was negligible in 58% of the study area and low in the rest of the area. The study also generated hot-spot maps of potential surface contamination for each vulnerability map by overlapping the potentially hazardous activity layer which corresponds to areas with higher industrial clusters. Human activities that commonly cause groundwater pollution need to be constrained by utilizing groundwater protection strategies in the identified vulnerable areas to ensure sustainable development.

Evaluation of Groundwater Vulnerability in the Upper Kelkit Valley (Northeastern Turkey) Using DRASTIC and AHP-DRASTICLu Models

This study aimed to investigate groundwater vulnerability to pollution in the Upper Kelkit Valley (NE Turkey). For this purpose, vulnerability index maps were created using the generic DRASTIC and AHP-DRASTICLu models. The latter model was suggested by adding a parameter to the DRASTIC model and weighting its parameters with the analytical hierarchy process with the GIS technique. The results showed that areas with high and very high vulnerabilities are concentrated around the Kelkit Stream, which flows from east to west in the central part of the study area. In contrast, areas with low and very low vulnerability classes are located in the northern and southern parts of the study area. To validate the model results, a physicochemical characterization of groundwater samples and their corresponding vulnerability index values were statistically compared using the Spearman correlation method. In addition, the single-parameter sensitivity method was applied to analyze the models’ sensitivities. Results revealed a stronger correlation between the vulnerability index values of the AHP-DRASTICLu model (compared to the DRASTIC model) in terms of sulfate (R2 = 0.75) and chloride (R2 = 0.76), while there was a slightly weaker correlation for the electrical conductivity (R2 = 0.65) values of the groundwater samples. Sensitivity analysis indicated that the vadose zone, aquifer media, and land use are the most influential parameters responsible for the highest variation in the vulnerability index. Generally speaking, the results indicated that the AHP-DRASTICLu model performs better than the DRASTIC model for investigating groundwater vulnerability to pollution in the Upper Kelkit Valley.

Coastal vulnerability assessment for the coast of Tamil Nadu, India-a geospatial approach.

A coastal region is a section of land that borders a significant body of water, often the sea or ocean. Despite their productivity, they are sensitive to even little alterations in the outside environment. This study aims to develop a spatial coastal vulnerability index (CVI) map for the Tamil Nadu coast of India, which has diverse coastal and marine environments that are ecologically fragile zones. Climate change is expected to increase the intensity and frequency of severe coastal hazards, such as rising sea levels, cyclones, storm surges, tsunamis, erosion, and accretion, severely impacting local environmental and socio-economic conditions. This research employed expert knowledge, weights, and scores from the analytical hierarchy process (AHP) to create vulnerability maps. The process includes the integration of various parameters such as geomorphology, Land use and land cover (LULC), significant wave height (SWH), rate of sea level rise (SLR), shoreline change (SLC), bathymetry, elevation, and coastal inundation. Based on the results, the very low, low, and moderate vulnerability regions comprise 17.26%, 30.77%, and 23.46%, respectively, whereas the high and very high vulnerability regions comprise 18.20% and 10.28%, respectively. The several locations tend to be high and very highdue to land-use patterns and coastal structures, but very few are contributed by geomorphological features. The results are validated by conducting a field survey in a few locations along the coast. Thus, this study establishes a framework for decision-makers to implement climate change adaptation and mitigation actions in coastal zones.

Learning simple additive weighting parameters for subsidence vulnerability indices in Tehran plain (Iran) by artificial intelligence methods

Rates and weightings are specified for every data layer to compensate for the data layer’s regional fluctuation and relative importance accordingly using simple additive weighting frameworks (SAWFs). To this end, the present article follows three learning steps using artificial intelligence (AI): In Level 1, SFL, ANN and SVM models are used to improve weighting factors from regional and observational data. In Level 2, the AI strategy of gene expression programming (GEP) is applied to understand further. In Level 3, the results from Level 1 are reused as inputs and observational data. Using the anticipated impact measurement and monitoring (AIMM) approach, the study paves the ground to create some frameworks for mapping indices (subsidence susceptibility). The main results of the AIMM analysis are three-fold: First, the NSE coefficient for SAWF obtained through land subsidence values is approximately 0.41. Second, the NSE value is enhanced to 0.88 by learning the weighting factors at Level 1 using SFL. Third, the NSE value is even further enhanced to 0.92 by Level 2 learning of the weighting factors via GEP. With the subsidence vulnerability index map, it is possible to manage underground water resources to prevent excessive subsidence in probable areas.

Modelling and Mapping Urban Vulnerability Index against Potential Structural Fire-Related Risks: An Integrated GIS-MCDM Approach

Identifying the regions with urban vulnerability to potential fire hazards is crucial for designing effective risk mitigation and fire prevention strategies. The present study aims to identify urban areas at risk of fire using 19 evaluation factors across economic, social, and built environment-infrastructure, and prior fire rates dimensions. The methods for “multi-criteria decision making” (MCDM) include the Analytic Hierarchy Process for determining the criteria’s importance and weight of the criteria. To demonstrate the applicability of this approach, an urban vulnerability index map of Ardabil city in Iran was created using the Fuzzy-VIKOR approach in a Geographic Information System (GIS). According to the findings, about 9.37 km2 (31%) of the city, involving roughly 179,000 people, presents a high or very high level of risk. Together with some neighbourhoods with low socioeconomic and environmental conditions, the city centre is the area where the level of risk is more significant. These findings are potentially very meaningful for decision-makers and authorities, providing information that can be used to support decision-making and the implementation of fire risk mitigation strategies in Ardabil city. The results of this research can be used to improve policy, allocate resources, and renew urban areas, including the reconstruction of old, worn-out, and low-income urban areas.

GIS-Based Vulnerability Analysis for Sustainable Fish Drying Cottage Industry in Southern Province, Sri Lanka

Dried fish manufacturing is a famous cottage industry in Sri Lanka. Due to various natural and manmade reasons, their locations are not set up in sustainable ways. As a result, industry locations have become more vulnerable. Therefore, hurdles are there to sustainably expanding this cottage industry. So, the industry's novelty and the enormous number of issues experienced have yet to be discovered or solved via research study. Therefore, the current study intends to conduct GIS-based vulnerability analysis of the fish drying cottage industry by identifying the socioeconomic, environmental, and industry-related backgrounds of existing manufacturers and the industry, challenges faced by manufacturers, and vulnerable sites in the village using geo-spatial analysis. The case study focuses on Kudawella of Tangalle local authority, in the southern province of Sri Lanka. Qualitative analysis was used to identify the existing situation using a structured questionnaire survey and descriptive analysis was performed on the obtained information. The constraints were revealed by a content analysis of qualitative data acquired from a judgemental sample via semi-structured and unstructured interviews. Furthermore, the relative positions of the cottages were determined using OpenStreet Map and the absolute coordinates of a sample of 27 cottages gathered using GPS. A Weighted Overlay Analysis was performed using Arc Map 10.8 to create a Vulnerability Index Map to identify vulnerable locations for the industry while taking nine socioeconomic and environmental parameters. The outcome indicated four categories of vulnerability: severe, moderately severe, moderate, and low vulnerability, with 4.13% of the village falling into the severely susceptible category. The index map assists relevant authoritative organisations in making industrial decisions to attain sustainability based on the amount of risk and underlines the importance of institutional and individual decision-making systems

1108. Feasibility and Impact of a Monoclonal Antibody Infusion Program in Reaching Vulnerable Underserved Communities

Abstract Background Monoclonal antibodies (mAbs) against SARS-CoV-2 are potent therapeutics that prevent progression of disease in high-risk patients. Deploying mAb infusion programs and reaching communities in need remains challenging. We established a mAb infusion program centered in the emergency department (ED) fast-track. We seek to assess the feasibility and impact of our mAb infusion program in reaching vulnerable underserved communities. Methods The ED fast-track was repurposed for mAb infusions. Indications and protocols were created by the infectious disease (ID) physician and antimicrobial stewardship program (ASP). Test to treat was deployed in the ED and a referral program connected patients from telehealth and providers in the community to the infusion program (Figure 1). Data on mAb use from November 2020 to February 2022 was reviewed. To assess the reach of the program in vulnerable communities, the COVID-19 Community Vulnerability Index (CCVI) map by zip code was used. The CCVI identifies communities at risk by using variables such as sociodemographic variables, occupational factors, cumulative COVID-19 burden, vaccine uptake and other factors. Figure 1.Monoclonal antibody infusion test-to-treat and referral process in the ED fast track Results mAbs were successfully deployed and sustained throughout the pandemic, increasing its use in each COVID-19 surge by >103% (Figure 2). A total of 267 patients were treated, with an average age of 52 years, of which 55% (147) were female. Ethnicity was 46% Hispanic and Race was 54% White, 28% Black, 1.4% Asian, 1.8% Multiracial and 14% Other. Zip codes by CCVI were 64% high, 18% medium, and 3% low CCVI index. 15% were outside of city limits without CCVI index (Figure 3). Figure 2.COVID-19 Community Vulnerability Index (CCVI) of patients who received monoclonal antibodies at our program Figure 3 Monoclonal antibody infusions by COVID-19 Community Vulnerability Index (CCVI) and Zip codes Conclusion A mAb infusion program with test to treat and referrals from the community was successfully deployed in an ED fast track. The ED space and staff can be leveraged for mAbs during surges amid clinic staffing challenges. The program adopted novel mAbs as new COVID-19 variants emerged. Uptake increased with each wave, likely reflecting awareness of mAbs and the infusion program by patients and providers in the community. The majority of patients served (82%) were of high and medium CCVI. Thus, this mAb infusion program led by ID and ASP successfully reached predominantly underserved vulnerable populations. Disclosures All Authors: No reported disclosures.

Integrated human behavior and tick risk maps to prioritize Lyme disease interventions using a 'One Health' approach

Lyme disease (LD) risk is emerging rapidly in Canada due to range expansion of its tick vectors, accelerated by climate change. The risk of contracting LD varies geographically due to variability in ecological characteristics that determine the hazard (the densities of infected host-seeking ticks) and vulnerability of the human population determined by their knowledge and adoption of preventive behaviors. Risk maps are commonly used to support public health decision-making on Lyme disease, but the ability of the human public to adopt preventive behaviors is rarely taken into account in their development, which represents a critical gap. The objective of this work was to improve LD risk mapping using an integrated social-behavioral and ecological approach to: (i) compute enhanced integrated risk maps for prioritization of interventions and (ii) develop a spatially-explicit assessment tool to examine the relative contribution of different risk factors.The study was carried out in the Estrie region located in southern Québec. The blacklegged tick, Ixodes scapularis, infected with the agent of LD is widespread in Estrie and as a result, regional LD incidence is the highest in the province. LD knowledge and behaviors in the population were measured in a cross-sectional health survey conducted in 2018 reaching 10,790 respondents in Estrie. These data were used to create an index for the social-behavioral component of risk in 2018. Local Empirical Bayes estimator technique were used to better quantify the spatial variance in the levels of adoption of LD preventive activities. For the ecological risk analysis, a tick abundance model was developed by integrating data from ongoing long-term tick surveillance programs from 2007 up to 2018. Social-behavioral and ecological components of the risk measures were combined to create vulnerability index maps and, with the addition of human population densities, prioritization index maps. Map predictions were validated by testing the association of high-risk areas with the current spatial distribution of human cases of LD and reported tick exposure. Our results demonstrated that social-behavioral and ecological components of LD risk have markedly different distributions within Estrie. The occurrence of human LD cases or reported tick exposure in a municipality was positively associated with tick density and the prioritization risk index (p < 0.001).This research is a second step towards a more comprehensive integrated LD risk assessment approach, examining social-behavioral risk factors that interact with ecological risk factors to influence the management of emerging tick-borne diseases, an approach that could be applied more widely to vector-borne and zoonotic diseases.

The Use of the DRASTIC-LU/LC Model for Assessing Groundwater Vulnerability to Nitrate Contamination in Morogoro Municipality, Tanzania

Groundwater is a useful source of water for various uses in different places. The major challenge in the use of this resource is how to manage and protect it from contamination. The current study was conducted in Morogoro Municipality to identify vulnerable groundwater areas by using DRASTIC-LU/LC model. The study applied eight input parameters, i.e., depth to water table, net recharge, aquifer media, soil media, topography, impact of vadose zone, hydraulic conductivity and land use/land cover patterns, which were overlaid in GIS to generate groundwater vulnerable map. The model used rating (R = 1–10) and weighting (W = 1–5) techniques to assess the effect of each parameter on groundwater contamination. The DRASTIC-LU/LC Vulnerability Index map was classified into low- (area = 29.2 km2), moderate- (area = 120.4 km2) and high-vulnerability zones (area = 124.4 km2). Nitrate analysis was conducted using the cadmium reduction method (DR 890) to assess the validity of the model and it was observed that 55%, 15% and 50% of the samples with unacceptable (&gt;50 mg/L), high (29–50 mg/L) and moderate (14–28 mg/L) nitrate concentrations, respectively, fall into the high-vulnerability zone. Furthermore, 45%, 70% and 50% of the samples with unacceptable, high and moderate nitrate concentrations, respectively, fall into the moderate-vulnerability zone. In the low-vulnerability zone, only 15% of samples were found with a high nitrate concentration.

Evaluating the climatic and socio-economic influences on the agricultural drought vulnerability in Jharkhand.

Environmental hazards like drought lead to degrading food production and adversely impact the agro-economy. This study investigates the contributions of different climatic and socio-economic variables to agricultural drought in Jharkhand. The three primary criteria, i.e., exposure (E), sensitivity (S), and adaptive capacity (AC), responsible for agricultural drought vulnerability, were examined to identify the drought-prone areas. Long-term (1958-2020) gridded climatic datasets obtained from the Terra-climate global dataset, MODIS vegetation index dataset (MOD13Q1) for the years 2001-2020, different soil parameters obtained from the ISRIC global soil database and state agricultural portal of Jharkhand, and different socio-economic datasets obtained from census data (2011) provided by Govt. of India, were utilized for this study. Analytic Hierarchy Process (AHP) was used to estimate the weighted contribution of the indicator variables falling under each criterion (E, S, and AC), and three criteria index maps were generated. These separate maps were further integrated to generate the final vulnerability index map. Finally, the study area was categorized into different zones based on the drought vulnerability index value ranging from 0 to 1, according to the severity of the drought. It was observed that about 4.05%, 28.12%, and 37.07% of the total geographical area is very highly, highly, and moderately vulnerable to agricultural drought, respectively. Amongst the three primary criteria, exposure showed a significant positive correlation (R = 0.61), and sensitivity showed a strong positive correlation (R = 0.55) with vulnerability. The adaptive capacity was negatively correlated (R = -0.75) with the vulnerability. However, putting equal weights to the variables to calculate the vulnerability, the exposure and sensitivity indicators showed a significant positive correlation with the vulnerability, with an R-value of 0.82 and 0.79, respectively. In contrast, the adaptive capacity showed a negative correlation with the vulnerability with R = -0.75.

Investigation of site properties of the Çaldıran (Van, Eastern Turkey) settlement area using surface wave and microtremor methods

Çaldıran (Van, eastern side of Turkey) which is a seismically very active region has more than active faults around and the areas under active tectonic effect. The earthquakes and soil characteristics are the significant factors that increase the severity of the damages ratio. For this reason, the characteristics of the engineering structure should be defined accurately and reliably. The shear wave velocity (Vs) obtained from geophysical methods is one of the most important parameters that determine the behavior of the soil during an earthquake. In this study, active and passive source surface wave methods were applied at different measurement points in the study area in order to determine soil properties of the settlement area and its surroundings in Çaldıran district the city of Van. As part of this study, 80 boreholes, 95 multichannel analysis of surface wave (MASW) profiles, and 66 microtremor (HVSR) measurements were performed in the study area. The Vs velocity with depth dependent could be determined by using the dispersive behavior of Rayleigh waves in surface wave analysis methods. In order to analyze the engineering characteristic of the region and earthquake-soil-structure relations parameters such as the amplification factor, predominant period (t0), Vs velocity, average velocity up to 30 m Vs30, and seismic vulnerability index (Kg) maps have been prepared. In order to reveal the variety of the engineering properties of the area in horizontal and vertical directions, depth cross-sections in different directions were created. When the tectonic structure and regional geology were examined, it was determined that all profile results were compatible with the geological structure.