Geographic Information Research Articles

Effects of The Cistern Program on Environmental Recovery and Desertification Mitigation in The Semi-Arid Region of Paraíba

Objective: This study aims to analyze the impact of the Cistern Program on mitigating desertification and promoting environmental recovery in the semi-arid region of Paraíba, focusing on the municipalities of Esperança, Lagoa Seca, and São Sebastião de Lagoa de Roça. Theoretical Framework: The research is based on studies addressing the effects of climate change and water scarcity in Brazil’s semi-arid region. Theories of public water policies and strategies for living with the semi-arid climate were also examined, with a focus on the role of cisterns in ensuring water security. Method: This descriptive study employs geotechnologies such as Geographic Information Systems (GIS) and remote sensing to develop desertification maps and track the distribution of cisterns. Data from the Trends.Earth plugin and images from MapBiomas were used to analyze soil degradation between 2004 and 2020. Results and Discussion: The results indicate that the Cistern Program significantly contributed to reducing soil degradation and increasing vegetation cover. Continuous access to water enabled the adoption of sustainable agricultural practices and enhanced environmental resilience in the studied areas. Research Implications: This study contributes to the formulation of public policies that promote water security and sustainable development, reinforcing the importance of social technologies for living in the semi-arid region. Originality/Value: The study provides a novel analysis of the effectiveness of the Cistern Program in combating desertification in the semi-arid region of Paraíba, highlighting its relevance for environmental sustainability policies.

Estimation of carbon stocks in the areas of a seasonally dry tropical forest in the Brazilian semi-arid region

In addition to its ecological importance, the Caatinga biome, one of the most extensive seasonally dry tropical forests (SDTF) in the world, has a relevant socioeconomic role as it is used as a primary natural resource by local communities. However, inadequate ecosystem management practices have resulted in gradual loss of natural vegetation in this ecosystem. Carbon stock estimation is a parameter that can contribute as a support tool for managing and maintaining the few remaining natural vegetated areas. In this study, we calibrated and validated the CENTURY model to simulate carbon stocks in areas of the Caatinga, in the state of Pernambuco, and compared the predictive capacity of the CENTURY model with available estimates. In the validation dataset, the average for biomass stocks was 33.1 Mg C ha−1, this value is close to those observed in the literature for the region. The model also performed well when estimating carbon stocks in the soil (r2 = 0.79, p = 0.017). Ecosystem modeling combined with Geographic Information Systems (GIS) is a promising tool for estimating carbon stocks in the Caatinga, where field sampling campaigns are generally expensive and have scarce research funding opportunities. Furthermore, it also allows the evaluation of the effect of environmental changes on C stocks in long-term studies, which is essential for creating and implementing public policies to mitigate and adapt to the impacts of climate change on the ecosystem. However, additional efforts are needed to improve C estimates, especially in areas with a strongly negative water balance.

Application of Analytical Hierarchy Process (AHP) and Multi-Criteria Evaluation (MCE) for a case study and scenario assessment of flood risk in the White Volta Basin of the Upper East Region, Ghana

In recent years, Ghana, particularly the inhabitants of the Upper East Region, has experienced profound impact of flooding, largely attributable to the complex interplay of climatic factors. This research embarks on a comprehensive assessment of flood risk zones nestled within the White Volta Basin, situated in the Upper East Region. The study employs advanced cartographic methodologies and uses Geographic Information Systems (GIS) in conjunction with the Analytical Hierarchy Process (AHP) to systematically categorize areas susceptible to inundation. Leveraging geospatial datasets acquired from satellites such as Landsat and Sentinel. Topographic, slope, and Land Use/Land Cover (LULC) maps have been constructed. The empirical findings underscore the susceptibility of specific regions, including the Talensi District, territories within Bawku West, and some segments of the Bolgatanga Municipal area, to escalated flood risk. Additionally, the research underscores the high vulnerability of communities such as Nunku, Tolla, Zaare, Pwalugu, Balungu, Winkongo, Biung, and Tongo to the negative impact of inundation. Significantly, the study unveils a pivotal factor in the perpetuation of flood devastation—namely, the role of water discharge. This intrinsic linkage between discharge rates and flood occurrences underscores the pressing need to address this critical component in mitigation strategies to reduce adverse impacts on the basin’s resident communities. The insights derived from the study offer some level of hope for residents, providing essential knowledge concerning flood-prone areas and optimal timing for agricultural activities to safeguard their cherished livelihoods.

3D Reconstruction of Geometries for Urban Areas Supported by Computer Vision or Procedural Generations

This work presents a numerical mesh generation method for 3D urban scenes that could be easily converted into any 3D format, different from most implementations which are limited to specific environments in their applicability. The building models have shaped roofs and faces with static colors, combining the buildings with a ground grid. The building generation uses geographic positions and shape names, which can be extracted from OpenStreetMap. Additional steps, like a computer vision method, can be integrated into the generation optionally to improve the quality of the model, although this is highly time-consuming. Its function is to classify unknown roof shapes from satellite images with adequate resolution. The generation can also use custom geographic information. This aspect was tested using information created by procedural processes. The method was validated by results generated for many realistic scenarios with multiple building entities, comparing the results between using computer vision and not. The generated models were attempted to be rendered under Graphics Library Transmission Format and Unity Engine. In future work, a polygon-covering algorithm needs to be completed to process the building footprints more effectively, and a solution is required for the missing height values in OpenStreetMap.

Biometric and Geographic Information-based Identity Authentication Method

With the ongoing advancements in computer technology and mobile internet, mobile payment has become an integral component of daily life. Identity authentication technology serves as a critical measure to ensure the security of mobile payments. This paper addresses the issue of low security associated with single-factor identity authentication while highlighting the substantial overhead involved in multi-factor authentication methods. We propose a novel identity authentication method that integrates biometric data and geographic information. In this framework, the identity authentication server first performs system initialization, configures system parameters, and stores them securely. Subsequently, the identity authentication client submits an authentication request to the server and completes its registration process. Finally, the server processes this request from the client, conducts user identity verification, and returns an authentication result. This approach effectively mitigates risks posed by attackers attempting to falsify biometric data or geographic information during the identity verification process and offers robust services for biometric- and geography-based identity authentication.

The Assessment of Land Suitability for Urban Expansion and Renewal for Coastal Urban Agglomerations: A Pilot Study of the Guangdong-Hong Kong-Macao Greater Bay Area

Effectively and rationally allocating land resources, while coordinating urban expansion with internal renewal strategies, is crucial for achieving high-quality regional development in coastal urban agglomerations. Land-use suitability assessment (LSA) is a key method for coastal land-use planning, but it is primarily used to delineate ecological redlines or areas for urban expansion, often overlooking the spatial analysis needed for urban renewal. This is particularly critical in coastal urban agglomerations facing land scarcity and ecological fragility. Here, we combined land use and the Analytical Hierarchical Process (to consider stakeholder priorities) in a Minimum cumulative resistance model (MCRM) to determine suitable coastal urban growth and renewal based on a suite of 12 indicators relevant to development intensity and stock space. Application to the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) indicates a dominance of the Ecological Buffer Zone (70.5%), and the available stock space in the GBA comprises only 9.2% of the total area. Our modeling framework tailored different development strategies for different cities: Huizhou and Zhaoqing had space for urban expansion to varying degrees, while other cities were found to be suitable for urban renewal due to low stock space and high development intensity. Our modeling approach, incorporating stakeholder input and objective evaluation of geographic land-use information, can assist planners in improving ecological security while promoting high-quality developments in coastal areas.

Intelligent Monitoring and Visualization System for High Building Nighttime Utilization Based on Image Processing

The rise of complex high-rise buildings has made building management increasingly challenging, especially the nighttime supervision of university laboratories. Idle occupation increases the risk of accidents and undermines campus sustainability. Effective occupancy detection is essential for optimizing campus building safety and energy efficiency. Environmental sensors for occupancy detection offer limited coverage and are costly, making them unsuitable for campuses. Surveillance cameras, as part of campus infrastructure, provide wide coverage. On this basis, we designed a detection algorithm that uses light brightness to assess nighttime building use. Experimental results showed that the algorithm achieves an average accuracy of 98.67%, enabling large-scale nighttime occupancy detection without the need for installing additional sensors, significantly improving the efficiency of campus building management. In addition, to address the limitations of indoor space representation in geographic information system (GIS) management models, this paper developed a comprehensive 3D GIS model based on a “building–floor–room” hierarchical structure, utilizing oblique photogrammetry and laser scanning technology. This study combined the detection results with real-world 3D data for visualization, providing a new perspective for the 3D spatiotemporal refinement of complex high-rise buildings, and providing a reference framework for the detection and analysis of other types of building environments.

OPEN-PIT LIMESTONE MINING AREAS MAPPING AND ASSESSMENT USING RANDOM FOREST ALGORITHM FOR SUSTAINABLE DEVELOPMENT

Limestone excavation is prone to land use/cover change, which impacts the ecosystem. The objective of this paper is to use a remote sensing dataset and the Quantum Geographic Information System (QGIS) to map and assess active captive limestone mining sites at the Yerraguntla in the YSR Kadapa district, Andhra Pradesh, India, for the year 2019. In this paper, the Limestone mining area was assessed as 379.57 ha with an overall accuracy of 95.79%, user accuracy of 97.25%, producer accuracy of 99.18%, and kappa coefficient of 0.957 by analyzing Sentinel-2A imagery dataset with Random Forest (RF) classifier. The findings were assessed in 2019 using very high-resolution (<1m) Google Earth images (486.47 ha) from Google Earth Pro and industrial field survey reports (487.10 ha). This research helps Limestone mine owners and Environmental engineers implement eco-friendly mining operations and monitor mining progress at low cost with less human efforts for sustainable development.

Delineation of Groundwater Potential Zones Using Geospatial Techniques. Case Study: Roman City and the Surrounding Area in the Northeastern Region, Romania

Effective groundwater management is crucial under the current climatic conditions, addressing both qualitative and quantitative aspects. An important step in delineating groundwater potential zones involves remote sensing (RS) data and geographic information systems (GISs), facilitating resource assessment, and the implementation of suitable field data management. This study introduces the delineation of potential groundwater zones using seven layers and the Multi-Criteria Decision Analysis (MCDA) method. Satty’s Analytic Hierarchy Process (AHP) was employed to rank the seven selected parameters, contributing to the advancement of groundwater research and resource assessment. All seven thematic layers (Rainfall, Geology, Land Use/Land Cover, Drainage Density, Elevation, Slope, and Soil) were prepared and analyzed to delineate groundwater potential zones. The resulting groundwater potential zone map was categorized into four classes, Very Good, Good, Moderate, and Poor, covering areas of 81.53 km2 (45.1%), 56.36 km2 (31.2%), 19.54 km2 (10.8%), and 23.17 km2 (12.8%) of the total area, respectively. The accuracy of the output was validated by comparing it with information on groundwater prospects in the area, and the overall accuracy of the method was approximately 72%. High-yield boreholes were drilled and concentrated in the Very Good groundwater potential zones, while low-yield ones were developed in the Poor areas.

Constructing Efficient Mesh-Based Global Grid Systems with Reduced Distortions

Recent advancements in geospatial technologies have significantly expanded the volume and diversity of geospatial data, unlocking new and innovative applications that require novel Geographic Information Systems (GIS). (Discrete) Global Grid Systems (DGGSs) have emerged as a promising solution to further enhance modern geospatial capabilities. Current DGGSs employ a simple, low-resolution polyhedral approximation of the Earth for efficient operations, but require a projection between the Earth’s surface and the polyhedral faces. Equal-area DGGSs are desirable for their low distortion, but they fall short of this promise due to the inefficiency of equal-area projections. On the other hand, efficiency-first DGGSs need to better address distortion. We introduce a novel mesh-based DGGS (MBD) which generalizes efficient operations over watertight triangular meshes with spherical topology. Unlike traditional approaches that rely on Platonic or Catalan solids, our mesh-based method leverages high-resolution spherical meshes to offer greater flexibility and accuracy. MBD allows high-resolution polyhedra (HRP) to be used as the base polyhedron of a DGGS, significantly reducing distortion. To address the operational challenges, we introduce a new hash encoding method and an efficient barycentric indexing method (BIM). MBD extends Atlas of Connectivity Maps to the BIM to provide efficient spatial and hierarchical traversal. We introduce several new base polyhedra with lower areal and angular distortion, and we experimentally validate their properties and demonstrate their efficiency. Our experimentation shows that we achieve constant-time operations for high-resolution MBD, and we recommend polyhedra to be used as the base polyhedron for low-distortion DGGSs, compact faces, and efficient operations.

Using Spatial Literacy for Disaster Management in Coastal Communities of Small Island Developing States (SIDS): A Case Study from Lavongai, Papua New Guinea

This study investigates the use of participatory geographic information systems (PGIS) for hazard assessment in small island developing states (SIDS), with a focus on spatial literacy and community-based disaster management. By partnering with the Lavongai community on Papua New Guinea, this research aimed to empower community members through skill development in geodata processing. The program leveraged local knowledge and the global positioning system to create participatory maps, enhancing both community capacity and researcher data quality. Workshops and focus group discussions (FGDs) were conducted to assess the community’s understanding of spatial concepts related to disaster risks. The core objective was a preliminary assessment of the community’s social and economic vulnerability to coastal disasters, using household data and GIS analysis. The results showed varied vulnerability levels within the community, highlighting the need for targeted disaster mitigation training and nature-based solutions. High-resolution satellite imagery and a simple bathtub model simulated sea level rise, identifying land-uses at risk. The program concluded with a community presentation of thematic maps, fostering collaboration and transparency. Future projects will address environmental challenges identified by local leaders and prioritize skill development, social data collection, and water resource mapping.

Multi-hazard modeling of erosion and landslide susceptibility at the national scale in the example of North Macedonia

Abstract Due to favorable natural conditions and human impact, the territory of North Macedonia is very susceptible to natural hazards. Steep hillslopes combined with soft rocks (schists on the mountains; sands and sandstones in depressions), erodible soils, semiarid continental climate, and sparse vegetation cover give a high potential for soil erosion and landslides. For this reason, this study presents a multi-hazard approach to geohazard modeling on the national extent in the example of North Macedonia. Utilizing Geographic Information Systems, relevant data about the entire research area were employed to analyze and assess soil erosion and susceptibility to landslides and identify areas prone to both hazards. Using the Gavrilović Erosion Potential Method (EPM), an average value of 0.36 was obtained for the erosion coefficient Z, indicating low to moderate susceptibility to erosion. However, a significant area of the country (9.6%) is susceptible to high and excess erosion rates. For the landslide susceptibility assessment (LSA), the Analytical hierarchy process approach is combined with the statistical method (frequency ratio), showing that 29.3% of the territory belongs to the zone of high and very high landslide susceptibility. Then, the accuracy assessment is performed for both procedures (EPM and LSA), showing acceptable reliability. By overlapping both models, a multi-hazard map is prepared, indicating that 22.3% of North Macedonia territory is highly susceptible to erosion and landslides. The primary objective of multi-hazard modeling is to identify and delineate hazardous areas, thereby aiding in activities to reduce the hazards and mitigate future damage. This becomes particularly significant when considering the impact of climate change, which is associated with increased landslide and erosion susceptibility. The approach based on a national level presented in this work can provide valuable information for regional planning and decision-making processes.

Peri-Urban Floodscapes: Identifying and Analyzing Flood Risk Areas in North Bhubaneswar in Eastern India

Peri-urban catchment areas are increasingly susceptible to floods due to rapid land use transformations and unplanned urban expansion. This study comprehensively examines flood vulnerability in the rapidly developing peri-urban areas of North Bhubaneswar, focusing on significant changes in Land Use/Land Cover (LULC) and hydrological dynamics from 2004 to 2024, utilizing Geographic Information System (GIS) tools. The analysis reveals substantial shifts in land use patterns, with the urban footprint expanding by 71.8%, cropland decreasing by 21.7%, and forest areas by 13.6%. These changes have led to increased impervious surfaces, resulting in higher surface runoff and decreased groundwater recharge, thereby exacerbating flood risks in the region. The GRID-based vulnerability analysis classifies 90 villages within the catchment area based on their vulnerability levels, identifying 20 villages as high-risk areas requiring urgent attention, 44 villages as medium vulnerable, and 26 villages as low vulnerable. These classifications are based on factors such as proximity to drainage networks, slope, geomorphology, and LULC characteristics, with areas near drainage channels and low-lying regions being prone to flooding. The analysis integrates multiple factors to provide a comprehensive assessment of flood risk, highlighting the need for sustainable land use planning, conservation of vegetated areas, and the implementation of advanced flood prevention strategies in the peri-urban areas. Extending this research to other fringe regions could offer further valuable insights, guiding flood prevention and sustainable development strategies for areas undergoing significant land use transformations to effectively mitigate future flood risks.

Integrating datasets from herbarium specimens and images to treat a Neotropical myrtle species complex.

Herbaria are the most important source of information for plant taxonomic work. Resources and technologies available today, such as digitised collections and herbarium DNA sequencing, can help accelerate taxonomic decisions in challenging plant groups. Here we employ an integrative methodology relying exclusively on herbarium specimens to investigate species boundaries in the Neotropical Myrcia neoobscura complex (Myrtaceae). We collected morphometric data from high-resolution images of herbarium sheets and analysed it using hierarchical clustering. We posteriorly tested the obtained morpho-groups with phylogenomics using the Angiosperms353 probe kit. We also gathered phenological and geographic information from specimen labels and built phenological histograms and ecological niche models to investigate ecological differences amongst taxa. Current circumscriptions of Myrcia arenaria, Myrcia neoglabra and Myrcia neoregeliana are confirmed in this study. Conversely, the four pieces of evidence together support Calyptranthes langsdorffii var. grandiflora, Marlierea regeliana var. parviflora and Marlierea warmingiana as separate from Myrcia marliereana, Myrcia neoriedeliana and Myrcia neoobscura, respectively, contrary to arrangements proposed by previous authors. Integrated analyses also support separation between Myrcia excoriata and two similar, undescribed taxa. Our data reveal the need for major changes in the systematics of the group, with recognition of 12 species. The successful delivery of our study aims was possible due to obtaining robust, high-quality data from museum specimens. We emphasise the importance of maintaining botanical collections physically and digitally available for taxonomic work and advocate their use to accelerate holistic taxonomic solutions of tropical species complexes. This is urgent, given the paucity of funds for fieldwork and unprecedented rates of habitat loss in the tropics.

Design and Implementation of a Geographic Scenario Computation System Based on Knowledge Base

Abstract. The promotion and expansion of Geographic Information System (GIS) applications have increased the complexity of geocomputation. The concept of a geographic scene, which encompasses both geographic entities and complex spatial interaction relationships, serves as a means of organizing and expressing geographic information. Existing methods of organizing and computing geographic scenes often focus on single geometric elements or single-category objects, neglecting the complexity of the geocomputation process and leading to biased results. To address this issue, this study designs and implements a geographic scenario computation system based on WebGIS to overcome the lack of integration of knowledge elements in traditional geographic scenario computation methods. The system consists of a data management module, a geographic scenario visualization module, and a geographic scenario computation module. It not only realizes the visualization of geographic scenarios but also introduces a knowledge base into the geographic scenario computation process, enabling accurate management of geographic scenarios. This paper uses a farmland scenario in Shandong Province, China, as a case study to verify the effectiveness and feasibility of the system. The application of this system provides new technical support for the computation process of geographic scenarios, promoting the refinement and intelligent development of geographic scenario management. This method can also be applied to similar applications of intelligent sensors and the Internet of Things in the organization and management of sensing information in the future.

Database Construction and Application of Large-scale Geospatial Data

Abstract. Geospatial data refers to data that related to spatial location. It is the abstract representation of objects, events, or other characteristic information located on or near the surface of the Earth. It characterizes attribute features, spatial features, and associated information of geospatial features. Geospatial data commonly include various data types such as point, polyline, polygon, and so on. Geospatial database is the basic method to store and manage geospatial data. It is also the important component of geographic information systems (GIS). Geospatial database generally stores data in a specific structure in a medium, which can achieve efficient management of geospatial data. Users can add, modify, and delete data efficiently. Furthermore, query retrieval and statistical analysis can be quickly performed based on geospatial database. In this paper, distributed geospatial database is constructed by using database partition tables storage technology. Meanwhile, database construction software is developed to achieve the construction, management, and application of large-scale geospatial data. It can improve data import, query and retrieval efficiency significantly. The database application service system can directly connect to the database for displaying statistical analysis results. At the same time, geospatial data are processed to vector tiles, which stored in the tile database. Furthermore, standard online service is then published, which can be used for database management and application service system. It can be used for natural resource management work such as basic surveying, survey and monitoring, and so on.

Inservice needs of selected Arkansas agriculture teachers related to precision agriculture

The purpose of this study was to determine selected Arkansas school-based agricultural education (SBAE) teachers’ perceptions of the importance, ability to teach, inservice needs, and barriers relative to incorporating precision agriculture (PA) into their programs. A non-probability sample (n = 44) of teachers participating in an introductory PA workshop completed the survey. Teachers rated each of the PA competencies as being above average or high importance but rated their ability to teach each competency as being none or below average. When competencies were grouped into seven PA topics, teachers had inservice needs for each topic with mean weighted discrepancy scores (MWDSs) ranging from 8.16 (guidance and autosteering systems) to 11.81 (geographic information systems). Years teaching experience, row-crop experience, and experience with PA had negligible to substantial negative correlations with inservice needs in each PA topic. A majority of teachers rated the lack of equipment (86.3%), curriculum materials (84.1%), personal knowledge (81.9%), and inservice opportunities (63.7%) as being either moderate or serious barriers to incorporating PA into their programs. These results indicated a perceived need for inservice education in PA, provided insight into priority topics, and identified potential barriers to incorporating PA into the curriculum.

Research on the Method of Managing Personal Massive Photos Using Geodatabase

Abstract. In view of the limitations of the existing methods for personal massive photos management and browsing, a method of using geodatabase to manage and browse massive personal photos is proposed. Firstly, vector geodatabase is created and defined based on geographic information technology. Then, attribute information including the file name, photo date and time, photo point longitude, photo point latitude, location name, photo storage path and so on of each photo in the file system are extracted. Finally, based on the longitude and latitude of the photo point, the geospatial location data of each photo is generated, and written into the vector geodatabase together with the attribute information. Based on the storage path of photos in the file system, the links between the geospatial database and photos are established. Furtherly realize the attribute information and geographical space location management of massive photos, as well as the browsing and viewing of photos. Take personal photos as an example to introduce the data processing and verify the practicability of the method. The result shows that this method uses the idea of geodatabase to manage massive personal photos, which is conducive to reflect the geographical spatial distribution and density characteristics of the photos, and can use the time axis to connect the track of personal history, which is convenient for browsing and analyzing the experience.

BG-LoA: A Benchmarking Framework for BIM/GIS Data Integration Based on Meta-Modelling Theory

Abstract. There are various studies on the integration of building information modelling (BIM) and geographic information system (GIS) that convert BIM data into GIS data. These studies used different technologies, data standards, data models, and data formats such as RDF (Resource Description Framework), graph, CityGML, and Shapefile to address the data incompatibility problem between BIM and GIS. This makes it difficult to compare these studies for the purpose of assessing research innovation, and sometimes, improper comparisons were carried out between studies. To solve this problem, this study adopted the metamodeling theory used in model-driven software engineering (MDSE) and extended its 4-layer framework of level of abstraction. The new benchmarking framework, referred to as level of abstraction for BIM/GIS data integration (BG-LoA), has 5 levels, including meta metamodel (M3), metamodel (M2), model (M1), reality (data) (M0), and reality (real world) (M00). The common types of IFC (Industry Foundation Classes) conversion were analysed using the new framework, including IFC-to-CityGML, IFC-to-CityJSON, IFC-to-Shapefile, IFC-to-GML, IFC-to-3DTiles, IFC-to-RDF, IFC-to-IfcGraph, and IFC-to-UML. The result shows that the new framework can show the difference between those conversion types, which makes it easier for future studies to benchmark their work and assess level of contribution to the body of knowledge.

A Systematic Review on Fluoride Contamination in Water Resources of Iran from 2016 to 2023: Spatial Distribution and Probabilistic Risk Assessment (Monte Carlo Simulation).

Because of significant variations and existence of high fluoride (F-) in some waters, Iran has been considered for various fluoride studies in recent decades. Nevertheless, there is a no updated review on fluoride levels especially including F- risk assessment. Thus, this systematic review is mainly aimed to evaluate the spatial distribution of F- content in water sources of Iran using geographic information system (GIS) and conduct the health risk assessment. Besides, the Monte-Carlo Simulation technique with 10,000 iterations was applied for determination of the non-carcinogenic effects of F- in different exposed groups (infant, children, teenagers, and adults). The results indicated that the maximum and minimum concentrations of F- content were related to Jazmourian (Roudbar plain) (4.8mg/L) and Sahneh (0.1mg/L) provinces, respectively, and F- content of more than ≅ 19% of the samples exceeds the Iranian standard value (1.5mg/L). The results showed that the HQ of F- in all groups were higher than 1 with the order of children > infants > teenager > adults in which children were the vulnerable group to F- consumption in study area. Therefore, it is necessary to monitor and continuously measure water supplies for fluoride content and control measures, including removal steps, be taken for human risk reduction.