Geographic Information System Methods Research Articles

The Use of Geographic Information Systems and Multi-Criteria Decision-Making Methods in the Creation of Forest Fire Susceptibility Maps: A Literature Review

Forest fires are natural disasters that cause significant environmental, economic, and social damage worldwide. This study provides a literature review examining how Geographic Information Systems (GIS) and Multi-Criteria Decision-Making (MCDM) methods are utilized in the prevention of forest fires and the identification of high-risk areas. GIS, as a system used for the collection and analysis of spatial data, enables the consideration of various factors influencing fire risk, such as climate change, topography, vegetation, and weather conditions. The spatial analysis capabilities offered by GIS play a critical role in identifying regions with high fire susceptibility when generating fire risk maps. Additionally, MCDM methods contribute significantly to the decision-making process by allowing the evaluation of multiple criteria in fire risk analysis. Logistic Regression and Frequency Ratio, which are frequently employed in the literature, are widely used in fire risk analysis and improve the accuracy of susceptibility maps. Furthermore, MCDM methods have been proven effective in estimating the likelihood of forest fire occurrences and identifying fire-prone areas. The integration of GIS and MCDM methods allows for more precise identification of risk zones and supports the development of fire prevention strategies. This literature review highlights the advantages of utilizing GIS and MCDM in the production of forest fire susceptibility maps and suggests that these methods may have broader applications in future research. The effective use of technology in combating forest fires enhances the accuracy of fire risk assessments, contributing significantly to environmental protection efforts.

Assessment of Land Suitability in Perennial Crops using FAHP and GIS in Central Vietnam

Background: Perennial crops including rubber and Thanh Tra grapefruit are highly effective land use that has occupied a large area (nearly 11,000 ha) in Thua Thien Hue province, Central Vietnam. Analyzing land suitability in agricultural production is one of the important tools for determining the effective use of land resources and sustainable development. This study aims to evaluate the land suitability of perennial crops in Phong Dien district, Thua Thien Hue province, Central Vietnam. Methods: The study was conducted in different communes in Phong Dien district, Thua Thien Hue province, Central Vietnam from 2022 to 2023 by collecting secondary and primary data together with soil samples from 45 profiles for analysis and finally application of the fuzzy multi-criteria analysis process (FAHP) tool and geographic information system method (GIS) to evaluate the land suitability. Result: The results indicated that economic criteria played the most important role with weights from 0.068 to 0.343, followed by the social criteria (weights from 0.014 to 0.147) and the environmental criteria (weights from 0.011 to 0.051). With land suitability evaluation, the very high suitability area of rubber and Thanh Tra grapefruit (S1) was 3,144.14 ha and 3,742.29 ha respectively. The FAHP and GIS integrated method is a useful method to assess land suitability for land use of perennial crops based on appropriate assessment of FAO land use requirements.

Integrated geophysical and GIS approaches for groundwater potential assessment: a case study of Aladja, Delta State, Nigeria

ABSTRACT This study explores the groundwater potential in Aladja, Udu, Local Government Area of Delta State, Nigeria, using an integrated approach combining electrical resistivity techniques with Remote Sensing (RS) and Geographic Information System (GIS) methods. The primary aim is to delineate aquifers and comprehensively assess groundwater resources. Methods include Vertical Electrical Sounding (VES) to gather resistivity data, RS and GIS for analyzing geological and hydrological parameters such as lineament density, drainage density, slope, soil type, and land use. The resistivity values ranged from 147 to 460.50 Ωm, with aquifer thicknesses varying between 6.50 and 36.30 m. The RS and GIS analysis indicated high groundwater potential in regions characterized by significant lineament density and moderate slopes. The study highlights the complementarity of VES and RS/GIS in groundwater exploration, revealing substantial groundwater resources in the northwestern regions of the study area. This approach underscores the importance of integrating these methods for a more accurate assessment of groundwater potential. The novelty of this study lies in validating RS and GIS findings with VES data, ensuring reliable groundwater potential mapping and effective resource management. This integrated methodology offers a robust framework for sustainable groundwater resource assessment and management in similar geological settings.

Identification of groundwater recharge potential zone using geospatial approaches and multi criteria decision models in Udham Singh Nagar district, Uttarakhand, India

Groundwater is a valuable natural resource, which is extensively used in agricultural, industrial as well as daily activities of human beings. It is an essential source of freshwater need of half of the world’s population. In this study Remote Sensing (RS) and Geographic Information Systems (GIS), along with Multi criteria decision models were used. The Analytical Hierarchy Process (AHP) was used to determine the weights of various themes and their classes in the study to identify the groundwater recharge zones in Udham Singh Nagar district. Nine thematic maps/layers were created using GIS methods and employed for the demarcation of groundwater potential zone (GWPZ). It was concluded that around 82% of the area is very suitable (very good and good) for ground water recharge, which suggests that there is a significant amount of potential for artificial recharge systems to be implemented. This can help to augment the natural recharge process, improve water quality, and reduce the strain on existing water sources. Whereas only 18% (very poor and poor) of the area has low potential to groundwater recharge is still important to consider, as it may require different approaches or strategies to improve its suitability. Validation shows that, wells data; (n=38) have water level <5 mbgl (83%), whereas only (n=8) have water less >5 mbgl (17%). This area could be prioritized for more extensive data collection or monitoring to better understand its hydrogeological characteristics. The findings will indeed be helpful for decision-makers in selecting suitable locations for groundwater recharge structures.

Potential assessment and development obstacle analysis of CCUS layout in China: A combined interpretive model based on GIS-DEMATEL-ISM

Although carbon capture, utilization, and storage (CCUS) has enormous emission reduction potential and application prospects, it still faces many obstacles and challenges in the actual deployment and promotion process. This study aims to evaluate the spatial suitability of the CCUS layout comprehensively and deeply analyze key development obstacles to provide a scientific basis for the promotion and layout of CCUS. Therefore, this article constructs a combination of Geographic Information Systems (GIS), Decision-making trial and evaluation laboratory (DEMATEL) method, and Interpretive structure model (ISM). Firstly, a comprehensive evaluation is conducted on the suitability of the CCUS layout in China based on GIS technology, identifying high-potential areas. The results indicate that when the threshold is 80 % of the highest score for suitability, a total area of 1.46 million square kilometers is suitable for the CCUS project layout. When it dropped to 60 %, this data increased to 2.04 million square kilometers. These areas are mainly distributed in large basin areas with abundant resources. Secondly, the DEMATEL-ISM analysis framework is improved to systematically identify and analyze the key obstacles that constrain the promotion of CCUS in China. The results indicate that there is a very complex relationship between the obstacles that constrain the development of CCUS, with the fundamental reasons being the lagging technological level and imperfect policies and regulations. Finally, goal-oriented suggestions and response measures were proposed, and targeted analysis was conducted on high-suitability areas. This study constructed an interdisciplinary methodology interpretive model to provide decision support for the CCUS layout and targeted strategies to break through current development barriers and promote the rapid development of CCUS.

AHP Optimization Method for Windy Site in Costal Annaba Region

The majority of countries worldwide rely on fossil fuels as a source of energy. Nevertheless, the utilization of this source of energy is associated with significant environmental problems. To reduce their impact, innovative clean energy solutions are needed. Wind power is currently the most cost-effective and competitive renewable energy source. The identification of an optimal location for a wind farm is a crucial aspect of the decision-making process. This involves considering numerous factors, including wind speed, terrain quality, distance to the power grid, road network, housing, agricultural land, and environmental concerns. The use of Multi-Criteria Decision Method (MCDM) and Geographic Information System (GIS), along with other techniques, facilitates the selection of sites that meet the desired criteria. This study employed GIS and MCDM-AHP methods in order to identify suitable sites for wind farm installation in an eastern Algerian coastal area. In the context of MCDM analysis, it is important to note that not all criteria are of equal importance. Consequently, each criterion is assigned a specific weight to reflect its importance. The weights assigned in this study were calculated using the Analytic Hierarchy Process (AHP). The result was a wind speed suitability map that combined all criteria into two site categories in fact, 20% of the land area is highly suitable and 10% is found to be most suitable.

Agricultural land suitability of Manimutha Nadhi watershed using AHP and GIS techniques

A precise land suitability assessment plays a vital role in modern agriculture to sustain production against population explosions worldwide without affecting natural resources or environmental factors. Further, recent urbanization has led to a reduction in arable land and forced the conversion of non-arable land to arable land. Therefore, developing land suitability with its potential and limitations is essential through advanced techniques like multi-criteria Decision Analysis for the particular area. The current research aimed to assess the land suitability of the Manimutha Nadhi watershed, Tamil Nadu, India, using geographical information system (GIS) and Analytic Hierarchy Process (AHP) methods. This study area, Manimutha Nadhi, covers a total area of 263.89 km2. Using a random sampling technique, 57 soil samples were obtained. The parameters for the AHP study were soil pH, EC, Soil organic carbon (SOC), available NPK, soil texture, and soil erosion. The climatic parameters, Annaula average rainfall (AAR) and Annual average temperature (AAT) and topographic parameter slope of the study area are also used as the criteria for generating final agricultural land suitability. The final suitability map through GIS and AHP was created using weighted overlay analysis. The land suitability class on the basis of FAO classification is highly suitable (S1), moderately suitable (S2), marginally suitable (S3), currently not suitable (N1), and permanently not suitable (N2) for agriculture. The results of the final land suitability map by AHP and GIS revealed that 150.58 km2 (57.09%) of the research region is highly suitable for agriculture (S1). The 29.60 km2 (10.08%) area was moderately suitable (S2) for irrigated agriculture. The 53.37 km2 (20.20%) area was marginally suitable (S3) for irrigated agriculture. The 10.56 km2 (7.53%) area was currently unsuitable (N1) for agricultural uses. The area of about 13.45 km2 (5.10%) was permanently unsuitable (N2) for agriculture. The outcomes can be used as baseline information on the land that could strengthen agriculture production in the Manimutha Nadhi watershed.

Mapping Segregation Patterns of Hospital Care among Patients with Limited English Proficiency.

Individuals with limited English proficiency (LEP) are disproportionately more likely to experience suboptimal care outcomes compared to English-proficient individuals, attributed to multi-level social determinants of health, including the quality of the hospital where LEP patients are more likely to receive care. Evidence demonstrates that racial minority patients are more often admitted to lower-quality hospitals serving high proportions of minority patients, despite living closer to higher-quality hospitals. Less is known about where individuals with LEP reside, where they seek hospital care, and the quality of care in these hospitals. Using Geographic Information Systems (GIS) methods, we developed a density map characterizing residential patterns of the LEP population across zip code tabulation areas in New Jersey and designated hospitals as high, middle, or low-LEP volume. We described differences in 30-day hospital wide readmission rates for hospitals across varying LEP volume status using Centers for Medicare and Medicaid Services Hospital Care Compare Data. Most hospitals in ZCTAs with higher LEP populations serve a high proportion of LEP patients (i.e. their patients' demographics are reflective of the community in which they are located). However, our results also show instances in which LEP patients may be forgoing receiving care at closer hospitals to instead receive care at further-distanced, high-LEP volume hospitals. significant. High-LEP volume hospitals have higher 30-day hospital wide readmission rates (20.1%) compared to middle (15%) and low (11.3%)-LEP volume hospitals (p < .001), indicating lower quality of care within high-LEP volume hospitals.

Selection of optimal areas for the installation of Wind Farms in the north-eastern part of Cuba

The present research aims to select optimal areas for the installation of wind farms in the north-eastern part of Cuba. The research is based on the free software QGIS, Hierarchical Process Analysis, databases of the information of the alternatives and criteria and Geographic Information Systems (GIS), which makes it possible to know the places where these farms will be installed. In addition, experts were consulted to obtain the weights or level of importance of the selected criteria, such as distance to ports, distances to common roads, distances to population centers, to electrical grids and finally the wind speed. As a result, a mathematical conceptual model was obtained for the selection of optimal zones for the installation of wind farms in the eastern north of Cuba, by means of the MCDM and GIS methods, where it was determined that, within the evaluated alternatives of wind farms, Gibara III is the optimal one. These models will allow the development of wind projects in the eastern region of Holguin, and to be able to manage human, financial and material resources.

The optimization of harm reduction services in Massachusetts through the use of GIS: Location-allocation analyses, 2019–2021

BackgroundFatal opioid-related overdoses (OOD) continue to be a leading cause of preventable death across the US. Opioid Overdose Education and Naloxone Distribution programs (OENDs) play a vital role in addressing morbidity and mortality associated with opioid use, but access to such services is often inequitable. We utilized a geographic information system (GIS) and spatial analytical methods to inform prioritized placement of OEND services in Massachusetts. MethodsWe obtained addresses for OEND sites from the Massachusetts Department of Public Health and address-level fatal OOD data for January 2019 to December 2021 from the Massachusetts Registry of Vital Records and Statistics. Using location-allocation approaches in ArcGIS Pro, we created p-median models using locations of existing OEND sites and fatal OOD counts to identify areas that should be prioritized for future OEND placement. Variables included in our analysis were transportation mode, distance from public schools, race and ethnicity, and location feasibility. ResultsThree Massachusetts communities – Athol, Dorchester, and Fitchburg – were identified as priority sites for new OEND locations using location-allocation models based on capacity to maximize OOD prevention. Communities identified by the models for OEND placement had similar demographics and overdose rates (42.8 per 100,000 vs 40.1 per 100,000 population) to communities with existing OEND programs but lower naloxone kit distribution rates (2589 doses per 100,000 vs 3704 doses per 100,000). Further models demonstrated differential access based on location and transportation. ConclusionOur analyses identified key areas of Massachusetts with greatest need for OEND services. Further, these results demonstrate the utility of using spatial epidemiological methods to inform public health recommendations.

Communication method of distribution network geographic information system

A communication method and system between the distribution network planning system and the grid geographic information system. After the change of the grid structure, the grid equipment change information is generated with the grid equipment as the unit. The production control region information structure is used to encapsulate the grid equipment change information, obtain the equipment change bus message, and send the equipment change bus message to the production control region service bus, This enables the distribution network planning system to receive equipment change bus messages from the production control area service bus, so that the distribution network planning system can directly communicate with the grid geographic information system.

Assessing the Impact of Climate Change on Glacial Lake Outburst Flood (GLOF) in Eastern Hindu Kush Region Using Integrated Geo-Statistical and Spatial Hydrological Approach

Glacier retreat, a major impact of climate change that continues to occur in many parts of the world, continues to increase the risk of glacial lake outburst floods (GLOFs) in northern Pakistan. The rapid melting of glaciers in the mountains of Northern Pakistan, including the Hindu Kush, the Himalayas and the Karakoram, the rapid melting of glacier has led to the formation of 3044 glacial lakes, with 33 identified as particularly vulnerable to GLOFs. This study uses remote sensing and geographic information systems (GIS) methods for mapping and representing GLOFs. Based on the observational data of lake area, volume, and depth, empirical equations are developed through statistical methods. Only two lakes, Chitral-GL2 and Swat-G31, are classified as lakes with high potential for GLOF. Through modeling techniques using HEC-RAS and HEC-GeoRAS spatial hydrological models integrated with GIS remote sensing, the spatial extent and depth of inundations under different lake volumes are assessed. The analysis reveals that a total area of 20.56 km2 is susceptible to submersion by GLOFs, with Chitral-GL2 flooding area of 14.80 km2 and Swat-GL31 5.79 km2. Different land types are impacted by critical water depths, with built-up and agricultural lands 2.7 km2 totally, and barren lands 8.93 km2 under different flood depths ranging from less than 5 m to over 15 m.

More Than Aging in Place: “Aging in Networks” in Singapore

Aging in place has become a popularly accepted logic and practice in both the academic and policy domains. Older adults’ preferred facilities and social networks, however, might not always be situated in the residential neighborhoods where their homes are located. While recognizing the value of aging in place, this article argues that geographical proximity should not be conflated with aging well, nor should a spatially delimited place be the only locus of “community.” We propose the concept of aging in networks to capture a range of social networks in the life-worlds of older adults, including those that extend beyond their neighborhoods. We combine qualitative and geographic information systems (GIS) methods (i.e., qualitative GIS) to analyze three types of activity spaces observed among older adults in Singapore. Our analysis elicits the reasons why older adults use facilities and cultivate social networks both within and beyond their neighborhoods. This approach highlights the importance of studying and supporting both the local and extralocal ties of older adults. The article draws on qualitative GIS research (interviews and Global Positioning System tracking) with fifty older adults in two neighborhoods in Singapore (from a wider survey with 1,199 older adults).

Flood risk assessment in arid and semi-arid regions using Multi-criteria approaches and remote sensing in a data-scarce region

Flooding is a natural disaster that poses a threat to both people and the environment, necessitating proactive assessment and mitigation strategies to protect vulnerable communities and ecosystems. These measures are necessary to reduce the risk of flooding and moderate the impact of rainfall. In this study, an Analytical Hierarchy Process (AHP) was used to evaluate flood risk in a data-limited region by integrating Remote Sensing (RS) and Geographic Information System (GIS) methods. The study identified several key flood risk indicators, including topographic wetness index, elevation, slope, land cover, precipitation, distance to river, distance to road, and NDVI. The flood risk map had a score range of 8.71–30.99 %, with higher scores indicating a greater susceptibility to flooding. These scores were then used to classify the flood risk into five categories: very low, low, moderate, high, and very high. The percentages of regions falling into each category were 8.71 %, 23.52 %, 30.99 %, 22.68 %, and 14.09 % respectively. The area under the Curve (AUC) approach was used to validate the flood risk map, which showed a high degree of accuracy (0.86). The results of this study provide valuable insights for monitoring, and forecasting the probability of floods in the Dosso Region.

A new method of estimating shelterbelt carbon storage on the regional scale: Combined the single tree carbon storage with tree numbers

The carbon storage potential of planted shelterbelts was gradually recognized in recent years, but there was a lack of shelterbelt knowledge for carbon inventory analyses because of their narrow linear feature and extensive distribution. Considering its own characteristics of the shelterbelt, this paper proposed a new remote sensing model for estimating the carbon storage of farmland shelterbelt (CSBelt) based on the single tree carbon storage and tree numbers. In this model, the single tree carbon storage model was established by shelterbelt age, which could be identified from time series images. Tree numbers of each shelterbelt, as another important parameter in the CSBelt model, was detected by numbers of rows, columns, and preservation rate, which were monitored by high resolution remote sensing image and geographic information system method. Finally, overall accuracy (OA), mean absolute error (MAE), and mean error (ME) was used to validate the remotely identified data. The results showed that, the OA of the single tree carbon storage was 80.4% within the error range of 0.05 MgC/tree, the MAE was 0.036 MgC/tree, and ME was 0.020 MgC/tree; the OA of tree numbers was 71.7% within the error range of 100 trees/km, the MAE was 94 trees/km, and ME was 73 trees/km; the OA of shelterbelt carbon storage was 82.6% within the error range of 100 MgC/km, the MAE was 49.63 MgC/km, and ME was 33.78 MgC/km. Compared to other carbon storage models, this model requires fewer parameters and is not restricted by the stand conditions, which has better universality and realizability. This research is helpful for the accurate assessment of shelterbelt ecosystem carbon storage, which is of great significance for predicting climate change and formulating strategies. The results of this research will also provide data support for shelterbelt management on a regional scale.

SPATIAL ANALYSIS OF LAND COVER CHANGES IN DEPOK CITY USING GEOGRAPHIC INFORMATION SYSTEMS

Depok City is an area undergoing significant changes in land cover due to rapid urban development. Additionally, population growth is contributing to these land cover changes. These factors exert pressure on land use changes, with implications for land cover. Therefore, it is necessary to conduct a spatial analysis of land cover changes in Depok City. This research employs Geographic Information System (GIS) methods and spatial analysis to examine land cover changes in Depok from 2017 to 2022. The objectives of this study are to identify the land cover conditions in Depok from 2017 to 2022 and analyze the changes in land cover during this period. The data source used is Landsat 8 satellite imagery. These satellite images were interpreted to produce land cover maps for the years 2017 and 2022. Subsequently, these two maps were overlaid to assess changes in land use. The research results revealed seven land cover classes in 2017, with the largest in terms of area being Residential/Built-up Areas (12,383.43 hectares), Green Open Spaces (3,479.66 hectares), Dryland Agriculture (2,218.65 hectares), Services/Industry (885.41 hectares), Paddy Fields (414.59 hectares), Open Land (373.66 hectares), and Water Bodies (239.23 hectares). Spatial analysis indicated a decrease in land cover for Green Open Spaces (238.84 hectares), Dryland Agriculture (122.67 hectares), and Paddy Fields (31.89 hectares). Conversely, an increase in land cover was observed for Residential/Built-up Areas (266.01 hectares), Services/Industry (91.84 hectares), and Open Land (35.55 hectares).

Flood hazard analysis in Mumbai using geospatial and multi-criteria decision-making techniques

ABSTRACT Flood risk assessment remains a crucial element, particularly within locations highly susceptible to repeated flood occurrences. This study seeks to conduct an elaborate flood risk analysis for Mumbai, India based on an integrated method of geographic information systems and analytic hierarchy process (AHP). In this study, land use/land cover, average annual rainfall, elevation, drainage density, normalized difference vegetation index, distance from rivers as well as distance from roads are identified and considered. For this reason, the expert survey utilizes the AHP weights so as to find out the significance of these factors towards flooding danger. Investigations show a flood risk index and a corresponding map for Mumbai, where all areas are divided into risk zones very low to very high. Very high risks are particularly situated along rivers. Such details offer critical knowledge to policymakers who will undertake informed emergency preparedness measures designed to shield Mumbai's citizens and assets. Therefore, this research is considered as one of the modern techniques for flood risk assessment, which can be applied in other flood-affected areas worldwide.

Artificial Neural Networks for Mapping Coastal Lagoon of Chilika Lake, India, Using Earth Observation Data

This study presents the environmental mapping of the Chilika Lake coastal lagoon, India, using satellite images Landsat 8-9 OLI/TIRS processed using machine learning (ML) methods. The largest brackish water coastal lagoon in Asia, Chilika Lake, is a wetland of international importance included in the Ramsar site due to its rich biodiversity, productivity, and precious habitat for migrating birds and rare species. The vulnerable ecosystems of the Chilika Lagoon are subject to climate effects (monsoon effects) and anthropogenic activities (overexploitation through fishing and pollution by microplastics). Such environmental pressure results in the eutrophication of the lake, coastal erosion, fluctuations in size, and changes in land cover types in the surrounding landscapes. The habitat monitoring of the coastal lagoons is complex and difficult to implement with conventional Geographic Information System (GIS) methods. In particular, landscape variability, patch fragmentation, and landscape dynamics play a crucial role in environmental dynamics along the eastern coasts of the Bay of Bengal, which is strongly affected by the Indian monsoon system, which controls the precipitation pattern and ecosystem structure. To improve methods of environmental monitoring of coastal areas, this study employs the methods of ML and Artificial Neural Networks (ANNs), which present a powerful tool for computer vision, image classification, and analysis of Earth Observation (EO) data. Multispectral satellite data were processed by several ML image classification methods, including Random Forest (RF), Support Vector Machine (SVM), and the ANN-based MultiLayer Perceptron (MLP) Classifier. The results are compared and discussed. The ANN-based approach outperformed the other methods in terms of accuracy and precision of mapping. Ten land cover classes around the Chilika coastal lagoon were identified via spatio-temporal variations in land cover types from 2019 until 2024. This study provides ML-based maps implemented using Geographic Resources Analysis Support System (GRASS) GIS image analysis software and aims to support ML-based mapping approach of environmental processes over the Chilika Lake coastal lagoon, India.

Developed analytic hierarchy process and multi criteria decision support system for wind farm site selection using GIS: A regional-scale application with environmental responsibility

As determined by empirical analysis, renewable energy sources account for less than 1.73 percent of the developing countries energy market. The western region of Iran, or province of Kermanshah, presents significant opportunities for power exports to neighboring country (Iraq). In this research, an appropriate location for wind farms in Western Iran is determined with the assistance of a combined method of Geographic Information Systems (GIS) and Multi-Criteria Decision Making (MCDM) description. Electrical, techno-economic, environmental, and geo-infrastructure aspects, as well as their respective sub-criteria, were subjected to a comprehensive analysis and weighting in this study. The Analytical Hierarchy Process (AHP) was utilized to achieve this result. The aforementioned conclusions were reached through an extensive examination of insightful interviews conducted with esteemed experts in the domain of wind energy. Significant suitability for the potential installation of large-scale wind turbines or wind farms is evident in six critical areas, encompassing more than 20 % of the evaluated region, according to the findings of this paper. Conversely, it was deduced that precisely 80 % of the areas examined were deemed inappropriate for such endeavors. The assessment yielded four locations that were deemed suitable, denoted as A, B, C, and D. The southern, western, and central regions of the province contain these locations. The proposed total capacity is 216 MW, which is determined by the combined surface areas of these regions (19 km2, 55 km2, 64 km2, and 40 km2, respectively). An anticipated reduction of 1,262,471 tons in greenhouse gas emissions is anticipated as a result of the implementation and substitution of the proposed wind farms for the gas turbine power plants that presently predominate in Iran's thermal power plant sector. The method used in this article can be extended to all cases of other faculties to measure the potential of wind farm resources.

Spatial mapping of indoor air quality in a light metro system using the geographic information system method

Abstract It is known that one of the greatest problems of developed countries in the twenty-first century is traffic. For this reason, engineers have searched for alternative solutions to the problem of traffic. One such solution is the construction and utilization of rail systems instead of main roads. From an engineering perspective, rail systems can be divided into three groups: metro, light metro, and tram systems. Light metro systems, which are a form of public transportation, are not directly inside the traffic. Their most important advantages include the fact that they do not release combustion products such as CO, and metro and light metro systems may be considered environmentally friendly based solely on their electricity consumption. In this study, measurements of parameters affecting indoor air quality were made inside light metro cars and in and around light metro stations belonging to the light metro system of the Metropolitan Municipality of Antalya, known as the tourism capital of Turkey. In February and March 2021, when the COVID-19 pandemic was first registered in Turkey, particulate matter (PM), temperature, and relative humidity measurements were made for testing indoor and outside air quality. Moreover, as outside air parameters, outside temperature, outside relative humidity, CO, normalized difference vegetation index, and ultraviolet aerosol index data were obtained from the General Directorate of Meteorology of Turkey. The measurement results were analyzed using the inverse distance weighting method in the geographic information system. Based on the results of the analyses, spatial maps were created for indoor and outside air quality parameters in the light metro system. Using these maps, the effects of passenger density and environmental factors both inside the metro cars and at the metro stations on indoor air quality were identified. In addition, the spread of the SARS-CoV-2 virus in the COVID-19 period was analyzed using spatial maps of the PM0.3 and PM0.5 parameters. It is believed that the results of this study will set an example for further indoor air quality studies worldwide, and this study is unique in that it employed a method that is used particularly in survey and geomatics engineering for analyzing indoor air quality in light metro systems.