Leveraging Remote Sensing, Geophysical Methods and AHP Model to Determine Optimal Locations for Green Hydrogen Production on Egypt's Mediterranean Coast

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El Hateem, Ahmad I. Diab, Hossam M. El -Sayed, Magdy M.S. El Maghraby, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8001705/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted 16 You are reading this latest preprint version Abstract Global efforts to decarbonize energy systems have intensified the search for renewable alternatives, green hydrogen is considered one of the best intriguing solutions. This research integrates satellite imaginary to identify optimal locations for production of green hydrogen along Egypt's Mediterranean coast. The methodology employs nine critical parameters: distance to sea, drainage density, geology, land use/land cover, elevation, lineament density, distance to roads, wind speed, and air temperature. These parameters were evaluated by utilizing analytic hierarchy process with a consistency ratio of 0.075 which confirms correctness of the weightage method. The resulting suitability map categorizes potential sites into three classes: least suitable, moderately suitable, and most suitable. Analysis revealed that the northern part of Marsa Matruh represents the most favorable location for green hydrogen production. Additionally, a geoelectrical survey using eleven vertical electrical soundings (VESs) with Schlumberger configuration validated the surface findings and provided crucial subsurface information, identifying dolomitic limestone as the optimal bedrock for facility construction. This study offers a thorough framework for the strategic advancement of green hydrogen production in Egypt, supporting the country's sustainable energy transition goals. Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Solid earth sciences Green Hydrogen production Renewable energy Vertical electrical sounding AHP Bedrock Marsa Matruh Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 1. Introduction The consistent rise in global energy demand, driven by population growth, elevated living standards, and industrial expansion in developing nations, presents significant environmental challenges (Dincer 2012 ; El-Shafie et al. 2019 ). Currently, greater than 95% of significant energy needs are satisfied by fossil fuels, leading to huge greenhouse gas emissions that intensify global warming problem and environmental deterioration (Lee et al. 2018 ; Sazali 2020 ). Global initiative has been established to mitigate these emissions and control the rise in the worldwide average temperature to less than 3°C (Braun et al. 2013 ; Khan et al. 2015 ; Fasullo et al. 2018 ). This imperative has spurred research institutions and organizations worldwide to develop breakthrough technologies that harness renewable energy sources for generating green energy and sustainable fuels (Vincent and Bessarabov 2018 ; Dawood et al. 2020 ; Pinsky et al. 2020 ). Hydrogen is well positioned to store and transfer renewable energy, taking a vital role in the world's energy transformation (Dincer and Acar 2015 ). The classification of hydrogen varies according to production methods and environmental impacts, with designations including blue (reforming with carbon capture from natural gas), gray (reforming from natural gas), brown (gasification from lignite), black (gasification from bituminous coal), and green (electrolysis from water) (Noussan et al. 2020 ; Ajanovic et al. 2022 ). As the most environmentally beneficial option, green hydrogen is generated using renewable electricity that comes from renewable energy sources to split water into hydrogen and oxygen that leads to zero carbon emissions while production (Carmo et al. 2013 ). Renewable energy sources like solar and wind energy represent ideal sources to generate green hydrogen through electrolysis process (Wang et al. 2014 ). This is especially important for green hydrogen production in areas that possess rich solar resources, like the Middle East and North Africa (Abbas et al. 2023 ; Arpino et al. 2024 ). Hydrogen possesses many advantages, for example, it is renewable, clean, non-polluting, adaptable, and storable. Hydrogen reserves are plentiful, also their unit calorific value is relatively high in contrast to other fuels (Liu et al. 2021 ; Jiangnan et al. 2021 ). Renewable hydrogen has diverse applications across multiple sectors, including renewable energy production for transportation, industrial processes such as steel manufacturing and petroleum refining, and chemical production like ammonia and methanol (Mazzeo et al. 2022 ). However, despite its numerous benefits, producing and managing hydrogen especially, green hydrogen presents significant challenges. These include the high costs of electrolysis equipment, the technical complexity of integrating intermittent renewable energy sources, and the substantial energy requirements for storage, compression, and transportation of hydrogen. Additionally, hydrogen's high flammability and low ignition energy necessitate specialized handling processes and safety systems (Nnabuife et al. 2025 ). To identify and categorize suitable areas for green hydrogen production, multiple datasets can be compiled and analyzed. The integration of satellite image analysis with GIS mapping enhances the strategic approach and improves understanding of results (Doorga et al. 2018 ). Statistical methodologies like the analytic hierarchy process increase the accuracy of results (Arnous et al. 2020 ), while combining analytic hierarchy process results with geophysical data substantially boosts reliability of the results (Kulandaisamy et al. 2020 ; Arunbose et al. 2021 ). This study aims to identify the optimal locations to produce green hydrogen on the Northwestern coast of Egypt especially, Marsa Matruh city (Fig. 1 ) by integrating remote sensing satellite imaginary and geophysical techniques to determine ideal areas with favorable conditions for facility development. 2. Study area and tectonic setting Matruh Governorate represents one of the most promising locations on Egypt's northern coast, particularly the Marsa Matruh city, which is pivotal for upcoming sustainable development projects. The population of Marsa Matruh reached 241.625 by the year 2024. It is situated 240 kilometers west of Alexandria and 222 kilometers east of Sallum, along the main highway connecting the Nile Delta and the Libyan border together and another highway extends in the south towards Siwa Oasis and Bahariya Oasis. Marsa Matruh International Airport and Gargoub port have granted the city significant logistical value, positioning it as an attractive place for industry, investment, and international trade. The research region ranges from latitude 28° 33' N to 31° 32' N and longitude 26° 10' E to 27° 52' E within the Matruh Basin. The Matruh Basin (Fig. 2 ) constitutes a segment of a sequence of intracratonic rift basins located along the northern African passive margin (Moustafa 2020 ). These rift basins formed during the Permian to Early Jurassic periods in the northern Western Desert of Egypt because of the fragmentation of Gondwana and the gradual opening of the Neotethys (Guiraud 1998 ; Yousef et al. 2019 ; Bosworth et al. 2020 ). These basins typically follow either NE SW or ENE-WSW orientations (Moustafa 1998 ), with the Matruh basin distinguished as a predominantly NNE-oriented formation (Moustafa 2020 ). This structural pattern presumably originated from pre-Jurassic structures; nevertheless, the idea requires more proof (Moustafa et al. 2002 ). During Late Cretaceous period, the junction of the Afro-Arabian and Eurasian tectonic plates generated compressional stress in these basins, leading to inversion of the basin (Moustafa 2020 ; Yousef et al. 2019 ; Guiraud and Bosworth 1997 ; Moustafa and Bevan 2012 ). This compressional deformation episode was later followed by a Neogene extension era (Bosworth et al. 2020 ; Yousef et al. 2010 ). 3. Materials and methods 3.1. Remote sensing This research employs a comprehensive methodology (Fig. 3 ) to determine the best places to produce green hydrogen. Nine critical parameters were selected for the weighted overlay analysis and evaluated using ArcGIS Desktop 10.8 software. The objective was to find the best sites for producing green hydrogen by evaluating key parameters including land use/land cover (LULC), climate data (Air temperature, wind speed), elevation, drainage density, geology, lineament density, distance to roads, and distance to sea. The methodology involved the following structured approach: 3.1.1. Data Collection To create the thematic layers required for analysis, high-resolution multispectral imagery from different sources such as Sentinel-2 satellites were utilized. These sources provided ideal data for generating detailed thematic maps. The specific layers were derived as follows: Land Use/Land Cover Map: Generated from Sentinel-2 imagery at 10m resolution. LULC was created to identify land availability for establishing green hydrogen production facilities such as infrastructure for producing hydrogen and establishing solar and wind energy projects. Geology: Geological data was acquired from the United States Geological Survey (USGS). It was used to identify rock units that could influence site selection and determine the age and characteristics of the bedrock. Elevation: Digital elevation models (DEMs) were derived from Copernicus GLO-30 data accessed through Open Topography. Elevation data are vital for understanding the topography of the site, which can affect the feasibility of infrastructure development. Drainage Density: This layer was created by analyzing drainage patterns derived from the DEM layer to assess water resource availability. Lineament Density: Derived from DEM analysis to identify geological lineaments that indicate fractures and faults in the subsurface, which may influence site stability. Proximity Maps: Transportation infrastructure data (railways and roads) was obtained to evaluate connectivity for hydrogen transportation and distribution. Proximity to the sea was derived from the Marsa Matruh coastline shapefile to determine distance from sea for getting water for electrolysis. Climate Data: Information on air temperature and wind speed was collected. These data are crucial for assessing the potential for wind and solar energy sources, that considering the main sources to generate electricity that is used in the electrolysis process at 2.5-minute spatial resolution (~ 21 km² at the equator) for air temperature map and at 10 meters (m/s) for wind speed map. 3.1.2. Data Processing The raw satellite imaginary was processed by utilizing (GIS) software through the following steps: Layer Generation: Each thematic layer was produced through specific algorithms and models. Air temperature and wind speed were calculated using Inverse Distance Weighted (IDW) interpolation, while drainage density and lineament density that obtained from digital elevation models by utilizing hydrological analysis tools. The Euclidean distance function was applied for proximity analysis, and unsupervised classification techniques were employed to generate the LULC map. Data Integration: The layers generated were integrated into a comprehensive GIS database, with standardization applied to ensure compatibility and consistency across the dataset. 3.1.3. Site Suitability Analysis Leveraging a multi-criteria decision-making (MCDM) to determine the optimal sites for green hydrogen production as illustrated through the following procedures: A. Criteria Weighting Each of the nine parameters was given a weight depending on their relative relevance for green hydrogen production. Weights of each parameter were determined by using pairwise comparisons method that resulted from Analytic Hierarchy Process (AHP). This quantitative scale, which followed the technique created by (Saaty 1980 ), varied from 1 to 9 based on the relative relevance of each parameter. In spatial decision-making, AHP represents a robust method for allocating weights to multiple parameters (Shebl et al. 2022 ; Vellaikannu et al. 2021 ). A numerical score is assigned to each criterion in relation to the others using the pairwise comparison approach, followed by consistency verification utilizing these equations (Saaty, 1980 ; Saaty, 2008 ): $$\:Consistency\:index\:\left(\text{C}\text{I}\right)=\frac{\lambda\:max-n}{\text{n}-1}$$ $$\:\text{C}\text{o}\text{n}\text{s}\text{i}\text{s}\text{t}\text{e}\text{n}\text{c}\text{y}\:\text{r}\text{a}\text{t}\text{i}\text{o}\:\left(\text{C}\text{R}\right)=\frac{\text{C}\text{I}}{\text{R}\text{I}}$$ where λ max stands for the maximum approximation of eigenvalue, the number of criteria is indicated by n, and the random consistency index for n criteria is called RI. The CR value ought to be below 0.1 for acceptable consistency. The final step involved applying weights derived from expert opinions to reclassified analyses in the ArcGIS environment (Kuru and Terzi 2018 ). B. Layer Overlay Analysis The weighted layers were overlaid in the GIS environment to create a composite suitability map. All nine parameter maps were reclassified into raster format with four classes (very low as 1 to very high as 4) but geology map was reclassified into three classes. The overlay analysis combined the layers using a weighted sum approach, with each pixel's suitability score calculated as the sum of the weighted values from individual layers. C. Site Selection The composite suitability map was classified into different categories (most suitable, moderately suitable, and least suitable). Sites with the highest suitability scores were identified as optimal locations for green hydrogen production. 3.2. Vertical electrical sounding (VES) survey The resistivity method represents a geophysical approach for investigating subsurface conditions based on resistivity values. This technique operates on the principle of injecting current from the surface into the subsurface using a transmitter through stainless steel electrode and recording the potential difference response using a receiver which is done by a copper electrode or at a well-conductive point within the soil or rock (Samouëlian et al. 2005 ). The current is injected into rock/soil formations and flows through pores or minerals, with ease of flow indicating conductive characteristics and resistance to flow suggesting resistive properties (Saminu 1999 ). The resistivity method has diverse applications in mineral exploration, environmental investigation, groundwater identification, and geotechnical assessment (Schepers et al. 2001 ). Vertical Electrical Sounding (VES) is specifically designed to study vertical variations in underlying rock resistivity (Loke 1999 ). This study used a Schlumberger array configuration that included eleven VES (Fig. 1 ), in which all four electrodes are aligned linearly and with half-current electrode spacing (AB/2) that ranged from 1.5 to 400 meters. The current electrodes were repositioned while the potential electrodes remained stationary, being adjusted only when signal strength diminished during each measurement after these processes, apparent resistivity data were acquired and evaluated to determine each geoelectric layer's resistivity and thickness (Sholichin and Prayogo 2019 ). From this survey, subsurface layers model was obtained, including resistivity values, thicknesses, and depths of the various strata. 4. Results and discussion 4.1. Remote sensing results 4.1.1. Proximity to Sea Marsa Matruh's location on Egypt's northwestern Mediterranean coast provides excellent access to seawater for the electrolysis process. Additionally, the area represents a strategic location for various industries and international trade due to the new Gargoub Port, which connects Egypt with European ports and would facilitate the export of green hydrogen to European markets (Fig. 4 a). 4.1.2. Proximity to Roads The Euclidean distance function was applied to generate proximity to roads map (Fig. 4 b) by merging road and railway layers into a single transportation network file. Site selection for industrial facilities necessitates careful consideration of road accessibility, as locations near major transportation routes enhance connectivity with regional and national commercial networks. 4.1.3. Geology Tertiary deposits cover the largest part of the area, particularly Miocene formations comprising a basal clastic section overlain by a carbonate unit (Fig. 4 c). Holocene deposits include sabkha formations, while Quaternary deposits (wadi and playa deposits) are predominantly distributed across the northern sites of the area. 4.1.4. Digital Elevation Model (DEM) Copernicus GLO-30 data was used to extract the Digital Elevation Model at a 30-meter resolution (Fig. 4 d). DEMs have many applications in different fields such as hydrologic and geological assessments, hazard monitoring, natural resource exploitation, and agricultural management (Balasubramanian 2017 ). For this research, the DEM played a crucial role in site selection for setting up green hydrogen manufacturing plants and related renewable energy infrastructure, such as wind farms and solar panels. The analysis revealed that elevations within the area ranged from − 135 to 267 meters above sea level, with the lowest values corresponding to the Qattara Depression which is considered Egypt's deepest (-145 m) and largest (~ 45,000 km 2 ) depression that is located in the northern portion of the Western Desert (Embabi 2018 ). 4.1.5. Drainage Density The quantity of channels used to carry surface water is measured by drainage (Roy et al. 2019 ; Ashmawy et al. 2018 ). Through digital elevation model analysis and GIS tools, a drainage density map was generated, revealing higher density concentrations along the coastal zone (Fig. 4 e). 4.1.6. Lineament Density Features on the earth's surface that are straight or almost straight are called lineaments that represent faults, folds, and fractures that reflect underlying geological structures (Hung et al. 2005 ). Lineament density analysis provides critical information about the frequency of lineaments for each unit area (Magesh et al. 2012 ; Yeh et al. 2016 ), with high density values indicating increased permeability. There was a high lineament density in the area's northern region (Fig. 4 f), suggesting favorable subsurface characteristics. 4.1.7. Land Use/Land Cover Decision-makers in a variety of industry sectors and emerging countries are finding land use/land cover mapping to be an increasingly helpful tool. The analysis identified four primary land use classes in the region including rangeland, water bodies, built-up areas, and barren land (Fig. 5 a), based on high resolution Sentinel-2 satellite imagery. 4.1.8. Air Temperature Solar power represents a critical factor in green hydrogen production site selection, as it provides the electrical energy required for water electrolysis to separate H 2 O into O 2 and H 2 . The Inverse Distance Weighted (IDW) interpolation results for air temperature demonstrated that the entire study area receives substantial solar radiation (Fig. 5 b), making it suitable for photovoltaic system installation. This favorable condition could incentivize the Egyptian government to develop solar energy infrastructure along Egypt's northwest coast, potentially creating employment opportunities, generating green electricity, and promoting sustainable development (Habib et al. 2020 ). 4.1.9. Wind Speed Recent advancements in wind turbine technology have enabled the establishment of larger wind farms in coastal and oceanic regions for increased energy generation (Majidi Nezhad et al. 2021 ). The integration of wind and solar energy can significantly enhance electric power generation for electrolyzer operation. The Inverse Distance Weighted (IDW) interpolation analysis indicated maximum wind speeds of 5.22 m/s in the northern section of the study area (Fig. 5 c), representing favorable conditions for wind energy development. 4.2. Delineation of Green Hydrogen Production Site Selection The pairwise comparison approach was employed to categorize and estimate the weight of parameter (El-Sayed and Elgendy 2024 ). Table 1 displays the allocated scores and weights for each parameter resulting from analytic hierarchy process methodology, with a consistency ratio (CR) of 0.075, indicating high reliability in the weighting procedure. The nine parameters are reclassified into four classes except geology into 3 classes Fig. 6 and then the reclassified maps were used to perform weighted overlay analysis to create a suitability map for green hydrogen production. The comprehensive analysis of parameters identified locations with high suitability for green hydrogen production within the study region, categorized into three distinct zones: least suitable, moderately suitable, and most suitable (Fig. 7 ). The most suitable sites are predominantly situated in the northern portion, whereas the least suitable site is concentrated in the southern regions. Table 1 Analytic heirarchy’s parameters weight Parameter Class Score Weight Distance to sea 0-74.3 74–151 152–227 228–316 4 3 2 1 25.9% LULC Water Bare ground Built-up area Rangeland 1 3 2 4 18.8% Geology Miocene Holocene Quaternary 3 1 4 9.1% Air temperature 25.1- 26.54 26.55–27.92 27.93–29.4 29.41–31.1 4 3 2 1 15.6% Wind speed (m/s) 3.46–3.79 3.8–4.11 4.12–4.53 4.54–5.23 1 2 3 4 12% Drainage density (km/km 2 ) 0.379–2.23 2.24–2.89 2.9–3.64 3.65–5.68 4 3 2 1 5.9% Lineament density 0- 0.05047 0.05048–0.1464 0.1465–0.3003 0.3004–0.6435 4 3 2 1 4.4% Distance to roads 0–7.026 7.027–16.86 16.87–28.91 28.92–5.19 4 3 2 1 5.1% Elevation (m) -135.8 - -23.54 -23.53–69.74 69.75–156.7 156.8–267.4 1 2 4 3 3.2% 4.3. Vertical Electrical Sounding (VES) Survey Results VES was used to identify types of subsurface rock units by interpreting their resistivity and the thickness of the layers. It also helped in determining the depth of bedrock. In this study, iso-apparent resistivity maps were created at spacings of 2.5, 8, 50, and 200 meters considering the apparent distribution of type AB/2 at 1.5, 5, 33, and 133 meters of depth to demonstrate how electrical resistivity varies laterally at certain depths. The resistivity value at a depth of 1.5 meters ranges from 3 to 376 Ω. m. (Fig. 8 a). Fractured limestone saturated by water possesses low resistivity value and medium resistivity value is assumed to be marly limestone and fossiliferous limestone. The northwest portion of the region showed moderate resistivity values as depth increased at AB/2 between 8 and 50 m (Fig. 8 b & 8 c). At greater depth (133 m), and the highest resistivity values became apparent in the central to northern sections (Fig. 8 d), with these high resistivity values likely corresponding to dolomitic limestone formations that constitute bedrock. The resistivity measurements were processed to develop a subsurface model for identifying bedrock layers, with readings used to characterize subsurface features and estimate rock composition. The results of one-dimensional (1D) inversion data processing were interpreted depending on data from nearby wells to create a comprehensive model for the subsurface layer of the region. There were six different units in this model: surface wadi deposits, marly limestone, and saturated fractured limestone, fossiliferous limestone, and dolomitic limestone (Fig. 9 ). The interpretation of the subsurface lithology at each sounding point is shown in Table 2 . Based on these results, dolomitic limestone was identified as the primary bedrock layer due to its superior strength and durability characteristics which found at a depth 20m in VES number 2 and ranging between 1.3 and 47 meters in all VESs, with true resistivity values varying from 309.2 to 2251 Ω. m (Fig. 10 a & 10 b). In the middle to north area of Marsa Matruh (zone A) as illustrated in Fig. 11 bedrock for green hydrogen infrastructure can be found to a depth of 14–20 meters. Table 2 Resulted parameters of the geoelectrical survey Layers Lithology Thickness (m) Resistivity (Ω. m) 1 2 3 4 5 Wadi deposits Marly limestone Saturated fractured limestone Fossiliferous limestone Dolomitic limestone 0.4–4.5 2.2- 132.8 14.3–57.2 1.7–66.7 - 12.9- 470.3 28.2-138.2 10.3–62.2 49.5-268.3 309.2–2251 This study demonstrated that hybrid renewable energy systems provide a dependable supply of green hydrogen to advance a sustainable and decarbonized energy sector. During the winter of December, January, and February, wind turbines generate more power than PV due to high wind speeds. In other months, however, the PV electricity is at its peak. As a result, the annual amount of power generated with a high PV percentage exceeds WT (Nasser et al. 2022 ). Egypt produces 8.48 kg/m 2 of hydrogen for the PV system, while the production of hydrogen for the WT system is 1.31 kg/m 2 (Gado et al. 2024 ). Thus, the integration between PV and WT in this project will enhance the production capacity of green hydrogen sustainably. The factors including geographical distribution, wind characteristics, topography, and local wind flow, are crucial for successful wind turbine deployment. The site that is economically feasible for power generation is characterized by an annual average wind speed of 20 km/h at a height of 30 m, and a power density of 150 W/m 2 (Herbert et al. 2007 ). Marsa Matruh is characterized by these suitable conditions as its annual average specific wind power of 180–230 W/m 2 at a height of 30 meters above ground. At heights of 30 and 50 meters, the average wind speed per year is estimated to be 6.98 and 7.93 m/s (Shata and Hanitsch 2006 ). Several factors influence the depth such as soil type, turbine size, wind load, and frost line. A well-designed base helps keep turbines stable. According to studies, most turbine foundations range in depth from 10 to 20 feet (~ 3–6 m) depending on soil conditions (Bob n.d.). Consequently, bedrock for the foundation of wind turbines can be found in (zone B) as illustrated in Fig. 11 . The International Energy Agency(International Energy Agency 2019 ) claims that, Egypt is a prospective hub for green hydrogen production, aiming to contribute 8% to the world hydrogen market and by 2050 the levelized cost of hydrogen (LCOH) will be1.7 $ /kg. Ultimately, it has been found that the most suitable land is near major roads, transmission lines, and urban areas. Also, it is close to Gargoub port that links Egypt with Europe which will help in exporting of green hydrogen to Europe and other countries that will enhance the economy and national income of Egypt. 5. Conclusion This paper sought to propose a realistic strategy for determining land availability of green hydrogen production for Egypt's Mediterranean coast by combining AHP technique with RS and GIS tools. The results can support the development of the green hydrogen sector, preserve energy for upcoming requirements, and support regional sustainable initiatives. As the worldwide demand for clean energy solutions grows, strategies that incorporate diverse data sources and analytical tools will become more relevant for accelerating the growth of renewable energy sources and supporting the shift to economies with lower carbon emissions. The integration of satellite imaginary and validating the results with geophysical data and AHP is a novel way to select the best places for green hydrogen production. Whereas electrical resistivity modeling adds a distinct subterranean dimension to the assessment. The synergy between these complementary techniques, combining both surface and subsurface viewpoints, significantly improves the dependability of the infrastructure chosen for green hydrogen production. In this study nine critical parameters are included for mapping potential sites for green hydrogen production: distance to sea, elevation, land use/land cover, geology, lineament density, wind speed, distance to roads, drainage density, and air temperature. The analytic hierarchy technique was used to determine appropriate weightings for parameters, resulting in the identification of northern part as the most optimal location for green hydrogen production. The electrical resistivity analysis provided detailed information regarding subsurface layer characteristics, with particular focus on the dolomitic limestone formation that constitutes the bedrock layer. It is recommended that this analysis be combined with other criteria such as geographical constraints, environmental impact evaluations, and economic concerns to provide a more extensive appropriateness assessment. Furthermore, long-term data analysis and on-site inspections are vital for making educated decisions about the growth of green hydrogen production projects. Declarations Conflict of interest The authors haven't any content of interests. Authors Contributions Yasmeen Y. ElHateem wrote the manuscript and conducted the data processing and interpretations. Ahmed I. Diab and Hossam M. El - Sayed carried out the field work and collected data for the study. Ahmed I. Diab supervised geophysical surveys, review and editing. Hossam El- Sayed contributed to the collection of the electrical resistivity soundings from the study area and supervised their processing. Amr S. Fahil contributed to the remote sensing data processing and methodology. Magdy M. El Maghraby conceived and planned the original research idea, review, editing and supervision. All authors commented on previous versions of the manuscript. All authors reviewed and approved the final manuscript for submission. Funding Declaration in the manuscript There is no external funding received. Availability of data and materials The used datasets are publicly available through the following websites: Land use/ land cover data at Esri Sentinel-2 Land Cover Explorer. Air temperature data on WorldClim website. Wind speed data at NASA website. Geological data at USGS website, Elevation data available at many sources, such as USGS and Open Topography website. Distance to roads at BBBike website. Distance to sea was derived from the Marsa Matruh coastline shapefile downloaded from DIVA-GIS. Furthermore, we will be pleased to share any additional data or methodology upon request. 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1","display":"","copyAsset":false,"role":"figure","size":560528,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of the vertical electrical soundings distribution in the study area\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8001705/v1/dab25ca7ebc56053513a87a6.png"},{"id":95888965,"identity":"e580b96a-f8b4-4eeb-9a27-009a13febca8","added_by":"auto","created_at":"2025-11-14 05:44:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":304740,"visible":true,"origin":"","legend":"\u003cp\u003eGlobal view of tectonic setting of Egypt showing location of the study area (modified after Yousef et al. 2010; Abd El-Fattah et al. 2021; Bosworth et al. 2008, 2015)\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8001705/v1/82fa2cf4f62f7d7a2129e1de.png"},{"id":96242792,"identity":"d0e728a3-1120-43b7-8252-28a2874b5243","added_by":"auto","created_at":"2025-11-19 07:14:20","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":590646,"visible":true,"origin":"","legend":"\u003cp\u003eMethodological Flow Chart\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8001705/v1/009289ce610c7101c5188f83.jpeg"},{"id":95888963,"identity":"98209bc0-752e-4569-b85b-c43dee4c6435","added_by":"auto","created_at":"2025-11-14 05:44:21","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":818032,"visible":true,"origin":"","legend":"\u003cp\u003eThematic layers of (a) Euclidean distance to sea shows the distance from each location to the coastline, (b) Euclidean distance to roads indicates proximity to transportation networks, (c) Geology represents three different geological formations, (d) Elevation (m) displays terrain height which affects construction and environmental conditions, (e ) Drainage density shows the concentration of streams, and (f) Lineament density identifies linear features (faults or fractures) which may impact structural integrity\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8001705/v1/45d361d44cb1e64f674cea82.png"},{"id":96241532,"identity":"16edbf49-a45d-4071-a9ee-6be684c54804","added_by":"auto","created_at":"2025-11-19 07:10:54","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":531688,"visible":true,"origin":"","legend":"\u003cp\u003eThematic layers of (a) LULC classifies the region into different categories (water, bare ground, built up area, and rangeland) based on unsupervised classification, (b) Air temperature illustrates the spatial variation of ambient temperature which influences energy demand, and (c) Wind speed helps in assessing wind energy potential\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8001705/v1/8f1f8a8231d9b8fa9f4562a7.png"},{"id":95888968,"identity":"fd72f4fb-5173-4c76-aa7f-0b426a23601a","added_by":"auto","created_at":"2025-11-14 05:44:21","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2673948,"visible":true,"origin":"","legend":"\u003cp\u003eReclassified thematic layers were integrated using AHP to assess site suitability for green hydrogen production: a) Euclidean distance to sea, b) Euclidean distance to roads, c) LULC, d) Elevation, e) Lineament density, f) Drainage density, g) Geology, h) air temperature and, I) Wind speed\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8001705/v1/7da24b087d7c5a3ff1692b4d.png"},{"id":96241487,"identity":"fd01aa7b-83c4-405b-8d25-2272738531db","added_by":"auto","created_at":"2025-11-19 07:10:48","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":280747,"visible":true,"origin":"","legend":"\u003cp\u003eGreen Hydrogen production suitability index map obtained from 9 integrated remote sensing parameters and identified 3 potential zones ranging from least suitable in the south to most suitable in the north\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8001705/v1/5c392bae56be99461f69ad29.png"},{"id":95888978,"identity":"8101ba1d-b3d1-41df-af6e-61591a54224d","added_by":"auto","created_at":"2025-11-14 05:44:21","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":276701,"visible":true,"origin":"","legend":"\u003cp\u003eIsoresistivity maps at (a) AB/2 =2.5 m; (b) 8 m; (c) 50 m; and (d) 200 m for the most suitable sites in the study area\u003c/p\u003e","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8001705/v1/cc54f02a1f22364686c7e7be.jpeg"},{"id":96242404,"identity":"b6368bf5-31bc-4523-a8a1-c55021c1bb34","added_by":"auto","created_at":"2025-11-19 07:12:54","extension":"jpeg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":197219,"visible":true,"origin":"","legend":"\u003cp\u003eInterpreted model of VES No. 2 and equivalent calculated geological model,showing the bedrock layer at depth of 20 m.\u003c/p\u003e","description":"","filename":"floatimage9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8001705/v1/cad98e7ac66e23db297b1373.jpeg"},{"id":96243011,"identity":"01cb447f-9cfd-4896-80f3-ebb1dda42f8a","added_by":"auto","created_at":"2025-11-19 07:15:11","extension":"jpeg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":161460,"visible":true,"origin":"","legend":"\u003cp\u003eBedrock- related thematic layers: (a) true resistivity map derived from calculated apparent resistivity; (b) depth map indicating variations in depth for the most suitable sites in the study area, used to identify the most suitable locations for infrastructure development within the study area\u003c/p\u003e","description":"","filename":"floatimage10.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8001705/v1/3a79750ba33cefddc7d92a9f.jpeg"},{"id":95888979,"identity":"2809e04a-d2f8-4be4-ad74-ff7daef31dfc","added_by":"auto","created_at":"2025-11-14 05:44:21","extension":"jpeg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":177821,"visible":true,"origin":"","legend":"\u003cp\u003eIso resistivity map illustrates bedrock depth variations across the most suitable sites in the study area. The map highlights zones A and B as potential locations for green hydrogen station infrastructure and wind turbine foundations\u003c/p\u003e","description":"","filename":"floatimage11.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8001705/v1/3839f03d4d017fa21990cfee.jpeg"},{"id":106343943,"identity":"789ef3ec-f494-42fa-8650-65cfe1b12090","added_by":"auto","created_at":"2026-04-07 16:10:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7462912,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8001705/v1/30cdc6ec-b8d5-4837-b398-8f4775d164c2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Leveraging Remote Sensing, Geophysical Methods and AHP Model to Determine Optimal Locations for Green Hydrogen Production on Egypt's Mediterranean Coast","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe consistent rise in global energy demand, driven by population growth, elevated living standards, and industrial expansion in developing nations, presents significant environmental challenges (Dincer \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; El-Shafie et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Currently, greater than 95% of significant energy needs are satisfied by fossil fuels, leading to huge greenhouse gas emissions that intensify global warming problem and environmental deterioration (Lee et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Sazali \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Global initiative has been established to mitigate these emissions and control the rise in the worldwide average temperature to less than 3\u0026deg;C (Braun et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Khan et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Fasullo et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This imperative has spurred research institutions and organizations worldwide to develop breakthrough technologies that harness renewable energy sources for generating green energy and sustainable fuels (Vincent and Bessarabov \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Dawood et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Pinsky et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHydrogen is well positioned to store and transfer renewable energy, taking a vital role in the world's energy transformation (Dincer and Acar \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The classification of hydrogen varies according to production methods and environmental impacts, with designations including blue (reforming with carbon capture from natural gas), gray (reforming from natural gas), brown (gasification from lignite), black (gasification from bituminous coal), and green (electrolysis from water) (Noussan et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ajanovic et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). As the most environmentally beneficial option, green hydrogen is generated using renewable electricity that comes from renewable energy sources to split water into hydrogen and oxygen that leads to zero carbon emissions while production (Carmo et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eRenewable energy sources like solar and wind energy represent ideal sources to generate green hydrogen through electrolysis process (Wang et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This is especially important for green hydrogen production in areas that possess rich solar resources, like the Middle East and North Africa (Abbas et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Arpino et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Hydrogen possesses many advantages, for example, it is renewable, clean, non-polluting, adaptable, and storable. Hydrogen reserves are plentiful, also their unit calorific value is relatively high in contrast to other fuels (Liu et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Jiangnan et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eRenewable hydrogen has diverse applications across multiple sectors, including renewable energy production for transportation, industrial processes such as steel manufacturing and petroleum refining, and chemical production like ammonia and methanol (Mazzeo et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, despite its numerous benefits, producing and managing hydrogen especially, green hydrogen presents significant challenges. These include the high costs of electrolysis equipment, the technical complexity of integrating intermittent renewable energy sources, and the substantial energy requirements for storage, compression, and transportation of hydrogen. Additionally, hydrogen's high flammability and low ignition energy necessitate specialized handling processes and safety systems (Nnabuife et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo identify and categorize suitable areas for green hydrogen production, multiple datasets can be compiled and analyzed. The integration of satellite image analysis with GIS mapping enhances the strategic approach and improves understanding of results (Doorga et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Statistical methodologies like the analytic hierarchy process increase the accuracy of results (Arnous et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), while combining analytic hierarchy process results with geophysical data substantially boosts reliability of the results (Kulandaisamy et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Arunbose et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This study aims to identify the optimal locations to produce green hydrogen on the Northwestern coast of Egypt especially, Marsa Matruh city (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) by integrating remote sensing satellite imaginary and geophysical techniques to determine ideal areas with favorable conditions for facility development.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"2. Study area and tectonic setting","content":"\u003cp\u003eMatruh Governorate represents one of the most promising locations on Egypt's northern coast, particularly the Marsa Matruh city, which is pivotal for upcoming sustainable development projects. The population of Marsa Matruh reached 241.625 by the year 2024. It is situated 240 kilometers west of Alexandria and 222 kilometers east of Sallum, along the main highway connecting the Nile Delta and the Libyan border together and another highway extends in the south towards Siwa Oasis and Bahariya Oasis. Marsa Matruh International Airport and Gargoub port have granted the city significant logistical value, positioning it as an attractive place for industry, investment, and international trade. The research region ranges from latitude 28\u0026deg; 33' N to 31\u0026deg; 32' N and longitude 26\u0026deg; 10' E to 27\u0026deg; 52' E within the Matruh Basin.\u003c/p\u003e\u003cp\u003eThe Matruh Basin (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) constitutes a segment of a sequence of intracratonic rift basins located along the northern African passive margin (Moustafa \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These rift basins formed during the Permian to Early Jurassic periods in the northern Western Desert of Egypt because of the fragmentation of Gondwana and the gradual opening of the Neotethys (Guiraud \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Yousef et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Bosworth et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These basins typically follow either NE SW or ENE-WSW orientations (Moustafa \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1998\u003c/span\u003e), with the Matruh basin distinguished as a predominantly NNE-oriented formation (Moustafa \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This structural pattern presumably originated from pre-Jurassic structures; nevertheless, the idea requires more proof (Moustafa et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDuring Late Cretaceous period, the junction of the Afro-Arabian and Eurasian tectonic plates generated compressional stress in these basins, leading to inversion of the basin (Moustafa \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Yousef et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Guiraud and Bosworth \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Moustafa and Bevan \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). This compressional deformation episode was later followed by a Neogene extension era (Bosworth et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Yousef et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"3. Materials and methods","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Remote sensing\u003c/h2\u003e\u003cp\u003eThis research employs a comprehensive methodology (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) to determine the best places to produce green hydrogen. Nine critical parameters were selected for the weighted overlay analysis and evaluated using ArcGIS Desktop 10.8 software. The objective was to find the best sites for producing green hydrogen by evaluating key parameters including land use/land cover (LULC), climate data (Air temperature, wind speed), elevation, drainage density, geology, lineament density, distance to roads, and distance to sea. The methodology involved the following structured approach:\u003c/p\u003e\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\u003ch2\u003e3.1.1. Data Collection\u003c/h2\u003e\u003cp\u003eTo create the thematic layers required for analysis, high-resolution multispectral imagery from different sources such as Sentinel-2 satellites were utilized. These sources provided ideal data for generating detailed thematic maps. The specific layers were derived as follows:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eLand Use/Land Cover Map: Generated from Sentinel-2 imagery at 10m resolution. LULC was created to identify land availability for establishing green hydrogen production facilities such as infrastructure for producing hydrogen and establishing solar and wind energy projects.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eGeology: Geological data was acquired from the United States Geological Survey (USGS). It was used to identify rock units that could influence site selection and determine the age and characteristics of the bedrock.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eElevation: Digital elevation models (DEMs) were derived from Copernicus GLO-30 data accessed through Open Topography. Elevation data are vital for understanding the topography of the site, which can affect the feasibility of infrastructure development.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eDrainage Density: This layer was created by analyzing drainage patterns derived from the DEM layer to assess water resource availability.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eLineament Density: Derived from DEM analysis to identify geological lineaments that indicate fractures and faults in the subsurface, which may influence site stability.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eProximity Maps: Transportation infrastructure data (railways and roads) was obtained to evaluate connectivity for hydrogen transportation and distribution. Proximity to the sea was derived from the Marsa Matruh coastline shapefile to determine distance from sea for getting water for electrolysis.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eClimate Data: Information on air temperature and wind speed was collected. These data are crucial for assessing the potential for wind and solar energy sources, that considering the main sources to generate electricity that is used in the electrolysis process at 2.5-minute spatial resolution (~\u0026thinsp;21 km\u0026sup2; at the equator) for air temperature map and at 10 meters (m/s) for wind speed map.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e3.1.2. Data Processing\u003c/h2\u003e\u003cp\u003eThe raw satellite imaginary was processed by utilizing (GIS) software through the following steps:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eLayer Generation: Each thematic layer was produced through specific algorithms and models. Air temperature and wind speed were calculated using Inverse Distance Weighted (IDW) interpolation, while drainage density and lineament density that obtained from digital elevation models by utilizing hydrological analysis tools. The Euclidean distance function was applied for proximity analysis, and unsupervised classification techniques were employed to generate the LULC map.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eData Integration: The layers generated were integrated into a comprehensive GIS database, with standardization applied to ensure compatibility and consistency across the dataset.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e3.1.3. Site Suitability Analysis\u003c/h2\u003e\u003cp\u003eLeveraging a multi-criteria decision-making (MCDM) to determine the optimal sites for green hydrogen production as illustrated through the following procedures:\u003c/p\u003e\u003cp\u003e\u003cb\u003eA. Criteria Weighting\u003c/b\u003e\u003c/p\u003e\u003cp\u003eEach of the nine parameters was given a weight depending on their relative relevance for green hydrogen production. Weights of each parameter were determined by using pairwise comparisons method that resulted from Analytic Hierarchy Process (AHP). This quantitative scale, which followed the technique created by (Saaty \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e1980\u003c/span\u003e), varied from 1 to 9 based on the relative relevance of each parameter. In spatial decision-making, AHP represents a robust method for allocating weights to multiple parameters (Shebl et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Vellaikannu et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). A numerical score is assigned to each criterion in relation to the others using the pairwise comparison approach, followed by consistency verification utilizing these equations (Saaty, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e1980\u003c/span\u003e; Saaty, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2008\u003c/span\u003e):\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:Consistency\\:index\\:\\left(\\text{C}\\text{I}\\right)=\\frac{\\lambda\\:max-n}{\\text{n}-1}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:\\text{C}\\text{o}\\text{n}\\text{s}\\text{i}\\text{s}\\text{t}\\text{e}\\text{n}\\text{c}\\text{y}\\:\\text{r}\\text{a}\\text{t}\\text{i}\\text{o}\\:\\left(\\text{C}\\text{R}\\right)=\\frac{\\text{C}\\text{I}}{\\text{R}\\text{I}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere λ max stands for the maximum approximation of eigenvalue, the number of criteria is indicated by n, and the random consistency index for n criteria is called RI. The CR value ought to be below 0.1 for acceptable consistency. The final step involved applying weights derived from expert opinions to reclassified analyses in the ArcGIS environment (Kuru and Terzi \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eB. Layer Overlay Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe weighted layers were overlaid in the GIS environment to create a composite suitability map. All nine parameter maps were reclassified into raster format with four classes (very low as 1 to very high as 4) but geology map was reclassified into three classes. The overlay analysis combined the layers using a weighted sum approach, with each pixel's suitability score calculated as the sum of the weighted values from individual layers.\u003c/p\u003e\u003cp\u003e\u003cb\u003eC. Site Selection\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe composite suitability map was classified into different categories (most suitable, moderately suitable, and least suitable). Sites with the highest suitability scores were identified as optimal locations for green hydrogen production.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Vertical electrical sounding (VES) survey\u003c/h2\u003e\u003cp\u003eThe resistivity method represents a geophysical approach for investigating subsurface conditions based on resistivity values. This technique operates on the principle of injecting current from the surface into the subsurface using a transmitter through stainless steel electrode and recording the potential difference response using a receiver which is done by a copper electrode or at a well-conductive point within the soil or rock (Samou\u0026euml;lian et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). The current is injected into rock/soil formations and flows through pores or minerals, with ease of flow indicating conductive characteristics and resistance to flow suggesting resistive properties (Saminu \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). The resistivity method has diverse applications in mineral exploration, environmental investigation, groundwater identification, and geotechnical assessment (Schepers et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eVertical Electrical Sounding (VES) is specifically designed to study vertical variations in underlying rock resistivity (Loke \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). This study used a Schlumberger array configuration that included eleven VES (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), in which all four electrodes are aligned linearly and with half-current electrode spacing (AB/2) that ranged from 1.5 to 400 meters. The current electrodes were repositioned while the potential electrodes remained stationary, being adjusted only when signal strength diminished during each measurement after these processes, apparent resistivity data were acquired and evaluated to determine each geoelectric layer's resistivity and thickness (Sholichin and Prayogo \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). From this survey, subsurface layers model was obtained, including resistivity values, thicknesses, and depths of the various strata.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Results and discussion","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e4.1. Remote sensing results\u003c/h2\u003e\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\u003ch2\u003e4.1.1. Proximity to Sea\u003c/h2\u003e\u003cp\u003eMarsa Matruh's location on Egypt's northwestern Mediterranean coast provides excellent access to seawater for the electrolysis process. Additionally, the area represents a strategic location for various industries and international trade due to the new Gargoub Port, which connects Egypt with European ports and would facilitate the export of green hydrogen to European markets (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003e4.1.2. Proximity to Roads\u003c/h2\u003e\u003cp\u003eThe Euclidean distance function was applied to generate proximity to roads map (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb) by merging road and railway layers into a single transportation network file. Site selection for industrial facilities necessitates careful consideration of road accessibility, as locations near major transportation routes enhance connectivity with regional and national commercial networks.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\u003ch2\u003e4.1.3. Geology\u003c/h2\u003e\u003cp\u003eTertiary deposits cover the largest part of the area, particularly Miocene formations comprising a basal clastic section overlain by a carbonate unit (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). Holocene deposits include sabkha formations, while Quaternary deposits (wadi and playa deposits) are predominantly distributed across the northern sites of the area.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\u003ch2\u003e4.1.4. Digital Elevation Model (DEM)\u003c/h2\u003e\u003cp\u003eCopernicus GLO-30 data was used to extract the Digital Elevation Model at a 30-meter resolution (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed). DEMs have many applications in different fields such as hydrologic and geological assessments, hazard monitoring, natural resource exploitation, and agricultural management (Balasubramanian \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). For this research, the DEM played a crucial role in site selection for setting up green hydrogen manufacturing plants and related renewable energy infrastructure, such as wind farms and solar panels. The analysis revealed that elevations within the area ranged from \u0026minus;\u0026thinsp;135 to 267 meters above sea level, with the lowest values corresponding to the Qattara Depression which is considered Egypt's deepest (-145 m) and largest (~\u0026thinsp;45,000 km\u003csup\u003e2\u003c/sup\u003e) depression that is located in the northern portion of the Western Desert (Embabi \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\u003ch2\u003e4.1.5. Drainage Density\u003c/h2\u003e\u003cp\u003eThe quantity of channels used to carry surface water is measured by drainage (Roy et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ashmawy et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Through digital elevation model analysis and GIS tools, a drainage density map was generated, revealing higher density concentrations along the coastal zone (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\u003ch2\u003e4.1.6. Lineament Density\u003c/h2\u003e\u003cp\u003eFeatures on the earth's surface that are straight or almost straight are called lineaments that represent faults, folds, and fractures that reflect underlying geological structures (Hung et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Lineament density analysis provides critical information about the frequency of lineaments for each unit area (Magesh et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Yeh et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), with high density values indicating increased permeability. There was a high lineament density in the area's northern region (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ef), suggesting favorable subsurface characteristics.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\u003ch2\u003e4.1.7. Land Use/Land Cover\u003c/h2\u003e\u003cp\u003eDecision-makers in a variety of industry sectors and emerging countries are finding land use/land cover mapping to be an increasingly helpful tool. The analysis identified four primary land use classes in the region including rangeland, water bodies, built-up areas, and barren land (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea), based on high resolution Sentinel-2 satellite imagery.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\u003ch2\u003e4.1.8. Air Temperature\u003c/h2\u003e\u003cp\u003eSolar power represents a critical factor in green hydrogen production site selection, as it provides the electrical energy required for water electrolysis to separate H\u003csub\u003e2\u003c/sub\u003eO into O\u003csub\u003e2\u003c/sub\u003e and H\u003csub\u003e2\u003c/sub\u003e. The Inverse Distance Weighted (IDW) interpolation results for air temperature demonstrated that the entire study area receives substantial solar radiation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb), making it suitable for photovoltaic system installation. This favorable condition could incentivize the Egyptian government to develop solar energy infrastructure along Egypt's northwest coast, potentially creating employment opportunities, generating green electricity, and promoting sustainable development (Habib et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section3\"\u003e\u003ch2\u003e4.1.9. Wind Speed\u003c/h2\u003e\u003cp\u003eRecent advancements in wind turbine technology have enabled the establishment of larger wind farms in coastal and oceanic regions for increased energy generation (Majidi Nezhad et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The integration of wind and solar energy can significantly enhance electric power generation for electrolyzer operation. The Inverse Distance Weighted (IDW) interpolation analysis indicated maximum wind speeds of 5.22 m/s in the northern section of the study area (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec), representing favorable conditions for wind energy development.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e4.2. Delineation of Green Hydrogen Production Site Selection\u003c/h2\u003e\u003cp\u003eThe pairwise comparison approach was employed to categorize and estimate the weight of parameter (El-Sayed and Elgendy \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e displays the allocated scores and weights for each parameter resulting from analytic hierarchy process methodology, with a consistency ratio (CR) of 0.075, indicating high reliability in the weighting procedure. The nine parameters are reclassified into four classes except geology into 3 classes Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and then the reclassified maps were used to perform weighted overlay analysis to create a suitability map for green hydrogen production. The comprehensive analysis of parameters identified locations with high suitability for green hydrogen production within the study region, categorized into three distinct zones: least suitable, moderately suitable, and most suitable (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The most suitable sites are predominantly situated in the northern portion, whereas the least suitable site is concentrated in the southern regions.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAnalytic heirarchy\u0026rsquo;s parameters weight\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eClass\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eScore\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWeight\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistance to sea\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0-74.3\u003c/p\u003e\u003cp\u003e74\u0026ndash;151\u003c/p\u003e\u003cp\u003e152\u0026ndash;227\u003c/p\u003e\u003cp\u003e228\u0026ndash;316\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003cp\u003e3\u003c/p\u003e\u003cp\u003e2\u003c/p\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25.9%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLULC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWater\u003c/p\u003e\u003cp\u003eBare ground\u003c/p\u003e\u003cp\u003eBuilt-up area\u003c/p\u003e\u003cp\u003eRangeland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e3\u003c/p\u003e\u003cp\u003e2\u003c/p\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18.8%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGeology\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMiocene\u003c/p\u003e\u003cp\u003eHolocene\u003c/p\u003e\u003cp\u003eQuaternary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.1%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAir temperature\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25.1- 26.54\u003c/p\u003e\u003cp\u003e26.55\u0026ndash;27.92\u003c/p\u003e\u003cp\u003e27.93\u0026ndash;29.4\u003c/p\u003e\u003cp\u003e29.41\u0026ndash;31.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003cp\u003e3\u003c/p\u003e\u003cp\u003e2\u003c/p\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.6%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWind speed (m/s)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.46\u0026ndash;3.79\u003c/p\u003e\u003cp\u003e3.8\u0026ndash;4.11\u003c/p\u003e\u003cp\u003e4.12\u0026ndash;4.53\u003c/p\u003e\u003cp\u003e4.54\u0026ndash;5.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e2\u003c/p\u003e\u003cp\u003e3\u003c/p\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrainage density (km/km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.379\u0026ndash;2.23\u003c/p\u003e\u003cp\u003e2.24\u0026ndash;2.89\u003c/p\u003e\u003cp\u003e2.9\u0026ndash;3.64\u003c/p\u003e\u003cp\u003e3.65\u0026ndash;5.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003cp\u003e3\u003c/p\u003e\u003cp\u003e2\u003c/p\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.9%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLineament density\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0- 0.05047\u003c/p\u003e\u003cp\u003e0.05048\u0026ndash;0.1464\u003c/p\u003e\u003cp\u003e0.1465\u0026ndash;0.3003\u003c/p\u003e\u003cp\u003e0.3004\u0026ndash;0.6435\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003cp\u003e3\u003c/p\u003e\u003cp\u003e2\u003c/p\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistance to roads\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u0026ndash;7.026\u003c/p\u003e\u003cp\u003e7.027\u0026ndash;16.86\u003c/p\u003e\u003cp\u003e16.87\u0026ndash;28.91\u003c/p\u003e\u003cp\u003e28.92\u0026ndash;5.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003cp\u003e3\u003c/p\u003e\u003cp\u003e2\u003c/p\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.1%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eElevation (m)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-135.8 - -23.54\u003c/p\u003e\u003cp\u003e-23.53\u0026ndash;69.74\u003c/p\u003e\u003cp\u003e69.75\u0026ndash;156.7\u003c/p\u003e\u003cp\u003e156.8\u0026ndash;267.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e2\u003c/p\u003e\u003cp\u003e4\u003c/p\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.2%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e4.3. Vertical Electrical Sounding (VES) Survey Results\u003c/h2\u003e\u003cp\u003eVES was used to identify types of subsurface rock units by interpreting their resistivity and the thickness of the layers. It also helped in determining the depth of bedrock. In this study, iso-apparent resistivity maps were created at spacings of 2.5, 8, 50, and 200 meters considering the apparent distribution of type AB/2 at 1.5, 5, 33, and 133 meters of depth to demonstrate how electrical resistivity varies laterally at certain depths. The resistivity value at a depth of 1.5 meters ranges from 3 to 376 Ω. m. (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ea). Fractured limestone saturated by water possesses low resistivity value and medium resistivity value is assumed to be marly limestone and fossiliferous limestone. The northwest portion of the region showed moderate resistivity values as depth increased at AB/2 between 8 and 50 m (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eb \u0026amp; \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ec). At greater depth (133 m), and the highest resistivity values became apparent in the central to northern sections (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ed), with these high resistivity values likely corresponding to dolomitic limestone formations that constitute bedrock.\u003c/p\u003e\u003cp\u003eThe resistivity measurements were processed to develop a subsurface model for identifying bedrock layers, with readings used to characterize subsurface features and estimate rock composition. The results of one-dimensional (1D) inversion data processing were interpreted depending on data from nearby wells to create a comprehensive model for the subsurface layer of the region. There were six different units in this model: surface wadi deposits, marly limestone, and saturated fractured limestone, fossiliferous limestone, and dolomitic limestone (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). The interpretation of the subsurface lithology at each sounding point is shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eBased on these results, dolomitic limestone was identified as the primary bedrock layer due to its superior strength and durability characteristics which found at a depth 20m in VES number 2 and ranging between 1.3 and 47 meters in all VESs, with true resistivity values varying from 309.2 to 2251 Ω. m (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003ea \u0026amp; \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eb). In the middle to north area of Marsa Matruh (zone A) as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e bedrock for green hydrogen infrastructure can be found to a depth of 14\u0026ndash;20 meters.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResulted parameters of the geoelectrical survey\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLayers\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLithology\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThickness (m)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eResistivity (Ω. m)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e2\u003c/p\u003e\u003cp\u003e3\u003c/p\u003e\u003cp\u003e4\u003c/p\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWadi deposits\u003c/p\u003e\u003cp\u003eMarly limestone\u003c/p\u003e\u003cp\u003eSaturated fractured limestone\u003c/p\u003e\u003cp\u003eFossiliferous limestone\u003c/p\u003e\u003cp\u003eDolomitic limestone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.4\u0026ndash;4.5\u003c/p\u003e\u003cp\u003e2.2- 132.8\u003c/p\u003e\u003cp\u003e14.3\u0026ndash;57.2\u003c/p\u003e\u003cp\u003e1.7\u0026ndash;66.7\u003c/p\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.9- 470.3\u003c/p\u003e\u003cp\u003e28.2-138.2\u003c/p\u003e\u003cp\u003e10.3\u0026ndash;62.2\u003c/p\u003e\u003cp\u003e49.5-268.3\u003c/p\u003e\u003cp\u003e309.2\u0026ndash;2251\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThis study demonstrated that hybrid renewable energy systems provide a dependable supply of green hydrogen to advance a sustainable and decarbonized energy sector. During the winter of December, January, and February, wind turbines generate more power than PV due to high wind speeds. In other months, however, the PV electricity is at its peak. As a result, the annual amount of power generated with a high PV percentage exceeds WT (Nasser et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Egypt produces 8.48 kg/m\u003csup\u003e2\u003c/sup\u003e of hydrogen for the PV system, while the production of hydrogen for the WT system is 1.31 kg/m\u003csup\u003e2\u003c/sup\u003e (Gado et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Thus, the integration between PV and WT in this project will enhance the production capacity of green hydrogen sustainably.\u003c/p\u003e\u003cp\u003eThe factors including geographical distribution, wind characteristics, topography, and local wind flow, are crucial for successful wind turbine deployment. The site that is economically feasible for power generation is characterized by an annual average wind speed of 20 km/h at a height of 30 m, and a power density of 150 W/m\u003csup\u003e2\u003c/sup\u003e (Herbert et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Marsa Matruh is characterized by these suitable conditions as its annual average specific wind power of 180\u0026ndash;230 W/m\u003csup\u003e2\u003c/sup\u003e at a height of 30 meters above ground. At heights of 30 and 50 meters, the average wind speed per year is estimated to be 6.98 and 7.93 m/s (Shata and Hanitsch \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Several factors influence the depth such as soil type, turbine size, wind load, and frost line. A well-designed base helps keep turbines stable. According to studies, most turbine foundations range in depth from 10 to 20 feet (~\u0026thinsp;3\u0026ndash;6 m) depending on soil conditions (Bob n.d.). Consequently, bedrock for the foundation of wind turbines can be found in (zone B) as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eThe International Energy Agency(International Energy Agency \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) claims that, Egypt is a prospective hub for green hydrogen production, aiming to contribute 8% to the world hydrogen market and by 2050 the levelized cost of hydrogen (LCOH) will be1.7 \u003cspan\u003e$\u003c/span\u003e/kg.\u003c/p\u003e\u003cp\u003eUltimately, it has been found that the most suitable land is near major roads, transmission lines, and urban areas. Also, it is close to Gargoub port that links Egypt with Europe which will help in exporting of green hydrogen to Europe and other countries that will enhance the economy and national income of Egypt.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis paper sought to propose a realistic strategy for determining land availability of green hydrogen production for Egypt's Mediterranean coast by combining AHP technique with RS and GIS tools. The results can support the development of the green hydrogen sector, preserve energy for upcoming requirements, and support regional sustainable initiatives. As the worldwide demand for clean energy solutions grows, strategies that incorporate diverse data sources and analytical tools will become more relevant for accelerating the growth of renewable energy sources and supporting the shift to economies with lower carbon emissions.\u003c/p\u003e\u003cp\u003eThe integration of satellite imaginary and validating the results with geophysical data and AHP is a novel way to select the best places for green hydrogen production. Whereas electrical resistivity modeling adds a distinct subterranean dimension to the assessment. The synergy between these complementary techniques, combining both surface and subsurface viewpoints, significantly improves the dependability of the infrastructure chosen for green hydrogen production.\u003c/p\u003e\u003cp\u003eIn this study nine critical parameters are included for mapping potential sites for green hydrogen production: distance to sea, elevation, land use/land cover, geology, lineament density, wind speed, distance to roads, drainage density, and air temperature. The analytic hierarchy technique was used to determine appropriate weightings for parameters, resulting in the identification of northern part as the most optimal location for green hydrogen production. The electrical resistivity analysis provided detailed information regarding subsurface layer characteristics, with particular focus on the dolomitic limestone formation that constitutes the bedrock layer.\u003c/p\u003e\u003cp\u003eIt is recommended that this analysis be combined with other criteria such as geographical constraints, environmental impact evaluations, and economic concerns to provide a more extensive appropriateness assessment. Furthermore, long-term data analysis and on-site inspections are vital for making educated decisions about the growth of green hydrogen production projects.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors haven't any content of interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYasmeen Y. ElHateem wrote the manuscript and conducted the data processing and interpretations. Ahmed I. Diab and Hossam M. El - Sayed carried out the field work and collected data for the study. Ahmed I. Diab supervised geophysical surveys, review and editing. Hossam El- Sayed contributed to the collection of the electrical resistivity soundings from the study area and supervised their processing. Amr S. Fahil contributed to the remote sensing data processing and methodology. Magdy M. El Maghraby conceived and planned the original research idea, review, editing and supervision. All authors commented on previous versions of the manuscript. All authors reviewed and approved the final manuscript for submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Declaration in the manuscript\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere is no external funding received.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe used datasets are publicly available through the following websites:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eLand use/ land cover data at Esri Sentinel-2 Land Cover Explorer.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eAir temperature data on WorldClim website.\u003c/li\u003e\n \u003cli\u003eWind speed data at NASA website.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eGeological data at USGS website,\u0026nbsp;Elevation data available at many sources, such as USGS and Open Topography website.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eDistance to roads at BBBike website.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eDistance to sea was derived from the Marsa Matruh coastline shapefile downloaded from DIVA-GIS.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eFurthermore, we will be pleased to share any additional data or methodology upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbbas MK, Hassan Q, Tabar VS, et al (2023) Techno-economic analysis for clean hydrogen production using solar energy under varied climate conditions. 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International journal of services sciences 1:83\u0026ndash;98\u003c/li\u003e\n\u003cli\u003eSaminu O (1999) Geophysical Investigation of Federal College of Education, Zaria. Unpublished M Sc Thesis, Department of Physics, Ahmadu Bello University, Zaria\u003c/li\u003e\n\u003cli\u003eSamou\u0026euml;lian A, Cousin I, Tabbagh A, et al (2005) Electrical resistivity survey in soil science: a review. Soil Tillage Res 83:173\u0026ndash;193. https://doi.org/10.1016/J.STILL.2004.10.004 \u003c/li\u003e\n\u003cli\u003eSazali N (2020) Emerging technologies by hydrogen: A review. Int J Hydrogen Energy 45:18753\u0026ndash;18771. https://doi.org/10.1016/J.IJHYDENE.2020.05.021 \u003c/li\u003e\n\u003cli\u003eSchepers R, Rafat G, Gelbke C, Lehmann B (2001) Application of borehole logging, core imaging and tomography to geotechnical exploration. International Journal of Rock Mechanics and Mining Sciences 38:867\u0026ndash;876. https://doi.org/10.1016/S1365-1609(01)00052-1 \u003c/li\u003e\n\u003cli\u003eShata ASA, Hanitsch R (2006) Evaluation of wind energy potential and electricity generation on the coast of Mediterranean Sea in Egypt. Renewable Energy 31:1183\u0026ndash;1202\u003c/li\u003e\n\u003cli\u003eShebl A, Abdelaziz MI, Ghazala H, et al (2022) multi-criteria ground water potentiality mapping utilizing remote sensing and geophysical data: A case study within Sinai Peninsula, Egypt. The Egyptian Journal of Remote Sensing and Space Sciences 25:765\u0026ndash;778. https://doi.org/10.1016/J.EJRS.2022.07.002 \u003c/li\u003e\n\u003cli\u003eSholichin M, Prayogo TB (2019) Field identification of groundwater potential zone by VES method in south Malang, Indonesia. International Journal of Civil Engineering and Technology 10:999\u0026ndash;1009\u003c/li\u003e\n\u003cli\u003eVellaikannu A, Palaniraj U, Karthikeyan S, et al (2021) Identification of groundwater potential zones using geospatial approach in Sivagangai district, South India. Arabian Journal of Geosciences 14:1\u0026ndash;17. https://doi.org/10.1007/S12517-020-06316-4/FIGURES/13\u003c/li\u003e\n\u003cli\u003eVincent I, Bessarabov D (2018) Low-cost hydrogen production by anion exchange membrane electrolysis: A review. Renewable and Sustainable Energy Reviews 81:1690\u0026ndash;1704. https://doi.org/10.1016/J.RSER.2017.05.258 \u003c/li\u003e\n\u003cli\u003eWang M, Wang Z, Gong X, Guo Z (2014) The intensification technologies to water electrolysis for hydrogen production \u0026ndash; A review. Renewable and Sustainable Energy Reviews 29:573\u0026ndash;588. https://doi.org/10.1016/J.RSER.2013.08.090 \u003c/li\u003e\n\u003cli\u003eYeh HF, Cheng YS, Lin HI, Lee CH (2016) Mapping groundwater recharge potential zone using a GIS approach in Hualian River, Taiwan. Sustainable Environment Research 26:33\u0026ndash;43. https://doi.org/10.1016/J.SERJ.2015.09.005 \u003c/li\u003e\n\u003cli\u003eYousef M, Moustafa AR, Shann M (2010) Structural setting and tectonic evolution of offshore North Sinai, Egypt. Geol Soc Spec Publ 341:65\u0026ndash;84. https://doi.org/10.1144/SP341.4 \u003c/li\u003e\n\u003cli\u003eYousef M, Yousef M, Sehim A (2019) Structural style and evolution of inversion structures of Horus field, Alamein Basin, northern Western Desert of Egypt. Mar Pet Geol 110:55\u0026ndash;72. https://doi.org/10.1016/J.MARPETGEO.2019.07.009 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Green Hydrogen production, Renewable energy, Vertical electrical sounding, AHP, Bedrock, Marsa Matruh","lastPublishedDoi":"10.21203/rs.3.rs-8001705/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8001705/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGlobal efforts to decarbonize energy systems have intensified the search for renewable alternatives, green hydrogen is considered one of the best intriguing solutions. This research integrates satellite imaginary to identify optimal locations for production of green hydrogen along Egypt's Mediterranean coast. The methodology employs nine critical parameters: distance to sea, drainage density, geology, land use/land cover, elevation, lineament density, distance to roads, wind speed, and air temperature. These parameters were evaluated by utilizing analytic hierarchy process with a consistency ratio of 0.075 which confirms correctness of the weightage method. The resulting suitability map categorizes potential sites into three classes: least suitable, moderately suitable, and most suitable. Analysis revealed that the northern part of Marsa Matruh represents the most favorable location for green hydrogen production. Additionally, a geoelectrical survey using eleven vertical electrical soundings (VESs) with Schlumberger configuration validated the surface findings and provided crucial subsurface information, identifying dolomitic limestone as the optimal bedrock for facility construction. This study offers a thorough framework for the strategic advancement of green hydrogen production in Egypt, supporting the country's sustainable energy transition goals.\u003c/p\u003e","manuscriptTitle":"Leveraging Remote Sensing, Geophysical Methods and AHP Model to Determine Optimal Locations for Green Hydrogen Production on Egypt's Mediterranean Coast","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-14 05:44:16","doi":"10.21203/rs.3.rs-8001705/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-03T16:47:52+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-12-03T06:07:51+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-02T14:14:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"213017539854067098360335821066454289258","date":"2025-12-01T05:03:38+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-30T12:04:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"312447261560214190644443010907745012345","date":"2025-11-29T01:10:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"182934393794889118226021755644347929776","date":"2025-11-28T11:43:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"203117740941565966570960700218896216936","date":"2025-11-28T05:21:09+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-10T12:31:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"273422379482999347715697937245845415696","date":"2025-11-10T03:34:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"311740786797576764251134585144612865108","date":"2025-11-04T17:01:45+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-04T15:15:21+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-04T14:58:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-01T07:47:28+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-01T07:45:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-10-31T21:13:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d84f66c7-72c5-49e4-83ed-584e87bde5ad","owner":[],"postedDate":"November 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":57774016,"name":"Earth and environmental sciences/Environmental sciences"},{"id":57774017,"name":"Earth and environmental sciences/Solid earth sciences"}],"tags":[],"updatedAt":"2026-04-07T16:06:24+00:00","versionOfRecord":{"articleIdentity":"rs-8001705","link":"https://doi.org/10.1038/s41598-026-41730-w","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-03-30 15:59:02","publishedOnDateReadable":"March 30th, 2026"},"versionCreatedAt":"2025-11-14 05:44:16","video":"","vorDoi":"10.1038/s41598-026-41730-w","vorDoiUrl":"https://doi.org/10.1038/s41598-026-41730-w","workflowStages":[]},"version":"v1","identity":"rs-8001705","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8001705","identity":"rs-8001705","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}