Impact of Ionospheric Delay on GNSS in a Low-Latitude Region

Impact of Ionospheric Delay on GNSS in a Low-Latitude Region | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Impact of Ionospheric Delay on GNSS in a Low-Latitude Region Efua Anthony Ogobor, Adebayo Segun Adewumi, Gbenro Benjamin Ayantunji, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7890225/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 15 You are reading this latest preprint version Abstract Accurate positioning and timing services are critically dependent on Global Navigation Satellite Systems (GNSS); however, their performance is often degraded by ionospheric delay, especially in low-latitude regions influenced by the Equatorial Ionization Anomaly and frequent scintillation. This study evaluates the impact of ionospheric delay on GNSS positioning accuracy over Ogbomoso, Nigeria (≈ 8.1° N, 4.2° E), using one year of GNSS observations data recorded with a u-blox ZED-F9P receiver. The data were converted to RINEX format using the RTKLIB suite and subsequently processed with the GPS-GOPI software to compute the Total Electron Content (TEC) and related parameters required for estimating the ionospheric delay. The positioning accuracy was then assessed using the Single Point Positioning technique, evaluated through key statistical performance metrics including the Two-Distance Root Mean Square (2DRMS), Circular Error Probable (CEP), Spherical Error Probable (SEP), and Mean Radial Spherical Error (MRSE) indices. The results revealed that ionospheric delay exhibited seasonal modulation, with higher magnitudes during the dry season (mean = 2.54 m) compared with the rainy season (mean = 2.39 m). Daily mean ionospheric delays ranged from 0.10 m to 3.70 m, while positioning accuracy metrics varied accordingly (2DRMS = 3.95–6.91 m; CEP = 1.64–2.89 m; SEP = 3.49–5.48 m; MRSE = 4.47–7.41 m). Moderate positive correlations (r = 0.29–0.36) between ionospheric delay and these accuracy indices confirmed that delay fluctuations significantly degrade positioning precision. The findings demonstrate that ionospheric delay remains a dominant source of error for GNSS users in equatorial regions GNSS ionospheric delay low-latitude positioning accuracy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 I. Introduction Modern positioning, navigation, and timing (PNT) applications in transportation, geodesy, telecommunications, and autonomous technologies now rely heavily on Global Navigation Satellite Systems (GNSS), which have become indispensable for achieving precise and reliable spatial referencing worldwide. Multi-constellation and multi-frequency systems such as GPS, Galileo, GLONASS, and BeiDou have greatly improved signal availability and spatial coverage [ 1 – 2 ]. However, GNSS performance remains highly susceptible to propagation errors in the ionosphere, where variations in the Total Electron Content (TEC) cause dispersive signal delays [ 3 ]. These ionospheric effects distort code and carrier phase measurements, increase tracking noise, and reduce positioning accuracy, particularly in equatorial and low-latitude regions [ 4 – 5 ]. Low-latitude ionospheres are among the most dynamic globally, influenced by the Equatorial Ionization Anomaly (EIA) and post-sunset electrodynamics that generate equatorial plasma bubbles (EPBs). These plasma irregularities produce intense amplitude and phase scintillation, leading to frequent cycle slips, signal fading, and loss of satellite lock [ 6 ]. As a result, positioning techniques such as Single Point Positioning (SPP), Real-Time Kinematic (RTK), and Precise Point Positioning (PPP) suffer reduced precision and reliability. The challenge is further exacerbated during geomagnetic disturbances when prompt penetration electric fields and disturbance dynamo effects intensify ionospheric currents and deepen the EIA crests [ 7 ]. Understanding the relationship between space weather activity and ionospheric behavior is therefore crucial for improving GNSS reliability in equatorial environments. The Disturbance Storm Time (DST) index serves as a key indicator of geomagnetic storm intensity and its coupling effects with the ionosphere [ 8 ]. Correlating DST variations with TEC demonstrates how geomagnetic force modulates ionospheric electron density and, consequently, GNSS signal integrity [ 9 – 10 ]. Despite global advancements in ionospheric modelling and GNSS error mitigation, the West African low-latitude region remains insufficiently studied. Nigeria, positioned near the EIA crest, experiences frequent equinoctial enhancements, strong pre-sunset plasma drifts, and storm-time scintillation that degrade GNSS performance. Limited location-based observational infrastructure and sparse long-term datasets have constrained regional understanding of ionospheric delay dynamics and their practical impact on positioning accuracy. This study investigates the impact of ionospheric delay on GPS system performance using the SPP technique in a low-latitude region, with emphasis on equatorial Africa. The study explores the relationship between ionospheric delay and the DST index to quantify space-weather influences on electron density over the region, quantifies seasonal and day-to-day variability of ionospheric delay using TEC-derived metrics, evaluates how these delays map onto practical accuracy indicators such as the Two-Distance Root Mean Square (2DRMS), Circular Error Probable (CEP), Spherical Error Probable (SEP), and Mean Radial Spherical Error (MRSE) for SPP solutions, and discusses mitigation strategies spanning higher sampling rate, multi-constellation, and multi-frequency usage together with disturbance-aware weighting models. II. Data Sources and Methods This study was conducted at the LAUTECH GNSS Laboratory (LGL), Ogbomoso, Nigeria (approximately 8.1°N, 4.2°E), a low-latitude region located within the EIA zone, using the SPP technique. A static-mode real-time GNSS dataset was logged using the u-blox ZED-F9P dual-frequency module in conjunction with a u-blox multiband GNSS patch antenna as shown in Fig. 1 . This GNSS module has been proven to be suitable and efficient in GPS-only and hybrid modes of operation with a 95% confidence level in low-latitude regions, as reported by [ 11 – 12 ]. The raw GNSS observations were recorded hourly in ubx format at a sampling rate of 4 Hz and subsequently converted to Receiver Independent Exchange (RINEX 3.03) format using the RTKLIB suite. Following data collection, the hourly dual-frequency RINEX files were merged to form continuous 24-hour daily datasets. Observations were acquired over fourteen randomly selected days per month, providing a representative one-year dataset covering both dry and rainy seasons. Data processing was carried out in two main phases. In the first phase, the GPS-GOPI software was employed to generate TEC data, which served as the foundation for estimating the ionospheric delay. In the second phase, the RTK-POST module of the RTKLIB suite was used to derive the position solutions (POS) through the SPP technique. The resulting POS data were then evaluated using the GNSS positioning accuracy analysis tool, where performance was assessed through standard statistical accuracy metrics. Finally, the obtained position accuracy results were compared with the corresponding daily ionospheric delay estimates to examine their relationship and assess the impact of ionospheric variability on GNSS positioning performance. During RTKLIB processing, broadcast ephemerides and satellite clock corrections were applied, and a 15° elevation mask was set to reduce low-elevation multipath effects. The ionospheric delay was estimated following the established computation approach. The precision of the GNSS POS was assessed using statistical parameters derived from the mean and standard deviation of the latitude, longitude, and altitude components across multiple epochs [ 13 ]. To maintain uniformity, latitude and longitude errors originally expressed in angular units were converted to meters. The positioning accuracy was characterized using four key performance indices: Two-Distance Root Mean Square (2DRMS), Circular Error Probable (CEP), Spherical Error Probable (SEP), and Mean Radial Spherical Error (MRSE), all defined in [ 14 ]. These indices were computed from the standard deviation components of latitude (σx), longitude (σy), and altitude (σz), providing a comprehensive measure of horizontal and vertical positioning accuracy. 2DRMS = \(\:2\sqrt{{\sigma\:}_{x}^{2}+{\sigma\:}_{y}^{2}}\) ​​, the horizontal error radius containing 95.8 to 98.2% of solutions. CEP \(\:=0.62{\sigma\:}_{y}+0.56{\sigma\:}_{z}\) Provided that \(\:\frac{{\sigma\:}_{y}}{{\sigma\:}_{x}}>0.3\) ​, the circle radius enclosing 50% of horizontal solutions. SEP = \(\:0.51\:\left({\sigma\:}_{x}+{\sigma\:}_{y}+{\sigma\:}_{z}\right)\) , the sphere radius enclosing 50% of 3D solutions. MRSE = \(\:\sqrt{\left({\sigma\:}_{x}^{2}+{\sigma\:}_{y}^{2}+{\sigma\:}_{z}^{2}\right)}\) ​​, the sphere radius enclosing 61% of 3D solutions. In addition, daily standard deviations of the latitude, longitude, and altitude positioning errors were computed to demonstrate the relative strength of horizontal versus vertical accuracy. The dataset DST March) seasons. More than 140 daily solutions were included, providing long-term coverage suitable for analyzing both climatological patterns and short-term disturbances. The Disturbance Storm Time Index (DST) used in this analysis was gotten from the World Data Centre for Geomagnetism website [ 15 ]. III. Results and Discussion Understanding the interaction between the Earth’s magnetosphere and ionosphere is crucial for assessing space-weather impacts on GNSS accuracy. Figure 2 illustrates the relationship between the TEC and the Disturbance Storm Time (DST) index over Ogbomoso, Nigeria. The results show that periods of intense geomagnetic activity (more negative DST) coincide with elevated TEC, confirming that storm-time disturbances strengthen ionospheric ionization. This correlation was most pronounced during months with intense and super storm classifications, such as May, August, and October 2024. In May 2024, the DST index dropped to − 285 nT, classifying the storm as ‘super,’ while the average TEC was about 37.8 TEC Unit (TECU) during this period. Although this value may appear relatively low, it likely represented an increase compared to the preceding months, and when contrasted with the June value of 33.8 TECU, it clearly indicates a rise in TEC. The full-scale TEC response appeared to lag slightly into June and July and started increasing in August and peaked in October 2024, when TEC rose to 47.6 TECU, the highest monthly value recorded. October's DST index reached as low as − 192 nT, falling within the "intense storm" category. Interestingly, not all storm months produced equally high TEC values, even during storms of similar or greater intensity. In August 2024, for instance, the DST index reached − 127 nT, yet TEC rose to about 40.4 TECU. In contrast, May 2024 recorded a stronger storm with a DST minimum of − 285 nT but a lower TEC of 37.8 TECU compared with October. This difference appears to be shaped by seasonal influences. May, being closer to the solstice, is affected by reduced solar zenith angle symmetry, which lowers the efficiency of electrodynamic coupling between the magnetosphere and ionosphere. As a result, even when geomagnetic disturbances were of similar strength, the corresponding increase in TEC could be limited. During magnetically quiet months such as June, July, and December 2024, when most daily DST values stayed above − 30 nT, TEC values dropped to around 33 to 34.9 TECU. This confirms the direct influence of geomagnetic forcing on ionospheric electron density. Seasonal effects also reinforced this behavior. December, occurring near the solstice, typically shows reduced ionization efficiency at low-latitudes because of weaker solar input. In March and April 2025, TEC remained moderately elevated at about 40–42 TECU despite only moderate geomagnetic activity, likely due to equinox-related solar symmetry that enhanced ionospheric conductivity. February 2025, however, showed slightly lower TEC under weak geomagnetic conditions, emphasizing that both storm strength and seasonal context influence the ionospheric response. The most intense storms occurred during the rainy season, whereas the dry season experienced only moderate or weak storms. Figure 3 presents the monthly averages of ionospheric delay derived from TEC measurements, which reveal distinct seasonal patterns. During the dry season, delays ranged from 2.13 m (December 2024) to 2.76 m (March 2025), gradually increasing toward the March equinox. This buildup corresponds to stronger electrodynamic forcing and a more pronounced EIA. In the rainy season, delays decreased to ~ 1.99 m in July 2024 but rose again to 2.85 m in October 2024, coinciding with equinoctial intensification. The seasonal means depicted in Fig. 4 indicate that the dry season recorded slightly higher average ionospheric delay (2.54 m) than the rainy season (2.39 m), representing a ~ 6% increase. Although day-to-day variability (σ ≈ 0.58–0.60 m) was similar in both seasons, the overall baseline delay was higher during the dry months, reflecting enhanced solar activity and reduced neutral density cooling. These findings confirm the seasonal modulation of ionospheric conditions in the equatorial environment. Table 1 summarizes the seasonal averages of ionospheric delay and related positioning accuracy metrics using the SPP technique. The mean ionospheric delay was slightly higher in the dry season than in the rainy season. Correspondingly, 3D accuracy indicators such as SEP and MRSE were also larger in the dry season, with values of 4.50 m and 5.90 m compared to 4.20 m and 5.37 m in the rainy season. This demonstrates that volumetric positioning errors become more pronounced when ionospheric delay increases. A similar trend was noted for the standard deviation of altitude error, which rose to 5.35 m in the dry season compared with 4.77 m in the rainy season. These results confirm that vertical positioning is especially sensitive to ionospheric effects, with dry season conditions amplifying this vulnerability. In contrast, the horizontal accuracy metrics showed only minor seasonal differences, even though values remained in the centimeter-to-meter range. The average 2DRMS was nearly identical across seasons, 4.93 m in the dry season and 4.92 m in the rainy season, indicating that horizontal accuracy is less affected by seasonal variations in ionospheric delay. Similarly, the standard deviations of the horizontal components were close: latitude error averaged 1.64 m in the dry season and 1.70 m in the rainy season, while longitude error averaged 1.83 m in the dry season and 1.77 m in the rainy season. The combined horizontal standard deviation was also nearly the same, at 2.47 m in the dry season and 2.46 m in the rainy season. These findings show that while horizontal errors remain relatively stable, the dry season consistently produces larger vertical and volumetric errors. This suggests that ionospheric delay has a stronger impact on degrading 3D accuracy than on horizontal positioning in low-latitude regions. Table 1 Seasonal Mean Values of Ionospheric Delay and POS Accuracy Metrics. Season Ionospheric delay (m) 2DRMS (m) CEP (m) SEP (m) MRSE (m) Lat. Error SD (m) Long. Error SD (m) Alt. Error SD (m) Horiz- ontal _SD (m) Dry 2.538 4.932 2.044 4.500 5.896 1.644 1.830 5.350 2.466 Rainy 2.393 4.919 2.045 4.201 5.367 1.704 1.767 4.766 2.460 Figure 5 a presents a time series plot of ionospheric delay plotted alongside 2DRMS and CEP. Periods of elevated ionospheric delay coincided with noticeable fluctuations in horizontal precision, although CEP showed relatively low variability compared to 2DRMS. Figure 5 b displays ionospheric delay together with SEP and MRSE, revealing stronger co-fluctuations between delay and 3D accuracy. In every case, spikes in ionospheric delay were followed by increases in SEP and MRSE, demonstrating that 3D errors are highly sensitive to ionospheric conditions. Figure 5 c compares ionospheric delay with the standard deviations of latitude, longitude, and altitude errors. Among these, the altitude error showed the largest variations, confirming that vertical accuracy is the most affected during disturbed ionospheric conditions. Figure 5 a: Ionospheric Delay alongside 2DRMS and CEP. Table 2 presents the general Pearson correlation analysis during the observation period, which revealed moderate positive relationships between ionospheric delay and positional-accuracy metrics: r ≈ 0.36 for 2DRMS and CEP, r ≈ 0.33 for SEP, and r ≈ 0.29 for MRSE. The correlations were slightly stronger in the dry season, indicating that increases in ionospheric delay generally coincide with reduced positioning precision. Horizontal metrics displayed the highest correlation coefficients, suggesting that delay effects first manifest in the horizontal plane before propagating into 3-D errors. Although the correlations were not exceptionally high, they consistently demonstrated that ionospheric delay is a dominant factor influencing GNSS positioning quality in low-latitude environments. The results also confirm that storm-time and equinoctial disturbances can significantly worsen GNSS performance, particularly for vertical positioning applications such as aviation guidance and geodetic leveling. Table 2 Pearson Correlation Analysis for POS Accuracy Metrics for Dry and Rainy Season Accuracy Metrics (m) Dry Pearson_r Rainy Pearson_r 2DRMS Mean 0.359482516 0.363953818 CEP Mean 0.373701683 0.353849126 SEP Mean 0.347692507 0.292692672 MRSC Mean 0.288339975 0.251266305 The results collectively show that ionospheric delay over Ogbomoso varies seasonally and responds strongly to geomagnetic conditions. The TEC and DST relationship confirms that enhanced storm-time activity leads to higher electron densities, which, in turn, increase ionospheric delay and degrade GNSS accuracy. Vertical components of position are most affected, while horizontal precision remains relatively robust. IV. Conclusion This study investigated the impact of ionospheric delay on GNSS positioning accuracy in a low-latitude region using one year (May 2024–April 2025) of dual-frequency observation data from Ogbomoso, Nigeria. The analysis provided an integrated view of how ionospheric behavior characterized through TEC, DST, and seasonal variations affects the quality of GNSS position solutions. The results showed that ionospheric delay exhibits a distinct seasonal pattern, with higher values during the dry season and notable peaks near the equinoxes. The TEC and DST correlation confirmed that enhanced geomagnetic disturbances and equinoctial electrodynamics significantly increase ionospheric density, thereby amplifying signal delays. Daily mean ionospheric delays ranged from 0.10 m to 3.70 m, averaging about 2.45 m. Corresponding SPP accuracy metrics revealed that 3-D positional errors (SEP ≈ 4–5.5 m, MRSE ≈ 5–7 m) increased with rising delay values, while horizontal errors (2DRMS ≈ 4.9 m, CEP ≈ 2 m) remained comparatively stable. Correlation analysis (r = 0.29–0.36) demonstrated a consistent positive relationship between ionospheric delay and positioning errors, indicating that delay fluctuations directly influence positional precision, particularly in the vertical component. This behavior was more pronounced during the dry season, when ionospheric gradients and equatorial electrodynamic activity were stronger. Generally, the study establishes that ionospheric delay remains a dominant error source in low-latitude GNSS applications, where equatorial ionization anomalies and space-weather disturbances introduce significant uncertainty in positioning performance. To mitigate these effects, the adoption of a higher sampling rate at 4 Hz, a higher elevation mask, multi-GNSS and dual-frequency processing, and the use of real-time data for ionospheric modeling are recommended. Such approaches can improve accuracy and reliability for navigation, geodetic surveying, and remote sensing operations in equatorial regions. Declarations Authors Contributions: Authors’ Contributions OEA: Study conception and design, data acquisition, data analysis and interpretation, original draft writing. AAS: Study conception and design, data acquisition, data analysis and interpretation, original draft writing, review and editing. AGB: Study conception and design, data analysis and interpretation, original draft writing, review and editing. HMS: Study conception and design, data analysis and interpretation, review and editing. OO: Study conception and design, data analysis and interpretation, original draft writing, review and editing. OEA: Study conception and design, data analysis and interpretation, review and editing. Funding The authors received no funding for this research. Data availability Data is provided within the manuscript files Declarations Ethics approval Ethics approval was not required for this study. Hence, no ethics approval declaration is available. Consent to publish Consent to publish was not required for this study. Hence, no consent to publish declaration is available. Consent to participate Consent to participate was not required for this study. Hence, no consent to participate declaration is available. Clinical trial number: Clinical trial is not applicable Competing interests The authors declare no competing interests References NovAtel Inc. In: Calgary, editor. An Introduction to GNSS: GPS, GLONASS, BeiDou, Galileo and other Global Navigation Satellite Systems. 2nd ed. Alberta, Canada: NovAtel Inc; 2015. Adewumi AS, Azeez IA. A Review of Global Navigation Satellite System and its Applications. International Journal of Scientific and Engineering Research; 2021. Taoufiq J, Mourad B, Rachid A, Amory-Mazaudier C. (2018). Study of Ionospheric Variability Using GNSS Observations. Positioning. Chen W, Gao S, Hu C, Chen Y, Ding X. (2008). Effects of ionospheric disturbances on GPS observation in low latitude area. Gps Solutions. Paziewski J, Høeg P, Sieradzki R, Jin Y, Jarmolowski W, Hoque MM, Orús-Pérez R. The implications of ionospheric disturbances for precise GNSS positioning in Greenland. Journal of Space Weather and Space Climate; 2022. Weng D, Ji S, Chen W, Liu Z. Assessment and mitigation of ionospheric disturbance effects on GPS accuracy and integrity. The Journal of Navigation; 2014. Astafyeva E, Zakharenkova I. Ionospheric response to the June 2015 St. Patrick’s Day storm: A global multi-instrumental overview. Journal of Geophysical Research: Space Physics; 2018. Bernegger C. Local Disturbance Storm Time Index for Ionospheric Forecasting: Comparison of Global vs. Local DST (Bachelor's thesis; 2024. Belehaki A, Tsagouri I, Altadill D, Blanch E, Borries C, Buresova D, Watermann J. An overview of methodologies for real-time detection, characterisation and tracking of traveling ionospheric disturbances developed in the TechTIDE project. J Space Weather Space Clim. 2020;10:42. Adewumi AS, Dan S, Mandal R, Bose A. (2025a). Effects of Ionospheric Delay on the Position Accuracy of GNSS Receiver: A Case Study at Burdwan University, India. In Proceedings of the 8th URSI-NG Annual Conference (URSI-NG 2024). Springer Nature. Dan S, Santra A, Mahato S, Koley C, Banerjee P, Bose A. On use of low cost, compact GNSS receiver modules for ionosphere monitoring. Radio Science; 2021. Adewumi AS, Atilade ÀG, Abiodun AI, Benjamin AG, Anthony OE, Oluwadara O, Emmanuel ED. (2025b). PPP Potential of a Compact, Low-Cost GNSS Receiver’s Module in a Low Latitude Region: A Case Study at the LAUTECH GNSS Laboratory, Nigeria. In 8th URSI-NG Annual Conference (URSI-NG 2024). Atlantis Press. Santra A, Mahato S, Dan S, Bose A. Precision of satellite based navigation position solution: a review using NavIC data. Journal of information and optimization sciences; 2019. Somnath Mahato A, Santra S, Dan P, Banerjee SK, Anindya Bose. Point Positioning Capability of Compact, Low-Cost GNSS Modules: A Case Study. IETE J Res. 2021. 10.1080/03772063.2021.1939801 . World Data Center Kyoto. Real-time and final DST index reports. Kyoto: World Data Center for Geomagnetism; 2025. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7890225","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":541876439,"identity":"3fd001ef-a5ab-4738-8be6-6807ddd4bb21","order_by":0,"name":"Efua Anthony Ogobor","email":"","orcid":"","institution":"Ladoke Akintola University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Efua","middleName":"Anthony","lastName":"Ogobor","suffix":""},{"id":541876440,"identity":"98e25a84-859d-467a-9319-157453469657","order_by":1,"name":"Adebayo Segun 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1","display":"","copyAsset":false,"role":"figure","size":97793,"visible":true,"origin":"","legend":"\u003cp\u003eExperimental Setup for GPS-only Ionospheric Delay study using u-blox ZED-F9P dual-frequency module\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7890225/v1/e36db8b1fbc673b986f0918f.png"},{"id":95543549,"identity":"991ff8df-8330-425b-b535-a3a236099881","added_by":"auto","created_at":"2025-11-10 12:03:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":83263,"visible":true,"origin":"","legend":"\u003cp\u003eDaily DST Index Average and Monthly TEC Overlay\u0026nbsp;\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7890225/v1/9a824f488d27ce824b18ea00.png"},{"id":95543552,"identity":"0e213e5e-cfb3-46bc-b567-b3a292e379c5","added_by":"auto","created_at":"2025-11-10 12:03:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":107073,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly Average Ionospheric Delay\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7890225/v1/d18189542b539fadbb15d528.png"},{"id":95654090,"identity":"03eeb604-9a04-4988-bb4b-dc3de9a6a8b7","added_by":"auto","created_at":"2025-11-11 16:09:39","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":43430,"visible":true,"origin":"","legend":"\u003cp\u003eSeasonal Mean of Ionospheric Delay (Dry and Rainy Season)\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7890225/v1/2502d0ccc0c0383a6e35fd2c.png"},{"id":95654108,"identity":"12a32385-c5d1-416b-a11f-53c940aa098d","added_by":"auto","created_at":"2025-11-11 16:09:44","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":171339,"visible":true,"origin":"","legend":"\u003cp\u003e5a: Ionospheric Delay alongside 2DRMS and CEP.\u003c/p\u003e\n\u003cp\u003e5b: Ionospheric Delay alongside SEP and MRSE.\u003c/p\u003e\n\u003cp\u003e5c: Ionospheric Delay Alongside Standard Deviations of Latitude, Longitude, and Altitude Errors.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7890225/v1/1322aa2d197a4ec2f17aa86e.png"},{"id":95659993,"identity":"9da3432c-f0ca-42b8-aa9e-c7a5023b0df0","added_by":"auto","created_at":"2025-11-11 16:30:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":970479,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7890225/v1/c72b81e5-e890-44a5-8e64-643200d3c2eb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of Ionospheric Delay on GNSS in a Low-Latitude Region","fulltext":[{"header":"I. Introduction","content":"\u003cp\u003eModern positioning, navigation, and timing (PNT) applications in transportation, geodesy, telecommunications, and autonomous technologies now rely heavily on Global Navigation Satellite Systems (GNSS), which have become indispensable for achieving precise and reliable spatial referencing worldwide. Multi-constellation and multi-frequency systems such as GPS, Galileo, GLONASS, and BeiDou have greatly improved signal availability and spatial coverage [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. However, GNSS performance remains highly susceptible to propagation errors in the ionosphere, where variations in the Total Electron Content (TEC) cause dispersive signal delays [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. These ionospheric effects distort code and carrier phase measurements, increase tracking noise, and reduce positioning accuracy, particularly in equatorial and low-latitude regions [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Low-latitude ionospheres are among the most dynamic globally, influenced by the Equatorial Ionization Anomaly (EIA) and post-sunset electrodynamics that generate equatorial plasma bubbles (EPBs). These plasma irregularities produce intense amplitude and phase scintillation, leading to frequent cycle slips, signal fading, and loss of satellite lock [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. As a result, positioning techniques such as Single Point Positioning (SPP), Real-Time Kinematic (RTK), and Precise Point Positioning (PPP) suffer reduced precision and reliability. The challenge is further exacerbated during geomagnetic disturbances when prompt penetration electric fields and disturbance dynamo effects intensify ionospheric currents and deepen the EIA crests [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Understanding the relationship between space weather activity and ionospheric behavior is therefore crucial for improving GNSS reliability in equatorial environments. The Disturbance Storm Time (DST) index serves as a key indicator of geomagnetic storm intensity and its coupling effects with the ionosphere [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Correlating DST variations with TEC demonstrates how geomagnetic force modulates ionospheric electron density and, consequently, GNSS signal integrity [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Despite global advancements in ionospheric modelling and GNSS error mitigation, the West African low-latitude region remains insufficiently studied. Nigeria, positioned near the EIA crest, experiences frequent equinoctial enhancements, strong pre-sunset plasma drifts, and storm-time scintillation that degrade GNSS performance. Limited location-based observational infrastructure and sparse long-term datasets have constrained regional understanding of ionospheric delay dynamics and their practical impact on positioning accuracy. This study investigates the impact of ionospheric delay on GPS system performance using the SPP technique in a low-latitude region, with emphasis on equatorial Africa. The study explores the relationship between ionospheric delay and the DST index to quantify space-weather influences on electron density over the region, quantifies seasonal and day-to-day variability of ionospheric delay using TEC-derived metrics, evaluates how these delays map onto practical accuracy indicators such as the Two-Distance Root Mean Square (2DRMS), Circular Error Probable (CEP), Spherical Error Probable (SEP), and Mean Radial Spherical Error (MRSE) for SPP solutions, and discusses mitigation strategies spanning higher sampling rate, multi-constellation, and multi-frequency usage together with disturbance-aware weighting models.\u003c/p\u003e"},{"header":"II. Data Sources and Methods","content":"\u003cp\u003eThis study was conducted at the LAUTECH GNSS Laboratory (LGL), Ogbomoso, Nigeria (approximately 8.1\u0026deg;N, 4.2\u0026deg;E), a low-latitude region located within the EIA zone, using the SPP technique. A static-mode real-time GNSS dataset was logged using the u-blox ZED-F9P dual-frequency module in conjunction with a u-blox multiband GNSS patch antenna as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. This GNSS module has been proven to be suitable and efficient in GPS-only and hybrid modes of operation with a 95% confidence level in low-latitude regions, as reported by [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The raw GNSS observations were recorded hourly in ubx format at a sampling rate of 4 Hz and subsequently converted to Receiver Independent Exchange (RINEX 3.03) format using the RTKLIB suite. Following data collection, the hourly dual-frequency RINEX files were merged to form continuous 24-hour daily datasets. Observations were acquired over fourteen randomly selected days per month, providing a representative one-year dataset covering both dry and rainy seasons. Data processing was carried out in two main phases. In the first phase, the GPS-GOPI software was employed to generate TEC data, which served as the foundation for estimating the ionospheric delay. In the second phase, the RTK-POST module of the RTKLIB suite was used to derive the position solutions (POS) through the SPP technique. The resulting POS data were then evaluated using the GNSS positioning accuracy analysis tool, where performance was assessed through standard statistical accuracy metrics. Finally, the obtained position accuracy results were compared with the corresponding daily ionospheric delay estimates to examine their relationship and assess the impact of ionospheric variability on GNSS positioning performance.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eDuring RTKLIB processing, broadcast ephemerides and satellite clock corrections were applied, and a 15\u0026deg; elevation mask was set to reduce low-elevation multipath effects. The ionospheric delay was estimated following the established computation approach. The precision of the GNSS POS was assessed using statistical parameters derived from the mean and standard deviation of the latitude, longitude, and altitude components across multiple epochs [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. To maintain uniformity, latitude and longitude errors originally expressed in angular units were converted to meters. The positioning accuracy was characterized using four key performance indices: Two-Distance Root Mean Square (2DRMS), Circular Error Probable (CEP), Spherical Error Probable (SEP), and Mean Radial Spherical Error (MRSE), all defined in [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. These indices were computed from the standard deviation components of latitude (σx), longitude (σy), and altitude (σz), providing a comprehensive measure of horizontal and vertical positioning accuracy.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003e2DRMS\u003c/b\u003e = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:2\\sqrt{{\\sigma\\:}_{x}^{2}+{\\sigma\\:}_{y}^{2}}\\)\u003c/span\u003e\u003c/span\u003e​​, the horizontal error radius containing 95.8 to 98.2% of solutions.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eCEP\u003c/b\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:=0.62{\\sigma\\:}_{y}+0.56{\\sigma\\:}_{z}\\)\u003c/span\u003e\u003c/span\u003e Provided that \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{{\\sigma\\:}_{y}}{{\\sigma\\:}_{x}}\u0026gt;0.3\\)\u003c/span\u003e\u003c/span\u003e​, the circle radius enclosing 50% of horizontal solutions.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eSEP\u003c/b\u003e = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:0.51\\:\\left({\\sigma\\:}_{x}+{\\sigma\\:}_{y}+{\\sigma\\:}_{z}\\right)\\)\u003c/span\u003e\u003c/span\u003e, the sphere radius enclosing 50% of 3D solutions.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eMRSE\u003c/b\u003e = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sqrt{\\left({\\sigma\\:}_{x}^{2}+{\\sigma\\:}_{y}^{2}+{\\sigma\\:}_{z}^{2}\\right)}\\)\u003c/span\u003e\u003c/span\u003e​​, the sphere radius enclosing 61% of 3D solutions.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eIn addition, daily standard deviations of the latitude, longitude, and altitude positioning errors were computed to demonstrate the relative strength of horizontal versus vertical accuracy. The dataset DST March) seasons. More than 140 daily solutions were included, providing long-term coverage suitable for analyzing both climatological patterns and short-term disturbances. The Disturbance Storm Time Index (DST) used in this analysis was gotten from the World Data Centre for Geomagnetism website [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e"},{"header":"III. Results and Discussion","content":"\u003cp\u003eUnderstanding the interaction between the Earth\u0026rsquo;s magnetosphere and ionosphere is crucial for assessing space-weather impacts on GNSS accuracy. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the relationship between the TEC and the Disturbance Storm Time (DST) index over Ogbomoso, Nigeria. The results show that periods of intense geomagnetic activity (more negative DST) coincide with elevated TEC, confirming that storm-time disturbances strengthen ionospheric ionization. This correlation was most pronounced during months with intense and super storm classifications, such as May, August, and October 2024. In May 2024, the DST index dropped to \u0026minus;\u0026thinsp;285 nT, classifying the storm as \u0026lsquo;super,\u0026rsquo; while the average TEC was about 37.8 TEC Unit (TECU) during this period. Although this value may appear relatively low, it likely represented an increase compared to the preceding months, and when contrasted with the June value of 33.8 TECU, it clearly indicates a rise in TEC. The full-scale TEC response appeared to lag slightly into June and July and started increasing in August and peaked in October 2024, when TEC rose to 47.6 TECU, the highest monthly value recorded. October's DST index reached as low as \u0026minus;\u0026thinsp;192 nT, falling within the \"intense storm\" category. Interestingly, not all storm months produced equally high TEC values, even during storms of similar or greater intensity. In August 2024, for instance, the DST index reached \u0026minus;\u0026thinsp;127 nT, yet TEC rose to about 40.4 TECU. In contrast, May 2024 recorded a stronger storm with a DST minimum of \u0026minus;\u0026thinsp;285 nT but a lower TEC of 37.8 TECU compared with October. This difference appears to be shaped by seasonal influences. May, being closer to the solstice, is affected by reduced solar zenith angle symmetry, which lowers the efficiency of electrodynamic coupling between the magnetosphere and ionosphere. As a result, even when geomagnetic disturbances were of similar strength, the corresponding increase in TEC could be limited. During magnetically quiet months such as June, July, and December 2024, when most daily DST values stayed above \u0026minus;\u0026thinsp;30 nT, TEC values dropped to around 33 to 34.9 TECU. This confirms the direct influence of geomagnetic forcing on ionospheric electron density. Seasonal effects also reinforced this behavior. December, occurring near the solstice, typically shows reduced ionization efficiency at low-latitudes because of weaker solar input. In March and April 2025, TEC remained moderately elevated at about 40\u0026ndash;42 TECU despite only moderate geomagnetic activity, likely due to equinox-related solar symmetry that enhanced ionospheric conductivity. February 2025, however, showed slightly lower TEC under weak geomagnetic conditions, emphasizing that both storm strength and seasonal context influence the ionospheric response. The most intense storms occurred during the rainy season, whereas the dry season experienced only moderate or weak storms.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the monthly averages of ionospheric delay derived from TEC measurements, which reveal distinct seasonal patterns. During the dry season, delays ranged from 2.13 m (December 2024) to 2.76 m (March 2025), gradually increasing toward the March equinox. This buildup corresponds to stronger electrodynamic forcing and a more pronounced EIA. In the rainy season, delays decreased to ~\u0026thinsp;1.99 m in July 2024 but rose again to 2.85 m in October 2024, coinciding with equinoctial intensification. The seasonal means depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e indicate that the dry season recorded slightly higher average ionospheric delay (2.54 m) than the rainy season (2.39 m), representing a\u0026thinsp;~\u0026thinsp;6% increase. Although day-to-day variability (σ\u0026thinsp;\u0026asymp;\u0026thinsp;0.58\u0026ndash;0.60 m) was similar in both seasons, the overall baseline delay was higher during the dry months, reflecting enhanced solar activity and reduced neutral density cooling. These findings confirm the seasonal modulation of ionospheric conditions in the equatorial environment.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the seasonal averages of ionospheric delay and related positioning accuracy metrics using the SPP technique. The mean ionospheric delay was slightly higher in the dry season than in the rainy season. Correspondingly, 3D accuracy indicators such as SEP and MRSE were also larger in the dry season, with values of 4.50 m and 5.90 m compared to 4.20 m and 5.37 m in the rainy season. This demonstrates that volumetric positioning errors become more pronounced when ionospheric delay increases. A similar trend was noted for the standard deviation of altitude error, which rose to 5.35 m in the dry season compared with 4.77 m in the rainy season. These results confirm that vertical positioning is especially sensitive to ionospheric effects, with dry season conditions amplifying this vulnerability. In contrast, the horizontal accuracy metrics showed only minor seasonal differences, even though values remained in the centimeter-to-meter range. The average 2DRMS was nearly identical across seasons, 4.93 m in the dry season and 4.92 m in the rainy season, indicating that horizontal accuracy is less affected by seasonal variations in ionospheric delay.\u003c/p\u003e\u003cp\u003eSimilarly, the standard deviations of the horizontal components were close: latitude error averaged 1.64 m in the dry season and 1.70 m in the rainy season, while longitude error averaged 1.83 m in the dry season and 1.77 m in the rainy season. The combined horizontal standard deviation was also nearly the same, at 2.47 m in the dry season and 2.46 m in the rainy season. These findings show that while horizontal errors remain relatively stable, the dry season consistently produces larger vertical and volumetric errors. This suggests that ionospheric delay has a stronger impact on degrading 3D accuracy than on horizontal positioning in low-latitude 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\u003eSeasonal Mean Values of Ionospheric Delay and POS Accuracy Metrics.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeason\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIonospheric delay (m)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2DRMS (m)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCEP (m)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSEP (m)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMRSE (m)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eLat. Error SD (m)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eLong. Error SD (m)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eAlt. Error SD (m)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eHoriz-\u003c/p\u003e\u003cp\u003eontal\u003c/p\u003e\u003cp\u003e_SD (m)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDry\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.538\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.932\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5.896\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.644\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.830\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e5.350\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e2.466\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRainy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.393\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.919\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.201\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5.367\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.704\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.767\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e4.766\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e2.460\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\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003ea presents a time series plot of ionospheric delay plotted alongside 2DRMS and CEP. Periods of elevated ionospheric delay coincided with noticeable fluctuations in horizontal precision, although CEP showed relatively low variability compared to 2DRMS. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eb displays ionospheric delay together with SEP and MRSE, revealing stronger co-fluctuations between delay and 3D accuracy. In every case, spikes in ionospheric delay were followed by increases in SEP and MRSE, demonstrating that 3D errors are highly sensitive to ionospheric conditions. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003ec compares ionospheric delay with the standard deviations of latitude, longitude, and altitude errors. Among these, the altitude error showed the largest variations, confirming that vertical accuracy is the most affected during disturbed ionospheric conditions.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003ea: Ionospheric Delay alongside 2DRMS and CEP.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the general Pearson correlation analysis during the observation period, which revealed moderate positive relationships between ionospheric delay and positional-accuracy metrics: r\u0026thinsp;\u0026asymp;\u0026thinsp;0.36 for 2DRMS and CEP, r\u0026thinsp;\u0026asymp;\u0026thinsp;0.33 for SEP, and r\u0026thinsp;\u0026asymp;\u0026thinsp;0.29 for MRSE. The correlations were slightly stronger in the dry season, indicating that increases in ionospheric delay generally coincide with reduced positioning precision. Horizontal metrics displayed the highest correlation coefficients, suggesting that delay effects first manifest in the horizontal plane before propagating into 3-D errors. Although the correlations were not exceptionally high, they consistently demonstrated that ionospheric delay is a dominant factor influencing GNSS positioning quality in low-latitude environments. The results also confirm that storm-time and equinoctial disturbances can significantly worsen GNSS performance, particularly for vertical positioning applications such as aviation guidance and geodetic leveling.\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\u003ePearson Correlation Analysis for POS Accuracy Metrics for Dry and Rainy Season\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAccuracy Metrics (m)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDry\u003c/p\u003e\u003cp\u003ePearson_r\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRainy\u003c/p\u003e\u003cp\u003ePearson_r\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2DRMS Mean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.359482516\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.363953818\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCEP Mean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.373701683\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.353849126\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSEP Mean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.347692507\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.292692672\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMRSC Mean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.288339975\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.251266305\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\u003eThe results collectively show that ionospheric delay over Ogbomoso varies seasonally and responds strongly to geomagnetic conditions. The TEC and DST relationship confirms that enhanced storm-time activity leads to higher electron densities, which, in turn, increase ionospheric delay and degrade GNSS accuracy. Vertical components of position are most affected, while horizontal precision remains relatively robust.\u003c/p\u003e"},{"header":"IV. Conclusion","content":"\u003cp\u003eThis study investigated the impact of ionospheric delay on GNSS positioning accuracy in a low-latitude region using one year (May 2024\u0026ndash;April 2025) of dual-frequency observation data from Ogbomoso, Nigeria. The analysis provided an integrated view of how ionospheric behavior characterized through TEC, DST, and seasonal variations affects the quality of GNSS position solutions. The results showed that ionospheric delay exhibits a distinct seasonal pattern, with higher values during the dry season and notable peaks near the equinoxes. The TEC and DST correlation confirmed that enhanced geomagnetic disturbances and equinoctial electrodynamics significantly increase ionospheric density, thereby amplifying signal delays. Daily mean ionospheric delays ranged from 0.10 m to 3.70 m, averaging about 2.45 m. Corresponding SPP accuracy metrics revealed that 3-D positional errors (SEP\u0026thinsp;\u0026asymp;\u0026thinsp;4\u0026ndash;5.5 m, MRSE\u0026thinsp;\u0026asymp;\u0026thinsp;5\u0026ndash;7 m) increased with rising delay values, while horizontal errors (2DRMS\u0026thinsp;\u0026asymp;\u0026thinsp;4.9 m, CEP\u0026thinsp;\u0026asymp;\u0026thinsp;2 m) remained comparatively stable. Correlation analysis (r\u0026thinsp;=\u0026thinsp;0.29\u0026ndash;0.36) demonstrated a consistent positive relationship between ionospheric delay and positioning errors, indicating that delay fluctuations directly influence positional precision, particularly in the vertical component. This behavior was more pronounced during the dry season, when ionospheric gradients and equatorial electrodynamic activity were stronger. Generally, the study establishes that ionospheric delay remains a dominant error source in low-latitude GNSS applications, where equatorial ionization anomalies and space-weather disturbances introduce significant uncertainty in positioning performance. To mitigate these effects, the adoption of a higher sampling rate at 4 Hz, a higher elevation mask, multi-GNSS and dual-frequency processing, and the use of real-time data for ionospheric modeling are recommended. Such approaches can improve accuracy and reliability for navigation, geodetic surveying, and remote sensing operations in equatorial regions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors Contributions:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors’ Contributions OEA: Study conception and design, data acquisition, data analysis and interpretation, original draft writing. AAS: Study conception and design, data acquisition, data analysis and interpretation, original draft writing, review and editing. AGB: Study conception and design, data analysis and interpretation, original draft writing, review and editing. HMS: Study conception and design, data analysis and interpretation, review and editing. OO: Study conception and design, data analysis and interpretation, original draft writing, review and editing. OEA: Study conception and design, data analysis and interpretation, review and editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors received no funding for this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData is provided within the manuscript files\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthics approval was not required for this study. Hence, no ethics approval declaration is available.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsent to publish was not required for this study. Hence, no consent to publish declaration is available.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsent to participate was not required for this study. Hence, no consent to participate declaration is available.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinical trial is not applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNovAtel Inc. In: Calgary, editor. An Introduction to GNSS: GPS, GLONASS, BeiDou, Galileo and other Global Navigation Satellite Systems. 2nd ed. Alberta, Canada: NovAtel Inc; 2015.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAdewumi AS, Azeez IA. A Review of Global Navigation Satellite System and its Applications. International Journal of Scientific and Engineering Research; 2021.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTaoufiq J, Mourad B, Rachid A, Amory-Mazaudier C. (2018). Study of Ionospheric Variability Using GNSS Observations. Positioning.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen W, Gao S, Hu C, Chen Y, Ding X. (2008). Effects of ionospheric disturbances on GPS observation in low latitude area. Gps Solutions.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePaziewski J, H\u0026oslash;eg P, Sieradzki R, Jin Y, Jarmolowski W, Hoque MM, Or\u0026uacute;s-P\u0026eacute;rez R. The implications of ionospheric disturbances for precise GNSS positioning in Greenland. Journal of Space Weather and Space Climate; 2022.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWeng D, Ji S, Chen W, Liu Z. Assessment and mitigation of ionospheric disturbance effects on GPS accuracy and integrity. The Journal of Navigation; 2014.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAstafyeva E, Zakharenkova I. Ionospheric response to the June 2015 St. Patrick\u0026rsquo;s Day storm: A global multi-instrumental overview. Journal of Geophysical Research: Space Physics; 2018.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBernegger C. Local Disturbance Storm Time Index for Ionospheric Forecasting: Comparison of Global vs. Local DST (Bachelor's thesis; 2024.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBelehaki A, Tsagouri I, Altadill D, Blanch E, Borries C, Buresova D, Watermann J. An overview of methodologies for real-time detection, characterisation and tracking of traveling ionospheric disturbances developed in the TechTIDE project. J Space Weather Space Clim. 2020;10:42.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAdewumi AS, Dan S, Mandal R, Bose A. (2025a). Effects of Ionospheric Delay on the Position Accuracy of GNSS Receiver: A Case Study at Burdwan University, India. In Proceedings of the 8th URSI-NG Annual Conference (URSI-NG 2024). Springer Nature.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDan S, Santra A, Mahato S, Koley C, Banerjee P, Bose A. On use of low cost, compact GNSS receiver modules for ionosphere monitoring. Radio Science; 2021.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAdewumi AS, Atilade \u0026Agrave;G, Abiodun AI, Benjamin AG, Anthony OE, Oluwadara O, Emmanuel ED. (2025b). PPP Potential of a Compact, Low-Cost GNSS Receiver\u0026rsquo;s Module in a Low Latitude Region: A Case Study at the LAUTECH GNSS Laboratory, Nigeria. In 8th URSI-NG Annual Conference (URSI-NG 2024). Atlantis Press.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSantra A, Mahato S, Dan S, Bose A. Precision of satellite based navigation position solution: a review using NavIC data. Journal of information and optimization sciences; 2019.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSomnath Mahato A, Santra S, Dan P, Banerjee SK, Anindya Bose. Point Positioning Capability of Compact, Low-Cost GNSS Modules: A Case Study. IETE J Res. 2021. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/03772063.2021.1939801\u003c/span\u003e\u003cspan address=\"10.1080/03772063.2021.1939801\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWorld Data Center Kyoto. Real-time and final DST index reports. Kyoto: World Data Center for Geomagnetism; 2025.\u003c/span\u003e\u003c/li\u003e\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":"discover-electronics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Electronics](https://www.springer.com/journal/44291)","snPcode":"44291","submissionUrl":"https://submission.nature.com/new-submission/44291","title":"Discover Electronics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"GNSS, ionospheric delay, low-latitude, positioning accuracy","lastPublishedDoi":"10.21203/rs.3.rs-7890225/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7890225/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccurate positioning and timing services are critically dependent on Global Navigation Satellite Systems (GNSS); however, their performance is often degraded by ionospheric delay, especially in low-latitude regions influenced by the Equatorial Ionization Anomaly and frequent scintillation. This study evaluates the impact of ionospheric delay on GNSS positioning accuracy over Ogbomoso, Nigeria (\u0026asymp;\u0026thinsp;8.1\u0026deg; N, 4.2\u0026deg; E), using one year of GNSS observations data recorded with a u-blox ZED-F9P receiver. The data were converted to RINEX format using the RTKLIB suite and subsequently processed with the GPS-GOPI software to compute the Total Electron Content (TEC) and related parameters required for estimating the ionospheric delay. The positioning accuracy was then assessed using the Single Point Positioning technique, evaluated through key statistical performance metrics including the Two-Distance Root Mean Square (2DRMS), Circular Error Probable (CEP), Spherical Error Probable (SEP), and Mean Radial Spherical Error (MRSE) indices. The results revealed that ionospheric delay exhibited seasonal modulation, with higher magnitudes during the dry season (mean\u0026thinsp;=\u0026thinsp;2.54 m) compared with the rainy season (mean\u0026thinsp;=\u0026thinsp;2.39 m). Daily mean ionospheric delays ranged from 0.10 m to 3.70 m, while positioning accuracy metrics varied accordingly (2DRMS\u0026thinsp;=\u0026thinsp;3.95\u0026ndash;6.91 m; CEP\u0026thinsp;=\u0026thinsp;1.64\u0026ndash;2.89 m; SEP\u0026thinsp;=\u0026thinsp;3.49\u0026ndash;5.48 m; MRSE\u0026thinsp;=\u0026thinsp;4.47\u0026ndash;7.41 m). Moderate positive correlations (r\u0026thinsp;=\u0026thinsp;0.29\u0026ndash;0.36) between ionospheric delay and these accuracy indices confirmed that delay fluctuations significantly degrade positioning precision. The findings demonstrate that ionospheric delay remains a dominant source of error for GNSS users in equatorial regions\u003c/p\u003e","manuscriptTitle":"Impact of Ionospheric Delay on GNSS in a Low-Latitude Region","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-10 12:03:02","doi":"10.21203/rs.3.rs-7890225/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-14T07:44:13+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-25T05:43:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"300882493699007071021856049946893259933","date":"2025-12-19T15:00:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-17T03:41:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"8804257890517423475985659191961690846","date":"2025-12-15T15:02:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"321157891529168712395607018098312923693","date":"2025-12-15T10:02:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"33553495269151844069757111437303543129","date":"2025-12-13T04:09:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"238176224872513968210061446175984034219","date":"2025-11-06T02:56:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"220587139653548952176213363955237227423","date":"2025-11-05T09:59:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"334495390329077375147794124990445554526","date":"2025-10-29T08:54:58+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-29T08:40:04+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-26T17:13:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-24T10:36:02+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-24T10:34:49+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Electronics","date":"2025-10-18T01:36:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-electronics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Electronics](https://www.springer.com/journal/44291)","snPcode":"44291","submissionUrl":"https://submission.nature.com/new-submission/44291","title":"Discover Electronics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6726e787-b7b6-45b1-a027-99d9c3ad33c4","owner":[],"postedDate":"November 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-20T04:09:08+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-10 12:03:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7890225","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7890225","identity":"rs-7890225","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}