Ion Transport from Soil to Air and Electric Field Amplitude of the Boundary Layer

dc.contributor.authorMuhammad, Ahmad
dc.contributor.authorKulahci, Fatih
dc.contributor.authorDanbatta, Salim Jibrin
dc.date.accessioned2026-08-12T17:07:46Z
dc.date.issued2024
dc.departmentFırat Üniversitesi
dc.description.abstractThe presence of ions within the atmospheric region near the soil surface has considerable implications for enhancing our understanding of Earth's complex systems. This study delves into the intricate relationship between the atmospheric electric field in the boundary layer and lithosphere. The focus was specifically on investigating how soil radon and its progeny influence the production rate of ions in both the soil and the atmosphere. To achieve this, we combined the radon transport equation with advanced machine learning techniques. Using a well-suited machine learning model, we effectively modeled the responses of soil radon and seamlessly integrated them into the radon transport equation. The resulting insights were used to predict the rates at which radon-induced ion pairs were produced. A particularly important parameter is the surface-ion production rate, which is crucial for estimating the amplitude of the near-surface electric field. This methodology was applied to analyze data from two radon monitoring stations in Turkey: Erzincan, located along the North Anatolian Fault (NAF), and Malatya, situated close to the East Anatolian Fault regions. The significance of this estimation approach resonates within the field of lithospheric-atmospheric studies. This innovative methodology holds promise as a valuable tool for future investigations in the domains of lithosphere-atmosphere-ionosphere coupling (LAIC), global electric circuits (GEC), and seismo-ionospheric coupling. Ultimately, this study underscores the importance of carefully considering the intricate interconnections that exist among different components of Earth's intricate system. This advocates the adoption of novel methods to shed light on these complex interactions.
dc.description.sponsorshipThe authors would like to thank Frat University for providing a suitable environment for the research, as well as Qatar University for providing additional research infrastructure.
dc.identifier.doi10.1134/S0016793223600613
dc.identifier.endpage591
dc.identifier.issn0016-7932
dc.identifier.issn1555-645X
dc.identifier.issue4
dc.identifier.orcid0000-0003-3886-7956
dc.identifier.orcid0000-0002-8913-5766
dc.identifier.scopus2-s2.0-85201313388
dc.identifier.scopusqualityQ3
dc.identifier.startpage581
dc.identifier.urihttps://doi.org/10.1134/S0016793223600613
dc.identifier.urihttps://hdl.handle.net/11508/49780
dc.identifier.volume64
dc.identifier.wosWOS:001291162000008
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherPleiades Publishing Ltd
dc.relation.ispartofGeomagnetism and Aeronomy
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20260511
dc.subjectsurface atmospheric electric field
dc.subjectradon transport
dc.subjection production rates
dc.subjectmachine learning
dc.titleIon Transport from Soil to Air and Electric Field Amplitude of the Boundary Layer
dc.typeArticle

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