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Analysis of institutional authors

Barrio-Parra, FernandoAuthorSerrano-García HAuthorIzquierdo-Diaz, MiguelAuthorDe Miguel EAuthor

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October 21, 2025
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Enhancing radon-deficit technique efficacy: machine learning applications for environmental variable analysis in soil gas monitoring.

Publicated to: ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH. 32 (41): 23694-23706 - 2025-10-15 32(41), DOI: 10.1007/s11356-025-37069-w

Authors:

Lorenzo; D; Barrio; F; Cecconi; A; Serrano-García; H; Izquierdo-Díaz; M; Santos López; A; De Miguel; E
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Affiliations

Department of Chemical Engineering and Materials, Facultad de Ciencias Químicas, Universidad Complutense de Madrid, Avenida Complutense S/N, 28040, Madrid, Spain. - Author
Department of Chemical Engineering and Materials, Facultad de Ciencias Químicas, Universidad Complutense de Madrid, Avenida Complutense S/N, 28040, Madrid, Spain. dlorenzo@quim.ucm.es. - Author
Laboratory of Environmental Engineering, Department of Civil Engineering and Computer Science Engineering, University of Rome Tor Vergata, Via del Politecnico 1, 00133, Rome, Italy. - Author
Prospecting & Environment Laboratory (PROMEDIAM), ETS de Ingenieros de Minas y Energía, Universidad Politécnica de Madrid, Alenza 4, 28003, Madrid, Spain. - Author
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Abstract

Soil contamination remains a critical environmental concern, necessitating efficient techniques for site characterization and remediation. The radon-deficit technique (RDT) offers a non-invasive approach to identifying organic contamination, relying on the behavior of radon-222 (222Rn) as a tracer. However, RDT results are influenced by environmental variables such as soil moisture, temperature, and atmospheric pressure, potentially leading to uncertainties. This study evaluates the application of machine learning (ML) models-including linear regression (LR), random forest (RF), artificial neural network (ANN), and gradient boosting machine (GBM)-to predict 222Rn activity in soil gas based on environmental parameters. A year-long dataset of continuous measurements was collected from an uncontaminated granite-based site in Madrid, encompassing variables such as soil moisture, ambient and soil temperatures, and atmospheric conditions. ANN and RF models exhibited superior performance in predicting 222Rn variability, identifying soil moisture and ambient temperature as the most influential predictors. The findings demonstrate that ML can significantly enhance the reliability of RDT by accounting for environmental variability, enabling more accurate identification of contamination hotspots. While the application of these models requires substantial datasets, they offer a promising tool for improving the efficacy of contamination screening and long-term remediation monitoring. Further studies are recommended to explore ML's predictive capacity in contaminated sites and expand the approach to diverse geological contexts.
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Keywords

%moisture222rnAdaptive boostingArtificial neural networkAs-soilsAtmospheric pressureAtmospheric temperatureChemistryContaminationEnvironmental monitoringEnvironmental remediationEnvironmental technologyEnvironmental variablesGradient boostingLearning systemsLinear regressionMachine learningMachine-learningMadridNeural networksNeural networks, computerNeural-networksPollution detectionProceduresRadonRadon-222Radon-deficit technologyRandom forestRandom forestsRemediationSite characterizationSoilSoil conservationSoil gasSoil moistureSoil pollutionSoil pollution controlSoil remediationSoil surveysSoil temperatureSoils remediation

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH due to its progression and the good impact it has achieved in recent years, according to the agency Scopus (SJR), it has become a reference in its field. In the year of publication of the work, 2025, it was in position , thus managing to position itself as a Q1 (Primer Cuartil), in the category Medicine (Miscellaneous).

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2026-04-25:

  • Scopus: 1
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Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2026-04-25:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 2.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 2 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

    It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

    • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: https://oa.upm.es/91542/

    As a result of the publication of the work in the institutional repository, statistical usage data has been obtained that reflects its impact. In terms of dissemination, we can state that, as of

    • Views: 56
    • Downloads: 2
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    Leadership analysis of institutional authors

    This work has been carried out with international collaboration, specifically with researchers from: Italy.

    There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: Last Author (MIGUEL GARCIA, EDUARDO DE).

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    Project objectives

    El estudio persigue los siguientes objetivos: analizar la influencia de variables ambientales como humedad del suelo, temperatura y presión atmosférica en la técnica de déficit de radón (RDT); evaluar la eficacia de modelos de aprendizaje automático (regresión lineal, random forest, redes neuronales artificiales y gradient boosting) para predecir la actividad de radón-222 en el gas del suelo; determinar los modelos con mejor desempeño en la predicción de la variabilidad del radón; identificar las variables ambientales más influyentes en la actividad de radón; y mejorar la fiabilidad de la RDT para la detección precisa de contaminación orgánica en suelos mediante la integración de técnicas de aprendizaje automático.
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    Most relevant results

    Los resultados más relevantes de este estudio se centran en la mejora de la técnica de déficit de radón (RDT) mediante aplicaciones de aprendizaje automático (ML). En primer lugar, los modelos de redes neuronales artificiales (ANN) y bosque aleatorio (RF) mostraron un rendimiento superior en la predicción de la actividad de radón-222 en el gas del suelo. En segundo lugar, se identificó que la humedad del suelo y la temperatura ambiental son las variables ambientales más influyentes en la variabilidad del radón. Finalmente, el uso de ML permite aumentar la fiabilidad de la RDT al considerar la variabilidad ambiental, facilitando una identificación más precisa de zonas contaminadas y mejorando el monitoreo a largo plazo.
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