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Python-Powered Environmental Intelligence: Computational Workflows for Soil Pollution Assessment Using ML Methods

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Τίτλος: Python-Powered Environmental Intelligence: Computational Workflows for Soil Pollution Assessment Using ML Methods
Συγγραφείς: Lemenkova, Polina
Συνεισφορές: Bureau de Recherches Géologiques et Minières (BRGM) (BRGM), LE STUDIUM Loire Valley Institute for Advanced Studies (LE STUDIUM), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université d'Orléans (UO)-Université de Tours (UT), NEOLAiA European University = Université Européenne NEOLAÏA (NEOLAiA)-NEOLAiA European University = Université Européenne NEOLAÏA (NEOLAiA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Ecole Supérieure d'art et de design d'Orléans (ESAD), La Technopole d’Orléans, Laboratoire pluridisciplinaire de recherche en ingénierie des systèmes, mécanique et énergétique (PRISME), Université d'Orléans (UO)-Institut National des Sciences Appliquées - Centre Val de Loire (INSA CVL), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)
Πηγή: ISSN: 3042-903X.
Στοιχεία εκδότη: CCSD
MDPI
Έτος έκδοσης: 2026
Συλλογή: Université François-Rabelais de Tours: HAL
Θεματικοί όροι: ecological risk assessment, soil ecosystem services, geospatial mapping, Monte Carlo simulation, heavy metals, PFAS, LSTM, convolutional neural network, random forest, contaminant detection, Python Computer program language, [SDE.IE]Environmental Sciences/Environmental Engineering, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]
Περιγραφή: International audience ; Soil pollution constitutes a critical global environmental challenge driven by industrialization, intensive agriculture, urban expansion, mining, and the application of synthetic agrochemicals. This article presents seven annotated Python-based Machine Learning (ML) workflows for soil pollution assessment, structured around five contaminant groups: heavy metals, pesticides, microplastics, per- and polyfluoroalkyl substances (PFAS), and excess macronutrients. The contribution has three distinct components. First, a literature synthesis drawing on more than 100 peer-reviewed studies contextualizes each contaminant group within current spectroscopic, geochemical, and ML-based detection frameworks. Second, a conceptual six-step workflow links field sampling, ML-based analysis, and scenario-based risk modelling to soil ecosystem service (SES) assessment. Third, seven executable Python scripts—implementing Random Forest regression, XGBoost with SHAP explainability, 1-D Convolutional Neural Networks, LSTM time-series forecasting, PCA-based dimensionality reduction, Monte Carlo uncertainty propagation, and GeoPandas geospatial mapping—serve as illustrative demonstrations using a benchmark dataset. All reported performance metrics are derived from synthetic data and represent workflow demonstrations, not validated field results. Radionuclides are acknowledged as an important contaminant class but fall outside the defined scope of this study. The scripts are provided as reproducible templates for adaptation to real contaminated-site datasets.
Τύπος εγγράφου: article in journal/newspaper
Γλώσσα: English
DOI: 10.3390/environremediat1020006
Διαθεσιμότητα: https://hal.science/hal-05686446
https://hal.science/hal-05686446v1/document
https://hal.science/hal-05686446v1/file/environremediat-01-00006.pdf
https://doi.org/10.3390/environremediat1020006
Rights: https://creativecommons.org/licenses/by/4.0/ ; info:eu-repo/semantics/OpenAccess
Αριθμός Καταχώρησης: edsbas.9738D67F
Βάση Δεδομένων: BASE
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  – Url: https://hal.science/hal-05686446#
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  Data: Python-Powered Environmental Intelligence: Computational Workflows for Soil Pollution Assessment Using ML Methods
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  Data: <searchLink fieldCode="AR" term="%22Lemenkova%2C+Polina%22">Lemenkova, Polina</searchLink>
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  Data: Bureau de Recherches Géologiques et Minières (BRGM) (BRGM)<br />LE STUDIUM Loire Valley Institute for Advanced Studies (LE STUDIUM)<br />Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université d'Orléans (UO)-Université de Tours (UT)<br />NEOLAiA European University = Université Européenne NEOLAÏA (NEOLAiA)-NEOLAiA European University = Université Européenne NEOLAÏA (NEOLAiA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Ecole Supérieure d'art et de design d'Orléans (ESAD)<br />La Technopole d’Orléans<br />Laboratoire pluridisciplinaire de recherche en ingénierie des systèmes, mécanique et énergétique (PRISME)<br />Université d'Orléans (UO)-Institut National des Sciences Appliquées - Centre Val de Loire (INSA CVL)<br />Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)
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  Data: <i>ISSN: 3042-903X</i>.
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  Data: CCSD<br />MDPI
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  Data: 2026
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  Data: Université François-Rabelais de Tours: HAL
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  Data: <searchLink fieldCode="DE" term="%22ecological+risk+assessment%22">ecological risk assessment</searchLink><br /><searchLink fieldCode="DE" term="%22soil+ecosystem+services%22">soil ecosystem services</searchLink><br /><searchLink fieldCode="DE" term="%22geospatial+mapping%22">geospatial mapping</searchLink><br /><searchLink fieldCode="DE" term="%22Monte+Carlo+simulation%22">Monte Carlo simulation</searchLink><br /><searchLink fieldCode="DE" term="%22heavy+metals%22">heavy metals</searchLink><br /><searchLink fieldCode="DE" term="%22PFAS%22">PFAS</searchLink><br /><searchLink fieldCode="DE" term="%22LSTM%22">LSTM</searchLink><br /><searchLink fieldCode="DE" term="%22convolutional+neural+network%22">convolutional neural network</searchLink><br /><searchLink fieldCode="DE" term="%22random+forest%22">random forest</searchLink><br /><searchLink fieldCode="DE" term="%22contaminant+detection%22">contaminant detection</searchLink><br /><searchLink fieldCode="DE" term="%22Python+Computer+program+language%22">Python Computer program language</searchLink><br /><searchLink fieldCode="DE" term="%22[SDE%2EIE]Environmental+Sciences%2FEnvironmental+Engineering%22">[SDE.IE]Environmental Sciences/Environmental Engineering</searchLink><br /><searchLink fieldCode="DE" term="%22[INFO%2EINFO-IR]Computer+Science+[cs]%2FInformation+Retrieval+[cs%2EIR]%22">[INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR]</searchLink><br /><searchLink fieldCode="DE" term="%22[INFO%2EINFO-LG]Computer+Science+[cs]%2FMachine+Learning+[cs%2ELG]%22">[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]</searchLink>
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  Label: Description
  Group: Ab
  Data: International audience ; Soil pollution constitutes a critical global environmental challenge driven by industrialization, intensive agriculture, urban expansion, mining, and the application of synthetic agrochemicals. This article presents seven annotated Python-based Machine Learning (ML) workflows for soil pollution assessment, structured around five contaminant groups: heavy metals, pesticides, microplastics, per- and polyfluoroalkyl substances (PFAS), and excess macronutrients. The contribution has three distinct components. First, a literature synthesis drawing on more than 100 peer-reviewed studies contextualizes each contaminant group within current spectroscopic, geochemical, and ML-based detection frameworks. Second, a conceptual six-step workflow links field sampling, ML-based analysis, and scenario-based risk modelling to soil ecosystem service (SES) assessment. Third, seven executable Python scripts—implementing Random Forest regression, XGBoost with SHAP explainability, 1-D Convolutional Neural Networks, LSTM time-series forecasting, PCA-based dimensionality reduction, Monte Carlo uncertainty propagation, and GeoPandas geospatial mapping—serve as illustrative demonstrations using a benchmark dataset. All reported performance metrics are derived from synthetic data and represent workflow demonstrations, not validated field results. Radionuclides are acknowledged as an important contaminant class but fall outside the defined scope of this study. The scripts are provided as reproducible templates for adaptation to real contaminated-site datasets.
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  Data: 10.3390/environremediat1020006
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  Data: https://hal.science/hal-05686446<br />https://hal.science/hal-05686446v1/document<br />https://hal.science/hal-05686446v1/file/environremediat-01-00006.pdf<br />https://doi.org/10.3390/environremediat1020006
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