Tracing source footprints of heavy metal(oid)s in coastal soils using traditional statistical techniques and machine learning data-driven models

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dc.contributor.authorIslam, Abu Reza Md Towfiqul
dc.contributor.authorVarol, Memet
dc.contributor.authorMallick, Javed
dc.contributor.authorMamun, Md. Abdullah-Al
dc.contributor.authorMia, Md. Yousuf
dc.contributor.authorSiddique, Md. Abu Bakar
dc.contributor.authorAktar, Mst. Nazneen
dc.date.accessioned2026-06-19T06:39:48Z
dc.date.available2026-06-19T06:39:48Z
dc.date.issued2026
dc.departmentMalatya Turgut Özal Üniversitesi
dc.description.abstractUnderstanding the source tracing of heavy metal(oid)s (HMs) in coastal soils is paramount for efficient pollution control and safety measures. In the current study, traditional statistical techniques such as principal component analysis (PCA), principal coordinate analysis (PCoA) and machine learning data-driven models, including self-organizing maps (SOMs), conditional inference trees (CITs), ridge regression (RR) and SHapley Additive ex-Planations (SHAP) were employed to detect and trace the sources of HMs in the soils along the northeast coast of Bangladesh. The concentrations of Pb (range: 11-44 mg/kg), Cd (range: 0.03-3.6 mg/kg), Mn (range: 199-981 mg/kg), and As (range: 0.13-13 mg/kg) exceeded the average shale values (ASV). This research found substantial spatial patterns of HM content and PCoA responsible for 75.46 % of total spatial variation. The SOM analysis identified natural sources for As, Ni, Fe, and Mn, and shipbreaking industry sources for Cu, Zn, and Pb in the study sites. The CIT showed that the effects of silt on soil Fe, Cd, and As, as well as the impact of soil pH on Mn and Ni, showed that these elements mostly came from geological processes. Furthermore, RR modeling provided high predictive performance for Pb, Zn, and Mn. The SHAP-based analysis quantified the importance of variables and identified potential physicochemical factors (soil pH, silt, and organic matter) that could contribute to the accumulation of HMs in soil. This paper integrates traditional and machine learning approaches to trace HM sources with a promise for future practice in other analogous areas, providing a theoretical basis for controlling soil HM contamination in coastal regions.
dc.description.sponsorshipDeanship of Research and Graduate Studies at King Khalid University [RGP2/168/46]
dc.description.sponsorshipThe authors extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University for funding this work through Large Research Project under grant number RGP2/168/46.
dc.identifier.doi10.1016/j.marpolbul.2025.118701
dc.identifier.issn0025-326X
dc.identifier.issn1879-3363
dc.identifier.orcid0009-0000-5451-7177
dc.identifier.orcid0000-0002-2609-6966
dc.identifier.orcid0000-0002-3598-0315
dc.identifier.orcid0000-0001-7947-1910
dc.identifier.orcid0009-0001-6749-9171
dc.identifier.pmid40945187
dc.identifier.scopus2-s2.0-105015420427
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.marpolbul.2025.118701
dc.identifier.urihttps://hdl.handle.net/20.500.12899/5800
dc.identifier.volume222
dc.identifier.wosWOS:001572214000001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherPergamon-Elsevier Science Ltd
dc.relation.ispartofMarine Pollution Bulletin
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20260612
dc.subjectSoil Pollution
dc.subjectHeavy Metal(Oid)S
dc.subjectRidge Regression
dc.subjectSelf-Organizing Map
dc.subjectNortheast Coast Of Bangladesh
dc.titleTracing source footprints of heavy metal(oid)s in coastal soils using traditional statistical techniques and machine learning data-driven models
dc.typeArticle

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