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. 2025 May 31;47(7):239.
doi: 10.1007/s10653-025-02533-6.

Identifying the key factors of mercury exposure in residents of southwestern Iran using machine learning algorithms

Narjes Okati  1 , Zohre Ebrahimi-Khusfi  2 , Samira Zandifar  3 , Ruhollah Taghizadeh-Mehrjardi  4
Affiliations

Identifying the key factors of mercury exposure in residents of southwestern Iran using machine learning algorithms

Narjes Okati et al. Environ Geochem Health. .

Abstract

It is necessary to predict hair mercury (Hg) levels and specify the related effective factors to develop preventive strategies to reduce Hg exposure in different regions. This study is the first effort to investigate the effectiveness of eight machine learning (ML) models (including multiple linear regression, decision tree regression, least absolute shrinkage and selection operator, multivariate adaptive regression splines, random forest, extreme gradient boosting, K-nearest neighbor, and Gaussian process) for predicting hair Hg levels and identifying the most important factors affecting them in residents of southwestern Iran. All ML models were trained with 70% of the dataset and their performance was evaluated using the determination coefficient (R2), root mean square error (RMSE), and mean absolute error (MAE) based on the remaining dataset. Finally, the Permutation Feature Importance (PFI) method was used to determine the relative importance (RI) of influencing factors. Mean hair Hg (3.31 µg g⁻1) was higher than the United States Environmental Protection Agency (US EPA) and World Health Organization (WHO) limits. It was indicated a high exposure risk for some people in this region. The extreme gradient boosting (XGB) model outperformed other algorithms in modeling hair Hg levels, with R2 = 0.61, RMSE = 2.2, and MAE = 1.25. According to the PFI analysis, weight (RI: 43.4%) and geographic place (RI: 41.8%) were found as the most important demographic factors influencing Hg variation in the study population. Additionally, occupation (RI: 46.1%) and the frequency of fish and canned fish consumption (RI: 22%) were identified as the most significant exposure factors controlling hair Hg variability in southwestern Iran. These findings can be useful for formulating appropriate strategies to reduce the health risk of Hg exposure and improve human health.

Keywords: Exposure factors; Hair mercury; Health risk; Machine learning; Southwest of Iran.

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Conflict of interest statement

Declarations. Conflict of interest: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Ethical approval: All procedures performed in this study involving human participants were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Consent to participate: Before the collection of hair samples, the objectives and protocol of the research were explained to participants and informed consent was obtained from all individual participants included in the study. Consent for publication: All authors have given their permission to publish this work.

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