DOI: 10.5281/zenodo.18061095
title: Supplementary Materials, Datasets, and Reproducible Code for: Arsenic Exposure in Abandoned Gold Mining Communities: Health Risk Assessment–Driven Machine Learning Risk Mapping in Loei, Thailand

description: Title: Supplementary Materials, Datasets, and Reproducible Code for: Arsenic Exposure in Abandoned Gold Mining Communities: Health Risk Assessment–Driven Machine Learning Risk Mapping in Loei, Thailand

Description: This repository contains the comprehensive supplementary materials, raw datasets, ethical documentation, and reproducible code workflows associated with the research article "Arsenic Exposure in Abandoned Gold Mining Communities: Health Risk Assessment–Driven Machine Learning Risk Mapping in Loei, Thailand", submitted to the International Journal of Environmental Research.

The study integrates quantitative Human Health Risk Assessment (HHRA) with Extreme Gradient Boosting (XGBoost) machine learning to evaluate arsenic exposure pathways across seven villages surrounding the abandoned Tungkum Ltd. gold mine in Loei Province, Thailand.

File Inventory

1. Research Datasets (Raw Data)

Primary data used for HHRA calculations and Machine Learning training:





Anonymized Survey Dataset.xlsx




Hasan_Supplementary_Data_As_Concen._9_Articles.xlsx (Meta-analysis data for Exposure Point Concentrations)




Supplementary_Data_HRA.xlsx (HHRA calculation matrices)



2. Manuscript Figures & Reproducible Code

Source files and code for figures appearing in the main text:





Figure 1 (Conceptual Framework): Fig 1. pdf, Fig 1. tex (LaTeX source), Fig 1. tif




Figure 2 (Map): Fig 2. pdf, Fig 2. python code, Fig 2. tif




Figure 3 (Risk Contribution): Fig 3. pdf, Fig 3. python code, Fig 3. tif




Figure 4 (SHAP Summary): Fig 4. pdf, Fig 4. python code, Fig 4. tif



3. Supplementary Figures & Reproducible Code

Source files and code for figures appearing in the Electronic Supplementary Materials (ESM):





Figure S1 (PCA Biplot): Fig S1. pdf, Fig S1. python code, Fig S1. tif




Figure S2 (ROC Curves): Fig S2. pdf, Fig S2. python code, Fig S2. tif




Figure S3 (SHAP Dependence): Fig S3. pdf, Fig S3. python code, Fig S3. tif




Figure S4 (Monte Carlo Simulation): Fig S4. pdf, Fig S4. python code, Fig S4. tif




Figure S5 (Downstream Map): Fig S5. pdf, Fig S5. python code, Fig S5. tif



4. Questionnaires & Ethical Documentation

Approved instruments and Institutional Review Board (IRB) certificates:





Appendix_English Questionnaire_Arsenic.pdf




Appendix_Thai Questionnaire_Arsenic.pdf




Appendix_IRB_COA P10137-64_Arsenic.pdf (Certificate of Approval)




Appendix_IRB_AF 05-10_Arsenic.pdf (Approved Consent Forms)



5. Compiled Supplementary Files

Full compiled documents for peer review:





ESM_1.pdf / ESM_1_Supplementary Tables S1-S30.docx (Contains Tables S1–S30)




ESM_2.pdf / ESM_2_Supplementary Figures S1-S5.docx (Contains Figures S1–S5 with captions)





Methodology Note: Data collection was conducted between March and August 20, 2022, under Naresuan University IRB Certificate No. 0384/2021. The "Weight of Evidence" framework synthesizes primary field data with a targeted review of nine site-specific studies.

Keywords: Arsenic; Mining legacy; Health Risk Assessment; XGBoost; Machine Learning; Loei Province; Environmental Justice; Rice Consumption.