Relating soil geochemical properties to arsenic bioaccessibility through hierarchical modeling

Area of Science:

• Environmental Science
• Geochemistry
• Soil Science

Background:

• Understanding soil properties and arsenic (As) bioaccessibility is crucial for environmental risk assessment.
• Previous regression models for As bioaccessibility prediction had limited applicability due to narrow soil type and condition ranges.
• Variability in As bioaccessibility across different geographic locations and contaminant sources necessitates advanced modeling approaches.

Purpose of the Study:

• To develop and evaluate hierarchical models for predicting arsenic (As) bioaccessibility in a diverse set of 139 soils.
• To improve the estimation of As bioaccessibility by considering variability across soil types and contaminant sources.
• To identify significant soil properties influencing As bioaccessibility.

Main Methods:

• Development of hierarchical models to predict As bioaccessibility on both mass fraction (mg/kg) and percentage (%) bases.
• Application of models to 139 soils with varying properties and arsenic contamination sources.
• Statistical analysis to identify significant soil elements affecting As bioaccessibility.

Main Results:

• The hierarchical modeling approach significantly improved the estimation of As bioaccessibility compared to previous methods.
• A broader range of soil elements were identified as statistically significant explanatory variables.
• Key significant elements included total soil Fe, P, Ca, Co, V, As, Cd, Cu, Ni, Zn, and Mg, depending on the model basis.

Conclusions:

• Hierarchical models offer a robust tool for exploring soil property-As bioaccessibility relationships across diverse environmental conditions.
• This approach enhances the understanding of arsenic behavior in soils and guides future mechanistic research.
• The findings provide a novel method for predicting As bioaccessibility and assessing environmental risks.