Health risk assessment of soil trace elements using the Sequential Gaussian Simulation approach

Area of Science:

• Environmental Science
• Geostatistics
• Public Health

Background:

• Heavy metal contamination poses significant health risks.
• Accurate spatial distribution of health risks is crucial for targeted interventions.
• Trace elements like V, Cr, Mn, Co, Ni, Cu, Zn, As, and Pb are key environmental contaminants.

Purpose of the Study:

• To evaluate the performance of Sequential Gaussian Simulation (SGS) for mapping local health risk distributions.
• To compare the accuracy of SGS with the conventional Ordinary Kriging (OK) method.
• To assess non-carcinogenic and carcinogenic health risks associated with trace elements.

Main Methods:

• Sequential Gaussian Simulation (SGS) was employed to model health risk distributions.
• Ordinary Kriging (OK) was used as a benchmark for comparison.
• Cross-validation was performed to assess the performance of both geostatistical methods.
• Health risk levels were calculated for 250 × 250 m² pixel sizes.

Main Results:

• SGS demonstrated superior performance in estimating both non-carcinogenic and carcinogenic health risks compared to OK.
• SGS yielded higher correlation coefficients (ρc) and lower Root Mean Square Errors (RMSE) and Mean Absolute Errors (MAE) for both risk types and age groups.
• For non-carcinogenic risks in children, SGS achieved ρc=0.57, RMSE=0.45, MAE=0.33, outperforming OK (ρc=0.23, RMSE=0.57, MAE=0.43).

Conclusions:

• Sequential Gaussian Simulation (SGS) is a more accurate geostatistical approach for determining local health risk distributions from trace elements.
• The findings highlight the importance of advanced geostatistical methods in environmental health risk assessment.
• Accurate mapping of heavy metal health risks can inform public health policies and environmental management strategies.