Optimising sampling strategies for emergency response: Soil sampling.

Area of Science:

• Environmental Science
• Radiochemistry
• Soil Science

Background:

• Optimizing soil sampling strategies is crucial for accurate radionuclide assessment in contaminated areas.
• Understanding factors like sample number, size, depth, and spatial resolution is key to efficient sampling plans.

Purpose of the Study:

• To develop and validate a novel approach for optimizing soil sampling strategies in radionuclide-affected areas.
• To examine factors influencing soil sampling efficiency for radionuclide assessment.

Main Methods:

• Conducted field studies across diverse landscapes (farmlands, forests) with varying Cesium-137 (137Cs) contamination levels.
• Collected 25 to 256 soil samples per unit, varying sample increments to determine standard deviation of log-transformed contamination density.
• Analyzed the influence of contamination density, deposition type, and landscape features on standard deviation.

Main Results:

• Standard deviation of log-transformed 137Cs soil contamination density was independent of mean contamination, deposition type, and landscape features.
• Mean standard deviation values were 0.44 ± 0.15 and 0.30 ± 0.10 for sampling areas of 0.001 m² and 0.005 m², respectively.
• Soil 137Cs concentrations were statistically independent beyond 1-meter sampling distances.

Conclusions:

• A simple method was developed to assess minimum sample sizes for estimating radionuclide soil contamination (median or geometric mean) with user-defined uncertainty.
• The approach is applicable for estimating uncertainty in composite soil samples.
• The methodology has been integrated into Ukrainian national soil quality assessment requirements.