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Comparison of common spatial interpolation methods for analyzing pollutant spatial distributions at contaminated
Pengwei Qiao1, Peizhong Li1, Yanjun Cheng1
1Beijing Key Laboratory of Remediation of Industrial Pollution Sites, Environmental Protection Research Institute of Light Industry, Beijing, 100089, China.
Environmental Geochemistry and Health
|May 31, 2019
Summary
Choosing the right soil pollutant interpolation method is key for effective site remediation. This study found specific methods like inverse distance weighting (IDW) and radial basis function (RBF) are best suited for different soil layers and pollutant distributions.
Area of Science:
- Environmental Science
- Geospatial Analysis
- Soil Science
Background:
- Accurate soil pollutant prediction is vital for contaminated site remediation.
- Determining the best interpolation method for complex soil pollutant distributions remains challenging.
- Pollutant spatial variability and characteristics influence prediction accuracy.
Purpose of the Study:
- To compare the prediction accuracies of three interpolation methods for benzo[b]fluoranthene (BbF) in four soil layers.
- To identify the most suitable interpolation methods based on soil layer and pollutant distribution characteristics.
- To provide guidance for selecting appropriate interpolation models in soil remediation.
Main Methods:
- Evaluated inverse distance weighting (IDW), radial basis function (RBF), and ordinary kriging (OK).
- Assessed prediction accuracy across four distinct soil layers.
- Analyzed the impact of parameter values and pollutant distribution patterns on interpolation performance.
Main Results:
- IDW1 was optimal for the first soil layer; RBF-IMQ excelled in the second, third, and fourth layers.
- IDW showed higher errors at intersections of high/low values, while RBF accuracy increased with consistent pollutant trends.
- Interpolation uncertainty correlated positively with contaminant concentration and local spatial variability.
Conclusions:
- The selection of interpolation models should consider the model's principles and the specific spatial distribution of soil pollutants.
- Different interpolation methods (IDW, RBF, OK) have varying suitability depending on soil layer and pollutant characteristics.
- Understanding interpolation uncertainty is crucial for reliable soil remediation strategies.
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