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Analyzing the spatial distribution of PCB concentrations in soils using below-quantification limit data.

Thomas G Orton¹, Nicolas P A Saby, Dominique Arrouays

¹INRA, US 1106 InfoSol, Orleans, France. Thomas.Orton@derm.qld.gov.au

Journal of Environmental Quality

|November 7, 2012

Summary

This summary is machine-generated.

This study introduces a geostatistical method for mapping soil polychlorinated biphenyls (PCBs) using censored data. This approach accurately represents uncertainty from below-quantification limit measurements, improving spatial distribution predictions.

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Area of Science:

Environmental Science
Geostatistics
Analytical Chemistry

Background:

Polychlorinated biphenyls (PCBs) are persistent environmental pollutants with significant soil accumulation.
Accurate spatial mapping of PCB contamination is crucial for environmental risk assessment.
Handling data below quantification limits (QL) presents a challenge in environmental monitoring.

Purpose of the Study:

To develop and demonstrate a geostatistical analysis framework for mapping PCB spatial distribution using censored data.
To accurately represent and account for uncertainty arising from measurements below the quantification limit.
To compare the performance of censored data analysis with traditional data imputation methods.

Main Methods:

Geostatistical modeling incorporating censored data for below-QL measurements.
Monte Carlo maximum likelihood estimation for geostatistical model parameterization.
Akaike Information Criterion (AIC) for explanatory variable selection.
Bayesian approach for spatial interpolation and prediction mapping.
Cross-validation for model performance assessment.

Main Results:

A geostatistical model was successfully implemented using censored data to analyze PCB-187 concentrations.
The Akaike Information Criterion (AIC) effectively identified key explanatory variables for spatial distribution.
Predictions generated using the censored data approach showed improved accuracy compared to imputed data methods.
Maps predicting the probability of exceeding contamination thresholds were produced.

Conclusions:

Geostatistical analysis using censored data provides a robust method for mapping environmental contaminants like PCBs.
This approach offers more reliable predictions and better uncertainty representation than methods relying on data imputation.
The findings support the use of censored data geostatistics for accurate environmental contamination assessment and risk management.