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Integrated Field Lysimetry and Porewater Sampling for Evaluation of Chemical Mobility in Soils and Established Vegetation

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Published on: July 4, 2014

Exploring environmental risk in soils: Leveraging open data for non-sampling assessment?

Silvia Aparisi-Navarro¹, Maria Moncho-Santonja¹, Beatriz Defez¹

¹Centro de Investigación en Tecnologias Gráficas. Universitat Politècnica de Valencia, Valencia, Spain.

|January 15, 2025

Summary

This summary is machine-generated.

Open data raster maps are not precise enough for assessing heavy metal (HM) soil contamination. Traditional soil sampling and lab analysis remain essential for accurate environmental risk assessment and decision-making.

Keywords:

Comparison of contamination indices Geostatistical raster maps Heavy metal contamination assessment Heavy metal pollution indices Soil sample data vs raster data

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Area of Science:

Environmental Science
Geochemistry
Geostatistics

Background:

Soil contamination by heavy metals (HM) poses significant environmental risks.
Traditional HM assessment methods are accurate but resource-intensive.
Exploring cost-effective alternatives like GIS and open data is crucial.

Purpose of the Study:

To evaluate the efficacy of geostatistical analysis with GIS and open data for HM contamination assessment.
To compare HM contamination data derived from field samples versus open data raster maps.
To determine if open data can replace traditional sampling for precise HM risk evaluation.

Main Methods:

Analysis of nine HM concentrations in 498 soil samples from Portuguese mining areas.
Comparison with HM data extracted from Lucas TOPSOIL 1 km raster maps.
Calculation and comparison of multiple contamination indices (Cf, mCd, Igeo, Pn, PERI, PLI) for both datasets.
Evaluation of classification accuracy using confusion matrices and concordance analysis.

Main Results:

Field data revealed very high contamination levels for Arsenic (As) and Antimony (Sb) in significant portions of soil samples.
Indices like Pollution Load Index (PLI) and Nemerow Pollution Index (Pn) indicated serious pollution in a majority of samples.
Lucas TOPSOIL data showed no high contamination levels and exhibited substantial misclassification rates (up to 97.55%) compared to field data.

Conclusions:

Open data raster maps provide rapid overviews but lack the necessary precision for reliable HM contamination assessment.
Significant discrepancies and high misclassification rates demonstrate the limitations of relying solely on open data for environmental decisions.
Traditional soil sampling and laboratory analysis are indispensable for accurate heavy metal risk assessment and effective environmental management.