SAMPLING OPTIMIZATION FOR MONITORING CONTAMINATED SOILED

V. Pelillo, L. Piper, A. Lay-Ekuakille, G. Griffo, A. Lanzolla, G. Andria

Abstract

Atmospheric deposition on soil is a matter of continuous study. Deposited pollution can be determined in various ways. But the first step is sampling; in case of sample destruction or lost or unvailability, it could be necessary to overcome the issue by a specific prediction. The paper presents prediction technique of dioxin deposition on ground using a geostatistic method called "Kriging". Kriging code is a regression method used in the context of the space analysis (geostatistics) which allows to interpolate a quantity in the space, minimizing the mean square error. In the statistics context it is better well known as gaussian process. Knowing the value of a quantity in a few points in the space (for example the analyte concentrations taken in every town of an area), we can determine the quantity value in other points for which measurements do not exist. The performed algorithm displays better results than many other ones because of geostatistic properties adopted in the procedure. Analytical data are taken from a campaign carried out in Lecce province.

Keywords

soil contamination, sampling modelling, statistical parameters, uncertainty, geostatistics

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DOI

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IMEKO TC

TC19 - Environmental Measurements

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