Exploratory functional data analysis of multivariate densities for the identification of agricultural soil contamination by risk elements

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61388980%3A_____%2F24%3A00583184" target="_blank" >RIV/61388980:_____/24:00583184 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989592:15310/24:73627870

Výsledek na webu

<a href="https://hdl.handle.net/11104/0351178" target="_blank" >https://hdl.handle.net/11104/0351178</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.gexplo.2024.107416" target="_blank" >10.1016/j.gexplo.2024.107416</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Exploratory functional data analysis of multivariate densities for the identification of agricultural soil contamination by risk elements

Popis výsledku v původním jazyce

Geochemical mapping of risk element concentrations in soils is performed in many countries around the world. It results in numerous large datasets of high analytical quality, which can be used to identify soils that violate individual legislative limits for safe food production. However, there is a lack of advanced data mining tools that would be suitable for sensitive exploratory data analysis of big data while respecting the natural variability of soil composition. To distinguish anthropogenic contamination from natural variations, the analysis of the entire data distribution for smaller subareas is key. In this article, we propose a new data mining methodology for geochemical mapping data based on functional data analysis of probability densities in the framework of Bayes spaces after post-stratification of a big dataset to smaller districts. The tools we propose allow us to analyse the entire distribution, going well beyond a superficial detection of extreme concentration anomalies. We illustrate the proposed methodology on a dataset gathered according to the Czech national legislation (1990–2009), whose information content has not yet been fully exploited. Taking into account specific properties of probability density functions and recent results for orthogonal decomposition of multivariate densities enabled us to reveal real contamination patterns that were so far only suspected in Czech agricultural soils. We process the above Czech soil composition dataset for Cu, Pb, and Zn by first compartmentalizing it into spatial units, the so-called districts, and by subsequently clustering these districts according to diagnostic features of their uni- and multivariate distributions at high concentration levels. These clusters were seen to correspond to compartments that show known features of contamination, such as historical metallurgy of non-ferrous metals and iron and steel production. Comparison between compartments, notably neighbouring districts with similar natural factors controlling soil variability, is key to the reliable distinction of diffuse contamination. In this work, we used soil contamination by Cu-bearing pesticides as an example for empirical testing of the proposed data mining approach. In general, there are no natural and justifiable thresholds of risk element concentrations that would be valid for geographical areas with too much natural heterogeneity. Therefore, national (or larger) soil geochemistry datasets cannot be processed as a whole. As we demonstrate in this paper, empirical knowledge and careful tailoring of statistical tools for the characteristic types of soil contamination are essential for unequivocal identification of the anthropogenic component in real datasets.

Název v anglickém jazyce

Exploratory functional data analysis of multivariate densities for the identification of agricultural soil contamination by risk elements

Popis výsledku anglicky

Geochemical mapping of risk element concentrations in soils is performed in many countries around the world. It results in numerous large datasets of high analytical quality, which can be used to identify soils that violate individual legislative limits for safe food production. However, there is a lack of advanced data mining tools that would be suitable for sensitive exploratory data analysis of big data while respecting the natural variability of soil composition. To distinguish anthropogenic contamination from natural variations, the analysis of the entire data distribution for smaller subareas is key. In this article, we propose a new data mining methodology for geochemical mapping data based on functional data analysis of probability densities in the framework of Bayes spaces after post-stratification of a big dataset to smaller districts. The tools we propose allow us to analyse the entire distribution, going well beyond a superficial detection of extreme concentration anomalies. We illustrate the proposed methodology on a dataset gathered according to the Czech national legislation (1990–2009), whose information content has not yet been fully exploited. Taking into account specific properties of probability density functions and recent results for orthogonal decomposition of multivariate densities enabled us to reveal real contamination patterns that were so far only suspected in Czech agricultural soils. We process the above Czech soil composition dataset for Cu, Pb, and Zn by first compartmentalizing it into spatial units, the so-called districts, and by subsequently clustering these districts according to diagnostic features of their uni- and multivariate distributions at high concentration levels. These clusters were seen to correspond to compartments that show known features of contamination, such as historical metallurgy of non-ferrous metals and iron and steel production. Comparison between compartments, notably neighbouring districts with similar natural factors controlling soil variability, is key to the reliable distinction of diffuse contamination. In this work, we used soil contamination by Cu-bearing pesticides as an example for empirical testing of the proposed data mining approach. In general, there are no natural and justifiable thresholds of risk element concentrations that would be valid for geographical areas with too much natural heterogeneity. Therefore, national (or larger) soil geochemistry datasets cannot be processed as a whole. As we demonstrate in this paper, empirical knowledge and careful tailoring of statistical tools for the characteristic types of soil contamination are essential for unequivocal identification of the anthropogenic component in real datasets.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10511 - Environmental sciences (social aspects to be 5.7)

Projekt

<a href="/cs/project/GF22-15684L" target="_blank" >GF22-15684L: Zobecněná relativní data a robustnost v Bayesových prostorech</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of Geochemical Exploration

ISSN

0375-6742

e-ISSN

1879-1689

Svazek periodika

259

Číslo periodika v rámci svazku

APR

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

17

Strana od-do

107416

Kód UT WoS článku

001187862100001

EID výsledku v databázi Scopus

2-s2.0-85184990454