Comparison of multivariate methods for arsenic estimation and mapping in floodplain soil via portable X-ray fluorescence spectroscopy

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41210%2F21%3A82486" target="_blank" >RIV/60460709:41210/21:82486 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0016706120325477?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0016706120325477?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.geoderma.2020.114792" target="_blank" >10.1016/j.geoderma.2020.114792</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Comparison of multivariate methods for arsenic estimation and mapping in floodplain soil via portable X-ray fluorescence spectroscopy

Popis výsledku v původním jazyce

Rapid, inexpensive, and equally reliable estimates of potentially toxic elements are a necessity, portable X ray fluorescence (pXRF) spectrometry is a handy tool to help achieve such. The current study sought to compare multiple linear regression with three regularized regression models Ridge, Lasso, and ElasticNet ENET for the estimation of total arsenic As using pXRF datasets in polluted temperate floodplain soils of Příbram, Czech Republic. A total of 158 (0-25 cm) floodplain surface soil samples were collected from a specific site in Příbram. Models were evaluated separately and compared based on mean absolute error (MAE), root mean squared error (RMSE) and the coefficient of determination (R2). All four models were able to predict As with good accuracy (MAE and RMSE values of 0,02 and 0,03, respectively, and R2 values ranging from 0,94 to 0,95). As measured via pXRF as well as predicted via the four regression models produced similar spatial variability as shown by the standard laboratory-measur

Název v anglickém jazyce

Comparison of multivariate methods for arsenic estimation and mapping in floodplain soil via portable X-ray fluorescence spectroscopy

Popis výsledku anglicky

Rapid, inexpensive, and equally reliable estimates of potentially toxic elements are a necessity, portable X ray fluorescence (pXRF) spectrometry is a handy tool to help achieve such. The current study sought to compare multiple linear regression with three regularized regression models Ridge, Lasso, and ElasticNet ENET for the estimation of total arsenic As using pXRF datasets in polluted temperate floodplain soils of Příbram, Czech Republic. A total of 158 (0-25 cm) floodplain surface soil samples were collected from a specific site in Příbram. Models were evaluated separately and compared based on mean absolute error (MAE), root mean squared error (RMSE) and the coefficient of determination (R2). All four models were able to predict As with good accuracy (MAE and RMSE values of 0,02 and 0,03, respectively, and R2 values ranging from 0,94 to 0,95). As measured via pXRF as well as predicted via the four regression models produced similar spatial variability as shown by the standard laboratory-measur

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

40104 - Soil science

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název periodika

Geoderma

ISSN

0016-7061

e-ISSN

1872-6259

Svazek periodika

384

Číslo periodika v rámci svazku

feb

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

13

Strana od-do

0-0

Kód UT WoS článku

000594244300004

EID výsledku v databázi Scopus

2-s2.0-85096657235