Using an ensemble model coupled with portable X-ray fluorescence and visible near-infrared spectroscopy to explore the viability of mapping and estimating arsenic in an agricultural soil

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00027073%3A_____%2F22%3AN0000114" target="_blank" >RIV/00027073:_____/22:N0000114 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/60460709:41210/22:91009 RIV/60460709:41330/22:91009

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/abs/pii/S0048969721068819" target="_blank" >https://www.sciencedirect.com/science/article/abs/pii/S0048969721068819</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.scitotenv.2021.151805" target="_blank" >10.1016/j.scitotenv.2021.151805</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Using an ensemble model coupled with portable X-ray fluorescence and visible near-infrared spectroscopy to explore the viability of mapping and estimating arsenic in an agricultural soil

Popis výsledku v původním jazyce

Increasing concentrations of potentially toxic elements (PTE) in agricultural soils remain a major source of public concern. Monitoring PTEs in an agricultural field with no history of contaminants necessitate adequate analysis utilizing a robust model to accurately uncover hidden PTEs. Detecting and mapping the distribution of soil properties using portable X-ray fluorescence (pXRF) and proximal sensing techniques is not only rapid, but also relatively inexpensive. In this study, an ensemble model, consisting of partial least square regression (PLSR), support vector machine (SVM), random forest (RF) and cubist, was used for the prediction and mapping of soil As content in an agricultural field with no history of pollution. The datasets were collected using pXRF and field spectroscopy techniques. The main goal was to compare the ensemble model to each of the calibration techniques in terms of prediction accuracy of As content in such a field. Other components [eg., soil organic carbon (SOC), Mn, S, soil pH, Fe] that are known to influence As levels in the soil were also retrieved to assess their correlation with soil As. The models were evaluated using the root mean squared error (RMSECV), the coefficient of determination (R-CV(2)) and the ratio of performance to interquartile range (RPIQ). In terms of prediction accuracy, the ensemble model outperformed each of the individual techniques (R-CV(2) = 0.80/0.75) and obtained the least error margin (RMSECV = 1.91/2.16). Overall, all the predictive techniques were able to detect both low and high estimated values of soil As within the study field, but with the ensemble model resembling the measurements better. The ensemble model, a promising tool as demonstrated by the current study, is highly recommended to be included in future studies for more accurate estimation of As and other FTEs in other agricultural fields.

Název v anglickém jazyce

Using an ensemble model coupled with portable X-ray fluorescence and visible near-infrared spectroscopy to explore the viability of mapping and estimating arsenic in an agricultural soil

Popis výsledku anglicky

Increasing concentrations of potentially toxic elements (PTE) in agricultural soils remain a major source of public concern. Monitoring PTEs in an agricultural field with no history of contaminants necessitate adequate analysis utilizing a robust model to accurately uncover hidden PTEs. Detecting and mapping the distribution of soil properties using portable X-ray fluorescence (pXRF) and proximal sensing techniques is not only rapid, but also relatively inexpensive. In this study, an ensemble model, consisting of partial least square regression (PLSR), support vector machine (SVM), random forest (RF) and cubist, was used for the prediction and mapping of soil As content in an agricultural field with no history of pollution. The datasets were collected using pXRF and field spectroscopy techniques. The main goal was to compare the ensemble model to each of the calibration techniques in terms of prediction accuracy of As content in such a field. Other components [eg., soil organic carbon (SOC), Mn, S, soil pH, Fe] that are known to influence As levels in the soil were also retrieved to assess their correlation with soil As. The models were evaluated using the root mean squared error (RMSECV), the coefficient of determination (R-CV(2)) and the ratio of performance to interquartile range (RPIQ). In terms of prediction accuracy, the ensemble model outperformed each of the individual techniques (R-CV(2) = 0.80/0.75) and obtained the least error margin (RMSECV = 1.91/2.16). Overall, all the predictive techniques were able to detect both low and high estimated values of soil As within the study field, but with the ensemble model resembling the measurements better. The ensemble model, a promising tool as demonstrated by the current study, is highly recommended to be included in future studies for more accurate estimation of As and other FTEs in other agricultural fields.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

40104 - Soil science

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název periodika

Science of The Total Environment

ISSN

0048-9697

e-ISSN

1879-1026

Svazek periodika

818

Číslo periodika v rámci svazku

20 April 2022

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

12

Strana od-do

151805

Kód UT WoS článku

000804813500005

EID výsledku v databázi Scopus

2-s2.0-85119897320