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

Result code in 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>

Alternative codes found

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

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

40104 - Soil science

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2022

Confidentiality

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

Name of the periodical

Science of The Total Environment

ISSN

0048-9697

e-ISSN

1879-1026

Volume of the periodical

818

Issue of the periodical within the volume

20 April 2022

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

12

Pages from-to

151805

UT code for WoS article

000804813500005

EID of the result in the Scopus database

2-s2.0-85119897320