Literature DB >> 33618476

Data fusion for the measurement of potentially toxic elements in soil using portable spectrometers.

Dongyun Xu1, Songchao Chen2, Hanyi Xu1, Nan Wang1, Yin Zhou3, Zhou Shi4.   

Abstract

Soil contamination posed by potentially toxic elements is becoming more serious under continuously development of industrialization and the abuse of fertilizers and pesticides. The investigation of soil potentially toxic elements is therefore urgently needed to ensure human and other organisms' health. In this study, we investigated the feasibility of the separate and combined use of portable X-ray fluorescence (pXRF) and visible near-infrared reflectance (vis-NIR) sensors for measuring eight potentially toxic elements in soil. Low-level fusion was achieved by the direct combination of the pXRF and vis-NIR spectra; middle-level fusion was achieved by the combination of selected bands of the pXRF and vis-NIR spectra using the Boruta feature selection algorithm; and high-level fusion was conducted by outer-product analysis (OPA) and Granger-Ramanathan averaging (GRA). The estimation accuracy for the eight considered elements were in the following order: Zn > Cu > Ni > Cr > As > Cd > Pb > Hg. The measurement for Cu and Zn could be achieved by pXRF spectra alone with Lin's concordance correlation coefficient (LCCC) values of 0.96 and 0.98, and ratio of performance to interquartile distance (RPIQ) values of 2.36 and 2.69, respectively. The measurement of Ni had the highest model performance for high-level fusion GRA with LCCC of 0.89 and RPIQ of 3.42. The measurements of Cr using middle- and high-level fusion were similar, with LCCC of 0.86 and RPIQ of 2.97. The best estimation accuracy for As, Cd, and Pb were obtained by high-level fusion using OPA, with LCCC >0.72 and RPIQ >1.2. However, Hg measurement by these techniques failed, having an unacceptable performance of LCCC <0.20 and RPIQ <0.75. These results confirm the effectiveness of using portable spectrometers to determine the contents of several potentially toxic elements in soils.
Copyright © 2020 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Granger–Ramanathan averaging; Machine learning; Outer-product analysis; Portable X-ray fluorescence; Proximal soil sensing; vis‒NIR

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Year:  2020        PMID: 33618476     DOI: 10.1016/j.envpol.2020.114649

Source DB:  PubMed          Journal:  Environ Pollut        ISSN: 0269-7491            Impact factor:   8.071


  2 in total

1.  Assessment of the Effect of Soil Sample Preparation, Water Content and Excitation Time on Proximal X-ray Fluorescence Sensing.

Authors:  Shuo Li; Jiali Shen; Thomas F A Bishop; Raphael A Viscarra Rossel
Journal:  Sensors (Basel)       Date:  2022-06-17       Impact factor: 3.847

2.  vis-NIR and XRF Data Fusion and Feature Selection to Estimate Potentially Toxic Elements in Soil.

Authors:  Asa Gholizadeh; João A Coblinski; Mohammadmehdi Saberioon; Eyal Ben-Dor; Ondřej Drábek; José A M Demattê; Luboš Borůvka; Karel Němeček; Sabine Chabrillat; Julie Dajčl
Journal:  Sensors (Basel)       Date:  2021-03-30       Impact factor: 3.576

  2 in total

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