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

Dongyun Xu; Songchao Chen; Hanyi Xu; Nan Wang; Yin Zhou; Zhou Shi

doi:10.1016/j.envpol.2020.114649

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

Environ Pollut. 2020 Aug;263(Pt A):114649. doi: 10.1016/j.envpol.2020.114649. Epub 2020 Apr 24.

Authors

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

Affiliations

¹ Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China.
² INRAE, Unité InfoSol, 45075, Orléans, France.
³ Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China; Institute of Land Science and Property, School of Public Affairs, Zhejiang University, Hangzhou, 310058, China.
⁴ Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China; Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture, Hangzhou, 310058, China. Electronic address: shizhou@zju.edu.cn.

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

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.

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

MeSH terms

Environmental Monitoring
Humans
Metals, Heavy* / analysis
Soil
Soil Pollutants* / analysis

Substances

Metals, Heavy
Soil
Soil Pollutants