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Modeling of Chromium, Copper, Zinc, Arsenic and Lead Using Portable X-ray Fluorescence Spectrometer Based on Discrete Wavelet Transform

Author

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  • Fang Li

    (Beijing Research Center for Agricultural Standards and Testing, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
    Beijing Municipal Key Laboratory of Agriculture Environment Monitoring, Beijing 100097, China)

  • Anxiang Lu

    (Beijing Research Center for Agricultural Standards and Testing, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
    Beijing Municipal Key Laboratory of Agriculture Environment Monitoring, Beijing 100097, China
    Risk Assessment Lab for Agro-products (Beijing), Ministry of Agriculture, Beijing 100097, China)

  • Jihua Wang

    (Beijing Research Center for Agricultural Standards and Testing, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
    Beijing Municipal Key Laboratory of Agriculture Environment Monitoring, Beijing 100097, China
    Risk Assessment Lab for Agro-products (Beijing), Ministry of Agriculture, Beijing 100097, China)

Abstract

A modeling method based on discrete wavelet transform (DWT) was introduced to analyze the concentration of chromium, copper, zinc, arsenic and lead in soil with a portable X-ray fluorescence (XRF) spectrometer. A total of 111 soil samples were collected and observed. Denoising and baseline correction were performed on each spectrum before modeling. The optimum conditions for pre-processing were denoising with Coiflet 3 on the 3rd level and baseline correction with Coiflet 3 on the 9th level. Calibration curves were established for the five heavy metals (HMs). The detection limits were compared before and after the application of DWT, the qualitative detection limits and the quantitative detection limits were calculated to be three and ten times as high as the standard deviation with silicon dioxide (blank), respectively. The results showed that the detection limits of the instrument using DWT were lower, and that they were below national soil standards; the determination coefficients ( R 2 ) based on DWT-processed spectra were higher, and ranged from 0.990 to 0.996, indicating a high degree of linearity between the contents of the HMs in soil and the XRF spectral characteristic peak intensity with the instrument measurement.

Suggested Citation

  • Fang Li & Anxiang Lu & Jihua Wang, 2017. "Modeling of Chromium, Copper, Zinc, Arsenic and Lead Using Portable X-ray Fluorescence Spectrometer Based on Discrete Wavelet Transform," IJERPH, MDPI, vol. 14(10), pages 1-12, September.
    • Handle: RePEc:gam:jijerp:v:14:y:2017:i:10:p:1163-:d:113829
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    References listed on IDEAS

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    1. Sung-Min Kim & Yosoon Choi, 2017. "Assessing Statistically Significant Heavy-Metal Concentrations in Abandoned Mine Areas via Hot Spot Analysis of Portable XRF Data," IJERPH, MDPI, vol. 14(6), pages 1-16, June.
    2. Yang, Zhang & Ce, Li & Lian, Li, 2017. "Electricity price forecasting by a hybrid model, combining wavelet transform, ARMA and kernel-based extreme learning machine methods," Applied Energy, Elsevier, vol. 190(C), pages 291-305.
    3. Jangwon Suh & Hyeongyu Lee & Yosoon Choi, 2016. "A Rapid, Accurate, and Efficient Method to Map Heavy Metal-Contaminated Soils of Abandoned Mine Sites Using Converted Portable XRF Data and GIS," IJERPH, MDPI, vol. 13(12), pages 1-18, December.
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    Cited by:

    1. Rafael López-Núñez & Fátima Ajmal-Poley & José A. González-Pérez & Miguel Angel Bello-López & Pilar Burgos-Doménech, 2019. "Quick Analysis of Organic Amendments via Portable X-ray Fluorescence Spectrometry," IJERPH, MDPI, vol. 16(22), pages 1-18, November.
    2. Fang Li & Jihua Wang & Li Xu & Songxue Wang & Minghui Zhou & Jingwei Yin & Anxiang Lu, 2018. "Rapid Screening of Cadmium in Rice and Identification of Geographical Origins by Spectral Method," IJERPH, MDPI, vol. 15(2), pages 1-12, February.

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