Quantitative Analysis of Pb in Soil Using Laser-Induced Breakdown Spectroscopy Based on Signal Enhancement of Conductive Materials

Abstract

Studying efficient and accurate soil heavy-metal detection technology is of great significance to establishing a modern system for monitoring soil pollution, early warning and risk assessment, which contributes to the continuous improvement of soil quality and the assurance of food safety. Laser-induced breakdown spectroscopy (LIBS) is considered to be an emerging and effective tool for heavy-metal detection, compared with traditional detection technologies. Limited by the soil matrix effect, the LIBS signal of target elements for soil heavy-metal detection is prone to interference, thereby compromising the accuracy of quantitative detection. Thus, a series of signal-enhancement methods are investigated. This study aims to explore the effect of conductive materials of NaCl and graphite on the quantitative detection of lead (Pb) in soil using LIBS, seeking to find a reliable signal-enhancement method of LIBS for the determination of soil heavy-metal elements. The impact of the addition amount of NaCl and graphite on spectral intensity and parameters, including the signal-to-background ratio (SBR), signal-to-noise ratio (SNR), and relative standard deviation (RSD), were investigated, and the mechanism of signal enhancement by NaCl and graphite based on the analysis of the three-dimensional profile data of ablation craters and plasma parameters (plasmatemperature and electron density) were explored. Univariate and multivariate quantitative analysis models including partial least-squares regression (PLSR), least-squares support vector machine (LS-SVM), and extreme learning machine (ELM) were developed for the quantitative detection of Pb in soil with the optimal amount of NaCl and graphite, and the performance of the models was further compared. The PLSR model with the optimal amount of graphite obtained the best prediction performance, with an Rp that reached 0.994. In addition, among the three spectral lines of Pb, the univariate model of Pb I 405.78 nm showed the best prediction performance, with an Rp of 0.984 and the lowest LOD of 26.142 mg/kg. The overall results indicated that the LIBS signal-enhancement method based on conductive materials combined with appropriate chemometric methods could be a potential tool for the accurate quantitative detection of Pb in soil and could provide a reference for environmental monitoring.

Keywords: Pb; conductive materials; laser-induced breakdown spectroscopy; soil.

Figure 1

Spectra of soil with different contents of sodium chloride (NaCl). (a) Full spectra of soil samples; (b) Pb I 283.31 nm spectrum of soil samples; (c) Pb I 368.35 nm spectrum of soil samples; (d) Pb I 405.78 nm spectrum of soil samples.

Figure 2

Spectra of soil samples with different contents of graphite. (a) Full spectra of soil samples; (b) Pb I 283.31 nm spectrum of soil samples; (c) Pb I 368.35 nm spectrum of soil samples; (d) Pb I 405.78 nm spectrum of soil samples; (e) C I 405.78 nm spectrum of soil samples; (f) CN 386.03 nm, CN387 nm and CN388.22 nm spectra of soil samples. G means graphite.

Figure 3

Comparison of Pb spectral-line parameters of soils with different contents of NaCl and graphite. (a,d) SBR; (b,e) SNR; (c,f) RSD. G means graphite.

Figure 4

Comparison of the three-dimensional profile of ablation craters of soil samples with different conductive materials. (a) NaCl; (b) graphite; (c) without additives.

Figure 5

(a) Instrument-broadening fitting curve; (b) Collision coefficient versus plasma temperature curve; (c) Comparison of plasma temperature; (d) Comparison of electron density. G means graphite.

Figure 6

Univariate detection models and prediction results of soil Pb content with the optimal addition of NaCl based on the three primary characteristic lines of the Pb element. (a) The univariate model of Pb I 283.31 nm; (b) Prediction results of the univariate model based on Pb I 283.31 nm; (c) The univariate model of Pb I 368.35 nm; (d) Prediction results of the univariate model based on Pb I 368.35 nm; (e) The univariate model of Pb I 405.78 nm; (f) Prediction results of the univariate model based on Pb I 405.78 nm.

Figure 7

Univariate detection models and prediction results of soil Pb content with the optimal addition of graphite based on the three primary characteristic lines of the Pb element. (a) The univariate model of Pb I 283.31 nm; (b) Prediction results of the univariate model based on Pb I 283.31 nm; (c) The univariate model of Pb I 368.35 nm; (d) Prediction results of the univariate model based on Pb I 368.35 nm; (e) The univariate model of Pb I 405.78 nm; (f) Prediction results of the univariate model based on Pb I 405.78 nm.

Figure 8

Univariate detection models and prediction results of soil Pb content without additives based on the three primary characteristic lines of the Pb element. (a) The univariate model of Pb I 283.31 nm; (b) Prediction results of the univariate model based on Pb I 283.31 nm; (c) The univariate model of Pb I 368.35 nm; (d) Prediction results of the univariate model based on Pb I 368.35 nm; (e) The univariate model of Pb I 405.78 nm; (f) Prediction results of the univariate model based on Pb I 405.78 nm.

Figure 9

Schematic diagram of LIBS system.