Detection of Pb Content in Water Using Graphite Enrichment Combined with Laser-Induced Breakdown Spectroscopy

Highlights Using graphite to enrich Pb elements in water provides a new idea for heavy metal element detection in agricultural water. Quantitative analysis of Pb using calibration curve methods and PLSR combined with LIBS technique has better applicability compared to single method detection results. Quantitative analysis of the calibration curve based on the fitting of the Lorentz function was most effective. The error in the quantitative analysis of all models was around 5%. Abstract. Industrial and human production cause heavy metal pollution of agricultural water, among which lead (Pb) contaminants have been considered to be one of the important influencing elements. Pb contaminates crops through water pollution, and subsequently enters the human body, leading to neurological and respiratory ailments. However, Pb in water typically present in low concentrations, which affects its detection accuracy. Therefore, ensuring precise detection of Pb in water is of paramount importance. In this study, we used graphite enrichment combined with laser-induced breakdown spectroscopy (LIBS) technique to achieve the quantitative analysis of heavy metals Pb in water. Graphite was used to enrich (from liquid to solid) Pb samples with 10 concentration gradients and the corresponding LIBS data were collected by LIBS system. The LIBS data were compared with the National Institute of Standards and Technology (NIST) atomic database, and Pb â 405.78 nm (â represents the atomic spectral line) was selected as the characteristic spectral line of Pb. The intensity of Pb characteristic spectral lines was found to increase with the increase of Pb concentration. Then, the calibration curves methods (spectral intensity, spectral peak area, and Lorentz function) were used to develop a quantitative analysis model for different concentrations of Pb elements, the correlation coefficients (R) of spectral intensity, spectral peak area, and Lorentz function were 0.975, 0.980, and 0.983, respectively. For comparison of this result, partial least squares regression (PLSR) was used for the quantitative analysis of Pb, and the correlation coefficient of calibration (R_C) was 0.989 and the correlation coefficient of prediction (R_P) was 0.987. The results showed that the calibration curves and PLSR methods both could achieve accurate quantitative analysis of heavy mental Pb in water. This study could provide a new idea for the detection of heavy metals in environmental and irrigation water used in agriculture, and offer data support and theoretical basis for the development of detectors to detect heavy metal elements in agriculture.