Estimation of the Fe and Cu Contents of the Surface Water in the Ebinur Lake Basin Based on LIBS and a Machine Learning Algorithm.

Xianlong Zhang,Hsiang-Te Kung,Ayinuer Yushanjiang,Ping Shi,Fei Zhang,Shidan Zhu

doi:10.3390/ijerph15112390

Abstract

Traditional technology for detecting heavy metals in water is time consuming and difficult and thus is not suitable for quantitative detection of large samples. Laser-induced breakdown spectroscopy (LIBS) can identify multi-state (such as solid, liquid, and gas) substances simultaneously, rapidly and remotely. In this study, water samples were collected from the Ebinur Lake Basin. The water samples were subjected to LIBS to extract the characteristic peaks of iron (Fe) and copper (Cu). Most of the quantitative analysis of LIBS rarely models and estimates the heavy metal contents in natural environments and cannot quickly determine the heavy metals in field water samples. This study creatively uses the Fe and Cu contents in water samples and the characteristics of their spectral curves in LIBS for regression modelling analysis and estimates their contents in an unknown water body by using LIBS technology and a machine learning algorithm, thus improving the detection rate. The results are as follows: (1) The Cu content of the Ebinur Lake Basin is generally higher than the Fe content, the highest Fe and Cu contents found within the basin are in the Ebinur Lake watershed, and the lowest are in the Jing River. (2) A number of peaks from each sample were found of the LIBS curve. The characteristic analysis lines of Fe and Cu were finally determined according to the intensities of the Fe and Cu characteristic lines, transition probabilities and high signal-to-background ratio (S/B). Their wavelengths were 396.3 and 324.7 nm, respectively. (3) The relative percent deviation (RPD) of the Fe content back-propagation (BP) network estimation model is 0.23, and the prediction ability is poor, so it is impossible to accurately predict the Fe content of samples. In the estimation model of BP network of Cu, the coefficient of determination (R2) is 0.8, the root mean squared error (RMSE) is 0.1, and the RPD is 1.79. This result indicates that the BP estimation model of Cu content has good accuracy and strong predictive ability and can accurately predict the Cu content in a sample. In summary, estimation based on LIBS improved the accuracy and efficiency of Fe and Cu content detection in water and provided new ideas and methods for the accurate estimation of Fe and Cu contents in water.

Highlights

Arid and semi-arid areas together constitute almost one-third of the world’s land area and half of China’s
It can be found that the content of Cu in the Ebinur Lake Basin is higher than that of Fe in general
This research creatively uses the contents of Fe and Cu in water samples, which are measured by an atomic absorption spectrophotometer, and the characteristic curves of Fe and Cu in Laser-induced breakdown spectroscopy (LIBS) for regression modelling are analysed to give Fe and Cu content estimation models

Summary

Introduction

Arid and semi-arid areas together constitute almost one-third of the world’s land area and half of China’s. Various techniques have been established to detect heavy metal ions (HMIs), including inductively coupled plasma mass spectrometry (ICP-MS) [6], inductively coupled plasma optical emission spectrometry (ICP-OES) [7], inductively coupled plasma atomic emission spectrometry (ICP-AES) [8], flameless atomic absorption spectrophotometry (FAAS) [9] and atomic absorption spectroscopy (AAS) [10]. These are all highly sensitive and selective techniques, they require relatively expensive instruments, the application of complex operational procedures, and long detection times. How to scientifically detect heavy metal content has become a research hotspot for relevant government departments, academics and environmentalists [11]

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Results

Discussion

Conclusion