Key Laboratory of Smart City and Environmental of Higher Education Institute, College of Resources and Environment Sciences, Xinjiang University, Urumqi 830046, China.
Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China.
Int J Environ Res Public Health. 2018 Oct 28;15(11):2390. doi: 10.3390/ijerph15112390.
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.
传统的水中重金属检测技术耗时长且困难，因此不适合用于大量样本的定量检测。激光诱导击穿光谱（LIBS）可以同时快速远程识别多态（如固、液、气）物质。本研究从艾比湖流域采集水样，采用 LIBS 提取水样中铁（Fe）和铜（Cu）的特征峰。LIBS 对大多数定量分析很少对自然环境中的重金属含量进行建模和估计，无法快速测定野外水样中的重金属。本研究创新性地利用水样中 Fe 和 Cu 的含量及其 LIBS 光谱曲线特征，通过回归建模分析对其进行估算，并利用 LIBS 技术和机器学习算法对未知水体的含量进行估计，从而提高了检测率。结果表明：（1）艾比湖流域的 Cu 含量普遍高于 Fe 含量，在流域内最高的 Fe 和 Cu 含量位于艾比湖流域，最低的位于精河。（2）从每个样本的 LIBS 曲线中发现了多个峰，最终根据 Fe 和 Cu 特征线的强度、跃迁概率和高信噪比（S/B）确定了 Fe 和 Cu 的特征分析线，它们的波长分别为 396.3nm 和 324.7nm。（3）Fe 含量反向传播（BP）网络估计模型的相对百分偏差（RPD）为 0.23，预测能力较差，因此无法准确预测样品的 Fe 含量。在 Cu 的 BP 网络估计模型中，决定系数（R²）为 0.8，均方根误差（RMSE）为 0.1，RPD 为 1.79。这表明 Cu 含量的 BP 估计模型具有较高的准确性和较强的预测能力，能够准确预测样本中的 Cu 含量。综上所述，基于 LIBS 的估算提高了水中 Fe 和 Cu 含量检测的准确性和效率，为水中 Fe 和 Cu 含量的准确估算提供了新的思路和方法。
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