Application of Energy Dispersive X-ray Fluorescence Spectroscopy in Analysis of Heavy Metals in Soil: A Review
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摘要:
伴随着城市化和工业化的进程，重金属通过各种途径进入生态环境并在土壤中大量富集，对土壤环境健康造成潜在风险。近年来随着能量色散X射线荧光光谱技术(ED-XRF)的发展，仪器的检出限明显降低，并能适用于土壤中各类重金属的检测，正逐渐成为测定土壤环境中重金属浓度的有效设备。然而，土壤基质的复杂性和仪器自身的限制会导致利用ED-XRF在测定目标重金属时存在准确度和精密度较低等问题。譬如土壤样品的类型及粒径、仪器和环境中的噪声均会对检测造成一定影响，同时在定量分析模型方法的建立上也存在一定难度，仍不能很好地应用于实验室等数据质量要求较高的环境。本文总结了XRF在土壤重金属检测领域的应用与研究进展，探究了不同土壤样品状态及检测条件对ED-XRF仪器检测精确性的影响，梳理了ED-XRF光谱的主要预处理方法以及定量分析模型的建立过程，分析了ED-XRF在评估土壤重金属有效性上的应用潜力。当前，应用于ED-XRF检测的土壤样品制备程序已经相对成熟，在操作手段上如何减少土壤基体效应的影响，学者们的意见相对统一，即尽量使用干样、低粒径或压片土壤样品，而对检测数据的处理和分析还存在一定优化的空间。因此，目前主流的关注领域是结合不同算法的优点，进行ED-XRF光谱的预处理分析及建立定量分析模型来提高ED-XRF检测的精准度。但当下针对不同类型土壤的ED-XRF检测研究只点明了存在差异的现象，对其中的机理尚缺明晰的探究。未来需要继续探明不同基体效应存在于各类型土壤的原因及其影响大小，优化完善ED-XRF光谱定量分析模型，是ED-XRF定量分析研究的两个重要方向，通过多元回归分析对土壤中重金属有效性进行预测研究亦应是今后重点关注的领域。
Abstract:With the process of urbanization and industrialization, heavy metals enter the ecological environment through various pathways and accumulate in large quantities in the soil, causing potential risks to soil environmental health. In recent years, with the development of energy dispersive X-ray fluorescence spectroscopy (ED-XRF), the detection limit of the instrument has been significantly reduced, and it can be effectively applied to the detection of various heavy metals in soil, and is gradually becoming an effective tool for determining the concentration of heavy metals in the soil environment. However, the complexity of the soil matrix and the limitations of the instrument itself will lead to problems such as low accuracy and precision in the determination of target heavy metals by ED-XRF, such as the type and particle size of soil samples and the noise in the instrument and the environment; there are also certain difficulties in the establishment of quantitative analysis model methods, which are still not well applied to laboratories and other environments with high data quality requirements. In this paper, we summarize the application and research progress of XRF in the field of soil heavy metal detection, explore and analyze the influence of different soil sample states and detection conditions on the detection accuracy of ED-XRF instruments, sort out the main pretreatment methods of ED-XRF spectroscopy and the establishment process of quantitative analysis models, and introduce and analyze the application potential of ED-XRF in evaluating the effectiveness of soil heavy metals. At present, the soil sample preparation procedures applied to ED-XRF detection have been relatively mature, and the opinions of scholars on how to reduce the impact of soil matrix effects on the operation methods are relatively unanimous, that is, to use dry samples, low particle size or pressed soil samples as much as possible, and there is still some room for optimization in the processing and analysis of detection data. Therefore, the current mainstream focus is on combining the advantages of different algorithms for the preprocessing analysis of ED-XRF spectroscopy and the establishment of quantitative analysis models to improve the accuracy of ED-XRF detection. At the same time, ED-XRF detection studies on different types of soils only highlighted the differences in the phenomenon, and did not clearly explore the underlying mechanisms. In the future, it is important to continue to explore the causes and magnitude of different matrix effects in various types of soils, and to optimize and improve the quantitative analysis model of ED-XRF spectroscopy; the prediction of the effectiveness of heavy metals in soil through multiple regression analysis is also a field that scholars should focus on.
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表 1 光谱预处理方法对比
Table 1. Comparison of spectral preprocessing methods
光谱
预处理
方法使用算法 特点与效果 参考文献 噪声扣除 切比雪夫滤波器 切比雪夫滤波器使用和编程快速简单，使用谱窗宽度作为唯一的参数来控制噪声数据的平滑度，可作为LSP等方法的替代 López-Camacho等［71］ 最佳波束形成-傅里叶变换 将X射线光谱视为角度序列，对谱线进行相应的逆傅里叶变换得到自相关函数，并构建自相关矩阵，根据矩阵重新估计空间谱，重新估计得到的谱图较原始谱图更加平滑 Wu等［72］ 小波变换-S-G滤波器 该方法能有效地剔除含噪光谱信号中的噪声信息，同时提高了光谱的平滑度 杨帆等［73］ 小波变换阈值法 与傅里叶变换法和移动平均法进行对比，其标准曲线的效果更好，且提高了模型的稳定性与准确度 李芳［74］ 双树复小波变换 通过双树结构消除丢失的数据，计算阈值并使用阈值函数对噪声信号进行滤波处理，然后对滤波处理后的小波系数进行逆变换重构信号。较离散小波变换去噪信噪比更高，均方根误差更小 黄素真等［75］ 微分非线性消除平滑 能显著提高能量分辨率和噪声分辨性能，并与原始值相比测试误差减低近50% Lu等［76］ 背景扣除 傅里叶变换法 经本底扣除后，XRF野外测定含量与实验室分析结果的平均误差仅为−11.0%，相较线性本底扣除法降低14.9% 王卓等［77］ 复小波变换 复小波变换方法比实小波变换方法能更有效地进行背景推断，从而大大提高了谱图测量的有效性和精度 Hu等［78］ 双树复小波变换 该方法具有平滑、无振荡、能有效地保持信号不变等优点，仿真和实验结果均表明该方法能有效地去除XRF光谱中的本底 Zhao等［79］ 非线性迭代削峰法
（SNIP）算法简单，且具有自适应于峰值区域宽度的裁剪窗口，较以往的算法能更准确地去除背景 Morháč［80］ 蒙特卡罗模拟
（MCNP）根据检测几何结构和实际测量条件，建立蒙特卡罗仿真计算系统模拟计算得到能量沉积谱和模拟谱，通过消除能量沉积谱中特征峰得到连续背景谱，视为真实背景值，在实际检测中效果较好 Jia等［81］ 生成对抗网络模型
（GNN）与原始光谱进行线性拟合对比显示，样品光谱信号的基线得到较好地校正 王欣然［82］ 
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表 2 X射线荧光光谱建模方法比较
Table 2. Comparison of modeling methods for X-ray fluorescence spectroscopy
目标元素 定量分析模型 模型应用效果 参考文献 Pb、Zn、Cu、Cr PLS 用PLS建立模型可以较好地预测不同类型土壤中Pb、Zn、Cu、Cr含量 钱原铬［52］ As、Pb DBN-RF 降低了光谱数据中的冗余，既保留DBN强大的特征提取能力，又提高了模型的预测能力， 较常用模型R2值至少提高0.0557 刘峥莹［83］ Pb CARS-PLS 预测决定系数R2达到0.9955，具有较好的预测能力 江晓宇等［84］ Cd、Hg PLSR 模型较为稳健，具有良好的预测能力 王清亚［57］ Cr、Cu、Zn、As、Pb PSO、SVM 模型训练集和测试集的决定系数R2分别在0.99和0.90以上，预测准确度显著提高 程惠珠等［85］ Cr、Cu、Zn、As、Pb SVM
LM-BP-ANN
MLR三种模型分析的决定系数R2均高于0.95，模型的拟合度高，准确度好，可以对土壤样品中5种重金属含量实现较好的预测 李芳［74］ Pb PLS 与传统的一元线性回归和多元线性回归分析相比，PLS回归分析能明显提高模型预测的准确度 黄启厅等［86］ Cr、Zn、As、Pb、Cd CNN 预测模型的决定系数R2达到0.9583，效果优于其他预测模型，预测效果良好 杨慧［87］ 稀土元素 PLSR、OLSR 偏最小二乘回归相比最小二乘回归的预测精度要好 Kirsanov等［88］
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