水体重金属污染对人体健康和生态环境构成严重威胁，并对国民经济造成重大损失。XRF光谱法作为一种非常重要的重金属现场快速检测技术，可实现重金属污染物实时、快速、在线检测。但该技术直接对水体重金属检测却存在光谱稳定性差、检出限不够低等问题，且多元素XRF光谱同时解析存在准确定量等难题。. 鉴于此，本项目提出一种基于藻富集的水体多种重金属XRF光谱同时快速定量分析方法，拟通过藻对不同重金属吸附性能实验研究，筛选出多种重金属可同时富集的合适藻种，以此建立水体多种重金属同时富集方法，去除水体复杂基体干扰，提高XRF光谱测量的灵敏度和稳定性；在此基础上，研究多元素XRF光谱特征变量筛选方法，并结合内标和多元非线性回归建立水体多种重金属浓度同时准确定量反演算法模型，校正多元素特征谱峰间交叉干扰和吸收增强效应，实现水体多种重金属同时准确定量解析，为水体重金属污染快速现场监测提供方法基础与技术支持。

Water heavy metal pollution poses a serious threat to human health and ecological environment, and causes significant losses to the national economy. X-ray fluorescence spectroscopy as a very important on-site and rapid detecting technology of heavy metals, can realize real-time, rapid and on-line detection of heavy metal pollutants. But if the technology is used to directly detect heavy metals in water, there are some problems, such as poor spectrum stability and low detection limit. Moreover, there is the problem of accurate quantification when XRF spectrum is used to simultaneous analyze the various heavy metal elements. For the above problems, this project proposed a simultaneous and rapid quantitative analysis method of various heavy metals in water based on preconcentration by algae and X-ray fluorescence spectroscopy. By the experimental study of absorptive property of algae for different heavy metals, the suitable algae species will be selected for the simultaneous preconcentration of various heavy metals. Then the selected algae species are used to establish simultaneous enrichment method of various heavy metals in water, which can effectively avoid the interference of water complex matrix and improve the sensitivity and stability of XRF spectrum measurement. On the basis, for the XRF spectrum containing various characteristic spectral lines of heavy metals, the method of selecting characteristic variables will be studied. Then the simultaneous and accurate quantitative inversion algorithm model for the concentrations of various heavy metals in water will be established by combining the selected multiple optimal variables, interior label and multivariant non-linear regression, in order to correct the cross interference of spectral peaks and absorption enhancement effect between the elements, and realize the simultaneous and accurate quantitative analysis of various heavy metals. This project will provide the method foundation and technical support for rapid and on-site monition of heavy metal pollution in water.