土壤石油类污染物的激光诱导荧光组分识别与快速检测方法研究

The petroleum pollution in soil becomes serious and turns into a great threat to human health and environment. Compared to traditional testing methods for petroleum pollution which all need complicated sample pretreatment, and are unable to realize rapid and in-situ measurement, Laser induced fluorescence (LIF) technique have several advantages including no need of sample pretreatment, fast detection and easy realization of filed measurement. But the influence of soil matrix to pollutant fluorescence spectrum, the weakness of fluorescence signal and the difficulties of qualitative and quantitative analysis still need to be solved. The research will start from the LIF spectrum. Combined with the optimized experimental system, adding surfactant and the use of two laser shots, the weak fluorescence signal will be obtained sensitively. The fluorescence spectrum correction model will be built under multiple conditions. The spectrum library including petroleum fluorescence, time-resolved fluorescence and its derivative spectrum will also be built. Combined with comprehensive similarity index model, rapid qualitative of petroleum in soil samples will be realized. The quantitative inversion model based on weighted nonnegative least squares will be built in order to realize the accurate and rapid quantitative analysis of petroleum in soil samples. The whole research will provide a new method for petroleum measurement in soil, methods and data support for rapid, field measurement equipment.

土壤中石油污染物对环境安全与人类健康都造成了巨大威胁。与传统土壤石油污染检测方法相比，激光诱导荧光技术具有检测速度快、无需样品预处理及可实现原位检测等优点，但目前该技术仍面临土壤复杂基质条件对荧光光谱干扰大、激光诱导荧光光谱信号弱和土壤中石油类污染物定性与定量分析困难等难题。项目从土壤污染物的激光诱导荧光光谱特性入手，通过优化实验参数，添加表面活性剂，以及双激光脉冲激发等增强荧光强度；通过研究土壤性质对荧光光谱的影响，建立土壤荧光光谱校正模型，消除系统与环境参数对光谱测量的影响；建立单一组分的石油污染物荧光、时间分辨荧光和荧光微分光谱库，结合综合相似度指数模型，实现土壤中石油污染物的多组分准确识别；建立基于加权非负最小二乘法的定量反演模型，实现未知干扰共存条件下土壤中石油污染物的准确、快速定量分析。项目为土壤中石油类污染物的快速检测及土壤有机污染物原位、快速监测设备研发提供方法与数据支撑。

土壤中石油污染物对环境与人类健康都造成了巨大威胁，与传统土壤石油污染检测方法相比，激光诱导荧光技术具有检测速度快、无需样品预处理以及可实现原位检测等优点。但目前该技术仍面临土壤复杂基质条件对荧光光谱干扰大、激光诱导荧光光谱信号弱和土壤中石油类污染物定性与定量分析困难等难题。项目从土壤污染物的激光诱导荧光光谱入手，通过优化实验参数，添加表面活性剂，显著增强了土壤石油烃荧光的强度与稳定性；研究了土壤理化组成、物理性状对石油烃荧光的影响，验证了LIF技术应用于实际土壤石油烃检测的可行性；建立了土壤中常见机油、柴油等石油烃和蒽、芘、菲等多环芳烃等有机污染物的荧光光谱库，研究了荧光光谱重叠谱线分离方法，以及基于主成分分析-BP神经网络的土壤石油烃污染物识别方法，实现了土壤中常见石油烃污染物的多组分准确识别，并通过建立加权非负最小二乘、支持向量机回归等定量反演模型，实现在未知干扰共存条件下土壤中石油类污染物的准确、快速定量分析；参与研制了移动式土壤石油烃污染物现场检测系统，在安徽省铜陵市进行了外场实验，验证了LIF技术对土壤常见石油烃污染物检测的可行性。该项目为土壤中石油类污染物的快速检测及便携式土壤有机污染物现场、快速监测设备研发提供方法与数据支撑。