矿床地球化学国家重点实验室知识库

Many instrumental quantifications for heavy metals require the establishment of the calibration curve between calibrator's signals and mass concentrations based on linear regression model. However, linear fitting based on the ordinary least squares regression model faces the challenges from the instruments, such as homoscedasticity of signals, which may result in the poor performance for the measurement of low mass concentration. In comparison, the linear weighted regression model in many studies has been proven to better address the problem of heteroscedasticity in inductively coupled plasma emission spectrometer (ICP-OES). This study developed a computational software incorporating the linear weighted regression model for the bottom-up evaluation of the measurement uncertainty from a few replicate signals of calibrators and sample. It includes a model of signal precision variation in calibration interval based on the adjustment factor from the previously detailed and daily calibration. In addition, normal distribution for signal repeatability in ICP-OES was confirmed by using the Jarque-Bera test. The Monte Carlo method (MCM) employed here is designed for simplicity and intuitiveness, thus minimizing complex mathematical maneuvers, although it does presuppose a basic proficiency in computer programming. The method was successfully applied to assess the mass concentration of heavy metals (Cu, Zn, Fe, and Mn) in acid mine drainage by ICP-OES after necessary sample dilution. The robustness of developed measurement uncertainty method, which will not be limited by this assumption of the quality of calibrators preparation, was validated through the analysis of control standards with known mass concentration and sample with several spiking levels. The metrological compatibility of mass concentration and respective uncertainty was given to show the feasibility, practicality and reliability of the MCM simulations. A user-friendly software for the MCM is provided in the supplementary material. Overall, this study makes a contribution to the objectivity of the evaluation of environmental sample analysis.