Heavy Metal Analysis Platform for Atmospheric Fine Particulate Matter Based on AHP Algorithm

Abstract

In this paper, the XRF inversion algorithm for heavy metal element detection in the atmosphere is studied to solve the problem that the calibration curve has complex nonlinear relationship caused by the difference of original spectral signal-to-noise ratio, spectral line overlap and soil matrix effect in XRF analysis. The Monte Carlo method is used to improve the accuracy of model prediction. Because 57 standard samples are not enough for intelligent algorithm analysis, the content information of 214 atmospheric standard samples is obtained through the national standard material resource sharing platform, and the spectra are generated by Monte Carlo simulation and normalized. The determination coefficients of Cr, Ni, Cu and Zn elements have been increased by 0.0036, 0.0065, 0.0117 and 0.0105 respectively based on the cross-validation method.