Correcting correlation quality of portable X-ray fluorescence to better map heavy metal contamination by spatial co-kriging interpolation.

ABSTRACT

High-precision mapping based on portable X-ray fluorescence (PXRF) data is currently being studied extensively; however, owing to poor correlation with soil metal concentration, the original PXRF data directly used for co-kriging interpolation (CKI) cannot accurately map contaminated sites with heterogeneous concentrations. Therefore, this study selected a landfill-contaminated site for research, explored the best correlation mode between PXRF variants and actual heavy metal concentration, analyzed the impact of improving the correlation model on the CKI of the spatial distribution of heavy metals, and explored the most appropriate CKI mode and point density. The results showed the following (1) After nonlinear transformation, the correlation model between PXRF and the actual concentration was significantly improved, and the correlation coefficients of five heavy metals increased from 0.214-0.232 to 0.936-0.986. (2) The introduction of corrected PXRF data significantly improves the accuracy of CKI. Compared with the original PXRF co-kriging interpolation (OP-CKI), the ME of the corrected PXRF co-kriging interpolation (CP-CKI) for Zn, Pb, and Cu decreased by 78.2 %, 45.5 %, and 65.3 %, respectively. In terms of the spatial distribution of heavy metal pollutant concentrations, CP-CKI effectively improved the influence of local anomalous high-value points on the interpolation accuracy. (3) When the sample density measured by inductively coupled plasma mass spectrometry (ICP-MS) was less than 4 boreholes/hm2, CKI accuracy decreased significantly, indicating that the sample density should not be less than a certain threshold during CKI. (4) When the sample density measured by PXRF exceeded 7 boreholes/hm2, the mean error and root mean square error of CKI continued to decrease, suggesting that the introduction of enough sample density measured by PXRF can effectively improve the accuracy of CKI.