Absolute metal concentrations after calibrating high resolution XRF core scanner data from highly polluted marine deposits offshore Portmán Bay and Barcelona, Spain

X-ray fluorescence core scanners (XRF-CS) allow rapid, non-destructive and continuous high-resolution analyses of the elemental composition of sediment cores. Since XRF-CS analyses are usually performed in fresh untreated materials, elemental intensities can be affected by the physical properties of the sediment (e.g. pore water content, grain size, sediment irregularities and changes in matrix) and the selected excitation parameters. Accordingly, the records of the measured elemental intensity cannot be considered quantitative. Nonetheless, these data can be converted to quantitative data through a linear regression approach using a relatively small number of discrete samples analyzed by techniques providing absolute concentrations. Such conversion constitutes a powerful tool to determine pollution levels in sediments at very high resolution. However, a precise characterization of the errors associated with the linear function is required to evaluate the quality of the calibrated element concentrations.

Here we present a novel calibration of high-resolution XRF-CS for Ti, Mn, Fe, Zn, Pb and As measured in heavily contaminated marine deposits. Three widely applied regression methods have been tested to determine the best linear function for XRF data conversion, which are: the ordinary least-squares (OLS) method, which does not consider the standard error in any variable (x and y), the weighted ordinary least-squares (WOLS) method, which considers the weighted standard error of the vertical variable (y), and the weighted least-squares (WLS) method, which incorporates the standard error in both x and y variables.

The results, derived from the analysis of metal-polluted sediments from offshore Portmán Bay and Barcelona, in the Mediterranean Sea off Spain, demonstrate that the applied calibration procedure improves the quality of the linear regression for any of the three regression methods (OLS, WOLS, and WLS), thus increasing correlation coefficients, which are higher than r²=0.94, and reducing data deviation from the linear function. Nonetheless, the WLS appears as the best regression method to minimize errors in the calibrated element concentrations. Our results open the door to use calibrated XRF-CS data to evaluate marine sediment pollution according to the sediment quality guidelines (SQG) with errors lower than 0.4% to 2% for Fe, 1% to 7% for Zn, 3 to 14% for Pb, and 5% to 16% for Mn, which highlight the robustness of the calibration procedure here presented. Our study incorporates and evaluates for the first time the analytical and statistical errors of XRF-CS data calibration, and evidences that the errors of the calibrated element concentrations must be properly assessed in future calibration efforts.