# Application of Corrected Methods for High-Resolution XRF Core Scanning Elements in Lake Sediments
## 5 Discussion
### Assessing the Influence of Sediment Properties on XRF-scan Elements within Lake Sediments  
The comparisons of XRF-scan and conventional-fractions indicate that artificial deviations occurred in the XRF-scan elemental detection. Such deviations are larger for the relatively light elements (e.g., Al, Si and K) rather than for the heavy elements (Figures 2 and 3). Deviations are thought to be primarily due to both specimen (i.e., inhomogeneous sediment properties, including water content and grain size) and matrix effects [11]. In order to assess the influence of sediment properties on XRF-scan intensities within lake sediments, core LGH2 was calibrated to element fractions using the MLC calibration, which effectively eliminates the influences of specimen effects and matrix effects. The XRF-scans were then scaled to the median value of a given XRF-calibrated element. The deviations (\(D_{ij}\)) between the XRF-scan element \(j\) and the calibrated-XRF element \(j\) of the same specimen \(i\) were calculated using the following equation:  
\[D_{ij}(\%)=\frac{\frac{I_{ij}}{I_{\text{MM}}}C_{jM}-C_{ij}}{C_{ij}}*100 \tag{4}\]  
where \(I_{ij}\) and \(C_{ij}\) represent the XRF-scan intensity and the XRF-calibrated element \(j\) in specimen \(i\), respectively and \(I_{jM}\) and \(C_{jM}\) are the median values of the XRF-scan intensity and the XRF-calibrated element \(j\), respectively. The element deviations and sediment properties were taken into account for redundancy analysis (RDA) in Canoco for windows 4.5 to examine the relationships between the element deviations and sediment properties (Figure 9), as expected sediment properties, including sorting, mean grain size and water content, explained significant proportions (\(p<0.05\)) of the element deviations (Figure 9). Factors of sorting and mean grain size only explained 6.6% and 0.7% of dataset deviations, respectively, similar to previous studies [11]. In contrast, water content explained 43% of the element deviations especially for the light elements (Al, Si, K, Ca, Ti and Fe; Figure 9). This phenomenon results from the pore water dilutive and absorption effects [25]. First, pore water may dilute the sediment solid phase [25; 26]. Second, it can absorb the X-ray radiation of the sediment causing lower XRF-scan intensities and low measurement accuracy. This effect predominantly influences the elements with weaker fluorescence energies, which in accordance with [PERSON]'s law relate primarily to elements with a lower atomic number such as Al and Si [8; 11]. Furthermore, a pore water film may form directly between the protective foil and sediment surface. This phenomenon could artificially enhance the absorption effect of pore water [8; 11; 13]. To assess the impacts of water content on the light elements, element deviations and water content were used in the generalized additive model (GAM) [27] (Figure 10). The GAM results reveal an inverse relationship between element deviations and water content. For Al (Figure 10a), Si (Figure 10b) and Ti (Figure 10e), when water content is below 52%, it has little impact on element deviation. However, a significant XRF-scan intensities decrease (up to 40%) was observed when water content was \(>52\%\). With respect to K and Ca, a significant XRF-scan intensities decrease appears (up to 30% and 20%,respectively) when water content is \(>\)47%. Fe is insensitive to changes in water content compared to the other five elements, with no significant decrease observed until water content was 56%. Figure 10: Smoothing curves (black curves) obtained by applying the GAM model based on element deviations and water content for core LGH2. (**a**–**f**) Represent the deviations of element Al, Si, K, Ca, Ti and Fe, respectively. The light-gray shade denotes the 95% confidence limits. Tick marks on the x-axis represent tested samples from core LGH2. Red dots indicate the threshold for the element deviations.