## 4 Results
Initially, the level of representativeness of the measured variables was analyzed using box plots to identify the contributions of the sources at each one of the mixing points. Figure 8 shows only one example of the box plots obtained in MIXSIAR for the mixing point PAN-M07. The criteria for this variable analysis (recommended or not) take into account whether the distribution of values is within or outside the range (mean, quartiles, upper and lower limits) of the sources and mixtures data. This analysis, carried out by the software using box plots, makes it possible to evaluate the principle of conservative behavior of the variables, to improve the possibilities of identifying the contributions of the soil deposition at the points and, additionally, to reduce the calculation times of the runs performed by discarding variables that do not allow determination of the contributions of the sources. The range of values in the box plots for each source contributors' type (forest, pasture, sediment) were developed using representative samples for each predominant contributors' type. The MIXSIAR model includes these samples in order to define the source distribution library for the soil apportions calculations. The box plots include all FAs of the sampling points associated with each source contributors and these are compared with each mixing points allowing to obtain the most relevant FA chain-length. From this initial analysis of the measured mixing samples, it was determined which FA carbon chain variables could be suppressed in the runs in order to evaluate the contributions of the sources at each one of the mixing points; these results are presented in Table 5.  
Mixing points 1 to 4 and 13 to 15 present the highest number of variables not recommended (out of range) for the analysis. This could be associated with the lack of capacity of these fatty acids for source determination for these points, and also to the lack of representativeness in the mixing points; all these factors could have an impact on the final uncertainty level of the results for these points. In addition, as part of the exploratory study of the data, a principal component analysis was performed to assess the variability of the data set. For most of the samples, more than 96% of the values of the variables in the first two (2) components were satisfactorily explained. An example of the analysis is shown in Figure 9. \begin{table}
\begin{tabular}{c c} \hline
**Mixture** & **Variables Not Recommended** \\ \hline PAN-M01 & C14, C18, C20; C22, C24, C28, C29, C31, C32, C33, C34 \\ \hline PAN-M02 & dC13, C16, C18, C20; C22, C23, C24, C25, C31, C32, C33, C34 \\ \hline PAN-M03 & dC13, C14, C16, C18, C20, C25, C26, C27, C28, C29, C30, C31, C32, C33, C34 \\ \hline PAN-M04 & dC13, C20, C22, C23, C24, C25, C26, C28, C32, C33, C34 \\ \hline PAN-M05 & - \\ \hline PAN-M06 & - \\ \hline PAN-M07 & dC13, C25, C33, C34 \\ \hline PAN-M08 & - \\ \hline PAN-M09 & - \\ \hline PAN-M10 & - \\ \hline PAN-M11 & C18, C20, C25, C31, C33, C34 \\ \hline PAN-M12 & C33 \\ \hline PAN-M13 & C18, C20, C22, C23, C24, C25, C26, C27, C29., C30, C31, C32, C33, C34 \\ \hline PAN-M14 & dC13, C18, C20, C22, C23, C24, C25, C26, C27, C28, C29, C30, C31, C33, C34 \\ \hline PAN-M15 & dC13, C20, C22, C23, C24, C25, C26, C27, C28, C29, C31, C33 \\ \hline PAN-M16 & C31, C32, C33, C34 \\ \hline \end{tabular}
\end{table} 
Table 5: Variables not recommended for the runs. Figure 8: Examples of box-plot graphs for variable selection. The final runs of the MIXSIAR model helped to identify the contribution levels of the different sources of land use in the selected mixing points. The program displays the results in textual and graphical form and includes probability density plots of the source inputs, histograms and correlation matrices between the different sources. Figure 10 presents an example of the graphical results. Figure 9: Examples of resulting principal component analyses for mixtures 3 and 16.