# Analysis of Influencing Characteristics of Biochars for Ammonium Adsorption
## 3 Results and Discussion
For the purposes of ammonium adsorption, there was not a clear feedstock material type that performed best on average (Figure 1) to be able to say whether this relationship between feedstock material, CEC and surface area is indicative of adsorption capacity. The PCA loading matrix in Figure 4 corroborated some of the relationships from the correlation matrix, though some of the principal components showed conflicting data. In addition, 80% of the total variance could be explained using the first five principal components (PC0-4), so subsequent PCs were not analyzed further. The relationships suggested by PC0 were positive between adsorption capacity and BET surface area, biochar dosage (g/mL biochar), ammonium concentration (NH4\({}^{+}\) mg/L), pH solution and pyrolysis temperature. Looking at PC1, a similar relationship between adsorption capacity and BET surface area and pyrolysis temperature was noted, but the opposite relationship between adsorption and biochar dosage, ammonium concentration and pH was determined. Figure 3: Correlation matrix for key influencing parameters; red indicates positive correlation and blue indicates negative correlation. Additionally, cation exchange capacity (CEC cmol/kg) showed a positive correlation according to PC1. PC3 confirms the positive relationship between adsorption and solution pH from PC0, but suggests an inverse relationship with pyrolysis temperature, particle size, biochar dosage, residence time, and BET surface area. As PC2 and PC4 have small coefficients for adsorption capacity, the relationships shown are likely less important than the other PCs. The contradictions seen in the various principal components in Figure 4 could be partially explained by bias in experimental parameters. For example, all experiments using high concentrations of ammonium also used a solution pH above 7. This creates a bias in the data that is unrelated to the mechanism of adsorption which can lead to incorrect conclusions. Therefore, it is suggested that both the PCA loading matrix and the correlation matrix relationships be considered, and those which are common for both are most likely to be correct. It can be concluded that ammonium adsorption is positively correlated with BET surface area, CEC, NH4+ concentration and possibly pyrolysis temperature. Again, it is important to stress that the relationship between CEC and adsorption capacity is supported by less data than the other relationships. A similar principal component analysis was performed on data separated by material type, though the results were highly dependent on the initial value for \(\Sigma^{(0)}\) used in the EM algorithm. Many of the converged covariance estimates had similar likelihoods despite significant differences in the converged parameters. The goal of such an analysis would be to determine if significantly different trends occur when considering different material type. Alternatively, a biplot for the first two PCs can offer a visual aid in seeking qualitative trends. Figure 4 shows such a biplot, where the Voronoi diagram of the datapoints has been used to color the plot area. This coloring shows the material type of the closest data point in, and can be used to visually identify trends in the data. Figure 4: PCA loading matrix for all data. Visual inspection of Figure 5 is sufficient to reveal some interesting trends. For example, the region where \(0.2<P\text{C0}<0.8\) and \(-0.2<PC1<0.2\) contains, from top to bottom,  
1. a sequence of five wood experiments showing increasing adsorption capacity;
2. a sequence of five agricultural waste experiments showing increasing adsorption capacity;
3. a sequence of five wood experiments showing increasing adsorption capacity;
4. a group of 10 agricultural waste experiments showing increasing adsorption capacity. Throughout the plot, there are other clear linearly arranged sequences of experiments, for example the series of industrial waste experiments in the top left, or a group of three food waste experiments in the bottom left.