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The impregnation solutions were a FA40 -mix (28 % FA, 68 % water and 4 % additives) from Kebony ASA, and a 2.07 % Wolmanit CX-8 solution from Dr. Wolman GmbH. The samples were weighed before impregnation. After impregnation the samples were dried with a cloth and weighed again. The uptake was recorded in grams. Table 2: Summary data of individual sapwood samples in the study Treatment Dimension N Mean Std dev min max CX-8 samples Density kg/m 3 181 473 60 359 640 FA40-mix samples Density kg/m 3 192 474 60 350 640 CX-8 samples RoF % 185 38.8 7.1 15.4 60.0 FA40-mix samples RoF % 196 25.4 10.5 7.8 58.9 To get a value of treatability the ratio of filling (RoF) was used. Level of treatment is often determined by retention of preservatives or liquid uptake per volume of wood. These variables are very much related to permeability and wood density. The lower the density, the higher the void volume, and the higher the permeability, the faster liquid will fill the void volume. Low permeability will also often result in restrained penetration depth of the wood. Treatability will therefore in this study be determined by the liquid ratio of filling of the void volume. Equation 1 expresses the calculated void volume of the sample, and Equation 2 expresses the ratio of filling (RoF). The RoF will vary upon permeability and the impregnation conditions. Since the impregnation conditions are kept constant for all samples, only the difference in permeability will influence on how fast the liquid flows into the wood. The RoF will therefore be a reasonable measure of treatability, as long as not all samples are fully treated. High permeability will lead to fast liquid flow and result in a high RoF. Given enough time for vacuum and pressure, it is believed that all these small wood samples would have been completely treated to a RoF of 100%. Therefore a pre-trial was conducted to find “mild” impregnation conditions, giving a range of uptakes. Vvoid = V12% – Vcell wall – Vwater + Vswell Equation 1. ROF = Vuptake / Vvoid Equation 2. Where: Vvoid: Void volume in wood V12%: Volume of the sample at 12 % MC Vcell wall: Volume of the cell wall components Vwater: Volume of the present water at 12 % MC Vswell: Potential increase in volume due to swelling Vuptake: Volume of the treating liquid The void volume was calculated at 12 % MC, were the specific gravity of wood substance was set to 1.5 and the specific gravity of bound water in cell wall was set to 1.1 (Stamm, 1964). The last term of Eq. 1, Vswell, represents the increased volume of the sample due to swelling from 12 % MC to fibre saturation. This term is relevant as long as the impregnation liquid is based on water and causing swelling of wood. In Eq. 2 the volume of the impregnated liquid was calculated by a specific gravity of 1.05. 6
2.3 Statistical analyses Initially variance component analyses of ROF with Dclass, vertical and radial positions as fixed effects and site and tree as random effects were performed to reveal the sources of variance. Simple statistical methods like one way ANOVA and simple regression were used to screen for main effects in the dataset. When the most promising variables were recognised, a linear mixed model was developed. The advantage of using linear mixed models is that the method handles a wide range of different variables simultaneously. Both fixed effect and random effect variables of various kinds like continuous and nominal (classes), independent and dependent variables can be modelled in the same model. Linear mixed models also allow for testing interactions between variables, i.e. one variable influences the importance of another variable. The parameter estimates let you know how much the variable contributes to the response, and the test statistics tells whether this effect is significant or not. Variables used to model the response are listed in Table 3. Table 3: Description of variables used on the mixed model Parameter Variable Description S Site Random, Nominal variable (i = 1-10) T TreeID Random, Nominal variable (j = 1-54) α Latitude (Lat) Fixed, Continuous variable β Vertical position Fixed, Nominal variable (k = bottom (1), middle (2) or top (3)) χ Horizontal position Fixed, Nominal variable (l = outer (1) or inner (2) sapwood) δ Liquid FA/CU Fixed, Nominal variable (m = FA or CU) μ Intercept ε Error Y Ratio of filling (RoF) Response variable The mixed model can then be written. Y = µ + Si + Tj + α(Lat) + βk + χl + δm + εijklm Due to few samples, the 3 rd and 4 th horizontal samples (close to heartwood), and the 4 th vertical sample (top of tree) were excluded. In addition 4 outliers were excluded. The statistical analyses were conducted with JMP 7.0 by SAS Institute Inc. 3. RESULTS AND DISCUSSION This sample set holds a lot of information and the main task was to find out which factor influenced the treatability significantly. Screening on possible variables to describe the RoF did not give any moderate correlation (R 2 >0.1) or significant differences (95 % confidence level) for tree age, density, diameter class, MOE, MOR, or dominance class. The screening did, however, reveal potential influence on RoF by location, latitude, height in tree, distance from outer rim and impregnation liquid used. A variance component analysis revealed a high significant level on Tree ID but not on Site as random variables. The variance due to site was explained by latitude Interaction between Latitude and Vertical position was also found to be significant. The lower section was more dependent upon latitude than the higher sections of the tree. Therefore the final model was expanded with this interaction effect. Y = µ + Tj + α(Lat) + βk + χl + δm + φk(Lat - µLat) + εijklm 7
Page 1 and 2: IRG/WP 08-40421 THE INTERNATIONAL R
Page 3 and 4: atio between depth of penetration a
Page 5: Table 1: Properties of the stands u
Page 9 and 10: Predicted RoF % 60 50 40 30 20 10 5
Page 11 and 12: 7. REFERENCES Beckers, E P J, Milit

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