# Effect of Spruce Wood Density on Selected Fire-Technical Parameters during Thermal Loading
## 4 Conclusions  
The study evaluated various fire-technical parameters of spruce wood with different densities, including some samples treated with wood flame retardants. The spruce wood samples were divided into several groups with identical treatments (or no treatment) but different average densities, gradually reducing the density difference to establish a limit for the dependence of experimental results on the difference in sample densities. The experiment utilized a simple method where the samples were exposed to a direct flame at a 45\({}^{\circ}\) angle. The monitored parameters were mass loss and mass loss rate, with an  
Figure 8: Dependence of the difference of fire spread rate on the difference of sample densities. additional parameter being the fire spread rate. The conclusions from the experiment can be summarised as follows:  
* Untreated samples exhibited significant variability in the measured data (mass loss variability of untreated samples with low density was 55%, with higher-density samples at 18%), unlike samples treated with retardants, which showed a lower variability of mass loss, approximately 5%. This significant variability in mass loss for untreated samples is mainly attributed to their ignition after a specific time, resulting in a subsequent rapid increase in the mass loss rate. The results confirm, among other findings, that flame retardants significantly prolong the ignition time. * Untreated samples with a density difference of about 100 kg\(\cdot\)m\({}^{-3}\) displayed a clear density dependence on the mass loss. Samples with a lower density have a significantly higher average mass loss rate over the entire duration of the experiment. * For samples treated with retardant 1 with a density difference of approximately 244 kg\(\cdot\)m\({}^{-3}\), a dependence of the mass loss rate on the sample density was observed. The most significant difference was in achieving the maximum mass loss rate, which was twice as high for the lower-density samples compared to the higher-density samples. Also, the mass loss dependence on the sample density was statistically confirmed by the ANOVA test (\(p\)-value = 8.46 \(\times\) 10\({}^{-5}\) at a testing time of 600 s). * Lower-density samples exhibited higher mass loss and mass loss rate. The study results further indicated that the sample density also affected the fire spread rate parameter (\(R_{\beta}\)). As the sample density decreased, the value of \(R_{\beta}\) increased, showing a linear relationship between the difference in \(R_{\beta}\) values and sample densities with a reliability coefficient of R\({}^{2}\) = 0.99. * For samples treated with retardants with a density difference of up to 50 kg\(\cdot\)m\({}^{-3}\), no dependence of the mass loss rate on sample density was observed. The ANOVA statistical analysis did not confirm the mass loss dependence on the sample density. Therefore, the difference in densities of up to 50 kg\(\cdot\)m\({}^{-3}\) no longer significantly affected experimental results, particularly parameters such as the mass loss rate and mass loss. Consequently, selecting the test samples with such a variation range of densities can be considered optimal without impacting the test results. The research was aimed at investigating the influence of spruce wood density on mass loss and mass loss rate when using flame retardants. The research was carried out on groups of samples with different densities using three flame retardants. In order to refine the research, it would be advisable to complement the experiments so that several flame retardants were applied for each density respective of the difference in densities. However, such research would be very challenging, especially due to the selection of samples with different densities. In our case of \"simplified research\", we had hundreds of samples available to us, from which we selected only 80 suitable for testing. Conceptualization, P.M. and L.M.O. ; methodology, P.M. and L.M.O. ; software, P.M.; validation, M.K. ; formal analysis, M.K. ; resources, P.M. and L.M.O.