## 3 Results
### ANOVA Results and Regression Models  
Table 2 shows the experimental design of BBD conducted to determine the effect of the different combinations of conditions on disinfection efficiency and the results for the response variables in each experiment. Over a total of 15 experiments, the ranges for disinfection efficiency for total bacteria in all areas were 0.77-2.72 log\({}_{10}\), 0.66-5.00 log\({}_{10}\), 1.32-5.00 log\({}_{10}\), 0.76-1.99 log\({}_{10}\), 0.69-3.40 log\({}_{10}\), and 1.03-5.00 log\({}_{10}\), at points 1, 2, 3, 4, 5, and 6, respectively, and the highest efficiency was observed for point 5, the wall in the direction of the spray. The average disinfection efficiency in all areas was 0.93-1.99 log\({}_{10}\). Figure 5: Disinfection efficiency (log\({}_{10}\) units) of total bacteria on indoor surfaces by locations and time after spraying. The results of the ANOVA and the goodness-of-fit of the models are presented in Table 3. The \(p\)-values for the models for all six response variables, except point 3, were less than 0.05 and had a nonsignificant lack-of-fit (\(p>0.05\)). Therefore, all response variables were fitted well by the quadratic model. In the model for which the response variable was average disinfection efficiency, the \(p\)-values of the linear coefficients (X\({}_{1}\), X\({}_{2}\), and X\({}_{3}\)), and the interaction coefficients (X\({}_{1}\)\(\cdot\)X\({}_{2}\), X\({}_{1}\)\(\cdot\)X\({}_{3}\), and X\({}_{2}\)\(\cdot\)X\({}_{3}\)) were all statistically significant (\(p<0.05\)). In the model for which the response variables were disinfection efficiency at locations 1 to 6, the significance of the \(p\)-values of linear coefficients and the interaction coefficients were varied. The results in which the \(p\)-values of coefficients of X\({}_{1}\), X\({}_{2}\), and X\({}_{1}\)\(\cdot\)X\({}_{2}\) were statistically significant at most locations were notable. All three parameters and their interactions affected the disinfection efficiency. In all models, the coefficients of determination (\(R^{2}\)), which explained the variability in the response values by the experimental parameters and their interactions [33], were high, close to 1 (\(R^{2}>0.8186\)). Therefore, all models were suitable for determining the optimum conditions for bacterial inactivation. \begin{table} \begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Treatment**}} & \multirow{2}{*}{**Value**} & \multicolumn{6}{c}{**Locations**} \\ \cline{3-10}  & & \multicolumn{2}{c}{**1**} & \multicolumn{2}{c}{**2**} & \multicolumn{2}{c}{**1**} & \multicolumn{2}{c}{**3**} & \multicolumn{2}{c}{**1**} & \multicolumn{2}{c}{**1**} & \multicolumn{2}{c}{**3**} & \multicolumn{2}{c}{**1**} \\ \hline Disinfectant & 50 & 1.50 (0.006) * & 1.65 (0.004) * & 5.00 (0.000) * & 1.24 (0.010) * & 1.65 (0.004) * & 1.52 (0.002) * \\ concentration & 100 & 1.41 (0.017) * & 1.27 (0.010) * & 2.46 (0.002) * & 1.21 (0.009) * & 0.93 (0.039) * & 1.42 (0.008) * \\ (times) 2 & 200 & 1.22 (0.032) * & 0.96 (0.032) * & 1.46 (0.014) * & 0.64 (0.023) * & 0.88 (0.029) * & 0.96 (0.013) * \\ \hline \multirow{3}{*}{Spray amount (L) 3  } & 2 & 1.25 (0.012) * & 1.08 (0.012) * & 2.01 (0.004) * & 1.12 (0.025) * & 0.95 (0.036) & 1.23 (0.015) * \\  & 5 & 1.71 (0.020) * & 1.27 (0.010) * & 2.46 (0.002) * & 1.21 (0.015) * & 0.93 (0.012) & 1.42 (0.009) * \\  & 8 & 1.93 (0.008) * & 1.63 (0.012) * & 5.00 (0.000) * & 1.35 (0.012) * & 1.34 (0.009) & 1.90 (0.003) * \\ \hline \multirow{3}{*}{Room size (m3) 4  } & 1.37 (0.008) * & 1.72 (0.008) & 1.38 (0.015) * & 2.63 (0.001) * & 2.51 (0.002) * \\  & 230 & 1.45 (0.013) * & 1.27 (0.008) * & 2.45 (0.002) & 1.21 (0.009) * & 0.93 (0.024) * & 1.42 (0.009) * \\  & 300 & 1.45 (0.011) * & 0.68 (0.016) * & 1.46 (0.019) & 0.87 (0.025) * & 0.92 (0.016) * & 0.96 (0.008) * \\ \hline \hline \end{tabular}