## 2 Materials and Methods
### Optimal Conditions for Beta-Cypermethrin Biodegradation by Strain BCC01  
As reported, BCC01 experienced the lag, exponential, and stationary phases sequentially within 48 h [22], and the inoculum was cultivated overnight until its optical density (OD) value at 600 nm (OD600) reached to 0.6 (diluted to 10 times when measured). Then the culture suspension was transferred into liquid LB media. After four days of cultivation, 85% of the added pesticide (initially at 50 mg/L) was degraded, which suggested the residual beta-cypermethrin was gradually reduced with the growth of bacteria. To facilitate the process of degradation, optimal conditions for beta-cypermethrin degradation by BCC01 were determined using the response surface method (RSM) based on central composite rotatable design (CCRD), as previously described [23]. Biodegradation may be affected by various conditions, so three parameters that seemed the most probable influencers were selected to investigate the optimal growth conditions for BCC01, including pH, temperature, and inoculum volume. The levels of the three independent variables were designed and listed in a Box-Behnken matrix (Table 1), in which \(X_{1}\), \(X_{2}\) and \(X_{3}\) represented pH, temperature, and inoculum volume (\(v\)/\(v\)), respectively. \(X_{1}\) refers to pH: \(-1\) (6), 0 (7.5), +1 (9); \(X_{1}\) refers to temperature: \(-1\) (25 \({}^{\circ}\)C), 0 (30 \({}^{\circ}\)C), +1 (35 \({}^{\circ}\)C); \(X_{3}\) refers to inoculum: \(-1\) (Inoculum (%) (\(v\)/\(v\)) = 5), 0 (Inoculum (%) (\(v\)/\(v\)) = 10), +1 (Inoculum (%) (\(v\)/\(v\)) = 15). Coded levels (at \(-1\), 0 and 1) were used for software model while uncoded levels were the experimental values. For each parameter, coded numbers represented different values. For the measurement of degrading ability, the degradation percentage (%) of beta-cypermethrin (at initial 100 mg/L) was analyzed according to different parameter combinations, and each treatment was repeated three times. Furthermore, degradation percentage (%) from the designed matrix was employed for regression analyses. The variable values were calculated using the following equation:  
\[x_{i}=(X_{i}-X_{0})\Delta X_{i}, \tag{1}\]  
where \(x_{i}\) is the dimensionless value of an independent variable, \(X_{i}\) is the real value of an independent variable, \(X_{0}\) is the real value of an independent variable at the center point, and \(\Delta X_{i}\) is the step change in the real value of the variable \(i\) corresponding to variation by a unit of the three independent variables (Table 1). The analyses were conducted as previously described [24]. Response surface regression was used to analyze the effects on degradation, using Statistical Analysis System (SAS, version 9.0, SAS Institute Inc., Cary, NC, USA) software and the following:  
\[Y_{i}=b_{0}+\sum b_{i}X_{i}+\sum b_{ij}X_{i}X_{j}+\sum b_{ii}X_{i}^{2}, \tag{2}\]  
where \(Y_{i}\) represented the predicted response, \(X_{i}\) and \(X_{j}\) are the variables, \(b_{0}\) is the constant, \(b_{i}\) is the linear coefficient, \(b_{ij}\) is the interaction coefficient, and \(b_{ii}\) is the quadratic coefficient.