## 5 Evaluation of Model Simulation Efficiency
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Model** & **Target Antibiotics** & **Application** & **Error** & **Deviation** & **Often Used** & **Range** & **Region** & **References** \\ \hline \multirow{9}{*}{AQUSAIM} & Carbamazepine & Predict concentration in river water & PBIAS & \(-1\) to \(-23\) & & & & & \\  & Venlafaxine & Predict concentration in river water & PBIAS & \(-28\) & & & & & \\  & Diclofenac & Degradation estimation of diclofenac & R\({}^{2}\) & 0.92 & – & – & Switzerland & [138] \\  & Carbamazepine & Modeling fate & MF & 0.5 & - & - & Canada & [108] \\  & Climbazole & Predict concentration in river water & MF & 4 & & & China & \\ QWASI & Amoxicillin & Antibiotic date modeling & LGMF & 1.32 & - & - & China & [5] \\  & Ciprofloxacinacin & Antibiotic date modeling & LGMF & \(-1.35\)-\(1.84\) & & & & \\  & Chlortetrazolodeling & Antibiotic fate modeling & LGMF & \(-0.88\)-\(2.13\) & & & & \\  & Ennofloxacin & Antibiotic fate modeling & LGMF & \(-0.24\)\(-\)\(2.54\) & & & & \\  & Erythromycin & Antibiotic fate modeling & LGMF & \(-1.28\) to \(2.02\) & & & & \\  & Norfolkacin & Antibiotic fate modeling & LGMF & \(-1.62\)-\(2.53\) & & & & \\  & Oxytetracycline & Antibiotic fate modeling & LGMF & \(-1.95\)-\(1.64\) & & & & \\  & Sulfachloryrridazine & Antibiotic fate modeling & LGMF & 0.29–\(1.91\) & & & & \\  & Sulfameter & Antibiotic fate modeling & LGMF & \(-0.75\)-\(2.04\) & & & & \\  & Sulfamomomethoxine & Antibiotic fate modeling & LGMF & \(-0.24\)-\(1.75\) & & & & \\  & Sulfamethoxazole & Antibiotic fate modeling & LGMF & \(-1.62\)-\(1.59\) & & & & \\  & Sulfathiazole & Antibiotic fate modeling & LGMF & \(-0.92\)-\(0.03\) & & & & \\  & Tetracycline & Antibiotic fate modeling & LGMF & \(-0.22\)-\(1.92\) & & & & \\  & Trimethoprim & Antibiotic fate modeling & LGMF & 0.12–\(2.27\) & & & & \\ ePiE & 30 pharmaceuticals & Model validation & MSE & 127 * & MSE & \(\leq\)150 & UK & [95] \\  & Ibuprofen & Evaluation of model prediction & MSE & 126 & & & UK & [113] \\  & Ibuprofen & Evaluation of model prediction & MSE & 88 & & & German & \\  & Ibuprofen & Evaluation of model prediction & MSE & 866 & & & Spain & \\  & Ibuprofen & Evaluation of model prediction & MSE & 419 & & & Slovenia & \\  & Ibuprofen & Evaluation of model prediction & MSE & 4427 & & & Croatia & \\ EUSES & Dichloromethane & Predict regional & MF & 6 & R\({}^{2}\) & \(\leq\)0.7 & Japan & [133] \\  & 1,2-dichloroethane & Predict regional & MF & 0.33 & & & & \\  & Trichosan & Predict regional & MF & 0.067 & & & Germany & [137] \\  & Trichosan & Precipitation concentration & MF & 16 & & & & \\  & Trichosan & Predict local concentration & MF & 16 & & & & \\ \hline \hline \end{tabular}  
Notes: R\({}^{2}\) is the correlation coefficient between the prediction and measured; MF is the Multiplication Factor, the ratio of predicted to measured concentration, MF \(<1\) is underprediction, and MF \(>1\) is overproduction; LGMF is the logarithmic value of MF, LGMG \(-1\), and \(1\) is supposed to be a good prediction of model; RE is a relative error in percent, RE \(<1\) is overprediction, and RE \(>1\) is underprediction; RMSE is the Root Mean Squared Error, zero value RMSE shows ideal representation, and minimum value shows a better picture of the observed statistics; PBIAS is the Percent Bias, negative PBIAS is under prediction and positive PBIAS is overestimation [134]; MSE is the Median Symmetric Error, MSE is the median absolute error where the relative error is defined to have the same direction, MSE assigns equal importance to deviations of the same order rather than the same magnitude, in the pair of predicted with measured concentration; (I, 10) ng/L and (100 ng/L, 1 ug/L) the absolute error is 9 and 900 ng/L but receives equal penalty [95]. More information on MSE can be found in [139].  
\end{table} 
Table 2: _Cont._different antibiotics produces a range of discrepancies in efficiency, ranging from a factor of 0.003 to 2 [11].