## 5 Evaluation of Model Simulation Efficiency
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Model** & **Target Antibiotics** & **Application** & **Error Metrics** & **Deviation** & **Often Used Metrics** & **Range** & **Region** & **References** \\ \hline phATE & Ibuprofen & Modeling concentration of antibiotics & R\({}^{2}\) & 0.48 & - & - & Canada & [42] \\  & Naproxen & Modeling concentration of antibiotics & R\({}^{2}\) & 0.68 & & & & \\  & Carbamazepine & Modeling concentration of antibiotics & R\({}^{2}\) & 0.68 & & & & \\  & Acetaminophen & Predict human antibiotics & & & & & & \\  & & in surface water & MF & 47 & & & USA & [136] \\ Erythromycin-H2O & Predict human antibiotics & MF & 4 & & & & & \\  & Oxytetracycline & Predict human antibiotics & MF & 0.02 & & & & \\  & Sulfamethoxazole & Predict human antibiotics & MF & 9 & & & & \\  & Tetracycline & Predict human antibiotics & & & & & & \\  & & in surface water & MF & 6.5 & & & & \\ GREAT-ER & Triclosan & Predict catchment & MF & 0.769 & R\({}^{2}\) & \(\geq\)0.7 & Germany & [137] \\  & Triclosan & Predict local concentration & MF & 1.5 & & & & \\  & Bezafibrate & Predict local concentration & MF & 0.03 & & & Spain & [11] \\  & Carbamazepine & Predict local concentration & MF & 0.5 & & & & \\  & Citazolam & Predict local concentration & MF & 0.1 & & & & \\  & Diclofenac & Predict local concentration & MF & 2 & & & \\  & Erythromycin & Predict local concentration & MF & 0.7 & & & & \\  & Kiteorofen & Predict local concentration & MF & 0.003 & & & & \\  & Trimethoprim & Predict local concentration & MF & 0.18 & & & \\  & Atorvastatin & Predict regional concentration & MF & 0.2 & & & \\  & Carbamazepine & Predict regional concentration & MF & 0.004 & & & \\  & Fluoxetine & Predict regional concentration & MF & 0.52 & & & \\  & Naproxen & Predict regional concentration & MF & 3 & & & \\  & Trimethoprim & Predict regional concentration & MF & 0.08 & & & \\  & Diclofenac & Predict local concentration & RMSE & 0 to 80 & & & Spain & [121] \\ QUAL-2K & Carbamazepine & Degradation study & RE & 5.85 to 6.82 & RMSE & \(\leq\)10\% & China & [123] \\  & Triclosan & Degradation study & RE & \(-\)7.18 to \(-\)157 & & & & \\ WASP & Venlafazine & Predict concentration in river water & PBIAS & \(-\)5 to \(-\)13 & PBIAS & \(\leq\)25\% & Canada & [2] \\  & Naproxen & Predict concentration in river water & PBIAS & \(-\)1 to 5 & & & \\  & Carbamazepine & Predict concentration in river water & & PBIAS & \(-\)22 & & & \\  & Venlafazine & Predict concentration in river water & & PBIAS & \(-\)9 to \(-\)26 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Representative model prediction performance evaluation of environmental antibiotic rate model simulation over a regional scale (country) application in different countries across the globe. Application of the same model in different regions has been observed to give different efficiency. Evaluation of ibuprofen prediction using ePiE in different Europe countries (UK, Germany, Spain, Slovenia, and Croatia) using the same simulation scenario has been observed to have variable efficiency among countries. Comparing predicted and observed concentrations gives a median symmetric error of 88% in Germany and 4427% in Croatia. On the other hand, applying the GREAT-ER model in the exact location (Spain) but for the same model in the same model, the difference between the two models is not significant.