## 5 Evaluation of Model Simulation Efficiency
A similar discrepancy has been observed in regional simulation as well; however, it can be noted that the discrepancy is higher in regional simulation as averaging emission scenarios affect the accuracy of the simulation. Further, this can be attributed to the spatial discretization, which might be inconsistent with the scale of ground physical phenomena. The phATE in USA, phATE, and iSTEAM in Canada, and GREAT-ER in Spain have been observed to have a relatively lower deviation between the predicted and observed data, as illustrated in Table 2. Further, the deviation in the majority of the models is positive, which is an overestimation. This has been observed mainly in the spatially explicit fate models (phATE, GREAT-ER, ePiE, and EUSES). Despite the expected acceptable uncertainties, both under and overprediction of the models are gaps that might be linked to various defects and require further investigation and improvement. The description of an area to be modeled requires defining its boundaries, describing the existing conditions and relationship, and characterizing the impact and relationship of the surrounding environment outside the model area. In addition, the characterization of the modeling area must be described in terms of space and time, as well as the selection and characterization of the processes that occur in the system. This necessitates the establishment of relationships and calibration to ensure that the model's perdition is consistent with the monitoring data. However, as the calibration is dependent on the monitoring data, uncertainties from sampling errors and measurement errors arising from the analytical detection equipment would also affect the prediction of the model. Moreover, due to uncertainties in representing the actual spatial mapping, temporal distribution, boundary conditions, and numerical approximation, the calibration of a model may be burdened with error and uncertainty. In explicit spatial models, due to coarse spatial resolution (e.g., phATE and WASP), model parameterization and site-specific predictions are challenging [140]. Further, site-specific contaminant fate mechanism evaluation is complex. Thus, leading to a range of errors and uncertainty regarding an individual perspective and the application of a model to a different environment. As a result, sensitivity analysis (examination of the impact of model errors and uncertainties) and verification is highly required. However, the spatial and temporal discretization, sensitivity analysis of environmental parameters, emphasis on the spatial distribution of emissions, and the depiction of appropriate spatial patterns of environmental drivers are essential factors that need to be addressed in future research and model development. The other factor is the conclusive adaptation of a first-order rate constant for all instream degradation mechanisms in natural water bodies. Despite the fact that the second-order decay rate requires extensive mathematical computation, the degradation (e.g., photodegradation, hydrolysis, and sorption) of various antibiotics in various environmental matrices is well expressed through the second-order decaying mechanism [51; 58; 65; 71; 141]. As discussed in Section 3, the rate constant of various removal processes is not independent of the particular combination of hydrological and geomorphologic properties of the system under study, which suggests a thorough evaluation of degradation kinetics. Therefore, a particular investigation of the environmental degradation kinetics of given antibiotics in a target area is important. Accounting variabilities and different scenarios allow for flexible characterization and estimation of different water quality state variables and parameters. Hence, considering variables, such as population, informal settlement, seasonal consumption rate, flow variability, chemical properties, degradation kinetics, sewer leakage, and WWTP removal rates, could help to observe the real phenomenon and improve model prediction [113].