## 3 Results and Discussion
### CECs
Nutrient Parameters (N-NO\({}_{3}\), N-NO\({}_{2}\), N-NH\({}_{4}\), P-PO\({}_{4}\), P total) are positioned in the negative F1 region, suggesting that they are negatively correlated with many pollutants. Sites like Mangalia and Costinesti are aligned with these parameters, indicating potentially nutrient-rich but less polluted environments. The pH is in the negative quadrant, showing a reverse correlation with pharmaceutical contaminants. The PCA confirms that certain locations are CECs pollution hotspots, particularly in tributary rivers and industrialized areas. Sulfamethoxazole, trimethoprim, carbamazepine, bisphenol A, ethinylestradiol, and diclofenac form a cluster in the positive F1 direction, suggesting they share common sources or behavior in the aquatic environment. They tend to group with locations like Siret R. upstream, Prut R. downstream, and Galati downstream, which could be indicative of wastewater discharges. The PCA analysis also highlights distinct spatial trends in pollution distribution across the sampling sites. Pollution hotspots, such as Prut R. downstream, Siret R. upstream, and Galati downstream, show a strong association with pharmaceutical pollutants, indicating high anthropogenic impact, likely driven by wastewater treatment plant (WWTP) effluents discharges. In contrast, less contaminated sites, including Sulina mouth, Sf. Gheorghe upstream, and Chilia veche downstream, exhibit lower pollution levels, suggesting they  
Figure 4: PCA (Principal Component Analysis) biplot for the correlations between physico-chemical parameters and pharmaceuticals pollutant concentrations, as well as their spatial distribution across sampling sites. represent more natural water bodies with reduced contamination. The marine area samples appear to be less contaminated compared to those from freshwater sources, due to lower anthropogenic influence or greater dilution effects in the marine environment. To better understand the relationships between physico-chemical parameters and pharmaceutical contamination, a Random Forest regression model was applied. This approach allows for the identification of key environmental factors influencing the distribution of contaminants while handling complex interactions and non-linear patterns in the data. The model was configured with regression-type analysis, random input selection, and a sampling method without replacement. The number of variables (predictors) considered at each tree split (Mtry) was set to 2, selecting two variables at each split, with a sample size of 29. A total of 300 trees were built out of the 500 initially required. The model's predictive performance was evaluated using the Out-of-Bag (OOB) error, residual analysis, and variable importance measures (Table S3, Supplementary Materials). The scatter plot of observed vs. predicted sulfamethoxazole concentrations reveals a moderate predictive power, with some deviations suggesting potential over- or underestimation (OBB 53.4) (Table S3, Supplementary Materials). The variable importance analysis identified P-PO\({}_{4}\) (phosphate), pH, total phosphorus (P total), and ammonium nitrogen (N-NH\({}_{4}\)) as key predictors, while N-NO\({}_{3}\) (nitrate) showed negative importance, suggesting it may introduce noise rather than contribute to predictions. These findings highlight the influence of nutrient levels on Sulfamethoxazole persistence in aquatic environments and suggest potential model refinements for improved accuracy. The Random Forest regression model applied to trimethoprim concentrations (Figure 5) showed an Out-of-Bag (OOB) error of 6.198, indicating a relatively low prediction error. The scatter plot of observed vs. predicted values demonstrates moderate predictive power, with points generally following the 1:1 diagonal. The variable importance analysis highlights P-PO\({}_{4}\) (phosphate), total phosphorus (P total), pH, and N-NH\({}_{4}\) (ammonium nitrogen) as the most influential parameters affecting trimethoprim concentrations.