## 3 Results and Discussion
### CECs
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Compound** & **Abbreviation** & **Min.** & **Max.** & **Lower P/NEC \({}^{*}\) ng/L** & **No. of Positive Results** \\  & & **(ng/L)** & **(ng/L)** & **Average (ng/L)** & **Fresh/Marine Water** & **No. of Positive Results** \\ \hline Sulfamethoxazole & SMO & 3.2 & 36 & 18.13 & 100/60 & 31 \\ Trimethoprim & TMP & 2.4 & 12 & 6.35 & 100 & 25 \\ Ciprofloxacin & CIP & 1.8 & 5.2 & 3.58 & 89/8.9 & 8 \\ Fluemquine & FLU & 4.6 & 6.4 & 5.5 & 1500/150 & 2 \\ Amoxicillin & AMX & 3.8 & - & - & 78/78 & 1 \\ Cervamine & CFX & 1.6 & 4.9 & 3.25 & 1290/130 & 2 \\ Dcloxacillin & DCX & 3.5 & 9.6 & 5.5 & 51.051 & 5 \\ Carbamazepine & CBZ & 5.6 & 38 & 18.05 & 50 & 28 \\ Pravastatin & PRV & 3.1 & 24.8 & 12.31 & 4570/460 & 15 \\ Erythromycin & ERV & 4.3 & - & - & 30/30 & 1 \\ Piroaxican & PIR & 5.6 & 28.6 & 17.62 & 490/49 & 6 \\ Ketoprofen & KET & 5.6 & 26 & 13.45 & 2100/210 & 6 \\ Dclofenac & DCF & 4.6 & 132 & 40.75 & 50/5 & 28 \\ Naporous & NAP & 4.9 & 8.6 & 6.2 & 1700/170 & 6 \\ Enilcanozide (Imazide) & IMEZ & 4.8 & 31.4 & 14.64 & 870/87 & 11 \\ Clotrimazole & CLO & 5 & 8.2 & 6.53 & 30/3 & 3 \\ Drespisprone & DRO & 1.04 & 3.4 & 2.09 & 120/12 & 8 \\
17a-Ethinylestradiol & EE2 & 1.15 & 3.05 & 1.91 & 0.035/0.0035 & 9 \\ Bisphenol A & BPA & 34.5 & 342 & 155.2 & 240/1600 & 32 \\ \hline \end{tabular}  
* lower P/NEC values according to NORMAN database ([[https://www.norman-network.com/nds/prioritisation/](https://www.norman-network.com/nds/prioritisation/)]([https://www.norman-network.com/nds/prioritisation/](https://www.norman-network.com/nds/prioritisation/))) (accessed on 13 May 2024) [50] and EU watch lists [16; 17; 18; 19; 20].  
\end{table} 
Table 5: Minimum (Min. ), maximum (Max. ), and average values of the CECs measured concentrations, the Predicted no-effect concentration (PNEC) values and the detection frequency of the identified substances. Figure 2: Ranges of concentrations of the determined pollutants in the Danube River and Black Sea surface water samples. confirmed the patterns among sampling locations based on contaminant concentrations (Figure 3). The analysis revealed three clusters (C1, C2, C3) as follows: cluster C1 (green): includes locations such as Mangalia, Costinesti, Gura Portitei, and Galati shipyard downstream, suggesting similarity in contamination patterns; cluster C2 (blue): includes Prut R. upstream, Siret R. upstream, and Chilia veche upstream and downstream, which may indicate distinct pollution sources; cluster C3 (red): includes Braila and Galati harbor downstream, Cernavoda bridge and Reni downstream, suggesting a unique pollution profile. The between-cluster variance is high (81.63%), indicating that the three clusters are well-separated and validate the chosen classification. Clusters 1 and 3 are more similar to each other, as indicated by their lower centroid distances. The mobility and persistence of pharmaceuticals in aquatic environments are strongly influenced by physico-chemical parameters such as pH, temperature, dissolved oxygen, and nutrient concentrations. These parameters affect the solubility, degradation, and adsorption of contaminants, ultimately shaping their spatial distribution. Understanding these interactions is essential for assessing pollution sources and potential environmental risks. To investigate these relationships, a Principal Component Analysis (PCA) and Random Forest regression models were conducted, examining the correlations between key physico-chemical parameters and pharmaceutical pollutants across different sampling sites. The PCA biplot provides an insight into how contaminants cluster together, which locations exhibit higher pollution levels, and which environmental factors may drive these trends (Figure 4). Figure 3: The hierarchical clustering analysis (HCA) dendrogram. The axes F1 (37.18%) and F2 (13.16%) together explain 50.34% of the total variance of the dataset's structure.