## 3 Results and Discussion
### Physical-Chemical Parameters of Investigated Drinking Water Samples
Among the sampling months, August was characterized by high values of Fe level and turbidity, March by high pH and concentrations of residual chlorine and ammonia, and January by high values of conductivity and nitrates but also Cd and oxidizability (Figure 6). The locality explained 37.6% (23.2%) of the variation in the physical and chemical parameters (pseudo-F = 2.6, \(p\) = 0.001), the first two constrained axes being significant, accounting for 39.6% (pseudo-F = 0.8, \(p\) = 0.001) and 27.1% (pseudo-F = 0.7, \(p\) = 0.002) of the explained variation (Figure 8). Along the first axis, conductivity and ammonia were negatively correlated with Fe and turbidity. Along the second axis, all response variables were positively correlated. The locality with the most distinctive chemical composition of water was Sadu. It was characterized by the highest values of turbidity, Fe, Cd, Al, and oxidizability, but also of ammonia and Mn. Seica-Mare also showed distinct water characteristics, with high values of conductivity, nitrates, pH, residual chlorine, ammonia, and Mn. On the other hand, Paltinis showed the lowest values of Cd, Al, oxidizability, ammonia, and Mn. Sibiu and the other large towns had similar water physical-chemical characteristics, with low values of conductivity, nitrates, pH, and residual chlorine. Figure 7: The t-value biplots for the physical and chemical parameters in relation to surface (Surf) and groundwater (Ground). The pink circle delimits the ordination space for significant positive response to the considered variable, while the blue circle marks the negative response. The national report highlights Mn, Fe, ammonia, and nitrates as the main chemical parameters exceeding the allowed values for some chemicals, from 15% for Mn to 7.8% for nitrates, of all samples collected in 2021 from small supply zones all over the country [73]. Similarly, other countries like Iran reported Fe and Mn as dominant metals exceeding allowed values [85]. According to the Council Directive 98/83/EC [86], a parameter's variability and the long-term trend of its concentration should determine the location and frequency of sampling. In our spatiotemporal model of analysis, the variation of physical and chemical parameters is explained by the water sources (Surf and Ground), sampling month, and locality. In the past, the city of Medias was polluted with heavy metals from a neighboring metallurgical plant, but recent research performed on local drinking water sources indicates a low level of pollution with Cd, Ni, Cr, Pb, and As [87]. In certain situations, chemical heterogeneity may refer to inadequate treatment in water plants, or post-treatment contamination in the distribution pipes, explaining the high concentrations of Mn, Fe, and nitrates [88]. Nitrates and nitrites, often associated with anthropogenic activities, are involved in the maintenance and development of microorganisms which can influence water turbidity [89; 90]. Thus, new methods for a combined evaluation of physical-chemical parameters and microbiome data have been developed [91]. In other countries, Spain for example, it has been found that the total water hardness, potassium, and pH influenced radioactivity levels [92]. Figure 8: The biplot of redundancy analysis (RDA) relating the physical and chemical parameters to the localities. The names of localities are written in full. The first two constrained axes are illustrated, both being significant. The co-inertia analysis between radioactivity and physical-chemical parameters showed a negative correlation between the gross alpha activity and nitrates, oxidizability, and conductivity, and a positive correlation with turbidity and residual chlorine. The gross beta activity was positively correlated with conductivity, Cd, and Mn, and negatively correlated with Fe (Figure 9).