Analysis of Variability of Water Quality Indicators in the Municipality Water Supply System—A Case Study

Abstract

This study investigated the variability of water quality indicators in four municipal water distribution systems near a medium-sized city. Despite the proximity of water intakes, water quality in different distribution systems can vary significantly due to local factors such as infrastructure conditions, treatment technology, and specific environmental conditions affecting water in each water supply network. Water samples were collected from multiple points in each system and analyzed for physicochemical properties. The results showed significant differences in total carbon, dissolved organic carbon, and ammonium nitrogen, indicating variability in water quality between systems. These results emphasize the need for integrated management strategies, innovative technologies, and real-time monitoring to maintain water quality. The study also highlights challenges such as aging infrastructure, pollution, and financial constraints in managing water supplies.

1. Introduction

The quality of water in distribution systems is a key issue that directly affects public health and the living comfort of the population. Maintaining the quality and stability of tap water in distribution systems is influenced by a variety of factors, including the water treatment technology used and the technical condition of the water supply infrastructure [1,2,3]. Transported water can change its properties, as water can stay in the distribution system from a few days to even weeks, which can lead to secondary contamination [4,5,6,7]. The technical condition and service life of individual components of the water supply infrastructure (pipes, pumps, valves) have a direct impact on the quality of drinking water. Problems such as pipe corrosion and the development of biofilms are of particular concern, as they can lead to the release of harmful substances and the proliferation of microorganisms [8,9,10,11].

Water distribution systems face many challenges, such as aging infrastructure, climate change, pollution, and limited financial resources. Effective management of these systems requires an approach that integrates different management strategies to ensure the stable quality of water delivered to consumers. As such, it is important to both:

• water quality aspect-monitoring water quality parameters (e.g., chlorine content, chemical and microbiological contaminants, and the presence of biofilms);
• environmental aspect-assessing the environmental impact of industrial and agricultural activities;
• economic aspect-analysis of costs related to the maintenance and modernization of infrastructure, as well as the operational efficiency of distribution systems [12].

Effective management of water quality in distribution systems requires integrated measures that address different operational challenges. There is an increasing emphasis on implementing innovative technologies and intelligent monitoring systems that can analyse water quality in real-time and respond immediately to detected changes. As evident, preventing the degradation of water quality within the network and ensuring its safe delivery to consumers are key challenges that the water supply industry must tackle.

The quality of water delivered to consumers is also influenced by the stability of the water entering the distribution system. Tap water should be stable both physicochemically and microbiologically. Physicochemical stability is related to the transformation and migration of inorganic elements in the network and the corrosion of pipelines. Meanwhile, biostability refers to the issue of microbial regrowth within the distribution system [13,14]. Drinking water quality indicators must be regularly monitored because they are crucial for the health and safety of consumers [15,16]. The most important physicochemical parameters include pH, which affects water corrosivity and taste, as well as electrical conductivity, which is an indicator of dissolved salts and mineral impurities [17,18]. Another important parameter is nitrogen, carbon, and phosphorus compounds, which can affect the development of microorganisms in the network [19]. It is worth emphasizing that phosphorus is not limited in the current regulations on drinking water quality, and its content can significantly contribute to the formation of biofilms in the network. For this reason, despite the lack of specific legal limits, phosphorus control can be an important element in ensuring the biological stability of drinking water.

The study aimed to determine changes in tap water quality at selected points in four municipal distribution systems located near a medium-sized city. The study included an assessment of the physical, chemical and biological stability of the analyzed water samples and a statistical analysis of the significance of differences between independent groups of physicochemical parameters.

2. Materials and Methods

2.1. Object of Study

The study was carried out in four water distribution systems (DS) supplying water to villages located close to each other. Details about the size of the supply area and the estimated number of residents receiving tap water are provided in

Table 1. Information on the public water supply (data for 2023).

	DS(I)	DS(II)	DS(III)	DS(IV)
Length of water mains [km] with house connections without connections	143.50 82.64	75.28 32.94	33.90 20.40	20.5 13.87
Average annual water production [m3/d]	819	695	283	207
Approximate number of people supplied with water from the water supply system	5642	3065	1556	583
Kind of building material	PVC, PE

. Four sampling locations were chosen within each distribution system for water analysis to evaluate quality variations based on the distance from the water treatment plant (WTP). The sampling points were located in public buildings such as schools or administrative buildings. The water drawn from WTP(I) and WTP(II) is groundwater containing high levels of iron and manganese compounds, and it undergoes treatment processes including aeration, filtration, and disinfection. In contrast, the water extracted at WTP(III) and WTP(IV) is groundwater that complies with drinking water standards [16] and is supplied directly to the network after undergoing disinfection. The analyzed distribution systems were located in residential and agricultural areas where the III and IV category of land dominates [20] (there are no large industrial plants in the vicinity). In the analyzed research period for the given area, the average monthly air temperature was 10 °C, while the monthly rainfall amounted to 80 mm [21].

2.2. Water Quality Analysis

The quality of tap water was evaluated using the methods outlined in

Table 2. Methods for evaluating the quality of tap water.

Parameter	Unit	Device/Method
Temperature	°C	HACCP digital thermometer
pH	-
Conductivity	µS/cm	HQ40D Digital multi-meter kit
Turbidity	NTU	EUTECHTM TN-100 Turbidimeter
Phosphorus	mg P-PO43−/L	Spectrophotometer UV VIS DR 6000, Hach-Lange
Ammonium nitrogen	mg N-NH4+/L
Nitrite nitrogen	mg N-NO2−/L
Nitrate nitrogen	mg N-NO3−/L
Sulfates	mg SO4−/L
Chlorides	mg Cl−/L	Titration method
Hardness	mval/L
Alkalinity	mval/L
TC, IC, DOC, BDOC *	mg C/L	Total organic carbon analyzer TOC-L, SHIMADZU
Calcium	mg Ca/L	Agilent 8900 ICP-MS Triple Quad

Note(s): * It was assumed that the BDOC concentration can be represented by the equation: BDOC = 9% DOC [22].

. Water samples were collected twice a week from four locations: one at the treatment plant and three at consumer sites within each DS system. Sampling was consistently performed on the same day and at the same time. The research was conducted from 20 September to 20 October.

2.3. Analysis Procedure

The study included the use of the non-parametric Kruskal-Wallis test [23] to compare medians between groups. In the context of the water quality study, data were analyzed from different points of the distribution system, which included water from waterworks (WTP(I), WTP(II), WTP(III), WTP(IV)) and from three control points for each distribution system: PT1, PT2, and PT3. The Kruskal-Wallis test does not require normality of distribution or homogeneity of variance, making it a suitable tool for analysing such data [24]. Interpretation of the Kruskal-Wallis test results is based on the p-value, where a low p-value (usually less than 0.05) indicates the existence of significant differences between the compared groups, which allows the rejection of the null hypothesis. In contrast, a high p-value suggests there are no significant differences between the groups, which means there are no grounds to reject the null hypothesis [25,26]. The application of this test made it possible to assess whether there were statistically significant differences in water quality between the measurement points studied. A post hoc Dunn’s test was used to further analyse significant differences between groups. The Dunn test is a non-parametric method used for multiple pairwise comparisons when a significant Kruskal-Wallis test result is obtained [27,28]. Where the Kruskal-Wallis test indicates differences between groups, the Dunn test is used to determine which specific groups are statistically different from each other [29,30]. In addition, time-series graphs were used as a tool to visualise changes over time in the study variables.

2.4. Assessment of Water Stability (Water Stability Indeks)

Chemical, physical, and biological stability were assessed based on the guidelines presented in

Table 3. Guidance for assessing water stability [31,32,33].

Chemical Stability
Langelier Index (IL)
IL = pH − pHs pHs = (9.3 + A + B) − (C + D) Where: pH = pH measured in situ. pHs = pH at saturation pHs A = (log10 [Total dissolved solids (TDS)]–1)/10, B = −13.12 × log10 (°C + 273) + 34.55, C = log10 [Ca2+ mg/L as CaCO3]-0.4 D = log10 [Alcal. as CaCO3] TDS = Cond. × Ke, Cond. –conductivity [µS/cm], Ke = 0.55–0.8, assumed: 0.64	IL > 0; Water can dissolve calcium compounds, which enhances its corrosive properties, IL = 0; (−0.5 to + 0.5 is considered as a “zero”) Water is stable: it does not tend to precipitate or dissolve calcium carbonate, the corrosion properties are weakened, IL < 0; Water can precipitate lime, which reduces its corrosive properties.
Ryznar Stability Index (IR)
IR = 2 pHs-pH pHs = pH at saturation pH = pH measured in situ.	IR ˃ 8.5     High corrosion 6.8 ˂ IR ˂ 8.5  Low corrosion 6.2 ˂ IR ˂ 6.8  Is considered neutral 5.5 ˂ IR ˂ 6.2 Moderate scale-forming IR ˂ 5.5     Heavy scale likely to form
Larson Skold Index (ILS)
$\mathrm{L}\mathrm{S}\mathrm{I}={\displaystyle \frac{\mathrm{C}{\mathrm{l}}^{-}\left[\frac{\mathrm{m}\mathrm{v}\mathrm{a}\mathrm{l}}{\mathrm{L}}\right]+\mathrm{S}{\mathrm{O}}_4^{2-}\left[\frac{\mathrm{m}\mathrm{v}\mathrm{a}\mathrm{l}}{\mathrm{L}}\right]}{\mathrm{H}\mathrm{C}{\mathrm{O}}_3^{-}\left[\frac{\mathrm{m}\mathrm{v}\mathrm{a}\mathrm{l}}{\mathrm{L}}\right]}}$	ILS < 0.8 Chlorides and sulfates do not participate in the formation of natural protective layers on steel surfaces (do not cause corrosion), ILS 0.8 ÷ 1.2 Chlorides and sulfates can contribute to the formation of natural layers on steel surfaces, potentially accelerating the corrosion rate. ILS > 1.2 A high rate of localized corrosion is anticipated.
Physical stability
Water was deemed physically stable if its turbidity was below 0.8 NTU [34,35].
Biological stability
Biological stability was evaluated based on the levels of biogenic compounds [22]: ∑Ninorg. ≤ 0.2 mg N/L, BDOC ≤ 0.25 mg C/L, P-PO43− ≤ 0.01 mg P-PO43−/L.

.

3. Results

The results presented in

Table 4. Average values of water quality indicators in DS(I) and DS(II).

Parameter	Unit	DS(I)				DS(II)
		WTP(I)	PT 1	PT 2	PT 3	WTP(II)	PT 1	PT 2	PT 3
Conductivity	µS/cm	504.88	506.86	508.88	502.75	628.86	639.50	637.25	644.86
pH	-	7.31	7.33	7.33	7.31	7.23	7.24	7.27	7.29
Turbidity	NTU	0.68	0.82	0.79	0.69	0.65	0.73	0.74	0.68
Phosphorus	mg P-PO43−/L	0.02	0.01	0.01	0.03	0.03	0.07	0.05	0.07
Ammonium nitrogen	mg N-NH4+/L	0.10	0.03	0.03	0.03	0.04	0.07	0.02	0.03
Nitrite nitrogen	mg N-NO2−/L	0.01	0.01	0.02	0.01	0.01	0.02	0.01	0.01
Nitrate nitrogen	mg N-NO3−/L	0.85	1.13	1.31	1.35	3.53	3.41	3.48	3.55
Chlorides	mg Cl-/L	13.98	14.20	14.64	13.76	23.52	23.74	24.41	24.09
Sulfates	mval/L	5.44	5.43	5.44	5.46	6.38	6.39	6.37	6.33
Hardness	mval/L	5.88	5.86	5.94	5.98	7.19	6.96	7.33	7.39
TC	mg C/L	59.01	51.33	52.09	52.70	68.62	61.51	61.188	62.99
IC	mg C/L	46.46	50.40	50.50	50.82	52.79	59.35	59.01	59.98
DOC	mg C/L	12.53	0.93	1.58	1.87	15.71	2.17	2.18	3.01

show that in DS(I), the mean values of water quality indicators such as pH, alkalinity, and hardness are similar at all measurement points. In the case of mean turbidity values, however, variation between points is observed, which may be due to the variable content of mineral or organic suspended solids in the water. Mean ammoniacal nitrogen values also vary between points, with the highest value recorded at station WTP(I). In addition, mean values for total carbon (TC) and dissolved organic carbon (DOC) show differences between points, with the highest values for these parameters also observed at station WTP(I).

At DS(II), mean values for pH, turbidity, ammonium and nitrate nitrogen, alkalinity, and hardness are similar at all measurement points. In contrast, differences between measurement points are observed in the mean values of conductivity and nitrite nitrogen, with the lowest value recorded at station WTP(I). Similarly to DS(I), the mean values of total carbon (TC) and dissolved organic carbon (DOC) show variation between points, with the highest values for these parameters observed at station WTP(II).

Analysing the mean values of the water quality indicators presented in

Table 5. Average values of water quality indicators in DS(III) and DS(IV).

Parameter	Unit	DS(III)				DS(IV)
		WTP(III)	PT 1	PT 2	PT 3	WTP(IV)	PT 1	PT 2	PT 3
Conductivity	µS/cm	643.38	653.50	652.25	657.00	633.38	626.13	631.25	644.14
pH	-	7.24	7.26	7.21	7.23	7.28	7.47	7.26	7.48
Turbidity	NTU	0.68	0.83	1.25	0.73	0.91	0.85	0.99	1.00
Phosphorus	mg P-PO43−/L	0.04	0.05	0.07	0.04	0.02	0.02	0.02	0.03
Ammonium nitrogen	mg N-NH4+/L	0.12	0.02	0.03	0.02	0.09	0.05	0.05	0.05
Nitrite nitrogen	mg N-NO2−/L	0.01	0.00	0.00	0.01	0.01	0.01	0.01	0.01
Nitrate nitrogen	mg N-NO3−/L	1.46	1.24	1.92	0.98	0.97	1.15	0.88	1.44
Chlorides	mg Cl−/L	3.99	3.11	3.55	2.54	23.08	24.85	24.85	24.34
Sulfates	mval/L	7.69	7.66	7.66	7.59	6.79	6.79	6.69	6.64
Hardness	mval/L	7.84	7.89	7.92	7.85	7.01	7.07	7.03	7.00
TC	mg C/L	74.21	71.49	74.34	71.42	64.09	63.58	64.22	66.55
IC	mg C/L	69.40	64.84	68.55	66.59	61.80	61.05	61.70	60.20
DOC	mg C/L	4.82	6.65	5.79	4.83	2.29	2.53	2.53	6.35

, it can be seen that there are differences in DS(III) mainly for turbidity and nitrogen compounds. The highest mean values for ammoniacal nitrogen and nitrite nitrogen were recorded at WTP(III). The other indicators are characterised by similar values at all points, with slight differences between them.

In DS(IV), differences between measurement points in mean values of phosphorus, ammoniacal nitrogen, and nitrate nitrogen are noticeable. In addition, the highest mean value for ammoniacal nitrogen and the lowest mean value for phosphorus are found in the WTP(IV). The other indicators show similar values at all points, differing slightly from each other.

In the DS(I) water distribution system, most of the quality indicators analysed, such as pH, conductivity, turbidity, nitrate nitrogen, nitrite nitrogen, phosphorus, alkalinity, hardness, chloride, and inorganic carbon (IC), showed no significant differences between sampling sites. The exceptions were total carbon (TC), dissolved organic carbon (DOC), and ammoniacal nitrogen, for which significant differences were found both in Kruskal-Wallis test results (p-value: 0.004, 0.015, 0.019; H-stat: 13.304, 10.537, 9.909) and visually in box plots (Figure 1).

Similarly, in DS(II), the Kruskal-Wallis test showed significant differences between sampling sites in TC, DOC and nitrite nitrogen. The p-values were significantly less than 0.05 (p-value: 0.006, 0.018, 0.004) and the high statistics (H-stat: 12.516, 10.012, 13.439) and box plots in Figure 2 confirm these differences. For the other variables, no significant differences were observed between sampling sites.

In both DS(I) and DS(II) distribution systems, indicators such as total carbon (TC) and dissolved organic carbon (DOC) showed significant differences between sampling sites. DS(I) additionally showed significant differences in ammonium nitrogen levels, while DS(II) showed differences in nitrite nitrogen. These results indicate local variation in key organic constituents and nitrogenous compounds.

DS(III) analysis using box plots (Figure 3) and the Kruskal-Wallis test revealed significant differences in nitrite nitrogen levels between the study sites. This was confirmed by a low p-value (0.002) and a high H-statistic (14.810). For the other variables, no significant differences were observed between sites, as their p-values were higher than the established significance level (0.05).

Similar results were obtained in the DS(IV) analysis, where the Kruskal-Wallis test showed significant differences in ammonia nitrogen, nitrate nitrogen, and phosphorus between the study sites. This is evidenced by low p-values (0.011, 0.047, 0.033) and high H-statistic values (11.122, 7.945, 8.704), as well as the graphs shown in Figure 4. For the other variables, no significant differences were found either. DS(III) and DS(IV) analyses reveal significant differences in the levels of different forms of nitrogen (nitrite nitrogen and ammonium nitrogen) between the study sites.

The water distribution systems DS(I) and DS(II) differ from DS(III) and DS(IV) mainly in the range of variables showing significant differences and in the type of indicators analysed. In DS(I) and DS(II), significant differences were observed mainly in the levels of total carbon (TC) and dissolved organic carbon (DOC), as well as in specific forms of nitrogen, such as ammonium nitrogen in DS(I) and nitrite nitrogen in DS(II). In contrast, in DS(III) and DS(IV), the differences were mainly in the different forms of nitrogen (nitrite nitrogen in DS(III) and ammoniacal nitrogen and nitrate nitrogen in DS(IV), as well as in phosphorus. These results indicate that DS(I) and DS(II) may have greater fluctuations in key organic constituents, which may affect the biological stability of the water. In DS(III) and DS(IV), on the other hand, significant differences in the levels of nutrients, such as nitrogen and phosphorus forms, suggest local variation in biological processes and potential changes in the dynamics of nutrient elements in the water. Differences occurring in water quality indicators may indicate different sources of pollution in each distribution system. In one system, the problem may be mainly biological and organic contaminants, while in another it may be chemical compounds. Despite these differences, in all systems most of the water quality indicators analysed, such as pH, conductivity, turbidity, alkalinity, hardness, chlorides, and inorganic carbon (IC), showed no significant differences between sampling sites. Where significant Kruskal-Wallis test results were obtained, post hoc tests were conducted using the Dunn’s test, which allows pairs of groups to be compared for significant differences. The results of Dunn’s tests are presented below, enriched with the corresponding time-series graphs, which visually illustrate significant differences in the content of the test substances between the different points. The DS(I) distribution system showed significant differences in TC (Figure 5a) and DOC (Figure 5b) between WTP(I) and PT 1 and PT 2, and in ammonia nitrogen between WTP(I) and PT 1 (Figure 5c).

In the DS(II) distribution system, significant differences in TC content are found between WTP(II) and PT 1 and PT 2 (Figure 6a). In DOC content, differences are shown between WTP(II) and PT 2 and PT 3 (Figure 6b), and in nitrite nitrogen content between WTP(II) and PT 3 and PT 1 and PT 3 (Figure 6c).

In the DS(III) distribution system, differences in nitrite nitrogen occur between WTP(III) and PT 3 and PT 1 and PT 3 (Figure 7). In the DS(IV) distribution system, significant differences in ammonium nitrogen content occur between WTP(IV) and PT 1 and PT 3 (Figure 8a), in phosphorus content between WTP(IV) and PT 2 (Figure 8b), and in nitrate nitrogen content between PT 2 and PT 3 (Figure 8c).

Post hoc analyses showed significant differences between the groups for the parameters tested (such as ammoniacal nitrogen, nitrite nitrogen, TC, DOC, phosphorus, nitrate nitrogen). These results indicate potential changes in the content of the substances tested between different points in the intake (WTP(I), WTP(II), WTP(III), WTP(IV)) and points (PT 1, PT 2, PT 3). Such detailed comparisons can be crucial in planning measures to improve the efficiency of water treatment processes in individual distribution systems. In the context of water system management, the water and sewage company needs to approach each of these systems individually. Our research shows that despite the proximity of intakes, and similar treatment technology, there are significant differences in water quality. Therefore, we recommend that individualized strategies and policies be developed that take into account these differences to improve water quality in each of the systems effectively.

The results indicate that the analysed tap water from the four intakes did not comply with the guidelines for biological stability of water due to increased contents of nutrients (in particular exceedences concerned inorganic nitrogen-in each sample taken N_inorg > 0.2 mg N/L (

Table 6. The content of nutrients in tap water of the analyzed distribution systems.

	N			p			BDOC
	Min-Max	Mean	Percentage of Samples <0.2 mg Ninorg/L	Min-Max	Mean	Percentage of Samples <0.01 mg P-PO43−/L	Min-Max	Mean	Percentage of Samples <0.25 mg C/L
DS(I)
WTP(I)	0.56–2.53	0.96	0	0.00–0.05	0.02	50	0.06–2.53	1.33	25
PT 1	0.61–2.42	1.18	0	0.00–0.04	0.01	62.5	0.01–0.35	0.10	87.5
PT 2	0.74–2.25	1.18	0	0.00–0.04	0.01	62.5	0.00–0.45	0.17	75
PT 3	0.76–2.73	1.39	0	0.00–0.06	0.03	37.5	0.05–0.63	0.20	75
DS(II)
WTP(II)	1.85- 4.73	3.58	0	0.00–0.07	0.03	37.5	0.36–2.28	1.66	0
PT 1	2.27–4.52	3.50	0	0.00–0.17	0.07	12.5	0.05–0.70	0.23	62.5
PT 2	2.26–5.81	3.50	0	0.00–0.11	0.05	12.5	0.01–0.97	0.23	87.5
PT 3	1.85–4.88	3.59	0	0.00–0.10	0.07	14.3	0.00–1.35	0.32	71.4
DS(III)
WTP(III)	0.17–1.93	1.08	0	0.00–0.003	0.02	25.0	0.04–0.68	0.24	62.5
PT 1	0.79–2.45	1.21	0	0.00–0.08	0.02	25.0	0.00–0.74	0.27	62.5
PT 2	0.51–2.31	0.94	0	0.00–0.05	0.02	25.0	0.00–0.75	0.27	62.5
PT 3	0.79–2.26	1.50	0	0.00–0.07	0.02	42.8	0.07–3.16	0.67	57.1
DS(IV)
WTP(IV)	0.95–2.33	1.59	0	0.00–0.07	0.04	25	0.03–1.34	0.51	25
PT 1	0.80–2.25	1.26	0	0.00–0.07	0.05	12.5	0.06–1.39	0.71	12.5
PT 2	0.80–2.93	1.83	0	0.02–0.10	0.07	0.0	0.08–1.67	0.61	37.5
PT 3	0.83–1.40	1.01	0	0.00–0.08	0.04	28.6	0.06–1.16	0.51	28.6

). The content of inorganic nitrogen compounds for DS(I), DS(III), and DS(IV) was comparable and ranged from 0.17–2.93 mg N/L, while water from DS(II) was characterised by significantly higher values of 1.85–3.59 mg N/L. The proximity of farmland around the intake may have caused the elevated content of nitrogen compounds for DS(II). The predominant form of nitrogen was N-NO₃⁻, accounting for an average of 93%-DS(I), 98%-DS(II), 92%-DS(III), and 96%-DS(IV). In contrast, phosphate concentrations in the analysed tap waters ranged from 0.00 to 0.17 mg PO₄³⁻/L.

The presence of nutrients in tap water promotes the growth of microorganisms and thus increases the risk of secondary water contamination. In the analyzed distribution systems, the percentage of samples meeting two of the three water biostability criteria set for nitrogen, carbon, and phosphorus was:

• DS(I): 25% WTP(I), 37.5% (PT 1), 37.5% (PT 2), 25% (PT 3),
• DS(II): 12.5% WTP(II), 12.5% (PT 1), 25% (PT 2), 0% (PT 3),
• DS(III): 37.5% WTP(III) 25% (PT 1), 25% (PT 2), 14,3% (PT 3),
• DS(IV): 12.5% WTP(IV), 0% (PT 1), 0% (PT 2), 0% (PT 3) (Table 6)

Water’s physical stability is primarily assessed by its turbidity, which should remain below 0.8 NTU [34,35]. However, according to drinking water quality guidelines, the maximum recommended turbidity level is 1.0 NTU, a threshold that ensures water is both safe and aesthetically acceptable to consumers. In the tap water tested, the mean turbidity was 0.75 NTU for DS(I) and 0.7 NTU for DS(II), which is close to the limit value of 0.8 NTU. In contrast, it was 0.94 NTU for DS(III) and 0.87 NTU for DS(IV). Based on the results, it can be suspected that only water transported by the DS(I) and DS(II) system could be characterised by the physical stability of the water. An increase in turbidity in itself does not pose a direct threat to human health, but in some situations it may indicate a disruption in the treatment process, resulting in an increased risk of water quality related diseases. Among consumer complaints about water services, more than 40 percent are related to concerns about deterioration of water aesthetics [17].

The calculated water corrosivity indices for the analysed points (

Table 7. Tap water corrosivity indicators of the analyzed distribution systems.

	Ryznar Index			Langelier Index			Larson-Skold Index
	Min	Max	Mean	Min	Max	Mean	Min	Max	Mean
DS(I)
WTP(I)	7.12	7.50	7.37	−0.09	0.09	−0.03	0.12	0.16	0.14
PT 1	6.93	7.18	7.07	0.07	0.18	0.13	0.13	0.16	0.14
PT 2	7.00	7.22	7.11	0.04	0.20	0.11	0.12	0.20	0.15
PT 3	6.97	7.18	7.18	0.07	0.16	0.10	0.12	0.18	0.14
DS(II)
WTP(II)	7.18	7.27	7.23	−0.04	0.04	0.00	0.16	0.20	0.18
PT 1	6.99	7.07	7.03	0.07	0.14	0.10	0.17	0.21	0.19
PT 2	6.83	6.99	6.92	0.12	0.26	0.18	0.17	0.21	0.19
PT 3	6.84	6.94	6.91	0.17	0.24	0.19	0.18	0.21	0.19
DS(III)
WTP(III)	7.03	7.23	7.12	0.03	0.15	0.08	0.04	0.19	0.16
PT 1	6.65	6.99	6.81	0.17	0.47	0.33	0.17	0.22	0.19
PT 2	6.82	7.15	7.00	0.06	0.21	0.13	0.17	0.22	0.19
PT 3	6.59	6.76	6.66	0.31	0.51	0.41	0.17	0.21	0.19
DS(IV)
WTP(IV)	6.78	7.09	6.95	0.06	0.29	0.15	0.07	0.1	0.08
PT 1	6.63	6.92	6.72	0.15	0.30	0.27	0.06	0.08	0.08
PT 2	6.68	6.92	6.79	0.16	0.30	0.21	0.07	0.1	0.08
PT 3	6.66	6.88	6.75	0.18	0.33	0.24	0.07	0.09	0.08

) indicate that the mean values of the Langelier Saturation Index LSI oscillate between −0.03 and 0.41, suggesting a tendency toward calcium sediment precipitation. Only at the WTP(I) and WTP(II) intake is the water chemically stable (mean LSI values range from −0.5 to 0.5). Temperature is one of the parameters that changed during water transport. In the analyzed WTPs, the average temperature was 11.18 °C, while at the consumer’s tap it was more than 1.5 times higher. The increase in temperature was mainly related to the stagnation of water in the internal installations of the buildings. The mean values obtained with the RSI obtained values of 7.07–7.37-DS(I), 6.91–7.23-DS(II) 6.66–7.12-DS(III) which allows the waters to be classified as poorly corrosive. In contrast, the water distributed by the DS(II) distribution system reached RSI values of 6.72–6.95, which corresponds to the chemical stability of the water (neutral nature of the water). Based on the LSI index, it can be concluded that the water is weakly corrosive and that the chlorides and sulphates present in the water will not impede the formation of a natural corrosion protection layer (the average LSI values were less than 0.2 in each case analysed). However, it should be noted that tap water transported by the DS(IV) system had the lowest chloride (0–7.1 mg Cl⁻/L) and sulphate (48–53 mg SO₄³⁻/L) contents.

Several indices should always be analysed when assessing the corrosivity of water, as a single one may lead to false conclusions. The LSI index, differs from IR and IL because it is based on the corrosive effect of chloride, sulphate, and bicarbonate ions, without taking into account other physicochemical factors such as pH, temperature, total dissolved solids, alkalinity, and calcium.

4. Discussion

Nitrogen and phosphorus compounds are prevalent groundwater contaminants, primarily resulting from agricultural practices and inadequate waste management [36]. For BDOC in DS(I) and DS(II), higher values were observed at the WTP compared to the consumer intake points (Table 6). This could be attributed to the partial degradation of organic compounds by microorganisms in the biofilm present on the water pipes. The biofilm creates a protective environment for bacteria, facilitating easier access to nutrients [37]. Biofilm formation is more likely in nutrient-poor distribution systems, as starving organisms can obtain a continuous supply of nutrients by adhering to surfaces [38]. Biofilm forms on all types of water pipes. This microbial growth can lead to the release of bacteria and other contaminants into the water, affecting its quality and safety. Using synthetic materials does not prevent biofilm growth, as organic compounds can leach from their surfaces. However, biofilms that form on smooth surfaces tend to adhere less strongly, making it easier for microorganisms to be flushed into the water with increased flow [39]. Additionally, the presence of biofilm has been linked to the spread of antibiotic-resistance genes, posing further health risks [40]. In addition to impacting water quality, biofilm formation can also increase operational costs by promoting corrosion in pipelines, further complicating water treatment. Addressing biofilm-related issues requires continuous monitoring and improved disinfection methods to minimize microbial growth and safeguard water.

Water quality is crucial for public health, as drinking water can carry numerous contaminants that pose a serious risk to consumers. Biological contaminants such as bacteria, viruses, and parasites can lead to serious diseases such as cholera, dysentery, and viral hepatitis [41]. Chemicals such as heavy metals (e.g., lead, mercury) and organic compounds can cause long-term health effects, including cancer, kidney and nervous system damage. Poor water quality can also negatively affect children, causing delays in physical and mental development [42,43]. Therefore, regular monitoring and maintaining high water quality is essential to prevent the spread of diseases and protect the health of consumers.

Traditional methods of assessing water quality have so far been used, which involve manual in-situ sampling and sending it to a laboratory for appropriate testing and analysis. Although this technique is widely used, it is time-consuming, labor-intensive, and involves long waiting times for results. However, technology is now evolving towards “smart” sensors that can be easily placed not only in rivers and lakes, but also in water distribution systems and wastewater treatment plants. These sensors, in addition to real-time monitoring, have the advantage that the data obtained can be shared with citizens via the Internet, which increases transparency and environmental awareness [44]. Real-time monitoring technologies such as IoT sensors enable real-time observation of physicochemical parameters of water, including total dissolved solids (TDS), pH, temperature, conductivity, and turbidity [45,46]. This type of monitoring allows for immediate response to changes in water quality. When used in conjunction with cloud-based data analysis systems, biofilm problems, and other contaminants can be identified more quickly, which in turn minimizes the risk of contamination spreading. Standalone devices that integrate all analysis steps into a single device, eliminating the need for traditional laboratory components, are being developed. An example of this approach is the Micro Total Analysis System (μTAS), which allows the functionality of an entire laboratory to be transferred onto a single chip [47]. Such systems and electrochemical sensors can provide an efficient alternative to conventional methods, enabling faster, cheaper, and more automated water quality analysis.

5. Conclusions

Analysis of water distribution systems reveals significant differences in water quality indicators, which can be crucial for effective water quality management and monitoring. Studies conducted on different distribution systems indicate significant differences both within and between systems. In particular, these differences relate to parameters such as concentrations of nitrogen, phosphorus and carbon compounds, which are key to the assessment of water status and the effectiveness of treatment processes. In addition, variation was noted in the biological, chemical and physical stability of the water. These results highlight the need for precise monitoring and the adaptation of control strategies to the specific conditions of each distribution system. Analysis of biological parameters indicated that water from four intakes did not meet the criteria for biological stability mainly due to elevated contents of nutrients, especially inorganic nitrogen. The highest concentrations of this element were recorded in the DS(II) intake, which may be due to the close proximity of agricultural fields. Chemical stability indices, such as the Langelier index and Ryznar index, revealed varying degrees of corrosion tendency in the water samples analysed, highlighting the need to monitor these parameters to prevent corrosion in distribution systems. The physical stability of the water was generally good, although variable turbidity values were observed at some measurement points, which could be due to the presence of mineral or organic suspended solids. Distribution systems DS(I) and DS(II) and DS(III) and DS(IV) appeared to exhibit similar chemical and physical properties, suggesting similar environmental conditions or treatment processes at these points.

The research was conducted in the autumn season, but in order to obtain a more complete picture of the variability of water quality, further research is planned, which will be conducted on an annual basis. This approach will allow for a comprehensive understanding of seasonal changes in physical, chemical and biological parameters of water, and will also enable the adjustment of management and monitoring strategies to dynamically changing environmental conditions. Thanks to this, the data obtained will allow for an in-depth analysis of the impact of seasonal factors on the stability of water in distribution systems and will contribute to a more precise definition of the requirements related to its treatment and protection.