Extended Abstract



Introduction:

Urban rivers are important components of surface water resources, and their quality can be affected by urban development, land-use changes, increased impervious surfaces, human activities, and surface runoff. These factors can introduce various pollutants into surface water systems and consequently alter water quality along the river course. In addition, changes in hydrological conditions between dry and wet periods can affect the transport and distribution of pollutants and, ultimately, water quality. Therefore, assessing water quality during different periods and investigating its spatial and temporal variations are essential for understanding surface water conditions and supporting appropriate pollution management and control strategies. The West Tehran watershed is an important area for surface runoff generation and transport due to extensive urban development, intensive human activities, and the presence of the main Kan, Hesarak, Farahzad, and Darakeh rivers. These rivers pass through mountainous and urban areas and eventually enter the surface runoff collection and conveyance network and the Western Tehran flood diversion channel. Therefore, assessing their water quality under dry and wet conditions can provide an appropriate representation of surface water quality in the watershed. This study aimed to assess the water quality of the main rivers in the West Tehran watershed using the Iranian Surface Water Quality Index (IRWQIsc), compare water quality between dry and wet periods, and investigate dominant patterns of co-variation among water quality parameters using Principal Component Analysis (PCA).

Materials and Methods:

The study area included the main rivers of the West Tehran watershed, namely the Kan, Hesarak, Farahzad, and Darakeh rivers, as well as the Western Tehran flood diversion channel. Ten sampling stations were established within the study area. Sampling was conducted during two periods: August 2025, representing the dry period, and February 2026, representing the wet period. Due to the absence of flow in the Hesarak River at stations 3S and 4S, these stations had no samples available for analysis during either period. Consequently, data from eight active sampling stations were analyzed. Water quality parameters were selected based on the requirements of the IRWQIsc and included BOD₅, COD, pH, electrical conductivity (EC), turbidity, dissolved oxygen (DO), nitrate, total ammonium, phosphate, total hardness, and fecal coliforms. Samples were collected using the grab sampling method, transferred to the laboratory, and analyzed using the designated analytical methods and laboratory equipment. The IRWQIsc was then calculated for each station, and water quality was classified according to the descriptive categories of the index. This index integrates 11 physical, chemical, and microbiological parameters into a single numerical value representing the overall status of surface water quality. For statistical analysis, the normality of the index data was assessed using the Shapiro–Wilk test. Because the data followed a normal distribution, a paired t-test was used to compare the mean IRWQIsc between the dry and wet periods. In addition, PCA was performed to identify dominant patterns of co-variation among water quality parameters and reduce the dimensionality of the dataset.

Results and Discussions:

The IRWQIsc results indicated a considerable difference in water quality between the dry and wet periods. During the dry period, IRWQIsc values ranged from 12.09 to 50.81; the highest value was observed at station 1S (50.81), while the lowest was recorded at station 10S (12.09). The mean IRWQIsc was 38.55, and water quality was predominantly classified as “moderate to relatively poor.” During the wet period, IRWQIsc values ranged from 10.64 to 40.96; the highest value was observed at station 2S (40.96), while the lowest was recorded at station 10S (10.64). The mean IRWQIsc was 29.46, and water quality was predominantly classified as “relatively poor to very poor.” Comparison of the two periods showed that the mean IRWQIsc was 38.55 during the dry period and 29.46 during the wet period, and the difference was statistically significant (p = 0.0207). Therefore, water quality during the wet period was poorer than during the dry period. Station 10S showed the lowest index value in both periods, indicating poorer water quality in the downstream section of the flow system after passing through urban areas. PCA results also revealed differences in the co-variation structure of water quality parameters between the two periods. During the dry period, the first three principal components collectively explained 85.9% of the total variance, with the first, second, and third components accounting for 38.2%, 26.5%, and 21.2%, respectively. During the wet period, the first three principal components collectively explained 84.8% of the total variance, while the contribution of the first component increased to 47.3%. In this component, COD, BOD₅, NH₄⁺, PO₄³⁻, and turbidity showed more prominent contributions. This change indicated that, during the wet period, a greater proportion of the variability in water quality parameters was organized along a common component. Overall, the decrease in mean IRWQIsc during the wet period and the increased contribution of the first PCA component indicated that the change in water quality between the two periods was accompanied by a change in the contribution pattern of water quality parameters. However, because of the limited number of samples and the lack of direct data on meteorological conditions, discharge, and pollutant sources, interpretation of the factors responsible for these variations should be considered exploratory. Therefore, the observed variations cannot be attributed to a specific pollutant source.

Conclusion:

The results demonstrated that the combined application of IRWQIsc and PCA provided an integrated assessment of surface water quality and revealed both seasonal water quality status and dominant patterns of variation among water quality parameters in the West Tehran watershed. Nevertheless, because of the limited number of samples and the absence of direct meteorological, hydrological, and pollutant-source data, the factors influencing these variations should be interpreted as exploratory and probable. Future studies involving more sampling events, simultaneous discharge and rainfall measurements, and direct identification of pollutant sources are recommended to improve understanding of water quality variations in the rivers of West Tehran.