Surface water quality evaluation of Mahanadi and its Tributary Katha Jodi River, Cuttack District, Odisha, using WQI, PLSR, SRI, and geospatial techniques

Abstract

Surface water depletion in Odisha tract poses significant challenges for sustainable water management. An important part of the effort to satisfy the growing demand for water is surface water quality control. For that purpose, this study’s primary goal is to assess the surface water quality for drinking and irrigation at nine different locations, via the use of the innovative techniques. In this regard, Drinking Water Quality Index (DWQI), Partial Least Square Regression (PLSR) and Spatial reflectance (SR) Indices (I), were considered to determine water suitability for different people’s activities. The samples were collected in the study area during the pre-monsoon season of period 2023–2024. The parameters analyzed: pH, DO, Alkalinity, Conductivity, Nitrate, Phosphate and Hardness. The results were subsequently contrasted with the water quality requirements, as instructed by World Health Organizations (WHO). The major anionic trend is expressed in the subsequent order: NO₃⁻ > PO₄³⁻. Finally, the analytical results were collected in order to produce the parameters’ numerical geographic distribution using the geographical information system (GIS) environment. According to the results of pH, the obtained average value is recorded as 8.0. This implies that the water is slight alkaline in nature. The results of the DWQI showed that 44.44% shared investigated locations, were classified as excellent to good, and 11% as poor, 22.22% as very poor and, 22.22% is indicated as unsuitable for drinking purpose classes. In addition, the new SRIs that were taken out of the VIS and NIR regions demonstrated a substantial correlation with DWQI, according to the results. The new SRIs and DWQI had R² correlations with values ranging from 0.65 to 0.82. The results from DWQI and SRI depicts that Nitrate and Phosphate concentration were higher and exceeds the WHO standards. At five sites, which confers as poor water quality, these parameters were recorded very high. Additionally, the main factors causing variations in water quality were fertilizer, organic waste, and soil leaching. Based on the values of R², the PLSR model generated an evaluation of DWQI that was more accurate. Furthermore, the PLSR model generated accurate predictions for DWQI, with an R² of 0.82 and 0.85, in the validation and calibration dataset. Hence, PLSR is efficient and provides us with a clear image for evaluating surface water’s fitness for drinking and its regulating elements. This study provides a quantitative framework for assessing surface water suitable potential zones in the chosen region. By identifying the hidden variables influencing water quality, the three approaches work together to maintain their advantages while also offering crucial information for water management. The results allow for the monitoring of restoration measures to be prioritized, the identification of the anthropogenic impact on the five locations (S-(1), (2), (3), (4), and (5)) and the type of anthropogenic pressure associated with each location, as well as the optimization of monitoring programs to reflect significant anthropogenic pressures. The resulting maps and data offer valuable insights for policy makers and water resource managers to develop targeted surface water management strategies. These findings have significant implications for sustainable water resource management in the region, particularly in addressing challenges related to drinking and agricultural water demand and climate change adaptation. A more thorough assessment of the surface water quality would result from the addition of more water quality indicators, such as hydrological, biological, and particular pollutants, to the straightforward and trustworthy assessment scheme that has been suggested.