Lakhanpur block, situated in Jharsuguda district of Odisha, faces serious deterioration due to massive human intervention. Nevertheless, the growing intensity of human activities—especially religious rituals—combined with inadequate waste disposal systems has created significant environmental concerns. These factors have led to increased bacterial pollution and a deterioration of water quality, highlighting the urgent need for corrective action. Owing to its severely degraded condition, only a minimal fraction of the river’s ecological and economic significance still persists. The present study focuses on evaluating pre-monsoon seasonal variations in water quality by conducting systematic field investigations at 20 designated sampling sites. Data collected over a 5-year period (2020–2025) for nine key water quality parameters were examined using the Water Quality Index (WQI), along with multivariate statistical techniques and machine learning (ML) approaches. Based on physicochemical outcomes, certain parameters like EC and HCO3- exceeded permissible limits set by WHO Standards, poses health risks, particularly to infants and local residents. Reported WA (Weighted Arithmetic)—WQI values during the pre-monsoon season ranged from 35 to 355, indicating a spectrum from excellent to unsuitable water quality. Notably, approximately 45% of the sampled locations exhibited water quality falling into the poor to unsuitable categories. In contrast, the calculated Numerow Pollution Index (NPI) values ranged from 5 to 65, similarly exhibiting water quality levels ranging from excellent to very poor. Here, the surface water chemistry is impacted by both anthropogenic activities, such as agricultural runoff, and geogenic processes, such as mineral dissolution. Entropy (E)–weighted WQI values derived from the present study range from 26 to 255, indicating water quality conditions spanning from excellent to extremely poor. Evaluated results show that five sites (25%) along the river meet standards for drinking and irrigation use, whereas ten sites (50%)—mostly situated near wastewater discharge points—fall within the very poor to extremely poor water quality categories. This deterioration is marked by elevated levels of EC, Na⁺, HCO₃⁻, Cl⁻, and SO₄²⁻, highlighting significant pollution. To assure the security of water essential for agriculture, several agricultural indicators such as SAR (sodium adsorption ratio), % Na (sodium percentage), RSC (residual sodium carbonate) and MH (magnesium hazard) were investigated. The computed score for all examined locations are recorded as: SAR (6.7 – 33), % Na (37 – 112), RSC (0.78 – 5.0), and MH (23 – 115). In a contrast, samples were considered suitable for irrigation, based on the observations gained from SAR (40%), MH (55%), RSC (70%), and MH (45%). The Piper diagram categorizes the study area into three distinct types, including Ca2+- Mg2+- Cl-, Ca2+- Cl- and Ca2+- HCO3- types. The generated outcomes suggests ions that are primarily released in water via the process of carbonate weathering, and rock-water interaction. Geochemical diagrams including Chadha’s plot, the distribution shows that carbonate weathering is predominant, leading to Ca2+ – Mg2+ – HCO3- type waters. The classification points to the dissolving of carbonate rocks as a result of surface water interaction with subsurface exposures such as overburden dumps. Pearson’s correlation coefficient is used to measure the degree of interaction between parameter pairs by classifying their associations according to the correlation magnitude. The relationship between Na+ and other factors is particularly significant. Na+ demonstrates robust positive associations with both EC, Ca2+, and Cl-, underscoring the significant influence of these ions on water salinity. Three clusters were identified through cluster analysis (CA), grouping twenty sampling locations into relatively low-polluted, medium-polluted, and highly polluted categories. While pollutant levels were comparatively higher in eight places, perhaps as a result of decreased river flow and pollutant concentration, the majority of water quality measures were found to be lower due to dilution from monsoon rainfall. Defining the dominant variability within the dataset, PCA (principal component analysis) yielded three principal components that explain 90.98% of the cumulative variance. Parameters such as EC (0.968), Na⁺ (0.936), Ca²⁺ (0.895), Mg²⁺ (0.871), HCO₃⁻ (0.865), and Cl⁻ (0.939) showed high loadings in PC-1, indicating that mineralization, ion dissolution, and rock weathering are the primary influences. Major agents identified through CA and PCA include natural processes as well as agricultural, municipal, and industrial discharges, which collectively contribute to pollution in the river basin. The Entropy-Weighted Water Quality Index (EWQI) was found to be more effective in minimizing the eclipsing effect compared to conventional approaches, making it a suitable foundation for developing a new Multiple Linear Regression (MLR)–based WQI model. The proposed EWQI-driven regression framework integrates six key water quality parameters and demonstrates excellent performance, achieving an adjusted R2 value of 0.994. This indicates that approximately 99.45% of the variation in the observed data is successfully explained by the model. To evaluate the consistency of the new model, a paired t-test was conducted between WQI values produced by the proposed equation and those obtained from the original WQI method. The dependability of the model was confirmed by the results, which showed no statistically significant differences with p-values exceeding 0.05 for the absolute WQI scores. Among the included variables, Electrical Conductivity (EC) exhibited the highest standardized beta coefficient (β = 0.38), highlighting its dominant influence on surface water quality assessment. This study highlights an opportunity of water reuse to promote environmentally friendly growth and urban landscaping, aligning with Odisha’s Vision for conserving natural resources, maintaining ecological balance, and fostering environmentally sustainable urban growth. So, this water quality assessment provides essential baseline information that can guide effective management planning for the region.
Das et al. (Thu,) studied this question.