WATER QUALITY MANAGEMENT OF GOMTI RIVER (INDIA)

Abstract

Many large and small rivers in Uttar Pradesh (India) pass through many cities and provide for the needs of their residents. However, the growing industrialization and urbanization in UP have resulted in a rapid deterioration of river conditions. River water quality is continuously declining, which has prompted numerous government agencies, NGOs, and researchers to conduct studies to address the issue. Some of the rivers, including Gomti, in UP, are among the most polluted rivers in India. The Gomti River routinely conveys pollutants from point and nonpoint sources throughout the river basin, including agriculture waste, sewage from households and offices, wastewater from industries, and other sources. Over the past few decades, the river Gomti has witnessed a surge in human activities, leading to a reduction in the flow of the river and a significant deterioration in its water quality. The objective of this study was to formulate a water quality management plan by assessing the existing condition and anticipated future state of water quality of the Gomti River, which flows through Lucknow, UP (India). The research was conducted to manage water quality in relation to assimilative capacity and climate change (effect of rising temperatures) by identifying, quantifying, and characterizing a subset of selected pollutants of river Gomti. This study is based on the physicochemical and biological monthly data from 2013-2017 for seven sampling stations collected from the Uttar Pradesh Pollution Control Board (UPPCB), Lucknow (UP), which monitors the water quality along the Gomti River in Lucknow. The data sets were further investigated using descriptive statistics and multivariate statistical techniques (PCA and CA). One way ANOVA was used to assess seasonal and spatial variation. The Gomti River's water quality was evaluated using four distinct water quality indices (Arithmetic WQI, CPI, SPI, and CPCB-WQI). The results were integrated with GIS to delineate several zones based on the severity of pollution. Four WQIs (Arithmetic WQI, CPI, SPI, and CPCB-WQI) for the river Gomti were forecasted using statistical modeling to help with future water quality conditions and identification of the most appropriate WQI through model performance indicators or metrics. The assimilative capacity of the river was evaluated using DO and BOD, and statistical modeling was used to predict assimilative capacity for future scenarios. IX The assessment of physical, chemical, and biological characteristics of the river water samples revealed significant parameter ranges exceeding the prescribed limits (BIS, 2012/WHO, 2011), including those for DO, BOD, COD, EC, TA, TC, and FC. It is abundantly apparent from these findings that the water is unsafe for human consumption. It was established that the entire river stretch was severely polluted, and pollution levels increased from upstream to downstream (S1 to S7), demonstrating the impact of Lucknow’s rapid industrialization and urbanization. One-way ANOVA analysis concluded all parameters increase from S1 to S7 except for pH and DO, which exhibit steady declines from S1 to S7. All parameters show temporal and spatial variation, although only a few parameters, including EC, TDS, Ca, Mg, and Cl, also show annual variation. The main principal components contributing to the decline in water quality throughout the study were pH, Cl, DO, BOD, COD, TC, and FC, with a total variance of 54.65% in the dataset. These elements reflected sewage contamination and organic pollutants from residential wastewater. To prioritize control efforts concerning different pollution sources, the PCA helped locate the study area's point and nonpoint sources of pollution., Cluster analysis of the river Gomti identified three distinct clusters representing areas with moderate (S1, S2, S3, and S4), high (S5 and S6), and very high (S7) levels of pollution. This categorization can reduce monitoring stations, with one per cluster, cutting river sampling costs in resource-limited countries like India. To help policymakers and stakeholders understand how various policy initiatives affect the water quality of a water body, WQIs simplify complex data. All of the water samples fell into category E (>100), which is unsuitable for drinking and fish culture except at S1, S2, and S3 during the monsoon season, which falls under category D (75- 100), according to the results of the Arithmetic WQIs. At all sampling sites, the SPI value indicated very poor (1-3) status and could only be used for irrigation. The CPI value was found in three categories: qualified (0.41-0.8), basically qualified (0.81-1.0), and polluted (1.01-2.0) at different locations and months. The mean values of CPCB WQI at S1 and S2 lie under the category medium to good, Class - B (50-63), S3 and S4 under the category bad, Class - C (38-50), S5, S6, and S7 under category bad to very bad, Class - D & E (<38). It was also noticed that the river Gomti water was found in all categories classified by CPCB-WQI for different sampling stations. Station S7 recorded the highest value for all estimated WQIs. Statistical analysis further X corroborated that the WQIs increased from S1 to S7. PCA investigation further confirms that anthropogenic activities primarily contribute to the deterioration of this region's water quality. As a result, it can be argued that apart from S1 and S2, WQIs are high at all sampling stations. Statistical modeling for the period of 10 years (2018-2027) based on calculated data of WQIs (Arithmetic WQI, SPI, CPI and CPCB-WQI) reveals similar results as the baseline period (2013-2017). In the case of all four projected WQIs, the maximum value was observed at station S7, followed by the minimum value at S1, and it rose from S1 to S7. RMSE, MAPE, MAE, MaxAPE, and MaxAE were employed as model performance indicators or metrics to track the model's effectiveness. SPI and CPI were determined to be the most appropriate WQIs out of the four based on model performance indicators or metrics values. Gomti River has an average daily flow of 1,500 MLD, rising to 55,000 MLD after rains and dropping to 500 MLD during the summer, resulting in a reduction of assimilative capacity. The minimum DO concentration at all sampling stations was below the reference limit (4 mg/l), while the maximum DO concentration was well above it. The sampling station S7 had a lower minimum and maximum DO concentration than the reference limit. According to the CPCB’s best-use criteria (IS 2296: 1992), river water at selected sampling stations was inappropriate for all purposes in respect of BOD. DO concentrations at S5, S6, and S7 are excessively low for the predicted period (2018- 2027). At S1, DO concentrations are higher than the reference limit for the predicted period, whereas at S2, S3, and S4, higher in the wet season and lower in the dry season. BOD exceeds the reference level (2 mg/l) at all sample locations over the predicted period. The water quality profile of BWQI for the four different climate change scenarios, RCP 4.5 (2040-2069), RCP 4.5 (2070-2099), RCP 8.5 (2040-2069), and RCP 8.5 (2070- 2099) are 38.79, 37.90, 37.75, and 34.83 respectively. It reveals that the BWQI is not significantly different from the previous scenario (2014-2017) as it lies in the bad (26- 50) category in the water quality classification; however, a slight decrease in BWQI is expected in the future under all scenarios. The selected WQIs have been studied through the GIS method. The maps of WQIs showed that 28 drains highly polluted the study area, discharging approximately 461.33 XI MLD wastewater. Therefore, the Gomti River water should not be used due to its high physicochemical and biological load. Prior treatment should be considered to meet water quality regulations, public expectations, environmental and public health concerns. Direct discharge of industrial and domestic wastewater into river through drains is the leading cause of the significantly polluted water quality. As a result, management alternatives are suggested to lessen pollution. A sufficient sewage treatment facility should be set up between S2 and S7. It is imperative to remove solid waste and to maintain adequate discharge at all times, particularly during the dry or non-monsoon seasons, to maintain its self-purification capacity.