A Simplified Equation for Calculating the Water Quality Index (WQI), Kalu River, Sri Lanka

Abstract

The water supply system plays a major role in the community. The water source is carefully selected based on quality, quantity, and reliability. The quality of water at its sources is continuously deteriorating due to various anthropogenic activities and is a major concern to public health as well. The Kalu River is one of the major water resources in Sri Lanka that supplies potable water to the Kalutara district (a highly populated area) and Rathnapura district. But, there has been no significant research or investigation to examine anthropogenic activities in the river. Due to this, it is difficult to find any proper study related to the overall water quality in the Kalu River. Therefore, this study covers a crucial part related to the water quality of the Kalu River. The spatiotemporal variation of river water quality is highly important not only to processing any treatment activities but also to implementing policy decisions. In this context, water quality management is a global concern as countries strive to meet the United Nations Sustainable Development Goal 6, which aims to ensure the availability and sustainable management of water and sanitation for all. Poor water quality can have severe consequences on human health, ecosystems, and economies. Contaminated water sources pose risks of waterborne diseases, reduced agricultural productivity, and ecological imbalances. Hence, assessing and improving water quality is crucial for achieving sustainable development worldwide. Therefore, this paper presents a comprehensive analysis of spatiotemporal analysis of the water quality of the Kalu River using the water quality data of eight locations for 6 years from 2017 to 2023. Nine water quality parameters, including the pH, electrical conductivity, temperature, chemical oxygen demand, biological oxygen demand, total nitrate, total phosphate, total sulfate, total chlorine, and hardness, were used to develop a simple equation to investigate the water quality index (WQI) of the river. Higher WQI values were not recorded near the famous Kalutara Bridge throughout the years, even though the area is highly urbanized and toured due to religious importance. Overall, the water quality of the river can be considered acceptable based on the results of the WQI. The country lockdowns due to COVID-19 might have impacted the results in 2020; this can be clearly seen with the variation of the annual WQI average, as it clearly indicates decreased levels of the WQI in the years 2020 and 2021, and again, the rise of the WQI level in 2022, as this time period corresponds to the lockdown season and relaxation of the lockdown season in the country. Somehow, for most cases in the Kalu River, the WQI level is well below 25, which can be considered acceptable and suitable for human purposes. But, it may need some attention towards the areas to find possible reasons that are not in the range. Nevertheless, the results suggest the importance of continuous water quality monitoring in the Kalu River.

Application of machine learning techniques to predict groundwater quality in the Nabogo Basin, Northern Ghana

“…The thirteen (13) significant parameters selected for the determination of the WQI were EC, TDS, pH, TA, TH, Mn, Fe, Ca 2+ , Mg 2+ , Cl − , F − , NO 3− , and SO 4 2− . Equation (1) ([ 49 , 50 ];, 2020; [ [19] , [20] , [21] , [22] ]) was used to calculate the weighted arithmetic water quality index based on Ghana Standard Authority (GSA) water quality requirements for the different parameters. Where W i denotes the unit weight for every parameter, q i is the 0–100 subindex rating for each variable, and n is the number of consolidated subindices.…”

“…Step 3: Using the weightage of each parameter, estimate the unit weight of parameters (W i ). The unit weight (Wi) of parameters (determined using equation (2) [ 21 ]) is proportional to the weightage assigned to each parameter i.e, And the constant of proportionality is given by equation (3) [ [19] , [20] , [21] , [22] ]. where K is a constant of proportionality; W i is the unit weight of the parameter; n is the number of water quality parameters.…”

“…Step 4: Determining the subindex value (q i ). The subindex value is using equation (4) [ [19] , [20] , [21] , [22] ]. Where;…”

“…Step 5: The overall WQI is calculated by aggregating the sub indices. WQI is the total of all the parameters' sub-indices (q i ) and unit weights (W i ) and was determined using equation (6) [ [19] , [20] , [21] , [22] ]. …”

“…In recent years, Water Quality Index (WQI) integrated with Machine Learning (ML) and Deep Learning, as many innovative techniques [ 18 ]. Deshpande et al [ 19 ], Goodarzi et al [ 20 ], Patel et al [ 21 ], Siriwardhana et al [ 22 ],Abba et al [ 23 ] and Xiong et al [ 24 ] assessed water, and groundwater quality in their various study areas, where their findings from WQI methods proved significant for water quality management, and it recommended the replication of these methods in different territories of the world.…”