Assessment of the quality of groundwater body of Neelangarai and Triplicane areas in Chennai, Tamil Nadu in the post-inundation circumstances

All life depends on fresh water. At the same time, strictly speaking, absolutely pure water does not exist for any considerable span of time in nature (Seifi et al. 2020). Pure water is believed to be that which has less suspended or floating solids and intolerable gases as well as little natural life (Dedic et al. 2020). Such high-class water may be essential only for consumption reasons while for other utilizations like industry and agriculture, the superiority of water can be reasonably bendable and water contaminated up to a certain degree of common sense can be stated as pure (Wator & Zdechlik 2021). It has been determined that almost 1% of the water in the world should supply human beings' demand for freshwater. Considering various issues related to the quality of drinking water, alongside the fact that there is a huge amount of brine water in the oceans and seas and local sources of brine water in deserts, researchers and desalination industries have been motivated to study and optimize different methods of water purification (Bazregari et al. 2022). Regular water quality examination of the water assets are enormously indispensable to evaluate the quality of water for hygiene, agricultural, industrial use, ecosystem health and hygiene and domestic use (Nowicki et al. 2020; Muthumanickam & Saravanathamizhan 2021). Chennai always faces severe innate resource paucity, particularly that of water because of population explosion and financial improvement (Uddin et al. 2021; Marlina et al. 2022). Heavy rainstorms hit the entire part of Chennai city and its outskirts in December 2015 which shook the entire state. Although it caused so much economic and financial loss, its impact on the quality of groundwater bodies was most significant (Banda & Kumarasamy 2020; Choudhury et al. 2021). Examining water quality can be an intricate procedure encompassing several factors capable of causing different pressure on aquatic quality (Shamsiev 2022). Assessing water quality from a large quantity of specimens, each having concentrations for several factors, is complicated (Ewaid et al. 2020; Samadi et al. 2021). There are as many as 35 parameters/indicators which are helpful in assessing the quality of the water system. However, it is not necessary to carry out the determination of all 35 parameters as many are interrelated in one way or another. For example, total hardness (Ca²⁺, Mg²⁺, ⁠), electrical conductivity (EC), and Cl⁻ are interrelated. DO and BOD are interrelated. Moreover, the quality of any water system greatly depends on the nearby environmental factors such as soil nature, geographical area, etc. Therefore, taking the above facts in mind, instead of wasting time and resources on carrying out all the 35 parameters, only the required parameters can be taken into account to testify the quality of the entire water system under investigation. According to the prescription given by the National Sanitary Foundation (NSF) and Water Quality Index (WQI), the following nine parameters are more than sufficient to explain the quality of water. They are broadly classified into three types. Physical parameters such as (i) temperature, (ii) turbidity, (iii) total solids, chemical parameters such as (iv) dissolved oxygen (DO), (v) biochemical oxygen demand (BOD), (vi) pH, (vii) total nitrate, (viii) total phosphate, and the biological parameters such as (ix) fecal coliform (Padmavathy et al. 2002). Usual approaches to understanding the quality of water are based on the association of experimentation with the accessible guiding principle (Koc 2022; Mbhele & Khuzwayo 2023). As a result, water quality indices (WQIs) are such approaches which minimize the information to a large degree and make the appearance of water health simpler (Jahin et al. 2020; Samadi et al. 2020). These indices are framed on the basis of the comparison of water quality pointers to dogmatic values and provide a distinct quantity to the water of a particular source (Opaluwa et al. 2020; Elhassan 2023). A number of approaches to this problem have been attempted which range from the use of subjectively applied water quality classifications to the development of mathematically derived water quality indices. A WQI allows the reduction of a vast amount of data on a range of physical–chemical and microbiological parameters to a single number in a simple and reproducible manner. It is mathematically derived and, therefore, allows the objective assessment of water quality and permits meaningful spatial and temporal comparisons to be made. This allows the classification of good and bad water quality. The summation of parameters effects and an indication of water reaches which have changed significantly in quality and which, if necessary, can be investigated in greater detail. Therefore, WQIs act not only as indicators of ecosystem change but can also effect changes on the use of water or the ability of this water to the life of human beings. In addition, the application of these indices to a variety of locations allows the cause of such changes to be detected. Finally, an index is suited to computerizations, thus reducing the amount of time involved in classifying the groundwater quality.

The WQI has emerged as a central way to convey water quality information to policymakers and the general public and is regularly used in US EPA regulatory impact analysis. It is a compound indicator that aggregates information from several water quality parameters. Several recent studies have criticized the aggregation function of the EPA WQI, arguing that it suffers from eclipsing and other problems.

NSF and WQI are used to express the quality of water samples. According to them only nine parameters, temperature, turbidity, total dissolved solids, DO, BOD, pH, nitrates, total phosphates, and fecal Coliform in three categories, namely physical, chemical, and biological parameters, are sufficient for calculating WQI since it has been formulated on the basis of their standard living, climatic conditions, soil properties, industrialization, food consumption pattern, etc. Two parameters, namely nitrates and total phosphates, were not analyzed, instead salinity and EC were measured but not included in the WQI table (Dandwate 2012). In this paper, to assess the quality of earth water taken from 10 places of Neelangarai and Triplicane which were the worst affected areas in the rainstorm, the Heber Water Quality Index (HWQI) was exploited. To calculate the Q-value, the test value of each parameter was located on the bottom of the respective weighting curve chart and a perpendicular streak was drawn until it traversed the weighting arc line. From the spot of the meeting point, a straight line was drawn to the Y-axis. The point of intersection gives the Q-value of that particular test result. This Q-value was recorded in column B on the WQI chart. The weighting arc quantity for each parameter was multiplied by the weighting factor listed on the chart for the particular test. This value was recorded in the total column of the WQI map. The weighting factor for each water quality test provides a relative gauge of a test's significance to the total water quality. For example, from the WQI chart, DO with a weighting factor of 0.17 is considered a more important test than total dissolved solids 0.07 in overall water quality determination. The overall WQI for the sampling station was determined by adding the total of the nine test results. The National Sanitation Foundation (NSF) has given a gauge of values for WQI to express the quality of the water samples. This range of values is also applied to express the quality in other water quality indices like HWQI – 1, HWQI – 2 and others (Malathy et al. 1998); one such WQI is given in Table 1 and the quality of water based on WQI values is presented in Table 2.

Water samples were collected from the bore wells at 15-feet depth during an ordinary day between 9 am and noon in a previously washed and dried plastic container. The containers were rinsed thoroughly with sample water in each sampling station. After the collection of samples, containers were labeled with information such as time, date and area, temperature, and pH. All the samples were preserved to resist changes that occurred during transportation to the laboratory and the time lapse between the collection and analysis. Temperature and pH were recorded at the time of sample collection, using a standardized thermometer and a digital pH meter, respectively. The water samples were instantly transported to a laboratory for the determination of various other physico-chemical pointers such as DO, TDS, BOD, turbidity, conductivity, and salinity in the laboratory by exploiting standard methods as given by APHA (2017).

To analyze the BOD, samples were taken in the BOD containers and were incubated immediately in the laboratory. Dissolved oxygen (% DO saturation) was set at the sampling spot itself at once after collection. To study Fecal Coliform, samples were collected separately and tested within one hour. Other parameters such as pH, turbidity, TDS, salinity, and EC were analyzed in the laboratory. Turbidity was measured using the Nephelometric method. To measure fecal coliform by MPN test, samples were gathered in uncontaminated containers. To investigate the TDS, the water sample was placed in a drying oven to dry and to leave the sediments. After all experiments were over, the outcomes of each parameter were tabulated. To frame a WQI, Q, the value of the outcome of each factor, was assessed using the Q-chart. In addition to the aforementioned nine parameters, salinity was also tested for all the samples. When the outcomes of all these pointers were presented, the comparative weights for each factor were cross-examined with the standard and the total was scaled so that the range remained between 0 and 100. This range gave an intimation of the inherent nature of water.

After collection, the samples were preserved to resist changes that occurred during transportation to the laboratory and in the time between collection and analysis. The samples were taken to the lab under a cool and dim environment within 4 hours of collection. Then the sample was refrigerated and analyses of all parameters were carried out within 7 days.

The water quality for the sampling station of the groundwater was formulated after the 11 water quality tests were completed and the results of each test recorded.

After the inspection of various water quality indices, it may be understood that the endeavor of WQI to present a value of water quality of a source along with minimizing several factors into an easy expression resulting in the easy elucidation of water quality scrutinizing statistics. It was proposed to analyze physico-chemical and biological parameters such as pH, temperature, BOD, DO, TDS, turbidity and fecal coliform and to interpret the results using the Water Quality Index as suggested by NSF and WQI. In addition to these parameters, EC and salinity were also determined. NSF has offered an assortment of values to express the WQI. The overall WQI values of the Neelangarai area for all the samples were in the range of 65.02–72.25 which divulges information that the nature of all the samples analyzed in this study is only moderate to good and could be used for drinking and other household uses only after suitable treatment. Among the various water samples analyzed, Neelangarai (Sivan Koil Street) had been registered with low WQI (65.02). Besides this low WQI value, salinity (1,450 mg/L) and EC (750 μs/cm) were also ascertained to be very high. Among the various samples analyzed Neelangarai (Second North cross street) had high WQI (72.25). These indicate that these waters are fit for drinking purposes after proper treatment. The overall WQI values of the Triplicane area for all the samples were in the range of 66.64–70.71, which discloses information that the quality of all the samples examined in this study is only moderate to good and could be used for drinking after proper treatment. None of the analyzed water samples were found to be excellent with regard to human consumption and other domestic uses. The probable reasons could be the long-term adverse impact of floods and tsunamis.

The authors thank the Principals and the managements of Sir Theagaraya College, Chennai 21, SDNB Vaishnav College for Women, Chennai-44 and the Panimalar Engineering College, Chennai-123 for their constant encouragement and support rendered.

None received.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.