Assessment of Groundwater Quality Status by using Hydrochemical and GIS approach for the feasible study of Coastal Reservoirs

doi:10.21203/rs.3.rs-2366584/v1

Groundwater qualities of coastal aquifers in the Netravati and Gurapura catchment of Dakshina Kannada district Karnataka have been extensively monitored in post-monsoon seasons in 2021 and Pre-Monsoon 2022 to assess their suitability for domestic and drinking uses in four regions. Thirty-two groundwater samples were analysed for various physicochemical parameters such as GWL, pH, Oxidation Reduction Potential (ORP), Electrical Conductivity (EC), Total Hardness (TH), Dissolved Oxygen (DO), Total Dissolved solids (TDS), Temperature, Calcium (Ca), Magnesium (Mg), Chloride (Cl), Sodium (Na), Potassium (K), Sulphate (SO₄) and Carbonate and Bicarbonate. Most of these parameters fall under the permissible limits of BIS and WHO standards. Using hydrochemistry and the GIS method, the current study aims to evaluate the quality of groundwater (well water samples) in the Netravati river basin. The quality of the groundwater, indicates that coastal aquifers have gained prominence over the past ten years as a result of the using global demand for groundwater. According to the findings of the study, groundwater depletion and the rising seawater level are the primary factors that contribute to the saline condition of coastal wells during the pre-monsoon period. The study demonstrates that groundwater levels have been trending downward for some time. Seawater intrusion appears to be the main problem in the study area's coastal region, according to the study. The water quality index (WQI) results show that the quality of well water samples during the pre-monsoon session is much worse than during the post-monsoon session due to fresh rainwater during the post-monsoon session raises the groundwater level and increases the water's physical and chemical parameters. Based on the recommendations of the Hortons water quality index, groundwater quality is divided into four categories: excellent water quality (90-100), good water quality (71-90), poor water quality (51-70), and bad water quality (31-51).

Water Quality Index (WQI)

Groundwater quality

Hydrochemistry

GIS

Coastal Reservoir

Groundwater is a prominent source of freshwater and groundwater is also a significant source for agricultural and domestic uses approximately 97% of the earth’s abundant freshwater is stored as Groundwater (Odukoya, 2015). The water quality index indicates the water quality in terms of a rating that offers a useful representation of the overall status of water quality. Horton defined the water quality index as a reflection of the composite influence of individual quality characteristics on the overall quality of water. The WQI is used to assess overall water quality trends for management purposes (Pradesh et al., 2011). Water's physical and chemical characteristics change through time and space as a result of natural phenomena, human activity, and seawater intrusion in coastal areas. Many variables, including human activity, agricultural activity, natural weathering processes, biogeochemical processes, and seawater intrusion, cause groundwater salinity to rise (Kanagaraj et al., 2018). Human instigation could be a point source or a non-point source. The management of water quality requires the collection and analysis of significant datasets about water quality, some of which can be challenging to analyse and synthesize. The water quality index model is one of many methods created to evaluate water quality data (Uddin et al., 2021). The physical parameters of well water and river water such as hydrogen ion concentration, Total Dissolved Solids (TDS), Oxidation-Reduction Potential (ORP is millivolts mV), Dissolved Oxygen (DO), Electric conductivity (EC is mS/cm), Salinity, Temperature (⁰C) is measured on-site by using HI98194 Multiparameter Meter. Chemical Parameters viz, Magnesium Hardness (Mg), Calcium Hardness (Cl), Total Hardness (TH), Chlorides (Ca), Potassium (K), Sodium (Na), Sulphate (SO₄), Carbonates (Co₃) and bicarbonates (HCo₃) were performed in the laboratory of water resources and ocean engineering, and the purpose of this research is to assess the water quality status of the Netravati and Gurapur river catchment and predict the seawater quality in the coastal region of Mangalore city. The laboratorial and in situ testing of physical, chemical, and biological parameters of groundwater and river water have been conducted. The present article demonstrates the water statistics and water quality index mapping for both pre-monsoon and post monsoon seasons.

1.1. Coastal Reservoir

Despite the region receiving a lot of rain from July to September of every year certain areas lack access to clean drinking water, while other areas of the region get excessive rain that causes flooding (Sitharam et al., 2017). Demand supply imbalance resulting from this geographical and seasonal variation in the natural water supply and the year round demand for irrigation, drinking, and industrial water gets worse as the population grows (Sitharam, 2017). Water pollution may also be the cause of a water shortage in places with abundant water resources. It is well known that there is a concern with the water because of its poor quality and scarcity (S. Yang, 2013). The coastal freshwater reservoir is a new emerging concept of storing flood water in the sea close to the shoreline (S.-Q. Yang & Lin, 2012). A coastal reservoir can be constructed in shallow waters at appropriate locations close to the mouth of the river along with a barrage at one or two ends (Parthasarathy et al., 2019).

The Netravati River is one of the important west-flowing rivers in the Dakshina Kannada district of Karnataka State. The geographical location of the Netravati River basin lies between 12°29′11″ to 13°11′11″N latitudes and 74°49′08″ to 75°47′53″ E longitudes. The Nethravati River is mainly used for irrigation and drinking water supply for more than 0.6 million people, petrochemical industries established in Mangalore city, and religious purposes in places like Dharmastala and Kukkesubramanya throughout the year (Babar & Ramesh, 2015). The Netravati River has a catchment area of 3,350 km² and begins in the Bangrabalige valley in the Chikkamagaluru district at an elevation of 1,000 m above MSL in the Western Ghats. It travels West for about 125 km before joining the Arabian Sea. The river has a rather steep slope of roughly 43.5 m/km for the first 20 km of its length, which gradually lessens to about 1.6 m/km. Geologically, the area is dominated by lateritic soil that is covered in gneiss.

Figure 1. Location map of the study area and observation wells (a & b)

The catchment receives 4,030 mm of precipitation annually on average, with temperatures ranging from 16 to 42 degrees Celsius. The catchment area is made up of a tonalitic gneisses basement from the early Precambrian that has been colonised by granites, granulites, and dolerite dykes. Granulites and granites have encroached over the older gneissic rocks (dating back to roughly 3000 Ma) (ca.2600 − 2500) (Ravindra & Venkat Reddy, 2011).

3.1. Groundwater Water Sample Collections

From the Mulki Gurpur river flow, the survey covered Mangalore's Haleyanagady, NITK, Surathkal, Kulai, Chitrapur, Tannirbhavi, and Uchal Bovi. 32 groundwater quality tests were carried out in and around the catchment areas of the Netravati and Gurapura Rivers. In ascending order, from NITK to Bengre Kasaba: Thumbe Road, BC; Puttur; Bellare; Subrahmanya, kukke; Nelliyadi; Dharmasthala; Ujire; Belthanagady; According to the groundwater sample location map (Fig. 1c), Bantwal Adyar Each sample was taken from 500 milliliters of acid-washed polyethylene, and water was poured into the bottles to stop air bubbles from forming. Additionally, care was taken to avoid sample agitation when transporting the water bottle to the laboratory for the examination of chemical parameters. The biological, physiochemical, and methodological parameters are listed in Table 1.

Figure 1c. Groundwater sample location map

3.2. Hydrcohemistry analysis of groundwater samples

Chemical characteristics such as magnesium hardness (Mg), calcium hardness (Cl), total hardness (TH), chlorides (Cl), potassium (K), sodium (Na), sulphate (SO₄), carbonates (Co₃), and bicarbonates (HCo3) were examined in the lab for total of 32 groundwater samples and 10 river water samples utilizing standards for drinking water quality the outcomes were evaluated with WHO 2004 and BIS 2012 standards. Table 1demonstrates the physiochemical and biological parameters of well water samples tested at the location and in the laboratory.

Table 1

Physio-chemical and biological parameters and their methods
Sl. No	Parameter	Units	Methods	Type of Test
1	GWL	Meter	Measuring Tape	In-situ Test
2	pH	Nil	HI98194 Multiparameter Meter
3	ORP	millivolts (mV)
4	DO	%
5	DO	mg/l
6	EC	µS/cm
7	TDS	mg/l
8	Temperature	⁰C
9	Salinity	ppm	Salinity Refractometer
10	Magnesium Hardness	Caco₃	EDTA Titrimetric Method	Laboratory Test
11	Calcium Hardness	Caco₃
12	Total Hardness	Caco₃
13	Chloride	mg/l	Titration Method
15	Carbonates	mg/l
16	Bicarbonates	mg/l
14	Potassium	mg/l	Flame Photometric Method
17	Sodium	mg/l	Flame Photometric Method
18	Sulphate	mg/l	Spectro Photometer

3.3. Groundwater Quality Indexing

In the beginning, Horton (1965) created the WQI in the United States by choosing the ten most often used water quality indicators, such as Dissolved Oxygen (DO), pH, coliforms, specific conductance, alkalinity, and chloride, among others, and has been widely used and accepted in the research community (Tyagi et al., 2013). The water quality index (WQI) model is a popular method for evaluating the quality of surface water and groundwater. It makes use of aggregation techniques to reduce vast amounts of data on water quality to a single value or index. The WQI model has been used globally to assess water quality using regional water quality standards (Uddin et al., 2021). The Index captures the overall impact of important physical and chemical characteristics, and the intended use of the water assesses the overall importance of several parameters in the production of the water quality index. During 1970, the National Sanitation Foundation created a mathematical formula known as a Water Quality Index (WQI). The index captures the overall influence of major physical and chemical parameters. Applying the index presents a comprehensive, understandable unit of measurement that adapts to water quality changes (BROWN et al., 1973).

3.4. Determination of Water Quality Index (WQI)

Brown completed a thorough analysis of the Horton index in 1970. The allocated weight had a significant impact on the index and showed the importance of a parameter for a specific purpose. Furthermore, the Brown group created a new WQI in 1970 that is comparable to Horton's index (Tyagi et al., 2013). The optimal method for assessing information about the quality behaviour of water based on the research findings of a physiochemical investigation is about Water Quality Index (Brown et al., 1970). By comparing the investigated parameters with the BIS quality standards, the Water Quality Index was calculated. Applying the following formulae (1,2,3 & 4), each parameter's relative weight (Wi) and quality rating scale (qi) were computed.

Step – 1. Calculation of Unit Weight (W_n) factors for each

$$Wn=\frac{K}{Sn}$$

1

Where $K=\left[\frac{1}{\frac{1}{S1}+\frac{1}{S2}+\frac{1}{S3}+\dots +\frac{1}{Sn}}\right] = \frac{1}{\sum \frac{1}{Sn}}$ (2)

Sn = Standard Desirable Value of the n^th Parameter

On summation of all selected parameter unit weight factors, W_s = 1 (Unity)

Step − 2. Calculate the sub-Index (Q_n) Value by using the formula

$$Qn= \frac{\left[\left(Vn-Vo\right)\right]}{\left[\left(Sn-Vo\right)\right]}*100$$

3

Where, V_n = Mean Concentration of the n^th Parameter

S_n = Standard desirable value of the n^th Parameter

V_O = Actual Value of the parameter in pure water (generally V_O = 0, for most parameter except pH)

Step − 3. Combining step-1 and Step-2, WQI is calculated as fallow

The water quality classification based on the Horton’s recommended Water Quality Index ranking and grading is demonstrated (Uddin et al., 2021) in the Table 2, in this study well water sample of study area is classified based on the following water quality category.

Table 2

Water Quality Indexing based on the Horton’s recommendation (Brown et al., 1970)
WQI Ranges	Water Quality Status	Grading
91–100	Excellent Water	A
71–90	Good Water	B
51–70	Poor Water	C
31–50	Bad Water	D
0–30	Unsuitable for Drinking purpose	E

The study of hydrochemistry is essential when determining the quality of the groundwater supply. Groundwater chemistry fluctuations with time and space are caused by hydrogeochemical processes, which can be analyzed using hydrochemistry (Curry & Stiff, 2021). The study of water chemistry focuses on how water interacts with other elements in the environment and how these other elements impact water quality. The health, richness, and diversity of life on earth are significantly influenced by the chemical composition of the water. Inadequate levels of some elements, like dissolved oxygen, or an excess of others, like nutrients, can lead to degraded circumstances and harm to life. Since water dissolves more things than any other liquid, it is referred to as the popular solvent. This implies that water carries vital chemicals, minerals, and nutrients everywhere it goes, whether through the ground or our bodies (Bourbonniere, 2009).

4.1. Hydrochemical Parameters of Groundwater

Groundwater Level (GWL)

Groundwater level in the study area varies from 0.4m to 12.2m, the water level has increased in the post-monsoon. Majority of the open well falls under rise of groundwater level and the water quality is good and suitable for drinking and domestic purpose.

Hydrogen Ion Concentration (pH)

The quality of ground water in some parts of the Dakshin a Kannada district particularly shallow ground water is changing as a result of human activities, Ground water is less susceptible to bacterial pollution than surface water because the soil and rocks through which ground water flows filter most of the bacteria. The normal range for pH concentration in surface water systems is 6.5 to 8.5 and for groundwater systems 6 to 8.5 The pH value of groundwater at coastal belt has observed highest 7.81 and lowest 5.5 at upstream of the catchment area, the pH of water decreases or increases a as the temperature varies.

Oxidation Reduction Potential (ORP)

Oxidation-reduction potential measures the ability of a lake or river to cleanse itself or break down waste products, such as contaminants and dead plants and animals. When the ORP value is high, there is lots of oxygen present in the water. This means that bacteria that decompose dead tissue and contaminants can work more efficiently.

Dissolved Oxygen (DO)

Dissolved oxygen levels in water depend, in part, on the chemical, physical, and biochemical activities occurring in the water. Oxygen has a limited solubility in water directly related to atmospheric pressure and inversely related to water temperature and salinity. Low-dissolved oxygen levels can limit the bacterial metabolism of certain organic compounds. In the study area DO varies from 1.3 mg/l to 4.6 mg/l. Low oxygen in water can kill fish and other organisms present in water. For living organism, about 4 mg/L of minimum DO should be in water. In the study area DO varies from 1.3 mg/l to 4.6 mg/l. Low oxygen in water can kill fish and other organisms present in water. For living organism, about 4 mg/L of minimum DO should be in water.

Electric Conductivity (EC)

Electrical Conductivity (EC) is an important indicator for water quality assessment, Electrical conductivity is a measure of water capacity to convey electric current. EC for groundwater is the ability of 1 cm³ water to conduct an electric current at 25˚C and is measured in micro Siemens per centimetre, so it depends on the total amount of soluble salts (TDS) as charged particles. In general, as the concentration of total dissolved solids (TDS) in the groundwater increases, its Electric conductivity also increases.

Total Dissolved Solids (TDS)

Total Dissolved Solids (TDS) usually refers to the mineral content of water, although it can also include dissolved organic material. TDS is the total amount of material remaining after evaporation of the water. TDS include common salts such as sodium, chloride, calcium, magnesium, potassium, sulphates and bicarbonates. In the study area TDS value varies from 29 mg/l to 590 mg/l indicating that most of the groundwater samples lies within the maximum permissible limit. High concentration of TDS in the groundwater sample is due to leaching of salts from soil and also domestic sewage may percolate into the groundwater, which may lead to increase in TDS values.

Temperature (^oC)

The temperature of groundwater is generally equal to the mean air temperature above the land surface. Overall 32 open wells are monitored in the present study area compiled that provide mean Ground Water Temperature (GWT). The distribution of GWT data is uneven, maximum temperature witnessed in the catchment area was 28.71 C^o and minimum temperature 25.74 ⁰C.

Salinity

The salinity of both surface water and groundwater is essential. The parameter's main goal is to evaluate the mass of dissolved salts in a specific mass of solution. Performing a comprehensive chemical analysis of a water sample is the only effective way to assess the actual or relative salinity of a natural water.

Total Hardness (TH)

The capacity of water to precipitate soap is defined as a measurement of water hardness. Especially, the concentration of calcium and magnesium ions induces soap. Calcium and, to a lesser extent of magnesium in solution are the main contributors to water hardness. Usually, it is expressed as the equivalent amount of calcium carbonate (CaCO₃)

Magnesium (Mg)

It is the second element in Group IIA of the periodic table, the quantity of magnesium is 2.1% on average in the earth's crust, 0.03 to 0.84% in soils, 4 mg/L in streams, and 5 mg/L in groundwaters. Magnesium can be predicted using the difference between calcium hardness and calcium carbonate (CaCO3).

Calcium (Ca)

It is present in the earth's crust on an average in abundances of 4.9%, 0.07 to 1.7% in soils, 15 mg/L in streams, and 1 to 500 mg/L in groundwater. The two most common calcium forms are calcium carbonate, or calcite, and calcium-magnesium carbonate (dolomite). Calcium compounds are widely used in pharmaceutics, photography, lime, salts, pigments, fertilisers, and plasters.

Chloride (Cl)

There are various concentrations of chlorides in all water streams. Increasing mineral content does not necessarily result in a decrease in chloride content (Rodger et al., 2016). One of the most common inorganic anions in water and wastewater is chloride. Chloride concentrations can produce a taste that either salty or sweet, depending on the chemical components of the water. Due to sodium chloride, the chloride concentration in wastewater is larger than in raw water (NaCl)

Potassium (K)

Potassium, a crucial nutrient, is very well retained by the soil's clay particles. Only soils with coarse soil textures have significant potassium absorption through the soil profile. Potassium is the seventh most abundant element; however most drinking water sources have 100 mg/l of the mineral. Potassium, a component of both plant and human nutrition, is released into groundwater as a result of mineral dissolution.

Sodium (Na)

The sixth most abundant element overall quantity, sodium, is found in most naturally occurring water. The amount may range from less than 1 mg Na/L to more than 500 mg Na/L. Relatively high levels may be found in brines and hard water softened by the sodium exchange process.

Sulphate (SO4)

Natural streams frequently include sulphate ions. In water, many sulphate compounds dissolve easily. The bulk of them are brought on by industrial waste, sulphate ore oxidation, gypsum and anhydrite solution, the existence of shales, particularly those rich in organic compounds, and other causes.

4.2. GIS Analysis

The geographical analysis of several physicochemical characteristics has been done in this work using a Geographic Information System (GIS) mapping technique; ArcGIS 10.8 software was employed for this. Groundwater resource assessments should include spatial analysis for accurate and straightforward visual depiction (Nath et al., 2021). Statistical interpolation and geostatistical interpolation are the two basic interpolation methods. Based on how similar the measured points are to one another, deterministic interpolation techniques build a surface from the measured points (El Mountassir et al., 2020). Thematic geographical spatial distributions for each parameter (GWL, pH, ORP, DO, EC, TDS, Temperature, Salinity, mg, Ca, Cl, K, HCO₃, Na, SO₄) were created for this study using the weighted inverse distance interpolation (IDW) and Kriging approach.

4.3. Methodology

Thirty groundwater samples were taken from the Netravathi and Gurapur river basin, nine of which were taken close to Mangalore City's coastal best and were continually monitored using digital water level recorders, and the remaining wells were inspected seasonally. Groundwater samples were analysed in the present research both before and after the monsoons of 2021 and 2022. All the groundwater samples were assessed for quality by considering eighteen hydrochemical parameters. The methodology seen in Fig. 2 is adopted to analyse and assess the groundwater quality index of study area.

The physicochemical parameters of water quality were investigated using the APHA guidelines (APHA, 2012). The quality of the groundwater is a clear indication of the water's potential for different purposes. There are standards for water quality for diverse uses. Since drinking water is the most sensitive, it must adhere to a wide variety of quality criteria. Standards for drinking water quality have been established by the World Health Organization and Bureau of Indian Standard (Anbazhagan & Nair, 2004).

5.1. Statistical analysis of groundwater samples

It is convenient to analyse large environmental data sets using multivariate statistical analysis of chemical parameters since it reduces dimensionality and skewness (Sharma et al., 2020). During a groundwater analysis, a wide range of chemical characteristics and data must be considered. It will be necessary to use specific statistical methods under these circumstances. One of the main advantages of multivariate analysis is its ability to reduce original data to data that only includes dominant features which affects groundwater quality (Nath et al., 2021). In the current study, 18 hydrogeochemical parameters from the post- and pre-monsoons of 2021 and 2022 were analysed in order to determine the study's groundwater quality index. The results are shown in Tables 3. The (WHO 2004 and BIS 2012) guidelines were taken into consideration when analysing the quality of groundwater for drinking water purposes.

5.2. Spatial distribution map of hydrogeochemical parameters

For understanding the concentration of each parameter over the study area for the post monsoon and pre monsoon seasons, the most popular interpolation techniques IDW and Kriging have been implemented, as shown in Figs. 3 and 4. represents spatially distributed maps of individual hydrogrochemical parameters of the study have been developed using ArcGIS software.

5.3. Statistical analysis and correlation coefficient matrix

The statistical programme XLSTAT-2022 was used to analyse the data. Origin Lab software was used to creates the graphs. For these thirty-two well water samples, correlation analysis has been applied to assess the water quality. The descriptive information for each of the examined water quality parameters is provided in Table 3 while the correlation coefficients between the WQI and the water quality metrics are shown in Table 5.

5.4. Box and Whisker Plot for Different Water Quality Parameters

The box and whiskers plot were used to analyse all eighteen hydrogeochemical parameters that were assessed for the water quality throughout the pre- and post-monsoon seasons. The results are shown in Fig. 5a and 5b for both pre monsoon and post monsoon seasons. The box and whiskers plot were used to examine trends and variance (Hema & Subramani, 2013) in the water quality measurement data over the period of the two seasons in the present study area.

5.5. Correlation Matrix Analysis

The relationships between the hydrochemical parameters and the sources of groundwater contamination for both the pre- and post-monsoon seasons are determined using a Pearson's correlation coefficient matrix(Bhuiyan et al., 2016) as shown in Tables 4a and 4b. It includes an estimation of the correlation between the two variables. It is computed by subtracting the sum of the standard deviations of the two variables from the covariance of the two variables. The range of values is 0 (perfectly negative correlation), -1 (no correlation), and + 1. (perfect positive correlation) (Shil et al., 2019). Nearly all of the parameters, including calcium, magnesium, sulphate, potassium, electrical conductivity (EC), and total hardness, have a substantial association with the parameters TDS, TH, Cl, and pH, according to the results of the current study's Pearson's correlation matrix

Table 3

Statistical summary of groundwater sample (Post-Monsoon’2021 and Pre-Monsson’22)
Sl. No	Parameter	Unit	Post-Monsoon’ 21					Pre-Monsoon' 22					WHO (2004)		BIS Std. (2012)
Sl. No	Parameter	Unit	Min	Max	Mean	Median	St. Dev.	Min	Max	Mean	Median	St. Dev.	Acceptable limit	Permissible limit	Acceptable limit	Permissible limit
1	GWL	meter	0.8	12.3	3.45	2.45	2.55	0.5	9.7	5	5.33	2.76			-	-
2	pH	pH-unit	4.8	7.2	6.14	6.005	0.6	4.88	7.55	6.39	6.43	0.64	7-8.5	9.2	6.5–8.5	-
3	ORP	millivolts	0.9	20.9	6.55	6.29	3.9	-1.3	26.9	10.5	10.2	7.91	-100	300	0	300
4	DO	%	20.2	103.6	41.75	39.05	16.89	3.7	11	8.07	8.4	2.16	-	-	80	120
5	DO	mg/l	1.57	8.84	3.25	2.955	1.42	0.34	0.86	0.63		0.15	1	5	6.5	8
6	EC	mS/cm	26	684	184.25	120.5	171.2	44	954	263.12	216	212.91	-	200	300	900
7	TDS	mg/l	13	308	82.72	56.5	72.85	22	316	118.19	108	79.87	1500	500	500	2000
8	Temp	^oC	26.92	30.09	28.29	28.08	0.87	27.06	32.55	29.52	29.28	1.29	10	20	10	20
9	Salinity	ppm	0	1	0.34	0	0.48	0	1	0.33	0	0.48	0.5	35	0.5	30
10	Mg	Caco3	-240	80	-61.25	-40	97.58	-480	360	-110.3	-80	192.36	50	150	30	100
11	Ca	Caco3	40	520	223.13	200	122.88	240	840	493.85	480	165.48	75	200	75	200
12	TH	Caco3	40	360	161.88	120	100.75	80	720	353.85	340	139.8	100	500	200	600
13	Cl	mg/l	40	200	78.13	60	34.59	19.99	159.9	64.59	59.96	38.47	200	600	75	1000
14	K	mg/l	-8.05	15.44	2.8	2.31	4.86	0	61.79	17.41	13.64	16.96	-	12	12	-
16	HCo₃	mg/l	48	352	154.72	138	84.25	0	0	0	0	0	500	-	125	-
17	Na	mg/l	4.25	64.54	21.56	16.965	13.82	28	208	78.09	60	46.1	-	200	45	-
18	SO₄	mg/l	0.048	89.7	9.37	4.65	15.9	0	0.172	0.03	0.01	0.05	200	400	200	400

Table 4

a. Correlation Coefficient Matrix of Water Quality Parameters (Post-Monsoon’21)
	Mg	salinity	Alkalinity	pH	EC	TDS	Ca	TH	Temp	K	Na	DO %	DO mg/l	Cl	SO₄
Mg	0.24
Salinity	0.59	-0.03
Alkalinity	-0.1	0.51	0.21
pH	-0.21	-0.21	0.39	0.41
EC	-0.1	0.24	0.4	0.62	0.57
TDS	-0.26	-0.31	0.57	0.42	0.48	0.79
Ca	-0.12	-0.6	0.07	0.43	0.13	0.43	0.31
TH	0.09	0.23	0.05	0.13	-0.04	0.29	0.08	0.64
Temp	-0.15	-0.17	-0.01	0.16	0.16	0.32	0.27	0.24	0.13
K	0.22	-0.31	-0.08	0.33	0.05	0.47	0.35	0.63	0.46	0.22
Na	0.23	-0.27	-0.08	0.29	0.23	0.58	0.39	0.38	0.2	0.3	0.72
DO %	0.06	-0.13	-0.07	0.13	0.29	0.34	0.05	0.19	0.11	0.39	0.33	0.57
DO mg/	0.03	-0.15	-0.09	0.09	0.27	0.31	0.04	0.19	0.09	0.35	0.33	0.57	0.99
Cl	-0.14	-0.08	0.19	0.49	0.34	0.67	0.32	0.24	0.21	0.29	0.29	0.35	0.44	0.41
SO₄	0.34	0.11	-0.17	0.38	0.19	0.52	-0.06	0.21	0.37	0.19	0.34	0.53	0.48	0.43	0.59

Bold values ≥ 0.5 reflecting significant correlation among the correlated hydrochemical parameters

Table 4

b. Correlation Coefficient Matrix of Water Quality Parameters (Pre-Monsoon’22)
	Mg	salinity	Alkalinity	pH	EC	TDS	Ca	TH	Temp	K	Na	DO %	DO mg/l	Cl	SO₄
Mg	0.24
salinity	-0.03	-0.39
Alkalinity	-0.41	0.21	-0.1
pH	-0.21	0.51	0.41	0.21
EC	-0.24	0.4	0.62	0.57	-0.1
TDS	-0.31	0.57	0.42	0.48	0.79	0.26
Ca	-0.6	0.07	0.43	0.13	0.43	0.31	0.12
TH	0.52	0.05	0.13	0.04	0.29	0.08	0.64	0.09
Temp	-0.17	-0.01	0.16	0.16	0.32	0.27	0.24	0.13	-0.15
K	-0.31	-0.08	0.33	0.05	0.47	0.35	0.63	0.46	0.22	0.22
Na	-0.27	-0.08	0.29	0.23	0.58	0.39	0.38	0.2	0.3	0.72	0.23
DO %	-0.13	-0.07	0.13	0.29	0.34	0.05	0.19	0.11	0.39	0.33	0.57	0.06
DO mg/l	-0.15	-0.09	0.09	0.27	0.31	0.04	0.19	0.09	0.35	0.33	0.57	0.99	0.03
Cl	-0.08	0.19	0.49	0.34	0.67	0.32	0.24	0.21	0.29	0.29	0.35	0.44	0.41	0.14
SO₄	0.11	-0.17	0.38	0.19	0.52	0.06	0.21	0.37	0.19	0.34	0.53	0.48	0.43	0.59	0.34

Bold values ≥ 0.5 reflecting significant correlation among the correlated hydrochemical parameters

5.6. Piper plot for hydrochemical analysis

A piper diagram consists of triangles representing anion and cation ions with a common baseline (Piper, 1944). The triangle's neighboring sides are 60^o distances apart. A diamond-shaped spacing is used between the analyses in order to plot them once more as circles with TDS-proportional areas. By analyzing its location on a piper diagram, one can make some reasonable assumptions about the origin of the water represented by an analysis (Hwang et al., 2017). Piper diagrams not only graphically represent the characteristics of a particular water sample, but also compare each sample to other samples to identify lithogenic and human-made contributions to the water and indicate how the chemistry of the water has changed along the flow path (Sayyed et al., 2013).

5.7. Groundwater Quality Assessment

When assessing the efficacy of water, ArcGIS maps are essential for virtually exhibiting the quality of the groundwater by location. It has been analysed the physicochemical variation and the water quality index (WQI) of the groundwater in various areas throughout the research region by generating a water quality index map for the pre and post monsoon seasons of 2021 and 2022. All the 18 hydrogeochemical samples were used to assess the quality of the groundwater throughout the catchment area based on the water's hardness, which is displayed in Table 5. Additionally, Table 6 shows that Horton's indexing recommendations are used to classify the water quality for the pre and post monsoon seasons.

Table 5

Classification of well water sample based on Hardness of water
Hardness as CaCo3 (mg/l)	Water Quality Status	% of water Post Monsoon’21	% of water in Pre Monsoon’22
0–75	Soft	12.5	0
75–150	Moderately hard	40.63	3.85
150–300	Hard	28.125	38.46
> 300	Very Hard	18.75	57.69

Table 6

Water Quality classification based on WQI Values (Brown et, al. 1970)
WQI Value	Water Quality	% water sample Post-monsoon’21	% water sample Pre-monsoon’22
90–100	Excellent Water Quality	36	13
71–90	Good Water Quality	52	46
51–70	Poor Water Quality	8	42
31–50	Bad Water Quality	4	0
0–30	Unsuitable for drinking water	0	0

Water Quality Index maps of both pre-monsoon and post monsoon for the study area which include 18 hydrogeochemical parameters of 32 seasonally monitored well water samples have been developed in Fig. 8a&b by using IDW interpolation technique in ArcGIS 10.8 software.

The primary objective of this study was to map and evaluate the quality of the groundwater in and around Mangalore City. The spatial distribution maps of groundwater quality parameters were created using geostatistical and GIS methods. Based on field experience and laboratory data analysis, it is concluded that the primary function of WQI is to provide a single value for a source's water quality and to condense multiple features into a digestible expression so that data on water quality monitoring is easy to understand. Table 6 shows that, compared to 36% of well water samples taken after the monsoon, 13% of well water samples taken before the monsoon have excellent water quality. Pre-monsoon water quality is higher than post-monsoon water quality (42% in pre-monsoon and 8% in post-monsoon). It has been found that pre-monsoon conditions have higher levels of pollutants in drinkable water than post-monsoon conditions. The study area's coastal belt is more susceptible to drinking water shortages during the pre-monsoon season, and it was observed that groundwater quality was deteriorating into brackish water close to the coast. The Hortons water quality index recommends categorizing groundwater quality into four categories: good water quality (71–90), bad water quality (51–70), and excellent water quality (90–100).

Acknowledgement

The authors thank DST-SERB Govt. of INDIA (Ref No: IMP/2018/001298/WR) for providing financial support to carry out this project work. This study is part of an ongoing R&D project titled "Impounding of River flood waters along Dakshina Kannada Coast: A Sustainable Strategy for water resource development IMPRINT-II Project.

Conflict of Interest Statement: The authors have no conflict of interest in publishing the article.

Declaration of interests

☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

☒The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

Dr. Ramesh H reports financial support was provided by Department of Science and Technology -Science and Engineering Research Board, New Delhi, India.

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No competing interests reported.

Assessment of Groundwater Quality Status by using Hydrochemical and GIS approach for the feasible study of Coastal Reservoirs

Status:

Version 1

Abstract

Figures

1. Introduction

1.1. Coastal Reservoir

2. Description Of The Study Area

3. Material And Methods