Abstract

Groundwater (GW) is a major source of drinking water in many parts of the National Capital Region (NCT) Delhi including the southwest part of the city. To assess the GW quality in the study area total 52 GW samples were collected for both pre and post monsoon season for two consecutive years (2019-20) from 13 sampling locations. These samples were then analyzed for 16 physical and chemical water quality parameters including pH, Temperature, EC, Salinity, TDS, alkalinity, TH, NO₃^-, PO₄^2-, SO₄^2-, F^-, Cl^-, Ca²⁺, Mg²⁺, Na⁺ and K⁺. Descriptive analysis and normality check was executed before subjected to multivariate analysis. Pearson correlation explained, positively high correlation coefficient(r) for both pre and post monsoon seasons between TDS and Cl^- (r=0.97(pre) and 0.93(post)); salinity and EC(r=0.82(pre) and 0.82(post)); TDS and TH(r=0.86(pre)and 0.83(post)); Na⁺ and Mg²⁺ (r=0.80(pre) and 0.81(post)). Factor Analysis depicted TDS, alkalinity, TH, SO₄^2-, F^-, Cl^-, Ca²⁺ were significantly loaded components for both the seasons, contributing for 77.84% (pre monsoon) and 82.31% (post monsoon) of component loading respectively. Degradation of water quality is majorly due to growing population and human interventions adding to the diversity of topography and the geology. The interpolation maps generated depict high concentration of alkalinity, salinity, total dissolved solids (TDS), and electrical conductivity (EC) across the study area’.

1. Introduction

As per the World Development Report, 2019, India is expected to become the most populous country and the Central National Capital Region (CNCR) of Delhi - the largest city in the world by 2030 ¹. The demographic details from the census of 2011 indicate that the southwest district of Delhi has the highest population of 2.292 million with the density 5445 per km², being the highest among the other districts of the of Delhi. This surge in population, leading to unplanned infrastructural development and land use practices, and the load on natural water resources has deteriorated both the quality and the quantity of groundwater ². One of the principal concerns is the degrading quality of underground water in semi-arid and arid area. Considering the Delhi, annual re-perishable groundwater (GW) is around 297 million cubic meters (mcm) while the annual draft is 480 mcm depicting the over-exploitation of the available resources ³. Mallick et. al., ⁴ highlighted the impending water crisis due to increasing population, urbanization, industrialization, and gradual depletion of GW tables resulting from inadequate recharge.

In recent years, there have been a number of studies on GW quality for drinking and irrigation purposes considering the physico-chemical parameters ⁵. The suitability of water for household use, agricultural and industrial use is often determined by the chemical characteristics of the specific geography ⁶. Rapidly changing hydro-geological conditions are leading to a water-stressed condition in Delhi with acute water contamination in south and southwest, especially in the Dwarka region. Research in the southwest Delhi district has indicated that the thickness of the freshwater zone is depleting at the rate of 50–300 cm/ year ⁷. As per the central ground water board (CGWB) report 2006, the exploitation of GW has reached to such an extreme level that an extent of an area (about 114 km²) in southwest and northwest districts are absolutely devoid of fresh GW. Dubey et. al., ⁸ observed in their study that GW of Dwarka is the only major source for domestic consumption in the region of southwest District of Delhi. Shekhar et. al., ⁹ recorded traces of significant amounts of Flouride (F^-) and Nitrate(NO₃^-) contamination in GW of Dwarka, mostly from areas where domestic effluent is discharged into open unlined drains. The presence of the Najafgarh drain in the district is the primary reason for the given situation too ¹⁰.

Given the above background, the study below examines the quality of GW for household as well as agricultural usage in the southwest region of Delhi (India) using both statistical and geospatial approach. The proposed area has been examined for physic-chemical parameters such as pH, temperature, EC, TDS, alkalinity, TH, NO₃^-, PO₄^2-, SO₄^2-, F^-, Cl^-, Ca²⁺, Mg²⁺, Na⁺ and K⁺ for the duration of 2019 to 2021. The data has been analyzed in accordance with the world health organization ¹¹ and bureau of Indian standards ¹² using different multivariate statistical tools.

2. Study Area

The location of Delhi lies between latitudes 28º24' to 28º53' N and longitudes 76º50' to 77º20' E with a total area of 1,483 sq. km. Present study has been carried out in the southwest district of Delhi depicting in Figure 1 which is surrounded by the west district on the north, south district on the east, Gurgaon of Haryana state on the south and Jhajjar of Haryana on the west. The district is divided into three subdivisions namely Delhi Cantonment, Najafgarh and Vasant Vihar. This district has a residing population of 22,92,363 as per the census 2011 ⁴⁴ and average population density is 5458 persons per sq. km. It also covers parts of Aravali range in the south and west of the district. The climate here is of semi-arid in character having extreme summer heat in June. The regional water trough near Kharkhari (west of the Najafgarh drain) and Papankalan (east of the Najafgarh drain) is the result of heavy groundwater abstraction in the southwest district ¹⁰.

3. Methodology

For the groundwater quality assessment, 52 water samples were collected from 13 different locations selected during pre and post monsoon season in the vicinity to CGWB tube well/ bore well covering the entire study area covering all different land use patterns (Figure 2). Also, the study area includes army restricted area where the access is restricted. The collected samples were then analysed for 15 physico-chemical parameters using APHA, 2012 manual and the results are subjected to statistical analysis as described by APHA ⁴². Below in Figure 3 is the flow chart of methodology for better understanding of the methodology implemented.

Descriptive statistics was performed for all the groundwater quality parameters and were compared with the standard guideline for drinking and irrigation recommended by world health organization ¹¹, and bureau of Indian standards ¹². The normality of data was checked using shapiro-wilk test and the p-value was found to be less than 0.05 and subjected to log normal transformation. The data was then subjected to multivariate analysis: pearson correlation analysis, Factor analysis (PCA) and hierarchical cluster analysis (HCA) were used as multivariate unbiased methods in the analysis in order to draw meaningful information ¹³. Pearson’s correlation coefficients (r) were calculated among all the parameters in order to check the relation between the parameters and understand how one parameter is impacting other ¹⁴. Correlation matrix is developed in order to understand the synergetic relation between the parameters and the link between sources of contamination ¹⁵. PCA was performed using varimax rotation with Kaiser Normalization using statistical package for the social science (SPSS) ¹⁶. Before PCA, KMO test was performed in order to measure the sampling adequacy. In the present study, KMO > 0.5 making it satisfactory for PCA ¹⁷ also for data interpretation the PCs with eigen value > 1 were taken into consideration ¹⁸.

To characterize the regional distribution of physico-chemical characteristics of water in southwest region of Delhi, geospatial interpolation maps were generated using ArcGIS software. The sampling points were imposed on the Delhi Boundary and then processed using spatial analyst tools. Among the different interpolation tools, deterministic interpolation method inverse distance weight (IDW) is used for multivariate interpolation based on the known concentration of water quality parameter for different sampling sites ^{18, 19, 20}. It helps in site specific category wise classification of water quality of the studied region on the basis of the measured values ²¹. This technique is widely used for spatial analysis for describing the distribution of contaminants and physicochemical determinant patterns ²². IDW computes the weights by taking the inverse of the distance from an observation’s location to the location of the point being estimated. On the basis of weighted average it generates expected value for each point ²³. It used the sample values for the 13 locations to predict the value for unsampled locations, based on the assumption that things that are close to one another are more alike than those that are farther apart. The interpolation technique can be represented as:

Where K is the interpolated value, k_i denotes the rate of sampling location, i=1, 2,3,…., n; D_i is the distance and a is a constant factor with value 1 or 2.

4. Results and Discussion

The results of descriptive statistics for both pre & post monsoon season depicted the variation in the chemical characteristics of the samples collected from different sources due the anthropogenic activities along with the climatic and geo-genic factors (Table 1). The physical parameters studied were pH, temperature, EC and salinity. For almost all the samples the pH is found in the range of 6.5-7.5 falling under the limit prescribed by BIS ¹² and WHO ¹¹. It depends on the temperature of the water at the time of sampling, relative acid - base concentration and their degree of dissolution ⁸. Also, it is controlled by carbon-dioxide, carbonate and bicarbonate equilibrium. This neutral pH of the water highlights its ability to react with both acid and basic materials existing ²⁴. The temperature is found to around 30° for the pre-monsoon season and 25° in the post-monsoon. Conductivity is the measure of presence of free ions in water in order to pass electric flow ²⁵. It ranges from 350 to 1115µS for the pre-monsoon season and 824 to 6150µS for the post monsoon season. High EC value more than 2000µS can be attributed by shallow water table depth indicating saline aquifer ²⁶. A study conducted by Selvakumar, 2017 depicted that higher values of EC contribute for the higher values for TDS and salinity ²⁷. For both the seasons lower values of EC were observed near the southern ridge whereas, higher values have been observed in the peri-urban areas of Najafgarh sub-division. Salinity is observed to be in the range of 150 to 475mg/l in the pre-monsoon season and 297 to 3540 mg/l in the post monsoon season. The concentration of salinity increases from pre to post due to percolation of the substrate from the surface to beneath. Also, saline ground water is present at a very shallow depth range and the fresh-saline water interface is observed at shallow depth in the south west region. Fluctuation in water level with change in season is due to evaporation that depresses the water level and concentrating the solute level in dry season whereas during winters, groundwater flow, precipitation and runoff dilutes the pollutant ²⁸.

Majorly, the inorganic salts of carbonates and bicarbonates of Ca²⁺, Mg²⁺, Na⁺ and K⁺ contributes for dissolved solids in groundwater (GW). In the present study, TDS value ranges from 430mg/l to 5336mg/l for pre-monsoon and 532mg/l to 4971mg/l in post monsoon season. As per the BIS and WHO standards, acceptable values for TDS are 500mg/l and 1000mg/l respectively. The concentration of TDS for all the sampling locations is showing very high concentration for both the seasons resultant of higher enrichment of salts ⁵. This can be contributed due to percolation of solid containing water, agricultural and industrial waste major source being Najafgarh drain ^{26, 27}. The problem associated with high value of TDS is bad odour and taste, hardness, corrosion and scaling in the water supply system.

The study area is observed to have hard water across with high concentration majorly contributed by salts of Ca²⁺ and Mg²⁺. The mean average concentration of total hardness is observed to be 685±443mg/l in pre monsoon season and 730±489mg/l in the post monsoon. This may be dissolution of CO₃^2- and HCO₃^- in GW during monsoon. Bicarbonates of Ca²⁺ and Mg²⁺ are responsible for temporary hardness and Cl^-, SO₄^2-, NO₃^- are responsible for permanent hardness. 88% of the samples exceed the Indian standard limit for the areas near Dwarka, Mahavir Enclave and certain villages like Raota are observed to high concentration falling under category of very hard type (>180 mg/l) ^{14, 43}. Alkalinity of water is majorly due to bicarbonate, carbonate and hydroxides of Ca²⁺, Na⁺ and K⁺. However, large quantities impart a bitter taste to water. BIS desired limit is 200 mg/l. The concentration of alkalinity is observed to be higher at all the sampling locations than the desirable limit for all the sampling locations except Rajokri, Vasant Kunj, Dilchaon Kalan and Mahavir Enclave. Although, the mean value for both the seasons is observed to be around 450 mg/l which is higher than the desirable limit for drinking. Alkalinity is influenced by bedrock salts and soil, hydrology and wastewater discharge ²⁹. The concentration of NO₃^- is varied at different sampling sites which ranges from 0.05mg/l to 77.56mg/l. The trend is observed to be similar in pre and post monsoon. As per the standards of both BIS and WHO, acceptable and permissible limits are 45mg/l yet for the natural conditions it doesn’t exceed 10mg/l ³⁰. The area surrounding Bhakargah village shows the maximum value for both the seasons due to use of chemical fertilizers, animal waste generation as the area is majorly covered with agricultural land and also due to livestock and human wastes ^{18, 31} Intake of high concentration of nitrate affects health causing thyroid disability and carcinogenicity.

Cl^- salt is observed to be soluble in groundwater and move freely through soil and rocks. Its significant concentration adds salty taste to GW and is also responsible for the corrosion in pipes and pumps. The chloride concentration in the study area varied from 67 mg/l to 2099.34mg/l in the pre-monsoon season and 69.97mg/l to 1749.45 mg/l in post monsoon season. The major reason for higher concentration could be due to over abstraction of GW for agriculture purpose leading to decline in the water table resulting in salt water intrusion. Also rainwater mixing and high evaporation rate in the region contributes to high chloride concentration ³¹. The standard limit prescribed by WHO and BIS is 200 mg/l which is exceeded near Dwarka region, Inderpuri and Mayapuri area along with some parts of peri-urban region across the study area. It may also be due to anthropogenic untreated domestic, irrigation water return flow, leaching of surface/sub-surface salts, rainwater mixing, evaporate deposits ^{19, 20}. Same results were observed by Kaur and Rani ³² chloride concentration was far above the permissible limits in the Najafgarh block ³². Similar trend is observed for the post monsoon season in the present study. A report by CGWB 2006 ⁴⁵, depicted that 30% of the Delhi region shows high concentration of F which is even more than the WHO permissible limits. In the present study, the Fluoride concentration is found to be above the acceptable limit prescribed by WHO and BIS at Najafgarh, Roata, Bhakargarh, Dichaon Kalan for the post monsoon season. High TDS enhances water’s ionic strength dealing to increased F solubility ³³. Also percolation down from phosphate fertilizers from the irrigation runoff contribute for fluoride concentration. SO₄^2- concentration changes significantly with time due to infiltration during rainfall ³⁴. In the sampling zlocations at Mahavir Enclave, Bhakargah, Dichaon Kalan, Shikarpur have higher concentration more than 200mg/l. This concentration is objectionable for domestic use as it is beyond the limit and causes gastro-intestinal irritation ³⁵. Also if the exceeds maximum allowable limit of 400mg/l, causing laxative effect on human system. Herein, in our study most of the sampling sites have SO₄^2- concentration below the desirable limits. Its high concentration also contributes for hardness of the ground water. This high concentration may be attributed by the prolonged evaporative enrichment of water, leading to high salinity ³⁶.

Ca²⁺ and Mg²⁺ are responsible for the hardness in the study area. The mean value observed for Ca²⁺ is 108±71 mg/l for pre monsoon and 115±70 mg/l for post monsoon season. Higher concentration of Ca²⁺ and Mg²⁺ is observed near the southern ridge area. Magnesium is also having higher concentration ranging from 14 mg/l to 249 mg/l and 12 mg/l to 182 mg/l for pre and post monsoon season respectively due to the presence of Mg²⁺ bearing rocks and minerals beneath. BIS and WHO prescribed standards for Ca²⁺ and Mg²⁺ are 75 mg/l and 30 mg/l respectively. High concentration of Ca²⁺ and Mg²⁺ at very high concentration causes calcification of arteries and also diseases related to kidney, stomach and bladder ³⁷. Similar trend is observed for monovalent Na⁺ and K⁺ for both the seasons (pre and post monsoon). There is no standard limit for K⁺ whereas for sodium only WHO has prescribed the limit to be 200 mg/l. The mean value for Na⁺ is found to be 552 mg/l in pre monsoon and 606.30 in the post monsoon season, depicting that major parts of the study area are having higher concentrations. Higher concentration may be attributed by the silicate weathering and dissolution of soil salts, ion-exchange processes and man-made discharge ¹⁴. Overall, cation abundance in groundwater is observed as Na⁺ > Ca²⁺ > Mg²⁺ > K⁺ for both the seasons.

Spatio-temporal variation in physical and chemical parameters are depicted by maps generated using Inverse distance weighted (IDW) interpolation technique and analyse the source of water pollution. This interpolation technique is widely applied in the field of spatial analysis for the distribution of contaminants ³⁸. The presented GIS based maps in Figure 4 and Figure 5 shows the geographic distribution of major chemical elemnts with the study area boundary ³⁹. Higher concentration is denoted by deeper colour and lower concentration with green in colour. It is done in order to easily understand the distribution of the chemical element. The seasonal variation is studied through the spatial maps developed. For the pre-monsoon season, the higher concentration is observed for alkalinity, EC, salinity, TDS for major part of the study area. High concentration of Na⁺, SO₄^2-, Mg²⁺ is observed in the western part areas near Delhi cantonment, ridge area and aroud inderpuri. Cl and hardness is observed to have high concentration near mahavir enclave area and parts of Dwarka region beyond the permissible limits. The results obtained through IDW have been validate with the ground water reports for the same sampling durtaion. As depicted in the maps for the Post Monsoon season, Cl^-, F^-, TDS, hardness, alkalinity all are observed to have high concentration along the eastern and central part of the study area as depicted in Figure 5. Also SO₄^2- is found to be high near Maya Puri and Mahavir Enclave area, Mg²⁺ is higher across Dwarka region and Sodium shows high concentration along the western part of southwest Delhi which is beyond the permissible limits.

Pearson correlation analysis is bivariate method used to evaluate the relationship between two parameters and establish the strength of a linear relationship they share ²⁵. A high correlation coefficient between two variables either positive or negative indicated a good/no relationship between them. The value of correlation coefficients(r) among various water quality parameters were calculated and given in the Table 2 and Table 3. The level of significance is taken to be 0.05 and correlation matrix is developed for both the seasons’ individually. For pre monsoon period, TDS is observed to have high positive correlation with hardness (r = 0.86), Cl^- (r = 0.97) and SO₄^2- (r = 0.81) as also observed in a study conducted by Shekhar for the same study area ⁹. Hardness also shows high positive correlation with Cl^- (r = 0.90) which possibly indicates that Cl^- contribute for the problem of hard water along with sulphate as it shows moderate correlation ^{14, 29}. It is also observed that conductivity and salinity show high positive correlation (r = 0.83) due to the presence of free ions. Na⁺ and Mg²⁺ (r = 0.80); Cl^- and SO₄^2- (r = 0.77) show strong positive correlation among each other indicating their release from the same source and anthropogenic activities ¹⁹. For the post monsoon season, high positive correlation is observed between salinity - EC (r = 0.82); TDS- Hardness (r = 0.83); TDS-Cl^- (r = 0.94); TDS–SO₄^2- (r = 0.82); Hardness-Cl^- (r = 0.77); Cl^--SO₄^2- (r = 0.79) and Na⁺-Mg²⁺ (r = 0.81), similar trend is observed in other studies ²⁵. For both pre and post monsoon, high positive correlation (r= 0.77 and 0.79) observed can be attributed by bed seepage from drains and wastewater ³⁶. A moderate correlation is observed between F^- and SO₄^2-; F^- and Ca²⁺; Ca²⁺ and Mg²⁺ for both pre and post monsoon season.

PCA was conducted to reduce the dimension of the data set and result in few factors that are important in order to explain the variation in the dataset. Prior to this Kaiser-Mayer-Olkin (KMO) and Barlett’s test was conducted ⁴⁶. All principal component with eigen value >1 was considered significant. The loadings more than ±0.75 are considered as strong correlation; between ±0.5 and ±0.74 is moderate correlation and, between 0 and ±0.49 is considered as weak correlation ^{27, 40}. On this basis, four principal components (PCs) were identified for pre monsoon season, accounted for cumulative of 77.84% of the total variance associated with the data set. PC1 explains 36.47% of the total variance with strong loading of TDS, hardness, Cl^-, SO₄^2- and moderate loading of ^-, NO₃^-. Factor 2 explains 19.91% with F^- and Ca²⁺ having high loading. PC3 explains 11.96% and PC4 explains 9.49% with strong loading of salinity and Na⁺ respectively. The component loading of >0.06 has been used for explaining the constituents of the different components of PCA. Likewise for the post monsoon season, five principal components (PC’s) were identified, accounting for cumulative 82.31% of the total variance. Herein, PC1 explains 31.64%; PC2 explains 20.88%; PC3 explains 12.35%, PC4 explains 9.21% and PC5 explains 8.21% as mentioned in Table 4.

The Eigenvalue, percentage variance, cumulative percentage variance and component loading of extracted factors for the dataset is mentioned for pre and post monsoon respectively. Strong loading of TDS, hardness, Cl^- and SO₄^2- for factor 1; Na⁺and K⁺ for factor 2 and Ca²⁺for factor 3 is observed. These may be attributed by weathering of mineral rock and is also supported by the strong correlation existing between one another. Strong loading of Cl^- is attributed by the anthropogenic activities. Also Cl^-, Ca²⁺ and SO₄^2- together contribute for the high loading of TDS and hardness. High loading of Na⁺ and Mg²⁺ can be observed for the post monsoon season due to dissolution of Na⁺ bearing minerals ⁴¹. Similar trend is observed for post monsoon season, factor 1 depicts high loading of TDS, hardness, Cl^- and SO₄^2- contributing around 28% of the total variance explained in Table 4.

5. Conclusion

Degradation of groundwater (GW) is a major issue of concern for environment and society both. For the study area, the physico-chemical analysis and statistical interpretation of GW quality indicates that all samples exceeds the acceptable limit (BIS) of TDS, alkalinity, Hardness for both pre and post monsoon seasons whereas, about 38% of the samples were exceeding for Mg, 21% for Cl and 15% for Ca. It shows that different salt and mineral groups along with organic and inorganic pollutants are the major source of GW pollution. The multivariate statistical analysis depicts that hardness, Cl, SO₄ and TDS were the chief factors affecting the GW quality. Also high positive correlation is observed for TDS with hardness, Cl and SO₄; salinity-EC; Na-Mg; SO₄-Cl. Four principal components were identified to explain 77.84% of the dataset and five principal components were identified to explain 82.31% of the dataset for pre and post monsoon season respectively. Though, the IDW interpolation shows that the spatial variation of the concentration of alkalinity, salinity, EC and TDS is found to be high all across the southwest region whereas NO₃ and F were found to be higher in western part of the study area. These water quality maps generated will enforce the standards and regulations, bringing it towards better pollution management. This could be due to consistent percolation of pollutants from nearby agricultural land. Hence, the above results concluded that the GW quality in the study area is undesirable and unhealthy for drinking and irrigation purpose. As there is no major other fresh water source, the quality of GW is to be improved for irrigation purpose and also to enhance the social and environmental aesthetics. As the water demand is high there should be increase in water supply in order to decrease the dependency on GW. There should be proper monitoring of illegal extraction of GW especially across the Najafgarh Drain for irrigation purpose. There is an utmost need to impose regular monitoring strategies and effective management resources in order to optimize the available resources.

Acknowledgements

This is to acknowledge that the data provided in the paper is authentic and not published in any other journal. The Financial Support for this study was provided by Guru Gobind Singh Indraprastha University as STRF. Thanks to Prof. Amarjeet Kaur and Dr. Priyanka Kumari for their guidance and support all through.

References