In an attempt to assess the groundwater quality of Vadodara and Chhota Udaipur districts and check its suitability for drinking purposes, a total of 162 samples (50 samples during pre-monsoon season and 54 samples during post-monsoon season from Vadodara district and 29 samples during both pre- and post-monsoon seasons from Chhota Udaipur district) were collected from 63 villages of both the districts for pre-monsoon and post-monsoon seasons during 2016–17. The analysis was carried out for physicochemical characteristics and the analytical results have been interpreted by graphical representation, correlation and regression analysis and water quality index so that the quality of groundwater can be easily understood. The analytical results were then compared with the Indian Standards Drinking Water-Specification (Second Revision). From this study, it is concluded that the overall groundwater quality of the region is comparatively good; however, elevated nitrate levels resulted in many of the samples having raised concern and the necessity to make all possible efforts to improve the quality of groundwater wherever deteriorated.

  • Elevated nitrate and fluoride concentration observed in the samples collected has raised concern about the quality of groundwater.

  • Physico-chemical analysis of groundwater quality of samples collected were compared with Indian Drinking Water Quality Standard.

  • Correlation and regression as well as water quality index was used to determine overall quality of water.

Graphical Abstract

Graphical Abstract
Graphical Abstract

One of the essential and natural resource for life on Earth is water. The human population has sustained itself for thousands of years because of water's complex interactions with the rest of the natural environment (Khatri et al. 2016). Also, the sustainable socioeconomic development of every community is dependent on the availability of freshwater resources (Sharma et al. 2016). Around 99.97% of the total freshwater available is groundwater, while the remaining is available as streams, lakes and rivers (Khatri et al. 2020b). Hence, out of other sources of fresh water like rivers, ponds and lakes, groundwater is the broadly used resource for drinking as well as irrigation and industrial purpose due to its quality and quantity considerations (Dohare et al. 2014). A significant fraction of the total supply for domestic, industrial and agricultural sectors is provided by groundwater in many countries. The groundwater is believed to be comparatively much cleaner and free from pollution than surface water; however, it is generally affected by anthropogenic activities. Pollution of groundwater is aggravated due to municipal, industrial, agricultural and other miscellaneous sources and causes. Also, the ever-increasing demand for groundwater due to rapid industrialization and urbanization results in overexploitation of groundwater resources causing depletion of water levels and also the degradation of groundwater (Chourasia 2018). Groundwater contamination results in poor drinking water quality, loss of water supply, high clean-up costs, high costs for alternative water supplies and/or potential health problems. Owing to poor drinking water quality, the world is affected with 80% of diseases, as per the WHO, 1984 (Kalaivanan et al. 2017).

Water quality, measured by assessing the physicochemical and biological properties of water against a set of standards, is used to determine whether water is suitable for consumption or safe for the environment (Khatri & Tyagi 2014). Hence, assessment of groundwater quality and quantity becomes necessary to be acquainted with the sustainability of groundwater resources (Shah & Mistry 2013). In the present study, we have selected Vadodara and Chhota Udaipur districts of Gujarat state as our study area. Vadodara is a well-known district in Gujarat, India and is located on the banks of the Vishwamitri river. It was the capital of Gaekwad state until 1947 and is prominent for Laxmi Vilas Palace, which served as the residence of the Maratha royal Gaekwad dynasty, that ruled over Baroda state (History of Vadodara district 2021). Chhota Udaipur, carved out of the Vadodara district, is a tribal district in the state of Gujarat with a rich indigenous history and culture. Chhota Udaipur district has a rich forest area that forms a part of Jambughoda and Ratanmahal wildlife sanctuaries. The district is also known for the Rathwa tribal community and is home to a large dairy industry (History of Chhota Udaipur district 2021). Both the districts selected for the study has enriched tourist sites explored by numerous visitors across the year amplifying the need for groundwater quality assessment. Groundwater collected from Vadodara and Chhota Udaipur districts was checked by analysing a total of 162 samples during the pre-monsoon (April–May) and post-monsoon (October–November) seasons.

Objective of the study

The primary objectives of this study are as follows: to assess the current status of groundwater quality of Vadodara and Chhota Udaipur districts by examining and evaluating the physicochemical characteristics of groundwater; to assess the overall quality of monitored sources by comparing the analytical results with the Indian Standards Drinking Water-Specification (Second Revision) (IS 10500:2012); to provide the current database with aid in decision-making for policy level change at different levels with respect to the current status of groundwater; to carry out statistical analysis using various data interpretation techniques, namely, seasonal comparison, water quality index and correlation and regression analysis methods.

Study area

Vadodara and Chhota Udaipur districts are located in the central part of mainland Gujarat. Chhota Udaipur district was carved out of the Vadodara district on August 15, 2013 with its headquarters at Chhota Udaipur town (Shah 2016–17). The districts are bounded to the north and northeast by Anand, Panchmahals and Dahod districts, to the east and southeast by Madhya Pardesh and Maharashtra state, to the southeast by Narmada district and to the south and west by Bharuch district (District Groundwater Brochure: Vadodara 2011). A brief district profile of both the districts is presented in Table 1.

Table 1

Brief profile of Vadodara and Chhota Udaipur districts

ParticularsVadodara districtChhota Udaipur district
Geographical area 7,548.50 sq. km 3,087 sq. km 
Latitudes 21°49′19″ to 22°48′37″ 20.49′ to 22.49′ 
Longitudes 72°51′05″ to 74°16′55″ 72.51′to 74.17′ 
Number of villages 657 888 
Name of talukas Dabhoi, Karjan, Padra, Savli, Shinor, Vadodara and Vaghodiya Chhota Udaipur, Pavi Jetpur, Kawant, Naswadi, Sankheda and Bodeli 
Maximum temperature 41 °C 45 °C 
Minimum temperature 12 °C 8 °C 
Average annual rainfall 965 mm 1,083 mm 
Soil type Black soil, alluvial soil and hilly soil Hard black soil, medium black soil, sandy loam soil and saline soil 
Hydrogeology Groundwater occurs both as unconfined and confined conditions. Saturated zones of unconsolidated shallow alluvium and weathered zones, shallow depth jointed and fractured rocks form unconfined aquifers, whereas multilayered aquifer below impervious clay horizons in alluvium formation and interflow zones of basalts, inter-trappean beds, deep seated fracture zones, shear zones in basalts, granites and gneisses give rise to semi-confined to confined conditions 
ParticularsVadodara districtChhota Udaipur district
Geographical area 7,548.50 sq. km 3,087 sq. km 
Latitudes 21°49′19″ to 22°48′37″ 20.49′ to 22.49′ 
Longitudes 72°51′05″ to 74°16′55″ 72.51′to 74.17′ 
Number of villages 657 888 
Name of talukas Dabhoi, Karjan, Padra, Savli, Shinor, Vadodara and Vaghodiya Chhota Udaipur, Pavi Jetpur, Kawant, Naswadi, Sankheda and Bodeli 
Maximum temperature 41 °C 45 °C 
Minimum temperature 12 °C 8 °C 
Average annual rainfall 965 mm 1,083 mm 
Soil type Black soil, alluvial soil and hilly soil Hard black soil, medium black soil, sandy loam soil and saline soil 
Hydrogeology Groundwater occurs both as unconfined and confined conditions. Saturated zones of unconsolidated shallow alluvium and weathered zones, shallow depth jointed and fractured rocks form unconfined aquifers, whereas multilayered aquifer below impervious clay horizons in alluvium formation and interflow zones of basalts, inter-trappean beds, deep seated fracture zones, shear zones in basalts, granites and gneisses give rise to semi-confined to confined conditions 

Groundwater availability

The taluka-wise details of available groundwater recharge per year, existing gross groundwater draft per year and level of groundwater development along with categorization for future groundwater development for both the districts is given in Table 2. The data were used as a primary source for selection of sampling locations at large.

Table 2

Details of groundwater availability

Sr. No.TalukasAvailable groundwater recharge in MCM/yearExisting gross groundwater draft for all uses in MCM/yearLevel of groundwater development (%)Category
Vadodara district 
1. Dabhoi 123.52 84.91 68.74 Safe 
2. Karjan 161.31 137.71 85.37 Semi-critical 
3. Padra 113.01 77.73 68.78 Safe 
4. Savli 71.35 37.56 52.65 Safe 
5. Shinor 80.99 64.76 79.96 Semi-critical 
6. Vadodara 100.99 104.12 103.10 Over-exploited 
7. Vaghodiya 63.84 14.69 23.01 Safe 
Chhota Udaipur district 
8. Chhota Udaipur 55.61 26.93 48.42 Safe 
9. Pavi Jetpur 99.96 60.45 60.47 Safe 
10. Kawant 29.36 19.14 65.20 Safe 
11. Naswadi 34.19 12.17 35.59 Safe 
12. Sankheda 66.26 35.96 54.27 Safe 
13. Bodeli – – – – 
Sr. No.TalukasAvailable groundwater recharge in MCM/yearExisting gross groundwater draft for all uses in MCM/yearLevel of groundwater development (%)Category
Vadodara district 
1. Dabhoi 123.52 84.91 68.74 Safe 
2. Karjan 161.31 137.71 85.37 Semi-critical 
3. Padra 113.01 77.73 68.78 Safe 
4. Savli 71.35 37.56 52.65 Safe 
5. Shinor 80.99 64.76 79.96 Semi-critical 
6. Vadodara 100.99 104.12 103.10 Over-exploited 
7. Vaghodiya 63.84 14.69 23.01 Safe 
Chhota Udaipur district 
8. Chhota Udaipur 55.61 26.93 48.42 Safe 
9. Pavi Jetpur 99.96 60.45 60.47 Safe 
10. Kawant 29.36 19.14 65.20 Safe 
11. Naswadi 34.19 12.17 35.59 Safe 
12. Sankheda 66.26 35.96 54.27 Safe 
13. Bodeli – – – – 

MCM, million cubic meter.

The hydro-geochemistry study of Vadodara and Chhota Udaipur districts was carried out by monitoring and analysing the groundwater samples from randomly selected villages. The methodology adopted includes site selection, sample collection, analysis, results, discussion and conclusion, followed by correlation and regression analysis and water quality index which were also found for both districts.

Site selection

About 50 sampling locations during the pre-monsoon season and 54 sampling locations during the post-monsoon season of Vadodara district were selected; whereas for Chhota Udaipur district, 29 samples were selected based on the stratified random sampling method and their respective geographical locations for sampling and monitoring. The selected villages represent the groundwater quality of the districts. The taluka-wise list of villages selected for sampling is given in Table 3, also the location map of the villages selected is shown in Figure 1.

Table 3

Taluka-wise details of villages selected for sampling in Vadodara and Chhota Udaipur districts

Sr. No.TalukaVillages
Vadodara district 
1. Waghodia Asoj, Jaroda, Waghodia, Sangadol, Timbi 
2. Savli Manjusar, Savli, Shihora, Vejpur, Mevli, Ghantiyal 
3. Nandod Poicha 
4. Becharaji Kanoda 
5. Sankheda Kasumbia 
6. Dabhoi Mohammadpura, Tentalav, Chandod, Shirola, Koyavarohan, Meghakuia, Borbara 
7. Sinor Awakhal, Sinor 
8. Vadodara Ranoli, Alkapuri, Por, Por Kayavarohan Road, Wadsalaa 
9. Karjan Choranda, Alampura, Motikoral, Samri, Divi, Sayara 
10. Padra Mobha, Muval, Ranu, Sarasvani, Padra 
Chhota Udaipur district 
11. Chhota Udaipur Puniyavant, Kasara, Kachhel, Gabadiya, Ganthiya, Oliamba 
12. Naswadi Naswadi, Nannupura, Akona, Rampuri, Kukavati, Kandva, Rayansingpura, Piplej, Anandpuri 
13. Sankheda Sankheda, Malu, Gajipur, Talakpur, Akakheda, Handod, Khandupura, Ratanpur, Bahadurpur 
Sr. No.TalukaVillages
Vadodara district 
1. Waghodia Asoj, Jaroda, Waghodia, Sangadol, Timbi 
2. Savli Manjusar, Savli, Shihora, Vejpur, Mevli, Ghantiyal 
3. Nandod Poicha 
4. Becharaji Kanoda 
5. Sankheda Kasumbia 
6. Dabhoi Mohammadpura, Tentalav, Chandod, Shirola, Koyavarohan, Meghakuia, Borbara 
7. Sinor Awakhal, Sinor 
8. Vadodara Ranoli, Alkapuri, Por, Por Kayavarohan Road, Wadsalaa 
9. Karjan Choranda, Alampura, Motikoral, Samri, Divi, Sayara 
10. Padra Mobha, Muval, Ranu, Sarasvani, Padra 
Chhota Udaipur district 
11. Chhota Udaipur Puniyavant, Kasara, Kachhel, Gabadiya, Ganthiya, Oliamba 
12. Naswadi Naswadi, Nannupura, Akona, Rampuri, Kukavati, Kandva, Rayansingpura, Piplej, Anandpuri 
13. Sankheda Sankheda, Malu, Gajipur, Talakpur, Akakheda, Handod, Khandupura, Ratanpur, Bahadurpur 

aVillages monitored in the post-monsoon season only.

Figure 1

Sampling locations for Vadodara and Chhota Udaipur districts.

Figure 1

Sampling locations for Vadodara and Chhota Udaipur districts.

Close modal

Sampling and monitoring

A total of 162 groundwater samples were collected from the identified villages during pre- and post-monsoon seasons, respectively, and the collection method used was ‘grab sampling’ method. The samples were collected in polyethylene carboys as per Gujarat Environment Management Institute (GEMI)'s sampling protocol for water and wastewater, and samples requiring preservation were preserved on-site using preservatives as prescribed in Standard Methods for the Examination of Water and Waste Water (2012).

The primary information collected by GEMI's sampling team during sampling includes allotment of unique sample IDs that are further used for representation of analytical data in graphical form for each sample collected from a distinct location along with its latitude and longitude, source type and depth of source and are summarized in Tables 4 and 5. The use of the groundwater sources from where the samples were collected was mostly drinking and domestic use followed by irrigation at a few locations. This implies direct dependency of the resident population in the study area on groundwater.

Table 4

Details collected during sampling and monitoring of Vadodara district

Sample IDVillageLatitudeLongitudeSource typeDepth of source (ft)
1. Ashoj 22.422051 73.208271 Borewell 160 
2. Manjusar 22.445154 73.19986 Borewell 150 
3. Savli_1 22.56367 73.22036 Borewell 160 
4. Savli_2 22.56797 73.22164 Borewell 160 
5. Poicha_1 22.58644 73.17814 Borewell 150 
6. Kanoda 22.59275 73.20281 Borewell 160 
7. Namisora 22.501895 73.186956 Borewell 170 
8. Poicha_2 22.58536 73.17581 Borewell 180 
9. Poicha_3 22.58097 73.19147 Hand pump 180 
10. Shihora 22.663892 73.280364 Borewell 150 
11. Vejpur 22.741369 73.350238 Hand pump 160 
12. Mevli 22.61174 73.326656 Hand pump 150 
13. Ghantiya 22.519921 73.380448 Hand pump 160 
14. Jarod 22.43925 73.33111 Borewell 170 
15. Kodarvaya 22.409305 73.37971 Hand pump 160 
16. Nimeta 22.351219 73.304914 Hand pump 180 
17. Waghodia (near school) 22.291346 73.233778 Borewell 170 
18. Sangadol_1 22.31664 73.43969 Borewell 160 
19. Sangadol_2 22.33378 73.43969 Hand pump 150 
20. Kasumbia 22.232665 73.515238 Hand pump 150 
21. Mohammadpura 22.290323 72.940151 Borewell 70 
22. Timbi 22.31784 73.279485 Borewell 40 
23. Dabhoi (Javapura) 22.11542 73.44839 Borewell 100 
24. Dharmpur 22.102905 73.444539 Borewell 80 
25. Tentalav 22.046044 73.423178 Borewell 70 
26. Chandod 21.985793 73.455587 Borewell 80 
27. Shirola 22.057356 73.378975 Borewell 180 
28. Awakhal 22.00757 73.317075 Borewell 120 
29. Koyavarohan 22.00757 73.317075 Borewell 160 
30. Ranoli 22.400377 73.13201 Borewell 170 
31. Alkapuri 22.313295 73.176588 Borewell 180 
32. Por Taluka 22.139507 73.189985 Borewell 120 
33. Por-Kayavarohan road 22.139567 73.189995 Borewell 230 
34. Karjan 22.05521 73.117257 Borewell 250 
35. Choranda 21.988927 73.173313 Borewell 250 
36. Sinor 21.9137 73.338729 Borewell 180 
37. Sursamal 22.30136 73.202022 Borewell 170 
38. Alampura 21.849902 73.209683 Borewell 80 
39. Motikoral 21.836598 73.208932 Borewell 110 
40. Samri 21.933566 73.197066 Borewell 150 
41. Divi 21.930128 73.108405 Borewell 90 
42. Kanbhu 22.061559 73.027245 Borewell 160 
43. Mobha 22.136074 72.9682 Borewell 170 
44. Muval 22.175358 72.963048 Borewell 170 
45. Ranu 22.210704 73.024524 Hand pump 190 
46. Sarasvani 22.173335 73.094389 Borewell 110 
47. Padra 22.239437 73.084798 Borewell 90 
48. Sayar 21.850664 73.235472 Borewell 160 
49. Meghakui 22.41667 73.268499 Borewell 150 
50. Borbar 22.02056 73.34389 Borewell 80 
51. Shihora 22.65777778 73.27194444 Borewell 80 
52. Narmisara 22.50305556 73.18500000 Borewell 200 
53. Poicha 22.58638889 73.17805556 Borewell 80 
54. Mevli 22.61194444 73.31972222 Borewell 85 
Sample IDVillageLatitudeLongitudeSource typeDepth of source (ft)
1. Ashoj 22.422051 73.208271 Borewell 160 
2. Manjusar 22.445154 73.19986 Borewell 150 
3. Savli_1 22.56367 73.22036 Borewell 160 
4. Savli_2 22.56797 73.22164 Borewell 160 
5. Poicha_1 22.58644 73.17814 Borewell 150 
6. Kanoda 22.59275 73.20281 Borewell 160 
7. Namisora 22.501895 73.186956 Borewell 170 
8. Poicha_2 22.58536 73.17581 Borewell 180 
9. Poicha_3 22.58097 73.19147 Hand pump 180 
10. Shihora 22.663892 73.280364 Borewell 150 
11. Vejpur 22.741369 73.350238 Hand pump 160 
12. Mevli 22.61174 73.326656 Hand pump 150 
13. Ghantiya 22.519921 73.380448 Hand pump 160 
14. Jarod 22.43925 73.33111 Borewell 170 
15. Kodarvaya 22.409305 73.37971 Hand pump 160 
16. Nimeta 22.351219 73.304914 Hand pump 180 
17. Waghodia (near school) 22.291346 73.233778 Borewell 170 
18. Sangadol_1 22.31664 73.43969 Borewell 160 
19. Sangadol_2 22.33378 73.43969 Hand pump 150 
20. Kasumbia 22.232665 73.515238 Hand pump 150 
21. Mohammadpura 22.290323 72.940151 Borewell 70 
22. Timbi 22.31784 73.279485 Borewell 40 
23. Dabhoi (Javapura) 22.11542 73.44839 Borewell 100 
24. Dharmpur 22.102905 73.444539 Borewell 80 
25. Tentalav 22.046044 73.423178 Borewell 70 
26. Chandod 21.985793 73.455587 Borewell 80 
27. Shirola 22.057356 73.378975 Borewell 180 
28. Awakhal 22.00757 73.317075 Borewell 120 
29. Koyavarohan 22.00757 73.317075 Borewell 160 
30. Ranoli 22.400377 73.13201 Borewell 170 
31. Alkapuri 22.313295 73.176588 Borewell 180 
32. Por Taluka 22.139507 73.189985 Borewell 120 
33. Por-Kayavarohan road 22.139567 73.189995 Borewell 230 
34. Karjan 22.05521 73.117257 Borewell 250 
35. Choranda 21.988927 73.173313 Borewell 250 
36. Sinor 21.9137 73.338729 Borewell 180 
37. Sursamal 22.30136 73.202022 Borewell 170 
38. Alampura 21.849902 73.209683 Borewell 80 
39. Motikoral 21.836598 73.208932 Borewell 110 
40. Samri 21.933566 73.197066 Borewell 150 
41. Divi 21.930128 73.108405 Borewell 90 
42. Kanbhu 22.061559 73.027245 Borewell 160 
43. Mobha 22.136074 72.9682 Borewell 170 
44. Muval 22.175358 72.963048 Borewell 170 
45. Ranu 22.210704 73.024524 Hand pump 190 
46. Sarasvani 22.173335 73.094389 Borewell 110 
47. Padra 22.239437 73.084798 Borewell 90 
48. Sayar 21.850664 73.235472 Borewell 160 
49. Meghakui 22.41667 73.268499 Borewell 150 
50. Borbar 22.02056 73.34389 Borewell 80 
51. Shihora 22.65777778 73.27194444 Borewell 80 
52. Narmisara 22.50305556 73.18500000 Borewell 200 
53. Poicha 22.58638889 73.17805556 Borewell 80 
54. Mevli 22.61194444 73.31972222 Borewell 85 
Table 5

Details collected during sampling and monitoring of Chhota Udaipur district

Sample IDVillageLatitudeLongitudeSource typeDepth of source (ft)
55. Malu 22.25621755 73.53268845 Borewell 100 
56. Gajpur 22.25036972 73.53278056 Hand pump 80 
57. Talakpur 22.19552059 73.62199615 Hand pump 90 
58. Akakheda 22.18400463 73.61262878 Borewell 110 
59. Sankheda 22.18518144 73.61233771 Borewell 120 
60. Handod 22.14591587 73.5723729 Hand pump 100 
61. Khandupura 22.14591587 73.5723729 Borewell 80 
62. Sanodiya 22.15318448 73.60934488 Borewell 130 
63. Ratanpur 22.11464018 73.50716918 Hand pump 100 
64. Akona 22.03775000 73.70936111 Hand pump 40–50 
65. Rampuri_1 22.04875000 73.71150000 Hand pump 50 
66. Rampuri_2 22.04841667 73.71144444 Hand pump 50 
67. Anandpuri 22.05444444 73.72875000 Borewell 70–80 
68. Kukavati 22.05586111 73.73641667 Borewell 70–80 
69. Kandva_1 22.05116667 73.75180556 Borewell 70–80 
70. Kandva_2 22.05027778 73.75158333 Hand pump 50 
71. Naswadi 22.02808333 73.77308333 Canal 100 
72. Nannupura 22.03430556 73.74675000 Hand pump 50 
73. Rayansingpura 22.02022222 73.69836111 Hand pump 50 
74. Puniyavant_1 22.33172222 73.96441667 Borewell 100 
75. Puniyavant_2 22.33161111 73.96408333 Hand pump 50 
76. Chhota Udaipur_1 22.31358333 74.01122222 Hand pump 40 
77. Chhota Udaipur_2 22.31291667 74.01347222 Borewell 180 
78. Kasara -1 22.34672222 74.01866667 Hand pump 50 
79. Kachhel 22.33877778 74.04855556 Hand pump 40 
80. Gabadiya 22.30000000 74.04636111 Hand pump 50 
81. Ganthiya 22.27536111 74.04769444 Hand pump 30 
82. Piplej 22.26650000 73.99819444 Hand pump 50 
83. Oliamba 22.28569444 73.97883333 Hand pump 30 
Sample IDVillageLatitudeLongitudeSource typeDepth of source (ft)
55. Malu 22.25621755 73.53268845 Borewell 100 
56. Gajpur 22.25036972 73.53278056 Hand pump 80 
57. Talakpur 22.19552059 73.62199615 Hand pump 90 
58. Akakheda 22.18400463 73.61262878 Borewell 110 
59. Sankheda 22.18518144 73.61233771 Borewell 120 
60. Handod 22.14591587 73.5723729 Hand pump 100 
61. Khandupura 22.14591587 73.5723729 Borewell 80 
62. Sanodiya 22.15318448 73.60934488 Borewell 130 
63. Ratanpur 22.11464018 73.50716918 Hand pump 100 
64. Akona 22.03775000 73.70936111 Hand pump 40–50 
65. Rampuri_1 22.04875000 73.71150000 Hand pump 50 
66. Rampuri_2 22.04841667 73.71144444 Hand pump 50 
67. Anandpuri 22.05444444 73.72875000 Borewell 70–80 
68. Kukavati 22.05586111 73.73641667 Borewell 70–80 
69. Kandva_1 22.05116667 73.75180556 Borewell 70–80 
70. Kandva_2 22.05027778 73.75158333 Hand pump 50 
71. Naswadi 22.02808333 73.77308333 Canal 100 
72. Nannupura 22.03430556 73.74675000 Hand pump 50 
73. Rayansingpura 22.02022222 73.69836111 Hand pump 50 
74. Puniyavant_1 22.33172222 73.96441667 Borewell 100 
75. Puniyavant_2 22.33161111 73.96408333 Hand pump 50 
76. Chhota Udaipur_1 22.31358333 74.01122222 Hand pump 40 
77. Chhota Udaipur_2 22.31291667 74.01347222 Borewell 180 
78. Kasara -1 22.34672222 74.01866667 Hand pump 50 
79. Kachhel 22.33877778 74.04855556 Hand pump 40 
80. Gabadiya 22.30000000 74.04636111 Hand pump 50 
81. Ganthiya 22.27536111 74.04769444 Hand pump 30 
82. Piplej 22.26650000 73.99819444 Hand pump 50 
83. Oliamba 22.28569444 73.97883333 Hand pump 30 

Analysis of groundwater

The samples collected were submitted to GEMI's laboratory with due procedure where analysis was carried out as per Standard Methods for the Examination of Water and Waste Water for the drinking water parameters. GEMI's laboratory is recognized as a ‘State Water Lab’, ‘Environmental Laboratory’, ‘National Accreditation Board for Testing and Calibration Laboratory (NABL)’, and as a ‘Scientific and Industrial Research Organization (SIRO)’. All the groundwater samples were analysed for the selected relevant physicochemical parameters. The physical parameters include pH and turbidity. The chemical parameters include electrical conductivity, total dissolved solids, chloride, total hardness, calcium hardness, magnesium hardness, alkalinity, fluoride, sulfate and nitrate concentration. Further analysis for heavy metals was also performed in a few selected groundwater samples. The parameters analysed were compared with Indian Standards Drinking Water-Specification (Second Revision) (IS 10500: 2012) since the groundwater of the study area is used for drinking and domestic purposes (Khatri et al. 2021).

The analytical details pertaining to the monitored parameters for both Vadodara and Chhota Udaipur districts including their acceptable and permissible limits, result range of pre-monsoon and post-monsoon seasons, sample IDs exceeding permissible limit and relevant inferences drawn for respective parameters are discussed further along with the graphical representation of the analytical results reported.

pH of solution is taken as the negative logarithm of hydrogen ion concentration for many practical purposes. The value range of pH from 7 to 14 is alkaline, from 0 to 7 is acidic and 7 is neutral. The pH of drinking water lies between 6.5 and 8.5. The overall pH of the pre-monsoon samples ranged between 6.65 and 8.74, whereas post-monsoon samples ranged from 6.72 to 8.22. Overall, the pH of the samples was found to be within the permissible limits of Indian Standards Drinking Water-Specification (Second Revision) except for a few samples. The graphical representation of the analytical results for all the monitored sources is illustrated in Figure 2.

Figure 2

Seasonal variations of pH in Vadodara and Chhota Udaipur districts.

Figure 2

Seasonal variations of pH in Vadodara and Chhota Udaipur districts.

Close modal

Electrical conductivity is the capacity of water to carry an electrical current and varies both with number and types of ions the solution contains. In contrast, the conductivity of distilled water is less than 1 μmhos/cm. This conductivity depends on the presence of ions, their total concentration, mobility, valence and relative concentration and on the temperature of the liquid. Solutions of most inorganic acids, bases and salts are relatively good conductors. The overall conductivity of the pre-monsoon samples ranged between 406 μS/cm and 3,370 μS/cm and for post-monsoon samples ranged between 294 μS/cm and 6,160 μS/cm.

Total dissolved solids (TDS) is generally not considered as a primary pollutant, but it is rather used as an indication of aesthetic characteristics of drinking water and as an aggregate indicator of the presence of a broad array of chemical contaminants. It indicates the general nature of water quality or salinity. The acceptable and permissible limit of TDS is 500 mg/L to 2,000 mg/L, respectively, according to the specifications of Indian Standards. The overall concentration of TDS was reported between 140 mg/L and 1,956 mg/L in the pre-monsoon samples. Post-monsoon samples showed a TDS range of about 136 mg/L to 3,604 mg/L. Only two samples from Vadodara district and collected during the post-monsoon season exceeded the permissible limit. The high TDS might be due to leaching of various pollutants into the groundwater, industrial effluents, agricultural runoff, etc. The graphical representation of the analytical results for all the monitored sources is shown in Figure 3.

Figure 3

Seasonal variations of TDS in Vadodara and Chhota Udaipur districts.

Figure 3

Seasonal variations of TDS in Vadodara and Chhota Udaipur districts.

Close modal

Chloride in excess quantity is usually taken as an index of pollution and considered as a tracer for groundwater contamination. All types of natural and raw water contain chlorides. It comes from activities carried out in agricultural areas, industrial activities and from chloride stones. As per IS 10500: 2012, the desirable limit for chloride is 250 mg/L and the permissible limit is 1,000 mg/L. The concentration of chloride ranged between 21 mg/L and 615 mg/L in the pre-monsoon samples. Post-monsoon samples showed a concentration of 0 mg/L to 1,154 mg/L. The higher concentration of chloride found in one sample of groundwater may be due to pollution sources such as domestic effluents, fertilizers, septic tanks, human waste, livestock waste and due to natural resources. Continuous consumption of higher chloride concentration may cause cardiac and kidney disease. The graphical representation of the analytical results for all the monitored sources is illustrated in Figure 4.

Figure 4

Seasonal variations of chloride in Vadodara and Chhota Udaipur districts.

Figure 4

Seasonal variations of chloride in Vadodara and Chhota Udaipur districts.

Close modal

The desirable and permissible limit for total hardness as per IS 10500: 2012 lies between 200 mg/L and 600 mg/L, respectively. The effect of hardness is demonstrated as scaling in utensils, hot water systems in boilers, etc. Soap scum sources are dissolved calcium and magnesium from soil and aquifer minerals containing limestone or dolomite. In the study, pre-monsoon samples showed a hardness range of 100 mg/L to 1,150 mg/L and post-monsoon samples a hardness range of about 120 mg/L to 1,290 mg/L. Figure 5 indicates the concentration of total hardness for different samples with respect to seasonal variations.

Figure 5

Seasonal variations of total hardness in Vadodara and Chhota Udaipur districts.

Figure 5

Seasonal variations of total hardness in Vadodara and Chhota Udaipur districts.

Close modal

Alkalinity is the sum total of components in the water that tend to elevate the pH to the alkaline side of neutrality. It is measured by titration with standardized acid to a pH value of 4.5 and is expressed commonly as milligrams per litre as calcium carbonate (mg/L as CaCO3). Commonly occurring materials in water that increase alkalinity are carbonate, phosphates and hydroxides. Pre-monsoon samples showed an alkalinity range of about 124 mg/L to 1,004 mg/L and post-monsoon samples alkalinity ranges of about 116 mg/L to 959 mg/L. Figure 6 indicates the value of alkalinity for different samples with respect to seasonal variations.

Figure 6

Seasonal variations of alkalinity in Vadodara and Chhota Udaipur districts.

Figure 6

Seasonal variations of alkalinity in Vadodara and Chhota Udaipur districts.

Close modal

Fluoride is more commonly found in groundwater than in surface water. Among factors which control the concentration of fluoride are the climate of the area and the presence of accessory minerals in the rock minerals’ assemblage through which the groundwater is circulating. Pre-monsoon and post-monsoon samples showed fluoride ranges of about 0–7.23 mg/L and 0–2.16 mg/L, respectively. The fluoride concentration of approximately less than or equal to 1 mg/L in drinking water is beneficial to human health, but if the fluoride concentration is more than the permissible limit, i.e., more than 1.5 mg/L, then it may cause dental fluorosis (tooth decay), bone fractures and, more seriously, skeletal fluorosis. The graphical representation of the analytical results for all the monitored sources is illustrated in Figure 7.

Figure 7

Seasonal variations of fluorides in Vadodara and Chhota Udaipur districts.

Figure 7

Seasonal variations of fluorides in Vadodara and Chhota Udaipur districts.

Close modal

Sulfate: Natural water contains sulfate ions and most of these ions are also soluble in water. Sulfate ion is one of the major anions occurring in natural water. Sulfate concentration was reported to be in the range of 0–203 mg/L in the pre-monsoon samples. Post-monsoon samples showed a sulfate concentration of about 0 mg/L–283 mg/L. Water with about 300 mg/L–400 mg/L sulfate concentration causes a bitter taste and if the concentration rises up to 1,000 mg/L or more it can cause intestinal disorders. Water containing appreciable amounts of sulfate can cause hard scale in boilers. The graphical representation of the analytical results for all the monitored sources is illustrated in Figure 8.

Figure 8

Seasonal variations of sulfates in Vadodara and Chhota Udaipur districts.

Figure 8

Seasonal variations of sulfates in Vadodara and Chhota Udaipur districts.

Close modal

Nitrate concentration is present in raw water and, mainly, it is a form of N2 compound (of its oxidizing state). Nitrate is produced by chemical and fertilizer factories, animal matter, decaying vegetables, domestic and industrial discharge. The method to measure quantity of nitrate is by UV spectrophotometer. As per IS 10500:2012, the desirable limit for nitrate is a maximum of 45 mg/L and there is no relaxation in permissible limit. Pre-monsoon samples showed a nitrate range of about 0–489.5 mg/L and post-monsoon samples a nitrate limit of 0–569 mg/L. A total of 12 and 21 samples of Vadodara district and 10 and 8 samples of Chhota Udaipur district analysed during pre-monsoon and post-monsoon seasons, resectively, exceeded the desirable limit. Elevated levels of nitrate found in many of the samples analysed raised concerns and have also influenced the overall groundwater quality of the region. The graphical representation of the analytical results for all the monitored sources is illustrated in Figure 9.

Figure 9

Seasonal variations of nitrates in Vadodara and Chhota Udaipur districts.

Figure 9

Seasonal variations of nitrates in Vadodara and Chhota Udaipur districts.

Close modal

Heavy metal analysis in groundwater

Heavy metals such as lead, cadmium, iron, nickel, chromium, zinc, arsenic were analysed to detect the heavy metal pollution of groundwater. The analytical results showed that the heavy metals were within the permissible limits except for iron and lead, where their concentration exceeded the permissible limit of 0.3 mg/L and 0.01 mg/L, respectively, as per Indian Standards of drinking water. The maximum concentration of iron detected was 9.9 mg/L and of lead was 0.057 mg/L. However, the iron concentration exceeded mainly in the range of 0.3 mg/L–0.8 mg/L. Heavy metals more than the permissible limits can be fatal and can cause even death after prolonged exposure.

Correlation and regression

Correlation is the mutual relationship between two variables. Correlation coefficient (r) measures the degree of association that exists between two variables, one taken as the dependent variable (Chaubey & Patil 2015). It determines the relationship of water quality parameters with each other of the water samples analysed (Dutta & Sarma 2018). It can be calculated by the equation given below. Here, x and y are any two variables (water quality parameters) and n the total number of observations (samples analysed). Now, between the selected variables x and y, the correlation coefficient (r) can be calculated as:
formula
where, , , and all the summations are to be taken from 1 to n.
Now, if the value of the correlation coefficient between two variables x and y is legitimately large, then it indicates that these two variables are highly correlated. In that case, it is likely to try a linear relation of the form
formula
The constant A and B are to be determined in order to correlate the variables x and y. According to the well-known method of least squares, the value of constants A and B are given by the relations
formula
and
formula
where,

The linear equation we get from this is also known as regression equation. The regression equation is used as a mathematical tool to calculate different dependent characteristics of water quality by substituting the values for the independent parameters in the equations. The regression analysis is usually carried out when the water quality parameters have a better and higher level of significance in their correlation coefficient.

Result of correlation and regression

For the present study a total of nine parameters are taken for correlation and regression analysis and the resulting correlation coefficients (r) are specified in Tables 6 and 7 for pre- and post-monsoon, respectively. As stated before, a regression equation needs to be found if the value of the correlation coefficient is fairly large; however, in this case, there is no need to find the linear regression equation as the value of correlation coefficients are not too large (>1).

Table 6

Correlation coefficient (r) among various water quality parameters for pre-monsoon season

ParameterspHTDSTHAlkalinityConductivityChlorideFluorideSulfateNitrate
pH         
TDS −0.23        
TH −0.39 0.67       
Alkalinity −0.19 0.76 0.41      
Conductivity −0.37 0.98 0.63 0.72     
Chloride −0.12 0.9 0.62 0.59 0.95    
Fluoride −0.1 −0 −0.08 0.06 0.18   
Sulfate −0.16 0.81 0.42 0.49 0.82 0.78 0.06  
Nitrate −0.33 0.56 0.69 0.17 0.52 0.41 −0.08 0.39 
ParameterspHTDSTHAlkalinityConductivityChlorideFluorideSulfateNitrate
pH         
TDS −0.23        
TH −0.39 0.67       
Alkalinity −0.19 0.76 0.41      
Conductivity −0.37 0.98 0.63 0.72     
Chloride −0.12 0.9 0.62 0.59 0.95    
Fluoride −0.1 −0 −0.08 0.06 0.18   
Sulfate −0.16 0.81 0.42 0.49 0.82 0.78 0.06  
Nitrate −0.33 0.56 0.69 0.17 0.52 0.41 −0.08 0.39 
Table 7

Correlation coefficient (r) among various water quality parameters for post-monsoon season

ParameterspHTDSTHAlkalinityConductivityChlorideFluorideSulfateNitrate
pH         
TDS −0.22        
TH −0.33 0.66       
Alkalinity −0.19 0.62 0.37      
Conductivity −0.44 0.99 0.61 0.63     
Chloride −0.1 0.89 0.58 0.43 0.91    
Fluoride −0.05 −0.02 −0.2 0.1 0.19 −0.1   
Sulfate −0.07 0.87 0.44 0.48 0.88 0.81 −0.03  
Nitrate −0.28 0.54 0.66 0.22 0.47 0.37 −0.08 0.44 
ParameterspHTDSTHAlkalinityConductivityChlorideFluorideSulfateNitrate
pH         
TDS −0.22        
TH −0.33 0.66       
Alkalinity −0.19 0.62 0.37      
Conductivity −0.44 0.99 0.61 0.63     
Chloride −0.1 0.89 0.58 0.43 0.91    
Fluoride −0.05 −0.02 −0.2 0.1 0.19 −0.1   
Sulfate −0.07 0.87 0.44 0.48 0.88 0.81 −0.03  
Nitrate −0.28 0.54 0.66 0.22 0.47 0.37 −0.08 0.44 

The method of linear correlation has been found to be a significant approach to get an idea of quality of the groundwater by determining a few parameters experimentally. From the result of pre-monsoon, it can be stated that alkalinity, conductivity, chloride and sulfate have strong correlation with TDS. Also, conductivity and alkalinity, chloride and conductivity, sulfate and conductivity and sulfate and chloride are strongly correlated as all these have the value of correlation coefficient >0.7. Alkalinity, conductivity, chloride, sulfate and nitrate have moderate correlation with TH. Also TH and TDS, nitrate and TDS, chloride and alkalinity, sulfate and alkalinity, nitrate and conductivity, nitrate and chloride and nitrate and sulfate are moderately correlated as the value of correlation coefficient varies between 0.3 and 0.7. Parameters other than these have a weak or negative correlation with each other.

While discussing the post-monsoon season, from the correlation analysis, it can be stated that conductivity, chloride and sulfate have strong correlation with TDS. Also, conductivity has strong correlation with chloride and sulfate and chloride has strong correlation with sulfate. These all have a value of correlation coefficient (r) >0.7. TH, alkalinity and nitrate have moderate correlation with TDS. Alkalinity, conductivity, chloride, sulfate and nitrate have moderate correlation with TH. Conductivity, chloride and sulfate have moderate correlation with alkalinity. Also, nitrate has moderate correlation with conductivity, chloride and sulfate. These can also be seen in the Table 7 as these all have correlation coefficient values between 0.3 and 0.7. Parameters other than these have a weak or negative correlation with each other.

Water quality index (WQI)

The Weighted Arithmetic Water Quality Index (WAWQI) was first proposed by Horton (Horton 1965), in which a weight is assigned to each parameter such that this weight influences the importance of the parameter in determining the water quality (Khatri et al. 2020a). However, WQI indicates the quality of water in terms of index number which represents the overall quality of water for any intended use (Falowo et al. 2019). It is defined as a rating reflecting the comprehensive influence of different water quality parameters taken into consideration for the calculation of WQI (Chaurasia et al. 2018). The indices are among the most effective ways to communicate the information on water quality status to the general public or to policymakers. In calculation of the WQI, the relative importance of various parameters depends on the intended use of the water (Hariharan 2007).

The calculation of WQI was made using the weighed arithmetic index method in the following steps.

Let there be n water quality parameters and quality rating (qn) corresponding to nth parameter is a number reflecting relative value of this parameter in the polluted water with respect to its standard permissible value. qn values are given by the relationship.

Calculation of quality rating (qn)

For calculation, the ideal value is taken as vi and the permissible value is vs . Similarly, the ideal value is zero for other parameters and the permissible value is taken from standards. Therefore, the quality rating is calculated from the following relation:
formula
where,
  • vo = observed value

  • vi = ideal value

  • vs= standard permissible value.

In most cases vi = 0 except in certain parameters like pH, dissolved oxygen, etc.

Calculation of unit weight (Wn)

The unit weight (Wn) to various water quality parameters is inversely proportional to the recommended standards for the corresponding parameters.
formula
where,
  • Wn = unit weight for nth parameter

  • Sn = standard permissible value for nth parameter

  • k= proportionality constant

  • k= 1/(1/vs1+ 1/vs2+ 1/vs3+ 1/vs4……. +1/vsn)

  • sn = ‘n’ number of standard values.

Calculation of water quality index (WQI)

WQI is calculated by the following equation:
formula

Assessment of water quality based on WQI

Application of WQI is a useful method in assessing the suitability of water for various beneficial uses, hence, WQI has been classified into five categories as shown in Table 8. The suitability of WQI values for human consumption according to Mishra & Patel (2001) is shown below.

Table 8

Water quality index of Vadodara and Chhota Udaipur districts during pre- and post-monsoon seasons

Water quality index (WQI)StatusSample ID
Pre-monsoon season 
0–25 Excellent 8, 9, 13, 16, 18, 19, 22, 23, 24, 25, 26, 27, 36, 37, 39, 49, 62, 67, 69 
26–50 Good 1, 2, 6, 7, 12, 14, 15, 17, 20, 21, 28, 29, 30, 31, 34, 35, 38, 40, 41, 42, 47, 48, 51, 53, 59, 61, 63, 70, 71 
51–75 Moderate 3, 4, 5, 10, 11, 32, 33, 43, 44, 46, 50, 54, 55, 56, 57, 60, 64, 72, 73, 74, 77 
76–100 Poor 52, 75 
100 and above Unfit for drinking 45, 58, 65, 66, 68, 76, 78, 79 
Post-monsoon season 
0–25 Excellent 2, 18, 19, 29, 39, 46, 50, 53, 55, 57, 58, 63 
26–50 Good 3, 4, 5, 6, 8, 9, 12, 13, 17, 20, 25, 26, 27, 28, 30, 31, 35, 37, 38, 40, 41, 42, 47, 48, 52, 54, 59, 60, 61, 62, 65, 67, 73, 75, 77 
51–75 Moderate 1, 7, 14, 15, 16, 21, 22, 23, 24, 32, 33, 34, 36, 43, 44, 45, 51, 56, 64, 66, 68, 74, 78, 81 
76–100 Poor 10, 11, 71, 79, 82 
100 and above Unfit for drinking 49, 69, 70, 72, 76, 80, 83 
Water quality index (WQI)StatusSample ID
Pre-monsoon season 
0–25 Excellent 8, 9, 13, 16, 18, 19, 22, 23, 24, 25, 26, 27, 36, 37, 39, 49, 62, 67, 69 
26–50 Good 1, 2, 6, 7, 12, 14, 15, 17, 20, 21, 28, 29, 30, 31, 34, 35, 38, 40, 41, 42, 47, 48, 51, 53, 59, 61, 63, 70, 71 
51–75 Moderate 3, 4, 5, 10, 11, 32, 33, 43, 44, 46, 50, 54, 55, 56, 57, 60, 64, 72, 73, 74, 77 
76–100 Poor 52, 75 
100 and above Unfit for drinking 45, 58, 65, 66, 68, 76, 78, 79 
Post-monsoon season 
0–25 Excellent 2, 18, 19, 29, 39, 46, 50, 53, 55, 57, 58, 63 
26–50 Good 3, 4, 5, 6, 8, 9, 12, 13, 17, 20, 25, 26, 27, 28, 30, 31, 35, 37, 38, 40, 41, 42, 47, 48, 52, 54, 59, 60, 61, 62, 65, 67, 73, 75, 77 
51–75 Moderate 1, 7, 14, 15, 16, 21, 22, 23, 24, 32, 33, 34, 36, 43, 44, 45, 51, 56, 64, 66, 68, 74, 78, 81 
76–100 Poor 10, 11, 71, 79, 82 
100 and above Unfit for drinking 49, 69, 70, 72, 76, 80, 83 

Results of water quality index

For calculation, the WQI of nine parameters, namely, pH, TDS, TH, Ca H, Mg H, fluorides, chlorides, sulfates and nitrates were taken into consideration. WQI thus calculated for the sampling points of pre-monsoon and post-monsoon seasons is listed in Table 8. Similarly, the chart representation for both seasons is given in Figure 10.

Figure 10

Water quality index of Vadodara and Chhota Udaipur districts during pre- and post-monsoon seasons.

Figure 10

Water quality index of Vadodara and Chhota Udaipur districts during pre- and post-monsoon seasons.

Close modal

A total of 162 samples was collected from the villages of Vadodara and Chhota Udaipur districts during pre-monsoon and post-monsoon seasons to assess the overall groundwater quality of the districts. The sampling was conducted as per the GEMI's Sampling Protocol for Water and Wastewater and the analysis of the samples was carried out in the GEMI's laboratory recognised as a ‘State Water Lab’ for different physicochemical parameters. The analytical findings were compared with Indian Standards Drinking Water-Specification (Second Revision) (IS 10500: 2012) to produce a view of overall groundwater quality of both the districts. The overall evaluation of the analytical resuts with standards is given in Table 9.

Table 9

Results of the parameters analysed

S. No.ParametersRequirement (acceptable limit)Permissible limit in the absence of alternate sourceVadodara district
Chhota Udaipur district
Range of present study (pre-monsoon)Range of present study (post-monsoon)Range of present study (pre-monsoon)Range of present study (post-monsoon)
pH 6.5–8.5 No relaxation 6.84–8.74 6.74–8.22 6.65–8.39 6.72–7.98 
EC (μS/cm) – – 406–3,370 294–6,160 743–2,470 – 
TDS (mg/L) 500 2,000 204–1,956 200–3,604 140–1,352 136–1,298 
Cl (mg/L) 250 1,000 21–615 21–1,154 24–399 0–394 
TH (mg/L) 200 600 100–1,150 140–1,290 120–910 120–630 
Ca++H (mg/L) 75 200 20–850 70–860 70–380 60–330 
Mg++H (mg/L) 30 100 20–850 70–860 30–690 10–530 
Alkalinity (mg/L) 200 600 156–1,004 116–959 124–858 168–812 
Turbidity (NTU) 1.10–76.9 0.1–90 – – 
10 F (mg/L) 1.0 1.5 0–2.25 0.4–1.73 0–7.23 0–2.16 
11 SO4 (mg/L) 200 400 2–203 10–283 0–95.32 0–111.52 
12 NO3 (mg/L) 45 No relaxation 0–489.5 0–569 0–100 0–115 
13 Pb (mg/L)  0.01 No relaxation 0–0.006 0.057 0–5.447 0–14.06 
14 Cd (mg/L) 0.003 No relaxation BDL BDL BDL BDL 
15 Fe (mg/L) 0.3 No relaxation 0–0.364 0.1–9.98 0–2.045 0–3.248 
16 Ni (mg/L) 0.02 No relaxation 0.001 0–0.01 BDL BDL 
17 Cr (mg/L) 0.05 No relaxation 0–0.004 BDL BDL 
18 Zn (mg/L) 15 0–0.0003 0–2.45 0–0.117 BDL 
19 As (mg/L) 0.01 0.05 0–0.003 0–0.005 BDL BDL 
S. No.ParametersRequirement (acceptable limit)Permissible limit in the absence of alternate sourceVadodara district
Chhota Udaipur district
Range of present study (pre-monsoon)Range of present study (post-monsoon)Range of present study (pre-monsoon)Range of present study (post-monsoon)
pH 6.5–8.5 No relaxation 6.84–8.74 6.74–8.22 6.65–8.39 6.72–7.98 
EC (μS/cm) – – 406–3,370 294–6,160 743–2,470 – 
TDS (mg/L) 500 2,000 204–1,956 200–3,604 140–1,352 136–1,298 
Cl (mg/L) 250 1,000 21–615 21–1,154 24–399 0–394 
TH (mg/L) 200 600 100–1,150 140–1,290 120–910 120–630 
Ca++H (mg/L) 75 200 20–850 70–860 70–380 60–330 
Mg++H (mg/L) 30 100 20–850 70–860 30–690 10–530 
Alkalinity (mg/L) 200 600 156–1,004 116–959 124–858 168–812 
Turbidity (NTU) 1.10–76.9 0.1–90 – – 
10 F (mg/L) 1.0 1.5 0–2.25 0.4–1.73 0–7.23 0–2.16 
11 SO4 (mg/L) 200 400 2–203 10–283 0–95.32 0–111.52 
12 NO3 (mg/L) 45 No relaxation 0–489.5 0–569 0–100 0–115 
13 Pb (mg/L)  0.01 No relaxation 0–0.006 0.057 0–5.447 0–14.06 
14 Cd (mg/L) 0.003 No relaxation BDL BDL BDL BDL 
15 Fe (mg/L) 0.3 No relaxation 0–0.364 0.1–9.98 0–2.045 0–3.248 
16 Ni (mg/L) 0.02 No relaxation 0.001 0–0.01 BDL BDL 
17 Cr (mg/L) 0.05 No relaxation 0–0.004 BDL BDL 
18 Zn (mg/L) 15 0–0.0003 0–2.45 0–0.117 BDL 
19 As (mg/L) 0.01 0.05 0–0.003 0–0.005 BDL BDL 

BDL, below detection limit.

The limits are as per Indian Standards Drinking Water-Specification (Second Revision) (IS 10500: 2012).

Based on the analysis of results, the following inferences are drawn:

  1. Overall, the pH of all the samples were found to be within the permissible limits according to Indian Standards (IS 10500: 2012) except that two samples showed a little higher pH.

  2. High variations are observed in the concentration of conductivity, TDS, chloride, total hardness, alkalinity, fluoride, sulfate parameters. The concentrations of the parameters were found to be within the permissible limits as prescribed by the Indian Standards almost for all the samples for the respective parameters. Moreover, slight variation was observed in the sulfate concentration.

  3. Nitrate concentrations, at places, were found to be higher than the permissible limits. High nitrate concentrations such as 569 mg/L was observed at Shihora village of Vadodara district. Moreover, many other villages have shown higher nitrate concentrations than the limits prescribed which is a matter of concern.

  4. Heavy metals were analysed in a few groundwater samples to detect whether the groundwater was contaminated with heavy metals. The analytical results of heavy metals for groundwater samples were found to be within the acceptable limits except for a few samples for iron and lead. In particular, iron was found to be high, in the range of 0.2 mg/L to 9.9 mg/L, whereas the permissible limit is only 0.3 mg/L.

  5. From the comparison study of pre- and post-monsoon groundwater quality it was concluded that EC, TDS, total hardness, alkalinity, fluoride, sulfate, nitrate concentrations, etc. were found to be a little higher in post-monsoon samples than the pre-monsoon samples.

  6. Results of the WQI of Vadodara and Chhota Udaipur districts for pre-monsoon season showed that 24.05% of the water samples fell in the excellent category, 36.70% the good category, 26.53% were found to be of moderate quality, 2.53% of poor quality and 10.12% were found to be unfit for drinking purposes.

  7. The results of the WQI of Vadodara and Chhota Udaipur districts for post-monsoon season showed that 14.45% of the water samples fell in the excellent category, 42.16% the good category, 28.91% were found to be of moderate quality, 6.02% of poor quality and 8.43% were found to be unfit for drinking purposes.

Human health concerns

The present study shows elevated nitrate and fluoride concentrations in many sources. Availability of both the elements in the human body may be essential; athough long-term consumption with excessive concentration may cause serious disease and sometimes even be life-threatening. Thus, it is important to understand the threat to human health due to elevated concentration of each parameter, as described in Table 10.

Table 10

Sources and health effects of drinking water characteristics

Sr. No.CharacteristicSourceHealth impact
1. pH Carbonate-rich rocks, effluent discharge, chemical dumping pH < 6.5 causes aesthetic problems, metallic taste 
2. EC Dissolved matter of inorganic salts, acids and bases High concentrations affect taste, damage crops, degrade drinking water 
3. TDS Landfill leachate, sewage Gastrointestinal irritation, corrosive, salty and brackish taste 
4. Chloride Domestic effluents, fertilizers, septic tanks, human waste, livestock waste Affects heart, kidney patients 
5. TH Weathering of limestone, sedimentary rocks, calcium bearing minerals, industrial effluents, application of lime to soil in agricultural areas Urolithosis, cardiovascular disorder, kidney problems, cancer 
6. Alkalinity Naturally occurring alkalis like CO32−, HCO3, OH, salts of Mg, Ca, K and Na, acid rain Bitter taste, slippery feel, dry skin 
7. Turbidity Inorganic sources like Fe, Mn from natural sources, geology and suspended matter Waterborne diseases 
8. Fluoride Weathering of rocks, fertilizers, liquid waste, volcanic ash, fly ash, industrial effluents Dental fluorosis, effects on skeletal tissues 
9. Sulfate Occurs naturally in soils, rocks, minerals, gypsum, decomposition of organic matter, fertilizers Diarrhoea, dehydration, sulfate >250 mg/L creates medicinal taste 
10. Nitrate Excessive use of inorganic nitrogenous fertilizers and manures Methaemoglobin. Long-term exposure causes pregnancy and neural tube defects, colorectal cancer, bladder, birth defects, thyroid disease 
11. Lead Carbonates and hydroxide complex in soil, erosion of natural deposits Poor muscle coordination, blood pressure, reproductive problems, damage nervous system, anaemia, liver and kidney damage 
12. Cadmium Erosion of natural deposits, discharge from metal refineries, runoff from waste batteries and paints Kidney damage 
13. Iron Naturally occurring, industrial effluents, sewage and landfill leachate Haemorrhagic necrosis, genetic disorder (haemochromatosis) 
14. Nickel Leakage from metals, dissolution from nickel ore-bearing rocks Long-term exposure causes decreased body weight, heart and liver damage 
15. Chromium Improper disposal of mining tools and industrial waste Skin rashes, nose irritations and nose bleeds, ulcers, weakened immune system, kidney and liver damage 
16. Zinc Occurs in small amounts in almost all igneous rocks Fever, nausea, vomiting, stomach cramps, and diarrhoea 
17. Arsenic By-products of agricultural and industrial activities Cancer of bladder, lungs, skin, kidney, nasal passages 
Sr. No.CharacteristicSourceHealth impact
1. pH Carbonate-rich rocks, effluent discharge, chemical dumping pH < 6.5 causes aesthetic problems, metallic taste 
2. EC Dissolved matter of inorganic salts, acids and bases High concentrations affect taste, damage crops, degrade drinking water 
3. TDS Landfill leachate, sewage Gastrointestinal irritation, corrosive, salty and brackish taste 
4. Chloride Domestic effluents, fertilizers, septic tanks, human waste, livestock waste Affects heart, kidney patients 
5. TH Weathering of limestone, sedimentary rocks, calcium bearing minerals, industrial effluents, application of lime to soil in agricultural areas Urolithosis, cardiovascular disorder, kidney problems, cancer 
6. Alkalinity Naturally occurring alkalis like CO32−, HCO3, OH, salts of Mg, Ca, K and Na, acid rain Bitter taste, slippery feel, dry skin 
7. Turbidity Inorganic sources like Fe, Mn from natural sources, geology and suspended matter Waterborne diseases 
8. Fluoride Weathering of rocks, fertilizers, liquid waste, volcanic ash, fly ash, industrial effluents Dental fluorosis, effects on skeletal tissues 
9. Sulfate Occurs naturally in soils, rocks, minerals, gypsum, decomposition of organic matter, fertilizers Diarrhoea, dehydration, sulfate >250 mg/L creates medicinal taste 
10. Nitrate Excessive use of inorganic nitrogenous fertilizers and manures Methaemoglobin. Long-term exposure causes pregnancy and neural tube defects, colorectal cancer, bladder, birth defects, thyroid disease 
11. Lead Carbonates and hydroxide complex in soil, erosion of natural deposits Poor muscle coordination, blood pressure, reproductive problems, damage nervous system, anaemia, liver and kidney damage 
12. Cadmium Erosion of natural deposits, discharge from metal refineries, runoff from waste batteries and paints Kidney damage 
13. Iron Naturally occurring, industrial effluents, sewage and landfill leachate Haemorrhagic necrosis, genetic disorder (haemochromatosis) 
14. Nickel Leakage from metals, dissolution from nickel ore-bearing rocks Long-term exposure causes decreased body weight, heart and liver damage 
15. Chromium Improper disposal of mining tools and industrial waste Skin rashes, nose irritations and nose bleeds, ulcers, weakened immune system, kidney and liver damage 
16. Zinc Occurs in small amounts in almost all igneous rocks Fever, nausea, vomiting, stomach cramps, and diarrhoea 
17. Arsenic By-products of agricultural and industrial activities Cancer of bladder, lungs, skin, kidney, nasal passages 

From the present study, it can be concluded that most of the groundwater sources analysed have satisfactory water quality and are suitable for human consumption as they meet the drinking water quality standards, except for the sources having elevated nitrate or fluoride concentrations. The water of the sources with elevated levels and not meeting the standards shall be consumed after receiving appropriate treatment, either at individual or village level, so as to minimize the health risk associated with consumption of such water. This study can be used to further collaborate with advanced hydrogeological and GIS studies to assess the sources and causes of such high nitrate and fluoride concentrations in the districts and ensure the safe quality of water with the application of suitable mitigation measures.

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

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