Rapid population growth, urban sprawl, and increasing developmental activities are leading to growing freshwater demands in the Kathmandu Valley. The limited availability of surface water has increased the people’s dependence on groundwater resources. Groundwater is an important source of drinking water in the Kathmandu Valley, contributing to roughly 50-70% of the total water supply (Chapagain et al., 2009). Unfortunately, the increasing extractions of groundwater have led to an abrupt decline in groundwater levels and also deterioration of groundwater quality. The variations in water quality are indicated by its physical, chemical and biological conditions, which are directly influenced by anthropogenic activities. These activities include introduction of untreated/partially treated wastewater, and excessive use of pesticides and chemical fertilizers in agriculture. The majority of people in Bhaktapur municipality in the Kathmandu Valley rely on groundwater for everyday domestic uses, but the degradation of groundwater quality has put the sustainability of groundwater resources under severe threat. Therefore, monitoring of groundwater quality should be done regularly so that the associated risks from the pollutants can be identified, better understood, and ultimately reduced or eliminated.
Groundwater quality is a complex subject to address, and a difficult water parameter to assess and evaluate, since it needs to consider multiple physico-chemical and biological parameters. Depending on the audience, assessment, evaluation, and communication of results is sometimes best done using a single metric that presents the information in simple terms. The water quality index (WQI) is one of the most effective ways of summarizing groundwater quality as it is a representative value reflecting the combined influence of various water quality parameters. WQI reduces the complexity of water quality data and presents the information in a simple and understandable manner to policymakers and concerned citizens. For those interested in specifics, the data inputs for determining the WQI can be evaluated individually.
A study was conducted by SmartPhones4Water Nepal (S4W-Nepal) to develop a WQI value in order to assess the groundwater quality of the Bhaktapur municipality. This study was performed as a part of a young researcher-led citizen science project called SmartPhones4Water (S4W) that was focused initially in Nepal (S4W-Nepal) and is now expanding into other areas. For data collection, 25 sample wells (both public and private) were selected from the entire municipality considering the spatial coverage and the human settlements. The samples were collected during the pre-monsoon (May), monsoon (July), and post-monsoon (November) time periods of 2018.
The public wells were typically open to the atmosphere, while the private wells typically had a covering. Groundwater samples were pulled up to the ground surface using a bucket and collected in 1 liter plastic bottles. The collected samples were subjected to a comprehensive analysis of 11 physico-chemical parameters including pH, electrical conductivity (EC), turbidity, total dissolved solids (TDS), total hardness, total alkalinity, chloride, phosphate, ammonia, iron, and nitrate. WQI was calculated by following the weighted arithmetic index method given by Brown et al., (1972) from the parametric values obtained from the analysis of physico-chemical parameters.
The WQI is expressed as a single number, with lower numbers representing better water quality and higher numbers representing poorer water quality. Numbers less than 25 are considered excellent quality, numbers between 25 and 50 are considered good quality, numbers between 50 and 75 are considered poor quality, numbers between 75 and 100 are considered very poor quality, and numbers over 100 are considered unsuitable for human consumption.
The WQI values in Bhaktapur municipality ranged from 23.03 to 943.15; the overall groundwater quality status of Bhaktapur municipality was determined to be of poor quality. 1.33% of water samples have excellent quality, 24% have good quality, 32% have poor quality, 13.33% have very poor quality and the remaining 29.33% were unsuitable for drinking purpose. The WQI values were found to be more sensitive to the parameters including turbidity, alkalinity and ammonia. The WQI distribution of sampling wells in pre-monsoon, monsoon and post-monsoon of 2018 is presented in figure 2. The seasonal fluctuation in water quality might be because of the monsoonal water influx and the dilution effect.
The reasons for the poor water quality of wells in the Bhaktapur municipality could be the cause of a variety of issues and influences. These include septic contamination, percolation of agricultural water containing pesticides and fertilizers, construction and demolition activities, and a direct influx of rainwater in open wells. In order to address any overland contamination, cover systems and regular maintenance and cleaning of the wells is recommended. The findings from this study may serve as an important foundation and frame of reference to guide future studies on the groundwater quality of the study area and efforts to improve water quality.
The three tables below include the water quality results for each of the wells from the three sampling periods and the resulting WQI values.
Table 1: Water quality results for sample wells during pre-monsoon 2018
| Sample Wells | PH | EC (μS/cm) | TDS(mg/L) | Turbidity (NTU) | Nitrate (mg/L) | Ammonia(mg/L) | Phosphate(mg/L) | Chloride(mg/L) | Iron(mg/L) | Hardness (mg/L) | Alkalinity(mg/L) | WQI |
| W1 | 6.9 | 459 | 216 | 21 | 0 | 0.2 | 0.05 | 31.36 | 0.3 | 128 | 340 | 63.09 |
| W2 | 6.8 | 808 | 380 | 7 | 0 | 0.2 | 0.05 | 62.72 | 0.3 | 216 | 570 | 48.28 |
| W3 | 7.1 | 648 | 305 | 145 | 10 | 3 | 0.5 | 39.2 | 0.3 | 192 | 410 | 312.8 |
| W4 | 7.3 | 1282 | 603 | 204 | 0 | 3 | 0.2 | 113.7 | 0.3 | 144 | 290 | 424.14 |
| W5 | 7.3 | 859 | 404 | 7 | 0 | 0.5 | 0.05 | 62.72 | 0 | 136 | 510 | 39.11 |
| W6 | 7.3 | 751 | 353 | 7 | 0 | 0 | 0.05 | 47.04 | 0 | 208 | 210 | 30.25 |
| W7 | 7.1 | 1121 | 527 | 161 | 0 | 3 | 0.2 | 90.16 | 0.3 | 160 | 280 | 343.31 |
| W8 | 7.1 | 544 | 256 | 6 | 0 | 1.5 | 0.2 | 35.28 | 0 | 128 | 350 | 36.08 |
| W9 | 7.7 | 712 | 335 | 7 | 0 | 3 | 0.2 | 47.04 | 0 | 144 | 830 | 58.9 |
| W10 | 6.8 | 1487 | 699 | 2 | 0 | 0 | 0.05 | 109.8 | 1 | 136 | 350 | 62.46 |
| W11 | 7.2 | 1344 | 632 | 42 | 0 | 3 | 1 | 0.5 | 0.3 | 304 | 630 | 136.57 |
| W12 | 7.7 | 1023 | 481 | 12 | 0 | 1.5 | 0.05 | 0.2 | 0.3 | 216 | 510 | 64.71 |
| W13 | 7.3 | 1231 | 579 | 25 | 0 | 3 | 0.5 | 0.5 | 0.3 | 440 | 270 | 99.64 |
| W14 | 7.9 | 1493 | 702 | 5 | 0 | 3 | 0.8 | 0.5 | 0 | 224 | 230 | 53.52 |
| W15 | 8.3 | 780 | 367 | 2 | 0 | 0.2 | 0.2 | 1 | 0 | 168 | 280 | 23.03 |
| W16 | 7.7 | 546 | 257 | 17 | 10 | 1.5 | 0.05 | 0.5 | 0.3 | 72 | 240 | 63.18 |
| W17 | 12 | 1668 | 784 | 35 | 100 | 0.5 | 0.5 | 0.5 | 0 | 168 | 250 | 116.18 |
| W18 | 8.8 | 668 | 314 | 10 | 10 | 0.2 | 0.5 | 0 | 0.3 | 120 | 880 | 58.47 |
| W19 | 8.2 | 1108 | 521 | 64 | 10 | 3 | 1 | 0 | 1 | 152 | 420 | 191.77 |
| W20 | 8.3 | 1295 | 609 | 34 | 10 | 3 | 1 | 0 | 1 | 368 | 270 | 140.49 |
| W21 | 9.1 | 1170 | 550 | 24 | 0 | 1.5 | 1 | 0.2 | 0 | 136 | 360 | 78.2 |
| W22 | 9.4 | 921 | 433 | 10 | 25 | 1.5 | 1 | 0 | 0.3 | 120 | 320 | 63.15 |
| W23 | 9.1 | 1319 | 620 | 25 | 75 | 1 | 1 | 0 | 1 | 152 | 310 | 121.84 |
| W24 | 9 | 1246 | 586 | 6 | 75 | 0.5 | 0.8 | 0 | 0 | 240 | 340 | 54.87 |
| W25 | 8.8 | 1021 | 480 | 5 | 10 | 0.5 | 0.05 | 0 | 1 | 168 | 570 | 70.48 |
Table 2: Water quality results for sample wells during monsoon 2018
| Sample Wells | PH | EC (μS/cm) | TDS(mg/L) | Turbidity (NTU) | Nitrate (mg/L) | Ammonia(mg/L) | Phosphate(mg/L) | Chloride(mg/L) | Iron(mg/L) | Hardness (mg/L) | Alkalinity(mg/L) | WQI |
| W1 | 6.7 | 742 | 349 | 19 | 0.2 | 75 | 0.05 | 31.36 | 0 | 184 | 50 | 62.92 |
| W2 | 7.4 | 1859 | 874 | 4 | 0.2 | 0 | 0.2 | 39.2 | 0 | 384 | 100 | 38.77 |
| W3 | 7.2 | 853 | 401 | 20 | 1 | 0 | 0.05 | 90.16 | 0 | 152 | 90 | 59.25 |
| W4 | 7.7 | 272 | 128 | 95 | 1.5 | 10 | 0.2 | 90.16 | 0.3 | 64 | 50 | 202.08 |
| W5 | 7.5 | 753 | 354 | 3 | 0 | 75 | 0.2 | 74.48 | 0 | 184 | 50 | 34.8 |
| W6 | 7.8 | 921 | 433 | 4 | 0.2 | 50 | 1 | 58.8 | 0 | 176 | 60 | 36.31 |
| W7 | 8.3 | 1863 | 863 | 8 | 0 | 100 | 1 | 39.2 | 0 | 264 | 40 | 62.2 |
| W8 | 8.3 | 1078 | 507 | 5 | 0 | 50 | 0.5 | 43.12 | 0 | 184 | 20 | 37.08 |
| W9 | 8.3 | 1065 | 501 | 8 | 1.5 | 50 | 0.05 | 31.36 | 0.3 | 144 | 200 | 61.88 |
| W10 | 9 | 940 | 442 | 12 | 0.2 | 75 | 1 | 35.28 | 0 | 120 | 50 | 54.99 |
| W11 | 8.2 | 604 | 284 | 9 | 0.2 | 50 | 1 | 43.12 | 0 | 64 | 25 | 39.36 |
| W12 | 8.7 | 1746 | 821 | 8 | 3 | 25 | 1 | 47.04 | 0.3 | 272 | 130 | 77.3 |
| W13 | 8.6 | 1193 | 561 | 1 | 1 | 10 | 0.8 | 54.88 | 0 | 80 | 50 | 29.78 |
| W14 | 8.4 | 1125 | 529 | 1 | 0 | 75 | 1 | 39.2 | 0 | 104 | 30 | 34.46 |
| W15 | 8.7 | 1504 | 707 | 11 | 0.5 | 50 | 0.8 | 43.12 | 0.3 | 120 | 70 | 65.29 |
| W16 | 8.9 | 965 | 454 | 3 | 0.2 | 75 | 0.8 | 43.12 | 0 | 136 | 40 | 38.8 |
| W17 | 8.7 | 1257 | 591 | 4 | 0 | 50 | 0.8 | 66.64 | 0 | 176 | 30 | 39.03 |
| W18 | 8.5 | 1023 | 481 | 32 | 1.5 | 0 | 0.8 | 54.88 | 1 | 168 | 70 | 117.73 |
| W19 | 8.8 | 921 | 433 | 8 | 0 | 10 | 0.5 | 47.04 | 1 | 112 | 70 | 64.29 |
| W20 | 8.4 | 944 | 444 | 2 | 0 | 25 | 0.2 | 101.92 | 0 | 136 | 70 | 27.45 |
| W21 | 8.6 | 823 | 387 | 5 | 1.5 | 10 | 0.05 | 113.68 | 0 | 96 | 70 | 37.68 |
| W22 | 8 | 1287 | 605 | 3 | 0 | 25 | 0.05 | 35.28 | 0 | 144 | 40 | 29.23 |
| W23 | 7.9 | 689 | 324 | 1 | 0 | 50 | 0.05 | 62.72 | 0.3 | 128 | 80 | 34.36 |
| W24 | 8.2 | 1078 | 507 | 20 | 1.5 | 0 | 0.05 | 70.56 | 0.3 | 96 | 90 | 73.17 |
| W25 | 8.3 | 870 | 409 | 34 | 3 | 0 | 0.05 | 125.44 | 0.3 | 96 | 40 | 106.77 |
Table 3: Water quality results for sample wells during post-monsoon 2018
| Sample Wells | PH | EC (μS/cm) | TDS(mg/L) | Turbidity (NTU) | Nitrate (mg/L) | Ammonia(mg/L) | Phosphate(mg/L) | Chloride(mg/L) | Iron(mg/L) | Hardness (mg/L) | Alkalinity(mg/L) | WQI |
| W1 | 7.5 | 444 | 209 | 19 | 50 | 3 | 0 | 39.2 | 0 | 192 | 300 | 77.38 |
| W2 | 7.6 | 519 | 244 | 155 | 25 | 1.5 | 0.05 | 47.04 | 0 | 248 | 640 | 319.46 |
| W3 | 7 | 568 | 266 | 74 | 10 | 3 | 0.05 | 50.96 | 0 | 256 | 560 | 177.17 |
| W4 | 7.7 | 714 | 336 | 16 | 10 | 1.5 | 0.2 | 50.96 | 0 | 320 | 800 | 70.8 |
| W5 | 8.4 | 246 | 116 | 9 | 25 | 3 | 0.5 | 23.52 | 0 | 144 | 350 | 54.07 |
| W6 | 7.6 | 723 | 340 | 5 | 0 | 1 | 0.05 | 82.32 | 0 | 384 | 660 | 45.41 |
| W7 | 7.9 | 700 | 329 | 31 | 75 | 1 | 1 | 54.88 | 0 | 128 | 410 | 97.97 |
| W8 | 7.7 | 787 | 370 | 24 | 100 | 3 | 0.8 | 86.24 | 0 | 160 | 250 | 101.02 |
| W9 | 7.5 | 1195 | 562 | 86 | 10 | 3 | 1 | 19.6 | 0 | 352 | 1590 | 227.14 |
| W10 | 7.6 | 817 | 384 | 21 | 25 | 1.5 | 0.8 | 90.16 | 0 | 168 | 600 | 79.68 |
| W11 | 7.7 | 848 | 399 | 12 | 25 | 3 | 0.8 | 109.76 | 0 | 120 | 800 | 76.33 |
| W12 | 7.8 | 980 | 461 | 16 | 10 | 1.5 | 1 | 129.36 | 0 | 128 | 680 | 72.31 |
| W13 | 7.5 | 919 | 432 | 1 | 75 | 3 | 0.8 | 90.16 | 0 | 176 | 570 | 62.03 |
| W14 | 7.8 | 1163 | 547 | 58 | 50 | 1 | 0.8 | 145.04 | 0 | 160 | 610 | 154.31 |
| W15 | 7.7 | 855 | 402 | 28 | 0 | 1.5 | 0.2 | 109.76 | 0 | 160 | 620 | 88.21 |
| W16 | 7.6 | 638 | 300 | 25 | 25 | 1.5 | 0.2 | 58.8 | 0 | 248 | 450 | 81.6 |
| W17 | 7.7 | 568 | 267 | 16 | 10 | 3 | 0.5 | 66.64 | 0 | 280 | 580 | 74.77 |
| W18 | 6.9 | 1082 | 509 | 143 | 0 | 1 | 0 | 164.64 | 0 | 408 | 570 | 301.11 |
| W19 | 7.5 | 738 | 347 | 406 | 10 | 1.5 | 0 | 98 | 0.3 | 320 | 460 | 784.08 |
| W20 | 7.8 | 961 | 452 | 19 | 50 | 1.5 | 0.05 | 86.24 | 0 | 288 | 430 | 79.9 |
| W21 | 7.7 | 1044 | 491 | 178 | 10 | 3 | 0.5 | 31.26 | 0 | 352 | 1510 | 391.14 |
| W22 | 7.3 | 972 | 457 | 412 | 10 | 1.5 | 0.05 | 160.72 | 5 | 256 | 590 | 943.15 |
| W23 | 7.5 | 910 | 428 | 46 | 10 | 1.5 | 0.05 | 74.48 | 0 | 320 | 1130 | 133.71 |
| W24 | 7.8 | 463 | 218 | 99 | 10 | 3 | 0.05 | 43.12 | 0 | 240 | 660 | 223.76 |
| W25 | 7.7 | 1129 | 531 | 15 | 10 | 1 | 0.2 | 196 | 0 | 352 | 610 | 72.64 |
References
Brown, R.M., McClelland, N.I., Deininger, R.A. and O’Connor, M.F. (1972) A water quality index—crashing the psychological barrier. Indicators of environmental quality [online]. pp. 173-182. Available from DOI: 10.1007/978-1-4684-1698-5_15 [Accessed 7th September 2019]
Chapagain, S. K., Pandey, V. P., Shrestha, S., Nakamura, T. and Kazama, F. (2009) Assessment of Deep Groundwater Quality in Kathmandu Valley Using Multivariate Statistical Techniques. Water, Air, & Soil Pollution [online]. 210(1-4), pp.277–288. Available from DOI: 10.1007/s11270-009-0249-8 [Accessed 1st September 2019]
