Introduction

Rivers are an important source of freshwater but are also vulnerable to kinds of pollution to both point and nonpoint sources. Anthropogenic activities related to extensive urbanization, agricultural practices, industrialization and population expansion have led to water quality deterioration in many parts of the world. The adjacent landscapes that act as an interface between the aquatic and terrestrial ecosystem called the ‘riparian zones play a significant role in controlling water and chemical exchange between surrounding land and stream systems (Burt and Pinay 2005). Disturbances in this landscape can lead to deterioration of water quality as they influence the flows of energy and material between the terrestrial and aquatic (Fausch et al. 2010) interface. Riparian zones form a unique ecosystem and act as ‘buffer zones’ between upland and streams (Hill 1996; Lowrance 1998) and are vital to the health of the watershed. The riparian forest along the river that receives and processes water, sediments and nutrients transports from upslope areas and effectively functions as sinks for sediment and nutrients, thus regulating the nutrient loading to the aquatic system (Luke et al. 2007; Mayer et al. 2007). Water quality of any specific area or source may be assessed using physical, chemical and biological parameters; it is considered harmful and unfit for different human usage and other agricultural activities once they occur more than the well-defined limits (ICMR 1975; BIS 2003). Accordingly, the suitability of water for its usage may be categorized or described in terms of Water Quality Index (WQI), which is one of the most effective ways to describe the status of water quality. It is calculated from the point of aptness of surface water for human consumption (Atulegwu and Njoku 2004).

WQI is a single number that expresses water quality by aggregating the measurements of water quality parameters (such as dissolved oxygen, pH, nitrate and total hardness). It reduces the bulk of information from the several water quality parameters into a single value and expresses the data in a simplified and logical form (Semiromi et al. 2011). Assessment of water quality could provide us the overall information on the quality of the water bodies and its potential threat to various uses. The application of WQI is a useful method in assessing the water quality of the river. It helps to understand the overall water quality status of individual sampling stations at a certain time (Yogendra and Puttaiah2008) and its suitability for various beneficial uses. The concept of indices to represent gradation in water quality was first proposed by Horton (1965), since then numerous water quality indices have been formulated that can easily evaluate the overall water quality of an area promptly and efficiently. The general WQI developed by Brown et al. (1970) has undergone much improved modification suitable for a different purpose. Many workers like Debels et al. (2005), Yisa and Jimoh (2010), Akoteyon et al. (2011), Othman et al. (2012), Naubi et al. (2016), Ewaid (2017) and Bouslah et al. (2017) have worked out the study of WQI of different rivers. Similarly, in India, Yogendra and Puttaiah (2008), Kumar et al. (2011), Sharma and Kansal (2011), Singh and Kamal (2014) and Shah and Joshi (2017) have also worked on WQI of rivers in different states of India. So far only a few studies on WQI from the northeastern part of India, mainly confined to Assam and Manipur (Singh et al. 2016, Bora and Goswami 2017), have been reported.

Nagaland state is dissected by a number of seasonal and perennial rivers and rivulets. Major rivers that flow westward into Brahmaputra River of Assam are Dhansiri, Doyang and Dikhu. The Doyang River passes through a great part of Wokha district of Nagaland and is called ‘POFU’ by the local inhabitants (Lothas) which simply means ‘encircle’ because the river flows right through the middle of the district touching all the three ranges encircling the whole district. The Dam area of Doyang River is an important eco-tourism spot for bird-watchers as it is a roosting place of a migratory bird Amur falcon (Falco amurensis). The falcons travel almost 22,000 km every year (October–November) from southeastern Siberia and Northern China in millions and spend nearly a month around the vicinity of the Dam. The river also has a strong economic and traditional attachment to the local people because of its sufficient fertile plains and slopes for cultivation. However, the changing landuse practices, increasing population and deforestation in the catchment and river banks, shifting cultivation along the river have threatened the riparian habitats as never before. This has drawn much attention in preserving the riparian vegetation along the streams and in other sensitive areas in order to protect the water quality and habitat value of these areas. Geomorphology and seasonal variation of physicochemical parameters of Doyang River had been worked out by Imnatoshi and Ahmed (2012); however, there has been no scientific investigation on water quality assessment of Doyang River till date. In the present study, the application of WQI would give us comparative results of the water quality status of Doyang River at different sampling stations in varying seasons. The main reason for using WQI in the present study is to test the hypothesis whether the riparian forest present along the stretch of Doyang River may help in improving the status of water quality besides several pockets of landuse practices being found. This study would provide us a comprehensive water quality status of the Doyang River. It would ultimately pave ways for future management and action plans so as to protect the riparian zones that face pressure from different landuse practices, and facilitate improvement of the water quality.

The study area

Nagaland has a total geographical area of 16,579 km2 extending from 25°6′ N to 27°4′ N Latitude and 93°20′ E–95°15′ E Longitude. The state is bounded by Assam in the north and west, by Myanmar and Arunachal Pradesh in the east and by Manipur in the south. Nagaland experiences heavy rainfall, and the annual rainfall varies from 100 to 300 cm. The monsoon seasons last for a period of 5 months from May to September with June, July and August experiencing the highest rainfall. The Doyang River is one of the major rivers in Nagaland and runs along the southern boundary of the state. It originates from the Japfü Hill near the southern slope of Mao in Manipur and moves in a southwest direction passing through Kohima district and flows northward into Zunheboto and Wokha. The river has a length of 167 km (from Gariphema/Ghathashi area to Liphi) and a catchment area of 3283 km2 (Laishram and Yumnam 2016). It passes through a great part of Wokha district of Nagaland and flows south westerly into Dhansiri in Sibsagar District of Assam and finally joins the mighty Brahmaputra River of Assam. The main tributaries of Doyang are Tsui, Tullo and Tishi. The present study was conducted within a stretch of 40–45 km of Doyang River under Wokha district, Nagaland. The Doyang hydroelectric project (DHEP) is located in this river at 26°14 N Latitude and 94°16 E Longitude of Wokha district. The large reservoir lake created for generating hydroelectric power is more than 20 km2, and it also comes under the present study area. There are several landuse practices around the catchment area of the Doyang hydroelectric dam and along the riparian zone of the river. Figure 1 shows the landuse/landcover (LULC) map of the present study area. The characteristics features of the selected sampling stations, their coordinates and elevation along the Doyang River are presented in Table 1.

Fig. 1
figure 1

Map showing the landuse/landcover (LULC) of the study area

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Table 1 Characteristic features of the sampling station, their coordinates and elevation along Doyang River
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Materials and methods

Along the stretch of Doyang River, surface water samples were collected from the eight sampling stations. Sampling was done during the first week of each month from June 2016 to May 2017 for a period of 1 year. The months were later categorized into three different seasons, namely pre-monsoon (PRM), monsoon (MON) and post-monsoon (POM) for interpretation of data. Figure 2 shows the sampling location selected along the river for the study of WQI. Water samples were collected from the first 20 cm of the water column using a bottom-weighted polyethylene flask, previously washed in the laboratory with lapoline, 10% HCl and then with a water sample from each spot. In this study, twelve physicochemical parameters of water were selected, namely pH, electrical conductivity (EC), total dissolved solids (TDS), total alkalinity (TA), total hardness (TH), calcium (Ca2+), magnesium (Mg2+), chloride (Cl−), nitrate (NO¯3), sulfate (SO42−), dissolved oxygen (DO) and biological oxygen demand (BOD) for generating the overall WQI of Doyang River. Parameters like pH and TDS were measured on the spot with the help of pen-type digital pH and TDS meter. Conductivity was analyzed with the help of a digital conductivity meter in the laboratory. Total alkalinity, total hardness, calcium, magnesium and chloride were analyzed by the titration method. For the measure of dissolved oxygen, fixatives were added on the spot and analyzed thereafter using Winkler’s method. Separate samples for BOD were also collected, incubated in the dark at 20 °C for 5 days and analyzed thereafter. Parameters like nitrate and sulfate were analyzed using the double-beam UV–visible spectrophotometer. All the parameters were analyzed using standard methods as prescribed by Trivedy and Goel (1986) and APHA (2005). Finally, the WQI was calculated by employing the Weighted Arithmetic Index method developed by Brown et al. (1970) which is given in the following equation:

$$ {\text{WQI}} = \sum Q_{i} W_{i} /\sum W_{i} $$
Fig. 2
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Map indicating the sampling stations located along Doyang River

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The quality rating scale (Qi) for each parameter was calculated by using the expression:

$$ Q_{i} = 100\left[ {\left( {V_{i} - V_{o} /S_{i} - V_{o} } \right)} \right] $$

where Vi = concentration of ith parameter in the water sample analyzed.

Vo= ideal value of parameter in pure water, i.e., Vo = 0 (except pH 7.0 and DO = 14.6 mg/l), Si= recommended standard value of ith parameter.

The unit weight (Wi) for each water quality parameter is calculated by using the following formula:

$$ W_{i} = K/S_{i} $$

where K = proportionality constant calculated by using the equation

$$ K = \frac{1}{{\sum (1/S_{i} )}} $$

The WQI range, its status and possible usage (Brown et al. 1972) are presented in Table 2.

Table 2 Water Quality Index (WQI) range, status and possible usage of water sample (Brown et al. 1972)
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Results and discussion

Water quality parameters

The values of water quality parameters obtained from all the sampling stations in three different seasons are presented in Table 3. The pH is a measure of the acidic or alkaline condition of water and serves as an important indicator of water quality and determines the suitability of water for various purposes. The experimental water bodies recorded approximately neutral or slightly alkaline in nature (Bouslah et al. 2017). The mean values of pH from all the sampling stations during PRM, MON and POM were found to be 8.09 ± 0.08, 7.84 ± 0.06 and 6.93 ± 0.06. In natural water, the common range of pH falls within 6–8 (Thakre et al. 2010); a similar range of values has also been observed of the current study as well. Electrical conductivity (EC) is an indirect measure of total dissolved salts. The presence of these salts greatly affects the taste and acceptance of the water as potable to the users (Pradeep 1998). Observed mean EC of Doyang River was found to be 188.67 ± 0.40 µS/cm during PRM, 151.50 ± 0.24 µS/cm during the MON and 155.05 ± 0.28 µS/cm in the POM. Season wise a maximum range of 171.32–271.49 µS/cm was observed in PRM across the different stations (Yogendra and Puttaiah 2008). Total dissolved solids (TDS) measure the dissolved particle present in the water sample and indicate the general nature of water quality or salinity. The values recorded were all under the desirable limits of 500 mg/l (BIS) with a maximum in PRM season (93.95 ± 0.62 mg/l). The MON season observed the least mean values of 70.35 ± 0.46 mg/l. Total alkalinity (TA) recorded the highest average value of 111.05 mg/l during the PRM ranging between 104.17 and 141.25 mg/l. However, there was a significant decrease in TA in the monsoon with the mean value of 82.87 ± 1.67 mg/l. This may be attributed to the influx of fresh water into the river system causing dilution (Chatterjee and Raziuddin 2002). In natural water, total hardness (TH) is contributed mainly by dissolved calcium and magnesium ions (Ikomi and Emuh 2000), with all other divalent cations contributing to its concentration. The majority of its source is contributed by the surrounding rocks of the water bodies. The maximum mean concentration of calcium observed was 16.79 ± 0.37 mg/l in PRM with values ranging from 15.36 to 23.11 mg/l. The increase in temperature, low level of water and other domestic waste may have contributed toward its higher concentration (Devi et al. 2015) during the PRM. Magnesium in natural water may be attributed to chemistry of the geological composition of the river bedrock. A mean value of 9.90 ± 0.21 mg/l, 9.72 ± 0.21 mg/l and 8.35 ± 0.20 mg/l was recorded during the PRM, MON and POM, respectively. No significant variation was observed in all the three seasons studied. Total hardness (TH) did not show much variation in their concentration in all three seasons. The mean values observed during the PRM, MON and POM seasons were 82.39 ± 0.89 mg/l, 72.52 ± 0.80 mg/l and 70.25 ± 0.81 mg/l, respectively, and generally fall under the category of moderately hard water (Bora and Goswami 2017). Chloride occurs naturally in all types of water; however, its main contributing sources are runoff of inorganic fertilizers from agricultural fields, sewage discharge, etc. The chloride content of the sample was found to be well within the permissible levels of 250 mg/l. Its highest concentration was recorded during the POM (average value of 20.01 ± 0.68 mg/l) and least (average concentration of 16.41 mg/l) during the MON. Nitrate is found in surface waters as a result of sewage, fertilizer runoff from agricultural land, etc. Excess of nitrate can cause eutrophication (WHO 1998) resulting in the death of aquatic animals and serious health hazards. The highest recorded mean value of nitrate was found to be 0.72 ± 0.02 mg/l during the POM. However, in the present study, the concentration of nitrate recorded relatively low with values ranging from 0.49 to 0.84 mg/l (Ewaid 2017). Sulfate naturally occurs in surface water as a result of weathering of igneous and sedimentary rock. Other sources may be leachate from abandoned mines, air deposition from the combustion of fossil fuels and industrial wastewater. The mean concentration of sulfate recorded during the study period was 16.66 ± 0.06 mg/l, 14.60 ± 0.04 mg/l and 12.17 ± 0.05 mg/l in PRM, MON and POM, respectively. The PRM recorded the maximum sulfate concentration with values ranging from 15.45 to 21.63 mg/l; nevertheless, all the observed values were within the tolerable limits of 150 mg/l (Yisa and Jimoh 2010). Dissolved oxygen (DO) is the measurement of the amount of oxygen dissolved in water and is a direct indicator of water quality. In a healthy water body that ensures good water quality, DO must be > 4 mg/l (Prasad and Bose 2001). DO along Doyang River was recorded significantly high from all the stations throughout the study period. The highest concentration of DO was observed during the PRM season range from 10.02 to 11.38 mg/l with a mean value of 10.56 ± 0.15 mg/l. The turbulent nature of the water bodies, photosynthesis and a decrease in temperature might have resulted in the increased concentration of DO (Bouslah et al. 2017). Biological oxygen demand (BOD) determines the strength of oxygen to stabilize domestic and industrial waste. A higher value of BOD levels represents a higher level of organic pollution (Patel et al. 1983) indicating higher organic pollution in a water sample. Observed BOD in PRM, MON and POM was 1.68 ± 0.08 mg/l, 2.18 ± 0.09 mg/l and 2.29 ± 0.08 mg/l, respectively. The low level of BOD in the present study indicates less organic matter in the water sample to be oxidized by microorganisms (Singh et al. 2016). All the twelve physicochemical parameters of water analyzed were well within the permissible limits of drinking water given by BIS (2003) and ICMR (1975).

Table 3 Seasonal mean value, range and statistical variation of water quality parameters of Doyang River
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Water Quality Index (WQI) calculation

The calculation of WQI using Weighted Arithmetic Index involves the estimation of ‘unit weight’ assigned to each physicochemical parameter selected. Different units and dimensions of the selected parameters are transformed into a common scale using the assigning units. Table 4 shows the drinking water quality standards and the unit weights assigned to each parameter used for the calculation of WQI. Considering the significance of water quality assessment and their impact on the value of WQI, a maximum weightage of 0.366 is assigned to both DO and BOD. Tables 5, 6, 7, 8, 9, 10, 11 and 12 depict the values observed for the selected physicochemical parameters from the eight sampling stations during each season and their corresponding WQI values. pH, DO and BOD were found to be the most significant parameters in the WQI scores worked out.

Table 4 Relative weights (Wi) of different parameters and their standards used for WQI determination
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Table 5 Calculation of WQI at station 1 (S 1)
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Table 6 Calculation of WQI at station 2 (S 2)
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Table 7 Calculation of WQI at station 3 (S 3)
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Table 8 Calculation of WQI at station 4 (S 4)
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Table 9 Calculation of WQI at station 5 (S 5)
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Table 10 Calculation of WQI at station 6 (S 6)
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Table 11 Calculation of WQI at station 7 (S 7)
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Table 12 Calculation of WQI at station 8 (S 8)
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The overall values of WQI of the water samples from all the eight sampling stations for each season are presented in Table 13. WQI were observed to have a positive relationship with the seasonal changes. Maximum WQI values were recorded during MON from all the eight stations followed by PRM and POM. A similar finding has also been reported by researchers like Singh and Kamal (2014), Bora and Goswami (2017) in their studies of assessment of surface water quality status. An average value of WQI for all the stations during PRM, MON and POM was 42.95, 47.13 and 36.66, respectively, as presented in Table 14. This result indicates that the quality of the water samples from all the stations falls under the class of good water samples (25 < WQI < 50) suitable for drinking, irrigation and industrial purpose (Fig. 3). Ranges of WQI values from all the eight stations during PRM, MON and POM were: 35.89 (S 6)–50.14 (S 2), 40.77 (S 7)–55.45 (S 4) and 33.00 (S 4)–44.35 (S 2), respectively (Table 13). In all the stations, both PRM and POM showed good water quality status. However, MON showed poor water quality status at stations 2, 3, 4 and 5 located around the vicinity of the hydroelectric dam.

Table 13 Summary of WQI of Doyang River along with its water quality status (WQS)
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Table 14 Seasonal WQI of Doyang River with its water quality status (WQI)
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Fig. 3
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WQI rating of Doyang River in various seasons

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The WQI value showed a mixed pattern of changes in all the seasons (Fig. 4). WQI of the upstream stations from 1 to 5 is higher than the downstream stations, i.e., 6–8 showing the decrease in pollution level while moving downstream of the river. Such observation was also made by Bora and Goswami (2017) in their studies of water quality assessment of Kolong River, Assam, where the water samples showed a decreasing pollution trend further downstream. Workers like Ewaid (2017) have observed better water quality status in upstream than downstream due to a decrease in water and accumulation of contaminants along the downstream of the river. However, the above case is not the same in the present study. This could be due to the absorption of contaminants by healthy riparian vegetation that is present along the downstream of the river. Despite witnessing several landuse practices along the riparian zones, there also observed abundant growth of riparian vegetation that might have positively mitigated in controlling pollution of the river. Workers like Othman et al. (2012) and Naubi et al. (2016) have shown encouraging results in the improvement of water quality due to proper management policy and remedial measures. Stations 2–5 experience an abrupt rise in pollution level as all these stations are located near the vicinity of the hydroelectric dam. The stagnant condition of water bodies due to the presence of hydroelectric dam and different landuse activities around these stations could have contributed to the deteriorating condition of water quality. Particularly at station 1, runoff of bridge construction materials (concrete, asphalt, etc.) from the ongoing construction of national highway bridge (NH-02) across the river and cutting down of riparian hill slope for the same have contributed to the increased concentration of many of the water quality parameters analyzed. The presence of some residential homes in the adjoining areas of station 1 has also played a vital role in influencing the physicochemical parameters of water. Different landuse activities located in the upstream of the river like Jhumming (S3), residential area (S1 and S5) and monoculture like teak plantation (S4) have imposed a serious threat to water quality deterioration. Besides, burning of forest annually for shifting cultivation, felling and logging of trees for timber, picnic spot along the river and fishing activities have also exerted much pressure in influencing the water quality of the river. Anthropogenic activities like sewage disposal by the communities residing in the catchment areas, agricultural runoff and unprotected river sites (Yisa and Jimoh 2010, Bouslah et al. 2017 and Shah and Joshi 2017) have also been contributing agents in the deterioration of water quality.

Fig. 4
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WQI value of various sampling stations showing a varied pattern of change across seasons

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Conclusion

The study provides us with valuable information about the overall water quality status of the Doyang River by calculating the WQI values. As per the observation, recorded WQI values fall in good water quality status during pre- and post-monsoon in all the sampling stations and poor water quality status during monsoon in some of the sampling stations that are located upstream of the river. No considerable changes in WQI were observed throughout the study period except in few sites, where a modest increase in WQI was observed during monsoon. The overall average WQI, however, indicated good water quality status. All the physicochemical parameters of water analyzed were within the permissible limit of drinking water quality, and at present, they do not pose a serious threat for different human usage. In the present study, pH, DO and BOD played a significant role in affecting the WQI of the river. Though in the case of nutrient parameters, no such significant roles were observed. Nevertheless, there are disturbances like Jhum cultivation, extensive teak plantation (monoculture) and increased settlements in the catchment area. Annual burning of the forest for shifting cultivation, logging of trees, eco-tourism, poisoning of rivers and use of explosives for fishing can impose a serious threat to the water quality. These activities, if not controlled, could lead to further deterioration of water quality in the near future. To further improve the water quality, proper management policy must be adopted on disposal of sewage by the communities residing in the catchment areas, agricultural runoff, unmanaged landuse practices and unprotected riparian areas. Special focus on community participation in conservation efforts could be helpful. Remedial measures along the riparian zones could play a positive role in future monitoring and improvement of Doyang River water quality.