Hydro-geochemical studies of water quality and identification of polluting agents present in the river system are important for the sustainable utilization of water resources. This study focuses on the evaluation and interpretation of major ion chemistry, hydro-geochemical processes, and the suitability of water quality in domestic and agricultural usage areas. The water quality parameters, total dissolved solids (TDS), electrical conductivity (EC) and NO3 have wide variations along the sampling sites. The river has Ca2+, Mg2+ and NO3 dominant ions. The chemistry of water is largely determined by rock weathering and ion exchange processes with low contribution from anthropogenic sources. The water of the Ajay River is not suitable for drinking and irrigation purposes due to high levels of TDS, EC, NO3, biochemical oxygen demand (BOD), chemical oxygen demand (COD), total coliform (TC) and fecal coliform (FC). Principal component analysis (PCA) suggests that rock weathering, atmospheric deposition and anthropogenic activities together influence the water quality/chemistry. The Water Quality Index (WQI) indicated that 78% of water falls under medium, 15.7% under bad and only 5.2% of water falls under good category. Thus, this study illustrates the identification of pollution sources and interpretation of the complex data of the Ajay River that will be helpful for river water quality management for this river in particular and other river systems of the globe in general.

  • Investigation of geochemical signature and anthropogenic sources to identify the pathway of co-contamination of the Ajay River Basin.

  • Assessment of co-bounding factors and releasing mechanism of contaminants in the groundwater using multivariate techniques.

  • Hydrochemical evolution of water under various environmental factors in the suburban and urban environment for the portability of water using pollution indices.

Rivers are vital and valuable freshwater systems, which play an important role in life from molecules to humans. They also play an imperative role in the transportation of 0.0002% of freshwater out of 0.006% of the total freshwater resources (Mir et al. 2016). In India, twenty major rivers and several minor rivers fulfill the water demands for agriculture and domestic and industrial purposes, which is the greatest strength of Indian freshwater. As per the National Commission for Integrated Water Resource Management, there is about 1,953 km3 average annual flow of Indian Rivers, whereas the utilizable quantity of water is only about 690 km3, i.e. around 36% of the total flow of the rivers (Kumar et al. 2005). However, in the present scenario, pollution of the rivers is a global problem due to the increase in human population, industrialization, urbanization and changes in land-use patterns (Suratman et al. 2015; Dominguez et al. 2016; Xu et al. 2017). In China, land creation by cutting off hilltops for urbanization and industrial setups has increased the risk for air pollution, water contamination, groundwater and surface water loss, soil erosion and geological hazards due to massive sediment formation, changing geological and hydro-geological conditions, landslides, flooding and altered watercourses (Li et al. 2014). The rebirth of the new Silk Road (the economic belt of China) also has caused collateral damage to the natural environment since a major part of this route is located in semi-arid and arid regions of China, where water resources are already in vulnerable condition due to high density of human population and climate change (Li et al. 2015a, b). Similarly, the Three Gorges Reservoir (TGR) over the Yangtze River is suffering from water quality degradation due to eutrophication problems and significant inputs of non-point and point sources of pollutants in the river (Tang et al. 2015). These consequences lead to more water demand and subsequently increase polluted water with different contaminants (Suratman et al. 2015). Rivers are loaded with different types of contaminants through agricultural, industrial and domestic activities worldwide (Gurgel et al. 2016). In India, it has been reported that about 70% of the available water is polluted and the prime source of pollution is sewage which contributes approximately 84–92% of the waste in the river system (Joshi et al. 2009). Approximately 10% of urban and rural Indians access unsafe drinking water (Suthar 2011). Around 3 million people die every year because of poor drinking water and unsanitary conditions worldwide (WHO 2004). Similarly, the accumulation of contaminants in the river system creates a high ecological risk for aquatic organisms and is responsible for high mortality rate, alteration in growth, damage to reproduction and loss of aquatic diversity (Devanesan et al. 2017; Mahmoud et al. 2017). Consequently, the assessment of river geochemistry is very important to identify the factors (weathering, seasonal variation, hydrological and anthropogenic) which are influencing the quality of river water (Gholizadeh et al. 2016; MacDonald et al. 2016; Wang et al. 2017). The chemical composition of river water is reflected through weathering products, mineralized organic material, soil profile washed out during excess water discharge and industrial effluent, sewage and agricultural discharges (Pettersson et al. 2000). The geochemical composition of river water mainly depends on weathering and dissolution of rock minerals, which are responsible for dissolved ions present in the river. Climatic condition also influences the river water chemistry due to elevation, temperature, precipitation and geographical features (Bowser & Jones 2002). A detailed study of the geochemical properties of river systems can provide significant information about the composition of ions, types of minerals, water types and history of the elemental cycle (Zhang et al. 1995; Gupta & Banarjee 2012).

The Ajay River flows through one of the richest coal-mining belts (Andal and Raniganj coalfield) of Jharkhand and West Bengal provinces, India and drains through Deoghar, Jamtara, Chittranjan, Illam Bazar and Katwa towns before joining the Bhagirathi River at Katwa, West Bengal. It also encounters (Gondwana land) the mineral-rich area (Jamtara and Dumka) which includes black stones, fireclay, china clay, silica sand, quartz, feldspar and coal (Singh et al. 2012). Therefore, the Ajay River is more contamination-prone due to surface activities for mineral exploration while industries, mining machinery, power plants and vehicular emissions are also additional sources of pollution. These issues clearly indicate that the water chemistry of the Ajay River can be strongly reflected by weathering, mineral exploration, agricultural runoff, atmospheric deposition and anthropogenic activities in the catchment area. However, a comprehensive assessment of the Ajay River Basin has largely been unstudied and very little literature is available on the hydro-geochemistry of the Ajay River Basin. So, it is very pertinent to assess and monitor the comprehensive environmental condition of the Ajay River Basin. Keeping in view the above gap, this study may provide insights so that any policy decision can be implemented at a larger scale for this river basin in particular and overall river basins in general.

The major objectives of this study are divided into five phases: (a) to identify the major ion chemistry and evolution of the contaminants in the river water; (b) to study the spatial and temporal distribution pattern of the major ion, (c) to identify possible sources of the physicochemical and biochemical parameters with the help of compositional analysis, (d) to apply the Water Quality Index (WQI) to identify the present water quality status in the study area, and (e) to verify whether principal component analysis (PCA) and geochemical indices can provide better understanding of the process occurring within the studied catchment than the simple statistical techniques.

Study area

The Ajay River originates from Chakai hill (East of the Chotanagpur plateau) an elevation of 600 meters and flows toward the south-east which is lying between 24° 27′ 763″ and 23° 27′ 342″ N latitude and 86° 38′156″ and 88° 7′ 729″ E longitude (Figure 1). It flows over a length of 132 km with the Archaean gneissic complex and encounters the Gondwana sedimentaries near the Raniganj and Andal Coalfields. The rest of the river course flows over older alluvial and recent alluvium planes and lastly submerged into the River Bhagirathi at Katwa, West Bengal, India (Roy et al. 2015). The important tributaries of the Ajay River are the Dudhwa, Partho, Jyanti, Hinglow, Tumoni, Kunur and Kana Rivers. The annual rainfall of the Ajay basin varies from 1,280 to 1,380 mm. The monsoon period starts in this area in June and ends in September and approximately 95% of rainfall occurs during this period. During the monsoon period (June to September), the Ajay basin receives excess water from different tributaries, especially the Kunur River (Mukhopadhyay et al. 2006). The carrying capacity of the Ajay River Basin results in flooding every year, especially in the lower basin and affects around 680.70 km2 of agricultural land in Birbhum and Burdwan districts, West Bengal (Roy 2012). The river water is trapped from several locations for domestic (26.92 million m3), industrial (1.63 million m3) and agriculture (3,561.04 million m3) purposes (Sharma & Chattopadhyay 1998). The Ajay River Basin is also well known for having a coal-bearing area and at least two important coalfields (Raniganj and Andal) are present in this region. Several major industries like fertilizer manufacturing, thermal power plants, locomotive industries and coal washeries are settled around the basin area. Some small-scale industries like copper bucket manufacturing by villagers, rice industries, brick industries, etc. are also located around the basin area. The climatic condition in this area is moderate and characterized by three distinct seasons viz. summer, monsoon and winter (Gupta et al. 2008). The summer season starts in March with hot and humid conditions and ends in June. The maximum highest temperature varies from 46 to 48 °C in May and June while the minimum temperature varies from 5 to 7 °C during December and January. The details of the study area and their land-use patterns are represented in Table 1.
Table 1

Land-use pattern of the Ajay River along the sampling sites

SitesLatitudeLongitudeLocation nameLand use in the adjoining area
S1 86.671287 24.524928 Deoghar Domestic and industrial discharged 
S2 86.715497 24.272522 Pansari Domestic waste from different nalas and sewerage 
S3 86.800515 24.204301 Sharath Vast agricultural area (especially paddy fields) & rice industries 
S4 86.688291 24.194996 Haribola Mineral exploration area (coal mines) 
S5 86.843024 23.988547 Jamtara Industrial effluent and sewage discharge from the urban center 
S6 86.941644 23.997868 Chitranjan Industrial effluent discharged (Indian rail locomotive industry) 
S7 86.975652 23.873538 Gour Bazar Proximity NH-19 and several Brick industries 
S8 87.055568 23.831549 Jaydev Vast Rural region with large agricultural field 
S9 87.118482 23.780211 Illam Bazar Copper buckets manufacturing by individual household 
S10 87.271514 23.745975 Bhedia Vast Agricultural area and well-known for sand mining 
S11 87.339528 23.7164 Majhkhara Vast agricultural area especially for paddy cultivation 
S12 87.390539 23.649442 Natunhat Agriculture area and associated with rice miles 
S13 87.455153 23.615171 Kunurpur Agriculture area and associated with rice miles 
S14 87.524867 23.616729 Chakulia Agriculture area and associated with rice miles 
S15 87.597983 23.598033 Kherua Agriculture area and associated with rice miles 
S16 87.6779 23.615171 Kandi Bridge Rural areas and land mainly used for paddy cultivation 
S17 87.741345 23.585837 Railway Bridge Rural areas and land mainly used for paddy cultivation 
S18 87.814766 23.561044 Kankurhat Large urban & industrial setup of the Katwa subdivision 
S19 87.895915 23.555731 Katwa Confluence points of the River Ajay and the Bhagirathi River 
SitesLatitudeLongitudeLocation nameLand use in the adjoining area
S1 86.671287 24.524928 Deoghar Domestic and industrial discharged 
S2 86.715497 24.272522 Pansari Domestic waste from different nalas and sewerage 
S3 86.800515 24.204301 Sharath Vast agricultural area (especially paddy fields) & rice industries 
S4 86.688291 24.194996 Haribola Mineral exploration area (coal mines) 
S5 86.843024 23.988547 Jamtara Industrial effluent and sewage discharge from the urban center 
S6 86.941644 23.997868 Chitranjan Industrial effluent discharged (Indian rail locomotive industry) 
S7 86.975652 23.873538 Gour Bazar Proximity NH-19 and several Brick industries 
S8 87.055568 23.831549 Jaydev Vast Rural region with large agricultural field 
S9 87.118482 23.780211 Illam Bazar Copper buckets manufacturing by individual household 
S10 87.271514 23.745975 Bhedia Vast Agricultural area and well-known for sand mining 
S11 87.339528 23.7164 Majhkhara Vast agricultural area especially for paddy cultivation 
S12 87.390539 23.649442 Natunhat Agriculture area and associated with rice miles 
S13 87.455153 23.615171 Kunurpur Agriculture area and associated with rice miles 
S14 87.524867 23.616729 Chakulia Agriculture area and associated with rice miles 
S15 87.597983 23.598033 Kherua Agriculture area and associated with rice miles 
S16 87.6779 23.615171 Kandi Bridge Rural areas and land mainly used for paddy cultivation 
S17 87.741345 23.585837 Railway Bridge Rural areas and land mainly used for paddy cultivation 
S18 87.814766 23.561044 Kankurhat Large urban & industrial setup of the Katwa subdivision 
S19 87.895915 23.555731 Katwa Confluence points of the River Ajay and the Bhagirathi River 
Figure 1

Description of the study area and sampling locations of the Ajay River.

Figure 1

Description of the study area and sampling locations of the Ajay River.

Close modal

Geological and lithological characteristics

The geology of the Ajay River Basin is characterized by three parts (upper, middle and lower parts of the basin in Jharkhand and West Bengal, respectively) and these are quite different (Figure 2). Geologically, the river basin is diversified into major geological setups such as Archaean gneissic, Gondwana sedimentary and upper tertiary with a small patch of Quaternary age rocks. The upper part of the river basin belongs to Archaean gneissic which is one of the aged rock sequences in India. The Archaean gneissic complex is composed of a vast stratified formation of biotite, hornblende, pyroxene-granulites, basic intrusive, pegmatite and quartzite (Ghosh & Guchhait 2015). The middle part of the Ajay basin lies within the Gondwana super-groups such as the Raniganj Formation, Talchir Formation and Durgapur bed which covers around 8.81% of the total basin area. The Raniganj formation is illustrated by the supremacy of kaolinite (70%) followed by illite (15%) and smectite (15%). Similarly, Talchir formation is dominated by chlorite (60%) followed by illite (30%) and kaolinite (10%) (Saha & Naskar 2002). The Gondwana contains a significant amount of valuable mineral and petroleum resources and is mainly composed of biotite, hornblende, pyroxene-graulite, quartz, feldspar, mica, etc. The lower basin of the Ajay River is entirely covered with broad coated younger and older alluvial soil over the rocks of tertiary age rocks with a small patch of Quaternary age rocks. Upper Tertiary and Quaternary age rocks contain pyroxene, amphibole, olivine, mica, orthoclase, biotite, garnet, etc.
Figure 2

General geological feature of the Ajay River Basin.

Figure 2

General geological feature of the Ajay River Basin.

Close modal

The lithology of the Ajay River Basin is mainly characterized by alluvial, major red and yellow loam sedimentary types. The younger alluvial is made up of clay, silt, sand, gravel, pebble and calcareous concretion, whereas the older alluvial is composed of laterite, sand, silt, ferruginous concretion, lithomargic clay and gravel. In the lower part of the basin at Illambazar, Birbhum district, the river has the tendency of laterisation which is commonly known as Illambazar formation and is mainly composed of ferruginous lateritic soil with sand, gravel and pebble. The lateritic soil is formed during the intense tropical weathering of minerals and has a composition dominated by iron oxide, aluminium-oxide, oxy-hydroxide, kaolinite and quartz (Tardy 1992). This part of the basin is characterized by chemical weathering (basalt) and crusting of aluminium (Al) and iron (Fe) minerals.

Sampling and analytical procedure

Nineteen replicate water samples were collected from each site in three consecutive seasons (pre-monsoon (PM) in May 2017), (monsoon (MON) in July 2017) and (post-monsoon (POM) in December 2017). The selection of sites was done considering the location of different project components, junction of stream courses, spots of high-water velocity and some of the stagnated pools along with the areas having human interference. Both sites were targeted based on the availability of human activities. The study includes the various baseline parameters of water quality. Integration of water quality parameters gives an overall perception of positive and negative impacts due to agriculture, industrial and some other human activities, if any. In the collection of samples for water samples, different methods and techniques were applied separately based on the international standards method by the American Public Health Association (APHA 2022). Selected physicochemical parameters have been analyzed at the site for projecting the status of existing water quality based on existing aquatic environmental conditions. Samples for chemical analysis were collected in 1 liter-sterilized polyethylene containers. Samples for bacteriological analysis were collected in sterilized dark brown glass bottles to avoid any oxidation process by bacteria.

Water samples were analyzed for twenty-three parameters to determine the overall quality with respect to physicochemical and biochemical parameters as per the standard procedure of APHA (2005). Electrical conductivity (EC), total dissolved solid (TDS) and pH were analyzed through a multi-parameters Orion Versa Star Pro bench-top meter with pH/ISE/mV module (A-214). All major ions such as calcium (Ca2+), magnesium (Mg2+), nitrate (), chloride (Cl), sodium (Na+), alkalinity (), sulfate (), potassium (K+), fluoride (F), boron (Br), nitrite (), ammonia (), and phosphate () were analyzed by ion chromatograph (IC-Plus, 5: Metrohom). The rest of the parameters such as dissolved oxygen (DO), biochemical oxygen demand (BOD), and chemical oxygen demand (COD) were examined by the titrimetric method suggested by (APHA 2005) while bacterial counts such as total coliform (TC) and fecal coliform (FC) were analyzed through the most probable number (MPN) method.

The reproducibility of the analytical procedures was checked by carrying out duplicate analysis. The variations in results were less than 5% of the mean. The ability to replicate samples was determined by collecting two samples at every station. All the reagents were of analytical grade and purchased from Merck, India. For the preparation of all reagents and calibration standards, Millipore water of the Synergy Water Purification System (resistivity of 18.2 MΩ.cm at 25 °C with TOC ≤5 ppb) was used. Percentage recovery (P) and measurement of uncertainty (MOU) were calculated and followed by ISO, 17025 (1999) for better laboratorial practices, as listed in (Table 2). To calculate the accuracy of chemical analysis, the ion balance error method was applied according to Mandel & Shiftan (1981) and Lloyd & Heathcote (1985). The percentage error of major ions during chemical analysis was within the range (≈ of 10%).

Table 2

List of parameters and their analytical procedure

ParametersUnitsAnalytical methodsBISWHO (2006) RangeMOUP. R.
pH – Multi-parameter analyzer 6.5–8.5 6.5–9 0.1–13.9 ± 0.84 100 
Temperature °C Thermo meter – – 1–100 –  
Turbidity NTU Turbidity meter – 1–1,000  98.6 
Alkalinity mg/L Ion chromatograph 200 – 1–5,000 ±0.68 102.2 
EC μS/cm Multi-parameter analyzer  1,400 1–4,000 ±0.12 97.9 
TDS mg/L Multi-parameter analyzer 500 1,500 1–10,000 ±0.63 – 
DO mg/L Winkler method – – 1–20 – – 
Ca2+ mg/L Ion chromatograph 70 200 1–1,000 – 101.4 
Mg2+ mg/L Ion chromatograph 30 150    
Cl mg/L Ion chromatograph 250 500 1–5,000 ± 0.55 99.2 
 mg/L Ion chromatograph 200 400 1–1,000 – 98.6 
 mg/L Ion chromatograph 45 40–70 1–100 – 104.4 
 mg/L Ion chromatograph – – 0.01–1 – – 
 mg/L Ion chromatograph – – 1–100 – – 
 mg/L Ion chromatograph – 10 0.01–100 – 102.2 
Na+ mg/L Ion chromatograph – 500 1–2,000  98.4 
K+ mg/L Ion chromatograph – 50 1–1,000  100.8 
F mg/L Ion chromatograph 1.5 – 0.01–100 – – 
COD mg/L Open reflux method <4 – 1–1,000 ± 1.0 100.2 
BOD mg/L Winkler method <2 – 1–1,000 – – 
TC and FC MPN/100mL MPN method Absent 10 – – – 
ParametersUnitsAnalytical methodsBISWHO (2006) RangeMOUP. R.
pH – Multi-parameter analyzer 6.5–8.5 6.5–9 0.1–13.9 ± 0.84 100 
Temperature °C Thermo meter – – 1–100 –  
Turbidity NTU Turbidity meter – 1–1,000  98.6 
Alkalinity mg/L Ion chromatograph 200 – 1–5,000 ±0.68 102.2 
EC μS/cm Multi-parameter analyzer  1,400 1–4,000 ±0.12 97.9 
TDS mg/L Multi-parameter analyzer 500 1,500 1–10,000 ±0.63 – 
DO mg/L Winkler method – – 1–20 – – 
Ca2+ mg/L Ion chromatograph 70 200 1–1,000 – 101.4 
Mg2+ mg/L Ion chromatograph 30 150    
Cl mg/L Ion chromatograph 250 500 1–5,000 ± 0.55 99.2 
 mg/L Ion chromatograph 200 400 1–1,000 – 98.6 
 mg/L Ion chromatograph 45 40–70 1–100 – 104.4 
 mg/L Ion chromatograph – – 0.01–1 – – 
 mg/L Ion chromatograph – – 1–100 – – 
 mg/L Ion chromatograph – 10 0.01–100 – 102.2 
Na+ mg/L Ion chromatograph – 500 1–2,000  98.4 
K+ mg/L Ion chromatograph – 50 1–1,000  100.8 
F mg/L Ion chromatograph 1.5 – 0.01–100 – – 
COD mg/L Open reflux method <4 – 1–1,000 ± 1.0 100.2 
BOD mg/L Winkler method <2 – 1–1,000 – – 
TC and FC MPN/100mL MPN method Absent 10 – – – 

The WQI

The WQI was constructed in the present study based on the index values, as suggested by Pesce & Wunderlin (2000) to examine the quality of water from measured parameters and to assess the temporal and spatial variation in water quality. In this study, the WQI considers 11 parameters of the river Ajay to characterize the water quality: pH, EC, TDS, DO, BOD, COD, , , , Cl and TC. It is a very useful technique to transform large data into a single number by a normalization factor (Ci) and relative weight (Pi), which represents the water quality level from 0 to 100 scales. The range from 0 to 25 is classified as ‘very bad’; 26–50 classified as ‘bad’; 51–70 classified as ‘medium’; 71–90 classified as ‘good’ and 91–100 classified as ‘excellent’ (Jonnalagadda & Mhere 2001; Sanchez et al. 2007). Therefore, the WQI gives results on 0–100 scale associated with a quality percentage that is very easy to understand for the public as it is based on logical and scientific criteria. WQI was calculated based on the following equation:
where k is the subjective constant (visual impression of river contamination). In this study, K has been taken one (1), which represents water is clear with natural suspended solid. Ci is the normalized weight and Pi represents the relative weight values of each parameter assigned by Pesce & Wunderlin (2000). The assigned values of Ci and Pi are listed in (Table 3).
Table 3

Parameters selected for WQI calculation and their normalization factors which are adopted from (Pesce & Wunderlin 2000)

ParameterPiNormalization factor (Ci)
1009080706050403020100
Range of analytical value
pH 7–8 7–8.5 7–9 6.5–7 6–9.5 5–10 4–11 3–12 2–13 1–14 
EC <750 <1,000 <1,250 <1,500 <2,000 <2,500 <3,000 <5,000 <8,000 ≤12,000 >12,000 
TDS <100 <500 <750 <1,000 <1,500 <2,000 <3,000 <5,000 <10,000 ≤20,000 >20,000 
DO ≥7.5 >7.0 >6.5 >6.0 >5.0 >4.0 >3.5 >3.0 >2.0 ≤1.0 <1.0 
BOD <0.5 <2 <3 <4 <5 <6 <8 <10 <12 ≤15 >15 
COD >5 >10 >20 >30 >40 >50 >60 <80 <100 ≤150 >150 
 <0.01 <0.05 <0.10 <0.20 <0.3 <0.40 <0.50 <0.75 <1.00 ≤1.25 >1.25 
 <0.5 <2.0 <4.0 <6.0 <8.0 <10.0 <15.0 <20.0 <50.0 ≤100 >100 
 <25 <50 <75 <100 <150 <250 <400 <600 <1,000 ≤1,500 >1,500 
Cl <25 <50 <100 <150 <200 <300 <500 <700 <1,000 ≤1,500 >1,500 
TC <50 <500 <1,000 <2,000 <3,000 <4,000 <5,000 <7,000 <10,000 <14,000 >14,000 
ParameterPiNormalization factor (Ci)
1009080706050403020100
Range of analytical value
pH 7–8 7–8.5 7–9 6.5–7 6–9.5 5–10 4–11 3–12 2–13 1–14 
EC <750 <1,000 <1,250 <1,500 <2,000 <2,500 <3,000 <5,000 <8,000 ≤12,000 >12,000 
TDS <100 <500 <750 <1,000 <1,500 <2,000 <3,000 <5,000 <10,000 ≤20,000 >20,000 
DO ≥7.5 >7.0 >6.5 >6.0 >5.0 >4.0 >3.5 >3.0 >2.0 ≤1.0 <1.0 
BOD <0.5 <2 <3 <4 <5 <6 <8 <10 <12 ≤15 >15 
COD >5 >10 >20 >30 >40 >50 >60 <80 <100 ≤150 >150 
 <0.01 <0.05 <0.10 <0.20 <0.3 <0.40 <0.50 <0.75 <1.00 ≤1.25 >1.25 
 <0.5 <2.0 <4.0 <6.0 <8.0 <10.0 <15.0 <20.0 <50.0 ≤100 >100 
 <25 <50 <75 <100 <150 <250 <400 <600 <1,000 ≤1,500 >1,500 
Cl <25 <50 <100 <150 <200 <300 <500 <700 <1,000 ≤1,500 >1,500 
TC <50 <500 <1,000 <2,000 <3,000 <4,000 <5,000 <7,000 <10,000 <14,000 >14,000 

GIS and statistical analysis

Maps of sampling sites, general geology and spatial analysis have been carried out using ArcGIS-10.3 software. For the spatial analysis, the algorithm interpolation method (IDW) was used through the spatial analyst module of Arc GIS 10.3 software. Some other indices and diagrams for physicochemical parameters have been developed by Aqua-Chem V-2010.1 software. The complex data were normalized by data standardization process using IBM-SPSS V. (20) software before applying the statistical techniques by Z score, which is expressed as follows:
where x indicates the observed value, X indicates the mean value and sd indicates the standard deviation (Yidana et al. 2012).
All standardized data are transformed into a new set of data tables (correlation matrix based on the linear combination) for the PCA. It is one of the important multivariate statistical techniques, which can extract critical variables from high-dimensional data of natural and social sciences (Nakamura et al. 2016). In this study, the varimax rotation method was used to maximize the participation of variables in the PCA. The obtained result of PCA is expressed as eigenvalues which are mainly known by factor loading or scores. PCA was calculated based on the following equation:
where y indicates the component score, a indicates the component loading, x indicates the measured value, i indicates the component number, j indicates the sample number and m indicates the total number of variables (Koklu et al. 2010).

Water quality analysis of the Ajay River

The climatic condition of the study region is mainly characterized by PM, MON and POM and is a highly influencing factor for physicochemical parameters of the river water. Descriptive statistics with a minimum, maximum, mean and standard deviation of physicochemical parameters, major ions and bacterial counts in 57 water samples of nineteen sampling locations are presented in Table 4. Spatial and temporal variation of physicochemical parameters in the Ajay River Basin is shown in (Figure 3).
Table 4

Statistical description of water's major ion and physicochemical parameters with min (minimum); max (maximum); SD (standard deviation)

ParametersSeasonal samplesTemppHDOTURBAlkECTDSCa2+Mg2+ClNa +K+FBrCODBODTCFC
°C-mg/LNTUmg/LμS/cmmg/Lmg/Lmg/Lmg/Lmg/Lmg/Lmg/Lmg/Lmg/Lmg/Lmg/Lmg/Lmg/Lmg/Lmg/LMPN/100 mLMPN/100 mL
S1 PM1 18.5 7.21 4.4 10 86.5 323 210.2 28.51 10.34 49.48 20.61 1.77 0.58 0.08 0.01 28.4 5.594 1.686 0.4 14.6 6.2 110 26 
 MON2 14.2 7.25 4.8 10 82.5 323 250.6 32.5 12.34 60.65 35.2 2.1 0.46 0.01 0.01 32.25 4.56 1.45 0.8 18.6 8.2 125 35 
 POM3 18.4 7.23 2.8 10 96.2 425 310 27.5 14.2 48.5 19.5 7.12 0.66 0.01 0.01 29.6 6.12 0.9 0.4 16 5.6 510 59 
S2 PM4 19.2 7.02 3.4 10 110 4,019 2,612.4 29.33 12.25 33 40 2,325 0.01 0.21 0.01 33.37 4.609 1.297 0.01 16.6 15.2 140 32 
 MON5 13.9 7.12 4.2 10 11.6 4,019 2,023.6 35.66 15.23 44.23 55.6 2,145.1 0.09 0.14 0.01 26.35 6.23 1.325 0.01 15.6 7.6 155 33 
 POM6 18.9 7.23 3.2 10 112 3,845 2,566 26.4 14.2 145 58.6 1,945 0.06 0.19 0.12 32.56 7.45 1.02 0.06 44 6.8 540 102 
S3 PM7 18.8 7.45 4.2 10 75.4 214 155.6 30.22 10.23 17.57 17.57 4.17 0.32 0.09 0.01 13.19 4.89 0.97 0.01 12.4 16 124 28 
 MON8 14 7.46 4.4 15 84 214 214.3 39.45 10.45 27.26 27.56 1,040.23 0.56 0.05 0.09 15.23 5.12 1.02 0.01 14.2 6.8 133 29 
 POM9 18.9 7.56 15 85.2 356.6 288.2 32.62 11.2 123 34.2 845 0.12 0.07 0.06 15.23 5.2 0.8 0.08 46 5.2 624 48 
S4 PM10 18.6 7.44 3.8 10 96.5 638 340.6 19.24 10.8 18.55 16.7 3.63 0.87 0.08 0.24 23.49 8.954 0.959 0.01 17.2 18.2 136 18 
 MON11 14.3 7.98 4.6 10 110 638 396.6 24.6 14.56 29.45 44.25 109.56 0.82 0.07 0.21 26.23 10.12 1.23 0.01 23.8 14.2 156 28 
 POM12 18.6 7.45 3.2 15 98.5 655 396.6 18.42 11.8 28 26.2 210 0.98 0.05 0.24 25.45 8.4 1.2 0.04 49 6.2 436 66 
S5 PM13 19.1 7.46 3.2 10 127.6 3386 2250.8 29.52 12.03 23.24 19.34 1949 0.01 0.45 0.01 24.9 6.611 0.01 0.01 19.8 17.2 210 56 
 MON14 14.2 7.74 3.9 15 125 3386 1860.5 38.54 18.4 32.1 29.12 1556.4 0.01 0.25 0.06 18.52 5.26 0.01 0.01 18.6 9.8 240 45 
 POM15 19 7.48 3.8 10 117.4 2,856 2,310 31.24 13.2 58.6 29.5 1,203 0.09 0.22 0.09 26.45 7.4 0.06 0.4 46 7.4 210 56 
S6 PM16 18.6 7.55 3.4 15 114.2 4,572 2,980.2 25.20 10.2 29 18.7 2,734 0.01 0.42 0.01 26.89 2.99 1.86 0.01 18.4 19.5 240 60 
 MON17 14.3 7.65 4.2 15 125.2 4,572 2,506.4 24.56 13.24 44.2 48.33 2,242.7 0.06 0.04 0.01 27.45 3.24 1.45 0.01 22.8 11.6 260 78 
 POM18 18.8 7.5 2.8 10 124.6 4,258 2,926 27.54 11.2 48.5 22.3 1,805 0.01 0.042 0.08 29.58 9.4 1.56 0.08 56 6.6 240 60 
S7 PM19 18.6 7.21 4.5 15 102.4 274 178.3 18.12 9.77 15.91 11.14 6.277 0.4 0.06 0.01 27.56 2.753 0.88 0.062 12.8 6.8 155 45 
 MON20 14 7.22 4.8 10 110.2 274 244.3 20.56 14.66 27.44 25.4 111.23 0.42 0.09 0.01 32.6 3.25 0.78 0.98 18.6 11.2 165 65 
 POM21 18.8 7.26 4.8 10 112.3 470 312.5 23.4 10.2 25.6 21.3 213 0.8 0.07 0.04 34.56 6.23 0.98 0.062 26 5.4 155 49 
S8 PM22 18.4 7.23 4.2 10 98.6 350 227.5 18.1 10.32 27.24 46.42 21.35 0.45 0.12 0.01 28.18 2.82 1.546 0.23 10.8 4.8 122 22 
 MON23 14 7.41 4.9 15 88.9 350 258.4 24.6 13.55 37.6 65.8 36.84 0.52 0.08 0.01 29.4 4.55 1.75 0.36 14.5 8.4 128 34 
 POM24 18.7 7.45 3.8 15 102.4 410.2 306.4 21.6 13.2 22.3 42.3 39.56 1.02 0.05 0.01 29.45 12.5 0.08 0.23 10.8 4.2 420 78 
S9 PM25 18.5 7.75 4.3 10 109 295 198.2 26.81 10.66 16.23 19.86 2.08 0.361 0.14 0.01 23.15 3.82 0.51 0.01 11.6 5.2 98 12 
 MON26 14.3 7.82 10 99.5 295 210.6 24.2 14.23 26.12 29.6 10.2 0.401 0.06 0.01 44.55 6.15 0.56 0.19 10.4 6.2 110 23 
 POM27 18.6 7.25 4.6 15 112.4 355 277.6 28.56 12.6 18.5 23.6 15.42 0.456 0.07 0.13 25.46 6.23 0.56 0.05 11.6 4.4 213 46 
S10 PM27 18.4 7.46 3.9 10 112.1 313 203.8 23.51 13.48 16.94 24.94 8.23 0.53 0.25 0.01 21.53 3.09 1.752 0.13 14.8 5.8 130 40 
 MON29 14.6 7.54 4.2 15 105.6 313 235.8 27.45 12.35 24.5 46.2 14.35 0.62 0.4 0.01 35.6 9.45 1.84 0.13 17.5 9.7 130 45 
 POM30 18.7 7.41 4.2 10 124.5 345 255.6 25.4 14.2 18.5 29.5 56.12 0.25 0.2 0.06 23.21 4.5 1.42 0.13 14.8 6.2 130 48 
S11 PM31 18.9 7.82 3.4 10 96.4 402 265.2 29.8 14.62 20.29 48.5 33.53 0.513 0.08 0.01 21.57 3.27 1.03 0.46 15.4 15.4 200 36 
 MON32 14.2 7.84 3.8 10 104.6 402 288.4 32.21 13.9 34.8 72.4 59.64 0.596 0.102 0.09 25.36 7.14 1.08 0.46 21.8 15.4 298 54 
 POM33 18.8 7.8 2.6 15 102.3 520 310.3 31.25 13.2 22.3 44.5 6.12 0.678 0.09 0.08 21.56 4.2 1.12 0.46 49.5 6.8 1,256 210 
S12 PM34 19.2 7.78 4.2 15 89.6 280 185 33.46 18.08 13.93 16.36 3.103 0.32 0.056 0.01 25.02 3.79 0.93 0.01 8.8 126 18 
 MON35 14.4 7.65 4.8 10 95.6 280 196.4 19.5 18.08 18.6 26.4 4.2 0.421 0.06 0.01 26.12 4.12 1.36 0.07 12.4 6.2 142 33 
 POM36 18.8 7.55 4.6 15 95.4 342 210.2 32.4 18.5 15.6 18.23 6.2 0.55 0.063 0.01 29.58 5.2 0.88 0.45 16.2 126 18 
S13 PM37 18.4 7.72 4.1 10 95.6 286 194.5 26.61 6.51 11.43 17.74 3.23 0.55 0.102 0.01 24.37 9.04 1.45 1.319 13.8 9.4 156 24 
 MON38 14 8.1 4.6 15 85.4 286 212.3 26.61 9.61 23.7 29.3 4.12 0.62 0.04 0.01 29.44 11.2 1.52 2.31 13.6 5.6 178 34 
 POM39 18.6 7.92 4.4 10 98.4 342 244.5 29.6 9.6 14.5 19.23 3.25 0.65 0.01 0.08 26.12 8.45 1.25 1.319 29.5 5.8 156 24 
S14 PM40 18.2 7.64 3.4 10 114 335 215.2 15.21 9.4 45.12 18.15 1.54 0.51 0.08 0.01 16.84 4.28 2.88 0.32 11.8 6.2 120 34 
 MON41 14.2 7.46 15 101.2 335 245.7 28.45 12.4 55.22 39.6 2.1 0.55 0.05 0.01 26.15 3.98 3.02 0.63 14.4 6.3 144 44 
 POM42 18.4 7.84 4.2 10 111.2 425 302.5 16.2 11.4 48.5 25.46 4.12 0.58 0.03 0.12 21.23 6.25 3.2 0.32 16.2 5.4 1,103 245 
S15 PM43 18.6 7.64 3.8 15 113.2 332 212.4 34.4 14.19 10.06 17.06 0.01 0.423 0.22 41.57 6.09 0.01 0.01 16.4 6.8 186 52 
 MON44 14.3 7.25 4.4 10 122.3 332 244.6 36.4 18.4 25.64 37.45 1.25 0.06 0.09 0.24 49.5 7.45 0.08 0.01 25.2 15.2 202 72 
 POM45 18.6 7.62 3.4 15 133.2 410 256.3 36.2 15.2 16.2 21.25 145 0.09 0.15 0.22 44.5 7.45 0.06 0.08 23.5 6.9 186 52 
S16 PM46 18.7 7.12 10 98.4 302 196.2 22.98 15.16 16.26 15.01 3.22 0.235 0.09 0.01 41.69 4.32 0.66 1.55 12.4 4.2 96 12 
 MON47 14.8 7.28 5.2 15 92.6 302 189.8 24.23 14.2 27.45 26.4 3.22 0.333 0.08 0.06 38.56 6.59 0.78 1.23 18.2 11.4 108 24 
 POM48 18.6 7.45 4.2 10 102.6 298 174.2 23.4 16.2 19.5 18.54 3.2 0.458 0.06 0.01 42.1 5.23 0.75 1.55 10.2 5.2 245 58 
S17 PM49 18.5 7.4 4.2 10 88.1 275 183.6 25.49 15.64 4.33 3.247 1.944 0.176 0.123 0.01 29.9 3.79 0.401 0.01 10.4 6.3 144 26 
 MON50 14.3 7.23 10 89.6 275 112.6 24.56 16.45 24.12 23.44 2.13 0.186 0.04 0.01 36.2 4.12 0.98 0.06 14.6 8.4 184 36 
 POM51 18.7 7.41 3.2 15 89.5 326 119.2 26.1 13.4 14.33 12.3 12.65 0.105 0.03 0.135 32.9 4.7 0.455 0.08 15.2 3.8 510 105 
S18 PM52 18.2 7.48 3.8 10 98.6 301 196.4 32.16 15.97 36 9.22 0.32 0.223 0.42 0.01 25.66 5.91 1.6 1.21 13.8 7.4 146 24 
 MON53 14.5 7.54 4.1 15 96.9 301 124.2 36.96 18.45 56.2 39.82 0.98 0.225 0.42 0.01 24.13 9.25 1.25 1.02 12.9 5.8 155 39 
 POM54 18.6 7.58 3.4 10 104.6 340.2 199.2 33.56 16.2 32.5 11.23 6.12 0.09 0.31 0.08 26.4 6.2 1.4 1.21 19.2 146 64 
S19 PM55 18.8 7.02 4.6 102.4 279 184.6 35.55 16.11 13.16 9.1 1.37 0.01 0.74 0.01 22.72 4.83 1.07 0.01 8.2 4.2 170 42 
 MON56 14 7.12 5.2 104.5 279 196.5 39.56 19.2 23.1 23.78 0.99 0.01 0.72 0.01 28.88 3.86 1.06 0.01 10.8 180 44 
 POM57 18.7 7.6 4.2 10 104.5 366 214.5 34.2 18.2 16.23 12.4 0.15 0.01 0.62 0.01 21.58 5.4 0.09 0.06 8.2 3.2 170 44 
 Min 13.90 7.02 2.60 5.00 11.60 214.0 112.60 15.21 6.51 4.33 3.25 0.15 0.01 0.01 0.01 13.19 2.75 0.01 0.01 8.20 3.20 96.00 12.00 
 Max 19.20 8.10 5.20 15.00 133.2 4,572 2,980.2 39.56 19.20 145.0 72.40 2,734.00 1.02 0.74 0.24 49.50 12.50 3.20 2.31 56.00 19.50 1,256.00 245.00 
 Average 17.1 7.49 4.06 11.67 101.6 906.9 582.14 27.90 13.57 31.51 28.17 368.04 0.36 0.16 0.05 28.25 5.85 1.08 0.37 19.06 8.07 234.70 49.72 
 ± SD 2.12 0.25 0.60 2.73 17.51 1319.7 829.63 6.00 2.88 23.92 14.65 749.33 0.28 0.17 0.07 7.50 2.22 0.69 0.52 11.17 4.04 216.33 39.65 
 BIS – 6.5–8.5 > 4 < 5 200 – 500 70 30 70 200 45 – < 1.2 – 200 50 1.5 – < 4 < 2 Absent Absent 
ParametersSeasonal samplesTemppHDOTURBAlkECTDSCa2+Mg2+ClNa +K+FBrCODBODTCFC
°C-mg/LNTUmg/LμS/cmmg/Lmg/Lmg/Lmg/Lmg/Lmg/Lmg/Lmg/Lmg/Lmg/Lmg/Lmg/Lmg/Lmg/Lmg/LMPN/100 mLMPN/100 mL
S1 PM1 18.5 7.21 4.4 10 86.5 323 210.2 28.51 10.34 49.48 20.61 1.77 0.58 0.08 0.01 28.4 5.594 1.686 0.4 14.6 6.2 110 26 
 MON2 14.2 7.25 4.8 10 82.5 323 250.6 32.5 12.34 60.65 35.2 2.1 0.46 0.01 0.01 32.25 4.56 1.45 0.8 18.6 8.2 125 35 
 POM3 18.4 7.23 2.8 10 96.2 425 310 27.5 14.2 48.5 19.5 7.12 0.66 0.01 0.01 29.6 6.12 0.9 0.4 16 5.6 510 59 
S2 PM4 19.2 7.02 3.4 10 110 4,019 2,612.4 29.33 12.25 33 40 2,325 0.01 0.21 0.01 33.37 4.609 1.297 0.01 16.6 15.2 140 32 
 MON5 13.9 7.12 4.2 10 11.6 4,019 2,023.6 35.66 15.23 44.23 55.6 2,145.1 0.09 0.14 0.01 26.35 6.23 1.325 0.01 15.6 7.6 155 33 
 POM6 18.9 7.23 3.2 10 112 3,845 2,566 26.4 14.2 145 58.6 1,945 0.06 0.19 0.12 32.56 7.45 1.02 0.06 44 6.8 540 102 
S3 PM7 18.8 7.45 4.2 10 75.4 214 155.6 30.22 10.23 17.57 17.57 4.17 0.32 0.09 0.01 13.19 4.89 0.97 0.01 12.4 16 124 28 
 MON8 14 7.46 4.4 15 84 214 214.3 39.45 10.45 27.26 27.56 1,040.23 0.56 0.05 0.09 15.23 5.12 1.02 0.01 14.2 6.8 133 29 
 POM9 18.9 7.56 15 85.2 356.6 288.2 32.62 11.2 123 34.2 845 0.12 0.07 0.06 15.23 5.2 0.8 0.08 46 5.2 624 48 
S4 PM10 18.6 7.44 3.8 10 96.5 638 340.6 19.24 10.8 18.55 16.7 3.63 0.87 0.08 0.24 23.49 8.954 0.959 0.01 17.2 18.2 136 18 
 MON11 14.3 7.98 4.6 10 110 638 396.6 24.6 14.56 29.45 44.25 109.56 0.82 0.07 0.21 26.23 10.12 1.23 0.01 23.8 14.2 156 28 
 POM12 18.6 7.45 3.2 15 98.5 655 396.6 18.42 11.8 28 26.2 210 0.98 0.05 0.24 25.45 8.4 1.2 0.04 49 6.2 436 66 
S5 PM13 19.1 7.46 3.2 10 127.6 3386 2250.8 29.52 12.03 23.24 19.34 1949 0.01 0.45 0.01 24.9 6.611 0.01 0.01 19.8 17.2 210 56 
 MON14 14.2 7.74 3.9 15 125 3386 1860.5 38.54 18.4 32.1 29.12 1556.4 0.01 0.25 0.06 18.52 5.26 0.01 0.01 18.6 9.8 240 45 
 POM15 19 7.48 3.8 10 117.4 2,856 2,310 31.24 13.2 58.6 29.5 1,203 0.09 0.22 0.09 26.45 7.4 0.06 0.4 46 7.4 210 56 
S6 PM16 18.6 7.55 3.4 15 114.2 4,572 2,980.2 25.20 10.2 29 18.7 2,734 0.01 0.42 0.01 26.89 2.99 1.86 0.01 18.4 19.5 240 60 
 MON17 14.3 7.65 4.2 15 125.2 4,572 2,506.4 24.56 13.24 44.2 48.33 2,242.7 0.06 0.04 0.01 27.45 3.24 1.45 0.01 22.8 11.6 260 78 
 POM18 18.8 7.5 2.8 10 124.6 4,258 2,926 27.54 11.2 48.5 22.3 1,805 0.01 0.042 0.08 29.58 9.4 1.56 0.08 56 6.6 240 60 
S7 PM19 18.6 7.21 4.5 15 102.4 274 178.3 18.12 9.77 15.91 11.14 6.277 0.4 0.06 0.01 27.56 2.753 0.88 0.062 12.8 6.8 155 45 
 MON20 14 7.22 4.8 10 110.2 274 244.3 20.56 14.66 27.44 25.4 111.23 0.42 0.09 0.01 32.6 3.25 0.78 0.98 18.6 11.2 165 65 
 POM21 18.8 7.26 4.8 10 112.3 470 312.5 23.4 10.2 25.6 21.3 213 0.8 0.07 0.04 34.56 6.23 0.98 0.062 26 5.4 155 49 
S8 PM22 18.4 7.23 4.2 10 98.6 350 227.5 18.1 10.32 27.24 46.42 21.35 0.45 0.12 0.01 28.18 2.82 1.546 0.23 10.8 4.8 122 22 
 MON23 14 7.41 4.9 15 88.9 350 258.4 24.6 13.55 37.6 65.8 36.84 0.52 0.08 0.01 29.4 4.55 1.75 0.36 14.5 8.4 128 34 
 POM24 18.7 7.45 3.8 15 102.4 410.2 306.4 21.6 13.2 22.3 42.3 39.56 1.02 0.05 0.01 29.45 12.5 0.08 0.23 10.8 4.2 420 78 
S9 PM25 18.5 7.75 4.3 10 109 295 198.2 26.81 10.66 16.23 19.86 2.08 0.361 0.14 0.01 23.15 3.82 0.51 0.01 11.6 5.2 98 12 
 MON26 14.3 7.82 10 99.5 295 210.6 24.2 14.23 26.12 29.6 10.2 0.401 0.06 0.01 44.55 6.15 0.56 0.19 10.4 6.2 110 23 
 POM27 18.6 7.25 4.6 15 112.4 355 277.6 28.56 12.6 18.5 23.6 15.42 0.456 0.07 0.13 25.46 6.23 0.56 0.05 11.6 4.4 213 46 
S10 PM27 18.4 7.46 3.9 10 112.1 313 203.8 23.51 13.48 16.94 24.94 8.23 0.53 0.25 0.01 21.53 3.09 1.752 0.13 14.8 5.8 130 40 
 MON29 14.6 7.54 4.2 15 105.6 313 235.8 27.45 12.35 24.5 46.2 14.35 0.62 0.4 0.01 35.6 9.45 1.84 0.13 17.5 9.7 130 45 
 POM30 18.7 7.41 4.2 10 124.5 345 255.6 25.4 14.2 18.5 29.5 56.12 0.25 0.2 0.06 23.21 4.5 1.42 0.13 14.8 6.2 130 48 
S11 PM31 18.9 7.82 3.4 10 96.4 402 265.2 29.8 14.62 20.29 48.5 33.53 0.513 0.08 0.01 21.57 3.27 1.03 0.46 15.4 15.4 200 36 
 MON32 14.2 7.84 3.8 10 104.6 402 288.4 32.21 13.9 34.8 72.4 59.64 0.596 0.102 0.09 25.36 7.14 1.08 0.46 21.8 15.4 298 54 
 POM33 18.8 7.8 2.6 15 102.3 520 310.3 31.25 13.2 22.3 44.5 6.12 0.678 0.09 0.08 21.56 4.2 1.12 0.46 49.5 6.8 1,256 210 
S12 PM34 19.2 7.78 4.2 15 89.6 280 185 33.46 18.08 13.93 16.36 3.103 0.32 0.056 0.01 25.02 3.79 0.93 0.01 8.8 126 18 
 MON35 14.4 7.65 4.8 10 95.6 280 196.4 19.5 18.08 18.6 26.4 4.2 0.421 0.06 0.01 26.12 4.12 1.36 0.07 12.4 6.2 142 33 
 POM36 18.8 7.55 4.6 15 95.4 342 210.2 32.4 18.5 15.6 18.23 6.2 0.55 0.063 0.01 29.58 5.2 0.88 0.45 16.2 126 18 
S13 PM37 18.4 7.72 4.1 10 95.6 286 194.5 26.61 6.51 11.43 17.74 3.23 0.55 0.102 0.01 24.37 9.04 1.45 1.319 13.8 9.4 156 24 
 MON38 14 8.1 4.6 15 85.4 286 212.3 26.61 9.61 23.7 29.3 4.12 0.62 0.04 0.01 29.44 11.2 1.52 2.31 13.6 5.6 178 34 
 POM39 18.6 7.92 4.4 10 98.4 342 244.5 29.6 9.6 14.5 19.23 3.25 0.65 0.01 0.08 26.12 8.45 1.25 1.319 29.5 5.8 156 24 
S14 PM40 18.2 7.64 3.4 10 114 335 215.2 15.21 9.4 45.12 18.15 1.54 0.51 0.08 0.01 16.84 4.28 2.88 0.32 11.8 6.2 120 34 
 MON41 14.2 7.46 15 101.2 335 245.7 28.45 12.4 55.22 39.6 2.1 0.55 0.05 0.01 26.15 3.98 3.02 0.63 14.4 6.3 144 44 
 POM42 18.4 7.84 4.2 10 111.2 425 302.5 16.2 11.4 48.5 25.46 4.12 0.58 0.03 0.12 21.23 6.25 3.2 0.32 16.2 5.4 1,103 245 
S15 PM43 18.6 7.64 3.8 15 113.2 332 212.4 34.4 14.19 10.06 17.06 0.01 0.423 0.22 41.57 6.09 0.01 0.01 16.4 6.8 186 52 
 MON44 14.3 7.25 4.4 10 122.3 332 244.6 36.4 18.4 25.64 37.45 1.25 0.06 0.09 0.24 49.5 7.45 0.08 0.01 25.2 15.2 202 72 
 POM45 18.6 7.62 3.4 15 133.2 410 256.3 36.2 15.2 16.2 21.25 145 0.09 0.15 0.22 44.5 7.45 0.06 0.08 23.5 6.9 186 52 
S16 PM46 18.7 7.12 10 98.4 302 196.2 22.98 15.16 16.26 15.01 3.22 0.235 0.09 0.01 41.69 4.32 0.66 1.55 12.4 4.2 96 12 
 MON47 14.8 7.28 5.2 15 92.6 302 189.8 24.23 14.2 27.45 26.4 3.22 0.333 0.08 0.06 38.56 6.59 0.78 1.23 18.2 11.4 108 24 
 POM48 18.6 7.45 4.2 10 102.6 298 174.2 23.4 16.2 19.5 18.54 3.2 0.458 0.06 0.01 42.1 5.23 0.75 1.55 10.2 5.2 245 58 
S17 PM49 18.5 7.4 4.2 10 88.1 275 183.6 25.49 15.64 4.33 3.247 1.944 0.176 0.123 0.01 29.9 3.79 0.401 0.01 10.4 6.3 144 26 
 MON50 14.3 7.23 10 89.6 275 112.6 24.56 16.45 24.12 23.44 2.13 0.186 0.04 0.01 36.2 4.12 0.98 0.06 14.6 8.4 184 36 
 POM51 18.7 7.41 3.2 15 89.5 326 119.2 26.1 13.4 14.33 12.3 12.65 0.105 0.03 0.135 32.9 4.7 0.455 0.08 15.2 3.8 510 105 
S18 PM52 18.2 7.48 3.8 10 98.6 301 196.4 32.16 15.97 36 9.22 0.32 0.223 0.42 0.01 25.66 5.91 1.6 1.21 13.8 7.4 146 24 
 MON53 14.5 7.54 4.1 15 96.9 301 124.2 36.96 18.45 56.2 39.82 0.98 0.225 0.42 0.01 24.13 9.25 1.25 1.02 12.9 5.8 155 39 
 POM54 18.6 7.58 3.4 10 104.6 340.2 199.2 33.56 16.2 32.5 11.23 6.12 0.09 0.31 0.08 26.4 6.2 1.4 1.21 19.2 146 64 
S19 PM55 18.8 7.02 4.6 102.4 279 184.6 35.55 16.11 13.16 9.1 1.37 0.01 0.74 0.01 22.72 4.83 1.07 0.01 8.2 4.2 170 42 
 MON56 14 7.12 5.2 104.5 279 196.5 39.56 19.2 23.1 23.78 0.99 0.01 0.72 0.01 28.88 3.86 1.06 0.01 10.8 180 44 
 POM57 18.7 7.6 4.2 10 104.5 366 214.5 34.2 18.2 16.23 12.4 0.15 0.01 0.62 0.01 21.58 5.4 0.09 0.06 8.2 3.2 170 44 
 Min 13.90 7.02 2.60 5.00 11.60 214.0 112.60 15.21 6.51 4.33 3.25 0.15 0.01 0.01 0.01 13.19 2.75 0.01 0.01 8.20 3.20 96.00 12.00 
 Max 19.20 8.10 5.20 15.00 133.2 4,572 2,980.2 39.56 19.20 145.0 72.40 2,734.00 1.02 0.74 0.24 49.50 12.50 3.20 2.31 56.00 19.50 1,256.00 245.00 
 Average 17.1 7.49 4.06 11.67 101.6 906.9 582.14 27.90 13.57 31.51 28.17 368.04 0.36 0.16 0.05 28.25 5.85 1.08 0.37 19.06 8.07 234.70 49.72 
 ± SD 2.12 0.25 0.60 2.73 17.51 1319.7 829.63 6.00 2.88 23.92 14.65 749.33 0.28 0.17 0.07 7.50 2.22 0.69 0.52 11.17 4.04 216.33 39.65 
 BIS – 6.5–8.5 > 4 < 5 200 – 500 70 30 70 200 45 – < 1.2 – 200 50 1.5 – < 4 < 2 Absent Absent 

PM, pre-monsoon; MON, monsoon; POM, post-monsoon.

Figure 3

Spatial and temporal variations of physicochemical parameters in the Ajay River Basin.

Figure 3

Spatial and temporal variations of physicochemical parameters in the Ajay River Basin.

Close modal

In this study, the fluctuation of temperature is mainly due to seasonal changes, geographical location and sampling time. The range of pH varies from 7.02 to 8.10 with a mean of 7.49, which indicates that the river water is moderately alkaline throughout the year. The presence of limestone bedrock along the basin area can increase the level of ions during water-rock interaction and lead to higher pH in the river water. pH value of the river water samples was found to be within the safe limit prescribed by BIS (1991) and WHO (2006). EC of the Ajay River water increased 17-fold along the basin route from 214 to 4,572 μS/cm with a mean value of 907.0 μS/cm. The EC values at three sites (S2, S5 and S6) were above the critical water conductivity of 3,000 μS/cm according to salinity classification (Selvakumar et al. 2017). The higher EC was observed in the upper part of the river basin. It may be due to the presence of gneissic rock, which leads to higher EC value because of the dissolution of carbonate minerals in the river water. Other sources like wastewater, urban runoff, application of nitrogenous content fertilizer and atmospheric deposition are also responsible for higher levels of EC in the river system. Similarly, the value of TDS varies from 112 to 2,980 mg/L with a mean value of 582.1 mg/L. The higher value of TDS suggests that the water of S2, S5 and S6 sites are not under fresh water type. Because the TDS level higher than >1,000 mg/L in water bodies is considered as brackish water type (Ayed et al. 2017). A higher amount of TDS was observed in the upper domain of the basin. An extreme level of TDS in this area is owing to anthropogenic sources like mining waste and urban and industrial effluents which can amplify the TDS level in the river water. Consequently, the river might get a significant amount of waste accomplished with ionized substances, biodegradable compounds and nutrients in the upper basin from urban and industrial centers and are responsible for a higher amount of TDS in the river system. Similarly, higher values of EC and TDS were obtained in the PM and POM seasons which may be the evidence of high temperature which enhances the evaporation rate, loss of water, decomposition and mineralization of organic materials during these periods.

The DO value ranges from 2.6 to 5.2 mg/L with a mean value of 4.06 mg/L. The values of DO indicated that the river water is not well oxygenated because all values were less than the regulatory standard (≥6.0 mg/L) as per BIS (1991) at all sites. The inferior amount of DO level in the river system is mainly due to the high amount of organic load and the presence of a high microbial population (Sharma et al. 2014). DO level of the river may also be affected by many environmental and biochemical factors such as temperature, photosynthesis and chemical and biological oxygen demands (Zhang et al. 2007). It may harm the self-purification capacity of the river and could form hypoxic conditions in aquatic systems (Suthar et al. 2009). However, in the monsoon period, DO levels showed a little higher value but did not fulfill the regulatory criteria. Elevated levels of DO in this period may be due to the high dispersion of organic pollutants as a result of the flushing effect and breakdown of organic waste. The value of BOD and COD varies from 3.2 to 19.5 mg/L with a mean value of 8.07 and 8.2 to 56 mg/L with a mean value of 19.06 mg/L, respectively. The values of both parameters indicated that all values exceeded the regulatory standard (<3 and <10 mg/L, respectively), as suggested by BIS (1991). The elevated level of both parameters suggests that river water is contaminated through point sources of decomposable organic matter from domestic, agricultural and industrial discharge. This is because both parameters are indicators of domestic and industrial discharge into the river system (Erturk et al. 2010). It was also noted that S2, S3, S4, S5, S6 and S11 sites are major contributors of organic load into the river basin that is responsible for elevated values of COD and BOD (>20 and >8 mg/L, respectively). These sites belong to urban and industrial centers, which are associated with different types of organic and inorganic compounds. TC and FC values of river water range from 130 to 585 MPN/100 mL with a mean value of 234.6 MPN/100 mL and 23 to 108 MPN/100 mL with a mean value of 49.6 MPN/100 mL, respectively. The value of both bacterial parameters did not fulfill the drinking water quality criteria and exceeded the permissible limit WHO (2006). The river water is contaminated due to significant counts of fecal bacteria present in the whole basin route. However, a higher concentration of these parameters was observed in the POM followed by the PM period. During this period, freshwater levels in river catchments are believed to be very low compared with other seasons. Consequently, it is possible that the organic wastes increase the microbial activities in the river and lead high concentration of these parameters.

Hydro-geochemistry of the Ajay River

Fourteen ionic compositions (anion and cation) have been studied along the Ajay River Basin, and their spatial and temporal variation is shown in Figures 4 and 5. Both these figures indicate that the water is characterized by > + > Cl > Na+ > > Ca2+ > Mg2+ > K+ > F > Br > > > facies.
Figure 4

Spatial and temporal variations of anions in the Ajay River Basin.

Figure 4

Spatial and temporal variations of anions in the Ajay River Basin.

Close modal
Figure 5

Spatial and temporal variations of cations in the Ajay River Basin.

Figure 5

Spatial and temporal variations of cations in the Ajay River Basin.

Close modal

The value of in the study area varies from 11.6 to 133.2 mg/L with a mean value of 101.6 mg/L. The results revealed that the river water quality is under alkaline conditions. The values of were within desirable limits (200 mg/L), as suggested by BIS (1991). The higher value of was noted in the POM followed by PM and monsoon periods. Availability of in river water generally depends on the rate of carbonates and silicate weathering, oxidation of organic matter and dissolution of atmospheric CO2 (Hartmann et al. 2013). The value of Ca2+ and Mg2+ in the study area varies from 15.21 to 39.56 mg/L and 6.51 to 19.2 mg/L with mean values of 27.9 and 13.57 mg/L, respectively. The values of Ca2+ and Mg2+ of all collected water samples were within the permissible limit prescribed by BIS (1991). The result suggested that the river water is not hard (<150–300 mg/L) according to hardness classification. However, enhancement of Ca2+ and Mg2+ values was observed in the upper domain of the basin, which may be due to the presence of a gneissic rock with an abundance of limestone, dolomite and calc-granulite. Chloride value in the study area varies from 4.33 to 145 mg/L with a mean value of 31.5 mg/L. Cl value of all collected water samples was less than the regulatory standard (200 mg/L) and under the safe limit. It is usually derived from rainfall sources, evaporites and atmospheric deposition (Mir et al. 2016). The temporal study indicated that the highest value of these parameters was obtained in the monsoon and POM period and sometime in the PM period.

Similarly, the Na+ and K+ value of the water samples in the study area varies from 13.19 to 48.5 mg/L and 2.75 to 12.5 mg/L with mean values of 28.2 and 5.8 mg/L, respectively. Na+ and K+ values of all collected water samples were within permissible limits according to WHO (2006). Both are very important to the human body for the proper functioning of the nervous and membrane system. The seasonal variations of both parameters were less but higher concentration was noted in the POM followed by PM, especially in the middle and lower part of the river basin. Na+ and K+ in the river water generally depend on the weathering of ferro-magnesium and feldspar minerals while anthropogenic inputs are additional sources (Breault et al. 2000). value of the collected water samples varies from 3.25 to 72.4 mg/L with a mean value of 27.9 mg/L. The results indicated that the value of all water samples was under the permissible limit prescribed by BIS (1991). The presence of in the river water generally depends on the geology of the area (gypsum containing rock) and land-use practices like application of fertilizer to agricultural land and input of domestic and industrial discharge. However, the higher concentration of in the monsoon period indicates that river water is slightly affected by agricultural runoff with sulfate-containing fertilizers from agricultural areas.

In the last few decades, the majority of river basins have been contaminated due to high concentrations of fluoride (F), nitrate (), nitrite (), ammonia () and phosphate () which are the most omnipresent chemical contaminants of the river water. The observed value of F varied from 0.01 to 3.2 mg/L with a mean value of 1.08 mg/L. The higher value of F was observed in nine samples of sites S1, S6, S8, S10 and S14 in all seasons and exceeded the water quality standard (1.5 mg/L) suggested by BIS (1991). The availability of fluoride in the river usually depends on the weathering of fluoride-bearing rocks (natural) and proximity to human emission sources (anthropogenic) along the basin area (Singh et al. 2008). Higher levels of F in collected river water samples along these regions may be due to the mineralization of granitic or sandstone and fluoride-bearing rocks (muscovite, biotite, fluorite and fluoro-apatite). However, the mining of coal, processing of phosphate rock and manufacturing of glass and ceramic products lead to the concentration of F in the basin area. However, the presence of a high concentration of F in the Ajay River Basin makes water unsuitable for drinking purposes. As a result, high fluoride-containing water for a long time causes chronic adverse effects on local public health. Therefore, it can be believed that the risk that we assumed in the present study may increase in future through geogenic and anthropogenic sources.

value of the river water in the study area varies from 0.15 to 2,734 mg/L with a mean value of 368.04 mg/L. value of collected samples was higher than regulatory standard (<45 mg/L) as per BIS (1991) in 17 samples from sites S2, S3, S4, S5, S6, S7 and S11 in all seasons. The elevated level of at these sites may be due to the increasing rate of mineral exploration, mining, agricultural and industrial development, which caused nitrogen accumulation in water. In this area, the river receives large amounts of waste from fertilizer industries, biodegradable wastes from domestic discharge, ionized substances from food and beverage industries and nitrogenous waste from agricultural fields. level in the river system is also positively influenced by biological fixation, precipitation and application of nitrogenous (N) fertilizers (Rao 2014). In addition, atmospheric deposition, sanitary landfills, garbage dumps, soil-containing organic matter, animal waste and poorly functioning septic systems are potential sources of . A higher amount of in the river can cause water quality degradation by the eutrophication process and create hypoxic conditions in the water body (Daisuke et al. 2014). It is also harmful to the human body and could be responsible for stomach cancer (Zeng & Wu 2015).

value varies from 0.01 to 0.74 mg/L with a mean value of 0.16 mg/L. The result indicated that all water samples have low value as compared to the regulatory standard (<1.2 mg/L), prescribed by ISI: 2296 (1982). The elevated level of was observed in the lower basin of the Ajay River Basin in all seasons. The land use of the Ajay River in the lower basin is commonly used for paddy cultivation. However, elevated levels of in the lower basin may be due to agricultural activities with the application of N-content fertilizers. The value of varies from 0.01 to 0.24 mg/L with a mean value of 0.05 mg/L. A higher concentration of was obtained in the upper and lower parts of the river basin in all the seasons. This is because that area is generally used for domestic and agricultural purposes. However, the presence of in the river system indicates that the water is affected by municipal wastewater discharges and domestic uses (bathing and washing purposes) because detergents are an important source of (Gebre 2017). The overall study showed that , , Cl, Na+ and are major dominant ions present in the river. The higher level of ions like , Cl, Na+ and in the river system may be due to weathering of aged rock-forming minerals. Furthermore, extreme level in the river system reveals the contribution of anthropogenic inputs such as fertilizer and urban and industrial wastes.

The evaluation of hydrochemical parameters of water can be understood by plotting the concentration of major cations and anions using a iper tri-linear diagram (Ige & Olasehinde 2011). The Piper tri-linear diagram was developed by Piper (1994) to characterize the water types. The water types along the sampling sites of the Ajay River are displayed in (Figure 6). The diagram consists of two lower triangles and a middle quadrilateral. The left and right triangles represent the distribution of major cations and major anions, respectively. The quadrilateral summarizes the dominant cations and anions to indicate the final water type and classified into (i) Ca2+–Mg2+– Cl, (ii) Na+–K+–Cl, (iii) Na+– K+ and (iv) Ca2+–Mg2+ groups (Ravikumar et al. 2010). The distribution of major ions indicates that the majority of samples belong to the Ca2+–Mg2+ type followed by Na+–K+–Cl and Ca2+–Mg2+– Cl types in the study area. The result indicates that the river water is significantly influenced by the weathering of calcite (CaCO3) and dolomite (CaMg(CO3)2)-containing rock (limestone) along the basin area. The atmospheric and biogenic CO2 have also influenced the availability of ions in the surface water during the process of dissolution of gases and minerals (Singh et al. 2011). Weathering of Na+–K+ minerals and the ion exchange process can be major contributors for Ca2+, Na+–and Mg2+ ions into the water (Singh et al. 2013). Some water samples of the Ajay River fall into Na+–K+–Cl water type. The occurrence of Na+–K+–Cl water type is mainly due to rock water interaction, weathering and mineralogical composition of the study area. The abundance of ferro-magnesium, feldspar and gypsum in the study area might be responsible for Na+–K+–Cl water type. However, the cation-exchange process is considered a major responsible factor for the dominance of Na+, K+, Cl and in the water (Reddy & Kumar 2009). However, broad lithologic and geological differences in the basin route could be responsible for different water types among the different sampling sites (Godsey et al. 2009).
Figure 6

Piper's diagram showing the characterization of water samples.

Figure 6

Piper's diagram showing the characterization of water samples.

Close modal
The concentration of major ions in the water mainly depends on the regional geology, weathering rate of mineral-containing rocks, water-rock interaction and precipitation rate (Li et al. 2015a, b). The Gibbs diagram is a useful technique to identify the water chemistry with chemical components present in the water system based on evaporation, precipitation and rock dominance. It was developed by Gibbs (1970) using the ratio of Cl/(Cl + ) for anions, (Na+ + K+)/(Na+ + K+ + Ca2+) for cations and TDS as a function was applied to assess the source of chemical constituent (Nguyen et al. 2014). The representation of the Gibbs diagram for Ajay River water samples is shown in (Figure 7). The result suggested that the majority of the water samples of river water are directly influenced by the dissolution of rocks during the water movement except at a few sites; only S2, S5 and S6 (nine samples of three seasons) represent evaporation dominance. It may be due to the presence of higher amount of TDS in the water. The minimal amount of freshwater in the river catchments with high anthropogenic inputs could induce anthropogenic TDS in the water. Consequently, anthropogenic TDS might be responsible for the shifting of some samples from the rock-dominance category to the evaporation-dominant category in the study area.
Figure 7

Gibbs diagram for water samples of the Ajay River.

Figure 7

Gibbs diagram for water samples of the Ajay River.

Close modal

Suitability of river water for irrigation

During the study period, the majority of water samples had high EC values (>250 μS/cm). The suitable value of EC (<250 μS/cm) in terms of irrigation purposes is very important because it is directly related to soil productivity (Joshi et al. 2009). Similarly, Na+ plays an important role in suitability for irrigation. The Wilcox diagram was proposed by USSL (1954) to describe the relation between SAR and conductivity using a rating scale with reference to irrigation water (Figure 8). SAR value in water ranging from 10 to 18 is considered good and acceptable for irrigation. Similarly, an EC value in water less than 750 μS/cm is considered good; between 750 and 2250 μS/cm is considered marginal and greater than 2250 μS/cm is considered poor quality for irrigation (Li et al. 2016). Salinity hazard for irrigation water quality can be classified into four categories viz. low (C1), medium (C2), high (C3) and very high (C4) (Gupta et al. 2008). The majority of samples fall into the C1 and C2 categories in terms of salinity hazard for crop productivity, which suggests that the water can be used for irrigation with little danger level of EC. However, it has been also observed that sites S2, S5 and S6 (12 samples of three seasons) fall into the C4 category, which indicates that the river water is not suitable for agricultural purposes. The higher amount of EC values found at these sites may be due to the extreme value of present in this area. However, the higher concentration of EC in the water can alter the osmotic action of plants and may interrupt the nutrient absorption process from the water and soil (Tatawat & Chandel 2008). The value of TDS is also more than 200 mg/L at maximum sites, which is noticeably high as compared with the worldwide average value of TDS in the river water (Lu et al. 2015).
Figure 8

Wilcox diagram for water samples of the Ajay River (USSL 1954).

Figure 8

Wilcox diagram for water samples of the Ajay River (USSL 1954).

Close modal

Agglomerative hierarchical cluster analysis

Agglomerative hierarchical cluster analysis (AHCA) displays the clustering of 19 sites with their physicochemical parameters, which is shown in the dendrogram (Figure 9). The objective of AHCA was to evaluate the groups of sites using particular water quality parameters in the form of dendrogram images using Ward's method. The AHCA identified four cluster groups from the 19 sites and each group is subdivided into subgroups. The dendrogram indicated that the sites S7, S8, S9, S10, S11 and S12 fall under the first cluster group, which mainly belongs to semi-urban areas. Result of the first group suggests that these sites belong to the same natural background and all studied parameters are derived from similar sources, which are responsible for the same water quality characteristic in this area. The possible sources of the physicochemical parameters are identical geology (Archaean gneissic and Gondwana sedimentaries) and similar land-use patterns (large agricultural fields and vast rural areas enclosed with small-scale industries) in this area. Furthermore, sites S14, S15, S16, S17 and S19 fall under the second cluster group which mainly belongs to an organic enriched area. All sites in this cluster belong to vast rural and large agricultural regions. These regions belong to the lower basin (downstream) of the Ajay River where the basin is dominated by new deposition of alluvial soil. The possible reasons for similar water quality characteristics at these sites are similar land-use patterns, comparable geology and climatic factors. Cluster third and fourth (S1, S3, S4, S13, S18 and S2, S5, S6, respectively) corresponded to the medium-polluted sites, which receive a significant amount of waste from point and non-point sources. These sites are mainly located in urban and industrial centers where partially treated or untreated wastewater is being discharged into the river through various drains. Besides, the majority of the sites are located in the upper part of the river basin, which encounters the Gondwana sediments. The upper part of the Ajay basin is well known for mineral exploration and settlement of large urban and industrial centers with many small-scale industries.
Figure 9

Agglomerative hierarchical cluster analyses of water quality parameters and classification of sites.

Figure 9

Agglomerative hierarchical cluster analyses of water quality parameters and classification of sites.

Close modal

Principal component analysis

PCAs for the water of the Ajay River include Scree plot, eigenvalue, percentage variance and cumulative percentage. Scree plot observed that the eigenvalues are associated with each component in descending order to identify important components or factors present in the dataset (Ledesma 2007). PCA develops a rotated component matrix in the expression of eigenvalues for each component with the percentage of variance and cumulative percentage to explain each component. Four components (PC) were obtained with a cumulative variance of 57.54% of the total variance in the dataset, in which PC1 accounts for 19.06%, PC2 accounts for 13.95%, PC3 accounts for 10.31% and PC4 accounts for 14.21% of the total variance. Eigenvalues of the eight principal components have been found more than one so they can be used to assess the dominant components present in the data set (Table 5). Eigenvalue of each factor has been characterized into four groups viz. >0.75 is considered as strong loading, 0.5–0.75 is considered as moderate loading and 0.3–0.5 is considered as weak loading (Liu et al. 2003).

Table 5

The rotated component matrix of water quality parameters of the Ajay River

ParametersD1D2D3D4
pH − 0.112 0.344 0.258 0.188 
DO − 0.355 0.007 − 0.176 0.548 
Alk 0.112 − 0.147 0.432 0.185 
EC 0.942 − 0.161 − 0.016 0.114 
TDS 0.947 − 0.153 0.015 0.132 
Ca2+ 0.059 0.648 0.136 − 0.012 
Mg2+ − 0.223 0.700 0.121 − 0.017 
Cl 0.428 0.060 − 0.177 0.449 
 0.353 0.288 − 0.002 0.154 
 0.941 − 0.166 − 0.076 0.096 
 − 0.385 0.806 0.079 0.008 
 0.034 0.707 − 0.076 − 0.048 
 − 0.019 0.093 0.765 0.266 
Na+ − 0.073 − 0.177 0.483 − 0.236 
K+ 0.047 0.312 0.611 0.042 
F 0.033 0.478 0.567 0.187 
COD 0.415 0.139 0.353 0.585 
BOD 0.544 0.167 0.236 − 0.254 
TC − 0.054 0.119 0.019 0.929 
FC − 0.026 0.050 0.043 0.879 
Eigenvalue 4.319 3.245 2.184 1.763 
% of Variance 19.061 13.958 10.315 14.214 
Cumulative % 19.061 33.020 43.335 57.549 
ParametersD1D2D3D4
pH − 0.112 0.344 0.258 0.188 
DO − 0.355 0.007 − 0.176 0.548 
Alk 0.112 − 0.147 0.432 0.185 
EC 0.942 − 0.161 − 0.016 0.114 
TDS 0.947 − 0.153 0.015 0.132 
Ca2+ 0.059 0.648 0.136 − 0.012 
Mg2+ − 0.223 0.700 0.121 − 0.017 
Cl 0.428 0.060 − 0.177 0.449 
 0.353 0.288 − 0.002 0.154 
 0.941 − 0.166 − 0.076 0.096 
 − 0.385 0.806 0.079 0.008 
 0.034 0.707 − 0.076 − 0.048 
 − 0.019 0.093 0.765 0.266 
Na+ − 0.073 − 0.177 0.483 − 0.236 
K+ 0.047 0.312 0.611 0.042 
F 0.033 0.478 0.567 0.187 
COD 0.415 0.139 0.353 0.585 
BOD 0.544 0.167 0.236 − 0.254 
TC − 0.054 0.119 0.019 0.929 
FC − 0.026 0.050 0.043 0.879 
Eigenvalue 4.319 3.245 2.184 1.763 
% of Variance 19.061 13.958 10.315 14.214 
Cumulative % 19.061 33.020 43.335 57.549 

Four principal components (PCs) accumulated 57.54% of the total variance in which PC1 represent 19.06%, PC2 represent 13.95%, PC3 represent 10.31% and PC4 represent 14.21%. Eigenvalues of these four PCs (D1-D4) with bold values considered as moderate to strong loading to demonstrate the compositional relationship and grouping pattern between variables.

However, in this study, two principle components (PC1 and PC2), which explain 19.06% and 13.95% of the cumulative variance have been employed in the biplot (Figure 10). This is a very useful technique to demonstrate the compositional relationship and grouping pattern between variables. In the PC1, the strong loading was obtained from EC, TDS and with moderate loading of BOD followed by weak loading of COD, Cl and . The remarkable association of these parameters in PC1 exemplifies agricultural, domestic and industrial sources. Therefore, possible sources of these parameters are industrial discharge, domestic waste, agricultural runoff and fertilizers, which are leading the concentration of these ions in the river system (Osei et al. 2010). In the PC2, Ca2+, Mg2+ and show moderate negative loading, while shows positive moderate loading. is widely used in N fertilizer and its interactions with exchangeable cations like Ca2+ and Mg2+ can enhance the protonation of ammonia in water bodies and play an important role in its environmental fate (Dontsova et al. 2005). A similar study conducted by Clow & Mast (2010) also suggests that the flushing can cause displacement of base cations like Ca2+, Mg2+, Na+ and K+ from exchange surfaces into the soil solution. Therefore, it is possible that due to the presence of a large agricultural area, such a reaction might be happening in the river catchments. The loading of , turbidity suggests that the river water may be affected by the agricultural runoff associated with the nitrogenous waste (Osei et al. 2010). Because the large portion of the lower basin of the Ajay River is used for agriculture practices, especially for paddy cultivation (Verma & Saksena 2010). Similarly, the strong loading of and moderate loading of Na+, K+, F and alkalinity in the PC2 may be due to mineral component that is predominately controlled by feldspar, calcite and dolomite equilibrium (Zheng et al. 2012). However, moderate loading of alkalinity and Na+ with F specifies the dissolution of fluorite at alkaline pH which supports the general mechanism of releasing F in water. In this mechanism, fluoride-bearing minerals in an alkaline environment enhance the mobilization of F from fluorite-rich rocks due to the exchange of ions between OH ions and F and increase the solubility of CaF2 and NaF in the water (Guo et al. 2007; Arveti et al. 2011). Further, a significantly strong loading of TC and FC with moderate negative loading of DO was obtained in the PC4. The association between TC and FC is mainly due to anthropogenic sources such as domestic sewage associated with bodily waste, animal droppings etc. However, a negative correlation of DO with TC and FC indicates that the depletion of oxygen is mainly due to the presence of high microbial diversity which consumes an excess amount of DO from the river water.
Figure 10

Biplot of the first two components which explained the 33.02% of the variance for water samples.

Figure 10

Biplot of the first two components which explained the 33.02% of the variance for water samples.

Close modal

WQI determination

WQI was determined involving eleven prime parameters of river water (pH, EC, TDS, DO, BOD, COD, , , , Cl and TC), which were normalized and weighted before index calculation. Overall, the WQI values can be categorized into five categories viz. excellent, good, medium, bad and very bad. The variation of Ajay River water quality in three seasons is significantly different from one another (Figure 11). In the PM season, 78% of water samples fell under the medium category, 15.7% of water samples fell under the bad and only 5.2% of water samples fell under the good category. In this season, the maximum deterioration of the water quality in the upper part of the river basin from sites S2 to S6 was observed. The change in river water characteristics is due to higher levels of COD, BOD, and bacterial parameters present at these locations. This is because of an extreme load of organic pollutants in the water body from agricultural runoff, as well as industrial and domestic discharge. In addition, low water levels, elevated temperature and high evaporation rate during this period also concentrate the ionized substances in the water (Rajmohan & Elango 2004). In the monsoon period, the result showed that 31.5% of water quality is under the medium category and about 68.5% under the good category. In this season, water quality has improved due to the flushing effect and dilution by rainwater. It was also observed that urban and industrial centers in the upper part of the river basin are more responsible factor to medium and bad water quality of the river. In the POM season, 42% of the river water quality falls under the medium category and 57% of water samples fall under the good category. In this period, water quality has slightly decreased as comparedwith the monsoon period. In general, WQI suggests that the water quality of the Ajay River is always vulnerable and rarely meets the desirable values for drinking water because it did not fulfill the excellent criteria (90–100) during three consecutive seasons. Consequently, the majority of the sites are not suitable for drinking purposes without conventional treatment. The decreasing trend line of Ajay River water quality indicates that the river system continuously suffering from a high load of organic contaminants, which could affect the self-purification capacity of the river in the near future. So, for the utilization of Ajay River water, proper management practices for drinking purposes are needed.
Figure 11

WQI variations during pre-monsoon, monsoon and post-monsoon period (2017–2018).

Figure 11

WQI variations during pre-monsoon, monsoon and post-monsoon period (2017–2018).

Close modal

Understanding of hydrochemical analysis suggests that multifaceted hydrochemical processes and numerous sources of contaminants influence the study area. The results indicated that mineral dissolution, climatic factors, geological setting, oxidation of ammonium and organic matter and anthropogenic inputs influence the water quality of the region. The weathering of minerals is the most imperative process that has controlled the water chemistry of the study area. The river water is moderately alkaline with high TDS and EC. The water type of the river is Ca2+–Mg2+ followed by Na+–K+–Cl and Ca2+–Mg2+– Cl which suggest the major role of carbonate, gypsum and dolomite weathering in the chemical composition of water. The availability of and showed that the river water is highly influenced by agricultural runoff and industrial discharge, which mainly includes nitrogenous fertilizer and organic wastes. The higher value of COD, BOD, TC and FC indicates organic pollution in the river due to domestic and industrial discharge. PCA determined seven PCs, which concluded the dominancy of geogenic activities, atmospheric deposition and anthropogenic sources on the river water quality. WQI and CA classified the 19 sites into four groups based on rating scale and statistical relation respectively. River water is trending toward deterioration condition and is presently unsuitable for drinking and agriculture purposes. Therefore, water should be treated before application for agriculture and drinking purposes. Specific technology for defluoridation, desalination and bacterial disinfection by conventional treatment methods needs to be considered in contaminated areas. The Ajay River water needs proper management practices with some inclusive techniques so that it can be utilized for drinking and agriculture purposes. This study introduced some environmental procedures for the interpretation of voluminous data, the effect on water quality according to land-use pattern, hydro-climatic factors on surface water chemistry, characterization and identification of pollution sources and comprehensive assessment of spatial and temporal variation. These findings will help significantly in better understanding the hydro-geochemistry of surface water bodies and their pollution sources at the global level.

The authors would like to thank the University Grants Commission (UGC) Government of India (F. No. 42- 437/2013 (SR)), for the financial grant through the major research project. We wish to thank the editor and the anonymous reviewers for their suggestions and critical comments.

The authors declare that the submitted manuscript is original. They also acknowledge that the current research has been conducted ethically and all authors have agreed on the final shape of the study.

The authors consent to participate in this research study.

The authors consent to publish this research study.

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

The authors declare there is no conflict.

American Public Health Association (APHA)
(
2005
)
Standard Methods for the Examination of Water and Wastewater
, 21st Centennial edn..
Washington, DC, USA
:
APHA, AWWA, WPCF
.
American Public Health Association (APHA) (2022) Standard Methods for the Examination of Water and Wastewater. 24th Edition, American Public Health Association.
Arveti
N.
,
Sarma
M. R. S.
,
Aitkenhead-Peterson
J. A.
&
Sunil
K
.
(2011)
Fluoride incidence in groundwater: a case study from Talupula, Andhra Pradesh, India
.
Environmental monitoring and assessment
,
172
,
427
443
.
Ayed
B.
,
Jmal
I.
,
Sahal
S.
,
Mokadem
N.
,
Saidi
S.
,
Boughariou
E.
&
Bouri
S.
(
2017
)
Hydrochemical characterization of groundwater using multivariate statistical analysis: the Maritime Djeffara shallow aquifer (Southeastern Tunisia)
,
Environ. Earth Sci.
,
76
(
24
),
821
.
Breault
R. F.
,
Waldron
M. C.
&
Striker
L. K.
(
2000
)
Relations Between Water-Quality Conditions and Drainage Basin Characteristics in the Scituate Reservoir Drainage Basin, Rhode Island, 1982–95: U.S. Geological Survey Water-Resources Investigations Report 00-4086
,
American Journal of Science, United States
p.
46
.
Bureau of Indian Standards (BIS, 10500)
(
1991
)
Indian Standard Specification for Drinking Water
. BIS, New Delhi.
Daisuke
K.
,
Takashi
O.
,
Nobutake
N.
,
Masanobu
M.
&
Kazuhiko
T.
(
2014
)
Utilization of ion-exclusion chromatography for water quality monitoring in a suburban river in Jakarta, Indonesia
,
Water
,
6
,
1945
1960
.
Devanesan
E.
,
Gandhi
M. S.
,
Selvapandiyan
M.
,
Senthilkumar
G.
&
Ravisankar
R.
(
2017
)
Heavy metal and potential ecological risk assessment in sedimentscollected from Poombuhar to Karaikal Coast of Tamilnadu using energy dispersive X-ray fluorescence (EDXRF) technique
,
Beni-Suef Univ. J. Basic Appl. Sci.
,
6
(
3
),
285
292
.
Domínguez
M. T.
,
Alegre
J. M.
,
Madejón
P.
,
Madejón
E.
,
Burgos
P.
,
Cabrera
F.
,
Marañón
T.
&
Murillo
J. M.
(
2016
)
Geoderma River banks and channels as hotspots of soil pollution after large-scale remediation of a river basin
,
Geoderma
,
261
,
133
140
.
doi: 10.1016/j.geoderma.2015.07.008
.
Dontsova
K.
,
Darrell
N. L.
&
Johnston
C. T.
(
2005
)
Calcium and magnesium effects on ammonia adsorption by soil clays calcium and magnesium effects on ammonia adsorption by soil clays
,
Soil. Sci. Soc. Am. J.
,
69
,
1225
1232
.
doi: 10.2136/sssaj2004.0335
.
Erturk
A.
,
Gurel
M.
,
Ekdal
A.
,
Tavsan
C.
,
Ugurluoglu
A.
,
Seker
D. Z.
,
Tanik
A.
&
Ozturk
I.
(
2010
)
Water quality assessment and meta model development in Melen watershed, Turkey
,
J. Environ. Manage.
,
91
,
1526
1545
.
Gibbs, R. J. (1970) Mechanisms controlling world water chemistry, Science, 170, 1088–1090.
Godsey
S. E.
,
Kirchner
J. W.
&
Clow
D. W
.
(2009)
Concentration -discharge relationships reflect chemostatic characteristics of US catchments
.
Hydrological Processes: An International Journal
,
23
(
13
),
1844
1864
.
Guo
Q.
,
Wang
Y.
,
Ma
T.
&
Ma
R
.
(2007)
Geochemical processes controlling the elevated fluoride concentrations in groundwaters of the Taiyuan Basin, Northern China
.
Journal of Geochemical Exploration
,
93
(
1
),
1
12
.
Gupta
S.
&
Banarjee
S. U.
(
2012
)
Geochemistry of the River Damodar – the influence of the geology and weathering environment on the dissolved load
,
Int. J. Geomatics. Geo-Sci.
,
2
(
3
),
853
867
.
Gupta
S.
,
Mahato
A.
,
Roy
P.
&
Datta
J. K.
(
2008
)
Geochemistry of groundwater, Burdhwan District, West Bengal, India
,
Environ. Geol.
,
53
,
1271
1282
.
Gurgel
P. D. M.
,
Julio
A. N.
,
Ferreira
D. D. M.
&
do Amaral
V. S.
(
2016
)
Ecotoxicological water assessment of an estuarine river from the Brazilian Northeast, potentially affected by industrial wastewater discharge
,
Sci. Total. Environ.
,
572
,
324
332
.
doi: 10.1016/j.scitotenv.2016.08.002
.
Hartmann, J., West, A. J., Renforth, P., Köhler, P., De La Rocha, C. L., Wolf-Gladrow, D. A., Dürr, H. H. & Scheffran, J. (2013) Enhanced chemical weathering as a geoengineering strategy to reduce atmospheric carbon dioxide, supply nutrients, and mitigate ocean acidification, Rev. Geophys., 51, 113–149.
Ige
O. O.
&
Olasehinde
P. I.
(
2011
)
Preliminary assessment of water quality in Ayede-Ekiti, Southwestern Nigeria
,
J. Geo. Min. Res.
,
3
(
6
),
147
152
. Journal of Geology and Mining Research.
ISI: Indian Standards Institute (ISI: 2296)
(
1982
)
Tolerance Limit for Inland Surface Water Subject to Pollution
.
New Delhi, India
.
ISO: International Organization of Standardization-17025
(
1999
)
General Requirements for the Competence of Testing and Calibration Laboratories
.
The International Organization of Standardization and the International Electrotechnical Commission (IEC)
,
New Delhi
.
Joshi
D. M.
,
Kumar
A.
&
Agrawal
N.
(
2009
)
Studies on physicochemical parameters to assess the water quality of river Ganga for drinking purpose in Haridwar district
,
Rasayan J. Chem.
,
2
(
1
),
195
203
.
Kumar
R.
,
Singh
R. D.
&
Sharma
K. D.
(
2005
)
Water resource of India
,
Current Sci.
,
89
(
5
),
794
811
.
Ledesma
R. D.
(
2007
)
Determining the number of factors to retain in EFA: an easy to use computer program for carrying out parallel analysis
,
Pract. Assess. Res. Eval.
,
12
(
2
),
1
11
.
Li
P.
,
Qian
H.
&
Wu
J.
(
2014
)
Accelerate research on land creation
,
Nature
,
510
(
7503
),
29
31
.
Li
P.
,
Qian
H.
,
Howard
K. W. F.
&
Wu
J.
(
2015b
)
Building a new and sustainable ‘Silk road economic belt’
,
Environ. Earth Sci.
,
74
(
10
),
7267
7270
.
Lloyd
J. A.
&
Heathcote
J. A.
(
1985
)
Natural Inorganic Hydrochemistry in Relation to Groundwater: an Introduction
.
New York
:
Oxford Uni. Press
, p.
296
.
Lu, J.-M., An, Y.-L., Wu, O.-X., Luo, J. & Jiang, H. (2015) Hydrochemical characteristics and sources of Qingshuijiang River Basin at wet season in Guizhou Province, Europe, PubMed. Central, 36 (5), 1565–1572.
Mahmoud
M. E.
,
El Zokm
G. M.
,
Farag
A. E.
&
Abdelwahab
M. S
.
(2017)
Assessment of heat-inactivated marine Aspergillus flavus as a novel biosorbent for removal of Cd (II)
,
Hg (II), and Pb (II) from water. Environmental Science and Pollution Research
,
24
,
18218
18228
.
Mandel
S.
&
Shiftan
Z. L.
(
1981
)
Groundwater Resources
.
New York
:
Academic
, p.
269
.
Mir, R. A., Jeelani, G. H. & Dar, A. D. (2016) Spatio-temporal patterns and factors controlling the hydrogeochemistry of the river Jhelum basin, Kashmir, Himalaya, Environ. Monit. Assess., 118 (438), 1–24.
Mukhopadhyay
M.
,
Mukhopadhyay
S.
&
Mukherjee
M.
(
2006
)
Study of River Pollution: Some Examples From Eastern India
.
Kolkata
:
ACB publisher
, pp.
51
58
.
Nakamura
K.
,
Kuwatani
T.
,
Kawabe
Y.
&
Komai
T.
(
2016
)
Extraction of heavy metals characteristics of the 2011 Tohoku tsunami deposits using multiple classification analysis
,
Chemosphere
,
144
,
1241
1248
.
doi: 10.1016/j.chemosphere.2015.09.078
.
Nguyen
T. A. H.
,
Ngo
H. H.
,
Guo
W. S.
,
Zhang
J.
,
Liang
S.
,
Lee
D. J.
,
Nguyen
P. D.
&
Bui
X. T
.
(2014)
Modification of agricultural waste/by-products for enhanced phosphate removal and recovery: potential and obstacles
.
Bioresource technology
,
169
, pp.
750
-
762
.
Osei
J.
,
Nyame
F. K.
,
Armah
T. K.
,
Osae
S. K.
,
Dampare
S. B.
,
Fianko
J. R.
,
Adomako
D.
&
Bentizl
N.
(
2010
)
Application of multivariate analysis for identification of pollution sources in the densu delta wetland in the vicinity of a landfill site in Ghana
,
J. Water Resour. Prot.
,
2
,
1020
1029
.
Pettersson
U. T.
,
Ingri
J.
&
Andersson
P. S.
(
2000
)
Hydrogeochemical processes in the Kafue River upstream from the Copper belt mining area, Zambia
,
Aquat. Geochem.
,
6
,
385
411
.
Piper
A. M.
(
1994
)
A geographic procedure in the geochemical interpretation of water analysis
,
Trans. Am. Geophys. Union
,
25
,
914
923
.
Rajmohan
N.
&
Elango
L.
(
2004
)
Identification and evolution of hydrogeochemical processes in the groundwater environment in an area of the Palar and Cheyyar River Basins, Southern India
,
Environ. Geol.
,
46
,
47
61
.
Rao
D. L.
(
2014
)
Recent advances in biological nitrogen fixation in agricultural systems
,
InProc. Indian Nat. Sci. Acad.
,
80
(
2
),
359
378
.
Ravikumar
P.
,
Venkatesharaju
K.
&
Somashekar
R. K.
(
2010
)
Major ion chemistry and hydrochemical studies of groundwater of Bangalore South Taluk, India
,
Environ. Monit. Assess.
,
163
,
643
653
.
Reddy
A. G. S.
&
Kumar
K. N.
(
2009
)
Identification of the hydrogeochemical processes in groundwater using major ion chemistry: a case study of Penna–Chitravathi river basins in Southern India
,
Environ. Monit. Assess.
,
163 (1), 643–653. doi:10.1007/s10661-009-1239-4
.
Roy
S.
(
2012
) '
Locating Archaeological Sites in the Ajay River Basin, West Bengal; An Approach Employing the Remote Sensing and Geographical Information System
',
14th Annual International Conference and Exhibition on Geospatial Information Technology and Applications
.
India geospatial forum
, pp.
1
10
.
Saha
D. K.
&
Naskar
D. C.
(
2002
)
Report on Geoenvironmental Appraisal in Parts of Damodar, Ajay Interfluve Including Asansol and Durgapur Areas for Development Activities of Adda and Wbpcb
.
Government of India geological survey of India central geophysics division.gsi-chq-35164
.
Sanchez
E.
,
Colmenarejo
M. F.
,
Vincete
J.
,
Rubio
A.
,
Garcia
M. G.
,
Travieso
L.
&
Borja
R.
(
2007
)
Use of the water quality index and dissolved oxygen deficit as simple indicators of watersheds pollution
,
Ecol. Indic.
,
7
,
315
328
.
Sharma
H. S.
&
Chattopadhyay
S.
(
1998
)
Sustainable Development Issues and Case Studies
.
New Delhi, India
:
Concept publishing company
.
Sharma
P.
,
Meher
P. K.
,
Kumar
A.
,
Gautam
Y. P.
&
Mishra
K. P.
(
2014
)
Changes in water quality index of Ganges river at different locations in Allahabad
,
Sustainability Water Ecol.
,
3
,
67
76
.
doi:10.1016/j.swaqe.2014.10.002
.
Singh
A. K.
,
Mondal
G. C.
,
Singh
S.
,
Singh
T. B.
,
Tewary
B. K.
&
Sinha
A.
(
2008
)
Major ion chemistry, weathering processes and water quality assessment in upper catchment of Damodar River Basin, India
,
Environ. Geol.
,
54
,
745
758
.
Singh
A. K.
,
Mondal
G. C.
,
Singh
T. B.
,
Singh
S.
,
Tewary
B. K.
&
Sinha
A.
(
2012
)
Hydrogeochemical processes and quality assessment of groundwater in Dumka and Jamtara districts, Jharkhand, India
,
Environ Earth Sci.
,
67
,
2175
2191
.
Singh
C. K.
,
Rina
K.
,
Singh
R. P.
&
Mukherjee
S.
(
2013
)
Geochemical characterization and heavy metal contamination of groundwater in Satluj River Basin
,
Environ Earth Sci.
67
, pp.
2175
2191
.
doi:10.1007/s12665-013-2424-x
.
Suratman, S., Sailan, M. I. M., Hee, Y. Y., Bedurus, E. A. & Latif, M. T. (2015) A pre-liminary study of water quality index in Terengganu River Basin, Malaysia, Sains Malaysiana, 44 (1), 67–73.
Suthar
S.
,
Nema
A. K.
,
Chabukdhara
M.
&
Gupta
S. K.
(
2009
)
Assessment of metals in water and sediments of Hindon River, India: impact of industrial and urban dis- charges
,
J. Hazard. Mater.
,
171
,
1088
1095
.
http://dx.doi.org/10.1016/j.jhazmat.2009.06.109
.
Suthar
S
.
(2011)
Contaminated drinking water and rural health perspectives in Rajasthan, India: an overview of recent case studies
.
Environmental monitoring and assessment.
,
173 (1), 837–849
.
Tang
J.
,
Wang
T.
,
Zhu
B.
,
Zhao
P.
,
Xiao
Y.
&
Wang
R.
(
2015
)
Tempo-spatial analysis of water quality in tributary bays of the Three Gorges Reservoir region (China)
,
Environ. Sci. Pollut. Res.
,
22
,
16709
16720
.
Tardy
Y.
, (
1992
)
Diversity and terminology of laterite profiles
. In:
Martini
I. P.
&
Chesworth
W.
(eds.)
Weathering, Soils and Paleosols
,
Amsterdam
:
Elsevier
, pp.
379
405
.
Tatawat
R. K.
&
Chandel
C. P. S.
(
2008
)
A hydrochemical profile for assessing the groundwater quality of Jaipur City
,
Environ. Monit. Assess.
,
143
,
337
343
.
United States Salinity Laboratory (USSL) Staff
(
1954
)
Diagnosis and Improvement of Saline and Alkaline Soils
, Vol.
60
. Amsterdam,
U.S. Department of Agriculture Hand Book
, p.
160
.
Verma
A. K.
&
Saksena
D. N.
(
2010
)
Assessment of water quality and pollution status of Kalpi (Morar) River, Gwalior, Madhya Pradesh: with special reference to conservation and management plan
,
Asian J. Exp. Biol. Sci.
,
1
(
2
),
419
429
.
Wang
J.
,
Liu
G.
,
Liu
H.
&
Lam
P. K. S.
(
2017
)
Multivariate statistical evaluation of dissolved trace elements and a water quality assessment in the middle reaches of Huaihe River, Anhui, China
,
Sci. Total Environ.
143
, 337–343.
doi: 10.1016/j.scitotenv.2017.01.088
.
WHO (World Health Organization)
(
1993, 2004
)
International Standards for Drinking Water
.
Geneva
:
World Health Organization
.
World Health Organization (WHO) Standard
(
2006
)
Drinking Water Guidelines
.
Geneva
, p.
6
.
Yidana
S. M.
,
Banoeng-Yakubo
B.
&
Sakyi
P. A.
(
2012
)
Identifying keyprocesses in the hydrochemistry of a basin through the combined useof factor and regression models
,
J. Earth Syst. Sci.
,
121
(
2
),
491
507
.
Zhang
Y. Y.
,
Zhang
J.
,
Wu
Y.
&
Zhu
Z. Y.
(
2007
)
Characteristics of dissolved oxygen and its affecting factors in the Yangtze Estuary
,
J. Environ. Sci.
,
8
,
1654
1679
.
Zheng
L.
,
Apps
J. A.
&
Spycher
N.
(
2012
)
Geochemical modeling of changes in shallow groundwater chemistry observed during the MSU-ZERT CO 2 injection experiment
,
Int. J. Greenhouse Gas Control.
,
7
,
202
217
.
doi: 10.1016/j.ijggc.2011.10.003
.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/).