GEOCHEMICAL ASSESSMENT OF GROUNDWATER QUALITY INTEGRATING MULTIVARIATE STATISTICAL ANALYSIS WITH GIS IN SHIWALIKS OF PUNJAB, INDIA
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C. K. Singh1 & S. Mukherjee 1 Remote sensing applications laboratory, School of Environmental Sciences, Jawaharlal Nehru University, New Delhi-110067, India Tel: + 91-11-2670-4312; Fax: + 91-11-2670-4312 E-mail:
[email protected],
[email protected]
Keywords: Factor analysis, Shiwaliks, Groundwater Abstract The dependency of people has increased on groundwater due to tremendous increase in crop production, population, industrialization and erratic rainfall due to climate change, in past few decades. The groundwater is the main source of irrigation in Shiwaliks of Punjab. In the present study the samples were collected from predetermined location as was located on satellite image on basis of spectral reflectance using GPS. The analysis of samples formed the attribute database for spatial distribution of water quality parameters using spatial analyst extension of ArcGIS 9.1. Principal components analysis (PCA) together with other factor analysis procedures, consolidate a large number of observed variables into a smaller number of factors that can be more readily interpreted. In the case of groundwater, concentrations of different constituents may be correlated based on underlying physical and chemical processes such as dissociation, ion-exchange, weathering or carbonate equilibrium reactions. The number of factors for a particular dataset is based on the amount of non-random variation that explains the underlying processes. The more factors extracted, the greater is the cumulative amount of variation in the original data. The PCA produced six significant components that explained 78% of the cumulative variance. Introduction Groundwater resource is a multidimensional concept; it is defined by its location, occurrence over time, size, properties, conditions of accessibility, the effort required to mobilize it and therefore, all of it is to be considered in the context of demand. Groundwater has become an essential commodity over the past few decades due to its increasing usage for drinking, irrigation and industrialization. Rural India has started facing water crisis due to its increasing dependency on depleting groundwater supply. Quality of groundwater is equally important as its quantity owing to the suitability of water for various purposes. Variation of groundwater quality in an area is a function of physical and chemical parameters that are greatly influenced by geological formations and anthropogenic activities. The quality of surface water and soil characteristics play a major role in determining the composition and quality of the groundwater. The chemical properties of groundwater also depend upon the chemistry of water in the recharge area as well as on the different geochemical processes that are occurring in the subsurface. These geochemical processes are responsible for the seasonal and spatial variations in groundwater chemistry (Matthess 1982). Factor analysis technique is useful in the analysis of groundwater data corresponding to large number of variables, analysis through this technique produce easily interpretable results, and data size is reduced to smaller number of variables which can explain all the parameters. Surface water, groundwater quality assessment employing multi-component techniques were widely used (Praus, 2005). Multivariate analysis of geochemical data operated on the concept that each aquifer zone has
its own unique groundwater quality signature, based upon the chemical makeup of the sediments that comprise it (Fetter, 1994). In the present study, the groundwater quality of Rupnagar district was evaluated from various deep aquifers (tube wells) and shallow aquifers i.e hand pumps to understand the geochemistry of the aquifers. Study Area Rupnagar district, falls between north latitude 30° 32' and 31° 24' and east longitude 76° 18' and 76° 55' (fig.1). Satluj is the most important river of Rupnagar district; it enters near Nangal the place where it leaves the Himalayas. The potential of the river has been largely tapped for development of power and irrigation. A major national project in the form of a gravity dam and reservoir has been established at Bhakra for generating hydroelectric power. Another dam at Nangal and barrages at Rupnagar and Harike divert the river water into canals. The rock formation found in the upper Shiwaliks consist of soft earth clay and boulder conglomerates. The middle Shiwalik are composed of massive sand rock and clay beds. The lower Shiwalik have grey micaceous sand stones and slabs and are non-fossiliferous. The piedmont plain area is composed of the alluvium derived from these Shiwaliks intercepted by seasonal rivulets or choes. The climate of Rupnagar District is characterized by its general dryness (except in the south-west monsoon season), a hot summer and a bracing cold winter. The average annual rainfall in district is 775.6 mm. About 78% of the annual rainfall is received during the period from June to September. Methodology The study area was divided in several grids and representative groundwater samples were taken from each grid. The grids were of 10 x 10 km2 and samples were collected on the basis of spectral signature as observed on satellite image from each grid. The study area was divided in several grids and representative groundwater samples were taken from each grid. The grids were of 10 x 10 km2 and samples were collected on the basis of spectral signature as observed on satellite image from each grid. Vegetation has a unique spectral signature which enables it to be distinguished readily from other types of land cover in an optical/near-infrared image. In the near infrared (NIR) region, the reflectance is much higher than that in the visible band due to the cellular structure in the leaves. Hence, vegetation can be identified by the high NIR but generally low visible reflectance. The healthy vegetation has the highest reflectance value while the severely stressed vegetation has the lowest reflectance value. The pixels which contained stressed vegetation and also pixels which showed high soil moisture content were selected for sampling as it can be discerned as surface manifestation of groundwater and geology of the area. Samples of groundwater were collected in polypropylene bottles (Tarsons) during the month of January of the year 2007 from adjoining areas of National Fertilizers Limited and Punjab Chemicals Limited and from the River Satluj where the discharge of above mentioned two factories are released (fig 2). GPS was used to map the location of each sampling site and finally the results were brought in GIS environment for further analysis. The samples were analyzed using AAS using standard procedures as given in APHA (1995). Results and Discussion Spatial variations of groundwater quality parameters The GIS based analysis of spatiotemporal behavior of the groundwater quality in the study area was done using the Spatial Analyst module of ArcGIS 9.1. The interpolation technique used in the analysis is inverse distance weighted (IDW) method. Factor Analysis The spatial variation of all the parameters is shown in fig. 3. Factor analysis is used to reduce the complex data to an easily interpretable form. Multivariate statistical approaches allow driving hidden information from the data set about their possible influences of the environment on water quality. Multivariate analysis was performed on matrix of hydro-geochemical data. The statistical
analysis was performed using SPSS software package. PCA aims to transform the observed variables to a new set of variables which are uncorrelated and arrange in decreasing order of importance to simplify the problem. Principal components analysis was performed on correlation matrix of the raw data in which a water sample is described by nineteen physical and chemical parameters. Factors with eigenvalues more than one were only considered. Six factors were sufficient to explain 78.64% of the variance for correlation matrix. It was observed that first six components are more significant which represent 78.64% of the variance in groundwater quality of Rupnagar 28.65% by PC1, 15.58% by PC2, 11.92% by PC3, 10.07% by PC4, 6.81% by PC5 and 5.58% by PC6. The rotation method used was varimax with Kaiser normalization. The largest component loading (which measure the degree of closeness between the variables and the PC) either positive or negative, suggests the meaning of the dimensions; positive loading indicates that the contribution of the variables increases with the increasing loading in dimension; and negative loading indicates a decrease (Lawrence, 1982). In general, component's loadings larger than 0.6 may be taken into consideration in the interpretation, in other words, the most significant variables in the components represented by high loadings have been taken into consideration in evaluation the components. Factor 1 (Fig. 4) explains 28.65% of the total variance and shows higher positive loading for EC, Chloride, Sulphate, Mg, Na and K (table 1). The processes such as ion-exchange, gas dissolution, soil leaching and excessive use of fertilizers are responsible for positive loading of above mentioned parameters. The dissolution of CO2 in water causes increase in total carbonate and a decrease in pH which is well explained by negative loading of pH. This explains that electrical conductivity is higher in the area due to Na, Cl, Mg, Sulphate and K. The most common sources of elevated sodium levels in groundwater are erosion of salt deposits and sodium bearing rock minerals, irrigation and precipitation leaching through soils high in sodium, infiltration of leachate from landfills or industrial sites. Factor 2 has a total variance of 15.58% with higher loading for Nitrate, Calcium and Total hardness. The higher loading of nitrate can be attributed to agricultural sources such as fertilizers, animal waste, crop residues and mineralization of soil organic nitrate and on the other hand non-agricultural sources such as septic tanks, effluents containing nitrogen discharged from industries. Factor 3 have 11.92% of the total variance and includes higher loading for Mn and Zn. Manganese and Zinc are found as trace elements in groundwater that occur as crystal structure of minerals found in the rocks. The occurrence of zinc and manganese in groundwater depends upon weathering conditions and their mobility in it. The district is dominated by agricultural practices and these elements get leached into groundwater from micronutrient fertilizers. Manganese gets adsorbed on clay particles, freshly precipitated calcite and silicates. Thus it can be assumed that their infiltration during the monsoon season, which is mainly responsible for recharging of groundwater in the area, is the possible reasons. Factor 4 with total variance of 10.07% shows higher loading for Cu & Fe. Iron loading may be due to dissolution of lithogenic or non-lithogenic materials by infiltrating water. Rock-water interaction, (ferrogenous quartzite, pyrites found in the area) is probably the reason for high loading of iron and copper. Copper loading may also be due to use of micronutrient fertilizers. Factor 5 and factor 6 contributes 6.81% and 5.58% to the total variance with loading of fluoride and chromium respectively. Fluoride commonly originates from dissolution of fluorapitite, fluorite, various silicates and volcanic ash (Hem, 1985). The sources for the fluoride consist of fluorite, which occurs in sedimentary and igneous rocks. Fluorite is also found in granite, gnesis and pegmatites. Amphiboles, such as hornblende and some of the micas, may contain fluoride, which has replaced part of the hydroxide. Fluoride is commonly associated with volcanic, plutonic or fumerolic gases, and in some areas these may be important sources of fluoride for natural water. Because of similarity of charge and radius, substitution of
fluoride for hydroxide ions at mineral surfaces is an obvious possibility. It can be inferred that fluoride in water is due to weathering of rocks. Since there is no natural source of chromium in the study area it may be due waste water pollution from industrial and domestic sources. In summary the six extracted principal components represents four different processes that are responsible for various component loadings: 1. Agricultural sources (fertilizers & micronutrient fertilizers) 2. Geological effects (due to weathering) and other processes (dissolution of halites etc) 3. Wastewater pollution from domestic and industrial effluents 4. Seasonal effects as monsoon season Conclusion The results suggest that natural processes as carbonate and silicate weathering, halite dissolution, ion exchange and reverse ion exchange are the dominant process occurring in the study area. The water has temporary hardness and is mainly of Ca-Mg-HCO3 type. The concentration of heavy metals like Manganese, Cadmium are more than permissible limit at all the places and metals like Lead and Iron are more than permissible limit at few places. Some of the areas have salinity content in groundwater which is not suitable for irrigation without prior treatment. The principle component analysis suggests processes responsible for occurrence of ions and metals in the study area are anthropogenic such as agricultural, water pollution from industrial discharge and natural processes as geological effects and seasonal effects. Infiltration of heavy metals during recharge due to precipitation appears to be the main transport mechanism for enrichment of metals in groundwater. References American Public Health Association (APHA) 1995. Standard methods for the examination of water and waste water, 19th edn. American Public Health Association, Washington DC Fetter, C.W., 1994. Applied hydrogeology, Prentice Hall, New York Hem, J.D., 1985. Study and interpretation of the chemical characteristics of natural water. US Geological Survey Water Supply Paper pp. 2254 Lawrence, F.W., Upchruch, S.B., 1982. Identification of recharge areas using geochemical factor analysis. Groundwater 20 pp. 680-687 Matthess, G., 1982. The properties of groundwater. Wiley, New York, pp. 498 Praus, P., 2005. Water Quality Assessment Using SVD- Based Principal Component Analysis of Hydrological Data, Water SA., 31(4), pp. 417- 422. Singh, S.K., Singh, C.K., Kewat, S.K., Gupta, R., Mukherjee, S., 2009. Spatial-temporal monitoring of groundwater using multivariate statistical techniques in Bareilly District of Uttar Pradesh, India Journal of Hydrology and Hydromechanics, 57(1) pp. 45–54
Fig. 1Study area
Fig. 2 Sampling sites
Fig 3 Spatial Variations of various physico-chemical parameters
Fig. 4 Spatial distribution of factor scores for six factors whose Eigen values are more than one. Factor 1
Factor 2
Factor 3
Factor 4
Factor 5
Factor 6
pH
-0.059
-0.862
0.124
-0.035
0.029
0.261
EC
0.889
0.359
-0.041
0.003
0.221
0.032
Bicarbonate
0.404
0.340
-0.191
0.439
0.533
-0.183
Chloride
0.843
0.228
0.173
-0.142
0.119
0.101
Sulphate
0.869
0.099
0.025
0.026
-0.240
-0.104
Nitrate
0.256
0.690
-0.115
-0.387
-0.076
0.353
Fluoride
-0.117
-0.130
0.398
-0.024
0.668
0.028 -0.038
Calcium
0.008
0.956
0.047
0.005
-0.172
Magnesium
0.623
0.286
-0.002
-0.008
0.420
0.338
Sodium
0.847
-0.074
0.075
0.225
0.241
-0.175 0.040
Potassium
0.674
-0.084
-0.471
-0.238
-0.064
Total Hardness
0.394
0.861
0.027
0.005
0.143
0.188
Cadmium
-0.173
0.051
-0.010
-0.073
-0.668
0.005
Chromium
-0.020
-0.037
-0.050
0.124
-0.003
0.816
Copper
0.048
0.045
0.065
0.797
-0.030
-0.013
Iron
-0.053
-0.191
-0.069
0.839
0.128
0.185
Manganese
0.025
-0.206
0.726
-0.146
0.456
0.064
Lead
0.208
-0.128
-0.657
-0.447
0.008
-0.204
Zinc
0.230
-0.046
0.789
-0.078
-0.008
-0.251
Eigen Value
5.444
2.961
2.265
1.914
1.296
1.062
% of Variance
28.653
15.586
11.922
10.075
6.819
5.589
Cumulative % of Variance
28.653
44.239
56.161
66.236
73.055
78.644
Table 1: Factor analysis of Routine Analysis Parameters
