Colten. (1998) observed the environmental litigation in the USA to establish the historical state ...... exposure tom organic compounds in a drinking water supply.
ASSESSMENT OF GROUNDWATER VULNERABILITY IN AN ALLUVIAL INTERFLUVE USING GIS
Ph.D. THESIS
By HUSSAIN MUSA HUSSAIN
DEPARTMENT OF HYDROLOGY INDIAN INSTITUTE OF TECHNOLOGY ROORKEE ROORKEE-247 667 (INDIA) OCTOBER, 2004
ASSESSMENT OF GROUNDWATER VULNERABILITY IN AN ALLUVIAL INTERFLUVE USING GIS A THESIS Submitted in fulfilment of the requirement for the award of the degree of DOCTOR OF PHILOSOPHY in HYDROLOGY
By HUSSAIN MUSA HUSSAIN
DEPARTMENT OF HYDROLOGY INDIAN INSTITUTE OF TECHNOLOGY ROORKEE ROORKEE-247 667 (INDIA) DECEMBER, 2004
© INDIAN INSTITUTE OF TECHNOLOGY, ROORKEE, 2004 ALL RIGHTS RESERVED
ii
ACKNOWLEDGEMENTS From the formative stages of this thesis, to the final draft, I owe an immense debt of gratitude to my supervisors, Dr. D.C. Singhal, Professor, Department of Hydrology, Dr. H. Joshi, Assoc. Professor, Department of Hydrology and Dr. S. Kumar, Scientist E, National Institute of Hydrology, Roorkee, who inspired me all along the course of work inspite of their busy schedules. The thought provoking discussions that I had with them cleared many concepts and were extremely fruitful in articulating the minute fabric of the work. They kindled in me a passion for research, and taught me among several things, to think by myself. This research venture could be taken up and sustained only because of their constant encouragement and worthy guidance. I wish to thank all the other faculty members of the Hydrology Department for generously providing the research facilities for carrying out the work and for their cooperation extended throughout the course of this work viz. Professor B.S. Mathur, Professor D.K. Srivastava, Professor Ranvir Singh, Professor N.K. Goel, Dr. M. Perumal and Dr. D.S. Arya. The prompt assistance rendered by Mr. J.K. Sharma, Mr. D.P. Sharma, Mr. M.L. Gupta, Mr. A.K. Singhal, Mr. J. Ali and other office staff of the Department of Hydrology is thankfully acknowledged. I would like to thank all the members of National Institute of Hydrology, Roorkee, especially Dr. Bhishm Kumar, Dr. M.S. Rao and Mr. S.K. Verma not only for sparing their time and showing extreme patience, but also for providing the available data. In addition, I would also like to acknowledge the help of Mr. Mohd. Furqan Ullah, the librarian of the Institute. I would like to thank Mr. S.K. Malhotra in Groundwater Department Division, Roorkee, for providing the geohydrologicl & meteorological data. I also got benefitted greatly by the cooperation from many colleagues here at the Department of Hydrology like Mr. Abdulzahra Alhello, Mr. R.K. Panigrahi and Mr. Vinay Kumar Sharma. I thank them sincerely. I am obliged to Indian government and the Indian Council for Cultural Relations (ICCR) for providing me this opportunity by sponsoring my name to pursue this research
iii
work. I sincerely thank the Director, ICCR and all the staff members for their valuable support and cooperation. I wish to express my gratitude to all my teachers and colleges back in my country and in India. Also, I would like to thank all the Indian people for their kind cooperation in making my four year stay in India pleasant. I thank, Dr. Abdul Hameed M. Jawad Al Obaidy and Dr. A.K. Seth from the bottom of my heart for illuminating, helping and educative discussions. Last, but not the least, because it is the kind of gratitude that is hardest to express in words, I would like to thank my sisters Mariam, Samera & Nathira, my brother-in-law Ahmad, my nephews and my close friends who deserve an award for their patience, understanding and prayers during my study and the writing of this thesis. I appreciate their support without which, my four year stay in India would not have been possible. I thank them for their faith in my capability, for offering me a soothing relief while being rational and realistic, and finally for always having something encouraging to say even from thousands of miles away.
Hussain Musa Hussain
iv
ABSTRACT The growth of human population often corresponds with change in land use, including expansion of urban areas, which necessitates increasing the available amount of drinking water. As the surface water sources are more amenable to pollution, it has become necessary to use groundwater at an increasing rate. Groundwater is normally abundant in the alluvial region where the urban areas are often located. Such areas face a greater risk of pollution of groundwater due to several factors. Keeping these aspects in view, groundwater vulnerability studies have been carried out in a selected alluvial area of northern India. The study area is situated in the upper part of the Ganga-Yamuna interfluve, and is considered to be the major recharge zone for the deep aquifers of the region. The aim of this study is to identify the groundwater vulnerability in the area so that the groundwater can be protected from pollution. In present work, it was envisaged to review the methods currently available for assessment of the groundwater vulnerability and to develop an appropriate method suitable for the alluvial aquifers of the Ganga-Yamuna interfluve area. Attempts were also to develop a multipurpose database in GIS environment, and to validate the developed method by comparing its findings against the observed water quality characteristics of the region. The study area is the northern part of the vast Indo – Gangetic Plain in India and lies o
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between latitudes 29 33 51 to 30 19 10 N and longitudes 77 06 20 to 78 20 15 E with a total geographical area of approximately 4900 km2. Administratively, the study area covers the districts of Hardwar in Uttaranchal and Saharanpur in Uttar Pradesh, and has a population of about 4.3 million as per Census of 2001. The Ganga River and its tributary, the Yamuna, are the two major rivers in the region. These two rivers are perennial in nature and form the eastern and western boundaries of the study area. Other small intermittent streams like Ratmau River, Solani, and Banganga – the tributaries of the Ganga, and Hindon River, a tributary of the Yamuna, drain the area. A network of canals exists in the study area for meeting the irrigation needs; the notable being the Upper Ganga canal and Eastern Yamuna canal along with their distributaries and branches. The climate of the area is humid and subtropical. The rains occur mainly during July to middle of September with annual average rainfall of about 100 cm.
v
LISS-III data of India Remote Sensing (IRS) satellite was used to prepare the land use map of the study area. On the basis of these data, the whole area was classified into three land use classes, i.e. urban, rural (including agriculture), and forest. The field monitoring was carried out during January 2002 to December 2003, through extensive field surveys covering entire study area. Groundwater samples (136 no.) were collected from various sites well distributed among various land use types in the study area and. Also, soil samples (48 no.) were collected from different sites covering all the land use categories. Geological and hydrogeological settings were examined for the study area. The depth to groundwater was monitored from the observation wells (119 no.). Besides, historic data available for past 10 years was also taken from Uttar Pradesh Groundwater Department, Roorkee. Groundwater recharge was estimated by Tritium tagging method at five locations during the period from Jun 2002 to Nov 2002. These, alongwith earlier data available at National Institute of Hydrology, Roorkee were used. A Digital Elevation Model (DEM) was prepared by digitization of bench mark and topographic points from relevant maps. The DEM was in turn, used for the construction of the slope map. The depth to groundwater map of the study area was generated from the observed data. The soil samples were analyzed to ascertain the soil texture and to prepare the soil map. This map showed that the most of the northern part, paleochannels and active floodplains of rivers have soils of sandy loam texture whereas the remaining part of the study area is covered by soils having silty loam. Considering the soil texture as an appropriate source factor of variation, the average recharge percentage was calculated as 6.3 % in silty loam soils and 15.5 % in sandy loam soils. The software RockWorks99 was used to prepare the geological fence diagram. Hydraulic conductivity map was prepared using Hydraulic conductivity data in GIS environment. The flow direction map, showed that the ground water flows from the northern and northeast part (the hilly area) to southern and southwestern part and follows the general topography of the study area. Hindon River and Solani River are fed by groundwater in the southern part. The hydraulic conductivity in the study area varies between 10 m/day and 48 m/day. The south-western part shows higher values in general, whereas the north-western part shows lower values. The quality of the soils in the study area was analyzed. The results exhibit that in general the urban soil has the highest values of all the physicochemical parameters followed by the rural and forest soils indicating an important role of urban activities The groundwater samples were analyzed for various physicochemical parameters like pH, EC, TDS and major ions (Ca2+, Mg2+, Na+, K+, HCO3-, Cl-, SO42-, CO32-, F-), nutrients (NO3-, vi
PO43-), total organic carbon (TOC) and heavy metals (Cd, Fe, Mn, Ni, Zn, Pb, Cr, Cu). In general, the groundwater quality in the study area does not indicate much variation between postmonsoon and premonsoon periods. Further, all major ions, except NO3-and K+, show an increasing trend from north to south and southwest. The groundwater is generally alkaline in nature with pH ranging from 7.01 to 8.90. The TDS values range between 117 to 1002 mg/l. HCO3- is the dominant major anion followed by Cl->SO42-> NO3->F->PO4 3- whereas Ca2+ is the major cation followed by Na+>Mg2+>K+. The heavy metal Zn is dominant followed by Mn>Fe>Pb>Cd. The calcium-bicarbonate facies are dominant in the groundwater of the study area, indicating that a substantial part of ground water is derived as recharge from the Bhabar zone in the north. Generally, the concentration of chemical parameters, except NO3- and SO42-, follow a decreasing trend for urban > rural > forest land use categories, whereas NO3- and SO42- follow the decreasing trend for forest > urban > rural land use. Besides, a few samples show some parameters like total alkalinity (TA), nitrate, TDS, and Calcium having higher ranges than acceptable limits of Indian standard (BIS: 10500) for drinking water. Among the heavy metals, tolerance limits of cadmium, manganese, lead and iron are violated in several samples. On the basis of demonstrated violation of the acceptable limits, the quality parameters like TDS, Ca2+, Total Alkalinity (TA), NO3-, Cd, Mn, Pb, and Fe have been selected for computation of an Index of Aquifer Water Quality (MIAWQ), utilizing the framework as proposed by Melloul and Collin (1998). The index was modified for the present case in the sense that the weights to these eight parameters were, however, assigned as per their analytical hierarchy in the human health (effecting) significance and not in a subjective manner (as attempted in the original work of Melloul and Collin). The values of MIAWQ (modified index) show an increase from north, north-east to south and south-western parts of the study area. The groundwater vulnerability mapping was carried out using two approaches viz. standard DRASTIC method and a modified DRASTIC-MOD method the study area. The DRASTIC parameters were evaluated in GIS environment as seven restart-map layers. The rating percentages were subsequently added to obtain the total cell rating. The DRASTIC index in the study area ranges from 122 to 183. The east and south-west corners of the study area and the paleochannels in the southern part show higher vulnerability index values. While applying the DRASTIC method on the present study area for assessment of vulnerability, following limitations were noticed: vii
!
The proposed rating scale for the parameter “Impact of vadose zone” did not adequately address the implicit variability among the geological constituents of the vadose zone viz. sand gravel, silt and clay, and the resulting complexity.
!
The “Hydraulic conductivity” values observed in study area mostly surpassed the highest limit/range of the rating scale rendering observed spatial variability meaningless with respect to the aquifer vulnerability.
!
Observing definitive signals about the influence of land use (urban > rural and agricultural > forest) on the soil and groundwater quality, the parameter “land use” appeared to also have on important bearing the status of aquifer vulnerability alongwith other parameters proposed earlier. Due modifications were incorporated in the original DRASTIC model in view of the
above and the modified (DRASTIC-MOD) index map was sub-divided into four classes, (i) 100 – 119 with low risk in the north part of the study area (forest area), (ii) 120 – 159 with moderate risk in the Bhabar zone with deep depth to groundwater and forest area, (iii) 160 – 199 with high risk in most parts of the study area, these values resulting mainly from cumulative effects of rural and agricultural land use, low to moderate depth to groundwater and high recharge coefficient. (iiiv) Indices of 200 and above with very high vulnerability in some parts of the study area, reflecting the shallow depth to groundwater, high recharge and high urbanization related activities. DRASTIC-MOD indicates high vulnerability in the southern parts of the study area indicating higher risk of groundwater pollution. In order to validate the projected risk of vulnerability with actual groundwater quality statues in the region, DRASTIC and DRASTIC-MOD maps were correlated with modified Index for Aquifer Water Quality (MIAWQ). The MIAWQ showed high significant correlation with DRASTIC-MOD map. The differences observed in the spatial distribution of vulnerability estimates obtained from both the methods (DRASTIC and DRASTIC-MOD) indicate that in the areas with existing well defined land use practices, vulnerability estimation should necessarily include “land use” as a parameter. Further, in view of a good correlation between the DRASTICMOD and MIAWQ maps, it may be inferred that the “risk of vulnerability” corresponds quite well with the existing water quality scenario in the study area, a finding not commonly reported by researchers earlier. This also highlights the need of initiating corrective measures in many parts of the study area as well as to establish a suitable monitoring protocoal to detect adverse quality trends in the future.
viii
CONTENTS Title
Page No.
CANDIDATE’S DECLARATION ACKNOWLEDGEMENTS ABSTRACT LIST OF TABLES LIST OF FIGURE LIST OF SYMBOLS ABBREVIATIONS / TECHNICAL TERMS
i iii v xv xix xxiii xxv
CHAPTER ONE: INTRODUCTION 1.1 INTRODUCTION
1
1.2 GROUNDWATER IN INDIA
1
1.3 GROUNDWATER VULNERABILITY
2
1.4 RATIONALE OF PRESENT STUDY
2
1.5 OBJECTIVES
3
1.6 APPROACH
3
1.7 ORGANIZATION OF THESIS
4
CHAPTER TWO: LITERATURE REVIEW 2.1 GROUNDWATER VULNERABILITY
5
2.1.1 Definition of Groundwater Vulnerability
5
2.1.2 Assessment of Groundwater Vulnerability
5
2.1.3 DRASTIC Method
9
2.1.3.1 Definition of DRASTIC method
9
2.1.3.2 Modification of DRASTIC and other similar approaches
16
2.1.4 Groundwater Vulnerability Assessment in India 2.2 GROUNDWATER QUALITY: ASSESSMENT AND REPRESENTATION
19 19
2.2.1 Assessment
19
2.2.2 Representation: Graphical and Numerical
27
2.3 GIS: CONCEPT AND APPLICATION IN GROUNDWATER VULNERABILITY ASSESSMENT
ix
34
2.4 DATABASE SYSTEM DESIGN AND MANAGEMENT
36
2.5 GROUNDWATER RECHARGE AND ITS ESTIMATION
37
CHAPTER THREE: STUDY AREA 3.1 GENERAL DESCRIPTION
39
3.1.1 Location
39
3.1.2 Climate and Rainfall
39
3.2 DEMOGRAPHIC CHARACTERISTICS
39
3.3 GEOLOGY
40
3.3.1 Siwalik Range
40
3.3.2 Bhabar Formation
40
3.3.3 Tarai Formation
44
3.3.4 Gangetic Alluvial Plain
44
3.4 REGIONAL GEOHYDROLOGY
45
3.5 WATER RESOURCES
46
3.5.1 The Rivers
46
3.5.1.1 Ganga River
46
3.5.1.2 Yamuna River
46
3.5.2 Canal System
47
3.5.3 Surface Water Resources
47
3.6 SOIL
47
3.7 AGRICULTURAL PRACTICES
48
3.8 SOCIO ECONOMIC FEATURES
48
3.9 CHARACTERISTICS OF WASTE GENERATION
48
CHAPTER FOUR: MATERIALS AND METHODS 4.1 SOIL MONITORING PROGRAMME
53
4.1.1 Collection of Samples
53
4.1.2 Soil Texture Analysis
53
4.1.2.1 Mechanical analysis by wet sieving
x
54
4.1.2.2 Mechanical analysis by pipette
54
4.1.2.3 Using USDA textural triangle
55
4.1.3 Soil Quality Analysis
56
4.2 GROUNDWATER MONITORING PROGRAMME
57
4.2.1 Preparation for Sampling
58
4.2.2 Water Table Level Measurements
58
4.2.3 Purging the Well or Hand Pump
59
4.2.4 Collection, Storage and Analysis of Samples
59
4.3 FIELD ESTIMATION OF GROUNDWATER RECHARGE
59
4.3.1 Tracer Injection and Site Sampling
61
4.3.2 Laboratory Experiments
61
4.3.2.1 Soil moisture content
61
4.3.2.2 Estimation of field capacity
63
4.3.2.3 Measurement of tritium activity in the samples
63
CHAPTER FIVE: DEVELOPMENT OF THE DATABASE 5.1 INTRODUCTION
65
5.2 THE DATABASE APPROACH
65
5.3 SOURCE AND FORMAT
67
5.4 DATABASE CONSTRUCTION
67
5.4.1 Location Database
71
5.4.2 Water Table Monitoring Database
73
5.4.3 Soil Database
75
5.4.4 Lithologs Database
76
5.4.5 Groundwater Recharge Database
77
5.4.6 Hydraulic Conductivity Database
78
5.4.7 Rainfall Database
79
5.4.8 Water Quality Database
80
xi
CHAPTER SIX: PREPARATION OF THEMATIC MAPS 6.1 GEOGRAPHIC INFORMATION SYSTEM (GIS)
83
6.2 DATA GENERALIZATION
85
6.2.1 Geocoding of Topographic Maps
85
6.2.2 Satellite Data Georeferencing
86
6.2.3 Map Projection
87
6.3 GENERATION OF THEMATIC LAYERS
90
6.3.1 Digital Elevation Model and Slope
90
6.3.2 Depth to Groundwater
90
6.3.3 Soil Map
92
6.3.4 Flow Direction
92
6.3.5 Drainage Networks
102
6.3.6 Hydraulic Conductivity
165
6.3.7 Land Use
165
6.3.8 Net Recharge
106
6.3.9 Hydro-Geological Setting
112
6.3.9.1 Cross section A-B
117
6.3.9.2 Cross section C-D
118
6.3.9.3 Cross section E-F
121
6.3.9.4 Cross section A-E
122
6.3.9.5 Cross section G-H
122
6.3.9.6 Cross section B-F
122
CHAPTER SEVEN: SOIL AND GROUNDWATER QUALITY 7.1 STUDY PLAN
129
7.2 SOIL QUALITY
129
7.2.1 Physical Properties
129
7.2.2 Major Ions
130
7.2.3 Nutrients
130
7.2.4 Heavy Metals
131
xii
7.3 GROUNDWATER QUALITY
143
7.3.1 Physical Properties
143
7.3.2 Major Ions
145
7.3.3 Nutrients
147
7.3.4 Heavy Metals
159
7.3.5 Groundwater Facies
169
7.4 DEVELOPMENT OF THE WATER QUALITY INDEX FOR THE STUDY AREA 7.4.1 Analytical Hierarchy Process (AHP)
171
7.4.2 Application of AHP: Calculation of the MIAWQ Parameter Weights
177
7.4.3 Calculation of the Final MIAWQ Map Using GIS
177
174
CHAPTER EIGHT: GROUNDWATER VULNERABILITY ASSESSMENT 8.1 ASSESSMENT AND MAPPING OF AQUIFER VULNERABILITY
185
8.1.1 Depth to Groundwater
185
8.1.2 Net Recharge
186
8.1.3 Aquifer Media
188
8.1.4 Soil Media
188
8.1.5 Topography
191
8.1.6 Impact of Vadose Zone
192
8.1.7 Hydraulic Conductivity
192
8.1.8 Consolidation and Computation of DRASTIC Index
193
8.2 LIMITATIONS OF DRASTIC MODEL AND MODIFICATIONS
199
8.2.1 Impact of the Vadose Zone
199
8.2.2 Hydraulic Conductivity
203
8.2.3 Land Use Parameter
206
8.2.4 Generation of DRASTIC-MOD map
211
8.3 COMPARISON AND VALIDATION 8.3.1 Comparing the DRASTIC and DRASTIC-MOD Results
xiii
211 211
8.3.2 Validation of DRASTIC and DRASTIC-MOD against the Groundwater Quality Index (MIAWQ)
212
CHAPTER NINE: SUMMARY AND CONCLUSIONS
221
REFERENCES
227
ANNEXURE- I: TRITIUM TAGGING DETAILS
259
ANNEXURE- II: PHOTOGRAPHS
267
Plate A: Land use Plate B: Monitoring and Analysis
xiv
LIST OF TABLES Table No. 2.1 2.2
Title Glossary of terms related to vulnerability
Page No. 6
2.3
Selected methods used in United States to Evaluate Groundwater 10 Vulnerability to contamination Weights for DRASTIC parameters 15
2.4
Reported modifications in the DRASTIC method
16
2.5
Methods similar to DRASTIC method
17
2.6
23
2.10
State wise consumption of nitrogenous fertilizers in India compared with NO3- in groundwater Physicochemical data of surface water, groundwater and wastewater in premonsoon and postmonsoon in District Haridwar, India Statistical summary of chemical analysis of water samples from shallow aquifers of Kali-Ganga sub-basin, India Statistical summary of chemical analysis of water samples from shallow aquifers of Roorkee Town Variables and Weights for Horton's Water Quality Index
2.11
Number of scores for water quality parameters and MAC
33
3.1
Population, area and density of the Saharanpur and Haridwar districts
43
3.2
The average value for aquifer parameters
46
4.1
Pipette withdrawal times calculated from Stokes law
56
4.2
Analytical methods for selected soil quality parameters
57
4.3
Analytical methods for selected water quality constituents
60
4.4
Range of available water holding capacity of soils
63
5.1
Sources and types of the data employed in this study
68
6.1
Soil table
101
6.2
List of experimental sites along with injection date, sampling date and data 111 sources Rainfall data 111
2.7 2.8 2.9
6.3
xv
24 26 26 28
6.4
Applied Irrigation
112
6.5
Legend of the geological units occurring in the Log type table
117
6.6
Well code, location, altitude and well depth for wells used in the six cross 121 sections in the study area Statistical summary of Soil-quality data 132
7.1 7.2 7.3
Comparison of heavy metal concentration data (mg/kg) in the study area 134 with other data from literature Classification of soils 140
7.4
Statistical summary of groundwater quality data
144
7.5
Statistical summary of groundwater quality data for different land use
146
7.6 7.7
Violation of Indian drinking water quality standards by groundwater 147 samples Samples of each land use falling in various classes of the piper diagram 169
7.8
Percentage and Violation of samples exceeding the Indian standards
173
7.9
Scale of relative importance
176
7.10
RCI values for different values of n
177
7.11 7.12
Classification of water quality parameters on the basis of human health 179 significance Analytic Hierarchy Process matrix 183
8.1
Assigned weights for DRASTIC parameters
186
8.2
Ranges and Ratings for Depth to groundwater
187
8.3
Ranges and Ratings for net Recharge
187
8.4
Ranges and Ratings for Aquifer media
188
8.5
Ranges and Ratings for Soil media
191
8.6
Ranges and Ratings for Topography
191
8.7
Ranges and Ratings for Impact of vadose zone
192
8.8
and Ratings for Hydraulic Conductivity
193
8.9
Color codes DRASTIC indexes
193
xvi
8.10
Impact of vadose zone range and rating for different strata type
8.11 8.12
Original (Aller et al., 1987 b) and modified ranges for hydraulic 203 conductivity Land use categories and the rating 206
8.13
Ranges and ratings for nitrogen fertilizer
207
8.14
Ratings of land use categories as modified by Secunda et al., (1998)
208
8.15
Land use categories ratings
208
8.16
217
8.17
Comparison of the number of pixel, the area in km2 and the area in percentage in the images representing the vulnerability classes obtained DRASTIC and DRASTIC-MOD Minimum, maximum, average and standard deviation of the three maps
217
8.18
Correlation matrix of the three maps
217
xvii
200
xviii
LIST OF FIGURES Figure No. 2.1
Title Definition of integrated vulnerability
Page No. 8
2.2
Visual explanation of Parameters of DRASTIC approach
11
2.3
13
2.4
Conceptual illustration of the DRASTIC method for aquifer vulnerability assessment GIS data model
2.5
GIS Application in PC-Based Environment
35
3.1
Index map of the study area
41
3.2
Monthly variation in rainfall data averaged for 18 years (1985-2002).
43
3.3
The variation in population for the 1991 and 2001 census
43
3.4
Geological map of the study area
44
4.1
The USDA soil textural triangle
56
4.2
Typical field sampling form
58
4.3
62
5.1
Diagram of Systematic injection layout and implements for artificial tritium injection at test site Schematic diagram of the system used for extraction of pore water from soil sample for tritium analysis Structure of an Environmental Data Base Management System
5.2
Organization of information as records in a Date file
66
5.3
Organization of a Data Base using the Hierarchical Data Model
67
5.4
Opening page of the database system
68
5.5
The database environment
69
5.6
Block table
71
5.7
Input Form for Location table.
72
5.8
Few records from Location table
72
4.4
xix
35
64 65
5.9
Input form for Observation well table
74
5.10
Input Form and some records for Water table data
74
5.11
Input Form for Soil table
75
5.12
Input Form for Soil type table
76
5.13
Input Form for Boreholes table
77
5.14
Input Form for Borehole log table
77
5.15
Input from for recharge table
78
5.16
Input form for Hydraulic conductivity table
78
5.17
Input form for Rainfall table
79
5.18
Input form for Monthly rainfall table
80
5.19
Input form for Water quality database
81
5.20
Water quality table
82
6.1
Geographic Information System Manipulation
84
6.2
84
6.3
The planning Process Geographic Information processing begins and ends with the real world Topographic map index
6.4
The Geographic grid
86
6.5
Digital Data of Satellite IRS-1D LISS-III Sensor
87
6.6
Map projections
89
6.7
Universal Transverse Mercator system
89
6.8
Index map of elevation points
91
6.9
Digital elevation Model (DEM) for study area
93
6.10
Slope map
93
6.11
Index map of the observation points
95
6.12
Distribution of depth to groundwater in postmonsoon
95
xx
85
6.13
Distribution of depth to groundwater in premonsoon
97
6.14
Distribution of groundwater fluctuation in premonsoon
97
6.15
Index map of location of test sites
99
6.16
Distribution of soil texture
99
6.17
Groundwater flow direction
103
6.18
Drainage network and water bodies
103
6.19
Distribution of hydraulic conductivity
107
6.20
Land use categories
107
6.21
Distribution of Recharge Percentage (from Tritium Tagging)
109
6.22
Distribution of annual rainfall (mm)
109
6.23
Distribution the mean of annual water application by irrigation
113
6.24
113
6.25
Distribution the mean of annual total water application (including rainfall) Distribution of net groundwater recharge
6.26
Geological map and index map of boreholes
115
6.27
Sub-surface correlation chart (section AB)
119
6.28
Sub-surface correlation chart (section CD)
119
6.29
Sub-surface correlation chart (section EF)
125
6.30
Sub-surface correlation chart (section AE)
125
6.31
Sub-surface correlation chart (section GH)
127
6.32
Sub-surface correlation chart (section BF)
127
7.1
Maps of soil quality parameters for the study area
135
7.2
Comparison of urban soil characteristics between various land use types
139
7.3
Soil classification maps indicating spatial variability of metals
141
7.4
Maps of groundwater quality parameters for the study area
149
xxi
115
7.5
Statistical distributions for different land use
157
7.6
Statistical distributions of metals for different land use
161
7.7
Maps of heavy metals for the study area
163
7.8
170
7.9
Piper Diagram of the chemistry of groundwater samples in the study area Percent of samples exceeding the Indian Standards
7.10
Xi map for the eight parameters
181
7.11
Relative index of water quality (MIAWQ) map
183
8.1
Depth to Groundwater rating map
189
8.2
Net Recharge rating map
189
8.3
Soil rating map
195
8.4
Slope rating map
195
8.5
Conceptual illustration of the DRASTIC method for the study area
173
197
8.6
Final DRASTIC index rating map
201
8.7
The impact of vadose zone rating using harmonic mean
201
8.8
204
8.10
Hydraulic conductivity and DRASTIC index correlation with a frequency analysis of hydraulic conductivity and DRASTIC index (2x, 3x, 4x, 5x, 6x and 7x) Alternate equation and correlation coefficients between hydraulic conductivity and DRASTIC index (2x, 3x, 4x, 5x, 6x and 7x) Hydraulic conductivity modified rating
8.11
Land use map
209
8.12
DRASTIC-MOD map
213
8.13
213
8.14
Comparison of the percentage areas in the vulnerability classes using DRASTIC and DRASTIC-MOD Cross section A-B
8.15
Composition of spatial variability along a common cross section
218
8.16
Regression between MIAWQ, DRASTIC and DRASTIC-MOD for cross section A-B
8.9
xxii
A-B
205 209
215
219
LIST OF SYMBOLS Symbol
Meaning
Unit
θv
Effective volumetric moisture content
-
Ar
Aquifer media rating
-
Aw
Aquifer media weight
-
CI
Consistency index
-
Cr
Hydraulic Conductivity rating
-
CR
Consistency ratio
-
Cw
Hydraulic Conductivity weight
-
Dr
Depth to water table rating
-
DS
Weight of dispersant
Dw
Depth to water table weight
ED
Weight of the clean evaporating dish
gm gm
Ir
Impact of Vadose Zone rating
-
Iw
Impact of Vadose Zone weight
-
Random Consistency index
-
Rr
Net recharge rating
-
Rw
Net recharge weight
-
SC
Weight of the silt and clay in the evaporating dish
Sr
Soil media rating
-
Sw
Soil media weight
-
Tr
Topography rating
-
Tw
Topography weight
-
Wi,
Weight for any given parameter
-
Wmax
Maximum possible weight
-
Wri
relative value of Wi/Wmax
-
Ymax
Maximum possible rating for any parameter
-
Yri
value of Yi/Ymax
-
λmax
Maximum eigenvalue
-
RCI
xxiii
gm
xxiv
ABBREVIATIONS / TECHNICAL TERMS Notation AHP
Analytic Hierarchy Process
APHA
American Public Health Association
AWWA
American Water Works Association
BARC
Bombay Atomic Research Center
C.G.
Center of gravity
DBMS
Database management system
DEM
GWD
Digital Elevation Model, a file with terrain elevations recorded at the intersections of a fine grid and organized by quadrangle to be the digital equivalent of the elevation data on a topographic based map. Modified DRASTIC method DRASTIC Index Geographic Information System. A computer system of hardware and software that integrates graphics with database and allows for display, analysis, and modeling. Global Positioning system, a system developed by the U.S. Department of Defense based on 24 satellites orbiting the Earth. Inexpensive GPS receiver can accurately determine once position on the Earth's surface. Groundwater Department (Uttar Pradesh)
H2O2
Hydrogen Peroxide
MIAWQ
Modified Index of Aquifer Water Quality
ID
Identification Code
IIRS
Indian Institute of Remote Sensing
IIT Roorkee
Indian Institute of Technology, Roorkee
MCDM
Multi-criteria decision-making
Nallahs
Open sewers
NBSSLUP
National Bureau of Soil Survey and Land Use Planning
ND
Not detected
NIH
National Institute of Hydrology (Roorkee)
NO3-N
Nitrogen as NO3
PI
Pollution Index
PO4-P
Phosphorous as PO4
DRASTIC-MOD DI GIS GPS
Meaning
xxv
Monsoon
SOI
Term monsoon is synonymous with the summer rainy season, which makes or breaks the lives of hundreds of millions of farmers on the subcontinent. The postmonsoon season begins with a slow withdrawal of the monsoon. This retreat leads to an almost complete disappearance of moist air by mid-October, thus ushering in generally cool, clear, and dry weather. The premonsoon season generally occurs during April and May; it is characterized by the highest temperatures Survey of India
TA
Total Alkalinity (HCO3- + CO32-)
TDS
Total dissolved solids
TOC
Total organic carbon
TP
Total phosphorus
TWD
Tube Well Division (Saharanpur and Haridwar)
U.P.
Uttar Pradesh State, India
U.T.
Uttaranchal State, India
USDA
United State Department of Agriculture
USEPA
United States Environmental Protection Agency
UTM
Universal Transverse Mercator
'W'
Scintillation cocktail
WEF
Water Environment Federation
Postmonsoon
Premonsoon
xxvi
CHAPTER - I
INTRODUCTION
1.1 INTRODUCTION Groundwater is a precious source of drinking water in many parts of the world. If contaminated, groundwater may pose a serious health hazard. Groundwater can be contaminated through a wide variety of human and other activities, which may include on land disposal of waste materials and sewage, and the leaching of fertilizers and pesticides. Since late 1970s, occurrences of nitrate, bacteria and pesticides in groundwater have exhibited significant increase in concentration and have stimulated research on the subsurface fate of contaminants. Prevention of groundwater contamination is the key to efficient and effective environmental management, as the groundwater remediation is expensive and slow. In order to protect groundwater resources, areas prone to contamination by human activity need to be delineated, which can be best accomplished through groundwater vulnerability assessment (National Research Council, 1993).
1.2 GROUNDWATER IN INDIA India has an area of about 3.3 x 106 km2 and the population of the country crossed one billion mark in the year 2000. The average annual precipitation is about 4000 km3 out of which about 700 km3 evaporates directly, 1150 km3 runs off as surface water and out of the remaining, about 2150 km3 percolates into the soil, 1650 km3 is used up in compensating soil moister deficiency and the remaining 500 km3 only joins the groundwater reservoir (Singh, 1997). Groundwater is the main source of water available for human consumption. Over the years due to swelling population, increasing industrialization and expanding agriculture, the demand for water has multiplied. Simultaneously the available per-capita water resources have been reduced due to the declining groundwater table, combined with inefficient use of water. The utilizable annual water resources of India are about 1140 km3 out of which 690 km3 is the surface water component and the remaining 450 km3 is the groundwater portion. Out of the total water utilized in the country, 84 % is used for irrigation, about 4.4 % for 1
drinking and municipal use, 4 % for industry, 3.6 % for energy development and the remaining 4 %for other purposes. The annual water demand in India was 750 km3 for year 2000 and the country would require about 1050 km3 by the year 2025 because of increased demand of water required to grow more food grains for the increasing population, which is estimated to reach upto 1.25 billion by the year 2025 (Singhal, 2002). Alongwith the indicated estimates of quantity, the necessity of conserving and providing good quality water to the people to meet their needs can also not be underscored. This calls for necessary steps for chalking out suitable strategies for planning, development, conservation and management of available water resources in an optimal way.
1.3 GROUNDWATER VULNERABILITY As water moves through the ground, natural processes are responsible for attenuation of concentration of many contaminants including harmful microorganisms. The degree to which attenuation occurs is dependent on the type of soil and aquifer characteristics, as well as the type of contaminant and the associated activity. In general, the term groundwater vulnerability is used to represent the intrinsic characteristics of the aquifer which determine whether it is likely to be affected by an imposed contaminant load (National Research Council, 1993). There are two categories of vulnerability viz. intrinsic vulnerability, which depends solely on the properties of the groundwater system, and specific vulnerability, where these intrinsic properties are referenced to a particular contaminant or human activity. Vulnerability assessment is based on the likely travel time for water to move from the ground surface to the water table. The greater the travel time, the greater is the opportunity for contaminant attenuation. Aquifer vulnerability can also be quantified by employing appropriate mathematical framework and further subdivided into broad classes like very high, high, low and very low, depending upon the governing criteria.
1.4 RATIONALE OF PRESENT STUDY Growth of human population often corresponds with change in land use, including expansion of urban areas, which necessitate increasing the available amount of drinking water. As the surface water sources are more amenable to pollution, it has become necessary to use groundwater at an increasing rate. Groundwater is normally abundant in the alluvial region where the urban areas are often located. Such areas face a greater risk of pollution of
2
groundwater due to several factors. Keeping these aspects in view, it was planned to carry out groundwater vulnerability studies in the Ganga-Yamuna interfluve area of northern India. The study area is an alluvial plain in the upper part of the Ganga-Yamuna interfluve, and is considered to be the major recharge zone for the deep aquifers of the region. The aim of this study is to identify the potential for groundwater contamination in the area so that the groundwater can be protected from contamination.
1.5 OBJECTIVES In the present work, it is proposed to review the methods currently available for assessment of the groundwater vulnerability, and to develop an appropriate method suitable for the alluvial aquifers of the Ganga-Yamuna interfluve area. Development of a multipurpose database in GIS environment is envisaged for this purpose.
It is further
proposed to validate this method by comparing the findings with the observed water quality characteristics of the region.
1.6 APPROACH The approach for the above study comprises of the following: •
Review available approaches and methods of aquifer vulnerability assessment.
•
Design and development of a database system for effective storage and processing of information.
•
Characterization of the geological and hydro-geological setting necessary for applying the vulnerability analysis.
•
Field investigation of soil, ground water quality and land use in the study area.
•
Development of an index for groundwater quality.
•
Preparation of the aquifer vulnerability map of the study area employing the available approach.
•
Development of a more appropriate method for vulnerability assessment.
•
Comparison of the groundwater vulnerability maps and validation using the existing groundwater quality scenario.
3
1.7 ORGANIZATION OF THESIS This thesis is organized into the following chapters: Chapter I
identifies the purpose and motivation for conducting this research. Establishes the objectives and highlights the approach for this thesis.
Chapter II
summarizes the literature review of the groundwater vulnerability analysis and groundwater quality, with particular emphasis on empirical approaches.
Chapter III
provides basic details about the study area viz. its location, climate, geologic and hydrogeologic features.
Chapter IV
describes the methods employed in the study.
Chapter V
describes the design and development of the database system.
Chapter VI
describes the preparation of thematic maps and the underlying processes (employing GIS), proposed to be analyzed in the research work.
Chapter VII
describes the soil and groundwater quality assessment and development of the groundwater quality index.
Chapter VIII
explains the preparation of the aquifer vulnerability map and development of a new method for vulnerability assessment. Its comparison with the prevailing groundwater quality scenario is also discussed.
Chapter IX
presents a summary conclusion and recommendations for further research.
4
CHAPTER - II
LITERATURE REVIEW
Groundwater vulnerability assessment and application requires understanding of various aspects like concept of vulnerability and its assessment; groundwater quality, its representation and its status evaluation; hydrogeological interpretation and assessment of groundwater recharge. Further, the vulnerability can be best studied in GIS environment for which suitable database needs to be developed. This chapter deals with a review of literature available on the above aspects.
2.1 GROUNDWATER VULNERABILITY 2.1.1 Definition of Groundwater Vulnerability The concept of groundwater vulnerability to contamination has different meaning for different groups of people. In its broadest context, groundwater vulnerability refers to whether or not an underlying aquifer will become contaminated as a result of activities at the land surface, or it’s a measure of how easy or hard it is for pollution or contamination at the land surface to reach a producing aquifer. Vulnerability is high if natural factors provide little protection to shield groundwater from contaminating activities at the land surface, and it is low, if natural factors provide relatively good protection and if there is little likelihood that contaminating activities will result in groundwater degradation (Harter and Walker, 2001). Some authors have attempted to avoid the term vulnerability altogether and substituted it with similar other terms like sensitivity. The related terminology has been separately explained in a glossary given in Table 2.1.
2.1.2 Assessment of Groundwater Vulnerability All ground water is vulnerable. Vulnerability is not an absolute property, but a relative indication of where contamination is likely to occur; no ground water, with possible exceptions such as deep sedimentary basin brines, is invulnerable. Furthermore, it may be necessary to consider long term effects on ground water quality, perhaps over decades, in carrying out vulnerability assessments. It is a probability (i.e., "the tendency or likelihood") of contamination occurring in the future, and thus must be inferred from surrogate
5
Table 2.1: Glossary of terms related to vulnerability Author/s & Year Albinet and Margat, 1970
Aquifer vulnerability
Villumsen et al., 1983
Vulnerability (1)
Vrba and Zoporozec, 1994 Bachmat and Collin, 1987 Foster, 1987 Foster, 1987
Term
Vulnerability (2) Groundwater vulnerability Aquifer pollution vulnerability Groundwater Pollution Risk
6
Vulnerability of a Hydrogeological system U.S. General Hydrogeological Accounting Office, 1991 Vulnerability U.S. General Total Vulnerability Accounting Office, 1991
The ability of this system to cope with external, natural and anthropogenic impacts that affect its state and character in time and space.
Sotornikova and Vrba, 1987
A function of geologic factors, as soil texture and depth to groundwater A function of these hydrogeologic factors as well as the pesticide use factors that influence the site’s susceptibility This last approach is even broader, for it incorporates the size of the population at risk from potential pesticide contamination- that is, the number of people who obtain their drinking water from groundwater in the area. Aquifer sensitivity is related to the potential for contamination, i.e. aquifer that has a high degree of vulnerability and are in areas of high population density, are considered to be the most sensitive. . The tendency or likelihood for contamination to reach a specified position in the groundwater system after introduction at some location above the uppermost aquifer.
U.S. General Total Risk Accounting Office, 1991 Pettyjohn et al. 1991
Aquifer Sensitivity
National Research Council, 1993
Groundwater Vulnerability to Contamination
Explanation The possibility of percolation and diffusion of contaminants from the ground surface into natural water table reservoirs, under natural conditions. The risk of chemical substance –used or disposed of on or near the ground surface –to influence groundwater quality. An intrinsic property of a groundwater system that depends on the sensitivity of that system to human and/or natural impacts. The sensitivity of groundwater quality to anthropogenic activities which may prove detrimental to the present and/ or intended usage or value of the resource. The intrinsic characteristic which determines the sensitivity of various parts of an aquifer to being adversely affected by an imposed contaminant load. The interaction between (a) the natural vulnerability of the aquifer, and (b) the pollution loading that is, or, will be applied on the subsurface environment as a result of human activity.
6
information that is measurable (National Research Council, 1993). In general, the assessment of vulnerability can be done in two ways •
The assessment of intrinsic vulnerability, which depends only upon the characteristics of the aquifer. The definition refers only to natural systems and may consider several variables, each having its own relative weight. The most important are the following (Napolitano, 1995): 1)
Hydro-lithology of the vadose zone. It defines the vertical and horizontal permeability and, consequently, it controls the speed of pollutant diffusion and the capability of the rocks to attenuate the action of the pollutant.
2)
Water table depth. It defines the thickness of the vadose zone (from the topographic surface) and is proportional to the capability of attenuation from pollutants;
3)
Hydro-lithologic characteristics of the aquifer. It controls the diffusion of the pollutants when they reach the saturated zone.
•
The assessment of specific (or integrated) vulnerability, which is a combination of the intrinsic vulnerability and of the potential or the actual sources of contamination (Figure 2.1, Civita, 1990). Many methods have been proposed by researchers for assessment of vulnerability.
These approaches range in complexity from a subjective evaluation of available spatial data to the application of complex contamination transport models. The choice of the methods depends on the following factors: •
The aim of the research in terms of scale of work and type of representation,
•
Distribution and type of the available data,
•
Geological and hydrogeological models. The National Research Council (1993) divided groundwater vulnerability assessment
methods into three categories: •
Overlay and Index Methods: These are based on combining maps of various physiographic attributes (e.g., geology, soil and depth to water) of the region by assigning a numerical index or score to each attribute. Each of these attributes has a range of possible values, indicating the degree to which that parameter indicates the possibility of protection of groundwater from contamination, or otherwise, in the region. Depth to the groundwater, for example, appears in many systems, with shallow water considered more vulnerable than deep. The simplest overlay systems
7
identify areas where parameters indicating manifestation of vulnerability have same implication, e.g. shallow groundwater and sandy soil would reflect similar measure of vulnerability. More sophisticated systems assign numerical scores based on several parameters. Variables used in the overlay index methods typically include approximate depth to the water table, groundwater recharge rate, and soil & aquifer material properties. The shorter the distance of groundwater movement, the lower is the possibility of soil and underlying unsaturated zone material to act as a filter or adsorbent. Depth to groundwater also affects the transit time available for various abiotic and biotic processes to degrade the chemicals. The identification of recharge and discharge zones may be particularly useful in assessing the potential for contaminants introduced at the water table to move deeper into the groundwater system. The properties of the unsaturated zone in general influence the potential for vertical transport of contaminants to groundwater, while properties of the aquifer influence the potential of lateral transport.
The National Research Council has
reported seven overlay and index methods as listed in Table 2.2. Out of these, the most popular is DRASTIC method (Aller et al., 1987a).
Real and Potential pollution Maps
Intrinsic Vulnerability Map
Integrated Vulnerability Map
Figure 2.1: Definition of integrated vulnerability (Source: Civita, 1990)
• Process – based simulation models: Process–based simulation models such as PRZM, GLEAMS and LEACHM can predict the fate and transport of contaminants from known sources with remarkable accuracy in a localized area by applying fundamental physical principals to predict the flow of water in porous media and the behavior of chemical constituents carried by that water. Methods in these categories range from 8
indices based on simple transport models to analytical solutions for one-dimensional transport of contaminants through the unsaturated zone to saturated zones on to multiple phases, two- or three-dimensional models (Table 2.2). • Statistical methods: These methods incorporate data on known or real contaminant distribution and provide characterization of contamination potential for the specific geographic area, from which the data were drawn. The statistical studies are often used as a validation test for other methods (Table 2.2).
2.1.3 DRASTIC Method 2.1.3.1 Definition of DRASTIC method The DRASTIC method is the most widely used method of indexing aquifer vulnerability in USA, Canada, and in some other countries (Evans and Myers, 1990; Rundquist et al. 1991; National Research Council, 1993; Napolitano, 1995; Secunda et al., 1998; NAWQA, 1999; Piscopo, 2001; Al-Adamat et al. 2003). A numerical rating scheme, called DRASTIC, has been developed by National Well Association under a cooperative agreement with the U.S. Environmental Protection Agency for evaluating the potential for groundwater pollution in given areas based on their hydrogeological setting (Aller et al., 1985; 1987a, b) defined as "a mappable unit with common hydrogeologic characteristics, and as a consequence, common vulnerability to contamination by introduced pollutants.” This rating scheme is based on seven hydrologic factors chosen by over 35 groundwater scientists from throughout the U.S.A. The information on these factors is presumed to represent all locations in the U.S. In addition, scientists also established relative importance: weights and ratings scale for each factor. The acronym DRASTIC is derived from the factors in the rating scheme (Figure 2.2) as explained bellow. These factors represent measurable parameters for which data are generally available from a variety of sources without detailed reconnaissance. Depth to water: Represents the depth of the water table from the topographic surface and it gives an idea of the minimum distance that a pollutant has to travel to reach the saturated zone. Net Recharge: Provides information about the amount of water that reaches the saturated zone. It is important because the infiltrating water is a pollutant transport vector and because a greater recharge gives a higher degree of saturation and thus a higher value of permeability of unsaturated zone.
9
Table 2.2: Selected methods used in United States to Evaluate Groundwater Vulnerability to contamination
Method Kansas Leachability Index DRASTIC California Hotspost Washington Map Overlay Vulnerability SEEPAGE Lowa Groundwater Vulnerability EPA/UIC PESTANS BAM MOUSE PRZM RF/AF GLEAMS CMLS RITZ/VIP LEACHM RUSTIC
Reference
Map Scale*
Reference Location
Overlay and Index methods Kissel et al., 1982 Small Soil Aller et al., Variable Groundwater 1985,1987a,1987b Cohen et al., 1986 Large Water Table Sacha et al., 1987
Small
Groundwater
Moore, 1988 Variable Groundwater Hoyer and Small Groundwater Hallberg, 1991 Pettyjohn et al., Small Groundwater 1991 Process-Based Simulation models Enfield et al., Large Soil 1982 Jury et al., 1983 Large Soil Steenhuis et al., Large Groundwater 1987 Carsel et al., 1984 Large Soil Rao et al., 1985 variable Soil Leonardo et al., Large Soil 1987 Nofziger and Large Soil Hornsby, 1986 McLean et al., Large Soil 1988 Wagenet and Large Large Hutson, 1987 Dean et al., 1989 Large Groundwater
Intrinsic and/or Specific Intrinsic Intrinsic Intrinsic and Specific Intrinsic and Specific Intrinsic Intrinsic Intrinsic Specific Specific Specific Specific Specific Specific Specific Specific Specific Specific and Intrinsic
Statistical methods Discriminant Analysis Teso et al. 1988 Small Groundwater Specific Regression Analysis Chen and Small Groundwater Specific Druliner, 1988 "Large Scale" means that the method is typically applied at a level of detail of at least a 1:24,000 scale map to a small spatial area. "Small Scale” means that the method is typically applied at a level of detail less than that of a 1:50,000 scale map to larger spatial area. (Source: National Research Council, 1993)
10
Aquifer media: The material of aquifer determines the mobility of the contaminant through it. Increases in the time of travel of pollutant through the aquifer results in increased attenuation of the contaminant. Soil media: Is the uppermost portion of the vadose zone characterized by significant biological activity. This along with the aquifer media decides the amount of percolation water to the groundwater surface. Soil with clays and silts have larger water holding capacity and thus increase the travel time of the contaminant through the root zone. Topography: Is represented by steepness. Areas with low slope tend to retain water longer. This allows greater infiltration of recharge water and greater potential for contaminant migration. Areas with steep slopes, having large amount of run-off and smaller amount of infiltration, are less vulnerable to groundwater contamination. Impact of the vadose zone: For an unconfined aquifer, the unsaturated zone above the water table is referred to as the vadose zone. The texture of the vadose zone determines the time of travel of the contaminant through it. Hydraulic Conductivity: Hydraulic conductivity of the soil media determines amount of water percolating to the groundwater through the aquifer. For highly permeable soils, the travel time of pollutant is decreased within the aquifer.
Rainfall Runoff
S I
T
Water table
A
D R
Aquiclude
C
Figure 2.2: Visual explanation of Parameters of DRASTIC approach (Modified from: Aller et al., 1987a) 11
Determination of the DRASTIC index for a given area (the smallest applicable size is about 40 hectares) involves multiplying each factor weight by its point rating and summing the total. The higher sum value represents greater potential for groundwater pollution, or greater aquifer vulnerability. For a given area being evaluated, each factor is divided into ranges viz. Depth to water, Net recharge, Topography and Hydraulic conductivity or significant media types viz. Aquifer media, Soil and Impact of vadose zone which have an impact on pollution potential. Ranges for each DRASTIC factor are evaluated with respect to the other factors in order to determine the relative significance of each range with respect to pollution potential. By using a correlation function (data vs. value), the range for each DRASTIC factor is assigned a subjective rating, which varies between 1 and 10. Table showing ranges and ratings for the seven variables are presented in section 8.1 (Table 8.2 to 8.8). Some factors D, R, S, T and C, are assigned one value per range, while the other two, A and I, are assigned a "typical" rating and a "variable" rating. The variable rating permits the user to choose either a typical value or to adjust the value based on more specific knowledge. Weights are assigned to each DRASTIC factor in order to determine the relative importance of each factor. The relative weights range from 5 (the most significant factor) to 1 (the least significant) (Table 2.3). Finally, the procedure to evaluate the potential pollution is the one shown in Figure 2.3, the seven thematic maps are prepared for the seven variables, and seven other maps are produced to assign a relative value according to the DRASTIC ratings. Once all factors have been assigned a rating, each rating is multiplied by the assigned weight, and the resultant numbers are summed to obtain the DRASTIC index (DI): DI = DrDw + RrRw + ArAw + SrSw + TrTw + IrIw + CrCw
. . . 2.1
Where: D, R, A, S, T, I and C are the seven parameters. r = rating for the area being evaluated, w = importance weight for the factor Based on the established ranges, the lowest possible score is 23 and the largest possible score is 226. Once the DRASTIC index has been computed, it is possible to identify areas that are relatively more likely to be susceptible to groundwater contamination. A higher DRASTIC index indicates greater groundwater pollution potential (Figure 2.3).
12
Rating
Weight
9 D
R
A
×5 3 ×4 6 ×3
S
T
5 ×2 5 ×1
I
6 ×5
C
4 ×3 DRASTIC INDEX
132
DRASTIC Index = (9×5) + (3×4) + (6×3) + (5×2) + (5×1) + (6×5) + (4×3) = 132
Figure 2.3: Conceptual illustration of the DRASTIC method for aquifer vulnerability assessment 13
14
The application of DRASTIC method envisages following assumption (Napolitano, 1995): •
The contaminant is introduced at the ground surface. DRASTIC, therefore, does not consider the situation in which the pollutants are introduced directly into the aquifer.
•
The contaminant is flushed into the groundwater by precipitation and it has the mobility of the water. The hydraulic conductivity of the aquifer, therefore, is expression of the velocity of the contaminant in the aquifer.
•
There is no interaction between chemical pollutants and the physical environment; attenuation phenomena such as dilution, dispersion, mechanical filtration, volatilization, biological assimilation and decomposition, precipitation, etc., are not considered.
•
The variables included in the model are critically related to groundwater vulnerability.
•
Data are available and process sufficient precision, resolution and accuracy for assignment of ratings.
•
The ratings, weightings and mathematical relationships between variables are adequately set forth in the DRASTIC procedure.
•
The area evaluated, i.e. the hydro-geological setting, should be 40 hectares or larger. Research workers have widely used DRASTIC approach through out the world (cf.
Northeast Ohio Environmental Data Exchange Network, 1989; Evans and Myers, 1990; Rundquist et al., 1991; Trent, 1999; Close, 1993; Brown et al., 1994; Atkinson and Thomlinson, 1994; Lobo-Ferreira and Oliveira, 1997; Snyder et al., 1998; Stenson and Strachotta, 1999; Medina, 2001; Piscopo, 2001; Lilly et al., 2001 and Al-Zabet, 2002). Table 2.3: Weights for DRASTIC parameters Parameters Depth to the water table Net Recharge of aquifer Aquifer media Soil media Topography Impact of Vadose Zone Hydraulic Conductivity
Weight 5 4 3 2 1 5 3
15
2.1.3.2 Modification of DRASTIC and other similar approaches Several researchers have reputedly applied DRASTIC method after making some modifications Table 2.4 illustrates these modifications. Table 2.4: Reported modifications in the DRASTIC method Authors & Year Secunda et al. (1998) Melloul and Collin (1998)
Area/ country
Method
Sharon region in Israel
DRASTIC
Sharon region in Israel
DRASTIC
Younggwang County in Korea
DRASTIC
Eastern Snake River Plain, Idaho
DRASTIC
Navulur and Engel (1994 and 2003)
Indiana, U.S.A.
DRASTIC method SEEPAGE Method
Leal and Castillo (2003)
Turbio river valley, in Guanajuato Atate, Mexico
DRASTIC method AVI method
Al-Adamat et al. (2003)
Azraq basin, Jordan
DRASTIC
Fraser Valley aquifer in southwestern British Columbia (BC)
DRASTIC method AVI (Aquifer Vulnerability Index) method
Lee et al. (1998) NAWQA (1999)
16 Wei (2003)
16
Modification Applied DRASTIC method by adding land use parameters. Developed an index of aquifer water quality and used this index to test the validation of the DRASTIC map. Modified the DRASTIC method by adding the lineament maps to consider the preferential migration of contamination through fractures. Three of the seven DRASTIC factors i.e. depth to water, net recharge (land use) and soil media were used. The final vulnerability map was correlated with NO2-N and NO3-N concentration. Validated the accuracy of these approaches by comparing the vulnerability maps with existing well water quality data sampled across the state. Modified the range of depth to water parameter by using a scale 5 times the original rating because the groundwater depth in their study area varied from 40 to 140m Omitted the hydraulic conductivity parameter and used other six parameters of DRASTIC method. Also the authors developed the DRASTIC risk assessment by introducing the land use factor in to DRASTIC index. Studied correlated between DRASTIC and AVI method with nitrate occurrence
Further, several researches have employed other methods similar to DRASTIC to evaluate groundwater pollution potential. These methods are summarized in Table 2.5. Table 2.5: Methods similar to DRASTIC method Authors & Year
Method
LeGrand (1964)
-
Schmidt (1987)
-
Foster and Hirata (1988)
GOD
17
Moore (1989)
SEEPAGE
Civita et al. (1990) Halliday and Wolfe (1991) Van Stempvoort et al. (1992) Ray and O’dell (1993)
SINTACS AVI DIVERSITY
Tickell (1994)
-
Hiscock et al. (1995)
-
Rine et al. (1998)
-
Bekesi and McConchie (1999 and 2000b)
-
Brief Explanation Developed an empirical point count system to evaluate the pollution potential of unconfined aquifers consisting of unconsolidated alluvium. Developed a new approach considered the overlaying and rating the following five resource characteristic maps; type of bedrock, soil characteristics, depth to bedrock, depth to water table and the surficial deposits. Developed the method to assess groundwater pollution risk. This method considered three factors viz. Groundwater occurrence (i.e. whether the aquifer is unconfined, semi confined, confined, etc.), Overall lithology (aquifer) class in terms of degree of consolidation and lithological character, and Depth to groundwater table. The SEEPAGE model considered the following parameters; Soil slope, Depth to water table, Vadose zone, Aquifer material, Soil depth, and Attenuation potential which further considered factors like texture of surface soil, texture of subsoil, pH of surface layer, organic content of the surface soils, soil drainage class, and soil permeability SINTACS, partially derived from DRASTIC, used the same seven parameters Used GIS for assessing groundwater pollution potential from nitrogen fertilizers Included only the vertical hydraulic conductivity and the thickness of the layers of the vadose zone The method is based on an assessment of three aquifer characteristic viz. recharge potential, flow velocity, and flow direction Developed an indicator for assessment of salinity hazard. The indicator includes groundwater salinity, vegetation type, aquifer, laterite, and median annual rainfall. Produced a series of groundwater vulnerability maps, covering England and Wales, to provide a framework for decision making. The approach defined vulnerability as a function of the nature of the overlying soil, the presence and nature of any overlying superficial or glacial deposits, the nature of the geological strata forming the aquifer, and the thickness of confining beds. Developed a new methodology to evaluate and map the contamination potential of the upper groundwater flow system of a portion of General Separation Area (GSA). provided a vulnerability assessment procedure scientifically based on four factors i.e. the soil, the unsaturated zone, rainfall recharge and the aquifer medium
17
Table 2.5: (Continued) Doerfliger et al. (1999) Kelly and Lunn (1999) Bekesi and McConchie (2000a)
EPIK CLASS -
Magiera (2000)
18
Magiera and Wolff (2001)
Developed a multi-attribute approach name EPIK method for water vulnerability assessment in karst environment using GIS. This method was based on a conceptual model of karst hydrological systems, which considered four karst aquifer attributes (1) Epikarst zone (located under any consolidated soil), (2) Protective cover, (3) Infiltration conditions and (4) Karst network development Developed a new approach using a similar approach to that of the DRASTIC system of considering physical and chemical properties of a site and classifying them into ranges focussed on estimating the effect of the soil, in particular its sorption capacity, on the fate of contaminants in the Manawatu area of New Zealand, and the uncertainty of the derived spatial estimates of soil sorption capacity. Also, the authors discussed the accuracy of the estimates of soil sorption, and consequently, the aquifer vulnerability. Tested three published methods in the northern Germany, one index method (Stempvoort et al.), one point rating method (Hölting et al. 1995) and one statistical method (Schleyer 1994) using correlating analysis, all based on similar input data (Soil types, Vadose zone lithology and groundwater recharge). In addition, the authors studied the correlation of the resulting maps with measured groundwater quality data (nitrate, ammonia, potassium, Sulphate and EC). Developed an assessment method programmed within the GIS framework.
Bekesi and McConchie (2002)
-
Daly et al. (2002)
OCKP
Davis et al. (2002)
KARSTIC
Thapinat and Hudak (2003) Collin and Melloul (2003
-
Lake et al. (2001and 2003)
-
Used the aquifer media characteristics to estimate the vulnerability of contamination in Manawatu region in New Zealand. The aquifer vulnerability was assessed on the basis of grain size, the larger grain size indicating higher hydraulic conductivity, thus promoting greater dilution. This resulted in lower vulnerability to contamination. Developed an approach for intrinsic and specific vulnerability assessment with the aim to introduce some consistency into the European approach. In this approach, three factors were considered when assessing the intrinsic vulnerability of the whole karst aquifer (Precipitation Factor, Flow Concentration Factor, and Overlying Layers Factor). Developed the method for examining the sensitivity of karstic aquifer to surface contamination in mountainous terrain of Rapid Creak Basin. The method was a modification of the DRASTIC method, and it used nine parameters, which were based on hydrogeological properties in karst terrains to define sensitivity. Used the GIS technology to evaluate the vulnerability to pesticide pollution in Thailand. The factors used by the authors for the vulnerability assessment included Soil texture, Slope, Land use, Well depth, Rainfall and Observed pesticide concentration in groundwater. These vulnerability factors were reclassified to a common scale, and a weighted average was computed to yield a vulnerability score Developed an approach to assess the groundwater vulnerability. This approach focusses upon the impact of land use planning and the sensitivity of rural and urban environment Demonstrated the use of GIS to identify areas vulnerable to groundwater pollution by combining information on the quantity and quality of the water leaving the root zone with data from Environment Agency's Groundwater Vulnerability Maps (GVMs) on soil, drift material and aquifer properties
18
2.1.4 Groundwater Vulnerability Assessment in India Besides substantial research effort on an international level, literature about groundwater vulnerability assessment is rather limited in India. Jayakumar (1996) applied DRASTIC method to evaluate the groundwater vulnerability in south India. Mishra and Richaria (1996) applied DRASTIC method in limestone aquifer in and around Rewa city. Nataraju et al. (2000) applied DRASTIC method in Bangalore North Taluk. Thirumalaivasan et al., (2003) developed a software package AHP-DRASTIC to derive ratings and weights of modified DRASTIC model parameters. Dey and Bhowmick (2002) applied DRASTIC and AVI to evaluate the groundwater protection strategy in Ghaziabad urban area (India). Chachadi et al., (2002) developed a new approach GALDIT (Groundwater occurrence, Aquifer hydraulic conductivity, Depth to water level, Distance from the shore, Impact of existing status of sea water intrusion, and Thickness of the mapped aquifer, to evaluate the vulnerability by using the factors that control seawater intrusion. Singhal et al. (2003) applied DRASTIC method to evaluate pollution potential of alluvial aquifer in Roorkee town. In view of the reviewed literature, it was felt that there is a need for ascertaining groundwater vulnerability in unconfined aquifer of alluvial areas involving additional dynamic factors like impact of land use changes along with its validation using realistic groundwater quality data especially in the Indian scenario.
2.2 GROUNDWATER QUALITY: ASSESSMENT AND REPRESENTATION 2.2.1 Assessment As water moves through the hydrologic cycle, its quality changes in response to the differences in the environment through which it passes. The changes may be either natural or human induced. Assessment of groundwater quality becomes imperative in order to initiate any further action. Further, considering that the magnitude of the change in groundwater quality is apparently related to the status of vulnerability, the results of the monitored water quality may also be used to validate the estimates of vulnerability. A review of the literature on the above stated aspects is presented below: Hem (1970) summarized the natural sources and concentrations of the principal chemical constituents found in groundwater and their effects on the usability of the water. Aulenbach and Tofflemire (1975); and Runnells (1976) studied the effects of primary and secondary wastewater effluent recharge on groundwater quality at several locations. Yeh and Yang (1980) studied in endemic areas of Blackfoot disease for the physical and chemical 19
characteristic of drinking water, such as pH level and levels of As, Na+, Ca2+, Mg2+, Mn, Fe, Pb, NO3- and HCO3-. Canter and Knox (1985) gave an overview of the effects of the septic tanks on groundwater quality. Feigin et al. (1991) described the processes and problems associated with irrigation with treated wastewater effluent. Tedaldi and Loehr (1992) studied the effects of long–term irrigation of wastewater on groundwater quality near Paris, Texas. They also developed a conceptual model using hydrogeologic investigations, soil and groundwater geochemical data and mass balances for development of a conceptual model for the development and recharge of a slightly saline aquifer below an operation overland flow (OLF) land treatment site. Aschenbrenner et al. (1992) developed a transport model to study the sulfate pollution in a catchments area located in the hilly country southwest of Hanover, Germany.
They mentioned that the calibration of the transport model showed good
agreement between the estimated and modeled sulfate input rate. Lagrerstedt et al. (1994) observed the nitrate pollution from the perspective of the general nitrogen circulation in the eastern Botswana. The authors presented the comparison of three cases of nitrate pollution in groundwater where the resulting concentrations were of similar magnitude but the causes were different. Swancar and Hutchinson (1995) described the water quality in the Upper Floridan aquifer of west-central Florida in terms of major ion and environmental isotope concentrations, and assessed the potential for contamination using DRASTIC method. The report mentioned the comparison between DRASTIC parameters with groundwater chemistry and also reported mineral saturation indices of groundwater and statistical analysis of groundwater. Bobba et al. (1995) developed three analytical pollution transport models applying Monte Carlo analysis. These models considered the transport mechanisms of advection, dispersion, adsorption, radioactive decay, hydrolysis, and biodegradation. Colten (1998) observed the environmental litigation in the USA to establish the historical state of knowledge about the potential for groundwater contamination resulting from industrial waste disposal. He described the practical understanding between land disposal and groundwater pollution. Gallegos et al. (1999) determined the environmental effects of wastewater irrigation on the subsurface at two locations in Mexico; Leon and the Mezquital valley. The subsurface sediment samples and groundwater samples were collected from various depths by the authors and analyzed for a range of physico-chemical and microbiological parameters. They concluded that wastewater irrigation have a negative impact on groundwater quality in the Mezquital Valley as faecal bacteria from the wastewater got transported through the subsurface into the underlying aquifer. The shallow aquifers were observed to be more affected by microbial contaminants than deep aquifers. 20
Pitt et al. (1999) evaluated the potential to adversely affect groundwater by the known stormwater contaminants. They evaluated this potential based on pollutant abundance in stormwater, pollutant mobility in the vadose zone, the treatability of the pollutants, and the infiltration procedure used. Zhang et al. (1999) investigated the situation and cause of the pollution in the Nanjing city which is the fastest developing region in terms of construction and economy in China. The objective of these investigations was to provide a scientific basis to prevent and solve the pollution problem in this city. They indicated that the heavy metals contained in the soil of the environmental unit of the highway were Pb, Co, and Cr; in the soil of the refinery Cr, V, Pb, Ni, and Co; in the soil of the rubbish plot Co, Cu, and Sb and in the sediments of the shoal Pb, Co, Cu, and Ni. The Document of Minnesota Pollution Control Agency (MPCA, 1999) contains a short discussion of sampling strategies (e.g. sample design, sample size), a summary of quality assurance /quality control procedures utilized in evaluating data, and step-by-step procedures for choosing a method of analyzing groundwater quality data. Thullner et al. (2000) applied the Dual Pumping Technique (DPT) to measure the level-determined solute concentration in groundwater using two pumps, one placed near the groundwater table and one placed near the bottom of fully screened well. This technique allowed determining vertical concentration profile for solutes in groundwater. They also developed an algorithm that allowed the computation of a single solute concentration profile incorporating measured data from both the pumps. Rahaman et al. (2000) studied the environmental impact assessment of low water flow in the river Ganges during a dry period at Khulna and Mongla port areas in southwestern Bangladesh. Also, the relationship between surface and ground water of the Khulna and Mongla port areas was observed by these authors. Cao et al. (2000) studied the fluoride concentration of 60 samples randomly selected from surface and underground water sources in major population regions of north-central, central, southern, and southern border regions of Tibet. They determined the fluoride concentration by using fluoride-ion selective electrode. The results of this study indicated that most drinking water sources in Tibet were low in fluoride and there no evidence responsibility for the widespread occurrence of dental flourosis that appeared to be caused by early childhood intake of fluoride from high-fluoride”brick tea”. Jeong (2001b) investigated the chemical characteristics and the contamination of groundwater in relation to the land use in Taejon area, Korea. He attempted to distinguish anthopogenic inputs from influence of natural chemical weathering on the chemical composition of groundwater in Taejon. Groundwater samples collected in this study at 170 locations in the Taejon area showed a highly variable chemical
21
composition of groundwater e.g. EC ranged from 65 to 1,290 µS/cm. Factor analysis was used in this study and is was showed that the HCO3- and NO3- concentrations has the highest factor loadings on factors 1 and factor 2, respectively. He mentioned that the factors 1 and 2 represented major contribution from natural processes and human activities, respectively and the final results of the factor analysis indicated that the high levels of Ca2+, Mg2+, Na+, Cl-, and SO42- were derived from both pollution sources and natural weathering reactions. Dalgaard et al. (2002) developed an enhanced, three steps, bottom-up methodology to map and simulate agricultural activity at the landscape-scale. Step one was the farm data setup, exemplified by the combination of administrative, digital database with agricultural statistics. Step two was the geological mapping of the farm data in combination with physical properties like soil type, classified within a geographical information system (GIS). Step three was the simulation of future scenarios for agricultural activity, which could be mapped by repeating the step two. The three- step methodology was demonstrated by mapping of two selected key indicators of agricultural activity. First, the farm gross margin (GM), defined as income deducted variable costs, was mapped as an indicator of the economic benefits of agriculture. Secondly, nitrogen (N) application via manure was mapped as an indicator of potential N-loss and environmental problems caused by agriculture. Trojan et al. (2003) studied and compared groundwater quality under irrigated and nonirrigated agriculture, sewered and nonsewered residential developments and industrial and, non development land use. The authors monitored twenty-three wells in upper layer of an unconfined sandy aquifer in the period between 1997 to 2000. They observed that there are no seasonal differences in water chemistry, but variations of water chemistry when land use changes occurred. So the land use is the dominant factor affecting shallow groundwater quality. In India, Seth and Singhal (1994) examined the status of groundwater quality in shallow aquifers upper Hindon Basin, in the context of prolific industrial activity around Saharanpur Town of Uttar Pradesh and reported that the groundwater of the area is only marginally affected, is at all, when compared to the quality criteria for drinking set by Regulatory Agencies like World Health. However, some toxic metals like Pb, Cd and total chromium showed high and erratic concentrations at a few localities. Kumar et al. (1995) also studied the groundwater quality status of shallow aquifer in Saharanpur District (U.P. State) and Haridwar District (Uttaranchal State), India. They collected twenty two samples from various wells in the period (Jun 1987 to Nov 1987), and analysed them for various physico – chemical parameters viz. pH, Specific conductance (EC), Colour, Odour, Hardness,
22
Alkalinity (Carbonates and bicarbonates), Temperature, and Major cations and anions. The authors indicated that there is not much variation in the premonsoon and postmonsoon quality of groundwater, which rich in Ca2+ + Mg2+ and HCO3- + CO32-, the water was reported to be good for drinking purpose except that a significant interaction of polluted waters of river Hindon with the groundwater of the area was noticed in the down stream direction (south of the Saharanpur). Agrawal et al. (1999) and Agrawal (1999) summarized the situation of nitrates in the groundwater. They also observed the correlation between the nitrate levels in groundwater over vast agriculture area with the intensive irrigated agriculture, corresponding use of nitrogenous fertilizers and groundwater development. The consumption of nitrogenous fertilizer on each state in India compared with NO3- in groundwater has been described by the authors (Table 2.6).
Table 2.6: State wise consumption of nitrogenous fertilizers in India compared with NO3- in groundwater.
State Jammu & Kashmir Average of seven Northeastern States Himachal Pradesh Punjab Haryana Uttar Pradesh and Uttaranchal* Bihar West Bengal Gujarat Madhya Pradesh Maharashtra Orrisa (state) Orissa (Ganjam District) Andhra Pradesh Kanataka Tamil Nadu * The study area lies in this state (Source: Agrawal et al., 1999)
Nitrogenous fertilizer consumption (kg/ha/yr) 1.4 0.92 4.20 182.33 91.06 52.56 23.6 43.00 22.16 8.40 10.59 8.53 22.60 14.60 20.43 30.70
Average NO3in groundwater (mg/l) 7.9 6.8 8.6 55.10 99.50 22.60 21.00 14.20 45.0 14.80 213.0 13.20 45.00 26.00
Maximum NO3reported in groundwater (mg/l) 275.0 45.0 177 567 1800 634 350 480 410 473 800 208 200 1030
A report of NIH (1999) presents the findings regarding the monitored physicochemical parameters of surface, groundwater and wastewater of district Haridwar. The total samples collected were 102 including 16, 60 and 26 samples from surface water, groundwater 23
and waste water respectively. Also, the affect of monsoon on the water quality was studied. The results presented in this study are given in Table 2.7. The conclusion of this report was that the quality of groundwater upto 50 feet was not good as it contained various inorganics and organics, while the quality of groundwater at deeper levels was good and safe. It could be due to the leaching of inorganics and organics in the aquifer till about 50 feet while the leaching of these contaminants upto 100 feet or more, though, not observed significantly, but could be possible after some time. The municipal wastewater was reported to be more polluted than the industrial wastewater and there was no marked effect of the rain on the water quality of the area. Table 2.7: Physico-chemical data of surface water, groundwater and wastewater in premonsoon and postmonsoon in District Haridwar, India Premonsoon Parameters pH EC (µS/cm) TDS (mg/l) Ca2+ (mg/l) Mg2+ (mg/l) Na+ (mg/l) K+ (mg/l) Alkalinity (mg/l) Cl- (mg/l) SO42- (mg/l) NO3 -(mg/l) PO4 (mg/l) Cd (µg/l) Pb (µg/l) Fe (µg/l)
Surface water Min. Max. 7.80 8.08 128 408 82 260 22 53 3 14 8 55 3 6 57 225 5 78 0.7 8 0.7 8 0.1 0.5 6 1 27 987 677 6120
Groundwater Min. 7.00 210 130 14 9 3 0.4 132 5 5 0.1 0.005 2 30 5
Max. 8.50 1361 855 190 55 47 19 458 151 74 20 0.230 109 200 12500
Postmonsoon Waste water Min. 7.00 500 319 48 2 5 1 140 7 7 1 0.1 2 8 359
Max. 8.15 1890 1209 162 25 96 2 400 116 61 5 0.7 100 564 12200
Surface water
Groundwater
Waste water
Min. 7.00 131 120 26 5 33 4 30 4 3 0.4 0.02 1 47 285
Min. 6.40 211.0 131.0 18 7 3 0.4 110 4 4 0.4 0.02 1 15 3
Min. 6.70 510 204 70 2 5 1 130 7 9 0.95 0.91 3 96 253
Max. 8.25 440 280 72 7.8 55.0 5.9 220.0 75.3 29.0 6.2 0.60 12.0 79.0 1450.0
Max. 8.10 1729 850 180 60 63 32 380 151 50 20 0.30 104 410 4989
Max. 7.60 2350 1506 136 27 96 69 290 202 73 4.9 0.91 110 119 4452
(Source: NIH, 1999) The CGWB and CPCB (2000) mentioned the water quality criteria, type of groundwater pollution and suitability of groundwater for various uses in the National Capital Territory (NCT), Delhi. In this study, the problematic zones were demarcated on the basis of groundwater quality. Also the groundwater of this area was classified on the basis of various classifications. The results of this study indicated that the groundwater in Delhi contained high concentration of total dissolved solids, chloride, nitrate, sulphate and fluoride etc., The incidence of heavy metals, organic matter and bacteriological contamination were also reported from a few localities. Umar et al. (2001) investigated the hydrogeological and hydrochemical framework of regional aquifer system in Kali-Ganga sub-basin, India. The authors studied the nature and character of alluvium from the fence diagram, prepared from 24
the borehole logs, which showed that the alluvial deposits consisted of alternate beds of sand and clay. A network of 186 dug wells was monitored for water level in pre and postmonsoon periods i.e. June and November, during the years 1996 and 1997. The final conclusion of this study was that generally two to three-tier aquifer systems merged with each other and behaved as a single-bodied aquifer system. The aquifers are unconfined to confined in nature. Also the groundwater of the basin is of alkali-bicarbonate type and is suitable for domestic and irrigation use. In certain areas higher concentration of heavy toxic trace metals was observed, which could entail various health hazards. The analytical results are given in Table 2.8. Singhal et al. (2001) studied the impact of sewage irrigation on groundwater quality of Roorkee town, Uttaranchal. A total of 21 groundwater samples were collected by the authors from Roorkee town as well as from its vicinity. The concentrations range of specific parameters and trace metals for this study given in Table 2.9. The final conclusions of this study were: •
From the groundwater flow studies, it is seen that groundwater is generally flowing towards Roorkee from Saliar village, and there is no notable riveraquiver interaction except towards the southern side.
•
The upper aquifer is contaminated at some places in the study area by toxic heavy metals like Cd, Cr, Mn and Pb as well as coliforms but no uniform pattern of variation is noticed.
•
The maximum concentration in case of coliforms is at Rampur village (toward southeast of Sailar sewage farm) and in case of heavy metals like Cd, Cr, Mn, and Pb, marginally high values are observed at place like Saliar village (upstream of sewage farms), Shekhpuri, Ganseshpur, Khanjarpur and Malakpur areas of Roorkee town.
•
The groundwater was of Ca2+- HCO3- type
•
The concentration of different parameters does not show uniform variation in any direction, this appears to be due to the localized effect of irregular effluent discharges from small-scale industrial units and unhygienic condition prevailing around the locations of such hand pumps.
25
Table 2.8: Statistical summary of chemical analysis of water samples from shallow aquifers of Kali-Ganga sub-basin, India Parameters Min. pH 7.72 200 EC (µmohs/cm) 2CO3 (mg/l) 0 HCO3 -(mg/l) 120 Cl- (mg/l) 8 SO42- (mg/l) 29 Na+ (mg/l) 20 + K (mg/l) 1 Ca2+ (mg/l) 14 Mg2+ (mg/l) 6 F- (mg/l) 0 106 Fe (µg/l) 20 Pb (µg/l) ND Zn (µg/l) 64 Cu (µg/l) ND Cd (µg/l) ND Cr (µg/l) ND Ni (µg/l) (Source: Umar et al., 2001)
Max. 8.85 1589 32 518 224 289 200 98 175 67 38 1432 518 54 554 128 64 80
Table 2.9: Statistical summary of chemical analysis of water samples from shallow aquifers of Roorkee Town Parameters Min. pH 7.1 402 EC (µmohs/cm TDS (mg/l) 260 Ca2+ (mg/l) 33 2+ Mg (mg/l) 5 Na+ (mg/l) 6.3 K+ (mg/l) .03 HCO3 (mg/l) 30 CO32- (mg/l) 2.9 SO42- (mg/l) 5 CL- (mg/l) 5 NO3- (mg/l) 0.15 PO4 (mg/l) ND 30 Cd (µg/l) ND Cr (µg/l) 32 Mn (µg/l) 0.053 Ni (µg/l) ND Pb (µg/l) ND Zn (µg/l) (Source: Singhal et al., 2001) 26
Max. 8.3 2850 1710 242 134 116 6.96 685 0.04 648 366 14 1.17 51 112 549 137 298 719
2.2.2 Representation: Graphical and Numerical Subsequent to assessment, the groundwater quality needs to be represented in a suitable format for end users and decision makers for better comprehension and further action. A range of methods from graphical to numerical (employing indices) have been reported in literature. The important ones are briefly described below: Piper (1944) developed one of the most useful graphs for representing and comparing water quality in the form of the trilinear diagram. Stiff (1951) suggested the pattern diagrams for representation of chemical analysis by four horizontal parallel axes along which concentration of cations are plotted to the left of a vertical zero axis and anions to the right. Water quality indexing system serves as a convenient means of holistic numerical representation of water quality data. It generally involves a mathematical framework used to transform large quantities of water quality data into a single number representing the consolidated water quality level, while eliminating the subjective assessment of water quality and bias of individual water quality experts. “A quality index system offers a means for measuring pollution abatement progress . . . .
the rating system can be useful for
administrative purpose and for meaningful communication with the public” (Horton, 1965). The first of the published water pollution indices appeared in 1974 in a doctoral thesis by Landwehr. Also, the council on Environmental Quality (CEQ) supported a survey and evaluation of water quality indices through contracts with Mathtech, Inc., and Energy Resources Co. Inc., the literature reviews appearing in the contractors' final reports by Rosen et al. (1976), Orlando and Wrightington (1976) and in a paper by Orland et al. (1976). The water quality indices reported in the literature can be broadly classified into four general categories (Wayne, 1978): •
General Water Quality Indices: These indices present an overall status of water considering “Quality" as a general attribute of “Water”, irrespective of the use to which the water is put.
•
Specific-Use Indices: These indices present a picture of water quality under influence of a specific use. Although, use specific selection of the water quality parameters and the governing structure apparently restricts the potential of application of these indices, but the researchers have tried to make them quite wide based.
•
Planning Indices: These indices are designed specifically for management decisionmaking. Unlike the general and specific-use indices, these indices usually do not depict ambient water quality or related conditions. Rather, they are "custom-designed" 27
to assist the user in making specific decisions or in solving particular problems. For example, planning index designed for allocating water pollution abatement funds might include the "cost of wastewater treatment facilities". •
Statistical Approaches: Numerous statistical approaches have also been suggested for evaluating and interpreting water quality data. These approaches usually employ some standard statistical procedures already available in the literature (e.g. factor analysis), although, the procedure often is adapted for use with water quality data. The statistical approaches have the advantage that they incorporate fewer subjective assumptions than the traditional indices; however, they are more complex and often more difficult to apply. Considering the applicability and relevance of the first category i.e. “General Water
Quality Indices” to the present study, details regarding the major indices proposed by various researchers in this category only have been presented in the following paragraphs: Horton (1965) proposed the first formal water quality index in the literature. He argued that "water quality" and "pollution" are relative terms, and presenting water quality in the "black and white" terms required by "stream classification" system is misleading because it does not allow for gradations in condition. Ten (10) variables selected for Horton's index (Table 2.10) resulted from his work with Ohio River Water Sanitation Commission (ORSANCO).
Table 2.10: Variables and Weights for Horton's Water Quality Index Variable Dissolved Oxygen Sewage Treatment (Percentage of the population served) pH Coliforms Specific Conductance Carbon Chloroform Extract Alkalinity Chloride
Weight 4 4 4 2 1 1 1 1
Horton's Quality Index (QI) uses a linear sum aggregation function. It consists of the weighted sum of subindices divided by the sum of the weights and multiplied by two coefficients, M1 and M2, which reflect temperature and "obvious pollution," respectively:
28
n
∑wI
i i
QI =
i =1 nn
∑w
M1× M 2
. . . 2.2
i
i =1
where wi is the variable weight (range from 1 to 4) , Ii is variable rating (range from 0 to 100). The Horton's index has an advantage that it is relatively easy to apply, although the coefficients M1 and M2 require some tailoring to fit individual situations. Brown et al (1970) presented a water quality index similar in structure to Horton's index. This effort was supported by the National Sanitation Foundation (NSF), and the resulting index is known as the National Sanitation Foundation's Water Quality Index (NSF WQI). The NSF WQI was developed using a formal procedure based on the Rand Corporation's Delphi technique. A series of three questions was mailed to 142 persons throughout the U.S. with expertise in various aspects of water quality management, In the first question, the respondents were asked to consider 35 pollutant variables for possible inclusion in a water quality index, whereas, in the second question they were asked to review their original ratings and modify their response if needed. In the third question, they were asked to develop a rating curve for each of the 9 individual variables. The final formula for the NSF WQI index was the weighted linear sum of the subindices: n
NSF WQIa = ∑ wiIi
. . . 2.3
i =1
where Ii = the subindex value from the appropriate rating curve for pollutant variable i, wi = weight for pollutant variable i. Prati et al. (1971) proposed an index for surface water based on the water quality
classification systems used in a number of different countries. In developing this index, the implicit index of pollution, the authors first reviewed the water quality classification systems adopted in England, Germany, the Soviet Union, Czechoslovakia, New Zealand, Poland and some states in the U.S. From this information, they developed their own classification system involving 13 pollutant variables (pH, Dissolved Oxygen, 5-day Biochemical Oxygen Demand, Chemical Oxygen Demand, Permanganate, Suspended Solids, Ammonia, Nitrates, Chlorides, Iron, Manganese, Alkyl Benzene Sulfonate and Carbon Chloroform Extract). The system has five different water quality classes (I-V), and subindex ranges are assigned to each class. The upper limits of the first four ranges are 1, 2, 4 and 8 (corresponding to geometric progression in which 2 is raised to successive integer powers 0,1,2,3). Toxic substances were not included, because the investigators felt that concentration above a 29
threshold level for any toxic substance should automatically result in the index being classified in highest category (heavily polluted). Prati’s index is computed as the arithmetic mean of the 13 subindices: I=
1 13 ∑ Ii 13 i =1
. . . 2.4
The index ranges from 0 to ≥ 14. Dinius (1972) proposed a water quality index as a first step towards designing
"rudimentary social accounting system", which would measure the costs and impact of pollution control efforts. Dinius water quality index was viewed as an initial step toward the development of the larger system. She suggested that it could be used as possible water quality reporting system for the State of Alabama. This water quality index includes 11 pollutant variables (Dissolved Oxygen, 5-day Biochemical Oxygen Demand, Fecal coliforms, Total coliforms, Specific Conductance, Chlorides, Hardness, Alkalinity, pH, Temperature and Colour). Like Horton's index and the NSF WQI, it had a decreasing scale, with values expressed as a percentage of "perfect water quality," which corresponded to 100%. The weights (w) ranged from 0.5 to 5.0 on a basic scale of importance. The index was calculated as the weighted sum of the subindices. I=
1 11 ∑ wiIi 21 i =1
. . . 2.5
The index was defined over the range from 0 to 100, although limits were required to be placed on the range of each variable to avoid values over 100. McDuffie and Haney (1973) presented a relatively simple water quality index which
they called the River Pollution Index (RPI). Six of eight subindices (Percent Oxygen Deficit; Biodegradable, Refractory and Nonvolatile Suspended Solids; Average Nutrient Excess and Dissolved Salts) described by McDuffie and Haney showed explicit linear functions and two (Coliform count and Temperature) showed explicit nonlinear functions. The formulation was described as:
RPI = where where
10 n ∑ Ii n + 1 i =1
. . . 2.6
⎛ X ⎞ Ii = 10⎜ ⎟i ⎝ XN ⎠
. . . 2.7
Ii = subindex for the ith pollutant variable X = observed value of the pollutant variable XN = natural level of the pollutant variable.
30
The RPI was applied on a test basis using data from New York State's Water Quality Surveillance Network and from other sources. Backman et al. (1998) developed an approach for assessment and visualization of areas
characterized by anomalous or hazardous concentration of defined elements and ionic species to calculate a contamination index. This index took into account both the number of parameters exceeding the upper permissible limit or guide values of the potentially harmful elements, and the concentrations exceeding these limit values. Calculation of the contamination degree, Cd, was made separately for each sample of water analyzed, as a sum of the contamination factors of individual components exceeding the upper permissible value. Hence, the contamination index summarized the combined effects of several quality parameters considered harmful. The scheme for the calculation of Cd is as follows: n
Cd = ∑ Cfi
. . . 2.8
i =1
where
Cfi =
CAi −1 CNi
. . . 2.9
Cfi = contamination factor for the ith component, CAi = analytical value of ith component, CNi = upper permissible concentration of the ith component (N denotes the "normative" value). An Index of Aquifer Water Quality (IAWQ) was proposed by Melloul and Collin (1998) within GIS environment. IAWQ was expressed as a summation of weights multiplied
by respective ratings of various parameters i for each cell j and the formulation was given as follows: ⎡n ⎤ IAWQ = C / n ⎢∑ (Wri.Yri )⎥ ⎣ i =1 ⎦
. . . 2.10
where: C = a constant used to ensure desired range of numbers (taken as 10), i = the chemical parameters n = number of parameters. Wri is the relative value of Wi/Wmax, where Wi is a weight for any given parameter and Wmax is the maximum possible weight (taken as 5). The weight is a numerical value given to a parameter to characterize its relative anticipated pollutant impact; lower numerical 31
values defining lower pollution potential and vice-versa. Higher value of Wi indicates toxic groundwater quality. Yri = the value of Yi/Ymax; where, Yi is the rating value for the ith chemical parameter and Ymax is the maximum possible rating for any parameter (taken as 10). In their study, Melloul and Collin developed the index for chloride and nitrate parameters to assess salinity and pollution in groundwater. More details of this index are presented in section 7.3.
Štambuk-Giljanoviċ (1999) described the process of determining the water quality index (WQI) for Dalmatian County waters as well as the result of the application of the index for water evaluation in Dalmatia (Southern Croatia) for a three year period (1995, 1996 and 1997). WQI included the following nine parameters [temperature, mineralization, corrosion coefficient {K=(Cl + SO4)/HCO3}, dissolved oxygen, biochemical oxygen demand, total nitrogen, protein N, total phosphorus and total coliform bacteria (MPN coli/100ml)], for which, the concentration C80 was calculated from the following formula: C80= Ĉ + t σ
. . . 2.11
Where Ĉ is the mean value, σ is the standard deviation and t is the value of a student t-test statistic for 80% of probability level. The results of C80 were recorded and transferred to the score table to obtain the qvalue. The q-value was an attempt to quantify environmental factors, which would otherwise be qualitative. For each parameter, the q-value was multiplied by a weighting factor based upon the relative significance of the parameter. The nine resulting values were then added to arrive at an overall water quality index. . . . 2.12
WQI= WQE / WQE (MAC) n
where WQE = ∑ qiwi
. . . 2.13
i =1
n
where
∑ q w = weighted sum, qi = water quality score of parameter i, i
i
i =1
wi = weighting factor of parameter i and n= number of parameters. Water quality evaluation (WQE)MAC highlights the relationship of the index to the maximum admissible concentration (MAC) of first class water (Table 2.11 ).
32
Table 2.11: Number of scores for water quality parameters and MAC S. No.
Parameters
Maximum number of scores
1 2 3 4 5 6 7 8 9
Temperature Mineralization K=(SO4+Cl)/HCO3 Oxygen (% saturation) BOD5 Total N Protein N Total P MPN coli/100 ml Total
7 7 6 16 10 16 10 12 16 100
MAC
Up to 17o 350 mg/l 0.4 90-105% 2 mg/l 0.3 mg/l 0.1 mg/l 0.1 mg/l 200
Number of scores for MAC 5 7 5 16 8 12 7 10 15 85
Said et al. (2002) defined some useful relationships between common water quality
constituents, which were used in evaluating water quality situation using a new water quality index. This index was applied on the big lost watershed in Idaho and on some other data from different watersheds. The index has the formulation as given below: ⎡ ⎤ ( DO)1.65 WQI = log ⎢ 0.5 0.15 0.5 0.5 ⎥ ⎣ 50(TP ) + (Turb) + 0.4( F .Coli ) + 0.15( SC ) ) ⎦
. . . 2.14
where: DO= Dissolved oxygen (% oxygen saturation), Turb= Turbidity (NTU) TP= Total phosphates (mg/l) F-Coli= Fecal coliform bacteria (counts/100ml) SC=Specific Conductivity (µS/cm at 25oC) This index was developed for the purpose of providing a simple method for expressing the significance of water quality data, and was designed to aid in the assessment of water quality for general uses.
33
2.3 GIS: CONCEPT AND APPLICATION IN GROUNDWATER VULNERABILITY ASSESSMENT GIS, Geographic Information System, represents a new, powerful set of tools that can significantly improve the usefulness of results obtained during the groundwater modeling process. Bridging the disciplines of modeling, computer graphics, cartography, and data management, it represents a computer-based set of tools to display and analyze spatial data in many areas including groundwater hydrology (e.g., water table elevations, groundwater quality and pollution potential). GIS can be defined as a computer-assisted system for the efficient acquisition, storage, retrieval, analysis, and representation of spatial data. Most GIS platforms consist of numerous subsystems that perform the listed tasks (Ross and Beljin, 1994).
GIS has been widely used for natural resources management and planning, primarily during the past decade. GIS can be combined with a ground-water quality model to identify and rank the areas vulnerable to pollution potential for different scenarios and land use practices. Many GIS software packages are available like GRASS. It's raster-based software in public domain developed by the U.S. Army Construction Engineers Research Laboratory (U.S. CERL, 1990). This software can assign different weights to, or reclass, the data layers
and combine map layers, and is suitable for implementing the DRASTIC and SEEPAGE models. ArcView is GIS software developed by Environmental Systems Research Institute (ESRI) in Redlands, California (Navulur and Engel, 1994). Three separate data models are supported by GIS: (1) vector data, (2) raster data, and (3) Triangulated Irregular Networks (TINs) (Figure 2.4). Vector data includes feature representation with points, lines, or polygons. For example, the monitoring wells for a site could be mapped as a point data source. Example line features include rivers, roads, and boundaries. Some polygon feature examples are buildings, lakes, and watersheds. While vector data are the most common format, other data sets are better represented with grids, where each cell in the grid has a particular value. This type of format is referred to as raster data and is effective for representing elevations and concentrations. Triangulated irregular Networks are the final type of data model and are particularly useful for surface representation and three-dimensional mapping. TINs are constructed by connecting a group of points, such as surveyed elevations. The lines that connect these points form triangles, and since each point in the TIN has an associated value, each triangle in the model (i.e., continuous surface of planar triangles) is sloped. This allows for powerful visualization 34
capabilities with a three dimensional viewer. The most common method of connecting points to form a TIN model is Delaney triangulation, which maximizes the minimum interior angles of the triangles formed, thereby avoiding long and thin triangles (Jones et al., 1990).
Figure 2.4: GIS data model
Any one of the commercially available GIS software packages can be used in the groundwater vulnerability assessment process. However, the prudent of Environmental Systems Research Institute, Inc. (ESRI) (ArcView GIS Version 3.2a) has been employed in this research work. This software has been chosen because of ease of use and worldwide availability. ArcView’s graphical interface allows a user to display spatial data, build maps, query data sets, create charts, and perform calculations. For purposes of a spatial environmental vulnerability assessment, ArcView is the most effective software tool. Its analytical capabilities have improved significantly over the past few years, and unless otherwise noted, all the methods discussed in this document can be performed with ArcView. One of the advantages of a GIS such as ArcView is its ability to connect with many different applications in a PC-based environment (Figure 2.5).
Figure 2.5: GIS Application in PC-Based Environment
(Source: Hay Wilson, 1998)
35
The examples of the application of GIS in groundwater vulnerability assessment can be broadly grouped into two categories. i. The first category includes those studies which combine geoscientific information, either raw data or results of groundwater modeling, with information characterizing the hazardous activities in order to assess either the environmental impact or risk of human exposure resulting from groundwater contamination (Hiscock et al., 1995). The literature in this group include; Schmidt (1987), Evans and Myers (1990), Halliday and Wolfe (1991), Fǘrst (1992), Harper et al. (1992), Padgett (1992),Von Braun (1993,1998), Pipes et al. (1994), Doerfliger et al. (1999), Kelly and Lunn (1999), Lake et al. (2001and 2003), Magiera and Wolff (2001),Thapinat and Hudak (2003) and Al-Adamat et al. (2003).
ii.
In the second category, geoscientific information is handled within the GIS and
then interfaced with a groundwater model to assist input and displayed with an amount of groundwater data at both the pre and post-processor stages of modeling. Baker et al. (1993,) Rifai et al. (1993) and El-Kadi et al. (1994), used GIS interfaced with a groundwater flow
model to delineate well head protection areas. Harris et al. (1993) demonstrated the coupling of GIS with a three dimensional, finite element model, and Turner (1989, 1992) discussed advanced use of three dimensional GIS for modeling groundwater contamination. Interactive groundwater and surface water resources modeling for environmental management, where systems are designed to integrate GIS, large databases, simulation models, expert systems and tools for graphical display, have been discussed by Loucks and Fedra (1987) and Fedra and Diersch (1989).
2.4 DATABASE SYSTEM DESIGN AND MANAGEMENT Basic information on a variety of spatial and nonspatial attributes of the physiographic setting of the area to be evaluated, is required to assess groundwater vulnerability to contamination. Although the type of data required depend on the specific technique employed, some combination of information will be needed on natural factors, such as topography, soil, climate, hydrogeology and land cover; and human factors such as land use and management (National Research Council, 1994).
To date, most vulnerability assessment studies have used existing data sources, and have rarely involved new data collection efforts in support of the vulnerability assessment (National Research Council, 1994). Further, since the data available for a particular region
are often meager, the attributes of interest are often derived by some type of interpolation of 36
information collected at sparsely distributed locations, sometimes from outside the region of interest, and frequently using data collected at different spatial scales (National Research Council, 1994). More details of Database are presented in chapter 5.
2.5 GROUNDWATER RECHARGE AND ITS ESTIMATION Groundwater recharge may be defined as “the downward flow of water reaching the water table, forming an addition to the groundwater reservoir” (Lerner et al., 1990). There are two main types of recharge, direct (vertical infiltration of precipitation where it falls on the ground) and indirect (infiltration following runoff). There are four groups of the approaches for estimating groundwater recharge viz. Inflow estimation, Aquifer response analysis, Outflow estimation and Groundwater Water budgeting. Each group includes different approaches e.g. the inflow estimation includes six approaches viz. Soil moisture budgets, Infiltration coefficients, Soil moisture flux approaches, Lysimeters, Tracer application, direct observations etc. (Misstear, 2000). In the Tracer application approach, the groundwater recharge can be estimated using both environmental and applied tracers (Lerner et al., 1990). However, environmental tracers have only been used to a limited extent (chloride mass balance, tritium, and carbon-14). In India groundwater budgeting has also been used extensively for estimation of recharge especially by agencies like Central Groundwater Board and State Groundwater Departments. Two methods are commonly applied for estimating groundwater recharge by tracer application viz. the signature method and Throughput method. Tritium is normally only used in the signature methods (whereby a parcel of water containing the tracer is racked and dated). Throughput methods involve a mass balance of tracer, comparing the concentration in precipitation with the concentration in soil water below the water table. Piston flow is generally assumed in most tracer studies. Tracers can also be used to investigate flow processes, including the occurrence of preferential pathways. Tritium is a beta ray emitter having half life of 12.43 years. In the present study, the artificial tritium has been used to estimate the vertical component of recharge to groundwater. Tritium Tracer technique was first applied by Zimmerman et al. (1967 a, b) in West Germany. In India, Sukhija (1972) and Sukhija and Rama (1973) ascertained the validity of this technique for semi-arid alluvial tracts of Gujarat. Datta et al. (1973) and Datta (1975) applied this method in several areas of U.P. and Indo-Gangetic Alluvial plains of Punjab. Sukhija and Shah (1976) applied this technique in western India in Gujarat. The recharge
37
rate was estimated as 11% of local rainfall. Goel et al. (1977) studied downward movement of soil moisture due to rainfall and supplemental irrigation in the unsaturated zone by tagging a layer of soil with tritiated water at a number of sites in the state of Haryana. The average recharge in this study was observed as 14% of the irrigation plus rainfall. Ali (1979) studied the groundwater recharge using tritium tagging method in IIT Roorkee campus, whereby the percentage of the ponding water, which went down as recharge within 60 hours was observed to be between 21 to 25%. Athavale et al. (1980) applied the tritium injection to estimate the groundwater recharge in the Lower Maner basin, India. The rate of the recharge value was reported within 10%. Datta et al. (1980) monitored the recharge to groundwater using the soil moisture movement in Sabarmati for a period of three years (1976-1979) and estimated the percentage recharge as 6% to 18% and the average of 14% for the total average rainfall. Gupta and Sharma (1984) estimated the ground recharge in the Mahi right-bank canal
command area, and the average percentage recharge was 23%. Mukherjee (1986) and Mukherjee et al. (1987) also carried out study of recharge to groundwater in rain-fed alluvial
area in IARI farm, New Delhi using tritium tagging technique. This group also carried out a few experiments to study the recharge at different places having similar soil conditions but different crops and irrigation practices. These studies showed that more recharge takes place in fields with irrigation watering and less recharge through fields with vegetation. Singh and Chandra (1978) studied the recharge to groundwater using tritium technique in Sharda
Command area of Uttar Pradesh. Raja et al. (1983) estimated the groundwater recharge using tritium tagging technique in various areas of Uttar Pradesh like Gandak command area, Ganga-Sarda area, Agra-Mathura area, Roorkee area, Deoband branch Command area, Eastern Yamuna Canal Command area, Sarda Sahayak command area and Saryu canal command area. The percentage recharges for these areas were reported as 21.38, 24.10, 22.54, 18.50, 18.20, 21.00, 20.85 and 21.25 respectively. Kumar and Nachiappan (1995) evaluated the recharge in the Bundelkhand region of U.P. state, India, using a tritium tagging technique at 25 locations. The results of this study showed a wide variation in the values of recharge to groundwater between 6.09 cm (8.47% from rainfall and irrigation) to 34.0 cm (25% from rainfall and irrigation). NIH (1999 and 2000) applied the tritium tagging method to estimate the groundwater recharge in Haridwar and Saharanpur Districts. The results of these studies showed varying recharge rates return 3.07% in Dhanauri in Haridwar to 17.01% in Saharanpur. Rangarajan and Athavale (2000) summarized the mean natural recharge values for 35 study areas in India, well distributed over 17 major river basins. The recharge rates showed wide variation from 4.1% to 19.7% of the local average seasonal rainfall. 38
CHAPTER - III
STUDY AREA This Chapter briefly describes the attributes of study area and its surrounding environment.
3.1 GENERAL DESCRIPTION 3.1.1 Location The study area is situated in the northern part of the vast Indo – Gangetic Plain in o
/
//
o
/
//
o
/
//
o
India a nd lies between latitudes 29 33 51 to 30 19 10 N and longitudes 77 06 20 to 78 /
//
20 15 E with total geographical area of approximately 5500 km 2. Administratively, the study area covers the districts of Haridwar in Uttaranchal and Saharanpur in Uttar Pradesh. The Saharanpur district is divided into 11 administrative blocks whereas the Haridwar district is divided into 6 administrative blocks (Figure 3.1). The study area is covered under the Survey of India Toposheets No. 53(G/1, G/2, G/5, G/6, G/9, G/10, G/13, G/14, K/1, K/2, F/8, F/11, F/12, F/15, F/16, and J/4) on the scale 1:50,000.
3.1.2 Climate and Rainfall The study area has a moderate to sub-tropical monsoon climate. The rainy season (monsoon) extends from 15th June to 15th September. The average annual rainfall of the study area is about 1000 mm, of which about 85% is received during the monsoon season (Figure 3.2). The region experiences higher temperatures during the months of May and June, with average maximum of 40 oC and minimum of 5 oC.
3.2 DEMOGRAPHIC CHARACTERISTICS The population of Saharanpur district (within the municipal limits) was 2,309,029 in 1991, while it increased to 2,848,152 in 2001. In Haridwar district of Uttaranchal, the population was 1,124,400 in 1991, while it went up to 1,444,213 in 2001 (Table 3.1 and Figure 3.3).
39
3.3 GEOLOGY Physiographically, the study area can be divided into hilly area comprised of the Siwalik range and Ganga basin. Taylor (1959) has divided the Ganga basin into three belts, which are termed as Bhabar, Tarai and Alluvial plan from north to south (Figure 3.4).
3.3.1 Siwalik Range: This forms the outermost range of Himalayas and belongs to Tertiary group of rocks. It commences with a gentle slope from Bhabar area from an altitude of about 500 m and then steeply rises northward attaining a height of about 900 m where it ends abruptly. The Siwalik range is further divided into Upper, Middle and Lower Siwalik zone. The Upper Siwalik zone is constituted of calcareous banded pebble -boulder conglomerates, sandy rocks and clay beds. The pebble and boulder are mostly of quartzite types. The Upper Siwalik zone is the most permeable and porous of the entire Siwalik sequence. Middle Siwalik zone comprises mainly of sandstone and serves as moderate to good aquifer. Lower Siwalik zone is made up of hard and massive sandstone, clays and shale beds. The Lower Siwalik zone bears lower water transmitting and storage capacity than the middle and upper Siwalik sandstone. In the deep borings conducted by the Oil and Natural Gas Commission (India), at certain localities in the Indo-Gangetic basin, the Upper and Middle Siwalik rocks found to underlie the alluvial deposits at a depth varying from 2800 m near the Himalayan foot hills, which gradually diminishes to about 1000 m at Ujhani (Budaun, U.P.) and to 620 m at Kasganj (Etah, U.P.) (Pandey et al., 1963; Mithal et al., 1973).
3.3.2 Bhabar Formation: Bhabar is the piedmont zone formed along the foot hills of Siwalik and is the upper most super group of formations. It is formed by flooding hill torrents and nullah (also locally termed as "rao"). Alluvial fans in these piedmont zones are wider and longer when formed along mature large streams. The topography is normally characterized by badlands (high undulations, noncohesive soils and sparse vegetation). The sediment matrix of Bhabar therefore , exhibits high porosity and permeability. The incisions by hill torrents and rushing nallahs have developed several longitudinal spurs and depressions all along the Bhabar zone. Considerable amount of groundwater recharge is expected to be taking place in this zone through direct infiltration of precipitation. Within the Gangetic Alluvial basin, the water table in the Bhabar zone occurs at high elevations and represents the maximum recharge head available to the various aquifer systems occurring within the plains (Pandey et al., 1963).
40
77°20'
77°30'
77°40'
mu na Ri ve r Ya
kR an ge Sadhuli Qadim T $
(H
Ca na
l
al
C an
na rn Y a mu
Ea st e
G anga
30°00' 29°50'
Up p er
Laksar T Khanpur $ T $
29°40'
29°40'
River
Narsen T $
29°50'
10
30°00'
0
Deoband T $
a Ga ng
41
Nanauta T $
10
Haridwar Bahadrabad U % T $ Roorkee T $
Bhagwanpur T $
Nagal T $
Rampur T $
Gangoh T $
ea Ar
T $
% U
N
)
Punwarka Saharanpur
ill
y
T $
Nakur T $
78°10'
Si w al i
Muzaffarabad Sarsawa T $
78°00'
30°10'
30°10'
77°50'
30°20'
30°20'
77°10'
20 Kilometers
T $
U %
77°10'
77°20'
77°30'
77°40'
77°50'
78°00'
78°10'
Figure 3.1: Index map of the study area 41
Haridwar District Saharanpur District Siwalik Range River Railway Line Canal District Boundary Block H. Q. District H. Q.
42
400
Mohamadpur Roorkee
350
Kalisia
Rainfall (mm)
300
Deoband Nakur
250
Saharanpur
200 150 100 50 0
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Months
Figure 3.2: Monthly variation in rainfall data averaged for 18 years (1985-2002).
Table 3.1: Population, area and density of the Saharanpur and Haridwar districts District
Population (2001 census )
Area (sq. km.) using GIS
Numbers of Towns
Numbers of Villages
Population density (persons / sq. km.)
Saharanpur
2,848,152
3721.89
14
1110
765
Haridwar
1,124,400
1872.99
5
461
600
3100000
1991 census
Population
2600000
2001 census
2100000 1600000 1100000 600000 100000 Saharanpur district
Haridwar district
Figure 3.3: The variation in population for the 1991 and 2001 census.
43
3.3.3 Tarai Formation: The Tarai is formed by the deposition of the finer outwash of Bhabar. It consists mainly of clays and clays with kankar (calcium carbonate concretion) (~70 %), coarse sand and pebbles (~20%) and little amount of fine sand and sandy clay (~7%). The granular beds occur mostly as lenses. Tarai is relatively flat with respect to Bhabar. Shallow water table and swampy grounds characterize the break in topographic slope. Along Bhabar-Tarai contact, a number of springs and seepage s occur in depression along the nullah. The southern limit of the Tarai is not clearly defined and is generally taken as the zone where flowing conditions
N
7 8 °1 0 '
7 8 °0 0 '
7 7 °5 0 '
7 7 °4 0 '
7 7 °3 0 '
7 7 °2 0 '
7 7 °1 0 '
cease to exist in the tube wells which indicates beginning of the plains (Pandey et al., 1963).
3 0 °2 0 '
3 0 ° 2 0 '
3 0 °1 0 '
3 0 °1 0 ' 3 0 °0 0 '
Saharanpur
U %
# % U
Roor kee
Haridwar
T $
3 0 °0 0 '
2 9 °5 0 '
2 9 ° 5 '0
2 9 °4 0 '
2 9 °4 0 ' 7 8 °1 0 '
7 8 °0 0 '
7 7 °5 0 '
20 Kilomet ers
7 7 °4 0 '
10
7 7 °3 0 '
0
7 7 °2 0 '
7 7 °1 0 '
10
Doon Alluvial Siwaliks Bahabar Tarai Gangetic Alluv ial
Figure 3.4: Geological map of the study area (Source: Pandey et al., 1963)
Tarai receives groundwater recharge by downward percolation and through lateral flow from the Bhabar belt. Thus, the groundwater storage capacity is large in this belt.
3.3.4 Gangetic Alluvial Plain: The region south of the Tarai is occupied by the Gangetic Alluvial plain, which forms greater part of the North Indian Plains. Lithologically, the alluvium is composed of unconsolidated and semi-consolidated deposits of sand, clay and kankar that provide a good groundwater reservoir (Pandey et al., 1963).
44
3.4 REGIONAL GEOHYDROLOGY The hydrology of the area is influenced by unconsolidated sediments consisting of clay, silt, sand and gravel of fluviatile origin with its northward extension to piedmont deposits known as Bhabar. The Bhabar region is characterized by deep water table. In the main alluvial plain south of the Bhabar zone, the sandy fraction increases from north-west to southeast. The lenticular and intercalated clay beds are a common feature of the study area. These clay beds are generally found dipping from north-west to southeast and influence the local occurrence of the ground water in the area. The deposits of the sandy horizons are the main source of groundwater in the study area (Shakeel, 1997). Based on 500 lithological logs and water table fluctuations, two types of aquifers have been delineated in this region (Mithal et al., 1973). The upper one is the shallow unconfined aquifer, which extends to a depth of about 25 m. The deeper aquifer system is made up to of confined aquifer, located between 30 to 140 m below ground level, and separated by 3 to 4 aquitards at average depths of 30 to 53, 67-90 and 122-140 meters (Singh et al., 1983). On a regional scale, aquifers are interconnected and hydraulically continuous through out the plain. The relative thickness of the aquifers gradually increases from north-west to southeast. A large number of the shallow wells tapping the top aquifers can be seen in the area, whereas some deep wells are also met, (Shakeel, 1997). Taylor (1959) believes that in the alluvial plains the aquifers are mostly under confined conditions, although flowing well conditions are very rare. Raghav Rao (1965) observed that the deeper aquifers in Saharanpur district are confined in nature. However, Singhal and Gupta (1966) concluded that deeper aquifers in Meerut and Muzaffarnagar area show leaky confined conditions locally. However, over long distances, the shallow and deeper aquifers may be interconnected thus representing a single hydraulic unit. The water level gradients are generally towards south-west in the Yamuna sub-basin and towards southeast in Ganga basin. The depth to water table varies from 1.20 to 22.25 m. the average minimum level being 1.30 m. The water table gradient is mainly dependent on the drainage of Ganga and Yamuna rivers. The Ganga and Yamuna rivers are effluent during the rainy season and become influent after the main rainy season (Mithal et al., 1973). The analysis of pumping test data carried out by Uttar Pradesh Ground Water Investigation Organization (U.P.G.W.I.O.), Roorkee division, Roorkee, and the correlation of lithologs of various drilled boreholes, suggest s towards the existence of extensive system of
45
water table aquifer along with deeper confined and leaky confined aquifer in the area. The average values of aquifer parameters as reported by them are, as under (Table 3.2):
Table 3.2: The average value for aquifer parameters 4 m to 100 m 2.16 m/day to 28.8 m/day 1 x 10 -4 to 3.74 x 10 -4 10 m2/day to 2880 m 2/day 0.13 to 0.26
Thickness of Shallow Aquifer Coefficient of Permeability Storativity Transmissivity Specific Yield (Source: Shakeel, 1997)
3.5 WATER RESOURCES 3.5.1 The Rivers Ganga and Yamuna are the two main perennial rivers in the region. Other rivers, like Ratmau River, Solani River, Banganga and Hindon Rivers are the tributaries of Ganga and Yamuna. These tributaries get water during monsoon season through rainfall only (NIH, 2002).
3.5.1.1 Ganga River The river Ganges (Ganga in Indian languages) is a major river. It originates as the Bhagirathi from Gangotri Glacier (Gangotri Glacier is located in Uttaranchal, India in the region bordering Tibet) and joins the Alaknanda near Devprayag to form the Ganga. Then on, the Ganga flows across the Gangetic plain and empties into the Bay of Bengal, after dividing up into distributaries. The Ganga River enters the study area from the northeastern end. Its tributaries are Ratmau Rao, Seetla Nu llah, and Solani river. The Solani river is the main source of natural drainage in Haridwar district. It remains dry most of time but carries large flows in rainy season. The Solani river basin covers an area of about 655 km2. The river originates in the Siwalik in dendritic form and becomes parallel in Bhabar due to its travel in folded rills. The river flows along the general slope of SW in Siwalik- Bhabar -Tarai region. On entering the plains, it flows in a SE direction influenced by the Solani fault. The river attains N-S direction after joining the river Ratmau due to ne otectonic movements (Pandey et al., 1963; Agrawal et al. , 2000; NIH, 2002)
3.5.1.2 Yamuna River The river Yamuna is a major river of northern India and with a total length of around 1370 km, it is the largest tributary of river Ganga. Its source is at Yamunotri, in the 46
Uttaranchal H imalayas. It flows through the states of Delhi, Haryana and Uttar Pradesh, before meeting the Ganga at Allahabad. The cities of Delhi, Mathura and Agra are on the banks of the river. The Yamuna River enters into the study region from the northwestern side . Major tributaries of this rive r are Tons, Chambal, Betwa, Sindh and Ken, Tons being the largest river. River Hindon, a tributary of Yamuna, like Kaluwala Rao and Nagde o Nullah originate s in the Siwalik while other streams like Kali, Krishni etc. originate in the plains. The Hindon River flows almost linearly from Saharanpur to its point of confluence with the river Yamuna, covering a distance of about 200 km (NIH, 2002).
3.5.2 Canal System The Upper Ganga Canal and Eastern Yamuna Canal are the two major canal systems in the area with numerous distributaries and minors. The Upper Ganga Canal originates in Haridwar and is branched into Deoband branch, Muhammadpur and Basera Distributaries. The Eastern Yamuna Canal originates a t Tajewala in Sadholi Quadim block and then gets branched into Eastern Yamuna canal and other distributaries like Randaul, Nagla Distributaries, Balpur, Hanguli, Sijud, Nalhera and Kallarpur. Most of the blocks get benefited from these canal systems, which somehow neutralize the lowering trend of the water table (NIH, 2002).
3.5.3 Surface Water Resources The basic source of surface water in the region is rainfall. Number of tanks, reservoirs and ponds are there, which store the rainwater. Numerous large marshy patches in the form of Jheels exist around Laksar, Khanpur and in the Solani catchments at places near the junction line between Bhabar and Tarai (NIH, 2002).
3.6 SOIL The study area is a part of the vast Indo – Gangetic plain, which is mainly composed of alluvium sediments transported by rivers from Himalayan region. The alluvium is composed of admixtures of sand, silt, clay and gravel. In general, the soil types in the study area are sandy loam and silt loam (Pandey et al., 1963).
47
3.7 AGRICULTURAL PRACTICES As the general topography of the area under study is plain, surface/sub-surface methods of irrigation are most commonly used. The land is used for agriculture in plains and the ploughing is done either by oxen or tractors. The plain area is readily cultivable due to intense irrigation through canal and tube-wells. The major crops grown in the study area are sugarcane, wheat and paddy. The study area is a major sugar belt in the country, where the small-scale sugar factories are located at every 15 kilometers alongwith few large sugar mills. Wheat and Gram are the important Rabi crops during winters, while Maize is an important cash crop grown in rainy season. In addition to these crops, fruits like Mango and Litchi are also widely grown in the study area (NIH, 2002). The region falls in Indo-Gangetic alluvial plain, which is agriculturally very fertile. Most of the deep tube wells tap the deeper (second and third) aquifers, whereas the private structures generally tap the shallow aquifer. Most of the aquifers are unconfined to semi confined in nature. The other mode of irrigation in the area is through Ganga and Yamuna Canal system.
3.8 SOCIO ECONOMIC FEATURES In the study area, rapid industrial and agricultural growth has taken place during last two decades. This is likely to become manifold in near future particularly in areas like Saharanpur and Haridwar where the necessary industrial nucleus already exists and industry – friendly policies are being pursued by the respective state governments. The major type of industries in the study area are paper and pulp, dairy products, distilleries, sugar, food-processing, small scale steel and cottage industries etc (Seth, 1993). In addition, Saharanpur district is famous for the wood-carving work in the state. It is known for its carving in hard Sheesham, and particularly for its famous leaf pattern (NIH, 2002) .
3.9 CHARACTERISTICS OF WASTE GENERATION With the rapid increase in population and growth of industrialization in the country, pollution of surface and groundwater by municipal and industrial wastes has increased tremendously. This has also re sulted in the most unsanitary conditions in the environment (Jain et al., 2002). Sources of water generation in the study area may be divided in three categories viz. municipal, agricultural and industrial. Coverage of the sewage collection system is only 48
practiced in the towns, whereas the villages do not have any such system at all. Further, the sewage treatment plants only exist in very few towns (e.g. Haridwar, Saharanpur) and operate at a much lower installed capacity than the designed one. The raw untreated sewage as well as the treated sewage is disposed off on land, in streams and ponds. Sewage farming is also common. In the absence of well designed sanitary landfills , municipal and industrial solid waste is invariably dumped on land, creating general nuisance and degradation of soil and water in quality. For example, the solid waste generated in Roorkee town is disposed in Saliar village (about 6 km away from Roorkee town) , and raw untreated sewage is employe d for farming in the adjacent area. Approximate number of tractor trolleys used to carry the solid waste from Roorkee Municipality to Saliar village is 17 to 20 per day, with each tractor trolley carrying about 30 to 40 quintals of waste and its volume has been reported as 5.5 m3 /day to 6.5 m3/day (Banerjee, 2001). With the expanding population during last few decades, agricultural production has also increased to meet their needs. The potential for irrigation has been increasingly tapped to raise both agr icultural productivity and the living standards of the rural and urban population. Irrigated agriculture occupies a major place in the study area, which has an extensive surface canal network (Ganga and Yamuna Canal system) and a large number of tubewells. The range of average groundwater application in the study area has been reported as 115 mm (minimum) to 405 mm (maximum) , whereas surface water application has been reported as almost nil to 291mm (maximum) (NIH, 2002; Singh et al., 1997). Extensive use of fertilizers and pesticides has been observed in the region, increasing the risk of contamination of groundwater by nutrients and toxic pesticide residues. In addition, sewage farming (employing raw as well as treated sewage) is extensively practiced in many locations. T he nutrients in wastewater, particularly nitrogen, phosphorus, potassium and minor elements supplement soil nutrients and are expected to reduce the requirement for commercial fertilizers (Roy, 2000). Associated environmental hazards with use of sewage are contamination of groundwater and accumulation of heavy metals and toxic organics in surface soils and water bodies (FAO, 1985 in Roy, 2000) . Shuval et al. (1986) have shown that sewage farm workers in India are exposed to raw wastewater in areas where Ancylostoma (hookworm) and Ascaris (nematode) infections are endemic , have significantly high levels of infection with these two parasites compared with other agricultural workers in similar occupations. Sewage farm workers are also liable to become infected with cholera if irrigation is practiced with raw wastewater derived from an urban area in which a cholera epidemic is in progress (Shuval et al., 1985). However, morbidity and serological studies on 49
wastewater irrigation workers or wastewater treatment plant workers occupationally exposed to wastewater directly and to wastewater aerosols have not been able to demonstrate excess prevalence of viral diseases. There are large number of industries in the study area especially in the upper part of Hindon river, related to paper, milk products, distillery and small-scale cottage industries pertaining to electroplating, paperboard, chemicals, and rubber. The waste effluents generated from these industries are discharged either directly or after partial treatment into the Hindon river and Solani river or into their tributaries or sometimes even injected into the abandoned wells inadvertently. Most of these effluents contaminate the receiving waters as can be sensed from the foul odor, apparent color , ill-health symptoms and violation of Indian drinking water quality standards especially in the stretches in the immediate downstream of their outfalls. More details about the industrial activity in and Saharanpur town, a part of the study area are presented below: • Major industries: Foremost Dairies, Star Paper Mill and C o-operative Company Ltd. (Distillery). • Small Scale Industries: Paper Board, Steel Rolling, Chemicals, Electroplating and Sugar mill. Foremost Dairies: Wastewater in dairy industry originates from washing of equipment, product spillage and losses. The effluent from the foremost dairies is milky white in color. The amount of discharge is reported to be about 2000 kilo liter per day. The discharge from the factory is released into nearby Nagdeo nullah at a distance of 1.25 km through covered pipes. The results of the laboratory analysis of wastewater (before treatment) indicate that this wastewater has high organic content in the form of BOD and total suspended solids. In addition, the wastewater also exhibits high content of phosphorus, oil and grease. Rajgopalan (1972) has pointed out that in dairies, which produce a variety of milk products such as butter, cheese, milk powder, condensed milk and casein, etc. the wastewater is relatively more concentrated and contain high amount of organic matter (BOD), suspended solids and fat. The dairy wastes also contain sodium as predominant inorganic constituent, which is derived from common salt and alkaline cleansing agents used in dairies. Star Paper Mill: The effluents from the Star Paper Mill, utilizing Kraft process, are dark to light brown, green or yellow in color and have pungent or irritating smell depending upon the nature of washing. They are highly alkaline in nature. The dissolved solids are
50
higher than suspended solids. The BOD and COD are high and contain enough amount of lignin. The effluents are discharged through a covered drain into the Hindon River. The effluent discharge from the mill amounts to 37, 950 kilo liter per day. The waste has been found to be having high total alkalinity, suspended solids, TDS, BOD and COD. Distillery: The effluents from the distillery unit contain large amounts of dissolved solids, organic matter, are decomposed by biological action and are discharged into the Dhamola nala through covered municipal drains. (Seth, 1993) . Electroplating Units: There are several small-scale electroplating units in the study area. In electroplating, the metal acts as the cathode while the plating metal in solution serves as the anode. The total liquid wastes are not voluminous but are extremely dangerous because of their toxic contents. The most important contamina nts are acids and metals such as Cr, Cd, Pb, Ni, Sn, and Cyanides. Alkaline cleaners, oil and grease are also found in these wastes. The wastes generated by these units are directly disposed into nearby drains, which carry the pollutants directly into the Hindon river system (Seth, 1993).
51
CHAPTER - IV
MATERIALS AND METHODS Water or soil data are only as good as the water or soil samples from which the measurements are made. Even the most precise laboratory analysis of a water or soil sample cannot compensate for improper or poorly executed sampling procedures or for physical and chemical alteration of a sample due to inappropriate sample collection, transport or storage. This chapter describes the monitoring program, selection of parameters and the analytical methods employed in the procurement and analysis of soil and groundwater samples for the present study.
4.1 SOIL MONITORING PROGRAMME 4.1.1 Collection of Samples Soil samples were collected from different types of land use, using hand auger from a depth ranging up to 50 cm and stored in clean self-sealing plastic bags before transportation from the sits. These were air-dried in the laboratory, and passed through a 2 mm sieve, after they had been disaggregated with a porcelain pestle and mortar. Subsequently, samples were stored in clean self-sealing plastic bags for further analysis.
4.1.2 Soil Texture Analysis Soil texture is a term commonly used to designate the proportionate distribution of different sizes of mineral particles in a soil. It dose not include any organic matter (Brown, 1990). Preparation of the samples for textural analysis consisted of the following steps (Carver, 1971): •
Breaking all clumps and mashing with fingers.
•
Mixing sample thoroughly and splitting.
•
Coning and Quartering the sample.
•
Taking 50-100 gm sample chunk, removing carbonates by adding 1N HCl with stirrer and rim washing, followed by decanting the HCl.
•
Removing organic matter by adding 6% to 30% H2O2, stirring and rim washing.
•
Adding distilled water and heating on hot plate for 12 hours (at 40oC temperature).
53
•
Removing Iron Oxide by adding distilled water, aluminum foil and 15 gm Oxalic acid with stirrer and heating on hot plate for 10 to 25 minutes followed by decanting excess clear water..
•
Drying and weighing.
4.1.2.1 Mechanical analysis by wet sieving Evaluation of the distribution of particle sizes can be accomplished by mechanical analysis accomplished analysis. This involves sieving particles coarser than 0.05 mm (62 µ) and measuring rates of settlement for smaller particles in suspension by the wet analysis (Todd, 1980). In the present study, size analysis was conducted by wet sieving as per the following procedure suggested by (Carver, 1971): Approximately 80 gm of soil sample was weighed. The sieve rack employing a 62 micron sieve was set up and the pan was attached to the bottom of the sieve rack. Sieving and washing was done using distilled water and washing was continued until nearly all fines were washed through screen. The sediments (silt and clay) that passed through the screen were collected alongwith the sand collected on sieve. The sand and finer sediments (silt and clay) were dried and weighed. Finally, the percentage of sand in the sample was calculated.
4.1.2.2 Mechanical analysis by pipette Sedimentation is based upon Stokes' law where the terminal velocity of a spherical particle settling in a fluid of a given density and viscosity, under the influence of gravity, is proportional to the square of the particle's radius (Hillel, 1982). Thus, the larger particles settle faster. If a soil sample is completely dispersed and suspended and then allowed to settle, the sand size particles will settle past the sample zone (within the top 10 cm) in 30 seconds, while the silt size particles will take up to 8 hours. There are two methods of particle fractionation based upon Stokes' law (Carver, 1971): •
The pipette method of analysis depends upon the premise that after a particular time, all particles greater than a specific diameter would have settled below a certain depth. If a pipette is inserted into the liquid at this depth, the sample will only contain particles of diameter smaller than this size.
•
For the hydrometer method, the depth to which a hydrometer sinks when placed in a suspension is dependent on the suspension density. As the larger particles settle beyond the depth at which the hydrometer is suspended, the liquid density becomes lower and the hydrometer drops giving lower readings.
54
In the present study, the Pipette analysis method has been used for sedimentary material for the grain size smaller than 62 micron (Carver, 1971). The method has been described in details in the following paragraph. About 10 to 20 gm of the soil sample (silt and clay) was placed in a beaker, moistened with a little distilled water and transferred to a larger container. Thereafter 300 ml of distilled water and 15 ml (10%) Calgon solutions were added. Stirring was done for 15 minutes to disperse the aggregates in the soil. The sample was then transferred to a 1000 ml graduated measuring cylinder, and the volume upto the 1000 ml mark was made up with distilled water. The stem of a 25 ml pipette was marked exactly 20 cm from its tip. The measuring cylinder was placed in a sink filled with water to maintain the temperature near a constant value. The suspension was vigorously stirred for about half a minute so that the soil got evenly distributed throughout the cylinder, care was taken to avoid introducing air bubbles into the suspension. The temperature of the suspension was noted and then stirring was done again for further 30 seconds. The timer (stop watch) was started as soon as the stirrer was withdrawn. Samples were withdrawn in the marked pipette from 20 cm depth after 20 seconds and from 5 cm depth after 3 hours 36 minutes (refer Table 4.1). The pipetted samples were transferred to a weighed crucible. The crucible was placed in an oven, evaporated to dryness, cooled and weighting. Subsequently the total weight of the evaporation dish plus its contents were recorded on the data sheet, and the weight of the clean evaporation dish (ED) and the weight of dispersant (DS) were subtracted from the total. The weight of the dispersant (DS) in the evaporation dish was calculated as follows: DS= [(molecular weight of dispersant) × (molarities of dispersant in 1000-ml cylinder)]/ 50 After subtracting ED and DS, the remainder was SC, the weight of the silt and clay in the evaporation dish. Because SC values was estimated from a sample volume of 20 ml (or 1/50 of the original volume of the 1000 ml cylinder), it was multiplied by 50 to get the net total weight in the cylinder (Carver, 1971).
4.1.2.3 Using USDA textural triangle After the completion of mechanical analysis in the laboratory and obtaining the percentage for each of the soil fractions (sand, silt and clay), USDA textural triangle (Brown, 1990) was used to correctly indicate the textural class name of mineral soil (Figure 4.1).
55
4.1.3 Soil Quality Analysis All glassware and plastic ware were acid washed over night, then washed with tap water and again washed three times with double distilled water in order to avoid any risk of contamination. The parameters selected and procedures followed for analysis of soil samples, have been briefly described in Table 4.2.
Table 4.1: Pipette withdrawal times calculated from Stokes law
Diameter in Microns Finer Than
Withdrawa l Depth in cm
4.0
62.5
20
9.0
1.95
5
Diameter in φ Finer Than
Elapsed Time for Withdrawal of Sample in Hours (h), Minutes (m) and Seconds (s) 22 o
20 s 3h 52 m
23 o
24 o
25 o
20 s
20 s
20 s
3h 46m
3h 41m
3h 36m
(Source: Carver, 1971)
Figure 4.1: The USDA soil textural triangle (Sources: Brown, 1990)
56
4.2 GROUNDWATER MONITORING PROGRAMME The modality of groundwater sampling depends on the purpose of sampling and the depth of the aquifer formation from which the well draws water. Wherever the large production wells require sampling every year to every few years (because changes in water quality for such a well would be gradual), the shallower wells, particularly domestic wells with smaller pumping rates, need to be sampled once or twice a year because they are increasingly prone to short-term variations in groundwater quality and contamination (Harter, 2003). In this study, the process of sampling from groundwater wells is comprised of four steps viz. preparation for sampling, measurement of the water level, purging the well and collection of the water samples; these are explained below:
Table 4.2: Analytical methods for selected soil quality parameters S.No Parameters
Methods
References Physical
1
pH
Measured in 1:2.5 (w/v), soil: water
Jackson (1973)
2
EC
Measured in 1:5 (w/v), soil: water
Jackson (1973)
Soluble ions 3
K+* Na+* Cl-*
Flame Photometric Method Flame Photometric Method AgNO3 Titration Method
Gupta (1999)
Nutrients 4
NO3-*
Method 4500-NO3 D, Nitrate Electrode Method
APHA, AWWA and WEF (1998)
Metal **
5 *
Bioavailable fraction
Extracted by 0.1 N HCl (dilute acid), Analysis by Flame Atomic Absorption Spectrometry
Ministry of Environment (1996)
Measured in 1:5 (w/v), soil: water Most widely used method (Sutherland and Tolosa, 2000; Jung et al., 2001; Kim et al., 2002).
**
Other methods reported in the literature (Beckett, 1989) are: i)
Solutions such as NH4NO3, CaCl2.
ii)
Chelating agents like EDTA and DTPA.
iii)
Dilute acids such as 1M HNO3.
57
4.2.1 Preparation for Sampling Prior to the commencement of sampling, the field sampling equipments were cleaned and calibrated. Field sampling equipment included Pumping or bailing equipment, water level meter, water quality measuring equipment (including probes and kits for measuring temperature, pH, electric conductivity etc.), sampling bottles (cleaned and preconditioned in the analytical laboratory), and storage containers alongwith preservation agents (coolers, ice, relevant chemicals etc. as per laboratory instructions). Field sampling labels and formats (Figures 4.2) were also designed to record the details.
4.2.2 Water Table Level Measurements Water level information is important because changes in water levels may be directly linked to the groundwater quality changes. Ground water levels were measured using a survey-grade groundwater level indicator. For reading water level, depth to water was measured from a fixed and identifiable reference point at the top of the well.
Figure 4.2: Typical field sampling form
58
4.2.3 Purging the Well or Hand Pump Before the collection of the groundwater sample, the well (or hand pump) was purged to remove any stagnant water to ensure that the water sample is representative of the aquifer formation being sampled. As a rule of thumb, a minimum of three to five well volumes of water were purged (Harter, 2003). Purging continued until temperature, electrical conductivity and pH readings stabilized. The readings were taken every few minutes and recorded in a field log book together with pumping method and volume of water pumped.
4.2.4 Collection, Storage and Analysis of Samples The groundwater samples were collected from the first unconfined aquifer in the study area by shallow hand pumps and open wells falling within the identified land use regions viz. urban, rural, agriculture and forest. The water samples were collected immediately after purging. All information concerning the samples was noted on the well sampling forms. All sample bottles were filled completely but (not allowed to overflow), capped, labeled, and the sealed sample containers were put into containers packed with ice and transported to analytical laboratory. Proper preservation, wherever needed, was done to ensure that the water quality of the sample did not change between the time of its collection (in the field) and the time of analysis in the laboratory. The constituents selected analytical and procedures followed have been briefly described in Table 4.3. Procedures followed for analysis were in accordance with the “Standard Methods for Examination of Water and Waste Water” (APHA, AWWA and WEF, 1998).
4.3 FIELD ESTIMATION OF GROUNDWATER RECHARGE The technique of estimation of recharge rate by using artificial tritium method was first applied by Zimmerman et al. (1967 a, b) in West Germany. The basic principle of this technique assumes that the soil water in the unsaturated zone moves downward "layer by layer" similar to a piston flow. Since the lateral molecular diffusion mixes the percolating water rather fast, this assumption is probably valid in most natural situations e.g. in the alluvial formations, except where the alluvial cover is too thin and the basement rock is at a shallow depth or where the fissures and/or discontinuities are present in the soil profile (NIH, 1986 and 2000).
59
Table 4.3: Analytical methods for selected water quality constituents S No.
Constituents
Methods Physical
1
pH*
Method 4500 H+ “pH Value”
EC*
Method 2510 B, “Electrical Conductivity”
TSS
Method 2540 D, “Total Suspended Solids dried at 103°C-105°C”
TDS
Method 2540 C, “Total Dissolved Solids dried at 103°C-105°C” Soluble ions
2
Ca2+
Method 3500-Ca B, “EDTA Titrimetric Method”
Mg2+
Method 3500-Mg B, “Calculation Method”
k+
Method 3500-K B, “Flame Photometric Method”
Na+
Method 3500-Na B, “Flame Photometric Method”
HCO3-
“Calculation Method”
Cl-
Method 4500-Cl B, “Argentometric Method” Nutrients
3
NO3-
Method 4500- NO3 D, “Nitrate Electrode Method”
PO43-
Method 4500 B, “Ascorbic Acid Method”
TP
Method 4500 E, “Ascorbic Acid Method”
TOC
Method 5310, “Total Organic Carbon Analyzer” Total heavy metals
4
(Cd, Cr, Cu, Mn, Ni, Pb and Zn)
Method 3030 E, “Acid Digestion and Analysis By Flame Atomic Absorption Spectrometry”
* Measured at the site
(Source: APHA, AWWA and WEF, 1998)
On the basis of this assumption, if fresh water labeled with tritium enters the top of the soil below the active root zone, and is not affected by sun heating (say below 75 cm to 1m), the tagged tritium will be mixed with the soil moisture available at that depth and act as an impermeable sheet. In this case, if any water is further added at the top of the soil surface, it will be infiltrated into the ground by pushing down the older water, thus the shift in the tritium peak can be observed after some time (say after lapse of one season). But, the tritium
60
peak will also be broadened due to molecular diffusion, stream line dispersion, asymmetrical flow and other heterogeneities of the soil media.
4.3.1 Tracer Injection and Site Sampling Tracer (tritiated water) was injected in 5 selected field sites during premonsoon period. Care was taken to see that the selected sites represent the area topographically and geomorphologically. At each site, three to four sets of injections were made, the distance between the two sets being three meter (Figure 4.3). The sets were located on a line fixed by choosing appropriate bench marks usually telephone or electrical poles. Each set of the injection consisted of one central injection on the line and four injections in a circle of radius 10 cm around it. This was done in order to make sure that the tracer is not lost due to possible slight misalignment in pin-pointing the injection point while sampling the site. About 2 ml of tritiated water (the tritium of specific activity of 200 mCi/cc bought from BARC, Mumbai was diluted to the specific activity of 40 µCi/cc) was injected at a depth of 75 cm, care being taken to have a minimum disturbance to the natural condition of the soil due to the injection. Each hole was completely filled up with soil after carrying out the tritium injection. At each site, few iron nails were hammered on the line of sites of injection and left in the ground, which acted as markers for subsequent identification of the sites. Sampling was done periodically, the first one at the time of injection from a place near the injection site, second one immediately after the monsoon (giving the recharge due to rainfall) and the third just before the onset of next rainy season (which gives the net annual recharge). Soil samples were taken out layer by layer (10 cm section) by hand auger of 7.5 cm diameter. The soil sample from each layer was carefully weighed (to an accuracy of one gm) and about 200 gm of the sample was collected in properly sealed polyethylene bottles so that there was no exchange of the moisture with the atmosphere. The sampling was done upto a desired depth (usually upto 3.5-4.0 m).
4.3.2 Laboratory Experiments The laboratory experiments consisted of estimation of soil moisture content, particle size analysis and measurement of tritium counts in the soil sample.
4.3.2.1 Soil moisture content The moisture content of the soil samples (on wet weight basis) was estimated by gravimetric method.
61
Wet weight of each soil sample was first determined by weighing the sampling using electronic balance. Subsequently, the bulk density of each sample was determined by dividing the wet mass of the sample by the volume of the each sample, which is equivalent to the volume of hand auger of known diameter for particular depth of soil column. Mass of the wet soil Bulk density = --------------------------Volume of the sample The moisture content of the sample was also estimated by gravimetric method. About 10 to 25 gm of the soil sample was accurately weighed and then kept for overnight heating in an oven at a temperature of 110 Co. The dried samples were cooled in desiccators and weighed again. The difference of the two weights gave the weight of the moisture content of the sample. Weight of the moisture Moisture content = --------------------------Weight of the field soil The volumetric moisture content for each soil sample was also estimated by multiplying the moisture content by Bulk density of the soil.
Main Road Iron Nail
Tree
5 injection 10 cm apart in each set
3m
30ml. syringe Plastic tube Brass tube needle Im long 0.4 cm O.D. 0.25 cm bore
Electrical pole as land mark
75 cm Steel rod 0.72 cm diameter 1m long
Figure 4.3: Diagram of Systematic injection layout and implements for artificial tritium injection at test site
62
4.3.2.2 Estimation of field capacity The particle size of the soil sample collected from the field was evaluated as mention in section (4.1.2) by sieve analysis and sizer analysis. On the basis of the soil texture, the field capacity was estimated (Table 4.4).
Table 4.4: Range of available water holding capacity of soils Soil type Field capacity Fine sand 3-5 Sandy loam 5-15 Silt loam 12-18 Clay loam 15-30 Clay 25-40 (Source: Michael, 1970) The effective volumetric moisture content (θv) was measured by multiplying volumetric soil moisture content and field capacity of the sample.
4.3.2.3 Measurement of tritium activity in the samples The measurement of tritium activity in the samples consisted of two parts;
•
The pore water from soil samples was extracted by distillation under low pressure
to avoid volatile impurities being collected alongwith the water (Figure 4.4). Water from each soil sample was extracted and stored in the plastic/glass vials. One ml of the soil water extracted from each 10 cm layer was mixed with 10 ml of scintillation cocktail 'W' (Sisco Research Laboratories {SRL}, Mumbai). The scintillation cocktail 'W' is commercially available and was prepared by dissolving 10 gm of [2,5 – Diphenyl oxazole (PPO)], 0.25 gm of [(1,4- Di-2, (5-phenyloxazolyl)-Benzene) (POPOP) and 100 gm of Naphthalene in one liter of [1,4-Dioxane]. The samples were stored in potassium free glass scintillation vials for counting.
•
The net tritium count rate was measured using the liquid scintillation system,
which is at present being used at Nuclear Hydrology Laboratory of National Institute of Hydrology, Roorkee. The model 'System 1409' of M/S Wallac Oy, Finland was employed whose efficiency is around 60% for tritium. The system provides an elegant way of counting the activity of tritium using 'Easy Count' approach. Each cell was counted for 300 second, the rate for each sample was calculated by subtracting the rate of cell (cocktail with sample) from the rate for cocktail 'W' only. The count rate of the samples was plotted as a histogram against the individual depth intervals. 63
The center of gravity (C.G.) of the profile gave the exact location of the tracer. The difference of C.G. and the point of injection gave the displacement (d) of the tracer at the time of sampling after the injection (Appendix 1). The recharge to groundwater for various sites was finally determined by multiplying the peak shift of tritium as calculated above and effective average volumetric moisture content in the peak shift region.
Capillary tube
Brass container
Heater
Dry Ice
Water sample
PVC bottle
Soil sample
Vacuum Pump Manometer
Figure 4.4: Schematic diagram of the system used for extraction of pore water from soil sample for tritium analysis (Source: Bhandari et al., 1986)
64
CHAPTER - V
DEVELOPMENT OF THE DATABASE
5.1 INTRODUCTION The field of information systems was developed during the late 1960s on words and the concept of the database management systems were developed and refined subsequently. Today, sophisticated data base management systems are used to handle enormous data, such as, national scale census information or global statistical data (Aronoff, 1991). GIS belongs to the category of data management systems that require building of large databases before they become useful. It is unlike many micro-computer applications where a user can start using only after the procurement of the hardware and software. These tasks are large and complex, and require substantial planning before the data collection (Becker et al., 2004).
5.2 THE DATABASE APPROACH A database is a collection of information about objects and their relationships to other objects. This information is stored as one or more computer files that is accessed by the special purpose database software in whatever manner the designer believes to be most efficient. In addition, the database management system (DBMS) is comprised of a set of programs that have the capability to store data when the databases are accessed through the DBMS. This file processing approach of database management is illustrated in Figure 5.1.
Figure 5.1: Structure of an Environmental Data Base Management System (Source: Aronoff, 1991)
65
There are many advantages of using the DBMS, such as centralized control, efficient data sharing, data independence, easier implementation of new data base application, direct user access, controlled redundancy and user views. In a database, the data files can be described in terms of records, fields, and keys. Small groups of related data items are stored together as a record. A record can be thought of as one row in a table, as shown for the location data in Figure 5.2. Each record is divided into fields, each of which contains an item of data. A field gets defined where a particular type of data can be found in the record. In addition, the record is retrieved from the data file by means of a key. The key is used to efficiently search the records that have a particular value. The conceptual organization of database is termed the data model. The classical data models used to organize the electronic databases can be summarized as follows (Aronoff, 1991): 1) Hierarchical data model: In this model (Figure 5.3), the data are organized in a tree structure and the information is retrieved by traversing the tree structure. There are higher-level elements termed Parents, and one or more subordinate elements, termed children. 2) Network data model: This model overcomes some of the inflexibility of the hierarchical model. In this model, an entity can have multiple Parent as well as multiple Child relations. 3) Relational data model: In this model, there is no hierarchy of data fields within records, and every data field can be used as a key.
Location ID Location Name Latitude Field Field Field
Longitude Field
Elevation Field
Record 1
001
MUTALPUR
29.87417
77.89028
262.89
Record 2
002
MADHOPUR
29.87639
77.88972
266.45
Record 3
003
SALEMPUR
29.87722
77.88778
263.22
Figure 5.2: Organization of information as records in a Data file
66
Figure 5.3: Organization of a Data Base using the Hierarchical Data Model (Source: Aronoff, 1991)
5.3 SOURCE AND FORMAT Data in the present study were of two types: spatial data and non-spatial data. Spatial data were taken from maps, satellite imageries, etc. They could be directly digitized into GIS environment. Non-spatial data were the tabular data taken from tables, points, lists, etc. and this data could be stored in the database. The Data were acquired during the preceding three years through field monitoring, laboratory analysis and from government offices, in different formats, scales and, levels of spatial completeness, temporal resolution etc. The sources and types of data employed in this study are given in Table 5.1.
5.4 DATABASE CONSTRUCTION The idea was to develop a database specifically for the purpose of this research, and at the same time to keep it as flexible as possible to eventually use the data for a variety of purposes. The database was developed using Visual FoxPro 6.0. The opening page of the database system is shown in Figure 5.4.
67
Table 5.1: Sources and types of the data employed in this study Data Depth to groundwater Groundwater Recharge Monthly rainfall Drill-holes Topography Soil Hydraulic conductivity Land use information Water quality
Data Source Field survey & GWD Field survey & NIH GWD TWD SOI Field survey & NBSSLUP GWD IRS Field survey
Format Table Point Table Table Map Table & Map Point Map Table
Date 1988-2002 1998, 1999, 2002 1985-2002 1950-2000 1967-1987 2002 1996 2002-2003
GWD = Ground Water Department (Uttar Pradesh); NIH = National Institute of Hydrology (Roorkee); TWD = Tube Well Division (Saharanpur and Haridwar); IRS = Indian Remote Sensing satellite. SOI = Survey of India, NBSSLUP = National Bureau of Soil Survey and Land Use Planning
Figure 5.4: Opening page of the database system The database systems was developed employing Hierarchical data model presented in Figure 5.5. The following sub-sections describe the attributes of the database in details included as tables, queries and forms.
68
69 Figure 5.5: The database environment
69
70
5.4.1 Location Database The attributes of the sites for different types of data were stored in Location database, linked with all other databases by primary key (Location ID). This database consists of the following Free tables: • State table, containing the following column fields; state ID (Sta_id), state name (Sta_name). • District table, containing the following column fields; district ID (Dis_id) and name of district (Dis_name). • Block table, containing the following column fields; block ID (Blo_id) and name of block (Blo_name). A sample of Block table is shown in Figure 5.6. • Landuse table, containing the following column fields; land use ID (Lan_id) and name of land use (Lan_name). • Location table, containing the following column fields; Location ID (Loc_id), location name (Loc_name), land use ID (Lan_id), state ID (Sta_id), block ID (Blo_id), District ID (Dis_id), Toposheets number (Top_num), longitude (Long_m), latitude (Lati_m), elevation (Elevation). Input form for Location table is showing Figure 5.7. The Location table contains one record for each site and the Location ID is unique for each site. In addition, location records are linked with the other four tables by the State ID, District ID, Block Id and Land use ID. Figure 5.8 shows some records from the location table. The Location database links all other databases pertaining to Water table monitoring, Soil, Lithologs, Groundwater Recharge, etc. through the Location ID.
Figure 5.6: Block table
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A
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Figure 5.7: Input Form for Location table. Part A highlights input field, whereas Part B highlights various windows displaying the data.
Figure 5.8: Few records from Location table
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5.4.2 Water Table Monitoring Database This database consists of the following two tables: • Observation well table (obs_well) highlights general information on observation wells and contains the following column fields: Location ID (Loc_id), Location name (Loc_name), Observation well ID (Obs_id) Longitude (Long_m), Latitude (Lati_m), R.L. of measuring point (Rlmp), R.L. of ground level (Rlmp), Construction year (Date), Total depth (Depth), Average depth for Premonsoon (Avr_pre), Average depth for postmonsoon (Avr_pos) and number of records (Rec). Figure 5.9 shows few records from this table. • Water table data table (Wat_tab_data) contains the following column fields: Serial number (Ser_num), Observation well ID (Obs_id), Location ID (Loc_id), Location name (Loc_name), Year of measurement (Year), Water table depth (Depth), and Season (Season). Figure 5.10 shows input form and some records from this table. In the Observation well table, the identifier is the Observation well ID, and for each observation well record, the water table data table contains as many records as the number of measurements which were identified by serial numbers. The relationship between the two tables is one-to-many and the common key that links the tables is Observation well ID. In addition, the Observation well table and the Location table are related and the command key linking these tables is Location ID. A program was also developed to calculate mean values of premonsoon and postmonsoon data from Water table data table and store the result in Observation well table.
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Figure 5.9: Input form for Observation well table
Figure 5.10: Input Form and some records for Water table data.
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5.4.3 Soil Database Soil database consists of the following two tables: • Soil table highlights general information for the sample locations and contains the following column fields: Serial number (Ser_num), Location ID (Loc_id), Location name (Loc_name), Longitude (Long_m), Latitude (Lati_m), Type soil code (Soi_id), Type Soil name (Soi_name). Figure 5.11 shows input form and some records for the Soil table. • Soil type table contains the following column fields: Code of soil type (Soi_id) and Soil type name (Soi_name). Figure 5.12 shows input form and some records for the Soil type table The identifier key in the Soil table is Location ID. Soil table is linked with Location database in one-to-one relationship. Soil table is also linked with Soil type by the Soil type ID and the relationship is one-to-many.
Figure 5.11: Input Form for Soil table.
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Figure 5.12: Input Form for Soil type table.
5.4.4 Lithologs Database The Lithologs database consists of the following three tables (Figure 5.12): • Boreholes table displays general information of the boreholes and consists of the following column fields: Borehole ID (Bor_id), Location ID (Loc_id), Location name (Loc_name), Longitude (Long_m), Latitude (Lati_m), Depth of borehole (Depth), Year of construction (Year) (Figure 5.13). • Boreholes logs table consists of the following columns: Location name (Loc_name), Borehole ID (Bor_id), Strata number (Str_num), Strata ID (Str_id), Name of strata (Str_name), Strata Top (Top) and Bottom (Bot), Strata thickness (Thc), and DRASTIC range (Range) (Figure 5.14). • Log type table (Decode table) consists of the following column fields: Type of strata (Str_id), Name of strata (Str_name), Strata code (Str_code), DRASTIC range (Range). In Boreholes table, there is one record for each borehole and the Identification (Bor_id) is linked with borehole log table (which has many records) in an one-to-many relationship. In addition, there is a relationship between borehole log table and Log type table using Type strata ID.
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Figure 5.13: Input Form for Boreholes table
Figure 5.14: Input Form for Borehole log table
5.4.5 Groundwater Recharge Database The groundwater recharge database consists of a single table of the following columns fields: Serial Number (Ser_num), Location ID (Loc_id), Location name (Loc_name), Longitude (Long_m), Latitude (Lati_m), Percent recharge (P_recharge) and recharge value (Rec_value). This table may be linked with location table through Location ID. Figure 5.15 shows the input form and some records for recharge table. 77
Figure 5.15: Input from for recharge table
5.4.6 Hydraulic Conductivity Database The Hydraulic conductivity (HC) database consists of a single table of the following column fields: Hydraulic conductivity ID (Hyc_id), Location ID (Loc_id), Location name (Loc_name), Longitude (Long_m), Latitude (Lati_m), Hydraulic conductivity value (Hyc_value), Aquifer thickens in m (Thc) and Transmissivity value (Tra_value). This table may be linked with location table through Location ID. Figure 5.16 shows the input form and some records for Hydraulic conductivity table
Figure 5.16: Input form for Hydraulic conductivity table.
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5.4.7 Rainfall Database The rainfall database contains the following two tables. • Rainfall table (main table) contains the following columns fields: Station ID (Stn_id), Station name (Stn_name), Location ID (Log_id), Location name (Loc_name), Longitude (Long_m) and Latitude (Lati_m). Also, it contains the monthly mean value of various months viz. January (Jan_av), February (Feb_av), March (Mar_av), April (Apr_av), May (May_av), June (Jun_av), July (Jul_av), August (Aug_av), September (Sep_av), October (Oct_av), November (Nov_av), December (Dec_av) and the Annual rainfall (Anu_av). Figure 5.17 shows the input form and few records from rainfall table. • Monthly rainfall table (Mon_rain) contains the following columns fields: Station ID (Stn_id), January (Jan_av), February (Feb_av), March (Mar_av), April (Apr_av), May (May_av), June (Jun_av), July (Jul_av), August (Aug_av), September (Sep_av), October (Oct_av), November (Nov_av), December (Dec_av) and the Annual rainfall (Anu_av). Figure 5.18 shows the input form and few records from Monthly rainfall table.
Figure 5.17: Input form for Rainfall table.
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In Rainfall table, there is one record for each station and the identifier key is Station ID. Through this key, the Rainfall table records may be linked with the Monthly rainfall table records. The relationship between these tables is one-to-many. The monthly and annual rainfall data of each year were stored in Monthly rainfall table. A programme was also developed to calculate the annual and monthly mean for all the years at each station and to store the results in the Rainfall table.
Figure 5.18: Input form for Monthly rainfall table.
5.4.8 Water Quality Database Water quality database was constructed in a different style, in which a separate table was created for each data set. The name of each table contains seven characters [letter t, two codes for the year, two codes for the month and two codes for premonsoon (pr) and postmonsoon (ps) respectively] i.e. t0301ps indicating data set of January, 2003 postmonsoon (Figure 5.19). In addition, a specific form was prepared to manage these tables (create, modify and delete), and also to validate and process the data as given bellow. 1. Ca and Mg concentration were calculated directly from the value of Total hardness and Calcium hardness as follows: Mg Hardness = Total Hardness – Ca Hardness Ca (mg/l) = (Ca Hardness / 2.497) Mg (mg/l) = (Mg Hardness / 4.116) 2. The Anion-Cation balance was calculated as follows: ⎡ %difference = 100 × ⎢ ⎣
(∑ cations − ∑ anions )
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3. The total dissolved solids (TDS) concentration (APHA, AWWA AND WEF, 1995) was calculated as follows Calculated Total dissolved solids (TDS) = 0.6 (alkalinity as CaCO3) + Na+ + K+ + Ca2+ + Mg2+ + Cl- + SO42- + NO3- + F The acceptable ratio is 1.0< (Measured TDS / Calculated TDS) < 1.2 4. The ratio of calculated EC to measured EC, (APHA, AWWA AND WEF, 1995) was checked against the acceptable ratio as follows: 0.9 < (calculated EC / measured EC) < 1.1 The table structure consists of the following columns fields: Serial
number
(Ser_num), Location ID (Loc_id), Location name (Loc_name), Number of samples in each location if available (Repeate), Land use ID (Lan_id), Land use name (lan_name), Longitude (Long_m), Latitude (Lati_m), Type of sample hand pump or well (Type), the physico chemical parameters (in mg/l) viz. T (Temperature), pH, EC, TDS, total hardness (TH), calcium hardness (Ca H.), magnesium hardness (Mg H.), Ca2+, Mg2+, Na+, K+, Cl-, CO32-, HCO3-, total alkalinity (TA), SO42-, NO3-, PO43-, F, Cd, Fe, Mn, Ni, Zn, Pb, Cr, Cu, TOC. For calculating the ionic balance, concentration of the major ions were calculated in meq/l form (converted from mg/l) and the fields were filled automatically. Figure 5.19 shows the input form and Figure 5.20 shown some records from a water quality table
Figure 5.19: Input form for Water quality database 81
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CHAPTER – VI
PREPARATION OF THEMATIC MAPS As discussed earlier, groundwater vulnerability of an area is dependent on the parameters like Groundwater depth, Net recharge, Slope, Soil type, Aquifer media, Vadose zone and Hydraulic conductivity. This chapter describes the procedures to generate the thematic maps depicting these parameters and the classifications therein.
6.1 GEOGRAPHIC INFORMATION SYSTEM (GIS) A geographic information system (GIS) may be defined as ". . . a computer – based information system which attempts to capture, store, manipulate, analyze and display spatially referenced and associated tabular attribute data, for solving complex research, planning and management problems." (Fischer and Nijkamp, 1993). The term geographical information system (GIS) is used generically for any computerbased capability for the manipulation of large volume of geographical information (Bernhardsen, 2002, Figure 6.1). GIS softwares are under continuous development, with respect not only to specific GIS program functions, but also operating system and other general program tools. These softwares may be divided into four main categories (Aronoff, 1991, Figure 6.2): • Functions for entering, registering, and storage of data. • Functions for correcting and adapting data for further use. • Functions for processing and analyzing data. • Functions for data presentation. The steps of data generalization and generation of thematic layers as undertaken in the present study have been discussed in following sections:
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Figure 6.1: Geographic Information System Manipulation (Source: Bernhardsen, 2002) .
Figure 6.2: The planning Process Geographic Information processing begins and ends with the real world. (Source: Aronoff, 1991)
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6.2 DATA GENERALIZATION 6.2.1 Geocoding of Topographic Maps Sixteen of topographic maps at 1:50,000 Scale cover entire the study area (Figure 6.3). These maps were available as paper maps (hard copies) and were scanned and a stored as raster file (tiff format). The scanned maps were georeferenced (Figure 6.4) using ERDAS 8.5 software employing the Ground Control Point (GCP) method. For each map file, four corner coordinates were entered as GCP and a first polynomial transformation was used to recalculate the coordinates of each pixel in the raster layer. Precaution was taken that the Root Main Square (RMS) error always remained less than the pixel value. After resampling (the method was applied to obtain smooth appearance of the map), a new raster file (geotiff format), with a resolution of 300dpi was obtained. An evaluation of the quality of georeferencing was carried out by loading all the topographic maps in ArcView. The process was repeated till the matching of the sides of the maps. These topographic maps were subsequently used for georeferencing of the satellite imagery.
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Figure 6.3 Topographic map index
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6.2.2 Satellite Data Georeferencing Digital data of satellite IRS 1D LISS – III sensor were available for the study area in BIL (Band Inter Leaved) format, without georeferencing. As the raw satellite image could not be integrated with other geocoded GIS layers, therefore, the first step was to geocode the satellite data. The geocoded topographic maps were used for this propose (Figure 6.5).
Figure 6.4: The Geographic grid (Source: Demers, 1997) The satellite scene was first imported in ERDAS and an FCC was prepared. As satellite scene covers larger area than a single topographic map, a number of topographic maps were combined together (in a mosaic) to cover the area of the satellite scene. The process of georeferencing was carried out as an image – to image registration, i.e. a single image registered on another single image. The procedure of image-to-image registration uses Ground Control Point (GCP) recognizable both on the satellite image and on the topographic maps in order to attribute ground coordinates (in a given coordinate system) to the first one. GCPs should preferably be located on the reference image, usually on features such as cross roads, railway lines and canal (Dana, 1994). In the present study, selected GCPs fell in the majority of cases on river confluences the hilly area or on road crossings in plains areas. A total of 8 to10 GCPs were identified for each scene scattered uniformity over the image. The RMS errors were always kept less than the pixel size. Location of different observation points and the GIS layers of land use, drainage network, soil, etc. for the whole study area were digitized at the computer screen by analyzing the satellite images and the rasterized topographic maps.
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Figure 6.5: Digital Data of Satellite IRS-1D LISS-III Sensor (Row: 96 and path: 50)
6.2.3 Map Projection: Map projection can be defined as a mathematical flat surface representation of the geodetic ellipsoid (Figure 6.6). There are different projection formulae that preserve either shape, area likeness or distance in a particular direction. Length preservation is not possible, where the scale is the same in all directions. Cylindrical, conical and azimuthal projections can be made equidistant, equal in area, or conformal. Because fixed mathematical formulae are used, it is possible to convert to and from different projections systems without loss of accuracy (Bernhardsen, 2002). The best-known map projection coordinate system is the Universal Transvers Mercator (UTM) Grid established by the U.S. Department of Defence in 1974, which is based on a special type of cylindrical projection. UTM covers the surface of the earth from 84o North to 80o South with the help of 60 zones, each zone having a width of 6o (Figure 6.7).
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Figure 6.6: Map projections (a) azimuthal projection; (b) conical projection; (c) cylindrical projection.
Figure 6.7: The Universal Transverse Mercator system (Source: Dana, 1994)
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The zones are numbered eastward from one to 60, starting west of continental Alaska. The central meridian in each zone represents the zone’s north-south axis, and the origin of the coordinate system lies at the meeting point of the axes and the equator. Thus, the equator represents the zone’s east-west axis (Bernhardsen, 2002). In this research work, all the GIS shapefiles (described in the next sections) were reprojected in ArcView software using UTM-1983 projection (zone 43 with central meridian at 75).
6.3 GENERATION OF THEMATIC LAYERS 6.3.1 Digital Elevation Model and Slope Digital Elevation model (DEM) generally refers to a regular array of elevations (squares or hexagons) and is represented as a raster/grid map. Each cell in the grid has its own elevation value. Topographic information was collected from Survey of India Toposheets at 1:50,000 scale in the form of elevation contours at 10 m intervals and benchmark points (Figure 6.8). These data were digitized and saved in shapefile as points in the UTM projection system. The elevation data were interpolated using Kriging algorithm employing spherical variogram with a lag interval of 200 m, search distance of 250 m, and pixel size of 100 m. A total of 1989 elevation points were used to generate the DEM of the study area. Kriging Interpolator extension for ArcView, developed by Marco Boeringa, was used. The elevation ranged between 234.0 m in the southern part of the study area to 410.0 m in the northern part (Figure 6.9). The DEM indicates that most of the study area exhibits flat topography except the northern region. The slope map (in percent) was prepared from DEM of the study area using the Hydrological Tools extension of ArcView GIS 3.2 (developed by Joebp Luijten). The percent Slope map was classified and most of the study area was categorized between 0.0% to 1.0% class. But some parts of northern region fall between slope categories of 1.0% to 4.0%. Slopes more than 4.0% also appeared in the distal northern part (Figure 6.10).
6.3.2 Depth to Groundwater Depth to ground water was monitored in 119 wells and piezometers during January 2002 to December 2003. Besides the field observations, historical data for past 10 years (1991-2000) were obtained from Uttar Pradesh Groundwater Department, Roorkee. The data were stored in the “Water table monitoring database” (cf. section 5.4.2) and the mean values for depth to groundwater were calculated for each observation point for premonsoon and
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postmonsoon seasons. The table Wat_tab_dat.dbf obtained from the database was imported into ArcView GIS and converted into shapefile (Figure 6.11). The observation points originally expressed as geographic coordinates were converted to UTM projection system for interpolation. Finally, the point data were interpolated using Kriging method (cf. section 6.2.3).
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In the study area, as the precipitation is concentrated mainly in monsoon season, depth to groundwater is generally reduced during monsoon and postmonsoon, whereas it gets deepened during premonsoon because discharge rates are greater than the recharge rates. Under natural conditions, depth to water-table is related to topography. According, water levels are closest to land surface in valleys (discharge areas) and deepest in highland recharge areas. Maps of depth to groundwater measured during postmonsoon and premonsoon periods presented in Figure 6.12 and Figure 6.13 respectively show that groundwater depths are maximum in the northeastern part of study area (29 m and 32 m bgl. respectively) in the Bhabar zone , and minimum (1 m and 2 m bgl. respectively) in the alluvium of the southern parts of the study area.
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The magnitude of seasonal fluctuations in water levels in response to monsoon recharge is related to aquifer porosity and storage. After recharge, the rise in water levels may be greater and sustained longer in aquifers with low permeability than in aquifers with high permeability. The range of seasonal fluctuation generally varied between 1 m to 3 m in the study area, reflecting the different hydrogeologic conditions and possible spatial variability in recharge rates or storage characteristics of the aquifer. It was found to be negative at few locations, where the discharge exceeded the recharge (Figure 6.14).
6.3.3 Soil Map The soil samples were collected from 48 locations. Adequate care was taken to follow the requisite procedures for preliminary treatment, coning and quartering as well as removal of substances that interfere with dispersal. The wet sieving and the pipette analysis were done for the samples to ascertain the soil texture (cf. section 4.1.2). The resulting data were stored in the soil database (cf. section 5.4.3) (Table 6.1). Table soil.dbf was imported to ArcView as a shapefile. The soil map was georeferenced with respect to the base map and brought to same coordinate system as that of other maps (Figure 6.15). The soil texture map, thus prepared was compared with the reference map obtained from National Bureau of Soil Survey and Land Use Planning (NBSSLUP). A good correlation was observed between the experimental soil map and the NBSSLUP soil map. A look at Figure 6.16 indicates that most of the northern part of the study area as well as the paleochannels and drainage portions illustrate sandy loam texture whereas the remaining part of the study area is covered by soils with silty loam texture.
6.3.4 Flow Direction Groundwater flows from a higher to lower head (water-level altitude), and therefore the general direction of horizontal ground-water flow can be estimated from a map of the water table or potentiometric surface. If there are no vertical head differences, then flow is strictly planar (two-dimensional). In isotropic aquifers, where hydraulic conductivity is independent of direction, the flow is parallel to hydraulic gradient. In anisotropic aquifers, where hydraulic conductivity depends on the direction, the flow can be oblique to the hydraulic gradient (towards the direction of highest permeability) (Senior and Goode, 1999).
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Figure 6.9: Digital elevation Model (DEM) for study area
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#
29°50'
6 # 116 103 39 3247 # 28 # # 112 # # # 14 # 25 42 # 44 # 46 53 # 96 # 8 # # # 15 # 73 # # # 21 76 # 78 ## # 58 57 87 79 90 # # 13 # 81 41 # # 92 # # 9 5 77 # 45 # # 2 # # 93 71 9155 31 2436 # # 22 # 72 # # # # # # 95 34 # 1 # # 62 23 # 48 # # # 65 # # 88 # 43 # 56 ## 26 # 86 54 # 60 89 # 106 # 61 # 3108 111 # 104 35 # # # # 69 # # # 11 59 105 101 64 # 109 # 29 # # # 4 # 50 # 74 49 63 38 # # 27 30 84 # # # # # 97 # 85 # 110 # #
10
30°00'
98
#
16 #
52
#
30°00'
118 117 70
#
30°10'
12 #
20
29°50'
119 #
#
100
29°40'
N
78°10'
30°20'
77°10'
#
20 Kilometers
77°20'
77°30'
77°40'
77°50'
78°00'
Observation Points
78°10'
Figure 6.11: Index map of the observation points
77°20'
77°30'
77°40'
77°50'
78°00'
30°20'
30°10'
30°10'
Sadhuli Qadim T $
Muzaffarabad T $
T $
30°00'
Saharanpur
% U
Gangoh T $
29°40' 77°10'
77°20'
77°30'
Deoband T $
Narsen T $
77°40'
77°50'
Laksar T Khanpur $ T $
29°40'
Nanauta T $
Nagal T $
29°50'
Rampur T $
Haridwar Bahadrabad U % T $ Roorkee T $
Bhagwanpur T $
30°00'
Punwarka
Sarsawa T $ Nakur T $
29°50'
N
78°10'
30°20'
77°10'
78°00'
Depth to Groundwater (m bgl) < 2.5 2.5 - 5 5 - 7.5 7.5 - 10 10 - 12.5 12.5 - 15 15 - 17.5 17.5 - 20 20 - 22.5 22.5 - 25 25 - 27.5 >= 27.5
78°10'
Figure 6.12: Distribution of depth to groundwater in postmonsoon
95
96
77°20'
77°30'
77°40'
77°50'
78°00'
N
78°10' 30°20'
30°20'
77°10'
30°10'
30°10'
Sadhuli Qadim T $
Muzaffarabad T $
T $
30°00'
Saharanpur
% U
Nagal T $
Rampur T $
Gangoh T $
29°40'
77°20'
Deoband T $
Narsen T $
77°40'
77°50'
77°30'
Laksar T Khanpur $ T $
29°40'
Nanauta T $
77°10'
Haridwar Bahadrabad U % T $ Roorkee T $
Bhagwanpur T $
29°50'
29°50'
Nakur T $
30°00'
Punwarka
Sarsawa T $
78°00'
Depth to Groundwater (m bgl) < 2.5 2.5 - 5 5 - 7.5 7.5 - 10 10 - 12.5 12.5 - 15 15 - 17.5 17.5 - 20 20 - 22.5 22.5 - 25 25 - 27.5 >= 27.5
78°10'
Figure 6.13: Distribution of depth to groundwater in premonsoon
77°20'
77°30'
77°40'
77°50'
78°00'
N
78°10' 30°20'
30°20'
77°10'
30°10'
30°10'
Sadhuli Qadim T $
Muzaffarabad T $
T $
30°00'
Saharanpur
% U
Rampur T $
Gangoh T $
29°40' 10
77°10'
0
10
77°20'
Nagal T $
Deoband T $
Narsen T $
Laksar T Khanpur $ T $
29°40'
Nanauta T $
Haridwar Bahadrabad U % T $ Roorkee T $
Bhagwanpur T $
29°50'
29°50'
Nakur T $
30°00'
Punwarka
Sarsawa T $
Groundwater Fluctuation (m) < -1.5 -1.5 - 0 0 - 1.5 1.5 - 3 >= 3
20 Kilometers
77°30'
77°40'
77°50'
78°00'
78°10'
Figure 6.14: Distribution of groundwater fluctuation in premonsoon
97
98
77°20'
77°30'
77°40'
77°50'
78°00'
N
78°10' 30°20'
30°20'
77°10'
MIRZAPUR RAIPUR # #
# ALIPUR BHAGUWALA
SADHAULI QADIM
30°10' 30°00'
SARSAWA #
GADARHERI #
NAKUR # #
#
#
MALAKPUR RAMNAGAR #### # MALAKPUR ## # SHEKHPURI JHABRERA PADLI GUJAR
29°50'
29°50'
CHANDARPUL (NAKUR) AMBAHTA NAGAL # MOHANPURA AHMADPUR # # MOHANPURA RAMPUR #
#
#
30°00'
MUZAFFARABAD JHANJHOLI # # BUGGAWALA # # KHAJNAWAR CHUNA # # RASULPUR PUNWARKA # # # BANDERJOOD CHHUTMALPUR JASAWALA RAUSHALABAD SAHARANPUR BAHERI # # # BHAGWANPUR # # TANDA # SALIAR
30°10'
#
LAKSAR LAKHNUTA
0
77°10'
10
DEOBAND #
29°40'
29°40'
#
10
#
#
NANAUTA
20 Kilometers
77°20'
77°30'
#
77°40'
77°50'
78°00'
soil sample location
78°10'
Figure 6.15: Index map of location of test sites.
77°20'
77°30'
77°40'
77°50'
78°00'
N
78°10' 30°20'
30°20'
77°10'
30°10'
30°10'
Sadhuli Qadim T $
Muzaffarabad T $
T $
30°00'
Saharanpur
% U
Rampur T $
Gangoh T $
29°40' 10
77°10'
0
10
77°20'
Nagal T $
Deoband T $
Narsen T $
Laksar T Khanpur $ T $
29°40'
Nanauta T $
Haridwar Bahadrabad U % T $ Roorkee T $
Bhagwanpur T $
29°50'
29°50'
Nakur T $
30°00'
Punwarka
Sarsawa T $
Soil Texture Sandy Loam Silty Loam
20 Kilometers
77°30'
77°40'
77°50'
78°00'
78°10'
Figure 6.16: Distribution of soil texture
99
100
Table 6.1 Soil table ser_num 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48
loc_id 152 136 148 181 135 119 107 154 110 170 050 175 024 016 034 124 147 165 149 117 100 053 003 167 174 054 032 119 153 129 001 073 127 046 096 110 098 193 160 020 031 162 070 084 036 005 125 074
loc_name RAMNAGAR PANIALA (CHANDAPUR) RAHIMPUR SHEKHPURI PADLI GUJAR MOHANPURA MAIN MARKET RAMPUR (ROORKEE) MALAKPUR SALIAR DOH - IIT, OORKEE SEWAGE FARM BHAGWANPUR BAHERI CHHUTMALPUR MUZAFFARABAD PUNWARKA SADHAULI QADIM RAIPUR MIRZAPUR MAGHANPUR Indrapur Talhero ALIPUR BHAGUWALA SAHARANPUR SARSAWA GADARHERI CHANDARPUL (NAKUR) MOHANPURA RAMPUR NANAUTA AHMADPUR JHABRERA NAKUR DEOBAND LAKHNUTA MALAKPUR LAKSAR TANDA RASULPUR BANDERJOOD BUGGAWALA RAUSHALABAD JASAWALA KHAJNAWAR CHUNA AMBAHTA NAGAL JHANJHOLI
long_m 77.86905 77.84731 77.85900 77.87888 77.87008 77.26294 77.88768 77.87427 77.90994 77.85970 77.89060 77.86180 77.81469 77.71014 77.75243 77.71480 77.62578 77.57609 77.58023 77.62556 77.64500 77.74111 77.70807 77.54141 77.40142 77.38095 77.30176 77.26294 77.45205 77.41747 77.54917 77.77673 77.30457 77.67961 77.79930 77.90994 78.02329 78.03861 77.96380 77.94070 77.90490 78.04790 77.95860 77.73060 77.59150 77.33640 77.63250 77.66540
101
lati_m 29.87624 29.85140 29.85510 29.86405 29.84237 29.83559 29.87398 29.88795 29.86540 29.89940 29.87190 29.89928 29.94157 29.95732 30.03434 30.12173 30.03478 30.20145 30.24752 30.25667 30.32500 30.25667 30.22538 29.97087 30.01705 29.96725 29.91328 29.83559 29.80562 29.71216 29.81911 29.80886 29.91923 29.69377 29.72959 29.86540 29.75000 29.92028 30.04200 30.05210 30.08450 29.96610 29.96190 30.09550 30.06720 29.85780 29.83840 30.10730
soi_id 08 06 06 08 08 06 08 06 06 06 08 06 06 08 06 08 08 08 08 08 06 06 06 08 06 06 06 06 08 08 08 08 06 08 08 06 06 06 08 08 06 06 06 08 08 08 08 08
soi_name Silty Loam Sandy Loam Sandy Loam Silty Loam Silty Loam Sandy Loam Silty Loam Sandy Loam Sandy Loam Sandy Loam Silty Loam Sandy Loam Sandy Loam Silty Loam Sandy Loam Silty Loam Silty Loam Silty Loam Silty Loam Silty Loam Sandy Loam Sandy Loam Sandy Loam Silty Loam Sandy Loam Sandy Loam Sandy Loam Sandy Loam Silty Loam Silty Loam Silty Loam Silty Loam Sandy Loam Silty Loam Silty Loam Sandy Loam Sandy Loam Sandy Loam Silty Loam Silty Loam Sandy Loam Sandy Loam Sandy Loam Silty Loam Silty Loam Silty Loam Silty Loam Silty Loam
To generate the flow direction map, a water level map was prepared by subtracting depth to groundwater map from the DEM. Next step was to remove the sinks or cells that had a lower value than the surrounding cells and giving them a higher value to average out the value of the neighboring cells. Thereafter, the flow direction was determined using SWBM Hydrologic Tools Extension (v1.0) from field groundwater level map. The flow direction command calculates the direction of flow out of each cell into one of its eight neighbors. The direction of flow is determined by finding the direction of the steepest descent from each cell. The map of flow direction for study area indicates that the ground water generally flows from the northern and northeastern hilly area to the southern and southwestern part. The flow direction of groundwater follows the surface drainage in the upper part and the groundwater is discharged to Hindon River and Solani River in the southern part (Figure 6.17).
6.3.5 Drainage Networks Drainage flow is naturally composed of base flow and direct runoff. Anthropogenic withdrawals from and discharges to streams increase or decrease drainage flow. The proportion of drainage flow that is base flow and direct runoff, as well as the relation between rainfall and runoff, depends on the hydrologic characteristics of a basin. The drainage network in the study area was digitized into two layers, the first one representing the network of main streams in the catchment (classified as first order channels), followed by the tributaries upto the last order; and the second one representing the other water bodies like ponds. The map of drainage network (Figure 6.18) shows the major rivers Ganga and Yamuna, and a large number of other streams draining this region. These originate either from the Siwalik or from the plains. In Haridwar district, Solani River is the major river channel. The river originates in Siwalik in ‘dendritic’ form and becomes ‘parallel’ in Bhabar due to its travel in the folded rills. The river flows along the general slope NE-SW in Siwalik – Bhabar -Tarai region. On entering into the plains, it flows in a NW-SE direction influenced by the Solani fault. The river becomes N-S after joining the River Ratmau. After the confluence with River Ratmau, the River Solani travels for a few kilometers and then vanishes in a swampy region and reemerges out in the form of a small channel to discharge into the river Ganga. Raghav Rao (1965) suggested formation of a swampy region because of filling up of an ancient lake due to sedimentation by the River Solani. Other minor rivers in this district are Ranipur Rao (originating in the Siwalik), Begam Rao (originating in the Plains) and Banganga (a paleochannels of river Ganga). In Saharanpur
102
77°20'
77°40'
< < < 50 mg/kg) for 98.4% and 87.6% of the study area respectively. It may, however, be noted that both urban and rural soil differ from forest soil by a substantial magnitude in respect of both these constituents.
Table 7.3: Classification of soils Category Zn Fe Mn Very Low 0.0 0.0 0.0 Low 3.3 0.0 0.0 Sufficient 80.8 0.2 0.5 High 13.6 1.43 12 Very High 2.3 98.4 87.6 Classification of soil samples according to Concentration of available Zn, Fe and Mn (mg/kg) Very Low 50 (Source: MAFF, 1988)
140
77°10'
77°20'
77°30'
77°40'
77°50'
78 °00'
78 °10 '
30°20'
30 °20'
Sadhuli Qadim T $
30°10'
3 0°10'
(a) Zn
Muzaffarabad T $
Punwarka
30°00'
Saharanpur
29°50' 29°40 '
0
10
Narson T $
Deoband T $
Laksar T Khanpur $ T $
29°4 0'
Nanauta T $
29 °50'
Nagal T $
Rampur T $
Gangoh T $
10
Haridwar Bahadrabad % U T $ Roorkee T $
Bhagwanpur T $
U %
Nakur T $
30°00'
T $
Sarsawa T $
20 Kilometers
77°20'
77°30'
77°40'
77°50'
78 °00'
78 °10 '
77°10'
77°20'
77°30'
77°40'
77°50'
78 °00'
78 °10 ' 30 °20'
30°20'
77°10'
(b) Mn
3 0°10'
30°10'
Sadhuli Qadim T $
Muzaffarabad T $
T $
30°00'
Sarsawa T $
Saharanpur
29°50' 29°40 ' 10
0
10
Narson T $
Deoband T $
Laksar T Khanpur $ T $
29°4 0'
Nanauta T $
29 °50'
Nagal T $
Rampur T $
Gangoh T $
Haridwar Bahadrabad % U T $ Roorkee T $
Bhagwanpur T $
U %
Nakur T $
30°00'
Punwarka
20 Kilometers
77°20'
77°30'
77°40'
77°50'
78 °00'
78 °10 '
77°10'
77°20'
77°30'
77 °40'
77 °50 '
78°00'
78 °10' 30°20'
30 °20 '
77°10'
(c) Fe
3 0°10'
30°10'
Sadhuli Qadim T $
Muzaffarabad T $
T $
30°00'
Sarsawa T $
Saharanpur
29 °50 ' 2 9°40' 10
77°10'
0
10
77°20'
Deoban d T $
Narson T $
Laksar T Khanpur $ T $
29°40'
Nanauta T $
Nagal T $
29°50'
Rampur T $
Gangoh T $
Haridwar Bahadrabad % U T $ Roorkee T $
Bhagwanpur T $
U %
Nakur T $
30°00'
Punwarka
Very Low Low Sufficient High Very high
20 Kilometers
77°30'
77 °40'
77 °50 '
78°00'
78 °10'
Figure 7.3: Soil classification maps indicating spatial variability of metals
141
142
7.3 GROUNDWATER QUALITY In the present study, groundwater quality of the shallow unconfined aquifer at different locations in the study area was evaluated after collecting a total of 136 groundwater samples and analyzing them for pH, TDS, major ions, nutrients, and heavy metals. A consolidated statistical summary of all measured water quality parameters is presented in Table 7.4 and their maps for the study area are presented in Figure 7.4 (a to h). Table 7.5 exhibits a statistical summary of groundwater quality for different types of land use, whereas the corresponding statistical distributions have been graphically represented in Figure 7.5 (a to l). It may be noticed that in the case of groundwater quality also, many constituents exhibit mean values in excess of median values and high standard deviation reflecting positively skewed distribution and a high degree of variation. An explanation of the observed characteristics is given in following sections with the term concentration implying median concentration, wherever employed. Further, the values of water quality parameters have been compared against corresponding Indian drinking water quality standards (BIS:10500, 1991), as the water is reportedly being used for drinking by a sizeable population in the study area.
7.3.1 Physical Properties The median pH value has been observed to be 7.71 (minimum value 7.01, maximum value 8.90). Low and high pH causes corrosion in water supply lines and household plumbing fixtures. Environmental Protection Agency has recommended a range of pH values of 5.0 to 9.0 as acceptable for domestic water supply. The observed values lie within the permissible pH range of the Indian standards. Dissolved solids include both organic and inorganic material dissolved in a sample of water (Bates and Jackson, 1984) and are commonly used as a general indicator of water salinity or quality. Water with a high dissolved solids concentration can produce scaly deposits and cause staining, wear, or corrosion of pipes and fittings. Excessively large concentrations of dissolved solids are objectionable in drinking water because of possible physiological effects, unpalatable mineral tastes, and higher costs due to corrosion or necessity for additional treatment (U.S. Environmental Protection Agency, 1986).
143
Table 7.4: Statistical summary of groundwater quality data
Characteristics
Min.
Max.
Median
Mean
SD
Indian water quality standard
Physical properties pH (standard units)
7.01
8.90
7.71
7.75
0.47
6.5-8.5
Specific conductance (µmhos/cm)
175
1530
677
751
302
-
Total dissolved solid (mg/L)
117
1002
437
491
199
500
Major ions (Dissolved) (mg/L) Calcium (Ca2+)
7.6
180.0
71.7
79.8
35.5
75
Magnesium (Mg2+)
2.4
86.0
24.3
27.4
14.3
-
Potassium (K +)
0.4
38.8
4.0
5.0
5.0
-
Sodium (Na+)
3.0
189.0
29.8
37.0
29.8
-
Bicarbonate (HCO3 -)
74.0
695.5
330.0
348.5
121.0
[TA=200]
Sulphate (SO4 2-)
2.9
380.6
29.2
43.5
46.3
200
Chloride (Cl-)
1.0
259.9
29.5
46.5
51.8
250
Fluoride (F-)
0.0
1.6
0.3
0.3
0.2
1.0
Nutrients (mg/L) Nitrate (NO3 -) Phosphate (PO4 )
2.07
335.0
11.65
34.11
47.88
45
0.0001
0.0744
0.004
0.0058
0.0084
-
Metals (Total) (µg/L) Cadmium (Cd)
ND
157.0
28.0
49.2
51.4
10
Iron (Fe)
ND
1233.0
166.0
248.1
265.4
300
Manganese (Mn)
ND
1898.0
247.0
357.9
385.8
100
Lead (Pb)
ND
599.0
96.0
109.0
113.9
50
Zinc (Zn)
ND
16030.0
418.5
1080.0
2076.7
5000
ND: Not detected
144
The median concentration of TDS has been observed to be 437 mg/L (minimum 117 mg/L, maximum 1002 mg/L). High variability in the concentration of dissolved solids has been exhibited by the groundwater of the study area. The TDS distribution map (Figure 7.4 a) shows a generally increasing trend from north to south and southwest of the study area, which corresponds to the groundwater flow direction. Groundwater samples from the urban land use have exhibited the highest median concentration and variation of TDS followed by rural and forest land use (Table 7.5). These clearly highlight the influence of anthropogenic activities in the urban settlements on groundwater quality. A look at table 7.6 reveals that a total of 41.2 % groundwater samples have been observed to violate the Indian drinking water standard (500 mg/L). The distribution of violation of these standards amongst various types of land use has been observed as 62.5 % and 35.2 % for urban and rural land use respectively (Table 7.6).
7.3.2 Major Ions The analyses indicates that HCO3 - and Ca
2+
are the dominant anion and cation
respectively, reflecting the chemical maturity of the water with the rock matrix. The groundwater displays a diverse range of quality and chemistry of the prominent ions reflecting the mineral composition of geologic material contacted by groundwater in the study area. The results indicate a moderate to high variation in the concentration of major ions. High magnitude of violation of Indian standards have been exhibited by Total alkalinity (as represented mainly by HCO3 - being 93.2 %, 92.5 % and 62.5 % from rural, urban and forest land use respectively) and Ca2+ (52.5 %, 46.6 % and 12.5 % from urban, rural and forest land use respectively). The spatial variation in concentration of major ions may be a result of non uniform mixing with natural or anthropogenic sources on one hand, as well as the presence of clay and other materials of low hydraulic conductivity on the other. Concentration of Ca2+ and HCO3 - ions has been observed to be relatively high in the groundwater (Figure 7.4 b and c).
This may be a result of a number of interrelated
geochemical processes like the dissolution and precipitation of calcite and dolomite minerals, which are present in the study area as impure calcium carbonate nodules viz. Kankar (a term used in tropical region for hard lime and iron nodular deposits formed as a result of precipitation of calcium carbonate material following the evapotranspiration of shallow groundwater saturated with carbonates from capillary zone) (Tyrrel, 1980). High concentration of HCO3 - in the groundwater may be explained by the following processes (Todd, 1988):
145
Table 7.5: Statistical summary of groundwater quality data for different land use LAND USE Characteristics
Forest No of samp.
Min
Max
Median
Rural Mean
STD.
No of samp.
Min
Urban
Max
Median
Mean
STD.
No of samp.
Min
Max
Median
Mean
STD.
Physical properties pH, (standard units)
8
8.10
8.90
8.41
8.48
0.26
88
7.01
8.90
7.72
7.74
0.43
40
7.02
8.51
7.58
7.61
0.46
Specific conductance, (µmhos/cm)
8
353
650
491
482
98
88
290
1456
652
713
265
40
175
1530
851
887
348
Total dissolved Solid, (mg/L)
8
232
430
324
318
65
88
192
965
423
464
173
40
117
1002
566
584
229
Major ions (Dissolved) (mg/L) 2+
8
30.0
78.1
51.9
52.5
15.5
88
7.6
166.6
71.3
79.0
34.7
40
32.0
180.0
81.3
87.1
37.8
Magnesium (Mg ) Potassium (K+)
8
9.7
25.0
17.7
17.4
6.2
88
6.0
86.0
24.0
26.9
14.3
40
2.4
62.2
30.4
30.4
14.6
8
2.0
25.0
3.1
8.0
9.6
88
0.8
38.8
3.9
4.8
5.2
40
0.4
13.5
5.2
5.0
3.0
Sodium (Na +)
8
4.0
19.2
11.5
11.5
4.9
88
5.6
95.4
27.3
31.0
17.1
40
3.0
189.0
44.7
55.4
43.1
Biocarbonate (HCO3-)
8
74.0
290.0
200.5
194.9
65.0
88
133.0
695.5
330.0
345.3
113.4
40
100.0
630.0
407.5
386.3
122.1
Carbonate (CO32-)
8
1.0
1.9
1.3
1.3
0.3
88
0.0
2.0
0.0
0.4
0.6
40
0.0
1.3
0.0
0.3
0.5
8
17.3
95.0
52.5
52.9
31.8
88
2.9
380.6
27.4
38.7
48.6
40
5.4
186.8
34.8
52.2
42.7
8
3.0
22.0
10.5
11.1
5.5
88
2.0
242.0
24.0
40.8
48.5
40
1.0
259.9
47.5
66.2
57.6
8
0.0
0.3
0.2
0.2
0.1
88
0.0
0.6
0.3
0.3
0.1
40
0.1
1.6
0.3
0.4
0.2
Calcium (Ca ) 2+
146
Sulphate
(SO 42-)
Chloride (Cl-) -
Flouride (F )
Nutrients (mg/L) Nitrate(NO 3 -)
8
10.50
72.00
43.00
40.11
19.38
88
2.35
335.00
9.16
27.27
46.15
40
2.07
206.00
24.65
47.98
52.99
Phosphate (PO 4)
8
0.000
0.000
0.000
0.000
0.000
88
0.000
0.074
0.005
0.006
0.009
40
0.000
0.026
0.004
0.006
0.007
69.350
0.000
1.820
10.174
40
0.000
28.360
0.000
0.746
4.481
Organic (%) Total organic carbon (TOC)
8
0.000
0.000
0.000
0.000
0.000
88
0.000
Metals (Total) (µg/L) Cadmium (Cd)L)
8
ND
85.0
0.5
11.7
29.6
88
ND
148.0
26.0
44.1
48.3
40
ND
157.0
62.5
67.8
55.5
Iron (Fe)
8
ND
803.0
2.0
247.9
346.2
88
ND
1233.0
156.5
232.4
248.7
40
ND
1189.0
242.0
282.6
287.3
Manganese (Mn)
8
ND
57.0
1.5
19.6
25.2
88
ND
1898.0
297.5
412.4
402.7
40
11.0
1619.0
182.5
305.5
346.8
Zinc (Zn) Lead (Pb)
8
ND
627.0
1.0
159.4
245.4
88
ND
5714.0
381.5
858.0
1048.0
40
5.0
16030.0
537.0
1752.7
3423.2
8
ND
139.0
1.0
25.4
50.1
88
ND
599.0
100.0
109.8
115.1
40
ND
465.0
112.0
123.9
114.9
ND: Not detected
146
(i)
The natural processes such as the dissolution of carbonate mineral and
(ii)
Dissolution of atmospheric and soil CO2 gas contributed by natural and anthropogenic sources in groundwater. Ca2+ + 2HCO 3-
CaCO3 + CO2 + H2O
H+ + HCO3-
CO2 + H2O
Concentration of anions like Cl-, SO4 2- and F- has been observed to be generally well within the Indian drinking water standards for most of the study area (Figure 7.4 e, f and g). Except for SO4 2-, which has displayed the highest concentration in forest land use, all other ions have been observed to follow a decreasing order of magnitude for urban > rural> forest land use (Figure 7.5). Alongwith the influence of various types of land use, the buildup of major ions has also been observed to be along the flow direction which highlights a major influence of the transport processes viz. advection and dispersion.
Table 7.6: Violation of Indian drinking water quality standards by groundwater samples Forest Characteristics
No.
%
pH Total dissolved solid
3 0
37.5 0.0
Calcium (Ca2+) Bicarbonate (HCO3 -) Sulphate (SO4 2-) Chloride (Cl -) Fluoride (F-)
1 5 0 0 0
12.5 62.5 0.0 0.0 0
Nitrate (NO3 -)
3
37.5
Cadmium (Cd) Iron (Fe) Manganese (Mn) Lead (Pb) Zinc (Zn) No. of Samples
3 3 0 2 0
37.5 37.5 0.0 25.0 0.0 8
Rural No.
Urban %
No.
Physical properties 1 1.14 1 31 35.2 25 Major ions 41 46.6 21 82 93.2 37 2 2.3 0 0 0.0 1 0 0 0 Nutrients 19 21.6 16 Metals 51 58.0 27 25 28.4 15 62 70.5 31 51 58.0 27 1 1.1 4 88 40
All
Water quality standard
%
No.
%
2.5 62.5
5 56
3.68 41.2
6.5 - 8.5 500
52.5 92.5 0.0 2.5 0
63 124 2 1 0
46.3 91.2 1.5 0.7 0
75 200 200 250 1.0
40.0
38
27.9
45
67.5 37.5 77.5 67.5 10.0
81 43 93 80 5
59.6 31.6 68.4 58.8 3.7
10 300 100 50 5000
136
7.3.3 Nutrients Nitrates in groundwater represent a widely distributed pollution concern; they are perhaps the most ubiquitous of all groundwater contaminants. Natural and human- induced sources of nitrates in groundwater are a result of water usage for irrigation, excessive applications of commercial fertilizers or manure, and waste disposal practices associated with 147
land application of sludge or wastewater effluents, municipal or industrial landfills, and septic tank systems (Keeney, 1989 and Canter, 1987). Sources of nitrate in groundwater have generally been grouped in four categories: (1) natural sources, (2) waste materials, (3) row crop agriculture and (4) irrigated agriculture (Keeney, 1986, 1989). The median concentration of NO3 -N in the study area has been observed to be 11.65 mg/L (minimum 2.07 mg/L, maximum 335.0 mg/L). It may be noted that the groundwater samples from the forest land use display the highest concentration of nitrate (median 43.00 mg/L) alongwith least variation in the values whereas samples from the rural land use display the lowest concentration (median 9.16 mg/L) (Figure 7.5). In addition to the other factors, the physical properties of soil and aquifer materials can affect the rate of vertically downward movement of water and nitrate. For example, groundwater in aquifers beneath the soil of low permeability usually show low nitrate levels due to low infiltration rates and/or anoxic condition (Postma et al., 1991; Smith et al., 1991; Panno et al., 2001). Figure 7.4 h indicates that there is no clear relation between the nitrate concentration and flow direction. It has also been reported that the movement of nitrate near the land surface is primarily subject chemical nature of to the soil (Starr and Gillham, 1993; Maeda et al., 2003). Further, in oxygenated sandy soils, nitrate is stable and usually poorly adsorbed and is, therefore, easily leached during precipitation. Maximum violation of the drinking water standards has been noticed by samples from urban land use (40 %) followed by forest (37.5%) and rural land use (21.6%) (Table 7.6). Phosphorus occurs in water in several forms including elemental phosphorus and dissolved orthophosphorus. Phosphorus in its elemental form may be toxic to aquatic organisms and may bioaccumulate in much the same manner as mercury (U.S. Environmental Protection Agency, 1986). Phosphorus is the nutrient most frequently cited as limiting algal growth in surface water. It is a common element that is needed in fairly small amounts compared with other nutrients. The solubility of rocks containing phosphorus is also low. However, once dissolved, phosphorus is quickly taken up by living organisms or adsorbed onto iron and aluminum hydroxides and oxides. Therefore, the amount of phosphorus available fo r plant growth at any given time being usually low, contributions from human activities greatly affect the phosphorus in water bodies. Median value of dissolved phosphorus concentration in the study area has been observed to be 0.004 mg/L (minimum not detected, maximum 0.0744 mg/L). Groundwater samples from forest land use have not exhibited any phosphorus, whereas, samples from rural as well as urban land use have displayed quite low but comparable phosphorus concentration. 148
77°10'
77°20'
77°30'
77°40'
77°50'
78°00'
N
78°10'
30°20'
30°20'
30°10'
30°10'
Sadhuli Qadim T $
Muzaffarabad T $
T $
30°00'
Saharanpur
29°50' 29°40' 10
0
77°10'
10
Deoband T $
Narsen T $
Laksar T Khanpur $ T $
29°40'
Nanauta T $
29°50'
Nagal T $
Rampur T $
Gangoh T $
Haridwar Bahadrabad % U T $ Roorkee T $
Bhagwanpur T $
U %
Nakur T $
30°00'
Punwarka
Sarsawa T $
TDS (mg/L) 300 - 350 350 - 400 400 - 450 450 - 500 500 - 550 550 - 600 600 - 650 650 - 700 700 - 750
20 Kilometers
77°20'
77°30'
77°40'
77°50'
78°00'
78°10'
(a): Total Dissolved Solid (TDS) map
77°10'
77°20'
77°30'
77°40'
77°50'
78°00'
N
78°10'
30°20'
30°20'
30°10'
30°10'
Sadhuli Qadim T $
Muzaffarabad T $
T $
30°00'
Sarsawa T $
Saharanpur
29°50'
Gangoh T $
29°40' 10
77°10'
0
10
77°20'
Deoband T $
Narson T $
Laksar T Khanpur $ T $
29°40'
Nanauta T $
Nagal T $
20 Kilometers
77°30'
77°40'
77°50'
29°50'
Rampur T $
Haridwar Bahadrabad % U T $ Roorkee T $
Bhagwanpur T $
U %
Nakur T $
30°00'
Punwarka
78°00'
Ca (mg/L) 50 - 55 55 - 60 60 - 65 65 - 70 70 - 75 75 - 80 80 - 85 85 - 90 90 - 95 95 - 100 100 - 105 105 - 110
78°10'
(b): Ca2+ map Figure 7.4: Maps of groundwater quality parameters for the study area 149
150
77°10'
77°20'
77°30'
77°40'
77°50'
78°00'
N
78°10'
30°20'
30°20'
30°10'
30°10'
Sadhuli Qadim T $
Muzaffarabad T $
T $
30°00'
Saharanpur
29°50' 29°40' 10
0
77°10'
10
Deoband T $
Narson T $
Laksar T Khanpur $ T $
29°40'
Nanauta T $
29°50'
Nagal T $
Rampur T $
Gangoh T $
Haridwar Bahadrabad % U T $ Roorkee T $
Bhagwanpur T $
U %
Nakur T $
30°00'
Punwarka
Sarsawa T $
Bicarbonate (mg/L) 230 - 260 260 - 290 290 - 310 310 - 340 340 - 370 370 - 400 400 - 430 430 - 460
20 Kilometers
77°20'
77°30'
77°40'
77°50'
78°00'
78°10'
(c): HCO3 - map
77°10'
77°20'
77°30'
77°40'
77°50'
78°00'
N
78°10'
30°20'
30°20'
30°10'
30°10'
Sadhuli Qadim T $
Muzaffarabad T $
T $
30°00'
Saharanpur
Rampur T $
Gangoh T $
29°40' 10
77°10'
0
10
77°20'
Deoband T $
Narson T $
Laksar T Khanpur $ T $
29°40'
Nanauta T $
Nagal T $
20 Kilometers
77°30'
77°40'
77°50'
78°00'
78°10'
(d): Na+ map Figure 7.4: (Continued)
151
29°50'
29°50'
Nakur T $
Haridwar Bahadrabad % U T $ Roorkee T $
Bhagwanpur T $
U %
30°00'
Punwarka
Sarsawa T $
Sodium (mg/L) 10 - 20 20 - 30 30 - 40 40 - 50 50 - 60 60 - 70 70 - 80 80 - 90 90 - 100 100 - 110 110 - 120
152
77°10'
77°20'
77°30'
77°40'
77°50'
78°00'
N
78°10'
30°20'
30°20'
30°10'
30°10'
Sadhuli Qadim T $
Muzaffarabad T $
T $
30°00'
Saharanpur
29°50' 29°40' 10
0
77°10'
10
Deoband T $
Narson T $
Laksar T Khanpur $ T $
29°40'
Nanauta T $
29°50'
Nagal T $
Rampur T $
Gangoh T $
Haridwar Bahadrabad % U T $ Roorkee T $
Bhagwanpur T $
U %
Nakur T $
30°00'
Punwarka
Sarsawa T $
Chloride (mg/L) 10 - 20 20 - 30 30 - 40 40 - 50 50 - 60 60 - 70 70 - 80 80 - 90 90 - 100
20 Kilometers
77°20'
77°30'
77°40'
77°50'
78°00'
78°10'
(e): Cl- map
77°10'
77°20'
77°30'
77°40'
77°50'
78°00'
N
78°10'
30°20'
30°20'
30°10'
30°10'
Sadhuli Qadim T $
Muzaffarabad T $
T $
30°00'
Saharanpur
U %
Rampur T $
Gangoh T $
29°40' 10
77°10'
0
10
77°20'
Deoband T $
Narsen T $
Laksar T Khanpur $ T $
29°40'
Nanauta T $
Nagal T $
20 Kilometers
77°30'
77°40'
77°50'
78°00'
78°10'
(f): SO4 2- map Figure 7.4: (Continued)
153
29°50'
29°50'
Nakur T $
Haridwar Bhagwanpur Bahadrabad % T $ U T $ Roorkee T $
30°00'
Punwarka
Sarsawa T $
Sulphate (mg/L) 3 - 26 26 - 49 49 - 71 71 - 94 94 - 116 116 - 139 139 - 162 162 - 184 184 - 207
154
77°10'
77°20'
77°30'
77°40'
77°50'
78°00'
N
78°10'
30°20'
30°20'
30°10'
30°10'
Sadhuli Qadim T $
Muzaffarabad T $
T $
30°00'
Saharanpur
U %
Nagal T $
Rampur T $
Gangoh T $
29°40' 10
0
77°10'
10
Deoband T $
Narson T $
Laksar T Khanpur $ T $
29°40'
Nanauta T $
29°50'
29°50'
Nakur T $
Haridwar Bahadrabad % U T $ Roorkee T $
Bhagwanpur T $
30°00'
Punwarka
Sarsawa T $
Fluoride (mg/L) 0 - 0.09 0.09 - 0.19 0.19 - 0.28 0.28 - 0.38 0.38 - 0.47 0.47 - 0.57 0.57 - 0.66 0.66 - 0.75 0.75 - 0.85
20 Kilometers
77°20'
77°30'
77°40'
77°50'
78°00'
78°10'
(g): F- Map
77°10'
77°20'
77°30'
77°40'
77°50'
78°00'
N
78°10'
30°20'
30°20'
30°10'
30°10'
Sadhuli Qadim T $
Muzaffarabad T $
T $
30°00'
Saharanpur
29°50'
Gangoh T $
29°40' 10
77°10'
0
10
77°20'
Deoband T $
Narson T $
Laksar T Khanpur $ T $
29°40'
Nanauta T $
Nagal T $
20 Kilometers
77°30'
77°40'
77°50'
78°00'
78°10'
(h): NO3 - map Figure 7.4: (Continued)
155
29°50'
Rampur T $
Haridwar Bahadrabad % U T $ Roorkee T $
Bhagwanpur T $
U %
Nakur T $
30°00'
Punwarka
Sarsawa T $
Nitrate (mg/L) 0 - 25 25 - 50 50 - 75 75 - 100 100 - 125 125 - 150 150 - 175 175 - 200
156
1600 1200 1000
Median
800 600 400
Min
200
.
0 Rural
Forest
Urban
Forest
Rural
Urban
100 90 80 70 60 50 40 30 20 10 0 Forest
Urban
(e) Na+
Rural
Urban
(f) K +
100 90 80 70 60 50 40 30 20 10 0
K+ (mg/L)
200 180 160 140 120 100 80 60 40 20 0
Rural
(d) Mg 2+
(c) Ca2+
200 180 160 140 120 100 80 60 40 20 0
Mg 2+ (mg/L)
Ca 2+ (mg/L)
Max.
1400
Forest
Na + (mg/L)
(b) EC 1800
EC
pH
(a) pH 9.0 8.8 8.6 8.4 8.2 8.0 7.8 7.6 7.4 7.2 7.0
Forest
Rural
Forest
Urban
Rural
Figure 7.5: Statistical distributions of groundwater quality parameters for different land use
157
Urban
(g) HCO3-
800
(h) Cl
300
-
250
600 500
Cl- (mg/L)
HCO3- (mg/L)
700
400 300
100 50
0
0 Forest
Rural
(i) SO4
400
Urban
Forest
2+
Rural
(j) F
1.8
Urban
-
1.6
350
1.4 F- (mg/L)
300 SO42- (mg/L)
150
200 100
250 200 150
1.2 1 0.8 0.6
100
0.4
50
0.2 0
0 Forest
Rural
(k) NO3
400
Forest
Urban
-
0.08
350
0.07
300
0.06 PO4 3- (mg/L)
NO3- (mg/L)
200
250 200 150
Rural
(l) PO 4
Urban
3-
0.05 0.04 0.03
100
0.02
50
0.01 0.00
0 Forest
Rural
Forest
Urban
Figure 7.5: (Continued)
158
Rural
Urban
7.3.4 Heavy Metals The most significant and natural source of heavy metals is weathering of rocks, as a result of which the released metals find their way into the groundwater. The anthropogenic influence is exerted through various domestic, industrial and agricultural activities. During the present study, analysis of groundwater samples has revealed presence of few heavy metals (total) i.e. Cd, Fe, Mn, Pb and Zn. These metals have generally been observed to violate Indian drinking water standards, upto a varying extent. Their distribution has been found to generally exhibit primary influence of localized natural and anthropogenic inputs, with a much lesser significance of the groundwater flow pattern. Figure 7.6 presents the statistical distributions parameters for different land use whereas Figure 7.7 (a to e) presents the maps showing heavy metal distribution in the study area. Discussion pertaining to each has been presented in the following sections:
Cadmium: Cd is highly toxic to man and animals (Friberg et al., 1974). Median concentration of Cd has been observed as 28.0 µg/L (minimum “not detected”, maximum 157.0 µg/L) in the groundwater of the study area. It should be noted that the possible source of Cd in the groundwater may be related to the high availability of Cd in the soil of study area (Although ”bioavailable Cd” in soil is not detectible in the study area as reported earlier in section 7.2.4, “total Cd” in soil has been reported by other researchers.). The pathways and migration of Cd could be governed by indiscriminate land dumping of untreated effluents or solid waste of small scale household industries scattered in the study area.. The highest Cd concentration has been observed in the samples from urban land use (62.5 µg/L) followed by rural (26.0 µg/L) and forest land use (0.5 µg/L) (Figure 7.6). A large difference in magnitude gives an indication that urban activities apparently serve as the major source of Cd in the groundwater. The percentage of groundwater samples violating the Indian drinking water standards have been observed as 67.5 %, 58.0 % and 37.5 % from urban, rural and forest land use respectively (Table 7.6) reflecting the manifestation of increasing industrial activity in urbanization as compared to rural and forest land uses.
Iron: Fe is essential in human nutrition, but it becomes highly toxic when the concentration increases (Fairbanks and Bentler 1971). Median concentration has been observed as 166.0 µg/L (“not detected” to maximum 1233.0 µg/L) (Figure 7.7 b). Violation of Indian drinking water standard (by the groundwater samples) has been observed to the extent of 37.5% in
159
urban and forest land use and 28.4% in rural land use respectively (Table 7.6). Reasons for such a high violation in urban areas are obviously greater industrial activity therein.
Manganese: Mn is a naturally occurring element that can be found ubiquitously in the air, soil and water. However, human activities are also responsible for much of the manganese contamination in groundwater in some areas (EPA, 2004). Median concentration of Mn in the groundwater of the study area has been observed to be 247.0 µg/L (minimum “not detected”, maximum 1898.0 µg/L) (Figure 7.7 c). The concentration Mn in groundwater of the study area is generally high and the highest Mn concentration has been observed in the rural land use followed by urban and forest land use (Figure 7.6), the levels detected being generally above the permissible level in drinking water. A total of 77.5 % and 70.5 % groundwater samples from urban and rural land use respectively, have been observed to violate the Indian drinking water standards, whereas no violation is exhibited by samples from forest land use (Table 7.6). This seems to be due to greater industrial and human activities in the urban and rural areas.
Lead: The concentration of Pb in natural water increases mainly through anthropogenic activity (Goel, 1974). Lead is also extensively used in some pesticides such as lead arsenate. The median Pb concentration in the groundwater samples has been observed as 96.0 µg/L (“not detected” to maximum 599.0 µg/L), with the water samples from urban and rural land use exhibiting almost similar concentration levels. The levels observed in the forest land use are extremely low in general, and highly localized indicating high level of spatial variability.
Zinc: Zn is essential for plant and animal metabolism. In the groundwater of the study area, median Zn concentration has been observed as 418.5 µg/L (“not detected” to maximum of 16030.0 µg/L) (Figure 7.7 e). The concentration of Zn in the groundwater of the study area has in general been found within the permissible limit of Indian drinking water standard, with the violation being only 10% and 1.1% in the urban and rural land use respectively (Table 7.6). To summarise, high concentration of heavy metals observed in the groundwater samples from urban and rural land use highlight the influence of anthropogenic activities resulting in transformation in natural soil properties and ultimately, groundwater quality through leaching / infiltration of contaminants (Burkart and Kolpin, 1993; Kolpin, 1997).
160
(b) Mn
(a) Cd
2000 1800 1600
180 160 120
Mn (µg/L)
Cd (µg/L)
140 100 80 60
1400 1200 1000 800 600
40
400 200 0
20 0
Forest
Rural
Forest
Urban
(c) Fe 1200
600
1000
500 Pb (µg/L)
700
800 600
400 300
400
200
200
100
0
0
Rural
Forest
Urban
Rural
Urban
(e) Zn 18000 16000 14000 Zn (µg/L)
Fe (µg/L)
Urban
(d) Pb
1400
Forest
Rural
12000 10000 8000 6000 4000 2000 0
Forest
Rural
Urban
Figure 7.6: Statistical distributionsof metals for different land use
161
162
77°10'
77°20'
77°30'
77°40'
77°50'
78°00'
N
78°10'
30°20'
30°20'
30°10'
30°10'
Sadhuli Qadim T $
Muzaffarabad T $
T $
30°00'
Saharanpur
U %
Nagal T $
Rampur T $
Gangoh T $
Laksar T $
29°40'
0
77°10'
10
Deoband T $
Narsen T $
T $
29°40'
Nanauta T $
10
29°50'
29°50'
Nakur T $
Haridwar Bhagwanpur Bahadrabad % T $ U T $ Roorkee T $
30°00'
Punwarka
Sarsawa T $
Cd (µg/L) Cadmium 600
166
77°10'
77°20'
77°30'
77°40'
77°50'
78°00'
N
78°10'
30°20'
30°20'
30°10'
30°10'
Sadhuli Qadim T $
Muzaffarabad T $
T $
30°00'
Saharanpur
Rampur T $
Gangoh T $
29°40' 10
77°10'
0
10
77°20'
Deoband T $
Narsen T $
Laksar T Khanpur $ T $
29°40'
Nanauta T $
Nagal T $
20 Kilometers
77°30'
77°40'
77°50'
78°00'
78°10'
(e): Zn map Figure 7.7: (Continued)
167
29°50'
29°50'
Nakur T $
Haridwar Bahadrabad % U T $ Roorkee T $
Bhagwanpur T $
U %
30°00'
Punwarka
Sarsawa T $
Zn (µg/L) Zinc
< 500 500 - 1000 1000 - 1500 1500 - 2000 2000 - 2500 2500 - 3000 >= 3000
168
7.3.5 Groundwater Facies The concept of hydrochemical facies was developed by Back (1966). The term hydrochemical facies is used to describe the bodies of groundwater in an aquifer that differ in their chemical composition. The facies are a function of lithology, solution kinetics and flow pattern of ground water through the aquifer (Back, 1966). The plot of chemical analysis on a Piper diagram shows that a majority of the groundwater samples belong to the bicarbonate type, a few samples fall in the 'non-dominant' class and only one sample falls in the chloride class in the anion facies (Figure 7.8 and Table 7.7). Among the cation facies, the majority of the water samples fall in the class of calcium type, a few samples belong to 'non-dominant' class, and only one sample falls in the class of sodium type. Finally, the Piper diagram shows that the groundwater in the study area is of calcium-bicarbonate type (carbonate hardness).
Table 7.7: Samples of each land use falling in various classes of the piper diagram Land use Forest Rural Urban All
No. % No. % No. % No. %
2+
Ca 8 100 80 91 33 83 121 89
Cation Facies Ca2+ - Na+ Na+ 0 0 0 0 8 0 9 0 6 1 15 3 14 1 10 1
169
Anion Facies HCO3 HCO3 - - Cl6 2 75 25 78 9 89 10 33 7 83 18 117 18 86 13 -
Cl0 0 1 1 0 0 1 1
Piper's trilinear digram
60
)+ (Ca
Ch lori de (Cl ) 60
m lciu Ca
80
80
Study Area
40
Su lfat e( SO 4) +
m siu gne Ma 40 g) (M
20
20
Mg 20 20
60
(CO 3) + Ca rbo 80 nat e 80
60
60
Na+K
Bic arb ona 40 te ( 40 HC O3 )
Ma gne siu m( Mg ) 40
CATIONS
20
HCO3 +CO 3 %meq/l
20
80
20
80
(K) m siu tas 60 Po
60
40
Calcium (Ca)
60
4
60
4) SO e( 40 lfat Su
)+ (Na 0
80
80
40
20
m diu So
80
20
Ca
SO 4
20
40
60
80
Cl
Chloride (Cl)
ANIONS
Figure 7.8: Piper Diagram of the chemistry of groundwater samples in the study area
170
7.4 DEVELOPMENT OF THE WATER QUALITY INDEX FOR THE STUDY AREA On the basis of the hydrogeological as well as the groundwater quality scenarios in the study area, the basic framework of the Index of Aquifer Water Quality (IAWQ) proposed by Melloul and Collin (1998) has been considered suitable for application in this study in order to fulfill two objectives: a) To get an overall status of groundwater quality. b) To have a basis for validating the results of vulnerability assessment. Details of the IAWQ index have been presented below: In order to relate data to global norms, each value of a parameter, Pij (field data value of parameter i in cell j), is related to its desired standard value Pid (Indian drinking water standards considered in present study). Each relative value, Xij, can be estimated as: Xij = Pij / Pid
. . . 7.1
To express Xij as a corresponding index rating value, related to groundwater quality, Yi has been assigned to each Xij value as follows: •
For good water quality, with Xij equal to 0·1, the corresponding index rating value would be around 1;
•
For acceptable water quality, with Xij equal to 1 (the raw value of the parameter Pi equal to its standard desired value), the corresponding index rating value would be 5; and
•
For unacceptable groundwater quality, with Xij equal to or higher than 3·5 (the initial value of the parameter Pi equal to or higher than 3·5 times its standard desired value), the corresponding index value would be 10. Operational hydrological experience indicates that Y1=1 for X1=0·1; Y2=5 for X2=1;
and Y3=10 for X3=3·5 (usually values of Yi lies between 1 to 10). For any parameter i in any cell j, an adjusted parabolic function of rates Yij = f(Xij) can be determined for each cell from 2nd order polynomial as in Equation (7.2): Yi = -0·712 Xi2 + 5·228 Xi + 0·484
171
. . . 7.2
From this equation the corresponding rating Yi can be estimated for any value of Xi. After this transformation of the field data, the index formula (IAWQ) involve s only Y values, representing input data for the IAWQ formula by summation of weights multiplied by respective ratings of various parameters i for each cell j as follows: n IAWQ = C / n ∑ (WriYri . ) i=1
. . . 7.3
where: C = a constant, used to ensure desired range of numbers (in this case, C=10) i, n = number of chemical parameters involved (i= 1, . . . , n) This value is incorporated in the denominator to average the data Wri
= the relative value of Wi/Wmax
Wi
= a weight for any given parameter
Wmax = the maximum possible weight (5) Yri
= the value of Yi/Ymax
Yi
= the rating as related to Xi [obtained from Equation (7.3)]
Ymax = the maximum possible rating for any parameter (Ymax=10) Few modifications made in the original framework towards development of the Modified Index of Aquifer Water Quality (MIAWQ have been explained in the following paragraphs : a) The range of water quality parameters considered in the original index was extended to include major ions and heavy metals [TDS, Ca2+, Cl-, NO3 -, SO4 2-, Total alkalinity (TA), Cd, Mn, Pb, Fe and Zn]. From the point of regional significance, only those parameters were included which reflected a violation of the Indian drinking water standard in more than 10% of the sample population distributed laterally across the region.
Accordingly, the chemical
parameters finally selected for MIAWQ were Cd, Mn, Pb, Fe, TA, NO3 -, TDS and Ca2+ (Figure 7.9 and Table 7.8). b) Weights (Wi) were assigned to these eight parameters as per their analytical hierarchy in the human health (effecting) significance and not in a subjective manner (as attempted in the original work of Melloul and Collin (Op Cit). Details of the Analytical Hierarchy Process and its application have been described in the following section.
172
Percent of samples %
100 90 80 70 60 50 40 30 20 10 0
Cut off limit
TDS
Ca
TA
SO4
Cl
NO3
Cd
Fe
Mn
Zn
Pb
Figure 7.9: Percent of samples exceeding the Indian Standards
Table 7.8: Percentage and Violation of samples exceeding the Indian standards
Parameter
Percent of samples exceeding the Indian standard
Indian water quality standard
TDS
41.2
500
Ca+2
46.3
75
TA
91.2
200
SO4-2
1.5
200
Cl-
0.7
250
NO3-
27.9
45
Cd
59.6
10
Fe
31.6
300
Mn
68.4
100
Zn
3.7
5000
Pb
58.8
50
173
7.4.1 Analytical Hierarchy Process (AHP) The Analytical Hierarchy Process (AHP) is a multi-criteria decision- making approach and was introduced by Saaty (1977 and 1994). The AHP has attracted the interest of many researchers mainly due to the mathematical properties of the method and the fact that the required input data are rather easy to obtain. The AHP is a decision support tool which can be used to solve complex decision problems. It uses a multi- level hierarchical structure of objective criteria, subcriteria and alternatives. The pertinent data are derived by using a set of pairwise comparisons. These comparisons are used to obtain the weights of importance of the decision criteria, and the relative performance measures of the alternatives in terms of each individual decision criteria. If the comparisons are not perfectly consistent, then it provides a mechanism for improving consistency. Some of the industrial engineering applications of the AHP include its use in integrated manufacturing (Putrus, 1990), in the evaluation of technology investment decisions (Boucher and McStravic, 1991), in flexible manufacturing systems (Wabalickis, 1988), layout design (Cambron and Evans, 1991), in other engineering problems (Wang and Raz, 1991) and also in groundwater pollution potential assessment (Chaudhary, 2000). Multi-Criteria Decision-Making (MCDM) plays a critical role in many real life problems. It is not an exaggeration to argue that almost any local or federal government, industry, or business activity, involves, in one way or other, the evaluation of a set of alternatives in terms of a set of decision criteria. Very often these criteria are conflicting with each other. Even more often, the pertinent data are expensive to collect (Triantaphyllou and Mann, 1995). The structure of the typical decision problem considered in this study consists of a number, say M, of alternatives and a number, say N, of decision criteria. Each alternative can be evaluated in terms of the decision criteria and the relative importance (or weight) of each criterion can be estimated as well. Let the values aij (i = 1,2,3…..M, and j = 1,2,3…..N) denote the performance values of the i- th alternative (i.e., Ai) in terms of the j-th criterion (i.e., Cj). Also, the values Wj denote the weight of the criterion Cj. Then, the core of the typical MCDM problem can be represented by the following decision matrix:
174
Alternatives
C1 W1
A1 A2 A3 .
a11 a21 a31 . .
AM
aM1
Criteria and weights C2 C3 …. CN W2 W3 …. WN a12 a22 a32 . . aM2
a13 a23 a33 . . aM3
a1N a2N a3N . .
. aMN
Given the above decision matrix, the decision problem considered in this study is “how to determine which the best alternative is”. A slightly different problem is to determine “the relative significance of the M alternatives when they are examined in terms of the N decision criteria combined”. In a simple MCDM situation, all the criteria are expressed in terms of the same unit. It is this issue of multiple dimensions, which makes the typical MCDM problem to be a complex one and the AHP, or its variants, may offer a great assistance in solving these types of problems. Although the AHP and its use of pairwise comparisons has inspired the creation of many other decision- making methods, it has also created some considerable criticism; both for theoretical and for practical reasons. Pairwise comparisons are used to determine the relative importance of each alternative in terms of each criterion. In this approach, the decision- maker has to express his opinion about the value of one single pairwise comparison at a time. Usually, the decisionmaker has to choose his answer among 10-17 discrete choices. Each choice is a linguistic phrase, for example "A is more important than B" or "A is of the same importance as B" , or "A is a little more important than B" and so on. Pairwise comparisons are quantified by using the scale shown in Table 7.9 .Such a scale provides a one to one mapping between the set of discrete linguistic choices available to the decision maker and a discrete set of numbers which represent the importance, or weight, of the previous linguistic choices. The relative importance is implied in the pairwise comparison matrix. The arrays of these comparisons comprise matrices of individual judgment by members of the expert panel. By application of the geometric mean principle, the eigenvectors for each row are estimated as follows: Ei = (a11 × a12 × a13 × . . . . × a1N) (1/N) Where: Ei = eigenvalue for the row i N = number of elements in the row i
175
. . . . 7.4
The priority vector can be determined by normalizing the eigenvalue (divided by their sum) as follows:
Pvi =
Ei
∑
i =1 M
. . . 7.5
Ei
An evaluation of the eigenvalue approach can be found in Triantaphyllou and Mann (1990). An alternative approach for evaluating the relative priorities from a judgment matrix is based on the least squares formulation and is described in slightly non-consistent pairwise comparisons. Perfect consistency rarely occurs in practice. In the AHP, the pairwise comparisons in judgment matrix are considered to be adequately consistent if the corresponding consistency ratio (CR) is less than 10% (Saaty, 1980). The CR coefficient is calculated as follows. First the consistency index (CI) needs to be estimated. This is done by adding the columns in the judgment matrix and multiplying the resulting vector by the vector of priorities (i.e., the approximated eigenvector) obtained earlier. This yields an approximation of the maximum eigenvalue, denoted by ? max . then, the CI value is calculated by using the formula: CI= (? max – n)/(n-1)
. . . 7.6
Table 7.9: Scale of relative importance Intensity of Importance 1 3 5 7 9 2,4,6,8
Definition
Equal importance Weak importance of one over another Essential or strong importance Demonstrated importance Absolute importance Intermediate values between the two adjacent judgments If activity I has one of the above nonzero numbers assigned to it Reciprocals of when compared with activity j, then j has the reciprocal values above nonzero when compared with i. (Source: Saaty, 1980) Next, the consistency ratio (CR) is obtained by dividing the CI value by the Random Consistency index (RCI) as follows: CR =
CI RCI
. . . . 7.7
where RCI values are as given in Table 7.10.
176
After the alternatives are compared with each other in terms of each one of the decision criteria, and the individual priority vectors are derived, the synthesis step is taken. The priority vectors become the columns of the decision matrix (not to be confused with the judgment matrices with the pairwise comparisons). Table 7.10: RCI values for different values of n Number of rows
1
2
3
4
5
6
7
8
9
RCI
0
0
0.58
0.90
1.12
1.24
1.32
1.41
1.45
(Source: Saaty, 1980)
7.4.2 Application of AHP: Calculation of the MIAWQ Parameter Weights The eight water quality parameters selected for computing the MIAWQ index were classified in five groups on the basis of the human health significance of these parameters (Table 7.11). The first group was considered relatively the most important, whereas the last group, the least important on the basis of available reports and references. As per the relative importance scheme of the AHP (Table 7.2; Saaty, 1980), the criteria of these parameters were transferred as input values for the AHP matrix (Table 7.9). The eigenvalue (column 9 of the Table 7.12) were normalized to obtain the priority (pollution impact) vector or relative unit vector. The highest priority vector was give n a weight 5 (due to the need of rescaling as per the 0-5 scale of MIAWQ) and weights of the other chemical parameters were deduced accordingly. The final weights, Wi, to be used in MIAWQ model are given in the column 11 of the Table 7.12.
7.4.3 Calculation of the Final MIAWQ Map Using GIS The process of calculation of MIAWQ index has been explained below: •
The geographic distribution (under GIS) of the aforementioned eight parameters was prepared (refer section 7.3), as earlier presented in Figure 7.4 and 7.7.
•
The Xj values were calculated for each cell (j) based upon equation 7.1 and using the geographic distribution of the parameters by using the spatial analyst extension in software ArcView GIS; where Cdd, Mnd, Pbd, Fe d, NO3 -d, TAd, TDS d and Ca2+d are desired Indian standards for drinking water (Table 7.8).
•
All the cells having values of Xij equal or more than 3.5 were given a value 3.5 using the map query tool in ArcView GIS and the final map for each parameter was prepared (Figure 7.10).
177
•
The Yi values were calculated for each cell based upon equation 7.2 and the Yi maps were prepared for each parameter. Subsequently, maps of Yri cell values for each parameter were prepared by dividing the Yi values by the values 10(Ymax).
•
Wri values in cells for each parameter were calculated based upon the weight values (from column 11 of Table 7.12) divided by 5 (Wmax). New maps were prepared for each parameter by multiplying the Yri values by the values of Wri.
•
The final values of the MIAWQ were calculated by employing equation 7.3, whereas the values arrived at after summation of the eight maps (Yri x Wri ) were multiplied by the value 1.25 (C/N; where C=10 and N=8). The value of CI was calculated as per equation 7.7 and the consistency ratio (C.R.)
was calculated as 0.09. As indicated earlier, the value of C.R. is extremely small (much smaller than 10%), and hence the pairwise comparisons may be considered fairly consistent. The final MIAWQ map is shown in the Figure 7.11. The figure exhibits a range of index values from less than 0.5 to more than 3.0 divided into 7 divisions with 0.5 division widths. The index with less than 0.5 indicates the best region (lowest pollution affected) and more than 3.0 as the worst region (maximum pollution affected). From the MIAWQ index map, it can be seen that the overall groundwater quality starts deteriorating towards southern direction and more intensely, towards south-west direction in the study area. The north-northwest region indicates the availability of fresh pure water, muc h within the drinking standard limits. Hydrologically, the groundwater movement in the study area is also in the southern direction in the eastern part of the study area and in the southwest direction in the western part of the study area. The north – northeastern region is mostly a hilly terrain (recharge area) with no urbanization whereas urbanization increases towards southern and southwest direction. Deterioration in groundwater quality is clearly exhibited (as per the MIAWQ index) apparently reflecting the progression of urban, industrial and agricultural activities alongwith the general direction groundwater flow in the study area.
178
Table 7.11: Classification of water quality parameters on the basis of human health significance Group
Parameter
Cd
I
Pb
II
NO3 -
Mn
III
Fe
IV
TDS
V
TA Ca+2
Water quality criteria
• Biologically, Cadmium is a nonessential, non beneficial element recognized to be of high toxic potential. • It is deposited and accumulated in various body tissues and is found in varying concentration throughout all areas where man lives. • The cadmium is toxic to man when ingested or inhaled. It is stored largely in the kidneys and liver and is excreted at an extremely slow rate (Train, 1979). • Most Lead salts are of low solubility and stable complexs result also from the interaction of Lead with the sulfhydryle group. • It has no beneficial or desirable nutritional effects. • It is a toxic metal that tends to accumulate in the tissues of man and other animals. Although seldom seen in the adult population, irreversible damage to the brain is a frequent result of lead intoxication in children. Such lead intoxication most commonly results from ingestion of leadcontaining paint still found in older homes. The major toxic effects of lead include anemia, neurological dysfunction, and renal impairment (EPA, 1973). • It becomes toxic only under conditions in which they are high nitrates concentration. Otherwise, at “reasonable” concentrations, nitrates are rapidly excreted in the urine. • High intake of nitrates constitutes a hazard primarily to warm blooded animals (Specially the younger ones) under conditions that are favorable to their reduction to nitrite (Train, 1979). • Manganese is a vital micro – nutrient for both plants and animals. • Very large doses of ingested manganese can cause some disease and liver damage but these are not still documented. • Few Manganese toxicity problems have been found throughout the world and these have occurred under unique circumstances, i.e. a well in Japan near a deposit of bur ied batteries (McKee and Wolf, 1963). • Iron is an essential trace element required by both plants and animals. • In some waters, it may be limiting factor for the growth of algae and other pla nts; this is especially true in some marl lakes where it is precipitated in high alkaline conditions (Train, 1979). • The human body has the ability to naturally store Iron. • Too much Iron in the body may be linked to heart disease, cancer, diabetes and other diseases (Joseph, 2004). • Excess dissolved solids are objectionable in drinking water because of possible physiological effects, unpalatable mineral tastes. • The physiological effects directly related to dissolved solids include laxative effects principally from sodium sulfate and magnesium sulfate and the adverse effect of sodium on certain patients afflicted with cardiac disease and women with toxemia associated with pregnancy (Train, 1979). • There are no direct effects on the human health. • There are no direct effects on the human health.
179
180
N
(a) Cd
(b) Mn
< 1.0 1.0 - 3.5 >= 3.5
(c) Pb
(d) Fe
(e) TA
(f) NO3 -
(h) Ca 2+
(g) TDS
10
0
10
20 Kilometer s
Figure 7.10: Xi map for the eight parameters 181
182
Table 7.12: Analytic Hierarchy Process matrix
77°10'
77°20'
77°30'
77°40'
77°50'
78°00'
N
78°10'
30°20'
30°20'
30°10'
30°10'
Sadhuli Qadim T $
Muzaffarabad T $
T $
30°00'
Saharanpur
29°50'
Gangoh T $
29°40' 10
0
77°10'
10
77°20'
Deoband T $
Narson T $
Laksar T Khanpur $ T $
MIAWQ MIAWQ < 0.5 0.5 - 1 1 - 1.5 1.5 - 2 2 - 2.5 2.5 - 3 >= 3.0
29°40'
Nanauta T $
Nagal T $
29°50'
Rampur T $
Haridwar Bahadrabad % U T $ Roorkee T $
Bhagwanpur T $
U %
Nakur T $
30°00'
Punwarka
Sarsawa T $
20 Kilometers
77°30'
77°40'
77°50'
78°00'
78°10'
Figure 7.11: Relative index of water quality (MIAWQ) map
183
184
CHAPTER - VIII
GROUNDWATER VULNERABILITY ASSESSMENT This chapter presents the assessment of aquifer vulnerability and generation of vulnerability maps by integrating multiple data sets. A modification of the DRASTIC method (DRASTIC-MOD) has also been incorporated in this chapter and results of validation of both the methods (DRASTIC and DRASTIC-MOD) against the computed groundwater quality index (MIAWQ) have been presented.
8.1 ASSESSEMENT AND MAPPING OF AQUIFER VULNERABILITY The method employed for the purpose, DRASTIC, is a relative evaluation tool, which was designed for producing pollution potential maps of the entire U.S.A by a standardized non-subjective method to compare pollution vulnerability over different areas. For this reason, weight classes and ratings were considered as constants that could not be changed, otherwise comparison between different areas would not have been possible. Only two parameters, aquifer media and impact of the vadose zone, were assigned a typical rating and a variable rating, wherein the variable rating permitted to select a typical value or to adjust the value as a function of more specific knowledge. The application of this method in the study area is discussed in the following sections. The DRASTIC parameters were entered into software ArcView GIS as vector map layers. The ratings and weights were assigned to the DRASTIC parameters, as given by Aller et al. (1987b). The ratings for all DRASTIC parameters were subsequently added to obtain the total cell rating. For each parameter, overlays were made between original layers and the layer of 100 m2 cells and the average parameter rating for each cell was calculated. For all parameters, the maps illustrate a rating variation from 1-10, darker shades indicating higher ratings. The process details in respect of all these parameters are presented in the following section.
8.1.1 Depth to Groundwater The water table is the expression of the surface where all the pore spaces are filled with water below the ground level (Aller et al., 1987b). The distance water must flow to reach the groundwater combined with the ease with which movement occurs, plays a considerable role in determining the vulnerability of an area to contamination. Areas with a
185
high water table are more vulnerable to contamination than are areas with deeper water table if the overlying materials are the same. Generally, high water table does not allow contaminated infiltrating waters enough contact time with aquifer material for their associated attenuation process to be effective in removing contamination. Therefore, the depth to groundwater was assigned the maximum weight (5) in determining the vulnerability using DRASTIC method (Table 8.1). Table 8.1: Assigned weights for DRASTIC parameters Parameters
Weight Scale
Depth to the water table Net Recharge of aquifer Aquifer media Soil media Topography Impact of Vadose Zone Hydraulic Conductivity
5 4 3 2 1 5 3
The depth to groundwater maps for the study area for pre- monsoon and postmonsoon seasons were prepared (refer section 6.3.2). Considering the postmonsoon season to be more critical with respect to the groundwater vulnerability (as the water table is shallowest), the waters table map for this period was considered. The depth to groundwater was classified according to DRASTIC rating (Table 8.2) and the final map for the study area, as generated, is shown in Figure 8.1. This map shows only four rating classes (3, 5, 7 and 9). The shallowest water table has been observed in the southern, eastern and central parts of the study area, resulting in maximum potential for groundwater pollution with high scores (9). The middle depth range, which has a rating of 5 & 7, has been observed in a very large portion of the study area, while the deeper water table, having a rating of 3, is only present in the northern part of the study area in Bhabar zone, having an unconfined deep aquifer.
8.1.2 Net Recharge The primary source of groundwater is typically precipitation, which infiltrates through the strata of the ground and percolates to the water table. Return flow from irrigation also adds up to the groundwater recharge. “Net recharge ” represents the total quantity of water, which is applied to the ground surface and infiltrates to reach the aquifer. It includes the average annual amount of infiltration and does not take into consideration distribution, intensity or duration of recharge events. The recharge is important because it is a principal
186
vehicle for leaching and transporting solid or liquid contaminants to the water table. Therefore, greater the recharge, higher is the potential for pollution. The net recharge was assigned a weight “4” in the DRASTIC method (Table 8.1). The reclassification of the net recharge map, prepared earlier (refer section 6.3.8, Figure 6.25), was done according to the DRASTIC rating (Table 8.3) and the second parameter layer was generated (Figure 8.2). Table 8.2: Ranges and Ratings for Depth to groundwater Depth to Groundwater Range (m) Rating 0-1.5 10 1.5-4.5 9 4.5-9.1 7 9.1-15.2 5 15.2-22.9 3 22.9-30.5 2 > 30.5 1 (Source: Aller et al., 1987b) The map for net recharge (Figure 8.2) shows three rating classes (3, 6 and 8). The highest score (8) corresponds to the southwestern part, which exhibits a high agricultural activity and also a number of paleochannels of river Ganga. High net recharge has also been observed in the northern part of the study area. Small patches have been observed displaying a recharge rating as 6, while the lowest score of 3 has been observed in few parts scattered over entire study area, including localized influences. Table 8.3 Ranges and Ratings for net Recharge. Net Recharge Range (mm) Rating 0-50.8 1 50.8-101.6 3 101.6-177.8 6 177.8-254.0 8 > 254.0 9 (Source: Aller et al., 1987b)
187
8.1.3 Aquifer Media Aquifer media refers to the consolidated or unconsolidated medium which serves as an aquifer, such as sand and gravel or limestone (Aller et al. 1987b). This parameter was assigned a weight “3” in the DRASTIC method (Table 8.1). The geological description of the study area (section 6.3.9) indicates that the aquifers are sandy in nature, having a rating between “4” and ”9” (typical rating is 8) (Aller et al., 1987b; Table 8.4). As the entire study area is a typical alluvial area, only one typical rating value of this DRASTIC parameter has been considered to apply for whole of the study area reflecting no significant spatial variation in the assessment of vulnerability with regard to aquifer media. Table 8.4: Ranges and Ratings for Aquifer media. Aquifer Media Range Massive Shale Metamorphic / Igneous Weathered Metamorphic/ Igneous Thin Bedded Sandstone, Limestone, Shale Sequences Massive Sandstone Massive Limestone Sand and Gravel Basalt Karst Limestone (Source: Aller et al., 1987b)
Rating 1-5 2-5 3-5 5-9 4-9 4-9 4-9 2-10 9-10
Typical Rating 2 3 4 6 6 6 8 9 10
8.1.4 Soil Media The soil has a significant impact on the amount of recharge, which can infiltrate into the groundwater and hence, influences the ability of contaminants to move vertically into the vadose zone. Moreover, where the soil zone is fairly thick, the attenuation processes of filtration, biodegradation, sorption and volatilization may be quite significant. This parameter was assigned a weight “2” in the DRASTIC method (Table 8.1). The reclassification of soil map prepared earlier (refer section 6.3.3, Figure 6.16), was done according to the DRASTIC rating (Table 8.5) and a new layer generated for this parameter. The Soil map (Figure 8.3) shows two rating classes (4 and 6). The high score “6” is seen to correspond to the sandy loam soils (present in paleochannels and the northern part of the study area). Lower score “4” represents the other parts of the study area, where the soil is silty loam.
188
77°10'
77°20'
77°30'
77°40'
77°50'
78°00'
N
78°10'
30°20'
30°20'
30°10'
30°10'
Sadhuli Qadim T $
Muzaffarabad T $
T $
30°00'
Saharanpur
29°50' 29°40' 10
0
77°10'
10
Deoband T $
Narsen T $
Laksar T Khanpur $ T $
29°40'
Nanauta T $
29°50'
Nagal T $
Rampur T $
Gangoh T $
Haridwar Bahadrabad % U T $ Roorkee T $
Bhagwanpur T $
U %
Nakur T $
30°00'
Punwarka
Sarsawa T $
Ratings (Depth in m) 3 ( 15.2 - 22.9 m) 5 ( 9.1 - 15.2 m) 7 ( 4.5 - 9.1 m) 9 (1.5 - 4.5 m)
20 Kilometers
77°20'
77°30'
77°40'
77°50'
78°00'
78°10'
Figure 8.1: Depth to Groundwater rating map.
77°10'
77°20'
77°30'
77°40'
77°50'
78°00'
N
78°10'
30°20'
30°20'
30°10'
30°10'
Sadhuli Qadim T $
Muzaffarabad T $
T $
30°00'
Saharanpur
29°50'
Gangoh T $
29°40' 10
77°10'
0
10
77°20'
Deoband T $
Narsen T $
Laksar T Khanpur $ T $
29°40'
Nanauta T $
Nagal T $
20 Kilometers
77°30'
77°40'
77°50'
29°50'
Rampur T $
Haridwar Bahadrabad % U T $ Roorkee T $
Bhagwanpur T $
U %
Nakur T $
30°00'
Punwarka
Sarsawa T $
78°00'
Ratings 3 6 8
78°10'
Figure 8.2: Net Recharge rating map.
189
(Recharge in mm) (50.8 - 101.6) (101.6 - 177.8) (177.8 - 254.0)
190
Table 8.5: Ranges and Ratings for Soil media. Soil Media Range Thin or Absent Gravel Sand Peat Shrinking and /or Aggregated Clay Sandy Loam Loam Silty Loam Clay Loam Muck Nonshrinking and Nonaggregated Clay (Source: Aller et al., 1987b)
Rating 10 10 9 8 7 6 5 4 3 2 1
8.1.5 Topography Topography refers to the slope and slope variability of the land surface. Topography helps controlling the likelihood that a pollutant will run off or remain on the surface in one area long enough to infiltrate. In the DRASTIC framework, the topography parameter was assigned a weight “1”. The reclassification of the slope map prepared earlier (refer section 6.3.1, Figure 6.10), was done according to the DRASTIC ratings (Table 8.6), and the layer for this parameter was generated. The map (Figure 6.3) indicates that the study area is flat in most parts, slope being generally very small. Since this type of topography would generally result in increased percolation time from ground surface to water table, DRASTIC ratings are correspondingly high. More than 90% of the study area has been observed to reflect a high score (10) where the slope percent age is less than “2”.
Table 8.6: Ranges and Ratings for Topography Topography (Slope) Range (%) Rating 0-2 10 2-6 9 6-12 5 12-18 3 > 18 1 (Source: Aller et al., 1987b)
191
8.1.6 Impact of Vadose Zone The vadose zone is defined as the zone above the water table and is unsaturated (Aller et al., 1987b). The type of vadose zone media determines the attenuation characteristics of the material below the typical soil horizon and above the water table. This parameter was assigned a weight “5” in the DRASTIC method. Based on the geological description of the study area (refer section 6.3.9), vadose zone has been observed to consist of varying proportion of clay, silt and sand, which has a rating between 4 and 8 (typical rating is 6) (Aller et al. 1987b; Table 8.7). Although the percentage of sand, silt and clay vary from place to place, but the ratings given by Aller et al. (1987b), do not account for inter-constituent variability. Hence, the available classification can only provide single unique value for vadose zone in the alluvial areas, considering that the vulnerability is not significantly influenced by the intrinsic spatial variability of the geological characteristics. Table 8.7: Ranges and Ratings for Impact of vadose zone Impact of Vadose Zone Media Range
Rating
Silt/Clay Shale Limestone Sandstone Bedded Sandstone, Limestone, Shale Sand and Gravel with significant Silt and Clay Metamorphic / Igneous Sand and Gravel Basalt Karst Limestone (Source: Aller et al., 1987b)
1-2 2-5 2-7 4-8 4-8 4-8 2-8 6-9 2-10 8-10
Typical Rating 1 3 6 6 6 6 4 8 9 10
8.1.7 Hydraulic Conductivity Hydraulic conductivity refers to the ability of the aquifer material to transmit water, which in turn, controls the rate at which groundwater would flow under a given hydraulic gradient. The rate at which the groundwater flows also controls the rate at which a contaminant would be moved away from the point at which it enters the aquifer. In the DRASTIC method this parameter was assigned a weight “3 ”.
192
On the basis of the hydraulic conductivity map prepared earlier (refer section 6.3.6, Figure 5.19), its values in most of the study area exceed 10 m/day. In view of the DRASTIC classification (Table 8.8), this parameter has been assigned a rating of “10” (Aller et al. 1987b; Table 8.8).
Table 8.8: Ranges and Ratings for Hydraulic Conductivity Hydraulic Conductivity (m/day) Range Rating 0.005-0.5 1 0.5-1.5 2 1.5-3.5 4 3.5-5.0 6 5.0-10.0 8 > 10.0 10 (Source: Aller et al., 1987b)
8.1.8 Consolidation and Computation of DRASTIC Index The process of consolidation of all the aforementioned layers and computation of DRASTIC index has been graphically presented in Figure 8.5. The final DRASTIC index map is presented in Figure 8.6, wherein the color coding has been derived from Table 8.9. Highest index values (larger than 160) have been displayed by the east and south-west corners of the study area and also the region characterized by paleochannels. This indicates a collective influence of low depth to groundwater, a high recharge coefficient, and sandy soil on the high estimates of aquifer vulnerability. The index values ranging between 122 to 183 have been, otherwise, observed in other parts of the study area (Table 8.16).
Table 8.9 Color codes DRASTIC indexes Classes Index ranges 1 Less than 79 2 80-99 3 100-119 4 120-139 5 140-159 6 160-179 7 180-199 8 200 and above (Source: Aller et al., 1987b)
193
Color codes Violet Indigo Blue Dark Green Light Green Yellow Orange Red
194
77°10'
77°20'
77°30'
77°40'
77°50'
78°00'
N
78°10'
30°20'
30°20'
30°10'
30°10'
Sadhuli Qadim T $
Muzaffarabad T $
T $
30°00'
Saharanpur
29°50' 29°40' 10
0
77°10'
10
Deoband T $
Narsen T $
Laksar T Khanpur $ T $
29°40'
Nanauta T $
29°50'
Nagal T $
Rampur T $
Gangoh T $
Haridwar Bahadrabad % U T $ Roorkee T $
Bhagwanpur T $
U %
Nakur T $
30°00'
Punwarka
Sarsawa T $
20 Kilometers
77°20'
77°30'
77°40'
77°50'
78°00'
Ratings (Soil texture) 4 (Silty Loam) 6 (Sandy Loam)
78°10'
Figure 8.3: Soil rating map.
77°10'
77°20'
77°30'
77°40'
77°50'
78°00'
N
78°10'
30°20'
30°20'
30°10'
30°10'
Sadhuli Qadim T $
Muzaffarabad T $
T $
30°00'
Saharanpur
29°50'
Gangoh T $
29°40' 10
77°10'
0
10
77°20'
Deoband T $
Narsen T $
Laksar T Khanpur $ T $
29°40'
Nanauta T $
20 Kilometers
77°30'
77°40'
77°50'
78°00'
78°10'
Figure 8.4: Slope rating map
195
29°50'
Nagal T $
Rampur T $
Haridwar Bahadrabad % U T $ Roorkee T $
Bhagwanpur T $
U %
Nakur T $
30°00'
Punwarka
Sarsawa T $
Ratings (Slope %) 1 ( > 18) 3 (12 - 18) 5 (6 - 12) 9 (2 - 6) 10 (0 - 2)
196
×5
D
+ ×4
R
+
×3
A
+ S
×2
+ ×1
T
+ ×5
I
+ ×3
C
= Normal DRASTIC
Figure 8.5: Conceptual illustration of the DRASTIC method for the study area
197
198
8.2 LIMITATIONS OF DRASTIC MODEL AND MODIFICATIONS While applying the DRASTIC method in the present study area for assessment of vulnerability, following limitations were noticed: 1. The proposed rating scale for the parameter “Impact of vadose zone” did not adequately address the implicit variability among the geological constituents of the vadose zone viz. sand gravel, silt and clay, and the resulting complexity. 2. The “Hydraulic conductivity” values observed in study area mostly surpassed the highest limit/range of the rating scale rendering observed spatial variability meaningless with respect to the aquifer vulnerability. 3. Observing definitive signals about the influence of land use (urban > rural and agricultural > forest) on the soil and groundwater quality, the parameter “land use” appeared to also have an important bearing on the status of aquifer vulnerability alongwith other parameters proposed earlier. Due modifications incorporated in the original DRASTIC model in view of the above and the results obtained are discussed in the following sections:
8.2.1 Impact of the Vadose Zone Aller et al. (1985, 1987a and 1987b) gave 10 ranges for classifying the impact of the vadose zone parameter (Table 8.7), wherein “silt and clay” were included in one range with rating “1” to “2” and a typical ranting “1”, and the second range included “sand and gravel with significant silt and clay” with ratings of 4 to 8 with typical rating “6”. In the present study, the area is alluvial and the vadose zone material is a mixture of sand, silt and clay layers. The geological signature of the study area indicates “sand and gravel with significant silt and clay”, which has typical rating value of “6” and the weight of “5”, this would give a constant score of “30”, which is to be added to the total DRASTIC index. However, in some parts, there are clay and silt layers without any sandy layer, while in some other parts, there are sandy layers without any silt and clay layers. Considering these complexities, it has been considered that the constant rating value of 6 and a score of “30” does not adequately represent the entire area. To address this anamoly, harmonic mean approach (Harr, 1962) has been employed to calculate the exact rating values at different locations. Following equation has been used: Ir=
T Ti ∑ Iri i =1 N
. . . . . . . . . (8.1)
199
where Ir = the weighted harmonic mean of the vadose zone, T = the total thickness of the vadose zone , Ti = thickness of layer i and Iri = rating of layer i. On the basis of the database of lithologs (refer section 5.4.4), the resulting geological units occurring in the Log type table (Table 6.5) and Borehole logs table for each borehole; weighted harmonic mean of the vadose zone rating has been calculated. The new ranges and ratings are given in Table 8.10. Figure 8.7 shows the final vadose zone rating map for the study area. In this map the vadose rating value get increased from northern to southern part of the study area, which is in agreement with the earlier groundwater quality results.
Table 8.10: Impact of vadose zone range and rating for different strata type Strata ID 01 02
Boulders Conglomerate
8 8
Strata ID 23 24
03
Gravel
8
25
Silty Sand
4
04
Pebble
8
26
Loam
4
05
Coarse Sand with Pebbles
6
27
Loamy Clay
4
06
Coarse Sand
6
28
Sandy Clay
4
07
Fine - Medium Sand
6
29
Sandy Clay with Pebbles
4
08 09
Fine Sand Fine Sand with Pebbles
6 6
30 31
Sandy Clay with Kankar Silty clay with pebbles
4 4
10
Gravelly Sand
6
32
Silt
2
11
6
33
Silt with Kankar
2
6
34
Silty Clay with Kankar
2
13
Medium - Coarse Sand Medium - Coarse Sand with Pebbles Medium Sand
6
35
Silty Clay
2
14
Medium Sand with Pebbles
6
36
Silty loam
2
15
Pebble with Sand
6
37
Silty loam with pebbles
2
16
Clay with Pebble
4
38
Surface Clay
2
17
Clayey Sand with pebble
4
39
Clay
1
18
Clayey Sand
4
40
Clay with Kankar
1
19 20
Clayey Sand with Kankar Coarse Sand with Kankar
4 4
41 42
Clayey Silt Kankar
1 1
21
Fine Sand with Kankar
4
43
Kankar with Silt
1
22
Kankar with Sand
4
44
Kankar with Clay
1
12
Strata Name
Rating
(Source: Kumar, 2004; personal communication)
200
Strata Name
Rating
Pebble with Clay Pebble with sand and clay
4 4
77°10'
77°20'
77°30'
77°40'
77°50'
78°00'
N
78°10'
30°20'
30°20'
30°10'
30°10'
Sadhuli Qadim T $
Muzaffarabad T $
T $
30°00'
Saharanpur
29°50' 29°40' 10
0
77°10'
10
Deoband T $
Narsen T $
Laksar T Khanpur $ T $
29°40'
Nanauta T $
29°50'
Nagal T $
Rampur T $
Gangoh T $
Haridwar Bahadrabad % U T $ Roorkee T $
Bhagwanpur T $
U %
Nakur T $
30°00'
Punwarka
Sarsawa T $
20 Kilometers
77°20'
77°30'
77°40'
77°50'
78°00'
DRASTIC Index 120 - 139 140 - 159 160 - 179 180 - 199
78°10'
Figure 8.6: Final DRASTIC index rating map
77°10'
77°20'
77°30'
77°40'
77°50'
78°00'
N
78°10'
30°20'
30°20'
30°10'
30°10'
Sadhuli Qadim T $
Muzaffarabad T $
T $
30°00'
Saharanpur
29°50'
Gangoh T $
29°40' 10
77°10'
0
10
77°20'
Deoband T $
Narsen T $
Laksar T Khanpur $ T $
20 Kilometers
77°30'
77°40'
77°50'
Ratings 29°40'
Nanauta T $
Nagal T $
29°50'
Rampur T $
Haridwar Bahadrabad % U T $ Roorkee T $
Bhagwanpur T $
U %
Nakur T $
30°00'
Punwarka
Sarsawa T $
78°00'
1-2 2-3 3-4 4-5
78°10'
Figure 8.7: The impact of vadose zone rating using harmonic mean
201
202
8.2.2 Hydraulic Conductivity The highest class of hydraulic conductivity has been assigned a rating of “10” (for values ≥ 10 m/day) values. However, in the study area, the hydraulic conductivity values have been observed to range from 10 m/day to 48 m/day. Adopting the original rating would imply that spatial distribution of hydraulic conductivity in the study area would not be adequately reflected while estimating the aquifer vulnerability, whereas, good correlation was observed between the hydraulic conductivity map (Figure 6.19) with the TDS concentration map (Figure 7.4 a) and the water quality index (MIAWQ) map (Figure 7.11). Therefore adjustment for the hydraulic conductivity was considered essential. The selection of new ranges for this parameter was made by trial and error looking for similar distributions of DRASTIC index of the hydraulic conductivity. The frequency distribution of the DRASTIC index for various scales (index values modified by multiplying factor) were calculated, as attempted earlier by Ramos and Castillo (2003), for adjustment of “depth to groundwater” parameter. The correlation between the hydraulic conductivity and its respective vulnerability index is shown in Figures 8.8 and 8.9. It can be seen from these figures that the best results in terms of similarity of the frequency distribution of the vulnerability index to the distribution of the hydraulic conductivity in the study area were obtained for a scale 6 times the original index value. The original and the rescaled ranges for hydraulic conductivity are given in Table 8.11. Hydraulic conductivity map on the basis of modified (reclassified) ranges is shown in Figure 8.10.
Table 8.11: Original (Aller et al., 1985) and modified ranges for hydraulic conductivity. Hydraulic conductivity (m/day) (Original range) 0.005 – 0.5 0.5 – 1.5 1.5 – 3.5 3.5 – 5 5.0 – 10.0 > 10.0
Hydraulic conductivity (m/day) (Modified range) 0.03 – 3.0 3.0 – 9.0 9.0 – 21.0 21.0 – 30 30.0 – 60.0 > 60.0
203
Rating 1 2 4 6 8 10
0.30 0.20 0.10
45 -50
40 -45
35 -40
30 -35
25 -30
20 -25
15 -20
0.00 10 -15
Frecuency (%)
0.40
HYDRAULIC CONDUCTIVITY (m/d)
DRASTIC X 3
Frecuency (%)
Frecuency (%)
DRASTIC X 2
1 0.8 0.6 0.4 0.2 0 4
6
8
10
1 0.8 0.6 0.4 0.2 0 1
DRASTIC X 2
Frecuency (%)
Frecuency (%)
1 0.8 0.6 0.4 0.2 0 6
8
4
8
10
DRASTIC X 5
DRASTIC X 7
Frecuency (%)
Frecuency (%)
6
10
DRASTIC X 6
1 0.8 0.6 0.4 0.2 0 6
4
1 0.8 0.6 0.4 0.2 0
DRASTIC X 4
4
3
DRASTIC X 5
DRASTIC X 4
4
2
DRASTIC X 3
8
10
DRASTIC X 6
1 0.8 0.6 0.4 0.2 0 4
6
8
10
DRASTIC X 7
Figure 8.8: Hydraulic conductivity and DRASTIC index correlation with a frequency analysis of hydraulic conductivity and DRASTIC index (2x, 3x, 4x, 5x, 6x and 7x)
204
y = 4.2961Ln(x) - 6.8295 R2 = 0.7254
12
12
10
10
DRASTIC X 5
DRASTIC X 2
y = 2.0239Ln(x) + 3.123 R2 = 0.4979
8 6 4 2
8 6 4 2
10 14 18 22 26 30 34 38 42 46 50
10 14 18 22 26 30 34 38 42 46 50
Hydraulic Condductivity (m/d)
Hydraulic Condductivity (m/d) y = 4.9954Ln(x) - 10.061 R2 = 0.792
12
12
10
10
DRASTIC X 6
DRASTIC X 3
y = 3.1021Ln(x) - 1.557 R2 = 0.6004
8 6 4
8 6 4 2
2 10 14 18 22 26 30 34 38 42 46 50
10 14 18 22 26 30 34 38 42 46 50
Hydrulic Conductivity (m/d)
Hydrulic Conductivity (m/d)
y = 1.1735Ln(x) + 2.3485 2
R = 0.2727
12
12
10
10
8
DRASTIC X 7
DRASTIC X 4
y = 2.9815Ln(x) - 1.9515 R2 = 0.6408
6
8
6
4 4
2 10 14 18 22 26 30 34 38 42 46 50
2 10
Hydrulic Conductivity (m/d)
14
18
22
26
30
34
38
42
46
50
Hydrulic Conductivity (m/d)
Figure 8.9: Alternate equation and correlation coefficients between hydraulic conductivity and DRASTIC index (2x, 3x, 4x, 5x, 6x and 7x)
205
8.2.3 Land Use Parameter Land use is one of the most important factors with respect to groundwater vulnerability. Some earlier attempts of integrating it with the original DRASTIC framework, as reported in literature, have been presented below: National Water Quality Assessment Program (NAWQA, 1999) developed an uncalibrated DRASTIC vulnerability map for Idaho by modifying DRASTIC method. Three of the seven DRASTIC factors [Depth to groundwater, net recharge (as a Land use) and soil media] were used. Land use was used as a surrogate for net recharge because areas having irrigated agriculture provided the largest amount of recharge. Table 8.12 gives the land use categories and the ratings developed by the NAWQA.
Table 8.12: Land use categories and the rating Land use Urban Irrigated agriculture Rangeland Dryland agriculture Forest (Source: NAWQA, 1999)
Rating 3 2 1 1 1
Ramolino (1988) developed a Nitrate Pollution Index for Groundwater based on a multiplicative –cum- additive model as follows. Nitrate Pollution Index = Ffi + Rri + Ssi + Ddi
. . . . .8.2
Where F, R, S and D = importance weights for Nitrogen fertilizer (F), net recharge (R), Soil texture (S) and depth to groundwater (D). fi, ri, si and di = factor ratings for the four factors. The net recharge and depth to groundwater were adapted from the DRASTIC methodology; the soil range and ratings were based on drainage characteristics. The nitrogen fertilizer ranges were not given in numerical values (Table 8.13), because crop requirement for nitrogen varied with the nature and the type of crops, the relative amount and distribution of inorganic forms of nitrogen in soil profile, climatic factors, and the type and number of management practices.
206
Secunda et al. (1998) and Al-Adamat et al. (2003) also developed a composite model combining DRASTIC with land use data. “Extensive land use” was incorporated into the DRASTIC model as eighth parameter. The land use ratings (Lr) were arranged on the basis of extensive land uses, as effluent irrigation of crops as potential sources of groundwater pollution (Table 8.14).
Land use parameter (Lw), was assigned weight of “5”, due to its
potential impact on pollutant - percolation to the groundwater table.
Assigned ratings and
weightings for the extensive agricultural land use parameter were added to the final DRASTIC Index (DI) to produce a Composite DRASTIC – Extensive Land Use Index (CDI) for each cell i. CDIi = DIi + Lr.Lwi
. . . . (8.3)
Table 8.13: ranges and ratings for nitrogen fertilizer Range Rating Over- fertilized 10 Fertilized to meet crop need 6 No fertilizer applied 1 (Source: Ramolino, 1988 in Canter, 1997)
For the present study area, limited information was available about the land use and soil quality. Therefore, the soil samples, collected from the different types of land use within the study area, were analyzed for the physico - chemical parameters (refer Section 7.2). Analysis for various parameters and the subsequent interpretations indicated that urban land use demonstrated maximum impact followed by rural (with agricultural) and forest land use. Further, the groundwater quality estimates also demonstrated maximum impact on the samples from urban land use followed by rural (with agricultural) and forest land use. Based on these observations, qualitative ratings were proposed for the three types of land use in the study area as given in the Table 8.15. Fresh land use map generated according to the values given in Table 8.15 is shown in Figure 8.11. This map was converted to raster grid and multiplied by the weight of this parameter (Lw=5). The resultant grid coverage was added to the DRASTIC index based on equation 8.3.
207
Table 8.14: Ratings of land use categories as modified by Secunda et al., (1998) Land use category Site-specific land usage Toxic – waste disposal Oil spillage Industries Soild-waste disposal (regional) Domestic-waste disposal (local) Effluent irrigated fields Effluent reservoirs Extensive land usage Cotton Built-up areas Irrigated fields crops Greenhouses/tomatoes Reservoirs Citrus orchards Orchards of other fruit Pasture or other land unsuitable for agricultural use Uncultivated land Vineyards Olives Non-irrigated fields Forests Natural areas or reserves Dune sand – open areas
Table 8.15: Land use categories ratings Land use
Ratings 10 8 1
Urban Rural and agriculture Forest
208
Rating 9 8 7 6 5 4 3 10 8 8 8 7 7 6 5 5 5 5 4 1 1 1
77°10'
77°20'
77°30'
77°40'
77°50'
78°00'
N
78°10'
30°20'
30°20'
30°10'
30°10'
Sadhuli Qadim T $
Muzaffarabad T $
T $
30°00'
Saharanpur
29°50'
Gangoh T $
29°40' 10
0
77°10'
10
77°20'
Deoband T $
Narson T $
Laksar T Khanpur $ T $
Ratings (H. C. m/day) 4 (9 - 21) 6 (21 - 30) 8 (30 - 60)
29°40'
Nanauta T $
29°50'
Nagal T $
Rampur T $
Haridwar Bahadrabad % U T $ Roorkee T $
Bhagwanpur T $
U %
Nakur T $
30°00'
Punwarka
Sarsawa T $
20 Kilometers
77°30'
77°40'
77°50'
78°00'
78°10'
Figure 8.10: Hydraulic conductivity modified rating
77°10'
77°20'
77°30'
77°40'
77°50'
78°00'
N
78°10'
30°00'
30°00'
30°10'
30°10'
30°20'
30°20'
Saharanpur Haridwar
29°50'
29°50'
29°40'
Roorkee
29°40'
10
77°10'
0
10
77°20'
20 Kilometers
77°30'
77°40'
77°50'
78°00'
78°10'
Figure 8.11: Land use map
209
Ratings (H. C. m/day)
1 (Forest) 8 (rural & Agricultural) 10 (Urban)
210
8.2.4 Generation of DRASTIC-MOD Map After incorporating all the above modifications, the new DRASTIC-MOD map was generated (Figure 8.12) Using the same classification methodology applied for the DRASTIC index (Aller et al. 1987b), the DRASTIC-MOD index was sub-divided into four classes, (i) 100 – 119 with low risk in the north part of the study area (forest area), (ii) 120 – 159 with moderate risk in the Bhabar zone with deep depth to groundwater and forest area, (iii) 160 – 199 with high risk in the most part of the study area, resulting mainly from cumulative effects of rural and agricultural land use, low to moderate depth to groundwater and high recharge coefficient. (iv) 200 and above with very high vulnerability in some parts of study area; such values are generally localized, resulting mainly from shallow depth to groundwater, high recharge and urban or rural/agricultural land use (Table 8.16 and Figure 8.13).
8.3 COMPARISON AND VALIDATION 8.3.1 Comparing the DRASTIC and DRASTIC-MOD Results The comparison of results from the original DRASTIC and modified DRASTICAMOD method is given in Table 8.16, and Figure 8.13. The values of DRASTIC and DRASTIC-MOD are distributed in four and six classes respectively. The DRASTIC-MOD index is distributed normally among the classes. Whereas the values of DRASTIC attain their peak in class 5, which also shows a higher numbers of values; DRASTIC-MOD values attain their peak in class 6, and classes 3, 7 and 8 show a higher number of values. This difference is apparently because of introduction of greater sensitivity in the original DRASTIC framework spatial variability displayed by hydraulic conductivity, vadose zone geology and land use.
211
8.3.2 Validation of DRASTIC and DRASTIC-MOD against the Groundwater Quality Index (MIAWQ) It was envisaged that comparison of the projected risk of groundwater pollution (vulnerability) to the actual groundwater quality status, would help validate the vulnerability approach on one hand, while also indicating the extent of risk for the carrying capacity of the system on the other. For this purpose, correlation between maps of DRASTIC, DRASTICMOD and the MIAWQ was attempted in two ways. In the first approach, the correlation coefficient between these maps was evaluated using the Grid Analyst Extension (Saraf, 1999) on ArcView GIS. The results of the correlation analysis are shown in Tables 8.17 and 8.18. The correlation of DRASTIC against MIAWQ has not been observed to be significant at the 0.01 level (0.26) whereas the correlation of DRASTIC-MOD against MIAWQ has been observed to be significant (0.54). The second approach examines the similarity of a spatial pattern of variability of these maps along a common cross section, A-B (Figure 8.14). The results show a better match between the patterns of the DRASTIC-MOD and the MIAWQ, wherein the maximum and minimum values for DRASTIC-MOD correspond with the maximum and minimum value of MIAWQ respectively (Figure 8.15 and 8.16). The differences observed in the spatial distribution of vulnerability estimates obtained from both the methods (DRASTIC and DRASTIC-MOD) indicate that in the areas with existing well defined land use practices, vulnerability estimation should necessarily include “land use” as a parameter. Further, in view of a good correlation between the DRASTICMOD and MIAWQ maps, it may be inferred that the “risk of vulnerability” corresponds quite well with the existing water quality scenario in the study area, a finding not commonly reported by researchers earlier. This also highlights the need of initiating corrective measures in many parts of the study area as well as to establish a suitable monitoring protocol to detect adverse quality trends in the future.
212
77°10'
77°20'
77°30'
77°40'
77°50'
78°00'
N
78°10'
30°20'
30°20'
30°10'
30°10'
Sadhuli Qadim T $
Muzaffarabad T $
Punwarka
30°00'
Saharanpur
29°50'
Gangoh T $
29°40' 10
0
77°10'
10
Deoband T $
Narson T $
Laksar T Khanpur $ T $
29°40'
Nanauta T $
Nagal T $
29°50'
Rampur T $
Haridwar Bahadrabad % U T $ Roorkee T $
Bhagwanpur T $
U %
Nakur T $
30°00'
T $
Sarsawa T $
DRASTIC-MOD
< 80 80 - 100 100 - 120 120 - 140 140 - 160 160 - 180 180 - 200 => 200
20 Kilometers
77°20'
77°30'
77°40'
77°50'
78°00'
78°10'
Figure 8.12: DRASTIC-MOD map
60
Area %
50 40
DRASTIC DRASTIC-MOD
30 20 10
200 and above
180 - 199
160 - 179
140 - 159
120 - 139
100 - 119
80 - 99
Less than 79
0
Vulnerability Classes
Figure 8.13: Comparison of the percentage areas in the vulnerability classes using DRASTIC and DRASTIC-MOD
213
214
215
7 8 °1 0 '
7 8 °0 0 '
7 7 °5 0 '
10
7 7 °4 0 '
0
7 8 °1 0 '
7 8 °0 0 '
7 7 °5 0 '
7 7 °4 0 '
7 8 °1 0 '
7 8 °0 0 '
7 7 °5 0 '
7 7 °4 0 '
10
7 7 °3 0 '
0
7 8 °1 0 '
7 8 °0 0 '
7 7 °5 0 '
7 7 °4 0 '
7 7 °3 0 '
7 7 °2 0 '
7 7 °1 0 '
7 8 °1 0 '
7 8 °0 0 '
7 7 °5 0 '
7 7 °4 0 '
7 7 °3 0 '
7 7 °2 0 '
7 7 °1 0 '
10
7 7 °3 0 '
7 7 °2 0 '
7 7 °1 0 ' 0
7 7 °3 0 '
10
7 7 °2 0 '
7 7 °1 0 ' 10
7 7 °2 0 '
7 7 °1 0 ' 10
3 0 °1 0 '
B
2 °9 4 '0
A
3 °0 1 0 '
B
2 9 ° 4 0 '
A
20 Kilometer s
(c): MIAWQ
Figure 8.14: Cross section A-B
7 8 °1 0 '
7 8 °0 0 '
7 7 °5 0 '
7 7 °4 0 '
7 7 °3 0 '
7 7 °2 0 '
7 7 °1 0 '
N
3 0 ° 2 0 ' 3 0 °2 0 '
3 0 °1 '0
B 3 0 °1 0 '
3 0 ° 0 0 ' 3 0 °0 0 '
2 °9 5 '0 2 9 °5 0 '
2 9 °4 0 '
A 2 9 °4 0 '
20 Kilometer s
(a): DRASTIC N
3 0 ° 2 '0 3 0 °2 0 '
3 0 °1 0 '
3 °0 0 '0 3 0 °0 0 '
2 9 °5 0 ' 2 9 °5 0 '
2 9 °4 0 '
20 Kilometer s
(b): DRASTIC-MOD
N
3 0 ° 2 0 ' 3 0 °2 0 '
3 0 °1 0 '
3 0 ° 0 0 '
3 0 °0 0 '
2 9 °5 0 '
2 9 °5 0 '
2 9 °4 0 '
216
Table 8.16: Comparison of the number of pixel, the area in km2 and the area in percentage in the images representing the vulnerability classes obtained DRASTIC and DRASTIC-MOD. DRASTIC Class
Index Ranges
DRASTIC-MOD
N. Pixel
Area
Area %
N. Pixel
Area
Area %
1
Less than 79
0
0.00
0.00
0
0.00
0.00
2
80 - 99
0
0.00
0.00
61
0.61
0.01
3
100 - 119
0
0.00
0.00
22215
222.15
4.43
4
120 - 139
44448
444.48
8.87
42826
428.26
8.55
5
140 - 159
257770
2577.70
51.43
55719
557.19
11.12
6
160 - 179
94455
944.55
18.85
215069
2150.69
42.92
7
180 - 199
104511
1045.11
20.85
138112
1381.12
27.56
8
200 and above
0
0.00
0.00
27182
271.82
5.42
Table 8.17: Minimum, maximum, average and standard deviation of the three maps
DRASTIC DRASTIC-MOD MIAWQ
Minimum 122.0 92.5 0.41
Maximum 183.0 213.5 3.49
Mean 160.7 166.5 2.64
SD 14.1 25.2 0.7
Table 8.18: Correlation matrix of the three maps
DRASTIC DRASTIC-MOD MIAWQ
Correlation Matrix DRASTIC DRASTIC-MOD 1 0.77 1 0.26 0.54
217
MIAWQ
1
Vulnerability Index Vulnerability Index MIAWQ
A
B
Figure 8.15: Composition of spatial variability along a common cross section A-B
218
3.5 3.0 MIAWQ
2.5 2.0 1.5 1.0 0.5 0.0 140.0
2
R = 0.034 150.0
160.0
170.0
180.0
190.0
DRASTIC
4.0
MIAWQ
3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 100.0
R2 = 0.67 120.0
140.0
160.0
180.0
200.0
DRASTIC-MOD
Figure 8.16: Regression between MIAWQ, DRASTIC and DRASTIC-MOD for cross section A-B
219
220
CHAPTER - IX
SUMMARY AND CONCLUSIONS
Groundwater is often treated, significantly less than surface water, as it tends to be a cleaner source. This also means that if contamination occurs, it is more likely to have a negative effect on the user population, as fewer steps are often taken to remove the contaminant before it reaches the public. Due to such reason, groundwater protection is significant importance especially in alluvial areas. Systematic studies have been conducted in the present work to develop an appropriate method suitable for assessment of the groundwater vulnerability of the alluvial aquifers of the Ganga-Yamuna interfluve area by developing a multipurpose database in GIS environment, and by validating the developed vulnerability method by comparing the findings with the observed water quality characteristics of the region. The study area is situated in the northern part of the vast Indo – Gangetic Plain in o
/
//
o
/
//
o
/
//
o
India and lies between latitudes 29 33 51 to 30 19 10 N and longitudes 77 06 20 to 78 /
//
20 15 E with total geographical area of approximately 5500 km2. Administratively, the study area covers the districts of Haridwar in Uttaranchal and Saharanpur in Uttar Pradesh. The area is characterized by the presence of hilly terrain composed of Siwalik formations in the northern part, which is not easily accessible. To the south of Siwaliks, a Piedmont zone is present, which can be divided into an upper Piedmont zone (called Bhabar) and a Lower Piedmont zone (towards south) known as Tarai, to the south of Tarai; the Gangetic alluvial plain is present. Subsurface formations are medium to fine sand and clay with thin beds of coarse sand, sand with pebbles, clay with kankar. In the northern part of the study area, i.e., in the Bhabar zone, boulders are quite common whereas the clay content increases southwards. There is a connection between the three formations viz. Bhabar, Tarai and Gangetic alluvial plain. Sand is dominant as compared to clay beds. Clay beds occur frequently between sandy formations or form lensoidal bodies due to which the sandy zone can behave as a semi confined aquifer. The hydraulic conductivity in the study area varies between 10 m/day and 48 m/day.
221
In most of the northern parts, paleochannels and active floodplains of rivers have soils of sandy loam texture whereas the remaining parts of the study area are covered by soils having silty loam. The depth to groundwater is generally deep (29 m to 32 m) in the northeastern part of study area (Bhabar zone) and shallow (1 m to 2 m) along Gangetic Alluvial Plain in the southern part, the seasonal ground water fluctuation ranging generally between 1 m to 3 m. Groundwater flows from the northern and northeast hilly part to southern and southwestern direction and follows the general topography of the study area. It discharges to Hindon River and Solani River in the southern parts. The north and northeastern parts receive higher rainfall as compared to southern and southwestern parts. Tritium tagging studies in the areas have revealed that higher groundwater recharge also occurs in the northern parts of the area and through paleochannels. Average recharge varies from 6.3 % in silty loam soils to 15.5 % in the sandy loam soils. Net yearly recharge in the study area is found to vary between 68 mm to 243 mm. In most of the study area, the net recharge is less than 80 mm. The analysis of the quality of the soils in the study area indicates that in general, the urban soil has higher concentration of physicochemical parameters, followed by the rural forest soils indicating an important influence of urban activities on soils. Chemical examination of shallow groundwater shows that generally, the ionic concentrations are found to increase from north to south, except NO3-, which shows higher concentration in the northern forested parts. Groundwater quality in the study area does not show much variation between postmonsoon and premonsoon period. TDS shows an increasing trend from north to south and southwestern parts of the study area, yet the overall salinity and the concentration of major ions is generally suitable for drinking purposes as per Indian standards. The groundwater in the area is alkaline in nature and is of calciumbicarbonate type reflecting the chemical maturity of the water. Groundwater in the urban landuse has the highest concentration of Ca2+, Mg2+, K+, Na+, HCO3- and F- while forest and rural landuse has the highest concentration of SO42- and Cl- respectively, indicating that the groundwater quality is affected both by human activities and natural processes (physical, chemical and microbial). The concentration of all the major ions in groundwater is high in urban areas as compared to the forest areas. Ionic concentrations increase along the flow gradient as a result of the addition of these ions through interaction with soil matrix and due to recharge. However, nitrate concentration does not show any relation with groundwater flow direction. 222
Concentration of some heavy metals, i.e., Cd, Mn and Pb in the groundwater exceeds the permissible level for drinking water as recommended by BIS 10500 (1991). The acceptable limits for concentration of the heavy metals (Cd, Pb and Mn) in shallow groundwater are violated in more than 55 % of the samples with the greater violations being found in urban and rural land use categories apparently due to increased human activity. On the basin of observed violation of the acceptable limits, parameters like TDS, Ca2+, Total Alkalinity (TA), NO3-, Cd, Mn, Pb, and Fe are have been selected for computation of a Modified Index of Aquifer Water Quality (MIAWQ). The Weights to these eight parameters were assigned as per their analytical hierarchy in the human health (effecting) significance and not in a subjective manner (as attempted in the earlier works). The values of MIAWQ show an increase from north, north-east to south and south-western parts of the study area. The groundwater vulnerability mapping has been carried out using two approaches: standard DRASTIC method and a modified DRASTIC-MOD method. For the assessment of the groundwater vulnerability in the study area, the DRASTIC parameters were evaluated in GIS environment as seven restart-map layers. The rating percentages were subsequently added to obtain the total rating for each cell. The DRASTIC index in the study area ranges from 122 to 183. The east and south-west corners of the study area and the paleochannels in the southern part show higher vulnerability index values. While applying the DRASTIC method on the present study area for assessment of vulnerability, following limitations were noticed: !
The proposed rating scale for the parameter “Impact of vadose zone” did not adequately address the implicit variability among the geological constituents of the vadose zone viz. sand gravel, silt and clay, and the resulting complexity.
!
The “Hydraulic conductivity” values observed in study area mostly surpassed the highest limit/range of the rating scale rendering observed spatial variability meaningless with respect to the aquifer vulnerability.
!
Observing definitive signals about the influence of land use (urban > rural and agricultural > forest) on the soil and groundwater quality, the parameter “land use” appeared to also have on important bearing the status of aquifer vulnerability alongwith other parameters proposed earlier. Due modifications were incorporated in the original DRASTIC model in view of the
above and the modified (DRASTIC-MOD) index map was sub-divided into four classes, (i) 100 – 119 with low risk in the north part of the study area (forest area), (ii) 120 – 159 with moderate risk in the Bhabar zone with deep depth to groundwater and forest area, (iii) 160 – 223
199 with high risk in most parts of the study area, these values resulting mainly from accumulative effects of rural and agricultural land use, low to moderate depth to groundwater and high recharge coefficient. (iiiv) 200 and above with very high vulnerability in some parts of the study area, reflecting the shallow depth to groundwater, high recharge and high urbanization related activities. DRASTIC-MOD indicates high vulnerability in the southern parts of the study area indicating higher risk of groundwater pollution. In order to investigate the relative empirical as well as theoretical hydroenvironmental evaluation of areas with regards to risk of groundwater pollution DRASTIC and DRASTIC-MOD maps were correlated with modified Index for Aquifer Water Quality (MIAWQ). High significant correlation was exhibited between MIAWQ and DRASTICMOD maps. The differences observed in the spatial distribution of vulnerability estimates obtained from both the methods (DRASTIC and DRASTIC-MOD) indicate that in the areas with existing well defined land use practices, vulnerability estimation should necessarily include “land use” as a parameter. Further, in view of a good correlation between the DRASTICMOD and MIAWQ maps, it may be inferred that the “risk of vulnerability” corresponds quite well with the existing water quality scenario in the study area, a finding not commonly reported by researchers earlier. This also highlights the need of initiating corrective measures in many parts of the study area as well as to establish a suitable monitoring protocoal to detect adverse quality trends in the future.
LIMITATIONS OF THE STUDY ! The piezometers and the boreholes yielding the lithologs were not observed to be as homogenously distributed across the study area as desired. ! Information about recharge from existing water bodies (e.g. Ganga and Yamuna canals) was not available. ! Historical data of soil and groundwater quality was not available. ! Data of contaminant loadings from various types of land use were not available.
FUTURE WORK ENVISAGED Groundwater vulnerability and the groundwater risk cannot be completely estimated without understanding of the hydrogeological condition as well as the groundwater balance, so it would be advisable to extend the above study by increasing the number of monitoring
224
wells both for optimal observation of water level and for lithologs. This may help in a better understanding of the interaction of the groundwater regime. Also, to assess the influence of the horizontal recharge from the drainage system including the canals, it is required to study the effect of these water bodies on the groundwater quality. A comprehensive inventory also needs to be prepared about the incident terrestrial contaminant loadings from different types of land use.
225
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257
ANNEXURE - I
TRITIUM TAGGING DETAILS
260
Plate I Figure: Movement of Tritium and soil moisture at Rahimpur
RAHIMPUR
2500
3000
10
10
30
30 50
50
Inj ection
70
90
90
110
110
130 150
CG
Depth (cm)
Depth (cm)
70
0.28
2000
0.23
1500
0.18
1000
0.08
500
0.03
0
0.13
Vol. moisture contane
Tritium activity (cpm)
130 150
170
170
190
190
210
210
230
230
250
250
08/07/2002 08/11/2002
Table: Volumetric soil moisture content, net tritium count rate and recharge to groundwater at Rahimpur
261
Plate II Figure: Movement of Tritium and soil moisture at Toda Kalyanpur
TODA KALYANPUR
Vol. moisture contane
Tritium activity (cpm) 0.00 10
500.00
1000.00
1500.00
2000.00
0.0000
0.2000
0.3000
0.4000
0.5000
10
30
30
50 70
0.1000
50
Inj ection
70
90
90
Depth(cm)
130 150
Depth (cm)
CG 110
110 130 150
170
170
190
190
210
210
230
230
250
250
089/07/2002 08/11/2002
Table: Volumetric soil moisture content, net tritium count rate and recharge to groundwater at Toda Kalyanpur
262
Plate III Figure: Movement of Tritium and soil moisture at Madhopur
MADHOPUR
Vol. moisture contane
Tritium activity (cpm) 0.00 10
1000.00
2000.00
3000.00
4000.00
5000.00
0.0000 10
30
0.2000
0.3000
0.4000
0.5000
0.6000
30
50 70
0.1000
50
Inj ection
70
90
90
Depth (cm)
130 150
Depth (cm)
C 110
110 130 150
170
170
190
190
210
210
230
230
250
250
09/07/2002 09/11/2002
Table: Volumetric soil moisture content, net tritium count rate and recharge to groundwater at Madhopur
263
Plate IV Figure: Movement of Tritium and soil moisture at Piran Kaliyar
PRIAN KALYR
Vol. moisture contane
Tritium activity (cpm) 0.00 10
500.00 1000.00 1500.00 2000.00 2500.00 3000.00 3500.00 4000.00
0.0000
0.2000
0.2500
0.3000
0.3500
50
Inj ection
70
90
90
110
110
Depth (cm)
Depth(cm)
0.1500
30
50
130 150 170
0.1000
10
30
70
0.0500
130 150
CG 170
190
190
210
210
230
230
250
250
09/07/2002 09/11/2002
Table: Volumetric soil moisture content, net tritium count rate and recharge to groundwater at Piran Kaliyar
264
Plate V Figure: Movement of Tritium and soil moisture at Roorkee
DOH - IIT, OORKEE
Vol. moisture contane
Tritium activity (cpm) 0.00 10
100.00 200.00 300.00 400.00 500.00 600.00 700.00 800.00
0.0000
30
0.1500
0.2000
0.2500
0.3000
0.3500
50 70
Inj ection
90
90
110
110
Depth (cm)
Depth(cm)
0.1000
30
50 70
0.0500
10
130 150 170
130 150 170
CG 190
190
210
210
230
230
250
250
09/11/2002 09/11/2002
Table: Volumetric soil moisture content, net tritium count rate and recharge to groundwater at Roorkee
265
ANNEXURE – II
PHOTOGRAPHS
PLATE: A LAND USE
A -1
A -2
A -3 A -3
A -4
A -5
A -6 A -6
A-1. A-2. A-3. A-4. A-5. A-6.
Forest land use Agricultural land use Rural land use Rural land use (sewage pond) Urban land use (commercial) Urban land use (residential)
PLATE: B MONITORING AND ANALYSIS
B -1
B -2
B -3
B -4
B -5
B-6
B-1. Groundwater sampling B-2. Measurement of groundwater depth B-3. Field analysis in progress B-4. Tritium injection in the field B-5. Soil sample collection B-6. Laboratory analysis in progress
