ANALYSIS OF WATER QUALITY AND BIOMONITORING OF TUIRIAL RIVER IN VICINITY OF THE HYDEL PROJECT IN MIZORAM
THESIS SUBMITTED IN FULFILLMENT FOR THE DEGREE OF DOCTOR OF PHILOSOPHY IN ENVIRONMENTAL SCIENCE
By S LALPARMAWII MZU/Ph.D/164/29.11.2007
DEPARTMENT OF ENVIRONMENTAL SCIENCE SCHOOL OF EARTH SCIENCES & NATURAL RESOURCES MANAGEMENT MIZORAM UNIVERSITY, AIZAWL MIZORAM – 796004 2012
CERTIFICATE
This is to certify that Miss S Lalparmawii has submitted the Ph.D Thesis entitled
“Analysis of Water Quality and Biomonitoring of Tuirial River in Vicinity of the Hydel
Project in Mizoram” under my supervision , for the requirement of the award of the Degree of Doctor of Philosophy in the Department of Environmental Science, Mizoram University,
Aizawl. The authenticity and content of the thesis is the original work of the Research
Scholar, and the nature and presentation of the work are the first of its kind in Mizoram. It is further certified that no portion(s) or part(s) of the content of the thesis is borrowed from any other resources nor reproduced from any other publications.
(PROF. H. LALRAMNGHINGLOVA) (DR. B. P. MISHRA) Head Supervisor Department of Environmental Science Department of Environmental Science Mizoram University Mizoram University
DECLARATION
I, Ms. S. Lalparmawii , hereby declare that the subject matter of this thesis entitled
“Analysis of Water Quality and Biomonitoring of Tuirial River in Vicinity of the Hydel
Project in Mizoram” is the record of work done by me. The contents of the thesis does not form basis of the award of any previous degree to me or to the best of my knowledge to anybody else, and that the thesis has not been submitted by me for any research degree in any other University/Institute.
This is being submitted to the Mizoram University for the award of the degree of
Doctor of Philosophy in the Department of Environmental Science.
(S. LALPARMAWII)
ACKNOWLEDGEMENTS
First and foremost, I would like to thank the Almighty God, who has been with me throughout this academic journey, and helped me make it through to this day. Lord, I express my heartfelt thanks.
I would like to express my sincere gratitude to my supervisor Dr. B. P. Mishra
(Department of Environmental Science, Mizoram University) for his guidance, expertise, support and above all his patience and understanding. He is my true mentor.
I would like to offer my sincere gratitude to Prof. H. Lalramnghinglova (Head,
Department of Environmental Science, Mizoram University) for his constant support and encouragement and also for providing access and facilities to the laboratory during my study.
During the course of this research, I have been fortunate enough to receive assistance and support from all corners, some of which have been unexpected but very much appreciated. I would like to take this moment to thank and offer my sincere gratitude to all who have contributed and helped me complete this long journey.
I am indebted and very grateful to the scientists and technical staffs of the Central
Pollution Control Board, New Delhi for their assistance and technical guidance during the course of study.
I extend my gratitude to NEEPCO, Tuirial Hydel Project, Mizoram for facilitating me to conduct this study at selected study sites and also for providing lodging in their guest house.
I would like to thank Mr. Lalruatfela Ralte, Mr. Lalrinchhana and Mr. David
Malsawmtluanga for accompanying and helping me during my visits to the sampling sites.
Your company not only helped me but also cheered and also extending security during the journeys. I would also like to thank all the faculty members and staffs of the Department of
Environmental Science at Mizoram University for their encouragements and cooperation during the course of study.
I am grateful and appreciate all the help which I received from my friends
Mr. Vanramliana, Dr. Saithantluangi Zote, Dr. Lalchhingpuii, Mr. H. T. Hnamtinkhuma,
Mr. David C. Vanlalfakawma, Mr. Vanlalfela Sailo, Mr. Samuel Lalronunga and
Ms. Lalremruati Ralte. I would be forever indebted for all your encouragements, constructive criticisms and moral support.
Most importantly, I would like to thank my parents, to whom this thesis is dedicated.
You have been an inspiration and pillars of my strength; I thank the Lord above everyday for blessing me with parents like you. My brothers, David and Joel, you’re the best brothers anyone would ask for and so much more. You are what kept me going to reach my goals. My sister and my best friend Gloria, thank you for your constant love, prayers and support throughout my study.
Last but not the least, I would like to thank the University Grants Commission for providing me financial support in form of the Rajiv Gandhi National Fellowship.
Dated:
Place: (S. LALPARMAWII)
CONTENTS Page No.
List of figures i-ii List of tables iii List of photo plates iv List of acronyms and abbreviations v-vi
Chapter 1: Introduction 1-11
Chapter 2: Objectives 12
Chapter 3: Review of Literature 13-18
Chapter 4: Study Area and Study Sites 19-29
Chapter 5: Methodology 30-38
Chapter 6: Results 39-61
Chapter 7: Discussion 62-81
Chapter 8: Summary and Conclusions 82-87
References 88-112
Appendices
Appendix I: Correlation coefficient between different parameters for two
years data (including macroinvertebrate families encountered)
Appendix II: Correlation coefficient of different parameters between various
sites for two years data
Appendix III: Correlation coefficient between different parameters at Site 1
for two years data
Appendix IV: Correlation coefficient between different parameters at Site 2
for two years data Appendix V: Correlation coefficient between different parameters at Site 3
for two years data
Appendix VI: Correlation coefficient between different parameters at Site 4
for two years data
Appendix VII: Biological assessment methods
List of figures
Figure Page No.
1.1 Schematic diagram of the hydrological cycle 4
1.2 Pathways of water moving downhill 5
4.1 Map showing Districts of Mizoram 20
4.2 Location map of study site 25
6.1 Seasonal variation in Temperature of water at selected study
sites (January 2008 – December 2009) 39
6.2 Seasonal variation in TDS of water at selected study
sites (January 2008 – December 2009) 41
6.3 Seasonal variation in EC of water at selected study
sites (January 2008 – December 2009) 42
6.4 Seasonal variation in pH of water at selected study
sites (January 2008 – December 2009) 43
6.5 Seasonal variation in DO content of water at selected study
sites (January 2008 – December 2009) 44
6.6 Seasonal variation in BOD of water at selected study
sites (January 2008 – December 2009) 45
i
6.7 Seasonal variation in Total Hardness of water at selected study
sites (January 2008 – December 2009) 46
6.8 Seasonal variation in Acidity of water at selected study
sites (January 2008 – December 2009) 47
6.9 Seasonal variation in Total Alkalinity of water at selected study
sites (January 2008 – December 2009) 48
6.10 Seasonal variation in Chloride content of water at selected study
sites (January 2008 – December 2009) 49
6.11 Seasonal variation in Fluoride content of water at selected study
sites (January 2008 – December 2009) 50
6.12 Seasonal variation in Nitrate-N content of water at selected study
sites (January 2008 – December 2009) 51
6.13 Seasonal variation in Phosphate-P content of water at selected study
sites (January 2008 – December 2009) 52
6.14 Taxa richness of communities during January 2008 – December 2009 60
6.15 Community Loss Index during January 2008 – December 2009 61
6.16 Jaccard Coefficient of Community Similarity during
January 2008 – December 2009 61
ii
List of tables
Table Page No.
1.1 Freshwater quality deterioration at the global level 1 7
1.2 Sources and significance of pollutants resulting from human activities 8
1.3 Major watersheds in Mizoram 10
5.1 Biological Water Quality Criteria 35
6.1 Biological Water Criteria of Tuirial river during January 2008 –
December 2009 53
6.2 Number of Macroinvertebrates Encountered at Site 1 54
6.3 Number of Macroinvertebrates Encountered at Site 2 55
6.4 Number of Macroinvertebrates Encountered at Site 3 56
6.5 Number of Macroinvertebrates Encountered at Site 4 57
7.1 Water quality standards given by various scientific agencies and
range of values recorded during present investigation 81
iii
List of photo plates
Plates Page No.
Photo plate 4.1: Site 1: Reference station which is situated at the upstream of dam 26
Photo plate 4.2: Site 2: Diversion inlet on river 27
Photo plate 4.3: Site 3: Power house outlet (downstream of dam) 28
Photo plate 4.4: Site 4: Diversion outlet situated downstream of river 29
Photo plate 6.1: Macroinvertebrates encountered at different study sites 58
Photo plate 6.2: Macroinvertebrates encountered at different study sites 59
iv
List of acronyms and abbreviations
% : percentage
˚C : degree Celsius
µS : microsiemens
Anon. : Anonymous
APHA : American Public Health Association
B.C : before Christ
BIS : Bureau of Indian Standards
BMWP : Biological Monitoring Working Party
BOD : Biological Oxygen Demand
BWQC : Biological Water Quality Criteria cm : centimetre
CPCB : Central Pollution Control Board
DO : Dissolved Oxygen
E : East e.g. : for example
EC : Electrical Conductance
EDTA : ethylene-diamine-tetra-acetic et al. : and others h : hour i.e., : that is
ICMR : Indian Council for Medical Research
v
km : kilometre km 2 : square kilometres m day -1 : metres per day mg : milligram mgL -1 : milligrams per litre mh -1 : metres per hour
MI : macroinvertebrates mm : millimetre
N : North nm : nanometres
OD : Optical Density
RBP : Rapid Bioassessment Protocol
S1 : Site 1
S2 : Site 2
S3 : Site 3
S4 : Site 4
SPANDS : Sodium 2-(parasulphophenylazo)-1, 8- dihydroxide-3, 6 napthalene dilsulphonate
TDS : Total Dissolved Solids
USEPA : United States Environment Protection Agency
USPH : United States Public Health
WHO : World Health Organisation
vi
INTRODUCTION CHAPTER 1
1.1 General
Water covers about three fourth area of the earth’s surface with volume of about one billion cubic kilometers. Of total surface water reserves, ocean constitutes 97%, permanent glaciers and ice-caps 2.1%, and remaining only 0.9% water is available as fresh water in the form of rivers, lakes, ponds, streams (Dugan, 1972 and Behura, 1981). Water and life are intricately linked, and water is the main constituent of the human body making up about 80% of total body weight and is the medium for all metabolic activities. More than half of the world’s species of plants and animals live in water, and even our terrestrial-derived food is totally dependent on water and often largely composed of water. Water is needed not only for drinking purpose, but also for production of food. Water is also used to generate electricity
(hydropower and cooling for thermal power), for navigation, and also for leisure. For this reason, most ancient civilizations grew near the bank of rivers and other perennial sources of water (Mishra, 1992).
1.2 Water properties
Water consists of two hydrogen atoms bound to an oxygen atom, forming an isosceles triangle. Water molecules are attracted to each other, creating hydrogen bonds, which influence physical as well as chemical properties of water. Pure water at sea level freezes at
0˚C and boils at 100˚C. At higher elevations, the boiling point of water decreases, due to the lower atmospheric pressure. If substances are dissolved in the water, the freezing point is lowered (John, 2008).
Perhaps the most striking feature of water is that it is less dense in its solid form (ice) than it is in liquid form, and so ice floats. The density of pure water approaches to 4˚C, and so water at this temperature is often found in the deep waters of a lake. The density of water increases if solutes are added (i.e., salty water may be denser than fresh water). Both these
1 features influence thermal and chemical stratification patterns in lakes, with important environmental consequences.
Water has a very high specific heat, which is the amount of energy needed to warm or cool a substance. People who live close to large bodies of water are often said to enjoy a maritime climate, with reduced climatic extremes between the seasons. On the contrary, regions far inland are often said to have continental climates, with striking seasonal changes in temperature. The large water bodies are significant heat reservoirs and heat exchangers.
People exploit the high specific heat capacity of water when they bring hot water bottles into their beds during cold nights.
Water has an extremely high surface tension, which is a measure of the strength of the water’s surface film. The epineuston is a community that takes advantages of this surface tension and lives on the surface of the water. Water striders (Gerridae) are insects commonly seen on the surface of lakes and ponds; they rely on this surface tension to walk on the water’s surface. Another group of organisms collectively referred to as the hyponeuston, live below the water line, but again attach themselves to the water’s surface. This surface tension can also be a deadly trap for some organisms. For example, insects that touch the water may not be able to release themselves from this force.
One of the important characteristics of water is that it is almost the universal solvent, with extraordinary abilities to dissolve other substances. Consequently, when water passes through soils or vegetation or a region of human activity (e.g., an agricultural field treated with fertilizers and insecticides, a mine tailings heap, a municipal or industrial landfill site.), it changes its characteristics as it dissolves solutes. Even a drop of water falling as rain will dissolve atmospheric gases, and its properties will be altered (e.g., carbon dioxide dissolves readily in water, forming a weak acid namely carbonic acid).
2
1.3 Hydrological cycle
The hydrological cycle describes the continuous cycling of water from atmosphere to earth and oceans back again (Figure 1.1). The hydrological cycle is powered by solar energy. This drives evaporation and evapotranspiration, transferring water from the surface of the land, from plant tissue, and especially from the oceans into the atmosphere. Precipitation, primarily as rain and snow, transfers water from the atmosphere to the land surface. These inputs immediately run off as surface water, or follow a number of alternative pathways. The groundwater is released to the stream channel slowly and so are, in effect, storage places as well (Allan, 1995).
Water moves downhill by various routes. Climate, vegetation, topography, geology, land use and soil characteristics determine how much surface runoff occurs compared with other pathways (Dunne and Leopold, 1978). This in turn affects the rate and the chemical composition of the runoff. Before tracing the pathways of water over or through the land, it should be noted that a substantial fraction of precipitation inputs return directly to the atmosphere by evaporation. Firstly, a fraction of rain-water evaporates from the surface of vegetation immediately during and after a rainstorm, never reaching the ground or being absorbed by plants. This is referred to as interception. Secondly, water on the surface of the ground and in lakes and streams evaporates directly back to the atmosphere. Thirdly, plants lose water to the atmosphere during the exchange of gases necessary for photosynthesis.
Water loss by transpiration constitutes a major flux back to the atmosphere. Plant leaves display special adaptations to minimize transpiration, and the geography of plant species and leaf forms reflect the importance of this water loss.
3
Figure 1.1 Schematic diagram of the hydrological cycle. (Redrawn from Dunne and Leopold, 1978.)
Once rain encounters the ground surface, it follows several pathways (Figure 1.2) in reaching a stream channel or groundwater. The rain water also percolates and reaches to certain depth in soil. This capacity declines during a rain event, normally approaching a constant some 0.5 to 2h into the storm (Free, Browning and Musgrave, 1940). The rainfall in excess of infiltration capacity accumulates on the land surface, and surface water in excess of depression storage capacity moves as an irregular sheet of overland flow. Overland flow is likely in arid areas due to low permeability of soil, when the soil surface is frozen, and where human activities make the land surface less permeable (Likens, 1984). In extreme cases, 50 –
100% of the rainfall can travel as overland flow (Horton, 1945), attaining velocity of 10 –
500mh -1. However, overland flow rarely occurs in undisturbed humid regions because soil has high infiltration capacity.
4
Figure 1.2 Pathways of water moving downhill. Overland flow (1) occurs when precipitation exceeds the infiltration capacity of the soil. Water that enters the soil adds to groundwater flow (2) and usually reaches streams, lakes, or the oceans. A relatively impermeable layer will cause water to move laterally through the soil (3) as shallow sub-surface stormflow. Saturation of the soil can force sub-surface water to rise to the surface where, along with direct precipitation, it forms saturation overland flow (4). The stippled area is relatively permeable topsoil. (Redrawn from Dunne and Leopold, 1978.)
Rain that penetrates the soil, particularly less intense rain that does not exceed the infiltration capacity, reaches the groundwater, from which it discharges to the stream slowly and over a long period of time. Baseflow or dry-weather flow in a river is due to groundwater entering the stream channel. However, if the soil structure includes relatively impermeable layer underlying highly permeable top-soil, water is accumulated at that layer and moves downhill. This movement is termed as shallow sub-surface stormflow and is slower than
Horton overland flow but faster than groundwater flow, moving at up to 11m day -1 through sandy loam soil on a steep hill (Linsley, Kohler and Paulhus, 1958). Finally, when there is a large enough rainstorm or a shallow enough water table, the water table rises to the ground surface, causing sub-surface water to escape from the saturated soil as saturation overland flow. This is composed of return flow forced up from the soil and direct precipitation onto the
5 saturated soil (Dunne and Leopold, 1978). Velocity is similar to the lower range of Horton overland flow.
The perennial rivers continue to flow throughout the year, and much of the water in the channel comes from groundwater. In humid regions the water table slopes toward the stream channel, with the consequence that groundwater discharges into the channel. Discharge from the water table into the stream accounts for baseflow during periods without precipitation, and also explains why baseflow increases as one proceeds downstream, even without tributary input. Such streams are called gaining or effluent. Streams originating at high altitudes often flow into drier areas where the local water table is below the bottom of the stream channel. Depending on permeability of materials underlying the stream bed, the stream may lose water into the ground. This is referred to as a losing or influent stream. The same stream can shift between gaining and losing conditions along its course due to changes in underlying lithology and local climate, or temporally due to alternation of baseflow and streamflow conditions. The exchange of water between the channel and groundwater will turn out to be important to the dynamics of nutrients and the ecology of the biota that dwells within the substrate of the stream bottom.
1.4 Water use pattern and water quality deterioration
In the past few decades, the entity of fresh water bodies is threatened due to input of pollutants from various point and non-point sources. Anthropogenic activities lead to discharge of a huge amount of foreign particles/substances in surface water bodies, causing ecological imbalance in aquatic ecosystem and making the water unfit for use. The increased concentration of organic and inorganic substances, pesticides, metals and heavy metals, nutrients, synthetic organics, chemicals, chlorinated solvents causes deterioration of quality
6 of surface water and resulting into adverse effects on human beings and other domesticated animals, using such polluted water for drinking purpose (Mishra and Tripathi, 2001).
The existing water use pattern has adverse impacts on the quality of the aquatic environment including hydrological changes such as storing water in reservoirs or transferring water from one drainage area to another. Human use of water for almost all purposes results in the deterioration of water quality and generally limits the further potential use of the water. Human activities are the source of particulate, dissolved and volatile materials which may eventually reach water. Dissolved materials and many particulates are discharged directly to water bodies, while the particulate and volatile materials that pollute the atmosphere and reaching to aquatic bodies through runoff. Some sources and the polluting materials (except radioactive and thermal wastes) released into water bodies are listed in Table 1.1
Table 1.1 Freshwater quality deterioration at the global level 1
Parameters Rivers Lakes Reservoirs Groundwaters Pathogens xxx x2 x2 x Suspended solids xx oo X oo Decomposable organic matter 3 xxx x xx x Eutrophication 4 x xx xxx oo Nitrate as a pollutant x o o xxx Salinisation x o o xxx Heavy metals xx xx xx xx 5 Organic micro-pollutants xx x xx xxx 5 Acidification x xx x o Changes to hydrological regimes 6 xx xx xx x
Source: Meybeck and Helmer (1996). (xxx) Globally occurring, or locally severe deterioration (xx)
Important deterioration (x) Occasional or regional deterioration (o) Rare deterioration (oo) Not relevant
(1) This is an estimate. At the regional level, these ranks may vary greatly according to the degree of economic development and the types of land use (2) Mostly in small and shallow water bodies (3) Other than that resulting from aquatic primary production (4) Algae and macrophytes ( 5) From landfills and mine tailings (6) Water diversion, damming, over-pumping, etc.
7
Specific locations where pollutants are discharged into aquatic bodies through human activities (discharges from sewage treatment works, industrial wastewater outlets, solid waste disposal sites, animal feedlots and quarries), can be described as point sources. The effect of a point source on the receiving water body is dependent on the population, and size and type of activity, the capacity of the water body to dilute the discharge, the ecological sensitivity of the receiving water body, and the uses to which the water may be put.
Pollutants may also be derived from diffuse and multi-point sources. Diffuse sources are often of agricultural origin and enter surface waters with run-off or infiltrate into ground waters (particularly pesticides and fertilisers). Multi-point sources, such as latrines and septic tanks in rural and urban areas may be treated as diffuse sources for the purposes of monitoring and assessment because it is not possible to monitor each source individually. The sources and significance of pollutants are given in Table 1.2.
Table 1.2 Sources and significance of pollutants resulting from human activities
Industrial Pesticides Trace organic Oils and Sources Bacteria Nutrients and metals micro- Greases Herbicides pollutants Atmos. transport x xxxG xxG xxG Point sources Urban sewage xxx xxx xxx x xxx Industrial effluent x xxxG x xxxG xx Diffuse sources Agriculture xx xxx x xxxG Urban waste and run-off xx xx xxx xx xx x Industrial waste disposal x xxx x xxx x Dredging x xxx x xxx x Navigation and harbours x x xx x xxx Internal recycling xxx xx x x
Source: Meybeck and Helmer (1996). (x) Low local significance (xx) Moderate local or regional significance (xxx) High local or regional significance (G) Global significance
8
Point sources of pollution can usually be identified and the polluting material eventually collected and treated. This cannot be done, however, with diffuse terrestrial sources, atmospheric depositions and the internal recycling of nutrients, metals and some organics. Pollution from these sources can be controlled only by prevention. Internal recycling is particularly difficult because it occurs mostly under the anoxic condition present in the interstitial water of some lake sediments and in some groundwaters. Pollution from accidental spills is unpredictable and its prevention requires the strict observance of safety procedures.
1.5 Biomonitoring of water and wastewater
Chemical monitoring gives discrete information on the effects of pollutants. However, biomonitoring provides a holistic picture about the ecological status and health on aquatic ecosystem and determines quality of water in terms of physicochemical attributes.
Knowledge on physico-chemical characteristics of water is however essential for proper utilization of riverine wealth (Kumar, 1997).
Biomonitoring is the latest emerging tool for instant and accurate monitoring of water quality. It not only acts as a supplement to the physicochemical and bacteriological characteristics, but also provides precious information about the overall health of a water body. The alterations produced in the physical and chemical status of the riverine ecosystem become recognizable through elasticity of the community structure of the organisms (Wilhm and Dorris, 1966, 1986; Cairns and Dickson, 1971). Thus, benthic macroinvertebrates make ideal subject for ecological studies and hence have often been used for biological assessment of water quality of aquatic ecosystems.
Environmentalists have carried out extensive researches in the field of water pollution and its management in India and abroad. But, there is paucity of data and lack of information on
9 status of aquatic bodies and their management in northeast India in general and in Mizoram in particular.
The state of Mizoram is drained by a number of rivers, streams and rivulets of various patterns and lengths. The width of the valley increases towards both ends on the north and south. Amidst the precipitous terrain of Mizoram, there are only few natural lakes, formed at places where hills and ridges serve as natural embankment on all sides. The area receives a considerable amount of rainfall during monsoon months. Most of the rivers and streams are ephemeral in nature. The volume is very low in dry season, whereas, they swell rapidly during monsoon season. Running water is the most decisive element which has sculptured landforms of the region.
According to the Institute of Resource Development and Social Management, geomorphic parameters for 22 watersheds have been worked out (Rao et al, 1994). The major watersheds in the state are presented in Table 1.3.
Table 1.3 Major watersheds in Mizoram.
Sl. No. Catchments Watershed Length of Ma ximum Total number area (km 2) watershed (kms) elevation (meters) of streams 1 Langkaih 395 47 2463 131 2 Teirei 678 72 2463 249 3 Tut 836 98 1200 372 4 Tlawng 1701 157 1536 702 5 Serlui 647 61 3813 326 6 Tuichhuahen 26 1 32 3622 76 7 Tuirial+ Tuirini 1795 107 1400 709 8 Tuivai+ Tuivawl 2310 106 2000 744 9 Mat 964 103 1423 342 10 Tuipui 879 67 1897 215 11 Tuichang 1601 90 1854 500 12 Ngengpui 712 59 1556 144 13 Tuilianpui 1270 97 990 523 14 Sazuklui (Bara Harina Chhara) 116 34 513 38 15 Khawthlangtuipui 149 19 606 30 16 Kau+ Deh 977 54 1387 354 17 Tuichawng 1276 110 1106 272 18 Kawrpui 356 76 720 84 19 Chhimtuipui 2741 136 2158 629 20 Tiau 875 88 1962 212 21 Sakeilui 256 42 770 58 22 Salalui+ Tinglo 290 29 600 68
Source: Rao et al. (1994).
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Water supply has always been a problem in Mizoram. Rivers, streams, and other water sources are exploited for drinking and domestic purposes as well as irrigation and agricultural uses. Monsoon season receives heavy rainfall (250 cm), this may lead to the high turbidity of water, as soil erosion and landslides are very common during rainy months.
In fact, the majority of people in the Mizoram depend on surface water bodies for their day to day life, as underground water is hardly assessable in most of the parts of the state, due to predominance of hilly terrain. Major portion of domestic, agriculture and other wastes are directly or indirectly discharged into the rivers situated in the vicinity, as no proper drainage system has been developed in the state so far. Thus, there is an ample need to determine status of fresh water bodies in the state, and to develop appropriate strategy for management of water bodies.
In view of this, the present research has been carried out with an aim to determine water quality of Tuirial river and biomonitoring in vicinity of Tuirial Hydel Project. This study is also aimed to determine how the bio-indicators are responding with respect to intensity of pollutants. Undoubtedly, the information procured from this study could provide a needful dimension towards formulation of appropriate water pollution abatement technique.
11
OBJECTIVES CHAPTER 2
The major objectives of the study envisage the followings,
1. To study water quality of Tuirial river in vicinity of Tuirial Hydel Project.
2. Biomonitoring of Tuirial river in vicinity of Tuirial Hydel Project.
3. To study impact of intensity of pollutants on biological communities of Tuirial river in vicinity of Tuirial Hydel Project.
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REVIEW OF LITERATURE CHAPTER 3
The available literature depicts that ecologists have paid much attention on the researches pertaining to the water pollution and management at a desired pace. Water pollution problem was first recognized by Hippocrates in 450 B.C and has suggested filtration and boiling as remedial measures (Borchardt and Walton, 1971). At the global level, the significant works on physicochemical characteristics of water have been carried out by Moyle (1946); Gaufin
(1958); Talling and Talling (1965); Horton (1965); Mercer (1966); Wilber (1970); Hickel
(1973); Young et al. (1973); Hem (1975); Hollis (1975); Lewis and Weibezahn (1976); Rai and Hill (1978); Adebisi (1981); Bass and Harlet (1981); Warren (1981); Wright (1982);
Steinitz-Kannan et al. (1983); Fauris (1985); Sakai et al . (1986); Kunishi (1988); Steele
(1989); Meybeck (1989); King and Ekeh (1990); Ayotamuno (1994); Dojlido et al. (1994);
Bukit (1995); Somlyody et al. (1998); Stambuk (1999).
A number of scientists and researchers have extensively studied ecology and water quality of aquatic bodies and established principles of ecology (Odum 1971; O’ Sullivan
1971; Reid and Wood 1976; Mortain and Baylay 1977; Fisher et al. 1982; Payne 1986; Kling
1988; Townsend 1989; Magurran 1991; Allan and Flecker, 1993; George 1997; Mackie
2001; Fytianos et al. 2002; Sood et al. 2008 and Jonathan et al. 2008).
Anthropogenic influences from municipal, industrial and agricultural activities and natural processes like weathering and erosion affect the quality of waters and threaten their use for drinking, irrigation and other economic and social purposes. In recent past, various studies pertaining to assessment of the impact of pollutants on aquatic environment have been carried out by Ekholm et al. (2000); Williams et al. (2000); Bordalo e t al. (2001); Izonfuo and Bariweni (2001); Jonnalagadda and Mhere (2001); Pronansky et al. (2002); Tsiouris et al. (2002); Daniel et al. (2002); Simeonov et al. (2003); Yu and Fang (2003); de Vlaming et
13 al. (2004); Carrera et al. (2004); Hector Hernandez-Romero et al. (2004); Said et al. (2004);
Debels et al. (2005); Djuikom et al. (2006); Fawell et al. (2006); Sanchez et al. (2006);
Kannel et al. (2007); Hasanzadeh (2008); Jassem and Raad (2008); Malakahmad et al.
(2008); Zheng et al. (2008); Wang et al. (2008); Moiseenko et al. (2008); Chang (2008);
Siyue et al. (2009);
The environmentalists have also carried out significant researches on aspects pertaining to water management (Perry 1939; Wisdom 1956; Henderson 1957; Toms 1975;
Boyd 1982; Canter 1987; Song et al. 1990; Johns 1993; Miyabara et al. 1993; Bachmat 1994;
Kellog 1994; Fan et al. 1996; Gasey 1997; Naiman and Billey, 1998; Alaerts 1999; Zheng and Chen 1998; Hu et al. 1999; Ongley and Booty 1999; Qasim 1999; Xia et al. 1999;
Carmichael and Strzepek 2000; Cude 2001; Paul 2001; Drolc and Zagorc 2002; Nandanal and Sakthivadivel 2002; Tchobanoglous 2003; Holda 2005; Lelo et al. 2005; Miranzadeh
2005; Parparov et al. 2006; MacQuarrie et al. 2008; Molle and Hoanh 2009; Bossio et al.
2010).
In India, the water quality of most of the important rivers has been studied by several workers. Mishra (1992) and Sikandar (1987) studied the water quality of river Ganga in
Varanasi; Ganapati (1964) and Mitra (1982) on river Godavari; Mahadevan and
Krishnaswamy (1983) on Vaigai river in Madurai; Nandan (1985) on Vishwamitri river at
Baroda; Rana and Palria (1988 a) on Ayad river at Udaipur; Pande et al. (1988) on Nana Kosi river in Kumaon region of Uttar Pradesh; Raina (1985) and Shah (1988) on Jhelum river in
Kashmir; Mishra (1990) on Morar river in Madhya Pradesh; Singh and Singh (1990) on
Subernrekha river at Ranchi; Unni et al. (1992) on Narmada river; Desai et al. (1995) on
Khandepur river in Goa; Sivasubramani (1999) on Periyar river in Tamil Nadu; Kumar
(2000) on Mayurakshi river in Santhal Pargana, Bihar; Mishra and Tripathi (2001), Zafar and
Sultana (2008) and Sood et al. (2008) on Ganga river; Jayaraman et al. (2003) on Karamana
14 river in Thiruvananthapuram District, South Kerela; Raginaa and Nabi (2004) on Cauvery and Bhavani river at a confluence point of Kooduthurai river; Sharma et al. (2008) on
Narmada river at Hosangabad city, India; Salve et al. (2009) and Deepti et al. (2010) on
Betwa river in Madhya Pradesh. Mishra and Tripathi (2000) studied the untreated and treated waste water of Varanasi city and they have evaluated the pollutants removal efficiency of a sewage treatment plant. Mishra and Yadav (1978) carried out a comparative study on physico-chemical characteristics of rivers and lakes in Central India. Santra (2001) has reported that most of the Indian rivers namely Ganga, Yamuna, Brahmaputra and their tributaries are highly polluted at some places due to direct discharge of untreated sewage and industrial effluents into the river. Mishra and Tripathi (2003) have worked out seasonal variation in pollution stress in water of river Ganga, and have reported that direct discharge of waste into river water leads to increase in pollution load and summer season showed high pollution intensity because of low water level during the summer months.
With respect to the rivers of N.E. India, the related published works are limited to certain aspects. The major contributors from the state of Meghalaya are Alfred and Thapa
(1995); Das et al. (1996); Sharma (1995, 1999); Sharma and Lyngdoh (1999) and Sharma and Wanswett (1999). Swer and Singh (2004, 2006) have reported that the water bodies of
Meghalaya are the greatest victims of coal mining. The rivers of Assam were studied by Dey and Kar (1987); Yadava et al . (1987); Yadava and Dey (1990); Sharma and Hussain (1999);
Sharma (2000); Sarma (2000) providing limited information. A limnological study was carried out on the Harora river in Tripura by Battacharya (1997); in Manipur, Singh and
Gupta (2010) studied the physico-chemical parameters of Imphal, Iril and Thoubal rivers.
Case studies based on biomonitoring were carried out by the Central Pollution Control Board in Meghalaya (2004) and Assam (2005) while the Arunachal Pradesh State Pollution Control
Board gave a report on biomonitoring of the important perennial rivers of the state (Anon,
15
2006). In Mizoram, Lalchhingpuii (2011) has worked on the status of water quality of
Tlawng river in the vicinity of Aizawl city.
The appropriate measures for proper management of the aquatic bodies in India have been suggested by Basak and Konar (1978); Bhargava, D.S. (1984); Trivedy and Goel
(1986); Bulushu (1987); Rana and Palria (1988 b); Apparao (1990); Handa and Rajesh
(1990); Kumar (1994); Joshi et al. (1996); Srivastva et al. (1996); Dua et al. (1999); Rao et al. (1999); Chhatwal et al. (2003); Iyer (2003); Doraiswamy and Gujja (2004); Mohile
(2007); Durvey and Sharma (2007); Reddy (2009); Sutapa et al. (2009).
Water quality monitoring has been initiated with a view of water quality management in order to restore and maintain the wholesomeness of natural water bodies. Minshall and
Minshall (1978) studied the role of chemical factors in determining distribution of benthic invertebrates in the river Duddon. According to Cairns (1982), biological monitoring is a regular and systematic use of organism to determine environmental quality. The history of biomonitoring can be traced back to Aristotle, who placed freshwater fishes into seawater to observe their reactions. In the early 1900s, Kolkwitz and Marsson introduced the concept of biological indicators of pollution. However, in an extremely comprehensive survey of the biological indicators, Hellawell (1986) has pointed out the popularity of macroinvertebrates.
Benthic macroinvertebrates are common inhabitants of water bodies (Frithsen and
Holland, 1990); they occupy every kind of freshwater habitat and also represent all functional feeding groups (Welch, 1992 and Mackie, 1998). They have been classified into functional feeding groups based on their food and feeding habits (Cummins, 1973; Cummins and Klug,
1979 and Merrit and Cummins, 1996). Ecology and classification of freshwater invertebrates have been worked out by Bouchard (2004), Thorp and Covich (1991), Williams and Felamte
(1992). Variations in the benthic populations from year to year appear to be common in freshwater impoundments (Anderson and Hooper, 1956; Ruggles, 1959 and Oliver, 1960).
16
Benthic macroinvertebrates vary greatly in their responses to change in water quality
(Boesch et al. , 1976; Wilhm and Dorris, 1966, 1986; Cairns and Dickson, 1971; Simboura et al. , 1995) and are the ideal subject for biological assessment of water quality (Hynes, 1974;
Hellawell, 1978; Bayly and Lake, 1979; Wright et al. , 1984; Abel, 1989; Friedrich et al. ,
1992; Lenat and Barbour, 1994; Barton, 1996; Magati, 1996 and Barbour et al. , 1997).
The survival of macroinvertebrates is influenced by various abiotic (physical and chemical features) and biotic (plant and animal) components of the aquatic ecosystem both naturally and human induced (Michael, 1968; Pip and Stewart, 1976; Ward and Stanford,
1982; Culp et al. , 1983; Resh et al. , 1988; Collier, 1993; Norris and Georges, 1993; Yanoviak and McCafferty, 1996; Hawkins et al. , 1997; Richards et al. , 1997; Beisel et al. , 1998;
Bouckaert and Davis, 1998; Minshall, 1998; Lake, 2000; Cole, 2002; Mayrink et al., 2002).
Macroinvertebrates have an ability to tolerate the environmental conditions in their habitat, based on which they have been allotted a tolerance value (Hilsenhoff, 1988; Plafkin et al. ,
1989; Bode et al. , 1996 and Hauer and Lamberti, 1996).
For benthic families tolerance value ranges from 0 to 10 and it is increased with intensity of pollutants. Based on tolerance values, Hilsenhoff (1988) developed Modified
Family Biotic Index to detect organic pollution, tolerance value for each family was developed by weighting species according to their relative abundance in the State of
Wisconsin. In India, Central Pollution Control Board (CPCB) in collaboration with Dutch experts defined Biological Monitoring Working party (BMWP) scores to the benthic macroinvertebrate families. BMWP score decreases as the resistance to pollution increases and vice versa (CPCB, 1999).
In India, biological monitoring has kept pace with physicochemical monitoring system.
Dutta et al. (1982) carried out extensive study on the diurnal rhythm of the some physico- chemical properties and zooplankton in a tropical fish water pond in Calcutta, West Bengal.
17
Trivedy and Goel (1984) investigated the chemical and biological methods for water pollution. The water quality of river Yamuna has been studied extensively by a number of researchers; Anand (1997) has studied bio-assessment of water quality of upstream and downstream stretches of river Yamuna in Delhi. Rao (1999) worked on the bio-assessment of entire stretch of river Yamuna from origin to confluence. Pandey et al. (2000) carried out ecological studies on river Panar of Araria (Bihar) with special emphasis on its biological components. Kumar (2003) and Mukhopadhyay (2002) have studied bio-assessment for water quality of river Yamuna using benthic macro-invertebrates. Sharma et al. (2003) carried out extensive research on micro-pollutants levels in macro-invertebrates collected from drinking water sources of Delhi, India.
Tyagi et al . (2006 a, b) have made significant contribution towards water quality assessment through occurrence of benthic macro-invertebrate families of river Hindon. The invertebrates are the best studied and reported as most diverse animals in streams and are reasonably sedentary, with comparatively long lives, so that they can be used to assess water quality at a single site over a long period of time. Semnal and Akolkar (2006 a, b) worked out on hydro-biological assessment of water quality of the rivers in Uttaranchal and Semnal et al.
(2008 a, b; 2009) carried out research on impact assessment for river Bhagirathi using benthic macroinvertebrates.
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STUDY AREA AND STUDY SITES CHAPTER 4
4.1 Mizoram: Location and Physiography
4.1.1 Location
Mizoram lies in the north eastern part of India; much of its southern part is sandwiched between Bangladesh and Myanmar. The state is situated between 21 ˚58’ to 24 ˚35’N latitudes and 92 ˚16’ to 93 ˚26’E longitudes, extending over a geographical area of 21,087 km 2. The length of the state from north to south is 277 km and the width from east to west is 121 km.
Its major length is in the west, sharing borders with the Chittagong Hill Tracts of Bangladesh, spanning 318 km. In the east and the south, its border with the Chin Hills and Northern
Arakans of Myanmar extends to about 404 km. On the Indian side, Mizoram has inter-state boundaries with Assam (123 km), Manipur (95 km) and Tripura (66 km). The Tropic of
Cancer passes just through the southern periphery of Aizawl City (state capital) at 23°30’N latitude; traversing places like Champhai, Chhawrtui, Darlung and Phuldungsei. This imaginary line divides the region into two almost equal parts (Pachuau, 1994).
Mizoram is divided into 8 administrative districts and 3 Autonomous District councils. According to the 2011 (provisional) census, population of the state is 10,91,014 with a population density of 52 persons per km 2 (Anon, 2011). Aizawl, the state capital is situated in the north-central part of Mizoram between 24 ˚25’16.04”N - 23 ˚18’17.78”N latitudes and 92 ˚37’03.27”E - 93 ˚11’45.69” E longitudes. Aizawl district is bound by
Champhai district of Mizoram and Manipur state on the east, on the west by Mamit and
Kolasib districts of Mizoram, by Assam state on the north and by Serchhip district of
Mizoram on the south. The total geographical area of Aizawl district is 3576.31 km 2, occupying 16.96 % area of the state with a population of 404,054 (Figure 4.1).
19
Figure 4.1: Map showing Districts of Mizoram (Census, 2011)
20
Mizoram falls under tropical monsoon type climate enjoying a moderate and pleasant climate. The temperature varies from 21˚C to 32˚C in summer and 10˚C to 17˚C in winter.
The entire state comes under the direct influence of the south west monsoon receiving an annual average rainfall of 250cm. Rainfall is generally prevalent in the southern part with the highest rainfall at Lunglei district (315cm) followed by Saiha district (243cm) and Aizawl district (235cm) Rai (2005). The year may be divided into four distinct seasons namely,
Spring (March-May), Rainy season (June-August), Autumn (September-November), Winter
(December- February).
4.1.2 Physiography
The mountain ranges in Mizoram run from north to south and largely taper from the middle of the state towards the north, the west and the south. The ranges in the west are steep and precipitous while those in the east are somewhat gentle. The average height of the hills in the west is about 1000 meters, gradually rising to 1,300 meters in the east. There are several mountain peaks of medium height. The highest peak in Mizoram is Phawngpui (Blue
Mountain), which is 2,157 m. above sea level and is located in the southeastern part of the state.
Mizoram is interspersed with numerous rivers, streams and brooks. The important rivers in the northern part of the state, flowing northwards, are Barak (Tuiruang) and its tributaries, Tlawng (Dhaleshwari), Tuirial (Sonai) and Tuivai. Tuivawl, a tributary of Tuivai, is another important river in the area. Barak, Dhaleshwari, Tuivai and Sonai are navigable for considerable stretches. Barak and Tuivai constitute the borderline between Manipur and
Mizoram and the two territories have through the centuries shared the facilities provided by these rivers. The most important river in the southern region of the state is the Chhimtuipui
(Kolodyne) with its four main tributaries - Mat, Tuichang, Tiau and Tuipui. Kolodyne river flows in Mizoram from Myanmar and turns west first and then southward within Mizoram
21 and re-enters Myanmar. Though interrupted by rapids, some stretches of the river in Mizoram are navigable. Khawthlangtuipui (Karnaphuli) and its tributaries – Tuichawng, Phaireng,
Kau, Deh and Tuilianpui – form the western drainage system. Karnaphuli enters Bangladesh at Demagiri; at its mouth sits the port city of Chittagong. The potential of the river systems and water resources in Mizoram remains largely unexploited. The utilization of the hydro- potential for generation of energy, for example, in whatever form, large scale, mini or micro, is still fractional. There has been a modest improvement in the supply of drinking water but the sector lacks rational and systematic approach. The current condition of water transportation is less than retrogressive, partly because of the non-availability of transit facilities through Bangladesh (Pudaite, 2005).
There are three small plains in the state scattered over the mainly hilly terrain. The plains have thick layers of rich alluvial soil. The largest of these plains is the Champhai
Plains, 10 km long and 5 km wide, and is situated near the Myanmar border, 150 km to the east of Aizawl. Another plain area is at Vanlaiphai, 90 km away to the southeast of Aizawl. It is 10 km long and 0.25 km wide on average. The third plain area is at Thenzawl, 100 km south of Aizawl (Anon, 2003). These plains have been put mainly for paddy cultivation. In addition, there are several small level grounds beside some of the rivers, which have been developed for wet rice cultivation.
The common rocks found in Mizoram are sandstone, shale, silt stone, clay stone and slates. The rock system is weak and unstable, prone to seismic influence. Soils of Mizoram are young, immature and sandy (Pachuau, 1994). Soil texture varies from sandy loam and clayey loam to clay, generally mature but leached owing to steep gradient and heavy rainfall.
The soils are porous with poor water holding capacity, deficient in potassium, phosphorous, nitrogen and even in humus content. The soil pH is normally in acidic range and sometimes approaching to neutral, this may be due to excessive leaching (Anon, 2003).
22
4.2 Description of Study Sites:
Tuirial river or Sonai is an important river and situated in the northern part of Mizoram. The span of the river is about 117 km and it originates from the North Chawilung Hills in Aizawl
District. It flows northward and merges in the Barak river in Cachar District in Assam. An important tributary is Tuirini, which joins the main stream from the eastern bank after flowing parallel to it for about 29 kms. Tuirial river flanks the eastern part of the Aizawl city, whereas, the western side is surrounded by the Tlawng river.
Tuirial river near the Tuirial Hydel Project has been selected as the study site. The Tuirial
Hydel Project is situated in the Aizawl District of Mizoram at latitudes 24°21.5' N and longitude 92°53.2' E (Figure 4.2). The Hydel Project is categorized as hydro-electric and has a capacity of 60 MW. The catchment area is about 1861 sq. km with an annual rainfall of
2540 mm. The area has a mean daily maximum temperature of 26.5°C and a mean daily minimum temperature of 11.3°C.
The project is accessible from Aizawl (163 km) and from Silchar (Assam) via Bagha
Bazar, Vairengte, Saiphai and Saipum to the project site. The location of the dam is in a well defined gorge, just downstream of a U-bend, 97 km upstream of the confluence point with the river Barak. The width of the river in the dam site is about 60 meters and the banks raise up to an elevation of about 120 m on either side. The course of the river is fairly straight at the dam site. The gorge is suitable to accommodate the entire dam and the topography offers scope for housing the off-take of power tunnels, as well as, for providing the spilling arrangements. The river bed consists of sandy soil with boulders and rock exposures are found on both the banks. The banks are formed of steeply sloping hills covered with forest growth.
Keeping in view the components of hydro electric power projects, four sampling points along the river bank have been selected (Figure 4.2)
23
(i) Site 1 (S 1): Site 1 is demarcated as reference station (control site) which is at
the upstream of dam with least human activities around and the river has its
natural flow (Photo plate 4.1).
(ii) Site 2 (S 2): Site 2 is demarcated as diversion inlet on river where the flow of
river recedes with the development of reservoir (Photo plate 4.2).
(iii) Site 3 (S 3): Site 3 is demarcated as power house outlet (downstream of dam).
There is little flow in the river at downstream of the dam (Photo plate 4.3).
(iv) Site 4 (S 4): Site 4 is demarcated as diversion outlet situated downstream of
river, where the desilted river water, after power generation is discharged back
into the river through a tunnel (Photo plate 4.4).
24
Figure 4.2: Location map of study sites.
25
Photo plate 4.1: Site 1: Reference (control site) station which is situated at the upstream of dam.
26
Photo plate 4.2: Site 2: Diversion inlet on river.
27
Photo plate 4.3: Site 3: Power house outlet (downstream of dam).
28
Photo plate 4.4: Site 4: Diversion outlet situated downstream of river.
29
METHODOLOGY CHAPTER 5
5.1 Collection of water samples
Water samples were collected at monthly intervals (in triplicate) for a period of two years
(i.e., from January 2008 to December 2009) for analysis of various physicochemical and biological characteristics namely, temperature, total dissolved solids, electrical conductance, pH, dissolved oxygen, biological oxygen demand, total hardness, acidity, total alkalinity, chloride, fluoride, nitrate-N and phosphate-P contents. The results have been expressed seasonally i.e., Spring (March - May), Rainy season (June - August), Autumn (September -
November) and Winter (December - February).
After collection, the water samples were packed in crushed and cubed ice and brought to the laboratory in ice boxes for analysis of various physicochemical and biological parameters. Temperature and pH of the water samples were measured at the site during sampling. For analysis of dissolved oxygen content, water samples were fixed immediately after collection.
5.2 Methods
For the analysis of various physico-chemical parameters of water samples, “The Standard
Methods for Examination of Water and Waste Water (APHA, 2005)” was adapted. Handbook of Method in Environmental Studies, Water and Waste Water Analysis (Maiti, 2001) was also followed for water sample analysis.
5.2.1 Physico-chemical characteristics
5.2.1.1 Temperature
The temperature of water was measured by using a digital EcoScan series Temp5, and result was expressed in ˚C.
30
5.2.1.2 Total dissolved solids (TDS)
The TDS of water samples was measured using Water Quality Analyser PE- 371 (Systronic), and result was expressed in mgL -1.
5.2.1.3 Electrical Conductance (EC)
The electrical conductance of water samples was measured by using a Water Quality
Analyser PE- 371 (Systronic), and result was expressed in µS.
5.2.1.4 pH
The pH of water was measured with help of digital ‘hydrogen ion electrode’.
5.2.1.5 Dissolved oxygen (DO)
The D.O content of water samples was measured by following “Modified Winkler’s Iodide
Azide method”. This titrimetric method is based on the oxidizing property of oxygen dissolved in water. The DO content of water samples was calculated by using the following formula, and result was expressed in mgL -1.
Dissolved oxygen (mgL -1) = V x N x 8 x 1000 ml of water sample used where.
V= Volume of titrant used; N= Normality of titrant.
5.2.1.6 Biological oxygen demand (BOD)
For the estimation of BOD content of water samples, initial and final DO of water samples were determined just after collection of sample and after 5 days incubation in BOD incubator,
31 at 20 ˚C respectively. Calculation of BOD was done by using the following formula, and the result was expressed in mgL -1.
BOD (mgL -1) = DO (I) – DO (F) Dilution factor, if any where,
DO (I) = DO initial; DO (F) = DO final (after 5 days incubation).
5.2.1.7 Total hardness
Total hardness of water samples was determined by Ethylene-Diamine-Tetra-Acetic (EDTA)
++ titration method. In alkaline condition, EDTA or its sodium salt (Na 2EDTA) reacts with Ca and Mg ++ to form a soluble complex. The Ca ++ and Mg ++ ions develop wine red colour when small amount of dye such as Erichrome Black T is added under alkaline condition. When
EDTA is added as a titrant, the Ca ++ and Mg ++ complexed with EDTA, resulting in sharp change from wine red to blue colour which indicates the end-point. Total hardness was
-1 calculated by using the following formula, and the result was expressed in mgL CaCO 3.
-1 Total hardness (mgL CaCO 3) = C x D x 1000 Volume of water sample used
where,
C= volume of EDTA used;
D= mg CaCO 3 equivalent to 1ml EDTA titrant (1 mg for 0.01N EDTA).
5.2.1.8 Acidity
The acidity of water samples was measured by using potentiometric titration method. Sodium hydroxide (0.02N) was used as a titrant. The concentration of mineral acids present and contributing to mineral acidity can be calculated by titrating or neutralizing samples to pH
32
4.3. The CO 2 and bicarbonates (carbonic acid) present in the sample can be neutralized completely by continuing the titration to pH 8.3. Acidity was calculated with the following
-1 formula, and result was expressed in mgL CaCO 3.
-1 Acidity (mgL CaCO 3) = Volume of titrant used (0.02N NaOH) x 1000 Volume of water sample used
5.2.1.9 Total Alkalinity
The total alkalinity of water samples was measured by using potentiometric titration method.
Standard sulphuric acid (0.02N) was used as a titrant to lower down the pH of sample at 8.3
(phenolphthalein alkalinity) and to pH 3.7 (methyl orange alkalinity). Total alkalinity was
-1 calculated with the following formula, and result was expressed in mgL CaCO 3.
-1 Total alkalinity (mgL CaCO 3) = [(A-B) x 1000] Volume of water sample used where,
A= Phenolphthalein alkalinity; B= Methyl orange alkalinity.
5.2.1.10 Chloride
The chloride content of water samples was determined by using modified Mohr’s
Argentometric titration method. Silver nitrate (0.041N) solution was used as a titrant. The
AgNO 3 reacts with chloride to form slightly soluble silver salts in a weak acid solution which precipitate as AgCl. A brick red silver chromate is formed at the end point. Chloride content of water samples was calculated by using the following formula, and result was expressed in mgL -1.
-1 Chloride (mgL CaCO 3) = Volume of titrant used x 0.041x 36.46x1000 Volume of water sample used where. 0.041 = Normality of titrant; 36.46 = atomic weight of chloride.
33
5.2.1.11 Fluoride
The fluoride content of water samples was determined by using SPANDS method. The absorbance was observed immediately at 570 nm OD in a spectrophotometer and compared with calibration curve. The fluoride content was calculated by using the following formula, and the result was expressed in mgL -1.
Fluoride (mgL -1) = mg of F determined photochemically x 1000 x B ml of water sample used C where, the ratio B/C applies only when a sample is diluted to a volume B and a portion C taken from it for colour development.
5.2.1.12 Nitrate-N
The UV-VIS Spectrophotometric method was used for estimating nitrate-N content in water samples. An ultraviolet (UV-VIS Spectrophotometer) technique measures the absorbance of nitrate-N at 220nm OD, which is suitable for screening uncontaminated water (low in organic matter). A second measurement made at 275nm OD may be used to correct the nitrate-N value (as 275nm is not absorbed by nitrate-N, but absorbed by other matter).
5.2.1.13 Phosphate-P
The stannous chloride colorimetric method was used for the determination of phosphate-P content of the water samples. The absorbance of colour was observed at 690nm OD in a spectrophotometer and compared with a calibration curve. Phosphate-P was calculated using the following formula, and result was expressed in mgL -1.
Phosphate-P (mgL -1) = mg of P (in approx. 104.5ml final volume) x 1000 ml of water sample used
34
5.2.2 Biological assessment
Biomonitoring was conducted using hand net (mesh size equivalent to that of sieve), shovel and sieve (0.6mm mesh size). The collected benthos were washed in water and transferred to a white enamel tray and taxonomically identified upto family level and scored for saprobity and diversity. Biological samples were preserved in 4% formalin or 70% alcohol.
5.2.2.1 Biological water quality assessment through qualitative biomonitoring
Bioassessment was done following Biological Water Quality Criteria (BWQC) developed by
CPCB in 1999 (Table 5.1). There are two methods involved in qualitative biomonitoring.
Table 5.1: Biological Water Quality Criteria (BWQC)
Range of Range of Indicator Water Quality Saprobic Score Diversity Score Water Quality Colour Class (0-10) (0-1.0)
7 and more 0.2 – 1.0 Blue Clean A
6 - 7 0.5 – 1.0 Light Blue Slight Pollution B
Moderate 3 - 6 0.3 – 0.9 Green C Pollution
2 – 5 0.4 – less Orange Heavy Pollution D
0 – 2 0 – 0.2 Red Severe Pollution E
35
5.2.2.1.1 Saprobic (BMWP) Score Method
This method involves inventory on benthic macroinvertebrate fauna upto the family level taxonomic precision (Appendix VII). The taxonomic identification was done by using various taxonomic identification keys (Tonapi, 1959; Needham and Needham, 1962; Quigley, 1986; de Zwart and Trivedi, 1995 and Ingram et al., 1997).
All possible families having saprobic indicator value are classified on a scale of 1 to
10, the families that the most sensitive to pollution are on the top of the list and scored the maximum. The scores of all families are averaged to produce BMWP (Biological Monitoring
Working Party) site score.
Saprobic (BMWP) score = Grand total multiplied score Grand total number of families encountered
Saprobic score ranges between 1 and 10.
5.2.2.1.2 Diversity (Sequential Comparison) Score Method
This method is based on pair wise comparison of sequentially encountered individuals and the difference of two benthic animals in terms of size, colour and shape, can be observed without any taxonomic skill. The diversity is the ratio of the total number of different animals
(runs) and the total number of organisms encountered (Appendix VII).
Diversity (Sequential Comparison) score = Number of runs Total number of individuals
The ratio of diversity has a value between 0 and 1. Normally high diversity of benthic animals always supports a good quality of water. Diversity alone cannot indicate the overall health of water body. For biological evaluation of water quality, the diversity of benthic animals is compared with Saprobic Score with the help of BWQC (Biological Water Quality
Criteria)
36
5.2.2.2 Taxa richness
The taxa richness was evaluated following the standard methods given by Plafkin et al.
(1989); RBP II metric – U.S.E.P.A (1998). The taxa richness (%) reflects health of the community through a measurement of the variety of taxa (total number of families) present at a site in comparison to reference site.
5.2.2.3 Community Similarity Indices
Community Loss Index and Jaccard Coefficient of Community Similarity were calculated as per methods defined by Plafkin et al. (1989) and RBP II metric – U.S.E.P.A (1998).
5.2.2.3.1 Community Loss Index
The loss of benthic taxa between a reference station and the station of comparison was measured by Community Loss Index. This is an index of compositional dissimilarity with values increasing as the degree of dissimilarity with the reference station increases. Values range from 0 to infinity.
Community Loss = d – a e
where, a = number of taxa common to both samples d = total number of taxa present at reference station e = total number of taxa present at station of comparison.
The Community Loss Index was developed by Courtemanch and Davies (1987) and it compares the diversity between reference site and other study sites. A higher number (value) indicates loss of species.
37
5.2.2.3.2 Jaccard Coefficient of Community Similarity
The degree of similarity in taxonomic composition between two stations in terms of presence or absence of taxa was evaluated by Jaccard Coefficient of Community Similarity.
Coefficient value ranges from 0 to 1.0 and increases as the degree of similarity with the reference station increases.
Jaccard Coefficient = a a+b+c where, a = number of taxa common to both samples b = number of taxa present at station of comparison but not at reference station c = number of taxa present at reference station but not at station of comparison.
5.3 Statistical Analysis
To check validity of the data and significance of results, two-way ANOVA and correlation coefficients were computed.
38
RESULTS CHAPTER 6
The results for various physicochemical characteristics and biological attributes of Tuirial river are presented as follows.
6.1 Physico-chemical characteristics
6.1.1 Temperature
Water temperature ranged from 21.1 ˚C to 23.2 ˚C during 2008; from 21.1 ˚C to 23 ˚C at Site 1, from 21.3 ˚C to 23.1 ˚C at Site 2, from 21.3 ˚C to 23.1 ˚C at Site 3 and from 21.2 ˚C to 23.2 ˚C at
Site 4. Similarly, water temperature ranged from 20.2 ˚C to 23.6 ˚C during 2009; from 20.2 ˚C to
23.2 ˚C at Site 1, from 20.2 ˚C to 23.5 ˚C at Site 2, from 20.4 ˚C to 23.6 ˚C at Site 3 and from
20.5 ˚C to 23.3 ˚C at Site 4. The water temperature at all the sites was recorded to be lower during winter and higher during rainy season (Figure 6.1).
A positive and significant correlation of temperature was established with TDS
(.987**), pH (.940**), TH (.958**), Acidity (.932**), TA (.974**), Fluoride (.984**),
39
Nitrate-N (.915**) and Phosphate-P (.939**). A positive correlation of temperature was observed with BOD. A negative and significant correlation of temperature was established with EC (-.977**) and Chloride (-.902**). A negative correlation of temperature was also observed with DO and MI (Appendix I).
*.Correlation is significant at the 0.05 level (2-tailed); **.Correlation is significant at the 0.01 level (2-tailed); - = negative correlation
40
6.1.2 Total dissolved solids (TDS)
Total dissolved solids of water ranged from 314 mgL-1 to 371 mgL -1 during 2008; from 314 mgL -1 to 362 mgL-1 at Site 1, from 318 mgL -1 to 365 mgL -1 at Site 2, from 321 mgL -1 to 368 mgL -1 at Site 3 and from 317 mgL -1 to 371 mgL -1 at Site 4. Similarly, TDS ranged from 301 mgL -1 to 379 mgL -1 during 2009; from 301 mgL -1 to 369 mgL -1 at Site 1, from 303 mgL -1 to
375 mgL -1 at Site 2, from 311 mgL -1 to 379 mgL -1 at Site 3 and from 315 mgL -1 to 371 mgL -1 at Site 4. It was also observed that the overall TDS content of water was lower during winter season and higher values were observed during rainy season. The Site 3 and Site 4 normally possessed higher values of TDS than the other sites in respective seasons (Figure 6.2).
A positive and significant correlation of TDS was established with temperature
(.987**), TH (.982**), acidity (.967**), TA (.992**), nitrate-N (.948**) and phosphate-P
(.959**). A positive correlation of TDS was observed with BOD. A negative and significant correlation of TDS was established with EC (-.990**), DO (-.730*) and chloride (-.933**). A negative correlation of TDS was observed with MI (Appendix I).
41
6.1.3 Electrical Conductance (EC)
Electrical conductance of water ranged from 124 µS to 175 µS during 2008; from 124 µS to
165 µS at Site 1, from 126 µS to 172 µS at Site 2, from 125 µS to 175 µS at Site 3 and from
127 µS to 168 µS at Site 4. Similarly, EC ranged from 114 µS to 182 µS during 2009; from
114 µS to 173 µS at Site 1, from 119 µS to 175 µS at Site 2, from 121 µS to 179 µS at Site 3 and from 115 µS to 182 µS at Site 4. It was also observed that EC was higher during winter season and lower values were observed during rainy season. The Site 3 and Site 4 normally possessed higher values than the other sites in respective season (Figure 6.3).
A positive and significant correlation of EC was established with acidity (.764*), fluoride (.804*) and phosphate-P (.857**). A positive correlation of EC was observed with
TDS, alkalinity, TH, nitrate-N and macroinvertebrates. A negative and significant correlation of EC was established with DO (-.872**). A negative correlation of EC was observed with pH, temperature, BOD and chloride (Appendix I).
42
6.1.4 pH
pH of water ranged from 7.27 to 7.91 during 2008; from 7.27 to 7.65 at Site 1, from 7.29 to
7.81 at Site 2, from 7.31 to 7.85 at Site 3 and from 7.29 to 7.91 at Site 4. Similarly, pH ranged from 7.19 to 7.95 during 2009; from 7.21 to 7.81 at Site 1, from7.19 to 7.92 at Site 2, from 7.24 to 7.95 at Site 3 and from 7.27 to 7.85 at Site 4. The winter season possessed lower values and higher values were recorded during rainy season. The pH values at Site 1 were normally lower in all seasons with some exceptions. On the contrary, pH of Site 3 and Site 4 were normally higher compared than other sites in respective season (Figure 6.4).
A positive and significant correlation of pH was established with temperature
(.940**), TDS (.976**), TH (.970**), acidity (.994**), TA (.981**), fluoride (.964**), nitrate-N (.961**) and phosphate-P (.907**). A positive correlation of pH was observed with
BOD. A negative and significant correlation of pH was established with EC (-.962**), DO
(-.732*) and chloride (-.925**). A negative correlation of pH was observed with MI
(Appendix I).
43
6.1.5 Dissolved oxygen (DO)
Dissolved oxygen content of water ranged from 6.2 mgL -1 to 8.1 mgL-1 during 2008; from 6.8 mgL -1 to 8.1 mgL -1 at Site 1, from 6.9 mgL -1 to 8 mgL -1 at Site 2, from 6.5 mgL -1 to 7.8 mgL -1 at Site 3 and from 6.2 mgL -1 to 8 mgL -1 at Site 4. Similarly, DO content ranged from 6.5 mgL -
1 to 8 mgL -1 during 2009; from 7 mgL -1 to 8 mgL -1 at Site 1, from 6.9 mgL -1 to 7.9 mgL -1 at
Site 2, from 6.8 mgL -1 to 7.8 mgL -1 at Site 3 and from 6.5 mgL -1 to 7.8 mgL -1 at Site 4. The
DO content of water at all sites was observed higher during winter season and lower during rainy season. The Site 1 possessed higher values than the other sites in respective season
(Figure 6.5).
A positive and significant correlation of DO was established with chloride
(.914**). A positive correlation of DO was observed with EC and MI. A negative and significant correlation of DO was established with TDS (-.730*), pH (-.723*), BOD
(-.998**), TH (-.793*), nitrate-N (-.721*) and phosphate-P (-.834*). A negative correlation of
DO was observed with temperature, acidity, TA and fluoride (Appendix I).
44
6.1.6 Biological oxygen demand (BOD)
Biological oxygen demand content of water ranged from 0.2 mgL -1 to 1.2 mgL -1 during 2008; from 0.2 mgL -1 to 0.9 mgL -1 at Site 1, from 0.3 mgL -1 to 1 mgL -1 at Site 2, from 0.4 mgL -1 to
1 mgL -1 at Site 3 and from 0.3 mgL -1 to 1.2 mgL -1 at Site 4. Similarly, BOD content ranged from 0.3 mgL -1 to 1.1 mgL -1 during 2009; from 0.3 mgL -1 to 0.8 mgL -1 at Site 1, from 0.3 mgL -1 to 0.9 mgL -1 at Site 2, from 0.4 mgL -1 to 0.9 mgL -1 at Site 3 and from 0.4 mgL -1 to 1.1 mgL -1 at Site 4. The BOD content of water showed a reverse trend in results with lower values during winter season and higher during rainy season. The Site 4 possessed higher values of BOD than the other sites in respective season. On the contrary, lower values were recorded at Site 1 and Site 2 (Figure 6.6).
A positive and significant correlation of BOD was established with TH (.763*) and phosphate-P (.804*). A positive correlation of BOD was observed with temperature,
TDS, pH, acidity, TA, fluoride and nitrate-N. A negative and significant correlation of BOD was established with DO (-.998**) and chloride (-.899**). A negative correlation of BOD was observed with EC and MI (Appendix I).
45
6.1.7 Total hardness (TH)
-1 -1 Total hardness of water ranged from 121 mgL CaCO 3 to 169 mgL CaCO 3 during 2008;
-1 -1 -1 -1 from 121 mgL CaCO 3 to 159 mgL CaCO 3 at Site 1, from 128 mgL CaCO 3 to 162 mgL
-1 -1 -1 CaCO 3 at Site 2, from 127 mgL CaCO 3 to 163 mgL CaCO 3 at Site 3 and from 124 mgL
-1 -1 CaCO 3 to 169 mgL CaCO 3 at Site 4. Similarly, values ranged from 118 mgL CaCO 3 to 164
-1 -1 -1 mgL CaCO 3 during 2009; from 118 mgL CaCO 3 to 155 mgL CaCO 3 at Site 1, from 120
-1 -1 -1 -1 mgL CaCO 3 to 162 mgL CaCO 3 at Site 2, from 125 mgL CaCO 3 to 164 mgL CaCO 3 at
-1 -1 Site 3 and from 127 mgL CaCO 3 to 160 mgL CaCO 3 at Site 4. The TH content of water at all sites was observed lower during winter season and higher during rainy season. The Site 3 and Site 4 normally possessed higher values than other sites in respective season (Figure 6.7).
A positive and significant correlation of TH was established with temperature
(.958**), TDS (.982**), pH (.970**), BOD (.763*), acidity (.947**), TA (.983**), fluoride
(.973**), nitrate-N (.915**) and phosphate-P (.959**). A negative and significant correlation of TH was established with EC (-.973**), DO (-.793*) and chloride (-.959**). A negative correlation of TH was observed with MI (Appendix I).
46
6.1.8 Acidity
-1 -1 Acidity values of water ranged from 41 mgL CaCO 3 to 62 mgL CaCO 3 during 2008; from
-1 -1 -1 -1 41 mgL CaCO 3 to 62 mgL CaCO 3 at Site 1, from 44 mgL CaCO 3 to 58 mgL CaCO 3 at
-1 -1 -1 Site 2, from 43 mgL CaCO 3 to 59 mgL CaCO 3 at Site 3 and from 42 mgL CaCO 3 to 61
-1 -1 -1 mgL CaCO 3 at Site 4. Similarly, acidity ranged from 37 mgL CaCO 3 to 69 mgL CaCO 3
-1 -1 -1 during 2009; from 38 mgL CaCO 3 to 63 mgL CaCO 3 at Site 1, from 37 mgL CaCO 3 to 67
-1 -1 -1 - mgL CaCO 3 at Site 2, from 39 mgL CaCO 3 to 69 mgL CaCO 3 at Site 3 and from 41 mgL
1 -1 CaCO 3 to 65 mgL CaCO 3 at Site 4. The acidity content was observed lower during winter season and higher during rainy season. The Site 3 and Site 4 normally possessed higher values than other sites in respective season (Figure 6.8).
A positive and significant correlation of acidity was established with temperature
(.932**), TDS (.967**), pH (.994**), TH (.947**), TA (.968**), fluoride (.953**), nitrate-N
(.980**) and phosphate-P (.892**). A positive correlation of acidity was observed with BOD.
A negative and significant correlation of acidity was established with EC (-.957**) and
Chloride (-.909**). A negative correlation of acidity was observed with DO and MI
(Appendix I).
47
6.1.9 Total Alkalinity
-1 -1 Total alkalinity values of water ranged from 38 mgL CaCO 3 to 68 mgL CaCO 3 during
-1 -1 -1 -1 2008; from 38 mgL CaCO 3 to 63 mgL CaCO 3 at Site 1, from 42 mgL CaCO 3 to 65 mgL
-1 -1 -1 CaCO 3 at Site 2, from 44 mgL CaCO 3 to 66 mgL CaCO 3 at Site 3 and from 44 mgL
-1 -1 -1 CaCO 3 to 68 mgL CaCO 3 at Site 4. Similarly, TA ranged from 36 mgL CaCO 3 to 70 mgL
-1 -1 -1 CaCO 3 during 2009; from 36 mgL CaCO 3 to 66 mgL CaCO 3 at Site 1, from 38 mgL
-1 -1 -1 CaCO 3 to 69 mgL CaCO 3 at Site 2, from 41 mgL CaCO 3 to 70 mgL CaCO 3 at Site 3 and
-1 -1 from 42 mgL CaCO 3 to 66 mgL CaCO 3 at Site 4. The TA value was observed lower during winter season and higher during rainy season. The Site 4 normally possessed higher values than other sites in respective season (Figure 6.9).
A positive and significant correlation of TA was established with temperature
(.974**), TDS (.992**), pH (.981**), TH (.983**), acidity (.968**), fluoride (.992**), nitrate-N (.929**) and phosphate-P (.935**). A positive correlation of TA was observed with
BOD. A negative and significant correlation of TA was established with EC (-.992**) and chloride (-.913**). A negative correlation of TA was observed with DO and MI (Appendix
I).
48
6.1.10 Chloride
-1 -1 Chloride content of water ranged from 47.17 mgL CaCO 3 to 75.09 mgL CaCO 3 during
-1 -1 -1 2008; from 47.17 mgL CaCO 3 to 73.63 mgL CaCO 3 at Site 1, from 47.33 mgL CaCO 3 to
-1 -1 -1 74.85 mgL CaCO 3 at Site 2, from 47.29 mgL CaCO 3 to 75.09 mgL CaCO 3 at Site 3 and
-1 -1 from 47.23 mgL CaCO 3 to 74.09 mgL CaCO 3 at Site 4. Similarly, chloride content ranged
-1 -1 -1 from 45.88 mgL CaCO 3 to 76.87 mgL CaCO 3 during 2009; from 45.88 mgL CaCO 3 to
-1 -1 -1 75.17 mgL CaCO 3 at Site 1, from 46.54 mgL CaCO 3 to 75.85 mgL CaCO 3 at Site 2, from
-1 -1 -1 46.87 mgL CaCO 3 to 76.21 mgL CaCO 3 at Site 3 and from 46.01 mgL CaCO 3 to 76.87
-1 mgL CaCO 3 at Site 4. The chloride content of water was observed higher during winter season and lower during rainy season. The Site 3 and Site 4 normally possessed higher chloride content than the other sites in respective season (Figure 6.10).
A positive and significant correlation of chloride was established with EC
(.910**) and DO (.914**). A positive correlation of chloride was observed with MI. A negative and significant correlation of chloride was established with temperature (-.902**),
TDS (-.933**), pH (-.925**), BOD (-.899**), TH (-.959**), acidity (-.909**), TA (-.913**), fluoride (-.913**), nitrate-N (-.912**) and phosphate-P (-.960**) (Appendix I).
49
6.1.11 Fluoride
Fluoride content of water ranged from .34 mgL -1 to .63 mgL -1 during 2008; from .34 mgL -1 to .61 mgL -1 at Site 1, from .36 mgL -1 to .62 mgL -1 at Site 2, from .36 mgL -1 to .62 mgL -1 at
Site 3 and from .35 mgL -1 to .63 mgL -1 at Site 4. Similarly, fluoride content ranged from .31 mgL -1 to .72 mgL -1 during 2009; from .31 mgL -1 to .63 mgL -1 at Site 1, from .32 mgL -1 to .69 mgL -1 at Site 2, from .32 mgL -1 to .72 mgL -1 at Site 3 and from .33 mgL -1 to .65 mgL -1 at Site
4. The fluoride content of water at all sites was observed lower during winter season and higher during rainy season. The Site 3 and Site 4 normally possessed higher values than other sites in respective season (Figure 6.11).
A positive and significant correlation of fluoride was established with temperature
(.984**), TDS (.994**), pH (.964**), TH (.973**), acidity (.953**), TA (.992**), nitrate-N
(.919**) and phosphate-P (.951**). A positive correlation of fluoride was observed with DO.
A negative and significant correlation of fluoride was established with EC (-.989**) and chloride (-.913**). A negative correlation of fluoride was observed with DO and MI
(Appendix I).
50
6.1.12 Nitrate-N
Nitrate-N content of water ranged from 0.09 mgL -1 to 0.21 mgL -1 during 2008; from 0.09 mgL -1 to 0.14 mgL -1 at Site 1, from 0.11 mgL -1 to 0.15 mgL -1 at Site 2, from 0.11 mgL -1 to
0.18 mgL -1 at Site 3 and from 0.09 mgL -1 to 0.21 mgL -1 at Site 4. Similarly, nitrate-N content ranged from 0.06 mgL -1 to 0.27 mgL -1 during 2009; from 0.06 mgL -1 to 0.15 mgL -1 at Site 1, from 0.07 mgL -1 to 0.22 mgL -1 at Site 2, from 0.08 mgL -1 to 0.27 mgL -1 at Site 3 and from
0.08 mgL -1 to 0.19 mgL -1 at Site 4. The nitrate-N content of water was observed to be lower during winter season and higher during rainy season. The Site 3 and Site 4 normally possessed higher nitrate-N content than other sites in respective season (Figure 6.12).
A positive and significant correlation of nitrate-N was established with temperature (.915**), TDS (.948**), pH (.961**), TH (.915**), acidity (.980**), TA
(.929**), fluoride (.919**) and phosphate-P (.899**). A positive correlation of nitrate-N was observed with BOD. A negative and significant correlation of nitrate-N was established with
EC (-.989**) and chloride (-.913**). A negative correlation of nitrate-N was observed with
DO (Appendix I).
51
6.1.13 Phosphate-P
Phosphate-P content of water ranged from 0.03 mgL -1 to 0.07 mgL -1 during 2008; from 0.03 mgL -1 to 0.06 mgL -1 at Site 1, from 0.04 mgL -1 to 0.06 mgL -1 at Site 2, from 0.04 mgL -1 to
0.06 mgL -1 at Site 3 and from 0.03 mgL -1 to 0.07 mgL -1 at Site 4. Similarly, phosphate-P content ranged from 0.03 mgL -1 to 0.07 mgL -1 during 2009; from 0.03 mgL -1 to 0.06 mgL -1 at
Site 1, from 0.03 mgL -1 to 0.07 mgL -1 at Site 2, from 0.04 mgL -1 to 0.07 mgL -1 at Site 3 and from 0.04 mgL -1 to 0.06 mgL -1 at Site 4. The phosphate-P content of water was observed lower during winter season and higher during rainy season. The Site 1 possessed lower values than other sites in respective season (Figure 6.13).
A positive and significant correlation of phosphate-P was established with temperature (.939**), TDS (.959**), pH (.907**), BOD (.804**), TH (.959**), acidity
(.892**), TA (.935**), fluoride (.951**) and nitrate-N (.899**). A negative and significant correlation of phosphate-P was established with EC (-.950**), DO (-.834*) and chloride
(-.960**). A negative correlation of phosphate-P was observed with MI (Appendix I).
The correlation coefficient was also observed among the sites for each parameter during two years period of study. Normally there was a positive and in majority of cases significant correlation among all sites with respect to the water quality attributes (Appendix
52
II). The correlation coefficient between different parameters at various study sites was also computed and presented in Appendix III-VI.
6.2 Biological characteristics
During the present investigation, benthic macroinvertebrates were collected from different study sites and biomonitoring indices were computed. The CPCB has derived a “Biological
Water Quality Criteria” (BWQC) for water quality evaluation. This system is based on the range of saprobic values obtained from the Saprobic (Biological Monitoring Working Party)
Score Method and diversity values obtained from the Diversity (Sequential Comparison)
Score Method of the benthic macro-invertebrate families with respect to water quality. For changes in water quality with respect to intensity of pollutants, the entire taxonomic groups with their range of Saprobic scores from 1 to 10 in combination with the range of diversity score from 0 to 1 have been classified into five different classes of water quality. The abnormal combination of Saprobic score and diversity score indicates sudden change in environmental conditions. The findings of present study have been given in Table 6.1 following the methods given in Appendix VII. The number of macroinvertebrates encountered at different study sites during the two years period has been given in Table 6.2 to
6.5. The photographs are given in photo plates 6.1 and 6.2, at the end of this chapter.
Table 6.1: Biological Water Criteria of Tuirial river during January 2008 – December 2009
Range of Range of Saprobic Indicator Water Sites Diversity Score Water Quality Score (0-10) Colour Quality Class (0-1.0) Site 1 7.46 0.64 Light Blue Slightly Polluted B Site 2 7.10 0.60 Light Blue Slightly Polluted B Site 3 7.04 0.6 Light Blue Slightly Polluted B Site 4 6.94 0.59 Light Blue Slightly Polluted B
Note: Reference Table 5.1 .
53
Table 6.2: Number of Macroinvertebrates Encountered at Site 1 Taxonomical Taxonomical Spring Spring Rainy Rainy Autumn Autumn Winter Winter Group Families 2008 2009 2008 2009 2008 2009 2008 2009 Ephemeroptera Heptageniidae 2 2 2 1
Leptophlebidae 5 8 6 2 7 2 10 8
Potamanthidae
Ephemeridae 2 2 9 4 2 3 15 5
Plecoptera Perlidae
Hemiptera Aphelocheiridae
Trichoptera Leptoceridae 6 10 7 7 3 3 6
Polycentropodidae
Philopotamidae
Hydropsychidae 1 1
Odonata Gomphidae 1 1
Mollusca Viviparidae
Thiaridae 20 14 12 14 8 12 17 21
Unionidae 1 1 1
Crustacea Atyidae 1 2 1 2 2 4
Palaemonidae 4 7 7 2 1 4 10 3
Diptera Tabanidae 1 1 1
Coleoptera Psephenidae 3
Elmidae 1 1 2
Megaloptera Corydalidae 1 1 1 1
During 2008, the number of families encountered at Site 1 was 8 in Spring, 8 in
Rainy season, 10 in Autumn and 11 in Winter season. Similarly, during 2009, the number of families encountered at Site 1 was 7 in Spring, 8 in Rainy season, 7 in Autumn and 11 in
Winter season. The results show that the number of families encountered was maximum during Winter season in both the years. The Spring and Rainy season normally possessed least number of families (Table 6.2).
54
Table 6.3: Number of Macroinvertebrates Encountered at Site 2 Taxonomical Taxonomical Spring Spring Rainy Rainy Autumn Autumn Winter Winter Group Families 2008 2009 2008 2009 2008 2009 2008 2009 Ephemeroptera Heptageniidae 3 4 3 2 1 2
Leptophlebidae 10 4 3 1 4 5 3 10
Potamanthidae 7 2 6 3
Ephemeridae 1 1 1 2 7 10
Plecoptera Perlidae 1 1
Hemiptera Aphelocheiridae 1
Trichoptera Leptoceridae 10 11 2 5 5 5 2
Polycentropodidae 1 1
Philopotamidae 1 1 1
Hydropsychidae 1 1 2 1 1
Odonata Gomphidae 4 1 2 2 3
Mollusca Viviparidae 1 1 1 3 2 2
Thiaridae 14 8 12 5 15 21 20 15
Unionidae 2 2 1 2 1 2
Crustacea Atyidae 3 1 1 1 3 6 1
Palaemonidae 5 2 11 6 5 2 7 9
Diptera Tabanidae 5 1 2 3 1 1
Coleoptera Psephenidae 2 2 2 3 1
Elmidae 1 3 1 1 2 1
Megaloptera Corydalidae 1 2 1 1 3 1
During 2008, the number of families encountered at Site 2 was 14 in Spring, 11 in
Rainy season, 18 in Autumn and 14 in Winter season. Similarly, during 2009, the number of families encountered at Site 2 was 10 in Spring, 10 in Rainy season, 13 in Autumn and 17 in
Winter season. The finding depicts that the number of families encountered was normally higher during Autumn during 2008 and Winter season during 2009. The Spring and Rainy season normally possessed least number of families (Table 6.3).
55
Table 6.4: Number of Macroinvertebrates Encountered at Site 3 Taxonomical Taxonomical Spring Spring Rainy Rainy Autumn Autumn Winter Winter Group Families 2008 2009 2008 2009 2008 2009 2008 2009 Ephemeroptera Heptageniidae 4 5 5 1
Leptophlebidae 26 20 21 4 11 3 15 10
Potamanthidae 2 5 2 9 9
Ephemeridae 4 3 1 1 5 6 8 6
Plecoptera Perlidae 1
Hemiptera Aphelocheiridae 2 1
Trichoptera Leptoceridae 10 14 4 1 3 5 8 8
Polycentropodidae 1 2
Philopotamidae 2 1 1 1 1
Hydropsychidae 2 1 1 3
Odonata Gomphidae 7 2 1 2 1 2 1
Mollusca Viviparidae 3 3 2 1 5 5 3
Thiaridae 19 13 27 17 11 17 35 26
Unionidae 2 1 2 1 2 1 1 1
Crustacea Atyidae 1 1 3 5 3 7
Palaemonidae 11 2 4 5 2 6 15 5
Diptera Tabanidae 3 1 1 1 1 1 2 2
Coleoptera Psephenidae 1 1 2 1 2 1
Elmidae 2 2 1 1 2 1 1
Megaloptera Corydalidae 2 1 1 2 1 1 1 1
During 2008 the number of families encountered at Site 3 was 13 in Spring, 16 in
Rainy season, 18 in Autumn and 18 in Winter season. Similarly, during 2009, the number of families encountered at Site 3 was 13 in Spring, 14 in Rainy season, 13 in Autumn and 15 in
Winter season. The finding reveals that the number of families encountered was normally higher during Winter season and lower during Spring (Table 6.4)
56
Table 6.5: Number of Macroinvertebrates Encountered at Site 4 Taxonomical Taxonomical Spring Spring Rainy Rainy Autumn Autumn Winter Winter Group Families 2008 2009 2008 2009 2008 2009 2008 2009 Ephemeroptera Heptageniidae 2 1 8 2
Leptophlebidae 20 18 42 10 15 10 29 20
Potamanthidae 4 1 2 3 7
Ephemeridae 8 4 3 2 10 19 6
Plecoptera Perlidae 1 1
Hemiptera Aphelocheiridae 2 1 1
Trichoptera Leptoceridae 9 4 1 10 4 8 11
Polycentropodidae 3 2 2 1
Philopotamidae 1 6 1 1 2
Hydropsychidae 1 1 2 1 1 1 1 1
Odonata Gomphidae 4 4 2 3 1 5 1
Mollusca Viviparidae 3 3 5 5 4 3 6 9
Thiaridae 29 14 12 15 26 29 23 31
Unionidae 2 1 2 1 1 2 3 1
Crustacea Atyidae 1 3 1 1 5 10 6
Palaemonidae 5 3 7 11 8 6 12 11
Diptera Tabanidae 1 2 1 1 3 1 2 1
Coleoptera Psephenidae 1 1 1 1 2 1 3
Elmidae 1 2 2 2 1 1 2
Megaloptera Corydalidae 1 2 2 1 2 1 1 1
During 2008, the number of families encountered at Site 4 was 13 in Spring, 15 in
Rainy season, 18 in Autumn and 17 in Winter season. Similarly, during 2009 the number of families encountered at Site 4 was 15 in Spring, 16 in Rainy season, 19 in Autumn and 17 in
Winter season. The finding reveals that the number of families encountered was maximum in
Autumn season, and lower during Spring season (Table 6.5).
57
Photo plate 6.1: Macroinvertebrates encountered at different sites.
58
Photo plate 6.2: Macroinvertebrates encountered at different study sites.
59
6.3 Taxa Richness
Taxa richness was determined following the standard methods by Plafkin et al. (1989) and
RBP II metric – U.S.E.P.A (1998). During the two years study, taxa richness at Site 1 was found minimum, and maximum taxa richness was reported at Site 3 during 2008 and at Site 4 during 2009 (Figure 6.14).
6.4 Community Similarity Indices
Community Loss Index (Figure 6.15) and Jaccard Coefficient of Community Similarity
(Figure 6.16) were calculated as per methods given by Plafkin et al. (1989) for a period of two years study. The Community Loss Index was recorded minimum at Site 3 and maximum at Site 4. However, Jaccard Coeeficient of Community Simlarity was minimum at Site 4 and minimum at Site 1.
60
A correlation of all the physicochemical parameters and the macroinvertebrates encountered during the two years was also computed. A positive correlation of macroinvertebrates (MI) was observed with EC, DO and chloride. On the contrary, a negative correlation of MI was observed with temperature, TDS, pH, BOD, TH, acidity, TA, fluoride, nitrate-N and phosphate-P (Appendix I).
61
DISCUSSION CHAPTER 7
Surface water bodies are very important and used for drinking and other domestic purposes, irrigation and industrial purposes, hydro electricity generation, means of transportation and dams. The quality of surface water is to be maintained to meet requirement for drinking purpose and also to maintain ecological processes in aquatic ecosystem (Rotimi and Iloba,
2010). The quality of water depends on its source and history. The history of water signifies the terrain through which water is flowing, its origin and most important the extent to which it is contaminated on its course of travel. To ascertain suitability of water for consumption, it is necessary to undertake examination of quality of water (Khopkar, 2005)
Water quality is the term widely used in multiple scientific publications and normative documents relating to the necessities of sustainable and optimal management of water resources. The concern that the fresh water may become a scarce resource in the future has forced the developing countries for evaluation of river water quality (Pesce and
Wunderlin, 2000). A comprehensive river water quality monitoring has become a necessity in order to safeguard public health and also to protect the valuable fresh water resources, however, overall water quality is sometimes difficult to evaluate from a large number of samples and parameters (Chapman, 1992). Traditional approaches for assessment of river water quality are based on the comparison of experimentally determined parameter values with the existing local normative, which however, does not provide a global vision on the spatial and temporal trends in the overall water quality (Debels et al. , 2005).
In 1921, the very first standard criterion for drinking water was reported in literature when United States published USPH standard for drinking water, specifying only on bacteriological parameters. Since then, water quality standards for different parameters have been established by various scientific agencies such as World Health organization (WHO,
2008), Bureau of Indian Standards (BIS, 2003), United States Public Health (USPH, 1962),
62
Indian Council for Medical research (ICMR, 1996) (Table 7.1). The Water (Prevention and
Control of Pollution) Act, 1974 is aimed to maintain and restore the wholesomeness of river water in terms of ecological sustainability. The water quality analysis helps in identification of water bodies requiring proper management.
The physicochemical parameters are not sufficient for determining quality of water.
The constraints of physicochemical parameters make it difficult to assess the quality status in terms of the health of a water body (Semwal and Akolkar, 2011). Over the years, it has been realized that the inclusion of biological attributes leads to determine quality of water at desired pace. The benthic macroinvertebrate communities are frequently used to assess water quality of surface water bodies (Semwal et al. , 2008 b). Biomonitoring in India has been initiated with the identification of best designated use of river water quality in terms of clean
(Class A), slight pollution (Class B), moderate pollution (Class C), heavy pollution (Class D) and severe pollution (Class E) CPCB (1999).
7.1 Physicochemical characteristics
7.1.1 Temperature
Temperature is one of the most important ecological factors that control the physiological behaviour and distribution of aquatic organisms. The variation in river water temperature usually depends on the season, geographic location, ambient air temperature and chemical reaction in a water body (Ahipathi and Puttaiah, 2006). The catabolic energy released in the form of heat during decomposition of organic matter and respiration may lead to rise in water temperature (Murthuzasab et al. , 2010). Water temperature in an aquatic ecosystem rarely exceeds 37˚C (Warren, 1981).
Present study reveals that there was no significant difference in water temperature between different sites in a particular season. There was a direct influence of seasonality on
63 water temperature. High temperature during rainy season may be due to the discharge of organic matter through runoff and subsequently microbial decomposition which results into increase in water temperature. Reference site (Site 1) possessed lower values than other sites.
Seasonal changes observed in the water temperature may be correlated with similar behaviour of atmospheric temperature (Zingde, 1981) and it has profound effects on DO and BOD contents (Hasan, 2008). The similar observations were also reported by Trivedy et al. (1990),
Ade and Wankhede (2001), Mishra and Tripathi (2000, 2001 and 2003), Abdel and Amaal
(2005), Zafar and Sultana (2008), Shraddha et al. (2008), Singh and Gupta (2010), Umavathi and Logankumar (2010) and Sharma et al. (2011).
7.1.2 Total Dissolved Solids (TDS)
The presence of TDS gives rise to physiological manifestations (Khopkar, 2005). High value of TDS may lead to change in taste of water and deteriorate plumbing and appliances. The
TDS level in water should not exceed more than 500mgL -1. The presence of TDS in water vary from season to season and affects the density of water and thereby the quality of water
(Imtiyaz et al. , 2012). TDS is a measure of impurities in a dissolved state and its value in water varies owing to different mineral solubilities in different geological regions, amount of rainfall and surface runoff. The concentration of TDS in water in contact with granite, siliceous sand, well-leached soil or other relatively insoluble materials is normally below
30mgL -1. In areas having Precambrian rock, TDS concentration in water is generally less than
65mgL -1. TDS level is high in regions of Palaeozoic and Mesozoic sedimentary rocks ranging from 195-1100 mgL -1, this may be due to the presence of carbonates, chlorides, calcium, magnesium and sulphates (Rainwater and Thatcher, 1960 and Durfor and Becker, 1972).
During the present investigation, the values of TDS did not vary significantly and were within the prescribed limit of WHO, BIS and ICMR (Table 7.1). Reference site
64 possessed lower values than other sites. The higher values during rainy and autumn seasons may be due to surface runoff containing organic and inorganic impurities. Similar trend of results was also reported by Patka and Rao Nasring (1997), Tiwari (2005), Nduka et al.
(2008), Kataria and Kumar (2010), Singh and Gupta (2010) and Imtiyaz et al. (2012).
7.1.3 Electrical Conductance (EC)
Electrical Conductance is a measure of capacity of a solution to conduct electrical current and it depends on the concentration of ions and intensity of nutrients. Water is capable to conduct current as most of the salts present in water are in ionic form. Increased level of conductivity and cations are the products of decomposition and mineralization of organic materials (Abida, 2008). Electrical conductance is a good and rapid measure of total dissolved ions and is directly related to total dissolved solids. Higher the value of dissolved solids, greater is the amount of ions in water (Bhatt et al. , 1999). Electrical conductance values of water higher than the permissible limits may cause reduced yield in agricultural crops (Jothivenkatachalam et al. , 2010).
In the present study, electrical conductance was found to be higher during winter season which may be due to low flow rate contributing to the subsequent increase in dissolved solids. Low values of electrical conductance during rainy season may be due to high water level. Reference site possessed lower values than other sites. The findings of present study are in conformity with the works of Marshall and Winterbourn (1979), Patra and Azadi (1987), Abbasi et al. (1999), Kulshrestha (1999), Pandey and Pandey (2003) and
Murthuzasab et al. (2010). The values are within the permissible limits set by scientific agencies (Table 7.1).
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7.1.4 pH
The pH of water is an important parameter, as all chemical and biochemical reactions are governed by pH. The range of pH of water is significant for the biotic communities because most of the plant and animal species can survive in a narrow range of pH i.e., from slightly acidic to slightly alkaline condition (George, 1997). Natural water with pH value between 6-8 can be used as potable water (Bulushu, 1987). The pH of an aqueous system is a measure of the acid-base equilibrium achieved by various dissolved compounds, and in most natural waters pH is controlled by the carbon dioxide-bicarbonate equilibrium system. Toxicity is greater in acidic water than in alkaline water (Singh et al. , 1989).
The present investigation depicts prevalence of alkaline earth metals. The water pH was slightly basic at all the sites and in all seasons. The higher values during rainy season could be attributed due to the leaching of rock material. Reference site possessed less alkaline water than other sites. The valuese within the prescribed limit of the scientific agencies
(Table 7.1). A similar trend of results was also recorded by Puttaiah and Somashekar (1985),
Unni et al. (1992), Singh (1995), Sivasubramani (1999) and Fakayode (2005).
7.1.5 Dissolved Oxygen (DO)
Dissolved oxygen (DO) plays a vital role in support of aquatic life, oxygen depletion often results during time of high community respiration and increased decomposition of organic matter (Rochford, 1951 and Jameel, 1998). Hence DO content of water has been extensively used as a parameter delineating water quality and to evaluate the degree of freshness of a river (Fakayode, 2005). It is also an important limnological parameter indicating level of water quality and organic matter pollution in the water body (Wetzel and Likens, 2006). DO content reveals the nature of the aquatic system and most of the physical, chemical and biological activities are directly linked with DO content in natural water and wastewater.
66
Many factors such as photosynthesis, chemical oxidation, exchange of oxygen between water and atmosphere and respiration of plants and bacteria in water, lead to change in DO content of water (Rawson, 1937). Seasonal variation in DO content is related to ambient air temperature and biological activities in aquatic body (Chapman and Kimstach, 1992).
In the present investigation, low DO content during rainy season may be due to high rate of organic matter decomposition, as more organic matter from surroundings is discharged into river water through surface runoff (Hannan, 1979). On the contrary, high values during winter season may be due to low decomposition rate and increased photosynthesis. Similar trend of result was also observed by Mishra (1992), Mishra and
Tripathi (2001, 2003), Singh and Gupta (2010), Umavathi and Logankumar (2010) and
Venkatesharaju et al. (2010). The values recorded were within the prescribed limit of USPH and BIS (Table 7.1).
7.1.6 Biological Oxygen Demand (BOD)
Biological oxygen demand is a measure of the oxygen required by the microorganisms for decomposition of organic matter present in water. The biodegradation of organic materials exerts oxygen tension in the water and increases the biochemical oxygen demand (Abida,
2008). Rivers with low BOD content have normally low organic matter, therefore, DO content is rather high. BOD directly affects the amount of dissolved oxygen in rivers and streams. The greater the BOD content, the more rapidly oxygen is depleted in the aquatic body resulting into low oxygen availability for aquatic life. The consequences of high BOD are the same as those for low dissolved oxygen; aquatic organisms become stressed, suffocate and die. Major sources of BOD in water include leaves and woody debris, dead plants and animals, faecal waste, effluents from pulp and paper mills, feedlots and food processing plants (Venkatesharaju et al., 2010).
67
The finding of the present study reveal that BOD content of water at all sampling stations and in all seasons was within the permissible limits given by scientific agencies
(Table 7.1). The higher values during rainy season and lower values during winter season, may be due to increased metabolic activities of microbes present in the water bodies and low decomposition rate of organic matter, respectively (Kumar and Sharma, 2005). Similar trend of results has been reported by Chatterjee (1992), Unni et al. (1992), Sivasubramani (1999),
Mishra (1992), Mishra and Tripathi (2000, 2001), Hassan (2008) and Murthuzasab et al .
(2010).
7.1.7 Total Hardness (TH)
The total hardness is the concentration of multivalent metallic cations in a solution. Although hardness is caused by cations, it is often discussed in terms of carbonate (temporary) and non- carbonate (permanent) hardness. Total Hardness is caused by the presence of calcium and magnesium. Hardness is the property of water that makes water form insoluble curd with soap. Hard water is primarily of concern because it requires more soap for effective cleaning, forms scum and curd, causes yellowing of fabrics, toughens vegetables cooked in the water and forms scales in boilers, water heaters, pipes and cooking utensils (Kataria and Kumar,
2010). Total Hardness is an important parameter of water quality whether it is used for domestic, industrial or agricultural purposes (Jothivenkatachalam et al. 2010). Sawyer (1960) classified water on the basis of hardness values into four types as follows.
-1 Water quality Total Hardness value (mgL CaCO 3)
Soft 0 – 75
Moderately hard 75 – 150
Hard 150 - 300
Very hard 300 and above
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During the present study, there was no significant change in the total hardness values between sites and seasons. Higher values during rainy season may be due to discharge of municipal sewage and surface runoff containing carbonates and bicarbonates, chlorides and sulphates of Ca ++ and Mg ++ . Reference site possessed lower values than other sites in specific season. All values were within permissible limits given by various scientific agencies
(Table 7.1). Similar trend of results was also observed by Unni et al . (1992), Sivasubramani
(1999), Mishra and Tripathi (2000, 2001, 2003), Zafar and Sultana (2008), Singh and Gupta
(2010).
7.1.8 Acidity
Acidity of water is the quantitative capacity to react with a strong base at designated pH. This is a measure of aggregate property of water. Acidity contributes to the corrosiveness, influences reaction rate and biological processes. The mineral acidity correspond to pH<4.0 and CO 2 acidity corresponding to pH 8.5 (due to dissolution of CO 2 in water and algal photosynthesis). Acidic water is less buffered and less productive because sufficient amount of bicarbonates are not dissolved to give CO 2 for a high rate of photosynthesis. Warren
(1981) argued that lowering of pH in water is a result of decomposition of organic matters, and resulting into release of CO 2 in water (Mishra, 1992).
During the present investigation, higher values during rainy season may be due to high organic load supporting decomposition process. The reference site normally possessed lower values with some exceptions. Similar trend of results has been reported by Srivastava and Kulshreshta (1990), Mishra and Tripathi (2000. 2001, 2003), Singh and Gupta (2010) and Venkatesharaju et al . (2010).
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7.1.9 Total Alkalinity
Total alkalinity is a measure of weak acid present in water and of the cations balanced against them (Sverdrap et al , 1942). This may be due to presence of mineral salts present in water.
Alkalinity is primarily caused by carbonate and bicarbonate ions, and bicarbonate ion is usually prevalent. However, the ratio of these ions is a function of pH, mineral composition, temperature and ionic strength. Water may have low alkalinity range but a relatively high pH or vice versa , so alkalinity alone should not be taken as a measure of water quality. Alkalinity is not considered detrimental to humans but generally is associated with high pH values, hardness and excessive dissolved solids. Water possessing high alkalinity value may have a distinctly flat and unpleasant taste. According to Schaeperclaus (1990), aquatic ecosystems have been categorized into three major categories.
-1 Water quality Total alkalinity values (mgL CaCO 3)
Less productive 0 – 15
Medium productive 15 – 100
Highly productive 100 – 250
The present investigation reveals high values of total alkalinity during rainy season which may be due to discharge of municipal waste through runoff. On the basis of the above classification, the water quality falls under medium productive at all the sites and in all seasons. All values were within the prescribed limit given by scientific agencies (Table 7.1).
The reference site possessed lower values in specific season. Similar trend of results has been reported by Mishra and Tripathi (2000, 2001, 2003), Zafar and Sultana (2008), Singh and
Gupta (2010) and Venkatesharaju et al . (2010).
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7.1.10 Chloride
Chloride is widely distributed in nature in the form of sodium potassium and calcium chloride. Chlorides occur naturally in all types of waters and it may be due to dissolving minerals. It may be found in surface water from road, salt fertilizers, domestic and industrial wastes or sewage (Kataria and Kumar, 2010). High concentration of chloride is considered to be indicator of pollution due to discharge of organic wastes of animal (faecal origin) or industrial effluents. Chloride is troublesome in irrigation water and also harmful to aquatic life (Rajkumar et al ., 2004). High concentration of chloride can cause water to have an objectionable salty taste and corrode hot-water plumbing systems. High chloride content in water has a laxative effect on human beings. The value above 250 mgL -1 imparts peculiar taste to water.
During present investigation low chloride content during rainy season may be due to dilution of water with rain. The reference site possessed lower values in specific season.
Chloride values recorded were within the permissible limit given by different scientific agencies (Table 7.1). Similar trend of results was also reported by Kumar (2000), Mishra and
Tripathi (2000, 2001, 2003), Zafar and Sultana (2008), Singh and Gupta (2010) and
Venkatesharaju et al . (2010).
7.1.11 Fluoride
Fluoride is normally present in soil strata due to natural geological formation in the form of fluorspar, fluorapatite, ampheboles such as hornblende, trimolite and mica. Weathering of alkali, silicate, igneous and sedimentary rocks especially shales contributes a major portion of fluorides to natural water flowing in catchment areas. The accumulation of fluoride in soil strata eventually results in its leaching with percolating water and resulting into increased fluoride content in ground water (Jain et al ., 2004). Fluoride concentrations between 0.7 and
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1.2 mgL -1 in drinking water may protect against dental cavities. The value between 1.5 and
3.0 mgL -1 causes mottling of teeth, 3.0 and 6.0 mgL -1 causes skeletal fluorosis and 10 mgL -1 causes crippling. The fluoride concentration less than 0.5 mgL -1 also causes dental cares
(Park and Park, 1980). Fluoride content in water and its health effects have been studied extensively by Dwarkanath and Subbaram (1991), Stanly and Pillai (1999) and Kataria
(2002).
The present investigation reveals higher values during rainy season which may be due to discharge of waste containing fluoride through runoff. The reference site normally possessed lower values in specific season. All the values recorded were within the permissible limits given by various scientific agencies (Table 7.1).
7.1.12 Nitrate-N
Nitrate-N in surface water is an important factor for water quality assessment (Johns and
Burt, 1993). It is the oxidised form of nitrogen and end product of aerobic decomposition of organic matter. The presence of nitrate-N in a lotic system mostly depends on the activity of nitrifying bacteria, stream currents and catchment characteristics, and domestic and agricultural sources. Water with high nitrate-N content may cause methemoglobinemia (blue- baby syndrome), and such water should not be consumed by pregnant women or for infant feeding. High content of nitrate-N in reservoir encourage growth of algae and other organisms that may produce undesirable tastes and odours in water (Kataria and Kumar
2010). High nitrate-N content in water leads to luxuriant growth of aquatic macrophytes and resulting into Eutrophication. Royal commission (Lester, 1969) classified water quality as follows.
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Water quality Nitrate-N content (mgL -1)
Very clean <0.5
Clean <2.0
Fairly clean <2.6
Doubtful <4.0
Bad >4.0
According to the above classification, the water quality under present investigation falls under clean water. Higher value during rainy season may be due to discharge of waste through runoff containing organic matter that results into high rate of organic matter decomposition. The reference site possessed lower values in specific season. Similar trend of results was also recorded by Srivastava and Kulshreshta (1990), Mishra and Tripathi (2000,
2001, 2003), Mahima and Pandey (2007), Murthuzasab et al . (2010), Singh and Gupta
(2010), Venkatesharaju et al . (2010). All values were within permissible limits given by various scientific agencies (Table 7.1).
7.1.13 Phosphate-P
Phosphorus is present in the form of phosphate-P in natural waters, and generally occurs in low concentration. Agricultural runoff containing phosphate fertilizers as well as the waste water containing the detergents tend to increase phosphate-P content in water. Phosphates are essential for the growth of an organism and a nutrient that limits primary productivity of the waterbody. In low concentration, phosphate-P behaves like most important nutrient but when it is in excess, in combination with nitrate-N, it causes algal blooms (Singh et al . 2010).
Present investigation reveals high content of phosphate-P during rainy season which may be due to discharge of wastes through runoff containing phosphorous and sediments.
The reference site possessed lower values in respective season. All values were within the
73 prescribed limit given by various scientific agencies (Table 7.1). A similar trend of result has been reported by Mishra (1992), Boominathan (1994), Mahima and Pandey (2007), Hassan
(2008), Murthuzasab et al . (2010), Singh and Gupta (2010), Singh et al. (2010),
Venkatesharaju et al. (2010) and Verma and Saksena (2010).
7.2 Biological characteristics
In any aquatic ecosystem, intensity of pollutants leads to change in biological characteristics namely, density and distribution of aquatic flora and fauna, and this change is linked with nature of organisms i.e., pollution sensitive and pollution tolerant. River stretches generally utilized for different purposes are exposed to diverse anthropogenic influences, thus, adversely affecting the aquatic ecosystems, and resulting in habitat destruction and disruption of aquatic ecosystem structure and function including change in aquatic life composition, distribution and widespread loss of biodiversity (Minshall, 1998). The Tuirial river stretch considered for present study is subject to diverse activities influencing the river’s biological organization as well as its habitability in terms of resources, space and physicochemical factors. Within the Tuirial hydel project stretch, the outlet of the powerhouse demarcated as site 3 is subject to more encroachment and anthropogenic influences compared to the other sites. This site receives discharge from domestic sewage from the hydel project area as well as from Saipum village. This may lead to alteration in normal condition of nutrients, dissolved oxygen and many other environmental attributes. Other sites also receive discharges from surrounding areas but Site 3 received maximum waste. The upper reach of the river stretch at the upstream is comparatively a cleaner stretch and is demarcated as the reference site or Site 1. Site 2 is demarcated as diversion inlet on river and Site 4 is demarcated as diversion outlet situated downstream of the river.
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Polluted water impairs benthic macroinvertebrate assemblage to a great extent
(Frondorf, 2001). A similar observation was reported during present study. Taxonomic composition of benthic macroinvertebrates of the Tuirial stretch in the hydel project area varied between the different demarcated sites. Taxonomic identification was done upto family level, and findings are in conformity with the work of Zamora and Alba (1996). Each site under natural and anthropogenic influences inhabits taxa as per their tolerance to that habitat. The entire stretch under study area was dominated by Molluscs followed by
Ephemeroptera. The intensity of pollutants led to change in benthic community organization from Site 1 to Site 4 which indicated an understanding of the major environmental processes in the river ecosystem (Cairns and Dickson, 1971; Wilhm and Dorris, 1966, 1986 and Hyland et al., 1996). The water quality impact was more prominent at site 3 but a remarkable feature of families inhabiting this site was the frequent appearance of Heptageniidae and
Leptophlebidae indicating only a slightly polluted water quality. There was an increase in percent tolerant taxon with increasing pollution stress, as tolerant taxonomical group
Megaloptera increases in the downstream of the study area. Stribling et al . (1998) also reported a similar kind of results.
The higher values of Saprobic and diversity score ranges for benthic macroinvertebrates at Site 1 indicating clean water. Taxa richness recorded for macroinvertebrates was increased towards downstream, a secondary effect of pollution is the prolific development of certain tolerant species or the presence of tolerant species at ordinary or low level of population development. (Jeffries and Mills, 1990; Peckarsky et al ., 1990 and
Gupta and Sharma, 2005). The intensity of pollutants is linked with change in the species diversity as well as density of the benthic population (Menon, 1990). The anthropogenic influences on river bank have adverse impact on number of benthic families especially at the
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Site 3. Such events are confirmed with urban impact relates studies on streams by Rosenberg and Weins (1978), Lamberti and Berg (1995), Shieh and Yang (2000) and Hamilton (2001).
The diversity and distribution of benthic macroinvertebrates were highly influenced by chemical characteristics of water. Minshall (1998) also reported a similar trend in results and argued that beyond a certain acceptable range, the river affects growth and survival of benthic communities inhabiting the river. In the present study, a correlation of all the physicochemical parameters and the macroinvertebrates indicated a positive correlation of macroinvertebrates (MI) with EC, DO and chloride. On the contrary, a negative correlation of macroinvertebrates was observed with temperature, TDS, pH, BOD, TH, acidity, TA, fluoride, nitrate-N and phosphate-P (Appendix I). Chung et al . (1992) and Lobinske et al .
(1997) established significant correlation between physicochemical parameters and benthic macroinvertebrates.
The benthic macroinvertebrates are widely used as water quality bioindicators due to their long life span in the bottom of freshwaters. Biomonitoring evaluates changes in genetic composition of specific populations, bioaccumulation of toxins and related occurrence of morphological deformities, changes in community composition and ecosystem functioning
(Marques and Barbosa, 2001). The use of these organisms as bioindicators is based on a simple feature: when submitted to adverse conditions, the organisms become adapted or die.
Therefore, the organisms that live in a determined ecosystem are adapted to the environmental conditions present on it and indicate the preservation level of natural conditions or changes caused by the existence of pollutants (Hynes, 1974). Biological communities are related with ecological features namely, chemical, physical, and biological characteristics, integrating effects from different stressors, and therefore providing a broad measure of combined impacts. Changes in species composition, dominance of pollution tolerant species, and frequency of deformity occurrences on larval head capsules are some of
76 the commonly used features in these types of evaluations (Marques et al ., 1999). Amongst the human impacts, eutrophication can determine a shift in the benthic organisms and a gradual replacement of species can be observed (Marques et al ., 2003).
The insect species in the orders Odonata, Coleoptera, and Diptera appear to show a certain relation to the condition of their location. The distribution of species in these orders can be correlated with the physicochemical characteristics of the habitats. Dissolved oxygen, electrical conductance, alkalinity and phosphate-P content of the waters could be the important factors determining distribution patterns at various study sites during present investigation. The restrictions of some species of these groups to definite habitat constitute them to be good indicator organisms of the condition of their habitats. The order Odonata increased in number as the river flowed downstream which indicates the health of the habitat.
Preponderance of Odonates is usually associated with organic enrichment (Ogbeibu and
Oribhabor, 2002; Rotimi and Iloba, 2003 and Duncan, 2005).
The members of family Coleoptera are able to survive in stressed conditions because they do not depend on dissolved oxygen of aquatic body and are considered as good indicators of water quality (Sharma et al . 1993; Kellog, 1994 and Mackie, 2001).
Dipterans in the aquatic ecosystem occur mostly at the benthic zone of streams and rivers. The bottom sediment usually contains most of the pollutants introduced into an aquatic ecosystem and reflects the level of pollution in an ecosystem. These macroinvertebrates occupying this zone are continually exposed to the high concentration of pollutants and are therefore ideal indicator at a specific location. Larvae of dipterans in water are frequently used as a tool to indicate pollution and to evaluate impacted ecosystem (Connel and Miller,
1984). The municipal waste adds organic matter that provides food for these tolerant benthic species. Association between aquatic dipterans and organic matter supported by low oxygen content has been argued by Ogbeibu (2001). High organic content and associated
77 deoxygenation tend to increase the number of dipteran species during the present investigation. The number of these organisms was much lower at Site 1, depicting cleaner water at the site.
During present investigation, the variation in occurrence of macroinvertebrates between different seasons can be explained by temporal changes that occur on the water level and flux affecting some abiotic parameters such as temperature, dissolved oxygen and nutrients availability (De Paula et al ., 1997; Barbosa et al ., 1997). In spite of the macroinvertebrates response to environmental factors, changes on abundance and taxonomic richness of community between periods can also be a consequence of biological factors (e.g., predation risk, temporal changes on macrophyte community during growth period and trophic resources availability) that vary along the year. Allan and Flecker (1993) proposed six factors as being of critical importance in lotic environments namely, habitat loss and degradation, spread of exotic species, overexploitation, secondary extinctions, chemical and organic pollution, and climate change. Among these, chemicals and organic matter pollutants are of a prime importance due to land use and human influences. The higher values of taxonomic richness are probably related with the high number of available habitats in the ecosystems
(Nessimian, 1996). Beisel et al. (2000) pointed out intense relationships between macroinvertebrate assemblages and substrate heterogeneity. In a heterogeneous environment, habitats produce high ecological niches diversity, which influences positively the benthic diversity (Callisto et al ., 2004). Richness measures are frequently included in multimetric systems (Roy et al. 2003). Taxa richness is more frequently used for the assessment of disturbance effects in streams and rivers than in stagnant water (Resh and McElvary, 1993).
The input of organic matter into rivers modifies the bottom substrate characteristics.
Sedimented particles of organic matter form patches that are dependent of the instream hydraulic conditions. Because fine organic matter particles have hydrological properties
78 similar to silt, food quantity is rarely a limiting factor in silt bottom habitats. As stated by
Brabec et al . (2004), the reach-scale geomorphic and chemical parameters are related to catchment land cover, as also observed in the Doce river sub-basins. Roy et al . (2003) and
Brabec et al . (2004) have pointed out that effects of organic matter pollution and eutrophication on stream benthic fauna are linked to each other by organic matter and nutrients transformation processes.
The Site 1 received low intensity of pollution, and subsequently is stable, representing environment that maintains natural biota at desired pace (Barbosa et al ., 1997). This results into high taxonomic richness than other sites. Callisto et al . (1996) have also suggested that vegetable surfaces are important substrate for the development of the periphytic community that becomes the main food source for macroinvertebrates. The river water receives large quantity of organic matter in the rainy months, increasing the availability of resources to the benthic community and providing new colonization opportunities (Callisto et al ., 2002;
Barbosa et al ., 2004 and Petrucio and Barbosa, 2004). The high population density during the rainy season may be due to the drift effect (Callisto and Goulart, 2005).
Coleoptera and Ephemeroptera showed wide distribution along the Tuirial river from
Site 1 to Site 4. Goulart and Callisto (2003) have suggested that these taxa are tolerant to some level of environmental contamination, caused by inputs of domestic untreated sewage.
Galdean et al . (2000), pointed out Diptera as filter-feedings of fine particulate organic matter
(FPOM), characteristic of rapid reaches, attached to mosses and aquatic macrophytes. This distribution pattern can be associated with the high density of organisms during the rainy season in a rocky bed and an intense water flux. Galdean et al . (2000), Goulart and Callisto
(2003) and Callisto and Goulart (2005) also argued high substrate heterogeneity, with respect to distributions of Plecoptera in any aquatic ecosystem. The study reveals that Plecopterans are pollution sensitive, and indicator of water quality,
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The use of benthic macroinvertebrate communities is a useful tool in the assessment of water quality and freshwater ecosystem health. According to Barbosa et al . (2001) and
Galdean et al . (2000), the taxonomic composition and community structure also offer important information for assessments, starting from the biological knowledge of nutritional requirements, utilization of available trophic resources and relationships with the diversity of available habitats. The taxonomic richness of benthic macroinvertebrates during 2009 was lower in comparison with 2008; this fact may suggest some changes in the structure of benthic communities that are probably associated with the continuous degradation process of the river water (Barbosa et al ., 1997).
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Table 7.1: Water quality standards given by various scientific agencies and range of values recorded during present investigation.
STANDARDS Range of values recorded Parameters during present investigation USPH WHO BIS ICMR 20.2˚C - 23.6˚C Temperature ˚C - - - - 500- 301 mgL -1 - 379 mgL -1 TDS (mgL -1) - 1000 500 1500 114 µS - 182 µS EC (µS) 300 - - - 6.5- 7.19 - 7.95 pH 6-8.5 6.5-8.5 7-8.5 8.5 6.2 mgL -1 - 8.1 mgL -1 DO (mgL -1) >4 - >5 - 0.2 mgL -1 - 1.2 mgL -1 BOD(mgL -1) + - <3 - -1 -1 Total Hardness 300- 118 mgL CaCO 3 - 169 mgL -1 500 500 300 (mgL CaCO 3) 1000 CaCO 3 -1 -1 Acidity 38 mgL CaCO 3 - 69 mgL -1 - - - - (mgL CaCO 3) CaCO 3 -1 -1 Total Alkalinity 36 mgL CaCO 3 - 70 mgL -1 - - 200 - (mgL CaCO 3) CaCO 3 -1 - -1 200- 45.88 mgL CaCO 3 - 76.87 mgL Chloride(mgL ) 250 200 250 1 1000 CaCO 3 0.31 mgL -1 - 0.72 mgL -1 Fluoride(mgL -1) - 1.5 1 1-1.5 0.06 mgL -1 - 0.27 mgL -1 Nitrate-N(mgL -1) 10 10 45 20 -1 -1 Phosphate-P 0.03 mgL - 0.07 mgL 0.1 - - - (mgL -1)
- Not available + No consensus on a single numerical value which is universally accepted.
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SUMMARY AND CONCLUSIONS CHAPTER 8
The majority of people in Mizoram depend on surface water bodies for their day to day life, as underground water is hardly assessable in most parts of the state, due to predominance of hilly terrain. The supply of treated water is limited, and majority of rural people depend on untreated water for drinking and other purposes. Major portion of domestic, agriculture, industrial and other wastes are directly or indirectly discharged into the rivers situated in the vicinity, as no proper drainage system has been developed in the state so far. Thus, there is an ample need to determine status of fresh water bodies in the state, and to develop appropriate strategy for management of surface water bodies.
In view of this, the present research was taken up to determine quality of water and biomonitoring of Tuirial river in vicinity of the Tuirial Hydel Project, Mizoram. This study is also aimed to determine how the bioindicators respond with intensity of pollutants.
Undoubtedly, the information procured from this study would provide a needful dimension towards formulation of appropriate water pollution abatement technique, which can be used as a measure for management of aquatic bodies.
Keeping in view the components of hydro electric power projects, four sampling points along the river bank namely, Site 1 - demarcated as reference station which is at the upstream of dam; Site 2 - demarcated as diversion inlet on river; Site 3 - demarcated as powerhouse outlet and Site 4 - demarcated as diversion outlet situated downstream of river were selected for detailed investigation.
Water samples were collected (at monthly interval) for a period of two years i.e., from
January 2008 to December 2009, and analyzed for various physicochemical (temperature, total dissolved solids, electrical conductance, pH, dissolved oxygen, biological oxygen demand, total hardness, acidity, total alkalinity, chloride, fluoride, nitrate-N and phosphate-P content) and biological characteristics (saprobic score, diversity score, taxa richness,
82 community loss index and Jaccard coefficient of community similarity). The result has been expressed seasonally i.e., Spring (March - May), Rainy season (June - August), Autumn
(September - November) and Winter (December - February). For the analysis of various physico-chemical parameters of water samples “The Standard Methods for Examination of
Water and Waste Water (APHA, 2005)” was adapted. The Handbook of Method in
Environmental Studies, Water and Waste Water Analysis (Maiti, 2001) was also followed for water sample analysis. Biological Water Quality Criteria (BWQC) derived by CPCB, for water quality evaluation was used which is based on the range of saprobic values obtained from the Saprobic (Biological Monitoring Working Party) Score Method and diversity values obtained from the Diversity (Sequential Comparison) Score Method of the benthic macro- invertebrate families. Taxa richness, community loss index and Jaccard coefficient of community similarity were calculated as per methods given by Plafkin et al (1989). To check validity of the obtained data and significance of results, two-way ANOVA and correlation coefficients were computed.
The findings of the present study can be summarized as follows;
1. The water temperature ranged from 20.2˚C to 23.6˚C. Higher values during rainy
season may be due to the discharge of runoff followed by microbial decomposition of
organic matter, which results into rise in water temperature. This could also be due to
impact of high ambient air temperature during June.
2. The TDS values ranged from 301 mgL -1 to 379 mgL -1. Higher values during rainy
and autumn seasons may be due to surface runoff containing organic and inorganic
impurities.
3. The EC values ranged from 114 µS to 182 µS. Higher values during winter season
may be due to low flow rate contributing to the subsequent increase in dissolved
83 solids. Low values of electrical conductance during rainy season may be due to dilution of river water through runoff.
4. The pH of water ranged from 7.19 to 7.95. The pH values during rainy season were markedly high, this could be attributed due to the leaching of rock material.
5. The DO content of water ranged from 6.2 mgL -1 to 8.1 mgL -1. Low DO content during rainy season may be due to high rate of organic matter decomposition as organic matter from surroundings is discharged into river water through surface runoff. On the contrary, high DO content during winter season may be due to low rate of decomposition of organic matter and increased photosynthesis.
6. The BOD content of water ranged from 0.2 mgL -1 to 1.2 mgL -1. The higher values during rainy season and lower values during winter season may be due to increased metabolic activities of microbes present in the water bodies and low decomposition rate of organic matter, respectively.
-1 -1 7. The total hardness ranged from 118 mgL CaCO 3 to 169 mgL CaCO 3. During the present study, there was no significant change in the total hardness values with respect to sites. Higher values during rainy season may be due to discharge of carbonates and bicarbonates chlorides and sulphates of Ca ++ and Mg ++ .
-1 -1 8. The acidity values ranged from 38 mgL CaCO 3 to 69 mgL CaCO 3. Higher values during rainy season could be attributed due to high organic load supporting decomposition process.
-1 -1 9. The total alkalinity values ranged from 36 mgL CaCO 3 to 70 mgL CaCO 3.
Higher values during rainy season may be due to discharge of municipal waste through runoff.
-1 -1 10. The chloride content ranged from 45.88 mgL CaCO 3 to 76.87 mgL CaCO 3.
Low chloride content during rainy season may be due to dilution of water with runoff.
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11. The fluoride content ranged from .31 mgL -1 to .72 mgL -1. Higher values during rainy season may be due to discharge of waste containing fluoride through runoff.
12. The nitrate-N content ranged from 0.06 mgL -1 to 0.27 mgL -1. Higher values during rainy season may be due to high rate of organic matter decomposition.
13. The phosphate-P content ranged from 0.03 mgL -1 to 0.07 mgL -1. Higher values during rainy season may be due to discharge of wastes through runoff containing phosphorous .
14. The Saprobic Score ranged from 6.94 to 7.46 and Diversity Score ranged from
0.59 to 0.64. The biological assessment depicts that the Tuirial river is slightly polluted. The number of families encountered was normally higher during winter and autumn season and maximum number of families is encountered at site 3 and 4.
15. The insect families in the orders Odonata, Coleoptera, and Diptera appear to show a certain relationship to the condition of their location.
16. The order Odonata increased in number as the river flowed downstream which indicates the health of the habitat. High taxa of Odonates are usually associated with organic enrichment.
17. The members of the family Coleoptera are able to survive with much diversity in stressed conditions because they do not depend on dissolved oxygen of their environment and are considered as good indicators of water quality.
18. High organic content and associated deoxygenation tend to increase as the number of dipteran species in the present study. High number of dipterans indicates pollution stress. The number of such organisms was much lower at Site 1, indicating the clean water.
85
19. Macroinvertebrates response to chemical and organic pollution are of a prime
importance, however unpredicted floods, high water discharges and velocities can
also disturb structure of the ecosystem.
20. The taxa richness was observed minimum at Site 1 and maximum at Site 3 and at
Site 4. This condition indicates successive increase in pollution intensity from Site 1
to Site 4. On the contrary, increased taxa richness at all sites compared to the
reference site is observed which may be due to the presence of numerous pollution
tolerant organisms indicating that the water quality is sufficient to maintain these
organisms and that another factor such as unstable habitat may be causing the variable
benthic macroinvertebrate communities.
21. The Community Loss Index recorded maximum at Site 4. This condition indicates
successive increase in pollution intensity from Site 1 to Site 4.
22. Jaccard Coefficient of Community Similarity was observed minimum at Site 4
indicating least similarity with reference site (Site 1). The presence of pollution
tolerant communities at Site 4 and disappearance of some intolerant communities
indicates more organic nutrients and pollution intensity of Site 4.
The overall observation depicts that the intensity of pollutants was maximum at Site 3 and Site 4, indicating that these sites receive wastes having more organic matter. The reference site (Site 1) was least polluted and intensity of pollutants was increased as the river flowed downstream. All the physico-chemical characteristics were normally within the prescribed limits laid down by various scientific agents. There was marked seasonal variation in the physico-chemical and biological attributes. It is recommended that the river water needs to be treated before use for drinking purpose, as the existing intensity of pollutants may cause adverse effects on human beings and other organisms continuing consumption of such
86 untreated water for long period. Moreover, direct discharge of water should be checked to protect aquatic life and also to maintain ecosystem stability.
Statistical analysis of results showed positive and significant correlation of TDS, pH, total hardness, acidity, total alkalinity, fluoride, nitrate-N and phosphate-P with temperature.
A positive correlation of BOD was observed with temperature. A negative and significant correlation of EC and chloride was established with temperature. A negative correlation of
DO and MI was observed with temperature.
.
87
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Appendix I: Correlation coefficient between different parameters for two years data (including macroinvertebrate families encountered) Parameters Temp TDS EC pH DO BOD TH Acidity TA Chlor Fluor Nitrate Phos MI Temp Pearson Correlation 1 .987 ** -.977 ** .940 ** -.684 .649 .958 ** .932 ** .974 ** -.902 ** .984 ** .915 ** .939 ** -.171 Sig. (2 -tailed) .000 .000 .001 .062 .082 .000 .001 .000 .002 .000 .001 .001 .685 TDS Pearson Correlation .987 ** 1 -.990 ** .976 ** -.730 * .698 .982 ** .967 ** .992 ** -.933 ** .994 ** .948 ** .959 ** -.170 Sig. (2-tailed) .000 .000 .000 .040 .054 .000 .000 .000 .001 .000 .000 .000 .687 EC Pearson Correlation -.977 ** -.990 ** 1 -.962 ** .677 -.643 -.973 ** -.957 ** -.992 ** .910 ** -.989 ** -.936 ** -.950 ** .087 Sig. (2-tailed) .000 .000 .000 .065 .085 .000 .000 .000 .002 .000 .001 .000 .837 pH Pearson Correlation .940 ** .976 ** -.962 ** 1 -.723 * .699 .970 ** .994 ** .981 ** -.925 ** .964 ** .961 ** .907 ** -.239 Sig. (2-tailed) .001 .000 .000 .043 .054 .000 .000 .000 .001 .000 .000 .002 .569 DO Pearson Correlation -.684 -.730 * .677 -.723 * 1 -.998 ** -.793 * -.692 -.685 .914 ** -.691 -.721 * -.834 * .121 Sig. (2-tailed) .062 .040 .065 .043 .000 .019 .057 .061 .001 .058 .044 .010 .775 BOD Pearson Correlation .649 .698 -.643 .699 -.998 ** 1 .763 * .672 .652 -.899 ** .656 .705 .804 * -.132 Sig. (2-tailed) .082 .054 .085 .054 .000 .028 .068 .080 .002 .077 .051 .016 .755 TH Pearson Correlation .958 ** .982 ** -.973 ** .970 ** -.793 * .763 * 1 .947 ** .983 ** -.959 ** .973 ** .915 ** .959 ** -.108 Sig. (2 -tailed) .000 .000 .000 .000 .019 .028 .000 .000 .000 .000 .001 .000 .799 Acidity Pearson Correlation .932 ** .967 ** -.957 ** .994 ** -.692 .672 .947 ** 1 .968 ** -.909 ** .953 ** .980 ** .892 ** -.270 Sig. (2-tailed) .001 .000 .000 .000 .057 .068 .000 .000 .002 .000 .000 .003 .517 TA Pearson Correlation .974 ** .992 ** -.992 ** .981 ** -.685 .652 .983 ** .968 ** 1 -.913 ** .992 ** .929 ** .935 ** -.116 Sig. (2-tailed) .000 .000 .000 .000 .061 .080 .000 .000 .002 .000 .001 .001 .785 Chlor Pearson Correlation -.902 ** -.933 ** .910 ** -.925 ** .914 ** -.899 ** -.959 ** -.909 ** -.913 ** 1 -.913 ** -.912 ** -.960 ** .118 Sig. (2-tailed) .002 .001 .002 .001 .001 .002 .000 .002 .002 .002 .002 .000 .781 Fluor Pearson Correlation .984 ** .994 ** -.989 ** .964 ** -.691 .656 .973 ** .953 ** .992 ** -.913 ** 1 .919 ** .951 ** -.084 Sig. (2-tailed) .000 .000 .000 .000 .058 .077 .000 .000 .000 .002 .001 .000 .843 Nitrate Pearson Correlation .915 ** .948 ** -.936 ** .961 ** -.721 * .705 .915 ** .980 ** .929 ** -.912 ** .919 ** 1 .899 ** -.330 Sig. (2-tailed) .001 .000 .001 .000 .044 .051 .001 .000 .001 .002 .001 .002 .425 Phos Pearson Correlation .939 ** .959 ** -.950 ** .907 ** -.834 * .804 * .959 ** .892 ** .935 ** -.960 ** .951 ** .899 ** 1 -.031 Sig. (2-tailed) .001 .000 .000 .002 .010 .016 .000 .003 .001 .000 .000 .002 .942 MI Pearson Correlation -.171 -.170 .087 -.239 .121 -.132 -.108 -.270 -.116 .118 -.084 -.330 -.031 1 Sig. (2-tailed) .685 .687 .837 .569 .775 .755 .799 .517 .785 .781 .843 .425 .942 **. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed)
Appendix III: Correlation coefficient between different parameters at Site 1 for two years data. Parameters Temp TDS EC pH DO BOD TH Acidity TA Chlor Fluor Nitrate Phos Temp Pearson Correlation 1 .990 ** -.984 ** .937 ** -.724 * .639 .960 ** .916 ** .968 ** -.917 ** .982 ** .984 ** .968 ** Sig. (2 -tailed) .000 .000 .001 .042 .088 .000 .001 .000 .001 .000 .000 .000 TDS Pearson Correlation .990 ** 1 -.982 ** .960 ** -.783 * .711 * .964 ** .947 ** .975 ** -.951 ** .987 ** .976 ** .979 ** Sig. (2-tailed) .000 .000 .000 .022 .048 .000 .000 .000 .000 .000 .000 .000 EC Pearson Correlation -.984 ** -.982 ** 1 -.958 ** .684 -.597 -.965 ** -.916 ** -.989 ** .901 ** -.993 ** -.964 ** -.983 ** Sig. (2-tailed) .000 .000 .000 .061 .118 .000 .001 .000 .002 .000 .000 .000 pH Pearson Correlation .937 ** .960 ** -.958 ** 1 -.687 .627 .940 ** .974 ** .975 ** -.896 ** .961 ** .935 ** .945 ** Sig. (2-tailed) .001 .000 .000 .060 .096 .001 .000 .000 .003 .000 .001 .000 DO Pearson Correlation -.724 * -.783 * .684 -.687 1 -.987 ** -.751 * -.758 * -.688 .926 ** -.734 * -.686 -.765 * Sig. (2-tailed) .042 .022 .061 .060 .000 .032 .029 .059 .001 .038 .060 .027 BOD Pearson Correlation .639 .711 * -.597 .627 -.987 ** 1 .677 .708 * .612 -.880 ** .655 .618 .688 Sig. (2-tailed) .088 .048 .118 .096 .000 .065 .049 .107 .004 .078 .102 .059 TH Pearson Correlation .960 ** .964 ** -.965 ** .940 ** -.751 * .677 1 .946 ** .983 ** -.928 ** .987 ** .933 ** .989 ** Sig. (2 -tailed) .000 .000 .000 .001 .032 .065 .000 .000 .001 .000 .001 .000 Acidity Pearson Correlation .916 ** .947 ** -.916 ** .974 ** -.758 * .708 * .946 ** 1 .952 ** -.914 ** .941 ** .910 ** .935 ** Sig. (2-tailed) .001 .000 .001 .000 .029 .049 .000 .000 .001 .000 .002 .001 TA Pearson Correlation .968 ** .975 ** -.989 ** .975 ** -.688 .612 .983 ** .952 ** 1 -.902 ** .995 ** .953 ** .988 ** Sig. (2-tailed) .000 .000 .000 .000 .059 .107 .000 .000 .002 .000 .000 .000 Chlor Pearson Correlation -.917 ** -.951 ** .901 ** -.896 ** .926 ** -.880 ** -.928 ** -.914 ** -.902 ** 1 -.928 ** -.889 ** -.937 ** Sig. (2-tailed) .001 .000 .002 .003 .001 .004 .001 .001 .002 .001 .003 .001 Fluor Pearson Correlation .982 ** .987 ** -.993 ** .961 ** -.734 * .655 .987 ** .941 ** .995 ** -.928 ** 1 .961 ** .996 ** Sig. (2-tailed) .000 .000 .000 .000 .038 .078 .000 .000 .000 .001 .000 .000 Nitrate Pearson Correlation .984 ** .976 ** -.964 ** .935 ** -.686 .618 .933 ** .910 ** .953 ** -.889 ** .961 ** 1 .939 ** Sig. (2-tailed) .000 .000 .000 .001 .060 .102 .001 .002 .000 .003 .000 .001 Phos Pearson Correlation .968 ** .979 ** -.983 ** .945 ** -.765 * .688 .989 ** .935 ** .988 ** -.937 ** .996 ** .939 ** 1 Sig. (2-tailed) .000 .000 .000 .000 .027 .059 .000 .001 .000 .001 .000 .001 **. Correlation is significant at the 0.01 level (2-ta iled). *. Correlation is significant at the 0.05 level (2-tailed).
Appendix IV: Correlation coefficient between different parameters at Site 2 for two years data. Parameters Temp TDS EC pH DO BOD TH Acidity TA Chlor Fluor Nitrate Phos Temp Pearson Correlation 1 .989 ** -.955 ** .935 ** -.667 .652 .970 ** .941 ** .973 ** -.895 ** .977 ** .843 ** .930 ** Sig. (2-tailed) .000 .000 .001 .071 .080 .000 .000 .000 .003 .000 .009 .001 TDS Pearson Correlation .989 ** 1 -.975 ** .967 ** -.707 * .687 .984 ** .963 ** .991 ** -.924 ** .989 ** .876 ** .958 ** Sig. (2-tailed) .000 .000 .000 .050 .060 .000 .000 .000 .001 .000 .004 .000 EC Pearson Correlation -.955 ** -.975 ** 1 -.931 ** .660 -.629 -.963 ** -.915 ** -.983 ** .898 ** -.979 ** -.816 * -.934 ** Sig. (2-tailed) .000 .000 .001 .075 .095 .000 .001 .000 .002 .000 .013 .001 pH Pearson Correlation .935 ** .967 ** -.931 ** 1 -.739 * .743 * .976 ** .983 ** .976 ** -.931 ** .955 ** .898 ** .936 ** Sig. (2-tailed) .001 .000 .001 .036 .035 .000 .000 .000 .001 .000 .002 .001 DO Pearson Correlation -.667 -.707 * .660 -.739 * 1 -.987 ** -.775 * -.646 -.683 .913 ** -.656 -.625 -.678 Sig. (2-tailed) .071 .050 .075 .036 .000 .024 .084 .062 .002 .077 .097 .065 BOD Pearson Correlation .652 .687 -.629 .743 * -.987 ** 1 .764 * .658 .668 -.904 ** .634 .648 .664 Sig. (2-tailed) .080 .060 .095 .035 .000 .027 .076 .070 .002 .092 .083 .072 TH Pearson Correlation .970 ** .984 ** -.963 ** .976 ** -.775 * .764 * 1 .946 ** .986 ** -.956 ** .969 ** .838 ** .929 ** Sig. (2-tailed) .000 .000 .000 .000 .024 .027 .000 .000 .000 .000 .009 .001 Acidity Pearson Correlation .941 ** .963 ** -.915 ** .983 ** -.646 .658 .946 ** 1 .962 ** -.879 ** .951 ** .943 ** .952 ** Sig. (2-tailed) .000 .000 .001 .000 .084 .076 .000 .000 .004 .000 .000 .000 TA Pearson Correlation .973 ** .991 ** -.983 ** .976 ** -.683 .668 .986 ** .962 ** 1 -.916 ** .993 ** .850 ** .936 ** Sig. (2-tailed) .000 .000 .000 .000 .062 .070 .000 .000 .001 .000 .007 .001 Chlor Pearson Correlation -.895 ** -.924 ** .898 ** -.931 ** .913 ** -.904 ** -.956 ** -.879 ** -.916 ** 1 -.899 ** -.817 * -.883 ** Sig. (2-tailed) .003 .001 .002 .001 .002 .002 .000 .004 .001 .002 .013 .004 Fluor Pearson Correlation .977 ** .989 ** -.979 ** .955 ** -.656 .634 .969 ** .951 ** .993 ** -.899 ** 1 .837 ** .921 ** Sig. (2-tailed) .000 .000 .000 .000 .077 .092 .000 .000 .000 .002 .010 .001 Nitrate Pearson Correlation .843 ** .876 ** -.816 * .898 ** -.625 .648 .838 ** .943 ** .850 ** -.817 * .837 ** 1 .951 ** Sig. (2-tailed) .009 .004 .013 .002 .097 .083 .009 .000 .007 .013 .010 .000 Phos Pearson Correlation .930 ** .958 ** -.934 ** .936 ** -.678 .664 .929 ** .952 ** .936 ** -.883 ** .921 ** .951 ** 1 Sig. (2 -tailed) .001 .000 .001 .001 .065 .072 .001 .000 .001 .004 .001 .000 **. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
Appendix V: Correlation coefficient between different parameters at Site 3 for two years data. Parameters Temp TDS EC pH DO BOD TH Acidity TA Chlor Fluor Nitrate Phos Temp Pearson Correlation 1 .983 ** -.946 ** .945 ** -.611 .552 .957 ** .931 ** .970 ** -.879 ** .984 ** .845 ** .862 ** Sig. (2-tailed) .000 .000 .000 .108 .156 .000 .001 .000 .004 .000 .008 .006 TDS Pearson Correlation .983 ** 1 -.976 ** .985 ** -.652 .593 .981 ** .971 ** .991 ** -.915 ** .993 ** .896 ** .853 ** Sig. (2 -tailed) .000 .000 .000 .080 .121 .000 .000 .000 .001 .000 .003 .007 EC Pearson Correlation -.946 ** -.976 ** 1 -.955 ** .666 -.624 -.977 ** -.950 ** -.984 ** .928 ** -.960 ** -.857 ** -.834 ** Sig. (2-tailed) .000 .000 .000 .072 .098 .000 .000 .000 .001 .000 .007 .010 pH Pearson Correlation .945 ** .985 ** -.955 ** 1 -.652 .590 .967 ** .974 ** .979 ** -.906 ** .969 ** .910 ** .786 * Sig. (2-tailed) .000 .000 .000 .080 .123 .000 .000 .000 .002 .000 .002 .021 DO Pearson Correlation -.611 -.652 .666 -.652 1 -.994 ** -.761 * -.616 -.599 .885 ** -.619 -.632 -.774 * Sig. (2-tailed) .108 .080 .072 .080 .000 .028 .104 .117 .004 .102 .093 .024 BOD Pearson Correlation .552 .593 -.624 .590 -.994 ** 1 .714 * .560 .544 -.856 ** .558 .576 .734 * Sig. (2-tailed) .156 .121 .098 .123 .000 .047 .149 .163 .007 .151 .135 .038 TH Pearson Correlation .957 ** .981 ** -.977 ** .967 ** -.761 * .714 * 1 .936 ** .973 ** -.967 ** .970 ** .854 ** .882 ** Sig. (2-tailed) .000 .000 .000 .000 .028 .047 .001 .000 .000 .000 .007 .004 Acidity Pearson Correlation .931 ** .971 ** -.950 ** .974 ** -.616 .560 .936 ** 1 .963 ** -.890 ** .960 ** .968 ** .808 * Sig. (2-tailed) .001 .000 .000 .000 .104 .149 .001 .000 .003 .000 .000 .015 TA Pearson Correlation .970 ** .991 ** -.984 ** .979 ** -.599 .544 .973 ** .963 ** 1 -.897 ** .985 ** .867 ** .811 * Sig. (2-tailed) .000 .000 .000 .000 .117 .163 .000 .000 .003 .000 .005 .015 Chlor Pearson Correlation -.879 ** -.915 ** .928 ** -.906 ** .885 ** -.856 ** -.967 ** -.890 ** -.897 ** 1 -.897 ** -.846 ** -.891 ** Sig. (2-tailed) .004 .001 .001 .002 .004 .007 .000 .003 .003 .003 .008 .003 Fluor Pearson Correlation .984 ** .993 ** -.960 ** .969 ** -.619 .558 .970 ** .960 ** .985 ** -.897 ** 1 .885 ** .877 ** Sig. (2 -tailed) .000 .000 .000 .000 .102 .151 .000 .000 .000 .003 .003 .004 Nitrate Pearson Correlation .845 ** .896 ** -.857 ** .910 ** -.632 .576 .854 ** .968 ** .867 ** -.846 ** .885 ** 1 .786 * Sig. (2 -tailed) .008 .003 .007 .002 .093 .135 .007 .000 .005 .008 .003 .021 Phos Pearson Correlation .862 ** .853 ** -.834 ** .786 * -.774 * .734 * .882 ** .808 * .811 * -.891 ** .877 ** .786 * 1 Sig. (2-tailed) .006 .007 .010 .021 .024 .038 .004 .015 .015 .003 .004 .021 **. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed).
Appendix VI: Correlation coefficient between different parameters at Site 4 for two years data Parameters Temp TDS EC pH DO BOD TH Acidity TA Chlor Fluor Nitrate Phos Temp Pearson Correlation 1 .978 ** -.976 ** .921 ** -.709 * .710 * .930 ** .920 ** .977 ** -.901 ** .988 ** .903 ** .779 * Sig. (2 -tailed) .000 .000 .001 .049 .048 .001 .001 .000 .002 .000 .002 .023 TDS Pearson Correlation .978 ** 1 -.974 ** .969 ** -.752 * .749 * .974 ** .963 ** .994 ** -.922 ** .990 ** .953 ** .811 * Sig. (2-tailed) .000 .000 .000 .031 .032 .000 .000 .000 .001 .000 .000 .015 EC Pearson Correlation -.976 ** -.974 ** 1 -.937 ** .680 -.675 -.914 ** -.965 ** -.963 ** .904 ** -.968 ** -.926 ** -.711 * Sig. (2-tailed) .000 .000 .001 .064 .066 .001 .000 .000 .002 .000 .001 .048 pH Pearson Correlation .921 ** .969 ** -.937 ** 1 -.767 * .762 * .970 ** .980 ** .976 ** -.926 ** .937 ** .982 ** .738 * Sig. (2-tailed) .001 .000 .001 .026 .028 .000 .000 .000 .001 .001 .000 .037 DO Pearson Correlation -.709 * -.752 * .680 -.767 * 1 -.995 ** -.846 ** -.706 -.749 * .919 ** -.726 * -.800 * -.896 ** Sig. (2-tailed) .049 .031 .064 .026 .000 .008 .050 .032 .001 .041 .017 .003 BOD Pearson Correlation .710 * .749 * -.675 .762 * -.995 ** 1 .834 * .706 .746 * -.915 ** .730 * .783 * .883 ** Sig. (2-tailed) .048 .032 .066 .028 .000 .010 .050 .034 .001 .040 .022 .004 TH Pearson Correlation .930 ** .974 ** -.914 ** .970 ** -.846 ** .834 * 1 .928 ** .975 ** -.944 ** .949 ** .970 ** .876 ** Sig. (2 -tailed) .001 .000 .001 .000 .008 .010 .001 .000 .000 .000 .000 .004 Acidity Pearson Correlation .920 ** .963 ** -.965 ** .980 ** -.706 .706 .928 ** 1 .958 ** -.910 ** .937 ** .956 ** .675 Sig. (2-tailed) .001 .000 .000 .000 .050 .050 .001 .000 .002 .001 .000 .066 TA Pearson Correlation .977 ** .994 ** -.963 ** .976 ** -.749 * .746 * .975 ** .958 ** 1 -.921 ** .987 ** .951 ** .793 * Sig. (2-tailed) .000 .000 .000 .000 .032 .034 .000 .000 .001 .000 .000 .019 Chlor Pearson Correlation -.901 ** -.922 ** .904 ** -.926 ** .919 ** -.915 ** -.944 ** -.910 ** -.921 ** 1 -.906 ** -.934 ** -.842 ** Sig. (2-tailed) .002 .001 .002 .001 .001 .001 .000 .002 .001 .002 .001 .009 Fluor Pearson Correlation .988 ** .990 ** -.968 ** .937 ** -.726 * .730 * .949 ** .937 ** .987 ** -.906 ** 1 .906 ** .806 * Sig. (2-tailed) .000 .000 .000 .001 .041 .040 .000 .001 .000 .002 .002 .016 Nitrate Pearson Correlation .903 ** .953 ** -.926 ** .982 ** -.800 * .783 * .970 ** .956 ** .951 ** -.934 ** .906 ** 1 .770 * Sig. (2-tailed) .002 .000 .001 .000 .017 .022 .000 .000 .000 .001 .002 .025 Phos Pearson Correlation .779 * .811 * -.711 * .738 * -.896 ** .883 ** .876 ** .675 .793 * -.842 ** .806 * .770 * 1 Sig. (2-tailed) .023 .015 .048 .037 .003 .004 .004 .066 .019 .009 .016 .025 **. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed).
Appendix II: Correlation coefficient of different parameters between various sites for two years data
Temperature Sites Site1 Site2 Site3 Site4
Site1 Pearson Correlation 1 .994 ** .990 ** .994 **
Sig. (2-tailed) .000 .000 .000
Site2 Pearson Correlation .994 ** 1 .998 ** .992 **
Sig. (2-tailed) .000 .000 .000
Site3 Pearson Correlation .990 ** .998 ** 1 .993 **
Sig. (2-tailed) .000 .000 .000
Site4 Pearson Correlation .994 ** .992 ** .993 ** 1
Sig. (2-tailed) .000 .000 .000
Total Dissolved Solids Sites Site1 Site2 Site3 Site4
Site1 Pearson Correlation 1 .997 ** .994 ** .984 **
Sig. (2-tailed) .000 .000 .000
Site2 Pearson Correlation .997 ** 1 .998 ** .982 **
Sig. (2-tailed) .000 .000 .000
Site3 Pearson Correlation .994 ** .998 ** 1 .986 **
Sig. (2-tailed) .000 .000 .000
Site4 Pearson Correlation .984 ** .982 ** .986 ** 1
Sig. (2-tailed) .000 .000 .000
**. Correlation is significant at the 0.01 level (2-tailed); *. Correlation is significant at the 0.05 level (2-tailed).
Electrical Conductance Sites Site1 Site2 Site3 Site4
Site1 Pearson Correlation 1 .994 ** .981 ** .993 **
Sig. (2-tailed) .000 .000 .000
Site2 Pearson Correlation .994 ** 1 .991 ** .987 **
Sig. (2-tailed) .000 .000 .000
Site3 Pearson Correlation .981 ** .991 ** 1 .986 **
Sig. (2-tailed) .000 .000 .000
Site4 Pearson Correlation .993 ** .987 ** .986 ** 1
Sig. (2-tailed) .000 .000 .000
pH Sites Site1 Site2 Site3 Site4
Site1 Pearson Correlation 1 .985 ** .983 ** .957 **
Sig. (2-tailed) .000 .000 .000
Site2 Pearson Correlation .985 ** 1 .991 ** .976 **
Sig. (2-tailed) .000 .000 .000
Site3 Pearson Correlation .983 ** .991 ** 1 .976 **
Sig. (2-tailed) .000 .000 .000
Site4 Pearson Correlation .957 ** .976 ** .976 ** 1
Sig. (2-tailed) .000 .000 .000
Dissolved Oxygen Sites Site1 Site2 Site3 Site4
Site1 Pearson Correlation 1 .993 ** .984 ** .998 **
Sig. (2-tailed) .000 .000 .000
Site2 Pearson Correlation .993 ** 1 .995 ** .995 **
Sig. (2-tailed) .000 .000 .000
Site3 Pearson Correlation .984 ** .995 ** 1 .986 **
Sig. (2-tailed) .000 .000 .000
Site4 Pearson Correlation .998 ** .995 ** .986 ** 1
Sig. (2-tailed) .000 .000 .000
Biological Oxygen Demand Sites Site1 Site2 Site3 Site4
Site1 Pearson Correlation 1 .985 ** .983 ** .986 **
Sig. (2 -tailed) .000 .000 .000
Site2 Pearson Correlation .985 ** 1 .982 ** .969 **
Sig. (2 -tailed) .000 .000 .000
Site3 Pearson Correlation .983 ** .982 ** 1 .956 **
Sig. (2-tailed) .000 .000 .000
Site4 Pearson Correlation .986 ** .969 ** .956 ** 1
Sig. (2 -tailed) .000 .000 .000
Total Hardness Sites Site1 Site2 Site3 Site4
Site1 Pearson Correlation 1 .983 ** .975 ** .973 **
Sig. (2-tailed) .000 .000 .000
Site2 Pearson Correlation .983 ** 1 .990 ** .964 **
Sig. (2-tailed) .000 .000 .000
Site3 Pearson Correlation .975 ** .990 ** 1 .978 **
Sig. (2-tailed) .000 .000 .000
Site4 Pearson Correlation .973 ** .964 ** .978 ** 1
Sig. (2-tailed) .000 .000 .000
Acidity Sites Site1 Site2 Site3 Site4
Site1 Pearson Correlation 1 .960 ** .960 ** .987 **
Sig. (2-tailed) .000 .000 .000
Site2 Pearson Correlation .960 ** 1 .991 ** .969 **
Sig. (2-tailed) .000 .000 .000
Site3 Pearson Correlation .960 ** .991 ** 1 .982 **
Sig. (2-tailed) .000 .000 .000
Site4 Pearson Correlation .987 ** .969 ** .982 ** 1
Sig. (2-tailed) .000 .000 .000
Total Alkalinity Sites Site1 Site2 Site3 Site4
Site1 Pearson Correlation 1 .992 ** .996 ** .982 **
Sig. (2-tailed) .000 .000 .000
Site2 Pearson Correlation .992 ** 1 .999 ** .986 **
Sig. (2-tailed) .000 .000 .000
Site3 Pearson Correlation .996 ** .999 ** 1 .985 **
Sig. (2-tailed) .000 .000 .000
Site4 Pearson Correlation .982 ** .986 ** .985 ** 1
Sig. (2-tailed) .000 .000 .000
Chloride Sites Site1 Site2 Site3 Site4
Site1 Pearson Correlation 1 1.000 ** .998 ** .999 **
Sig. (2-tailed) .000 .000 .000
Site2 Pearson Correlation 1.000 ** 1 .999 ** .999 **
Sig. (2-tailed) .000 .000 .000
Site3 Pearson Correlation .998 ** .999 ** 1 .999 **
Sig. (2-tailed) .000 .000 .000
Site4 Pearson Correlation .999 ** .999 ** .999 ** 1
Sig. (2-tailed) .000 .000 .000
Fluoride Sites Site1 Site2 Site3 Site4
Site1 Pearson Correlation 1 .986 ** .979 ** .989 **
Sig. (2-tailed) .000 .000 .000
Site2 Pearson Correlation .986 ** 1 .997 ** .992 **
Sig. (2-tailed) .000 .000 .000
Site3 Pearson Correlation .979 ** .997 ** 1 .986 **
Sig. (2-tailed) .000 .000 .000
Site4 Pearson Correlation .989 ** .992 ** .986 ** 1
Sig. (2-tailed) .000 .000 .000
Nitrate-N Sites Site1 Site2 Site3 Site4
Site1 Pearson Correlation 1 .880 ** .864 ** .925 **
Sig. (2-tailed) .004 .006 .001
Site2 Pearson Correlation .880 ** 1 .990 ** .812 *
Sig. (2-tailed) .004 .000 .014
Site3 Pearson Correlation .864 ** .990 ** 1 .829 *
Sig. (2-tailed) .006 .000 .011
Site4 Pearson Correlation .925 ** .812 * .829 * 1
Sig. (2 -tailed) .001 .014 .011
Phosphate-P Sites Site1 Site2 Site3 Site4
Site1 Pearson Correlation 1 .911 ** .845 ** .822 *
Sig. (2-tailed) .002 .008 .012
Site2 Pearson Correlation .911 ** 1 .824 * .657
Sig. (2-tailed) .002 .012 .077
Site3 Pearson Correlation .845 ** .824 * 1 .874 **
Sig. (2-tailed) .008 .012 .005
Site4 Pearson Correlation .822 * .657 .874 ** 1
Sig. (2-tailed) .012 .077 .005
APPENDIX VII: BIOLOGICAL ASSESSMENT METHODS
SAPROBIC [BIOLOGICAL MONITORING WORKING PARTY (BMWP)] SCORE:
Ample care should be taken to ensure that all indicator families of benthic macroinvertebrates, which are present, are actually encountered. This method involves a quantitative inventory of the presence of macroinvertebrate benthic fauna upto family level of taxonomic precision. All possible families having saprobic indicator value are classified on a score scale of 1 to 10 according to their preference for saprobic water quality. The families which are most sensitive to pollution are on the top of the list and are getting a score of 10 while the most pollution tolerant families are getting a score of 1 and 2. The other immediately sensitive families are placed in between the scoring scale of 10 to 1.
ENTER DIFFERENT SPECIES WITHIN ONE FAMILY SEPARATELY, AND INDICATE ABUNDANCY AS:
Abundance scale: A= single (one individual)
B= scarce (2-10 individuals)
C= common (10-15 individuals)
D= abundant (50-100 individuals)
E= excessive (more than 100 individuals or only one species)
MARK ENCOUNTERED FAMILIES AND IF TOTAL MULTI TAXONOMICAL TAXONOMICAL POSSIBLE SPECIES FAMILIES/ BMWP -PLIED GROUP FAMILIES WITHIN FAMILIES SPECIES SCORE SCORE ALSO MARK ENCOUNTERED ABUNDANCY AS 1A, 1B, 1C, 1D, 1E Ephemeroptera Siphonuridae Heptageniidae Leptophlebiidae Ephemerellidae Pothamanthidae Ephemeridae Prosopistomatidae Plecoptera Taeniopterygidae Leuctridae Capniidae Perlodidae Perlidae Hemiptera Aphelocheiridae Trichoptera Leptoceridae Goeridae Lepidostomatidae Brachycentridae Sericostomatidae TOTAL FAMILIES ENCOUNTERED & TOTAL MULTIPLIED SCORE x10
MARK ENCOUNTERED FAMILIES AND IF TOTAL MULTI- TAXONOMICAL TAXONOMICAL POSSIBLES SPECIES FAMILIES/ BMWP PLIED GROUP FAMILIES WITHIN FAMILIES SPECIES SCORE SCORE ALSO MARK ENCOUNTERED ABUNDANCY AS 1A, 1B, 1C, 1D, 1E Odonata Euphaeldae Lestidae Plathycnemididae Gomphidae Cordulegasteridae Aeschnidae Cordullidae Libellulidae Trichoptera Psychomylidae Philopotamidae TOTAL FAMILIES ENCOUNTERED & TOTAL MULTIPLIED SCORE X 8 Ephemeroptera Caenidae Plecoptera Nemouridae Trichoptera Rhyacophilidae Polycentropodidae Limnephilidae TOTAL FAMILIES ENCOUNTERED & TOTAL MULTIPLIED SCORE X 7 Mollusca Nerltidae Viviparidae Hydrobiidae Thiaridae Bithynidae Ancylidae Unionidae Trichoptera Hydroptillidae Crustacea Atyidae Gammaridae Palaemonidae Polychaeta Nereidae Nephthyidae Odonata Agriidae Coenagriidae TOTAL FAMILIES ENCOUNTERED & TOTAL MULTIPLIED SCORE X 6
MARK ENCOUNTERED FAMILIES AND IF POSSIBLES TOTAL MULTI TAXONOMICAL TAXONOMICAL SPECIES FAMILIES/ BMWP -PLIED GROUP FAMILIES WITHIN SPECIES SCORE SCORE FAMILIES ALSO ENCOUNTERED MARK ABUNDANCY AS 1A, 1B, 1C, 1D, 1E Hemiptera Mesovelidae Hydrometridae Gerridae Nepidae Naucoridae Notonectidae Pleidae Veliidae Hebridae Belastomatidae Corixdae Coleoptera Haliplidae Hygrobidae Dytiscidae Gyrinidae Hydrophilidae Dryopidae Eliminthidae Noteridae Psepheniade Trichptera Hydropsychidea Diptera Tipulidae Culicidae Blepharoceridae Simulidae Planaria Planaridae Dendroceolidae TOTAL FAMILIES ENCOUNTERED & TOTAL MULTIPLIED SCORE x 5 Ephemeroptera Baetidae Megaloptera Sialidae Hirudinea Piscicolidae TOTAL FAMILIES ENCOUNTERED & TOTAL MULTIPLIED SCORE X4
MARK ENCOUNTERED FAMILIES AND TOTAL IF POSSIBLES MULTI- TAXONOMICAL TAXONOMICAL FAMILIES/ BMWP SPECIES WITHIN PLIED GROUP FAMILIES SPECIES SCORE FAMILIES ALSO SCORE ENCOUNTERED MARK ABUNDANCY AS 1A, 1B, 1C, 1D, 1E Mollusca Lymnaeidae Physides Planorbidae Shaeridae Hirudinea Glossiphonidae Hirudidae Erpobdellidae Planaria Dugesiidae Crustacea Asellidae TOTAL FAMILIES ENCOUNTERED & TOTAL MULTIPLIED SCORE x 3 Diptera Syrphidae Chironomidae Ephydridae TOTAL FAMILIES ENCOUNTERED & TOTAL MULTIPLIED SCORE x 2 Oligochaeta All families TOTAL FAMILIES ENCOUNTERED & TOTAL MULTIPLIED SCORE x 1 GRAND TOTAL FAMILIES ENCOUNTERED & GRAND TOTAL MULTIPLIED SCORE
Saprobic Score: GRAND TOTAL MULTIPLIED SCORE GRAND TOTAL OF FAMILIES ENCOUNTERED
SAPROBIC SCORE :
DIVERSITY SCORE (SEQUENTIAL COMPARISON):
The evaluation of the benthic fauna diversity level can easily be done utilizing the same animals collected for estimating the Saprobic score. Since the method only involves a pair-wise comparison of sequentially encountered individuals, and the differences of two specimens can easily be observed up to the family level, no taxonomic skill is required. First observed animal is always different and scored as 1 run. When the next observed animal is different from the last, a new run starts. The encounter of an individual which cannot be discerned from the last does not increment the number of runs. Size differences only do not change the run.
SAME RUN IS 0 (Organism is the same as the previous).
NEXT RUN IS 1 (organism is different from the previous).
When a row is full, continue on next row. Enter the number of runs over all rows (sum of 1s).
Total Total Diversity No. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Runs Org, Score 1 15 2 30 3 45 4 60 5 75 6 90 7 105 8 120 9 135 10 150 11 165 12 180 13 195 14 210 15 225 16 240 17 255 18 270 19 285 20 300
DIVERSITY SCORE = Number of runs Number of organisms
DIVERSITY SCORE =
BIOLOGICAL WATER QUALITY CRITERIA (BWQC)
To assess the actual health of water bodies, CPCB has derived a Biological Water Quality Criteria (BWQC) for water quality evaluation. This system is based on the range of saprobic values and diversity of the benthic macroinvertebrate families with respect to water quality. The system has been developed after extensive field trials and calibration on the saprobity and diversity information of different taxonomic groups of benthic animals collected from artificial substratum and natural substratum of various water bodies. To indicate changes in water quality to different grades of pollution level, the entire taxonomic groups, with their range of saprobic score from 1 to 10, in combination with the range of diversity score from 0 to 1 has been classified into five different classes of water quality. The abnormal combination of saprobic score and diversity score indicates sudden change in environmental conditions.
Range of Range of Indicator Water Quality Saprobic Score Diversity Score Water Quality Colour Class (0-10) (0-1.0)
7 and more 0.2 – 1.0 Blue Clean A
6 - 7 0.5 – 1.0 Light Blue Slight Pollution B
3 - 6 0.3 – 0.9 Green Moderate Pollution C
2 – 5 0.4 – less Orange Heavy Pollution D
0 – 2 0 – 0.2 Red Severe Pollution E
CRITERIA FOR BIOLOGICAL WATER QUALITY EVALUATION
The biological water quality evaluation using benthic fauna can easily be done by combining the observed saprobic score and diversity score and the biological water quality class can be determined through comparing the results with the ranges of saprobic and Diversity Score prescribed in Biological Water Quality Criteria (BWQC).
Range of Range of Indicator Water Saprobic Diversity Water Quality Colour Quality Class Score (0-10) Score (0-1.0)
Results: