pollution load studies due to power plants of...
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POLLUTION LOAD STUDIES DUE TO
POWER PLANTS OF UNDIVIDED
SAMBALPUR DISTRICT
2013
P.G. Dept. of Chemistry
Sambalpur University JyotiVihar, Burla
Orissa-768019
POLLUTION LOAD STUDIES DUE TO POWER PLANTS OF UNDIVIDED SAMBALPUR
DISTRICT
DOCTOR OF PHILOSOPHY
IN SCIENCE (ENVIRONMENTAL CHEMISTRY)
2013
By
Sanjib Kumar Naik Regd. No: 14/2005/Chemistry
POLLUTION LOAD STUDIES
DUE TO POWER PLANTS OF UNDIVIDED SAMBALPUR DISTRICT
2013
Thesis Submitted To Sambalpur University
for the Degree Of
DOCTOR OF PHILOSOPHY
IN SCIENCE (ENVIRONMENTAL CHEMISTRY)
By
Sanjib Kumar Naik Regd. No: 14/2005/Chemistry
Under Supervision of
Dr. Amitabh Mahapatra P.G. Department of Chemistry
Sambalpur University Jyoti Vihar- 768019
To My Loving
Parents
Dr.Amitabh Mahapatra
(M.Sc. M.Phil, Ph.D)
Reader School of Chemistry
Sambalpur University
Jyoti Vihar, Burla-768019
Odisha (INDIA).
Email: [email protected]
CERTIFICATE
This is to certify that the thesis entitled “POLLUTION LOAD STUDIES
DUE TO POWER PLANTS OF UNDIVIDED SAMBALPUR DISTRICT” being
submitted by Sri Sanjib Kumar Naik, to the Sambalpur University, Jyoti Vihar for
the award Doctor of Philosophy in science is a record of bonafide research work
carried out by him under my supervision and guidance. In my opinion, the thesis has
reached the standard fulfilling the requirements for submission. He has worked
more than five years in the school of Chemistry, Sambalpur University, Jyoti Vihar.
The results embodied in this thesis have not been submitted for the award of any
other degree or diploma.
(Amitabh Mahapatra)
ACKNOWLEDGEMENT
First I would like to thank my supervisors Dr. Amitabh
Mahapatra. You have guided, inspired and supported me
throughout these years and provided the necessary resources for
the preparation of this thesis.
I owe a profound sense of gratitude and indebtedness to Prof.
K.C. Satpathy (Retd.) for constant encouragement and necessary
advice. His timely suggestions and pragmatic approach to research
have helped in carrying out this thesis.
My deep sense of gratitude goes to successive Head of Dept. of
Chemistry, Sambalpur University Prof. B. K. Mishra and Prof.
(Mrs) P.K. Misra for providing me the necessary laboratory
facilities for my research work.
I am grateful to Er. Debraj Sahu, the Managing Director,
Sundargarh Engineering College for giving me necessary
permission to carry out the Ph.D work
It is an excellent opportunity to express my thanks to all the
faculties of the department, friends and colleagues for their help
and encouragement.
Finally, I would like to add a word of thanks to my parents,
wife, brother my loving son and other family members for their
cooperation, blessing and encouragement, without which it would
have been difficult for me to accomplish the ambition I have been
nourishing since long.
(Sanjib Kumar Naik)
CONTENTS
Page No.
1. CHAPTER-I
Section-A: Introduction 01-04
Section-B: Brief Review of impact on air and water
quality of the Environment due to coal-fired
thermal power plants 05-32
Section-C: Aim and Objectives of the present study 33-34
References 35-42
2. CHAPTER-II
Section-A: Methods of Evaluation of air quality 43-54
Section-B: Method of Evaluation of water quality 55-62
References 63
3. CHAPTER-III
Evaluation of Impact due to Coal- Fired
Thermal Power Plants on the Air Quality
of the Environment. 64-98
4. CHAPTER-IV
Evaluation of Impact due to Coal-Fired
Thermal Power Plants on the Water Quality
of the Environment. 99-143
5. CHAPTER-V
Evaluation of Impact due to Coal-Fired
Thermal Power Plants on the Flora and Fauna
of the Cluster Area. 144-167
Reference 168-169
4. SUMMARY 170-184
CHAPTER-1
A. INTRODUCTION
B. BRIEF REVIEW OF IMPACT ON AIR AND
WATER QUALITY OF THE ENVIRONMENT
DUE TO COAL FIRED THERMAL POWER
PLANTS
B. AIM AND OBJECTIVES OF THE PRESENT STUDY.
CHAPTER-II
METHODS OF EVALUATIONS OF
AIR AND WATER QUALITY OF THE
CLUSTER AREA
SECTION-A: METHODS OF EVALUATIONS OF AIR
QUALITY
SECTION-B: METHODS OF EVALUATIONS OF
WATER QUALITY
CHAPTER-III
EVALUATION OF IMPACT DUE TO THE COAL-
FIRED THERMAL POWER PLANTS ON THE AIR
QUALITY OF THE ENVIRONMENT.
CHAPTER-IV
EVALUATION OF IMPACT DUE TO THE
COAL-FIRED THERMAL POWER PLANTS
ON THE
WATER QUALITY OF THE ENVIRONMENT.
CHAPTER-V
EVALUATION OF IMPACT DUE TO THE COAL-
FIRED THERMAL POWER PLANTS ON THE
FLORA AND FAUNA OF THE CLUSTER AREA.
SUMMARY
1
SECTION-A
INTRODUCTION
Pollution is defined as an undesirable change in physical, chemical and
biological characteristics of air, water and land that may be harmful to living
organisms, living conditions and cultural assets. The pollution control board
defined pollution as unfavourable alternation of our surrounding, largely as a by-
product of human activities. The pollution may be due to human activities of
natural eco-system. Natural pollution contaminates the air by storms, forest fire,
volcanoes and natural process (methane from marshy lands). Nature by and large
treats, recycles and makes good use of the pollutants and renders them less
harmful, whereas man-made pollutants threaten the integrity of the nature.
There are various types of pollutions based on sources of pollutants or
nature of pollutants. Air pollution, water pollution, soil pollution are the three
major types of pollution based on environment. Based on sources of pollutants they
are classified as automobile pollution, agricultural pollution and industrial
pollution (tanneries, distilleries, thermal and nuclear power plants, chemical
industries etc.).Further based on nature of pollutants, pollution is classified as
pesticide pollution, plastic pollution, heavy metal pollution, radiation pollution, oil
pollution, sewage pollution, noise pollution etc.
2
Sources of Air pollutions:
The following are a few sources and emission of air pollution:
A major source of air pollution is particulates and gaseous matter which are
emitted due to the burning of fossils fuels. From this combustion a variety of
emission
a. Fine particles (<100mm) which include carbon particles, metallic
dust, resins, aerosols, solid oxides, nitrates and sulphate.
b. Coarse particles (>200mm) largely carbon particles and heavy dust
c. Sulphur compounds
d. Nitrogen compounds
e. Oxygen compounds
f. Halogen Compounds
g. Radioactive Substances
These pollutants are artificial and they enter into the atmospheric air due to
the following fuel burning sources.
i. Automobile emits carbon monoxide, hydrocarbon and nitrous oxides. It
also exhausts leads gas and lead particle.
ii. Power plants burns fossil-fuel; coal and sometimes petrol and diesel and
produce sulphur dioxide.
3
iii. Industrial processors metallurgical plants and smelter, chemical plants,
Petroleum refineries, pulp and paper mills, sugar mills, cottons mills,
Synthetic rubber manufacturing plants etc.
iv. Transportation industry- rail roads, ship, air crafts, trucks, buses.
v. Heating plants for homes, apartments, schools and industrial buildings.
Besides the above mentioned artificial sources of pollution of air, there are
also some natural sources. The pollutants of natural sources are pollens,
hydrocarbons released by vegetation, dusts from deserts, storms and volcanic
activity.
Sources of Water pollution:
Water undergoes pollution both physically and chemically. The physical
pollution of water brings about changes with regards to its colour, odour, density,
taste, turbidity and thermal properties.
The chemical pollution of water is due to the presence of inorganic and
organic chemicals such as acids, alkalies, toxic inorganic compounds, dissolves
organic compounds etc. The chemical pollution of water caused change in acidity,
alkalinity or pH, DO, etc. It may be caused either by organic pollutants or
inorganic pollutants or by both.
Water pollutants includes non-biodegradable organic pollutants include
pesticides, fungicides, bacteria, etc. which persists for long period. Several gases,
4
toxic metals and compounds are also included as inorganic pollutants as they also
degrade water quality.
Some biological pollutants are pathogens bacteria, fungi, virus, parasites
worms etc. The sources of these types of pollutant are due to domestic sewage and
industrial waste.
Physiological pollution is due to several chemical agents such as chlorine,
sulphur dioxide, hydrogen sulphide, mercaptants, phenol and hydroxyl benzene.
The different resources of water are polluted by different sources of pollutants. The
ground water is polluted by domestic waste, industrial waste and agricultural
waste, runoff from urban and soluble effluents from different sources. Surface
water when direct come in contact with atmosphere streams etc. Lake water gets
polluted by Sewage treatment plants, toxic and hazardous effluents from industries,
urban areas. Organic waste comes from hills etc. Sludge from factories, washing
and dumping of tailings directly mixed with surface water. Water of river mostly
polluted due to increase in population, industrialization, urbanization and broad
ranges of human activities. Marine pollution is associated with the change in
physical, chemical and biological conditions of the sea water.
5
SECTION-B
BRIEF REVIEW OF IMPACT ON
AIR AND WATER QUALITY OF THE ENVIRONMENT
DUE TO COAL FIRED THERMAL POWER PLANTS
In the foregoing section-A descriptions on the environmental pollution due
to various reasons have been given. Industrialisation is one of the main reasons for
environmental pollution is well known. Because of industrialisation and for other
purpose electric power is most essential for which several thermal power plants are
being erected throughout India. Odisha is not excluded from the thermal power
industries. In recent years several coal-fired thermal power plants have been
erected in Odisha for which Odisha environment is polluting intensively in the last
years. Undivided Sambalpur district of Odisha where several coal-fired power
plants are erected for industrialisation and polluting the environment miserable.
Since our present study is on “pollution load studies due to power plants of
undivided Sambalpur district”. It is desirable to give a brief review of earlier
work reported on the impact on air and water of the environment due to coal-fired
thermal power plants before describing the present work.
I. Review of impact on Air
Florkowski and Kuc[1]
have reported that increasing power production by
burning fossil fuels is accompanied by increasing emission of CO2 and SO2which
are toxic pollutants to the atmosphere. The predicted concentrations of SO2 due to
the emissions from the thermal power plant, at Tuticorin, south India studied by
6
L. T. Khemaniand Coworkers [2]
. Thermal power plant and its possible
impact on the inhabitants and climate in the downwind region were evaluated.
Also, the predicted concentrations downwind of a Petrochemical Industrial
Complex (PIC) located in the vicinity of the thermal power plants were computed
and compared with the measured concentrations.
Coal consumption in the electric utility industry provides a context to
examine the relationships between coal and electricity production industry. The
industry has the dual distinction of being the largest consumer of coal in the USA
as well as the single largest source of sulphur oxides and a primary source of
particulates; nitrogen oxides and carbon dioxide are also reported [3]
.
In Brazil, total installed capacity has grown 57.6GW in 1997 to 90.2 GW in
2006 with hydro-electric share declining from 92 to 83% and thermoelectric share
growing from 8 to 17% over same period. The studied dealt with analysis of
atmospheric pollution from coal and natural gas power plants and policy related in
the power plants[4]
.
The methodology and results of a dynamic individual air pollution
exposure model (DINEX) that calculates the hourly exposure for each adult in a
panel study has been reported [5]
. Each of over 260 participants, through the use of
a diary, provided information used in the model to calculate his/her personal,
individualized exposure in an industrial area in Norway. The estimated air
pollution concentration from the dispersion model based on continuous
metrological measurement, were calibrated with air pollutant concentration
measured continuously.
7
Bernard E. A. Fisher[6]
have reported that power industry was more
concerned with the concentrations of the acid gas SO2 around power stations,
which is released from power stations in much greater quantities than dust. None
of the measures arising from the Clean Air Act was specifically directed to SO2,
which is emitted as an inevitable consequence of burning coal.
The conversion of energy with the highest possible efficiency is of primary
importance in processes with high intensity of energy flows, among which the
most significant are those in thermal power plants have been reported [7]
.The paper
deals with the influence of coal composition on the conversions taking place in
power plants. The analysis of energy conversions treats, above all, the heat loss
due to the exhaust flue gas. The analyses of the power plants use of electric powers
are focused on the processes of coal pulverizing and exhaust flue gas cleaning. The
same workers in another communication reported[8]
that when lignite coal with
lower heating values ranging between 9 and 10 MJ/kg and with contents of ash
about 20% and moisture about 38%. The influences of significant lignite
composition components on heating value, boiler losses, boiler specific production
of steam, own consumption of electric power, power consumption for coal
grinding and flue gas desulphurization etc. are discussed. The results of analyses
and measurements are graphically presented.
A comprehensive study report[9]
resolved fossil fuel consumption database
and emissions inventory was constructed, for India. Emissions of sulphur dioxide
and aerosol chemical constituents were estimated for 1996–1997 and extrapolated
to the Indian Ocean Experiment (INDOEX) study period (1998–1999). District
8
level consumption of coal/lignite, petroleum and natural gas in power plants,
industrial, transportation and domestic sectors was 9411 PJ, with major
contributions from coal (54%) followed by diesel (18%). Emission factors for
various pollutants were derived using India specific fuel characteristics and
information on combustion/air pollution control technologies for the power and
industrial sectors.Domestic and transportation emission factors, appropriate for
Indian source characteristics, were compiled from literature. SO2 emissions from
fossil fuel combustion for 1996–1997 were 4.0Tg SO2yr-1
, with 756 large point
sources (e.g. utilities, iron and steel, fertilisers, cement, refineries and
petrochemicals and non-ferrous metals), accounting for 62%. PM2.5 emitted was
0.5 and 2.0 Tg yr-1
for the 100% and the 50% control scenario, respectively,
applied to coal burning in the power and industrial sectors. Coal combustion was
the major source of PM2.5 (92%) primarily consisting of fly ash, accounting for
98% of the „„inorganic fraction‟‟ emissions of 1.6 Tg yr-1
. Black carbon emissions
were estimated at 0.1 Tg yr-1
, with 58% from diesel transport, and organic matter
emissions at 0.3 Tg yr-1
, with 48% from brick-kilns. Fossil fuel consumption and
emissions peaked at the large point industrial sources and 22 cities, with elevated
area fluxes in northern and western India. The spatial resolution of this inventory
makes it suitable for regional-scale aerosol-climate studies.
Johann P. Engelbrecht and Coworkers reported[10]
the emissions from
residential coal combustion have been a major cause of increase of air pollution
levels in the industrialized areas of South Africa. The adverse health effects
resulting from exposure to residential coal combustion emissions have been a
major public concern for many years. To address this, the Department of Minerals
9
and Energy of South Africa conducted a macro-scale experiment in the township
of Qalabotjha during the winter of 1997 to assess the technical and social benefits
of combusting low-smoke fuels. This paper reports the PM2.5 and PM10 chemical
mass-balance (CMB) source apportionment results from Qalabotjha during a 30
days sampling period, including a 10 days period when a large proportion of low-
smoke fuels were combusted. Biomass burning is also a major sources accounting
for 13.8% PM2.5 and 19.9% of PM10. Fugitive dust is only significant in coarse
particle fraction accounting for 11.3% of PM10. Contribution from secondary
ammonium sulphate and three–four times greater than from ammonium nitrate
accounting for 5-6% of PM10. Minor contributions of PM10 were found form power
plant fly ash, motor vehicles exhaust and agricultural lime was reported.
It is reported that coal combustion is the greatest atmospheric pollution
source in China [11]
. The authors analyzed the evolution of the coal fired thermal
power plant in China. The results indicate that it is more efficient to introduce a de-
sulfur installation rather than to introduce de-nox or de-carbon systems for a CFP
(Coal Fired Power Plants) burning fuel with relatively high sulfur content. The
plant is globally evaluated from the point of view of its energy ecological
efficiency. Using the result of the analysis for CFP the author proposed to improve
the energy ecological efficiency of exiting CFPS.
Rajesh Kumar and Coworkers[12]
have reported the environmental
impacts of 260MW coal based Thermal Power Plant(TPP) at Dewas in Madhya
Pradesh, India. Through monitoring of existing environmental parameters such as
ambient air quality, water quality, land environment, noise environment, biological
10
environment, socio-economic environment, etc. Environmental Impact Assessment
(EIA) study has been carried out which reveals that in general the site conditions
are suitable for establishment of proposed TPP in the region as it will have net
positive environmental impacts if proper control measures are adopted.
World primary energy demand increases with increases in population and
economic development. Within the last 25yrs., the total energy consumption has
almost doubled. For the purpose of meeting this demand fossil energy sources are
used and various pollutants are generated. CO2 is also one of these gases, which
cannot be removed like other pollutants, and it causes greenhouse effect and
climate change. Reducing the CO2 emission is very important because of the
environmental concerns and regulations, especially the Kyoto Protocol. A
review[13]
estimated world carbon emission amounts estimated until the year 2020
and emission factors for dust, SO2, NOx and CO2. The estimated results show that
CO2 emissions from thermal power plants in Turkey will make about 0.66% of the
global CO2 emissions in 2020.
Sarath K.Guttikunda and Coworkers[14]
reported the contribution of
megacities to sulphur emissions and pollution in Asia over a 25-year period (1975-
2000) using a multi-layer Lagrangian puff transport model. Asian megacities cover
of 2% of the land area but emit 16% of the total anthropogenic sulphur emissions
of Asia. It was shown that urban sulphur emissions contribute over 30% to the
regional pollution levels in large parts of Asia.
Local air quality impacts of a proposed conventional coal-fired power plant
in the˙Içel region have been investigated by Mustafa og˘ uz[15]
. Using numerical
11
dispersion modeling studies coupled with a GIS application within the impact area
of the facility, Industrial Source Complex Short Term (ISCST2) dispersion model
has been used to estimate ground-level concentrations of air pollutants originating
from the power plant. For the same impact area, GIS applications have been
utilised to determine the agricultural yield distribution. Based on the predicted
ground-level pollutant concentrations and sensitivity of the agricultural crops to the
pollutants, agricultural yield loss was estimated for the impact area.
B.R. Gurjarand Coworkers[16]
have reported comprehensive emission
inventory for megacity Delhi, India, for the period 1990–2000 has been developed
in support of air quality, atmospheric chemistry and climate studies. It appears that
SO2 and Total Suspended Particles (TSP) are largely emitted by thermal power
plants ~ 68% and ~ 80%, respectively, while the transport sector contributes most
to NOx, CO and non-methane volatile organic compound (NMVOC) emissions.
Further, while CO2 has been largely emitted by power plants in the past was about
60% in 1990, and 48% in 2000. The relative strong growth of NOx emissions
indicates that photochemical O3 formation in the regions environment may be
increase substantially in the dry season was reported.
A communication [17]
deals with the work carried out on six pulverized
coal-fired power plants in western Canada burning sub-bituminous coal for the
mass-balance and speciation of mercury. The main objectives of this study were to
determine the total gaseous mercury (TGM) emitted from stacks of power plants
using the Ontario Hydro method; identify the speciation of emitted mercury such
as metallic (Hg0) and gaseous elemental (GEM) mercury; and perform mass-
12
balance calculations of mercury for milled-coal, bottom ash, electrostatic
precipitators (ESP) fly ash and stack-emitted mercury based on three tests.
Sampling of mercury was carried out using the Ontario Hydro method and mercury
was determined using the USEPA method 7473 by cold vapor atomic absorption
spectroscopy (CVAAS). The variation in mass-balance of mercury for the six
power plants is mostly related to the variability of coal feed rate.
The work reported by P. Goyal and Sidhartha [18]
had focused on the
seasonal evaluation of suspended particulate matter (SPM) produced by Badarpur
thermal power station (BTPS) within a radius of 5 km of its source. The work
using monitoring and modeling; experimental measurements were obtained from a
monitoring network in and around the power station. Numerical simulations were
carried out employing a Gaussian plume point source model. SPM is considered to
be the main pollutant emitted by the power station. Two years (1998–1999) of
wind speed, wind direction and cloud cover data recorded by India Meteorological
Department (IMD) were used for concentration computations. These computed
values of particulate matter at six receptors, in and around BTPS, were used to
evaluate the seasonal impact of BTPS on air quality. Comparison of numerical
results with experimental data show a marked seasonal trend along the study
period which is characterized by SPM levels that were higher in winter and
decreased progressively through the pre-monsoon, post-monsoon and monsoon
seasons.
The dominant use of coal in power sectors has an adverse environmental
impacts have been studied by R. Sharma & S. Pervez [19]
. Ambient air monitoring
13
for the two size fractions of particulate matter, respirable suspended particulate
matter (RSPM) and non-respirable suspended particulate matter (NRSPM) in the
downwind and upwind directions of a large coal-fired power station in central
India, was carried out. Collected samples of ambient particulate matter were
analysed atomic absorption spectrophotometrically (AAS) for 15 elements. Spatial
variability in elemental composition of RSPM and NRSPM and the degree of
enrichment of these toxic metals in RSPM were investigated. A significant spatial
variability for the elements in RSPM and NRSPM and higher degrees of
enrichment of the elements were observed.
A.D. Bhanarkar [20]
has studied the comprehensive and spatial emission
inventory was carried out for SO2, particulate matter PM and toxic metals from
industrial sources in Greater Mumbai, India. Fuel consumption database was
developed for industrial sources. Emission factors for various pollutants were
compiled from the literature, scrutinized and used appropriately as applicable
under Indian conditions. Emissions of SO2, PM and toxic metals were estimated
for 2001–02 and extrapolated to 2010. SO2 emissions from fossil fuel combustion
covering 215 points sources for 2001–02were computed as 55.591Ggy-1
whereas
those for PM were calculated as 9.794Ggy-1
. The total metal emissions from
industrial sources were computed as 0.375Ggy-1
. Total fossil fuel energy
consumption in industrial sector during 2001–02 was 145 PJ, which included fuel
consumption (29%) in power plants. Amongst the industries, thermal power plants
(TPP) were the major source of emissions in the region contributing 27% share
towards SO2, 19% PM and 62%metals. Projected scenario for 2010 indicates that
14
there would be substantial reduction in PM and metal emission load while the
gaseous pollutant concentration would show a decreasing trend.
Works on atmospheric deposition have also been reported by R.K. Singh
and M. Agrawal [21]
. They have observed the significant spatial and temporal
variations in depositions of cations and anions were observed. Depositions were
higher near the thermal power stations and coalmines as compared to distantly
situated site. Seasonally summer samples showed maximum cation and anion
depositions followed by winter and minimum in rainy season. The mean pH of the
depositions indicates that rainfall in the area is alkaline. Among the anions,
maximum deposition was recorded for SO42-
followed by NO3- and minimum for
Cl-. Among the cations, Ca2C deposition was maximum followed by NH4
+ , Na
+,
K+ and Mg
2+ deposition rates showed more or less similar values. The depositions
of cations and anions as well as pH were higher in through fall than clear fall
samples. Results of the present study suggest that atmospheric depositions are
strongly modified due to thermal power stations and coal mines in the area.
A study on volatile organic compounds which is major group of air
pollutants play critical role in atmospheric chemistry [22]
. It contributes to toxic
oxidants which are harmful to ecosystem human health and atmosphere. Data on
levels of VOCs in developing countries is lacking. In India information at target
VOCs as defined in USEPA compendium method TO-14 is almost totally lacking.
In the work deals with estimation of target VOCs at 15locations in five categories
namely residential, industrial, commercial, traffic intersections and petrol refueling
stations in Delhi, the capital of India. The monitoring was carried out during peak
15
hours in morning and evening each month for a year in 2001. Ambient air was
adsorbed on adsorbent tubes, thermally desorbed and analyzed on GC–MS. The
results show that levels of VOCs are high and stress the need for regular
monitoring programme of VOCs in urban environment.
Mrinal K. Ghose and Coworkers [23]
have analysed the significant effects
on exacerbation of asthma, allergy and other respiratory diseases. Like many other
megacities in the world the ambient air quality of Kolkata is also being deteriorated
day by day. Automobile exhausts and certain industrial pollutants produce O3 by
photochemical reactions. The particulate matter, particularly less than 10μ in size,
can pass through the natural protective mechanism of human respiratory system
and plays an important role in genesis and augmentation of allergic disorders.
The analysis of carbon dioxide emitted as a product of combustion of coal
(fossil fuels) is currently responsible for over 60% of the enhanced greenhouse
effect have been studied by Shiv Pratap Raghuvanshi and Coworkers[24]
. In the
communication they were made to attempted brief investigation of CO2 emission
from coal based power generation in India. Energy indicators, trends in energy
consumption and carbon dioxide emissions have been thoroughly investigated.
Methodology for analysis of carbon emissions and possible sinks are also
provided.
The trends of greenhouse gas (GHG) and local air pollutant emissions of
India for 1985-2005 have also been reported [25]
. The GHGs covered are six Kyoto
gases, namely carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O),
perfluorocarbons(PFCs), hydrofluorocarbons (HFCs) and sulphur hexafluoride
16
(SF6). The local air pollutants are sulphur dioxide (SO2), nitrogen oxides (NOX),
carbon monoxide (CO) and total suspended particulates (TSP). These estimates
incorporate some of the most recent scientific assessments for India. The multi gas
emissions have varied sectoral and fuel-based dominance, as well as regional
distribution patterns. Coal consumption in power sector dominates CO2 and SO2
emissions, while power and road transport equally contribute to NOX emissions.
Rice cultivation and livestock-related emissions from the agriculture sector
dominate CH4 emissions, while synthetic fertilizer use in the same sector is the
major source of N2O emissions. PFC emissions are dominated by C2F6 andCF4
emissions from aluminum production. The majority of HFC emissions are
contributed by HFC-23, a by-product during the production of HCFC-22 that is
widely used in refrigeration industry. CO emissions have dominance from biomass
burning. Particulate emissions are dominated by biomass burning (residential
sector), road transport and coal combustion in large plants.
The Indo-Gangetic basin is characterized by dense fog, haze and smog
during the winter season [26]
. The author have shown one to one correspondence
during the winter season of aerosol optical properties with the location of thermal
power plants which are single small spatial entities compared to the big cities. Our
results indicate that power plants are the key point source of air pollutants. The
detailed analysis of aerosol parameters deduced from the Multi angle Imaging
Spectro Radiometer (MISR) level 3 remote sensing data show the existence of
absorbing and non-absorbing aerosols emitted from these plants. Analysis of
higher resolution Moderate Resolution Imaging Spectro Radiometer (MODIS)
level 2 aerosol optical depth over thermal power plants supports the findings.
17
An attempt has been made by Barnali Nag[27]
to develop framework for
estimating realistic baseline for carbon emissions from power generation in India
till the end of the Eleventh Five Year Plan Period (2010–2011). Such a supply side
framework for estimation of baselines is useful for developing countries like India
where the electricity markets are supply constrained. Also the paper demonstrates
the evaluation of additional emission reductions over and above the business as
usual baseline by identification and quantification of future possibilities of changes
in specific coal consumption and auxiliary consumption of around 70 existing
thermal power plants using data envelopment analysis (DEA).
Methods of estimation of the composition of flue gases involving the use of
portable gas analyzers are considered, and some ways of interpreting the primary
information obtained are also reported [28]
.
The work reported by L. Bay´on [29]
presents an environmental dispatch
algorithm in a hydrothermal system and addresses the problem of minimization of
emissions of SO2 and NOx caused by the operation of thermal plants. Several
models have been used to represent the emissions function. In this paper, author
has first constructed a quadratic model for both emissions: E (P) = α +β P +γ P2,
where P is the power generated and the parameters were computed via the least-
square criteria from several tests at thermal plants have seen that the problem
consists in the minimization of a functional F(z) within the set of piecewiseC1
functions that satisfy boundary conditions and non-holonomic inequality
constraints.An optimal control technique is applied and Pontryagin‟s theorem is
18
employed. The algorithm proposed is easily implemented using the Mathematica
Package and is applied to a sample system
The monitoring of air quality in large urban agglomerations is the key to
the prevention of air pollution related problems in emerging mega-cities is reported
[30]. The city of Wuhan is a highly industrialised city with49 million inhabitants in
Central China. Simultaneous PM10 sampling was performed during 1year at one
urban and one industrial site. Mean PM10 daily levels exceed the USEPA or EU
annual limit values by 3-4times. PM10 at the industrial site was reported 34%
followed by coal fired power plant (20%) and the anthropogenic regional
background (16%). At the urban site the major PM10 source is a mixed coal
combustion source (31%), followed by the anthropogenic regional background
(28%) and traffic (16%).
The evaluation and environmental impact resulting from the natural gas and
diesel combustion in thermoelectric power plants that utilize the combined cycle
technology has been reported by Iraı´desand Coworkers [31]
. The ecological
efficiency concept, which evaluated by and large the environmental impact, caused
by CO2, SO2, NOx and particulate matter (PM) emissions. The resulting pollution
from natural gas and diesel combustion is analyzed, considering separately the
CO2, SO2, NOx and particulate matter gas emission and comparing them with the
international standards regarding the air quality. It can be concluded that it is
possible to calculate thermoelectric power plant quantitative and qualitative
environment factor, and on the ecological standpoint.
19
K. S. Patel Coworkers [32]
have reportedairborne lead is one of the most
harmful particulate pollutants. The main sources of airborne lead are industrial
processes i.e. ferrous and nonferrous metallurgy, automobile and coal combustion.
Central India is one of the largest sources of aerosols because in this region
millions of tons of coal are burnt annually from its abundant coal deposits by coal-
burning power plants and coal-fired heavy metal industries. The mass distribution,
concentration, variations and source pattern of Pb associated to the ambient coarse
particulate matters (PM10) and fine particulate matters (PM2.5)in the residential site
of Raipur city (capital of Chhattisgarh state) Central India is described. The PM10
and PM2.5 samples were collected for one year (i.e. June, 2005 - May, 2006) by
using Partisol sequential speciation air sampler over Teflon paper. The samples
were collected for 24-hr (6 am - 6 am) duration weekly on Wednesday -Tuesday
except rainy season. The ambient annual mass distribution of PM10 and
PM2.5inambient air is ranged from 37.0 - 501 and 27.0 - 293 μgm-3
with mean
values of 209±38 and 95±18 μg m-3
, respectively. The Pb content in the PM was
analysed by technique: proton induced X-ray emission spectroscopy (PIXE). The
PM10Pb andPM2.5Pb mass distribution in the ambient air is ranged from 21.0 -
5582 and 13.0-5234 ngm-3
with annual mean value of 909±370 and 730±323 ngm-
3, respectively. The seasonal variations, enrichment factor, correlation coefficient
and sources are discussed.
Ambient suspended particles (TSP) were collected from January to June
2001 at seven sampling sites in western Macedonia, Greece, where four thermal
power stations are located. TSP samples were chemically analysed elements Fe,
Al, Mg, Ca, K, Ti and Si, and trace elements P, Cd, Cr, Cu, Mn, Pb, V, Zn,Te, Co,
20
Ni, Se, Sr, As, and Sb, water-soluble ions Cl-,NO3
-,SO4
2-,Na
+,K
+,NH4
+,Ca
2+,Mg
2+
carbonaceous compounds(OC/EC) and polycyclic aromatic hydrocarbons (PAHs)
and reported[33]
. These classes of compounds were consequently compared with
PM mass concentrations of TSP in order to perform mass closure.
K. Sivaramasundaram & P. Muthusubramanian[34]
have estimated
respirable particulate matter (RPM; PM2.5)and total suspended particulate matter
(TSP) concentrations in ambient air in Tuticorin, India. RPM and TSP was
measured in the location of thermal Power Station and about 6 km from the Port of
Tuticorin in the southwest direction.The result indicates that the RPM
concentrations ranged between 20.9 and198.2μg/m3, while the TSP concentrations
varied from51.5 to 333.3μg/m3 during the study period. There was a better
correlation between PM10–100 and TSP concentrations than that of PM10 (RPM) and
TSP concentrations, but the correlation of RPM fraction was also acceptable. It
was found that wind speed was the most important meteorological factor affecting
the concentrations of the pollutants. Significant seasonal variations in the pollutant
concentrations of present interest were found at 5% significance level except for
TSP concentrations in the year 2006 have been reported.
The combustion of fossil fuels and land use change in Africa for various
time periods have been reported by J. G. Canadell and Coworker [35]
. Africa was
responsible for an average of 500TgCy−1
for the period 2000–2005. An
understanding of the regional contributions and trends of anthropogenic CO2
emissions is critical to design mitigation strategies aimed at stabilizing atmospheric
greenhouse gases has been discussed.
21
Lee and Coworkers [36]
have reported the emission of nitrogen compounds
from power plants accounts for a significant proportion of the total emissions of
nitrogen to the atmosphere. The slant column density (SCD) of power plant
generated NO2 was derived using imaging differential optical absorption
spectroscopy (I-DOAS) with scattered sunlight as a light source. The vertical
structure of NO2 SCD from power plant stacks was simultaneously probed using a
push-broom sensor from the two stacks of the Pyeongtaek Power Plant, northwest
South Korea.
The fraction of ambient PM10 that is due to the formation of secondary in
organic particulate sulphate and nitrate from the emissions of two large, brown
coal-fired power stations in Saxony (East Germany) was examined[37]
. The
exhausted gas-steam mixture contained the gases CO, CO2, NO, NO2, and SO2 was
reported. The directly emitted primary particles, and additionally, an excess of
„free‟ sulphate ions in water solution, which, after the desulfurization steps, remain
non-neutralized by cations. The precursor gases NO2 and SO2 were capable of
forming nitric and sulphuric acid by several pathways. The acids can be neutralized
by ammonia and generate secondary particulate matter by heterogeneous
condensation on pre-existing particles has been reported.
Sharma and Coworkers [38]
have reported that air pollution control in
Delhi have largely neglected, the emission reduction measures from thermal power
plants (TPPs), which are the second most polluting sources. The present study
investigates how the ambient air quality of Delhi would improve if the World Bank
emission guidelines (WBEG) for the TPPs were to be implemented. To accomplish
22
this, a comprehensive inventory of point, area, and line sources was conducted in
the selected study area, primarily aiming to estimate the sectoral emission
contributions to ambient air quality. The Industrial Source Complex Short-Term
Model, Version 3 (ISCST3) was used to predict the ambient concentrations of total
suspended particulates (TSP), sulphur dioxide (SO2), and nitrogen dioxide (NO2) at
seven monitoring sites operated by the Central Pollution Control Board (CPCB)
for the period from July 2004 to June2005. The ISCST3 model predictions for TSP
and NO2 were satisfactory at all receptor locations. However, for SO2, the model
predictions were satisfactory at only two receptor locations. The vehicles
contributed 58% of the total ambient air pollution, followed by TPPs contributing
30%. The study estimates that adoption of WBEG may reduce the ambient air
pollution due to TPPs emissions by 56% to 82%, bringing it within the National
Ambient Air Quality Standards (NAAQS) set for industrial areas in India, except at
one location where TPP‟s contribution to ambient air pollution is negligible
compared to vehicular emissions.
The impact assessment of Barapukuria thermal power and coal mining
project through environmental, socio-economical and meteorological study has
been reported [39]
. The analysis showed that, the Mn concentration was found in the
satisfactory range. The pH was found slightly alkaline and surface water was
bacteria contaminated. SO42-
concentration was in the range of WHO standard.
Calculated SOx loading was almost same of monitored emission. Corresponding
estimated concentration of SOx was in acceptable range, which may not bring any
matter of concern. In the study, an attempt was also made to evaluate the health
impacts of SPM (suspended particulate matter) emitted from the combustion of
23
coal in the power plant. The socio economic condition was also considered a
dominating factor, for the EIA along with the chemical parameters since increased
employment for the project. In general, this study includes comprehensive baseline
data for decision makers to evaluate the feasibility of coal power industry at
Barapukuria and the coalmine itself.
Khamsimak and Coworkers [40]
have studied the dispersion of sulphur
dioxide (SO2) in the vicinity of Mae Moh power plant, the largest fossil fuel power
plant in northern Thailand, was investigated using well known air dispersion
model. The area of 2,500 km2 around the plant was studied, with spatial resolution
of 200 x 200 m2. Publicly available MM5 and CALMET software were used to
provide meteorological conditions within the study domain, while CALPUFF was
used to simulate the patterns of SO2 dispersion, based on actual plant operations in
winter, summer and rainy seasons of the year 2009. Comparison against
measurements from monitoring stations was made. Simulated results were found to
agree qualitatively and quantitatively well with measured data. Root mean squared
errors were found in the range between 2.19 to 8.32µg/m3. The CALPUFF model
can be used for SO2 dispersion prediction with satisfactory accuracy.
The fossil fuel and bio fuel burning in a developing country like India can
have a significant impact on global climate[41]
. In the current work, they have setup
a more realistic, accurate and spatial distributed, all India, NOX emissions from
different fuel combustion and industrial activities at 1°×1° grid resolution by
incorporating the most recently available micro level activity data as well as
country specific emission factors at high resolution. The emission scenarios and
24
their trends are studied in a comprehensive way for approximately 593 districts
(sub-region) in India. Authors have developed three scenarios to construct the
possible range of past and present NOX emissions using Geographical Information
System (GIS) based methodology.
II. Review of impact on Water
Coal based thermal power plant are water intensive industries. In mega power
plant consumption of water is 3.5-8.0 liters/KWh. Which is used in the boiler to
generate steam to run turbine. A portion of the water undergoes loss during the
process and the rest are recycled or discharge to the nearby water bodies. Thermal
power plants are also main culprits to pollute surrounding water sources for human
use. In view of this in the global level a lot of work are being conducted to prevent
this pollution or to minimize the pollution load on water bodies. Hence, we thought
of undertaking a project to study the impact of a large no of thermal power plants
in a cluster area of Sambalpur and Jharsguda district of Odisha. In this section the
earlier work carried out to evaluate the quality of water of the power plants the
region and the findings obtained has been described.
The physico-chemical properties of water Ganga canal, Kasimpur coal fired
thermal power plant (530MW) have been reported by Mahammad and Khan[42]
.
As per the findings of the work it reveals that water and soil of the locality is
affected and due to that the pea and wheat crops have been reduced.
25
A preliminary study on the trace elements such as As, Hg, Pb, Cd, Cu, Cr,
Fe, Mn and Zn and natural radioactivity has been made to determine from drinking
water in eight locations of sampling site in and around Tunçbi lek coal-fired power
plant in Kütahya, Turkey. Attentions have been focused in particular on trace
elements for most of which no data were available in the studied area. The
obtained results showed that, in general, the trace elements concentration in eight
sampling site from drinking water did not exceed WHO limits. It was observed that
global activity for sampling site 2-4 did exceed WHO limits. Only one location for
one sampling site was the global α activity within limits, in others WHO guidelines
were not reached. Was reported by N. Ozturk, and Z.Yilmaz,[43]
.
V. V. Golovina and Coworkers[44]
have studied the distribution of
elements in the Beresh River result indicated the concentrations of elements to
decrease according to the series Ca> Na > Mg > Si > K >Sr> Fe > Al and the river
water self-purification capacity for these elements to be inadequate. Concentrations
of elements dramatically increase in the river reach near the Thermal Power
Station (GRES) and next increase even greater in the lower reach of the river
before its emptying into the Uryup River. It is shown that the seasonal variations,
typical of the river upper and middle reaches and determining the high level of
anthropogenic impact of the fuel and energy complex facilities onto the river, even
out in the lower reaches of the river. Specific features of seasonal and spatial
variations in the element concentrations for the 8 years of observations were
discussed.
26
Yatagan thermal power plant consumes annually 5.4 million tons of coal
and the annual production capacity of the plant is 3.78 billion KWh. The thermal
power plant uses 15,000 tons of coal and discharges 5,000 tons of fly and bottom
ash daily to the disposal site. However, as the waste hills formed, the water level
reached the karstic marbles that over lay schist. Water leaches through dried waste
hills and karstic marbles, ultimately adversely affecting the quality of ground and
surface waters was reported by A. Baba and Coworkers[45]
. The concentrations of
major and minor ions were determined on water samples taken at 2points in the
dam, 5 points in surface water and at 21 points in groundwater located in the
vicinity of the waste disposal site, total of 28 samples, for three years. The
chemical analyses revealed that the concentrations of Ca2+
, Cd2+
, Pb2+
, Sb2+
and
SO2−
in some samples exceed the Turkish Drinking Water, the U.S. EPA and WHO
limits. Isotope analyses were carried out to determine the origins of waters, which
showed that contamination is taking place in the vicinity of the waste disposal site.
Ahmet Demirak and Coworkers[46]
estimated trace metals such as Hg,
Pb, As, Cd, Cu, Fe, Mn and Zn in underground water samples obtained from three
wells, an ash-pond and drinking water located near the Yatagan Thermal Power
Plant were measured. As, Hg, Cu and Zn contents of the underground water were
lower than those reported in the EEC (European Economic Community) and WHO
guidelines. In contrast, the levels of Fe, Pb, Cd and Mn in some groundwater
samples were higher than EEC and WHO guideline values. The trace metal
concentrations in coal ash-pond water were observed to be lower than water
quality standards with the exception of Pb. The obtained results indicated that the
27
trace metal concentration in the sampled drinking water site did not exceed WHO
limits was reported by.
The construction of the Almaraz nuclear power plant in Spain in the 1970s
posed interesting environmental problems concerning the construction of a cooling
reservoir (Arrocampo reservoir) to cool the steam condensers and the consequent
heating of the reservoir‟s water. The socio-political context forced decision makers
to set up a project for the monitoring and management of the environmental,
impacts derived from the construction and operation of the power plant J.F.
Lavado Contador[47]
, have reported the control of the water eutrophy is one of the
most important due to its repercussion on the rest of the ecosystem, especially the
fish fauna. The development of the shore vegetation slowly increased the
patchwork nature of the reservoir, leading to a greater diversity of the avian
species. This paper describes the monitoring and management of the Arrocampo
ecosystem, the condition before and after the construction of the reservoir and the
results obtained concerning some biological communities.
The chemical composition of the rain water in Yatağan, which is a region
surrounding a coal power plant was investigated from February to April 2002.
Rainwater samples were obtained from Yatağan, located northwest of Muğla City
in Turkey. pH values and concentrations of major ions Ca2+
, Na+ , K
+,SO4
2−, NO3
−,
NH4+ in the rainwater samples were analyzed. The pH varied from 5.1 to 7.9 with
an average of 6.7. In the total of 30 rain events, only three events were observed
with water in the acidic range (<5.6), which occurred after continuous rains. The
equivalent concentration of components followed the order: Ca2+
> SO42−
> Na+>
28
NH4+> NO3
−> K
+> H
+. The anion and cation concentrations in the rainwater
samples showed a high sulphate concentration (131μEq/l), as well as high sodium
(40μEq/l) and calcium (298μEq/l) concentrations. These values indicate that one
probable source of the high sodium concentration is fly ash, after the coal burning
process and the power plant can be effective on level SO42−
concentrations in
rainwater. In addition, the dust-rich local and surrounding limestone environment
might have caused the high concentration of Ca2+
in rainwater of the Yatağan
Basin. Due to a large contribution of these cations to the sulphate neutralization
action, the rainwater of this region displays only a moderate acidity, which does
not cause significant environmental impact was reported by A.Demirak[48]
.
The River Adyar is almost stagnant and do not carry enough water except
during rainy season. Rapid industrialization and urbanization along the river course
during 80s and 90s of last century has increased the pollution of the river water
was reported byT. Venugopal[49]
. The main objective of this study was to identify
and assess the nature of pollution. In order to achieve this objective, necessary
geochemical parameters were determined and the quality of water is evaluated
using various tools, such as Wilcox diagram, USIS, Piper, sodium absorption ratio
(SAR), 3D scattered diagrams, and seasonal variation diagrams. The monsoonal
variations in the data matrix of the river water (River Adyar) were monitored at 33
stations for the pre-monsoon and post-monsoon periods during September 2005
and February 2006.
Prabhat Kumar Rai[50]
has studied the concentrations of heavy metals
(Cu, Cr,Fe, Pb, Zn, Hg, Ni, and Cd) and macronutrients (Mn) were measured in
29
industrial effluents, water, bottom sediments, and wetland plants from a reservoir,
Govind Ballabh (G.B.) Pant Sagar, in Singrauli Industrial region, India. The
discharge point of a thermal power plant, a coal mine, and chlor-alkali effluent into
the G.B. Pant Sagar were selected as sampling sites with one reference site in order
to compare the findings. The concentrations of heavy metals in filtered water,
sieved sediment samples (0.4–63μm), and wetland plants were determined with
particle induced X-ray emission. The collected plants were Aponogetonnatans, L.
Engl. & Krause, Cyperusrotundus, L., Hydrillaverticillata, (L.f.) Royle, Ipomoea
aquatica, Forssk., Marsilea quadrifolia, L., Potamogeton pectinatus, L.,
Eichhornia crassipes, (Mart.) Solms Monogr., Lemna minor, L., Spirodela
polyrhiza (L.) Schleid. Linnaea, Azolla pinnata, R.Br. Vallisneria spiralis, L., and
Polygonum amphibium, L. In general, metal concentration showed a significant
positive correlation between industrial effluent, lake water, and lake sediment
(p<0.01). Likewise, significant positive correlation was recorded with metals
concentration in plants and lake ambient, which further indicated the potential of
aforesaid set of wetland macrophytes for pollution monitoring.
Yunfeng Li, and Coworkers[51]
have investigated groundwater
contamination in the Yuxi River Valley in northern Shaanxi Province, one of
largest energy resource centres in China. Groundwater samples collected from 129
locations in the Yuxi River Valley area were analyzed and evaluated to establish
the local groundwater quality zonings. Results indicated that groundwater in the
Yuxi River Valley was contaminated and the dominant contaminants in the
groundwater are ammonium and nitrite (
Maximal concentration of
was detected at 0.019 and3.50 mg/L in the samples collected up-gradient and down
30
gradient, respectively, of the segment of Yuxi River that flows through Yulin City.
Concentration of
was detected at 0.0015 and 1.522 mg/L, respectively from
the same samples. Zones I through IV, from non-polluted to seriously polluted,
were identified for groundwater quality in the Yuxi River Valley. We attribute the
groundwater contamination in the Yuxi River valley to sources in the Yulin
Township, presumably its wastewater discharge.
Heavy metals concentration is increasing in the environment due to
increased anthropogenic activity. The risk of heavy metal contamination is
pronounced in the environment adjacent to large industrial complexes. In a
combined case study, the environmental pollution by heavy metals was related to
children‟s health in the vicinity of an industrial area located 4 km south-east from
Bucharest about 2 km east from the nearest town Pantelimon was done by T.
Veleaand Coworkers[52]
.
Ranipet industrial area is about 120KM from Chennai on Chennai-
Bangalore highway and is a chronic polluted area identified by Central Pollution
Control Board of India has been studied by S. S. Gowd and Coworkers[53]
. It is
one of the biggest exporting centers of tanned leather in India. The total number of
industries located in and around Ranipet town are 240 tanneries along with TPS.
A.G.S.Reddy and Coworkers[54]
have studied the ground water samples
from different hydro geological set-up of north eastern part of Anantapur, Andhra
Pradesh. They have been collected during the pre and post monsoon seasons and
analysed for the major ions such as Ca, Mg, Na, K, CO32-
, HCO3-, Cl
-, SO4
2-, NO3
-
31
and F-. The study revealed that 65% of the samples were found to be unsuitable for
drinking.
R.Reza and G. Singh[55]
have studied the impact of various industrial
developments on water resources may be in the range of minimal to severe. An
intensive investigation was conducted in highly industrialized area of Angul to
determine the water quality status. The industries such as coal mines of MCL,
Aluminium Plant of NALCO and its CPP, Talcher Super Thermal Power Station
and Talcher Thermal Power Station of NTPC etc. are situated along with the river
stretches. The deterioration of river Brahmani water quality may give adverse
effect on human health and aquatic ecosystem directly or indirectly. This work
attempted to be focused on the overall status of river water resources and their
management strategies.
S.Sengupta[56]
have studied the bioaccumulation of some elements in
Mangifera indica tree leaves grown in the region of a coal fired Thermal Power
Plant.. Toxic group of metals (Pb, Cd and Cr) in an average displayed higher levels
(28.0, 2.4 and 3.2 ppm for Pb, Cd and Cr respectively).The plants grown in
affected sites with distinct temporal and spatial variation in comparison to samples
in control areas was 0.09 to 2.2 ppm for Pb, 0.01 to 1.1 ppm for Cd and 0.01 to
0.65 ppm for Cr was reported.
P.M. Nalawade[57]
have studied the underground and surface water
samples were collected from the surrounding areas of fly ash dumping site near
Parli Thermal Power Station (PTPS). The heavy metal pollution index (HPI) of the
underground and surface water samples shows that, concentration of certain heavy
32
metals is above permissible limit. The heavy metals like As, Hg and Zn shows
highest concentration, while metals such as Cu, Cd and Pb shows low
concentration. The HPI of ground water were found comparatively low as compare
with critical pollution limit of 100. The HPI of surface water near fly ash dumping
site were ranged 5.56. The heavy metal pollution index indicates that leaching of
fly ash contaminates the groundwater as well as surface water.
33
SECTION-C
Aim and Objectives of the Present Study
In the forgoing section, the impact on air and water on the environment due
to coal-fired thermal power plants are reviewed. The review reveals that no
researchers have studied the impact of thermal power plants on the environment(air
and water) of an industrial cluster of undivided Sambalpur district of Odisha even
though there are as many as ten coal fired thermal plants producing 4505MW per
day in the area. Besides, there are also many major, middle and small industrial
production industries in the area. Hence, we thought to start some research work on
the cluster area and the impact of thermal power plants and other industrial units of
the area and their impact on the air and water.
In the present study, we will monitor the air samplings of the cluster
area, samples outside the cluster area in a distance range of 05-07 KM from the
boundary and also samples from faraway places from the boundary of the cluster
area which are at a distance range of 10-20KM away from the boundary of cluster
area. Our objective is to evaluate the suspended particulate matters, sulphur
dioxide, nitrogen dioxide and metals like Pb, Hg, Cd of all the samplings. Based on
the data of the above parameters of air the impact on the air of the cluster area will
be evaluated and how the order of impact decreases with respect to distance will be
also known.
34
In case of water samples from different water resources (Ponds, and ground
waters) will be collected from different locations of cluster area and beyond. The
pH, Electrical Conductivity, Total Dissolved solids, Dissolve oxygen(DO),
Biological Oxygen Demand (BOD), Chemical Oxygen Demand (COD), and Total
hardness etc. will be analysed and basing on the analytical data the impact of the
coal fired thermal power plants on the water resources of the area will be
evaluated.
35
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42
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43
SECTION-A
METHODS OF EVALUATIONS OF AIR QUALITY.
The analytical methods adopted for the evaluation of different parameters of
air, of the cluster area has been described in this section. The data of different
parameters of air, of the area are essential to know the quality of air.
Ambient Air Quality (AAQ) monitoring stations were set up at ten location of
each area with due consideration to the topography of the area, meteorological
conditions and representative of likely impact areas. The sampling locations are
selected in all direction of the respective areas.
Frequency and Parameters of Sampling
Ambient air quality monitoring has been carried out with a frequency of two
days per week at all location during the study periods October-2009 to December -
2011. The samples are collected and analysed for three sessions i.e. post monsoon,
winter and summer except rainy season. The base line data of air quality for the
following parameters have been generated.
Suspended Particulate Matters (SPM).
Sulphur Dioxide (SO2).
Oxide of Nitrogen (NOx).
Metals like Pb, Hg, and Cd
44
Duration of Sampling:
The sampling duration for SPM, SO2 and NO2 were done for 24 hours with 4
hourly sampling for SO2 and NO2, 8 hourly sampling for SPM. The metals are
estimated from the collected SPM samples.
The pollution load of a particular area can be known from the air quality of the
area. In our present work, we study the impact of coal fired power plant on its
surrounding air. Hence, it is essential to evaluate a few parameters of air quality of
the surrounding plants area. The procedure of determination of suspended
particulate matters (SPM), SO2,NO2, heavy metals are discussed below.
Suspended Particulate Matter (SPM)[1]
The high volume air sampler is popular and frequently used equipment for the
determination of suspended particulate matter. The principle involved in this
method is that the particles are filtered from known volume of an air sample by
vaccum pump and the particles are made to deposit on a porous filter paper. The
commonly used high volume sampler consists of the following Fig- 2.1. It consists
of a.face plate and gasket b.filter paper(Preferably Whatman) and fitting assembly
c.vaccum pump(air sucker) with air flow measuring device d. casing with roof.
45
Fig-2.1 Assemble of Vacuum Sampler
The filter plate provides the base for sitting the filter paper of size
200mmx250mm, through which air sample is collected by creating suction in the
filter area. This suction is created by a vaccum pump with a flow rate of 25 lpm.
This condition will permit the sampling of ambient air for a period of 8h. The
suspended particle of size less than 10 micron and greater than 3 microns are
retained on the filter. The duration of sampling is measured in an elapsed time
meter, which is placed in series with the blower. A rota-meter or mono meter is
provided to measure the volume of air passed through the filter. Generally
“Whatman make” filter papers are widely used. The concentration of the pollutant
is expressed as micro gram per cubic meter which is calculated from the observed
data as per given below.
46
Where
C= Concentration of suspended particulate matter(SPM) in micro gram per
cubic meter.
W1= Weight of filter paper before sampling or initial mass of filter in gm
W2= Weight of filter paper after sampling, g(Kept the filter paper in a desiccator
for 24-h after sampling).
V= Volume of air sampled in cubic meter at STP=
Here
Q1= air flow rate through clean filter (initial flow rate,) in m3/minute.
Q2 = air flow rate at the end of the sampling in m3/Minute.
t = Sampling period in minute.
High Volume Sampler(Envirotech APM460BL) with size selective inlet of
size20.3 X 25.4 cm and at a flow rate, which is typically 0.9 – 1.4 m3 /minwas
used.
Sulphur dioxideSO2 [2]
The modified West-Gaeke spectro photometric method is a standard method
for SO2 in ambient air. It is suitable for short term sampling like 5minutes to 6
hoursThe principle involved in this method is that when air containing sulphur
dioxide from air is absorbed in a solution of sodium tetra chloromercurate(TCM).
47
It forms a stable dichlorosulphitomercurate. If p-rosaniline hydrochloride is added,
a colour is produced and the intensity of colour is proportional to the sulphur
dioxide present in air. The colour is estimated by using spectrophotometer and the
value are reported to the nearest 0.005 ppm at concentration below 0.15ppm and to
the nearest to 0.01 ppm above 1.5ppm.
If ozone and nitrogen dioxide present in air with concentration more than
sulphur dioxide they interfere in estimation of sulphur dioxide. Hence interference
of nitrogen dioxide is eliminated by using 0.06% sulphamic acid in the absorbing
reagents. Heavy metal especially iron salts also interfere in in oxidising
dichlorosulphitomercurate during sampling collection. So interference is
eliminated by adding ethylene diamine tetra acetic acid in the absorbing reagents
Procedure: First recorded the gas meter reading. Pipette exactly 10 ml of
absorbing reagents in to the absorber. Aspirate the air samples through the absorber
at a rate of 0.2 to 2.5 liter per minute. After sampling period of 8hours, the samples
contain 2 to 4 micro liter of sulphur dioxide in 10 ml of absorbing reagents. Stop
the pump and read the final reading of the gas meter.
If necessary, filter the samples to get clarity and make up with distilled water
up to 10 ml mark. Add 1.0 ml of p-rosaniline solution and 1.0 ml of the
formaldehyde solution and mix well. After 20 minutes, find the absorbance at 560
nm wavelength in a spectrophotometer and read the value from the calibration
chart.
48
Calculation:
The air is sampled at ambient conditions. Hence, the volume of air is corrected
to standard temperature and pressure (STP) conditions, which is as follows:
Based on Boyles‟s and Charles‟s law,
Similarly the volume of air at STP (250C and 760mm Hg) is obtained as
Where Vm= Volume of air in litres at standard conditions
V= Volume of air in litres at ambient conditions
P= biometric pressure in mm Hg.
Pm= Suction at meter in mm Hg
T= Temperature of sampled air in Celsius degrees.
Results:
The concentration of sulphur dioxide in air is calculated as follows:
SO2 ppm by volume =
,
Where Vm=Volume of sampled air at STP.
49
SO2 µg/m3 =
Nitrogen dioxide (NO2) [3]
Principle:
Nitrogen oxides as nitrogen dioxide are collected by bubbling air through a
sodium hydroxide solution to form a stable solution of sodium nitrite. The nitrite
produced during sampling is determined calorimetrically by reacting the exposed
absorbing reagent with phosphoric acid, sulphanilamide, and N (1-napthyl)
ethylenediamine dihydrochloride.
The interference of sulphur dioxide is eliminated by converting it to sulphuric
acid with hydrogen peroxide before analysis.
Procedures:
Sampling: Assemble the sampling train including the absorber, critical flow
control device and pump. Added 50ml absorbing reagents to absorber. Disconnect
funnel, insert calibrated flow meter, and measure flow before sampling. If flow rate
before sampling is less than 85 percent of needle calibration, check the leak or
change filter as necessary. Remove flow meter and replace funnel samples for 24
hours from mind night to mid night and measure flow at the end of sampling
period.
50
Analysis:
Replace any water lost by evaporation during sampling. Pipette 10ml of the
collected sample into a test tube. Add 1.0 ml of hydrogen peroxide solution, 10.0
ml of sulphanilamide solution and 1.4 ml of NEDA solution with through mixing
after the addition of each reagent. Prepared a blank in the same manner using 10ml
of absorbing reagents. After a 10 minutes colour development interval, measured
the absorbance at 540nm against the blank read mg NO2/ ml from the standard
curve.
Calculation:
Volume of air Sampled V=
Where V=Volume of air sample, m3.
F1=Measured flow rate before sampling, ml/min.
F2=Measured flow rate sampling, ml/min and
T= Time of sampling, min.
Mass of nitrogen dioxide in µgper m3=
V=Volume of air sampled, m3 and 0.35 is overall average efficiency.
50=Volume of absorbing reagent used in sampling, ml.
51
Metals (Pb, Hg, and Cd):[4]
Heavy metals ions, in particular e.g. Hg2+
, Pb2+
, Cd2+
ions acts as effective
enzymes inhibitors. In the polluted air the above mentioned metal ions have
affinity for sulphur containing ligands i.e –SCH3 and –SH in methionine and
cysteine ammino acid, which are part of the enzymes structure. In view of this we
have evaluated these metals ions in the air of study area with the help of Atomic
Absorption Spectroscopy (AAS).
Atomic Absorption Spectroscopy
It is a method of chemical analysis where no samples preparation is necessary
for which it is ideal tools for non-chemist like engineers, biologist and clinician. It
is particularly useful for determination of trace metals in liquid and almost
independents of the molecular form of the metal sample. The method is very
sensitive and can detect different metals in lower than 1ppm concentration. It is
high sensitivity and comparatively easy to get result.
Principle:
The absorption of energy by ground state atoms in the gaseous state forms the
atomic absorption spectroscopy (AAS). When a solution containing metallic
species ions introduced into a flame, the vapour metallic species will be
obtained.Some of the metals may be raised to an energy level sufficient high to
emit the characteristics radiation of the metal, which is utilized in the emission
52
flame photometry.A large percentage of metal of the metal atom will remain in the
non-emitting ground state. These ground state atoms of a particular element are
receptive the light radiation of their own specific resonance wave length. When a
light of this wavelength is allowed to pass through a flame, heavy atoms of the
metallic species, part of the light will be absorbed. The absorption will be
proportional to density of the atoms in the flame. Hence in AAS determines the
amount of light absorbed. Knowing the value of absorption this concentration of
the metallic elements is know
Mathematically, the total amount of light absorbed is expressed as;
At v the total amount of light absorbed =
Nf -------------------(i)
Where e is the charge on the electron, m is the mass of the electron, c is the
velocity of light, N is the total number of atoms that can absorb at frequency v in
the light path and f is the ability for each atom to absorb at frequency v. As π, c, m
and e are constants the above equation can be reduced to
Total amount of light absorbed=constant x N x f --------------------(ii)
From the equation(ii) it is clear that there is no term involve like wave length
frequency of absorption other than indication of the actual absorption wave length.
Here is no term of temperature. Thus it is follows that absorption by atoms is
independent of the wave length of absorption and the temperature of the atoms.
The principle is based on the measurement of the decreased in light intensity from
a source (Hollow Cathode Lamp) when it passes through a layer of the atoms of
the analyte element.
53
Instrumentation:
The basic component of an AAS and the key component Hollow Cathode
Lamp are as shown in Fig-2.2 and Fig 2.3 respectively.
Fig-2.2: Arrangement of Atomic Absorption Spectroscopy(AAS).
Fig-2.3: Schematic Diagram of a Hallow Source Lamp for Atomic Absorption of Sodium.
54
Hollow Cathode Lamp:
It consists of a glass tube containing noble gas (Argon) at several mm
presences. The cathode consists of hollow cup, inside of which is coated with the
metal to be determined. The anode is tungsten wire. The two electrodes are housed
in the glass tube. When a high potential is applied between the two electrodes a
current in the milli-ampere range arises. The inert gas is charged (Ar+)at the anode
and the charged gas is attracted at high velocity to the cathode. The impact with the
cathode vaporizes of the metal atoms. These metal atoms produced are highly
energized and emit radiation with a very narrow wave length. The radiation from
the hollow cathode lamp passes through a flame into which the sample in solution
is aspirated. The solution gets dispersed into a mist of very small droplets which
evaporates in the flame to give the dry salt and then the vapour of the salts. A part
of the metallic vapour dissociate into atoms of the metal to be determined. The
flame possesses free unexcited atoms which are capable of absorbing radiation
from the external source (from Hollow Cathode Lamp). Then the unabsorbed
radiation from the flame is allowed to pass through a monochromaton to eliminate
extraneous light from the flame and finally to a detection and recorder. Atomic
Absorption Spectrophotometry (AAS) (Model no ELICO, India No: SL243).
55
SECTION-B
METHODS OF EVALUATIONS OF WATER QUALITY.
Coal fired thermal power plants are water intensive industry. In such industry
huge quantity of water is used for steam generation and there after water is either
recycled or discharged to the nearby water bodies. In view of this the water bodies
of the locality undergo pollution. Hence it is necessary to evaluate a few water
samples collected from the nearby samples stations of the plant the different
analytical methods used have described in this section.
Sampling Procedure:
Sampling of water was done adopting the procedure (APHA, 1989) [5]
. All
the procedure like labeling of samples with respect to sample collection points,
date and time were also followed so as to avoid any error between collection and
analysis. For sampling, 2.5 liter capacity plastic bottles were used. The pH and
dissolved oxygen of the samples were measured on the spot with the help of a
portable water testing kit. All other parameters were determined in the laboratory
after transporting the samples. In some cases, recommended preservatives were
added
56
Analytical Methods
Water samples were analysed as per the procedure specified in „Standard
Methods for Examination of water and waste water published by American public
Health Association (APHA, 1989). The following parameters of water samples
were analysed as per the procedure given;
PH[5]
It is a convenient parameter to express the intensity of acidity or alkalinity; it
has been measured by ORION ion selective meter model no: 720A plus. In water
pH is important parameters which determine the growth of plants, availability of
nutrients, bacteria activity and its physical conditions. If strongly acidic or alkaline,
nothing may grow. For each species of plant, there is a pH range favourable for the
growth [6, 8]
.
Electrical Conductivity (EC)[5]
Salinity is caused of soluble salts, mostly of inorganic in nature. High salinity
retards plant growth. EC is a measured of salinity in micro mhos/cm at 250C and is
used as a standard parameter for evaluating the irrigational water.EC has been
determine by the digital conductivity meter (Hanna Instruments model no
HI98188, USA).
Suspended solid (SS)[5]
In high concentration, it is likely to deposit on the bed of the water resources
leading to silting and blanketing. It can cause destruction of aquatic life. In case of
57
high organic in it, putrefication may occur and stream be denuded of DO. It is the
residual retained over whatman filter paper number 54 on filtration and drying it
over 100-1500C.
Dissolve oxygen(DO)[6]
Dissolve oxygen is generally considered to be one of the most important
parameters of water quality in streams, rivers and lakes. It is usually abbreviated
simply as DO. Just as people need oxygen of the air to breath, fish and other
aquatic organisms need DO in the water to survive. With most other substances,
the less there is in water, the better is the quality. But the situation is reversed for
DO. The higher the concentration of dissolve oxygen, the better is the water
quality.
Oxygen is only slightly soluble in water. For example, the saturation
concentration at 200C is about 9mg/l or 9ppm. Because if this very slightly
solubility, there is usually quite a bit of competition among aquatic organisms
including bacteria, for the available dissolve oxygen. Bacteria will use up the DO
very rapidly if there is much DO level drops. Another factor to remember is that
oxygen solubility is very sensitive on DO concentration. Dissolve oxygen has no
direct effect on public health, but drinking water with very little or no oxygen
tastes flat and may be objectionable to some people. Dissolved oxygen does play a
part in the corrosion or rusting of metal pipes; it is an important factor in the
operation and maintenance of water distribution networks.
58
Dissolve oxygen is used extensively in biological waste water treatment
facilities. Air or sometimes pure oxygen is mixed with sewage to promote the
aerobic decomposition of the organic wastes.
Estimation (Winkler or Iodometric Method)
DO is allowed to react with iodide ions to form iodine which is then titrated
with standard Na2S2O3 solution. By adding Mn(II) salts in strongly alkaline
medium the reaction process is made fast.
4Mn(OH)2+O2+2H2O→Mn(OH)3
Mn(OH)3+I-+3H
+→Mn
2++0.5I2 +3H2O
I2+2S2O32-
→ 2I- +S4O6
2-
5 ml of 0.025M N2S2O3≡1 mg I- DO
Interference due to oxidizing agents such as NO2- and SO3
2- present in water
may be eliminated by addition of N3H to alkaline I- solution . On acidification
NO2- is decomposed.
N3H +HNO3→N2+N2O +H2O
59
Biological Oxygen Demand (BOD)[6]
Bacteria and other microorganism use organic substances as food. As they
metabolize organic material, they consume oxygen. The organics are broken down
into simpler compounds such as CO2 and H2O and the microbes use the energy
released for growth and reproduction. When this process occurs in water, the
oxygen consumed is the DO. If oxygen is not continuously replaced in the water
by artificial or natural means, then the DO level will decrease as the organics are
decomposed by microbes. This need for oxygen is called the biochemical oxygen
demand. In effect, the microbes “demand” the oxygen for use in the biochemical
reaction that sustains them. The abbreviation for biochemical oxygen demand is
BOD; this is one of the most commonly used terms in water quality and pollution
control technology.
Organic waste in sewage is one of the major types of water pollutants. .It is
impractical to isolate and identify each specific organic chemical in these wastes
and to determine its concentration. Instead, the BOD is used as an indirect measure
of the total amount of biodegradable organic in the water. The more organic
material there in in the water, the higher the BOD exerted by the microbes will be.
In addition to being used as a measure of the amount of organic pollution in
stream or lakes, the BOD is used as a measure of the strength of sewage. This is
one of the important parameters for the design and operation of a water pollution
control plants. A strong sewage has a high concentration of organic material and a
corresponding high BOD. A weak sewage, with a low BOD, may not require as
much treatment.
60
The complete decomposition of organic material by microorganisms takes
time, usually 20 days more under ordinarily circumstances. The amount of oxygen
used to completely ordinary or stabilse all the biodegradable organics in a given
volume of water is called the ultimate BOD, or BODL. For example, if one liter
volume of municipal sewage requires 300 mg of oxygen for complete
decomposition of the organics the BODL would be expressed as 300 mg/l. One litre
of waste water from an industrial or food processing plant may require as much as
1500 mg of oxygen for complete stabilization of water. In this case, the BODL
would be 1500mg/L, indicating a much stronger waste than ordinary municipal or
domestic sewage. In general, the BOD is expressed in terms of mg/L of oxygen.
The BOD is a function of time. At the very beginning of a BOD test, or
time=0, no oxygen will have been consumed and the BOD =0. As each day goes
by, oxygen is used by the microbes and the BOD increase. Ultimately the BODL is
reached and the organics are completely decomposed.
Five Day BOD test:
The five day BOD is the total amount of oxygen consumed by
microorganism during the first 5 days of biodegradation. In its simplest form, a
bod test would involve putting a sample of waste into a stoppered bottle,
measuring the concentration of dissolved oxygen in the samples at the beginning of
the test and again 5 days later. The difference in DO would be the 5-days BOD.
Light must be kept out of the bottle to keep algae from adding oxygen by
photosynthesis and the stoppered is used to keep air from replenishing DO that has
been removed by bio-degradation. To standardise the procedure, the test is run at a
61
fixed temperature of 200C. Since the oxygen demand of typical waste is several
hundred mg per liter, and since the saturated value of DO for water at 200C is only
9.1 mg/l. it is usually necessary to dilute the sample to keep final DO above zero. If
during the 5 days the DO drops to zero then the test is valid.
The five day BOD5=
Where DOi=Initial dissolve oxygen of the diluted waste water.
DOf=Final DO of the diluted water
P=Dilution Factor=
Chemical Oxygen Demand(COD)[6]
The BOD test provides a measure of the biodegradable organic material in
water, that is of the substance that microbe can readily use for food. There also
might be non-biodegradable or slowly biodegradable substances that would not be
detected by the conventional BOD test.
The chemical oxygen demand (COD), is another parameters of water quality,
which measure all organics, including the non-biodegradable substances. It is a
chemical test using a strong oxdising agent (Potassium dichromate) sulphuric acid,
and heat. The result of the COD test can be available in just 2hours, a definite
advantage over the 5 days required for the standard BOD test.
62
COD values are always higher than BOD values for the same samples, but
there is generally no consistent correlation between the two tests for different
waste water. In other words, it is not feasible to simplify measure the COD and
then predict the BOD. Because most waste water treatment plants are biological in
their mode of operation, the BOD is more representative of the treatment process
and remains a more commonly used parameters than COD. The method is based
on the chemical oxidation of material in the presence of a catalyst by Cr2O72-
in
50% H2SO4.
3[CH2O] +16H++2Cr2O7
2-→4Cr
3+ +3CO2+11H2O
The amount of unreacted Cr2O72-
is then determined by titration with a
standard Mohr‟s salt solution.
Total Hardness (TH)[5][7]
:
It represent the concentration of Ca2+
and Mg2+
expressed as CaCO3 and has
been determine by the titration in a buffered solution against ethylenediamine
tetraacetate (EDTA) using alcoholic solution of Erichrome Black T indicator.
Metals The metals Pb, Hg and Cd can be analysed by using Atomic Absorption
Spectroscopy(AAS).
63
REFERENCE
1. IS: 5182 Part IV- 1973, code of India Standard measurement of air
pollution.
2. IS: 5182 Part VIII- 1976, code of India Standard measurement of air
pollution.
3. IS: 5182 Part VI- 1975, code of India Standard measurement of air
pollution.
4. A.K. De, Wiley Eastern Limited New Delhi, India(1987)
5. Standard methods for the examination of water and waste water, Ed.18
APHA, AWWA, WPCF, American Public Health Association,
Washington, DC(1989).
6. K.C.Sathpathy and U.Jha, “Environmental Science and Engineering”,
Vrinda Publication, New Delhi, India (2008).
7. Indian Standard, “Methods for sampling and test for industrial effluents;
part V” IS: 2488, ISI, New Delhi (1976).
8. Indian Standard “tolerance limits for inland surface water subjected to
pollution” IS: 2296, ISI, New Delhi (1982).
64
EVALUATION OF IMPACT DUE TO
COAL-FIRED THERMAL POWER PLANTS ON
THE AIR QUALITY OF THE ENVIRONMENT.
Environmental pollution in an industrial cluster is a national issue
particularly in a period which is witnessing a rapid industrial growth. The
environmental pollution in a cluster is a complex multi-dimensional problem
which is often difficult to measure and manage. In order to address such a
complex problem we have made an attempt to study a cluster of Odisha which is
located in the district of Jharsguda and Sambalpur (Fig-3.1) which is considered
as a critically polluted area.
In the study area the locations of ten coal-fired thermal power plants
shown in (Fig-3.1) and their power generation capacity is mentioned in (Table-
3.1). Huge deposits of coal in the IB-Valley are located in the area and a vast
water reservoir (Hirakud) is also in very close proximity to this. The area is a
most ideal site for the production of thermal power. There are many sponge irons,
very big iron and steel plants aluminium and cement industry are also located in
this region. Small scale industries like rice mills, bricks kilns and stone crushers
are also operating in the area.
Hirakud reserviour is the life line of the entire industrialization process in
the region. The major problem in the region is the runoff contamination which is
likely to be fluoride and cyanides since aluminum smelters are in the operation.
65
Besides, runoff from various stock piles like coal, iron etc. also have potential for
water pollution.
At present the solid waste (fly-ash) generation is mostly from power
plants, steel plants and aluminum smelters. Conversion of natural land into dumps
sites would enhance soil erosion and the rate of siltation of the reservoir.
The centering place (Kherual) of the cluster area located near as (840 00’
31’’ E and 210 47’ 00’’N) with location of different station as shown in Fig-3.2.
The meteorological data of the IMD Jharsguda is recorded in table(Table- 3.2 and
3.3) and the figure-3.3 is the wind rose annual pattern of the said place.
In this chapter, the analytical data of the air parameters such as SPM, SO2,
NO2 and three metals (Pb, Hg, Cd) are evaluated of the cluster area, beyond the
boundary of the cluster area(boundary-boundary around 5KM) and faraway from
the boundary of cluster area(around 15 KM). The sampling stations of the areas
are recorded in Table 3.4 a, b, c). The analytical data are recorded in (Table-3.5,
Table-3.6, and Table-3.7) respectively. The graphical representation of the
parameters are shown in (Fig-3.4 to 3.9), (Fig-3.10 to 3.15) and (Fig-3.16 to 3.21)
for the areas.
The symbols S’, S” and S”’ of the analytical tables stands for cluster,
beyond the boundary of cluster and the stations faraway from the boundary of the
cluster respectively. In the graphical representations of the data, the red, green and
yellow denote maximum, minimum and average values respectively. However,
the yellow colour graph in Figs. 3.22 – 3.27, stands for standard “S”value.
66
Fig. 3.1 Location of Power plants in the cluster study area
67
Fig-3.2: Location of sampling stations in the cluster area and outside
68
Fig 3.3: Wind rose diagram of IMD- Jharsguda station (Annual Pattern)
69
Result and Discussion:
The ambient air quality standard are prequisite for effective management
of ambient air quality and to reduce the damaging effect of air pollution. These
standard are prescribed and enforced by CPCB as per the section 16(2) (h) of the
Air (Prevention and Control of Pollution) Act-1981. The standard first adopted by
the CPCB on 11th
November, 1982 and further revised by the CPCB on 11th
April
1994 and finally the recent guideline was issued on 16th
November-2009
regarding the monitoring of ambient air quality Table-3.9. These standards are
based on land use and other factors of the area. The recent revised standard on
18th
November, 2009 is provided in is provided in the Table-3.10 below.
Suspended Particulate Matters(SPM)
The analytical data of the SPM of the ten sampling stations of the cluster
area are recorded in Table-3.5. A survey of the data reveals that the annual
average of the ten sampling stations of the cluster area is 403.5 µg/m3. Out of the
ten stations the maximum SPM is 460µg/m3 at station
and lowest is 340µg/m3
at the station . The Vedant Captive Power Plant where generation capacity is
1215 MW is nearer to . So there is every reason for generation of more SPM
nearer the Station . Stations
inside the Brajarajnagar township, where no
power plant is closer to the town, hence, the SPM percentage is oveiously less
(340µg/m3) in the town area. Among the other stations, the next higher SPM
percentages are 450µg/m3at stations
and 445µg/m3 at stations
(Table-3.5
and Fig-3.4). These stations are closely to Sterllite power plant and Bhusan
70
power plant respectively which are producing 2400MW and 376MW
respectively. Hence, the SPM percentage also higher in those stations.
Annual average of the ten sampling stations located beyond the boundary
of cluster area is 230µg/m3(Fig-3.10, Table-3.6). Out of the ten stations, the
maximum percentage of SPM is 280µg/m3 at station
and lowest 170µg/m3 at
the station . At the station
the percentage is maximum may be due to north
-east(NE) direction of wind which is the predominant direction of wind in the day
time nearer to the sampling stations. Another reason for maximum SPM may
be due to presence cement producing unit(UltraTech, Arda) which is just 03KM
away from the monitoring station. Station which is 20KM away from the
reference point (Kherual) have the minimum SPM (170µg/m3). Among the other
stations the SPM is 276µg/m3
at ( ) and the next one is 260µg/m
3 of
(Table-3.5, Fig-3.10). These stations are also very close to Sterllite, Vedant
and Bhusan Power plants and dominant direction of wind blow. Hence, the SPM
percentage is quit high in these monitoring stations.
SPM value of sampling stations faraway from the boundary of cluster
area shows annual average of 126.5µg/m3 . Among all the sampling stations,
faraway from the boundary cluster area, shows the maximum of 170µg/m
3
(Fig-3.16, Table-3.7). This sampling station is in the predominat direction of
wind blow and the station is nearer to the railway line and market area. The
sampling stations at Raidhi shows minimum of 90.0µg/m
3 at the distance
25KM away from the reference point (Kherual) and also the locality is free from
industries. The other stations ,
SPM concentrations are 150µg/m
3
71
, 125µg/m3 and 120µg/m
3 repectively (Table-3.7 Fig-3.16). These stations are
nearer to highway such as SH10, NH200 and they are also nearer to the market
area.
On analysis of SPM of cluster area, beyond cluster area and far- away
from cluster area the annual maximum value are 403.5µg/m3, 230µg/m
3 and
126.5µg/m3 respectively. The data indicate that the industrial area is highly
polluted as the permissible concentration of SPM in ambient air should be
100µg/m3. As regards the boundary and faraway from the boundary area the
concentration values are still higher than the permissible value of concentration of
SPM in ambient air (100µg/m3). The three values of SPM of cluster area,
beyond cluster and far away from cluster it can be concluded that the SPM
value decreses with respect to distance from the cluster area. The SPM of
cluster area 403.5 µg/m3
> boundary of cluster area 230.5 > faraway from
cluster area 126.5µg/m3.
Sulphur Dioxide(SO2)
The analytical data recorded in the (Table-3.5) reveals that the three years
annual average value of ten monitoring stations for SO2 in the cluster area is
103.81µg/m3. Among all the stations
and have the higher concentration of
120.5µg/m3 and 115.6µg/m
3 respectively. The minimum concentration is
80.2µg/m3 at
(Table-3.5, Fig-3.5). The other monitoring stations such as
, have concentrations 115.4µg/m
3 and 110.6µg/m
3 and 105.6µg/m
3 for
. The
concentration in the stations ,
, and
are all in the range 105.6-120.5µg/m3
72
i.e the density of SO2 is the higher and this is due to all the major coal-fired
power plants (Table-3.1) are located in this region. Besides this, at stations the
concentration of SO2 is 105.6µg/m3, the higher value is due to the power-plant at
OPGC, Banaharpali (420MW).
Beyond the bounary of cluster area, the annual average concentration of
SO2 is 92.63µg/m3. The maximum and minimum concentration in the area are
114.8µg/m3 at
and 68.8µg/m3 at
respectively (Table 3.6 and Fig-3.11). The
average concentration of SO2 of the stations
is 108.5µg/m3
which is less by 3.7µg/m3
from the average value of the same stations of the
cluster regions. The air samples collected from all sampling stations which are at
a distance of 25.7 KM from the boundary of the cluster area. The annual average
of SO2 concentration of those stations is 85.5µg/m3. The concentration is
maximum at (103.1µg/m
3) and minimum at
(66.5µg/m3) (Table-3.7, Fig-
3.17). The stations which show higher concentration are 100.5µg/m3 and 100.0
µg/m3 at
and respectively.
The SO2 concentration is maximum 120.5 µg/m3 at
and and minimum
80.2 at respectively. The annual average value is 103.81µg/m
3 in the cluster
area. In the beyond cluster the maximum value is 114.8µg/m3 and minimum is
68.8µg/m3 and average 92.63µg/m
3. The stations faraway from cluster area the
maximum and minimum values of concentration of SO2 are 103.1µg/m3
66.5µg/m3 respectively. On comparing the SO2 concentration with national
standard the concentration is much higher than the national standard 80µg/m3. So
it is clear, even at distance places from the cluster area the SO2 concentration
73
persists in the ambient air. However, when we compare the concentration of all
the three areas the concentration of SO2 is in the order 103.81µg/m3
> beyond
the boundary 92.63µg/m3
> faraway from the cluster area 85.5µg/m3.
Nitrogen dioxide(NO2):
The analytical data of nitrogen dioxide are recorded in (Table-3.5) and
the graphical representations of the data are shown in the Fig-3.6. It is noticed
from the three years annual average value of all the monitoring stations for NO2
in the cluster area is 105.55µg/m3. Among all the monitoring stations
is
having maximum concentration of NO2 (127.2µg/m3) and next to that
is
120.6µg/m3. The minimum concentration is 86.3µg/m
3 at
. (Table 3.5, Fig 3.6).
The higher concentration of NO2 in the cluster area around the stations ,
,
are most reasonable, since the mega coal-fired power stations are around
those stations i.e more coal burning in the plants. At the NO2 concentration is
also higher. Since there is also a major plants. The minimum concentration is
around the stations as the station is located inside the Brajrajnagar town. Since
no power plant nearer the township, it is natural NO2 concentration is low.
Outside the bounary of cluster area the annual average concentration of
NO2 is 88.75µg/m3
. The maximum concentration is at (112.5µg/m
3) and
minimum is at station (62.6µg/m
3 ) (Table- 3.6 and Fig-3.12). The other
monitoring stations which are having higher concentration of NO2 are
108.5µg/m3 and 104.5µg/m
3 at
and resepectively because they are also
nearer to mega power plants and they are also nearer to highways and rail lines.
74
Air samples were also collected from an average distance of 25.7 KM
from the boundary of the cluster. The analytical data were recorded in (Table-3.7)
and the graphical representation in Fig-3.18. The annual average of all the stations
is 78.6µg/m3. Maximum concentration is shown at
96.8µg/m3)
and
minimum at (53.8µg/m
3)(Table-3.7 Fig 3.18). Other monitoring stations
which show higher concentration are 92.8 µg/m3 and 90.6µg/m
3 at
and
respectively.
The analysis of the NO2 concentrations is given above indicates that in the
cluster area annual average concentration is 105.55µg/m3, beyond cluster is
88.75µg/m3 and that of area faraway from the boundary is 78.6µg/m
3. Based on
the maximum concentration of NO2 it can be said that the values are in the
order cluster area > beyond boundary of cluster area > faraway from the
boundary of cluster area.
Lead(Pb)
The annual average of all samplings of the cluster area is 2.087µg/m3. At
the station the maximum lead is 2.86 µg/m
3and lowest is 1.44µg/m
3 at the
stations . The stations
is nearer to the two mega plants such as Vedant
Captive Power and Sterlite Power plant, so there is every reason for generation of
more lead near the station . Stations
is inside the Brajarajnagar township
where no power stations is closer to the town, so it is natural that the lead
concentration is less (1.44µg/m3) (Table-3.5 Fig-3.7). Among the other stations
the lead concentration is 2.56µg/m3 at
and the next one is 2.16µg/m3 for both
75
the stations at and
. These stations are very close to Sterllite, Bhusan and
Vedant power plant respectively. Hence, the lead concentration is quite high in
these stations.
Annual average of the ten sampling stations out side the boundary of the
cluster area is 1.754µg/m3. Out of all the stations the maximum lead
concentration is 2.24µg/m3 at stations
and lowest to 1.31µg/m3 at the stations
. The stations
in the direction of north-east (NE) from power plants which is
the predominant direction of wind towards the sampling stations. Stations
which is 20KM away from the reference point have the minimum lead
concentration is 1.31 µg/m3. Among the other stations the lead is 1.82µg/m
3
both at and
. Station is close to Vedant, Sterllite and nearer to SH10,
whereas station is nearer to the OPGC plant. The lead concentration of
1.71µg/m3
is at . This station is also very close to mega power plants and
dominant direction of air blow. Hence, the lead percentage is quite high in these
monitoring stations (Table-3.6 and Fig- 3.13).
Lead concentration of sampling stations faraway from the boundary of
cluster area shows annual average of 0.969µg/m3 . Among them
shows the
maximum of 1.33µg/m3
(Table-3.7, Fig-3.19). Again the sampling stationsis in the
predominat direction of air and monitoring stations is near to the railway stations
and market area. The station which show minimum of 0.43µg/m3 is
. which is
far away from the reference point and the locality is free from industies. The other
stations ,
. have concentration of 1.16µg/m
3, 1.01µg/m
3 and
1.03µg/m3 repectively which may be due to the location of the station nearer to
76
SH10, NH200 and also nearer to the market area. The decrese trends of Pb in
three areas are given in Fig-3.25.
Mercury(Hg)
The analytical data are embodied in Table 3.5 and Fig-3.26. It is observed
that the annual average of mercury is 2.712µg/m3. Equal concentration of
3.06µg/m3 are observed at stations
and it may be due to location of three
mega power plants and burning of huge quantites of coal by the respective power
plants. Besides above, the other most polluted locations are at and
which
have pollution load of this metal are 2.88µg/m3 and 2.86µg/m
3 respectively
(Table-3.5, Fig-3.8).
Annual average of the monitoring stations out side the boundary of
industrial cluster area is 1.784µg/m3. The maximum concentration is 2.22µg/m
3 at
the monitoring stations and minimum is 1.1µg/m
3 at
. Other stations, which
have higher values of 2.1µg/m3 at stations
,
(Table-3.6 and Fig-3.14).
Stations faraway from the boundary of cluster area show annual average
value of 0.9µg/m3. Stations
and have maximum and minimum
concentration of 1.16µg/m3 and 0.41µg/m
3 respectively (Table-3.7, Fig-3.20).
Other stations which have higher value of 1.08 µg/m3 and 1.03µg/m
3 at
and
respectively. Comparative study of three areas is in Fig-3.26
Cadmium(Cd)
77
It is observed that the annual average of all the sampling stations of the
cluster area is 2.511 µg/m3. Out of all the stations the maximum cadmium is 2.86
µg/m3 at stations
and lowest is 1.52 µg/m3 at the stations
. S’3 is nearer to the
three mega power plants and in the direction of wind. So there is every reason for
generation of more cadmium nearer the station . Station
is inside
Brajarajnagar township where no power station is closer to the town, so it is
natural that cadmium concentration is less (1.52µg/m3) (Table-3.5 Fig-3.9)
Among the other stations the cadmium concentration is 2.76µg/m3 at
and the
next one is 2.75 µg/m3 and 2.73 for both
and . These stations are very close
to three mega power plants. Hence, the cadmium concentration is quite high
which may be due to burning of coal as fuel.
Annual average of all the stations outside the boundary of the cluster area
is 1.772µg/m3. Out of all the stations, the maximum cadmium concentration is
2.08µg/m3 at stations
and lowest 1.26µg/m3 at station (
). The station is
in the direction of northeast (NE) which is the predominant direction of air in the
day time nearer to the sampling stations. Stations which is faraway from the
reference point is minimum (1.26µg/m3). Among the other stations the metal
value is 2.03µg/m3 at stations
, and
and that of is 2.01µg/m
3. These
stations are very close to mega power plants and also in the dominant direction of
wind. Hence, the metal concentration is quite high in these monitoring stations
(Table-3.6 and Fig- 3.15).
Stations faraway from the boundary of cluster area shows annual average
of 0.847µg/m3. Among the stations,
shows the maximum value of 1.06µg/m3
78
(Table-3.7, Fig-3.21), this may be due to the predominat direction of air and
nearer to the railway station and market area. The station which shows minimum
of 0.43µg/m3 is
which is faraway from the reference point and locality is free
from industies. The other stations ,
have concentration of 1.01µg/m
3 ,
0.93µg/m3 and 0.84µg/m
3 repectively it is due to the position of the station the
near to SH10, NH200 and near to the market respectively.
Basing on the result and discussion on SPM, SO2, NO2 and metals
(Pb,Hg,Cd) it can be concluded that impact of power plants in the cluster
region is much higher than outside the boundary of the cluster area and the
load decreses at a distance of more than 25KM.
79
Table3.1:
Location of Thermal power plants and Power generation capacity in (MW) in Cluster area.
Sl No
Name of Power Plants Power Generation in (MW)
Sl No
Name of Power Plants
Power Generation in (MW)
1 Vedanta Captive Power
Plant.(CPP),
Bhurkhamunda,
Jharsguda
1215 6 Shyam DRI Pvt.
Ltd. Pandloi,
Sambalpur
30
2
Sterllite Energy Ltd.,
Bhurkhamunda,
Jharsguda
2400 7 OPGC,
Banaharpali,
Jharsguda
420
3 Bhusan Steel and
Power Ltd. Thelkoloi,
Jharsuguda
376 8 SMC, Steel and
Power, Hirma,
Jharsguda
20
4 Aryan Ispat and Energy
Ltd.,Bamloi,
Sambalpur
08 9 Action Ispat and
Power,Marakuta
Jharsguda
08
5 Viraj Steel and Energy,
Gurupali, Sambalpur
20 10 Eastern Steel
and Power Ltd.
Lahandabud.
08
Total power generation
(MW)
4019 Total power
generation
(MW)
486
Grand Total of Power generation in the Cluster area(MW) 4505
80
Table-3.2
Weather data of IMD-Jharsguda monitoring Station:
Month Temperature in Degree Celsius
Relative Humidity (%)
Average Wind Speed in Kmph
Total Rainfall
in Millimeter
Avg. Max
Avg. Min
0830hrs 1730hrs 0830hrs 1730hrs
January 29.0 11.9 62.0 40.6 2.6 1.3 1.0
February 32.7 15.2 50.3 29.0 3.3 3.0 3.9
March 35.7 18.4 48.3 27.6 3.6 5.3 0.2
April 40.8 24.4 38.3 2.0 3.6 5.3 14.7
May 41.4 27.3 49.3 30.3 4.3 6.0 48.9
June 38.3 27.4 63.3 46.6 5.0 5.3 140.2
July 31.8 25.2 82.6 76.0 4.0 4.0 421.7
August 31.8 25.2 84.3 79.3 4.0 4.0 254.1
September 32.3 24.7 81.3 77.3 4.0 3.0 287.3
October 32.7 21.1 70.3 63.0 2.3 1.0 60.4
November 31.0 17.4 68.6 55.6 3.0 0.6 4.7
December 28.3 13.7 68.3 51.0 2.3 0.6 11.8
Avg. =Average, Max. =Maximum, Min= Minimum
81
Table-3.3
Wind Direction IMD-Jharsguda monitoring Station:
N=North, S=South, E=east, W=West, C=Calm
Months Time Year
2009 2010 2011
January 0830 NE N NW
1730 SW C SW
February 0830 N N NW
1730 SW NW SW
March 0830 NE N NW
1730 S SW SW
April 0830 NE NE NE
1730 SW SW SW
May 0830 SE SE S
1730 SW SE S
June 0830 SW SW SSW
1730 S SW SW
July 0830 SW SE SW
1730 SW SE SW
August 0830 SW NE SW
1730 SW NE SW
September 0830 SW NE SW
1730 SW NE SW
October 0830 NE NE NE
1730 NE NE C
November 0830 NE NE NW
1730 NE NE C
December 0830 NE NE NE
1730 C NE C
82
Table-3.4(a):
List of Monitoring stations in the cluster area
Station
code Stations Name
Direction from
Reference place
Distance from
Reference
place
S.P. office Building,
Jharsguda N 11 KM
Jharsguda Engineering
School, Badheimunda NE 9 KM
Village School Building,
Banjari NE 6.5 KM
Village School
Building,Katikela SE 8 KM
Police station, Thelkoli S 3 KM
SBI, Building, Lapanga SE 7 KM
Village School Building,
Pandloi SE 12 KM
Village School Building,
Banaharpali SW 12 KM
SBI,office Building,
Brajarajnagar NW 10 KM
Municipality office
Building, Jharsguda N 8 KM
Table-3.4(b)
Monitoring Stations beyond the boundary of Cluster Area
Station
code Stations Name
Direction from
Reference
place
Distance
from
Reference
place
Airport office, Durlaga NE 15 KM
Village School Building , Arda NE 17KM
Village School Building ,
Badimal NE 18KM
Village School Building ,
Raghunathpur NE 15KM
Panchyat Office, Samasingha E 18KM
Police Station,
Katarbaga SE 19KM
Village School Building ,
Remenda SW 24KM
Village School Building ,
Bikramkhol W 20KM
Village School Building ,
Jamkani NW 20KM
Village School Building ,
Chichinda NW 20KM
83
Table 3.4(c)
Monitoring Stations far away from the boundary of ClusterArea
Station
code Stations Name
Direction from
Reference place
Distance from
Reference
place
Sundargarh Engineering
College Building, Kirei N 30KM
Police Station, Dharuadihi NE 30KM
Panchyat Office, Bagdihi NE 28KM
Police stations, Laikera NE 24KM
Panchyat office, Jhirlapali NE 23KM
Panchyat office, Laira SE 23KM
Village School building,
Gumlai SE 24KM
Panchyat office, Sason S 26KM
Block office, Lakhanpur W 25KM
Panchyat office, Raidihi NW 25KM
Table-3.5:
Analytical data of air of samples of the cluster area*
Parameters
Mean
Suspended
Particulate
Matters(SPM)(µg/m3)
392 424 460 445 450 380 376 410 340 358 403.5
Sulphur Dioxide SO2
(µg/m3) 102.2 110.6 120.5 115.4 115.6 96.6 98.6 105.6 80.2 92.8 103.81
Oxide of Nitrogen
NOx(µg/m3) 108.5 104.4 127.2 120.6 116.4 88.6 95.2 112.5 86.3 95.8 105.55
Lead (Pb)( µg/m3) 2.16 2.14 2.86 2.16 2.56 1.78 1.82 2.12 1.44 1.83 2.087
Mercury (Hg)( µg/m3) 2.04 2.86 3.06 2.88 3.06 2.76 2.66 2.97 1.98 2.85 2.712
Cadmium(Cd)( µg/m3) 2.02 2.73 2.86 2.75 2.76 2.68 2.52 2.72 1.52 2.55 2.511
*Average data of three consecutive years S’= Samples of cluster area. Numerical figure indicates
the sampling stations number
84
Table-3.6:
Analytical data of air samples beyond cluster Area
Parameters
Mean
Suspended Particulate Matters(SPM)(µg/m3)
240 276 280 260 224 220 240 200 190 170 230
Sulphur Dioxide SO2
(µg/m3) 100.6 109.8 114.8 108.8 91.5 79.7 99.8 80.7 68.8 71.8 92.63
Oxide of Nitrogen
NOx(µg/m3) 108.5 104.5 112.5 98.8 82.8 75.2 98.6 80.5 63.5 62.6 88.75
Lead (Pb)( µg/m3)
1.82 1.71 2.24 2.02 2.14 1.64 1.82 1.42 1.42 1.31 1.754
Mercury (Hg)(
µg/m3)
1.95 1.91 2.22 2.1 1.62 1.86 2.1 1.5 1.48 1.1 1.784
Cadmium(Cd)
( µg/m3)
2.03 2.01 2.08 2.03 1.72 1.7 2.03 1.56 1.3 1.26 1.772
*Average data of three consecutive years S’’= Location of samples collected beyond cluster area
around (5-7 KM) from the boundary of cluster area). Numerical figure indicates the sampling
stations number
Table-3.7
Analytical data of air of the far away Sampling Stations:
Parameters
Mean
Suspended Particulate Matters(SPM)(µg/m3)
150 120 170 125 120 110 105 130 145 90 126.5
Sulphur Dioxide SO2
(µg/m3) 100 100.5 103.1 90.3 80.4 78.5 66.5 82.6 82.6 70.5 85.5
Oxide of Nitrogen
NOx(µg/m3) 90.6 92.8 96.8 80.7 76.6 58.6 53.8 81.8 83.8 70.5 78.6
Lead (Pb)( µg/m3) 1.16 1.03 1.33 1.01 1.01 0.84 0.84 1.02 1.02 0.43 0.969
Mercury (Hg)
( µg/m3) 1.08 1.03 1.16 0.86 0.86 0.81 0.75 1.02 1.02 0.41 0.9
Cadmium(Cd)
( µg/m3) 1.01 0.93 1.06 0.84 0.78 0.73 0.65 1.01 1.03 0.43 0.847
*Average data of three consecutive years S’’’: Sampling stations far away from Cluster area around (20-
25KM) from the boundary
Numerical figure indicates the sampling stations number
85
Table-3.8:
Comparisons of Analytical data of air pollution from Industrial sampling stations
faraway sampling stations:
Parameters Standard
Mean S' Mean S'' Mean S''' WHO NAAQS EU
Suspended
Particulate
Matters(SPM)(µg/m3)
50 100 40 403.5 230.5 126.5
Sulphur Dioxide SO2
(µg/m3) 20 80 - 103.61 92.63 85.5
Oxide of Nitrogen
NO2(µg/m3) 40 80 40 105.55 88.75 78.6
Lead (Pb)( µg/m3) - 1.0 0.5 2.087 1.754 0.969
Mercury (Hg)( µg/m3) - - - 2.712 1.808 0.9
Cadmium(Cd)(
µg/m3) - - - 2.511 1.772 0.847
Table 3.9
National Ambient Air Quality Standard (1994) Act
Pollutant
Time Weighted
average Concentration in ambient air
Industrial Area Residential. Rural &
other areas,
Sensitive Area
SO2 Annual Average* 80 g/m³ 60g/m ³ 15 µg/m³
24 hours ** 120 µg/m³ 80 µg/m³ 30µg/m³
N02 Annual Average* 80 g/m³ 60g/m ³ 15 µg/m³
24 hours ** 120 µg/m³ 80 µg/m³ 30µg/m³
SPM Annual Average* 360 µg/m³ 140 µg/m³ 70 µg/m³
24 hours ** 500 µg/m³ 200 µg/m³ 100 µg/m³
RPM Annual Average* 120 µg/m³ 60g/m ³ 50g/m ³
24 hours ** 150g/m ³ 100g/m ³ 75g/m ³
Lead (Pb) Annual Average* 1.0g/m ³ 0.75g/m ³ 0.50g/m ³
24 hours ** 1.5g/m ³ 1.00g/m ³ 0.75g/m ³
CO 8 hours 5.0g/m ³ 2.0g/m ³ 1.0g/m ³
I hour 10.0g/m ³ 4.0g/m ³ 2.0g/m ³
* Annual Arithmetic mean of minimum 104 measurements in a year taken twice a week 24 hourly at
uniform interval
** 24 boundary/8 hourly values should be met 98% of the time in a year. However, 2% of the time, it
may exceed but not on two consecutive days.
86
Table 3.10
National Ambient Air Quality Standard (2009) Act Sl. No.
Pollutant Time weighted Average
Concentraion in ambient air
Industrial Residential
Rural & Other Areas
Ecological Sensitive areas
(notified by Central Govt.)
Methods of Measurement
1 SO2 g/m³ Annual* 50 20 -Improved West and
Gaeke
-Ultravilet fluorescence 24 hours** 80 80
2 N02 g/m³ Annual* 40 40 Modified Jacob
&Hochheiser(Na-
Arsenite) 24 hours** 80 80
3 PM10g/m³ Annual* 60 60 -Gravimetric
-TOEM
-Beta attenuation 24 hours** 100 100
4 PM2.5g/m³ Annual* 40 40 -Gravimetric
-TOEM
-Beta attenuation 24 hours** 40 60
5 O3g/m³ 8 hours** 100 100 -UV photometric
-Chemiluminescence
-Chemical method 1 hour* 180 180
6 Lead (Pb) g/m³ Annual* 0.50 0.50 AAS/ICP method after
sampling on EPM 200
or equavalent filter
paper
-ED-XRF using Teflon
filter
24 hours** 1.0 1.0
7 (CO) g/m³ 8 hours** 02 2.0 Non Dispersive
Infrared Spectroscopy 1 hour** 04 4.0
8 NH3 g/m³ Annual* 100 100 Chemiluminescence
-Indophenol blue
method 24 hours** 400 400
9 C6H6g/m³ Annual* 05 05 -Gas Chromatography
based continuous
analyser
-Adsorption and
Desorption followed by
GC analysis
10 BenzoPyreneg/
m³
Annual* 01 01 -Solvent extraction
followed by HPLC/GC
analysis
11 As ng/m3
Annual* 06 06 AAS/ICP method after
sampling on EPM 200
or equavalent filter
paper
12 Ni
ng/m3
Annual* 20 20 AAS/ICP method after
sampling on EPM 200
or equavalent filter
paper
* Annual Arithmetic mean of minimum 104 measurements in a year at a particular site
taken twice a week 24 hourly at uniform interval.
** 24 boundary or0 8 hourly monitored values as applicable should be complied with 98%
of the time in a year. 2% of the time, it may exceed but not on two consecutive days of
monitoring.
87
ANALYSIS OF AIR POLLUTION:
S’1 S
’2 S
’3 S
’4 S
’5 S
’6 S
’7 S
’8 S
’9 S
’10 M
Fig-3.4 : Suspended Particulate Matter(SPM) µg/m3
S
’1 S
’2 S
’3 S
’4 S
’5 S
’6 S
’7 S
’8 S
’9 S
’10 M
Fig-3.5 : Sulphur dioxide(SO2) µg/m3
392 424
460 445 450
380 376
410
340 358
403.5
0
50
100
150
200
250
300
350
400
450
500
(SP
M)(
µg/
m3 )
102.2 110.6
120.5 115.4 115.6
96.6 98.6 105.6
80.2
92.8
103.81
0
20
40
60
80
100
120
140
SO2
(µg/
m3 )
88
S
’1 S
’2 S
’3 S
’4 S
’5 S
’6 S
’7 S
’8 S
’9 S
’10 M
Fig-3.6 : Nitrogen dioxide(NO2) µg/m3
S
’1 S
’2 S
’3 S
’4 S
’5 S
’6 S
’7 S
’8 S
’9 S
’10 M
Fig-3.7: Lead(Pb) µg/m3
108.5 104.4
127.2 120.6
116.4
88.6 95.2
112.5
86.3
95.8
105.55
0
20
40
60
80
100
120
140
NO
2(µ
g/m
3)
2.16 2.14
2.86
2.16
2.56
1.78 1.82
2.12
1.44
1.83
2.087
0
0.5
1
1.5
2
2.5
3
3.5
(Pb
)( µ
g/m
3 )
89
S
’1 S
’2 S
’3 S
’4 S
’5 S
’6 S
’7 S
’8 S
’9 S
’10 M
Fig-3.8 : Mercury(Hg) µg/m3
S
’1 S
’2 S
’3 S
’4 S
’5 S
’6 S
’7 S
’8 S
’9 S
’10 M
Fig-3.9 : Cadmium(Cd)µg/m3
2.04
2.86 3.06
2.88 3.06
2.76 2.66
2.97
1.98
2.85 2.712
0
0.5
1
1.5
2
2.5
3
3.5
(Hg)
( µ
g/m
3 )
2.02
2.73 2.86
2.75 2.76 2.68 2.52
2.72
1.52
2.55 2.511
0
0.5
1
1.5
2
2.5
3
3.5
(Cd
)( µ
g/m
3 )
90
M
Fig-3.10 : Suspended Particulate Matter(SPM) µg/m3
M
Fig-3.11: Sulphur dioxide(SO2) µg/m3
240
276 280
260
224 220
240
200 190
170
230
0
50
100
150
200
250
300
(SP
M)(
µg/
m3 )
100.6
109.8 114.8
108.8
91.5
79.7
99.8
80.7
68.8 71.8
92.63
0
20
40
60
80
100
120
140
SO2
(µg/
m3 )
91
M
Fig-3.12 : Nitrogen dioxide(NO2) µg/m3
M
Fig-3.13 : Lead(Pb) µg/m3
108.5 104.5
112.5
98.8
82.8 75.2
98.6
80.5
63.5 62.6
88.75
0
20
40
60
80
100
120
NO
2(µ
g/m
3)
1.82 1.71
2.24
2.02 2.14
1.64
1.82
1.42 1.42 1.31
1.754
0
0.5
1
1.5
2
2.5
(Pb
)( µ
g/m
3 )
92
M
Fig-3.14 : Mercury(Hg) µg/m3
M
Fig-3.15 : Cadmium(Cd)µg/m3
1.95 1.91
2.22 2.1
1.62
1.86
2.1
1.5 1.48
1.1
1.784
0
0.5
1
1.5
2
2.5
(Hg)
( µ
g/m
3
2.03 2.01 2.08 2.03
1.72 1.7
2.03
1.56
1.3 1.26
1.772
0
0.5
1
1.5
2
2.5
(Cd
)( µ
g/m
3)
93
S
’’’1 S
’’’2 S
’’’3 S
’’’4 S
’’’5 S
’’’6 S
’’’7 S
’’’8 S
’’’9 S
’’’10 M
Fig-3.16 : Suspended Particulate Matter(SPM) µg/m
3
S
’’’1 S
’’’2 S
’’’3 S
’’’4 S
’’’5 S
’’’6 S
’’’7 S
’’’8 S
’’’9 S
’’’10 M
Fig-3.17 : Sulphur dioxide(SO2) µg/m3
150
120
170
125 120
110 105
130
145
90
126.5
0
20
40
60
80
100
120
140
160
180
(SP
M)(
µg/
m3
100 100.5 103.1
90.3
80.4 78.5
66.5
82.6 82.6
70.5
85.5
0
20
40
60
80
100
120
SO2
(µg/
m3 )
94
S
’’’1 S
’’’2 S
’’’3 S
’’’4 S
’’’5 S
’’’6 S
’’’7 S
’’’8 S
’’’9 S
’’’10 M
Fig-3.18 : Nitrogen dioxide(NO2) µg/m3
S
’’’1 S
’’’2 S
’’’3 S
’’’4 S
’’’5 S
’’’6 S
’’’7 S
’’’8 S
’’’9 S
’’’10 M
Fig-3.19 : Lead(Pb) µg/m3
90.6 92.8 96.8
80.7 76.6
58.6 53.8
81.8 83.8
70.5
78.6
0
20
40
60
80
100
120
NO
2(µ
g/m
3 )
1.16
1.03
1.33
1.01 1.01
0.84 0.84
1.02 1.02
0.43
0.969
0
0.2
0.4
0.6
0.8
1
1.2
1.4
(Pb
)( µ
g/m
3)
95
S’’’
1 S’’’
2 S’’’
3 S’’’
4 S’’’
5 S’’’
6 S’’’
7 S’’’
8 S’’’
9 S’’’
10 M
Fig-3.20 : Mercury(Hg) µg/m3
S
’’’1 S
’’’2 S
’’’3 S
’’’4 S
’’’5 S
’’’6 S
’’’7 S
’’’8 S
’’’9 S
’’’10 M
Fig-3.21 : Cadmium(Cd)µg/m3
1.08 1.03
1.16
0.86 0.86 0.81
0.75
1.02 1.02
0.41
0.9
0
0.2
0.4
0.6
0.8
1
1.2
1.4
(Hg)
( µ
g/m
3)
1.01
0.93
1.06
0.84 0.78
0.73
0.65
1.01 1.03
0.43
0.847
0
0.2
0.4
0.6
0.8
1
1.2
(Cd
)( µ
g/m
3 )
96
S
’ S
’’ S
’’’ S
Fig-3.22 : Mean Suspended particulate matter(SPM) µg/m3
S
’ S
’’ S
’’’ S
Fig 3.23 : Mean concentration of Sulphur dioxide(SO2) µg/m3
403.5
230
126.5 100
0
50
100
150
200
250
300
350
400
450
(SP
M)(
µg/
m3 )
103.61
92.63 85.5
80
0
20
40
60
80
100
120
SO2
(µg/
m3 )
97
S
’ S
’’ S
’’’ S
Fig-3.24 : Mean concentration of NOx in µg/m
3
S
’ S
’’ S
’’’ S
Fig-3.25 : Mean concentration of Lead(Pb) in µg/m3
105.55
88.75
78.6 80
0
20
40
60
80
100
120
NO
x(µ
g/m
3 )
2.087
1.754
0.969 1
0
0.5
1
1.5
2
2.5
(Pb
)( µ
g/m
3 )
98
S
’ S
’’ S
’’’ S
Fig-3.26 : Mean concentration of Mercury(Hg) in µg/m3
S
’ S
’’ S
’’’ S
Fig -3.27 : Mean concentration of Cadmium(Cd) in µg/m3
2.712
1.784
0.9 1
0
0.5
1
1.5
2
2.5
3
(Hg)
( µ
g/m
3)
2.511
1.772
0.847 1
0
0.5
1
1.5
2
2.5
3
(Cd
)( µ
g/m
3 )
99
EVALUATION OF IMPACT DUE TO COAL-FIRED
THERMAL POWER PLANTS ON THE WATER QUALITY OF
THE ENVIRONMENT
In the previous chapter, the pollution load on air due to a number of coal-
fired thermal power plants in the cluster area of the undivided portion of
Sambalpur district of Odisha has been discussed. One of our other objectives of the
study is to investigate the impact of those coal-fired thermal power plants of said
cluster on the water quality of the area. The experimental works on water quality
determination, the findings and the discussion on the results will be described in
this chapter.
The Water Resources
United Nation’s Water Conference of March,1997, held in Argentina,
recorded that, “If the world’s water were represented by half-gallon bottle the
quantity of fresh water would be about half a tea spoon and a single droplet would
sufficient to represent the surface–flowing waters(rivers and streams), the rest
being ground water”. Similarly, Rao (1975) in his book entitled, “Water wealth of
India” pointed out that, “of the total available water, approximately 97.3% is
contained in the oceans and the remaining 2.7%is mostly in solid form. The
amount of water actually available over the ground is a very small fraction and is
estimated to be 1x10-5
% of the total water resources of the world.
100
Surface water systems have formed lifeline for the growth of human
civilization. The industrialization, urbanization, a fast-growing population and lack
of comprehensive liquid & solid waste disposal systems and sanitation facilities
have contributed to the pollution of surface water system. The contaminations of
water with hazardous substances create health havoc as surface water forms life
line of civilisation.
Odisha is blessed with abundant resources both surface and ground water
as compared to its size and population at national level. However, the water
resources of Odisha, depends upon the rainfall which is unevenly distributed. A
part of the rainfall is lost by evaporation, transpiration and deep percolation, while
the other part is stored as ground water resources and the balance flow down to sea
as surface runoff. During summer, most of the water resources get dry due to high
temperature. During monsoon there are very wet days as well as long spells.
The study area is dominated by Bheden and IB-river system. There is a
large variation in ground water potential and therefore, water table over the area is
highly variable. It lies below 4-8 meters from ground during pre-monsoon, while
during post-monsoon; it ranges between 1.5-3 meters below ground levels (Central
Ground Water Board, CGWB).
Surface Water Quality
A total of ten coal-fired thermal power plants are located in the cluster area.
Other than these plants there are small, medium and major production industries
101
located in this cluster area. All the industries located in the cluster area are more or
less water intensive industries and they all require water for operation in general
but after the use the unused water may be discharged in to common water bodies
resulting water pollution. So, coal-fired thermal plants are no way less culprit
for water pollution. One can observe in the vicinity of an industrial cluster, large
patches of very dirty and unhealthy swampy areas without any common
boundaries, where the cluster of local industries discharges their effluent.
Whatever pollution load observed in water of the study area is due to flow
of pollutants from the industries to the water bodies of the surface. Therefore, the
surface water quality in the cluster area is conducted to assess the quality of
surface water in the area in accordance with the standard prescribed by Central
Pollution Control Board (CPCB). The different standard for different parameters is
described in Table-4.1. The water can be classified based on use which is
mentioned in Table-4.2. In Table-4.3 the tolerance limit of surface water subject to
pollution
In the present study the surface water and underground water of the cluster
area and beyond cluster area (Fig-3.2) have been examined. The water samples of
the two areas were analysed for a few important water quality parameters. The
sampling stations are recorded in Table-4.4 and Table-4.5
102
Significance of a few Important Physico-Chemical Parameters of
Water:
pH
The pH of natural water is affected by various physical and biological
processes both natural and anthropogenic. Acidic or alkaline water have the ability
to leach many metals and can be detrimental to certain vital biological processes.
pH range for fresh water for aquatic life should be 6.5- 8.5
Conductivity
Conductivity of the ground water samples was measured to have some idea
about dissolve solid present in water sample. A higher value of conductivity
indicates presence of more soluble solids and hence more pollutants.
Biochemical Oxygen Demand(BOD)
Water containing high organic substances encourages the growth of
decomposers which required excess oxygen to decompose the organic material
present in water bodies. The amount of oxygen required for this activity is known
is Biochemical Oxygen Demand (BOD). It is a measure of the contamination of
water.
103
Chemical Oxygen Demand (COD)
The chemical oxygen demand (COD) is another parameters of water
quality, which measure all organics, including the non-biodegradable substances. It
is a chemical test using a strong oxdising agent (Potassium dichromate) sulphuric
acid, and heat. The result of the COD test can be available in just 2hours.
Total Dissolved Solid (TDS)
Total dissolved solids are generally due to the soluble inorganic salts
present in water. Excess TDS are objectionable in drinking water because of
physiological effect and unpalatable taste. Though dissolved solids have negligible
effects on aquatic life, but unsettle able and suspended solids should not reduce the
depth of light penetration by more than 10%.
Hardness
Hardness is caused due to the presence of chloride, sulphates and bicarbonates, etc.
of calcium, magnesium and iron. Generally the total hardness below 75 mg/l is
termed as soft water and above 150 mg/l is termed as hard water. If the value of
hardness is more than 300 mg/l, it is classified as very hard water, which should
not be used for domestic purpose.
104
Total Alkalinity
The alkalinity of water is its capacity to neutralise the acid. Alkalinity itself
is not harmful to human health but the portable water from the pipe line should
have alkalinity below 100 mg/l.
SAMPLING PROCEDURE
Sampling and preservation of water samples were done strictly in
accordance with standard methods adopted by APHA (1989). All the formalities
like labelling of samples with respect to collecting points, date and times were also
followed to overcome possible error between collection and analysis. In specific
terms, the whole of the sampling procedure was as follows:
Sample Containers
The samples were collected from each sampling stations between 7AM to
9AM in the clean, screw-capped plastic bottles (Kudesia, 1985) for physico-
Chemical analysis.
Sample Labelling
As soon as sampling was over, the sample containers were labelled with the
following details:
(a) Sampling stations
105
(b) Sampling date and time
(c) pH, temperature, conductivity and dissolved oxygen of the samples (which
were measured on the spot)
Sample Collection
Water samples were collected from all the stations. For this purpose the
samples were always collected from just below the surface of water. Prior to
sampling, the collection bottles were rinsed well and then filled upto neck and
stoppered immediately to prevent accidental entry or escape as well as contact with
outside atmosphere.
Spot Analysis
In anticipation of possible changes in certain water quality parameters with
respect to time, these were measure immediately after sample collection.
Parameters which are analysed on the spot are pH, temperature, conductivity and
dissolved oxygen. All the others parameters were determine in the laboratory after
transporting the samples there, for a few parameters, the samples were preserved
by adding recommended preservatives as per the standard method, APHA(1989).
106
Sampling Frequency
In order to examine the variation and trends of the different parameters
over time, samples were collected for two years (24 months) on a monthly basis,
starting from January-2011 to December-2012. Three grab samples were collected
from each of the sampling stations every month throughout the year 2011 and
2012. The values listed in Table-4.6, to Table 4.9, various water quality parameters
are the average values obtain from three grab samples collected each month from
each sampling stations. In these Tables, all values are in mg/l except for pH and
conductivity. The procedure adopted is equal to both for the samples of the cluster
area and that of beyond the cluster area.
Analysis of Samples
The samples were analysed for the following water quality parameters:
(i) pH
(ii) Conductivity
(iii) Dissolved Oxygen (DO)
(iv) Biological Oxygen Demand (BOD)
(v) Chemical Oxygen Demand (COD)
(vi) Total Dissolved Solids (TDS)
107
(vii) Total Hardness
(viii) Alkalinity,
(ix). TC (MPN/100ml)
(x) FC (MPN/100ml)
Experimental Methods
The pH was determined with the help of ORION ion selective meter,
Model No.720A PLUS. Conductivity was measured with conductivity meters
(SYSTRONICS, Model No 306). Total dissolved solids were determined by the
gravimetric method. The total alkalinity was obtained by titrating against sulphuric
acid solution using methyl orange as an indicator. Hardness was determined by
using complexometric technique, where known aliquot of water samples were
titrated against EDTA with Erichrome black-T indicators. Hardness of water was
calculated in terms of mg CaCO3 per litre. Dissolve Oxygen was measured by
Wrinkler titrimetric azidemodification(Iodometric) method. Biochemical Oxygen
Demand (BOD) was measured by the method which consists of filing with
samples, to overflowing, in an airtight bottle of the specified size, and incubating it
at 270C for 3 days. Dissolve Oxygen is measured initially and after incubation, and
the BOD is computed from the difference between initial and final DO. Because
the initial DO is determined immediately after the dilution is made, all oxygen
uptake including that occurring during the first 15 minutes is included in the BOD
measurement. Chemical Oxygen Demand (COD) was measured using potassium
108
dichromate as an oxidant in the presence of sulphuric acid. The excess dichromate
remaining after oxidation was titrated against standard ferrous ammonium sulphate
solution using ferroin indicators. COD was measured by closed reflux, titrimetric
method with the help of HACH COD Reactor Model No.45600.
In general, the methods recommended by APHA (1989) were followed for
the analysis of various parameters.
In the graphical representation of the different parameters of surface water
and ground water, the maximum, minimum and average values are denoted in the
colour red, green and yellow respectively (Figs. 4.1 to 4.40)
Results and Discussion
The analytical data of a few important parameters of surface water and
ground water of the cluster area are recorded in the Table-4.6 and Table-4.7
respectively. The minimum, maximum and average analytical data of those
parameters are recorded in Table-4.10. The graphical representations of the
parameters are shown in Fig-4.1 to Fig-4.20and that of average data in Fig-4.41.
The analytical data of different parameters of surface water and ground
water of the area beyond the cluster are recorded in Table-4.8 and Table-4.9
respectively. The minimum, maximum and average data of those parameters are
recorded in Table-4.11. The graphical representations of the above data are shown
in Fig-4.21 to Fig-4.40 and that of the average value is in Fig-4.42. All the values
are in mg/l, except pH, conductivity and total Coliform (TC)
109
The pH values of eight surface water samples of the cluster area is in the
range 6.1-8.8 and the average value is 7.21. On comparing these data with the
standard data (6.5-8.5) for drinking purpose the surface water can be used for
drinking only after disinfection. However, the water is suitable for outdoor use like
bathing, swimming and sports purpose. In case of ground water of cluster area the
pH range of all the eight samples is in the range 6.3- 8.6 and their average value is
7.9. As above the source of ground water of the cluster area need treatment before
using it for drinking. However this water of the cluster area can be used like
surface water for outdoor bathing, swimming etc.
The pH values of both types of water of the area beyond cluster are
recorded in Table-4.8 and Table-4.9. The minimum, maximum and average value
are 7.5, 8.3 7.8 and 7.3, 8.0 7.61 respectively. The water of either of sources cannot
be used for drinking directly from the sources but can be done after conventional
treatment. But the water can be used for outdoor activities.
The analytical data of DO of the sources of water in the cluster area as well
as that of outside the boundary reveals that like pH, the water can be used for
drinking with necessary conventional treatment. But the water can be used without
treatment for outdoor activities.
The BOD values of surface water and that of ground water quality of the
cluster area is in the range 2.7-4.3 with average value 3.6 and 1.8- 3.3 and 2.9
respectively. BOD values of both types of water of the area beyond cluster are
recorded in Table-4.8 and Table-4.9. The minimum, maximum and average values
110
are 1.4, 3.0, 2.2 and 1.3, 2.5, 1.8 respectively. The water either of sources cannot
be used for drinking directly from the sources since the BOD values exceeds in all
the cases. However, the water can be used for drinking after necessary
conventional treatment.
The COD values of surface water quality of the cluster area is in the range
18.5-22.5 with average value 20.04 of all the samples and that of ground water of
cluster area the COD value range is 17.5-20.6 and their average value is 19.9.
COD values of both types of water beyond cluster are recorded in Table-4.8 and
Table-4.9. The minimum, maximum and average values are 14.2, 20.5, 17.8 and
12, 18.5, 14.6 respectively. Since there is no standard data for COD it can be told
about the quality of the water.
The total coliform (TC) organism for drinking water without conventional
treatment should be 50 MPN/100ml but the values of all the samples in the present
study are around 2-3 folds more than the standard values of drinking water. Hence,
no water sample of the cluster area or beyond should be taken for drinking.
However, after conventional treatment the water can be used for drinking.
111
Table 4.1
Drinking water standard (Manual on Water Supply and Treatment)
Sl
No
Characteristics Unit Acceptable Cause for
Rejection
1 Turbidity - 2.5 10
2 Colour hazness 5.0 2.5
3 Taste and odour - unobjectionable unobjectionable
4 pH 7.5-8.5 >8.5
5 Total dissolve Solids mg/l 500 1500
6 Total Hardness mg/l 200 600
7 Chloride as Cl- mg/l 200 1000
8 Sulphate as SO42-
mg/l 200 400
9 Fluoride as F- mg/l 1.0 1.5
10 Nitrate as NO3- mg/l 45 45
11 Calcium as Ca2+
mg/l 75 200
12 Magnesium Mg 2+
mg/l 30 150
13 Iron as Fe3+
mg/l 0.1 1.0
14 Manganese as Mn2+
mg/l 0.05 0.5
15 Copper as Cu2+
mg/l 0.05 1.5
16 Zinc as Zn2+
mg/l 5.0 15
17 Phenolic compounds mg/l 0.001 0.002
18 Anionic detergent mg/l 0.2 1.0
19 Minerals oil mg/l 0.01 0.3
20 Arsenic as As3+
mg/l 0.05 0.05
21 Cadmium as Cd2+
mg/l 0.01 0.01
22 Chromium as Cr3+
mg/l 0.05 0.05
23 Cyanide as CN-
mg/l 0.05 0.05
24 Lead as Pb2+
mg/l 0.1 0.1
25 Selenium as Se2+
mg/l 0.01 0.01
26 Mercury as Hg2+
mg/l 0.001 0.001
112
Table 4.2
Classification of water based on use
Class Mode of Use Required Parameters
A Drinking water source without
conventional treatment but after
disinfection
(i) Total coliform organism MPN/100ml
shall be 50 or less
(ii) pH between 6.5-8.5
(iii) DO= 6 mg/L or more
(iv) BOD= 2mg/L or less
(v) There shall be no visible discharge
of domestic or industrial waste
B Outdoor bathing, swimming
and water contact sports.
(i) Total coliform organism MPN/100 ml
shall be 50 less.
(ii) pH between 6.5-8.5.
(iii) DO= 5 mL or more
(iv) BOD= 3 mg/L or less
(v) All domestic and industrial waste water
discharged upstream of bathing place
shall be so regulated that the standard are
maintained and there is no visible floating
matter including oil in
bathing places.
C Drinking water sources with
conventional treatment
followed by disinfection
(i) Total coliform organism MPN/100 ml shall
be 5000 or less.
(ii) pH between 6.0-9.0.
(iii) DO= 4 mg/L or more
(iv) BOD= 2 mg/L or less
D Propagation of wind life and
Fisher
(i) pH between 6.5-8.5.
(ii) DO= 4 mg/L or more
(iii) Free ammonia (as N) i= 1.2 mg/L or less
E Irrigation, industrial cooling
and controlled
(i) pH between 6.0-8.5.
(ii) Electrical connectivity at 250 C, Max
2250 mho/cm
(ii) Sodium absorption ratio max;36
(iii) Sodium absorption ratio: max 26.
(iv) Boron max=2 mg/l
113
Table 4.3
Tolerance limits for Land surface Water subjected to pollution:
Sl
No
Characterstics Unit Tolerence of different classes
A B C D E
1 pH value 6.5-8.5 6.5-8.5 6.5-8.5 6.5-8.5 6.0-8.5
2 Colour hazens 10 300 300 - -
3 Odour - Unobject
-ionable
unobject-
ionable
Unobject
-ionable
Unobject
-ionable
Unobject
-ionable
4 Taste - Tasteless Tastele
ss
Tastele
ss
Tasteless Tasteless
5 DO mg/l 6 5 4 -
6 BOD mg/l 2 3 3 -
7 TDS mg/l 500 - 1500 - 2100
8 Chloride mg/l 250 - 600 - 600
9 Total Hardness mg/l 300 - - -
10 Ca-Hardness mg/l 200 - - -
11 Mg Hardness mg/l 100 - - -
12 Iron mg/l 3.3 - 5.0 - -
13 Manganese mg/l 0.5 - - - -
14 Copper mg/l 1.5 - 1.5 - -
15 Sulphate mg/l 400 - 400 - 1000
16 Nitrate mg/l 20 - 50 - -
17 Chloride mg/l 1.5 1.5 1.5 - -
18 Phenolic Comp. mg/l 0.002 0.005 0.005 - -
19 Mercury mg/l 0.001 - 0.001 - -
20 Cadmium mg/l 0.01 - 0.01 - -
21 Selenium mg/l 0.01 - 0.05 - -
22 Arsenic mg/l 0.05 0.2 0.2 - -
23 Cyanides mg/l 0.05 0.5 0.05 - -
24 Lead mg/l 0.05 - 0.1 - -
25 Zinc mg/l 15.0.05 - 15 - -
26 Chromium mg/l 0.05 0.05 0.05 - -
27 Anionic
detergent
mg/l 0.2 1 1 - -
28 PAH µg/l 0.2 - - - -
29 Minerals oil mg/l 0.01 - - - -
30 Barium mg/l 1 - - - -
31 Silver mg/l 0.05 - - - -
32 Pesticides&
insect.
mg/l Absent - - - -
33 Alpha centre uc/ml 10 10 10 10 -
34 Beta emitter uc/ml 10 10 10 10 10
35 Total coliform MPN/l 50 500 - - -
114
Table-4.4(a)
List of Monitoring stations of surface water in the cluster area
Station code Stations Name
Direction and
Distance from Kherual
Direction Distance
Pond, behind Collecteriate, Jharsguda N 11 KM
Pond,Debadihi, village NE 9 KM
Pond, Banjari, village NE 6.5 KM
Pond,Katikela, village SE 8 KM
Pond, Thelkoli, village S 3 KM
Pond, Pandloi, village SE 12 KM
Banaharpali, Village School Building, NE 12 KM
Pond near Municipality office,
Jharsguda N 8 KM
Table-4.4(b):
List of Monitoring stations of Ground water in the cluster area
Station
code Stations Name
Direction and
Distance Kherual
Direction Distance
Tube well near Collecteriate,
Jharsguda N 11 KM
Well(6.5mts), Debadihi, village NE 9 KM
Well (6.3mts), Banjari village NE 6.5 KM
Well (6.0mts),in Katikela in the
village SE 8 KM
Well(6.5mts), Thelkoli village S 3 KM
Well(5.5mts), Pandloi village S 12 KM
Well(5.0mts), Banaharpali in the
village SW 12 KM
Tube well Muncipality office,
Jharsguda N 8 KM
115
Table-4.5(a)
Monitoring Stations of Surface water beyond the boundary of Cluster Area
Station
code Stations Name
Distance and Direction
from Kherual
Direction Distance
Pond, Durlaga, Village NE 15 KM
Pond, Arda, Village NE 17KM
Pond , Badimal, Village NE 18KM
Pond, Raghunathpurvilage NE 15KM
Pond, Samasingha Village E 18KM
Pond,Katarbaga, Village SE 19KM
Pond, Remenda, Village SW 24KM
Pond, Chichinda, Village NW 20KM
Table-4.5(b)
Monitoring Stations of Ground water beyond the boundary of Cluster Area
Station
code Stations Name
Direction and
Distance from Kherual
Direction Distance
Well (5.5mts), Durlaga of Village NE 15 KM
Well(4.6mts), Arda, Village NE 17KM
Well (4.3mts), Badimal Village NE 18KM
Well(4.0mts), Raghunathpur Village NE 15KM
Well (3.6mts), Samasingha Village E 18KM
Tube wellof Katarbaga Village SE 19KM
Well(3.0mts), Remenda Village SW 24KM
Well(3.6mts), Chichinda, Village NW 20KM
116
Table-4.6
Surface water quality of the Cluster area*
Sl No.
Parameters Concentration of pollutant
Mean
1 pH 6.1 6.9 8.8 7.8 7.6 6.8 6.2 7.5 7.21
2 Conductivity
(µScm-1) 200.8 280.2 320.8 315.6 310.5 300.4 280.2 270.6 284.9
3 DO 9.2 7.2 6.1 6.3 6.5 7.1 7.2 7.4 7.13
4 BOD 2.7 3 4.3 4.2 4 3.5 3.1 4.0 3.6
5 COD 18.5 18.8 22.5 20.2 20.6 20.1 19.4 20.2 20.04
6 TDS 180 186 220 200 190 180 170 175 187.6
7 Total Hardness 72.4 75.2 94.6 88.4 87.2 84.2 72.6 83.2 82.2
8 Alkalinity 58.8 61 77.8 65.4 68.2 66.4 68 65.2 66.4
9 TC(MPN/100ml) 120 115 150 130 135 124 110 125 126.1
10 FC(MPN/100ml) 75 80 100 90 105 80 70 85 85.6
All values are in mg/l except pH, conductivity, TC and FC.
*Average data of two consecutive years
= Samples of cluster area. Numerical figure indicates the sampling stations number
Table-4.7
Ground water quality of the Cluster area* Sl No.
Parameters Concentration of pollutant
Mean
1 pH 6.3 7.4 8.6 8.3 7.9 8.1 8.3 8.2 7.9
2 Conductivity(µScm-1) 120.8 160.2 208.8 202.6 200.5 155.4 130.2 140.6 164.9
3 DO 8.2 8 6.5 6.6 6.7 7.2 7.1 7.9 7.3
4 BOD 2.7 2.8 3.3 3.2 3.1 3 2.9 1.8 2.9
5 COD 17.5 19.8 21.5 20.2 20.6 20.1 20.4 19.2 19.9
6 TDS 160 175 223 215 185 170 162 160 181.3
7 Total Hardness 52.4 55.2 70.6 68.4 56.2 64.2 62.4 61.2 61.3
8 Alkalinity 50.8 51 70.8 68 60.2 66.4 66 68.2 62.7
9 TC(MPN/100ml) 120 110 140 130 125 115 120 130 123.8
10 FC(MPN/100ml) 75 60 75 71 81 65 70 57 69.3
All values are in mg/l except pH, conductivity, TC and FC.
*Average data of two consecutive years = Samples of cluster area.
Numerical figure indicates the sampling stations number
117
Table-4.8
Surface water quality of beyond the Cluster area* Sl
No. Parameters Concentration of pollutant
Mean
1 pH 7.6 8.1 8.3 8 7.8 7.8 7.6 7.5 7.8
2 Conductivity(µScm-1) 110.8 120.2 140.8 135.6 120.5 105.4 100.2 100.6 116.8
3 DO 10.2 9.3 8.1 8.2 8.8 9.8 9.2 9.8 9.2
4 BOD 2.2 2.8 3 2.4 1.8 1.6 2 1.4 2.2
5 COD 18.5 19.8 20.5 19.2 16.6 15.1 18.4 14.2 17.8
6 TDS 162 180 200 175 125 115 150 110 152.1
7 Total Hardness 50.4 58.2 60.6 48.4 46.2 44.2 52.2 53.2 51.7
8 Alkalinity 66.8 60.8 62.8 71 52.2 51.4 55 55.2 59.4
9 TC(MPN/100ml) 100 115 120 115 110 95 90 104 106.1
10 FC(MPN/100ml) 50 55 65 50 48 50 40 50 51
All values are in mg/l except pH, conductivity, TC and FC. *Average data of two consecutive years
= Samples of cluster area. Numerical figure indicates the sampling stations number
Table-4.9
Ground water quality of beyond the Cluster area* Sl
No. Parameters Concentration of pollutant
Mean
1 pH 7.5 7.7 8.0 7.9 7.6 7.4 7.5 7.3 7.61
2 Conductivity(µScm-1) 100.8 105.2 106.8 118.2 100.5 96.4 98.2 95.6 102.7
3 DO 11.9 10.5 9.1 8.9 9.8 10.8 10.4 10.9 10.3
4 BOD 1.8 2.2 2.5 2.1 1.5 1.4 1.7 1.3 1.8
5 COD 15.5 16.8 18.5 16.2 12.6 12.1 13.4 12 14.6
6 TDS 145 160 180 150 125 120 136 112 141
7 Total Hardness 50.2 55.2 60.6 50.4 47.2 44.2 48.4 43.2 49.9
8 Alkalinity 52.8 61 65.8 60 55.2 60.4 60 55.2 58.8
9 TC(MPN/100ml) 85 90 122 110 105 100 101 90 100.4
10 FC(MPN/100ml) 60 70 75 70 60 72 73 58 67.3
All values are in mg/l except pH, conductivity, TC and FC. *Average data of two consecutive years
= Samples of cluster area. Numerical figure indicates the sampling stations number
118
Table-4.10
Minimum, maximum and average value of Surface and
Ground water of cluster area.
Parameters SW' GW'
Minimum Maximum Average Minimum Maximum Average
pH 6.1 8.8 7.2 6.3 8.6 7.9
Conductivity(µScm-1) 200.8 320.8 284.9 120.8 208.8 164.9
DO 6.1 9.2 7.1 6.5 8.2 7.3
BOD 2.7 4.2 3.6 1.8 3.3 2.9
COD 18.5 22.5 20.0 17.5 21.5 19.9
TDS 170 220 187.6 160 223 181.3
Total Hardness 72.4 94.6 82.2 52.4 70.6 61.3
Alkalinity 58.8 77.8 66.4 50.8 70.8 62.7
TC(MPN/100ml) 100 150 126.1 110 140 123.8
FC(MPN/100ml) 70 105 85.6 57 81 69.3
Table-4.11
Minimum, maximum and average value of Surface and
Ground beyond the cluster area
Parameters SW'' GW''
Minimum Maximum Average Minimum Maximum Average
pH 7.5 8.3 7.8 7.3 8.0 7.6
Conductivity(µScm-1) 100.6 140.8 116.8 95.6 118.2 102.7
DO 8.1 10.2 9.2 8.9 11.9 10.3
BOD 1.4 3.0 2.2 1.3 2.5 1.8
COD 14.2 20.5 17.8 12.0 18.8 14.6
TDS 110.0 200.0 152.1 112.0 180.0 141.0
Total Hardness 44.2 60.6 51.7 43.2 60.6 49.9
Alkalinity 51.4 71.0 59.4 52.8 65.8 58.8
TC(MPN/100ml) 90.0 120.0 101.6 85.0 122 100.4
FC(MPN/100ml) 40.0 65.0 51.0 58.0 75.0 67.3
119
Table-4.12
Minimum, maximum and average value of Surface water of cluster and
beyond cluster area.
Parameters SW' SW''
Minimum Maximum Average Minimum Maximum Average
pH 6.1 8.8 7.21 7.5 8.3 7.8
Conductivity(µScm-1) 200.8 320.8 284.9 100.6 140.8 116.8
DO 6.1 9.2 7.13 8.1 10.2 9.2
BOD 2.7 4.2 3.6 1.4 3.0 2.15
COD 18.5 22.5 20.0 14.2 20.5 17.8
TDS 170 220 187.6 110.0 200.0 152.1
Total Hardness 72.4 94.6 82.2 44.2 60.6 51.7
Alkalinity 58.8 77.8 66.4 51.4 71.0 59.4
TC(MPN/100ml) 100 150 126.1 90.0 120.0 106.1
FC(MPN/100ml) 70 105 85.6 40.0 65.0 51.0
Table-4.13
Average values of Ground water of cluster and beyond the cluster area.
Parameters GW' GW''
Minimum Maximum Average Minimum Maximum Average
pH 6.3 8.6 7.9 7.3 8.0 7.6
Conductivity(µScm-1) 120.8 208.8 164.9 95.6 118.2 102.7
DO 6.5 8.2 7.3 8.9 11.9 10.3
BOD 1.8 3.3 2.9 1.3 2.5 1.8
COD 17.5 21.5 19.9 12.0 18.8 14.6
TDS 160 223 181.25 112.0 180.0 141
Total Hardness 52.4 70.6 61.3 43.2 60.6 49.9
Alkalinity 50.8 70.8 62.675 52.8 65.8 58.8
TC(MPN/100ml) 110 140 123.8 85.0 122 100.4
FC(MPN/100ml) 57 81 69.3 58.0 75.0 67.3
120
SW’1 SW’2 SW
’3 SW
’4 SW
’5 SW
’6 SW
’7 SW
’8 M
Fig-4.1 : pH
SW’1 SW
’2 SW
’3 SW
’4 SW
’5 SW
’6 SW
’7 SW
’8 M
Fig-4.2 : Conductivity in (µScm-1
)
6.1 6.9
8.8
7.8 7.6 6.8
6.2
7.5 7.21
0
1
2
3
4
5
6
7
8
9
10
200.8
280.2
320.8 315.6 310.5 300.4 280.2 270.6
284.9
0
50
100
150
200
250
300
350
121
SW’1 SW
’2 SW
’3 SW
’4 SW
’5 SW
’6 SW
’7 SW
’8 M
Fig-4.3 : Dissolve oxygen in mg/l.
SW’1 SW
’2 SW
’3 SW
’4 SW
’5 SW
’6 SW
’7 SW
’8 M
Fig-4.4 : Biological oxygen Demand mg/l
9.2
7.2
6.1 6.3 6.5 7.1 7.2 7.4
7.12
0
1
2
3
4
5
6
7
8
9
10
2.7 3
4.3 4.2 4
3.5
3.1
4
3.6
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
122
SW’1 SW
’2 SW
’3 SW
’4 SW
’5 SW
’6 SW
’7 SW
’8 M
Fig-4.5 : Chemical Oxygen Demand in mg/l
SW’1 SW
’2 SW
’3 SW
’4 SW
’5 SW
’6 SW
’7 SW
’8 M
Fig-4.6 : Total Dissolve Solid in mg/l
18.5 18.8
22.5
20.2 20.6 20.1 19.4
20.2 20.04
0
5
10
15
20
25
180 186
220
200 190
180 170 175
187.625
0
50
100
150
200
250
123
SW’1 SW
’2 SW
’3 SW
’4 SW
’5 SW
’6 SW
’7 SW
’8 M
Fig-4.7 : Total Hardness in mg/l
SW’1 SW
’2 SW
’3 SW
’4 SW
’5 SW
’6 SW
’7 SW
’8 M
Fig-4.8 : Alkalinity in mg/l
72.4 75.2
94.6 88.4 87.2
84.2
72.6
83.2 82.2
0
10
20
30
40
50
60
70
80
90
100
58.8 61
77.8
65.4 68.2 66.4 68
65.2 66.4
0
10
20
30
40
50
60
70
80
90
124
SW’1 SW
’2 SW
’3 SW
’4 SW
’5 SW
’6 SW
’7 SW
’8 M
Fig-4.9 : Total coliforms in MPN/100ml
SW’1 SW
’2 SW
’3 SW
’4 SW
’5 SW
’6 SW
’7 SW
’8 M
Fig-4.10 : Fecal Coliforms in MPN/100ml
120
100
150
130 135
124
110
125 124.3
0
20
40
60
80
100
120
140
160
75 80
100
90
105
80
70
85 85.6
0
20
40
60
80
100
120
125
GW’1 GW
’2 GW
’3 GW
’4 GW
’5 GW
’6 GW
’7 GW
’8 M
Fig-4.11 : pH
GW’1 GW
’2 GW
’3 GW
’4 GW
’5 GW
’6 GW
’7 GW
’8 M
Fig-4.12 : Conductivity in (µScm-1)
6.3
7.4
8.6 8.3
7.9 8.1 8.3 8.2 7.9
0
1
2
3
4
5
6
7
8
9
10
120.8
160.2
208.8 202.6 200.5
155.4
130.2 140.6
164.9
0
50
100
150
200
250
126
GW’1 GW
’2 GW
’3 GW
’4 GW
’5 GW
’6 GW
’7 GW
’8 M
Fig-4.13 : Dissolve oxygen in mg/l.
GW’1 GW
’2 GW
’3 GW
’4 GW
’5 GW
’6 GW
’7 GW
’8 M
Fig-4.14 : Biological oxygen Demand mg/l
8.2 8
6.5 6.6 6.7 7.2 7.1
7.9
7.28
0
1
2
3
4
5
6
7
8
9
2.7 2.8
3.3 3.2
3.1 3
2.9
1.8
2.85
0
0.5
1
1.5
2
2.5
3
3.5
127
GW’1 GW
’2 GW
’3 GW
’4 GW
’5 GW
’6 GW
’7 GW
’8 M
Fig-4.15 : Chemical Oxygen Demand in mg/l
GW’1 GW
’2 GW
’3 GW
’4 GW
’5 GW
’6 GW
’7 GW
’8 M
Fig-4.16 : Total Dissolve Solid in mg/l.
17.5
19.8
21.5 20.2 20.6 20.1 20.4
19.2 19.91
0
5
10
15
20
25
160 175
223 215
185 170
162 160
181.25
0
50
100
150
200
250
128
GW’1 GW
’2 GW
’3 GW
’4 GW
’5 GW
’6 GW
’7 GW
’8 M
Fig-4.17 : Total Hardness in mg/l.
GW’1 GW
’2 GW
’3 GW’4 GW’5 GW
’6 GW
’7 GW
’8 M
Fig-4.18 : Alkalinity in mg/l
2.7 3
4.3 4.2 4
3.5
3.1
4
3.6
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
50.8 51
70.8 68
60.2
66.4 66 68.2 62.675
0
10
20
30
40
50
60
70
80
129
GW’1 GW
’2 GW
’3 GW’4 GW’5 GW
’6 GW
’7 GW
’8 M
Fig-4.19 : Total coliform in MPN/100ml
GW’1 GW
’2 GW
’3 GW’4 GW’5 GW
’6 GW
’7 GW
’8 M
Fig-4.20 : Fecal Coliform in MPN/100ml
120 110
140 130
125 115
120 130
123.75
0
20
40
60
80
100
120
140
160
75
60
75 71
81
65 70
57
69.25
0
10
20
30
40
50
60
70
80
90
130
SW’’1 SW’’
2 SW’’
3 SW’’
4 SW’’
5 SW’’
6 SW’’
7 SW’’
8 M
Fig-4.21 : pH
SW’’1 SW’’
2 SW’’
3 SW’’
4 SW’’
5 SW’’
6 SW’’
7 SW’’
8 M
Fig-4.22 : Conductivity in (µScm-1)
7.6
8.1
8.3
8
7.8 7.8
7.6 7.5
7.8
7
7.2
7.4
7.6
7.8
8
8.2
8.4
110.8 120.2
140.8 135.6
120.5
105.4 100.2 100.6
116.8
0
20
40
60
80
100
120
140
160
131
SW’’1 SW’’
2 SW’’
3 SW’’
4 SW’’
5 SW’’
6 SW’’
7 SW’’
8 M
Fig-4.23 : Dissolve oxygen in mg/l.
SW’’1 SW’’
2 SW’’
3 SW’’
4 SW’’
5 SW’’
6 SW’’
7 SW’’
8 M
Fig-4.24 : Biological oxygen Demand mg/l
10.2
9.3
8.1 8.2 8.8
9.8 9.2
9.8 9.18
0
2
4
6
8
10
12
2.2
2.8 3
2.4
1.8 1.6
2
1.4
2.15
0
0.5
1
1.5
2
2.5
3
3.5
132
SW’’1 SW’’
2 SW’’
3 SW’’
4 SW’’
5 SW’’
6 SW’’
7 SW’’
8 M
Fig-4.25 : Chemical Oxygen Demand in mg/l
SW’’1 SW’’
2 SW’’
3 SW’’
4 SW’’
5 SW’’
6 SW’’
7 SW’’
8 M
Fig-4.26 : Total dissolves Solid in mg/l.
18.5 19.8
20.5 19.2
16.6 15.1
18.4
14.2
17.79
0
5
10
15
20
25
162
180
200
175
125 115
150
110
152.13
0
50
100
150
200
250
133
SW’’1 SW’’
2 SW’’
3 SW’’
4 SW’’
5 SW’’
6 SW’’
7 SW’’
8 M
Fig-4.27 : Total Hardness in mg/l
SW’’1 SW’’
2 SW’’
3 SW’’
4 SW’’
5 SW’’
6 SW’’
7 SW’’
8 M
Fig-4.28 : Alkalinity in mg/l
50.4
58.2 60.6
48.4 46.2
44.2
52.2 53.2 51.675
0
10
20
30
40
50
60
70
56.8
65.8 67.8 71
52.2 51.4 55 55.2
59.4
0
10
20
30
40
50
60
70
80
134
SW’’1 SW’’
2 SW’’
3 SW’’
4 SW’’
5 SW’’
6 SW’’
7 SW’’
8 M
Fig-4.29 : Total coliform in MPN/100ml
SW’’1 SW’’
2 SW’’
3 SW’’
4 SW’’
5 SW’’
6 SW’’
7 SW’’
8 M
Fig-4.30 : Fecal Coliform in MPN/100ml
100
115 120
115 110
95 90
104 106.13
0
20
40
60
80
100
120
140
50
55
65
50 48
50
40
50 51
0
10
20
30
40
50
60
70
135
GW’’
1 GW’’
2 GW’’
3 GW’’
4 GW’’
5 GW’’
6 GW’’
7 GW’’
8 M
Fig-4.31 : pH
GW’’
1 GW’’
2 GW’’
3 GW’’
4 GW’’
5 GW’’
6 GW’’
7 GW’’
8 M
Fig-4.32 : Conductivity in (µScm-1)
7.5
7.7
8
7.9
7.6
7.4
7.5
7.3
7.61
6.8
7
7.2
7.4
7.6
7.8
8
8.2
100.8 105.2 106.8
118.2
100.5 96.4 98.2 95.6
102.7
0
20
40
60
80
100
120
140
136
GW’’
1 GW’’
2 GW’’
3 GW’’
4 GW’’
5 GW’’
6 GW’’
7 GW’’
8 M
Fig-4.33 : Dissolve oxygen in mg/l.
GW’’
1 GW’’
2 GW’’
3 GW’’
4 GW’’
5 GW’’
6 GW’’
7 GW’’
8 M
Fig-4.34 : Biological oxygen Demand mg/l
11.9
10.5
9.1 8.9
9.8
10.8 10.4
10.9 10.3
0
2
4
6
8
10
12
14
1.8
2.2
2.5
2.1
1.5 1.4
1.7
1.3
1.8
0
0.5
1
1.5
2
2.5
3
137
GW’’
1 GW’’
2 GW’’
3 GW’’
4 GW’’
5 GW’’
6 GW’’
7 GW’’
8 M
Fig-4.35 : Chemical Oxygen Demand in mg/l
GW’’
1 GW’’
2 GW’’
3 GW’’
4 GW’’
5 GW’’
6 GW’’
7 GW’’
8 M
Fig-4.36 : Total dissolves Solid in mg/l.
15.5
16.8
18.5
16.2
12.6 12.1
13.4
12
14.6
0
2
4
6
8
10
12
14
16
18
20
145
160
180
150
125 120
136
112
141
0
20
40
60
80
100
120
140
160
180
200
138
GW’’
1 GW’’
2 GW’’
3 GW’’
4 GW’’
5 GW’’
6 GW’’
7 GW’’
8 M
Fig-4.37 : Total Hardness in mg/l
GW’’
1 GW’’
2 GW’’
3 GW’’
4 GW’’
5 GW’’
6 GW’’
7 GW’’
8 M
Fig-4.38 : Alkalinity in mg/l
50.2
55.2
60.6
50.4 47.2
44.2 48.4
43.2
49.9
0
10
20
30
40
50
60
70
52.8
61
65.8
60
55.2
60.4 60
55.2 58.8
0
10
20
30
40
50
60
70
139
GW’’
1 GW’’
2 GW’’
3 GW’’
4 GW’’
5 GW’’
6 GW’’
7 GW’’
8 M
Fig-4.39 : Total coliform in MPN/100ml
GW’’
1 GW’’
2 GW’’
3 GW’’
4 GW’’
5 GW’’
6 GW’’
7 GW’’
8 M
Fig-4.40 : Fecal Coliform in MPN/100m
85 90
122
110 105
100 101
90
100.4
0
20
40
60
80
100
120
140
60
70 75
70
60
72 73
58
67.3
0
10
20
30
40
50
60
70
80
99
Fig.4.41 : Comparative Figure of Surface and Ground water of Industrial Cluster Area.
7.2
284.9
7.1 3.6 20.0
187.6
82.2 66.4
126.1
85.6
7.9
164.9
7.3 2.9 19.9
181.3
61.3 62.7
123.8
69.3
0.0
50.0
100.0
150.0
200.0
250.0
300.0
pH Conductivity DO BOD COD TDS TotalHardness
Alkalinity TC FC
SW' GW'
100
Fig.4.42 : Comparative Figure of Surface and Ground water Quality of Beyond the boundary of Cluster Area
7.8
116.8
9.2 2.2
17.8
152.1
51.7 59.4
101.6
51
7.6
102.7
10.3
1.8
14.6
141
49.9
58.8
100.4
67.3
0.0
20.0
40.0
60.0
80.0
100.0
120.0
140.0
160.0
pH Conductivity DO BOD COD TDS TotalHardness
Alkalinity TC FC
SW'' GW''
101
Fig.4.43 : Comparative Figure of Surface water Quality of Industrial and Beyond the boundary of Cluster Area
7.21
284.9
7.13 3.6
20.0
187.6
82.2 66.4
126.1
85.6
7.8
116.8
9.2
2.15 17.8
152.1
51.7 59.4
106.1
51.0
0.00
50.00
100.00
150.00
200.00
250.00
300.00
pH Conductivity DO BOD COD TDS TotalHardness
Alkalinity TC FC
SW' SW''
102
Fig.4.44 : Comparative Figure of Ground water Quality of Industrial and Beyond the boundary of Cluster Area
7.9
164.9
7.3 2.9
19.9
181.25
61.3 62.675
123.8
69.3
7.6
102.7
10.3 1.8
14.6
141
49.9 58.8
100.4
67.3
0.0
20.0
40.0
60.0
80.0
100.0
120.0
140.0
160.0
180.0
200.0
pH Conductivity DO BOD COD TDS TotalHardness
Alkalinity TC FC
GW' GW''
140
EVALUATION OF IMPACT DUE TO COAL-FIRED THERMAL
POWER PLANTS ON THE FLORA AND FAUNA OF THE
CLUSTER AREA.
This chapter deals with the impact of coal-fired power plants on Flora and
Fauna of the cluster area. The literature review and the other aspects of the study are
discussed in this chapter
A brief review of literature
Forest plays a vital role in the maintenance of quality of environment. The
rainfall, one of the most important of climatic parameters, is known to be controlled
by the forests. Studies of Meher-Homji [1, 2]
on some Indian tropical forests reveal that
the deforestation is responsible for the diminishing tendency of rainfall and annual
number of rainy days. It has been established that reconstruction of forest vegetation
by afforestation leads to gradual increase in rainfall. Role of vegetation on
microclimate has also been reported by Jenik [3]
. According to Bray [4]
, changes in the
plant density and distribution can be considered as a factor for bringing about climatic
change along with human interference and natural disturbance. Murthy [5]
gave a very
clear relationship between climate, soil, man and forest crop, where the latter is the net
resultant of interactions between the former. As per the report of Amundson and
Wright [6]
, forest transformation is one of the prime causes of climatic change over a
particular zone. Gringer [7]
reported long-term climatic change due to tropical forest
141
denudation with adequate examples from India. Mishra and Dash[8]
from their
observations of desertification around Hirakud reservoir of Sambalpur district of
Orissa had correlated decrease in rain fall, number of rainy days and morning relative
humidity cover. Kin Che [9]
highlighted the consequences of forest denudation in
South-Eastern China in relation to soil erosion and sediment production. Relationship
between the climate and the forest vegetation is also sufficiently evident from the
findings of Warren [10-14]
Howard [15]
, Gaussen coworker[16]
, Baigartmer [17]
, Olembo
[18] and Meher – Homji
[19] .
On global basis, forest occupies a geographical area of 4000 million hectres.
Around 70% of the total global forest are in the tropics and it has been estimated that
the forests of tropical region contribute around 80% of the World’s total requirements
of forest resources. Rapid industrialization and subsequent urbanization has done a
great injustice to the forest by denuding it at an alarming rate. Grainger[7]
in a detailed
analytical study of tropical forests per minute has indicated a loss of 30 hect area of
tropical forest. Considering the above fact, the Food and Agricultural Organization
(FAO) declared the year 1985 as the “International Year of Forest” to draw the
attention of masses to the problem of deforestation.
The forest has played an important role in shaping the economy of Odisha. As
per the compilation of Padhi [20]
forests of Odisha state produce annually 5 lakh cubic
meters of timber, 7 lakh cubic meters of fire wood, 350 lakh metric tons of bamboo,
40,000 metric tons of Kendu (Diospyros melnoxylon) leaves and 80,000 metric tons of
142
Sal (Shorea robusta) seeds. Other non-edible oil seeds of tree origin such as Neem
(Azadirachta indica), Karanja(Pongamia pinnata), Mahua(Madhuka indica) and
Kusum(Carthamus tinctorius) are collected every year from the forest of Odisha to
nearly equals to 70,000 metric tons. These forest are also produce 50,000 metric tons
of Mahua flowers and 10,000 metric tons of Myrobalan fruits every years. Minor
forest products like gum, resin , wax, lac, brooms, medicinal plants, pulp and fiber-
yielding grasses are also collected in bulk quantities every years. It has been further
estimated that the forest contribute around Rs 2458 lakh of revenue to this state’s
exchequer annualy20
(Padhi 1981).
As per the bulletin of forest department of Odisha the total area of forest is
1637 sq. km in Sambalpur forest division. This forest division has the maximum area
of reserve forest (1251sq.km) in comparison to other three divisions of undivided
Sambalpur district. The forest at Sambalpur Division has been subjected to severe
denudation because of the Hirakud Dam project and consequent urbanization and
industrialization after 1950. Barpahar forest range of Sambalpur division which had a
thick forest cover of dry deciduous nature, has lost much of its forest because of its
proximity to the Hirakud Dam project. Particularly the reservoir of the Dam project
has drawned a significant portion of the barpahar forest range.
143
STUDY METHODOLOGY
Flora and Fauna diversity of the cluster area has been noted down from field
observations and inputs from local sources. The Forests Department list, which does
not cover full inventory, but only those which are conspicuous only used for study.
The major species of trees of the Forests of Sambalpur Division in and around
the study area in descending order of dominance are listed in Table – 5.1.
Table – 5.1
Major Species of Trees of the Sambalpur Forests Division
Botanical Name Family
Shorea robusta Dipterocarpaceae
Terminalia tomentosa Combretaceae
Diospyros malanoxylon Ebenaceae
Boswellia serrate Burseraceae
Lagerstroemia peryiflora Lythraceae
Terminalia chebula Combretceae
Bombax ceiba Bombacaceae
Madhuka indica Sapindaceae
Terminalia arjuna Combretaceae
Pterocarpus marsupium Papillionaceae
Anogeissus latifolia Combretaceae
Buchanania langan Anacardiaceae
Bambusa sp. Gramineae
Phoenix sp. Palmae
Borassus flabellifera Palmae
144
A set of physico-environmental conditions suitable for successful growth of a
species or a group of species is called Habitat. Habitat may be divided into many
types, such as aquatic, terrestrial, aerial, arboreal, etc. A terrestrial habitat may
comprise of forest, grassland, agricultural land, tundra, desert, etc. The plants growing
in these habitats are known as terrestrial flora. In the past, Theophrastus tried to
classify the plants. During 23 – 79 A.D., Pliny classified the plants into trees, shrubs
and herbs, on the basis of differences in the form and size of the plants. A woody
perennial plant with a single main trunk (stem) having a tap root system is known as
Tree. A perennial woody plant of short structure is known a Shrub. There are several
Trees and Shrubs predominant in the study area. A few selected are given in Table –
5.2.
FLORA
There are various classes of Flora. A brief description of the same is given
bellow:
145
(a) Terrestrial Flora
(i) Trees and Shrubs
Table 5.2
Sl . No. Botanical Name Local Name Family
1 Annona squmosa Sitaphal Annonaceae
2 Annona Verticulate Ramphal Annonaceae
3 Polyalthia pendula Ashok Annonaceae
4 Nerium indicum Kaneer Apocyanaceae
5 Adhatoda vasica Vasaka Acanthaceae
6 Acacia catechu Khaira Mimosaceae
7 Acacia nilotica Babul Mimosaceae
8 Acacia leucophloea - Mimosaceae
9 Mimosa pudica Lajkuli Mimosaceae
10 Adina cordifolia Kelikadamba Rubiaceae
11 Mitragyna parviflora Kadam Rubiaceae
12 Aegla marmelos Bela Rutaceae
13 Citrus limon Lemon Rubiaceae
14 Citrus medica Badanimbu Rubiaceae
15 Terminalia arjuna Arjuna Combretaceae
16 Terminalia bellirica Bahera Combretaceae
17 Terminalia chebula Harida Combretaceae
18 Terminalia tomentosa Asan Combretaceae
19 Strebulus asper Sahada Moraceae
20 Ficus glomerata Dumer Moraceae
21 Ficus religiosa Aswatha Moraceae
22 Azadirachta indica Neem Maliaceae
23 Emblica officinalis Amla Euphorbiaceae
24 Tectona grandis Teak Verbinaceae
25 Dalbergia latifolia Shisam Papilionaceae
26 Dalbergia sissoo Sissoo Papilionaceae
27 Shorea robusta Sal Dipterocarpaceae
28 Madhuca indica Mahula Sapotaceae
29 Eugenia jambolana Jamun Myrtaceae
30 Dendrocalamus
strictus
Solid bamboo Poaceae
31 Diospyrous
melanoxylon
Kendu Ebenaceae
32 Zizyphus mauritiana Barakoli Rhamnaceae
33 Ocimum basilicum Bana tulsi Labiatae
146
(ii) Herbs
These are the plant that do not have much woody tissue and are of very short
height. There are many species of herbs predominant in the study area which are given
below:
Sl .
No.
Botanical Name Local Name Family
1 Achyranthus aspera - Amaranphaceae
2 Tridax procumbers Bisalyakarani Compositae
3 Argemone Mexicana Prickly poppy Papaveraceae
4 Papaver sominiferum Opium Papaveraceae
5 Vetiveria zizanioides Khus – khus Poaceae
6 Dendrocalamus strictus dans Poaceae
7 Musa paradisiaca Banana Musaceae
8 Cuscuta reflexa Dodder Convolvulaceae
9 Entada phaseoloides Nickerean Mimosaceae
10 Zizyphus vulgaris - rhamnaceae
147
(iii) Climbers
When a weak stem climbs a support by means of some spetial organs of
attachment like tendrils, hooks or climbing roots, it is known as climber. Some
important climber predominant in the study area are given below (Table 5.3):
Table-5.3
Sl .
No.
Botanical Name Local Name Family
1 Argyreia speciosa - Convolvulaceae
2 Bauhinia vahlii - Caesalpiniaceae
3 Capparis zeylanica - Capparidaceae
4 Combratum deandrum - Combretaceae
5 Entada phaseaoloideas - Mimosaceae
6 Uraria narum - Anonaceae
7 Zizyphus vulgaris - Rhamnaceae
8 Zizyphus jujube Ber Rhamnaceae
9 Bougainviallea sp. Glory of Garden Nyctaginaceae
10 Pothos sp. Money plant Araceae
148
(iv) Grasses
A few examples of grasses are recorded in (Table-5.4)
Table 5.4
Sl . No. Botanical Name Family
1 Arundinaria falcate Poaceae
2 Cenchrus ciliaris Poaceae
3 Cynodon dactylon Poaceae
4 Dichanthiun annulatum Poaceae
5 Heteropogon contortus Poaceae
6 Saccharum munja Poaceae
7 Saccharum spontaneum Poaceae
8 Vetiveria zizanioides Poaceae
9 Axonopus affinis Poaceae
10 Cortaderia selloana Poaceae
149
(b) Weed of Marshes
A few of weeds of marshes are the following (Table-5.5):
Table 5.5
Sl . No. Botanical Name Family
1 Ammania baccifera Lythraceae
2 Aeschynomenae aspera Papilionaceae
3 Linmophila indica Scrophulariaceae
4 Vallisnaria spiralis Hydrocharitaceae
5 Hydrilla verticellata Hydrocharitaceae
6 Hydrocharis cellulose Hydrocharitaceae
7 Hydroccharis morsus Hydrocharitaceae
8 Linmophila heterophylla Scrophulariaceae
9 Lindernia sp. Scrophulariaceae
10 Aerides sp. Orchidaceae
150
(c) Parasites
Parasites is an interaction between two species where one, called parasite, live
for a part or whole of its life-cycle on or in the body of another species, called host,
and from where it derives its nourishment more or less permanently fom the tissue of
the later. A parasite is usually smaller in size than its host and majority of them are
micro-organisms such as viruses, protozoans, mycoplasma, bacteria, fungi etc. Some
bigger plants also acts as parasites of other plants. Thus a wide variety of plants are
parasites in their mode of existence. A typical parasite may seriously harm its host, but
usually it does not kill the host until its life-cycle is completed. However, hosts may
develop resistance to the pathogens and hence the affect become mild. Parasitism
maintains a balance between the hosts and parasites and controls the population. Some
important parasites of the study area are given below.
Sl . No. Botanical Name Family
1 Cuscuta reflxa Convolvulaceae
2 Dendrophthoe falcate Loranthaceae
3 Viscum album Loranthaceae
4 Viscum monoicum Loranthaceae
5 Utricularia Lentibulariaceae
151
(d) Lithophytes
There are the plants which grow on the rocks. They require least amount of
water and soil for their growth. There are three predominant species of lithophytes at
present study area, which are given below.
Sl . No. Botanical Name Family
1 Pogostemon perilloides Lamiaceae
2 Sarcostemma acidum Asclepiadaceae
3 Tephrosia maxima papilionaceae
(e) Pteridophytes
These are considered to be most advanced among the cryptogames, since the
plant body is well differentiated into true stem, leaf and root. The stems may be either
underground or aerial. The leaves usualy bear spore-bearing organs. Generaly these
plants grow in rocky places under shady habitates. Ther few important species of
pteridophytes predominantly present in the study area, which given below.
Sl . No. Botanical Name Family
1 Adiantum incisum Adintaceae
2 Adiantum lunulatum Adintaceae
3 Drynaria rigidula Polypodiaceae
4 Hemionitis palmate Polypodiaceae
5 Lygodium palmatum Schizaeceae
6 Pteris biaurita Pteridaceae
7 Pteris vitta Pteridaceae
152
(f) Bryophytes
These plants usually grow on damp soil, rock walls or on the tree trunks. They
form a green carpet on such surfaces. The plant body is well differentiated and they
are considered to be more advanced than the thallophytes in their structural
organization as well as in their reproductive methods. These plants may have thick
plate like thallus, as in the liver worts or may be differentiated into minute stem, leaf
and root-like structures. In both of these types small filamentous root-like structures
are developed, known as rhizoids. Some important species of Brophytes of the study
area are given below.
Sl . No. Botanical Name Family
1 Riccia sp. Ricciaceae
2 Marchantia sp Marchantiaceae
(g) Lichens
These are greyish green patches of plants occurring on the bank of trees or on
barren rocks .These are peculiar and composite plants. Each lichen consists of two
parameters, one is an alga, and the other is fungus. They lead a symbiotic life, one
helping the other. The following are the few .
153
Sl . No. Botanical Name Family
1 Parmelia flavicans Parmeliaceae
2 Graphis scripta Graphidiaceae
3 Pyrenula nitida Pyrenulaceae
4 Trypethelium sp. Trypetheliaceae
(h) Aquatic Flora
An aquatic habitat may be a fresh water, estuarine or marine or river, pond, etc.
The plants growing in such habitat are called aquatic flora. Some important aquatic
floras available in the study area are given below:
Sl . No. Botanical Name Family
1 Najas indica Najadaceae
2 Aponogetun sp. Aponogetonaceae
3 Potamogetun sp. Potamogetonacee
4 Lemna minor Lemnaceae
5 Lemna paucicostata Lemnaceae
6 Wolffia arrhiza Lemnaceae
7 Nymphaea alba Nymphaeceae
8 Marsilea quadrifolia Marsileaceae
9 Alisma plantago Alismataceae
10 Nymphaea stellate Nymphacaceae
154
h(i) Floating Plants
These plants usually float on the water surface and they are not fixed to the
soil. They usually do not developed any root or they may develop very few roots,
which also float on the water, e.g., eichhornia(Water Hyacinth), Lemna(Duck weed),
Trapa(Pani singda), etc.
Sl . No. Botanical Name Family
1 Pistia stratiotes Araceae
2 Azolla pinnata salviniaceae
h(ii) Submerged Plants
Plants which grow inside water and all their organs remain within water with
their roots fixed to the bottom are known as submerged plant. Some important
submerged plant of the study area are given below:
Sl . No. Botanical Name Family
1 Najas indica Najadaceae
2 Hydrilla verticillate Hydrocharitaceae
3 Nymphaea stellate Nymphaeaceae
4 Fimdristylis milliaceae Cyperaceae
5 Cyperus papyrus syteraceae
155
Cultivated plant
Paddy is the most widely cultivated crop in Odisha. Most of the cultivated
plants of the study area are the sugarcane and wheat. Various kinds of pulses such as
mung, pea, cowpea and Masuria along with different kinds of vagetables are also
cultivated. Among the fruits, mango is the most popular fruit. Banana, papaya, guava,
jamun, tal, coconut and palms are also largely grown in the study area by the local
people.
FAUNA
(a) Annelids
Annelids are the invertebrate organism which have bilaterally symmetrical and
metamerically segmented body. These are mostly aquatic animals. Some are
terrestrial, borrowing or tubiculous, sedentary or free living, some are commensal and
parasite. Some of the important annelids of the present study area are given below:
Sl . No. Zoological name Status
1 Hirudinaria viridis Common
2 Pheretima posthuma Common
3 Pheretima sambalpuriensis Common
4 Hirudinaria granulose Common
156
(b) Arthropods
These are the metamerically segmented invertebrate organism having jointed
legs, exoskeleton of chitinous cuticle. The body is divisible into head, thorax and
abdomen. These are found in all habitats. The largest class of phylum arthropoda is
class insect. Some of the important arhropods of the presence study are given below:
Sl . No. Zoological name Status
1 Palamnaeus sp. common
2 Mastigotroctus sp. common
3 Scolopendr sp. common
4 Periplaneta Americana common
5 Palaemon idea common
6 Gulusterrestris common
7 Mantis religiosa common
8 Carausius sp. common
(c) Mollusces
Mollusces are the unsegmented animals with a calcareous hard body cover
whose body is divisible into head, mantle, food and visceral mass. These are either
terrestrial or aquatic. Some of the mollusces of the presence study are as follows:
Sl . No. Zoological name Status
1 Pila globosa common
2 Lmellidens marginalis common
157
(d) Fish
In the study area of the major sources of fish faun are the Hirakud reservoir
and Ib river and their tributaries. Fishes are also available in the private ponds of the
villages. A few common fish fauna of the study area re given below:
Sl . No. Zoological name Common Name
1 Catla catla Catla
2 Labeo rohita Rohu
3 Cirrhina mrigala Mirgal
4 Labeo kalbasu Kala bainsi
5 Clarius batrachus Magur
6 Heteropneustus fossilis Singhi
7 Notopterus notopterus Pholui
8 Notopterus chitala chital
(e) Amphibia
The greatest event in the phylogenetic history was a transition from aquatic to
terrestrial mode of life and Amphibian were the first animals to attempt this transition.
But they are not fully terrestrially adapted and hover between aquatic and land
environment. The name of the class also indicates the double life. Structurally,
amphibians are between the fish on one hand and the reptiles on the other. Some of the
important amphibians of the present study area are given below:
158
Sl . No. Zoological name Status
1 Rana tigrina Common
2 Rana cyanothlyctis Common
3 Rana erythraea Common
4 Rana limnocris Common
(f) Reptiles
Reptiles represent the first class of vertebrate fully adopted for life in dry
places on land. They have no obvious diagnostic characteristics of their own that
immediately separate them from other classes of vertebrates. The characteristics of
reptiles are in fact a conmition of characters that are found in fish and amphibians on
one hand and birds and mammals on the other. The class name refers to the mode of
locomotion. A few important reptiles of the present study area are given below.
Sl . No. Zoological name Status
1 Naja naja Occasional
2 Vipera russelli Common
3 Bungarus Coeruleus Common
4 Python morulus Occasional
5 Bungarus fasciatus Common
6 Ptyas mucosus Occasional
7 Natrix piscator Occasional
159
(g) Aves
Birds constitutes a well-defined group of vibrate animals. They show less
diversification than any other group of vertebrate animals. This singular uniformity of
structure is imposed upon them by the demand of flight. About 9,000 living species of
birds are known at present. Major Avifauna of the study area is given below:
Sl . No. Zoological name Common name
1 Cypsiurus parvas Palm swift
2 Merops orientalis Small bee eater
3 Phalacrocorax niger Little commarant
4 Centropus sinensis Crow pheasant
5 Clamator jacobinus Cuckoo pied Crested
6 Ceryle rudis Pied king Fisher
7 Halistur indus Brahmany Kite
8 Eudynamys scolopacea Koel
Mammals
(a) Domestic Animals
Sl . No. Zoological name Local name
1 Ovis folic Sheep
2 Capra hiscus Goat
3 Bos indus Cow
4 Bubalus indicus Buffalo
5 Felis domestic Cat
6 Canis fmiliaris Dog
7 Equus asinus Pig
160
(b) Wild Life
The district of Sambalput has been the home of elephants for centuries and also
had good population of tiger. At present there are no tiger in the area.
Sl . No. Zoological name Status
1 Presbytis entellus Common
2 Mecaea mullate Occasional
3 Hyaena Striata Rare
4 Herpestes edwardsi Occasional
5 Axis axis Rare
6 Sus scrofa Common
7 Lepus ruficaudatus Occasional
8 Canis lupus Common
(c) Rodents
These are usually small gnawing mammals of rat family. Some of the
important Rodents of the present study are given below
Sl . No. Zoological name Status
1 Funambulus pennanti Common
2 Rattus rattus Common
3 Bandicota indica Common
4 Bandicota benghalensis Common
161
A systematic statistics on Flora and Fauna of the cluster area that we have been
collected may be having some theoretical importance, but practically the above
mention flora and fauna are not at all sufficient to help to maintain a normal
environment in the region. The reasons are manifold, some of which are as follows:
(i) The population of individual species of flora and fauna is very negligible.
Forexample, if we take the number of big trees in the region, the average may
not exceed 100-120 per sq. Km. The forest in the area as per Forest
Department record are very few in numbers but in practice very few trees are
seen in the forest. In some cases only a few shurbs are seen here and there. It
may be the fact that the above identified forest were dense forests in long past,
but there present condition is measurable.
(ii) In view of the negligible tree population, the jungles are almost deserted and
incidence of wild animal species is very rare. There is practically very little or
no food survival and growth of such animals.
(iii) The same is the position of less developed classes of animals and plants
including microscopic organism, whose abundance is inevitable for
maintaining ecology balance. The injudicious application of Fertilizers and
pesticides for agriculture purpose is the main cause of the reduction of their
population.
162
(iv) The study area has hosted three townships, Jharsguda, Brajarajnagar and
Belpahar and a few villages init and majority of area has been occupied by
cement concrete roads. Only a very small space left for natural activities in the
township area. The three towns are continuously expanding and they may
cover the entire regions in a few years. Apart from this, the expansions of
industries in the region are taking a rapid course and every day some large or
small industries is being added to it.
The above findings clearly indicate a very alarming situation so far as pollution
of environment, ecological imbalance and reduced bio-population are
concerned. However, in some areas steps have been taken to increase forest
area by plantation of teak and other plants by Forest Department or even by the
public. But in no way it is adequate or is able to solve the problem and unless
adequate plantation in the hills. Forest river banks, roadside plantation and
town streets plantation are encouraged, there is very little chance of recovery
of normal environment in the area.
In short it can be said that our ignorance of the bio-diversity and its
function in ecosystem is overwhelmingly large. It requires serious and urgent
effort to remedy the situation. First and foremost, there is an urgent need for
detailed taxonomic studies of all biota and preparation of identification keys
for all groups of organism. Detailed studies are required on all type of
ecosystems. These studies need to emphasize on the ecosystem prosesses like
163
production pathways of energy transfer and biochemical cycle in relation to
major environmental variable such as hydrology, nutrients and biotic
interference. Aquatic microbial diversity and its role in the ecosystem
functioning have to be accorded high priority ecological response of different
communities and individual taxa, both common and rare, need to be
investigated. Rare, threatened and endangered species have to be identified and
their population biology is to be understood. Studied on genetic variability in
widely distributed species would greatly help conserving genetic diversity.
Further, while a better understanding of the aquatic ecosystems and
their bio-diversity will be required some times, urgent steps have to be taken to
ensure that the aquatic systems are not degraded further by anthropogenic
activities in situ or in their watersheds. Also there is a need for an extensive
programme of training and education, creating public awareness about bio-
diversity conservation.
164
REFERENCE
1. Meher-Homiji Variablity, an aspect of Bioclimatology with reference to the
Indian Sub-continents. In: Proc.Symp. Rec. Adv. Trop. Ecol. Part-1(Eds. R.
Mishra and B. Gopal) Intern. Soc, Trop. Ecol. Publ 144-153(1968).
2. Meher-Homiji, Bio climatic variability with special reference to Indian
Trop. Ecol, 12(2) 155-176(1970).
3. J. Jenik Wind action and vegetation in Tropical west Africa In: Proc Symp
Rec. adv Trop. Ecol part-I (Eds R.Mishra and B.Gopal) Inter Soc. Trop
Ecol. 108-113(1968).
4. J.R. Bray Vegetational distrubtation, tree growth and crop success in
relation to recent climate changes In: Advance in Ecological Research(Ed.
J.B Crag) Academic press 178(1971).
5. N.S. Murthy, Relationship between Vegetation and climate in the upper
catchment area of the Narmada river , Central India vegetation (36), (1)53-
60(1978).
6. D.C Amundson and H.C. Wright jr Forest change in Minnesota at the
end of the Pleistocene Ecol. Monogor 49(1): 1-16(1979).
7. A. Grainger The state of the world tropical forest The Ecologist 10, 6-
12(1980).
8. A. Mishra and M.C.Das, Desertification around Hirakud Reservoir. The
Environmentalist 4, 51-58:(1984).
9. Kin-Che. Soil erosion, suspended sedimention and solute production in
three Hongkong carchment, J. Trop. Geogr, 47, 51-62(1978).
10. Warren W.D.M, The influence of forest on climate Sal Regeneration De
Novo, Indian Forester LXVIII(G) 292-330(1941).
11. Warren W.D.M, Studies in climate change Amelioration by contour
trenching arid forest slopes Indian Forester LXVIII(6) 422-429(1942).
165
12. Warren W.D.M, Studies in climate change-II, The proof of the climatic
change. Indian Forester LXVIII, 17-23(1942).
13. Warren W.D.M, A Study of climate and Forest in Ranchi platue Part-I
Indian Forester LXVIII, 100(4)229-234(1974a).
14. Warren W.D.M, A Study of climate and Forest in Ranchi platue Part-II
Indian Forester LXVIII, 100(5)291-314(1974b).
15 W.L. Howard Ecological and Silviculture aspect. In Hand book of Applied
Hydrology, Mc Graw-Hill, 61-130(1964).
16. Gaussen, H.V.M. Meher-Homji, P.Legris, A. Delacourt, J.P. Troy,
J.F.Blasco and J. Foutannel, International maps of vegetation and
Environmental condtion Sheet, Wainganga ICAR and Inst. Fr. Pondicherr.
Jr. Sect. Sci. Sci. Tech. Hors Series NO: 13-18(1972).
17. A. Baigartmer The climatic functions of Woodland , Quantification
climatic and hygienic function of woodland, Befland Wirtch, 5(4)708-
771(1977).
18. R.J. Olembo, Environmental issue in forest and wild land management in
FAO, The Ecologist 10-48(1978).
19. Meher-Homiji, The link between rainfall and forest clearance case studies
from Western Karnataka Trans Inst. Indian Geographers,2(1)59-65(1980).
20. G.Padhi, Forest Resources of Orissa, Government of Orissa, Bhubaneswar,
Orissa (India) (1981).
166
SUMMARY
The thesis is consists of five chapters. Chapter-I is divided into three
sections (A, B& C). In section–A, the sources of air and water of the
environmental pollution has been described as an introduction. The section-B of
the chapter deals with the literature review of earlier work reported on coal-fired
thermal power plants in the global level and their impact on the environment. The
review work has been done to set a back ground of the present study and also to
ascertain that no researcher has done this work before this study started. The aim
and objectives of the study is in Section-C.
The materials and methods used in the study have been described in
Chapter-II. In order to know the quality of air, generally the parameters such as
suspended particulate matters, sulphur dioxide, nitrogen dioxide and also the
polluting metals of the ambient air has been analysed. In the present study the
analysis of those parameters have been done of the ambient air of the cluster area,
outside the cluster and faraway places from the cluster area adopting literature
methods. Similarly in order to evaluate the quality of surface and underground
water of the cluster area and outside area of the cluster has been analysed for a few
important parameters of water adopting standard methods.
In chapter-III, the analytical data of samples collected from a number of
sampling stations of cluster area where several coal-fired thermal power plants are
located and the data are recorded in tables and also recorded in graphical form.
Basing on those experimental data the necessary discussion follows as below:
167
Suspended Particulate Matters (SPM) the analytical data of the SPM of the
ten sampling stations of the cluster area are recorded in Table-3.5. A survey of the
data reveals that the annual average of the ten sampling stations of the cluster area
is 403.5µg/m3. Out of the ten stations the maximum SPM is 460µg/m
3 at
station and lowest is 340µg/m
3 at the station
. The Vedant Captive Power Plant
where generation capacity is 1215MW is nearer to . So there is every reason for
generation of more SPM nearer the station . Stations
inside the Brajarajnagar
township, where no power plant is closer to the town, hence, the SPM percentage
is oveiously less (340µg/m3) in the town area. Among the other stations, the next
higher SPM percentages are 450µg/m3at stations
and 445µg/m3 at stations
(Table-3.5 and Fig-3.4). These stations are very closely to Sterllite power and
Bhusan power which are producing 2400MW and 376MW respectively. Hence,
the SPM percentage also higher in those stations.
Annual average of the ten sampling stations located beyond the boundary
of cluster area is 230µg/m3(Fig-3.10, Table-3.6). Out of the ten stations, the
maximum percentage of SPM is 280µg/m3 at station
and lowest 170µg/m3 at the
station . At the station
the percentage is maximum may be due to north-
East(NE) direction of wind which is the predominant direction of wind in the day
time nearer to the sampling stations. Another reason for maximum SPM may be
due to presence cement producing unit (UltraTech, Arda) which is just 03KM
away from the monitoring station. Station whichn is 20KM away from the
reference point (Kherual) have the minimum SPM (170µg/m3). Among the other
stations the SPM is 276µg/m3at (
) and the next one is 260µg/m3
(Table-3.5,
Fig-3.10). These stations are also very close to Sterllite, Vedant and Bhusan Power
168
plants and dominant direction of wind blow. Hence, the SPM percentage is quit
high in these monitoring stations.
SPM values of sampling stations faraway from the boundary of cluster area
shows annual average of 126.5µg/m3. Among all the sampling stations, faraway
from the boundary cluster area, shows the maximum of 170µg/m
3 (Fig-3.16,
Table-3.7). This sampling station is in the predominate direction of wind blow and
the station is nearer to the railway line and market area. At samplings at Raidhi
shows minimum of 90.0µg/m3at the distance 25KM away from the reference point
(Kherual) andalso the locality is free from industries. The other stations ,
SPM concentrations are 150µg/m3, 125µg/m
3 and 120µg/m
3 respectively
(Table-3.7 Fig-3.16). These stations are nearer to highway such as SH10, NH200
and they are also near to the market area.
On analysis of SPM of cluster area, beyond cluster area and faraway from
cluster area the annual maximum value are 403.5µg/m3, 230µg/m
3 and 126.5µg/m
3
respectively. The data indicate that the industrial area is highly polluted as the
permissible concentration of SPM in ambient air should be 100µg/m3. As
regards the boundary and faraway from the boundary areas the concentration
values are still higher than the permissible value of concentration of SPM in
ambient air (100µg/m3). The three values of SPM of cluster area, beyond
cluster and faraway from cluster it can be concluded that the SPM value
decreases with respect to distance from the cluster area. SPM cluster area
403.5 µg/m3>boundary of cluster area 230.5>faraway from cluster area are
126.5µg/m3.
169
The Sulphur Dioxide (SO2) analytical data recorded in the (Table-3.5)
reveals that the three years annual average value of ten monitoring stations for SO2
in the cluster area is 103.81µg/m3. Among all the stations
and have the higher
concentration of 120.5µg/m3 and 115.6µg/m
3 respectively. The minimum
concentration is 80.2µg/m3at
(Table-3.5, Fig-3.5). The other monitoring
stations such as ,
have concentrations115.4µg/m3,110.6µg/m
3 and 105.6µg/m
3
for .The concentration in the stations
, ,
and are all in the range 105.6-
120.5µg/m3
i.e. the density of SO2 is the higher and this is due to all the major
coal-fired power plants(Table-3.1) are located in this region. Besides this, at
stations the concentration of SO2 is 105.6µg/m
3, the higher value is due to the
power-plant of OPGC, Banaharpali (420MW).
Beyond the boundary of cluster area, the annual average concentration of
SO2 is 92.63µg/m3
. The maximum and minimum concentration in the area are
114.8µg/m3 at
and 66.8µg/m3
for stations and
respectively (Table 3.6
and Fig-3.11). The average concentration of SO2 of the stations
is
108.5µg/m3 which is less 3.7µg/m
3 from the average value of the same stations of
the cluster regions. The air samples collected from all sampling stations which are
at a distance of 25.7 KM from the boundary of the cluster area. The annual average
of SO2 concentration of those stations is 85.5µg/m3. The concentration is maximum
at (103.1µg/m
3) and minimum at
(66.5µg/m3) (Table-3.7, Fig-3.17). The
stations which show higher concentration are 100.5µg/m3 and 100.0 µg/m
3 at
and respectively.
170
The SO2 concentration is maximum 120.5 µg/m3 at
and and minimum
80.2µg/m3 at
respectively. The annual average value is 103.81µg/m3 in the
cluster area. In the beyond cluster the maximum value is 114.8µg/m3, minimum
66.8µg/m3 and average 92.63µg/m
3. The stations faraway from cluster area the
maximum and minimum values of concentration of SO2 are 103.1µg/m3 66.5
µg/m3respectively. On comparing the SO2 concentration with national standard the
concentration is much higher than the national standard 80µg/m3. So it is clear,
even at distance place from the cluster area the SO2 concentration persists in the
ambient air. However, when we compare the concentration of all the three
areas the concentration ofSO2 in the order 103.81µg/m3> concentration of
area beyond the boundary 92.63µg/m3> concentration of area faraway from
the cluster area 85.5µg/m3.
Nitrogen dioxide (NO2) analytical data of nitrogen dioxide are recorded in
(Table-3.5) and the graphical representations of the data are shown in the Fig-3.6.
It is noticed from the three years annual average value of all the monitoring
stations for NO2 in the cluster area is 105.55µg/m3. Among all the monitoring
stations is having maximum concentration of NO2 (127.2µg/m
3) and next to
that is 120.6µg/m
3. The minimum concentration is 86.3µg/m
3 at (
), (Table
3.5, Fig 3.6). The higher concentration of NO2 in the area of the cluster area around
the stations ,
, are most reasonable, since the mega coal-fired power
stations are around those stations i.e more coal burning in the plants. At the NO2
concentration is also higher. Since there is also a major plant located. The
minimum concentration is around the stations as the station is located inside the
171
Brajrajnagar town. Since no power plant nearer the township, it is natural NO2
concentration is low.
Outside the boundary of cluster area the annual average concentration of
NO2 is 88.75µg/m3
. The maximum concentration is at (112.5µg/m
3) and
minimum is at station (62.6µg/m
3) (Table- 3.6 and Fig-3.12). The other
monitoring stations which are having higher concentration of NO2 are 108.5µg/m3
and 104.5µg/m3
at and
because they are also nearer to mega power plants
and they are also nearer to highways and rail lines.
Air samples were also collected from an average distance of 25.7 KM from
the boundary of the cluster. The analytical data were recorded in (Table 3.7) and
the graphical representation in (Fig-3.18). The annual average of all stations is
78.6µg/m3. Maximum concentration is shown at
96.8µg/m3 and minimum at
is 70.5µg/m3
(Table-3.7 Fig 3.18). Other monitoring stations which show higher
concentration are 92.8 µg/m3 and 90.6µg/m
3 by
and respectively.
The analysis of the NO2 concentrations is given above indicated that in the
cluster area it is maximum 127.2µg/m3. The annual average concentration of NO2
in the area beyond cluster is 112.5µg/m3 and that of area faraway from the
boundary is 90.6µg/m3. Based on the maximum concentration of NO2 it can be
said that the values are in the order cluster area > beyond boundary of cluster
area >far away from the boundary of cluster area.
The annual average of Lead (Pb) for all samplings of the cluster area is
2.087 µg/m3. At the station
the maximum lead is 2.86µg/m3
and lowest 1.44
µg/m3 at the stations
. The stations is near to the two mega plant such as
172
Vedant Captive Power and Sterlite Power plant, so ther is every reason for
generation of more lead near the Station . Stations
is inside the Brajarajnagar
township where no power stations is closer to the town so it is natural that the lead
concentration is less 1.44µg/m3(Table-3.5 Fig-3.7)Among the other stations the
lead concentration is 2.56µg/m3 at
and the next one is 2.16 µg/m3 for both the
stations at and
. These stations are very close Sterllite, Bhusan and Vedant
power plant respectively. Hence, the lead concentration is quit high in these
stations.
Annual average of the ten sampling stations outside the boundary of the
cluster area is 1.754µg/m3. Out of all stations the maximum lead concentration is
2.24µg/m3 at stations
and lowest 1.31µg/m3 at the stations
. The station in
the direction of north-east (NE), from power plants which is the predominant
direction of wind towards the sampling station. Stations which is 20KM away
from the reference point have the minimum lead concentration is 1.31 µg/m3.
Among the other stations the lead is 1.82µg/m3 both at
and .Station
to close
Vedant, Sterllite and near to SH10, where as station is nearer to the OPGC plant.
The lead concentration is 1.71µg/m3at
. These stations are also very close to
mega power plants and dominant direction of air blow. Hence, the lead percentage
is quit high in these monitoring stations (Table-3.6 and Fig- 3.13).
Lead concentration of sampling stations faraway from the boundary of
cluster area shows annual average of 0.969µg/m3. Among them
shows the
maximum of 1.33µg/m3
(Table-3.7, Fig-3.19). Again the sampling station is in the
predominate direction of air and monitoring stations is near to the railway stations
173
and market area. The stations which show minimum of 0.43µg/m3 at
is
faraway from the reference point and locality is free from industries. The other
stations ,
have concentration of 1.16µg/m
3, 1.01µg/m
3 and
1.03µg/m3respectively which may due to the location of the station near to SH10,
NH200 and nearer to the market respectively.
Mercury (Hg) analytical data are embodied in Table 3.5 and Fig-3.26. It is
observed that the annual average of mercury is 2.712µg/m3. Equal concentration of
3.06µg/m3 are observed at stations
and it may be due to presence of three
nearby mega power plants and burning of huge quantity of coal by the respective
power plants. Besides above the other most polluted locations are at and
which
have pollution load of this metal is 2.88µg/m3 and 2.86µg/m
3respectively (Table-
3.5, Fig-3.8).
Annual average of the monitoring stations outside the boundary of
industrial cluster area is 1.784µg/m3. The maximum concentration is 2.22µg/m
3 at
the monitoring stations and minimum 1.1µg/m
3at
. Stations which have
higher values 2.1µg/m3 at stations
respectively (Table-3.6 and Fig-3.14).
Stations faraway from the boundary of industrial cluster area show annual average
value of 0.9µg/m3. Stations
and have maximum and minimum
concentration of 1.16µg/m3 and 0.41µg/m
3 respectively (Table-3.7, Fig-3.20).
Other stations which have higher value are 1.08 µg/m3 and 1.03µg/m
3 at
and
respectively.
Cadmium (Cd) is observed that the annual average of all the sampling
stations of the cluster area is 2.511µg/m3. Out of all the stations the maximum
174
cadmium is 2.86µg/m3 at station
and lowest is 1.52µg/m3 at the stations
. S’3 is
nearer to the three mega power plants and direction of wind. So there is every
reason for generation of more cadmium nearer the Station . Stations
inside
Brajarajnagartownship where no power stations is closer to the town so it is natural
that cadmium concentration is less 1.52µg/m3
(Table-3.5 Fig-3.9). Among the
other stations the cadmium concentration is 2.76µg/m3at
and the next one is
2.75 µg/m3 and 2.73 for both
and . These stations are very close to three mega
power plants. Hence, the cadmium concentration is quit high which may be due to
burning of coal as fuel.
Annual average of all the stations outside the boundary of the cluster area is
1.772µg/m3. Out of all the stations the maximum cadmium concentration is
2.08µg/m3 at stations
and lowest 1.26µg/m3at station (
). The station in the
direction of north- east (NE), which is the predominant direction of air in the day
time near to the sampling stations. A station which is faraway from the reference
point is minimum (1.26µg/m3). Among the other stations the metal value is
2.03µg/m3 at stations
, and
the next one is 2.01µg/m3
at . These stations
are very close to nearby mega power plants and dominant wind direction. Hence,
the metal concentration is quit high in these monitoring stations (Table-3.6 and
Fig- 3.15).
Stations faraway from the boundary of cluster area shows annual average of
0.847µg/m3.Among the stations
shows the maximum of 1.06µg/m3 (Table-3.7,
Fig-3.21). Stations are in the predominate direction of air and nearer to the railway
station and market area. The station which show minimum of 0.43µg/m3 is
175
which is far away from the reference point and locality is free from industries. The
other stations ,
have concentration of 1.01µg/m
3, 0.93µg/m
3 and 0.84µg/m
3
respectively it is due to the position of the station the near to SH10, NH200 and
near to the market respectively.
Basing on the result and discussion on SPM, SO2, NO2 and
metals(Pb,Hg,Cd) it can be concluded that impact of power plants in the
cluster region is much higher than outside the boundary of the cluster area
and the load decrease at a distance of more than 25KM.
The most important parameters to evaluate the quality of water such as pH,
DO, BOD, etc. are analysed of the cluster area as well as that of outside the cluster
area and the analytical data are recorded in the tabular form and the data are also
represented in the graphical pattern. Basing on the experimental data of surface and
ground water of both the areas studied then necessary evaluation of the quality of
water for the purpose of human activities of the two sources has been done as
describe below.
The analytical data of a few important parameters of surface water and
ground water of the cluster area are recorded in the Table-4.6 and Table-4.7
respectively. The minimum, maximum and average analytical data of those
parameters are recorded in Table-4.10. The graphical representations of the
parameters are shown in Fig-4.1 to Fig-4.20and that of average data in Fig-4.41.
The analytical data of different parameters of surface water and ground
water of the area beyond the cluster are recorded in Table-4.8 and Table-4.9
respectively. The minimum, maximum and average data of those parameters are
176
recorded in Table-4.11. The graphical representations of the above data are shown
in Fig-4.21 to Fig-4.40 and that of the average value is in Fig-4.42.
The pH values of eight surface water samples of the cluster area is in the
range 6.1-8.8 and their average value is 7.2 . On comparing these data with the
standard data (6.5-8.5) for drinking purpose the surface water can be used for
drinking only after disinfection. However, the water is suitable for outdoor use like
bathing, swimming and sports purpose. In case of ground water of cluster area the
pH range of all the eight samples is in the range 6.3- 8.6 and their average value is
7.9. As above the source of ground water of the cluster area need treatment before
using it for drinking. However this water of the cluster area can be used like
surface water for outdoor bathing, swimming etc.
The pH values of both types of water of the area beyond cluster are
recorded in Table-4.8 and Table-4.9. The minimum, maximum and average value
are 7.5, 8.3 7.8 and 7.3, 8.0 7.6 respectively. The water of either of sources cannot
be used for drinking directly from the sources but can be done after conventional
treatment. But the water can be used for outdoor activities.
The analytical data of DO of the sources of water in the cluster area as well
as that of outside the boundary reveals that like pH, the water can be used for
drinking with necessary conventional treatment. But the water can be used without
treatment for outdoor activities.
The BOD values of surface water and that of ground water quality of the
cluster area is in the range 2.7-4.2mg/l with average value 3.6mg/l and 1.8- 3.3mg/l
and 2.9mg/l respectively. BOD values of both types of water of the area beyond
177
cluster are recorded in Table-4.8 and Table-4.9. The minimum, maximum and
average values are 1.4mg/l, 3.0mg/l, 2.2mg/l and 1.3mg/l, 2.5mg/l, 1.8mg/l
respectively. The water either of sources cannot be used for drinking directly from
the sources since the BOD values exceeds in all the cases. However, the water can
be used for drinking after necessary conventional treatment.
The COD values of surface water quality of the cluster area is in the range
18.5-22.5 with average value 20.04 of all the samples and that of ground water of
cluster area the COD value range is 17.5-20.6 and their average value is 19.9.
COD values of both types of water beyond cluster are recorded in Table-4.8 and
Table-4.9. The minimum, maximum and average values are 14.2, 20.5, 17.8 and
12, 18.5, 14.6 respectively. Since there is no standard data for COD it can be told
about the quality of the water.
The total coliform organism for drinking water without conventional
treatment should be 50 MPN/100ml but the values of all the samples in the present
study are around 2-3 folds more than the standard values of drinking water. Hence,
no water sample of the cluster area or beyond should be taken for drinking.
However, after conventional treatment the water can be used for drinking.
The cluster area is located in the Forest division of Sambalpur and beyond
the boundary area of the cluster is in the close proximity of Hirakud Dam project.
The Flora and Fauna of the forest and the Mega water body of this area has been
collected and recorded in Chapter-V. The statistics collected on Flora and Fauna is
not sufficient for maintaining a normal environment in the industrial cluster area
and outside the boundary of the cluster area for the reasons:
178
(i) The population of individual species of flora and fauna is very negligible.
For example, if we take the number of big trees in the region, the average
may not exceed 100-120 per sq. Km. The forest in the area as per Forest
Department record are very few in numbers but in practice very few trees
are seen in the forest. In some cases only a few shurbs are seen here and
there. It may be the fact that the above identified forest were dense forests
in long past, but there present condition is measurable.
(ii) In view of the negligible tree population, the jungles are almost deserted
and incidence of wild animal species is very rare. There is practically very
little or no food survival and growth of such animals.
(iii) The same is the position of less developed classes of animals and plants
including microscopic organism, whose abundance is inevitable for
maintaining ecology balance. The injudicious application of Fertilizers and
pesticides for agriculture purpose is the main cause of the reduction of their
population.
(iv) The study area has hosted three townships, Jharsguda, Brajarajnagar and
Belpahar and a few villages init and majority of area has been occupied by
cement concrete roads. Only a very small space left for natural activities in
the township area. The three towns are continuously expanding and they
may cover the entire regions in a few years. Apart from this, the expansions
of industries in the region are taking a rapid course and every day some
large or small industries is being added to it.
179
The above findings clearly indicate a very alarming situation so
far as pollution of environment, ecological imbalance and reduced bio-
population are concerned. However, in some areas steps have been taken
to increase forest area by plantation of teak and other plants by Forest
Department or even by the public. But in no way it is adequate or is able to
solve the problem and unless adequate plantation in the hills. Forest river
banks, roadside plantation and town streets plantation are encouraged,
there is very little chance of recovery of normal environment in the area.
In short it can be said that our ignorance of the bio-diversity and its
function in ecosystem is overwhelmingly large. It requires serious and
urgent effort to remedy the situation. First and foremost, there is an urgent
need for detailed taxonomic studies of all biota and preparation of
identification keys for all groups of organism. Detailed studies are required
on all type of ecosystems. These studies need to emphasize on the
ecosystem possesses like production pathways of energy transfer and
biochemical cycle in relation to major environmental variable such as
hydrology, nutrients and biotic interference. Aquatic microbial diversity
and its role in the ecosystem functioning have to be accorded high priority
ecological response of different communities and individual taxa, both
common and rare, need to be investigated. Rare, threatened and endangered
species have to be identified and their population biology is to be
understood. Studied on genetic variability in widely distributed species
would greatly help conserving genetic diversity.
180
Further, while a better understanding of the aquatic ecosystems and
their bio-diversity will be required some times, urgent steps have to be
taken to ensure that the aquatic systems are not degraded further by
anthropogenic activities in situ or in their watersheds. Also there is a need
for an extensive programme of training and education, creating public
awareness about bio-diversity conservation.
COMMUNICATIONS
1. S.K. Naik, A.Mahapatra, “Analysis of Pollution load studies on
Mahanadi due to Municipal Sewage in 2004 and Remedial
Measures”, Int. J .Chem. Sci., (2005), 333-341.
2. A.Mahapatra, D.C.Dash, S.K.Naik, S.Nanda “Monitoring of Air
Quality Status in and around OPGC Thermal power Plant,
Belpahar, Orissa, (India)”, J. of Env. Res. & Dev. (2008), 3, 143-
155.
3. S. K. Naik , A. Mahapatra “Assessment of Noise Pollution Of
Vedanta Thermal Power Plant, Jharsguda, Odisha, India” Int. J.
Chem. Sci.: 11(2), (2013), 1016-1028.
64
ANNEXURE
CHAPTER-III
EVALUATION OF IMPACT DUE TO
COAL-FIRED THERMAL POWER PLANTS ON
THE AIR QUALITY OF THE ENVIRONMENT.
Environmental pollution in an industrial cluster is a national issue
particularly in a period which is witnessing a rapid industrial growth. The
environmental pollution in a cluster is a complex multi-dimensional problem
which is often difficult to measure and manage. In order to address such a
complex problem we have made an attempt to study a cluster of Odisha which is
located in the district of Jharsguda and Sambalpur (Fig-3.1)1 which is considered
as a critically polluted area2.
In the study area the locations of ten coal-fired thermal power plants
shown in (Fig-3.1) and their power generation capacity is mentioned in (Table-
3.1). Huge deposits of coal in the IB-Valley3 are located in the area and a vast
water reservoir (Hirakud) is also in very close proximity to this. The area is a
most ideal site for the production of thermal power. There are many sponge irons,
very big iron and steel plants aluminium and cement industry are also located in
this region. Small scale industries like rice mills, bricks kilns and stone crushers
are also operating in the area.
Hirakud reserviour is the life line of the entire industrialization process in
the region. The major problem in the region is the runoff contamination which is
likely to be fluoride and cyanides since aluminum smelters are in the operation.
65
Besides, runoff from various stock piles like coal, iron etc. also have potential for
water pollution.
At present the solid waste (fly-ash) generation is mostly from power
plants, steel plants and aluminum smelters. Conversion of natural land into dumps
sites would enhance soil erosion and the rate of siltation of the reservoir.
The centering place (Kherual) of the cluster area located near as (840 00‟
31‟‟ E and 210 47‟ 00‟‟N)
4 with location of different station as shown in Fig-3.2
1.
The meteorological data5 of the IMD Jharsguda is recorded in table (Table- 3.2
and 3.3) and the figure-3.3 is the wind rose annual pattern of the said place5.
In this chapter, the analytical data of the air parameters such as SPM, SO2,
NO2 and three metals (Pb, Hg, Cd) are evaluated of the cluster area, beyond the
boundary of the cluster area(boundary-boundary around 5KM) and faraway from
the boundary of cluster area(around 15 KM). The sampling stations of the areas
are recorded in (Table 3.4 a, b, c). The analytical data are recorded in (Table-3.5,
Table-3.6, and Table-3.7) respectively. The graphical representation of the
parameters are shown in (Fig-3.4 to 3.9), (Fig-3.10 to 3.15) and (Fig-3.16 to 3.21)
for the areas.
The symbols S‟, S” and S”‟ of the analytical tables stands for cluster,
beyond the boundary of cluster and the stations faraway from the boundary of the
cluster respectively. In the graphical representations of the data, the red, green and
yellow denote maximum, minimum and average values respectively. However,
the yellow colour graph in Figs. 3.22 – 3.27, stands for standard “S”value.
66
Fig. 3.1 Location of Power plants in the cluster study area
67
Fig-3.2: Location of sampling stations in the cluster area and outside
68
Fig 3.3: Wind rose diagram of IMD- Jharsguda station (Annual Pattern)
69
Result and Discussion:
The ambient air quality standard are prequisite for effective management
of ambient air quality and to reduce the damaging effect of air pollution. These
standard are prescribed and enforced by CPCB as per the section 16(2) (h) of the
Air (Prevention and Control of Pollution) Act-1981. The standard first adopted by
the CPCB on 11th
November, 1982 and further revised by the CPCB on 11th
April
19946 and finally the recent guideline was issued on 16
th November-2009
regarding the monitoring of ambient air quality Table-3.9. These standards are
based on land use and other factors of the area. The recent revised standard on
18th
November, 20097 is provided in is provided in the Table-3.10 below.
Suspended Particulate Matters(SPM)
The analytical data of the SPM of the ten sampling stations of the cluster
area are recorded in Table-3.5. A survey of the data reveals that the annual
average of the ten sampling stations of the cluster area is 403.5 µg/m3. Out of the
ten stations the maximum SPM is 460µg/m3 at station
and lowest is 340µg/m3
at the station . The Vedant Captive Power Plant where generation capacity is
1215 MW is nearer to . So there is every reason for generation of more SPM
nearer the Station . Stations
inside the Brajarajnagar township, where no
power plant is closer to the town, hence, the SPM percentage is oveiously less
(340µg/m3) in the town area. Among the other stations, the next higher SPM
percentages are 450µg/m3at stations
and 445µg/m3 at stations
(Table-3.5
and Fig-3.4). These stations are closely to Sterllite power plant and Bhusan
70
power plant respectively which are producing 2400MW and 376MW
respectively. Hence, the SPM percentage also higher in those stations.
Annual average of the ten sampling stations located beyond the boundary
of cluster area is 230µg/m3(Fig-3.10, Table-3.6). Out of the ten stations, the
maximum percentage of SPM is 280µg/m3 at station
and lowest 170µg/m3 at
the station . At the station
the percentage is maximum may be due to north
-east(NE) direction of wind which is the predominant direction of wind in the day
time nearer to the sampling stations. Another reason for maximum SPM may
be due to presence cement producing unit(UltraTech, Arda) which is just 03KM
away from the monitoring station. Station which is 20KM away from the
reference point (Kherual) have the minimum SPM (170µg/m3). Among the other
stations the SPM is 276µg/m3
at ( ) and the next one is 260µg/m
3 of
(Table-3.5, Fig-3.10). These stations are also very close to Sterllite, Vedant
and Bhusan Power plants and dominant direction of wind blow. Hence, the SPM
percentage is quit high in these monitoring stations.
SPM value of sampling stations faraway from the boundary of cluster
area shows annual average of 126.5µg/m3 . Among all the sampling stations,
faraway from the boundary cluster area, shows the maximum of 170µg/m
3
(Fig-3.16, Table-3.7). This sampling station is in the predominat direction of
wind blow and the station is nearer to the railway line and market area. The
sampling stations at Raidhi shows minimum of 90.0µg/m
3 at the distance
25KM away from the reference point (Kherual) and also the locality is free from
industries. The other stations ,
SPM concentrations are 150µg/m
3
71
, 125µg/m3 and 120µg/m
3 repectively (Table-3.7 Fig-3.16). These stations are
nearer to highway such as SH10, NH200 and they are also nearer to the market
area.
On analysis of SPM of cluster area, beyond cluster area and far- away
from cluster area the annual maximum value are 403.5µg/m3, 230µg/m
3 and
126.5µg/m3 respectively. The data indicate that the industrial area is highly
polluted as the permissible concentration of SPM in ambient air should be
100µg/m3. As regards the boundary and faraway from the boundary area the
concentration values are still higher than the permissible value of concentration of
SPM in ambient air (100µg/m3). The three values of SPM of cluster area,
beyond cluster and far away from cluster it can be concluded that the SPM
value decreses with respect to distance from the cluster area. The SPM of
cluster area 403.5 µg/m3
> boundary of cluster area 230.5 > faraway from
cluster area 126.5µg/m3.
Sulphur Dioxide(SO2)
The analytical data recorded in the (Table-3.5) reveals that the three years
annual average value of ten monitoring stations for SO2 in the cluster area is
103.81µg/m3. Among all the stations
and have the higher concentration of
120.5µg/m3 and 115.6µg/m
3 respectively. The minimum concentration is
80.2µg/m3 at
(Table-3.5, Fig-3.5). The other monitoring stations such as
, have concentrations 115.4µg/m
3 and 110.6µg/m
3 and 105.6µg/m
3 for
. The
concentration in the stations ,
, and
are all in the range 105.6-120.5µg/m3
72
i.e the density of SO2 is the higher and this is due to all the major coal-fired
power plants (Table-3.1) are located in this region. Besides this, at stations the
concentration of SO2 is 105.6µg/m3, the higher value is due to the power-plant at
OPGC, Banaharpali (420MW).
Beyond the bounary of cluster area, the annual average concentration of
SO2 is 92.63µg/m3. The maximum and minimum concentration in the area are
114.8µg/m3 at
and 68.8µg/m3 at
respectively (Table 3.6 and Fig-3.11). The
average concentration of SO2 of the stations
is 108.5µg/m3
which is less by 3.7µg/m3
from the average value of the same stations of the
cluster regions. The air samples collected from all sampling stations which are at
a distance of 25.7 KM from the boundary of the cluster area. The annual average
of SO2 concentration of those stations is 85.5µg/m3. The concentration is
maximum at (103.1µg/m
3) and minimum at
(66.5µg/m3) (Table-3.7, Fig-
3.17). The stations which show higher concentration are 100.5µg/m3 and 100.0
µg/m3 at
and respectively.
The SO2 concentration is maximum 120.5 µg/m3 at
and and minimum
80.2 at respectively. The annual average value is 103.81µg/m
3 in the cluster
area. In the beyond cluster the maximum value is 114.8µg/m3 and minimum is
68.8µg/m3 and average 92.63µg/m
3. The stations faraway from cluster area the
maximum and minimum values of concentration of SO2 are 103.1µg/m3
66.5µg/m3 respectively. On comparing the SO2 concentration with national
standard the concentration is much higher than the national standard 80µg/m3. So
it is clear, even at distance places from the cluster area the SO2 concentration
73
persists in the ambient air. However, when we compare the concentration of all
the three areas the concentration of SO2 is in the order 103.81µg/m3
> beyond
the boundary 92.63µg/m3
> faraway from the cluster area 85.5µg/m3.
Nitrogen dioxide(NO2):
The analytical data of nitrogen dioxide are recorded in (Table-3.5) and
the graphical representations of the data are shown in the Fig-3.6. It is noticed
from the three years annual average value of all the monitoring stations for NO2
in the cluster area is 105.55µg/m3. Among all the monitoring stations
is
having maximum concentration of NO2 (127.2µg/m3) and next to that
is
120.6µg/m3. The minimum concentration is 86.3µg/m
3 at
. (Table 3.5, Fig 3.6).
The higher concentration of NO2 in the cluster area around the stations ,
,
are most reasonable, since the mega coal-fired power stations are around
those stations i.e more coal burning in the plants. At the NO2 concentration is
also higher. Since there is also a major plants. The minimum concentration is
around the stations as the station is located inside the Brajrajnagar town. Since
no power plant nearer the township, it is natural NO2 concentration is low.
Outside the bounary of cluster area the annual average concentration of
NO2 is 88.75µg/m3
. The maximum concentration is at (112.5µg/m
3) and
minimum is at station (62.6µg/m
3 ) (Table- 3.6 and Fig-3.12). The other
monitoring stations which are having higher concentration of NO2 are
108.5µg/m3 and 104.5µg/m
3 at
and resepectively because they are also
nearer to mega power plants and they are also nearer to highways and rail lines.
74
Air samples were also collected from an average distance of 25.7 KM
from the boundary of the cluster. The analytical data were recorded in (Table-3.7)
and the graphical representation in Fig-3.18. The annual average of all the stations
is 78.6µg/m3. Maximum concentration is shown at
96.8µg/m3)
and
minimum at (53.8µg/m
3)(Table-3.7 Fig 3.18). Other monitoring stations
which show higher concentration are 92.8 µg/m3 and 90.6µg/m
3 at
and
respectively.
The analysis of the NO2 concentrations is given above indicates that in the
cluster area annual average concentration is 105.55µg/m3, beyond cluster is
88.75µg/m3 and that of area faraway from the boundary is 78.6µg/m
3. Based on
the maximum concentration of NO2 it can be said that the values are in the
order cluster area > beyond boundary of cluster area > faraway from the
boundary of cluster area.
Lead(Pb)
The annual average of all samplings of the cluster area is 2.087µg/m3. At
the station the maximum lead is 2.86 µg/m
3and lowest is 1.44µg/m
3 at the
stations . The stations
is nearer to the two mega plants such as Vedant
Captive Power and Sterlite Power plant, so there is every reason for generation of
more lead near the station . Stations
is inside the Brajarajnagar township
where no power stations is closer to the town, so it is natural that the lead
concentration is less (1.44µg/m3) (Table-3.5 Fig-3.7). Among the other stations
the lead concentration is 2.56µg/m3 at
and the next one is 2.16µg/m3 for both
75
the stations at and
. These stations are very close to Sterllite, Bhusan and
Vedant power plant respectively. Hence, the lead concentration is quite high in
these stations.
Annual average of the ten sampling stations out side the boundary of the
cluster area is 1.754µg/m3. Out of all the stations the maximum lead
concentration is 2.24µg/m3 at stations
and lowest to 1.31µg/m3 at the stations
. The stations
in the direction of north-east (NE) from power plants which is
the predominant direction of wind towards the sampling stations. Stations
which is 20KM away from the reference point have the minimum lead
concentration is 1.31 µg/m3. Among the other stations the lead is 1.82µg/m
3
both at and
. Station is close to Vedant, Sterllite and nearer to SH10,
whereas station is nearer to the OPGC plant. The lead concentration of
1.71µg/m3
is at . This station is also very close to mega power plants and
dominant direction of air blow. Hence, the lead percentage is quite high in these
monitoring stations (Table-3.6 and Fig- 3.13).
Lead concentration of sampling stations faraway from the boundary of
cluster area shows annual average of 0.969µg/m3 . Among them
shows the
maximum of 1.33µg/m3
(Table-3.7, Fig-3.19). Again the sampling stationsis in the
predominat direction of air and monitoring stations is near to the railway stations
and market area. The station which show minimum of 0.43µg/m3 is
. which is
far away from the reference point and the locality is free from industies. The other
stations ,
. have concentration of 1.16µg/m
3, 1.01µg/m
3 and
1.03µg/m3 repectively which may be due to the location of the station nearer to
76
SH10, NH200 and also nearer to the market area. The decrese trends of Pb in
three areas are given in Fig-3.25.
Mercury(Hg)
The analytical data are embodied in Table 3.5 and Fig-3.26. It is observed
that the annual average of mercury is 2.712µg/m3. Equal concentration of
3.06µg/m3 are observed at stations
and it may be due to location of three
mega power plants and burning of huge quantites of coal by the respective power
plants. Besides above, the other most polluted locations are at and
which
have pollution load of this metal are 2.88µg/m3 and 2.86µg/m
3 respectively
(Table-3.5, Fig-3.8).
Annual average of the monitoring stations out side the boundary of
industrial cluster area is 1.784µg/m3. The maximum concentration is 2.22µg/m
3 at
the monitoring stations and minimum is 1.1µg/m
3 at
. Other stations, which
have higher values of 2.1µg/m3 at stations
,
(Table-3.6 and Fig-3.14).
Stations faraway from the boundary of cluster area show annual average
value of 0.9µg/m3. Stations
and have maximum and minimum
concentration of 1.16µg/m3 and 0.41µg/m
3 respectively (Table-3.7, Fig-3.20).
Other stations which have higher value of 1.08 µg/m3 and 1.03µg/m
3 at
and
respectively. Comparative study of three areas is in Fig-3.26
77
Cadmium(Cd)
It is observed that the annual average of all the sampling stations of the
cluster area is 2.511 µg/m3. Out of all the stations the maximum cadmium is 2.86
µg/m3 at stations
and lowest is 1.52 µg/m3 at the stations
. S‟3 is nearer to the
three mega power plants and in the direction of wind. So there is every reason for
generation of more cadmium nearer the station . Station
is inside
Brajarajnagar township where no power station is closer to the town, so it is
natural that cadmium concentration is less (1.52µg/m3) (Table-3.5 Fig-3.9)
Among the other stations the cadmium concentration is 2.76µg/m3 at
and the
next one is 2.75 µg/m3 and 2.73 for both
and . These stations are very close
to three mega power plants. Hence, the cadmium concentration is quite high
which may be due to burning of coal as fuel.
Annual average of all the stations outside the boundary of the cluster area
is 1.772µg/m3. Out of all the stations, the maximum cadmium concentration is
2.08µg/m3 at stations
and lowest 1.26µg/m3 at station (
). The station is
in the direction of northeast (NE) which is the predominant direction of air in the
day time nearer to the sampling stations. Stations which is faraway from the
reference point is minimum (1.26µg/m3). Among the other stations the metal
value is 2.03µg/m3 at stations
, and
and that of is 2.01µg/m
3. These
stations are very close to mega power plants and also in the dominant direction of
wind. Hence, the metal concentration is quite high in these monitoring stations
(Table-3.6 and Fig- 3.15).
78
Stations faraway from the boundary of cluster area shows annual average
of 0.847µg/m3. Among the stations,
shows the maximum value of 1.06µg/m3
(Table-3.7, Fig-3.21), this may be due to the predominat direction of air and
nearer to the railway station and market area. The station which shows minimum
of 0.43µg/m3 is
which is faraway from the reference point and locality is free
from industies. The other stations ,
have concentration of 1.01µg/m
3 ,
0.93µg/m3 and 0.84µg/m
3 repectively it is due to the position of the station the
near to SH10, NH200 and near to the market respectively.
Basing on the result and discussion on SPM, SO2, NO2 and metals
(Pb,Hg,Cd) it can be concluded that impact of power plants in the cluster
region is much higher than outside the boundary of the cluster area and the
load decreses at a distance of more than 25KM. The mean concentration of all
three types of location have been compared with inernational standard like WHO8
European Union(EU)9 and national standard like NAAQS
7 (Table-3.8)
79
Table3.1:
Location of Thermal power plants and Power generation capacity in (MW) in Cluster area.
Sl No
Name of Power Plants Power Generation in (MW)
Sl No
Name of Power Plants
Power Generation in (MW)
1 Vedanta Captive Power
Plant.(CPP),
Bhurkhamunda,
Jharsguda
1215 6 Shyam DRI Pvt.
Ltd. Pandloi,
Sambalpur
30
2
Sterllite Energy Ltd.,
Bhurkhamunda,
Jharsguda
2400 7 OPGC,
Banaharpali,
Jharsguda
420
3 Bhusan Steel and
Power Ltd. Thelkoloi,
Jharsuguda
376 8 SMC, Steel and
Power, Hirma,
Jharsguda
20
4 Aryan Ispat and Energy
Ltd.,Bamloi,
Sambalpur
08 9 Action Ispat and
Power,Marakuta
Jharsguda
08
5 Viraj Steel and Energy,
Gurupali, Sambalpur
20 10 Eastern Steel
and Power Ltd.
Lahandabud.
08
Total power generation
(MW)
4019 Total power
generation
(MW)
486
Grand Total of Power generation in the Cluster area(MW) 4505
80
Table-3.2
Weather data of IMD-Jharsguda monitoring Station:
Month Temperature in Degree Celsius
Relative Humidity (%)
Average Wind Speed in Kmph
Total Rainfall
in Millimeter
Avg. Max
Avg. Min
0830hrs 1730hrs 0830hrs 1730hrs
January 29.0 11.9 62.0 40.6 2.6 1.3 1.0
February 32.7 15.2 50.3 29.0 3.3 3.0 3.9
March 35.7 18.4 48.3 27.6 3.6 5.3 0.2
April 40.8 24.4 38.3 2.0 3.6 5.3 14.7
May 41.4 27.3 49.3 30.3 4.3 6.0 48.9
June 38.3 27.4 63.3 46.6 5.0 5.3 140.2
July 31.8 25.2 82.6 76.0 4.0 4.0 421.7
August 31.8 25.2 84.3 79.3 4.0 4.0 254.1
September 32.3 24.7 81.3 77.3 4.0 3.0 287.3
October 32.7 21.1 70.3 63.0 2.3 1.0 60.4
November 31.0 17.4 68.6 55.6 3.0 0.6 4.7
December 28.3 13.7 68.3 51.0 2.3 0.6 11.8
Avg. =Average, Max. =Maximum, Min= Minimum
81
Table-3.3
Wind Direction IMD-Jharsguda monitoring Station:
N=North, S=South, E=east, W=West, C=Calm
Months Time Year
2009 2010 2011
January 0830 NE N NW
1730 SW C SW
February 0830 N N NW
1730 SW NW SW
March 0830 NE N NW
1730 S SW SW
April 0830 NE NE NE
1730 SW SW SW
May 0830 SE SE S
1730 SW SE S
June 0830 SW SW SSW
1730 S SW SW
July 0830 SW SE SW
1730 SW SE SW
August 0830 SW NE SW
1730 SW NE SW
September 0830 SW NE SW
1730 SW NE SW
October 0830 NE NE NE
1730 NE NE C
November 0830 NE NE NW
1730 NE NE C
December 0830 NE NE NE
1730 C NE C
82
Table-3.4(a):
List of Monitoring stations in the cluster area
Station
code Stations Name
Direction from
Reference place
Distance from
Reference
place
S.P. office Building,
Jharsguda N 11 KM
Jharsguda Engineering
School, Badheimunda NE 9 KM
Village School Building,
Banjari NE 6.5 KM
Village School
Building,Katikela SE 8 KM
Police station, Thelkoli S 3 KM
SBI, Building, Lapanga SE 7 KM
Village School Building,
Pandloi SE 12 KM
Village School Building,
Banaharpali SW 12 KM
SBI,office Building,
Brajarajnagar NW 10 KM
Municipality office
Building, Jharsguda N 8 KM
Table-3.4(b)
Monitoring Stations beyond the boundary of Cluster Area
Station
code Stations Name
Direction from
Reference
place
Distance
from
Reference
place
Airport office, Durlaga NE 15 KM
Village School Building , Arda NE 17KM
Village School Building ,
Badimal NE 18KM
Village School Building ,
Raghunathpur NE 15KM
Panchyat Office, Samasingha E 18KM
Police Station,
Katarbaga SE 19KM
Village School Building ,
Remenda SW 24KM
Village School Building ,
Bikramkhol W 20KM
Village School Building ,
Jamkani NW 20KM
83
Village School Building ,
Chichinda NW 20KM
Table 3.4(c)
Monitoring Stations far away from the boundary of ClusterArea
Station
code Stations Name
Direction from
Reference place
Distance from
Reference
place
Sundargarh Engineering
College Building, Kirei N 30KM
Police Station, Dharuadihi NE 30KM
Panchyat Office, Bagdihi NE 28KM
Police stations, Laikera NE 24KM
Panchyat office, Jhirlapali NE 23KM
Panchyat office, Laira SE 23KM
Village School building,
Gumlai SE 24KM
Panchyat office, Sason S 26KM
Block office, Lakhanpur W 25KM
Panchyat office, Raidihi NW 25KM
Table-3.5:
Analytical data of air of samples of the cluster area*
Parameters
Mean
Suspended
Particulate
Matters(SPM)(µg/m3)
392 424 460 445 450 380 376 410 340 358 403.5
Sulphur Dioxide SO2
(µg/m3) 102.2 110.6 120.5 115.4 115.6 96.6 98.6 105.6 80.2 92.8 103.81
Oxide of Nitrogen
NOx(µg/m3) 108.5 104.4 127.2 120.6 116.4 88.6 95.2 112.5 86.3 95.8 105.55
Lead (Pb)( µg/m3) 2.16 2.14 2.86 2.16 2.56 1.78 1.82 2.12 1.44 1.83 2.087
Mercury (Hg)( µg/m3) 2.04 2.86 3.06 2.88 3.06 2.76 2.66 2.97 1.98 2.85 2.712
Cadmium(Cd)( µg/m3) 2.02 2.73 2.86 2.75 2.76 2.68 2.52 2.72 1.52 2.55 2.511
84
*Average data of three consecutive years S‟= Samples of cluster area. Numerical figure indicates
the sampling stations number
Table-3.6:
Analytical data of air samples beyond cluster Area
Parameters
Mean
Suspended Particulate Matters(SPM)(µg/m3)
240 276 280 260 224 220 240 200 190 170 230
Sulphur Dioxide SO2
(µg/m3) 100.6 109.8 114.8 108.8 91.5 79.7 99.8 80.7 68.8 71.8 92.63
Oxide of Nitrogen
NOx(µg/m3) 108.5 104.5 112.5 98.8 82.8 75.2 98.6 80.5 63.5 62.6 88.75
Lead (Pb)( µg/m3)
1.82 1.71 2.24 2.02 2.14 1.64 1.82 1.42 1.42 1.31 1.754
Mercury (Hg)(
µg/m3)
1.95 1.91 2.22 2.1 1.62 1.86 2.1 1.5 1.48 1.1 1.784
Cadmium(Cd)
( µg/m3)
2.03 2.01 2.08 2.03 1.72 1.7 2.03 1.56 1.3 1.26 1.772
*Average data of three consecutive years S‟‟= Location of samples collected beyond cluster area
around (5-7 KM) from the boundary of cluster area). Numerical figure indicates the sampling
stations number
Table-3.7
Analytical data of air of the far away Sampling Stations:
Parameters
Mean
Suspended Particulate Matters(SPM)(µg/m3)
150 120 170 125 120 110 105 130 145 90 126.5
Sulphur Dioxide SO2
(µg/m3) 100 100.5 103.1 90.3 80.4 78.5 66.5 82.6 82.6 70.5 85.5
Oxide of Nitrogen
NOx(µg/m3) 90.6 92.8 96.8 80.7 76.6 58.6 53.8 81.8 83.8 70.5 78.6
Lead (Pb)( µg/m3) 1.16 1.03 1.33 1.01 1.01 0.84 0.84 1.02 1.02 0.43 0.969
Mercury (Hg)
( µg/m3) 1.08 1.03 1.16 0.86 0.86 0.81 0.75 1.02 1.02 0.41 0.9
Cadmium(Cd)
( µg/m3) 1.01 0.93 1.06 0.84 0.78 0.73 0.65 1.01 1.03 0.43 0.847
*Average data of three consecutive years S‟‟‟: Sampling stations far away from Cluster area around (20-
25KM) from the boundary
Numerical figure indicates the sampling stations number
85
Table-3.8:
Comparisons of Analytical data of air pollution from Industrial sampling stations
faraway sampling stations:
Parameters Standard
Mean S' Mean S'' Mean S''' WHO NAAQS EU
Suspended
Particulate
Matters(SPM)(µg/m3)
50 100 40 403.5 230.5 126.5
Sulphur Dioxide SO2
(µg/m3) 20 80 - 103.61 92.63 85.5
Oxide of Nitrogen
NO2(µg/m3) 40 80 40 105.55 88.75 78.6
Lead (Pb)( µg/m3) - 1.0 0.5 2.087 1.754 0.969
Mercury (Hg)( µg/m3) - - - 2.712 1.808 0.9
Cadmium(Cd)(
µg/m3) - - - 2.511 1.772 0.847
Table 3.9
National Ambient Air Quality Standard (1994) Act
Pollutant
Time Weighted
average Concentration in ambient air
Industrial Area Residential. Rural &
other areas,
Sensitive Area
SO2 Annual Average* 80 g/m³ 60g/m ³ 15 µg/m³
24 hours ** 120 µg/m³ 80 µg/m³ 30µg/m³
N02 Annual Average* 80 g/m³ 60g/m ³ 15 µg/m³
24 hours ** 120 µg/m³ 80 µg/m³ 30µg/m³
SPM Annual Average* 360 µg/m³ 140 µg/m³ 70 µg/m³
24 hours ** 500 µg/m³ 200 µg/m³ 100 µg/m³
RPM Annual Average* 120 µg/m³ 60g/m ³ 50g/m ³
24 hours ** 150g/m ³ 100g/m ³ 75g/m ³
Lead (Pb) Annual Average* 1.0g/m ³ 0.75g/m ³ 0.50g/m ³
24 hours ** 1.5g/m ³ 1.00g/m ³ 0.75g/m ³
CO 8 hours 5.0g/m ³ 2.0g/m ³ 1.0g/m ³
I hour 10.0g/m ³ 4.0g/m ³ 2.0g/m ³
86
* Annual Arithmetic mean of minimum 104 measurements in a year taken twice a week 24 hourly at
uniform interval
** 24 boundary/8 hourly values should be met 98% of the time in a year. However, 2% of the time, it
may exceed but not on two consecutive days.
Table 3.10
National Ambient Air Quality Standard (2009) Act Sl. No.
Pollutant Time weighted Average
Concentraion in ambient air
Industrial Residential
Rural & Other Areas
Ecological Sensitive areas
(notified by Central Govt.)
Methods of Measurement
1 SO2 g/m³ Annual* 50 20 -Improved West and
Gaeke
-Ultravilet fluorescence 24 hours** 80 80
2 N02 g/m³ Annual* 40 40 Modified Jacob
&Hochheiser(Na-
Arsenite) 24 hours** 80 80
3 PM10g/m³ Annual* 60 60 -Gravimetric
-TOEM
-Beta attenuation 24 hours** 100 100
4 PM2.5g/m³ Annual* 40 40 -Gravimetric
-TOEM
-Beta attenuation 24 hours** 40 60
5 O3g/m³ 8 hours** 100 100 -UV photometric
-Chemiluminescence
-Chemical method 1 hour* 180 180
6 Lead (Pb) g/m³ Annual* 0.50 0.50 AAS/ICP method after
sampling on EPM 200
or equavalent filter
paper
-ED-XRF using Teflon
filter
24 hours** 1.0 1.0
7 (CO) g/m³ 8 hours** 02 2.0 Non Dispersive
Infrared Spectroscopy 1 hour** 04 4.0
8 NH3 g/m³ Annual* 100 100 Chemiluminescence
-Indophenol blue
method 24 hours** 400 400
9 C6H6g/m³ Annual* 05 05 -Gas Chromatography
based continuous
analyser
-Adsorption and
Desorption followed by
GC analysis
10 BenzoPyreneg/
m³
Annual* 01 01 -Solvent extraction
followed by HPLC/GC
analysis
11 As ng/m3
Annual* 06 06 AAS/ICP method after
sampling on EPM 200
or equavalent filter
paper
12 Ni
ng/m3
Annual* 20 20 AAS/ICP method after
sampling on EPM 200
or equavalent filter
paper
87
* Annual Arithmetic mean of minimum 104 measurements in a year at a particular site
taken twice a week 24 hourly at uniform interval.
** 24 boundary or0 8 hourly monitored values as applicable should be complied with 98%
of the time in a year. 2% of the time, it may exceed but not on two consecutive days of
monitoring.
ANALYSIS OF AIR POLLUTION:
S‟1 S
‟2 S
‟3 S
‟4 S
‟5 S
‟6 S
‟7 S
‟8 S
‟9 S
‟10 M
Fig-3.4 : Suspended Particulate Matter(SPM) µg/m3
S
‟1 S
‟2 S
‟3 S
‟4 S
‟5 S
‟6 S
‟7 S
‟8 S
‟9 S
‟10 M
392 424
460 445 450
380 376
410
340 358
403.5
0
50
100
150
200
250
300
350
400
450
500
(SP
M)(
µg/
m3 )
102.2 110.6
120.5 115.4 115.6
96.6 98.6 105.6
80.2
92.8
103.81
0
20
40
60
80
100
120
140
SO2
(µg/
m3)
88
Fig-3.5 : Sulphur dioxide(SO2) µg/m3
S
‟1 S
‟2 S
‟3 S
‟4 S
‟5 S
‟6 S
‟7 S
‟8 S
‟9 S
‟10 M
Fig-3.6 : Nitrogen dioxide(NO2) µg/m3
S
‟1 S
‟2 S
‟3 S
‟4 S
‟5 S
‟6 S
‟7 S
‟8 S
‟9 S
‟10 M
Fig-3.7: Lead(Pb) µg/m3
108.5 104.4
127.2 120.6
116.4
88.6 95.2
112.5
86.3
95.8
105.55
0
20
40
60
80
100
120
140N
O2(
µg/
m3)
2.16 2.14
2.86
2.16
2.56
1.78 1.82
2.12
1.44
1.83
2.087
0
0.5
1
1.5
2
2.5
3
3.5
(Pb
)( µ
g/m
3 )
89
S
‟1 S
‟2 S
‟3 S
‟4 S
‟5 S
‟6 S
‟7 S
‟8 S
‟9 S
‟10 M
Fig-3.8 : Mercury(Hg) µg/m3
S
‟1 S
‟2 S
‟3 S
‟4 S
‟5 S
‟6 S
‟7 S
‟8 S
‟9 S
‟10 M
Fig-3.9 : Cadmium(Cd)µg/m3
2.04
2.86 3.06
2.88 3.06
2.76 2.66
2.97
1.98
2.85 2.712
0
0.5
1
1.5
2
2.5
3
3.5
(Hg)
( µ
g/m
3 )
2.02
2.73 2.86
2.75 2.76 2.68 2.52
2.72
1.52
2.55 2.511
0
0.5
1
1.5
2
2.5
3
3.5
(Cd
)( µ
g/m
3 )
90
M
Fig-3.10 : Suspended Particulate Matter(SPM) µg/m3
M
Fig-3.11: Sulphur dioxide(SO2) µg/m3
240
276 280
260
224 220
240
200 190
170
230
0
50
100
150
200
250
300
(SP
M)(
µg/
m3 )
100.6
109.8 114.8
108.8
91.5
79.7
99.8
80.7
68.8 71.8
92.63
0
20
40
60
80
100
120
140
SO2
(µg/
m3 )
91
M
Fig-3.12 : Nitrogen dioxide(NO2) µg/m3
M
Fig-3.13 : Lead(Pb) µg/m3
108.5 104.5
112.5
98.8
82.8 75.2
98.6
80.5
63.5 62.6
88.75
0
20
40
60
80
100
120
NO
2(µ
g/m
3)
1.82 1.71
2.24
2.02 2.14
1.64
1.82
1.42 1.42 1.31
1.754
0
0.5
1
1.5
2
2.5
(Pb
)( µ
g/m
3 )
92
M
Fig-3.14 : Mercury(Hg) µg/m3
M
Fig-3.15 : Cadmium(Cd)µg/m3
1.95 1.91
2.22 2.1
1.62
1.86
2.1
1.5 1.48
1.1
1.784
0
0.5
1
1.5
2
2.5
(Hg)
( µ
g/m
3
2.03 2.01 2.08 2.03
1.72 1.7
2.03
1.56
1.3 1.26
1.772
0
0.5
1
1.5
2
2.5
(Cd
)( µ
g/m
3)
93
S
‟‟‟1 S
‟‟‟2 S
‟‟‟3 S
‟‟‟4 S
‟‟‟5 S
‟‟‟6 S
‟‟‟7 S
‟‟‟8 S
‟‟‟9 S
‟‟‟10 M
Fig-3.16 : Suspended Particulate Matter(SPM) µg/m
3
S
‟‟‟1 S
‟‟‟2 S
‟‟‟3 S
‟‟‟4 S
‟‟‟5 S
‟‟‟6 S
‟‟‟7 S
‟‟‟8 S
‟‟‟9 S
‟‟‟10 M
Fig-3.17 : Sulphur dioxide(SO2) µg/m3
150
120
170
125 120
110 105
130
145
90
126.5
0
20
40
60
80
100
120
140
160
180
(SP
M)(
µg/
m3
100 100.5 103.1
90.3
80.4 78.5
66.5
82.6 82.6
70.5
85.5
0
20
40
60
80
100
120
SO2
(µg/
m3 )
94
S
‟‟‟1 S
‟‟‟2 S
‟‟‟3 S
‟‟‟4 S
‟‟‟5 S
‟‟‟6 S
‟‟‟7 S
‟‟‟8 S
‟‟‟9 S
‟‟‟10 M
Fig-3.18 : Nitrogen dioxide(NO2) µg/m3
S
‟‟‟1 S
‟‟‟2 S
‟‟‟3 S
‟‟‟4 S
‟‟‟5 S
‟‟‟6 S
‟‟‟7 S
‟‟‟8 S
‟‟‟9 S
‟‟‟10 M
Fig-3.19 : Lead(Pb) µg/m3
90.6 92.8 96.8
80.7 76.6
58.6 53.8
81.8 83.8
70.5
78.6
0
20
40
60
80
100
120
NO
2(µ
g/m
3 )
1.16
1.03
1.33
1.01 1.01
0.84 0.84
1.02 1.02
0.43
0.969
0
0.2
0.4
0.6
0.8
1
1.2
1.4
(Pb
)( µ
g/m
3)
95
S‟‟‟
1 S‟‟‟
2 S‟‟‟
3 S‟‟‟
4 S‟‟‟
5 S‟‟‟
6 S‟‟‟
7 S‟‟‟
8 S‟‟‟
9 S‟‟‟
10 M
Fig-3.20 : Mercury(Hg) µg/m3
S
‟‟‟1 S
‟‟‟2 S
‟‟‟3 S
‟‟‟4 S
‟‟‟5 S
‟‟‟6 S
‟‟‟7 S
‟‟‟8 S
‟‟‟9 S
‟‟‟10 M
Fig-3.21 : Cadmium(Cd)µg/m3
1.08 1.03
1.16
0.86 0.86 0.81
0.75
1.02 1.02
0.41
0.9
0
0.2
0.4
0.6
0.8
1
1.2
1.4
(Hg)
( µ
g/m
3)
1.01
0.93
1.06
0.84 0.78
0.73
0.65
1.01 1.03
0.43
0.847
0
0.2
0.4
0.6
0.8
1
1.2
(Cd
)( µ
g/m
3 )
96
S
‟ S
‟‟ S
‟‟‟ S
Fig-3.22 : Mean Suspended particulate matter(SPM) µg/m3
S
‟ S
‟‟ S
‟‟‟ S
Fig 3.23 : Mean concentration of Sulphur dioxide(SO2) µg/m3
403.5
230
126.5 100
0
50
100
150
200
250
300
350
400
450
(SP
M)(
µg/
m3 )
103.61
92.63 85.5
80
0
20
40
60
80
100
120
SO2
(µg/
m3 )
97
S
‟ S
‟‟ S
‟‟‟ S
Fig-3.24 : Mean concentration of NOx in µg/m
3
S
‟ S
‟‟ S
‟‟‟ S
Fig-3.25 : Mean concentration of Lead(Pb) in µg/m3
105.55
88.75
78.6 80
0
20
40
60
80
100
120
NO
x(µ
g/m
3 )
2.087
1.754
0.969 1
0
0.5
1
1.5
2
2.5
(Pb
)( µ
g/m
3 )
98
S
‟ S
‟‟ S
‟‟‟ S
Fig-3.26 : Mean concentration of Mercury(Hg) in µg/m3
S
‟ S
‟‟ S
‟‟‟ S
Fig -3.27 : Mean concentration of Cadmium(Cd) in µg/m3
2.712
1.784
0.9 1
0
0.5
1
1.5
2
2.5
3
(Hg)
( µ
g/m
3)
2.511
1.772
0.847 1
0
0.5
1
1.5
2
2.5
3
(Cd
)( µ
g/m
3 )
99
References:
1. Indian Map Service Sector „G‟ Shastri Nagar, Near Ram Mandir,
Jodhpur-3, Rajasthan(India) P- 30, 2008.
2. Final Report “Critically Polluted Industrial Clusters(Ib Valley-Jharsuguda
Area), Orissa Pollution Control Board,Bhubaneswar,December 2010.
3. http://www.mcl.gov.in/
4. http://earth.google.com/
5. Indian Meteorological Department(IMD), Bhubaneswar vide letter No:CS-
03006/NR(P)/XXII/063.
6. National Ambient Air Quality Standards(NAAQS), Central Pollution
Control Board(CPCB), Notification,Delhi, the 11th
April, 1994.
7. National Ambient Air Quality Standards(NAAQS), Central Pollution
Control Board (CPCB), Notification, New Delhi 18th
November 2009.
8. WHO Air quality guidelines for particulate matter, ozone, nitrogen dioxide
and sulfur dioxide Global update 2005 Summary of risk assessment, WHO
Press, World Health Organization, 20 Avenue Appia, 1211 Geneva 27,
Switzerland, PP 09,16,18,2006.
9. http://ec.europa.eu/environment/air/quality/standards.htm.
99
ANNEXURE
CHAPTER-IV
EVALUATION OF IMPACT DUE TO COAL-FIRED
THERMAL POWER PLANTS ON THE WATER QUALITY OF
THE ENVIRONMENT
In the previous chapter, the pollution load on air due to a number of coal-
fired thermal power plants in the cluster area of the undivided portion of
Sambalpur district of Odisha has been discussed. One of our other objectives of the
study is to investigate the impact of those coal-fired thermal power plants of said
cluster on the water quality of the area. The experimental works on water quality
determination, the findings and the discussion on the results will be described in
this chapter.
The Water Resources
United Nation’s Water Conference of March,19971, held in Argentina,
recorded that, “If the world’s water were represented by half-gallon bottle the
quantity of fresh water would be about half a tea spoon and a single droplet would
sufficient to represent the surface–flowing waters(rivers and streams), the rest
being ground water”. Similarly, Rao (1975)2 in his book entitled, “Water wealth of
India” pointed out that, “of the total available water, approximately 97.3% is
contained in the oceans and the remaining 2.7% is mostly in solid form. The
amount of water actually available over the ground is a very small fraction and is
estimated to be 1x10-5
% of the total water resources of the world.
100
Surface water systems have formed lifeline for the growth of human
civilization. The industrialization, urbanization, a fast-growing population and lack
of comprehensive liquid & solid waste disposal systems and sanitation facilities
have contributed to the pollution of surface water system. The contaminations of
water with hazardous substances create health havoc as surface water forms life
line of civilisation.
Odisha is blessed with abundant resources both surface and ground water
as compared to its size and population at national level5. However, the water
resources of Odisha, depends upon the rainfall which is unevenly distributed. A
part of the rainfall is lost by evaporation, transpiration and deep percolation, while
the other part is stored as ground water resources and the balance flow down to sea
as surface runoff. During summer, most of the water resources get dry due to high
temperature. During monsoon there are very wet days as well as long spells.
The study area is dominated by Bheden and IB-river system. There is a
large variation in ground water potential and therefore, water table over the area is
highly variable. It lies below 4-8 meters from ground during pre-monsoon, while
during post-monsoon; it ranges between 1.5-3 meters below ground levels (Central
Ground Water Board, CGWB)3.
Surface Water Quality
A total of ten coal-fired thermal power plants are located in the cluster area.
Other than these plants there are small, medium and major production industries
101
located in this cluster area. All the industries located in the cluster area are more or
less water intensive industries and they all require water for operation in general
but after the use the unused water may be discharged in to common water bodies
resulting water pollution. So, coal-fired thermal plants are no way less culprit
for water pollution. One can observe in the vicinity of an industrial cluster, large
patches of very dirty and unhealthy swampy areas without any common
boundaries, where the cluster of local industries discharges their effluent.
Whatever pollution load observed in water of the study area is due to flow
of pollutants from the industries to the water bodies of the surface. Therefore, the
surface water quality in the cluster area is conducted to assess the quality of
surface water in the area in accordance with the standard prescribed by Central
Pollution Control Board (CPCB)4. The different standard for different parameters
is described in Table-4.15. The water can be classified based on use which is
mentioned in Table-4.24. In Table-4.3 the tolerance limit of surface water subject
to pollution6
In the present study the surface water and underground water of the cluster
area and beyond cluster area (Fig-3.2) have been examined. The water samples of
the two areas were analysed for a few important water quality parameters. The
sampling stations are recorded in Table-4.4 and Table-4.5
102
Significance of a few Important Physico-Chemical Parameters of
Water:
pH
The pH of natural water is affected by various physical and biological
processes both natural and anthropogenic. Acidic or alkaline water have the ability
to leach many metals and can be detrimental to certain vital biological processes7.
pH range for fresh water for aquatic life should be 6.5- 8.5
Conductivity
Conductivity of the ground water samples was measured to have some idea
about dissolve solid present in water sample. A higher value of conductivity
indicates presence of more soluble solids and hence more pollutants7.
Biochemical Oxygen Demand(BOD)
Water containing high organic substances encourages the growth of
decomposers which required excess oxygen to decompose the organic material
present in water bodies. The amount of oxygen required for this activity is known
is Biochemical Oxygen Demand (BOD). It is a measure of the contamination of
water8.
103
Chemical Oxygen Demand (COD)
The chemical oxygen demand (COD) is another parameters of water
quality, which measure all organics, including the non-biodegradable substances. It
is a chemical test using a strong oxdising agent (Potassium dichromate) sulphuric
acid, and heat. The result of the COD test can be available in just 2 hours8.
Total Dissolved Solid (TDS)
Total dissolved solids are generally due to the soluble inorganic salts
present in water. Excess TDS are objectionable in drinking water because of
physiological effect and unpalatable taste9. Though dissolved solids have
negligible effects on aquatic life, but unsettle able and suspended solids should not
reduce the depth of light penetration by more than 10% .
Hardness
Hardness is caused due to the presence of chloride, sulphates and bicarbonates, etc.
of calcium, magnesium and iron. Generally the total hardness below 75 mg/l is
termed as soft water and above 150 mg/l is termed as hard water. If the value of
hardness is more than 300 mg/l, it is classified as very hard water, which should
not be used for domestic purpose10
.
104
Total Alkalinity
The alkalinity of water is its capacity to neutralise the acid. Alkalinity itself
is not harmful to human health but the portable water from the pipe line should
have alkalinity below 100 mg/l10
.
SAMPLING PROCEDURE
Sampling and preservation of water samples were done strictly in
accordance with standard methods adopted by APHA (1989)11
. All the formalities
like labelling of samples with respect to collecting points, date and times were also
followed to overcome possible error between collection and analysis. In specific
terms, the whole of the sampling procedure was as follows:
Sample Containers
The samples were collected from each sampling stations between 7AM to
9AM in the clean, screw-capped plastic bottles (Kudesia, 1985) for physico-
Chemical analysis.
Sample Labelling
As soon as sampling was over, the sample containers were labelled with the
following details:
(a) Sampling stations
105
(b) Sampling date and time
(c) pH, temperature, conductivity and dissolved oxygen of the samples (which
were measured on the spot)
Sample Collection
Water samples were collected from all the stations. For this purpose the
samples were always collected from just below the surface of water. Prior to
sampling, the collection bottles were rinsed well and then filled upto neck and
stoppered immediately to prevent accidental entry or escape as well as contact with
outside atmosphere.
Spot Analysis
In anticipation of possible changes in certain water quality parameters with
respect to time, these were measure immediately after sample collection.
Parameters which are analysed on the spot are pH, temperature, conductivity and
dissolved oxygen. All the others parameters were determine in the laboratory after
transporting the samples there, for a few parameters, the samples were preserved
by adding recommended preservatives as per the standard method, APHA(1989)11.
106
Sampling Frequency
In order to examine the variation and trends of the different parameters
over time, samples were collected for two years (24 months) on a monthly basis,
starting from January-2011 to December-2012. Three grab samples were collected
from each of the sampling stations every month throughout the year 2011 and
2012. The values listed in Table-4.6, to Table 4.9, various water quality parameters
are the average values obtain from three grab samples collected each month from
each sampling stations. In these Tables, all values are in mg/l except for pH and
conductivity. The procedure adopted is equal to both for the samples of the cluster
area and that of beyond the cluster area.
Analysis of Samples
The samples were analysed for the following water quality parameters:
(i) pH
(ii) Conductivity
(iii) Dissolved Oxygen (DO)
(iv) Biological Oxygen Demand (BOD)
(v) Chemical Oxygen Demand (COD)
(vi) Total Dissolved Solids (TDS)
107
(vii) Total Hardness
(viii) Alkalinity,
(ix). TC (MPN/100ml)
(x) FC (MPN/100ml)
Experimental Methods
The pH was determined with the help of ORION ion selective meter,
Model No.720A PLUS. Conductivity was measured with conductivity meters
(SYSTRONICS, Model No 306). Total dissolved solids were determined by the
gravimetric method. The total alkalinity was obtained by titrating against sulphuric
acid solution using methyl orange as an indicator. Hardness was determined by
using complexometric technique, where known aliquot of water samples were
titrated against EDTA with Erichrome black-T indicators. Hardness of water was
calculated in terms of mg CaCO3 per litre. Dissolve Oxygen was measured by
Wrinkler titrimetric azidemodification(Iodometric) method. Biochemical Oxygen
Demand (BOD) was measured by the method which consists of filing with
samples, to overflowing, in an airtight bottle of the specified size, and incubating it
at 270C for 3 days. Dissolve Oxygen is measured initially and after incubation, and
the BOD is computed from the difference between initial and final DO. Because
the initial DO is determined immediately after the dilution is made, all oxygen
uptake including that occurring during the first 15 minutes is included in the BOD
measurement. Chemical Oxygen Demand (COD) was measured using potassium
108
dichromate as an oxidant in the presence of sulphuric acid. The excess dichromate
remaining after oxidation was titrated against standard ferrous ammonium sulphate
solution using ferroin indicators. COD was measured by closed reflux, titrimetric
method with the help of HACH, COD Reactor Model No.45600.
In general, the methods recommended by APHA (1989)11
were followed
for the analysis of various parameters.
In the graphical representation of the different parameters of surface water
and ground water, the maximum, minimum and average values are denoted in the
colour red, green and yellow respectively (Figs. 4.1 to 4.40)
Results and Discussion
The analytical data of a few important parameters of surface water and
ground water of the cluster area are recorded in the Table-4.6 and Table-4.7
respectively. The minimum, maximum and average analytical data of those
parameters are recorded in Table-4.10. The graphical representations of the
parameters are shown in Fig-4.1 to Fig-4.20and that of average data in Fig-4.41.
The analytical data of different parameters of surface water and ground
water of the area beyond the cluster are recorded in Table-4.8 and Table-4.9
respectively. The minimum, maximum and average data of those parameters are
recorded in Table-4.11. The graphical representations of the above data are shown
in Fig-4.21 to Fig-4.40 and that of the average value is in Fig-4.42. All the values
are in mg/l, except pH, conductivity and total Coliform (TC)
109
The pH values of eight surface water samples of the cluster area is in the
range 6.1-8.8 and the average value is 7.21. On comparing these data with the
standard data (6.5-8.5) for drinking purpose the surface water can be used for
drinking only after disinfection. However, the water is suitable for outdoor use like
bathing, swimming and sports purpose. In case of ground water of cluster area the
pH range of all the eight samples is in the range 6.3- 8.6 and their average value is
7.9. As above the source of ground water of the cluster area need treatment before
using it for drinking. However this water of the cluster area can be used like
surface water for outdoor bathing, swimming etc.
The pH values of both types of water of the area beyond cluster are
recorded in Table-4.8 and Table-4.9. The minimum, maximum and average value
are 7.5, 8.3 7.8 and 7.3, 8.0 7.61 respectively. The water of either of sources cannot
be used for drinking directly from the sources but can be done after conventional
treatment. But the water can be used for outdoor activities.
The analytical data of DO of the sources of water in the cluster area as well
as that of outside the boundary reveals that like pH, the water can be used for
drinking with necessary conventional treatment. But the water can be used without
treatment for outdoor activities.
The BOD values of surface water and that of ground water quality of the
cluster area is in the range 2.7-4.3 with average value 3.6 and 1.8- 3.3 and 2.9
respectively. BOD values of both types of water of the area beyond cluster are
recorded in Table-4.8 and Table-4.9. The minimum, maximum and average values
110
are 1.4, 3.0, 2.2 and 1.3, 2.5, 1.8 respectively. The water either of sources cannot
be used for drinking directly from the sources since the BOD values exceeds in all
the cases. However, the water can be used for drinking after necessary
conventional treatment.
The COD values of surface water quality of the cluster area is in the range
18.5-22.5 with average value 20.04 of all the samples and that of ground water of
cluster area the COD value range is 17.5-20.6 and their average value is 19.9.
COD values of both types of water beyond cluster are recorded in Table-4.8 and
Table-4.9. The minimum, maximum and average values are 14.2, 20.5, 17.8 and
12, 18.5, 14.6 respectively. Since there is no standard data for COD it can be told
about the quality of the water.
The total coliform (TC) organism for drinking water without conventional
treatment should be 50 MPN/100ml but the values of all the samples in the present
study are around 2-3 folds more than the standard values of drinking water. Hence,
no water sample of the cluster area or beyond should be taken for drinking.
However, after conventional treatment the water can be used for drinking.
111
Table 4.1
Drinking water standard (Manual on Water Supply and Treatment)
Sl
No
Characteristics Unit Acceptable Cause for
Rejection
1 Turbidity - 2.5 10
2 Colour hazness 5.0 2.5
3 Taste and odour - unobjectionable unobjectionable
4 pH 7.5-8.5 >8.5
5 Total dissolve Solids mg/l 500 1500
6 Total Hardness mg/l 200 600
7 Chloride as Cl- mg/l 200 1000
8 Sulphate as SO42-
mg/l 200 400
9 Fluoride as F- mg/l 1.0 1.5
10 Nitrate as NO3- mg/l 45 45
11 Calcium as Ca2+
mg/l 75 200
12 Magnesium Mg 2+
mg/l 30 150
13 Iron as Fe3+
mg/l 0.1 1.0
14 Manganese as Mn2+
mg/l 0.05 0.5
15 Copper as Cu2+
mg/l 0.05 1.5
16 Zinc as Zn2+
mg/l 5.0 15
17 Phenolic compounds mg/l 0.001 0.002
18 Anionic detergent mg/l 0.2 1.0
19 Minerals oil mg/l 0.01 0.3
20 Arsenic as As3+
mg/l 0.05 0.05
21 Cadmium as Cd2+
mg/l 0.01 0.01
22 Chromium as Cr3+
mg/l 0.05 0.05
23 Cyanide as CN-
mg/l 0.05 0.05
24 Lead as Pb2+
mg/l 0.1 0.1
25 Selenium as Se2+
mg/l 0.01 0.01
26 Mercury as Hg2+
mg/l 0.001 0.001
112
Table 4.2
Classification of water based on use
Class Mode of Use Required Parameters
A Drinking water source without
conventional treatment but after
disinfection
(i) Total coliform organism MPN/100ml
shall be 50 or less
(ii) pH between 6.5-8.5
(iii) DO= 6 mg/L or more
(iv) BOD= 2mg/L or less
(v) There shall be no visible discharge
of domestic or industrial waste
B Outdoor bathing, swimming
and water contact sports.
(i) Total coliform organism MPN/100 ml
shall be 50 less.
(ii) pH between 6.5-8.5.
(iii) DO= 5 mL or more
(iv) BOD= 3 mg/L or less
(v) All domestic and industrial waste water
discharged upstream of bathing place
shall be so regulated that the standard are
maintained and there is no visible floating
matter including oil in
bathing places.
C Drinking water sources with
conventional treatment
followed by disinfection
(i) Total coliform organism MPN/100 ml shall
be 5000 or less.
(ii) pH between 6.0-9.0.
(iii) DO= 4 mg/L or more
(iv) BOD= 2 mg/L or less
D Propagation of wind life and
Fisher
(i) pH between 6.5-8.5.
(ii) DO= 4 mg/L or more
(iii) Free ammonia (as N) i= 1.2 mg/L or less
E Irrigation, industrial cooling
and controlled
(i) pH between 6.0-8.5.
(ii) Electrical connectivity at 250 C, Max
2250 mho/cm
(ii) Sodium absorption ratio max;36
(iii) Sodium absorption ratio: max 26.
(iv) Boron max=2 mg/l
113
Table 4.3
Tolerance limits for Land surface Water subjected to pollution:
Sl
No
Characterstics Unit Tolerence of different classes
A B C D E
1 pH value 6.5-8.5 6.5-8.5 6.5-8.5 6.5-8.5 6.0-8.5
2 Colour hazens 10 300 300 - -
3 Odour - Unobject
-ionable
unobject-
ionable
Unobject
-ionable
Unobject
-ionable
Unobject
-ionable
4 Taste - Tasteless Tastele
ss
Tastele
ss
Tasteless Tasteless
5 DO mg/l 6 5 4 -
6 BOD mg/l 2 3 3 -
7 TDS mg/l 500 - 1500 - 2100
8 Chloride mg/l 250 - 600 - 600
9 Total Hardness mg/l 300 - - -
10 Ca-Hardness mg/l 200 - - -
11 Mg Hardness mg/l 100 - - -
12 Iron mg/l 3.3 - 5.0 - -
13 Manganese mg/l 0.5 - - - -
14 Copper mg/l 1.5 - 1.5 - -
15 Sulphate mg/l 400 - 400 - 1000
16 Nitrate mg/l 20 - 50 - -
17 Chloride mg/l 1.5 1.5 1.5 - -
18 Phenolic Comp. mg/l 0.002 0.005 0.005 - -
19 Mercury mg/l 0.001 - 0.001 - -
20 Cadmium mg/l 0.01 - 0.01 - -
21 Selenium mg/l 0.01 - 0.05 - -
22 Arsenic mg/l 0.05 0.2 0.2 - -
23 Cyanides mg/l 0.05 0.5 0.05 - -
24 Lead mg/l 0.05 - 0.1 - -
25 Zinc mg/l 15.0.05 - 15 - -
26 Chromium mg/l 0.05 0.05 0.05 - -
27 Anionic
detergent
mg/l 0.2 1 1 - -
28 PAH µg/l 0.2 - - - -
29 Minerals oil mg/l 0.01 - - - -
30 Barium mg/l 1 - - - -
31 Silver mg/l 0.05 - - - -
32 Pesticides&
insect.
mg/l Absent - - - -
33 Alpha centre uc/ml 10 10 10 10 -
34 Beta emitter uc/ml 10 10 10 10 10
35 Total coliform MPN/l 50 500 - - -
114
Table-4.4(a)
List of Monitoring stations of surface water in the cluster area
Station code Stations Name
Direction and
Distance from Kherual
Direction Distance
Pond, behind Collecteriate, Jharsguda N 11 KM
Pond,Debadihi, village NE 9 KM
Pond, Banjari, village NE 6.5 KM
Pond,Katikela, village SE 8 KM
Pond, Thelkoli, village S 3 KM
Pond, Pandloi, village SE 12 KM
Banaharpali, Village School Building, NE 12 KM
Pond near Municipality office,
Jharsguda N 8 KM
Table-4.4(b):
List of Monitoring stations of Ground water in the cluster area
Station
code Stations Name
Direction and
Distance Kherual
Direction Distance
Tube well near Collecteriate,
Jharsguda N 11 KM
Well(6.5mts), Debadihi, village NE 9 KM
Well (6.3mts), Banjari village NE 6.5 KM
Well (6.0mts),in Katikela in the
village SE 8 KM
Well(6.5mts), Thelkoli village S 3 KM
Well(5.5mts), Pandloi village S 12 KM
Well(5.0mts), Banaharpali in the
village SW 12 KM
Tube well Muncipality office,
Jharsguda N 8 KM
115
Table-4.5(a)
Monitoring Stations of Surface water beyond the boundary of Cluster Area
Station
code Stations Name
Distance and Direction
from Kherual
Direction Distance
Pond, Durlaga, Village NE 15 KM
Pond, Arda, Village NE 17KM
Pond , Badimal, Village NE 18KM
Pond, Raghunathpurvilage NE 15KM
Pond, Samasingha Village E 18KM
Pond,Katarbaga, Village SE 19KM
Pond, Remenda, Village SW 24KM
Pond, Chichinda, Village NW 20KM
Table-4.5(b)
Monitoring Stations of Ground water beyond the boundary of Cluster Area
Station
code Stations Name
Direction and
Distance from Kherual
Direction Distance
Well (5.5mts), Durlaga of Village NE 15 KM
Well(4.6mts), Arda, Village NE 17KM
Well (4.3mts), Badimal Village NE 18KM
Well(4.0mts), Raghunathpur Village NE 15KM
Well (3.6mts), Samasingha Village E 18KM
Tube wellof Katarbaga Village SE 19KM
Well(3.0mts), Remenda Village SW 24KM
Well(3.6mts), Chichinda, Village NW 20KM
116
Table-4.6
Surface water quality of the Cluster area*
Sl No.
Parameters Concentration of pollutant
Mean
1 pH 6.1 6.9 8.8 7.8 7.6 6.8 6.2 7.5 7.21
2 Conductivity
(µScm-1) 200.8 280.2 320.8 315.6 310.5 300.4 280.2 270.6 284.9
3 DO 9.2 7.2 6.1 6.3 6.5 7.1 7.2 7.4 7.13
4 BOD 2.7 3 4.3 4.2 4 3.5 3.1 4.0 3.6
5 COD 18.5 18.8 22.5 20.2 20.6 20.1 19.4 20.2 20.04
6 TDS 180 186 220 200 190 180 170 175 187.6
7 Total Hardness 72.4 75.2 94.6 88.4 87.2 84.2 72.6 83.2 82.2
8 Alkalinity 58.8 61 77.8 65.4 68.2 66.4 68 65.2 66.4
9 TC(MPN/100ml) 120 115 150 130 135 124 110 125 126.1
10 FC(MPN/100ml) 75 80 100 90 105 80 70 85 85.6
All values are in mg/l except pH, conductivity, TC and FC.
*Average data of two consecutive years
= Samples of cluster area. Numerical figure indicates the sampling stations number
Table-4.7
Ground water quality of the Cluster area* Sl No.
Parameters Concentration of pollutant
Mean
1 pH 6.3 7.4 8.6 8.3 7.9 8.1 8.3 8.2 7.9
2 Conductivity(µScm-1) 120.8 160.2 208.8 202.6 200.5 155.4 130.2 140.6 164.9
3 DO 8.2 8 6.5 6.6 6.7 7.2 7.1 7.9 7.3
4 BOD 2.7 2.8 3.3 3.2 3.1 3 2.9 1.8 2.9
5 COD 17.5 19.8 21.5 20.2 20.6 20.1 20.4 19.2 19.9
6 TDS 160 175 223 215 185 170 162 160 181.3
7 Total Hardness 52.4 55.2 70.6 68.4 56.2 64.2 62.4 61.2 61.3
8 Alkalinity 50.8 51 70.8 68 60.2 66.4 66 68.2 62.7
9 TC(MPN/100ml) 120 110 140 130 125 115 120 130 123.8
10 FC(MPN/100ml) 75 60 75 71 81 65 70 57 69.3
All values are in mg/l except pH, conductivity, TC and FC.
*Average data of two consecutive years = Samples of cluster area.
Numerical figure indicates the sampling stations number
117
Table-4.8
Surface water quality of beyond the Cluster area* Sl
No. Parameters Concentration of pollutant
Mean
1 pH 7.6 8.1 8.3 8 7.8 7.8 7.6 7.5 7.8
2 Conductivity(µScm-1) 110.8 120.2 140.8 135.6 120.5 105.4 100.2 100.6 116.8
3 DO 10.2 9.3 8.1 8.2 8.8 9.8 9.2 9.8 9.2
4 BOD 2.2 2.8 3 2.4 1.8 1.6 2 1.4 2.2
5 COD 18.5 19.8 20.5 19.2 16.6 15.1 18.4 14.2 17.8
6 TDS 162 180 200 175 125 115 150 110 152.1
7 Total Hardness 50.4 58.2 60.6 48.4 46.2 44.2 52.2 53.2 51.7
8 Alkalinity 66.8 60.8 62.8 71 52.2 51.4 55 55.2 59.4
9 TC(MPN/100ml) 100 115 120 115 110 95 90 104 106.1
10 FC(MPN/100ml) 50 55 65 50 48 50 40 50 51
All values are in mg/l except pH, conductivity, TC and FC. *Average data of two consecutive years
= Samples of cluster area. Numerical figure indicates the sampling stations number
Table-4.9
Ground water quality of beyond the Cluster area* Sl
No. Parameters Concentration of pollutant
Mean
1 pH 7.5 7.7 8.0 7.9 7.6 7.4 7.5 7.3 7.61
2 Conductivity(µScm-1) 100.8 105.2 106.8 118.2 100.5 96.4 98.2 95.6 102.7
3 DO 11.9 10.5 9.1 8.9 9.8 10.8 10.4 10.9 10.3
4 BOD 1.8 2.2 2.5 2.1 1.5 1.4 1.7 1.3 1.8
5 COD 15.5 16.8 18.5 16.2 12.6 12.1 13.4 12 14.6
6 TDS 145 160 180 150 125 120 136 112 141
7 Total Hardness 50.2 55.2 60.6 50.4 47.2 44.2 48.4 43.2 49.9
8 Alkalinity 52.8 61 65.8 60 55.2 60.4 60 55.2 58.8
9 TC(MPN/100ml) 85 90 122 110 105 100 101 90 100.4
10 FC(MPN/100ml) 60 70 75 70 60 72 73 58 67.3
All values are in mg/l except pH, conductivity, TC and FC. *Average data of two consecutive years
= Samples of cluster area. Numerical figure indicates the sampling stations number
118
Table-4.10
Minimum, maximum and average value of Surface and
Ground water of cluster area.
Parameters SW' GW'
Minimum Maximum Average Minimum Maximum Average
pH 6.1 8.8 7.2 6.3 8.6 7.9
Conductivity(µScm-1) 200.8 320.8 284.9 120.8 208.8 164.9
DO 6.1 9.2 7.1 6.5 8.2 7.3
BOD 2.7 4.2 3.6 1.8 3.3 2.9
COD 18.5 22.5 20.0 17.5 21.5 19.9
TDS 170 220 187.6 160 223 181.3
Total Hardness 72.4 94.6 82.2 52.4 70.6 61.3
Alkalinity 58.8 77.8 66.4 50.8 70.8 62.7
TC(MPN/100ml) 100 150 126.1 110 140 123.8
FC(MPN/100ml) 70 105 85.6 57 81 69.3
Table-4.11
Minimum, maximum and average value of Surface and
Ground beyond the cluster area
Parameters SW'' GW''
Minimum Maximum Average Minimum Maximum Average
pH 7.5 8.3 7.8 7.3 8.0 7.6
Conductivity(µScm-1) 100.6 140.8 116.8 95.6 118.2 102.7
DO 8.1 10.2 9.2 8.9 11.9 10.3
BOD 1.4 3.0 2.2 1.3 2.5 1.8
COD 14.2 20.5 17.8 12.0 18.8 14.6
TDS 110.0 200.0 152.1 112.0 180.0 141.0
Total Hardness 44.2 60.6 51.7 43.2 60.6 49.9
Alkalinity 51.4 71.0 59.4 52.8 65.8 58.8
TC(MPN/100ml) 90.0 120.0 101.6 85.0 122 100.4
FC(MPN/100ml) 40.0 65.0 51.0 58.0 75.0 67.3
119
Table-4.12
Minimum, maximum and average value of Surface water of cluster and
beyond cluster area.
Parameters SW' SW''
Minimum Maximum Average Minimum Maximum Average
pH 6.1 8.8 7.21 7.5 8.3 7.8
Conductivity(µScm-1) 200.8 320.8 284.9 100.6 140.8 116.8
DO 6.1 9.2 7.13 8.1 10.2 9.2
BOD 2.7 4.2 3.6 1.4 3.0 2.15
COD 18.5 22.5 20.0 14.2 20.5 17.8
TDS 170 220 187.6 110.0 200.0 152.1
Total Hardness 72.4 94.6 82.2 44.2 60.6 51.7
Alkalinity 58.8 77.8 66.4 51.4 71.0 59.4
TC(MPN/100ml) 100 150 126.1 90.0 120.0 106.1
FC(MPN/100ml) 70 105 85.6 40.0 65.0 51.0
Table-4.13
Average values of Ground water of cluster and beyond the cluster area.
Parameters GW' GW''
Minimum Maximum Average Minimum Maximum Average
pH 6.3 8.6 7.9 7.3 8.0 7.6
Conductivity(µScm-1) 120.8 208.8 164.9 95.6 118.2 102.7
DO 6.5 8.2 7.3 8.9 11.9 10.3
BOD 1.8 3.3 2.9 1.3 2.5 1.8
COD 17.5 21.5 19.9 12.0 18.8 14.6
TDS 160 223 181.25 112.0 180.0 141
Total Hardness 52.4 70.6 61.3 43.2 60.6 49.9
Alkalinity 50.8 70.8 62.675 52.8 65.8 58.8
TC(MPN/100ml) 110 140 123.8 85.0 122 100.4
FC(MPN/100ml) 57 81 69.3 58.0 75.0 67.3
120
SW’1 SW’2 SW
’3 SW
’4 SW
’5 SW
’6 SW
’7 SW
’8 M
Fig-4.1 : pH
SW’1 SW
’2 SW
’3 SW
’4 SW
’5 SW
’6 SW
’7 SW
’8 M
Fig-4.2 : Conductivity in (µScm-1
)
6.1 6.9
8.8
7.8 7.6 6.8
6.2
7.5 7.21
0
1
2
3
4
5
6
7
8
9
10
200.8
280.2
320.8 315.6 310.5 300.4 280.2 270.6
284.9
0
50
100
150
200
250
300
350
121
SW’1 SW
’2 SW
’3 SW
’4 SW
’5 SW
’6 SW
’7 SW
’8 M
Fig-4.3 : Dissolve oxygen in mg/l.
SW’1 SW
’2 SW
’3 SW
’4 SW
’5 SW
’6 SW
’7 SW
’8 M
Fig-4.4 : Biological oxygen Demand mg/l
9.2
7.2
6.1 6.3 6.5 7.1 7.2 7.4
7.12
0
1
2
3
4
5
6
7
8
9
10
2.7 3
4.3 4.2 4
3.5
3.1
4
3.6
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
122
SW’1 SW
’2 SW
’3 SW
’4 SW
’5 SW
’6 SW
’7 SW
’8 M
Fig-4.5 : Chemical Oxygen Demand in mg/l
SW’1 SW
’2 SW
’3 SW
’4 SW
’5 SW
’6 SW
’7 SW
’8 M
Fig-4.6 : Total Dissolve Solid in mg/l
18.5 18.8
22.5
20.2 20.6 20.1 19.4
20.2 20.04
0
5
10
15
20
25
180 186
220
200 190
180 170 175
187.625
0
50
100
150
200
250
123
SW’1 SW
’2 SW
’3 SW
’4 SW
’5 SW
’6 SW
’7 SW
’8 M
Fig-4.7 : Total Hardness in mg/l
SW’1 SW
’2 SW
’3 SW
’4 SW
’5 SW
’6 SW
’7 SW
’8 M
Fig-4.8 : Alkalinity in mg/l
72.4 75.2
94.6 88.4 87.2
84.2
72.6
83.2 82.2
0
10
20
30
40
50
60
70
80
90
100
58.8 61
77.8
65.4 68.2 66.4 68
65.2 66.4
0
10
20
30
40
50
60
70
80
90
124
SW’1 SW
’2 SW
’3 SW
’4 SW
’5 SW
’6 SW
’7 SW
’8 M
Fig-4.9 : Total coliforms in MPN/100ml
SW’1 SW
’2 SW
’3 SW
’4 SW
’5 SW
’6 SW
’7 SW
’8 M
Fig-4.10 : Fecal Coliforms in MPN/100ml
120
100
150
130 135
124
110
125 124.3
0
20
40
60
80
100
120
140
160
75 80
100
90
105
80
70
85 85.6
0
20
40
60
80
100
120
125
GW’1 GW
’2 GW
’3 GW
’4 GW
’5 GW
’6 GW
’7 GW
’8 M
Fig-4.11 : pH
GW’1 GW
’2 GW
’3 GW
’4 GW
’5 GW
’6 GW
’7 GW
’8 M
Fig-4.12 : Conductivity in (µScm-1)
6.3
7.4
8.6 8.3
7.9 8.1 8.3 8.2 7.9
0
1
2
3
4
5
6
7
8
9
10
120.8
160.2
208.8 202.6 200.5
155.4
130.2 140.6
164.9
0
50
100
150
200
250
126
GW’1 GW
’2 GW
’3 GW
’4 GW
’5 GW
’6 GW
’7 GW
’8 M
Fig-4.13 : Dissolve oxygen in mg/l.
GW’1 GW
’2 GW
’3 GW
’4 GW
’5 GW
’6 GW
’7 GW
’8 M
Fig-4.14 : Biological oxygen Demand mg/l
8.2 8
6.5 6.6 6.7 7.2 7.1
7.9
7.28
0
1
2
3
4
5
6
7
8
9
2.7 2.8
3.3 3.2
3.1 3
2.9
1.8
2.85
0
0.5
1
1.5
2
2.5
3
3.5
127
GW’1 GW
’2 GW
’3 GW
’4 GW
’5 GW
’6 GW
’7 GW
’8 M
Fig-4.15 : Chemical Oxygen Demand in mg/l
GW’1 GW
’2 GW
’3 GW
’4 GW
’5 GW
’6 GW
’7 GW
’8 M
Fig-4.16 : Total Dissolve Solid in mg/l.
17.5
19.8
21.5 20.2 20.6 20.1 20.4
19.2 19.91
0
5
10
15
20
25
160 175
223 215
185 170
162 160
181.25
0
50
100
150
200
250
128
GW’1 GW
’2 GW
’3 GW
’4 GW
’5 GW
’6 GW
’7 GW
’8 M
Fig-4.17 : Total Hardness in mg/l.
GW’1 GW
’2 GW
’3 GW’4 GW’5 GW
’6 GW
’7 GW
’8 M
Fig-4.18 : Alkalinity in mg/l
2.7 3
4.3 4.2 4
3.5
3.1
4
3.6
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
50.8 51
70.8 68
60.2
66.4 66 68.2 62.675
0
10
20
30
40
50
60
70
80
129
GW’1 GW
’2 GW
’3 GW’4 GW’5 GW
’6 GW
’7 GW
’8 M
Fig-4.19 : Total coliform in MPN/100ml
GW’1 GW
’2 GW
’3 GW’4 GW’5 GW
’6 GW
’7 GW
’8 M
Fig-4.20 : Fecal Coliform in MPN/100ml
120 110
140 130
125 115
120 130
123.75
0
20
40
60
80
100
120
140
160
75
60
75 71
81
65 70
57
69.25
0
10
20
30
40
50
60
70
80
90
130
SW’’1 SW’’
2 SW’’
3 SW’’
4 SW’’
5 SW’’
6 SW’’
7 SW’’
8 M
Fig-4.21 : pH
SW’’1 SW’’
2 SW’’
3 SW’’
4 SW’’
5 SW’’
6 SW’’
7 SW’’
8 M
Fig-4.22 : Conductivity in (µScm-1)
7.6
8.1
8.3
8
7.8 7.8
7.6 7.5
7.8
7
7.2
7.4
7.6
7.8
8
8.2
8.4
110.8 120.2
140.8 135.6
120.5
105.4 100.2 100.6
116.8
0
20
40
60
80
100
120
140
160
131
SW’’1 SW’’
2 SW’’
3 SW’’
4 SW’’
5 SW’’
6 SW’’
7 SW’’
8 M
Fig-4.23 : Dissolve oxygen in mg/l.
SW’’1 SW’’
2 SW’’
3 SW’’
4 SW’’
5 SW’’
6 SW’’
7 SW’’
8 M
Fig-4.24 : Biological oxygen Demand mg/l
10.2
9.3
8.1 8.2 8.8
9.8 9.2
9.8 9.18
0
2
4
6
8
10
12
2.2
2.8 3
2.4
1.8 1.6
2
1.4
2.15
0
0.5
1
1.5
2
2.5
3
3.5
132
SW’’1 SW’’
2 SW’’
3 SW’’
4 SW’’
5 SW’’
6 SW’’
7 SW’’
8 M
Fig-4.25 : Chemical Oxygen Demand in mg/l
SW’’1 SW’’
2 SW’’
3 SW’’
4 SW’’
5 SW’’
6 SW’’
7 SW’’
8 M
Fig-4.26 : Total dissolves Solid in mg/l.
18.5 19.8
20.5 19.2
16.6 15.1
18.4
14.2
17.79
0
5
10
15
20
25
162
180
200
175
125 115
150
110
152.13
0
50
100
150
200
250
133
SW’’1 SW’’
2 SW’’
3 SW’’
4 SW’’
5 SW’’
6 SW’’
7 SW’’
8 M
Fig-4.27 : Total Hardness in mg/l
SW’’1 SW’’
2 SW’’
3 SW’’
4 SW’’
5 SW’’
6 SW’’
7 SW’’
8 M
Fig-4.28 : Alkalinity in mg/l
50.4
58.2 60.6
48.4 46.2
44.2
52.2 53.2 51.675
0
10
20
30
40
50
60
70
56.8
65.8 67.8 71
52.2 51.4 55 55.2
59.4
0
10
20
30
40
50
60
70
80
134
SW’’1 SW’’
2 SW’’
3 SW’’
4 SW’’
5 SW’’
6 SW’’
7 SW’’
8 M
Fig-4.29 : Total coliform in MPN/100ml
SW’’1 SW’’
2 SW’’
3 SW’’
4 SW’’
5 SW’’
6 SW’’
7 SW’’
8 M
Fig-4.30 : Fecal Coliform in MPN/100ml
100
115 120
115 110
95 90
104 106.13
0
20
40
60
80
100
120
140
50
55
65
50 48
50
40
50 51
0
10
20
30
40
50
60
70
135
GW’’
1 GW’’
2 GW’’
3 GW’’
4 GW’’
5 GW’’
6 GW’’
7 GW’’
8 M
Fig-4.31 : pH
GW’’
1 GW’’
2 GW’’
3 GW’’
4 GW’’
5 GW’’
6 GW’’
7 GW’’
8 M
Fig-4.32 : Conductivity in (µScm-1)
7.5
7.7
8
7.9
7.6
7.4
7.5
7.3
7.61
6.8
7
7.2
7.4
7.6
7.8
8
8.2
100.8 105.2 106.8
118.2
100.5 96.4 98.2 95.6
102.7
0
20
40
60
80
100
120
140
136
GW’’
1 GW’’
2 GW’’
3 GW’’
4 GW’’
5 GW’’
6 GW’’
7 GW’’
8 M
Fig-4.33 : Dissolve oxygen in mg/l.
GW’’
1 GW’’
2 GW’’
3 GW’’
4 GW’’
5 GW’’
6 GW’’
7 GW’’
8 M
Fig-4.34 : Biological oxygen Demand mg/l
11.9
10.5
9.1 8.9
9.8
10.8 10.4
10.9 10.3
0
2
4
6
8
10
12
14
1.8
2.2
2.5
2.1
1.5 1.4
1.7
1.3
1.8
0
0.5
1
1.5
2
2.5
3
137
GW’’
1 GW’’
2 GW’’
3 GW’’
4 GW’’
5 GW’’
6 GW’’
7 GW’’
8 M
Fig-4.35 : Chemical Oxygen Demand in mg/l
GW’’
1 GW’’
2 GW’’
3 GW’’
4 GW’’
5 GW’’
6 GW’’
7 GW’’
8 M
Fig-4.36 : Total dissolves Solid in mg/l.
15.5
16.8
18.5
16.2
12.6 12.1
13.4
12
14.6
0
2
4
6
8
10
12
14
16
18
20
145
160
180
150
125 120
136
112
141
0
20
40
60
80
100
120
140
160
180
200
138
GW’’
1 GW’’
2 GW’’
3 GW’’
4 GW’’
5 GW’’
6 GW’’
7 GW’’
8 M
Fig-4.37 : Total Hardness in mg/l
GW’’
1 GW’’
2 GW’’
3 GW’’
4 GW’’
5 GW’’
6 GW’’
7 GW’’
8 M
Fig-4.38 : Alkalinity in mg/l
50.2
55.2
60.6
50.4 47.2
44.2 48.4
43.2
49.9
0
10
20
30
40
50
60
70
52.8
61
65.8
60
55.2
60.4 60
55.2 58.8
0
10
20
30
40
50
60
70
139
GW’’
1 GW’’
2 GW’’
3 GW’’
4 GW’’
5 GW’’
6 GW’’
7 GW’’
8 M
Fig-4.39 : Total coliform in MPN/100ml
GW’’
1 GW’’
2 GW’’
3 GW’’
4 GW’’
5 GW’’
6 GW’’
7 GW’’
8 M
Fig-4.40 : Fecal Coliform in MPN/100m
85 90
122
110 105
100 101
90
100.4
0
20
40
60
80
100
120
140
60
70 75
70
60
72 73
58
67.3
0
10
20
30
40
50
60
70
80
140
REFERENCE:
1. Report on the United Nations Water conference, Mardel Plata, Argentina
14-25 March (1977).
2. K.L. Rao “India’S water wealth” Orient Longman Limited 1/24 Asaf Ali
Road New Delhi 110002 PP 01, (1995).
3. Dynamic Ground Water Resources of India (As on 31 March 2009),
published by Central Ground Water Board Ministry of Water Resources
Government of India, Faridabad. PP 32-35, (2011).
4. http://www.cpcb.nic.in/Water_Quality_Criteria.php.
5. Manual on Water supply and Treatment 3rd
Edition, Published by Central
Public health and Environmental Engineering Organisation, Ministry of
Urban Development, New Delhi,PP 14-15, (1999).
6. ISI-IS: 2296-1982, Bureau of Indian Standards, Manak Bhavan 9 Bahadur
shah Zafar Marg New Delhi 110002.
7. Gerard kiely “Environmental Engineering” Tata McGraw Hill Education
Pvt Ltd. New Delhi. PP 63-69 and 71-73, (2007).
8. Gerard kiely “Environmental Engineering” Tata McGraw Hill Education
Pvt Ltd. New Delhi, PP 76 & 301-310, (2007).
9. Guidelines for Drinking-water Quality,. Health criteria and other
supporting information. 2nd
ed. Vol. 2, World Health Organization (WHO),
Geneva, (1996).
10. Gerard kiely “Environmental Engineering” Tata McGraw Hill Education
Pvt Ltd. New Delhi, PP 66, 70 & 283-288, (2007).
141
11. Standard methods for the examination of water and waste water, Ed.18 APHA,
AWWA, WPCF, American Public Health Association, Washington, DC(1989).
99
Fig.4.41 : Comparative Figure of Surface and Ground water of Industrial Cluster Area.
7.2
284.9
7.1 3.6 20.0
187.6
82.2 66.4
126.1
85.6
7.9
164.9
7.3 2.9 19.9
181.3
61.3 62.7
123.8
69.3
0.0
50.0
100.0
150.0
200.0
250.0
300.0
pH Conductivity DO BOD COD TDS TotalHardness
Alkalinity TC FC
SW' GW'
100
Fig.4.42 : Comparative Figure of Surface and Ground water Quality of Beyond the boundary of Cluster Area
7.8
116.8
9.2 2.2
17.8
152.1
51.7 59.4
101.6
51
7.6
102.7
10.3
1.8
14.6
141
49.9
58.8
100.4
67.3
0.0
20.0
40.0
60.0
80.0
100.0
120.0
140.0
160.0
pH Conductivity DO BOD COD TDS TotalHardness
Alkalinity TC FC
SW'' GW''
101
Fig.4.43 : Comparative Figure of Surface water Quality of Industrial and Beyond the boundary of Cluster Area
7.21
284.9
7.13 3.6
20.0
187.6
82.2 66.4
126.1
85.6
7.8
116.8
9.2
2.15 17.8
152.1
51.7 59.4
106.1
51.0
0.00
50.00
100.00
150.00
200.00
250.00
300.00
pH Conductivity DO BOD COD TDS TotalHardness
Alkalinity TC FC
SW' SW''
102
Fig.4.44 : Comparative Figure of Ground water Quality of Industrial and Beyond the boundary of Cluster Area
7.9
164.9
7.3 2.9
19.9
181.25
61.3 62.675
123.8
69.3
7.6
102.7
10.3 1.8
14.6
141
49.9 58.8
100.4
67.3
0.0
20.0
40.0
60.0
80.0
100.0
120.0
140.0
160.0
180.0
200.0
pH Conductivity DO BOD COD TDS TotalHardness
Alkalinity TC FC
GW' GW''
103