health effects of coal (autosaved)2

8/12/2019 Health Effects of Coal (Autosaved)2

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CHAPTER 1

Introduction and Outline:

1.1 IntroductionIn today s scenario environmental degradation can be considered as a by -

product of economic activities. Several forms of environmental degradationcause real costs to the economy and to people s welfare. Yet these costsoften go unmeasured due to several uncertainties and knowledge gaps andthus their magnitudes are largely unknown. The environmental damagecategories due to any economic activity can be summarized as damages tothe following:

Air quality: impacts of air pollution on health (costs of mortality andmorbidity from airborne diseases) and the environment ( throughreduced visibility and aesthetic value of landscape)

Agricultural land: losses of agricultural productivity on croplandsand the rangelands due to unsustainable practices.

Forests: losses of forest goods (for example timber, firewood, and

non-wood forest products) and services (such as watershed protection and recreation) due to deforestation and forestdegradation.

Water: impacts on major economic sectors of water salinity,contamination, waterlogging, dam sedimentation, andoverexploitation of groundwater.

Waste: impacts on the environment and public welfare of

inappropriate waste collection, transport, and disposal.

Coastal zone: losses of recreational and landscape value due tounsustainable coastal activities.

Air pollution is one of the most serious environmental problems aroundthe world. The rapid economic expansion and population growth overthe past few decades has made extensive energy utilization giving riseto several pollution generating economic activities (e.g. mining).Theeffects of air pollution have multifaceted consequences for humanwelfare in areas such as health, agriculture and the ecosystem. Air


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pollutants such as carbon monoxide (CO), nitrogen dioxide (NO 2), particulates (PM 10 and PM 2.5), sulphur dioxide (SO 2), and ozone haveserious impacts on health. Epidemiological evidence supports anassociation between exposure to these ambient air pollutants and

various health effects, such as respiratory symptoms or illness (e.g.asthma), impaired cardiopulmonary function, reduction of lungfunction, and premature mortality. In particular, the most serious healthimpacts include a significant reduction in life expectancy and prematuredeath both of which are strongly linked to exposure to PM. Althoughexposure to air pollution damages the health of everyone especiallyvulnerable people (e.g. elderly people, children, and those withunderlying disease) are at greater risk of being affected by air pollutants.

Uncertainties in the estimates of external costs of air pollution areendemic, but following the extensive research and subsequent review process a certain degree of consensus has emerged in the literature as tothe accounting for external effects of air pollution [1].

1.2 Outline of the thesis: In the given thesis we have estimated thesocial cost of air pollution due to opencast coal mine. Chapter 1 coversthe major impacts of air pollution due to coal mines in the surroundingareas. Chapter 2 covers the most widely used methods around the worldfor quantification of mortality and morbidity effects. Under chapter 3,the monetization techniques commonly used are elaborated. In chapter4, a methodology is provided for the work to be performed. Chapter 5covers the calculation part. Lastly, chapter 6 contains the result,discussions and conclusion part.


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CHAPTER 2

Impact due to coal mines

In India, Coal is considered as an important fossil fuel for generation ofelectricity and for other industrial purposes. Its importance in electricitygeneration has become more prominent after increase in international priceof crude oil. Therefore, coal mining is now essential part of civilization.Traditionally, coal mining and coal fired power plants are considered to bemost polluted industry.

Mining of coal releases a number of toxic pollutants in the atmosphere andwater bodies, some of which remain behind as solid waste, and some ofwhich are released into the atmosphere. These pollutants are responsible fora large number of illnesses and premature deaths, both to people directlyinvolved in the industry and the people living in the surrounding areas.

Coal combustion emissions released into the atmosphere contain nitrousoxides which are responsible for industrial and urban smog, sulfur dioxidewhich is the primary reactive agent behind acid rain, mercury whichaccumulates in the food chain, and large amounts of carbon dioxide which

is the most important greenhouse gas contributing to climate change. Coalmining itself also releases significant amounts of methane, anotherextremely potent greenhouse gas resulting in global warming.

Coal dust in mines and near storage and transport facilities contributes toserious respiratory illness such as asthma and pneumoconiosis (black lung).Solid combustion wastes such as fly ash pollute groundwater near storagefacilities, contaminating individual and community water supplies.

In almost every case of international literature ,the studies and associatedreports on the health effects of exposure to coal dust of respirable particlesize (i.e. PM10 and smaller) relate to coal miners and coal mine sites, eitherunderground or opencast. In many cases the actual extent of exposure,usually expressed as time in years but also on occasions as total body

burden of inhaled dust, has been established. In turn, this has been used todraw conclusions on the dose-response relationship for the onset of adversehealth effects from respirable coal dust exposure.


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2.1.2 Cardiovascular Effects

Air pollution is known to negatively impact cardiovascular health.Pollutants produced by coal combustion can lead to cardiovasculardisease, such as artery blockages leading to heart attacks, and tissue

death and heart damage due to oxygen deprivation. Nitrogen oxides and PM2.5, along with other pollutants, are

associated with hospital admissions for potentially fatal cardiacrhythm disturbances according to recent researches.

There are also cardiovascular effects from long-term air pollutionexposure. Exposure to chronic air pollution over many yearsincreases cardiovascular mortality.

2.1.3 Nervous System Effects The nervous system is also a target for coal pollution s health effects,

as the same mechanisms thought to mediate the effect of air pollutants on coronary arteries also apply to the arteries that nourishthe brain. These include stimulation of the inflammatory responseand oxidative stress, which can lead to stroke and other cerebralvascular disease.

Several studies have shown a correlation between coal-related air pollutants and stroke.

Coal contains trace amounts of mercury that, when burned, enter theenvironment and can act on the nervous system to cause loss of intellectualcapacity. Coal-fired power plants are responsible for approximately one-third of all mercury emissions attributable to human activity.

Apart from these, there are occupational health effects resulting fromaccidents and diseases of persons working in coal mines. Accident causesimmediate impacts while occupational diseases generally occur as a moreor less delayed response following a long term exposure to an external

burden, e.g. airborne pollutants, noise, vibration etc. Exposure to radon andlung cancer may result in coal miners mortality. Exposure to dust is relatedto several different epidemiological measures of lung disease in coalminers. It may lead to coal workers simple pneumoconiosis (CWSP) andto its advanced or complicated form of Progressive Massive Fibrosis(PMF) or to chronic bronchitis as determined from the presence ofrespirable symptoms, and to the respiratory symptoms, and to therespiratory symptoms of breathlessness. Exposure to coalmine dust is also
http://www.sourcewatch.org/index.php/Mercury_and_coalhttp://www.sourcewatch.org/index.php/Mercury_and_coal


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associated with loss of lung function. The accidental injury may becategorised as fatalities (defined as mortality or permanentdisabilities),major injuries (defined to include major fractures, amputations,serious eye injuries, some causes of loss of consciousness and any injury

requiring hospital treatments for more than 24 hours) and minor injuries (defined to include other accidents responsible for the loss of more thanthree working days).

2.2 Impact on agriculture due to coal mines:

The air pollutants due to coal mines cause severe damage to the agriculturalland present in the buffer zone and surrounding areas. Most damage arisesdue to foliar uptake of pollutants rather than through the deposition to the

soil. Apart from this soil acidification is also caused due to pollutantswhich depend on the type of vegetation, soil parent material and climate.Effects of SO 2 on crops are complicated because they may stimulate orreduce growth, depending on concentration and the presence of other

pollutants or stresses which creates uncertainty in measurements.

The emissions from the mines are transported and exposed to the cropsaccording to the wind direction and climate. Sometimes these pollutantsreact among themselves in the existing atmosphere to form more complex

pollutants due to persisting conditions (temperature and humidity). These pollutants interact with the crops either through dry deposition or wetdeposition.

Dry deposition results in several damages in plants like foliar necrosis, physiological damage, chlorosis, pest performance, leaching, growthstimulation, climate interactions etc. Wet deposition on the other handleads to soil acidification and mobilization of heavy metals and nutrients.

This ultimately results in root damage, leaching from foliage, nutrients lossfrom soil, nutritional balance, climate interactions, pest performance etc.Both dry and wet depositions finally results in yield loss, degradation insoil quality and appearance etc.


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species/individuals. This ultimately results in loss of recreational andexistence values and other commercial losses.

2.4 Impacts on building materials due to coal mines:

The impacts for most materials generally fall into three categories i.e.discoloration, material loss and structural failure. For most materials, thedry deposition of SO 2 exerts the strongest corrosive effect of atmospheric

pollutants. Wet deposition of pollutants expressed as rain acidity has acorrosive effect on certain materials, but is generally weaker. The role ofatmospheric NO 2 has not yet been clarified. Although a strong synergisticeffect with SO 2 has been observed in laboratory studies. However ozonehas recently been observed to act synergistically with SO 2 in the field [1].

Ozone is also known to damage some polymeric materials such as paints, plastic and rubbers [2].

Apart from this, the extraction of coal results in ground subsidence whichcauses damage to buildings and the sewer systems. The vibration from theuse of heavy machinery also causes cracks in the window panes andcement layers. So, damage assessment can be done for utilitarian buildings(houses, shops, factories, offices, schools etc.), historical buildings andother structures prone to corrosion due to pollution in the coal miningareas.

2.5 Impacts of coal mining on ground and surface water:

Mining activity can contaminate aquatic ecosystems from draining andleaching of refuse piles, and from the contamination of surface water with

pit water. Any acid precipitation percolating through the pile andsubsequently through the soil is buffered by carbonates and silicates. This

process releases a quantity of cations (Fe2+

, Ca2+

, Na2+

, and Mg2+

) and CO 2equivalent to the buffered acid. When ferric sulphide is oxidised in therefuse pile, the pH can decrease further as this process produces sulphuricacid and can mobilise trace elements.

Once all the buffering capacity is used up in the pile, the underlying soilwill be acidified, leading to the mobilisation of the trace elements. Thetransport of the chemicals depends on the underground water flow regimeand on its adsorption and desorption capacity, as well as on the mobility

and persistency of the chemicals. Chloride can be regarded as conservative,i.e. moving similarly to a water molecule.


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Similarly, the surface water bodies in the surrounding areas of a mine areaffected when mine water is pumped from the pit. These increase theambient concentration of pollutants in the receiving stream which makesthe water unfit for drinking and other uses. Even aquatic ecosystem is also

affected by the same.2.6 Impact due to noise pollution in coal mining areas:

Each stage of mining activity is associated with some kind of emissions ofsound. The use of heavy earth moving machinery in the opencast minesaggravates the problem. Noise basically results from the emission of sound,which is vibration of air (or any other medium) due to perturbation by somemechanical vibration. Some sound is generated naturally in the

environment, notably by the action of wind, but much is anthropogenic inorigin. The ears of humans and many other animals are sensitive to thesevibrations and therefore can detect sound.

Unwanted anthropogenic sound is generally recognised as a disbenefit. Atvery high levels, characteristic of some working environments, it canimpair hearing unless appropriate health and safety measures areintroduced. At lower levels it can interrupt sleep or hinder verbalcommunication. In general, unwanted sound affects human amenity.


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CHAPTER 3

Methods to estimate health impacts

3.1 Intake Fraction Approach : An intake fraction measures the change in population-weighted ambientconcentrations of a pollutant (e.g. PM 2.5) per unit of primary pollutantemitted from a pollution source. For example, if Q= emissions of PM 2.5from a power plant in grams per second, Ci is the change in ambientPM2.5 in grid cell I resulting from Q, P i is the population of the grid celland BR is the average breathing rate, then the intake fraction is defined as,

(1) IF= [P iC iBR]/Q,Where the sum (1) is taken over all grid cells for which C i>0. The IFcorresponding to an air pollution source depends on the distribution of

population around the source, on meteorological conditions, and oncharacteristics of the source that affect {Ci /Q} [3].

Once the intake fraction has been estimated for a particular source and pollutant, it can be used to calculate health impacts. Rearranging equation

(1), the population-weighted average change in ambient concentrations,P iC i , is given by

(2) IF*Q/BR= P iC i

Thus, once IF has been calculated and annual emissions (Q) are knownP iC i can be calculated. In most epidemiological studies of the healtheffects of air pollution, the relative risk (RR) of death or illness associatedwith a change in pollutant concentration is given by

(3) RR=exp ( P iC i),

Where is estimated from an epidemiol ogical study. The number ofcase (E) of premature mortality or illness associated with P iC i isgiven by

(4) E= (( RR-1)/R)*BaseCases

Implying that (RR-1)/RR) is the fraction of existing cases attributable to

the source [3].


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3.2 Dose Response Relationships :

A DRR (Dose Response Relationship) is usually a function of a largenumber of variables, such as air pollution: nitrogen oxides, sulphur dioxide,sulphate aerosol, black smoke, particulates, ozone and ammonia accordingto the literature of health impacts of air pollution. In addition to thesevariables, the number of WLDs (Work Loss Days) is influenced by othervariables as well such as education and occupation, income, job situation(employed or not), race, sex, age and habits such as drinking and smoking.A DRR can be represented by the following formula [4]:

WLD=f (P 1 .P N, X1 XM )

WLD= annual work loss days;

Pi= air pollutant i;

Xi= other variables j.

Based on the above formula Netherlands formulated the following healthmodel for estimating an empirical DRR.

WLD= f (P 1 P6 , X1 X4 )

WLD= annual work loss days; P 1=sulphur dioxide;

P2=sulphate aerosol; P 3=black smog;

P4=particulates; P 5= ammonia;

P6=ozone; X 1=unemployment percentage in a region;

X2= percentage of labour force in a region receiving a pension under theDutch Disablement Insurance Act;

X3= population density as an indicator for the urbanization rate of a region;

X4= average annual gross income per capita in a region.

The DRR is then estimated by means of two techniques: the ordinary leastsquares method (OLS) and the one-way fixed-effects method (OWFEM).

In Jakarta the following model was developed with the help of damagefunction approach using dose-response relationships to estimate the healthimpacts of air pollution. [5]


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dH i = b i *POP i*dA

Where: dH i = change in population risk of health effect I;

bi = slope from the dose-response curve for health impact I;

POP i = population at risk of health effect I;

dA = change in ambient air pollutant under consideration.

3.3 Disability Adjusted Life Years (DALY):

There are certain measures of population health that combine information

on mortality and non-fatal health outcomes to represent the health of a particular population as a single number. These may be categorised asfollow [6]-[8]:

Health expectancies.

Health expectancies measure years of life gained or years of improvedquality of life. In this group of measures, among others, followingmeasures are classified:

Active Life Expectancy (ALE),

Disability-Free Life Expectancy (DFLE),

Disability-Adjusted Life Expectancy (DALE),

Healthy Adjusted Life Expectancy (HALE),

Quality Adjusted Life Expectancy (QALE).

Health gaps.

Health gaps measure lost years of full health in comparison with someideal health status or accepted standard. In this group of measures amongothers, following measures (indicators) are classified:

Potential Years of Life Lost (PYLL),


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Healthy Years of Life Lost (HYLL),

Quality Adjusted Life Years (QALY),

Disability Adjusted Life Years (DALY).

3.3.1 Disability Adjusted Life Year (DALY) concept:

DALY can be defined as an indicator of Burden of Disease in a populationfrom certain externalities. The DALY is a time-based measure thatcombines years of life lost due to premature mortality and years of life lostdue to time lived states less than ideal health. One DALY can be thought ofas one lost year of healthy life, and the BoD can be thought o f as ameasurement of the gap between current health status and an ideal situationwhere everyone lives into old age, free of disease and disability. In otherwords, DALYs are the combination (more precisely the sum) of twodimensions: the present value of future years of lifetime lost through

premature mortality, and the present value of years of future lifetimeadjusted for the average severity (frequency and intensity) of any mental or

physical disability caused by a disease or injury [6].

The DALY measures health gaps as opposed to health expectancies. Itmeasures the difference between a current situation and an ideal situationwhere everyone lives up to the age of the standard life expectancy, and in

perfect health. Based on life tables, the standard life expectancy at birth isset at 80 years for men and 82.5 for women [7].

The DALY combines in one measure the time lived with disability and the

time lost due to premature mortality:

DALY = YLL + YLD

Where:YLL = years of life lost due to premature mortality.YLD = years lived with disability.

The DALY is based on the premise that the best approach for measuringthe burden of disease is to use units of time. Having chosen units of time as


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the unit of measure, the burden of disease can still be calculated usingincidence or prevalence measures. Time lost due to premature mortality is afunction of the death rate and the duration of life lost due to a death at eachage. Because death rates are incidence rates, there is no obvious alternative

for mortality than to use an incidence perspective. By contrast, for non-fatalhealth outcomes, both incidence and prevalence measures have beenroutinely used. Thus, it is possible to calculate the number of healthy yearsof life lost because of people living in disease states, in terms of prevalentcases of disease in the population in the year of interest, or in terms of theincident stream of healthy years of life lost into the future for incident casesof the disease in the year of interest [8].

3.3.2 The years of life lost dimension

The standard life expectancy for the DALY measure is already mentionedearlier which is defined as living in a completely healthy state until death atage around 80 years for males and 82.5 years for females. It is taken fromthe country having highest life expectancy in the world i.e. Japan. If wehave to represent it graphically then perfect health is 1 on the y-axis anddeath is 0 on the DALY diagram. The ideal life is quantified as the total

area in the box which is a combination of the number of years lived and thefull quality of life without disability [9]-[12].

If a person dies prematurely, the number of years lost is counted up tothe standardised maximum life span. Such a measure of premature death innumber of years lost is known as "years of life lost" (YLL) [12].

Fig.3.1 Graphical presentation of a life in full health for male


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The calculation of YLL can be understood using the following scenario:A man dies due to heart attack at 30 years of age. In terms of years of lifelost, 50 years are lost due to this premature death (YLLs = 80-30 years).This could be illustrated in the following figure:

Fig.3.2 the grey area represents the time lost due to premature death.

On a population basis, the YLL metric essentially corresponds to thenumber of deaths multiplied by the standard life expectancy at the age atwhich death occurs, and it can be rated according to social preferences. The

basic formula for calculating the YLL for a given cause, age or sex, is:

YLL = N x L

Where: N = number of deathsL = standard life expectancy at age of death (in years).

3.3.3 Quantifying time lived with disability

There are at least two ways of measuring the aggregate time lived with adisability. One method is to take point prevalence measures of disability,adjusting for seasonal variation if present, and express them as an annual

prevalence. The alternative is to measure the incidence of disabilities andthe average duration of each disability. The product of the incidence and


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the duration will then provide an estimate of the total time lived withdisability. This is the approach used for the DALY [13].

The disability is measured in length in years and in severity. Severity

weights have been appointed for each disabling condition on a scale fromone to zero. The disability severity weight for each disease reflects theaverage degree of disability a person suffers with each condition. Panels ofhealthy experts with knowledge about disease conditions have determinedthe weights [14].

The severity weight is then multiplied by the average time a person issuffering from the disability from each disease [9-12]. A measure of years

lived in health states less than ideal health is known as "years lived withdisability" (YLD). The following example will improve our understandingon YLDs [12].

At the age of 30, a man gets a knee injury and his health is jeopardized witha weighted severity of 0.1. The injury is incurable and a man suffers untilhe dies at the age of 80 years.In terms of years lost due to disability this man s health is only 0.9 of the

maximum of 1.0 for the entire 50-year period. This could be illustrated inthe following figure. The grey area in Figure represents his life years lostdue to disability, and YLDs corresponds to 5 years (YLDs = 0.150= 5years).

Fig.3.3 Illustration of life of a man who gets a knee injury at the age of 30.


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To estimate YLD on a population basis, the number of disability cases ismultiplied by the average duration of the disease and a weight factor thatreflects the severity of the disease on a scale from 0 (perfect health) to 1(dead). The basic formula (without applying social preferences) for one

disabling event is [13]:

YLD = I x DW x L

Although the disability weights used in DALY calculations quantifysocietal preferences for different health states, the weights do not representthe lived experience of any disability or health state, or imply any societalvalue for the person in a disability or health state. Rather, they quantify

societal preferences for health states in relation to the societal ideal of goodhealth. Thus, a weight for paraplegia of 0.57 does not mean that a person inthis health state is half dead, that they experience their life as halfway

between life and death, or that society values them less as a personcompared to healthy people. It means that, on average, society judges ayear with blindness (weight 0.43) to be preferable to a year with paraplegia(weight 0.57), and a year with paraplegia to be preferable to a year withunremitting unipolar major depression (weight 0.76). It also means that, on

average, society would prefer a person to have a year in good healthfollowed by death, than a year with paraplegia followed by death. Societywould also prefer a person to live three years with paraplegia followed bydeath (3 years x 0.57 = 1.7 lost healthy years), than have one year ofgood health followed by death (2 lost years of good health) [9].

Following the GBD terminology, and consistent with the WHOInternational Classification of Functioning, Disability and Health (ICF), the

term disability is used broadly in BoD analyses to refer to departuresfrom good or ideal health in any of the important domains of health. These


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Table 3.1 Examples of disability weights a

Disease or sequelae

AIDS 0.50 Infertility 0.18 Diarrhoea disease, episodes

0.11

Measles episode 0.15 Tuberculosis 0.27 Malaria, episodes 0.20 Trachoma, blindness 0.49 Trachoma, low vision 0.24 Lower respiratory tract infection, episodes 0.28 Lower respiratory tract infection, chronic 0.01

sequelae Cancers, terminal stage 0.81 Diabetes mellitus cases (uncomplicated) 0.03 Unipolar major depression, episodes 0.30

Alcohol dependence syndrome 0.18 Parkinson disease cases 0.32

Alzheimer disease cases 0.64 Post-traumatic stress disorder 0.11

Angina pectoris 0.10 Congestive heart failure 0.17 Chronic obstructive lung disease, 0.39

symptomatic cases Asthma, cases 0.06 Deafness 0.17 Benign prostatic hypertrophy 0.04 Osteoarthritis, symptomatic hip or knee 0.11 Brain injury, long-term sequelae 0.35

Spinal cord injury 0.73 Sprains 0.06 Burns (>60%) long term 0.25

a Adapted from Murray & Lopez (1996).


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number of years of lost production/consumption is high, and that means theresult is sensitive to the rate used to discount the value of future life-years,which is usually assumed by the research rather than estimated on the basic ofactual behaviour [20].

The adjusted human capital approach (AHC), which is widely used in China,represents an important departure from the traditional human capital approach.Because the use of foregone earnings would assign a value of zero to the livesof the retired and the disabled, the AHC approach avoids this problem byassigning the same value-per capita GDP- to a year of life lost by all persons,regardless of age. For this reason, the adjusted human capital approach can beviewed as a social statement of the value of avoiding premature mortality [21].

The AHC values a life lost at any age by the present discounted value of percapita GDP over the remainder of the individual s expected life. In computingthe AHC measure, real per capita GDP is assumed to grow at rate annuallyand is discounted to the present at the rate r. AHC, is thus given by followingequation:

AHC= GDP 0 [(1+) t/(1+r) t]

Where GDP 0 is per capita GDP in the base year and t is remaining lifeexpectancy. In the base case calculations =7% and r= 8 % [21].

4.2 WTP and value of statistical life (VSL)Generally economists use the method of value of statistical life for monetisingthe mortality part of the health damage of pollution for monetising the value ofhealth damage. VSL represents the loss of value due to the shortening of life.For valuing morbidity, on the other hand, two alternative approaches are used:

Observed capital approach: This includes techniques that rely ondemand and cost functions, market prices, and observed behaviourand choices. Household production functions and cost of illnessstudies illustrate this approach [22-24]. According to this method, thecost of illness studies considers the wage or earning loss due to theloss of working days and the cost of treatment to monetise morbidity.

Constructed market approach: In this method the people are

directly asked about their willingness to pay or accept compensation


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for certain assumed change in the level of pollution or risk ofmorbidity. The contingent valuation method (CVM) is an example ofthis method.While the observed market valuation is based on actual behaviour, itdoes not capture the valuation of those aspects of morbidity likesuffering or pain for which there is no market signal. The observedmarket often values a part of the totality which we wish to value. Forexample, the cost of illness studies cannot capture the cost ofsuffering and pain due to illness which cannot be observed in themarket. The constructed market approach as illustrated by the CVMcan avoid this problem only if the questionnaire is precise andfocused on all the relevant issues and also if the respondents revealtheir true preferences and be realistic while structuring their

preferences. As a consequence of all these difficulties, the valuationof health damage cost has often been found to be unreliable anduncertain as arrived at in either approach. It is, in fact, difficult togive a point estimate of the damage cost with reliability. It istherefore often a range of interval estimates of such valuation that aregiven assuming different scenarios of values for the uncertain factorsrelating to the environmental parameters of exposure or ambientconcentration effect or to the ones relating to the epidemiologicalimpact or to the monetisation of damage [25, 26].

Comparing with HCA, the main advantage of WTP approach relies in itsfoundation on the individual viewpoint of concerned population. It attempts toestimate the demand (WTP) for an improved environmental quality. Actually itis only measured by how much the concerned individuals are READY to pay

(probably not a true payment) in order to improve their own security. Thendoing statistical amounts of all concerned individuals results in a value that agroup of concerned individuals attributes to the improvement in security or thereduction environmental impacts [19].

However, this approach requires a high quality for those researchers who useCVM to elicit the people s WTP. Researchers must be professional ineconomics theory and method and skilled in designing questionnaires andexperimental in a real survey. Unbelievable, any one engineer or scientist cando best this work [19].


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In Sommer et al. (1999) Opinion, the main difficult of the WTP approachconsists of obtaining reliable and correct empirical estimations. A multitude ofempirical assessment conducted so far for the VSL has provided a very largerange. It also appears that according to the questions and the starting valuesdesigned, a direct interview with the individual persons (CVM) may lead tounrealistic and biased results.

In addition, besides starting-point bias, strategic bias and hypothetical bias,there are other kind biases due to (1) a payment amount is not realizable, sincerespondents give a lower or higher amount; (2) a question mode is not liked byrespondents, so they give a confuse answer [27].

4.2.1 Example of CVM questionnaire:

(a) Consider the following two areas in which your village could belocated. Which do you prefer, taking into account the differences in thelife years lost and the differences in your cash income? Remember thatall other aspects of the two areas are the same and similar to the currentarea in which you live.

If area2 is chosen initially then tick the symbol (+) in the box. Change theamount of money in area2 and ask the interviewee to reconsider.

Area1 Area2

You have a cash income of RS20,000 per month

You have a cash income of RS.18400 per month

There is a risk of 40 life years lost inthe area

There is a risk of 20 life years lost inthe area

You have same amounts of your own products to consume as in your ownvillage

You have same amounts of your own products to consume as in your own village


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(b). Ask: What value of cash income would make Area One and Area Twoequally desirable for you given the difference in the life years lost from mine

pollution? (Please Specify : -). Go to the end of this question.

Carrying out this survey on a greater mass of people, we will get the averagevalue of DALY according to the residents in the mine area.

4.3 Transfer WTP approachSome researchers would like to transfer an estimate of WTP from othercountries and correct it using ratio of GDP per capita. Indeed, in mostdeveloping countries researchers have to transfer an estimate of WTP fromdeveloped countries, which usually were corrected for ratio of GDP per capita

or for other economic indicators. It is a common knowledge that there are manydifferences between two countries, especially between a developing country anda developed country. Some of differences may be corrected for some ratios,such as GDP per capita and income per capita. But some of differences may not

be corrected directly by using quantitative method, such as cultural backgroundand education background, payment custom and consumer behaviour,understanding security and danger, life value sense, society open level, etc. [28][29]


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5.1 Collection of air quality data and the estimates of its health impactsfor major polluted cities in India.

There is a paucity of data regarding morbidity and mortality impacts due to pollution in coal mines in our country. So here we extrapolated the estimates ofmortality and morbidity from 15 most polluted cities in the country assumingthe similar demographic profile and sensitive population as in the mine area.

The data was collected from a study using more detailed air monitoring datacollected by NEERI and Central Pollution Control Board (CPCB).The impact ofindoor air pollution from the residential use of biofuels was not considered eventhough it is potentially a large public health factor in both urban and rural areas.The health impacts of air pollutants are most easily estimated through the use ofdose-response functions drawn from epidemiological studies done around theworld. Similar reviews from the World Bank studies were used for estimatinghealth impacts in India .By using dose-response functions estimated in the citiesin more developed countries, the estimates derived in the study are likely to beconservative: given the lower standard of living, nutrition, and health indeveloping countries, there is a higher percent of the population in the marginalhealth that would be more susceptible to negative health impacts from air

pollution [30].

Here we were having the data for the cities- Agra, Ahmedabad, Bangalore,Bhilai, Bhopal, Bombay, Kolkata, Dhanbad, Delhi, Jaipur, Kanpur, Jamshedpur,Mysore, Varanasi and Nagpur. The corresponding estimates for prematuremortality, hospital admissions and sickness requiring medical treatment andcases for incidence of minor sickness were available.


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Table 5.1.Estimated data for premature mortality [30]

.No. City Population Prematuremortality

TSP andRSPM/ PM10

Mortality/population

Agra 8,91,790 1569 451.93 0.175Ahmedabad 46,31,200 2979 146.94 0.064

Bangalore 57,01,446 254 90.83 0.004

Bhilai 6,85,474 464 226.46 0.067

Bhopal 50,32,450 663 214.92 0.013

Bombay 99,25,891 4477 100.81 0.045

Calcutta 43,99,819 5726 120.33 0.130

Dhanbad 19,49,526 995 364.64 0.051Delhi 94,20,644 7491 229.73 0.079

0 Jaipur 38,88,000 1145 142.3 0.029

1 Kanpur 18,74,409 1894 153.97 0.101

2 Jamshedpur 4,60,577 118 106.5 0.025

3 Mysore 22,81,653 72 105.1 0.003

4 Varanasi 9,29,270 1851 489.23 0.199

5 Nagpur 90,21,129 506 92 0.005


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Table 5.2 Estimated data for incidence of minor sickness [30]

.No. City Population PM level Cases Cases/population

Agra 891790 451.93 40794073 45.744

Ahmedabad 4631200 146.94 72177644 15.585

Bangalore 5701446 90.83 8326282 1.460

Bhilai 685474 226.46 11917298 17.385

Bhopal 5032450 214.92 17024691 3.382

Bombay 9925891 100.81 156452916 15.762

Calcutta 4399819 120.33 179479908 40.792

Dhanbad 1949526 364.64 26864178 13.779

Delhi 9420644 229.73 241958219 25.683

0 Jaipur 3888000 142.3 31708958 8.155

1 Kanpur 1874409 153.97 49247224 26.273

2 Jamshedpur 460577 106.5 3172627 6.888

3 Mysore 2281653 105.1 2376599 1.041

4 Varanasi 929270 489.23 48125143 51.788

5 Nagpur 9021129 92 17681765 1.960


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Table 5.3 Estimated data for hospital admissions and sickness requiringmedi cal treatment [30]

5.2 Formulating exposure response curves from the given data. From the data collected in step 1, we formulated an exposure response

curve for the morbidity and mortality due to air pollution for Indianscenario. The population data for the cities was collected for the givenyear [31].

For the type of estimates the ratio of cases v/s population was found out. Using this ratio and the estimate cases, best fit exposure-response curves

were made using Microsoft Excel. Here we had formulated three doseresponse curves for various estimates.

Equation for fig.5.1 is given as:

Y= (ATAN ((X-250)*0.008)-ATAN (-2)))*0.09)

.No. City Population PM level Cases Cases/population

Agra 891790 451.93 665769 45.744Ahmedabad 4631200 146.94 1183033 15.585

Bangalore 5701446 90.83 135887 1.460

Bhilai 685474 226.46 194493 17.385

Bhopal 5032450 214.92 277847 3.382

Bombay 9925891 100.81 2579210 15.762

Calcutta 4399819 120.33 3022786 40.792Dhanbad 1949526 364.64 421663 13.779

Delhi 9420644 229.73 3990012 25.683

0 Jaipur 3888000 142.3 520947 8.155

1 Kanpur 1874409 153.97 812381 26.273

2 Jamshedpur 460577 106.5 51778 6.888

3 Mysore 2281653 105.1 38787 1.0414 Varanasi 929270 489.23 785414 51.788

5 Nagpur 9021129 92 290710 1.960


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Equation for fig.5.2 is given as: Y= (ATAN ((X-150)*0.013) +1.09)*0.35)

Equation for fig.5.3 is given as:Y= (ATAN ((X-110)*0.035) +1.32)*18)

Fig.5.1 Exposure response for premature mortality cases

Fig.5.2 Exposure response for incidence of minor sickness cases

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 100 200 300 400 500 600

Series2

PM Concentration

Ratio

-10

0

10

20

30

40

50

60

0 100 200 300 400 500 600

Series2

PM Concentration

Rati

o


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Fig.5.3 Exposure response for hospital admissions and sickness requiringmedical treatment

5.3 Collection of air pollutants dispersion data in ambient atmosphere for

mine area through air dispersion modeling and population data.

Case Study:Mine A was a proposed coal mine in some hilly and undulating terrain of India(fig.9) .The drainage pattern of the core zone was dendritic. PM 10 was present

between 11 to 55 g/m 3 .The concentrations of SO 2 and NO x were considerablylow compared to the 80 g/m 3 NAAQS limit for residential, rural and otherareas. Air modelling data of the locations around the proposed mine area was

made available to us from the environmental impact assessment report.We were having the population data of all the locations along with theconcentration of particulates before mining and after mining conditions. So, theStep 3 of the methodology that we have adopted here was satisfied.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 100 200 300 400 500 600

Series2

PM Concentration

R

atio


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VillageFig.5.4 Topography of Mine A

5.4 Deducing the estimate cases from the previously made exposureresponse curves extrapolated for the mine area.

Now from the dose response curves formulated in step 2, the estimate cases of premature mortality and morbidity along with hospital admissions were derivedfor the villages in the Mine A. For every change in PM concentration, thecorresponding ratio would come for the villages through which the estimatecases were generated by multiplying the given population for each village. Thetotal cases were generated for each kind by summation of cases of all villagesgenerated before as shown in tables A1, A2 and A3.


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5.5 Calculation of DALYs for the respective cases. We got the total cases of mortality and morbidity from previous step.

Now using table 6.we calculated the total DALYs lost [32]. The mortality cases include mortality of adults and mortality of children

under age 5. The cases were divided according to the ratio of both typesof mortality given in the table 6.Then DALYs lost for total cases ofmortality were calculated.

The hospital admissions include emergency room visits and chronic bronchitis is the disease requiring medical treatment. Total DALYs lostwere calculated similar to the last step.

The Incidence of minor sickness include restricted activity days, lowerrespiratory illness in children and respiratory symptoms (adults) andhence DALYs lost were calculated.

Total DALYs lost were calculated by summation of all the DALYs lost indifferent cases.

5.6 Monetization of the total DALYs calculated. Now since no concrete WTP and HCA studies were available for the

country so we had extrapolated the cost of DALY in US to Indianscenario.

The cost of DALY (in $) is 2,510 for lower bound HCA and 18,310 forupper bound WTP method in 2006 [33].

PPP conversion factor was then multiplied to the given DALY cost toconvert it in rupees. The PPP conversion factor is 11.41 for India.Purchasing power parity (PPP ) is a component of some economictheories and is a technique used to determine the relative value ofdifferent currencies. The concept of purchasing power parity allows one

to estimate what the exchange rate between two currencies would have to be in order for the exchange to be at par with the purchasing power of thetwo countries' currencies. Using that PPP rate for hypothetical currencyconversions, a given amount of one currency thus has the same

purchasing power whether used directly to purchase a market basket ofgoods or used to convert at the PPP rate to the other currency and then

purchase the market basket using that currency [34, 35]. The indexation factor was calculated for the current scenario. The

indexation of currency or exchange rate often refers to a country pegging


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its currency to the US dollar. The ratio of cost inflation index in 2006-07to 2013-14 is calculated as indexation factor [36-38].

The total cost was then calculated as = indexation factor total DALYslost DALY cost (in rupees).


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(1* 160)+ (1*45) + (168*22000)/10000 = (3696000+160+45)/10000= 369.6

6.3 Incidence of minor sickness:

Total incidence cases =9975

Restricted activity days (adults) = (3*9975)/ (3+65+0.75)

Lower respiratory illness in children = (65*9975)/ (3+65+0.75)

Respiratory symptoms (adults) = (0.75*9975)/(3+65+0.75)Corresponding DALYs =

(3*435) + (65*9431) + (0.75*109)/10000= (1305+613015+81.75)/10000=61.4

Total DALYs lost =1318 + 369.6 + 61.4 = 1749

6.4 Monetization of impacts:

PPP conversion factor in 2006 = 11.41

Cost inflation index 2006-07 = 519

Cost inflation index 2013-14 = 939

Indexation factor = 939/519 = 1.8Total cost = 1.8*1749*DALY cost


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Synergistic effects of several pollutants: We had only considered thedirect effects of particulate air pollution in our study. There was a gapregarding the combined effects of pollutants regarding mortality andmorbidity.

Paucity of survey data in the country: The survey data in developingcountries like India is not available in sufficient amounts. Therefore wehave to extrapolate the data generated in developed countries to carry onour studies.

7.4 Conclusion:Thus, in the given thesis we had provided an estimate of the range of cost to the

society in future impacted by the proposed opencast coal mine. The lower bound was set for HCA method since it provides the cost for the materialisticaspect and other intangible costs are not included in it. On the other hand, theupper bound cost was calculated keeping in mind all the tangible and non-tangible losses due to air pollution in the mine area. The overall work was doneto check the profitability of the mine development for the people in the minearea and surrounding areas.

7.5 Future Suggestions:

Instead of extrapolating data from developed countries we can have ourown data using survey methods.

Further research for exposure response relationships can be done. Monetization of other impacts apart from health impacts can be done. Impacts of other sorts of pollution must be brought into account. We can use DALY calculator from R statistical tool for calculating

DALYs. It requires a thorough survey and more reliable data for more

robust calculation [19].


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Appendix

Table A. Air quality data for Mine A

S. No.Locations population

Increment in

PM (g)

beforemining

(g)

Aftermining

(g)1. V1 385 2 12 142. V2 658 5 20 253. V3 552 5 23 284. V4 1417 5 28 335. V5 420 10 45 55

6. V6 2003 10 55 657. V7 1211 3 20 238. V8 611 10 50 609. V9 396 2 23 2510. V10 1496 2 45 4711. V11 309 2 18 2012. V12 544 2 16 1813. V13 772 1 14 1514. V14 333 1 11 1215. V15 1943 4 40 4416. V16 654 4 32 3617. V17 592 1 35 3618. V18 912 1 34 3519. V19 556 1 33 3420. V20 796 1 36 3721. V21 459 1 32 3322. V22 639 1 30 31

23. V23 1363 2 45 4724. V24 643 1 23 2425. V25 576 1 22 2326. V26 2121 1 21 2227. V27 1377 1 26 2728. V28 421 1 27 2829. V29 1113 4 39 4330. V30 659 1 21 22

31. V31 1502 1 34 3532. V32 1990 1 36 37


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33. V33 1223 1 37 3834. V34 944 2 25 2735. V35 648 1 19 2036. V36 1308 1 28 29

37. V37 325 1 22 2338. V38 628 1 24 2539. V39 1485 1 27 2840. V40 924 1 21 2241. V41 955 4 48 5242. V42 278 1 23 2443. V43 1295 1 47 4844. V44 1088 1 43 44

TOTAL 40524


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Table A1. Deduced mortality cases for Mine A

S.No. cases before mining cases after mining Premature mortality due tomining

1. 0.691755 0.812414 0.120659 12. 2.023827 2.573162 0.549335 1

3. 1.972434 2.44274 0.470305 1

4. 6.270583 7.519877 1.249294 2

5. 3.171973 4.02165 0.849677 1

6. 19.17944 23.53715 4.35771 5

7. 3.724703 4.327207 0.602504 1

8. 5.221394 6.50298 1.281586 2

9. 1.415007 1.54859 0.133583 1

10. 11.29827 11.88631 0.588047 1

11. 0.849617 0.950399 0.100782 1

12. 1.320691 1.495765 0.175074 1

13. 1.629048 1.751226 0.122178 1

14. 0.546656 0.598323 0.051667 0

15. 12.81152 14.29633 1.484808 2

16. 3.353799 3.826355 0.472556 1

17. 3.355556 3.463612 0.108055 1

18. 5.00406 5.169371 0.16531 1

19. 2.950636 3.050721 0.100085 1

20. 4.657154 4.80346 0.146307 1

21. 2.353813 2.435867 0.082054 0

22. 3.050763 3.163431 0.112667 1

23. 10.29381 10.82957 0.535767 1

24. 2.2976 2.405684 0.108084 1

25. 1.962023 2.058192 0.096169 0

26. 6.872998 7.224741 0.351743 1

27. 5.619496 5.855726 0.23623 1

28. 1.790313 1.863031 0.072719 0

29. 7.129855 7.97441 0.844555 1

30. 2.135458 2.244745 0.109288 1

31. 8.241336 8.513591 0.272255 1

32. 11.64288 12.00865 0.365766 1

33. 7.380191 7.606556 0.226366 1

34. 3.691588 4.014383 0.322795 1


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Table A2.Deduced morbidity cases for Mine A (a)S.No. cases before

miningcases aftermining

hospital admissions and sickness requiringmedical treatment due to mining

1. 3.735389 4.575185 0.839796 1

2. 12.32329 16.31875 3.995462 43. 12.32598 15.79612 3.470139 44. 40.5491 49.99516 9.446061 105. 22.26696 29.37668 7.109716 86. 140.0988 178.6008 38.50206 397. 22.68009 27.04124 4.361145 58. 37.40222 48.42002 11.01781 119. 8.842551 9.82101 0.978458 110. 79.3128 84.12698 4.814177 511. 5.066093 5.787075 0.720982 112. 7.678037 8.918948 1.240911 213. 9.174138 10.03027 0.856136 114. 2.873672 3.230869 0.357197 115. 88.03938 99.9426 11.90322 1216. 22.18195 25.81788 3.635927 417. 22.53265 23.37031 0.837664 118. 33.43741 34.71246 1.275043 2

19. 19.61701 20.38509 0.768076 120. 31.42359 32.56357 1.139979 221. 15.56807 16.19462 0.626554 122. 19.95945 20.81124 0.851791 123. 72.2616 76.64778 4.386179 524. 14.35798 15.14777 0.789787 125. 12.16251 12.86189 0.699387 126. 42.23994 44.78589 2.545955 3

27. 35.88132 37.63259 1.75127 228. 11.50568 12.0474 0.541723 129. 48.77815 55.51348 6.735333 730. 13.12405 13.91509 0.791034 131. 55.06907 57.16898 2.099907 232. 78.55899 81.40893 2.849948 333. 50.03172 51.80453 1.772813 234. 23.4117 25.79896 2.387258 335. 11.37573 12.136 0.760272 136. 37.42994 39.13287 1.702933 2


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Table A3. Deduced morbidity cases for Mine A (b)S.No. cases before

miningcases aftermining

incidence cases of minor sicknesses due tomining

1. 227.8988 266.6295 38.73075 39

2. 670.384 870.2156 199.8316 2003. 660.8851 839.403 178.5179 1794. 2154.772 2665.72 510.9481 5115. 1234.867 1726.054 491.1864 4926. 8231.632 11353.59 3121.958 31227. 1233.792 1449.876 216.0847 2168. 2129.464 2951.311 821.8465 8229. 474.1132 523.7164 49.60316 5010. 4398.479 4711.77 313.2908 31411. 279.8757 314.8156 34.93986 3512. 433.6056 492.7262 59.12054 6013. 534.6441 574.6013 39.95717 4014. 180.8359 197.1176 16.28175 1715. 4781.907 5517.043 735.1361 73616. 1181.032 1385.217 204.1849 20517. 1206.074 1253.897 47.82273 4818. 1786.029 1858.006 71.97699 72

19. 1045.971 1088.851 42.88049 4320. 1685.983 1751.815 65.83259 6621. 828.8896 863.4901 34.60048 3522. 1060.802 1106.852 46.04986 4623. 4007.438 4292.876 285.4381 28624. 769.8353 809.6849 39.84953 4025. 654.6551 689.6192 34.96412 3526. 2284.503 2410.631 126.128 127

27. 1910.112 2001.038 90.92688 9128. 611.7917 640.1969 28.40514 2929. 2640.276 3051.033 410.7563 41130. 709.8008 748.9891 39.18828 4031. 2941.465 3060.006 118.5411 11932. 4214.957 4379.539 164.5815 16533. 2691.546 2795.125 103.5792 10434. 1248.455 1371.808 123.3533 12435. 623.1933 660.1958 37.00244 3736. 1989.02 2079.212 90.19214 91


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37. 369.38 389.1081 19.72802 2038. 790.7964 830.5401 39.74368 4039. 2157.983 2258.177 100.1939 10140. 995.229 1050.176 54.94685 55

41. 3111.851 3556.998 445.1466 44642. 332.837 350.0659 17.22888 1843. 4078.705 4219.735 141.0305 14144. 2982.501 3089.317 106.816 107

Total 9975


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