impact of drinking water contamination caused by hattar...

Impact of Drinking Water Contamination

Caused by Hattar Industrial Estate on Health and

Household Utility

By

Aziz Ullah

CIIT/FA10-R67-001/ATD

PhD Thesis

in

Environmental Sciences

COMSATS Institute of Information Technology

Spring, 2017

ii




Household Utility

A Thesis presented to


in partial fulfillment

of the requirement for the degree of

PhD (Environmental Sciences)

By

Aziz Ullah


Spring, 2017

iii



Household Utility

A Post Graduate Thesis submitted to the Department of Environmental Sciences

as partial fulfillment of the requirements for the award of Degree of Ph.D in

Environmental Sciences.

Name Registration Number

Aziz Ullah CIIT/FA10-R67-001/ATD

Supervisor

Dr. Shehla Amjad

Professor, Department of Management Sciences

COMSATS Institute of Information Technology (CIIT)

Abbottabad.

iv

Certificate of Approval

This is to certify that the research work presented in this thesis, entitled ―Impact of

Drinking Water Contamination Caused by Hattar Industrial Estate on Health and

Household Utility‖ was conducted by Mr. Aziz Ullah CIIT/FA10-R67-001/ATD, under

the supervision of Dr. Shehla Amjad. No part of this thesis has been submitted anywhere

else for any other degree. This thesis is submitted to the Department of Environmental

Sciences, COMSATS Institute of Information Technology, Abbottabad, in the partial

fulfillment of the requirement for the degree of Doctor of Philosophy in the field of

v

Author’s Declaration

I Aziz Ullah, CIIT/FA10-R67-001/ATD, hereby state that my PhD thesis titled ―Impact

of Drinking Water Contamination Caused by Hattar Industrial Estate on Health and

Household Utility‖ is my own work and has not been submitted previously by me for

taking any degree from this University i.e. COMSATS Institute of Information

Technology or anywhere else in the country/world.

At any time if my statement is found to be incorrect even after I graduate the University

has the right to withdraw my PhD degree.

Date: September 28, 2017

Aziz Ullah


vi

Plagiarism Undertaking

I solemnly declare that research work presented in the thesis titled ―Impact of Drinking

Water Contamination Caused by Hattar Industrial Estate on Health and Household

Utility‖ is solely my research work with no significant contribution from any other

person. Small contribution/help wherever taken has been duly acknowledged and that

complete thesis has been written by me.

I understand the zero tolerance policy of HEC and COMSATS Institute of Information

Technology towards plagiarism. Therefore, I as an author of the above titled thesis

declare that no portion of my thesis has been plagiarized and any material used as

reference is properly referred/ cited.

I undertake if I am found guilty of any formal plagiarism in the above titled thesis even

after award of PhD Degree, the University reserves the right to withdraw/revoke my PhD

degree and that HEC and the university has the right to publish my name on the

HEC/university website on which names of students are placed who submitted

plagiarized thesis.

Date: September 28, 2017

Aziz Ullah


vii

Certificate

It is certified that Aziz Ullah, CIIT/FA10-R67-001/ATD has carried out all the work

related to this thesis under my supervision at the Department of Environmental Sciences,

COMSATS Institute of Information Technology, Abbottabad and the work fulfills the

requirements for award of PhD degree.

viii

DEDICATION

My wife, daughters and son for their love, care, support, sacrifices

and encouragement that made me capable to achieve this remarkable

and uphill task of my life

ix

ACKNOWLEDGEMENTS

I am thankful to Almighty Allah (JJH) who blessed me with the capabilities and strengths

to complete this thesis and gave me the optimism even in hardships and difficulties. At

every sphere and step of my life, I will need the help and guidance of my Almighty

Allah.

I am highly thankful to my supervisor Dr.Shehla Amjad, Professor, Department of

Management Sciences, Abbottabad for her genuine and excellent supervision and

guidance throughout the research process. She has been very encouraging,

accommodating and kind in the entire academic and research activities. I pay my

gratitude to my supervisory committee including Dr.Heman Das Lohano, Dr.Arshid

Pervez, Dr.Muhammad Bilal and Dr.Adnan Ahmad Dogar for their valuable help in my

research. Particularly, I feel pleasure to pay my immense thanks to Dr.Muhammad Bilal

and Dr.Muhammad Asim Afridi for their extraordinary support and help. I express my

sincere feelings of thanks to Professor Dr.Iftikhar. A. Raja who put me on the track of

research. I am grateful to Dr.Shehzad Sarfraz, Associate Professor, and University of

Gujarat for his help in GIS applications.

I express my thanks to South Asian Network for Development and Environmental

Economics (SANDEE), Kathmandu, Nepal and their resource persons, Professsor Dr.

Partha Das Gupta, Professor Dr. Enamul Haq, Dr.Pryashamsunder and Dr.Mani Nepal for

providing me the opportunity of attending a workshop relevant to my PhD at Asian

Institute of Technology (AIT), Bangkok, Thailand. I am grateful to Mr. Sajid Khan

Masroor, Laboratory technician, Instrumental Lab, and Mr. Sohail Akhter, Laboratory

Assistant, Micro Biological Lab, CIIT, Abbottabad for helping me in laboratory

experiments.

I am obliged to my elder brother Mr. Saeed Ullah Sayal, younger brother Mr.Obaid Ullah

Sayal and nephew Mr. Owais Sayal who supported and helped me in my life.

Aziz Ullah


x

ABSTRACT

Impact of Drinking Water Contamination Caused by Hattar

Industrial Estate on Health and Household Utility

Economic activity of industry is posing a serious threat to the health and lives of

communities living around. The compromised and unaware behavior of the people is

affecting the human health and life and its impact spreads from individual to society in

various dimensions. This multidisciplinary study attempts to identify, quantify and

analyze the problem of industrial wastewater contamination into the drinking water of the

communities and its impact on health and utility at household level. The study is based on

primary data and in total 950 households interviewed and 305 drinking water samples

collected from area affected by Hattar Industrial Estate (HIE) in Pakistan. The data is

collected from two affected villages (Dingi and Motian) situated on the bank of industrial

wastewater channel and from one reference village (Khanpur) located upstream with the

same socio-economic characteristics as target villages. The study used Geographical

Information Systems (GIS), epidemiology, environmental sciences and economic

approach to analyze data. Based on laboratory tests, in drinking water of target villages,

lead and Nickel is found above the guideline values of the World Health Organization

(WHO). Single difference approach of with and without is adopted to analyze its impact.

The associated diseases in both selected villages found to be high blood pressure,

lipominingocele, renal disease, black gums, skin and joint pains. The statistical

relationship between the contaminants and diseases was positive. Other variables of high

significance included location, pollution awareness and perception of risk. Based on the

principle of household‘s utility maximization three demand equations were estimated: 1.

demand for health status, 2. Demand for mitigating activities and 3. Demand for avertive

activities. The three demand equations are quantified in terms of marginal willingness to

pay; opportunity cost of avertive measures and leisure; economic cost of water pollution;

and welfare loss to the community. Marginal willingness to pay is estimated as Rs.

4142.03/- and Rs. 819.6/- per household/per annum for the target villages of Dingi and

Motian respectively. Total opportunity cost of avertive measures and leisure for Dingi

xi

was Rs. 7955/- where for Motian Rs.4260/-. Total economic cost for Dingi and Motian

was Rs.11889/- and Rs.10442 per household per annum respectively. The welfare loss to

the community due to industrial water contamination was calculated as Rs. 5.8 million

per annum for Dingi where Rs. 0.2 million per annum for Motian. The total welfare loss

due to water contamination in both selected villages was Rs.6 million per annum. This

welfare quantification, if extrapolated to other villages situated on the wastewater

channel, can result in high monetary loss to the community. This monetary loss is in

addition to the physical and psychological sufferings associated with diseases. The

findings of the study cannot be generalized for all pollutants or all locations since it is

based on data collected from households affected by heavy metal water contamination

caused by HIE in Pakistan. However the majority of developing countries are facing

similar types of problems and the results of this study can be helpful in the choice of

better policy options. The results suggest that apart from environmental pollution, the

economic and behavioral factors also contribute to the prevalence of disease. Therefore,

policy initiatives should be focused on specific issues such as decisions on the location of

industrial estates; inclusion of environmental awareness in the formal education;

motivation to the community for participation in government or Non Government

Organization(NGO) run programs for environmental awareness; and adoption of aversion

and mitigation measures to save the communities from harmful effects of pollution.

xii

TABLE OF CONTENTS

Chapter 1 Introduction .................................................................................................... 1

1.1. Water Contamination and its Impact ........................................................................ 4

1.2. Estimation of Health Damages ................................................................................. 5

1.3. Water Contamination in Pakistan ............................................................................. 7

1.4. Objectives ................................................................................................................. 8

1.5. Organization of the Study ........................................................................................ 8

Chapter 2 Literature Review ......................................................................................... 10

2.1 Lead Contamination, Source and Associated Illness .............................................. 11

2.2 Nickel Contamination, Source and Associated Diseases ........................................ 13

2.3. Water Contamination ............................................................................................. 13

2.4. Cost of Illness ......................................................................................................... 16

2.5. Health Production Function (HPF) ........................................................................ 17

2.6 Willingness to Pay (WTP).................................................................................. 20

2.7 Avertive Measures ................................................................................................... 22

2.8. Welfare Loss .......................................................................................................... 25

Chapter 3 Research Methodology ................................................................................. 30

3.1 The Study Area ........................................................................................................ 31

3.2 Universe of the Study .............................................................................................. 34

3.3 Sample Design ......................................................................................................... 38

3.4 Zoning of Households and GIS based Sampling .................................................... 39

3.5 Water Sampling, Handling and Quality Testing ..................................................... 42

3.6 Data Collection ........................................................................................................ 43

3.7 Estimated Model ..................................................................................................... 46

3.8 Hypotheses .............................................................................................................. 50

3.9 Statistical Techniques used for Data Analysis ........................................................ 53

Chapter 4 Results and Discussion ................................................................................. 55

4.1 Water Contamination and its Effect on Household ................................................. 56

4.1.1 Zone Wise Contamination in Dingi .................................................................. 56

4.1.2 Zone Wise Contamination in Motian ............................................................... 57

xiii

4.1.3 Descriptive Statistics ........................................................................................ 58

4.1.4 Correlation Analysis: Dingi .............................................................................. 64

4.1.5 Correlation Analysis: Motian ........................................................................... 67

4.1.6 Correlation Analysis: Khanpur ......................................................................... 69

4.2 Health Production Function, Medical Costs and Avertive Costs ............................ 71

4.2.1 Estimation for Dingi ......................................................................................... 73

4.2.2 Estimation for Motian ....................................................................................... 78

4.2.3 Estimation for Khanpur .................................................................................... 84

4.3 Marginal Willingness to Pay and Welfare Loss ...................................................... 87

4.3.1 Marginal Willingness to Pay ............................................................................ 87

4.3.2 Opportunity Cost .............................................................................................. 93

4.3.3 Economic Cost .................................................................................................. 94

4.3.4 Welfare Loss ..................................................................................................... 96

4.4 Analysis and Discussion ........................................................................................ 102

Chapter 5 Conclusions .................................................................................................. 111

5.1 Conclusion ............................................................................................................. 112

5.2 Significance of the Study ...................................................................................... 114

5.3 Limitations of the Study ........................................................................................ 114

5.4 Future research application and benefits to community ........................................ 115

References ...................................................................................................................... 116

Annexure A (Household Survey Questionnaire) ........................................................ 131

AnnexuresB1 – B19 ....................................................................................................... 137

List of Publications ....................................................................................................... 146

xiv

LIST OF FIGURES

Figure 3.1 Province (KPK) Map Showing Area of Study ................................................ 31

Figure 3.2 Combined Maps showing All the Three Villages at One Place ...................... 33

Figure 3.3 Graphical Map of Target Village: Dingi ......................................................... 35

Figure 3.4 Graphical Map of Target Village: Motian ....................................................... 36

Figure 3.5 Graphical Map of Reference Village: Khanpur............................................... 36

Figure 3.6 GIS-based Sampling for Target Village: Dingi ............................................... 40

Figure 3.7 GIS-based Sampling Target Village: Motian .................................................. 40

Figure 3.8 GIS-based Sampling for Reference Village: Khanpur .................................... 41

Figure 4.1 Percentage Share of Adoption of Avertive Measures:Dingi ........................... 60

Figure 4.2 Percentage share of Cost incurred on Avertive Measures: Dingi .................... 61

Figure 4.3 Percentage Share of Avertive Measure Adopted by Household ..................... 62

Figure 4.4 Percentage Share of Cost incurred on Avertive Measures: Motian ................ 63

Figure 4.5 Map showing joining of Channel NOR to Channel JHAR ............................. 90

Figure 4.6 Distribution of Total Economic Cost .............................................................. 95

xv

LIST OF TABLES

Table 3.1. Profile of sample villages ............................................................................... 37

Table 3.2: Household and water sampling ........................................................................ 39

Table 3.3: Zone wise GIS based-water/household sampling ............................................ 42

Table 3.4. List of variables ............................................................................................... 52

Table 4.1: Zone Wise Average Concentration of Lead (Pb) and Nickel (Ni) for Dingi. .. 56

Table 4.2.Zone wise Average Concentration of Lead (Pb) and Nickel( Ni) for Motian .. 58

Table 4.3. Descriptive Statistics: Dingi village ................................................................ 59

Table 4.4.Descriptive Statistics: Motian ........................................................................... 59

Table 4.5. Descriptive Statistics: Khanpur ....................................................................... 64

Table 4.6.Correlation Analysis Dingi ............................................................................... 66

Table 4.7. Correlation Analysis: Motian ........................................................................... 68

Table 4.8.Correlation Analysis: Khanpur ......................................................................... 70

Table 4.9.Probability of Sickness: Dingi .......................................................................... 73

Table 4.10. Probability of Medical Cost: Dingi ................................................................ 75

Table 4.11.Probability of Avertive Cost: Dingi ................................................................ 77

Table 4.12.Probability of Sickness: Motian ...................................................................... 79

Table 4.13.Probability of Medical Cost: Motian .............................................................. 82

Table 4.14.Probability of Avertive Cost: Motian ............................................................. 84

Table 4.15. Probability of Sickness: Khanpur .................................................................. 85

Table 4.16. Probability of Medical Cost: Khanpur ........................................................... 85

Table 4.17.Indicators for Marginal Willingness to Pay: Dingi ........................................ 88

Table 4.18. Indicators for Marginal Willingness to Pay: Motian Pb ................................ 91

Table 4.19.Marginal Willingness to Pay (MWTP): Motian Pb ........................................ 92

Table 4.20.Marginal Willingness to Pay (MWTP): Motian Ni ........................................ 92

xvi

Table 4.21. Welfare Loss due to Lead Contamination: Dingi .......................................... 97

Table 4.22.Welfare Loss due to Nickel Contamination:Dingi ......................................... 98

Table 4.23. Welfare Loss due to Contamination of Lead: Motian ................................... 99

Table 4.24. Welfare Loss due to Contamination of Nickel: Motian ............................... 100

Table 4.25.Total Welfare Loss due to Water Contamination ......................................... 101

xvii

LIST OF ABBREVIATIONS

AM Avertive Measures

BHU Basic Health Unit

Coeff Coefficient

COI Cost Of Illness

Col Column

COMSATS Commission on Science and Technology for the

sustainable development in South

DF Disease Frequency

DRF Dose Response Function

EDU Education

GIS Geographical Information System

GPS Geographical Positioning System

HD Health Damage

HIE Hattar Industrial Estate

HPF Health Production Function

M2 Square Kilometer

LPG Liquid Purified Gas

MC Medical/Mitigating Cost

mg/L Milligram per Litter

MWTP Marginal Willingness To Pay

NGO Non-Governmental Organization

Ni Nickel

OPC Opportunity Cost

OPCAM Opportunity Cost of Avertive Measures

OPCL Opportunity Cost of Leisure

PKR Pakistan‘s Rupee

POA Probability of Avertive Cost

POLAWAR Pollution Awareness

POM Probability of Medical Cost

POS Probability of Sickness

Pr Perception of Risk

Probit Probability Unit model

SD Sick Days

SMOK Smoking

Std Err Standard Error

TAVC Total Avertive Cost

xviii

TEC Total Economic Cost

WC Water Contamination

WDS Work Days Lost

WHO World Health Organization

WL Welfare Loss

WTA Willingness To Accept

Chapter 1

Chapter 1Introduction

2

Provision of clean drinking water is essential for the survival of human being and

sustainable environment. The major anthropogenic activity is unplanned industrialization

and lack of ensuring the environmental regulations compliance of the country. The

industrial water contamination is posing serious threat to human and environmental

health especially in developing country like Pakistan (Ahriz et al., 2010). Every year

200,000 children die due to diarrheal disease (Nils, 2005). Similarly 1.8 million people

die every year from diarrhea as well as cholera and 90 percent of them are children under

the age of five years. Most of these children belong to developing countries (WHO,

2004). Use of contaminated water in food preparation can result in contaminated food,

because high cooking temperatures do not affect the toxicity of most chemical

contaminants (Fong and Lipp, 2005).

Drinking contaminated water is the most direct route of exposure to pollutants in

water because pathogens contact directly via gastrointestinal tract(Fong and Lipp, 2005).

The actual exposure via drinking water depends on the amount of water consumed,

usually 2 to 3 liters per day for an adult, with higher amounts for people living in hot

areas or people engaged in heavy physical work(Jamison et al., 2006).As per report of

WHO, 768 million people in the world have no access to drinking water of acceptable

standards (WHO, 2012) and this practice of drinking poor quality of water leads to

widespread diseases across the globe (WHO, 2013). Toxic levels of Pb have been

reported worldwide in drinking water including Maxico (Buschmann et al., 2008),

southeastern Sweden (Augustsson et al.), India (Gowd and Govil, 2008), France (Le Bot

et al.), Bangladesh (Chakraborti et al., 2010), Mailsi, Pakistan (Rasool et al., 2016),

Malakand agency (Nawab et al., 2016) and Hazara region of Pakistan (Sayal et al.,

2016). Inhalation and skin exposures to volatile compounds have been reported during

bathing or taking hot showers (Xu and Weisel, 2003), whereas the use of recreational

water is also associated with waterborne disease and outbreaks and marked as potential

route of exposure to water pollutants (Yoder et al., 2008). Toxic chemicals in water can

affect unborn by crossing the placenta or being ingested through breast milk(Howard and

Lawrence, 1998). Estimating actual exposure via water involves analyzing the level of

the contaminant in the water consumed and assessing daily water intake(WHO, 2004).

Biological monitoring using blood or urine samples can be a precise tool for measuring

3

total exposure from water, food, and air (Barbosa Jr et al., 2005; Paustenbach and

Galbraith, 2006). Pb contamination of pond and well water has been closely associated

with abandoned e-waste recycling sites in longtang, Shanghai, China (Wu et al., 2015).

Industrial development has been found as the main route of water contamination for Pb,

for instance, surface water contamination in Bhopal, India (Virha et al., 2011), surface

and groundwater, as source of drinking water, affected by fertilizers and paint industries

in Hyderabad, India (Krishna et al., 2009), drinking water contamination by Jhar

wastewater channel of HIE of Haripur Hazara, Pakistan (Sayal et al., 2016). The adverse

effects of Pb have been reported on central nervous system, blood cells and may cause

brain damage (Lars, 2003), a chronic renal disease in Maharashtra, India (Lin et al.,

2003), intellectual malfunctioning of children (Lanphear et al., 2005), cardiovascular

mortality in adults of USA (Menke et al., 2006), developmental neurotoxicity,

reproductive dysfunction and toxicity to the kidneys, blood and endocrine systems

(Sanborn et al., 2002).

Ni contamination in drinking water is reported as a worldwide pollution threat to

the humans‘ health. For instance Ni contamination in drinking water has been found in

Nowshera, Swabi districts of Khyber Pukhtoon Khowh (KPK) province (Khan et al.,

2015; Tariq et al., 2015), drinking waters of Subarnarekha river basin India (Giri and

Singh, 2015). Literature reflect that industrial activities and untreated effluents are mainly

responsible for Ni contamination of surface and ground waters, for example, Ni

contamination of groundwater has been associated with industrial activities in north India

(Bhutiani et al., 2016), drinking water and health risk assessment of Ni in Pakistan

(Bhowmik et al., 2015), sewage drains impact the groundwater contamination of Ni in

Faisalabad, Pakistan (Zara et al., 2015). Ni has been found responsible for higher

prevalence of end-stage kidney disease (Liu et al., 2015), atopic dermatitis in children

(Goldenberg et al., 2015), allergic contact dermatitis in children (Jacob et al., 2015),

intrinsic atopic dermatitis (Yamaguchi et al., 2015).

4

1.1. Water Contamination and its Impact

The untreated industrial , agricultural and animal husbandry wastes are the major

sources of microbial pollution and environmental degradation (Abid and Jamil, 2005;

Kahlown et al., 2004; Sun et al., 2001). The toxic metals can contaminate the freshwater

and become a worldwide environmental problem. Rapid industrialization comes with a

negative consequence of environmental pollution which in turn affects human health,

aquatic community and environment via untreated discharge into soil, groundwater,

sediments, surface water and air (Ansari and Malik, 2009). Unlike organic pollutants,

majority of which are susceptible to biological degradation, metal ions do not degrade

into harmless end products and can accumulate in the environment such as food chain,

therefore even at low concentrations, metals can pose a significant danger to human

health and environment. It also damages the agriculture and livestock (Ensink et al.,

2004). A very small amount of heavy metals can even lead to drastic physiological and

neurological damage to human body (Akar et al., 2009).

There are three factors, i.e., host, agent and environment that are responsible for

the imbalance in pathogenesis. These factors are interdependent: host contaminated water

has more chances to be exposed; the human with poor immune system is more vulnerable

to the exposed pollution; the minute level of the agent can induce the morbidity (Atreya

et al., 2013). This water born morbidity necessitates the safe provision of clean drinking

water which in turn will reduce the morbidity/illness and thereby heavy expenditure of

the affected community.

Morbidity is a general term that refers to cases of disease or being in less than

―good‖ health. It can be classified on the basis of duration of condition (chronic or acute),

degree of impairment of activity, or type of symptoms. An episode of acute morbidity

will last only a matter of days with well-defined beginning and end. Chronic morbidity

refers to cases of longer term illness with indefinite duration. The degree of impairment

can be defined in terms of restricted activities days in which a person is able to undertake

some, but not all, normal activities; bed disability days in which a person is confined to

bed, either at home or in an institution; work loss days in which a person is unable to

5

engage in ordinary gainful employment. These measurements of morbidity reflect

responses to ill health rather than the health condition itself. Clinical manifestation of ill

health due to restriction on activity, bed disability, or work loss depends upon a number

of socioeconomic variables such as employment and labor force status, on labor sources

of income, the presence of other income earners in the household. Morbidity can also be

measured by ―symptom days‖ such as an asthma attack, a headache, a cough, throat

irritation or diarrhea. The choice of symptoms for defining and measuring morbidity has

implications for chronic valuation of health effects (Remoundou and Koundouri, 2009).

The economic perspective on health focuses attention on effects that people are aware of

and wish to avoid, i.e., effects that will reduce their utility. Measures of the morbidity

must be taken into account the fact that morbidity is not a discrete event but a process

involving time unlike mortality.

1.2. Estimation of Health Damages

Economic quantification for multidimensional reasons is required for the

estimation of this environmental degradation (Brajer et al., 2006). Economists are

contributing their share in this quantification along with some additional aspects of

recreational loss, ecosystem health damage, deforestation damages, time value of natural

resource, natural resources extraction like oil and gas, hedonic pricing and travel costs

(Remoundou and Koundouri, 2009). For the quantification of health damages, there is

need to determine the health impacts of water contamination. Although epidemiologists

have done a lot pertaining to relationship between pollution and exposure but

quantification of this relation in terms of economic damage to health is still to be

investigated and solved by the economists. Moreover, they have developed the

fundamental theories and valuation techniques. Such quantification will enhance the

public awareness and ability of policy makers.

Multiple economic approaches have been devised for the quantification of health

damages due to environmental degradation. These approaches include revealed

preference, stated preferences, contingent valuation(Loomis et al., 2000), willingness to

pay(Hite et al., 2002), willing to accept and cost of illness(Maria, 2003) and avertive

6

costs(Ahmad et al., 2003). Cost of illness can be quantified by calculating restricted

activity days, loss of productivity, work days loss, opportunity cost, and medical

treatment expenditure. Whereas, the avertive cost estimation is based on all the

precautionary measures to avoid the lethal effects of pollution.

Theoretically utility of individuals is effected due to illness or morbidity in

relation to water pollution. Thereby the affected individual takes health as an output

which is considered as a health production function. This health production function or

dose response function provides the fundamental basis for the economic quantification.

Incidence rate is more appropriate for investigating causal relationships. Estimates of the

monetary values of reduced morbidity take one of two forms, those based on individual

preferences, i.e., willingness to pay and willingness to accept and those on the resource

and opportunity costs associated with illness: typically known as cost of illness or

sometimes called damage cost measure. It seeks to identify the real cost of illness in the

form of lost productivity. These all quantifying strategies are associated with the damage

of human health, when environmental quality is marked as the responsible factor because

the environmental quality is taken as an input in the production of human health through

health production function (HPF).

HPF is the formal model used for deriving the value of reduced morbidity

(Grossman, 1972). Alberini et al.,(1997) introduced a pollution variable into the health

production function. Harrington and Portney(1987)extended the model to examine

explicit relationship among willingness to pay for a reduction in pollution and cost of

illness as well as changes in the avertive or defensive expenditures. Freeman et al.(2014)

developed an expanded version of the Harrington and Portney(1987)model. The health

production function relates exogenous variables (including environmental variables) like

water contamination and choice variables such as avertive expenditure, and treatment

cost (Thornton, 2002). Individuals know their health production function, choose the

output level optimally and choose inputs to minimize the cost of production of any level

of health. Health at any time period is measured by the number of sick days whereas

other determinants of health status are the level of exposure or dose of some

environmental contaminants (Fayissa and Gutema, 2005). Dose is represented by scalar

7

variable which depends on concentration of pollution and the amount of avertive activity

undertaken to avoid or reduce exposure to pollution (Grandjean and Landrigan, 2006).

Mitigating activities are undertaken to reduce health effects of any given exposure to

pollution.

1.3. Water Contamination in Pakistan

Safe drinking water and sanitation have been the longing problems in developing

countries. In Pakistan, industrialization has boosted the economic growth in terms of

higher gross national product and gross domestic product but it causes the losses to well-

being and welfare in terms of environmental degradation. Most of Pakistan‘s community

does not have access to clean drinking water. The rural and urban people‘s access to safe

drinking water is estimated to be 23.5 percent and 30 percent respectively. Pakistan is

facing unprecedented shortage of clean drinking water due to the lowest recorded levels

of clean water availability in the country (WHO, 2008).

Water contamination affects the economic activities of human beings like double

edged sword i.e. as an input into the productive process and the damage to the human

health. The effected individuals due to above mentioned diseases spend most of their time

and resources to manage clean water and medical treatment. The combination of unsafe

and contaminated water consumption and poor hygiene practices require treatments for

water borne illnesses as well as affecting the workability by reducing the working days. It

also contributes to lowering of educational and economic well-being achievement due to

loss of productivity of human capital, consequently lead to the poverty.

People of two selected villages Dingi and Motian are reported to have the water

contamination with heavy metals in their drinking water released from the main Hattar

Industrial Estate‘s wastewater channel, named Jhar(Manzoor, 2006). This water

contamination is the main reason for the prevalence and incidence of water borne

diseases (DHQ, 2010). Because of these diseases, affected people incur heavy cost of

illness and thereby, directly and indirectly their utility decrease. The fundamental reason

of water contamination in these two villages is the geographical location. Dingi and

8

Motian are situated on the downstream banks of the industrial wastewater channel.

Whereas the village Khanpur is situated upstream with the same socioeconomic

characteristics but geographically located differently has not been reported any water

contamination and diseases. These health costs and productivity loss require the sound

quantification.

This study attempts to assess health impacts of water contamination due to

industrial wastewater pollution using cost of illness and avertive behavior techniques in

Dingi and Motian villages of district Haripur, Pakistan. This study is purely based on

primary data where the villages of Dingi and Motian are taken as target group, while

Khanpur village as reference group. The current study is to estimate economic cost of

illness due to water contamination in the drinking water of the inhabitants of villages

Dingi and Motian.

1.4. Objectives

The objectives of the present study are:

1. To assess the level of water contamination in the study area.

2. To investigate the prevalence of illness and their association with the drinking

water contamination in the study area.

3. To estimate the economic cost of water contamination.

4. To analyze the impact of water contamination on household utility in the study

area.

1.5. Organization of the Study

This study is organized in to five chapters. Chapter 1 describes the introduction of

the problem globally, nationally and regionally along with the quantifiable objectives of

the study. Chapter 2 describes the research work relevant to the study under

consideration. Chapter 3 is about the methodology adopted to achieve the objectives. It

9

also describes the profile and digital maps of the study area. Chapter 4 presents the

results, discussion and analysis. This chapter contains three sections which describe the

descriptive statistics for targeted and reference villages. It also presents econometric

estimation for targeted and reference villages followed by the interpretation of estimated

models for each village. This includes estimates of marginal willingness to pay for each

village, estimates for opportunity cost for each village, estimates for economic cost of

contamination and estimates for welfare loss due to water contamination in each village.

Chapter 5 is about the conclusion of the study and chapter 6 contains references.

Chapter 2

Chapter 2Literature Review

11

Provision of safe drinking water mode can yield a range of possible benefits for

household. One such area of economic benefits and cost is with and without pollution in

the drinking water. People either incur additional cost for the negative impact of pollution

on their health or they gain the benefit through removing the pollution. Health costs to

community due to water pollution has various forms like averting and mitigating

expenditure, cost of illness (COI), loss of productivity, disability, restricted activities

days, utility and welfare loss etc. Averting expenditure is the cost of measures taken to

counter act the negative health impacts and other consequences of the impurities in

drinking water whereas the mitigating expenditure is the cost of medical treatment after

the illness; this cost includes the cost of the doctor‘s fee, medicine, work days lost, pains

and suffering etc. Households are compelled to incur expenditure on purifying water.

They generally face water of undesirable quality either in the form of direct or indirect

pollution. Though households incur expenditure for water purification, yet waterborne

diseases may continue to occur.

2.1 Lead Contamination, Source and Associated Illness

Pb Contamination has been reported worldwide in drinking waters of Mexico

(Buschmann et al., 2008), southeastern Sweden (Augustsson et al.;2016), Malakand

Agency, Pakistan (Nawab et al., 2016), Northern Greece (Simeonov et al., 2003),

Turkey (Demirak et al., 2006), Cambodia (Buschmann et al., 2008), India (Gowd and

Govil, 2008), Egypt (Mona et al. 2008), Riyadh, Saudi Arabia (Alabdula‘aly and Khan,

2009), Bangladesh (Chakraborti et al., 2010), Mailsi, Pakistan (Rasool et al., 2016),

Hazara Pakistan (Sayal et al., 2016), Bihar India (Kumar et al., 2016), Shenzhen China

(Lu et al., 2015), Thailand (Wongsasuluk et al., 2014), Quebec Canada (Levallois et al.,

2014), Islampur area of Swat, Pakistan (Hussain et al., 2014). Mining area has been

reported as the main source of Pb contamination in the Karakaya Dam Lake and plant

samples near Keban Town of Elazığ of Turkey (Bakırdere et al., 2016). Similarly Pb

contamination of pond and well water has been closely associated with abandoned e-

waste recycling sites in long tang, Shanghai, China (Wu et al., 2015). In a study of first

12

draw drinking water samples (n=212) in new south Wales, Australia, 56 percent water

samples have been found exceeding the Australian drinking water guidelines of Pb

contamination by Pb(Harvey et al., 2016).

The main route of water contamination is industrial development in many areas ,

for instance, surface water contamination in Bhopal, India (Virha et al., 2011), surface

and groundwater, as source of drinking water, affected by fertilizer and paint industries in

Hyderabad, India (Krishna et al., 2009), southeast Asian River systems contamination by

Pb and other metals closely associated with urban activities (Chanpiwat and

Sthiannopkao, 2014), groundwater of Pb from wastewater Shikrat region Morocco

(Muamar et al., 2014), drinking water contamination by Jhar wastewater channel of

Hattar Industrial Estates of Haripur Hazara, Pakistan (Sayal et al., 2016). A close

association has been observed between the Industrial processing activities and Pb

contamination of surface waters, residential wells and test wells in Pennsylvania, USA

(Ayuso and Foley, 2016). Moreover, rivers have been found heavily polluted with Pb and

Ni contaminations and the industrial, ship breaking yard and gas production plant, and

urban wastes were found the main sources of pollution. These rivers polluted the Bay of

Bengal (Hasan et al., 2016). Lead contamination has also been found in Nahand dam,

Karkaj and Azarshahr well, tab and mineral waters in Iran (Abolhasani et al., 2014).

As per report of WHO, 768 million people in the world have not access to

drinking water of acceptable standards (WHO, 2012) and this practice of drinking poor

quality water Leads to widespread diseases across the globe (WHO, 2013). The adverse

effects of Pb have been reported on central nervous system, blood cells and may cause

brain damage (Lars, 2003). Lead has also been linked to cause a chronic renal disease in

Maharashtra, India (Lin et al., 2003), intellectual malfunctioning of children (Lanphear et

al., 2005), cardiovascular mortality in adults of USA (Menke et al., 2006), developmental

neurotoxicity, reproductive dysfunction and toxicity to the kidneys, blood and endocrine

systems (Sanborn et al., 2002), renal and degenerative diseases like cataract (Rosin,

2009), blood pressure (Hara et al., 2015), kidney failure (Pollack et al., 2015),

neurotoxicants (Grandjean and Herz, 2015), abdominal pain and jaundice (Mohamed et

al., 2016), headache, joint pain, and confusion (Porter et al., 2015), miscarriage in

13

women (Tang et al., 2016), hypertension, elevated intracranial pressure and headache,

anemia, and renal dysfunction (Yıldızgören et al., 2015).

2.2 Nickel Contamination, Source and Associated Diseases

Nickel contamination in drinking water is reported as a worldwide pollution threat

to the humans‘ health, for instance Ni contamination in drinking water has been found in

Nowshera and Swabi districts of KPK province (Khan et al., 2015; Tariq et al., 2015),

drinking waters of Subarnarekha river basin India (Giri and Singh, 2015), drinking water

contamination of major cities of Kurdistan (Ebrahimi and Ebrahimzadeh, 2015), drinking

water of Gombe State, northeast Nigeria (Ebrahimi and Ebrahimzadeh, 2015).

Literature reflect that industrial activities and untreated effluents are mainly

responsible for Ni contamination of surface and ground waters, for example, Ni

contamination of groundwater has been associated with industrial activities in north India

(Bhutiani et al., 2016), sugarcane juice upon the industrial effluent irrigated sugarcane

crop Uttar akhand India (Pandey et al., 2016), drinking water and health risk assessment

of Ni in Pakistan (Bhowmik et al., 2015), sewage drains impact the groundwater

contamination of Ni in Faisalabad, Pakistan (Zara et al., 2015).

Celiac disease has been reported in patients, who were suffering from systemic Ni

hypersensitivity (Darlenski et al., 2013; Picarelli et al., 2011). Similarly Ni has been

found responsible for higher prevalence of end-stage kidney disease (Liu et al., 2015),

atopic dermatitis in children (Goldenberg et al., 2015), vein thrombosis in the legs

(Spiezia et al., 2016), allergic contact dermatitis in children (Jacob et al., 2015), intrinsic

atopic dermatitis (Yamaguchi et al., 2015), insurgence of nickel-derived allergies and

nickel-induced carcinogenesis in humans (Zambelli and Ciurli, 2013).

2.3. Water Contamination

Water borne diseases are the serious and fatal consequence of water pollution.

Zhou et al.. (2013) studied the acute gastrointestinal illness induced by water pollution.

14

They used the cross-section data through questionnaire in the province of Jiangsu, China.

They investigated the monthly prevalence of disease per person per year. The significant

risks factors in multivariable model were gender, education, season, place and travel.

Lora-Wainwright (2013) conducted the study on villagers‘ awareness and respond to

pollution related health risks. He investigated that community does not respond to the

harm impacts of pollution as a collective unit. Main factors, social, political, economic

and development were responsible for uncertainty about pollution. Therefore villagers

engage in a form of lay epidemiology instead of population epidemiology and consisting

collective action against the pollution. The health impacts of environmental pollution

were also reviewed in China (Holdaway, 2013). Ahmed and Sattar (2007) studied the

infectious intestinal diseases due to polluted water. They modeled as a smooth function of

temperature relative humidity and average monthly fecal indicator organisms. They

found the season as a fundamental basis for gastrointestinal illness whereas weather did

not show any association with viral infection. Osvaldo (2013) studied the seasonal

variations for ecological concerns and concluded that if incidence of a disease is not

uniform over time, then environmental factor should be considered as etiology.

Mane et al. (2011) carried a research on the toxicity of heavy metals in the drinking water

of the people. They proved that presence of heavy metals in the drinking water sources

and in the vicinity of the people`s residence is dangerous for the health of the community.

They selected six heavy metals, i.e., (Zn), (Fe), (Pb), (Cd) and (Cr). We also attempted

to investigate four metals Pb, Ni, Cr and Cd but Cd and Cr were found below the

threshold values; however, in our study Pb and Ni were the only heavy metals, which

were found above the standard guideline value. The reason is that our study was specific

to the Industrial state and its effluents. We found strong correlation between the heavy

metals concentration and the residential location of the household.

Agricultural problems like soil degradation and deforestation are directly linked to human

health (Pearce et al., 1993). A comparative analysis of developed and developing

countries reflected that the damage costs in the developed countries are lower than the

developing countries. They supported the argument that the dangerous outcomes of

environmental degradation in the developing countries are damage to the human health.

15

Yongguanet al., (2001) studied the environmental degradation in the Chongqing city of

China. The study reflected that the industrial pollution affected not only the human and

agriculture but also the activities of industry itself; they also found exposure and damage

relationship by using dose-response relationship for illness and deaths due to pollution

(Alberini et al., 1997; Cropper et al., 1997; Lvovsky, 1998; Ostro, 1995; Quah and Boon,

2003). Kumar et al.,(2013) explicitly found that cadmium is one of the toxic metals that

could enter the water through industrial waste. Yeet al., (2013)presented the estimates

that Lead poisoning could be accompanied by hepatitis. They investigated that Lead

metal has no biological function useful for human body. Oyetiboet al. (2013) presented

the results for heavy metal chromium. According to their study, chromium accounts for

rapid and high generation of reactive oxygen species and its resultant toxicity.

Wedgworth and Brown (2013)found the waterborne diseases due to contamination in

drinking water. They conducted the cross section study of 305 household and examined

the relationship between drinking water quality and highly credible gastrointestinal

illness. They analyzed the drinking water quality for fecal coli forms. PhD study was

carried out on chemical profiling of wastewater in the Hattar Industrial estate and its

vicinity (Manzoor, 2006). He made the water quality analysis. Although his analysis was

confined to the wastewater channel and point source pollution but it is that wastewater

channel, which passes through our target villages. The study endorses the presence of

heavy metals in the wastewater channel which has the seepage into the drinking water

resources of communities. All the parameters (pH, conductivity, BOD, COD, nitrates and

heavy metals) analyzed were found above the national environmental quality standards.

Manzoor (2006) also suggested that this water could not be used for any purpose. For

example, if the water is used for irrigation, it will cause the problem of salinity and

sodality and even toxic to fish. It is a proven fact that this groundwater is highly toxic for

the drinking purpose. Sial et al.(2006) presented a laboratory analysis about the

toxicology of industrial wastes and analyzed for various pollutants. He found that

industries in the area of Hattar are releasing toxic heavy metal and these metals and other

coli forms are above the threshold values given by World Bank and Pakistan‘s National

Environmental Quality Standards.

16

2.4. Cost of Illness

Cost of illness (COI) is a quantitative technique used by a various disciplines to

assess monetary valuation of health impacts particularly associated with environmental

valuation. It consists of loss of productivity, medical expenditure, treatment cost, hospital

cost, pathological cost etc. Medical expenses are incurred to treat waterborne diseases.

The avoidance of COI, borne by the households, is an important area of economic

benefits. Alberini and Krupnik (2000) made a cohort study to estimate the COI associated

with air pollution. Harrigton et al.(1989) conducted a pioneering study to estimate

averting expenditure and COI jointly. Alberini et al. (1996) focused the attention on one

of the most common waterborne diseases, diarrhea and investigated the explanatory

factors of averting behavior and illness. While estimating the total losses borne, both the

out-of-pocket expenditure and the opportunity cost of lost time are calculated. Results of

econometric investigations are also incorporated in these estimations for the total

economic cost borne by households. Such estimates reveal that households bear sizeable

expenditure due to undesirable quality water and hence expected to pay for water quality

improvement program.

Freeman (1993) provided the basis for the estimation of COI, mitigating and avertive

expenditure. An important aspect of water quality in relation to human health has not

received due attention. It was the Freeman who provided the basis for the valuation of

non-market good. Ostro (1983) estimated DRF to assess the impact of particulate and

sulfates on morbidity in the USA. To measure the association he estimated the workdays

lost and restricted activity days. Su and Flessa (2013) investigated the main determinants

of household's direct and indirect COI and recommended to have the complete

information about the household cost in order to measure the total economic damage to

health. Dasgupta (2004) identified and estimated the environmentally attributable cost

associated with diseases and disabilities. They used COI approach through

environmentally attributable fractions and extrapolated from national estimates.

Based on the dose-response model of Gerking and Stanley (1986), Murty et al.

(2003) estimated household‘s HPF for the estimation of economic benefits from reduced

air pollution in Indian urban cities of Delhi and Kolkata. They estimated a system of

17

simultaneous equations using the method of three stages least square using six months‘

data relating to sick days, averting and mitigating activities. Sun et al.(2013) applied the

threshold concentration of pollution. They calculated economic cost by applying the COI

and exposure response approach using GIS sampling approach based on spatial gradients.

Atreya et al. (2012) estimated the health cost by applying the COI and calculated the

economic burden on household in Nepal.

2.5. Health Production Function (HPF)

The HPF model is the actual strong footings that provided the new avenue for economists

to incorporate the environmental quality as in input in the health function of human

being. HPF was first developed by Grossman (1972) and subsequently used by many

other for their respective field. Cropper (1981) was the first who used HPF provided by

Grossman and he took pollution as one of the inputs in his modified model. Harrington

and Portney(1987)and Gerking and Stanley (1991)have used this model to examine the

relationships among Willingness to pay (WTP) for reduction in pollution, reduction in

COI, and changes in defensive expenditures. Lvovsky et al. (1998) analyzed health

damages from exposure to the high levels of particulates in126 cities worldwide.

Empirically health production function can also be called as dose response function.

Dose-response relations were extrapolated analytically to various cities and the health

damages were assessed comprehensively, and finally they included mortality, morbidity,

and chronic illness. Gerking and Stanley (1986) were among those initial researchers who

used individuals as a producer of health and therefore they developed choice model. They

estimated the WTP for improved air quality in St. Louis. The WTP gives both lower and

the higher bounds of estimation subject to procedures adopted by the researchers.

Griebler and Avramov (2015) described the natural quality of water like purified

water, stock of clean water for centuries and biodegradation of anthropogenic

contaminants which clearly means that if any economic activity (industrial) or any other

human activity does not affect the natural ecosystem, human can be blessed with clean

water. Dasgupta (2004) valued the health damages from water pollution in urban Delhi

18

using HPF. The study estimates revealed that a representative household would gain

annual benefits from reductions in pollution to the safe level.

Ostro(1983) studied the impact of particulates and sulphate through dose-response

relationship. He established this association by estimating the non-productive days in the

form of work days lost and restricted activities days. He explicitly argued that the HPF

could be used to value the reduced pollution by the household. HPF was applied to study

the relationships among reduce pollution, illness, WTP and avertive expenses. It was at

first devised by the Grossman in 1972 and later on modified and improved by Cropper

(1981); Gerking and Stanley (1986). Harrington and Portney(1987) took the health as an

input in the production of individual‘s health.

United State Environmental Protection Agency has been playing a Leading role in

regulating the clean water standards and threshold values but the situation has reached to

the point that federal and state agencies of USA were to the supreme court for three times

without any resolution for kind of the body water (Adler, 2015).They extrapolated the

exposure-damage association among morbidity, mortality and chronic illness. They

summed up the COI and avertive cost of the people in Kolkata. Alberini et al. (1997)

applied the WTP approach by using contingent valuation techniques for the estimation of

health impacts of pollution in Taiwan. They quantified the model in which WTP depends

upon disease and characteristics of respondent. They estimated the mitigating behavior,

determined within the model. They used the benefit transfer approach from USA. For

estimation they used the Logit model.

Industrialization is also another factor that has contributed to the degradation of

ecosystem. It has resulted in heavy losses to the society in terms of human health

damage. Naturally and fundamentally we cannot deny the fact that water is one of the

universe‘s elements. Therefore its impact is in various dimensions, particularly in the

agricultural countries, the importance of water becomes much higher. For example

Pakistan is among those developing countries where people are facing double edged

sword of water quality and its scarcity.

19

Though there are various empirical studies on agriculture related environmental

degradation, only a few reflected the environmental problems associated with water

pollution and its impact on health. Pearce et al. (1993) quantified the costs of the

environmental deterioration in terms of human health. They further investigated that such

damage costs in developing countries are higher than those in developed countries. They

also argued that the immediate outcome of environmental degradation in the developing

countries is that it affects the human health. Yongguan et al.(2001) carried the research in

the impacts of industrial pollution on agriculture, human health and industrial activities in

chine‘s city Chongqing, which is heavily polluted city in China. Although they estimated

the health damage using dose-response function (DRF) but it was calibrated instead of

statistical. Traditionally most of the economists took the help from epidemiologist but

after the requirement of multidisciplinary research economist have taken this in their

hands and started to quantify the impacts. They use statistical dose-response model which

has greater degree of accuracy in terms economic quantification. (Gerking and Stanley,

1986) established the choice model through HPF. Aunan et al. (2000) analyzed the

energy saving program in Hungry and made the cost benefit analysis. They studied the

vulnerability of the public health to the air pollution released by this project. They used

dose-response function by taking the data from the rural and urban areas. But for

valuation they obtained the data from western studies and concluded that reduction in the

air pollution caused the reduction in chronic respiratory disease. Dasgupta (2004) used

the same model of HPF of Grossman (1972) and valued the damages to health in urban

Delhi. Majumdar and Gupta (2009) studied the drinking water pollution effect on the

public health. They applied the revealed preference approach to estimate the cost born by

the society. Murty et al. (2007) used the household HPF approach for two cities in India;

Delhi and Kolkata. Gupta (2008) also estimated that benefits to the communities can be

obtained from reduction in the pollution. They estimated the total benefits by estimating

precautionary, medical expenditure and household HPF. For estimation they applied three

stage least Square methods for simultaneous equations.

Haque et al. (2011) also estimated the DRF and demand for mitigating

expenditure through HPF approach for air quality and cement production in Srilanka.

However, they did not estimate demand for avertive expenditure due to non-availability

20

of reliable and adequate information on avertive costs. Adhikari et al. (2012) studied the

impact of pollution on the human health and estimated the individual gains when the

amount of pollution was reduced. He used the theoretical framework of consumer utility

and HPF. He also established the physical relationship between the pollution and illness

and medical expenditure function for quantitative economic benefits.

2.6 Willingness to Pay (WTP)

Quantification of economic value is of the various ways to determine and measure

any impact. Economic values are important when there is a making of economic choices,

because choices often have tradeoffs in allocation of resources. Valuations of economic

impacts are based on the peoples' preferences. Fundamental theory of economics also

considers the individual choices and preferences. Economic value of a good or service is

valued by the maximum amount of other things that a person is willing to give up to have

other good. The number of monetary units of currency that a person is willing to pay for

something, i.e., clean water tells how much of all other goods and services, a person is

willing to give up to get that good. This is generally called as willingness to pay (WTP).

Alberini et al.(1997) and Chesnutt et al. (1997) have provided empirical evidences that

benefit transfer approaches are unsatisfactory and potentially misleading for WTP to

avoid illness is higher in Taiwan and Thailand than United States. Therefore, in order to

obtain reliable estimates of gains from water pollution reduction, they emphasized that

for scientific and reliable results, specific study is required. As COI approach is used in

various disciplines like public health policy, epidemiology, medical etc but economists

have taken a right and productive advantage of this approach. Therefore this is a frequent

practice of the economists to apply COI strategy when it pertains to health impacts

(Alberini and Krupnick, 2000).The method initially establishes cause-effect or HPF or

dose-response relationships and then values the impacts of changes in environmental

quality. Many researchers have also used statistical dose-response functions in estimating

the morbidity and mortality impacts of pollution (Alberini et al. 1997; Cropper et al.,

1997; Lvovsky, 1998; Ostro, 1995; Quah and Boon, 2003).

21

Aunan et al.(2000) considered the possible reduced damage to public health in

Hungary. It was estimated together with exposure response functions. Their study reveals

that by reducing the concentration of pollution, public health will be improved and

respiratory disease will be minimized. Their cost benefit analysis indicates that by

reduction the pollution benefits will be larger than costs. They also suggested that huge

damage could be avoided to agriculture, human health and buildings from the reduction

of air pollution. Majumdar and Gupta (2009) applied the revealed preference analysis in

Kolkata, India for polluted drinking water. He also calculated the avertive cost.

Alberini et al. (1997) compared dose response function for two health studies

followed by the same study after a year in Los Angeles and Taiwan. They applied logit

model for estimation after carrying epidemiological study. He predicted the probability of

acute respiratory symptoms as a function of pollution, weather, individual characteristics,

health background and proxies for reporting effects. Empirical evidences of benefit

transfer approach have been found unsatisfactory and potentially misleading that WTP to

avoid illness is found higher in Taiwan and Thailand than United States(Alberini et al.,

1997; Tsai et al., 2000). Therefore a specific study is required which may represent the

actual socio economic and other factors of the respective country in order to obtain

reliable estimates of gains from water pollution reduction.. Therefore in valuing the

health impacts of pollution, COI approach is used by many researchers (Alberini and

Krupnick, 2000) the method initially establishes cause-effect or HPF. (Freeman III et al.,

2014) wrote an award winning book and first time provided the basis for the estimation

of mitigating and avertive expenditure.

Anderson et al. (2013) estimated the monetary abatement value of noise pollution.

They used the hedonic regression technique through property market for WTP. Their

focus was to compare the WTPs estimates to avoid noise pollution between the two

sources i.e., rail and road.

22

2.7 Avertive Measures

Avertive measure is a practice exercised by the people to avoid certain health risk.

Expenditure incurred on these avertive measures are called avertive expenditure. For

example household takes any measure to clean drinking water is an avertive activity, i.e.,

using a filter cartridge to purify the drinking water. The sum of the expenditures on

averting activities and illness can be used as a measure of the cost that the society bears

due to undesirable quality of drinking water. Courant and Porter (1981) investigated the

suitability of averting expenditure as a measure of benefits arising from some

improvement in the quality of environment. They argued that utility maximizing

consumers demand environmental quality as it reduces the need for averting expenditure.

But there is no assurance that the averting expenditure alone will approximate users'

WTP correctly. Swartz and Strand (1981) offered an analysis of the avoidance costs when

consumers make decisions with imperfect information. Watson and Jaksch (1982) used

empirical estimates and Harford (1984) developed a theoretical model to calculate

benefits of reduced pollution when averting actions are practiced. Bartik (1988)

concluded that the upper and the lower bounds to benefits of reduction in pollution could

be estimated using household's avertive expenditure, if information on averting

expenditure technology is available. Bartik (1988) also pointed out that the divergence

between averting expenditure and WTP is due to the fact that averting measures cannot

control some effects of pollution. Households take averting activities such as home

filtration of water or boil water in response to impurity in drinking water. Yet households

have to incur costs on waterborne illness. Harrington and Portney (1987) also

investigated the relationship between the sum of precautionary expenditure and explicit

and implicit cost of illness and WTP. Harrington et al. (1989) later extended the earlier

formulation of Harrington and Portney (1987). The extension incorporated the effect of

illness on productivity of a worker even in days when work is not missed. Alberini et al.

(1996), and Alberini and Krupnick (2000) developed similar structural models of averting

behavior and illness. Whereas Abdalla et al. (1992) and Larson and Gnedenko (1999)

investigated behavior of households to estimate averting expenditure empirically. Due to

advances in research and knowledge, importance of precautionary measures particularly

23

pertaining to health has got attention. Therefore avertive expenditure techniques are

frequently used for valuing ecosystem services. Avertive expenditure or cost is a

literature term which is frequently used for any precautionary step adopted by the people

to avoid the lethal effects of pollution. This Pollution can be of air, water, soil etc.

Researchers have proved that taking of avertive measures can reduce the risk of illness or

in mitigating the severe effects of pollution on health. The avertive expenditure can also

be used to estimate the WTP (Bartik, 1988). Avertive cost estimates appear to provide a

conceptually valid estimation of actual costs or benefits of changes in drinking water

quality. Haque et al. (2011) used COI and avertive expenditure approach to value the

benefits of arsenic removal from the contaminated drinking water. They estimated the

dose-response function or HPF, mitigating expenditure. However, they did not find any

statistical relationship between the adaptation of averting activities and the source of

drinking water thereby did not estimate the demand function for avertive activities.

Kumar and Rao (2001) estimated the economic value improving the air quality for the

community of Panipat thermal power station colony, India. They applied the same dose-

response model of (Gerking and Stanley, 1986) to find the relationship between exposure

and pollution. Zuidema and Nentjes (1997) studied the exposure response relationship for

the Holland‘s labor population in twenty nine districts. They estimated the loss of

productive days during illness. Roy (2008) estimated the economic cost of arsenic

removal from the drinking water in west Bengal; He also used the individual‘s utility

maximization through HPF based on the concept of (Freeman III et al., 2014). Alberini

and Krupnick (2000) made a comparative analysis between COI and WTP estimates of

the damages respiratory symptoms due air pollution. They used a stated preferences

technique, i.e., contingent valuation for the estimation of WTP to quantify the impacts of

minor respiratory illness. They made their analysis on the basis of health diaries to

predict the likelihood and cost of seeking relief from illness and of absentees from work.

(Ostro, 1983) estimated dose response function to assess the impact of particulates and

sulfates on morbidity in USA. To measure this association, the study took into

consideration work days lost (WDL) for employed people and restricted activity days

(RAD) for the combined sample of adults and other non-workers (Chestnut et al., 1997).

Lvovsky (1998) analyzed human exposure to the high levels of pollution in 126 cities

24

worldwide, where the annual mean levels exceeded 50 (μg/m3). Using meta-analytical

technique, the results of dose-response relationship were extrapolated to various cities

suffering from the higher levels of air pollution in different parts of the world. To assess

the health damages comprehensively, the study included mortality, morbidity and chronic

illnesses. Gupta (2011) calculated the welfare loss due to pollution. Chestnut et al.(1996)

investigated the usefulness of averting-behavior as compare to the directly-elicited WTP,

providing a test of the validity of the contingent-valuation approach in heart patients for

changes in their angina symptoms. They used the actual expenditures and perceived

angina episodes avoidance to infer an averting-behavior measurement. Secondly, a

contingent-valuation approaches regarding a hypothetical medical treatment to avoid

additional episodes of angina. They found negligible COI changes with small changes in

angina frequency, however, WTP was found significant to avoid increases in

Angina,Gang et al. (2004) estimated losses and damages caused by the water pollution

using the human capital approach in Chongqing Southwest China. Yongguan et al.

(2001) estimated the resource cost of water pollution in Chongqing, China and found 20

percent damage to human health using the calibrated dose -response function.

Dwight et al. (2005) applied the COI approach to HPF and quantified the health

burden from illness with exposure to polluted recreational marine water for residents of

orange county, California. They estimated the economic burden of gastrointestinal illness

amount. Malik et al. (2012) found that 40percent masses of his sample were unaware of

the pollution. Community below the poverty line was incurring much health expenditure.

They collected the information through questionnaire and used community awareness,

the occurrence water born diseases and community participation as a health intervention

for water born diseases. Azizullah et al. (2011) discussed the various pollutants in the

drinking water particularly total coli forms bacteria and heavy metals concentration in the

drinking water and concluded that such pollutants are responsible for health problems.

Behera and Reddy (2002) used economic and pollution data and estimated the technical

efficiency, pollution control instruments and prices by applying econometric analysis.

They found the association of increase of labor cost and decrease in capital cost with an

improvement in technical efficiency. Pfister et al. (2009) found that environmental

25

impacts on health directly affected individuals and families and their livelihood. He also

placed the costs and benefits towards china‘s economic development.

2.8. Welfare Loss

An environmental quality is an input in to the health production of human being.

Therefore whenever there is degradation in the environmental quality it affects the

household‘s utility at individual level. Marginal willingness to pay is calculated at

individual level but when this individual level disutility is extrapolated over the society as

a whole, it leads to substantial welfare loss in monetary terms. Bogahawatte and Herath

(2008) estimated the dose response function and calculated the welfare loss of $29600

due to air pollution in Srilanka, Haque et al.(2011) estimated the welfare loss due to

arsenic contaminated drinking water from the source of well. He incorporated the

environmental quality in the disutility of the household in Bangladesh. Fleisher et al.,

(1998) calculated the welfare loss due to water pollution. They investigated that exposure

to polluted water is a serious risk to human health. Dasgupta (2004) also calculated the

welfare loss to the society due to pollution. Kampa and Castanas (2008) estimated the

welfare gains from reductions in air pollution in the urban area. He estimated household

HPF, demand for mitigating activities and gains for non-working individuals on the basis

of daily minimum wage rate in India.

Pakistan is facing much devastating situation regarding environmental

degradation particularly water pollution and thereby the water borne diseases.

Relationship between exposure to water pollution and health damage is well recognized

and established in United States and European countries. Most of the studies in the past

used benefit transfer approach to quantify the pollution impacts. But this is an admitted

fact on scientific grounds that as developed countries like USA,UK, Holland, France

have different country specific socioeconomic characteristics, role of genetics in health

production, behavioral responses, institutional circumstances, levels of pollution,

environmental conditions, cultural barrier, weather conditions etc, and thereby the

benefits transfer approach to the study in developing countries will definitely yield

misleading results either in authenticity and/or for policy purposes. Developed countries

26

have taken serious measures to combat water pollution but the worst situation has been

observed in the developing countries.

The current study is a multidisciplinary research; therefore, extensive studies have

been reviewed in a systematic way which encompasses environmental sciences,

econometrics, epidemiology and environmental economics. The present literature was

synthesized and analyzed (from 1972 to 2015) keeping in view the methods, materials,

data sources, diseases and theoretical framework of focused areas, i.e., COI, dose-

response function mitigating activities, avertive activities, epidemiology; estimation and

policy implications. The damages due to nitrate groundwater pollution have been

transformed into monetary form reviewed for the period of 1972-1976 for Beijing city,

china(BI et al. 2010).

Economic quantification has emerged as a breakthrough in the impact assessment of

environmental degradation. Many advances have occurred in the estimation of

environmental losses. In the period of 1987-1989, regional institutes of China calculated

the environmental damage in the province of Liaoning and Vantai city. For both regions,

they estimated the losses of 2.2percent of China's GDP. Based on these studies, china

held a conference of international cooperation committee for china development and

environment in 1992. Prominent feature of this conference was to present the quantitative

figures of economic losses of 95 billion Yuan per annum. Out of 95 billion economic

losses 30 billion (31.57 percent) losses were accounted to water pollution. A childhood

deaths accounted to 11 million. Magnificent work was carried out by the Auster et al.

(1969), who incorporated the health production to determine the health impacts due to

various factors. Although our focused is the environmental factor that affects the human

health but instead of environmental factor they considered the medical expenditure as a

determinant factor of health status. Basically they hypothesized health as a function of

medical services. The current study has also used various variables, which Auster and his

colleagues used. For example education, medical services, income, employment and bad

habit like smoking. As up to the 1972, probit or logit models were not frequently used

particularly in the damage function. Therefore they used ordinary least square, and two

stage least square methods. But in our estimation, we used probit models for estimation.

27

Auster and his colleagues concluded that environmental factors have greater impact on

health status as compare to the medical expenditure. We used different models for

estimation of health impacts. Auster took only education, but we also incorporated the

pollution awareness and perception of risk. With reference to the Auster study, we

investigated that formal education is not as significant as informal education like

pollution awareness and perception of risk. The other reason for our difference is that our

study is regional. However, Siddique and Muhammad (1991) carried the study across the

country analysis, but they studied the health status in terms of socio economic variables.

Instead of Auster and his colleagues, Siddique and Muhammad used generalized least

squares as a function of socio economic variables. They adopted macro variables like

GDP, per capita expenditure, population per nurse and poverty parameter. These results

were consistent in education as their variables literacy rate emerged as a significant

variable. Again our result is not consistent with Siddique and Muhammad. The reason is

that our particular community was very much concerned of their residential location in

the vicinity of Industrial estate, where they are using directly the degraded quality of

drinking water for the last decades. Our study is different from Auster, Siddique and

Muhammad in respect of data input, especially considering the individual household`s

data. We endorsed the conclusion of both studies and found the consistent results to their

opinion of accurate results. Bischoff-Ferrari et al. (2006) quantified the impact of

pollution in monetary terms using the benefit transfer approach in Hongkong. Alam

(2009) studied the causes of diarrhea disease in capital Dhaka of Bangladesh. He

investigated the community living in the slum area, which is badly affected by the water

born disease diarrhea. For the investigation of association between diarrhea and cause he

used dose-response function.

Richardson et al. (2013) established morbidity with exposure to wildfire smoke

using a comparison of revealed and stated preference techniques. Guh et al. (2008)

conducted a survey in a rural area in China. They found that respondents COI for

shigellosis, a bacterial infection caused by water contamination, actually approximated an

upper bound estimate than lower bound of WTP to avoid the illness. The authors explain

the fact that preventative expenditures and disutility from pain and suffering are low for

this illness.

28

Environmental economics, ecological economics and evolutionary economics emerged as

multidisciplinary research to counter the various critiques and offer viable alternatives

(Bebbington et al., 2007; Nelson and Winter, 2009; Van den Bergh, 2007). Evolutionary

and ecological economics encompasses various dimensions and disciplines. Most

important discovery is relation between human and natural systems (Daily et al., 1997).

Environmental economics has become the part of environmental sciences (Pimentel et

al., 2005). Social scientists in collaboration with environmental sciences are not familiar

about evolutionary thought (Ehrlich, 2002). Thereby it can lead to misleading and

unproductive outcomes. In a very informative overview of the meaning of evolution for

the environmental sciences, growth theory with environmental and resource factors to

address sustainability issues (Adams, 2003). A logical starting point for evolutionary

content of environmental economics is the work of Georgescu-Roegen(1975), who got

training in economics at Harvard. It has stimulated study of the relationship between

thermodynamics, economics and growth (Hornborg, 1998).

Dwight et al. (2005) estimated the COI due to water pollution in California, USA.

The economic burden per gastrointestinal illness was at 31.9 €, the burden per acute

respiratory disease at 66.94 €, the burden per ear ailment at 32.95 €, and the burden per

eye ailment at 23.81€.Maddison et al, (2005) applied the value of statistical life for

groundwater contamination in Bangladesh. He reported an aggregate of WTP 2.26 €

billion annually to avoid mortality and morbidity cases. Tseng et al.(2009) used the

contingent valuation technique for non-market good in Taiwan. Their focused area was

climate change but in their research they estimated that people would pay 15.78€ (12%),

70.35€ (43%) and 111.62€ (87%) per year in order to reduce the probabilities of dengue

fever inflection.

Summary

The literature was initially searched for required relevancy and research

objectives. The literature review is focused on heavy metals Pb and Ni contamination

and its association with the diseases and economic utility at household level. It also

includes the studies about epidemiology and dose-response function, econometric

modeling, marginal willingness to pay, welfare loss and over all community‘s welfare

29

loss in terms of economic quantification. Based on literature, It can be concluded that

heavy metal contaminations have adverse effects on human health resulting in welfare

loss in the communities.

Chapter 3

Chapter 3Research Methodology

31

The core and pivotal base for the valuation of economic cost of water

contamination is to investigate the physical relationship between water contamination

exposure and health damages in terms of diseases. Composition of sampling techniques is

multi staged and divided in to two parts; first water quality analysis to find out the

toxicity and its association with the diseases and secondly household survey to assess the

impact of this toxicity on human health damages and its quantification.

3.1 The Study Area

Figure 3.1 Province (KPK) Map Showing Area of Study

32

The study is based on primary data. Three villages i.e., Dingi, Motian and Khanpur were

selected purposely for the current study. Population of these villages are 5021, 1781 and

11800 respectively. Whereas total numbers of households in Dingi are 1600, in Motian

400 and in Khanpur. The total numbers of households are 2500. Out of these households,

350 were selected as a sample for village Dingi, 150 for village Motian and 450 from

village Khanpur. Figure 3.1 shows the study area in relation to the Pakistan where bottom

left figure is the province of Khyber Pakhtunkhwa in which study areas are situated.

Khyber Pakhtunkhwa is one of the four provinces of Pakistan. Geographically, two

villages Dingi and Motian are situated on the left banks of industrial wastewater channel

Jhar (local name) and have been taken as polluted whereas third village Khanpur is taken

as reference village. Both the villages are at the downstream flow of industrial

wastewater channel. Two major heavy metals Lead and Nickel and are found in the

wastewater channel and in the drinking water of the households of the both villages.

Concentrations of contaminants, i.e., Lead and Nickel are beyond the limit set by the

EPA and World Bank (≤0.05 of lead and ≤0.02 of nickel) that is why the diseases

associated with these pollutants are prevailing in both the villages. Major and common

diseases prevailing in these villages are of renal, high blood pressure, skin, joint pain and

stomach. The reference village Khanpur which has the same socio economic

characteristics except the source of contamination, industrial wastewater channel(Fig.3.2)

Although the people have been living adjacent to the wastewater channel since the

establishment of Hattar industrial estate in 1980 but the people of Dingi and Motian are

not much formalized and disciplined in keeping their medical history. Moreover, there

are no Government or private hospitals in these villages to maintain the health diaries.

Therefore the double difference approach could not be applied and is relied on the single

difference approach of with and without. Same approach was adopted by Reddy and

Behera (2006). Both polluted villages were studied on these diseases in association of

drinking water and it was found that these diseases are frequent in both the villages.

Although community at its own level takes some measures to mitigate the effect of water

contaminated and it has got success to some extent. For example, communities of both

the villages adopt some avertive measure like boiling of water, chemical chlorination,

filer cartridge or they purchase the sealed water bottled from the market. But adopting of

33

any single measure could not produce fruitful results until the combination of two to three

adopting measures. Scientifically, it is proved that boiling of water alone cannot remove

the presence of heavy metals like Lead from the drinking water and therefore the

household adopting avertive measure can mitigate the degree of impact but cannot

eradicate the fatal impacts of contaminants completely. However if condensation process

is applied can reduce the effect of contamination.

Figure 3.2 Combined Maps showing All the Three Villages at One Place

Among 22 of villages around the industrial estate , two villages Dingi and Motian

(target group) are considered to be most affected areas of industrial water contamination

(Manzoor, 2006). Khanpur village which has the same socioeconomic and demographic

characteristics except water contamination was taken as a non-polluted reference area.

The researchers normally use three types of approaches i.e. with and without approach,

before and after approach and double difference approach. In the present study, there

were some limitations of secondary data availability; therefore, with and without

34

approach i.e. comparison of reference group (Khanpur) and targeted group (Dingi and

Motian) was adopted. The majority of the people in these two villages are facing various

common diseases i.e. diarrhea, neural, renal, high blood pressure, stomach, joint pains,

liver and skin allergy etc. The main cause of these diseases in the villages was considered

to be the industrial wastewater contamination which is reflected from the collected data,

econometric estimation and additional health associated cost incurred by the household.

For stratification, water and household samples were digitized on geographical

information system (GIS) using Arc- GIS software.

3.2 Universe of the Study

There are three large industrial estates across the province of Khyber

Pakhtunkhwa i.e. Gadoon Amazie Industrial Estate, Mardan; Hayatabad Industrial Estate,

Peshawar and Hattar Industrial Estate, Haripur (HIE). Using convenient sampling,

universe of the study is the surrounding areas of HIE, in Pakistan .Hattar Industrial Estate

(HIE) is situated 16 kilometer from Union Council Kot Najibullah, Haripur district,

Pakistan. It has been established in 1985-86 at total area of 1,032 acres (4180 m2) of land.

In HIE there are around 200 operational units, and mainly composed of food and

beverage, textile, crockery, paper printing, chemical, fertilizers, stainless steel , cement,

publishing, chemical, rubber, carpets, battery and leather products.

Village Dingi is one of 8 union counsels of district Haripur of Pakistan and is

located on the southern part of the Haripur district, on both northern and southern banks

of the industrial wastewater channel and because of this reason Dingi along with other

areas situated in this topography is highly prone to the industrial contamination, i.e.,

heavy metals contamination. Majority of population of village Dingi is living on the

southern bank of the wastewater channel. Geographical location of Motian is different

from Dingi, as it is situated on the one bank (southern) of wastewater channel Jhar

(Fig.3.3). Untraditionally both household and drinking water sampling were made

through Garmin GPS coordinates and subsequently in the software of Arc-GIS. The

whole community living of village Dingi around the wastewater channel is scattered over

the area of 190 m2 with 177.6

°, whereas geographically community is occupying the area

35

of 500 m2 with 176.26°from the wastewater channel. Therefore the whole population of

Dingi has been distributed in three buffer zones of variants of 100, 200 and 350 m2 r

distances and for Motian it is at 100,200 and 300 m2 (Fig.3.6) from the industrial

wastewater channel, however household and water sampling from the village Khanpur

was made randomly throughout the village(Fig.3.2). Sampling points from wastewater

channel and household along with their drinking water sources were marked on Garmin

GPS 60.

Figure 3.3 Graphical Map of Target Village: Dingi

36

Figure 3.4 Graphical Map of Target Village: Motian

Figure 3.5 Graphical Map of Reference Village: Khanpur

37

Representative sampling of villages: Dingi, Motian and Khanpur for the total

population of 8 union councils, of district Haripur has been done for the study purpose.

Among these union councils three villages Dingi, Motian (Targeted) and Khanpur

(Reference) were selected through a purposive sampling. All the three villages have the

same socio economic characteristics except the industrial water contamination and

prevalence of diseases in two villages; Dingi and Motian. Two selected investigations

were carried out in these villages; one for the quality of drinking water and second

pertains to other relevant information about household through face to face questionnaire

interviewing.

Villages Dingi and Motian are located at the downstream of industrial wastewater

channel Jhar (Local name) and are vulnerable to industrial water contamination. There

are more than 22 villages situated around the industrial wastewater channel but the

degree of closeness and health impacts in other villages are not as higher as of two

villages; Dingi and Motian. Therefore, villages Dingi and Motian were selected as target

group and the Khanpur village which has the same socioeconomic characteristics except

the water contamination was selected as reference group.

Table 3.1. Profile of sample villages

Village

Name

Area

(m2)

Total

Populat

ion

No of

House

holds

Househol

d sampled

Union

Council/

Ward

Digital Arial

Coordinates

Dingi

(Target) 279.8 5021 790 350 Dingi

33° 54' 9.38"N

72° 48' 20.88"E

Motian

(Target) 81.1 1781 258 150 Dingi

33° 54' 01.83" N

72° 47' 52.33"E

Khanpur

(Referenc

e)

728 11800 1685 450 Khanpur

33° 48' 16.25"N

72° 54' 06.77"E

Table 3.1 shows the profiles of all the three villages. Village Dingi is situated on

the both sides of the industrial wastewater channel Jhar, however, 20 percent population

is situated on the north bank of the channel whereas 80 percent population is on the south

bank. Geography of Motian is different from Dingi, as the whole village is situated on the

left side of the channel but 60 meter above the industrial wastewater bed, which has been

38

proved scientifically in during the study that dug well depth and wastewater channel bed

have the same level.

The only difference of pollution makes the topography of study areas distinct and

therefore for sampling of water and households, population was distributed in three

variant distances i.e., total targeted area of village Dingi is 0.2738 m2 (Table3.1) and of

Motian is 0.0811 m2 (Table 3.1) around the wastewater channel. The distribution of the

sampling has been random at three variant distances from the industrial wastewater

channel: 100,200 and 350 meters from the source of contamination for the village Dingi

where as in Motian the variant distances were little different because of the geographical

size. These variant distances for Motian were: 100, 200 and 300 meters. For the village

Khanpur, random sampling was adopted in the whole village.Hence100, 200 and 350

meters on the left side of the channel have been selected for sampling, whereas the area

of Khanpur is 0.72 m2 (Table 3.1).Sampling points both for Water and households

marked on GPS-60,Garmin device to obtain the coordinates (Table 3.1), which further

transformed in to Google earth to map the area.

3.3 Sample Design

For the determination of sampling, sampling design was required to be in two parts:

water sampling and household sampling. Water sampling was carried out in two phases

one from the source of water contamination, i.e., main wastewater channel; and the 2nd

drinking water sampling from households.

The total target number of households was 2733 in all the three villages and overall

950 households were sampled. Number of sampled households was determined through

survey software depending upon the total population of the selected village. Sample of

350 households in village Ding, 150 households in village Motian and 450 households in

village Khanpur (Table 3.2) was taken.

As there is no industrial wastewater channel in the village Khanpur, therefore,

simple random sampling was used in the whole area for household and water sampling

39

(Table 3.2). Households and water sample size was determined by the survey system

calculator.

Table 3.2: Household and water sampling

Village Total No. of

Households

Confidence

level

Water

sample

Sample

size

Percentage of

Population

Dingi 790 95percent 105 350 44

Motian 258 95percent 45 150 58

Khanpur 1685 95percent 155 450 26

Total 2733 305 950

Second phase of investigation was of water quality. For the purpose of water

sampling, the water samples were collected from the two major sources, one from the

main industrial wastewater channel and the second from the drinking water source of the

household. Over all 305 water samples were collected from all the villages.105 from the

village Dingi, 45 from the village Motian and 155 from the village Khanpur (Table 3.2).

Primary data of 350 households from Dingi, 150 from Motian and 450 from

Khanpur covering their demographic, socioeconomic, cultural, diseases, education,

pollution awareness, risk perception, exposure to the pollution and water contamination

was collected during the period of 2013-14. Data was collected through a well-structured

and purpose built questionnaire.

3.4 Zoning of Households and GIS based Sampling

Geographically two villages Dingi and Motian are located on the banks of

industrial wastewater channel. Therefore the communities living close to the channel

were considered to be more vulnerable to contamination and diseases. Both the villages

were distributed in to three zones of variant distances. These distances were marked by

GPS with respect to the proportion of population. Figure 3.6, 3.7 and 3.8 shows GIS

based sampling for Dingi, Motian and Khanpur villages respectively.

40

Figure 3.6 GIS-based Sampling for Target Village: Dingi

Figure 3.7 GIS-based Sampling Target Village: Motian

41

Figure 3.8 GIS-based Sampling for Reference Village: Khanpur

For village Dingi these distances were of 100,200 and 350 m2 whereas for Village

Motian these were 100, 200 and 300 m2as it is smaller than Dingi village whereas village

Khanpur was marked randomly. Purpose of this marking was to obtain coordinates

which later converted on the Google earth, which were used to develop GIS map for the

study area. This methodology with the help of technically experts was adopted to collect

the water samples. Water samples collected in sealed sterilized bottles and were

immediately commuted to the well-established and international instrumental and

microbiological lab of COMSATS Institute of Information Technology, where all the

drinking water samples were tested for suspected presence of heavy metals concentration

of Lead, Nickel, chromium and cadmium and it was found that more than 80percent

households' drinking water has the presence of Lead and Nickel concentration. The

concentration of contamination traced in the industrial wastewater was also found in the

drinking water sources of the households. This concentration was of variant degrees,

based on location from the wastewater channel. The whole procedure of water sampling

was performed on atomic absorption spectrometer, Model NO.AA700 Analyst, made by

PerkinElmer, USA. Initially the investigation was carried out for four heavy metals:

cadmium, chromium, Nickel and Lead but the traces of Lead and Nickel concentration

42

with variant degrees was spread over the whole sample. Most of the samples were above

the threshold guide line values set by the WHO and EPA, Pakistan.

Table 3.3: Zone wise GIS based-water/household sampling

Location Distances from main

wastewater channel

m2

Number of

Household

samples

Water

samples

Number of

Household

Sick

Sickness

percentage

Dingi 100 132 35 105 79

200 99 35 55 55

350 231 35 28 12

Total

350 105 128 53

Motian 100 48 15 36 75

200 43 15 16 37

300 59 15 5 8.47

Total 150 45 57 38

Khanpur NA 450 155 30 6

The evidence from the data shows that people who are living more close to the

industrial wastewater channel have higher percentage of sickness as compare to the

people living farther. As in Village Dingi the percentage of sickens in three variant zones

was found as : 79 percent, 55 percent and 12 percent respectively. Where as in the village

Motian these percentages are: 75 percent, 37 percent and 8.4 percent. Overall percentage

of sickness in Dingi is 53 percent which is higher than Motian' 38 percent (Table 3.3).

However in village Khanpur, this percentage is 6 percent and it is not because of

industrial wastewater channel.

3.5 Water Sampling, Handling and Quality Testing

Water quality tests of collected samples were carried out in equipped labs of the

Environmental Sciences Department, COMSATS Institute of Information Technology

Abbottabad, Pakistan. These samples were collected from the different locations of the

household‘s drinking water (n = 305) based on the GIS sampling of the three villages:

43

Dingi; Motian and Khanpur, as well as from the source of contamination (n = 15):

industrial wastewater channel (Jhar).

The 200 ml of water samples were collected from each source of drinking water

as mentioned above in small plastic bottles. After the collection of samples, these were

immediately commuted to the lab in 2 hours drive and were stored in the refrigerator.

Contaminant of Lead (Pb) and Nickel (Ni) in the water samples were determined

by Atomic Absorption Spectrophotometer (AA 700, Perkin Elmer, USA)in an air-

acetylene flame. Calibration curves were developed for each Pb using1, 5, 10, 15, 20

mg/L standards, and for Ni using1, 3, 5, 7, 9mg/L standards. Lead Sulphate and Nickel

Chloride salts were taken for the preparation of standards for Pb and Ni respectively.

Samples were analyzed once the calibration curve reflect the characteristic coefficient =

0998.The operating parameters for working elements were set as recommended by the

manufacturer. Stock and working solutions were prepared using the formula below:

C1V1 = C2V2

Where,

C1 = 1000 ppm (stock solution)

V1 = volume of stock solution

C2 = required concentration of the contaminant

V2 = volume of the flask (50 mL) in which standards were prepared

3.6 Data Collection

The data for the study pertaining to household includes six categories:

1 Household level information to determine the general characteristics of

household in terms of income, age education family size etc.

44

2 Health, demographic and socio economic characteristics by individuals.

3 Information pertains to illness, private and government doctor‘s fee, hospitals

fee, Pathology fee, transportation, treatment, medicine and laboratory

expenses. Loss of their income, sick days, disease frequency, absents from

job, work days lost, leisure time, holidays etc.

4 Information about the pollution and its dangers.

5 Their perception about the water born and Lead induced diseases‘ information

regarding various avertive measures and expenditure incurred on them.

6 Avertive measures adopted by the households, expenditure incurred on these

avertive measures, Time spent on avertive measures.

A pilot survey was conducted. Data pertaining to above information was collected

through well-structured and well-designed questionnaire. Based on the feedback of pilot

survey, questionnaire (see annexure 1) was further modified.

Household level information was collected from the head of household. There

were some subjective technically limitations to collect unbiased information from the

households. For example, a person who is an employee in the industry and the sole

source of bread and butter for his dependents was of the response that there is no

pollution in this area. The reason was that household was scared of losing his job but the

percentage of the employed household was not as much as to affect the biasness. The

other limitation was of taking time from the villagers for filling the questionnaire as all

the week long they do either job or on daily wage work or they are shopkeeper. Their

village life routine was observed for several days in the whole month and concluded that

the most suitable day to obtain maximum response rate is Friday. As on Friday most of

people offer a obligatory prayer, ―Friday Prayer‖ on this day response rate was of 90

percent.

For the calculation of cost of illness, data was collected from four sources:(i)

Basic Health Units( BHU);(ii) questionnaire;(iii) pilot survey; (iv) district hospitals. Data

on household‘s disease frequency and actual expenditure on the illness was collected

directly from the household for recall period of 6 months.

45

Household avertive action decision is usually taken in three steps: first step for the

household is to decide whether averting actions should be taken or not; the second step is

to choose the number and type of averting actions to be undertaken; third step is the to

employ averting actions and about the size of avertive expenditure.

Explanatory variables explaining avertive expenditure included the location of

residence; concentration of Lead and Nickel in their drinking water, knowledge of

pollution pertains to water born disease, Lead related decision which creates the

perception of risk and exposure to the contaminated water.

Average time of boiling water was calculated by performing the actual activity on

the spot to obtain the accurate time utilized in the activity. People of these villages use

four types of fuels to boil the water or to cook the meal, i.e., wood, LPG Gas cylinder,

electric heater and animal‘s dugs. In order to assess the correct time, activity of boiling

water was exorcized and water was boiled repeatedly using all the types of fuels and the

average time of boiling water was calculated which found to be of 6 to 7 minutes.

Similarly to determine the average cost of market bottles and chemicals, market bottles

and chemicals were purchased from the community‘s accessed markets on different

occasions.

Cost of illness accounts for real cost in terms of productivity loss and the increase

in the resources used for medical care. The environmental effects of water contamination

on health can reduce people‘s wellbeing through the following channels: medical cost or

cost of disease treatment caused by the environmental quality changes and the

opportunity cost of time on treatment (direct cost); loss of wages during illness (indirect

cost); avertive or defensive expenditures; activities associated with attempts to prevent

diseases caused by the environmental quality changes.

Medical activities refer to the measures taken after illness for the reduction of

disease and it after effects. It includes various components of expenditure: visits to the

doctor consultancy fee, pathological tests expenditures, treatment expenditures, hospital

fee and transportation cost etc.

46

Avertive activities are those adoptions made by the household to prevent

themselves from the impact of contamination. These avertive activities were of 4 types:

boiling of water, chlorination tablets, purchase of bottled water, and filter cartridge

methods. Amongst, 2 methods, i.e., filter cartridge and market bottles, were found to be

expensive for the households. The most frequent, effective and cheaper method opted by

households was organized boiling water at family level. However, chlorination is also

less expensive but it is not as much opted as boiling water.

3.7 Estimated Model

The health production function model was first developed by Grossman (1972)

and later on improved by Cropper (1981); Gerking and Stanley (1986); Harrington and

Portney(1987). Freeman (1993) proposed a model in which environmental quality, and

mitigating activity, avertive activity, stock of health capital, and stock of social capital,

like education level of a household are inputs of the health production function.

Household‘s utility function is given as follows:

(3.1)

Where is a private good other than M and A consumed by the household and L

is leisure. The price of private good Y is taken as a numeraire.

Household‘s health production function is given as follows:

(3.2)

Where H represents number of sick days in which environmental quality C, and

mitigating activity M, avertive activity A, stock of health capital K, and stock of social

capital S, like education level are the inputs. Pollution affects individual utility indirectly

through the health production function.

The household‘s budget constraint is given as:

(3.3)

47

Given the environmental quality level C, the health capital K, human resource

capital S, income I, and prices w (wage rate), Pm (Price of medical activities), and

Pa(price of avertive activities), the individual maximizes (I) with respect to Y, M, A and

L given the budget constraint.

Solution to this problem yields the demand function for mitigating activities and

averting activities by the household.

(3.4)

This expression shows that marginal willingness to pay (MWTP) for health

benefits from reduction in pollution is the sum of observable reductions in the cost of

illness, cost of mitigating activities and cost of avertive activities.

In order to estimate the MWTP for safe water in the following equation:

(3.5)

(3.6)

For the estimation of MWTP for contamination free water given in equation (3.6),

there is need to estimate three functions. Freeman‘s (1993) model provides the basis for

the estimating of dose-response function for sickness and two demand functions for

mitigating and avertive functions.

The estimation of MWTP (

) using this equation requires the estimation of:

1. Health production function/dose-response function for work days lost is:

(3.7)

2. Demand function for mitigating activities

(3.8)

48

3. Demand function for avertive activities

(3.9)

Where w is the wage income, y is the non-wage income. Pm is the cost price of

mitigating activities, PA is the cost price of avertive measures opted by household is C is

the level of level of contamination concentration in to the drinking water of household.

HS is the health status and Hi is a household characteristics. For all the three demand

functions binary dependent variables were used. Mitigating expenditure takes the value 1

if it incurs the medical expenditure on his sickness otherwise it is 0.Similarly avertive

expenditure is a binary variable that takes the value of 1 if any avertive measure is opted

by the household , otherwise 0.

For the estimation of dose response function, sickness is also taken as binary

variable which takes the value 1 if contamination induced sickness exists, otherwise 0.

Empirical model of equation (3.2) for an individual is amended as:

(

) (

) (

) (3.10)

MWTP= I + II + III

Where

(

) measures the marginal impact in terms of income loss due

to change in the level of exposure to contamination (∆C);

(

) easures the marginal effect in mitigating expenditure due to

change in environmental quality.

(

) measures the marginal effect on avertive expenditure at the

individual level due to changes in exposure to contamination (∆C).All these functions

have been estimated by probit models (Haque et al., 2011).

First and fundamental requirement for our empirical analysis is to determine the

probability of sickness defined in equation (3.4).These probability estimations are derived

49

using a probit model. This model is used when the dependent variable is dichotomous

and we need to transform the dichotomous "Y" into continuous variable, i.e., given any

real value it produces a number (probability) between 0 and 1. This is cumulative normal

distribution which takes the form of link function as F(Y) = Φ−1(Y). This link function is

known as the probit link. The word was coined in 1930s by biologists studying the

dosage-cure rate link, in short, probability unit or Probit. "Probit" is used for the

estimation of dependent variables when it is binary. This econometric model has been

used in the literature frequently which describes the probability of occurrence of the

event. In this case, as probability of sickness, probability of medical cost and probability

of avertive cost were dichotomous dependent variables. The probit model is applied by

maximizing the following log-likelihood function.

∑ ( ) (3.11)

In above equation is a vector of independent variables. F is the cumulative

probability function for probit model. The dependent variable Yi = 1 if any contaminant

induced sickness exists and = 0 if absent for the ith household. Coefficients of the

variables for the relationships have been estimated using probit model. Unlikely ordinary

least square, coefficients in probit modeling, do not interpret the impacts of the variables.

Therefore for the estimation of impacts of the variables we estimated the marginal effects

along with the coefficients. Marginal effects interpret the unit change in dependent

variable with respect to the unit change of independent variable. Using the probit model,

the marginal effect due to change in the concentration of contaminant i.e., Lead and

Nickel in the drinking water. The marginal effect for contaminant ∆F=(F│contm=1)-

(F│contm=0),which shows the effect on changes in the probability of reducing the

incidence of contaminant related sickness when l contaminated water replaced to safe

mode. Probit model is a linear probability model .The assumption underlying the probit

analysis is that there is a response function of the form Y*t=+βXt+ut, where Xt is

observable butY*tis an unobservable variable. ut/ has the standard normal distribution.

It was observed in practice that Yt which takes the value 1 if Y*t> 0, otherwise 0.

Therefore, the equation becomes:

50

(3.12)

(3.13)

The cumulative distributions function of the standard normal distribution that is:

(3.14)

Then the equation becomes:

(3.15)

(3.16)

The joint probability density of the sample of observation is called the likelihood

function which is given as:

(

) (3.17)

Where π denotes the product operator. The parameters and β are estimated by

maximizing this expression, which is highly nonlinear in the parameters and cannot be

estimated by conventional regression programs. The model is estimated using, STATA

13 for nonlinear optimization.

3.8 Hypotheses

The following hypotheses are formulated for testing in the current study:

1. There is negative relationship between distance from the source of contamination

and level of contamination.

2. There is a positive relationship between prevalence of illness and level of water

contamination.

3. There is a positive relationship between cost of illness and level of water

contamination.

4. There is a negative relationship between household income and level of water

contamination.

51

5. There is a negative relationship between household utility and level of water

contamination.

Variables

Table 4.3 describes the variables used in the estimation and analysis of demand

equations for all the three villages, i.e., Dingi, Motian and Khanpur.

Income (INCOME) of the households was monthly income measured in Pakistani

rupees. Medical or mitigating cost(MC) of the household was the expenditure incurred by

the household on the various activities like doctor‘s consultant fee, doctor‘s visits fee,

pathology tests fee, treatment costs, hospitals cost etc. Household‘s exposure

(EXPOSURE) to the contaminated water was determined on five types of exposures:

drinking, cooking, bathing, washing, and ablution (religious cleanness).

Each exposure was assigned 1 weight and in analysis average of exposures was

used. Sickness was taken as a binary variable, which interprets 1 if the disease exists,

otherwise 0. Avertive measure (AM) was also taken as a binary variable which means if

household adopts any avertive measure is considered as 1, otherwise 0.Lead (Pb) and

Nickel (Ni) were water quality parameters and were obtained in the concentration units

(mg/L).

Avertive cost was calculated on the basis of four avertive measures adopted by

the households. These avertive measures were boiling of water, chlorination, market

bottles and filter cartridge. Average avertive cost incurred by the household on the

adoption of any one or all avertive measures by the household was calculated.

Location is the distance of household living within three marked zones: I,II and

III from the industrial wastewater channel. Pollution awareness (POLOWAR)) of the

household was determined on the basis of five types of information: (i) presence of

radio,(ii) TV, (iii) internet,(iv) participation of any government or NGO program of

awareness,(v)general awareness of pollution. If any household who has 3 or more than

three levels of information was marked 1 otherwise 0.Perception of risk(Pr) was

determined on the scales of five types of information: (i) specific knowledge of heavy

52

metals contamination;(ii) knowledge of waterborne diseases; (iii)knowledge of metals

associated diseases; (iv) verification of contamination through water quality test; (v)

presence of metal associated disease in family. Like Pollution, awareness of the

household was 1, if responded greater than 3 otherwise marked as 0.Family size is the

number of members in the family of a household. Education (EDU) of the household was

determined on five grades: (0) illiterate; (1) primary; (2) middle; (3) matriculation;(4)

above matriculation. Bad habits of the household was determined on binary variable

smoking(SMOK), if does, then assigned 1, otherwise marked as 0. Age (AGE) was taken

as the number of years of the household‘s age.

Sick days (SD) determine the duration of sickness across various episodes in the

year. Disease frequency (DF) interprets either number of ill people or the number of

episodes of illness in the family in a year. Cost of illness (COI) was calculated on the

basis of loss of productivity, treatment cost, hospital cost, pathology cost etc. These all

costs were measured in PKR rupees.

Table 3.4. List of variables

Variable Description

INCOME Household‘s income Household‘s per month income in Pak .Rs

(PKR)

MC Medical cost (binary dependent variable)

EXPOSURE Contaminated water exposure (No of exposures)

SIKNES Sickness (binary dependent variable)

AM Avertive measures (No of avertive measures opted by household)

NICKEL (Ni) Contaminant (Pollution, measured in mg/L)

LEAD (Pb) Contaminant (Pollution, measured in mg/L)

AC Avertive cost (Binary dependent variable)

LOCATION Distance of drinking water source from source of contaminant.

(meters)

POLAWAR Pollution awareness (General Knowledge of environment and

pollution)

PR Specific to the contamination, Water borne diseases, perception of

risk specifically contamination induced diseases

FS Family size (No of persons in the household‘s family)

EDU Education of the household.(No of stages of education)

SMOK Smoking (Binary behavior for bad habit)

AGE Age of the household (Actual number of years)

SD Sick Days (No of sick days in the last six months)

DF Disease frequency (No of the people having the disease)

53

COI (A) Cost of Illness: Loss of productivity ,Medical treatment (PKR)

TAVC (B) Total Avertive Cost (PKR)

OPCL (C ) Opportunity cost of leisure (PKR)

OPCOAM (D ) Opportunity cost of avertive Measures (Time taken for Avertive

measures) (Per hour wage * total number of hours ; PKR)

WDL Work days Lost (No of days lost during illness) (No of work days

lost*wage rate) (PKR)

∆L Change in level of Lead contamination

∆N Change in Level of Nickel contamination

TEC Total economic cost(PKR)= A+B+C+D

Total avertive cost (TAVC) is the total cost incurred by the household in adopting

the one or more avertive measures. Opportunity cost of leisure (OPCL) was calculated by

distributing the time into two categories, i.e., active leisure time and passive leisure time.

Active leisure time is that in which household remains engage in home affairs and

productive tasks; whereas passive time is when he sleeps. We took the active hours for

the calculation of average leisure time affected during illness.

Opportunity cost of avertive measures (OPCOAM) is calculated on the average

time utilized to boil the drinking water. Work days lost (WDL) were calculated for those

working days during which household remained ill and absent from the work. Holidays

during the illness were deducted. ∆L and ∆N are the change in the contamination levels

of Lead and Nickel in the drinking water of the household. Total economic cost (TEC) is

the cost incurred by the household due to water contamination.

3.9 Statistical Techniques used for Data Analysis

For the quantification of objectives, it was essential to apply scientific

methodologies and techniques to analyze the data. A multidisciplinary approach was

adopted including physical, statistical and econometric techniques that have been already

used in the literature. For sampling design and mapping of the location GIS technique

using the Arc-GIS software was used.

54

In statistical techniques, descriptive statistics and correlation analysis was used.

Descriptive statistics describes the averages of the actual data whereas correlation

analysis presents the association among the variables used in the study; It was found that

the correlation among the various variables in the study was positive and significant.

Dose-response function was used for epidemiology study. Dose response function is

study of relationship between the exposure and contaminants.

In econometric study there are various models used for the analysis of the data. In

this study, Probability model named as probit model was used. Probit model determines

the probability of the occurrence of dependent variable, the probability of sickness among

the community due to their close residence with the water contamination source. The data

was analyzed using econometric software STATA 13.

Chapter 4

Chapter 4Results and Discussion

56

This chapter is divided into four broad sections. Section 4.1 is about the water

contamination and its effects on the human health. Section 4.2 describes the Health

Production Function and it describes the relationship between disease and exposure.

Section 4.3 is about the welfare loss to the society due adverse health effects in terms of

monetary cost. Section 4.4 presents discussion and analysis.

4.1 Water Contamination and its Effect on Household

Instead of using benefit transfer approach, and to provide solid basis for

econometric estimation, water samples were analyzed for heavy metals' contamination.

Water from the source of contamination, i.e., wastewater channel, and from the drinking

source of household was used for the investigation of contamination.

Table 4.1: Zone Wise Average Concentration of Lead (Pb) and Nickel (Ni)

for Dingi. Pb Wastewater

Channel(mg/L) Pb in Household drinking water source(mg/L)

Distances 100m 200m 350 m Safe Limit (WHO,2004)

Zone 1 Zone II Zone III

1.58 0.253275 0.180461 0.07977 ≤ 0.05

Ni Wastewater

Channel(mg/L)

Ni in Household drinking water source(mg/L)


Zone 1 Zone II Zone III

10.028 5.84 2.036 1.19152 ≤ 0.02

4.1.1 Zone Wise Contamination in Dingi

Table 4.1 describes the concentration of two contaminants: Pb and Ni with respect

to geographical distribution for village Dingi. During pilot project, four contaminants

were identified: Lead, nickel, cadmium and chromium. The concentrations of chromium

and cadmium were below the threshold limits whereas concentrations of Pb and Ni were

above the given values. The average concentrations of Pb and Ni from the industrial

wastewater channel in Dingi were found as 1.58mg/L for Pb and 10.03 mg/L for Ni.

Whereas average concentration of Pb in household‘s drinking water at three different

57

zones (100 m, 200 m, 350 m from wastewater channel) in Dingi were found as 0.25, 0.18

and 0.07 mg/L and average concentration of Ni at three different zones are 5.84, 2.036

and 1.191 mg/L respectively. Concentration of both Pb and Ni at all the zones were

beyond the threshold values given by World Health Organization (WHO 2004).

4.1.2 Zone Wise Contamination in Motian

Table 4.2 describes the concentration of two contaminants: Pb and Ni water

samples taken from Motian village. Average concentration of Pb and Ni from the

industrial wastewater channel in village Motian is found as 1.62mg/L and 1.40 mg/L

respectively. The average concentration of Pb in household‘s drinking water at three

different zones in Motian is 0.647, 0.095 and 0.008 mg/L respectively. Average

concentrations of Ni (Ni) at three different zones are 1.12, 0.607 and 0.31 mg/L

respectively. Concentrations of both Pb and Ni at all the zones exceed the threshold

values given by World Health Organization : 0.05 and 0.02 respectively.

Average concentration of Pb 1.62 mg/L in industrial wastewater channel at

Motian is little bit higher than that found in Dingi, i.e., 1.58 mg/L .The reason is that

before entrance of industrial wastewater channel Jhar into the village Motian, there is a

small stream called Nor that joins the wastewater channel Jhar at point called a

Domail(See Fig.2).This small stream contains the effluent of Pb from the battery

industry, that is why concentration of Pb in the industrial wastewater channel at Motian is

higher than Dingi. Concentration of Pb and Ni all are beyond the WHO‘s value except at

the zone III, where Pb is below the threshold value.

It is evident from the tables that the concentration of both contaminants varies

with respect to distance from the source of contamination. It was further investigated that

the location and the distance from the wastewater channel matter. Guideline value for Pb

given by WHO is 0.05mg/L and for Ni it is 0.02 mg/L. In the present study, although

there is variation in the concentration with respect to distances from wastewater channel

(source of metal contamination) but in all the three zones both Pb and Ni are above the

guideline values in the drinking water.

58

Table 4.2.Zone wise Average Concentration of Lead (Pb) and Nickel( Ni) for

Motian Pb Wastewater

Channel(mg/L) Pb in Household drinking water source(mg/L)


Zone I Zone II Zone III

1.62 0.647 0.0.095204 0.008951 ≤ 0.05

Ni Wastewater

Channel(mg/L)

Ni in Household drinking water source(mg/L)


Zone I Zone II Zone III

1.40 1.12247 0.60781 0.305954 ≤ 0.02

4.1.3 Descriptive Statistics

Table.4.3 shows the descriptive summary of statistics for village Dingi. Total

number of household observations for village Dingi is 350. Interpretation of all the

variables have been explained in 4.2.1.The mean value of the exposure is 3.36 for the

community of Dingi which shows that people are highly exposed to the contaminated

water. Mean value of avertive cost incurred by the household in a year is Rs.4512/-.

Three values assigned to location based on distances of residence of households form the

industrial wastewater channel. Zone I for the people living at the distance of 100 meter,

Zone II at the distance of 200 meters and Zone III for the people living at the distance of

350 meters. Table 4.4 reveals that most of the households are living in Zone I and Zone

II. Only 38 percent households have the pollution awareness where as 49 percent

households perceived the industrial contamination as a risk to their lives. Opportunity

cost of the avertive measure per household per annum is Rs.5653/-.This is the time

utilized by the household to manage the clean water. Average sick days are 11 per

household per year. Average household‘s age is 40 years. Mean values of Lead and Ni in

the village Dingi are 0.24 and 3.29 mg/L respectively. Education of the household was

measured on the 5 scales. Descriptive statistics in table 4.4 shows that average education

59

of the household in the village Dingi is 1.88, .which means they are in the first two stages

of education i.e., primary and middle.

Table 4.3. Descriptive Statistics: Dingi village

Variable Obs Mean Std. Dev. Min Max

Income/Household/Annum(PKR) 350 233528.6 120725 81000 660000

Avertive Cost/Household/Annum(PKR) 350 4512.18 11153.5 0 108980

Medical Cost/Household/annum(PKR) 350 10270.04 12777.26 0 64786

Opportunity Cost of Av. Measure(PKR) 350 5653.99 9130.94 0 44016

Sick Days/Household 350 10.92 12.02 0 42

Family Size/Household 350 4.13 1.52 1 9

Exposure 350 3.37 1.47 0 5

Location (m) 350 1.95 0.84 1 3

Education 350 1.89 0.90 0 4

Smoking 350 0.19 0.40 0 1

Age (Year) 350 40.75 11.56 21 72

Pb mg/L 350 0.25 0.41 0 2.235

NI mg/L 350 2.87 3.30 0 11.39

Table 4.4.Descriptive Statistics: Motian


Income/Household/Annum(PKR) 150 228720 71962.81 96000 384000

Avertive Cost /Household/ Annum(PKR) 150 1883.6 5050.078 0 30840


Opportunity Cost of Av. Measure(PKR) 150 2900.43 7577.52 0 33872

Sick Days/Household / annum 150 6.22 9.67 0 40

Family Size per Household 150 3.62 1.43 1 8

Exposure 150 3.97 1.12 0 5

Location 150 2.09 0.84 1 3

Education 149 1.91 0.80 1 4

Smoking 150 0.11 0.31 0 1

Age 150 42.71 10.18 23 66

Pb(Lead) mg/L 150 1.78 4.39 0 19.24

Ni(Nickel) mg/L 150 0.64 0.67 0 2.34

Fig.4.1 describes the percentage share of every avertive measure adopted by the

household in village Dingi. The most prevailing avertive measure in the village Dingi is

boiling of water, which is 41percent. The second prevailing avertive measure is use of

60

chemical, i.e., chlorination and its percentage is 29 percent. Only 16 percent households

adopt the filter cartridge method which is still higher than the actual functioning of this

method. The reason is installing of filter cartridge is considered to be item, which sheds

the effect of demonstration. As the bottled water from the market is expensive, therefore,

the percentage share, i.e., 14 percent that is smaller than all the options of avertive

measures taken.

Figure 4.2 represents the percentage share of cost of each avertive measure in the

total avertive cost incurred by the household in village Dingi. The highest share is of

boiling water as it contributes to 75 percent of the total avertive cost. Bottled water, filter

cartridge and chlorination contribute as 14, 4 and 7 percent respectively.

Figure 4.1 Percentage Share of Adoption of Avertive Measures:Dingi

.

BOILW 41%

CHLORIN 29%

FILT.CRTG 16%

BOTTLW 14%

Avertive Measures composition

61

Figure 4.2 Percentage share of Cost incurred on Avertive Measures: Dingi

Table 4.4 describes the summary statistics for the village Motian. As compare to

the village Dingi, sick days per household per annum in Motian are 6 whereas average of

Lead and Nickel concentration in the Motian village is 4389 and 0.67 mg/L respectively

which are higher than the guide line value given by the WHO. Average exposure for

Motian is 3.97 out of 5 exposures which mean people of Motian are more exposed as

compare to village Dingi. Effect of smoking is not as influential as the other variables

which are a dummy variable. Average age of the household in the Motian village is 42.

As avertive measures are the outcomes based on four variables: income, education,

pollution awareness and perception of risk. Most of households were at the 1st and

second stage of education i.e., either illiterate or have primary education. There are 29

percent people who have the knowledge of general pollution, but 20 percent people have

the specific knowledge of water contamination and its associated diseases. Although the

education level of the households in the village Motian is a bit high as compare to village

Dingi but we have proved it through impacts of estimation that informal knowledge, i.e.

perception of risk and pollution awareness play significant role in making the decision

pertains to health production. Again Location variable displays that majority of

households live in Zone I and Zone II. There are 39 percent households who incur the

medical cost but there are only 19 percent people who incur the avertive cost. Average

avertive cost incurred by the household per annum in Motian is Rs.1883/-, whereas the

BOILW 75%

CHLORIN 7%

FILT.CRTG 4%

BOTTLW 14%

Avertive Cost share (Rs)

62

opportunity cost of avertive measure per annum per household in Motian is Rs.2900/-.

Average income of the household in Motian village is Rs.228720 per annum. Average

opportunity cost of avertive measures is Rs.1883/- per annum.

Figure 4.3 Percentage Share of Avertive Measure Adopted by Household

Figure 4.3 describes the numbers of avertive measures adopted by the household

and the percentage contribution of every avertive measure in village Motian. Figure 4.3

shows that percentage share of boiling avertive measure is the highest one which is 50

percent of the sampled population. Filter cartridge and bottled water both are of 12

percent and use of chlorination is found as 26 percent. Reason for the high percentage of

boiling water and chlorination is their prices as both the methods are cheaper relatively.

FLTC 12% BOTLW

12%

CHLO 26%

BOILW 50%

63

Figure 4.4 Percentage Share of Cost incurred on Avertive Measures: Motian

Figure 4.4 shows the relative percentage contribution of cost of every avertive

measure adopted by the household in Motian. Share of boiling water is of 54 percent

followed by chlorination (14 percent), filter cartridge (15 percent) and market purchased

water bottles (17 percent).

Table 4.5 describes the descriptive statistics for village Khanpur. It was expected

that the determining factors of the various variables like location, exposure, sick days,

pollution and their behavior were due to presence of industrial wastewater channel and its

contamination into the drinking water of the households living in villages Dingi and

Motian. But village Khanpur with the same social economic characteristics except this

industrial wastewater and its contamination. Therefore, in Khanpur the trend of the

variables is reflected different. Average income of the household is Rs. 413866.7 per

household per annum. The most decisive factor is the number of sick days which is only

1 in the Khanpur where as in Dingi and Motian the average sick days per annum were

11and 6 respectively. Average family size in Khanpur is 4, which is not as much different

from Dingi and Motian. Further more, there is only 6 percent sickness of kidney in

Khanpur but not associated with water contamination. Although the Lead traces were

investigated in the drinking water but this Lead concentration is absolutely different from

FC 15%

BOTW 17%

BOILW 54%

CHLO 14%

Avertive cost share (Rs)

64

the concentration in the Dingi and Motian. This is because of two reasons:(i) Lead

presence is due to old rusted water pipes in the old part of Khanpur where small

population is living and most of the people have replaced them; (ii) average concentration

is 0.02 mg/L which is very smaller than the threshold value of 0.05 mg/L given by WHO.

Nickel was not found in water. Average education level is at second stage which means

average household possess primary education. Average age of household is almost same

as of other two targeted villages. Water contamination has never been found like in Dingi

and Motian that‘s why people of Khanpur do not think to have any need of pollution

awareness or perception of risk. Hence there are 6 percent people who have the general

pollution awareness and 3 percent households are those who have perception of risk.

Table 4.5. Descriptive Statistics: Khanpur


Income/Household/Annum(PKR) 450 413866.70 204320.00 120000 1080000

Avertive Cost /Household/ Annum(PKR) 450 673.33 2890.34 0 20000


Sick Days/Household / annum 450 1.036 4.637 0 36

Family Size per household 450 4.08 1.35 2 9

Exposure 450 0.21 0.84 0 5

Education 450 2.05 0.90 0 4

Smoking 450 0.11 0.32 0 1

Age 450 42.34 11.55 22 72

Pb(Lead) mg/L 450 0.04 0.15 0 1

4.1.4 Correlation Analysis: Dingi

Table 4.6 describes the relationship among the thirteen variables. Variable

exposure has a highest positive correlation of 0.46 with sickness and medical cost, which

is highly significant at 1percent level of significance (t-test). After the sickness and

medical cost, exposure has a negative relationship with location which is highly

significant. This negative relationship shows that farther the location of household`s

residence, lesser is the exposure to the water contamination.

65

Exposure has a negative relationship with pollution awareness, education, income,

risk perception and avertive cost but exposure variables do not have any relation with age

and Lead. However exposure has some relation with contaminant of Nickel. Exposure

has a week relationship with avertive cost, but it is significant at 10percent level of

significance (t-test).

Variable avertive measure has a strongest correlation with avertive cost, which is

highly significant at 1percent level of significance. Avertive measure has no relation with

age and Nickel.

Sickness is one of those variables, which highly correlated with the various

influential variables. It has a strong correlation with location, avertive measures and

pollution awareness. These variables are highly significant at 1percent level of

significance. Sickness has a negative sign with avertive measure, which means that high

the adoption of avertive measure, lower the sickness. There is also negative relationship

between sickness, location and with pollution awareness. Medical cost has a significant

relationship with exposure. Location is highly correlated with sickness but it is negative.

66

Table 4.6.Correlation Analysis Dingi

Exposure

A.

Measure Location

Pol.

Awareness Education Income

Risk

Perception Age Sickness Pb(Lead) Ni(Nickel)

Med.

Cost

Avertive

Cost

Exposure 1

A. measure -0.123** 1

Location -0.243*** 0.084 1

Pol. Awareness -0.179*** 0.368*** 0.147*** 1

Education -0.117** 0.287*** 0.064 0.269*** 1

Income -0.094* 0.317*** 0.038 0.240*** 0.233*** 1

Risk.

Perception -0.143*** 0.268*** 0.176*** 0.220*** 0.447*** 0.129** 1

Age 0.053 -0.052 0.021 0.01 -0.053 0.058 -0.01 1

Sickness 0.464*** -0.395*** -0.530*** -0.446*** -0.237*** -0.159*** -0.308*** -0.067 1

Pb(Lead) 0.086 -0.089* -0.407*** -0.172*** -0.078 -0.131** -0.106** 0.003 0.307*** 1

Ni(Nickel) 0.20*** -0.104** -0.722*** -0.149*** -0.109** -0.06 -0.155*** -0.03 0.47*** 0.334*** 1

Med. Cost 0.464*** -0.395*** -0.529*** -0.446*** -0.237*** -0.158*** -0.308*** -0.067 1*** 0.307*** 0.47*** 1

Avertive Cost -0.0928* 0.9038*** 0.005 0.327*** 0.274*** 0.283*** 0.2282*** -0.078 -0.336*** 0.0317 -0.0238 -0.336*** 1

Signifiance: *** P<1percent, ** P<5percent, * P<10percent

67

4.1.5 Correlation Analysis: Motian

In case of Motian village, sickness has a strong correlation with medical cost, Ni,

Pb and exposure. These correlations are highly significant at 1percent level of

significance. In estimation, sickness has a negative correlation with location, income,

pollution awareness and risk perception. However, it is highly significant statistically in

case of location, income, and risk perception. Sickness has small correlation up to the

sign with formal education. In the estimation, formal education does not have any impact

on the sickness of household. Lead has strong correlation with location and medical cost

but with location, it is negative and in medical cost, it is positive (See Table 4.7).

Location has a strong correlation with medical cost, Nickel and Lead. All the four

variables are also influential in leaving the impact on the households in various

dimensions. These all variables are highly significant at 1percent level of significance.

Exposure has a positive relation with sickness and Lead, whereas negative relationship

with location. But all these relations are highly significant. Smoking and age do not have

any relation with any variable. Medical cost has a strong relationship of 0.58 with Ni,

which is higher than the Pb (0.52). Avertive measure has a relationship with pollution

awareness and risk perception, but weak relationship with the formal school education.

Relationship with pollution awareness and risk perception are highly significant at

1percent level of significance. Both contaminants Ni and Pb have a strong correlation of

0.65 and 0.51 respectively and are highly significant at 1percent level of significance.

68

Table 4.7. Correlation Analysis: Motian

Sickness PB Location Exposure Smoking Age Income

Pol.

Awareness

Risk

Perception Education Med. Cost Ni (Nickel)

A.

Measure

A.

Cost

Sickness 1.000

PB 0.515*** 1.000

Location -0.645*** -0.521*** 1.000

Exposure 0.408*** 0.302*** -0.325*** 1.000

Smoking 0.066 0.014 -0.168** 0.066 1.000

Age -0.038 -0.036 0.044 0.042 -0.139* 1.000

Income -0.249*** -0.047 0.090 -0.070 -0.043 0.159* 1.000

Pol. Awareness -0.206** -0.185** 0.121 -0.024 0.204** -0.055 0.094 1.000

Risk Perception -0.278*** -0.172** 0.124 -0.167** -0.011 -0.050 0.182** 0.117 1.000

Education -0.105 0.012 0.117 -0.138* -0.029 0.086 0.146* 0.090 0.088 1.000

Med. Cost 0.917*** 0.520*** -0.629*** 0.446*** 0.031 -0.044 -0.264*** -0.249*** -0.266*** -0.123 1.000

Ni (Nickel) 0.651*** 0.368*** -0.527*** 0.266*** 0.233*** -0.104 -0.153* -0.141* -0.145* -0.045 0.589*** 1.000

Measure -0.027 -0.015 -0.176** -0.109 -0.060 -0.003 0.238*** 0.278*** 0.304*** 0.146* -0.049 -0.048 1.000

Cost -0.027 -0.015 -0.176** -0.109 -0.060 -0.003 0.238*** 0.278*** 0.304*** 0.146* -0.049 -0.048 1.000*** 1.000


69

4.1.6 Correlation Analysis: Khanpur

Table 4.8 describes the correlation analysis of the households in Khanpur village.

As the Khanpur village is our reference village, which is free from industrial

contamination. Therefore most of the variables like sickness, avertive measures, medical

cost and pollution knowledge do not have any correlation. It is found that the general

sickness of the people is strongly related with the medical cost and it is highly significant

but this sickness is not because of water pollution.

Three villages were selected: Dingi, Motian and Khanpur for the study of impacts

of industrial water pollution. Among these villages, third village Khanpur having the

same socio economic conditions was taken as a reference village. In the estimation of

probability of sickness, medical cost and avertive cost it was already proved that location,

Lead and Nickel contamination, sickness, pollution awareness and perception of risk are

found to be significant statistically. In the correlation analysis, these variables were found

highly correlated in polluted villages, however, in case of non-polluted villages, these all

variables are uncorrelated.

70

Table 4.8.Correlation Analysis: Khanpur

Med.

Cost Sickness

A.

Measure

Pb

(Lead) Exposure A. Cost

Pol.

Awarenes

s

Risk

Perceptio

n

Educatio

n Smoking Age Income

Med. Cost 1

Sickness 0.880*** 1

A. Measure -0.080* -0.07 1

Pb (Lead) 0.007 0.016 -0.055 1

Exposure -0.471*** -0.505*** 0.025 0.061 1

A. Cost -0.075 -0.066 0.788*** -0.029 -0.005 1

Pol. Awareness 0.024 0.008 0.315*** -0.046 -0.002 0.298*** 1

Risk

Perception -0.018 -0.006 0.090* -0.047 0.079* 0.101** 0.097** 1

Education -0.045 -0.115* 0.055 -

0.143*** 0.075 0.038** 0.037 0.001 1

Smoking -0.006 -0.009 0.051 -0.011 0.048 0.003 -0.03 -0.033 0.05 1

Age 0.05 0.051 0.019 0.012 0.064 -0.003 0.007 -0.03 0.056 0.057 1

Income -0.138*** -0.179*** 0.072 -0.089* 0.080* 0.03 -0.062 -0.051 0.504*** -0.031 0.102** 1


71

4.2 Health Production Function, Medical Costs and Avertive Costs

First and fundamental requirement for empirical analysis is to determine the

probability of sickness defined in equation given in methodology. These probability

estimations are derived using a probit model explained in section 3.5. Probit model is

used when the dependent variable is dichotomous. This is cumulative normal distribution

which takes the form of link function as F(Y) = Φ−1(Y). This link function is known as

the probit link. The word was coined in 1930s by biologists studying the dosage-cure rate

link; Probit is used for the estimation of dependent variables when it is binary. This

econometric model has been used in the literature frequently which describes the

probability of occurrence of the event. As probability of sickness, probability of medical

cost and probability of avertive cost are dichotomous dependent variables. The probit

model is applied by maximizing log-likelihood function given in equation 3.12:

∑ ( )

All three equations are estimated: probability of sickness, probability of medical

cost and probability of avertive cost for both the villages using the above function. The

dependent variables sickness (sickness), avertive cost (AC) and medical cost (MC) are

the binary variables and therefore the probit model is the most suitable model used for the

case of binary variable.

In equation 3.12, are the coefficients that include:

(a) Individual level information such as age measured in years;

(b)Education measured on 5 stages, i.e., Illiterate, primary, middle, secondary and

above secondary;

(c) Income measured monthly in Pakistan‘s rupees (PKR);

(d) Concentration of Pb and Ni measured in mg/L;

72

(e) Avertive measure (AM) are the binary variable if household adopts 1,

otherwise 0

(f) Perception of risk (Pr) based on four types of information; specific knowledge

to contamination, knowledge of water borne diseases, contamination of Lead and Nickel

found in their drinking source through lab analysis and presence of contamination

associated disease;

(g) Pollution awareness based on available on five sources of information and

knowledge: television; internet, mobile, radio and participation in any government or

non-government organization;

(h) Location is measured in 3 variant distances of household‘s drinking source

from the contamination source in meters;

(i) Family size (FS) measures the number of persons in household‘s family;

(j) Exposure based on five types measures the contaminated water exposure of

household in drinking, cooking, bathing, ablution and washing.

In general, the coefficients cannot be interpreted from the output of a probit

regression. It needs to interpret the marginal effects of the regressors, that is, how much

the conditional probability of the outcome variable changes, when we change the value of

a regressor, holding all other regressors constant at some values. This is different from

the linear regression case where it is directly interpreted the estimated coefficients. This

is so because in the linear regression case, the regression coefficients are the marginal

effects.

In the probit regression, there is an additional step of computation required to get

the marginal effects, once we have computed the probit regression fit. Therefore, after

having estimated the models, the marginal effects thorough out the estimation were also

calculated.

73

4.2.1 Estimation for Dingi

Probability of Sickness which is also called as dose- response function or health

production function due to water contamination was estimated using probit model. The

estimated probit equation and the marginal effects are shown in Table 4.9

Table 4.9.Probability of Sickness: Dingi

Independent variables Coeff. Std. Err. z P>z dy/dx Std. Err. ey/ex

Exposure 0.4881 0.0743 6.56 0.000***

0.0788 0.0098 1.2097

Avertive measures -1.1096 0.2188 -5.07 0.000***

-0.1792 0.0309 -0.8137

Location -0.7207 0.1879 -3.84 0.000***

-0.1164 0.0284 -1.8339

Pollution awareness -1.065 0.2206 -4.82 0.000***

-0.1720 0.0315 -0.7352

Perception of risk -0.3919 0.2099 -1.87 0.062***

-0.0633 0.0333 -0.2739

Education -0.0905 0.1187 -0.76 0.446 --- --- ---

Age -0.022 0.0087 -2.49 0.013* -0.0035 0.0014 -0.9368

Pb(Lead) 0.8537 0.4009 2.13 0.033* 0.1379 0.0633 0.0897

Ni(Nickel) 0.0977 0.0497 1.97 0.049* 0.0158 0.0079 0.1100

_cons 1.6867 0.7207 2.34 0.019* --- --- ---

* Means significant at 10percent level ** means significant at 5percent *** means significant at

1percent level. No of observations=350,LR chi-square(9)= 280.16,Prob> chi-square=0.0000,Pseudo

R2=0.5801 ,Log Likelihood= -101.39976

Table 4.9.shows the reduction in the probability of sickness to 13percent due to

one unit decrease in the concentration of Lead while Nickel reduces the probability of

sickness by1.5percent this measures the benefit in the terms of kidney and skin disease

prevalence reduction by bringing the quality of water to "safe mode‖. Both Nickel and

Lead are positively related with the probability of sickness. There were five kinds of

activities which define the exposure of the household: normal drinking of water, taking

of bath, cooking, washing and ablution .The major role of ablution activity contributes

to exposure in which people take water in to their mouth which goes inside the body

and intact with gums and throat thereby causes black gums and kidney diseases. In

estimation exposure has a positive sign with probability of sickness and describes the

reduction of sickness by 7 percent if one unit increase of exposure happens. It is

understood that seepage of polluted water into the adjacent community. Soil of

74

community living close to the industrial wastewater channel is very vulnerable and

exhibiting high concentration of Pb as compared to the community located farther to the

wastewater channel.

Location of residence of the household also plays a significant role for disease.

People who live closer to the main source of contamination, i.e. wastewater channel have

more probability of sickness where as people who live away from this source have low

probability. The parameter emerged with negative sign which satisfies the theory. One

unit distance away from the source of contamination reduces the probability of sickness

by 11percent.On the part of household, two categories of the knowledge: one is general

awareness of pollution; and other specific to the disease with cause and effect of

prevailing disease. General awareness about the pollution among the community is based

on the sources of media and their participation in awareness programs it is incorporated

as pollution awareness which gives the negative sign and reduces the probability of

sickness by17percent ,whereas disease specific awareness describes the sensitive and

concerned response of the household to the causes and effects of water contamination and

water born disease induced by heavy metals, such category of awareness is incorporated

as a perception of risk Pr. Perception of risk has also emerged as statistically significant

factor, people are well aware of the fact that poison of heavy metal is present in their

water, they have the patient of Lead associated diseases at their home and they have got

their water tested for the presence of heavy metals that‘s why Produces the probability of

sickness by 6 percent with negative sign.

Distinctive result of the study is the effect of informal education i.e. pollution

awareness and perception of risk over the formal school education. Education does not

seem to affect the behavior of household towards water contamination. Although

education has a negative sign but it does not have any impact on the sickness and

therefore it is comes insignificant.

Medical expenses are those which are incurred due to illness when any member of

household is affected by heavy metal (Lead and Nickel) induced disease: In the sample of

350, 188 households reported the medical expenditure incurred due to contaminated

induced diseases of kidney and skin. Probability of incurring mitigating expenditure due

75

to water contamination was estimated using probit model. The estimated probit equation

and the marginal effects are shown in Table 4.10.

Table 4.10. Probability of Medical Cost: Dingi

Independent variables Coeff. Std. Err. z P>z dy/dx Std. Err. ey/ex

Exposure 0.4714 0.0728 6.48 0.000*** 0.078 0.010 1.145

Avertive measures -1.0770 0.2116 -5.09 0.000*** -0.179 0.031 -0.775

Location -0.6978 0.1845 -3.78 0.000*** -0.116 0.029 -1.738

Pollution awareness -1.0885 0.2157 -5.05 0.000*** -0.181 0.032 -0.736

Perception of risk -0.4256 0.1979 -2.15 0.032* -0.071 0.032 -0.290

Pb(Lead) 0.7375 0.3736 1.97 0.048* 0.123 0.061 0.077

Ni(Nickel) 0.0997 0.0492 2.02 0.043* 0.017 0.008 0.110

_cons 0.6597 0.5723 1.15 0.249 --- --- ---

*Means significant at 10percent level ** means significant at 5percent *** means significant at

1percent level. No of observations=350,LR chi-square(7)= 273.44,Prob> chi-square=0.0000,Pseudo

R2= 0.5662,Log Likelihood= -104.7625

Table 4.10 shows that incurring medical cost influenced by the concentration of

Lead and the Nickel in drinking water and the location of their residence. The

concentration of Lead and Nickel can reduce the probability of medical expenditure by

12percent and 1.6percent respectively. Their location is contributing for 11 percent

reduction in the mitigating expenditure. Coefficient of AM is contributing a very vital

role in reducing the probability of mitigating expenditure by 17 percent because of the

reason that people of this village opted the AM as a render of the last resort. They do not

have other option they have made compromised to live with it and have strong belief in

avertive measures particularly boiling water.

Knowledge of water contamination and pollution awareness is highly significant

statistically; change in pollution awareness can bring the reduction of probability of

incurring mitigating expenditure by 18 percent. As household has more knowledge of

pollution awareness it lowers the probability of incurring mitigating expenses. Another

most vital parameter Pr (Perception of risk) has emerged as influential factor, people are

well aware of the fact that poison of heavy metal is present in their water, they have the

patient of Lead associated diseases at their home and they have got their water tested for

76

the presence of heavy metals that‘s why Pr reduces the probability of sickness by 7

percent. Income of the household is not significant the reason is that village has the

different culture as compare to urban and city. Majority of the community live in a joint

family system and has a strong bond. So at the time of sickness they may borrow from

any of family member and sometimes sell their personal assets. So sickness and its cure is

their first priority). More in the village it is also a culture to make common Pool of

money which is drawn on lucky draw basis so every month there is draw of that pool and

one gets but if anybody who has some emergency can take to cope the emergency and

that is why they prefer to cure sickness.

Although most of the literature showed the lower bound of willingness to pay by

calculating the cost of illness and health production function. As for as this study is

concerned, household perceives the spillover effects of industrial contamination as a life

partner and he has the thinking of taking precautionary measures to minimize the risks

of health impacts. Households incur handsome amount of monetary resources on the

avertive measures. Household averting behavior decisions in the village Dingi were

considered as a two-step process. The household's first step is to decide whether actions

should be taken to reduce the exposure to contaminated water.

There are the two possibilities either the household decides that the contamination

is not significant enough to warrant these practice. Probit regression model was used to

determine factors influencing to option avertive actions. The dependent variable was

equal to 1. If the household took at least one averting actions as a specific response to

contamination and equal to zero of no specific action is taken.

The probit regression results in the Table 4.10 describe that income, perception of

risk and education. As they are more aware about the water contamination as pollution

with its fatal implication they are more conscious about the avertive measures. Boiling of

water is an essential activity of like other routine activities.

Almost all the community is well aware of heavy metal toxicity (Lead) and

dangers in drinking water. They have also perceived that risk that they can reduce the

effects of contamination for severe disease. This is the main reason seem to be for "Pr"

77

significant. Household has the risk perception, takes more avertive actions and that is

why more probability of avertive expenditure. Only individuals who made the decision to

avert considered a second decision step- selection of intensity and feasibility of averting

actions. Parameter location is also significant and with negative sign which means people

who are living close to the source of drinking water contamination have the thinking of

higher concentration of Lead. All the parameters except age are significant statistically.

Table.4.10.also shows that the coefficient values and change in probability of sickness

(marginal effects) associated with the avertive measures, location pollution awareness,

education, risk perception and smoking bad habits of individual household.

Table 4.11.Probability of Avertive Cost: Dingi

Independent variables Coef. Std. Err. z P>z dy/dx Std. Err ey/ex

Pollution Awareness 0.7247 0.1533 4.73 0.000***

0.2352 0.0449 0.1458

Perception of Risk 0.3737 0.1607 2.32 0.020* 0.1213 0.0510 0.1266

Education 0.1748 0.0900 1.94 0.052* 0.0567 0.0288 0.2659

Income 0.00002 7.43E-06 3.93 0.000***

9.43E-06 2.27E-06 0.4457

_cons -1.4972 0.2062 -7.1 9 0.000***

--- --- ---

* Means significant at 10percent level ** means significant at 5percent *** means significant at

1percent level. No of observations=350,LR chi-square(4)= 82.80 ,Prob> chi-square=0.0000,Pseudo

R2=0.1717 ,Log Likelihood= -199.73825

In terms of impact on the probability of sickness the model in Table 4.11 shows

that if avertive measures induced by pollution awareness, Perception of risk. Education

and vulnerability of heavy metals are adopted; it can reduce the probability of sickness.

Because of three reasons; persistent contamination to their source of drinking water.

Secondly, the majority of population adopts home practiced and cheap measure of

avertive adaptations (boiling water) thirdly, one being a poor community, well aware that

there will be no intervention of third party because of economically strong industrial

group and low voice in political and local government. Pollution awareness is the highly

significant statistically with positive sign. The estimated model further reveals interesting

results about pollution knowledge and formal education. Both the variables show the

negative sign for their coefficients. But as far as impact on the probability of incurring

avertive cost is concerned, informal education (pollution) awareness about the water

78

contamination can reduce the probability of sickness by 23 percent whereas for school

education can reduce by 6.6 percent. This is because water pollution and its associated

diseases are so common that household along with their adult have been made conscious

for adopting the avertive measures like to boil water regularly. As boiling water practice

is much common. Hence majority of people are well aware of the pollution and its health

impacts.

The other reason is that wastewater channel and its contamination into drinking

water has become the ecosystem of this particular vicinity and the whole community sees

the phenomena and its fatal implication practically, whereas in school education they do

not get any such particular awareness.

4.2.2 Estimation for Motian

Geographically Motian is situated on the GPS coordinates 33 5401.83 N and 72

47.52 33E.Total area of the Motian is 0.0811 m2

and its population is 1781 whereas total

no of households in the Motian are 258.Out of 258, 150 households were sampled for the

study.

In context of pollution effect, Motian is situated on the left bank of the industrial

wastewater channel. Majority of the people are using the drilled bore water source

however there are few families who have the dug wells but these dug wells are very old.

Both dug wells and drilled water bores are having the concentration of industrial

contaminants. Two contaminants are vital: Lead and Nickel. Unlike village Dingi, Motian

has lower concentration level of Lead and the Nickel. One reason of this lower

concentration is that village Motian is farther from the industrial wastewater coming from

industries and the second reason is that before entering of this channel in to the village of

Motian there is another rain water channel named as NOR (Local name) joins the Jhar

and the junction of this joining is called as Domail (local name).Joining of rain water

channel nor in to Industrial wastewater channel Jhar dilutes the water.

79

The same sampling and estimation methodology was used in village Motian as it

was in village Dingi.

Three probit models along with their marginal effects were estimated for village

Motian. However unlikely Dingi, people of Motian have low level of income and

education and they do not have much knowledge of pollution awareness (see Table 4.12

and Table 4.13) that is why in the probability of sickness, AM parameter was observed as

insignificant. Out of 150 households only 30 people (20 percent) have been observed in

opting the avertive measures. Out of these 20 percent households only 7 percent

households were those who opted more than 2 avertive measures activities. There was not

even a single household who opted all the 4 avertive measures technology.

Table 4.12.Probability of Sickness: Motian

Independent

variables

Coef. Std.

Err.

Z P>z dy/dx Std. Err. ey/ex

Lead(Pb) 0.2773 0.1130 2.45 0.014 0.0403 0.0162 0.0643

Location -0.5307 0.2594 -2.05 0.041 -0.0771 0.0356 -1.8847

Exposure 0.3247 0.1805 1.8 0.072 0.0472 0.0254 1.5459

Perception of

Risk -0.9475 0.4626 -2.05 0.041 -0.1376 0.0643

-0.4421

Nickel(Ni) 1.2209 0.2838 4.3 0.000 0.1773 0.0329 0.3867

_cons -1.5157 0.9993 -1.52 0.129 --- --- ---

Probit regression Number of obs = 150

LR chi2(5) = 126.58

Prob> chi2 = 0.0000

Log likelihood = -38.054058 Pseudo R2 = 0.6245

Table.4.12. also shows the coefficient and marginal for the probability of sickness

(marginal effects) along with the elasticities associated with Lead and nickel

concentration, since Lead and Nickel concentration in the drinking water is witnessed the

valid reason for the various diseases. Exposure to high levels of Pb causes various

diseases, and evidence suggests that lower levels can damage kidneys as well. The toxic

effect of Pb causes nephropathy in which the proximal tubular function of the kidney gets

impaired (Goyer, 1971). Long-term exposure at levels lower than those that cause Lead

nephropathy have also been reported asnephro - toxicin patients from developed

countries that had chronic kidney disease or were at risk because of hypertension

http://en.wikipedia.org/wiki/Proximal_tubule

http://en.wikipedia.org/wiki/Nephrotoxicity

80

or diabetes mellitus. Nickel is used mainly in the production of stainless steels, non-

ferrous alloys, and super alloys. Other uses of Nickel and Nickel salts are in

electroplating, as catalysts, in Nickel–cadmium batteries, in coins, in welding products,

and in certain pigments and electronic products (IARC, 1990).It is estimated that 8

percent of Nickel is used for household appliances (IPCS, 1991).

In terms of its impact on the probability of sickness the model shows that as

concentration of contaminants increases the probability of sickness also increases. That is

why the sickness percentage in the village of Dingi is 38 percent. Out of 150 households

57 were found sick and majority of people are suffering from the kidney, high blood

pressure, stomach and gastrointestinal diseases. Similar results were obtained by (Khan

and Haq, 2011) but they studied the poisonous impacts of arsenic in the drinking water of

the households.

Estimated model further shows (Table 4.12) that residential location of the

household is negatively related with the probability of sickness in the household which is

consistent to our theory and expectations which clearly shows that people living farther

from the industrial wastewater channel have less chances of illness which describes

higher the distance of living lower the probability of sickness. Each additional unit of

farther distance will bring the probability of sickness lower to 7 percent.

The exposure, people of Motian like Dingi are exposed to the contaminated water

in 4 ways; drinking of water, Bathing and religious cleanness, eating and cooking.

Exposure of the household and the probability of sickness are positively related which

means higher the exposure will have higher probability of sickness. It is also an

investigation that people who are more exposed to the contaminated water are more

vulnerable to the probability of sickness. Table 4.12 shows that one unit reduction in the

exposure will reduce the probability of sickness by 4 percent.

Purpose to include perception of risk along with the awareness of pollution

variable was to obtain the information about opting of avertive measures by the

household. Although perceiving of risk in the community of Motian was there but it did

not come as output for adopting the avertive measures as was in village Dingi. They have

http://en.wikipedia.org/wiki/Diabetes_mellitus

81

lower income level and lower level of education. That is why the results show the

negative relationship of risk perception with the probability of sickness which shows that

the perception of risk will reduce the probability of sickness. In the study result shows

that one unit increase in the risk perception reduces the probability of sickness by

13percent which is the highly influential variable. The reason was that they are highly

exposed to the concentration of contamination.

Nickel contaminant has been found highly significant statistically at 1 percent

level of significance whereas concentration of Lead, location and risk perception are

significant at 5 percent level of significance. Exposure emerged as a significant variable

at 10 percent .Theoretically; the results are absolutely consistent in signs. Lead and

Nickel emerged to be positive signs which depicts that higher the concentration of water

contaminants.

Mitigating activities include expenditures incurred due to sickness when any

person in the household‘s family is affected by contaminant-related diseases. In the

sample of 150 households, 57 reported medical expenditure related to the diseases

induced by Lead and Nickel concentration in the drinking water. Using the probit model,

the probability of incurring mitigating expenditure due to exposure to contaminated

drinking water is estimated. The estimated probit equation and the marginal effects are

given in Table 4.13.

Table 4.13 shows that probability of incurring medical expenditure is influenced

by concentration of contaminants Lead (Pb), Nickel (Ni), location and exposure. Unlike,

the village Dingi where the knowledge of pollution and perception were found as

significant variables but in the case of Motian all the three variables of education,

pollution awareness and perception of risk have not been found significant which clearly

shows that people of Motian have lower income level and less knowledge about the

contamination and water borne diseases that‘s why these variables have no impact on the

probability of incurring medical expenditure. Based on small knowledge about the water

contamination and its dangers and low level of income, people of Motian cannot rule out

the presence of diseases and thereby do not report the diseases. However, coefficients of

Lead, Nickel, exposure and location are significant.

82

Table 4.13.Probability of Medical Cost: Motian

Independent

variables

Coeff. Std. Err. Z P>z dy/dx Std. Err. ey/ex

Lead(Pb) .2234 .1117 2.00 0.045 .0366 .01804 .0569

Location -.5421 .2447 -2.21 0.027 -.0889 .0376 -1.8557

Exposure .4258 .1823 2.34 0.020 .0699 .02894 1.9361

Pollution

awareness -.4978 .3493 -1.43 0.154 --- --- ---

Perception of

Risk -.5633 .4146 -1.36 0.174 --- --- ---

Education -.1009 .1717 -0.59 0.557 --- --- ---

Nickel(Ni) .8389 .2597 3.23 0.001 .1376 .0378 .3066

Cons -1.3520 1.0447 -1.29 0.196 --- --- ---


LR chi2(7) = 116.95

b > chi2 = 0.0000


Lead and the Nickel both have the positive sign which are consistent to the theory

and show that higher the concentration of Lead and Nickel in the drinking water of the

household will increase the probability of medical expenditure. In the estimated results

one unit increase in the concentration of Lead and Nickel will increase the probability of

medical expenditure by 3 percent and 13 percent respectively. As the average

concentration of Ni in the drinking water of the Motian is higher than the Lead that‘s why

its impact is higher as compare to the other contaminant, Pb. The reason for the higher

concentration of Ni in the drinking water has already been described..

Location of the residential people from the industrial wastewater channel has been

the most influential parameter in both the villages of Dingi and Motian. Like village

Dingi, again, variable location is negatively related with the probability of medical

expenditure. Which clearly indicates that as the distance from the wastewater channel

83

gets increases it reduces the chances of illness and thereby reduces the probability of

incurring medical expenditure.

In estimated probit equation Table 4.13 shows that one unit farther living of the

household from the industrial pollutants reduces the probability of incurring medical

expenditure by 8 percent. Exposure is another variable which is significant, the people

who are most exposed to pollution have more chances of illness. Therefore in our

estimation exposure has a positive relationship with the medical expenditure. Higher the

exposure will bring higher probability of incurring medical expenditure. In probit

estimation one unit increase in the exposure increases the probability of incurring medical

expenditure by 6.95 percent (See Table 4.13).

Avertive expenses are incurred when adopting any alternative technology to

reduce the impact of contamination. It is precautionary step on the part of the household

and expenses are often incurred at the household level rather than individual level.

Similar studies carried by Aftab et al. (2006).The similarity in current and their study is

that they also measured that the increased level of pollution awareness on the arsenic-

related health risks Leads to the adaptation of averting technologies where as in this study

the perception of risk and general pollution knowledge were also included and

investigated the same trend. In Motian, only one third of the households (in sample)

adopted one or 2 avertive measures however unlike Dingi, people of Motian has lower

level of income and education and they do not have much knowledge of pollution

awareness that is why in the probability of sickness , AM parameter was observed as

insignificant. Out of 150 households only 30 people have been observed in opting the

avertive measures. Out of these 20 percent households only 7 percent households were

those who adopted more than 2 avertive measures activities.

Table.4.14 shows the estimated probit equation for the probability of incurring

avertive expenses. Three variables Lead concentration. Location and Perception of risk

are appeared to be significant in the determination of Probability of incurring avertive

expenditure. Probability of avertive measures is positively related to the concentration of

pollutants which means higher the concentration higher is the avertive expense. One unit

increase in the concentration will increase the probability of incurring avertive

84

expenditure by 1.4 percent. Location is negatively related to the probability of avertive

cost and it shows that people who live farther from industrial wastewater channel do not

think themselves to be the pray of illness that‘s why they do not adopt the avertive

measures. Hence one unit farther from the pollutants will reduce the probability of

incurring avertive cost by18 percent. Whereas unit increase of risk perception will

increase the probability by 28 percent.

Table 4.14.Probability of Avertive Cost: Motian

Independent

variables

Coef. Std.

Err.

z P>z dy/dx Std. Err. ey/ex

Location -0.5782 0.1793 -3.22 0.000 -0.117 0.0332 -2.1405

Income .00005 .00002 2.39 0.017 .00001 4.72E-06 1.7164

Pollution

awareness .9115 .2857 3.19 0.001 .1848 .0536 .3064

Perception

of Risk 1.0407 0.3095 3.36 0.001 0.2110 0.0557 0.2023

_cons 0.5872 0.3691 1.59 0.112 --- --- ---


LR chi2(4) = 37.76

P > chi2 = 0.0000


4.2.3 Estimation for Khanpur

Village Khanpur was taken as reference village which has the same socio

economic conditions except the industrial pollution. People of Khanpur are enjoying

good living standards and health. Their income level is also higher than the polluted

villages: Dingi and Motian.

Table 4.15 shows the probit results of for the estimated health production

function. This is clearly as expected. No traces of contaminants were observed in their

drinking water. Total population of the Khanpur is 11800, whereas total no of the

households are 1685.Out of 1685 households 450 households were surveyed for

questionnaire. Probability of sickness was estimated against, contaminant (Pb), pollution

awareness, perception of risks, education, age and income. No pollutants found in the

85

drinking water of the households and thereby the diseases associated with the specific

pollutant Lead did not appear to be present. However there were common and ordinary

diseases like fever, cough or the diseases due to any other reasons were also found in the

community. Therefore only income variable occurred to be significant. These results are

according to hypothesis.

Table 4.15. Probability of Sickness: Khanpur

Independent variables Coef. Std. Err. Z P>z 95% CI.

(Lead)Pb 0.1708 0.6195 0.28 0.783 1.38514 1.043

Pollution awareness 0.0934 0.4048 0.23 0.818 0.88688 0.88688

Perception of Risk 0.0842 0.5159 0.16 0.87 1.09557 0.927

Education 0.0829 0.12099 0.69 0.493 0.32014 0.15416

Aeg 0.011702 0.0079 1.47 0.412 0.003 0.027

Income 2.5E-05 7.81E-06 3.19 0.001 4E-05 9.57E-06

_cons 1.100 12369 2.67 0.008 1.909 0.2925


LR chi2(6) = 19.24 Prob> chi

2 = 0.0038


Table 4.16. Probability of Medical Cost: Khanpur

Independent variables Coef. Std. Err. z P>z 95% CI

Lead(Pb) 0.1262 0.599 0.21 0.833 1.301 1.048

Pollution awareness 0.0565 0.348 0.16 0.871 0.6268 0.739

Perception of risk 0.26402 0.507 0.52 0.603 1.258 0.730

Education 0.069 0.110 0.63 0.529 0.147 0.286

Age 0.009 0.007 1.27 0.205 0.005 0.023

Income 1.9E-05 6.51E-06 2.87 0.004 3.2E-05 5.95E-06

_cons 1.315 0.381443 3.45 0.001 2.063 0.568


LR chi2(6) = 11.41 Prob> chi2 = 0.0765


86

Although the only variable Income has emerged significant in the incurring of

medical cost by the people of Khanpur but the purpose to display of the estimated Table

4.15 along with the other insignificant variables is to emphasis on the comparison of the

location surrounded by the industrial wastewater channel with the location free from

industrial wastewater channel and different behavior of the variables as compare to

village Dingi and Motian.

For reference purpose, we selected a village Khanpur which has the same socio

economic characteristics except the industrial wastewater source of contamination. Due

to absence of contamination source, health status of people living in the Khanpur is good

as compared to the other two targeted villages: Dingi and Motian. This good health of the

people reflects in various aspects. They do not have to incur the additional burden of

imposed cost of illness due to contamination. People have more of income at their

disposal for other non-health goods and services. As the income and living standard of

Khanpur (non-polluted) is higher than both the polluted villages of Dingi and Motian.

Therefore average wage rate is also higher, i.e. PKR. 1149/- for Khanpur. That is why

only the income variable has emerged as a significant variable whereas all the other

variables in the reference village Khanpur found to be insignificant as expected (See

Table 4.16.) Average wage rate of Rs 635 is in Motian and Rs 648 is in Dingi, which is

clearly associated with the illness due to contamination in drinking water. The villages

Dingi and Motian are exposed to industrial pollution and thereby average sick days per

households per annum are larger than non-polluted areas. Average sick days for villages

Dingi and Motian are 11 and 6 days respectively whereas general routine sick days in

non-polluted area, Khanpur are 4.

Educational level of these people is also higher and thereby there living standard

is higher. It was statistically found that general and normal day to day diseases like flu,

blood pressure or stomach just for temporary duration of time exist in the village of

Khanpur but there is no any pollution induced diseases as compare to the villages of

Dingi and Motian. There by there were no diseases associated with heavy metals in

Khanpur. Some traces of metal Lead were also found in the drinking water of the

household in Khanpur but it was not because of industrial wastewater contamination. It

87

was because of the old water pipes installed in the old locality of Khanpur as Khanpur

village is divided into two parts. Old Khanpur and new Khanpur, some traces of Pb were

found in the drinking water of the household in Khanpur. This Lead presence was

because of rusted water pipes in the old Khanpur. In the new Khanpur and in some area

of old Khanpur water is provided by the government water supply scheme known as

―SUKH NAIN‖ water supply scheme which is free from contamination. This study

attempted to investigate the same contaminants in Khanpur which were investigated in

the targeted villages of Dingi and Motian but was not able to find any.

4.3 Marginal Willingness to Pay and Welfare Loss

This section presents the marginal willingness to pay and welfare loss due to

water contamination in both villages, Dingi and Motian. Marginal willingness to pay is

based on workdays lost, average wage rate, average work days lost per individual per

year, total medical expenditure on illness and avertive expenditure. Based on all the

medical activities performed by the household either to avoid the effects of contamination

or to mitigate, total medical and avertive expenditure were calculated. Three

probabilities: sickness, medical cost and avertive cost with respect to change in the

environmental quality i.e. Lead and Nickel are also calculated.

4.3.1 Marginal Willingness to Pay

Table 4.17 shows that average wage rate of the house hold in Dingi is Rs.648/-

and average work days lost per year per individual are 9. Marginal change in the sickness

quality for Lead (Pb) is 0.137 whereas for medical cost and avertive cost. It is 0.061 and

0.144 respectively. Marginal change in the sickness due to environmental quality

(Nickel) is 0.015 and for medical cost it is 0.016. However Nickel does not affect the

avertive cost. Total medical expenditure per household per year is Rs. 12807.9 and

avertive expenditure is Rs. 10260/-. On the basis of indicators and the estimated values

used in the table 6.1 we put the values in the main equation of marginal willingness to

pay. Main equation of MWTP has three terms: I, II, III. Term I and II in equation

collectively describes the cost of illness whereas term III represents the avertive cost.

88

After summing up all the three terms we finally calculated the marginal willingness to

pay in monetary terms in village Dingi due to environmental quality Lead (Pb) which is

Rs.3062.3146/-. Marginal willingness to pay in village Dingi due to Nickel is different

from Lead. The reason is that as compare to Lead, marginal effect of Nickel on avertive

cost did not show any impact and that is why the value of parameter was insignificant.

Marginal willingness to pay is calculated as:

(

) (

) (

) (4.1)

MWTP= (I) + (II) + (III)

Table 4.17.Indicators for Marginal Willingness to Pay: Dingi

Indicators ( MWTP) Estimated Value Comments

Average wage rate (Rs)648 Actual average wage rate

Average Work days lost per

individual per year

9 Days per year

P(S/∆L) 0.1378 Marginal effects of

Contamination on Sickness

from Table.4.7

P(M/∆L) 0.061 Marginal effect Table.4.8

P(A/∆L) 0.1440

P(S/∆N) 0.015 Marginal effects of


from Table.4.7

P(M/∆N) 0.016 Marginal effects of


from Table.4.8

Mitigating Expenditure Rs.12807.9

Avertive Expenditure Rs.10260

Marginal willingness to pay for change in environmental quality of Pb (Lead)

Dingi is calculated as:

(

) (

) (

)

(4.2)

(I) (II) (III)

648*09*0.1378 12807*0.061 10260*0.1440

803.6496 781.225 1477.44 = Rs. 3062.32/-

(Loss of income) (Mitigating

Expenditure)

(Avertive

Expenditure)

(Indirect cost) (Direct Cost)

89

Therefore, we applied the same procedure as was done in the case of Lead except

the third term in equation. This term was not calculated because the marginal effect of the

environmental quality is the term which is multiplied by the relevant expenditure and

therefore there is no marginal effect of Nickel on avertive cost as a multiplicative.

However, on the basis of the two terms of the marginal willingness to pay equation were

also calculated and the quantitative value of MWTP due to Nickel in Dingi which is Rs.

1079.71/-. Total marginal willingness to pay in village Dingi due to Lead and Nickel

arrives after the summation of both at Rs. 4142.024/- per household per annum. In case of

Motian marginal willingness to pay for the change in environmental quality due to Lead

is Rs.201.26 and of Nickel is Rs. 617.05. We adopted the same procedure as in Dingi for

the calculation of total marginal willingness to pay. Hence the total marginal willingness

to pay for both Lead and Nickel in village Motian is Rs.819.6 and the total opportunity

cost in the village Motian is Rs.4260/- and economic cost of water contamination for the

village Motian is Rs. 10441.56/- per household per year.

Marginal willingness to pay for change in environmental quality of Ni

(Nickel) is calculated as:

(

) (

)

(4.3)

(I) (II)

648*09*0.015 + 12807*0.016

874.8 + 204.91 Rs.1079.71/-

MWTP in village Dingi due to Lead and Nickel =3062.32 + 1079.71 = Rs.4142.03/- per

household /per annum.

90

Figure 4.5 Map showing joining of Channel NOR to Channel JHAR

Table 4.18 displays the same indicators and procedure was used in the

calculations of marginal willingness to pay in the case of village Dingi. However, all the

values of indicators for the village Motian are different from village Dingi. Average wage

rate of the household in village Motian is Rs. 635 and average work days loss is of 5.49.

Marginal change in the sickness due to Lead contamination is 0.04 which is lower as

compare to village Dingi. Marginal change in medical expenditure and avertive

expenditure are 0.036 and 0.014. All the probabilities of sickness, medical cost and

avertive cost with respect to Lead contamination are lower against the probabilities in

village Dingi. However, probabilities of sickness and medical cost in the village Motian

are higher against the Dingi due to contamination of Nickel. Reasons of lower

probabilities for Lead and higher probabilities of Nickel in the village Motian have a

strong geographical justification. Concentrations of contaminations are higher in the

village Dingi, as the industrial wastewater channel enters into the Dingi first and later it

goes along the other villages and Motian (See figure 4.5) therefore, concentration of Lead

is higher in the village Dingi.

91

Table 4.18. Indicators for Marginal Willingness to Pay: Motian Pb

Indicators Estimated Value Comments

Average wage rate Rs. 635/- Actual average wage rate

Average Work days lost per

individual per year

5.49 Days per year

P(S/∆L) 0.04 mg/L Marginal effects Contamination on

Sickness from Table.4.10

P(M/∆L) 0.037 mg/L Marginal effects Contamination on


P(A/∆L) 0.02 mg/L

P(S/∆N) 0.18 mg/L Marginal effects Contamination on


P(M/∆N) 0.13 mg/L Marginal effects Contamination on

medical expenses from Table.4.11

COI 3486.15

Mitigating Expenditure(Rs) 812.41

Avertive Expenditure(Rs) 1883

Total opportunity cost of

Leisure(Rs)

1360

Total economic cost(Rs) 10441.56

Marginal willingness to pay for change in environmental quality of Pb (Lead) Motian

(

) (

) (

)

(4.4)

(I) (II) (III)

635*5.49*0. 0403 812.41*0. 0403 1883*0. 015

140.381 32.714 28.17 = Rs.201.27/-

(Loss of income) (Mitigating

Expenditure)

(Avertive

Expenditure)

(Indirect cost) (Direct Cost)

Same would have happened for Nickel in Motian but before the entrance of industrial

wastewater into Motian, there is another wastewater channel called NOR which joins the

earlier industrial wastewater channel JHAR.. Effluents of NOR contain higher

concentration of Nickel (see Figure4.5). Therefore, concentration of Nickel is higher in

village Motian.

92

Table 4.19.Marginal Willingness to Pay (MWTP): Motian Pb A:Lead(Pb)

Loss of income Medical Expenditure Avertive expenditure Willingness to pay

635*5.49*0. 0403 812.41*0. 0403 1883*0. 015 = Rs.201.27/-

140.381 32.714 28.17

Marginal willingness to pay for change in environmental quality of Ni (Nickel)

(

) (

)

(4.5)

(I) (II)

635*5.49*0.177 812.41*0.131741 = Rs.617.05

Table 4.20.Marginal Willingness to Pay (MWTP): Motian Ni B:Nickel(Ni)

Loss of income Medical Expenditure Avertive Expenditure Willingness to pay

635*5.49*0.177 812.41*0.131741 NA

Rs.617.05 Per

household/annum 874.8 204.91 NA

Avertive cost is not included in the calculation of Nickel effect as the Nickel‘s

impact on the avertive cost has been found insignificant. Therefore, TMWTP for the

village Motian for both Lead and Nickel= Rs.201.27+ Rs.617.05= Rs.819.6/-.

Reasons for the Different results of Marginal willingness to pay for different

villages:

Marginal willingness to pay is the minimum amount paid by the household to

avoid the damage. Mathematically it is the summation of various terms in the equation.

6.3

A + B + C

In this equation first all the components are calculated separately and then added

up to find the marginal willingness to pay.

93

W is the wage rate of household in any village , S is the no of sick days in the

villages, is the probability of incurring avertive cost, is the probability of

incurring the medical expenditure by the household , C is the value of concentration of

contamination, is the marginal utility of income and utility is the household‘s utility.

As the is the total of all the terms A,B and C and based on the

collected data and after the application of econometric models all these terms A ,B and C

are different for every respective village ,Therefore the marginal willingness to pay for

Dingi and Motian are different.

4.3.2 Opportunity Cost

Two types of opportunity cost are calculated one is leisure time effected due to

sickness and another is the time spent on managing the avertive measure. Leisure time is

distributed in two parts; active leisure time and passive leisure time. Active leisure time is

that time in which households spends in performing the home responsibilities, either at

home or outside of home whereas passive leisure hours of ill person and these hours are

multiplied by the hour wage rate to quantify in monetary terms.

Opportunity costs of avertive measures were calculated on the basis of time taken

to avertive measures. There are four types of avertive measures adopted by the household

among all the avertive measures boiling water is time consuming. It is a regular

organized activity and performed on daily basis. Therefore total time spent by the

household to perform the activity of boiling water was converted into hours which further

was multiplied by the wage rate and is the way opportunity cost of avertive measure was

calculated. All the other avertive measures opportunity cost was negligible. Opportunity

cost of leisure hour is Rs. 2302/- and of avertive measures is Rs. 5653/-. Hence total

opportunity cost for village Dingi was calculated by summing up both which is Rs.

7955/-.

Opportunity cost= Opportunity cost of leisure hour +Opportunity cost of Avertive

Measures

Total O.C = OPCLH+OPCAM = 2302 + 5653 = Rs.7955/-

94

Opportunity cost= Opportunity cost of leisure hour +Opportunity cost of avertive

measures

The same cost is calculated for Motian as:

Total O.C = OPCLH+OPCAM = 1360 + 2900 = Rs.4260/-

4.3.3 Economic Cost

Economic cost is the composition of five types of costs: loss of productivity,

medical cost, avertive cost, opportunity cost leisure hour and opportunity cost of avertive

measure, loss of productivity contributes for 8percent. Opportunity cost of leisure hour

contributes to 13percent and opportunity cost of avertive measures contributes to

28percent.

Effects of water contamination are calculated in terms of economic cost in

monetary terms born by the household. This economic cost is the composition of five

types of costs, loss of productivity, medical cost, avertive cost opportunity cost of leisure

and opportunity cost of the avertive measures adopted by the households.

Figure 4.6 presents the share of each type of cost born by the household. It is

depicted in the figure that the major share of the cost is opportunity cost of avertive

measure which is 51percent of the total economic cost. The reason for the major chunk of

OPCAM in total economic cost is the industrial wastewater channel which is perceived

by the household as a life partner. Community cannot change the geographical location of

their residence due to one or other reasons. Therefore, people have made their habit to

adopt the avertive measure irrespective of the fact that how much degree of positive

results. Cost in terms of loss of productivity and medical cost is of 7 percent each. Second

large share of cost is again opportunity cost of leisure hour which is of 21percentand the

contribution of avertive cost is of 14 percent.

Total economic cost is calculated on the basis of both direct cost (out of pocket)

and indirect cost (opportunity cost). Direct cost is of Rs.3933.67/- and indirect cost is

Rs.7955/-, hence total economic cost accumulates to Rs.11888.67/-.

95

Total economic cost = Cost of illness (Loss of productivity (LOP)+ Medical cost

(MC)) + Avertive cost (AC) + Opportunity cost of Leisure (OCLH) + opportunity cost of

avertive measures(OCAM).

Figure 4.6 Distribution of Total Economic Cost

Total Economic Cost= Direct Cost + Indirect Cost

3933.68 + 7955 = Rs. 11888.68/-

For village Motian calculation is:

Economic cost =Cost of illness+ Avertive cost+ opportunity cost of leisure +

Opportunity cost of avertive Measures.

3486.15+812.41+1883+1360+2900 = Rs.10441.56/- per household per annum in

village Motian

Based on cost of illness, avertive cost ,opportunity cost of avertive cost and

opportunity cost of the leisure three probit equations were initially estimated to

investigate the impact of parameters and their relation and finally with respect to change

in the environmental quality (Lead and Nickel) in the drinking water of the households

living in the village Dingi and Motian. The total economic costs of Rs. 31023/- per

7% 7%

14%

21%

51%

LOP MC AC OCLH OCAM

96

household per year for Dingi and Rs.9422/- for the village Motian were calculated.

Economic cost per household/per annum for Dingi is: Rs.31023/- Economic cost per

household/per annum for Motian is: Rs.9038/- Total economic cost of Industrial water

contamination is Rs. 40061/-.

4.3.4 Welfare Loss

It is one of our objectives to estimate the quantitative impact of water

contamination on the household`s health and utility. Health impacts of water

contamination in terms of willingness to pay cost of illness and economic costs have

already Dingi and Motian. For the calculation of utility to the whole community the

impact of water contamination in terms of welfare loss was calculated. Welfare loss for

both the villages separately and then calculated the total welfare loss by summing the

losses in both the villages. Further as there were two contaminations: Lead (Pb) and

Nickel (Ni) responsible for the damages to the health of the community. Therefore

welfare impacts associated to Lead and Nickel were required to be estimated separately.

Tables4.21 and Table 4.22 describe the welfare loss due to Lead contamination in

the village Dingi. Column I of the table shows the total population of the village Dingi.

Column 2 has been obtained from the multiplication of column 1 by the probability of

sickness with respect to the change in environmental quality (Lead) 0.1378 and this

column describes the affected population of Dingi which is 691.89. For the obtaining of

column 3, total affected population was multiplied by work days lost that accounted for

Rs. 6221.04/-. There are total numbers of sick days in a year. Column 4 in table 6.5

shows represents the monetary quantification of sick days loss in terms average loss of

earnings. Average loss of earnings in column 4 is Rs. 4 million in a year for the village

Dingi.

Column 5 is the monetary valuation of average medical expenditure in rupees and

was obtained from the multiplication of average cost of illness by the probability of

medical cost with respect to the change in environmental quality Lead (0.061) and which

further was multiplied by the affected population in column 2.

97

Column 6 in Table 4.21 shows the average avertive expenditure per year which

was obtained from the multiplication of average avertive cost with respect to the change

in environmental quality Lead (0.144) which was further multiplied by the affected

population of the village Dingi in column 2. This total average avertive expenditure is Rs.

1.2 million. The last column 7 of the Table 4.21 represents the total welfare loss to the

community of Dingi due to Lead. This welfare loss was calculated by adding the

monetary value in column 4, 5 and 6. So the total welfare loss due to Lead contamination

in the village Dingi was Rs. 5.74 million. Table 4.22 represents the same procedure for

the calculation of welfare loss due to contamination of Ni.

Welfare Loss Dingi:

Welfare Loss due to Contamination of Lead for the village Dingi:

Population at risk: 5021

Probable affected people: 691

Probability of sickness with respect to change in Lead concentration(S/∆L): 0.1378

Probability of medical cost with respect to change in Lead concentration P(M/∆L):0.061

Probability of avertive cost with respect to change in Lead concentration (A/∆L): 0.1440

Mean work days Lost: 9

Mean wage rate (Rs): 648/-

Average medical expenses (Rs): 10260/-

Average avertive cost (Rs): 12807/-

Welfare Loss due to Contamination of Nickel for the village Dingi:

Probability of sickness with respect to change in Ni concentration P(S/∆N): 0.015

Probability of medical cost with respect to change in concentration of Ni P(M/∆N):

0.016

Table 4.21. Welfare Loss due to Lead Contamination: Dingi Population

at Risk

Affected

population

No of sick

days

WDL(Rs) Medical

expenditure

(Rs)

Avertive

expenditure

(Rs)

Welfare loss

(Rs)

(1) (2) (3) (4) (5) (6) (7)

5021 691.893 6227.044 4035124.641 433028.65 1275996.081 5744149.376

98

Procedure to calculate:

Col.no.1. Total Population of the Village Dingi 5021

Col.no.2. Col.1*P(S/∆L) 0.1378

Col.no.3 WDL*col.2

Col.no.4 Average Loss of earnings * col.2

Col.no.5 Average cost of Illness*P (M/∆L)0.061*col.2

Col.no.6 Average Avertive cost*P (A/∆L)0.1440*col.2

Col.no.7 col.4+col.5+col.6

Table 4.22.Welfare Loss due to Nickel Contamination:Dingi Population

at Risk

Affected

Population

No of

sick days

WDL

(Rs)

Medical

Expenditure

(Rs)

Avertive

expenditure

(Rs)

Welfare Loss

(Rs)

(1) (2) (3) (4) (5) (6) (7)

5021 75.32/- 413.48/- 30201.3/- 978.99/- NA 31180.30/-

Procedure to calculate:

Col.no.1. Total Population of the Village Dingi 5021

Col.no.2. Col.1*P(S/∆N) 0.015

Col.no.3 WDL*col.2

Col.no.4 Average Loss of earnings * col.2

Col.no.5 Average cost of Illness*P (M/∆N)0.016*col.2

Col.no.6 Average Avertive cost*P (A/∆N)*col.2

Col. no.7 col.4+col.5+col.6

Welfare loss to the Village Dingi due to Water contamination =

loss due to Lead contamination + loss due to Nickel contamination =

5744149.38 + 31180.30 = Rs. 5775329.68/-

99

Welfare Loss: Motian

We adopted the same procedure as we did for Dingi to obtain the estimates of

welfare loss in the village Motian. Table 4.23 and Table 4.24 describe the welfare loss in

Motian for Lead and Nickel contaminants.

Welfare Loss due to Contamination of Lead for the village Motian:

Population at Risk: 1781

Probable Affected People: 71.72

Probability of sickness with respect to change in Pb concentration (S/∆L) =0.04

Probability MC with respect to change in concentration of Pb P(M/∆L ) = 0.037

Probability of Avertive cost with respect to change in Lead concentration(A/∆L) = 0.015

Mean WDL: 5.49

Mean wage rate (Rs): 635

Average medical expenses (Rs): 812.41

Average AC (Rs): 1883

Welfare Loss due to Contamination of Nickel for the village Motian:

Population at Risk: 1781

Affected People: 71

Probability of sickness with respect to change in Ni concentration (S/∆N) : 0.177

Probability of medical cost with respect to change in Ni concentration (M/∆N):0.137

Probability of Avertive cost with respect to change in Ni concentration: NA

Table 4.23. Welfare Loss due to Contamination of Lead: Motian

Population

at Risk

Affected

Population

No of

sick days

WDL(Rs) Mitigating

Expenditure

(Rs)

Avertive

expenditure

(Rs)

Welfare

Loss

(Rs)

(1) (2) (3) (4) (5) (6) (7)

1781 71.72 393.73 28758.64/- 2134.70/- 2020.12/- 32913.44/-

100

Table 4.24. Welfare Loss due to Contamination of Nickel: Motian

Population

at Risk

Affected

population

No of

sick days

WDL(Rs) Mitigating

Expenditure

(Rs)

Avertive

expenditure

(Rs)

Welfare

Loss

(Rs)

(1) (2) (3) (4) (5) (6) (7)

1781 315.24 393.73 126410.04/- 35059.55/- NA 161469.59/-

Procedure to calculate the Welfare loss is

Col.no.1. Total Population of the Village Motian

Col.no.2. Col.1*P(S/∆L) 0.040268

Col.no.3 WDL*col.2

Col.no.4 Average loss of earnings * col.2

Col.no.5 Average cost of Illness*P (M/∆L) .0366385 *col.2

Col.no.6 Average avertive cost* P (A/∆L) 0.014959 *col.2

Col. no.7 col.4+col.5+col.6

Welfare loss due to water contamination in the village Motian =

Welfare Loss due contaminant of Lead + Nickel =

32913.46 + 161469.59 = Rs.194383.05/-

Total Welfare Loss due to Water Contamination

Using the results of the estimated equations for contaminants in equation 13 and

14, the welfare gain to the population of Dingi and Motian from reduction in the water

contamination to the safe level was calculated. The current research attempts to value the

sick days in monetary terms by taking in to account work days lost (absent from work).

The amount of daily wages lost may be considered as the cost of work days lost

due to contamination induced illness. This study uses the per day minimum average wage

101

rate of the unskilled labor as Rs.648/-, Rs. 635/- and Rs.1149/- for village Dingi, Motian

and Khanpur respectively.

Mitigation of this problem will ensure the health and wellbeing of millions of the

people. As the industrial wastewater channel length is more than 40 km and at last it falls

in to the Herro River. The villages situated along with this channel up to the Herroriver

will be safe from the vulnerability of exposure and sickness. As villages Dingi and

Motian are situated along side bank of this industrial wastewater channel. Therefore any

remedy for these villages will make safer all the other communities and villages come

across the channel .In financial terms, as estimated from willingness to pay, this is huge

amount.

It is estimated that total medical expenditure from water contamination exposure

is potentially in the range of Rs.471201.89/- per year. It is essential to emphasis, that

these figures are just for the two small villages whose population is of 7000 but if its

spread distributes over the 32 more villages which are situated in the catchment area of

industrial wastewater channel the problem will more aggravate and the spillover effects

will damage the human beings in various aspects.

In addition there are costs in the range of Rs.4.2/- million per annum in terms of

work day‘s losses. Avertive cost is another major chunk of welfare loss. In the

investigations, avertive cost to mitigate the effects of contamination on human health was

also calculated. The range of avertive cost has been calculated to Rs.1.27/- million per

annum.

Thus the total Welfare loss due to water contamination is around 6 million per

annum (PKR). This means that if it is possible to mitigate this problem using suitable

technologies there is likely to be a net social gain of more than Rs.6 million per annum.

(See Table 4.25)

Table 4.25.Total Welfare Loss due to Water Contamination Welfare loss in village

Dingi(Rs)

Welfare loss in village

Motian(Rs)

Total welfare Loss due

water Contamination(Rs)

5775329.67794 194383.0499 5969712.7279

US$58,673.3237

102

4.4 Analysis and Discussion

The main reason for the higher sick days in the polluted areas is of contaminated

drinking water. Average concentration of Pb in the drinking water of household is 0.310

mg /L where as in Motian it is 0.177 mg/L and in Khanpur, it is 0.03 mg/L. Both in Dingi

and Motian average concentration of Pb is above the guideline value of 0.05 mg/L and

0.02 mg/L respectively. It is the evidence from the literature that presence of Lead in

drinking water is dangerous and causes various diseases like, renal, high blood pressure

joint pain and stomach. All these diseases are prevailing in the village of the Dingi.

People of Dingi have been living for centuries but the establishment of the Hattar

industrial estate took place 30 years back. Lead contamination has impacts on the masses

in both farms, acute and chronic. Acute effect is for short run exposure where as in

chronic, it mixes with the blood and ultimately deposits in the joints of the bones and

thereby a patient feels pains in the joints of its skeleton .It is important to note that village

Motian is farther and 1km ahead of village Dingi but concentration of Pb in the drinking

of Motian's household is higher than the Dingi`s drinking water. There are two reasons

for higher concentration of Pb into Motian's drinking water. Village Dingi is situated on

the banks of the industrial wastewater channel ‗‘JHAR‘‘ (local name) whereas Motian is

situated on this channel after 1 km. But after 800 meters this wastewater channel takes

turn towards Motian, and on this turn another wastewater channel containing of Pb joins

this JHAR Industrial wastewater. That is why the name of the industrial wastewater at

this point becomes (DOMAIL) (local name) means ‗‘junction of two‘‘. The effluent that

comes in this stream is of Pb from battery industry.

Location and pollution concentration played a significant role in presence of

diseases and thereby economic implications. People living close to the wastewater

channel are highly prone to contamination and thereby the diseases and economic cost. In

this study location played a very significant role in defining the impact of contamination

in both the polluted villages. Haque et al. (2011) carried the study pertains to the air

quality and cement production, they distributed the location within the three kilo meters

103

range but it was essential in case of air pollution as for as in our case it was spatial

distribution of the community on the banks of industrial wastewater channel. Location

parameter in both villages appeared to be the significant statistically (Table 4.2 and Table

4.12). Similar study was done by Reddy and Behera (2006) in which they used two

command areas to collect the water samples for the investigation of industrial water

pollution where as in this study GIS based sampling was applied to obtain accurate

geographical data of focused communities.

The communities of the both villages (target) could make their water clean to

some extent by adopting some avertive measures which was also proved by the

econometric estimation that the people who adopted the avertive measures could reduce

the effect of pollution (Table4.2). The results of estimations are interesting in the sense

that the people who had the specific knowledge of pollution, diseases, heavy metals and

risk, were more convinced towards adoptive measures as compared to the people who

have only formal school education.

Average sickness among the households of Dingi is 53.71percent and 38percent

is in the village of Motian. Where as in Khanpur village, it is 6.6percent, but still this

sickness is found, however, not pollution induced. We have also calculated the indicators

in the economics perspective, i.e., cost of illness, workdays lost, hospital days, average

loss of earnings. Average opportunity cost of leisure and average opportunity cost of

adopting avertive measures were also calculated.

People of these areas are facing number of diseases like joint pains, renal, high

blood pressure, skin etc. The sickness duration is also different among the villages and

within villages. Average numbers of sick days are 11 for Dingi, 6.21 for Motian, and 4.07

for Khanpur. However, it is important to note that sick days in Dingi or Motian are due to

pollutant induced illness, While in Khanpur it is not pollution induced diseases. These

sick days of the people in Dingi and Motian are the days in which they lost their

productive days and thereby loss of earnings.

However, it was observed that there was a difference between the average sick

days and the average work days lost. The reason is that collected data was for 3 months

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of recall period and then distributed over a year .There were some days on which

household was sick and despite of sickness he attended his job and on some days,

household was took leave due to sickness. There are two reasons which created the

difference between the sick days and workdays lost.

Highest average loss of earnings is in Ding which is because of its closeness to

the source of contamination and thereby the prevailing of sickness. Out of 350

households of Dingi, 188 reported to be sick and percentage of sickness is 53.71 percent.

In Motian, out of 150 households, 57 households were sick and the percentage of

sickness is 38percent. Whereas Lead induced sickness is not visible in the non-polluted

area Khanpur. In Khanpur among 450 households only 30 people had common day to

day illnesses like flu, cough, fever and the percentage of this sickness is 6 percent. For

calculating the cost of illness and the productive days lost by the sick persons were taken

for the quantification of loss of those particular days. It was based on the average wage

rate of unskilled labor per day.

Cost of illness for the village Dingi and Motian is Rs. 10260/- and Rs. 812/-

respectively. This clearly indicates the people living in the polluted area are suffering

more of diseases of kidney, high blood pressure and of stomach. These peoples are

incurring additional medical expenses due to the water contamination. Same the average

opportunity cost of leisure and avertive measures is also higher in the polluted villages as

compare to the non-polluted villages. Average opportunity cost of leisure is Rs. 2302/-

and Rs. 1360/- in Dingi and Motian respectively. Where as in the non-polluted village

Khanpur, the average cost of illness per household per annum is only Rs. 290/-

.Moreover, the people of Khanpur does not have the risk of pollution, there by the do not

take avertive measures, hence, their opportunity cost of leisure is negligible of Rs. 355/-

.per household per annum.

Knowing sources of pollutants Lead, Chromium, Nickel and cadmium metals

were selected. But on repeated sampling two heavy metals: Lead and Nickel were found

above the guideline values of World Bank for drinking water of Pakistan. Average

concentration of both Lead and Nickel are the main contaminants which are responsible

105

for the prevailing diseases. We compared the concentration of Nickel and Lead with the

guideline value (WHO, 2004).

Similar study was carried out by (Roy, 2007) who estimated the same three

demand equations of dose response function, demand for medical expenditure and

demand for avertive expenditure but Roy‘s study is not specifically for the industrial

pollution. Results of present study pertain to specifically industrial contamination in to

the drinking water. This study estimates three demand equations of dose response

function,, medical expenditure and avertive expenditure of those households who are

living adjacent to the industrial wastewater channel and the community is prone to the

wastewater contamination. The other peculiarity of our study is that it studied the

chemistry aspect of water on one hand and the contingent valuation of its impact on the

economic aspect on the other hand.

A key aspect of this study was to collect contamination induced information

through both direct and indirect questioning of the household and the observations. It was

observed in the study area, majority of the people were well aware of the general

pollution knowledge and water contamination; associated diseases and adopted avertive

measures to avoid some specific diseases. This was the actual reason that people adopt

the avertive measures and sensitive about the risks of water borne diseases.

In case of avertive measures by Roy (2008) and results of the current study have

the similarity in significance but not in the signs.The reason for this difference is that the

study is focused and specific to Lead and Nickel associated diseases where as she studied

all the diseases prevailing in the village. However causality of the impact of sick days in

relation to avertive behavior shows the same results. It was found that higher the

exposure to contaminated water the more is the likeliness of the water carrying diseases.

The estimated ―probability of sickness‖ was found to have positive relation with the

variable ―exposure‖ to contaminated water at target villages. The results show that a one

unit increase in the exposure to the contaminated water by the residents had 7.8percent

more chances to have water borne diseases in Dingi and 4 percent in Motian.

106

In case of avertive measures adopted by the residents to avoid the lethal effects of

polluted water; the households were using four different measures; boiling the water, use

of chemicals, i.e., chlorine tablets; use of minerals bottled water; and filter plant. Among

all these four avertive measures, boiling of water was the most frequently used method

because of less cost, less efforts and easy to perform. The least used measure was the

purchase of bottled water. In case of filter plant, this method of prevention was least

effective because the effectiveness of this plant depends upon the two stages, one the

time of installation of filter plant and the other periodic change of its filter cartridge. Most

of households installed this filter cartridge just for demonstration effect but they do not

care and bother of periodic change of filter cartridge and that is why this method has not

been found effective. Variable ―Avertive measures (AM)‖ had negative relationship with

the probability of sickness and shows that one unit increase in the avertive measure will

decrease the probability of sickness by 17 percent in Dingi 4percent in Motian..

The probability of sickness was also estimated for the location/distance of the

inhabitant and it was found that closeness of residence to the source of contaminated

water, i.e., wastewater channel, the more is the probability of sickness. Statistically

negative relationship was found between location and probability of sickness, with the

result that one unit increase in the distance away from the source of contaminated water

the probability of sickness decreased by 11 percent in Dingi7Percent in Motian.. The

variable pollution awareness (POLAWAR) indicates that the more aware the inhabitants

are, the less is the sickness. The use of TV, radio, mobile phones, internet and the

participation in to local NGOs have made the people more aware about the diseases and

thus they have fewer chances of diseases as compared to those who have less access to

these sources of information. This variable was also negatively related to the probability

of sickness implying that one unit increase in the pollution awareness results in the 17

percent less probability to sickness in Dingi and 4 percent in Motian.

Another variable tested was perception of risk (Pr). In the locals, questions were

asked about their knowledge about the different diseases, regular medical checkups etc. It

was found that those who had better risk perception were less prone to diseases because

of concern against contaminated water. Negative relationship was found between risk

107

perception and probability of sickness. One unit increase in the risk perception causes the

probability of sickness to decreases by 6 percent in the Dingi and 13percent in the

motian..

The impact of formal education on the probability of sickness was found to be

insignificant. This means that informal education and the awareness of contaminated

water were more important for the household to be away from diseases caused by

polluted water. The factor of age though having negative relationship with probability of

sickness seems less appropriate with the observed data because the target population was

household and average age were taken into consideration in the data. The most interesting

results were from the tests of Lead (Pb) found in the drinking water of household in

Dingi. Impact of Lead on the probability of sickness was found to be positive as one unit

increase in Lead in the drinking water will increase the probability of sickness by 13

percent in Dingi and 4 percent in Motian. Ni had positive relationship with the

probability of sickness although with less impact than Lead of 1 percent and 17 percent in

the villages of Dingi and Motian respectively.

The village khanpur with same socio economic characteristics except industrial

waste water was selected as reference is found to be better as compared to target villages:

Dingi and Motian. Status of people living in the Khanpur is good as compared to the

other two targeted villages: Dingi and Motian. Due to this hey do not have to incur the

additional burden of imposed cost of illness due to contamination. People have more of

income at their disposal for other non-health goods and services. As the income and

living standard of Khanpur is higher than both the polluted villages of Dingi and Motian.

Therefore average wage rate is also higher, i.e., Rs. 1149/- for Khanpur as compared to

Rs 635 in Motian and Rs 648 in Dingi. Average sick days are 11 and 6 for villages Dingi

and Motian respectively whereas 4 for, Khanpur.

Educational level of Khanpur is also higher. It was found that the diseases like flu,

blood pressure or stomach just for temporary duration of time exist in the village of

Khanpur but there is no any pollution induced diseases as compared to the villages of

Dingi and Motian. Some traces of metal Lead in the drinking water of the household in

Khanpur were found but it was due to the old water pipes installed in the old locality of

108

Khanpur. In new Khanpur and in some area of old Khanpur water is provided by the

government water supply scheme known as ―SUKH NAIN‖ water supply scheme which

is free from contamination. The study attempts to calculate the welfare gain due to

reduction in the Pb and Ni concentration that can reduce the mitigating expenditure,

avertive expenditure and probability of sickness 50percent reduction in the Lead

concentration can reduce the mitigating expenditure, avertive expenditure and probability

of sickness. The biggest economic implication of water contamination is in is

community‘s welfare loss. Based on estimation welfare loss Rs. 5.76 million and Rs. 0.19

million were calculated for village Dingi and motian respectively.

This study is based on single difference approach is attempt to bring socio-

economic and water contamination together to establish the relation between water

contamination and health in Pakistan. This study is also different in the calculation of

opportunity cost of Leisure time and the opportunity cost of the time taken for avertive

measures.

One interesting thing was observed in this study is that people who are more

involved in the religious practices are obligatorily required to clean themselves by water,

have higher risks of illness due to polluted water. Similarly the people who are educated

and literate but do not have the knowledge of pollution are more ill as compared to who

are not educated but have better knowledge of pollution. This clearly indicates that

formal education system needs to be revised for the environmental awareness and its

impacts on human health.

The study found that variable location is highly significant statistically in all the

equations. Although farther distance of residents from the contaminating source reduces

the illness and costs but people of villages are not much inclined to migration as they

have been living there for the last 100 years and they will never be ready to move away

from their forefather‘s place and heritage. This type of study also suggests that Pakistan

needs to revise its Environmental protection agency act, 1997, particularly pertains to the

establishment of industrial estates. Informing policy makers about the tangible health

costs individuals bear may help them make reasonable decision about the water pollution

standards. Few studies take in to account the opportunity cost of time. Very few studies

109

take the opportunity cost of the leisure. As most of studies have calculated the mitigating

cost of water pollution but in this study along with the mitigating cost avertive cost

incurred by the household was also calculated. Dasgupta (2004) estimated the direct cost

of child diarrhea to US$1.94 per illness in India. Gokhale et al., (1999) found the cost of

US $5.64 per illness in India. Taking the direct costs on mitigation, avertive measures,

opportunity cost of the leisure and opportunity cost of avertive measures, the current

study estimates the total economic cost per household and per annum is to be US

$.392.7/-.This seems high with respect to the referred studies, because all the referred

studies estimated the cost per episode of the illness and they have not calculated the

avertive cost and the opportunity cost of the avertive measures along with the avertive

opportunity cost of the leisure.

Compensating affected individuals for their health losses would be one option in

overcoming the damages to the households. It might also be less expensive for the

individual factory to contribute in the common pool for the building of treatment plant

equivalent to the household‘s loss. It is found that socio-economic variables such as

owing of radio, TV, newspaper, knowledge of water contamination, water borne diseases

and participation in NGO pollution awareness behavior reduce the probability of sickness

prevalence.

Behavioral factors such as adaptation of avertive measures the probability of

illness but one interesting thing was observed during this study is of cleanness of body

before the obligation of prayer that all those people who are more involved in the

religious practice have to clean themselves by water. But all these people have one or

other illness, the reason behind is that available water to them for cleanness is polluted

water.

This study does not only suggest about the water pollutant standards but the most

important aspect is geographical selection of Industrial zones and informal training about

the pollution .In this study it was found the variable location highly significant in all the

equations. Which clearly indicates residential location of the household matters in facing

the illness.. People who live farther are facing less sickness where as those people who

live just close to the industrial wastewater channel have larger sickness.

110

The analyses of factors that affect disease prevalence suggest that behavioral

factors have more influence on the risk of disease attack than the engineering factors.

Therefore policy measures should focus on specific issues such as decisions on location

of industrial estates; inclusion of environmental awareness in the formal education of

school and colleges; motivation to the community for the participation in government or

NGO initiated programs for environment awareness.

Chapter 5

Chapter 5Conclusions

112

5.1 Conclusion

The main focus of the study is to investigate the association of prevailing diseases

in the two affected villages with the industrial contamination in the drinking water and its

economic impact on the household. This multidisciplinary study attempts to identify,

quantify and analyze the problem of industrial wastewater contamination into the

drinking water of the communities and its impact on health and utility at household level.

The study is based on primary data and in total 950 households interviewed and 305

drinking water samples collected from three villages two affected by industrial waste and

one as a reference in Pakistan. The study used GIS, epidemiology, environmental

sciences and economic approach to analyze data. Based on laboratory tests, in drinking

water of target villages, Lead and Nickel is found above the guideline values of the WHO

Village Dingi is situated at the start of the industrial waste water channel, village

Motian is farther as compared to Dingi. But village Khanpur does not have any link with

the contaminating waste water channel. Therefore average concentration of

contamination in Dingi is higher than the in Motian and no traces of industrial

contamination were found in the drinking water of Kanpur‘s household.

Other component of the analysis was the concentration of contamination within

the villages. The higher concentration was found in the drinking water sources of those

household who are living nearer to the industrial waste water channel. As the distance

from the source increases the lower is the concentration of contaminants. In both the

analyses, the concentrations of (Pb) and (Ni) were found to be higher than the threshold

values given by World Bank and Environmental Protection Agency.

Due to this higher concentration of contaminants in the drinking water of the

households, large numbers of people suffering from the some specific diseases, namely :

renal disorder, high blood pressure, joint pains, skin, stomach etc. At least one or more

members of a household found to be suffering from of these diseases. Households have to

bear heavy expenses for the treatment of these diseases. The expenditure is increased on

various heads like doctor‘s visits, hospitals stay, pathology tests and sometimes visits to

113

other cities with advanced medical facilities. Percentage of sickness related to these

diseases is different for every village. Dingi was found to have higher percentage of

sickness as compared to village Motian and Khanpur. Although some routine diseases

were also observed in the village of Khanpur but these diseases are not associated with

industrial contamination. Based on the primary data collected, numbers of sick days were

calculated for every village .Number of patients and the sick days were higher in the

village Dingi and Motian as compared to the village Khanpur.

Marginal willingness to pay is estimated as Rs. 4142.03/- and Rs. 819.6/- per

household/per annum for the target villages of Dingi and Motian respectively. Total

opportunity cost of avertive measures and leisure for Dingi is found to be Rs. 7955/- and

for Motian Rs.4260/-. Total economic cost for Dingi and Motian is Rs.11889/- and

Rs.10442 per household per annum respectively. The welfare loss to the community due

to industrial water contamination is calculated as Rs. 5.8 million per annum for Dingi and

Rs. 0.2 million per annum for Motian. Moreover, the total welfare loss due to water

contamination in both the villages is Rs. 6 million per annum.

The inhabitants of Dingi and Motian are living at the locations before the

establishment of industrial estate; therefore they have historically ancestral attachment to

their present residence. Instead of migration from the polluted place, they adopted ways

to cope the situation either by compromise or measures to reduce the impact of pollution.

The avertive measures adopted are boiling water, installation of filter cartridge, using of

chlorine tablets purchasing sealed water bottles from the market. The presence of

contamination in the drinking water and the prevalence of specific diseases analyzed for

the epidemiology study. To investigate the association of contamination with the

prevailing diseases, statistical dose-response function was applied. For medical cost and

avertive measures, probit modeling was used. Based on the estimations, it emerged that

major parameters like location of the residents from the pollution source, knowledge of

pollution, perception of disease risk, exposure and the contaminants have statistically

significant influence on the probability of sickness.

114

The closeness to the contamination source, exposure have positive impact

whereas, pollution awareness, risk perception, presence of Lead (Pb) and Nickel (Ni)

have negative impact on the probability of sickness.

This total economics cost of both the villages Ding and Motian is millions of

rupees per annum which is a substantial reduction in the money and real income of the

people living in the polluted area. It clearly indicates that if this study is extrapolated to

other villages in the vicinity, this cost will be a heavy burden to the society.

5.2 Significance of the Study

This study is a multidisciplinary research and based on primary data of two

categories. One category is of household survey and second is of water

contamination.

Primary data was collected for 950 households survey comprises of various

socio economic indicators, diseases, expenditure, income etc. Questioners

based survey was conducted for collecting household data.

The water quality tests for 350 samples were carried out on the samples from

main industrial waste water channel and household‘s drinking water sources.

Both household survey and water sampling is based on GIS by using Arc-Gis.

Instead of adopting benefit transfer approach, real epidemiology study is

carried out for the relationship between contamination and diseases.

All the water samples were put for contamination tests in the instrumental

laboratory.

Quantitative and qualitative analysis were made to quantify the objectives.

5.3 Limitations of the Study

Current study is confined to the drinking water contamination and its health

impacts in the targeted areas of Khyber Pakhtunkhwa province, Pakistan. Whereas the

externality effects of thi industrial estate are also affecting the other adjacent areas of

Khyber Pakhtunkhwa and the province of Punjab. However due to time and resources

115

constraints, we opted the single difference approach, i.e., with or without. Double

difference approach, i.e., with and without and before and after could have been adopted.

But due to constraints of resources and non-availability of organized data, more

dimensions could not be covered. Further, the pilot study was conducted at the outset of

the research and the investigation was made for the presence of heavy metals like Lead

(Pb), Nickel (Ni), Cadmium (Cd) and Chromium (Cr). Two of these metal (Pb and Ni)

were found above the threshold values set by WHO 2010 (0.05 mg/L and 0.02 mg/L) and

is considered as unsafe for human health.

5.4 Future research application and benefits to community

Although research has been carried out to specific objectives pertaining to

contamination in the drinking water and its impacts on the health of household but Study

can be extended to the other areas of Pakistan. By utilizing our research study can be

extended to other aspects like agricultural productivity, child and women study, live stalk

etc.

The study indicates that there are significant health impacts due to industrial water

contamination on the locals living within 500 m2 around the industrial zone.

Reducing the current contamination levels in drinking water in significant

margins determined by the estimated models could reduce the medical, avertive,

opportunity cost and economic cost of illness which would in turn lead to welfare gain

that will come to the society.

This study is attempt to bring together socio-economic and pollution data to

understand the links between water contamination and health in the surrounding of

industrial estates in Pakistan.

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131

Annexure A (Household Survey Questionnaire)

Greetings! We are conducting a research on the impact of drinking water pollution on

the Health of the people in the areas of Dingi, Motian and Khanpur and need to

conduct interviews with the households to know about the drinking water quality and

behavioral variables related to the diseases. This research is first ever in this area

regarding the water-related diseases which is solely for academic purposes and all

your responses will remain confidential. We will try our best to share the results of

our this research once we have completed our study. We will be extremely grateful

if you agree to collaborate with us and give some time to answer a set of questions

we have. Your answers to our questions will make us understand how you and your

family are facing and coping with the diseases and results of our research may

Lead to the utilization by the policy makers to rectify the pollution problem if

found.

Section.1 Survey Information:

Household NO

Union Counsel

Patwarkhana

Village

Name Of Household Male Female

Name Of Respondent Male Female

Address

Name Of Enumerator

Date

PINCODE:

132

Section2

Socio-Economic Characteristics:

2.1 Household:

Accommodation

Area No of rooms Construct: Concrete Mud / Clay

Family Background migrated By birth

2.2Details Of Household Members

S.N

O

Nam

e

Se

x

Relation

With(H

H)

Ag

e

Educatio

n

(Adults)

Marrie

d

(Y/N)

Smokin

g

(Y/N)

Exercis

e

Place of

Employme

nt

Q.NO.1.Do you think that there is pollution to your drinking water Yes No

Section3 Source of Drinking Water:

Source Of

Drinking Water

Govt.

Public

Health

Tap

water

Pond Hand

Pump

Tube

well

Drilled Dug

Well

Industrial Waste water

channel near

Distance Between

channel and source

Water level/Table

133

Section4 Health Production Model:

Q.NO.2. Are You Aware that water Pollution Causes Illness? Yes No

Kindly Mark the diseases in your family Members:

Diseases

Q.No.3.Have you ever gone to the laboratory or hospitals for the

tests of these diseases for cause?

Yes No

Q.No.4 If yes what was the result: ________________________________

Q.NO.5.In your opinion what are the factors responsible for these diseases?

Factors

Q.NO.6.Is there any Water Pollution In your Area? Yes No

Q.NO.7.How did you Know:

Laboratory Test:

Govt: Test: NGO Test:

Q.NO.8.If yes what was the result: ___________________________________

Q.NO.9.Was there any Contamination? Yes No

Q.NO.10. Which Was?______________________________________________

Q.NO 11 .Have you ever got any disease due to drinking water ? Yes No

Q.NO.12.How many times? _________________________

Q.NO.13.Does your any other family Member has the same

disease?

Yes No

Q.NO.14.Did your Father/Mother had the same disease? Yes No

134

Section. 5 General Health

Chronic Disease

S.No Diseases

Section.6 Avertive/Mitigating Activities

6.1 Avertive Activities:

Extra

Distance

Traveled

to fetch

water

Others

Measures

to Avoid

Water

pollution

Use

Boiling

water

Y/N

Filter/Cloth chlorination Transportation/Public/own

Pedestrian

6.2 Mitigating Activities:

Name

Of

Diseas

e

No.

of

Time

s

Sick

No Of

Sick

days

in

last

three

month

s

Travel

Cost to

Doctor‘

s

Clinic

(Rs)

Total

Time

Waitin

g and

Travel

Perso

n

with

Y/N

Doctor‘

s

Fee/Per

Visit

(Rs)

Cost of

Medicine

Taken/La

b Tests

(Rs)

Total

No

days

Absenc

e from

work

Tota

l

cost:

(Rs)

6.3Hospitalization:

Turns Disease No of

days

stayed at

hospital

No Of

Days Rest

at Home

and leave

from

working

days

Govt/Private Attendant

Paid/Family

Member

Total

Cost

135

Section.7 Household Income:

7.1Consumer Durable:

S.No Item Yes/No

Washing Machine

Sewing machine

Refrigerator

Filter Plant

Computer

T.V

Telephone

Heater

Vehicle

Motorcycle

Piece of land

Any other specify

Section.8 Household Expenditure:

Annual Expenditure Incurred by the household on the following categories(In

Rs)

S.NO Expense Head Expenditure(Rs)

Education

Household Living

Recreation

Travel

entertainment

House rented/owned

Q.NO.15. Would you like to contribute any remedy for the

Reduction of this pollution?

Yes No

Q.NO.16. If Yes How Much? ____________________

Water Related Information:

Q.NO.17. Do you follow any purification method?

1.Yes

2.No

136

Q.NO.18. Why Are you purifying the water?

Serial No Reasons 1= Yes 2=No

1 Pollution

2 Precautionary

3 Any Other

Q.No.19. Water Purification methods

Serial

No

Different

Methods

Mark 1.Yes

2.No

In Rs. Length Of time

Drinking water

From Source

Do you

think it

removes

pollution

Money spent Before

Sick

After

Sick

Initial Recurring Years Years

Rs/PM Rs/PM

1 Boiling

2 Filter(Cartridge)

3 Filter(Iron)

4 Filter(Cloth)

5 Storage

6 Alum

7 Pitcher Method

8 Treating with

chemicals

Thanks and Best wishes

137

AnnexuresB1 – B19

B-1

Domail is a place

where Industrial

waste water

neither channel

nor is joining in

to industrial

waste water

channel Jhar and

the entrance of

combined waste

water channel in

to Village

Motian.

B-2

Community is

given the briefing

about the various

sections of

questionnaire and

interpretation of

the items.

138

B-3

Data Collection

Using

Questionnaire

B-4

Surveying Data

B-5

Questionnaire

Filling

139

B-6

Muhammad

Hashim is Patient

of renal failure

and of

Lipominingocele

(Neural Tube

Defect).

Mr.Hashim is of

46 years old but

his learning

behavior is slow

in relation to his

age.

B-7

Mr. Maqsood,

aged 54 years, is

a skin disease

patient in the

village of Motian.

140

B-8

Mr. Akram,

Renal Disease

Patient who has

got died during

dialysis

B-9

Water Sampling

141

B-10

Laboratory work

for Water Quality

Analysis

B-11

Human exposure

to water

contamination.

142

B-12

Bathing Exposure

to the

Contaminated

Water of

Industrial

Channel

B-13

Industrial water

Pollution

143

B-14

Onion along with

the other

vegetables and

crops are mostly

irrigated with

contaminated

water.

B-15

Industrial Waste

Water Channel

Jhar

144

B-16

Industrial Waste

Water Channel

Jhar

B-17

Industrial Waste

Water Channel

Jhar

145

B-18

Crop irrigation

with Polluted

water

B-19

Obtaining of GIS

Points for

Sampling

146

List of Publications

1 AzizullahSayal, ShehlaAmjad, Muhammad Bilal, Arshid

Pervez,QaisarMahmood, Muhammad AsimAfridi. 2015. Industrial Water

Contamination and Health Impacts: Economic Perspective. Polish Journal of

Environmental Science, Vol. 25, No. 2 (2016), 765-775

impact of drinking water contamination caused by hattar...

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