Supplementary appendix · 2018. 4. 10. · 1 Appendix Introduction Climate Change, Pollution, and Health Climate change and pollution are closely linked. Both arise from the current

Supplementary appendixThis appendix formed part of the original submission and has been peer reviewed. We post it as supplied by the authors.

Supplement to: Landrigan PJ, Fuller R, Acosta NJR, et al. The Lancet Commission on pollution and health. Lancet 2017; published online Oct 19. http://dx.doi.org/10.1016/S0140-6736(17)32345-0.

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Appendix

Introduction

Climate Change, Pollution, and Health

Climate change and pollution are closely linked. Both arise from the current linear,

unsustainable resource-intensive model of economic development, and they are both

produced by many of the same industrial and agricultural sources. Climate change is

expected to alter pollutants’ fate, transport, use, and environmental distribution, magnify

the risk of toxic exposures and thus increase risk for PRD. The following are specific

examples of links between climate change and pollution:

Air pollution. Rising temperatures will result in increased need for electricity for cooling

and air conditioning and thus will increase airborne emissions of carbon dioxide, short-

lived climate pollutants, particulates, and toxic chemical pollutants from electric power

plants. Rising temperatures will also increase long- range atmospheric transport and

distant deposition of toxic chemicals. Rising temperatures will melt permafrost leading

to increased mobilization of Persistent Organic Pollutants (POPs) and mercury

sequestered in permafrost.1 Rising temperatures, drought and desertification will lead to

increased formation of airborne dust 2.Air stagnation increases pollutant concentrations

and aggravates the health effects of air pollution.3

Pesticides. Rising temperatures and increased precipitation will result in expanded

geographic ranges and longer active seasons for insect pests and weeds. Increased

applications of insecticides and herbicides will follow.4 Rising temperatures will also

increase the volatility of some pesticides, leading to further exposure risk.5

Toxic Metals. Ocean acidification may modify the bioavailability of contaminated

sediments,6 increasing movement of toxic chemicals up the food chain,7 thus

increasing exposures of marine species to these pollutants. Extreme weather events

such as floods, cyclones and hurricanes have been shown to liberate dioxins, heavy

metals and toxic chemical wastes from storage depots and waste sites 8,9 thus

increasing runoff into lakes and rivers results that can result in human exposure.10

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Wildfires release legacy toxins into the atmosphere that have been sequestered in

forest soil and plants (including depositions of lead from gasoline as well as

mercury).11,12

Climate change will further increase incidence and prevalence of pollution-related

disease by increasing human sensitivity to pollutants. For example, high summer

temperatures may increase cardiovascular sensitivity to airborne particulate pollution.13

On a positive note, measures taken to mitigate climate change and encourage transition

towards a more circular economy such as a switch to renewable non-polluting sources

of energy, upgrading and incentivizing of active commuting and public transport, and

designing waste out of industrial processes will help to control pollution.

Emerging Chemical Pollutants

Synthetic chemicals have been responsible for repeated episodes of disease, death and

environmental degradation. A recurrent theme in these episodes is that new chemicals

have been brought to market with great enthusiasm but with little premarket assessment

of their potential hazard, used widely, and then found belatedly to have caused harm to

human health or the environment.14 A root cause of virtually all these episodes has

been failure to conduct adequate premarket evaluations of the safety of new chemicals

before they came to market.14 The consequences are that:

• Information on potential toxicity is publicly available for only about half of the

synthetic chemicals in current wide use; and

• Information on developmental toxicity or capacity to harm infants and children is

available for fewer than 20% of the most widely used synthetic chemicals.15

Developmental neurotoxicants. Evidence is strong that a number of widely used

industrial chemicals and pesticides have been responsible for injury to the brains of

millions of children worldwide and that these exposures have resulted and continue

today to result in a global ‘pandemic of neurodevelopmental toxicity’.16,17 Loss of

cognition – expressed as reduction in IQ, shortening of attention span, impairment of

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executive function, and behavioral disorders are among the consequences of exposures

to toxic chemicals in early life, and chemical exposures are associated also with

attention deficit/hyperactivity disorder, learning disabilities, dyslexia, and autism.

Prospective epidemiological birth cohort studies that enroll women during pregnancy,

measure prenatal exposures to chemicals in real time as they occur, and then follow

children longitudinally with periodic examinations to assess growth and development

have been powerful engines for the discovery of etiologic associations between prenatal

exposures to chemical pollutants and neurodevelopmental disorders.18 Examples of

pollution-related diseases in children that have been identified through prospective

studies are:

• Cognitive impairment with loss of IQ in children exposed prenatally to PCBs;19

• Reduced IQ and shortening of attention span in children exposed prenatally to

methyl mercury;16

• Microcephaly at birth, anatomic and functional delays in brain development, and

autistic behaviors in children exposed prenatally to the organophosphate

pesticide, chlorpyrifos;20,21

• Autistic behaviors in children exposed prenatally to phthalates;22

• Cognitive impairment, shortened attention span, and disrupted behavior in

children exposed prenatally to brominated flame retardants;23 and

• Neurodevelopmental delays in children exposed prenatally to polycyclic aromatic

hydrocarbons (PAHs).24,25

In total, approximately twelve chemicals have been shown to date through clinical and

epidemiologic studies to be developmental neurotoxicants.

An important yet unanswered question is whether there are additional chemicals in use

today whose ability to cause silent injury to human health has not yet been

discovered.26,27 Particular attention has focused on the possibility that there may be

undiscovered neurotoxicants capable of causing injury to the developing human brain.

Such toxicants may be found among the 200 chemicals in current use that have been

shown to cause neurotoxicity in adult workers and among the 1,000 chemicals known to

cause neurotoxic effects in experimental animals.16,17

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Endocrine disruptors. Endocrine disruptors are synthetic chemical pollutants that

mimic, block, or alter the actions of normal hormones such as estrogen, testosterone,

growth hormone, insulin, and thyroid hormone.28

Initial recognition of the power of an endocrine disruptor to produce widespread

ecological damage and injure health came in 1962 with publication of Rachel Carson’s

Silent Spring,29 which described widespread contamination of the environment with DDT

and the consequent near extinction of the American bald eagle. It has subsequently

been shown that DDT decimated the eagle pollution by interfering with estrogen

function and thus impairing the birds’ ability to produce viable eggs; the eagle

population has rebounded in the United States since the banning of DDT in 1972.30

DDT has subsequently been found to interfere with estrogen function in humans, and

adult women in California who were exposed in utero to high levels of DDT 40-50 years

ago have been shown to be at heightened risk for breast cancer.31

The first recognition of the ability of an endocrine disruptor to alter the course of human

development and increase risk of disease emerged from the diethylstilbestrol (DES)

tragedy. DES, a synthetic estrogen, was prescribed to as many as 5 million pregnant

women in the US in the 1960s and early 1970s to block spontaneous abortion and

promote fetal growth. A decade later, gynecologists began to observe cases of a rare

malignancy, adenocarcinoma of the vagina, in young women. Peak incidence occurred

in the years after puberty. Epidemiologic analysis found that the great majority of these

young women had been exposed in utero to DES.32 Their mothers were physically

unaffected. Further long-term follow-up has shown that after age 40 DES daughters

have a 2.5-fold increased incidence of breast cancer.

Synthetic endocrine disruptors include phthalates, bisphenol A, perchlorate, certain

pesticides, brominated flame retardants, certain metals, and dioxins. These chemicals

are manufactured in volumes of millions of pounds per year. They are widespread in

consumer products such as soaps, shampoos, perfumes, and plastics. Exposures to

even extremely low doses of endocrine disruptors during sensitive periods in early

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development can lead to permanent impairments in organ function and to increased risk

of disease.

Phthalates are a class of endocrine disruptors in current wide use that have been

shown to cause disease and dysfunction but whose contributions to the global burden of

disease have not yet been quantified. Phthalates are used as plasticizers to confer

flexibility to rigid plastics and in personal-care products, lacquers, varnishes, and timed-

release coatings for some medications. Several phthalates have been shown to

possess anti-androgenic activity and reduce testosterone levels. Evidence of anti-

androgenic effects associated with early-life exposures to phthalates in animal studies

include impaired Leydig cell function, hypospadias, and undescended testicles. In

humans, prenatal exposure to phthalates has been linked to subnormal serum

testosterone levels in newborn and adult males and with adverse effects on adult

sperm. Prenatal exposure to phthalates in baby boys has been linked to shortening of

the ano-genital distance, a marker of in utero feminization.33,34 Prenatal exposures to

phthalates are deleterious also to infant brain development and increase risk for autistic

behaviors.22

Pesticides. Pesticides, including insecticides, herbicides, fungicides, and rodenticides

are compounds deliberately engineered to be toxic. Pesticides come in many classes,

and more than 600 unique pesticide chemicals and more than 20,000 commercial

pesticide products are currently on world markets. Pesticide chemicals are used in large

and ever-increasing quantities worldwide – more than 1.1 billion pounds are used in the

United States each year and an estimated 5.2 billion pounds globally.35 Some of the

heaviest applications occur in low- and middle-income countries where use and

exposure data are often scant.

Chronic, lower-level exposures to pesticides can cause chronic toxicity and increase

risk for non-communicable diseases, including neurodevelopmental disorders

(organophosphate pesticides), chronic lung disease (the herbicide, paraquat),36 and

cancers (multiple herbicides).36 However, despite their wide use and known adverse

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effects on human health, the contributions of pesticide pollution to the global burden of

disease cannot yet be estimated, except in the case of acute poisonings, because data

are not available in most countries on the sizes of the populations chronically exposed

to pesticides or on their levels of exposure. Gaps exist also in knowledge of the toxicity

of these agents and thus in knowledge of the full range of their effects on human health.

Experience with three categories of pesticides – the organophosphate insecticides, the

neonicotinoid insecticides, and the synthetic herbicide, glyphosate - illustrate these

challenges.

Organophosphate insecticides. The organophosphate insecticides are a large and

widely used class of pesticides. These compounds were deliberately designed to be

neurotoxic, and in both insects and mammalian species they cause neurotoxicity by

inhibiting the enzyme acetylcholinesterase. The chemical warfare agents sarin and

soman are members of the organophosphate family.

Evidence is now strong that organophosphate insecticides are developmental

neurotoxicants. Initial recognition of the developmental toxicity of these compounds

emerged from an anthropological study undertaken among 4-5-year-old children in an

agricultural community in Mexico who were exposed to high levels of organophosphate

as well as organochlorine pesticides prenatally and in early childhood.37 Compared to

genetically similar children of the same tribe in the nearby foothills that did not use

chemical pesticides, children in the agricultural community manifested multiple aspects

of neurotoxicity including reduced hand-eye coordination, impairment in ability to draw

simple figures, and deficits in short-term memory (Figure A1).

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Figure A1. Human figures drawn by Mexican children exposed (Valley) and not

exposed (Foothills) to organophosphate and organochlorine pesticides.37

Further strong evidence for the developmental neurotoxicity of the organophosphates is

provided by three epidemiological studies of children exposed prenatally to chlorpyrifos

in different regions in the United States. These studies, each of which measured

chlorpyrifos exposures prenatally and then followed children longitudinally, found that

prenatal exposures are associated with persistent deleterious effects on cognitive and

behavioral function in children through at least the age of 7 years.21 Further evaluation

of children in one of these populations using magnetic resonance imaging (MRI) found

that even low to moderate prenatal exposures to chlorpyrifos are associated with long-

term, potentially irreversible changes in brain structure.20 Toxicological studies of

rodents exposed perinatally to chlorpyrifos also find strong anatomical and functional

evidence for developmental neurotoxicity.38 Despite this strong evidence for the

developmental neurotoxicity of the organophosphate insecticides, the contribution of

these compounds to the global burden of disease has not yet been estimated.

Neonicotinoids. The neonicotinoids are a novel class of neurotoxic pesticides that

were developed in the 1980’s to replace the organophosphates and carbamates. Use of

neonicotinoids has risen dramatically in the past decade and the neonicotinoid

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insecticide, Imidacloprid is now the most widely used insecticide in the world.39 In the

United States, agricultural use of neonicotinoids in 2014 was nearly 8 million pounds.40

Neonicotinoids target nicotinic acetylcholine receptors (nAChRs) in the insect nervous

system,41,42 acting as potent agonists of these receptors and impairing neural

transmission.43 Neonicotinoids are most commonly applied by prophylactic seed

coating. This application leads to systemic absorption by plants, and neonicotinoids can

be detected in relevant concentrations in plant tissues for many months after

application.44 Neonicotinoids are water-soluble and can persist for years in soils, dust,

wetlands, and groundwater.45 They enter rivers, lakes and the oceans, and they are

detected in fish.46 They are detected in commonly consumed foods.

Substantial evidence from both laboratory and field studies indicates that neonicotinoids

can have negative impacts on the behavior, health, and abundance of bees and other

pollinators at environmentally relevant concentrations.43,47,48 Neonicotinoid

concentrations in the field, including in nectar and pollen consumed by bees, are

typically between 2 and 6 ppb47 although they can occasionally reach much higher

values.43,47,49 While these concentrations are generally below the levels acutely toxic

for bees,43,47 these sub-lethal exposures have nonetheless been shown to significantly

affect bees’ immune function,50,51 neuronal activity,52 learning capacity,53,54 ability to

navigate,55,56 and foraging performance.57,58 Field studies and studies under semi-field

conditions have shown also that chronic neonicotinoid exposure can reduce rates of

colony growth in bumblebees49,59,60 and reduce the abundance of wild bees.49,61

Studies of the effects of neonicotinoid exposure on colony growth in honeybees are less

definitive49,62 and suggest that effects may depend on factors such as colony size63 or

genetic background.62 Lastly, neonicotinoid exposure reduces delivery of pollination

services to food crops.64 Because both biodiversity65 and global agricultural yields27

depend heavily on pollination services, a reduction in pollinators (especially bees) is

predicted to have significant adverse effects on human health28.

Neonicotinoids were originally thought to pose only a minimal direct threat to vertebrate

wildlife and to humans because of their selective affinity for the insect nAChR receptor

and their inability to cross the blood-brain barrier4,29. Since their approval for commercial

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use, neonicotinoids (and their metabolites) have however been linked to adverse effects

in vertebrates even at sub-lethal levels. These effects include impaired growth, slowed

development, and impaired reproduction. 30-33 Sub-lethal exposure also has effects on

neurobehavioral function in both mammals and birds. 30,34,35 In rats, neonicotinoids alter

brain function with effects similar to those of nicotine36, a known developmental

neurotoxicant. Neonicotinoids also have indirect effects on vertebrate wildlife, for

example by reducing availability of prey. 30

Despite their extensive use in agriculture, only very limited information is available on

the possible human health effects of the neonicotinoids. A recent systematic review was

able to identify only 8 published studies of human toxicity, 4 examining acute poisoning

episodes and 4 examining chronic effects. The studies of chronic effects produced

“suggestive but methodologically weak findings” mainly in the area of

neurodevelopmental outcomes.38 Clearly more research on this class of pesticides is

needed. It is not possible at this time to estimate the possible contribution of

neonicotinoid exposures to the global burden of disease.

Chemical herbicides. Herbicides account for nearly 40% of global pesticide use and

herbicide use is increasing sharply.35 A major use of herbicides today is in the

production of genetically modified (GM) food crops, mainly corn and soybeans that have

been engineered to be tolerant to glyphosate (Roundup), the world’s most widely used

herbicide. Glyphosate-resistant, “Roundup-Ready” crops now account for more than

90% of all corn and soybeans planted in the United States and their use is growing

globally. Their advantage, especially in the first years after introduction, is that they

greatly simplify weed management. Farmers can spray herbicide on “Roundup-Ready”

crops both before and during the growing season to kill weeds while leaving their crops

unharmed. Glyphosate is widely detected today in air and water in agricultural areas,

and glyphosate residues are detected in foods.66

The adoption of glyphosate-resistant crops has led to overreliance on herbicides and to

the emergence of glyphosate-resistant weeds, which now infest more than 100 million

acres of North American cropland. In the United States, glyphosate use has increased

by more than 250-fold in the past 40 years — from 0.4 million kg in 1974 to 113 million

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kg in 2014. Over the same time, global use has increased more than 10-fold. Use is

projected to grow still further as glyphosate-resistant weeds continue to emerge and

spread. Simultaneous application of multiple herbicides is proposed as a strategy to

overcome glyphosate resistance.67

Epidemiologic studies of agricultural workers exposed occupationally to glyphosate and

other herbicides have found evidence for increased incidence of non-Hodgkin’s

lymphoma. Toxicological studies of experimental animals exposed to glyphosate show

strong evidence of dose-related carcinogenicity at multiple anatomical sites. On the

basis of these findings, the International Agency for Research on Cancer has

determined that glyphosate is a “probable human carcinogen”.36 The contribution of

herbicides to the global burden of disease cannot yet be estimated.

Chemicals at the Pole

Indigenous peoples of the Arctic are among the world’s most chemically contaminated

populations. Mercury, polychlorinated biphenyls (PCBs) and other Persistent Organic

Pollutants (POPs) are the chemicals of greatest concern. These chemicals originate

from industrial sources in Europe, Asia, and North America and are transported to the

Arctic through long-range migration in air and water.

In the Arctic ecosystem, persistent chemicals biomagnify in the food chain and reach

extremely high concentrations in marine mammals, such as ringed seals and polar

bears, which contain some of the highest levels of these chemicals on the planet.68

These same animals are integral components of the diets and lifestyles of Arctic

Indigenous Peoples.68 Their consumption has long provided basic nutrition as well as

social, cultural and economic well-being.69 The presence of chemical contaminants in

traditional foods has caused many Indigenous Peoples to shift away from their ancient

diets and lifestyles. This shift has resulted in rising rates of obesity, diabetes and

cardiovascular disease.70

Arctic contamination has been the focus of initiatives such as the Northern

Contaminants Program initiated in 1991 by the Canadian government and the multi-

country circumpolar Arctic Monitoring and Assessment Programme. These programs

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have provided solid evidence that the concentrations of many legacy and emerging

chemicals are increasing in the Arctic ecosystem and peoples, and that current levels

represent a hazard to human health.71,72

Pollution at the pole is also an environmental justice issue, since the Arctic communities

that suffer chemical exposure and adverse health outcomes derive no benefit from the

industrial processes that emit the chemicals nor do they have any opportunity to

participate in decisions on how to prevent pollution or mitigate risk

Pollution and children’s health. Children are exquisitely sensitive to pollution in all its

forms and are highly susceptible to pollution-related disease.14,73 Four key differences

have been identified between children and adults that contribute to children’s

heightened vulnerability:74

• Children have proportionately heavier exposures to pollutants compared to adults.

This reflects children’s greater intake kilogram-for-kilogram of food, water and air

coupled with their unique age-related behaviors, in particular, their oral-exploratory

behaviors.

• Children’s metabolic pathways, especially in the first months after birth, are

immature. In many instances, children are less able than adults to excrete and

detoxify toxic compounds.

• Children are undergoing rapid growth and development. Early development creates

windows of great vulnerability.

• Because children have more years of future life than most adults, they have more

time to develop chronic diseases that may be initiated by early exposures.

Rates of a number of non-communicable diseases are increasing in children worldwide.

Chemical pollutants are among the forces responsible for these increases. Two

recent reports from the World Health Organization further document the

disproportionate effects of pollution and other unhealthy environmental exposures on

the health of children. These analyses find that more than 1 in 4 deaths of children

under 5 years of age - 1.7 million deaths per year - are attributable to environmental

risks – air pollution, second-hand smoke, unsafe water, lack of sanitation, and

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inadequate hygiene. These include 570,000 children under the age of 5 years who

die from respiratory infections, and 361,000 children under 5 years of age who die of

diarrheal disease.28,75 Pollution exposures in early life are linked also to increased

risks of non-communicable disease in later life.76,77

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The power of prevention.

In 2007, 1.7 million people died from HIV/AIDS, 1.3 million from tuberculosis and 1.1

million from malaria - 4.1 million deaths total.78 HIV/AIDS, tuberculosis and malaria

appeared to be unstoppable. Today thanks to bold, science-driven, well-funded

interventions by UN agencies, the Global Fund to Fight AIDS, Tuberculosis and Malaria,

the President’s Emergency Plan for AIDS Relief (PEPFAR), and the work of many other

organizations and individuals, the number of people who die each year from these

diseases has been reduced to about 3 million, a 33% reduction in mortality within a

decade, an extraordinary accomplishment.79,80

Successes in Managing Toxic Chemicals

Mandatory testing of chemicals for safety and potential toxicity coupled with strict

regulation of toxic chemicals are the linchpins of chemical safety policies intended to

protect human health and the environment.14 In response to rising concern about the

effects on human health of widespread exposures to untested and potentially

dangerous chemicals, high-income countries have begun in recent years to enact

legislation that requires the testing of new and existing chemicals.14

In 2007, the European Union enacted the Registration, Evaluation, Authorisation and

Restriction of Chemical Substances (REACH) legislation.81 This legislation places

responsibility on industry to generate data on potential risks of commercial chemicals

and to register this information with the European Chemical Agency in Helsinki.82 The

European Union is using this information to craft regulations to protect health, and it has

banned and restricted certain toxic products.

In June 2016, the United States passed new legislation to revamp the obsolete and

outdated Toxic Substances Control Act of 1976.83 This law - the Frank R. Lautenberg

Chemical Safety for the 21st Century Act84 requires that the US Environmental

Protection Agency make an affirmative finding on the safety of any new chemical before

it is allowed to enter the market; prioritize existing chemicals for safety testing within

clear and enforceable deadlines; and use a risk-based standard to evaluate the safety

of chemicals that is blind to cost-benefit ratios or the costs of protective action.

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Implementation of this new law is barely beginning and close vigilance will be required

to determine whether it fulfills its promise.

Because they are faced with a large backlog of untested chemicals, the recently

enacted chemical safety policies in the European Union and the United States will need

to rely heavily on new, highly efficient approaches to chemical testing such as those that

have been recommended by the National Academy of Sciences and are now coming

on-line at the National Institute of Environmental Health Sciences and the US

Environmental Protection Agency through the Tox21 program.85–87 These new high-

throughput approaches incorporate multiple new technologies including exposure

modeling, sensors, biomonitoring, omics technologies, novel computational methods,

big data mining and bioinformatics as well as the integration of toxicological findings

with genomic and health outcome data.

Many countries, especially low-and middle-income countries do not yet have health-

based chemical policies. As a result of global variations in chemical policy, hazardous

materials no longer permitted in Western Europe or North America can be shipped

overseas and sold into developing markets often in contravention of existing global

conventions.82 To address these challenges, UN Environment has called for “a global

commitment to the sound management of chemicals” and UN Environment’s report,

Global Chemicals Outlook report, presents case studies showing that sound

management of chemicals in low- and middle-income countries can produce substantial

economic returns.88 UN Environment also supports international agreements limiting the

manufacture, environmental release and global transport of persistent pollutants,89

pesticides,90 hazardous waste,91 and mercury.92 The Strategic Approach to International

Chemicals Management (SAICM) process housed within UN Environment93 provides a

platform for discussion on control of chemical pollution and toxic waste amongst a broad

range of stakeholders.

.

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The Commission on Pollution and Health.

This Lancet Commission on Pollution & Health was formed in August, 2015. Its launch

was announced simultaneously in Geneva at the meeting of the International

Conference on Chemicals Management and in Jakarta at the annual meeting of the

Pacific Basin Consortium for Environment and Health.

This Commission has brought together a diverse group of Commissioners and

contributors from many disciplines including health, environmental science, economics,

law, social science, political science, and engineering; academia, government,

international organizations; and diverse geographical backgrounds. The Commission

met twice - in New York on November 9-11, 2015 and in New York again on June 16-

17, 2016.

The Global Alliance on Health and Pollution94 has served as secretariat for the

Commission. The Global Alliance is a collaborative body that facilitates the provision of

technical and financial resources to governments and communities to reduce the effects

of pollution on health in low- and middle-income countries.

This Commission sought input and consultation from the members of the Global

Alliance on Health and Pollution, experts at the World Bank, the World Health

Organization, UN Environment, the Consortium of Universities in Global Health, the

Pacific Basin Consortium for Environment and Health, the Superfund Research

Program of the US National Institute of Environmental Health Sciences, and a number

of non-governmental organizations.

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Section 1

Additional Details on Contaminated Sites Methodology.

The Blacksmith Institute/Pure Earth version of this protocol has been streamlined to

accommodate application in low- and middle-income countries by trained, non-

professional, local investigators.95 At each site, these investigators use standardized

procedures to identify the principal pollutant and to measure levels of environmental

contamination. The investigators use publicly available demographic data to estimate

the size and age distribution of the exposed population.95,96 The toxic pollutants

identified most commonly are lead (found at 44% of sites worldwide), mercury,

hexavalent chromium, asbestos, cadmium, and the pesticides: DDT, lindane, and

Aldrin. To date 2,591 sites in 49 countries have been assessed.4 The methodology is

well described in Ericson et al.95,97

Two particularly common types of contaminated sites are used lead-acid battery (ULAB)

recycling sites, where lead is the principal pollutant, and Artisanal and Small Scale Gold

Mining (ASGM) sites, where the principal pollutant is elemental mercury used to extract

gold from ore. To assess the burden of disease associated with exposures to heavy

metals at these sites, the Commission used the methodology of Ericson et al.97 for lead

battery recycling sites and the methodology and data of Steckling et al.98,99 for gold

mining sites.

Assessing Lead Poisoning at Used Lead-Acid Battery (ULAB) Recycling Sites.

Used lead-acid battery recycling sites are major loci of lead production in low- and

middle-income countries worldwide and are important sources of both occupational and

community exposure to lead. To assess the burden of disease associated with lead

17

exposure at these sites, the Commission used the methodology of Ericson et al.97

described.

Step 1. The first step in the methodology developed by Ericson et al.97 is to estimate the

amount of lead recycled each year in each country and globally. This estimation is

undertaken by obtaining data on the total amount of lead entering the recycling market

in each country and then subtracting out the amount recycled in commercial smelters;

information on commercial smelters is obtained from the United States Geological

Service. The total amount of lead entering the recycling market in a country is estimated

from information on the number of the metric tons of lead recycled annually from

automotive and other batteries, uninterrupted power supplies, electric bicycle batteries,

and other applications such as green energy storage units. Data provided by the

Organisation Internationale des Constructeurs d’Automobiles were used to determine

the number of cars, trucks, and buses on the road in each country. Information on the

number of motorbikes was provided by Ministries of Transport for several South Asian

countries, and estimated elsewhere. For China, a widely quoted estimate of 200 million

electric bicycles was used. A model developed by the International Lead Association is

used to determine the amount of lead contained in each type of battery.

Step 2. Estimate soil lead levels at recycling sites. For this purpose, Ericson et al97

developed a series of nine exposure scenarios, based on three smelter sizes and three

levels of exposure. This distribution of smelter sizes and exposure levels was based on

data collected through 28 field assessments carried out by Blacksmith Institute/Pure

Earth at informal used lead-acid battery recycling sites in 11 countries.

Smelting operations were divided into three categories: small, accounting for 50% of

recyclers globally, with 500 people exposed per site; medium, accounting for 35% of

sites with 500 persons exposed per site; and large, accounting for 15% of all operations,

with 2000 persons exposed per site. Then to capture information on varying degrees of

exposure, sites of each size were divided into three exposure categories - low, medium

and high on the basis of soil lead concentrations. Fifty per cent of sites fell into the low

exposure category (mean soil lead concentration 850 mg/ kg); 35% fell into the medium

18

exposure category (2500 mg/ kg); and 15% into the high exposure category (5000

mg/kg).

Step 3. Estimate the distribution of blood lead levels at sites from soil lead data using

the US Environment Protection Agency’s Integrated Exposure Uptake Biokinetic Models

for Lead in Children (IEUBK) and Adults.

Step 4. Estimate the burden of disease at each site by combining blood lead data with

data on population size and age structure and using exposure-response algorithms

developed by the World Health Organization. No mortality from lead poisoning was

assumed in this analysis, and therefore the estimated burden of disease is based solely

on Years Lived with Disability (YLDs)

Artisanal and Small-Scale Gold Mining has become the world’s largest source of

mercury pollution. Chronic occupational mercury poisoning caused by inhalation of

mercury vapor is a major health problem among gold miners. Symptoms are mainly

neurological and involve both central and peripheral neurologic dysfunction. To assess

the burden of disease resulting from mercury exposure in gold mining, the Commission

relied on the methodology and data of Steckling et al.98,99

Step 1. The first step in the methodology developed by Steckling et al.98,99 is to estimate

the size of the global population of artisanal and small-scale gold miners. This

information was derived from a country-by-country census of gold mining populations

undertaken by Seccatore et al.100 supplemented by a structured literature review.

In step 2 estimates were developed of the prevalence of moderate mercury poisoning

among miners by compiling a dataset of all available studies that measured urine

mercury levels and that had also assessed frequency of symptoms of mercury

poisoning in miners using a standardized diagnostic tool developed by Drasch and

Doering.101,102 Severe cases were not included in the analysis of because severely

poisoned miners can no longer be employed. Cases in workers' families and

communities were also not considered.

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In step 3, the burden of disease associated with moderate mercury poisoning among

miners was quantified in each country and globally by multiplying the number

of prevalent cases by the appropriate disability weight and the number of miners. No

mortality was assumed in this analysis, and therefore the estimated burden of disease is

based solely on Years Lived with Disability (YLDs).

In future iterations, this methodology will be expanded to include severe cases of

mercury poisoning caused by elemental mercury in artisanal and small-scale gold

mining as well as community cases.

Note that the totals for air pollution, water pollution and all pollution are less than the arithmetic sum of the individual risk factors within each of these categories due to overlapping contributions e.g. household air pollution also contributes to ambient air pollution and vice versa.

Table 1. Global estimated deaths (millions) by pollution risk factor and region, 2015103

Household

Air Pollution

Ambient Particulate Pollution

Total Air

Total Water Occupation Total

Lead Total

Africa 0.6 0.3 0.8 0.7 0.03 0.02 1.4

Eastern Mediterranean 0.2 0.4 0.5 0.1 0.03 0.1 0.7

Europe 0.1 0.6 0.6 0.0 0.1 0.1 0.8

Americas 0.1 0.3 0.3 0.1 0.1 0.1 0.5

South East Asia 1.3 1.4 2.3 0.8 0.2 0.1 3.2

Western Pacific 0.7 1.3 1.9 0.1 0.4 0.1 2.2

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Section 2

2A. Measurement of Productivity Losses

A. Methodology

We estimate the present discounted value of the loss in GDP attributable to premature deaths associated with each pollutant. The loss in GDP in the present year if a worker dies, I(0), is equal to labor’s share of GDP (s) multiplied by GDP, divided by the number of persons who are employed (e). To measure the loss in GDP if a person of working age dies, we weight I(0) by the fraction of persons of working age who are employed, i.e., by e divided by the working age population of the county, WPop. The loss in GDP in the current year if a person of working age dies is therefore given by

(A.1) I′(0) = [sGDP/e][e/WPop],

which may be written as the product of labor’s share of GDP times per capita GDP, divided by the fraction of the country’s population that is of working age,

(A.2) I′(0) = s[GDP/Pop] / [Wpop/Pop].

If a person of working age dies in the current year, their contribution to GDP will be lost for all future years of their working life. To compute the value of GDP lost in future years we assume that GDP per capita in country i grows at rate gi. If labor’s share of GDP and the fraction of population of working age remain constant, this implies that lost GDP t years hence will equal I′(0)(1+gi)t. This must be weighted by the probability that an individual would have survived to year t. We therefore weight the loss in GDP in future years by the probability that an individual who dies this year would have survived to each future year of his working life.

To discount the value of GDP lost t years in the future to the current year we use the Ramsey formula, which forms the basis for discounting future benefits and costs in many economic applications104 and now forms the basis for discounting project benefits by the World Bank.105 The formula says that the rate at which future losses should be discounted in country i, ri, equals society’s pure rate of time preference (di) plus the rate of growth in per capita GDP (gi) multiplied by a term that measures how the value of additional output declines as output grows. If we assume that the latter term equals 1,

(A.3) ri = di + gi .

When equation (A.3) is used to discount future GDP losses, which are growing at rate gi, the rate of increase in per capita GDP cancels out the gi term in the discount rate and the discount rate reduces to di. We assume that the pure rate of time preference is

21

constant for all countries, and use d = 3.0 for our base case and d = 1.5 as a sensitivity analysis. A value of d = 3 is consistent with the World Bank’s choice of a discount rate of 6% when the rate of growth in per capita income is 3% .105

B. Data

For each country and source of pollution we compute the present value of GDP lost due to premature mortality for persons who die between the ages of 15 and 64. We assume that a person’s contribution to GDP continues until age 65; i.e., the present value of lost GDP is calculated from the current age through age 64. This is consistent with international practice that views the working age population as extending from age 15 to age 64. For a child who dies before age 15 we discount the present value of lost GDP beginning at age 15 to the present.

Data on GDP per capita, population and working-age population come from the World Bank World Development Indicators.106,107 Labor’s share of GDP is taken from the World Bank.108 Survival probabilities are calculated from country-specific life tables provided by IHME.

C. Results

Table A.1 which reports productivity losses in dollar terms, by pollutant and WB income group, indicates that productivity losses are greatest in dollar terms for AAP—approximately $51.6 billion in 2015.1 More adults of working age die due to AAP than any other category of pollutant studied (see Table A.2). While it is true that there are more child deaths for other pollutant categories, it is many years before children reached working age; hence their future output is more heavily discounted than the future output of persons who die between 15 and 64. Half of deaths due to AAP occur in High and Upper Middle income countries, which have higher per capita GDP. It is, therefore perhaps not surprising that over 75% of the productivity losses due to AAP occur in High and Upper Middle income countries (see Table A.3).

HAP results in reductions in future GDP equal to $20.5 billion in 2015. HAP results in fewer deaths than AAP and 74% of deaths due to HAP occur in Low and Low Middle income countries, which have lower GDP per person of working age than in higher income countries. This results in lower dollar productivity losses. Overall, 90% of the dollar value of productivity losses due to HAP occur in Low Middle and Upper Middle income countries (Table A.3) with India and China accounting for 23% and 30% of lost GDP associated with HAP.

1 Thesumofproductivitylossesfromthefivepollutantcategoriesis65.970(AAP)+23.646(WSH)+3.045billion(Lead).

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According to the GBD103 deaths due to unsafe water and unsafe sanitation combined are lower than either deaths due to AAP or HAP; moreover, these deaths occur primarily in Low and Low Middle income countries. It is therefore not surprising that productivity losses from unsafe water and sanitation are lower than losses due to air pollution. In 2015 losses due to unsafe water were $16.5 billion and losses due to unsafe sanitation $10 billion; the losses due to both risk factors together was $23.6 billion. In Low income countries, however, losses due to unsafe water and sanitation are greater than losses due to air pollution. The larger productivity losses reflect the age distribution of deaths due to unsafe water and sanitation (Table A.2): only 33% of deaths due to lack of safe water and sanitation occur after age 64. The larger number of YLLs below age 65 gives rise to larger productivity losses. Given that fact that over 90% of deaths due to unsafe water and sanitation occur in Low and Low Middle income countries,103 it is not surprising that these countries account for 87% of the productivity losses associated with unsafe water and 91% of the productivity losses due to unsafe sanitation (Table A.3).

The $3 billion in productivity losses associated with lead reported in Table A.1 represent the deaths of adults of working age primarily from cardiovascular disease. Close to 70% of these deaths occur in Low and Low Middle income countries, which together account for approximately 23% of productivity losses. About a third of deaths of people under 65 due to lead exposure occur in Upper Middle income countries which account for roughly a third of productivity losses due to lead.

Table A.4 displays productivity losses due to pollution as a fraction of GDP. Productivity losses due to pollution are greatest as a percent of GDP in Low and Low Middle income countries. In our preferred specification (d = 3%), all five pollution categories together account for productivity losses equal to 1.3% of GDP in Low Income and 0.61% of GDP in Low-Middle income countries. The largest sources of productivity losses in these countries are lack of access to safe water and sanitation and the use of solid fuels for cooking. Productivity losses are greatest in dollar terms for High and Upper Middle income countries (Table A.1), but remain small as a percent of GDP (Table A.4). Productivity losses from all sources are only 0.05% of GDP in high income countries and 0.15% of income in Upper Middle income countries. Ambient air pollution is the largest source of productivity losses in both High and Upper Middle countries, although household air pollution is also important in Upper Middle income countries.

Using a lower discount rate significantly increases the magnitude of productivity losses. We use as a sensitivity analysis a pure rate of time preference of 1.5%. This implies that, if per capita GDP were growing at 2%—the current rate in many high income countries—the discount rate applied to future earnings would be 3.5%. This, coincidentally, is the forward rate used by the UK government in evaluating project over the next 30 years.109 This discount rate raises productivity damages for all pollution

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categories. Total productivity losses worldwide rise from $93 billion to $114 billion. The percent of GDP lost due to premature deaths increases to 1.90 % in Low and 0.82% in Low Middle income countries.

Results by WHO Region

Tables A.5 and A.6 summarize productivity losses by WHO region. Table A.5 shows the percent of productivity losses by pollutant accounted for by each of the six regions. Table A.6 presents productivity losses as a percent of GDP for each region. Europe, the Western Pacific region and the Americas account for 74% of productivity losses due to AAP. The Western Pacific region has second largest number of deaths due to AAP (after South-East Asia), so it is not surprising that it accounts for a large share (28%) of productivity losses. Europe and the Americas combined have significantly fewer deaths than South Asia, but much higher per capita GDP, which accounts for the higher productivity losses in these regions. The majority of productivity losses due to HAP occur in the Western Pacific (34%) and Southeast Asia (31%) regions, followed by Africa (22%), mirroring the pattern of deaths by WHO region, but reflecting the higher average per capita GDP in the Western Pacific. Productivity losses in Africa and Southeast Asia account for the highest percent of productivity losses worldwide from lack of access to safe water and sanitation, while Europe and the Americas account for over half of the productivity losses from premature mortality due to lead exposure.

Losses as a percent of GDP (Table A.6) are highest in Africa (0.92% of GDP), reflecting the large burden imposed by lack of access to safe water and sanitation, but also a considerable burden from household air pollution. Interestingly, productivity losses due to AAP, as a percent of GDP, are higher in Africa than in Europe and the Americas. Productivity losses due to pollution are 0.55% of GDP in Southeast Asia. This reflects a multiplicity of pollution problems—household air pollution, ambient air pollution but also lack of access to safe water and sanitation.

Uncertainties in the Estimates

Our productivity estimates depend on a number of assumptions, which, if changed, would alter the estimates. For simplicity, we assume that labor’s share of GDP in each country remains constant over time at its current value. We also assume that the country-specific life tables remain constant over the period of the analysis, which for children is up to 60 years. This will understate losses in low and low-middle income countries, where survival probabilities are likely to increase in the future.

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Our assumption that working age ends at 64 is also likely to understate estimates of productivity losses. In equation (A.2), raising the working age will raise the ratio of the working age population to total population, which will lower I’(0). At the same time, the number of years of working life over which productivity losses are computed will increase for all persons who die prematurely from pollution-related illnesses, and the number of these persons will also increase. The latter two effects are likely to outweigh the first. We therefore view our estimates of productivity losses as a conservative one.

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2B. Willingness to Pay to Avoid Premature Mortality

A. Methodology

The willingness to pay (WTP) approach captures individuals’ preferences for avoiding increases in risk of death by analyzing their behavior in risky situations (the revealed preference approach) or in hypothetical choice situations involving changes in their risk of death (the stated preference approach).110 An example of the former approach is a labor market study of jobs with different mortality risks, in which the analyst uses knowledge of those different risks and the wages that different jobs command to derive a wage premium (the willingness to be paid) for bearing extra risk.111 A good example of the latter is a survey that asks participants to choose among several hypothetical situations where mortality risks can be reduced at a cost.112

Either of these approaches yield values consistent with the centrality of individual preferences in modern welfare economics, in contrast to the productivity approach discussed above, which is regarded as embedded in WTP values. The amount that a person would pay (or accept) in exchange for a small change in risk of death should reflect losses in output when the individual dies—losses that may exceed the person’s contribution to GDP. WTP should also reflect the utility received from living, and should therefore exceed the value of productivity losses.2

The value of mortality risk reductions is typically expressed in terms of the Value per Statistical Life (VSL)—the sum of what people would pay for small risk reductions that sum to one statistical life saved. To illustrate, if each of 10,000 people were willing to pay $100 over the coming year to reduce their risk of dying by 1 in 10,000 during this period, on average, one statistical life would be saved and the VSL would equal $100x10,000 or $1,000,000. To evaluate WTP to reduce risk of death by 1 in 10,000, one would multiply the VSL by .0001.

There is a large literature using revealed and stated preference approaches to estimating the VSL, primarily in OECD countries but also in middle income countries.112,113 Because many countries have no studies representing preferences of their population towards reducing risk of death, analysts typically transfer estimates from one country (a base country) to other countries, adjusting for differences in per capita income.113 This adjustment is made using equation (B.1), where Y denotes per capita income and ε denotes the elasticity of the VSL with respect to income.

(B.1) VSLTransfer = VSLBase*(YTransfer/YBase)ε

2WTPmaynotreflectcoststothirdpartiesassociatedwithanindividual’sdeath.

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We select a base VSL based on a meta-analysis of stated-preference studies reported in OECD.114 This meta-analysis forms the basis of the VSL used by the OECD for policy analysis and is also the basis of VSL transfers by the International Monetary Fund in their computation of health-based fuel taxes.115 It is the same baseline value used in the IHME-World Bank study The Cost of Air Pollution.116 The base VSL is $3.83 million international dollars. In transferring the VSL to other countries, per capita income is usually measured in international (i.e., in purchasing power parity or PPP) dollars. We measure per capita incomes using 2015 Gross National Income (GNI) in PPP dollars from the World Development Indicators. This implies that the VSL is also measured in PPP terms. Because the costs of pollution control projects are in general measured using market exchange rates, we convert the VSL to US dollars, using 2015 market exchange rates.

The elasticity of the VSL with respect to income (ε) represents the percentage change in the VSL for a one percent change in income (Y). If the VSL were proportional to income (i.e., if ε = 1) then the ratio of the VSL to income (Y) would be the same in all countries. Using the OECD VSL as a base value implies a ratio of VSL/Y of ~ 96:1. Studies in Low Middle income countries, however, suggest that the ratio of the VSL/Y falls as per capita income falls,113 implying a value of ε > 1. We therefore use a value of ε > 1 when transferring the base VSL to Low and Low Middle income countries. Studies in the United States, however, suggest a ratio of the VSL/Y that is greater than our base value, implying a value of ε < 1 for high income countries. We therefore use an ε < 1 when transferring the OECD value to countries with PPP incomes above the OECD average ($40,002 PPP).

Treatment of Age

We use the same VSL irrespective of age at death, and use the same VSL for children as for adults. The age distribution of deaths associated with pollution varies widely, raising the question of whether the same VSL should be used to evaluate the deaths of children and the elderly, who lose very different numbers of life years. There is limited and contradictory evidence that VSLs are lower for elderly people than for younger adults.117 In the case of children, the VSL should be based on parents’ WTP to reduce risks of death to their children. There is a growing literature on parents’ WTP however, it consists primarily of studies in high income countries.118 Because of the lack of studies in low and middle income countries and differences in child mortality between high and low income countries we do not attempt to transfer studies of parents’ WTP to reduce child mortality to low and middle income countries.

B. Results

Table B.1 shows country-specific VSLs obtained by transferring the OECD base VSL of $3.83 million ($2015) to countries based on the ratio of their PPP GNI to the

27

OECD GNI ($40,002). Transfers assume an elasticity of the VSL with respect to income of 0.8 for high income countries, 1.0 for high middle income countries and 1.2 for low middle and low income countries, as defined by the World Bank. These elasticities imply median ratios of VSL/Y of 97, 96, 64 and 50 for High, Upper Middle, Low Middle and Low income countries. Values are listed in both PPP and MER terms.

As a check on our results we compare the transferred VSL for China and India with the results of original VSL studies conducted in these countries. A recent study by Krupnick, Hoffman and Qin119 yields an estimate of the VSL for China in 2010 PPP terms of $1 million dollars, a value quite close to the transferred VSL reported in Table B.1. Estimates of the VSL for India vary by an order of magnitude—from $150,000120 to $1.5 million121 in PPP terms—but bracket the amount reported in Table B.1.

To generate values that could be used for policy evaluation we use the VSL to value the average risk of death associated with each pollutant in each country. The average risk of death from, e.g., AAP, is the estimated number of deaths due to AAP, divided by the size of the exposed population. Multiplying this by the country’s VSL provides an estimate of WTP to reduce the average risk of death from AAP.3 Equivalently, it is per capita WTP to eliminate deaths due to AAP. We perform similar calculations for each pollutant. Results are summarized, by World Bank income group in PPP terms in Table B.2, and in MER terms in Table B.3.4 Which set of numbers is relevant depends on the context. Expressing damages in PPP terms is useful for making comparisons across countries; however, when evaluating pollution control policies, values calculated using market exchange rates are appropriate if costs are also calculated using market exchange rates.

Table B.2 indicates that in Low income countries per capita WTP to eliminate deaths due to AAP is $34 in PPP terms and $13 at market exchange rates (MER). The figure is even higher for deaths due to unsafe water and unsafe sanitation and highest for HAP. In Low Middle income countries per capita WTP is highest for AAP ($85 MER), followed by HAP ($66 MER) and unsafe water ($39 MER). In Upper Middle income countries WTP per capita is also highest for AAP ($523 MER) followed by HAP ($214 MER) and lead ($47). This ranking is consistent with the ranking of pollutants in terms of deaths. Table B.4 presents per capita WTP by WHO region.

Aggregate Damages

3ThisisequivalenttocalculatingtheproductoftheVSLtimesthenumberofAAPdeathsandthendividingbytheexposedpopulation;i.e.,itisequivalenttoexpressingVSLdamagesinpercapitaterms.4Theaverageforeachincomegroupandpollutantiscomputedas∑iVSLi(Deathsi)]/∑iExposedPopulationiforallcountriesiintheincomegroup.

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Table B.5 presents per capita WTP aggregated across the exposed population—i.e., deaths per pollutant multiplied by the VSL. When aggregated across the exposed populations of low and low middle income countries the VSL yields much higher benefits than indicated by the productivity losses discussed above. To illustrate, aggregate WTP to eliminate deaths due to either APP or HAP in low income countries is approximately 8 times as large as the productivity losses estimated in Table 1 of the main text. This reflects the fact that productivity losses ascribe no value to the deaths of persons over age 64, who constitute a high fraction of air pollution deaths. In the case of Low Middle income countries aggregate WTP to eliminate deaths due to AAP or HAP is approximately 20 times the value of productivity losses. Added across all countries and income groups the social value of eliminating all deaths due to the five pollution categories we consider is approximately $4.6 billion, or approximately 6.2% of 2015 global GDP.

Uncertainties in the Estimates

Extrapolating estimates of the VSL from high- to low- and middle-income countries involves many judgment calls, both in the choice of a base VSL value to be transferred and in the choice of income elasticities to be used in the transfer. Our choice of the base VSL matches the one used in the World Bank-IHME study The Cost of Pollution116 as do the income elasticities that we use for the transfer. Arguments could be made for choosing a higher baseline VSL122 and also higher income elasticities.123 Our goal, however, is to match the VSL/Y ratio implied by the joint choice of baseline VSL and income elasticities to VSL/Y estimates in the literature.113 A higher baseline VSL will, other things equal, require a higher income elasticity when transferring the VSL to lower income countries in order to achieve a given VSL/Y ratio. We view our VSL transfer as a conservative one, which yields median VSL/Y ratios of 64:1 for low-middle income countries and 50:1 for low-income countries.

Comparison with IHME-World Bank Estimates

Although our methodology for measuring welfare losses is identical to that used in The Cost of Pollution care must be taken in comparing results from the two studies. The Cost of Pollution evaluates health risks from the 2013 GBD, whereas we evaluate risks from the 2015 GBD. We translate all values into 2015 USD using market exchange rates, whereas values are reported in 2011 purchasing power parity (PPP) dollars in The Cost of Pollution. Our estimate of the welfare costs from ambient and household air pollution in 2013, using 2013 GBD health risks and purchasing power dollars is $5.16 billion compared to $5.07 billion in The Cost of Pollution.

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2C. Measurement of Health Expenditures Attributable to Pollution

Healthcare expenditures account for a sizeable fraction of world GDP. The fraction of GDP spent on healthcare in 2013 ranged from 12% in high income countries to 5% in Low and Low-Middle income countries (see Table C.1). The amount of this expenditure associated with pollution constitutes an additional economic burden which we attempt to quantify.

To estimate health care expenditures associated with pollution requires estimating expenditures associated with each disease affected by pollution and then attributing a portion of expenditure on the disease to pollution. Table C.2 shows the diseases underlying the IHME estimates of deaths due to pollution in 2015.124 For AAP and HAP estimates of deaths are based on integrated exposure response functions for ischemic heart disease (IHD) and stroke, lower respiratory infections, chronic obstructive pulmonary disease (COPD) and cancers of the lung, bronchus and trachea. Cardiovascular disease (IHD and ischemic and hemorrhagic stroke) accounts for 53% of AAP deaths and 46% of HAP deaths worldwide. Lower respiratory infections and COPD account for 49% of HAP deaths and 40% of AAP deaths. Cardiovascular disease is responsible for the majority of deaths associated with lead exposure. Deaths due to lack of safe water and sanitation are due primarily to diarrhea (88-89%), with the remainder attributed to typhoid and paratyphoid fever.

Table C.3 shows the percent of DALYs attributable to AAP and HAP combined, by disease and WB income group. AAP and HAP together are estimated to cause about 40% of DALYs due to IHD and stroke in Low income and about 30% of DALYs due to IHD and stroke in Low-Middle income countries. AAP and HAP together account for over 50% of DALYs due to COPD in Low and Low Middle income countries. The percent of IHD, stroke and COPD deaths attributed to air pollution is somewhat lower in Upper Middle income countries, but is still substantial.

Table C.4 shows the percent of DALYs attributable to unsafe water and unsafe sanitation combined. The IMHE attributes virtually all DALYs associated with diarrhea to unsafe water and sanitation in low and middle income countries: 97% in Low, 96% in Low Middle and 92% in Upper Middle income countries. This is likewise true for typhoid and paratyphoid fever.

In the case of lead exposure (Table C.5) most DALYs are associated with cardiovascular disease in adults. Lead exposure is, however, also associated with chronic kidney disease and accounts for 19% of the DALYs associated with idiopathic intellectual disability in Low income countries. Table C.6 summarizes the percent of DALYs attributable to each risk factor, by World Bank income group.

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The fraction of DALYs, by disease, attributable to each pollutant is similar to the percent of deaths attributable to each pollutant. This is due to the fact that the DALYs attributed to each pollutant are primarily life years lost (YLLs). Worldwide, 97% of the DALYs associated with AAP are YLLs. The figure is slightly lower for HAP (93%), which includes YLDs due to cataracts. Approximately 92% of the DALYs associated with unsafe water, unsafe sanitation and lead exposure are YLLs. Thus, while a significant fraction of DALYs by disease are attributable to pollution, it is not clear that this attributable fraction will equal the fraction of disease expenditures attributable to pollution.

To investigate the relationships between DALYs by disease as estimated by the IHME125 and health expenditures, we examine health expenditures by disease category for countries for which these data are available.5 In the United States the Bureau of Economic Analysis has provided this information since 2010.126 Other OECD countries127 also provide such information. Table C.7 summarizes expenditures on cancer, cardiovascular diseases and respiratory diseases, as a percent of total health expenditures, for seven OECD countries. Taking an arithmetic average across countries, cancer accounts for approximately 7% of total health expenditures, cardiovascular disease for approximately 12% and respiratory illness approximately 6% of total health expenditures.6

Table C.7 also compares the percent of a country’s DALYs associated with each disease category as estimated by IHME with the percent of heath care expenditure on that disease category. As Table C.7 indicates, the fraction of DALYs attributable to each disease category in general differs from the fraction of expenditures attributable to the disease. In general, DALYs lost due to cancer exceed the percent of health care expenditures allocated to cancer. The same is true for cardiovascular disease (CVD). For respiratory illness, where DALYs are more evenly divided between YLLs and YLDs, the fraction of DALYs attributable to respiratory illnesses is approximately equal to the fraction of health care expenditures associated with these illnesses.

Table C.7 can be used to calculate an average cost per DALY for cancer, CVD and respiratory illness based on the seven OECD countries in the table.7 If these weights are applied to the DALYs attributable to the specific diseases in Table C.3 for high income countries, the total expenditure associated with air pollution in high income countries is approximately $103 billion, or 1.7% of total health care expenditures. This is

5Thisanalysisisconductedusinghealthexpendituredatafor2013,as2015datawerenotavailableatthetimeoftheanalysis.Wethereforecomparehealthexpendituresin2013withDALYsbycauseforthesameyear(2013).6Asnotedinthetable,thesumofhealthexpendituresallocatedtovariousdiseasesisonaverage79%oftotalhealthexpenditures.Respiratorydiseaseincludesbothchronicrespiratorydiseaseandlowerrespiratoryinfection.7ThecostperDALYforcancer=(Totalhealthexpenditure/TotalDALYs)*(0.07/0.18).Forhighincomecountries,this=($5,932billion/365.0millionDALYs)*0.39.

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clearly an approximation—the cancer weight based on Table C.7 may not apply to all cancers, and likewise for individual cardiovascular diseases; however, it provides an order of magnitude estimate of medical expenditures associated with air pollution in High income countries.

To estimate the healthcare expenditures associated with pollution in Low and Middle income countries requires estimates of expenditures by disease in those countries. Sri Lanka is the only such country for which we were able to find medical expenditures by disease category. As Table C.7 indicates, the percent of healthcare expenditure on cancer and CVD in Sri Lanka is much lower than in the OECD countries in the table, while the percent spent on respiratory illness is much higher. The associated weights attached to cancer, CVD and respiratory illness thus differ from those based on the OECD country averages. Calculating healthcare expenditures for lack of access to safe water and sanitation would require similar expenditure data on diarrheal disease and typhoid fever. We do not attempt to quantify the fraction of health care expenditures due to pollution in Low and Middle income countries due to lack of data on health care expenditures by disease in these countries.

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Table A.1 – Productivity Losses by Pollutant and World Bank Income Group (billions 2015 USD)

World Bank Income Region

Ambient Air

Pollution (AAP)

Household Air

Pollution (HAP)

Unsafe Water

Source Unsafe

Sanitation Lead

Exposure AAP and

HAP Combined

Unsafe Water and Sanitation Combined1

High income 20.059 0.188 0.063 0.006 1.245 20.125 1.265 Upper middle income 19.393 8.390 2.014 0.881 1.077 25.396 3.816

Lower middle income 11.216 10.145 12.457 7.651 0.680 18.144 15.952

Low income 0.945 1.820 1.955 1.459 0.043 2.304 2.613

Total 51.613 20.545 16.489 9.996 3.045 65.970 23.646 1Includes no handwashing with soap.

Table A.2 – Pollution Deaths by Pollutant and Age at Death

Risk factor 0-14 15-64 65+ Total

Ambient Air Pollution (AAP) 222,000 1,391,000 2,882,000 4,495,000

Household Air Pollution (HAP) 319,000 942,000 1,593,000 2,854,000

AAP and HAP Combined 449,000 2,044,000 3,993,000 6,485,000 Unsafe Water Source 565,000 326,000 360,000 1,251,000

Unsafe Sanitation 370,000 209,000 229,000 808,000

Unsafe Water and Sanitation Combined1 749,000 442,000 576,000 1,767,000 Lead Exposure 0 123,000 372,000 495,000

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1Includes no handwashing with soap. Table A.3 – Percent of Productivity Losses by Pollutant and World Bank Income Group

World Bank Income Region Ambient Air

Pollution (AAP)

Household Air Pollution

(HAP) Unsafe Water

Source Unsafe

Sanitation Lead

Exposure

High income 38.9% 0.917% 0.383% 0.0587% 40.9%

Upper middle income 37.6% 40.8% 12.2% 8.81% 35.4%

Lower middle income 21.7% 49.4% 75.5% 76.5% 22.3%

Low income 1.83% 8.86% 11.9% 14.6% 1.42%

Total 100.0% 100.0% 100.0% 100.0% 100.0%

Table A.4 – Productivity Losses as a Percent of GDP by Pollutant and World Bank Income Group


Ambient Air

Pollution (AAP)

Household Air

Pollution (HAP)

Unsafe Water

Source Unsafe

Sanitation Lead

Exposure AAP and

HAP Combined


High income 0.044% 0.0068% 0.0023% 0.00021% 0.0027% 0.044% 0.0028% Upper middle income 0.098% 0.042% 0.0101% 0.0044% 0.0054% 0.13% 0.019%

Lower middle income 0.197% 0.18% 0.22% 0.13% 0.012% 0.32% 0.28%

Low income 0.25% 0.49% 0.52% 0.39% 0.012% 0.62% 0.699%

Total 0.072% 0.072% 0.057% 0.035% 0.0042% 0.092% 0.033% 1Includes no handwashing with soap.

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Table A.5 – Percent of Productivity Losses by Pollutant and WHO Region

WHO Region Ambient Air

Pollution (AAP)


(HAP)

Unsafe Water

Source Unsafe

Sanitation Lead

Exposure

African Region 5.36% 22.03% 41.7% 46.96% 2.19%

Eastern Mediterranean Region 6.38% 4.996% 8.81% 6.16% 9.46%

European Region 22.8% 4.14% 0.639% 0.444% 24.5%

Region of the Americas 22.9% 4.27% 3.77% 2.19% 29.7%

South-East Asia Region 14.6% 30.99% 40.8% 41.04% 14.02%

Western Pacific Region 27.9% 33.6% 4.34% 3.198% 20.1%

Total 100.00% 100.00% 100.00% 100.00% 100.00% Table A.6 – Productivity Losses as a Percent of GDP by Pollutant and WHO Region

WHO Region Ambient

Air Pollution

(AAP)

Household Air

Pollution (HAP)

Unsafe Water

Source

Unsafe Sanitatio

n Lead

Exposure

AAP and HAP

Combined

Unsafe Water and

Sanitation

Combined1

African Region 0.17% 0.28% 0.42% 0.29% 0.0041% 0.38% 0.54%

Eastern Mediterranean 0.11% 0.035% 0.049% 0.021% 0.0098% 0.13% 0.066%

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Region

European Region 0.058% 0.022% 0.0027% 0.0011% 0.0037% 0.061% 0.0043%

Region of the Americas 0.049% 0.018% 0.013% 0.0046% 0.0037% 0.051% 0.0075%

South-East Asia Region 0.203% 0.17% 0.18% 0.11% 0.012% 0.31% 0.23%

Western Pacific Region 0.076% 0.059% 0.0061% 0.0027% 0.0032% 0.101% 0.0079%

Total 0.072% 0.072% 0.057% 0.035% 0.0042% 0.092% 0.033% 1Includes no handwashing with soap. Table B.1 – VSL Values Transferred from OECD to Low and Middle Income Countries

World Bank Income Region VSL (PPP) VSL (MER)

Upper middle income (ε=1) 1,351,932 628,093

Lower middle income (ε=1.2) 342,554 145,345

Low income (ε=1.2) 80,473 31,686

India (ε=1.2) 394,657 104,237

China (ε=1) 1,355,761 748,733 Table B.2 – VSL Damages Per Capita by Pollutant and World Bank Income Group (PPP)


Pollution (AAP)


(HAP) Unsafe Water

Source Unsafe

Sanitation Lead

Exposure

High income 1,615 178 21 2 283

36

Upper middle income 996 394 25 10 94

Lower middle income 285 218 122 74 32

Low income 34 59 38 29 3

Average 732 274 71 41 92 Table B.3 – VSL Damages Per Capita by Pollutant and World Bank Income Group (MER)


Pollution (AAP)


(HAP) Unsafe Water

Source Unsafe

Sanitation Lead

Exposure

High income 1,472 98 11 1 264

Upper middle income 523 214 13 5 47


Low income 13 23 15 11 1

Average 459 123 25 14 64 Table B.4 – VSL Damages Per Capita by Pollutant and WHO Region (MER)

WHO Region Ambient Air

Pollution (AAP)


(HAP) Unsafe Water

Source Unsafe

Sanitation Lead

Exposure

African Region 47 52 51 33 4 Eastern Mediterranean Region 168 26 18 7 31

37

European Region 1,168 102 4 1 212

Region of the Americas 715 69 21 6 142

South-East Asia Region 96 74 38 23 9

Western Pacific Region 659 281 5 2 48

Average 459 123 25 14 64 Table B.5 – VSL Damages by Pollutant and World Bank Income Group (MER, billions)


Ambient Air

Pollution (AAP)

Household Air

Pollution (HAP)

Lead Exposure

AAP and HAP

Combined


High income 1,693 15 303 1,691 159 Upper middle income 1,309 535 118 1,691 89


Low income 7.6 14 0.7 18 12

Total 3,249 749 451 3,767 404 1Includes no handwashing with soap.

38

Table C.1 – 2013 Total Health Expenditures (THE) by World Bank Income Group


Total, billions USD

As a Percent of the World THE

As a Percent of GDP

High income 5,932 81% 12% Upper middle income 1,131 15% 6%

Lower middle income 0.238 3% 5%

Low income 0.029 0.4% 5%

Total 7,330 100% 10% Table C.2 – Percent of Deaths Attributable to Pollution Globally, by Disease

Ambient

Air Pollution

(AAP)

Household Air

Pollution (HAP)

Unsafe Water

Source Unsafe

Sanitation Lead

Exposure

Lower respiratory infections 15% 26%

Tracheal, bronchus, and lung cancer 6% 5%

Ischemic heart disease 34% 27% 49%

Ischemic stroke 8% 7% 14%

Hemorrhagic stroke 11% 12% 18%

Chronic obstructive pulmonary disease 25% 23%

39

Diarrheal diseases 88% 89%

Typhoid fever 10% 9%

Paratyphoid fever 2% 2%

Hypertensive heart disease 10% Other cardiovascular and circulatory diseases 3%

Chronic kidney disease 6%

Total1 100% 100% 100% 100% 100% 1The parts may not add up to the total due to rounding. Table C.3 – DALYs Attributable to Air Pollution (AAP and HAP), by Disease and World Bank Income Group

High income Upper middle income

Lower middle income

Low income Global

Lower respiratory infections 12% 34% 57% 64% 53%

Tracheal, bronchus, and lung cancer 8% 29% 38% 48% 24%

Ischemic heart disease 13% 24% 35% 43% 28%

Ischemic stroke 9% 20% 28% 36% 22%

Hemorrhagic stroke 11% 24% 31% 37% 27% Chronic obstructive pulmonary disease 16% 41% 52% 51% 44%

Cataract 0.019% 14% 25% 35% 19%

40

Table C.4 – DALYs Attributable to Unsafe Water and Sanitation, by Disease and World Bank Income Group


Lower middle income

Low income Global

Diarrheal diseases 34% 92% 96% 97% 96%

Typhoid fever 83% 90% 95% 97% 95%

Paratyphoid fever 75% 90% 95% 97% 95% Table C.5 – DALYs Attributable to Lead Exposure, by Disease and World Bank Income Group


Lower middle income

Low income Global

Ischemic heart disease 2% 2% 3% 3% 3%

Ischemic stroke 2% 2% 3% 3% 2%

Hemorrhagic stroke 2% 2% 3% 3% 3%

Hypertensive heart disease 4% 4% 5% 5% 4%

Rheumatic heart disease 1% 1% 1% 1% 1%

Cardiomyopathy and myocarditis 1% 1% 1% 1% 1%

Atrial fibrillation and flutter 2% 1% 2% 2% 2%

Aortic aneurysm 1% 1% 2% 2% 1%

Peripheral vascular disease 1% 1% 2% 1% 1%

41

Endocarditis 1% 1% 1% 1% 1% Other cardiovascular and circulatory diseases 1% 1% 2% 2% 1%

Chronic kidney disease 2% 1% 2% 1% 2%

Idiopathic intellectual disability 4% 9% 15% 19% 12% Table C.6 – Percent of DALYs Attributable to Each Risk Factor, by World Bank Income Group


Ambient Air

Pollution (AAP)

Household Air

Pollution (HAP)

Unsafe Water

Source Unsafe

Sanitation Lead

Exposure AAP and

HAP Combined


High income 2.3% 0.44% 0.12% 0.013% 0.32% 2.3% 0.18%

Upper middle income 4.8% 2.1% 0.44% 0.19% 0.39% 6.3% 0.78%

Lower middle income 5.0% 4.5% 4.4% 2.7% 0.43% 8.0% 5.6%

Low income 3.2% 6% 5.8% 4.4% 0.22% 7.7% 7.8%

Total 4.3% 3.5% 2.9% 1.9% 0.38% 6.8% 3.9% 1Includes no handwashing with soap.

42

Table C.7 – Percent of Total Health Expenditures (THE) and DALYs by Disease Category, 2013

% of THE

due to cancer

% of DALYs due to cancer

% of THE due to CVD

% of DALYs due to CVD

% of THE due to Resp

diseases

% of DALYs due to Resp

diseases

Disease expenditure

/ THE

France 5% 20% 9% 13% 6% 5% 82%

Germany 6% 17% 12% 18% 4% 5% 79%

Hungary 7% 20% 16% 24% 5% 5% 88%

Korea 10% 18% 11% 12% 10% 5% 85%

Netherlands 4% 21% 7% 14% 3% 7% 62%

Slovenia 8% 18% 14% 17% 6% 5% 85%

United States 7% 15% 13% 16% 8% 7% 72% Average (w/o Sri Lanka) 7% 18% 12% 16% 6% 6% 79%

Sri Lanka 3% 1% 7% 19% 14% 10% 83%

Productivity Losses as a Percentage of GDP by Pollutant

3%DiscountRate 1.5%DiscountRate

Country

WorldBank

IncomeGroup

AAPandHAP

Combined

UWandUS

Combined

LeadExposure Total

AAPandHAP

Combined

UWandUS

Combined

LeadExposure Total

AntiguaandBarbuda H 0.0461% 0.0181% 0.0033% 0.0674%

0.0531% 0.0256% 0.0035% 0.0822%

Australia H 0.0135% 0.0011% 0.0031% 0.0176%

0.0149% 0.0014% 0.0033% 0.0196%

Austria H 0.0419% 0.0011% 0.0013% 0.0442%

0.0457% 0.0014% 0.0014% 0.0485%

Bahrain H 0.0503% 0.0062% 0.0003% 0.0568%

0.0582% 0.0085% 0.0003% 0.0670%

Barbados H 0.0795% 0.0205% 0.0048% 0.1048%

0.0901% 0.0272% 0.0052% 0.1224%

Belgium H 0.0517% 0.0027% 0.0048% 0.0591%

0.0563% 0.0032% 0.0051% 0.0646%

Brunei H 0.0155% 0.0028% 0.0003% 0.0186%

0.0176% 0.0038% 0.0004% 0.0218%

Canada H 0.0230% 0.0016% 0.0010% 0.0256%

0.0251% 0.0019% 0.0011% 0.0281%

Chile H 0.0356% 0.0031% 0.0007% 0.0394%

0.0399% 0.0040% 0.0008% 0.0447%

Croatia H 0.1096% 0.0031% 0.0059% 0.1186%

0.1193% 0.0037% 0.0063% 0.1294%

Cyprus H 0.0455% 0.0016% 0.0034% 0.0505%

0.0503% 0.0021% 0.0036% 0.0560%

CzechRepublic H 0.0707% 0.0046% 0.0016% 0.0769%

0.0769% 0.0054% 0.0017% 0.0840%

Denmark H 0.0372% 0.0021% 0.0016% 0.0410%

0.0405% 0.0026% 0.0017% 0.0448%

Estonia H 0.0635% 0.0055% 0.0070% 0.0759%

0.0692% 0.0064% 0.0074% 0.0830%

Finland H 0.0275% 0.0009% 0.0008% 0.0292%

0.0298% 0.0010% 0.0009% 0.0317%

France H 0.0368% 0.0024% 0.0024% 0.0416%

0.0403% 0.0028% 0.0025% 0.0457%

Germany H 0.0484% 0.0021% 0.0018% 0.0523%

0.0528% 0.0024% 0.0019% 0.0571%

Greece H 0.0615% 0.0012% 0.0045% 0.0672%

0.0682% 0.0014% 0.0049% 0.0745%

Hungary H 0.1450% 0.0038% 0.0075% 0.1563%

0.1580% 0.0047% 0.0081% 0.1707%

Iceland H 0.0247% 0.0024% 0.0019% 0.0290%

0.0272% 0.0029% 0.0020% 0.0322%

Ireland H 0.0278% 0.0018% 0.0021% 0.0317%

0.0307% 0.0022% 0.0023% 0.0351%

Israel H 0.0284% 0.0020% 0.0008% 0.0311%

0.0316% 0.0025% 0.0008% 0.0349%

Italy H 0.0392% 0.0010% 0.0028% 0.0430%

0.0431% 0.0012% 0.0030% 0.0473%

Japan H 0.0296% 0.0020% 0.0007% 0.0322%

0.0328% 0.0023% 0.0007% 0.0358%

Kuwait H 0.0417% 0.0033% 0.0001% 0.0451%

0.0486% 0.0046% 0.0001% 0.0533%

Latvia H 0.1600% 0.0090% 0.0124% 0.1813%

0.1751% 0.0105% 0.0133% 0.1988%

Lithuania H 0.1394% 0.0080% 0.0102% 0.1576%

0.1533% 0.0094% 0.0110% 0.1737%

Luxembourg H 0.0273% 0.0014% 0.0008% 0.0295%

0.0300% 0.0018% 0.0009% 0.0327%

Netherlands H 0.0437% 0.0020% 0.0017% 0.0474%

0.0477% 0.0024% 0.0018% 0.0519%

NewZealand H 0.0135% 0.0010% 0.0025% 0.0170%

0.0149% 0.0013% 0.0026% 0.0189%

Norway H 0.0197% 0.0010% 0.0010% 0.0218%

0.0215% 0.0013% 0.0011% 0.0239%

Oman H 0.0426% 0.0076% 0.0013% 0.0514%

0.0496% 0.0099% 0.0014% 0.0608%

Poland H 0.1172% 0.0053% 0.0078% 0.1303%

0.1275% 0.0062% 0.0083% 0.1420%

Portugal H 0.0385% 0.0050% 0.0077% 0.0511%

0.0426% 0.0058% 0.0084% 0.0568%

Qatar H 0.0175% 0.0016% 0.0000% 0.0192%

0.0202% 0.0022% 0.0000% 0.0223%

SaudiArabia H 0.0604% 0.0091% 0.0024% 0.0718%

0.0690% 0.0124% 0.0026% 0.0840%

44

Seychelles H 0.0694% 0.0210% 0.0064% 0.0968%

0.0802% 0.0272% 0.0069% 0.1143%

Singapore H 0.0268% 0.0026% 0.0018% 0.0312%

0.0296% 0.0030% 0.0020% 0.0346%

Slovakia H 0.0866% 0.0066% 0.0047% 0.0979%

0.0950% 0.0081% 0.0050% 0.1081%

Slovenia H 0.0605% 0.0025% 0.0017% 0.0647%

0.0657% 0.0030% 0.0018% 0.0705%

SouthKorea H 0.0382% 0.0013% 0.0016% 0.0411%

0.0424% 0.0015% 0.0017% 0.0456%

Spain H 0.0336% 0.0021% 0.0034% 0.0390%

0.0372% 0.0024% 0.0037% 0.0433%

Sweden H 0.0145% 0.0012% 0.0007% 0.0165%

0.0158% 0.0015% 0.0008% 0.0182%

Switzerland H 0.0300% 0.0014% 0.0012% 0.0327%

0.0329% 0.0018% 0.0013% 0.0361%

TheBahamas H 0.0504% 0.0128% 0.0039% 0.0672%

0.0577% 0.0171% 0.0042% 0.0791%

TrinidadandTobago H 0.0498% 0.0114% 0.0011% 0.0623%

0.0560% 0.0157% 0.0012% 0.0729%

UnitedArabEmirates H 0.1440% 0.0055% 0.0004% 0.1499%

0.1648% 0.0069% 0.0004% 0.1721%

UnitedKingdom H 0.0424% 0.0027% 0.0022% 0.0473%

0.0467% 0.0033% 0.0024% 0.0523%

UnitedStates H 0.0500% 0.0035% 0.0038% 0.0573%

0.0548% 0.0041% 0.0040% 0.0629%

Uruguay H 0.0391% 0.0076% 0.0055% 0.0523%

0.0436% 0.0102% 0.0060% 0.0598%

Albania UM 0.0966% 0.0067% 0.0115% 0.1148%

0.1116% 0.0100% 0.0125% 0.1341%

Algeria UM 0.0392% 0.0181% 0.0038% 0.0611%

0.0462% 0.0264% 0.0042% 0.0768%

Angola UM 0.6215% 0.5794% 0.0077% 1.2086%

0.8641% 0.8500% 0.0085% 1.7226%

Argentina UM 0.0486% 0.0099% 0.0098% 0.0684%

0.0549% 0.0132% 0.0106% 0.0787%

Azerbaijan UM 0.0935% 0.0173% 0.0064% 0.1172%

0.1151% 0.0268% 0.0069% 0.1488%

Belarus UM 0.2372% 0.0076% 0.0249% 0.2697%

0.2594% 0.0089% 0.0267% 0.2950%

Belize UM 0.0996% 0.0385% 0.0036% 0.1418%

0.1205% 0.0549% 0.0040% 0.1794%

BosniaandHerzegovina UM 0.2321% 0.0036% 0.0286% 0.2643%

0.2561% 0.0047% 0.0310% 0.2919%

Botswana UM 0.1282% 0.1209% 0.0024% 0.2515%

0.1511% 0.1584% 0.0026% 0.3122%

Brazil UM 0.0855% 0.0289% 0.0047% 0.1191%

0.0984% 0.0403% 0.0051% 0.1438%

Bulgaria UM 0.1674% 0.0072% 0.0114% 0.1860%

0.1850% 0.0095% 0.0122% 0.2066%

China UM 0.1438% 0.0073% 0.0042% 0.1553%

0.1632% 0.0104% 0.0045% 0.1782%

Colombia UM 0.0522% 0.0166% 0.0022% 0.0711%

0.0621% 0.0242% 0.0024% 0.0887%

CostaRica UM 0.0452% 0.0115% 0.0039% 0.0606%

0.0515% 0.0162% 0.0042% 0.0719%

DominicanRepublic UM 0.0699% 0.0335% 0.0070% 0.1104%

0.0842% 0.0506% 0.0076% 0.1424%

Ecuador UM 0.0261% 0.0187% 0.0039% 0.0487%

0.0325% 0.0276% 0.0042% 0.0643%

EquatorialGuinea UM 0.5050% 0.2334% 0.0088% 0.7472%

0.6706% 0.3327% 0.0095% 1.0129%

Fiji UM 0.1823% 0.1039% 0.0110% 0.2972%

0.2152% 0.1455% 0.0121% 0.3729%

Gabon UM 0.1014% 0.0868% 0.0051% 0.1933%

0.1293% 0.1190% 0.0057% 0.2539%

Georgia UM 0.2059% 0.0093% 0.0189% 0.2341%

0.2315% 0.0132% 0.0205% 0.2652%

Grenada UM 0.0811% 0.0299% 0.0083% 0.1193%

0.0955% 0.0408% 0.0090% 0.1453%

Guyana UM 0.1275% 0.0687% 0.0176% 0.2138%

0.1458% 0.0984% 0.0190% 0.2632%

Iran UM 0.0627% 0.0078% 0.0138% 0.0843%

0.0719% 0.0108% 0.0151% 0.0978%

Iraq UM 0.1373% 0.0561% 0.0166% 0.2099%

0.1662% 0.0839% 0.0183% 0.2684%

Jamaica UM 0.0791% 0.0213% 0.0076% 0.1080%

0.0920% 0.0305% 0.0083% 0.1307%

Jordan UM 0.0752% 0.0189% 0.0005% 0.0945%

0.0918% 0.0279% 0.0005% 0.1202%

Kazakhstan UM 0.1591% 0.0151% 0.0097% 0.1840%

0.1822% 0.0212% 0.0106% 0.2140%

Lebanon UM 0.0623% 0.0071% 0.0024% 0.0718%

0.0704% 0.0098% 0.0027% 0.0829%

Macedonia UM 0.1877% 0.0054% 0.0098% 0.2030%

0.2095% 0.0079% 0.0106% 0.2280%

Malaysia UM 0.0929% 0.0387% 0.0026% 0.1342%

0.1051% 0.0497% 0.0028% 0.1576%

45

Maldives UM 0.0382% 0.0165% 0.0011% 0.0558%

0.0453% 0.0237% 0.0012% 0.0702%

Mauritius UM 0.0699% 0.0121% 0.0067% 0.0887%

0.0784% 0.0161% 0.0072% 0.1017%

Mexico UM 0.0453% 0.0183% 0.0027% 0.0662%

0.0542% 0.0260% 0.0029% 0.0831%

Montenegro UM 0.1769% 0.0028% 0.0092% 0.1889%

0.1952% 0.0037% 0.0099% 0.2087%

Namibia UM 0.2725% 0.3605% 0.0016% 0.6346%

0.3468% 0.5186% 0.0018% 0.8673%

Panama UM 0.0411% 0.0374% 0.0030% 0.0814%

0.0504% 0.0551% 0.0032% 0.1087%

Paraguay UM 0.1326% 0.0417% 0.0056% 0.1799%

0.1577% 0.0611% 0.0060% 0.2249%

Peru UM 0.0595% 0.0177% 0.0025% 0.0797%

0.0753% 0.0252% 0.0027% 0.1033%

Romania UM 0.1611% 0.0127% 0.0129% 0.1867%

0.1807% 0.0168% 0.0139% 0.2114%

Russia UM 0.2178% 0.0196% 0.0125% 0.2500%

0.2409% 0.0237% 0.0135% 0.2781%

SaintLucia UM 0.0567% 0.0207% 0.0049% 0.0823%

0.0660% 0.0288% 0.0053% 0.1001%SaintVincentandtheGrenadines UM 0.0753% 0.0307% 0.0096% 0.1157%

0.0869% 0.0436% 0.0104% 0.1409%

Serbia UM 0.2326% 0.0056% 0.0115% 0.2497%

0.2561% 0.0073% 0.0124% 0.2757%

SouthAfrica UM 0.1676% 0.1767% 0.0059% 0.3503%

0.2004% 0.2377% 0.0064% 0.4445%

Suriname UM 0.0974% 0.0449% 0.0073% 0.1495%

0.1147% 0.0660% 0.0079% 0.1886%

Thailand UM 0.1047% 0.0292% 0.0035% 0.1374%

0.1176% 0.0361% 0.0038% 0.1574%

Turkey UM 0.0587% 0.0097% 0.0050% 0.0734%

0.0675% 0.0139% 0.0055% 0.0868%

Turkmenistan UM 0.2176% 0.0633% 0.0159% 0.2968%

0.2755% 0.0973% 0.0175% 0.3904%

Armenia LM 0.1649% 0.0154% 0.0203% 0.2006%

0.1879% 0.0225% 0.0219% 0.2323%

Bangladesh LM 0.3930% 0.1700% 0.0228% 0.5857%

0.4880% 0.2427% 0.0253% 0.7560%

Bhutan LM 0.3760% 0.3359% 0.0070% 0.7190%

0.4609% 0.4738% 0.0078% 0.9425%

Bolivia LM 0.1307% 0.0811% 0.0087% 0.2206%

0.1673% 0.1233% 0.0096% 0.3002%

Cambodia LM 0.2959% 0.1279% 0.0051% 0.4289%

0.3711% 0.1837% 0.0056% 0.5604%

Cameroon LM 0.6723% 0.6503% 0.0049% 1.3275%

0.9359% 0.9612% 0.0054% 1.9026%

CapeVerde LM 0.1572% 0.0943% 0.0041% 0.2556%

0.1953% 0.1326% 0.0045% 0.3325%

Congo LM 0.4097% 0.2686% 0.0131% 0.6913%

0.5340% 0.3760% 0.0145% 0.9245%

Coted'Ivoire LM 0.6540% 0.6677% 0.0055% 1.3273%

0.8948% 0.9770% 0.0061% 1.8779%

Djibouti LM 0.4751% 0.5932% 0.0588% 1.1270%

0.6284% 0.8310% 0.0657% 1.5251%

Egypt LM 0.1798% 0.0636% 0.0212% 0.2647%

0.2239% 0.0980% 0.0232% 0.3451%

ElSalvador LM 0.0943% 0.0301% 0.0068% 0.1312%

0.1131% 0.0411% 0.0075% 0.1617%

FederatedStatesofMicronesia LM 0.1512% 0.0496% 0.0039% 0.2048%

0.1751% 0.0667% 0.0043% 0.2461%

Ghana LM 0.3857% 0.2121% 0.0042% 0.6020%

0.4950% 0.3007% 0.0046% 0.8003%

Guatemala LM 0.2148% 0.1833% 0.0033% 0.4014%

0.2952% 0.2705% 0.0036% 0.5693%

Honduras LM 0.1958% 0.0987% 0.0120% 0.3066%

0.2367% 0.1418% 0.0132% 0.3917%

India LM 0.4078% 0.3245% 0.0116% 0.7439%

0.4992% 0.4525% 0.0127% 0.9644%

Indonesia LM 0.1789% 0.1374% 0.0134% 0.3298%

0.2106% 0.1909% 0.0147% 0.4163%

Kenya LM 0.4920% 0.9555% 0.0024% 1.4498%

0.6897% 1.3295% 0.0026% 2.0218%

Kiribati LM 0.1930% 0.1986% 0.0168% 0.4084%

0.2338% 0.2806% 0.0186% 0.5329%

Kyrgyzstan LM 0.2595% 0.0684% 0.0317% 0.3596%

0.3208% 0.1058% 0.0347% 0.4612%

Laos LM 0.4590% 0.2850% 0.0054% 0.7494%

0.6215% 0.4296% 0.0060% 1.0571%

Lesotho LM 0.5955% 0.8835% 0.0046% 1.4836%

0.7571% 1.2235% 0.0050% 1.9856%

Mauritania LM 0.3590% 0.4082% 0.0019% 0.7691%

0.4937% 0.6051% 0.0021% 1.1009%

Moldova LM 0.1769% 0.0190% 0.0344% 0.2303%

0.1967% 0.0241% 0.0370% 0.2579%

Mongolia LM 0.3699% 0.0262% 0.0309% 0.4270%

0.4494% 0.0393% 0.0338% 0.5225%

46

Morocco LM 0.0846% 0.0373% 0.0109% 0.1328%

0.0989% 0.0518% 0.0119% 0.1627%

Myanmar LM 0.3459% 0.1645% 0.0144% 0.5248%

0.4350% 0.2273% 0.0158% 0.6780%

Nicaragua LM 0.0797% 0.0298% 0.0019% 0.1114%

0.1018% 0.0449% 0.0021% 0.1488%

Nigeria LM 0.3947% 0.8593% 0.0013% 1.2553%

0.5582% 1.2998% 0.0014% 1.8595%

Pakistan LM 0.4295% 0.3780% 0.0203% 0.8278%

0.5486% 0.5627% 0.0223% 1.1336%

Palestine LM 0.1041% 0.0309% 0.0062% 0.1412%

0.1226% 0.0433% 0.0069% 0.1728%

PapuaNewGuinea LM 0.2630% 0.1137% 0.0022% 0.3789%

0.3264% 0.1564% 0.0024% 0.4852%

Philippines LM 0.2423% 0.0904% 0.0114% 0.3441%

0.2954% 0.1304% 0.0126% 0.4383%

Samoa LM 0.1069% 0.0356% 0.0023% 0.1448%

0.1243% 0.0481% 0.0025% 0.1749%

SaoTomeandPrincipe LM 0.6674% 0.5365% 0.0134% 1.2174%

0.8747% 0.7827% 0.0150% 1.6725%

SolomonIslands LM 0.4048% 0.1138% 0.0050% 0.5236%

0.4803% 0.1575% 0.0055% 0.6434%

SriLanka LM 0.2013% 0.0520% 0.0031% 0.2564%

0.2277% 0.0698% 0.0034% 0.3009%

Sudan LM 0.1680% 0.1252% 0.0189% 0.3121%

0.2191% 0.1954% 0.0210% 0.4355%

Swaziland LM 0.5682% 0.6737% 0.0021% 1.2440%

0.7166% 0.9509% 0.0023% 1.6698%

Tajikistan LM 0.2140% 0.1152% 0.0141% 0.3434%

0.2859% 0.1794% 0.0156% 0.4809%

Timor-Leste LM 0.2799% 0.2415% 0.0047% 0.5261%

0.3932% 0.3689% 0.0052% 0.7673%

Tonga LM 0.0964% 0.0438% 0.0025% 0.1427%

0.1151% 0.0606% 0.0027% 0.1785%

Tunisia LM 0.0816% 0.0164% 0.0069% 0.1049%

0.0933% 0.0223% 0.0075% 0.1232%

Ukraine LM 0.2462% 0.0129% 0.0073% 0.2664%

0.2704% 0.0156% 0.0079% 0.2939%

Uzbekistan LM 0.2258% 0.0374% 0.0246% 0.2878%

0.2891% 0.0563% 0.0269% 0.3723%

Vanuatu LM 0.3608% 0.1052% 0.0046% 0.4706%

0.4347% 0.1483% 0.0051% 0.5881%

Vietnam LM 0.1913% 0.0639% 0.0079% 0.2630%

0.2243% 0.0878% 0.0086% 0.3207%

Zambia LM 0.5163% 0.6457% 0.0095% 1.1714%

0.6916% 0.8903% 0.0105% 1.5923%

Afghanistan L 0.9970% 0.3756% 0.1283% 1.5009%

1.3086% 0.5755% 0.1432% 2.0274%

Benin L 0.8799% 0.7869% 0.0078% 1.6746%

1.2047% 1.1478% 0.0086% 2.3611%

BurkinaFaso L 0.9395% 1.2861% 0.0017% 2.2273%

1.3676% 1.9310% 0.0018% 3.3004%

Burundi L 0.8686% 1.1156% 0.0014% 1.9857%

1.2325% 1.6292% 0.0016% 2.8632%

CentralAfricanRepublic L 0.3478% 0.3122% 0.0090% 0.6690%

0.4695% 0.4366% 0.0099% 0.9160%

Chad L 1.7549% 3.0469% 0.0026% 4.8044%

2.5954% 4.6635% 0.0029% 7.2618%

Comoros L 0.3647% 0.4386% 0.0057% 0.8090%

0.4922% 0.6067% 0.0063% 1.1052%DemocraticRepublicoftheCongo L 0.6889% 0.5976% 0.0106% 1.2971%

0.9793% 0.8795% 0.0117% 1.8704%

Ethiopia L 0.4554% 0.5036% 0.0034% 0.9625%

0.6283% 0.7174% 0.0038% 1.3494%

Guinea L 0.6950% 0.5860% 0.0036% 1.2846%

0.9793% 0.8576% 0.0039% 1.8409%

Guinea-Bissau L 0.9024% 1.0836% 0.0059% 1.9920%

1.2384% 1.6216% 0.0065% 2.8665%

Haiti L 0.4017% 0.3246% 0.0226% 0.7490%

0.5208% 0.4991% 0.0249% 1.0448%

Liberia L 0.4267% 0.5991% 0.0081% 1.0339%

0.5969% 0.8836% 0.0090% 1.4894%

Madagascar L 0.5650% 0.6364% 0.0095% 1.2109%

0.7583% 0.9232% 0.0106% 1.6921%

Malawi L 0.4868% 0.6403% 0.0015% 1.1286%

0.7064% 0.9373% 0.0017% 1.6454%

Mali L 0.4539% 0.7211% 0.0023% 1.1774%

0.6489% 1.0924% 0.0026% 1.7439%

Mozambique L 0.3669% 0.4572% 0.0106% 0.8347%

0.4906% 0.6367% 0.0116% 1.1389%

Nepal L 0.3306% 0.2602% 0.0008% 0.5917%

0.4261% 0.3630% 0.0009% 0.7900%

Niger L 1.0729% 2.0023% 0.0013% 3.0765%

1.5812% 3.0819% 0.0015% 4.6646%

Rwanda L 0.7856% 0.5914% 0.0019% 1.3790%

1.1146% 0.8687% 0.0021% 1.9854%

Senegal L 0.3867% 0.5620% 0.0039% 0.9526%

0.5246% 0.8327% 0.0043% 1.3616%

47

SierraLeone L 1.0406% 1.0325% 0.0088% 2.0819%

1.4576% 1.5316% 0.0097% 2.9989%

Somalia L 1.2083% 2.0873% 0.0243% 3.3199%

1.7613% 3.0229% 0.0268% 4.8110%

SouthSudan L 0.8247% 1.4377% 0.0014% 2.2637%

1.1699% 2.0632% 0.0015% 3.2346%

Tanzania L 0.4287% 0.4106% 0.0027% 0.8420%

0.6039% 0.5770% 0.0030% 1.1839%

TheGambia L 0.4103% 0.4209% 0.0043% 0.8356%

0.5682% 0.6361% 0.0048% 1.2091%

Togo L 1.0563% 1.0164% 0.0080% 2.0806%

1.4297% 1.4826% 0.0088% 2.9210%

Uganda L 0.5131% 0.5059% 0.0042% 1.0232%

0.7195% 0.7375% 0.0046% 1.4616%

Zimbabwe L 0.3914% 0.6761% 0.0029% 1.0704%

0.5141% 0.9957% 0.0032% 1.5130%

48

Welfare Damages in Billion Dollars and as a Percentage of GNI by Pollutant

BillionDollars

Percent

Country

WorldBank

IncomeGroup

AAPandHAPCombined

UWandUSCombined

LeadExposure Total

AAPandHAP

Combined

UWandUS

Combined

LeadExposure Total

AntiguaandBarbuda H 0.032570076 0.007459991 0.00672394 0.046754007

2.6492% 0.6068% 0.5469% 3.8029%

Australia H 19.02641408 1.906293676 12.95867926 33.89138701

1.3319% 0.1334% 0.9071% 2.3725%

Austria H 18.03206729 0.554619854 2.875613562 21.4623007

4.4441% 0.1367% 0.7087% 5.2895%

Bahamas,The H 0.23664404 0.043066449 0.048154274 0.327864762

2.8619% 0.5208% 0.5824% 3.9651%

Bahrain H 0.44931481 0.034348269 0.005292501 0.488955579

1.6032% 0.1226% 0.0189% 1.7446%

Barbados H 0.183726778 0.045599818 0.027753724 0.25708032

4.3678% 1.0841% 0.6598% 6.1117%

Belgium H 24.31454679 2.425941989 6.537549871 33.27803865

4.8567% 0.4846% 1.3059% 6.6472%

Canada H 34.43880708 3.553788628 4.453260895 42.4458566

2.0223% 0.2087% 0.2615% 2.4925%

Chile H 9.607526878 1.116030035 0.521460859 11.24501777

3.8072% 0.4423% 0.2066% 4.4561%

Croatia H 5.838160409 0.14534064 0.947803077 6.931304126

10.8905% 0.2711% 1.7680% 12.9297%

Cyprus H 0.750585513 0.034292351 0.159135969 0.944013833

2.4840% 0.1135% 0.5267% 3.1242%

CzechRepublic H 14.92329938 0.814421215 0.86471384 16.60243443

7.8358% 0.4276% 0.4540% 8.7175%

Denmark H 12.20922075 1.196544731 1.497067104 14.90283259

3.6713% 0.3598% 0.4502% 4.4813%

Estonia H 1.625962826 0.035672366 0.405516064 2.067151256

6.7062% 0.1471% 1.6725% 8.5258%

Finland H 7.026883263 0.346672945 0.634757755 8.008313963

2.7649% 0.1364% 0.2498% 3.1511%

France H 82.06845947 10.36727406 19.70740882 112.1431424

3.0271% 0.3824% 0.7269% 4.1365%

Germany H 189.3323442 13.78811349 19.53608217 222.6565398

5.0788% 0.3699% 0.5240% 5.9727%

Greece H 15.18826536 0.540471493 2.939862528 18.66859938

6.9159% 0.2461% 1.3387% 8.5007%

Hungary H 13.96199128 0.269345842 1.627933526 15.85927065

10.9178% 0.2106% 1.2730% 12.4014%

Iceland H 0.284528816 0.034346173 0.076085324 0.394960313

1.7295% 0.2088% 0.4625% 2.4007%

Ireland H 5.14031905 0.559135872 1.081653975 6.781108897

2.3729% 0.2581% 0.4993% 3.1303%

Israel H 7.842409983 0.660618155 0.969709081 9.472737219

2.6405% 0.2224% 0.3265% 3.1895%

Italy H 115.9499346 4.278096204 36.01107342 156.2391043

5.8158% 0.2146% 1.8062% 7.8366%

Japan H 221.2772908 39.60369704 18.02719562 278.9081834

4.7501% 0.8502% 0.3870% 5.9873%

Korea,Rep. H 50.7893513 3.158840239 5.750791562 59.6989831

3.6567% 0.2274% 0.4140% 4.2982%

Kuwait H 2.360710671 0.161245617 0.007363699 2.529319988

1.4819% 0.1012% 0.0046% 1.5877%

Latvia H 3.74128145 0.061515909 0.589694537 4.392491895

12.6914% 0.2087% 2.0004% 14.9005%

Lithuania H 4.817200843 0.098341307 0.763632766 5.679174916

11.0352% 0.2253% 1.7493% 13.0098%

Luxembourg H 1.20164714 0.085442307 0.130009062 1.417098509

2.7394% 0.1948% 0.2964% 3.2306%

Netherlands H 32.16776747 3.000276988 3.70611995 38.87416441

3.8809% 0.3620% 0.4471% 4.6900%

NewZealand H 2.415302142 0.241624327 1.271919744 3.928846213

1.3113% 0.1312% 0.6905% 2.1330%

Norway H 11.83153036 1.592718756 2.012233989 15.43648311

2.4271% 0.3267% 0.4128% 3.1666%

Oman H 1.520931377 0.20055014 0.116853388 1.838334905

2.0018% 0.2640% 0.1538% 2.4195%

Poland H 46.17777331 1.937793291 6.952430531 55.06799713

9.0892% 0.3814% 1.3684% 10.8390%

Portugal H 8.177766776 1.518775506 3.60756864 13.30411092

3.8491% 0.7149% 1.6980% 6.2620%

Qatar H 1.586860558 0.084735608 0.003839196 1.675435362

0.8310% 0.0444% 0.0020% 0.8773%

49

SaudiArabia H 17.22063468 1.779871337 1.36784065 20.36834667

2.3184% 0.2396% 0.1842% 2.7422%

Seychelles H 0.045606314 0.013116936 0.008666105 0.067389355

3.3260% 0.9566% 0.6320% 4.9146%

Singapore H 7.318658869 1.456864649 0.974176732 9.74970025

2.5384% 0.5053% 0.3379% 3.3816%

SlovakRepublic H 6.700944093 0.398258787 0.919097309 8.018300189

7.1370% 0.4242% 0.9789% 8.5401%

Slovenia H 2.86487902 0.210094481 0.253904392 3.328877893

6.1397% 0.4502% 0.5441% 7.1341%

Spain H 42.3144972 4.01828066 14.92968486 61.26246272

3.1963% 0.3035% 1.1277% 4.6276%

Sweden H 12.16669868 1.567900024 2.447158162 16.18175687

2.1478% 0.2768% 0.4320% 2.8566%

Switzerland H 19.19859705 1.306331835 3.778593276 24.28352216

2.7521% 0.1873% 0.5417% 3.4810%TrinidadandTobago H 0.944326964 0.122549732 0.055515228 1.122391924

3.7329% 0.4844% 0.2194% 4.4367%

UnitedArabEmirates H 9.779663032 0.413039055 0.037123265 10.22982535

2.4739% 0.1045% 0.0094% 2.5878%

UnitedKingdom H 117.3044401 13.231359 17.76098248 148.2967816

4.1552% 0.4687% 0.6291% 5.2530%

UnitedStates H 486.3584027 39.72241004 103.2347691 629.3155819

2.7534% 0.2249% 0.5844% 3.5627%

Uruguay H 2.484368882 0.434782127 0.771349454 3.690500462

4.6055% 0.8060% 1.4299% 6.8413%

Albania UM 0.888104616 0.023921732 0.215656842 1.127683191

7.1653% 0.1930% 1.7399% 9.0982%

Algeria UM 5.739390116 1.209865598 1.163981084 8.113236798

2.9711% 0.6263% 0.6025% 4.1999%

Angola UM 9.383136008 5.674006407 0.273306306 15.33044872

8.9712% 5.4249% 0.2613% 14.6574%

Azerbaijan UM 4.526888148 0.274662303 0.717340448 5.518890899

7.1500% 0.4338% 1.1330% 8.7169%

Belarus UM 7.731750455 0.082340368 1.625065199 9.439156022

12.5814% 0.1340% 2.6444% 15.3597%

Belize UM 0.047973833 0.009991149 0.004477171 0.062442154

3.0209% 0.6291% 0.2819% 3.9320%

BosniaandHerzegovina UM 2.155824071 0.023759974 0.432139277 2.611723323

12.0891% 0.1332% 2.4233% 14.6457%

Botswana UM 0.931940914 0.491442836 0.032290693 1.455674444

6.3273% 3.3366% 0.2192% 9.8832%

Brazil UM 66.66321708 14.44324356 9.989506957 91.0959676

3.2569% 0.7056% 0.4880% 4.4506%

Bulgaria UM 7.51415864 0.157914753 1.182103775 8.854177169

14.4991% 0.3047% 2.2809% 17.0847%

China UM 1188.867629 25.30837604 47.49759183 1261.673597

11.0889% 0.2361% 0.4430% 11.7680%

Colombia UM 10.21988117 1.172376462 1.147680265 12.5399379

2.9720% 0.3409% 0.3338% 3.6467%

CostaRica UM 1.10780724 0.14988744 0.210907746 1.468602426

2.2568% 0.3053% 0.4297% 2.9918%

Dominica UM 0.015440852 0.004044024 0.005252223 0.024737099

3.1427% 0.8231% 1.0690% 5.0349%DominicanRepublic UM 2.362372726 0.418328866 0.624109516 3.404811108

3.6604% 0.6482% 0.9670% 5.2756%

Ecuador UM 1.993686461 0.70250714 0.647536061 3.343729663

2.0548% 0.7240% 0.6674% 3.4462%

EquatorialGuinea UM 0.604799737 0.173955944 0.025624141 0.804379822

9.1873% 2.6425% 0.3892% 12.2190%

Fiji UM 0.166359185 0.063520593 0.019734615 0.249614392

3.8848% 1.4833% 0.4608% 5.8290%

Gabon UM 0.958296933 0.421270428 0.099962604 1.479529965

6.0308% 2.6512% 0.6291% 9.3111%

Georgia UM 2.43030139 0.034099056 0.460813617 2.925214063

15.8795% 0.2228% 3.0109% 19.1132%

Grenada UM 0.032518767 0.007804613 0.009017414 0.049340794

3.6111% 0.8667% 1.0013% 5.4791%

Guyana UM 0.133918859 0.033907558 0.033518984 0.201345402

4.2685% 1.0808% 1.0684% 6.4176%

Iran,IslamicRep. UM 21.56411317 1.432660701 7.353193829 30.3499677

4.1616% 0.2765% 1.4191% 5.8572%

Iraq UM 8.506053182 1.409907147 2.184621538 12.10058187

4.2078% 0.6975% 1.0807% 5.9859%

Jamaica UM 0.557109649 0.068594344 0.132562541 0.758266534

4.0793% 0.5023% 0.9707% 5.5522%

Jordan UM 0.631478892 0.071314744 0.015111574 0.71790521

1.7767% 0.2006% 0.0425% 2.0199%

Kazakhstan UM 14.34495115 0.445146959 1.738305704 16.52840381

7.0609% 0.2191% 0.8556% 8.1356%

Lebanon UM 1.649264869 0.069329415 0.183254757 1.901849041

3.5547% 0.1494% 0.3950% 4.0991%

Macedonia,FYR UM 1.103580848 0.011842347 0.134566241 1.249989436

10.3300% 0.1108% 1.2596% 11.7005%

50

Malaysia UM 9.942554591 2.403166246 0.534090997 12.87981183

3.1012% 0.7496% 0.1666% 4.0174%

Maldives UM 0.050915979 0.008779387 0.003541066 0.063236433

1.8657% 0.3217% 0.1298% 2.3171%

MarshallIslands UM 0.011553539 0.002667214 0.002192918 0.016413671

4.9663% 1.1465% 0.9426% 7.0554%

Mauritius UM 0.446157357 0.056228891 0.08596843 0.588354679

3.6770% 0.4634% 0.7085% 4.8490%

Mexico UM 35.37834397 6.250340021 4.198681077 45.82736507

2.8689% 0.5069% 0.3405% 3.7162%

Montenegro UM 0.511047308 0.005145062 0.058734638 0.574927009

11.3413% 0.1142% 1.3035% 12.7589%

Namibia UM 0.785535408 0.489455927 0.014005867 1.288997201

6.1320% 3.8207% 0.1093% 10.0620%

Panama UM 1.187768275 0.332990832 0.2327443 1.753503406

2.5087% 0.7033% 0.4916% 3.7036%

Paraguay UM 1.204728333 0.173790178 0.129159815 1.507678326

4.3000% 0.6203% 0.4610% 5.3813%

Peru UM 8.16153329 1.72259039 0.660049717 10.5441734

4.1954% 0.8855% 0.3393% 5.4202%

Romania UM 22.29062511 0.603714939 4.426103057 27.3204431

11.8311% 0.3204% 2.3492% 14.5007%RussianFederation UM 162.5417973 4.69567351 20.40529594 187.6427668

9.8946% 0.2858% 1.2422% 11.4225%

Serbia UM 6.16964022 0.087602389 0.713491265 6.970733874

15.8032% 0.2244% 1.8276% 17.8552%

SouthAfrica UM 20.75941693 10.21981175 1.79644408 32.77567277

6.2436% 3.0737% 0.5403% 9.8577%

St.Lucia UM 0.047442472 0.010061297 0.011357416 0.068861185

3.4702% 0.7359% 0.8307% 5.0369%

St.VincentandtheGrenadines UM 0.02685168 0.005534317 0.008040703 0.040426699

3.6778% 0.7580% 1.1013% 5.5371%

Suriname UM 0.182899043 0.03983057 0.032944308 0.25567392

3.6220% 0.7888% 0.6524% 5.0632%

Thailand UM 28.1457387 5.505216602 1.190906255 34.84186155

7.3693% 1.4414% 0.3118% 9.1225%

Turkey UM 27.73539644 1.708809406 5.322972886 34.76717873

3.5434% 0.2183% 0.6801% 4.4418%

Turkmenistan UM 2.241087691 0.259616032 0.383358813 2.884062535

5.5534% 0.6433% 0.9500% 7.1467%

Armenia LM 0.681333265 0.017204808 0.176210987 0.874749059

5.8190% 0.1469% 1.5050% 7.4709%

Bangladesh LM 14.67450544 2.698775774 1.243181614 18.61646283

7.6593% 1.4086% 0.6489% 9.7168%

Bhutan LM 0.088516372 0.02753833 0.003683969 0.119738671

4.8202% 1.4996% 0.2006% 6.5205%

Bolivia LM 1.226010143 0.323942518 0.178971326 1.728923988

3.7116% 0.9807% 0.5418% 5.2341%

CaboVerde LM 0.072693255 0.017782339 0.004222328 0.094697922

4.2450% 1.0384% 0.2466% 5.5300%

Cambodia LM 0.791584308 0.163707081 0.028738784 0.984030173

4.7490% 0.9821% 0.1724% 5.9036%

Cameroon LM 1.841497478 1.050368976 0.03860945 2.930475903

5.9312% 3.3831% 0.1244% 9.4386%

Congo,Rep. LM 0.833010711 0.287290605 0.058969853 1.179271169

7.0981% 2.4480% 0.5025% 10.0486%

Coted'Ivoire LM 1.79685441 1.131793219 0.037817417 2.966465047

5.6136% 3.5358% 0.1181% 9.2675%

Egypt,ArabRep. LM 14.94885148 1.943115521 3.328395431 20.22036243

4.8910% 0.6358% 1.0890% 6.6158%

ElSalvador LM 0.787795413 0.136946773 0.128594485 1.05333667

3.2636% 0.5673% 0.5327% 4.3637%

Ghana LM 2.129878239 0.655854257 0.053218663 2.838951159

5.2503% 1.6167% 0.1312% 6.9982%

Guatemala LM 2.058618992 1.00249331 0.128932031 3.190044333

3.5088% 1.7087% 0.2198% 5.4372%

Honduras LM 0.723728069 0.152041681 0.098488332 0.974258082

3.9482% 0.8295% 0.5373% 5.3150%

India LM 188.5697265 67.42076037 9.990308622 265.9807955

9.0463% 3.2344% 0.4793% 12.7600%

Indonesia LM 32.67950205 16.72933077 4.269572402 53.67840522

3.6878% 1.8879% 0.4818% 6.0575%

Kenya LM 1.881082809 2.880376305 0.029092997 4.79055211

3.0484% 4.6678% 0.0471% 7.7633%

Kiribati LM 0.008275721 0.006384446 0.001069683 0.015729849

2.2790% 1.7582% 0.2946% 4.3318%

KyrgyzRepublic LM 0.300915625 0.023202238 0.063664657 0.38778252

4.3175% 0.3329% 0.9135% 5.5638%

LaoPDR LM 0.70098205 0.21528767 0.020370305 0.936640025

5.9569% 1.8295% 0.1731% 7.9595%

Lesotho LM 0.218919841 0.162755304 0.004851702 0.386526846

7.7096% 5.7317% 0.1709% 13.6121%

Mauritania LM 0.218162875 0.138992995 0.003259088 0.360414958

3.9149% 2.4942% 0.0585% 6.4677%

Micronesia,Fed. LM 0.008097882 0.001553195 0.000399427 0.010050504

2.4225% 0.4647% 0.1195% 3.0067%

51

Sts.

Moldova LM 0.482783247 0.013236191 0.16219534 0.658214779

6.1188% 0.1678% 2.0557% 8.3422%

Mongolia LM 0.748684728 0.022656201 0.113298517 0.884639446

6.6060% 0.1999% 0.9997% 7.8055%

Morocco LM 2.290321257 0.462463368 0.597624455 3.350409079

2.1915% 0.4425% 0.5718% 3.2059%

Nicaragua LM 0.266963796 0.037360538 0.018735942 0.323060276

2.2626% 0.3166% 0.1588% 2.7380%

Nigeria LM 20.84216075 30.16681214 0.21807579 51.22704868

4.0564% 5.8713% 0.0424% 9.9702%

Pakistan LM 20.69792305 6.897357932 2.1484318 29.74371278

7.6091% 2.5356% 0.7898% 10.9345%PapuaNewGuinea LM 1.08519056 0.3914566 0.016130713 1.492777873

6.3583% 2.2936% 0.0945% 8.7464%

Philippines LM 18.62143939 3.392534766 1.305851924 23.31982608

5.2237% 0.9517% 0.3663% 6.5417%

Samoa LM 0.01742724 0.003411531 0.000794377 0.021633149

2.2949% 0.4492% 0.1046% 2.8488%

SaoTomeandPrincipe LM 0.015296581 0.005787528 0.000677458 0.021761567

4.8121% 1.8207% 0.2131% 6.8460%

SolomonIslands LM 0.047831199 0.008652221 0.001111082 0.057594502

4.2247% 0.7642% 0.0981% 5.0871%

SriLanka LM 4.125776975 0.370002113 0.144594798 4.640373886

5.1785% 0.4644% 0.1815% 5.8244%

Sudan LM 3.688211803 1.229193015 0.646536021 5.56394084

4.9819% 1.6603% 0.8733% 7.5156%

Swaziland LM 0.30994929 0.183004975 0.003256823 0.496211088

7.4562% 4.4024% 0.0783% 11.9370%

Tajikistan LM 0.423592252 0.096111413 0.065988436 0.5856921

4.0275% 0.9138% 0.6274% 5.5687%

Timor-Leste LM 0.083296868 0.03011143 0.004605003 0.118013302

3.4846% 1.2597% 0.1926% 4.9369%

Tonga LM 0.009543538 0.002662892 0.000555461 0.012761891

2.1101% 0.5888% 0.1228% 2.8217%

Tunisia LM 1.350173874 0.151527125 0.231675333 1.733376331

3.0618% 0.3436% 0.5254% 3.9307%

Ukraine LM 11.63808854 0.140755849 0.894336538 12.67318092

9.8279% 0.1189% 0.7552% 10.7020%

Uzbekistan LM 3.092751878 0.174775176 0.766755021 4.034282074

4.5959% 0.2597% 1.1394% 5.9950%

Vanuatu LM 0.041009003 0.007211079 0.001041821 0.049261902

4.9036% 0.8623% 0.1246% 5.8905%

Vietnam LM 8.599280234 0.94197976 0.694593173 10.23585317

4.7360% 0.5188% 0.3825% 5.6373%WestBankandGaza LM 0.200316607 0.027909821 0.03105124 0.259277668

1.4660% 0.2043% 0.2272% 1.8975%

Zambia LM 1.368362623 1.195540216 0.063839353 2.627742191

5.6270% 4.9163% 0.2625% 10.8059%

Afghanistan L 1.780760892 0.357929729 0.313057788 2.451748409

8.6901% 1.7467% 1.5277% 11.9646%

Benin L 0.503833959 0.277276521 0.010579506 0.791689986

5.3848% 2.9634% 0.1131% 8.4613%

BurkinaFaso L 0.543158167 0.527411575 0.003349834 1.073919577

4.5454% 4.4136% 0.0280% 8.9870%

Burundi L 0.122046532 0.110776759 0.00070127 0.233524561

4.1991% 3.8113% 0.0241% 8.0345%

CentralAfricanRepublic L 0.128968969 0.072893792 0.007168266 0.209031028

8.2246% 4.6486% 0.4571% 13.3303%

Chad L 0.86160469 1.102107274 0.004911911 1.968623875

6.9749% 8.9218% 0.0398% 15.9364%

Comoros L 0.018556457 0.015483322 0.000792595 0.034832374

2.9791% 2.4857% 0.1272% 5.5920%

Congo,Dem.Rep. L 1.497973373 0.825929115 0.067224833 2.391127321

4.7285% 2.6071% 0.2122% 7.5479%

Ethiopia L 2.370945206 1.693359879 0.064755432 4.129060517

4.0432% 2.8877% 0.1104% 7.0413%

Gambia,The L 0.030693941 0.017565276 0.000831316 0.049090533

3.3515% 1.9180% 0.0908% 5.3603%

Guinea L 0.327748718 0.183762252 0.004967154 0.516478124

5.5307% 3.1009% 0.0838% 8.7154%

Guinea-Bissau L 0.075574403 0.058184064 0.001169815 0.134928282

6.9452% 5.3471% 0.1075% 12.3998%

Haiti L 0.486816826 0.165844937 0.060473139 0.713134902

5.5427% 1.8882% 0.6885% 8.1194%

Liberia L 0.054102322 0.047653497 0.00250842 0.104264239

3.1615% 2.7846% 0.1466% 6.0927%

Madagascar L 0.513933178 0.340401595 0.019447531 0.873782304

5.0490% 3.3442% 0.1911% 8.5843%

Malawi L 0.230925622 0.222935596 0.003519666 0.457380885

3.8326% 3.7000% 0.0584% 7.5910%

Mali L 0.527357828 0.544401455 0.009117746 1.080877029

3.7929% 3.9155% 0.0656% 7.7740%

52

Mozambique L 0.60602061 0.460974329 0.039627437 1.106622376

3.7346% 2.8408% 0.2442% 6.8196%

Nepal L 1.272231078 0.431934713 0.00824644 1.712412231

6.1121% 2.0751% 0.0396% 8.2268%

Niger L 0.391664234 0.508573963 0.001761785 0.901999982

5.0468% 6.5532% 0.0227% 11.6227%

Rwanda L 0.342141756 0.173856248 0.003009098 0.519007103

4.2101% 2.1393% 0.0370% 6.3864%

Senegal L 0.625496245 0.494248703 0.017074952 1.1368199

4.1343% 3.2668% 0.1129% 7.5140%

SierraLeone L 0.217208956 0.145092553 0.00477042 0.36707193

5.3427% 3.5689% 0.1173% 9.0289%

SouthSudan L 0.591591032 0.77914861 0.00359744 1.374337082

6.0686% 7.9925% 0.0369% 14.0980%

Tanzania L 2.10782838 1.511358012 0.045944829 3.665131221

4.3319% 3.1061% 0.0944% 7.5324%

Togo L 0.172337868 0.100723608 0.003158851 0.276220327

4.3691% 2.5535% 0.0801% 7.0027%

Uganda L 1.083627081 0.709756168 0.028479233 1.821862481

4.1436% 2.7140% 0.1089% 6.9665%

Zimbabwe L 0.469323777 0.391922514 0.010155771 0.871402063

3.5388% 2.9552% 0.0766% 6.5705%

53

WillingnesstoPay,Dollars

DeathRiskReducedper10,000

Country

WorldBank

IncomeGroup

AmbientAir

Pollution(AAP)

HouseholdAir

Pollution(HAP)

UnsafeWaterSource

UnsafeSanitation

LeadExposure

AmbientAir

Pollution(AAP)

HouseholdAir

Pollution(HAP)

UnsafeWaterSource

UnsafeSanitation

LeadExposure

AntiguaandBarbuda H 329.022 29.410 25.823 7.396 73.231

2.282 0.204 0.179 0.051 0.508

Australia H 800.407 544.913

1.422 0.968Austria H 2110.068 333.943

4.841 0.766

Bahamas,The H 590.249 23.488 19.750 3.782 124.103

2.588 0.103 0.087 0.017 0.544Bahrain H 329.577 1.698 9.214 0.664 3.843

1.684 0.009 0.047 0.003 0.020

Barbados H 643.781 3.251 18.538 5.486 97.650

3.779 0.019 0.109 0.032 0.573Belgium H 2171.760 579.276

5.214 1.391

Canada H 966.979 124.213

2.167 0.278Chile H 508.402 34.055 10.793 3.000 29.054

3.343 0.224 0.071 0.020 0.191

Croatia H 1185.138 242.795 3.287 0.370 224.364

8.633 1.769 0.024 0.003 1.634Cyprus H 650.735 136.562

2.488 0.522

CzechRepublic H 1371.287 57.201 10.410 0.294 81.954

7.512 0.313 0.057 0.002 0.449

Denmark H 2166.858 263.754

4.003 0.487Estonia H 983.197 283.228 1.139 0.374 309.083

5.156 1.485 0.006 0.002 1.621

Finland H 1284.106 115.789

2.905 0.262France H 1236.217 294.984

3.189 0.761

Germany H 2340.505 239.962

5.543 0.568Greece H 1417.886 271.613

6.736 1.290

Hungary H 1235.494 228.905 8.251 1.981 165.362

9.015 1.670 0.060 0.014 1.207Iceland H 861.966 229.988

1.863 0.497

Ireland H 1112.593 233.080

2.564 0.537Israel H 946.909 115.712

2.716 0.332

Italy H 1934.567 592.267

6.028 1.846Japan H 1751.317 141.946

4.958 0.402

Korea,Rep. H 1012.991 113.614

3.748 0.420Kuwait H 607.832 0.997 11.241 0.449 1.892

1.782 0.003 0.033 0.001 0.006

Latvia H 1675.210 253.908 1.295 0.580 298.060

10.621 1.610 0.008 0.004 1.890Lithuania H 1479.031 209.392 1.898 0.835 262.399

9.496 1.344 0.012 0.005 1.685

Luxembourg H 2125.519 228.216

3.231 0.347Netherlands H 1915.438 218.824

4.247 0.485

NewZealand H 525.639 276.763

1.339 0.705Norway H 2285.526 387.272

2.800 0.474

Oman H 334.692 8.328 11.640 1.242 26.022

2.038 0.051 0.071 0.008 0.158Poland H 1048.641 205.175 2.512 0.286 182.961

7.480 1.464 0.018 0.002 1.305

Portugal H 795.407 348.603

3.784 1.658Qatar H 716.877 0.175 17.380 1.020 1.717

1.127 0.000 0.027 0.002 0.003

SaudiArabia H 545.164 4.531 20.038 1.819 43.368

2.574 0.021 0.095 0.009 0.205Seychelles H 473.120 20.374 18.038 4.547 93.284

3.066 0.132 0.117 0.029 0.604

Singapore H 1326.715 176.003

3.065 0.407SlovakRepublic H 1169.089 82.331 3.590 0.275 169.449

6.578 0.463 0.020 0.002 0.953

Slovenia H 1200.369 233.765 2.335 0.333 123.030

5.263 1.025 0.010 0.001 0.539Spain H 923.224 321.634

3.282 1.143

54

Sweden H 1244.440 249.739

2.326 0.467Switzerland H 2334.988 455.968

3.162 0.617

TrinidadandTobago H 681.168 15.406 30.735 6.951 40.817

3.602 0.081 0.163 0.037 0.216

UnitedArabEmirates H 1084.716 0.224 22.515 0.683 4.054

2.940 0.001 0.061 0.002 0.011

UnitedKingdom H 1812.114 272.666

4.379 0.659

UnitedStates H 1529.857 321.203

3.114 0.654Uruguay H 707.321 19.546 19.272 4.628 224.781

4.106 0.113 0.112 0.027 1.305

Albania UM 217.123 106.598 0.580 0.053 74.643

5.286 2.595 0.014 0.001 1.817Algeria UM 144.444 1.151 16.218 4.942 29.344

3.098 0.025 0.348 0.106 0.629

Angola UM 170.695 268.797 154.888 97.594 10.923

4.265 6.716 3.870 2.439 0.273Azerbaijan UM 402.171 85.011 9.615 4.918 74.325

6.403 1.353 0.153 0.078 1.183

Belarus UM 799.739 17.173 0.554 0.265 170.826

12.930 0.278 0.009 0.004 2.762Belize UM 101.347 40.157 9.916 3.863 12.461

2.395 0.949 0.234 0.091 0.294

BosniaandHerzegovina UM 341.938 288.998 0.435 0.109 113.410

7.631 6.450 0.010 0.002 2.531

Botswana UM 192.845 260.900 144.451 94.073 14.272

3.094 4.186 2.318 1.509 0.229Brazil UM 251.403 79.201 23.769 6.464 48.073

2.666 0.840 0.252 0.069 0.510

Bulgaria UM 939.910 136.076 1.437 0.230 164.684

13.597 1.968 0.021 0.003 2.382China UM 644.374 322.235 3.623 2.032 34.644

8.606 4.304 0.048 0.027 0.463

Colombia UM 160.271 62.649 10.681 1.947 23.797

2.348 0.918 0.156 0.029 0.349CostaRica UM 211.929 22.644 16.749 1.491 43.867

2.168 0.232 0.171 0.015 0.449

Dominica UM 174.600 43.185 22.088 9.310 72.265

2.698 0.667 0.341 0.144 1.117DominicanRepublic UM 187.287 44.352 19.266 8.372 59.279

3.191 0.756 0.328 0.143 1.010

Ecuador UM 108.526 17.206 12.419 4.120 40.109

1.886 0.299 0.216 0.072 0.697EquatorialGuinea UM 315.544 535.613 113.717 29.743 30.322

4.231 7.181 1.525 0.399 0.407

Fiji UM 107.019 86.892 41.322 9.619 22.120

2.329 1.891 0.899 0.209 0.481Gabon UM 404.180 198.672 133.558 80.860 57.940

4.583 2.253 1.515 0.917 0.657

Georgia UM 434.446 269.481 2.313 1.063 125.255

10.907 6.766 0.058 0.027 3.145Grenada UM 274.465 34.552 13.610 5.331 84.413

3.400 0.428 0.169 0.066 1.046

Guyana UM 144.312 35.653 24.168 11.768 43.697

3.685 0.910 0.617 0.301 1.116Iran,IslamicRep. UM 272.112 3.562 7.251 2.026 92.950

4.339 0.057 0.116 0.032 1.482

Iraq UM 231.116 4.161 24.455 4.661 59.979

4.349 0.078 0.460 0.088 1.129Jamaica UM 159.781 52.036 6.846 2.732 48.630

3.331 1.085 0.143 0.057 1.014

Jordan UM 83.632 0.059 3.190 0.040 1.990

1.866 0.001 0.071 0.001 0.044Kazakhstan UM 654.474 196.881 5.927 3.136 99.082

5.903 1.776 0.053 0.028 0.894

Lebanon UM 283.732 0.059 6.063 0.407 31.322

3.737 0.001 0.080 0.005 0.413Macedonia,FYR UM 390.332 178.726 1.107 0.234 64.743

7.931 3.632 0.022 0.005 1.316

Malaysia UM 323.015 6.570 19.066 1.728 17.609

3.192 0.065 0.188 0.017 0.174Maldives UM 112.583 16.611 12.142 4.322 8.654

1.763 0.260 0.190 0.068 0.136

MarshallIslands UM 139.423 88.899 22.316 8.378 41.381

3.317 2.115 0.531 0.199 0.985

Mauritius UM 329.164 28.490 14.267 2.524 68.088

3.577 0.310 0.155 0.027 0.740Mexico UM 225.842 67.010 27.275 6.626 33.060

2.429 0.721 0.293 0.071 0.356

Montenegro UM 493.166 387.869 0.388 0.065 94.370

7.114 5.595 0.006 0.001 1.361Namibia UM 130.625 229.790 142.828 81.052 5.696

2.619 4.607 2.863 1.625 0.114

55

Panama UM 224.618 88.923 48.280 20.155 59.235

1.947 0.771 0.418 0.175 0.513Paraguay UM 98.465 96.842 11.244 5.518 19.454

2.437 2.397 0.278 0.137 0.481

Peru UM 160.494 125.228 8.212 4.314 21.036

2.704 2.110 0.138 0.073 0.354Romania UM 850.748 324.352 1.840 0.810 223.175

9.353 3.566 0.020 0.009 2.454

RussianFederation UM 1045.627 96.931 1.902 1.129 141.605

9.580 0.888 0.017 0.010 1.297

Serbia UM 545.842 383.867 1.625 0.598 100.517

10.365 7.290 0.031 0.011 1.909SouthAfrica UM 269.359 136.832 118.357 57.135 32.688

4.650 2.362 2.043 0.986 0.564

St.Lucia UM 211.038 51.820 14.444 6.882 61.392

2.983 0.732 0.204 0.097 0.868St.VincentandtheGrenadines UM 209.720 40.839 19.038 7.725 73.457

3.284 0.639 0.298 0.121 1.150

Suriname UM 273.185 74.366 32.392 9.153 60.674

3.068 0.835 0.364 0.103 0.681Thailand UM 308.158 134.844 23.427 1.083 17.524

5.727 2.506 0.435 0.020 0.326

Turkey UM 342.704 19.315 7.885 2.097 67.666

3.597 0.203 0.083 0.022 0.710Turkmenistan UM 414.317 4.448 21.191 7.722 71.342

5.762 0.062 0.295 0.107 0.992

Armenia LM 214.733 14.975 1.520 0.540 58.392

7.839 0.547 0.055 0.020 2.132Bangladesh LM 56.816 53.489 12.645 8.351 7.722

8.094 7.620 1.802 1.190 1.100

Bhutan LM 80.965 49.652 21.378 14.708 4.755

4.972 3.049 1.313 0.903 0.292Bolivia LM 80.759 42.773 10.082 6.143 16.688

3.899 2.065 0.487 0.297 0.806

CaboVerde LM 82.042 74.217 16.078 9.142 8.112

3.759 3.400 0.737 0.419 0.372Cambodia LM 24.806 32.575 6.001 3.812 1.845

3.990 5.240 0.965 0.613 0.297

Cameroon LM 42.605 53.472 31.206 22.383 1.654

5.587 7.012 4.092 2.935 0.217Congo,Rep. LM 90.416 122.580 38.960 28.133 12.763

5.381 7.295 2.319 1.674 0.760

Coted'Ivoire LM 28.327 62.129 34.373 23.536 1.666

3.469 7.608 4.209 2.882 0.204Egypt,ArabRep. LM 164.540 0.054 11.813 0.927 36.373

6.699 0.002 0.481 0.038 1.481

ElSalvador LM 102.348 34.887 7.949 3.814 20.990

3.723 1.269 0.289 0.139 0.764Ghana LM 28.814 59.714 12.806 8.735 1.942

3.212 6.656 1.427 0.974 0.216

Guatemala LM 74.439 67.692 37.011 18.394 7.889

3.026 2.752 1.505 0.748 0.321Honduras LM 55.965 43.338 13.870 6.812 12.197

3.945 3.055 0.978 0.480 0.860

India LM 95.265 77.688 40.059 25.814 7.620

9.139 7.453 3.843 2.476 0.731Indonesia LM 78.184 57.518 51.890 24.932 16.574

3.091 2.274 2.052 0.986 0.655

Kenya LM 11.678 33.977 48.748 36.202 0.632

1.522 4.428 6.353 4.718 0.082Kiribati LM 7.692 66.919 38.985 21.249 9.515

0.391 3.404 1.983 1.081 0.484

KyrgyzRepublic LM 32.305 21.897 1.846 1.311 10.687

4.750 3.219 0.271 0.193 1.571

LaoPDR LM 50.629 67.434 19.249 9.949 2.995

4.566 6.081 1.736 0.897 0.270Lesotho LM 42.402 73.684 57.138 43.118 2.272

5.532 9.613 7.455 5.626 0.296

Mauritania LM 31.834 32.729 24.230 16.971 0.801

3.905 4.015 2.972 2.082 0.098Micronesia,Fed.Sts. LM 35.412 45.874 6.533 2.163 3.824

1.871 2.424 0.345 0.114 0.202

Moldova LM 117.664 21.908 0.297 0.192 45.635

8.278 1.541 0.021 0.013 3.211Mongolia LM 141.040 136.117 0.439 0.293 38.288

4.973 4.799 0.015 0.010 1.350

Morocco LM 60.377 7.779 5.124 1.917 17.384

2.885 0.372 0.245 0.092 0.831Nicaragua LM 24.640 23.644 3.093 1.890 3.081

2.007 1.926 0.252 0.154 0.251

Nigeria LM 51.813 83.682 130.942 88.451 1.197

2.824 4.560 7.136 4.820 0.065Pakistan LM 68.394 59.118 28.829 11.793 11.373

7.418 6.412 3.127 1.279 1.234

PapuaNewGuinea LM 49.539 106.791 33.920 25.118 2.117

3.932 8.475 2.692 1.993 0.168

Philippines LM 101.776 102.575 16.240 4.138 12.968

4.056 4.087 0.647 0.165 0.517Samoa LM 10.735 81.195 7.871 1.155 4.111

0.421 3.184 0.309 0.045 0.161

56

SaoTomeandPrincipe LM 24.800 64.040 18.109 11.757 3.559

2.577 6.654 1.882 1.222 0.370

SolomonIslands LM 18.086 68.543 7.785 5.229 1.904

1.742 6.603 0.750 0.504 0.183

SriLanka LM 106.868 111.989 9.426 4.795 6.897

3.770 3.951 0.333 0.169 0.243Sudan LM 53.826 51.292 21.745 15.887 16.069

4.823 4.596 1.948 1.424 1.440

Swaziland LM 93.007 178.495 102.956 66.543 2.531

4.145 7.955 4.589 2.966 0.113Tajikistan LM 34.797 20.552 6.173 4.838 7.780

4.822 2.848 0.855 0.670 1.078

Timor-Leste LM 25.453 48.563 13.265 10.457 3.699

2.215 4.226 1.154 0.910 0.322Tonga LM 14.994 76.694 9.177 3.058 5.232

0.551 2.818 0.337 0.112 0.192

Tunisia LM 121.839 1.026 4.322 0.770 20.857

4.145 0.035 0.147 0.026 0.710Ukraine LM 239.430 21.782 0.259 0.130 19.787

13.233 1.204 0.014 0.007 1.094

Uzbekistan LM 80.609 23.730 0.787 0.672 24.497

5.702 1.679 0.056 0.048 1.733Vanuatu LM 45.263 120.101 13.535 8.449 3.937

2.505 6.646 0.749 0.468 0.218

Vietnam LM 61.078 40.557 5.253 2.864 7.574

4.759 3.160 0.409 0.223 0.590WestBankandGaza LM 44.035 1.774 2.166 0.421 7.022

2.250 0.091 0.111 0.022 0.359

Zambia LM 31.456 66.293 55.034 37.836 3.938

3.534 7.447 6.182 4.250 0.442Afghanistan L 30.485 33.395 6.183 4.346 9.625

9.210 10.089 1.868 1.313 2.908

Benin L 18.533 36.102 17.442 13.165 0.972

4.058 7.905 3.819 2.883 0.213BurkinaFaso L 12.398 23.447 21.855 16.541 0.185

3.717 7.029 6.551 4.959 0.055

Burundi L 4.642 8.513 7.484 5.665 0.063

4.153 7.616 6.696 5.069 0.056CentralAfricanRepublic L 11.478 20.216 10.636 8.082 1.463

8.677 15.283 8.040 6.110 1.106

Chad L 26.190 48.092 61.204 46.628 0.350

5.599 10.281 13.084 9.968 0.075Comoros L 7.153 19.193 14.355 10.908 1.005

1.841 4.940 3.695 2.808 0.259

Congo,Dem.Rep. L 8.390 14.979 7.226 5.374 0.870

4.773 8.522 4.111 3.057 0.495

Ethiopia L 9.407 18.572 12.713 9.837 0.652

3.162 6.243 4.274 3.307 0.219Gambia,The L 6.955 11.677 6.163 4.082 0.418

3.014 5.060 2.670 1.769 0.181

Guinea L 8.193 21.603 9.677 7.095 0.394

3.722 9.814 4.397 3.223 0.179Guinea-Bissau L 15.300 32.826 23.398 16.728 0.634

5.259 11.282 8.042 5.749 0.218

Haiti L 18.498 33.008 10.674 7.785 5.646

4.401 7.852 2.539 1.852 1.343Liberia L 1.855 10.953 7.858 5.489 0.557

1.139 6.723 4.823 3.369 0.342

Madagascar L 6.433 17.598 10.241 8.153 0.802

3.128 8.556 4.979 3.964 0.390Malawi L 4.445 11.076 9.587 7.457 0.204

2.702 6.733 5.828 4.534 0.124

Mali L 12.583 23.222 24.724 18.586 0.518

2.930 5.407 5.757 4.328 0.121Mozambique L 6.562 17.853 12.469 9.498 1.416

2.395 6.515 4.550 3.466 0.517

Nepal L 28.229 25.860 11.392 6.619 0.289

7.032 6.442 2.838 1.649 0.072Niger L 9.457 14.921 20.150 15.660 0.089

5.351 8.443 11.402 8.861 0.050

Rwanda L 12.985 22.716 9.593 7.278 0.259

3.635 6.360 2.686 2.037 0.073Senegal L 18.169 30.275 24.737 16.594 1.129

3.334 5.555 4.539 3.045 0.207

SierraLeone L 9.455 28.637 15.583 11.497 0.739

2.999 9.084 4.943 3.647 0.234SouthSudan L 18.046 38.542 50.084 38.471 0.292

4.525 9.664 12.558 9.646 0.073

Tanzania L 13.023 32.189 19.688 15.417 0.859

2.578 6.372 3.898 3.052 0.170

Togo L 9.076 18.614 9.683 6.940 0.432

3.473 7.122 3.705 2.655 0.165Uganda L 13.391 20.742 12.715 9.614 0.730

3.890 6.025 3.694 2.793 0.212

Zimbabwe L 10.859 23.370 18.473 11.519 0.651

2.509 5.401 4.269 2.662 0.150

57

DALYs Attributable to Air Pollution by Disease

CountryLower

respiratoryinfections

Tracheal,bronchus,andlungcancer

Ischemicheartdisease

Ischemicstroke

Hemorrhagicstroke

Chronicobstructivepulmonarydisease

Cataracts

HighIncomeCountriesAntiguaandBarbuda

14% 9.3% 14% 9.8% 11% 15% 1.8%

Australia 3.7% 2.3% 6.8% 3.8% 4.5% 5.5% 0%Austria 15% 10.2% 13% 8.9% 10.4% 20.1% 0%Bahrain 33% 24% 25% 19% 20.2% 35% 0.22%Barbados 13% 9.0% 14% 9.4% 10.4% 15% 0.10%Belgium 14% 9.4% 13% 8.6% 10.0% 19% 0%Brunei 2.7% 1.6% 6.6% 3.4% 4.3% 4.0% 0%Canada 5.4% 3.5% 8.6% 5.0% 6.3% 11% 0%Chile 18% 13% 17% 11% 13% 20.0% 3.2%Croatia 23% 16% 17% 12% 14% 28% 7.5%Cyprus 16% 11% 15% 9.6% 11% 22% 0%CzechRepublic

19% 13% 15% 11% 12% 23% 1.6%

Denmark 9.6% 6.5% 11% 7.3% 8.5% 14% 0%Estonia 13% 9.2% 12% 8.7% 10.2% 16% 8.3%Finland 5.6% 3.7% 8.8% 5.4% 6.5% 9.3% 0%France 11% 7.4% 12% 7.5% 9.0% 17% 0%Germany 12% 8.4% 12% 8.4% 9.5% 17% 0%Greece 12% 8.1% 13% 7.9% 9.2% 19% 0%Hungary 23% 17% 17% 13% 15% 28% 7.1%Iceland 6.1% 4.0% 9.2% 5.4% 6.3% 9.1% 0%Ireland 8.4% 5.6% 11% 6.6% 8.2% 13% 0%Israel 17% 12% 15% 10.2% 12% 23% 0%Italy 17% 12% 14% 9.2% 11% 24% 0%Japan 12% 8.0% 12% 7.8% 9.8% 17% 0%Kuwait 36% 27% 28% 21% 22% 34% 0.072%Latvia 21% 15% 17% 12% 14% 23% 6.3%Lithuania 19.5% 14% 16% 12% 14% 23% 5.7%Luxembourg 14% 9.9% 14% 8.9% 10.4% 19% 0%Netherlands 13% 8.8% 13% 8.6% 10.0% 17% 0%NewZealand 3.2% 2.0% 6.3% 3.5% 4.1% 4.6% 0%Norway 7.6% 5.1% 10.2% 6.2% 7.2% 11% 0%

58

Oman 32% 24% 24% 17% 19% 32% 0.85%Poland 24% 17% 18% 13% 16% 27% 7.3%Portugal 8.3% 5.6% 10.4% 6.7% 7.8% 14% 0%Qatar 44% 35% 29.7% 25% 27% 41% 0.0092%SaudiArabia 43% 34% 28% 22% 25% 40.0% 0.43%Seychelles 12% 8.1% 15% 9.2% 11% 13% 1.2%Singapore 16% 11% 15% 9.6% 12% 20.1% 0%Slovakia 19% 13% 16% 12% 13% 23% 2.7%Slovenia 21% 15% 15% 11% 13% 26% 6.3%SouthKorea 22% 16% 17% 12% 15% 26% 0%Spain 8.3% 5.6% 11% 6.4% 7.7% 16% 0%Sweden 4.1% 2.6% 6.9% 4.2% 4.8% 7.0% 0%Switzerland 11% 7.7% 12% 7.6% 8.9% 17% 0%TheBahamas 13% 8.5% 14% 9.6% 11% 16% 0.82%TrinidadandTobago

13% 8.7% 15% 9.8% 11% 14% 0.53%

UnitedArabEmirates

35% 27% 30.0% 24% 26% 40.0% 0.0053%

UnitedKingdom

11% 7.3% 12% 7.4% 8.8% 15% 0%

UnitedStates 6.9% 4.5% 11% 6.3% 7.8% 13% 0%Uruguay 10.4% 7.1% 12% 7.9% 9.4% 13% 1.0%

UpperMiddleIncomeCountriesAlbania 27% 18% 20.0% 14% 15% 29.6% 13%Algeria 24% 18% 19% 14% 15% 26% 0.32%Angola 58% 50.0% 40.5% 32% 33% 44% 29.5%Argentina 12% 8.5% 14% 8.8% 11% 15% 1.7%Azerbaijan 28% 19% 22% 16% 17% 29% 7.1%Belarus 17% 12% 16% 11% 13% 21% 1.0%Belize 29% 21% 23% 17% 18% 26% 8.5%BosniaandHerzegovina

51% 39% 32% 26% 29% 50.1% 28%

Botswana 39% 32% 29% 21% 24% 38% 19%Brazil 15% 11% 15% 9.7% 12% 19% 5.6%Bulgaria 26% 18% 19% 14% 15% 30.1% 6.1%China 46% 35% 31% 25% 28% 46% 21%Colombia 24% 17% 19% 13% 15% 25% 8.6%CostaRica 19% 14% 16% 11% 14% 20.0% 2.6%DominicanRepublic

22% 16% 18% 13% 14% 21% 5.4%

59

Ecuador 14% 9.6% 15% 10.1% 11% 15% 2.7%EquatorialGuinea

64% 57% 45% 36% 38% 49.5% 32%

Fiji 15% 9.9% 15% 9.9% 10.3% 13% 12%Gabon 37% 30.0% 25% 18% 21% 34% 11%Georgia 32% 22% 23% 17% 19% 33% 18%Grenada 15% 11% 16% 11% 12% 15% 2.5%Guyana 19.7% 14% 19% 13% 14% 18% 5.1%Iran 29% 21% 22% 16% 18% 31% 0.56%Iraq 31% 22% 24% 18% 18% 31% 0.88%Jamaica 22% 15% 17% 13% 14% 22% 6.6%Jordan 27% 19% 22% 15% 16% 29% 0.030%Kazakhstan 23% 16% 19.5% 14% 16% 25% 9.0%Lebanon 24% 17% 19% 13% 15% 27% 0.0086%Macedonia 40.2% 29% 27% 20.0% 23% 40.4% 17%Malaysia 14% 9.4% 16% 10.1% 12% 16% 0.61%Maldives 26% 18% 20.3% 14% 16% 26% 6.1%Mauritius 15% 10.0% 16% 10.0% 12% 16% 3.1%Mexico 24% 16% 19% 14% 16% 26% 6.6%Montenegro 37% 27% 26% 18% 21% 38% 22%Namibia 50.3% 43% 34% 26% 29% 47% 27%Panama 19% 13% 16% 11% 13% 18% 6.9%Paraguay 32% 23% 23% 16% 20.0% 30.4% 19%Peru 37% 28% 25% 19% 20.4% 33% 17%Romania 26% 18% 19% 14% 16% 29% 12%Russia 16% 11% 16% 11% 13% 19% 3.3%SaintLucia 17% 12% 15% 11% 12% 17% 4.5%SaintVincentandtheGrenadines

16% 11% 16% 11% 12% 16% 3.8%

Serbia 34% 25% 23% 18% 20.0% 36% 20.3%SouthAfrica 34% 23% 23% 18% 20.0% 32% 11%Suriname 21% 15% 18% 12% 14% 18% 5.8%Thailand 29.6% 21% 23% 17% 19.8% 31% 15%Turkey 27% 19.5% 21% 15% 16% 29.9% 2.5%Turkmenistan 23% 16% 19.8% 12% 16% 23% 0.42%

LowerMiddleIncomeCountriesArmenia 21% 15% 17% 12% 14% 25% 2.6%Bangladesh 67% 54% 46% 37% 42% 62% 32%Bhutan 49% 38% 32% 25% 28% 43% 18%

60

Bolivia 33% 25% 23% 18% 19.7% 34% 12%Cambodia 48% 35% 37% 27% 31% 39% 36%Cameroon 66% 57% 44% 37% 36% 55% 35%CapeVerde 48% 39.7% 29.8% 24% 26% 42% 21%Congo 61% 55% 42% 34% 37% 53% 29.6%Coted'Ivoire 58% 50.0% 41% 34% 33% 49.8% 28%Djibouti 45% 36% 30.1% 23% 27% 39% 15%Egypt 43% 34% 28% 22% 21% 40.3% 0.021%ElSalvador 35% 25% 24% 17% 19.7% 32% 9.9%FederatedStatesofMicronesia

19.9% 13% 18% 12% 13% 17% 18%

Ghana 57% 50.1% 41% 34% 37% 52% 32%Guatemala 45% 33% 29.9% 23% 23% 36% 22%Honduras 46% 35% 30.5% 24% 18% 40.2% 19.8%India 62% 46% 39% 32% 35% 56% 26%Indonesia 28% 18% 26% 17% 20.0% 26% 19.8%Kenya 55% 47% 37% 33% 36% 49% 32%Kiribati 18% 12% 14% 10.2% 10.3% 14% 22%Kyrgyzstan 29.8% 20.5% 23% 18% 20.4% 32% 17%Laos 51% 37% 39% 29% 31% 35% 35%Lesotho 51% 44% 35% 27% 30.4% 48% 28%Mauritania 63% 55% 41% 34% 34% 53% 28%Moldova 21% 15% 17% 13% 15% 25% 6.7%Mongolia 41% 29% 31% 23% 27% 34% 23%Morocco 22% 15% 18% 14% 14% 23% 4.0%Myanmar 56% 41% 40.4% 31% 35% 51% 34%Nicaragua 42% 31% 28% 21% 25% 37% 21%Nigeria 60.2% 52% 39.6% 34% 32% 48% 31%Pakistan 59% 46% 39.9% 32% 36% 47% 26%Palestine 18% 12% 19% 12% 13% 22% 1.4%PapuaNewGuinea

41% 29% 36% 26% 26% 33% 32%

Philippines 39% 27% 32% 22% 26% 34% 27%Samoa 24% 16% 18% 13% 14% 20.1% 26%SaoTomeandPrincipe

50.0% 43% 34% 28% 29% 45% 29.5%

SolomonIslands

36% 25% 30.3% 22% 23% 29% 29.7%

SriLanka 44% 32% 33% 23% 27% 41% 30.3%

61

Sudan 54% 40.1% 38% 29.8% 26% 41% 28%Swaziland 52% 45% 36% 28% 31% 47% 28%Tajikistan 45% 33% 31% 24% 27% 40.1% 17%Timor-Leste 45% 33% 33% 25% 26% 31% 32%Tonga 19% 12% 14% 9.2% 9.4% 15% 22%Tunisia 29% 21% 19.9% 16% 17% 31% 0.39%Ukraine 18% 12% 16% 11% 13% 22% 3.8%Uzbekistan 34% 24% 24% 19% 21% 34% 10.5%Vanuatu 35% 24% 29% 21% 22% 28% 29%Vietnam 36% 25% 25% 19% 23% 35% 25%Zambia 61% 52% 42% 35% 38% 53% 32%

LowIncomeCountriesAfghanistan 57% 43% 43% 35% 37% 46% 30.5%Benin 64% 56% 45% 38% 38% 56% 37%BurkinaFaso 66% 57% 44% 39% 38% 48% 37%Burundi 67% 59% 45% 39% 42% 52% 36%CentralAfricanRepublic

68% 59.8% 48% 40.1% 43% 58% 39.6%

Chad 67% 59% 45% 39% 33% 49% 37%Comoros 56% 49% 39% 32% 36% 48% 32%DemocraticRepublicoftheCongo

67% 59.5% 46% 39% 40.0% 52% 39.5%

Ethiopia 65% 56% 43% 36% 39% 54% 35%Guinea 63% 54% 44% 38% 36% 54% 35%Guinea-Bissau

65% 57% 46% 39% 35% 52% 35%

Haiti 56% 44% 39.9% 32% 33% 40.2% 29%Liberia 58% 51% 39% 33% 33% 48% 39%Madagascar 62% 54% 44% 37% 40.0% 45% 35%Malawi 64% 55% 41% 35% 35% 49% 35%Mali 67% 59% 46% 39% 34% 60.0% 36%Mozambique 61% 53% 41% 35% 38% 49% 36%Nepal 62% 51% 41% 33% 36% 56% 31%Niger 70.0% 61% 47% 41% 36% 55% 34%Rwanda 68% 59.7% 45% 39% 42% 53% 38%Senegal 62% 53% 42% 35% 37% 53% 33%SierraLeone 62% 54% 44% 38% 35% 50.5% 38%Somalia 62% 54% 42% 36% 39% 46% 35%

62

SouthSudan 65% 57% 44% 38% 41% 51% 39.5%Tanzania 62% 54% 41% 35% 38% 49.6% 33%TheGambia 68% 58% 46% 39.5% 39% 57% 33%Togo 64% 56% 46% 38% 38% 56% 36%Uganda 69.9% 61% 46% 39.8% 43% 52% 37%Zimbabwe 55% 48% 37% 29% 21% 48% 29.7%

63

Section 4

Effective Interventions - Prioritizing Solutions, Recognizing Co-Benefits

This Annex provides more detail, case studies and examples relevant to the issues of

pollution control, and disease prevention addressed in Chapter 4, including information

on prioritization and planning processes, benefit-cost and cost-effectiveness analyses,

effective interventions and preparation of Health and Pollution Action Plans.

The Annex is broken into two sections. The first reviews important elements for

success of pollution control and prevention programs. The second section outlines

examples of interventions related to various types of pollution.

Part 1. Planning and Prioritization.

The experience of this Commission suggests that a number of elements contribute to

successful pollution control programs:

1. Visionary leadership by the head of government and a clear mandate to all ministries and agencies calling for intervention against pollution and pollution-related disease;

2. A clear action plan for pollution control and prevention pollution-related disease that is data-driven, subject to constant evaluation and public input, and guided by clear priorities, measurable targets, clearly identified deliverables and specific timelines;

3. A sound body of environmental laws and policies backed by an effective and independent judiciary.

4. A cabinet-level environmental agency with responsibility for collecting data on sources and levels of pollution and the power to enforce environmental laws and regulations;

5. Robust and transparent environmental and public health data systems for monitoring the sources, levels and geographic distribution of pollution and for tracking pollution-related disease;

64

6. An active civil society and independent media that can direct public attention to problems of pollution, pollution-related disease and environmental injustice;

7. Transdisciplinary training programs that build future capacity in pollution prevention and upgrade technical skills; and

8. Sufficient financial resources to sustain pollution control programs and prevention of pollution-related disease now and in the future.

The following expand upon these elements.

1. Visionary leadership by the head of government. Visionary leadership by the head of government – the President, Prime Minister,

Governor or Mayor – is critical to pollution control. Major changes can result when the

top leadership of a city or country commit to ambitious and inspiring goals and issue a

clear mandate to ministries and agencies requiring immediate interventions against

pollution and pollution-related diseases. In other cases where government does not

take the lead, the emergence of a local leader, a champion, sometimes the leader of a

civil society organization may provide the catalyst to raise the profile of the issue and to

bring the challenges to the attention of the government’s leadership.

Clear and committed leadership can help to drive progress in tackling pollution, energy,

and climate challenges. In Canada, the “Clean50” awards are made each year in 16

categories covering many industries, academia, different levels of government and

other advocates and leaders. The awards recognize the individuals for their

contributions to broad sustainability over the previous two years.128

Leadership also has a key role in trying to drive progress in the direction of a less

resource intensive and more sustainable economy. Authorities in Denmark, for

example, are moving beyond merely regulating pollution and instead are actively

seeking to identify and promote better alternatives for harmful chemicals in the

environment. The Government is supporting an approach that they term “Circulating

65

Resources” and is setting up systems to help businesses to substitute better

substances for harmful chemicals and to seek for different approaches which eliminate

completely the harmful chemicals.129

Leadership is important also in the control of toxic chemical pollution. Canada has

recognized the importance of early-stage assessment of new chemicals that have

potential to harm health and the environment. This “Preventative Principle” aims to

address such substances before they are released to the market for general use in

consumer products. The Canadian Environmental Protection Act (CEPA 1999) sets out

a range of risk management instruments and regulations that can be used by the

Government to minimize the risks associated with potentially harmful substances.

The UN’s Sustainable Energy for All (SEA)130 initiative lays out a shared framework for

accelerating progress, achieving energy goals, reducing pollution and slowing the pace

of global climate change. Under this initiative, countries must adopt an “energy

efficiency first” approach that focuses on control of ambient and household air pollution.

At the same time, access must be placed at heart of countries’ energy strategies. And

finally, there needs to be a global scaling up of renewables.”131

Two structural issues that heads of government will need to confront if they are to be

successful in controlling pollution and preventing pollution-related disease are (1) the

separation in most governments of environmental protection from public health, and (2)

the fact that Ministries of Health and Environment seldom have the financial resources

that they need to effectively control pollution. The formation of interagency task forces

that include all relevant ministries including Finance has been a strategy used by some

leaders to successfully overcome these barriers. A further challenge is to balance

economic development that will lift people out of poverty against prevention of pollution

and pollution-related disease.

2. A clear, data-driven action plan for pollution control and prevention of pollution-related disease.

High-level commitment to dealing with a pollution challenge is often expressed in a

government policy or a strategy, which typically identifies the major concerns and sets

66

out broad targets for dealing with them. The Health and Pollution Action Plan then

translates the strategy into a specific work program, designed to achieve the stated

aims of the strategy.

A well-articulated Health and Pollution Action Plan with clear goals, well defined

priorities, a is essential for the control of pollution and detailed roadmap, and a timetable

the prevention of pollution-related disease. The plan should define specific pollution

problems that are then prioritized for action, and given targets for reduction in exposure

and benefits to health. The plan should specify appropriate regulatory agencies as well

as other strategies, such as economic development, tax incentives and industrial policy

to be used to achieve the goals. Plans will be most effective when they assign high

priority to controlling pollution sources that directly impact health. The specific structure

and the level of detail of the Health and Pollution Action Plan will vary according to

context and purpose but it typically addresses the following.

• Clear statement of the context/challenge and the related objectives.

• Identification and assessment of priority problems, and possible interventions

• Agreement on set of activities which will achieve progress to the objectives

• Establishment and agreement of institutional responsibilities for each

action/activity

• Definition of measurable Specific, Measurable, Attributable, Realistic, and Time-

bound (SMART) indicators to be used to determine progress

• Clear timeline for achievement of outcomes for each activity

• Implementation of a focused stakeholder awareness and involvement process

• Identification of the financial and other resources available and required for

successful action

• Procedure for monitoring and reporting on progress against targets

• Process for review of progress and adaptive management of activities

A good Action Plan is a collective agreement to work to achieve a common goal: it

should not be just a “wish list”. There is no one ideal model for an Action Plan but the

67

variety and usefulness of different types of Plans can be seen from consideration of a

non-systematic sample of publicly available examples.

An important aspect of preparation of a Health and Pollution Action Plan is to strike the

appropriate balance between policy/institutional activities and the implementation of

specific projects and programs. This balance usually reflects the level at which the

planning is being carried out.

Identification of priority issues. Governments (and communities) face many

challenges in establishing a Health and Pollution Action Plan and in balancing pollution

control against other demands. Careful prioritization is therefore of critical importance.

It is essential that effort and resources are applied to those pollution issues where

impacts are greatest and where the potential benefits to action are highest. This

requires examination of a broad range of potential control targets using the most current

and comprehensive health and environmental data. Targets for intervention are

selected by giving heavy weight to those pollution sources that have greatest health

impacts; examining the range of possible pollution control options; determining

opportunities and constraints; and identifying institutions and resources that will guide

and assist the process of pollution control and disease prevention.

Selection of invention targets – Choosing ‘best buys’. In weighing the pros and

cons of various possible interventions against pollution there are two broad kinds of

economic analyses that can be carried out. The first is a standard benefit-cost analysis,

which calculates the costs and the estimated benefits of an intervention in monetary

terms and compares the two. At a minimum a positive benefit/cost ratio is required,

which indicates that the intervention would provide a net return for the investment of

public money. There are challenges in carrying out a credible and comprehensive

benefit-cost analysis but a major advantage is that the approach is well known and

understood by finance officials and is widely used in planning and budgeting.

A second approach to evaluating interventions is through a cost-effectiveness analysis.

This analysis examines the effectiveness of an intervention in reducing health risks and

other deleterious outcomes. A cost-effectiveness analysis can be used where different

68

interventions have the same or comparable outcomes. In the health sector, where

DALYs are often used for measuring and comparing the results of treatments or other

interventions, cost-effectiveness estimated as $/DALY is a clear and well understood

measure.132 Where it can be calculated, this $/DALY number provides a very good

indicator of the value of the intervention, since a range of figures are available in the

literature.133,134

Health and Pollution Action Plans for Urban Air Pollution. Air quality is mainly an

urban issue and many metropolitan areas have prepared and implemented Health and

Pollution Action Plans over recent decades. There are also several networks for cities

dealing with air pollution, including Clean Air Asia and the Instituto del Aire Limpio

(Clean Air Institute). These networks foster the sharing of experience and material

among cities. Current “Plans” for the larger cities (Bangkok, Delhi, Mexico, Santiago)

tend to be new initiatives building on past progress and current information, often

focused on very specific technical interventions. As an example, in 2016 the Mayor of

London put out for consultation new plans, including the extension of an “Ultra-Low

Emissions Zone” in the metropolitan area. In other cases, such as Tehran, lessons

learned from implementation of a First Action Plan are used to refine subsequent plans.

Shortcomings in levels of institutional cooperation and active public involvement that

were identified in the First Plan were given increased attention in the Second Action

Plan. By contrast, the Air Action Plan for the Province of British Colombia (Canada) has

selected “clean communities” as one of its three areas of activity – not just supporting

individual actions but also engaging the population and civil society in the entire

process. At the more local end of the spectrum of Plans, English local councils are

required to address air quality but have limited jurisdiction and resources. As a result,

their Action Plans typically identify some generic actions, such as traffic management

and promoting non-motorized options, and then apply these in detail to local hotspots.

Health and Pollution Action Plans for Water Pollution. For water pollution, there is

often a mismatch between physical extent of the relevant river or water body and the

administrative boundaries. This adds an extra complication to the process of preparing

69

an Action Plan. It presents a challenge but a cooperative Action Planning process may

be a way to define and agree on a set of cross-boundary activities. The Great Lakes

process between the USA and Canada has resulted in a number of specific Action

Plans to address identified priorities, with considerable success in reducing pollution.

Health and Pollution Action Plans in China. China has developed a number of

national “Action Plans” to address major issues related to pollution and health. These

include the National Action Plan on Environment and Health (2007 – 2015) which set

out basic principles, targets and actions. In 2013, the State Council issued an Action

Plan on Control of Air Pollution, followed in 2015 by an Action Plan for Water Pollution

Prevention and Control, which targeted key polluters and emphasized the need to

improve enforcement and to engage local stakeholders. In 2016, a further Action Plan

was issued for Soil Pollution Prevention and Control,135,136 following a once-in-a-decade

national soil survey and aimed at making contaminated arable land safe for human use.

These plans set goals and targets for different contexts (for 2020, 2030 and beyond)

and identify typical policies and interventions which Provincial and local authorities are

expected to adapt and apply as appropriate. A key element, especially of the water

plan, is the designation of a lead ministry for each specific activity, together with named

ministries and other agencies who are to support the implementation, as a way to

promote the necessary coordination which is often a challenge in any country. The

specification of defined targets is a critical mechanism to help track progress in such a

large and diverse country. The new Environment Protection Law which came into effect

at the beginning of 2015 has been described by the Ministry of Environmental

Protection as innovative, noting that it specifically takes into account: relevance to

reality, a view to the future, and the balance of rights and obligations for organizations

and citizens.137 The Government has acknowledged that the next level of progress in

managing environmental challenges will have to come from “strengthening of the

institutions, incentives and instruments that enable effective enforcement across sectors

and at an appropriate geographic scale”.138 This will involve strengthening “green

governance”, including setting binding targets, providing greater authority and resources

for enforcement efforts, replacing subsidies which discourage energy efficiency with

70

support to targeted groups, and providing more mechanisms for citizen involvement and

access to the judicial system.

An Action Plan is broadly consistent with a Logical Framework (LogFrame) or Results

Framework approach, which is often required for large projects, especially those

supported by international agencies. In practice, the Action Plan would normally put

more emphasis on the specific activities that are to be undertaken, detailing the parties

responsible and the resources to be applied. Both of these complementary approaches

provide a structured framework for planning, implementing, communicating and

monitoring major programs.

External Review of Health and Pollution Action Plans. Health and Pollution Action

Plans always benefit from expert scrutiny by disinterested outside experts with

experience in pollution control and pollution-related disease prevention. An effective

high-level review can be conducted in a relatively short time, including one or two days

of workshop with the appropriate officials and other key players. The outputs of the

review include refining the roadmap and creating or expanding a comprehensive Action

Plan to improve health outcomes for the population.

Key elements of this review are as follows:

• Include all relevant agencies. Senior representatives of Ministries of Health,

Environment, Industry, Development, Finance, Transportation, Planning, and

legislative branches if appropriate should be involved.

• Make health impacts the focus of the review and focus interventions on those types

of pollution that cause the most premature death or disability.

• Review each pollution aspect to understand the sources of exposures and to clarify

the linkages to health.

• Review existing programs for efficacy and reach. Determine where there are

performance gaps: areas where expansion can impact more people, or further

reduce pollution-related disease. Review legal, regulatory and enforcement gaps.

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• Outline potential interventions (new and expanded) for those exposures for which

there is high health impact, significant co-benefits, and an appetite for solutions, and

evaluate these for cost–effectiveness.

• Focus not only on traditional pollution aspects, but also on pollution aspects that

have likely had less attention historically. These include:

o Household air, especially with respect to enhancing fuel types, such as

support for bottled gas instead of burning dung or wood.

o Contaminated sites. Not just those near large industry, but a focus on

toxins from smaller industry, especially in high-density areas. These may

include mercury contamination from artisanal mining, and lead

contamination from battery recycling.

o Distributed lead exposure issues, which include lead in pottery glazes,

lead in paint, and other pathways that may be specific to a particular

culture.

o Occupational risks, including those of asbestos.

• Review co-benefits of interventions aside from health improvement. Gender, climate

change, tourism, economic growth, education, and political factors need to be

included in discussions.

Citizen Participation in Health and Pollution Action Plans. Governments extend

their reach and increase the effectiveness of pollution control programs when they

reach out to non-governmental organizations, the private sector, academia and other

stakeholders and use the tools of information technology to aggregate good practices

online that can be used by individuals, companies, and governments. The public should

have the right to contribute to formulation of the pollution action plan and to access

information and records held by the environmental agency and other relevant

authorities, subject to only carefully limited and defined exceptions, and with the right to

prompt judicial review if the information and records are not provided. Members of the

public should have the right to participate in administrative proceedings to issue

regulations and permits. Environmental and other citizen groups and/or individual

members should have the right to judicial review of standards, regulations, permits,

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environmental impact assessments, and agency disposition of petitions to take actions.

Modest changes by the many will have a profound impact on pollution reduction.

Government agencies should conduct an environmental impact assessment based on

international standards for major new facilities or projects that may significantly affect

public health and/or the environment.

1. A sound body of environmental laws and policies backed by an effective and independent judiciary. Institutions and coordination.

A fundamental requirement for effective pollution control and management is a sound

body of environmental laws and policies backed by an effective and independent

judiciary. Most countries and cities have adequate basic environmental legislation and

pollution control regulations in place.

Key components of a model environmental law could include the following:

1. Framing the law. The preamble of such a law must state clearly the societal purpose

of the legislation and must define what is a pollutant. Such a clear preamble will

ensure that future judicial interpretation of cases brought under the law will be

required to consider both the technical and the societal elements of offences against

the law.

2. Sanctions. The law must establish sanctions for failure to comply and establish

penalties commensurate with the health and safety harms that each pollutant causes

both to individuals and to communities. In this aspect of the law, scientific and

environmental evidence is key.

3. ‘Polluter pays’ principle. The law should state clearly that the legal onus is on the

polluter and not those suffering from the polluter’s actions. This reverse onus of the

burden of proof in cases of environmental pollution and degradation is best suited to

situations of low resourced communities and offers a real chance for social justice as

an outcome.

4. Targets and timelines. A model law would include specific targets and timelines. The

ultimate goal is both control and elimination of pollution that harms the health and

safety of individuals and communities.

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5. Monitoring. A model law must establish ongoing monitoring. While a law and its

subsequent regulations are essential, they are but a first step. Rigorous and

enforceable monitoring must be a part of all proposals and projects designed to

reduce the health and safety risks posed by locally created and driven polluting

practices. This process must be adequately funded, and implemented in a way that

provides training and employment to those living within affected communities in the

future. Several monitoring protocols have been developed for environmental

projects. Those that already exist internationally should be identified and amended

to meet specific local circumstances. These, then, can be used as templates for

action.

6. Legal redress. A model law must provide options for judicial review through the

courts. Yet while the courts have an important role to play, they should not be the

only option for redress. Alternative dispute resolution mechanisms exist in several

jurisdictions, and should be used as primary tools to address and resolve such

issues. The court process is lengthy and expensive, focussing inevitably on

individual rights. Effective ways to articulate and protect community interests in

healthier and safer conditions and environments are essential. Most pollution and

environmental cases involve entire communities, and the health and safety issues

affect most of the families who live there. Even choosing to pursue a class action on

their behalf ultimately leads to a lengthy judicial process, and only includes the rights

of individuals who form part of, or who have opted into, the class.

Ultimately, the purpose of the law will be to identify the pollution and polluter, offer legal

and alternative options and solutions to address the health and safety impacts of the

polluting activity, and to ensure that environmentally sound alternatives are offered and

undertaken directly by those within the affected community to benefit that larger

community and empower it to further action and control.

Every country has its own specific characteristics and institutions, and new legislation

and regulation must be designed and implemented to suit the local circumstances.

However, there are relevant models available in the legislation of a number of

developed countries. Canada has a broad suite of environmental legislation, covering

environmental protection, pollution prevention, biodiversity and conservation, and

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sustainable development broadly. This legislation and associated regulations may help

countries in the design of their own systems.139

Laws and regulations140,141 are not sufficient142 to control pollution and prevent pollution-

. They need to be backed by the head of government, buttressed by related disease

strong and impartial enforcement and supported by an independent judiciary that has

effective authority, knowledge and capacity to enforce their decisions. Enforcement staff

may need to be expanded and trained.143 Fines or penalties need to have sufficient

substance to force polluters to change their behavior. They must be legally applied.

Regulatory and remediation programs should be based on Polluter Pays principle, but

some public funding should be provided where needed for public waste treatment and

other public facilities, laggard industry sectors undergoing modernization, and “orphan”

hazardous waste sites.

2. A cabinet-level environmental agency with responsibility for collecting data on sources and levels of pollution and the power to enforce environmental laws and regulations. A national, regional or city-wide environmental regulatory body

with general authority over pollution in air water and soils and a mission to protect

health and the environment is of central importance to successful intervention

against pollution. In addition to data collection and enforcement powers, this agency

should ideally have capacities also for scientific research and risk analysis,

engineering and economic analysis. The environmental regulatory body must have clear constitutional and statutory

authority to adopt standards and regulations to protect health and the environment and

to enforce regulations through requiring control and cleanup measures from industry to

achieve standards. It must also have the authority to impose meaningful and effective

sanctions, liabilities, and orders requiring specific actions on its own writ or by

application to the courts. There must also be mechanisms for effective cooperation with

other relevant government authorities in public health and medical care, toxicology and

epidemiology, finance, economic development, industrial policy and location, energy,

agriculture, mining, drinking water, maritime activities and environmental health and

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management research institutions. The environmental regulatory body should have

regional branches and an appropriate allocation of authority with state, provincial or

regional authorities, and local authorities. The allocation of authority and responsibility

among these different units should be designed and implemented at scales appropriate

to the health and environmental problems and their solution.

The environmental regulatory body must have adequate staffing and resources,

including staffing in disciplines such as environmental health science, risk assessment,

engineering, economic analysis, and data management.

3. Robust, transparent environmental and public health data systems. High quality metrics that monitor pollution and and track pollution-related disease

progress towards national and local pollution prevention and disease control goals are

essential to the success of any Pollution Action Plan. Early establishment of public

health and environment monitoring systems should therefore be a priority

Two types of data are needed for effective intervention against pollution, and they both

need to be continually updated and monitored:

• Environmental data that monitor sources, levels and geographic distribution of

the various types of pollution; and

• Public health data that track geographic and temporal patterns of diseases

related to pollution.

This Commission encourages governments to consider creation of a Central

Data Bank (CDB) that acts as a repository of data on pollution – household,

ambient and occupational. The CDB should have controlled and restricted

accessibility to general public and full accessibility to researchers, regulators, and

scientists and policy makers.

Environmental data systems. Considerable expertise has been developed worldwide

in the design and operation of environmental monitoring, sampling and testing systems.

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Advice can therefore be provided to cities and countries by donors, development banks,

and technical organizations.144,145

Modern technology continues to reduce the costs of data collection and information

processing and basic monitoring systems can often be established for relatively limited

costs. The use of satellite-based remote sensing, for example, to measure air pollution

is gaining increased attention although the coverage and interpretation of satellite data

is still being refined.146 Europe and the USA have acquired significant experience with

the use of satellite-derived data, which have been reported to be capable of accounting

for year-to-year variability, and are population-weighted and useful for long-term trends.

Satellite-based data have also been used to provide spatial coverage in sparsely

populated areas, such as in the mid-west region of USA, which lacks a robust network

of monitors. It is important to note that the use of satellite-derived estimates is

nonetheless built on a strong foundation of existing ground-level monitoring networks.

The use of satellite technology for measuring air quality is not without challenges.

Satellite-derived estimates have been reported to “average out” high particulate matter

concentrations leading to low bias in estimates of particulate matter concentrations in

cities where PM concentrations are known to be high. This has been observed in

Ulaanbaatar, a city known to have some of the highest PM2.5 concentrations ever

measured, and cities in China, Ukraine and Kazakhstan. Furthermore, satellite-derived

data have led to over-estimates of PM concentrations in desert countries affected by

natural dust and sea salt. For example, according to the GBD 2013 data sets,

Mauritania has the highest annual PM2.5 concentration of 70 µg/m3 and Mongolia 8

µg/m3, which is incorrect. Other shortcomings of application of satellite technology relate

to use of only day-time observations, no observations on cloudy days, and averaging

out of PM concentrations over large areas (typically 10x10km). At the same time,

satellite-based data has the potential to provide estimates for regions of the world with

no or limited ground-level monitoring (GLM) networks, if discrepancies between

satellite-based and GLM can be resolved. Also, satellite-based observations could

potentially reduce the cost of air quality monitoring in certain locations by reducing the

density of GLM, but this issue needs to be better understood.

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Public health data systems. While there have been substantial improvements in the

quality of public health data worldwide, there are still many gaps especially in poor

countries with insufficient resources for systematic data collection.147 Thus only one-

third of the world’s population and only 5% of Africa is covered by usable information on

causes of death. China and India have both been redeveloping their verbal autopsy

registration systems in which cause of death is based on data provided by field-trained

personnel, and these data systems are improving.148 Limitations in the quality of public

health data reduce the accuracy of global estimates of the burden of disease related to

pollution.

Estimates of disease frequency from the Global Burden of Disease Study coordinated

by the Institute for Health Metrics and Evaluation can serve as a starting point for

tracking the burden of pollution-related disease in a city or country, but these modeled

estimates need to be supplemented by data collected on the ground by local agencies.

This Commission is of the strong opinion that environmental and public health

monitoring data should be made available to the wider public in clear and accessible

form. Such “democratization” of the accumulated knowledge is essential to engage

society in understanding the issues and supporting key actions.149,150 Information should

be reported in a format that is readily understandable to the general public – data from

monitoring systems can often be highly technical.

Data collection and analysis can be an intensive and expensive exercise and new

sampling and monitoring systems have to be well justified. There would be strong

value in having a Central Data Coordination System (CDCS) whose role would be to

liaise with the many different collectors of data and to commission and publish

integrated reports.

4. An active civil society and independent media who can bring attention to pollution problems, and environmental pollution-related diseasediscrimination.

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Civil society organizations (non-governmental organizations and community-based

organizations) are a playing an increasing role in holding governments and companies

to account for pollution control and prevention of . Heads of pollution-related disease

government and government agencies can gain powerful allies and great leverage

when they empower civil society to combat pollution. This can be achieved by improving

transparency (making information on air, soil, and water quality publicly available) and

participation (consulting private sector and civil society representatives in pollution

policy making).

Neglected in global health circles is advocacy for strengthening human rights

protections, enforced by a competent, independent, non-corrupt judiciary. Such rights

and the judicial mechanisms needed to support them are essential to protect people

and their lands from the damaging effects they may be subjected to through certain

industrial activities. This is particularly important for indigenous peoples who often

endure some of the lowest socio-economic conditions yet whose lands contain a large

percentage of the world’s natural resources. It also enables individuals and

governments to receive compensation for damages sustained and recoup funds needed

to rehabilitate lands damaged through industrial activities.

Private industry is also finding that responsible corporate citizenship (i.e., a sound

commercial, social, and environmental bottom-line) is good for business. Enlightened

business leaders can be powerful advocates for pollution control and disease

prevention. The creation by government of incentives for non-polluting industries can be

powerful catalysts for innovative action, as has been seen in the recent rapid

development of solar power systems and the organic food industry.

This Commission emphasizes that multiple stakeholders should have a role in

controlling pollution and preventing s, including not only top pollution-related disease

government leaders but also key civil servants, business and academia, and civil

society. Mapping the most important and influential stakeholders (both formal and

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informal) can help to ensure that all the parties who can advance (or derail) programs

are taken into account.151

5. Transdisciplinary training programs that build future capacity in pollution prevention and upgrade technical skills.

Capacity building is essential to the success of Pollution Action Plans and to attainment

of the Sustainable Development Goals. UNDP has identified four main components to

upgrading national capacity in pollution control and prevention of pollution-related

disease: institutions; leadership; knowledge; and accountability.152 Education underlies

every aspect of capacity building.

To strengthen the capacity of governments and academic institutions in low- and

middle-income countries to control pollution and prevent pollution-related disease, this

Commission encourages countries to develop transdisciplinary education programs in

the environmental health sciences. These programs can build knowledge of the role

of toxic environmental exposures in human biology and disease and focus on

pathophysiologic, clinical, and public health endpoints. Regional partnerships across

countries, possibly supported by international organizations, are an effective way to

launch these programs and support research.

This Commission also encourages additional institutional capacity-building efforts

such as in-country training workshops in advanced techniques; distance learning; and

interaction with other national and regional efforts to strengthen the ability of

institutions to identify and undertake successful research and research training in

to influence teaching, implementation, and country policy. pollution-related disease

Specific training models include the following:

• Long-term (6 months or longer) training to build the full range of skills

necessary to plan, conduct, manage and disseminate the results of

research on with the understanding that the pollution-related disease

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focus of thesis and training-related research will be in the low-and

middle-income country.

• Medium-term (3-6 months) training or mentoring, which may include

specialized research, policy, or administrative/business skills necessary

to conduct research that is planned or ongoing and translate results for

interventions and policy.

• Short-term (less than 3 months) training or mentoring that focuses on

building research skills.

International experts and agencies can guide and inform the building of professional

capacity within countries. The World Bank has gained great experience through their

Country Environmental Analysis (CEA) and more recently through studies specifically

focused on health and economic aspects.153 The Global Burden of Disease team

(GBD)103,124,154 project has developed detailed health impact data down to a country

level.

6. Sufficient financial resources to sustain prevention of pollution-related and pollution control programs now and in the future. disease

Interventions against pollution and are multi-year endeavors pollution-related disease

that require sustainable, reliable, long-term funding. Government funding is needed to

support core regulatory, data collection and capacity building operations. Beyond that

core, a range of financing mechanisms is typically required and the mix will vary from

country to country and also by type of pollution.

City-wide or regional systems for pollution control and pollution-related disease

prevention such as drinking water reservoirs, central sanitation, improved power plants,

or remediation of contaminated sites will typically require some form of physical upgrade

or up-front new investment. These types of interventions often require substantial public

financing. Assessing the cost-effectiveness of these interventions is critical, and they

need to be weighed against other government priorities. Economic impacts, social

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impacts, and other priorities all come into play. Calculation of the costs of pollution and

of pollution-related disease can help put the costs of pollution control into perspective

and counter one-sided arguments against pollution control that are based solely on the

costs of control.

Government funds to support pollution control and infrastructure improvement activities

can be raised through general revenues (taxes or debt) supplemented in some cases by

fees or specific taxes levied on polluters. The use of government funds for pollution

control and prevention is justified by the benefits that these of pollution-related disease

programs provide to societal health and well-being. Basic funding of a government

department will normally come from the general budget but additional funding is often

required for special programs or for capital items. Where possible, regulatory and

remediation programs should be based on Polluter Pays principle, including recovery of

costs incurred in managing the process. However, some public funding is likely to be

needed for public waste treatment and other public facilities, laggard industry sectors

undergoing modernization, and “orphan” hazardous waste sites

A baseline estimate of the scale of government funding needed to sustain a successful

pollution control program comes from the EU countries where in in 2014 the average

annual expenditure of governments on ‘environmental protection’ amounted to 0.8 % of

GDP, with range from 0.3% to 1.6%.155 Pollution action programs in the EU countries

tend to be highly sophisticated and include expenditures on control of air and water

pollution, site remediation, pollution abatement, and biodiversity protection, all of them

buttressed by extensive data collection programs.

Policy actions may bring in charges, fees or other financial instruments and their impact

on different groups needs to be carefully assessed. Pollution charges requiring

industries to pay for their actions generally have broad public support. On the other

hand, user charges and taxes are often controversial. Fuel and similar taxes are often

seen as regressive – impacting the poor to a greater extent. Charges for water and

sanitation can make the services unaffordable for the poor, who would benefit most.156

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Subsidies can be important tools in combatting pollution – both removal of subsidies to

activities that pollute (in sectors such as energy, agriculture and transport) and provision

of subsidies for “cleaner” or pollution abatement activities. Subsidies to bring the cost of

bottled gas in reach can mitigate health impact on poor families using wood and dung

as cooking fuel. Decisions are usually based on a complex mix of considerations.157,158

The use of pollution discharge fees or taxes and tradable pollution permits should be

seriously considered.

Substantial sanctions should be imposed for violation by regulated facilities or sites of

requirements for monitoring, record-keeping and reporting pollution discharges or site

conditions to the regulatory authorities. Sanctions and liabilities must be set at levels to

provide effective compliance incentives. Experience shows that relying exclusively or

primarily on criminal sanctions is generally not effective. Criminal sanctions should be

reserved for the most serious violations of law. Severe liabilities and penalties should be

imposed for all substantial violations, calibrated at levels that will recoup the economic

benefits that the violator gained from the violation plus an additional amount based on

the gravity of the violation.

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SECTION 4: INTERVENTIONS

A wide range of experience in controlling pollution and preventing pollution-related

disease has been gained worldwide in countries and cities at every level of income.

Some of these processes, interventions, and successful strategies are summarized

here.

Control of active, large-scale industrial pollution: The principal strategy for control

of active large-scale industrial pollution is enforcement of laws and regulations and is

based on the ‘polluter pays’ principle that requires polluters to use their own internal

resources to reduce their pollution. Polluters can reduce their emissions by multiple

approaches that include increasing process efficiency, changing processes or

feedstocks, or capturing emissions by stack scrubbers or wastewater treatment. Modest

levels of pollution reduction may be financed out of an industry’s operational budgets,

but major improvements typically require allocation of capital. Such allocation of capital

is usually resisted by polluting industries whose managers use threats of job losses or

relocation to avoid pollution control.159 Visionary and forceful leadership is required to

overcome the ‘political economy’ of such vested interests.

Remediation of widely dispersed pollutants. Cost is a particular problem in dealing

with persistent and toxic substances that are dispersed on a large or global scale. Lead

is a key example, as even low levels of exposure to lead as occur broadly around the

world have been found to have profound impacts on children’s health and intelligence

and on adults’ risk of hypertension and cardiovascular disease. Persistent organic

pollutants such as PCBs, brominated flame retardants and chlorinated pesticides

provide another example. Remediation of these types of toxic substances at

concentrations that are relatively low but still pose a significant health risk is difficult and

costly.

Primary prevention of environmental dissemination needs to be the mainstay of

protection of planetary health against persistent pollutants. Such prevention is most

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effectively achieved by bans on the production of highly hazardous substances, such as

are mandated under the Stockholm Convention on Persistent Organic Pollutants

(POPs).

Control of small-scale industrial and artisanal pollution: The difficulties in

identifying individual polluters and in bringing about improvements in their performance

can be substantial in the case of smaller active polluters, especially those in the informal

sector such as battery recyclers and gold miners. These polluters typically have no

capital reserves, and the strategies that can be used to curb emissions by larger

polluters have little traction. Some of the most successful approaches to control of

emissions from small polluters have involved market-based initiatives.160

Control of household pollution: Governments can play an important role in the

control of pollution at the household level in assisting households and communities to

secure alternative fuels for cooking and in accelerating the provision of clean water

sources and better sanitary facilities. Such programs can be moved forward by

redirecting and targeting ongoing infrastructure and service delivery programs or by

providing new support. A recent initiative in India to finance the provision of clean fuels

to 50 million households161 is notable for a number of reasons, not least that it is

planned to be funded by re-direction of liquefied petroleum gas subsidies voluntarily

from wealthier consumers to the targeted poor households.162 Non-governmental

organizations can play important roles in such efforts.

Provision of water and sanitation facilities to poorer communities and households has

for decades been implemented by government finances, with donor support.163 In urban

areas with large utilities, there may be an element of cross-subsidy, from industry to

households and from wealthier to poorer. However, since poor communities are often

difficult to service and generate limited revenue, there has often been slow progress by

utilities in reaching targets for coverage of poor communities.164

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Behavioral changes for control of pollution: Behavioral strategies for pollution

control and prevention of pollution-related diseases that require individuals to change

the established patterns of their daily lives, such as changes in cooking methods, fuel

sources or sanitation behaviors, require no major physical investments, but can yield

great benefits for health.165,166 Successful interventions incorporate local community

involvement often coordinated by civil society, along with media and public awareness

campaigns.167

Interventions based on behavior change and technical interventions for clean cooking,

sanitation, or trash disposal must negotiate complex social, economic, and cultural

preferences in low- and middle-income countries. In resource-poor communities and

households, behavior change interventions have to be particularly mindful of how

livelihood risk affects behavioral outcomes. It is not uncommon for households and

communities to abandon the use of better practices or technologies in times of a

livelihood crisis. The history of household air pollution is illustrative of how sound and

elegant technical solutions of clean combustion or clean fuels are difficult to sustain

under real world conditions. Household air pollution interventions have to overcome

cheap availability of wood and other solid fuels secured daily by women and girls.

Complicating behavioral and technical interventions in low- and middle-income

countries are gender preferences and varying time horizons involved in the decision-

making around technical and social interventions. Provided that an intervention is

evidence-based, it is necessary to focus on understanding why behavior change is hard

to realize or technology that is beneficial is abandoned after a short duration of

experimentation. Research must also learn from the behavior of positive deviants who

shift voluntarily to better practices like hand washing or cooking with clean fuels.

There is an emerging body of methodologies in system science to engage communities

and vulnerable households to understand the underlying mechanism of feedback

structures that drive desired or bad behavior over time.168,169 Institutional level factors

include household and community level characteristics. The household level

characteristics can be awareness levels, gender-based decisions, willingness to pay,

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and perceived/real opportunity costs. Community level characteristics include capacity

to mobilize, investments from a community, and social networks. System-level factors

include supply side and policy related determinants.

Control of ambient air pollution. Ambient air pollution is the product of a range of

emissions from stationary sources, principally industrial sources, as well as from mobile

sources – cars, trucks and buses. In rapidly industrializing cities and countries where

there is a mix of household air pollution and ambient air pollution, the burden of disease

can be high.161 Interventions in such locales need to be broad-based and encompass

control of pollution from both sources.

Each year, an estimated 6.5 million deaths worldwide are linked to ambient air pollution.

Inefficient and poorly regulated fuel combustion is the primary source of the main

pollutants. Despite the growing challenges, there are examples where major

improvements have been achieved.

Mexico City —In 1992, Mexico City was named the most polluted city in the

world by the World Health Organization. Since then, after some key interventions

both air quality has improved170 and the rate of greenhouse gas emission has

reduced significantly.171 The “Hoy no Circula” (“No Driving Today”) program

controls the city’s vehicle circulation by restricting diesel- and gasoline-fueled

vehicles from driving according to the day of the week and the last digit of the

vehicle’s license plate. It reduces vehicle circulation by 20% every day from

Monday to Friday.172

London — The city’s air was seriously polluted with the Great Smog in

December 1952 that killed approximately 4,000 people. The peak in number of

deaths coincided with peaks in the levels of airborne smoke and sulfur dioxide. In

the wake of this disaster, the first Clean Air Act was introduced in 1956 to combat

the bad air quality in London. The main points of this policy were regulations on

chimney heights, smoke control areas, and emissions from industrial

premises.173 The great reduction in both smoke and sulfur dioxide levels in

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London that followed has been attributed to both the Clean Air Act and to a

general switchover from smoke-producing fuels such as coke and coal to gas.

California — Ambient ozone levels in Los Angeles in the 1960’s were the

highest levels ever recorded in the world. Thick smog was present year-round

and contained harmful substances such as formaldehyde, and peroxyacetyl

nitrate. To reduce this toxic pollution, the California Air Resources Board adopted

an aggressive strategy. They implemented source controls for cleaner engines

for cars, regulations on industrial emissions, and vapor recovery systems in cars.

Between the 1960’s and 2008 air pollution had been reduced by 75%.

Reductions in childhood asthma and improvements in lung function have been

documented.

Ambient air pollution, climate change, and health are closely linked problems that share

a number of common causes and solutions.174 Highlighting and leveraging the co-

benefits of air pollution reduction strategies encourages collaboration, increases funding

opportunities, and ensures greater traction, acceptance, and compliance. Actions to

reduce air pollution can have additional global benefits.175

While gaps in understanding details of sources and associated risks should not be a

reason for delaying actions to begin to tackle obvious problems, detailed source

apportionment studies are important for large and complex problems. These take time,

technical capacity, and resources. They provide a better understanding of sources of

toxicants, often identifying new issues, including those outside the immediate study

area.

Control of stationary sources of air pollution. Many countries have made real

progress in dealing with large stationary sources of ambient air pollution, although more

remains to be done. Emissions regulations – if they are consistently applied – can bring

about operational changes (including fuel improvements) and attract capital investment.

Thus large electricity generating plants in most countries around the world are now fitted

with or soon will have filters (for particulates) and scrubbers (mainly for sulphur gas and

other volatile pollutants). Fuel upgrades that can be introduced include coal washing (to

reduce dust in emissions) and transition to lower sulphur liquid fuels.176

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For many low- and middle-income countries, the first step in an air pollution control

program is to reduce emissions of particulate matter. China, which has already done a

great deal to reduce emissions, still faces major challenges and has been planning for

additional efforts to reduce particulate emissions. Measures being implemented

include: demand-side management and structural measures; cleaner technology and

fuel; end-of-pipe control; and addressing general dust from other sources.177

Emissions controls can often improve the overall efficiency of electricity generating

plants. India has historically had relatively poor efficiency in its coal-fired plants.178 A

public rating of the performance of these179 reported that the best plants were 15%

more efficient than the average, with a number well below the average, indicating that

increasing performance of the poorer plants would not only provide more energy but

would also reduce both particulate and CO2 emissions.

Widespread small and medium polluting industries such as brickmaking may also need

to be regulated and upgraded. Small sources, such as district heating, can be

addressed by upgrading systems, using new heating equipment, and/or better fuels. In

low-income countries, burning of solid fuel contributes not only to household air

pollution, but also to ambient pollution and therefore needs specific attention.

Practical implementation of a national air quality monitoring (AQM) plan. Implementation of a pollution control plan is challenging. In Thailand, four main steps

were identified: (1) establishing the basic air quality system; (2) strengthening

compliance and enforcement; (3) carrying out action plans for major cities and specific

issues; and, (4) public participation and involvement. The national environmental

protection agency in Thailand has a set of existing and emerging strategies to take

these four steps and to continue to improve air quality in Bangkok and other target

areas,180 as summarized in the Table.

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TABLE 2. Thailand’s Strategy for Controlling Ambient Air Pollution180

Strategies for AQM in Thailand

Existing Emerging

Ambient and emissions standards Improved fuel quality standards

Diesel emissions reductions Environmental Impact assessment

Control of high polluting buses and

trucks

VOCs emissions controls

Inspection and Maintenance

program

Continuous Emissions Monitoring

systems

Alternative fuels Enhanced capacity for compliance

Control of open burning City Action Plan and implementation

Co-benefits with Climate Change

strategies

Policies can be defined on paper, but often significant difficulties may be faced in

implementation. It is very useful to identify potential barriers early in the policy process

and to prepare some practical responses. In the context of urban transportation

policies, a useful practical checklist could be as outlined in the following Table derived

from the International Energy Agency.181 The most important aspect is to anticipate and

prepare for the process of negotiating a policy change through the various different

practical steps necessary for effective intervention.

TABLE 3. Strategic Considerations in Upgrading Public Transportation.

IDENTIFY

BARRIERS

What the potential barriers: financial; legal, regulatory, public

opposition, bureaucratic resistance?

Which existing policies or regulations could impede effective policy

action?

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Are there identifiable groups which are likely to oppose the policy?

Where is the political support for implementation (and the opposition)?

How reliable or secure is the needed funding?

Is any critical data lacking? How will progress be monitored?

PREPARE Which actions can be designed to respond to specific barriers?

What changes to legal, regulatory or policy frameworks are needed?

Can the new policy process be adjusted to accelerate implementation?

How to engage and then involve stakeholders in overcoming barriers?

What concessions or compromises can be considered while still

achieving policy aims?

RESPOND How should responses be organised and orchestrated?

What is the best sequence and timing of responses?

How many different types of activities are required and are the

resources available?

Who needs to be involved and how will they be

supported/coordinated?

Are the relevant target audiences being addressed?

Is the overall process of dealing with barriers decisively managed and

adjusted as needed?

Control of mobile air pollution sources: Mobile sources are becoming the most

important contributors to ambient air pollution in many cities. Reflecting this increase,

the global percentage share of total oil demand by the road transport sector is projected

to increase from 57 percent in 2010 to 60 percent in 2035.151 Control of mobile

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pollution sources is complex because of the number and variety of individual units and

because of the range of alternatives that have to be taken into account.

An overview of progress by governments in 193 countries around the world in

implementing Urban Air Quality Management Plans (UAQMP) has identified ten

successful policy elements, which include: clear objectives; robust monitoring;

establishing an emissions inventory; modeling; developing control strategies; and

obtaining public participation.182 This review also identified important challenges to

such plans: weak enforcement; limited cooperation between national and urban

authorities; inadequate monitoring; and failure to involve the public.

A range of strategies for controlling vehicular air pollution have been used – often in

tandem with one another - in developed countries and in some middle-income

countries.183 They are listed in the table below. To date few of these strategies have

been adopted in low- and lower-middle income countries (see Table 4). These proven

strategies are ready to be adopted ‘off-the-shelf’ today by countries at every level of

income.

4: Strategies implemented under UAQMPs for vehicular sources183

UMAQM STRATEGIES US UK EU Aus B.C. Jap

H

K Sin MX TH ZA CN IN

Alternative vehicle fuel x x x x x x x x x x

x x

Fuel quality improvement x x x x x x x x x

x x

Catalytic converter x x x x x x x x x

Inspection and

maintenance program x x x x x x x x x

Low emission vehicles x x x x x x x

x

Street parking banned x x x x x x x x x

Support for cycling x x x x x x x x

x

x

Congestion charging x x x x

x

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Bus corridors x x x x x x

x

Pedestrian corridors x x x x x x

x

Low emission zones

x x

Old vehicles banned in

AQ zones x x x x x x x x x

HGV banned in AQ

zones x x x x x x x x x

No idling at traffic signals x x x

x

Traffic signal optimization

x x

Intelligent transport

systems x x x

Ban on smoky vehicles x x x x x x x x x x

On board vehicle

diagnostics x x x x x x x x x x

Registration subsidy for

clean vehicles x x x

x x

Mass rapid transit x x x x x x x x x x

x x

Support for carpools x x x x x

x

Road infrastructure

maintenance x x x

x x

x x

Stringent emissions

standards x x x x x x x x x

x x

Reduction in diesel

vehicles x x x x x x x x x

In Africa vehicular sources are estimated to contribute over 90 percent of urban air

pollution.182 Initiatives such as the Partnership for Clean Fuels and Vehicles have been

instrumental in helping to reduce vehicular air pollution in developing countries.184

In Mexico City, the government acted to cut emissions from vehicles by mandating the

installation of catalytic converters in automobiles, removing lead from gasoline, reducing

the sulfur content of diesel, and strengthening a vehicle inspection program that was

designed to reduce emissions from old, obsolete, and/or poorly maintained vehicles,

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among other interventions.171 As a consequence, by 2010, Mexico had cut ambient

concentrations of most pollutants by half.

In Peru, the government implemented a set of pollution control measures identified with

support from the World Bank, taking into account cost effectiveness and practicality. As

a result, improvements in air quality in Lima between 2002 and 2012 resulted in a 15%

decline in population exposure to ambient air pollution, despite ongoing population

growth.

A critical option to be considered in many locations is to switch to low-sulfur fuels in

order to lower direct emissions of particulate matter from on-road traffic. While

developed countries have reduced fuel sulfur levels to 50 or even 10 parts per million

(ppm), in developing and transitional countries the average sulfur levels (particularly in

diesel fuels) remain high (~10,000 ppm). A World Bank assessment of benefits and

costs of selected interventions to control PM air pollution in Karachi showed that moving

to lower sulfur diesel and retrofitting large vehicles with diesel oxidation catalysts would

have benefit-cost ratios of about 1.7.185 It was also recommended to convert from two-

stroke to four-stroke rickshaws and motorcycles.

Controlling urban air pollution by upgrading public transport. Transportation

accounts for nearly 20% of world energy use and urban transportation is responsible for

about 40% of this transportation-related consumption and is expected to double by

2050.186 Cities of different sizes and densities in countries at different levels of

development around the world have used a broad range of policy options to reduce

vehicular pollution and to increase active transportation (walking and bicycling) and the

use of public transport.181 The table below,181 from the International Energy Agency,

summarizes some common policy targets for different types of cities and the possible

responses.

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Table 5: Policy options for reducing urban traffic-related air pollution181

Developing Cities Sprawling Cities Congested Cities Multi-modal Cities

Increase

Density

Minimum density requirements, transit-oriented

development, mixed-use zoning,

clustering

Affordable housing programs, zoning

reforms, builder incentives, smart growth

reforms

Improve

transport

network

Park-and-ride facilities Bus and taxi only lanes

Bus rapid transit network with feeder routes

Formal transit system

development

Light/commuter rail Trolley/Metro/Light Rail options

Prioritised bus lanes and signalisation Complete streets designs

Dedicated pedestrian

infrastructure and

cycling lanes

High-occupancy

vehicle (HOV) lanes

Cycling lanes

Seamless transport interconnectivity: easy, accessible, demarcated connections between

travel modes, e.g. bus to metro

Road freight to rail facilities

Reduce

driving

Teleworking facilities Transit incentives

Parking maximums/restrictions, fees and levies

Road pricing/tolls Congestion pricing and vehicle quotas

Vehicle registration tax/pay-go fees/fuel prices and taxes

Improved public transport services and increased frequency/reliability

Carpool/rideshare programs Integrated ticketing for transit

Freight delivery restrictions

Improve

safety

Non-motorised transport facilities: separated cycling lanes, sidewalk improvements, zebra

crossings, median barriers/islands (mid-road protection for crossing pedestrians)

Safe routes to transit/school programs

Traffic calming measures: lane narrowing, road

“diets” (reduction in lanes), speed reductions,

one-way to two-way streets, street closures,

reduced speed zones, improved signalisation

Traffic calming measures: speed bumps,

curb extensions, “shared space” roads,

cyclist/pedestrian priority roads, chokers

(narrowing at crossroads), pedestrian

zones (reduced speed), car-free zones

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Controlling Household Air Pollution. About 3 billion people around the world, mostly

in low- and middle-income countries, still depend on open fires and stoves burning

biomass and coal to cook and to heat their homes.187 Exposure to household air

pollution is high in these populations, especially among women and children who spend

the most time indoors close to polluting cook stoves. Their daily high exposure to

combustion by-products is linked to a range of diseases including pneumonia, stroke,

ischemic heart disease, chronic obstructive pulmonary disease, and lung cancer.188–190

Open fires and stoves are also responsible for many serious burns in children in

developing countries who accidently fall into them.

Since the 1970s, scores of non-governmental organizations as well as several large

national programs supported by international agencies and foundations have worked to

reduce household air pollution by promoting various clean cook stove technologies.191

Initially, the goals of these programs were to save time and effort in fuel collection and

to reduce deforestation, but after establishment of the Millennium Development Goals,

they expanded their focus to include protection of public health. Establishment of the

Global Alliance for Clean Cookstoves in 2009 accelerated activities in this arena by

funding research and development while also directing global attention and resources to

the issue of household air pollution.187 The World Health Organization has recently

developed peer-reviewed, evidence-based design and evaluation criteria for cookstoves

that are intended to reduce exposures to household air pollutants and protect human

health.192

The health impacts of household fuel use extend well beyond the household. Evidence

is growing that household sources play major roles in contributing to ambient pollution in

many countries. In India, for example, which faces the worst average ambient pollution

in the world in terms of fine particles, recent estimates indicate that 25-50% of ambient

air pollution comes from the use of solid fuels in two-thirds of Indian households.161

A barrier to the introduction and use of new and improved stoves and fuels has been

peoples’ preference to continue to use their traditional stoves. Studies have shown that

securing adoption and lasting use of clean and efficient stoves and fuels can be very

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challenging, for reasons that involve a wide range of factors.134 Programs and projects

need to understand these barriers and design culturally appropriate strategies to

overcome them.193 Information and marketing campaigns can enhance initial adoption

of new stoves and fuels, but sustained use requires that the new technologies match

local preferences.194

With growing recognition that improved stoves could achieve only limited success in

reducing household air pollution and improving health,195 interest has grown in finding

ways to make clean fuels - gas, electricity and renewable sources of energy - available

to the large number of poor populations still using coal and biomass fuels. Household

air pollution is virtually eliminated when households and communities are able to adopt

and sustain the use of alternative fuels for cooking and heating.

In some areas and in some countries, the shift to cleaner fuels comes about as income

and resources increase as a consequence of economic development, but it is argued

that rural populations are not moving away rapidly enough from traditional solid fuel

cookstoves despite increasing incomes.196 Barriers to the adoption of alternative fuels

are costs, logistics and cultural preferences. In spite of considerable development in the

last 30 years, the same number of people worldwide rely on solid cooking fuels today as

in the 1980s, with some parts of the world – China - seeing a decline in users, others an

increase - Sub-Saharan Africa - and some unchanging - South Asia.197

In the last two years, there have been major advances to make clean fuels available in

a range of countries.

Controlling indoor air pollution by reducing tobacco use and exposure to second-hand smoke. The United Kingdom has one the highest tobacco tax rates in the world.

Since the implementation of high tobacco taxation, the proportion of adults over 16 who

admit to smoking has decreased steadily since the 1970s.198 In 2003, more anti-

smoking initiatives were put into place such as a ban on advertising, a large-scale

health campaign, and making smoking cessation aids available on NHS prescription. In

the overall population, the prevalence of smoking decreased about 10 percent from

1980 to 2002.

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Controlling water pollution and improving sanitation and hygiene. Provision of

modern water and sanitation infrastructure can dramatically decrease risks of disease

and premature death from waterborne diseases. The challenge is to promote and

accelerate water, sanitation and hygiene interventions within the development process

so as to address specifically the health risk factors.

Protecting water resources at a national level requires a comprehensive approach.

Canada introduced a national Action Plan for Clean Water in 2007, under which a range

of programs and specific actions have been implemented. These include pollution

reduction and control, monitoring and water quality, investment in infrastructure,

regulations, and research. Within this broad national framework, more detailed action

plans have been developed for individual watersheds, including several large

transboundary systems such as the North American Great Lakes.

In 2000, the UN General Assembly set the basis for the Millennium Development Goals

(MDGs) which included a target that challenged the global community to halve, by 2015,

the number of people without sustainable access to safe drinking water and basic

sanitation. A Joint Monitoring Program established by the World Health Organization

and the United Nations Children’s Fund (UNICEF) tracks progress to this target. The

latest report from this program199 concludes that while progress continues to be made,

the net result falls short of the MDG target by almost 700 million people, leaving about

2.4 billion people around the world short of adequate sanitation. A detailed analysis in

2007 estimated that the reduction in avoidable deaths in the developing world if the

MDGs were met could be on the order of 3 million per year (in 2006).200

A brief unpublished analysis carried out under the auspices of this Commission

indicated that upgrading of water and sanitation to the levels of the developed world

would result in a saving of over 200 million years of life lost annually, by focusing

prevention efforts on only the 22 most populous developing nations. This estimate is

consistent with previous global estimates made in 2006,200 which concluded that the

numbers of premature deaths that could be avoided in the developing world if adequate

water and sewer services were provided would be on the order of 3 million per year.

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This Commission’s analysis indicates that the range of effort/success in providing

improved sanitation and water in sub-Saharan nations is generally comparable to that

observed elsewhere in the developing world. Most striking, however, is the conclusion

on disparity between countries in the rate of progress which suggests that improved

access to sanitation and reduction in child mortality is not related to access to funds,

estimated as either Gross National Income or reported direct investment in water and

sanitation. If wealth alone is not a direct driver of sanitation and of health outcomes,

then cultural and institutional factors would appear to be important. It is possible to

identify nations whose progress in child mortality is far ahead of sanitation

improvements, including Burkina Faso, Niger and Rwanda.

The Global Analysis and Assessment of Sanitation and Drinking-Water was developed

to analyze the institutional and financial readiness of developing nations for investment

in water, sanitation and hygiene.201 This framework evaluates the adequacy or

effectiveness of (i) requisite inputs—human resources and financing and (ii) the

“enabling environment”—laws, plans, policies, institutional arrangements and

monitoring— specific to each nation. Along those lines, this project initiated a survey by

country among the developing nations to produce a mixed quantitative/qualitative

snapshot of national readiness for program level investment in nationwide improved

water and sanitation. It is possible that assessment of national readiness for investment

can be used to help to maximize the return on external investment in water, sanitation

and hygiene in the developing world.

Where physical infrastructure is lacking or inadequate, household level hygiene and

water treatment interventions such as point of use disinfection systems (using chlorine

tablets or similar) and basic hand washing can be effective.

Upgrading water and sanitation services:

Urban areas. The principal aim of water and sanitation programs in the urban context is

to provide full coverage of household water and sanitation connection. A growing

number of different institutional and financial models are being utilized to take

advantage of local conditions and entrepreneurial efforts to achieve this goal.

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The model for urban water and sanitation is increasingly one of corporatization or

privatization, with a stand-alone body responsible for service delivery, within a

government regulatory framework. Service agreements typically include requirements

for expansion of water and sewerage to underserved communities. However,

expansion of infrastructure into poor (and often technically difficult) neighborhoods does

not provide high returns on the investment and commercial agencies are often slow to

meet targets.156 More recent models are allowing smaller local providers to operate on

the fringes of large schemes as they may be able to achieve better results through

implementing locally more appropriate solutions.

Rural areas. For rural areas, where provision of convenient and safe drinking water has

been the primary objective, a range of successful approaches are being implemented,

although financial sustainability has been harder to establish. Increasing attention is

now being paid to rural sanitation and hygiene because of the large public health

benefits. Efforts are increasingly focused on campaigns to eliminate open defecation at

the community level (since high levels of compliance are necessary to achieve the

health benefits).

In many rural areas, particularly in Africa and South Asia, the focus of many sanitation

efforts is less on the hardware (latrines and so on) and more on promotion of communal

efforts to see the value in better sanitation – often in the context of ending open

defecation - and in implementing solutions that are beneficial for the whole

community.202 There are many programs, often supported by international donors and

NGOs, that work with local leaders in various forms of Community Led Total Sanitation

(CLTS).203–207 These efforts are often small scale and time-consuming but are

successful in changing attitudes and increasing the uptake of better habits and facilities.

The keys to uptake usually require careful attention to local culture and conditions and

therefore scaling up takes time.208

In China, rural water and sanitation coverage has increased dramatically over the past

thirty years. Coverage of improved water supply went from 76% in 1986 to 92% in

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2000, while sanitary latrine provision went from unknown (but low) in the 1980’s to 49%

in 2000, with efforts continuing to reach a target of 65%.209

Peri-urban areas. The peri-urban areas and large towns have particular challenges,

being neither full urbanization nor low-density rural. A specific strategy for these areas

is required, perhaps a transition approach as suggested by Water Aid.210 A problem in

these areas is that the informal context cannot provide the legal basis required for the

agency to enter into a contract to provide regular water and sewerage services. Private

water vendors are common in such contexts and there are now some emerging small-

scale providers of sanitation services.

One example in Nairobi, set up by a social enterprise, Sanergy, covers the construction

of basic facilities and the collection and treatment of wastes. It is reported to provide

user satisfaction and community benefits, demonstrating the potential for such market

based services. However, scaling up these efforts continues to be a challenge.211 The

IFC with the multi-donor Water and Sanitation Program has identified the potential

market and is providing advice and support to businesses and governments, under a

“Selling Sanitation” initiative.

Non-piped sanitation systems. There is considerable interest in non-piped sanitation

systems, especially for slum or peri-urban areas where the difficulties and cost of

providing a traditional sewer network are prohibitive. Over the past decade, the

municipality of eThekwini in South Africa has provided about 75,000 waterless toilets to

unserviced properties in their area in efforts to increase the levels of sanitation

coverage. Surveys of usage and opinions among the households with these toilets

showed that general satisfaction with the systems was not high (about 30%) despite

considerable efforts by the Municipality to provide appropriate solutions and to inform

and educate users. The reasons for this outcome are varied but include an attitude

that the waterless systems are a second-class solution, continuing use of nearby pit

latrines (from habit or other reasons), and perceptions of odors, poor construction and

other practical problems. Review of other systems in South Africa and elsewhere

showed that there were practical problems that could be addressed but that awareness

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and education on hygiene and environmental aspects could increase user

satisfaction.212

The multiple benefits of clean drinking water and adequate sanitation

Sanergy, which operates in Kenya under the leadership of David Auerbach, makes

hygienic sanitation accessible and affordable in the slums of Nairobi, where 8 million

people resort to open defecation and unsanitary pit latrines, and where 4 million tons of

waste is dumped daily into waterways. Sanergy’s “build, collect, convert” approach

solves the sanitation problem in a sustainable manner. Specially designed toilets are

provided to local residents, who run them as viable businesses, collecting about $40 per

week for every two toilets. Waste is collected in cartridges and delivered to a central

location where it is converted into low-cost organic fertilizer, animal feed, and renewable

energy that is sold to local-area electrical grids.

Controlling soil, chemical and metals pollution from industrial activities. Industrial

activities, including minerals extraction and processing, are responsible for several

different types of pollution and the release of many pollutants. Historically, industrial

pollution has been regulated on the basis of the medium (air, water, waste). This

approach has often resulted in pollutants being moved from one medium to another

rather than being fully controlled. To deal with this fragmentation, an Integrated

Pollution Prevention and Control strategy was introduced formally in Europe, initially in

1996, where the overall process is regulated and requirements are imposed for total

pollutant reduction. The European Union Directive mandating this strategy introduced

four main principles: an integrated approach, use of Best Available Techniques,

flexibility to suit the context, and public participation.

Effectiveness of the integrated pollution control strategy was examined in a review

undertaken in England and Wales.213 This review identified a number elements that

were crucial in putting a high-performing system in place, including designating a single

agency to be responsible for program implementation; phasing in the approach over

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about six years by sector; clear guidance (developed in consultation with each sector)

on information requirements and application tools; and a risk-based appraisal system

which also establishes priorities for compliance assessments. A significant success of

the integrated pollution control framework in the UK was seen in the clear resource

efficiency savings made by industry. A review of this approach resulted in the

integrated strategy being supplanted by an Industrial Emissions Directive (2010/75/EU)

which added requirements on inspections and on pollutant release information. The

integrated approach is preferable from the point of view of resource efficiency.

There now is sufficient experience gained in Europe and elsewhere to provide a very

sound basis for recommending that countries around the world develop and implement

practical and effective systems for integrated control of soil pollution.

Contaminated Sites and toxic contamination: Contamination of land and water by

toxic metals and chemicals is a problem that has come on to the global pollution agenda

relatively recently. The extent of the problem is not yet fully defined and is likely to

continue to grow in size until systematic monitoring and effective controls can be put in

place and applied globally. Contaminants in soil can consist of a very wide range of

materials but those of greatest concern are persistent toxic substances, including heavy

metals (particularly lead, mercury and chromium), persistent organics such as some

pesticides (POPs), and radionuclides.

Remediation of hazardous waste sites: The Polluter Pays Principle is the first option

for securing financial support for the remediation of hazardous industrial and mining

sites. This approach may be feasible when the polluter is still the owner of the site or

facility. In other cases where the polluter cannot be identified or lacks resources to fulfill

their clean-up responsibilities, particularly old industrial sites, the possibility of

“brownfields” development needs to be explored. In the case of complex brownfield

sites, initial government funding is often needed to initiate and manage the development

process.214 In the case of abandoned sites where no responsible party or willing

investor can be identified, then the government may be required to step in to pay for a

minimum level of containment or isolation until long-term solutions can be found.215

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Identification and prioritization of contaminated sites: Contaminated sites are

widely scattered and management of their risks requires a process to identify and

address priority sites. The Toxic Sites Identification Program process developed by

Pure Earth/Blacksmith institute is a good starting point but taking action should not be

delayed until an inventory is complete. The inventory system needs to include a formal

process for proposing priorities for intervention - typically some form of Hazard Ranking

System. A number of other factors will influence the selection of initial interventions,

including readiness for action, public and political concerns and availability of finance.

Identification and remediation of contaminated sites needs strong institutional

capabilities. A lead department or agency needs to be designated to drive forward the

process, and mechanisms to ensure cross-agency coordination and to address funding

must be established. Toxic exposures at contaminated sites can be categorized into a

series of commonly encountered problems, where solutions to each are replicable.

Generally, cleaner technologies and better enforcement of environmental regulations

are needed in coordination with low-cost clean-up of legacy contamination.

Four of the most serious types of toxic site contamination worldwide are:

• Lead pollution from backyard smelting and industry;

• Mercury pollution from artisanal and small-scale gold mining;

• Toxic releases from e-waste recycling; and

• Hazardous waste dumps.

Lead pollution from backyard smelting and industry. Major sources of

environmental lead exposure in low- and middle-income countries include mining (both

legacy and active), car battery processing (primarily informal), and the use lead-based

ceramic glazes.216–219

Informal used lead-acid battery processing has resulted in very high blood lead levels

(>40 ug/dL) among both workers and community residents, including young children, in

developing countries.220,221 Battery recycling often takes place in residential areas and is

characterized by poor dust and emission control, little or no personal protection

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equipment and migration of material offsite as a result of fugitive emissions, waste

transport and contaminated clothing.

Pilot interventions to control lead pollution in battery recycling have been undertaken in

the Dominican Republic, Indonesia, and Vietnam with good success and have

demonstrated that affordable and practical cleanups are possible. In practice, technical

solutions are coupled with institutional controls and other measures such as public

education, high quality home cleaning, and workplace improvements.

Mercury pollution from artisanal and small-scale gold mining. Artisanal and small-

scale gold mining has proliferated across the world’s low- and middle-income countries

and has become a major source of mercury pollution and toxicity. Elemental mercury is

used in artisanal gold mining to amalgamate gold and extract it from ore.

An estimated 14 to 19 million workers are occupationally involved in small scale gold

mining and an additional 100 million people, many of them women and children, are

reliant upon gold mining for their income and live in mining communities.222–224 Most of

these mining communities are deeply impoverished and are situated in rural areas that

lack basic health care services and clean potable water.100,222,224 Mining activities

ravage landscapes and contaminate ecosystems.224 Miners face multiple occupational

hazards in addition to mercury, such as exposure to cyanide, which is also used to

extract gold from ore. 225

Mercury from artisanal and small-scale gold mining vaporizes into the atmosphere and

disseminates globally. Gold mining is now estimated to be the world’s largest source of

mercury pollution and receives special mention under the Minamata Convention on

Mercury.88 Article 7 of the Minamata Convention requires that parties “reduce and

where feasible eliminate the use of mercury in ASGM.” Those countries with “more than

insignificant” artisanal and small-scale gold mining activity must develop and implement

a National Action Plan. This plan requires not only the implementation of technical

measures to reduce mercury use, but also the creation of an enabling policy framework

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to support the transition of communities away from mercury and the creation of a public

health strategy.

Improvements in mercury release from artisanal and small scale gold mining can be

achieved only by coordinated initiatives that include not only technical measures to

reduce mercury use, but also the formalization/legalization of the sector, support for a

transition away from mercury, and a public health strategy to deal with the deeply

impoverished, highly vulnerable mining communities.

A practical step towards encouraging more sustainable artisanal and small-scale gold

mining is to train miners in mercury-free technologies. However, these approaches must

show increased gold yields and/or reduced effort if they are to be accepted by the

miners. Considerable progress in reducing mercury loss and related exposures has

been achieved by use of retorts to contain and recover mercury during the final stages

of processing.

A number of mining communities using mercury-free approaches and processing

methods involving relatively harmless compounds such as borax have been successful

in certain mining conditions. A mercury-free methodology from the Philippines is being

demonstrated and adapted in sites in Indonesia, Bolivia, Tanzania and Ethiopia, where

miners from the Philippines are working with local miners.

Toxic releases from e-waste recycling. In 2014, global production of e-waste was

estimated to amount to 41.8 million tons, and substantial future increases are

projected.226 While most e-waste is generated in Europe and North America, more than

80% of e-waste recycling and processing takes place in China, India, and certain

African countries.227 The recycling and disposal of e-waste is largely an informal sector

activity, and is thus poorly documented, monitored, and regulated. Evidence is strong

that informal recycling activities can be hazardous to workers, cause widespread

environmental contamination,228 and result in toxic exposures to nearby communities.229

Burning cables to remove plastic and recover copper is a widespread and particularly

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hazardous component of poor recycling and can result in exposures to dioxins and

other toxic products of plastics combustion.

The only practical approach to reducing pollution and health hazards at large e-waste

recycling centers is to upgrade and formalize the work and to try to relocate the most

highly polluting activities at a distance from densely populated urban centers. Improved

recovery technologies have been developed, although these may require the availability

of electric power and large-scale operation. Incentives and training for recyclers can

support improvements, but these need to be integrated into the complex networks of

waste transportation, storage, and processing that constitute the recycling industry.230

Hazardous waste dumps. Thousands of “toxic” dumps, containing large quantities of

hazardous chemical wastes are found in countries worldwide. An additional number of

general waste dumps contain undocumented hazardous and toxic materials. Estimates

of the total amounts of waste generated in developing countries total almost 2 million

tons per day, and this volume is expected to double by 2025.231 Only about 20% of this

waste is taken to designated disposal sites.

Effective solutions to waste and dumps are well known and routinely used in

industrialized countries. Modern integrated and sustainable waste management

addresses both the technological aspects and the governance aspects - including

institutions, stakeholder involvement, and finance. Hazardous waste management has

specific regulations, techniques, and institutional aspects intended to keep dangerous

materials out of the main domestic/municipal waste systems.

An overview of solid waste management worldwide232 examined the challenges and

progress made in selected cities in moving from open dumps – the common starting

point – to controlled disposal systems. Cities cited in the report as making very good

progress include Quezon City in the Philippines, Ghorhai in Nepal and Belo Horizonte in

Brazil. These are cities where strong political commitment has been a key factor. Some

Indian cities have been coming to grips with some of their very large dumps as a result

of public complaints and the subsequent adoption of national legislation, in the form of

the Municipal Solid Waste Rules 2000.233 Strong and effective local leadership, with the

involvement of the community and some external financial and technical support, can

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achieve steady improvement in waste management and disposal and therefore can

improve the health and living conditions of the waste workers and the communities

surrounding waste dumps.

Other activities that are associated with contamination of land and water include

industrial estates, municipal/industrial dumpsites, chemicals manufacturing,

electroplating industries, and smelters. Technical approaches exist for addressing

these problems, but commitment and finance are essential.

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Documents

Supplementary appendix · 2018. 4. 10. · 1 Appendix Introduction Climate Change, Pollution, and Health Climate change and pollution are closely linked. Both arise from the current