1

Valuation of the Health Effects of Particulate Matter Pollution

in the Pearl River Delta, China

Desheng Huang, Shiqiu Zhang∗

College of Environmental Sciences and Engineering, Peking University, Beijing,

100871, PR China

Abstract: Air pollution in the Pearl River Delta (PRD) is serious, and as one of the most

important air pollutants, particulate matter does harm to the health and causes great

economic loss. To quantitatively evaluate the extent of the damage, this paper estimates the

adverse health effects and corresponding economic loss applying different valuation

methods including contingent valuation (CV), amended human capital (AHC) and cost of

illness (COI). The results show that the total economic loss of health effects from PM10

pollution in PRD cities in 2006 is estimated to be 29.214 (Confidence Interval (CI): 9.552,

45.013) billion Chinese Yuan, to be equivalent to 1.35% (CI: 0.44%, 2.08%) of the total

GDP of these cities by the methods of CV and COI; and 15.508 (CI: 5.153, 23.846) billion

Chinese Yuan, to be equivalent to 0.72% (CI: 0.24%, 1.10%) of the GDP by the methods of

AHC and COI. Economic loss in Guangzhou, Foshan and Dongguan is greater than other

cities in PRD, as higher population density and relative severe particulate air pollution in

these three cities. And economic loss of premature death and chronic respiratory disease

accounts for more than 95% in all the health effects. Considering the uncertainties, the

∗ Corresponding author. Tel: +86 10 62764974.

Email address: Zhangshq@pku.edu.cn

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results indicate the severity of the health effects of particulate pollution in PRD, which is in

urgent need of more effective control and governance.

Keywords: Economic loss, Air pollution, PM10, Health effects, Valuation, Pearl River Delta

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1. Introduction

Pearl River Delta (PRD) region (with longitude from 112°E to 115°E, and latitude

from 21°N to 24°N) lies in the central southern coastal part of Guangdong, a southern

province in China adjacent to Hongkong, where there are nine prefecture level cities (Fig.

1). As the forerunner of economic reform, this region has made tremendous economic

achievements since China started its reform and opening policy about 30 years ago. From

1978 to 2007, the gross domestic product (GDP) of PRD had been increasing at an average

annual rate of 21.2%. In 2007, PRD produced 10.3% of the total GDP (mainland China’s

total GDP) in its less than 0.6% of the total land (Mao, 2009). However, these

achievements were mostly depending on the development of energy-, resource- and

labor-intensive industries. As a byproduct, the air quality in the PRD cities deteriorated.

Severe haze pollution has been haunting PRD frequently in the recent years, and greatly

impaired the visibility in this region (Huang et al., 2008; Wu et al., 2007b). For example,

about 150 days per year occurred to be the haze days (daily mean visibility < 10km and

daily mean relative humidity < 90%) in Guangzhou between 1980 and 2006 (Deng et al.,

2008).The culprit of the haze is particulate matter (PM), especially fine particulates less

than 2.5 microns in aerodynamic diameter (PM2.5) (Huang et al., 2008; Wu et al., 2007a;

Zhang et al.,

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2008).

Fig. 1. The Pearl River Delta with its nine prefecture level cities, which are Guangzhou

(GZ), Shenzhen (SZ), Zhuhai (ZH), Foshan (FS), Jiangmen (JM), Zhongshan (ZS),

Dongguan (DG), Huizhou (HZ) and Zhaoqing (ZQ), and locations of the air quality

monitoring stations.(Picture revised of Pearl River Delta Regional Air Quality Monitoring

Network: A Report of Monitoring Results in 2006)

The adverse health effects of PM pollution have been well-documented (Jia et al.,

2004; Kan and Chen, 2002; Li et al., 2003; Yang and Pan, 2008.). The portal of entry for

PM air pollution is the lung, and PM interactions with respiratory epithelium likely

mediate a wide range of effects, including respiratory as well as systemic and

cardiovascular effects (Lippmann et al., 2003). The monetized health effects associated

with PM pollution has been documented as well. World Bank (2007) estimated that the

health cost attributed to urban PM pollution in China in 2003 was 157 billion Chinese Yuan

(mean value) by using the adjusted human capital (AHC), 520 billion Chinese Yuan (mean

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value) by using the value of statistical life (VSL), equivalent to 1.2% and 3.8% of the 2003

national GDP, respectively; while Yu et al. (2004) estimated that the health cost of PM10

pollution over 659 cities in China was 170.3 billion Chinese Yuan by using AHC,

equivalent to 1.02% of the 2004 national GDP. Kan et al. (2004a) reported the health loss

associated with PM pollution in Shanghai in 2004, which was 5.15 billion Chinese Yuan,

accounting for 1.03% of Shanghai’s GDP in the same year. Zhang et al. (2007) estimated

the health cost of PM10 in Beijing from 2000 to 2004 by using the adjusted VSL from

previous studies, and the estimated yearly monetized health cost ranges from 1.67 to 3.66

billion Chinese Yuan which accounted for 5.58%～7.06% of Beijing’s GDP. Albeit the

inherent uncertainties in estimating and monetizing health damage, these numbers

manifested the magnitude of health damage caused by PM pollution.

The PRD accommodates about 4% of China’s total population and the PM pollution in

PRD is among the severest in China (Zhang et al., 2008). As the living standard and

people’s environmental awareness improve, cleaner air is cried in the PRD as indicated by

the large amount of complaints regarding air pollution in the recent years. In this paper, we

valuate the health loss due to PM pollution in PRD for two purposes: (1) the results can be

used as justify the proposal and implementation of more stringent air pollution abatement

actions, and (2) the results can serve as a benchmark for the alternative air pollution control

policy options. Though fine particles (PM2.5) is reported to be most strongly associated

with mortality (Kan et al., 2007; Pope et al., 2002; Schwartz et al., 1996; Xie et al., 2009),

we select PM10 (with PM2.5 being part of it) as the target pollutant given that PM10 instead

of PM2.5 is monitored in the current routine monitoring practice. The year 2006 is chosen

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as the study year mainly because we have monitored PM10 data in 2006 available for the

cities in the PRD and the PM pollution in this area has not been improved significantly

since 2006.

In the following, we introduce the methods that we use to estimate and valuate health

effects, describe the data collection in details including the exposure of the population, the

baseline concentration of PM10 for assessment, exposure-response coefficients for different

health outcomes and the economic loss per case of different health outcomes, we would

also discuss uncertainty and conduct sensitivity analysis to the estimation results

considering different scenarios and valuation approaches, and finally, conclusions are

drawn with policy implications.

2. Methods

Following the general approach to environmental damage assessment (Tietenberg,

2005), we identify the exposed population and the health outcomes related to the PM10

exposure, estimate the adverse health effects by using exposure-response functions, and

monetize the estimated health effects.

2.1. Estimating the Health Effects

In using exposure-response functions to estimate the health effects associated with

PM10 pollution, we need to identify health outcomes, select exposure-response functions,

assess the exposure of the population, choose baseline PM10 concentration, obtain the

measured PM10 concentration and estimate health effects of PM10 pollution. Selecting

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exposure-response function is the core part because it affects the precision of the eventual

valuation results (Zou and Zhang, 2010). The incidence of morbidity or mortality among

the population can be regarded as small probability event, and is consistent with Poisson

distribution statistically. Most epidemiologic studies linking air pollution and health effects

are based on a relative risk model in the form of Poisson regression (Kan et al., 2004b).

Since we chose the exposure-response function and coefficients of epidemiological studies

of Pope et al.(2002), Kan and Chen (2002) and Xie et al. (2009), which used a log-linear

function to estimate the health effects, we apply the same function to our calculation

formula in this paper.

The incidence of each health outcome (I) in the actual concentration of PM10 can be

expressed as follows:

(1)

Where I is health incidence rate (such as mortality or morbidity) of the population

exposed in the actual situation, I0 refers to health incidence rate of the population exposed

in the baseline scenario (air with baseline PM10 concentration), β is the exposure-response

coefficients, C is the actual concentration of PM10, and C0 is the baseline concentration of

PM10.

Then, the health incidence rate attributed to the actual PM10 pollution can be estimated

by Eq. (2), as follows:

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(2)

The health effect (E) for each health outcome attributed to PM10 pollution among the

population (P), which is the number of people with the studied health outcome attributed to

PM10 pollution, can be calculated by using Eq. (3), as follows:

(3)

Once β, C, C0 and P are obtained, the number of people with the studied health

outcome attributed to PM10 pollution can be estimated accordingly.

2.2. Monetizing the Health Effects

Welfare economics assumes that life (or health) has values like other goods and the

values can be compared. Specifically, it assumes that he individuals are rational enough

and various choices they made in their daily life involve the trade-offs between the changes

in health risk and money or other economic goods that can be measured by money. Then,

economic loss due to the health damage attributed to PM10 pollution can be estimated by

using Eq. (4).

(4)

Where i is the index of health outcome attributed to PM10 pollution, e.g., cases of

premature death, chronic or acute bronchitis, asthma, etc., M is the total number of types

health outcome attributed to PM10 pollution, is the economic loss of health outcome i,

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is the health outcome i due to PM10 pollution, and the economic loss associated

per case of health outcome i.

The key is to determine the value of life/health. Given that health is irreplaceable and

has no market price, indirect approaches such as contingent valuation (CV), human capital

(HC) or amended human capital (AHC) approach, and the cost of illness (COI) method are

practically used for health valuation.

2.2.1. Contingent valuation (CV) method

CV method is a simple, flexible nonmarket valuation method that is widely used in

cost-benefit analysis and environmental impact assessment, which application in

environmental economics including estimation of non-use values, nonmarket values, or

both of environmental resources (Venkatachalam, 2004). It can effectively measure the

money that individuals are willing to pay for improving their own and others’ safety or

health, though it has the limitation that it’s difficult to obtain reliable and accurate results,

and needs to spend much time and resources in practice.

There are ample studies conducted on using CV method to estimate the value of life

internationally. However, there are only a few such studies conducted in a Chinese setting

(Hammitt and Zhou, 2006; Wang and Mullahy, 2006) and none of them were conducted in

the PRD. The valuation results obtained in other nations are not suitable for our estimation

given the differences in the economic and social development and the perceptions on

environmental problems between nations. As a second best choice, we use the value of

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statistical life (VSL) obtained with the CV study conducted in Chongqing (a southwestern

Chinese city) in 1998 by Wang and Mullahy (2006). They estimated that on average the

VSL of Chongqing residents is US$34,458 in 1998, and the VSL increases by US$14 434

for every US$144.6 increase in annual income. Taking into account the differences in

residential income between cities and years, we use the following equation to estimate the

VSL in PRD cities based on the VSL in Chongqing.

(5)

Where , , , , e represents the VSL of residents in the PRD

cities in 2006, the VSL of residents in Chongqing in 1998, the yearly per capita income in

the PRD cities in 2006, the yearly per capita income in Chongqing in 1998, and the income

elasticity coefficient, respectively. The income elasticity coefficient is normally given a

value of 1.

2.2.2. Human capital (HC) and amended human capital (AHC) approach

In the HC approach, individuals are considered as the basic unit of human capital

providing products and services. HC measures the loss of life and health according to the

general standards to assess common physical capital (usually represented as wage or labor

capital). HC approach merely regards expected income loss as the loss of premature death.

So there is an implicit assumption that value of human life of individuals with different

incomes are different, which quite often raises concerns over fairness issues. To fix this,

the amended human capital (AHC) approach was put forward, which use per capita GDP to

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measure the value of a statistical year of life (Eq. 6). It estimates human capital from the

perspective of the whole society, neglecting individual differences. AHC approach gains

advantages in data collection, and is currently widely used in life valuation, especially in

developing countries. The estimation results are more conservative than those obtained

with CV method.

(6)

Where HCL represents the human capital or life value of individuals based on GDP

per capita, t is the loss of life years per capita, is the discounted value of GDP per

capita in the future year i, represents GDP per capita in the base year, α is the

growth rate of GDP per capita, and r is the social discount rate.

2.2.3. Cost of illness (COI) method

COI method directly estimates the minimum value of health damage by calculating

these disease costs (including pharmaceutical, diagnostic, treatment and hospitalization

costs) and loss of income due to illness. Cost of illness (COI) method is widely and

frequently used to measure the cost of different diseases in various regions with different

levels of economic and social development.

COI method mainly uses the yearbook, literature, or hospital medical information in

PRD cities to estimate the cost of health outcomes such as acute bronchitis, asthma,

outpatients and inpatients, and working time lost. Basic formulae are as follows:

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(7)

(8)

Where is the cost of acute bronchitis or asthma, is the cost of outpatients or

inpatients, is the cost per case of medical treatment for different disease i, is

the GDP per capita, is the working time lost due to each disease i, and represents

the increased cases of corresponding disease attributed to PM10 pollution.

Therefore, CV, AHC and COI method are introduced and applied to the valuation work

in this paper, where CV and AHC methods are mainly used to estimate the loss of

premature deaths and chronic diseases, and the COI method for other health outcomes. In

this paper, we use CV (actually we use value of statistical life which is benefit transfer

from the results of Chongqing CV study in 1998, detail information as bellow) and AHC

method to evaluate the chronic and acute mortality and morbidity in chronic bronchitis,

and compare the estimation results of these two methods, while COI method applied to the

disease cost estimation of other health outcomes.

3. Data Description

3.1. Ambient Concentration of PM10

The hourly PM10 concentration measured in the 13 monitoring stations in PRD

throughout the year 2006 (Jan. 1, 2006 – Dec. 31, 2006) is provided by Guangdong

Provincial Environmental Monitoring Center. The locations of the 13 monitoring stations

are illustrated in Fig. 1. We then calculated the daily average and annual average of PM10

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concentrations in each of the 13 monitoring stations (data are available upon request).

Since a few days of the monitoring data are not available except Guangzhou, most of them

are neglected in our calculation because of insignificant impact to the estimation results.

However, as we only have the PM10 concentration of Dongguan from Aug. 1 to Dec. 31,

we firstly calculate health effect of each city excluding Dongguan from Aug. 1 to Dec. 31

for each health outcome and then calculate the average proportion of the whole year, which

is applied to estimate the total health effect of Dongguan for the whole year. This treatment

is reasonable as the seasonal distribution of PM10 concentration is similar in the same

region of PRD. Annual average concentration is used for estimation of chronic effects of

long-term exposure such as premature death and chronic bronchitis, while daily average

concentration for acute effects of short-term exposure as other health outcomes.

3.2. Choice of Baseline PM10 Concentration

To assess the health effects of PM10 pollution, a baseline concentration of PM10 must

be chosen. Then the incremental health effects (compare with the baseline case) can be

estimated. Generally the threshold (the lowest concentration at which individual’s adverse

health effects can be observed) is taken as the baseline concentration for assessment.

However, a large number of studies have shown that there seemed to be no health effect

threshold concentration for ambient PM (Morgan et al., 2003; Pope et al., 1995b; Quah and

Boon, 2003). WHO implied that existing literature did not support the existence of a

concentration level below which there was no observable effects (WHO, 2000), which

means that the health risk exists at any level of exposure concentration of ambient PM.

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Therefore, we select zero as the baseline PM10 concentration to estimate the health effects

in this paper. However, alternative baseline concentrations are discussed and used to

conduct a sensitivity analysis in Section 5.

3.3. Selecting Exposure-response Coefficients

Only two cohort studies on the relationship between long-term exposure to air

pollution and population mortality are well recognized, which are the Harvard Six Cities

study (Dockery et al., 1993) and the American Cancer Society cohort study (Pope et al.,

1995a). But results of both the studies were conditioned on the low concentration of

particulate matter in USA, and the exposure-response functions and coefficients cannot be

applied directly to areas of high PM concentration. In fact, some studies discussed the

trend of the exposure-response coefficient changing with PM concentration, and supported

the view that relative risk (RR) curve would become flatter (less steep) as the increase of

PM concentration (Ostro, 2004). What’s more, WHO’s study showed that relative risk (RR)

curve became horizontal as the PM10 concentration came to 100µg/m3 and above (Cohen et

al., 2004). Since the PM10 concentration in PRD broadly ranges from 10µg/m3 below to

300µg/m3 above, directly applying the coefficients of Pope’s or Dockery’s study is

inappropriate.

However, there are no relevant cohort studies in China. Most of the studies are

cross-sectional studies on chronic health effects and time-series studies on acute health

effects. Aunan and Pan (2004) conducted a comprehensive analysis of a number of studies

on the acute effects of PM10 in China, and used meta-analysis method to calculate the

exposure-response coefficients of health effects of PM10. According to the chronic health

effects in long-term exposure and acute effects in short-term exposure of PM10, Kan and

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Chen (2002) integrated the related national epidemiological studies into a meta-analysis,

and derived relative risk from PM10 exposure and health effects of Chinese population;

Similarly, Xie et al. (2009) also conducted meta analysis to a large number of domestic

studies on health effects from PM10 and PM2.5, and derived exposure-response coefficients

that suitably applied in China. Among them, only Kan and Chen distinguish between

chronic mortality under long-term exposure and acute mortality under short-term exposure,

and Xie analyze the epidemiological study of the association directly to PM10

concentration instead of transferring TSP or PM2.5 to PM10. Therefore, based on the

comprehensive analysis and comparison of the various studies on exposure-response

functions and coefficients, the reliable and suitable coefficients derived from last two latest

Meta researches are selected for each health outcome and applied to our estimation (Table

2).

3.4. Health Outcomes and Basic Health Information

According to medical and epidemiological study, the adverse health effects of PM10

include mortality (induced by cardiovascular or respiratory disease) and morbidity (e.g.

acute and chronic bronchitis, asthma attacks, etc.), and these morbidity changes are usually

measured as increased internal medicine and pediatric outpatient visits, emergency room

visits, hospital admissions, and also restricted activity days (Pope et al., 1995a). Only those

health outcomes that can be quantitatively estimated and monetized are selected in this

paper, while those outcomes with unavailable data or difficult to assess are excluded, such

as decreased lung function, pain and suffering, restricted activity days and other

sub-clinical symptoms. Neglecting these health outcomes would underestimate the final

results to some extent, although they are known to be associated with PM pollution.

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Therefore, we select all cause mortality (including chronic and acute mortality), chronic

bronchitis, acute bronchitis, asthma, outpatient visits (including pediatrics and internal

medicine) and hospital admissions (including respiratory and cardiovascular disease) as

main health outcomes in this paper, shown in Table 2.

Basic health information for each health outcome can be calculated from statistics

available. The mortality of each city and morbidity of corresponding diseases are

calculated according to the data of Health Statistical Yearbook of Guangdong Province

2007 and the Fifth Population Census of Guangdong Province. While the information of

disease incidence in different cities is not available, the average health information of

Guangdong Province is used instead. The basic health information is all listed in Table 2.

Table 2 Exposure-response coefficients and basic health information

Health outcomes Coefficients β (95%CI)

Data source Incidence (person times/year)

Chronic effect All cause mortality 0.00148(0.00038,0.00252) ** Chronic bronchitis 0.00505(0.00183,0.0078)

Kan and Chen, 2002 0.00148

Acute effect All cause mortality 0.00046(0.00013,0.00079) ** Acute bronchitis 0.00505(0.00192,0.00904)

Kan and Chen, 2002 0.0372

Asthma 0.0019(0.00145,0.00235) Xie et al., 2009 0.0094 Hospital admissions Respiratory disease 0.00124(0.00086,0.00162) 0.00797 Cardiovascular disease 0.00066(0.00036,0.00095)

Xie et al., 2009 0.00325

Outpatient visits Internal medicine 0.00042(0.00025,0.00061) 0.14856 pediatrics 0.00047(0.00017,0.00077)

Kan and Chen, 2002 0.54261

**Refer to mortality of each city in PRD excluding accidental death, shown as Table 3.

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3.5. Exposed population

Most of the people live in the PRD cities are affected by PM10 pollution, though we

are not exactly aware of the exposed time and degree individually. Due to a large number

of migrating populations in PRD cities, registered resident population data are unsuitable

for the estimation of exposed population. Thus, we take all the resident population

(registered and unregistered) of PRD cities at the end of 2005 as exposed population and

divide them into two groups (children and adults) to calculate the health effects separately

(as the exposure-response functions for older people are not available, older people are not

treated specially here). The exposed population information of nine PRD cities is shown in

Table 3.

Table 3 Basic information of exposed population in PRD cities

City

Resident population (person)

Child

(＜15 year-old,

%)

Adult

(≥15 year-old,

%)

Population density (person/km2)

Mortality (‰)

GZ 9 496 800 14.91 85.09 1277 3.5734 SZ 8 277 500 9.21 90.79 4239 0.0734 ZH 1 415 700 16.39 83.61 839 1.2734 FS 5 800 300 14.19 85.81 1507 3.3834 JM 4 102 900 18.38 81.62 430 3.5834 DG 6 560 700 7.65 92.35 2662 1.9534 ZS 2 434 600 13.65 86.35 1352 2.7634 HZ 3 706 900 19.90 80.10 332 2.3634 ZQ 3 676 000 26.55 73.45 247 3.5434

(Source: Year Book of Guangdong Province 2006 and the Fifth Population Census of

Guangdong Province, author calculated)

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3.6. Economic loss per case of health outcomes

As mentioned above, we use the findings of Chongqing survey and convert to the VSL

of PRD residents through benefit transfer method based on the disposable income per

capita of urban residents in these cities. The data needed can be obtained from the

Statistical Yearbook of China and Guangdong Province. Meanwhile, we also adopt AHC

approach to estimate the value of human capital according to the GDP per capita of PRD

cities as alternative measurement of value of a life, compared with the estimation results of

VSL derived from WTP. For calculation of human capital in Eq. (6), three key parameters

should be determined: the loss years of life per capita (t), the growth rate of GDP per capita

(α) and the social discount rate (r). According to calculation method in Han’s research

(Han et al., 2006) and the data of Health Statistics Yearbook of China and Guangdong

Province, t can be estimated to be about 14 years, and α is forecasted to be 10.89% in PRD

cities based on the historical data of Guangdong Statistics Yearbook. And r is suggested to

be 8% in Han’s study (Han et al., 2006), which is accepted in our calculation.

Chronic bronchitis has no exact time limit of suffering, so the cost is difficult to

estimate by COI method. However, Viscusi et al. (1991) suggest an alternative approach

successfully establishing equivalence between the utility of good health and the utility of

the disease through risk-risk trade-offs. Since chronic bronchitis leads to reducing

significantly the quality of life, as in a US study, when individuals are asked to make

trade-offs between the risk of contracting chronic bronchitis and risk of dying in an auto

accident, their choices implied that the utility of living with chronic bronchitis was about

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0.68 of the utility of living in good health (Viscusi et al., 1991). That means if good health

is scaled to equal 1 and death scaled to equal 0, living a year with chronic bronchitis is

equal to losing 0.32 of a year of life. Thus, this number can be converted to the value of a

statistical case of chronic bronchitis by multiplying the VSL by 0.32.

71 867 asthmatics in Guangdong were investigated in 1999 and medical cost per capita

was 634.2 Chinese Yuan (Tang et al., 2000). Considering the price and inflation from 1999

to 2006, we estimate that the cost per case of asthma was 1113 Chinese Yuan in PRD cities

in 2006, according to the conversion by the increased ratio of the disposable income per

capita. As no information of cost per case of acute bronchitis can be used in PRD, we

convert the research results of acute bronchitis and asthma medical cost per case in

Shanghai (52.56 Chinese Yuan and 38.69 Chinese Yuan, respectively) (Kan and Chen,

2004a) to calculate the cost per case of acute bronchitis in PRD, which was 1512 Chinese

Yuan in 2006.

Based on the data information of cost per case of outpatient and hospital admission,

frequency per capita of annual diagnosis and treatment, disease-specific average hospital

stay and cost in Guangdong Health Statistical Yearbook 2006, combining with the time

delayed (averagely half a day for outpatients, 9.5 days and 11.5 days for hospital stay of

respiratory diseases and cardiovascular diseases, separately), we can estimate the cost per

case of outpatient and inpatient of different cities.

Economic loss per case of all selected health outcomes are summarized in Table 4.

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Table 4 Economic loss per case of health outcomes in PRD cities (Chinese Yuan)

Premature

death

(million)

Chronic

bronchitis

(million)

outpatient Inpatient City

VSL AHC VSL AHC

Acute

bronchitis Asthma

Adult Child respiratory cardiovascular

GZ 1.54 1.08 0.49 0.35 1512 1113 1122 708 12253 12598 SZ 1.75 1.19 0.56 0.38 1512 1113 1130 674 7444 7824 ZH 1.37 0.90 0.44 0.29 1512 1113 1015 672 7248 7534 FS 1.51 0.86 0.48 0.28 1512 1113 951 621 9282 9558 JM 1.24 0.39 0.40 0.13 1512 1113 635 484 6501 6627 DG 1.87 0.68 0.60 0.22 1512 1113 688 428 4924 5140 ZS 1.47 0.72 0.47 0.23 1512 1113 964 688 4967 5198 HZ 1.08 0.43 0.34 0.14 1512 1113 535 371 6364 6501 ZQ 0.90 0.24 0.29 0.35 1512 1113 418 326 5019 5095

4. Summary of the results

According to all the methods and data summarized above, we establish a database in

Microsoft EXCEL software and calculate the health effects of each health outcome and

corresponding economic loss, with 95% confidence interval (CI) based on the

exposure-response coefficients. The results of assessment are shown in Table 5 and Table

6.

Avoiding double counting in different health outcomes as possible as we can, we

summary the economic loss of four most important health outcomes (premature deaths

attributed to long-term exposure, chronic bronchitis, outpatient visits and hospital

admissions) as the total economic loss of health effects from PM10 pollution in PRD cities.

The loss of both premature deaths in chronic exposure and chronic bronchitis is estimated

by AHC and CV methods, while the loss of other health outcomes is estimated by COI

method. The results showed that economic loss of health effects from PM10 pollution in

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nine cities of PRD in 2006 were 29.214 (CI: 9.552, 45.013) billion Chinese Yuan, to be

equivalent to 1.35% (CI: 0.44%, 2.08%) of the total GDP of the these cities by the methods

of CV and COI; and 15.508 (CI: 5.153,23.846) billion Chinese Yuan, to be equivalent to

0.72% (CI: 0.24%, 1.10%) of the GDP by the methods of AHC and COI.

4.1. Health effects of PM10 pollution

From the perspective of health outcomes, we can see from Table 5 that about 12800

premature deaths (chronic effect), 21600 cases of chronic bronchitis, 496900 cases of acute

bronchitis, 55000 cases of asthma, 292200 outpatient visits and 38500 hospital admissions

increased in 2006 due to the air pollution of PM10 in all nine cities of PRD. Considering the

total health effects of PM10 pollution in the whole PRD cities, the cases of premature

deaths attributed to long-term exposure are much greater than that attributed to short-term

exposure (more than 3 times). Meanwhile, the increased cases of acute bronchitis are

overwhelmingly more than that of chronic bronchitis (more than 20 times) and about 10

times as many as asthma, and the increased hospital admissions attributed to respiratory

disease are about 4 times more than that attributed to cardiovascular disease; and the

increased outpatient visits of adults are 2.5 times as many as that of children, while the

total population of adults in PRD cities is 5.5 times more than children, implying that

children are more sensitive to PM10 pollution to some extent

From the perspective of health damage in different cities, the cases of increased

premature deaths (both the acute and chronic effects) in Guangzhou and Foshan are the

greatest and significantly greater than that in other cities. In light of the increased cases of

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all selected diseases, Guangzhou, Foshan, Shenzhen and Dongguan are most seriously

affected, and Zhuhai and Zhongshan are least affected. This difference may relate to not

only the degree and complexity of the PM10 pollution in different cities, but also the age

structure and density of the population and even people’s behaviors in their daily life.

4.2. Economic loss of health effects

Economic loss of each health outcome estimated by different methods of CV, AHC

and COI is listed separately in Table 6. Considering different health outcomes, economic

loss of premature death and chronic bronchitis is the overwhelming majority of the total

loss, far more than that of other health outcomes; economic loss of acute bronchitis is

much greater than asthma while increased costs of outpatient visits and hospital admissions

are roughly equal in magnitude. Comparing the loss in different cities, we are confirmed

that Guangzhou, Foshan and Dongguan suffer the most serious economic loss of health

damage from PM10 pollution in 2006, while Zhuhai and Zhongshan suffer the least, similar

to the results of the physical health effects. In addition to the factors related to degree of

health damage, the difference in economic loss is also affected by the economic

development and medical expenses in different cities. Comparing different valuation

methods, the loss of the same health outcome estimated by CV method is more than that by

AHC method, nearly twice as much. This is reasonable as CV method is based on

investigation of individual willingness to pay for reducing the risk of death or diseases and

reflects all the loss of individual welfares caused by death or diseases (including time cost

and income loss, medical expenses, pain suffering, etc.), while the AHC method is based

23

on GDP per capita, only considering the loss of individual contribution to the productivity

of society, so the estimation may be lower than the CV method to a certain extent as

expected.

24

Table 5 Health effects attributed to PM10 pollution in PRD cities in 2006 (95% CI, hundred cases)

Outpatient visits Hospital admissions City

Premature

death A1

Premature

death B2

Chronic

bronchitis

Acute

bronchitis asthma

adult child sum respiratory cardiovascular sum

GZ 35.54

(9.52,58.26)

11.3

(3.4,19.3)

44

(18,62)

1064

(464,1631)

116

(90,141)

432

(258,619)

178

(66,286)

610

(324,906)

66

(47,85)

15

(8,21)

81

(55,106)

SZ 0.52

(0.14,0.85)

0.2

(0,0.3)

32

(13,46)

709

(303,1112)

76

(59,92)

279

(167,401)

115

(42,186)

394

(209,587)

43

(30,55)

10

(5,14)

52

(35,69)

ZH 1.05

(0.28,1.76)

0.3

(0.1,0.5)

4

(2,6)

92

(37,151)

9

(7,11)

34

(20,48)

14

(5,23)

48

(25,71)

5

(4,7)

1.2

(0.6,1.7)

6

(4,8)

FS 31.64

(8.68,50.77)

10

(3,16.9)

39

(17,52)

890

(416,1274)

104

(82,125)

403

(242,575)

166

(62,264)

569

(304,839)

60

(43,77)

14

(8,19)

74

(51,96)

JM 14.42

(3.85,23.7)

4.5

(1.4,7.8)

18

(7,25)

430

(186,663)

47

(36,56)

173

(103,248)

72

(26,115)

245

(130,363)

27

(19,34)

6

(3,8)

32

(22,42)

DG 16.66

(4.51,27.05)

4.9

(1.5,8.3)

37

(15,50)

790

(361,1151)

90

(70,108)

341

(205,488)

141

(52,225)

482

(257,713)

52

(37,66)

12

(6,16)

63

(43,82)

ZS 3.97

(1.04,6.62)

1.2

(0.4,2)

7

(3,10)

154

(64,249)

16

(12,19)

58

(34,83)

24

(9,39)

82

(43,122)

9

(6,12)

2

(1,3)

11

(7,14)

HZ 9.77

(2.63,15.98)

3.1

(0.9,5.3)

18

(7,25)

436

(192,663)

48

(37,58)

179

(107,257)

74

(27,119)

253

(134,375)

27

(19,35)

6

(3,9)

33

(23,44)

ZQ 14.29

(3.84,23.38)

4.4

(1.3,7.5)

18

(7,25)

405

(180,611)

45

(35,54)

170

(102,244)

70

(26,112)

240

(128,356)

26

(18,33)

6

(3,8)

32

(22,41)

SUM 128

(34,208)

40

(12,68)

216

(89,302)

4969

(2203,7503)

550

(429,665)

2068

(1239,2963)

854

(315,1368)

2922

(1554,4331)

315

(223,403)

70

(39,100)

385

(262,503)

1. refer to increased cases of premature deaths attributed to chronic impact with long-term exposure

25

2. refer to increased cases of premature deaths attributed to acute impact with short-term exposure

Table 6 Economic loss of health effects attributed to PM10 pollution in PRD cities in 2006 (95% CI, million Chinese Yuan)

Premature death A1 Premature death B2 Chronic bronchitis Acute

bronchitis Asthma

Outpatient

visits

Hospital

admissions Total loss

City

CV AHC CV AHC CV AHC COI COI COI COI CV+COI AHC+COI

GZ 5474

(1467,8972)

3849

(1032,6310)

1740

(519,2971)

1223

(365,2090)

2176

(887,3059)

1531

(624,2152)

161

(70,247)

13

(10,16)

61

(34,90)

99

(67,130)

7810

(2455,12252)

5541

(1756,8682)

SZ 90

(24,149)

61

(16,101)

26

(8,45)

18

(5,31)

1791

(714,2562)

1219

(486,1744)

107

(46,168)

8

(7,10)

39

(22,58)

39

(27,52)

1960

(786,2821)

1359

(551,1955)

ZH 144

(38,241)

94

(25,157)

43

(13,74)

28

(8,48)

170

(66,250)

111

(43,163)

14

(6,23)

1

(0.8,1.3)

4

(2,6)

5

(3,6)

324

(109,503)

215

(73,333)

FS 4782

(1312,7673)

2728

(748,4377)

1506

(454,2548)

859

(259,1454)

1872

(812,2508)

1068

(463,1431)

135

(63,193)

12

(9,14)

49

(27,71)

69

(47,89)

6772

(2198,10341)

3913

(1286,5968)

JM 1790

(478,2941)

568

(152,933)

564

(168,963)

179

(53,305)

716

(289,1012)

227

(92,321)

65

(28,100)

5

(4,6)

14

(8,21)

21

(14,28)

2541

(790,4002)

830

(266,1303)

DG 3113

(843,5055)

1129

(306,1832)

912

(274,1548)

330

(99,561)

2194

(920,3018)

796

(333,1094)

119

(55,174)

10

(8,12)

30

(16,43)

31

(21,41)

5368

(1800,8157)

1985

(677,3011)

ZS 582

(153,970)

287

(75,478)

171

(51,294)

84

(25,145)

317

(123,463)

156

(60,228)

23

(10,38)

1.8

(1.4,2.2)

7

(4,11)

5

(4,7)

911

(283,1452)

455

(143,724)

HZ 1052

(283,1720)

420

(113,687)

333

(100,569)

133

(40,227)

627

(257,876)

250

(103,350)

66

(29,100)

5

(4,6)

12

(7,18)

21

(14,28)

1713

(561,2642)

704

(237,1083)

ZQ 1280

(344,2094)

343

(92,561)

395

(118,673)

106

(32,180)

510

(209,714)

137

(56,191)

61

(27,92)

5

(4,6)

9

(5,14)

16

(11,21)

1815

(569,2843)

505

(164,787)

26

SUM 18307

(4941,29816)

9479

(2559,15437)

5690

(1705,9686)

2961

(887,5041)

10373

(4278,14463)

5494

(2261,7674)

751

(333,1135)

61

(48,74)

226

(124,332)

308

(209,402)

29214

(9552,45013)

15508

(5153,23846)

1. refer to increased cases of premature deaths attributed to chronic impact with long-term exposure

2. refer to increased cases of premature deaths attributed to acute impact with short-term exposure

27

5. Uncertainty discussion and sensitivity analysis

There are some uncertain factors that may affect the results of estimation, mainly lie in

the fundamental assumptions and data processing. The uncertainty comes from the

estimation of both physical health effects and monetary valuation. The current study still

has some limitations and sources of uncertainty such as incomplete health outcomes,

selection of exposure-response functions and coefficients, selection of baseline

concentration of PM10 for assessment, lag time specification and exact exposure with

people’s daily behaviors. In addition, the VSL elicited from the CV survey involves a high

degree of uncertainty in the terms of the method itself as well as the Benefit Transfer

process. Some important quantifiable uncertainty is discussed below and even sensitivity

analysis is conducted to quantify the level of the uncertainty.

5.1. Uncertainty of exposure-response functions and coefficients

The mechanism of health effects of PM is not completely clear to date, and the

exposure-response functions and coefficients derived from epidemiological or

toxicological studies with many uncertain factors need to be studied much further. In

addition, the Meta results of studies in various regions may be not entirely appropriate to

apply to the PRD region, because the differences in population structure, exposure

condition and human behaviors may lead to unquantifiable uncertainty, which involves in

the valuation work inevitably. Though we use the 95% confidence interval of

exposure-response coefficients to obtain the mean estimation with an interval of lowest and

highest value, choice of different coefficients is so crucial that it would have significantly

28

impact on the final calculation results.

5.2. Uncertainty of baseline concentration of PM10

Due to the threshold concentration of health effects of particulate matter is

inconclusive currently, WHO (2000) has proposed several feasible thresholds for

environment impact assessment: zero, non-zero “clean” concentration, the background

concentration of a site, or some mandated air quality standard. Based on the meta-analysis

of majority of worldwide epidemiological studies results, WHO (2006) carefully made an

air quality guideline (AQG) of ambient particulate matter for human health and safety,

which indicated that PM10 concentration should not be more than 20µg/m3 for annual

average and 50µg/m3 for 24-hour average. In addition, WHO also proposed three interim

targets (IT) of PM10 concentration for air quality management and improvement. Choosing

different baseline concentration inevitably introduces some deviation into the results of the

estimation of both health effects and economic loss. Thus, We choose other five alternative

PM10 concentration baselines to conduct a sensitivity analysis for the calculation of health

effects and corresponding economic loss as different scenarios, e.g. WHO air quality

guideline (AQG), interim targets 1 (IT-1), interim targets 2 (IT-2), interim targets 3 (IT-3)

and the first-class of China National Air Quality Standard (NAQS) (see Table 7),.

Table 7 Alternative baseline concentration of PM10 for assessment

PM10 concentration (µg/m3) Scenario Alternative baseline

Annual average 24-hours average 1 Zero 0 0 2 WHO AQG 20 50 3 WHO IT-3 30 75

29

4 WHO IT-2 50 100 5 WHO IT-1 70 150 6 China NAQS 40 50

Scenario 1 (zero) represents the most stringent scenario as is assessed above, although

there is hardly an area with zero PM10 concentration in PRD region. Scenario 2-5 are

designed by WHO for health risk assessment and policy development of air quality

management, including a WHO AQG level and three WHO interim target level of PM10

concentration. And Scenario 6 (China NAQS) considers much more about the current

development of China and has targeted policy implication and support to decision-making

for PM10 pollution control and air quality management in the reality of PRD cities. The

results of health effects and economic loss under six scenarios are all listed in Table 8.

Total economic loss of health effects greatly changes as different baseline concentration are

chosen. Generally, taking scenario 1 as the base, the total economic loss under scenario 2-6

are about 76.85% (75.79%), 64.63% (63.63%), 40.11% (39.06%), 14.15% (13.01%) and

52.94% (52.26%) of that under scenario 1 by the estimation method of CV and COI (AHC

and COI), respectively.

30

31

Table 8 Total economic loss of the health effects of PM10 pollution in PRD under different scenarios of baseline concentration (95% CI)

(million Chinese Yuan)

Scenario 1 (Zero) 2(AQG) 3(IT-3) 4(IT-2) 5(IT-1) 6(NAQS)

(CV+COI) 29214

(9552,45013)

22374

(7143,34939) 18881

(5962,29679) 11718

(3635,18634) 4134

(1257,6574) 15466

(4877,24435) Total loss

(AHC+COI) 15508

(5153,23846)

11753

(3788,18334) 9868

(3137,15501) 6057

(1888,9627) 2018

(612,3208) 8105

(2588,12787)

32

6. Conclusions

Ambient particulate matter pollution in PRD region is extremely severe and has led to

great adverse health effects and economic loss to the local people. Summarizing the

fundamental methods of evaluating the health damage of PM, we establish a general

framework and approaches of assessment for the health effects and economic loss.

Considering the actual concentration and selected baseline concentration of PM10, the

health outcomes, exposure-response functions and coefficients, population exposure and

other factors related to the calculation, we estimate the economic loss of health effects of

PM10 pollution in PRD cities in 2006, applying different methods of CV, AHC and COI.

Taking account of various uncertain factors throughout the evaluation process, we focus

on the uncertainty from the exposure-response functions and coefficients, baseline

concentration of PM10 for assessment. We also conduct a sensitivity analysis to quantify

and discuss the uncertainty of the results.

The results show that economic loss of health effects of PM10 pollution in nine cities

of PRD in 2006 are 29.214 (CI: 9.552, 45.013) billion Chinese Yuan, to be equivalent to

1.35% (CI: 0.44%, 2.08%) of the total GDP of these cities by the methods of CV and COI;

and 15.508 (CI: 5.153,23.846) billion Chinese Yuan, to be equivalent to 0.72% (CI: 0.24%,

1.10%) of the GDP by the methods of AHC and COI. The cities of greatest economic loss

of health effects are Guangzhou, Foshan and Dongguan, which have higher population

density and relative centralized air pollution, while the two cities with least loss are Zhuhai

and Zhongshan. And among all the health effects, economic loss of premature death and

33

chronic respiratory disease accounts for the most, more than 95 percent of the total loss.

Considering a variety of uncertainties in the assessment process, the results to some extent

have implied the severity of the PM10 pollution adversely effecting on human health in

PRD. Facing the complicated air pollution status quo of PRD megalopolises, particulate

matter pollution control and management is becoming much more important and urgent.

Acknowledgements

The author would like to thank the members of Environmental Economics and Policy

Study Group, Peking University, including Zou Wenbo, Chen Xiaolan, Wu Dan, Xie

Xuxuan, Wan Wei, Yu Jialing, Mu Quan, Yi Ru, Ma Xunzhou and Zhang Xiuli, who

contributed to discussion and suggestion to the study. Special thanks for the critical

comments and patiently revision by Doctor Xu Jianhua from College of Environmental

Science and Engineering, Peking University. Supported by the Research Fund of National

High Technology Research and Development Projects (“863” Projects, Grant No.

2006AA06A309): Synthesized Prevention Techniques for Air Pollution Complex and

Integrated Demonstration in Key City-Cluster Region (3C-STAR).

References

Aunan, K., Pan, X., 2004. Exposure-response functions for health effects of ambient air

pollution applicable for China-a meta-analysis. Science of Total Environment 329,

3-16.

Cohen, A.J., Anderson, H.R., Ostro, B., Pandey, K.D., Krzyzanowski, M., Künzli, N.,

34

Gutschmidt, K., Pope III, C.A., Romieu, I., Samet, J.M., 2004. Mortality impacts of

urban air pollution. Comparative quantification of health risks: global and regional

burden of disease attributable to selected major risk factors. Geneva, World Health

Organization 2.

Deng, X., Tie, X., Wu, D., Zhou, X., Bi, X., Tan, H., Li, F., Jiang, C., 2008. Long-term

trend of visibility and its characterizations in the Pearl River Delta (PRD) region,

China. Atmospheric Environment 42(7), 1424-1435.

Dockery, D.W., Pope III, C.A., Xu, X., Spengler, J.D., Ware, J.H., Fay, M.E., Ferris, B.G.,

Speizer, F.E., 1993. An association between air pollution and mortality in six US cities.

The New England journal of medicine 329, 1753.

Hammitt, J.K., Zhou, Y., 2006. The economic value of air-pollution-related health risks in

China: a contingent valuation study. Environmental and Resource Economics 33(3),

399-423.

Han, M., Guo X., Zhang, Y., 2006. The human capital loss of air pollution in cities, China.

China Environmental Science 26, 509-512(in Chinese).

Huang, J., Wu, D., Huang, M., Li, F., Bi, X., Tan, H., Deng, X., 2008. Visibility Variations

in the Pearl River Delta of China During the Period of 1954--2004. Journal of Applied

Meteorological Science 19, 61-70(in Chinese).

Jia, J., Kan, H., Chen, B., Xu, W., Xia, D., 2004. Association of Air Pollution With Daily

35

Mortality in Zhabei District of Shanghai: a Case-Crossover Analysis. Journal of

Environment and Health 21, 279-282(in Chinese).

Kan, H., Chen, B., 2002. Analysis of Exposure-Response Relationships of Air Particulate

Matter and Adverse Health Outcomes in China. Journal of Environment and Health 19,

422-424(in Chinese).

Kan, H., Chen, B., 2004a. Particulate air pollution in urban areas of Shanghai, China:

health-based economic assessment. Science of Total Environment 322, 71-79.

Kan, H., Chen, B., Chen, C., Fu, Q., Chen, M., 2004b. An evaluation of public health

impact of ambient air pollution under various energy scenarios in Shanghai, China.

Atmospheric Environment 38, 95-102.

Kan, H., London, S.J., Chen, G., Zhang, Y., Song, G., Zhao, N., Jiang, L., Chen, B., 2007.

Differentiating the effects of fine and coarse particles on daily mortality in Shanghai,

China. Environment International 33, 376-384.

Li, Y., Yuan, D., Kan, H., Lu, W., Ruan, S., 2003. Progress in epidemiological study on

urban particulate air pollution and mortality. Journal of Environmental and

Occupational Medicine 20, 47-49(in Chinese)

Mao, Y., 2009. Growth model of Pearl River Delta: characteristics, impact and

transformation. Social Sciences in Guangdong 5, 10-17(in Chinese)

Morgan, G., Lincoln, D., Sheppeard, V., Jalaludn, B.,Beard, J.F., Simpson, R.,

36

Petroeschevsky, A., O'Farrell, T., Corbett, S., 2003. The effects of low level air

pollution on daily mortality and hospital admissions in Sydney, Australia, 1994 to

2000. Epidemiology, 14(5, Supplement), S111-S112.

Ostro, B., 2004. Outdoor air pollution: assessing the environmental burden of disease at

national and local levels. Environmental Burden of Disease Series 5.

Pope III, C.A., Bates, D.V., Raizenne, M.E., 1995a. Health effects of particulate air

pollution: time for reassessment?. Environmental Health Perspectives 103, 472-480.

Pope III, C.A., Burnett, R.T., Thun, M.J., Calle, E.E., Krewski, D., Ito, K., Thurston, G.D.,

2002. Lung cancer, cardiopulmonary mortality, and long-term exposure to fine

particulate air pollution. Journal of the American Medicine Association 287, 1132.

Pope III, C.A., Dockery, D.W., 1999. Epidemiology of particle effects. In: Holgate, S.T.,

Koren, H., Maynard, R., Samet, J., (eds.), Air Pollution and Health. Academic Press;

London, pp. 673-705.

Pope III, C.A., Thun, M.J., Namboodiri, M.M., Dockery, D.W., Evans, J.S., Speizer, F.E.,

Heath Jr, C.W., 1995b. Particulate air pollution as a predictor of mortality in a

prospective study of US adults. American Journal of Respiratory and Critical Care

Medicine 151, 669.

Quah, E., Boon, T.L., 2003. The economic cost of particulate air pollution on health in

Singapore. Journal of Asian Economics 14, 73-90.

37

Schwartz, J., Dockery, D.W., Neas, L.M., 1996. Is daily mortality associated specifically

with fine particles? Journal of the Air & Waste Management Association (1995) 46,

927.

Tang, T.,Ding, Y., Zheng, J., Wang, X., Ma, Q., Liang, B., Chen, S., Cai, D., 2000.

Epidemiological survey and analysis on bronchial asthma in Guangdong province.

Chinese Journal of Tuberculosis and Respiratory Diseases 12(in Chinese).

Tietenberg, T.H., 2005. Environmental and natural resource economics, 6th ed, pp. 35.

Tsinghua University Press, Beijing.

Venkatachalam, L., 2004. The contingent valuation method: a review. Environmental

Impact Assessment Review 24(1), 89-124.

Viscusi, W.K., Magat, W.A., Huber, J., 1991. Pricing environmental health risks: survey

assessments of risk-risk and risk-dollar trade-offs for chronic bronchitis. Journal of

Environmental Economics and Management 21, 32-51.

Wang, H., Mullahy, J., 2006. Willingness to pay for reducing fatal risk by improving air

quality: a contingent valuation study in Chongqing, China. Science of Total

Environment 367, 50-57.

World Bank and China SEPA, 2007. Cost of pollution in China. Economic Estimates of

Physical Damages, Available online: www.worldbank.org/epaenvironment.

World Health Organization (WHO), 2000. Air quality guidelines for Europe, 2nd ed.

38

Copenhagen, World Health Organization Regional Office for Europe, 2000 (WHO

Regional Publications, European Series, No. 91).

World Health Organization (WHO), 2006. WHO Air quality guidelines. Global update

2005. Particulate matter, ozone, nitrogen dioxide and sulfur dioxide. EURO Nonserial

Publication. http://www.euro.who.int/__data/assets/pdf_file/0005/78638/E90038.pdf

(accessed 30 Aug 2010)

Wu, D., Deng, X., Bi, X., Li, F., Tan, H., Liao, G., 2007a. Study on the visibility reduction

caused by atmospheric haze in Guangzhou area. Journal of Tropical Meteorology 23,

1-6(in Chinese).

Wu, D., Bi, X., Deng, X., Li, F., Tan, H.B., Liao, G.L., Huang, J., 2007b. Effect of

atmospheric haze on the deterioration of visibility over the Pearl River Delta. Acta

Meteorologica Sinica-English Edition- 21, 215.

Xie, P., Liu, X., Liu, Z.,Li, T., Bai., Y., 2009. Exposure-response functions for health

effects of ambient particulate matter pollution applicable for China. China

Environmental Science, 10(in Chinese).

Yang, M., Pan, X., 2008. Time-series analysis of air pollution and cardiovascular mortality

in Beijing, China. Journal of Environment and Health, 25(4): 294-297(in Chinese)

Yu, F., Guo, X., Zhang, Y., Pan, X., Zhao, Y., Wang, J., Cao, D., Cropper, M.L., Aunan,

K., 2007. Assessment on economic loss of health effect from air pollution in China in

39

2004. Journal of Environment and Health 24, 999-1003(in Chinese).

Zhang, M., Song, Y., Cai, X., 2007. A health-based assessment of particulate air pollution

in urban areas of Beijing in 2000-2004. Science of Total Environment 376, 100-108.

Zhang, Y., Hu, M., Zhong, L., Wiedensohler, A., Liu, S., Andreae, M.O., Wang, W., Fan,

S., 2008. Regional integrated experiments on air quality over Pearl River Delta 2004

(PRIDE-PRD2004): Overview. Atmospheric Environment 42, 6157-6173.

Zou,W., Zhang, S., 2010. Economic Valuation of Health Impact of PM10 Pollution in

Beijing from 2001 to 2006. Chinese Journal of Population, Resources and

Environment 8.(in press).