particulate matter air pollution reduction scenarios in osaka, houston ...

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Journal of Environmental Assessment Policy and Management Vol. 10, No. 3 (September 2008) pp. 265–289 © Imperial College Press

PARTICULATE MATTER AIR POLLUTION REDUCTION SCENARIOS IN OSAKA, HOUSTON, BANGKOK AND SEOUL: A PROSPECTIVE HEALTH BENEFITS ANALYSIS

A. SCOTT VOORHEES Department of Urban and Environmental Engineering Kyoto University, 5109 Lansdowne Drive, Durham NC 27712, USA [email protected]

NGUYEN THI KIM OANH Environmental Engineering & Management School of Environment, Resources and Development Asian Institute of Technology, P. O. Box 4, Klong Luang Pathumthani 12120, Thailand [email protected]

PRAPAT PONGKIATKUL Environmental Engineering & Management School of Environment, Resources and Development Asian Institute of Technology, Thailand

YOON SHIN KIM Department of Occupational & Environmental Medicine College of Medicine, Hanyang University, Korea

WANIDA JINSART Department of General Science, Faculty of Science Chulalongkorn University, Thailand

IWAO UCHIYAMA Department of Urban and Environmental Engineering Graduate School of Engineering, Kyoto University, Japan

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A. S. Voorhees et al.

WONGPUN LIMPASENI Department of Environmental Engineering Faculty of Engineering, Chulalongkorn University, Thailand

Received 28 January 2006 Revised 20 August 2008 Accepted 21 August 2008 The objectives of this study were to assess potential health and productivity benefits for the year 2010 with five scenarios for reducing particulate matter (PM10 and PM2.5 ) air pollution in the cities of Osaka, Houston, Bangkok and Seoul. Assuming a uniform 10% decline in ambient PM levels, the preventible cases of: (1) premature mortality ranged from 35 in Houston to 379 in Seoul, (2) chronic bronchitis ranged from 95 in Houston to 1,631 in Seoul, (3) cardiovascular disease ranged from 68 in Houston to 818 in Seoul, (4) pneumonia ranged from 28 in Houston to 336 in Seoul, (5) asthma attacks ranged from 388 in Osaka to 96,876 in Seoul, and (6) acute bronchitis ranged from 186 in Houston to 2,973 in Seoul. The per million population central estimate of the purchasing power parity adjusted value of health and productivity benefits ranged from $25 million in Bangkok to $160 million in Osaka. There was a wide variability in measured PM10 levels across cities. Percentages of active monitors reporting concentrations above 50 µg/m3 (annual average) or 150 µg/m3 (24-hour average) in 2001–2002 were 0% in Houston, 5% in Osaka, 33% in Bangkok and 92% in Seoul. Assuming a non-uniform reduction in PM only at concentration hotspots with levels above air quality standards, the number of preventible cases of mortality ranged from 0 in Houston to 1,104 in Seoul. The central estimate of total benefits ranged from $0 in Houston to $240 million in Seoul. Keywords: Air pollution; particulate matter; benefits analysis; health effects; cost benefit analysis.

Introduction Particulate matter (PM) is an air pollutant that exhibits adverse health effects in humans, including premature mortality, bronchitis, and asthma attacks (US EPA, 2000). Smaller particles are a major contributor to respiratory problems, especially in children, the elderly and people with existing illnesses (The Associated Press, 2005). At very high levels, as occurred in the summer of 2005 in Malaysia due to forest burning in Indonesia, schools closed, airports and shipping lanes were impacted, and hospitals were “inundated with people complaining of eye, throat and chest problems” (BBC News, 2005). In spite of inroads made in controlling PM over the past three decades, particles remain a major concern in many urban areas (World Bank, 2001a) and especially in large Asian cities (World Bank, 1997). Scientists at the US National Aeronautics and Space Administration have reported a possible link between Asian soot pollution and climate changes at the North Pole (Bustillo, 2005). As a prelude to additional pollution controls, it may be informative to identify and assign value to potential health and productivity impacts.


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Particulate Matter Air Pollution Reduction Scenarios

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Materials and Methods In this study, Ostro’s model as applied in Jakarta (Ostro, 1994) was used for estimating numbers of cases of prevented mortality and morbidity, in order to calculate the benefits in the year 2010 of improved air quality for the cities of Osaka, Japan; Houston, United States; Bangkok, Thailand and Seoul, Korea. A directive of the fellowship under which this work was conducted was to compare urban areas in Japan, the US and select other countries. Osaka was chosen because it ranks as Japan’s third largest city which has received little attention for PM impact assessment and Houston was included because of its complex mix of particulate matter sources. Bangkok and Seoul were chosen as urban centers in industrializing nations at different stages in PM air quality mangement and with differing PM source types. The US Environmental Protection Agency’s (EPA) benefits model was selected for the types of health effects and for assigning values to those effects (US EPA, 1997; US EPA, 1999). The Mexico Air Quality Management Team’s benefits assessment of Mexico City served as a model to identify appropriate pollution reduction scenarios and also for its inter-country benefits transfer methodology (Mexico Air Quality Management Team, 2002). Three uniform city-wide reduction scenarios of 5%, 10% and 25% were selected to characterize the impact of modest, intermediate, and aggressive pollution reduction policies. Similar to the Mexico City work, two additional reduction scenarios were addressed in this analysis. One identified the single highest monitored annual mean and highest 24-hour mean (95th or 98th percentile) value in each city, calculated the percent reduction needed to reach a 50 µg/m3 (annual) or 150 µg/m3 (24 hour) level, and applied that percentage across the entire city. The other scenario identified all monitored values above 50 µg/m3 or 150 µg/m3 , calculated the percent reductions needed to reach those threshold levels at each “hotspot,” and assumed site-specific reductions only at locations above the concentration thresholds. Particulate matter with a diameter less than 10 µm (PM10 ) concentrations data in 2001 for Osaka and 2002 for the other three cities were collected (Japan Environment Agency, 2003; US EPA, 2004; Pollution Control Department — Thailand, 2002a; Pollution Control Department — Thailand, 2002b; National Institute of Environmental Research — Korea, 2005). At the time of the analysis, these data were the most current available. This analysis relied on monitored concentrations and assumed that 2010 concentrations were a predetermined fraction of the measured 2001/2002 levels. In the absence of fine particle monitoring data, the PM2.5 (particulate matter with a diameter less than 2.5 µm) fraction was assumed to be 60% of total PM10 (Levy and Spengler, 2002), except in the case of Houston, where monitored fine particle data were used. Only data from monitors located within the city boundaries were used, and those data were more or less representative,


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depending on the city. Data for Bangkok were limited, with 2002 PM10 data available for 9 monitors (3 roadside; 6 general) in 7 of 50 political districts. An additional 4 roadside and 6 general monitors in Bangkok had no reported PM data for the year 2002. Data for Houston were also limited, with PM10 data available for 5 monitors and PM2.5 data available for 10 monitors (3 of them collocated) in 6 of 9 political districts. The most representative city was Seoul, with data available for 26 monitors in 24 of 25 political districts. Osaka was also well characterized with data available from 22 monitors (7 roadside; 15 general) in 16 of 24 districts. Where only one monitor was located in a district, that value was used for that district. For districts with multiple monitors, a district average was used. For districts with no monitoring data, a city-wide average was used. Roadside monitoring data and general ambient monitoring data were averaged together. Six C-R functions (long-term mortality, chronic bronchitis, cardiovascular disease, pneumonia, asthma, acute bronchitis) associated with exposure to PM were applied (Krewski et al., 2000; Schwartz, 1993; Abbey et al., 1995; Samet et al., 2000; Whittemore and Korn, 1980; Dockery et al., 1996) as cited in the EPA = s analysis of heavy duty engines and diesel fuel (US EPA, 2000). All these studies were based on exposed populations in North America, and they were described in a report from the National Research Council of the National Academies as being reasonable choices for use in estimating expected health benefits (National Research Council, 2002). Krewski et al. (2000) studied long-term exposure to PM2.5 in 51 US cities and their work was selected for use by the EPA due to strong statistical methods, large sample size, long exposure interval and large number of study locations. Chronic bronchitis, expected to last from initial onset throughout one’s life, was examined by Schwartz (1993) and Abbey et al. (1995). Schwartz examined the relationship between PM10 exposure and prevalence of chonic bronchitis. Abbey et al. examined the relationship between PM2.5 and new incidences of chronic bronchitis. Due to the relative strengths and weaknesses of both analyses, EPA pooled the results and devised a combined C-R function (US EPA, 2000). A study by the Health Effects Institute (Samet et al., 2000) examined cardiovascular disease and pneumonia in each of 14 US cities, and pooled the results across the cities. Wittemore and Korn (1980) examined asthma attacks associated with PM exposure in 443 children and adults in six southern California communities. Dockery et al. (1996) examined the relationship between PM exposure and bronchitis in a study of 13,369 children aged 8–12 in 24 US and Canadian communities. At the time of this analysis, few epidemiological studies were available that examined the impact of PM exposure specifically in these four cities. For cross-city comparative purposes, applying the same six functions to all four populations, allows for comparisons based on differences due to varying pollutant exposures. Hypothetical cases of prevented pollution


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illness were estimated using these six concentration-response (C-R) functions multiplied by the exposed populations and the assumed decrease in PM. The estimated human mortality impacts were valued using either Willingness To Pay (WTP) or Human Capital Loss (HCL), and the estimated human morbidity impacts were valued by either WTP for prevention of illness, which included the cost of pain and suffering, or Cost Of Illness (COI), which did not include pain and suffering, plus Productivity Loss. The WTP values for Japan, Thailand and Korea were adjusted based on income equivalency and elasticity and the COI values were adjusted using Purchasing Power Parity (PPP) income equivalency. Values were assigned to the human health and productivity impacts by multiplying the number of cases of mortality by WTP or HCL, and the number of cases of morbidity by either WTP or COI, plus Productivity Loss. Figure 1 outlines the pollution reduction scenarios and the assumptions of WTP, HCL and COI. The figure identifies each of the six health endpoints, lists assumptions of elasticity, and describes each of four estimation scenarios. This follows the procedure of the Mexico Air Quality Management Team in its World Bank sponsored assessment. In that analysis, High, Central and Low scenarios were identified and quantified for controlling ozone and PM using income elasticities of 1.0 and 0.4 (Mexico Air Quality Management Team, 2002). This methodology was chosen due to its application

Mortality WTP High Estimate (1)

VSL (US) (elasticity = 0.4)

High Estimate (2)

VSL (US) (elasticity = 1.0)

Central Estimate

VSL (Euro) (elasticity = 1.0)

Low Estimate

Human Capital Loss

Morbidity WTP

COI

Chronic bronchitis Cardiovascular Asthma attacks disease Acute bronchitis Pneumonia (elasticity = 0.4) Chronic bronchitis Asthma attacks Acute bronchitis (elasticity = 1.0)

Productivity Loss Lost wages

Lost wages

COI = Cost Of Illness Elasticity = Income elasticity Euro = European derived VSL US = United States derived VSL VSL = Value of Statistical Life WTP = Willingness To Pay

Fig. 1. Estimation scenarios for valuing health and productivity benefits of particulate matter air pollution.


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A. S. Voorhees et al. Table 1. Health endpoint values applied in estimating particulate matter health benefits. Health endpoint

Long term mortality Chronic bronchitis Cardiovascular disease Pneumonia Asthma attacks Acute bronchitis

Health valuation per case (1999$)

Valuation method

$6,120,000 $3,360,000a $331,000 $18,387 $14,693 $40.79 $57.34

WTP WTP WTP COI COI WTP WTP

COI = cost of illness (medical expenses, lost earnings) USD = United States Dollars WTP = willingness to pay (medical expenses, lost earnings, pain and suffering) Health valuations from US EPA (2000). a Value from Mexico Air Quality Management Team (2002).

of developed country analytical procedures to a developing country context, using local data where available. Table 1 lists the values assigned to each endpoint. For mortality, two separate estimates of Value of Statistical Life (VSL) were utilized — a US value from EPA, and a Western Europe value applied by the Mexico Air Quality Management Team. Benefits transfer procedures were applied using the Mexico Air Quality Management Team approach. For Japan, Thailand and Korea, the WTP values were adjusted based on income equivalency and elasticity [Eq. (1)] and the COI values were adjusted using income equivalency. Using international dollars of equivalent purchase power, the PPP-adjusted per capita Gross National Income figures in 1999 were $25,170 (Japan), $31,910 (US), $5,950 (Thailand) and $15,530 (Korea) (World Bank, 2001b). WTP CountryB = WTP CountryA [Income CountryB/Income CountryA] γ

(1)

where γ = income elasticity. Figure 1 presents the assumptions used in the four valuation estimates. High estimate 1, high estimate 2, and the central estimate of WTP-derived mortality valuation used VSL. The low estimate used HCL (i.e., lost wages), where it was assumed that 5 years of employment were lost, that wages increased 2.45% per year, that the affected population was 30 years of age and older and that no economic loss occurred for the elderly population. For morbidity, all valuations included lost wages. The high estimates of WTP-derived morbidity valuation used an income elasticity of 0.4 or 1.0. The central and low estimates used an income elasticity of 1.0.


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PM-10 Annual Mean (µg/m3)

Particulate Matter Air Pollution Reduction Scenarios 100 90 80 70 60 50 40 30 20 10 0 Osaka

PM-10 24-Hour Mean (µg/m3)

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Houston Bangkok Seoul (citywide range and mean concentrations)

200 180 160 140 120 100 80 60 40 20 0 Osaka (98%) Houston (95%) Bangkok (95%) Seoul (95%) (citywide range and mean concentrations)

Fig. 2. Concentration ranges, mean values, and ambient standards for PM10 in four cities (2001/2002).

Ranges and mean monitored values for PM10 in the years 2001 and 2002 are shown graphically in Fig. 2. Included for comparative purposes are annual average standards of 50 µg/m3 (US and Thailand), 60 µg/m3 (Seoul), and 70 µg/m3 (Korea); plus 24-hour average standards of 100 µg/m3 (Japan), 120 µg/m3 (Thailand and Seoul), and 150 µg/m3 (US and Korea) (Japan Environment Ministry, 2005; Korea Environment Institute, 2002; Pollution Control Department — Thailand, 2005; US EPA, 2005). The World Health Organization maintains that any exposure can potentially result in adverse effects (World Health Organization, 2005). Table 2 lists 1-hour, 24-hour and annual PM10 concentration data. One site in Bangkok (Thonburi Substation Itrapitak) and all 26 of Seoul’s monitors recorded 24-hour average levels above 150 µg/m3 . At the 95th percentile, the highest Bangkok site recorded 108 µg/m3 , whereas six of Seoul’s monitors remained above 150 µg/m3 . Estimated annual mean concentrations of PM2.5 were 23 µg/m3 (Osaka), 29 µg/m3

2001 2002 2002 2002

Osaka Houston Bangkok Seoul

22 5 9 26

Number of monitors with data reported N/A–390 N/A 5–314 1–998

Range of 1-hour values (µg/m3 ) N/A 110 196 870

Highest 24-hour mean (µg/m3 ) 68–101 25–69 55–108 113–173

Range of 24-Hour means* (µg/m3 ) 82 49 80 140

City-wide 24-hour mean* (µg/m3 )

*Data reported as 95th percentile except Osaka reported as 98th percentile. N/A = Not Available.

Data year

Location

26–51 15–34 35–60 57–88

Range of annual means (µg/m3 ) 38 26 49 71

City-wide annual mean (µg/m3 )

JEM, 2003 US EPA, 2004 PCD-T, 2002a NIER-K, 2005

References

272

Table 2. PM-10 concentrations monitored in four Pacific Rim cities.

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(Bangkok), and 42 µg/m3 (Seoul) and measured levels averaged 12 µg/m3 in Houston. The only country of the four with ambient standards for PM2.5 is the US — 15 µg/m3 (annual average) and 65 µg/m3 (24-hour average) (US EPA, 2005). The relative risk values, associated changes in PM concentration, along with input data are found in Table 3. Exposure populations were identified for each health endpoint and data were collected (Bangkok Metropolitan Government, 2005; City of Houston, 2004; National Statistical Office — Korea, 2005; National Statistical Office — Thailand, 2005; Osaka Prefectural Government, 2004; Texas State Data Center, 2004). Long term mortality and chronic bronchitis were estimated for populations of adults 30 years old and older. Cardiovascular disease and pneumonia were estimated for populations of adults 65 years old and older. Asthma attacks were assumed to occur in existing asthmatic populations. Acute bronchitis was estimated for populations of children ages 8–12. City-specific data were collected for mortality rates, except for Osaka, which relied on mortality rates for Tokyo (City of Houston, 2005; National Health Insurance Corporation — Korea, 2005; National Statistical Office — Korea, 2005; Texas Department of Health, 2004; Tokyo Metropolitan Government, 1999; Vichit-Vadakan, 2005) and for the asthmatic population in Osaka (Uchiyama, 2003).

Results Assuming an annual average threshold of 50 µg/m3 and a 24-hour average (95% percentile) of 150 µg/m3 , Houston had no high ambient level districts and Osaka had one district above 50 µg/m3 . Bangkok had three districts above the 50 µg/m3 level (one district above 150 µg/m3 for two days in the year); Seoul had 24 districts above the 50 µg/m3 level and six districts above the 95% percentile 150 µg/m3 level (all 26 Seoul monitors recorded 24-hour mean values above 150 µg/m3 at least once during the year). Table 4 lists the reduction percentages. Table 4 also outlines the expected number of prevented cases of premature mortality and morbidity. A three percent discount rate was used for premature mortality under the assumption that there would be a time lag between changes in PM exposures and changes in mortality rates. The estimated values of health and productivity impacts are presented in Table 5. For the 10% citywide scenario, estimated benefits in 2010 per million population ranged from $25 million in Bangkok to $160 million in Osaka (1999$). For the city specific citywide scenario, estimated benefits in 2010 ranged from $0 in Houston to $350 million in Seoul. For the city specific hotspots scenario the benefit for Seoul was valued at $240 million. At the other extreme, there were no monitored concentrations in Houston above the 50 µg/m3 and 150 µg/m3 levels and the resulting benefit was $0.

1.50 (0.91–2.47)

0.00144 (beta logistic coefficient)

1.07 (1.02–1.12)

20.7 µg/m3

—

10 µg/m3

10 µg/m3

10 µg/m3

24.5 µg/m3

Associated change in PM concentration

Health effect data inputs

annual PM2.5 Osaka death rate for persons ages 30 and older — 0.008262 (Osaka Pref. Govt., 1999) Houston death rate for persons ages 30 and older — 0.00716 (City of Houston, 2005) Bangkok death rate for persons ages 30 and older — 0.00586 (Vichit-Vadakan, 2005) Seoul death rate for persons ages 30 and older — 0.00365 (NSO-Korea, 2005) annual PM10 US chronic bronchitis prevalence rate for persons ages 18 and older — 0.0535 (US EPA, 2000) US annual chronic bronchitis incidence rate per person — 0.00378 (US EPA, 2000) daily PM10 US daily hospital admission rate for CVD per person 65 and older — 0.000223 (US EPA, 2000) daily PM10 US daily hospital admission rate for pneumonia per person 65 and older — 0.000053 (US EPA, 2000) daily PM10 US daily incidence of asthma attacks per asthmatic — 0.027 (US EPA, 2000) Osaka asthmatics in population — 0.136% (Uchiyama, 2003) Houston / Bangkok / Seoul asthmatics in population — 5.61% (US EPA, 2000) annual PM2.5 US annual acute bronchitis incidence rate per person — 0.044 (US EPA, 2000)

Averaging time

CI = confidence interval; PM = particulate matter; TSP = total suspended particulates. a Defined as the probability of the health effect occurring in the exposed population relative to an unexposed population. b Deaths All Causes (all ages) assumed to be equal to deaths in persons ages 30 and older.

Dockery et al.

Samet et al.

Acute bronchitis

Samet et al.

Cardiovascular disease (CVD) Pneumonia

Whittemore & Korn

1.021 (1.009–1.033)

Schwartz; Abbey et al.

Chronic bronchitis

Asthma attacks

1.012 (1.010–1.014)

Krewski et al.

Long term mortalityb

1.12 (1.06–1.19)

Researchers

Health endpoint

274

Relative riska (95% CI)

Table 3. Particulate matter health impact concentration-response functions.

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Table 4. Mortality and morbidity impacts in 2010 from reduced levels of particulate matter pollution. Osaka (24 districts)

Populationb

City-wide percent reductions (%)

Hotspots (%)

5

10

25

2/0c

2/0c

1,708,000 1,708,000 444,600 444,600 3,523 114,500

62 141 170 70 192 129

126 270 344 142 388 260

356 622 854 350 963 709

2 71 0 0 0 5

2 3 0 0 0 5

Population

5

10

25

0/0

0/0

Long term mortality Chronic bronchitis Cardiovascular disease Pneumonia Asthma Acute bronchitis

1,010,000 1,010,000 164,065 164,065 111,000 147,000

28 41 31 13 3,047 154

35 95 68 28 6,758 186

91 224 166 68 16,317 474

0 0 0 0 0 0

0 0 0 0 0 0

Bangkok (50 districts)

Population

5

10

25

17/0

Long term mortality Chronic bronchitis Cardiovascular disease Pneumonia Asthma Acute bronchitis

3,212,735 3,212,735 324,854 324,854 356,900 379,900

88 274 126 52 19,591 460

245 642 250 103 39,072 1,250

573 1,510 623 255 97,107 2,807

412 1,032 0 0 0 2,058

Population

5

10

25

43/13

5,727,816 5,727,816 612,783 612,783 509,000 655,520

185 812 416 171 49,168 1,475

379 1,631 818 336 96,876 2,973

940 3,788 2,026 825 239,235 6,918

1,597 6,118 1,063 436 125,716 10,920

Long term mortalitya Chronic bronchitis Cardiovascular disease Pneumonia Asthma Acute bronchitis Houston (9 districts)

Seoul (25 districts) Long term mortality Chronic bronchitis Cardiovascular disease Pneumonia Asthma Acute bronchitis

2∼17/0 25 62 0 0 0 124 12∼43 / 2∼13 1,104 4,335 143 59 16,535 7,950

a Mortality estimated using 3% discount rate. b Study population, i.e., adults aged 30+ or 65+; existing asthmatics; children 8–12 years old. c Percent reduction in annual average/percent reduction in 95th percentile 24-hour average.


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A. S. Voorhees et al. Table 5. Predicted benefits in 2010 from reduced particulate matter pollution.

City (total population)

City-wide reductions

Hotspots only

Osaka (2,598,774)

(million 1999$ per million population) 5% 10% 25% 2%/0%a

2%/0%a

High estimate (1) High estimate (2) Central estimate Low estimate

150 130 80 18

300 260 160 35

850 730 440 86

13 11 9.2 7.1

5.4 4.6 2.7 0.51

5

10

25

0/0

0/0

94 94 55 9.6

130 130 78 21

320 320 200 48

0 0 0 0

0 0 0 0

5

10

25

17/0

2∼17/0

51 18 12 3.3

140 49 25 7.9

320 110 72 19

230 81 51 12

13 4.5 2.6 0.19

5

10

25

43/13

12∼43 / 2∼13

100 68 42 15

210 140 86 30

520 340 210 71

870 570 350 110

600 390 240 78

Houston (1,953,631) High estimate (1) High estimate (2) Central estimate Low estimate Bangkok (6,355,144) High estimate (1) High estimate (2) Central estimate Low estimate Seoul (10,207,295) High estimate (1) High estimate (2) Central estimate Low estimate

a Percent reduction in annual average/percent reduction in 95th percentile 24-hour average.

Uncertainties The health impacts estimated in this study relied on information about ambient concentration, population, and C-R functions, all of which have the potential to introduce uncertainty. Ambient PM levels and population numbers were based on data reported from each environmental agency and municipal government. To quantify the uncertainty in the C-R functions, the lower and upper confidence limits were applied for the case of Osaka. Applying the 5th percentile lower confidence limit reduced the valued benefits by 51% and applying the 95th percentile upper confidence limit increased the benefits estimates by 55%. Likewise, to quantify uncertainty in the PM2.5 fraction (assumed in the analysis to be 60% of PM10 ), 50% and


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75% ratios were assumed as lower and upper bounds. Benefits valuation estimates dropped only slightly by 2% or increased by 34%. A 75% ratio was reasonable as an upper bound limit, exceeding the 67% reported in a residential center and the 71% reported at a traffic location in Bangkok (Kim Oanh, 2005). Only rarely is 75% an underestimate, as in the reported personal exposures to PM consisting of nearly 90% fine particles at one traffic-impacted site in Bangkok (Jinsart et al., 2002).

Discussion Assumptions The actual populations in each of these four cities are exposed to a mix of air pollutants, and the health effects that are expressed in those urban locations will included effects not only from PM, but also from other pollutants such as sulfur oxides, nitrogen oxides and ozone. Furthermore, cigarette smoking will have a health impact as well. As such, it would be unrealistic to state that PM is the only pollutant of concern in these four real cities. The intent of this analysis is to isolate and estimate the effects from PM that would not occur if PM levels were lowered. This is a hypothetical exercise limited to PM impacts, and the health functions used in the analysis specifically separated out the impact due to PM from other confounding factors. Krewski et al. reported separate mortality rate ratios for particles (total and fine), sulfur dioxide, sulfate particles, aerosol acidity and ozone while simultaneously adjusting for cigarette smoking (Krewski et al., 2000). Similarly, Schwartz reported an increased risk of chronic bronchitis after controlling for race, age, sex and cigarette smoking (Schwartz, 1993). A number of assumptions were made in this analysis regarding PM monitoring and particle composition. With only 5 active PM10 monitors in Houston and 9 in Bangkok, it was difficult to ascertain whether the limited PM concentration data were typical of urban ambient levels. On the other hand, Osaka reported data from 22 monitors and Seoul had 26 active sites, which provides a more comprehensive concentration profile for the metropolis. Houston was unique in having a PM2.5 monitoring network with 10 active monitors in 4 political districts. Fine particles have been shown to cause the greatest damage to human health in a number of cities, including Cairo, Mexico City, Quito and Bangkok (Kojima and Lovei, 2001) and mortality estimates in this study (with their associated high monetary valuations) were based on PM2.5 exposure assumptions. Particle size distribution was assumed to be identical across cities and within cities. Kim Oanh et al. (2003) has reported variability in the PM2.5 /PM10 fraction for Bangkok that ranged from 1/3 to over 2/3. Emission sources and chemical composition were assumed to be identical. However, PM emission sources


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will differ between cities. In Bangkok, diesel vehicles, biomass burning, secondary particles, sea salt and construction activities are major contributors to ambient PM (Kim Oanh et al., 2003). In Seoul, aerosols and secondary aerosols, ammonium sulfates and nitrates, motor vehicles, and biomass burning are major contributors (Lee et al., 2005). Chemical composition will differ as well. Particulates in Bangkok contain diesel particles, ammonium sulfate and sodium nitrate (Kim Oanh et al., 2003). Polycyclic aromatic hydrocarbons (PAH) have also been reported near Bangkok (Kim Oanh et al., 2000) but PAH compounds are a common component of PM in urban areas worldwide. In Seoul, sand storms originating in upwind regions contribute to long range transport of fine aerosols, high silica content particles and heavy metals (Youngsin and Lim, 2004; French, 2002). Temporal differences within cities can be marked, as in the case of Bangkok where both particle size distribution and total concentrations differ markedly between the rainy season and the dry season (Kim Oanh et al., 2003; Thongsanit et al., 2003) and Seoul, where PM2.5 mass from biomass burning is 2.5 times higher in autumn than it is in spring (Lee et al., 2005). Epidemiological studies of air pollution and human health typically rely on outdoor monitored concentrations — assuming that people are always exposed at those levels — an assumption used in this analysis. In reality, people spend much time indoors. Nonetheless, residents of the three Asian cities in this analysis spend some portion of nearly every day outdoors. In Osaka, there are large numbers of pedestrians, along with more cycling commuters and shoppers than automobile drivers in the residential districts. In Bangkok, street vendors hawk their wares and roadside restaurants open up at meal times, with chairs and tables set out on the wide sidewalks. The open air auto-rickshaws known as tuk-tuks and numerous non-air conditioned public buses in Bangkok also represent opportunities for exposure by passengers and drivers to the higher outdoor PM concentrations typical of roadways. Seoul has large numbers of pedestrians and its open air markets may register higher ambient concentrations than exist indoors. Furthermore, even for those residents who spend most of their time indoors, it is not always the case that PM exposure will be less. For indoor locations in Bangkok without air conditioning and with some indoor PM source present (such as cigarette smoke or charcoal), PM levels are equal to or higher than outdoor levels (Chestnut et al., 1998). Architectural preferences such as the open air markets prevalent in Seoul and Bangkok, zoning limitations on roadside residences, and amount of recreational time in public spaces and outdoor nightlife all impact exposure. Even with equal amounts of exposure, the baseline health status of individuals in different cities with different living standards and varied health care systems will be different. The health effects resulting from exposure may differ across countries, or the effects may be the same, but with different concentration thresholds for the occurrence of those effects. It requires site-specific epidemiological evidence to


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address these questions. Initial evidence suggests that effects observed in Asian cities are similar to those seen in Western country studies (Chestnut et al., 1998; Health Effects Institute, 2005). Each of the six C-R functions required information on health occurrence, such as death rates, bronchitis prevalence, and hospital admissions (see Table 3). For mortality, country-specific data on all cause mortality rates were available. Chronic bronchitis prevalence rate for adults 18 years and older and annual chronic bronchitis incidence rate per person were based on US data. Daily hospital admission rates for cardiovascular disease and pneumonia per person 65 and older were based on US data. Daily incidence of asthma attacks per asthmatic and annual acute bronchitis incidence rate per person were based on US data, with the exception of Osaka, where data on the local asthmatic population were used. For comparative purposes, Table 6 lists four studies that valued mortality and morbidity benefits. Olsthoorn et al. (1999) calculated that reducing PM to a

Table 6. Comparison of benefits associated with reducing particulate matter air pollution. Investigator (year) Olsthoorn et al.

Analytical year 2010

(1999)

Target illness (number of cases)

Study location Value of (study pop.) statistical life (million )

Mortality (LT) 25 Euro. cities (1,925–11,550) Mortality (ST) (468–650) (16,000,000) Emergency room visits (ST) Morbidity (other) Total

2.8–4.7

Econ. impact (million) (1999$) 5,568–53,970 0–3,037 1–4 1 5,571–57,012

Chang et al. (2002)

2020

Mortality (1,790–2,808) Morbidity Work loss days Total

Shanghai (14,000,000)

0.15

225–353 37–58 8–13 270–424

Mexico Team (2002)

2010

Mortality (2,826–13,229) Hospital admissions Emergency room visits Work loss days Morbidity (other)

Mexico City (17,000,000)

1.85–4.28

43–3,980 3.19–3.36 2.4–16.32 20.46–95.76 69.95–11,815.56

Chestnut et al. (1998)

1998

Mortality (4,000–5,500) + morbidity

Bangkok (10,000,000)

Not reported

2,600–7,000

Present study

2010

Mortality (2–356) Morbidity Mortality (0–91) Morbidity Mortality (25–573) Morbidity Mortality (185–1,597) Morbidity

Osaka (1,708,000) Houston (1,010,000) Bangkok (3,212,735) Seoul (5,727,816)

2.7–5.6

0.33–1,990 1–204 0–555 0–79 0.85–1,780 0.38–263 17–7,350 140–1,550

LT = long term; ST = short term.

3.36–6.12 0.63–3.1 1.6–4.6


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proposed European Commission ambient standard in 25 European cities would result in an annual mortality benefit in 2010 (per million population) of $350–3,600 million. Chang et al. (2002) estimated mortality benefits from controlling power plant emissions in Shanghai in 2020 to be $16–25 million (per million population). The Mexico Air Quality Management Team (2002) valued the reduced mortality benefit from reducing PM in Mexico City to a level of 50 µg/m3 and 150 µg/m3 at between $2.6 and 230 million (per million population). Chestnut et al. (1998) estimated a combined mortality and morbidity benefit in Bangkok associated with a 20 µg/m3 reduction in annual average PM10 concentrations to be $260–700 million (per million population). In the present study, mortality was valued at $0.19–1,200 million (Osaka), $0–550 million (Houston), $0.27–550 million (Bangkok) and $3.0– 1,300 million (Seoul) (per million population). There are two principal reasons for the variability in these valuations, namely, use of a wide range of VSL numbers (see Table 6), and adjustments to valuations for income and cost-of-living differences between countries. Possible biases in risks calculations This study presumes that VSL provides the most reasonable single estimate of willingness to trade off money for reductions in mortality risk. The VSL as applied assigned the same value to individuals of all ages. Uniform mortality valuation remains a standard practice in benefits analysis (Ballaman, 1998; Chang et al., 2002; Danielis and Chiabai, 1998; Levy and Spengler, 2002; Olsthoorn et al., 1999; Raufer, 1997; US EPA, 2000). However, it is generally recognized that the value to an individual of a reduction in mortality risk may differ based on culture, nationality, age and other characteristics (Cropper and Sussman, 1990; Moore and Viscusi, 1988). The uncertainty in risk valuation associated with applying developed nation health values in a developing country context was addressed by the Mexico Air Quality Management Team in the form of alternative valuation scenarios. To account for uncertainty in the income elasticity of WTP for mortality, two income elasticity estimates were included: 1.0 and 0.4. To account for uncertainty about the size of WTP, conservative lower bound estimates of the value of mortality and morbidity were derived using a Human Capital/foregone earnings approach (Mexico Air Quality Management Team, 2002). A similar approach was applied in this analysis. Accurate valuation of mortality risk reduction has a potentially large impact on the benefits calculation, since the value of prevented mortality accounted for a high percentage of total benefits — ranging from 68% to 92% in the uniform PM reduction scenarios, and as high as 99% in the non-uniform reduction scenario. The impact on overall benefits is less for valued lost Human Capital where mortality accounted for less than half the total benefit.


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This study, while using city-specific death rates in its calculation of avoided premature mortality, relied on US rates for avoided cases of morbidity due to an absence of local data. The C-R functions for chronic and acute bronchitis used US prevalence and incidence rates. The impact on the risks estimate could be significant for chronic bronchitis, where up to 30% of the benefits were attributable to chronic bronchitis for uniform control scenarios. The impact on overall benefits is greater in the lost Human Capital estimate where chronic bronchitis accounted for up to 98% of the total benefit. The impact on benefits is small in scale for acute bronchitis, given that avoided cases accounted for less than 0.01% of benefits. This study did not include an estimate of benefits to agricultural productivity from reducing levels of PM, nor did it estimate ecosystem health or productivity effects due to a lack of sufficient C-R data for PM exposure. This has also been a limitation of other PM benefit studies (Ballaman, 1998; Chang et al., 2002; Danielis and Chiabai, 1998; Levy and Spengler, 2002; Olsthoorn et al., 1999; Raufer, 1997; US EPA, 2000; Mexico Air Quality Management Team, 2002). Policy implications The four cities in this analysis represented four countries at different stages of economic development, and with varying levels of ambient PM pollution. The results reported here reflect those differences in numbers of cases of preventible illness and mortality, and the value of PM impacts. Relying on monitored concentrations alone as an indicator of air quality may not capture the resulting exposure or the value of the benefits associated with reducing ambient levels. Though Houston had the lowest concentrations and Seoul had the highest (Fig. 2) and the preventible cases of premature mortality were lowest in Houston and highest in Seoul (Table 4), the adjusted value of health and productivity benefits ranged from a low of $25 million in Bangkok to a high of $160 million in Osaka (Table 5). Particulate matter remains a serious concern in many urban areas, especially in large Asian cities (World Bank, 1997). Developing a plan to reduce PM levels has many facets and each plan will have site-specific components. This has been evident during the 2008 Beijing Summer Olympics, where pollution control measures included alternate driving days for roughly a third of Beijing’s vehicles, factory closures in the city and in surrounding provinces (due to regional pollutant transport), and short term construction bans in the capital. In addition, 300,000 heavily polluting vehicles, such as older and poorly maintained industrial trucks, were banned a month prior to the opening ceremonies (CNN News, 2008). Such targetted approaches to pollution reduction can work well at the urban-scale, and have varying degrees of difficulty for implementation, depending on the source type. Also, the toxicity of PM components and the sensitivity of some persons should be


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considered in pollution remediation planning. Sources of construction dust and sea salt particles should be of lesser concern than diesel particulates, whereas particles that induce bronchitis in children or premature mortality in the elderly should receive greater attention in air quality management strategies. Ambient levels in developing country cities can be several times higher than those in cities of developed countries (Gwilliam et al., 2004). This study noted PM levels in excess of ambient standards for Bangkok and Seoul, and to a lesser extent in Osaka. Government officials in Korea have recently committed to return visibility in the Seoul metropolitan area to levels not seen in three or four decades (The Korea Herald, 2005). Seoul and Bangkok are included in a benchmark characterization of Air Quality Management as part of the Air Pollution in the Megacities of Asia (APMA) project (Korea Environment Institute, 2002). The Asian Institute of Technology has conducted intensive air pollution research in Bangkok (AIRPET), and in the DIESEL Project (Developing Integrated Emission Strategies for Existing Land Transport), the Asian Development Bank has been working to address diesel vehicle emissions and present control options for Bangkok (Asian Development Bank, 2005). In 1999 Bangkok opened its elevated rail system, the “Skytrain.” In 2004 a new subway line was completed, and in 2006 the Skytrain will be expanded and the new Suvarnabhumi International Airport is scheduled to open to the east of the city. These measures will help mitigate ambient PM10 in the central city areas by reducing use of the bus network that currently serves as the main public transit option. The Health Effects Institute is supporting the Public Health and Air Pollution in Asia (PAPA) program, in which local investigators in seven Asian cities (including Bangkok) are conducting time-series studies of the health effects of short term exposure to air pollution (Health Effects Institute, 2005). Preparation of a PM Air Quality Management Plan involves answering to a sequential series of questions. (i) Are the ambient levels of PM in populated areas high? — This is a subjective question and should be answered jointly by government, scientists, politicians and citizen groups. (ii) How widespread is the problem and is this determination made using [representative] monitoring data, or by dispersion modeling? (iii) What are the constituents of the PM mix and how much is PM2.5 ? (iv) What are the dominant sources and are they located nearby? (v) What are the health and productivity benefits of reducing PM? (vi) What are the costs? (vii) If a plan is prepared, will the controls be tailored to reduce pollution hotspots or the whole area, and should the focus be on the most toxic particle component?


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In developed countries, stationary sources of PM no longer pose the threat they did in past years. Control technologies for large industrial sources (e.g., electrostatic precipitators, fabric filters, scrubbers, cyclones) have been in use for many years. Also, large stationary sources are often located away from populated areas and frequently emit their PM from tall smokestacks. Assuming that stationary sources are also controlled in developing nations, the impact on ambient PM of motor vehicle emissions — both direct tailpipe and indirect (e.g., reentrained road dust, tire wear, brake pad/shoe dust) — will continue to increase in importance, especially as emerging economies become more motorized. Once lead additives have been banned from gasoline, then “the reduction of fine particulate matter [from motor vehicles] is by far the highest priority” (Kojima and Lovei, 2001). A targetted control strategy for motor vehicles — as might appear appropriate for Houston and Osaka — is more difficult to implement than a targetted approach for stationary sources. As new vehicle emissions approach ultra low levels, the identification and removal of older and poorly maintained motor vehicles (i.e., “gross polluters”) through inspection and maintanence (I/M) programs and roadside enforcement becomes a more cost effective way to reduce mobile source PM (Kojima and Lovei, 2001; Gwilliam et al., 2004). A citywide approach to controlling motor vehicle PM that includes traffic management strategies and land use management would seem to be best for Bangkok and Seoul. The situation in Seoul is complicated by the presence of high levels of particles transported long distances from upwind sources outside of Korea. Others have explained in detail the means of controlling mobile source pollution (Eskeland and Devarajan, 1996; World Bank, 1997; Kojima and Lovei, 2001). The policy instruments fall into two broad categories: (a) Reducing Emissions through Transport System Improvement, and (b) Reducing Emissions at the Vehicle Level. Transport system improvements include synchronized traffic signals, segregated traffic lanes, restrained vehicle movements, bans on certain vehicle types, and land use plans that reduce trip lengths. Providing convenient, clean and safe public transportation by bus or train helps influence consumer modal choice. Differential taxation of more polluting fuels (e.g., leaded gasoline, conventional diesel) helps discourage purchase and use, and is more effective than subsidizing “clean” technologies. Reducing emissions at the vehicle level involve measures such as I/M programs, mandatory fuel quality standards, requirements for improved vehicle emission control and engine technology, and use of alternative transport fuels (e.g., gaseous fuels, biofuels, electricity) (Gwilliam et al., 2004). Work sponsored by the Health Effects Institute, the World Bank and others is adding to knowledge of localized health impacts in developing countries. Asian researchers are documenting the fine particle fraction and chemical composition of PM. The selection of control policies should consider the toxicity of


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components, and not just total PM. Though Seoul recorded higher concentrations of PM than Bangkok, the presence of diesel particles and PAH’s in Bangkok should receive added weight in decisions to allocate resources for environmental improvement. Devising a metric for cross-country analyses that can account for disparate income levels and population exposures could provide a tool to assist international aid agencies and transnational quasi-governmental organizations in provision of environmental assistance. Reducing a cross-country analysis to comparing per capita economic benefits is an imperfect tool, in particular when comparing a higher income-lower population city (such as Osaka) with a lower income-higher population city (such as Bangkok or Seoul). Evidence suggests that some persons are more sensitive than others to the effects of air pollution. The EPA recognizes this in its Air Quality Index, which is an index for reporting daily air quality. Using color-coded categories from “good” to “hazardous,” the AQI links air concentrations of ozone, PM, carbon monoxide, sulfur dioxide and nitrogen dioxide to an index from 0 to 500. An AQI value of 100 approximates the level EPA has set to protect public health. According to the EPA, “AQI values below 100 are generally thought of as satisfactory. When AQI values are above 100, air quality is considered to be unhealthy-at first for certain sensitive groups of people, then for everyone as AQI values get higher.” (US EPA, 2008). In this analysis, we selected health effect studies that incorporated sensitive subpopulations. Krewski et al. (2000) assessed deaths among a random sample of men and women due to respiratory disease, cardiovascular disease, lung cancer, and from all other causes. Their results represented PM mortality risks to both sensitive and non-sensitive individuals. Dockery et al. (1996) identified the risk of developing acute bronchitis from exposure to acidic air pollution in children, a recognized sensitive population. Another vulnerable group is the aged. The elderly were singled out by Samet et al. (2000) for their assessment of the impact of PM on cardiovascular disease. Also considered to be vulnerable are asthmatics. The Whittemore and Korn (1980) study used in this analysis singled out exposure to PM as a risk factor in a cohort of over 400 juvenile and adult asthmatics.

Conclusions Particulate matter exposure causes human and ecological impacts and disrupts commerce. This study assessed the prospective benefits for air pollution reductions in Osaka, Houston, Bangkok and Seoul, four cities with differing histories of control and at different stages of economic development. There is no single strategy that applies equally for all cities. For the industrialized nation cities of Osaka and Houston, the concentrations of PM were already relatively low at the beginning of this decade and even a small uniform 5% reduction demonstrated larger benefits than


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ensuring that only the most polluted sites reached ambient standards. If the costs of control are shown to be sufficiently high, a hotspots strategy may be more appropriate than a uniform control strategy. At the other extreme, some locations in Seoul reported PM levels so high that a large uniform 25% reduction would be insufficient to reach ambient standards. With such significant reductions needed, it is premature to focus on hotspots. Bangkok has a situation somewhere between the two extremes, but with PM sources that are both mobile (diesel engines) and stationary (biomass burning) in nature, a broad city-wide strategy seems appropriate.

Acknowledgements This research was assisted by a grant from the Abe Fellowship Program of the Social Science Research Council and the American Council of Learned Societies with funds provided by the Japan Foundation Center for Global Partnership. The authors are grateful to Ms Jeongsuk Moon at the Department of Occupational & Environmental Medicine, College of Medicine, Hanyang University, Seoul, and to Patcharawadee Suwanathada, PhD in the Royal Thailand Pollution Control Department for providing valuable data. The authors thank Youn-Joo An, PhD at Konkuk University for providing office space and a sounding board for the Korean component of this work, and also Jong-Tae Lee, PhD at Hanyang University and Young-Man Roh, Dr Med. Sc. at Hanyang University for their suggestions. The authors thank Ms Yamasaki Akiko at the Osaka Prefecture Department of Environment, Forestries and Fisheries, Dr Hari Srinivas, PhD at the United Nations Environment Programme in Osaka, Assist. Prof Nuntavarn Vichit-Vadakan, Dr P. H. at Chulalongkorn University in Bangkok, and Mr Bill Frietsche at the US Environmental Protection Agency in Research Triangle Park for the valuable time and data they provided. They also thank the Institute for Global Environmental Strategies (IGES) and its fellows in both Kanagawa and Kitakyushu, Japan (Matsumoto Naoko, PhD, Shobhakar Dhakal, PhD, Prof Imura Hidefumi, Ms Kono Noriko, MUP, Sudhakar Yedla, PhD, Shirakawa Hiroaki, PhD) for sharing their expertise in sustainable development.

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