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Sulfur Dioxide and Public Health in China Cameron Ball EECE Interna.onal Experience 2008 Research project

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Page 1: CSB Eece Presentation

Sulfur Dioxide and Public Health in China

Cameron Ball EECE Interna.onal Experience 2008 

Research project 

Page 2: CSB Eece Presentation

Outline

•  Ques%ons to answer •  Introduc%on, background and significance •  Cri%que of HRA methods and suggested improvements 

•  Data from sample studies 

•  Stressing the need for a comprehensive HRA of SO2 in China 

•  Interven%ons 

Page 3: CSB Eece Presentation

Outline

•  Ques%ons to answer •  Introduc%on, background and significance •  Cri%que of HRA methods and suggested improvement 

•  Data from sample studies 

•  Stressing the need for a comprehensive HRA of SO2 in China 

•  Interven%ons 

Page 4: CSB Eece Presentation

Project Aims •  ASSESS THE STATE OF SO2 

RESEARCH ON HEALTH EFFECTS IN CHINA. 

•  SUGGEST CONCRETE IMPROVEMENTS FOR FUTURE STUDIES. 

•  IDENTIFY THE QUALITATIVE AND QUANTITATIVE HEALTH RISKS ASSOCIATED WITH SO2 IN CHINA.  

•  DETERMINE HOW TO DECREASE HEALTH RISKS IN AN IMMEDIATE AND PRAGMATIC MANNER. 

•  PREDICT HOW THESE RISKS WILL CHANGE IN THE FUTURE. 

Page 5: CSB Eece Presentation

Outline

•  Ques%ons to answer •  Introduc%on, background and significance •  Cri%que of HRA methods and suggested improvements (longest sec%on) 

•  Data from sample studies 

•  Stressing the need for a comprehensive HRA of SO2 in China 

•  Interven%ons 

Page 6: CSB Eece Presentation

Introduction

•  China’s moderniza%on 

•  GDP‐ 8‐9% increase per year since 1978 

•  Projected growth is staggering 

!

CHAPTER 2 CURRENT STATUS OF ENERGY USE AND

AIR POLLUTION

2.1 Rapid Economic Development

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! BE

Page 7: CSB Eece Presentation

Projected GDP Growth

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Page 8: CSB Eece Presentation

Energy Demands

•  From 2000 to 2004, average increase in energy usage was 10.8% per year. 

•  Efficiency is necessary, but insufficient. 

•  With increased power output  increased emissions. 

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2.3 Air quality status

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Page 9: CSB Eece Presentation

Atmospheric Brown Clouds 

•  Since 1950: –  5x soot emissions –  7x sulfur emissions 

•  95% sulfur emissions are SO2 

•  Crea%on of ABC “hot spot” •  Impacts on agriculture, 

hydrology, climate change, etc. 

•  Great impact on health and ecology (direct and indirect) 

Page 10: CSB Eece Presentation

ABCs

•  Dimming means 15 W/m2 less solar energy shine on India and China than in 1950 (6% decrease) 

•  Upper atmosphere warming by 20‐50% 

Page 11: CSB Eece Presentation

China is aging

•  Older popula%ons much more suscep%ble to air pollu%on 

•  Increasing age poses great social challenge to China 

Page 12: CSB Eece Presentation

Development Model

•  China is developing •  Model for other Asian na%ons concerned about health and pollu%on associated with coal 

•  Coal derived pollutants important‐ cheap 

Page 13: CSB Eece Presentation

SO2 Background & Significance

•  SO2 considered most dangerous gaseous pollutant 

•  Sources: coal, oil, biofuels, nonferrous smel%ng 

•  Soluble‐ 11.3g in 100ml H2O 

•  De novo nuclea%on of H2SO4 par%culates 

Page 14: CSB Eece Presentation

Standards •  SO2 level standards 

–  China (Tsinghua, Peking, NREL, 2008) •  Class I 

–  Daily avg. ≤ 50μg/m3 –  Yearly avg. ≤ 20μg/m3 

•  Class II –  Daily avg. ≤ 150μg/m3 –  Yearly avg. ≤ 60μg/m3 

•  Class III –  Daily avg. ≤ 250μg/m3 –  Yearly avg. ≤ 100μg/m3 

–  WHO (World Health Organiza%on, 2006) •  Interim target 1 

–  Daily avg. ≤ 125μ/m3 •  Interim target 2 

–  Daily avg. ≤ 50μ/m3 •  WHO Guidelines, 2005 

–  Daily avg. ≤ 20μ/m3 –  10‐minute avg. ≤ 500μ/m3 

Page 15: CSB Eece Presentation

Pass/Fail

•  2003‐ more than 26% of Chinese ci%es s%ll failed to meet class III requirements.  

•  31.5% met class III requirements but did not meet class II requirements  

Page 16: CSB Eece Presentation

SO2 Trends A I R Q U A L I T Y G U I D E L I N E S5 0

The introduction of restrictions on sulfur in fuels is one of the reasons for the decline in sulfur dioxide concentrations. This is clearly seen in Fig. 12 showing ambient concentrations in Hong Kong, China, where restrictions were imposed from 1990. Furthermore, cleaner fuels such as natural gas, which contains less ash and sulfur, have replaced coal and high-sulfur oils. Also, light transport sys-tems, as mentioned above, have contributed to this improvement in some cities. A study by the Clean Air Initiative for Asian Cities, summarizing air qual-ity data from 20 cities in Asia, shows that on average there has been a slight to

Fig. 11. Average concentrations of PM10, nitrogen dioxide, sulfur dioxide and ozone at five Hong Kong monitoring stations

Fig. 10. The development of annual average sulfur dioxide concentrations in Chinese cities from 1990 to 2002

Source: Hao & Wang (45).

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Source: reprinted from Hedley et al. (59) with permission from Elsevier.

Page 17: CSB Eece Presentation

SO2 Trends

•  Sulfur dioxide concentra%ons in China fell on average by 44.3% (from 93 μg/m3 in 1990 to 52 μg/m3 in 2002)  

•  S%ll very high compared to most of the developed world. 

Page 18: CSB Eece Presentation

SO2 Emissions Distribution (approximate)

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Page 19: CSB Eece Presentation

Major Indirect Effects of SO2 on Health

•  SO2 impact on environment and ecosystems –  Via acid deposi%on 

•  Water pollu%on •  Soil acidifica%on •  Plant life & Agriculture 

–  Crop yields sensi%ve to pollu%on concentra%ons (especially Ozone) •  Biodiveristy  

–  Human health and animal health –  Ecological or climate change 

•  Rains shimed south in China due to ABCs •  Hindu Kush‐Himalayan‐Tibetan (HKHT) glacier retreat will 

cause loss of 75% of snowcaps by 2050 –  large water shortages throughout India and East Asia.  –  Currently, about 80% of Western Tibetan glaciers are in retreat 

Page 20: CSB Eece Presentation

Outline

•  Ques%ons to answer •  Introduc%on, background and significance •  Cri%que of HRA methods and suggested improvements 

•  Data from sample studies 

•  Stressing the need for a comprehensive HRA of SO2 in China 

•  Interven%ons 

Page 21: CSB Eece Presentation

Study Types

•  Time series studies 

•  Toxological studies •  Cohort studies •  Mul%na%onal metadata 

•  Interven%on monitoring! 

Page 22: CSB Eece Presentation

Data Acquisition

– Data collec%on (physical) •  Loca%on in 3‐space •  Loca%on rela%ve to sources 

–  Shanghai 2008 study presents urban background measurements from 6 fixed monitoring sites. »  Six measurement sites used to study nine city districts. No informa%on on spa%al mapping provided (Kan, 2008). 

•  Indoor vs. outdoor •  Resolu%on requirements •  Measurement equipment •  Calcula%ons and valida%on for remote sensing •  Regularity of posi%on and methods •  Faithful recording •  Appropriate for popula%on of study 

Page 23: CSB Eece Presentation

Data Acquisition

–  Data compila%on (literature) •  Comprehensive search of literature •  Considera%on of spa%otemporal con%nuity •  Considera%on of variability of source data •  Considera%on of source reliability •  Data type considera%on and quality 

–  AQI –  Mean concentra%ons for provinces, etc. 

–  Data es%ma%on or modeling •  Resolu%on requirements •  Sectors of interest •  Es%mate or measure a subset of a class of objects for extrapola%on to other class members 

•  Implementa%on of modeling of diffusion, terrain, climate 

Page 24: CSB Eece Presentation

Data Source reliability

– Government data •  SEPA versus USEPA or European organiza%ons 

–  Censoring of data –  Doctoring of data – Movement of measurement sites –  Historical precedent within China 

»  Cultural aspect of government structure and policy control. Effect on trust of the government. 

–  Independent researcher data •  Peer review •  Errors included with results •  No major conflicts of interest (usually) 

Page 25: CSB Eece Presentation

Time averaging

•  Time course of SO2 health effects •  Effects can be seen within minutes of increased exposures 

•  Data compression for manageability 

•  Compromise between increased resolu%on and feasibility  

Page 26: CSB Eece Presentation

GIS implementation or lack thereof –  For berer correla%ons with 

exposure and increasing precision. 

–  Increasing accessibility to source data and methods. 

–  GIS has been implemented to study the spa%o‐temporal distribu%on of SO2 throughout the Chengdu plain, although monitoring sta%ons used in the study experienced malfunc%ons and may not have been properly managed (Song, 2008).  

Page 27: CSB Eece Presentation

Suggested Improvements •  Construc%on of online GIS database 

for remote sensing data and high‐resolu%on city‐based data (from monitoring sta%ons) along with informa%on on hospital admissions and deaths on a daily basis. System would be automated. –  Provide high resolu%on data for 

correla%ons and ease of use –  Could be validated by ground 

measurements and calibrated by atmospheric modeling 

•  Increasing accessibility to and reliability of government data. –  Shim governor’s no%on of informa%on 

disclosure •  Responsibility to people of China •  Increasing access will result in berer 

solu%ons to problem 

Page 28: CSB Eece Presentation

Exposure estimation methods

•  popula%on loca%on –  rela%vely simple task to 

locate households in China due to great government oversight, although data is likely available only on a case by case request from regional offices. 

•  Although numbers of individuals may be obtained, informa%on on age‐composi%on, occupa%on, etc. missing. 

Page 29: CSB Eece Presentation

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! @O

Example of GIS use to catalog popula%on density 

Page 30: CSB Eece Presentation

Population, cont.

–  Increase in migrant popula%on and economic development •  Shim of popula%on from rural to urban 

•  Architecture and customs bring different parerns of exposure to pollutants. 

–  Hong Kong study (2003) es%mated that its eight monitoring sites covered 73% of the popula%on. 

Page 31: CSB Eece Presentation

Outdoor versus Indoor Exposure

•  Chinese spend more %me outside 

•  Ven%la%on system reduc%ons in pollutant concentra%ons 

•  Solid fuel usage complica%on 

•  Outdoor exercise •  Age‐bias: whole other ball game 

Page 32: CSB Eece Presentation

Exposure based on SES •  Loca%on of industry •  Roadways •  Occupa%onal hazards •  Educa%on may affect risk 

–  Shanghai PAPA study suggests increase in risk of cardio and pulmonary deaths based on educa%on level 

high-education group. The educationaldifferences in respiratory mortality were notsignificant for any pollutants.

DiscussionAlthough the associations between outdoorair pollution and daily mortality have beenwell established in developed countries, thequestion of the potential modifiers remainsinconclusive. As the U.S. National ResearchCouncil (1998) pointed out, it is importantto understand the characteristics of individu-als who are at increased risk of adverse eventsdue to outdoor air pollution. Our results sug-gest that season and individual sociodemo-graphic factors (e.g., sex, age, SES) maymodify the health effects of air pollution inShanghai. Specifically, the associationbetween air pollution and daily mortality wasgenerally more evident for the cool seasonthan the warm season; females and the elderly(! 65 years of age) appeared to be more vul-nerable to air pollution than males andyounger people; and disadvantaged SES mayintensify the adverse health effects of outdoorair pollution.

Our finding of a stronger associationbetween air pollution and daily mortality inthe cool season is consistent with several priorstudies in Hong Kong (Wong et al. 1999,2001) and Athens, Greece (Touloumi et al.1996), but in contrast with others reportinggreater effects in the warm season (Andersonet al. 1996; Bell et al. 2005; Nawrot et al.2007). In Shanghai, the concentrations ofPM10, SO2, and NO2 were higher and morevariable in the cool season than in the warmseason (Table 1). Because these three pollu-tants were highly correlated, greater effectsobserved during the cool season may also bedue to other pollutants that were also at higherlevels during that season. In contrast, the O3level was higher in the warm season than inthe cool season, and our exposure–responserelationship also revealed a flatter slope athigher concentrations of O3 for both sexes(data not shown). At higher concentrations,the risks of death could be reduced becausevulnerable subjects may have died before theconcentration reached the maximum level(Wong et al. 2001).

Exposure patterns may contribute to ourseason-specific observation. During the warmseason, Shanghai residents tend to use air con-ditioning more frequently because of the rela-tively higher temperature and humidity, thusreducing their exposure. For example, in a sur-vey of 1,106 families in Shanghai, 32.7% ofthe families never turn on air conditioners inthe winter compared with 3.7% in the summer(Long et al. 2007). Heavy rain in the warmseason may reduce time outdoors, thus reduc-ing personal exposure. In contrast, the coolseason in Shanghai is drier and less variable, sopeople are more likely to go outdoors andopen the windows. Nevertheless, the fact thata consistently significant health effect of airpollution was observed only in the cool seasonin two subtropical Asian cities [Shanghai (pre-sent study) and Hong Kong (Wong et al.1999, 2001)] suggests that the interaction ofair pollution exposure and season may vary bylocation.

Unlike the gaseous pollutants, the con-stituents of the complex mix of PM10 may varyby season. Therefore, another potential expla-nation for the seasonal difference in the effectsof PM10 is that the most toxic particles mayhave a cool-season maximum in Shanghai.

We found a greater effect of ambient airpollution on total mortality in females than inmales. Results of prior studies on sex-specificacute effects of outdoor air pollution were dis-cordant. For example, Ito and Thurston(1996) found the highest risk of mortalityrelated with air pollution exposure amongblack women. Hong et al. (2002) found thatelderly women were most susceptible to theadverse effects of PM10 on the risk of acutemortality from stroke. However, Cakmaket al. (2006) found that sex did not modifythe hospitalization risk of cardiac diseases dueto air pollution exposure.

The reasons for our sex-specific observa-tions are unclear and deserve further investiga-tion. In Shanghai, females have a much lowersmoking rate than males (0.6% in females vs.50.6% in males) (Xu 2005). One study sug-gested that effects of air pollution may bestronger in nonsmokers than in smokers(Künzli et al. 2005). Oxidative and inflamma-tory effects of smoking may dominate to such

an extent that the additional exposure to airpollutants may not further enhance effectsalong the same pathways in males. In addi-tion, females have slightly greater airway reac-tivity than males, as well as smaller airways(Yunginger et al. 1992); therefore, dose–response relations might be detected more eas-ily in females than in males. Deposition ofparticles in the lung varies by sex, with greaterlung deposition fractions of 1-µM particles inall regions for females (Kim and Hu 1998;Kohlhaufl et al. 1999). Sunyer et al. (2000)suggested that differing particulate depositionpatterns between females and males maypartly explain the difference between the sexes.Moreover, compared with males, females inShanghai had a lower education level (73.9%in females vs. 41.0% in males); thus, lowerSES might contribute to the observed largereffects of air pollution in females.

As in a few other studies (Gouveia andFletcher 2000; Katsouyanni et al. 2001), wefound the elderly were most vulnerable to theeffects of air pollution. Low numbers of deathsin the 0- to 4-year age group limited ourpower to detect the effects of air pollution onmortality, even if they exist. Two groups, theelderly and the very young, are presumed to beat greater risk for air pollution–related effects(Gouveia and Fletcher 2000; Schwartz 2004).For the elderly, preexisting respiratory orcardiovascular conditions are more prevalentthan in younger age groups; thus, there issome overlap between potentially susceptiblegroups of older adults and people with heartor lung diseases.

It has long been known that SES canaffect health indicators such as mortality(Mackenbach et al. 1997). Recently, studieshave started to examine the role of SES inthe vulnerability of subpopulations to out-door air pollution, especially for particlesand O3, although the results remain incon-sistent (O’Neill et al. 2003). For example,Zeka et al. (2006) found that individual-level education was inversely related to therisk of mortality associated with PM10.Another cohort study with small-area meas-ures of SES in Hamilton, Ontario, Canada,found important modification of the particleeffects by social class (Finkelstein et al. 2003;

Kan et al.

1186 VOLUME 116 | NUMBER 9 | September 2008 • Environmental Health Perspectives

Table 4. Percent increase in number of deaths due to total, cardiovascular, and respiratory causes associated with a 10-µg/m3 increase in air pollutants by edu-cational attainment.a

Educational Mean daily PollutantMortality attainment deaths (n) PM10 SO2 NO2 O3

Total Low 67.3 0.33 (0.19 to 0.47) 1.19 (0.77 to 1.61) 1.27* (0.89 to 1.66) 0.26 (–0.09 to 0.60)High 42.1 0.18 (0.01 to 0.36) 0.66 (0.16 to 1.17) 0.62 (0.15 to 1.09) 0.30 (–0.11 to 0.71)

Cardiovascular Low 27.8 0.30 (0.10 to 0.51) 1.08 (0.47 to 1.69) 1.15 (0.58 to 1.72) 0.39 (–0.13 to 0.90)High 16.4 0.23 (–0.03 to 0.50) 0.57 (–0.20 to 1.35) 0.73 (0.01 to 1.45) 0.26 (–0.38 to 0.91)

Respiratory Low 8.9 0.36 (0.00 to 0.72) 1.54 (0.43 to 2.66) 1.59 (0.57 to 2.62) 0.20 (–0.74 to 1.16)High 5.4 0.02 (–0.43 to 0.47) 0.73 (–0.61 to 2.09) 0.34 (–0.89 to 1.60) 0.27 (–0.86 to 1.41)

aWe used current day temperature and humidity (lag 0) and 2-day moving average of air pollutants concentrations (lag 01) and we applied 3 df to temperature and humidity. *Significantlydifferent from high educational attainment (p < 0.05).

Page 33: CSB Eece Presentation

Seasonal Variations

strong day-to-day correlation between PM10and PM2.5 between February 1995 andAugust 1996, with higher median values forboth during the colder season. Jorquera et al.(2004) reported that because of geography andclimatic conditions, Santiago has a higherratio of estimated PM10 emissions (tons peryear) to annual mean PM10 (micrograms percubic meter) than Mexico City, Buenos Aires,and São Paulo. A low-lying inversion layer inthe winter reduces dispersion of pollutants.Although the present study is based on out-door-area monitoring, it probably reflects per-sonal exposure. During the winters of 1988and 1989, Rojas-Bracho et al. (2002) carriedout an exposure study of Santiago children10–12 years of age; personal, indoor, and out-door PM2.5 concentrations were all within 5%at 69.5, 68.5, and 68.1 µg/m3, respectively.Even if the mean outdoor and indoor valuesare different, our results would be valid as longas the exposures changed in the same direc-tion. The present daily time-series analysisexamines the effects of day-to-day differencesin air pollution, not absolute values.

Air pollution–related mortality. The pre-sent findings averaged over seven urban centersare similar to those of previous air pollutionstudies in Chile. In 1989 and 1991, cardiacand respiratory mortality were higher on daysof increased PM10 (Ostro et al. 1996). Ostroet al. (1996) reported that a 10-µg/m3 changein daily mean PM10 was associated with a 1%

increase in total daily mortality. During thesame period, total mortality was associatedwith PM2.5 (Salinas and Vega 1995). Between1988 and 1996, nonaccidental deathsincreased on days of higher air pollution(Cifuentes et al. 2000). Cifuentes et al. (2000)reported that changes in mean levels of pollu-tants were related to 4–11% changes in mor-tality. In the present study we found that achange in 24-hr mean PM10 of 10 µg/m3 wasassociated with a 1% mortality change, using asingle-day lag. This effect occurred largely inthe colder months when particulate concentra-tions were higher, with no statistically signifi-cant findings in the warmer months. Afteradjusting for PM10, we also detected an effectof pollutant gases O3, CO, and SO2 on mor-tality. An increase in air pollution was associ-ated with an approximate 50% relative increasein respiratory compared to cardiac deaths forCO, SO2, and PM10.

Seasonal influences. The mortality effectsof CO, SO2, and PM10 appeared greater dur-ing April–September, the colder months,although differences were significant for onlyPM10. Ilabaca et al. (1999) also reported a sea-sonal modification of the PM2.5 effect onpediatric emergency department visits, greatestin the colder months. In the present study, achange in PM10 of about 85 µg/m3 was associ-ated with a 12.2% change in mortality duringthe warmer months and 1.3% in the coldermonths, using unconstrained distributed lags.

During the warmer months, October–March,a change in 1-hr maximum daily O3 ofapproximately 100 ppb was associated with a4.9% change in daily mortality, comparedwith 2.1% in the colder months. PM2.5 ishigher in the colder season and O3 higher inthe warmer season. This could affect the esti-mate of effect if the exposure–response rela-tion was not linear or if there was a thresholdeffect. However, the pattern of residuals in ouranalysis was consistent with a linear effect.Threshold effects have not been documentedeven at levels of air pollution lower than seenin the present study. Another possible reasonfor seasonal differences may be differenttime–activity patterns resulting in differentexposures. Finally, there is evidence for sea-sonal differences in particulate mass composi-tion (crustal vs. combustion sourced) anddirection (Celis et al. 2004).

Effect modification by age. Bell et al.(2005) reported increased mortality effectsin the elderly from O3 in The NationalMorbidity, Mortality, and Air PollutionStudy of 95 U.S. cities. Bateson and Schwartz(2004) reported that the risk of mortalityassociated with PM10 in Cook County,Ilinois, appeared to increase among elderlywomen but decreased among elderly men.Filleul et al. (2004) reported a greater effect ofair pollution mortality in those > 65 years ofage, but it did not reach conventional levels ofstatistical significance. Others also reportedincreased susceptibility of those > 65 years ofage (Gouveia and Fletcher 2000; Spix et al.1998). We studied the extremes of old age.Compared with those < 65 years of age, thoseat least 85 years of age were observed to beover twice as likely to die from acute increasesin PM10 and > 50% more likely to die fromincreases in O3 and SO2. Age-related suscep-tibility was further magnified when uncon-strained distributed lags were considered. Wealso observed a generally monotonic increasein susceptibility with increasingly older agegroups. These findings suggest that the deter-mination of air quality guidelines designed toprotect the general population may be insuffi-cient to protect the elderly in our society.

In summary, more recent air pollutiondata in Santiago, Chile, indicate that air pol-lution levels continues to be high by compari-son with those in North America and areassociated with stronger mortality effects fromrespiratory than cardiac disease. Dailyincreases in gases and particles are associatedwith increased mortality. The extremelyelderly appear to be at greater risk than thosewho are younger. We recommend that thedegree of susceptibility to air pollution in thevery elderly be investigated in other countriesto determine whether this finding is generaliz-able across different climates and air pollutioncharacteristics.

Cakmak et al.

526 VOLUME 115 | NUMBER 4 | April 2007 • Environmental Health Perspectives

Table 5. Percent change (t-ratio) in nonaccidental daily mortality associated with changes in pollutantconcentrations equivalent to population-weighted averages by cause of death, age at death, and season.

Classification PM10 O3 SO2 COCause of death

NonaccidentalSingle-day lag 8.54 (5.14) 5.64 (2.78) 5.65 (4.97) 5.88 (6.42)Distributed lag 11.68 (5.22) 4.38 (2.18) 9.28 (6.64) 9.39 (6.89)

CardiacSingle-day lag 10.06 (3.25) 8.78 (2.42) 7.24 (3.55) 7.79 (4.56)Distributed lag 13.33 (3.35) 2.30 (0.78) 10.53 (4.29) 11.22 (4.8)

RespiratorySingle-day lag 18.58 (4.51) 8.21 (1.46) 12.45 (4.19) 12.93 (5.78)Distributed lag 29.66 (4.88) 15.63 (2.50) 20.44 (5.21) 21.31 (6.34)

Age at death (years)! 64

Single-day lag 4.53 (1.52) 4.96 (1.17) 4.77 (2.50) 4.10 (2.52) Distributed lag 4.26 (1.29) 1.84 (0.71) 4.27 (2.49) 4.76 (2.19)

65–74Single-day lag 9.47 (2.81) 8.00 (1.77) 5.99 (2.49) 6.24 (3.17)Distributed lag 11.72 (3.01) 2.15 (0.86) 7.21 (2.55) 8.12 (3.88)

75–84Single-day lag 12.61 (3.80) 9.42 (2.28) 8.73 (4.00) 8.64 (4.82)Distributed lag 17.62 (3.72) 3.32 (0.92) 11.2 (4.25) 13.12 (5.12)" 85

Single-day lag 14.03 (3.87) 8.56 (2.02) 7.92 (3.23) 8.58 (4.45)Distributed lag 19.73 (3.75) 5.92 (1.92) 11.13 (4.38) 13.20 (4.82)

SeasonApril–September

Single-day lag 9.12 (3.35) 3.21 (1.14) 6.47 (3.92) 7.09 (4.02)Distributed lag 12.20 (3.75) 2.14 (1.25) 10.23 (4.72) 9.65 (4.50)

October–MarchSingle-day lag 0.60 (0.45) 6.19 (1.92) 2.62 (1.19) 5.45 (1.14)Distributed lag 1.27 (1.46) 4.89 (1.82) 4.25 (1.75) 7.80 (1.89)

Page 34: CSB Eece Presentation

For personal use. Only reproduce with permission from The Lancet Publishing Group.

ARTICLES

1648 THE LANCET • Vol 360 • November 23, 2002 • www.thelancet.com

ResultsIn the first year after introduction of the intervention,mean fall in SO2 concentration at five stations was 53%(table 1). Reduction in SO2 concentration was sustainedbetween 35% and 53% (mean 45%) of the mean valuebefore the intervention, over 5 years. At eight stations forwhich complete data were available for up to 2·5 years,the average reduction in SO2 concentration over thisperiod was 50%.

Mean concentration of sulphate in respirableparticulates at five stations for 2 years before theintervention was 8·9 !g/m3. This concentration fell by15–23% for 2 years but rose again to between 110% and114% of the concentration before 1990 in years 3–5 afterthe intervention (data not shown). No significant changein mean concentration of PM10 (p=0·926) and NO2

(p=0·205)—but a significant increase of O3 (p<0·0001)—was noted over the 5 years after the restriction on fuelsulphur content (figure 1).

Over the 5 years before the intervention, number ofdeaths per month showed a stable seasonal pattern for allcauses and cardiorespiratory diseases. In the year after therestriction on fuel sulphur content was introduced, theexpected cool season peak was absent (figure 2).

The noted seasonal mortality cycle closely fitted themodel for the 5 years before introduction of theintervention. In the first 12 months after the intervention,amplitude of the cycle was low compared with thatpredicted because of a striking reduction in deaths in thecool season (figure 3). This fall was associated with areduction in the warm to cool season mortality gradient,for every age-group, for all causes, respiratory, andcardiovascular deaths. For example, the seasonalpercentage increase for all causes and all ages declinedfrom the average 5-year baseline of 10·3% to 4·2% andrespiratory deaths from 20·3% to 5·3% (table 2). Inpeople aged 65 or older, seasonal deaths for all causesdeclined from 14·7% to 6·1% and respiratory deaths from22·7% to 5·4%. No consistent change in seasonal patternof deaths in any age-group for neoplasms or other causeswas noted. In the second 12 months a striking rebound indeaths in the cool season deaths arose, followed by agradual return during years 3–5 to the seasonal patternbefore intervention.

The reduction in cool-season deaths in the first yearafter the intervention showed a consistent pattern across

the eight stations, except in one district, which onlycontributed 1·3% of total deaths covered by air-pollutantmonitoring.

The average annual proportional change in number ofdeaths, for all causes and all ages, was an increase of 3·5%per year in 1985–90, in accordance with the increase insize and ageing of the population. After the intervention

80

60

40

20

0

12

8

4

0Pollu

tant

con

cent

ratio

n (!

g/m

3 ) SO

4 concentration (!g/m

3)1988

Year1989

19901991

19921993

19941995

PM10NO2SO4SO2O3

Figure 1: Average of pollutant concentrations at fivemonitoring stations Vertical line represents date of introduction of fuel regulation.

1000

750

500

250

0

Mon

thly

dea

ths

July,1985

July,1990

June,1995

Neoplasms and other causes

1000

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Respiratory

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0

Cardiovascular

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NeoplasmsOther causes

Figure 2: Number of deaths per month for all ages from July, 1985, to June, 1995, for all causes, respiratory,cardiovascular, and neoplasms and other causesVertical line represents date of introduction of fuel regulation.

Page 35: CSB Eece Presentation

Suggested Improvements –  The majority of studies assume that measured concentra%ons 

are representa%ve of average concentra%ons over an en%re region (which may be of varying size). Of course, this is not true, but it is omen the only method available. The popula%on distribu%on is some%mes geographically correlated with the pollu%on distribu%on (study from San%ago, Chile). Other %mes, it is disregarded, and the popula%on is thought of as a one‐dimensional parameter (PAPA studies).  

–  What needs to be done is to simultaneously increase the resolu%on of pollu%on concentra%on data and make a concerted effort to calculate exposure based upon the geographic overlay of popula%on or popula%on density with pollu%on concentra%ons. 

–  Rela%ve exposures based on popula%on surveys 

Page 36: CSB Eece Presentation

Improvements –  Indoor vs. outdoor %me expenditures by the popula%on (while 

preserving age classifica%ons) should be conducted by survey in regions under study, along with indoor measurements, to more closely approximate exposure.  •  Then modeling could be done to examine how change in habits or architecture 

of a certain locale may lead to an indirect benefit in health for the en%re region of interest. 

•  Shanghai paper discussion reveals that 67.3% of Shanghai residents use air condi%oning in the winter, while 96.7% do so in the summer. Thus, significant increases in risk to health for 10 μg/m3 increases in pollutant concentra%ons were only seen in cooler months, when the average temperature dropped from 24.3 C to 11.2 C. For subtropical coastal ci%es in China, the parern of staying indoors in the summer and opening windows in the cooler season may be a regional varia%on in culture that affects exposure assessment (Kan, 2008). –  Similar results were seen in papers from Hong Kong. (Hedley, 2002) –  Personal experience in Shenzhen also leads me to this conclusion on cultural varia%on. –  Opposite effects in Bangkok and Wuhan due to the rela%vely low incidence of air 

condi%oning usage (Wong, 2008). 

Page 37: CSB Eece Presentation

•  Such improvements in understanding of exposure are necessary for the normaliza%on of %me series results, cross comparison, policy crea%on, etc. 

Page 38: CSB Eece Presentation

C-R functions

•  Log‐linear regressions common 

•  Non‐linear lsq curve fisng 

•  Spline interpola%ons •  Ideally‐ spa%otemporal C‐R func%ons integrated into GIS platorm. 

y = B ⋅ eβ ⋅x

ln(y) = α+ β ⋅ x

Δy = B(eβ ⋅x − eβ ⋅x0 )Δy = B ⋅ eβ ⋅x0 (e(β ⋅x−β ⋅x0 ) −1)Δy = B ⋅ eβ ⋅x0 (eβ (x−x0 ) −1)

∴Δy = y0(eβ ⋅Δx −1)

Δx Δy = y− y0

dy =∂y∂xdx+

∂y∂β

dβ +∂y∂B

dB

Page 39: CSB Eece Presentation

Bangkok than in the other cities (Ostro et al.1999). With relatively higher mortality due toinfectious diseases [Supplemental Material,Table 1 (available online at http://www.ehponline.org/members/2008/11257/suppl.pdf)] and with more deaths at younger ages, itis also likely that the Bangkok population isexposed to a larger number of other risk factorsand may be more susceptible to the risks associ-ated with air pollution. Tsai et al. (2000)reported that exposure levels for indoor andoutdoor particulates in shopping areas wereunderestimated by the ambient monitoring sta-tions in Bangkok, and therefore that the excessrisk per air pollutant concentration wouldbe higher than if it were a well-calibrated

measurement. The higher ratio of PM2.5 (PM! 2.5 µm in aerodynamic diameter) to PM10may suggest that the proportion of smaller par-ticles in the PM10 composition in Bangkok ismore important and might be more stronglyrelated to adverse health effects than in theother cities (Jinsart et al. 2002).

In all the three Chinese cities, the maxi-mum effects always occurred at lag 0–1 days,except for O3 in Shanghai, where maximumeffects were recorded at longer lags. The lagpattern is consistent with other reports indemonstrating a maximum at lag 1 day formost pollutants (Samoli et al. 2005, 2006).However, for O3, the effect estimates are maxi-mal at longer lags, showing that the pattern is

also consistent with the literature (Goldberget al. 2001; Wong et al. 2001). The lag pat-terns of SO2 and O3 in Bangkok are consistentwith those of the three Chinese cities; however,the Bangkok lag patterns for NO2 and PM10,with greater effects at longer lags, are differentfrom those of the three Chinese cities. For thetraffic-related pollutants NO2 and PM10, theeffects appear to be stronger, and they alsoseem to last longer in Bangkok than in thethree Chinese cities.

In all cities in the PAPA study, the effects ofair pollution are stronger for cardiopulmonarycauses than for all natural causes. This is consis-tent with results from most North Americanand Western European studies (Anderson et al.

Wong et al.

1200 VOLUME 116 | NUMBER 9 | September 2008 • Environmental Health Perspectives

0.3

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Figure 4. CR curves for all natural-cause mortality at all ages in all four cities for the average concentration of lag 0–1 days for NO2 (A), SO2 (B), PM10 (C), and O3 (D).The thin vertical lines represent the IQR of pollutant concentrations. The thick lines represent the WHO guidelines (WHO 2005) of 40 µg/m3 for 1-year averaging timefor NO2 (A), 20 µg/m3 for 24-hr averaging time for SO2 (B), 20 µg/m3 for 1-year averaging time for PM10 (C), and 100 µg/m3 for daily maximum 8-hr mean for O3 (D).

Page 40: CSB Eece Presentation

Dependence on outcomes

p-values ! 0.05), but the effect in Wuhan wasnot significant. The excess risk showed trendsof increasing risk with increasing age for allfour pollutants. The trends for the age-specificeffects were the strongest in Bangkok, lessstrong in Hong Kong and Wuhan, but absentin Shanghai (Figure 3). For all four pollutants,the excess risk in Bangkok was higher thanthose in the three Chinese cities. When thepollutant concentrations were expressed as theinterquartile range (IQR; i.e., 75th per-centile–25th percentile), Bangkok estimateswere comparable to those of the three Chinesecities, particularly in all ages. Within cities, theeffect estimates of different pollutants were alsocomparable to each other (data not shown).

In all cities, there was heterogeneity ineffect estimates for NO2 and PM10 on allnatural-cause mortality and for PM10 oncardiovascular mortality (Table 3). For allnatural-cause mortality, the combined randomeffects excess risk were 1.23, 1.00, 0.55, and0.38% for NO2, SO2, PM10, and O3, respec-tively (all p-values ! 0.05). The results forcardiovascular mortality (Table 3) followed agenerally similar pattern, with the highestexcess risk per 10-µg/m3 in Bangkok for PM10and O3, and in Wuhan for NO2 and SO2. Allof the cities demonstrated significant associa-tions for each pollutant except SO2 inBangkok and O3 in Wuhan, whereas all of thecombined estimates were statistically signifi-cant. A similar pattern was shown for respira-tory mortality, for which the highest estimateswere found in Wuhan for NO2 and SO2 andin Bangkok for PM10 and O3. All the randomeffects estimates were statistically significant atthe 5% level except for O3.

For the lag effects in the three Chinesecities, with a few exceptions, the average lag0–1 days usually generated the highest excessrisk. However, for Bangkok the longer cumu-lative average of lag 0–4 days generated thehighest excess risk for all of the pollutantsexcept SO2. For the combined estimates,effects at the lag 0–1 days showed the highest

excess risk, except O3, for which the effect atlag 0–4 days was the greatest (data not shown).

Sensitivity analyses for PM10 showed that,in general, the results were fairly robust forvarious concentrations, monitors, specifica-tions for temperature, methods of aggregatingdaily data, df used in the smoothers, and alter-native spline models. In all cases, the effectestimates were statistically significant. In allcities, the effect estimates for PM10 were sensi-tive to exclusion of the higher concentrations.For the Chinese cities, this increased the excessrisk > 20% for PM10, but in Bangkok theeffect estimate decreased, with the excess riskchanging from 1.25% to 0.73% per 10-µg/m3

increase in average concentration of lag0–1 days (Table 4). Examination of the warmseason (which varied for each city) resulted insignificant increases in effect estimates forBangkok and Wuhan but decreases in HongKong and, to a lesser extent, in Shanghai

(excess risk changed from 0.26% to 0.24%).Adjusting for temperature through use oflonger-term cumulative averages tended todecrease the PM10 effect.

The smoothed concentration-response(CR) relationship, between all natural-causemortality and concentration of each pollu-tant, appeared to be positive. Most CR curvesshowed linear relationships over the IQR ofthe concentrations (Figure 4). At all ages, testsfor nonlinearity for the entire curve showedthat linearity could not be rejected at the 5%level for most of the associations between airpollution and mortality (data not shown).

DiscussionReview of PAPA project results. In the city-specific main effects for the five main healthoutcomes under study, there were variationsin effect estimates between cities. For NO2the estimates were similar in magnitude and

Wong et al.

1198 VOLUME 116 | NUMBER 9 | September 2008 • Environmental Health Perspectives

Table 3. Excess risk (ER; %) of mortality (95% CI) for a 10-µg/m3 increase in the average concentration of lag 0–1 days by main effect estimates of individual citiesand combined random effects.

Random effects Random effectsBangkok Hong Kong Shanghai Wuhan (4 cities) (3 Chinese cities)

Pollutant ER 95% CI ER 95% CI ER 95% CI ER 95% CI ER 95% CI ER 95% CI

All natural causes NO2 1.41 0.89 to 1.95 0.90 0.58 to 1.23 0.97 0.66 to 1.27 1.97 1.31 to 2.63 1.23 0.84 to 1.62* 1.19 0.71 to 1.66*(all ages) SO2 1.61 0.08 to 3.16 0.87 0.38 to 1.36 0.95 0.62 to 1.28 1.19 0.65 to 1.74 1.00 0.75 to 1.24 0.98 0.74 to 1.23

PM10 1.25 0.82 to 1.69 0.53 0.26 to 0.81 0.26 0.14 to 0.37 0.43 0.24 to 0.62 0.55 0.26 to 0.85# 0.37 0.21 to 0.54O3 0.63 0.30 to 0.95 0.32 0.01 to 0.62 0.31 0.04 to 0.58 0.29 –0.05 to 0.63 0.38 0.23 to 0.53 0.31 0.13 to 0.48

Cardiovascular NO2 1.78 0.47 to 3.10 1.23 0.64 to 1.82 1.01 0.55 to 1.47 2.12 1.18 to 3.06 1.36 0.89 to 1.82 1.32 0.79 to 1.86SO2 0.77 –2.98 to 4.67 1.19 0.29 to 2.10 0.91 0.42 to 1.41 1.47 0.70 to 2.25 1.09 0.71 to 1.47 1.09 0.72 to 1.47

PM10 1.90 0.80 to 3.01 0.61 0.11 to 1.10 0.27 0.10 to 0.44 0.57 0.31 to 0.84 0.58 0.22 to 0.93** 0.44 0.19 to 0.68O3 0.82 0.03 to 1.63 0.62 0.06 to 1.19 0.38 –0.03 to 0.80 –0.07 –0.53 to 0.39 0.37 0.01 to 0.73 0.29 –0.09 to 0.68

Respiratory NO2 1.05 –0.60 to 2.72 1.15 0.42 to 1.88 1.22 0.42 to 2.01 3.68 1.77 to 5.63 1.48 0.68 to 2.28 1.63 0.62 to 2.64*SO2 1.66 –3.09 to 6.64 1.28 0.19 to 2.39 1.37 0.51 to 2.23 2.11 0.60 to 3.65 1.47 0.85 to 2.08 1.46 0.84 to 2.08

PM10 1.01 –0.36 to 2.40 0.83 0.23 to 1.44 0.27 –0.01 to 0.56 0.87 0.34 to 1.41 0.62 0.22 to 1.02 0.60 0.16 to 1.04O3 0.89 –0.10 to 1.90 0.22 –0.46 to 0.91 0.29 –0.44 to 1.03 0.12 –0.89 to 1.15 0.34 –0.07 to 0.75 0.23 –0.22 to 0.68

p-Values (homogeneity test): *0.01 < p ! 0.05; **0.001 < p ! 0.01; and #p ! 0.001.

Figure 3. Excess risk (%) of mortality [point estimates (95% CIs)] for a 10-µg/m3 increase in averageconcentration of lag 0–1 days for three age groups.

4

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sk (%

)

" 65 " 75 " 65 " 75 " 65 " 75 " 65 " 75 " 65 " 75 " 65 " 75 " 65 " 75

" 65 " 75 " 65 " 75 " 65 " 75 " 65 " 75 " 65 " 75 " 65 " 75 " 65 " 75 " 65 " 75

Bangkok Hong Kong Shanghai Wuhan Bangkok Hong Kong Wuhan

Bangkok Hong Kong Wuhan Bangkok Hong Kong Wuhan

Shanghai

Shanghai Shanghai

Page 41: CSB Eece Presentation

Outcome dependency

p-values ! 0.05), but the effect in Wuhan wasnot significant. The excess risk showed trendsof increasing risk with increasing age for allfour pollutants. The trends for the age-specificeffects were the strongest in Bangkok, lessstrong in Hong Kong and Wuhan, but absentin Shanghai (Figure 3). For all four pollutants,the excess risk in Bangkok was higher thanthose in the three Chinese cities. When thepollutant concentrations were expressed as theinterquartile range (IQR; i.e., 75th per-centile–25th percentile), Bangkok estimateswere comparable to those of the three Chinesecities, particularly in all ages. Within cities, theeffect estimates of different pollutants were alsocomparable to each other (data not shown).

In all cities, there was heterogeneity ineffect estimates for NO2 and PM10 on allnatural-cause mortality and for PM10 oncardiovascular mortality (Table 3). For allnatural-cause mortality, the combined randomeffects excess risk were 1.23, 1.00, 0.55, and0.38% for NO2, SO2, PM10, and O3, respec-tively (all p-values ! 0.05). The results forcardiovascular mortality (Table 3) followed agenerally similar pattern, with the highestexcess risk per 10-µg/m3 in Bangkok for PM10and O3, and in Wuhan for NO2 and SO2. Allof the cities demonstrated significant associa-tions for each pollutant except SO2 inBangkok and O3 in Wuhan, whereas all of thecombined estimates were statistically signifi-cant. A similar pattern was shown for respira-tory mortality, for which the highest estimateswere found in Wuhan for NO2 and SO2 andin Bangkok for PM10 and O3. All the randomeffects estimates were statistically significant atthe 5% level except for O3.

For the lag effects in the three Chinesecities, with a few exceptions, the average lag0–1 days usually generated the highest excessrisk. However, for Bangkok the longer cumu-lative average of lag 0–4 days generated thehighest excess risk for all of the pollutantsexcept SO2. For the combined estimates,effects at the lag 0–1 days showed the highest

excess risk, except O3, for which the effect atlag 0–4 days was the greatest (data not shown).

Sensitivity analyses for PM10 showed that,in general, the results were fairly robust forvarious concentrations, monitors, specifica-tions for temperature, methods of aggregatingdaily data, df used in the smoothers, and alter-native spline models. In all cases, the effectestimates were statistically significant. In allcities, the effect estimates for PM10 were sensi-tive to exclusion of the higher concentrations.For the Chinese cities, this increased the excessrisk > 20% for PM10, but in Bangkok theeffect estimate decreased, with the excess riskchanging from 1.25% to 0.73% per 10-µg/m3

increase in average concentration of lag0–1 days (Table 4). Examination of the warmseason (which varied for each city) resulted insignificant increases in effect estimates forBangkok and Wuhan but decreases in HongKong and, to a lesser extent, in Shanghai

(excess risk changed from 0.26% to 0.24%).Adjusting for temperature through use oflonger-term cumulative averages tended todecrease the PM10 effect.

The smoothed concentration-response(CR) relationship, between all natural-causemortality and concentration of each pollu-tant, appeared to be positive. Most CR curvesshowed linear relationships over the IQR ofthe concentrations (Figure 4). At all ages, testsfor nonlinearity for the entire curve showedthat linearity could not be rejected at the 5%level for most of the associations between airpollution and mortality (data not shown).

DiscussionReview of PAPA project results. In the city-specific main effects for the five main healthoutcomes under study, there were variationsin effect estimates between cities. For NO2the estimates were similar in magnitude and

Wong et al.

1198 VOLUME 116 | NUMBER 9 | September 2008 • Environmental Health Perspectives

Table 3. Excess risk (ER; %) of mortality (95% CI) for a 10-µg/m3 increase in the average concentration of lag 0–1 days by main effect estimates of individual citiesand combined random effects.

Random effects Random effectsBangkok Hong Kong Shanghai Wuhan (4 cities) (3 Chinese cities)

Pollutant ER 95% CI ER 95% CI ER 95% CI ER 95% CI ER 95% CI ER 95% CI

All natural causes NO2 1.41 0.89 to 1.95 0.90 0.58 to 1.23 0.97 0.66 to 1.27 1.97 1.31 to 2.63 1.23 0.84 to 1.62* 1.19 0.71 to 1.66*(all ages) SO2 1.61 0.08 to 3.16 0.87 0.38 to 1.36 0.95 0.62 to 1.28 1.19 0.65 to 1.74 1.00 0.75 to 1.24 0.98 0.74 to 1.23

PM10 1.25 0.82 to 1.69 0.53 0.26 to 0.81 0.26 0.14 to 0.37 0.43 0.24 to 0.62 0.55 0.26 to 0.85# 0.37 0.21 to 0.54O3 0.63 0.30 to 0.95 0.32 0.01 to 0.62 0.31 0.04 to 0.58 0.29 –0.05 to 0.63 0.38 0.23 to 0.53 0.31 0.13 to 0.48

Cardiovascular NO2 1.78 0.47 to 3.10 1.23 0.64 to 1.82 1.01 0.55 to 1.47 2.12 1.18 to 3.06 1.36 0.89 to 1.82 1.32 0.79 to 1.86SO2 0.77 –2.98 to 4.67 1.19 0.29 to 2.10 0.91 0.42 to 1.41 1.47 0.70 to 2.25 1.09 0.71 to 1.47 1.09 0.72 to 1.47

PM10 1.90 0.80 to 3.01 0.61 0.11 to 1.10 0.27 0.10 to 0.44 0.57 0.31 to 0.84 0.58 0.22 to 0.93** 0.44 0.19 to 0.68O3 0.82 0.03 to 1.63 0.62 0.06 to 1.19 0.38 –0.03 to 0.80 –0.07 –0.53 to 0.39 0.37 0.01 to 0.73 0.29 –0.09 to 0.68

Respiratory NO2 1.05 –0.60 to 2.72 1.15 0.42 to 1.88 1.22 0.42 to 2.01 3.68 1.77 to 5.63 1.48 0.68 to 2.28 1.63 0.62 to 2.64*SO2 1.66 –3.09 to 6.64 1.28 0.19 to 2.39 1.37 0.51 to 2.23 2.11 0.60 to 3.65 1.47 0.85 to 2.08 1.46 0.84 to 2.08

PM10 1.01 –0.36 to 2.40 0.83 0.23 to 1.44 0.27 –0.01 to 0.56 0.87 0.34 to 1.41 0.62 0.22 to 1.02 0.60 0.16 to 1.04O3 0.89 –0.10 to 1.90 0.22 –0.46 to 0.91 0.29 –0.44 to 1.03 0.12 –0.89 to 1.15 0.34 –0.07 to 0.75 0.23 –0.22 to 0.68

p-Values (homogeneity test): *0.01 < p ! 0.05; **0.001 < p ! 0.01; and #p ! 0.001.

Figure 3. Excess risk (%) of mortality [point estimates (95% CIs)] for a 10-µg/m3 increase in averageconcentration of lag 0–1 days for three age groups.

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" 65 " 75 " 65 " 75 " 65 " 75 " 65 " 75 " 65 " 75 " 65 " 75 " 65 " 75

" 65 " 75 " 65 " 75 " 65 " 75 " 65 " 75 " 65 " 75 " 65 " 75 " 65 " 75 " 65 " 75

Bangkok Hong Kong Shanghai Wuhan Bangkok Hong Kong Wuhan

Bangkok Hong Kong Wuhan Bangkok Hong Kong Wuhan

Shanghai

Shanghai Shanghai

Page 42: CSB Eece Presentation

Outcomes, cont.

•  Morbidity outcomes include: – decreases in ven%lator capacity –  increases in specific airway resistance – Wheezing – shortness of breath – HRV – LBW, IUGR 

Page 43: CSB Eece Presentation

C-R uncertainties, complications

•  Slope varia%on at low levels 

•  Toxicological uncertain%es •  Simplis%c data (pop, 

pollutant) •  IQR and extrapola%on •  Mul%ple pollutants •  Local weather, pollu%on 

mix •  Wish to produce 

significant results –  Gender study in Shanghai 

Page 44: CSB Eece Presentation

Gender in Shanghai –  Shanghai 2008 study claims to have 

shown that SO2 produces greater affect in females. However, the data error is far too large to make this conclusion (95% CI are [.43, 1.28] and [.62, 1.51] for males and females, respec%vely). From an objec%ve standpoint, berer data is needed before such claims may be made (Kan, 2008).  •  lack of consensus over whether 

gender is a determining factor in C‐R func%ons for SO2.  

•  Thought that because 50% shanghai males smoke, and only .6% of females smoke, increase in pollu%on will be more dras%c for females. Also, PM (<1μm) deposi%on in respiratory tract is greater for females (Kan, 2008). 

Page 45: CSB Eece Presentation

Extrapolation across cities

models. We regarded a change of excess risk> 20% from that of the analysis as an indica-tion of sensitive results. Specifically, the sensi-tivity analysis included the following items:• Exclude the daily concentration of PM10

> 95th percentile• Exclude the daily concentration of PM10

> 75th percentile• Exclude the daily concentration of PM10

> 180 µg/m3

• Exclude monitoring stations with high trafficsources (highest nitric oxide/nitrogen oxidesratio)

• Assess warm season effect with dummyvariables of seasons in the core model

• Add temperature at average lag 1–2 days or3–7 days into the model

• Use a centered daily concentration of PM10(Wong et al. 2001)

• Use natural spline with degrees of freedom(df) of time trend per year, temperature, andhumidity fixed at 8, 4, and 4, respectively

• Use penalized spline instead of natural spline.Combined estimates of excess risk of mor-

tality and their standard errors were calculatedusing a random-effects model. Estimates wereweighted by the inverse of the sum of within-and between-study variance.

Concentration–response curves for theeffect of each pollutant on each mortality out-come in the four cities were plotted. Weapplied a natural spline smoother with 3 df onthe pollutant term. We assessed nonlinearityby testing the change of deviance between anonlinear pollutant (smoothed) model with3 df and linear pollutant (unsmoothed) modelwith 1 df.

The main analyses and the combinedanalysis were performed using R, version2.5.1 (R Development Core Team 2007). Wealso used mgcv, a package in R.

ResultsTable 1 summarizes the mortality data for thefour cities, and Table 2 summarizes the pollu-tion and meteorological variables. The dailymortality counts for all natural causes at allages for each city showed more marked sea-sonal variations in the cities farther north.Shanghai (mean daily deaths, 119; population,7.0 million) and Bangkok (95; 6.8 million)had higher daily numbers of deaths than HongKong (84; 6.7 million) and Wuhan (61;

4.2 million). The ratios for causes of death dueto cardiovascular disease relative to respiratorydisease were the highest in Wuhan (4:1) fol-lowed by Shanghai (3:1), Bangkok (2:1), andHong Kong (1.5:1). The proportion of totalcardiorespiratory mortality was also the highestin Wuhan (57%) followed by Shanghai (49%),Hong Kong (48%), and Bangkok (23%)[Table 1; Supplemental Material, Table 1(available online at http://www.ehponline.org/members/2008/11257/suppl.pdf)]. Deathsoccurring at ! 65 years of age were less fre-quent in Bangkok (36%) than in the threeChinese cities (72–84%).

As indicated in Table 2 and Figure 2,Wuhan showed the highest concentrations ofPM10 and O3, whereas Shanghai had thehighest concentrations of NO2 and SO2. Thelatter was probably due to the significant localcontribution of power plants in Shanghai’smetropolitan area. To provide an indication ofthe relative magnitude of the pollution con-centrations in these four large Asian cities, wecompared them to the 20 largest cities in the

United States using data from 1987 to 1994from the National Morbidity, Mortality, andAir Pollution Study (NMMAPS) (Samet et al.2000). Generally, in the PAPA cities, the con-centrations of PM10 and SO2 were muchhigher than those reported in the UnitedStates (PM10 means of 52–142 µg/m3 in thecities of the PAPA study vs. 33 µg/m3 inNMMAPS, and SO2 means of 13–45 µg/m3

vs. 14 µg/m3); comparisons of NO2 and O3showed a fairly similar pattern.

We demonstrated the adequacy of the coremodels with partial autocorrelation functionplots of the residuals in the previous 2 days, allwithin |0.1| [Supplemental Material, Figure 1(available online at http://www.ehponline.org/members/2008/11257/suppl.pdf)].

In individual cities, for all natural causes atall ages (Table 3) the percentage of excess riskper 10-µg/m3 associated with NO2 rangedfrom 0.90 to 1.97 (all p-values " 0.001); withSO2, from 0.87 to 1.61 (all p-values " 0.05);with PM10, from 0.26 to 1.25 (all p-values" 0.001); and with O3, from 0.31 to 0.63 (all

Public health and air pollution association

Environmental Health Perspectives • VOLUME 116 | NUMBER 9 | September 2008 1197

Table 2. Summary statistics of air pollutant concentrations and meteorological conditions.

Mean Median IQR Minimum, maximumHong Hong Hong Hong

Bangkok Kong Shanghai Wuhan Bangkok Kong Shanghai Wuhan Bangkok Kong Shanghai Wuhan Bangkok Kong Shanghai Wuhan

NO2 (µg/m3) 44.7 58.7 66.6 51.8 39.7 56.4 62.5 47.2 23.1 24.4 29.0 24.0 15.8, 139.6 10.3, 167.5 13.6, 253.7 19.2, 127.4SO2 (µg/m3) 13.2 17.8 44.7 39.2 12.5 14.7 40.0 32.5 5.5 12.6 28.7 30.8 1.5, 61.2 1.4, 109.3 8.4, 183.3 5.3, 187.8PM10 (µg/m3) 52.0 51.6 102.0 141.8 46.8 45.5 84.0 130.2 20.9 34.9 72.0 80.2 21.3, 169.2 13.7, 189.0 14.0, 566.8 24.8, 477.8O3 (µg/m3) 59.4 36.7 63.4 85.7 54.4 31.5 56.1 81.8 36.2 31.6 45.1 67.4 8.2, 180.6 0.7, 195.0 5.3, 251.3 1.0, 258.5Temperature (°C) 28.9 23.7 17.7 17.9 29.1 24.7 18.3 18.5 1.8 8.0 14.4 16.3 18.7, 33.6 6.9, 33.8 –2.4, 34.0 –2.5, 35.8RH (%) 72.8 77.9 72.9 74.0 73.0 79 73.5 74.0 10.8 10.0 15.5 19.0 41.0, 95.0 27, 97.0 33.0, 97.0 35.0, 99.0

Abbreviations: IQR, interquartile range; RH, relative humidity. NO2, SO2, and PM10 are expressed as 24-hr averages, and O3 is an 8-hr average.

Figure 2. Box plots of the air pollutants for the four cities. Boxes indicate the interquartile range (25th per-centile–75th percentile); lines within boxes indicate medians; whiskers and circles below boxes representminimum values; and circles above boxes indicate maximum values.

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Outline

•  Ques%ons to answer •  Introduc%on, background and significance •  Cri%que of HRA methods and suggested improvements 

•  Data from sample studies 

•  Stressing the need for a comprehensive HRA of SO2 in China 

•  Interven%ons 

Page 47: CSB Eece Presentation

Hong Kong Intervention

For personal use. Only reproduce with permission from The Lancet Publishing Group.

ARTICLES

1648 THE LANCET • Vol 360 • November 23, 2002 • www.thelancet.com

ResultsIn the first year after introduction of the intervention,mean fall in SO2 concentration at five stations was 53%(table 1). Reduction in SO2 concentration was sustainedbetween 35% and 53% (mean 45%) of the mean valuebefore the intervention, over 5 years. At eight stations forwhich complete data were available for up to 2·5 years,the average reduction in SO2 concentration over thisperiod was 50%.

Mean concentration of sulphate in respirableparticulates at five stations for 2 years before theintervention was 8·9 !g/m3. This concentration fell by15–23% for 2 years but rose again to between 110% and114% of the concentration before 1990 in years 3–5 afterthe intervention (data not shown). No significant changein mean concentration of PM10 (p=0·926) and NO2

(p=0·205)—but a significant increase of O3 (p<0·0001)—was noted over the 5 years after the restriction on fuelsulphur content (figure 1).

Over the 5 years before the intervention, number ofdeaths per month showed a stable seasonal pattern for allcauses and cardiorespiratory diseases. In the year after therestriction on fuel sulphur content was introduced, theexpected cool season peak was absent (figure 2).

The noted seasonal mortality cycle closely fitted themodel for the 5 years before introduction of theintervention. In the first 12 months after the intervention,amplitude of the cycle was low compared with thatpredicted because of a striking reduction in deaths in thecool season (figure 3). This fall was associated with areduction in the warm to cool season mortality gradient,for every age-group, for all causes, respiratory, andcardiovascular deaths. For example, the seasonalpercentage increase for all causes and all ages declinedfrom the average 5-year baseline of 10·3% to 4·2% andrespiratory deaths from 20·3% to 5·3% (table 2). Inpeople aged 65 or older, seasonal deaths for all causesdeclined from 14·7% to 6·1% and respiratory deaths from22·7% to 5·4%. No consistent change in seasonal patternof deaths in any age-group for neoplasms or other causeswas noted. In the second 12 months a striking rebound indeaths in the cool season deaths arose, followed by agradual return during years 3–5 to the seasonal patternbefore intervention.

The reduction in cool-season deaths in the first yearafter the intervention showed a consistent pattern across

the eight stations, except in one district, which onlycontributed 1·3% of total deaths covered by air-pollutantmonitoring.

The average annual proportional change in number ofdeaths, for all causes and all ages, was an increase of 3·5%per year in 1985–90, in accordance with the increase insize and ageing of the population. After the intervention

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Page 48: CSB Eece Presentation

Hong Kong Intervention 

For personal use. Only reproduce with permission from The Lancet Publishing Group.

ARTICLES

1648 THE LANCET • Vol 360 • November 23, 2002 • www.thelancet.com

ResultsIn the first year after introduction of the intervention,mean fall in SO2 concentration at five stations was 53%(table 1). Reduction in SO2 concentration was sustainedbetween 35% and 53% (mean 45%) of the mean valuebefore the intervention, over 5 years. At eight stations forwhich complete data were available for up to 2·5 years,the average reduction in SO2 concentration over thisperiod was 50%.

Mean concentration of sulphate in respirableparticulates at five stations for 2 years before theintervention was 8·9 !g/m3. This concentration fell by15–23% for 2 years but rose again to between 110% and114% of the concentration before 1990 in years 3–5 afterthe intervention (data not shown). No significant changein mean concentration of PM10 (p=0·926) and NO2

(p=0·205)—but a significant increase of O3 (p<0·0001)—was noted over the 5 years after the restriction on fuelsulphur content (figure 1).

Over the 5 years before the intervention, number ofdeaths per month showed a stable seasonal pattern for allcauses and cardiorespiratory diseases. In the year after therestriction on fuel sulphur content was introduced, theexpected cool season peak was absent (figure 2).

The noted seasonal mortality cycle closely fitted themodel for the 5 years before introduction of theintervention. In the first 12 months after the intervention,amplitude of the cycle was low compared with thatpredicted because of a striking reduction in deaths in thecool season (figure 3). This fall was associated with areduction in the warm to cool season mortality gradient,for every age-group, for all causes, respiratory, andcardiovascular deaths. For example, the seasonalpercentage increase for all causes and all ages declinedfrom the average 5-year baseline of 10·3% to 4·2% andrespiratory deaths from 20·3% to 5·3% (table 2). Inpeople aged 65 or older, seasonal deaths for all causesdeclined from 14·7% to 6·1% and respiratory deaths from22·7% to 5·4%. No consistent change in seasonal patternof deaths in any age-group for neoplasms or other causeswas noted. In the second 12 months a striking rebound indeaths in the cool season deaths arose, followed by agradual return during years 3–5 to the seasonal patternbefore intervention.

The reduction in cool-season deaths in the first yearafter the intervention showed a consistent pattern across

the eight stations, except in one district, which onlycontributed 1·3% of total deaths covered by air-pollutantmonitoring.

The average annual proportional change in number ofdeaths, for all causes and all ages, was an increase of 3·5%per year in 1985–90, in accordance with the increase insize and ageing of the population. After the intervention

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Figure 2: Number of deaths per month for all ages from July, 1985, to June, 1995, for all causes, respiratory,cardiovascular, and neoplasms and other causesVertical line represents date of introduction of fuel regulation.

For personal use. Only reproduce with permission from The Lancet Publishing Group.

ARTICLES

1650 THE LANCET • Vol 360 • November 23, 2002 • www.thelancet.com

associated with air-pollution episodes are in individualswho are frail and already have a short life-expectancy.

In addition to short-term seasonal fluctuations in deathrates, we recorded a decline in the average annualproportional increase in deaths in the period after theintervention, which also provides evidence of a longer-term benefit from removal of sulphur. As with the earlyeffect on seasonal deaths, the largest decline over 5 yearswas for respiratory deaths. Reduction in risk for overallmortality was greater in districts that had large reductionsin SO2 than in those that did not.

Differences in age-specific death rates before and afterthe intervention suggest that it led to an average gain inlife expectancy for men aged 25–100 years of 0·73 yearsfor 15 years’ exposure per 10 !g/m3 reduction in SO2. Fora man aged 25–29 years, the lifetime gain would be 1·14 years. Brunekreef 8 applied a relative risk of 1·10—derived from US cohort studies, with relative risk per 10 !g/m3 for PM10 ranging from 1·074 to 1·173—to the

1992 life-table for people aged 25–100 years to obtain anestimated gain of 1·51 years for a 25-year old Dutch manwith 15 years of exposure. This finding indicates theexpected difference in life expectancy betweenpopulations living in polluted or clean air. On the basis ofthe relative risk of 1·18 from our Poisson regression, thecomparable gain in life expectancy per 10 !g/m3 SO2 for a25-year old Hong Kong man is 2·58 years. The short-term analysis of changes in risk of death could haveunderestimated the benefits, in terms of life expectancy, ofthe restriction on sulphur in fuel.

Further benefits arising from reductions in the otherpollutants, including respirable particulates and the othergaseous pollutants, could be expected in addition to thosederived from sulphur sources. The strong associationbetween reduced risk of death and the acute fall in sulphuroxides contrasts strikingly with the conclusions of otheranalyses—based on time series and cohort studies—of SO2

and deaths in the USA and the Netherlands. Schwartz20

Cool season increase in mortality (%) (95% CI)

All causes Respiratory Cardiovascular Neoplasms Other causes

PeriodBaseline(July, 1985, to June, 1990) 10·2 (9·5 to 11·0) 20·3 (18·4 to 22·2) 18·0 (16·6 to 19·4) 1·1 (–0·3 to 2·4) 4·2 (2·9 to 5·6)Year 1(July, 1990, to June, 1991) 4·2 (2·5 to 5·8) 5·3 (1·2 to 9·4) 12·3 (9·2 to 15·4) 2·8 (–0·2 to 5·8) 3·7 (0·7 to 6·6)Year 2(July, 1991, to June, 1992) 14·6 (13·0 to 16·2) 27·7 (23·9 to 31·5) 24·1 (21·2 to 27·1) 1·1 (–1·9 to 4·0) 3·6 (0·7 to 6·6)Year 3(July, 1992, to June, 1993) 12·5 (10·8 to 14·1) 26·6 (22·8 to 30·4) 20·1 (17·0 to 23·2) 2·0 (–0·9 to 4·9) 4·9 (2·0 to 7·8)Year 4(July, 1993, to June, 1994) 11·3 (9·6 to 12·9) 17·2 (13·3 to 21·0) 19·8 (16·7 to 22·9) 2·7 (–0·2 to 5·5) 6·8 (3·9 to 9·7)Year 5(July, 1994, to June, 1995) 11·3 (9·7 to 12·9) 24·2 (20·4 to 28·1) 21·4 (18·4 to 24·4) 2·1 (–0·7 to 5·0) 5·1 (2·2 to 7·9)

Table 2: Cool season increase in mortality and 95% CI for all ages after intervention compared with mean (baseline) for all causes,respiratory, and cardiovascular, neoplasms, and other causes

Average annual proportional change (%) (95% CI)* Relative change (%) (95% CI) per year

Pre-intervention Post-intervention From pre-intervention to Intrapolated to 10 !g/m3

post-intervention period† change in SO2‡

All causesAge 15–64 years 0·65 (–0·01 to 1·31) –1·16 (–1·83 to –0·48) –1·75 (–2·98 to –0·50) –0·89Age 65 years and older 5·40 (4·93 to 5·88) 2·40 (1·96 to 2·83) –2·81 (–4·20 to –1·39) –1·44All ages 3·50 (3·12 to 3·88) 1·20 (0·84 to 1·56) –2·11 (–3·32 to –0·89) –1·08

RespiratoryAge 15–64 years 2·28 (0·12 to 4·44) –3·36 (–5·64 to –1·07) –4·80 (–8·28 to –1·18) –2·47Age 65 years and older 7·79 (6·75 to 8·83) 2·91 (1·97 to 3·85) –4·17 (–6·59 to –1·69) –2·14All ages 6·55 (5·62 to 7·48) 1·88 (1·02 to 2·74) –3·94 (–6·23 to –1·60) –2·02

CardiovascularAge 15–64 years –1·33 (–2·78 to 0·12) –3·12 (–4·64 to –1·59) –1·64 (–3·95 to 0·72) –0·84Age 65 years and older 4·17 (3·36 to 4·99) 1·81 (1·04 to 2·57) –2·44 (–4·20 to –0·65) –1·25All ages 2·79 (2·08 to 3·49) 0·77 (0·09 to 1·45) –2·01 (–3·66 to –0·33) –1·03

Neoplasm, without lung cancerAge 15–64 years 0·73 (–0·47 to 1·94) –0·64 (1·90 to 0·63) –1·34 (–2·95 to 0·30) –0·68Age 65 years and older 3·53 (2·16 to 4·91) 4·53 (3·44 to 5·64) 1·06 (–0·64 to 2·79) 0·54All ages 2·04 (1·03 to 3·05) 2·16 (1·34 to 2·99) 0·17 (–1·08 to 1·44) 0·09

Lung cancerAge 15–64 years –0·48 (–2·54 to 1·63) –0·52 (–2·62 to 1·63) 0·17 (–2·67 to 3·08) 0·09Age 65 years and older 5·41 (3·60 to 7·25) 3·00 (1·32 to 4·70) –2·16 (–4·40 to 0·12) –1·10All ages 3·12 (1·82 to 4·43) 1·83 (0·50 to 3·19) –1·08 (–2·89 to 0·77) –0·55

Other causesAge 15–64 years 0·73 (–0·29 to 1·76) –1·28 (–2·42 to –0·13) –1·95 (–3·43 to –0·45) –0·99Age 65 years and older 3·99 (2·88 to 5·16) 3·53 (2·46 to 4·62) –0·50 (–2·04 to 1·06) –0·25All ages 2·41 (1·51 to 3·31) 1·55 (0·70 to 2·41) –0·85 (–2·06 to 0·37) –0·43

*Estimate obtained from fitting of the Poisson regression model in the stratified pre-intervention and post-intervention period. †Estimated by the intervention by trendinteraction term in the Poisson regression model. ‡Estimates derived from column 4, which show reduction in excess risk (relative proportional change) after theintervention. Reduction in excess risk was converted to be associated with 10 µg/m3 by the log linear assumption.

Table 3: Average annual percentage change in mortality and 95% CI before and after the intervention, with relative change in annualtrend from before to after the intervention

Page 49: CSB Eece Presentation

WHO Reviewed Studies 

Page 50: CSB Eece Presentation

PAPA

models. We regarded a change of excess risk> 20% from that of the analysis as an indica-tion of sensitive results. Specifically, the sensi-tivity analysis included the following items:• Exclude the daily concentration of PM10

> 95th percentile• Exclude the daily concentration of PM10

> 75th percentile• Exclude the daily concentration of PM10

> 180 µg/m3

• Exclude monitoring stations with high trafficsources (highest nitric oxide/nitrogen oxidesratio)

• Assess warm season effect with dummyvariables of seasons in the core model

• Add temperature at average lag 1–2 days or3–7 days into the model

• Use a centered daily concentration of PM10(Wong et al. 2001)

• Use natural spline with degrees of freedom(df) of time trend per year, temperature, andhumidity fixed at 8, 4, and 4, respectively

• Use penalized spline instead of natural spline.Combined estimates of excess risk of mor-

tality and their standard errors were calculatedusing a random-effects model. Estimates wereweighted by the inverse of the sum of within-and between-study variance.

Concentration–response curves for theeffect of each pollutant on each mortality out-come in the four cities were plotted. Weapplied a natural spline smoother with 3 df onthe pollutant term. We assessed nonlinearityby testing the change of deviance between anonlinear pollutant (smoothed) model with3 df and linear pollutant (unsmoothed) modelwith 1 df.

The main analyses and the combinedanalysis were performed using R, version2.5.1 (R Development Core Team 2007). Wealso used mgcv, a package in R.

ResultsTable 1 summarizes the mortality data for thefour cities, and Table 2 summarizes the pollu-tion and meteorological variables. The dailymortality counts for all natural causes at allages for each city showed more marked sea-sonal variations in the cities farther north.Shanghai (mean daily deaths, 119; population,7.0 million) and Bangkok (95; 6.8 million)had higher daily numbers of deaths than HongKong (84; 6.7 million) and Wuhan (61;

4.2 million). The ratios for causes of death dueto cardiovascular disease relative to respiratorydisease were the highest in Wuhan (4:1) fol-lowed by Shanghai (3:1), Bangkok (2:1), andHong Kong (1.5:1). The proportion of totalcardiorespiratory mortality was also the highestin Wuhan (57%) followed by Shanghai (49%),Hong Kong (48%), and Bangkok (23%)[Table 1; Supplemental Material, Table 1(available online at http://www.ehponline.org/members/2008/11257/suppl.pdf)]. Deathsoccurring at ! 65 years of age were less fre-quent in Bangkok (36%) than in the threeChinese cities (72–84%).

As indicated in Table 2 and Figure 2,Wuhan showed the highest concentrations ofPM10 and O3, whereas Shanghai had thehighest concentrations of NO2 and SO2. Thelatter was probably due to the significant localcontribution of power plants in Shanghai’smetropolitan area. To provide an indication ofthe relative magnitude of the pollution con-centrations in these four large Asian cities, wecompared them to the 20 largest cities in the

United States using data from 1987 to 1994from the National Morbidity, Mortality, andAir Pollution Study (NMMAPS) (Samet et al.2000). Generally, in the PAPA cities, the con-centrations of PM10 and SO2 were muchhigher than those reported in the UnitedStates (PM10 means of 52–142 µg/m3 in thecities of the PAPA study vs. 33 µg/m3 inNMMAPS, and SO2 means of 13–45 µg/m3

vs. 14 µg/m3); comparisons of NO2 and O3showed a fairly similar pattern.

We demonstrated the adequacy of the coremodels with partial autocorrelation functionplots of the residuals in the previous 2 days, allwithin |0.1| [Supplemental Material, Figure 1(available online at http://www.ehponline.org/members/2008/11257/suppl.pdf)].

In individual cities, for all natural causes atall ages (Table 3) the percentage of excess riskper 10-µg/m3 associated with NO2 rangedfrom 0.90 to 1.97 (all p-values " 0.001); withSO2, from 0.87 to 1.61 (all p-values " 0.05);with PM10, from 0.26 to 1.25 (all p-values" 0.001); and with O3, from 0.31 to 0.63 (all

Public health and air pollution association

Environmental Health Perspectives • VOLUME 116 | NUMBER 9 | September 2008 1197

Table 2. Summary statistics of air pollutant concentrations and meteorological conditions.

Mean Median IQR Minimum, maximumHong Hong Hong Hong

Bangkok Kong Shanghai Wuhan Bangkok Kong Shanghai Wuhan Bangkok Kong Shanghai Wuhan Bangkok Kong Shanghai Wuhan

NO2 (µg/m3) 44.7 58.7 66.6 51.8 39.7 56.4 62.5 47.2 23.1 24.4 29.0 24.0 15.8, 139.6 10.3, 167.5 13.6, 253.7 19.2, 127.4SO2 (µg/m3) 13.2 17.8 44.7 39.2 12.5 14.7 40.0 32.5 5.5 12.6 28.7 30.8 1.5, 61.2 1.4, 109.3 8.4, 183.3 5.3, 187.8PM10 (µg/m3) 52.0 51.6 102.0 141.8 46.8 45.5 84.0 130.2 20.9 34.9 72.0 80.2 21.3, 169.2 13.7, 189.0 14.0, 566.8 24.8, 477.8O3 (µg/m3) 59.4 36.7 63.4 85.7 54.4 31.5 56.1 81.8 36.2 31.6 45.1 67.4 8.2, 180.6 0.7, 195.0 5.3, 251.3 1.0, 258.5Temperature (°C) 28.9 23.7 17.7 17.9 29.1 24.7 18.3 18.5 1.8 8.0 14.4 16.3 18.7, 33.6 6.9, 33.8 –2.4, 34.0 –2.5, 35.8RH (%) 72.8 77.9 72.9 74.0 73.0 79 73.5 74.0 10.8 10.0 15.5 19.0 41.0, 95.0 27, 97.0 33.0, 97.0 35.0, 99.0

Abbreviations: IQR, interquartile range; RH, relative humidity. NO2, SO2, and PM10 are expressed as 24-hr averages, and O3 is an 8-hr average.

Figure 2. Box plots of the air pollutants for the four cities. Boxes indicate the interquartile range (25th per-centile–75th percentile); lines within boxes indicate medians; whiskers and circles below boxes representminimum values; and circles above boxes indicate maximum values.

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Bangkok than in the other cities (Ostro et al.1999). With relatively higher mortality due toinfectious diseases [Supplemental Material,Table 1 (available online at http://www.ehponline.org/members/2008/11257/suppl.pdf)] and with more deaths at younger ages, itis also likely that the Bangkok population isexposed to a larger number of other risk factorsand may be more susceptible to the risks associ-ated with air pollution. Tsai et al. (2000)reported that exposure levels for indoor andoutdoor particulates in shopping areas wereunderestimated by the ambient monitoring sta-tions in Bangkok, and therefore that the excessrisk per air pollutant concentration wouldbe higher than if it were a well-calibrated

measurement. The higher ratio of PM2.5 (PM! 2.5 µm in aerodynamic diameter) to PM10may suggest that the proportion of smaller par-ticles in the PM10 composition in Bangkok ismore important and might be more stronglyrelated to adverse health effects than in theother cities (Jinsart et al. 2002).

In all the three Chinese cities, the maxi-mum effects always occurred at lag 0–1 days,except for O3 in Shanghai, where maximumeffects were recorded at longer lags. The lagpattern is consistent with other reports indemonstrating a maximum at lag 1 day formost pollutants (Samoli et al. 2005, 2006).However, for O3, the effect estimates are maxi-mal at longer lags, showing that the pattern is

also consistent with the literature (Goldberget al. 2001; Wong et al. 2001). The lag pat-terns of SO2 and O3 in Bangkok are consistentwith those of the three Chinese cities; however,the Bangkok lag patterns for NO2 and PM10,with greater effects at longer lags, are differentfrom those of the three Chinese cities. For thetraffic-related pollutants NO2 and PM10, theeffects appear to be stronger, and they alsoseem to last longer in Bangkok than in thethree Chinese cities.

In all cities in the PAPA study, the effects ofair pollution are stronger for cardiopulmonarycauses than for all natural causes. This is consis-tent with results from most North Americanand Western European studies (Anderson et al.

Wong et al.

1200 VOLUME 116 | NUMBER 9 | September 2008 • Environmental Health Perspectives

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Shanghai Wuhan

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50 150 120806040200

150500 200150100500

O3 concentration (µg/m3)

O3 concentration (µg/m3) O3 concentration (µg/m3)

Figure 4. CR curves for all natural-cause mortality at all ages in all four cities for the average concentration of lag 0–1 days for NO2 (A), SO2 (B), PM10 (C), and O3 (D).The thin vertical lines represent the IQR of pollutant concentrations. The thick lines represent the WHO guidelines (WHO 2005) of 40 µg/m3 for 1-year averaging timefor NO2 (A), 20 µg/m3 for 24-hr averaging time for SO2 (B), 20 µg/m3 for 1-year averaging time for PM10 (C), and 100 µg/m3 for daily maximum 8-hr mean for O3 (D).

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PAPA

p-values ! 0.05), but the effect in Wuhan wasnot significant. The excess risk showed trendsof increasing risk with increasing age for allfour pollutants. The trends for the age-specificeffects were the strongest in Bangkok, lessstrong in Hong Kong and Wuhan, but absentin Shanghai (Figure 3). For all four pollutants,the excess risk in Bangkok was higher thanthose in the three Chinese cities. When thepollutant concentrations were expressed as theinterquartile range (IQR; i.e., 75th per-centile–25th percentile), Bangkok estimateswere comparable to those of the three Chinesecities, particularly in all ages. Within cities, theeffect estimates of different pollutants were alsocomparable to each other (data not shown).

In all cities, there was heterogeneity ineffect estimates for NO2 and PM10 on allnatural-cause mortality and for PM10 oncardiovascular mortality (Table 3). For allnatural-cause mortality, the combined randomeffects excess risk were 1.23, 1.00, 0.55, and0.38% for NO2, SO2, PM10, and O3, respec-tively (all p-values ! 0.05). The results forcardiovascular mortality (Table 3) followed agenerally similar pattern, with the highestexcess risk per 10-µg/m3 in Bangkok for PM10and O3, and in Wuhan for NO2 and SO2. Allof the cities demonstrated significant associa-tions for each pollutant except SO2 inBangkok and O3 in Wuhan, whereas all of thecombined estimates were statistically signifi-cant. A similar pattern was shown for respira-tory mortality, for which the highest estimateswere found in Wuhan for NO2 and SO2 andin Bangkok for PM10 and O3. All the randomeffects estimates were statistically significant atthe 5% level except for O3.

For the lag effects in the three Chinesecities, with a few exceptions, the average lag0–1 days usually generated the highest excessrisk. However, for Bangkok the longer cumu-lative average of lag 0–4 days generated thehighest excess risk for all of the pollutantsexcept SO2. For the combined estimates,effects at the lag 0–1 days showed the highest

excess risk, except O3, for which the effect atlag 0–4 days was the greatest (data not shown).

Sensitivity analyses for PM10 showed that,in general, the results were fairly robust forvarious concentrations, monitors, specifica-tions for temperature, methods of aggregatingdaily data, df used in the smoothers, and alter-native spline models. In all cases, the effectestimates were statistically significant. In allcities, the effect estimates for PM10 were sensi-tive to exclusion of the higher concentrations.For the Chinese cities, this increased the excessrisk > 20% for PM10, but in Bangkok theeffect estimate decreased, with the excess riskchanging from 1.25% to 0.73% per 10-µg/m3

increase in average concentration of lag0–1 days (Table 4). Examination of the warmseason (which varied for each city) resulted insignificant increases in effect estimates forBangkok and Wuhan but decreases in HongKong and, to a lesser extent, in Shanghai

(excess risk changed from 0.26% to 0.24%).Adjusting for temperature through use oflonger-term cumulative averages tended todecrease the PM10 effect.

The smoothed concentration-response(CR) relationship, between all natural-causemortality and concentration of each pollu-tant, appeared to be positive. Most CR curvesshowed linear relationships over the IQR ofthe concentrations (Figure 4). At all ages, testsfor nonlinearity for the entire curve showedthat linearity could not be rejected at the 5%level for most of the associations between airpollution and mortality (data not shown).

DiscussionReview of PAPA project results. In the city-specific main effects for the five main healthoutcomes under study, there were variationsin effect estimates between cities. For NO2the estimates were similar in magnitude and

Wong et al.

1198 VOLUME 116 | NUMBER 9 | September 2008 • Environmental Health Perspectives

Table 3. Excess risk (ER; %) of mortality (95% CI) for a 10-µg/m3 increase in the average concentration of lag 0–1 days by main effect estimates of individual citiesand combined random effects.

Random effects Random effectsBangkok Hong Kong Shanghai Wuhan (4 cities) (3 Chinese cities)

Pollutant ER 95% CI ER 95% CI ER 95% CI ER 95% CI ER 95% CI ER 95% CI

All natural causes NO2 1.41 0.89 to 1.95 0.90 0.58 to 1.23 0.97 0.66 to 1.27 1.97 1.31 to 2.63 1.23 0.84 to 1.62* 1.19 0.71 to 1.66*(all ages) SO2 1.61 0.08 to 3.16 0.87 0.38 to 1.36 0.95 0.62 to 1.28 1.19 0.65 to 1.74 1.00 0.75 to 1.24 0.98 0.74 to 1.23

PM10 1.25 0.82 to 1.69 0.53 0.26 to 0.81 0.26 0.14 to 0.37 0.43 0.24 to 0.62 0.55 0.26 to 0.85# 0.37 0.21 to 0.54O3 0.63 0.30 to 0.95 0.32 0.01 to 0.62 0.31 0.04 to 0.58 0.29 –0.05 to 0.63 0.38 0.23 to 0.53 0.31 0.13 to 0.48

Cardiovascular NO2 1.78 0.47 to 3.10 1.23 0.64 to 1.82 1.01 0.55 to 1.47 2.12 1.18 to 3.06 1.36 0.89 to 1.82 1.32 0.79 to 1.86SO2 0.77 –2.98 to 4.67 1.19 0.29 to 2.10 0.91 0.42 to 1.41 1.47 0.70 to 2.25 1.09 0.71 to 1.47 1.09 0.72 to 1.47

PM10 1.90 0.80 to 3.01 0.61 0.11 to 1.10 0.27 0.10 to 0.44 0.57 0.31 to 0.84 0.58 0.22 to 0.93** 0.44 0.19 to 0.68O3 0.82 0.03 to 1.63 0.62 0.06 to 1.19 0.38 –0.03 to 0.80 –0.07 –0.53 to 0.39 0.37 0.01 to 0.73 0.29 –0.09 to 0.68

Respiratory NO2 1.05 –0.60 to 2.72 1.15 0.42 to 1.88 1.22 0.42 to 2.01 3.68 1.77 to 5.63 1.48 0.68 to 2.28 1.63 0.62 to 2.64*SO2 1.66 –3.09 to 6.64 1.28 0.19 to 2.39 1.37 0.51 to 2.23 2.11 0.60 to 3.65 1.47 0.85 to 2.08 1.46 0.84 to 2.08

PM10 1.01 –0.36 to 2.40 0.83 0.23 to 1.44 0.27 –0.01 to 0.56 0.87 0.34 to 1.41 0.62 0.22 to 1.02 0.60 0.16 to 1.04O3 0.89 –0.10 to 1.90 0.22 –0.46 to 0.91 0.29 –0.44 to 1.03 0.12 –0.89 to 1.15 0.34 –0.07 to 0.75 0.23 –0.22 to 0.68

p-Values (homogeneity test): *0.01 < p ! 0.05; **0.001 < p ! 0.01; and #p ! 0.001.

Figure 3. Excess risk (%) of mortality [point estimates (95% CIs)] for a 10-µg/m3 increase in averageconcentration of lag 0–1 days for three age groups.

4

3

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0All

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)

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" 65 " 75 " 65 " 75 " 65 " 75 " 65 " 75 " 65 " 75 " 65 " 75 " 65 " 75 " 65 " 75

Bangkok Hong Kong Shanghai Wuhan Bangkok Hong Kong Wuhan

Bangkok Hong Kong Wuhan Bangkok Hong Kong Wuhan

Shanghai

Shanghai Shanghai

Page 53: CSB Eece Presentation

PAPA

Page 54: CSB Eece Presentation

Tsinghua, Peking study (PM)

Page 55: CSB Eece Presentation

Outline

•  Ques%ons to answer •  Introduc%on, background and significance •  Cri%que of HRA methods and suggested improvements 

•  Data from sample studies 

•  Stressing the need for a comprehensive HRA of SO2 in China 

•  Interven%ons 

Page 56: CSB Eece Presentation

Call for HRA and Precedent •  The study released in March, 2008 by Tsinghua and Peking 

Universi%es performs a Health Risk Assessment (HRA) of PM in China, and arempts to es%mate the benefits of adop%ng changes in energy use, policy, efficiency, etc. on human health. 

•  The work done by these universi%es should be extended to study SO2 directly. 

•  In the report, the authors state that although SO2 concentra%ons in China are tradi%onally very high, they have seen marked decline over the past ten years in most ci%es, and SO2’s health effects may par%ally be accounted for in studying PM due to the role of SO2 in sulfuric acid par%cle forma%on. 

•  The report goes on to say that its analysis most likely underes%mates the full impact of adop%ng the specified policies of interest, as the full effects of SO2 are not taken into account. (Tsinghua, Peking, NREL, 2008) 

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Need for HRA

•  It would be advantageous to adapt the study performed by these groups and apply es%mates of exposure and C‐R from the mul%tude of epidemiological studies that have arempted to quan%fy SO2’s impact on health. Regrerably, the raw data sets resul%ng from the report’s modeling of SO2 emissions are not easily obtained.  

•  Several efforts to increase emissions inventories, etc. throughout China  opportunity 

•  Poten%al for guiding policy and changing public opinion 

Page 58: CSB Eece Presentation

HRA 

Page 59: CSB Eece Presentation

Outline

•  Ques%ons to answer •  Introduc%on, background and significance •  Cri%que of HRA methods and suggested improvements 

•  Data from sample studies 

•  Stressing the need for a comprehensive HRA of SO2 in China 

•  Interven%ons 

Page 60: CSB Eece Presentation

Improving Health

•  Ven%la%on, filters •  Gasifica%on stoves •  SCR, FGD •  Awareness of risk •  Increasing stringency of AQI for no%fica%on of at‐risk groups 

Page 61: CSB Eece Presentation

Acknowledgements 

•  Department of EECE, WUSTL •  Tsinghua University •  Peking University 

Page 62: CSB Eece Presentation

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Management Associa.on , 55, 1298‐1305. •  Hedley, A. J. (2002). Cardiorespiratory and all‐cause mortality amer restric%ons on sulphur content of fuel in Hong 

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•  Song, S. (2008). A GIS‐based approach to spa%o‐temporal analysis of urban air quality in Chengdu plain. The Interna.onal Archives of the Photogrammetry, Remote Sensing and Spa.al Informa.on Sciences , 37, 1447‐50. 

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•  Wong, C.‐M. e. (2008). Public Health and Air Pollu%on in Asia (PAPA): A Mul%city Study of Short‐Term Effects of Air Pollu%on on Mortality. Environmental Health Perspec.ves , 116 (9), 1195‐1202. 

•  World Health Organiza%on. (2006). Air Quality Guidelines, Global Update 2005. WHO. Copenhagen: WHO. •  Yu, M.‐H. (2005). Environmental Toxicology: Biological and Health Effects of Pollutants (2nd Edi%on ed.). Boca 

Raton, FLorida: CRC Press. •  Zhang, J., & Smith, K. R. (2007). Household Air Pollu%on from Coal and Biomass Fuels in China: Measurements, 

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