CSB Eece Presentation

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

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

Outline
improvement
of SO2 in China
•  Interven%ons

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.

Outline
improvements (longest sec%on)
of SO2 in China
•  Interven%ons

Introduction
!

•  China’s moderniza%on CURRENT STATUS OF ENERGY USE AND
CHAPTER 2

AIR POLLUTION
•  GDP‐ 8‐9% increase per
year since 1978 Economic Development
2.1 Rapid

•  Projected growth is
quot;#$%&! '(&! #$')*+,%'#*$! *-! &%*$*.#%! )&-*).! /$+! *0&$#$1! 0*2#%#&34! '(&! 5(#$&3&!
&%*$*.6!(/3!&70&)#&$%&+!)/0#+!/$+!3#1$#-#%/$'!1)*8'(9!:(&!/$$,/2!;<=!1)*8'(!)/'&!
)&/%(&+! >?@A! -)*.! B@C>! '*! DEEF9! G$! DEED4! 5(#$/H3! ;<=! &7%&&+&+! BE! ')#22#*$! IJK!

staggering LBIJK! M! E9BD>! Nquot;<O4! /3! P#1,)&! D?B! 3(*839! G$! DEEF4! '(&! ;<=! 0&)! %/0#'/! #$! 5(#$/!
8/3!BE4QRB!IJK9!
16000
14000
12000
GDP (billion RMB)

10000
8000
6000
4000
2000
0
1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004

Year
!
P#1,)&!D?B!S#3'*)#%/2!;<=!;)*8'(!#$!5(#$/!Lquot;*,)%&3T!5(#$/!quot;'/'#3'#%/2!UV3')/%'!DEEQO!

U-'&)!5(#$/!/+*0'&+!'(&!0*2#%6!*-!)&-*).!/$+!&%*$*.#%!2#V&)/2#W/'#*$4!/!%)*33?%&$',)6!
&%*$*.#%! +&X&2*0.&$'! 3')/'&16! 8/3! +&X&2*0&+! '*! )&/2#W&! .*+&)$#W/'#*$! #$! '()&&!
3'/1&39!:(&!1*/23!*-!'(&!-#)3'!/$+!3&%*$+!3'/1&3!8&)&!'*!3*2X&!'(&!0)*V2&.3!*-!-**+!/$+!
%2*'(#$1! *-! '(&! &$'#)&! 5(#$&3&! 0&*02&! /$+! '*! &$/V2&! '(&.! '*! 2#X&! /! )&2/'#X&26!

I#'3!+,-,#+=/,+-!/#<,!-$16%,6!)/%'#A-!.1$1+,!,=&'&0%=!6,<,(&@0,'$!#'6!6,<,(&@,6!
6%..,+,'$! 6,<,(&@0,'$! -=,'#+%&-D! I&-$! &.! $/,-,! -=,'#+%&-! #+,! ;#-,6! &'! $/,! =&00&'!
;,(%,.!$/#$!&<,+!$/,!',H$!9:!3,#+-!)/%'#!?%((!=&'$%'1,!$&!6,<,(&@!%$-!,=&'&03!#$!#!/%*/!
*+&?$/!+#$,>!$/,!,=&'&0%=!*#@!;,$?,,'!)/%'#!#'6!6,<,(&@,6!=&1'$+%,-!?%((!;,!.1+$/,+!

Projected GDP Growth
+,61=,6>! #'6! )/%'#! ?%((! ;,=&0,! &',! &.! $/,! $&@! ,=&'&0%=! =&1'$+%,-! %'! $/,! ?&+(6D!
J%*1+,! 9B9! -/&?-! -&0,! &.! $/,! @+%0#+3! +,-1($-! &.! $/,-,! 782! @+&G,=$%&'! -$16%,-D!
F==&+6%'*! $&! $/,-,! @+&G,=$%&'->! )/%'#A-! #''1#(! 782! *+&?$/! +#$,! ?%((! ;,! #;&1$! KL!
&<,+!$/,!',H$!9:!3,#+-D! !

45000

Chinese Academy of Social Science, medium scenario, 1995
40000
Chinese Academy of Sciences, 1995
35000 Beijing University of Technology, 2000
GDP (billion RMB, 2000price)

DRC, Medium scenario, 2003
30000
ERI, medium scenario, 2003,
25000

20000

15000

10000

5000

0
1990 1995 2000 2005 2010 2015 2020
Year
!
J%*1+,!9B9!M=&'&0%=!8,<,(&@0,'$!E+,'6!#'6!2+,6%=$%&'-!.&+!)/%'#!

2.2 Energy consumption status

01.$!/$%1quot;2K!761(&!6&,!3$+quot;.$!06$!,$+quot;(2!'&%)$,0!$($%)*!+quot;(,-.$%!1(!06$!Dquot;%'2!&90$%!
06$! H4I4K! 2-$! 0quot;! 06$! quot;#$%D6$'.1()! 1(+%$&,$! 1(! $($%)*! +quot;(,-./01quot;(4! L(! B;MCK! 06$!
quot;#$%&''! $($%)*! +quot;(,-./01quot;(! quot;9! 761(&! D&,! BJ4F! 31''1quot;(! =@4! L(! :CCCK! 06&0! 91)-%$!
1(+%$&,$2! 0quot;! <M4B! 31''1quot;(! =@4! N1)-%$! :O<! ,6quot;D,! 06$! 0quot;0&'! +quot;(,-./01quot;(! quot;9! /%1.&%*!
$($%)*!1(!761(&!9%quot;.!B;MC!0quot;!:CCP4!N%quot;.!:CCC!0quot;!:CCPK!761(&8,!$($%)*!+quot;(,-./01quot;(!
2$.quot;(,0%&0$2!$#$(!,0%quot;()$%!)%quot;D06!.quot;.$(0-.4!Q-%1()!061,!/$%1quot;2K!06$!quot;#$%&''!$($%)*!
+quot;(,-./01quot;(!1(+%$&,$2!3*!B;4F!31''1quot;(!=@K!%$/%$,$(01()!&(!&((-&'!1(+%$&,$!quot;9!BC4MRK!
S6$! $'&,01+10*! +quot;$991+1$(0 B ! D106! $+quot;(quot;.1+! 2$#$'quot;/.$(0! D&,! B4BF4! S6$,$! (-.3$%,!

Energy Demands
,6quot;D!06&0!10!D1''!3$+quot;.$!1(+%$&,1()'*!21991+-'0!9quot;%!761(&8,!$($%)*!,-//'*!0quot;!.$$0!06$!
2$.&(2,! quot;9! $+quot;(quot;.1+! 2$#$'quot;/.$(04! T$+&-,$! 761(&! 1,! $(0$%1()! 1(0quot;! 06$! /$%1quot;2! quot;9!
1(2-,0%1&'1U&01quot;(! quot;9! 6$&#*! 1(2-,0%*K! 06$! .&>quot;%10*! quot;9! 06$! 2quot;.1(&(0! 1(2-,0%1$,! 1(! 06$!
(&01quot;(&'!$+quot;(quot;.*!&%$!,01''!6$&#1'*!$($%)*O+quot;(,-.1()4!S61,!)$($%&0$,!-%)$(0!($$2,!9quot;%!
9-0-%$! $($%)*! ,-//'*4! L9! 761(&! &++quot;./'1,6$,! $+quot;(quot;.1+! .quot;2$%(1U&01quot;(! 3*! 06$! .122'$!
quot;9! 06$! :B,0! +$(0-%*K! 10! .-,0! 91(2! &! ($D! D&*! 0quot;! &+61$#$! $+quot;(quot;.1+! )%quot;D06! D106! 'quot;D$%!
$($%)*!&(2!%$,quot;-%+$!+quot;(,-./01quot;(!/$%!+&/10&!+quot;./&%$2!0quot;!06$!2$#$'quot;/$2!+quot;-(0%1$,4!

•  From 2000 to 2004,

Trillion RMB

Billion GJ
16 70

14 )*+ 60

average increase in
12 +, - . 01 232, 401 56378. - 63
/, 9:
50
10
40
8

energy usage was 10.8% 6

4
30

20

per year. 2
0
!quot;#! #$ #% #& #quot; quot;! quot;$ quot;% quot;& quot;quot; '((! '(($
10

0

•  Eﬃciency is necessary, !
N1)-%$!:O<!Squot;0&'!7quot;(,-./01quot;(!quot;9!V%1.&%*!W($%)*!1(!761(&! !

but insuﬃcient.
(Sources: China Statistical Yearbook 2003 and data from the official website of China’s State
Statistical Administration)

•  With increased power
2.3 Air quality status

L(!B;;FK!761(&!%$#1,$2!10,!50.quot;,/6$%1+!5.31$(0!X-&'10*!I0&(2&%2,K!&,!,6quot;D(!1(!S&3'$!

output  increased
:OB4! Y6$(! 06$! /quot;''-01quot;(! '$#$',! quot;9! &((-&'! &#$%&)$! Z[! +quot;(+$(0%&01quot;(! 3$0D$$(! B;;G!
&(2! :CCB! D$%$! &(&'*U$2K! 06$! B;M:! ,0&(2&%2,! D$%$! -,$2! 9quot;%! B;;G! /quot;''-0&(0!

emissions.
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
B
! W'&,01+10*!+quot;$991+1$(0]!06$!%&01quot;!quot;9!06$!1(+%$.$(0!quot;9!$($%)*!0quot;!06$!1(+%$.$(0!quot;9!=QV!

! B:

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)

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%

China is aging
•  Older popula%ons much
more suscep%ble to air
pollu%on
•  Increasing age poses
great social challenge to
China

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

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

Standards
•  SO2 level standards
–  China (Tsinghua, Peking, NREL, 2008)
•  Class I
–  Daily avg. ≤ 50μg/m3
–  Yearly avg. ≤ 20μg/m3
•  Class II
•  Class III
–  WHO (World Health Organiza%on, 2006)
•  Interim target 1
–  Daily avg. ≤ 125μ/m3
•  Interim target 2
•  WHO Guidelines, 2005
–  10‐minute avg. ≤ 500μ/m3

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

50
SO2 Trends AIR QUALITY GUIDELINES

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

120

100
Concentration (µg/m3)

80

60
Grade III standard

40

Northern cities
20 Average
Southern cities
0
1990 1992 1994 1996 1998 2000 2002

Year

Source: Hao & Wang (45).

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

80
PM10 Restriction on sulfur in fuel

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.

J3361@*+,!-6!-./!/5*))*6+!*+7/+-6189!-./!/5*))*6+!@*)-1*A=-*6+!6<!-./!:6;;=-2+-)!*+!
K.*+2!*+!-./!A2)/!8/21!*)!).6?+!*+!I*,=1/!B>L'!G*,.!/5*))*6+)!6<!%MN9!OMN!2+@!4PN'B!
633=11/@!*+!-./!5*@@;/!2+@!/2)-!6<!K.*+29!?.*3.!.27/!12:*@;8!@/7/;6:*+,!/36+65*/)!
2+@!).6?!-./!3.2123-/1*)-*3)!6<!*+@=)-1*2;!/5*))*6+!2+@!1/)*@/+-*2;!/5*))*6+'!I61!%MN9!
SO Emissions Distribution
.*,.! /5*))*6+)! 633=11/@! *+! -./! )6=-.?/)-! 21/2)! ?./1/! .*,.! )=;<2-/! 36+-/+-! 362;! *)!
2
A=1+*+,'! I61! 4PN'B9! 5*@@;/! 2+@! /2)-/1+! K.*+2! ).6?/@! .*,.! /5*))*6+)! *+-/+)*-8! <61!
(approximate)
*+@=)-1*2;! 2+@! 1/)*@/+-*2;! /5*))*6+'! %6=-.?/)-! K.*+2! ).6?/@! /):/3*2;;8! .*,.!
/5*))*6+)!*+-/+)*-8!@=/!-6!-./!?*@/):1/2@!=)/!6<!A*6<=/;!*+!-./!1/)*@/+-*2;!)/3-61'! !

!

quot;2&! ! %MN!

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

Study Types
•  Time series studies
•  Toxological studies
•  Cohort studies
•  Mul%na%onal
metadata
•  Interven%on
monitoring!

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

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

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)

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

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).

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

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.

P',/Q!R./2:>!S/1/+294!T.1,-,),/U1!%AAD!&2'B/9,-'.!'.!;+1-9!/9'.'6-9!+.5!/./2:>!-.E'26+,-'.!
!

!

V-:)2/!quot;F%!&'()*+,-'.!5/.1-,>!+.5!5-1,2-;),-'.!-.!74-.+!-.!%AAC!
!
Example of GIS use to catalog popula%on density
T.!,4-1!4/+*,4!;/./E-,!+.+*>1-18!,4/!/0('1/5!('()*+,-'.!-1!,4/!<4'*/!('()*+,-'.8!-.9*)5-.:!2/1-5/.,1!*-=-.:!-.!
)2;+.! +.5! 2)2+*! +2/+1! -.! 74-.+#! W'</=/28! +6;-/.,! ('**),-'.! */=/*1! -.! ,4/! 2)2+*! +2/+! +2/! ).9*/+2! ;/9+)1/!
,4/2/! +2/! E/<! +-2! 6'.-,'2-.:! 1,+,-'.1! *'9+,/5! ,4/2/#! ! X'6/! 1,)5-/1! -.! 74-.+! 4+=/! 14'<.! ,4+,! -.5''2! +-2!
('**),-'.!-1!+!6'2/!-6('2,+.,!E+9,'2!-.!2)2+*!+2/+18!+.5!,4/!94+2+9,/2-1,-91!'E!2)2+*!'),5''2!+-2!('**),-'.!6+>!

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.

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

10
between air pollution and daily mortality in have a cool-season maximum in Shanghai. elderly and the very young, are presumed to be
the cool season is consistent with several prior We found a greater effect of ambient air at greater risk for air pollution–related effects
studies in Hong Kong (Wong et al. 1999, pollution on total mortality in females than in (Gouveia and Fletcher 2000; Schwartz 2004).
2001) and Athens, Greece (Touloumi et al. males. Results of prior studies on sex-specific For the elderly, preexisting respiratory or
1996), but in contrast with others reporting acute effects of outdoor air pollution were dis- cardiovascular conditions are more prevalent
greater effects in the warm season (Anderson cordant. For example, Ito and Thurston than in younger age groups; thus, there is

Exposure based on SES
et al. 1996; Bell et al. 2005; Nawrot et al. (1996) found the highest risk of mortality some overlap between potentially susceptible
2007). In Shanghai, the concentrations of related with air pollution exposure among groups of older adults and people with heart
PM10, SO2, and NO2 were higher and more black women. Hong et al. (2002) found that or lung diseases.
variable in the cool season than in the warm elderly women were most susceptible to the It has long been known that SES can
season (Table 1). Because these three pollu- adverse effects of PM10 on the risk of acute affect health indicators such as mortality
•  Loca%on of industry
tants were highly correlated, greater effects
observed during the cool season may also be
mortality from stroke. However, Cakmak
et al. (2006) found that sex did not modify
(Mackenbach et al. 1997). Recently, studies
have started to examine the role of SES in

Roadways
due to other pollutants that were also at higher the hospitalization risk of cardiac diseases due the vulnerability of subpopulations to out-
• 
levels during that season. In contrast, the O3 to air pollution exposure. door air pollution, especially for particles
level was higher in the warm season than in The reasons for our sex-specific observa- and O3, although the results remain incon-
•  Occupa%onal hazards
the cool season, and our exposure–response
relationship also revealed a flatter slope at
tions are unclear and deserve further investiga-
tion. In Shanghai, females have a much lower
sistent (O’Neill et al. 2003). For example,
Zeka et al. (2006) found that individual-
•  Educa%on may affect risk
higher concentrations of O3 for both sexes
(data not shown). At higher concentrations,
smoking rate than males (0.6% in females vs.
50.6% in males) (Xu 2005). One study sug-
level education was inversely related to the
risk of mortality associated with PM 10.
–  Shanghai PAPA study suggests increase in risk of cardio and
the risks of death could be reduced because
vulnerable subjects may have died before the
gested that effects of air pollution may be
stronger in nonsmokers than in smokers
Another cohort study with small-area meas-
ures of SES in Hamilton, Ontario, Canada,
pulmonary deaths based on educa%on level
concentration reached the maximum level (Künzli et al. 2005). Oxidative and inflamma- found important modification of the particle
(Wong et al. 2001). tory effects of smoking may dominate to such effects by social class (Finkelstein et al. 2003;

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 Pollutant
Mortality 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 a
pplied 3 df to temperature and humidity. *Significantly
different from high educational attainment (p < 0.05).

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

are different, our results would be valid as long CO, SO2, and PM10. Effect modificat
as the exposures changed in the same direc- Seasonal influences. The mortality effects (2005) reported incr
tion. The present daily time-series analysis of CO, SO2, and PM10 appeared greater dur- in the elderly from
examines the effects of day-to-day differences ing April–September, the colder months, Morbidity, Mortali
in air pollution, not absolute values. although differences were significant for only Study of 95 U.S. citie
Air pollution–related mortality. The pre- PM10. Ilabaca et al. (1999) also reported a sea- (2004) reported tha
sent findings averaged over seven urban centers sonal modification of the PM 2.5 effect on associated with PM
are similar to those of previous air pollution pediatric emergency department visits, greatest Ilinois, appeared to

Seasonal Variations
studies in Chile. In 1989 and 1991, cardiac
and respiratory mortality were higher on days
of increased PM10 (Ostro et al. 1996). Ostro
et al. (1996) reported that a 10-µg/m3 change
in the colder months. In the present study, a
change in PM10 of about 85 µg/m3 was associ-
ated with a 12.2% change in mortality during
the warmer months and 1.3% in the colder
women but decrease
Filleul et al. (2004) re
air pollution mortalit
age, but it did not rea
in daily mean PM10 was associated with a 1% months, using unconstrained distributed lags. statistical significanc
increased susceptibilit
Table 5. Percent change (t-ratio) in nonaccidental daily mortality associated with changes in pollutant age (Gouveia and Fl
concentrations equivalent to population-weighted averages by cause of death, age at death, and season. 1998). We studied t
Classification PM10 O3 SO2 CO Compared with those
Cause of death at least 85 years of a
Nonaccidental over twice as likely to
Single-day lag 8.54 (5.14) 5.64 (2.78) 5.65 (4.97) 5.88 (6.42) in PM10 and > 50%
Distributed lag 11.68 (5.22) 4.38 (2.18) 9.28 (6.64) 9.39 (6.89) increases in O3 and S
Cardiac tibility was further m
Single-day lag 10.06 (3.25) 8.78 (2.42) 7.24 (3.55) 7.79 (4.56) strained distributed la
Distributed lag 13.33 (3.35) 2.30 (0.78) 10.53 (4.29) 11.22 (4.8)
Respiratory
also observed a gener
Single-day lag 18.58 (4.51) 8.21 (1.46) 12.45 (4.19) 12.93 (5.78) in susceptibility with
Distributed lag 29.66 (4.88) 15.63 (2.50) 20.44 (5.21) 21.31 (6.34) groups. These finding
Age at death (years) mination of air qualit
≤ 64 protect the general po
Single-day lag 4.53 (1.52) 4.96 (1.17) 4.77 (2.50) 4.10 (2.52) cient to protect the el
Distributed lag 4.26 (1.29) 1.84 (0.71) 4.27 (2.49) 4.76 (2.19) In summary, mo
65–74
Single-day lag 9.47 (2.81) 8.00 (1.77) 5.99 (2.49) 6.24 (3.17)
data in Santiago, Chi
Distributed lag 11.72 (3.01) 2.15 (0.86) 7.21 (2.55) 8.12 (3.88) lution levels continue
75–84 son with those in N
Single-day lag 12.61 (3.80) 9.42 (2.28) 8.73 (4.00) 8.64 (4.82) associated with strong
Distributed lag 17.62 (3.72) 3.32 (0.92) 11.2 (4.25) 13.12 (5.12) respiratory than c
≥ 85 increases in gases and
Single-day lag 14.03 (3.87) 8.56 (2.02) 7.92 (3.23) 8.58 (4.45) with increased mor
Distributed lag 19.73 (3.75) 5.92 (1.92) 11.13 (4.38) 13.20 (4.82)
Season
elderly appear to be a
April–September who are younger. W
Single-day lag 9.12 (3.35) 3.21 (1.14) 6.47 (3.92) 7.09 (4.02) degree of susceptibilit
Distributed lag 12.20 (3.75) 2.14 (1.25) 10.23 (4.72) 9.65 (4.50) very elderly be invest
October–March to determine whether
Single-day lag 0.60 (0.45) 6.19 (1.92) 2.62 (1.19) 5.45 (1.14) able across different c
Distributed lag 1.27 (1.46) 4.89 (1.82) 4.25 (1.75) 7.80 (1.89) characteristics.

526 VOLUME 115 | NUMBER 4 | April 2007 • Environm

ARTICLES

Results the eight stations, except in one district, which only
In the first year after introduction of the intervention, contributed 1·3% of total deaths covered by air-pollutant
mean fall in SO2 concentration at five stations was 53% monitoring.
(table 1). Reduction in SO2 concentration was sustained The average annual proportional change in number of
between 35% and 53% (mean 45%) of the mean value deaths, for all causes and all ages, was an increase of 3·5%
before the intervention, over 5 years. At eight stations for per year in 1985–90, in accordance with the increase in
which complete data were available for up to 2·5 years, size and ageing of the population. After the intervention
the average reduction in SO2 concentration over this
All causes
period was 50%. 4000
Mean concentration of sulphate in respirable
particulates at five stations for 2 years before the
intervention was 8·9 g/m3. This concentration fell by 3000
15–23% for 2 years but rose again to between 110% and
114% of the concentration before 1990 in years 3–5 after
the intervention (data not shown). No significant change 2000
in mean concentration of PM10 (p=0·926) and NO2
(p=0·205)—but a significant increase of O3 (p<0·0001)—
1000
was noted over the 5 years after the restriction on fuel
sulphur content (figure 1).
Over the 5 years before the intervention, number of 0
deaths per month showed a stable seasonal pattern for all
causes and cardiorespiratory diseases. In the year after the
restriction on fuel sulphur content was introduced, the Respiratory
1000
expected cool season peak was absent (figure 2).
The noted seasonal mortality cycle closely fitted the
model for the 5 years before introduction of the 750
intervention. In the first 12 months after the intervention,
amplitude of the cycle was low compared with that
predicted because of a striking reduction in deaths in the 500
cool season (figure 3). This fall was associated with a
reduction in the warm to cool season mortality gradient,
250
for every age-group, for all causes, respiratory, and
cardiovascular deaths. For example, the seasonal

Monthly deaths
percentage increase for all causes and all ages declined 0
from the average 5-year baseline of 10·3% to 4·2% and
respiratory deaths from 20·3% to 5·3% (table 2). In
people aged 65 or older, seasonal deaths for all causes Cardiovascular
1000
declined from 14·7% to 6·1% and respiratory deaths from
22·7% to 5·4%. No consistent change in seasonal pattern
of deaths in any age-group for neoplasms or other causes 750
was noted. In the second 12 months a striking rebound in
deaths in the cool season deaths arose, followed by a
gradual return during years 3–5 to the seasonal pattern 500
before intervention.
The reduction in cool-season deaths in the first year
250
after the intervention showed a consistent pattern across

PM10 0
NO2
SO4
SO2 Neoplasms and other causes
O3 1000
80 12
Pollutant concentration ( g/m3)

SO4 concentration ( g/m3)

750
60
8

40 500
Neoplasms
Other causes
4
20 250

0 0 0
July, July, June,
88 89 99
0 91 992 993 994 995 1985 1990 1995
19 19 1 19 1 1 1 1
Year Figure 2: Number of deaths per month for all ages from
Figure 1: Average of pollutant concentrations at five July, 1985, to June, 1995, for all causes, respiratory,
monitoring stations cardiovascular, and neoplasms and other causes
Vertical line represents date of introduction of fuel regulation. Vertical line represents date of introduction of fuel regulation.

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

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

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

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).

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

C-R functions
y = B ⋅ eβ ⋅x •  Log‐linear regressions
ln(y) = α + β ⋅ x common
Δx  Δy = y − y0 •  Non‐linear lsq curve
∂y ∂y ∂y ﬁsng
dy = dx + dβ + dB
∂x ∂β ∂B •  Spline interpola%ons
Δy = B(eβ ⋅x − eβ ⋅x0 ) •  Ideally‐ spa%otemporal
Δy = B ⋅ eβ ⋅x0 (e (β ⋅x −β ⋅x0 ) − 1) C‐R func%ons integrated
Δy = B ⋅ eβ ⋅x0 (eβ (x −x 0 ) − 1) into GIS platorm.

∴ Δy = y0 (eβ ⋅Δx − 1)

outdoor particulates in shopping areas were pattern is consistent with other reports in In all cities in the PAPA study, the effects of
underestimated by the ambient monitoring sta- demonstrating a maximum at lag 1 day for air pollution are stronger for cardiopulmonary
tions in Bangkok, and therefore that the excess most pollutants (Samoli et al. 2005, 2006). causes than for all natural causes. This is consis-
risk per air pollutant concentration would However, for O3, the effect estimates are maxi- tent with results from most North American
be higher than if it were a well-calibrated mal at longer lags, showing that the pattern is and Western European studies (Anderson et al.
0.3 Bangkok 0.3 Hong Kong 0.3 Bangkok 0.3 Hong Kong
A B
0.2 0.2 0.2 0.2

Log-risk

Log-risk
0.1 0.1 0.1 0.1

0.0 0.0 0.0 0.0

–0.1 –0.1 –0.1 –0.1

20 40 60 80 100 120 20 40 60 80 100 120 140 0 10 20 30 40 50 0 20 40 60 80 100
NO2 concentration (µg/m3) NO2 concentration (µg/m3) SO2 concentration (µg/m3) SO2 concentration (µg/m3)
0.3 Shanghai 0.3 Wuhan 0.3 0.3
Shanghai Wuhan

0.2 0.2 0.2 0.2
Log-risk

Log-risk
0.1 0.1 0.1 0.1

0.0 0.0 0.0 0.0

–0.1 –0.1 –0.1 –0.1

50 100 150 200 20 40 60 80 100 120 50 100 150 50 100 150
NO2 concentration (µg/m3) NO2 concentration (µg/m3) SO2 concentration (µg/m3) SO2 concentration (µg/m3)
0.25 Bangkok 0.25 Hong Kong 0.20 Bangkok 0.20 Hong Kong
C D
0.20 0.20 0.15 0.15
0.15 0.15
0.10 0.10

Log-risk
Log-risk

0.10 0.10
0.05 0.05
0.05 0.05
0.00 0.00
0.00 0.00
–0.05 –0.05 –0.05 –0.05

–0.10 –0.10 –0.10 –0.10
20 40 60 80 100 120 140 160 50 100 150 50 100 150 0 20 40 60 80 100 120
PM10 concentration (µg/m3) PM10 concentration (µg/m3) O3 concentration (µg/m3) O3 concentration (µg/m3)

0.25 Shanghai 0.25 Wuhan 0.20 Shanghai 0.20 Wuhan

0.20 0.20 0.15 0.15
0.15 0.15 0.10 0.10
Log-risk
Log-risk

0.10 0.10
0.05 0.05
0.05 0.05
0.00 0.00
0.00 0.00
–0.05 –0.05
–0.05 –0.05
–0.10 –0.10
–0.10 –0.10
100 200 300 400 100 200 300 400 0 50 100 150 200 0 50 100 150 200

PM10 concentration (µg/m3) PM10 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 NO (A), SO2 (B), PM10 (C), and O3 (D).
2
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 time
for NO2 (A), 20 µg/m 3 for 24-hr averaging time for SO (B), 20 µg/m3 for 1-year averaging time for PM (C), and 100 µg/m3 for daily maximum 8-hr mean for O (D).
2 10 3


centrations were expressed as the cities, the effect estimates for PM10 were sensi- the concentrations (Figure 4). At all ages, tests
e range (IQR; i.e., 75th per- tive to exclusion of the higher concentrations. for nonlinearity for the entire curve showed
percentile), Bangkok estimates For the Chinese cities, this increased the excess that linearity could not be rejected at the 5%
able to those of the three Chinese risk > 20% for PM 10 , but in Bangkok the level for most of the associations between air
larly in all ages. Within cities, the effect estimate decreased, with the excess risk pollution and mortality (data not shown).
es of different pollutants were also changing from 1.25% to 0.73% per 10-µg/m3
o each other (data not shown). increase in average concentration of lag Discussion
ies, there was heterogeneity in
ates for NO 2 and PM 10 on all
e mortality and for PM 10 on
Dependence on outcomes
0–1 days (Table 4). Examination of the warm
season (which varied for each city) resulted in
significant increases in effect estimates for
Review of PAPA project results. In the city-
specific main effects for the five main health
outcomes under study, there were variations
ar mortality (Table 3). For all Bangkok and Wuhan but decreases in Hong in effect estimates between cities. For NO2
mortality, the combined random Kong and, to a lesser extent, in Shanghai the estimates were similar in magnitude and
risk were 1.23, 1.00, 0.55, and
O2, SO2, PM10, and O3, respec- NO2 8 SO2
values ≤ 0.05). The results for 4

r mortality (Table 3) followed a
Excess risk (%)

Excess risk (%)
6
3
milar pattern, with the highest
4
r 10-µg/m3 in Bangkok for PM10 2
in Wuhan for NO2 and SO2. All 2
demonstrated significant associa- 1

ch pollutant except SO 2 in 0
0
O3 in Wuhan, whereas all of the All ≥ 65 ≥ 75 All ≥ 65 ≥ 75 All ≥ 65 ≥ 75 All ≥ 65 ≥ 75 All ≥ 65 ≥ 75 All ≥ 65 ≥ 75 All ≥ 65 ≥ 75 All ≥ 65 ≥ 75
timates were statistically signifi- ages ages ages ages ages ages ages ages
Bangkok Hong Kong Shanghai Wuhan Bangkok Hong Kong Shanghai Wuhan
ar pattern was shown for respira-
y, for which the highest estimates PM10 2.5 O3
n Wuhan for NO2 and SO2 and 3
2.0
or PM10 and O3. All the random
Excess risk (%)

Excess risk (%)
tes were statistically significant at 2 1.5

except for O3. 1.0
ag effects in the three Chinese 1
0.5
few exceptions, the average lag
ally generated the highest excess 0 0
r, for Bangkok the longer cumu- All ≥ 65 ≥ 75 All ≥ 65 ≥ 75 All ≥ 65 ≥ 75 All ≥ 65 ≥ 75 All ≥ 65 ≥ 75 All ≥ 65 ≥ 75 All ≥ 65 ≥ 75 All ≥ 65 ≥ 75
e of lag 0–4 days generated the ages ages ages ages ages ages ages ages

ss risk for all of the pollutants Bangkok Hong Kong Shanghai Wuhan Bangkok Hong Kong Shanghai Wuhan

For the combined estimates, Figure 3. Excess risk (%) of mortality [point estimates (95% CIs)] for a 10-µg/m3 increase in average
lag 0–1 days showed the highest concentration of lag 0–1 days for three age groups.

s 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 cities
random effects.
Random effects Random effects
Bangkok 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
s 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*

tions for each pollutant except SO 2 in 0
0
Bangkok and O3 in Wuhan, whereas all of the All ≥ 65 ≥ 75 All ≥ 65 ≥ 75 All ≥ 65 ≥ 75 All ≥ 65 ≥ 75 All ≥ 65 ≥ 75 All ≥ 65 ≥ 75 All ≥ 65 ≥ 75 All ≥ 65 ≥ 75
combined estimates were statistically signifi- ages ages ages ages ages ages ages ages
cant. A similar pattern was shown for respira-
tory mortality, for which the highest estimates PM10 2.5 O3
were found in Wuhan for NO2 and SO2 and 3
2.0
in Bangkok for PM10 and O3. All the random

Excess risk (%)

Excess risk (%)
Outcome dependency
effects estimates were statistically signiﬁcant at 2 1.5

the 5% level except for O3. 1.0
For the lag effects in the three Chinese 1
0.5
cities, with a few exceptions, the average lag
0–1 days usually generated the highest excess 0 0
risk. However, for Bangkok the longer cumu- All ≥ 65 ≥ 75 All ≥ 65 ≥ 75 All ≥ 65 ≥ 75 All ≥ 65 ≥ 75 All ≥ 65 ≥ 75 All ≥ 65 ≥ 75 All ≥ 65 ≥ 75 All ≥ 65 ≥ 75
lative average of lag 0–4 days generated the ages ages ages ages ages ages ages ages

highest excess risk for all of the pollutants Bangkok Hong Kong Shanghai Wuhan Bangkok Hong Kong Shanghai Wuhan

except SO 2 . For the combined estimates, Figure 3. Excess risk (%) of mortality [point estimates (95% CIs)] for a 10-µg/m3 increase in average
effects at the lag 0–1 days showed the highest concentration of lag 0–1 days for three age groups.

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 cities
and combined random effects.
Random effects Random effects
Bangkok 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 0.54
O3 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 1.86
SO2 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 0.68
O3 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 1.04
O3 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.


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

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

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).

• Exclude monitoring stations with high traffic org/members/2008/11257/suppl.pdf)]. Deaths showed a fairly similar pattern.
sources (highest nitric oxide/nitrogen oxides occurring at ≥ 65 years of age were less fre- We demonstrated the adequacy of the core
ratio) quent in Bangkok (36%) than in the three models with partial autocorrelation function
• Assess warm season effect with dummy Chinese cities (72–84%). plots of the residuals in the previous 2 days, all
variables of seasons in the core model As indicated in Table 2 and Figure 2, within |0.1| [Supplemental Material, Figure 1
• Add temperature at average lag 1–2 days or Wuhan showed the highest concentrations of (available online at http://www.ehponline.org/
3–7 days into the model PM 10 and O 3 , whereas Shanghai had the members/2008/11257/suppl.pdf)].
• Use a centered daily concentration of PM10 highest concentrations of NO2 and SO2. The In individual cities, for all natural causes at

Extrapolation across cities
(Wong et al. 2001) latter was probably due to the significant local all ages (Table 3) the percentage of excess risk
• Use natural spline with degrees of freedom contribution of power plants in Shanghai’s per 10-µg/m 3 associated with NO 2 ranged
(df) of time trend per year, temperature, and metropolitan area. To provide an indication of from 0.90 to 1.97 (all p-values ≤ 0.001); with
humidity fixed at 8, 4, and 4, respectively the relative magnitude of the pollution con- SO2, from 0.87 to 1.61 (all p-values ≤ 0.05);
• Use penalized spline instead of natural spline. centrations in these four large Asian cities, we with PM10, from 0.26 to 1.25 (all p-values
Combined estimates of excess risk of mor- compared them to the 20 largest cities in the ≤ 0.001); and with O3, from 0.31 to 0.63 (all
tality and their standard errors were calculated
200
using a random-effects model. Estimates were 250
weighted by the inverse of the sum of within-
NO2 concentration (µg/m3)

SO2 concentration (µg/m3)
and between-study variance. 200 150
Concentration–response curves for the
effect of each pollutant on each mortality out- 150
100
come in the four cities were plotted. We
applied a natural spline smoother with 3 df on 100
the pollutant term. We assessed nonlinearity 50
50
by testing the change of deviance between a
nonlinear pollutant (smoothed) model with
0 0
3 df and linear pollutant (unsmoothed) model
with 1 df.
The main analyses and the combined
analysis were performed using R, version 600
250
2.5.1 (R Development Core Team 2007). We
PM10 concentration (µg/m3)

O3 concentration (µg/m3)
500
also used mgcv, a package in R. 200
400
Results 150
Table 1 summarizes the mortality data for the 300

four cities, and Table 2 summarizes the pollu- 200
100
tion and meteorological variables. The daily
mortality counts for all natural causes at all 100 50

ages for each city showed more marked sea-
0 0
sonal variations in the cities farther north.
Shanghai (mean daily deaths, 119; population, Bangkok Hong Kong Shanghai Wuhan Bangkok Hong Kong Shanghai Wuhan

7.0 million) and Bangkok (95; 6.8 million) Figure 2. Box plots of the air pollutants for the four cities. Boxes indicate the interquartile range (25th per-
had higher daily numbers of deaths than Hong centile–75th percentile); lines within boxes indicate medians; whiskers and circles below boxes represent
Kong (84; 6.7 million) and Wuhan (61; minimum values; and circles above boxes indicate maximum values.

Table 2. Summary statistics of air pollutant concentrations and meteorological conditions.
Mean Median IQR Minimum, maximum
Hong 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, 127.4
SO2 (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, 187.8
PM10 (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, 477.8
O3 (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, 258.5

people aged 65 or older, seasonal deaths for all causes Cardiovascular
1000

Mon
declined from 14·7% to 6·1% and respiratory deaths from
22·7% to 5·4%. No consistent change in seasonal pattern
of deaths in any age-group for neoplasms or other causes 750
was noted. In the second 12 months a striking rebound in
deaths in the cool season deaths arose, followed by a

Hong Kong Intervention
gradual return during years 3–5 to the seasonal pattern 500
before intervention.
The reduction in cool-season deaths in the first year
250
after the intervention showed a consistent pattern across

PM10 0
NO2
SO4
SO2 Neoplasms and other ca
O3 1000
80 12
Pollutant concentration ( g/m3)

SO4 concentration ( g/m3)
750
60
8

40 500

4
20 250

0 0 0
8 July, J
98 89 990 991 992 993 994 995 1985 1
1 19 1 1 1 1 1 1
Year Figure 2: Number of deaths per mo
Figure 1: Average of pollutant concentrations at five July, 1985, to June, 1995, for all c
monitoring stations cardiovascular, and neoplasms an
Vertical line represents date of introduction of fuel regulation. Vertical line represents date of introduc

1648 THE LANCET • Vol 360 • Novemb

CSB Eece Presentation

Recomendados

Recomendados

Más contenido relacionado

La actualidad más candente

La actualidad más candente (20)

Similar a CSB Eece Presentation

Similar a CSB Eece Presentation (20)

Último

Último (20)

CSB Eece Presentation