CSB Eece Presentation

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Published on November 21, 2008

Author: 391445

Source: slideshare.net

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 •  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 

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 •  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 

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  –  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 

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. 

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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 

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 

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

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

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