particulate matter air pollution: science, controversy ... meetings/fall13/westar denver.pdf ·...
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Particulate Matter Air Pollution: Science,
Controversy, and Impact on Public Health
C. Arden Pope III, PhD
Mary Lou Fulton Professor of Economics
Presented at the
Western States Air Resources Council (WESTAR)
Denver, Colorado
November 7, 2013
Illustration for Charles Dickens’
Sketches by Boz, by
George Cruikshank, 1838
The chimney, smoke Stack, and
smoke and fog (smog) have long
played important iconic roles in the art
and literature of London.
Coffee Stall by
Gustave Doré,
In a description of a
representative London Fog day
(April 6, 1870) Doré’s friend
Blanchard Jerrold say’s of an
experience with Doré:
“We were well into the morning—
and it was as dark as the darkest
midnight. . . It was choking: it
made the eyes ache.”
From:
London: A pilgrimage, 1872 and
Doré’s London: All 180
illustrations from London, A
Pilgrimage, 2004
Waterloo Bridge, London, July 2006 (St Paul’s Cathedral, Tower 42, 30 St Mary Axe)
Danora, PA. Noon, Oct 29, 1948.
http://www.weather.com/multimedia/videoplayer.html?clip=12603&collection=257&fro
m=sitemap
The killer episodes spurred air pollution policies in the U.S.
Killer Smog (and related research)
Public Policy Results 1955-1967: Air pollution control
acts
1967, 1970: Clean Air Act,
Major Amendments, National
Environmental Policy Act
1977 & 1990: Major
Amendments to Clean Air Act
1980’s: Elimination of
extreme “Killer Smog”
episodes & Improved air
quality
1930: Meuse Valley, Belgium
1948: Donora, PA
1952: London, England
By the 1980’s, many thought the air pollution problem
had been solved…
Particulate matter air
pollution is a mixture of
particles suspended in
the air. These particles
vary in:
-Size,
-Shape,
-Surface area,
-Chemical composition,
-Solubility, and
-Origin.
Electron Microscope images of soot
particles From: Park K, Cao F,
Kittelson DB, McMurry PH. Environ
Sci Technol 2003
Utah Valley, 1980s
• Winter inversions trap local pollution
• Natural test chamber
• Local Steel mill contributed ~50% PM2.5
• Shut down July 1986-August 1987
• Natural Experiment
mg
/m3/N
um
be
rs o
f A
dm
issio
ns
0
50
100
150
200
250
300
PM10 concentrations Children's respiratory hospital admissions
Mean PM10
levels forMonthsIncluded
Mean HighPM
10
levels forMonthsIncluded
PneumoniaandPleurisy
Bronchitisand Asthma
Total
Children's Respiratory Hospital AdmissionsFall and Winter Months, Utah Valley
Sources: Pope. Am J Pub Health.1989; Pope. Arch Environ Health. 1991
When the steel mill was open, total children’s hospital
admissions for respiratory conditions approx. doubled. m
g/m
3/N
um
bers
of A
dm
issio
ns
0
50
100
150
200
250
300
PM10 concentrations Children's respiratory hospital admissions
Mean PM10
levels forMonthsIncluded
Mean HighPM
10
levels forMonthsIncluded
PneumoniaandPleurisy
Bronchitisand Asthma
Total
Children's Respiratory Hospital AdmissionsFall and Winter Months, Utah Valley
Sources: Pope. Am J Pub Health.1989; Pope. Arch Environ Health. 1991
Mill
Open
Mill
Closed
Health studies take advantage of highly variable air
pollution levels that result from inversions.
98 99 00 01 02 03 04 05 06 07 08 09 10
0
10
20
30
40
50
60
70
80
90
100
98 99 00 01 02 03 04 05 06 07 08 09 10
0
10
20
30
40
50
60
70
80
90
100
PM2.5 concentrations January 1 1998-December 12 2009. Black dots, 24-hr PM2.5; Red line, 30-day moving average PM2.5;
Green line, 1-yr moving average PM2.5.
Utah Valley (Lindon Monitor)
g/m
3
Salt Lake Valley (Hawthorn Monitor)
g/m
3
Salt Lake Valley (Hawthorn Monitor)
Poisson Regression
Count data (non-negative integer values). Counts of independent and
random occurrences classically modeled as being generated by a Poisson
process with a Poisson distribution:
Prob (Y = r) = e(-λ)
Note: λ = mean and variance. If λ is constant across time, we have a
stationary Poisson process. If λ changes over time due to changes in
pollution (P), time trends, temperature, etc., this non-stationary Poisson
process can model as:
ln λt = α + β(w0Pt + w1Pt-1 + w2Pt-2 + . . .) + s1(t) + s2(tempt) + . . .
λr
r!
How to construct
the lag structure?
(MA, PDL, etc.)
How aggressive do you
fit time? (harmonics vs
GAMs, df, span, loess,
cubic spline, etc.)
How to control for
weather? (smooths of
temp & RH, synoptic
weather, etc.)
Modeling
controversies
Also: How to combine or integrate information from multiple cities
% incre
ase in m
ort
alit
y
0
1
2
3
Estimates frommeta analysis
Estimates from Multicity studies
29 c
itie
s(L
evy e
t a
l. 2
00
0)
GA
M-b
ase
d s
tud
ies
(Stie
b e
t a
l. 2
00
2,
200
3)
Un
ad
juste
d(A
nd
ers
on
et
al. 2
00
5)
6 U
.S.
citie
s(K
lem
m a
nd
Ma
so
n 2
00
3)
8 C
ana
dia
n c
itie
s(B
urn
ett
an
d G
old
be
rg 2
00
3)
9 C
alif
orn
ian c
itie
s(O
str
o e
t a
l. 2
00
6)
10 U
.S c
itie
s(S
ch
wa
rtz 2
00
0,
200
3)
14 U
.S c
itie
s,
ca
se
-cro
sso
ve
r(S
ch
wa
rtz 2
00
4)
NM
MA
PS
, 2
0-1
00
U.S
. citie
s(D
om
inic
i e
t a
l. 2
00
3)
AP
HE
A-2
, 1
5-2
9 E
uro
pe
an
citie
s(K
ats
ouya
nn
i e
t a
l. 2
00
3)
9 F
rench
citie
s(L
e T
ert
re e
t a
l. 2
00
2)
13 J
apa
ne
se
citie
s(O
mo
ri e
t a
l. 2
00
3)
No
n G
AM
-base
d s
tud
ies
(Stie
b e
t a
l. 2
00
2,
200
3)
Pub
lica
tio
n b
ias a
dju
ste
d
(And
ers
on
et
al. 2
00
5)
7 K
ore
an
citie
s(L
ee
et
al. 2
00
0)
20
g/m
3 P
M1
0
20
g/m
3 P
M1
0
20
g/m
3 P
M1
0
20
g/m
3 P
M1
0
20
g/m
3 P
M1
0
10
g/m
3 P
M2.5
20
g/m
3 P
M1
0
10
g/m
3 P
M2.5
10
g/m
3 P
M2.5
20
g/m
3 P
M1
0
20
g/m
3 P
M1
0
20
g/m
3 P
M1
0
20
g/m
3 B
S
40
g/m
3 T
SP
20
g/m
3 S
PM
Re
vie
w o
f A
sia
n L
it.-
-8 s
tudie
s(H
EI
Re
po
rt,
Ta
ble
TS
2)
20
g/m
3 P
M1
0
20
g/m
3 P
M1
0
20
g/m
3 P
M1
0
Estimates frommeta analysisfrom Asian Lit
PA
PA
Stu
die
s--
4 s
tudie
s(H
EI
Re
po
rt,
Ta
ble
TS
2)
Asia
n L
it.
inco
rpora
tin
g P
AP
A s
tud
ies
(HE
I R
epo
rt,
Ta
ble
TS
2)
18 L
atin
Am
. stu
die
s
(PA
HO
20
05)
20
g/m
3 P
M1
0
10 mg/m3 PM2.5 or 20 mg/m3 PM10 → 0.4% to 1.5%
increase in relative risk of mortality—Small but
remarkably consistent across meta-analyses and multi-city studies.
Daily time-series studies ***of over 200 cities***
Methods:
Case-crossover study of acute
ischemic coronary events (heart
attacks and unstable angina) in
12,865 well-defined and followed up
cardiac patients who lived on Utah’s
Wasatch Front
…and who underwent coronary
angiography
Jeffrey Anderson
2006;114:2443-48
yt
0
1
t
Binary Data, classic time-series
yt
0
1
t
Binary Data, case-crossover
Conditional Logistic Reg.
Each subject serves as his/her own control.
Control for subject-specific effects, day of week, season,
time-trends, etc.—by matching
Prob (Yt = 1)
1 - Prob (Yt = 1)
α1 + α2 + α3 + . . . + α12,865 + β(w0Pt + w1Pt-1 + w2Pt-2 + . . .)
Control by matching for:
All cross-subject differences
(in this case, 12,865 subject-level fixed effects),
Season and/or month of year,
Time trends,
Day of week
( ) = ln
Conditional logistic regression:
Modeling controversies: How to select control or referent periods. Time
stratified referent selection approach (avoids bias that can occur due to time
trends in exposure) (Holly Janes, Lianne Sheppard, Thomas Lumley
Statistics in Medicine and Epidemiology 2005)
%
0.00
5.00
10.00
15.00
Figure 1. Percent increase in risk (and 95% CI) of acute coronary
events associated with 10 g/m3 of PM
2.5, or PM
10 for
different lag structures.
Co
ncu
rre
nt d
ay
Co
ncu
rre
nt d
ay
1-d
ay
lag
1-d
ay
lag
2-d
ay
lag
2-d
ay
lag
3-d
ay
lag
3-d
ay
lag
2-d
ay
mo
vin
g a
v.
2-d
ay
mo
vin
g a
v.
4-d
ay
mo
vin
g a
v.
3-d
ay
mo
vin
g a
v.
3-d
ay
mo
vin
g a
v.
4-d
ay
mo
vin
g a
v.
PM2.5
PM10
%
-10.00
-5.00
0.00
5.00
10.00
15.00
20.00
Figure 2. Percent increase in risk (and 95% CI) of acute coronary events associated with
10 mg/m3 of PM
2.5, stratified by various characteristics.
All
acu
te c
oro
na
ry
Su
bse
que
nt M
I
Un
sta
ble
An
gin
a
Ag
e<
65
Ag
e>
=6
5
Ma
le
Fem
ale
Sm
okin
g
No
n S
mo
kin
g
BM
I<3
0
BM
I>=
30 C
HF
,ye
sC
HF
,no
Hyp
ert
ensio
n,y
es
Hyp
ert
ensio
n,n
o
Hyp
erlip
idem
ia,y
es
Hyp
erlip
idem
ia,n
o
Dia
be
tes,y
es
Dia
be
tes,n
o
Fam
ily h
isto
ry,y
es
Fam
ily h
isto
ry,n
o
# ofDiseasedVessels
# of Risk Factors
01
2
3
0
1
2
3
4+
Inde
x M
I
Short-term PM exposures contributed to acute coronary events,
especially among patients with underlying coronary artery disease.
Short-term changes in air pollution exposure
are associated with:
• Daily death counts (respiratory and cardiovascular)
• Hospitalizations
• Lung function
• Symptoms of respiratory illness
• School absences
• Ischemic heart disease
• Etc.
Median PM2.5
for aprox. 1980
8 10 12 14 16 18 20 22 24 26 28 30 32 34
Ad
juste
d M
ort
alit
y f
or
19
80
(D
ea
ths/Y
r/1
00
,00
0)
600
650
700
750
800
850
900
950
1000
Mean sulfate for aprox. 1980
4 6 8 10 12 14 16 18 20 22 24
Ad
juste
d M
ort
alit
y
for
19
80
(D
ea
ths/Y
r/1
00
,00
0)
600
650
700
750
800
850
900
950
1000
Mean PM2.5
for aprox. 1980
8 10 12 14 16 18 20 22 24 26 28 30 32 34
600
650
700
750
800
850
900
950
1000
Mean TSP for aprox. 1980
15 30 45 60 75 90 105 120 135 150 165
600
650
700
750
800
850
900
950
1000
Figure 1. Age-, sex-, and race-adjusted population-based mortality rates in U.S. cities for 1980 plotted over various indices of particulate air pollution.
Age-, sex-, and race- adjusted population-
based mortality rates in U.S. cities for 1980
plotted over various indices of particulate
air pollution (From Pope 2000).
An Association Between Air Pollution and
Mortality in Six U.S. Cities
1993
Dockery DW, Pope CA III, Xu X, Spengler JD,
Ware JH, Fay ME, Ferris BG Jr, Speizer FE.
Methods:
14-16 yr prospective follow-up of 8,111adults living in six
U.S. cities.
Monitoring of TSP PM10, PM2.5, SO4, H+, SO2, NO2, O3 .
Data analyzed using survival analysis, including
Cox Proportional Hazards Models.
Controlled for individual differences in: age, sex, smoking,
BMI, education, occupational exposure.
Cox Proportional Hazards Survival Model
Cohort studies of outdoor air pollution have commonly used the CPH Model
to relate survival experience to exposure while simultaneously controlling for
other well known mortality risk factors. The model has the form
)()()( )(0
)()( txexptt l
i
Tll
i
Hazard function
or instantaneous
probability of
death for the ith
subject in the lth
strata.
Baseline
hazard
function,
common to all
subjects within
a strata.
Regression equation that
modulates the baseline
hazard. The vector Xi(l)
contains the risk factor
information related to the
hazard function by the
regression vector β which
can vary in time.
Adjusted risk ratios (and 95% CIs) for
cigarette smoking and PM2.5
Cause of
Death
Current Smoker,
25 Pack years
Most vs. Least
Polluted City
All 2.00 (1.51-2.65)
1.26 (1.08-1.47)
Lung
Cancer
8.00 (2.97-21.6)
1.37 (0.81-2.31)
Cardio-
pulmonary
2.30 (1.56-3.41)
1.37 (1.11-1.68)
All
other
1.46 (0.89-2.39)
1.01 (0.79-1.30)
Particulate Air Pollution as a Predictor of Mortality in a
Prospective Study of U.S. Adults
Pope CA III, Thun MJ, Namboodiri MM,
Dockery DW, Evans JS, Speizer FE, Heath CW Jr.
1995
Methods: Linked and
analyzed ambient air
pollution data from 51-
151 U.S. metro areas
with risk factor data for
over 500,000 adults
enrolled in the ACS-
CPSII cohort.
Clark Heath Michael Thun
Adjusted mortality risk ratios (and 95% CIs) for cigarette
smoking the range of sulfates and fine particles
Cause of
Death
Current
Smoker
Sulfates Fine
Particles
All 2.07 (1.75-2.43)
1.15 (1.09-1.22)
1.17 (1.09-1.26)
Lung
Cancer
9.73 (5.96-15.9)
1.36 (1.11-1.66)
1.03 (0.80-1.33)
Cardio-
Pulmonary
2.28 (1.79-2.91)
1.26 (1.16-1.37)
1.31 (1.17-1.46)
All other 1.54 (1.19-1.99)
1.01 (0.92-1.11)
1.07 (0.92-1.24)
Reduction in fine particulate air pollution: Extended follow-up of the Harvard Six Cities Study (Laden, Schwartz, Speizer, Dockery. AJRCCM 2006)
0.7
0.8
0.9
1.0
1.1
1.2
1.3
1.4
0 5 10 15 20 25 30 35
PM2.5 (mg/m3)
Mo
rtali
ty R
isk R
ati
o
Steubenville Topeka
Watertown
Kingston
St. Louis
Portage
Francine Laden Joel Schwartz
2004. R
R (
95
% C
I)
0.65
0.70
0.75
0.80
0.85
0.90
0.95
1.00
1.05
1.10
1.15
1.20
1.25
1.30
1.35
1.40
All
Card
iovascula
rplu
s D
iabete
s
Ischem
ic h
eart
dis
ease
Dysrh
yth
mia
s,H
eart
failu
re,
Card
iac a
rrest
Hypert
ensiv
edis
ease
Oth
er
Ath
ero
scle
rosis
,aort
ic a
neury
sm
s
Cere
bro
-vascula
r
Oth
er
Card
io-
vascula
r
Dia
bete
s
Respirato
ryD
iseases
CO
PD
and
alli
ed c
onditio
ns
Pneum
onia
,In
fluenza
All
oth
er
respirato
ry
Figure 1. Adjusted relative risk ratios for cardiovascular and respiratory mortality associated with
a 10 mg/m3 change in PM
2.5 for 1979-1983, 1999-2000, and the average of the two periods.
(Relative size of the dots correspond to the relative number of deaths for each cause.)
John Godleski
Blood • Altered rheology
• Increased coagulability
• Translocated particles
• Peripheral thrombosis
• Reduced oxygen saturation
Systemic Inflammation
Oxidative Stress
• Increased CRP
• Proinflammatory mediators
• Leukocyte & platelet activation
PM Inhalation
Brain
• Increased cerebrovascular
ischemia
Heart
• Altered cardiac
autonomic function
• Increased dysrhythmic
susceptibility
• Altered cardiac
repolarization
•Increased myocardial
Ischemia
•Heart failure
exacerbation
Vasculature • Atherosclerosis,
accelerated progression of and
destabilization of plaques
• Endothelial dysfunction
• Vasoconstriction and Hypertension
Lungs • Inflammation
• Oxidative stress
• Accelerated progression
and exacerbation of COPD
• Increased respiratory symptoms
• Effected pulmonary reflexes
• Reduced lung function
Pope and Dockery,
JAWMA 2006.
The Global Burden of Disease 2010
Bre
ath
ing
Conta
min
ants
The Global Burden of Disease 2010
Breathing contaminates contributes to global burden of disease (GBD)
Number of
attributable
deaths
Disability
adjusted
life-years
(DALYs)
Tobacco Smoking 5.7 mil. 5.7%
Second Hand Smoke 0.6 mil. 0.6%
Household air pollution from solid fuels 3.5 mil. 4.5%
Ambient PM air pollution 3.2 mil. 3.1%
Ambient Ozone 0.2 mil. 0.1%
So, an obvious question—
Has reducing air pollution resulted in
substantial and measurable
improvements in human health?
- Matching PM2.5 data for
1979-1983 and 1999-2000 in
51 Metro Areas
- Life Expectancy data for
1978-1982 and 1997-2001 in
211 counties in 51 Metro areas
- Evaluate changes in Life
Expectancy with changes in
PM2.5 for the 2-decade period
of approximately 1980-2000.
Fine-Particulate Air Pollution and Life
Expectancy in the United States
C. Arden Pope, III, Ph.D., Majid Ezzati, Ph.D., and Douglas W.
Dockery, Sc.D.
January 22, 2009
Do cities with bigger improvements in air quality have bigger
improvements in health, measured by life expectancy?
A 10 µg/m3 decrease in PM2.5 was associated with
a 7.3 (± 2.4) month increase in life expectancy.
This increase in life expectancy persisted even after controlling for
socio-economic, demographic, or smoking variables
Finally—
We seem to be making major progress
understanding the health effects of air
pollution.
In the U.S. reducing air pollution has
resulted in substantial and measurable
improvements in human health.
Good News, Right?
Smith and Stewart Subpoena EPA’s Secret Science Aug 2, 2013 Press Release (http://stewart.house.gov/)
Washington, D.C. – Science, Space, and Technology Committee Chairman Lamar Smith (R-Texas) and Environment Subcommittee Chairman Chris Stewart (R-Utah) issued a subpoena to the EPA, forcing the agency to release the secret science it uses as the basis for costly air regulations
House Subpoenas Personal Medical Information in Continued Assault on Clean Air Policies by Sam Abbott, 8/6/2013 Citizen Health & Safety, Safeguarding Public Health and the Environment, Setting and
Enforcing Regulations, Fostering Scientific Integrity, Environmental Protection Agency
(EPA)
Will House Science Panel Need an Ethical Review? Kelly Servick, Aug. 12, 2013
Eddie Bernice Johnson and Lamar Smith
Two Utahns have stake in pollution health data fight Environment » Is congressional inquiry into pollution-health studies after truth or partisan gain?
By Judy Fahys The Salt Lake Tribune Aug 27 2013
Chris Stewart
Harvard Six-Cities Study Dockery et al. New England Journal of Medicine (NEJM), 1993
ACS CPS-II Cohort Study Pope, et al. Am J Respir Crit Care Med (AJRCCM), 1995
Independent Re-analyses of Harvard Six-Cities and ACS CPS-II Studies Krewski et al. HEI Special Report , 2000; J Tox Enviro Health, Special Issue, 2003 3-yr reanalysis by a team of 31 independent researchers with oversight from a 9-member expert panel and peer review by a special panel of the HEI Health Review Committee. Included full data access that insured the privacy and confidentiality of research participants. Reanalyses include data audits, full replication and validation, and extensive sensitivity analyses.
Harvard Six-Cities and ACS CPS-II Cohort Studies of Air Pollution and Mortality: Initial, Independent, Extended, and Replicative Analyses
Extended analyses of Harvard Six-Cities study Laden et al. AJRCCM, 2006 Schwartz et al. EHP, 2008 Lepeule et al. EHP, 2012
Extended analyses of ACS CPS-II study Pope et al. JAMA, 2002; Pope et al. Circulation, 2004; Jerrett et al. Epidemiology, 2005; Krewski et al. HEI Rep. 2009 Jerrett et al. NEJM, 2009; Smith et al. Lancet, 2009 Turner et al. AJRCCM, 2011; Jerrett et al. AJRCCM, 2013
Replicative studies in other cohorts: Miller et al. (Women’s Health Initiative) NEJM, 2007; Beelen et al.
(Netherlands) EHP, 2008; Zeger et al. (U.S. National Medicare) EHP, 2008; Puett et al. (Nurses Health Study) EHP, 2009; Puett et al. (Health Professionals) EHP, 2011; Hart et al. (U.S. Truckers) AJRCCM, 2011; Lipsett et al. (California Teachers) AJRCCM, 2011; Crouse et al. (Canadian ) EHP, 2012; Cesaroni et al. (Rome) EHP, 2013
**See recent meta-analytic review by Hoek et al. Environ Health, 2013**
Working Draft, Aug. 2, 2013, C. Arden Pope III, PhD
Episode studies of morbidity and mortality Meuse Valley, Belgium, 1930 Donora, PA, 1948 London Smog, 1952
Cohort-based Mortality Studies
Population-based cross-sectional mortality studies Lave, Seskin. Science, 1970 Evans et al. Environ Int, 1984 Ozkaynak, Thurston. Risk Anal, 1987 --and others
Time-series and case-crossover daily mortality studies Dominici et al. (NMMAPS) HEI, 2003 Zanobetti et al. (112 US cities) EHP, 2009 Analitis et al. (15-29 Euro cities) Epi, 2006 Anderson et al. (meta anal) Epi. 2005 --and many, many others
Intervention/Natural Experiment studies -Intermittent operation of a steel mill. Pope AJPH, 1989, Ghio JAM, 2004 -US Life Expectancy and pollution reduction. Pope et al. NEJM, 2009 -Ireland coal ban. Dockery et al. HEI, 2013 -China Huai river RD. Chen et al. PNAS, 2013 --and others
Time-series and case-crossover hospitalization studies Schwartz (8 cities). Epi, 1999 Dominici et al. (204 US counties) JAMA, 2006 --and many others
Lung function/resp. symptoms studies Dockery et al. JAWMA, 1982 Pope et al. ARRD, 1991 Hoek et al. Eur Respir J, 1998 Gauderman et al. NEJM, 2004 --and many others
Others The literature is large, complex, and growing.
Cardio- and cerebrovascular disease events studies Peters et al. Circulation 2001 Pope et al. Circulation 2006 Wellenius, et al. Arch Intern Med, 2012 --and many others
Subclinical markers of cardiovascular health studies Including systemic inflammation, oxidative stress, endothelial cell activation, thrombosis and coagulation, vascular dysfunction and atherosclerosis, blood pressure, altered cardiac autonomic function/HRV, etc.) For overview see Brook et al. Circulation 2010
Controlled human exposure and animal toxicological studies For overview see Brook et al. Circulation 2010
A Broader View of the PM Air Pollution and Human Health Scientific Literature
Working Draft, Aug. 2, 2013, C. Arden Pope III, PhD
The assertion that the Harvard Six-Cities and the ACS cohort studies
are secret is simply false on its face. The methodology, protocol, and
results of these studies are openly published in peer-reviewed scientific
journals.
As is common in medical and public health studies, these studies
include analysis of some private data. Releasing private data is
generally unethical and often in violation of confidentiality agreements
with research participants, other data providers, and mandates of
Institutional Review Boards for human subjects.
For the Harvard Six-Cities and the ACS cohort studies, full data access
has been provided for independent data audits, validation, and
reanalysis under conditions that protected the privacy and
confidentiality of research subjects.
For approximately two decades, multiple research teams have been
actively involved in open, collaborative, extended analyses. Results of
the studies have been reproduced, replicated, extended, and
documented in multiple peer-reviewed publications.
Biggest criticisms regarding the overall results:
1. The effects aren’t big enough to be compelling (need RR > 2.0)
2. The effects are too large to be biologically plausible based on
an extrapolation of smoking literature.
0 60 120 180 240
Ad
juste
d R
ela
tive R
isk
1.0
1.5
2.0
estimated daily dose of PM2.5
, mg
18-22cigs/day
Pack-a-day smoker:
RR ~ 2
Daily inhaled dose ~ 240 mg
Live in polluted city or
With smoking spouse
RR ~ 1.15 – 1.35
Daily inhaled dose ~ 0.2–1.0 mg
0 60 120 180 240 300
Ad
jus
ted
Re
lati
ve
Ris
k
1.0
1.5
2.0
2.5
<3cigs/day
estimated daily dose of PM2.5
, mg
23+cigs/day
8-12cigs/day
13-17cigs/day
18-22cigs/day
4-7cigs/day
Pope, Burnett, Krewski, et al. 2009.
Figure 1. Adjusted relative
risks (and 95% CIs) of IHD
(light gray), CVD (dark
gray), and CPD (black)
mortality plotted over
estimated daily dose of
PM2.5 from different
increments of current
cigarette smoking.
Diamonds represent
comparable mortality risk
estimates for PM2.5 from air
pollution. Stars represent
comparable pooled relative
risk estimates associated
with SHS exposure from
the 2006 Surgeon
General’s report and from
the INTERHEART study.
0.1 1.0 10.0 100.0
Ad
jus
ted
Re
lati
ve
Ris
k
1.0
1.5
2.0
2.5
estimated daily dose of PM2.5
, mg
Exposure from
Second hand cigarette smoke: Stars, from 2006 Surgeon General Report and INTERHEART studyAnd air pollution: Hex, from Womens Health Initiative cohort Diamonds, from ACS cohort Triangles, Harvard Six Cities cohort
Exposure from smoking<3, 4-7, 8-12, 13-17, 18-22, and 23+
cigarettes/day
Figure 2. Adjusted relative
risks (and 95% CIs) of
ischemic heart disease
(light gray), cardiovascular
(dark gray), and
cardiopulmonary (black)
mortality plotted over
baseline estimated daily
dose (using a log scale) of
PM2.5 from current
cigarette smoking (relative
to never smokers), SHS,
and air pollution.
0 60 120 180 240 300 360 420 480 540
Ad
jus
ted
Re
lati
ve
Ris
k
1.0
1.5
2.0
2.5
3.0
(<3)
Estimated daily dose of PM2.5
, mg (cigarettes smoked per day)
(4-7)
Ad
jus
ted
Re
lati
ve
Ris
k
5
10
15
20
25
30
35
40
(8-12) (13-17) (18-22) (23-27) (28-32) (33-37) (38-42) (>42)
Lung Cancer
Ischemic heart (light gray)Cardiovascular (dark gray)Cardiopulmonary (black)
0.0 0.5 1.0
1.00
1.25
1.50
0.0 0.5 1.0
1.00
1.25
1.50
Ambient Concentrations ( g/m3)
$
C* CH
Marginal Costsof Abatement
Marginal HealthCosts of Pollution
CNC
Pollution Abatement
Ambient Concentrations ( g/m3)
$
C* CH
Marginal Costsof Abatement
Marginal HealthCosts of Pollution
CNC