532 VOLUME 114 | NUMBER 4 | April 2006 • Environmental Health Perspectives

Research

Tropospheric ozone is a common urban area

pollutant linked to numerous harmful health

effects, including reduced lung function,

increased frequency of respiratory symptoms,

and development of asthma [Broeckaert et al.

1999; Brunekreef and Holgate 2002;

McConnell et al. 2002; U.S. Environmental

Protection Agency (EPA) 1996]. Recent metaanalysis and time-series studies have linked

short-term O3 exposure to premature mortality (Anderson et al. 2004; Bell et al. 2004,

2005; Huang et al. 2005; Ito et al. 2005; Levy

et al. 2005), but the exposure–response curve

for O3 remains unknown. More than 100 million people in the United States live in areas

that exceed the current health-based U.S.

National Ambient Air Quality Standard

(NAAQS) for O3 (U.S. EPA 2004). Elevated

concentrations of O3 are also a growing concern for rapidly developing nations with rising

emissions of O3 precursors from expanding

transportation networks.

The U.S. EPA is currently reviewing the

scientific evidence on O3 and health to determine if the current NAAQS (80 ppb for the

daily 8-hr maximum) should be revised to

meet the goal mandated in the Clean Air Act

Amendments (1990) to protect human health

with an “adequate margin of safety” (U.S.

EPA 1997). There are several critical questions regarding the association between O3

and mortality as the current NAAQS is reexamined: Can O3 affect mortality even at

low levels? Are current regulations sufficiently

stringent to prevent premature mortality?

Is there an attainable threshold O3 level that

does not affect mortality, and if so, is it below

current regulatory limits? Evidence relevant

to these questions can be obtained by estimating the exposure–response curve for O3 and

mortality. The shape of this curve can provide

a basis for a) understanding the impacts of

low levels of O3 pollution on health, b) assessing the adequacy of regulatory standards,

c) designing other health-based studies on O3,

d) estimating the health consequences associated with emissions scenarios and policies

(e.g., Hubbell et al. 2005), and e) assessing

how climate change might affect human health

through altered O3 levels (e.g., Knowlton

et al. 2004).

Materials and Methods

Data and hierarchical model. To investigate

the exposure–response relationship between

O3 and mortality, we applied several modeling

structures to daily time-series data on all-cause

nonaccidental mortality, weather (temperature

and dew point), and O3 pollution levels for the

period 1987–2000 for 98 large U.S. urban

communities (Figure 1). The communities are

listed in the Appendix and consist of urban

areas based on a county or a set of contiguous

counties. Our database includes > 40% of the

total U.S. population and is part of the

National Morbidity, Mortality, and Air

Pollution Study (NMMAPS) (Daniels et al.

2000, 2004; Dominici et al. 2000; Samet et al.

2000a, 2000b, 2000c). We obtained air pollution data by request from the U.S. EPA, and

weather data from the U.S. National Climatic

Data Center.

We used measurements from ambient

monitors as a surrogate for community-level

exposure. The measure of exposure was the

average of the same and previous days’ O3 levels (lag 01—). First, 24-hr averages were calculated for each day within each community,

and then the lag 01— concentrations were calculated. The use of any single day’s O3 level as

the exposure metric would underestimate the

relationship between O3 and mortality (Bell

et al. 2004). We aggregated measurements

from multiple monitors within a community

using a 10% trimmed mean to estimate a

community-level exposure.

We obtained mortality data by request

from the National Center for Health

Statistics. The mortality outcome is the number of daily deaths within the community

excluding nonresidents and excluding those

caused by injuries and other external causes

corresponding to International Classification of

Diseases, 9th Revision (ICD-9) [World Health

Organization (WHO) 1978], codes 800 and

above, and International Classification of

Diseases, 10th Revision (ICD-10) (WHO

1993), codes S and above. Additional information on the generation of the air pollution

data set and the entire database is available

through the Internet-Based Health & Air

Pollution Surveillance System (iHAPSS)

(iHAPSS 2006).

We used a Bayesian hierarchical model to

evaluate the relationship between ambient O3

levels and mortality rates within each community (community-specific relative rate estimate)

and to combine information across communities to produce a national average relative rate

Address correspondence to M.L. Bell, School of

Forestry and Environmental Studies, Yale University,

205 Prospect St., New Haven, CT 06511 USA.

Telephone: (203) 432-9869. Fax: (203) 432-3817.

E-mail: michelle.bell@yale.edu

We thank J. Samet, S. Zeger, R. White,

R. Brookmeyer, and D. Moffitt.

Funding was provided by U.S. Environmental

Protection Agency (EP05C000125, EPA-3D-6867-

NAEX), Johns Hopkins Centers for Disease Control

Center of Excellence for Environmental Public Health

Tracking (U50/CCU322417), National Institute of

Environmental Health Sciences (ES012054-01, P30

ES 03819), and the Health Effects Institute (4720-

RFA04-2/04-16).

Views expressed are those of the authors.

The authors declare they have no competing

financial interests.

Received 4 November 2005; accepted 23 January

2006.

The Exposure–Response Curve for Ozone and Risk of Mortality and the

Adequacy of Current Ozone Regulations

Michelle L. Bell,1 Roger D. Peng,2 and Francesca Dominici2

1School of Forestry and Environmental Studies, Yale University, New Haven, Connecticut, USA; 2Department of Biostatistics, Johns

Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA

Time-series analyses have shown that ozone is associated with increased risk of premature mortality,

but little is known about how O3 affects health at low concentrations. A critical scientific and policy

question is whether a threshold level exists below which O3 does not adversely affect mortality. We

developed and applied several statistical models to data on air pollution, weather, and mortality

for 98 U.S. urban communities for the period 1987–2000 to estimate the exposure–response

curve for tropospheric O3 and risk of mortality and to evaluate whether a “safe” threshold level

exists. Methods included a linear approach and subset, threshold, and spline models. All results

indicate that any threshold would exist at very low concentrations, far below current U.S. and

international regulations and nearing background levels. For example, under a scenario in which

the U.S. Environmental Protection Agency’s 8-hr regulation is met every day in each community,

there was still a 0.30% increase in mortality per 10-ppb increase in the average of the same

and previous days’ O3 levels (95% posterior interval, 0.15–0.45%). Our findings indicate that

even low levels of tropospheric O3 are associated with increased risk of premature mortality.

Interventions to further reduce O3 pollution would benefit public health, even in regions that

meet current regulatory standards and guidelines. Key words: mortality, ozone, regulations,

threshold. Environ Health Perspect 114:532–536 (2006). doi:10.1289/ehp.8816 available via

http://dx.doi.org/ [Online 23 January 2006]
estimate, accounting for the uncertainty of

each community’s relative rate (Dominici et al.

2000; Everson and Morris 2000). The first

stage estimates the relationship between shortterm exposure to O3 and daily nonaccidental

mortality rates within each community, using a

Poisson regression model (McCullagh and

Nelder 1989) of the form:

log(μc

t) = βc

xc

t + yc

DOWt + ns(timet,7/year) 

+ ns(Tc

t ,6) + ns(Tct –1,t –3,6) 

+ ns(Dc

t ,3) + ns(Dct –1,t –3,3) 

+ interaction terms for age and time,

[1]

where μc

t is the expected number of deaths

for community c on day t, based on an overdispersed Poisson distribution; x c

t is the

average of the same and previous days’ daily

O3 concentrations in community c on day t;

DOWt is the categorical variable for day of the

week on day t; ns(timet,7/year) is the natural

cubic spline function of calendar time with

7 degrees of freedom per year; ns(Tc

t ,6) is the

natural cubic spline function for temperature

with 6 degrees of freedom; ns(Tc

t –1,t –3,6) is the

natural cubic spline function of the average of

the 3 previous days’ temperature (adjusted for

current day temperature); ns(Dc

t ,3) is the natural cubic spline function for dew point with

3 degrees of freedom; and ns(Dc

t –1,t –3,3) is the

natural cubic spline function of the average of

the 3 previous days’ dew point (adjusted for

current day dew point). Interaction terms for

age and time are the interaction terms between

natural cubic spline functions of time- and

age-specific indicators (< 65, 65–74, and

≥ 75 years).

In the first stage, we estimated the effect of

O3 on mortality for each community, β

ˆc

, (an

estimate of the true community-specific relative rate, βc), and the corresponding variance

vˆc

. We assume:

β

ˆc

|βc,vˆc ∼ N(βc,vˆc) [2]

βc|μ,τ2 ∼ N(μ,τ2), [3]

where μ is the true national average relative rate

and τ2 is the variance of the true communityspecific relative rates, βc

. Sensitivity analyses

and characteristics of the first-stage statistical

model for confounding adjustment have been

explored for particulate matter (PM), with

results indicating that national average estimates are robust to model specification for

weather and seasonal confounding (Peng et al.

2005; Welty and Zeger 2005). Earlier analysis

showed national-average and communityspecific estimates for O3 and mortality to be

robust to inclusion of PM10 (PM with an aerodynamic diameter < 10 μm) in the first-stage

model (Bell et al. 2004). Results were also

robust to exclusion of days with high temperature (Bell et al. 2004).

As a second stage, we generated a national

relative rate estimate that accounts for the statistical uncertainty of each community’s relative rate estimate and for the variability across

communities of the true relative rates. We fit

this two-stage normal–normal model by use

of a two-level normal independent sampling

estimation (TLNise 2006) with noninformative priors (Everson and Morris 2000).

Using this two-stage approach, we performed four analyses that make different modeling assumptions about the communityspecific exposure–response curve for O3 and

mortality. Under each analysis, we estimated a

national relative rate and/or a national exposure–response curve by combining information across the 98 communities.

Linear approach. For the first analysis, the

linear approach, we estimated a linear association between the log of the expected mortality

rate and O3 levels as described in Equation 1.

This model assumes that any change in O3

concentration, even at very low levels, can be

associated with mortality. For example, a

10-ppb increase in O3 levels from 5 to 15 ppb

would lead to the same percentage increase in

mortality as a 10-ppb rise from 50 to 60 ppb.

This is the modeling approach used in most

epidemiologic studies of air pollution and in

most health and impact assessments of air

pollution policies. We then relaxed this

assumption of linearity across the entire range

of O3 levels with the three approaches

described below.

Subset approach. Under the second analysis, the subset approach, we estimated a linear

relationship between the log of the expected

mortality rate and O3 levels as in Equation 1

but using a subset of the data including only

days with lag 01— O3 levels below a specified

concentration, s. We performed this analysis for

values of s ranging from 5 to 60 ppb. Under

this approach, we assume that “safe” O3 levels

are those lower than the specific s value that

leads to lack of evidence of an association

between O3 and mortality.

We also used the subset approach to assess

the relationship between O3 and mortality

under several idealized policy scenarios in

which various O3 regulations and guidelines

were met every day in each community.

Because O3 regulations are expressed in different metrics, we proceeded in three steps.

First, we used hourly O3 concentrations to

calculate daily O3 levels under the same metric specified by the standard (e.g., daily 8-hr

maximum or daily 1-hr maximum). Second,

we constructed a subset of the data set that

includes only days that meet the regulatory

standard or guideline. For example, for the

U.S. EPA O3 standard, we first calculated a

daily time series of 8-hr maximum O3 levels,

and then we constructed a subset of the data

set that only includes days with an 8-hr maximum O3 level < 84 ppb (U.S. EPA 1997).

Third, using only days that met the standard,

we estimated the percentage increase in mortality associated with a 10-ppb increase in

lag 01— O3 levels on average across the 98 communities, with the 95% posterior interval,

which is the Bayesian analogue of the 95%

confidence interval. This strategy allows us to

analyze the subset of days that meet a regulatory requirement using the metric specified in

the standard but to present results with a single metric for the exposure variable (lag 01— of

the 24-hr averages) to maintain a common

interpretation of the relative rate estimates.

The NAAQS for O3 is “80 ppb” for the

daily 8-hr maximum, but U.S. EPA regulations specify that values between 80 and

84 ppb can be rounded down and are not

considered exceedances (U.S. EPA 1997).

Thus, for our analysis of the NAAQS, we

considered a standard of 84 ppb for the daily

8-hr maximum. Regulations generally do not

require every monitor to meet the standard

every day. For example, a standard can allow

a specified number of exceedances and require

that a certain percentile (e.g., 98th) meet the

Ozone and mortality: exposure response and regulations

Environmental Health Perspectives • VOLUME 114 | NUMBER 4 | April 2006 533

Figure 1. Locations of the 98 U.S. urban communities examined in this study.
requirement on a 3-year average. In actual

compliance with a regulatory standard for a

given area, the levels of pollution would

follow an uneven spatial distribution (U.S.

EPA 2005). Our analysis considers a more

stringent application in that it incorporates

only days with O3 levels at or below the specific standard for both the same and previous

days. However, the regulatory standard

requires compliance from every monitor,

whereas this analysis considers averages across

communities rather than individual monitor

exceedances.

Threshold approach. If a threshold (h)

exists, we would expect to detect an association

between O3 and mortality for O3 levels > h but

not for O3 levels < h. Our threshold approach

has the same structure of Equation 1, but with

the pollution term replaced by

(xc

t –h)+, [4]

where

(xc

t –h)+ = (xct –h) if xct ≥ h [5]

= 0 otherwise.

Under this model, we assume no association between O3 and mortality for days with

O3 concentrations below h and a linear relationship for days with O3 levels above h. We

performed this analysis for values of h ranging

from 0 to 60 ppb at increments of 5 ppb. For

each community-specific model and threshold

level (h), we calculated the Akaike Information

Criterion (AIC) (Akaike 1973) as

AICc

(h) = deviance + 2(number of parameters).

[6]

Note that the number of parameters can differ

by urban community because of the varying

frequencies with which O3 is measured and

the variables for time. We then calculated the

average AIC for each h value as

, [7]

where n = number of communities (98). The

rationale for this approach is that if an O3

threshold exists, the threshold approach with

the appropriate value for h will have the best

fit and therefore the minimum AIC—(h) (Akaike

1973).

Spline approach. Under the fourth analysis, the spline approach, we allow the relationship between O3 and mortality to fluctuate

for different ranges of pollution levels, using a

nonlinear function of O3. This model can be

defined as Equation 1 but replacing βc

xc

t with

ns(xc

t), where ns is a natural cubic spline of O3

levels (Daniels et al. 2000, 2004; Dominici

et al. 2002). Boundary knots were specified at

0 and 80 ppb, with interior knots at 20 and

40 ppb. The spline approach extends the linear approach because here the relative rate

corresponding to a 10-ppb increase in O3 levels from 5 to 15 ppb is allowed to differ from

the relative rate corresponding to a 10-ppb

increase from 50 to 60 ppb. Visual inspection

of the estimated exposure–response curve can

provide evidence about whether a safe level

exists and at what concentration.

Results

We found that daily increases in ambient O3

levels were significantly associated with daily

increases in the number of deaths, on average,

across the 98 U.S. communities. Specifically,

under the linear approach, we found that the

percentage increase in all-cause mortality associated with a 10-ppb increase in the lag 01— O3

levels was 0.32% (95% posterior interval,

0.17–0.46%). We also found that the largest

relative rate estimates occur on more recent

days: the percentage increases (95% posterior

intervals) in all-cause mortality associated

with a 10-ppb increase in lag 01— daily O3 levels were 0.25% (0.12 to 0.38%), 0.18% (0.07

to 0.30%), 0.14% (0.03 to 0.26%), and

0.04% (–0.07 to 0.16%) for single-day lags of

0, 1, 2, and 3 days, respectively. The community-specific maximum likelihood estimates

under the linear approach displayed no association with the communities’ long-term O3

concentrations over the study period, as tested

by correlation and weighted second-stage

regression.

Our results show that daily increases

in ambient O3 were significantly associated

with daily increases in the number of deaths,

on average, across the 98 U.S. communities

for the idealized policy scenarios under which

every community meets current O3 regulatory

standards and guidelines (California Environmental Protection Agency 2005; Canadian

Council of Ministers of the Environment

2000; European Commission 2002; U.S. EPA

1997; WHO 2000) for every day of the study

period, 1987–2000 (Table 1). For example,

the percentage increase in all-cause mortality

associated with a 10-ppb increase in lag 01— O3

levels was 0.30% (0.15–0.45%) when we

used a data set including only days with a

daily 8-hr maximum O3 concentration lower

than U.S. O3 regulations. We also found that

daily increases in ambient O3 exposure are

linked to premature mortality under compliance with other O3 regulations, including

some more stringent than the U.S. standards.

In summary, these results indicate that current regulations, even California’s new, more

stringent standards, are not sufficiently low to

provide complete protection against the risk

of premature mortality from O3.

Daily changes in ambient O3 were significantly associated with daily changes in the

number of deaths, on average, across the 98

U.S. communities, even when we used data

that include only days with lag 01— average O3

levels < 15 ppb. Figure 2 shows the estimated

percentage increase in mortality for a 10-ppb

increase in the lag 01— O3 level for different

Bell et al.

534 VOLUME 114 | NUMBER 4 | April 2006 • Environmental Health Perspectives

Table 1. National effect estimates (95% posterior interval) under the scenario that a specific regulation or

guideline is met every day in each community.

Increase in mortality 

for 10-ppb increase

Organization/government Regulation/guideline in lag 01— O3 (%)

U.S. EPA 84 ppb daily 8-hr maximum 0.30 (0.15–0.45)

WHO (guideline) 120 μg/m3 (~ 61 ppb) daily 8-hr maximum 0.25 (0.06–0.43)

European Commission (target value for 2010) 120 μg/m3 (~ 61 ppb) daily 8-hr maximum 0.25 (0.06–0.43)

Canada (to be achieved by 2010) 65 ppb daily 8-hr maximum 0.28 (0.11–0.45)

California 70 ppb daily 8-hr maximum 0.30 (0.14–0.46)

90 ppb daily 1-hr maximum 0.29 (0.14–0.44)

Both of California’s above standards 0.31 (0.14–0.47)

All standards All of the above standards and guidelines 0.24 (0.06–0.42)

All days of dataa NA 0.32 (0.17–0.46)

NA, not applicable.

aConsidered regardless of whether they meet a standard or guideline.

2

0

–2

–4

0 20 40 60 80

Cutoff value, s (ppb)

Percent increase in mortality for 10 ppb O3

*

** ***

All data

Figure 2. Percentage increase in daily nonaccidental mortality per 10-ppb increase in lag 01— O3

obtained by using the subset approach. Diamonds

denote the point estimates, and vertical lines represent the 95% posterior intervals. Each estimate is

obtained by including in the analysis only days with

24-hr average lag 01— O3 levels below the s value

specified on the x-axis. Not all communities had

sufficient data for analysis at all s values: *25 communities; **74 communities; ***92 communities. All

other estimates used 98 communities. The estimate

at the far right marked by a square uses all data.
values of s. National relative rate estimates for

s values ranging from 35 to 60 ppb are similar

to the ones obtained by using all data. The

95% posterior interval increases as s is lowered because of the decreasing sample size.

For example, at an s value of 40 ppb, 30% of

days are excluded from analysis, on average,

across the 98 communities. At an s of 20 ppb,

73% of days are excluded. The estimates

decline and lose significance only when s is

equal to very low concentrations (≤ 10 ppb).

Therefore, the subset approach suggests that a

“safe” O3 level would be lower than approximately 10 ppb, for the lag 01— daily O3 level,

which is roughly 15–19 ppb for the maximum 8-hr average. However, relative rate

estimates for s ≤ 10 ppb have large statistical

uncertainty because of the very small number

of days with O3 concentrations so low. In

fact, 73 communities were excluded entirely

at an s of 5 ppb because of insufficient data.

Results from the threshold and spline

approaches are consistent with those from

the subset approach and provide evidence that

a “safe” O3 level can only exist at very low

concentrations. We found that the model fit

under the threshold approach for values of h

from 5 to 60 ppb never provides more than a

nominal improvement (< 1% difference in

the AIC—) over the model fit under the linear

approach (analogous to the threshold

approach with h = 0) for the national average

and each individual community. In other

words, a model that allows for a “safe” O3

level fit the data only marginally better than a

model that assumes any level of O3 pollution,

even low concentrations, can be associated

with mortality. The spline approach indicates

that the national average exposure–response

curve obtained using natural cubic splines

is near horizontal, indicating the lack of

evidence for an association, only at the very

low concentrations (from 0 to ~10 ppb) and

then becomes approximately linear at higher

concentrations (Figure 3).

Discussion

In summary, our nationwide study provides

strong and consistent evidence that daily

changes in ambient O3 exposure are linked to

premature mortality, even at very low pollution levels, including an idealized scenario of

complete adherence to current O3 regulations. We also found robust evidence of this

relationship between O3 exposure and mortality when we used data that included only

O3 levels nearing background concentrations,

which typically range from 10 to 25 ppb

(Fiore et al. 2003, 2004). Therefore, any

anthropogenic contribution to ambient O3,

however slight, still presents an increased risk

for premature mortality.

Results from this multisite national study

are consistent with single-site time-series studies that found no evidence of a “safe” O3 level

at concentrations higher than background

levels. Consistent with the results obtained

under our spline approach, Kim et al. (2004)

found that a spline model indicated a threshold around 20–30 ppb for the daily 1-hr

maximum, which is approximately equal to

8–12 ppb for the 24-hr average, using 5 years

of data for Seoul, Korea. Hoek et al. (1997)

found that relative risk estimates of mortality

associated with daily changes in O3 were

robust to exclusion of days with a 24-hr average ≥ 40 μg/m3 (about 20 ppb) in a study of

Rotterdam, the Netherlands, and concluded

that should a threshold exist, it may be at a

low concentration. Adverse health responses

such as decreases in pulmonary function,

alterations in the respiratory tract, and declines

in lung function have been observed at O3

levels close to background concentrations

(Chan and Wu 2005; WHO 2000). O3 levels

below U.S. EPA regulations have been associated with increased frequency of respiratory

symptoms in children with asthma (Gent

et al. 2003).

Pollution levels below air quality regulatory standards should not be misinterpreted

as safe for human health. For instance, the

San Joaquin Valley Air Pollution Control

District refers to the standards as the “highest

level of O3 that can be present without

adverse health effects” (San Joaquin Valley

Air Pollution Control District 2006).

However, decision makers and the public

should distinguish between the complete

absence of harm and a lessened or acceptable

risk. In fact, the interpretation of an “adequate margin of safety” and what is a “safe”

level could depend on the individual, because

people may differ in their susceptibility

to air pollutants, and could depend on the

evolving knowledge about the health impacts

of air pollution at low levels (American

Thoracic Society 2000). This research shows

that any reduction in ambient O3 levels,

such as through transportation planning in

urban areas, should yield important benefits

to public health, even in areas that meet current regulatory standards. Persons may be

adversely affected by O3 pollution, even at

very low levels including days that meet

current regulatory requirements.

Ozone and mortality: exposure response and regulations

Environmental Health Perspectives • VOLUME 114 | NUMBER 4 | April 2006 535

6

5

4

3

2

1

0

0 20 40 60 80

Average of same and previous days’ O3 (ppb)

Percent increase in mortality risk

Central estimate

95% posterior interval

Figure 3. Exposure–response curve for O3 and

mortality using the spline approach: percentage

increase in daily nonaccidental mortality at various

O3 concentrations. 

Appendix. List of 98 U.S. urban communities.

Descriptive statistics for each community are given in iHAPSS (2006).

Akron, Ohio

Albuquerque, New Mexico

Arlington, Virginia

Atlanta, Georgia

Austin, Texas

Bakersfield, California

Baltimore, Maryland

Baton Rouge, Louisiana

Biddeford, Maine

Birmingham, Alabama

Boston, Massachusetts

Buffalo, New York

Cedar Rapids, Iowa

Charlotte, North Carolina

Chicago, Illinois

Cincinnati, Ohio

Cleveland, Ohio

Colorado Springs, Colorado

Columbus, Georgia

Columbus, Ohio

Corpus Christi, Texas

Coventry, Rhode Island

Dallas/Fort Worth, Texas

Dayton, Ohio

Denver, Colorado

Des Moines, Iowa

Detroit, Michigan

District of Columbia

El Paso, Texas

Evansville, Indiana

Fort Wayne, Indiana

Fresno, California

Grand Rapids, Michigan

Greensboro, North Carolina

Honolulu, Hawaii

Houston, Texas

Huntsville, Alabama

Indianapolis, Indiana

Jackson, Mississippi

Jacksonville, Florida

Jersey City, New Jersey

Johnstown, Pennsylvania

Kansas City, Kansas

Kansas City, Missouri

Kingston, New York

Knoxville, Tennessee

Lafayette, Louisiana

Lake Charles, Louisiana

Las Vegas, Nevada

Lexington, Kentucky

Lincoln, Nebraska

Little Rock, Arkansas

Louisville, Kentucky

Los Angeles, California

Madison, Wisconsin

Memphis, Tennessee

Miami, Florida

Milwaukee, Wisconsin

Mobile, Alabama

Modesto, California

Muskegon, Michigan

Nashville, Tennessee

New Orleans, Louisiana

New York, New York

Newark, New Jersey

Oakland, California

Oklahoma City, Oklahoma

Omaha, Nebraska

Orlando, Florida

Philadelphia, Pennsylvania

Phoenix, Arizona

Pittsburgh, Pennsylvania

Portland, Oregon

Providence, Rhode Island

Raleigh, North Carolina

Riverside, California

Rochester, New York

Sacramento, California

Salt Lake City, Utah

San Antonio, Texas

San Bernardino, California

San Diego, California

San Jose, California

Santa Ana/Anaheim,

California

Seattle, Washington

Shreveport, Louisiana

Spokane, Washington

St. Louis, Missouri

St. Petersburg, Florida

Stockton, California

Syracuse, New York

Tacoma, Washington

Tampa, Florida

Toledo, Ohio

Tucson, Arizona

Tulsa, Oklahoma

Wichita, Kansas

Worcester, Massachusetts
Bell et al.

536 VOLUME 114 | NUMBER 4 | April 2006 • Environmental Health Perspectives

REFERENCES

Akaike H. 1973. Information theory and extension of the maximum likelihood principle. In: International Symposium on

Information Theory (Petrov BN, Caski F, eds). Budapest:

Akademiai Kaiado, 267–281.

American Thoracic Society. 2000. What constitutes an adverse

health effect of air pollution? Am J Respir Crit Care Med

161:665–673.

Anderson HR, Atkinson RW, Peacock JL, Marston L,

Konstantinou K. 2004. Meta-Analysis of Time-Series Studies

and Panel Studies of Particulate Matter (PM) and Ozone

(O3). Report of a WHO Task Group. Copenhagen:World

Health Organization.

Bell ML, Dominici F, Samet JM. 2005. A meta-analysis of timeseries studies of ozone and mortality with comparison to

the National Morbidity, Mortality, and Air Pollution Study.

Epidemiology 4:436–445.

Bell ML, McDermott A, Zeger SL, Samet JM, Dominici F. 2004.

Ozone and short-term mortality in 95 US urban communities,

1987–2000. JAMA 292:2372–2378.

Broeckaert F, Arsalane K, Hermans C, Bergamaschi E, Brustolin

A, Mutti A, et al. 1999. Lung epithelial damage at low concentrations of ambient ozone. Lancet 353:900–901.

Brunekreef B, Holgate ST. 2002. Air pollution and health.

Lancet 360:1233–1242.

California Environmental Protection Agency. 2005. California

Adopts New Ozone Standard. Sacramento:California Air

Resources Board.

Canadian Council of Ministers of the Environment. 2000.

Canada-wide standards for particulate matter (PM) and

ozone. Quebec City:Canadian Council of Ministers of the

Environment.

Chan CC, Wu TH. 2005. Effects of ambient ozone exposure on

mail carriers’ peak expiratory flow rates. Environ Health

Perspect 113:735–738.

Clean Air Act Amendments of 1990. 1990. Public Law 101-549.

Daniels MJ, Dominici F, Samet JM, Zeger SL. 2000. Estimating

particulate matter-mortality dose-response curves and

threshold levels: an analysis of daily time-series for the 20

largest US cities. Am J Epidemiol 152:397–406.

Daniels MJ, Dominici F, Zeger SL, Samet JM. 2004. The National

Morbidity, Mortality, and Air Pollution Study Part III: PM10

Concentration-Response Curves and Thresholds for the 20

Largest US cities. Report 94, pt 3. Cambridge, MA:Health

Effects Institute.

Dominici F, Daniels M, Zeger SL, Samet JM. 2002. Air pollution

and mortality: estimating regional and national doseresponse relationships. J Am Stat Assoc 97:100–111.

Dominici F, Samet JM, Zeger SL. 2000. Combining evidence on

air pollution and daily mortality from the largest 20 US

cities: a hierarchical modeling strategy. J R Stat Soc A

163:263–302.

European Commission. 2002. Directive 2002/3/EC of the European

Parliament and of the Council. Directive 2002/3/EC.

Brussels, Belgium:European Commission.

Everson PJ, Morris CN. 2000. Inference for multivariate normal

hierarchical models. J R Stat Soc B 62:399–412.

Fiore A, Jacob DJ, Liu H, Yantosca RM, Fairlie TD, Li Q. 2003.

Variability in surface ozone background over the United

States: implications for air quality policy. J Geophys Res

Atmos. doi:10.1029/2003JD003855.

Fiore A, Jacob D J, Liu H, Yantosca RM, Fairlie TD, Li Q. 2004.

Correction to “Variability in surface ozone background

over the United States: implications for air quality policy.”

J Geophys Res Atmos. doi:10.1029/2004JD004567.

Gent JF, Triche EW, Holford TR, Belanger K, Bracken MB,

Beckett WS, et al. 2003. Association of low-level ozone and

fine particles with respiratory symptoms in children with

asthma. JAMA 290:1859–1867.

Hoek G, Schwartz JD, Groot B, Eilers P. 1997. Effects of ambient

particulate matter and ozone on daily mortality in Rotterdam,

the Netherlands. Arch Environ Health 52:455–463.

Huang Y, Dominici F, Bell ML. 2005. Bayesian hierarchical distributed lag models for summer ozone exposure and cardiorespiratory mortality. Environmetrics 16:547–562.

Hubbell BJ, Hallberg A, McCubbin DR, Post E. 2005. Healthrelated benefits of attaining the 8-hr ozone standard.

Environ Health Perspect 113:73–82.

iHAPSS. 2006. Internet-Based Health & Air Pollution Surveillance

System. Baltimore, MD:Johns Hopkins Bloomberg School of

Public Health. Available: http://www.ihapss.jhsph.edu/

[accessed 5 January 2006].

Ito K, De Leon SF, Lippmann M. 2005. Associations between

ozone and daily mortality: analysis and meta-analysis.

Epidemiology 4:446–457.

Kim SY, Lee JT, Hong YC, Ahn KJ, Kim H. 2004. Determining the

threshold effect of ozone on daily mortality: an analysis of

ozone and mortality in Seoul, Korea, 1995–1999. Environ Res

94:113–119.

Knowlton K, Rosenthal JE, Hogrefe C, Lynn B, Gaffin S,

Goldberg R, et al. 2004. Assessing ozone-related health

impacts under a changing climate. Environ Health Perspect

112:1557–1563.

Levy JI, Chemerynski SM, Sarnat JA. 2005. Ozone exposure

and mortality: an empiric Bayes metaregression analysis.

Epidemiology 4:458–468.

McConnell R, Berhane K, Gilliland F, London SJ, Islam T,

Gauderman WJ, et al. 2002. Asthma in exercising children

exposed to ozone: a cohort study. Lancet 359:386–391.

McCullagh P, Nelder JA. 1989. Generalized Linear Models. 2nd

ed. London:Chapman & Hall/CRC.

Peng RD, Dominici F, Pastor-Barriuso R, Zeger SL, Samet JM.

2005. Seasonal analyses of air pollution and mortality in

100 US cities. Am J Epidemiol 161:585–594.

Samet JM, Dominici F, Curriero FC, Coursac I, Zeger SL. 2000a.

Fine particulate air pollution and mortality in 20 U.S. cities,

1987–1994. N Engl J Med 343:1742–1749.

Samet JM, Dominici F, Zeger SL, Schwartz J, Dockery DW.

2000b. The National Morbidity, Mortality, and Air Pollution

Study Part I: Methods and Methodologic Issues. Report 94,

pt 1. Cambridge, MA:Health Effects Institute.

Samet JM, Zeger SL, Dominici F, Curriero F, Coursac I,

Dockery DW, et al. 2000c. The National Morbidity, Mortality,

and Air Pollution Study Part II: Morbidity and Mortality from

Air Pollution in the United States. Report 94, pt 2.

Cambridge, MA:Health Effects Institute.

San Joaquin Valley Air Pollution Control District. 2006. Ozone.

Fresno, CA:San Joaquin Valley Air Pollution Control District.

Available: http://www.valleyair.org/Air_Quality_Plans/

AQ_plans_Ozone_standards.htm [accessed 5 January 2006].

TLNise. 2006. Two Level Normal Independent Sampling

Estimation. Swarthmore, PA:Swarthmore College. Available:

http://www.swarthmore.edu/NatSci/peverso1/TLNise/tlnise.

htm [accessed 5 January 2006].

U.S. EPA. 1996. Air Quality Criteria for Ozone and Related

Photochemical Oxidants. EPA/600/P-93/004a-cF. Washington,

DC:U.S. Environmental Protection Agency.

U.S. EPA (U.S. Environmental Protection Agency). 1997.

National ambient air quality standards for ozone. Fed Reg

62:38855–38896.

U.S. EPA. 2004. The Ozone Report: Measuring Progress through

2003. EPA/454/K-04–001. Research Triangle Park, NC:U.S.

Environmental Protection Agency.

U.S. EPA. 2005. Review of the National Ambient Air Quality

Standards for Particulate Matter: Policy Assessment of

Scientific and Technical Information. EPA-452/R-05/005.

Research Triangle Park, NC:U.S. Environmental Protection

Agency.

Welty LJ, Zeger SL. 2005. Are the acute effects of particulate

matter on mortality in the National Morbidity, Mortality,

and Air Pollution Study the result of inadequate control for

weather and season? A sensitivity analysis using flexible

distributed lag models. Am J Epidemiol 162:80–88.

WHO. 1978. International Classification of Diseases, 9th

Revision. Geneva:World Health Organization.

WHO. 1993. International Classification of Diseases, 10th

Revision. Geneva:World Health Organization.

WHO. 2000. Air Quality Guidelines for Europe. 2nd ed.

WHO Regional Publications, European Series, no. 91.

Copenhagen:World Health Organization Regional Office

for Europe.