Environmental Health Perspectives • VOLUME 110 | NUMBER 10 | October 2002 1025

The Concentration–Response Relation between PM2.5 and Daily Deaths

Joel Schwartz,1,2,3 Francine Laden,1,3 and Antonella Zanobetti1

1Environmental Epidemiology Program, Department of Environmental Health, Harvard School of Public Health, Boston, Massachusetts,

USA; 2Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA; 3Channing Laboratory, Brigham and

Women’s Hospital, and Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA

In the last decade, a series of studies reported

associations between daily concentrations of

airborne particles and daily deaths (1–3). The

magnitude of the regression coefficients in

those studies indicated that particulate air pollution was associated with between 50 and

100,000 early deaths per year in the United

States, and similar numbers were found in

Europe. More recently, a number of large,

multicity studies (4–7) have reported associations between airborne particles, measured in

various ways, and daily deaths. The largest

study demonstrated that gaseous air pollutants

did not confound the association, and that

none of the gaseous air pollutants showed an

independent effect on daily deaths (7). These

studies assumed a linear concentration–

response relation between airborne particles

and daily deaths and did not address the question of what the association looked like for

particle constituents, characterized by size,

physiochemical composition, or source.

In a recent study of six U.S. cities (5), we

demonstrated that daily mortality was associated with fine particulate matter (aerodynamic diameter ≤ 2.5 μm; PM2.5) and not

with coarse particulate matter (aerodynamic

diameter between 2.5 and 10 μm; PM2.5–10).

Each 10 μg/m3 increase in the 2-day mean

concentration of PM2.5 was associated with a

1.5% (95% confidence interval, 1.1–1.9%)

increase in daily mortality.

Ambient PM2.5 consists mainly of combustion particles from motor vehicles and the

burning of coal, fuel oil, and wood, but also

contains some crustal particles from finely pulverized road dust and soils. These sources produce particles with different characteristics,

and the relative toxicity of those sources and

characteristics is an area of relative recent but

intense interest. In a follow-up study (8), we

used the elemental composition of size-fractionated particles to identify several distinct

source-related fractions of fine particles. We

then examined the association of these fractions with daily mortality in each of the six

cities and combined the city-specific results in

a meta-analysis to derive overall relative risks

for each fraction. We found positive associations with particles from traffic, particles from

coal, and particles from residual oil combustion when included jointly in the model predicting daily deaths (8). The largest effect size

was for residual oil particles, followed by traffic

particles and then coal particles. Only the latter

two associations were statistically significant,

however. Again, as traditional, these analyses

assumed a linear association between the various particle constituents and daily deaths.

The shape of the concentration–response

relationship is critical for public health assessment, and in particular, some have speculated

that thresholds might exist.

Recently, three reports have explored this

question for particulate air pollution, using

multicity studies in the United States. In one

study, Daniels et al. (9) used data from 20 U.S.

cities, five of which had daily measurements of

PM10, with the rest having measurements

only one day in six. They used regression

splines to model the concentration–response

curve in each city and combined the results

across cities. They found no evidence for a

threshold. In fact, the concentration–response

relation was quite linear across the entire

range of exposure. In another report,

Schwartz and Zanobetti (10) used data from

10 cities, all of which had daily measurements

of PM10, resulting in slightly more days of

study than in the first report. They used nonparametric smoothing to model the concentration–response curve between air pollution

and daily deaths in each city and combined

the results across cities. Again, a linear, nothreshold relationship was seen. Schwartz and

Zanobetti also performed simulations to confirm the ability of this approach to detect

thresholds and other types of nonlinearity

(10). Schwartz et al. (11), using data from

eight Spanish cities, similarly reported a linear

association between daily deaths and black

smoke, an optical measure of black particles.

These results held after adjusting for SO2. To

date, no similar examination of the concentration–response curve has been done for

PM2.5, or for any source components.

Because PM2.5 is now the regulated form of

particulate air pollution in the United States,

we here report results of such an analysis.

Materials and Methods

Air pollution data. As part of the Harvard Six

Cities studies (12), dichotomous virtual

impactor samplers were placed at a central

residential monitoring site in six U.S. metropolitan areas: Boston, Massachusetts;

Knoxville, Tennessee; St. Louis, Missouri;

Stuebenville, Ohio; Madison, Wisconsin; and

Topeka, Kansas. Separate filter samples were

collected of fine particles (PM2.5) and of the

coarse mass (PM2.5–10) fraction. Integrated

24-hr samples were collected at least every

other day from 1979 until the late 1980s,

with daily sampling during health survey

periods. For fine and coarse particle samples,

mass concentration was determined separately

by beta-attenuation (13). Except for a period

Address correspondence to J. Schwartz, Environmental

Epidemiology Program, Harvard School of Public

Health, 665 Huntington Ave., Boston, MA 02115

USA. Telephone: (617) 384-8752. Fax: (617) 384-

8745. E-mail: jschwrtz@hsph.Harvard.edu

This work was supported by U.S. EPA Grant

R827353 and NIEHS Grant ES 00002.

Received 21 December 2001; accepted 20 March

2002.

Articles

Particulate air pollution at commonly occurring concentrations is associated with daily deaths.

Recent attention has focused on the shape of the concentration–response curve, particularly at low

doses. Several recent articles have reported that particulate matter with aerodynamic diameter 

≤ 10 μm (PM10) was associated with daily deaths with no evidence of a threshold. These reports

have used smoothing or spline methods in individual cities and pooled the results across multiple

cities to obtain estimates that are more robust. To date, fine particulate matter (aerodynamic

diameter ≤ 2.5 μm; PM2.5), a component of PM10, has not been examined in this regard. We

examined this association in a hierarchical model in six U.S. cities. In the first stage, we fit log-linear models including smooth functions of PM2.5 in each city, controlling for season, weather, and

day of the week. These smooth functions allowed for nonlinearities in the city-specific associations. We combined the estimated curves across cities using a hierarchical model that allows for

heterogeneity. We found an essentially linear relationship down to 2 μg/m3. The same approach

was applied to examine the concentration response to traffic particles, controlling for particles

from other sources. Once again, the association showed no sign of a threshold. The magnitude of

the association suggests that controlling fine particle pollution would result in thousands fewer

early deaths per year. Key words: meta-analysis, mortality, particulate air pollution, smoothing,

time series, traffic. Environ Health Perspect 110:1025–1029 (2002). [Online 27 August 2002]

http://ehpnet1.niehs.nih.gov/docs/2002/110p1025-1029schwartz/abstract.html
between October 1981 and January 1984 in

all cities, elemental composition of fine and

coarse mass was determined by X-ray fluorescence (14). Elemental composition was available on 97% of these samples. In the fine

fraction, 15 elements were routinely found

above the limit of detection: silicon, sulfur,

chlorine, potassium, calcium, vanadium,

manganese, aluminum, nickel, zinc, selenium, bromine, lead, copper, and iron.

Source identification. In separate analyses

for each city, we used specific rotation factor

analysis to identify up to five common factors

from the 15 specified elements. We specified a

single element as the tracer for each factor and

maximized the projection of these elements

using the Procrustes rotation, a variant of the

oblique rotation method (15). The Procrustes

method allows us to use known tracers for different sources as targets for the different factors and to maximize their loadings on those

factors instead of having factors defined in an

entirely data-driven manner. To rescale the

factor scores from the normalized scale to the

mass scale (in micrograms per cubic meter),

we regressed the total daily fine particle concentrations on the daily factor scores for all of

the factors in separate regression models for

each city and took the product of each factor

score with its regression coefficient (16). Only

sources that were significant predictors of total

fine particle mass (p < 0.10) were considered

in the mortality analyses. Further details have

been published previously (8).

Meteorologic data. We obtained meteorologic data from the National Center for

Atmospheric Research, including hourly

measures of temperature, dew point temperature, and precipitation from the

National Oceanographic and Atmospheric

Administration weather station nearest to

each city (17). We calculated 24-hr mean

values for temperature and dew point

temperature.

Mortality data. We defined the six metropolitan areas in this study as the county containing the air pollution monitor and

contiguous counties (5). We extracted daily

deaths from annual detail mortality tapes

(National Center for Health Statistics) (18)

for people who lived and died in the selected

counties for the time periods with fine particulate measurements. After excluding all

deaths caused by accidents and other external

causes [International Classification of Diseases,

9th Revision (ICD-9) (19), clinical modification codes 800–999], we analyzed the

remaining total daily deaths.

Poisson regression of mortality. We investigated the association of daily deaths with

sources of fine particles separately for each

city using Poisson regression in a generalized

additive model (GAM) (20,21). That is, in

each city we assumed

[1]

where Yt is the number of deaths in the city

on day t and Xit is the value of covariate i on

day t. GAMs are distinguished by allowing us

to use smooth functions Si instead of linear

terms to control for covariates, such as temperature, that may affect daily deaths in a

nonlinear way. Linear functions may be used

where appropriate. This approach was introduced for time series of counts in 1994 (22)

and is now standard (23,24).

To control for trend and season, we used a

locally weighted linear regression (LOESS)

smooth function of date with a span of 0.05

(25). For the smooth functions of temperature

and dew point temperature, we used LOESS

functions with spans of 0.80. Indicator variables for day of the week also were included in

the models. This is the identical model used

by Schwartz et al. (5) and Laden et al. (8), and

more details are provided there. To these models we added a smooth function of the mean

PM2.5 concentration on the day of death and

the previous day, instead of the linear term

previously used by Schwartz et al. (5). The

smoothing window included 50% of the data,

which corresponds to between four and five

degrees of freedom for the air pollution relation in each city. Alternatively, we added the

estimated mass for each of the source factor

scores (in micrograms per cubic meter ) simultaneously in the model. That is, the estimate of

the mobile source factor is in a model controlling for coal-derived particles, crustal particles,

and the other source factors, and vice versa.

Because only the particles from traffic showed

a strong linear association, and because the

exposure ranges for the exposures to coal particles did not overlap sufficiently, we only used a

smooth function for the traffic particles and

followed Laden et al. (8) in treating the particle

mass from the other sources as linear terms.

Hierarchical model. To combine the

smooth curves across cities, we applied the

approach of Schwartz and Zanobetti (10), as

modified by Schwartz et al. (11). In each city,

the predicted log relative risk and its pointwise standard error was computed for each 2

Log[ ] EY S X ( )t i it = +∑ βο ( ),

Articles • Schwartz et al.

1026 VOLUME 110 | NUMBER 10 | October 2002 • Environmental Health Perspectives

Table 1. Mean daily deaths in six U.S. cities and mean concentrations of PM2.5 overall, and from the three

source categories showing evidence of an association with daily deaths in Laden et al. (8).

PM2.5 Traffic Coal Residual oil Dates

City Deaths (μg/m3) (μg/m3) (μg/m3) (μg/m3) (month/year)

Boston 59 16.5 4.8 8.3 0.5 5/79–1/86

Knoxville 12 21.1 4.4 6.8 — 1/80–12/87

St. Louis 55 19.2 2.9 5.6 — 9/79–1/87

Steubenville 3 30.5 1.5 19.2 0.9 4/79–9/87

Madison 11 11.3 3.1 4.9 — 3/79–12/97

Topeka 3 12.2 2.1 7.0 — 9/79–10/88

Figure 1. Overall estimated dose–response relation between total PM2.5 and daily deaths in six U.S. cities.

The estimate is obtained by combining the estimated smoothed curves in each of the cities, after controlling for weather, season, and day of the week. The shaded area indicates the pointwise 95% confidence

intervals at each point. The line shown is a least-squares regression line through the estimated points.

6

4

2

0

–2

0 10 20 30

Percent increase in deaths

PM2.5 (μg/m3)
μg/m3 increment in exposure. These estimates are provided by the GAM function in

S-plus (MathSoft, Inc., Seattle, WA). To successfully combine data across cities, we need

to use a range of exposures that is common to

all cities. Because high concentrations of

PM2.5 were rare, the curves were combined

only in the range of 0–35 μg/m3. The first

phase of the analysis produced estimated

effect sizes (log relative risks) Y

ˆ

ij in each city i

for each exposure category j. A pointwise

standard error of the estimate is also estimated by GAM. To produce the combined

curve, we regressed these estimates against

indicator variables for each level, using inverse

variance weighting and allowing for a random

variance component to capture heterogeneity

in the association across cities. That is, we

assumed

[2]

where dj are dummy variables for the j exposure levels, Vij is the estimated variance in city

i at level j, and δ is the estimated random

variance component.

We used the iterative meta-regression

approach of Berkey et al. (26) to obtain a

maximum likelihood estimate of the random

variance component.

The nonparametric smooth functions we

use to estimate the shape of the concentration

response relation use four to five degrees of

freedom, and it is not clear that the sourcespecific relations can support so many degrees

of freedom, which would entail a total of 20

degrees of freedom for all the PM2.5 sources.

In our previous report (8), the relation

between PM2.5 from traffic and daily deaths

was estimated with considerably greater precision than for particles from other sources,

most of which were not significant. Further,

the range of overlap in exposures across cities

was lower for coal, crustal, and residual oil

factors. Therefore, in our source-specific

models, we only modeled the traffic source

particles using a nonparametric smooth,

while controlling for PM2.5 from the other

sources using linear terms, as in Laden et al.

(8). We then combined the estimated concentration–response relations for traffic particles similarly to what we did for PM2.5 from

the other sources.

Results

Table 1 shows the daily deaths, PM2.5 levels,

and estimated concentrations of PM2.5 from

each source. Figure 1 shows the meta-smooth

dose–response relation between PM2.5 and

daily deaths in the six cities. There is no evidence of a threshold, and the relation occurs

well below the U.S. Environmental Protection

Agency standard of 65 μg/m3 (27). The line

shows the least-squares fit of a linear relation

through the estimated points.

The next results come from the source

component models. These models had a

smooth function of PM2.5 from traffic and

linear functions of PM2.5 from the other

sources in each city. Figure 2 shows the

results when we combined the estimated

dose–response curves for traffic particles

across the six cities. Again, there is no evidence of a threshold, and the association is

essentially linear. If anything, the slope is

steeper at lower concentrations. To test the

robustness of the association with traffic particles to our method of controlling for particles from other sources, we re-estimated the

relationship controlling for smooth functions

of the estimated particle mass from other

sources, rather than the linear terms. This

association is shown in Figure 3 and differs

little from that shown in Figure 2. We also fit

linear regressions through the points shown

on Figures 1 and 2. We obtained a slope of

1.5% increase in deaths per 10 μg/m3

increase in PM2.5 and 3% increase in deaths

per 10 μg/m3 increase in particles from traffic, which is the same as the results reported

by Laden et al. (8). These lines are shown on

the figures. This supports the assumption of a

linear relationship.

Discussion

We have explored the concentration–response

relation between PM2.5 and daily deaths in six

U.S. cities and combined the results to obtain

greater stability, while accounting for heterogeneity in response. The population mean

curve shows no evidence of a threshold down

to the lowest levels of PM2.5. In fact, the curve

is quite linear over the exposure range from 0

to 35 μg/m3. This is consistent with previous

results using a similar methodology but with

PM10 (10) and black smoke (11) as the exposure metric. In addition, a different methodology, using regression splines, was applied by

Daniels et al. (9) to PM10 data in different

cities. They combined these spline models

across 20 cities. Again, the association

appeared to be quite linear without any evidence of a threshold. A spline model had previously been applied by Schwartz (22) to the

PM2.5 data from Boston, with a similar finding. Indeed, the original study of these data by

Schwartz, Dockery, and Neas (5) found a significant association when limited to days

below 30 μg/m3, with a slightly larger slope.

The consistency of the results on two continents, and using different techniques, suggests

that this finding is robust. The concentration–response curve seen here for PM2.5 is

steeper than that previously reported (per

μg/m3) for PM10 (10). This is consistent with

the previous report from this study (5) that

coarse mass (the difference between PM10 and

PM2.5) is not associated with daily deaths. We

note that Schwartz and Zanobetti (10)

demonstrated in simulation studies that measurement error was not likely to distort the

shape of the association. Similarly, recent

studies of “harvesting” have shown that effect

sizes increase rather than decrease when longer

lags are taken into account; for example, high

ˆY N d d dV ~ ,, ij k k ij ββ β δ ( ) 11 2 2 + +⋅⋅+ +

Articles • PM2.5 and daily deaths

Environmental Health Perspectives • VOLUME 110 | NUMBER 10 | October 2002 1027

Figure 2. Overall estimated dose–response relation between PM2.5 from traffic and daily deaths in six U.S.

cities. The estimate is obtained by combining the estimated smoothed curve in each of the cities, after

controlling for weather, season, and day of the week and for PM2.5 from crustal sources, coal combustion,

residual oil, salt, and metal processes as linear terms. The line shown is a least-squares regression line

through the estimated points.

10

5

0

0 10 20

Percent increase in deaths

Traffic particles (μg/m3)

5 15
days producing harvesting that mutes the

effect on the next high day is unlikely to have

distorted the shape of the association.

These results are also biologically plausible. Schwartz (28) pointed out that if thresholds exist in individuals, but there is a

distribution of those thresholds among individuals, and if multiple genetic and predisposing illnesses each contributed to the

distribution of those thresholds, then by the

central limit theorem, the distribution of

thresholds should approach a normal distribution. Hence, the population concentration–

response curve should approach a cumulative

normal curve. But the low-dose end of the

cumulative normal curve is linear. To see this,

consider that typical death rates in U.S. cities

are 8/1,000 per year, or 2 × 10–6 per day. The

normal range of variation in daily deaths in

U.S. cities is a factor of two or less. Hence,

the normal range of daily death probabilities

in response to all risk factors is from 1 to 3 ×

10–6. Figure 4 shows the cumulative normal

curve in that range of probabilities, which is

quite linear. Because we are clearly in the lowdose regime, in the sense that the exposures

to particles are well below the threshold for

mortality for most people, this linearity is

exactly what would be expected.

Figure 1 also indicates that the association

reported here has public health significance.

The difference between mean PM2.5 concentrations of 10 μg/m3 and 20 μg/m3, which is

a difference found between U.S. cities, is

associated with about a 1.5% increase in

deaths. In a metropolitan area of a million

inhabitants, this would amount to about 130

additional early deaths per year, and in the

country as a whole, these results indicate that

a reduction of 10 μg/m3 would be expected to

result in about 36,000 fewer early deaths per

year. Although this study does not indicate

the extent to which these deaths are brought

forward, other studies of the harvesting issue

(29–32) suggest that they are considerable.

The association of daily deaths with traffic particles also has no threshold and is somewhat steeper than the association with all

PM2.5. This is consistent with the results of

Laden et al. (8), except that they used linear

terms instead of smooth functions. This study

confirms that this association extends to low

levels. This result has considerable public

policy relevance. Recently, automotive

companies have proposed using diesel engines

to achieve higher fuel economy in the future.

However, diesel engines produce substantially

greater emissions of particles and particle precursors such as NOx. The present results indicate that such an expansion of diesel engine

use in the United States before diesel engines

can meet the same particle emission levels as

gasoline engines may result in important public health problems. A 1 μg/m3 increase in the

concentration of traffic particles in the

United States, for example, could be associated with about 7,000 additional early deaths

per year in the United States.

REFERENCES AND NOTES

1. Schwartz J, Dockery DW. Increased mortality in

Philadelphia associated with daily air pollution concentrations. Am Rev Respir Dis 145:600–604 (1992).

2. Pope CA, Dockery DW, Schwartz J. Review of epidemiologic evidence of health effects of particulate air pollution.

Inhal Toxicol 7:1–18 (1995).

3. Touloumi G, Pocock SJ, Katsouyanni K, Trichopoulos D.

Short-term effects of air pollution on daily mortality in Athens:

a time-series analysis. Int J Epidemiol 23:957–967 (1994).

4. Katsouyanni K, Touloumi G, Spix C, Schwartz J, Balducci

F, Medina S, Rossi G, Wojtyniak D, Sunyer J, Bacharova L,

et al. Short term effects of ambient sulphur dioxide and

particulatematter on mortality in 12 European cities:

results from time series data from the APHEA project. Br

Med J 314:1658–1663 (1997).

5. Schwartz J, Dockery DW, Neas LM. Is daily mortality

associated specifically with fine particles? J Air Waste

Manag Assoc 46:2–14 (1996).

6. Schwartz J. Assessing confounding, effect modification,

and thresholds in the association between ambient particles and daily deaths. Environ Health Perspect

108:563–568 (2000).

7. Samet JM, Zegar SL, Dominici F, Curriero F, Coursac I,

Dockery DW, Schwartz J, Zanobetti A. The National

Morbidity, Mortality, and Air Pollution Study Part II:

Morbidity, Mortality, and Air Pollution in the United States.

Report 94. Boston, MA:Health Effects Institute, 2000.

8. Laden F, Neas LM, Dockery DW, Schwartz J. Association

of fine particulate matter from different sources with daily

mortality in six U.S. cities. Environ Health Perspect

108(10):941–947 (2000).

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

particulate matter mortality dose response curves and

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

largest U.S. cities. Am J Epidemiol 152:397–406 (2000).

10. Schwartz J, Zanobetti A. Using meta-smoothing to estimate dose-response trends across multiple studies,

with application to air pollution and daily death.

Epidemiology 11(6):666–672 (2000).

11. Schwartz J, Ballester F, Saez M, Pérez-Hoyos S, Bellido J,

Cambra K, Arribas F, Cañada A, Pérez-Boillos MJ, Jordi

Sunyer J. The concentration–response between air pollution and daily deaths. Environ Health Perspect

109:1001–1006 (2001).

12. Ferris BG Jr, Speizer FE, Spengler JD, Dockery DW,

Bishop YMM, Wolfson M, Humble C. Effects of sulfur

oxides and respirable particulates on human health:

methodology and demography of populations in study. Am

Rev Respir Dis 120:767–779 (1979).

13. Courtney WJ, Shaw RW, Dzubay TD. Precision and accuracy of a beta-gauge for aerosol mass determinations.

Environ Sci Technol 16:236–239 (1982). 

14. Spengler JD, Thurston G. Mass and elemental composition of fine and coarse particles in six U.S. cities. J Air

Pollut Control Assoc 33:1162–1171 (1983).

15. Browne MW. On oblique Procrustes rotation. Psychometrika

32:125–132 (1967).

16. Thurston GD, Spengler JD. A quantitative assessment of

source contributions to inhalable particulate matter pollution in metropolitan Boston. Atmos Environ 19:9–25 (1985).

17. U.S. National Climate Data Center. EarthInfo CD. NCDC

Surface Airways. Boulder, CO:EarthInfo Inc.

18. U.S. Department of Health and Human Services, Public

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Figure 4. Cumulative normal curve versus a standardized predictor (the sum of the effects of all risk

factors) over the range of exposures that correspond to daily death rates of between 1 and 3 per

million, which is the observed range of variation in

U.S. cities. It is quite linear in the predictor.

3.0

2.5

2.0

1.5

0

10 20

Probability of death (× 10–6)

5 15

Scaled linear predictor

Figure 3. Overall estimated dose–response relation between PM2.5 from traffic and daily deaths in six U.S.

cities. The estimate is obtained by combining the estimated smoothed curve in each of the cities, after

controlling for weather, season, and day of the week. Instead of linear terms for particles from other

sources, in this analysis we controlled for smoothed terms for PM2.5 from crustal sources, coal combustion, residual oil, and salt. The line shown is a least-squares regression line through the estimated points.

10

5

0

–5

0 10 20

Percent increase in deaths

Traffic particles (μg/m3)

5 15
Health Service, Centers for Disease Control and

Prevention, National Center for Health Statistics. Public

Use Data Tape Documentation. Hyattsville, MD:U.S.

Department of Health and Human Services, 1993.

19. International Classification of Diseases, 9th revision, Vol 1.

Commission on Professional and Hospital Activities. Ann

Arbor, MI:Edward Brother Inc, 1979.

20. Hastie T, Tibshirani R. Generalized Additive Models.

London:Chapman and Hall, 1990.

21. Schwartz J. Generalized additive models in epidemiology. Invited Papers: Proceedings of the 17th

International Biometric Conference. Hamilton, Ontario,

Canada:International Biometric Society, 1994:55–80.

22. Schwartz J. Air pollution and daily mortality: a review and

meta analysis. Environ Res 64:36–52 (1994).

23. Hoek G, Schwartz J, Groot B, Eilers P. Effects of ambient

particulate matter and ozone on daily mortality in

Rotterdam, The Netherlands. J Arch Environ Health

52:455–463 (1997).

24. Michelozzi P, Forastiere F, Fusco D, Perucci CA, Ostro B,

Ancona C, Pallotti G. Air pollution and daily mortality in

Rome, Italy. Occup Environ Med 55(9):605–610 (1998).

25. Cleveland WS, Devlin SJ. Robust locally-weighted regression and smoothing scatterplots. J Am Stat Assoc

74:829–836 (1988).

26. Berkey CS, Hoaglin DC, Mosteller F, Colditz GA. A random–effects regression model for meta-analysis. Stat

Med 14:395–411 (1995).

27. U.S. EPA. National Ambient Air Quality Standards for

Ozone and Particulate Matter, Proposed Decision. 

Fed Reg 62:7743–7744 (1997). Available: http://www.epa.gov/

fedrgstr/EPA-AIR/1997/February/Day-20/a4330.htm [cited

31 July 2002].

28. Schwartz J. Health effects of particulate air pollution: is

there a threshold? In: Relationship between Respiratory

Disease and Exposure to Air Pollution (Mohr U, ed).

Washington, DC:ILSI Press, 1998;195–206.

29. Zeger SL, Dominici F, Samet JM. Harvesting resistent estimates of air pollution effects on mortality. Epidemiology

10:171–175 (1999).

30. Zanobetti A, Wand MP, Schwartz J, Ryan L. Generalized

additive distributed lag models: quantifying mortality displacement. Biostatistics 1:279–292 (2000).

31. Schwartz J. Is there harvesting in the association of airborne particles with daily deaths and hospital admissions? Epidemiology 12:55–61 (2001).

32. Zanobetti A, Schwartz J, Samoli E, Gryparis A, Touloumi G,

Atkinson R, Le Tertre A, Bobros J, Celko M, Goren A, et al.

The temporal pattern of mortality responses to air pollution. Epidemiology 13(1):87–93 (2002).

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