Environmental Health Perspectives • VOLUME 110 | NUMBER 1 | January 2002 43

Air Conditioning and Source-Specific Particles as Modifiers of the Effect of

PM10 on Hospital Admissions for Heart and Lung Disease

Nicole A.H. Janssen, Joel Schwartz, Antonella Zanobetti, and Helen H. Suh

Department of Environmental Health, Harvard School of Public Health, Boston, Massachusetts, USA

In the last decade, epidemiologic studies

have documented associations between particulate matter (PM) air pollution and

increases in hospital admissions for respiratory and cardiovascular disease in studies all

over the world (1–4). The magnitude of the

estimated PM10 effects, however, has differed substantially among different studies.

A recent multicity study, conducted in 14

cities thoughout the United States, has documented significant heterogeneity among

the city-specific effect estimates (5). This

heterogeneity could not be attributed to

sociodemographic factors such as the percentage of the population with college education, percentage of unemployment,

percentage living below the federal poverty

level, or the percentage of the population

that was nonwhite (5).

Exposure studies have shown an influence of ventilation on personal/indoor–outdoor relations. Sarnat et al. (6) found that in

a panel of 15 nonsmoking older subjects in

Baltimore, Maryland, associations between

personal PM2.5 and ambient concentrations

were strongest for well-ventilated indoor

environments and decreased with decreasing

ventilation. For sulfate, which can be considered a tracer for particulate matter of ambient origin, correlations between personal and

ambient concentrations showed less decrease

with decreasing ventilation, producing strong

personal–ambient associations even in poorly

ventilated indoor environments. Personal to

ambient ratios and personal–ambient slopes,

however, decreased with decreasing ventilation, both for PM2.5 mass and for sulfate,

with the personal–ambient regression slopes

of poorly ventilated indoor environments

being almost half the value of the well-ventilated indoor environment (0.46 vs. 0.83 and

0.39 vs. 0.70 for PM2.5 and sulfate, respectively) (6).Consistent with these findings,

previous studies have also shown that personal and/or indoor concentrations of sulfate

are lower and less well correlated with outdoor concentrations for homes with air conditioning (AC) than homes without AC

(7,8), likely because air conditioned homes

typically have lower air exchange rates than

homes that use open windows for ventilation

(8,9). For example, in a study by Suh et al.

(8), the median air exchange rate, measured

over 12-hr daytime periods in 47 homes in

State College, Pennsylvania, during the summer of 1991, was about six times higher for

non-air conditioned homes compared to airconditioned homes. In the same study,

regression of indoor on outdoor sulfate levels

yielded a lower slope in air conditioned

homes than in non-air conditioned homes

(0.36 vs. 0.78)(8). These results suggest that

the fraction of PM2.5 from ambient origin

that penetrates indoors is lower in homes

with AC than in homes without AC.

Because people spend most of their time

indoors, persons living in homes with AC

will, at the same outdoor concentrations, be

exposed to lower levels of particles from

ambient origin than persons living in homes

without AC. Consequently, a change in

ambient levels of, for example, 10 μg/m3 will

correspond to a smaller change in personal

exposures for subjects living in homes with

AC than for subjects living in homes

without AC, which should cause concentration–effect relationships to be attenuated.

Besides the difference in the levels of exposures, a poorer correlation between personal

and ambient levels for subjects living in

homes with AC could further attenuate the

observed exposure–response relationships

through misclassification of exposure.

Several recent studies have suggested that

fine particles are more responsible than

coarse particles for the observed associations

between PM air pollution and health effects

(10–12). Another recent study suggests that

combustion particles from mobile sources

and coal combustion sources are specifically

associated with increased mortality (13). In

addition, the traffic-related particles were

more strongly associated with cardiovascular

deaths (13). Recent toxicologic studies also

suggest that specific components of concentrated air particles may be responsible for

specific biologic responses (14,15).

We therefore evaluated whether differences in prevalence of AC and/or the contribution of different combustion sources to

total PM10 emissions could explain part of

the observed variability in exposure–effect

relations among different cities. 

Address correspondence to H. Suh, Department of

Environmental Health, Harvard School of Public

Health, 677 Huntington Avenue, Room I-1309,

Boston, MA 02115 USA. Telephone: (617) 384-

8805. Fax: (617) 384-8819. E-mail: hsuh@

hsph.harvard.edu

This study was conducted during a research fellowship of the Netherlands Organization for

Scientific Research (NWO) for N. Janssen at the

Harvard School of Public Health. The study was

supported by the Harvard–EPA Particle Health

Effects Center (grant R827353-01-0).

Received 20 March 2001; accepted 21 June

2001.

Articles

Studies on acute effects of particulate matter (PM) air pollution show significant variability in

exposure–effect relations among cities. Recent studies have shown an influence of ventilation on

personal/indoor–outdoor relations and stronger associations of adverse effects with combustionrelated particles. We evaluated whether differences in prevalence of air conditioning (AC) and/or

the contribution of different sources to total PM10 emissions could partly explain the observed

variability in exposure–effect relations. We used regression coefficients of the relation between

PM10 and hospital admissions for chronic obstructive pulmonary disease (COPD), cardiovascular

disease (CVD), and pneumonia from a recent study in 14 U.S. cities. We obtained data on the

prevalence of AC from the 1993 American Housing Survey and data on PM10 emissions by

source category, vehicle miles traveled (VMT), and population density from the U.S. EPA. We

analyzed data using meta-regression techniques. PM10 regression coefficients for CVD and

COPD decreased significantly with increasing percentage of homes with central AC when cities

were stratified by whether their PM10 concentrations peaked in winter or nonwinter months.

PM10 coefficients for CVD increased significantly with increasing percentage of PM10 emission

from highway vehicles, highway diesels, oil combustion, metal processing, decreasing percentage

of PM10 emission from fugitive dust, and increasing population density and VMT/mile2. In multivariate analysis, only percentage of PM10 from highway vehicles/diesels and oil combustion

remained significant. For COPD and pneumonia, associations were less significant but the patterns of the assocations were similar to that for CVD. The results suggest that air conditioning

and proportion of especially traffic-related particles significantly modify the effect of PM10 on

hospital admissions, especially for CVD. Key words: air conditioning, air pollution, combustion

sources, hospital admissions, meta-regression. Environ Health Perspect 110:43–49 (2002).

[Online 15 December 2001]

http://ehpnet1.niehs.nih.gov/docs/2002/110p43-49janssen/abstract.html
Methods

Data collection. We used regression coefficients of the relation between ambient PM10

and hospital admissions for chronic obstructive pulmonary disease (COPD), cardiovascular disease (CVD), and pneumonia from a

recent study in 14 U.S. cities (Table 1) (5).

Briefly, daily counts of hospital admissions

for CVD [International Classification of

Diseases, 9th Revision (ICD-9), 390–429],

COPD (ICD-9, 490–492, 494–496), and

pneumonia (ICD-9, 480–487) (16), in persons ≥ 65 years were obtained from 14 cities

with extended daily PM10 measurements for

the period 1985 through 1994. For each

city, the associations between hospital

admissions and PM10 were investigated with

a generalized additive robust Poisson regression model. We built a model for each city

to allow for city-specific differences. The

variables included in each model were season, weather variables (24-hr means of temperature, relative humidity, and barometric

pressure) and day of week. Because weather

and season vary across the cities, however,

the smoothing parameter for each variable

was optimized separately in each location.

We calculated effects of several lags, including distributed lags (both unconstrained and

quadratic). For this study we chose lag 0/1

for CVD and lag 1/2 for COPD and pneumonia because PM effects on these outcomes have generally been most prominent

for these lags. Details about the methods

used to calculate the city-specific effect estimates are given elsewhere (5).

We obtained information about the

prevalence of AC from the 1993 American

Housing Survey of the United States Census

Bureau (17), which we used to calculate the

percentage of homes with central AC for each

metropolitan area. We obtained data on

PM10 emissions by source category and vehicle miles traveled (VMT) by county from the

U.S. EPA emissions and air quality data website (18). In addition, we obtained data on

population density from the 1990 census

(19). We calculated the percentages of total

PM10 emission from total highway vehicles,

highway diesels, coal fuel combustion (electric

utilities, industrial, and commercial/industrial), oil fuel combustion (electric utilities,

industrial, commercial/industrial, and

residential), residential wood combustion,

metal processing, and fugitive dust by dividing the PM10 emissions from these sources

by the counties’ total PM10 emissions. We

divided vehicle miles traveled in 1996 and

the total population in 1990 by the areas of

the respective counties to obtain VMT per

square mile and population density.

The seasonal pattern in ambient PM

concentrations can differ among different

cities, with the highest PM10 concentrations

occuring in summer in some cities and in

winter in other cities. Air exchange rates are

generally lower in winter than in summer

because people keep their windows closed

(20), so use of outdoor concentrations could

cause higher exposure misclassification in

cities with winter peaking PM10 concentrations compared to cities with nonwinter

peaking PM10 concentrations. This could

reduce the exposure–response relationship in

cities with winter peaking PM10 concentrations. Hoek et al. (21) suggested an explanation for the lower effect estimates that are

generally found in Europe compared to the

Articles • Janssen et al.

44 VOLUME 110 | NUMBER 1 | January 2002 • Environmental Health Perspectives

Table 1. Mean PM10 concentrations in summer and winter and regression coefficients (natural log of relative risk for 1 μg/m3 increase, multiplied by 10,000) of the

effect of PM10 on hospital admissions for CVD, COPD, and pneumonia in 14 U.S. cities.

Mean PM10 concentration (μg/m3) CVD, COPD, Pneumonia, 

Winter/ PM10 Lag 0/1 PM10 Lag 1/2 PM10 Lag 1/2

City County Summera Winterb summer Coefficient SE Coefficient SE Coefficient SE

Birmingham, AL Blount, Shelby, Walker, 

St. Clair, Jefferson 40.0 27.4 0.69 4.2 3.2 –13.2 10.3 6.2 5.8

Boulder, CO Boulder 26.8 36.3 1.35 16.8 13.9 105.0 37.2 7.6 25.4

Canton, OH Stark 36.6 25.8 0.70 7.3 6.8 29.7 18.1 11.0 13.7

Chicago, IL Cook 42.5 30.4 0.71 13.1 1.7 8.8 5.1 13.0 3.0

Colorado Springs, CO El Paso 21.3 37.3 1.75 11.6 8.9 36.2 22.4 40.8 14.6

Detroit, MI Wayne 42.8 32.8 0.77 15.1 1.8 22.7 4.9 21.1 3.3

Minneapolis, MN Hennepin, Ramsey 30.5 23.0 0.75 7.3 3.8 24.8 11.7 27.1 7.0

Nashville, TN Davidson 40.1 31.9 0.80 2.0 6.0 –5.3 19.1 –14.4 12.7

New Haven, CT New Haven 30.3 31.6 1.04 21.0 4.2 35.4 17.4 23.6 8.4

Pittsburgh, PA Allegheny 46.6 29.4 0.63 12.4 1.7 16.6 4.6 11.7 3.5

Seattle, WA King 23.8 43.3 1.82 10.7 2.6 16.5 8.0 9.8 5.1

Spokane, WA Spokane 32.7 42.2 1.29 6.0 3.3 6.7 7.7 4.5 4.9

Provo-Orem, UT Utah 31.4 66.3 2.11 3.5 5.7 –45.9 25.8 0.8 8.8

Youngstown, OH Columbiana, Mahoning 40.7 30.1 0.74 10.2 6.2 32.6 17.3 6.3 13.5

aAverage of June, July, and August. bAverage of January, February, and December.

Table 2. Percentage of homes with central AC, population density, VMT/mile2, and percentage of PM10 from different sources per city.

PM10 from different sources (%)

Central Highway Highway Coal Oil Wood Metal Fugitive Population VMT/

City AC (%) vehicles diesels combustion combustion burning processing dust Density mile2

Birmingham, AL 70.2 1.23 0.74 5.30 0.05 1.06 2.63 81.9 228 3.2

Boulder, CO 6.3 1.36 0.85 0.56 0.06 1.09 0.03 78.6 303 2.5

Canton, OH 29.7 2.34 1.43 0.40 0.13 1.22 1.66 76.4 638 5.8

Chicago, IL 43.2 4.26 2.16 0.44 0.51 0.09 6.82 62.3 5,398 46.4

Colorado Springs, CO 13.4 1.29 0.73 0.48 0.03 0.64 0.07 85.8 187 1.6

Detroit, MI 41.0 6.02 3.06 2.40 0.35 0.57 8.25 54.0 3,439 32.2

Minneapolis–St. Paul, MN 48.4 2.62 1.36 0.44 0.13 0.47 0.30 82.3 2,131 21.1

Nashville, TN 72.2 2.28 1.18 0.27 0.34 1.36 0.02 91.4 1,017 15.6

New Haven, CT 23.9 4.34 2.45 0.00 1.07 26.15 0.06 60.6 1,327 10.4

Pittsburgh, PA 33.4 4.21 2.38 2.64 0.21 0.78 9.05 70.0 1,830 15.5

Seattle, WA 6.2 2.44 1.31 0.05 0.03 3.73 0.36 86.4 709 7.6

Spokane, WA 28.2 1.69 1.00 0.03 0.03 6.13 1.57 59.2 205 1.9

Provo-Orem, UT 26.9 1.58 0.92 0.41 0.24 0.58 4.78 70.4 132 1.4

Youngstown, OH 22.8 2.40 1.53 0.49 0.10 1.95 1.31 83.8 394 3.5
United States: In the United States PM

concentrations typically peak in summer

whereas in Europe they peak in the winter.

To evaluate this, we characterized the cities

as either winter or nonwinter peaking. We

calculated average PM10 concentrations per

month using the daily ambient PM10 concentrations that were also used to calculate

the city-specific effect estimates (5). In addition to visual inspection of plots of the specific mean PM10 concentrations by month,

we calculated the ratios between the mean

concentrations during summer (June, July,

and August) and winter (January, February,

and December). 

Statistical analysis. We analyzed the data

as a two-stage hierarchic model. The first

stage produced the city-specific coefficients

published previously (5). In the second

stage, an ecologic regression was fit where we

assumed

βi = c0 + γZi + εi ,

where βi is the PM10 coefficient in city i, Zi

are the predictors in city i, and we assumed

E (εi

) = 0 and 

Var (εi

) = δi

2 + δ,

where δi

2 is the within city estimate of the

standard error of βi and δ represents the heterogeneity in the βi not explained by the

predictors Z. We estimated c0 and γ using

inverse variance weighted least squares

regression. We estimated the between-city

variance δ using an iterative maximum likelihood approach (22).

Results

Table 2 shows the distributions of the independent variables. The percentage of homes

with central AC ranged from 6% in Seattle

to 72% in Nashville. On average, 57% of

the PM10 emissions from highway vehicles

came from highway diesels. In total, the different combustion sources on average

accounted for about 10% of the total primary PM10 emission, whereas on average

74% was from fugitive dust.

Table 3 shows the correlation among the

different independent variables. Percentage

of homes with central AC was not strongly

correlated with any of the combustionrelated variables. Percentage of PM10 from

highway vehicles and highway diesels were

highly correlated with population density

and VMT per square mile, and also with

PM10 from oil combustion (r, 0.68–0.87).

Percentage of homes with AC was not significantly correlated with mean temperature

during the study period (r = 0.13).

Air conditioning. Five cities (Boulder and

Colorado Springs, Colorado; Seattle and

Spokane, Washington; and Provo-Orem,

Utah) were classified as having winter peaking PM10 concentrations, with winter/summer concentration ratios ranging from 1.3 to

2.1 (Table 1). For all cities, central AC was

not strongly associated with PM10 coefficients (Table 4). However, when we analyzed

the data for cities with nonwinter peaking

and winter peaking PM10 concentrations separately (Table 4, Figure 1), coefficients for

CVD-related hospital admissions decreased

significantly with increasing percentage of

central AC for both nonwinter peaking and

winter peaking citites. A model that adjusted

for whether the cities were characterized by

winter peaking PM10 concentrations (yes/no)

yielded results that were similar to those of

the stratified analysis. For COPD, a significant association was found for the nonwinter

peaking cities, whereas for the winter peaking

cities a very high but nonsignificant percentage change of 204% was found. As can be

seen in Table 1, an important part of the

variance between the COPD coefficients was

caused by extreme high and low values found

for Boulder and Provo-Orem respectively,

both of which are winter peaking cities and

are also the two smallest cities (total population < 300,000). As a result, the variance in

the COPD coefficients for winter peaking

cities was much higher than the average

within-city variance. Adding the betweencity variance component to the weights

therefore produced similar weights for all five

cities and a very steep slope. When only the

within-city variance was included in the

weights (fixed effects model), we found an

estimated percentage decrease in the COPD

coefficients of 89% (SE 96%) per interquartile range increase in central AC, which is

similar to the value for nonwinter peaking

cities of 82% (Figure 1B).

Coefficients for pneumonia were only

marginally significantly associated with percentage of homes with central AC in the

model that adjusted for whether the cities

were characterized by winter peaking PM10

concentrations (yes/no). The pattern of all

nonsignificant associations, however, was

similar to that found for CVD. 

In univariate analyses, we did not find

any significant differences between PM10

coefficients of nonwinter peaking and winter

Articles • PM10 and hospital admissions effect modification

Environmental Health Perspectives • VOLUME 110 | NUMBER 1 | January 2002 45

Table 3. Spearman correlation matrix.

PM10 from different combustion sources (%) PM10 PM10

Highway Highway Wood from metal from fugitive Population VMT/

Source vehicles diesels Coal Oil burning processing dust (%) density mile2

Central AC 0.15 0.17 0.21 0.42 –0.41 0.28 –0.11 0.43 0.54**

PM10 from highway vehicles 0.96# –0.15 0.68# –0.11 0.30 –0.50** 0.87# 0.81#

PM10 from highway diesels –0.07 0.69# –0.02 0.38 –0.57** 0.81# 0.75#

PM10 from coal combustion –0.08 –0.55** 0.42 0.01 0.05 0.04

PM10 from oil combustion –0.29 0.19 –0.38 0.67# 0.66**

PM10 from wood burning –0.47* 0.13 –0.30 –0.31

PM10 from metal processing –0.57** 0.21 –0.18

PM10 from fugitive dust –0.25 –0.16

Population density 0.98#

VMT/mile2

*p < 0.10. **p < 0.05. #p < 0.01. 

Table 4. Percentage change in the coefficient of the effect of ambient PM10 on hospital admissions for CVD, COPD, and pneumonia for an interquartile range

increase in the percentage of homes with central AC.

β CVD, Lag 0/1 β COPD, Lag 1/2 β Pneumonia, Lag 1/2 

Homes with AC No. Change (%) SE (%) Change (%) SE (%) Change (%) SE (%)

All cities 14 –15.2 14.8 –50.8* 23.5 –16.7 24.4

Nonwinter peaking cities 9 –50.3** 17.4 – 82.1** 25.1 –49.9 32.1

Winter peaking cities 5 –51.7** 13.8 –203.6 118.3 –71.2 74.4

Adjusted for winter peaking PM10 (yes/no) 14 –50.5# 0.46 – 91.6# 27.9 –52.8* 28.8

*p < 0.10. **p < 0.05. #p < 0.01.
peaking cities. After adjustment for central

AC, however, PM10 coefficients of nonwinter

peaking cities were significantly higher than

those of winter peaking cities for CVD (p <

0.01) and COPD (p < 0.05) and to a lesser

extent for pneumonia (p = 0.06) as well. 

Mean temperature during the study

period was not significantly associated with

any of the three hospital admission coefficients. 

Source-related variables. Table 5 shows

the results of the regression analysis of the

source-related variables. All results were

adjusted for central AC percentage. The univariate relationship with percentage of PM10

from highway vehicles is shown in Figure 2.

Coefficients for hospital admissions for CVD

increased significantly with increasing percentage of PM10 from highway vehicles, highway diesels, oil combustion, metal processing,

increasing population density, and VMT per

square mile and with decreasing percentage of

PM10 from fugitive dust. All of these variables

were significantly correlated with one another,

except metal processing. Given the number of

observations available (n = 14), the number of

independent variables we could simultaneously include in the model was limited. The

associations were strongest for PM10 from

highway vehicles/diesels (highest t-values).

When central AC and percentage of PM10

from highway vehicles/diesels were included

in the model together with one of the other

significant variables, all variables except oil

combustion lost significance, and the percentage change in PM10 coefficient for an

interquartile range change in the explanatory

variable fell to < 6%. In contrast, the highway and central AC variables remained stable. In the model with oil combustion, the

estimated percentages change in PM10 coefficient per interquartile range change in the

explanatory variable decreased from 56–58%

to about 40% (SE 10%) for highway vehicles/diesels and from 38% to 21% (SE 6%)

for oil combustion, but both variables as well

as the AC variable remained significant. 

None of the source-specific variables were

significantly associated with coefficients for

COPD. As mentioned previously, an important part of the heterogeneity in the COPD

coefficients was caused by Boulder and

Provo-Orem. When these two cities were

excluded from the analysis, the variance in

the COPD coefficients decreased by > 75%.

Furthermore, the percentage increase in

PM10 coefficients associated with an

interquartile range change in the independent variables became more similar to those

for CVD and pneumonia, with a significant

association found for percentages of PM10

from highway vehicles (percentage change

50%; SE 19%) and highway diesels (percentage change 49%; SE 18%). Coefficients for

pneumonia were only marginally significantly associated with percentage of PM10

from highway vehicles/diesels, although, as

was the case for the associations with AC, the

pattern of nonsignificant associations for

pneumonia was similar to that for CVD. 

Discussion

Air conditioning. Regression coefficients of

the relation between ambient PM10 and hospital admissions for CVD and COPD of 14

different cities throughout the United States

decreased significantly with increasing percentage of homes with central AC. The associations became most apparent when cities

were characterized by their PM10 concentrations as either nonwinter peaking or winter

peaking. We found a similar pattern of associations for pneumonia, although the standard errors of the estimates were larger those

for CVD and COPD.

To our knowledge, no other studies have

looked at AC use as an effect modifier of the

relation between PM10 and hospital admissions. In a recent meta-analysis of the timeseries PM-mortality literature, Levy et al.

(23) did include percentage of homes with

central AC as one of the variables to explain

the variability in effect estimates. With the

“t-to-enter” statistic, percentage of homes

with central AC did not enter the model,

although stratified analyses showed higher

effect estimates for cities with < 30% of

homes with central AC [effect estimate

0.76% per 10 μg/m3; confidence interval

(CI), 0.54–0.98] than for cities with > 30%

of homes with central AC (effect estimate

0.57% per 10 μg/m3; CI, 0.39–0.74). That

Articles • Janssen et al.

46 VOLUME 110 | NUMBER 1 | January 2002 • Environmental Health Perspectives

Figure 1. Univariate relation between percentage of homes with central AC and regression coefficients for (A) CVD, (B) COPD, and (C) pneumonia, for cities with

nonwinter peaking PM10 concentrations (red) and winter peaking PM10 concentrations (yellow). Circle area is proportional to the inverse of the variance of the

effect estimate. Lines represent inverse variance weighted regression equations (fixed-effects model).

0

0.0025

0.0020

0.0015

0.0010

0.0005

0.0000 10 20 30 40 50 60 70 80

CVD coefficient

0 10 20 30 40 50 60 70 80

0.012

0.010

0.008

0.006

0.004

0.002

0.000

–0.002

–0.004

–0.006

COPD coefficient

Central AC (%)

0 10 20 30 40 50 60 70 80

0.005

0.004

0.003

0.002

0.001

0.000

–0.001

–0.002

Pneumonia coefficient

A B C

Central AC (%) Central AC (%)

Table 5. Percentage change in the coefficient of the effect of ambient PM10 on hospital admissions for CVD, COPD, and pneumonia for an interquartile range

increase in the percentage of PM10 from different sources, population density, and VMT/mile2, adjusted for central AC (%).

β CVD, Lag 0/1 β COPD, Lag 1/2 β Pneumonia, Lag 1/2 

Source Change (%) SE (%) Change (%) SE (%) Change (%) SE (%)

PM10 from highway vehicles 58.0** 9.9 48.0 29.7 60.7* 29.5

PM10 from highway diesels 55.6** 9.4 47.4 28.2 55.5* 28.7

PM10 from coal combustion 0.6 2.6 –0.4 4.7 1.9 5.0

PM10 from oil combustion 37.5** 9.3 2.3 24.9 26.5 22.9

PM10 from wood burning 2.7 3.2 1.4 5.7 2.3 5.1

PM10 from metal processing 29.0** 13.0 2.7 27.3 12.1 29.3

PM10 from fugitive dust –49.4** 16.5 –10.9 36.3 –31.3 37.9

Population density 22.4** 7.8 16.6 19.9 27.6 18.6

VMT /mile2 21.2** 7.4 16.4 17.9 26.4 17.7

*p < 0.10. **p < 0.05.
study, however, used 19 effect estimates

from 13 different U.S. cities, published in

12 different papers by 6–7 different research

groups. As a result, the study incorporated

coefficients obtained using a variety of statistical models, including different lag times

and control strategies for confounders (23).

In our study, we used the same statistical

methods for all cities and included the same

confounding variables in all models. Only

the smoothing parameters for each variable

were optimized seperately in each location,

to allow for city-specific differences (5).

Moreover, Levy et al. (23) did not stratify or

control their analysis for winter versus summer peaking PM10 concentrations.

Several studies that have included more

than one city also found substantial differences

in the city-specific effect estimates (24,25). For

cardiovascular disease, Moolgavkar (24)

recently documented significant associations

between daily hospital admissions and PM10

in single-pollutant models in Cook County,

Illinois, and Los Angeles County, California,

communities with prevalences of central AC

of 43% and 31%, respectively. In contrast,

the same study found no significant associations in Maricopa County, Arizona, where

almost 90% of the homes have central AC.

Similarly, for COPD and pneumonia,

Moolgavkar et al. (25) found significant association between PM10 and hospital admissions in Minneapolis–St. Paul, Minnesota,

where 48% of the homes have central AC,

whereas no effect was found in Birmingham,

Alabama, which has a higher prevalence of

AC (70%). This between-city variability in

the associations is consistent with findings

from our study.

The relation between AC and PM10

coefficient became more apparent when the

cities were classified as having either nonwinter peaking or winter peaking PM10 concentrations. The slopes of the relations between

PM10 coefficients and percentage of homes

with central AC were similar for nonwinter

and winter peaking cities, but the y-intercepts for the nonwinter peaking cities were

higher than those for the winter peaking

cities. The difference in the intercepts

implies that, for cities with no AC, the effect

of PM10 is stronger in cities with nonwinter

peaking concentrations than for cities with

winter peaking concentrations. This may be

explained by the fact that air exchange rates

are generally lower in winter than in summer

because people keep their windows closed

(20). The fraction of ambient particles that

penetrates indoors is therefore probably

lower in winter than in summer, producing

a smaller increase in indoor and personal

concentrations per increase in ambient concentrations in winter, especially for homes

without AC. Although the similarity of the

slopes suggests that the stronger effect in

nonwinter peaking than in winter peaking

cities is also present between any two cities

with the same percentage of homes with AC,

the relatively low percentages of AC in the

winter peaking cities (< 30%) make it difficult to predict to what extent the difference

found in our study can be extrapolated to

winter peaking cities with higher percentages

of homes with AC. Both the decrease in

PM10 coefficients in locations with more

central AC and the lower PM10 coefficients

in locations with winter peaking particle

concentrations support the conclusion that

factors that reduce the slope between outdoor PM and indoor PM of ambient origin

also reduce the slope of the association

between ambient PM10 and health outcomes. This also supports the causality of the

exposure–effect associations, because if outdoor PM10 were a surrogate for something

else, such as seasonality or epidemics, this

pattern across cities would not be expected.

The associations observed with AC could

also be related to other city-specific characteristics associated with percentage of homes

with AC. Because of the limited sample size

(14 cities), the extent to which we could

adjust for potential confounders was limited.

Because use of AC is related to temperature,

temperature would be the most obvious

potential confounder. The model we used to

calculate the city-specific PM10 regression

coefficients in the first stage of the analysis,

however, included variables for 24-hr means

of temperature, relative humidity, and barometric pressure, among others, and the

smoothing parameters for these variables

were optimized separately in each location.

The resulting city-specific regression coefficients are therefore already adjusted for the

effect of day-to-day variations in temperature (and other weather variables), so the

results of our meta-regressions (second stage)

are not likely to be confounded by differences in the temporal patterns of these

meteorologic conditions. Because mean temperature during the study period was not significantly correlated with percentage of

homes with AC (r = 0.13) and no significant

associations between mean temperature and

any of the hospital admission coefficients

were found, our results are also not likely to

be confounded by long-term differences in

temperature between the cities.

Source-related variables. PM10 regression coefficients for CVD increased significantly with increasing percentage of PM10

emission from highway vehicles, highway

diesels, oil combustion, and metal processing, decreasing percentage of PM10 emission

from fugitive dust, and increasing population density and VMT per square mile. In

multivariate analysis, however, only percentages of PM10 from highway vehicles/diesel

and oil combustion were significantly related

to coefficients for CVD. None of the variables were significantly associated with hospital admissions for COPD, and hospital

admissions for pneumonia were only

marginally significantly associated with percentage of PM10 emission from highway

vehicles/diesels. As was the case for percentage of homes with central AC, however, the

patterns and magnitude of the nonsignificant

associations for COPD and pneumonia were

similar to those for CVD. Furthermore, when

Boulder and Provo-Orem were excluded from

the analysis for COPD, associations for motor

vehicle-related PM10 became significant. 

Source-specific morbidity effects are consistent with findings from particle mortality

and toxicology studies. Using factor analysis

Articles • PM10 and hospital admissions effect modification

Environmental Health Perspectives • VOLUME 110 | NUMBER 1 | January 2002 47

Figure 2. Univariate relation between percentage of PM10 from highway vehicles and regression coefficients for (A) CVD, (B) COPD, and (C) pneumonia. Circle

area is proportional to the inverse of the variance of the effect estimate. Lines represent inverse variance weighted regression equations (fixed-effects model).

PM10 emission from highway vehicles (%)

0 1 23 4 567 01 23 4 567

PM10 emission from highway vehicles (%)

0 12 3 4 5 67

PM10 emission from highway vehicles (%)

CVD coefficient

A B C

0.0025

0.0020

0.0015

0.0010

0.0005

0.0000

0.012

0.010

0.008

0.006

0.004

0.002

0.000

–0.002

–0.004

–0.006

COPD coefficient

0.005

0.004

0.003

0.002

0.001

0.000

–0.001

–0.002

Pneumonia coefficient
techniques, Laden et al. (13) also found significant associations between several distinct

combustion source-related fractions, determined using the elemental composition of

PM2.5 samples, and daily mortality in six

eastern U.S. cities. A 10 μg/m3 increase in

PM2.5 from mobile sources accounted for a

3.4% increase in daily mortality, with the

strongest associations found for deaths caused

by ischemic heart disease compared to those

caused by COPD or pneumonia. In comparison, the same 10 ug/m3 increase from coal

combustion sources produced a smaller mortality increase of 1.1%, with the strongest

associations found for respiratory deaths. For

PM10 from oil combustion, associations were

less significant, but even larger per microgram per cubic meter, with a nonsignificant

summary effect of 5.6%. No associations

were found for PM2.5 from crustal particles

(13). These results are consistent with our

findings of greater-than-average impacts of

traffic particles, particularly for heart disease,

greater impact of oil combustion particles,

and smaller effects of fugitive dust. Sourcerelated fine particles were also significantly

associated with subtle alterations in pulmonary and systemic cell profiles in canines

exposed to concentrated air particles (14).

Among other source-related effects, combustion-related metals were linked to peripheral

blood parameters, and coal-related particles

were related to hematologic alterations.

It is interesting to note that in both our

study and Laden et al.’s (13) mortality study,

the source-specific associations were

strongest for vehicle exhaust-related particles.

In our study, highway diesels comprised over

50% of the total percentage of PM10 emission from highway vehicles. Because these

two variables were very highly correlated

(Spearman r = 0.96), our data do not allow

any conclusions about the relative importance of diesel exhaust particles compared to

other vehicle exhaust particles. Several studies, however, have suggested that diesel

exhaust particles in particular are associated

with health outcomes (26,27). An epidemiologic study in the Netherlands showed that

exposure to diesel exhaust particles, measured as either truck traffic density or classroom black smoke concentrations, was

significantly associated with reduced lung

function and increased prevalence of chronic

respiratory symptoms in school children

(26,27). In addition, deaths from cardiovascular disease in European cities have been

associated with ambient concentrations of

black smoke, for which the major source is

diesel-powered vehicles, with the effect

estimates of black smoke being more stable

than those for PM10 (21,28).

Of the source-specific parameters, only

PM10 emissions from fugitive dust were

negatively associated with coefficients for

CVD. Because PM10 from fugitive dust

consists primarily of coarse mode particles,

this finding is consistent with the observed

importance of motor vehicle-related and oil

combustion particles, which are primarily

fine particles, and with the growing evidence

of stronger associations for fine than for

coarse particles (10–12,23).

For each of the source-specific parameters, associations were strongest for CVD,

suggesting that the observed relations may be

disease specific. Because the patterns of the

associations were generally similar for all

three disease categories, however, results may

also be related to the power of the statistical

analyses, where hospital admission rates for

CVD are much higher than those for

COPD and pneumonia. For the cities and

time period included in this study, mean

daily Medicare admissions for CVD were

over 10 times higher than those for COPD

and almost four times higher than those for

pneumonia (5). As a result, the PM10 effect

estimates for COPD and pneumonia are

more subject to random error than those for

CVD, which could explain the higher standard errors of our meta-regression results for

COPD and pneumonia. 

One of the limitations of this study is

that we relied on U.S. EPA emissions summaries to characterize the contribution of

different sources to total PM10 emissions.

These estimates may not be the best

indicators of the actual differences in the

composition of the PM mixtures. For the

traffic-related particles we also evaluated two

other measures of exposure—vehicle miles

traveled per square mile and population density—which were highly correlated with each

other and also with the highway emission

variables (Spearman r, 0.74–0.98). These

alternative variables also yielded significant

associations with coefficients for cardiovascular disease. Although these associations

lost significance when entered in the model

together with the U.S. EPA estimate, they

do provide additional support for a stronger

effect of traffic-related particles on hospital

admissions for cardiovascular events in particular. The greater explanatory power of the

U.S EPA estimates of percentage of PM10

emission from traffic or diesel than that of

the VMT and population density variables

provide some assurance that the EPA estimates are reasonable.

Conclusions

This study suggests that while sociodemographic factors previously have explained

little of the variation in city-specific coefficients for PM10, exposure-related differences

could potentially explain a substantial fraction of that variability. In particular, the

variability in the observed effects of ambient

PM10 on hospital admissions for heart and

lung disease among different cities was attributed to differences in percentage of homes

with central AC and percentage of PM10

emissions from specific combustion sources,

especially for CVD. Because of the ecologic

nature and the limited sample size of our

study, the role of central AC as a modifier of

the effect of PM10 and the potentially higher

toxicity of particularly traffic-related particles

must be further investigated.

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