Environmental Health Perspectives • VOLUME 108 | NUMBER 10 | October 2000 941

Association of Fine Particulate Matter from Different Sources with Daily

Mortality in Six U.S. Cities

Francine Laden,1 Lucas M. Neas,2 Douglas W. Dockery,1,3 and Joel Schwartz1,3

1Channing Laboratory, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA;

2Epidemiology and Biomarkers Branch, Human Studies Division, National Health and Environmental Effects Research Laboratory, U.S.

Environmental Protection Agency, Research Triangle Park, North Carolina, USA; 3Environmental Epidemiology Program, Department of

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

In a study of six U.S. cities, we previously

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

coarse particulate matter (aerodynamic

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

(1). Each 10 μg/m3 increase in the 2-day

mean concentration of PM2.5 was associated

with a 1.5% [95% confidence interval (CI),

1.1–1.9%) increase in daily mortality. An

equivalent change in PM2.5–10 was associated with only a 0.4% increase in total mortality (CI, 0.1–1.0%) and no increase after

control for PM2.5. 

Particle size is an important determinant

of the site and efficiency of pulmonary deposition, but particle size is also a surrogate for

particle source and composition. PM2.5–10

consists mainly of crustal particles mechanically generated from agriculture, mining,

construction, road traffic, and related

sources, as well as particles of biological origin. 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. Although a

convincing body of evidence has accumulated on the adverse effects of PM2.5 (1–11), the

specific sources and constituents responsible

for these adverse effects have not been

determined.

In this study, 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.

Materials and Methods

Air pollution data. As part of the Harvard

Six Cities Studies (12), dichotomous virtual impactor samples were placed at a central residential monitoring site in

Watertown, Massachusetts; KingstonHarriman, Tennessee; St. Louis, Missouri;

Steubenville, Ohio; Portage, Wisconsin;

and Topeka, Kansas. Separate filter samples

were collected of fine particles (PM2.5) and

of the coarse mass (PM2.5–10) fraction. The

2.5 μm cutpoint for dichotomous samplers

represents the aerodynamic diameter at

which 50% of the particles are sent to the

fine particle filter and 50% to the coarse

mass filter. The cutoff is relatively sharp, but

some larger particles, including some crustal

material, will still be deposited on the fine

fraction filter. In addition, there is crustal

material between 1 and 2.5 μm, representing

the low end of the coarse-mode particle size

distribution. Integrated 24-hr samples were

collected at least every other day from 1979

until the late 1980s (1), with daily sampling

during health survey periods. For fine and

coarse particle samples, mass concentration

was determined separately by beta-attenuation (13). With the exception of a period

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 5 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 that is commonly used in psychometric testing (15,16).

This method is similar to the factor analytic

approach used by Koutrakis and Spengler

(17), but it uses standard software (18). 

We selected tracer elements in three

steps. First, we identified tracers on the basis

of the chemical composition of fine particulate matter from known sources and the previous source apportionment research in

Address correspondence to F. Laden, Channing

Laboratory, 181 Longwood Avenue, Boston, MA

02115 USA. Telephone: (617) 525-2711. Fax:

(617) 731-1541. E-mail: francine.laden@

channing.harvard.edu

We thank D. Sredl and A. Heff for their expert

assistance with the analysis.

Supported by U.S. EPA grant R826245-01 and

National Institute of Environmental Health

Sciences grant ES-000002. F. Laden was supported

in part by a National Institute of Health National

Research Service Award T32HL07427. The views

expressed in this article are those of the individual

authors and do not necessarily reflect the views and

policies of the U.S. EPA. The research described in

this article has been subject to U.S. EPA peer and

administrative review and it has been approved for

publication. 

Received 6 January 2000; accepted 30 May 2000.

Articles

Previously we reported that fine particle mass (particulate matter ≤ 2.5 μm; PM2.5), which is primarily from combustion sources, but not coarse particle mass, which is primarily from crustal

sources, was associated with daily mortality in six eastern U.S. cities (1). In this study, we used

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

fractions of fine particles and examined the association of these fractions with daily mortality in

each of the six cities. Using specific rotation factor analysis for each city, we identified a silicon

factor classified as soil and crustal material, a lead factor classified as motor vehicle exhaust, a selenium factor representing coal combustion, and up to two additional factors. We extracted daily

counts of deaths from National Center for Health Statistics records and estimated city-specific

associations of mortality with each source factor by Poisson regression, adjusting for time trends,

weather, and the other source factors. Combined effect estimates were calculated as the inverse

variance weighted mean of the city-specific estimates. In the combined analysis, a 10 μg/m3

increase in PM2.5 from mobile sources accounted for a 3.4% increase in daily mortality [95%

confidence interval (CI), 1.7–5.2%], and the equivalent increase in fine particles from coal combustion sources accounted for a 1.1% increase [CI, 0.3–2.0%). PM2.5 crustal particles were not

associated with daily mortality. These results indicate that combustion particles in the fine fraction from mobile and coal combustion sources, but not fine crustal particles, are associated with

increased mortality. Key words: air pollution, coal combustion, fine particles, mobile sources,

mortality, PM2.5, source apportionment. Environ Health Perspect 108:941–947 (2000). [Online

29 August 2000]

http://ehpnet1.niehs.nih.gov/docs/2000/108p941-947laden/abstract.html
Watertown by Thurston and Spengler (19)

and in Steubenville by Koutrakis and

Spengler (17). Second, we regressed total

fine mass on the identified factors and rejected any factors that had negative regression

coefficients. Third, we attempted to identify

additional, possibly local, sources by examining elements that did not load heavily on the

positive factors from step 2 and created the

additional factors that were both positive

predictors of total fine mass and maximized

the model R2. In steps 1 and 2, we defined

three sources of fine particles in all six cities:

a silicon factor classified as soil and crustal

material, a lead factor classified as motor

vehicle exhaust, and a selenium factor representing coal combustion sources. In city-specific analyses, we also considered vanadium

(fuel oil combustion), chlorine (salt), and

selected metals (nickel, zinc, or manganese)

as possible targets and sources. We identified

five source factors for each city, except for

Topeka, where we were only able to identify

three positive predictors of total PM2.5.

Daily factor scores. For each metropolitan area, we calculated daily scores for each

of the identified factors. Information on the

relative concentration of each of the 15 elements in each factor is provided by the standardized scoring coefficients. Therefore, the

score for each factor for each day was calculated by multiplying the normalized concentration of each element by the respective

standardized scoring coefficient for that element and factor and then summing across

these 15 products. We recentered these daily

factor scores by calculating a factor score for

a hypothetical day on which all of the elemental concentrations were zero and then

subtracting this minimum factor score from

the daily factor score (19). To rescale the factor scores from the normalized scale to the

mass scale (in μg/m3), 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 obtained

the product of each factor score with its

regression coefficient (19). Only sources that

were significant predictors of total fine particle mass (p < 0.10) were considered in the

mortality analyses.

As in our earlier study (1), we assumed

that mortality was associated with the 2-day

mean of the non-missing particle concentrations on the same and on the previous day.

Because much of the monitoring was conducted every other day during the study

period, this technique imputed missing days

using the non-missing values on the previous

days, which increased the number of days

included in the mortality analysis from

6,211 to 9,108. For this imputation, we

assumed that the sampling schedule, and

hence the missing values, were random with

respect to daily mortality.

Meteorological data. We obtained meteorological data from the National Center

for Atmospheric Research (Boulder, CO)

including hourly measures of temperature,

dew point temperature, and precipitation

from the National Oceanographic and

Atmospheric Administration (Washington,

DC) weather station nearest to each city.

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 (1). For the mortality

analysis, each study area is identified by the

name of its largest city (e.g., Watertown as

Boston; Kingston-Harriman as Knoxville;

Portage as Madison). We extracted daily

deaths for people who lived and died in the

selected counties from annual detail mortality tapes (from the National Center for

Health Statistics, Hyattsville, MD) for the

time periods with fine particulate measurements. After excluding all deaths due to accidents and other external causes [International

Classification of Diseases, Ninth Revision (ICD9), clinical modification codes 800–999)], we

analyzed the remaining total daily deaths as

well as daily deaths from ischemic heart disease (ICD-9 410–414), pneumonia (ICD-9

480–486), and chronic obstructive pulmonary disease (ICD-9 490–496). Since the

1996 analysis, the mortality data were updated to restrict deaths of those individuals who

both lived and died within the specified counties and to correct for administrative changes

in the county codes for St. Louis in 1982.

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 (20,21). To control

for trend and season, we used a locally

weighted linear regression (Loess) smooth

function of date with a span of 0.05 (22).

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. The relative risks for each

source were evaluated by including the

absolute 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. To

obtain summary estimates of the association

between the different sources of fine particles and daily mortality, we combined the

city-specific regression coefficients using

inverse variance weights. This method produces similar summary estimates as weighting by the number of deaths in each city

Articles • Laden et al.

942 VOLUME 108 | NUMBER 10 | October 2000 • Environmental Health Perspectives

Table 1. Means and standard deviations of total fine particulate matter < 2.5 μm diameter (in μg/m3) and selected elements (in ng/m3) by metropolitan area, six

U.S. cities, 1979–1988.

KingstonWatertown Harriman Portage

(Boston) St. Louis (Knoxville) (Madison) Steubenville Topeka

Mean ± SD Mean± SD Mean ± SD Mean ± SD Mean ± SD Mean± SD

PM2.5 16.5 ± 9.2 19.2 ± 10.1 21.1 ± 9.3 11.3 ± 7.5 30.5 ± 22.4 12.2 ± 7.1

Silicon 114.2 ± 107.8 195.9 ± 256.9 203.4 ± 188.4 109.1 ± 155.7 283.3 ± 360.4 202.4 ± 281.2

Aluminum 65.5 ± 88.7 161.2 ± 211.7 152.5 ± 150.2 70.5 ± 127.0 186.8 ± 238.0 130.1 ± 205.2

Calcium 33.9 ± 31.6 78.8 ± 54.6 98.7 ± 142.3 35.2 ± 33.7 101.7 ± 155.3 134.0 ± 200.0

Iron 62.2 ± 53.5 143.7 ± 132.7 116.9 ± 89.1 44.1 ± 45.7 542.4 ± 738.3 72.0 ± 88.2

Manganese 3.7 ± 2.8 19.2 ± 27.3 8.8 ± 21.8 3.2 ± 3.1 30.4 ± 41.5 4.7 ± 3.5

Potassium 75.6 ± 56.5 118.0 ± 87.2 109.4 ± 71.8 59.6 ± 36.4 344.4 ± 411.1 84.5 ± 99.0

Lead 240.3 ± 212.1 212.7 ± 223.9 108.7 ± 95.6 33.3 ± 33.7 184.5 ± 195.3 71.5 ± 104.9

Bromine 58.7 ± 66.5 39.8 ± 59.3 21.5 ± 22.4 6.1 ± 5.7 30.2 ± 31.4 18.2 ± 32.1

Copper 11.0 ± 14.5 29.7 ± 46.6 12.7 ± 16.4 6.4 ± 8.3 11.9 ± 10.7 6.7 ± 9.7

Zinc 24.8 ± 18.7 57.0 ± 62.6 33.8 ± 54.1 15.9 ± 13.2 138.4 ± 214.6 13.8 ± 15.5

Sulfur 1921.6 ± 1,391.3 2350.3 ± 1,583.4 2555.9 ± 1491.4 1481.5 ± 1327.2 4,248.4 ± 3,185.3 1,368.3 ± 1,168.9

Selenium 0.7 ± 0.9 2.2 ± 1.9 1.9 ± 1.5 0.9 ± 0.8 5.2 ± 4.2 0.8 ± 0.7

Vanadium 23.2 ± 19.8 2.0 ± 4.4 1.4 ± 3.3 0.1 ± 2.9 10.5 ± 20.4 0.6 ± 2.8

Nickel 8.8 ± 7.0 2.2 ± 4.8 1.0 ± 1.1 0.5 ± 0.7 3.7 ± 4.7 0.6 ± 1.0

Chlorine 49.3 ± 148.6 20.5 ± 98.1 6.7 ± 15.8 9.1 ± 101.3 58.7 ± 263.0 10.6 ± 49.5
and takes into account the precision of each

individual estimate.

As an alternative approach, we evaluated

the association of daily deaths with individual elements. Based on previous toxicological

research suggesting which elements might be

important in ambient particle toxicity

(23–26), we built city-specific models that

included daily measurements of lead, iron,

sulfur, nickel, vanadium, manganese, and

zinc individually and in combination.

Trend, season, and weather were controlled

for as described above.

Results

Source apportionment. The mean and standard deviations for the 15 elements varied

across the six metropolitan areas (Table 1).

As the most common element in the fine

fraction, the mean sulfur concentration varied by a factor of two across five areas

(1.5–2.6 μg/m3) and was much higher in

Steubenville (4.2 μg/m3). Because these

measurements were conducted before the

removal of lead from gasoline, the lead concentrations were relatively high (> 0.1

μg/m3) in every city except Portage and

Topeka. Vanadium was much higher in

Watertown and Steubenville than in the

other metropolitan areas.

The identified sources and their contribution to total daily fine mass in each

community are presented in Table 2. Using

silicon, lead, and selenium as tracer elements, we identified crustal, mobile, and

coal combustion factors, respectively, in all

six metropolitan areas. Coal and mobile

sources account for the majority of fine

particles in each city; the proportion of fine

particulate matter accounted for by these two

factors combined ranged from 0.44 in St.

Louis to 0.79 in Watertown. In Watertown

the crustal factor accounted for < 1% of the

fine particle mass and was not a significant

predictor in the regression model. Therefore, in Boston, the larger metropolitan area

represented by Watertown, this factor was

not included in the mortality analyses. We

identified a vanadium factor, representing

fuel oil combustion in Watertown and

Steubenville. A chlorine factor (salt) was

identified in Watertown, KingstonHarriman, and Portage. We identified a

metal factor in St. Louis (zinc), Steubenville

(zinc), and Kingston-Harriman (nickel),

presumably related to local manufacturing.

Finally, we found a manganese factor in St.

Louis and Portage. In Topeka, which had a

low average PM2.5 concentration, we were

able to identify only the crustal, mobile, and

coal combustion factors. The source of at

least 18% of the total mass remained

unexplained in all cities, except Steubenville

(8% unexplained).

The crustal factor consisted predominantly of silicon (the tracer element), aluminum, iron, potassium, and calcium. The

elemental composition of road dust identified by published chemical analyses was similar (27), and the relative contributions of

each element (the standardized scoring coefficients) were fairly consistent across the six

metropolitan areas (Table 3). The coefficient

for silicon, the marker for sandy soils, varied

only slightly from 0.25 to 0.36, whereas the

coefficient for aluminum, a marker for clay

soils, varied much more—from 0.17 in

Watertown to 0.50 in Steubenville. Except

for Kingston-Harriman, the contribution of

calcium was relatively constant across the

metropolitan areas. These differences in the

standardized scoring coefficients for the

crustal factor may reflect regional differences

in the composition of soils.

Because these measurements were conducted while leaded gasoline was still in use,

lead provided a good marker for mobile

sources. To minimize the possibility of noise

in the data in the later years when leaded

gasoline was being phased out, we evaluated a

measurement of lead adjusted by time. This

correction did not substantially affect the

results, so it was not used in the main analyses and is not presented here. For the mobile

source factor, the standardized scoring coefficients were similar for lead and bromine (a

constituent of commercial tetra-ethyl lead

mixtures), except in Steubenville and

Kingston-Harriman, where the coefficients

for bromine were low (Table 4). Copper had

surprisingly high scoring coefficients for this

source factor, especially in Steubenville and

Kingston-Harriman. Furthermore, sulfur

loaded particularly high on the lead factor in

Kingston-Harriman. Thus, we must be careful in the interpretation of this factor as a

mobile source in Kingston-Harriman and

perhaps in Steubenville.

For the selenium or coal combustion factor, the standardized scoring coefficients

reflected the important contribution of sulfur from this source factor in every metropolitan area except Kingston-Harriman

(Table 5). In Kingston-Harriman, this factor

had high coefficients for vanadium, manganese, and potassium that were not seen in

the other metropolitan areas. Again, as with

the mobile factor, we should be cautious in

interpreting this factor as a coal combustion

source factor in Kingston-Harriman.

For the three communities in which we

identified metal source factors, the pattern of

high coefficients was different for each. In St.

Louis, the zinc factor also had high coefficients

Articles • Fine particles and mortality

Environmental Health Perspectives • VOLUME 108 | NUMBER 10 | October 2000 943

Table 2. Proportional composition of fine particulate matter (PM2.5) by source and metropolitan area, six

U.S. cities, 1979–1988.

KingstonWatertown Harriman Portage

Source Target (Boston) St. Louis (Knoxville) (Madison) Steubenville Topeka

Crustal Silicon < 0.01 0.14 0.08 0.03 0.14 0.08

Mobile Lead 0.29 0.15 0.21 0.27 0.05 0.17

Coal Selenium 0.50 0.29 0.32 0.45 0.63 0.57

Fuel oil Vanadium 0.03 — — — 0.03 —

Metals a — 0.12 0.14 — 0.08 —

Salt Clorine 0.01 — 0.04 0.01 — —

Manganese Manganese — 0.03 — 0.07 — —

Residual 0.18 0.28 0.21 0.18 0.08 0.19

aThe target element varied by metropolitan area: zinc in St. Louis and Steubenville and nickel in Knoxville.

Table 3. Crustal factor with silicon as the target element: standardized scoring coefficients by element

and metropolitan area, six U.S. cities, 1979–1988.

KingstonWatertown Harriman Portage

Element (Boston) St. Louis (Knoxville) (Madison) Steubenville Topeka

Silicon 0.28 0.33 0.25 0.30 0.36 0.27

Aluminum 0.17 0.29 0.29 0.27 0.50 0.24

Calcium 0.26 0.25 –0.04 0.26 0.29 0.17

Iron 0.26 0.20 0.11 0.24 –0.01 0.27

Manganese 0.14 0.00 0.30 0.00 0.11 0.17

Potassium 0.08 0.18 0.26 0.13 0.06 0.12

Lead 0.00 0.00 0.00 0.00 0.00 0.00

Bromine 0.02 0.00 0.05 0.01 –0.17 0.01

Copper 0.06 –0.01 –0.28 –0.02 –0.04 0.00

Zinc 0.00 0.00 0.19 –0.03 0.00 –0.01

Sulfur 0.00 –0.01 –0.14 0.05 0.02 –0.02

Selenium 0.00 0.00 0.00 0.00 0.00 0.00

Vanadium 0.00 0.02 0.38 –0.14 0.00 0.04

Nickel 0.02 –0.06 0.00 –0.14 –0.25 0.03

Chlorine 0.00 0.15 0.00 0.00 –0.24 –0.01
for nickel, copper, and vanadium as well as

potassium and chlorine. In Steubenville, the

zinc factor had high coefficients for nickel,

iron, manganese, chlorine, bromine, and calcium. Finally, in Kingston-Harriman, the

nickel factor had high coefficients for iron

and copper, but not for zinc.

In Watertown, vanadium and nickel were

highly correlated, and both loaded high on

the factor targeted by vanadium and defined

as fuel oil combustion (19). In Steubenville,

the vanadium factor had moderate scoring

coefficients only for aluminum and calcium.

The manganese factors in Portage and St.

Louis had high factor scoring coefficients for

nickel and vanadium. Therefore, manganese

may serve to identify a fuel oil combustion

factor in some midwestern communities.

Association of mortality with specific

source factors. Because elemental composition was not available on every day in which

fine mass was measured, the current data set

is a subset of the data described in the 1996

analysis (1). Results for the combined analysis of total fine mass were similar to the

previously published results: total daily mortality increased by 1.6% (CI, 1.1–2.1%)

with each 10 μg/m3 increase in the 2-day

mean of PM2.5, as compared to the previous

report of a 1.5% increase (CI, 1.1–1.9%)

using the full data set (1). The magnitude of

the associations with ischemic heart disease

and chronic obstructive pulmonary disease

were similar to those for total mortality

(approximately 2). These point estimates

were slightly attenuated and the confidence

intervals wider in these data compared to the

earlier analysis.

For the three source factors identified in

all six metropolitan areas, we found the

strongest increase in daily mortality associated with the mobile source (lead) factor

(Table 6). In the combined analysis across

the six cities, daily mortality increased by

3.4% (CI, 1.7–5.2%) with each 10 μg/m3

increase in the 2-day mean of the mobilesource factor. The communities are presented

in the table in order of decreasing population

size, and consequently decreasing contribution to the summary measure. In St. Louis,

Knoxville (Kingston-Harriman), and

Madison (Portage), the estimated effects were

4.3, 5.2, and 6.3%, respectively. The associations in Steubenville and Topeka were

inverse, but the standard errors were large

and the contribution of mobile sources to

total PM2.5 was modest in Steubenville

(Table 2). Similar to the association observed

with total PM2.5, there was evidence of a 2%

increase in daily mortality from ischemic

heart disease with each 10 μg/m3 increase in

mobile sources; however, it was not statistically significant (CI, –1.1–5.2). We did not

identify adverse effects for respiratory deaths

(chronic obstructive pulmonary disease or

pneumonia).

The coal combustion (selenium) factor

was positively associated with mortality in all

metropolitan areas, with the exception of

Topeka (Table 6). The summary relative risk

indicated that a 10 μg/m3 increase in the 2-

day mean mass concentration from coal combustion sources was associated with a 1.1%

increase in daily mortality (CI, 0.3–2.0%).

Deaths from chronic obstructive pulmonary

disease and pneumonia increased by higher

percentages than did deaths from all causes:

4.5% (CI, –0.4–9.3%) and 7.9% (CI,

3.1–12.7%), respectively. Unlike the motor

source factor, there was no evidence of an

increased effect of exposure to coal combustion sources for ischemic heart disease.

Articles • Laden et al.

944 VOLUME 108 | NUMBER 10 | October 2000 • Environmental Health Perspectives

Table 4. Mobile source factor with lead as the target element: standardized scoring coefficients by element and metropolitan area, six U.S. cities, 1979–1988.

KingstonWatertown Harriman Portage

Element (Boston) St. Louis (Knoxville) (Madison) Steubenville Topeka

Silicon 0.00 0.00 0.00 0.00 0.00 0.00

Aluminum –0.16 –0.09 –0.10 –0.06 0.26 –0.06

Calcium 0.09 0.19 0.34 0.15 0.04 0.16

Iron 0.11 0.11 0.05 0.09 –0.08 0.07

Manganese 0.10 0.00 –0.37 0.00 0.05 0.10

Potassium 0.04 0.10 –0.17 0.09 0.26 0.08

Lead 0.28 0.33 0.11 0.27 0.44 0.33

Bromine 0.26 0.37 0.01 0.25 0.03 0.31

Copper 0.30 –0.03 0.42 0.21 0.40 0.20

Zinc 0.14 0.00 –0.23 0.15 0.00 0.18

Sulfur 0.06 –0.07 0.24 0.08 0.01 0.05

Selenium 0.00 0.00 0.00 0.00 0.00 0.00

Vanadium 0.00 –0.32 –0.50 –0.30 0.00 –0.11

Nickel 0.01 –0.11 0.00 –0.13 –0.54 0.06

Chlorine 0.00 0.16 0.00 0.00 –0.50 0.11

Table 5. Coal combustion factor with selenium as the target element: standardized scoring coefficients by

element and metropolitan area, six U.S. cities, 1979–1988.

KingstonWatertown Harriman Portage

Element (Boston) St. Louis (Knoxville) (Madison) Steubenville Topeka

Silicon 0.00 0.00 0.00 0.00 0.00 0.00

Aluminum 0.10 0.05 0.11 0.08 –0.26 0.04

Calcium –0.12 –0.10 –0.29 –0.08 –0.26 –0.06

Iron –0.04 –0.03 –0.11 –0.04 0.06 0.00

Manganese 0.16 0.00 0.28 0.00 –0.04 0.14

Potassium 0.07 –0.07 0.26 0.09 –0.01 0.09

Lead 0.00 0.00 0.00 0.00 0.00 0.00

Bromine –0.06 –0.10 0.09 –0.12 0.14 –0.04

Copper –0.04 0.09 –0.42 –0.17 0.05 0.08

Zinc 0.21 0.00 0.15 0.12 0.00 0.22

Sulfur 0.41 0.56 –0.08 0.57 0.40 0.39

Selenium 0.43 0.39 0.26 0.46 0.31 0.41

Vanadium 0.00 –0.12 0.50 –0.08 0.00 0.19

Nickel –0.01 –0.25 0.00 –0.24 0.41 0.14

Chlorine 0.00 –0.47 0.00 0.00 –0.03 –0.04

Table 6. Percent increase in daily deaths and 95% CIs associated with a 10 μg/m3 increase in mass concentration from a specific major source of fine particles by metropolitan area, six U.S. cities, 1979–1988.a

Mean Crustal (Si) Motor (Pb) Coal (Se)

daily no. Percent Percent Percent

City deaths increase 95% CI increase 95% CI increase 95% CI

Bostonb 59 — — 1.2 –1.9–4.3 2.8 1.2–4.4

St. Louis 55 –3.0 –7.7–1.6 4.3 1.6–7.0 0.3 –1.1–1.6

Knoxville 12 –1.7 –20.0–17.0 5.2 1.2–9.2 0.8 –2.7–4.3

Madison 11 40.5 –26.8–112.5 6.3 –3.1–15.8 0.9 –2.5–4.2

Steubenville 3 –1.4 –7.1–4.2 –0.2 –20.5–20.5 1.1 –1.2–3.5

Topeka 3 –7.9 –42.4–27.9 –8.2 –29.0–13.1 –3.9 –11.2–3.5

Summaryc –2.3 –5.8–1.2 3.4 1.7–5.2 1.1 0.3–2.0

aEach multivariate model includes Loess smooth function of date with a span of 0.05, Loess functions of temperature and

dew point temperature with spans of 0.80, indicator variables for day of the week, and all source factors simultaneously.

bThe crustal factor was not a statistically significant predictor of fine mass in Watertown (Boston); therefore, it was not

included in the analysis. cSummary estimates obtained by combining the city-specific regression coefficients using

inverse variance weights.
As we mentioned before, the elemental

profiles for the lead (mobile) and selenium

(coal) factors in Knoxville differed from the

profiles in the other cities. When Knoxville

is omitted from the meta-analysis, the

increase in daily mortality attributed to

mobile sources and coal combustion

changed only slightly. 

In all of the metropolitan areas combined, 46% of the total fine particle mass

was attributed to coal combustion and 19%

to mobile sources. Therefore, the difference

between the 5th and 95th percentile is considerably different for coal combustion (28.7

μg/m3) than for mobile sources (12.9

μg/m3). However, the summary estimate of

the change in daily mortality across this

range of exposure still indicated a difference

in magnitude; the increase was 4.5% (CI,

2.2–18.3) for mobile sources and 3.2% (CI,

0.7–5.8%) for coal combustion. 

The crustal factor in fine particulate matter was not associated with mortality. If anything, the summary relative risk suggested a

protective association between crustal particles and mortality. However, the confidence

intervals were wide and the association far

from statistical significance. This factor was

not included in the mortality analysis in

Boston (Watertown) because, even though it

was identified in that city, it was not a significant predictor of and accounted for < 1% of

total fine particulate matter.

There is a suggestion of a large positive

association of fuel oil combustion with daily

mortality in the cities in which the vanadium factor was identified; however, the confidence intervals were wide and included a

null effect (Boston: 27.3% increase, CI,

–2.0–57.5%; Steubenville: 13.6% increase,

CI, –34.2–63.8%). If we consider the manganese factor as defining a fuel oil combustion source in Madison and St. Louis, then

the summary effect estimate for the four

cities is 5.6% (CI, –1.8– 13.2%) for a 10

μg/m3 increment in PM2.5 from this factor.

Association of mortality with specific elements. In models with measurements for the

individual elements included simultaneously,

sulfur, nickel, and lead were significantly

associated with total mortality (Table 7). An

increase of sulfur across its range of exposure

(5916 ng/m3: 5th–95th percentile for all

cities combined) was associated with a 3.0%

(CI, 0.9–5.2%) increase in total daily mortality. The equivalent increases in nickel

(10.3 ng/m3) and lead (461.4 ng/m3) were

associated with a 1.5% (CI, 0.5– 2.6%) and

1.6% (CI, 0.2–2.9%) increase in mortality,

respectively. Vanadium and iron, although

statistically significant predictors when evaluated separately, were not significant when

included simultaneously with nickel, lead,

and sulfur. 

Discussion

Using specific rotation factor analysis, we

were able to identify crustal, mobile, and coal

combustion source of fine particles in each of

six cities and to estimate the percentage of

daily deaths attributable to changes in the

concentration of each factor. In the combined

analysis across the six cities, controlling for

the other sources, a 10 μg/m3 increase in

PM2.5 from mobile sources accounted for a

3.4% increase in daily mortality (CI,

1.7–5.2%) and from coal combustion sources

a 1.1% increase (CI, 0.3–2.0%). PM2.5 from

crustal particles was not associated with

increased daily mortality. A possible residual

oil combustion source was identified in four

of the six cities, and the summary effect estimate was positive but not statistically significant (5.6% increase, CI, –1.8–13.2%).

Results from element-specific mortality analyses were consistent with the analysis of

sources; lead and sulfur, markers for mobile

and coal combustion sources, respectively,

were independently associated with daily

deaths. Additionally, nickel was positively

associated with daily deaths.

The association between particulate air

pollutants, either measured as total suspended particulates (TSP) or inhalable particles

(PM10; aerodynamic diameter ≤ 10 μg/m3),

has been extensively studied and is well

established. The magnitude of association

has been consistent across studies with the

weighted mean percent change in daily mortality for each 10 μg/m3 increase in PM10

concentration of approximately 0.8% (28).

Most of the fine particles (PM2.5) are

deposited in the alveolar region and are

therefore not cleared efficiently. These particles include toxic sulfates, nitrates, and

bioavailable transition metals (25,26).

Prospective studies (6,10) and studies of daily

deaths (1,2,5,9,11) have observed associations of PM2.5 with mortality. Associations

with morbidity also have been reported

(3,4,7,8).

Our previous analysis demonstrating

that PM2.5 was more strongly associated

with all-cause mortality than was PM2.5–10

has been criticized because the larger particles

in the PM2.5–10 filters were more likely to fall

off the filter before being measured. Hence, it

has been suggested that poorer predictive

power was due to measurement error (29). In

this study, we found that crustal material in

the fine size range is not associated with daily

deaths. Boston was not included in this

analysis because the mass attributed to the

crustal factor in that city was small and was

not a significant predictor of total fine mass.

The percent increase in mortality in the other

cities associated with this factor were consistently null and in the same direction in all

communities except Madison, Wisconsin.

The crustal material on the PM2.5 filters

tends to be in particles that are > 1 μm in

aerodynamic diameter. This suggests that a

reduction in the size cutoff for the particles

(e.g., to 1.5 μm) would more specifically

focus on the most toxic particles.

In 1987, Ozkaynak and Thurston (2)

concluded that particles from industrial

sources (iron and steel emissions) and from

coal combustion were more significant contributors to mortality than soil-derived particles based on analyses using 1980 U.S. Vital

Statistics data and available ambient air pollution data. Recently, Schwartz and co-workers

(30) examined dust storms in Spokane,

Washington. In 17 episodes with very high

concentrations of coarse but not fine particles,

they found no evidence of excess mortality

(30). In another recent study in Salt Lake

City, Utah, and the neighboring Wasatch

Front communities, the estimated association

between PM10 and mortality was stronger

when pollution episode days that were obviously dominated by windblown-dust were

excluded (31). Similarly, Schwartz and Neas

(32) reported that coarse particles were not

associated with lower respiratory symptoms or

peak flow deficits in panel studies of children.

We believe these results present a consistent

pattern of less toxic reactions to crustal particles, even when in the fine range, and no

association with daily deaths.

Articles • Fine particles and mortality

Environmental Health Perspectives • VOLUME 108 | NUMBER 10 | October 2000 945

Table 7. Percent increase in daily deaths and 95% CIs associated with an increase in mass of elements

across the total range of exposure (5th to 95th percentile) by metropolitan area, six U.S. cities,

1979–1988.a

Nickel Lead Sulfur

Percent Percent Percent

increase 95% CI increase 95% CI increase 95% CI

Boston 1.9 0.4–3.4 –0.5 –2.8–1.8 7.9 3.9–12.1

St. Louis 1.0 –0.4–2.5 2.2 0.5–3.9 0.8 –2.4–4.2

Knoxville 12.2 –5.8–33.8 15.0 5.0–26.0 1.0 –6.8–9.4

Madison –23.9 –38.1––6.5 35.7 –1.8–81.0 4.6 –3.0–12.7

Steubenville 8.2 0.3–16.6 3.9 –4.7–13.3 0.5 –6.8–8.3

Topeka –27.8 –50.9–6.2 –5.2 –19.3–11.3 –10.3 –23.1–4.6

Summaryb 1.5 0.5–2.6 1.6 0.2–2.9 3.0 0.9–5.2

The equivalent increases for nickel, lead, and sulfur were 10.3, 461.4, and 5,916, respectively. 

aEach multivariate model includes Loess smooth function of date with a span of 0.05, Loess functions of temperature and

dew point temperature with spans of 0.80, indicator variables for day of the week, and nickel, lead and sulfur. bSummary

estimates obtained by combining the city-specific regression coefficients using inverse variance weights.
The finding of a greater effect per microgram per cubic meter for mobile source

particles than for coal combustion source

particles, which are predominantly sulfate, is

intriguing. Sulfates have long been implicated as the major toxicologic component in

fine particulates (23) and have been shown

to be associated with respiratory disease and

mortality (24). However, Peters et al. (33)

reported a stronger association of some

health endpoints with ultrafine particles than

with sulfate particles. Ultrafine particles are

primarily from mobile sources (34), and our

mobile source factor may be standing for

variations in ultrafine particles. Alternatively,

the organic aerosol associated with traffic

pollution may be more toxic even if it is not

in the ultrafine component, or an identical

ambient concentration increase of mobile

source particles may have a stronger relationship to personal exposure than do particles

from power plant sources. 

Another important issue is possible confounding by gaseous air pollutants. The traffic particles in particular may reflect carbon

monoxide effects. CO is an important constituent of motor-vehicle emissions and has

been shown to be correlated with adverse

respiratory effects (35). However, a recent

analysis found no confounding of the PM10

mortality relationship by CO in 10 U.S.

cities (36). A second recent study (37) found

no evidence of confounding, and also no evidence of an effect of CO on daily deaths,

when examining 20 cities. Hence, we think

that confounding by CO is unlikely to

explain our results.

Particles from vehicle exhaust were more

strongly associated with deaths due to

ischemic heart disease but not with deaths

due to chronic obstructive disease or pneumonia (diseases less common than ischemic heart

disease). In contrast, the coal-derived particles

were more strongly associated with respiratory

deaths than with deaths due to ischemic heart

disease. This suggests that these particles may

affect health by different mechanisms. In a

recent study, dogs exposed to concentrated air

particles had more cardiovascular responses to

the traffic-derived than to the sulfate/coalderived particles (38). Further examination of

this issue is warranted.

We also note that although the residual

oil factor was not significant, it had a large

slope. Residual oil is rich in metals that have

been implicated in some of the toxicity studies (25,26). More investigation of oil burning seems warranted.

The primary limitation in this study is

the identification and interpretation of the

factors. In the factor analysis, we chose a priori the elements to target to identify the first

three sources: silicon (crustal), lead (mobile),

and selenium (coal). These choices and their

interpretation are supported by the published

source apportionment and chemical composition literature. Silicon is a crustal element

unlikely to be related to combustion sources.

In other source apportionment studies, silicon has consistently been correlated with

other crustal elements, that together have

been determined to be derived from soil

(19,39). Lead is considered a reliable marker

of mobile source particles during the years

when leaded gasoline was in widespread use

(19,39). Coal combustion is responsible for

between 62% (40) and 85% (41) of selenium

emissions in the United States.

We do not have direct confirmation that

these three major factors truly represent the

sources we have attributed to them. However,

we are confident that, in all cities, with the

possible exception of Kingston-Harriman

(Knoxville), the silicon, lead, and selenium

factors are identifying crustal, mobile, and

coal combustion sources, respectively. The

elemental profile of the crustal factor was

qualitatively similar to published chemical

analyses of road dust (27). Bromine was correlated with lead in all cities except

Steubenville and Knoxville, and in chemical

analyses of emissions from noncatalyst automobiles, both lead and bromine accounted

for high percentages of the total mass (42). In

all cities except for Knoxville, sulfur, a major

component of coal combustion, was found

predominantly on the selenium factor.

After these first three major source factors, the selection of additional factors was

more problematic. We chose the targets for

the remaining two factors for each city on an

individual basis, guided primarily by statistical considerations. A previous source apportionment study in Watertown identified a

residual oil fly-ash source with high loadings

of nickel and vanadium and a salt or chlorine source (19). Therefore, nickel, vanadium, and chlorine were considered first as

potential targets for the other factors in all

cities. Manganese also would appear to be a

good target for an oil source because the

manganese factor identified in two cities had

high loadings of nickel and vanadium. 

We did not tailor our factor analysis in

each city to specific local sources, and this

limitation could influence the appropriateness

of our selection of factor targets. As a sensitivity analysis, we performed nontargeted

oblique rotation factor analysis [the “promax”

rotation option of SAS software (18)] for each

city. Factors with high relative concentrations

of silicon, lead, and selenium or sulfur were

identified in each city by this method as well.

The choice of targets for each of the factors influences the relative concentration of

each element attributed to each factor,

which in turn could affect the conclusions

from the mortality analysis. We evaluated

the association of factors identified by the

nontargeted (promax) rotation described

above with daily mortality and observed similar results for crustal, mobile and coal combustion sources. Results were also similar if,

based on the source apportionment in

Watertown, we selected vanadium and chlorine a priori as the targets for the fourth and

fifth factors in the other cities. 

The elemental profiles of the sources in

Kingston-Harriman were not consistent

with those for the other cities. However,

mortality results from the meta-analysis were

similar when we omitted this city.

Finally, we analyzed the elements thought

to be of toxicological significance rather than

focusing on sources. Two of the elements that

appeared most important in this analysis were

sulfates and lead. The difference in these

results from the factor analysis results is the

significant association with nickel. Nickel

never emerged as its own factor, and it usually

loaded on several factors in each city. Hence,

the association of nickel with mortality may

indicate a direct toxic effect of that metal.

Alternatively, it may reflect the effect of particles from metal industries and residual oil

combustion more broadly.

It is also important to acknowledge that

this is an analysis of six communities. The

results for these six cities may not be generalizable to all urban areas in the United States;

however, they are internally consistent. The

cities did not have equal weights in the metaanalysis due to their size; Boston and St.

Louis were the largest communities included.

The use of inverse variance weights is similar

to weighting by the number of deaths and

accounts for the precision of the estimate.

Therefore, the conclusions are more heavily

influenced by the results observed in Boston

and St. Louis.

In conclusion, particulate matter from

combustion sources was associated with

increased mortality in these six U.S. cities.

The relative potency of combustion-related

particles were inversely related to their relative abundance, but particulate matter from

mobile sources and coal combustion were

associated with increased mortality.

Conversely, particulate matter of crustal origin, even in the fine fraction (PM2.5) was not

associated with increased mortality.

Although a small portion of the particles

containing silica, aluminum, and iron may

be produced from crustal inclusions in coal,

most of the PM2.5 mass associated with the

crustal factor is from the lower tail of the

coarse mode distribution < 2.5 μm (43).

Thus, lack of an association between mortality and our crustal factor in the fine fraction

is consistent with our earlier finding that

mortality was not associated with coarse particles between 2.5 and 10 μm (2) 

Articles • Laden et al.

946 VOLUME 108 | NUMBER 10 | October 2000 • Environmental Health Perspectives
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