Many time-series studies have shown that ambient particulate matter (PM) pollution, generally measured as particles with aerodynamic

diameter ≤ 10 μm (PM10), is associated with

increased risk of deaths for cardiovascular or

respiratory causes in both Europe and the

United States (Katsouyanni et al. 1997; Samet

et al. 2000; Schwartz 1991, 2000). More

recent multicity studies have shown the associations generally persist unchanged after control

for other air pollutants (Dominici et al. 2005),

although some single-city studies have shown

some sensitivity to this choice. Further, this

association remains after matching on other air

pollutants and temperature in case–crossover

analyses (Schwartz 2004a, 2004b). 

Less work has been reported using particles from specific sources, which is a critical

need for regulators. Several studies have examined intermediary markers and reported that

traffic particles seemed to be more toxic than

others. In a case–crossover analysis of 772

patients presenting to Boston-area hospitals

with strictly defined myocardial infarction,

Peters et al. (2001) reported that elevated concentrations of ambient particulates were associated with higher risk. The risk was even

greater if black carbon (BC), a surrogate for

traffic particles, was used as the exposure.

Schwartz and coworkers (2005) reported a

stronger association of BC than of PM with

aerodynamic diameter ≤ 2.5 μm (PM2.5) with

heart rate variability. Less directly, Janssen and

coworkers (2002) showed that the increased

risk of cardiovascular hospitalization in 14

U.S. cities associated with a fixed increment in

exposure to airborne particles varied with the

particle composition in the city. It increased

with the percent of the particles from traffic

sources, and decreased with the percent of

particles from windblown dust. Laden and

coworkers (2000) analyzed data from the

Harvard Six Cities study and found different

toxicity for particles from different sources.

They found that a 10-μg/m3 increase in ambient particles was associated with an increase in

daily deaths of 3.4% for mobile source particles and 1.1% for coal combustion particles.

Further, the sulfate particles were more

strongly associated with respiratory deaths.

However, in this study particles from both

traffic and coal-burning power plants were

estimated from central-site monitoring (Laden

et al. 2000). 

The ability of stationary air monitoring

(SAM) site PM concentrations to reflect corresponding exposures depends largely on the

extent of spatial heterogeneity in PM levels,

which varies by particulate component.

Sulfate, a major component of PM2.5 in

Boston, is a stable particle species that varies

little outdoors across wide geographic areas

(Suh et al. 1995). 

Studies of traffic-related particles have

used tracers to reflect total traffic-related particulate emissions, of which elemental carbon

(EC) and filter blackness are the most

common, particularly for diesel exhaust. Both

markers have been shown to vary substantially

within a city, with a 4-fold range of variations

across different streets in Harlem, New York,

related to bus and truck counts on adjacent

streets (Kinney et al. 2000). Another study

(Brauer et al. 2003) showed that traffic variables explained 81% of the variability of filter

blackness across 41 sites within Munich,

Germany, further validating the measure as a

marker of traffic particles. 

This suggests that substantial exposure

misclassification could result from the use of a

central measuring site to assess exposure to

traffic particles, and that a geographically

based approach could substantially improve

the assessment. We have conducted a

case–crossover analysis of approximately

100,000 hospital deaths in the Boston metropolitan area over the years 1995–2002, using

a spatial–temporal land use regression estimate of the daily concentrations of traffic particles at the home of each decedent and

central-site readings of sulfate levels. 

Data and Methods

Health data. Individual mortality records

were obtained from the Massachusetts

Department of Public Health, for the years

1995–2002. These records included information such as residential location, place of

death, age, sex, date of death, ethnicity, education, and primary cause of death. Specific

cause mortality was derived from the

International Classification of Diseases (ICD)

codes [9th Revision before 1999 (World

Health Organization 1975) and 10th Revision

1999 to 2002 World Health Organization

1993)]. Cardiovascular disease (CVD) was

defined as ICD-9 390–429, and ICD-10

Environmental Health Perspectives • VOLUME 115 | NUMBER 5 | May 2007 751

Research

Address correspondence to J. Schwartz, Department

of Environmental Health, Exposure Epidemiology

and Risk Program, Harvard School of Public Health,

401 Park Dr., Landmark Center, Suite 415, PO Box

15698, Boston, MA 02215 USA. Telephone: (617)

384-8752. Fax: (617) 384-8745. E-mail: joel@

hsph.harvard.edu 

We are thankful for the input of S. Melly and

H. Suh. 

This work was funded by the National Institute of

Environmental Health Sciences (ES09825/

ES012004/ES00002), and the U.S. Environmental

Protection Agency (R827353). 

The authors declare they have no competing

financial interests.

Received 20 July 2006; accepted 29 January 2007.

Mortality Risk Associated with Short-Term Exposure to Traffic Particles

and Sulfates

Dan Maynard,1 Brent A. Coull,1 Alexandros Gryparis,1 and Joel Schwartz2,3

1Department of Biostatistics, 2Department of Environmental Health, and 3Department of Epidemiology, Harvard School of Public Health,

Boston, MA, USA

BACKGROUND: Many studies have shown that airborne particles are associated with increased risk of

death, but attention has more recently focused on the differential toxicity of particles from different

sources. Geographic information system (GIS) approaches have recently been used to improve

exposure assessment, particularly for traffic particles, but only for long-term exposure. 

OBJECTIVES: We analyzed approximately 100,000 deaths from all, cardiovascular, and respiratory

causes for the years 1995–2002 using a case–crossover analysis. 

METHODS: Estimates of exposure to traffic particles were geocoded to the address of each decedent

on the day before death and control days, with these estimates derived from a GIS-based exposure

model incorporating deterministic covariates, such as traffic density and meteorologic factors, and a

smooth function of latitude and longitude. 

RESULTS: We estimate that an IQR increase in traffic particle exposure on the day before death is

associated with a 2.3% increase [95% confidence interval (CI), 1.2 to 3.4%] in all-cause mortality

risk. Stroke deaths were particularly elevated (4.4%; 95% CI, –0.2 to 9.3%), as were diabetes

deaths (5.7%; 95% CI, –1.7 to 13.7%). Sulfate particles are spatially homogeneous, and using a

central monitor, we found that an IQR increase in sulfate levels on the day before death is associated with a 1.1% (95% CI, 0.1 to 2.0%) increase in all-cause mortality risk. 

CONCLUSIONS: Both traffic and powerplant particles are associated with increased deaths in Boston,

with larger effects for traffic particles.

KEY WORDS: air pollution, black carbon particles, fine particles, GIS-based exposure, land use

regression, mortality, particulate matter, traffic. Environ Health Perspect 115:751–755 (2007).

doi:10.1289/ehp.9537 available via http://dx.doi.org/ [Online 29 January 2007]
I01–I52. Stroke was defined as ICD-9

430–438 and ICD-10 I60–I69. Respiratory

deaths were ICD-9 460–519 and ICD10

J00–J99. Diabetes was defined as ICD-9 250

or ICD-10 E10–E14.

Each individual’s residential location at

time of death was geocoded (in terms of latitude and longitude) using a commercial

geocoding firm (Tele Atlas, Lebanon, NH). 

No BC data were available from March

1997 to April 1999, so deaths during this

period were not included in the analysis. Of

the remaining 192,822 deaths, we had an

imprecise place of residence for 6,230 of these

(a necessary variable to predict location-specific

individual BC levels), so these deaths were

excluded from the analysis (Table 1). In addition, we excluded inpatient deaths because

these subjects were unlikely to have been at

their residence the day before death. This

excluded an additional 78,667 deaths, resulting

in 107,925 total deaths for the final analysis.

Stationary air monitoring. Daily measurements of BC were obtained from a measuring

location at the Harvard School of Public

Health (HSPH) using an aethalometer

(Magee Scientific, Berkeley, CA). From

March 1997 to December 1999 the HSPH

pollution monitor was shutdown, so no data

are available for this time period. During

April–December 1999, the Massachusetts

Department of Environment operated an

identical monitor at a nearby site (Roxbury).

We used monitoring data from that site for

that period, after calibrating the measurements

to the HSPH monitor using a linear regression

during the period when both monitors were

operating. Overall, 12.6% of the days in our

study used BC data from the Roxbury monitor. Hence, our analysis focuses on mortality

between 1995–1997 and 1999–2002. 

Sulfate data were measured at HSPH

using HEADS impactors (URG, Chapel Hill,

NC), and were available from 25 September

1999 to the end of the study (31 December

2002). Weather data were obtained from the

National Climatic Data Center. 

Exposure modeling. In order to predict local

BC level, we used a validated spatial–temporal

land use regression model to predict 24-hr

measures of traffic exposure data (BC) at > 80

locations in the Boston area. Three-quarters

of the sites were residential, and the rest were

at commercial or government facilities. The

data consist of > 6,021 BC observations from

2,127 unique exposure days. A detailed

description of all exposure data sources are

provided in Section 2 of the study by Gryparis

et al. (2007). Predictors in the regression were

the BC value at the stationary monitor (to

capture average concentrations in the area on

that day), meteorologic conditions and other

characteristics (e.g., weekday/weekend) of a

particular day, as well as measures of the

amount of traffic activity [e.g., GIS (geographic information system)-based measures

of cumulative traffic density within 100 m,

population density, distance to nearest major

roadway, percent urbanization] at a given

location. Cumulative traffic density measure

is recorded once per location. We used nonparametric regression methods to allow these

factors to affect exposure levels in a potentially nonlinear way. Finally, we used thinplate splines, a two-dimensional extension of

nonparametric regression terms, to model

longitude and latitude and capture additional

spatial variability unaccounted for after

including our deterministic spatial predictors

in the model. This approach is a form of universal kriging (i.e., kriging extended to incorporate covariates) or a geoadditive model

(Kammann and Wand 2003) for daily concentrations of particle levels. We had complete

information on all these factors for 2,114 of

the 2,127 unique exposure days. 

Specifically, let Yij be the log-transformed

BC concentration for the j

th location on day i.

The model expresses bc (BC) concentration as

a function day of the week, ambient levels

from the monitor located at the HSPH, meteorologic conditions [Temp (temperature),

R.Hum (relative humidity)], day of the year,

cumulative traffic density within 100 m

(CumTraffic), and a nonparametric function of

longitude and latitude (lat,lon). The model is:

Yij = β0 + β1Mondayi + . . . + β6Saturdayi

+ β7bc.HSPHi + f1(Tempi) + f2(R.Humi) 

+ f3(DayofYeari) + f4 (Windi) 

+ f5(CumTraffici) + g(lat,lon)j + εij. [1]

Gryparis et al. (2007) showed that the

residuals obtained from the fitted model do

not exhibit additional residual correlation

over either time or space, indicating that the

temporal and spatial components of the

model adequately characterize correlations

among the observed pollution. A separate

model was fit for the warm (May–October)

and cold (November–April) seasons. The R2

of the model (over both seasons) was 0.82,

and the cross-validated R2 between the daily

measurements taken outside the residential

locations and corresponding predictions

obtained from fitting the model to the data,

excluding data from a particular residential

location, was 0.36. This is in contrast to an

R2 of 0.09 for the association between the

central site and each residential reading. No

other traffic variable was significant after controlling for cumulative traffic. 

It is important to emphasize that the proposed model specifies a lack of space–time

interaction on the log scale, so that the percent

change over time, not the raw concentration,

is constant across locations. Thus, the model

implies that locations with higher levels have

larger differences in concentrations over time,

and these different changes in time are used in

the health effects models. 

Analytical methods. We investigated the

association between daily concentrations of

BC and sulfate and mortality risk using a

bidirectional case–crossover design (Bateson

and Schwartz 1999) with time-stratified control sampling (Levy et al. 2001). By definition

of the case–crossover design, each case acted

as its own control on a set of predefined days

(called a reference period) around the time it

became a case. For a particular subject, the

reference period was chosen to consist of the

pollution concentrations for every third day

within the same month of the same year that

death occurred. For example, if a subject died

on 23 November 2000, then November 2, 5,

8, 11, 14, 17, 20, 26, and 29 of 2000 were

defined as controls days. Every third day was

chosen to reduce autocorrelation between

concentrations on successive days, while

maintaining power. More details on this

approach have been published previously

(Zanobetti and Schwartz 2005; Zeka et al.

2006). Because a particular subject is his or

her own control, the case–crossover design

inherently accounts for subject-specific, timeinvariant confounders. Using a month-long

control period has been shown by simulation

to produce unbiased estimates of effect sizes

even in the presence of strong seasonal confounding (Schwartz et al. 2003). 

The National Morbidity, Mortality, and

Air Pollution Study (Dominici et al. 2005)

Maynard et al.

752 VOLUME 115 | NUMBER 5 | May 2007 • Environmental Health Perspectives

Table 2. Descriptive statistics: deaths out of hospital in Boston metropolitan area, 1995–2002.

Characteristic No. (%)

Sex

Male 46,377 (43)

Female 61,548 (57)

Race

White 101,976 (94.4)

Black 4,164 (3.9)

Other 1,785 (1.7)

Education (years)

0–12 78,496 (72.7)

> 12 28,181 (26.1)

Missing 1,248 (1.2)

Cause of death

CVD 33,785 (31.3)

Stroke 6,070 (5.6)

Respiratory 9,878 (9.2)

Diabetes 2,694 (2.5)

Other 55,498 (51.4)

Table 1. Deaths in the Boston metropolitan area

1995–1997 and 1999–2002 by exclusion criteria.

Criteria No.

Total cases (deaths) 230,506

Missing BC data 37,684

Inpatient deaths 78,667

Imprecise residence location 6,230

Total restricted cases 107,925
indicates the strongest particle–mortality association with the previous day’s concentration,

and we used that lag in our models. Thus, we

report results that compare the exposure the

day before each subject’s death with the exposures the subject experienced the day before

control days. We express effect size estimates

as the percent increase in risk of mortality

associated with an interquartile range (IQR)

increase in particle concentrations. 

Risk may vary nonlinearly with apparent

temperature (defined as an individual’s perceived air temperature given the humidity),

given by the formula:

AT = –2.653 + (0.994 × Ta) + (0.0153 × Td 2),

where Ta is air temperature and Td is dew

point temperature (O’Neill et al. 2003). We

therefore used a regression spline with

4 degrees of freedom for apparent temperature

on the day of death. Apparent temperature the

previous day was treated linearly because previous analyses in Boston have shown a linear

relation. A 4-degree-of-freedom spline was also

considered for previous day’s temperature. Day

of the week was controlled using dummy variables for each day. The data were analyzed

using a conditional logistic regression (PROC

PHREG in SAS (SAS release 8.2; SAS Institute

Inc., Cary, NC).

In addition to analyzing all-cause mortality, we ran a series of stratified analyses to

investigate how the pollution–mortality association varies among different causes of death.

We stratified the data according to categories

of causes of death: CVD, respiratory deaths,

stroke, and diabetes. We conducted the same

case–crossover analysis within each stratum,

in case the temperature dependence varied by

cause of death.

We estimated effect modification due to

factors on both the personal level and at the

census tract level. Specifically, we estimated

whether sex and low individual education

(defined as < 12 years of education), or poverty

rate and median income, defined at the census

tract level, modified the association between

death and particle levels. Last, to assess whether

the association between BC levels and death is

driven by high air pollution days, we ran a

broken stick model that specified two slopes for

BC levels, one below and one above the median

BC level. Because sulfate data were more

limited, this analysis was only done for BC. 

Results

Of the 107,925 deaths included in our analyses, 43.0% of subjects were male, 94.4% were

white, and 26.1% had higher than a high

school education (Table 2). The average age

among these 107,925 subjects was 76.6 years. 

Table 3 contains a summary of the exposure and meteorologic variables used in the

analysis. Sulfate data were available only for

about half as many days as data for BC. The

correlation between HSPH BC levels and

predicted BC levels for an individual was

fairly high, approximately 0.80, whereas the

correlations with apparent temperature were

0.22 and 0.05 for predicted BC and HSPH

BC levels, respectively. Sulfate levels were

moderately correlated with both predicted BC

levels and HSPH-BC levels, at approximately

0.44 for both variables. Mean predicted BC at

residences was lower than the mean BC level

at the central-site monitor, reflecting its

downtown location.

Table 4 shows the results from the regression analyses for all-cause mortality. We estimate that an IQR increase in previous day’s

predicted BC level is associated with a 2.3%

[95% confidence interval (CI), 1.2 to 3.4%]

increase in all-cause mortality risk. These

results were essential unchanged when we

used a 4-degree-of-freedom spline for previous day’s temperature. 

Table 4 also shows estimated associations

between mortality and sulfate levels at the central site. We estimate that an IQR increase in

sulfate level on the day before death is associated with a 1.1% (95% CI, 0.01 to 2.0%)

increase in mortality risk. The sample size was

reduced substantially in the sulfate analysis. In

the bivariate analysis that included both sulfate

and predicted BC in the model, we observed a

similar effect estimate for an IQR increase BC

level, at 2.2% (95% CI, 0.16 to 4.2%),

although the effect of an IQR increase in sulfate

levels dropped to 0.45% (95% CI, –0.45 to

1.6%). Because of limitations on when monitors were operated, this analysis contained

fewer deaths than either univariate analysis. 

Table 5 shows results of the stratified

cause-specific analyses. We estimate the

largest BC associations for respiratory deaths,

stroke, and diabetes, with the association with

stroke being statistically significant. Diabetes

showed a particularly large increase (5.7%;

95% CI, –1.7 to 13.7%), but was based on a

small number of deaths (Table 2). We estimate that the association between mortality

and an IQR increase in sulfate on the day

before death is largest for respiratory deaths

and death by stroke, although none of these

associations were statistically significant. 

Because some studies have suggested that

the sulfate–mortality association has a different lag than for other particles (e.g., Ito et al.

2006), we examined longer lags; however, the

largest association was seen at lag 1 (results

not shown).

We also estimated possible effect modification on both the personal and census tract levels. Personal variables evaluated included sex

and education, whereas census tract variables

considered were poverty rate and median

income. None of these interaction terms were

even marginally significant (p > 0.50). 

Further, the broken stick model specifying

two BC slopes defined below and above the

median BC level (0.22 μg/m3), showed no evidence of nonlinearity of the BC slope (p = 0.32

for a change in slope). 

Traffic pollution and mortality in the Greater Boston area

Environmental Health Perspectives • VOLUME 115 | NUMBER 5 | May 2007 753

Table 3. Descriptive statistics for exposure and meteorologic covariates, Boston 1995–2002.

Days of data 

Covariate Mean Median SD Range IQR Q1 Q3 available

Apparent temp (°C) 9.715 8.232 10.315 45.793 17.724 0.849 18.573 2,705

HSPH BC (g/m) 1.059 0.897 0.679 7.188 0.757 0.591 1.348 2,127

GIS-based BC (g/m) 0.255 0.218 0.171 2.619 0.203 0.132 0.334 2,114

HSPH sulfate 3.087 2.378 2.514 28.958 2.259 1.531 3.79 1,136

temp, temperature. Q1 and Q3 are quartiles.

Table 4. Percent increase in deaths for IQR increase in exposure, Boston 1995–2002.

Exposure measure p-Value Percent increase for IQR increase (95% CI) No. of cases in analysis

Univariate

GIS-based BC < 0.0001 2.3 (1.2 to 3.4) 107,925

HSPH sulfate 0.0169 1.1 (0.01 to 2.0) 64,080

Bivariate

GIS-based BC 0.0339 2.2 (0.16 to 4.2) 57,029

HSPH sulfate 0.2991 0.45 (–0.45 to 1.6)

Table 5. Stratified cause of death-specific regression results (lag-1).

Cause of death Particle type p-Value Percent increase for IQR increase (95% CI)

CVD BC 0.13 1.5 (–0.4 to 3.4)

Sulfate 0.72 –0.2 (–1.5 to 1.0)

Stroke BC 0.06 4.4 (–0.2 to 9.3)

Sulfate 0.39 2.0 (–2.4 to 6.1)

Respiratory BC 0.04 3.7 (0.1 to 7.4)

Sulfate 0.20 2.1 (–1.1 to 5.3)

Diabetes BC 0.13 5.7 (–1.7 to 13.7)

Sulfate 0.36 2.9 (–3.1 to 9.5)
Discussion

We have shown that both traffic particles, as

indexed by BC, and sulfate particles are associated with increased risk of death in Boston.

The association with traffic particles derived

from a GIS-based exposure assessment, which

has not previously been used in examining the

acute effects of particle exposure. The association with traffic particles was independent of

the sulfate association. The association with

sulfate was reduced, and lost significance,

after control for traffic particles. The lack of

significance may be attributed partly to the

reduced sample size. Moreover, the effects of

traffic particles were seen for respiratory, cardiovascular, and stroke deaths, whereas the

sulfate associations were predominantly with

respiratory deaths and strokes, with no association with cardiovascular deaths, similar to

the results of Laden et al. (2000). 

The finding of an association with traffic

particles, especially the finding regarding vascular health, is consistent with recent acute

exposure studies suggesting that traffic particles may be associated with myocardial infarctions (Peters et al. 2001, 2004), arrhythmias

(Peters et al. 2000), alterations in heart rate

variability (Schwartz et al. 2005), and

impaired endothelial function (O’Neill et al.

2005). It is also consistent with chronic exposure studies such as that of Hoek et al. (2002),

who found that relative risk of death associated with spatial variability in traffic particles

was larger than the risk reported for PM2.5 in

other cohort studies. The recent finding in the

American Cancer Society study that spatial

variability of PM2.5 exposure in Los Angeles,

California, had a larger risk coefficient than

for PM2.5 across cities is also suggestive,

because traffic particles account for a larger

fraction of within-city variation in concentration (Jerrett et al. 2005). 

Roemer and van Wijnen (2001) examined

the association with mortality indirectly, by

reporting a steeper slope between ambient PM

and daily deaths for persons who lived near

major roads, where more of the PM was likely

produced by traffic. The effect of an interquartile increase in BC in our study was larger than

has previously been noted for IQR increases in

PM2.5, again suggesting greater toxicity.

The association between sulfate particles

and daily deaths in our study is consistent

with the results published by Laden et al.

(2000), analyzing data from two decades earlier. Indeed, given that the IQR for sulfates in

our study was 2.26 μg/m3, the effect size we

found, even after controlling for traffic particles, is about twice as big as reported in the

Laden study. Hence, the lack of significance in

the model adjusting for traffic particles is most

likely a power issue. Other studies, using

source apportionment techniques, have also

reported associations with sulfate factors (e.g.,

Ito et al. 2006). Although their inclusion of

mass other than SO4

2– per se precludes a

quantitative comparison, they qualitatively

support these results. These results are also

consistent with findings of associations of sulfate particles in Montreal (Goldberg et al.

2001). Hence, even at current concentrations,

sulfate particles may be a major public health

risk. Sulfate particles in the Northeast are predominantly the result of long-range transport

from coal-burning power plants. Indeed, a

study comparing sulfate concentrations in

Western Massachusetts, far from any local

sources, and Boston found that the concentrations were essentially identical (Salmon et al.

1997). Therefore, control of sulfur emissions

from such power plants remains a major public health concern. 

Our findings do differ from the results of

a study in Atlanta, Georgia (Klemm et al.

2004). However, sulfate particles are formed

by photochemical processes, resulting in

much higher concentrations in the summer

than other periods. Studies have shown that

low ventilation conditions, such as those

found when central air conditioning is operating, substantially reduce both the correlation

of indoor with outdoor particles and the slope

of the association (e.g., Sarnat et al. 2000).

Hence, ambient sulfate particle concentrations in Atlanta (the prevalence of central air

conditioning was 87% in 1999) are likely a

much poorer surrogate for exposure to sulfate

than in Boston, where central air conditioning prevalence was 17% in 1999. Thus these

differences in results could result from greater

exposure error in Atlanta. 

Developing data also suggest that urban

sulfate particles, which are not simply ammonium bisulfate, are associated with adverse

responses. For example, a recent study in

Boston examined endothelial function in diabetics, as assayed using flow mediated dilation

of the brachial artery (O’Neill et al. 2005).

The association with sulfate particles was as

strong as (and more significant than) with BC

in that study. Another study reported an association between sulfates and ventricular

arrhythmias in subjects with implanted defibrillators (Dockery et al. 2005). 

The two-pollutant model we fit to estimate the independent effects of BC and sulfate requires some caution. First, the degree of

measurement error in the two exposures may

well be different, which can distort the association of collinear exposures. For instance,

Zeger et al. (2000) showed that upward bias

can occur in multipollutant models, but only

when there is strong correlation among the

pollutants or among the multiple measurement errors. In our case the correlation was

modest, suggesting bias toward the null. In

addition, the reduced power in such models

makes chance a plausible explanatory factor for

differences from the single pollutant models,

particularly in a single-city analysis. 

The finding of an imprecise but considerably larger association with diabetes deaths is

consistent with previous studies showing that

diabetics are a susceptible subgroup (e.g.,

Goldberg et al. 2001), as well as recent studies

of deaths from diabetes (e.g., Ostro et al. 2006). 

Previous studies using GIS-based land use

regressions to estimate exposures have focused

on estimating long-term exposure, and studies

of health effects associated with such exposures have focused on longer-term effects.

Often, the GIS models have been deterministic (e.g., depending on predictors such as traffic density), based on dispersion models, or

based on empirical smoothing of observed

data. Recently some models have tried to

incorporate at least two of these approaches,

and we have used land use regression predictors plus smoothing. 

Our model differs in that it seeks to capture short-term temporal variations, using

concentrations at a central site and weather

parameters as predictors. Spatial variability in

the temporal effects are captured by fitting

separate spatial models for warm and cold season, where differences in average mixing

heights produce different spatial patterns, and

by using a log-linear model, with weather

parameters predicting different percent

changes in concentrations at different locations, rather than different absolute changes.

This allowed us to explain 67% of the temporal variation at the 80 different locations for

which we had multiday measurements of BC.

Using this approach, we have captured some

additional variation in exposure between event

and control days that would not have been

possible using a central-site monitor for each

subject. We believe this represents an advance

in exposure assessment for short-term effects.

The size of the estimate for traffic particles

deserves some comment. Because BC is a

proxy for traffic particles, the true fine mass

concentration (in micrograms per cubic meter)

of traffic particles in Boston is greater than the

concentration of BC. This is one major reason

we have focused on the IQR rather than a specific increment (the other is for comparison

with sulfates). However, the size of effect for an

IQR change in exposure is large compared

with other studies of the acute effects of PM on

mortality. This is likely attributed to a combination of less measurement error (compared

with central-site measurements), reducing the

downward bias produced by measurement

error, and greater toxicity of traffic particles

than particles from other sources. 

A potential statistical issue that arises

when using spatial–temporal predictions of

exposure is that these quantities are uncertain

rather than measured quantities. Thus, there

is some uncertainty associated with these

Maynard et al.

754 VOLUME 115 | NUMBER 5 | May 2007 • Environmental Health Perspectives
Traffic pollution and mortality in the Greater Boston area

Environmental Health Perspectives • VOLUME 115 | NUMBER 5 | May 2007 755

measures, which could represent a different

type of measurement error that could bias the

resulting health effect estimates. We have

begun to investigate this issue, and preliminary simulation studies suggest that in logistic

regression settings in which the true effect is

relatively small, this bias is neglible. Such

empirical findings agree with the bias formulas for logistic regression in the work of

Carroll et al. (1995).

In sum, we find evidence that both traffic

particles and particles from coal burning power

plants are associated with increased risk of

mortality in the Boston metropolitan area,

with the traffic particle association more significant, and larger. Because Boston currently is

in compliance with the current and proposed

U.S. Environmental Protection Agency (2006)

PM2.5 standard, this suggests that current standards are not protective of public health. 

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