Cifuentes
et al.
ISSN 1047-3289 J. Air & Waste Manage.
Assoc. 50:1287-1298
TECHNICAL PAPER
Copyright 2000 Air & Waste Management Association
Effect of the Fine Fraction of Particulate Matter versus the
Coarse Mass and Other Pollutants on Daily Mortality in
Santiago, Chile
Luis A. Cifuentes, Jeanette Vega, and Katherine Köpfer
Pontificia Universidad Católica de Chile, Santiago, Chile
Lester B. Lave
Carnegie Mellon University, Pittsburgh, Pennsylvania
ABSTRACT
Daily counts of non-accidental deaths in Santiago, Chile,
from 1988 to 1996 were regressed on six air pollutants—
fine particles (PM2.5), coarse particles (PM10-2.5), CO, SO2,
NO2, and O3. Controlling for seasonal and meteorological conditions was done using three different models—
a generalized linear model, a generalized additive model,
and a generalized additive model on previously filtered
data. Single- and two-pollutant models were tested for lags
of 1–5 days and the average of the previous 2–5 days.
The increase in mortality associated with the mean
levels of air pollution varied from 4 to 11%, depending
on the pollutants and the way season of the year was considered. The results were not sensitive to the modeling
approaches, but different effects for warmer and colder
months were found. Fine particles were more important
than coarse particles in the whole year and in winter, but
not in summer. NO2 and CO were also significantly associated with daily mortality, as was O3 in the warmer
months. No consistent effect was observed for SO2. Given
particle composition in Santiago, these results suggest that
combustion-generated pollutants, especially from motor
vehicles, may be associated with increased mortality. Temperature was closely associated with mortality. High temperatures led to deaths on the same day, while low
temperatures lead to deaths from 1 to 4 days later.
IMPLICATIONS
Significant statistical associations were found between daily
mortality in Santiago for 1988–1996 and several measures
of ambient air pollution (PM2.5, PM10–2.5, CO, NO2, and O3 ).
The risks for PM2.5, NO2, and CO were comparable. PM10–2.5
and O3 were significant only during the warmer months.
SO2 did not show a consistent statistically significant risk.
The results suggest that combustion sources may be responsible for increased mortality in Santiago.
Volume 50 August 2000
INTRODUCTION
In recent years, many studies have reported an association between different air pollutants and the incidence of
numerous health endpoints, ranging from pulmonary
function decrements to respiratory symptoms and premature mortality. A consistent effect of air pollution on
mortality has been found in studies of different design
and in different parts of the world.1-3 Several meta-analyses4-8 have focused on the association between suspended
PM and mortality, while relatively less emphasis has been
given to gaseous pollutants.
Data availability limited early studies to characterizing PM by total suspended particles (TSP). In the last decade, extensive data on respirable particles have become
available, measured mainly as PM10 in the United States
and as black smoke (BS) in Europe. Still, little data on fine
particles (PM2.5) has been available. Two prospective studies2,3 and a time-series study9 have found associations between PM2.5 and premature mortality in several U.S. cities.
Besides the U.S. studies, several recent time-series studies10-15 have found associations between different measures
of PM in other parts of the world.
Chile’s capital, Santiago, has high levels of particulate air pollution, due in part to its geographic situation
and climatic conditions and in part to its more than
5 million inhabitants and close to 800,000 motor vehicles.
The city is situated in a valley surrounded by mountains
and has stable atmospheric conditions, including low
speed and infrequent winds. An inversion layer prevails
on most winter days 600–900 m above the city. This layer
decreases the ventilation and dispersion of pollutants.16
As a result, the ambient levels of PM during the winter
months are among the highest observed in any urban area
in the world, averaging 111 µg/m3 of PM10 from 1988 to
1996. The Chilean National Environmental Commission
has embarked on a 15-year plan to clean the air of the
Journal of the Air & Waste Management Association 1287
Cifuentes et al.
city, at an estimated cost of more than $1.5 billion.17 This
expensive abatement program will take resources from
other social programs and from the private sector.
Three studies investigated the relationship between
levels of particulate air pollution and mortality in
Santiago.18-20 All of them examined the relationship between PM10 and daily deaths using time-series analysis.
The results show a strong, consistent association between
PM10, daily total mortality, and mortality due to cardiovascular and respiratory diseases. The effects occur for all
age groups, but especially among those over 65. To date,
however, the specific impact of the fine and the coarse
fraction of PM and of other pollutants on daily deaths
has not been explored adequately. The analysis by
Sanhueza et al.20 appears to find a stronger effect of PM10
than of PM2.5.
The main objectives of this study are to determine
(1) whether there was a differential impact of the fine
fraction and of the coarse mass of PM and (2) whether
there was an impact of the other air pollutants on daily
mortality.
DATA AND METHODS
Mortality Data
Mortality data were obtained for residents of the Santiago
metropolitan area from death certificates processed by the
Instituto Nacional de Estadísticas, the official source of statistical data in Chile. The metropolitan area of Santiago includes
34 municipalities. Two were excluded, because they are mostly
rural and are outside the urban area of the region. Deaths of
residents that occurred outside of Santiago were also excluded,
as were accidental and trauma deaths (ICD9 [International
Classification of Disease, 9th revision] ≥ 800).
Air Pollution and Meteorological Data
Air pollution and meteorlogical data were obtained from
the records of the air pollution-monitoring network operated by the Servicio de Salud Metropolitano del
Ambiente. From 1988 to 1996, this network operated five
monitoring stations. Four of the monitors are closely located around downtown Santiago, and the fifth is in the
far northeast corner of the city, 15 km from downtown.
Hourly measures of SO2, NO2, CO, and O3 were obtained
in all five stations. Daily measures of fine (PM2.5) and coarse
(PM10–2.5) particles were obtained by dichotomous samplers at all five locations. Missing data for particles were
more common in the summer months, when the levels
are lower. During the early years, sampling in the summer was performed every other day. For the high pollution season, the data had few omissions.
Approximately 70% of the days had daily measurements for particles. The missing data fell disproportionately in the summer months, due to the sampling strategy.
1288 Journal of the Air & Waste Management Association
For data missing for a particular day, we substituted the
average of the two surrounding observations, increasing
the availability of data to 89%. For the gases, we discarded
the days with less than 18 hr of data, resulting in approximately 94–98% of days with data, except for ozone, which
had only 78% of days with data.
Measures of temperature and relative humidity were
obtained hourly in four of the five monitoring stations
and were available for 99% of the days.
Exposure Assessment
For the gaseous pollutants, the 1-hr maximum, the 24-hr
average, and the maximum 8-hr average for each monitor were computed. For PM, only the 24-hr average was
available. Since we had data for five monitors, we considered each monitor independently, as well as the average
of all monitors and of the four central monitors that had
data for any given day. For all variables, we tested the
same day and up to 5 previous days (lags 1–5), and the
average of the previous days, from 1 to 5.
Statistical Models
As in previous studies, we modeled the association between the number of daily deaths and explanatory factors using a Poisson model. In this model, it is assumed
that log(E(Y))= β0 + β1X1 + …+ βmXm, where Y is the daily
count of deaths, Xi are the explanatory variables, and βi
are the regression coefficients for those predictors (usually referred to as “betas”).
A number of specifications are plausible. We fitted a
range of models to examine the sensitivity of the results
to model specification. We fitted generalized linear models21 using SAS22 and S-Plus.23 The standard errors of the
coefficients were corrected for overdispersion by dividing
them by the square root of the overdispersion parameter
φ, defined as the deviance of the model over the residual’s
degrees of freedom. We also adjusted generalized additive
models24 that permitted us to model the association between the dependent variable and the explanatory variables in a non-parametric way.
Seasonal Controls
Seasonality has been controlled in several ways in previous studies: (1) by using different order continuous time
terms and “indicator” variables (which are yearly, quarterly, and day-of-the-week variables),25 (2) by including
sine/cosine terms in the model, 26 (3) by including
weighted moving averages or non-parametric smoothers
of the dependent variables in the model,27,28 or (4) by
pre-filtering the data using different types of filters.29-32
Schwartz and colleagues33 provide a good summary of
these approaches and suggest ways to check the adequacy
of the seasonal controls.
Volume 50 August 2000
Cifuentes et al.
Clearly, season per se does not affect mortality. Temperature, air pollution, and behavior associated with holidays affect mortality. We prefer models that contain causal
variables rather than indicator variables, which are surrogates for the underlying causal variables. However, we find
that indicator variables for time, season, month, and day
of the week improve the model fit. Insofar as these indicator variables are surrogates for the causal variables of
interest, they generally bias the estimated effect of air
pollution and temperature downward. For example, the
central air pollution monitors are an imperfect indicator
of population exposure; thus, the surrogate variables may
encompass the unmeasured or imprecisely measured effects of air pollution. We report the models with indicator variables because they improve the fit; however, we
caution that the estimated effects of the causal variables
are likely to be biased downward.
In Santiago, daily deaths and most air pollutant concentrations peak in winter. This presents a potentially serious problem of confounding. Previous studies of Santiago
showed the importance of seasonal controls—the estimated effect of PM10 decreased almost 4-fold when more
controls for seasonality were included in the model (see
Ostro et al.,19 Table 6).
We tried three different approaches to control for
seasonal confounding. In our generalized linear model,
we used linear and quadratic terms for day of the study
and binary variables for each year (eight variables), quarter (three variables), day of the week (six variables), and
all holidays (one variable). We also tested adding variables for each month of the study (107 variables in total). We tested the inclusion of each of these variables
sequentially, checking the effect on the autocorrelation
of the residuals and in the Akaike Information Criteria
of the model.
In our generalized additive models (GAM), we used
two approaches:
(1) including in the model a locally weighted regression (LOESS) of time,34 and
(2) pre-filtering the variables using a LOESS of time,
month, and day-of-the-week indicator variables.
The LOESS method is a generalization of a weighted
moving average over a window of the data surrounding
the observation. The method places a greater weight on
the observations closer to the center of the window. Determining the length of the window is crucial. The shorter
the window, the higher the frequency of the temporal
cycle controlled or removed from the data. In both approaches, we determined the length of the window by
minimizing the autocorrelation of the residuals. The filtering was done by subtracting the predicted value of the
model from each observation. In that way, we obtained a
series with 0 mean and with almost no seasonal components.
Volume 50 August 2000
For the dependent variable, we added the mean of the
variable, so we could still use a Poisson model. We also
tested the use of the ratio of the observed value over the
predicted value, with similar results.
Weather Controls
After we had adjusted the seasonal control model, we included the controls for weather effects. The influence of
temperature on mortality is well documented.35 The association is not linear, with some studies reporting a hockeystick shape.36
In our lineal models, we tried several ways to control
for weather effects. First, we used linear and quadratic
terms of the daily average, maximum and minimum temperature, and relative humidity for the same day and the
average of up to the 5 days before the date of death, and
binary variables for hot and cold days. Second, we tested
piecewise linear variables for average temperature of the
same day and of the previous days, varying the location
of the knot from 18 to 25 °C in half degrees. We selected
the weather variables by stepwise selection, using the
stepwise procedure in SAS.22
In our generalized additive models, we used smoothed
terms for the same-day temperature and relative humidity and the average of 1–5 previous days. The smoothing
was performed using a smoothing spline with 8 degrees
of freedom. No indicator variables for extreme weather
conditions were used in this case. In the filtered model,
we pre-filtered the temperature and humidity data prior
to including it in the model.
Pollution Effects
After the seasonal and weather controls had been established, pollution variables were added to the models. The
differential effect of fine and coarse particles were our main
interest, but we also considered SO2, CO, NO2, and O3 as
potential predictors. We tested each of the pollutant specifications defined previously, first in single-pollutant models and then in models that included two pollutants
simultaneously. We selected the specification that had the
highest t-ratio from all the specifications tested.
We investigated the effect of pollution separately for winter and summer (summer includes October–March while winter includes April–September). Two variables were defined for
each pollutant—one variable for the summer months and
the other for the winter months. In this way, the control for
seasonality and weather is the same for the whole year, with
only the pollution effect varying by season.
RESULTS
The statistics for the analysis variables are shown in Table
1 and the time-series plots are shown in Figure 1. The
average number of daily deaths across the study interval
Journal of the Air & Waste Management Association 1289
Cifuentes et al.
was 56.6. The data show a strong Table 1. Summary statistics for analysis variables in Santiago, 1988–1996.
seasonal component, as is eviPercentiles
dent in Figure 1 and in Table 2,
Obs
Mean
5%
50%
95%
Max
IQRa
which shows the mean and Variable
Disease
Deaths
3288
56.6
39.0
55.0
79.0
116.0
15.0
interquartile range for winter
Avg. Temperature (°C)
3286
15.9
8.3
15.9
23.1
27.2
8.1
and summer months. Daily PM (µg/m3)
2303
64.0
19.5
50.0
148.7
282.3
54.8
2.5
deaths increased slightly over
Completed Data
2927
58.3
19.3
42.6
144.5
282.3
47.6
3
2303
47.3
20.0
44.8
83.5
177.8
22.3
the study period and were 30% PM10-2.5 (µg/m )
Completed Data
2927
46.4
21.3
44.3
80.3
177.8
19.4
higher in winter than in sum- CO 24-Hr Avg. (ppm)
3247
2.5
0.6
1.9
6.2
12.8
2.2
mer. CO and PM2.5 winter aver- SO2 24-Hr Avg. (ppb)
3092
18.1
5.2
14.3
43.6
86.6
11.7
3197
40.8
14.3
32.8
95.0
298.5
27.3
ages were 2.8 and 2.7 times the NO2 8-Hr Avg. 4 mon (ppb)
O3 Max. 1-Hr mon 5 (ppb)
2564
90.9
16.0
85.0
175.9
963.0
62.0
summer averages. Only PM10–2.5
does not show a marked seasonal cycle. Ozone is high in Notes: Averages for all five monitoring stations, except NO2 and O3. aIQR refers to the interquartile range = percentile 75–percentile 25.
summer but also remarkably
it (due to colinearity, it is not possible to use terms for
high in winter. Winter levels may be due to relatively warm
each month of the study in this case). The results of the
temperatures and to the high percentage of days with sunGAM filtered model are shown at the bottom of Table 4.
shine in Santiago. Average PM10 (computed as the sum of
These models include only the two filtered temperature
PM2.5 and PM10–2.5) during the study period was more than
terms (same day and average of the previous four days)
twice the U.S. annual National Ambient Air Quality Stanand one filtered pollution variable. The results are more
dards (NAAQS) (Chile has only a daily standard for PM10).
stable than in the previous models.
Average PM2.5 was more than 4 times the new U.S. annual
This analysis shows that results for the different modNAAQS.
eling approaches are quite similar, and that the control
Table 3 presents the correlations among all pollutfor seasonality has by far the biggest impact on the estiants and temperature. Fine particles are highly correlated
mated association of particulate air pollution with morwith CO and SO2, but not as much with the coarse fractality. The model with the best fit in terms of φ and the
tion and with NO2. Ozone has a low correlation with all
autocorrelation of the residuals is the filtered GAM model,
other pollutants. PM2.5, CO, SO2, and NO2 are all negaalthough the GAM model is close. Due to its relative simtively correlated with temperature. PM10–2.5 does not show
plicity, and since the results were not very different, the
a correlation with temperature, confirming its almost null
GAM model was used as the base model for all subsequent
seasonal variation. The correlation between monitors of
analysis, with the generalized linear model and the GAM
PM2.5 was quite high for the centrally located monitors,
filtered model considered for sensitivity analysis.
ranging from 0.90 to 0.95. However, the northeast moniAll the models controlled seasonality of the data reator had lower correlations with the other monitors, from
sonably well, as shown in Figure 2, which gives the time0.65 to 0.78. As expected, the correlations between PM10–2.5
series plot of the residuals of the GAM model. The final
monitors were much lower.
models show a significant association of temperature
with deaths; high temperatures increased deaths on the
Seasonal and Weather Control
Table 4 shows the model fit and the sensitivity of the coefficient for same-day PM2.5 for different model specificaTable 2. Mean and IQR of analysis variables for summer and winter.
tions used to control for seasonality and weather. The
generalized linear model included six terms for high and
Mean
IQR
Variable
Winter
Summer
Winter
Summer
low temperatures (above and below the knot, whose optimal value was 22.5 °C), both linear and quadratic; indiDisease Deaths
63.4
49.8
16.0
10.0
cators for cold days; indicators for day of the week and
Avg. Temperature (°C)
12.3
19.5
4.5
4.3
PM2.5 (µg/m3)
82.4
32.8
60.0
15.8
for years; and a quadratic time term, for a total of 17 variCompleted Data
83.0
31.9
60.7
14.8
ables. Including day-of-the-week indicators increased the
PM10-2.5 (µg/m3)
49.9
42.9
28.5
15.5
PM coefficient. When a term for each month of the study
Completed Data
49.6
43.0
28.3
13.4
CO 24-Hr Avg. (ppm)
3.6
1.3
2.6
0.8
was added, the PM coefficient decreased to half the value.
SO2 24-Hr Avg. (ppb)
22.5
13.7
17.2
6.6
The results for the GAM model, shown next in Table 4,
NO2 8-Hr 4 Mon (ppb)
52.4
29.3
39.9
16.0
show similar effects—including the temperature and dayO3 Max. 1-Hr Mon 5 (ppb)
72.8
108.2
65.0
48.0
of-the-week terms increased the PM coefficient, while inNote: Averages for all five monitoring stations, except NO2 and O3.
cluding indicators for each month of the year decreased
1290 Journal of the Air & Waste Management Association
Volume 50 August 2000
Cifuentes et al.
Table 3. Correlation of pollutants and temperature.
PM2.5
PM10-2.5
CO
O3
SO2
NO2
Temperature
PM2.5
PM10-2.5
CO
O3
SO2
NO2
Temp.
1.00
0.52
0.80
-0.02
0.70
0.58
-0.50
0.52
1.00
0.47
0.11
0.40
0.61
0.04
0.80
0.47
1.00
-0.01
0.62
0.65
-0.49
-0.02
0.11
-0.01
1.00
0.19
0.01
0.40
0.70
0.40
0.62
0.19
1.00
0.41
-0.30
0.58
0.61
0.65
0.01
0.41
1.00
-0.28
-0.50
0.04
-0.49
0.40
-0.30
-0.28
1.00
specification, however, was the
maximum 1-hr concentration of
monitor 5, with a t-ratio of 2.5. This
was expected because the prevailing
winds in Santiago during the day are
from the southwest and, therefore,
O3 is formed in the northeast section of the city, where monitor 5 is
located. In all cases except O3, the
differences among the exposure
specifications were small.
Lag Structure
Table 5 shows the regression coeffecients for one-pollutant models, for lags 0-5 days before the date of death, and
for the moving average of the previous 2–5 days. The examination of models including each pollutant alone indicated that a 1-day lag was the best predictor for CO and
O3, and a 2-day lag was the best specification for the other
pollutants. When the average of the previous days was
considered, in almost all cases the coefficient and the
t-ratio kept increasing up to 5 days before the date of death.
However, for the average of more than 1 day, the differences were small. The same-day value had the smallest
coefficient and the smallest t-ratio in all pollutants. All
the subsequent analyses were performed using the average of the previous 2 days.
same day. Low temperatures increased deaths 1–4 days
later, as shown in Figure 3. Relative humidity did not
have a significant effect in any of the models tested.
Specification of Exposure
Having selected our base model, we tested different specifications for exposure to air pollutants. The regression
coefficients for each monitor of PM2.5 were similar, except
for the northeast monitor, which had the lowest beta. The
results for the average of the four central monitors (beta =
0.73, t-ratio = 6.7) were slightly better than for the average of all monitors (beta = 0.71, t-ratio = 6.4), so we used
the average of the central four monitors in the analysis.
The same was true for PM10–2.5.
For CO and SO2, the best specifications were the average of all monitors’ 24-hr mean. For NO2, the best specification was the 8-hr average of the central four monitors.
For O3, the 24-hr average was less significant, with tratios of 1.7 for both the maximum and the average of
all monitors. For both the geographical average and maximum of all monitors, the 8-hr average had a t-ratio of
2.2, and the maximum 1-hr had a t-ratio of 2.4. The best
Two-Pollutant Models and Season Interaction
The main goals of the study were to differentiate the
effects of the fine and coarse fractions of PM and to assess the potential effect of other pollutants. Table 6 shows
the relative risk and the t-ratio for each combination of
the pollutants in a two-pollutant model, plus the results
Table 4. Sensitivity of the model fit and PM2.5 coefficient to alternative controls for season and weather. Average of the previous 2 days of PM2.5 completed data for missing
observations.
Beta
x1000
t-ratio
φ
AIC
Generalized Linear Model
High and low temperature, year, quarter
Day of the week indicators
Each month of the study indicators (108 vars)
1.20
1.26
0.57
12.9
13.4
4.5
1.332
1.318
1.092
3802.3
3771.3
3235.4
0.195
0.192
-0.015
0.174
0.180
0.021
0.092
0.101
-0.025
GAM Model
LOESS of time (span = 68 days)
Spline of temp. and spline of avg. temp. prev. 4 days (8 days of each)
Day of the week indicators
Month of the year indicators
0.76
0.92
1.03
0.72
11.6
11.4
12.8
6.8
1.108
1.099
1.086
1.079
3247.0
3233.9
3203.6
3193.7
0.010
0.001
-0.006
-0.022
0.023
0.028
0.031
0.022
-0.038
-0.030
-0.022
-0.026
GAM Filtered Model
Filtered with LOESS of time (span = 68 days)
Filtered by time and day of week
Filtered by time, day of week, and month
0.48
0.62
0.63
3.7
4.7
4.9
1.104
1.083
1.056
3124.2
3065.6
3038.3
-0.002
-0.007
-0.004
0.027
0.029
0.033
-0.026
-0.019
-0.016
Volume 50 August 2000
Partial AC of Residuals
Lag 1
Lag 2
Lag 3
Journal of the Air & Waste Management Association 1291
O3 1hr max [ppb] NO2 8hr [ppb]
SO2 24hr [ppb]
CO 24hr [ppm]
PM10-2.5 [µg/m3]
PM2.5 [µg/m3]
Temp [°C]
Deaths
Cifuentes et al.
Figure 1. Time-series plots for all disease mortality, average daily temperature, PM2.5, PM10-2.5, CO, SO2, NO2, and O3 for the years 1988–1996. The
verticle lines indicate the first day of each year.
for single-pollutant models. The results are shown for
the whole year and separately for summer and winter.
The relative risks are computed for an increase of ambient concentrations of each pollutant equal to the average concentrations for the corresponding data period,
so that the magnitude of the effects of each pollutant
can be compared.
The first thing to notice in Table 6 is that for the singlepollutant models (shown in the diagonal of each panel),
the effects of all pollutants are significant, with the exception of ozone in winter months. When the pollutants
are combined in pairs, however, the results change. For
the whole year, the effects of PM2.5, CO, and NO2 are robust to the inclusion of other pollutants, in contrast to
PM10–2.5, SO2, and O3. The total risks of the mean concentrations for these air pollutants are quite stable, ranging
from 1.039 to 1.042 when considered alone and from
1292 Journal of the Air & Waste Management Association
Figure 2. Time-series plot of the residuals of the GAM cycle. Note
that all seasonal cycles have been removed.
Volume 50 August 2000
Cifuentes et al.
Table 5. Regression coefficients for different lags and number of days in previous moving average for each pollutant.
PM2.5
Beta x1000 t-ratio
PM10-2.5
Beta x1000 t-ratio
CO
Beta x1000 t-ratio
SO2
Beta x1000 t-ratio
NO2
Beta x1000 t-ratio
O3
Beta x1000 t-ratio
Lags
0
1
2
3
4
5
0.27
0.43
0.54
0.28
0.14
0.29
3.0
4.7
6.1
3.1
1.6
3.3
-0.09
0.41
0.86
0.44
0.34
0.24
-0.5
2.6
5.5
2.8
2.1
1.5
7.72
12.21
10.88
5.55
5.42
3.55
3.8
6.1
5.6
2.8
2.7
1.8
0.47
0.88
1.13
0.76
0.82
0.56
2.0
3.8
5.0
3.4
3.6
2.5
0.07
0.60
0.62
0.41
0.26
0.21
0.6
5.3
5.6
3.6
2.3
1.9
0.01
0.10
0.06
0.04
0.03
0.01
0.1
2.1
1.4
0.8
0.6
0.3
Number
of previous
days in
average
2
3
4
5
0.73
0.81
0.83
0.96
6.7
7.0
6.6
7.1
0.91
1.10
1.25
1.36
4.9
5.3
5.5
5.5
16.25
18.15
21.35
23.71
7.2
7.2
7.7
8.0
1.37
1.50
1.78
1.83
5.6
5.7
6.4
6.3
0.95
1.09
1.21
1.25
7.2
7.4
7.5
7.2
0.15
0.18
0.27
0.33
2.5
2.4
3.2
3.5
Notes: The base model includes the following terms: LOESS of time of span 68 days, spline of same-day temperature, spline of the average temperature for the previous four days, and
indicators for day of the week and month of the year. Each pollutant included alone in the model. PM2.5 and PM10-2.5 data completed for missing observations.
1.050 to 1.051 when considered in pairs, for the whole
period of analysis. This pattern is similar in winter, although the risks increase—for the pollutants alone, they
range from 1.048 to 1.055, and in pairs from 1.059 to
1.064. The combination with the highest risks is PM2.5
plus CO.
In summer, different results emerge. Some of the following results are due to summer conditions, but some
are due to the fact that PM data are collected only every
2nd day in summer. We fill in the missing days by taking
the average of the measured PM values on the adjacent
days. Since the PM data have only half the information
of the other pollutants, we expect that the estimated coefficients will display greater variability with significance
biased toward zero. In contrast, the colinearity among the
pollution variables will mean that the other estimated
coefficients might be biased upward.
Given this caveat, we note that almost all combinations of pollutants have significant risks, and these are
higher than for winter. Most noteworthy is that PM10–2.5
has a greater risk and is more significant than the fine fraction. In fact, the combination of pollutants with the highest risk of all is PM10–2.5 plus CO, with a total risk of 1.127.
Ozone and SO2 also have significant risks, but of a magnitude about half that of the other pollutants. The significance of PM2.5 and NO2 decrease, while CO remains stable.
This may be due to the fact that now both PM10–2.5 and O3
have significant effects, and PM10–2.5 is correlated (r = 0.50)
with PM2.5, while O3 is correlated (r = 0.41) with NO2.
Figure 4 shows the association between daily mortality
and each air pollutant when they are simultaneously included
in a model with PM2.5. The shape of the PM2.5 concentrationresponse function does not change much with any of the other
pollutants. The biggest change in the shape occurs in the model
1.20
1.20
1.10
1.10
1.00
1.00
0.90
0.90
Figure 3. Association between daily mortality and same-day temperature (left) and average temperature of the previous 4 days (right), using a spline
of 8 degrees of freedom. The outer curves show the 95% confidence interval. The other terms in the model are a LOESS of time of span 68 days and
indicators for day of the week and month of the year.
Volume 50 August 2000
Journal of the Air & Waste Management Association 1293
Cifuentes et al.
Table 6. Relative risk (RR) and t-ratios for each pollutant in single- and two-pollutant models, for the whole year and for each season of the year.
All
PM2.5
t-ratio
RR
PM2.5
PM10-2.5
CO
SO2
NO2
O3
1.042
1.006
1.025
1.006
1.031
1.006
6.7
0.5
3.5
1.1
4.5
1.0
1.039
1.043
1.035
1.013
1.038
1.009
RR
t-ratio
1.055
0.990
1.025
0.999
1.036
0.997
6.1
-0.7
2.1
-0.2
4.0
-0.5
Winter
PM2.5
PM10-2.5
CO
SO2
NO2
O3
RR
1.058
1.065
1.053
1.027
1.043
1.027
PM10-2.5
t-ratio
3.9
3.0
5.3
3.0
3.4
2.4
SO2
NO2
O3
RR
t-ratio
RR
t-ratio
RR
t-ratio
3.3
1.2
7.2
1.8
3.8
0.9
1.039
1.034
1.038
1.026
1.037
1.011
5.2
3.3
6.0
5.6
5.9
1.9
1.019
1.009
1.026
1.006
1.039
1.008
2.5
0.8
3.9
1.1
7.2
1.4
1.036
1.031
1.036
1.010
1.026
1.013
4.9
3.0
4.8
1.8
3.8
2.3
1.026
1.013
1.041
1.009
1.024
1.005
RR
t-ratio
RR
t-ratio
RR
t-ratio
RR
t-ratio
RR
t-ratio
1.062
1.037
1.049
1.011
1.054
1.001
5.1
3.7
4.3
1.6
5.4
0.2
1.039
1.004
1.052
1.008
1.032
0.998
3.4
0.3
5.9
1.2
3.9
-0.3
1.057
1.029
1.049
1.029
1.047
1.003
5.0
2.5
5.0
4.7
5.8
0.6
1.025
0.993
1.027
1.001
1.048
1.001
2.3
-0.5
2.6
0.2
6.7
0.1
1.052
1.028
1.051
1.016
1.034
1.007
4.8
2.4
4.4
2.1
3.8
1.2
RR
t-ratio
RR
t-ratio
RR
t-ratio
RR
t-ratio
RR
t-ratio
1.036
1.096
1.053
1.025
1.037
1.026
2.3
4.7
5.3
2.9
2.8
2.3
1.038
1.074
1.053
1.017
1.028
1.018
2.5
3.6
6.0
2.2
2.4
1.8
1.055
1.092
1.050
1.028
1.042
1.027
3.3
4.2
5.2
3.5
3.4
2.4
1.037
1.077
1.047
1.016
1.045
1.021
2.4
3.5
5.2
2.0
3.9
2.1
1.037
1.070
1.042
1.002
1.023
1.024
2.1
3.0
3.6
0.2
1.7
2.4
PM10-2.5
PM2.5
t-ratio
CO
t-ratio
RR
4.7
4.9
4.9
2.3
5.3
1.5
PM2.5
Summer
PM2.5
PM10-2.5
CO
SO2
NO2
O3
RR
CO
PM10-2.5
SO2
CO
NO2
SO2
O3
NO2
O3
Notes: The rows indicate the coeficient for the pollutant when it is included simultaneously with the pollutant indicated in each column. The diagonals of each panel show the results for a
single-pollutant model. For example, in a model with SO2 and CO for winter, the t-ratio for SO2 is 1.2, while the t-ratio for CO is 5.0. The t-ratio of SO2 in a single-pollutant model is 4.7. The
base model includes the following terms: LOESS of time of span 68 days, spline of same-day temperature, spline of the average temperature for the previous 4 days, and indicators for day
of the week and month of the year. Pollutant specification is the average of the previous 2 days. Relative risks were computed for an increment equal to the mean for the whole year or the
respective season (shown in Tables 1 and 2). The regression coefficient can be computed as ln(RR)/mean. Results significant at the 5% level or more are indicated in bold.
shown at the bottom, which is for PM2.5 and ozone in summer, but this is due mainly to the effect of season.
Sensitivity to Modeling Approaches
Table 7 shows the summary results for the three models
tested. The top section shows the results for the GAM
model (our base model), the middle section the results
for the GAM model with filtered variables, and the bottom section the results for the generalized linear model.
Generally, the size of the effects for the GAM model
and the t-ratios are slightly higher than for the other two
models. The generalized linear model has the less significant results, with only PM2.5 and NO2 being consistently
significant. In that model, CO is significant only when
alone and with SO2, and O3 is never significant.
DISCUSSION
This study supports and adds to the evidence that exposure
to ambient air pollutants poses a risk to the inhabitants of
1294 Journal of the Air & Waste Management Association
Santiago. Ambient concentrations of respirable PM, as well
as of other gaseous pollutants, were found to be significantly
associated with increases in daily mortality, after controlling for confounding factors like seasonal patterns and
weather effects. The effects were shown to be higher during
the warmer months.
While the observed risks for PM are consistent with
previous results obtained in other cities of the world, our
estimates are somewhat lower than previously published
results. A previous study conducted in Santiago by Ostro et
al.19 found a 10% increase in total mortality associated with
the average levels of PM10 (115 µg/m3 for 1989–1991). This
is more than double our estimate of a 4.2% increase for
average levels of PM2.5. However, when seasonal effects were
controlled in Ostro’s work, including variables for months
or trigonometric terms, the increase in mortality dropped
to 4%, which is consistent with our estimates. While the
studies conducted in the United States have found increases
in mortality of around 0.7% per 10 µg/m3 of average 24-hr
Volume 50 August 2000
Cifuentes et al.
Figure 4. Association between daily mortality and air pollutants considered simultaneously in the GAM model. Each pair of graphs shows the
results for one model which includes PM2.5 and another pollutant simultaneously. Results are for the whole year, except for the model with PM2.5
and O3, which corresponds to summer. Each dot represents one day. The base model includes a LOESS of time of span 68 days, a spline of sameday and previous four days of temperature, and indicators for day of the week and month of the year.
Volume 50 August 2000
Journal of the Air & Waste Management Association 1295
Cifuentes et al.
Table 7. Summary relative risk (RR) and t-ratios for each pollutant in single- and two-pollutant models.
Model
GAM (base)
GAM Filtered Variables
GLM
Pollutant
PM2.5
PM10-2.5
CO
SO2
NO2
O3a
PM2.5
PM10-2.5
CO
SO2
NO2
O3a
PM2.5
PM10-2.5
CO
SO2
NO2
O3a
Pollutant alone
RR
t-ratio
1.042
1.043
1.041
1.026
1.039
1.024
1.038
1.030
1.030
1.024
1.035
1.023
1.032
1.030
1.023
1.003
1.028
1.003
6.7
4.9
7.2
5.6
7.2
2.4
4.9
2.8
4.3
3.4
5.2
2.1
3.7
2.3
2.4
0.3
3.4
0.3
Average for models
with other pollutants
RR
t-ratio
1.032
1.019
1.032
1.009
1.031
1.024
1.033
1.005
1.022
1.010
1.028
1.023
1.029
1.012
1.013
0.989
1.024
0.999
4.1
1.8
4.6
1.6
4.7
2.2
3.4
0.4
2.4
1.2
3.3
2.0
2.6
0.8
1.1
-1.1
2.4
-0.1
Sign with other pollutants
p < 0.05
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
Notes: For the other pollutants, the RR and t-ratio are the simple average of the results for all models with other pollutants. Pollutant specification is the average of the previous 2 days.
Effects are for the whole year, except for the O3 model, which corresponds to summer only. The rightmost section of the table indicates whether the pollutant was significant at the 5%
level in models with each of the other pollutants (in the same order as shown in the rows). PM2.5 and PM10-2.5 data completed for missing observations. Results significant at the 5%
level or more are indicated in bold.
aFor summer months.
PM10 concentrations,37 for European cities the increase is
around 0.4–0.5%.38 Our risk estimates are closer to the risks
found in European cities than those in U.S. cities.
Previous studies have shown that the fine fraction
of PM10 has stronger effects than the coarse mass.2,9 However, recent analyses have more ambiguous results on
the relative effect of each fraction. Some studies show a
stronger effect of the fine fraction—the analysis by Pope
et al. of three metropolitan areas in Utah39 concluded
that higher concentrations of combustion-source particles were more associated with mortality than were
coarse particles; Schwartz et al.40 showed that high episodes of windblown coarse particles do not affect mortality. In contrast, Loomis et al.41 found that the coarse
mass rather than the fine fraction affects mortality in
Mexico City. Effects of PM10 dominated by coarse mass
have been reported in a California community by Ostro
et al.42 Schwartz and Neas43 found PM2.5 associated with
acute lower respiratory symptoms, but PM10–2.5 associated
with cough. Our results for the whole year and for colder
months are consistent with the first set of studies. But
we found an important effect of coarse mass in the
warmer months, which is partially consistent with the
Mexico and California results.
An interesting finding of this study is the association
between gaseous pollutants (singly and in pairs) and all
1296 Journal of the Air & Waste Management Association
disease mortality (CO, NO2, and O3 during summer). These
associations were robust to alternative model specifications and seasonal considerations. This consistency was
not observed in previous analyses of Santiago. These
results are unlikely to come from a pollutant acting as a
surrogate for an omitted pollutant, such as PM2.5. For example, SO2 has a high correlation with PM2.5 (0.70), but
CO has an even higher correlation with PM2.5 (0.80). However, SO2 generally is not significant in the two-pollutant
models, even though it is significant when it is the only
pollution variable.
Several studies have previously reported associations
of daily mortality with some of these pollutants. However, the associations of specific pollutants are not consistent across studies and sometimes not even across
specifications within a study. For example, an early study31
found associations between NO2 and CO in Los Angeles
for the period 1970–1979. Kelsall et al.28 found significant associations between TSP, SO2, CO, and O3 on total
mortality, but no consistent NO2 effect was found. Burnett
et al.,15 analyzing data for 11 Canadian cities, found the
biggest effect for NO2, followed by SO2 and O3, and no
effect for CO (unfortunately there was no daily PM data
available). The risks for each pollutant varied across cities, so no single responsible pollutant could be identified.
In Toronto, Burnett et al.32 found a 4.7% increase in deaths
Volume 50 August 2000
Cifuentes et al.
associated with average levels of CO and a 1.1% increase
associated with TSP.
These results suggest that a mixture of pollutants
may be responsible for the increased mortality. A recent study sheds some light in this direction. Using factor analysis, Ozkaynak et al. found a relationship among
daily mortality for 20 years of data in Toronto and factors representing auto emissions, power plants/fossil
fuel, and ozone/regional haze.44 The main factor was
auto emissions, with an average contribution to mortality of 2%.
Our results are consistent with these results. Source
apportionment of PM in Santiago during the winter of
199645 indicates that most of the fine fraction is related to
transportation activities (64%), while only a small fraction (15%) corresponds to windblown crustal matter. The
coarse mass is composed mainly of resuspended crustal
material. According to the emissions inventory for
Santiago in 1997,17 gasoline vehicles were responsible for
83% of CO emissions and 38% of NOx emissions, while
diesel-powered vehicles were responsible for 10% of CO
and 32% of NOx emissions. Our finding of stronger effects of all pollutants in summer than in winter, especially of the coarse fraction of PM, is consistent with
previous studies, mostly in European cities.11,46-48
There may be physiological mechanisms responsible for the increase in effects of air pollution during
high-temperature weather, such as those found in Athens.49 Also, during warmer months, people spend more
time outdoors and ventilate their homes more than
during the winter. This last point may help explain the
high risk of the coarse mass observed for summer
months. Santiago has temperate weather, so there is
little air conditioning. We conjecture that open windows equalize indoor and outdoor concentrations of
coarse particles in the summer, while in the winter, indoor and outdoor air have relatively similar concentration of fine particles, but the coarse particles stay outside
(the infiltration factor, which gives the fraction of outdoor particles found indoors, is greater for fine than
for coarse particles).50 However, this explanation assumes the same inherent toxicity of both coarse and
fine particles, which is unlikely.
Using two-pollutant models and separating the effects by season leads to better quantification of the effects of these air pollutants. Using the risks shown in Table
6 (for the average pollution levels) and the average number of deaths shown in Tables 1 and 2, we computed the
excess deaths, assuming no threshold in the associations.
We found that a single-pollutant analysis gives an estimate of 813–871 excess deaths per year (considering the
effects of PM2.5, CO, and NOx average concentrations during the study period).
Volume 50 August 2000
When excess deaths are computed based on the twopollutant models (computing the number of excess deaths
for each pollutant and then adding them together), the
numbers increase from 1024 to 1054, depending on the
combination of pollutants considered. The separate analysis by season of the year also increases the estimated
number of deaths considerably—for example, for PM2.5,
the total number increases from 871 to 1165, with 642 in
winter and 524 in summer. The highest estimate of excess deaths corresponds to the combination of the separate effects by season of PM2.5 and CO, with a mean
estimate of 1568 excess deaths, of which 740 occur in
winter and 828 in summer. These results show that the
estimated excess deaths can be underestimated by a factor of 2 if a single pollutant is used or the seasons are
required to have the same effect. Fine particles are more
significant than are coarse particles, although coarse particles are estimated to kill more people in the summer. In
winter, fine particles are associated with 717 excess deaths.
In summer, fine and coarse particles are associated with
328 and 594 deaths, respectively.
We warn readers of the need for caution in interpreting these results. We indicated above that PM data had to
be interpolated during summer, possibly biasing the estimated effects of PM downward and the other pollutants
upward. We also caution that these results are for a single
city for the period 1988–1996. Analysis of the association
of daily air pollution and daily mortality is powerful in
identifying associations that are likely to be causal but
are not powerful in estimating the extent of life-shortening implied by the air pollutants. In particular, the ambient air measurements are for outdoor sites, not for the
actual air that people breathe. Inferring causality here
implies, implicitly or explicitly, that potential confounding variables are controlled.
In conclusion, this study found a consistent association of particulate air pollution, mainly fine particles, and
the gaseous pollutants CO, NO2, and O3 with total daily
mortality. These results are consistent with previous analysis of European and Canadian cities whose air pollution
is dominated by traffic and combustion sources.
ACKNOWLEDGMENTS
This research was funded by Fondecyt Project 197-0114
and by the Center for Integrated Study of the Human
Dimensions of Global Change, a joint creation of the
National Science Foundation (SBR-9521914) and Carnegie
Mellon University. The authors would like to thank Richard Burnett and Marc Golberg for useful advice on the
filtering of variables. We also would like to thank Ignacio
Olaeta from the Servicio de Salud Metropolitano del
Ambiente for providing access to the air pollution data.
Of course, all remaining errors are our own.
Journal of the Air & Waste Management Association 1297
Cifuentes et al.
REFERENCES
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
36.
Lave, L.B.; Seskin, E.P. Science 1970, 169, 723-733.
Dockery, D.W.; Pope, C.A., III; Xu, X.; Spengler, J.D.; Ware, J.H.; Fay,
M.E.; Ferris, B.G., Jr.; Speizer, F.E. New Engl. J. Med. 1993, 329, 17531759.
Pope, C.A., III; Thun, M.J.; Nambordini, M.M.; Dockery, D; Evans,
J.S.; Speizer, F.E.; Heath, C.W., Jr. Am. J. Resp. Crit. Care Med. 1995,
151, 669-674.
Ostro, B.D. Arch. Environ. Health 1993, 48, 336-342.
Schwartz, J. Environ. Res. 1994, 64, 36-52.
Dockery, D.W.; Pope, C.A., III. Annu. Rev. Pub. Health 1994, 15, 101132.
Thurston, G.D. J. Expos. Anal. Environ. Epidemiol. 1996, 6, 3-21.
Air Quality Criteria for Particulate Matter; EPA/600/P-95/00lcF; U.S. Environmental Protection Agency, Office of Health and Environmental
Assessment: Research Triangle Park, NC, 1996.
Schwartz, J.; Dockery, D.W.; Neas, L.M. J. Air & Waste Manage. Assoc.
1996, 46, 927-939.
Borja-Aburto, V.H.; Castillejos, M.; Gold, D.R.; Bierzwinski, S.; Loomis,
D.P. Environ. Health Perspect. 1998, 106, 849-855.
Michelozzi, P.; Forastiere, F.; Fusco, D.; Perucci, C.A.; Ostro, B.; Ancona,
C.; Pallotti, G. Occup. Environ. Med. 1998, 55, 605-610.
Pönkä, A.; Savela, M.; Virtanen, M. Arch. Environ. Health 1998, 53,
281-286.
Verhoeff, A.P.; Hoek, G.; Schwartz, J.; Wijnen, J.H. v. Epidemiol. 1996,
7, 225-230.
Zmirou, D.; Schwartz, J.; Saez, M.; Zanobetti, A.; Wojtyniak, B.;
Touloumi, G.; Spix, C.; Ponce de Leon, A.; Le Moullec, Y.; Bacharova,
L.; Schouten, J.; Ponka, A.; Katsouyanni, K. Epidemiol. 1998, 9, 495503.
Burnett, R.T.; Cakmak, S.; Brook, J.R. Can. J. Pub. Health 1998, 89,
152-156.
Prendez, M.; Ortiz, J.; Zolezzi, C.; Campos, C.; Apablaza, N. Rev. Chi.
Enf. Resp. 1991, 7, 224-237.
Plan de Prevención y Descontaminación Atmosférica de la Región
Metropolitana; Comisión Nacional del Medio Ambiente: Santiago,
Chile, 1997.
Salinas, M.; Vega, J. World Health Stat. 1995, 48, 118-125.
Ostro, B.; Sanchez, J.M.; Aranda, C.; Eskeland, G.S. J. Expos. Anal.
Environ. Epidemiol. 1996, 6, 97-114.
Sanhueza, P.; Vargas, C.; Jimenez, J. Rev. Med. Chile 1998, 1/27, 235242.
McCullagh, P.; Nelder, J.A. Generalized Linear Models; 2nd ed.;
Chapman and Hall: London, 1989.
SAS/STAT User’s Guide; SAS Institute: Cary, NC, 1990.
S-Plus User’s Guide Version 4.5; Mathsoft Inc., Seattle, WA, 1998.
Hastie, T.; Tibshirani, R. Generalized Additive Models; Chapman and
Hall, London, 1990.
Schwartz, J.; Dockery, D.W. Am. Rev. Respir. Dis. 1992, 145, 600-604.
Schwartz, J. Am. J. Epidemiol. 1993, 137, 1136-1147.
Burnett, R.T.; Dales, R.E.; Raizenne, M.E.; Krewski, D.; Summers, P.W.;
Roberts, G.R.; Raad-Young, M.; Dann, T.; Brook, J. Environ. Res. 1994,
65, 172-194.
Kelsall, J.E.; Samet, J.S.; Zeger, S.; Xu, J. Am. J. Epidemiol. 1997, 146,
750-762.
Mazumdar, S.; Schimmel, H.; Higgins, I.T.T. Arch. Environ. Health 1982,
37, 213-220.
Schwartz, J.; Marcus, A. Am. J. Epidemiol. 1990, 131, 185-194.
Kinney, P.L.; Ozkaynak, H. Environ. Res. 1991, 54, 99-120.
Burnett, R.T.; Cakmak, S.; Raizenne, M.E.; Stieb, D.; Vincent, R.;
Krewski, D.; Brook, J.R.; Phillips, O.; Ozkaynak, H. J. Air & Waste
Manage. Assoc. 1998, 48, 689-700.
Schwartz, J.; Spix, C.; Touloumi, G.; Bacharova, L.; Barumamdzadeh,
Y.; Le Tretre, A.; Pierkarski, T.; Ponce de Leon, A.; Ponka, A.; Rossi, G.;
Saez, M.; Shouten, J.P. J. Epidemiol. Comm. Health 1996, 50 (suppl A),
S3-S11.
Cleveland, W.S.; Devlin, S.J. J. Am. Stat. Assoc. 1988, 74, 829-836.
Kalkstein, L.S.; Green, J.S. Environ. Health Perspect. 1997, 105, 84-93.
Kunst, A.E.; Looman, C.W.N.; Mackenbach, J.P. Am. J. Epidemiol. 1993,
137, 331-341.
1298 Journal of the Air & Waste Management Association
37. Levy, J.I.; Hammitt, J.K.; Spengler, J.D. Environ. Sci. Tech. 1999, 33,
4364.
38. Katsouyanni, K.; Touloumi, G.; Spix, C.; Schwartz, J.; Balducci, F.;
Medina, S.; Rossi, G.; Wojtyniak, B.; Sunyer, J.; Bacharova, L.;
Schouten, J.P.; Ponka, A.; Anderson, H.R. Brit. Med. J. 1997, 314, 16581663.
39. Pope, C.A., III; Hill, R.W.; Villegas, G.M. Environ. Health Perspect. 1999,
107, 567-73.
40. Schwartz, J.; Norris, G.; Larson, T.; Sheppard, L.; Claiborne, C.; Koenig,
J. Environ. Health Perspect. 1999, 107, 339-342.
41. Loomis, D.; Castillejos, M.; Borja-Aburto, V.H.; Dockery, D.W. In Third
Colloquium on Particulate Matter and Human Health; Phalen, R.F.; Bell,
Y.M. Eds.; Air Pollution Health Effects Laboratory, University of California, 1999, pp 5.13-5.18.
42. Ostro, B.D.; Hurley, S.; Lipsett, M.J. Environ. Res. 1999, 81, 231-238.
43. Schwartz, J.; Neas, L.M. Epidemiol. 2000, 11, 6-10.
44. Ozkaynak, H.; Xue, J.; Zhou, H.; Raizenne, M.; Burnett, R. Presented
at PM 2000: Particulate Matter and Health–The Scientific Basis for
Regulatory Decision-Making, Charleston, SC, January 2000; submitted for publication, 2000.
45. Artaxo, P.; Oyola, P.; Martinez, R. Nucl. Inst. Meth. Phys. Res. B 1999,
150, 409-416.
46. Anderson, H.R.; de Leon, A.P.; Bland, J.M.; Bower, J.S.; Strachan, D.P.
Brit. Med. J. 1996, 312, 665-669.
47. Touloumi, G.; Pocock, S.J.; Katsouyanni, K.; Trichopoulos, D. Int. J.
Epidemiol. 1994, 23, 957-967.
48. Zmirou, D.; Barumandzadeh, T.; Balducci, F.; Ritter, P.; Laham, G.;
Ghilardi, J.P. J. Epidemiol. Comm. Health 1996, 50 Suppl. 1, S30-35.
49. Katsouyanni, K.; Pantazopoulou, A.; Touloumi, G.; Tselepidaki, I.;
Moustris, K.; Asimakopoulos, D.; Poulopoulou, G.; Trichopoulos, D.
Arch. Environ. Health 1993, 48, 235-242.
50. Wilson, W.E.; Suh, H.H. J. Air & Waste Manage. Assoc. 1997, 47, 12381249.
About the Authors
Luis A. Cifuentes is assistant professor, Industrial and Systems Engineering Department, Pontificia Universidad
Católica de Chile, Casilla 306 Correo 22, Santiago, Chile
(email: [email protected]). Lester B. Lave is university professor and Higgins Professor of Economics, Graduate School
of Industrial Administration, Carnegie Mellon University,
Pittsburgh, PA 15213 (email: [email protected]).
Jeanette Vega is associate professor, School of Public
Health, Pontificia Universidad Católica de Chile (email:
[email protected]). Katherine Köpfer is a master of science,
Industrial and Systems Engineering Department, Pontificia
Universidad Católica de Chile.
Volume 50 August 2000