-------
when the coldest days were omitted. Including additional trigonometric terms, or including 36
dummy variables for combinations of year and month reduced the RR for PM, but did not
eliminate PM as a significant contributor to total mortality. Control of seasonality by use of a
generalized additive model to adjust for time effects gave a somewhat larger RR for PM 10, with
small uncertainty. Figure 12-23b shows that the estimated TSP effect has little sensitivity to the
inclusion of copollutants: NO2, SO2, O3.
Figure 12-23c evaluates a number of lag and moving average models for PM. The relative
risks corresponding to each term have been recalculated from the regression coefficients (denoted
b) in their Table 8, for a basis of 100 to 150 //g/m3, by the formula
= exp(b*log(150/100)),
with confidence limits estimated analogously. All of the PM effects are statistically significant,
with the exception of the 3-day lag term in the 4-day polynomial distributed lag (PDL) model.
The 0-day and 2-day single lag models and the 3-day and 4-day moving average models perform
almost as well at predicting total mortality as does the PDL model, of which they are each a
special case.
Model Specification for the St. Louis and Eastern Tennessee Mortality Studies
(Dockery et al, 1992)
The daily mortality data for St. Louis and for eastern Tennessee analyzed by Dockery et al.
(1992) were discussed in Section 12.3.1. Additional results contributing to the analysis were
presented by Dockery in a report presented at the EPA-sponsored workshop on PM-related
mortality in November, 1994 (Dockery, 1995). Figure 12-24a,b illustrates the sensitivity of the
PM10 RR to the lag time or moving average model in the Poisson regression for St. Louis total
mortality, and Figure 12-25a,b shows the analogous plot for the eastern Tennessee area. Models
were fitted for lags from 0 to 4 days, and for the lagged moving average from the two preceding
days. The lag 1 and 2 RR estimates for St. Louis, and the lagged 2-day moving average were
statistically significant for the St. Louis mortality series, but no PM indicator had a statistically
significant RR for PM 10 in the eastern Tennessee mortality series even though the RR estimates
were numerically very similar.
12-273
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(a) PM1D Relative Risk
(b) PM2.5 Relative Risk
o>
•o
o>
2
$
o> 1+2
o
Relative Risk per 50 \iglm3 PM10
Relative Risk per 25 M9/m3 PM25
I Lower 95% CL • Relative Risk I Upper 95% CL
Figure 12-24. Relative risk of tAal mortality for particulate matter in St. Louis, as a function of moving average and lag times:
(a)PM10and(b)PM25.
Source: U.S. EPA graphical depiction of results from Dockery et al. (1992) and Dockery (1995).
-------
to
to
(a) PM10 Relative Risk
TJ
O
D) 1+2
'>
o
s
1
+
0.8 0.9 1.0 1.1 1.2 1.3 1.4
Relative Risk per 50 jjg/m 3 PM10
(b) PM_, Relative Risk
0.8 0.9 1.0 1.1 1.2 1.3
Relative Risk per 50 pg/m 3 PM 2 5
I Lower 95% CL • Relative Risk I Upper 95% CL
Figure 12-25. Relative risk of total mortality for particulate matter eastern Tennessee as a function of moving average and lag
times: (a) PM10 and (b) VM25.
Source: U.S. EPA graphical depiction of results from Dockery et al. (1992) and Dockery (1995).
-------
Longer-term moving averages were not evaluated, but the effects of PM 10 would probably have
been much smaller than the RR calculated using the average of 1- and 2-day lagged PM. As in
the Utah Valley and Santiago studies, PM lag structure needed to be identified in order to obtain a
significant PM effect.
Model Specification for the New York City Respiratory Mortality Study
(Thurston andKinney, 1995)
Thurston and Kinney (1995) compared several Gaussian OLS time series models with a
Poisson regression model, using respiratory mortality data for New York City for 1972 to 1975.
Time series were done using both unfiltered mortality and pollution data, and filtered mortality
and pollution time series using a 19-day moving average. Analyses were done using year-round
unfiltered OLS, April-September OLS, April-September filtered OLS, April-September
adjustments by sines and cosines, and April-September Poisson regression adjusted with sines and
cosines. During the April-September ozone season, the unfiltered OLS model showed a strong
significant COH effect, but the COH effect size decreased to small and nonsignificant values when
the filtered or detrended analyses were performed. The ozone effect size decreased somewhat
from the unfiltered OLS analysis, but was similar in magnitude and statistically significant using
filtered or detrended OLS, or Poisson regression models.
Model Specification for the Los Angeles Mortality Studies
(Kinney et al, 1995; Ito et al, 1995)
Kinney et al. (1995) have discussed a number of important model specification issues for an
air pollution time series model. Figure 12-26a,b, taken from their paper, shows the RR estimates
for 100 ug/m3 PM10, with alternative methods to control for temporal cycles. In general, most
such adjustments for seasonal cycles using dummy variables or Fourier series (sines and cosines)
reduced the RR slightly. Subsetting the data into winter and summer groups increased the
uncertainty, but did not greatly affect the RR estimate. However, the summer-only RR adjusted
with 4 sine/cosine terms was larger than the unadjusted annual RR, and statistically significant.
Figure 12-26b shows the results of including co-pollutants, O 3 and CO. Including O3 in the
model, along with PM 10, did not
12-276
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(a) Seasonal Model
(b) Model for Copollutants
to
to
Seasonal dummy variables h| • 1
4 sine/cosine waves | • 1
10 sine/cosine waves I • 1
Winter 1 ^ 1
1 1 1 1
0.9 1 1.1 1.2
Relative Risk
™ i
PM10 only • '
Ozone only H — • — I
r-> n i 1 • 1
CO only ' • '
I ^ i r>n/i
PM + 0 IPM10
PM1D + O3
I ^ 1 n nil
I • I PM10
PM1ft + CO . _
10 I • 1 CO
0.9 0.95 1 1.05 1.1 1.15
Relative Risk
Figure 12-26. Relative risk of total mortality for PMin Los Angeles, as a function of (a) seasonadiodel and (b) models including
co-pollutants.
Source: Adapted from Kinney et al. (1995).
-------
change the RR for PM, but increased its uncertainty slightly so that the RR for PM became only
marginally significant (two-tailed test, p < 0.05). Including CO in the model reduced the RR for
PM, which was also less significant. CO and O3 were too highly correlated to use in a three-
pollutant model.
Ito et al. (1995) have evaluated alternative model specifications for combining data from a
network of urban monitoring stations, when one station collects data daily and others at an
irregular schedule, such as once-every-6-days with different days at different stations. While an
important subject, this is not the primary source of concern about possible model mis-
specification. The optimal use of monitoring data distributed over space and time is more likely
to appear as a problem in exposure measurement error arising when any surrogate is used instead
of the actual individual exposure.
Model Specification for the Chicago Mortality Studies
(Ito et al, 1995; Styer et al, 1995)
Styer et. al. evaluated several alternative models for the Chicago PM 10 study discussed in
Section 12.3.1, including models that assess the effects of dividing data by season. Figure 12-27
shows the RR for total elderly mortality per 50 //g/m3 of PM10 in ten different models. Model 0 is
their basic best-fitting model using all of the data and assuming a common PM effect for all
seasons. The next eight models deal with pairs of model specifications for PM in each season.
Models 1, 4, 6, 8 are based on a single model using all of the data with dummy variables for each
season that allows separate PM effects in fall, spring, winter, and summer respectively. Models 2,
5, 7, and 9 are similar models fitted independently using subsets of the data for each season.
Model 3 is also a separate model for elderly mortality in fall, similar to Model 2 except that the
moving average for PM is 5 days, whereas all of the other models used 3-day moving averages.
In general, the RR for each season did not show large differences when different estimation
methods were used, but there were large differences among seasons in these analyses. The only
statistically significant RR were for fall and spring. The PM RR for winter and summer seasons
did not differ significantly from 1.0.
Ito et al. (1995) also evaluated alternative model specifications for combining data from a
network of urban monitoring stations in Chicago. Relative risks for models with daily
12-278
-------
s
Winter -
d
s
Summer -
d
s
Spring
d
s5
Fall s
d
Whole Year d
»d
(A
•
, 1
i •
i
i
/
/
/ .
' '
A i
T 1
1 1 1
1 A 1
/
1 g| i
V
1 * 1
1 V 1
1^ 1
0.9 9.5 1.0 1.05 1.1
1.15 1.2
Relative Risk per 50 |jg/rrr PM10
I Lower 95% CL • Relative Risk I Upper 95% CL
Figure 12-27. Relative risk of total mortality for PM 10 in Chicago as a function of the
model for seasons. Abbreviations: d, all of the data; s, subset of the data;
S5, for models of 3 to 5 day moving average, whereas all other models used
3-day moving average.
Source: U.S. EPA graphical depiction of results from Styer et al., 1995.
PM10 were statistically significant using any of several alternative averaging models, such as
averaging from all non-missing sites or averaging from all sites using regression-imputed PM 10 for
missing sites. Data from some individual sites also gave significant PM effects, but models using
every-6-day data were generally not significant, typically because the estimated RR had greater
uncertainty when only 1/6 as many data were available.
12-279
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Model Specification for the Steubenville Mortality Studies (Schwartz and Dockery, 199 2b;
Moolgavkar et al 1995a)
Two papers have assessed alternative treatments of a single data base, air pollution and
mortality data from Steubenville, OH for 1974-1984 and, more recently, for 1981-1988
(Moolgavkar et al. 1995a). The initial analyses by Schwartz and Dockery (1992b) evaluated
several Poisson regression model specifications, including a basic model with mean temperature
and dewpoint (same day and lagged one day), and seasonal indicators. Neither same-day nor
lagged temperature and dewpoint were statistically significant, nor the square of these variables,
nor indicators for hot days (> 70 °F). However, humidity measured by mean dewpoint
temperature was nearly statistically significant at the p < 0.05 level, and an indicator for days that
were both hot (> 70 °) and humid (dewpoint > 65 °) was a statistically significant predictor of
mortality. Sensitivity analyses included putting both average of same-day and previous day TSP
and SO 2 in the model, omitting weather and season variables, including year of the study as either
a random effect or as a fixed effect (no year was significant) and including an autocorrelation
structure. As expected, including SO 2 reduced the TSP effect, but the decrease was small; RR for
TSP decreased from 1.04 without including SO 2 to 1.03 per 100 ug/m 3 when SO2 was included,
and the SO2 coefficient was not significant whereas the TSP coefficient was still statistically
significant. As shown in Figure 12-28a, these had little effect on the estimated relative risk for
100 ug/m3 TSP. This paper also demonstrated the use of TSP quartiles for displaying a
relationship between the PM indicator and adjusted mortality or morbidity. However, TSP was
used as a continuous covariate in the models because the grouping of continuous measurements
into groups or categories must involve a loss of information, whether large or small.
Moolgavkar et al. (1995a) evaluated a number of Poisson regression models, with
particular emphasis on seasonal subsets of the data. The whole-year models analogous to those of
Schwartz and Dockery (1992b) are also shown in Figure 12-28a. The results are close to those of
Schwartz and Dockery, but are not identical. The RR for 100 ug/m 3 TSP are somewhat smaller,
but the decrease is only from about 1.032 to 1.025 when SO 2 is included in the model. These
coefficients are for what Moolgavkar et al. define as the "restricted" mortality data set, which
consists of deaths in Steubenville of people who resided
12-280
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(a) Relative Risk From Different Models
(b) Relative Risk as Function of Season from Moolgavkar Models
to
to
oo
Schwartz Model
+ SO2
Schwartz Model +
Year (Fixed Effects)
Schwartz Model +
Autocorrelation
Schwartz Model +
Year (Random Effects)
Schwartz Model
Without Covariates
Schwartz: Winter,
Spring, Hot/Humid Days
Moolgavkar: Winter,
Spring, Hot/Humid Days
Moogavkar Model
+ SO2
0.65 1.0 1.05 1.
Relative Risk per 100 ug/m3 TSP
1 Lower 95% CL • Relative Risk 1 Upper 85% CL
+SO2
w
LL
-S02
5>+so2
E
E
M-S02
S+so2
0)
|-S02
^+S02
3 2
c
*-S02
C,+S°2
c
"-S02
3>+so2
0)
|-S02
0.85 1.0 1.05 1.1 1.15
Relative Risk per 100 ug/m3 TSP
0.95 1.0 1.05 1.1 1.15
Relative Risk per 100 ug/m3 TSP
I Lower 95% CL • Relative Risk I Upper 95% CL |
Figure 12-28. Relative risk of total mortality for total suspended particle (TSP) in Steubenville: (a) different models (left) and
(b) as a function of season (center, right).
Sources: U.S. EPA graphical depiction of results from Schwartz and Dockery (1992b) and Moolgavkar et al. (1995a).
-------
there. This is comparable to the data set used by Schwartz and Dockery in this study, and by
Schwartz or Dockery and their associates in many other studies. The argument for use of the
"restricted" mortality data is that community-based air monitors provide better exposure
indicators for people who live in the community most of the time, as opposed to commuters or to
other visitors who die in the community. Also, since many metropolitan areas contain medical
facilities that may be better equipped than those in more remote areas, it is possible that some
excess number of the deaths in elderly or ill patients transported from the more remote areas
occur in urban centers such as Steubenville. Moolgavkar et al. (1995a) also show results for
analyses of "full" mortality data, which includes individuals who did not reside in the location at
which they died.
It is clear that season-to-season effects are present in these data. Schwartz and Dockery
found that winter and spring mortality was significantly higher than summer and fall mortality.
Moolgavkar separated the analyses by season. He found that whole-year RR for TSP was nearly
the same as RR in the separate summer and fall models, with or without SO 2 in the model, and
nearly the same in the spring model when SO 2 was included. However, TSP coefficients were
higher in the winter, and in the spring model when SO 2 was not included. In fact, as shown in
Figure 12-28b, the RR for TSP increased slightly in the winter model when SO 2 was included.
There is a possibility that the weather models used by Schwartz and Dockery, and by
Moolgavkar et al. are not adequate to remove all of the seasonal effects. It is possible that
additional variance reduction could have been achieved with the use of additional weather data,
emphasizing more extreme conditions than the very moderate cutpoints of temperature and
dewpoint, since temperature extremes are known to have effects on mortality (Kalkstein, 1991;
Kalkstein et al., 1995; Kunst et al., 1993). Variables used by other investigators, such as
barometric pressure, could have been tested. The flexibility of the model to fit nonlinear
relationships could be improved by the use of nonparametric or semi-parametric models, and
classifying data by synoptic weather category may provide a useful alternative approach to
evaluating the interaction between season and weather.
Moolgavkar found that the TSP coefficients were not statistically significant (two-tailed
tests at 0.05 level) in any season except winter, nor in the whole-year model, when SO 2 was
included in the model. However, the season-by-season TSP coefficients were not tested in a
whole-year model. Part of the non-significance may be attributable to the fact that confidence
12-282
-------
intervals for a regression parameter in a separate seasonal model, with about 1/4 of the data in a
whole-year model, may be on the order of twice (= reciprocal square root of 1/4) as wide as the
confidence interval for the corresponding season-by-pollutant regression coefficient in a whole-
year model, everything else being equal.
The method of adjusting the mortality series for weather effects and for other time-related
effects (detrending) may be important in explaining why the RR estimates for TSP vary seasonally
and why those derived by Moolgavkar et al. are quantitatively different from those derived by
Schwartz and Dockery (1992b), even though the differences are small in these studies. There may
exist some residual confounding with weather, since other studies have found that substantial
adjustment of weather by use of temperature and dewpoint categories, or nonparametric
smoothers of temperature, humidity, and time can effectively eliminate seasonal variations in
residuals and in PM effect. Even so, the estimated TSP effect on RR of mortality is positive in
most seasons, even in Steubenville models including the collinear co-pollutant SO 2. No
adjustments were made for other pollutants such as CO, NO x, and O3.
These analyses of the Steubenville data set are primarily useful for demonstrating the results
of different data analysis strategies and methods, since the PM indicator was TSP, not PM 10.
These analyses have shown the desirability of adequately adjusting the analysis of pollution effects
for weather and for long-term and medium-term time trends and variations. When co-pollutants
were evaluated, it was evident that only part of the TSP effect could be attributed to SO 2.
Differences in RR of TSP between analyses presented in the two papers are not regarded as large.
Model Specification for Philadelphia Mortality Studies (Schwartz and Dockery, 199 2a; Li and
Roth, 1995; Moolgavkar et al. 1995b; Wyzga andLipfert, 1995b; Cifuentes and Lave, 1996)
Several papers have recently appeared that allow assessment of alternative treatments of a
single data base, the air pollution and mortality data from Philadelphia for the years 1973 to 1980,
and more recently 1981 to 1988 (Moolgavkar et al. 1995b). The initial analyses by Schwartz and
Dockery (1992a) evaluated several Poisson regression model specifications, including a basic
model with mean temperature and dewpoint (lagged one day), winter season temperature (same
day), and an indicator for hot days (> 80 F). Sensitivity analyses included putting both average of
same-day and previous day TSP and SO 2 in the model, stratifying analyses as above or below
median SO2 level (18 ppb), omitting weather and season variables, and including day of week. As
12-283
-------
shown in Figure 12-29, these had little effect on the estimated relative risk for 100 //g/m3 TSP.
RR for mortality in the elderly was greater than for other age groups. A more detailed assessment
of the age structure was presented by Schwartz (1994c), showing clearly that there was increased
mortality in ages 65 to 74, and again higher at ages 75+. There was also significantly increased
mortality at ages 5 to 14 years, based on a small number of cases. This paper also demonstrated
the use of TSP quantiles for displaying a relationship between the PM indicator and adjusted
mortality or morbidity. However, TSP was used as a continuous covariate in the models because
the grouping of continuous measurements into groups or categories must involve a loss of
information, whether large or small.
Schwartz and Dockery Study
Moolgavkar whole-year model
Schwartz and Dockery
Model 1 when age < 65
Model 1 forage 65+
Model 1 + day of week
Model 1 w/o weather
Model 1 when SO2 > 18ppb
Model 1 when SO2 < 18ppb
Model 1 + SO2
Model 1: hot day, mean temp, winter temp,
mean dewpoint, year, 4 seasons, linear trend
-• 1
0.95 1.00 1.05 1.10 1.15
Relative Risk per 100 |jg/m 3TSP
I I Lower 95% CL • Relative Risk I Upper 95% CL I
Figure 12-29. Relative risk of total mortality for total suspended particles (TSP) in
Philadelphia.
Sources: U.S. EPA graphical depiction of results from Schwartz and Dockery (1992a) and
Moolgavkar et al. (1995b).
12-284
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Li and Roth (1995) reanalyzed these data, but only reported results in the form of
t-statistics. A wide range of model specifications were tested, although some models (such as
those using deviations from mean values for day of year, or from monthly average) appear to
assume an unrealistic level of seasonal recurrence of air pollution and weather effects in the model
most directly comparable to the Poisson regression models used by Schwartz and Dockery
(1992a). The autoregressive model they denoted AR(6) was somewhat comparable to models
tested in the London analyses of Schwartz and Marcus (1990). However, the models with
residual deviations of mortality from 7-, 15-, or 29-day moving averages did not have comparably
filtered predictors on the "right" side of the prediction equation; so the regression coefficients are
not readily interpretable as predictions of mortality deviations from mean pollution levels. The
Poisson log models that are most comparable to those used by other investigators involved
comparisons of model specifications for averaging times. The results only indicate statistical
significance by use of statistics, not effect size in any form more useful in epidemiologic studies
(Greenland et al., 1986). In a model that includes TSP, SO 2, and O3, statistical significance of
TSP is clearly highest with the moving average of 0+1 day lags, and diminishes sharply for all
pollutants when longer lags are included. Models with longer weather averages are also more
predictive. The lower significance of the TSP term may be related to the fact that it may have
greater exposure measurement error than the gaseous pollutants. The models evaluated in this
paper were not adjusted for collinearity, even though there are some fairly strong collinearities in
the data, such as between TSP and SO 2 and between temperature and ozone, so that inclusion of
several collinear variables is almost certain to greatly inflate the variance and thus reduce the
statistical significance of many of the regression coefficients.
Moolgavkar et al. (1995b) evaluated a number of Poisson regression models, with
particular emphasis on seasonal subsets of the data. These are shown in Figure 12-30a-d. It is
clear that season-to-season effects are present in these models. The models were adjusted for
weather and time trend by using quintiles of temperature and indicators of year. There is a
possibility that the weather model is not adequate to remove all of the seasonal effects.
Subdividing the temperature range by quintiles will result in three or four closely spaced quintiles
corresponding to moderate temperatures which have little effect on mortality, and will not
adequately take into account temperature extremes. Quintiles of temperature are not
12-285
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(a) Moolgavkar: Spring
(b) Moolgavkar: Summer
SO2 quintiles
+ Ozone
Both SO 2
and Ozone
No Ozone,
+ SO2
NoSO2
or Ozone
1
i 1 1
0.90 0.95 1.00 1.05 1.10 1.15 0.90 0.95 1.00 1.05 1.10 1.15
Relative Risk per 100 |jg/m 3 TSP Relative Risk per 100 |jg/m 3 TSP
(c) Moolgavkar: Fall
(d) Moolgavkar: Winter
SO2 quintiles
+ Ozone
Both SO 2
and Ozone
No Ozone,
+ SO2
NoSO2
or Ozone
1
1 1 1
1
1 1 1
0.90 0.95 1.00 1.05 1.10 1.15 0.90 0.95 1.00 1.05 1.10 1.15
Relative Risk per 100 |jg/m 3 TSP Relative Risk per 100 |jg/m 3 TSP
| I Lower 95% CL • Relative Risk I Upper 95% CL |
Figure 12-30. Relative risk of total mortality for total suspended particles (TSP) in
Philadelphia, in the (a) spring, (b) summer, (c) fall, and (d) winter.
Source: U.S. EPA graphical depiction of results from Moolgavkar et al, 1995b.
12-286
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given here, but Li and Roth (1995) report values of maximum temperature at the 10th, 25th, 50th,
75th, and 90th percentiles as 37, 48, 63, 78, and 84 degrees F; and Schwartz and Dockery
(1992a) report mean temperature percentiles corresponding to 25, 36, 52, 66, and 73 degrees F,
respectively. This paper finds a significant effect for the highest quintile in the summer and the
lowest quintile in the other seasons, suggesting that additional variance reduction could have been
gained by the use of additional weather data, emphasizing more extreme conditions than the 20th
and 80th percentiles and possibly including information on dewpoint or barometric pressure (as
used by other investigators). In general, replacing numeric data by grouped equivalents such as
quintile classes involves some loss of information. The loss of information may be acceptable if
there is a corresponding increase in the flexibility of the model to fit nonlinear relationships, but in
this instance the loss of information may be substantial since extreme temperatures are known to
have a quantifiable and increasing relationship with mortality as the temperatures become more
extreme (Kunst et al., 1993). The method of adjusting the mortality series for weather effects and
for other time-related effects (detrending) may be important in explaining why the RR estimates
for TSP vary seasonally and are quantitatively different from those derived by Schwartz and
Dockery (1992a). There may exist some residual confounding with weather, since other
investigators have found that substantial adjustment for weather by use of temperature and
dewpoint categories or by nonparametric smoothers of temperature, humidity, and time can
effectively eliminate seasonal variations in residuals and in PM effect. Even so, the estimated TSP
effect on RR of mortality is positive in most seasons, even in models including collinear co-
pollutants SO 2 and O3.
Neither the Schwartz and Dockery (1992a) study nor the Moolgavkar et al. (1995b) study
allows a complete assessment of the actual role of co-pollutants as confounders of a PM effect.
While SO2 is not as strongly correlated with temperature as is O 3, it is also subject to weather
conditions that affect atmospheric dispersion along with TSP. Therefore, if there is an incorrect
assignment of weather effects on mortality, some part of the mortality that could have been
explained with weather-related variables will probably be allocated to various other predictors of
mortality used in the models, especially TSP and the co-pollutants. The development of a
predictive model for mortality using weather and other time-varying covariates would probably
have required use of humidity, since humidity along with temperature had been predictive of
mortality in earlier studies, such as for London (Schwartz and Marcus, 1990) and Steubenville
12-287
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(Schwartz and Dockery, 1992b). If confounding can be explained by an unobserved (or in this
case, unused) covariate, then omission of humidity from any of the models in the Moolgavkar et
al. (1995b) study is certainly another candidate explanation for the differences in results between
these papers. However, both papers may also have provided an inadequate adjustment for other
medium-term effects on a scale longer than a day and shorter than a season or quarter, such as for
epidemics. The use of nonparametric smoothers such as LOESS, or GAM models of time, would
allow subtraction of such trends. Even a simple alternative, such as including a dummy variable
for every month in every year (96 parameters for the 1973-1980 series; another 96 parameters for
the 1981-1988 series) would probably have greatly improved the ability of these analyses to
evaluate short-term responses to short-term changes in air pollution and weather. The parameters
that relate mortality to pollution and weather over intervals of a few days were likely the same or
similar over periods of some years and would require only a few more parameters. In view of
these questions, we regard potential confounding among TSP, SO 2> and summer ozone in
Philadelphia that was identified in the Moolgavkar et al. (1995b) study as possible, but not yet
proven.
Another unresolved issue is that TSP may have relatively larger exposure measurement
error than the gaseous pollutants. Since TSP includes large particles, TSP levels are more
associated with local sources and transport near the air pollution monitors and show a weaker
correlation with TSP at other monitors than is the case for smaller particles. In particular, TSP
would be expected to show less correlation within the Philadelphia area than would PM 10, and
even less yet than would PM 2 5 across the area. Therefore, TSP may be less predictive of
individual PM exposure than the smaller size PM indicators in Philadelphia. Since variables with
larger exposure measurement error are more likely to show attenuated effects (bias towards
smaller RR) than covariates with smaller measurement errors, it is at least possible that SO 2 may
spuriously appear to be a more important predictor of pollution-related mortality than does TSP.
There does not seem to be any way to evaluate these possibilities from the published reports.
Wyzga and Lipfert (1995b) also reanalyzed the Philadelphia time series data for 1973 to
1990, using Gaussian OLS regression models with time-lagged predictors. In view of the
moderately large number of deaths per day (21 deaths at ages less than 65 years, 34.5 deaths at
ages 65 and older), the OLS regression coefficients are probably sufficiently accurate
approximations to regression coefficients estimated from Poisson regression models. They
12-288
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evaluated model specifications for daily mortality, log mortality, and deviations of mortality from
15-day moving averages. The regression models were adjusted for maximum temperature dummy
variables in 6 categories, winter season, daily changes in barometric pressure, and time trend.
Maximum hourly O 3 was evaluated as a co-pollutant. The RR estimates for TSP were calculated
using regression coefficients and standard errors in their Table 3, plus data from their Figures 14
and 20. Figure 12-3 la shows RR for ages <65 years, Figure 12-3 Ib for ages 65+ years, for all
days (N = 2380) and for N=390 hot days (maximum temperature at least 85 degrees), for
different averaging times. The largest and most significant estimates of TSP effect, measured as
deviations from 15-day moving averages, are in the elderly, especially on hot days. For the
elderly on hot days, the TSP effect is nearly the same for averaging times from 2 to 5 days on hot
days, but the 0+1 day moving average has only slightly greater statistical significance than thee
0+1+2+3 and 0+1+2+3+4 day averages. When all days are considered, the RR for TSP is only
half as large and statistically significant only for 0+1 day TSP averages. For deaths at age <65,
none of the all-day TSP RR values were significant; on hot days, the 0+1 average TSP was nearly
significant, and the 0+1+2 day average TSP effect nearly as large, but other RR estimates were
much smaller. The estimates were not calculated using filtered pollution series, but the moving
averages of TSP had some of the same effect of removing long-term trends and effects. These
estimates are in general similar to those found by Schwartz and Dockery, but larger differences
were found for other model specifications. This paper did not attempt to include SO 2 as a
covariate, since TSP was clearly collinear with SO 2.
These analyses of the Philadelphia data set are primarily useful for demonstrating the results
of different data analysis strategies and methods, since the PM indicator was TSP, not PM 10.
These analyses have shown the desirability of adequately adjusting the analysis of pollution effects
for weather and for long-term and medium-term time trends and variations. When co-pollutants
were evaluated, it was evident that only part of the TSP effect could be attributed to O 3, and that
the O3 effect was more nearly confounded with temperature and season than with TSP. However,
there was a substantial degree of confounding between
12-289
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(a) Age < 65
(b) Age 65+
» 3
< to
^ Q n
to 2
to =
o „
1
0
1.
1 A
ig
h
1
• 1
1 • 1
>l
M
1 1
1_
h
IA 1
1
• 1
•H
1 1 1
0.8 0.9
Relative Risk per 100 pg/m3 TSP
1.3 0.9 1.0 1.1 1.2 1.3 1.4
Relative Risk per 100 M9/rn3 TSP
I Lower 95% CL • Relative Risk I Upper 95% CL
Figure 12-31. Relative risk of mortalit^or TSP in Philadelphia, as a function of age, averaging time, and temperature: (a) age
< 65; (b) age > 65.
Source: U.S. EPA graphical depiction of results from Wyzga and Lipfert (1995b).
-------
TSP and SO 2 effects, which could be separated in some analyses but not in all analyses. The best
averaging time for pollution was 0+1 days, but longer averages seemed useful in estimating RR
among the elderly during hot weather.
Models with Additive Linear Specification for Multiple Pollutants
The relationship between Philadelphia mortality and some potentially confounding pollutants
has recently been reexamined by Samet et al. (1996a). The results from tables 7,8, and 11 of their
report are summarized in Table 12-26. They fitted models for total mortality, cardiovascular
mortality, respiratory mortality, and mortality for other non-external causes, for the period 1974
to 1988. Models were fitted for whole-year data using adjustments for weather, season, time
trends, and for five pollutants: TSP, SO 2, O3, NO2, and CO. The results shown here in Table 12-
26 are for whole-year total mortality averages of current-day and previous-day pollutant
concentrations, and a lagged CO variable denoted LCO that includes the 2-day average CO from
3 and 4 days earlier, as predictors of total mortality in a Poisson regression model with seasonal
adjustments. They report results from their models somewhat differently than in this document,
as the percent increase in mortality per increase over the inter-quartile range (IQR) of the
pollutant. While we have established standard increments for TSP and SO 2, we have not defined
standard increments for the effects of the other pollutants and so we report their results in the
same form as in their report. The most important findings from Samet et al. (1996a) are: (a) CO
never has a significant concurrent effect; (b) LCO has a stable significant effect; (c) O 3 has a
stable significant effect; (d) the TSP coefficient is reduced if SO 2 is in the model increased when
NO2 is in the model; (e) the SO 2 coefficient is reduced when TSP is in the model and increased
when NO 2 is in the model; and (f) the NO 2 coefficient is small and not significant unless TSP or
SO2 are in the model.
Table 12-26 shows the IQR effects for 17 models reported by Samet et al. (1996a). Model
1 shows the regression coefficients using all six pollutant averages. The superscript" 1" shows
that the coefficients would be regarded as statistically significant, with the t statistic (ratio of
coefficient estimate to asymptotic standard error estimate) between 2 and 4, except for TSP with t
= 1.962, and CO which is not at all statistically significant. Models 2 and 3 show results with
omission of LCO and omission of CO respectively. To compare
12-291
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TABLE 12-26. EXCESS RISK ESTIMATES FOR SIX AIR POLLUTION INDICES, FOR PHILIDELPHIA, 1973-1988.
COEFFICIENTS ARE PERCENT EXCESS TOTAL MORTALITY PER INTERQUARTILE RANGE IN
POLLUTANT CONCENTRATION.
to
to
VO
to
Model 1
P
O
L
L
U
T
A
N
T
S
TSP
S02
03
N02
CO
LCD
AIC3
TSP
SO2
03
N02
CO
LCD
1.041
1.081
1.951
-1.141
0.08
1.071
—
'2 < t\ <4, except
2
TSP
SO2
03
N02
CO
0.95
1.081
2.15
-1.151
0.09
74.9
for Model
3 45678 9 10 11 12 13 14 15 16 17
TSP TSP TSP — TSP TSP TSP TSP
SO2 SO2 — SO2 — — — — SO2 SO2 SO2 SO2
O3 — — — O3 — — — O3 — — — O3 O3 O3
MO — — — — NO — — — NO — — — NO
CO CO CO
T CO — — — — — — T CO — — — T CO T CO
1 061 074 1 151 0 96 ' 1 792 1 431 1 91 '
1.081 0.60 — 1.081 — — — — 1.051 1.451 1.231 1.121 —
1.91i ___ ___ ___ 2.041 — — — 2.251 — — — 2.1 11 2.271 2.371
.1 in1 — — — — -0931 — — — -063 — — — 014
0 54 038 0 97
1 in1 — — — — — — 1 172 — — — 1 162 1 041
66.5 — 81.7 82.0 74.4 — — 72.5 — — — 71.4 78.8 78.7
1, which has t = 1.04/0.53 = 1.962 from Table 11.
3AIC - 16,400, from Table 8, where the Akaike Information Criteria (AIC) assesses goodness of fit.
Source: Samet et al. (1996a).
-------
competing mortality prediction models, Samet et al. (1996a) used Akaike's Information Criterion
(AIC) (15, 16). This index of model fit combines the deviance, which measures the fidelity of the
model predictions to the observed data, with a penalty for adding more predictor variables. In the
comparison of two models A and B, the model with lower AIC is preferred. If the AIC for model
A is 5(10) units smaller than that for model b, this means that a new observed mortality series
would be 10(150) times more likely to have occurred under model a than model b. Model 4
shows that fitting total mortality only with TSP and SO 2 results in greatly reduced and non-
significant coefficients, although the TSP coefficient is reduced less than is the SO 2 coefficient.
Model 5 shows that fitting the mortality time series with only TSP produces a goodness of fit that
is somewhat inferior to the goodness of fit of Models 2 and 3 with 5 pollutants, where the AIC for
Model 5 is 16,481.7 compared to AIC = 16,474.9 for Model 2 and AIC = 16,466.5 for Model 3.
Relatively small differences in AIC should not be overinterpreted. Model 6 with only SO 2
produces a slightly worse fit than Model 5. Model 7, with TSP and O 3, produces a sightly better
fit than Model 2 after adjusting for the fact that Model 2 includes three additional pollutants: SO 2,
NO2, and CO. However, Model 11 with SO 2 and O3 produces a somewhat better fit than Model 7
with TSP and O 3, and Model 15 with O 3 and LCO a better fit than Model 11. The other models
in Table 12-26 show all pairwise combinations of pollutants including either TSP, SO 2, or O3.
Samet et al. (1996a) conclude that"... a single pollutant of the group TSP, SO 2, NO2, and
CO cannot be readily identified as the best predictor of mortality, because concentrations of the
four pollutants were moderately correlated in Philadelphia during the years of this study ... We
advise caution in interpreting model coefficients for individual pollutants in models including such
correlated pollutants. Insights into the effects of individual criteria pollutants can be best gained
by assessing effects across locations having different pollutant mixtures and not from the results of
regression models of data from single locations."
Some of the issues related to confounding from co-pollutants are discussed in
Sections 12.63.4 and 12.6.3.5, and the usefulness of assessments from multiple sites with
different pollutant mixtures is noted there. However, further study of the analyses in Samet et al.
(1996a) suggests that at least some of these issues may be capable of resolution by more complete
analyses of the Philadelphia data. Our purpose in evaluating the potential for confounding among
co-pollutants is to determine whether different pollutants are so closely related in every season as
to preclude any possibility of separating their effects on health. While confounding is not the
12-293
-------
same as collinearity in general, there is little reason to believe that pollutant concentrations
adjusted for weather and season have non-monotonic or strongly nonlinear relationships, so that
we may use collinearity diagnostics as convenient characterizations for potential confounding in
this case. Season-specific differences among potentially confounded pollutants may also be
present. The pollutant indices used in the Poisson regression models are averages of same-day
and previous-day concentrations, whereas the correlation matrices in Samet et al. (1996a) are for
each single day.
EPA has evaluated the potential for copollutant confounding using the correlation matrices
reported in Table 6 of Samet et al. (1996a). The authors reported partial correlation coefficients
of TSP, SO 2, O3, NO2, and CO adjusted for weather and time trends, for the whole year and by
season. EPA carried out principal components analyses of these correlation matrices (shown in
Table 12-27). The principal values of a correlation matrix are all non-negative and add up to the
number of variables in the matrix, so that the average principal value = 1. Many textbooks and
papers (Belsley et al., 1980) suggest that for ordinary least squares regression analyses,
collinearity is unlikely to be a problem if the condition number of the correlation matrix (ratio of
largest to smallest principal value) is less than about 30, or roughly speaking, if the smallest
principal value is greater than about 0.05. The smallest principal value for any copollutant
correlation matrix is 0.228 and the largest condition number for any season is 14.5 for winter, as
shown in Table 12-28. In other words, at worst, copollutants can only moderately confound the
TSP effect.
Detailed assessment of principal components of the seasonal correlation matrices in Table
12-29 shows some important similarities. First of all, O 3 is virtually absent from the main factor
(explaining 52 to 58% of the variance of the five pollutants) for spring and autumn, and O 3 is the
virtually unique second factor explaining 22 to 24% of the variance. Thus, O3 does not confound
any of the TSP or other copollutant findings for these seasons. During summer and winter, O 3 is
a somewhat larger component of the overall pollutant factor (which only accounts for 50% of the
variance), but CO is a somewhat smaller component, whereas the second principal component for
summer is primarily the difference between O 3 and CO. The third principal component is
primarily the difference between SO 2
12-294
-------
TABLE 12-27. CORRELATION MATRICES FOR FIVE POLLUTANTS IN
PHILADELPHA FOR THE YEARS 1974-1988, ADJUSTED FOR
TIME TRENDS AND WEATHER, FOR EACH SEASON
TSP
SO,
03
NO,
CO
Spring
TSP
SO2
03
NO2
CO
1.000
0.584
0.198
0.624
0.388
0.584
1.000
0.031
0.578
0.344
0.198
0.031
1.000
-0.065
-0.294
0.624
0.578
-0.065
1.000
0.664
0.388
0.344
-0.294
0.664
1.000
Summer
TSP
SO2
03
NO2
CO
1.000
0.552
0.368
0.572
0.308
0.552
1.000
0.293
0.551
0.214
0.368
0.293
1.000
0.279
0.026
0.572
0.551
0.279
1.000
0.482
0.308
0.214
0.026
0.482
1.000
Fall
TSP
SO2
03
NO2
CO
1.000
0.697
0.134
0.757
0.564
0.697
1.000
0.035
0.660
0.442
0.134
0.035
1.000
0.041
-0.241
0.757
0.660
0.041
1.000
0.657
0.564
0.442
-0.241
0.657
1.000
Winter
TSP
SO2
03
NO2
CO
1.000
0.716
-0.367
0.700
0.574
0.716
1.000
-0.470
0.683
0.535
-0.367
-0.470
1.000
-0.516
-0.462
0.700
0.683
-0.516
1.000
0.727
0.574
0.535
-0.462
0.727
1.000
Source: Sametetal., 1996a.
12-295
-------
TABLE 12-28. PRINCIPAL VALUES OF THE PRINCIPAL COMPONENTS OF TSP
AND ITS COPOLLUTANTS IN PHILADELPHIA FOR THE YEARS 1974-1988,
BASED ON CORRELATION MATRICES IN TABLE 12-27
Season
Spring
Summer
Fall
Winter
Component Number
1
2.606
2.537
2.899
3.326
2
1.233
1.016
1.129
0.679
O
0.558
0.660
0.491
0.501
4
0.353
0.427
0.253
0.265
Condition Number
5
0.250
0.359
0.228
0.229
10.42
7.07
12.71
14.52
Source: U. S. EPA calculations based on results reported by Samet et al. (1996a).
and CO except in summer, where it is the difference between SO 2 and O3 plus CO. The fourth
principal component is primarily the difference between TSP and SO 2 and accounts for 5 to 8% of
the variance, which may explain in part why separating these pollutants in the absence of other
information may be difficult. The fifth principal component, accounting for 5 to 7% of the
pollutant variance, includes NO 2 as the major component, but with differences between NO 2 and
TSP in autumn, between NO 2 and CO in spring. Additional analyses without ozone in the
pollutant mixture are shown in Table 12-30. The principal values are not shown because they are
quite similar to those in Table 12-27 except for the absence of component 2, representing ozone,
and a corresponding increase in the principal value for component 3. The principal components of
the four-pollutant mixture in Table 12-30 are quite similar from season to season. There is a
primary component 1 in which all four pollutants are given similar weight, representing 65 to 72%
of the non-ozone variance. This corresponds to overall high or low levels in all four pollutants,
and is likely to be inversely related to wind speed. Component 2 largely reflects the differences
between SO 2 (representing stationary sources) and CO (representing mobile sources) in all
seasons, explaining 15 to 20% of the variance, with TSP making a relatively minor contribution to
the SO2 component loading. Component 3 largely represents the difference between TSP and
SO2 in all seasons, and explains 7 to 10% of the non-ozone pollutant variance in each season.
Component 4 consists primarily of NO 2 in spring, summer, and winter; it appears to contrast NO 2
with TSP in the fall, and it explains 6 to 9% of the variance. Thus, it seems
12-296
-------
TABLE 12-29. PRINCIPAL COMPONENTS OF THE POLLUTANTS
PHILADELPHIA IN THE YEARS 1974-1988, BASED ON
THE CORRELATION MATRICES IN TABLE 12-27
FOR
Season
Spring
Component Loadings
Pollutant
TSP
SO2
03
NO2
CO
1
0.803
0.773
-0.058
0.899
0.742
2
0.363
0.203
0.923
-0.067
-0.451
O
0.006
-0.534
0.315
0.155
0.387
4
-0.462
0.255
0.209
0.075
0.159
5
0.102
0.102
0.039
-0.396
0.266
Summer
TSP
SO2
03
NO2
CO
0.822
0.771
0.506
0.845
0.546
0.112
0.146
0.676
-0.197
-0.698
0.117
0.472
-0.519
0.020
-0.391
-0.542
0.315
0.123
0.126
0.062
0.072
0.251
0.043
-0.481
0.242
Fall
TSP
SO2
03
NO2
CO
0.894
0.824
-0.000
0.909
0.772
0.184
0.122
0.963
0.037
-0.387
-0.032
-0.490
0.235
0.113
0.427
-0.292
0.259
0.128
-0.155
0.246
0.284
-0.006
0.005
-0.367
0.109
Winter
TSP
SO2
03
NO2
CO
0.836
0.843
-0.664
0.901
0.814
0.361
0.175
0.717
0.054
-0.027
-0.171
-0.365
0.183
0.156
0.530
-0.358
0.345
0.099
0.002
0.088
0.113
0.075
0.037
-0.401
0.219
Source: U.S. EPA calculations based on results reported by Samet et al, 1996a.
that TSP effects can be substantially distinguished from those of NO 2 (except possibly in the
autumn) and can be reasonably distinguished from those of CO in all seasons. O3 may be a
potential confounder in summer, but not otherwise. The most consistent potential confounder
12-297
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TABLE 12-30. PRINCIPAL COMPONENTS OF FOUR POLLUTANTS FOR
PHILADELPHIA IN THE YEARS 1974-1988, BASED ON
THE CORRELATION MATRICES IN TABLE 12-27, EXCLUDING OZONE
Season
Spring
Component Loadings
Pollutant
TSP
SO2
NO2
CO
1
0.810
0.776
0.899
0.734
2
-0.331
-0.444
0.167
0.629
3
0.464
-0.439
0.018
-0.070
4
0.139
0.091
-0.405
0.246
Summer
TSP
SO2
NO2
CO
0.810
0.771
0.864
0.608
0.245
0.435
-0.084
-0.758
-0.521
0.396
0.102
0.048
0.112
0.254
-0.486
0.230
Fall
TSP
SO2
NO2
CO
0.894
0.824
0.909
0.772
0.160
0.443
-0.054
-0.595
0.301
-0.355
0.195
-0.200
0.291
-0.011
-0.364
0.103
Winter
TSP
SO2
NO2
CO
0.869
0.852
0.906
0.818
0.279
0.377
-0.149
-0.523
0.402
-0.344
-0.043
-0.021
0.072
0.119
-0.391
0.237
Source: Sametetal., 1996a.
for TSP is SO 2, but even here, the collinearity is not so severe as to discourage further analyses.
It would therefore seem possible that a structured approach to evaluating copollutant
interrelationships would allow construction of more realistic TSP exposure indices than simply
using the mean of TSP, SO 2, NO2, and CO. A conceptual basis for modelling discussed in
Section 12.6.3.5 illustrates what these data suggest, i.e., that NO 2 and SO2 are primary pollutants
and that TSP is partly a secondary pollutant - including components generated from SO 2 and NO2.
The analyses by Samet et al. (1996a) represent a first step in this direction.
12-298
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A more direct assessment of potential confounders is based on simply evaluating the stability
of the TSP effect or other pollutant effects when other pollutants are included in the model. This
is, in many contexts, among the least biased of all confounder selection methods (Mickey and
Greenland, 1989). A review of Table 12-26 shows that the TSP effect changes by more than 10%
from a base value of 1.06 (Model 3) when O 3 is not included as a covariate, and when SO 2, NO2,
CO, or LCO are included (Models 4, 8, 9, 10). The TSP effect is: reduced by 30% when SO 2 is
included; reduced by 10% when O 3 is included; increased by 14% when LCO is included;
increased by 35% when CO is included (although the CO effect is negative); and increased by
70% when NO 2 is included (although the NO 2 effect is significantly negative, more likely
indicative of collinearity than a beneficial health effect from NO 2 exposure). The SO2 effect is
more sensitive to the inclusion of TSP, a 44% reduction, than is the TSP effect to inclusion of
SO2. There is also a smaller increase of the SO 2 effect when NO 2 or CO are included. The O3
and LCO effects are nearly invariant, suggesting that they may be important covariates, but not
confounders of TSP or SO 2 effects. We cannot assess the effects of models that include O 3, LCO,
and some combination of two or more pollutants including TSP. In particular, the effects of O 3
and LCO on the simultaneous estimates of TSP and SO 2 would be of interest.
Cifuentes and Lave (1996) also evaluated additive linear models using combinations of TSP,
SO2, and O3, but with results for the years 1983 to 1988 that differ somewhat from those derived
by Moolgavkar et al. (1995b). The results are summarized in Table 12-31 for each season and for
the whole year, showing all combinations in which at least one pollutant was used to fit the total
mortality time series. The time series were adjusted for weather and for time trends. The most
consistently predictive pollutant in these models is TSP. Model 1, with all three pollutants, shows
a significant TSP effect for spring and for autumn, with smaller effects that are not quite
statististically significant at the 0.05 level for summer and winter. SO2 effects are positive only in
winter, but not significant. Model 2 shows similar results without including O 3, with a somewhat
larger and statistically significant effect for TSP in summer. When TSP is the only pollutant used
as a predictor of mortality in Model 3, it is similar in magnitude and statistically significant in all
seasons. When SO2 is used
12-299
-------
o
o
TABLE 12-31. RELATIVE RISKS OF TOTAL NON-EXTERNAL MORTALITY FOR ADDITIVE LINEAR MODELS
USING TSP, SO2, AND O3 IN PHILADELPHIA, 1983 TO 1988. INCREMENTS ARE 100//g/m3 FOR TSP,
100 //g/m3 FOR SO2,100 ppb FOR O3
Season
Spring
Summer
Autumn
Winter
All Year
TSP
SO2
03
TSP
SO2
O3
TSP
SO2
03
TSP
SO2
03
TSP
SO2
03
1
TSP
SO2
O3
1.0871
0.956
1.010
1.079
0.979
1.013
1.0901
0.986
1.1501
1.056
1.050
1.108
1.0761
1.011
1.0321
2
TSP
SO2
___
1.0901
0.960
—
1.0881
0.984
___
1.1141
0.977
—
1.050
1.039
—
1.0881
1.008
—
3
TSP
—
___
1.0801
—
—
1.0811
—
___
1.0931
—
—
1.0811
—
—
1.0931
___
—
Model
4
___
SO2
___
—
1.036
—
—
1.067
___
—
1.050
—
___
1.0641
—
—
1.0581
—
5
TSP
—
03
1.0781
—
1.005
1.0741
—
1.010
1.0761
—
1.1301
1.0951
—
1.094
1.0821
___
1.0311
6 7
___
SO2
O3 O3
—
1.012
1.039 1.041
—
1.027
1.037 1.0401
—
1.044
1.1841 1.1601
___
1.0791
1.115 1.081
—
1.0521
1.0451 1.0501
'95% Confidence Interval > 1.
Source: Cifuentes and Lave (1996) Tables 6 and 7.
-------
alone in Model 4, it is statistically significant only in winter. The TSP effect is nearly the same in
Model 5 when O 3 is also used as a covariate, suggesting little confounding between O 3 and TSP.
Concentration-Response Models with Piecewise Linear Components
Cifuentes and Lave (1996) have evaluated several important classes of alternatives to the
log-linear Poisson models fitted by other investigators, using Philadelphia TSP data for 1983-
1988 in which both daily SO 2 and ozone data were also available. Their results are shown in two
forms in the paper: (1) restriction to subsets of data below a given level of TSP; (2) piecewise
linear models with specified values of the joint point c. The results for the latter are shown in
their Table 10 and Figure 4. These models fit in the more general form:
s(PM) = aPM ifPM c.
A continuous piecewise linear function or linear spline has d = ac, and a discontinuous function
has d /= ac. The subset and piecewise continuous functions for c = 59 //g/m3 (the 50th percentile
of TSP) and for c = 91 //g/m3 (the 90th percentile of TSP) are shown in Figure 12-32. For the
subset of analyses using same-day TSP values the apparent statistical significance of the estimated
RR shows a slight decrease with decreasing sample size as the cutoff concentration c (not
necessarily a concentration at which the two linear segments intersect) decreases to 100 //g/m3.
The results for continuous piecewise linear models are shown in Figure 4 of Cifuentes and
Lave (1996). The upper half of the the piecewise linear relationship is consistently high (RR of
1.04 to 1.08 for all cut points c = 30 to 90 //g/m3), and is statistically significant except for the
small number of data points above 90 //g/m3. The lower half of the piecewise linear model also
shows a strong relationship to TSP (RR of 1.047 to 1.055) for TSP at cut points of 90 //g/m3 or
above, with general statistical significance. There is little relationship in the lower part of the
piecewise linear fit for cutpoints between 30 and 60 //g/m3. This suggests that there are some
substantial deviations from a purely linear additive models involving TSP and certain covariates or
copollutants at TSP levels below 100 //g/m3, but that the deviations are not necessarily indicative
of a "threshold" model with slope 0
12-301
-------
I.US
w
re
03
Qi
1 00 •
Piecewis
0 50
TS
a
/""
P(Mfl
_.
<-••"
/
/
100 15C
/m3)
I.US
M
Of
1 1-°4-
n
0)
o:
1 00 •
)
Piecewise
S* ..•••'
X^ • ' ' '
^""
0 50
TSP (uc
x
l/rr
/
/
7
100 15C
i3)
Figure 12-32. Relative risk of death versus total suspended particles (TSP) level for each
of the models used to explore the threshold levels, for disease deaths. The
model includes SO 2 and O3, as well as control for weather using the full
specification. The breakpoints are at 59 //g/m3, the 50th percentile of TSP,
and at 91 //g/m3, the 90th percentile of TSP.
Source: Cifuentes and Lave (1996).
(RR = 1.00) below some cutoff level c as shown in Figure 12-32. While Cifuentes and Lave show
the results of fitting a nonparametric loess smooth model of temperature and dewpoint, there is no
analogous nonparametric model for TSP shown in the paper.
The HEI study (Samet et al., 1995) largely confirmed the additive linear model estimates
derived by other investigators, and so will not be shown here. A major new finding from this
study is that the marginal mortality-pollution curves for TSP and the two-dimensional smooth
response surfaces fitted to mortality data using both of these pollutants was significantly nonlinear
and nonadditive. This is discussed below in evaluating copollutant models.
Model Specification Using Fine Versus Coarse Versus Thoracic Particle Indices
The recent reanalyses of the Six City Study by Schwartz et al. (1996) allow evalution of the
effects of thoracic particles (PM 15), fine particles (FP = PM 25), or coarse particles (CP = PM 15 -
PM2 5) as exposure indices. The reported results were transformed to standard increments of 50
|ig/m3 PM15, 25 |ig/m3 PM25, and 25 |ig/m3 for CP, as shown in Figure 12-33.
12-302
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Relative Risk for 50 m/m PM15
in Six City Acute Study
Topeka
Portage
Steubenville
St.Louis
Harriman
Boston
1— 0— 1
0.9
1.1
Relative Risk
Relative Risk for 25 pg/m Fine Particles
(PM2.5) in Six City Acute Study
Relative Risk for 25 pg/m 3 Coarse Particles *
(PM15-PM2.5) in Six City Acute Study
Topeka
Portage
Steubenville
St.Louis
Harriman
Boston
1
— 0 1
I-OH
I-OH
Topeka
Portage
Steubenville
" St.Louis
Harriman
Boston
1 O
1
1-
1-
H
O 1
•>— 1
0—1
0.9
1 1.1
Relative Risk
09
1 1 1
Relative Risk
Figure 12-33. Relative risks of acute mortality in the Six City Study, for inhalable
particles (PM10, PM15), fine particles (PM 25) and coarse particles (PM 15-
PM25).
Source: U.S. EPA graphical depiction of results from Schwartz et al. (1996).
It is clear that, across the six cities, PM 2 5 is the most predictive of the three PM indices
except in Steubenville, where a more significant CP effect was found (although the FP effect size
for Steubenville was nearly as large as in most other cities). In spite of very considerable
differences among the cities in terms of climate and demographics, the FP effect sizes were rather
consistent. The CP effect sizes were positive, small, and not significant except for Steubenville
(positive, significant) and Topeka (negative, nearly significant). Since PM15 was the sum of FP
and CP, it had an intermediate significance, with positive and significant effects except for Portage
and Topeka. The St. Louis and Harriman/Knoxville associations for PM 15 and FP were both
significant, possibly because of the use of nonparametric smoothers to adjust for weather and time
trends. Overall, the pattern of results obtained most strongly implicates fine particles (PM 25) as
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contributing to PM-mortality relationships in the subject six cities. The Steubenville results
suggest that, in some cases, CP may also need to be considered as well as FP in evaluating PM
health risks.
Model Specification for Other Mortality Studies
Other studies on acute mortality have evaluated alternative model specifications. A number
of OLS and time series regression models for COH in Santa Clara were compared by Fairley
(1990). Mortality studies for Detroit (Schwartz 199la) and Birmingham (Schwartz, 1993a)
evaluated other regression and time series approaches. These are not reported in as much detail
as the studies cited here, and the Detroit study also uses estimates for PM. Lipfert and Wyzga
(1995a,b) also compare many of the above mortality studies using elasticity as a risk index.
12.6.2.2 Model Specification for Morbidity Studies
There have been a large number of recent studies on hospital admissions related to PM
(Schwartz, 1993b, 1994b, 1994e, 1995a, 1995b, 1996). These have used generally similar
strategies for evaluating alternative Poisson regression models. The basic model includes a set of
variables for temperature and dewpoint (usually in 6 to 8 categories), linear and quadratic time
trends, indicators or dummies for each month in each year (so that no assumptions need to be
made about recurrent seasonal or monthly effects), and the PM indicator. Alternative model
specifications usually include: (1) piecewise cubic spline functions for time trend, temperature,
and dewpoint; (2) generalized additive models (GAM) for time trend, temperature, and dewpoint;
(3) basic model, excluding all non-attainment days (PM 10 > 150 ug/m3, or ozone > 120 ppb, etc.);
(4) basic model without hot days; (5) extended range of lag times or moving averages; (6) basic
model plus co-pollutants. Differences in RR for PM among most specifications is small. RR
estimates from the GAM method tend to be higher than most other specifications, but the
conclusions about RR are fairly insensitive to alternative specifications. Since there have been no
studies that disagree with these conclusions, these are not reviewed below in detail, since the
assessments are in many ways similar to those for the acute mortality studies.
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12.6.2.3 Model Specification Issues: Conclusions
Published research articles have provided a substantial amount of evidence about the
consequences of different model specifications for short-term and long-term models. The short-
term studies have been generally consistent across many different kinds of model specifications.
The general concordance of PM effects, particularly in analyses of short-term mortality studies, is
a consequence of certain appropriate choices in modelling strategy that most authors have
adopted, but the results are not dictated by the use or misuse of any specific model. While it is
conceivable that different plausible model specifications could lead to markedly different
conclusions, this has not emerged thus far.
12.6.3 Other Methodological Issues for Epidemiology Studies
The issues in air pollution epidemiology for PM are similar to those of many other
pollutants. No single air pollutant, nor any mixture such as PM or an identifiable component of
PM, is uniquely related to a specific health outcome. Also, in the PM studies individual exposure
measurements are generally lacking, with exposure to PM typically measured at only one site in an
urban or regional airshed or, at most, at a few widely spaced sites. U.S. studies of acute
mortality typically depend on combining three data bases: (1) mortality data tapes provided by
the National Center for Health Statistics (NCHS); (2) air pollution data sets for urban areas,
accessed through the Aerometric Information Retrieval System (AIRS) network; and (3)
meteorological data for urban areas and smaller SMSA's, obtained from the National Climatic
Data Center (NCDC). Hospital admissions data involve a more diverse set of sources. Merging
the data sets has not always been a straight-forward task, and attempts to replicate results have
sometimes been complicated by the fact that different investigators have used different approaches
to creating a merged data set for subsequent analyses. As a simple example, the PM 10 monitoring
data for Chicago consists of every-day monitoring at one site and every-6-days monitoring at up
to eight other sites. In that case, different investigators may calculate different PM 10
concentrations according to how the data from the intermittent monitoring sites are combined
with data from the every-day site. In this section, specific methodology issues encountered in the
studies reviewed earlier are discussed.
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12.6.3.1 Particulate Matter Exposure Characterization
PM10 measures the inhalable particles better than TSP. The U.S. EPA NAAQS are specified
by PM10 concentrations, which were not generally available before 1986. PM10 is also a better
index of ambient fine particle exposure than TSP because it is more uniformly distributed in an
urban area or region than TSP. Since fine particles from outside can also penetrate indoors and
constitute a major fraction of indoor air concentrations, PM 10 is also likely to be a better index of
indoor air exposure to ambient fine particles than TSP. Currently, PM data on AIRS do not allow
discrimination among important components of PM 10, including fine particles, coarse particles, or
sulfates. In the absence of any clearly demonstrated mechanistic relationship between PM
components (by size, composition, or source) and specific health endpoints, there is little a-priori
reason to believe that health endpoints related to PM should not be predicted well in different
studies by different PM indices. The indices include PM 10, fine particles (defined as PM 2 5), coarse
fraction of PM 10 particles (defined as PM 10 - PM2 5), or surrogates (e.g., sulfates, SO 2, or H+) that
may be more closely correlated with fine particles than to coarse particles. Results presented by
Dockery and Pope (1994b) suggest that PM 2 5 may be a more appropriate "proxy" of exposure to
particles that are predictive of health effects. This has also been generally supported by Schwartz
et al. (1996), although there appears fully to be some situations, such as in Steubenville, where
coarse particles cannot be ruled out as contributing to observed PM-health effects relationships.
PM2 5 particles are more likely to be uniformly distributed within an urban airshed and, upon
penetrating indoors, to be removed less rapidly from indoor air than coarse particles, so that
outdoor ambient fine particle concentration becomes a better predictor of total fine particle
exposure than ambient coarse particle concentration does for total coarse particle exposure.
However, it is not clear that inhalable coarse particle fractions (i.e., PM 10-PMZ5) can be entirely
discounted in terms of their potential health effects. While sulfates are a significant part of fine
particle levels in some places, they may be of more limited value as an indicator of a toxic
component of PM 10 due to measurement artifacts (filter artifacts). The usefulness of sulfate data
may also be limited because of regional and seasonal differences of sulfate levels. Information on
other components of PM 10, including acidity, metal ions, and organic components, is often not
available. Similarly, data deficiencies exist for most co-pollutants. In studies where SO 2 is a good
proxy for PM 10, it may be difficult to assign effects to one or the other without evaluating the
relationships linking the two, since SO2 is the source of some fraction of particle sulfates.
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Chapter 7 on Human Exposure to PM indicates that variations in ambient PM concentration
can be significantly correlated, on a longitudinal (day-to-day) basis, with the variation of
individual personal PM exposures as measured by personal monitors. However, cross-sectional
correlations of individual exposures with ambient PM concentrations are typically low. In terms
of community air pollution, a properly sited ambient PM measurement is reasonably related to the
mean personal PM exposure of the community, and on a time series basis it may be a good
indicator of the variability of any single individuals' daily PM exposure. An important
consideration here is that the ambient monitors be properly sited in relation to the populations
they are intended to represent. This would have to be evaluated study by study, which can be
difficult or impossible if pertinent data were not been reported for the study. There must be limits
to the acceptability to using a monitor for daily level changes in regards to both the distance from
the population and the terrain between the population and the monitoring site (e.g., a mountain
range).
Data are available at more than one monitoring site in a few studies, including Birmingham
AL (Schwartz, 1993a, 1994e), Utah Valley (Pope et al., 1992, 1994), Los Angeles (Kinney et al.,
1995; Kinney and Ozkaynak, 1991), San Francisco Bay area (Fairley, 1994), Philadelphia (Wilson
and Suh, 1995), and Chicago (Ito et al., 1993, 1995; Styer et al., 1995). While PM10 varies from
place to place, with a decreasing correlation with increasing distance across a metropolitan area,
measurements are well correlated up to a few kilometers (Burton et al., 1996). Fine particle
measures (e.g., PM 2 5 and sulfate) are particularly well correlated across a metropolitan region.
Exposure Relevance
The majority of the PM data used in the PM/mortality literature are daily observations,
rather than the standard every-6th-day observations. The ambient daily mean PM levels reported
in these PM/mortality studies of U.S. cities range from 28 //g/m3 (St. Louis, MO) to 58 //g/m3
(Los Angeles, CA) for PM 10; 76 //g/m3 (Cincinnati, OH) to 111 //g/m3 (Steubenville, OH) for
TSP. Other PM indices in the literature include CoH (monthly mean range = 9 to 12) in Santa
Clara County, CA; and KM (mean = 25) in Los Angeles County, CA. The data description
reported for these PM indices indicate a generally skewed distribution, and the maximum daily
values deviate about 50 to 150 //g/m3 from these means. The current 24-h NAAQS, 150 /-ig/m3,
is rarely exceeded in these communities. Many of these communities studied were urban, but the
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PM levels observed appeared to be representative of metropolitan areas where substantial
fractions of the U.S. population reside.
Size and Chemistries
In theory, since TSP includes particle sizes (d a < 50 //m) that exceed those having thoracic
deposition (da < 10 //m), it is expected that TSP would be a less reliable measure of paniculate
matter for health effects analyses. However, comparison of the significance of the PM regression
coefficients in the recent U.S. PM/mortality studies do not show systematically lower significance
for TSP than PM 10. This may be because, so long as TSP levels fluctuate together with smaller
particles over time, TSP may still be a reasonable surrogate for thoracic or fine particles, albeit
not as good as PM 10. The error introduced by large particles depends on their local availability
and, therefore, it is site-specific.
In most of the PM/mortality time series studies, only one PM index was employed. An
exception is the study conducted in St. Louis, Mo. and Kingston, TN (Dockery et al., 1992).
In this study, PM 10, PM2 5, sulfates, and aerosol acidity were available. The regression results
indicate that, for both cities, PM 10 showed the most significant mortality associations, and the
significance declined as the size of the index decreased. However, the sample size of this study
was relatively small (n = 300; PM 10 coefficient t-ratio = 2.17 in St. Louis, and 1.07 in Kingston),
and the sample size for the aerosol acidity was even smaller (n = 200). Furthermore, we currently
do not know the extent to which the measurement errors of these different PM measures affect
PM/mortality significance. Thus, it is as yet premature to relate the significance of various PM
measures to size or chemistry specific causality from this study. Cross-sectional studies reported
more significant mortality associations for fine particles (PM 25: Dockery et al., 1993; sulfates:
Ozkaynak and Thurston, 1987). However, significant PM/mortality associations have also been
reported in areas where summertime sulfates are not the major component of PM (e.g., winter
analysis of Santa Clara, CA; Los Angeles, CA). All the PM measures in the U.S. studies do
include some type of combustion source originated particles (e.g., automobile emissions in Los
Angeles, sulfates in the eastern U.S.). Overall, PM composition varies widely, not only between
sites, but also over time at a single site. This represents a major challenge to any attempts to
quantify PM-related health impacts.
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12.6.3.2 Exposure-Response Functions, Including Thresholds
A PM threshold for mortality is difficult to detect because of small numbers of deaths
(especially when broken down by age group and cause of death), and because the observed PM
concentration is only a surrogate for exposure. In general, the threshold question has not been
extensively examined except by Cifuentes and Lave (1996). Even in their analyses, there is no
precise estimate of a change point in the relationship, with values of TSP in the range of 60 to 90
//g/m3 as possible cutpoints. Other model specification issues that have had little examination
include non-linear transformations of pollutant variables, interactions among pollutant variables,
and interactions between meteorological variables and pollutants.
Thresholds
Many of the recent U.S. PM/mortality studies have reported PM/mortality "exposure-
response" curves of the data, after controlling for weather and seasonal variables (Schwartz,
1993a, 1994a; Schwartz and Dockery, 1992a; Pope et al., 1992). Measurement error is a limiting
factor in the ability to detect thresholds, no matter what methods are used. Some of the smoothed
curves are shown in Figure 12-34. Some estimates were constructed by using quintile or quartile
indicator variables in the regression, or by nonparametric smoothing (in the Generalized Additive
Models), both of which should allow for possible non-linear relationships. In all the figures
presented, a generally monotonic increase in mortality, as PM increases, is suggested. However,
a search for a threshold from these results is difficult because of the distribution of the available
number of datapoints. For example, in the plots of the quintile (or quartile) PM versus relative
risk, the resolution of the shape of slope is determined by the number (5 or 4) of indicator
categories. The lowest quintile (or quartile) could be higher than a potential threshold level (e.g.,
the lowest quintile of TSP was about 50 //g/m3 in Philadelphia), or other discontinuities might be
present at some higher level if finer level breakdown of the data were feasible. However, because
estimation of more stable
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A. Birmingham, AL
B. Cincinnati, OH
C. Philadelphia, PA
1.15 '
S 1.10
tf.
I 105 '
1.00
1.10 •
1.10'
£1.08'
at
Q
o 1.06
-^
I 1.041
» 1.02'
tr
1.00
20 40 60 80 100 120 140
PM10
20 40 60 80 100 120 140
Total Suspended Particulates
0 50 100 150
Total Suspended Particulate Matter
(M9/m3)
Figure 12-34. Smoothed nonparametric estimate of relative risk of mortality in three
studies, where the particulate matter index is either total suspended
particulates or PM 10, in micrograms per cubic meter.
Source: A. Schwartz (1993a), B. Schwartz (1994a), C. Schwartz andDockery (1992a).
coefficients requires greater numbers of cases, even a large dataset may not allow smaller data
division than quintiles. Thus, from these results, we cannot determine if any threshold exists
below approximately 50 //g/m3 for TSP or 20 //g/m3 for PM10 or if other discontinuities exist in
the range of the observed data. Samet et al. (1995) derived quintile estimates for many of the
studies cited here; but the quintile estimates derived by Samet et al. (1995) were based on the
observed PM values in each study, whereas those derived by Schwartz and by Pope were based
on adjusted PM values on weather and time.
Nonparametric smoothing of relative risk versus PM can, in theory, allow greater resolution
of the shape. However, the stability of the results also depends on the weights of neighborhood
and the interval of the PM, or "span", used to compute each segment of the curve (these
parameters are not described in the relevant publications). Again, these smoothed curves, as with
the quintile approach, cannot describe the shape of the curve where data do not exist. For
example, the smoothed curve shown in Figure 12-34 for Cincinnati, OH, appears to suggest a
threshold around 40 //g/m3 of TSP, but the distribution (25th percentile TSP = 53 //g/m3)
indicates that there are not enough data points below 40 //g/m3 to obtain stable curve shape below
this level. Lack of data densities and confidence intervals makes any detailed examination more
difficult. Thus, while these figures do collectively suggest a linear-like PM/mortality relationship,
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any examination of a threshold level is limited by the data. Most other studies did not consider or
present graphical examination of the possible shape of any exposure/response relationship and,
thus, the results could have been constrained by the functional form specified in the regression
model.
Samet et al. (1995) exhibits smooth nonparametric concentration-response functions for
TSP and SO 2 (Figure 11 in their report), for all ages, ages < 65, and 65+. For all ages mortality
and over 65 mortality, there appears to be a piecewise linear response which increases only for
TSP > 100 //g/m3 (all ages) or TSP > 60 //g/m3 (age 65+). The SO2 relationship is quadratic.
However, the nonparametric smooth response surface for TSP and SO 2 differs significantly from
this simple threshold model.
12.6.3.3 Adjustments for Seasonally, Time Lags, and Correlation Structure
Trends, long-term and medium-term recurrent or cyclical effects, and effects of medium-
term non-recurrent or random events such as influenza epidemics are removed from the data so
that short-term responses to short-term changes in PM concentration can be detected without
confounding or interference from longer-term effects. For Gaussian time series models, this can
usually be done well by filtering. However, filtering has the potential to remove longer-term
effects of PM exposure, and therefore may underestimate the true PM effect. For example, death
may occur from PM exposure during the first few days after exposure because the PM exposure
may exacerbate pulmonary insufficiency in individuals whose respiratory capacity has already been
compromised, especially the elderly and the ill. This may also contribute to excess short-term
cardiovascular mortality. However, if PM exposure also compromises the immune system, the
exposed individual may succumb to an infectious disease some weeks after the PM exposure, an
effect that would be more likely to be cancelled out by application of filtering or other detrending
techniques. Detrending could also be done by using regressors that are functions of the time or
day of study. Candidate regressors are Fourier series (sums of sine and cosine terms), polynomial
functions of time, dummy variables for year, season or quarter, month, or day of week. Fourier
series are mathematically convenient, but require many terms in order to fit asymmetric seasonal
variations, and cannot include random year-to-year differences in seasonal effects. Dummy
variables for year, season, and month provide a great deal of flexibility, but may still be too
"rough" in that such models allow abrupt changes between December 31, 1980 and January 1,
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1981, between June 30 and July 1, and so on. Non-parametric smoothers such as spline
functions, LOESS smoothers, and generalized additive models are often good choices, but as with
any other detrending procedure, the scale or span of the smooth detrender determines what
medium-term effects are removed from the model.
A number of short-term studies have provided reasonable control over time-related
exogenous changes. The use of tapered high-pass filters in Gaussian time series models, in
connection with linear time trends or dummy variables for season or day of week, has been
demonstrated in numerous papers, for example, among acute mortality studies: (Shumway et al.,
1988; Schwartz and Marcus, 1990; Kinney and Ozkaynak, 1991; Ito et al., 1993; Thurston and
Kinney, 1995; Kinney et al. 1995; Ito et al., 1995).
Mortality Displacement
It is possible that there is a causal effect of airborne PM, but rather than altering the long
term average mortality rate, peaks in exposure simply advance the date of death of otherwise
terminally ill subjects. The terms "mortality displacement" or "harvesting" have generally been
applied to this hypothesis. Under this scenario, lowering particulate matter concentrations might
grant a few extra days life to a small part of the population, but have no effect on the general
mortality rate. It is obviously extremely important for policy making purposes to resolve whether
this is indeed the case.
Although the possibility has been discussed in several of the papers reviewed (Lipfert and
Wyzga, 1995a,b), only a few (Spix et al., 1993; Cifuentes and Lave, 1996) seem to have offered a
serious test of the hypothesis. They point out that the effect of harvesting should be to induce a
negative effect on the autocorrelation, since "a high number of deaths on one day may leave a
smaller number of vulnerable individuals at risk of dying on succeeding days." They further
suggest that the magnitude of this effect should be proportional to the excess deaths due to
pollution. Hence, they test the hypothesis by adding an interaction between the pollutant level on
that day and the last k days mortality deviation from the expected value, where the expected value
is based on a previously fitted model including trend, season, and influenza epidemics. A negative
estimate for this interaction term would be interpreted as evidence for this phenomenon.
Applying this test to data from Erfurt, East Germany, Spix et al. found a weak effect for
suspended particles in the expected direction (nominal one-tailed p = 0.07 ignoring the multiple
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testing for k): the RR comparing the 5th and 95th percentiles of the exposure distribution was
1.51 if the previous 18 days mortality was above expected, 1.26 if it was below expected. A
somewhat similar approach, examining mortality displacement from summer heat waves, has been
described by Kalkstein et al. (1994).
Cifuentes and Lave (1996) examined short-term mortality displacement in two different
ways. One method was to look at mortality autocorrelation coefficients. Total mortality showed
a negative correlation at lag 2 days, and deaths outside of hospital inpatients had negative
autocorrelation for lags 1 and 2 days. This is consistent with depletion of a potentially susceptible
population by acceleration of death by 1 or 2 days, but is not a strong demonstration of the
hypothesis.
A much more detailed analysis was based on the definition of "episodes" by Cifuentes and
Lave. Episodes are contiguous periods of time in which pollution levels tend to be relatively
elevated. They identified more than 100 such 3-day "episodes" during the 6 year period. Positive
residuals (excess mortality) during the episode and negative residuals after the episode suggest
displacement of mortality during that episode. However, the number of deaths occurring after the
episode was typically smaller than the number occurring during the episodes suggesting that some
of the excess deaths occurring during the episode were not among people who were certain to die
within a few days anyway. Different methods for estimating the number of deaths, for time lags
etc., produce different estimates of short term displacement. Alternative explanations such as
unusual weather events cannot account for the mortality deviations observed during that period of
time. Additional analyses of this reported effect would be of great interest including evaluation of
out-of-hospital deaths.
The estimates comparing the first day of a three-day episode and the first day after an
episode are shown in Table 12-32, for three age groups (Cifuentes and Lave, 1996; Table 10).
The mean residuals are based on the best fitted model, using TSP, SO 2, and O3. The mean
number of deaths is greated than predicted on the first day of the episode. For total mortality, the
excess is 0.874 deaths against 1.98 predicted for the given TSP level, or an excess of 0.874 /1.98
= 44%. For the first day after the episode, there is a deficit of 0.895 deaths less than the 1.68
expected at the smaller post-episode value of TSP, or a
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TABLE 12-32. MEAN OF TSP, MODEL RESIDUALS, AND PREDICTED AND
OBSERVED DEATHS FOR THE FIRST DAY OF THE EPISODES AND THE FIRST
DAY AFTER THE EPISODES, FOR THE THREE AGE GROUPS.
Age
Group
Period
n
Avg.
TSP
(//g/m3)
Mean of
Residuals
(deaths/day)
Daily
Predicted
(deaths/day)
Deaths
Observed
(deaths/day)
Res./
predicted
All
Episode
After
109
109
71.9
61.3
0.874
-0.895
1.98
1.68
2.85
0.78
0.44
0.53
Age 18-64 years
Age GS+
Episode
After
years
Episode
After
82
82
120
120
70.5
65.7
73.7
62.0
0.642
-0.591
0.759
-0.503
0.73
0.68
1.61
1.35
1.38
0.09
2.37
0.85
0.87
0.87
0.47
0.37
Source: Cifuentes and Lave (1996).
deficiency of 0.895 /1.68 = 53%. There is a large effect in adults of ages 18 to 64 years, with an
87% excess during the first day of a three-day episode and an 87% deficiency in the first day after
the episode. The effect for older adults is also large, with a 47% excess during the episode and
37% fewer deaths than expected in the first day after the episode. This strongly suggests that
some of the individuals who would have otherwise been expected to die on the first day after the
episode may have died 3 days prematurely, on the first day of the episode.
One should also be quite clear about what Table 12-32 does not show. The effect of an
episode in causing premature deaths is focussed primarily on deaths that occurred within a day or
two after exposure, but does not preclude premature deaths that may have occurred more than
two days after exposure. There is no estimate here of the cumulative excess of episode-related
deaths that were displaced by more than a few days. While acute responses following exposure
suggests a cause-effect relationship with at least some short-term displacement of mortality, the
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question of long-term excess mortality over times greater than a few days must be addressed by
the long-term mortality studies.
The statistical properties of this test merit further research. However, a full investigation of
the performance of the test in realistic settings with the more sophisticated time series and GEE
methods, including estimation of the harvesting parameter k is beyond the scope of this
assessment.
In addition to statistical research, further epidemiologic research is warranted to better
characterize the excess deaths in terms of age, cause of death, hospitalization status, prior
morbidity, etc. It may be necessary to develop a multistage model, with recruitment of individuals
from a healthy stage through one or more stages of morbidity until they reach a susceptible stage
at which acute air pollution exposure may cause deaths.
There is, at present, relatively little basis for quantifying the shortening of life in some
individuals by periods of months or years using time series data.
12.6.3.4 Adjustments for Meteorological Variables and Other Confounders
There has been only limited progress in developing a systematic approach to the use of
weather-related variables in daily mortality or morbidity studies. A variety of ad hoc procedures
have been used. While various statistical methods for adjusting daily mortality or morbidity time
series for weather effects appear to be successful on a case-by-case basis, there is little
understanding of how to do this systematically in a way that appropriately characterizes current
knowledge about the relationship between weather, weather changes, and changes in mortality.
The empirical adjustments used in most studies are made with little theoretical basis and may be
arguable for that reason alone. It is clear that the effects of some variables, such as temperature,
are intrinsically nonlinear, and that it may be more useful to define the likelihood of excess
weather-related mortality by the presence of clusters of related meteorological variables, such as
the synoptic classes suggested by Kalkstein et al. (1994) and used by Pope and Kalkstein (1996)
for the Utah Valley. While the synoptic class approach appears promising, it has so far been
applied to relatively few cities, and may require further modification to be applicable in a general
health effects modelling framework. The problem is that meteorological variables are confounded
with other pollutants as well as with PM, so that any misspecifications of the relationship between
health effects and weather can provide a distorted set of residual effects to be modelled using air
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pollution variables. A causal or mechanistic model could be useful in relating weather, season,
pollutant emissions, pollutant concentrations, behavior as it affects exposure, and health
endpoints. Remarkably, weather continues to be significantly related to mortality and other health
effects, in spite of increasing use of air conditioning.
One interesting possibility in the use of synoptic categories has been demonstrated by
Kalkstein et al. (1994). They showed that during the most offensive synoptic weather category,
there may be little detectable relationship between PM and excess mortality since most of the
excess is attributable to weather. During non-offensive weather categories, however, the excess
mortality attributable to PM is readily detected since the weather effect is much smaller and there
is a quantitative dose-response relationship between PM and excess mortality.
Weather/climate control between studies has been discussed by Schwartz (1994a,b),
Dockery and Pope (1994b) and others as a qualitative issue rather than as a formal numerical
evaluation. These papers present global comparisons of RR between cities studied that are
labeled as warm or cold cities, based on longer term mean temperature. Since the actual study
analysis looked at day to day changes, long-term comparisons of means may not be as informative
or appropriate to examine in such a global manner. First, it is not clear that the classification of a
city as a warm or cold climate is correct. This dichotomy does not consider moderate climates in
a continuum as a factor, so the comparison may not be appropriate. Second, the mortality in the
studies is examined on a daily basis as is the temperature. Mean comparison over several months
of temperature is an inappropriate control for the study design.
Interrelationships Between Weather, PM, and Mortality
A number of studies have concluded that both extreme weather and high pollution adversely
affect mortality. While a majority of this research has examined the independent effect of these
stresses on mortality, few studies have successfully separated weather-induced from
pollution-induced mortality. This has been especially true in the evaluation of acute mortality.
There have been some efforts to evaluate these differential impacts (e.g., Ramlow and Kuller,
1990; Shumway et al., 1988; Schwartz and Dockery, 1992a,b).
Some authors have conducted weather/pollution/mortality evaluations in Steubenville, OH;
Philadelphia, PA; London, England; Birmingham, AL; and Utah County, UT as well as other
locales. In all of these investigations, they have reported significant associations between human
12-316
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mortality and PM, and in some cases, the relationship extends to levels well below the current
National Ambient Air Quality Standard. In several, they have also alluded to a weather-mortality
relationship. For Steubenville, a positive non-linear relationship between both temperature and
dew point temperature and mortality was detected. When dummy variables were used to denote
hot days, humid days, and hot/humid days, the hot/humid days were a significant predictor of
mortality. When seasonal variations were controlled for in their Poisson regression models
however, neither temperature nor dew point proved to be significant predictors of mortality
(Schwartz and Dockery, 1992b). In a study of British Smoke in London, Schwartz and Marcus
(1990) controlled for temperature and humidity and improved the model results significantly over
the results of a model with no meteorological variables.
More recent studies indicate that controls for weather may probably not have been adequate
to determine true meteorological impacts in the evaluations cited above. Many PM/mortality
studies utilize rank-ordered temperatures, squared temperature and dewpoint values, moving
averages of temperature, and mean temperatures for groupings of days (see Table 12-33 for
further details), which may not provide the detail to detect true weather/mortality relationships.
In addition, it is probably not feasible to assume that cities within a wide range of climates
demonstrate similar weather/PM impacts on mortality, and there are possibly some regional
similarities in response which have not been adequately explored. In a reanalysis of Philadelphia
mortality/PM relationships, Schwartz (1994b,c) took a more direct approach to examine the
possibility of confounding weather impacts. The reanalysis utilized Hastie and Tibshirani's (1990)
"Generalized Additive Model" to detect and control for nonlinearities in the dependence of daily
mortality on weather; nevertheless, this study uncovered findings similar to the original
Philadelphia study. In addition, Moolgavkar et al. (1995a) assert that the role of weather was
improperly evaluated within the Steubenville study, and suggest a more sophisticated evaluation
of meteorology in future PM/mortality analyses.
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TABLE 12-33. ADJUSTMENTS FOR METEOROLOGICAL FACTORS IN SOME
RECENT STUDIES RELATING MORTALITY TO PARTICULATE MATTER
Location
Studied and Authors
London
Schwartz and
Marcus (1990)
Philadelphia
Schwartz and
Dockery(1992a)
Steubenville
Schwartz and
Dockery(1992b)
Utah
Popeetal. (1992)
Erfurt.
East Germany
Spixetal. (1993)
Birmingham
Schwartz (1993a)
Steubenville
Moolgavkar et al. (1995a)
Philadelphia
Wyzga and Lipfert (1995b)
Pollution
Data and Treatment
British Smoke measurements
from 7 stations;
logarithmic and square root
transformations ;
TSP samples collected
routinely at two monitors;
supplemented by sampling
every sixth day at several
sites; daily means and lags
used
TSP from one monitor;
ranked by levels and sorted
into quartiles;
PM10 level from one site;
up to 7-day lagged moving
averages; divided into
quintiles used as dummy
variables;
Suspended particulates;
0-3 day lags; logarithmic
transformation;
PM10 level averaged from all
(1-2) city monitors; divided
into quartiles used as dummy
variables;
TSP from one monitor and
SO2 (two series)
24 h averages of O3 and TSP
from several city monitors;
lags of 0 to 4 days tested
Mortality
Data and Treatment
Daily total death counts,
including respiratory and
cardiovascular causes;
sensitivity to filtering
Daily total, elderly, <65,
pulmonary disease,
pneumonia,
cardiovascular and cancer
mortality;
Poisson regression using
GEE;
Daily total mortality;
Poisson and weather
model regressions;
Daily total, non-
accidental respiratory,
cardiovascular, and all
other causes of non-
accidental mortality;
Poisson regression;
Daily total mortality;
Poisson regression;
autocorrelation
adjustment for
"harvesting"
Daily total mortality;
Poisson regression using
GEE; dummy variables
for year and day of week;
biannual cycle filters;
Daily total non-accidental
mortality;
Poisson regression; with
and without GEE; full
year and season; serial
correlation unimportant.
Daily non-accidental
deaths (non-elderly and
elderly); linear filtering;
variable for time over
entire period in stepwise
regression and forced
OLS.
Weather Data
and Treatment
Temperature and RH;
grouped plots of
temperature and humidity
versus mortality;
autoregressive model.
Mean 24 h temp and DP
including squared
transformation;
indicator variables for
season, hot, cold, humid,
and hot humid days, and
year.
Mean 24 h temp and DP;
year as random effect;
indicator variables for hot,
humid, and hot humid
days;
Temp and RH; dummy
variables used for 10 °F
ranges, previous day's
temp, 5-day temp moving
average, and humidity;
linear time trend, random
year effect;
Daily mean temp, RH,
precipitation; indicator
variables used for very cold
days and hot days for
different thresholds,
various lags;
Mean 24 h temp and DP;
dummy variables same as
Utah study, plus cold days;
3-day moving lags;
Mean 24 h temp and DP;
indicator variable for hot
and humid days;
temperature quintiles;
Daily maximum temp.;
daily change in barometric
pressure; dummy variables
for winter and seasonality
Pollution/
Weather Impact
British Smoke is significant
predictor of mortality;
temp/humidity control
increased significance, as did
autocorrelation adjustment
Significant TSP association
mortality, strongest among
elderly and respiratory
patients; hot days, mean DP,
other weather factors also
associated
Nonlinear association
between TSP and daily
mortality; hot humid days
associated with daily total
mortality;
Relative risk of death
increased monotonically
with the mean PM10 level for
each quintile; also observed
when weather controlled;
Effects of air pollution
smaller than influenza and
weather effect; significant
SO2
Significant association
between PM10 and daily
mortality; extremely hot
weather also associated with
excess mortality; relation to
temperature.
TSP influence on mortality
greatly reduced when SO2
included in analysis; choice
of SO2 series and season had
large impact on mortality
results.
Strong relationship between
temp, and mortality;
seasonal adjustments very
important; TSP-temp.
interaction; most mortality
with TSP on hot days.
To further control for weather, Schwartz (1994b,c) stated that the similar responses to air
pollution in the "mild" weather of Philadelphia (based on a mean daily temperature of 57 °F) and
"cold" weather of London reduce the confounding role of weather. Furthermore, Schwartz
(1994b,c) notes that similarity in temperature and humidity on high and low air pollution days
12-318
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(when different mortality response are noted)"... also would seem to eliminate weather as a
potential confounder." However, it is possible that these studies do not remove the total
confounding influence of weather, especially because of their dependence on mean temperatures
and other meteorological surrogate which may not truly reflect weather variation.
There have been other studies which have attempted to assess the differential impact of PM
and weather on acute mortality. For example, Ostro (1993) summarized studies which show
strong associations between exposures to PM 10 and total daily mortality for many urban areas in
the United States, Europe, and Canada. In addition, he notes that results are remarkably
consistent across regions. However, the impact of weather as a confounding influence is
implicitly considered rather unimportant. Ito et al. (1993) showed that daily mortality in London
was significantly associated with aerosol acidity levels and British Smoke. Weather played a
lesser role, and Ito's work confirms results obtained by others who have evaluated London's
mortality/PM/weather relationship (Schwartz and Marcus, 1990; Thurston et al., 1989;
Mazumdar et al., 1982). However, it should be noted that London's marine climate is rather
benign when compared to many large American cities, as thermal extremes are unusual.
Some studies for cities exhibiting higher climate variation yielded somewhat different
results. Wyzga (1978) used the Coefficient of Haze (COH) as a surrogate measure of
PM concentration, and determined that high COH values are associated with increased mortality
in Philadelphia. However, he recognized the potential impact of extreme weather as well, and
noted that heat waves may also be responsible for large numbers of extra deaths. In a recent
study by Wyzga in which weather was treated in a more sophisticated manner (Wyzga and
Lipfert, 1995a), the impact of ozone concentrations and weather on acute mortality were
evaluated and results were compared to TSP. The authors conclude that a determination of
ozone and TSP impacts is most difficult because of the influence of confounders, particularly
weather. In addition, use of different explanatory models yields disparate results, with pollution
impacts ranging, "...from essentially no effect to response similar to that associated with a 10 °F
increase in ambient temperature" (Wyzga and Lipfert, 1995a). This evaluation appeared to
uncover a synergistic relationship between weather and pollution, as days with maximum
temperatures exceeding 85 °F contributed most to the associations between TSP and mortality.
Several other studies have uncovered synergistic relationships, and some of these consider
pollution to be of secondary importance to weather in affecting acute mortality. Ramlow and
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Kuller (1990) found that daily mortality was most closely associated with the daily average
temperature of the previous day rather than any pollution measure in Allegheny County, PA. In a
study which attempts to determine synergistic relationships between weather and pollution on
mortality in Los Angeles, Shumway et al. (1988) determined that mortality is, "...an additive
nonlinear function of temperature and pollution, whereas there may be significant interactions
present, especially when low or high temperatures are combined with high pollution levels." The
authors found that model-predicted average mortality values increased at both temperature
extremes when particulate levels were held constant. Two evaluations in the Netherlands found
temperature extremes in summer and winter to be primary determinants in mortality variation.
Kunst et al. (1993) and Mackenbach et al. (1993) determined that the relationship between
temperature and mortality is linear, producing a U-shaped temperature curve, with minimum
mortality rates observed between 10 to 15 °C. The Kunst evaluation determined that summer
acute mortality is not influenced by variations in air pollution concentration.
Although weather seems to induce mortality increases when temperatures are either very
warm or very cold, the impact of weather as a confounder varies seasonally. For example, the
impact of weather on acute mortality in winter is much more difficult to evaluate, and thermal
relationships are decidedly weaker.
Controlling for Weather in PM/Mortality Analyses: The Use of Synoptic Climatological
Methods
A number of procedures have been utilized to control for weather in PM/mortality studies,
and although the variety has been great, they generally suffer from common shortcomings. First,
many depend on arbitrary decisions to remove extreme weather events from the dataset. The
definition of extreme weather to include, for example, days above 90 °F may be proper for a city
in the north, but not for a locale further south. Thus, these arbitrary delineations consider weather
as an absolute, rather than a relative, factor affecting human health. It is therefore possible that
some stressful weather days are not identified, contaminating a PM/mortality dataset which is
considered controlled for weather. Second, the use of weather "dummy variables" to control for
meteorology within PM/mortality analyses categorizes weather within groupings which may not
duplicate meteorological reality. Kalkstein et al. (1991, 1994) propose that the meteorology of a
locale is defined by discrete, identifiable situations, which represent frequency modes for
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combinations of weather elements. Meteorological delineation that recognize the existence of
such modes can be used to control for weather within this context. Third, the use of mean
weather elements (e.g., mean daily temperature) does not permit a proper evaluation of, or
control for, daily weather extremes. Finally, most all consideration of weather in PM/mortality
studies are thermal (temperature), and, less frequently, moisture (humidity) dependent. This
creates a potential weather control problem, as certain meteorological phenomena, such as stormy
situations associated with mid-latitude cyclones, are not associated with thermal extremes, yet
may be very important contributors to acute mortality (Kalkstein et al., 1994). These are rarely
controlled for in PM/mortality studies, as they cannot be identified on the basis of temperature
and humidity.
A completely different approach is that adjustment for weather-related variables is needed
only insofar as it provides a basis for removing potential confounding of excess mortality with PM
and other air pollutants, and that any empirical adjustment for weather is adequate. One of the
most completely empirical methods for adjusting daily time series data for covariates is by use of
nonparametric functions, such as LOESS smoothers, generalized splines, or generalized additive
models (GAM), as demonstrated in Schwartz (1994d,e,f,g,h; 1995a,b); and Schwartz and Morris
(1995). These are empirically satisfactory and may provide a better fit to data than synoptic
categories, but at the loss of a basis for defining weather "episodes" as a characterization of
duration of exposure.
Application of synoptic climatological procedures to control for weather has the potential to
compensate for these difficulties and add further insight by defining an entire set of meteorological
conditions which lead to increases in mortality. Many U.S. cities tend to be especially affected by
a single type of "offensive" summer air mass associated with unusually high mortality
(e.g., Philadelphia, Table 12-34). This "moist tropical" air mass in
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TABLE 12-34. MEANS AND STANDARD DEVIATION FOR SUMMER AIR MASSES IN PHILADELPHIA
Total Mortality
to
oo
to
to
Air Mass
Category Number
1
2d
3
4
5
6
7
8
9
10
Mean 3 PM
Temperature
77.0
89.0
82.4
79.0
82.6
85.0
80.6
85.5
74.7
83.6
Mean
Mortality3
-4.11
8.89
1.63
-4.43
-2.57
3.92
0.70
2.47
-4.49
0.13
Standard
Deviation
12.87
16.14
12.82
10.19
11.14
16.83
11.82
12.49
12.53
11.80
% of Top 50
Mortality11
2.00
46.00
14.00
0.00
4.00
14.00
2.00
8.00
0.00
6.00
% Top 50
% Frequency °
0.14
3.77
1.27
0.00
0.45
1.99
0.14
1.08
0.00
1.07
aValues are evaluated against a baseline of 0.
bRepresents the percentage of top 50 mortality days within a particular synoptic category.
°Ratio of percentage of top 50 days within the synoptic category over the seasonal frequency of the
Mean
Mortality
-0.
6,
1
-2.
-1.
3,
0,
2,
-2.
1
category.
,91
.72
.59
,82
,36
.84
.52
.70
,56
.28
A
Elderly
Standard
Deviation
9.99
12.58
10.76
9.33
11.10
13.77
10.41
9.88
10.39
10.86
Mortality
% of Top 50
Mortality
0.00
46.0
14.00
2.00
4.00
10.00
4.00
10.00
2.00
4.00
% Top 50
% Frequency
0.00
3.79
1.28
0.23
0.45
1.42
0.67
1.33
0.31
0.67
number greater than one indicates that a larger
proportion of days in the synoptic category are among the top 50 mortality days than might be expected based on the frequency of the category.
d"Offensive" category.
Source: Kalkstem (1993).
-------
Philadelphia, possessing the highest maximum and minimum temperatures, was also associated
with the greatest standard deviation in mortality of all air masses evaluated. Thus, although many
days within the offensive air mass were associated with high mortality totals, a number of days
showed little mortality increase. The greatest daily mortality totals during moist tropical air mass
incursions occurred as part of a lengthy string of consecutive days of the air mass, and when
minimum temperatures were particularly high. This type of information may be important when
controlling for weather in PM/mortality analysis.
Offensive air masses which lead to mortality totals significantly higher than the long-term
baseline have been identified for a number of U.S. cities (Table 12-35). In most cases moist
tropical air masses were deemed offensive (especially in the East), but the very oppressive "dry
tropical" air mass was often associated with the greatest increases in mortality, especially in New
York, St. Louis, Philadelphia, and in southwestern cities (Kalkstein, 1993b). In some cases, daily
mortality totals are over 50% above the baseline (World Health Organization, 1996). The air
mass analyses support the notion that acute mortality increases only after a meteorological
threshold is exceeded. This threshold is not only temperature dependent; it represents an overall
meteorological situation which is highly stressful. It is noteworthy that most cities demonstrate
only one or two offensive air masses which possesses meteorological characteristics exceeding
this threshold.
In a PM study where stressful weather days are removed from the data base, synoptic
categorization provides an efficient means to remove such days with greater security that very few
meteorologically offensive days are contaminating the remaining dataset. In studies where
weather is stratified based on certain meteorological elements, synoptic categorization allows for
a meteorologically realistic control, and may be preferable to the use of arbitrary dummy variables
when identifying meteorological conditions with an elevated mortality risk.
The Effect of Different Weather and Time Trend Model Specifications on Concentration-
Response Models for PM 10
A recent study by Pope and Kalkstein (1996) allows detailed assessment of the effects of the
substantially different approaches to modeling conentration-response and weather variables. The
original analyses and reanalyses of the Utah Valley data by Samet et al. (1995) use quintiles of
PM10 as the indicator. The reanalyses reported by Pope and Kalkstein as Models 1-8 used a
linear model for 5-day moving average PM 10, and
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TABLE 12-35.
DAILY EXCESSIVE MORTALITY (SUMMER SEASON) DURING
OFFENSIVE AIR MASSES
City
Birmingham
Phoenix
Los Angeles
Riverside
San Francisco
Hartford
Tampa
Atlanta
Chicago
Indianapolis
Louisville
Boston
Baltimore
Detroit
Minneapolis
Offensive Air
Mass
MT
MT
MT
DM
DT
DT
MT
MM
MT*
DP
MT*
DT
MT*
MT*
MT*
MT*
MT*
DT
MT
DT
MT*
Mortality Above
Baseline3
+2
+1
+9
+3
+2
+9
+3
+1
+3
+4
+3
+9
+14
+3
+2
+8
+5
+10
+8
+4
+6
City
Kansas City
StLouis
Newark
Buffalo
Nassau, NY
New York
Cincinnati
Columbus
Portland
Philadelphia
Providence
Memphis
Dallas-
FtWorth
Houston
San Antonio
Offensive Air
Mass
DT
MT*
DT
MT*
DT
MT*
MT*
DT
MT*
DT
MT*
MT*
MT*
DT
DT
MT*
MT*
DT
MT*
DT
DT
DT
Mortality Above
Baseline2
+5
+3
+15
+2
+6
+4
+3
+6
+5
+49
+30
+2
+3
+5
+32
+10
+7
+3
+1
+3
+8
+1
aMean daily deaths above the long-term baseline.
Air Mass Abbreviations: MT = Moist Tropical; DM = Dry Temperate; DT = Dry Tropical; MM = Moist Temperate;
DP = Dry Polar. Asterisks denote a particularly offensive subset of MT.
Source: Kalkstein(1993b).
8 different weather models: (1) no adjustment; (2) indicator variables for 20 seasons (1985-1990);
(3) indicators for 20 seasons, and indicators for for quintiles of temperature and relative humidity;
(4) indicators for 20 seasons, and indicators for 19 synoptic weather categories; (5) linear time
trend,, and indicators for 19 synoptic categories; (6) LOESS smooth of time (span =10 percent of
days); (7) LOESS smooths of time (span = 10 percent of days), temperature (span = 50 percent of
days), and relative humidity (span = 50 percent of days); (8) LOESS smooth of time (10 percent
of days), and indicator variables for 19 synoptic categories. The results are shown in Table 12-36.
The results are relatively insensitive to the form of time trend and adjustment for weather
12-324
-------
variables, with RR for 50 //g/m3 increments in PM 10 varying only from about 1.058 (Model 2) to
1.077 (Model 7) for total mortality, all of them statistically significant. The pulmonary mortality
models are somewhat more sensitive to the form of the covariate adjustments, with RR for 50
ug/m3 ranging from 1.132 (Model 6) to 1.221 (Model 7); Model 2 shows only a marginally
significant PM 10 coefficient, the others significant one-tailed (Models 3 and 4) or two-tailed. The
cardiovascular mortality models have RR ranging from 1.076 (Models 3 and 7) to 1.116 (Model
1), with Model 3 one-tailed significant and all other models showing a significant PM 10 effect on
cardiovascular mortality. While the authors comment that other communities may show greater
sensitivity to the statistical methods for adjusting for time trend and weather, the relative lack of
sensitivity of the estimated PM 10 effect over a very wide range of models is noteworthy.
Table 12-36 also shows subset models corresponding to Models 7 and 8. Cold season
models called Models 9 and 11 by Pope and Kalkstein (1996, Table 4) consist of Models 7 and 8
respectively, limited to the months of October to March. Intra-seasonal differences are adjusted
by LOESS smoothers of time, and daily weather variation either by LOESS smoothers of
temperature and relative humidy (Model 9) or by indicators for synoptic categories. Total
mortality is highly significant in either case (1.070 for Model 9 and 1.059 for Model 11).
Pulmonary mortality is higher (1.145 for Model 9 and 1.120 for Model 11) and marginally
significant. Cardiovascular mortality has RR = 1.062 in Model 9 (not significant) but RR = 1.075
(significant) in Model 11. The corresponding Models 10 and 12 for the warm season (April-
September) shows higher RR effects for total and pulmonary mortality, but the effects are not at
all statistically significant. The lower statistical significance may reflect the halving of the sample
size in these data sets, since the effect size estimates must be similar to those obtained by
averaging the whole-data analyses across the corresponding seasons, with cold season = fall +
winter approximately, and warm season = spring + summer approximately.
Pope and Kalkstein (1996) also show four nonparametric smooth regression plots
corresponding to Models 1, 6, 7, and 8, respectively. All of the models using a nonparametric
regression for daily mortality on PM 10 are approximately linear, showing
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TABLE 12-36. EFFECTS OF DIFFERENT MODELS FOR WEATHER AND TIME
TRENDS ON MORTALITY IN UTAH VALLEY STUDY
to
00
to
ON
Model
Identity
Basel
Base II
1
2
3
4
5
6
7
8
9
10
11
12
Relative Risk for PM10 50 Ag/m3
Time Model
-
-
None
20 seasons
20 seasons
20 seasons
Linear
LOESS
LOESS
LOESS
Cold season, LOESS
Warm season, LOESS
Cold Season, LOESS
Warm season, LOESS
Weather Model
-
-
None
None
Quintile
Synoptic
Synoptic
None
LOESS
Synoptic
LOESS
LOESS
Synoptic
Synoptic
Total Mortality
1.076
1.083
1.074
1.058
1.062
1.068
1.068
1.059
1.077
1.068
1.070
1.112
1.059
1.091
(1.044,
(1.030,
(1.032,
(1.002,
(1.003,
(1.009,
(1.020,
(1.017,
(1.028,
(1.021,
(1.015,
(0.918,
(1.009,
(0.947,
1.109)
1.139)
1.118)
1.118)
1.124)
1.130)
1.118)
1.102)
1.129)
1.117)
1.129)
1.346)
1.111)
1.258)
Pulmonary Mortality
1.198
1.215
1.185
1.133
1.150
1.169
1.183
1.131
1.221
1.166
1.145
1.529
1.120
1.394
(1.035,
(1.049,
(1.056,
(0.963,
(0.972,
(0.988,
(1.032,
(1.006,
(1.063,
(1.018,
(0.981,
(0.813,
(0.971,
(0.794,
1.386)
1.408)
1.331)
1.333)
1.361)
1.382)
1.356)
1.273)
1.402)
1.335)
1.337)
2.877)
1.291)
2.577)
Cardiovascular Mortality
1.094(1.019, 1
1.094(1.020, 1
1.116(1.054, 1
1.081 (1.000, 1
1.076(0.992, 1
1.090(1.005, 1
1.100(1.030, 1
1.085(1.024, 1
1.076(1.006, 1
1.099(1.029, 1
1.062(0.984, 1
1.053(0.789, 1
1.075(1.003, 1
1.024(0.780, 1
.174)
.174)
.181)
.169)
.167)
.183)
.175)
.150)
.152)
.173)
.146)
.404)
.153)
.343)
Source: Pope and Kalkstein (1996)
-------
some suggestion of nonlinear structure between roughly 60 and 100 /-ig/mS PM10, but in no case
suggesting a threshold or consistent flattening of the concentration-response relationship at any
PM10 concentration. The authors note that a chi-squared test comparing each non-parametric
regression model for PM 10 with the corresponding linear model shows no statistically significant
deviation from linearity.
Samet et al. (1996b) have recently published another study of different methods for
estimating the modifying effects of different weather models on the relationship of TSP and SO 2
to total mortality in Philadelphia from 1973 to 1980. The models included the original Schwartz
and Dockery (1992a) weather specification, a nonparametric regression model, LOESS
smoothing of temperature and dewpoint, and Kalkstein's Temporal Synoptic Index (TSI) or
Spatial Synoptic Category (SSC) models. The first three methods allowed the weather model to
be adjusted so as to provide an optimal prediction of mortality, whereas the latter two models
were based completely on external criteria and the classification of days by SSC or TSI categories
was not adjusted to improve prediction of mortality. The authors conclude the "... the association
between air quality as measured by either TSP alone, SO 2 alone, or TSP and SO 2 together, cannot
be explained by replacing the original Schwartz and Dockery weather model with either a
nonparametric regression, LOESS, or by synoptic categories using either Kalkstein's TSI or SSC
systems. In addition, there is little evidence in the Philadelphia total mortality data to support the
hypothesis that the pollution effects are modified by the type of weather conditions as measured
either by TSI or by strata created from the predicted weather-induced mortalities using the
Dockery and Schwartz model or the LOESS model. ... We did not fine variation of the effect of
pollution across categories of weather." Their results are not shown here.
Additional studies systematically evaluating the differential effects of PM and other
pollutants by weather category would be of interest. The Philadelphia study by Samet et al.
(1996b) used only TSP and SO 2, whereas the Utah Valley study by Pope and Kalkstein (1996) did
not look at the effects of weather as a modifier with other pollutants as well as PM 10.
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Confounding by Epidemics
Concern exists that the increased incidence of illness or mortality associated with changes in
air pollution during the winter season may not indicate a causal relationship because of
confounding influences of contagious illnesses epidemics. Infectious respiratory illness (e.g., the
"flu") strongly influences mortality. An underlying or contributing cause for changes in air
pollution or in contagious illness may be weather changes. Confounding due to epidemics may be
adjusted statistically to some extent by use of filtering, but this is at best suitable for time series
with a normally distributed response, and filtering of time series may perform better when there is
some recurrent medium-to-long wave pattern to outbreaks of the disease in a given population.
Without some recurrence pattern, filtering may only eliminate evidence of longer-term persistence
of health effects related to air pollution. Close inspection of the time course of infectious
respiratory illness outbreaks in populations reveals that outbreaks do not appear on a regular
schedule from year to year (Henderson et al., 1979a,b; Murphy et al., 1981; Chapman et al., 1981;
Denny et al., 1983). In any given year, a number of important respiratory pathogens may not
appear at all in a given population. Thus, fixed-cycle curve-smoothing techniques may not
accurately describe the time course of respiratory illness outbreaks in populations. Several
investigators have subsequently used long-term nonparametric methods such as loess smoothers
or generalized additive models (GAM) to adjust mortality series for aperiodic fluctuations that
may include time-extended outbreaks of respiratory disease (Pope, 1994; Schwartz, 1994b,c,
1995b).
It is sometimes possible to evaluate the effect of epidemics on health outcome time series by
comparison with adjacent communities. Pope (1991) evaluated the possible effect on hospital
admissions of contagious illnesses such as influenza (which is known to cause a substantial
number of deaths in the elderly) and respiratory syncytial virus (RSV, which affects a substantial
number of children and is often mistakenly diagnosed as influenza). There was particular interest
in the possibility that infectious diseases occurred more often during the winters when the Utah
Valley steel mill was open, and less often during the winter when the steel mill was closed, purely
by chance. Pope writes that "The few diagnoses where the agent of disease was specified limited
opportunities to directly observe epidemics of any specific infectious agent. Bronchitis and
asthma admissions for preschool-age children were more than twice as high in Utah Valley during
periods when the mill was operating than when it was closed. The potential of highly localized
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epidemics of contagious respiratory disease that were correlated coincidentally with the operation
of the steel mill cannot be completely ruled out. If the association were strictly spurious,
however, the same correlation would probably be observed in neighboring communities
unaffected by the mill's pollution. Such correlations were not observed."
The ability to directly observe diagnosed cases of influenza or RSV would allow a direct
adjustment of health outcome time series for community-wide incidence of occurrence of the
disease, which could in turn be exacerbated by co-occurring air pollution. Some progress in
obtaining data on outbreaks of influenza-like illnesses (ILI) may be possible using recently
established data bases, such as the CDC volunteer physician surveillance network. Evidence
exists that these 140 family physicians make good sentinels for epidemics of ILI (Buffington et al,
1993). Data are provided to CDC on a weekly basis, which seems appropriate to the level of
filtering that may be needed to adjust daily time series of health outcomes and air pollution for co-
occurring respiratory diseases.
Confounding: Is It a Real Problem?
In developing criteria for assessing epidemiologic studies, we have paid a great deal of
attention to the potential confounding of PM effects on human health with the effects of other
agents that are associated with PM. Confounding has both conceptual and technical aspects. We
will first discuss some of the conceptual aspects.
There are three distinct options by which an analyst can deal with confounding in an
epidemiology study: (1) control; (2) avoid; or (3) adjust by analysis. It is obviously preferable to
control confounding by designing a study in such a way that all of the potential confounding
effects are anticipated and avoided. If confounding is unavoidable, then all levels of the nominal
causal agent (PM) and its confounding factors should be included in the study, preferably in a
balanced design so as to simplify the analyses of the data. Since the PM studies are all
observational studies, study design rarely allows a representative sampling of all levels of all
factors. For example, in a city or region where there are large stationary sources that burn fossil
fuels containing sulfur, both PM and SO 2 are likely to be high at the same time or low at the same
time, being governed by similar patterns of generation and dispersion. Likewise, if mobile sources
burning fossil fuel are the primary source of PM in a region, then PM during the summer is likely
to be associated with some or all of the following factors: high temperatures, low wind speed,
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high concentrations of ozone, CO, and airborne nitrates. Therefore, avoiding situations in which
confounding occurs is not usually an option.
However, there are some situations in which certain kinds of confounding are minimized.
One example occurred in the Utah Valley studies. During the year that the mill was closed due to
a strike, PM emissions from the mill were greatly reduced, but not quite eliminated since the coke
ovens were banked during the closure, and not shut down. The years before and after the closure
were years with high PM 10 concentrations and typical weather. The year during the closure had
generally typical seasonal weather, but much lower PM 10 levels. Hence, confounding between
PM10 and weather was relatively minimal during the study. Other studies by Pope et al. (1991)
and Pope (1989) in surrounding counties showed little evidence of any change in the incidence of
respiratory infections during the year of closure, so that confounding of winter health effects with
epidemics of respiratory infection seems unlikely. Other pollutants were at low levels even when
the mill was operating, particularly SO 2. Summer levels of ozone were high enough to merit
covariate adjustment, but had little effect on the estimated RR for various health effects of PM 10.
In general, the potential for confounding of PM effects with the effects of other air
pollutants is regionally distributed, with sulfates forming a higher percentage of particle mass in
areas of the eastern U.S. and Canada, and nitrates a larger percentage than sulfates in the western
U.S. and Canada. Thus, the potential for confounding with SO 4= and with SO2 is greater in
studies in eastern states, and the potential for confounding of PM effects with effects of NO x, and
(presumably) with other air pollutants such as CO and O 3 that are generated largely by mobile
sources, varies with location. Likewise, there is some confounding of health effects of PM with
health effects from weather, since weather conditions may affect both generation of PM and its
atmospheric dispersion (that is, concentration). For this reason, it may also be helpful to take a
multi-city or multi-study perspective in comparing the effects of potential confounding variables
onRRforPM.
Schwartz (1994c,d; 1995a,b) has emphasized a multi-study and multi-endpoint perspective
from several points of view. We believe that comparisons of study results across different studies
is very useful, but the approach still leaves some unresolved questions about confounding. For
example, a completely factorial design controlling for effects of weather and co-pollutants might
require finding studies in both "hot" and "cold" cities, in "wet" and "dry" cities, in cities with
"high" SO2 and "low" SO2, with "high" O3 and "low" O3. Thus, even a simple factorial design
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would require comparisons of at least 2 4 = 16 cities, counties or SMSA's. Since the variables used
in describing the cities are numeric, combining the results would be more appropriately done using
a "meta-regression" in the same sense as in the cross-sectional analyses done for the long-term
exposure studies, rather than a "meta-analysis". Meta-analyses are discussed below. In general,
there have not been enough reported studies to do this "meta-regression". There are also
problems in defining levels of weather effects, since Kalkstein et al. (1994) have shown that
thresholds for excess mortality from high temperatures are different in different cities. That is, a
"high" temperature in Minneapolis-St. Paul or Seattle, may not have the same effect in
Birmingham or Los Angeles, and that differences may depend on other weather variables and on
climate conditions. This approach also shares another concern about population-based cross-
sectional studies, that populations in different cities are demographically different in ways that
affect population-based health outcomes. Even the measure of effect size that we have used for
most of our comparisons, relative risk of health outcome for PM or other factors, is relative to a
base rate for the health outcome that one would expect to differ somewhat among different
populations in different cities.
Avoidance of confounding is also possible for some co-pollutants. Gaseous chemical
compounds such as SO 2, CO, and O 3 are likely to have very similar effects in different conditions,
everything else (such as temperature and humidity) being equal. When levels of these pollutants
are very low, such as SO 2 in most western studies, there is virtually no chance that these
pollutants have a causal effect on health endpoints such as mortality and hospital admissions.
While such effects cannot be absolutely excluded, the fact that they are often found at levels very
far below the NAAQS should control their contribution to some extent.
In spite of these concerns, the general similarity of RR estimates for acute mortality in
different studies and the large differences in potential confounding variables among the studies,
along with the similarity of RR to that found in studies where confounding effects seem relatively
minimal, adds a great deal of credibility to the conclusion that the PM mortality effects are real,
and similar in many locations, even if their magnitude is small and somewhat uncertain. This is
not to say that there is no confounding with co-pollutants, particularly where pollutants such as
SO2 are generated by the same process that generates PM. Differences in RR for hospital
admissions are somewhat greater, possibly reflecting differences in demographic factors or
regional differences in hospital admissions criteria, but for similar reasons these estimates are not
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so seriously confounded in every study as to preclude concluding that, in some studies, there are
real increases in hospital admissions rates for the elderly, and for certain classes of respiratory and
cardiovascular conditions.
Control of Confounding By Covariate Adjustment
For most of the short-term studies, there is some unavoidable confounding with co-
pollutants, with weather, and possibly with other medium-term and long-term events such as
epidemics and seasons. Different model specifications of some studies in Section 12.3 were
compared at length in Section 12.6.2. Weather variables and temporal variations over times
longer than a few weeks can be adequately modeled using any of several approaches discussed
above, such as polynomials, sinusoids, indicator variables for each month and year, indicators of
synoptic climatological categories, nonparametric smoothers or generalized additive models, or
high-pass filtering for Gaussian models. Careful examination of residuals for Poisson or Gaussian
models have found that a large number of alternative models can provide regression residuals or
Poisson expectations apart from air pollution variables that are independent of season, so that
seasonal subsetting of time series data in short-term studies may not be necessary for adequately
adjusted models. Sometimes, as in analyses of the London mortality series (U.S. Environmental
Protection Agency, 1986a; Schwartz and Marcus, 1990), only seasonal monitoring data are
available, but one should not make a virtue of necessity by subsetting time series, since statistical
tests to detect PM effects of the magnitude currently observed in the U.S. require long series of
data, roughly at least 800 values. Apart from this sample size requirement, different methods for
adjusting for weather and time trends provided adequate levels of adjustment to control for these
factors. In addition to controlling confounding with air pollution, it is also important to fit very
good models for weather and time trends in time series data, however, so as to help reduce
residual variability in daily response data to the limiting or irreducible Poisson minimum variance,
which is equal to the expected number on that day.
12.6.3.5 Adjustments for Co-pollutants
Not all studies contain data on the major co-pollutants, and a wide variety of approaches has
been used to assess the importance of these co-pollutants as predictors of health effects that
compete with PM in terms of explanatory power. Studies in which no other co-pollutant is
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assessed probably over-estimate the PM effect, but the use of a large number of more or less
closely related pollutants to predict the health outcome almost guarantees that the statistical
significance and size of the PM effect will be under-estimated. So far, few of these studies have
used effective diagnostic techniques or alternative methods for dealing with correlated (e.g.
multicollinear) predictor data.
Earlier discussion has indicated that other pollutants such as SO 2 are factors that play a role
in modifying the relationship between PM and mortality when they are incorporated into models
examining these relationships such that the RR is usually smaller. Other pollutants such as O 3 and
CO also need to be considered. Indeed as more studies incorporate these other pollutants into the
studies, concern for the role they play becomes more important. This applies to hospitalization
studies where possible relationships with CO may be evident. The biological plausibility of CO
and sudden death is established. The earlier major air pollution episode events in London
involved relatively high levels of CO (Commins and Waller, 1967). Section 12.6.2 conducted an
intense examination of the roles of copollutants with a focus on SO 2 but also O3 and CO to
determine what roles these copollutants play and what summary statements are possible to allow
conclusions about PM effects to be stronger.
One of the more difficult problems in interpreting the analyses of the studies discussed here
is that of separating the effects of several air pollutants. These pollutants are often fairly highly
correlated, and the correlation is often causal, in that several pollutants may be emitted by the
same mix of sources in a community, or that one pollutant is a precursor to another pollutant or
to a component of that pollutant, such as the fractions of sulfates and nitrates in PM that are
secondary pollutants formed from SO 2 or NOX. There have been a number of studies in which
several different model specifications were tested, involving PM as the only air pollutant, versus
PM and other pollutants used jointly in the model. In many studies, such as TSP in Philadelphia
(Schwartz and Dockery, 1992a) there was little effect of SO 2 on the RR for TSP, whereas other
authors have found that SO 2 appeared to modify the TSP effect in some seasons, using a similar
approach and data set, but with less comprehensive adjustment for weather variables and time
trends. There are two ways in multi-pollutant models can cause differences in interpretation from
a single-pollutant model: (1) the correlation between PM and the other pollutant(s) is (are)
sufficiently high that the effect or health outcome attributable is shared among the pollutants and
the individual RR for any one pollutant may be seriously biased. Measurement error in pollutants
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or other covariates may also bias the result, not necessarily towards the null, and the most poorly
measured exposure covariate is usually the one that is driven towards no effect; (2) parameter
variance estimates are seriously inflated among the entire group of nearly collinear covariates,
increasing estimated standard errors and the width of the confidence intervals for the RR
estimates and thereby also attenuating their apparent statistical significance.
Collinearity diagnostics have been developed for Gaussian OLS regression models (Belsley
et al., 1980) and are implemented in most modern statistical programs. Analogous methods for
Gaussian, logistic, or Poisson time series models are less well developed. Most programs allow
calculation of the correlation coefficient between estimates of regression parameters (denoted B)
based on the asymptotic covariance matrix. However, as noted in Table 12-5, correlation of the
B's was given in only two out often studies relating acute mortality to PM 10. Pollutants with
similar patterns and effects can be identified by B-correlation values close to -1. Numeric
diagnostics for confounding of co-pollutants could be easily included in reports of long-term
studies, many of which use Gaussian OLS linear or nonlinear regression methods for which these
diagnostics are readily calculated.
Some investigators have noted that similarity of PM regression coefficients in single- and
multiple-pollutant models is sufficient to show that PM is not confounded with the other
pollutants. This is not the whole story, since there is a possibility that the B coefficient or RR for
PM is unchanged, but the confidence limits are much wider because of the variance inflation of
the parameter estimate for collinear pollutants. When the RR estimate for PM is relatively
unchanged and there is little increase in the width of the confidence interval, then one can say
there is little evidence of confounding. This has been done in a number of analyses discussed in
this section, for example in the Utah Valley mortality study as shown in Figure 12-21. The RR
estimates for the summer season and the width of the confidence intervals for PM 10 are similar
without ozone in the model, with daily average ozone, or with maximum daily one-hour ozone as
the co-pollutant. The summer PM coefficient, with or without ozone, is similar to the winter
value, when ozone levels were so low as to have little probable effect on mortality, which
illustrates both covariate adjustment and confounder avoidance strategies in the same study.
There is some question about whether the confounding of certain co-pollutants such as PM
and SO2 should be regarded as true confounding when one pollutant is part of a causal pathway
from pollution source to pollution monitor (Rothman, 1986). Our assessment of probable causal
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pathways in a hypothetical multivariate model relating source emissions, weather, air pollution,
and health outcomes is shown in Figure 12-35. This could serve as a framework for a statistical
analysis in which the direct and indirect effects of air pollutants and other factors could be
disentangled using substantive scientific hypotheses and data.
Concentration-Response Surfaces for Two or More Pollutants
A recent study by the Health Effects Institute (Samet et al., 1995) shows how additive and
interactive models can differ. An example of an additive linear model is one in which s(x) = bx
and S(x) = dx, so that
log(E(Y)) = XB + b PM + d OP
is constant along any line in which b PM + d OP is constant. When Samet et al. fitted a two-
dimensional smoothing model to Philadelphia mortality counts against PM = TSP and OP = SO 2,
where the general form of the model was defined by a two-dimensional nonparametric smoothing
function ss,
log(E(Y)) = XB + ss(TSP, SO 2),
the resulting models differed substantially from an additive linear form and showed evidence of
very strong non-linearity as well as non-additivity.
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WEATHER
T, RH, BP, WS
T=TEMPERATURE
RH = RELATIVE HUMIDITY
BP = BAROMETRIC PRESSURE
WS = WIND SPEED
ELECTRIC
POWER
AGE
GENDER
RACE
EDUCATION
Figure 12-35. A conceptual model of sources and pathways for air pollution health effects
such as mortality, including a causal model of potential confounding by co-
pollutants. No attempt is made to differentiate strength of evidence for each
pathway.
In general, most papers have provided very little empirical basis for the reader to assess the
adequacy of the fitted model, especially for analyses involving copollutants. The most data-driven
display would consist of a three-dimensional scatterplot, whose axes are the PM index, the
copollutant, and the response variable (mortality). The HEI report comes closest to this by
presenting three-dimensional surfaces showing the smoothed or fitted mortality response versus
TSP and SO 2 for the 1973 to 1980 Philadelphia data set (see Figures 12-36 and 12-37). The
smoothed surfaces are based on LOESS smoothers whose bandwidth includes 50 percent of the
data, reflecting a substantial degree of smoothing of daily mortality counts. The smoothed actual
data surface falls considerably above the linear model
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Figure 12-36. Smooth surface depicting relative effects of sulfur dioxide (SO 2) and total
suspended particles (TSP) levels on total mortality for Philadelphia, 1983 to
1988. Surface was estaimated from a generalized additive model (Hastie
and Tibshirani, 1990) using a LOESS smoother (bandwidth 50% of data,
10.2 degrees of freedom). Deviations from a plane surface suggest a
nonlinear concentration-response function.
Source: Samet et al. (1995).
surface for TSP greater than about 100 //g/m3 and almost all SO 2 levels above 20 ppb. Below
about 75 //g/m3 TSP, the plane surface generally lies above the smoothed data surface. This
suggests that there is a very complex pattern of dependence on the j oint values of TSP and SO 2
that is not adequately captured by an additive linear model.
A somewhat different way of looking at the results from Figure 12-36 is shown in Figure
12-38. Figure 12-38 shows the contours of the same two surfaces projected onto the plane with
TSP and SO 2 values for each day in the data set. The contour lines represent TSP and SO 2
combinations for which the estimated excess risk of mortality in Philadelphia is equal to the value
shown. The parallel lines are estimates from the regression plane in the additive linear model.
The curved contours represent smoothed estimates from the LOESS
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Figure 12-37. Smooth surface depicting Philadelphia mortality in winter relative to sulfur
dioxide (SO2) and total suspended particles (TSP), 1973 to 1980. Surface
was estimated from a generalized additive model (Hastie and Tibshirani
1990) using a LOESS smoother with 9.6 equivalent degrees of freedom,
controlling for temperature, dew point, and day of the week.
Source: Samet et al. (1995).
smoothing model and may be thought of as simplified representations of the data. The two sets of
curves appear quite different, and in fact the difference in deviance of the mortality counts
between the LOESS model (with 10.2 equivalent degrees of freedom) and the additive linear
model (with 2 degrees of freedom) is 28.0 with 8.2 degrees of freedom, which is a statistically
significant difference at level P = 0.01 after adjustment for overdispersion of the mortality counts
(Samet et al., 1995, p. 31).
The nature of the nonlinear and nonadditive response surface provides additional
information. If the contour lines in Figure 12-38 are roughly parallel to the horizontal axis (TSP),
then the figure suggests that mortality is changing in relation to the variable on the vertical axis
(SO2), as is suggested for TSP less than about 75 £tg/m3. If the contour lines in Figure 12-38 are
roughly parallel to the vertical axis (SO 2), then the figure suggests that
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100-
80-
60-
40-
20-
0-
°-04>
°-08>
-0.06
50
100
TSP
150
Figure 12-38. Curved contours depicting the excess risk of total mortality in Philadephia,
by season, for 1983 to 1988. Straight lines show the excess risk from an
additive linear model fitted to the same data, which exhibits significantly inferior
goodnesss of fit relative to the GAM model.
Source: Adapted from Samet et al. (1995).
mortality is changing in relation to the variable on the horizontal axis (TSP), as is suggested for
TSP greater than about 125 //g/m3. The contours change orientation between 75 and 125 //g/m3
TSP. It is clearly not correct to conclude from the additive linear model that one pollutant is
always (or never) a better predictor of excess mortality in Philadelphia than is the other pollutant.
Seasonal differences also seem to play an important role. Figures 12-39 through 12-42
show analogous results from the HEI report for spring, summer, fall, and winter 1973 to 1980
data. In Figure 12-39 (spring), the nonparametric contours for TSP greater than about 125 //g/m3
are roughly parallel to the straight lines from the additive linear model but spaced irregularly,
suggesting an additive but somewhat nonlinear model for TSP and SO 2 in this range. For TSP
below about 75 //g/m3, there seems to be little relationship of mortality of TSP.
The summer results are shown in Figure 12-40. For TSP greater than about 110 //g/m3 and
SO2 less than about 20 ppb, the nonparametric surface contours are roughly parallel to the
additive linear model contours, but more closely spaced. For TSP greater than about
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o
CO
80-
60 -
40 -
20-
0 -
0.04i
•0.08
50
100
TSP
150
Figure 12-39. Contours depicting the fractional change in Philadelphia mortality in spring
by levels of total suspended particles (TSP) and sulfur dioxide (SO 2)-
Straight lines show contours predicted by an additive model. Contours predicted
by LOESS with 10.2 equivalent degrees of freedom are shown in the curved
lines.
Source: Adapted from Samet et al. (1995).
80-
60-
o 40
CO
20-
0-
0.02
0.03
°-04>
i
50
100
150
TSP
Figure 12-40. Contours depicting the fractional change in Philadelphia mortality in
summer by levels of total suspended particles (TSP) and sulfur dioxide
(SO2). Straight line show contours predicted by an additive linear model.
Contours predicted by LOESS with 10.2 equivalent degrees of freedom are
shown in curved lines.
Source: Adapted from Samet et al. (1995).
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Figure 12-41.
80 -
60 -
O
to 40 -
20 -
0-
-0.02
-0.02\ 0 0.02
50
100
150
TSP
Contours depicting the fractional change in Philadelphia mortality in fall by
levels of total suspended particles (TSP) and sulfur dioxide (SO 2-) Straight
lines show contours predicted by an additive linear model. Contours
predicted by LOESS with 10.2 equivalent degrees of freedom are shown in
curved lines.
Source: Adapted from Samet et al. (1995).
100-
80-
60-
o
CO
40-
20-
o-
-0.02 >
-0.03 >• \0.02
% n r\A ^
50
100
TSP
150
Figure 12-42.
Contours depicting the fractional change in Philadelphia mortality in
winter by levels of total suspended particles (TSP) and sulfur dioxide (SO
Straight lines show contours predicted by an additive linear model.
Contours predicted by LOESS with 10.2 equivalent degrees of freedom are
shown in curved lines.
Source: Adapted from Samet et al. (1995).
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110 //g/m3 and SO2 greater than about 20 ppb, the nonparametric surface contours are roughly
parallel to the vertical axis, suggesting a fairly strong dependence of mortality on TSP with little
additional effect of SO 2. For TSP less than 110 //g/m3, the contours are very complex and
suggest a small excess of mortality for SO 2 between 20 and 40 ppb, with results for higher values
of SO2 somewhat uncertain because of the virtual absence of high SO 2 data on days with low
TSP.
Fall results are shown in Figure 12-41. For TSP greater than about 100 //g/m3 and SO2 less
than about 40 ppb, the nonparametric surface contours are roughly parallel to the vertical axis,
suggesting a strong TSP effect in this range. For TSP less than about 100 /-ig/m3, the
nonparametric surface contours are roughly parallel to the horizontal axis, showing little effect of
TSP in this range.
Winter results are shown in Figure 12-42. For TSP between about 80 and 100 //g/m3 and
SO2 less than about 30 ppb, the nonparametric surface contours are roughly parallel to the vertical
axis suggesting some TSP effect, but otherwise SO 2 appears to be the dominant pollutant for
winter mortality since the contour lines generally parallel the horizontal axis. This can be
visualized more effectively using the three-dimensional plot in Figure 12-37. One-
dimensional nonparametric models for mortality versus TSP and mortality versus SO 2 are shown
in the HEI report (Samet et al., 1995; Figure 11). These figures, based on generalized additive
models, suggest a somewhat complex relationship with lower RR for total mortality at TSP less
than 90 to 100 /-ig/m3, a sharp increase at higher TSP levels, whereas the relationship of excess
mortality to SO 2 is sharply increasing at SO 2 below 20 ppb, flat above 20 ppb. The relationship of
mortality to TSP is flat for people less than 65 years of age, but sharply increasing at TSP greater
than 50 //g/m3 for people age 65 years or greater. Age and other factors affecting the susceptible
subpopulation(s) such as weather and copollutant stresses may be contributing factors in the
apparent nonlinear and interaction between PM and other variables that was observed in the
multidimensional mortality concentration-response surfaces plotted in Figures 12-36 through
12-42.
While these plots may invite some overinterpretation several important points have been
established by the nonparametric modelling of concentration-response surfaces for the
Philadelphia mortality data:
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(1) Both TSP and SO 2 were associated with significant increases in mortality in
Philadelphia during 1973-1980, even after adjustments for weather-related effects, but
there were important differences in effect depending on season and on the range of TSP
or SO2 values;
(2) There was indication of a relatively large relationship between TSP and excess mortality
during spring and summer, for TSP larger than about 100 //g/m3; even during these
seasons, there was little evidence for a TSP relationship with mortality at substantially
smaller TSP concentrations;
(3) There was a relationship between SO 2 and excess mortality at TSP concentrations
below 75 //g/m3, but the relationship was not evident at SO 2 concentrations above about
50 ppb or TSP concentrations above about 75 to 100 //g/m3;
(4) There is little basis for assuming that analogous results would be obtained for other PM
indices, such as PM 10 or PM2 5.
In the studies discussed in Sections 12.3 and 12.4, many of the analyses are based on
additive linear models for the copollutants. Based on the preceding discussion, there may be
some unresolved questions about the adequacy of the fitted models to accurately characterize the
joint effects of the PM index and other pollutants. Therefore the estimated RR and statistical
significance of PM and other pollutants as predictors of health endpoints may be biased by the
misspecifi cation of the joint or multivariate concentration-response surface for the multiple
pollutants.
The excess risk contours change orientation between 75 and 125 |ig/m 3 TSP. It is clearly
not correct to conclude from the additive linear model results that one pollutant is always (or
never) a better predictor of excess mortality in Philadelphia than is the other pollutant. Seasonal
differences also seem to play an important role.
The Samet et al. analyses suggest that interpretation of the results of fitting additive linear
models using two or more pollutants may be premature without considering in some detail the
exact nature of the interactions among the pollutants, and possibly also the effects of interactions
(i.e., adjustments and effect modifications) involving weather and other covariates. In particular,
the conclusion from an additive linear model that inclusion of copollutants generally lowers the
effect attributable to PM may not apply to a more accurate nonparametric model. It is possible
that for certain ranges of PM concentrations, inclusion of copollutants in the model makes little or
no difference for the estimated PM effect, and for some ranges of the copollutants, the estimated
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PM effect might even be larger than the overall PM effect estimated from a linear model. These
differences or PM effect modifications may vary from city to city or from season to season. There
is little basis for generalizing these findings beyond these 8 years of data from one city.
The underlying problem of modeling multiple pollutants is very similar whether the study
data are derived from daily time series, from long-term prospective studies, or from population-
based studies. In most analyses of population-based data, an additive linear model for the
logarithm of the pollutant concentration is used, which may not alter the fundamental problem
that the additive linear model may still be a misspecification of the relationship.
The conclusion that the RR estimates from fitting a linear model with a single pollutant are
upper bounds of that pollutant's RR should not be taken as true in general, for all pollutants and
all concentration ranges. Tests of the adequacy of the additive linear model specification have not
been reported in general, and it is likely that investigations of other data sets will find more
situations in which the standard additive linear model is not adequate for evaluating the health
effects of multiple pollutants.
Summary
In summary, confounding by weather and by time effects can be adjusted statistically so as
to remove a substantial amount of confounding, but possibly at the expense of reducing the
estimated PM effect by attributing it to weather or longer-term time effects not related to short-
term PM exposure. Confounding by co-pollutants sometimes cannot be avoided, but should be
diagnosed and reported more completely than in most studies now available. In studies where
sensitivity analyses demonstrate that including other pollutants in the model causes little change in
either the RR estimate for PM or on the width of the confidence interval for the PM effect, one
may conclude that the model is not seriously confounded by co-pollutants. Since a number of
mortality and morbidity studies have shown that the PM effect on health is not sensitive to other
pollutants, we may conclude that the PM effects in these studies are real. This adds some
credibility to the claim that a significant PM effect exists in the remaining studies where PM is
statistically significant in a model without other pollutants, though similar in magnitude to the PM
effect found in other studies with less co-pollutant confounding, but is not statistically significant
when other pollutants are included in the model. This then provides a basis for the meta-analyses
discussed below.
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12.6.3.6 Ecological Study Design
Most of the studies considered are ecologic in design. Even in the daily longitudinal studies,
individuals are grouped by region, SMSA, or catchment area for hospital admissions, and all are
assumed to have exposure to PM and other covariates characterized by a single numerical value
for the area on that day. The "ecological fallacy" refers to the biases inherent in making
individual-level predictions from aggregate-level data. However, such studies are often used
because of the availability of data bases for air pollution, weather, and mortality or hospital
admissions on a daily basis. Relative risk estimates for individuals should therefore be regarded as
subject to much uncertainty, even for age-specific sub-populations, in the absence of subject-
specific exposure and covariate data. Recent additions to the NCHS mortality data base,
including demographic information such as educational attainment, may allow better resolution of
the effects of socio-demographic covariates. While residential location might improve estimates
of exposure in communities with several monitoring sites, there would still be considerable
uncertainty about individual non-residential exposures in the absence of information about daily
activity. Better individual exposure information would still be needed to reduce the substantial
uncertainties about exposure.
12.6.3.7 Measurement Error
While there has been much discussion about the effects of measurement error, particularly
with respect to exposure misclassification, few suggestions have been made as to how to deal
with this question.
There have been few quantitative assessments of errors in measurements of particulate
matter or other copollutants. There are at least two major components of these errors.
(1) Instrument error: Errors in measurement of pollutant levels at the point of
measurement.
(2) Proxy error: Error in using levels at a point (even if correctly measured) as the levels to
which study population members are exposed.
For studies of chronic effects, another potentially important problem is sometimes dealt with
under the heading of "exposure definition":
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(3) Construct error: Error in using a particular exposure summary other than the
biologically relevant exposure (for example, using time-weighted average level when
only time above a critical threshold is biologically relevant). This is also encountered in
constructing moving averages for short-term studies.
It is often assumed that any measurement error is nondifferential, and that consequently any
bias produced by the error would be towards the null. Neither assumption is necessarily correct.
There are several possible scenarios under which proxy measurement errors will be differential.
For example, suppose monitor readings in low-pollution, low-mortality areas tend to understate
exposure more than in high-pollution, high mortality areas because many residents of low-
mortality areas commute to jobs in high-mortality areas. Then measurement errors will be
differentially higher for low-mortality populations (and among noncases in an individual-level
study based on these measurements and areas).
Contrary to popular treatments, nondifferential error does not guarantee that the resulting
bias in effect estimates is towards the null. In ecologic designs, nondifferential error in individual-
level exposure measurements can easily produce very large bias away from the null. In individual-
level designs, nondifferential error may produce bias away from the null if errors are
interdependent or if the dependence of measured on true levels is not monotonic.
Interdependence of errors seem likely. For example, wind patterns would induce correlated proxy
errors in all atmospheric pollutants. Effects of confounder errors can be in either direction,
whether or not the errors are nondifferential. Under the best of circumstances the only
predictable effect of nondifferential confounder errors is that they will tend to leave the exposure
effect estimates partially confounded. A recent study by Schwartz et al. (1996) suggests that the
effects may be small in daily mortality studies.
In summary, there has been no evidence presented that measurement errors are
nondifferential. Even if there were such evidence, it would not imply that the biases produced by
the errors are toward the null. Bias due to measurement error can be profound.
12.6.4 Assessment Issues for Epidemiology Studies
12.6.4.1 Significance of Health Effects/Relevancy
The "relative risks" derived from the regression coefficients in recent short-term
PM/mortality studies appear to be consistently "small" (i.e., 1.025 to 1.05 per 50 //g/m3 increase
in PM10), compared (at a face value) to the relative risks in other types of studies. In cancer
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epidemiology, for example, some (Shapiro, 1994) consider a relative risk of 1.7 as weak support,
"at most", for a causal inference. However, much lower RR estimates of 1.2 to 1.3 have been
regarded as sufficient for establishing a presumption of a causal relationship for health effects
from environmental pollutants in recent EPA studies on environmental tobacco smoke (U.S.
Environmental Protection Agency, 1992) and nitrogen oxides (U.S. Environmental Protection
Agency, 1993).
The fact that a relationship is weak, or that an effect is small, does not mean that the
relationship is not causal. As Rothman (1986, pp. 17-18) points out, "By 'strength of association',
Hill [1965] means the magnitude of the ratio of incidence rates. Hill's argument is essentially that
the strong associations are more likely to be causal than weak associations because if they were
due to confounding or some other bias, the biasing association would have to be even stronger
and would therefore presumably be evident. Weak associations, on the other hand, are more
likely to be explained by undetected biases. Nevertheless, the fact that an association is weak
does not rule out a causal connection." Many of the studies cited in this chapter included
substantial assessments of the effects of potential confounding factors, particularly age group,
identifiable cause of death or hospital admission, weather or climate, and the levels of co-
pollutants. In some cases, potentially confounding factors were either not present or present at
such levels as to have an negligible effect on the health outcome. Even when potential
confounders were present, it was often possible to carry out a statistical adjustment for the
confounder, with the PM effect size estimated with and without the potential confounder in the
model. The PM effect size estimates and their statistical uncertainty in many studies showed little
sensitivity to the adjustment for confounding variables. In a few other studies, there was
substantial confounding with some co-pollutants such as SO 2 or O3, but estimates of RR for PM
without inclusion of the confounders in the statistical concentration-effect model used in these
studies were quantitatively similar to RR estimates from other studies where confounding was
either avoided or was shown statistically to have little effect. This bears out the comment by
Rothman (1986, p. 18) that"... the strength of an association is not a biologically consistent
feature, but rather a characteristic that depends on the relative prevalence of other causes," which
here includes confounders such as weather and co-pollutants.
However, these two types of relative risks are not directly comparable. The "relative risk"
estimates used in these short-term PM exposure studies are not only "acute" in their
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exposure/response relationship, but also represent "indirect" cause of deaths. A healthy person
does not develop respiratory disease and die from an exposure to 100 //g/m3 PM10 in one day.
The causal hypothesis is that people with chronic respiratory or cardiovascular diseases, who may
be near death from the preexisting conditions, are pushed toward death prematurely by the
additional stress on the respiratory system imposed by an increased level of air pollution. This is
in contrast to a cancer risk from exposures to a chemical, through which a perfectly healthy
person may develop cancer and die at the age of 50, when the person may otherwise have lived up
to 70 years old. This difference may be obvious to the researchers analyzing these data, but needs
to be clarified when such "risk estimates" are communicated to people who are not familiar with
this field. Estimates of life shortening attributable to short-term and chronic PM exposure are not
available.
With this difference taken into consideration, there are several reasons why we may be
concerned about the estimated "relative risks":
The apparent "relative risk" estimates are often calculated for the entire death categories.
Cause-specific "relative risk" estimates are often greater than for total mortality (e.g., in
Pope et al.'s Utah study, the excess relative risk calculated for the respiratory category
mortality was 43% as opposed to 16% for total mortality). If susceptible populations
were defined and categorized, for example by age, the risk estimate would be even
higher than for the general population.
The apparent "relative risk" tacitly assumes a baseline death population in which all are
subject to the change in PM exposures. It is likely that this is not the case. An unknown
fraction of the population are not subject to the change in exposure levels of outdoor
PM, thereby causing an underestimation of the risk of those actually exposed.
There may be a downward bias in the estimated PM/mortality regression coefficients
(and, therefore, in the estimated relative risk) due to the PM measurement errors. The
extent of this bias is not known.
The extent of prematurity of the deaths, which may range from days to years, is not
known.
12.6.4.2 Biological Mechanisms
Most of speculation on the biological mechanism of PM mortality effects were made in the
earlier major air pollution episodes. According to Firket's report (1936) on the fog episode of
Meuse Valley in 1930, the autopsies with microscopical examinations found local and superficial
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irritation of the mucus membrane of the respiratory ducts and the inhalation of fine particles of
soot in the pulmonary alveoli. The chemists concluded that "the SO 2 in the presence of oxidation
catalysts such as ferric and zinc oxide, must have been partly transformed to sulfuric acid". The
discussion of the report suggested sulfuric acid to be "the most probable cause" of deaths. In the
1952 London fog episode (United Kingdom Ministry of Health, 1954), the association of the air
pollution and the observed increase in deaths, estimated to be 4,000 excess deaths, was rather
obvious. The report suggested "it is probable that sulphur trioxide dissolved as sulphuric acid in
fog droplets, appreciably reinforced the harmful effects of sulphur dioxide." One immediate cause
of death was speculated to be acute anoxia from bronchospasm.
Health effects observed at current air pollution levels are more subtle, as in recent
PM/mortality studies. There are some speculations regarding possible mechanisms, identifying
specific chemical components responsible for the effects such as acid aerosols. The pattern that
does appear to resemble the past episodes in these more recent observational studies is the age
and cause specificity of the deaths associated with PM. Both cardiovascular and respiratory
deaths in the elderly population increased in the 1952 London episode. The estimated relative
risks for these categories were found to be disproportionately higher and more significant in the
analysis of Philadelphia (Schwartz, 1994b,c). Other cause specific analyses (e.g., Fairley, 1990;
Pope et al., 1992; Schwartz, 1994b) also reported higher estimated relative risks for respiratory
and cardiovascular categories than total or other categories. While the excess deaths in the
cardiovascular category, which was also apparent in past episodes, do not provide direct
information on possible causal mechanisms, the analysis of contributing causes (Schwartz, 1994h)
appears to suggest that the respiratory illness is contributing to the deaths of people with
cardiovascular conditions. If a person has been suffering from a major cardiovascular disease,
that person's death may be still categorized as cardiovascular, even if the respiratory condition
causes the death. Such misclassification may also occur for other categories (e.g., cancer). More
analyses using the contributing cause of deaths are needed to further characterize such
mechanisms.
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12.6.4.3 Coherence
Factors involved in evaluating both the data and the entire group of epidemiological studies,
include the strength of association, the consistence of the association, as evidenced by its repeated
observation by different persons, in different places, circumstances and time, and the coherence
with other known facts (Bates, 1992). One can look for interrelationships between different
health indices to provide a stronger and more consistent synthesis of available information. The
various findings that support a picture of coherence would provide a stronger case with
quantitative studies as opposed to qualitative studies. Other studies may be inappropriate to use
in such a discussion, the quality of the study should be considered. Bates (1992) states that the
difficulty with discussing any index of internal coherence is that this requires a series of
judgements on the reliability of the individual findings and observations. The outcome of a
coherence discussion then is a qualitative presentation.
Bates (1992) also noted that the strength of different health indexes are important as are
difficulties in assessing exposure. Bates (1992) also suggests three areas to look for coherence:
(1) within epidemiological data, (2) between epidemiological and animal toxicological data, and
(3) between epidemiological, controlled human and animal data.
Coherence by its nature considers biological relationships of exposure to health outcome.
The biologic mechanism underlying an acute pulmonary function test reduction in children is most
likely not part of the acute basis for a change in the mortality rate of a population exposed in an
older group of individuals. In looking for coherence one can compare outcomes that look at
similar time frames—daily hospitalizations compared to daily mortality or acute versus chronic
outcomes. Overall the data indicates that PM has a relationship with a continuum of health
outcomes, but the underlying mechanisms may be different.
Coherence in the overall data base can be considered within the endpoint and/or in other
endpoints. The principal health outcome for which coherence is desirable is mortality, the death
rate in a population. Of the various morbidity outcomes studied and discussed in the earlier part
of the chapter, hospitalization studies reviewed in the chapter support this notion. The mortality
studies suggest that these specific causes provide stronger relationships (i.e., larger RR estimates)
than total mortality. The outcome potentially most related is hospital admission for respiratory or
cardiovascular causes in the older age group (i.e., > 65 years old). In a qualitative sense, the
increased mortality found in that age group are paralled by increased hospital admissions.
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Partial coherence is supported by those studies in which increased incidence of different
health outcomes associated with PM are found in elderly populations in different cities, as is the
case for the following examples, based on currently published studies:
• Detroit: Mortality mainly in elderly populations, hospital admissions for respiratory
causes and for cardiovascular causes in the elderly;
• Birmingham: Mortality mainly in the elderly, hospital admissions for the elderly;
• Philadelphia: Mortality and hospital admissions for pneumonia in the elderly;
In the Utah Valley, several studies have been conducted. Mortality and hospital admissions
for respiratory causes in adults have been associated with PM in the Utah Valley. Also,
pulmonary function, respiratory symptoms, and medication use in asthmatic subjects of all ages;
hospital admissions for respiratory symptoms, pulmonary function, respiratory symptoms, and
medication use in healthy school children, pulmonary function in symptomatic and asymptomatic
children; and elementary school absences in children were found to be associated with PM
exposures in Utah Valley. Another study found a PM effect on pulmonary function in smokers
with COPD in Salt Lake Valley. The Utah Valley population was largely non-smoking, so
smoking was not likely to be a source of confounding.
While these multiple outcomes did not occur in strictly identical subgroups of each
population, there was probably a sufficient degree of overlap to indicate that PM was a significant
predictor of a wide range of health outcomes within a specific community. The symptoms serious
enough to warrant hospitalization and the major part of the excess mortality occurred in the
elderly sub-group of the population. However, a significant decrement in pulmonary function and
increased incidence of symptoms associated with daily increases in PM occurred in children in
Utah Valley, along with a "quality of life" effect measured by lost school days. Thus, there is
evidence for increased risk of health effects related to PM exposure ranging in seriousness from
asymptomatic pulmonary function decrements, to respiratory symptoms and cardiopulmonary
symptoms sufficiently serious to warrant hospitalization, and to excess mortality from respiratory
and cardiovascular causes, especially in those older than 65 years of age.
Children may also be at increased risk of pulmonary function changes and increased
incidence of symptoms associated with PM exposure. While we have arrayed these health
outcomes in order of increasing severity, there is as yet little indication that there is a progression
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of effects in any single individual associated with increasing exposure to PM. The "exposure-
response" relationship that is derived in most studies must be understood as characterizing
population risk from population exposure. Additional studies are needed to define the
relationship(s) among individual exposure to PM and other stress factors, individual risk, and
individual progression among disease states. Differences in PM dosimetry in the developing,
aged, or diseased respiratory tract may also contribute to increased susceptability.
12.6.5 Meta-Analyses and Other Methods for Synthesis of Studies
12.6.5.1 Background
Several reports have appeared in which results from different studies have been combined,
formally or informally, to present an overall effect size estimate for acute health effects. For
example, a synthesis of daily mortality studies for seven cities was published by Schwartz (1992a).
The seven cities included four TSP studies (Steubenville, Philadelphia, Detroit, Minneapolis) and
three PM10 studies (St. Louis, Eastern Tennessee, Utah Valley). The daily mortality studies were
further analyzed by Schwartz in a later paper (1994b), which added studies from New York, from
Birmingham, Alabama, later London studies (1959 to 1972), and a study in Athens, Greece. The
RR estimates were combined in formal quantitative meta-analyses, using either unweighted RR
estimates, or using a smaller set of estimates weighted by inverse of the estimation variance of the
RR coefficient from studies in which the standard error was reported. Several methods were
used, and several subsets of the data were tested according as to whether or not the study city
was "warm" or the TSP coefficient was adjusted for copollutants.
A recent paper by Dockery and Pope (1994b) extends the research synthesis to a variety of
health outcomes, including hospital admissions studies and respiratory function tests. This paper
is also based on conversion of different PM measures to an equivalent PM 10 by applying a scaling
factor: 1.0 for PM 15 and BS, 0.55 for TSP, 4 for sulfates (SO 4), 1/0.60 for PM25, and 1/0.55 for
COH. This synthesis paper uses eight cities for total mortality, four cities for respiratory mortality
and for cardiovascular mortality, three cities for hospital admissions for respiratory symptoms,
four studies for asthma admissions, and combines three cities with different reasons for emergency
room visits. The paper examines the effects of PM on exacerbation of asthma by combining
results of two cities for bronchodilator use, and combining three studies for asthmatic attacks.
Pulmonary function tests are synthesized from four studies for Forced Expired Volume (FEV x and
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FEV0 75), and six studies for Peak Expiratory Flow (PEF daily, weekly, or longer). Respiratory
symptom results are divided into combining six studies reporting lower respiratory symptom
results, upper respiratory symptom results, and six studies reporting cough symptom results. The
authors conclude that these results demonstrate a coherence of effects across a range of related
health outcomes, and a consistency of effects across independent studies by different investigators
in different settings.
The synthesis of the epidemiologic evidence in this document presents some unusual
problems. Many of the studies showing mortality and morbidity effects are based on relatively
small increases estimated with great precision resulting from sophisticated analyses of long series
of infrequent events. As a result, relative risks (or odds ratios) of 1.06 are common and often
statistically significant. A value of 1.06 would indicate that mortality (or morbidity) is increased
by 6% when PM10 is increased a specified amount (usually 50 //g/m3). Traditionally, relative risks
less than 1.5 were considered to be of questionable biological meaning. Although relative risks
near 1.06 are not large in magnitude, they may represent a large net effect because the events are
so common. The question remains: are these effects real or are they an artifact of the analysis?
A careful review of the analysis techniques in Section 12.6.3 suggests that similar results are
obtained as long as similar covariates and independent variables are included in the analysis.
There are remaining questions about the accuracy of the variances and the assumptions upon
which they are based. Even allowing for these problems, the estimated regression coefficients are
consistently estimating the correct quantities although the exact p-values may be slightly in error.
The results do not appear to depend heavily on the form in which covariates were included
in the model. Analyses that included the known covariates such as temperature and season
usually gave similar results. The one factor which appeared to make a consistent difference was
the inclusion of one or more copollutant(s) in addition to particulate matter. The inclusion of SO 2
tend to reduce the effect of particulate matter in most analyses, while O 3 generally had less of an
impact on PM regression coefficients. This would be expected because O 3 tends to be less
correlated with PM than does SO 2. Although the PM coefficients were reduced by the inclusion
of SO 2, most remained statistically significant.
One unresolved question is the possibility that the effects seen were the result of some
covariate which, had it been included, would have reduced the PM coefficients to a non-
significant level. Although this is always a concern with epidemiologic studies, the concern is
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often dismissed as improbable when the relative risks are large as 1.5 or 2.0. When the relative
risks are less than 1.1, the question is of greater concern.
12.6.5.2 Meta-Analyses Using Studies Reviewed in This Document
In order to compare the results of the various studies relating acute exposure to PM to
excess mortality, we selected studies that satisfied certain criteria: (1) the study has been
published or is in press; (2) the study used PM 10 or TSP as an index of paniculate matter
exposure; and (3) the study included adequate adjustments for seasonality, weather, other effects.
The first criterion was imposed to provide adequate access to a description of study data,
methods, and results; and the second so as to restrict consideration to studies with pollutants for
which EPA has extensive air monitoring data. Even here, analyses were performed separately for
PM10 studies and for TSP studies, so as to avoid having to make any assumptions about site-
specific calibrations of one PM concentration or index into another. It may be possible to extend
the meta-analyses to a wider range of studies when methods are developed for assessing the
uncertainty associated with generic versus city-specific calibrations of one PM index to another.
The results of the analyses have been standardized for purposes of comparison. All of the
acute exposure studies used Poisson or equivalent regression methods with the expected mortality
an exponential function of a linear combination of predictors, or with the logarithm of the
mortality rate as a linear combination of predictors including the PM index. This means that the
relative risk (RR) — the fractional increase in the mortality rate relative to a baseline value
without pollution, everything else being equal — can be expressed in terms of changes per unit of
pollution. The base unit for change in risk was chosen differently for each pollutant. For PM10
studies, the effect was the odds ratio for mortality corresponding to an increase of 50 //g/m3 in
PM10. Other ranges have been used in published papers, most commonly 10 or 100 /-ig/m3. We
selected 50 //g/m3 because it is closer to the range of values in various morbidity studies, whereas
the range in mortality studies usually is larger than 100 //g/m3. Since the range of values in TSP
studies is typically much larger than in PM 10 studies, we used 100 //g/m3 as the base unit for TSP
studies of mortality.
The basic data on effects size estimates, in appropriate units, are shown earlier in
Tables 12-2 and 12-4. Note that the confidence intervals derived in the various papers are not
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always symmetric about the estimated RR. The data could be naturally sorted into six distinct
groups:
• Estimates of PM10 effect on RR, not adjusted for copollutants, lags <2 d
(4 studies, 4 cities)
• Estimates of PM 10 effect on RR, not adjusted for copollutants, lags >2 d (6
studies, 6 cities)
• Estimates of TSP effect on RR, not adjusted for copollutants (4 studies, 3 cities)
• Estimates of PM 10 effect on RR, adjusted for copollutants (3 studies, 3 cities)
• Estimates of TSP effect on RR, adjusted for SO 2 (3 studies, 2 cities)
• Estimates of PM 10 effects on RR short, long lags (3 studies, 3 cities)
There are presently no methods for using results of different analyses of the same data set, such as
the two studies on Steubenville (Schwartz and Dockery, 1992b; Moolgavkar et al., 1995a). (For
this assessment, we report results using each separately.)
The meta-analysis methods were similar to those used in the nitrogen oxides criteria
document (U.S. Environmental Protection Agency, 1993; Hasselblad et al., 1992). Differences
among studies are regarded as random effects. The U.S. EPA meta-analyses results are shown in
Figures 12-43 through 12-48 and Table 12-33. The relative risk for
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Touloumi Athens
Ozkaynak Toronto
Kinney Los Angeles
Ostro Santiago
Combined
IS
to
2S
30
3d
0
1 • 1
99 1.01 1.03 1.05
Relative Risk per 50 |jg/m 3 PM 10
1 Lower 95% CL • Relative Risk 1 Upper 95% CL
Figure 12-43. Summary of studies used in a combined U.S. Environmental Protection
Agency meta-analysis of PM 10 effect on mortality with short averaging times
(0 to 1 day), and co-pollutants in the model.
Source: Touloumi et al. (1994); Ozkaynik et al. (1994); Kinney et al. (1995), and Ostro et al. (1996).
Dockery St. Louis
Dockery E. Tenn.
Pope Utah
Schwartz Birmh.
Ostro Santiago
Styer Chicago
Combined
••
••
H«H
D.9 1.0 1.1 1.2 1.3
0
Relative Risk per 50 |jg/m PM 10
1 Lower 95% CL • Relative Risk 1 Upper 95% CL
Figure 12-44. Summary of studies used in a combined U.S. Environmental Protection
Agency meta-analysis of PM 10 effects on mortality with longer averaging
times (3 to 5 days), and no co-pollutants in the model.
Source: Dockery et al. (1992); Pope et al. (1992); Schwartz et al. (1993a); Ostro et al. (1995b); and Styer et al.
(1995).
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Schwartz Cine
Schwartz Phila
Schwartz Steub
Moolg. Steub
Combined (Schwartz)
Combined (Moolgav.)
1.0 1.02 1.04 1.06 1.08 1.1
Relative Risk per 100 |jg/m 3TSP
| I Lower 95% CL • Relative Risk I Upper 95% CL |
Figure 12-45. Summary of studies used in a combined U.S. Environmental Protection
Agency meta-analysis of total suspended particles (TSP) effects on
mortality, with no co-pollutants in the model.
Source: Schwartz (1994a); Schwartz and Dockery (1992a); Schwartz and Dockery (1992b); Moolgarvkar et al.
(1995a).
Schwartz Phila
Schwartz Steub
Moolg. Steub
Combined (Schwartz)
Combined (Moolgav.)
a
b
b
'-)
.)
0.
38 1.0 1.02 1.04 1.06 1.08 1
Relative Risk per 100 ug/m3 TSP
1 Lower 95% CL • Relative Risk 1 Upper 95% CL
Figure 12-46. Summary of studies used in combined U.S. Environmental Protection
Agency meta-analysis of total suspended particles (TSP) effects on
mortality, with sulfur dioxide in the model.
Source: Schwartz and Dockery (1992a); Schwartz and Dockery (1992b); Moolgarvkar et al. (1995a).
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Touloumi Athens
Kinney Los Angeles
Ito Chicago
Combined
0.99 1.00 1.01 1.02 1.03 1.04 1.05
Relative Risk per 50 |jg/m 3 PM 10
Lower 95% CL • Relative Risk
Upper 95% CL
Figure 12-47. Summary of studies used in a combined EPA meta-analysis of PM 10 effects
on mortality, with other pollutants in the model.
Source: Touloumi et al. (1994); Kinney et al. (1995); Ito et al. (1995).
PM10only, <2d lag
PM10only, >2d lag
PM10with other pol.
1.0 1.02 1.04 1.06 1.08
Relative Risk per 50 |jg/m 3 PM 10
Lower 95% CL • Relative Risk I Upper 95% CL
Figure 12-48. Summary of PM 10 effects on mortality.
Source: U.S. Environmental Protection Agency meta-analyses.
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PM10 exposure averaged <2 days is estimated as 1.031 per 50 //g/m3 PM10, with a 95%
confidence interval of 1.025 to 1.038 per 50 //g/m3 PM10. There is overall evidence of an effect,
even though one of the four studies in Figure 12-43 is not significant. The relative risk for PM 10
exposure with longer averaging times, 3 to 5 d, is estimated as 1.064 with 95% confidence
interval of 1.047 to 1.082. In Figure 12-44, one study is negative and another marginally
significant. The combined estimate for TSP effect in Figure 12-45 depends on which study is
used for the Steubenville estimate; with the Schwartz study, the effect is 1.051 per 100 //g/m3
TSP, whereas with the Moolgavkar study, the estimate is 1.050, but is less certain. However,
none of these studies included SO 2, the most probable confounding co-pollutant. The analogous
estimates for a TSP effect with copollutants in the model is less significant across three studies, as
shown in Figure 12-46. Also, Figure 12-47 shows that when SO 2 is included in the model,
estimated PM 10 effects still remain significant. RR = 1.018 with a 95% confidence interval from
1.007 to 1.029 per 50 //g/m3 PM10. The overall EPA meta-analyses results are summarized in
Table 12-37 and Figure 12-48.
TABLE 12-37. U.S. EPA META-ANALYSES: COMBINED ESTIMATES OF
RELATIVE RISK OF INCREASED MORTALITY FROM
ACUTE EXPOSURE TO AIR POLLUTANTS
Pollutant
PM10
PM10
TSP
TSP
PM10
TSP
TSP
Increment
50 //g/m3
50 //g/m3
100 //g/m3
100 //g/m3
50 //g/m3
100 //g/m3
100 //g/m3
Model
No copollutant
No copollutant
NoSO2
NoSO2
+copollutants
+SO2
+SO2
Averaging
Time
0-1 days
3-5 days
Relative Risk
Estimate Per
Increment
1.031
1.064
1.0511
1.0502
1.018
1.038
1.030
95 Percent
Confidence Limits
1.025 to 1.038
1.047 to 1.082
1.035 to 1.067
1.029 to 1.072
1.007 to 1.029
1.016 to 1.059
1.008 to 1.053
'Including Schwartz Steubenville study.
Including Moolgavkar Steubenville study.
We conclude that there is a short-term increase in mortality in response to acute PM
exposures. This appears to be at least partly confounded with other pollutants, especially SO 2,
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but even with SO 2 included in the model the effect is on the order of 1 to 5% increase in relative
risk per 100 //g/m3 TSP. This is probably a minimum estimate of effect size. If SO 2 is in fact a
proxy for fine particle exposure through the SO 2 to sulfate to fine particle pathway, then adjusting
for SO 2 may overcontrol the estimate of PM effect, which could be as large as 1 to 5% per 100
//g/m3 TSP, or 2 to 6% per 50 //g/m3 PM10. This also depends on PM 10 averaging times, with a
3% increase for averages of current and preceding day PM 10 and 6% effect for 3 to 5 day moving
averages.
These analyses suggest that there is an identifiable effect of PM exposure on increases in
acute mortality, even when characterized by TSP, a relatively insensitive index of thoracic particle
concentration. The role of SO 2 as a possible proxy for fine particle exposure remains to be
clarified. It is also not possible to overlook the potential confounding effects of other pollutants
such as O3 and NO2.
12.6.5.3 Synthesis of Prospective Cohort Mortality Studies
The results of the prospective cohort mortality studies are shown in Table 12-16. The
California nonsmoker study is not readily compared quantitatively to the other two studies and so
will not be used in a quantitative synthesis. The ACS and Six City studies have many points of
similarity, as demonstrated in Table 12-38. Two kinds of relative risk comparison are shown for
all causes of death, for death by lung cancer and by cardiopulmonary causes, and for all other
internal and external causes. The first comparison between the ACS and Six City studies is the
relative risk of smoking for current smokers compared to never-smokers. Even though these two
studies were completely independent, covering different populations with different recruitment
strategies, the general and disease-specific risk rates of smoking for the two studies are strikingly
similar, suggesting that other results from the studies may be sensibly compared or combined.
The last three columns in Table 12-38 compare the risk rates of the least polluted and most
polluted cities in the respective studies. There are two comparisons for the ACS study, based on
151 cities with sulfate data and 50 cities with fine particle data, and 6 cities in the other study.
Steubenville OH was the most polluted comparison city in the ACS sulfate and Six City
comparison, and the community of
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TABLE 12-38. ADJUSTED MORTALITY RISK RATIOS FOR SMOKING AND FOR
PARTICIPATE MATTER EXPOSURE BY CAUSES OF DEATH IN TWO RECENT
PROSPECTIVE COHORT STUDIES
Current Smokers Versus
Cause of
Death
All Causes
Lung
Cancer
Cardio-
pulmonary
All other
Non-smokers
ACS
2.07
(1.75,
9.73
(5.96,
2.28
(1.79,
1.54
(1.19,
2.43)
15.9)
2.91)
1.99)
6-City
2.00
(1.51,
8.00
(2.97,
2.30
(1.56,
1.46
(0.89,
2.65)
21.6)
3.41)
2.39)
Most Versus Least
Polluted City
ACS
Sulfate
1.15
(1.09,
1.36
(1.11,
1.26
(1.16,
1.01
(0.92,
1.22)
1.66)
1.37)
1.11)
i
6-City
PM7,
1.17
(1.09,
1.03
(0.50,
1.31
(1.17,
1.07
(0.92,
1
1
1
1
.26)
.33)
.46)
.24)
1.26
(1.08,
1.37
(0.81,
1.37
(1.11,
1.01
(0.79,
1.47)
2.31)
1.68)
1.30)
'ACS sulfates, 151 cities (Great Falls, MT versus Steubenville, OH); ACS fine particles, 50 cities,
(Albuquerque, NM versus Huntington, WV); Six City study (Portage, WI versus Steubenville, OH).
Huntingdon, WV (also in Ohio River valley) the most polluted community in the fine particle
comparison. The RR for 25 //g/m3 PM2 5 is 1.17 (1.09, to 1.26) in the ACS study and 1.31 (1.11
to 1.68) in the Six-City Study. The RR for 15 //g/m3 sulfate is 1.10 (1.06 to 1.16) in the ACS
study and 1.46 (1.16 to 2.16) in the Six City Study. The average for the two studies (random
effects weighting) is RR = 1.18 (1.04, 1.33) for 25 //g/m3 PM2 5 and RR = 1.11 (0.90 to 1.36) for
15 //g/m3 sulfate.
12.6.5.4 Discussion
In general, there appears to be a range of acute health responses to air pollution exposure as
characterized by some PM indicator. Dockery and Pope (1994b) have stated that "It is ...
presumptuous to assign these adverse health effects solely to the mass concentration of
parti culates. ... Many health effects of particles are thought to reflect the combined action of the
diverse components of the pollutant mix." Since pollutant mixes and exposed populations differ
from one location to another, it is more probable that there are real differences among different
studies.
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Several approaches to estimating a combined PM effect as a weighted average of study-
specific effects may be considered: (1) regard each effect size estimate as a measurement in an
ecological study and adjust for differences in effect size among cities as a function of differences
in climate, mixture of other air pollutants, and differences in demographic characteristics; (2) carry
out multiple comparisons of effect size estimates and group together those estimates that are not
significantly different; (3) perform combined analyses in which the PM effect size parameter(s) are
constrained to be equal in different data sets.
With the first approach (1), it may be possible to model the differences in PM effect size
estimates by multiple regression on known quantitative differences in climate, copollutant mix,
and population. This would require a "meta-regression" in which some assumptions would need
to be made about the relationship between PM effect size and the inter-study variables that
distinguish different cities, adding yet another layer of uncertainty about model specification. It
would not be feasible to carry out this analysis unless there were a large enough number of
studies, since multiple linear regression models do not perform well unless there are several times
as many data values (effect size estimates from different studies) as there are variables that are
used for adjustment.
With approach (2), each effect size estimate for which there was an attached standard error
estimate would be compared with each other effect size estimate, as if each effect size estimate
was a separate group mean in an analysis of variance. The effect size estimates would then be
grouped into clusters in which the cluster members (studies) were not statistically different from
each other, although some methods allow for the possibility of partially overlapping clusters. A
variety of multiple comparison procedures are available, using either methods based on normally
distributed data or more robust methods (e.g., Hochberg and Tamhane, 1987). Some
comparisons of a multiple hypothesis testing approach with a metaanalysis approach are described
by Westfall and Young (1993), who prefer computer-intensive resampling methods such as
bootstrap estimation or permutation testing that may not be feasible unless raw data were
available. Conventional multiple testing methods can be done without raw data when standard
error estimates are available, and may be especially suitable when there are only a few effect size
estimates.
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With alternative approach (3), it is essential that raw data be available. It is unlikely that
raw data for all studies of any specified health outcome could be assembled within a short period
of time, and even then it would likely take months to conduct such an analysis adequately.
The formal meta-analytic methods used to combine effect size estimates for acute mortality
(Schwartz, 1994c) or for a variety of health outcomes (Dockery and Pope, 1994b) could possibly
be improved by including more information when weighing the studies, as suggested above.
There are still many unresolved questions about how the synthesis of PM health effects data from
different studies should be carried out.
12.7 SUMMARY AND CONCLUSIONS
Several uncertainties need to be considered in interpreting the PM epidemiology studies
individually and as a group. Measurement error in exposure is potentially one of the most
important methodological problems, and potential confounding due to weather, copollutants and
other factors also needs to be considered. Important potential covariates should be adequately
controlled, and the response variable should vary as a function of increasing PM exposure. In
addition, quantitative studies must estimate PM exposure with reasonable accuracy as a
continuous variable. While individual PM studies may not fully take into account the above
uncertainties and considerations, as a group, especially within one study type (i.e., acute
mortality), PM studies present a relatively consistent picture. Their use in establishing
concentration-response parameters, however, still argues for caution in interpreting these studies
because no biological mechanism is known for the increases in mortality related to low level
ambient PM exposure.
12.7.1 Mortality Effects of Participate Matter Exposure
The time-series mortality studies reviewed in this and past PM criteria documents provide
strong evidence that ambient air pollution is associated with increases in daily human mortality.
Recent studies provide confirmation that such effects occur at routine ambient levels, extending to
24 h concentrations below 150 //g/m3 (the level of the present U.S. air quality standards).
Furthermore, these new PM studies are consistent with the hypothesis that PM is the air pollutant
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class most closely associated with the mortality impacts of air pollution. One of the more
important findings is that longer averaging times (3 to 5 day moving averages) predict larger and
more significant effects on total, respiratory, or cardiovascular mortality in many studies than do
PM concentrations on the same or preceding day. Overall as noted in Table 12-4, the PM 10
relative risk estimates derived from the recent PM 10 total mortality studies suggest a 24-h average
50 //g/m3 PM10 increase in acute exposure has an effect on the order of RR = 1.025 to 1.05 in the
general population. Higher relative risks are indicated for the elderly and for those with
pre-existing respiratory conditions, both of which represent sub-populations at special risk for
mortality implications of acute exposures to air pollution, including PM. Results are very similar
over a range of specifications of statistical models used in the analyses, and are not artifacts of the
methods by which the data were analyzed.
A growing body of evidence suggests that fine particles (PM 2 5) are most strongly related to
excess mortality in both acute and chronic studies. However, while coarse inhalable particles are
less strongly implicated in excess mortality, there appears to be some situations in which they may
also be predictive of excess mortality.
Evidence for or against threshold effects or other nonlinearities in response is as of yet
equivocal. Statistical significance tests for piecewise linear models with a range of cut points
(possible thresholds) for effects of TSP on mortality in Philadelphia (Cifuentes and Lave, 1996)
show some indication of a nonlinear relationship, with a generally flatter linear relationship
between mortality and TSP below the cutpoint than above the cutpoint. However, both linear
segments have statistically significant positive regression coefficients at cutpoints around 90
//g/m3 TSP, even when other pollutants (SO 2, O3) are included in the model and the TSP
regression coefficients do not appear to be significantly different between the two segments,
suggesting that there may not be a threshold for effect. Other analyses of Philadelphia daily
mortality series (Samet et al., 1995) suggest that the relationship is moderately nonlinear and
nonadditive, but do not provide evidence for either a TSP threshold or a SO 2 threshold. There is
strong evidence that the relationships vary by season, however. The Philadelphia results may
reflect seasonal or daily changes in the composition and size distribution of TSP. Other acute
studies suggest that the relationship between mortality or hospital admissions and PM 10 do not
differ significantly from a linear relationship. On the other hand, some long-term mortality studies
suggest a possible threshold for TSP or sulfates. However, because of possible exposure
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measurement errors and limited numbers of quantile observations available in piecewise analyses,
the detection of a threshold or strongly nonlinear concentration-response relationships may be
essentially impossible even if such a relationship actually exists.
There is an indication among these various analyses that children may be more susceptible to
the mortality effects of air pollution exposure than the population in general, but it is difficult,
given the limited and somewhat conflicting results available at this time, to ascribe any such
association to PM pollution in particular. This is an area where further research is clearly needed
to broaden the base upon which to assess the potential for PM to increase mortality among
children.
Long-term exposure to air pollution was studied by use of cross-sectional studies,
comparing rates of mortality or morbidity at a point in time against differences in annual average
pollutant concentrations. Most older mortality studies were population-based cross-section
studies. These studies used outcome rates for entire cities or SMSA's. Several recent prospective
cohort-based cross-sectional studies allow use of subject-specific information about other health
risk factors, such as cigarette smoking or occupational exposure; and subject-specific outcome
measures. The relative risk estimates show some sensitivity to model specification. For models
of 1980 mortality from all natural causes, the RR from separate OLS regression models using
TSP, PM15, PM25 or SO4 as PM indicators all showed a positive but statistically, non-significant
effect. The PM15 RR is 1.036 at PM 15 = 50 //g/m3 (95% confidence interval 0.98 to 1.10),
whereas a log-linear model for the same 62 SMSA's found a larger and statistically significant RR
for TSP of 1.066 (95% confidence interval 1.006 to 1.13 at TSP = 100 //g/m3). The relative risk
of major cardiovascular disease (CVD) for sulfate particles was 1.19 at SO 4=15 //g/m3 (interval
1.03 to 1.35) when adjusted for one set of demographic covariates, but smaller and not significant
after adjustment with a larger set of covariates. The relative risk of COPD for TSP at TSP =100
/-ig/m3 or for non-sulfur TSP was highly significant, 1.50 and 1.43 with confidence intervals (1.22,
1.83) and (1.20, 1.71), respectively.
Although most of these studies covered the entire U.S. using the basic paradigm of Lave
and Seskin (1970), there are major differences in the numbers of independent variables
considered, including the air pollutants. Most of the studies found pollutant elasticities (i.e., mean
effects) of 0.02 to 0.08, although the specific pollutants associated with mortality varied.
However, all of these studies found at least some association between air pollution and mortality
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on an annual average basis. There was a slight suggestion that elasticities may be decreasing over
time (1960 to 1980). It was not possible to determine whether the mortality associations were
stronger for pollution measured the same year or in previous years. Analyses by age and cause of
death were limited; the most consistent associations found by Lipfert (1994a) were for the elderly,
especially ages 75+, and for respiratory disease mortality and TSP.
Two older and three newer prospective cohort studies of mortality associated with chronic
PM exposures were also evaluated. Table 12-16 summarizes the three newer prospective studies
considered. The two early studies not shown in Table 12-16 were largely inconclusive, and the
studies of California nonsmokers by Abbey et al. (1991a, 1995a,b,c) found no significant mortality
effects of previous air pollution exposure. That study, however, and the Six-City chronic
mortality study, suffer from small sample sizes and inadequate degrees of freedom, which partially
offset the specificity gained by considering individuals instead of population groups. The Six
Cities and ACS studies agree in their findings of strong associations between fine particles and
excess mortality, but it is unfortunate that the ACS study did not consider a wider range of
pollutants so as to also evaluate the extent to which other air pollutants may have contributed to
the reported PM effects.
The RR estimates for total mortality are large and highly significant in the Six-Cities study.
With their 95 percent confidence intervals, the RR for 50 //g/m3 PM15 is 1.42 (1.16, 2.01), the RR
for 25 //g/m3PM25is 1.31 (1.11, 1.68), and the RR for 15 //g/m3 SO4 is 1.46(1.16,2.16). The
estimates for total mortality in the ACS study are much smaller, but also much more precise, 1.17
for 25 //g/m3 PM25 (RR 1.09, 1.26), and 1.10 for 15 //g/m3 SO4 (RR 1.06, 1.16). Both studies
used Cox regression models and were adjusted for rather similar sets of individual covariates. In
each case, however, caution must be applied in use of the stated quantitative risk estimates, given
that the life-long cumulative exposures of the study cohorts (especially in the dirtiest cities)
included distinctly higher past PM exposures than those indexed by the more current PM
measurements used to estimate the chronic PM exposures of the study cohorts. Thus, lower risk
estimates than the published ones are apt to apply.
An additional line of evidence concerning long-term effects may be seen in comparing some
specific causes of death in the prospective cohort studies. Table 12-38 shows relative risk for
total mortality, lung cancer deaths, cardiopulmonary deaths, and other deaths in the Six City
Study and the ACS study. The RR for current smokers in these two independent studies is very
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similar, with no significant differences. The RR for most and least polluted cities in the two
studies is the same for total, cardiopulmonary, and other causes of mortality, and the same for
lung cancer for sulfates in the ACS study and the Six City study, but not for PM 2 5 in the ACS
study. It is interesting that Abbey et al. (1991a) found a statistically significant relationship
between female cancer and TSP in the AHSMOG study, although not for heart attacks or non-
external mortality.
Cross-sectional studies may find a significant association between mortality and a specific air
pollutant for any of several reasons:
• The association may reflect a non-zero integral of the acute effects of that pollutant over
the period of study.
• The association may reflect a chronic effect from long-term exposures.
• The association may have resulted from confounding, either with another pollutant, with
the characteristics of the sources that produced that pollutant (occupational hazards or
exposures), or with human elements spatially associated with pollution sources such as
differential migration of the healthy, less desirable housing near sources, or other
socioeconomic factors.
The studies reviewed above probably all reflect some varying combinations of these possibilities.
Some of the prospective studies demonstrated that including additional pollutant exposures
in a statistical model (smoking, occupational exposure) not reflected in the outdoor measurements
leads to a stronger statistical mortality relationship with the outdoor measurements. This suggests
two possibilities (there may be others):
• The indoor and outdoor exposures may reinforce each other and thus may have similar
physiological effects. This may provide some clues as to the most likely of several
collinear outdoor pollutants. The responses could be either chronic or acute.
• The indoor or occupational exposures may have created a disease state (independent of
the outdoor exposures) that makes the individual more susceptible to outdoor pollution
effects.
Distinguishing between these two scenarios will likely require additional research, probably
including temporal studies of long-term changes in air quality in different places.
At this time, the results of the long-term studies provide support for the existence of short-
term increases in mortality which are not subsequently canceled by decreases below normal rates,
as well as for the existence of chronic effects above and beyond the acute PM exposures. Also,
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they provide no convincing evidence as to the specific pollutant(s) involved, and they do not rule
out the existence of pollutant thresholds. Displacement of mortality on a time scale of one or
more years is difficult to infer from ordinary population-based studies because there are a variety
of other factors that are also affecting changes in mortality rates. Some long-term changes
include demographic changes in the affected population, and changes in the incidence of disease
and in cause-specific mortality because of changes in the health care system. The extent to which
changes in the relation between mortality rate and air pollution may be confounded with changes
in these other factors is uncertain. Prospective studies can in principle account for some of the
more important individual risk factors, but the advantage of the prospective design may be lost if
changes in individual or personal health risk factors such as smoking status, exercise, habits, and
obesity are not included as time-varying covariates in the analyses of the data. These factors may
also differ significantly among communities. Long-term changes in mortality could also in
principle be detected by changes in air pollution over a shorter time scale than the changes in
demographics and in baseline mortality rates.
The chronic exposure studies, taken together, suggest that there may be increases in
mortality in disease categories that are consistent with long-term exposure to airborne particles,
and that at least some fraction of these deaths are likely to occur between acute exposure
episodes. If this interpretation is correct, then at least some individuals may experience some
years of reduction of life as a consequence of PM exposure. Unfortunately, without knowing the
age and the prior disease state of the decedents, it is not obvious that this information can be
usefully quantified.
12.7.2 Morbidity Effects of PM Exposure
Several morbidity health effect endpoints have been studied to examine their association
with PM exposure. These studies provide a measure of the respiratory morbidity status of a
community in relation to PM exposure. Principle endpoints include hospitalization for a
respiratory illness, respiratory symptoms and disease, and changes in lung function. The
relationship with these endpoints and PM exposure indicates that ambient exposure to PM
impacts the respiratory system. Acute exposure studies show an effect more than chronic
exposure studies, but more recent chronic studies are also indicative of an effect. No relationship
between acute exposures and chronic health outcomes have been demonstrated.
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Hcapitalization
Potentially, the most severe morbidity measure is hospitalization for respiratory and
cardiovascular illness diagnosis, especially for COPD and pneumonia specifically. This outcome
is coherent with the mortality PM relationship discussed above. The hospitalization studies
usually compared daily fluctuations in admissions about a long term (e.g., 19 day) moving
average. These fluctuations were regressed on PM estimates for the time period immediately
preceding or concurrent with the admissions. Some authors considered lags up to 5 days, but the
best predictor usually was the most recent exposure. Some morbidity outcomes associated with
hospitalization may be appropriately associated with concurrent admission, while others may
require several days of progression to end in an admission. Exposure-response lag periods are not
yet well examined for hospital admissions related to PM exposures. Both COPD and pneumonia
hospitalization studies show moderate but statistically significant relative risks in the range of 1.06
to 1.25 resulting from an increase of 50 |ig/m 3 in PM10 or its equivalent. The admission studies of
respiratory and cardiovascular disease show a similar effect. The hospitalization studies in general
use similar analysis methodologies. There is evidence of a relationship to heart disease, but the
estimated relative risks are somewhat smaller than those for respiratory endpoints. Overall, these
studies are indicative of morbidity effects being related to PM exposure (see Figure 12-1).
While a substantive number of hospitalizations for respiratory related illnesses occur in those
>65 years of age, there are also numerous hospitalizations for those under 65 years of age.
Several of the PM 10 hospitalization studies restricted their analysis by age of the individuals.
These studies are clearly indicative of health outcomes related to PM for individuals >65 years of
age, but did not explicitly examine other age groups that would allow directly comparable
estimates as some mortality studies did. The limited available analyses examining young age
groups, especially children < 14 years of age, constrain possible conclusions about this age group.
Studies by Thurston et al. (1992, 1994a,b) and Burnett et al. (1994, 1995) examining acid
aerosols and sulphates however did show results differing by age.
The EPA ozone criteria document (U.S. Environmental Protection Agency, 1996) examines
several of these same studies for an O 3 effect; it concludes that, collectively, the specific studies
evaluated indicate that ambient O 3 often has a significant effect on hospital admissions for asthma
and other respiratory causes (with a relative risk ranging from 1.1 to 1.36/100 ppb O 3). The
present PM document examines a broader group of studies, which collectively are indicative of
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consistent PM effects on hospital admissions for all respiratory causes (COPD, pneumonia, etc.)
and for cardiovascular causes. Also, in a very recently reported study which used two pollutant
models to evaluate which pollutants made contributions to explaining respiratory hospital
admissions, the PM 10 and O3 associations appeared to be independent of each other, with no
reduction in the relative risk for one pollutant after control for the other.
Respiratory Illness Studies
Acute respiratory illness and the factors determining its occurrence and severity are
important public health concerns. This effect is of public health importance because of the
widespread potential for exposure to PM and because the occurrence of respiratory illness is
common. Of added importance is the fact that recurrent childhood respiratory illness may be a
risk factor for later susceptibility to lung damage.
The PM studies generally used several different standard respiratory questionnaires that
evaluated respiratory health by asking questions about each child's and adult's respiratory disease
and symptom experience daily, weekly or over a longer recall period. The reported symptoms
and diseases characterize respiratory morbidity in the cohorts studied. Respiratory morbidity
typically includes specific diseases such as asthma and bronchitis, and broader syndromes such as
upper and lower respiratory illnesses.
Acute respiratory illness studies typically include several different endpoints, but most
investigators reported results for at least two of: (1) upper respiratory illness, (2) lower
respiratory illness, or (3) cough. The following relative risks are all estimated for an increase of
50 |ig/m3 in PM10 or its equivalent. The studies of upper respiratory illness do not show a
consistent relationship with PM. Two of the studies showed no effect, three studies estimated an
odds ratio near 1.2, and one study estimated the odds ratio of 1.55. Some of inconsistency could
be explained by the fact that the studies included very different populations. The studies of lower
respiratory disease gave odds ratios which ranged from 1.10 to 1.28 except for the Six-Cities
study which gave a value over 2.0. Although the lower respiratory disease studies also include a
variety of populations, it is difficult to explain the large range of estimates. The studies of cough
were more consistent, having odds ratios ranging from 0.98 to 1.51. Again, the Six City study
produced the largest value. The second highest value was that of a Utah study at 1.29.
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All three endpoints had the same general pattern of results. Nearly all odds ratios were
positive, and the 95% confidence intervals for about half were statistically larger than 1.0 (i.e.,
they were statistically significant at p < 0.05). Each endpoint had one study with a very high odds
ratio. This can be contrasted with the hospital admission studies, which all resulted in very similar
estimates. There are several factors which could account for this. The respiratory disease studies
used a wide variety of designs and, as a result, the models for analysis were also varied. Finally,
the populations included several different subgroups, whereas the hospitalization studies tended to
include similar populations. There were few studies of respiratory symptoms in adults as
compared with those in children.
Acute exposures to PM are associated with increased reporting of respiratory symptoms and
with small decrements in several measures of lung function. As a consequence, cross-sectional
studies of the relationship between long-term exposure to PM (or any air pollutant) and
consequent chronic effects on respiratory function and/or respiratory symptoms may be limited by
the inability to control for effects of recent exposures on function and symptoms. Moreover, such
studies are further handicapped by: (1) limited or no ability to characterize accurately lifetime
exposure to PM other than through "area-based" ecological assignments or assignments inferred
from short-term, acute measurements; and (2) their inherent limited ability to characterize
correctly other relevant exposure histories (e.g., past histories of respiratory illnesses, passive
exposure to tobacco smoke products, active smoking in older subjects, etc.).
Longitudinal studies offer numerous obvious advantages over cross-sectional studies in
terms of PM exposure characterization and characterization of relevant covariates. Nonetheless,
to the extent to which such studies base their inference with regard to the occurrence of long-term
morbidity on effects observed over relatively short durations of cohort follow-up (e.g., incident
respiratory illness in relationship to ambient PM, short-term relationship between ambient PM and
lung function, etc.), their results need to be viewed with circumspection. These approaches do
not definitively establish long-term exposure effects but only suggest the coherence of the
possibility of such long-term effects.
Three chronic respiratory disease studies were based on a similar type of questionnaire but
were done by Harvard University at three different times as part of the Six Cities and 24-Cities
Studies. The studies provide data on the relationship of chronic respiratory disease to PM. All
three studies suggest a chronic effect of PM on respiratory disease. The analyses for chronic
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cough, chest illness and bronchitis tended to be significantly positive. These studies suffer from
the usual difficulty of cross sectional studies. The effect of paniculate matter is based on
variations in exposure which are determined by the different number of locations. The results
seen in all studies were consistent with a PM gradient, but it is difficult to separate out clearly the
effects of PM versus any other factors or pollutants which have the same gradient. The recent 24
North American City study is strongly suggestive of an effect on bronchitis from acidic particles
or from PM which is consistent with the results of the Six Cities study and thus tends to support
the gradient observed.
Pulmonary Function Studies
Pulmonary function studies are part of any comprehensive investigation of possible air
pollutant effects. Guidelines for standardized testing procedures and for reference values and
interpretative strategies of lung function tests exist. Various factors are important determinants of
lung function measures. Lung function in childhood is primarily related to age and, especially, to
general stature (as measured by height). The growth patterns differ between males and females.
Lung function begins to decline with age in the 3rd to 4th decades and continues to do so
monotonically as people age. Cigarette smoking, the presence of COPD and, in some cases,
asthma are some factors related to more rapid declines in lung function in adults. Environmental
factors undoubtedly influence the natural history of the growth and decline of lung function.
Pulmonary function results are somewhat easier to compare because most studies used peak
flow (PEFR) or forced expiratory volume (FEV) as the health end-point measure. Acute
pulmonary function studies (summarized in Figure 12-6) are suggestive of a short term effect
resulting from particulate pollution. Peak flow rates show decreases in the range of 30 to 40
ml/sec resulting from an increase of 50 |ig/m 3 in PM10 or its equivalent. The results appear to be
larger in symptomatic groups such as asthmatics. The effects are seen across a variety of study
designs, authors, and analysis methodologies. Effects using FEV t or FVC as endpoints are less
consistent. For comparison, a study of over 16,000 children found that maternal smoking
decreased a child's FEV by 10 - 30 ml. An estimate of the effect of PM on pulmonary function in
adults found a 29 (±10) ml decrease in FEV t per 50 //g/m3 increase in PM 10, which is similar in
magnitude to the changes found in children.
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The chronic pulmonary function studies are less numerous than the acute studies. The Six
City studies, which had good monitoring data, found no statistically significant PM effect.
However, another recent paper found a small but significant decrease in F VC in healthy non-
smokers. Yet another recent study is strongly indicative of a PM effect either from acidic
particles or from PM itself. Cross sectional studies require very large sample sizes to detect
differences because the studies cannot eliminate person to person variation which is much larger
than the within person variation. Thus, the lack of statistical significance in some long-term
studies cannot be taken as proof of no effect.
Overall, the morbidity studies as a group qualitatively indicate that acute PM exposures are
associated with hospitalization admission for respiratory and cardiovascular disease, increased
levels of respiratory symptoms and disease, and pulmonary function decrements. The quantitative
magnitude of these relationships and their public health meaning are important aspects to
consider.
12.7.3 Comparison of Human Health Effects of PM 10 Versus PM 2 5 Exposure
Recent reanalyses of the Six City Study by Schwartz et al. (1996) evaluated the effects of
using fine particles (FP = PM 2 5), inhalable particles (PM 15), or coarse particles (CP = PM 15 -
PM25) as exposure indices. The results were transformed to standard increments of 25 |ig/m 3
PM25 and 50 |ig/m3 PM15, and 25 |ig/m3 for CP, with results for short-term (24-h) PM exposures
as depicted earlier in Figure 12-33. Across the six cities, PM 25 was the most predictive of the
three PM indices for daily mortality RR increases except in Steubenville, where a more significant
CP effect was found (although the FP effect size was as large as in most other cities). In spite of
very considerable differences among the cities in terms of climate and demographics, the FP effect
sizes were rather consistent. The CP effect sizes were positive, small, and not significant except
in Steubenville (positive, significant) and Topeka (negative, nearly significant). In some cases, CP
may need to be considered as well as FP in evaluating PM health risks. Since PM15 was the sum
of FP and CP, it had an intermediate significance, with positive and significant effects except for
Portage and Topeka. The St. Louis and Eastern Tennessee associations for PM 15 and FP were
both significant, possibly because of the use of nonparametric smoothers to adjust for weather and
time trends.
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Relationships between chronic PM exposures indexed by different particle size indicators
(PM15, PM2 5, PM15 - PM2 5) and mortality effects as observed in the Harvard Six City Study were
earlier depicted graphically in Figure 12-8. More specifically, the adjusted risks are plotted in
Figure 12-8, so as to emphasize the increasing correlation of long-term mortality with PM as the
size cut of the particles decreases. The figure shows a modest positive association between RR
and TSP, but a stronger association between RR and inhalable particles (IP or PM 15) and a
weaker association between RR and non-inhalable particles (TSP-IP) than between RR and TSP.
The figure also shows that there is a stronger association between RR and IP, although the coarse
particle relationship is almost linear if Topeka is dropped. The figure also shows that both sulfate
and non-sulfate components of fine particles appear to be closely associated with increased PM-
related RR.
While numerous morbidity studies have been conducted examining PM health effects for
PM10 as discussed above, limited numbers of studies have been published that examine fine
particles such as PM 25. The most direct comparison of the effect of PM 10 to PM25 results when
studies include both exposure measures in their analyses. For acute exposure studies, this
occurred in the Six City study, the Tucson study, and the Uniontown study. None of these
studies could directly show that one of these measures was a significantly better predictor than the
other. The Six City study suggested that PM 10 was a better predictor of respiratory disease. The
Tucson study suggested that PM 25 was a better predictor of lung function change. The
Uniontown study used PM 2 5, FT, and SO4= values in their analysis, but not PM 10 which may have
been due to the fact that the PM 10 values were not available as 12 h averages whereas the other
pollutants were.
Two other studies used PM 25 as a measure of particulate exposure. A study of respiratory
disease in Denver found an effect that fell in the middle of the range of effects found by the PM 10
studies. A study of lung function found a slightly larger effect for asthmatics and slightly smaller
effect for non-asthmatics when compared with the PM 10 studies.
Two recent chronic exposure studies provide results for PM 10, PMZ1, and particulate acidity.
One respiratory symptoms study in 24 North American communities reported that children living
in communities with the highest levels of particle strong acidity were significantly more likely (OR
= 1.66, 95% CI = 1.11, 2.48) to report at least one episode of bronchitis in the past year
compared to children living in communities with the lowest levels of acidity. For PM215 the odds
12-374
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ratio for bronchitis was 1.50 (95% CD = 0.91, 2.47). No other respiratory symptoms were
significantly associated with any of the pollutants. In particular, there was no evidence that the
presence of asthma or asthmatic symptoms was associated with the measured pollutants. No
sensitive subgroups were identified. The strong correlations of several pollutants in this study,
especially particle strong acidity in the sulfate (r = 0.90) and PM 2l (r = 0.82), make it difficult to
distinguish the agent of interest.
A study of pulmonary function test results from 22 North American communities described
above indicated that a 52 nmole/m 3 difference in annual mean particle strong acidity was
associated with a 3.5% deficit in adjusted FVC and a 3.1% deficit in adjusted FEV t. The deficit
was larger (but not statistically larger) in lifelong residents of their communities. Deficits were
also found in PEFR and MMEFR although these deficits were not statistically significant. Ratios
of FEV and FVC were not statistically significant. Slightly smaller deficits were seen using total
sulfate, PM2!, and PM10 as pollutant exposure measures, and these deficits were also statistically
significant. The data did not allow for the separation of effects of the various particulate matter
exposures.
These few studies on PM 2 5 show effects that are difficult to separate both from PM 10
measures and acid aerosols measures which are briefly discussed in the next section. The PM2 5
studies do show effects related to exposure to the fine fraction. The high correlation between
PM2 5, PM10, and acid aerosols may make it very difficult to separate out differences.
Health Effects of Acid Aerosols
While most epidemiology studies of PM measure or estimate mass of PM, several studies
measured the mass of acid aerosols. Presently this represents the main chemical characterization
of PM. However, this mass would primarily be found in the fine fraction of PM, that is PM 2 5.
Earlier and present-day studies suggest that there can be both acute and chronic effects by
strongly acidic PM on human health. Studies of historical pollution for episodes, notably the
London Fog episodes of the 1950's and early 1960's, indicate that extremely elevated daily acid
aerosol concentrations may be associated with excess acute human mortality when present as a
co-pollutant with elevated concentrations of PM and SO 2. In addition, significant associations
were found between acid aerosols and mortality in London during non-episode pollution levels ( <
7.5 //g/m3 as H2SO4, or < approximately 150 nmoles/m 3 FT), though these associations could not
12-375
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be separated from those for BS or SO 2 (Lippman and Ito, 1995). The attempts to-date to
associate present-day levels of acidic aerosols with acute and chronic mortality were unable to do
so, but there may not have been a sufficiently long series of H + data to detect H + associations.
Increased hospital admissions for respiratory causes were also documented during the London
Fog episode of 1952, and this association has now been observed under present-day conditions, as
well. In these studies, H + effects were estimated to be the largest during 1 to 3-day acid aerosol
episodes (H+ > 10 //g/m3 as H2SO4, or »200 nmoles/m 3 FT), which occur roughly 2 to 3 times per
year in eastern North America. These studies suggest that present-day strongly acidic aerosols
can represent a portion of PM which is particularly associated with significant acute respiratory
disease health effects in the general public.
Results from recent acute symptoms and lung function studies of healthy children indicate
the potential for acute acidic PM effects in this population. The 6-City study of diaries kept by
parents of children's respiratory and other illness show H + associations with lower respiratory
symptoms at H+ above 110 moles/m3. Some, but not all, recent summer camp and school children
studies of lung function have also indicated significant associations between acute exposures to
acidic PM and decreases in the lung function of children independent of those associated with O 3.
Studies of the effects of chronic H + exposures on children's respiratory symptoms and lung
function are generally suggestive of effects due to chronic H + exposure. Preliminary analyses of
bronchitis prevalence rates as reported across the 6-City study locales were found to be more
closely associated with average H + concentrations than with PM in general. Furthermore, in a
study of children in 24 U.S. and Canadian communities in which the analysis was adjusted for the
effects of gender, age, parental asthma, parental education, and parental allergies, bronchitic
symptoms were confirmed to be significantly associated with strongly acidic PM (relative odds =
1.66, 95% CI: 1.11 to 2.48). It was also found in the 24-Cities study that mean FVC and FEV L0
were lower in locales having high particle strong acidity. Thus, chronic exposures to strongly
acidic PM may have effects on measures of respiratory health in children. The acid levels,
however, were highly correlated to other PM indicators such as PM 2l, as noted above.
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13. INTEGRATIVE SYNTHESIS OF KEY POINTS:
PM EXPOSURE, DOSIMETRY, AND HEALTH RISKS
13.1 INTRODUCTION
This chapter integrates key information on exposure-dose-response risk assessment
components drawn from the preceding detailed chapters, in order to provide a coherent
framework for assessment of human health risks posed by ambient particulate matter (PM) in the
United States. More specifically, this chapter first provides background information on key
features of atmospheric particles, highlighting important distinctions between fine and coarse
mode particles with regard to their size, chemical composition, sources, atmospheric behavior,
and potential human exposure relationships—distinctions which collectively suggest that fine
and coarse mode particles should be treated as two distinct subclasses of air pollutants.
Information on recent trends in U.S. concentrations of different ambient PM size/composition
fractions and ranges of variability seen in U.S. regions and urban air sheds is also summarized to
place the ensuing health effects discussions in perspective.
The chapter next summarizes key points regarding respiratory tract dosimetry, followed by
discussion of the extensive PM epidemiologic database that has evolved during the past several
decades. The latter includes recent studies providing evidence that serious health effects
(mortality, exacerbation of chronic disease, increased hospital admissions, etc.) are associated
with exposures to ambient levels of PM found in contemporary U.S. urban air sheds even at
concentrations below current U.S. PM standards. Evaluations of other possible explanations for
the reported PM epidemiology results (e.g., effects of weather, other co-pollutants, choice of
models, etc.) are also discussed, ultimately leading to the conclusion that the reported
associations of PM exposure and effects are valid. Evidence is then reviewed that (a) clearly
substantiates associations of such serious health effects with U.S. ambient PM10 levels and (b)
less extensively points toward fine particles (as indexed by various indicators) as likely being
important contributors to the observed human health effects. The overall coherence of the
epidemiologic data base is also discussed, suggesting a likely causal role of ambient PM in
contributing to the reported effects.
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The nature of the observed effects and hypothesized potential mechanisms of action
underlying such effects are then discussed in subsequent sections. The discussion of potential
mechanisms of injury examines ways in which PM could induce health effects. The current
limited availability of much experimental evidence necessary to evaluate or directly substantiate
the viability of the hypothesized mechanisms is noted. Limited information concerning possible
contributions of particular classes of specific ambient PM constituents is also summarized.
The chapter also provides information on the identification of population groups at special
risk for ambient PM effects, factors placing them at increased risk, and other key components
that need to be considered in generating risk estimates for the possible occurrence of PM-related
health events in the United States. An examination of risk factors includes those affecting
exposure risk and mechanistic determinants of dose, as well as individual factors affecting
susceptibility related to age or disease.
One of the present problems of "integrating" PM health effects research results is the
current disparity between evidence from epidemiologic studies and from experimental human
exposure and laboratory animal studies. On the one hand, epidemiologists have examined
relationships between regionally and temporally variable mixtures of ambient air particles and
broad classes of health effects (e.g., mortality, hospital admissions, respiratory illness, etc.),
whose target population largely includes the elderly and individuals with cardiopulmonary
disease. Extremely high exposure levels associated with historic air pollution "disasters"
indicate that severe illness and death are clearly linked with high levels of air pollution,
including PM. Also, children have been studied for respiratory symptomatology and mechanical
pulmonary function changes in relation to ambient PM concentrations. On the other hand,
experimental human studies have focused mainly on reversible physiologic and biochemical
effects in young healthy people that result from controlled exposures to laboratory-generated
acidic aerosols, sulfates or nitrates. Laboratory animal studies cover a broader range of specific
health endpoints than the human studies, but again typically evaluate individual particle species
that comprise the ambient mixture called particulate matter and their effects on healthy animals.
Much more experimental research data are needed on effects of ambient (or quasi-ambient) PM
on diseased humans or animal models of disease.
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13.2 AIRBORNE PARTICLES: DISTINCTIONS BETWEEN FINE AND
COARSE PARTICLES AS SEPARATE POLLUTANT SUBCLASSES
As discussed in detail in Chapter 3 of this document, airborne PM is not a single pollutant
but many classes of pollutants, each class consisting of several to many individual chemical
species. One classification is based on the natural division of the atmospheric aerosol into fine-
mode and coarse-mode particles. Fine-mode particles, in general, are smaller than coarse-mode
particles, but they also differ in many other aspects such as formation mechanisms, chemical
composition, sources, physical behavior, human exposure relationships, and control approaches
required for risk reduction. Such differences alone are sufficient to justify consideration of fine-
mode and coarse-mode particles as separate pollutants, regardless of the extent or lack of
evidence regarding differences in composition, respiratory tract dosimetry, or associated health
effects in laboratory animals or humans. Table 13-1 compares several key points that
differentiate fine-mode and coarse-mode particles. Various physical and chemical differences
between fine-mode particles and coarse-mode particles, their sources, factors affecting human
exposure, and their respiratory tract deposition are also concisely summarized below as a prelude
to more in-depth discussion of key health effects associated with ambient PM exposures and
other information useful in assessing PM-related public health risks in the United States.
13.2.1 Size Distinctions
Three approaches are used to classify particles by size: (1) modes, based on formation
mechanisms and the modal structure observed in the atmosphere; (2) size cut point, based on the
50% cut point of the specific sampling device; and (3) dosimetry, based on the ability of
particles to enter certain regions of the respiratory tract. The modal structure is shown in Figure
13-1. In the ambient atmosphere the fine particle mode is composed of the nuclei mode and the
accumulation mode. The nuclei mode is clearly observable only near sources of condensible
gases. Particles in the nuclei mode rapidly grow into the accumulation mode but the
accumulation mode does not grow further into the coarse particle mode. The lognormal
distribution (in units of particle diameter) is frequently used to approximate the distribution of
particle number, surface area, volume, or mass. The accumulation mode may contain varying
amounts of ultrafine particles (<0.1 //m) aggregated from the nuclei mode.
1O "
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TABLE 13-1. COMPARISON OF AMBIENT FINE AND COARSE
MODE PARTICLES
Fine
Coarse
Formed from:
Formed by:
Composed of:
Solubility:
Sources:
Atmospheric half-life:
Travel distance:
Gases
Chemical reaction
Nucleation
Condensation
Coagulation
Evaporation of fog and cloud
droplets in which gases have
dissolved and reacted
Sulfate,
Nitrate, NO;,
Ammonium,
Hydrogen ion, H+
Elemental carbon,
Organic compounds
(e.g., PAHs, PNAs)
Metals, (e.g., Pb, Cd, V,
Ni, Cu, Zn, Mn, Fe)
Particle-bound water
Largely soluble, hygroscopic
and deliquescent
Combustion of coal, oil,
gasoline, diesel, wood
Atmospheric transformation
products of NOX, SO2, and
organic compounds including
biogenic organic species,
e.g., terpenes
High temperature processes,
smelters, steel mills, etc.
Days to weeks
100s to 1000s of km
Large solids/droplets
Mechanical disruption
(crushing, grinding, abrasion
of surfaces, etc.)
Evaporation of sprays
Suspension of dusts
Resuspended dusts
(Soil dust, street dust)
Coal and oil fly ash
Oxides of crustal elements,
(Si, Al, Ti, Fe) CaCO3, NaCl,
sea salt
Pollen, mold, fungal spores
Plant/animal fragments
Tire wear debris
Largely insoluble and
non-hygroscopic
Resuspension of industrial dust
and soil tracked onto roads and
streets
Suspension from disturbed
soil, e.g., farming, mining,
unpaved roads
Biological sources
Construction and demolition,
coal and oil
combustion, ocean spray
Minutes to hours
<1 to 10s of km
Source: Adapted from Wilson and Suh (1996).
13-4
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O.
Q
at
Mechanically
Generated
2
1
0
0.002
0.01
Nuclei Mode
0.1 1 10
Geometric Diameter, D, , |jm
100
Accumulation Mode
Coarse Mode
Fine-Mode Particles
Coarse-Mode Particles
Figure 13-1. Measured volume size distribution showing fine-mode and coarse-mode
particles and the nuclei and accumulation modes within the fine-particle
mode. DGV (geometric mean diameter by volume, equivalent to volume
median diameter) and og (geometric standard deviation) are shown for each
mode. Also shown are transformation and growth mechanisms
(e.g., nucleation, condensation, and coagulation).
Source: Wilson et al. (1977).
Particle diameters are usually given as aerodynamic equivalent diameter, dae, defined as the
diameter of a particle with equal settling velocity to that of a sphere with unit density (1 g/cm3).
This is the most appropriate diameter for discussion of lung deposition and particle collection.
The accumulation mode typically has a mass median aerodynamic diameter (MMAD) of 0.3 to
0.7//m and a geometric standard deviation, og (a measure of the size dispersion), of 1.5 to 1.8.
The coarse particle mode may also contain multiple modes but they are not readily
distinguished. Therefore, the coarse particle mode tends to have a broader size distribution, with
a og = 2.2 to 2.4. Measured MMADs typically range from 6 to 20 //m diameter in the ambient
13-5
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atmosphere, but these values may be low because of the difficulty of collecting particles in the
upper tail of the coarse-mode distribution.
The indicator for the current PM standard is PM10. Since neither the respiratory tract nor
particle samplers can separate particles with a sharp cut, PM10 is defined as having a 50%
cutpoint at 10 //m dae. PM10 samplers collect all fine-mode particles. They collect a decreasing
fraction of particles as the diameter increases above 10 //m dae and an increasing fraction of
particles as the diameter decreases below 10 //m dae. The mass of the coarse fraction ranges
from 20% of PM10 in some eastern urban areas to 80% of PM10 in dry western areas.
Agreement has been reached between the International Standards Organization (ISO) and
American Council of Government Industrial Hygienists (ACGIH) who have also promulgated
definitions of particle size fractions that are based on the ability of particles to penetrate to
various depths within the respiratory tract (Vincent, 1995). Inhalable refers to particles which
can enter beyond the external airway openings and, as discussed in Chapter 10, has a practical
upper limit of 40 to 60 //m. Thoracic particles refer to those particles which can penetrate
beyond the larynx; 50% of particles of 10 //m aerodynamic diameter will penetrate beyond the
larynx.
The appropriate division between the fine and coarse fractions is not sharply defined, but
falls in the range between 1.0 and 3.0 //m dae, where fine-mode and coarse-mode particles
overlap but where particle mass is at a minimum. Thus, in general, particles less than 1.0 //m dae
are fine-mode particles and particles greater than 2.5//m dae are coarse-mode particles. However,
as the relative humidity approaches 100%, fine particles may grow beyond 1.0 //m and even
beyond 2.5 //m dae; and, in very dry environments, it may also be possible to find particles less
than 1.0 //m dae in the small size tail of the coarse particle mode. It is important to note that
PM2 5 may sometimes contain an appreciable quantity of coarse-mode particles in the 1 to
2.5 fj,m dae size range.
PM2 5 particles are frequently referred to as fine, while the difference between PM2 5 and
PM10 (PM10_2 5), is sometimes referred to as coarse or as the coarse fraction of PM10. In the
present discussion, fine-mode particles and coarse-mode particles are used to emphasize that
important distinctions include not just size but also other additional fundamental differences in
sources, formation mechanisms, and chemical composition.
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13.2.2 Formation Mechanisms
Fine particles are formed from gases by nucleation (gas molecules coming together to form
a new particle), by condensation (gas molecules condensing onto a pre-existing particle), or by
liquid phase reactions. Gases may dissolve in a liquid droplet (either a solution particle or a
cloud or fog droplet), react with another dissolved gas, and form a low vapor pressure product.
When fog and cloud droplets evaporate, paniculate matter remains, usually in the fine particle
mode.
Coarse particles are formed by mechanical processes which produce small particles from
large ones. Energy considerations normally limit coarse mode particle sizes to greater than
about 1.0//m dae.
Particles are designated as primary if they are emitted directly into the air as particles or as
vapors which condense to form particles without chemical reaction. Examples of primary
particles are (a) elemental carbon chain agglomerates formed during combustion and (b)
chemical species such as lead, cadmium, selenium, or sulfuric acid which are volatile at
combustion temperature but form PM rapidly as the combustion gases cool.
Particles are designated as secondary if they form following a chemical reaction in the
atmosphere which converts a gaseous precursor to a product which either has a low enough
saturation vapor pressure to form a particle or reacts further to form a low saturation vapor
pressure product. Examples are the conversion of sulfur dioxide (SO2) to sulfuric acid (H2SO4)
which nucleates or condenses on existing particles, or the conversion of nitrogen dioxide (NO2)
to nitric acid (HNO3) which may react further with ammonia (NH3) to form particulate
ammonium nitrate (NH4NO3).
Coarse particles are normally primary since they are formed by mechanical rather than by
chemical processes. An exception is the reaction of acid gases with carbonate (CO;) containing
particles in which the CO; may be replaced by sulfate (SO4), nitrate (NO3), or chloride (Cl").
Other exceptions are the reaction of HNO3 with NaCl to form NaNO3 and HC1 gas and the
reaction of SO2 with wet NaCl to form Na2SO4 and HC1 gas.
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13.2.3 Chemical Composition
13.2.3.1 Fine-Mode Participate Matter
In the ambient atmosphere, fine-mode particulate matter is mainly composed of varying
proportions of six major components (sulfates, acids, nitrates, elemental carbon, organic carbon,
and trace elements such as metals) and varying amounts of water.
Sulfates/Acid. Sulfur dioxide (SO2), mainly from combustion of fossil fuel, is oxidized in
the atmosphere to form sulfuric acid (H2SO4) particles. The H2SO4 may be partially or
completely neutralized by reaction with ammonia (NH3). Since the particles usually contain
water, the actual species present are H1", HSO4, SO4, and NH4, in varying proportions depending
on the amount of NH3 available to neutralize the H2SO4. Particle strong acidity is due to free H+
or H+ available from HSO4 or H2SO4.
Nitrates. Nitrogen oxides (NOX= NO + NO2) are formed during combustion or any high
temperature process involving air. The NO is converted to NO2 by ozone (O3) or other
atmospheric oxidants. During the daytime, NO2 reacts with the hydroxyl radical (OH) to form
nitric acid (HNO3). During nighttime, it forms nitric acid through a sequence of reactions
involving ozone and the nitrate radical (NO3). Ammonia reacts preferentially with sulfuric acid,
but, if sufficient NH3 is available, paniculate ammonium nitrate (NH4NO3) will form.
Elemental Carbon. Chain agglomerates of very small elemental carbon (EC) particles are
formed during combustion, such as in open hearth fireplaces, wood stoves and diesel engines.
Organic Carbon. Several heterogenous categories of organic carbon (OC) compounds are
also often found in ambient air, as follows:
• Primary-anthropogenic. Incomplete combustion also leads to hundreds of organic
compounds with low enough vapor pressure to be present in the atmosphere as
particles, including mutagenic species such as polyaromatic hydrocarbons (PAHs).
• Secondary-anthropogenic. Some organic compounds, including aromatics (larger
than benzene), cyclic olefins and diolefins, and other C7 or higher hydrocarbons, react
with O3 or OH to form polar, oxygenated compounds with vapor pressures low enough
to form particles.
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• Primary biogenic. Viruses, some bacteria, and plant and/or animal cell fragments may be
found in the fine mode.
• Secondary biogenic. Terpenes, C10 cyclic olefms released by plants, also react in the
atmosphere to yield organic particulate matter.
Trace Elements. A variety of transition metals and non-metals are volatilized during the
combustion of fossil fuels, smelting of ores, and incineration of wastes and are emitted as fine
particles (or vapors which rapidly form fine particles).
Water. Sulfates, nitrates, and some organic compounds are hygroscopic, i.e., they absorb
water and form solution droplets. A variety of atmospheric pollutant gases can dissolve in the
water component of the particle. This provides a mechanism for carrying into the lung species
such as SO2, H2O2, HCHO, etc., which, when in the gas phase, would normally be removed in
the nose, throat, or upper airways.
13.2.3.2 Coarse-Mode Particulate Matter
Coarse-mode PM sources are primarily crustal, biological, or industrial in nature.
Crustal. Crustal material, from soil or rock, primarily consists of compounds that contain
Si, Al, Fe, Mg, and K (small amounts of Fe and K are also found among fine-mode particles but
come from different sources). In urban areas, much crustal material arises from soil which is
tracked onto roads during wet periods and is suspended in the air by vehicular traffic. In rural
areas, tilling, wind blowing over disturbed soil, or vehicles traveling on unpaved roads can
generate coarse particles. Where farms have been treated with persistent pesticides or
herbicides, these materials may also be present in suspended soil particles.
Biological. Biological materials such as bacteria, pollen, spores, and other plant and
animal fragments are mostly found in the coarse size range (i.e., 2.0 to 10 //m dae for most, >20
//m dae for some).
Industrial. A variety of industrial operations generate coarse particles. Examples are
construction and demolition, open pit mining, grain handling, coal handling, etc. Also, coal and
oil combustion generate fly ash which is similar in chemical composition to soil and crustal
material but can be differentiated by microscopic examination.
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13.2.4 Atmospheric Behavior
Coarse-mode particles are large enough so that the force of gravity exceeds the buoyancy
forces of the air. Therefore, large particles tend to rapidly fall out of the air. Coarse-mode
particles are also too large to follow air streams, so they tend to be easily removed by impaction
on surfaces. The atmospheric half-life of coarse particles depends on their size, but is usually
only minutes to hours. However, vigorous mixing and convection, such as occurs during dust
storms, can lead to longer lifetimes for the smaller size range of coarse-mode particles.
In contrast, fine-mode particles are small enough that gravitational forces are largely
overcome by the random forces from collisions with gas molecules. Thus fine particles tend to
follow air streams and are typically not removed by impaction. Accumulation-mode particles
are sufficiently larger than gas molecules that their diffusion velocity is low. Removal by dry
deposition is inefficient since they do not readily diffuse through the boundary layer of still air
next to surfaces. Therefore, accumulation-mode particles have very long half-lives in the
atmosphere, travel long distances, and tend to be more uniformly distributed over large
geographic areas than coarse-mode particles. The atmospheric half-life of accumulation-mode
particles with respect to dry deposition is on the order of weeks. Removal of accumulation-
mode particles occurs when the particles absorb water, grow into cloud droplets, grow further to
rain drops, and fall out as rain. This process reduces the atmospheric half-life of accumulation-
mode particles to a few days.
Ultrafine or nuclei-mode particles, formed by nucleation of low saturation-vapor-pressure
substances, tend to exist as disaggregated individual particles for very short periods of time
(
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reaction products. Coarse particles, on the other hand, are produced mainly by the abrasion of
surfaces (e.g., wind erosion, tire friction).
For a variety of reasons, concentrations of aerosol constituents measured at specific
monitoring sites do not reflect the composition that would be obtained from a straightforward
comparison of the source strengths shown in Chapter 5. Although windblown dust, from
whatever source, represents the largest single category of PM10 emissions by mass (accounting
for roughly 88% of the total), it does not often account for more than half of the mass of ambient
samples. This discrepancy reflects in part the shorter residence time of dust in the atmosphere.
Dust is found mainly in the coarse fraction, while secondary constituents are mainly found in the
fine fraction. Monitoring sites are frequently located near specific sources such as roadways and
less frequently away from areas where there is a perceived need for monitoring.
In general, emissions of primary PM10 components and gaseous precursors to PM10 are
estimated to have decreased from 1984 to 1993. Ambient PM10 levels have also decreased in
major urban areas during the same time period. However, a number of factors preclude a
detailed comparison between trends in PM10 emissions and trends in ambient PM10 levels. These
factors include long term variations in transformation rates of precursor gases to secondary
particulate matter, wet and dry deposition rates, and effects of meteorological variability on dust
emissions. As an example, nationwide emissions of dust by wind erosion decreased by almost a
factor of eight between 1992 and 1993, because of the severe wet weather in the central United
States. The large effect of meteorological variability on the magnitude of fugitive dust places
severe constraints on the magnitude of trends in ambient dust concentrations that can be
discerned. Because of the large secondary component of PM10 in the eastern United States, the
concentration of PM10 reflects the emission of gaseous precursors by widely dispersed sources,
followed by their conversion to parti culate matter. The conversion of gases to secondary
parti culate matter occurs over distances of up to a few thousand kilometers, thereby uncoupling
variability in the emissions of local sources from that of ambient concentrations.
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13.2.6 Patterns and Trends in United States Particulate Matter
Concentrations
PM10 Trends and Concentrations
Annual average PM10 mass concentrations throughout the United States, for different
regions within the United States, and for most subregions or cities have generally decreased
from 1988 to 1994. For the contiguous United States, the PM10 decrease has been greater in the
western United States (approximately 30%) than in the eastern United States (about 15 to 20%).
With few exceptions, the same range of percentage decreases have occurred for most subregions
within the eastern and western United States. Smaller decreases in PM10 concentrations occurred
for a few eastern subregions or cities and larger decreases in PM10 occurred for a few cities in the
west. These decreases in annual average PM10 levels ranged from 25 to 35 //g/m3 for all U.S.
regions and most U.S. cities by 1994.
In general, annual mean PM10 concentrations in urban areas, found in EPA's Air
Information Retrieval System (AIRS, 1995) database, are greater than about 20 |ig/m3. The
highest annual mean concentrations in the eastern United States were found in Atlanta, GA;
Paterson, NJ; Roanoke, VA; Philadelphia, PA; and Atlantic City, NJ. The overall annual mean
concentration from these urban areas was about 34 |ig/m3. The five urban areas in the central
United States with the highest annual mean concentrations were St. Joseph, MO; Steubenville,
OH; Cleveland, OH; Omaha, NE; and Chattanooga, TN. The overall annual mean PM10
concentration for these five cities was 36 |ig/m3. The five areas with the highest annual mean
PM10 concentrations in the western United States were Bakersfield, CA; Visalia, CA; Fresno,
CA; Riverside, CA; and Stockton, CA. The average concentration in these five areas was about
50 |ig/m3. This value is significantly higher than corresponding values in the eastern and central
United States. All averages given above were taken over the five year period from 1990 to
1994. At least one monitoring site was located in each area listed above, most areas had data
from several sites. The sites themselves are located in areas representing a variety of different
activities (e.g., industrial, commercial, agricultural and residential). The lowest annual mean
PM10 concentrations found at sites in populated areas in the United States (Penobscot Co., ME;
Marquette, MI; and Lakeport, CA) averaged about 12 |ig/m3 during the period from 1990 to
1994. Concentrations in all other areas in the United States fell within the limits given above.
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All of the annual means stated above were calculated on the basis of sampling schedules
that varied from every day to every sixth day, depending on the likelihood of exceedances of the
PM10 NAAQS. The range of annual mean values shown above is consistent with the range
found at the central sites used in the Harvard Six-City Study, where measurements were made
every other day. The six cities along with their annual means are: Steubenville, OH (46.5
Mg/m3); Harriman, TN (32.5 //g/m3); St. Louis, MO (31.4 //g/m3); Topeka, KS (26.4 //g/m3);
Watertown, MA (24.2 //g/m3); and Portage, WI (18.2 //g/m3).
The lowest annual mean PM10 concentrations listed in AIRS (1995) were all below
10 |ig/m3. Examples of areas where annual mean concentrations this low were found include:
Campbell Co., WY; Pima Co., AZ; Rosebud Co., MT; and Washington Co., ME. There was
interannual variability in concentrations in these areas which sometimes resulted in annual
averages greater than 10 |ig/m3 during the period from 1990 to 1994. At rural sites in national
parks, wilderness areas, and national monuments, the annual average PM10 concentrations in the
western United States during 1988 to 1991 were in the range of 5 //g/m3 to 10 //g/m3. Higher
PM10 concentrations have been reported at some rural sites in the eastern United States. The
corresponding PM2 5 concentrations in western rural or remote sites were approximately 3 //g/m3
and in eastern rural or remote sites were in the range of 5 //g/m3 to 10 //g/m3'
A few attempts to infer various types of "background" levels of PM2 5 and PM10 have been
made. The background levels most relevant to the present criteria document include a "natural
background" which excludes all anthropogenic sources anywhere in the world, and a
"background" which excludes anthropogenic sources in North America, but not elsewhere.
Annual average natural background levels of PM10 have been estimated to range from 4 to
8 |ig/m3 in the western United States and 5 to 11 |ig/m3 in the eastern United States.
Corresponding PM2 5 levels have been estimated to range from 1 to 4 |ig/m3 in the western
United States and from 2 to 5 |ig/m3 in the eastern United States. Twenty-four hour average
concentrations may be substantially higher than the annual or seasonal average background
concentrations presented in Chapter 6.
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Fine and Coarse Particulate Matter Trends and Patterns
There are a few sites where information on both fine and coarse PM is available over
extended time periods. Most of these data were obtained with dichotomous samplers which
measure PM25 and PM10_25 (i.e., the coarse fraction of PM10). Note that PM25 will contain some
coarse-mode particles as indicated earlier.
Examples were provided in Chapter 6 (Section 10) of PM25 (fine), the coarse fraction of
PM10 (coarse), and PM10 yearly arithmetic means and 90th percentiles and, where daily data were
available, daily or every 6th day values for one year. Sources used are EPA's Aerometric
Information Retrieval System, California Air Resources Board data, the Harvard Six-City data
base, and the Harvard Philadelphia data base.
The Harvard Six-City Study provided data during 1980 to 1986. In the dirtier cities,
Steubenville, St. Louis, and Harrison, there were decreases in all PM indicators, especially in the
earlier years. There was also an apparent decrease in Topeka, one of the cleaner cities. No trend
could be discerned in Watertown or Portage. It was difficult to determine whether there was a
greater trend in fine or coarse particles.
AIRS provided some data on fine and coarse PM from 1989 to 1994. No significant trends
were evident in PM2 5 or PM10_2 5 either in the means or the 90th percentile values. PM10 and
PM10_25 at the dirtier site in New York City appeared to have decreased from 1988 to 1992 but to
have increased between 1992 and 1994. Other data from a number of sites in California from
1989 to 1995 also showed very slight downward trends for both fine and coarse PM. The
California sites, however, showed substantial seasonal variability in both fine and coarse-mode
particle concentrations.
Several data sets from Philadelphia were combined to show TSP trends from 1973 to 1990
and changes in fine and coarse PM from the 1980 period to the 1990 period. TSP came down
rapidly between 1973 and 1981 and leveled off thereafter. Fine particle concentrations were
approximately 30% higher in the 1980-1982 period than in the 1992-1993 period.
The data base of fine and coarse PM allowed an analysis of the fractions of PM10 due to
both fine and coarse PM. The annual ratios of PM2 5 to PM10 were within the range of 0.5 to 0.6
for most eastern U.S. urban stations, but there was considerable spatial and seasonal variability.
In Philadelphia, the fine fraction of PM was fairly stable over the year.
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During the 1993-1994 period, the mean PM2 5/PM10 ratio was 0.71, with a coefficient of
variation (CV) of 18%. In contrast, the fine fraction of PM10 was seasonally quite variable in
California, in general being higher in the winter and lower in the summer. For example, a mean
ratio of 0.50 and CV of 26% was found in Azusa, a mean ratio of 0.44 and CV of 43% in
Bakersfield, and a mean of 0.29 and CV of 34% for El Centre. This illustrates limitations in
trying to infer PM2 5 concentrations from PM10 or TSP measurements unless site-specific ratios
are available. The ratio of PM2 5 to PM10 values may vary substantially from location to location
or from one season to another at the same site.
Day-to-Day Variability ofPM Concentrations
The only data set from which the daily variability in PM2 5 and PM10 concentrations could
be assessed, based on daily measurements, was obtained in Philadelphia, PA from 1992 to 1995.
Average day-to-day concentration differences obtained were 6.8±6.5 //g/m3 for PM25 and
8.6±7.5 //g/m3 for PM10. Maximum day-to-day differences obtained were 54.7 //g/m3 for PM2 5
and 50.4 Mg/m3 for PM10.
13.2.7 Community and Personal Exposure Relationships
As discussed in Chapters 6 and 7, atmospheric behavior differences between fine-mode
and coarse-mode particles lead to important differences in relationships between personal
exposure and ambient concentrations measured at a central fixed-site monitor. Fine particles
tend to have long atmospheric half-lives, can travel long distances, and therefore can result from
distant or widely distributed sources. Evidence from one eastern city, Philadelphia, suggests that
the concentrations of fine particles may be uniform over that urban area. Therefore, a
measurement at one site may give a reasonable estimate of the fine particle concentration across
a city or even wider regional areas, assuming the site is not unduly influenced by a local source
of fine particles. Coarse particles, however, have more localized and variable sources and
because such particles are rapidly removed, their concentration decreases with distance from the
source and the distribution may not be uniform across a city or region. Thus, people in one part
of a city may experience high concentrations of coarse fraction particles on one day while people
in a different part of the city may experience high concentrations on another day, even though
the city-wide average
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concentration may be the same on both days. This unevenness of coarse mode particles across a
city may need to be taken into account when assessing health impacts in community
epidemiological studies.
A further consideration arises with regard to relationships between ambient (outdoor) PM
concentrations and personal or indoor exposures. Because people spend most of their time
indoors, the particle concentrations indoors tend to dominate personal exposures. However,
indoor exposure is due both to particles generated indoors and to ambient particles generated
outdoors but which have infiltrated indoors. Major indoor sources of fine particles are smoking
and cooking. The major indoor sources of coarse particles are indoor activities that resuspend
previously settled PM and that stir up and suspend other materials, including a variety of
biological materials such as mold spores and insect debris. Household cleaning, especially
dusting and vacuuming, can dramatically increase coarse particle concentrations. When doors
and windows are open, both fine-mode and coarse-mode particles will penetrate from outdoors
to indoors. When doors and windows are closed, particle penetration might be expected to be
dependent on size and air exchange rate, but two experimental studies (Thatcher and Layton,
1995; Koutrakis et al., 1993) suggest that particle penetration may be independent of particle
size up to about 10 //m dae. Once indoors, however, particle size becomes important. Coarse-
mode particles are rapidly removed by deposition, whereas accumulation-mode particles have
longer half-lives. The production of indoor-generated particles is controlled by daily indoor
activities. Therefore, the exposure to indoor-generated particles will not be correlated with the
concentration of ambient (outdoor-generated) particles, and time-series epidemiology based on
ambient measurements are unlikely to identify health effects related to indoor-generated
particles.
The various penetration and removal processes can be modeled, and the equilibrium ratio
of the concentration of ambient particles which have penetrated indoors and remained suspended
to the concentration of ambient particles outdoors (called the infiltration ratio) can be calculated
as a function of the air exchange rate, the penetration factor (assumed to be 1.0 for PM <
10 //m), and the removal rates which are a function of particle size. Infiltration ratio
calculations, based on data from the Particle Total Exposure Assessment Methodology Study
(PTEAM), reviewed in Chapter 7, are graphically depicted in Figure 13-2. As is evident, the
infiltration ratio of sulfate, which is almost completely of outdoor origin and
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0.2 0.4 0.6 0.8
1.2 1.4 1.6 1.8
Air Exchange Rate, hr
D SO|, Accumulation mode + PM25 ° PM10 25
Figure 13-2. Ratio of indoor concentration of ambient PM to outdoor concentration
(infiltration ratio) for sulfate (an indicator of accumulation-mode particles),
PM2 5, and the coarse fraction of PM10 (PM10_2 5), as a function of air
exchange rate. Based on data from PTEAM.
expected to be in the fine-mode, is greater than that of PM2 5, which may contain some coarse-
mode material from both indoor and outdoor sources and thus have a larger effective dae than
sulfate. PM2 5 in turn has a greater infiltration ratio than PM10_2 5.
The more uniform distribution of ambient fine-mode particles across a city and the higher
infiltration ratio for fine particles, means that an ambient measure of fine particles at a central
site may provide a useful estimate of the average exposure of people in the community to
ambient fine-mode particles. For example, experimental data on personal exposure to sulfate,
which are predominantly of outdoor origin and in the fine-mode particle size range, show
consistently high correlation of total human exposure to sulfate with outdoor central-site
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measurements of ambient sulfates (0.78 < R2 < 0.92) (Suh et al., 1993). However, because of
the non-uniform regional concentrations and lower infiltration ratios, an ambient measure of
coarse particles at a central site may not provide nearly as good an indication of exposure of
people in the community to ambient coarse particles. Much of the time-series epidemiology
currently available is based on ambient TSP or PM10 measurements, which represent the sum of
fine and coarse (in the case of TSP) or the sum of fine particles and the coarse-mode fraction of
PM10 (in the case of PM10). In Philadelphia, and to a lesser extent some other cities (where PM10
is not dominated by coarse wind-blown dust), it has been shown that TSP and PM10
concentrations correlate better with PM2 5 concentrations than with the coarse fraction of PM10.
It is thus possible that the observed statistical relationships between various ambient particle
indicators and health outcomes are largely due to an underlying relationship between fine-mode
particles and health outcomes. This hypothesis is supported by recent epidemiological analyses
for cities where both PM2 5 and PM10_2 5 data are available (Schwartz et al., 1996a).
13.3 CONSIDERATION OF FACTORS AFFECTING DOSIMETRY
Because the tissue dose of a putative toxic moiety is not always proportional to the ambient
exposure of a compound and because the response is more likely related to the tissue dose,
contemporary health risk assessment emphasizes the need to clearly distinguish between
exposure concentration and internal doses to critical target tissues. The term "exposure-dose-
response" assessment has been recommended as more accurate and comprehensive (Andersen et
al., 1992). Characterization of the exposure-dose-response continuum is advocated as a way to
reduce the uncertainty in extrapolations required from laboratory animal data or from typical
humans to susceptible members of the human population. In the case of PM, such
characterization requires the elucidation and understanding of the mechanistic determinants of
particle deposition and clearance, toxicant-target interactions, and tissue responses.
13.3.1 Factors Determining Deposition and Clearance
Particles are deposited in the respiratory tract by mechanisms of impaction, sedimentation,
interception, diffusion, and electrostatic precipitation. Differences in ventilation rates, in the
upper respiratory tract structure, and in the size and branching pattern of the lower respiratory
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tract between species and among humans of different ages and disease states result in
significantly different patterns of particle deposition due to the effects of these geometric
variations on air flow patterns. The relative contribution of each deposition mechanism to the
fraction of particles deposited varies for each region of the respiratory tract (extrathoracic, ET;
tracheobronchial, TB; and alveolar, A). Air flow in the ET region is characterized by high
velocity and abrupt directional changes, so that the predominant deposition mechanism in this
region is inertial impaction. Although, for ultrafine particles, the dominant mechanism in the ET
region is diffusion. In the A region, diffusional deposition is also important since many smaller
particles penetrate to this region.
Disposition and retention of initially deposited particles depends on clearance and
translocation mechanisms that also vary with each region of the respiratory tract. Sneezing and
nose wiping or blowing and mucociliary transport to the gastrointestinal tract via the pharynx are
important clearance processes for particles deposited in the ET region, whereas coughing,
mucociliary transport, endocytosis by macrophages or epithelial cells and dissolution and
absorption into the blood or lymph are important in the TB region. Smoking reduces the rate of
respiratory tract clearance. Endocytosis by macrophages or epithelial cells and dissolution and
absorption into the blood or lymph are the dominant mechanisms in the alveolar region.
Depending on their solubility, particles deposited in the alveolar region could have long
residence times. The ultimate disposition and retention of a deposited dose is thus dependent on
the initial site of deposition, physicochemical properties of the particles (e.g., solubility), and on
time since deposition.
The influence of different airway geometry on airflow patterns and subsequent deposition
have been documented both empirically and with theoretical modeling. Simulations discussed in
Chapter 10 suggest deposition differences among children and adults, with adolescents (age 14
to 18) predicted to have greater respiratory tract daily mass deposition (//g/d) of submicron
particles than adults. Changes in respiratory tract architecture, especially in the smaller
conducting airways and gas exchange regions, can be critical factors affecting the dosimetry of
inhaled particles. Ambient particles will be deposited in the lung to varying degrees depending
on their aerodynamic and physicochemical properties. Changes in architecture or geometry of
the respiratory tract
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with disease affect airflow and thereby the aerodynamic behavior of inhaled particles. A
mismatch of ventilation and perfusion in lung diseases, such as emphysema, chronic obstructive
pulmonary disease (COPD), and asthma has been noted (Bates et al., 1971; Bates, 1989).
Chronic bronchitis, emphysema, and chronic airways obstruction all fall within the aegis of
COPD, and both it and asthma result in altered airflow. In more severe stages of these diseases,
the healthy portion of the lung receives more of the tidal volume which can result in some
ventilatory units receiving an increased particle burden compared to others. Kim et al. (1988)
demonstrated greater particle deposition, using an aerosol rebreathing test, in COPD patients
versus healthy subjects. The increase in deposition correlated with the degree of airway
obstruction. Anderson et al. (1990) also showed that the deposition of ultrafine particles in
patients with COPD is greater than in healthy subjects. Svartengren et al. (1994) showed
enhanced deposition in asthmatics. Bennett et al. (1996) reported a greater deposition rate
(particles/time) in COPD patients relative to healthy subjects and that these patients under
resting breathing conditions receive an increasing dose of inhaled fine particles with increased
severity of their airways disease. Model simulations discussed in Chapter 10 predict that dose
expressed in terms of numbers of particles per anatomical unit would be increased in individuals
with compromised lungs relative to healthy subjects (Miller et al., 1995).
Not only may patients with preexisting COPD be susceptible because of an enhanced or
altered deposited dose pattern, but their disease may also predispose these patients to altered
responses to the toxic effects of ambient PM (discussed in the next section). To the extent that
cigarette smoke contributes to changes in architecture and response, smokers can also be
considered a potentially susceptible population for the effects of PM.
Physicochemical characteristics of particles (e.g., particle diameter, distribution,
hygroscopicity) interact with the anatomic (e.g., branching pattern) and physiologic (e.g.,
ventilation rate, clearance processes) factors to influence deposition and retention of inhaled
aerosols. For a given aerosol, the two most important parameters which characterize size
distribution, and hence deposition, are the MMAD and the og of the particles. It must be
emphasized that the relative contribution of these anatomic, physiologic, and physicochemical
determinants is a dynamic relationship. Further, the relative contribution of these determinants
is also influenced by exposure conditions such as concentration and duration.
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The influence of the particle size distribution on the fraction of particles deposited in the
respiratory tract is illustrated in Figure 13-3. This figure depicts the predicted deposition
fractions for an adult male, using a general population ventilation activity pattern, in the alveolar
(A), tracheobronchial (TB), and thoracic (A + TB) regions. The difference between total
respiratory tract and total thoracic deposition fractions represents the extrathoracic (ET) or upper
airway deposition fraction. The deposition fraction in the respiratory tract, relative to unit mass
concentration in air, is shown for particles of different MMAD, in the range of 0.1 to 100 //m,
for two different geometric standard deviations (og = 1.8 in the top panel and og = 2.4 in the
bottom panel).
These simulations show that alveolar deposition fraction is fairly uniform for aerosols
between 0.5 and 4.0 //m MMAD. Deposition fraction of particles in the A region increases for
particles less than 0.5 //m because diffusion becomes the dominant mechanism. In the
aerodynamic range of particles (> 1.0 //m MMAD), deposition fraction increases as particle size
increases and sedimentation and impaction become important deposition mechanisms, especially
for the larger particles (> 5 //m MMAD) in the TB region. This pattern is altered slightly for
mouth breathing versus normal breathing, in that mouth breathers have a greater TB deposition
of particles greater than 2.5 //m (i.e., the coarse fraction of PM10) than they would if breathing
PM only via the nose. The pattern is also influenced by the degree of dispersion of the particle
sizes. Polydispersity decreases the deposition fraction of particles in the aerodynamic range as
shown by decrements in the bottom panel for the polydisperse aerosol (og = 2.4) compared to the
more monodisperse aerosol (og = 1.8) in the top panel.
The collection fraction for PM10 and PM2 5 samplers are also depicted in Figure 13-3. As
considered for the basis of the previous PM standard, the PM10 sampler collection curve shows
that this sample accounts well for thoracic (TB + A) deposition but excludes many of the larger
particles which would be deposited in the ET region. Also, the PM2 5 cutpoint does not capture
some larger particles that would be deposited in the TB and A regions, especially in mouth
breathers under the simulated conditions. These simulations corroborate that the 10 //m cut
point is appropriate to separate ambient particles that have the potential to deposit in the lower
respiratory tract versus those in ET regions. However, these results also
13-21
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Total Respiratory
PM2.5 X PM10 Xr-^Jt Tract
0.01
0.1 1 10
MMAD (\m) with ofl= 1.8
100
Total Respiratory
Tract
0.01
0.1 1 10
MMAD (\m) with og= 2.4
• Alveolar (Normal) * TB (Normal) • Total Thoracic (Normal)
0 Alveolar (Mouth) A TB (Mouth) n Total Thoracic (Mouth)
Figure 13-3. Human respiratory tract PM deposition fraction and PM10 or PM2 5
sampler collection versus mass median aerodynamic diameter (MMAD)
with two different geometric standard deviations (a = 1.8 or a = 2.4).
Alveolar, tracheobronchial, or total thoracic deposition fractions predicted
for normal augmenter versus mouth breather adult male using a general
population (ICRP66) minute volume activity pattern and the 1994 ICRP66
model.
13-22
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suggest that an intermediate cut point that is directly comparable to separation of fine- and
coarse-mode particles is not supported on the basis of considering particle deposition alone, due
to the fact that particles in the coarse fraction of PM10 also have some efficiency for deposition
in both the A and TB regions. As discussed previously, construction of the exposure-dose-
response continuum is also dependent on defining a dose metric that is relevant to the
mechanism of action for a compound.
13.3.2 Factors Determining Toxicant-target Interactions and Response
Differences in susceptibility can be due to factors influencing deposited and retained
particle mass or number, toxicant-target interaction, or tissue sensitivity (e.g., conditions causing
altered or enhanced target tissue response). Discussion of various individual risk factors that
might influence tissue response to a delivered dose is provided in Section 13.6. Since the target
tissue has been identified as the lower respiratory tract, however, some generalizations for the
definition of dose can be useful in trying to ascertain if one metric may be more appropriate than
another to describe a given toxicant-target interaction.
The biologically-effective dose resulting from inhalation of particles can be defined as the
time integral of total inhaled particle mass, particle number, or particle surface area per unit of
respiratory tract surface area or per unit mass of the respiratory tract. Choice of the metric to
characterize the biologically-effective dose should be motivated by insight into the mechanisms
of action of the compound (or particles) in question. The biologically-effective dose may be
accurately described by particle mass or number deposition alone if the particles exert their
primary action on the surface contacted (Dahl et al., 1991). For longer-term effects, the
deposited dose may not be a decisive metric, since particles clear at varying rates from the
different respiratory tract regions. When considering the epidemiologic data, dose metrics could
be separated into two major categories, pattern and quantity of acute deposition and the pattern
and quantity of retained dose. The deposited dose may be more important for daily mortality,
hospital admissions, work loss days, etc. On the other hand the retained dose may be more
important for chronic responses such as induction of chronic disease, shortening of life-span
("premature" mortality), or diminished quality of life although repeated acute responses may
also be related to chronic responses.
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To date, most analyses have relied upon the particle mass concentration (//g/m3) breathed
by exposed individuals. If relative risk (RR) estimates were calculated based on various internal
dose metrics (e.g., deposited dose [mass] normalized per unit tracheobronchial or alveolar
surface area or normalized per critical cell type such as the alveolar macrophage), some of these
relationships could change or be modified. Moreover, not only is there a question about how the
doses should be normalized (e.g., by body mass, lung epithelial surface area, etc.), but also as to
whether the PM dose should be expressed as numbers of particles, aggregate particle surface
area, or total particle mass in a given size fraction. The fine fraction contains by far the largest
number of particles, and those particles generally have a larger aggregate surface area than
coarse-mode particles. Such considerations may be important when trying to ascertain the
appropriate dose metric for evaluation of lower respiratory tract health outcomes. For example,
retardation of alveolar macrophage phagocytosis due to particle overload appears to be better
correlated with particle surface area than particle mass (Morrow, 1988; Oberdorster et
al., 1995a,b). Also, ultrafine particles have been shown to be less effectively phagocytosed by
macrophages than larger particles (Oberdorster et al., 1992a,b).
Figure 13-4 presents an example which illustrates the complexities of considering PM
"dose" using different metrics (e.g., such as mass, surface area, and number of particles) that are
typical for a Southern California urban aerosol (Whitby, 1978). For the accumulation mode,
which constitutes about 40% of the total mass in the illustrated sample, the geometric mean for
the volume distribution, DGV, equivalent to the volume median diameter, is 0.31 //m. When the
median diameter is expressed in terms of surface area, or count, the respective median diameters
of the fine mode are 0.19 //m and 0.07 //m. By far the largest number of particles are contained
in the nuclei mode, which is inconsequential in terms of mass. It must be remembered that the
composition of the particles in each mode is different as are their hygroscopicity, solubility,
translocation pathways, and toxicity.
Table 13-2 shows the predicted deposition efficiency in various regions of the respiratory
tract for the aerosol depicted in Figure 13-4, which illustrates different particle diameters and
size distributions that are typical of the nuclei, accumulation, and coarse modes of ambient
particles. These are predicted from simulations as performed in
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15
o
X
E 10
i- O
!l 5
Nn = 7.7x10
DGNn = 0.013
O gn= 1.7
Na = 1.3x10
DGNa = 0.069
Oga=2.03
CO
CO
U
«g 30
0)
E
0*20
O)
10
Vn = 0.33
DGVn = 0.031
i i i I in
'0.001
0.01
Sa = 535
DGSa = 0.19
Sc = 41
DCS c = 3.1
- DCS n = 0.023
« < 200
100
Figure 13-4. Distribution of coarse (c), accumulation (a), and nuclei or ultrafine (n),
mode particles by three characteristics, volume (V), surface area (S), and
number (N). DVG = geometric mean diameter by volume; DGS = geometric
mean diameter by surface area; DGN = geometric mean diameter by
number; Dp = geometric diameter.
Source: Whitby (1978).
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TABLE 13-2. PREDICTED RESPIRATORY TRACT DEPOSITION AS A
PERCENTAGE OF TOTAL INHALED MASS FOR THE THREE PARTICLE
SIZE MODES IN THE AEROSOL DEPICTED IN FIGURE 13-4
MMAD = 0.029 f^m MMAD = 0.27
= 2-03
MMAD = 6.9
o = 2.15
Deposition Site
Extrathoracic Region
Tracheobronchial
Region
Alveolar Region
Exhaled
p = 1.4 g cm
Nuclei Mode
0.05
0.07
0.02
0.02
p = 1.2 g cm
Accumulation Mode
1.8
0.8
2.8
23.8
p = 2.2 g cm*
Coarse Mode
52.5
2.1
3.4
12.4
TJynamic shape factor (used to calculate MMAD from measured MMD) assumed for all three particle size
modes to be 1.5 (ICRP, 1994).
MMD = mass median diameter (equivalent geometric).
MMAD = mass median aerodynamic diameter.
og = geometric standard deviation.
p = particle density.
Chapter 10 for the aerosols of Phoenix, AZ and Philadelphia, PA. The patterns are different for
the different modes.
How could particle size be important in biological activity? The mass of the particle may
be important if the mechanism of action of the particle is related to its persistence. For example,
large acid droplets require a much longer time to undergo neutralization than very small droplets
and therefore would be more likely to reach intrathoracic airways as acid rather than as a
neutralization product. Larger particles will take longer to dissolve or to be degraded
enzymatically. If presentation of active groups to cell surfaces is important in the mechanisms
of action, then the total surface area of the particles may be important. The largest aggregate
surface area is contained in the accumulation mode. The particle mode with the largest surface
area will be able to present the largest number of reactive surface groups to the cell surface.
This feature would presumably be most important for relatively less soluble particles.
Biological effects on epithelial cells or macrophages may depend on
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the number of cell surface receptors that are stimulated or occupied. The number of particles
may be related to their toxic effect. For example, if the number of separate phagocytotic events
determines the capacity of a cell to ingest particles, then number becomes important. Numbers
may also be important with regard to particles interacting with surface receptors of epithelial or
phagocytic cells.
13.3.3 Construction of Exposure-Dose-Response Continuum for PM
It is clear that the characterization of the exposure-dose-response continuum from PM
exposure data to human morbidity/mortality risk is far from complete. As defined by the
National Research Council Board on Environmental Studies and Toxicology, a "biologic
marker" is any cellular or molecular indicator of toxic exposure, of an adverse health effect, or
of susceptibility (National Research Council, 1987). The markers represent signals — generally
biochemical, molecular, genetic, immunologic, symptomatic (e.g., cough), or physiologic — in a
continuum of events between a causal exposure and resultant disease.
The events in the progression from exposure to disease are not necessarily discrete, nor the
only events in the continuum, and represent a conceptual temporal sequence. The paradigm of a
continuum is only meant to illustrate a single pathway among many pathways to a biologic
endpoint from a given exposure. Whether the progression is exactly linear or some other form,
such as a multidimensional network, is debatable (Schulte, 1989). In most exposure-disease
relationships, the linear causal sequence is an implied framework for research purposes.
Appraisal of the validity of the components of the sequence requires that the framework be made
explicit and that the existence of causal relationships be tested. That is, to better model the
situation, one would consider that there may be multiple pathways leading to a given disease
outcome. This is especially true for the etiology of most ambient air pollution-related
biomedical outcomes. The effect of interest is often small in comparison to effects of other
etiologic factors, and exposure itself may be confounded with that to other compounds and by
inadequate characterization of temporal relationships.
Many advances in the understanding and quantification of the mechanistic determinants of
toxicant-target interactions and tissue responses (including species sensitivity) are required
before an overall model of a pathogenesis continuum can be constructed for ambient air PM. As
our understanding is supplemented by identification of intervening relationships and components
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are characterized more precisely or with greater detail, health events are less likely to be viewed
as dichotomous (e.g., death or not; presence or absence of disease) but rather as a series of
changes in a continuum from homeostatic adaptation, through dysfunction, to disease and death.
The critical effect could become that biologic marker deemed most pathognomonic or of
prognostic significance, based on a validated hypothesis of the role of the marker in the
development of disease. As more causal component linkages are identified, it becomes more
possible to elucidate quantitative relationships of the kinetics, natural history, and rates of
transition along the continuum. Multiple markers may be more efficacious than a single marker
for characterizing any given component.
Supplementary independent studies (typically lexicological), required to establish the
validity of postulated intermediate components (markers) between exposure and disease,
relevant to the observed mortality and morbidity in PM epidemiologic investigations, have been
encumbered by methodologic difficulties. For example, differences in dosimetry due to altered
flow patterns caused by geometric variation of the respiratory tract in different species have
important implications for interspecies extrapolation. Toxicological data in laboratory animals
typically can aid the interpretation of human clinical and epidemiological data because they
provide concentration- and duration-response information on a more complex array of effects
and exposures than can be evaluated in humans. However the use of laboratory animal
toxicological data has typically been limited because of difficulties in quantitative extrapolation
to humans. The various species used in inhalation toxicological studies do not receive identical
doses in comparable respiratory tract regions (ET, TB, A) when exposed to the same aerosol
(same composition, mass, concentration, and size characteristics). Such interspecies differences
are important because the adverse toxic effect is likely related more to the quantitative pattern of
deposition within the respiratory tract than to the exposure alone; this pattern determines not
only the initial respiratory tract tissue dose, but also the specific pathways by which the inhaled
particles are cleared and redistributed. Until these differences can be quantified, these
dosimetric interspecies differences will impede characterization of the exposure-dose-response
continuum for PM components and mixtures.
Another difficulty in elucidating the exposure-dose-response continuum using laboratory
animal data is that different endpoints are typically assayed in the laboratory animals and the
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relationship of these endpoints to the human health outcomes of interest have not been
established. For example, the epidemiological studies evaluate endpoints such as illness,
hospital admissions, and emergency room/doctor visits whereas the homologous biochemical or
pathological endpoints in the laboratory animal models are unknown. Although the ultimate
goal, for example, may be to estimate the responses of elderly persons with cardiopulmonary
disease, most laboratory animal studies are normally performed on homogeneous populations of
healthy animals and the majority of human clinical studies are performed on healthy young
subjects or those with only mild disease.
In summary, until the mechanism(s) of action for effects induced by ambient PM or its
important constituents can be characterized, the linkage between exposure and response provided
by dosimetry will remain weak and only qualitative at best. Until dose metrics can be defined
that correlate well with PM mechanism(s) of action, insights from dosimetry will be limited.
Clearly, inhaled dose is important, but the best exposure/dose metric(s) to relate quantitatively to
acute or chronic health outcomes awaits elucidation of pertinent mechanisms. Should the dose
be normalized to regional surface area, for example, or expressed relative to some other critical
mechanistic determinant (e.g., possibly per alveolar macrophage)? Once pertinent mechanism(s)
of action are delineated, different biomedical indices can be used to characterize intermediate
linkages to mortality or morbidity outcomes and to quantify relationships across the exposure-
dose-response continuum. Towards that ultimate objective, both improved epidemiologic
studies, using more refined measures of PM exposure (e.g., for fine versus coarse mode fractions
of PM10, for ultrafme particles, for particle number concentration, or for various classes of
chemical constituents) and more laboratory animal studies evaluating effects of real-world
concentrations of ambient PM mixtures or constituents are needed.
13.4 HEALTH EFFECTS OF PARTICULATE MATTER
This section evaluates available scientific evidence regarding the health and physiologic
effects of exposure to ambient PM. The main objectives of this evaluation are as follows: (1) to
summarize and evaluate the strengths and limitations of available epidemiologic findings; (2) to
assess the biomedical coherence of findings across studied endpoints and
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scientific disciplines; (3) to evaluate the plausibility of available evidence in light of
mechanistic, pathophysiologic, and dosimetric considerations; and (4) to assess the extent to
which observed effects can be attributed to PM and to specific size fractions and chemical
constituents within the PM complex. Epidemiologic findings are emphasized first because they
provide the largest body of evidence directly relating ambient PM concentrations to biomedical
outcomes.
By far the strongest evidence for ambient PM exposure health risks is derived from
epidemiologic studies. Many epidemiologic studies have shown statistically significant
associations of ambient PM levels with a variety of human health endpoints, including mortality,
hospital admissions and emergency room visits, respiratory illness and symptoms measured in
community surveys, and physiologic changes in mechanical pulmonary function. Associations
of both short-term and long-term PM exposure with most of these endpoints have been
consistently observed. The general internal consistency of the epidemiologic data base and
available findings have led to increasing public health concern, due to the severity of several
studied endpoints and the frequent demonstration of associations of health and physiologic
effects with ambient PM levels at or below the current U.S. NAAQS for PM10. The weight of
epidemiologic evidence suggests that ambient PM exposure has affected the public health of
U.S. populations. However, there remains much uncertainty in the published data base
regarding the shapes of PM exposure-response relationships, the magnitudes and variabilities of
risk estimates for PM, the ability to attribute observed health effects to specific PM constituents,
the time intervals over which PM health effects are manifested, the extent to which findings in
one location can be generalized to other locations, and the nature and magnitude of the overall
public health risk imposed by ambient PM exposure.
The etiology of most air pollution-related health outcomes is highly multifactorial, and the
effect of ambient air pollution exposure on these outcomes is often small in comparison to that
of other etiologic factors (e.g., smoking). Also, ambient PM exposure in the U.S. is usually
accompanied by exposure to many other pollutants, and PM itself is composed of numerous
physical and chemical components. Assessment of the health effects attributable to PM and its
constituents within an already-subtle total air pollution effect is difficult even with well-designed
studies. Indeed, statistical partitioning of separate pollutant effects may
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somewhat artificially describe the etiology of effects which actually depend on simultaneous
exposure to multiple air pollutants. Furthermore, identification of anatomic sites at which
particles trigger end-effects and elucidation of biological mechanisms through which these
effects may be expressed are still at an early stage. Thus, it remains difficult to form incisive a
priori hypotheses to guide epidemiologic and experimental research. Lack of clear mechanistic
understanding also increases the difficulty with which available findings can be integrated in
assessing the coherence of PM-related evidence.
In this regard, several viewpoints currently exist on how best to interpret the epidemiology
data: one sees PM exposure indicators as surrogate measures of complex ambient air pollution
mixtures and reported PM-related effects represent those of the overall mixture; another holds
that reported PM-related effects are attributable to PM components (per se) of the air pollution
mixture and reflect independent PM effects; or PM can be viewed both as a surrogate indicator
as well as a specific cause of health effects. In any case, reduction of PM exposure would lead
to reductions in the frequency and severity of the PM-associated health effects.
Several other key questions and problems also must be considered when attempting to
interpret the data reviewed in this document. While the epidemiology data provide strong
support for the associations mentioned above, no credible supporting toxicologic data are yet
available that provide insight into potential mechanisms. There is also a paucity of information
of either a biological or clinical nature that argues for the biologic plausibility of the
epidemiologic results. Nor is there much toxicologic data that elucidates the role of specific PM
constituents in mediating responses of the type demonstrated by the epidemiologic analyses at
low ambient PM concentrations. More specifically, although several hypotheses are discussed
later with regard to possible mechanisms by which ambient PM may exert human health effects,
little non-epidemiologic evidence is presently available to support or refute a causal relationship
(i.e., to construct an exposure-dose-response continuum) between low ambient concentrations of
PM and observed increased mortality or morbidity risks. Thus, specific causal agents cannot
presently be confidently identified among typical ambient PM constituents, nor can mechanisms
be clearly specified by which health effects of ambient PM are exerted.
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Due to these uncertainties much caution is warranted with regard to derivation or extrapolation
of quantitative estimates of increased risks for mortality or morbidity related to low level
ambient PM exposures based on available epidemiology information.
13.4.1 Epidemiologic Evidence for Ambient PM Health Impacts
The health effects of short (24 h) and long-term (annual) PM exposure on mortality,
hospitalization, respiratory symptom/illness, and pulmonary function change are examined
across epidemiological, laboratory animal and controlled human studies. Where the information
is available, the data for these health endpoints are also related to particle size, including PM10,
PM2 5, and PM(10_2 5), as well as to specific chemical constituents such as SOJ or H+.
13.4.1.1 Ambient PM Mortality Effects
Early epidemiology studies of severe air pollution episodes in Europe and the U.S. from
the 1930's to 1950's indicated that exposure to high ambient levels of urban air pollution can
produce serious human health effects. By far, the most clearly defined health effects attributable
to ambient PM exposure are the marked increases in daily deaths that occurred during episodes
of high pollution (e.g., in the Meuse Valley in 1930, in Donora in 1948, and in London in 1952).
During a London episode in the 1950s, for example, more than 4,000 excess deaths during a 4 to
5-day period were attributed to air pollution, with the greatest increase in death seen most clearly
among patients over 45 years with lung and heart disease. The early episode studies
demonstrated, as subsequently confirmed in several re-analyses, that primary and secondary
particulate combustion products and sulfur oxide air pollution at sufficiently high concentrations
(in excess of 500 to 1,000 //g/m3 BS), exert lethal effects even though conclusively substantiated
mechanisms of action underlying the observed episodic mortality have yet to be elucidated.
Recent studies in a variety of locations, summarized in Chapter 12, further implicate air
pollution exposure in mortality at much lower ambient levels, including levels well below the
current 24-h PM10 NAAQS of 150 //g/m3 and annual PM10 NAAQS of 50 //g/m3. More than 20
time-series analyses published in the late 1980s and early 1990s demonstrate significant positive
associations between daily mortality and 24-h concentrations of ambient
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particles indexed by various measures (black smoke, TSP, PM10, PM25, etc.) in numerous U.S.
metropolitan areas and in other countries (e.g., Athens, Sao Paulo, Santiago). These studies
collectively suggest that PM alone or in combination with other commonly occurring air
pollutants (e.g., SO2) is associated with daily mortality, the effect of PM appearing to be most
constituent. In both the historic and recent studies, the association of air pollution exposure with
mortality has been strongest in the elderly and for respiratory and cardiovascular causes of death.
Furthermore, the recent analyses suggest a major role of PM relative to other air pollutants in
terms of increased risk of mortality.
Time-series analyses strongly suggest a positive effect on daily mortality across the entire
range of ambient PM levels. Relative risk (RR) estimates for daily mortality in relation to daily
ambient PM concentration are consistently positive, and statistically significant (at P < 0.05),
across a variety of statistical modeling approaches and methods of adjustment for effects of
relevant covariates such as season, weather, and co-pollutants. Examination of Table 12-4 in
Chapter 12 shows that relative risk estimates (RR) for non-accidental mortality in the total
population associated with a 50 //g/m3 increase in 24-h average PM10 range from 1.015 to 1.085.
Relative risk estimates with PM10 as the only pollutant index in the model range from RR =
1.025 to 1.085, while the PM10 RR with multiple pollutants in the model range from 1.015 to
1.025. Higher relative risks are indicated for the elderly and for those with pre-existing
respiratory conditions.
Mortality effects associated with chronic, long-term exposure to PM air pollution have
been assessed in cross-sectional studies and more recently, in prospective cohort studies. A
number of older cross-sectional studies provided indications of increased mortality associated
with chronic (annual average) exposures to ambient PM (indexed mainly by TSP or sulfate
measurements). However, unresolved questions regarding adequacy of statistical adjustments
for other potentially important covariates (e.g., cigarette smoking, economic status, etc.) across
cities tended to limit the degree of confidence that could be placed on such studies or on
quantitative estimates of PM effects derived from them.
Several more recent studies, in contrast, have used subject-specific information about
relevant covariates (such as cigarette smoking, occupational exposure, etc.), and appear to
provide more reliable findings of long-term PM exposure effects. In particular, three new
prospective cohort studies of mortality associated with chronic PM exposures were evaluated in
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Chapter 12 as yielding especially useful information. The studies of California nonsmokers by
Abbey et al. (1991) and Abbey (1994) found no significant mortality effects of previous TSP
exposure in a small, young cohort. On the other hand, the larger and more extensive Harvard
Six Cities (Dockery et al., 1993) and American Cancer Society (ACS) (Pope et al., 1995) studies
agree in their findings of statistically significant positive associations between fine particles and
excess mortality, although the ACS did not evaluate the contribution of other air pollutants. The
RR estimates for total mortality in the Six-Cities study (with their 95 percent confidence
intervals) per increments in PM indicator levels are as follows: the RR for 50 //g/m3 PM15 is
1.42(1.16, 2.01), the RR for 25//g/m3PM2 5 is 1.31 (1.11, 1.68), and the RRfor 15//g/m3 SO4
is 1.46 (1.16, 2.16). The estimates for total mortality derived from the ACS study are 1.17
(1.09, 1.26) for 25 //g/m3 PM25, and 1.10 (1.06, 1.16) for 15 //g/m3 SO=4. In some cases, the
life-long cumulative exposure of the study cohorts included distinctly higher past PM exposures,
especially in the cities with historically higher PM concentrations; but more current PM
measurements were used to estimate the chronic PM exposures. Thus, caution must be exercised
regarding the use of the reported quantitative risk estimates, since somewhat lower risk estimates
than the published ones are apt to apply. However, the chronic exposure studies, taken together,
suggest that there may be increases in mortality in disease categories that are consistent with
long-term exposure to airborne particles and that at least some fraction of these deaths reflect
cumulative PM impacts above and beyond those exerted by acute exposure events.
The weight of epidemiologic evidence suggests that short-term ambient PM exposure
likely contributes to increased daily mortality, and it also suggests that long-term PM exposure
reduces survival time. It is extremely unlikely that study designs not yet employed, covariates
not yet identified, or statistical techniques not yet developed could wholly negate the large and
consistent body of epidemiologic evidence relating short-term PM exposure to daily mortality in
U.S. urban areas. Similarly, although relatively few cohort studies of long-term PM exposure
and mortality are available, they are consistent in direction and magnitude of excess risk with a
larger body of cross-sectional annual mortality studies, and most show positive associations of
PM exposure with mortality. In view of the consistency with which they are observed, it is
unlikely that these associations could result entirely from important confounding factors as yet
unidentified.
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Variation in relative risks exists among the estimates for PM-related daily mortality. These
estimates would be expected to vary if PM exposure truly affects daily mortality for the
following reasons: (1) the toxicity of PM likely depends on its size distribution and chemical
composition, and these characteristics differ among geographic areas; (2) local populations differ
in demographic and socioeconomic characteristics; (3) the distribution of diseases differs among
geographic locations; and (4) ambient PM means and ranges differ among geographic areas.
Somewhat different RR estimates are therefore derived across varying PM ranges in different
studies, even when they have been standardized to the same PM increment. This results in
different site-specific RR estimates, as would be expected unless PM-mortality relationships are
truly linear throughout the entire PM range and represent a general non-specific (i.e., chemical
composition-independent) PM effect. On balance, the observed variations in RR estimates are
not inconsistent with a real effect of PM exposure on daily mortality.
In many studies, daily mortality has been most strongly associated with PM levels
occurring shortly (0 to 5 days) before death. These short intervals have been invoked as
evidence that PM-induced mortality occurs primarily in persons who would have died soon,
even without PM exposure. However, there is no pathophysiologic reason why the exposure-to-
death interval need be related to the time by which the death itself is hastened. The existence of
short exposure-to-mortality intervals neither requires nor excludes the possibility that at least a
portion of PM-associated deaths are advanced by long time intervals. At the same time,
available evidence does not allow confident quantitative inference as to PM-associated
shortening of life.
Comparison of Size-Specific and Chemical-Specific Particle Effects on Mortality
An important objective of this chapter is to evaluate different exposure metrics based on
size-specific and chemical-specific information. However, only a limited number of studies
have included direct measurements of indicators of fine particle mass (i.e., PM25, PM2A).
Additional indirect support for fine particle effects is derived from studies that used BS, COH,
KM, or sulfate measurements, which are primarily associated with components of fine particles.
Information on chemical-specific PM constituents is limited to a few studies that included
measures of particle strong acidity and/or sulfates; but the results of such analyses may best be
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interpreted in terms of the exposure metrics being reflective of fine particle effects in general,
rather than of acids or sulfates in particular.
Early indications that fine particles are likely important contributors to observed PM-
mortality and morbidity effects came from evaluation of past serious air pollution episodes in
Britain and the United States. The most severe episodes, as discussed in the 1982 Criteria
Document (U.S. Environmental Protection Agency, 1982), were characterized by several
consecutive days of very low wind speed conditions, during which large coarse mode particles
rapidly settle out of the atmosphere and concentrations of fine mode particles dramatically
increase. Even during non-episode conditions, mortality associations with BS or COH readings
in Britain or the U.S. during the 1950s to 1970s most likely reflected contributions of fine mode
particles. This is based on the low D50 cutpoints («4.5 //m) for the BS and COH methods
described in Chapter 4, although some contribution of small inhalable particles (up to ~ 10 //m)
cannot be entirely ruled out.
Table 13-3 summarizes effect estimates (relative risk information) derived from more
recent epidemiology studies demonstrating health effects (mortality, morbidity) associations
with ambient 24-h PM10 concentrations in U.S. and Canadian cities. The evidence summarized
in Table 13-3 leaves little doubt that short-term PM10 concentrations typical of contemporary
U.S. urban air sheds are correlated with detectable increases in risk of human mortality and
morbidity. Less extensive evidence summarized in Table 13-4 also suggests that fine particles
may be important contributors to the observed PM-health effects associations given the increased
risks (of mortality, hospitalization, respiratory symptoms, etc.) associated with several different
fine particle indicators (e.g., PM2 5, SOJ, IT").
Because of the potential impact of particle size on their observations, some investigators
have attempted to determine what size and/or chemical form of particles had the strongest
association with health effects. For example, in initial of data from St. Louis and eastern
Tennessee (part of the Six-Cities Study), the strongest associations of daily mortality rates were
seen with PM10 while progressively weaker associations were seen with PM2 5, sulfate, and
aerosol acidity (Dockery et al., 1992). However, because of the limited statistical power of the
latter study and the lesser quantity of aerosol acidity data (only one year versus seven years for
the other PM measures), the observation of weaker association of aerosol acidity with mortality
is inconclusive.
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TABLE 13-3. EFFECT ESTIMATES PER 50 ^g/m3 INCREASE
IN 24-h PM,n CONCENTRATIONS FROM U.S. AND CANADIAN STUDIES
Study Location
RR (± CI) RR (± CI)
Only PM Other Pollutants
in Model in Model
Reported
PM10 Levels
Mean (Min/Max)T
Increased Total Acute Mortality
Six Cities3
Portage, WI
Boston, MA
Topeka, KS
St. Louis, MO
Kingston/Knoxville, TN
Steubenville, OH
St. Louis, MOC
Kingston, TN*
Chicago, ILh
Chicago, ILg
Utah Valley, UTb
Birmingham, ALd
Los Angeles, CAf
—
1.04(0.98,1.09) —
1.06(1.04,1.09) —
0.98(0.90,1.05) —
1.03(1.00,1.05) —
1.05(1.00,1.09) —
1.05(1.00,1.08) —
1.08(1.01,1.12) 1.06(0.98,1.15)
1.09(0.94,1.25) 1.09(0.94,1.26
1.04(1.00,1.08) —
1.03(1.02,1.04) 1.02(1.01,1.04)
1.08(1.05,1.11) 1.19(0.96,1.47)
1.05(1.01,1.10) —
1.03 (1.00, 1.055) 1.02 (0.99, 1.036)
18 (±11. 7)
24 (±12.8)
27 (±16.1)
31 (±16.2)
32 (±14.5)
46 (±32.3)
28 (1/97)
30 (4/67)
37 (4/365)
38(NR/128)
47(11/297)
48 (21, 80)
58( 15/177)
Increased Hospital Admissions (for Elderly > 65 yrs.)
Respiratory Disease
Toronto, CAN1
Tacoma, WAJ
New Haven, CTJ
Cleveland, OHK
Spokane, WAL
COPD
Minneapolis, MNN
Birmingham, ALM
Spokane, WAL
Detroit, MI°
1.23 (1.02, 1.43)* 1.12 (0.88, 1.36)*
1.10(1.03,1.17) 1.11(1.02,1.20)
1.06(1.00,1.13) 1.07(1.01,1.14)
1.06(1.00,1.11) —
1.08(1.04,1.14) —
1.25(1.10,1.44) —
1.13(1.04,1.22) —
1.17(1.08,1.27) —
1.10(1.02,1.17) —
30-39*
37 (14, 67)
41 (19, 67)
43 (19, 72)
46(16,83)
36(18,58)
45 (19, 77)
46(16, 83)
48 (22, 82)
13-37
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TABLE 13-3 (cont'd). EFFECT ESTIMATES PER 50 ^g/m3 INCREASE
IN 24-h PM,n CONCENTRATIONS FROM U.S. AND CANADIAN STUDIES
Study Location
Pneumonia
Minneapolis, MNN
Birmingham, ALM
Spokane, WAL
Detroit, MI°
Ischemic HP
Detroit, MIP
Increased Respiratory
Lower Respiratory
Six Cities'2
Utah Valley, UTR
Utah Valley, UTS
Cough
Denver, COX
Six Cities'2
Utah Valley, UTS
RR (± CI)
Only PM
in Model
1.08(1.01, 1.15)
1.09(1.03, 1.15)
1.06(0.98, 1.13)
—
1.02(1.01, 1.03)
Symptoms
2.03(1.36,3.04)
1.28(1.06, 1.56)T
1.01 (0.81, 1.27)71
1.27(1.08, 1.49)
1.09(0.57,2.10)
1.51 (1.12,2.05)
1.29(1.12, 1.48)
RR (± CI) Reported
Other Pollutants PM10 Levels
in Model Mean (Min/Max)T
— 36(18,58)
— 45 (19, 77)
— 46(16,83)
1.06(1.02,1.10) 48(22,82)
1.02(1.00,1.03) 48(22,82)
Similar RR 30(13,53)
— 46(11/195)
— 76(7/251)
— 22 (0.5/73)
Similar RR 30(13,53)
— 76(7/251)
Decrease in Lung Function
Utah Valley, UTR
Utah Valley, UTS
Utah Valley, UTW
55 (24, 86)"
30 (10, 50)"
29(7,51)*"
— 46(11/195)
— 76(7/251)
— 55(1,181)
References:
"Schwartz et al. (1996a).
"Pope et al. (1992, 1994)/O3.
'Dockery et al. (1992)/O3.
"Schwartz (1993).
slto and Thurston (1996)/O3.
'Kinney et al. (1995)/O3, CO.
"Styer et al. (1995).
'Thurston et al. (1994)/O3.
'Schwartz (1995)/SO2.
KSchwartz et al. (1996b).
LSchwartz (1996).
"Schwartz (1994e).
"Schwartz (1994f).
"Schwartz (1994d).
QSchwartz et al. (1994).
'Schwartz and Morris (1995)/O3, CO, SO2.
RPope et al. (1991).
sPope and Dockery (1992).
TSchwartz (1994g)
"Pope and Kanner (1993).
xOstroetal. (1991)
TMin/Max 24-h PM10 in parentheses unless noted
otherwise as standard deviation (± S.D), 10 and
90 percentile (10, 90). NR = not reported.
Children.
"Asthmatic children and adults.
'Means of several cities.
"PEFR decrease in ml/sec.
'"FEVj decrease.
*RR refers to total population, not just>65 years.
13-38
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TABLE 13-4. EFFECT ESTIMATES PER VARIABLE INCREMENTS IN 24-h
CONCENTRATIONS OF FINE PARTICLE INDICATORS (PM2 5, SO^ H+)
FROM U.S. AND CANADIAN STUDIES
Acute Mortality
Six CityA
Portage, WI
Topeka, KS
Boston, MA
St. Louis, MO
Kingston/Knoxville, TN
Steubenville, OH
Indicator
PM2.5
PM2.5
PM2.5
PM2.5
PM2.5
PM,,
RR (± CI) per 25 ,ug/m3
PM Increase
1.030(0.993, 1.071)
1.020(0.951, 1.092)
1.056(1.038, 1.0711)
1.028(1.010,1.043)
1.035(1.005, 1.066)
1.025(0.998, 1.053)
Reported PM
Levels Mean
(Min/Max)1
11. 2 (±7.8)
12.2 (±7.4)
15.7 (±9.2)
18.7 (±10.5)
20.8 (±9.6)
29.6 (±21. 9)
Increased Hospitalization
Ontario, CANB
Ontario, CAN0
NYC/Buffalo, NYD
Toronto13
so;
so;
03
so;
H+ (Nmol/m3)
so;
PM,,
1.03(1.02, 1.04)
1.03(1.02, 1.04)
1.03(1.02, 1.05)
1.05(1.01, 1.10)
1.16(1.03, 1.30)*
1.12(1.00, 1.24)
1.15(1.02,1.78)
R = 3.1-8.2
R = 2.0-7.7
NR
28.8 (NR/391)
7.6 (NR, 48.7)
18.6(NR, 66.0)
Increased Respiratory Symptoms
Southern CaliforniaF
Six Cities0
(Cough)
Six Cities0
(LowerResp. Symp.)
so;
PM25
PM2 5 Sulfur
H+
PM25
PM25 Sulfur
H+
1.48(1.14, 1.91)
1.19(1.01, 1.42)**
1.23(0.95, 1.59)**
1.06(0.87, 1.29)**
1.44(1.15-1.82)**
1.82(1.28-2.59)**
1.05(0.25-1.30)**
R = 2-37
18.0(7.2,37)***
2.5(3.1,61)***
18.1 (0.8,5.9)***
18.0 (7.2, 37)***
2.5 (0.8, 5.9)***
18.1 (3.1,61)***
Decreased Lung Function
Uniontown, PAE
PM25
PEFR 23. 1 (-0.3, 36.9) (per 25 //g/m3)
25/88 (NR/88)
References:
ASchwartz et al. (1996a)
BBurnett et al. (1994)
cBurnett et al. (1995) O3
DThurston et al. (1992, 1994)
ENeasetal. (1995)
FOstroetal. (1993)
°Schwartz et al. (1994)
24-h PM indicator level shown in parentheses unless
otherwise noted as (± S.D.), 10 and 90 percentile (10,90)
or R = range of values from min-max, no mean value reported.
'Change per 100 nmoles/m3
"Change per 20 ,ug/m3 for PM25; per 5 //g/m3 for
PM2 5 sulfur; per 25 nmoles/m3 for H+.
***50th percentile value (10,90 percentile)
13-39
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More recent reanalyses of the Harvard Six-City Study by Schwartz et al. (1996a)
examined the effects on daily mortality of 24-h concentrations of fine particles (PM25), inhalable
particles (PM15/10), or coarse fraction particles (PM15/10 minus PM2 5) as exposure indices. Note
that inhalable particles are denoted here by PM15/10 to reflect the change from the use of PM15 cut
point dichotomous samplers to PM10 cut point samplers for later years of the study. The results
were transformed to standard increments of 25 |ig/m3 PM25, 50 |ig/m3 PM15/10, and 25 |ig/m3 for
the coarse fraction (PM15/10_25) and are graphically depicted in Chapter 12, Figure 12-33. Of the
three PM indices, PM2 5 had the highest RR for daily mortality across the six cities. The only
exception was for Steubenville, where a statistically significant coarse particle effect was found
(although the fine particle effect size was as large as in most other cities and the fine and coarse
particle concentrations were highly correlated in Steubenville). The acid aerosol relationships
were weaker than were fine particle relationships, possibly because the acid aerosol time series
were much shorter than the PM time series, as noted above.
In spite of differences in climate and demographics, the results showed that there were
similar increases in daily mortality associated with fine particles in all six cities, with RR
ranging from 1.020 to 1.056 per 25 //g/m3 PM25. The results were statistically highly significant
in Harriman-Kingston, St. Louis, Watertown, nearly so in Portage and Steubenville, but less so
in Topeka where the fine particle concentrations were low. The excess risk of death by ischemic
heart disease associated with PM2 5 was about 40% higher than for all-cause nonexternal
mortality. For death due to pneumonia or due to COPD the excess risk was more than twice as
high as for other causes. Only Steubenville, which had an RR = 1.061 per 25 //g/m3 coarse-mode
particles, showed results suggestive of possible excess risk from coarse particles. Overall, these
analyses suggest that, in general, the association between excess mortality and thoracic particles
appears to be stronger for the fine than the coarse fraction.
When data for all six cities were combined, the estimate of the effects of PM15/10 and PM2 5
were even more significant, with PM2 5 having a higher associated risk than PM15/10. The
combined estimate for coarse mode particles (PM15/10-PM2 5), on the other hand, was only
marginally significant. The combined effects estimates derived for the sulfate component was a
statistically significant predictor of excess mortality (although less so than either PM15/10 or
PM25), but H+ was not statistically significant, even with 1,183 days of data in four cities. These
results do not necessarily implicate sulfates as the key fine particle component associated with
13-40
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mortality effects; rather, sulfates may represent a surrogate index for fine particles in general.
Other studies in areas with low sulfate levels suggest that increased risk is also associated with
non-sulfate fine particle components.
Relationships between chronic (annual average) PM exposures (Dockery et al., 1993)
indexed by different particle size indicators (PM15, PM2 5, PM15 to PM2 5) and mortality effects as
observed in the Harvard Six City Study were depicted graphically in Figure 12-8 of Chapter 12,
emphasizing that there tends to be an increasing correlation of long-term mortality with PM
indicators as they become more reflective of fine particle levels. These results are summarized
in Table 13-5, along with findings from other key studies of U.S. and Canadian cities
demonstrating associations between increased risk of mortality/morbidity and chronic (annual
average) exposures to PM10 or fine particle indicators in contemporary North American urban air
sheds.
The effect estimate results for the studies in Table 13-3 are characterized in terms of
relative risks (RR) corresponding to a specific PM increment (50 //g/m3 PM10) that generally
encompass the range of the data within each study. As seen in Table 13-3, the mean 24-h PM10
concentrations that were present during the studies generally ranged from 18 to 76 //g/m3, with
many of the highest daily values exceeding 100 //g/m3. An indication of the potential for the
occurrences of changes/increases of 24-h PM10 levels of the magnitude of 50 //g/m3 can be
drawn from a data set of three years of daily levels of PM10 in Philadelphia. During this study,
the mean day-to-day differences seen in PM10 concentration was 8.6 //g/m3 with a maximum
day-to-day variation of 50.4 //g/m3. Maximum daily values by season were: summer - 82
//g/m3; winter - 77.5 //g/m3; spring - 54.7 //g/m3; and fall - 54.4 //g/m3. The difference between
the median and maximum value for summer was 54.4 //g/m3 and for winter, 58.3 //g/m3.
Acid Aerosol Mortality Effects
Several epidemiologic studies have measured the mass of acidic aerosols or sulfates. This
acid aerosol mass would primarily be found in the fine PM fraction, that is in ambient fractions
< PM2 5. Studies of past episodes suggest that there can be both acute and chronic
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TABLE 13-5. EFFECT ESTIMATES PER INCREMENTS3 IN
ANNUAL MEAN LEVELS OF FINE PARTICLE INDICATORS FROM
U.S. AND CANADIAN STUDIES
Type of Health
Effect & Location
Increased total chronic
Six Cityb
ACS Study0
(151 U.S. SMSA)
Increased bronchitis in
Six Cityd
Six City6
24 Cityf
24 Cityf
24 Cityf
24 Cityf
Southern California8
Indicator
mortality in adults
PM15/10
PM25
so;
PM2.5
so;
children
PM15/10
TSP
H+
so;
PM21
PM10
so;
Change in Health Indicator per
Increment in PMa
Relative Risk (95% CI)
1.42(1.16-2.01)
1.31 (1.11-1.68)
1.46(1.16-2.16)
1.17(1.09-1.26)
1.10(1.06-1.16)
Odds Ratio (95% CI)
3.26(1.13, 10.28)
2.80(1.17,7.03)
2.65(1.22,5.74)
3.02(1.28,7.03)
1.97(0.85,4.51)
3.29(0.81, 13.62)
1.39(0.99, 1.92)
Range of City
PM Levels
Means (,ug/m3)
18-47
11-30
5-13
9-34
4-24
20-59
39-114
6.2-41.0
18.1-67.3
9.1-17.3
22.0-28.6
—
Decreased lung function in children
Six Cityd'h
Six City6
24 Citylj
24 City1
24 City1
24 City1
PM15/10
TSP
H+ (52 nmoles/m3)
PM21(15//g/m3)
SO; (7 Mg/m3)
PM,n(17Mg/m3)
NS Changes
NS Changes
-3.45% (-4.87, -2.01) FVC
-3.21% (-4.98, -1.41) FVC
-3.06% (-4.50, -1.60) FVC
-2.42% (-4.30, -.0.51) FVC
20-59
39-114
—
—
—
—
Estimates calculated annual-average PM increments assume: a 100 ,wg/m3 increase for TSP; a 50 ,ug/m3
increase for PM10 and PM15; a 25 //g/m3 increase for PM25; and a 15 //g/m3 increase for SO;, except where
noted otherwise; a 100 nmole/m3 increase for H+.
bDockeryetal. (1993)
Topeetal. (1995)
dDockeryetal. (1989)
6Wareetal. (1986)
TJockery et al. (1996)
8Abbeyetal. (1995a,b,c)
hNS Changes = No significant changes.
'Raizenne et al. (1996)
jPollutant data same as for Dockery et al. (1996)
13-42
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health effects of strongly acidic PM. Studies of historical pollution episodes, notably the
London Fog episodes of the 1950's and early 1960's, indicate that acute exposures to extreme
elevations of 24-h acid aerosol concentrations may be associated with excess daily human
mortality when present at times of elevated concentrations of BS and SO2. In addition,
significant associations were found between acid aerosols (< 30 //g/m3 as H2SO4, 24-h or
<~600 nmoles/m3 FT, 24-h) and mortality in London during non-episode pollution periods of the
1960s and 1970s, though these associations could not be separated from those for BS or SO2.
Studies evaluating present-day U.S. levels of acidic aerosols have not found associations
between acid aerosols and acute and chronic mortality, but the series of H+ data used may not
have been long enough to detect H+ associations.
Based on laboratory animal toxicology studies, it is known that sulfuric acid aerosols exert
their action throughout the respiratory tract, with the site of deposition dependent upon particle
size and the response dependent on mass and number concentration at specific deposition sites.
At very high concentrations that are not environmentally realistic, mortality can occur in
toxicological studies following acute exposure, due primarily to laryngospasm or
bronchoconstriction; larger acidic particles may be somewhat more potent in this regard than
smaller ones. As seen in these studies, extensive pulmonary damage, including edema,
hemorrhage, epithelial desquamation, and atelectasis can also cause death, but even in the most
sensitive animal species, lethal concentrations are at least a thousand-fold greater than current
ambient levels.
The available laboratory animal findings regarding acid aerosols provide no evidence that
ambient acidic PM components contribute to mortality and essentially no quantitative guidance
as to the ambient PM levels at which mortality would be expected to occur in either healthy or
diseased humans. The laboratory animal effects were observed at acid levels that exceed worst-
case ambient concentrations by more than ten-fold. Also, since the inhalable particle size range
for common laboratory animals is generally < 2 to 4 //m, only comparisons between inhalable
and ultrafine particles were possible. There were no obvious differences between responses of
laboratory animals exposed to ultrafine acid aerosol as compared to larger inhalable acidic
aerosols (see Section 13.6.7).
13-43
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Shortening of Life Associated with Ambient PM Exposure
The public health burden of ambient PM-mediated mortality depends on both the number
of deaths and the shortening of life that PM exposure causes or promotes. Knowledge of the
true excess mortality and prematurity of death attributable to PM would be valuable to
environmental risk managers and scientists in predicting and monitoring the public health benefit
of reducing ambient PM exposure.
Epidemiologic findings suggest that short-term ambient PM exposure can trigger terminal
events. Also, long-term PM exposure could conceivably promote life-shortening chronic illness.
The relative risk ratios derived from long-term U.S. cohort studies of PM exposure and mortality
are considerably larger than those from daily mortality studies. This suggests that a portion of
deaths associated with long-term PM exposure may be independent of the daily deaths associated
with short-term exposure and/or that some factor not accounted for may be contributing to these
effects. In both long-term and short-term studies, the PM associations with mortality are
strongest in the elderly for respiratory and cardiovascular causes of death.
Available experimental evidence provides only minimal biological understanding of PM's
true role in influencing mortality. At the same time, several general pathways by which long-
term and short-term PM exposure might plausibly increase mortality have been postulated. For
example, long-term PM exposure might promote life-shortening chronic respiratory illness, the
terminal event of which could be infection or other insult unrelated to recent PM exposure.
Conversely, episodic short-term PM exposure might trigger death in highly susceptible persons
with preexisting severe illnesses unrelated to long-term PM exposure. Or, in some individuals,
ambient PM exposures might both promote chronic illness and trigger death. Emerging
experimental evidence indicates that all of these should be considered as possibilities.
Confident quantitative determination of years of life lost to ambient PM exposure is not yet
possible; life shortening may range from days to years. Two recent epidemiologic analyses
(Spix et al., 1993; Cifuentes and Lave, 1996) suggest that some portion of PM-induced daily
mortality occurs in people who are already so ill that they would soon die even without PM
exposure. In addition to non-episodic increase in PM-related mortality, Cifuentes and Lave
estimate that 37 to 87% of the adult deaths occurring during identifiable short-term PM episodes
may be premature by only a few days. The public health implications of this estimate are not yet
clear because the proportion of all PM-associated daily deaths occurring during episodes, and the
13-44
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strengths of PM-daily mortality relationships during episodes relative to other periods, have not
been determined.
The upper limit of PM-associated life shortening is not known and will also be difficult to
determine. Available evidence regarding the effect of smoking on mortality may be of some
contextual use in estimating this limit. Davis and Novotny (1989) investigated smoking-
attributable mortality and years of life lost to smoking in chronic obstructive pulmonary disease
(COPD). They reported that, in 1984, 51,013 (79.4%) of a calculated total of 64,211 COPD
deaths in the U.S. were attributable to smoking and that 82% of these deaths occurred in persons
aged at least 65 years. These smoking-attributable deaths represented a total of 501,290 years of
life lost in relation to average life expectancy. These figures yield an average of 9.8 years of life
lost per smoking-attributable COPD death. It is highly unlikely that PM-attributable life
shortening would approach or exceed this average at current ambient U.S. PM levels.
Nevertheless, life shortening could conceivably be on the order of years, especially if smoking
and PM exposure exert synergistic long-term effects in COPD.
In summary, most available epidemiologic evidence suggests that increased mortality
results from both short-term and long-term ambient PM exposure. Limitations of available
evidence prevent quantification of years of life lost to such mortality in the population. Life
shortening, lag time, and latent period of PM-mediated mortality are almost certainly distributed
over long time periods, although these temporal distributions have not been characterized.
Increased biological understanding of PM's role in relevant mechanisms is essential to guide
further epidemiologic study of these complex issues.
13.4.1.2 Ambient PM Morbidity Effects
Consistent with the above-noted observations of PM-induced mortality effects, numerous
epidemiologic studies in the U.S. and elsewhere have demonstrated significant associations
between ambient PM exposures indexed by a variety of indicators (BS, TSP, PM10, PM2 5,
sulfates, etc.) and various acute and chronic morbidity outcomes. Such outcomes include, for
example, hospital admissions, increased respiratory symptoms, and decreased lung function.
Tables 13-3 to 13-5 provide effect estimates for various PM indicators drawn from recent U.S.
and Canadian studies thought to provide reasonably credible quantitative estimates that are likely
13-45
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representative of the range of increased mortality and morbidity risks associated with ambient
exposures to PM in contemporary U.S. urban air sheds.
Hospitalization and Outpatient Visits
Potentially, the most severe morbidity measure evaluated with regard to PM exposure is
hospitalization with a cardiopulmonary diagnosis. This outcome is relevant to the PM-mortality
relationships discussed above. Some morbidity outcomes require hospitalization immediately,
while others may require several days of progression to end in an admission. Exposure-response
lag periods are not yet well examined for hospital admissions related to PM exposures.
Both COPD and pneumonia hospitalization studies show moderate but statistically
significant relative risks in the range of 1.06 to 1.25 resulting from an increase of 50 |ig/m3 in
PM10 or its equivalent. There is a suggestion of a relationship between ambient PM10 and heart
disease admissions, but the estimated effects are smaller than those for other endpoints (see
Figure 12-1 in Chapter 12). While a substantial number of hospitalizations for respiratory
illnesses occur in those >65 years of age, there are also numerous hospitalizations for those
under 65 years of age. Several of the hospitalization studies restricted their analysis by age of
the individuals, but did not explicitly examine younger age groups. One exception was Pope
(1991) who reported an increase in hospitalization for Utah Valley children (aged 0 to 5) for
monthly numbers of admissions in relation to PM10 monthly averages, as opposed to daily
admissions in relation to daily PM levels used in other studies.
Studies examining associations between other indicators of fine particles, e.g., British
smoke (BS), or indicators of total particle concentrations (TSP) and hospital admissions also
report finding significant relationships. One study in Spain, for example, found a statistically
significant association between changes in hospital admissions and BS during the winter season.
Also, in Finland, TSP was found to be significantly correlated with hospitalization admissions
for asthma. For those age 65 or older in Philadelphia, hospitalization for pneumonia showed a
RR at about 1.22 (1.10 to 1.36) corresponding to an increase of 100 //g/m3 of TSP.
Increased hospital admissions for respiratory causes documented during the 1952 London
Fog episode suggested an association with sulfuric acid aerosols as well as with BS and SO2
measurements. More recent studies have shown a consistent relationship between summertime
levels of both sulfates and O3 with hospital admissions. Two Canadian studies estimated a 3 to
13-46
-------
4% increase in annual respiratory hospital admissions for about a 13 to 14 //g/m3 increase in
concentration of the sulfate fraction. A corresponding 2 to 3% increase in cardiac admissions
was reported in one of these studies. While sulfates have been predictive of health effects in
some studies, it is not clear whether the sulfate-related effects can be attributed to their acidity or
other characteristics, or if they are more broadly related to fine particles in general. Another
study found associations between ambient acidic aerosols and summertime respiratory hospital
admissions both in New York State and Toronto, Canada, even after controlling for potentially
confounding temperature effects. In the Toronto analysis, the increase in respiratory hospital
admissions associated with IT was roughly six times that for non-acidic PM10 (per unit mass).
In these analyses IT" effects were estimated to be the largest during acid aerosol episodes (days)
(H+ > 10 //g/m3 as H2SO4, or -200 nmoles/m3 If), which occur roughly 2 to 3 times per year in
eastern North America. Sulfate concentrations that were previously found to be correlated with
respiratory admissions are associated with acidic aerosols in Eastern North America. In these
recent analyses, the IT" associations with respiratory hospital admissions were found to be
stronger than for sulfates or any other PM component monitored. This Toronto study showed no
associations for PM10-PM2 5 or for TSP-PM10 measures of coarse particles. Other studies,
which did not directly measure coarse particles, have evaluated situations where PM was
dominated by coarse particles. Gordian et al. (1996) reported increased outpatient visits for
asthma and upper respiratory illness, but not for bronchitis in Anchorage, Alaska where PM10
contains primarily coarse particle-crustal material and volcanic ash. Hefflin et al. (1994)
determined that the maximum observed/expected ratio was 1.2 for respiratory disorders resulting
from dust storms on October 16 and 21, 1991 which produced the highest PM10 levels of 1991
(i.e., 1,689 and 1,035 //g/m3, respectively) in southeast Washington state. PM10 was considered
to be mostly from natural sources as compared to industry or combustion sources. In both of
these studies, numerous marked exceedances of the PM10 standards occurred.
Community-Based Respiratory Illness and Pulmonary Function Studies
Acute respiratory illness studies may include several different endpoints, but typically
present results for: (1) upper respiratory illness, (2) lower respiratory illness, or (3) cough (as
summarized earlier in Chapter 12, Figure 12-5). The studies of upper respiratory illness do not
show a consistent relationship with PM, although some of this inconsistency could be explained
13-47
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by the differences in populations studied. The studies of lower respiratory disease, however,
yielded odds ratios (OR) which ranged from 1.10 to 1.28, and studies of cough gave odds ratios
ranging from 0.98 to 1.29 (note that the odds ratios were estimated for a 50 //g/m3 increase in
PM10 or its equivalent). An exception in each of the latter two categories was the Six City study
which produced ORs of 2.0 and 1.51 for lower respiratory disease and cough, respectively.
These three respiratory illness endpoints had similar general patterns of results. The odds ratios
were generally positive, the 95% confidence intervals for about half of the studies were
statistically significant (i.e., the lower bound exceeded 1.0) and, for each endpoint, one study
had a high odds ratio. Limited data were available relating PM exposure to asthma or
respiratory symptoms in adults.
As part of the Six Cities studies, three analyses done for different time periods suggest a
chronic effect of PM exposure on respiratory disease. Chronic cough, chest illness, and
bronchitis showed positive associations with PM for the earlier surveys. A recent study is
strongly suggestive of an effect on bronchitis from acidic particles or from other PM.
Pulmonary function studies (summarized in Chapter 12, Figure 12-6) are suggestive of
short term effects resulting from particulate exposure. Peak expiratory flow rates show
decreases in the range of 2 to 5 1/min resulting from an increase of 50 |ig/m3 in PM10 or its
equivalent, with somewhat larger effects in symptomatic groups such as asthmatics. Studies
using FEVj or FVC as endpoints show less consistent effects. For comparison, a passive
smoking study of over 16,000 children found that maternal smoking decreased a child's FEVj by
10 to 30 ml. An estimate of the effect of PM on pulmonary function in adults found a 29 (±10)
ml decrease in FEVj per 50 //g/m3 increase in PM10, which is similar in magnitude to the
changes found in children, although a smaller percent change.
The chronic pulmonary function studies are less numerous than the acute studies and the
results are inconclusive. The Six-City studies, which had good monitoring data, showed no
associations of chronic pulmonary function effects with long-term particulate pollution
measurements. Other studies found small, but statistically significant, decreases in FVC in
healthy non-smokers or other pulmonary function effects that may be attributed to either acidic
particles or PM in general. The absence of a strong association between chronic pulmonary
function changes and PM calls into question the viability of one of the hypothetical mechanisms
13-48
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for chronic PM-mortality relationships, namely the acceleration of the age-related decline in
pulmonary function.
In addition to respiratory symptoms, bronchitis prevalence rates reported in the Six-City
study were found to be more closely associated with annual average IT" concentrations than with
PM in general. As mentioned earlier, in a study of children in 24 U.S. and Canadian
communities, bronchitis symptoms were shown to be significantly associated with strongly
acidic PM. Thus, chronic exposures to strongly acidic PM may have effects on measures of
respiratory health in children. The acid levels were highly correlated to other fine particle
indicators such as PM21, as noted previously.
Overall, the morbidity studies qualitatively indicate that acute PM exposures are associated
with hospital admission for respiratory disease, increased occurrence of respiratory disease
symptoms, and pulmonary function decrements. As stated above, hospitalization studies and
acute pulmonary function changes suggest quantitative relationships. Also, some limited
evidence exists for association of ambient acidic aerosol exposures with increased acute or
chronic respiratory symptoms.
Comparison ofPM10 Versus PM25 Exposure Effects on Morbidity
Dosimetry models predict that total deposition of fine mode particles in the alveolar region
of the lower respiratory tract (alveoli, terminal bronchioles) is somewhat greater than in the
tracheobronchial region. It is therefore important to consider whether exposure indices for the
fine fraction (e.g., PM2 5) show larger and more significant effects than indices that also include
coarse particles (e.g., PM10), which may have a greater deposition efficiency in the larger and
more proximal airways. Mechanistic effects caused by PM in these different lower respiratory
tract regions may be different, potentially leading to different health outcomes. While numerous
studies of PM related respiratory morbidity have been conducted using PM10 as an indicator,
only limited numbers of studies have examined the effects of fine particle indicators such as
PM2 5. Obviously, the only meaningful direct comparison of the effect of PM10 to PM2 5 is
provided when a study includes both exposure measures and evaluates effects in relation to the
coarse fraction PM(10_2 5) as well. PM10 was a better predictor of respiratory disease in the Six-
City study, whereas PM25 was a better predictor of pulmonary function effects in Tucson, where
coarse particles likely represent a larger fraction of PM10 than in eastern U.S. cities. Other
13-49
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studies using PM2 5 to evaluate acute morbidity have not provided information that permits
assessment of these two exposure indices with regard to health outcomes.
Two more recent chronic exposure studies permit comparison of results for PM10, PM213
and particulate acidity. Children living in communities with the highest levels of particle strong
acidity were more likely (OR = 1.66, 95% CI = 1.11, 2.48) to report at least one episode of
bronchitis in the past year compared to children living in communities with the lowest levels of
acidity. The odds ratios for bronchitis were similar at 1.50 (increment of 15 //g/m3; 95% CI =
0.91, 2.47) for PM2, and 1.50 (increment of 17 //g/m3; 95% CI = 0.93 to 2.43) for PM10,
respectively. No other respiratory symptoms, including asthma symptoms, were significantly
associated with any of the pollutants. The strong correlations between several of the pollutants
in this study, especially particle strong acidity with sulfate (r = 0.90) and PM2 x (r = 0.82), make
it difficult to distinguish the agent of most interest.
In children, a 52 nmole/m3 difference in annual mean particle strong acidity was associated
with a 3.5% deficit in FVC (adjusted) and a 3.1% deficit in FEVj (adjusted) with a slightly
larger deficit in lifelong residents of their communities. Slightly smaller deficits were seen using
total sulfate, PM2 j, and PM10 as pollutant exposure measures, and these deficits were also
statistically significant.
These few studies on PM2 5 show morbidity effects that are difficult to separate both from
PM10 measures and acid aerosol measures discussed above. The PM2 5 studies do show effects
related to exposure to the fine fraction. However, high correlations among PM2 5, PM10, and acid
aerosols make it very difficult to distinguish among these exposure indicators.
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Other information suggests that coarse PM effects may warrant continued attention. There are
epidemiological findings of physician visits for asthma associated with coarse crustal PM (e.g.,
Gordian et al., 1996). Also, therapeutic aerosols used in the treatment of asthma are generally in
a size range from 2.5 to 5 //m, although greatest penetration into the lung is with the particles at
the lower end of this range (i.e., 2.5 to 3.0 //m) (Kim et al., 1985). Thus, particles in the coarse
fraction of PM10 appear to be associated with the exacerbation of asthma via ambient exposure,
and analogous sized aerosols are used in the treatment of asthma via metered-dose inhalers.
13.4.2 Assessment of Validity and Coherence of Epidemiologic Findings
13.4.2.1 Human Exposure Assessment: Uncertainties and Implications
To varying extents, all available epidemiologic studies are subject to uncertainty in
assessment of individual subjects' exposures to ambient PM and other air pollutants. Studies of
PM are especially prone to such uncertainty because PM is physically and chemically far more
complex than any other NAAQS pollutant. Such uncertainty tends to be greatest in
hospitalization and mortality studies, because measurements from limited numbers of ambient
monitoring stations have generally been applied to large populations in broad geographic areas,
without adjustment for factors affecting individuals' indoor and personal exposures. Individual
exposure estimates have seldom been made in available epidemiologic studies, and remain
subject to much uncertainty even when available.
Even at fixed outdoor stations, accurate, thorough measurement of ambient PM size
distributions and chemical constituents is technologically challenging and expensive. Ambient
measurements are not yet available in sufficient accuracy or detail to enable thorough
comparison of the potencies of specific constituents of the PM complex. For example, few
direct measurements of PM25 and inhalable coarse fraction PM, and no size-specific
measurements of PM < 1.0 //m, are yet available for epidemiologic assessment. Similarly,
beyond sulfates, nitrates, and to some extent H+ and organic compounds, specific chemical
components of PM have yet to be extensively epidemiologically assessed. Thus, for example,
very little biomedical information has yet been analyzed against levels of the non-sulfate fraction
of PM2 5. Despite these limitations, several salient points appear to be emerging from assessment
of currently available information.
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For example, although generally useful for qualitative epidemiologic demonstration of PM
effects, TSP measurements can include large coarse-mode particles that exceed the inhalable
range. Thus, TSP can reasonably be expected to provide "noisy" estimates of exposure-effect
relationships if such relationships are due to inhalable particle fractions of the measured TSP
mass. PM10 is a better index of the inhalable particles than is TSP, and PM10 may be a better
index of ambient fine particle exposure than TSP because the smaller particulate fraction
contained in PM10 is more uniformly distributed in an urban area or region than are larger coarse
particles also indexed by TSP.
As discussed in Section 13.2.6, PM25 particles are generally likely to be more uniformly
distributed than coarse particles within an urban airshed. For example, while PM10 levels vary
from site to site, PM2 5 levels have been shown to be particularly well correlated across at least
one eastern metropolitan region, i.e., Philadelphia (Burton et al., 1996; Wilson and Suh, 1996).
Also as noted earlier, fine particles are at least as likely to infiltrate indoors as are coarse
particles, but the fine particles are removed less rapidly from indoor air than coarse particles.
Thus, outdoor ambient fine particle concentrations may be better predictors of total human
exposure to ambient fine particles than ambient coarse particle concentrations are of total
exposure to ambient coarse particles.
Overall, then, it appears that size-specific fixed-station ambient PM measurements
generally approximate total ambient fine PM exposure more closely than coarse PM exposure.
Within the fine fraction, fixed-station measurements of ambient SO=4 likely approximate total
exposure to sulfates better than similar measurements of FT would index total H+ exposure,
because a higher proportion of SO=4 persists indoors (FT is neutralized by indoor ammonia).
Furthermore, because misclassification of exposure tends to bias toward the null hypothesis, the
larger error in ambient coarse PM and FT estimates could produce more underestimation of
effects of coarse than of fine PM, and of H+ than of SO4. On balance, available health effects
estimates, whatever their magnitude and direction, are more subject to uncertainty for coarse
than for fine PM, and for FT than SOJ.
Difficulties in distinguishing between possible differences in health effects from particles
of various sizes and chemistries that fluctuate together also represent a limitation in interpreting
existing long-term PM exposure studies. Cross-sectional and prospective cohort studies have
reported significant mortality associations for fine particles, indexed by PM25 (Dockery et al.,
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1993) or sulfates (Ozkaynak and Thurston, 1987). However, significant PM/mortality
associations have also been reported in areas where summertime sulfates are not the major
component of PM (e.g., winter analysis of Santa Clara, CA; Los Angeles, CA).
13.4.2.2 Model Selection/Specification Issues
Model selection/specification issues assume many forms, including distributional
assumptions, assumptions about temporal structure or correlation, assumptions about random
and systematic components of variability, assumptions about the shape of the relationship
between response and covariate, and assumptions about additivity and interactions of covariates.
Most studies evaluate some of these model specification issues, but rarely provide enough
information for the reader to independently assess the conclusions. Some of the model
specification issues have been shown to have the potential for substantially modifying the
conclusions reached by the analyses. The most sensitive model specification issues appear to be:
adjustments for seasonality and for long-term time trends; adjustments for co-pollutants; and
adjustments for weather variables. An in depth discussion of model specification for acute
mortality studies, is presented in Section 12.6.2, where PM10 studies of mortality are reviewed
and analyzed (Pope et al., 1992; Ostro et al., 1996; Dockery et al., 1992; Thurston and Kinney,
1995; Kinney et al., 1995; Ito et al., 1995; Styer et al., 1995). Also, importantly, alternative TSP
mortality analyses for the same city, Philadelphia (Moolgavkar et al., 1995, Li and Roth, 1995;
Wyzga and Lipfert, 1995; Cifuentes and Lave, 1996; Samet et al., 1995; Schwartz and Dockery,
1992b) are reviewed and analyzed.
Differences in model specification may produce important differences in estimates of PM
effects. The general concordance of PM effects estimates, particularly in the analyses of short-
term mortality studies, is a consequence of certain appropriate choices in modelling strategy that
most investigators have adopted using several different types of standardized models (GLM,
LOESS, etc.) and a variety of specific specifications. For example, in short-term studies of
mortality or hospital admissions, it is important that large differences occurring over time be
extracted before assessing short-term changes in health effects attributable to concurrent short-
term changes in air pollution. However, several methods appear to be adequate for carrying out
such adjustments, including nonparametric detrending, use of indicator variables for season and
year, and (in older studies) filtering. The largely consistent specific results, indicative of
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significant positive associations of ambient PM exposures and human mortality/morbidity
effects, are not model-specific, nor are they artifactually derived due to misspecification of any
specific model. The robustness of the results of different modelling strategies and approaches
increases confidence in their validity.
13.4.2.3 Evaluation of Potential Influences Due to Weather
A variety of methods also appear to be capable of adequately adjusting time series data for
the effects of weather. Most PM epidemiology studies use temperature and dewpoint as
covariates, with several parametric models (possibly differing by season) and nonparametric
smoothing models appearing to be adequate. Other weather variables, such as changes in
barometric pressure, may also be predictive. Models that used synoptic weather categories as
indicator variables, in which the categories were defined independently of information about the
health effect, provide a plausible a priori basis for weather covariate adjustments as Pope and
Kalkstein (1996) have shown for the Utah Valley study. At this time, relatively few studies have
examined possible statistical interactions between weather and air pollution (Lipfert and Wyzga,
1995). While the role of weather-related variables is clearly important, this issue appears to
have been adequately addressed in most of the recent studies reviewed in Chapter 12, and the
relative insensitivity of PM coefficients to different methods of weather adjustment has been
demonstrated in these studies including recently reported reanalyses of several data sets by HEI.
While weather clearly affects human health, there does not seem to be much basis for believing
that weather can explain a substantially greater part of the health effects attributed to PM than
has already been accounted for by the empirical models used in the health studies assessed in
Chapter 12.
13.4.2 A Evaluation of Potential Influences of Co-pollutants
Other pollutants such as SO2, O3, and CO play a role in modifying the relationship between
PM and mortality. When they are incorporated into models examining these relationships, the
RR is usually smaller. Multi-pollutant models can cause differences in interpretation for a
single-pollutant model such as when the correlation between PM and the other pollutants is
sufficiently high that attributed health outcomes are shared among the pollutants. The most
13-54
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poorly measured pollutant is usually the one that is driven toward no statistically significant
estimate of effect.
Some of the studies cited in Chapter 12 include substantial assessments of the effect of
potential confounding from co-pollutants. It was possible to carry out a statistical adjustment for
co-pollutants in some studies, with the PM effect size estimated with and without the potential
confounder in the model. The PM effect size estimates and their statistical uncertainty in many
studies showed little sensitivity to the adjustment for co-pollutants. However, in some other
analyses where was substantial confounding with co-pollutants such as SO2 or O3, estimates of
RR for PM without inclusion of the confounders in the statistical concentration-effect model
used in these studies were quantitatively similar to RR estimates from other studies where
confounding was either avoided or was shown statistically to have little effect. This includes
cases where PM effects were demonstrated in cities with very low levels of other major
copollutants present, as well as in cities with moderate to high levels of one or another
copollutant.
Some investigators have noted that similarity of PM regression coefficients in single and
multi-pollutant models is sufficient to show that PM is not confounded by the other pollutants.
When the RR estimates for PM are relatively unchanged and there is little increase in the width
of the confidence interval, then one can say there is little evidence of confounding. For
example, in the Utah Valley mortality study (Pope et al., 1992), the RR estimates for the summer
season and the width of the confidence intervals for PM10 were similar whether the model did
not include ozone, included daily average ozone, or used maximum daily 1-h ozone as the co-
pollutant measure. The summer PM coefficient, with or without ozone, is similar to the winter
value, when ozone levels were so low as to have little probable effect on mortality, which
illustrates both covariate adjustment and confounder avoidance strategies in the same study.
The model for the Los Angeles mortality studies (Kinney et al., 1995) evaluated the results
of including co-pollutants, O3 and CO. Including O3 in the model along with PM10, did not
change the RR for PM, but increased its uncertainty slightly so that the RR for PM was now only
marginally significant. Including CO in the model reduced the RR for PM which was also less
significant. Thus, the PM-mortality association was not completely separable from other
copollutants. A sensitivity analysis by Schwartz and Dockery (1992b) for mortality in
Steubenville indicates that including SO2 reduced the TSP effect. However, the decrease was
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small with RR for TSP only decreasing from 1.04 without including SO2 to 1.03 per 100 //g/m3
when SO2 was included.
Most studies have provided very little empirical basis for the reader to assess the adequacy
of the fitted model, especially for analyses involving copollutants. The HEI report (Samet et al.,
1995) presents three-dimensional surfaces showing the smoothed or fitted mortality response
versus TSP and SO2 for the 1973 to 1980 Philadelphia data set. These analyses indicate that
both TSP and SO2 were associated with significant increases in mortality but there were
important differences in effect depending on season and on the range of TSP or SO2 values.
There was a relationship between SO2 and excess mortality at TSP concentrations below 75
Mg/m3, but the relationship was not evident at above 50 ppb SO2 or above 75 to 100 //g/m3 TSP
concentration. Thus, it is clearly not correct to conclude from the additive linear model results
that one pollutant is always (or never) a better predictor of excess mortality in Philadelphia than
is the other pollutant. The Samet et al. (1995) analyses suggest that concluding from an additive
linear model that inclusion of copollutants generally lowers the effect attributable to PM may not
always apply to a more accurate nonparametric model.
Recent reanalyses of the Philadelphia mortality-TSP data (Moolgavkar et al., 1995b;
Wyzga and Lipfert, 1995; Samet et al., 1995, 1996a; Cifuentes and Lave, 1996) have elucidated
some of the complex issues relating to analyses of urban air pollution mixtures. The first point
is that the relationship between mortality and different air polluitants may be different from
season to season. This may be due, in part, to substantial seasonal differences in the correlation
structure among the multiple pollutants in the urban airshed (Samet et al., 1996a, discussed in
Section 12.6). Furthermore, there may be additional interactions within each season involving
TSP and temperature (Wyzga and Lipfert, 1995), although a study of TSP and synoptic weather
categories in Utah Valley found little evidence for interaction of PM10 and weather (Pope and
Kalkstein, 1996).
Secondly, while some studies find that including O3 in a model with TSP can modify the
estimated TSP seasonal effect (Moolgavkar et al., 1995b), other studies find that O3 has a
significant additive effect on mortality that is largely unconfounded with the TSP or SO2 effects
(Cifuentes and Lave, 1996; Samet et al., 1996a). CO has little effect on mortality, as does NO2
by itself, but including NO2 in a model with either TSP and SO2 tends to increase the effects of
both (Samet et al., 1996a). While TSP, SO^ NOj, CO, and O3 are modestly correlated in the
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Philadelphia studies, these correlations are not so high as to preclude the possibility of
identifying separate air pollutant effects in different seasons (discussed in Section 12.6).
Thirdly, the relationship between TSP, SO2, and mortality may be intrinsically nonlinear
(Samet et al., 1995; Cifuentes and Lave, 1996). The additive linear models used in most studies
to assess effects of copollutants may therefore not be adequate to characterize the more complex
nonlinear interrelationships among them.
Finally, the estimated TSP effects for Philadelphia are quantitatively similar to those in
other studies. Estimates of effects using PM indicators in communities where SO2
concentrations are low, such as Utah Valley, are also similar. While there is difficulty in
separating TSP and SO2 effects in Philadelphia, the results are not anomalous compared to those
in other cities.
Confounding by co-pollutants sometimes cannot be avoided. In studies where sensitivity
analyses demonstrate that including other pollutants in the model cause little change in either the
RR estimate for PM or the width of the confidence interval for the PM effect, one may conclude
that the model is not seriously confounded by co-pollutants. Some studies of PM-related
mortality or morbidity have shown the specific relative risk estimates for PM only in the
respective models to be little changed by inclusion of other co-pollutants in the model,
suggesting little confounding in those cases. On the other hand, in those analyses where the RR
estimate for PM was notably diminished by inclusion of other co-pollutants in the model
(indicative of some confounding), the PM effect typically still remains statistically significant,
although reduced. Since a number of mortality and morbidity studies have shown that the PM
effect on health is not sensitive to other pollutants, we may conclude that findings regarding the
PM effects are valid.
13.4.2.5 Coherence of Epidemiologic Findings
Factors involved in evaluating both the data and the associations between exposure
variables and outcome variables derived from epidemiological studies, include the strength of
the association; the consistency of the association, as evidenced by its repeated observation by
different investigators, in different places, circumstances and time; and the consistency of the
association with other known facts (Bates, 1992). To provide a more comprehensive synthesis
of available information, coherence or the logical or systematic interrelationships between
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different health indices, should be evaluated. Making the case for causality in regard to
observed epidemiologic associations would be further strengthened by biological plausibility,
consistency or replication of findings, and coherence. The difficulty with discussing any index
of internal coherence is that it requires a series of judgments on the reliability of the individual
findings and observations. Thus the outcome of a coherence discussion is qualitative not
quantitative. Bates (1992) also noted that the strength of the association of different health
indices with exposure are important, as are difficulties in assessing exposure, and suggests three
areas to look for coherence: (1) within epidemiological data, (2) between epidemiological and
animal toxicological data, and (3) among epidemiological, controlled human and animal data.
Coherence considers the logical and systematic relationships among various health
outcomes that may be related to exposure. For example, the biologic mechanism underlying a
reversible acute pulmonary function test reduction in children is most likely not part of the acute
basis for a change in the mortality rate in adults. In assessing coherence, one should compare
outcomes that look at similar time frames—daily hospitalizations compared to daily mortality
rather than monthly hospitalizations.
There are now available a large number of community epidemiologic studies that
specifically assess health effects of ambient exposure to at least one of the following four PM
indicators: (1) thoracic PM (PM10 or PM15); (2) fine PM; (3) coarse PM; (4) sulfate and acid
PM. Most of this body of indicator-specific evidence has appeared since the previous PM
AQCD and promulgation of the U.S. EPA air quality standards for PM10. To assist in the
assessment of overall coherence across the relevant available epidemiologic database, it is
helpful to summarize this evidence qualitatively.
Tables 13-6 and 13-7 present qualitative summaries of findings from community
epidemiologic studies that specifically assess health effects of ambient exposure to one or more
of the above four PM indicators. Table 13-6 summarizes findings on short-term exposure and
table 13-7 summarizes findings on long-term PM exposure. For each PM indicator, the tables
summarize findings for the health measures in the indicated population groups. The first step in
preparing these tables was to develop separate layouts of cells for findings on short-term and
long-term ambient PM exposures. The next step was to identify citations in the reference list of
Chapter 12 that pertained to each individual cell. Community epidemiologic studies were
included regardless of location and magnitude of ambient air pollution exposures. Review
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articles, abstracts, and occupational studies were not included. For each table, all references
used to derive the rating for each cell are presented in Appendix 13 A.
Studies in which the analyzed PM exposure variable was TSP, BS, COH or some other PM
surrogate were not included, unless gravimetric PM measurements had also been made in the
study location which could serve as a basis for quantitative conversion to, or confident
qualitative inference as to, levels of one or more of the PM indicators considered in these tables
as per footnotes for each table.
Within each cell, the identified citations were qualitatively evaluated as a whole. In this
evaluation, first consideration was given to the consistency of findings pertinent to a given cell.
The following additional factors were also considered in this evaluation: (1) magnitude and
statistical significance of observed effects estimates; (2) statistical power of study designs
(dependent mainly on clarity of exposure-based comparisons, numbers of subjects, and durations
of studies); and (3) pertinent information allowing reasonably confident relating of reported
health effects to one or another of the specified PM indicators versus other pollutant measures.
Finally, each cell received a qualitative summary rating within the following 6-category
scale: +++; ++; +; +/-; ID; and 0. This scale does not include a rating of "negative" because
uniformly negative results were not observed in any cell for which pertinent studies were
identified. The rating categories are described below.
Rating Description
+++ Many studies identified and findings highly consistent across most or all
studies, or fewer studies identified but findings highly reproducible and
observed effects relatively large and statistically significant at p < 0.05.
++ Findings generally consistent across two or more studies and observed effects
generally statistically significant, or relatively few studies identified and
observed effects highly reproducible and statistically significant.
+ Findings somewhat mixed but generally consistent and at least some
observed effects statistically significant, or few studies identified but incisive
tests of effect were possible and results were generally statistically significant
atp < 0.05.
+/- Few pertinent studies identified, weight of evidence somewhat positive but
uncertain. Usually at least one or more marginally significant (p < 0.10)
PM-related effects reported.
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ID Insufficient data: at least 1 pertinent study identified but inference as to
weight of evidence not warranted.
0 No pertinent studies identified.
Tables 13-6 and 13-7 may be useful in providing the reader with an overview of the more
specifically-targeted available epidemiologic studies, in assessing the relative health effects of
specific components of the thoracic PM complex, in assessing the relative sensitivity of different
subpopulations to ambient PM exposure, and in identifying needs for future epidemiologic
research.
It is emphasized that Tables 13-6 and 13-7 are intended to assist in the overall evaluation
of available epidemiologic evidence, not to substitute for it. The reader is strongly cautioned not
to interpret these tables beyond their appropriate limits of inference. For example, these tables
are silent with respect to many other epidemiologic studies of clear, continuing relevance in the
PM risk assessment and risk management process, including important recent studies for which
the sole PM exposure index was TSP and most other studies in which indices for ambient PM
mass were not gravimetric measurements. These studies should be considered, together with the
studies identified in tables 13-6 and 13-7, in assessing both the overall coherence of
epidemiologic evidence and the potential public health consequences of ambient PM exposure.
Furthermore, the cell rating criteria did not include consistency of epidemiologic findings
across different PM indices or other air pollutants, health indices, or population groups, or
biological coherence of epidemiologic findings with experimental findings. Thus, these tables,
alone, are not intended to yield conclusions bearing on important broader issues
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TABLE 13-6. QUALITATIVE SUMMARY OF COMMUNITY EPIDEMIOLOGIC FINDINGS ON
SHORT-TERM EXPOSURE TO AMBIENT THORACIC PARTICLES AND SELECTED CONSTITUENTS
Oi
Health Measure and Pollutant
Population
Group
Adults
Children
Asthmatics
Subgroup
General Population
Elderly
Respiratory3
Cardiovascular
General Population
Pre-existing
Respiratory Conditions
Regardless of Age
Mortality
ThP FP1 CP2
+++ ++ +/-*
+ + 0
++ + 0
+ + 0
ID 0 0
000
000
SO4=
Acid
+
0
0
0
0
0
0
Hospitalization and
Outpatient Visits
SO4=
ThP FP1 CP2 Acid
+ 0 ID 0
++00 0
++ +/- ID ++
+ 00 +
+ 0 ID +/-
0000
++ +/- +/-•' +
Community -Based
Morbidity /Symptoms
SO4=
ThP FP1 CP1 Acid
+/- 0 0 +/-
0000
+ / +/ Q +/
0000
+ + 0 +/-
+ +/- 0 +/-
+ +/- ID +/-
Changes in
Lung Function
S04=
ThP FP1 CP2 Acid
+ 000
0000
0000
0000
++ + 0 +
+ ID 0 +/-
+ +/- ID +/-
'FP = Indicator of fine-mode particles, usually PM2 5, and ThP = Indicator of thoracic particles, typically PM10.
2CP = Indicator of inhalable fraction of coarse-mode particles, usually (PM10-PM25) or (PM15-PM25).
3Respiratory causes of death.
4Cardiovascular causes of death.
ID = insufficient data, inference not warranted.
+/- = Few studies available, weight of evidence uncertain, but somewhat positive.
+ to +++ = Increasingly stronger, more consistent positive evidence for PM effects.
0 = No pertinent studies identified.
'Based on signficant positive association for Steubenville with CP found by Schwartz et al. (1966); but CP highly correlated with FP.
**CP not measured directly in Gordian et al. (1996) and/or Hefflin et al. (1994), but PM measured in CP-dominated polluted air.
/ThP designation based on London BS having D50cut point = 4.5 that includes some ThP particles, but probably more closely indexed FP along with acid actually
measured as H2SO4 in Lawther et al. (1970) study.
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TABLE 13-7. QUALITATIVE SUMMARY OF COMMUNITY EPIDEMIOLOGIC FINDINGS ON
LONG-TERM EXPOSURE TO AMBIENT THORACIC PARTICLES AND SELECTED CONSTITUENTS
OJ
I
to
Health Measure and PM Indicator
Population
Group
Adults
Children
Asthmatic-
Atopic
Subgroup
General population
Elderly
Cardiopulmonary3
General population
Regardless of Age
Community -Based
Mortality Morbidity/Symptoms
Acid- Acid-
ThP FP1 CP2 SO4= ThP FP1 CP2 SO4=
++ ++ +/-* ++ +/- +/- 0 +
00 00 0000
++ +++ 0 ++ 0000
+/- 0 00 + + 0 ++
00 00 + +/- 0 +/-
Changes in
Lung Function
ThP FP1 CP2
+/- 0 0
000
000
+/- ID 0
000
Acid-
scv
ID
0
0
+
0
'FP = Indicator of fine-mode particles, usually PM25.
2CP = Indicator of inhalable fraction of coarse-mode particles, usually (PM10-PM25) or (PM15-PM25).
3Combined cardiovascular and non-malignant respiratory causes of death.
0 = No pertinent studies identified.
ID = Insufficient data, inference not warranted.
5+/- = Few studies available, weight of evidence somewhat positive.
+ to +++ = Increasingly stronger, more consistent positive evidence for PM effect.
'Based on supplemental reanalysis by U.S. EPA of results from Dockery et al. (1993); see Figure 12-8 in Chapter 12.
-------
such as biological plausibility of the epidemiologic findings or possible underlying mechanisms
of action (which are discussed elsewhere in this chapter).
Within these important limitations, the tables suggest the following:
Short-term exposure to ambient thoracic PM is consistently associated with adverse
health effects ranging from mortality to changes in lung function. Long-term thoracic
PM exposure is also strongly associated with increased mortality;
• Available evidence, though limited, suggests stronger associations of ambient fine PM
exposure than coarse PM exposure with adverse health effects;
• The association of ambient PM exposure with total mortality is due primarily to its
association with mortality due to respiratory and cardiovascular causes;
• There is reasonable consistency between findings on sulfate-acid exposure with
findings on fine PM exposure. Because sulfates and airborne acid occur primarily in
the fine PM fraction, this consistency reinforces observed associations of fine PM
exposure with adverse health effects;
• Most available evidence regarding PM effects in adults comes from studies of
mortality, hospitalization, and outpatient visits. Most evidence for children comes
from community-based studies of morbidity, symptoms, and lung function. This
impedes systematic assessment of the relative sensitivity of children and adults to
ambient PM exposure;
• Very little is known about effects of long-term ambient PM exposure on chronic
respiratory disease and stable lung function decrements in adults. Enhanced
understanding in these areas will be especially important in assessing the biological
coherence and credibility of observed associations of ambient PM exposure with
increased mortality.
Table 3-8 provides further information indicative of quantitative coherence across several
health endpoints, as observed in various PM epidemiology studies. The entries in the upper half
of the table are for the whole population, including all age groups (designated as ALL). Overall,
the data indicate that PM does have a relationship with a continuum of several health outcomes.
Elevated mortality is the endpoint most clearly demonstrated to be affected in numerous studies,
and represents the key endpoint for which coherence is sought in relation to other endpoints.
The mortality studies suggest that mortality attributed to specific causes (respiratory,
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cardiovascular) show stronger relationships (i.e., larger RR estimates) to PM measures than total
mortality.
The health outcome potentially most related to cardiorespiratory mortality is hospital
admissions for respiratory or cardiovascular causes in older age groups (i.e., > 65 years). In a
qualitative sense, the increased mortality associated with ambient PM found in that age group
should also be paralleled by increased hospital admissions within a similar time frame.
Unfortunately, this issue has not been addressed specifically in relation to PM10 exposures by
those studies yielding the above results for the population as a whole. Information from other
studies directly evaluating increased mortality and morbidity risk among the elderly in related to
PM10 measures is presented in the bottom half of Table 13-8.
A general way to assess quantitative coherence is to compare reported acute mortality and
acute hospitalization risk estimates. One would expect that hospitalization would occur
substantially more frequently than mortality, even though many deaths attributed to air pollution
probably do not occur in hospital. Table 13-8 shows that this is indeed the case, using RR
estimates developed in Chapter 12. For all age groups, expected respiratory mortality
attributable to a 50 //g/m3 increment in PM10 is about 0.3 deaths per day per million people
(based on analyses without copollutants at sites with 3 to 5 d averaging times), whereas 2.0 daily
hospital admissions per million people for respiratory conditions attributable to PM10 would be
expected in the whole population. Similarly, 0.9 cardiovascular deaths per million per day can
be projected to be associated with a 50 //g/m3 increase in PM10, compared to 2.3 hospital
admissions for cardiovascular causes attributed to a comparable PM10 increase. For age 65+, a
total of 1.0 deaths per day from all causes attributed to PM10 exposure might occur, whereas a
larger number of daily hospital admissions would be expected for the two most common first-
listed diagnoses, total respiratory conditions and heart disease. While there are some small
numerical inconsistencies in Table 13-8, the coherence between the daily mortality results and
the daily hospital admissions results is reassuring, considering the great diversity in study
populations and analytical methods on which these estimates are based.
More specifically, we would expect 23.6 deaths per day per million people, of whom 17.0
would be age 65+ years, and 23.6 - 17.0 = 6.6 less than 65 years. Both the absolute number
(17.0 per million) and the age-specific rate (17.0/126,000) are higher in the elderly.
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TABLE 13-8. QUANTITATIVE COHERENCE OF ACUTE
MORTALITY AND HOSPITALIZATION STUDIES
Age
Group
Population
Annual Baseline
Health Per Million Total
Endpoint
Population
Population Daily
Baseline
Per Million Total
Population
PM10
Lag
Time
Excess
Risk per
50 Mg/m3
PMIO Incr.
Possible Number of
PM-Related Events
Per Day Per 1 Mil.
Pop. for 50 ,ug/m3
PMln Increment
Whole Population
All
All
All
Total mortality
Total hospit.
Resp. mortality
Total resp.
hospitalization
Cardiovascular
mortality
Heart disease
hospitalization
8,603'
124,1103
6761
12, ISO3
3,635'
21,3103
23.6
340.0
1.85
33.4
10.0
58.4
<2d
3-5d
-
3-5d
<2d
3-5d
<2d
0.032
0.062
-
0.194
0.065
0.094
0.046
0.7
1.5
-
0.3
2.0
0.9
2.3
Elderly
65+
65+
Total mortality
Total hospit.
Total resp.
hospitalization
Pneumonia hospit.
COPD hospit.
Heart disease
hospitalization
6,20 17
42,8459
5,1019
2,3359
2,560"
13,5029
17.0
117.4
14.0
6.4
7.0
37.0
2d
-
-------
short averaging time, is 0.03 (23.6) = 0.7 deaths per million, compared to 0.06 (17.0) =1.0
deaths per million in the elderly. However, the difference of -0.3 is not attributable to beneficial
effects of PM10, but to uncertainty in the relative risk estimates and to the superposition of results
from different studies; the difference is not statistically significant. The observation that there is
not a significant excess of total deaths attributable to PM10 beyond deaths of elderly people
attributable to PM10 suggests that the number of deaths in younger people attributable to PM10 is
relatively small. There have been few efforts to establish age-specific PM mortality rates,
however, (Lyon et al., 1995)
with a little evidence for excess mortality in young children. Some studies for TSP suggest little
excess mortality for young adults (Schwartz, 1994b; Wyzga and Lipfert, 1995) and increasing
attributable excess risk with increasing age.
The element of coherence is further strengthened by those studies in which increased
frequency of different health outcomes associated with PM are found in the same population. If
the PM effect on mortality and hospitalization were real, we would expect to observe PM-
associated mortality and hospitalization from the same conditions in the same populations. This
has indeed been observed in several populations, as summarized below:
•Detroit: Mortality mainly in elderly populations, hospital admissions for respiratory
causes and for cardiovascular causes in the elderly;
•Birmingham: Mortality mainly in the elderly, hospital admissions for the elderly;
•Philadelphia: Mortality and hospital admissions for pneumonia in the elderly;
•Utah Valley: Mortality and hospital admissions for respiratory causes in adults.
In the latter study, in addition to hospital admissions, other outcomes were associated with
PM episodes including decrements in peak flow, increased respiratory symptoms and medication
use in asthmatics, and elementary school absences. The presence of a primarily non-smoking
population more or less eliminates smoking as a source of confounding. While these multiple
outcomes did not occur in strictly identical subgroups of each population, there was probably a
sufficient degree of overlap to indicate that PM was a significant predictor of a broad range of
related health outcomes within this community. Significant decrements in pulmonary function
and increased incidence of symptoms were associated with daily increases in PM in children in
Utah Valley, along with a "quality of life" effect measured by lost school days. Thus, there is
evidence for increased risk of health effects associated with PM exposure that range in severity
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from asymptomatic pulmonary function decrements, to respiratory and cardiopulmonary illness
requiring hospitalization, to excess mortality from respiratory and cardiovascular causes,
especially in those older than 65 years of age.
13.5 POTENTIAL MECHANISMS AND EFFECTS OF SELECTED PM
CONSTITUENTS
Epidemiologic studies have suggested that ambient particulate exposure may be associated
with increased mortality and morbidity at PM concentrations below those previously thought to
affect human health (Chapter 12). This section discusses the nature of observed effects reported
in the above-discussed epidemiologic observational studies and attempts to interrelate such
findings to available supporting information on hypothesized potential mechanisms of action that
might contribute to increased human morbidity and mortality. Also discussed is information
from limited controlled human and laboratory animal studies pertaining to identification of
specific ambient PM constituents as possible etiologic contributors to reported ambient PM
effects.
13.5.1 Characteristics of Observed Morbidity and Mortality
To approach the difficult problem of determining if the association between low-level PM
concentrations and daily morbidity and mortality is biologically plausible, one must consider:
the chemical and physical characteristics of the particles in the inhaled atmospheres; the
characteristics of the morbidity/mortality observed and the affected population; as well as
potential mechanisms that might link the two. Several salient considerations related to the
evaluation of biological plausibility of the epidemiology findings are discussed below.
If daily mortality rates are associated with elevated ambient particulate concentrations, it is
important to examine the specific causes of death to determine if they could plausibly be
contributed to by inhaled PM. Schwartz (1994b,c) compared causes of death in Philadelphia on
high pollution days (average TSP = 141 |ig/m3) with causes of deaths on lower pollution days
(average TSP = 47 |ig/m3). On the high pollution days there was a higher relative increase in
deaths due to: COPD (RR = 1.25); pneumonia (RR = 1.13); cardiovascular disease (RR = 1.09);
and stroke (RR = 1.15). There was also a higher relative age at death and an increase in reports
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that respiratory factors may have contributed to the cause of death. The causes of death and age
at death were found to be similar to those observed in the London smog deaths of 1952.
Studies of associations of morbidity with particulate pollution noted small decreases (2 to
2.5%) in spirometry (FVC or FEVj) in smokers and nonsmokers on high pollution days (60 to
100 |ig/m3; Pope and Kanner, 1993; Chestnut et al., 1991), an increased number of asthma
attacks (Ponka, 1991), and increased outpatient visits for asthma (Gordian et al., 1996) and
bronchitis (but not for asthma) (Hefflin et al., 1994). Thus, the characteristics of health effects
on high particle pollution days are mainly cardiopulmonary in nature and are the types of effects
that can be considered plausibly related to airborne toxicants.
Data on the lung function effects of particle exposures in persons with pre-existing
pulmonary disease compared to healthy persons do not yield a clear picture although they are
logically likely to be more susceptible to effects from exposure to particulate pollutants. Pope
and Kanner (1993) reported an approximate 2% decline in FEVj in smokers with mild to
moderate COPD during an increased concentration in ambient PM10 of 100 //g/m3 in Salt Lake
City. However, in controlled exposures to similar concentrations of H2SO4, persons with mild
COPD (average FEVj/FVC ratio 56%) had no reduction in spirometry (Morrow et al., 1994).
Exercising mild asthmatics may (Morrow et al., 1994; Koenig et al., 1989) or may not (Avol et
al., 1990) experience slight bronchoconstriction following similar acid aerosol exposures. Using
an elastase-induced rat model of emphysema, Mauderly et al. (1990) found that exposure to
diesel exhaust, which contains aggregates of ultrafme soot particles, resulted in less particle
deposition in the lungs of emphysematous rats than in normal rats, thus sparing the
emphysematous rats the health effects induced by the soot particles in normal animals.
A portion of PM-related deaths may occur during short-term ambient PM episodes in
persons who would have died within days or weeks. For this portion, a "harvesting effect"
would logically be expected in the daily mortality statistics. That is, after the episode-related
increase in mortality, the daily mortality count should decline below baseline, because some of
those at risk would already have died. This decline would be expected within the period of PM-
induced life shortening.
Kunst et al. (1993) have reported a harvesting effect with temperature-related mortality,
and some epidemiologic studies (Cifuentes and Lave, 1996 and Spix et al., 1993) have reported
such effects to be associated with episodic ambient PM exposure. Even if true PM-related
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harvesting exists, epidemiologic studies may generally not be sensitive enough to detect it,
because the PM effect on overall mortality is relatively small, and because it is likely that
multiple mechanisms with variable time courses are involved in PM-related mortality. For
example, in the 1952 London fog episode, daily mortality did not quickly return to baseline
following the peak in excess deaths. Rather, mortality remained somewhat elevated in the days
after pollution levels had returned to baseline (Logan, 1953). This observation suggests that
among the deaths associated with short-term ambient PM exposure, the time of life lost is
variable.
Particle exposure could conceivably increase susceptibility to infection with bacteria or
respiratory viruses, leading to an increased incidence of respiratory infections such as pneumonia
in susceptible members of the population. Potential mechanisms could include slowing of
mucociliary clearance, impairment of alveolar macrophage function, and other specific or
nonspecific effects on the immune response. Incubation periods for common pneumonias are in
the range of 1 to 3 days although some forms require substantially longer (Benenson, 1990) and
the relative risk of death from pneumonia was positively associated with ambient PM in
Philadelphia. If pollutant exposure increased susceptibility to infectious disease, it might be
possible to detect differences in the incidence of such diseases in communities with low versus
high PM concentrations (Utell and Framptom, 1995). If so, emergency room visits and
hospitalizations for pneumonia caused by the relevant agent could be measurably higher on days
following elevated ambient particle concentrations. Schwartz (1994a,b) reported increased risk
(RR 1.19; 95% CI, 1.07 to 1.32) for pneumonia hospitalization associated with PM10 (100
|ig/m3) in Birmingham for patients aged 65 and older. On the other hand, although bronchitis
and asthma admissions for children were increased approximately twofold in association with
operation of a steel mill in the Utah valley, pneumonia admissions for all ages were not
increased. Laboratory animal data to support a direct causal link between PM exposure and
death induced by pneumonia pathogens are not available. Although exposure to acidic aerosols
has been linked with alterations in mucociliary clearance, non-acidic aerosols and other PM
species have not been shown experimentally to cause increased susceptibility to infection in
otherwise healthy young animals. Infectivity studies in old animals, as models of chronic
respiratory disease, could be potentially instructive in this regard.
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Particulate air pollution might also aggravate the severity of underlying chronic lung
disease. This mechanism could explain increases in daily mortality and longitudinal increases in
mortality if individuals with chronic airways disease experienced more frequent or severe
exacerbation of their disease, or more rapid loss of function as a result of parti culate exposure. If
so, increased hospital admissions for specific respiratory causes should be associated with PM.
There are numerous examples (cited in Chap. 12) of increased hospital admissions for COPD,
bronchitis, and asthma being associated with variations in PM levels.
13.5.2 Possible Mechanisms of PM-Induced Injury
Several potential pathophysiologic mechanisms can be proposed by which low level
ambient particle concentrations could conceivably contribute to morbidity and mortality. As
discussed in Chapter 11, PM has been identified as causing a variety of health effects including
respiratory symptoms, mechanical changes in lung function, alteration of mucociliary clearance,
pulmonary inflammatory responses and morphological alterations in the lung. In addition, PM
has been associated with respiratory illness, hospital admissions, and increased daily mortality.
In this section, attention is directed at pulmonary and cardiovascular mechanisms which
could hypothetically contribute to increased morbidity and mortality, although it is
acknowledged that specific mechanisms of action for PM are not yet well known. The
phenomenon of particle related mortality may include: (1) "premature" death (or mortality
displacement), that is the hastening of death for individuals already near death (i.e., hastening of
certain death by hours or days); (2) increased susceptibility to infectious disease; and
(3) exacerbation of chronic underlying cardiac or pulmonary disease (Utell and Frampton;
1995). The distribution of deposition of particles inhaled into the respiratory tract depends on
their size, shape, chemical composition, and the airway geometry and pulmonary ventilation
characteristics of the organism. The mechanisms responsible for the broad range of particle-
related health affects will vary depending on the site of deposition. Once deposited, the particles
may be cleared from the lung, translocated into the interstitium, sequestered in the lymph nodes,
metabolized or otherwise transformed by mechanisms described in Chapter 10.
Deposition of parti culate matter in the human respiratory tract could initiate events leading
to increased airflow obstruction, impaired clearance, impaired host defenses, or increased
epithelial permeability. Airflow obstruction could result from laryngeal constriction or
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bronchoconstriction secondary to stimulation of receptors in extrathoracic or intrathoracic
airways. In addition to reflex airway narrowing, reflex or local stimulation of mucus secretion
could lead to mucus hypersecretion and could eventually contribute to mucus plugging in small
airways. Finally, in airways disease with localized airway narrowing or obstruction, PM will
tend to accumulate more rapidly.
One component of PM, namely acid aerosols, is known to cause slowing of mucociliary
clearance. Since this mechanism is important in clearing particles from the lung, including
biologically active particles such as spores, fungi, and bacteria, impairment of mucociliary
clearance could lead to increased PM burdens, inflammation, and infection. Alveolar clearance
may also be impaired through alterations in macrophage function including decreased
phagocytosis, depression of mobility, and decreased adherence to surfaces. Macrophages play
an important role in removing and digesting particles and may be involved in facilitating
translocation of PM to either other parts of the lung or into the vascular system.
PM may transport reactive oxygen species or increase their formation. PM may induce or
enhance an inflammatory response in the lung; such an effect may depend on particle size and
hence deposition site as well as on chemical or biological composition of the particles.
Inflammatory responses can lead to increased permeability and possibly diffusion abnormality.
Retention of PM may be associated with the initiation and/or progression of COPD. In addition,
mediators released during an inflammatory response could cause release of factors in the
clotting cascade that may lead to an increased risk of thrombus formation in the vascular system
(Seaton et al., 1995).
Pulmonary changes that contribute to cardiovascular responses include a variety of
mechanisms which can lead to hypoxemia, including bronchoconstriction, apnea, impaired
diffusion, and production of inflammatory mediators. Hypoxia can lead to cardiac arrhythmias
and other cardiac electrophysiologic responses that in turn may lead to ventricular fibrillation
and ultimately cardiac arrest. Additionally, many respiratory receptors have direct
cardiovascular effects. Stimulation of C-fibers leads to bradycardia and hypertension, while
stimulation of laryngeal receptors can result in hypertension, cardiac arrhythmia, bradycardia,
apnea, and even cardiac arrest. Nasal receptor or pulmonary J-receptor stimulation can lead to
vagally mediated bradycardia and hypertension (Widdicombe, 1988). Unfortunately, little is
known about the effects of aging on airway receptor reflexes and their cardiac effects, and
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limited research evaluating potential triggering of terminal cardiac events (e.g., arrhythmias) by
inhaled ambient PM is only now beginning to yield preliminary results.
In addition to possible acute toxicity of particles in the respiratory tract, particles that
deposit in the lung may induce inflammation. The response of the respiratory tract to such
particles includes the release of numerous cytokines from alveolar macrophages and epithelial
lining cells that promote healing and repair. With repeated cycles of acute lung injury and repair
or with the persistence of toxic particles chronic lung injury could develop. Although such acute
responses are well known, they typically occur only after several days or weeks of exposure to
airborne particle concentrations many fold higher than those ambient exposures that have been
shown to be associated with increased mortality and morbidity in epidemiology studies.
13.5.3 Specific PM Constituents: Acid Aerosols
Acid aerosol exposure in controlled human exposure and laboratory animal toxicology
studies has been shown to cause a variety of effects on the respiratory system. In humans
acutely exposed to acid aerosols, these include decrements in lung function, slowing of
mucociliary clearance, and increased airway responsiveness and respiratory symptoms.
Human experimental studies indicate that healthy subjects experience only very modest
decrements in respiratory mechanics following single exposures to H2SO4 at levels up to
2,000 //g/m3 for 1 h. Acid aerosol deposition and neutralization models suggest that with the
ammonia present in the mouth and respiratory tract of humans, a large portion of inhaled acids
will be neutralized during inhalation. Nevertheless, even with exercise to decrease the time for
neutralization and the use of acidic gargles to minimize the levels of oral ammonia available for
neutralization, lung function and symptom responses are not appreciably enhanced in healthy
subjects. Mild lower respiratory symptoms occur at exposure concentrations in the > 1,000
|ig/m3 range, particularly with larger particle sizes. These observations are consistent with
deposition models that indicate greater deposition of larger aerosols in the tracheobronchial
region, the origin of many of the respiratory symptoms such as cough and irritation. However,
these observations do not provide an explanation for the observed lower levels of FVC and FEVj
seen in children who reside in communities with high levels of acidic PM. The only studies of
controlled acid exposures in non-adults are those of adolescent asthmatics, discussed below.
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Both acute and chronic exposure to H2SO4 can produce functional changes in the
respiratory tract, some of which have a greater pathological significance than others. Acute
exposure will alter pulmonary function, largely due to bronchoconstriction. However, attempts
to produce changes in airway resistance in healthy animals at levels below 1,000 //g/m3 have
been largely unsuccessful. With the exception of guinea pigs, these findings in laboratory
animals are similar to those for healthy humans. The lowest effective level of H2SO4 producing
a small transient change in airway resistance in the guinea pig is 100 //g/m3 (1-h exposure).
In general, the smaller size droplets were more effective in altering pulmonary function,
especially at low concentrations. Deposition models predict that only smaller aerosols (< 2-4
Aim) would have appreciable tracheobronchial and alveolar deposition in small laboratory
animals. Chronic exposure to H2SO4 is also associated with alterations in pulmonary function
(e.g., changes in the distribution of ventilation and in respiratory rate in monkeys). However, in
these cases the effective concentrations are >500 f^g/m3. Hyperresponsive airways have been
induced with repeated exposures to 250 //g/m3 H2SO4 in rabbits. Acute exposures to higher
concentrations did not affect responsiveness in healthy humans but exposures in the 500 to 1,000
Aig/m3 range in asthmatics can result in changes in airway responsiveness. Because droplet
aerosols are highly soluble, it is unlikely that any appreciable lung burden of these particles
would accumulate.
Asthmatic subjects appear to be more sensitive than healthy subjects to the effects of acid
aerosols on lung function, but the effective concentration differs widely among studies.
Adolescent asthmatics may be more sensitive than adults, and may experience small decrements
in lung function in response to H2SO4 at exposure levels only slightly above peak ambient levels.
Mild bronchoconstriction has been reported after brief exposures to as low as 68 fj-g/m3 H2SO4 in
exercising adolescent asthmatics and 90 //g/m3 in exercising adult asthmatics (Morrow et al.,
1994; Koenig et al., 1989), although this has not always been observed (Avol, et al., 1990).
These observations may be consistent with the association of pulmonary function decrements
with acidic PM exposure in children attending summer camps. Acid aerosol probably acts as an
irritant in the tracheobronchial region and increased responsiveness in this region is the likely
cause of increased response of asthmatics to acids. If chronic acid exposure were to exacerbate
asthma in children, this could partially account for the reduced lung function levels found in
communities with higher levels of acidic PM. In very limited studies, the elderly and people
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with COPD do not appear to be unusually susceptible to the effects of acid aerosols on lung
function.
Acid aerosols typically cause slowing of mucociliary clearance in healthy subjects,
although the effects are dependent on exposure concentration and exposure duration and on the
region of the lung being studied (brief exposure to low concentrations of acid may accelerate
clearance of particles deposited primarily in the tracheobronchial region). The bronchial
mucociliary clearance system in laboratory animals is also very sensitive to inhaled acids. The
lowest level shown to have an effect on mucociliary transport rates in healthy laboratory animals
(100 //g/m3 with repeated exposures) is well below that which results in other physiological
changes in most laboratory animals and is consistent with the findings in humans exposed to acid
aerosols.
The lungs have an array of defense mechanisms to detoxify and physically remove inhaled
material, and available evidence indicates that certain of these defenses may be altered by
exposure to H2SO4 levels <1,000 //g/m3. Defenses such as resistance to bacterial infection may
be altered even by acute exposure to concentrations of H2SO4 around 1,000 //g/m3. Limited data
also suggest that exposure to acid aerosols may affect the functioning of alveolar macrophages at
levels as low as 500 //g/m3 H2SO4. However, in humans, exposure to acid aerosol (1,000
Mg/m3) did not appear to induce an inflammatory response or to cause any changes in
macrophage function (Frampton et al., 1992). Alveolar region particle clearance is affected by
repeated H2SO4 exposures to as low as 125 //g/m3, although these are still higher than currently
observed ambient acid U.S. concentrations. One would expect effects from impaired pulmonary
defense mechanisms to develop over an extended period of continuing exposure. Impairment of
pulmonary host defense mechanisms by acidic particles is consistent with the observations of
increased prevalence of bronchitis in communities with higher levels of acidic PM.
The assessment of the toxicology of acid aerosols requires some examination of potential
interactions with other air pollutants. Such interactions may be antagonistic, additive, or
synergistic. Evidence for interactive effects may depend upon the sequence of exposure as well
as on the endpoint examined. Low levels of H2SO4 (40 to 100 //g/m3) have been shown to react
synergistically with O3 in simultaneous exposures using biochemical endpoints. In this case, the
H2SO4 enhanced the damage due to the O3. Two recent studies have examined the effects of
exposure to both H2SO4 and ozone on lung function in healthy and asthmatic subjects. In
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contrast with several previous studies conducted at higher acid concentrations, both studies
suggested that 100 //g/m3 H2SO4 may cause slight potentiation of the pulmonary function
response to ozone.
The surface of a particle is primarily in contact with respiratory cells and surfaces and thus
any coating on a solid particle, such as acid, would be presented to the respiratory surfaces.
Acid coating of ultrafine zinc oxide particles appears to enhance the effects of acid in the guinea
pig, for both permeability, inflammation, and some functional responses such as changes in
diffusing capacity (Chen et al., 1992; 1995). However, acid coating of fine (<1 //m) carbon
particles did not enhance the responses of humans to acid aerosols (Anderson et al., 1992). The
process of acid coating used by Anderson et al. (1992) is different form that used by Chen et al.
(1995) and these data may not be comparable. It is unclear whether these differences can be
attributed to the acid coating alone since the carrier particle (ZnO versus C) may play a role and,
in the case of the ultrafine coated particles, the total number of particles/>er se may play a role in
the response. Moreover, Chen et al. (1995) noted changes in intracellular pH of macrophages,
which may affect phagocytosis, following exposure to aerosols of H2SO4 layered on carbon
particles. This effect was dependant both upon the number of particles as well as the total mass
concentration of H+ in the exposure atmosphere; a threshold existed for both exposure
parameters. Similar amounts of (larger) droplet acid aerosol did not produce these responses in
guinea pigs. This latter
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finding is consistent with a single human study of very fine acid/sulfate particle exposure in
which no spirometry responses were observed at levels in excess of 1000 |ig/m3 (Horvath et al.,
1987).
Human exposure studies of particles other than acid aerosols provide insufficient data to
draw conclusions regarding health effects. However, available data suggest that inhalation of
inert particles in the respirable range, including three studies of carbon particles, have little or no
effect on symptoms or lung function in healthy subjects. Although, coating of micron-sized
carbon particles with sulfuric acid did not increase pulmonary function responses, carbon
particles impregnated with formaldehyde did increase the delivery of formaldehyde and
consequently increased irritant responses in human subjects.
13.5.4 Specific PM Constituents: Ultrafine Aerosols
Ultrafme aerosols (<0.1 //m) are a class of particles that have the potential to cause toxic
injury to the respiratory tract as seen in studies conducted both in vivo and in vitro. At high
concentrations, ultrafme particles, as a metal or polymer "fume", are associated with toxic
respiratory responses both in humans and in laboratory animals. Occupational exposures to high
levels of polymer fumes (>1,000 //g/m3; size <1 //m) can lead to fever, diffusion impairment,
and respiratory symptoms (Dahlqvist et al., 1992; Goldstein et al., 1987). Such exposures are
associated with cough, dyspnea, pulmonary edema, and acute inflammation.
Ultrafine (11 nm) particles of copper oxide inhaled at 109 particles/cm3 for 60 minutes in
hamsters were dispersed throughout the lung including the interstitium, the alveolar capillaries
and the pulmonary lymphatics (Stearns et al., 1994). During the exposure pulmonary resistance
increased four-fold and the increase persisted for 24 h. These results indicate that ultrafme
particles of low solubility can rapidly breach epithelial cell barriers and penetrate to interstitial
and endothelial sites.
The potential for toxicity of ultrafme particles has been studied using a polymer ultrafme
particle as a model (Oberdorster et al., 1995a,b; Warheit et al., 1990). These studies indicate
that freshly generated insoluble ultrafme particles, when inhaled as single particles in low
concentrations (<50 //g/m3) can cause severe injury to the lung. In addition there are studies on
a number of relatively insoluble ultrafme particles (diesel, carbon black) that are present in the
ambient atmosphere as aggregated ultrafmes. These studies, reviewed in Chapter 11, indicate
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that inhalation exposures of laboratory animals to aggregated particles, including TiO2, carbon
black particles and diesel soot are associated with epithelial cell proliferation, occlusion of
interalveolar pores (of Kohn), impairment of alveolar macrophages, chronic pulmonary
inflammation, pulmonary fibrosis, and induction of lung tumors. No acute effects were
observed, however, even at the highest exposure concentrations.
As reviewed in Chapter 11, mechanisms which could enhance the toxicity of ultrafine
particles include: the high pulmonary deposition efficiencies of inhaled singlet ultrafine
particles; the large numbers of these particles per unit mass; their increased surface area
available for reaction; their rapid penetration of epithelial layers and access to pulmonary
interstitial sites; and the presence of radicals and perhaps acids on the particle surface. When
inhaled at the same mass concentration, ultrafine particles with a diameter of 20 nm have a
number concentration that is approximately 6 orders of magnitude higher than for a 2.5 jim
particle; the collective particle surface area is also greatly increased (Table 11-1) Ultrafine
particles present a problem to the respiratory tract because of their large collective surface area
and because they can evade macrophage phagocytosis and penetrate into the interstitium more
easily than larger sized particles (Takenaka et al., 1986; Ferin et al., 1990). There is evidence
that some aggregated insoluble ultrafine particles may dissociate into singlet ultrafine particles in
the lung (Takenaka et al., 1986; Ferin et al., 1990; Oberdorster, et al., 1994) which would
facilitate transport across the epithelium. Even though the deposition of aggregated ultrafmes
would be similar to particles in the fine range, their behavior in the lung would be that of singlet
ultrafine particles.
The occurrence of ultrafine particles as well as their sources are reviewed in Chapters 3
and 6. Single ultrafine particles occur regularly in the urban atmosphere at high number
concentrations (5 x 104 - 3 x 10s particles/cm3) but very low mass concentrations (Brand et al.,
1991; 1992; Castellani, 1993). Particle number concentrations may vary from less than
1000/cm3 at clean, background sites to over 100,000 cm3 in polluted urban areas. Geometric
mean diameter ranged from 12 to 43 nm in Long Beach, CA and 47 to 75 nm in clean air in the
Rocky Mountains. Although ultrafine particles are not stable because they quickly aggregate to
form larger particles, they continue to be freshly generated from a number of anthropogenic
sources (e.g., gas to particle conversion; combustion processes; incinerator emissions).
Moreover, the presence of ultrafine particles in human alveolar macrophages indicates
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widespread exposures to ultrafines, either as singlet particles or aggregates in ambient air (Hatch
etal., 1994).
At present there are no studies with ambient ultrafine particles. An important aspect of the
potential toxicity of ultrafine particles is their low solubility whether they are present in the
exposure atmosphere as singlet particles or as aggregates. At this point the limited data base
does not permit a judgment to be made on the potential for ultrafine particles to contribute to
morbidity and/or mortality consistent with the epidemiologic findings for ambient particle
exposures.
13.5.5 Specific PM Constituents: Crystalline Silica
The limited data on air concentrations of silica in the United States indicate that silica
particles arising from natural, industrial, and farming activities can result in estimated ambient
annual average and high ambient quartz levels of 3 and 8 //g/m3, respectively. However, silica is
one of the most common substances to which workers are exposed and several extensive
occupational studies clearly define the exposure levels and resultant health effects.
Consequently, a causal relationship between inhalation of dust containing crystalline silica and
pulmonary inflammation and the consequent development of fibrosis (silicosis) is well-
established. Although a correlation between silicosis and increased risk of neoplasia is
suggested by the results of recent occupational studies, experimental evidence that quartz can
cause lung cancer without silicosis, has only been seen in rats. Rats appear to be more sensitive
to the development of silica-induced lung injury and lung tumors than other rodent species such
as mice and hamsters. Although the pulmonary pathological effects of inhaled crystalline silica
are well-established, there is little information on the effects of inhaled amorphous silica. The
limited information suggests that, in the absence of continuing exposures, the respiratory tract
effects following exposures to amorphous silicates are reversible, and occur only in laboratory
animals exposed to silica in excess of 10,000 //g/m3 for periods ranging from days to years. The
results demonstrate that the crystalline forms of silica dust were substantially more potent in
producing pulmonary toxicity compared to the amorphous or colloidal forms of silica.
Differences in sensitivity to inhaled silica are apparent not only across and within rodent species,
but also between rodents and humans; this limits the utility of laboratory animal data for
extrapolation of silica risk to ambient level exposures.
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The effects of crystalline silica exposure (CSE) have been extensively studied in mining
environments, and there are some clear differences between the mining environments and the
ambient environment. These differences generally suggest that silica in the ambient
environment is less toxic, primarily because of the larger particle sizes associated with ambient
sources, the reduced likelihood of exposure to more potent "freshly fractured" silica, and less
frequent peak exposures. In any case, a thorough analysis of the most extensive occupational
studies available, each of which examined the medical histories of thousands of miners, suggests
that the cumulative risk of silicosis among South Dakotan, Canadian, and South African miners
from exposures at or below 1000 //g crystalline silica/m3 • years is very nearly 0%. Using a high
estimate of 10% for the crystalline silica fraction in PM10 from U.S. metropolitan areas, 1000 //g
crystalline silica/m3 • years is the highest CSE expected from continuous lifetime exposure at or
below the annual PM10 NAAQS of 50 //g/m3. Thus, current data suggest that, for healthy
individuals not compromised by other respiratory ailments and for ambient environments
expected to contain 10% or less crystalline silica fraction in PM10, maintenance of the 50 //g/m3
annual NAAQS for PM10 would be adequate to protect against silicotic effects from ambient
crystalline silica exposures.
13.5.6 Specific PM Constituents: Bioaerosols
Ambient bioaerosols include fungal spores, pollen, bacteria, viruses, endotoxin, and animal
and plant debris. Bacteria, viruses and endotoxin are mainly found attached to aerosol particles,
while entities in the other categories are found as separate particles. Data for characterizing
ambient concentrations and size distributions of bioaerosols are sparse. Matthias-Maser and
Jaenicke (1994) found that bioaerosols constituted about 30% of the total number of particles in
samples collected on a clean day in Mainz, Germany. The proportion of particles that were
bioaerosols was higher in the fine size mode (as much as a third) and slightly lower in the coarse
size mode. In Brisbane, Australia, Glikson et al. (1995) found that fungal spores dominate the
bioaerosol count in the coarse fraction of PM10 and that the overall contribution of bioaerosols to
total PM10 particulate mass was on the order of 5 to 10%. However, the cytoplasmic content of
spores and pollen was often found to be adhered to particles emitted by motor vehicles and
particles of crustal origin.
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Fungal spores range in size from 1.5 jim to > 100 |im, although most are 2 to 4 jim MMAD.
They form the largest and most consistently present component of biological aerosols in ambient
air. Levels vary seasonally, usually being lowest when snow is on the ground. Fungal spores
often reach levels of 1000 to 10,000 spores/m3 during the summer months (Lacey and
Dutkiewicz, 1994; Madelin, 1994) and may be as high as 100,000/m3 near some anthropogenic
sources (agriculture activities, compost, etc.).
Bioaerosols can contribute to increased mortality and morbidity. Asthma mortality has
been associated with ambient levels of fungal spores, unadjusted OR of 2.16 (95% CI = 1.31 to
3.56) per increment of 1000 spores/m3; controlling for time and pollen counts reduced the RR to
1.2 (95% CI = 1.07 to 1.34) (Targonski et al., 1995). Asthma mortality in Scotland shows a
seasonal peak that follows the peak in ambient pollen levels (Mackay et al., 1992). Exposure to
fungal spores has also been identified as a possible precipitating factor in respiratory arrest in
asthmatics (O'Hollaren et al., 1991).
Exposure to fungal spores in healthy individuals can lead to allergic alveolitis
(hypersensitivity pneumonitis) or pulmonary mycoses such as coccidioidomycosis or
histoplasmosis (Lacey and Dutkiewicz, 1994). Induction of hypersensitivity generally requires
exposure to concentrations that are substantially higher than in ambient air, although subsequent
antigenic responses require much lower concentrations. Association of fungal and pollen spores
with exacerbations of asthma or allergic rhinits is well established (Ayres, 1986). The incidence
of many other diseases (e.g., coccidioidomycosis) induced by fungal spores is relatively low,
although there is no doubt about the causal organisms (Lacey and Dutkiewicz, 1994). The
potential for fungal induced diseases is much higher in immunocompromised patients and those
with unusually high exposures to crustal dust in the breathing zone, such as military personnel.
In addition to fungal spores and pollen, other bioaerosol material can exacerbate asthma
and can also induce responses in nonasthmatics. For example, in grain workers who experience
symptoms, spirometry decrements, and airway hyperresponsiveness in response to breathing
grain dust, the severity of responses is associated with levels of endotoxin in the bioaerosol
rather than the total dust concentration (Schwartz et al., 1995). A classic series of studies (Anto
and Sunyer, 1990) proved that airborne dust from soybean husks was responsible for asthma
epidemics and increased emergency room visits in Barcelona, Spain. These studies indicate that
airborne fragments of biological substances can produce severe health effects.
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Bacterial aerosol counts may range as high as 30,000 bacteria/m3 downwind of sewage
treatment facilities, composting areas, waterfalls from polluted rivers, or certain agricultural
activities. Typical levels in urban areas range from several hundred to several thousand
bacteria/m3 (Lighthart and Mohr, 1994). Human pathogenic activity of such bacteria is not well
understood or characterized. Infective potential of aerosolized bacteria depends on size (smaller
are more effective), virulence, host immune status, and host species sensitivity (Salem and
Gardner, 1994). Aerosolized bacteria can cause bacterial infections of the lung including
tuberculosis and legionnaire's disease. The Legionella pneumophila bacterium is one of the few
infectious agents known to reside outside an infected host and is commonly found in water,
including lakes and streams. Levels of bioaerosols (fungi and bacteria) are generally higher in
urban than in rural areas (Lighthart and Stetzenbach, 1994).
Exposures to bioaerosols of the above types, are clearly capable of producing serious
health effects especially at high concentrations encountered in indoor environments.
Because of the extremely limited knowledge of ambient levels of bioaerosols and their
composition and relative potency of various components, the small number of well conducted
epidemiologic studies of bioaerosols, and the absence of controlled studies of ambient
bioaerosols, the relative contribution of bioaerosols to the observed PM-associated morbidity
and mortality effects cannot be determined with any confidence at the present time. However, it
seems unlikely that bioaerosols play more than a minor role in such effects. This conclusion is
based on
1. The seasonal variability in concentration of some bioaerosols whose general trends are
different from the seasonal trends in mortality.
2. The subpopulation most afflicted by bioaersols is asthmatics who are not identified as a
sensitive subgroup for PM-associated mortality.
3. Many of the specific diseases induced by bioaerosols have an extremely low incidence
and, for many, the mortality rate is also very low.
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13.6 INDIVIDUAL RISK FACTORS AND POTENTIALLY
SUSCEPTIBLE SUBPOPULATIONS
In addition to risk associated with activity, location, and dosimetry, inherent individual
characteristics may also affect risk from inhaled PM. For example, elderly individuals or
persons with pre-existing cardiovascular or respiratory disease, particularly chronic obstructive
pulmonary disease (COPD), are likely to be at greater risk from PM exposure. Both the
incidence of and the death rates from cardiovascular and pulmonary diseases increase with age.
The following section discusses individual risk factors, including: age, asthma, COPD, and
cardiovascular disease. The incidence of selected cardiopulmonary diseases by age and
geographic region is presented in Table 13-9 to help place the following discussion in
perspective.
13.6.1 Age
Certain population groups such as the elderly may be more sensitive to changes in
pulmonary or cardiovascular function because of age-related decrements in physiological
reserve. For example, cardiorespiratory function, including lung volumes, FEVl3 maximum
oxygen uptake, and cardiac output reserve decline with age (Folkow and Svanborg, 1993; Dice,
1993; Lakatta, 1993; Kenney, 1989), even in a healthy active population. Morphological
changes in the lung lead to loss of lung elasticity, increased stiffness of the chest wall,
enlargement of alveolar ducts and loss of alveolar septa including diminished numbers of
pulmonary capillaries, and increased numbers of mucous glands. Many of the decrements in
physiological function associated with the aging process also may be associated with
pathological changes caused by disease or other environmental stressors impacting a person over
their lifespan.
If the pulmonary clearance mechanisms are impaired due to pulmonary disease, aging, or
repeated inhalation exposures that are toxic to the normal clearance mechanisms, then particles
and their metabolic or degradation products may persist. The degree to which an added particle
burden may impact an individual will likely be affected by their age, health status, medication
usage and their overall susceptibility to this inhalation exposure. One factor that may promote
increased risk in the older population is that, over their lifespan,
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TABLE 13-9. INCIDENCE OF SELECTED CARDIORESPIRATORY
DISORDERS BY AGE AND BY GEOGRAPHIC REGION
(reported as incidence per thousand population and as number of cases in thousands)
Chronic Condition/Disease
COPD
Incidence/1 ,000 persons
No. cases x 1,000
Asthma
Incidence/1 ,000 persons
No. cases x 1,000
Heart Disease
Incidence/1 ,000 persons
V° No. cases x 1,000
oo
°-* HD-ischemic
Incidence/1 ,000 persons
No. cases x 1,000
HD-rhythmic
Incidence/1 ,000 persons
No. cases x 1,000
Hypertension
Incidence/1 ,000 persons
No. cases x 1,000
All Ages
61
15,400
49
12,370
86
21,600
32
8,160
33
8,160
111
27,820
Under 45
50
8,650
52
9,000
29
5,050
3
490
20
3,500
34
5,830
Age
45-64
63
3,550
45
2,180
135
6,540
61
2,970
44
970
226
10,980
Over 65
104
3,210
40
1,230
325
10,000
153
4,702
83
2,550
358
11,000
Over 75
107
1,200
34
420
404
4,980
184
2,270
104
1,275
352
4,300
Regional
NE MW S W
56 63 63 61
48 49 48 52
89 84 93 74
37 29 37 24
33 35 32 31
106 115 123 91
Source: National Center for Health Statistics (1994).
-------
they have had more exposure and hence more opportunity to accumulate particles or damage in
their lungs.
Cardiorespiratory system function may be compromised and become less efficient in older
people and as a result of disease. For example in people over 75 years, 40% have some form of
heart disease and 35% have hypertension. Approximately 10% of the population in this age
group has COPD. (See table 13-9) Responses to particle inhalation could, conceivably, further
compromise the functional status in such individuals. The terminal event(s) of life must
presumably result from a triggering or exacerbating of a lethal failing of a critical function, such
as ventilation, gas exchange, pulmonary circulation, lung fluid balance, or cardiovascular
function in subjects already approaching the limits of tolerance due to preexisting conditions.
13.6.2 COPD
The conditions most likely to be affected by inhaled PM are the chronic airways diseases,
particularly COPD. COPD is the fourth leading cause of death in the United States, and is the
most common cause of non-malignant respiratory deaths, accounting for more than 100,000
deaths in 1993 (National Center for Health Statistics, 1996). According to the International
Classification of Disease (ICD) definitions and classification codes, asthma is included along
with emphysema, chronic bronchitis, and pnuemonitis under the classification of COPD (490-
496). In discussions of epidemiological studies that included this range of ICD codes, asthma is
included under COPD unless Code 493 is specifically excluded. In the discussion in Chapters 11
and 13, we have included only emphysema and chronic bronchitis in accord with the view
espoused in a recent official statement of the American Thoracic Society (1995).
This group of diseases encompasses emphysema and chronic bronchitis, but information on
death certificates may not allow differentiation between these diagnoses. The pathophysiology
includes chronic inflammation of the distal airways as well as destruction of the lung
parenchyma. Loss of supportive elastic tissue leads to airway closure during expiration,
resulting in obstruction of flow. Processes that enhance airway inflammation or edema lead to
constriction of the conducting airways or slowing of mucociliary clearance that could adversely
affect gas exchange and host defense. Moreover, the uneven matching of ventilation and
perfusion characteristic of this disease, with dependence on fewer functioning airways and
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alveoli for gas exchange, means inhaled particles may be directed to the remaining functional
lung units in higher concentration than in healthy lungs (Bates, 1992)
In comparison to healthy people, individuals with chronic respiratory disease have greater
deposition of inhaled aerosols that would be contained in the fine (PM25) mode (see Chapter 10).
The deposition of particles in the lungs of a COPD patient may be as much as three-fold greater
than in a healthy adult. Thus, the potential for greater target tissue dose in susceptible patients is
present. The lungs of individuals with chronic lung diseases, such as asthma, bronchitis, or
emphysema are often in a chronic state of inflammation. In addition to the fact that particles can
induce an inflammatory response in the respiratory region, the influence of particles on
generation of proinflammatory cytokines is enhanced by the prior existence of inflammation.
Phagocytosis by alveolar macrophages is down-regulated both by inflammation and the
increased volumes of ingested particles. Therefore, people with lung disease not only have
greater particle deposition, but the conditions that exist in their lungs prior to exposure are
conducive to amplification of the effects of particles and depression of their clearance.
Particles, especially submicron particles, could also act at the level of the pulmonary
vasculature by eliciting changes in pulmonary vascular resistance that could exacerbate
ventilation perfusion abnormalities in people with COPD. Emphysema destroys alveolar walls
and pulmonary capillaries causing a progressive increase in pulmonary vascular resistance,
pulmonary blood pressure, and interstitial edema, eventually leading to systemic hypoxia. This
results in an increased workload on the heart and increases the risk of heart failure.
Patients admitted to an intensive care unit for acute COPD exacerbations have a substantial
hospital mortality (possibly as high as 25%) rising to an overall mortality that may approach
60% within one year of the admission. For patients 65 years and older, the mortality is
substantially higher than for younger patients (Seneff et al., 1995). Mortality is often associated
with non-respiratory system organ dysfunction and thus causes of death may be misclassified.
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13.6.3 Cardiovascular Disease
Particulate pollutants have been associated with increases in cardiovascular mortality both
in the historic major air pollution episodes and in the more recent time-series analysis.
Approximately eight times as many deaths are caused by heart disease as by chronic respiratory
disease. Bates (1992) has postulated three ways in which pollutants could affect cardiovascular
mortality statistics. These include: acute airways disease misdiagnosed as pulmonary edema;
increased lung permeability, leading to pulmonary edema in people with underlying heart
disease and increased left atrial pressure; and acute bronchiolitis or pneumonia induced by air
pollutants precipitating congestive heart failure in those with pre-existing heart disease.
Moreover, the pathophysiology of many lung diseases is closely intertwined with cardiac
function. Many individuals with COPD also have cardiovascular disease caused by: smoking,
aging, or pulmonary hypertension accompanying COPD. Terminal events in patients with end-
stage COPD are often cardiac, and may therefore be misclassified as cardiovascular deaths.
Furthermore, hypoxemia associated with abnormal gas exchange can precipitate cardiac
arrhythmias and lead to sudden death.
13.6.4 Asthma
Asthma is a common chronic obstructive respiratory disease that may be exacerbated by air
pollution. Asthmatics are known to be more sensitive to certain gaseous pollutants such as
sulfur dioxide and ozone. General trends in asthma mortality (increasing) have not paralleled
changes in air pollution (decreasing) (Lang and Polansky, 1994). Atmospheric particle levels
have been linked with increased hospital admissions for asthma, worsening of symptoms,
decrements in lung function, and increased medication use. Asthma-related mortality is
relatively uncommon, accounting for approximately 5000 deaths annually or about 5% of total
chronic respiratory deaths in 1991. Asthma accounts for only a small percentage of overall
respiratory death in older adults. Although PM-related mortality may have a component related
to asthma, the observed mortality increases cannot be accounted for by increased deaths due to
asthma alone.
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13.6.5 Estimating Public Health Impacts of Ambient PM Exposures in the
United States
Efforts to quantify the number of deaths attributable to, and the years of life lost to,
ambient PM exposure are currently subject to much uncertainty. Determination of the number
of deaths attributable to a risk factor requires knowledge of the following entities: (1) the
number of deaths in the population; (2) variations in the extent of exposure of the population to
the factor; and (3) the relative risk of mortality that exposure to the factor confers; and (4) the
shape of the underlying exposure-response relationship.
In the case of PM exposure, uncertainty arises primarily with regard to the second, third,
and fourth entities. While available monitoring information provides rough estimates of likely
exposures of the general population or susceptible subpopulations for PM10 in a number of U.S.
urban locations, much less extensive information exists with regard to ambient measures of
PM25 or other indicators of fine particles or specific PM constituents.
As for the third entity, several sources of uncertainty would affect derivation of and
application of population relative risk estimates for PM and mortality. First, risk ratios from
various short-term mortality studies, while generally falling within a range of 1.02 to 1.10 (i.e., 2
to 10% increase in risk of death over background risk), do vary somewhat from site to site.
Hence, it is probably most credible to use site-specific relative risk estimates in projecting
numbers of PM-related health events for any particular U.S. city, rather than broad application
of a single "best estimate" relative risk value across various locations. Lastly, the proportions of
total PM-mediated mortality attributable to short-term and long-term PM exposure are not
known, and the overlap between short-term and long-term mortality studies, that is the
proportion of all PM-mediated mortality detected in both types of studies, is not known.
Therefore, it would be difficult to achieve appropriate weighting of the widely-divergent short-
term and long-term mortality risk ratios in projecting potential PM public health impacts.
The interpretation of the underlying exposure-response relationships is probably the most
problematic issue for risk assessment purposes at this time. In the absence of clear toxicologic
evidence regarding possible mechanisms of action that would plausibly explain the observed
epidemiologic associations between mortality or morbidity and low-level ambient PM
concentrations, one is left with a dilemma of how to interpret the underlying exposure-response
relationship based only on the available epidemiologic findings.
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As shown in Figure 13-5, several alternative interpretations of reported relative risk
findings are reasonable with regard to possible underlying PM exposure (concentration)-health
effects relationships. Most published studies report results (RR estimates) based on linear
models (as illustrated by Line A in the figure), implying a possible linear, no-threshold
underlying relationship that may extend to essentially zero PM concentrations (line B).
However, the existing PM epidemiology data do not allow one to rule out the possible existence
of an underlying non-linear relationship (e.g., the "threshold" function illustrated by Line C).
The choice of one or another interpretation for risk assessment purposes has important ultimate
implications. Choice of a linear, no-threshold function implied by Line B may overestimate
numbers of health events (e.g., numbers of PM-related deaths or hospital visits per day or year),
given the absence of evidence substantiating increased risk below the lowest observed PM
concentrations used in generating the risk estimates. On the other hand, far fewer health events
would be estimated for the lowest PM concentrations before any "threshold" breakpoint if the
relationship implied by Curve C is assumed. Another intermediate possibility would be to
assume a linear relationship down to an estimated PM "background level", as a means of
projecting the number of health events that would be associated with theoretically controllable
PM concentrations above "background" levels.
Unfortunately, only very limited information now exists from published analyses that
might aid in resolving this interpretational dilemma. As noted earlier, most of the PM
epidemiology studies report only the results of fitting a linear model for PM for the relative risk
of a health effect for a specific PM increment. Only a few studies provide additional
information by which to assess the adequacy of the linear model assumption. Nonlinear
smoothing splines have been shown by Schwartz (1994b) and by Samet et al. (1995) for their
Philadelphia mortality studies, by Schwartz (1994a) for the Cincinnati mortality study, by
Schwartz (1993) for the Birmingham PM10 mortality study, and by Schwartz for a number of
hospital admissions studies. Linear splines were shown by Cifuentes and Lave (1996) for a
different set of Philadelphia TSP mortality data. The TSP mortality curves shown in Chapter 12
are compared in Figure 13-6, along with linear models based in part on the same studies. Both
the TSP models fitted without copollutants (Cincinnati, Philadelphia 1973 to 1980) and
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r
20 30 40 50 60 70 80
Particulate Matter Concentration ((jg/m3)
Figure 13-5. Schematic representation of alternative interpretations of reported
epidemiologic relative risk (RR) findings with regard to possible underlying
PM mortality concentration-response functions. Published studies typically
only report results from linear models that estimate RR over a range of
observed PM concentrations as represented by Line A (specific PM values
shown are for illustrative purposes only), compared against baseline risk
(RR = 1.0) at the lowest observed PM level. One alternative interpretation is
that the RR actually represents an underlying linear, no-threshold PM-
mortality relationship (Line B) with the same slope as Line A but extending
below the lowest observed PM level essentially to 0 Mg/m3. Another
possibility is that the underlying functional relationship may have a
threshold (illustrated by Curve C), with an initially relatively flat segment,
not statistically distinguishable from the baseline risk (1.0) until some PM
concentration where it sharply increases (or more likely somewhat less
sharply ascends in the vicinity of the breakpoint as shown by the dashed
lines).
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1.16
1.14
= 1.10
l_
o
i i-°8
> i-°6
'•^
JB
5 1.04-
1.02-
1.0-
-1.16
-1.14
EPA Metaanalysjs
Philadelphia (1973-8C
Cincinnati (1977-82)^
EPA Metaanalysis
with Copollutants
Philadelphia (1983-88)
50
100
TSP |jg/m3
150
200
Figure 13-6. Comparison of smoothed nonlinear and linear mathematical models for
relative risk of total mortality associated with short-term TSP exposure.
Curves show smoothed nonparametric models for Philadelphia (based on
Schwartz 1994b and for Cincinnati (based on Schwartz, 1994a), and
piecewise linear models for Philadelphia (based on Cifuentes and Lave,
1996). Solid curve shows linear model from EPA metaanalysis using studies
with no copollutants, dash-dot curve shows linear model from EPA
metaanalysis using studies with SO2 as a copollutant (described in Chapter
12).
with copollutants (Philadelphia 1983 to 1988, EPA metaanalysis) show some tendency for a
linear model to over estimate mortality at low concentrations and to underestimate mortality at
higher concentrations. The differences between linear and nonlinear models are sometimes
statistically significant (Samet et al., 1995). Not enough comparisons are available to determine
whether nonlinear models may be needed for PM10 or PM2 5 concentration-effect relationships,
but some assessments reported for Birmingham (Schwartz, 1994g) and Utah Valley (Pope and
Kalkstein, 1996) find no significant improvement by fitting LOESS models instead of a linear
model. Additional tests of the adequacy of the additive linear model for PM and its copollutants
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would be desirable. The additive linear model and the corresponding RR estimates appear
adequate for assessments of PM10 and PM25 effects.
13.7 SUMMARY AND CONCLUSIONS
The chemical and physical differences between fine-mode and coarse-mode particles have
important implications for evaluation of the health and welfare effects of such particles as
distinct pollutant subclasses. For example, as discussed in Section 13.3, the differences in
removal of fine and coarse particles from air streams leads to differences in respiratory tract
deposition, although both fine and coarse particles penetrate into and deposit in all regions of the
respiratory tract. According to the available empirical evidence and deposition models, particles
above 15 //m are largely removed by impaction in the nose, throat and larynx. The efficiency of
removal in this region falls as particle size decreases from 10//m to l/^m dae and reaches a
minimum between 0.5 and 0.1 //m dae. As the particle size decreases below 0.1 //m dae, the
removal efficiency increases again due to diffusion of the very small particles to surfaces. The
larger particles in the coarse fraction are deposited more in the tracheobronchial region (TB)
and, as particle size decreases, TB deposition decreases and alveolar deposition increases,
reaching a peak between approximately 1 and 5 //m dae. Both TB and alveolar deposition reach
a minimum in the accumulation-mode size range between 0.5 and 1.0 //m dae with alveolar
deposition being greater than TB deposition. For particle sizes below 0.5 //m dae, both TB and
alveolar deposition increase due to diffusion and reach a peak below 0.1 //m.
Our current understanding of the toxicology of ambient particulate matter suggests that
fine and coarse particles may have different biological effects. For example, as discussed more
fully in Chapter 11 and Section 13.5, differences in chemical composition of fine and coarse
particles lead to the prediction of different biological effects. Acids, metals which generate
hydroxyl radicals and reactive oxidant species in the lung, and dissolved reactive species may all
be carried into the respiratory tract by fine particles. On other hand, silica (which may produce a
distinctive lung pathology) and biological materials such as spores, pollens, bacteria, and other
biological fragments which may produce immune responses are found primarily among
coarse-mode particles, many of which may be larger than 10 //m. Some epidemiology studies
tend to show stronger associations with fine particle indicators than with coarse particles.
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However, clear differentiation between PM25 and PM10 is difficult from currently available
analyses and is complicated by the fact that PM25 is part of PM10. Direct assessment of coarse
PM effects (i.e., PM15/10-PM2 5) is especially limited in available epidemiologic studies.
The evidence for PM-related effects from epidemiologic studies is fairly strong, with most
studies showing increases in mortality, hospital admissions, respiratory symptoms, and
pulmonary function decrements associated with several PM indices. These epidemiologic
findings cannot be wholly attributed to inappropriate or incorrect statistical methods,
misspecification of concentration-effect models, biases in study design or implementation,
measurement errors in health endpoint, pollution exposure, weather, or other variables, nor
confounding of PM effects with effects of other factors. While the results of the epidemiology
studies should be interpreted cautiously, they nonetheless provide ample reason to be concerned
that there are detectable human health effects attributable to PM at levels below the current
NAAQS.
There is considerable agreement among different studies that the elderly are particularly
susceptible to effects from both short-term and long-term exposures to PM, especially if they
have underlying respiratory or cardiac disease. These effects include increases in mortality and
increases in hospital admissions. Children, especially those with respiratory diseases, may also
be susceptible to pulmonary function decrements associated with exposure to PM or acid
aerosols. Respiratory symptoms and reduced activity days have also been associated with PM
exposures in some studies.
A number of studies using multiple air pollutants as predictors of health effects have not
completely resolved the role of PM as an independent causal factor. PM concentrations are
often correlated with concentrations of other pollutants, in part because of common emissions
patterns and in part because of weather patterns. There are seasonal differences within any
community, however, and differences exist among various communities that allow at least some
separation of PM effects from those of other pollutants. Unfortunately, most of the analyses of
multiple pollutants within cities have used additive linear models that may not adequately
characterize the interactions among pollutants, so that confident assignment of specific fractions
of variation in health endpoints to specific air pollutants may still require additional study.
Within the overall PM complex, the indices that have been most consistently associated
with health endpoints are fine particles (indexed by BS, COH, and PM25), inhalable particles
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(PM10 or PM15), and sulfate (SO4). Less consistent relationships have been observed for TSP,
strong acidity (H+), and coarse PM (PM10_25). For reasons discussed above, none of these indices
can completely be ruled out as a biologically relevant indicator of PM exposure.
Based on current evidence from epidemiologic, controlled human, human occupational,
and laboratory animal studies, no conclusions can be reached regarding the specific chemical
components of PM10 that may have the strongest biologic activity. Various subclasses of PM
have been considered including acid aerosols, bioaerosols, metals (including transition metals),
and insoluble ultrafine particles. On the basis of currently available information, none of these
can be specifically implicated as the sole or even primary cause of specific morbidity and
mortality effects.
Recent analyses have substantiated the previous selection of PM10 as an indicator of
particle-related health effects. The strong and consistent association of mortality and various
morbidity endpoints with PM10 exposure clearly demonstrates that this indicator of inhalable
particle mass and the associated PM standard are appropriate for the protection of public health.
There is evidence that older adults with cardiopulmonary disease are more likely to be
impacted by PM-related health effects (including mortality) than are healthy young adults. The
likelihood of ambient fine mode particles being significant contributors to PM-related mortality
and morbidity among this elderly population is bolstered by: (1) the more uniform distribution
of fine particles across urban areas and their well-correlated variation from site to site within a
given city; (2) the penetration of ambient particles to indoor environments (where many
chronically ill elderly individuals can be expected to spend most of their time), and (3) the
longer residence time of ambient fine particles in indoor air, enhancing the probability of indoor
exposure to ambient fine particles more so than for indoor exposure to ambient coarse particles.
In addition to the above rather broad classification of elderly individuals (including -50%
of adults over 65) as being at special risk, identification of other specific sensitive populations by
age group or specific disease entity may also be warranted based on currently available analyses.
These clearly include younger (i.e., < 65) individuals with acute or chronic respiratory disease
(e.g., pneumonia, COPD, etc.) and/or cardiovascular diseases, current and former smokers (who
account for about 80 to 85% of COPD deaths and many cardiovascular disease deaths), and
possibly young children in regard to acute pulmonary function decrements being induced by low
level PM exposures.
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The above-noted differences indicate that it would be appropriate to consider fine and
coarse mode particles as separate subclasses of pollutants. For this reason it would be desirable
to monitor each class separately. Because fine and coarse particles are derived from different
sources, it is also necessary to quantify ambient levels of fine and coarse particles separately in
order to plan effective control strategies.
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13-106
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APPENDIX 13A
REFERENCES USED TO DERIVE CELL RATINGS IN
TEXT TABLES 13-6 AND 13-7 FOR ASSESSING
QUALITATIVE STRENGTH OF EVIDENCE FOR
PM-RELATED HEALTH EFFECTS
13A-1
-------
TABLE 13A-1 (ANALOG OF TABLE 13-6). QUALITATIVE SUMMARY OF COMMUNITY EPIDEMIOLOGIC
FINDINGS ON SHORT-TERM EXPOSURE TO AMBIENT THORACIC PARTICLES
AND SELECTED CONSTITUENTS
Population
Group
Adults
Children
Asthmatics
Subgroup
General Population
Elderly
Respiratory
Cardiovascular
General Population
Pre-existing
Respiratory Conditions
Regardless of Age
Health Measure and PM Indicator
Mortality
ThP
1
+++
5
+
9
++
13
+
65
ID
69
0
97
0
FP
2
++
6
+
10
+
14
+
66
0
70
0
98
0
CP
3
+/-*
7
0
11
0
15
0
67
0
71
0
99
0
S04= or
Acid
4
+
8
0
12
0
16
0
68
0
72
0
100
0
Hospitalization and Outpatient
Visits
ThP
17
+
21
++
25
++
29
+
73
+
77
0
101
++
FP
18
0
22
0
26
+/-
30
0
74
0
78
0
102
+/-
CP
19
ID"
23
0
27
ID
31
0
75
ID"
79
0
103
+/-**
SO4= or
Acid
20
0
24
0
28
++
32
+
76
+/-
80
0
104
+
Community -Based
Morbidity/Symptoms
ThP
33
+/-
37
0
41
+'
45
0
81
+
85
+
105
+
FP
34
0
38
0
42
+'
46
0
82
+
86
+/-
106
+/-
CP
35
0
39
0
43
0
47
0
83
0
87
0
107
ID
SO4= or
Acid
36
+/-
40
0
44
+'
48
0
84
+/-
88
+/-
108
+/-
Changes in Lung Function
ThP
49
+
53
0
57
0
61
0
89
++
93
+
109
+
FP
50
0
54
0
58
0
62
0
90
+
94
ID
110
+/-
CP
51
0
55
0
59
0
63
0
91
0
95
0
111
ID
SO4= or
Acid
52
0
56
0
60
0
64
0
92
+
96
+/-
112
+/-
to
ThP = Index of thoracic particles, usually measured PM 10 or PMjj.
FP = Index of fine-mode fraction of thoracic particles, usually measured PM 2J or PM21.
CP = Index of coarse-mode fraction of thoracic particles, usually the calculated or measured difference between PM 10 or PM15 and PM2 5 or PM2 A.
Cells 1, 2, 3, 4: Effect of specified indicator on total mortality or total non-accidental mortality, regardless of age.
Cells 5, 6, 7, 8: Generally, effect of specified indicator on mortality in persons at least 65 years old.
Cells 9, 10, 11, 12: Effect of specified indicator on respiratory causes of death.
Cells 13, 14, 15, 16: Effect of specified indicator on cardiovascular causes of death.
Cells 17 to 64: Effect of specified indicator on total hospital admissions or outpatient visits, respiratory symptoms or changes in lung function in adults.
Cells 69-72, 77-80, 85-88, 93-96: Effect of specified indicator in children with history of pre-existing respiratory illness or symptoms, excluding asthma.
Cells 97 to 112: Mortality or exacerbation of existing asthma (not increased incidence of new asthma) among asthmatic individuals, regardless of age.
0 = No pertinent studies identified.
ID = Insufficient data: at least 1 pertinent study identified but inference as to weight of evidence not warranted.
+/- = Few pertinent studies identified, weight of evidence uncertain but somewhat positive.
+ to +++ = Increasingly stronger, more consistent positive evidence.
'Based on significant positive association for mortality in Steubenville with CP found by Schwartz et al. (1996); but CP highly correlated with FP.
"CP not measured directly in Gordian et al. (1996) and/or Hefflin et al. (1994), but PM 10 measured in CP-dominated polluted air.
'ThP designation based on BS in London having D 50 cutpoint = 4.5 /j,m that includes some ThP particles, but probably more closely indexes FP as also designated along with acid actually measured
as H2SO4 in Lawther et al. (1970) study.
-------
TABLE 13A-2. REFERENCES USED IN RATING CELLS OF MAIN TEXT TABLE 13-6 (AND TABLE 13A-1):
QUALITATIVE SUMMARY OF COMMUNITY EPIDEMIOLOGIC FINDINGS ON SHORT-TERM
EXPOSURE TO AMBIENT THORACIC PARTICLES AND SELECTED CONSTITUENTS1
THORACIC PM
ADULTS: MORTALITY
CELL 1 (+++-)
Dockery et al., 1992
Itoetal., 1995
Kinney etal., 1995
Lyonetal., 1995
Ostroetal., 1996
Pope etal., 1992
Pope and Kalkstein, 1996
Schwartz, 1993
Schwartz etal., 1996a
Sty er etal., 1995
CELLS (+*)
Lyonetal., 1995
Ostroetal., 1996
Saldivaetal, 1995
Sty er etal, 1995
CELL 9 (++}
Ostroetal., 1996
Pope etal., 1992
Pope and Kalkstein, 1996
CELL 13 (+)
Pope etal, 1992
Pope and Kalkstein, 1996
ADULTS: HOSPITAL IZATION AND
OUTPATIENT VISITS
CELL 17 (+}
Gordian et al., 1996
Hefflm et al., 1994
FINEPM
CELL 2 (++-)
Dockery et al., 1992
Schwartz etal., 1996a
CELL 6 (+-)
Schwartz etal., 1996a
CELL 10 (+-)
Schwartz etal., 1996a
CELL 14 (+)
Schwartz etal., 1996a
CELL 18 CO)
No pertinent studies identified.
COARSE PM
CELLS (+/-•)
Schwartz etal., 1996a
CELL? (V)
No pertinent studies identified.
CELL 1 1 (0)
No pertinent studies identified.
CELL 15 CO)
No pertinent studies identified.
CELL 19 (+/-•)
Gordian et al., 1996
Hefflm et al., 1994
SULFATESORACID
CELL 4 (+•)
Dockery et al., 1992
Itoetal., 1993
Lippmann and Ito, 1 995
Schwartz et al., 1996a
Thurstonetal, 1989
CELLS CO)
No pertinent studies identified.
CELL 12 CO)
No pertinent studies identified.
CELL 16 (0)
No pertinent studies identified.
CELL 20 (V)
No pertinent studies identified.
-------
TABLE 13A-2 (cont'd). REFERENCES USED IN RATING CELLS OF MAIN TEXT TABLE 13-6 (AND TABLE 13A-1):
QUALITATIVE SUMMARY OF COMMUNITY EPIDEMIOLOGIC FINDINGS ON SHORT-TERM
EXPOSURE TO AMBIENT THORACIC PARTICLES AND SELECTED CONSTITUENTS1
THORACIC PM
ADULTS: HOSPITAL IZATION AND
OUTPATIENT VISITS
CELL 21 (++}
Schwartz, 1994d
Schwartz, 1994e
Schwartz, 1994f
Schwartz, 1995
Schwartz, 1996
Schwartz and Morris, 1 995
Schwartz etal, 1996b
ADULTS: HOSPITAL IZATION AND
OUTPATIENT VISITS, CONT.
CELL 25 (++}
Lamm etal., 1994
Pope, 1989
Pope, 1991
Schwartz, 1994d,e,f
Schwartz, 1995
Schwartz, 1996
Schwartz etal., 1996b
Thurstonetal., 1994
CELL 29 (+}
Schwartz and Morris, 1 995
ADULTS: COMMUNITY-BASED
MORBIDITY AND SYMPTOMS
CELL 33 (+/-}
Dusseldorpetal, 1994
CELL 37 (0)
No pertinent studies identified.
CELL 41 (+}
Lawtheretal, 1970
FINEPM
CELL 22 (V)
No pertinent studies identified.
CELL 26 (+/-•)
Thurstonetal., 1994
CELL 30 (V)
No pertinent studies identified.
CELL 34 CO)
No pertinent studies identified.
CELL 38 CO)
No pertinent studies identified.
CELL 42 (+}
Lawtheretal., 1970
COARSE PM
CELL 23 CO)
No pertinent studies identified.
CELL 27 (ID)
Thurstonetal., 1994
CELL 31 CO)
No pertinent studies identified.
CELL 35 (V)
No pertinent studies identified.
CELL 39 (0)
No pertinent studies identified.
CELL 43 CO)
No pertinent studies identified.
SULFATESORACID
CELL 24 (V)
No pertinent studies identified.
CELL 28 (++•)
Bates and Sizto, 1987
Bates and Sizto, 1989
Burnett etal., 1994
Burnett etal., 1995
Delfino et al, 1994
Thurstonetal., 1992
Thurstonetal., 1994
CELL 32 (+-)
Burnett etal., 1995
CELL 36 (+/-•)
Ostroetal, 1993
CELL 40 CO)
No pertinent studies identified.
CELL 44 (+}
Lawtheretal., 1970
-------
TABLE 13A-2 (cont'd). REFERENCES USED IN RATING CELLS OF MAIN TEXT TABLE 13-6 (AND TABLE 13A-1):
QUALITATIVE SUMMARY OF COMMUNITY EPIDEMIOLOGIC FINDINGS ON SHORT-TERM
EXPOSURE TO AMBIENT THORACIC PARTICLES AND SELECTED CONSTITUENTS1
THORACIC PM
ADULTS: COMMUNITY-BASED
MORBIDITY AND SYMPTOMS
CELL 45 CO)
No pertinent studies identified.
ADULTS: CHANGES IN LUNG
FUNCTION
CELL 49 (+}
Dusseldorpetal., 1994
Pope and Kanner, 1993
CELL 53 CO)
No pertinent studies identified.
CELL 57 CO)
No pertinent studies identified.
CELL 61 (ff)
No pertinent studies identified.
CHILDREN: MORTALITY
CELL 65 (ID)
Lyonetal., 1995
Saldivaetal., 1994
CELL 69 (ff)
No pertinent studies identified.
CHILDREN: HOSPITALIZATION AND
OUTPATIENT VISITS
CELL 73 (+}
Gordian et al., 1996
Hefflm et al, 1994
Lammetal., 1994
Pope, 1989
Pope, 1991
FINEPM
CELL 46 (0)
No pertinent studies identified.
CELL 50 CO)
No pertinent studies identified.
CELL 54 (0)
No pertinent studies identified.
CELL 58 (0)
No pertinent studies identified.
CELL 62 CO)
No pertinent studies identified.
CELL 66 (0)
No pertinent studies identified.
CELL 70 CO)
No pertinent studies identified.
CELL 74 CO)
No pertinent studies identified.
COARSE PM
CELL 47 CO)
No pertinent studies identified.
CELL 51 (0)
No pertinent studies identified.
CELL 55 CO)
No pertinent studies identified.
CELL 59 CO)
No pertinent studies identified.
CELL 63 (0)
No pertinent studies identified.
CELL 67 CO)
No pertinent studies identified.
CELL 71 (0)
No pertinent studies identified.
CELL 75 (+/-)
Gordian et al., 1996
Hefflm et al., 1994
SULFATES OR ACID
CELL 48 (0)
No pertinent studies identified.
CELL 52 CO)
No pertinent studies identified.
CELL 56 (0)
No pertinent studies identified.
CELL 60 (ff)
No pertinent studies identified.
CELL 64 CO)
No pertinent studies identified.
CELL 68 (0)
No pertinent studies identified.
CELL 72 CO)
No pertinent studies identified.
CELL 76 (+/-)
Burnett et al., 1994
>
l^ft
-------
TABLE 13A-2 (cont'd). REFERENCES USED IN RATING CELLS OF MAIN TEXT TABLE 13-6 (AND TABLE 13A-1):
QUALITATIVE SUMMARY OF COMMUNITY EPIDEMIOLOGIC FINDINGS ON SHORT-TERM
EXPOSURE TO AMBIENT THORACIC PARTICLES AND SELECTED CONSTITUENTS1
THORACIC PM
CHILDREN: HOSPITALIZATION AND
OUTPATIENT VISITS
CELL 77 CO)
No pertinent studies identified.
CHILDREN: COMMUNITY-BASED
MORBIDITY AND SYMPTOMS
CELL 81 (+}
Hoek and Brunekreef, 1 993, 1 994, 1 995
Pope and Dockery, 1992
Popeetal, 1991
Schwartz etal, 1994
CELL 85 (+}
Pope and Dockery, 1992
Roemeretal, 1993
CHILDREN: CHANGES IN LUNG
FUNCTION
CELL 89 C++)
Hoek and Brunekreef , 1993, 1994
Johnson et al, 1990
Neasetal, 1995
Pope and Dockery, 1992
Popeetal., 1991
Spektoretal, 1988
Studmckaetal, 1995
CELL 93 (+}
Neasetal., 1995
Pope and Dockery, 1992
Roemeretal., 1993
ASTHMATICS: MORTALITY
CELL 97 (0)
No pertinent studies identified.
FINEPM
CELL 78 (V)
No pertinent studies identified.
CELL 82 (+-)
Neasetal, 1995
Schwartz etal., 1994
CELL 86 (+/-•)
Neasetal., 1995
CELL 90 (-K)
Dassen et al., 1986
Johnson et al., 1990
Koemgetal, 1993
Neasetal., 1995
CELL 94 (ID)
Neasetal., 1995
CELL 98 CO)
No pertinent studies identified.
COARSE PM
CELL 79 CO)
No pertinent studies identified.
CELL 83 (V)
No pertinent studies identified.
CELL 87 (V)
No pertinent studies identified.
CELL 91 CO)
No pertinent studies identified.
CELL 95 CO)
No pertinent studies identified.
CELL 99 (0)
No pertinent studies identified.
SULFATESORACID
CELL 80 (V)
No pertinent studies identified.
CELL 84 (+/-•)
Hoek and Brunekreef , 1994, 1995
Neasetal., 1995
Schwartz etal., 1994
CELL 88 (+/-•)
Neasetal., 1995
CELL 92 (-K)
Bock etal., 1985
Hoek and Brunekreef, 1 994
Neasetal., 1995
Raizenneetal, 1989
Spektoretal., 1988
Studnickaet al., 1995
CELL 96 (+/-•)
Neasetal., 1995
CELL 100 CO)
No pertinent studies identified.
-------
TABLE 13A-2 (cont'd). REFERENCES USED IN RATING CELLS OF MAIN TEXT TABLE 13-6 (AND TABLE 13A-1):
QUALITATIVE SUMMARY OF COMMUNITY EPIDEMIOLOGIC FINDINGS ON SHORT-TERM
EXPOSURE TO AMBIENT THORACIC PARTICLES AND SELECTED CONSTITUENTS1
THORACIC PM
ASTHMATICS: HOSPITALIZATION
AND OUTPATIENT VISITS
CELL 101 (++}
Delfino et al., 1994
Gordian et al., 1996
Pope, 1989
Pope, 1991
Schwartz, 1994d
Schwartz etal., 1993
Thurstonetal., 1994
White etal., 1994
ASTHMATICS: COMMUNITY-BASED
MORBIDITY/SYMPTOMS
CELL 105 (+)
Ostroetal, 1995
Pope etal., 1991
Roemeretal, 1993
ASTHMATICS: CHANGES IN LUNG
FUNCTION
CELL 109 (+)
Pope etal., 1991
Roemeretal., 1993
Silver-man etal., 1992
Studnickaet al., 1995
FINEPM
CELL 102 (+/-)
Thurstonetal., 1994
CELL 106 (+/-)
Ostroetal., 1991
Perry etal., 1983
CELL 110 (+/-)
Koemgetal, 1993
Perry etal., 1983
COARSE PM
CELL 103 (+/-)
Gordian et al., 1996
Thurstonetal., 1994
CELL 107 (ID)
Perry etal., 1983
CELL 1 1 1 (ID)
Perry etal., 1983
SULFATESORACID
CELL 104 (+)
Bates and Sizto, 1987
Thurstonetal., 1992
Thurstonetal., 1994
CELL 108 (+/-)
Ostroetal., 1991
Perry etal., 1983
CELL 112 (+/-)
Perry etal., 1983
Raizenneetal, 1989
Studnickaetal, 1995
'References are cited as in the reference list of Chapter 13.
-------
TABLE 13A-3 (ANALOG OF TABLE 13-7). QUALITATIVE SUMMARY OF COMMUNITY EPIDEMIOLOGIC
FINDINGS ON LONG-TERM EXPOSURE TO AMBIENT THORACIC PARTICLES AND SELECTED CONSTITUENTS
Population Group
Adults
Children
Asthmatics
Subgroup
General Population
Elderly
Cardiopulmonary
General Population
Regardless of Age
Health Measure and PM Indicator
Mortality
ThP
1
++
5
0
9
++
37
+/-
49
0
FP
2
++
6
0
10
+++
38
0
50
0
CP
o
3
+/-*
7
0
11
0
39
0
51
0
SO4= or
Acid
4
++
8
0
12
++
40
0
52
0
Community -Based Morbidity/
Symptoms
ThP
13
+/-
17
0
21
0
41
+
53
+
FP
14
+/-
18
0
22
0
42
+
54
+/-
CP
15
0
19
0
23
0
43
0
55
0
SO4= or
Acid
16
+
20
0
24
0
44
++
56
+/-
Changes in Lung Function
ThP
25
+/-
29
0
33
0
45
+/-
57
0
FP
26
0
30
0
34
0
46
ID
58
0
CP
27
0
31
0
35
0
47
0
59
0
SO4= or
Acid
28
ID
32
0
36
0
48
+
60
0
>
oo
ThP = Index of thoracic particles, usually measured PM 10 or PM15.
FP = Index of fine-mode fraction of thoracic particles, usually measured PM 2 5 or PM2 j.
CP = Index of coarse-mode fraction of thoracic particles, usually the calculated or measured difference between PM 10 or PM15 and PM2 5 or PM21.
Cells 1, 2, 3, 4: Effect of specified indicator on total mortality or mortality due to natural causes, regardless of age.
Cells 9 to 12: Effect of specified indicator on combined cardiovascular and non-malignant respiratory causes of death.
Cell 37: Only infant mortality studied.
Cells 49 to 60: Mortality, exacerbation of existing asthma, increased incidence of new asthma, or lung function changes in asthmatics regardless of age.
0 = No pertinent studies identified.
ID = Insufficient data: at least 1 pertinent study identified but inference as to weight of evidence not warranted.
+/- = Few pertinent studies identified, weight of evidence uncertain, but somewhat positive.
+ to +++ = Increasingly stronger, more consistent positive evidence.
'Based on supplemental reanalysis by U.S. EPA of results from Dockery et al. (1993), see Figure 12-8 in Chapter 12.
-------
TABLE 13A-4. REFERENCES USED IN RATING CELLS OF TABLE 13-7 (AND TABLE 13A-3)
QUALITATIVE SUMMARY OF COMMUNITY EPIDEMIOLOGIC FINDINGS ON LONG-TERM EXPOSURE
TO AMBIENT THORACIC PARTICLES AND SELECTED CONSTITUENTS1
THORACIC PM
ADULTS: MORTALITY
CELL 1 (++)
Dockery et al., 1993
Lipfert et al., 1988
Lipfert, 1993
Ozkaynak and Thurston, 1 987
CELL 5 CO)
No pertinent studies identified.
CELL 9 (++)
Dockery et al., 1993
ADULTS: COMMUNITY-BASED
MORBIDITY AND SYMPTOMS
CELL 13 (+/-*)
Abbey etal., 1995a
Abbey etal., 1995b
CELL 17 (0)
No pertinent studies identified.
CELL 21 CO)
No pertinent studies identified.
ADULTS: CHANGES IN LUNG
FUNCTION
CELL 25 (+/-}
Ackermann-Liebrich et al., 1996
CELL 29 (0)
No pertinent studies identified.
CELL 33 CO)
No pertinent studies identified.
FINEPM
CELL 2 (++)
Dockery et al., 1993
Lipfert etal., 1988
Lipfert, 1993
Ozkaynak and Thurston, 1 987
Pope etal., 1995
CELL 6 (0)
No pertinent studies identified.
CELL 10 (+++)
Dockery et al., 1993
Pope etal., 1995
CELL 14 (+/-)
Abbey etal., 1995a
Abbey etal., 1995b
CELL 18 CO)
No pertinent studies identified.
CELL 22 CO)
No pertinent studies identified.
CELL 26 CO)
No pertinent studies identified.
CELL 30 CO)
No pertinent studies identified.
CELL 34 (0)
No pertinent studies identified.
COARSE PM
CELL 3 (+/-)
Supplemental EPA analysis of Dockery et
al., 1993 (see Chapter 12, Figure 12-8).
CELL 7 CO)
No pertinent studies identified.
CELL 1 1 CO)
No pertinent studies identified.
CELL 15 CO)
No pertinent studies identified.
CELL 19 (V)
No pertinent studies identified.
CELL 23 CO)
No pertinent studies identified.
CELL 27 CO)
No pertinent studies identified.
CELL 31 (ff)
No pertinent studies identified.
CELL 35 CO)
No pertinent studies identified.
SULFATES OR ACID
CELL 4 (++)
Dockery et al., 1993
Lipfert etal., 1988
Lipfert, 1993
Ozkaynak and Thurston, 1 987
Pope etal, 1995
CELLS (01
No pertinent studies identified.
CELL 12 (++)
Dockery et al., 1993
Lipfert, 1993
Pope etal., 1995
CELL 16 (+)
Abbey etal., 1995a
Abbey etal., 1995b
Chapman etal., 1985
CELL 20 CO)
No pertinent studies identified.
CELL 24 (0)
No pertinent studies identified.
CELL 28 (ID)
Jedrychowski and Krzyzanowski, 1 989
CELL 32 CO)
No pertinent studies identified.
CELL 36 (0)
No pertinent studies identified.
-------
TABLE 13A-4 (cont'd). REFERENCES USED IN RATING CELLS OF TABLE 13-7 (AND TABLE 13A-3)
QUALITATIVE SUMMARY OF COMMUNITY EPIDEMIOLOGIC FINDINGS ON LONG-TERM EXPOSURE
TO AMBIENT THORACIC PARTICLES AND SELECTED CONSTITUENTS1
THORACIC PM
CHILDREN: MORTALITY
CELL 37 (+/-)
Bobak and Leon, 1992
CHILDREN: COMMUNITY-BASED
MORBIDITY AND SYMPTOMS
CELL 41 (+)
Dockery et al., 1989
Dockery et al., 1996
Speizer, 1989
CHILDREN: CHANGES IN LUNG
FUNCTION
CELL 45 (+/-)
Dockery et al., 1989
Johnson et al., 1990
Raizenne et al., 1996
Spektoretal, 1991
ASTHMATICS: MORTALITY
CELL 49 (0)
No pertinent studies identified.
ASTHMATICS: COMMUNITY-BASED
MORBIDITY/SYMPTOMS
CELL 53 (+}
Abbey etal, 1995a, 1995b
Dockery et al., 1989
ASTHMATICS: CHANGES IN LUNG
FUNCTION
CELL 57 CO)
No pertinent studies identified.
FINEPM
CELL 38 CO)
No pertinent studies identified.
CELL 42 (+)
Dockery et al., 1989
Dockery et al., 1996
Speizer, 1989
CELL 46 (ID)
Dockery et al., 1989
Johnson et al., 1990
Raizenne et al., 1996
CELL 50 (0)
No pertinent studies identified.
CELL 54 (+/-•)
Abbey etal., 1995a
Abbey etal., 1995b
CELL 58 (0)
No pertinent studies identified.
COARSE PM
CELL 39 (0)
No pertinent studies identified.
CELL 43 (0)
No pertinent studies identified.
CELL 47 (0)
No pertinent studies identified.
CELL 51 (0)
No pertinent studies identified.
CELL 55 (0)
No pertinent studies identified.
CELL 59 (0)
No pertinent studies identified.
SULFATESORACID
CELL 40 (0)
No pertinent studies identified.
CELL 44 (++)
Dockery et al., 1989, 1996
Dodge etal., 1985
Speizer, 1989
Stern etal., 1989, 1994
Ware etal., 1986
CELL 48 (+)
Dockery et al., 1989
Raizenne et al., 1996
Stern etal., 1989, 1994
CELL 52 (0)
No pertinent studies identified.
CELL 56 (+/-•)
Abbey etal., 1995a
Abbey etal., 1995b
CELL 60 (0)
No pertinent studies identified.
-------