Environmental Health Perspectives • VOLUME 111 | NUMBER 1 | January 2003 45

Air Pollution and Daily Mortality in a City with Low Levels of Pollution 

Sverre Vedal,1 Michael Brauer,2,3 Richard White,4 and John Petkau4

1Department of Medicine, National Jewish Medical and Research Center, Denver, Colorado, USA; 2Department of Medicine, 3School of Occupational and Environmental Hygiene, and 4Department of Statistics, University of British Columbia, Vancouver, 

British Columbia, Canada

An association between short-term changes in

ambient pollutant concentrations, especially

particle concentrations, and daily mortality has

been observed in many settings (1,2). These

settings have included a range of mean particle

concentrations and of particle emission sources

and different types and concentrations of other

ambient pollutants. When concentration–

response relationships between particle concentration and daily mortality have been evaluated, most studies have observed that the

relationship is reasonably linear, even down to

the lowest concentrations observed in any

given study (3–5). One implication of such

observations is that no clear threshold concentration can be identified below which no

effects of particle pollution are observed.

Conclusions about the linearity of the

concentration–response relationship extending

down to the lowest observed concentrations

should be tempered by the relative scarcity of

observations at the lowest, as well as the highest, particle concentrations in most studies.

This is reflected in the larger confidence intervals around the fitted relationship typically

observed at both low and high concentration

extremes (6). Further, error in measuring individual particle exposures due to the use of

only a few pollution monitors to reflect individual exposures would be expected to blur

any threshold concentration, if one exists.

This occurs because some individuals will

have true exposures above their “thresholds”

when measured exposures are below these

thresholds, and some will have true exposures

below their “thresholds” when measured

exposures are above these thresholds (7). The

observed linearity of the concentration–

response relationships may therefore partly be

a result of measurement error.

Vancouver, British Columbia, Canada, is a

large urban area that has low levels of air pollution relative to other large urban areas (2,8).

For example, the mean daily PM10 (particulate

matter ≤ 10 μm in diameter) concentration in

Vancouver during 1994–1996 was lower than

that reported for any of the 90 cities studied in

the National Morbidity and Mortality Air

Pollution Study (NMMAPS) (2). A large number of observations are therefore available at

very low concentrations of ambient particles

and other pollutants, which should enhance the

ability to assess effects at the low end of the

concentration–response relationship. The presence of an association at these low concentrations would argue in favor of the linearity of the

concentration–response relationship extending

down to very low concentrations. Conversely,

the absence of an association would argue that a

threshold concentration is present.

Methods

Mortality data. Vancouver is a metropolitan

area with a population of approximately 1.8

million located on the southwest coast of

British Columbia. Daily mortality data for

the lower mainland of British Columbia,

including Vancouver, for the 3-year period

January 1994–December 1996 were obtained

from the Centre for Health Services and

Policy Research at the University of British

Columbia, with approval from the British

Columbia Vital Statistics Agency, Ministry of

Health Services, and included date of death,

underlying cause of death according to the

International Classification of Diseases, Ninth

Revision (ICD-9), local health area of residence and of death, and date of birth. Total

deaths were defined as all deaths except those

with an underlying cause of trauma or suicide

(ICD-9 codes 800–999). Respiratory deaths

were defined as all deaths coded with ICD-9

codes of 460–519, and cardiovascular deaths

were defined as deaths with ICD-9 codes of

390–459.

Pollution and meteorology data. Mean

hourly pollutant concentration data [inhalable particulate matter (PM10), ozone, nitrogen dioxide, sulfur dioxide, and carbon

monoxide] were obtained from the Greater

Vancouver Regional District for the same 3-

year period for which mortality data were

available. The pollutant monitoring network

spanned the entire region extending from

Vancouver proper eastward to Chilliwack, 90

km east of Vancouver. The network for continuous pollutant monitoring included 10

monitoring sites for PM10 [using tapered element oscillating microbalance (TEOM)

monitors], 19 O3 monitoring sites, 12 SO2

sites, 19 NO2 sites, and 16 CO sites. Mean

hourly meteorologic data on temperature (16

sites), relative humidity (7 sites), barometric

pressure (7 sites), and rainfall (8 sites) were

obtained from Environment Canada and the

Greater Vancouver Regional District.

Analysis. We filled in missing hourly data

values for each pollutant and each meteorologic variable (except rainfall) separately using

the EM algorithm, after preprocessing to

remove systematic patterns in the data.

Specifically, temporal patterns were first

removed from the observed data at each of

the available sites using sine and cosine functions with periods of 1, 1/2, 1/3, 1/4, 1/6,

1/12 and 1/24 of a year, as well as indicators

for the hour of the day as predictors. We

Address correspondence to S. Vedal, Division of

Environmental and Occupational Health Sciences,

National Jewish Medical and Research Center, 1400

Jackson Street, Denver, CO 80206 USA. Telephone:

(303) 398-1520. Fax: (303) 398-1452. E-mail:

vedals@njc.org

We thank the British Columbia Vital Statistics

Agency, Ministry of Health Services, for providing

the vital statistics data for Vancouver.

This work was supported by a grant from the

British Columbia Lung Association. 

Received 5 October 2001; accepted 7 October 2002.

Articles

The concentration–response relationship between daily ambient inhalable particle (particulate

matter ≤ 10 μm; PM10) concentrations and daily mortality typically shows no evidence of a

threshold concentration below which no relationship is observed. However, the power to assess a

relationship at very low concentrations of PM10 has been limited in studies to date. The concentrations of PM10 and other air pollutants in Vancouver, British Columbia, Canada, from January

1994 through December 1996 were very low: the 50th and 90th percentiles of daily average PM10

concentrations were 13 and 23 μg/m3, respectively, and 27 and 39 ppb, respectively, for 1-hr maximum ozone. Analyses of 3 years of daily pollution (PM10, ozone, sulfur dioxide, nitrogen dioxide,

and carbon monoxide) concentrations and mortality counts showed that the dominant associations were between ozone and total mortality and respiratory and cardiovascular mortality in the

summer, and between nitrogen dioxide and total mortality in the winter, although some association with PM10 may also have been present. We conclude that increases in low concentrations of

air pollution are associated with increased daily mortality. These findings may support the notion

that no threshold pollutant concentrations are present, but they also raise concern that these

effects may not be effects of the measured pollutants themselves, but rather of some other factor(s)

present in the air pollution–meteorology mix. Key words: air pollution, mortality, nitrogen dioxide,

doi:10.1289/ehp.5276 available via http://dx.doi.org/

ozone, particulate matter. Environ Health Perspect 111:45–51 (2003). [Online 14 November 2002]
removed any autoregressive (AR) structure in

the residuals from this initial fit by fitting an

AR model; the AR order was chosen to adequately minimize residual autocorrelation,

with fourth-order AR models being adequate

for all variables. We then applied the EM

algorithm to these residuals from all sites

simultaneously to take advantage of any spatial autocorrelation that might be present in

the variables. This yields an imputed residual

for each missing hourly value, which, when

added to the predictions from the temporal

and AR fits, provides the filled-in value. The

available rainfall data were converted to an

indicator (present or absent) for each hour at

each site. The estimated probability of rainfall

was imputed for missing hourly values of the

indicator using a logistic regression separately

at each site, incorporating temporal patterns

as described above for the other variables.

The estimation procedure for missing data

was carried out on the log scale for the pollution variables because their distributions were

skewed. To overcome the minor complication

of some sites reporting zero readings for some

variables, we added a small positive constant

(one-half the smallest nonzero hourly measurement of that variable in the entire data

set) to all the observations before the data

were log transformed. After exponentiating to

transform back to the original scale, we subtracted this same constant from each of the

filled-in hourly values.

Because the mortality data were available

on a daily basis, all analyses were carried out

with daily data. We obtained overall daily values of the pollutants (except O3) and the

meteorologic variables by first averaging the

filled-in hourly values across the day at each

site and then averaging these site-specific

daily average values across the available sites.

For O3, the maximum hourly value over the

day was first determined at each site, and

these site-specific maxima were then averaged

across the available sites. For rainfall, the

overall daily value is the proportion of hours

for which rain was recorded.

Because of the marked seasonality of some

of the pollutants, we stratified analyses by season. Only two “seasons” were chosen given

the relatively moderate marine climate in

Vancouver: summer ranging from May

through September, and winter from October

through April. We used both Poisson regression and generalized additive models for count

data (9) to estimate the effects of the air pollutants on daily mortality. A systematic battery

of preliminary analyses were carried out separately on the total, respiratory, and cardiovascular mortality counts in summer and winter

to identify appropriate forms to adjust for

temporal trends and meteorologic effects. The

primary approach taken to removing the temporal trends was to fit a loess smooth. Initially,

we explored a wide range of window widths

using total mortality, with more detailed

exploration subsequently of window widths

ranging from 90 to 180 days for all mortality

outcomes. Examination of the partial autocorrelation functions suggested a window width

of 120 days sufficed to remove almost all of

the autocorrelation in the residuals and was

therefore used for all subsequent loess-based

temporal adjustments. We then explored various combinations of loess smooths of the

Articles • Vedal et al.

46 VOLUME 111 | NUMBER 1 | January 2003 • Environmental Health Perspectives

Figure 1. The time series of daily total, respiratory, and cardiovascular deaths from January 1994 through December 1996 show an annual cyclical pattern that is

most pronounced for respiratory deaths [dotted lines separate summer (S) from winter (W)]. The corresponding residual time series plots after use of loess

smoothing of long-term temporal trends show that no meaningful cyclical patterns persist.

WS W S W SW

WS W S W SW

WS W S W SW

WS W S W SW

WS W S W SW

WS W S W SW

1994 1995 1996 1994 1995 1996

1994 1995 1996 1994 1995 1996

1994 1995 1996 1994 1995 1996

Year of study Year of study

Year of study Year of study

Year of study Year of study

60

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Cardiovascular deaths Respiratory deaths Total deaths

Residuals Residuals Residuals

Time series Time series after loess smoothing
meteorologic variables as additional adjustments. The impact of these adjustments was

much more modest than that for the temporal

trends in the winter, but comparable in the

summer. In each case, there was little to

choose among many of the different possible

adjustments. Results reported here are for

models in which the meteorologic adjustment

is a sum of separate loess smooths of temperature, relative humidity, barometric pressure,

and rainfall on the same day, each based on

the default window width of one-half.

To allow for easy interpretation and significance testing, we then entered linear terms

for each pollutant in separate models for day

lags ranging from no lag (same day) to 2 days.

We examined the simultaneous effects on

mortality of two pollutants by fitting models

with linear terms for each of the two pollutants

together.

We performed sensitivity analyses to

assess the influence on the results of using a

different approach to accounting for temporal

trends. For these analyses, a linear trend and

sine and cosine terms with periods of 1 year,

and 6, 4, and 3 months were included in the

models instead of a loess smooth to capture

the temporal trends. Exploration of various

loess smooths of the meteorologic variables

indicated that the same sum of separate loess

smooths of temperature, relative humidity,

barometric pressure, and rainfall on the same

day provided adequate adjustment for the

meteorologic variables. 

We used S-Plus (Insightful Corp., Seattle,

WA, USA) for all analyses. Model fits using

loess smooths were based on stringent convergence criteria (10–9 for both the local scoring

and backfitting algorithms) to eliminate

recently identified difficulties with the use of

the default convergence criteria in the S-Plus

generalized additive models (GAM) module

(10). Standard errors for the pollutant effects

in these model fits were determined by a

Monte Carlo approach (based on 1,000 simulated Poisson data sets for each model fit) to

avoid reliance on the potentially inaccurate

approximation used to evaluate these standard errors in the S-Plus implementation of

GAM (10,11).

Results

Mortality, pollution, and meteorology. Total

daily deaths showed an annual cyclical pattern with peaks occurring in the winter

(Figure 1). Numbers of daily deaths ranged

largely from 30 to 40 per day. Respiratory

deaths showed an even more pronounced

cyclical pattern, with peaks again occurring in

the winter. Cardiovascular deaths also showed

an annual cyclical pattern, but this was much

less pronounced than for respiratory deaths.

After removal of long-term temporal trends

using a loess smooth (with a window width of

120 days) separately for summer and winter,

residual plots showed that the annual patterns

seemed to be adequately removed (Figure 1).

Each of the monitored pollutants and

meteorologic variables had some data missing. For PM10, 8.9% of the monitor-hours

had missing concentration data. For O3, SO2,

NO2, and CO, 4.7%, 6.4%, 6.5%, and 5.0%

of the monitor-hours, respectively, were missing. For temperature, humidity, barometric

pressure, and rainfall, 5.3%, 12.9%, 14.7%,

and 2.7% of the hourly measurements,

respectively, were missing. We estimated the

missing hourly data using an EM algorithm

as detailed in “Methods,” with the overall

daily values based on the complete set of pollution and meteorologic data, both measured

and estimated, used in the analyses.

The overall daily air pollution concentrations and the meteorologic measures exhibited

various temporal patterns (Figure 2). Of the

pollutants, O3, with higher concentrations in

summer, and CO, with higher concentrations in winter, exhibited the most seasonal

variability. Air pollution concentrations were

uniformly low (Table 1). The pollution variables, apart from O3, were highly correlated

(Table 2). Correlations between pollutants

were generally similar for the summer and

winter seasons, with the exception of O3, for

which correlations with other pollutants were

negative in winter but positive in summer.

Single-pollutant models. Linear terms for

the pollution variables were added singly to

the regression models in which separate loess

smooths were used to remove the long-term

Articles • Low levels of air pollution and mortality

Environmental Health Perspectives • VOLUME 111 | NUMBER 1 | January 2003 47

WS W S W

W

1994 1995 1996

Year of study

20

10

0

–10

Temperature (C) Humidity (%) Pressure (kPas)

S W

1994 1995 1996

Year of study

1994 1995 1996

Year of study

1994 1995 1996

Year of study

1994 1995 1996

Year of study

1994 1995 1996

Year of study

1994 1995 1996

Year of study

1994 1995 1996

Year of study

1994 1995 1996

Year of study

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Rain (% hr/day) 100

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Max O3 (ppb) NO2 (ppb) SO2 (ppb) 15

10

5

0

1.5

1.0

0.5

CO (ppm)

Figure 2. The time series of the overall daily values

of the five air pollutants and the four meteorologic

measures [dotted lines separate summer (S) from

winter (W)] show varying degrees of an annual

cyclical pattern. kPas, kiloPascals. Concentrations

of the air pollutants were uniformly low. 
temporal patterns and adjust for the same day

temperature, relative humidity, barometric

pressure, and rainfall. Estimated effects for

total, respiratory, and cardiovascular deaths by

season for a standard deviation change in concentration for each pollutant at lags of 0, 1,

and 2 days are presented in Figure 3. In the

summer, we observed a statistically significant

effect on total deaths only for O3 at lag 0, and

a nearly significant effect (p < 0.10) for SO2 at

lag 0. Statistically significant effects on respiratory deaths in the summer were observed for

PM10, O3, and SO2, but effects of NO2 and

CO were also nearly significant. Effects on

cardiovascular deaths were seen only for O3,

and then only for lag 0 (p < 0.10). In the winter, we observed significant effects on total

deaths for PM10 (lag 2), NO2 (lag 2), and SO2

(lag 1). No significant deleterious effects on

respiratory deaths were observed. Effects on

cardiovascular deaths were observed for NO2

(lags 1 and 2) and SO2 (lag 1).

Two-pollutant models. Because pollutant

concentrations are often highly correlated,

findings from single-pollutant models may be

difficult to interpret. We fit models including

linear terms for all pollutant lag pairs (lags 0,

1, 2) to attempt to identify independent pollutant effects. We focused attention on those

models in which both pollutant effects were

observed in the single-pollutant models. In

the summer, of the significant effects detected

in the single-pollutant models, only the

effects of O3 on total mortality at lag 0 and

on respiratory mortality at lag 2 remained statistically significant in all two-pollutant models (Figure 4 shows the estimates of effect

from the most relevant two-pollutant models). In the winter, only the effect for NO2 on

total mortality at lag 2 was largely unchanged

after the addition of other pollutants (Figure

4). Other effects that were significant in the

single-pollutant models were substantially

diminished after addition of some of the

other pollutants in the two-pollutant models:

for example, the greatly diminished effect of

PM10 at lag 1 on respiratory mortality in the

summer after the addition of O3, and the

greatly diminished effect of SO2 at lag 1 on

total mortality in the winter after the addition

of NO2 (Figure 4).

Sensitivity analyses. As detailed in the

“Methods,” another approach taken to removing the long-term temporal trends was to fit a

linear trend and sine and cosine functions

with frequencies ranging from 1 year to 3

months to the mortality series. Residuals from

these fits showed that the annual pattern for

total, respiratory, and cardiovascular deaths

was removed (data not shown). Although, in

general, there was little difference in estimates

of pollution effect between the two approaches

to removing the long-term temporal trends,

there were exceptions. For example, Figure 5

illustrates the smaller effect estimates obtained

for total mortality in the summer with the

sine–cosine function approach. If differences

were observed, loess smooth models almost

always resulted in larger estimates of pollution

effect. However, no qualitative differences in

the study findings resulted from the use of the

trigonometric function approach rather than

loess smoothing for the removal of long-term

temporal trends.

Discussion

In Vancouver, where ambient concentrations

of all major air pollutants are low relative to

other large urban areas (2,8), the principal

finding of this 3-year study was that increases

in the concentrations of some of the gaseous

air pollutants, particularly O3 in the summer,

were associated with increases in daily mortality. For PM10, the only relatively robust

effect was for increased respiratory mortality

in the summer at lag 1, but even that was

sensitive to inclusion of O3 (at lag 2) in the

two-pollutant model.

Estimated effects of ambient particulate

matter have been less consistently observed in

some more recently reported time-series studies, whereas gaseous pollutant effects have been

observed more consistently (12–16). It is not

known whether these effects reflect those of

the gaseous pollutants themselves, or whether

the gaseous pollutants are acting as surrogate

markers of pollutant sources that contain more

toxic compounds. In Vancouver, given the low

concentrations of these pollutants, it seems

unlikely that the observed effects are due to the

measured pollutants themselves. The mean

concentrations of both PM10 and O3 (mean

24-hr average was 13.7 ppb) during the 3-year

period of the study (1994–1996) were lower

than those in any of the 90 cities studied in

NMMAPS (2). Further, only one of the cities

in NMMAPS in which data on carbon

monoxide were available had a lower mean

concentration of carbon monoxide than

Vancouver. Mean concentrations of NO2 and

SO2 in Vancouver were lower than 72% and

76% of the cities in NMMAPS, respectively.

Because the mean concentrations reported in

NMMAPS were trimmed means in which the

upper and lower 10th percentiles were

excluded, and because the distribution of concentration data is typically skewed to the right,

it would be expected that the trimmed means

would be lower than those calculated from the

complete set of data. If trimmed values had

been calculated for Vancouver, mean concentrations relative to the cities included in

NMMAPS would have been even lower.

In Vancouver, the most notable effects on

mortality were those associated with increases

in O3 concentrations. It is noteworthy that,

of the pollutants, O3 was least strongly correlated with the other pollutants. Two-pollutant models with O3 also typically had the

lowest correlations between pollutant effect

estimates; effect estimate correlations may be

more relevant because they reflect pollutant

correlations after long-term temporal trends

and effects of meteorology have been

removed. The weaker correlations associated

with O3 may have contributed to the ability

Articles • Vedal et al.

48 VOLUME 111 | NUMBER 1 | January 2003 • Environmental Health Perspectives

Table 1. Distribution of daily mortality counts and overall daily pollutant and meteorologic measures.

Min 10% 50% 90% Max Mean (SD)

Total mortality 16 27 35 43 60 35.0 (6.6)

Respiratory mortality 0 1 4 7 13 3.8 (2.2)

Cardiovascular mortality 4 9 14 19 28 14.0 (4.0)

PM10 (μg/m3) 4.1 7.8 13.1 22.8 37.2 14.4 (5.9)

O3 [ppb (1-hr max)] 3.1 14.6 27.3 39.4 75.1 27.4 (10.2)

NO2 (ppb) 4.3 11.8 16.1 22.9 33.9 16.9 (4.5)

SO2 (ppb) 0.3 1.2 2.4 4.9 15.4 2.8 (1.7)

CO (ppm) 0.3 0.4 0.5 0.9 1.9 0.6 (0.2)

Temperature (°C) –8.8 2.7 10.1 17.8 24.2 10.2 (6.0)

Relative humidity (%) 24.2 65.4 79.9 91.3 97.3 78.6 (11.1)

Barometric pressure (kPas) 97.4 100.7 101.7 102.4 103.9 101.6 (0.7)

Rainfall (% hours/day) 0 0 2.6 48.1 97.9 14.7 (20.9)

Abbreviations: Max, maximum; Min, minimum; kPas, kiloPascals.

Table 2. Pearson correlations among the overall daily pollutant and meteorologic measures by season.a

PM10 O3 SO2 NO2 CO Temperature Humidity Pressure Rainfall

PM10 0.48 0.76 0.84 0.71 0.61 –0.35 0.01 –0.47

O3 –0.32 0.44 0.45 0.12 0.41 –0.59 –0.02 –0.33

SO2 0.78 –0.41 0.80 0.67 0.59 –0.36 0.13 –0.47

NO2 0.73 –0.38 0.68 0.81 0.45 –0.24 –0.01 –0.31

CO 0.76 –0.65 0.83 0.78 0.28 0.12 –0.01 –0.19

Temperature –0.11 0.28 –0.13 –0.34 –0.29 –0.42 0.00 –0.41

Humidity –0.38 –0.38 –0.21 –0.21 –0.05 0.21 –0.20 0.55

Pressure 0.40 –0.25 0.35 0.24 0.28 –0.04 –0.21 –0.40

Rainfall –0.55 –0.02 –0.40 –0.31 –0.31 0.13 0.56 –0.40

aSummer (May–September) above diagonal; winter (October–April) below diagonal.
to detect effects of O3 that were relatively

insensitive to effects of other pollutants. The

difficulty of disentangling the effects of the

other pollutants is not surprising in view of

the high correlations among them. Because of

concerns regarding the interpretability of estimated effects in models containing several

strongly correlated variables, we did not

attempt to estimate effects from models that

included any more than two pollutant terms.

Findings from a study of the acute mortality effect of short-term increases in PM10 concentrations in the 88 largest cities in the

United States have recently been reported

(2,10) (in only 88 of the 90 cities was an effect

for PM10 estimated). Because these cities were

selected only on the basis of population size

and availability of pollution concentration

data, and a standardized approach was taken to

the analysis, these data provide the most comprehensive picture to date of the consistency of

acute PM10 effects on mortality. An overall

effect of a 0.21% increase in total mortality (at

a lag of 1 day) for each 10 μg/m3 increase in

PM10 concentration was estimated. However,

a substantial degree of heterogeneity of effect

across cities was observed, with the estimated

effect being zero or negative (that is, a decrease

in mortality effect associated with an increase

in PM10 concentration) in 32 of the 88 cities

(36%) in the single-pollutant models. The

presence of a positive effect in any given city

was not clearly related to the average PM10

concentration in that city, although there was

a trend for stronger effects to be present in

cities with lower PM10 concentrations, suggesting that the inability to observe a PM10

effect in the 33% of cities was not due to the

PM10 concentrations being below a certain

threshold concentration below which effects

were not present.

Based on our single-pollutant models, an

effect of PM10 was present for respiratory

mortality in the summer and for total mortality in the winter. However, these effects were

sensitive to the addition of other pollutants in

the two-pollutant models. The effects of

PM10 were present even though the average

daily PM10 concentration in Vancouver from

1994 to 1996 (14.4 μg/m3) was lower than

that of any of the 88 American cities during

1987–1994, where average daily PM10 concentrations ranged from a low of 15.3 μg/m3

to a high of 53.2 μg/m3 (2). In a recent analysis of the concentration–response relationship

in the 20 largest of these U.S. cities (4), the

investigators argued that no evidence for a

threshold for PM10 could be found, at least

above a concentration of 15 μg/m3. Similar

findings were reported for an analysis of all

88 cities (5). The effects of PM10 in this

study are consistent with the contention that

there is no threshold concentration for PM10,

nor for some of the other pollutants.

Because the absence of a concentration–

response threshold for most air pollutants

seems biologically implausible, consideration

should be given to possible reasons that timeseries studies seem to be unable to detect such

thresholds. First, a “blurring” of a threshold

might be expected if the use of ambient pollutant concentrations measured with a few

Articles • Low levels of air pollution and mortality

Environmental Health Perspectives • VOLUME 111 | NUMBER 1 | January 2003 49

Figure 3. Estimated increases (and 95% confidence intervals) in daily mortality (total, respiratory, and cardiovascular) corresponding to a standard deviation

increase in each pollutant from single-pollutant models for three lag periods for summer (May–September) and winter (October–April) periods.

Lag

Total mortality (summer)

8

6

4

2

0

–2

–4

–6

Lag Lag Lag Lag

01 2 01 2 01 2 01 2 01 2

PM10 O3 SO2 NO2 CO

Lag Lag Lag Lag Lag

01 2 01 2 01 2 01 2 01 2

Total mortality (winter)

4

2

0

–2

–4

Lag Lag Lag Lag Lag

01 2 01 2 01 2 01 2 01 2

Lag Lag Lag Lag Lag

01 2 01 2 01 2 01 2 01 2

Respiratory mortality (summer) Respiratory mortality (winter)

Cardiovascular mortality (summer) Cardiovascular mortality (winter)

Lag Lag Lag Lag Lag

01 2 01 2 01 2 01 2 01 2

Lag Lag Lag Lag Lag

01 2 01 2 01 2 01 2 01 2

30

20

10

0

–10

10

5

0

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–10

Percent change/SD change 10

5

0

–5

–10

6

4

2

0

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–4

–6

PM10 O3 SO2 NO2 CO

PM10 O3 SO2 NO2 CO PM10 O3 SO2 NO2 CO

PM10 O3 SO2 NO2 CO PM10 O3 SO2 NO2 CO Percent change/SD change

Percent change/SD changePercent change/SD change

Percent change/SD changePercent change/SD change
pollution monitors results in error in the

measurement of exposure. Such measurement

error must be present to some extent. Recent

findings of a simulation study based on actual

ambient and personal monitoring data suggest that measurement error can have a substantial effect on the ability to detect a

threshold (7). Others have not found that

measurement error causes any difficulty in

identifying a threshold within a meta-analysis

context using a collection of cities (3). The

detection of effects in Vancouver, where concentrations of all of these pollutants are low,

suggests that measurement error may not be

solely responsible for the inability to detect

threshold concentrations in settings with

higher pollutant concentrations.

Second, ambient concentrations of air pollutants may be acting as surrogate measures of

exposure to other agents or to specific pollution sources that are in fact responsible for the

observed effects. For example, there is no

known mechanism whereby exposure to

ambient O3 might produce adverse cardiac

effects, although some have been suggested

(17), yet O3 was associated with cardiac mortality in Vancouver. One could postulate that

ambient O3 concentrations in this setting may

be reflecting other pollutants in the photochemical smog mix that might potentially

have adverse cardiac effects, although the

identity of these agents is not known. No

apparent threshold O3 concentration might

therefore be observed if O3 were acting as

such a surrogate measure. Similarly, if PM10

concentrations or concentrations of other pollutants are acting as surrogate measures of

another unmeasured toxic pollutant or pollutants, or of specific sources, absence of an

apparent threshold for these pollutants could

also be observed. It has recently been suggested

(18) that in some settings concentrations of

gaseous pollutants may be better measures of

exposure to particle pollution than the particle

mass concentrations, in which case the apparent gaseous effects merely reflect unmeasured

effects of particles. For example, others have

also reported an inverse association between

O3 concentrations and mortality in the winter

(19) (see Figure 3). It has been proposed that

this effect is an example of negative confounding due to the negative correlation between

wintertime O3 and fine PM (20). This seems

unlikely in our data given the winter

correlation between O3 and PM10 of –0.32

(Table 2), although the negative correlation

with PM2.5 could have been stronger. There

was little change in the estimate of effect of

O3 on respiratory mortality in the winter in

the two-pollutant models (data not shown).

We had no data on PM2.5 during the 3 years

of the study to allow us to specifically address

this hypothesis. Additional monitoring in

multiple settings is needed to determine

whether this is likely.

Third, because meteorology is a strong

determinant of air pollutant concentrations in

an urban setting, with variation in pollutant

emissions being relatively minor, ambient pollutant concentrations might conceivably also

be acting as surrogate measures of meteorologic factors. If some of the observed effects of

the pollutants are due to meteorology rather

than to the pollutants themselves, again, a

threshold for the pollutants might not be

observable. The addition of daily meteorologic

variables to the regression models in this study

was aimed at controlling for effects of meteorology in the estimation of air pollutant

effects. Alternative approaches to controlling

for the effects of meteorology have not significantly affected estimates of pollution effects in

other studies (21), suggesting that significant,

uncontrolled confounding by meteorology is

unlikely. However, when effects of pollutants

are detected at low concentrations at which no

adverse effects would be expected (that is, no

threshold concentrations are detected), and

the primary determinant of pollutant concentrations is meteorology, then a case can be

made that the pollutant concentrations are

Articles | Vedal et al.

50 VOLUME 111 | NUMBER 1 | January 2003 • Environmental Health Perspectives

Figure 4. Estimated increases (and 95% confidence intervals) in daily mortality (total, respiratory, and cardiovascular) for a standard deviation increase in each pollutant from selected two-pollutant models. Corr,

correlation between the pollutant effect estimates.

Corr

–0.32

20

10

0

–10

Percent change/SD change

O3

lag 2

Total (summer)

8

6

4

2

0

–2 Corr

–0.67

Cardiovascular (winter)

Respiratory (summer)

Corr

Percent change/SD change

–0.58

Corr

–0.13

Corr

–0.22

PM10

lag 1

PM10

lag 1

SO2

lag 1

O3

lag 2

SO2

lag 1

O3

lag 0

SO2

lag 0

6

4

2

0

–2

–4

Corr

–0.76

Corr

–0.47

SO2

lag 1

PM10

lag 2

NO2

lag 2

PM10

lag 2

NO2

lag 2

SO2

lag 1

SO2

lag 1

NO2

lag 1

6

4

2

0

–2 Corr

–0.69

Total (winter)

Figure 5. Estimated increases (and 95% confidence intervals) in daily total mortality for a standard deviation increase in pollutant concentration estimated from

the single-pollutant models for the summer using (A) loess smoothing or (B) a trigonometric function approach to removing long-term temporal trends. Estimates

of effect using the trigonometric functions resulted in smaller estimates of pollution effect in this case.

Lag

5

0

–5

–10

Percent change/SD change

Lag Lag Lag Lag

01 2 01 2 01 2 01 2 01 2

PM10 O3 SO2 NO2 CO

Lag Lag Lag Lag Lag

01 2 01 2 01 2 01 2 01 2

CO

5

0

–5

–10

A B Percent change/SD change

PM10 O3 SO2 NO2
serving as better measures of the meteorologic

factors influencing mortality than the meteorologic measures themselves (in this case daily

temperature, humidity, barometric pressure,

and rainfall). Exposure measurement error of

the relevant meteorologic factors as measured

by pollutant concentrations would also presumably be less than that of the ambient pollutants. Given the multitude of adverse health

effects attributed to changes in meteorology

(22,23), confounding by meteorology still

seems plausible.

Confidence in the study findings can be

enhanced by demonstrating that the method

of data analysis did not substantially influence

the findings. The findings did not exhibit

much sensitivity to the approach taken to

removing the long-term temporal trends from

the data. Findings may have been influenced,

however, by the decision to use a seasonally

stratified analysis. The decision to stratify the

analysis by season was prompted by the obvious annual cycles in much of the time series

data (Figures 1 and 2). Rather than attempting to incorporate this seasonal complexity in

a single model, an approach that may not

succeed in adequately removing all of the seasonal correlations between the time-varying

measures (24), it seems justified to stratify by

season. A potential disadvantage of stratification is the loss of statistical power and the

associated instability of the estimates of effect,

but this concern seems relatively unimportant

in the current study.

For loess smoothing of the long-term

temporal trend, our Monte Carlo estimates of

the standard errors of the pollutant effects

were typically about 10% larger than those

reported by the S-Plus GAM module, though

for a few models they were as much as 30%

larger. A number of additional pollutant

effects would have been judged to be statistically significant based on the smaller standard

errors reported by the S-Plus GAM module.

The latter should not be used for model fits

involving loess smoothing (10,11). For the

present context of overdispersed Poisson

regression models, the only currently available

alternative for evaluating accurate standard

errors is a Monte Carlo approach. This is

computationally intensive and time-consuming. This fundamental limitation of the

S-Plus GAM module may encourage the use

of parametric approaches to smoothing (e.g.,

natural splines) for which explicit evaluation

of exact standard errors of pollutant effects is

straightforward even for this context.

In conclusion, assuming that the findings

from Vancouver are generalizable to other

cities with low pollutant concentrations,

increases in air pollutant concentrations, even

when concentrations are low, are associated

with adverse effects on daily mortality.

Although this observation may support the

argument that there are no threshold concentrations of air pollution below which adverse

effects cannot be detected, it also raises concern that the associations are not reflecting the

effects of the measured pollutants, but rather

some factor or combination of factors, such as,

for example, unmeasured air pollutants or

uncontrolled features of meteorology that are

correlated with the measured pollutants.

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Articles • Low levels of air pollution and mortality

Environmental Health Perspectives • VOLUME 111 | NUMBER 1 | January 2003 51