Articles

Daily Mortality and Air Pollution in Santa Clara County, California: 1989-1996

David Fairley

Bay Area Air Quality Management District, San Francisco, California, USA

Since the last revision of the national particulate standards, there has been a profision of epidemiologic research showing associations between particulates and health effects-mortality in particular. Supported by this research, the U.S. Environmental Protection Agency promulgated a

national standard for particulate matter C2.5 pm in aerodynamic diameter (PM25;). Nevertheless,

the San Francisco Bay Area of California may meet this new standard. This study investigates the

relationship between daily mortality and air pollution in Santa Clara County (a Bay Area county)

using techniques similar to those utilized in earlier epidemiologic studies. Stastically sigificant

associations persist in the early 1990s, when the Bay Area met national air pollution standards for

every criteria polutant Of the various pollutants, the strongest associations occur with particulates, especially ammonium nitrate and PM25. The coninuing presence of associations between

morulity and air pollutants calls into question the adequacy of national standards for protectig

public helth. Key wordr. air pollution, ammonium nitrate, cabon monoxide, epidemiology,

National Ambient Air Quality Standards (NAAQS), ozone, PM25, Poisson regession. Environ

Healtb Perspet 107:637-641 (1999). [Online 25 June 1999]

tsp:llehpnlt.niehs. nib.gpvdoc/1999/107p637-64lfairley/absaract.btml

The past decade has seen a burgeoning of

epidemiologic research investigating the relationship between air pollution and health

effects. Dozens of these studies have analyzed the relationship of daily mortality to

various air pollutants, especially particulates.

The U.S. EPA analyzed many of these studies in Chapter 12 of Air Quality Criteria for

Particulate Matter (1). This criteria document and the later staff report (2) concluded

that the preponderance of evidence supports

a causal connection between fine particulate

levels and various health effects, including

mortality. This led to the establishment of

national standards for particulate matter

< 2.5 pm in aerodynamic diameter (PM2.5). A previous study (3) showed that an association existed between particulates [measured

as coefficient of haze (COH)] and mortality

in Santa Clara County (SCC), California,

during the years 1980-1986. Since that time,

the Bay Area Air Quality Management

District (BAAQMD) has monitored particulate matter < 10 pm in aerodynamic diameter

(PM1O), and since 1990, the California Air

Resources Board has operated PM25 monitors, including one in SCC. An analysis of

SCC PM2.5 data shows that SCC would have

met the PM2.5 standard between 1991 and

1996. The present study is motivated by the

concern that, although SCC may attain the

new PM2 5 standard, particulates there may

still cause substantial health effects.

Air quality in SCC. Most of the studies of

mortality and air quality have been based on

eastern or midwestern U.S. cities, whose air

quality dynamics differ markedly from those

of the San Francisco Bay Area. Among the

gaseous pollutants, ozone and carbon monoxide levels are similar, but Bay Area sulfur

dioxide levels are an order of magnitude lower

than in the eastern United States. In fact, sulfur dioxide is so low that it is no longer measured in SCC, but nearby San Francisco's 24-

hr design value is < 0.01 ppm, compared

with typical design values of approximately

0.05 ppm in many eastern cities (4).

SCC's particulate composition, dynamics,

and sources also differ markedly from those of

eastern cities. In eastern cities, ammonium

sulfate represents approximately 45% of

PM25 (1), whereas in SCC it represents 5%.

For many eastern and midwestern cities, particulate levels peak in the summer months

(1). For SCC, however, particulates (especially fine particulates) are higher in winter.

Specifically, mean San Jose, California, PM2.5

levels in November, December, and January

averaged 25 pg/m3 in 1990-1996, but < 10

pg/mi during the rest of the year.

Wood-burning and ammonium nitrate

each contribute approximately 40% of

SCC's wintertime PM2.5 (5). These sources,

combined with wintertime stagnation periods, are the main causes of SCC's elevated

wintertime particulate levels. As a result of

this seasonality, the new SCC 15 pg/m3

annual standard appears no more stringent

than the 65 pg/m3 24-hr standard (6). This

is in spite of the EPA's stated intention to

make the annual average the more stringent

controlling standard (4.

Particle size also varies by season. During

the winter, SCC PM2.5 averages approximately 70% of PMIO compared with 50%

for the year as a whole. Wintertime PM1O is

dominated by combustion sources, with

approximately 10% coming from geological

dust. During the rest of the year, geological

dust makes up a larger fraction, marine sea

salt becomes significant, and the amount of

ammonium nitrate decreases by half.

For several years during the early 1990s,

SCC and, in fact, the entire Bay Area, had air

quality that complied with air quality standards for all criteria pollutants. Moreover, the

Bay Area would have attained the new 8-hr

ozone standard and, based on research monitoring results, would have attained the new

PM2.5 standard, had those standards been in

effect. In contrast, in the early 1980s, when

the previous study was done, SCC violated the

8-hr CO standard, the 1-hr ozone standard,

and would have violated the 8-hr ozone standard had that been in effect. Although PM1O

and PM25 were not measured in the 1980s,

SCC violated the 150 pg/m3 total suspended

particulate (TSP) standard almost every year.

Methodology

This study attempts to draw on the extensive

experience of previous studies to determine a

modeling approach. The sensitivity of conclusions to model choice, meteorological adjustment, and covariates has been extensively

investigated [e.g., (1,8-10)]. These studies

have reached similar conclusions, namely that

the choices of (reasonable) model and (reasonable) meteorological adjustment do not

appear to greatly affect conclusions on the

relationship between mortality and particulates, but the inclusion of other air contaminants often causes a substantial increase in the

standard error of the particulate regression

coefficient and sometimes a drop in the level

of the coefficient. In other words, there can be

substantial confounding of these variables.

Based on these considerations, various

models were tried, including Poisson regression with either linear predictors or generalized additive models (GAMs) for temporal

and weather variables, and models with an

overdispersion fit using quasi likelihood.

Address correspondence to D. Fairley, Bay

Area Air Quality Management District, 939 Ellis

Street, San Francisco, CA 94109 USA. Telephone:

(415) 749-4656. Fax: (415) 749-4741. E-mail:

dfairley@baaqmd.gov

The author wishes to thank J. Mann and T.

Porco from the San Francisco Department of

Public Health for their help and suggestions on the

use of generalized additive models and modeling

building simulations.

The opinions expressed here are those of the

author, and not necessarily those of the Board of

Directors or staff of the Bay Area Air Quality

Management District.

Received 19 January 1999; accepted 31 March

1999.

Environmental Health Perspectives * Volume 107, Number 8, August 1999 637
Articles * Fairley

The disadvantage of the GAM approach is

that it does not provide simple coefficients.

Because the focus is on pollutant variables,

however, this lack is not of great concern.

The advantage is that the GAM approach is

less likely to induce lack of fit. Thus, we will

use the GAM approach. Models with an

overdispersion parameter are useful for certain deviations from the Poisson model.

However, if the Poisson model appeared

adequate, it would be tised.

The modeling strategy follows that of

Samet et al. (10), first fitting terms for season

anid trend, then adding terms for meteorology, and finally adding pollutant terms, with

the number of seasonal, trend, and meteorology terms determined by optimizing Akaike's

information criterion (AIC).

Tests ofgoodness offit. A goodness of fit

test of the Poisson model was performed

based on deviance. Under the null hypothesis that the data derive from this model, the

deviance has an approximately X2 distribution with the residual degrees of freedom.

Specifically, the x2 test is a likelihood ratio

test versus a saturated model, where each day

is fitted with a different mean. Seriouis lack

of fit would result in unusually large values

of the deviance.

Residuals were checked for extreme values. The CAM approach minimizes problems

with any nonlinearity between the response

and the temporal and weather variables.

To test the sensitivity of the results to the

use of CAM, a parallel modeling approach

was performed using sine and cosine terms

for time and day of year and polynomials in

minitnum and maximum temperature.

A simulation ofthe model-fitting process.

The statistical significance level for testing a

parameter in a model is based on the assumption that the selection of the model was made

before the data were gathered. In practice,

this is rarely the case, so the defacto assumption is that the process of model-building has

a minimal effect on the significance level.

An approach to finding more realistic

significance levels is to simulate the modelbuilding process itself. To that end, an SPlus function was developed to simulate the

following approximation of model-building.

The idea was to simulate data from a true

model that contains no pollutant term, then

simulate the building up of the model and

the fitting of a pollutant variable. The set of

pollutant variable coefficients thus obtained

should form a more realistic distribution

than the simple one where model-building

is ignored.

The steps of the simulation were as follows. Initially, a vector of Poisson means was

generated by fitting daily mortality data to the

seasonal, trend, and weather variables using

the (AM approach. An S-Plus function was

then invoked repeatedly with different random seeds that performed the following steps:

1. The function simulates a vector of Poisson

variates from the initial mean vector.

2. It fits this simulated variate vector to

CAM terms for time and day of year in a

Poisson regression, increasing the degrees

of freedom until there is no improvement

in AIC from the addition of another

degree of freedom in either GAM term.

3. It uses the optimal number of degrees of

freedom for time and day of year from step

2, and adds GAM terms for minimum and

maximum temperature, again adding terms

until there is no improvement in AIC.

4. It fits the simulated variates to PM,J) in

addition to the optimal number of time,

day of year, and minimum and maximum

temperature GAM terms found in steps 2

and 3, wvith the fitted PM-, coefficienlt

ouLtput.

The coefficient found from the actual

data is then compared to the resulting distribution of simulated coefficients, providing

what may be a more realistic p-value.

The data. California mortality data were

obtained from the California Department of

Health Services (Sacramento, CA) for the

years 1989-1996. Counts of daily total

nonaccidental mortality (henceforth described

as mortality), respiratory mortality, and

cardiovascular mortality were extracted for

SCIC residents who died in-county, using the

same International Classification of Diseases,

Ninth Revision (11) codes as in the previous

study (3).

Pollutant data were obtained from the

BAAQMD pollutant database. Long-term

PM1O data were available for only one SCC

site San Jose 4th Street. These data cover

the full period on an every-6-day schedule,

with an every-other-day schedule during the

first 3 years. This site also provided PM1o

constituents nitrate and sulfate on the 6-day

schedule and daily COH values. PM215 and

PM10-2)5 were also available from a research

model dichotomous sampler that operated at

this site from 1990 through 1996 on the

satne 6-day schedule.

Ozone, carbon monoxide, and nitrogen

dioxide data were also obtained for the 4th

Street site. Although data for ozone were available from some other SCC sites, these were

not included in the interests of simplicity.

Because national standards are health-based it

seemed reasonable to include variables with

averaging times as defined in the standards,

namely maximum 8-hr ozone, maximum 8-hr

CO, and 24-hr NO,. Nevertheless, 24-hr CO

and ozone were also considered.

Comparisons of 4th Street ozone with

other SCC' sites conlsistentlytI show correlationls above 0.8 in seasonally, adjtisted ozoie

concentrations. Thus, 4th Street ozoone concentrations represent a reasonably good surrogate for outdoor ozone exposure in SCC.

Based on data from the late ]970s when

the district operated a number of (OH

monitors in SCC, correlations with the 4th

Street site were quite high. The correlation

between season- and trend-adjusted PM-)

for Fremonit and 4th Street was 0.86.

Weather data were obtained from the

BAAQMD meteorological database for San

Jose Airport. Previous studies have found

inonlinear relationships between mortality

and weather variables. Because mortalitr

can be affected by both hot anid cold weather, it seemied reasoInable to consider both

minimunm and maximtum temperature as

variables. 'Fherefore, both daily maxinmunm

and minimum temperature as well as 24-hr

average relative humidity data (rh) were

obtained. Missing values were filled in by

regressing against temperature and rh values

at other nearby BAAQMD meteorological

sites Alviso and Union City.

Comparison with previous results. To

compare the results for 1989-1996 with the

previous 1980-1986 resuilts it was necessary

to reanalyze the earlier results paralleling the

nlew analysis as closely as possible.

For the 1980-1986 reanalysis, I'M-1, and

PM10 and its species were not available. COH

was used along with NO3 from the TSP filter.

The other pollutants-NO2, 03, and COwere measured, although the results were read

from strip charts and recorded with one less

significant digit. San Jose Airport data were

not available for this time period; therefore,

San Jose city temperatures were used.

Results

Table 1 provides summary statistics for

PM, related to the new PM,, statndards.

Note that from 1993 through 1996 the 4th

Street site would have met the new I'M',

standards, based on results from the dichotomous sampler. Although the new PM21

network will include many sites, the 4thi

Street site has historically had the highest

particulate (PMIO) levels in the Bay Area.

Table 1. PM25 design values (pg/m3): San Jose, CA, 4th Street, 1990-1996.

1990 1991 1992 1993 1994 1995 1996

98th percentile 88 51 48 50.0 44 32 25

3-Year average 62.4 50.0 47.2 42.0 33.6

Annual mean 18.4 15.5 13.8 12.9 12.6 10.3 9.5

3-Year average 15.9 14.1 13.1 11.9 Au.'

PM2 5' particulate matter < 2.5 pm in aerodynamic diameter.

638 Volume 07 Number 8 August 999 * Environmental Health Perspectives
Articles * Daily mortality and air pollution in Santa Clara County

Thus, there is a good chance that the Bay

Area (including SCC) would have met the

standards had PM2.5 samplers been in operation at other BAAQMD monitoring sites.

Table 2 presents summary statistics for all

the variables considered. Note that PM2.5 and PMI102.5 do not sum to PM1O, and that

the number of observations for the PM1O

fractions was 408 compared with 823 for

PM o. This is because the fractions were

measured with a dichotomous sampler

whereas the PM1O was measured with the

district high-volume sampler. Although the

dichotomous sampler averages lower than the

district sampler, the two PM1O measurements

have a correlation of 0.94.

Season and trendfits. Rather than predicting mortality from a single temporal GAM

term, separate CAM terms in time and day of

year were fit because a good-fitting model

could be obtained using many fewer degrees of

freedom. Terms were added sequentially until

there was no further improvement in AIC.

The best model contained a GAM term for

time with 7 degrees of freedom (dfi and a day

of year term with 12 df. The resulting

deviance was 3,038.5, with AIC 3,078.5.

Meteorological variables. GAM terms

were fit for minimum and maximum temperature in addition to a 7-degree term for time

and a 12-degree term for day of year, yielding

an optimum AIC with 3 dffor minimum

temperature and 2 df for maximum.

(Subsequendy, this set of GAM terms will be

referred to as the optimal GAM terms.) The

inclusion of a minimum-maximum crossproduct term did not improve the AIC, nor

did the inclusion of relative humidity. The

model had a deviance of 2,998.1 on 2,897 df,

and an AIC of 2,897 df

To test for lack of fit, a quasi likelihood

model was fit. The overdispersion parameter

of 1.02 is barely larger than 1, the value for

the Poisson. The p-value for a X2 of 2,998.1

with 2,897 dfis 0.09, so that the Poisson

model cannot be rejected at the 0.05 level.

Table 2. Summary statistics for mortality, weather variables, and pollutant concentrations, San Jose CA,

1989-1996.

Variablea

Mortality (count)

Respiratory mortality (count)

Cardiovascular (count)

mortality

Min temperature (°F)

Max temperature ('F)

Relative humidity (%)

PM10b (pg/m3)

PM2 5b (pg/m3)

PM10.2 5b (pg/M3)

COH (COH units)

NO3 (Pg/M3)

S04 (pg/Mi3)

03 (ppb)

8-hr 03 (ppb)

CO (ppm)

8-hr CO (ppm)

NO2 (ppb)

Obs

(no.)

2,922

2,922

2,922

2,922

2,922

2,922

823

408

408

2,780

523

534

2,856

2,908

2,849

2,865

2,888

Mean ± SD

20 ± 5

2.5±1.7

8.7 ± 3.2

50 ± 8

70 ± 10

70 ± 12

34 ± 23

13 ± 13

11 ±6

0.5 ± 0.4

3.0 ± 3.7

1.8 ± 1.3

16±9

29 ± 15

1.4±1.0

2.1 ± 1.6

28 ± 13

Min

6

0

0

22

39

24

6

2

0

0.0

0.0

0.0

0

0

0.0

0.2

5

Lower

quartile

17

1

6

45

62

63

19

6

7

0.2

0.9

0.9

10

19

0.8

1.0

19

Med

20

2

9

51

70

70

27

9

11

0.3

1.8

1.5

16

29

1.1

1.5

25

Upper

quartile

24

3

11

56

77

77

40

14

15

0.5

3.7

2.5

22

37

1.7

2.5

34

Max

40

14

24

70

101

100

165

105

45

3.1

33.1

7.9

51

105

7.6

12.0

96

Abbreviations: COH, coefficient of haze; Max, maximum; Med, median; Min, minimum; Obs, observations; PMI.5, particulate

matter < 2.5 pm in aerodynamic diameter; PM10, particulate matter s 10 pm in aerodynamic diameter; SD, standard deviation.

aValues are 24-hr averages unless otherwise noted. blhe fine and coarse fraction of PM10 do not add to total PM10 because

they derive from the dichotomous sampler, whereas total PM10 was collected with a separate high-volume sampler.

Pollutant variables. Table 3 presents

partial correlations between mortality and

the pollutant variables. Specifically, mortality and each pollutant variable were regressed

against the optimal GAM terms, and the

residuals saved. Several of the Poisson regressions did not converge, so least squares

regressions were used. The table presents the

correlations among these residuals.

Of the pollutant measures, PM2 5 and

NO3 have the highest partial correlations

with mortality. There are also reasonably

high correlations with PM1O and SO4.

Interestingly, in contrast to other studies,

there are actually negative correlations

between mortality and the lags (previous

day) of these variables. Another change is

that COH is only weakly correlated with

mortality, although there is a statistically significant correlation with lagged COH. The

relationship with 24-hr CO is similar to that

of COH. NO2 is also highly correlated with

COH, but lag NO2 has a lower partial correlation with mortality than unlagged NO2.

The correlation between ozone and mortality is weak, although the correlation with 8-

hr ozone is borderline significant.

Except for ozone, there are positive correlations between the other pollutant variables,

with high correlations between some of the

particulate measurements (PM2.5 and PM10,

PMIO and COH, and PM2 5 and NO3).

There are also high correlations between

NO2 and PM1O, and NO2 and COH.

Various combinations of pollutants were

tried in Poisson regressions that also included

the optimal GAM terms. The results are

shown in Table 4. As with the partial correlations, both NO3 and PMIO were highly significant. PM2 5, SO4 and 8-hr ozone were

also marginally statistically significant. Among

lagged variables, COH and CO were highly

significant and NO2 was marginally so.

PM10-2.5 was not significant, nor was its lag.

Because NO3 and PM25 had the highest

partial correlations with mortality, these were

included in regressions with other pollutants.

Table 3. Partial correlations among mortality and pollutant variables.a

Mortality vs.

Variable Mortality lagged variable PM10 PM25 PM10-25 COH NO3 S04 03 8-hr 03 CO 8-hr CO

PM10 0.116 -0.026

PM2.5 0.142 -0.084 0.849

PM10.2 5 0.037 -0.031 0.649 0.508

COH 0.026 0.041 0.827 0.772 0.476

NO3 0.141 -0.061 0.689 0.830 0.496 0.493

S04 0.100 -0.040 0.509 0.499 0.444 0.318 0.614

03 0.025 -0.013 -0.064 -0.122 -0.026 -0.289 -0.103 0.102

8-hr 03 0.037 -0.016 -0.102 -0.153 -0.070 -0.330 -0.108 0.085 0.844

CO 0.033 0.048 0.609 0.435 0.326 0.736 0.270 0.146 -0.215 -0.210

8-hr CO 0.029 0.024 0.620 0.542 0.378 0.750 0.285 0.155 -0.236 -0.277 0.810

NO2 0.036 0.022 0.800 0.662 0.590 0.826 0.459 0.293 -0.228 -0.219 0.651 0.643

Abbreviations: COH, coefficient of haze; PM25, particulate matter < 2.5 pm in aerodynamic diameter; PM10, particulate matter < 10 pm in aerodynamic diameter.

"Correlations of residuals from regressions on generalized additive model terms of degree 12 for day of year, degree 7 for time, degree 3 minimum temperature, and degree 2 for maximum

temperature.

Environmental Hea/th Perspectives . Volume 107, Number 8. August 1999 639
Articles * Fairley

NO3 was statistically significant paired with

every other pollutant except PM,,. PM,

was statistically significant paired with every

pollutant except NO3 and PM11. No other

pollutant was statistically significant in

regressions with either of these pollutants.

Two runs of all four categories of pollutant

were made (particulate, NO,, CO, and

ozone), using either PM2,5 or NO3 as the

particulate variable. In these regressions, the

particulate variable was highly statistically sig

nificant and the others were not.

Goodness offit. In the fitted models in

Yable 4 that included PM,,, the deviance

is actually 364.7- less than its degrees of

freedom (382). This implies p-values of 0.5

or higher, i.e., there is no indication of lack

of fit. Similarly, for the model with NO3,

the deviance is 503.4, with 497 df the pvalue is 0.31.

There are no large y-outliers, the largest

daily mortality value being 40, approximately 4 standard deviations (SDs) above

the mean. Several of the pollutant variables

are right-skewed-PM2 5 and NO3 in particular. However, taking the log of PM s

(after first adding 5 pg/m3 to reduce undue

influence of small PM25 values) eliminates

much of the skewness, and the transformed

variable is still statistically significant in a

Poisson regression with the trend, seasonal,

and meteorological terms.

The raw mortality values have an autocorrelation of 0.18, but the residuals from

the multiple regression of mortality On

trend, season, and weather terms has an

autocorrelation of only 0.04. Thus, autocorrelation of residuals is not a significant issue.

The fact that the deviance is approximately equal to the value expected under the

null hypothesis suggests that it would be

difficult to improve the fit substantially. The

lack ofY-outliers, the lack of influential x-values. and the lack of autocorrelation stiggest

that the Poisson model fits reasonably well.

Analysis using a parametric app-oach.

10o check the adequacy, of the GAM

approach, a parallel analysis was performed

using sine/cosine ftinctions for season and

trend, and polynomials for weather variables.

[he results were similar both qualitatively

aind quantitatively to those found in Table 4.

A simulation of the model-fitting process.

The simutlation described in "A Simulation of

the Model-Fitting Process" in "Methodology"

was repeated 1,000 times. It yielded four fitted coefficients greater than that observed so

that, based on the simulationi, the p-value is

approximately 0.004. This p-value is, if anything, smaller than that found using statistical

theory, where the p-value was 0.012.

Comparison with 1980-1986 results.

ITable 5 presents a reanalysis of the

1980-1986 data using methods paralleling

those of Table 4. In particular, the same

variables for season, trend, and weather were

used (although they were refit with

1980 19986 data). To make coefficients

comparable, the same deltas are used; that is,

50 x S[)(p)/SD(PM,()), where the SDs are

from the 1989-1996 data.

Generally, the results for the 1980-1986

period are similar to those of 1989-1996. In

particular, with the exceptioni of ozone, the

coefficient for every pollutant or the lagged

pollutant is statistically significanit. In pairw ise models with lagged COH, the other pollutants are no longer statistically significacnt.

l agged C(OH remains highly significanit in

conbin.ation with NO, and ozone; with CO,

it is nlot statistically significant, but its regressioin coefficienlt is little changed. NO2 is

Table 4. Pollutant relative risksa for modelsb with pollutant alone, lagged, and with other pollutants, Santa

Clara County, CA, 1989-1996.

Pollutant PM10 PM25 PM10 2. COHC NO3 So4 NO2C COc 8-hr 03

Alone 108** 109* 102 103 107"' 105* 103 102 103*

Lagged 099 096 098 1.05** 098 098 1.03* 1.04** 099

With

PM10 - 1.13 - - 107* - -

PM2,5 096 - 097 099 1.09 1.00 096 098 1.04

PM1025 - 113* - - 11**

Lag COHc - 1 11* - - 1 07

NO3 1,02 1.00 095 1.01 1.01 099 1.01 1.05

S04 - 1.10* - - 1.07* - - -- -

Lag NO,` 1 12** 1 08**

LagCO 11** 1 07**

8-hr03 - 1 10** 1 08**

Four-pollutant' - 1 13** 0.96 1.00 1.05 Four-pollutant" - 1.09** 095 1.06 1.07

Abbreviations: SD, standard deviation; COH, coefficient of haze; PM2 5, particulate matter < 2.5 pm in aerodynamic diameter; PM10, particulate matter < 10 pm in aerodynamic diameter. aRelative risks calculated by exp(b x Ap) - 1, where b is the pollutant coefficient from the Poisson regression, and Ap

50 for PM10, corresponding to the increment used in the criteria document 1). For other pollutants, p, the increment was

50 x SD(pI/SD(PM10); e.g., SD(PM2.5) = 13, SD(PM10) = 23, so for PM2 5, Ap = 50 x 13/23 = 28. bAll models include 7 generalized additive model terms for trend, 12 for season, 3 for minimum temperature, and 2 for maximum temperature. cLagged

variables were used if they appeared to fit better lagged than unlagged. Thus, lagged CO and lagged COH were used

when fitting jointly with other pollutants. dPollutants are lagged CO, lagged NO2, 8-hr ozone, and either PM2 or NO3 *Statistical significance at the 0.05 level. **Statistical significance at the 0.01 level.

borderline significanit (p= 0.06) with the

ozoIne, but not significant with CO or NO,.

Oddly, in combination with NO3, NO, was

significant. Note that the sample i7,c for No,

is only 354, comnpared with over 2-000 for thc

other pollutanits. [he small sample size miakes

it more difficult to detect an effect. When

both lag COH and NO, are in the model,

the COH coefficient is smnaller and nio longet- statistically significant, whereas the NO, coef

ficient changes only slightly and is borderlilne

significant (p= 0.09).

One difference wx ith the 1989-1996

results is that the 1980-1986 CO(H coefficient is highly signiificanit. with a relatiVe

risk of 1 .06, comipared wvith 1.03 for

1 989-1996. A comparison of the tswso coefficients takinig theii differencce and dividing

by the square root of the SUIml of tlhcir- sample

variances yields a value of z= 1.36, not statistically significant. Pooling the two periods, fitting the same coefficient for season, trend,

and weather, but with different COH slopes

and intercepts did not result in a statistically

significant difference in COH coefficients.

One possible reason that the COH coefficient might have changed is that COH has

diminished from the early 1980s to the

1990s. Thus, if the effect of COH is not linear, this could result in different coefficients.

However, neither a quadratic nor a hockeystick function of C(OH was sigiificuint in the

pooled regressionis for either period.

Respiratoiy and cardiovascular regr.essions.

Table 6 shows relative risks fiom ioisson

regressions iSing eaclh pOlluIltant or their lags

(dependinig on wlhich hadl the greotter risk

based ont Fable 4). So04 anid CO were significantlv associated rirespiratoryi mortalits

Table 5. Pollutant relative risks' for modelsb with

pollutant alone, lagged, and with other pollutants,

Santa Clara County, CA, 1980-1986.

COH NO3 NO2 CO 8-hr 03

Alone 105** 104` 104' 1.04`' 101

Lag 1 1.06** 1.00 103`' 105'' 1.02

Lag COH' 1.03 1.01 1.00 1.02

NO 103 108 103 103

NO2 105** 101

Lag CO 1.06 1.03

Lag 03' 1.06** 1.04

Four-poll2 106 - 101 100 102

Abbreviations: COH, coefficient of haze; poll, pollutant;

PM10, particulate matter < 10 pm in aerodynamic diameter; SD, standard deviation.

aRelative risks calculated by exp(b x Ap) - 1, where b is

the pollutant coefficient from the Poisson regression, and

Ap = 50 x SD(p)/SD(PM10) for the pollutant, p, where

SD(p) and SD(PM1O) are computed on 1989-1996 data. bAll

models include 7 generalized additive model terms for

trend, 12 for season, 3 for minimum temperature, and 2 fot

maximum temperature. cLagged variables were used if

they appeared to fit better lagged than unlagged. Thus,

lagged CO, COH, and 8-hr 03 were used when fitting joint- ly with other pollutants. dPollutants are lagged CO, NO2,

lagged 8-hr 03, and lagged COH.

*Statistical sigilificarice at the 0.05 level. Statistici31

significariri' a,, the 001 level

640 Volume 107 Nurmtber 8 August ') . LEnvirorietcil HuAth P r
Articles * Daily mortality and air pollution in Santa Clara County

Table 6. Respiratory and cardiovascular mortality relative risksa for modelsb with pollutantc alone, Santa

Clara County, CA, 1989-1996.

PM10 PM2.5 PM102.5 Lag COH NO3 S04 Lag NO2 Lag CO 8-hr 03

Respiratory 1.11 1.13 1.16 1.07 1.10 1.15* 1.07 1.08* 0.96

Cardiovascular 1.09* 1.07 1.03 1.03 1.09* 1.04 1.02 1.04* 1.02

Abbreviations: COH, coefficient of haze; PM25, particulate matter c 2.5 pm in aerodynamic diameter; PM10, particulate

matter < 10 pm in aerodynamic diameter.

&Relative risks calculated by exp(b x Ap) - 1, where b is the pollutant coefficient from the Poisson regression and Ap = 50

for PM10 and 50 x SD(p)/SD(PM10J for other pollutants, p, e.g., SD(PM251 = 13, SD(PM10) = 23, so for PM25, Ap = 50 x 13/23

= 28. bAll models include 7 generalized additive model terms for trend, 12 for season, 3 for minimum temperature, and 2

for maximum temperature. cLagged variables were used if they appeared to fit better lagged than unlagged. Thus, lagged

CO and lagged COH were used when fitting jointly with other pollutants.

*Statistical significance at the 0.05 level.

and PM2 5, NO3, and CO were associated

with cardiovascular mortality. For PM1O,

PM1I2.5, NO3, and CO the point estimates

for risk were higher than those in Table 4.

Analyses by season. Analyses were performed by season for pollutants with the

highest partial correlations with mortality

(Table 7). In most cases, the change in relative risk is not statistically significant. Based

on Tukey's studentized range distribution,

the risks differ significantly from season to

season for NOY For the other pollutants,

the differences in risk between seasons are

not statistically significant.

Discussion

One striking result of the analysis is that

although the Bay Area met every air quality

standard in the early 1990s (and would have

met the new 8-hr ozone and PM2.5 standards had they been in effect), there is a statistically significant correlation between each

pollutant considered (except coarse fraction

PM1O) and mortality. Second, the regression

coefficients of other pollutants that are correlated with particulates-CO and NO2-

drop to nonsignificance in a regression that

also includes some measure of fine particulates (either PM2.5 or NO3), whereas there is

little change in the fine particulate coefficients. This suggests that fine particulates (or

what fine particulates may be a surrogate for)

may be the real culprits. The result that

NO3 had the strongest association with mortality is clearly of practical importance and

worth investigating for other areas.

The level of PMIO effect found-a relative risk of 1.08 for an increase of 50 pg/m3

PM10-is larger than that found in many

other studies [see the EPA's Table 12-37 and

Figure 12-43 (1)]. This may reflect a better

correlation between monitored values and

exposure in SCC. Part of the explanation

may be that buildings in SCC are not as

tight because of its mild climate, which

could lead to a higher correlation of indoor

and outdoor particulate levels. A second

point is that the correlation between particulate values measured at the San Jose 4th

Street monitor and other SCC monitors is

high. Particulate levels at the 4th Street

monitor exceed those of other SCC monitors (12); therefore, the relative risk as a

function of SCC average levels could be

higher than 1.08.

No evidence for a threshold was found.

Although the COH coefficient was substantially lower for the 1989-1996 period than

for 1980-1986, the result did not appear to

be due to the lower particulate levels in the

later period. One point that is important to

keep in mind is the role of chance in these

comparisons; because there is marginal power

to detect effects of this magnitude, some data

sets may yield nonsignificant results whereas

others yield highly significant results.

Although the results for respiratory and

cardiovascular mortality showed fewer significant results than for mortality as a whole,

the level of effects appeared somewhat higher. The number of cardiovascular and respiratory deaths is considerably smaller than all

deaths (Table 2), so the power to detect an

effect is less unless the effect is much larger.

The results by season were ambiguous.

The lack of statistical significance of most of

the coefficients can be attributed to lack of

power. The criteria document (1) found that

a minimum sample size of 400 was necessary

to achieve reasonable power in epidemiologic studies such as this. For PM2 5, NO3, and

S04, there were approximately 100 observations per season, far below the 400 observations necessary to achieve reasonable statistical power. Nevertheless, a statistically significant difference in effect was found for NO3,

with positive effects for winter, spring, and

summer, and a negative effect for fall;

although only the winter coefficient was statistically significant, the range of coefficients

was larger than expected by chance.

This analysis has found associations

between air pollution variables and mortality-especially with fine particulate variables-similar to the levels of associations

found in the studies that were used to justify

the new PM2 5 standards. Yet the Bay Area

probably meets these new standards. The

Table 7. Relative risks by season.a,b

Lag

Season PM10 PM2.5 COH NO3 S04

Springc 1.08 1.07 1.02 1.07 1.06

Summerd 1.10 1.05 1.13 1.32* 1.11*

Falle 1.07 1.04 1.08 0.87 1.03

Winterf 1.06 1.05 1.04* 1.07* 1.00

Abbreviations: COH, coefficient of haze; PM25, particulate matter < 2.5 pm in aerodynamic diameter; PM10, particulate matter < 10 pm in aerodynamic diameter.

&Relative risks calculated by exp(b x Ap) - 1, where b is

the pollutant coefficient from the Poisson regression and

Ap = 50 for PM10 and 50 x SD(p)/SD(PM10) for other pollutants, p e.g., SD(PM25) = 13, SD(PMJ0) = 23, so for PM25,

Ap = 50 x 13/23 = 28. bAll models include 7 generalized

additive model terms for trend, 12 for season, 3 for minimum temperature, and 2 for maximum temperature.

CFebruary, March, April. dMay, June, July. *August,

September, October. 'November, December, January.

*Statistical significance at the 0.05 level.

new PM25 standards may be protective in

other areas where seasonal PM variations are

not as great. In the Bay Area, however, the

seasonal variation in PM2.5 is large, with

winter concentrations averaging more than

double that of summer concentrations. The

results of this analysis suggest that current

national air quality standards, specifically

those for particulates, may not be protective

of public health for the Bay Area.

REFERENCES AND NOTES

1. U.S. EPA. Air Quality Criteria for Particulate Matter.

EPA/600/P-95/OOlaF. Washington, DC:U.S. Environmental

Protection Agency, 1996.

2. U.S. EPA. Review of the National Ambient Air Quality

Standards for Particulate Matter: Policy Assessment of

Scientific and Technical information. OAQPS Staff Paper

EPA-452\R-96-013. Washington, DC:U.S. Environmental

Protection Agency, 1996.

3. Fairley D. The relationship of daily mortality to suspended

particulates in Santa Clara County, 1980-86. Environ Health

Perspect 89:159-168 (19901.

4. AIRS database. AIRSWEB query-annual summary

http:/www.epa.gov/aqspubil/annual-summary.html (1998)

[cited 11 May 19981.

5. Fairley D. Source Apportionment of Bay Area Particulates.

BAAQMD Draft Technical Report. San Francisco, CA:Bay

Area Air Quality Management District, 1996.

6. BAAQMD. Comments on New Proposed National Air

Quality Standards. Attachment 4: Stringency of Proposed

Standards: Particulates. San Francisco, CA.Bay Area Air

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7. U.S. EPA. 40 CFR 50. National Ambient Air Quality

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7:59-65(1995).

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12. Fairley D. Unpublished data, 1994.

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