Articles



# Daily 

# Mortality and Air 

# Pollution in 

# Santa 

# Clara County, 

# California: 1989-1996

DavidFairley

BayArea Air Quality Management District, San Francisco, California, USA

Sincethelastrevisionofthenationalparticulate standards, therehasbeenaprofisionofepidemiologic

### research showing

### associations betweenparticulates and

### health

### effects-mortality inparticular. Supported by this research, the U.S. Environmental Protection Agencypromulgated a 

### national

### standardfor

### particulatematter

## C2.5pmin

### aerodynamicdiameter 

## (PM25;). Nevertheless, the SanFrancisco BayAreaofCaliforniamaymeet thisnewstandard. This study

### investigates the relationship between

### dailymortalityandairpollution inSanta

### Clara

### County (aBayAreacounty) usingtechniques similar to those utilized in earlier 

### epidemiologic studies. 

### Stastically sigificant associations

### persist in

### theearly 1990s, whenthe

### BayAreametnationalairpollutionstandards for


### everycriteria

### polutant Ofthe various 

### pollutants, the 

### strongest associations occurwith

### particulates, especially ammoniumnitrate and PM25. The coninuingpresence ofassociations between 

### morulityand airpollutants calls into question the adequacyofnational standards forprotectig 

### public helth. Key wordr. air 

### pollution, ammonium nitrate, 

### cabon monoxide, epidemiology, National Ambient Air Quality Standards 

### (NAAQS), ozone, PM25, Poisson 

### regession. Environ 

### Healtb 

### Perspet107:637-641 (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 rela

### tionship between air 

### pollution and health effects. Dozens 

### of these 

### studies have ana

### lyzed the 

### relationship 

### ofdaily mortality to 

### various 

### air 

### pollutants, 

### especially particulates. The U.S. EPA

### analyzed many 

### ofthese 

### studies in 

### Chapter 12 

### ofAir 

### Quality Criteria

### for Particulate Matter 

### (1). This criteria 

### document 

### and the later 

### staffreport (2) 

### concluded that the 

### preponderance ofevidence 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

### existedbetween

### particulates 

### [measured

as 

### coefficient ofhaze 

### (COH)] and 

### mortality in Santa 

### Clara County 

### (SCC), 

### California, during theyears 

### 1980-1986. Since 

### that time, 

### the Bay Area Air 

### Quality Management District 

### (BAAQMD) has monitored particu

### late matter < 10 

### pm in

### aerodynamicdiameter 

## (PM1O), and since 

### 1990, 

### the 

### California Air Resources 

### Board 

### has 

### operated PM25 moni

### tors, 

### including one in SCC. An 

### analysis of SCC

## PM2.5 data shows that SCC

### wouldhave 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

### qualityin SCC. Most 

### ofthe 

### 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 monox

### ide levels are 

### similar, but 

### Bay Area 

### sulfur 

### dioxide levels are an

### orderof

### magnitude lower than in 

### the eastern

### UnitedStates. In 

### fact, sul

### furdioxide is so 

### lowthat it is 

### no 

### longermea

### sured in 

### SCC, 

### but

### nearbySan

### Francisco's 24- hr 

### design value is < 

### 0.01 

### ppm, 

### compared with typical 

### design values 

### of approximately 0.05 ppm in

### manyeastern

### cities (4). SCC's 

### particulate composition, 

### dynamics, andsources 

### also

### differmarkedlyfrom 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 (especial

### ly 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 duringthe rest

### oftheyear. 

### Wood-burning and ammonium nitrate 

### each contribute 

### approximately 40% of SCC's wintertime

## PM2.5 (5). These sources, 

### combined with wintertime 

### stagnation 

### peri

### ods, 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 

### alsovaries 

### byseason. During 

### the winter, SCC 

## PM2.5 averages 

### approxi

### mately 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 ammoniumnitrate

### decreases by

### half. For several years during the early 1990s, SCC 

### and, in 

### fact, the entire 

### BayArea, had air 

### quality that 

### complied with air 

### quality stan

### dards 

### for all criteria

### pollutants. Moreover, the 

### BayArea 

### 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 

### studywas

### done, SCC

### violatedthe 8-hr CO 

### standard, the 1-hr ozone 

### standard, 

### andwould have 

### violated the 8-hr ozone stan

### dard 

### 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) 

### standardalmostevery

### year. 

### Methodology This 

### studyattempts to 

### draw on the extensive experience 

### ofprevious 

### studies to 

### determine a 

### modeling approach. The sensitivityofconclusions 

### 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 (rea

### sonable) meteorological adjustment do not 

### appear to 

### greatly affect conclusions on 

### the relationship between 

### mortality and particu

### lates, 

### 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 

### ofthe

### coefficient. In 

### otherwords, there can

### be 

### substantial confoundingofthesevariables. 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 637Articles * 

### 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 offit. 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, itwould 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 

### ofseasonal, 

### trend, and meteorology terms 

### determined

### byoptimizingAkaike's 

### information criterion (AIC). Tests 

### ofgoodness 

### of

### fit. 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 ofthe deviance. 

### Residuals were checked for extreme values. The CAM 

### approach minimizes 

### problems with any 

### nonlinearity between the 

### response and the 

### temporal andweather

### variables.
To test the sensitivity 

### ofthe results to the

use 

### of CAM, a 

### parallel modeling 

### approach

was 

### performed using sine and cosine terms for time and 

### day ofyear and 

### polynomials in minitnum

### andmaximum temperature. 

### A 

### simulation ofthemodel-fitting

### process. The statistical 

### significance level 

### for testing a parameter in a

### model is based on the assumption that the selection ofthe 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 model

### building 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 ofa 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 

### generatedby

### fittingdaily mortalitydata to the 

### seasonal, trend, and weather 

### variables 

### using the 

### (AM approach. An S-Plus function was then invoked 

### repeatedly with different randomseeds that

### performed the followingsteps: 1. The function simulates avector ofPoisson variates from the initial mean vector. 2. It fits this simulated variate vector to 

### CAM 

### terms for 

### time and 

### day ofyear in a Poisson regression, increasing the degrees of freedom until there is no improvement in AIC from the 

### addition of another 

### degree 

### offreedom in 

### either GAM term. 3. It uses the optimal number of degrees of freedom for time andday ofyear from step 2, and adds GAM terms for minimum and maximum temperature, again 

### adding terms until there is no improvement inAIC. 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 maybe amore 

### 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-dayschedule. Ozone, carbon monoxide, and nitrogen dioxide data were also obtained for the 4th Street site. 

### Althoughdata for ozonewere 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, namelymaximum 8-hr 

### ozone, maximum 8-hr CO, and 24-hr

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

### and ozonewerealso 

### considered. 

### Comparisons of 4th Street 

### ozone 

### with other 

### SCC' sites 

### conlsistentlytI show correla

### tionls 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 rhvalues
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 specieswere 

### not available. COH

was 

### usedalong

### 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, SanJose 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
98thpercentile 88 51 48 50.0 44 32 25 3-Yearaverage 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-Yearaverage 15.9 14.1 13.1 11.9 Au.'

PM25' particulate matter < 2.5 pm in aerodynamic diameter.

Volume 07 Number 8 August 999 * Environmental Health Perspectives 638Articles * 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 opera

### tion at 

### other 

### BAAQMD 

### monitoringsites. 

### Table 2 

### presents 

### summarystatistics 

### forall 

### 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

## PM1Omeasurements have a

### correlation 

### of0.94. 

### Seasonand

### trendfits. Rather than

### predicting 

### mortality 

### from a 

### single temporal GAM 

### term, separate 

### CAM terms in time 

### and

### dayof year were 

### fit because a 

### good-fitting model couldbe

### obtainedusing

### 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 deviancewas 

### 3,038.5, 

### withAIC 

### 3,078.5. Meteorological variables. GAM terms

were 

### fit forminimum 

### and maximum temperature in 

### addition to a 

### 7-degree term 

### for time 

### and a 

### 12-degree term 

### for

### dayofyear, 

### yielding

an 

### optimum AIC 

### with 3 

### dffor minimum temperature 

### and 2 

### df for maximum. 

### (Subsequendy, this set 

### ofGAM terms 

### will 

### be 

### referred to as 

### the 

### optimal GAM terms.) 

### The inclusion of a minimum-maximum cross

### product term 

### did not improve 

### the AIC, nor 

### did the inclusion of 

### relative 

### humidity. The model had a

### deviance of2,998.1 on 2,897 

### df, and anAIC 

### of2,897

## 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 

### rejectedat 

### the 0.05 

### level.

Table 2. Summary statistics for mortality, weather variables, and pollutant concentrations, San Jose CA, 1989-1996.

Variablea Mortality (count) Respiratorymortality (count) Cardiovascular (count) mortality Min temperature (°F) Maxtemperature ('F) Relative humidity (%) 

### PM10b (pg/m3) PM25b(pg/m3) PM10.2 5b(pg/M3) COH (COHunits) 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 < 2.5pmin aerodynamic diameter; PM10, particulate matters 10pminaerodynamic diameter; SD, standard deviation. aValues are 24-hr averages unless otherwise noted. blhefine and coarse fraction of PM10 do not add tototal PM10 because theyderive fromthedichotomous sampler, whereas total PM10 was collectedwith 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 ofthe 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 

### onlyweakly correlated with 

### mortality, although there is a

### statisticallysignificant correlation with 

### lagged COH. 

### The 

### relationshipwith 

### 24-hr CO is 

### similar to 

### that of

### COH. 

### NO2 is 

### also

### highly correlated with 

### COH, but 

### lagNO2 has a 

### lower 

### partial cor

### relation with 

### mortality than 

### unlaggedNO2. The correlation 

### between ozone 

### and mortality is 

### weak, although the 

### correlation with 8- 

### hr ozone is

### borderline

### significant. Except forozone, 

### there arepositive corre

### lations 

### between the other

### pollutantvariables, 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 correla

### tions, both 

### NO3 and 

### PMIO were 

### highly significant. 

### PM2 5, 

### SO4 and 8-hr ozone were 

### also

### marginallystatisticallysignificant. 

### Among lagged variables, COH and CO were 

### highly significant and 

### NO2 was 

### marginally so. PM

### 10-2.5 was not

### significant, norwas 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 mortalityand pollutantvariables.a

Mortalityvs. 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 

### PM10.25 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, coefficientofhaze; 

### PM25, particulate matter < 2.5 pm inaerodynamicdiameter; PM10, particulate matter < 10 pm inaerodynamic diameter. "Correlations of residuals fromregressions ongeneralized additive model terms ofdegree 12 fordayofyear, degree 7 fortime, degree 3 minimumtemperature, anddegree 2 formaximum temperature.

Environmental 

### Hea/th Perspectives

. Volume 107, Number 

### 8. August 1999 639Articles * Fairley

NO3 was statistically significant paired with every other pollutant except 

### PM,,. PM,

was statistically significant 

### paired with every pollutant except NO3 and PM

### 11. 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 

### highlystatistically 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, approxi

### mately 4 

### standard deviations (SDs) above the mean. Several of the 

### pollutant variables

are 

## right-skewed-PM2 5 

### and NO3 in par

### ticular. 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 auto

### correlation 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, autocor

### relation ofresiduals is not a 

### significant issue. The 

### fact that the deviance is approxi

### mately equal to the value 

### expected under the null 

### hypothesis suggests that it would be difficult to improve the fit 

### substantially. The lack of

### Y-outliers, the lack of

### influential x-values. and the lack of 

### autocorrelation stiggest that the Poisson 

### model fits 

### reasonablywell. 

### 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 forweathervariables. 

### [he results were similar both 

### qualitatively aind

### quantitatively to those found in 

### Table 4. A 

### simulation 

### ofthe 

### model-fittingprocess. 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 pairwise models with lagged COH, the other 

### pollutants are no longer statistically 

### significacnt. lagged 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 modelsbwith 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 1 11** 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 xSD(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 jointlywith other pollutants. dPollutants are lagged CO, lagged NO2, 8-hr ozone, and either PM2 orNO3 *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, 

### comnparedwith 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 wxith 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 1989-1996. A 

### comparison ofthe tswso coefficients 

### takinig 

### theii differencce and 

### dividing by the square root ofthe

### SUIml of

### tlhcir-

### sample
variances 

### yields avalue of z= 

### 1.36, not sta

### tisticallysignificant. 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, ifthe effect ofCOH 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. Respiratoiyand

### cardiovascularregr.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 

### LagCO 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 2fot 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

Volume 107 

## Nurmtber 8 August ') L

. Envirorietcil 

### HuAth P r 640Articles * 

### Daily

### mortality and air 

### pollution in Santa Clara 

### County

Table 6. Respiratory and cardiovascular mortality relative risksa for modelsbwith 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 byexp(bxAp)

- 1, where bis the pollutant coefficient fromthe Poisson regression andAp = 50 for PM10 and 50 x

### SD(p)/SD(PM10Jforother pollutants, p, e.g., SD(PM251 = 13, SD(PM10) = 23, so forPM25, Ap = 50 x 13/23

= 28. bAll models include 7 generalized additive model terms fortrend, 12 for season, 3 for minimum temperature, and 2 formaximumtemperature. cLaggedvariables were used iftheyappearedtofitbetter lagged thanunlagged. Thus, lagged CO and lagged COHwere usedwhenfittingjointlywith otherpollutants. *Statistical significance atthe 0.05 level.



### and PM2 

### 5, NO3, 

### and CO were 

### associated with 

### cardiovascular mortality. For 

## PM1O, 

## PM1I2.5, NO3, and CO 

### the point estimates 

### forriskwere

### higher than those in

### Table4. 

### Analyses 

### by season. Analyses 

### were per

### formed 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 

### statisticallysignificant. 

### Discussion

One 

### striking result of the 

### analysis is 

### that 

### although the BayArea met 

### every air 

### quality standard in 

### the 

### early 1990s 

### (andwouldhave met 

### the new 

### 8-hr ozone 

### and 

## PM2.5 stan

### dards had 

### theybeen in 

### effect), there is a sta

### tistically

### significant correlation 

### between each 

### pollutant considered (except coarse 

### fraction 

## PM1O) and

### mortality. Second, 

### the regression 

### coefficients of other 

### pollutants that are cor

### related 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 fineparticulates may

### bea

### surrogate 

### for)
may 

### be the 

### real culprits. The result 

### that NO3 

### had the strongest association

### withmor

### tality is 

### clearly of

### practical importance and worth 

### investigating for

### otherareas. 

### 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 

### correlationbetweenparticulate 

### 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 substan

### tially 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 

### ofthis

### magnitude, some 

### data sets 

### may

### yield 

### nonsignificant results 

### whereas others

### yieldhighly

### significant results. Although the results 

### for respiratory 

### and cardiovascularmortality

### showed fewer

### significant results 

### than for mortality as a whole, 

### the level 

### ofeffects appeared somewhat 

### higher. 

### The 

### number ofcardiovascular and respi

### ratory

### 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

### ofstatistical 

### significance ofmost 

### of the 

### coefficients can 

### be attributed to 

### lack of
power. 

### The criteria

### document (1) 

### found that

aminimum 

### sample size 

### of400 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 statisti

### cal 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 sta

### tistically

### significant, the range 

### of

### coefficients

was 

### larger than

### expectedbychance. This 

### analysis has found associations 

### between air 

### pollution variables 

### and mortali

### ty-especially with 

### fine 

### particulate vari

### ables-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 byseason.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(bxAp)

- 1, where b is the pollutant coefficient fromthe Poisson regression and Ap = 50 for PM10 and 50 xSD(p)/SD(PM10) forotherpollutants, 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 fortrend, 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 atthe 0.05 level.



### new 

### PM25 standards may 

### be protective in 

### other areas 

### where 

### seasonal PM variations are 

### not as great. In 

### the BayArea, 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

### publichealth forthe BayArea.

REFERENCESAND

### NOTES

1. U.S. EPA. Air Quality Criteria for Particulate Matter. EPA/600/P-95/OOlaF. Washington, DC:U.S. Environmental ProtectionAgency, 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 ProtectionAgency, 1996. 3. Fairley D. The relationship of daily mortality to suspended particulates in Santa Clara County, 1980-86. Environ Health Perspect89:159-168 (19901. 4. AIRS database. AIRSWEB query-annual summary http:/www.epa.gov/aqspubil/annual-summary.html (1998) [cited 11 May19981. 5. Fairley D. SourceApportionment of BayArea Particulates. BAAQMD Draft Technical Report. San Francisco, CA:Bay AreaAirQualityManagementDistrict, 1996. 6. BAAQMD. Comments on New Proposed National Air Quality Standards. Attachment 4: Stringency of Proposed Standards: Particulates. San Francisco, CA.BayArea Air QualityManagementDistrict, 1997. 7. U.S. EPA. 40 CFR 50. National Ambient Air Quality Standards for Particulate Matter; final rule. Fed Reg 62(138):38651-38701 (1997). 8. KinneyPL, ItoK, Thurston GD. Asensitivityanalysis ofmortality/PM10 associations in Los Angeles. Inhal Toxicol 7:59-65(1995). 9. SametJM, Zeger SL Berane K. The association ofmortality and particulate airpollution. In: Particulate Air Pollution and DailyMortality. Replication andValidation of Selected Studies. Cambridge, MA:Health Effects Institute, 1995. 10. Samet, JM, Zeger, SL, Kelsall JE, Xu J, Kalkstein, LS. Analysis of the effects of weather and multiple air pollutants. In: Particulate Air Pollution and Daily Mortality: Replication andValidation of Selected Studies. Cambridge, MA:Health Effects Institute, 1997. 11. Worid HealthOrganization. The International Classification of Diseases, 9th revision, Clinical Modification, 2nd ed. Bethesda, MD:U.S. Department of Health and Human Services, 1980. 12. FairleyD. Unpublished data, 1994.

Environmental Health Perspectives * Volume 107, Number 8, August 1999 641