j 
Arricles 

Associations between Air Pollution and Mortality in Phoenix, 1995-1997 

Therese F. Mar,1 Gary A. Norris,2 Jane Q. Koenig,1 and Timothy V. Larson3 

'Department of Environmental Health, University of Washington, Seattle, Washington, USA; 2U.S. Environmental Protection Agency, 

Research Triangle Park, North Carolina, USA; 3Department of Civil and Environmental Engineering, University of Washington, Seattle, 

Washington, USA 

We evaluated the association between mortality outcomes in elderly individuals and particulate mat· 

ter (PM) of varying aerodynamic diameters (in micrometers) [PM.D, PM2.

5

, and PMCF (PMIO 

minus PM2

.

S

)]' and selected particulate and gaseous phase pollutants in Phoenix, Arizona, using 3 

years of daily data (1995-1997). Although source apportionment and epidemiologic methods have 

been previously combined to investigate the effects of air pollution on mortality, this is the first 

study to use detailed PM composition data in a time-series analysis of mortality. Phoenix is in the 

arid Southwest and has approximately 1 million residents (9.7% of the residents are > 65 years of 

age). PM data were obtained from the U.S. Environmental Protection Agency (EPA) National 

Exposure Research Laboratory Platform in central Phoenix. We obtained gaseous pollutant data, 

specifically carbon monoxide, nitrogen dioxide, ozone. and sulfur dioxide data, from the EPA 

Aeromeuic Information Retricval System Database. We used Poisson regression analysis to evaluate 

me associations bemreen air pollution and nonaccidental mortality and cardiovascular mortality. 

Total mortality was significantly associated wim co and N02 (p < 0.05) and weakly associated 

wim S02' PM.o' and PMCF (p < 0.10). Cardiovascular monalitywas significandy associated with 

CO, N02, S02' PM2.

5

, PM.o' PMCF (p < 0.05), and demental carbon. Factor analysis revealed that 

both combwtion·related pollutants and secondary aerosols (sulfates) were associated with cardiovascular mortaliry. Key words; r;ardiovascular, composition! factor analysis, particulate matter! PMz

.

S

) 

PM1o' sources. Envjron Health Perpect 108:347-353 (2000). [Online 25 February 2000] 

http://ehpmtl.1Jjehs.nih.gov!docs/2000/108p347·353m.ar/abdract.lmnl 

The associations between air pollution, especially particulate matter (PM), and adverse 

human health effects have been well documented (1-10). PM is associated with 

decreased respiratory function, aggravation 

of existing respiratory and cardiovascular 

conditions, altered defense mechanisms, and 

even premature death. The most susceptible 

populations include those with preexisting 

respiratory or catdiovascular conditions, 

asthmatics, children, and me dderly (8.11). 

To date, few epidemiology studies have 

used PM measures other man size·segregated 

mass as me exposure metric. Schwa.m; et al. 

(l2) looked at episodes ofhigh coarse p;J.rcide 

concentration in Spokane, Washington, and 

found that windblown dust episodes were 

not associated with increased mortality. In 

{he Harvard Six Cities Study, Schwanz et al. 

(3) found a significant association benveen 

nonaccidemal mortality and particulate mat· 

ter :s 2.5 Ilm in aerodynamic diameter 

(PM2.

5

) and sulfur. They did nOt find a sig. 

nificanr association with particulate matter 

:s 10 j.lm in aerodynamic diameter (PM 10) or 

the coarse fraction of PM {PMCF (PM 10 

minus PM2,5)]' In contrast, Osuo er aI. (1J} 

found that PM IO dominated by coarSe parti. 

cles was associated with an increase in mor· 

taliry in the Coachella Valley in California. 

The differences in the reswts from these two 

studies may be due to me particulate composition as well as the difference in the amount 

ofPMcF. In the eas~ern United Scares, PM2.

5 

is dominated by sulfates (34%), whereas in 

the western and central Uniced States it is 

dominated by organic carbon (OC) from 

motor vehicles and vegetative burning (39%) 

(14). The average PM2.

5

'PMIO racio for the 

Six Cities Study (3) was 0.6 (based on the 

50th percentiles) as compared to a ratio of 

0.3 for Phoenix, Arizona (15). 

The goal of the present study was co 

evaluate the associations between daily air 

pollution and total nonaccidental and car· 

diovascwar mortaliey in Phoenix. Phoenix is 

an arid southwestern ciey with a popwation 

of approximately 1 million residents (16). lc 

is an interesting location because of ic.s large 

proportion of elderly people (9.7% of the 

popwation is > 65 years of age). The elderly 

are more susceptible to air pollution than the: 

general public (2). The primary sources of 

PM in Phoenix are motor vehicles, paved 

road dust, and vegetative burning (15). 

This study focused on the effecc.s of air 

pollution on cardiovascular mortality for 

several reasons. First, the association between 

air pollutants and cardiovascular morraliry 

has been consistent in previous scudies 

(I.2,9,1l). Second, a smdy in Baltimore, 

Maryland, found that hean rate variability 

was associated wim PM2.? in elderly subjecc.s 

with cardiovascwar conditions (lB). Finally, 

in this smdy cardiovascular mortality had 

the largest sample size, accounting for 45% 

of che [oeal nonacciden tal deaths in the 

study region (based on zip codes). This may 

Environmental HeCllrh Perspecrives • Volume 108, Number 4, April 2000 

be reflective of the increased si:ze of Phoenix's 

elderly popularion, which is more prone to 

cardiovascular disease. 

A unique aspect of this study is thac our 

pollution data include daily information nor 

only on traditional gaseous pollutants, but 

also on PM in various size fractions and the 

chemical composition of PM2S From 1995 

to 1997, me U.S. Environmental Protection 

Agency (EPA) National Exposure Research 

Laborarory (NERL) operated a comprehen· 

sive monitoring platform in Phoenix. They 

collected daily PM2.

5 samples and subsequently analyzed them for various dlemicai 

components of PM. This provided an oppor· 

mnity co examine more specific mercies for 

PM man simply mass, as well as an oPPOrtUnity [Q idenril)r selected chemical components 

of PM mat are associated with mortality. 

In addition to PM, iliis study also evalu· 

ated the association berween total nonacci· 

dental and cardiovascular morcaliry and other 

measured air pollutants: carbon monoxide, 

nitrogen dioxide, sulfur dioxide, and ozone. 

These EPA criteria pollutants are also 

associated with mortality (7, 17, 19,2{]). 

Methods 

Study area and dara. Morrality data for aU 

of Maricopa County from 1995 to 1997 

were obtained from the Arizona Ceocer 

for 'Health Statistics in Phoenix. Death 

Address correspondence to T.F Mar, Departmem 

of Environmental HeaJth, Box 357234, Universi£), 

of Washington. Seatde, WA 98195·7234 USA. 

Telephone: (206) 685-1596. Fax: (206) 685-3990. 

E-mail: therese@u.washington.edu 

We thank D. Bates for his advice and commems 

regarding the manuscript. We also thank T. Moore 

for advice regarding the representative spatial scale 

of the platform parriculate maner measurements, 

C. Mrela fOf tne morrali£), darn, and B. O'Brian for 

help with assembling the data. 

This publication was made possible in part by 

gram 5T32 ES07262 from the NIEHS, NIH. The 

U.S. EPA Office of Research and Development 

partially funded and collaborated in the research 

described here under assistance agreement R 

827355 to the University of Washington. 

The coments of this paper are solely the responsibility of rhe authors and do not necessarily represent [he official views of [he NIEHS, NIH. This 

paper has been subjected to EPA review and 

approved for publication. Mention of reade names 

or commercial products docs nor constitute an 

endorsement or recommendation for use. 

Received 8 September 1999: accepted 9 

November 1999. 

347 
Arricles • Mar er al. 

ccrtifi-cate data included residence zip code 

and the primary cause of death as identified 

by {he lnftrnntional Cwsijicfttion o/DiJtllJts, 

Ninth Rtyision (JCD-9, World Health 

Organi-z.ation, Geneva). Only the deaths of 

residents in the zip codes located near the air 

pollution platform were included in this 

study. This zip-code region was recommended by [he Arizona Department of 

Environmental Quality (Phoenix, AZ). We 

evaluated toral nonaccidental mortality 

(JCD-9 codes < 800) and cardiovascular 

morrality (lCD-9 codes 390-44B.9) in this 

study. Summary statistics for the mortality 

outcomes are presented in Table 1. 

We obtained PM 2.

S

' PM 10' PMcp and 

PM2.

5 chemical composition data from the 

EPA NERL platform in central Phoenix. 

Chemical composition was only available for 

PM2

.

l

. The monitoring platform is approximate y 10 km west-northwest of downtown 

Phoenix at a state and local air monitoring station. Standard meteorologic parameters such 

as wind speed and direction, temperature, and 

relative humidity were continuously measured. 

The average temperature in Phoenix from 

1995 (Q 1997 was 23.7 ± B.l"C. The average 

relative hwnidity was 32 ± 15%. 

NERL investigators made hourly PM2.

5 

and PMlO measurements each day using rwo 

colocated capered element oscillation 

microbalance (TEaM) monitors (Rupprecht 

& Patasnick Co., Albany, NYJ. The TEOMPM10 was fined with-an EPA-approved federal reference method PM10 impactor inlet 

(model 24Gb; Andersen Instruments, 

Smyrna, GA). The TEOM-PM2.5 was fiued 

with a PM2.

5 cyclone inlet (University 

Research Glassware, Chapel Hill, NC). The 

PM2.

S cydone on the TEOM was replaced 

with a well-impactor ninery-six (WINS) inlet 

on 20 December 1996. The WINS inlet has a 

sharper cut point as compared to the cyclone. 

We averaged the hourly concentrations to 

create a 24-hr average (0700--0700 hr), and 

we calculated the concentration of coarse 

fraction (TEOM PMCF) as TEOM PM iO 

minus TEaM PM2.

5

. 

NERL investigawrs collec[ed the daily 

gravimetric integrated 24-hr (starring at 

0700) fine particle filter samples using a dual 

fine particle seguential sampler (DFPSS; 

University Research Glassware). The DFPSS 

was fitted with a cyclone that was identical 

to the cyclone on rhe TEOM-PM2.

S

' The 

DFPSS collected daily samples on both 

Tenon and quarn filters. The Teflon filter 

was used for maSs and elemental analysis, 

whereas the quarn filter was used for carbon 

analysis. In addi{Lon to [he DFPSS, NERL 

investigators operated a dichotomous sampler 

(Andersen Instruments, Inc.) every third day 

beginning 17 June 1996. Both the PM2

.

S and 

PMCF samples were collected on Teflon 

348 

filters. The investigarors measured elemental 

concentrations at the EPA (Research 

Triangle Park, NC) with energy dispersive 

X-ray fluorescence. OC and elemental carbon (EC) were measured by Sunset 

Laboratory (Forese Grove, OR) using thermal optical transmittance (21). 

PM and gaseous pollutant concentrations (range and mean 1 SD) from 1995 to 

1997 arc presented in Table 2. We obcained 

gaseous criteria pollutant data for CO, N02, 

03' and 502 from the EPA Aerometric 

Information Retrieval System (AIRS) database (22) for residential sites in the Phoenix 

region. We averaged CO values over four 

monitoring sites and we averaged N02 over 

two sites. Only one residential monitoring 

site was available for 502, We averaged {he 

hourly averages for CO, N02, and 502 over 

24 hr from 0700 to 0700. We used [he maximum hourly 0 3 (03 max) concentration in 

the same 24-hr period in the analysis. 

The PM2

.

5 constituents that we evaluated for effects on monaiity were sulfur, zinc, 

lead, soil-corrected potassium (Ks) (23), 

OCI Ee, toeal carbon (Te), and reconstructed soil. Soil was reconstructed by summing the oxides of AI, Si, Ca, Fe, ana Ti 

using the formula recommended by Malm et 

al. (24). We also considered PM2

. that was 

corrected for soil content (nonsoi~ PM2

.

s = 

PM2.

S - reconstructed soil). Table 3 presents 

the percent ?f the total mass of PM2.

5 

accounted for by each component. The elements aluminum, silicon, calcium, titanium, 

and iron were nOt evaluated separately in me 

mortality analysis because they are the major 

elemental components of soil. 

Statistical analysis. I n our zi p-code 

regions, we analyzed a rotal of 9,276 nonaccidental deaths from 1995 to 1997. Poisson 

regression was used ro evaluate [he association 

between the air pollutant exposure variables 

and [he mortality outcomes (2,,5). 

We used Poisson regression because 

mortaliry data are discrete counts and death 

is a rare event. Poisson regression assumes 

the variance is equal to the mean. When the 

variance exceeds the mean, the variance is 

overdispersed. We adjusted standard errors 

for overdispersion; however, the amount of 

overdispersion was small. The Qverdispersion 

parameter was 1.05 and 1.00 for nonaccidental and cardiovascular mortality, respectively. We calculated all relative risks (RRs) 

for an interquarrile increase (25th-75th 

percentile) in pollutant concenrration. 

The effect of air pollution on mortality is 

small and can be influenced by confounders. 

Therefore, base models for total mortality 

and cardiova!icular mortaJiry were consUUcted by adjusting for day of rhe week with. indicator variables, and time trends, temperature, 

and relative humidity with smoothing functions (25). We determined degrees of freedom (d/) for the function used to smooth 

rime trend by minimiting autocorrelation as 

well as the Akaike information criterion 

(AlC) (26). We chose the dfand bg for the 

smoothing functions for temperature and 

Tabla 1. Mortality counts for individuals ~ 65 years of age in Phoenix. 

Total Average nonaccidental 

Year nonaccidantal deaths/day 

1995 3.072 8.45 

1996 3.101 B.74 

1997 3.003 B.45 

199&-1997 9.176 B.55 

Table 2. Annual range of pollutant concentrations (1995-1997). 

Particulate matter pollutant, year 

PM,.s (OFPSSI 

1995 

1996 

1997 

J·year mean 

PMIO(TEOMI 

1995 

1996 

1997 

J·year mean 

PM,., (TEOMI 

199, 

1996 

1997 

J·year mean 

PMCF(TEOMI 

1995 

1996 

1997 

J·year mean 

Range 

4-37 

3-39 

1-35 

12.0 ± 6.6 

9-129 

&-213 

7-1B6 

46.5 ± 22.3 

1-40 

0-42 

1-34 

13.0±7.2 

&-104 

&-lB7 

&-159 

33.5117.3 

Total Average cardiovascular 

cardiovascular 

1.391 

1.473 

1.31B 

4.1B1 

Gaseous pollutant 

CO (ppml 

NO, (ppbl 

OJ max (ppbJ 

SO, (ppbl 

deaths/day 

3.B6 

3.9B 

3.73 

3.B5 

Range 

0.5-4.0 

0.3-4.0 

0.3-3.7 

1.510.B 

8-£4 

9-59 

8-£1 

JO± 10 

10-131 

14-112 

14-104 

57.0 ± 17.7 

0-11 

1-17 

2-12 

3.1 ± 2.2 

Volume 108. Number 4, April 2000 • EnvironmentClI Health Perspectives 
relative humidity [Q minimize the Ale. The 

base model for total mortality used indicator 

variables for day of the week, 10 difor rime 

trends, 2 dJfor temperature with 2 days lag, 

and 2 dJfer relative humidity with 0 days lag. 

The base model for cardiovascular mortality 

used indicacor variables for the day of the 

week, 10 dJfor rime trends, 2 dJfor temperarure with 1 day lag, and 2 dJror relative 

humidi£y with 0 days lag. 

We included continuous daily data from 

1995 ro 1997 (1,097 days) in rhe "udy. 

Each day was coded and included in the 

model co adjust for rime erends. Liede aurocorrelation was observed after adjusting for 

day of week, time trends, temperature, and 

relative humidity. The aurocorrelarion for 

days 1-25 for both total and cardiovascular 

morraJiry were within me 95% confidence 

intervaJ for an independent series. 

We evaluated air pollution exposure 

variables by adding them individually as 

linear terms to the base model. The air 

pollution exposure menics that were evaluated in this analysis included CO, N02, 03' 

SO" TEaM PMlO, TEaM PM,.5' TEaM 

PMcF' PM,.; (DFP5S), 5, Zn, Pb, soil, K" 

nonsoil PM, OC, EC, al'!d Te. Lag days 

Table 3. Percent of total mass of PMt5 accounted 

for by each component. 

Component 

S' 

Mn 

Zn 

8r 

Pb 

OCi! 1.4 

EC 

Ks 

Soilb 

PM" 1%1 

3.69 

0.05 

0.15 

0.03 

0.06 

38.37 

10.78 

0.52 

17.5Il 

'If S is assumed to be inlhe form 01 (NH41,50" the mllss 

percent would be 151%. 1I;!.2D% AI + 2.49% Si + 1.63% ea + 2.42% Fa + 1.9~% Ti (23). 

Table 5. Correlation coefficient matrix of air pollufants. 

S In Pb DC EC 

S 1.00 0.14 0.25 0.12 0.04 

In 1.00 0.63 0.62 0.71 

Pb 1.00 0.69 0.69 

OC 1.00 0.91 

EC 1.00 

TC 

Ks 

PM lOa 

PM2Si! 

PMC'Fa 

Nonsoil PMz.s 

Soil 

CO 

NO, 

OJ °Oma, 

S 1 

118ased on TEOM measuremenls. 

Articles • Air pollution and mortality in Phoenix 

ranging from 0 to 4 were investigated. We 

evaluaced the assumption of a linear relationship using a smooth function. This assump· 

tion was met if a straight line could be 

placed within the 95% confidence intervaJs 

(CIs). A p-vaJue < 0.05 associated with the 

pollution exposure variable was considered 

significant. We conducted Poisson regression analyses using S-PLUS 4 (Marhsoft, 

Inc., Seattle, WA) 

Foctor analysis. We conducted a facror 

analysis on the daily concentrations of the 

chemical components of PM2.

5 from samples colieered by the DFP55 (Al, Si, 5 Ca, 

Fe, Zn, Mn, Pb, Br, Ks' OC, and EC). The 

analysis also included the daily averages of 

the gaseous species emitted by combustion 

sources (CO, N02, and S02)' Factor analysis 

is a technique used [Q explain me corrciations 

between variables in terms of underlying factors thar are not directly measurable. Each 

factor is a linear combination of che originaJ 

variables and all such facrors are orthogonal 

to each other. The factors were extracted 

using principal component analysis with a 

varimax rotation. We conducted factor 

analysis using SAS (SAS Institute Inc. Cary, 

NC). We used the resultant fac[Qr scores as 

surrogate exposure variables in predicting 

mortality outcomes with the Poisson regression model. Each facror was evaluated in a 

single source model. However, because the 

factor scores formed a set of orthogonaJ variables, we performed a separate regression 

analysis wim all of me scores included in one 

mulrifactor model. 

We also conducted a facror analysis on 

the daily concentrations of the chemical 

components ofPMeF from samples collecred 

by the dicar (AI, Si, el, S, K Ca, Mn, Fe, Zn, 

Br, Pb, Sr, Cu, and Rb). We did not use rhe 

scores from this analysis in the time-series 

anaJysis because me sampling period starred 

in June 1996 and samples were only collected 

every third day. 

Results 

Table 4 shows the correlation coefficients 

between PM, gaseous pollutants, temperature, and relative humidity for Phoenix in 

1995-1997. PM,.; (obrained hom the 

DFPSS) was highly correlated with CO (r;::: 

0.85) and NO, (r = 0.79), but less so with 

SO, (r =0.43). PM,.; from the DFPSS was 

highly correlated with that measured with 

rhe TEaM (r = 0.93). Table 5 shows the 

Table 4. Correlation coefficients between PM, gaseous pollutants, temperature, and relative humidity (RH) 

for Phoenix, 1995-1997. 

PM IO PM2.S PMCF 

PMz~ • Temp ITEOMI RH ITEOMI ITEOMI CO NOZ O~mn S02 

PM2.

5 

, 1.00 ·0.31 0.69 0.16 0.93 0.50 0.85 0.79 ·0.24 0.43 

Temp 1.00 ·0.08 ·0.55 ·0.25 0.00 -0.49 ·0.40 0.71 ·0.38 

PMIOITEOMI 1.00 ·0.12 0.77 0.97 0.53 0.53 -0.12 0.41 

RH 1.00 0.09 ·0.19 0.23 O.OB ·0.54 0.10 

PM"ITEOMI 1.00 0.59 0.82 0.77 ·0.20 0.48 

PMCF ITEOMI 1.00 0.34 0.37 -0.08 0.33 

CO 1.00 0.87 ·0.40 0.53 

NO, 1.00 -0.24 0.57 

0 1.00 ·0.37 

SOl 1.00 

-Measured with the OFPSS. 

Nonsoil 

TC K, PM]II PMH PMt;E PM25 Soil CO NOz 0, Damn S02 

0.10 0.02 0.19 0.27 0.13 0.26 0.25 0.01 0.04 0.13 0.31 ·0.07 

0.65 0.30 0.46 0.61 0.33 0.63 0.49 0.65 0.62 -0.49 ·0.27 0.26 

0.71 0.39 0.48 0.67 0.34 0.71 0.49 0.71 0.63 -0.51 ·0.30 0.33 

0.99 0.65 0.5B 0.89 0.38 0.96 0.52 0.89 0.81 ·0.57 -0.32 0.49 

0.95 0.57 0.58 0.84 0.40 0.89 0.52 0.90 0.82 ·0.64 ·0.41 0.46 

1.00 0.64 0.59 0.90 0.39 0.96 0.53 0.91 0.83 ·0.60 -0.35 0.49 

1.00 0.34 0.59 0.19 0.64 0.26 0.52 0.45 -0.27 ·0.14 0.25 

1.00 0.79 0.97 0.62 0.72 0.55 0.56 ·0.25 ·0.11 0.42 

1.00 0.60 0.91 0.64 0.82 0.77 -0.44 ·0.19 0.47 

1.00 0.41 0.66 0.37 0.39 ·0.14 -0.07 0.35 

1.00 0.54 0.87 0.80 -0.54 ·0.29 0.46 

1.00 0.4B 0.49 ·0.17 0.05 0.09 

1.00 0.87 ·0.68 ·0.39 0.51 

1.00 ·0.60 ·0.24 0.56 

1.00 0.81 ·0.46 

1.00 ·0.37 

1.00 

Environmental Health Perspe([ives • Volume r 08. Number 4, April 2000 349 
Articles . Mar et aL 

correlation coefficiems between selected were associated with an excess risk of cardio~ furrher investigated the associations between 

chemical composition components ofPM2,5 vascular death. The strongest associations me m~mality outcomes and PM by evaluatand the other air pollurants, TEaM PM10 were with PM2., (TEOM), followed by ing [he associadon between the mortality 

was correlated with fine soil (r = 0.72), OC PM10 and PMCI" PM2.~ adjusted for soil outcomes and the PM composition. The 

(r= 0.5B), EC (r= 0.5B), and TC (r= 0.59). comem (nonsoil PM2.

S was also related PM2.

5 composition data analysis revealed 

TEOM PM2.

S was highly correlated with with cardiovascular mortality with 1 day lag that EC and TC were significamly associated 

OC (r= 0.B9), EC (r= 0.B4), TC (r= 0.90), (p < 0.10). Table 7 lists all of the statistically with cardiovascular mortality (l day lag). 

and [Q a lesser extent with Zn (r: 0.61), Pb significant associations with cardiovascular Weaker associations were also evident with 

(r = 0.67), and Ks (r = 0.59). The high cor- mortality, Cardiovascular morrality showed OC at 1 and 3 days lag and TC at 3 days lag. 

relation coefficients between carbon and a more consisrent associarion with panicwate Ks had a signiflClnt positive association with 

PM2.

5 indicate that the majority of the varia- mass concentrations th<U1. total mortaJity. We cardiovascular mortality (3 day lag). 

rion in PM2.

5 is due to combustion producLS. Table 6. RR for lotal mortality in Phoenix from an interquanile range (IQR) increase in pollutants. PMCF was correlated with soil (r: 0.66). 

OC and EC concentrations follow a sea- Pollutant lag days ~ SE t lOA AA LCI UCI 

sonal pattern-they arc high in (he colder CO 0 4,50 x 10'2 1.48)(10.2 3.05 1.19 1.06 1.02 1.09 

months and low in [he warmer months. This 1 4.15)( 10'2 1.48 x 10'2 2.81 1.19 1.05 1.01 1.09 

pattern is due to increased com-buscion emis- NO, 0 2.64)( 100 1.15 x IOD 2.31 0.02 1.05 1.01 1.10 

sions from space heating and me decreased 1 3,29 x 100 1.13)(100 2.91 0.02 1.07 1.02 1.12 

mixing height during the wimer months. 3 1.80 x 111' 1.06 x 10D 1.69 0.02 1.04 0.99 1.08 

Particulate sulfur concentrations peak in the 4 2.20 x 100 1.07 x 100 2.05 0.02 1.04 1.00 1.09 

warmer months. Soil concentration also fol- SO, 0 1.17)( 10'2 6.37 x 10.3 1.84 2.78 1.03 1.00 1.07 

lows a seasonal trend, with higher conCentra- 5 3 ,1.38 x 10"" 6.24 x 10,5 -2.21 280.60 0.96 0.93 1.00 

tions in the spring and fall. Measured soil 4 -1.10)( 10"4 6,10 x 10,5 -1.80 279.90 0.97 0.94 1.00 

concentrations decreased after 20 December Soil 1 ·1.75 x 10.5 6.67 x 10.6 -2.01 1,767.45 0.97 0.94 1.00 

1996 because of me use of the WINS inlet. 2 ·1.76)( 10.5 6.59 x 10.6 -2.05 1,769.33 0.97 0.94 1.00 

Summaries of me RR between the expo- 3 ·1.75 x 10" 8.56 x 10.6 -2.04 1,772.4B 0.97 0.94 1.00 

sure variables and both total and cardiovas- 4 ·1.47)(10,5 6.54 x 10.6 -1.72 1,775.62 0.97 0.95 1.00 

cwar mortality are presented in Tables 6 and PMIOITEOM) 0 1.06 x 10.3 5.35 x 10"" 1.98 24.88 1.03 1.00 Ins 

7, respectively. Because of space limitations, PMCFITEOM) 0 1.17 x 10"3 6.99 x 10'4 1.68 18.39 1.02 1.00 1.05 

we only prescm statistically significam (p Pb 3 -2,70 x 10.3 1.59)( 10'3 -1.69 6.00 0.9B 0.97 1.00 

< 0.05) and marginally significant (p < 0.10) Abbreviations:~. regression coefficient; lCI, lower 95% confidonce interval; r. t-statistic from the regression model; UCI, results in the tables, although models were upper 95% conlidence interval. run using all ohhe pollutants listed in Table 

5. Tables of all of the nonsignificant resulLS Table 7. RR for cardiovascular mortality from an interquartile range (JaR) increase in pollutants. 

are available from the authors by request. We Pollutant Lag days ~ SE t IQR AA LCI UCI -=valuated the associations between tOtal and 

cardiovascw,_: morrality and the gaseous pol- CO a 4,49)( 10'2 2,14)(10.2 2.10 1.19 1.05 1.00 1.11 

1 7.66)( 10.2 2.07)( 10'2 3.71 1.19 1.10 1.04 1.15 lutanc.s, PM mass metrics, and PM composi- 2 5,79 x 10'2 2,00 x 10.2 2.89 1.19 1.07 1.02 1.12 

tion merries using single-pollurant models. 3 5.32)( 10'2 2.03)( 10,2 2.63 1.19 1.07 1.02 1.12 

We found significant associations 4 6.43 x 10'2 2.06 x 10'2 3.12 1.19 1.08 1.03 1.13 

between both morrality outcomes and select- NO, I 4,88 x 100 1.59 x 10° 3.08 0.02 1.10 1.04 1.17 

eel gaseous air pollutants. CO and N02 were 2 2.53)( 100 1.54)( 10° 1.64 0.02 1.05 0.99 1.12 

positively associared with coral mortality at 0- 3 2.76)( 10° 1.55 x 100 1.78 0.02 1.06 0.99 1.12 

and I-day lags. There was evidence of a weak 4 5.74 x 100 1.57 x 10° 3.66 0.02 1.12 1.05 1.19 

association with S02 at 0 days lag (p 0;; 0.10). SO, 2 1.63)( 10.2 8.64 x 10'3 I.B8 2.78 1.05 1.00 1.10 

We found several strong associations with car- 3 1.85 x 10'2 6.65 x 10,3 2.14 2.79 1.05 1.00 1.10 

diovascwar mortality. Cardiovascwar morral i- 4 2.49 x 10.2 8.58 x 10'3 2.90 2.)9 1.07 1.02 1.12 

ty was positively associated with CO (0-4 Ks J 5.81 X 10.4 2.96)( 10.4 1.97 55.62 1.03 1.00 1.07 

days lag). This was the most consistent associ- PMIOITEOM) 0 l.88 x 10,3 7,66 x 10.4 2.46 24.88 1.05 1.01 1.09 

ation because the associarion was significant I 1.47 x 10'3 7.56 x 10'4 1.95 24.88 1.04 1.00 1.08 

for all 5 lag days. Statistically significant asso- PM'.5ITEOM) a 3.91 x 10,3 2,38 x 10.3 1.64 8.52 1.03 0.99 1.08 

ciadons (p 0;; 0,05) were also evident with I 6,65)( 10,3 2.36 x 10.3 2.90 8.52 1.06 1.02 1.10 

N02 on lag days 1 and 4, alrhough the associ- J 4.86)( 10.3 2.35 x 10'3 2.07 8.51 1.04 1.00 1.08 

ation was weaker on lag days 2 and 3. 4 5.43 x 10.3 2.35 x 10'3 2.31 B.47 1.05 1.01 1.09 

Cardiovascular mortality was also associated PMCF 0 2,50)( 10,3 9.88 x 10'4 2.54 18.39 1.05 1.01 1.09 

with S02 (lag days 2, 3, and 4). 1 1.62 x 10'] 9.78 x 10'4 1.66 18.39 1.03 0.99 1.07 

We also found significant associations NonsoiJ PM2.5 5,56 x 10.6 3.12xlO·6 1.78 6,601.06 1.04 1.00 1.08 

between the mortality outcomes and DC 1.46)( 10,5 6,82 x 10,6 2.15 2,976.50 1.04 1.00 1.09 

particulate mass. The associations between 1.39 x 10.5 6.89 x 10.6 2.02 2,960.00 1.04 1.00 1.08 

PM 10 and total mortality, and between EC 4.40)(10.5 1.82)( 10'5 2.42 1.165.50 1.05 1.01 1.10 

PMCF and rotal morralir:y, were marginal (p TC 1 1.15 x 10.5 5.05 x 10.6 2.28 4,169.00 1.05 1.01 1.09 

< 0.10). Toral mortality was nor significantly 3 9.71 x 10'6 5.10)( 10'5 1.90 4.170.00 1.04 1.00 1.09 

associated with PM2.

5

; however, the RR was Abbreviations: fl, regression coefficient; lCI, lower 95% con/idence interval; t, I'statistic Irorn the rogression model; UCI, 

1.02 (CI, 1.00--1.05). All PM mass merrics upper 95% confidence interval. 

350 Volume! 08, Number 4, April 2000 • Environmental Health Perspectives 
Articles • Air pollution and mortality in Phoenix 

We also found that soil, S, and Pb were 

negativc:ly associated with total mortali[),. 

That is, these exposure variables were associated with a decrease in excess deaths. 

We further evaluated the associations 

between the mortality outcomes and sources 

of both particulate and gas-phase pollutants 

using the scores from a factor analysis in 

place of the individual pollutant concentrations. The results from the analysis with five 

facmrs are presented in Table 8. Factor 1 

probably represents the influence of moror 

vehicle exhaust and resuspended road dust 

with high loadings (loading> 0.5) on Mn, 

Fe, Zn, Pb, OC, EC, CO, and NO,. Factor 

2 represents soil with high loadings on AI, Si, 

and Fe. Factor 3 represents vegetative burning wim high loadings on OC and Ks. Factor 

4 represents a local source of S02 with a high 

loading on S02' Factor 5 represents predominately regional sulfate with a high loading 

on S. The RR.s associated with an interquartile range increase in each factor are presented 

in Table 9. Total mortality had both a positive and a negative: association with the factor 

representing regional sulfate, positive: on lag 

day 0 (same day) and negative on lag day 3. 

The factor representing S02 had a negative 

Table 8. loadings from factor analysis. 

Factor I Factor 2 

AI 0.14 0.96 

Si 0.19 0.96 

S 0.04 0.15 

C, 0.26 0.93 

Mn 0.66 0.62 

Fe 0.57 0.76 

2n 0.86 0.24 

Br 0.46 0.31 

Pb 0.74 0.21 

OC 0.66 0.13 

EC 0.76 0.15 

~D 0.20 0.08 0.76 0.20 

NO, 0.69 0.24 

SO, 0.24 -0.04 

Percent variance 30.5 17.5 

explained by factor 

association with mtal mortali[),. We also 

found a significant negative association for 

fine soil on lag days I and 2, and a nearly 

significant negative association on lag days 3 

and 4. Cardiovascular disease was significandy associated with me factOrs representing mocor vehicles (lag day I) and vegetative 

burning (lag day 3). Regression analysis with 

all of rhe factors included in a multisource 

modc:l produced similar result5. 

Table 10 presents me results from the factor analysis on [he daily concentrations of me 

chemical components of PMCF from samples 

collected by me dichotomous sampler. Factor 

1 represents soil with high loadings on AI, Si, 

K, Ca, Mn, Fe, Sr, and Rb. Factor 2 represents a soutce of coarse fraction merals with 

high loadings on Zn, Pb, and Cu. Factor 3 

represents a marine influence with a high 

loading on Cl. These three factors explain 

91.8% of the variance in me PMCF dara. 

Sensitivity 4n4lysis. As a sensitivity 

analysis, we analyzed temperature as a 

cofactor ramer man a confounder. That is, we 

evaluated the effects of temperature on 

mortali[), as an independent variable rather 

than adjusting for it in the model as a 

confounding variable. We evaluated'the 

Factor 3 Factor 4 Factor 5 

0.08 ·0.01 0.07 

0.11 -0.01 0.10 

0.01 -0.03 0.96 

0.15 ·0.01 0.09 

0.05 0.13 0.07 

0.19 0.19 0.05 

0.03 ·0.03 0.03 

0.59 0.01 O.lB 

0.15 0.11 0.16 

0.55 0.33 0.01 

0.41 0.2B -O.OB 

0.91 0:08 -0.04 

0.39 0.35 -0.09 

0.31 0.45 -0.05 

0.09 0.93 -0.02 

13.7 9.7 7.4 

Table 9. RR for total and cardiovascular mortality from an interquartile range (lOR) increase in each factor. 

Outcome, factor lag days p SE lOR RR lCI UCI 

Total mortality 

Factor 2 1 ·0.03 0.01 -2.03 1.26 0.96 0.93 1.00 

2 -0.04 0.01 -2.45 1.26 0.96 0.92 0.99 

3 ·0.02 0.01 ·1.67 1.26 0.97 0.94 1.01 

4 -0.02 0.01 -1.74 1.26 0.97 0.94 1.00 

Factor 4 2 -0.03 0,01 -2.01 1.09 0.97 0.94 1.0{) 

4 ·0.03 0.01 -1.72 1.09 0.97 0.94 1.00 

Factor 5 0 0.03 0.01 2.13 1.3B 1.04 1.01 1.08 

3 -0.03 0.01 ·2.22 1.39 0.96 0.92 0.99 

Cardiovascular mortality 

Factor 1 1 0.05 0.02 2.59 1.11 1.06 1.01 1.10 

Factor 3 3 0.05 0.02 2.67 1.02 1.05 1.01 1.09 

Factor 5 0 0.04 0.02 2.03 1.38 1.06 1.00 1.12 

Abbreviations: f~, regrllssion coefficient; lCI, lower 95% confidence interval; r. ('statistic from the rllgression model; UCI, 

upper 95% confidence interval. 

Environmenral Heal{h Per5pe([ive~ - VolUme J 08. Number 4. April 2000 

significance of temperature after adjusting 

for day of the:: week, time trends, and relative humidity. For tOtal and cardiovascular 

monaliry, we found thar temperature 

was not associated with excess deaths. 

Temperature was not correlated with either 

PM 10 (, = -0.08) or PM,., (T = -0.25). A 'ecand analysis examined me effect of extreme 

temperatures. If the average daily temperature: 

was greater than or equal to the 95th percentile (35.4 D

C), we: assigned a I to me predictor variable; otherwise we assigned a O. We 

did not find an association between extreme 

temperature and total mortality. However, 

with cardiovascular morrali[)" extreme temperature was associated with excess dc:ams at 0 

and 2 days lag (p < 0.1). To further assess the 

imporrance of me high temperature days to 

our analysis, we evaluated- the association 

between PM2.

5 and cardiovascular mortali[), 

after excluding me days when me temperature 

was above the 95th percentile. The effect of 

eliminating the high temperature days was 

negligible. The RR for cardiovascular mortality associated with PM2.

5 (I day lag) induding 

all days was the same as mat excluding rhe 

hones, day, (RR = ],06, cr, ],02-1.10). 

We also conducted a sensitivity analysis 

wim relative humidi[)1 as a cofactor, wim the 

model controlling for time trends and temperature. As a cofactor, rdative humidity was not 

associated wich either tOtal mortality or cardiovascular mortality. To further assess the effectS 

of extreme relative humidi[)" we eliminated 

me driest days (rdative humidity < 25m percentile) from me data. We men found mat me 

coarse fraction was no longer associated wim 

mtal mortality. The association between cardiovascular mortality and coarse fraction was 

statistically significant (p < 0.05) on me concurrent day, but nonsignificant with I-day lag. 

We also used dew point rather than relative humidity in the base model. Controlling 

for dew point rather man relative humidity 

did not alter our results. We obtained similar 

regression coefficients. 

Table 10. Factor analysis results for PMCf' 

Element Factor 1 Factor 2 Factor 3 

AI 0.91 0.33 0.22 

Si 0.90 0.36 0.24 

CI 0.25 ·0.35 0.82 

S 0.59 0.55 0.41 

K 0.91 0.33 0.23 

C, 0.84 0.41 0.31 

Mn 0.88 0.42 0.17 

Fe 0.84 0.50 0.19 

2n 0.47 0.83 0.07 

8r 0.23 0.30 0.85 

Pb 0.40 0.80 ·0.02 

Sr 0.83 0.42 0.26 

Cu 0.41 0.82 ·0.02 

Rb 0.91 0.27 0.17 

Percent variance 51.1 26.5 14.2 

explained 

by faclor 

351 
Articles • Mar et aJ. 

To assess the effect of replacing the PMz 5 

cyclone on the TEOM with [he WINS, we 

evaluated the association berween soil and 

rotal mortality from 1 January 1995 ro 31 

December 1996 and from 1 January 1997 [0 

31 December 1997. The latter period represented the WINS inlet measurements. The 

association becween soil and mortality was 

nor significant for the cyclone measurements 

alone. Analysis with only the WINS data 

revealed chat rhe association berween soil 

and morcality was positive and significanr at 

o days lag, bur not significant for any of the 

other days. 

We estimated soil-related potassium using 

a correction ratio = KlSi (23). We then reevaluated me RR for cardiovascular mortality associated with Ks using Ks ca1cwaced from three 

slightly different values of KlSi. This correction ratio is dependent on where the soil was 

obtained: PM2.

5 paved road dust (KISi = 

1.85113.69), an agricultural field (KISi ~ 

1.98114.35), or Phoenix desert soil (KlSi = 

1.89/14.00) (27). We found similar RRs for 

cardiovascular mortality associated with Ks 

when we used any of these three approaches. 

In contrast, total porassium was noc associated 

wim either total or cardiovascular monality. 

Discussion 

To our knowledge this is the first time-series 

analysis [hat has looked at the association 

between PM chemical composition and 

mortality and the association berween the 

underlying facrors influencing that composition and monality. Ozkaynak and Thursron 

(28) combined source apportionment and 

epidemiologic methods to assess the effects 

of air pollution on mortality. However, their 

study was a cross-sectional analysis rather 

than a time-series a~alysis. The present 

study found significant associations between 

air pollutants and total nonaccidental and 

cardiovascular mortality. The association 

becween PM10 and cardiovascular mortality 

is consistent with previous studies. Zmirou 

et aI. (J7) reported an RR for cardiovascular 

mortality from a 50-llgfm3 increase in PM iD 

(RR = 1.04) in a study of air pollution in 10 

large Western European cities. Pope et al. 

(5) found an association between respiratory 

disease death and cardiovascular deaths with 

PM 10 in Utah. Schwartz (2) also found chat 

an high-pollution days {increased tOtal suspended particulates) there was an increased 

risk of death from cardiovascular disease (RR 

= 1.09) in Philadelphia, Pennsylvania, and 

Birmingham. Alabama (1). Furthermore. 

Anderson et al. (29) found that black smoke 

was associated with a 0.58% increase in 

cardiovascular deaths in London. 

The association between PM2.

5 and 

cardiovascular mortality is similar m that 

of Schwartz et at. (3). who found that a 

352 

10_j.lg/m3 increase in PMZ.

5 was associated 

with a 1.5% increase in total mortality and 

2.1 % increase in mortality from ischemic 

hean disease in a study of six eastern U.S. 

cities. In contrast to Schwartz er al. (3), the 

present study also found a significant association between PMCF and total and cardiovascular mortality. Although Schwartz et aI. 

(3) did not find a significant association 

between coarse fraction and mortality when 

the results from all six cities were combined, 

there was an association in Steubenville, 

Ohio. alone. Such observed differences may 

have been due to differences in regional 

coarse fraction composition. In Spokane. 

Schwan, et at. (12) also found no association between coarse particle concenttation 

and total mortality. However, that srudy 

only looked ar high episodes of coarse particle concentrations resulting from dust 

storms. Our findings are in agreement with 

Ostra et al. {l3}, who found a signifacant 

association between daily PM10 dominated 

by coarse particles and mortality. 

We investigated the possibility that 

PMCF was a surrogate for dryness by dim inating the days with humidity less than the 

25[h percentile. Although the association 

with total mortality was no longer significam, 

we found a significant association with 

cardiovascular mortality. 

The reason for the negative association 

between soil and total mortality is unclear. 

One possible explanation for this observation is related to the fact that the PM2.

5 

cyclone on the DFPSS was replaced with a 

WINS on 20 December 1996. The sharper 

cut point reduced the amount of sail intrusion into the PM z

.

5 sample, which could 

produce soil data mat are essentially different 

between the 1995-1996 period and 1997. 

To assess this effect, we eliminated all 1997 

soil data and reevaluated the RR for toral 

mortality. After removing che WINS data, 

the association becween reconstructed soil 

and total mortality was not significant. 

These observations are similar ro thar of 

Ozkaynak and Thurston (28), who in a 

study of the association between U.S. 

mortality rates and particle pollution levels 

in 1980 found that sail was the least significant predictor of morrality. We also evaluated the association between sail and mortality 

with only soil data obtained with the WINS. 

The association was positive and significant 

(p < 0.05) on the concurrent day. but not 

significant on any other lag days. However, 

this observation may be due co [he low 

number of days used to evaluate [he association between WINS PM2.

5 soil and total 

morraliry (n· 377). 

With respect ra rhe elemental components of PM z.j ' we found that EC was 

significantly associated with cardiovascular 

mortality. EC is found in combustionderived particles. most notably diesel exhaust 

(21). We found that Ks [potassium from vegetative burning (23)] was also associated with 

cardiovascular monality. 

We found several associations that are 

potentially spurious. The associations with 

these variables were found with only total 

mortality and nat with cardiovascular 

monaliey. Lead was negatively associated with 

toral mortality at lag day 3, although this may 

be reflective of the moderate correlation 

between Pb and soil in Phoenix (r = 0.49). Pb 

may have accWllulated in me soil or in road 

dust from the past use of leaded gasoline. 5 

was also negatively associated with ra~al mortality on lag day 3. At present, the reason for 

the negative association with S is unclear. 

However, S accounts for a rela-tivdy small 

percentage of the mass of PM2.$ (I 5%). The 

significant negative associations between total 

mortality and Pb and S were not consistent 

wich the lack of association betwet::n these exposure variables and cardiovascular monality. 

For the gaseous species, we found thar 

fot:u nonaccidental mortality and cardiovascular mortality were strongly associated with 

CO and N02. These obst::rvations are similar to those ofBurne[c et al. (30), who found 

associations between CO and N02 and 

total nonaccidenral mortality in Toronto. 

Canada. Burnett et al. (30) also found that 

cardiac mortality was associaced with CO. 

CO exacerbaces cardiac conditions (10). CO 

concentrations are also associated with hospital admissions for cardiovascular disease 

(31). In Phoenix the primary sources of CO 

and NOz are morar vehicles. 

The association between S02 and cardiovascular mortality was similar to that of 

Zmirou et aI. (J7), who also found thac an 

increase in 502 was associated with an 

increase in cardiovascular deaths (RR = 

1.04). In addition, Zmirou et al. (J 7) found 

weak but significant association between 1-

hr maximum 0 3 concentrations and cardiovascular mortality (RR = 1.02). Hoek et al. 

(32) also found an association becween total 

mortality and 0 3 in the Netherlands. We 

found no significant associations with 0 3, 

The present study demonstrated rhe use 

of factor analysis in an epidemiologic study. 

Using facror analysis, we were able to idencify those underlying factors of measured air 

pollution composition variability that were 

associated with excess mortality. Poisson 

regression with factor scores as exposure variables revealed that combustion-related pollutants associated with motor vehicles and 

vegetative burning as well as fine particwate 

50 4 concentrations were significantly 

associated with cardiovascular monality. The 

soil facrar. however, was associated wim fewer 

than expected [otal deaths. These results are 

Volume 108, Number 4, April 2000 • Environmental HeCllth Perspectives 
Articles • Air pollution and mortality in Phoenix 

consistent with our time-series results for 

individual pollmams, specifJcally CO, N02, 

KS> EC, OC, and reconstructed soil. It is 

intercsting to nore rhac me factor repn:-se:nring S was significantly associ aced with 

cardiovascular monality, whereas S alone: in 

an individual pollucanr model was nor associared with cardiovascular mortality. This may 

be reflective of the contribution of Pb and Br 

[0 the S factor. 

A unique: aspecc of this study was rhe use 

of the chemical composition dara of PM2,S 

Using such data, we found positive associations berween cardiovascular mortality and 

Ks

' OC, and EC as well as che more traditionally measured pollutants CO, NO z• 

SOz' PMlO' PM2.5' and PMCF' Significant 

associations were also found with factors 

associated with incomplete combustion 

products and particulate S compounds. A 

limitation of [his study is thar [he factor 

analysis results are only in terms of the variance explained by each £aCtOr. radler than in 

terms of the quamitative comribution from 

a specific source category. Although methods 

are available [0 include quantitative source 

apportionments in a time-series framework 

(33), such an analysis is beyond the scope of 

this initial invesrigacion. 

REFERENCES AND NOTES 

1. Schwartz J. Air pollution and daily mona1i1y in Birmingham, 

Alabama. Am J Ep'ldEmiol lJ7:11J6-1146 0993). 

2. Schwartz J. What are people dying of on hIgh air polllltion days? Environ Ras 64:26-35(1994). 

J. Schwam: J, OockeryO, Neas L Is daily monaIity associated 

specifically with fine panicles? J Air Waste Manag Assoc 

46:927-9J9 (1996). 

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

partlculates In Santa Clara COllnty. 1980-1986. EnvIron 

Health Pcrspect 89:159-168 /1990). 

5. Pope CA. Schwanz J, Ransom MR. Daily mortality and 

PM10 pollution in Utah Valley. Arch Environ Health 

47:211-217 (1992). 

6. Dockery OW, Pope CA III. ACllto respirator effects of 

particUlate air polllltion. Annu Rev PlIblic Health 

15:107-1J2(1994). 

7. DSlro B. Fino particulate air polllrtion and monality in two 

SOllthern California cOllntios. Environ Res 70:98-104 {1995). 

8. Vedal S. Ambient penicles and hoallh: lines that divide. J 

Air Waste Manag Assoc 47:551-581 /1!Hl7). 

9. Wordley J, Walters S. Ayres JG. Shon term variations in 

hospital admissions and mortality and particulate air pollution. Dccup Environ Mod 54:108-116(1997). 

10. Pope CA III, Hill RW, Villogu GM. Particulate air pollulion and daily monality on Utah's Wasatch Front. Environ 

Health PorspectI07:567-57J (1999). 

11. Bascom R. Health eftects 01 olltdoor air pollution. Am J 

Respir Crit Care Mod 153:411-498 /1996)_ 

12. Schwam: J, Norris G.larson T, Sheppard l. Claiborne C. 

Koenig J. Episodes of high coarSE particle concentra· 

tions are not assoc'lated with increased mortality. 

Environ Health Perspccl107:339-J42 /1999). 

lJ. Dstro BD. Hurley S, Lipsett MJ. Air polilltion and daily 

mortalily in the Coachella Valley. California: a study of 

PM 1D dominated by coarse particles. Environ Res 

1I1:231-238/1999}. 

14. U.S. EPA. The Air Quality Criteria for Paniclllate Matter. 

EPN6OO1P-951OO1 bF. Washington, DC:U.S. EMironmental 

Protection Agency. 1996. 

15. Norris GA, Zweidinger AB. Bloemen HJTh. PlIrdlle LJ, 

Bowser J. Stevens RK. Unpublished data. 

16. U.S. Census Bureau. U.S. Census 1990. Available: 

tlllp:llwww.census.gov/cgi·bin/gazetteer I cited 18 

January 2000J. 

17. Zmiroll D. Schwartz J. Saez M. Zanobeni A. Wojtyniak B. 

TOlllollmi G. Spilt C. Ponce de LEon A. Le MoulJec y, 

8acharova l, et aL TImo-series analysis of air pclllrtion and 

cause'specific monalily. E Fidem'lol ogy 9".495-503 II GG81. 

18. Liao 0, Creason J, Shy C, Williams R, Walls R, 

Zweidinger R. Daily variation of perticulate air pollution 

and poor cardiac autonomic conlrol in the elderly. 

Environ Hoalth PerspecI 101:521-525 (19991. 

19. BlIrnett AT. Cakmak S, Ra'lzenne ME, Stieb 0, Vincent R. 

Krewski D, Brook JR. Philips O. Olkeynak H. The 

association between ambient carbon monOltide lEvels 

and daily mortality '~ Toronto, Canada. J Air Waste 

Mana!} Assoc 48:689-700 {199B). 

Environmental He",lrh Perspectives • Volume I DB, Number 4, April 2000 

20. Touloumi G, Katsollyanni K. Zmirou 0, Schwanz J, Spill; 

C. de Loon AP. Tobias A, QlIennel p. Rabczenko D. 

Bachelllva l. et al. Shon·lerm effects 01 amb'lunt oltldant 

exposure on monality: a combined analysis withIn thE 

APHEA projecl Am J EpidemioI14B:I77-185 (1S97). 

21. Birch ME. Cary AA Elemental carbon-based meThod for 

monitoring occupational exposures to panielliate diesel 

exhaust. Aerosol Sci Teehnol 25:221-241 (1995). 

22. U.S. Environmental Protection Agency. Aerometric 

Informotion Retrieval System. Avallable: htlp:j/ 

l'M'W.opa.gov/airs,fairs.html Icited 14 December 19991_ 

2J. Lewis CW, Baumgardner RE, Stevens AI(, Russworm GM. 

Recoptor modeflng Stlldy 01 Denver winter haze. Environ 

Sci Technol 20:1126-1136 (1985). 

24. Maim WC. Sisler JF. HlIliman D, Eldred RA, Cahill TA. 

Spatial and seasonal trends In particle concentralion 

and optical extinction in the United Stetos. J Geophys 

Res 99:1347-1J70 119941. 

25. Hastie T, Tibshirani R. Generalized Additive Models. 

London·.Chapman and Hall, 1990. 

26. Akaike H. Statistic predictor identilication. Ann Inst Sial 

Math 22:203-217 (1970). 

27. Chow JC. Watson JG, Richards OW. Haaso DL, McDade 

C. Dietrich DL, Moon D. Sloane C. The 1989-90 Phoenix. 

PM 1D Stlldy, Valli. SOllrce Apportionment Appendices, 

DR! Docllment No. 89Jl 6Ft REno. NV:Oesert Research 

InsliMe.1991. 

28. Dzkaynak H. ThUrston G. Assoclalions between 1980 U.S. 

mona lily rates and alternative measllres of airborne par· 

ticle concentration. Risk Anal 7:44!1--461 119871. 

29. Anderson HR, Ponce de Leon A. Bland JM, Bower JS. 

Strachan DP. Air polllltion and daily monality in London: 

1967-92. Br Med J JI2:655--669 /1996). 

JO. Bumon RT, Smith-Ooiren M, Sti~b 0, Cakmak S, Brook 

JR. Effects of particlllate and gaseolls air pollution on 

cardiorespiratory hospilalizations. Arch Environ Health 

54:1JO-I39/1999). 

31. Schwartz J. Air Polllltion and Hospital Admissions lor 

Cardiovasclliar Disease in TlIcson. Epidemiology 

8:J71-377 /1g971. 

J2. Hoek G, Schwartz JD. Groot S, Eilers P. Effects of ambi· 

ent particulate mattEr and ozone on daily mortality in 

Rotterdam, the Netherlands. Arch Environ Health 

52:455--46J (1997). 

JJ. Henry RC. Lewis CW, Collins JF. Vehicle·related hydrocarbon source compositions from ambient data: the 

GRECE/SAFER Method. Environ Sci Technol 28:823-8J2 

11994). 

353